diff --git a/assets/css/atom.css.map b/assets/css/atom.css.map
index 4f3f9e5cc3..3fb5ed84c7 100644
--- a/assets/css/atom.css.map
+++ b/assets/css/atom.css.map
@@ -1 +1 @@
-{"version":3,"sourceRoot":"","sources":["../../../../../../../../../tmp/jekyll-remote-theme-20241010-3005-owiwgx/_sass/_02_settings_typography.scss","../../../../../../../../../tmp/jekyll-remote-theme-20241010-3005-owiwgx/_sass/_03_settings_mixins_media_queries.scss","atom.scss","../../_sass/_01_settings_colors.scss"],"names":[],"mappings":"AAuDA,wBAPoB,QAQpB,wBAPoB,QAQpB,wBAPoB,QAQpB,wBAPoB,OAQpB,wBAPoB,QC8RlB,wBACE,sBAGF,yBACE,4BACA,UAGF,8BACE,kDACA,UAGF,0BACE,qDACA,eAGF,+BACE,0EACA,eAGF,yBACE,qDACA,eAGF,8BACE,0EACA,eAGF,0BACE,qDACA,eAGF,+BACE,2EACA,eAGF,2BACE,sDACA,gBAGF,yCACE,kBC1XJ,EACC,cAGD,MACC,WACA,WCKqB,QDJrB,MCEqB,KDDrB,YFMwB,mDEFxB,WACC,kBACA,cACA,0CACA,mBACA,mBACA,mBACC,yBACA,oBAED,kBACC,sFACA,cACA,YACA,iBCHmB,QDInB,WACA,oBACA,kBACA,uBAIF,wEAMC,aAIF,MACC,cACA,gCACA,iBACC,mBAGD,YACC,MCxBoB,QDyBpB,YFtCkB,8BEuClB,UFhBkB,QEiBlB,mBAGD,kBACC,mBACA,MCzCoB,QD4CrB,cACC,MCdoB,QDepB,gBAGD,cACC,eAGD,mEAKC","sourcesContent":["@charset \"utf-8\";\n/* TOC – Typography variables\n\nModular Scale › http://www.modularscale.com//?16,36&px&1.25&web&table\n\n- Fonts\n- Font Weight\n- Font Size Variables\n\n*/\n\n@import \"functions\"; // Allows the use of rem-calc() or lower-bound() in your settings\n\n\n\n/* Fonts\n------------------------------------------------------------------- */\n\n$base-font-size: 16px;\n$rem-base: $base-font-size;\n// $base-line-height is 24px while $base-font-size is 16px\n$base-line-height: 1.5 !default;\n\n\n$font-family-sans-serif: \"Lato\", \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n$font-family-serif: \"Volkhov\", Georgia, Times, serif;\n$font-family-monospace: \"Lucida Console\", Monaco, monospace;\n\n$body-font-family: $font-family-sans-serif;\n$body-font-weight: normal;\n$body-font-style: normal;\n\n$header-font-family: $font-family-serif;\n\n\n\n/* Font Weight\n------------------------------------------------------------------- */\n\n$font-weight-normal: normal;\n$font-weight-bold: bold;\n\n\n\n/* Font Size Variables\n------------------------------------------------------------------- */\n\n$font-size-p:    \t$base-font-size;\n$font-size-h1:      2.441em;\n$font-size-h2:      1.953em;\n$font-size-h3:      1.563em;\n$font-size-h4:      1.25em;\n$font-size-h5:      1.152em;\n$font-size-small:   0.8em;\n\n.font-size-h1   { font-size: $font-size-h1; }\n.font-size-h2   { font-size: $font-size-h2; }\n.font-size-h3   { font-size: $font-size-h3; }\n.font-size-h4   { font-size: $font-size-h4; }\n.font-size-h5   { font-size: $font-size-h5; }\n","@charset \"utf-8\";\n// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n//\n// Foundation Variables\n//\n\n// Data attribute namespace\n// styles get applied to [data-mysite-plugin], etc\n$namespace: false !default;\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n    -webkit-#{$side}-left-radius: $radius;\n    -webkit-#{$side}-right-radius: $radius;\n    border-#{$side}-left-radius: $radius;\n    border-#{$side}-right-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We can control whether or not we have inset shadows edges.\n// $active - Default: true, Options: false\n@mixin inset-shadow($active: true) {\n  box-shadow: $shiny-edge-size $shiny-edge-color inset;\n\n  @if $active {\n    &:active {\n      box-shadow: $shiny-edge-size $shiny-edge-active-color inset;\n    }\n  }\n}\n\n// @mixins\n//\n// We use this to add transitions to elements\n// $property - Default: all, Options: http://www.w3.org/TR/css3-transitions/#animatable-properties\n// $speed - Default: 300ms\n// $ease - Default:ease-out, Options: http://css-tricks.com/almanac/properties/t/transition-timing-function/\n@mixin single-transition($property: all, $speed: 300ms, $ease: ease-out) {\n  transition: $property $speed $ease;\n}\n\n// @mixins\n//\n// We use this to add box-sizing across browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -($width/2);\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n\n\n\n// d. Media Query Ranges\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n$small-range: (\n  0em,\n  40em\n);\n$medium-range: (\n  40.063em,\n  64em\n);\n$large-range: (\n  64.063em,\n  90em\n);\n$xlarge-range: (\n  90.063em,\n  120em\n);\n$xxlarge-range: (\n  120.063em,\n  99999999em\n);\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-small-only {\n    font-family: \"/\" + unquote($small-only) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-medium {\n    font-family: \"/\" + unquote($medium-up) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-medium-only {\n    font-family: \"/\" + unquote($medium-only) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-large {\n    font-family: \"/\" + unquote($large-up) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-large-only {\n    font-family: \"/\" + unquote($large-only) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-xlarge {\n    font-family: \"/\" + unquote($xlarge-up) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xlarge-only {\n    font-family: \"/\" + unquote($xlarge-only) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xxlarge {\n    font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n    width: lower-bound($xxlarge-range);\n  }\n\n  meta.foundation-data-attribute-namespace {\n    font-family: #{$namespace};\n  }\n\n  @if $include-html-global-classes {\n\n    // Must be 100% for off canvas to work\n    html,\n    body {\n      height: 100%;\n    }\n\n    // Set box-sizing globally to handle padding and border widths\n    *,\n    *:before,\n    *:after {\n      @include box-sizing(border-box);\n    }\n\n    html,\n    body {\n      font-size: $base-font-size;\n    }\n\n    // Default body styles\n    body {\n      background: $body-bg;\n      color: $body-font-color;\n      padding: 0;\n      margin: 0;\n      font-family: $body-font-family;\n      font-weight: $body-font-weight;\n      font-style: $body-font-style;\n      line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n      position: relative;\n      cursor: $cursor-auto-value;\n    }\n\n    a:hover {\n      cursor: $cursor-pointer-value;\n    }\n\n    // Grid Defaults to get images and embeds to work properly\n    img {\n      max-width: 100%;\n      height: auto;\n    }\n\n    img {\n      -ms-interpolation-mode: bicubic;\n    }\n\n    #map_canvas,\n    .map_canvas {\n\n      img,\n      embed,\n      object {\n        max-width: none !important;\n      }\n    }\n\n    // Miscellaneous useful HTML classes\n    .left {\n      float: left !important;\n    }\n\n    .right {\n      float: right !important;\n    }\n\n    .clearfix {\n      @include clearfix;\n    }\n\n    // Hide visually and from screen readers\n    .hide {\n      display: none !important;\n      visibility: hidden;\n    }\n\n    // Hide visually and from screen readers, but maintain layout\n    .invisible {\n      visibility: hidden;\n    }\n\n    // Font smoothing\n    // Antialiased font smoothing works best for light text on a dark background.\n    // Apply to single elements instead of globally to body.\n    // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n    .antialiased {\n      -webkit-font-smoothing: antialiased;\n      -moz-osx-font-smoothing: grayscale;\n    }\n\n    // Get rid of gap under images by making them display: inline-block; by default\n    img {\n      display: inline-block;\n      vertical-align: middle;\n    }\n\n    //\n    // Global resets for forms\n    //\n\n    // Make sure textarea takes on height automatically\n    textarea {\n      height: auto;\n      min-height: 50px;\n    }\n\n    // Make select elements 100% width by default\n    select {\n      width: 100%;\n    }\n  }\n}","@charset \"utf-8\";\n\n@import \"functions.scss\";\n\n$include-html-classes: false;\n@import \"01_settings_colors.scss\";\n@import \"02_settings_typography.scss\";\n@import \"03_settings_mixins_media_queries.scss\";\n@import \"04_settings_global.scss\";\n\n* {\n\tdisplay: block;\n}\n\n:root {\n\tmargin: 3em;\n\tbackground: $body-bg;\n\tcolor: $body-font-color;\n\tfont-family: $body-font-family;\n}\n\nfeed {\n\t> title {\n\t\ttext-align: center;\n\t\tcolor: lighten($primary-color, 25%);\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1 * 1.5;\n\t\tfont-weight: bolder;\n\t\t&::before {\n\t\t\tcontent: 'Atom Feed for ';\n\t\t\tfont-weight: initial;\n\t\t}\n\t\t&::after {\n\t\t\tcontent: \"This Atom feed is meant to be used by RSS reader applications and websites.\";\n\t\t\tdisplay: block;\n\t\t\tpadding: 1em;\n\t\t\tbackground-color: $alert-color;\n\t\t\tcolor: #fff;\n\t\t\tfont-family: initial;\n\t\t\tfont-size: initial;\n\t\t\tletter-spacing: initial;\n\t\t}\n\t}\n\t\n\t> id,\n\t> updated,\n\t> subtitle,\n\t> author,\n\t> link,\n\t> generator {\n\t\tdisplay: none;\n\t}\n}\n\nentry {\n\tpadding: 1em 0;\n\tborder-bottom: 1px solid invert($body-bg);\n\t&:last-child {\n\t\tborder-bottom: none;\n\t}\n\n\t> title {\n\t\tcolor: $secondary-color;\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1;\n\t\tmargin-bottom: 0.5em;\n\t}\n\n\t> link::after {\n\t\tcontent: attr(href);\n\t\tcolor: $primary-color;\n\t}\n\n\t> updated {\n\t\tcolor: $grey-5;\n\t\tfont-size: small;\n\t}\n\n\t> summary {\n\t\tmargin-top: 1em;\n\t}\n\n\t> id,\n\t> author,\n\t> category,\n\t> published,\n\t> content {\n\t\tdisplay: none;\n\t}\n}\n","/// from https://github.com/Phlow/feeling-responsive/raw/gh-pages/_sass/_01_settings_colors.scss\n@charset \"utf-8\";\n/* TOC – Color Variables\n\n- Basics\n- Corporate Identity Colorpalette\n- Foundation Color Variables\n- Grey Scale\n- Topbar-Navigation\n- Footer\n- Code\n\n*/\n\n\n\n/* Basics\n------------------------------------------------------------------- */\n\n$text-color         : #111;\n$body-font-color    : $text-color;\n$body-bg            : #fdfdfd;\n\n\n\n/* Corporate Identity Colorpalette\n   https://color.adobe.com/de/Flat-Design-Colors-v2-color-theme-4341903/\n------------------------------------------------------------------- */\n\n$ci-1               : #334D5C; // dark turquoise\n$ci-2               : #45B29D; // turquoise\n$ci-3               : #EFC94C; // yellow\n$ci-4               : #E27A3F; // orange\n$ci-5               : #DF4949; // red\n$ci-6               : #A1D044; // green\n\n/// CIL overrides\n$ci-2               : #c92c99;\n$ci-6               : #e50695;\n\n\n/* Foundation Color Variables\n------------------------------------------------------------------- */\n\n$primary-color      : $ci-1;\n$secondary-color    : $ci-6;\n$alert-color        : $ci-5;\n$success-color      : $ci-6;\n$warning-color      : $ci-4;\n$info-color         : $ci-1;\n\n\n\n/* Grey Scale\n------------------------------------------------------------------- */\n\n$grey-1             : #E4E4E4;\n$grey-2             : #D7D7D7;\n$grey-3             : #CBCBCB;\n$grey-4             : #BEBEBE;\n$grey-5             : #A4A4A4;\n$grey-6             : #979797;\n$grey-7             : #8B8B8B;\n$grey-8             : #7E7E7E;\n$grey-9             : #646464;\n$grey-10            : #575757;\n$grey-11            : #4B4B4B;\n$grey-12            : #3E3E3E;\n$grey-13            : #313131;\n$grey-14            : #242424;\n$grey-15            : #171717;\n$grey-16            : #0B0B0B;\n\n/// CIL overrides\n$grey-8             : #043852;\n$grey-13            : #510c76;\n\n\n/* Topbar-Navigation\n------------------------------------------------------------------- */\n\n$topbar-bg-color    : $body-bg;\n$topbar-bg          : $topbar-bg-color;\n\n\n$topbar-dropdown-toggle-color: $ci-1;\n\n$topbar-link-color  : #000;\n$topbar-link-color-hover: #000;\n$topbar-link-color-active: #000;\n$topbar-link-color-active-hover: #000;\n\n$topbar-dropdown-label-color: $ci-2;\n$topbar-dropdown-link-bg-hover: $ci-6;\n\n$topbar-link-bg-active: $ci-6; // Active Navigation Link\n$topbar-link-bg-hover: $ci-6;\n$topbar-link-bg-active-hover: $ci-2;\n\n\n$topbar-dropdown-bg: $ci-6; // Background Mobile Navigation\n$topbar-dropdown-link-color: #000;\n$topbar-dropdown-link-bg: $ci-2;\n\n$topbar-menu-link-color-toggled: $ci-1;\n$topbar-menu-icon-color-toggled: $ci-1;\n$topbar-menu-link-color: #000;\n$topbar-menu-icon-color: #000;\n$topbar-menu-link-color-toggled: $ci-6;\n$topbar-menu-icon-color-toggled: $ci-6;\n\n\n\n/* Footer\n------------------------------------------------------------------- */\n\n$footer-bg          : $grey-8;\n$footer-color       : #fff;\n$footer-link-color  : $ci-6;\n\n\n$subfooter-bg       : $grey-13;\n$subfooter-color    : $grey-8;\n$subfooter-link-color: $grey-8;\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\n$code-background-color: scale-color($secondary-color, $lightness: 70%);\n\n$highlight-background: #ffffff;\n$highlight-comment: #999988;\n$highlight-error: #a61717;\n$highlight-comment-special: #999999;\n$highlight-deleted: #000000;\n$highlight-error-2: #aa0000;\n$highlight-literal-string: #d14;\n$highlight-literal-number: #009999;\n$highlight-name-attribut: #008080;\n$highlight-error-background: #e3d2d2;\n$highlight-generic-deleted: #ffdddd;\n$highlight-generic-deleted-specific: #ffaaaa;\n$highlight-generic-inserted: #ddffdd;\n$highlight-generic-inserted-specific: #aaffaa;\n$highlight-generic-output: #888888;\n$highlight-generic-prompt: #555555;\n$highlight-subheading: #aaaaaa;\n$highlight-keyword-type: #445588;\n$highlight-name-builtin: #0086B3;\n$highlight-name-class: #445588;\n$highlight-name-entity: #800080;\n$highlight-name-exception: #990000;\n$highlight-name-function: #990000;\n$highlight-name-namespace: #555555;\n$highlight-name-tag: #000080;\n$highlight-text-whitespace: #bbbbbb;\n$highlight-literal-string-regex: #009926;\n$highlight-literal-string-symbol: #990073;\n"],"file":"atom.css"}
\ No newline at end of file
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// Allows the use of rem-calc() or lower-bound() in your settings\n\n\n\n/* Fonts\n------------------------------------------------------------------- */\n\n$base-font-size: 16px;\n$rem-base: $base-font-size;\n// $base-line-height is 24px while $base-font-size is 16px\n$base-line-height: 1.5 !default;\n\n\n$font-family-sans-serif: \"Lato\", \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n$font-family-serif: \"Volkhov\", Georgia, Times, serif;\n$font-family-monospace: \"Lucida Console\", Monaco, monospace;\n\n$body-font-family: $font-family-sans-serif;\n$body-font-weight: normal;\n$body-font-style: normal;\n\n$header-font-family: $font-family-serif;\n\n\n\n/* Font Weight\n------------------------------------------------------------------- */\n\n$font-weight-normal: normal;\n$font-weight-bold: bold;\n\n\n\n/* Font Size Variables\n------------------------------------------------------------------- */\n\n$font-size-p:    \t$base-font-size;\n$font-size-h1:      2.441em;\n$font-size-h2:      1.953em;\n$font-size-h3:      1.563em;\n$font-size-h4:      1.25em;\n$font-size-h5:      1.152em;\n$font-size-small:   0.8em;\n\n.font-size-h1   { font-size: $font-size-h1; }\n.font-size-h2   { font-size: $font-size-h2; }\n.font-size-h3   { font-size: $font-size-h3; }\n.font-size-h4   { font-size: $font-size-h4; }\n.font-size-h5   { font-size: $font-size-h5; }\n","@charset \"utf-8\";\n// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n//\n// Foundation Variables\n//\n\n// Data attribute namespace\n// styles get applied to [data-mysite-plugin], etc\n$namespace: false !default;\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n    -webkit-#{$side}-left-radius: $radius;\n    -webkit-#{$side}-right-radius: $radius;\n    border-#{$side}-left-radius: $radius;\n    border-#{$side}-right-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We can control whether or not we have inset shadows edges.\n// $active - Default: true, Options: false\n@mixin inset-shadow($active: true) {\n  box-shadow: $shiny-edge-size $shiny-edge-color inset;\n\n  @if $active {\n    &:active {\n      box-shadow: $shiny-edge-size $shiny-edge-active-color inset;\n    }\n  }\n}\n\n// @mixins\n//\n// We use this to add transitions to elements\n// $property - Default: all, Options: http://www.w3.org/TR/css3-transitions/#animatable-properties\n// $speed - Default: 300ms\n// $ease - Default:ease-out, Options: http://css-tricks.com/almanac/properties/t/transition-timing-function/\n@mixin single-transition($property: all, $speed: 300ms, $ease: ease-out) {\n  transition: $property $speed $ease;\n}\n\n// @mixins\n//\n// We use this to add box-sizing across browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -($width/2);\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n\n\n\n// d. Media Query Ranges\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n$small-range: (\n  0em,\n  40em\n);\n$medium-range: (\n  40.063em,\n  64em\n);\n$large-range: (\n  64.063em,\n  90em\n);\n$xlarge-range: (\n  90.063em,\n  120em\n);\n$xxlarge-range: (\n  120.063em,\n  99999999em\n);\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-small-only {\n    font-family: \"/\" + unquote($small-only) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-medium {\n    font-family: \"/\" + unquote($medium-up) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-medium-only {\n    font-family: \"/\" + unquote($medium-only) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-large {\n    font-family: \"/\" + unquote($large-up) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-large-only {\n    font-family: \"/\" + unquote($large-only) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-xlarge {\n    font-family: \"/\" + unquote($xlarge-up) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xlarge-only {\n    font-family: \"/\" + unquote($xlarge-only) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xxlarge {\n    font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n    width: lower-bound($xxlarge-range);\n  }\n\n  meta.foundation-data-attribute-namespace {\n    font-family: #{$namespace};\n  }\n\n  @if $include-html-global-classes {\n\n    // Must be 100% for off canvas to work\n    html,\n    body {\n      height: 100%;\n    }\n\n    // Set box-sizing globally to handle padding and border widths\n    *,\n    *:before,\n    *:after {\n      @include box-sizing(border-box);\n    }\n\n    html,\n    body {\n      font-size: $base-font-size;\n    }\n\n    // Default body styles\n    body {\n      background: $body-bg;\n      color: $body-font-color;\n      padding: 0;\n      margin: 0;\n      font-family: $body-font-family;\n      font-weight: $body-font-weight;\n      font-style: $body-font-style;\n      line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n      position: relative;\n      cursor: $cursor-auto-value;\n    }\n\n    a:hover {\n      cursor: $cursor-pointer-value;\n    }\n\n    // Grid Defaults to get images and embeds to work properly\n    img {\n      max-width: 100%;\n      height: auto;\n    }\n\n    img {\n      -ms-interpolation-mode: bicubic;\n    }\n\n    #map_canvas,\n    .map_canvas {\n\n      img,\n      embed,\n      object {\n        max-width: none !important;\n      }\n    }\n\n    // Miscellaneous useful HTML classes\n    .left {\n      float: left !important;\n    }\n\n    .right {\n      float: right !important;\n    }\n\n    .clearfix {\n      @include clearfix;\n    }\n\n    // Hide visually and from screen readers\n    .hide {\n      display: none !important;\n      visibility: hidden;\n    }\n\n    // Hide visually and from screen readers, but maintain layout\n    .invisible {\n      visibility: hidden;\n    }\n\n    // Font smoothing\n    // Antialiased font smoothing works best for light text on a dark background.\n    // Apply to single elements instead of globally to body.\n    // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n    .antialiased {\n      -webkit-font-smoothing: antialiased;\n      -moz-osx-font-smoothing: grayscale;\n    }\n\n    // Get rid of gap under images by making them display: inline-block; by default\n    img {\n      display: inline-block;\n      vertical-align: middle;\n    }\n\n    //\n    // Global resets for forms\n    //\n\n    // Make sure textarea takes on height automatically\n    textarea {\n      height: auto;\n      min-height: 50px;\n    }\n\n    // Make select elements 100% width by default\n    select {\n      width: 100%;\n    }\n  }\n}","@charset \"utf-8\";\n\n@import \"functions.scss\";\n\n$include-html-classes: false;\n@import \"01_settings_colors.scss\";\n@import \"02_settings_typography.scss\";\n@import \"03_settings_mixins_media_queries.scss\";\n@import \"04_settings_global.scss\";\n\n* {\n\tdisplay: block;\n}\n\n:root {\n\tmargin: 3em;\n\tbackground: $body-bg;\n\tcolor: $body-font-color;\n\tfont-family: $body-font-family;\n}\n\nfeed {\n\t> title {\n\t\ttext-align: center;\n\t\tcolor: lighten($primary-color, 25%);\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1 * 1.5;\n\t\tfont-weight: bolder;\n\t\t&::before {\n\t\t\tcontent: 'Atom Feed for ';\n\t\t\tfont-weight: initial;\n\t\t}\n\t\t&::after {\n\t\t\tcontent: \"This Atom feed is meant to be used by RSS reader applications and websites.\";\n\t\t\tdisplay: block;\n\t\t\tpadding: 1em;\n\t\t\tbackground-color: $alert-color;\n\t\t\tcolor: #fff;\n\t\t\tfont-family: initial;\n\t\t\tfont-size: initial;\n\t\t\tletter-spacing: initial;\n\t\t}\n\t}\n\t\n\t> id,\n\t> updated,\n\t> subtitle,\n\t> author,\n\t> link,\n\t> generator {\n\t\tdisplay: none;\n\t}\n}\n\nentry {\n\tpadding: 1em 0;\n\tborder-bottom: 1px solid invert($body-bg);\n\t&:last-child {\n\t\tborder-bottom: none;\n\t}\n\n\t> title {\n\t\tcolor: $secondary-color;\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1;\n\t\tmargin-bottom: 0.5em;\n\t}\n\n\t> link::after {\n\t\tcontent: attr(href);\n\t\tcolor: $primary-color;\n\t}\n\n\t> updated {\n\t\tcolor: $grey-5;\n\t\tfont-size: small;\n\t}\n\n\t> summary {\n\t\tmargin-top: 1em;\n\t}\n\n\t> id,\n\t> author,\n\t> category,\n\t> published,\n\t> content {\n\t\tdisplay: none;\n\t}\n}\n","/// from https://github.com/Phlow/feeling-responsive/raw/gh-pages/_sass/_01_settings_colors.scss\n@charset \"utf-8\";\n/* TOC – Color Variables\n\n- Basics\n- Corporate Identity Colorpalette\n- Foundation Color Variables\n- Grey Scale\n- Topbar-Navigation\n- Footer\n- Code\n\n*/\n\n\n\n/* Basics\n------------------------------------------------------------------- */\n\n$text-color         : #111;\n$body-font-color    : $text-color;\n$body-bg            : #fdfdfd;\n\n\n\n/* Corporate Identity Colorpalette\n   https://color.adobe.com/de/Flat-Design-Colors-v2-color-theme-4341903/\n------------------------------------------------------------------- */\n\n$ci-1               : #334D5C; // dark turquoise\n$ci-2               : #45B29D; // turquoise\n$ci-3               : #EFC94C; // yellow\n$ci-4               : #E27A3F; // orange\n$ci-5               : #DF4949; // red\n$ci-6               : #A1D044; // green\n\n/// CIL overrides\n$ci-2               : #c92c99;\n$ci-6               : #e50695;\n\n\n/* Foundation Color Variables\n------------------------------------------------------------------- */\n\n$primary-color      : $ci-1;\n$secondary-color    : $ci-6;\n$alert-color        : $ci-5;\n$success-color      : $ci-6;\n$warning-color      : $ci-4;\n$info-color         : $ci-1;\n\n\n\n/* Grey Scale\n------------------------------------------------------------------- */\n\n$grey-1             : #E4E4E4;\n$grey-2             : #D7D7D7;\n$grey-3             : #CBCBCB;\n$grey-4             : #BEBEBE;\n$grey-5             : #A4A4A4;\n$grey-6             : #979797;\n$grey-7             : #8B8B8B;\n$grey-8             : #7E7E7E;\n$grey-9             : #646464;\n$grey-10            : #575757;\n$grey-11            : #4B4B4B;\n$grey-12            : #3E3E3E;\n$grey-13            : #313131;\n$grey-14            : #242424;\n$grey-15            : #171717;\n$grey-16            : #0B0B0B;\n\n/// CIL overrides\n$grey-8             : #043852;\n$grey-13            : #510c76;\n\n\n/* Topbar-Navigation\n------------------------------------------------------------------- */\n\n$topbar-bg-color    : $body-bg;\n$topbar-bg          : $topbar-bg-color;\n\n\n$topbar-dropdown-toggle-color: $ci-1;\n\n$topbar-link-color  : #000;\n$topbar-link-color-hover: #000;\n$topbar-link-color-active: #000;\n$topbar-link-color-active-hover: #000;\n\n$topbar-dropdown-label-color: $ci-2;\n$topbar-dropdown-link-bg-hover: $ci-6;\n\n$topbar-link-bg-active: $ci-6; // Active Navigation Link\n$topbar-link-bg-hover: $ci-6;\n$topbar-link-bg-active-hover: $ci-2;\n\n\n$topbar-dropdown-bg: $ci-6; // Background Mobile Navigation\n$topbar-dropdown-link-color: #000;\n$topbar-dropdown-link-bg: $ci-2;\n\n$topbar-menu-link-color-toggled: $ci-1;\n$topbar-menu-icon-color-toggled: $ci-1;\n$topbar-menu-link-color: #000;\n$topbar-menu-icon-color: #000;\n$topbar-menu-link-color-toggled: $ci-6;\n$topbar-menu-icon-color-toggled: $ci-6;\n\n\n\n/* Footer\n------------------------------------------------------------------- */\n\n$footer-bg          : $grey-8;\n$footer-color       : #fff;\n$footer-link-color  : $ci-6;\n\n\n$subfooter-bg       : $grey-13;\n$subfooter-color    : $grey-8;\n$subfooter-link-color: $grey-8;\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\n$code-background-color: scale-color($secondary-color, $lightness: 70%);\n\n$highlight-background: #ffffff;\n$highlight-comment: #999988;\n$highlight-error: #a61717;\n$highlight-comment-special: #999999;\n$highlight-deleted: #000000;\n$highlight-error-2: #aa0000;\n$highlight-literal-string: #d14;\n$highlight-literal-number: #009999;\n$highlight-name-attribut: #008080;\n$highlight-error-background: #e3d2d2;\n$highlight-generic-deleted: #ffdddd;\n$highlight-generic-deleted-specific: #ffaaaa;\n$highlight-generic-inserted: #ddffdd;\n$highlight-generic-inserted-specific: #aaffaa;\n$highlight-generic-output: #888888;\n$highlight-generic-prompt: #555555;\n$highlight-subheading: #aaaaaa;\n$highlight-keyword-type: #445588;\n$highlight-name-builtin: #0086B3;\n$highlight-name-class: #445588;\n$highlight-name-entity: #800080;\n$highlight-name-exception: #990000;\n$highlight-name-function: #990000;\n$highlight-name-namespace: #555555;\n$highlight-name-tag: #000080;\n$highlight-text-whitespace: #bbbbbb;\n$highlight-literal-string-regex: #009926;\n$highlight-literal-string-symbol: #990073;\n"],"file":"atom.css"}
\ No newline at end of file
diff --git a/assets/css/rss.css.map b/assets/css/rss.css.map
index d5a0a3fec7..eeb0a936ad 100644
--- a/assets/css/rss.css.map
+++ b/assets/css/rss.css.map
@@ -1 +1 @@
-{"version":3,"sourceRoot":"","sources":["../../../../../../../../../tmp/jekyll-remote-theme-20241010-3005-owiwgx/_sass/_02_settings_typography.scss","../../../../../../../../../tmp/jekyll-remote-theme-20241010-3005-owiwgx/_sass/_03_settings_mixins_media_queries.scss","rss.scss","../../_sass/_01_settings_colors.scss"],"names":[],"mappings":"AAuDA,wBAPoB,QAQpB,wBAPoB,QAQpB,wBAPoB,QAQpB,wBAPoB,OAQpB,wBAPoB,QC8RlB,wBACE,sBAGF,yBACE,4BACA,UAGF,8BACE,kDACA,UAGF,0BACE,qDACA,eAGF,+BACE,0EACA,eAGF,yBACE,qDACA,eAGF,8BACE,0EACA,eAGF,0BACE,qDACA,eAGF,+BACE,2EACA,eAGF,2BACE,sDACA,gBAGF,yCACE,kBC1XJ,EACC,cAGD,MACC,WACA,WCKqB,QDJrB,MCEqB,KDDrB,YFMwB,mDEFxB,cACC,kBACA,cACA,0CACA,mBACA,mBACA,sBACC,wBACA,oBAED,qBACC,qFACA,cACA,YACA,iBCHmB,QDInB,WACA,oBACA,kBACA,uBAIF,iCAEC,aAIF,KACC,cACA,gCACA,gBACC,mBAGD,WACC,MCpBoB,QDqBpB,YFlCkB,8BEmClB,UFZkB,QEalB,mBAGD,UACC,MCpCoB,QDuCrB,aACC,MCToB,QDUpB,gBAGD,iBACC,eACA,gBACA,mBACA,uBAGD,wBAEC","sourcesContent":["@charset \"utf-8\";\n/* TOC – Typography variables\n\nModular Scale › http://www.modularscale.com//?16,36&px&1.25&web&table\n\n- Fonts\n- Font Weight\n- Font Size Variables\n\n*/\n\n@import \"functions\"; // Allows the use of rem-calc() or lower-bound() in your settings\n\n\n\n/* Fonts\n------------------------------------------------------------------- */\n\n$base-font-size: 16px;\n$rem-base: $base-font-size;\n// $base-line-height is 24px while $base-font-size is 16px\n$base-line-height: 1.5 !default;\n\n\n$font-family-sans-serif: \"Lato\", \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n$font-family-serif: \"Volkhov\", Georgia, Times, serif;\n$font-family-monospace: \"Lucida Console\", Monaco, monospace;\n\n$body-font-family: $font-family-sans-serif;\n$body-font-weight: normal;\n$body-font-style: normal;\n\n$header-font-family: $font-family-serif;\n\n\n\n/* Font Weight\n------------------------------------------------------------------- */\n\n$font-weight-normal: normal;\n$font-weight-bold: bold;\n\n\n\n/* Font Size Variables\n------------------------------------------------------------------- */\n\n$font-size-p:    \t$base-font-size;\n$font-size-h1:      2.441em;\n$font-size-h2:      1.953em;\n$font-size-h3:      1.563em;\n$font-size-h4:      1.25em;\n$font-size-h5:      1.152em;\n$font-size-small:   0.8em;\n\n.font-size-h1   { font-size: $font-size-h1; }\n.font-size-h2   { font-size: $font-size-h2; }\n.font-size-h3   { font-size: $font-size-h3; }\n.font-size-h4   { font-size: $font-size-h4; }\n.font-size-h5   { font-size: $font-size-h5; }\n","@charset \"utf-8\";\n// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n//\n// Foundation Variables\n//\n\n// Data attribute namespace\n// styles get applied to [data-mysite-plugin], etc\n$namespace: false !default;\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n    -webkit-#{$side}-left-radius: $radius;\n    -webkit-#{$side}-right-radius: $radius;\n    border-#{$side}-left-radius: $radius;\n    border-#{$side}-right-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We can control whether or not we have inset shadows edges.\n// $active - Default: true, Options: false\n@mixin inset-shadow($active: true) {\n  box-shadow: $shiny-edge-size $shiny-edge-color inset;\n\n  @if $active {\n    &:active {\n      box-shadow: $shiny-edge-size $shiny-edge-active-color inset;\n    }\n  }\n}\n\n// @mixins\n//\n// We use this to add transitions to elements\n// $property - Default: all, Options: http://www.w3.org/TR/css3-transitions/#animatable-properties\n// $speed - Default: 300ms\n// $ease - Default:ease-out, Options: http://css-tricks.com/almanac/properties/t/transition-timing-function/\n@mixin single-transition($property: all, $speed: 300ms, $ease: ease-out) {\n  transition: $property $speed $ease;\n}\n\n// @mixins\n//\n// We use this to add box-sizing across browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -($width/2);\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n\n\n\n// d. Media Query Ranges\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n$small-range: (\n  0em,\n  40em\n);\n$medium-range: (\n  40.063em,\n  64em\n);\n$large-range: (\n  64.063em,\n  90em\n);\n$xlarge-range: (\n  90.063em,\n  120em\n);\n$xxlarge-range: (\n  120.063em,\n  99999999em\n);\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-small-only {\n    font-family: \"/\" + unquote($small-only) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-medium {\n    font-family: \"/\" + unquote($medium-up) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-medium-only {\n    font-family: \"/\" + unquote($medium-only) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-large {\n    font-family: \"/\" + unquote($large-up) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-large-only {\n    font-family: \"/\" + unquote($large-only) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-xlarge {\n    font-family: \"/\" + unquote($xlarge-up) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xlarge-only {\n    font-family: \"/\" + unquote($xlarge-only) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xxlarge {\n    font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n    width: lower-bound($xxlarge-range);\n  }\n\n  meta.foundation-data-attribute-namespace {\n    font-family: #{$namespace};\n  }\n\n  @if $include-html-global-classes {\n\n    // Must be 100% for off canvas to work\n    html,\n    body {\n      height: 100%;\n    }\n\n    // Set box-sizing globally to handle padding and border widths\n    *,\n    *:before,\n    *:after {\n      @include box-sizing(border-box);\n    }\n\n    html,\n    body {\n      font-size: $base-font-size;\n    }\n\n    // Default body styles\n    body {\n      background: $body-bg;\n      color: $body-font-color;\n      padding: 0;\n      margin: 0;\n      font-family: $body-font-family;\n      font-weight: $body-font-weight;\n      font-style: $body-font-style;\n      line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n      position: relative;\n      cursor: $cursor-auto-value;\n    }\n\n    a:hover {\n      cursor: $cursor-pointer-value;\n    }\n\n    // Grid Defaults to get images and embeds to work properly\n    img {\n      max-width: 100%;\n      height: auto;\n    }\n\n    img {\n      -ms-interpolation-mode: bicubic;\n    }\n\n    #map_canvas,\n    .map_canvas {\n\n      img,\n      embed,\n      object {\n        max-width: none !important;\n      }\n    }\n\n    // Miscellaneous useful HTML classes\n    .left {\n      float: left !important;\n    }\n\n    .right {\n      float: right !important;\n    }\n\n    .clearfix {\n      @include clearfix;\n    }\n\n    // Hide visually and from screen readers\n    .hide {\n      display: none !important;\n      visibility: hidden;\n    }\n\n    // Hide visually and from screen readers, but maintain layout\n    .invisible {\n      visibility: hidden;\n    }\n\n    // Font smoothing\n    // Antialiased font smoothing works best for light text on a dark background.\n    // Apply to single elements instead of globally to body.\n    // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n    .antialiased {\n      -webkit-font-smoothing: antialiased;\n      -moz-osx-font-smoothing: grayscale;\n    }\n\n    // Get rid of gap under images by making them display: inline-block; by default\n    img {\n      display: inline-block;\n      vertical-align: middle;\n    }\n\n    //\n    // Global resets for forms\n    //\n\n    // Make sure textarea takes on height automatically\n    textarea {\n      height: auto;\n      min-height: 50px;\n    }\n\n    // Make select elements 100% width by default\n    select {\n      width: 100%;\n    }\n  }\n}","@charset \"utf-8\";\n\n@import \"functions.scss\";\n\n$include-html-classes: false;\n@import \"01_settings_colors.scss\";\n@import \"02_settings_typography.scss\";\n@import \"03_settings_mixins_media_queries.scss\";\n@import \"04_settings_global.scss\";\n\n* {\n\tdisplay: block;\n}\n\n:root {\n\tmargin: 3em;\n\tbackground: $body-bg;\n\tcolor: $body-font-color;\n\tfont-family: $body-font-family;\n}\n\nchannel {\n\t> title {\n\t\ttext-align: center;\n\t\tcolor: lighten($primary-color, 25%);\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1 * 1.5;\n\t\tfont-weight: bolder;\n\t\t&::before {\n\t\t\tcontent: 'RSS Feed for ';\n\t\t\tfont-weight: initial;\n\t\t}\n\t\t&::after {\n\t\t\tcontent: \"This RSS feed is meant to be used by RSS reader applications and websites.\";\n\t\t\tdisplay: block;\n\t\t\tpadding: 1em;\n\t\t\tbackground-color: $alert-color;\n\t\t\tcolor: #fff;\n\t\t\tfont-family: initial;\n\t\t\tfont-size: initial;\n\t\t\tletter-spacing: initial;\n\t\t}\n\t}\n\t\n\t> description,\n\t> link {\n\t\tdisplay: none;\n\t}\n}\n\nitem {\n\tpadding: 1em 0;\n\tborder-bottom: 1px solid invert($body-bg);\n\t&:last-child {\n\t\tborder-bottom: none;\n\t}\n\n\t> title {\n\t\tcolor: $secondary-color;\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1;\n\t\tmargin-bottom: 0.5em;\n\t}\n\n\t> link {\n\t\tcolor: $primary-color;\n\t}\n\n\t> pubDate {\n\t\tcolor: $grey-5;\n\t\tfont-size: small;\n\t}\n\n\t> description {\n\t\tmargin-top: 1em;\n\t\toverflow: hidden;\n\t\twhite-space: nowrap;\n\t\ttext-overflow:ellipsis;\n\t}\n\n\t> guid,\n\t> category {\n\t\tdisplay: none;\n\t}\n}\n","/// from https://github.com/Phlow/feeling-responsive/raw/gh-pages/_sass/_01_settings_colors.scss\n@charset \"utf-8\";\n/* TOC – Color Variables\n\n- Basics\n- Corporate Identity Colorpalette\n- Foundation Color Variables\n- Grey Scale\n- Topbar-Navigation\n- Footer\n- Code\n\n*/\n\n\n\n/* Basics\n------------------------------------------------------------------- */\n\n$text-color         : #111;\n$body-font-color    : $text-color;\n$body-bg            : #fdfdfd;\n\n\n\n/* Corporate Identity Colorpalette\n   https://color.adobe.com/de/Flat-Design-Colors-v2-color-theme-4341903/\n------------------------------------------------------------------- */\n\n$ci-1               : #334D5C; // dark turquoise\n$ci-2               : #45B29D; // turquoise\n$ci-3               : #EFC94C; // yellow\n$ci-4               : #E27A3F; // orange\n$ci-5               : #DF4949; // red\n$ci-6               : #A1D044; // green\n\n/// CIL overrides\n$ci-2               : #c92c99;\n$ci-6               : #e50695;\n\n\n/* Foundation Color Variables\n------------------------------------------------------------------- */\n\n$primary-color      : $ci-1;\n$secondary-color    : $ci-6;\n$alert-color        : $ci-5;\n$success-color      : $ci-6;\n$warning-color      : $ci-4;\n$info-color         : $ci-1;\n\n\n\n/* Grey Scale\n------------------------------------------------------------------- */\n\n$grey-1             : #E4E4E4;\n$grey-2             : #D7D7D7;\n$grey-3             : #CBCBCB;\n$grey-4             : #BEBEBE;\n$grey-5             : #A4A4A4;\n$grey-6             : #979797;\n$grey-7             : #8B8B8B;\n$grey-8             : #7E7E7E;\n$grey-9             : #646464;\n$grey-10            : #575757;\n$grey-11            : #4B4B4B;\n$grey-12            : #3E3E3E;\n$grey-13            : #313131;\n$grey-14            : #242424;\n$grey-15            : #171717;\n$grey-16            : #0B0B0B;\n\n/// CIL overrides\n$grey-8             : #043852;\n$grey-13            : #510c76;\n\n\n/* Topbar-Navigation\n------------------------------------------------------------------- */\n\n$topbar-bg-color    : $body-bg;\n$topbar-bg          : $topbar-bg-color;\n\n\n$topbar-dropdown-toggle-color: $ci-1;\n\n$topbar-link-color  : #000;\n$topbar-link-color-hover: #000;\n$topbar-link-color-active: #000;\n$topbar-link-color-active-hover: #000;\n\n$topbar-dropdown-label-color: $ci-2;\n$topbar-dropdown-link-bg-hover: $ci-6;\n\n$topbar-link-bg-active: $ci-6; // Active Navigation Link\n$topbar-link-bg-hover: $ci-6;\n$topbar-link-bg-active-hover: $ci-2;\n\n\n$topbar-dropdown-bg: $ci-6; // Background Mobile Navigation\n$topbar-dropdown-link-color: #000;\n$topbar-dropdown-link-bg: $ci-2;\n\n$topbar-menu-link-color-toggled: $ci-1;\n$topbar-menu-icon-color-toggled: $ci-1;\n$topbar-menu-link-color: #000;\n$topbar-menu-icon-color: #000;\n$topbar-menu-link-color-toggled: $ci-6;\n$topbar-menu-icon-color-toggled: $ci-6;\n\n\n\n/* Footer\n------------------------------------------------------------------- */\n\n$footer-bg          : $grey-8;\n$footer-color       : #fff;\n$footer-link-color  : $ci-6;\n\n\n$subfooter-bg       : $grey-13;\n$subfooter-color    : $grey-8;\n$subfooter-link-color: $grey-8;\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\n$code-background-color: scale-color($secondary-color, $lightness: 70%);\n\n$highlight-background: #ffffff;\n$highlight-comment: #999988;\n$highlight-error: #a61717;\n$highlight-comment-special: #999999;\n$highlight-deleted: #000000;\n$highlight-error-2: #aa0000;\n$highlight-literal-string: #d14;\n$highlight-literal-number: #009999;\n$highlight-name-attribut: #008080;\n$highlight-error-background: #e3d2d2;\n$highlight-generic-deleted: #ffdddd;\n$highlight-generic-deleted-specific: #ffaaaa;\n$highlight-generic-inserted: #ddffdd;\n$highlight-generic-inserted-specific: #aaffaa;\n$highlight-generic-output: #888888;\n$highlight-generic-prompt: #555555;\n$highlight-subheading: #aaaaaa;\n$highlight-keyword-type: #445588;\n$highlight-name-builtin: #0086B3;\n$highlight-name-class: #445588;\n$highlight-name-entity: #800080;\n$highlight-name-exception: #990000;\n$highlight-name-function: #990000;\n$highlight-name-namespace: #555555;\n$highlight-name-tag: #000080;\n$highlight-text-whitespace: #bbbbbb;\n$highlight-literal-string-regex: #009926;\n$highlight-literal-string-symbol: #990073;\n"],"file":"rss.css"}
\ No newline at end of file
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// Allows the use of rem-calc() or lower-bound() in your settings\n\n\n\n/* Fonts\n------------------------------------------------------------------- */\n\n$base-font-size: 16px;\n$rem-base: $base-font-size;\n// $base-line-height is 24px while $base-font-size is 16px\n$base-line-height: 1.5 !default;\n\n\n$font-family-sans-serif: \"Lato\", \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n$font-family-serif: \"Volkhov\", Georgia, Times, serif;\n$font-family-monospace: \"Lucida Console\", Monaco, monospace;\n\n$body-font-family: $font-family-sans-serif;\n$body-font-weight: normal;\n$body-font-style: normal;\n\n$header-font-family: $font-family-serif;\n\n\n\n/* Font Weight\n------------------------------------------------------------------- */\n\n$font-weight-normal: normal;\n$font-weight-bold: bold;\n\n\n\n/* Font Size Variables\n------------------------------------------------------------------- */\n\n$font-size-p:    \t$base-font-size;\n$font-size-h1:      2.441em;\n$font-size-h2:      1.953em;\n$font-size-h3:      1.563em;\n$font-size-h4:      1.25em;\n$font-size-h5:      1.152em;\n$font-size-small:   0.8em;\n\n.font-size-h1   { font-size: $font-size-h1; }\n.font-size-h2   { font-size: $font-size-h2; }\n.font-size-h3   { font-size: $font-size-h3; }\n.font-size-h4   { font-size: $font-size-h4; }\n.font-size-h5   { font-size: $font-size-h5; }\n","@charset \"utf-8\";\n// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n//\n// Foundation Variables\n//\n\n// Data attribute namespace\n// styles get applied to [data-mysite-plugin], etc\n$namespace: false !default;\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n    -webkit-#{$side}-left-radius: $radius;\n    -webkit-#{$side}-right-radius: $radius;\n    border-#{$side}-left-radius: $radius;\n    border-#{$side}-right-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We can control whether or not we have inset shadows edges.\n// $active - Default: true, Options: false\n@mixin inset-shadow($active: true) {\n  box-shadow: $shiny-edge-size $shiny-edge-color inset;\n\n  @if $active {\n    &:active {\n      box-shadow: $shiny-edge-size $shiny-edge-active-color inset;\n    }\n  }\n}\n\n// @mixins\n//\n// We use this to add transitions to elements\n// $property - Default: all, Options: http://www.w3.org/TR/css3-transitions/#animatable-properties\n// $speed - Default: 300ms\n// $ease - Default:ease-out, Options: http://css-tricks.com/almanac/properties/t/transition-timing-function/\n@mixin single-transition($property: all, $speed: 300ms, $ease: ease-out) {\n  transition: $property $speed $ease;\n}\n\n// @mixins\n//\n// We use this to add box-sizing across browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -($width/2);\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n\n\n\n// d. Media Query Ranges\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n$small-range: (\n  0em,\n  40em\n);\n$medium-range: (\n  40.063em,\n  64em\n);\n$large-range: (\n  64.063em,\n  90em\n);\n$xlarge-range: (\n  90.063em,\n  120em\n);\n$xxlarge-range: (\n  120.063em,\n  99999999em\n);\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-small-only {\n    font-family: \"/\" + unquote($small-only) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-medium {\n    font-family: \"/\" + unquote($medium-up) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-medium-only {\n    font-family: \"/\" + unquote($medium-only) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-large {\n    font-family: \"/\" + unquote($large-up) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-large-only {\n    font-family: \"/\" + unquote($large-only) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-xlarge {\n    font-family: \"/\" + unquote($xlarge-up) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xlarge-only {\n    font-family: \"/\" + unquote($xlarge-only) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xxlarge {\n    font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n    width: lower-bound($xxlarge-range);\n  }\n\n  meta.foundation-data-attribute-namespace {\n    font-family: #{$namespace};\n  }\n\n  @if $include-html-global-classes {\n\n    // Must be 100% for off canvas to work\n    html,\n    body {\n      height: 100%;\n    }\n\n    // Set box-sizing globally to handle padding and border widths\n    *,\n    *:before,\n    *:after {\n      @include box-sizing(border-box);\n    }\n\n    html,\n    body {\n      font-size: $base-font-size;\n    }\n\n    // Default body styles\n    body {\n      background: $body-bg;\n      color: $body-font-color;\n      padding: 0;\n      margin: 0;\n      font-family: $body-font-family;\n      font-weight: $body-font-weight;\n      font-style: $body-font-style;\n      line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n      position: relative;\n      cursor: $cursor-auto-value;\n    }\n\n    a:hover {\n      cursor: $cursor-pointer-value;\n    }\n\n    // Grid Defaults to get images and embeds to work properly\n    img {\n      max-width: 100%;\n      height: auto;\n    }\n\n    img {\n      -ms-interpolation-mode: bicubic;\n    }\n\n    #map_canvas,\n    .map_canvas {\n\n      img,\n      embed,\n      object {\n        max-width: none !important;\n      }\n    }\n\n    // Miscellaneous useful HTML classes\n    .left {\n      float: left !important;\n    }\n\n    .right {\n      float: right !important;\n    }\n\n    .clearfix {\n      @include clearfix;\n    }\n\n    // Hide visually and from screen readers\n    .hide {\n      display: none !important;\n      visibility: hidden;\n    }\n\n    // Hide visually and from screen readers, but maintain layout\n    .invisible {\n      visibility: hidden;\n    }\n\n    // Font smoothing\n    // Antialiased font smoothing works best for light text on a dark background.\n    // Apply to single elements instead of globally to body.\n    // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n    .antialiased {\n      -webkit-font-smoothing: antialiased;\n      -moz-osx-font-smoothing: grayscale;\n    }\n\n    // Get rid of gap under images by making them display: inline-block; by default\n    img {\n      display: inline-block;\n      vertical-align: middle;\n    }\n\n    //\n    // Global resets for forms\n    //\n\n    // Make sure textarea takes on height automatically\n    textarea {\n      height: auto;\n      min-height: 50px;\n    }\n\n    // Make select elements 100% width by default\n    select {\n      width: 100%;\n    }\n  }\n}","@charset \"utf-8\";\n\n@import \"functions.scss\";\n\n$include-html-classes: false;\n@import \"01_settings_colors.scss\";\n@import \"02_settings_typography.scss\";\n@import \"03_settings_mixins_media_queries.scss\";\n@import \"04_settings_global.scss\";\n\n* {\n\tdisplay: block;\n}\n\n:root {\n\tmargin: 3em;\n\tbackground: $body-bg;\n\tcolor: $body-font-color;\n\tfont-family: $body-font-family;\n}\n\nchannel {\n\t> title {\n\t\ttext-align: center;\n\t\tcolor: lighten($primary-color, 25%);\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1 * 1.5;\n\t\tfont-weight: bolder;\n\t\t&::before {\n\t\t\tcontent: 'RSS Feed for ';\n\t\t\tfont-weight: initial;\n\t\t}\n\t\t&::after {\n\t\t\tcontent: \"This RSS feed is meant to be used by RSS reader applications and websites.\";\n\t\t\tdisplay: block;\n\t\t\tpadding: 1em;\n\t\t\tbackground-color: $alert-color;\n\t\t\tcolor: #fff;\n\t\t\tfont-family: initial;\n\t\t\tfont-size: initial;\n\t\t\tletter-spacing: initial;\n\t\t}\n\t}\n\t\n\t> description,\n\t> link {\n\t\tdisplay: none;\n\t}\n}\n\nitem {\n\tpadding: 1em 0;\n\tborder-bottom: 1px solid invert($body-bg);\n\t&:last-child {\n\t\tborder-bottom: none;\n\t}\n\n\t> title {\n\t\tcolor: $secondary-color;\n\t\tfont-family: $header-font-family;\n\t\tfont-size: $font-size-h1;\n\t\tmargin-bottom: 0.5em;\n\t}\n\n\t> link {\n\t\tcolor: $primary-color;\n\t}\n\n\t> pubDate {\n\t\tcolor: $grey-5;\n\t\tfont-size: small;\n\t}\n\n\t> description {\n\t\tmargin-top: 1em;\n\t\toverflow: hidden;\n\t\twhite-space: nowrap;\n\t\ttext-overflow:ellipsis;\n\t}\n\n\t> guid,\n\t> category {\n\t\tdisplay: none;\n\t}\n}\n","/// from https://github.com/Phlow/feeling-responsive/raw/gh-pages/_sass/_01_settings_colors.scss\n@charset \"utf-8\";\n/* TOC – Color Variables\n\n- Basics\n- Corporate Identity Colorpalette\n- Foundation Color Variables\n- Grey Scale\n- Topbar-Navigation\n- Footer\n- Code\n\n*/\n\n\n\n/* Basics\n------------------------------------------------------------------- */\n\n$text-color         : #111;\n$body-font-color    : $text-color;\n$body-bg            : #fdfdfd;\n\n\n\n/* Corporate Identity Colorpalette\n   https://color.adobe.com/de/Flat-Design-Colors-v2-color-theme-4341903/\n------------------------------------------------------------------- */\n\n$ci-1               : #334D5C; // dark turquoise\n$ci-2               : #45B29D; // turquoise\n$ci-3               : #EFC94C; // yellow\n$ci-4               : #E27A3F; // orange\n$ci-5               : #DF4949; // red\n$ci-6               : #A1D044; // green\n\n/// CIL overrides\n$ci-2               : #c92c99;\n$ci-6               : #e50695;\n\n\n/* Foundation Color Variables\n------------------------------------------------------------------- */\n\n$primary-color      : $ci-1;\n$secondary-color    : $ci-6;\n$alert-color        : $ci-5;\n$success-color      : $ci-6;\n$warning-color      : $ci-4;\n$info-color         : $ci-1;\n\n\n\n/* Grey Scale\n------------------------------------------------------------------- */\n\n$grey-1             : #E4E4E4;\n$grey-2             : #D7D7D7;\n$grey-3             : #CBCBCB;\n$grey-4             : #BEBEBE;\n$grey-5             : #A4A4A4;\n$grey-6             : #979797;\n$grey-7             : #8B8B8B;\n$grey-8             : #7E7E7E;\n$grey-9             : #646464;\n$grey-10            : #575757;\n$grey-11            : #4B4B4B;\n$grey-12            : #3E3E3E;\n$grey-13            : #313131;\n$grey-14            : #242424;\n$grey-15            : #171717;\n$grey-16            : #0B0B0B;\n\n/// CIL overrides\n$grey-8             : #043852;\n$grey-13            : #510c76;\n\n\n/* Topbar-Navigation\n------------------------------------------------------------------- */\n\n$topbar-bg-color    : $body-bg;\n$topbar-bg          : $topbar-bg-color;\n\n\n$topbar-dropdown-toggle-color: $ci-1;\n\n$topbar-link-color  : #000;\n$topbar-link-color-hover: #000;\n$topbar-link-color-active: #000;\n$topbar-link-color-active-hover: #000;\n\n$topbar-dropdown-label-color: $ci-2;\n$topbar-dropdown-link-bg-hover: $ci-6;\n\n$topbar-link-bg-active: $ci-6; // Active Navigation Link\n$topbar-link-bg-hover: $ci-6;\n$topbar-link-bg-active-hover: $ci-2;\n\n\n$topbar-dropdown-bg: $ci-6; // Background Mobile Navigation\n$topbar-dropdown-link-color: #000;\n$topbar-dropdown-link-bg: $ci-2;\n\n$topbar-menu-link-color-toggled: $ci-1;\n$topbar-menu-icon-color-toggled: $ci-1;\n$topbar-menu-link-color: #000;\n$topbar-menu-icon-color: #000;\n$topbar-menu-link-color-toggled: $ci-6;\n$topbar-menu-icon-color-toggled: $ci-6;\n\n\n\n/* Footer\n------------------------------------------------------------------- */\n\n$footer-bg          : $grey-8;\n$footer-color       : #fff;\n$footer-link-color  : $ci-6;\n\n\n$subfooter-bg       : $grey-13;\n$subfooter-color    : $grey-8;\n$subfooter-link-color: $grey-8;\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\n$code-background-color: scale-color($secondary-color, $lightness: 70%);\n\n$highlight-background: #ffffff;\n$highlight-comment: #999988;\n$highlight-error: #a61717;\n$highlight-comment-special: #999999;\n$highlight-deleted: #000000;\n$highlight-error-2: #aa0000;\n$highlight-literal-string: #d14;\n$highlight-literal-number: #009999;\n$highlight-name-attribut: #008080;\n$highlight-error-background: #e3d2d2;\n$highlight-generic-deleted: #ffdddd;\n$highlight-generic-deleted-specific: #ffaaaa;\n$highlight-generic-inserted: #ddffdd;\n$highlight-generic-inserted-specific: #aaffaa;\n$highlight-generic-output: #888888;\n$highlight-generic-prompt: #555555;\n$highlight-subheading: #aaaaaa;\n$highlight-keyword-type: #445588;\n$highlight-name-builtin: #0086B3;\n$highlight-name-class: #445588;\n$highlight-name-entity: #800080;\n$highlight-name-exception: #990000;\n$highlight-name-function: #990000;\n$highlight-name-namespace: #555555;\n$highlight-name-tag: #000080;\n$highlight-text-whitespace: #bbbbbb;\n$highlight-literal-string-regex: #009926;\n$highlight-literal-string-symbol: #990073;\n"],"file":"rss.css"}
\ No newline at end of file
diff --git a/assets/css/styles_feeling_responsive.css.map b/assets/css/styles_feeling_responsive.css.map
index 14ee78c95a..294fc47365 100644
--- a/assets/css/styles_feeling_responsive.css.map
+++ b/assets/css/styles_feeling_responsive.css.map
@@ -1 +1 @@
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defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n  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browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -($width/2);\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n\n\n\n// d. Media Query Ranges\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n$small-range: (\n  0em,\n  40em\n);\n$medium-range: (\n  40.063em,\n  64em\n);\n$large-range: (\n  64.063em,\n  90em\n);\n$xlarge-range: (\n  90.063em,\n  120em\n);\n$xxlarge-range: (\n  120.063em,\n  99999999em\n);\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-small-only {\n    font-family: \"/\" + unquote($small-only) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-medium {\n    font-family: \"/\" + unquote($medium-up) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-medium-only {\n    font-family: \"/\" + unquote($medium-only) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-large {\n    font-family: \"/\" + unquote($large-up) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-large-only {\n    font-family: \"/\" + unquote($large-only) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-xlarge {\n    font-family: \"/\" + unquote($xlarge-up) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xlarge-only {\n    font-family: \"/\" + unquote($xlarge-only) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xxlarge {\n    font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n    width: lower-bound($xxlarge-range);\n  }\n\n  meta.foundation-data-attribute-namespace {\n    font-family: #{$namespace};\n  }\n\n  @if $include-html-global-classes {\n\n    // Must be 100% for off canvas to work\n    html,\n    body {\n      height: 100%;\n    }\n\n    // Set box-sizing globally to handle padding and border widths\n    *,\n    *:before,\n    *:after {\n      @include box-sizing(border-box);\n    }\n\n    html,\n    body {\n      font-size: $base-font-size;\n    }\n\n    // Default body styles\n    body {\n      background: $body-bg;\n      color: $body-font-color;\n      padding: 0;\n      margin: 0;\n      font-family: $body-font-family;\n      font-weight: $body-font-weight;\n      font-style: $body-font-style;\n      line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n      position: relative;\n      cursor: $cursor-auto-value;\n    }\n\n    a:hover {\n      cursor: $cursor-pointer-value;\n    }\n\n    // Grid Defaults to get images and embeds to work properly\n    img {\n      max-width: 100%;\n      height: auto;\n    }\n\n    img {\n      -ms-interpolation-mode: bicubic;\n    }\n\n    #map_canvas,\n    .map_canvas {\n\n      img,\n      embed,\n      object {\n        max-width: none !important;\n      }\n    }\n\n    // Miscellaneous useful HTML classes\n    .left {\n      float: left !important;\n    }\n\n    .right {\n      float: right !important;\n    }\n\n    .clearfix {\n      @include clearfix;\n    }\n\n    // Hide visually and from screen readers\n    .hide {\n      display: none !important;\n      visibility: hidden;\n    }\n\n    // Hide visually and from screen readers, but maintain layout\n    .invisible {\n      visibility: hidden;\n    }\n\n    // Font smoothing\n    // Antialiased font smoothing works best for light text on a dark background.\n    // Apply to single elements instead of globally to body.\n    // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n    .antialiased {\n      -webkit-font-smoothing: antialiased;\n      -moz-osx-font-smoothing: grayscale;\n    }\n\n    // Get rid of gap under images by making them display: inline-block; by default\n    img {\n      display: inline-block;\n      vertical-align: middle;\n    }\n\n    //\n    // Global resets for forms\n    //\n\n    // Make sure textarea takes on height automatically\n    textarea {\n      height: auto;\n      min-height: 50px;\n    }\n\n    // Make select elements 100% width by default\n    select {\n      width: 100%;\n    }\n  }\n}","/// from https://github.com/Phlow/feeling-responsive/raw/gh-pages/_sass/_01_settings_colors.scss\n@charset \"utf-8\";\n/* TOC – Color Variables\n\n- Basics\n- Corporate Identity Colorpalette\n- Foundation Color Variables\n- Grey Scale\n- Topbar-Navigation\n- Footer\n- Code\n\n*/\n\n\n\n/* Basics\n------------------------------------------------------------------- */\n\n$text-color         : #111;\n$body-font-color    : $text-color;\n$body-bg            : #fdfdfd;\n\n\n\n/* Corporate Identity Colorpalette\n   https://color.adobe.com/de/Flat-Design-Colors-v2-color-theme-4341903/\n------------------------------------------------------------------- */\n\n$ci-1               : #334D5C; // dark turquoise\n$ci-2               : #45B29D; // turquoise\n$ci-3               : #EFC94C; // yellow\n$ci-4               : #E27A3F; // orange\n$ci-5               : #DF4949; // red\n$ci-6               : #A1D044; // green\n\n/// CIL overrides\n$ci-2               : #c92c99;\n$ci-6               : #e50695;\n\n\n/* Foundation Color Variables\n------------------------------------------------------------------- */\n\n$primary-color      : $ci-1;\n$secondary-color    : $ci-6;\n$alert-color        : $ci-5;\n$success-color      : $ci-6;\n$warning-color      : $ci-4;\n$info-color         : $ci-1;\n\n\n\n/* Grey Scale\n------------------------------------------------------------------- */\n\n$grey-1             : #E4E4E4;\n$grey-2             : #D7D7D7;\n$grey-3             : #CBCBCB;\n$grey-4             : #BEBEBE;\n$grey-5             : #A4A4A4;\n$grey-6             : #979797;\n$grey-7             : #8B8B8B;\n$grey-8             : #7E7E7E;\n$grey-9             : #646464;\n$grey-10            : #575757;\n$grey-11            : #4B4B4B;\n$grey-12            : #3E3E3E;\n$grey-13            : #313131;\n$grey-14            : #242424;\n$grey-15            : #171717;\n$grey-16            : #0B0B0B;\n\n/// CIL overrides\n$grey-8             : #043852;\n$grey-13            : #510c76;\n\n\n/* Topbar-Navigation\n------------------------------------------------------------------- */\n\n$topbar-bg-color    : $body-bg;\n$topbar-bg          : $topbar-bg-color;\n\n\n$topbar-dropdown-toggle-color: $ci-1;\n\n$topbar-link-color  : #000;\n$topbar-link-color-hover: #000;\n$topbar-link-color-active: #000;\n$topbar-link-color-active-hover: #000;\n\n$topbar-dropdown-label-color: $ci-2;\n$topbar-dropdown-link-bg-hover: $ci-6;\n\n$topbar-link-bg-active: $ci-6; // Active Navigation Link\n$topbar-link-bg-hover: $ci-6;\n$topbar-link-bg-active-hover: $ci-2;\n\n\n$topbar-dropdown-bg: $ci-6; // Background Mobile Navigation\n$topbar-dropdown-link-color: #000;\n$topbar-dropdown-link-bg: $ci-2;\n\n$topbar-menu-link-color-toggled: $ci-1;\n$topbar-menu-icon-color-toggled: $ci-1;\n$topbar-menu-link-color: #000;\n$topbar-menu-icon-color: #000;\n$topbar-menu-link-color-toggled: $ci-6;\n$topbar-menu-icon-color-toggled: $ci-6;\n\n\n\n/* Footer\n------------------------------------------------------------------- */\n\n$footer-bg          : $grey-8;\n$footer-color       : #fff;\n$footer-link-color  : $ci-6;\n\n\n$subfooter-bg       : $grey-13;\n$subfooter-color    : $grey-8;\n$subfooter-link-color: $grey-8;\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\n$code-background-color: scale-color($secondary-color, $lightness: 70%);\n\n$highlight-background: #ffffff;\n$highlight-comment: #999988;\n$highlight-error: #a61717;\n$highlight-comment-special: #999999;\n$highlight-deleted: #000000;\n$highlight-error-2: #aa0000;\n$highlight-literal-string: #d14;\n$highlight-literal-number: #009999;\n$highlight-name-attribut: #008080;\n$highlight-error-background: #e3d2d2;\n$highlight-generic-deleted: #ffdddd;\n$highlight-generic-deleted-specific: #ffaaaa;\n$highlight-generic-inserted: #ddffdd;\n$highlight-generic-inserted-specific: #aaffaa;\n$highlight-generic-output: #888888;\n$highlight-generic-prompt: #555555;\n$highlight-subheading: #aaaaaa;\n$highlight-keyword-type: #445588;\n$highlight-name-builtin: #0086B3;\n$highlight-name-class: #445588;\n$highlight-name-entity: #800080;\n$highlight-name-exception: #990000;\n$highlight-name-function: #990000;\n$highlight-name-namespace: #555555;\n$highlight-name-tag: #000080;\n$highlight-text-whitespace: #bbbbbb;\n$highlight-literal-string-regex: #009926;\n$highlight-literal-string-symbol: #990073;\n","@charset \"utf-8\";\n/*! normalize.css v3.0.2 | MIT License | git.io/normalize */\n\n/**\n * 1. Set default font family to sans-serif.\n * 2. Prevent iOS text size adjust after orientation change, without disabling\n *    user zoom.\n */\n\nhtml {\n  font-family: sans-serif; /* 1 */\n  -ms-text-size-adjust: 100%; /* 2 */\n  -webkit-text-size-adjust: 100%; /* 2 */\n}\n\n/**\n * Remove default margin.\n */\n\nbody {\n  margin: 0;\n}\n\n/* HTML5 display definitions\n   ========================================================================== */\n\n/**\n * Correct `block` display not defined for any HTML5 element in IE 8/9.\n * Correct `block` display not defined for `details` or `summary` in IE 10/11\n * and Firefox.\n * Correct `block` display not defined for `main` in IE 11.\n */\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\n\n/**\n * 1. Correct `inline-block` display not defined in IE 8/9.\n * 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n */\n\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block; /* 1 */\n  vertical-align: baseline; /* 2 */\n}\n\n/**\n * Prevent modern browsers from displaying `audio` without controls.\n * Remove excess height in iOS 5 devices.\n */\n\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n\n/**\n * Address `[hidden]` styling not present in IE 8/9/10.\n * Hide the `template` element in IE 8/9/11, Safari, and Firefox < 22.\n */\n\n[hidden],\ntemplate {\n  display: none;\n}\n\n/* Links\n   ========================================================================== */\n\n/**\n * Remove the gray background color from active links in IE 10.\n */\n\na {\n  background-color: transparent;\n}\n\n/**\n * Improve readability when focused and also mouse hovered in all browsers.\n */\n\na:active,\na:hover {\n  outline: 0;\n}\n\n/* Text-level semantics\n   ========================================================================== */\n\n/**\n * Address styling not present in IE 8/9/10/11, Safari, and Chrome.\n */\n\nabbr[title] {\n  border-bottom: 1px dotted;\n}\n\n/**\n * Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n */\n\nb,\nstrong {\n  font-weight: bold;\n}\n\n/**\n * Address styling not present in Safari and Chrome.\n */\n\ndfn {\n  font-style: italic;\n}\n\n/**\n * Address variable `h1` font-size and margin within `section` and `article`\n * contexts in Firefox 4+, Safari, and Chrome.\n */\n\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\n\n/**\n * Address styling not present in IE 8/9.\n */\n\nmark {\n  background: #ff0;\n  color: #000;\n}\n\n/**\n * Address inconsistent and variable font size in all browsers.\n */\n\nsmall {\n  font-size: 80%;\n}\n\n/**\n * Prevent `sub` and `sup` affecting `line-height` in all browsers.\n */\n\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\n\nsup {\n  top: -0.5em;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\n/* Embedded content\n   ========================================================================== */\n\n/**\n * Remove border when inside `a` element in IE 8/9/10.\n */\n\nimg {\n  border: 0;\n}\n\n/**\n * Correct overflow not hidden in IE 9/10/11.\n */\n\nsvg:not(:root) {\n  overflow: hidden;\n}\n\n/* Grouping content\n   ========================================================================== */\n\n/**\n * Address margin not present in IE 8/9 and Safari.\n */\n\nfigure {\n  margin: 1em 40px;\n}\n\n/**\n * Address differences between Firefox and other browsers.\n */\n\nhr {\n  -moz-box-sizing: content-box;\n  box-sizing: content-box;\n  height: 0;\n}\n\n/**\n * Contain overflow in all browsers.\n */\n\npre {\n  overflow: auto;\n}\n\n/**\n * Address odd `em`-unit font size rendering in all browsers.\n */\n\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\n\n/* Forms\n   ========================================================================== */\n\n/**\n * Known limitation: by default, Chrome and Safari on OS X allow very limited\n * styling of `select`, unless a `border` property is set.\n */\n\n/**\n * 1. Correct color not being inherited.\n *    Known issue: affects color of disabled elements.\n * 2. Correct font properties not being inherited.\n * 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n */\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit; /* 1 */\n  font: inherit; /* 2 */\n  margin: 0; /* 3 */\n}\n\n/**\n * Address `overflow` set to `hidden` in IE 8/9/10/11.\n */\n\nbutton {\n  overflow: visible;\n}\n\n/**\n * Address inconsistent `text-transform` inheritance for `button` and `select`.\n * All other form control elements do not inherit `text-transform` values.\n * Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n * Correct `select` style inheritance in Firefox.\n */\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n/**\n * 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n *    and `video` controls.\n * 2. Correct inability to style clickable `input` types in iOS.\n * 3. Improve usability and consistency of cursor style between image-type\n *    `input` and others.\n */\n\nbutton,\nhtml input[type=\"button\"], /* 1 */\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button; /* 2 */\n  cursor: pointer; /* 3 */\n}\n\n/**\n * Re-set default cursor for disabled elements.\n */\n\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\n\n/**\n * Remove inner padding and border in Firefox 4+.\n */\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\n\n/**\n * Address Firefox 4+ setting `line-height` on `input` using `!important` in\n * the UA stylesheet.\n */\n\ninput {\n  line-height: normal;\n}\n\n/**\n * It's recommended that you don't attempt to style these elements.\n * Firefox's implementation doesn't respect box-sizing, padding, or width.\n *\n * 1. Address box sizing set to `content-box` in IE 8/9/10.\n * 2. Remove excess padding in IE 8/9/10.\n */\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Fix the cursor style for Chrome's increment/decrement buttons. For certain\n * `font-size` values of the `input`, it causes the cursor style of the\n * decrement button to change from `default` to `text`.\n */\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n * 2. Address `box-sizing` set to `border-box` in Safari and Chrome\n *    (include `-moz` to future-proof).\n */\n\ninput[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  -moz-box-sizing: content-box;\n  -webkit-box-sizing: content-box; /* 2 */\n  box-sizing: content-box;\n}\n\n/**\n * Remove inner padding and search cancel button in Safari and Chrome on OS X.\n * Safari (but not Chrome) clips the cancel button when the search input has\n * padding (and `textfield` appearance).\n */\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * Define consistent border, margin, and padding.\n */\n\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\n\n/**\n * 1. Correct `color` not being inherited in IE 8/9/10/11.\n * 2. Remove padding so people aren't caught out if they zero out fieldsets.\n */\n\nlegend {\n  border: 0; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Remove default vertical scrollbar in IE 8/9/10/11.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * Don't inherit the `font-weight` (applied by a rule above).\n * NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n */\n\noptgroup {\n  font-weight: bold;\n}\n\n/* Tables\n   ========================================================================== */\n\n/**\n * Remove most spacing between table cells.\n */\n\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\n\ntd,\nth {\n  padding: 0;\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-grid-classes: $include-html-classes !default;\n$include-xl-html-grid-classes: false !default;\n\n$row-width: rem-calc(1000) !default;\n$total-columns: 12 !default;\n\n$last-child-float: $opposite-direction !default;\n\n//\n// Grid Functions\n//\n\n// Deprecated: We'll drop support for this in 5.1, use grid-calc()\n@function gridCalc($colNumber, $totalColumns) {\n  @warn \"gridCalc() is deprecated, use grid-calc()\";\n  @return grid-calc($colNumber, $totalColumns);\n}\n\n// @FUNCTION\n// $colNumber - Found in settings file\n// $totalColumns - Found in settings file\n@function grid-calc($colNumber, $totalColumns) {\n  @return percentage(calc($colNumber / $totalColumns));\n}\n\n//\n// @mixins\n//\n\n// For creating container, nested, and collapsed rows.\n//\n//\n// $behavior - Any special behavior for this row? Default: false. Options: nest, collapse, nest-collapse, false.\n@mixin grid-row($behavior: false) {\n\n  // use @include grid-row(nest); to include a nested row\n  @if $behavior ==nest {\n    width: auto;\n    margin-#{$default-float}: - calc($column-gutter/2);\n    margin-#{$opposite-direction}: - calc($column-gutter/2);\n    margin-top: 0;\n    margin-bottom: 0;\n    max-width: none;\n  }\n\n  // use @include grid-row(collapse); to collapsed a container row margins\n  @else if $behavior ==collapse {\n    width: 100%;\n    margin: 0;\n    max-width: $row-width;\n  }\n\n  // use @include grid-row(nest-collapse); to collapse outer margins on a nested row\n  @else if $behavior ==nest-collapse {\n    width: auto;\n    margin: 0;\n    max-width: none;\n  }\n\n  // use @include grid-row; to use a container row\n  @else {\n    width: 100%;\n    margin-#{$default-float}: auto;\n    margin-#{$opposite-direction}: auto;\n    margin-top: 0;\n    margin-bottom: 0;\n    max-width: $row-width;\n  }\n\n  // Clearfix for all rows\n  @include clearfix();\n}\n\n// Creates a column, should be used inside of a media query to control layouts\n//\n// $columns - The number of columns this should be\n// $last-column - Is this the last column? Default: false.\n// $center - Center these columns? Default: false.\n// $offset - # of columns to offset. Default: false.\n// $push - # of columns to push. Default: false.\n// $pull - # of columns to pull. Default: false.\n// $collapse - Get rid of gutter padding on column? Default: false.\n// $float - Should this float? Default: true. Options: true, false, left, right.\n@mixin grid-column($columns: false,\n  $last-column: false,\n  $center: false,\n  $offset: false,\n  $push: false,\n  $pull: false,\n  $collapse: false,\n  $float: true,\n  $position: false) {\n\n  // If positioned for default .column, include relative position\n  // push and pull require position set\n  @if $position or $push or $pull {\n    position: relative;\n  }\n\n  // If collapsed, get rid of gutter padding\n  @if $collapse {\n    padding-left: 0;\n    padding-right: 0;\n  }\n\n  // Gutter padding whenever a column isn't set to collapse\n  // (use $collapse:null to do nothing)\n  @else if $collapse ==false {\n    padding-left: calc($column-gutter / 2);\n    padding-right: calc($column-gutter / 2);\n  }\n\n  // If a column number is given, calculate width\n  @if $columns {\n    width: grid-calc($columns, $total-columns);\n\n    // If last column, float naturally instead of to the right\n    @if $last-column {\n      float: $opposite-direction;\n    }\n  }\n\n  // Source Ordering, adds left/right depending on which you use.\n  @if $push {\n    #{$default-float}: grid-calc($push, $total-columns);\n    #{$opposite-direction}: auto;\n  }\n\n  @if $pull {\n    #{$opposite-direction}: grid-calc($pull, $total-columns);\n    #{$default-float}: auto;\n  }\n\n  @if $float {\n    @if $float ==left or $float ==true {\n      float: $default-float;\n    }\n\n    @else if $float ==right {\n      float: $opposite-direction;\n    }\n\n    @else {\n      float: none;\n    }\n  }\n\n  // If centered, get rid of float and add appropriate margins\n  @if $center {\n    margin-#{$default-float}: auto;\n    margin-#{$opposite-direction}: auto;\n    float: none;\n  }\n\n  // If offset, calculate appropriate margins\n  @if $offset {\n    margin-#{$default-float}: grid-calc($offset, $total-columns) !important;\n  }\n\n}\n\n// Create presentational classes for grid\n//\n// $size - Name of class to use, i.e. \"large\" will generate .large-1, .large-2, etc.\n@mixin grid-html-classes($size) {\n\n  @for $i from 0 through $total-columns - 1 {\n    .#{$size}-push-#{$i} {\n      @include grid-column($push: $i, $collapse: null, $float: false);\n    }\n\n    .#{$size}-pull-#{$i} {\n      @include grid-column($pull: $i, $collapse: null, $float: false);\n    }\n  }\n\n  .column,\n  .columns {\n    @include grid-column($columns: false, $position: true);\n  }\n\n\n  @for $i from 1 through $total-columns {\n    .#{$size}-#{$i} {\n      @include grid-column($columns: $i, $collapse: null, $float: false);\n    }\n  }\n\n  @for $i from 0 through $total-columns - 1 {\n    .#{$size}-offset-#{$i} {\n      @include grid-column($offset: $i, $collapse: null, $float: false);\n    }\n  }\n\n  .#{$size}-reset-order {\n    margin-#{$default-float}: 0;\n    margin-#{$opposite-direction}: 0;\n    left: auto;\n    right: auto;\n    float: $default-float;\n  }\n\n  .column.#{$size}-centered,\n  .columns.#{$size}-centered {\n    @include grid-column($center: true, $collapse: null, $float: false);\n  }\n\n  .column.#{$size}-uncentered,\n  .columns.#{$size}-uncentered {\n    margin-#{$default-float}: 0;\n    margin-#{$opposite-direction}: 0;\n    float: $default-float;\n  }\n\n  // Fighting [class*=\"column\"] + [class*=\"column\"]:last-child\n  .column.#{$size}-centered:last-child,\n  .columns.#{$size}-centered:last-child {\n    float: none;\n  }\n\n  // Fighting .column.<previous-size>-centered:last-child\n  .column.#{$size}-uncentered:last-child,\n  .columns.#{$size}-uncentered:last-child {\n    float: $default-float;\n  }\n\n  .column.#{$size}-uncentered.opposite,\n  .columns.#{$size}-uncentered.opposite {\n    float: $opposite-direction;\n  }\n\n  .row {\n    &.#{$size}-collapse {\n\n      >.column,\n      >.columns {\n        @include grid-column($collapse: true, $float: false);\n      }\n\n      .row {\n        margin-left: 0;\n        margin-right: 0;\n      }\n    }\n\n    &.#{$size}-uncollapse {\n\n      >.column,\n      >.columns {\n        @include grid-column;\n      }\n    }\n  }\n}\n\n@include exports(\"grid\") {\n  @if $include-html-grid-classes {\n    .row {\n      @include grid-row;\n\n      &.collapse {\n\n        >.column,\n        >.columns {\n          @include grid-column($collapse: true, $float: false);\n        }\n\n        .row {\n          margin-left: 0;\n          margin-right: 0;\n        }\n      }\n\n      .row {\n        @include grid-row($behavior: nest);\n\n        &.collapse {\n          @include grid-row($behavior: nest-collapse);\n        }\n      }\n    }\n\n    .column,\n    .columns {\n      @include grid-column($columns: $total-columns);\n    }\n\n    [class*=\"column\"]+[class*=\"column\"]:last-child {\n      float: $last-child-float;\n    }\n\n    [class*=\"column\"]+[class*=\"column\"].end {\n      float: $default-float;\n    }\n\n    @media #{$small-up} {\n      @include grid-html-classes($size: small);\n    }\n\n    @media #{$medium-up} {\n      @include grid-html-classes($size: medium);\n\n      // Old push and pull classes\n      @for $i from 0 through $total-columns - 1 {\n        .push-#{$i} {\n          @include grid-column($push: $i, $collapse: null, $float: false);\n        }\n\n        .pull-#{$i} {\n          @include grid-column($pull: $i, $collapse: null, $float: false);\n        }\n      }\n    }\n\n    @media #{$large-up} {\n      @include grid-html-classes($size: large);\n\n      @for $i from 0 through $total-columns - 1 {\n        .push-#{$i} {\n          @include grid-column($push: $i, $collapse: null, $float: false);\n        }\n\n        .pull-#{$i} {\n          @include grid-column($pull: $i, $collapse: null, $float: false);\n        }\n      }\n    }\n  }\n\n  @if $include-xl-html-grid-classes {\n    @media #{$xlarge-up} {\n      @include grid-html-classes($size: xlarge);\n    }\n\n    @media #{$xxlarge-up} {\n      @include grid-html-classes($size: xxlarge);\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"../functions\";\n//\n// Foundation Variables\n//\n\n// Data attribute namespace\n// styles get applied to [data-mysite-plugin], etc\n$namespace: false !default;\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n// $base-line-height is 24px while $base-font-size is 16px\n$base-line-height: 1.5 !default;\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n    -webkit-#{$side}-left-radius: $radius;\n    -webkit-#{$side}-right-radius: $radius;\n    border-#{$side}-left-radius: $radius;\n    border-#{$side}-right-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We can control whether or not we have inset shadows edges.\n// $active - Default: true, Options: false\n@mixin inset-shadow($active: true) {\n  box-shadow: $shiny-edge-size $shiny-edge-color inset;\n\n  @if $active {\n    &:active {\n      box-shadow: $shiny-edge-size $shiny-edge-active-color inset;\n    }\n  }\n}\n\n// @mixins\n//\n// We use this to add transitions to elements\n// $property - Default: all, Options: http://www.w3.org/TR/css3-transitions/#animatable-properties\n// $speed - Default: 300ms\n// $ease - Default:ease-out, Options: http://css-tricks.com/almanac/properties/t/transition-timing-function/\n@mixin single-transition($property: all, $speed: 300ms, $ease: ease-out) {\n  transition: $property $speed $ease;\n}\n\n// @mixins\n//\n// We use this to add box-sizing across browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -(calc($width / 2));\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n$white : #FFFFFF !default;\n$ghost : #FAFAFA !default;\n$snow : #F9F9F9 !default;\n$vapor : #F6F6F6 !default;\n$white-smoke : #F5F5F5 !default;\n$silver : #EFEFEF !default;\n$smoke : #EEEEEE !default;\n$gainsboro : #DDDDDD !default;\n$iron : #CCCCCC !default;\n$base : #AAAAAA !default;\n$aluminum : #999999 !default;\n$jumbo : #888888 !default;\n$monsoon : #777777 !default;\n$steel : #666666 !default;\n$charcoal : #555555 !default;\n$tuatara : #444444 !default;\n$oil : #333333 !default;\n$jet : #222222 !default;\n$black : #000000 !default;\n\n// We use these as default colors throughout\n$primary-color: #008CBA !default; // bondi-blue\n$secondary-color: #e7e7e7 !default; // white-lilac\n$alert-color: #f04124 !default; // cinnabar\n$success-color: #43AC6A !default; // sea-green\n$warning-color: #f08a24 !default; // carrot\n$info-color: #a0d3e8 !default; // cornflower\n\n// We use these to define default font stacks\n$font-family-sans-serif: \"Helvetica Neue\", Helvetica, Roboto, Arial, sans-serif !default;\n$font-family-serif: Georgia, Cambria, \"Times New Roman\", Times, serif !default;\n$font-family-monospace: Consolas, \"Liberation Mono\", Courier, monospace !default;\n\n// We use these to define default font weights\n$font-weight-normal: normal !default;\n$font-weight-bold: bold !default;\n\n// We use these to control various global styles\n$body-bg: #fff !default;\n$body-font-color: #222 !default;\n$body-font-family: $font-family-sans-serif !default;\n$body-font-weight: $font-weight-normal !default;\n$body-font-style: normal !default;\n\n// We use this to control font-smoothing\n$font-smoothing: antialiased !default;\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to make sure border radius matches unless we want it different.\n$global-radius: 3px !default;\n$global-rounded: 1000px !default;\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n// Media Query Ranges\n$small-range: (\n  0,\n  40em) !default;\n$medium-range: (\n  40.063em,\n  64em) !default;\n$large-range: (\n  64.063em,\n  90em) !default;\n$xlarge-range: (\n  90.063em,\n  120em) !default;\n$xxlarge-range: (\n  120.063em,\n  99999999em) !default;\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\" !default;\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range\n    );\n}\n\nmeta.foundation-mq-small-only {\n  font-family: \"/\" + unquote($small-only) + \"/\";\n  width: lower-bound($small-range);\n}\n\nmeta.foundation-mq-medium {\n  font-family: \"/\" + unquote($medium-up) + \"/\";\n  width: lower-bound($medium-range);\n}\n\nmeta.foundation-mq-medium-only {\n  font-family: \"/\" + unquote($medium-only) + \"/\";\n  width: lower-bound($medium-range);\n}\n\nmeta.foundation-mq-large {\n  font-family: \"/\" + unquote($large-up) + \"/\";\n  width: lower-bound($large-range);\n}\n\nmeta.foundation-mq-large-only {\n  font-family: \"/\" + unquote($large-only) + \"/\";\n  width: lower-bound($large-range);\n}\n\nmeta.foundation-mq-xlarge {\n  font-family: \"/\" + unquote($xlarge-up) + \"/\";\n  width: lower-bound($xlarge-range);\n}\n\nmeta.foundation-mq-xlarge-only {\n  font-family: \"/\" + unquote($xlarge-only) + \"/\";\n  width: lower-bound($xlarge-range);\n}\n\nmeta.foundation-mq-xxlarge {\n  font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n  width: lower-bound($xxlarge-range);\n}\n\nmeta.foundation-data-attribute-namespace {\n  font-family: #{$namespace};\n}\n\n@if $include-html-global-classes {\n\n  // Must be 100% for off canvas to work\n  html,\n  body {\n    height: 100%;\n  }\n\n  // Set box-sizing globally to handle padding and border widths\n  *,\n  *:before,\n  *:after {\n    @include box-sizing(border-box);\n  }\n\n  html,\n  body {\n    font-size: $base-font-size;\n  }\n\n  // Default body styles\n  body {\n    background: $body-bg;\n    color: $body-font-color;\n    padding: 0;\n    margin: 0;\n    font-family: $body-font-family;\n    font-weight: $body-font-weight;\n    font-style: $body-font-style;\n    line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n    position: relative;\n    cursor: $cursor-auto-value;\n  }\n\n  a:hover {\n    cursor: $cursor-pointer-value;\n  }\n\n  // Grid Defaults to get images and embeds to work properly\n  img {\n    max-width: 100%;\n    height: auto;\n  }\n\n  img {\n    -ms-interpolation-mode: bicubic;\n  }\n\n  #map_canvas,\n  .map_canvas {\n\n    img,\n    embed,\n    object {\n      max-width: none !important;\n    }\n  }\n\n  // Miscellaneous useful HTML classes\n  .left {\n    float: left !important;\n  }\n\n  .right {\n    float: right !important;\n  }\n\n  .clearfix {\n    @include clearfix;\n  }\n\n  // Hide visually and from screen readers\n  .hide {\n    display: none !important;\n    visibility: hidden;\n  }\n\n  // Hide visually and from screen readers, but maintain layout\n  .invisible {\n    visibility: hidden;\n  }\n\n  // Font smoothing\n  // Antialiased font smoothing works best for light text on a dark background.\n  // Apply to single elements instead of globally to body.\n  // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n  .antialiased {\n    -webkit-font-smoothing: antialiased;\n    -moz-osx-font-smoothing: grayscale;\n  }\n\n  // Get rid of gap under images by making them display: inline-block; by default\n  img {\n    display: inline-block;\n    vertical-align: middle;\n  }\n\n  //\n  // Global resets for forms\n  //\n\n  // Make sure textarea takes on height automatically\n  textarea {\n    height: auto;\n    min-height: 50px;\n  }\n\n  // Make select elements 100% width by default\n  select {\n    width: 100%;\n  }\n}\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-button-classes: $include-html-classes !default;\n\n// We use these to build padding for buttons.\n$button-tny: rem-calc(10) !default;\n$button-sml: rem-calc(14) !default;\n$button-med: rem-calc(16) !default;\n$button-lrg: rem-calc(18) !default;\n\n// We use this to control the display property.\n$button-display: inline-block !default;\n$button-margin-bottom: rem-calc(20) !default;\n\n// We use these to control button text styles.\n$button-font-family: $body-font-family !default;\n$button-font-color: $white !default;\n$button-font-color-alt: $oil !default;\n$button-font-tny: rem-calc(11) !default;\n$button-font-sml: rem-calc(13) !default;\n$button-font-med: rem-calc(16) !default;\n$button-font-lrg: rem-calc(20) !default;\n$button-font-weight: $font-weight-normal !default;\n$button-font-align: center !default;\n\n// We use these to control various hover effects.\n$button-function-factor: -20% !default;\n\n// We use these to control button border styles.\n$button-border-width: 0 !default;\n$button-border-style: solid !default;\n$button-bg-color: $primary-color !default;\n$button-bg-hover: scale-color($button-bg-color, $lightness: $button-function-factor) !default;\n$button-border-color: $button-bg-hover !default;\n$secondary-button-bg-hover: scale-color($secondary-color, $lightness: $button-function-factor) !default;\n$secondary-button-border-color: $secondary-button-bg-hover !default;\n$success-button-bg-hover: scale-color($success-color, $lightness: $button-function-factor) !default;\n$success-button-border-color: $success-button-bg-hover !default;\n$alert-button-bg-hover: scale-color($alert-color, $lightness: $button-function-factor) !default;\n$alert-button-border-color: $alert-button-bg-hover !default;\n$warning-button-bg-hover: scale-color($warning-color, $lightness: $button-function-factor) !default;\n$warning-button-border-color: $warning-button-bg-hover !default;\n$info-button-bg-hover: scale-color($info-color, $lightness: $button-function-factor) !default;\n$info-button-border-color: $info-button-bg-hover !default;\n\n// We use this to set the default radius used throughout the core.\n$button-radius: $global-radius !default;\n$button-round: $global-rounded !default;\n\n// We use this to set default opacity and cursor for disabled buttons.\n$button-disabled-opacity: 0.7 !default;\n$button-disabled-cursor: $cursor-default-value !default;\n\n\n//\n// @MIXIN\n//\n// We use this mixin to create a default button base.\n//\n// $style - Sets base styles. Can be set to false. Default: true.\n// $display - Used to control display property. Default: $button-display || inline-block\n\n@mixin button-base($style:true, $display:$button-display) {\n  @if $style {\n    border-style: $button-border-style;\n    border-width: $button-border-width;\n    cursor: $cursor-pointer-value;\n    font-family: $button-font-family;\n    font-weight: $button-font-weight;\n    line-height: normal;\n    margin: 0 0 $button-margin-bottom;\n    position: relative;\n    text-decoration: none;\n    text-align: $button-font-align;\n    -webkit-appearance: none;\n    border-radius:0;\n  }\n  @if $display { display: $display; }\n}\n\n// @MIXIN\n//\n// We use this mixin to add button size styles\n//\n// $padding - Used to build padding for buttons Default: $button-med ||= rem-calc(12)\n// $full-width - We can set $full-width:true to remove side padding extend width - Default: false\n\n@mixin button-size($padding:$button-med, $full-width:false) {\n\n  // We control which padding styles come through,\n  // these can be turned off by setting $padding:false\n  @if $padding {\n    padding-top: $padding;\n    padding-#{$opposite-direction}: $padding * 2;\n    padding-bottom: $padding + rem-calc(1);\n    padding-#{$default-float}: $padding * 2;\n\n    // We control the font-size based on mixin input.\n    @if      $padding == $button-med { font-size: $button-font-med; }\n    @else if $padding == $button-tny { font-size: $button-font-tny; }\n    @else if $padding == $button-sml { font-size: $button-font-sml; }\n    @else if $padding == $button-lrg { font-size: $button-font-lrg; }\n  }\n\n  // We can set $full-width:true to remove side padding extend width.\n  @if $full-width {\n    // We still need to check if $padding is set.\n    @if $padding {\n    padding-top: $padding;\n    padding-bottom: $padding + rem-calc(1);\n    } @else if $padding == false {\n      padding-top:0;\n      padding-bottom:0;\n    }\n    padding-right: 0;\n    padding-left: 0;\n    width: 100%;\n  }\n}\n\n// @MIXIN\n//\n// we use this mixin to create the button hover and border colors\n\n// @MIXIN\n//\n// We use this mixin to add button color styles\n//\n// $bg - Background color. We can set $bg:false for a transparent background. Default: $primary-color.\n// $radius - If true, set to button radius which is $global-radius || explicitly set radius amount in px (ex. $radius:10px). Default: true\n// $disabled - We can set $disabled:true to create a disabled transparent button. Default: false\n// $bg-hover - Button Hover Background Color. Default: $button-bg-hover\n// $border-color - Button Border Color. Default: $button-border-color\n@mixin button-style($bg:$button-bg-color, $radius:false, $disabled:false, $bg-hover:null, $border-color:null) {\n\n  // We control which background styles are used,\n  // these can be removed by setting $bg:false\n  @if $bg {\n\n    @if $bg-hover == null {\n      $bg-hover: if($bg == $button-bg-color, $button-bg-hover, scale-color($bg, $lightness: $button-function-factor));\n    }\n\n    @if $border-color == null {\n      $border-color: if($bg == $button-bg-color, $button-border-color, scale-color($bg, $lightness: $button-function-factor));\n    }\n\n    // This find the lightness percentage of the background color.\n    $bg-lightness: lightness($bg);\n    $bg-hover-lightness: lightness($bg-hover);\n\n    background-color: $bg;\n    border-color: $border-color;\n    &:hover,\n    &:focus { background-color: $bg-hover; }\n\n    // We control the text color for you based on the background color.\n    color: if($bg-lightness > 70%, $button-font-color-alt, $button-font-color);\n\n    &:hover,\n    &:focus {\n      color: if($bg-hover-lightness > 70%, $button-font-color-alt, $button-font-color);\n    }\n  }\n\n  // We can set $disabled:true to create a disabled transparent button.\n  @if $disabled {\n    cursor: $button-disabled-cursor;\n    opacity: $button-disabled-opacity;\n    box-shadow: none;\n    &:hover,\n    &:focus { background-color: $bg; }\n  }\n\n  // We can control how much button radius is used.\n  @if $radius == true { @include radius($button-radius); }\n  @else if $radius { @include radius($radius); }\n\n}\n\n// @MIXIN\n//\n// We use this to quickly create buttons with a single mixin. As @jaredhardy puts it, \"the kitchen sink mixin\"\n//\n// $padding - Used to build padding for buttons Default: $button-med ||= rem-calc(12)\n// $bg - Primary color set in settings file. Default: $button-bg.\n// $radius - If true, set to button radius which is $global-radius || explicitly set radius amount in px (ex. $radius:10px). Default:false.\n// $full-width - We can set $full-width:true to remove side padding extend width. Default:false.\n// $disabled - We can set $disabled:true to create a disabled transparent button. Default:false.\n// $is-prefix - Not used? Default:false.\n// $bg-hover - Button Hover Color - Default null - see button-style mixin\n// $border-color - Button Border Color - Default null - see button-style mixin\n// $transition - We can control whether or not to include the background-color transition property - Default:true.\n@mixin button($padding:$button-med, $bg:$button-bg-color, $radius:false, $full-width:false, $disabled:false, $is-prefix:false, $bg-hover:null, $border-color:null, $transition: true) {\n  @include button-base;\n  @include button-size($padding, $full-width);\n  @include button-style($bg, $radius, $disabled, $bg-hover, $border-color);\n\n  @if $transition {\n    @include single-transition(background-color);\n  }\n}\n\n\n@include exports(\"button\") {\n  @if $include-html-button-classes {\n\n    // Default styles applied outside of media query\n    button, .button {\n      @include button-base;\n      @include button-size;\n      @include button-style;\n\n      @include single-transition(background-color);\n\n      &.secondary { @include button-style($bg:$secondary-color, $bg-hover:$secondary-button-bg-hover, $border-color:$secondary-button-border-color); }\n      &.success   { @include button-style($bg:$success-color, $bg-hover:$success-button-bg-hover, $border-color:$success-button-border-color); }\n      &.alert     { @include button-style($bg:$alert-color, $bg-hover:$alert-button-bg-hover, $border-color:$alert-button-border-color); }\n      &.warning   { @include button-style($bg:$warning-color, $bg-hover:$warning-button-bg-hover, $border-color:$warning-button-border-color); }\n      &.info      { @include button-style($bg:$info-color, $bg-hover:$info-button-bg-hover, $border-color:$info-button-border-color); }\n\n      &.large  { @include button-size($padding:$button-lrg); }\n      &.small  { @include button-size($padding:$button-sml); }\n      &.tiny   { @include button-size($padding:$button-tny); }\n      &.expand { @include button-size($padding:null,$full-width:true); }\n\n      &.left-align  { text-align: left; text-indent: rem-calc(12); }\n      &.right-align { text-align: right; padding-right: rem-calc(12); }\n\n      &.radius { @include button-style($bg:false, $radius:true); }\n      &.round  { @include button-style($bg:false, $radius:$button-round); }\n\n      &.disabled, &[disabled] { @include button-style($bg:$button-bg-color, $disabled:true, $bg-hover:$button-bg-hover, $border-color:$button-border-color);\n        &.secondary { @include button-style($bg:$secondary-color, $disabled:true, $bg-hover:$secondary-button-bg-hover, $border-color:$secondary-button-border-color); }\n        &.success { @include button-style($bg:$success-color, $disabled:true, $bg-hover:$success-button-bg-hover, $border-color:$success-button-border-color); }\n        &.alert { @include button-style($bg:$alert-color, $disabled:true, $bg-hover:$alert-button-bg-hover, $border-color:$alert-button-border-color); }\n        &.warning { @include button-style($bg:$warning-color, $disabled:true, $bg-hover:$warning-button-bg-hover, $border-color:$warning-button-border-color); }\n        &.info { @include button-style($bg:$info-color, $disabled:true, $bg-hover:$info-button-bg-hover, $border-color:$info-button-border-color); }\n      }\n    }\n\n    //firefox 2px fix\n    button::-moz-focus-inner {border:0; padding:0;}\n\n    @media #{$medium-up} {\n      button, .button {\n        @include button-base($style:false, $display:inline-block);\n        @include button-size($padding:false, $full-width:false);\n      }\n    }\n  }\n}\n","@charset \"utf-8\";\n\n$spacing-unit: 30px;\n\n\n// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n//\n\n// Table of Contents\n// Foundation Settings\n//\n// a. Base\n// b. Grid\n// c. Global\n// d. Media Query Ranges\n// e. Typography\n// 01. Accordion\n// 02. Alert Boxes\n// 03. Block Grid\n// 04. Breadcrumbs\n// 05. Buttons\n// 06. Button Groups\n// 07. Clearing\n// 08. Dropdown\n// 09. Dropdown Buttons\n// 10. Flex Video\n// 11. Forms\n// 12. Icon Bar\n// 13. Inline Lists\n// 14. Joyride\n// 15. Keystrokes\n// 16. Labels\n// 17. Magellan\n// 18. Off-canvas\n// 19. Orbit\n// 20. Pagination\n// 21. Panels\n// 22. Pricing Tables\n// 23. Progress Bar\n// 24. Range Slider\n// 25. Reveal\n// 26. Side Nav\n// 27. Split Buttons\n// 28. Sub Nav\n// 29. Switch\n// 30. Tables\n// 31. Tabs\n// 32. Thumbnails\n// 33. Tooltips\n// 34. Top Bar\n// 36. Visibility Classes\n\n// a. Base\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// This is the default html and body font-size for the base rem value.\n// $rem-base: 16px;\n\n// Allows the use of rem-calc() or lower-bound() in your settings\n@import \"functions\";\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n// $base-font-size: 100%;\n\n$base-font-size: 16px;\n$rem-base: $base-font-size;\n\n\n// The $base-font-size is 100% while $base-line-height is 150%\n// $base-line-height: 150%;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true;\n// $include-print-styles: true;\n$include-html-global-classes: $include-html-classes;\n\n// b. Grid\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-grid-classes: $include-html-classes;\n// $include-xl-html-grid-classes: false;\n\n// $row-width: rem-calc(1000);\n// $total-columns: 12;\n// $column-gutter: rem-calc(30);\n\n// c. Global\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// We use these to define default font stacks\n// $font-family-sans-serif: \"Lato\", \"Helvetica Neue\", \"Helvetica\", Helvetica, Arial, sans-serif;\n// $font-family-serif: \"Volkhov\", Georgia, Times, serif;\n// $font-family-monospace: \"Lucida Console\", Monaco, monospace;\n\n// We use these to define default font weights\n// $font-weight-normal: normal !default;\n// $font-weight-bold: bold !default;\n\n// $white       : #FFFFFF;\n// $ghost       : #FAFAFA;\n// $snow        : #F9F9F9;\n// $vapor       : #F6F6F6;\n// $white-smoke : #F5F5F5;\n// $silver      : #EFEFEF;\n// $smoke       : #EEEEEE;\n// $gainsboro   : #DDDDDD;\n// $iron        : #CCCCCC;\n// $base        : #AAAAAA;\n// $aluminum    : #999999;\n// $jumbo       : #888888;\n// $monsoon     : #777777;\n// $steel       : #666666;\n// $charcoal    : #555555;\n// $tuatara     : #444444;\n// $oil         : #333333;\n// $jet         : #222222;\n// $black       : #000000;\n\n// We use these as default colors throughout\n// $primary-color: #008CBA;\n// $secondary-color: #e7e7e7;\n// $alert-color: #f04124;\n// $success-color: #43AC6A;\n// $warning-color: #f08a24;\n// $info-color: #a0d3e8;\n\n// We use these to control various global styles\n// $body-bg: $white;\n// $body-font-color: $jet;\n// $body-font-family: $font-family-sans-serif;\n// $body-font-weight: $font-weight-normal;\n// $body-font-style: normal;\n\n// We use this to control font-smoothing\n// $font-smoothing: antialiased;\n\n// We use these to control text direction settings\n// $text-direction: ltr;\n// $opposite-direction: right;\n// $default-float: left;\n// $last-child-float: $opposite-direction;\n\n// We use these to make sure border radius matches unless we want it different.\n$global-radius: 3px;\n// $global-rounded: 1000px;\n\n// We use these to control inset shadow shiny edges and depressions.\n// $shiny-edge-size: 0 1px 0;\n// $shiny-edge-color: rgba($white, .5);\n// $shiny-edge-active-color: rgba($black, .2);\n\n// // d. Media Query Ranges\n// // - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $small-range: (0em, 40em);\n// $medium-range: (40.063em, 64em);\n// $large-range: (64.063em, 90em);\n// $xlarge-range: (90.063em, 120em);\n// $xxlarge-range: (120.063em, 99999999em);\n\n// $screen: \"only screen\";\n\n// // $landscape: \"#{$screen} and (orientation: landscape)\";\n// // $portrait: \"#{$screen} and (orientation: portrait)\";\n\n// $small-up: $screen;\n// $small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n// $medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\";\n// $medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\";\n\n// $large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\";\n// $large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\";\n\n// $xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\";\n// $xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\";\n\n// $xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\";\n// $xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\";\n\n// Legacy\n// $small: $medium-up;\n// $medium: $medium-up;\n// $large: $large-up;\n\n// We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n// $cursor-crosshair-value: crosshair;\n// $cursor-default-value: default;\n// $cursor-pointer-value: pointer;\n// $cursor-help-value: help;\n// $cursor-text-value: text;\n\n// e. Typography\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-type-classes: $include-html-classes;\n\n// We use these to control header font styles\n// $header-font-family: $font-family-serif;\n// $header-font-weight: $font-weight-normal;\n// $header-font-style: normal;\n// $header-font-color: $jet;\n// $header-line-height: 1.4;\n// $header-top-margin: .2rem;\n// $header-bottom-margin: .5rem;\n// $header-text-rendering: optimizeLegibility;\n\n// We use these to control header font sizes\n// $h1-font-size: rem-calc(54);\n// $h2-font-size: rem-calc(36);\n// $h3-font-size: rem-calc(29);\n// $h4-font-size: rem-calc(24);\n// $h5-font-size: rem-calc(19);\n// $h6-font-size: 1rem;\n\n// We use these to control header size reduction on small screens\n// $h1-font-reduction: rem-calc(10) !default;\n// $h2-font-reduction: rem-calc(10) !default;\n// $h3-font-reduction: rem-calc(5) !default;\n// $h4-font-reduction: rem-calc(5) !default;\n// $h5-font-reduction: 0 !default;\n// $h6-font-reduction: 0 !default;\n\n// These control how subheaders are styled.\n// $subheader-line-height: 1.4;\n// $subheader-font-color: scale-color($header-font-color, $lightness: 35%);\n// $subheader-font-weight: $font-weight-normal;\n// $subheader-top-margin: .2rem;\n// $subheader-bottom-margin: .5rem;\n\n// A general <small> styling\n// $small-font-size: 60%;\n// $small-font-color: scale-color($header-font-color, $lightness: 35%);\n\n// We use these to style paragraphs\n// $paragraph-font-family: inherit;\n// $paragraph-font-weight: $font-weight-normal;\n// $paragraph-font-size: 1rem;\n// $paragraph-line-height: 1.6;\n// $paragraph-margin-bottom: rem-calc(20);\n// $paragraph-aside-font-size: rem-calc(14);\n// $paragraph-aside-line-height: 1.35;\n// $paragraph-aside-font-style: italic;\n// $paragraph-text-rendering: optimizeLegibility;\n\n// We use these to style <code> tags\n// $code-color: $oil;\n// $code-font-family: $font-family-monospace;\n// $code-font-weight: $font-weight-normal;\n// $code-background-color: scale-color($secondary-color, $lightness: 70%);\n// $code-border-size: 1px;\n// $code-border-style: solid;\n// $code-border-color: scale-color($code-background-color, $lightness: -10%);\n// $code-padding: rem-calc(2) rem-calc(5) rem-calc(1);\n\n// We use these to style anchors\n// $anchor-text-decoration: none;\n// $anchor-text-decoration-hover: none;\n// $anchor-font-color: $primary-color;\n// $anchor-font-color-hover: scale-color($primary-color, $lightness: -14%);\n\n// We use these to style the <hr> element\n// $hr-border-width: 1px;\n// $hr-border-style: solid;\n$hr-border-color: $grey-3;\n// $hr-margin: rem-calc(20);\n\n// We use these to style lists\n// $list-font-family: $paragraph-font-family;\n// $list-font-size: $paragraph-font-size;\n// $list-line-height: $paragraph-line-height;\n// $list-margin-bottom: $paragraph-margin-bottom;\n// $list-style-position: outside;\n$list-side-margin: 1.3rem;\n// $list-ordered-side-margin: 1.4rem;\n// $list-side-margin-no-bullet: 0;\n// $list-nested-margin: rem-calc(20);\n// $definition-list-header-weight: $font-weight-bold;\n// $definition-list-header-margin-bottom: .3rem;\n// $definition-list-margin-bottom: rem-calc(12);\n\n// We use these to style blockquotes\n// $blockquote-font-color: scale-color($header-font-color, $lightness: 35%);\n// $blockquote-padding: rem-calc(9 20 0 19);\n// $blockquote-border: 1px solid $gainsboro;\n// $blockquote-cite-font-size: rem-calc(13);\n// $blockquote-cite-font-color: scale-color($header-font-color, $lightness: 23%);\n// $blockquote-cite-link-color: $blockquote-cite-font-color;\n\n// Acronym styles\n// $acronym-underline: 1px dotted $gainsboro;\n\n// We use these to control padding and margin\n// $microformat-padding: rem-calc(10 12);\n// $microformat-margin: rem-calc(0 0 20 0);\n\n// We use these to control the border styles\n// $microformat-border-width: 1px;\n// $microformat-border-style: solid;\n// $microformat-border-color: $gainsboro;\n\n// We use these to control full name font styles\n// $microformat-fullname-font-weight: $font-weight-bold;\n// $microformat-fullname-font-size: rem-calc(15);\n\n// We use this to control the summary font styles\n// $microformat-summary-font-weight: $font-weight-bold;\n\n// We use this to control abbr padding\n// $microformat-abbr-padding: rem-calc(0 1);\n\n// We use this to control abbr font styles\n// $microformat-abbr-font-weight: $font-weight-bold;\n// $microformat-abbr-font-decoration: none;\n\n// 01. Accordion\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-accordion-classes: $include-html-classes;\n\n$accordion-navigation-padding: rem-calc(12);\n// $accordion-navigation-bg-color: #ffffff;\n// $accordion-navigation-hover-bg-color: $grey-1;\n// $accordion-navigation-active-bg-color: $grey-1;\n// $accordion-navigation-font-color: $jet;\n// $accordion-navigation-font-size: rem-calc(16);\n// $accordion-navigation-font-family: $body-font-family;\n\n// $accordion-content-padding: $column-gutter/2;\n$accordion-content-active-bg-color: $body-bg;\n\n// 02. Alert Boxes\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-alert-classes: $include-html-classes;\n\n// We use this to control alert padding.\n// $alert-padding-top: rem-calc(14);\n// $alert-padding-default-float: $alert-padding-top;\n// $alert-padding-opposite-direction: $alert-padding-top + rem-calc(10);\n// $alert-padding-bottom: $alert-padding-top;\n\n// We use these to control text style.\n// $alert-font-weight: $font-weight-normal;\n$alert-font-size: rem-calc(15);\n// $alert-font-color: $white;\n// $alert-font-color-alt: scale-color($secondary-color, $lightness: -66%);\n\n// We use this for close hover effect.\n// $alert-function-factor: -14%;\n\n// We use these to control border styles.\n// $alert-border-style: solid;\n// $alert-border-width: 1px;\n// $alert-border-color: scale-color($primary-color, $lightness: $alert-function-factor);\n// $alert-bottom-margin: rem-calc(20);\n\n// We use these to style the close buttons\n// $alert-close-color: $oil;\n// $alert-close-top: 50%;\n// $alert-close-position: rem-calc(4);\n// $alert-close-font-size: rem-calc(22);\n// $alert-close-opacity: 0.3;\n// $alert-close-opacity-hover: 0.5;\n// $alert-close-padding: 9px 6px 4px;\n\n// We use this to control border radius\n// $alert-radius: $global-radius;\n\n// We use this to control transition effects\n// $alert-transition-speed: 300ms;\n// $alert-transition-ease: ease-out;\n\n// 03. Block Grid\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-block-grid-classes: $include-html-classes;\n// $include-xl-html-block-grid-classes: false;\n\n// We use this to control the maximum number of block grid elements per row\n// $block-grid-elements: 12;\n// $block-grid-default-spacing: rem-calc(20);\n// $align-block-grid-to-grid: false;\n\n// Enables media queries for block-grid classes. Set to false if writing semantic HTML.\n// $block-grid-media-queries: true;\n\n// 04. Breadcrumbs\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-nav-classes: $include-html-classes;\n\n// We use this to set the background color for the breadcrumb container.\n$crumb-bg: $grey-1;\n\n// We use these to set the padding around the breadcrumbs.\n// $crumb-padding: rem-calc(9 9 14 0);\n// $crumb-side-padding: rem-calc(12);\n\n// We use these to control border styles.\n// $crumb-function-factor: -10%;\n$crumb-border-size: 0;\n// $crumb-border-style: solid;\n$crumb-border-color: $grey-1;\n$crumb-radius: 0;\n\n// We use these to set various text styles for breadcrumbs.\n// $crumb-font-size: rem-calc(11);\n// $crumb-font-color: $primary-color;\n// $crumb-font-color-current: $oil;\n// $crumb-font-color-unavailable: $aluminum;\n// $crumb-font-transform: uppercase;\n// $crumb-link-decor: underline;\n\n// We use these to control the slash between breadcrumbs\n// $crumb-slash-color: $base;\n$crumb-slash: \"/\";\n\n// 05. Buttons\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// We use these to build padding for buttons.\n// $button-tny: rem-calc(10);\n// $button-sml: rem-calc(14);\n// $button-med: rem-calc(16);\n// $button-lrg: rem-calc(18);\n\n// We use this to control the display property.\n// $button-display: inline-block;\n// $button-margin-bottom: rem-calc(20);\n\n// We use these to control button text styles.\n// $button-font-family: $body-font-family;\n// $button-font-color: $white;\n// $button-font-color-alt: $oil;\n// $button-font-tny: rem-calc(11);\n// $button-font-sml: rem-calc(13);\n// $button-font-med: rem-calc(16);\n// $button-font-lrg: rem-calc(20);\n// $button-font-weight: $font-weight-normal;\n// $button-font-align: center;\n\n// We use these to control various hover effects.\n// $button-function-factor: -20%;\n\n// We use these to control button border and hover styles.\n// $button-border-width: 0px;\n// $button-border-style: solid;\n// $button-bg-color: $primary-color;\n// $button-bg-hover: scale-color($button-bg-color, $lightness: $button-function-factor);\n// $button-border-color: $button-bg-hover;\n// $secondary-button-bg-hover: scale-color($secondary-color, $lightness: $button-function-factor);\n// $secondary-button-border-color: $secondary-button-bg-hover;\n// $success-button-bg-hover: scale-color($success-color, $lightness: $button-function-factor);\n// $success-button-border-color: $success-button-bg-hover;\n// $alert-button-bg-hover: scale-color($alert-color, $lightness: $button-function-factor);\n// $alert-button-border-color: $alert-button-bg-hover;\n\n// We use this to set the default radius used throughout the core.\n// $button-radius: $global-radius;\n// $button-round: $global-rounded;\n\n// We use this to set default opacity and cursor for disabled buttons.\n// $button-disabled-opacity: 0.7;\n// $button-disabled-cursor: $cursor-default-value;\n\n// 06. Button Groups\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// Sets the margin for the right side by default, and the left margin if right-to-left direction is used\n// $button-bar-margin-opposite: rem-calc(10);\n// $button-group-border-width: 1px;\n\n// 07. Clearing\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-clearing-classes: $include-html-classes;\n\n// We use these to set the background colors for parts of Clearing.\n// $clearing-bg: $oil;\n// $clearing-caption-bg: $clearing-bg;\n// $clearing-carousel-bg: rgba(51,51,51,0.8);\n// $clearing-img-bg: $clearing-bg;\n\n// We use these to style the close button\n// $clearing-close-color: $iron;\n// $clearing-close-size: 30px;\n\n// We use these to style the arrows\n// $clearing-arrow-size: 12px;\n// $clearing-arrow-color: $clearing-close-color;\n\n// We use these to style captions\n// $clearing-caption-font-color: $iron;\n// $clearing-caption-font-size: 0.875em;\n// $clearing-caption-padding: 10px 30px 20px;\n\n// We use these to make the image and carousel height and style\n// $clearing-active-img-height: 85%;\n// $clearing-carousel-height: 120px;\n// $clearing-carousel-thumb-width: 120px;\n// $clearing-carousel-thumb-active-border: 1px solid rgb(255,255,255);\n\n// 08. Dropdown\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-dropdown-classes: $include-html-classes;\n\n// We use these to controls height and width styles.\n// $f-dropdown-max-width: 200px;\n// $f-dropdown-height: auto;\n// $f-dropdown-max-height: none;\n\n// Used for bottom position\n// $f-dropdown-margin-top: 2px;\n\n// Used for right position\n// $f-dropdown-margin-left: $f-dropdown-margin-top;\n\n// Used for left position\n// $f-dropdown-margin-right: $f-dropdown-margin-top;\n\n// Used for top position\n// $f-dropdown-margin-bottom: $f-dropdown-margin-top;\n\n// We use this to control the background color\n// $f-dropdown-bg: $white;\n\n// We use this to set the border styles for dropdowns.\n// $f-dropdown-border-style: solid;\n// $f-dropdown-border-width: 1px;\n// $f-dropdown-border-color: scale-color($white, $lightness: -20%);\n\n// We use these to style the triangle pip.\n// $f-dropdown-triangle-size: 6px;\n// $f-dropdown-triangle-color: $white;\n// $f-dropdown-triangle-side-offset: 10px;\n\n// We use these to control styles for the list elements.\n// $f-dropdown-list-style: none;\n// $f-dropdown-font-color: $charcoal;\n// $f-dropdown-font-size: rem-calc(14);\n// $f-dropdown-list-padding: rem-calc(5, 10);\n// $f-dropdown-line-height: rem-calc(18);\n// $f-dropdown-list-hover-bg: $smoke  ;\n// $dropdown-mobile-default-float: 0;\n\n// We use this to control the styles for when the dropdown has custom content.\n// $f-dropdown-content-padding: rem-calc(20);\n\n// Default radius for dropdown.\n// $f-dropdown-radius: $global-radius;\n\n\n// 09. Dropdown Buttons\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// We use these to set the color of the pip in dropdown buttons\n// $dropdown-button-pip-color: $white;\n// $dropdown-button-pip-color-alt: $oil;\n\n// $button-pip-tny: rem-calc(6);\n// $button-pip-sml: rem-calc(7);\n// $button-pip-med: rem-calc(9);\n// $button-pip-lrg: rem-calc(11);\n\n// We use these to style tiny dropdown buttons\n// $dropdown-button-padding-tny: $button-pip-tny * 7;\n// $dropdown-button-pip-size-tny: $button-pip-tny;\n// $dropdown-button-pip-opposite-tny: $button-pip-tny * 3;\n// $dropdown-button-pip-top-tny: -$button-pip-tny / 2 + rem-calc(1);\n\n// We use these to style small dropdown buttons\n// $dropdown-button-padding-sml: $button-pip-sml * 7;\n// $dropdown-button-pip-size-sml: $button-pip-sml;\n// $dropdown-button-pip-opposite-sml: $button-pip-sml * 3;\n// $dropdown-button-pip-top-sml: -$button-pip-sml / 2 + rem-calc(1);\n\n// We use these to style medium dropdown buttons\n// $dropdown-button-padding-med: $button-pip-med * 6 + rem-calc(3);\n// $dropdown-button-pip-size-med: $button-pip-med - rem-calc(3);\n// $dropdown-button-pip-opposite-med: $button-pip-med * 2.5;\n// $dropdown-button-pip-top-med: -$button-pip-med / 2 + rem-calc(2);\n\n// We use these to style large dropdown buttons\n// $dropdown-button-padding-lrg: $button-pip-lrg * 5 + rem-calc(3);\n// $dropdown-button-pip-size-lrg: $button-pip-lrg - rem-calc(6);\n// $dropdown-button-pip-opposite-lrg: $button-pip-lrg * 2.5;\n// $dropdown-button-pip-top-lrg: -$button-pip-lrg / 2 + rem-calc(3);\n\n// 10. Flex Video\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-media-classes: $include-html-classes;\n\n// We use these to control video container padding and margins\n// $flex-video-padding-top: rem-calc(25);\n// $flex-video-padding-bottom: 67.5%;\n// $flex-video-margin-bottom: rem-calc(16);\n\n// We use this to control widescreen bottom padding\n// $flex-video-widescreen-padding-bottom: 56.34%;\n\n// 11. Forms\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-form-classes: $include-html-classes;\n\n// We use this to set the base for lots of form spacing and positioning styles\n// $form-spacing: rem-calc(16);\n\n// We use these to style the labels in different ways\n// $form-label-pointer: pointer;\n// $form-label-font-size: rem-calc(14);\n// $form-label-font-weight: $font-weight-normal;\n// $form-label-line-height: 1.5;\n// $form-label-font-color: scale-color($black, $lightness: 30%);\n// $form-label-small-transform: capitalize;\n// $form-label-bottom-margin: 0;\n// $input-font-family: inherit;\n// $input-font-color: rgba(0,0,0,0.75);\n// $input-font-size: rem-calc(14);\n// $input-bg-color: $white;\n// $input-focus-bg-color: scale-color($white, $lightness: -2%);\n// $input-border-color: scale-color($white, $lightness: -20%);\n// $input-focus-border-color: scale-color($white, $lightness: -40%);\n// $input-border-style: solid;\n// $input-border-width: 1px;\n// $input-border-radius: $global-radius;\n// $input-disabled-bg: $gainsboro;\n// $input-disabled-cursor: $cursor-default-value;\n// $input-box-shadow: inset 0 1px 2px rgba(0,0,0,0.1);\n\n// We use these to style the fieldset border and spacing.\n// $fieldset-border-style: solid;\n// $fieldset-border-width: 1px;\n// $fieldset-border-color: $gainsboro;\n// $fieldset-padding: rem-calc(20);\n// $fieldset-margin: rem-calc(18 0);\n\n// We use these to style the legends when you use them\n// $legend-bg: $white;\n// $legend-font-weight: $font-weight-bold;\n// $legend-padding: rem-calc(0 3);\n\n// We use these to style the prefix and postfix input elements\n// $input-prefix-bg: scale-color($white, $lightness: -5%);\n// $input-prefix-border-color: scale-color($white, $lightness: -20%);\n// $input-prefix-border-size: 1px;\n// $input-prefix-border-type: solid;\n// $input-prefix-overflow: hidden;\n// $input-prefix-font-color: $oil;\n// $input-prefix-font-color-alt: $white;\n\n// We use this setting to turn on/off HTML5 number spinners (the up/down arrows)\n// $input-number-spinners: true;\n\n// We use these to style the error states for inputs and labels\n// $input-error-message-padding: rem-calc(6 9 9);\n// $input-error-message-top: -1px;\n// $input-error-message-font-size: rem-calc(12);\n// $input-error-message-font-weight: $font-weight-normal;\n// $input-error-message-font-style: italic;\n// $input-error-message-font-color: $white;\n// $input-error-message-font-color-alt: $oil;\n\n// We use this to style the glowing effect of inputs when focused\n// $input-include-glowing-effect: true;\n// $glowing-effect-fade-time: 0.45s;\n// $glowing-effect-color: $input-focus-border-color;\n\n// Select variables\n// $select-bg-color: $ghost;\n// $select-hover-bg-color: scale-color($select-bg-color, $lightness: -3%);\n\n// 12. Icon Bar\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// We use these to style the icon-bar and items\n// $include-html-icon-bar-classes: $include-html-classes;\n// $icon-bar-bg: $oil;\n// $icon-bar-font-color: $white;\n// $icon-bar-font-size: 1rem;\n// $icon-bar-hover-color: $primary-color;\n// $icon-bar-icon-color: $white;\n// $icon-bar-icon-size: 1.875rem;\n// $icon-bar-image-width: 1.875rem;\n// $icon-bar-image-height: 1.875rem;\n// $icon-bar-active-color: $primary-color;\n// $icon-bar-item-padding: 1.25rem;\n\n// 13. Inline Lists\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-inline-list-classes: $include-html-classes;\n\n// We use this to control the margins and padding of the inline list.\n// $inline-list-top-margin: 0;\n// $inline-list-opposite-margin: 0;\n// $inline-list-bottom-margin: rem-calc(17);\n// $inline-list-default-float-margin: rem-calc(-22);\n// $inline-list-default-float-list-margin: rem-calc(22);\n\n// $inline-list-padding: 0;\n\n// We use this to control the overflow of the inline list.\n// $inline-list-overflow: hidden;\n\n// We use this to control the list items\n// $inline-list-display: block;\n\n// We use this to control any elements within list items\n// $inline-list-children-display: block;\n\n// 14. Joyride\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-joyride-classes: $include-html-classes;\n\n// Controlling default Joyride styles\n// $joyride-tip-bg: $oil;\n// $joyride-tip-default-width: 300px;\n// $joyride-tip-padding: rem-calc(18 20 24);\n// $joyride-tip-border: solid 1px $charcoal;\n// $joyride-tip-radius: 4px;\n// $joyride-tip-position-offset: 22px;\n\n// Here, we're setting the tip font styles\n// $joyride-tip-font-color: $white;\n// $joyride-tip-font-size: rem-calc(14);\n// $joyride-tip-header-weight: $font-weight-bold;\n\n// This changes the nub size\n// $joyride-tip-nub-size: 10px;\n\n// This adjusts the styles for the timer when its enabled\n// $joyride-tip-timer-width: 50px;\n// $joyride-tip-timer-height: 3px;\n// $joyride-tip-timer-color: $steel;\n\n// This changes up the styles for the close button\n// $joyride-tip-close-color: $monsoon;\n// $joyride-tip-close-size: 24px;\n// $joyride-tip-close-weight: $font-weight-normal;\n\n// When Joyride is filling the screen, we use this style for the bg\n// $joyride-screenfill: rgba(0,0,0,0.5);\n\n// 15. Keystrokes\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-keystroke-classes: $include-html-classes;\n\n// We use these to control text styles.\n// $keystroke-font: \"Consolas\", \"Menlo\", \"Courier\", monospace;\n// $keystroke-font-size: inherit;\n// $keystroke-font-color: $jet;\n// $keystroke-font-color-alt: $white;\n// $keystroke-function-factor: -7%;\n\n// We use this to control keystroke padding.\n// $keystroke-padding: rem-calc(2 4 0);\n\n// We use these to control background and border styles.\n// $keystroke-bg: scale-color($white, $lightness: $keystroke-function-factor);\n// $keystroke-border-style: solid;\n// $keystroke-border-width: 1px;\n// $keystroke-border-color: scale-color($keystroke-bg, $lightness: $keystroke-function-factor);\n// $keystroke-radius: $global-radius;\n\n// 16. Labels\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-label-classes: $include-html-classes;\n\n// We use these to style the labels\n// $label-padding: rem-calc(4 8 4);\n// $label-radius: $global-radius;\n\n// We use these to style the label text\n// $label-font-sizing: rem-calc(11);\n// $label-font-weight: $font-weight-normal;\n// $label-font-color: $oil;\n// $label-font-color-alt: $white;\n// $label-font-family: $body-font-family;\n\n// 17. Magellan\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-magellan-classes: $include-html-classes;\n\n// $magellan-bg: $white;\n// $magellan-padding: 0 !important;\n\n// 18. Off-canvas\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-off-canvas-classes: $include-html-classes;\n\n// $tabbar-bg: $oil;\n// $tabbar-height: rem-calc(45);\n// $tabbar-icon-width: $tabbar-height;\n// $tabbar-line-height: $tabbar-height;\n// $tabbar-color: $white;\n// $tabbar-middle-padding: 0 rem-calc(10);\n\n// Off Canvas Divider Styles\n// $tabbar-right-section-border: solid 1px scale-color($tabbar-bg, $lightness: 13%);\n// $tabbar-left-section-border: solid 1px scale-color($tabbar-bg, $lightness: -50%);\n\n// Off Canvas Tab Bar Headers\n// $tabbar-header-color: $white;\n// $tabbar-header-weight: $font-weight-bold;\n// $tabbar-header-line-height: $tabbar-height;\n// $tabbar-header-margin: 0;\n\n// Off Canvas Menu Variables\n// $off-canvas-width: rem-calc(250);\n// $off-canvas-bg: $oil;\n// $off-canvas-bg-hover: scale-color($tabbar-bg, $lightness: -30%);\n\n// Off Canvas Menu List Variables\n// $off-canvas-label-padding: 0.3rem rem-calc(15);\n// $off-canvas-label-color: $aluminum;\n// $off-canvas-label-text-transform: uppercase;\n// $off-canvas-label-font-size: rem-calc(12);\n// $off-canvas-label-font-weight: $font-weight-bold;\n// $off-canvas-label-bg: $tuatara;\n// $off-canvas-label-border-top: 1px solid scale-color($tuatara, $lightness: 14%);\n// $off-canvas-label-border-bottom: none;\n// $off-canvas-label-margin:0;\n// $off-canvas-link-padding: rem-calc(10, 15);\n// $off-canvas-link-color: rgba($white, 0.7);\n// $off-canvas-link-border-bottom: 1px solid scale-color($off-canvas-bg, $lightness: -25%);\n// $off-canvas-back-bg: $tuatara;\n// $off-canvas-back-border-top: $off-canvas-label-border-top;\n// $off-canvas-back-border-bottom: $off-canvas-label-border-bottom;\n// $off-canvas-back-hover-bg: scale-color($off-canvas-back-bg, $lightness: -30%);\n// $off-canvas-back-hover-border-top: 1px solid scale-color($off-canvas-label-bg, $lightness: 14%);\n// $off-canvas-back-hover-border-bottom: none;\n\n// Off Canvas Menu Icon Variables\n// $tabbar-menu-icon-color: $white;\n// $tabbar-menu-icon-hover: scale-color($tabbar-menu-icon-color, $lightness: -30%);\n\n// $tabbar-menu-icon-text-indent: rem-calc(35);\n// $tabbar-menu-icon-width: $tabbar-height;\n// $tabbar-menu-icon-height: $tabbar-height;\n// $tabbar-menu-icon-padding: 0;\n\n// $tabbar-hamburger-icon-width: rem-calc(16);\n// $tabbar-hamburger-icon-left: false;\n// $tabbar-hamburger-icon-top: false;\n// $tabbar-hamburger-icon-thickness: 1px;\n// $tabbar-hamburger-icon-gap: 6px;\n\n// Off Canvas Back-Link Overlay\n// $off-canvas-overlay-transition: background 300ms ease;\n// $off-canvas-overlay-cursor: pointer;\n// $off-canvas-overlay-box-shadow: -4px 0 4px rgba($black, 0.5), 4px 0 4px rgba($black, 0.5);\n// $off-canvas-overlay-background: rgba($white, 0.2);\n// $off-canvas-overlay-background-hover: rgba($white, 0.05);\n\n// Transition Variables\n// $menu-slide: \"transform 500ms ease\";\n\n// 19. Orbit\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-orbit-classes: $include-html-classes;\n\n// We use these to control the caption styles\n// $orbit-container-bg: none;\n// $orbit-caption-bg: rgba(51,51,51, 0.8);\n// $orbit-caption-font-color: $white;\n// $orbit-caption-font-size: rem-calc(14);\n// $orbit-caption-position: \"bottom\"; // Supported values: \"bottom\", \"under\"\n// $orbit-caption-padding: rem-calc(10 14);\n// $orbit-caption-height: auto;\n\n// We use these to control the left/right nav styles\n// $orbit-nav-bg: transparent;\n// $orbit-nav-bg-hover: rgba(0,0,0,0.3);\n// $orbit-nav-arrow-color: $white;\n// $orbit-nav-arrow-color-hover: $white;\n\n// We use these to control the timer styles\n// $orbit-timer-bg: rgba(255,255,255,0.3);\n// $orbit-timer-show-progress-bar: true;\n\n// We use these to control the bullet nav styles\n// $orbit-bullet-nav-color: $iron;\n// $orbit-bullet-nav-color-active: $aluminum;\n// $orbit-bullet-radius: rem-calc(9);\n\n// We use these to controls the style of slide numbers\n// $orbit-slide-number-bg: rgba(0,0,0,0);\n// $orbit-slide-number-font-color: $white;\n// $orbit-slide-number-padding: rem-calc(5);\n\n// Hide controls on small\n// $orbit-nav-hide-for-small: true;\n// $orbit-bullet-hide-for-small: true;\n// $orbit-timer-hide-for-small: true;\n\n// Graceful Loading Wrapper and preloader\n// $wrapper-class: \"slideshow-wrapper\";\n// $preloader-class: \"preloader\";\n\n// 20. Pagination\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-pagination-classes: $include-html-classes;\n\n// We use these to control the pagination container\n// $pagination-height: rem-calc(24);\n// $pagination-margin: rem-calc(-5);\n\n// We use these to set the list-item properties\n// $pagination-li-float: $default-float;\n// $pagination-li-height: rem-calc(24);\n// $pagination-li-font-color: $jet;\n// $pagination-li-font-size: rem-calc(14);\n// $pagination-li-margin: rem-calc(5);\n\n// We use these for the pagination anchor links\n// $pagination-link-pad: rem-calc(1 10 1);\n// $pagination-link-font-color: $aluminum;\n// $pagination-link-active-bg: scale-color($white, $lightness: -10%);\n\n// We use these for disabled anchor links\n// $pagination-link-unavailable-cursor: default;\n// $pagination-link-unavailable-font-color: $aluminum;\n// $pagination-link-unavailable-bg-active: transparent;\n\n// We use these for currently selected anchor links\n// $pagination-link-current-background: $primary-color;\n// $pagination-link-current-font-color: $white;\n// $pagination-link-current-font-weight: $font-weight-bold;\n// $pagination-link-current-cursor: default;\n// $pagination-link-current-active-bg: $primary-color;\n\n// 21. Panels\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-panel-classes: $include-html-classes;\n\n// We use these to control the background and border styles\n$panel-bg: $grey-1;\n// $panel-border-style: solid;\n// $panel-border-size: 1px;\n\n// We use this % to control how much we darken things on hover\n// $panel-function-factor: -11%;\n// $panel-border-color: scale-color($panel-bg, $lightness: $panel-function-factor);\n\n// We use these to set default inner padding and bottom margin\n// $panel-margin-bottom: rem-calc(20);\n// $panel-padding: rem-calc(20);\n\n// We use these to set default font colors\n// $panel-font-color: $oil;\n// $panel-font-color-alt: $white;\n\n// $panel-header-adjust: true;\n// $callout-panel-link-color: $primary-color;\n\n// 22. Pricing Tables\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-pricing-classes: $include-html-classes;\n\n// We use this to control the border color\n// $price-table-border: solid 1px $gainsboro;\n\n// We use this to control the bottom margin of the pricing table\n// $price-table-margin-bottom: rem-calc(20);\n\n// We use these to control the title styles\n// $price-title-bg: $oil;\n// $price-title-padding: rem-calc(15 20);\n// $price-title-align: center;\n// $price-title-color: $smoke;\n// $price-title-weight: $font-weight-normal;\n// $price-title-size: rem-calc(16);\n// $price-title-font-family: $body-font-family;\n\n// We use these to control the price styles\n// $price-money-bg: $vapor  ;\n// $price-money-padding: rem-calc(15 20);\n// $price-money-align: center;\n// $price-money-color: $oil;\n// $price-money-weight: $font-weight-normal;\n// $price-money-size: rem-calc(32);\n// $price-money-font-family: $body-font-family;\n\n// We use these to control the description styles\n// $price-bg: $white;\n// $price-desc-color: $monsoon;\n// $price-desc-padding: rem-calc(15);\n// $price-desc-align: center;\n// $price-desc-font-size: rem-calc(12);\n// $price-desc-weight: $font-weight-normal;\n// $price-desc-line-height: 1.4;\n// $price-desc-bottom-border: dotted 1px $gainsboro;\n\n// We use these to control the list item styles\n// $price-item-color: $oil;\n// $price-item-padding: rem-calc(15);\n// $price-item-align: center;\n// $price-item-font-size: rem-calc(14);\n// $price-item-weight: $font-weight-normal;\n// $price-item-bottom-border: dotted 1px $gainsboro;\n\n// We use these to control the CTA area styles\n// $price-cta-bg: $white;\n// $price-cta-align: center;\n// $price-cta-padding: rem-calc(20 20 0);\n\n// 23. Progress Bar\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-media-classes: $include-html-classes;\n\n// We use this to set the progress bar height\n// $progress-bar-height: rem-calc(25);\n// $progress-bar-color: $vapor  ;\n\n// We use these to control the border styles\n// $progress-bar-border-color: scale-color($white, $lightness: 20%);\n// $progress-bar-border-size: 1px;\n// $progress-bar-border-style: solid;\n// $progress-bar-border-radius: $global-radius;\n\n// We use these to control the margin & padding\n// $progress-bar-pad: rem-calc(2);\n// $progress-bar-margin-bottom: rem-calc(10);\n\n// We use these to set the meter colors\n// $progress-meter-color: $primary-color;\n// $progress-meter-secondary-color: $secondary-color;\n// $progress-meter-success-color: $success-color;\n// $progress-meter-alert-color: $alert-color;\n\n// 24. Range Slider\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-range-slider-classes: $include-html-classes;\n\n// These variables define the slider bar styles\n// $range-slider-bar-width: 100%;\n// $range-slider-bar-height: rem-calc(16);\n\n// $range-slider-bar-border-width: 1px;\n// $range-slider-bar-border-style: solid;\n// $range-slider-bar-border-color: $gainsboro;\n// $range-slider-radius: $global-radius;\n// $range-slider-round: $global-rounded;\n// $range-slider-bar-bg-color: $ghost;\n\n// Vertical bar styles\n// $range-slider-vertical-bar-width: rem-calc(16);\n// $range-slider-vertical-bar-height: rem-calc(200);\n\n// These variables define the slider handle styles\n// $range-slider-handle-width: rem-calc(32);\n// $range-slider-handle-height: rem-calc(22);\n// $range-slider-handle-position-top: rem-calc(-5);\n// $range-slider-handle-bg-color: $primary-color;\n// $range-slider-handle-border-width: 1px;\n// $range-slider-handle-border-style: solid;\n// $range-slider-handle-border-color: none;\n// $range-slider-handle-radius: $global-radius;\n// $range-slider-handle-round: $global-rounded;\n// $range-slider-handle-bg-hover-color: scale-color($primary-color, $lightness: -12%);\n// $range-slider-handle-cursor: pointer;\n\n// 25. Reveal\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-reveal-classes: $include-html-classes;\n\n// We use these to control the style of the reveal overlay.\n// $reveal-overlay-bg: rgba($black, .45);\n// $reveal-overlay-bg-old: $black;\n\n// We use these to control the style of the modal itself.\n// $reveal-modal-bg: $white;\n// $reveal-position-top: rem-calc(100);\n// $reveal-default-width: 80%;\n// $reveal-max-width: $row-width;\n// $reveal-modal-padding: rem-calc(20);\n// $reveal-box-shadow: 0 0 10px rgba($black,.4);\n\n// We use these to style the reveal close button\n// $reveal-close-font-size: rem-calc(40);\n// $reveal-close-top: rem-calc(8);\n// $reveal-close-side: rem-calc(11);\n// $reveal-close-color: $base;\n// $reveal-close-weight: $font-weight-bold;\n\n// We use this to set the default radius used throughout the core.\n// $reveal-radius: $global-radius;\n// $reveal-round: $global-rounded;\n\n// We use these to control the modal border\n// $reveal-border-style: solid;\n// $reveal-border-width: 1px;\n// $reveal-border-color: $steel;\n\n// $reveal-modal-class: \"reveal-modal\";\n// $close-reveal-modal-class: \"close-reveal-modal\";\n\n// 26. Side Nav\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-nav-classes: $include-html-classes;\n\n// We use this to control padding.\n$side-nav-padding: rem-calc(0 0 0 0);\n\n// We use these to control list styles.\n// $side-nav-list-type: none;\n// $side-nav-list-position: inside;\n$side-nav-list-margin: rem-calc(0 0 0 0);\n\n// We use these to control link styles.\n$side-nav-link-color: $primary-color;\n$side-nav-link-color-active: scale-color($side-nav-link-color, $lightness: -40%);\n$side-nav-link-color-hover: scale-color($side-nav-link-color, $lightness: -40%);\n$side-nav-font-size: rem-calc(16);\n\n// $side-nav-link-bg-hover: hsla(0, 0, 0, 0.025);\n// $side-nav-link-margin: 0;\n// $side-nav-link-padding: rem-calc(7 14);\n// $side-nav-font-size: rem-calc(14);\n// $side-nav-font-weight: $font-weight-normal;\n// $side-nav-font-weight-active: $side-nav-font-weight;\n// $side-nav-font-family: $body-font-family;\n// $side-nav-font-family-active: $side-nav-font-family;\n\n// We use these to control heading styles.\n// $side-nav-heading-color: $side-nav-link-color;\n// $side-nav-heading-font-size: $side-nav-font-size;\n// $side-nav-heading-font-weight: bold;\n// $side-nav-heading-text-transform: uppercase;\n\n// We use these to control border styles\n$side-nav-divider-size: 1px;\n$side-nav-divider-style: solid;\n$side-nav-divider-color: $grey-1;\n\n\n\n// 27. Split Buttons\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// We use these to control different shared styles for Split Buttons\n// $split-button-function-factor: 10%;\n// $split-button-pip-color: $white;\n// $split-button-pip-color-alt: $oil;\n// $split-button-active-bg-tint: rgba(0,0,0,0.1);\n\n// We use these to control tiny split buttons\n// $split-button-padding-tny: $button-pip-tny * 10;\n// $split-button-span-width-tny: $button-pip-tny * 6;\n// $split-button-pip-size-tny: $button-pip-tny;\n// $split-button-pip-top-tny: $button-pip-tny * 2;\n// $split-button-pip-default-float-tny: rem-calc(-6);\n\n// We use these to control small split buttons\n// $split-button-padding-sml: $button-pip-sml * 10;\n// $split-button-span-width-sml: $button-pip-sml * 6;\n// $split-button-pip-size-sml: $button-pip-sml;\n// $split-button-pip-top-sml: $button-pip-sml * 1.5;\n// $split-button-pip-default-float-sml: rem-calc(-6);\n\n// We use these to control medium split buttons\n// $split-button-padding-med: $button-pip-med * 9;\n// $split-button-span-width-med: $button-pip-med * 5.5;\n// $split-button-pip-size-med: $button-pip-med - rem-calc(3);\n// $split-button-pip-top-med: $button-pip-med * 1.5;\n// $split-button-pip-default-float-med: rem-calc(-6);\n\n// We use these to control large split buttons\n// $split-button-padding-lrg: $button-pip-lrg * 8;\n// $split-button-span-width-lrg: $button-pip-lrg * 5;\n// $split-button-pip-size-lrg: $button-pip-lrg - rem-calc(6);\n// $split-button-pip-top-lrg: $button-pip-lrg + rem-calc(5);\n// $split-button-pip-default-float-lrg: rem-calc(-6);\n\n// 28. Sub Nav\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-nav-classes: $include-html-classes;\n\n// We use these to control margin and padding\n// $sub-nav-list-margin: rem-calc(-4 0 18);\n// $sub-nav-list-padding-top: rem-calc(4);\n\n// We use this to control the definition\n// $sub-nav-font-family: $body-font-family;\n// $sub-nav-font-size: rem-calc(14);\n// $sub-nav-font-color: $aluminum;\n// $sub-nav-font-weight: $font-weight-normal;\n// $sub-nav-text-decoration: none;\n// $sub-nav-padding: rem-calc(3 16);\n// $sub-nav-border-radius: 3px;\n// $sub-nav-font-color-hover: scale-color($sub-nav-font-color, $lightness: -25%);\n\n// We use these to control the active item styles\n// $sub-nav-active-font-weight: $font-weight-normal;\n// $sub-nav-active-bg: $primary-color;\n// $sub-nav-active-bg-hover: scale-color($sub-nav-active-bg, $lightness: -14%);\n// $sub-nav-active-color: $white;\n// $sub-nav-active-padding: $sub-nav-padding;\n// $sub-nav-active-cursor: default;\n\n// $sub-nav-item-divider: \"\";\n// $sub-nav-item-divider-margin: rem-calc(12);\n\n// 29. Switch\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-form-classes: $include-html-classes;\n\n// Controlling border styles and background colors for the switch container\n// $switch-border-color: scale-color($white, $lightness: -20%);\n// $switch-border-style: solid;\n// $switch-border-width: 1px;\n// $switch-bg: $white;\n\n// We use these to control the switch heights for our default classes\n// $switch-height-tny: rem-calc(22);\n// $switch-height-sml: rem-calc(28);\n// $switch-height-med: rem-calc(36);\n// $switch-height-lrg: rem-calc(44);\n// $switch-bottom-margin: rem-calc(20);\n\n// We use these to control default font sizes for our classes.\n// $switch-font-size-tny: 11px;\n// $switch-font-size-sml: 12px;\n// $switch-font-size-med: 14px;\n// $switch-font-size-lrg: 17px;\n// $switch-label-side-padding: 6px;\n\n// We use these to style the switch-paddle\n// $switch-paddle-bg: $white;\n// $switch-paddle-fade-to-color: scale-color($switch-paddle-bg, $lightness: -10%);\n// $switch-paddle-border-color: scale-color($switch-paddle-bg, $lightness: -35%);\n// $switch-paddle-border-width: 1px;\n// $switch-paddle-border-style: solid;\n// $switch-paddle-transition-speed: .1s;\n// $switch-paddle-transition-ease: ease-out;\n// $switch-positive-color: scale-color($success-color, $lightness: 94%);\n// $switch-negative-color: $white-smoke;\n\n// Outline Style for tabbing through switches\n// $switch-label-outline: 1px dotted $jumbo;\n\n// 30. Tables\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-table-classes: $include-html-classes;\n\n// These control the background color for the table and even rows\n// $table-bg: $white;\n$table-even-row-bg: $grey-1;\n\n// These control the table cell border style\n// $table-border-style: solid;\n// $table-border-size: 1px;\n// $table-border-color: $gainsboro;\n\n// These control the table head styles\n$table-head-bg: $grey-2;\n// $table-head-font-size: rem-calc(14);\n// $table-head-font-color: $jet;\n// $table-head-font-weight: $font-weight-bold;\n// $table-head-padding: rem-calc(8 10 10);\n\n// These control the row padding and font styles\n// $table-row-padding: rem-calc(9 10);\n// $table-row-font-size: rem-calc(14);\n// $table-row-font-color: $jet;\n// $table-line-height: rem-calc(18);\n\n// These are for controlling the layout, display and margin of tables\n// $table-layout: auto;\n// $table-display: table-cell;\n// $table-margin-bottom: rem-calc(20);\n\n// 31. Tabs\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-tabs-classes: $include-html-classes;\n\n// $tabs-navigation-padding: rem-calc(16);\n// $tabs-navigation-bg-color: $silver  ;\n// $tabs-navigation-active-bg-color: $white;\n// $tabs-navigation-hover-bg-color: scale-color($tabs-navigation-bg-color, $lightness: -6%);\n// $tabs-navigation-font-color: $jet;\n// $tabs-navigation-active-font-color: $tabs-navigation-font-color;\n// $tabs-navigation-font-size: rem-calc(16);\n// $tabs-navigation-font-family: $body-font-family;\n\n// $tabs-content-margin-bottom: rem-calc(24);\n// $tabs-content-padding: $column-gutter/2;\n\n// $tabs-vertical-navigation-margin-bottom: 1.25rem;\n\n// 32. Thumbnails\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-media-classes: $include-html-classes;\n\n// We use these to control border styles\n// $thumb-border-style: solid;\n// $thumb-border-width: 4px;\n// $thumb-border-color: $white;\n// $thumb-box-shadow: 0 0 0 1px rgba($black,.2);\n// $thumb-box-shadow-hover: 0 0 6px 1px rgba($primary-color,0.5);\n\n// Radius and transition speed for thumbs\n// $thumb-radius: $global-radius;\n// $thumb-transition-speed: 200ms;\n\n// 33. Tooltips\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-tooltip-classes: $include-html-classes;\n\n// $has-tip-border-bottom: dotted 1px $iron;\n// $has-tip-font-weight: $font-weight-bold;\n// $has-tip-font-color: $oil;\n// $has-tip-border-bottom-hover: dotted 1px scale-color($primary-color, $lightness: -55%);\n// $has-tip-font-color-hover: $primary-color;\n// $has-tip-cursor-type: help;\n\n// $tooltip-padding: rem-calc(12);\n// $tooltip-bg: $oil;\n// $tooltip-font-size: rem-calc(14);\n// $tooltip-font-weight: $font-weight-normal;\n// $tooltip-font-color: $white;\n// $tooltip-line-height: 1.3;\n// $tooltip-close-font-size: rem-calc(10);\n// $tooltip-close-font-weight: $font-weight-normal;\n// $tooltip-close-font-color: $monsoon;\n// $tooltip-font-size-sml: rem-calc(14);\n// $tooltip-radius: $global-radius;\n// $tooltip-rounded: $global-rounded;\n// $tooltip-pip-size: 5px;\n// $tooltip-max-width: 300px;\n\n// 34. Top Bar\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-top-bar-classes: $include-html-classes;\n\n// Height and margin\n$topbar-height: rem-calc(50);\n// $topbar-margin-bottom: 0;\n\n// Controlling the styles for the title in the top bar\n$topbar-title-weight: $font-weight-bold;\n$topbar-title-font-size: rem-calc(19);\n\n// Style the top bar dropdown elements\n// $topbar-dropdown-bg: $oil;\n// $topbar-dropdown-link-color: $white;\n// $topbar-dropdown-link-bg: $ci-2;\n// $topbar-dropdown-link-weight: $font-weight-normal;\n// $topbar-dropdown-toggle-size: 5px;\n// $topbar-dropdown-toggle-color: $ci-2;\n// $topbar-dropdown-toggle-alpha: 0.4;\n\n// Set the link colors and styles for top-level nav\n// $topbar-link-color: #000;\n// $topbar-link-color-hover: #000;\n// $topbar-link-color-active: #000;\n// $topbar-link-color-active-hover: #000;\n// $topbar-link-weight: $font-weight-normal;\n$topbar-link-font-size: rem-calc(15);\n// $topbar-link-hover-lightness: -10%; // Darken by 10%\n// $topbar-link-bg: $topbar-bg;\n// $topbar-link-bg-color-hover: #ff0;\n// $topbar-link-bg-hover: #f00;\n// $topbar-link-bg-active: $primary-color;\n// $topbar-link-bg-active-hover: scale-color($primary-color, $lightness: -14%);\n// $topbar-link-font-family: $body-font-family;\n$topbar-link-text-transform: uppercase;\n// $topbar-link-padding: $topbar-height / 3;\n// $topbar-back-link-size: $h5-font-size;\n// $topbar-link-dropdown-padding: 20px;\n\n// $topbar-button-font-size: 0.75rem;\n// $topbar-button-top: 7px;\n\n// $topbar-dropdown-label-color: #f77;\n// $topbar-dropdown-label-text-transform: uppercase;\n// $topbar-dropdown-label-font-weight: $font-weight-bold;\n// $topbar-dropdown-label-font-size: rem-calc(10);\n// $topbar-dropdown-label-bg: $oil;\n\n// Top menu icon styles\n$topbar-menu-link-transform: uppercase;\n// $topbar-menu-link-font-size: rem-calc(13);\n// $topbar-menu-link-weight: $font-weight-bold;\n// $topbar-menu-link-color: $white;\n// $topbar-menu-icon-color: $white;\n// $topbar-menu-link-color-toggled: $ci-6;\n// $topbar-menu-icon-color-toggled: $ci-6;\n\n// Transitions and breakpoint styles\n// $topbar-transition-speed: 300ms;\n// Using rem-calc for the below breakpoint causes issues with top bar\n$topbar-breakpoint: #{lower-bound($large-range)}; // Change to 9999px for always mobile layout\n$topbar-media-query: \"only screen and (min-width: #{$topbar-breakpoint})\" !default;\n\n// Divider Styles\n$topbar-divider-border-bottom: solid 0px scale-color($topbar-bg-color, $lightness: 23%);\n$topbar-divider-border-top: solid 0px scale-color($topbar-bg-color, $lightness: -50%);\n\n// Sticky Class\n// $topbar-sticky-class: \".sticky\";\n// $topbar-arrows: true; //Set false to remove the triangle icon from the menu item\n\n// 36. Visibility Classes\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-visibility-classes: $include-html-classes;\n// $include-table-visibility-classes: true;\n// $include-legacy-visibility-classes: true;\n// $include-accessibility-classes: true;\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"buttons\";\n\n//\n// @variables\n//\n$include-html-form-classes: $include-html-classes !default;\n\n// We use this to set the base for lots of form spacing and positioning styles\n$form-spacing: rem-calc(16) !default;\n\n// We use these to style the labels in different ways\n$form-label-pointer: pointer !default;\n$form-label-font-size: rem-calc(14) !default;\n$form-label-font-weight: $font-weight-normal !default;\n$form-label-line-height: 1.5 !default;\n$form-label-font-color: scale-color($black, $lightness: 30%) !default;\n$form-label-small-transform: capitalize !default;\n$form-label-bottom-margin: 0 !default;\n$input-font-family: inherit !default;\n$input-font-color: rgba(0, 0, 0, 0.75) !default;\n$input-font-size: rem-calc(14) !default;\n$input-bg-color: $white !default;\n$input-focus-bg-color: scale-color($white, $lightness: -2%) !default;\n$input-border-color: scale-color($white, $lightness: -20%) !default;\n$input-focus-border-color: scale-color($white, $lightness: -40%) !default;\n$input-border-style: solid !default;\n$input-border-width: 1px !default;\n$input-border-radius: $global-radius !default;\n$input-disabled-bg: $gainsboro !default;\n$input-disabled-cursor: $cursor-default-value !default;\n$input-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1) !default;\n$input-include-glowing-effect: true !default;\n\n// We use these to style the fieldset border and spacing.\n$fieldset-border-style: solid !default;\n$fieldset-border-width: 1px !default;\n$fieldset-border-color: $gainsboro !default;\n$fieldset-padding: rem-calc(20) !default;\n$fieldset-margin: rem-calc(18 0) !default;\n\n// We use these to style the legends when you use them\n$legend-bg: $white !default;\n$legend-font-weight: $font-weight-bold !default;\n$legend-padding: rem-calc(0 3) !default;\n\n// We use these to style the prefix and postfix input elements\n$input-prefix-bg: scale-color($white, $lightness: -5%) !default;\n$input-prefix-border-color: scale-color($white, $lightness: -20%) !default;\n$input-prefix-border-size: 1px !default;\n$input-prefix-border-type: solid !default;\n$input-prefix-overflow: hidden !default;\n$input-prefix-font-color: $oil !default;\n$input-prefix-font-color-alt: $white !default;\n\n// We use this setting to turn on/off HTML5 number spinners (the up/down arrows)\n$input-number-spinners: true !default;\n\n// We use these to style the error states for inputs and labels\n$input-error-message-padding: rem-calc(6 9 9) !default;\n$input-error-message-top: -1px !default;\n$input-error-message-font-size: rem-calc(12) !default;\n$input-error-message-font-weight: $font-weight-normal !default;\n$input-error-message-font-style: italic !default;\n$input-error-message-font-color: $white !default;\n$input-error-message-bg-color: $alert-color !default;\n$input-error-message-font-color-alt: $oil !default;\n\n// We use this to style the glowing effect of inputs when focused\n$glowing-effect-fade-time: 0.45s !default;\n$glowing-effect-color: $input-focus-border-color !default;\n\n// Select variables\n$select-bg-color: $ghost !default;\n$select-hover-bg-color: scale-color($select-bg-color, $lightness: -3%) !default;\n\n//\n// @MIXINS\n//\n\n// We use this mixin to give us form styles for rows inside of forms\n@mixin form-row-base {\n  .row {\n    margin: 0 calc((-1 * $form-spacing) / 2);\n\n    .column,\n    .columns {\n      padding: 0 calc($form-spacing / 2);\n    }\n\n    // Use this to collapse the margins of a form row\n    &.collapse {\n      margin: 0;\n\n      .column,\n      .columns {\n        padding: 0;\n      }\n\n      input {\n        @include side-radius($opposite-direction, 0);\n      }\n\n    }\n  }\n\n  input.column,\n  input.columns,\n  textarea.column,\n  textarea.columns {\n    padding-#{$default-float}: calc($form-spacing / 2);\n  }\n}\n\n// @MIXIN\n//\n// We use this mixin to give all basic form elements their style\n@mixin form-element {\n  background-color: $input-bg-color;\n  font-family: $input-font-family;\n\n  border: {\n    style: $input-border-style;\n    width: $input-border-width;\n    color: $input-border-color;\n  }\n\n  box-shadow: $input-box-shadow;\n  color: $input-font-color;\n  display: block;\n  font-size: $input-font-size;\n  margin: 0 0 $form-spacing 0;\n  padding: calc($form-spacing / 2);\n  height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n  width: 100%;\n  @include box-sizing(border-box);\n\n  @if $input-include-glowing-effect {\n    @include block-glowing-effect(focus, $glowing-effect-fade-time, $glowing-effect-color);\n  }\n\n  // Basic focus styles\n  &:focus {\n    background: $input-focus-bg-color;\n    border-color: $input-focus-border-color;\n    outline: none;\n  }\n\n  // Disabled Styles\n  &:disabled {\n    background-color: $input-disabled-bg;\n    cursor: $input-disabled-cursor;\n  }\n\n  // Disabled background input background color\n  &[disabled],\n  &[readonly],\n  fieldset[disabled] & {\n    background-color: $input-disabled-bg;\n    cursor: $input-disabled-cursor;\n  }\n}\n\n// @MIXIN\n//\n// We use this mixin to create form labels\n//\n// $alignment - Alignment options. Default: false. Options: [right, inline, false]\n// $base-style - Control whether or not the base styles come through. Default: true.\n@mixin form-label($alignment: false, $base-style: true) {\n\n  // Control whether or not the base styles come through.\n  @if $base-style {\n    font-size: $form-label-font-size;\n    color: $form-label-font-color;\n    cursor: $form-label-pointer;\n    display: block;\n    font-weight: $form-label-font-weight;\n    line-height: $form-label-line-height;\n    margin-bottom: $form-label-bottom-margin;\n  }\n\n  // Alignment options\n  @if $alignment ==right {\n    float: none !important;\n    text-align: right;\n  }\n\n  @else if $alignment ==inline {\n    margin: 0 0 $form-spacing 0;\n    padding: calc($form-spacing / 2) + rem-calc($input-border-width) 0;\n  }\n}\n\n// We use this mixin to create postfix/prefix form Labels\n@mixin prefix-postfix-base {\n  display: block;\n  position: relative;\n  z-index: 2;\n  text-align: center;\n  width: 100%;\n  padding-top: 0;\n  padding-bottom: 0;\n  border-style: $input-prefix-border-type;\n  border-width: $input-prefix-border-size;\n  overflow: $input-prefix-overflow;\n  font-size: $form-label-font-size;\n  height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n  line-height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n}\n\n// @MIXIN\n//\n// We use this mixin to create prefix label styles\n// $bg - Default:$input-prefix-bg || scale-color($white, $lightness: -5%) !default;\n// $is-button - Toggle position settings if prefix is a button. Default:false\n//\n@mixin prefix($bg: $input-prefix-bg, $border: $input-prefix-border-color, $is-button: false) {\n\n  @if $bg {\n    $bg-lightness: lightness($bg);\n    background: $bg;\n    border-#{$opposite-direction}: none;\n\n    // Control the font color based on background brightness\n    @if $bg-lightness >70% or $bg ==yellow {\n      color: $input-prefix-font-color;\n    }\n\n    @else {\n      color: $input-prefix-font-color-alt;\n    }\n  }\n\n  @if $border {\n    border-color: $border;\n  }\n\n  @if $is-button {\n    padding-#{$default-float}: 0;\n    padding-#{$opposite-direction}: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    text-align: center;\n    border: none;\n  }\n\n}\n\n// @MIXIN\n//\n// We use this mixin to create postfix label styles\n// $bg - Default:$input-prefix-bg || scale-color($white, $lightness: -5%) !default;\n// $is-button - Toggle position settings if prefix is a button. Default: false\n@mixin postfix($bg: $input-prefix-bg, $border: $input-prefix-border-color, $is-button: false) {\n\n  @if $bg {\n    $bg-lightness: lightness($bg);\n    background: $bg;\n    border-#{$default-float}: none;\n\n    // Control the font color based on background brightness\n    @if $bg-lightness >70% or $bg ==yellow {\n      color: $input-prefix-font-color;\n    }\n\n    @else {\n      color: $input-prefix-font-color-alt;\n    }\n  }\n\n  @if $border {\n    border-color: $border;\n  }\n\n  @if $is-button {\n    padding-#{$default-float}: 0;\n    padding-#{$opposite-direction}: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    text-align: center;\n    border: none;\n  }\n\n}\n\n// We use this mixin to style fieldsets\n@mixin fieldset {\n  border: $fieldset-border-width $fieldset-border-style $fieldset-border-color;\n  padding: $fieldset-padding;\n  margin: $fieldset-margin;\n\n  // and legend styles\n  legend {\n    font-weight: $legend-font-weight;\n    background: $legend-bg;\n    padding: $legend-padding;\n    margin: 0;\n    margin-#{$default-float}: rem-calc(-3);\n  }\n}\n\n// @MIXIN\n//\n// We use this mixin to control border and background color of error inputs\n// $color - Default: $alert-color (found in settings file)\n@mixin form-error-color($color: $alert-color) {\n  border-color: $color;\n  background-color: rgba($color, 0.1);\n\n  // Go back to normal on focus\n  &:focus {\n    background: $input-focus-bg-color;\n    border-color: $input-focus-border-color;\n  }\n}\n\n// @MIXIN\n//\n// We use this simple mixin to style labels for error inputs\n// $color - Default:$alert-color. Found in settings file\n@mixin form-label-error-color($color: $alert-color) {\n  color: $color;\n}\n\n// @MIXIN\n//\n// We use this mixin to create error message styles\n// $bg - Default: $alert-color (Found in settings file)\n@mixin form-error-message($bg: $input-error-message-bg-color) {\n  display: block;\n  padding: $input-error-message-padding;\n  margin-top: $input-error-message-top;\n  margin-bottom: $form-spacing;\n  font-size: $input-error-message-font-size;\n  font-weight: $input-error-message-font-weight;\n  font-style: $input-error-message-font-style;\n\n  // We can control the text color based on the brightness of the background.\n  $bg-lightness: lightness($bg);\n  background: $bg;\n\n  @if $bg-lightness < 70% or $bg ==yellow {\n    color: $input-error-message-font-color;\n  }\n\n  @else {\n    color: $input-error-message-font-color-alt;\n  }\n}\n\n// We use this mixin to style select elements\n@mixin form-select {\n  -webkit-appearance: none !important;\n  border-radius: 0;\n  background-color: $select-bg-color;\n\n  // Hide the dropdown arrow shown in newer IE versions\n  &::-ms-expand {\n    display: none;\n  }\n\n  // The custom arrow has some fake horizontal padding so we can align it\n  // from the right side of the element without relying on CSS3\n  background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgeD0iMTJweCIgeT0iMHB4IiB3aWR0aD0iMjRweCIgaGVpZ2h0PSIzcHgiIHZpZXdCb3g9IjAgMCA2IDMiIGVuYWJsZS1iYWNrZ3JvdW5kPSJuZXcgMCAwIDYgMyIgeG1sOnNwYWNlPSJwcmVzZXJ2ZSI+PHBvbHlnb24gcG9pbnRzPSI1Ljk5MiwwIDIuOTkyLDMgLTAuMDA4LDAgIi8+PC9zdmc+);\n\n  // We can safely use leftmost and rightmost now\n  background-position: if($text-direction =='rtl', 0%, 100%) center;\n\n  background-repeat: no-repeat;\n\n  border: {\n    style: $input-border-style;\n    width: $input-border-width;\n    color: $input-border-color;\n  }\n\n  padding: calc($form-spacing / 2);\n  font-size: $input-font-size;\n  font-family: $body-font-family;\n  color: $input-font-color;\n  line-height: normal;\n  @include radius(0);\n\n  &.radius {\n    @include radius($global-radius);\n  }\n\n  &:hover {\n    background-color: $select-hover-bg-color;\n    border-color: $input-focus-border-color;\n  }\n\n  // Disabled Styles\n  &:disabled {\n    background-color: $input-disabled-bg;\n    cursor: $input-disabled-cursor;\n  }\n}\n\n// We use this mixin to turn on/off HTML5 number spinners\n@mixin html5number($browser, $on: true) {\n  @if $on==false {\n    @if $browser==webkit {\n      -webkit-appearance: none;\n      margin: 0;\n    }\n\n    @else if $browser==moz {\n      -moz-appearance: textfield;\n    }\n  }\n}\n\n@include exports(\"form\") {\n  @if $include-html-form-classes {\n\n    /* Standard Forms */\n    form {\n      margin: 0 0 $form-spacing;\n    }\n\n    /* Using forms within rows, we need to set some defaults */\n    form .row {\n      @include form-row-base;\n    }\n\n    /* Label Styles */\n    label {\n      @include form-label;\n\n      &.right {\n        @include form-label(right, false);\n      }\n\n      &.inline {\n        @include form-label(inline, false);\n      }\n\n      /* Styles for required inputs */\n      small {\n        text-transform: $form-label-small-transform;\n        color: scale-color($form-label-font-color, $lightness: 15%);\n      }\n    }\n\n    /* Attach elements to the beginning or end of an input */\n    .prefix,\n    .postfix {\n      @include prefix-postfix-base;\n    }\n\n    /* Adjust padding, alignment and radius if pre/post element is a button */\n    .postfix.button {\n      @include button-size(false, false);\n      @include postfix(false, false, true);\n    }\n\n    .prefix.button {\n      @include button-size(false, false);\n      @include prefix(false, false, true);\n    }\n\n    .prefix.button.radius {\n      @include radius(0);\n      @include side-radius($default-float, $button-radius);\n    }\n\n    .postfix.button.radius {\n      @include radius(0);\n      @include side-radius($opposite-direction, $button-radius);\n    }\n\n    .prefix.button.round {\n      @include radius(0);\n      @include side-radius($default-float, $button-round);\n    }\n\n    .postfix.button.round {\n      @include radius(0);\n      @include side-radius($opposite-direction, $button-round);\n    }\n\n    /* Separate prefix and postfix styles when on span or label so buttons keep their own */\n    span.prefix,\n    label.prefix {\n      @include prefix();\n    }\n\n    span.postfix,\n    label.postfix {\n      @include postfix();\n    }\n\n    /* We use this to get basic styling on all basic form elements */\n    #{text-inputs(all, 'input')} {\n      -webkit-appearance: none;\n      border-radius: 0;\n      @include form-element;\n\n      @if $input-include-glowing-effect ==false {\n        @include single-transition(all, 0.15s, linear);\n      }\n\n      &.radius {\n        @include radius($input-border-radius);\n      }\n    }\n\n    form {\n      .row {\n        .prefix-radius.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-radius);\n          }\n\n          .prefix {\n            @include radius(0);\n            @include side-radius($default-float, $button-radius);\n          }\n        }\n\n        .postfix-radius.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($default-float, $button-radius);\n          }\n\n          .postfix {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-radius);\n          }\n        }\n\n        .prefix-round.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-round);\n          }\n\n          .prefix {\n            @include radius(0);\n            @include side-radius($default-float, $button-round);\n          }\n        }\n\n        .postfix-round.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($default-float, $button-round);\n          }\n\n          .postfix {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-round);\n          }\n        }\n      }\n    }\n\n    input[type=\"submit\"] {\n      -webkit-appearance: none;\n      border-radius: 0;\n    }\n\n    /* Respect enforced amount of rows for textarea */\n    textarea[rows] {\n      height: auto;\n    }\n\n    /* Not allow resize out of parent */\n    textarea {\n      max-width: 100%;\n    }\n\n    /* Add height value for select elements to match text input height */\n    select {\n      @include form-select;\n      height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n    }\n\n    /* Adjust margin for form elements below */\n    input[type=\"file\"],\n    input[type=\"checkbox\"],\n    input[type=\"radio\"],\n    select {\n      margin: 0 0 $form-spacing 0;\n    }\n\n    input[type=\"checkbox\"]+label,\n    input[type=\"radio\"]+label {\n      display: inline-block;\n      margin-#{$default-float}: $form-spacing * .5;\n      margin-#{$opposite-direction}: $form-spacing;\n      margin-bottom: 0;\n      vertical-align: baseline;\n    }\n\n    /* Normalize file input width */\n    input[type=\"file\"] {\n      width: 100%;\n    }\n\n    /* HTML5 Number spinners settings */\n    input[type=number] {\n      @include html5number(moz, $input-number-spinners)\n    }\n\n    input[type=\"number\"]::-webkit-inner-spin-button,\n    input[type=\"number\"]::-webkit-outer-spin-button {\n      @include html5number(webkit, $input-number-spinners);\n    }\n\n    /* We add basic fieldset styling */\n    fieldset {\n      @include fieldset;\n    }\n\n    /* Error Handling */\n\n    #{data('abide')} {\n\n      .error small.error,\n      .error span.error,\n      span.error,\n      small.error {\n        @include form-error-message;\n      }\n\n      span.error,\n      small.error {\n        display: none;\n      }\n    }\n\n    span.error,\n    small.error {\n      @include form-error-message;\n    }\n\n    .error {\n\n      input,\n      textarea,\n      select {\n        margin-bottom: 0;\n      }\n\n      input[type=\"checkbox\"],\n      input[type=\"radio\"] {\n        margin-bottom: $form-spacing\n      }\n\n      label,\n      label.error {\n        @include form-label-error-color;\n      }\n\n      small.error {\n        @include form-error-message;\n      }\n\n      >label {\n        >small {\n          color: scale-color($form-label-font-color, $lightness: 15%);\n          background: transparent;\n          padding: 0;\n          text-transform: $form-label-small-transform;\n          font-style: normal;\n          font-size: 60%;\n          margin: 0;\n          display: inline;\n        }\n      }\n\n      span.error-message {\n        display: block;\n      }\n    }\n\n    input.error,\n    textarea.error,\n    select.error {\n      margin-bottom: 0;\n    }\n\n    label.error {\n      @include form-label-error-color;\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"grid\";\n@import \"buttons\";\n@import \"forms\";\n\n//\n// Top Bar Variables\n//\n$include-html-top-bar-classes: $include-html-classes !default;\n\n// Background color for the top bar\n$topbar-bg-color: $oil !default;\n$topbar-bg: $topbar-bg-color !default;\n\n// Height and margin\n$topbar-height: rem-calc(45) !default;\n$topbar-margin-bottom: 0 !default;\n\n// Controlling the styles for the title in the top bar\n$topbar-title-weight: $font-weight-normal !default;\n$topbar-title-font-size: rem-calc(17) !default;\n\n// Set the link colors and styles for top-level nav\n$topbar-link-color: $white !default;\n$topbar-link-color-hover: $white !default;\n$topbar-link-color-active: $white !default;\n$topbar-link-color-active-hover: $white !default;\n$topbar-link-weight: $font-weight-normal !default;\n$topbar-link-font-size: rem-calc(13) !default;\n$topbar-link-hover-lightness: -10% !default; // Darken by 10%\n$topbar-link-bg: $topbar-bg !default;\n$topbar-link-bg-hover: $oil !default;\n$topbar-link-bg-color-hover: $charcoal !default;\n$topbar-link-bg-active: $primary-color !default;\n$topbar-link-bg-active-hover: scale-color($primary-color, $lightness: -14%) !default;\n$topbar-link-font-family: $body-font-family !default;\n$topbar-link-text-transform: none !default;\n$topbar-link-padding: calc($topbar-height / 3) !default;\n$topbar-back-link-size: rem-calc(18) !default;\n$topbar-link-dropdown-padding: rem-calc(20) !default;\n$topbar-button-font-size: 0.75rem !default;\n$topbar-button-top: 7px !default;\n\n// Style the top bar dropdown elements\n$topbar-dropdown-bg: $oil !default;\n$topbar-dropdown-link-color: $white !default;\n$topbar-dropdown-link-color-hover: $topbar-link-color-hover !default;\n$topbar-dropdown-link-bg: $oil !default;\n$topbar-dropdown-link-bg-hover: $oil !default;\n$topbar-dropdown-link-weight: $font-weight-normal !default;\n$topbar-dropdown-toggle-size: 5px !default;\n$topbar-dropdown-toggle-color: $white !default;\n$topbar-dropdown-toggle-alpha: 0.4 !default;\n\n$topbar-dropdown-label-color: $monsoon !default;\n$topbar-dropdown-label-text-transform: uppercase !default;\n$topbar-dropdown-label-font-weight: $font-weight-bold !default;\n$topbar-dropdown-label-font-size: rem-calc(10) !default;\n$topbar-dropdown-label-bg: $oil !default;\n\n// Top menu icon styles\n$topbar-menu-link-transform: uppercase !default;\n$topbar-menu-link-font-size: rem-calc(13) !default;\n$topbar-menu-link-weight: $font-weight-bold !default;\n$topbar-menu-link-color: $white !default;\n$topbar-menu-icon-color: $white !default;\n$topbar-menu-link-color-toggled: $jumbo !default;\n$topbar-menu-icon-color-toggled: $jumbo !default;\n\n// Transitions and breakpoint styles\n$topbar-transition-speed: 300ms !default;\n// Using rem-calc for the below breakpoint causes issues with top bar\n$topbar-breakpoint: #{lower-bound($medium-range)} !default; // Change to 9999px for always mobile layout\n$topbar-media-query: $medium-up !default;\n\n// Top-bar input styles\n$topbar-input-height: rem-calc(28) !default;\n\n// Divider Styles\n$topbar-divider-border-bottom: solid 1px scale-color($topbar-bg-color, $lightness: 13%) !default;\n$topbar-divider-border-top: solid 1px scale-color($topbar-bg-color, $lightness: -50%) !default;\n\n// Sticky Class\n$topbar-sticky-class: \".sticky\" !default;\n$topbar-arrows: true !default; //Set false to remove the triangle icon from the menu item\n$topbar-dropdown-arrows: true !default; //Set false to remove the \\00bb >> text from dropdown subnavigation li\n\n// Accessibility mixins for hiding and showing the menu dropdown items\n@mixin topbar-hide-dropdown {\n  // Makes an element visually hidden by default, but visible when focused.\n  display: block;\n  @include element-invisible();\n}\n\n@mixin topbar-show-dropdown {\n  display: block;\n  @include element-invisible-off();\n  position: absolute !important; // Reset the position from static to absolute\n}\n\n@include exports(\"top-bar\") {\n\n  @if $include-html-top-bar-classes {\n\n    // Used to provide media query values for javascript components.\n    // This class is generated despite the value of $include-html-top-bar-classes\n    // to ensure width calculations work correctly.\n    meta.foundation-mq-topbar {\n      font-family: \"/\" + unquote($topbar-media-query) + \"/\";\n      width: $topbar-breakpoint;\n    }\n\n    /* Wrapped around .top-bar to contain to grid width */\n    .contain-to-grid {\n      width: 100%;\n      background: $topbar-bg;\n\n      .top-bar {\n        margin-bottom: $topbar-margin-bottom;\n      }\n    }\n\n    // Wrapped around .top-bar to make it stick to the top\n    .fixed {\n      width: 100%;\n      #{$default-float}: 0;\n      position: fixed;\n      top: 0;\n      z-index: 99;\n\n      &.expanded:not(.top-bar) {\n        overflow-y: auto;\n        height: auto;\n        width: 100%;\n        max-height: 100%;\n\n        .title-area {\n          position: fixed;\n          width: 100%;\n          z-index: 99;\n        }\n\n        // Ensure you can scroll the menu on small screens\n        .top-bar-section {\n          z-index: 98;\n          margin-top: $topbar-height;\n        }\n      }\n    }\n\n    .top-bar {\n      overflow: hidden;\n      height: $topbar-height;\n      line-height: $topbar-height;\n      position: relative;\n      background: $topbar-bg;\n      margin-bottom: $topbar-margin-bottom;\n\n      // Topbar Global list Styles\n      ul {\n        margin-bottom: 0;\n        list-style: none;\n      }\n\n      .row {\n        max-width: none;\n      }\n\n      form,\n      input {\n        margin-bottom: 0;\n      }\n\n      input {\n        height: $topbar-input-height;\n        padding-top: .35rem;\n        padding-bottom: .35rem;\n        font-size: $topbar-button-font-size;\n      }\n\n      .button,\n      button {\n        padding-top: .35rem + rem-calc(1);\n        padding-bottom: .35rem + rem-calc(1);\n        margin-bottom: 0;\n        font-size: $topbar-button-font-size;\n        // position: relative;\n        // top: -1px;\n\n        // Corrects a slight misalignment when put next to an input field\n        @media #{$small-only} {\n          position: relative;\n          top: -1px;\n        }\n      }\n\n      // Title Area\n      .title-area {\n        position: relative;\n        margin: 0;\n      }\n\n      .name {\n        height: $topbar-height;\n        margin: 0;\n        font-size: $rem-base;\n\n        h1,\n        h2,\n        h3,\n        h4,\n        p,\n        span {\n          line-height: $topbar-height;\n          font-size: $topbar-title-font-size;\n          margin: 0;\n\n          a {\n            font-weight: $topbar-title-weight;\n            color: $topbar-link-color;\n            width: 75%;\n            display: block;\n            padding: 0 $topbar-link-padding;\n          }\n        }\n      }\n\n      // Menu toggle button on small devices\n      .toggle-topbar {\n        position: absolute;\n        #{$opposite-direction}: 0;\n        top: 0;\n\n        a {\n          color: $topbar-link-color;\n          text-transform: $topbar-menu-link-transform;\n          font-size: $topbar-menu-link-font-size;\n          font-weight: $topbar-menu-link-weight;\n          position: relative;\n          display: block;\n          padding: 0 $topbar-link-padding;\n          height: $topbar-height;\n          line-height: $topbar-height;\n        }\n\n        // Adding the class \"menu-icon\" will add the 3-line icon people love and adore.\n        &.menu-icon {\n          top: 50%;\n          margin-top: -16px;\n\n          a {\n            @if $text-direction ==rtl {\n              text-indent: -58px;\n            }\n\n            height: 34px;\n            line-height: 33px;\n            padding: 0 $topbar-link-padding+rem-calc(25) 0 $topbar-link-padding;\n            color: $topbar-menu-link-color;\n            position: relative;\n\n            & {\n              // @include hamburger icon\n              //\n              // We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n              // $width - Width of hamburger icon\n              // $left - If false, icon will be centered horizontally || explicitly set value in rem\n              // $top - If false, icon will be centered vertically || explicitly set value in rem\n              // $thickness - thickness of lines in hamburger icon, set value in px\n              // $gap - spacing between the lines in hamburger icon, set value in px\n              // $color - icon color\n              // $hover-color - icon color during hover, here it isn't set b/c it would override $topbar-menu-icon-color-toggled\n              // $offcanvas - Set to false of @include in topbar\n              @include hamburger(16px, false, 0, 1px, 6px, $topbar-menu-icon-color, \"\", false);\n            }\n          }\n        }\n      }\n\n      // Change things up when the top-bar is expanded\n      &.expanded {\n        height: auto;\n        background: transparent;\n\n        .title-area {\n          background: $topbar-bg;\n        }\n\n        .toggle-topbar {\n          a {\n            color: $topbar-menu-link-color-toggled;\n\n            span::after {\n              // Shh, don't tell, but box-shadows create the menu icon :)\n              // Change the color of the bars when the menu is expanded, using given thickness from hamburger() above\n              box-shadow: 0 0 0 1px $topbar-menu-icon-color-toggled,\n                0 7px 0 1px $topbar-menu-icon-color-toggled,\n                0 14px 0 1px $topbar-menu-icon-color-toggled;\n            }\n          }\n        }\n      }\n    }\n\n    // Right and Left Navigation that stacked by default\n    .top-bar-section {\n      #{$default-float}: 0;\n      position: relative;\n      width: auto;\n      @include single-transition($default-float, $topbar-transition-speed);\n\n      ul {\n        padding: 0;\n        width: 100%;\n        height: auto;\n        display: block;\n        font-size: $rem-base;\n        margin: 0;\n      }\n\n      .divider,\n      [role=\"separator\"] {\n        border-top: $topbar-divider-border-top;\n        clear: both;\n        height: 1px;\n        width: 100%;\n      }\n\n      ul li {\n        background: $topbar-dropdown-bg;\n\n        &>a {\n          display: block;\n          width: 100%;\n          color: $topbar-link-color;\n          padding: 12px 0 12px 0;\n          padding-#{$default-float}: $topbar-link-padding;\n          font-family: $topbar-link-font-family;\n          font-size: $topbar-link-font-size;\n          font-weight: $topbar-link-weight;\n          text-transform: $topbar-link-text-transform;\n\n          &.button {\n            font-size: $topbar-link-font-size;\n            padding-#{$opposite-direction}: $topbar-link-padding;\n            padding-#{$default-float}: $topbar-link-padding;\n            @include button-style($bg: $primary-color);\n          }\n\n          &.button.secondary {\n            @include button-style($bg: $secondary-color);\n          }\n\n          &.button.success {\n            @include button-style($bg: $success-color);\n          }\n\n          &.button.alert {\n            @include button-style($bg: $alert-color);\n          }\n\n          &.button.warning {\n            @include button-style($bg: $warning-color);\n          }\n        }\n\n        >button {\n          font-size: $topbar-link-font-size;\n          padding-#{$opposite-direction}: $topbar-link-padding;\n          padding-#{$default-float}: $topbar-link-padding;\n          @include button-style($bg: $primary-color);\n\n          &.secondary {\n            @include button-style($bg: $secondary-color);\n          }\n\n          &.success {\n            @include button-style($bg: $success-color);\n          }\n\n          &.alert {\n            @include button-style($bg: $alert-color);\n          }\n\n          &.warning {\n            @include button-style($bg: $warning-color);\n          }\n        }\n\n        // Apply the hover link color when it has that class\n        &:hover:not(.has-form)>a {\n          background-color: $topbar-link-bg-color-hover;\n\n          @if ($topbar-link-bg-hover) {\n            background: $topbar-link-bg-hover;\n          }\n\n          color: $topbar-link-color-hover;\n        }\n\n        // Apply the active link color when it has that class\n        &.active>a {\n          background: $topbar-link-bg-active;\n          color: $topbar-link-color-active;\n\n          &:hover {\n            background: $topbar-link-bg-active-hover;\n            color: $topbar-link-color-active-hover;\n          }\n        }\n      }\n\n      // Add some extra padding for list items contains buttons\n      .has-form {\n        padding: $topbar-link-padding;\n      }\n\n      // Styling for list items that have a dropdown within them.\n      .has-dropdown {\n        position: relative;\n\n        &>a {\n          &:after {\n            @if ($topbar-arrows) {\n              @include css-triangle($topbar-dropdown-toggle-size, rgba($topbar-dropdown-toggle-color, $topbar-dropdown-toggle-alpha), $default-float);\n            }\n\n            margin-#{$opposite-direction}: $topbar-link-padding;\n            margin-top: -(calc($topbar-dropdown-toggle-size / 2)) - 2;\n            position: absolute;\n            top: 50%;\n            #{$opposite-direction}: 0;\n          }\n        }\n\n        &.moved {\n          position: static;\n\n          &>.dropdown {\n            @include topbar-show-dropdown();\n            width: 100%;\n          }\n\n          &>a:after {\n            display: none;\n          }\n        }\n      }\n\n      // Styling elements inside of dropdowns\n      .dropdown {\n        padding: 0;\n        position: absolute;\n        #{$default-float}: 100%;\n        top: 0;\n        z-index: 99;\n        @include topbar-hide-dropdown();\n\n        li {\n          width: 100%;\n          height: auto;\n\n          a {\n            font-weight: $topbar-dropdown-link-weight;\n            padding: 8px $topbar-link-padding;\n\n            &.parent-link {\n              font-weight: $topbar-link-weight;\n            }\n          }\n\n          &.title h5,\n          &.parent-link {\n            // Back Button\n            margin-bottom: 0;\n            margin-top: 0;\n            font-size: $topbar-back-link-size;\n\n            a {\n              color: $topbar-link-color;\n              // line-height: ($topbar-height / 2);\n              display: block;\n\n              &:hover {\n                background: none;\n              }\n            }\n          }\n\n          &.has-form {\n            padding: 8px $topbar-link-padding;\n          }\n\n          .button,\n          button {\n            top: auto;\n          }\n        }\n\n        label {\n          padding: 8px $topbar-link-padding 2px;\n          margin-bottom: 0;\n          text-transform: $topbar-dropdown-label-text-transform;\n          color: $topbar-dropdown-label-color;\n          font-weight: $topbar-dropdown-label-font-weight;\n          font-size: $topbar-dropdown-label-font-size;\n        }\n      }\n    }\n\n    .js-generated {\n      display: block;\n    }\n\n\n    // Top Bar styles intended for screen sizes above the breakpoint.\n    @media #{$topbar-media-query} {\n      .top-bar {\n        background: $topbar-bg;\n        @include clearfix;\n        overflow: visible;\n\n        .toggle-topbar {\n          display: none;\n        }\n\n        .title-area {\n          float: $default-float;\n        }\n\n        .name h1 a {\n          width: auto;\n        }\n\n        input,\n        .button,\n        button {\n          font-size: rem-calc(14);\n          position: relative;\n          height: $topbar-input-height;\n          top: calc(($topbar-height - $topbar-input-height) / 2);\n        }\n\n        &.expanded {\n          background: $topbar-bg;\n        }\n      }\n\n      .contain-to-grid .top-bar {\n        max-width: $row-width;\n        margin: 0 auto;\n        margin-bottom: $topbar-margin-bottom;\n      }\n\n      .top-bar-section {\n        @include single-transition(none, 0, 0);\n        #{$default-float}: 0 !important;\n\n        ul {\n          width: auto;\n          height: auto !important;\n          display: inline;\n\n          li {\n            float: $default-float;\n\n            .js-generated {\n              display: none;\n            }\n          }\n        }\n\n        li {\n          &.hover {\n            >a:not(.button) {\n              background-color: $topbar-link-bg-color-hover;\n\n              @if ($topbar-link-bg-hover) {\n                background: $topbar-link-bg-hover;\n              }\n\n              color: $topbar-link-color-hover;\n            }\n          }\n\n          &:not(.has-form) {\n            a:not(.button) {\n              padding: 0 $topbar-link-padding;\n              line-height: $topbar-height;\n              background: $topbar-link-bg;\n\n              &:hover {\n                background-color: $topbar-link-bg-color-hover;\n\n                @if ($topbar-link-bg-hover) {\n                  background: $topbar-link-bg-hover;\n                }\n              }\n            }\n          }\n\n          &.active:not(.has-form) {\n            a:not(.button) {\n              padding: 0 $topbar-link-padding;\n              line-height: $topbar-height;\n              color: $topbar-link-color-active;\n              background: $topbar-link-bg-active;\n\n              &:hover {\n                background: $topbar-link-bg-active-hover;\n                color: $topbar-link-color-active-hover;\n              }\n            }\n          }\n        }\n\n        .has-dropdown {\n          @if($topbar-arrows) {\n            &>a {\n              padding-#{$opposite-direction}: $topbar-link-padding + $topbar-link-dropdown-padding !important;\n\n              &:after {\n                @include css-triangle($topbar-dropdown-toggle-size, rgba($topbar-dropdown-toggle-color, $topbar-dropdown-toggle-alpha), top);\n                margin-top: -(calc($topbar-dropdown-toggle-size / 2));\n                top: calc($topbar-height / 2);\n              }\n            }\n          }\n\n          &.moved {\n            position: relative;\n\n            &>.dropdown {\n              @include topbar-hide-dropdown();\n            }\n          }\n\n          &.hover,\n          &.not-click:hover {\n            &>.dropdown {\n              @include topbar-show-dropdown();\n            }\n          }\n\n          >a:focus+.dropdown {\n            @include topbar-show-dropdown();\n          }\n\n          .dropdown li.has-dropdown {\n            &>a {\n              @if ($topbar-dropdown-arrows) {\n                &:after {\n                  border: none;\n                  content: \"\\00bb\";\n                  top: 1rem;\n                  margin-top: -1px;\n                  #{$opposite-direction}: 5px;\n                  line-height: 1.2;\n                }\n              }\n            }\n          }\n        }\n\n        .dropdown {\n          #{$default-float}: 0;\n          top: auto;\n          background: transparent;\n          min-width: 100%;\n\n          li {\n            a {\n              color: $topbar-dropdown-link-color;\n              line-height: $topbar-height;\n              white-space: nowrap;\n              padding: 12px $topbar-link-padding;\n              background: $topbar-dropdown-link-bg;\n            }\n\n            &:not(.has-form):not(.active) {\n              &>a:not(.button) {\n                color: $topbar-dropdown-link-color;\n                background: $topbar-dropdown-link-bg;\n              }\n\n              &:hover>a:not(.button) {\n                color: $topbar-dropdown-link-color-hover;\n                background-color: $topbar-link-bg-color-hover;\n\n                @if ($topbar-dropdown-link-bg-hover) {\n                  background: $topbar-dropdown-link-bg-hover;\n                }\n              }\n            }\n\n            label {\n              white-space: nowrap;\n              background: $topbar-dropdown-label-bg;\n            }\n\n            // Second Level Dropdowns\n            .dropdown {\n              #{$default-float}: 100%;\n              top: 0;\n            }\n          }\n        }\n\n        &>ul>.divider,\n        &>ul>[role=\"separator\"] {\n          border-bottom: none;\n          border-top: none;\n          border-#{$opposite-direction}: $topbar-divider-border-bottom;\n          clear: none;\n          height: $topbar-height;\n          width: 0;\n        }\n\n        .has-form {\n          background: $topbar-link-bg;\n          padding: 0 calc($topbar-height / 3);\n          height: $topbar-height;\n        }\n\n        // Position overrides for ul.right and ul.left\n        .#{$opposite-direction} {\n          li .dropdown {\n            #{$default-float}: auto;\n            #{$opposite-direction}: 0;\n\n            li .dropdown {\n              #{$opposite-direction}: 100%;\n            }\n          }\n        }\n\n        .#{$default-float} {\n          li .dropdown {\n            #{$opposite-direction}: auto;\n            #{$default-float}: 0;\n\n            li .dropdown {\n              #{$default-float}: 100%;\n            }\n          }\n        }\n      }\n\n      // Degrade gracefully when Javascript is disabled. Displays dropdown and changes\n      // background & text color on hover.\n      .no-js .top-bar-section {\n        ul li {\n\n          // Apply the hover link color when it has that class\n          &:hover>a {\n            background-color: $topbar-link-bg-color-hover;\n\n            @if ($topbar-link-bg-hover) {\n              background: $topbar-link-bg-hover;\n            }\n\n            color: $topbar-link-color-hover;\n          }\n\n          // Apply the active link color when it has that class\n          &:active>a {\n            background: $topbar-link-bg-active;\n            color: $topbar-link-color-active;\n          }\n        }\n\n        .has-dropdown {\n          &:hover {\n            &>.dropdown {\n              @include topbar-show-dropdown();\n            }\n          }\n\n          >a:focus+.dropdown {\n            @include topbar-show-dropdown();\n          }\n        }\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n\n$include-html-accordion-classes: $include-html-classes !default;\n\n$accordion-navigation-padding: rem-calc(16) !default;\n$accordion-navigation-bg-color: $silver !default;\n$accordion-navigation-hover-bg-color: scale-color($accordion-navigation-bg-color, $lightness: -5%) !default;\n$accordion-navigation-active-bg-color: scale-color($accordion-navigation-bg-color, $lightness: -3%) !default;\n$accordion-navigation-font-color: $jet !default;\n$accordion-navigation-font-size: rem-calc(16) !default;\n$accordion-navigation-font-family: $body-font-family !default;\n\n$accordion-content-padding: calc($column-gutter / 2) !default;\n$accordion-content-active-bg-color: $white !default;\n\n\n//  Mixin: accordion-container()\n//  Description: Responsible for the container component of accordions, generating styles relating to a margin of zero and a clearfix\n//  Explicit Dependencies: a clearfix mixin *is* defined.\n//  Implicit Dependencies: None\n\n@mixin accordion-container() {\n  @include clearfix;\n  margin-bottom: 0;\n}\n\n//  Mixin: accordion-navigation( $bg, $hover-bg, $active-bg, $padding, $active_class,  $font-color, $font-size, $font-family){\n//    @params $bg-color: [ color or string ]: Specify the background color for the navigation element\n//    @params $hover-bg-color [ color or string ]: Specify the background color for the navigation element when hovered\n//    @params $active-bg [ color or string ]: Specify the background color for the navigation element when clicked and not released.\n//    @params $active_class [ string ]: Specify the class name used to keep track of which accordion tab should be visible\n//    @params $font-color [ color or string ]: Color of the font for accordion\n//    @params $font-size [ number ]: Specify the font-size of the text inside the navigation element\n//    @params $font-family [ string ]: Specify the font family for the text of the navigation of the accordion\n\n@mixin accordion-navigation($bg: $accordion-navigation-bg-color, $hover-bg: $accordion-navigation-hover-bg-color, $active-bg: $accordion-navigation-active-bg-color, $padding: $accordion-navigation-padding, $active_class: 'active', $font-color: $accordion-navigation-font-color, $font-size: $accordion-navigation-font-size, $font-family: $accordion-navigation-font-family ) {\n  display: block;\n  margin-bottom: 0 !important;\n\n  @if type-of($active_class) !=\"string\" {\n    @warn \"`#{$active_class}` isn't a valid string. A valid string is needed to correctly be interpolated as a CSS class. CSS classes cannot start with a number or consist of only numbers. CSS will not be generated for the active state of this navigation component.\"\n  }\n\n  @else {\n    &.#{ $active_class }>a {\n      background: $active-bg;\n    }\n  }\n\n  >a {\n    background: $bg;\n    color: $font-color;\n\n    @if type-of($padding) !=number {\n      @warn \"`#{$padding}` was read as #{type-of($padding)}\";\n\n      @if $accordion-navigation-padding !=null {\n        @warn \"#{$padding} was read as a #{type-of($padding)}\";\n        @warn \"`#{$padding}` isn't a valid number. $accordion-navigation-padding (#{$accordion-navigation-padding}) will be used instead.)\";\n        padding: $accordion-navigation-padding;\n      }\n\n      @else {\n        @warn \"`#{$padding}` isn't a valid number and $accordion-navigation-padding is missing. A value of `null` is returned to not output an invalid value for padding\";\n        padding: null;\n      }\n    }\n\n    @else {\n      padding: $padding;\n    }\n\n    display: block;\n    font-family: $font-family;\n\n    @if type-of($font-size) !=number {\n      @warn \"`#{$font-size}` was read as a #{type-of($font-size)}\";\n\n      @if $accordion-navigation-font-size !=null {\n        @warn \"`#{$font-size}` is not a valid number. The value of $accordion-navigation-font-size will be used instead (#{$accordion-navigation-font-size}).\";\n        font-size: $accordion-navigation-font-size;\n      }\n\n      @else {\n        @warn \"`#{$font-size}` is not a valid number and the default value of $accordion-navigation-font-size is not defined. A value of `null` will be returned to not generate an invalid value for font-size.\";\n        font-size: null;\n\n      }\n    }\n\n    @else {\n      font-size: $font-size;\n    }\n\n    &:hover {\n      background: $hover-bg;\n    }\n  }\n}\n\n//  Mixin: accordion-content($bg, $padding, $active-class)\n//    @params $padding [ number ]: Padding for the content of the container\n//    @params $bg [ color  ]: Background color for the content when it's visible\n//    @params $active_class [ string ]: Class name used to keep track of which accordion tab should be visible.\n\n@mixin accordion-content($bg: $accordion-content-active-bg-color, $padding: $accordion-content-padding, $active_class: 'active') {\n  display: none;\n\n  @if type-of($padding) !=\"number\" {\n    @warn \"#{$padding} was read as a #{type-of($padding)}\";\n\n    @if $accordion-content-padding !=null {\n      @warn \"`#{$padding}` isn't a valid number. $accordion-content-padding used instead\";\n      padding: $accordion-content-padding;\n    }\n\n    @else {\n      @warn \"`#{$padding}` isn't a valid number and the default value of $accordion-content-padding is not defined. A value of `null` is returned to not output an invalid value for padding.\";\n      padding: null;\n    }\n  }\n\n  @else {\n    padding: $padding;\n  }\n\n  @if type-of($active_class) !=\"string\" {\n    @warn \"`#{$active_class}` isn't a valid string. A valid string is needed to correctly be interpolated as a CSS class. CSS classes cannot start with a number or consist of only numbers. CSS will not be generated for the active state of the content. \"\n  }\n\n  @else {\n    &.#{$active_class} {\n      display: block;\n      background: $bg;\n    }\n  }\n}\n\n@include exports(\"accordion\") {\n  @if $include-html-accordion-classes {\n    .accordion {\n      @include clearfix;\n      margin-bottom: 0;\n\n      .accordion-navigation,\n      dd {\n        display: block;\n        margin-bottom: 0 !important;\n\n        &.active>a {\n          background: $accordion-navigation-active-bg-color;\n        }\n\n        >a {\n          background: $accordion-navigation-bg-color;\n          color: $accordion-navigation-font-color;\n          padding: $accordion-navigation-padding;\n          display: block;\n          font-family: $accordion-navigation-font-family;\n          font-size: $accordion-navigation-font-size;\n\n          &:hover {\n            background: $accordion-navigation-hover-bg-color;\n          }\n        }\n\n        >.content {\n          display: none;\n          padding: $accordion-content-padding;\n\n          &.active {\n            display: block;\n            background: $accordion-content-active-bg-color;\n          }\n        }\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Alert Box Variables\n//\n$include-html-alert-classes: $include-html-classes !default;\n\n// We use this to control alert padding.\n$alert-padding-top: rem-calc(14) !default;\n$alert-padding-default-float: $alert-padding-top !default;\n$alert-padding-opposite-direction: $alert-padding-top + rem-calc(10) !default;\n$alert-padding-bottom: $alert-padding-top !default;\n\n// We use these to control text style.\n$alert-font-weight: $font-weight-normal !default;\n$alert-font-size: rem-calc(13) !default;\n$alert-font-color: $white !default;\n$alert-font-color-alt: scale-color($secondary-color, $lightness: -66%) !default;\n\n// We use this for close hover effect.\n$alert-function-factor: -14% !default;\n\n// We use these to control border styles.\n$alert-border-style: solid !default;\n$alert-border-width: 1px !default;\n$alert-border-color: scale-color($primary-color, $lightness: $alert-function-factor) !default;\n$alert-bottom-margin: rem-calc(20) !default;\n\n// We use these to style the close buttons\n$alert-close-color: $oil !default;\n$alert-close-top: 50% !default;\n$alert-close-position: rem-calc(4) !default;\n$alert-close-font-size: rem-calc(22) !default;\n$alert-close-opacity: 0.3 !default;\n$alert-close-opacity-hover: 0.5 !default;\n$alert-close-padding: 9px 6px 4px !default;\n$alert-close-background: inherit !default;\n\n// We use this to control border radius\n$alert-radius: $global-radius !default;\n\n$alert-transition-speed: 300ms !default;\n$alert-transition-ease: ease-out !default;\n\n//\n// Alert Mixins\n//\n\n// We use this mixin to create a default alert base.\n@mixin alert-base {\n  border-style: $alert-border-style;\n  border-width: $alert-border-width;\n  display: block;\n  font-weight: $alert-font-weight;\n  margin-bottom: $alert-bottom-margin;\n  position: relative;\n  padding: $alert-padding-top $alert-padding-opposite-direction $alert-padding-bottom $alert-padding-default-float;\n  font-size: $alert-font-size;\n  @include single-transition(opacity, $alert-transition-speed, $alert-transition-ease)\n}\n\n// We use this mixin to add alert styles\n//\n// $bg - The background of the alert. Default: $primary-color.\n@mixin alert-style($bg: $primary-color) {\n\n  // This finds the lightness percentage of the background color.\n  $bg-lightness: lightness($bg);\n\n  // We control which background color and border come through.\n  background-color: $bg;\n  border-color: scale-color($bg, $lightness: $alert-function-factor);\n\n  // We control the text color for you based on the background color.\n  @if $bg-lightness >70% {\n    color: $alert-font-color-alt;\n  }\n\n  @else {\n    color: $alert-font-color;\n  }\n\n}\n\n// We use this to create the close button.\n@mixin alert-close {\n  font-size: $alert-close-font-size;\n  padding: $alert-close-padding;\n  line-height: 0;\n  position: absolute;\n  top: $alert-close-top;\n  margin-top: -(calc($alert-close-font-size / 2));\n  #{$opposite-direction}: $alert-close-position;\n  color: $alert-close-color;\n  opacity: $alert-close-opacity;\n  background: $alert-close-background;\n\n  &:hover,\n  &:focus {\n    opacity: $alert-close-opacity-hover;\n  }\n}\n\n// We use this to quickly create alerts with a single mixin.\n//\n// $bg - Background of alert. Default: $primary-color.\n// $radius - Radius of alert box. Default: false.\n@mixin alert($bg: $primary-color, $radius: false) {\n  @include alert-base;\n  @include alert-style($bg);\n  @include radius($radius);\n}\n\n@include exports(\"alert-box\") {\n  @if $include-html-alert-classes {\n    .alert-box {\n      @include alert;\n\n      .close {\n        @include alert-close;\n      }\n\n      &.radius {\n        @include radius($alert-radius);\n      }\n\n      &.round {\n        @include radius($global-rounded);\n      }\n\n      &.success {\n        @include alert-style($success-color);\n      }\n\n      &.alert {\n        @include alert-style($alert-color);\n      }\n\n      &.secondary {\n        @include alert-style($secondary-color);\n      }\n\n      &.warning {\n        @include alert-style($warning-color);\n      }\n\n      &.info {\n        @include alert-style($info-color);\n      }\n\n      &.alert-close {\n        opacity: 0\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Breadcrumb Variables\n//\n$include-html-nav-classes: $include-html-classes !default;\n\n// We use this to set the background color for the breadcrumb container.\n$crumb-bg: scale-color($secondary-color, $lightness: 55%) !default;\n\n// We use these to set the padding around the breadcrumbs.\n$crumb-padding: rem-calc(9 14 9) !default;\n$crumb-side-padding: rem-calc(12) !default;\n\n// We use these to control border styles.\n$crumb-function-factor: -10% !default;\n$crumb-border-size: 1px !default;\n$crumb-border-style: solid !default;\n$crumb-border-color: scale-color($crumb-bg, $lightness: $crumb-function-factor) !default;\n$crumb-radius: $global-radius !default;\n\n// We use these to set various text styles for breadcrumbs.\n$crumb-font-size: rem-calc(11) !default;\n$crumb-font-color: $primary-color !default;\n$crumb-font-color-current: $oil !default;\n$crumb-font-color-unavailable: $aluminum !default;\n$crumb-font-transform: uppercase !default;\n$crumb-link-decor: underline !default;\n\n// We use these to control the slash between breadcrumbs\n$crumb-slash-color: $base !default;\n$crumb-slash: \"/\" !default;\n\n//\n// Breadcrumb Mixins\n//\n\n// We use this mixin to create a container around our breadcrumbs\n@mixin crumb-container {\n  display: block;\n  padding: $crumb-padding;\n  overflow: hidden;\n  margin-#{$default-float}: 0;\n  list-style: none;\n  border-style: $crumb-border-style;\n  border-width: $crumb-border-size;\n\n  // We control which background color and border come through.\n  background-color: $crumb-bg;\n  border-color: $crumb-border-color;\n}\n\n// We use this mixin to create breadcrumb styles from list items.\n@mixin crumbs {\n\n  // A normal state will make the links look and act like clickable breadcrumbs.\n  margin: 0;\n  float: $default-float;\n  font-size: $crumb-font-size;\n  line-height: $crumb-font-size;\n  text-transform: $crumb-font-transform;\n  color: $crumb-font-color;\n\n  &:hover a, &:focus a { text-decoration: $crumb-link-decor; }\n\n  a {\n    color: $crumb-font-color;\n  }\n\n  // Current is for the link of the current page\n  &.current {\n    cursor: $cursor-default-value;\n    color: $crumb-font-color-current;\n    a {\n      cursor: $cursor-default-value;\n      color: $crumb-font-color-current;\n    }\n\n    &:hover, &:hover a,\n    &:focus, &:focus a { text-decoration: none; }\n  }\n\n  // Unavailable removed color and link styles so it looks inactive.\n  &.unavailable {\n    color: $crumb-font-color-unavailable;\n    a { color: $crumb-font-color-unavailable; }\n\n    &:hover,\n    &:hover a,\n    &:focus,\n    a:focus {\n      text-decoration: none;\n      color: $crumb-font-color-unavailable;\n      cursor: $cursor-default-value;\n    }\n  }\n\n  &:before {\n    content: \"#{$crumb-slash}\";\n    color: $crumb-slash-color;\n    margin: 0 $crumb-side-padding;\n    position: relative;\n    top: 1px;\n  }\n\n  &:first-child:before {\n    content: \" \";\n    margin: 0;\n  }\n}\n\n@include exports(\"breadcrumbs\") {\n  @if $include-html-nav-classes {\n    .breadcrumbs {\n      @include crumb-container;\n      @include radius($crumb-radius);\n\n      &>* {\n        @include crumbs;\n      }\n    }\n  }\n}\n\n/* Accessibility - hides the forward slash */\n[aria-label=\"breadcrumbs\"] [aria-hidden=\"true\"]:after {\n  content: \"/\";\n  }\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Block Grid Variables\n//\n$include-html-block-grid-classes: $include-html-classes !default;\n$include-xl-html-block-grid-classes: false !default;\n\n// We use this to control the maximum number of block grid elements per row\n$block-grid-elements: 12 !default;\n$block-grid-default-spacing: rem-calc(20) !default;\n\n$align-block-grid-to-grid: false !default;\n\n@if $align-block-grid-to-grid {\n  $block-grid-default-spacing: $column-gutter;\n}\n\n// Enables media queries for block-grid classes. Set to false if writing semantic HTML.\n$block-grid-media-queries: true !default;\n\n//\n// Block Grid Mixins\n//\n\n// Create a custom block grid\n//\n// $per-row - # of items to display per row. Default: false.\n// $spacing - # of ems to use as padding on each block item. Default: rem-calc(20).\n// $base-style - Apply a base style to block grid. Default: true.\n@mixin block-grid($per-row: false,\n  $spacing: $block-grid-default-spacing,\n  $include-spacing: true,\n  $base-style: true) {\n\n  @if $base-style {\n    display: block;\n    padding: 0;\n\n    @if $align-block-grid-to-grid {\n      margin: 0;\n    }\n\n    @else {\n      margin: 0 calc(-1 * $spacing / 2);\n    }\n\n    @include clearfix;\n\n    &>li {\n      display: block;\n      height: auto;\n      float: $default-float;\n\n      @if $include-spacing {\n        padding: 0 calc($spacing / 2) $spacing;\n      }\n    }\n  }\n\n  @if $per-row {\n    &>li {\n      width: calc(100% / $per-row);\n\n      @if $include-spacing {\n        padding: 0 ($spacing/2) $spacing;\n      }\n\n      list-style: none;\n\n      &:nth-of-type(1n) {\n        clear: none;\n      }\n\n      &:nth-of-type(#{$per-row}n+1) {\n        clear: both;\n      }\n\n      @if $align-block-grid-to-grid {\n        @include block-grid-aligned($per-row, $spacing);\n      }\n    }\n  }\n}\n\n@mixin block-grid-aligned($per-row, $spacing) {\n  @for $i from 1 through $block-grid-elements {\n    @if $per-row >=$i {\n      $grid-column: '+'+$i;\n\n      @if $per-row ==$i {\n        $grid-column: '';\n      }\n\n      &:nth-of-type(#{$per-row}n#{unquote($grid-column)}) {\n        padding-left: ($spacing - (($spacing / $per-row) * ($per-row - ($i - 1))));\n        padding-right: ($spacing - (($spacing / $per-row) * $i));\n      }\n    }\n  }\n}\n\n// Generate presentational markup for block grid.\n//\n// $size - Name of class to use, i.e. \"large\" will generate .large-block-grid-1, .large-block-grid-2, etc.\n@mixin block-grid-html-classes($size, $include-spacing) {\n  @for $i from 1 through $block-grid-elements {\n    .#{$size}-block-grid-#{($i)} {\n      @include block-grid($i, $block-grid-default-spacing, $include-spacing, false);\n    }\n  }\n}\n\n@include exports(\"block-grid\") {\n  @if $include-html-block-grid-classes {\n\n    [class*=\"block-grid-\"] {\n      @include block-grid;\n    }\n\n    @if $block-grid-media-queries {\n      @media #{$small-up} {\n        @include block-grid-html-classes($size: small, $include-spacing: false);\n      }\n\n      @media #{$medium-up} {\n        @include block-grid-html-classes($size: medium, $include-spacing: false);\n      }\n\n      @media #{$large-up} {\n        @include block-grid-html-classes($size: large, $include-spacing: false);\n      }\n\n      @if $include-xl-html-block-grid-classes {\n        @media #{$xlarge-up} {\n          @include block-grid-html-classes($size: xlarge, $include-spacing: false);\n        }\n\n        @media #{$xxlarge-up} {\n          @include block-grid-html-classes($size: xxlarge, $include-spacing: false);\n        }\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"buttons\";\n\n//\n// Button Group Variables\n//\n$include-html-button-classes: $include-html-classes !default;\n\n// Sets the margin for the right side by default, and the left margin if right-to-left direction is used\n$button-bar-margin-opposite: rem-calc(10) !default;\n$button-group-border-width: 1px !default;\n\n//\n// Button Group Mixins\n//\n\n// We use this to add styles for a button group container\n@mixin button-group-container($styles: true, $float: false) {\n  @if $styles {\n    list-style: none;\n    margin: 0;\n    #{$default-float}: 0;\n    @include clearfix();\n  }\n\n  @if $float {\n    float: #{$default-float};\n    margin-#{$opposite-direction}: $button-bar-margin-opposite;\n\n    & div {\n      overflow: hidden;\n    }\n  }\n}\n\n// We use this to control styles for button groups\n@mixin button-group-style($radius: false, $even: false, $float: false, $orientation: horizontal) {\n\n  >button,\n  .button {\n    border-#{$default-float}: $button-group-border-width solid;\n    border-color: rgba(255, 255, 255, 0.5);\n  }\n\n  &:first-child {\n\n    button,\n    .button {\n      border-#{$default-float}: 0;\n    }\n  }\n\n  // We use this to control the flow, or remove those styles completely.\n  @if $float {\n    margin: 0;\n    float: $float;\n    display: list-item;\n\n    // Make sure the first child doesn't get the negative margin.\n    &:first-child {\n      margin-#{$default-float}: 0;\n    }\n  }\n\n  @else {\n    margin: 0 -2px;\n    display: inline-block;\n  }\n\n  @if $orientation ==vertical {\n    display: block;\n    margin: 0;\n\n    >button,\n    .button {\n      border-top: $button-group-border-width solid;\n      border-color: rgba(255, 255, 255, 0.5);\n      border-left-width: 0;\n      margin: 0;\n      display: block;\n    }\n\n    &:first-child {\n\n      button,\n      .button {\n        border-top: 0;\n      }\n    }\n  }\n\n  // We use these to control left and right radius on first/last buttons in the group.\n  @if $radius ==true {\n\n    &,\n    &>a,\n    &>button,\n    &>.button {\n      @include radius(0);\n    }\n\n    &:first-child,\n    &:first-child>a,\n    &:first-child>button,\n    &:first-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(top, $button-radius);\n      }\n\n      @else {\n        @include side-radius($default-float, $button-radius);\n      }\n    }\n\n    &:last-child,\n    &:last-child>a,\n    &:last-child>button,\n    &:last-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(bottom, $button-radius);\n      }\n\n      @else {\n        @include side-radius($opposite-direction, $button-radius);\n      }\n    }\n  }\n\n  @else if $radius {\n\n    &,\n    &>a,\n    &>button,\n    &>.button {\n      @include radius(0);\n    }\n\n    &:first-child,\n    &:first-child>a,\n    &:first-child>button,\n    &:first-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(top, $radius);\n      }\n\n      @else {\n        @include side-radius($default-float, $radius);\n      }\n    }\n\n    &:last-child,\n    &:last-child>a,\n    &:last-child>button,\n    &:last-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(bottom, $radius);\n      }\n\n      @else {\n        @include side-radius($opposite-direction, $radius);\n      }\n    }\n  }\n\n  // We use this to make the buttons even width across their container\n  @if $even {\n    width: percentage(calc((100/$even) / 100));\n\n    button,\n    .button {\n      width: 100%;\n    }\n  }\n}\n\n@include exports(\"button-group\") {\n  @if $include-html-button-classes {\n    .button-group {\n      @include button-group-container;\n\n      &>li {\n        @include button-group-style();\n      }\n\n      &.stack {\n        &>li {\n          @include button-group-style($orientation: vertical);\n          float: none;\n        }\n      }\n\n      &.stack-for-small {\n        &>li {\n          @include button-group-style($orientation: horizontal);\n\n          @media #{$small-only} {\n            @include button-group-style($orientation: vertical);\n          }\n        }\n      }\n\n      &.radius>* {\n        @include button-group-style($radius: $button-radius, $float: null);\n      }\n\n      &.radius.stack>* {\n        @include button-group-style($radius: $button-radius, $float: null, $orientation: vertical);\n      }\n\n      &.radius.stack-for-small>* {\n        @media #{$medium-up} {\n          @include button-group-style($radius: $button-radius, $orientation: horizontal);\n        }\n\n        @media #{$small-only} {\n          @include button-group-style($radius: $button-radius, $orientation: vertical);\n        }\n      }\n\n      &.round>* {\n        @include button-group-style($radius: $button-round, $float: null);\n      }\n\n      &.round.stack>* {\n        @include button-group-style($radius: $button-med, $float: null, $orientation: vertical);\n      }\n\n      &.round.stack-for-small>* {\n        @media #{$medium-up} {\n          @include button-group-style($radius: $button-round, $orientation: horizontal);\n        }\n\n        @media #{$small-only} {\n          @include button-group-style($radius: $button-med, $orientation: vertical);\n        }\n      }\n\n      @for $i from 2 through 8 {\n        &.even-#{$i} li {\n          @include button-group-style($even: $i, $float: null);\n        }\n      }\n    }\n\n    .button-bar {\n      @include clearfix;\n\n      .button-group {\n        @include button-group-container($styles: false, $float: true);\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-clearing-classes: $include-html-classes !default;\n\n// We use these to set the background colors for parts of Clearing.\n$clearing-bg: $oil !default;\n$clearing-caption-bg: $clearing-bg !default;\n$clearing-carousel-bg: rgba(51,51,51,0.8) !default;\n$clearing-img-bg: $clearing-bg !default;\n\n// We use these to style the close button\n$clearing-close-color: $iron !default;\n$clearing-close-size: 30px !default;\n\n// We use these to style the arrows\n$clearing-arrow-size: 12px !default;\n$clearing-arrow-color: $clearing-close-color !default;\n\n// We use these to style captions\n$clearing-caption-font-color: $iron !default;\n$clearing-caption-font-size: 0.875em !default;\n$clearing-caption-padding: 10px 30px 20px !default;\n\n// We use these to make the image and carousel height and style\n$clearing-active-img-height: 85% !default;\n$clearing-carousel-height: 120px !default;\n$clearing-carousel-thumb-width: 120px !default;\n$clearing-carousel-thumb-active-border: 1px solid rgb(255,255,255) !default;\n\n@include exports(\"clearing\") {\n  @if $include-html-clearing-classes {\n    // We decided to not create a mixin for Clearing because it relies\n    // on predefined classes and structure to work properly.\n    // The variables above should give enough control.\n\n    /* Clearing Styles */\n    .clearing-thumbs, #{data('clearing')} {\n      @include clearfix;\n      margin-bottom: 0;\n      margin-#{$default-float}: 0;\n      list-style: none;\n\n      li {\n        float: $default-float;\n        margin-#{$opposite-direction}: 10px;\n      }\n\n      &[class*=\"block-grid-\"] li {\n        margin-#{$opposite-direction}: 0;\n      }\n    }\n\n    .clearing-blackout {\n      background: $clearing-bg;\n      position: fixed;\n      width: 100%;\n      height: 100%;\n      top: 0;\n      #{$default-float}: 0;\n      z-index: 998;\n\n      .clearing-close { display: block; }\n    }\n\n    .clearing-container {\n      position: relative;\n      z-index: 998;\n      height: 100%;\n      overflow: hidden;\n      margin: 0;\n    }\n\n    .clearing-touch-label {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      color: $base;\n      font-size: 0.6em;\n    }\n\n    .visible-img {\n      height: 95%;\n      position: relative;\n\n      img {\n        position: absolute;\n        #{$default-float}: 50%;\n        top: 50%;\n        margin-#{$default-float}: -50%;\n        max-height: 100%;\n        max-width: 100%;\n      }\n    }\n\n    .clearing-caption {\n      color: $clearing-caption-font-color;\n      font-size: $clearing-caption-font-size;\n      line-height: 1.3;\n      margin-bottom: 0;\n      text-align: center;\n      bottom: 0;\n      background: $clearing-caption-bg;\n      width: 100%;\n      padding: $clearing-caption-padding;\n      position: absolute;\n      #{$default-float}: 0;\n    }\n\n    .clearing-close {\n      z-index: 999;\n      padding-#{$default-float}: 20px;\n      padding-top: 10px;\n      font-size: $clearing-close-size;\n      line-height: 1;\n      color: $clearing-close-color;\n      display: none;\n\n      &:hover,\n      &:focus { color: $iron; }\n    }\n\n    .clearing-assembled .clearing-container { height: 100%;\n      .carousel > ul { display: none; }\n    }\n\n    // If you want to show a lightbox, but only have a single image come through as the thumbnail\n    .clearing-feature li {\n      display: none;\n      &.clearing-featured-img {\n        display: block;\n      }\n    }\n\n    // Large screen overrides\n    @media #{$medium-up} {\n      .clearing-main-prev,\n      .clearing-main-next {\n        position: absolute;\n        height: 100%;\n        width: 40px;\n        top: 0;\n        & > span {\n          position: absolute;\n          top: 50%;\n          display: block;\n          width: 0;\n          height: 0;\n          border: solid $clearing-arrow-size;\n          &:hover { opacity: 0.8; }\n        }\n      }\n      .clearing-main-prev {\n        #{$default-float}: 0;\n        & > span {\n          #{$default-float}: 5px;\n          border-color: transparent;\n          border-#{$opposite-direction}-color: $clearing-arrow-color;\n        }\n      }\n      .clearing-main-next {\n        #{$opposite-direction}: 0;\n        & > span {\n          border-color: transparent;\n          border-#{$default-float}-color: $clearing-arrow-color;\n        }\n      }\n    \n      .clearing-main-prev.disabled,\n      .clearing-main-next.disabled { opacity: 0.3; }\n\n      .clearing-assembled .clearing-container {\n\n        .carousel {\n          background: $clearing-carousel-bg;\n          height: $clearing-carousel-height;\n          margin-top: 10px;\n          text-align: center;\n\n          & > ul {\n            display: inline-block;\n            z-index: 999;\n            height: 100%;\n            position: relative;\n            float: none;\n\n            li {\n              display: block;\n              width: $clearing-carousel-thumb-width;\n              min-height: inherit;\n              float: $default-float;\n              overflow: hidden;\n              margin-#{$opposite-direction}: 0;\n              padding: 0;\n              position: relative;\n              cursor: $cursor-pointer-value;\n              opacity: 0.4;\n              clear: none;\n\n              &.fix-height {\n                img {\n                  height: 100%;\n                  max-width: none;\n                }\n              }\n\n              a.th {\n                border: none;\n                box-shadow: none;\n                display: block;\n              }\n\n              img {\n              cursor: $cursor-pointer-value !important;\n              width: 100% !important;\n              }\n\n              &.visible { opacity: 1; }\n              &:hover { opacity: 0.8; }\n            }\n          }\n        }\n\n        .visible-img {\n          background: $clearing-img-bg;\n          overflow: hidden;\n          height: $clearing-active-img-height;\n        }\n      }\n\n      .clearing-close {\n        position: absolute;\n        top: 10px;\n        #{$opposite-direction}: 20px;\n        padding-#{$default-float}: 0;\n        padding-top: 0;\n      }\n    }\n\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-dropdown-classes: $include-html-classes !default;\n\n// We use these to controls height and width styles.\n$f-dropdown-max-width: 200px !default;\n$f-dropdown-height: auto !default;\n$f-dropdown-max-height: none !default;\n\n// Used for bottom position\n$f-dropdown-margin-top: 2px !default;\n\n// Used for right position\n$f-dropdown-margin-left: $f-dropdown-margin-top !default;\n\n// Used for left position\n$f-dropdown-margin-right: $f-dropdown-margin-top !default;\n\n// Used for top position\n$f-dropdown-margin-bottom: $f-dropdown-margin-top !default;\n\n// We use this to control the background color\n$f-dropdown-bg: $white !default;\n\n// We use this to set the border styles for dropdowns.\n$f-dropdown-border-style: solid !default;\n$f-dropdown-border-width: 1px !default;\n$f-dropdown-border-color: scale-color($white, $lightness: -20%) !default;\n\n// We use these to style the triangle pip.\n$f-dropdown-triangle-size: 6px !default;\n$f-dropdown-triangle-color: $white !default;\n$f-dropdown-triangle-side-offset: 10px !default;\n\n// We use these to control styles for the list elements.\n$f-dropdown-list-style: none !default;\n$f-dropdown-font-color: $charcoal !default;\n$f-dropdown-font-size: rem-calc(14) !default;\n$f-dropdown-list-padding: rem-calc(5, 10) !default;\n$f-dropdown-line-height: rem-calc(18) !default;\n$f-dropdown-list-hover-bg: $smoke !default;\n$dropdown-mobile-default-float: 0 !default;\n\n// We use this to control the styles for when the dropdown has custom content.\n$f-dropdown-content-padding: rem-calc(20) !default;\n\n// Default radius for dropdown.\n$f-dropdown-radius: $global-radius !default;\n\n//\n// @mixins\n//\n//\n// NOTE: Make default max-width change between list and content types. Can add more width with classes, maybe .small, .medium, .large, etc.;\n// We use this to style the dropdown container element.\n// $content-list - Sets list-style. Default: list. Options: [list, content]\n// $triangle - Sets if dropdown has triangle. Default:true.\n// $max-width - Default: $f-dropdown-max-width || 200px.\n@mixin dropdown-container($content:list, $triangle:true, $max-width:$f-dropdown-max-width) {\n  position: absolute;\n  left: -9999px;\n  list-style: $f-dropdown-list-style;\n  margin-#{$default-float}: 0;\n  outline: none;\n\n  > *:first-child { margin-top: 0; }\n  > *:last-child { margin-bottom: 0; }\n\n  @if $content == list {\n    width: 100%;\n    max-height: $f-dropdown-max-height;\n    height: $f-dropdown-height;\n    background: $f-dropdown-bg;\n    border: $f-dropdown-border-style $f-dropdown-border-width $f-dropdown-border-color;\n    font-size: $f-dropdown-font-size;\n    z-index: 89;\n  }\n  @else if $content == content {\n    padding: $f-dropdown-content-padding;\n    width: 100%;\n    height: $f-dropdown-height;\n    max-height: $f-dropdown-max-height;\n    background: $f-dropdown-bg;\n    border: $f-dropdown-border-style $f-dropdown-border-width $f-dropdown-border-color;\n    font-size: $f-dropdown-font-size;\n    z-index: 89;\n  }\n\n  @if $triangle == bottom {\n    margin-top: $f-dropdown-margin-top;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, bottom);\n      position: absolute;\n      top: -($f-dropdown-triangle-size * 2);\n      #{$default-float}: $f-dropdown-triangle-side-offset;\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, bottom);\n      position: absolute;\n      top: -(($f-dropdown-triangle-size + 1) * 2);\n      #{$default-float}: $f-dropdown-triangle-side-offset - 1;\n      z-index: 88;\n    }\n\n    &.right:before {\n      #{$default-float}: auto;\n      #{$opposite-direction}: $f-dropdown-triangle-side-offset;\n    }\n    &.right:after {\n      #{$default-float}: auto;\n      #{$opposite-direction}: $f-dropdown-triangle-side-offset - 1;\n    }\n  }\n\n  @if $triangle == $default-float {\n    margin-top: 0;\n    margin-#{$default-float}: $f-dropdown-margin-right;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, #{$opposite-direction});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset;\n      #{$default-float}: -($f-dropdown-triangle-size * 2);\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, #{$opposite-direction});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset - 1;\n      #{$default-float}: -($f-dropdown-triangle-size * 2) - 2;\n      z-index: 88;\n    }\n\n  }\n\n  @if $triangle == $opposite-direction {\n    margin-top: 0;\n    margin-#{$default-float}: -$f-dropdown-margin-right;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, #{$default-float});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset;\n      #{$opposite-direction}: -($f-dropdown-triangle-size * 2);\n      #{$default-float}: auto;\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, #{$default-float});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset - 1;\n      #{$opposite-direction}: -($f-dropdown-triangle-size * 2) - 2;\n      #{$default-float}: auto;\n      z-index: 88;\n    }\n\n  }\n\n  @if $triangle == top {\n    margin-top: -$f-dropdown-margin-bottom;\n    margin-left: 0;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, top);\n      position: absolute;\n      top: auto;\n      bottom: -($f-dropdown-triangle-size * 2);\n      #{$default-float}: $f-dropdown-triangle-side-offset;\n      #{$opposite-direction}: auto;\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, top);\n      position: absolute;\n      top: auto;\n      bottom: -($f-dropdown-triangle-size * 2) - 2;\n      #{$default-float}: $f-dropdown-triangle-side-offset - 1;\n      #{$opposite-direction}: auto;\n      z-index: 88;\n    }\n\n  }\n\n  @if $max-width { max-width: $max-width; }\n  @else { max-width: $f-dropdown-max-width; }\n\n}\n\n// @MIXIN\n//\n// We use this to style the list elements or content inside the dropdown.\n\n@mixin dropdown-style {\n  font-size: $f-dropdown-font-size;\n  cursor: $cursor-pointer-value;\n\n  line-height: $f-dropdown-line-height;\n  margin: 0;\n\n  &:hover,\n  &:focus { background: $f-dropdown-list-hover-bg; }\n\n  &.radius { @include radius($f-dropdown-radius); }\n\n  a {\n    display: block;\n    padding: $f-dropdown-list-padding;\n    color: $f-dropdown-font-color;\n  }\n}\n\n@include exports(\"dropdown\") {\n  @if $include-html-dropdown-classes {\n\n    /* Foundation Dropdowns */\n    .f-dropdown {\n      @include dropdown-container(list, bottom);\n\n      &.drop-#{$opposite-direction} {\n        @include dropdown-container(list, #{$default-float});\n      }\n\n      &.drop-#{$default-float} {\n        @include dropdown-container(list, #{$opposite-direction});\n      }\n\n      &.drop-top {\n        @include dropdown-container(list, top);\n      }\n      // max-width: none;\n\n      li { @include dropdown-style; }\n\n      // You can also put custom content in these dropdowns\n      &.content { @include dropdown-container(content, $triangle:false); }\n\n      // Sizes\n      &.tiny    { max-width: 200px; }\n      &.small   { max-width: 300px; }\n      &.medium  { max-width: 500px; }\n      &.large   { max-width: 800px; }\n      &.mega    {\n        width:100%!important;\n        max-width:100%!important;\n\n        &.open{\n          left:0!important;\n        }\n      }\n    }\n\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-button-classes: $include-html-classes !default;\n\n// We use these to set the color of the pip in dropdown buttons\n$dropdown-button-pip-color: $white !default;\n$dropdown-button-pip-color-alt: $oil !default;\n\n$button-pip-tny: rem-calc(6) !default;\n$button-pip-sml: rem-calc(7) !default;\n$button-pip-med: rem-calc(9) !default;\n$button-pip-lrg: rem-calc(11) !default;\n\n// We use these to style tiny dropdown buttons\n$dropdown-button-padding-tny: $button-pip-tny * 7 !default;\n$dropdown-button-pip-size-tny: $button-pip-tny !default;\n$dropdown-button-pip-opposite-tny: $button-pip-tny * 3 !default;\n$dropdown-button-pip-top-tny: calc(-1 * $button-pip-tny / 2) + rem-calc(1) !default;\n\n// We use these to style small dropdown buttons\n$dropdown-button-padding-sml: $button-pip-sml * 7 !default;\n$dropdown-button-pip-size-sml: $button-pip-sml !default;\n$dropdown-button-pip-opposite-sml: $button-pip-sml * 3 !default;\n$dropdown-button-pip-top-sml: calc(-1 * $button-pip-sml / 2) + rem-calc(1) !default;\n\n// We use these to style medium dropdown buttons\n$dropdown-button-padding-med: $button-pip-med * 6 + rem-calc(3) !default;\n$dropdown-button-pip-size-med: $button-pip-med - rem-calc(3) !default;\n$dropdown-button-pip-opposite-med: $button-pip-med * 2.5 !default;\n$dropdown-button-pip-top-med: calc(-1 * $button-pip-med / 2) + rem-calc(2) !default;\n\n// We use these to style large dropdown buttons\n$dropdown-button-padding-lrg: $button-pip-lrg * 5 + rem-calc(3) !default;\n$dropdown-button-pip-size-lrg: $button-pip-lrg - rem-calc(6) !default;\n$dropdown-button-pip-opposite-lrg: $button-pip-lrg * 2.5 !default;\n$dropdown-button-pip-top-lrg: calc(-1 * $button-pip-lrg / 2) + rem-calc(3) !default;\n\n// @mixins\n//\n// Dropdown Button Mixin\n//\n// We use this mixin to build off of the button mixin and add dropdown button styles\n//\n// $padding - Determines the size of button you're working with. Default: medium. Options [tiny, small, medium, large]\n// $pip-color - Color of the little triangle that points to the dropdown. Default: $white.\n// $base-style - Add in base-styles. This can be set to false. Default:true\n\n@mixin dropdown-button($padding: medium, $pip-color: $white, $base-style: true) {\n\n  // We add in base styles, but they can be negated by setting to 'false'.\n  @if $base-style {\n    position: relative;\n    outline: none;\n\n    // This creates the base styles for the triangle pip\n    &::after {\n      position: absolute;\n      content: \"\";\n      width: 0;\n      height: 0;\n      display: block;\n      border-style: solid;\n      border-color: $dropdown-button-pip-color transparent transparent transparent;\n      top: 50%;\n    }\n  }\n\n  // If we're dealing with tiny buttons, use these styles\n  @if $padding ==tiny {\n    padding-#{$opposite-direction}: $dropdown-button-padding-tny;\n\n    &:after {\n      border-width: $dropdown-button-pip-size-tny;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-tny;\n      margin-top: $dropdown-button-pip-top-tny;\n    }\n  }\n\n  // If we're dealing with small buttons, use these styles\n  @if $padding ==small {\n    padding-#{$opposite-direction}: $dropdown-button-padding-sml;\n\n    &::after {\n      border-width: $dropdown-button-pip-size-sml;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-sml;\n      margin-top: $dropdown-button-pip-top-sml;\n    }\n  }\n\n  // If we're dealing with default (medium) buttons, use these styles\n  @if $padding ==medium {\n    padding-#{$opposite-direction}: $dropdown-button-padding-med;\n\n    &::after {\n      border-width: $dropdown-button-pip-size-med;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-med;\n      margin-top: $dropdown-button-pip-top-med;\n    }\n  }\n\n  // If we're dealing with large buttons, use these styles\n  @if $padding ==large {\n    padding-#{$opposite-direction}: $dropdown-button-padding-lrg;\n\n    &::after {\n      border-width: $dropdown-button-pip-size-lrg;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-lrg;\n      margin-top: $dropdown-button-pip-top-lrg;\n    }\n  }\n\n  // We can control the pip color. We didn't use logic in this case, just set it and forget it.\n  @if $pip-color {\n    &::after {\n      border-color: $pip-color transparent transparent transparent;\n    }\n  }\n}\n\n@include exports(\"dropdown-button\") {\n  @if $include-html-button-classes {\n\n    .dropdown.button,\n    button.dropdown {\n      @include dropdown-button;\n\n      &.tiny {\n        @include dropdown-button(tiny, $base-style: false);\n      }\n\n      &.small {\n        @include dropdown-button(small, $base-style: false);\n      }\n\n      &.large {\n        @include dropdown-button(large, $base-style: false);\n      }\n\n      &.secondary:after {\n        border-color: $dropdown-button-pip-color-alt transparent transparent transparent;\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-media-classes: $include-html-classes !default;\n\n// We use these to control video container padding and margins\n$flex-video-padding-top: rem-calc(25) !default;\n$flex-video-padding-bottom: 67.5% !default;\n$flex-video-margin-bottom: rem-calc(16) !default;\n\n// We use this to control widescreen bottom padding\n$flex-video-widescreen-padding-bottom: 56.34% !default;\n\n//\n// @mixins\n//\n\n@mixin flex-video-container {\n  position: relative;\n  padding-top: $flex-video-padding-top;\n  padding-bottom: $flex-video-padding-bottom;\n  height: 0;\n  margin-bottom: $flex-video-margin-bottom;\n  overflow: hidden;\n\n  &.widescreen { padding-bottom: $flex-video-widescreen-padding-bottom; }\n  &.vimeo { padding-top: 0; }\n\n  iframe,\n  object,\n  embed,\n  video {\n    position: absolute;\n    top: 0;\n    #{$default-float}: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@include exports(\"flex-video\") {\n  @if $include-html-media-classes {\n    .flex-video { @include flex-video-container; }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-inline-list-classes: $include-html-classes !default;\n\n// We use this to control the margins and padding of the inline list.\n$inline-list-top-margin: 0 !default;\n$inline-list-opposite-margin: 0 !default;\n$inline-list-bottom-margin: rem-calc(17) !default;\n$inline-list-default-float-margin: rem-calc(-22) !default;\n$inline-list-default-float-list-margin: rem-calc(22) !default;\n\n$inline-list-padding: 0 !default;\n\n// We use this to control the overflow of the inline list.\n$inline-list-overflow: hidden !default;\n\n// We use this to control the list items\n$inline-list-display: block !default;\n\n// We use this to control any elements within list items\n$inline-list-children-display: block !default;\n\n//\n// @mixins\n//\n// We use this mixin to create inline lists\n@mixin inline-list {\n  margin: $inline-list-top-margin auto $inline-list-bottom-margin auto;\n  margin-#{$default-float}: $inline-list-default-float-margin;\n  margin-#{$opposite-direction}: $inline-list-opposite-margin;\n  padding: $inline-list-padding;\n  list-style: none;\n  overflow: $inline-list-overflow;\n\n  & > li {\n    list-style: none;\n    float: $default-float;\n    margin-#{$default-float}: $inline-list-default-float-list-margin;\n    display: $inline-list-display;\n    &>* { display: $inline-list-children-display; }\n  }\n}\n\n@include exports(\"inline-list\") {\n  @if $include-html-inline-list-classes {\n    .inline-list {\n      @include inline-list();\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-keystroke-classes: $include-html-classes !default;\n\n// We use these to control text styles.\n$keystroke-font: \"Consolas\", \"Menlo\", \"Courier\", monospace !default;\n$keystroke-font-size: inherit !default;\n$keystroke-font-color: $jet !default;\n$keystroke-font-color-alt: $white !default;\n$keystroke-function-factor: -7% !default;\n\n// We use this to control keystroke padding.\n$keystroke-padding: rem-calc(2 4 0) !default;\n\n// We use these to control background and border styles.\n$keystroke-bg: scale-color($white, $lightness: $keystroke-function-factor) !default;\n$keystroke-border-style: solid !default;\n$keystroke-border-width: 1px !default;\n$keystroke-border-color: scale-color($keystroke-bg, $lightness: $keystroke-function-factor) !default;\n$keystroke-radius: $global-radius !default;\n\n//\n// @mixins\n//\n// We use this mixin to create keystroke styles.\n// $bg - Default: $keystroke-bg || scale-color($white, $lightness: $keystroke-function-factor) !default;\n@mixin keystroke($bg:$keystroke-bg) {\n  // This find the lightness percentage of the background color.\n  $bg-lightness: lightness($bg);\n\n  background-color: $bg;\n  border-color: scale-color($bg, $lightness: $keystroke-function-factor);\n\n  // We adjust the font color based on the brightness of the background.\n  @if $bg-lightness > 70% { color: $keystroke-font-color; }\n  @else { color: $keystroke-font-color-alt; }\n\n  border-style: $keystroke-border-style;\n  border-width: $keystroke-border-width;\n  margin: 0;\n  font-family: $keystroke-font;\n  font-size: $keystroke-font-size;\n  padding: $keystroke-padding;\n}\n\n@include exports(\"keystroke\") {\n  @if $include-html-keystroke-classes  {\n    .keystroke,\n    kbd {\n      @include keystroke;\n      @include radius($keystroke-radius);\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-panel-classes: $include-html-classes !default;\n\n// We use these to control the background and border styles\n$panel-bg: scale-color($white, $lightness: -5%) !default;\n$panel-border-style: solid !default;\n$panel-border-size: 1px !default;\n\n// We use this % to control how much we darken things on hover\n$panel-function-factor: -11% !default;\n$panel-border-color: scale-color($panel-bg, $lightness: $panel-function-factor) !default;\n\n// We use these to set default inner padding and bottom margin\n$panel-margin-bottom: rem-calc(20) !default;\n$panel-padding: rem-calc(20) !default;\n\n// We use these to set default font colors\n$panel-font-color: $oil !default;\n$panel-font-color-alt: $white !default;\n\n$panel-header-adjust: true !default;\n$callout-panel-link-color: $primary-color !default;\n$callout-panel-link-color-hover: scale-color($callout-panel-link-color, $lightness: -14%) !default;\n\n//\n// @mixins\n//\n// We use this mixin to create panels.\n// $bg - Sets the panel background color. Default: $panel-pg || scale-color($white, $lightness: -5%) !default\n// $padding - Sets the panel padding amount. Default: $panel-padding || rem-calc(20)\n// $adjust - Sets the font color based on the darkness of the bg & resets header line-heights for panels. Default: $panel-header-adjust || true\n@mixin panel($bg: $panel-bg, $padding: $panel-padding, $adjust: $panel-header-adjust) {\n\n  @if $bg {\n    $bg-lightness: lightness($bg);\n\n    border-style: $panel-border-style;\n    border-width: $panel-border-size;\n    border-color: scale-color($bg, $lightness: $panel-function-factor);\n    margin-bottom: $panel-margin-bottom;\n    padding: $padding;\n\n    background: $bg;\n\n    @if $bg-lightness >=50% {\n      color: $panel-font-color;\n    }\n\n    @else {\n      color: $panel-font-color-alt;\n    }\n\n    // Respect the padding, fool.\n    &>:first-child {\n      margin-top: 0;\n    }\n\n    &>:last-child {\n      margin-bottom: 0;\n    }\n\n    @if $adjust {\n\n      // We set the font color based on the darkness of the bg.\n      @if $bg-lightness >=50% {\n\n        h1,\n        h2,\n        h3,\n        h4,\n        h5,\n        h6,\n        p,\n        li,\n        dl {\n          color: $panel-font-color;\n        }\n      }\n\n      @else {\n\n        h1,\n        h2,\n        h3,\n        h4,\n        h5,\n        h6,\n        p,\n        li,\n        dl {\n          color: $panel-font-color-alt;\n        }\n      }\n\n      // reset header line-heights for panels\n      h1,\n      h2,\n      h3,\n      h4,\n      h5,\n      h6 {\n        line-height: 1;\n        margin-bottom: calc(rem-calc(20) / 2);\n\n        &.subheader {\n          line-height: 1.4;\n        }\n      }\n    }\n  }\n}\n\n@include exports(\"panel\") {\n  @if $include-html-panel-classes {\n\n    /* Panels */\n    .panel {\n      @include panel;\n\n      &.callout {\n        @include panel(scale-color($primary-color, $lightness: 94%));\n\n        a:not(.button) {\n          color: $callout-panel-link-color;\n\n          &:hover,\n          &:focus {\n            color: $callout-panel-link-color-hover;\n          }\n        }\n      }\n\n      &.radius {\n        @include radius;\n      }\n\n    }\n\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"grid\";\n\n//\n// @name _reveal.scss\n// @dependencies _global.scss\n//\n\n$include-html-reveal-classes: $include-html-classes !default;\n\n// We use these to control the style of the reveal overlay.\n$reveal-overlay-bg: rgba($black, .45) !default;\n$reveal-overlay-bg-old: $black !default;\n\n// We use these to control the style of the modal itself.\n$reveal-modal-bg: $white !default;\n$reveal-position-top: rem-calc(100) !default;\n$reveal-default-width: 80% !default;\n$reveal-max-width: $row-width !default;\n$reveal-modal-padding: rem-calc(20) !default;\n$reveal-box-shadow: 0 0 10px rgba($black,.4) !default;\n\n// We use these to style the reveal close button\n$reveal-close-font-size: rem-calc(40) !default;\n$reveal-close-top: rem-calc(10) !default;\n$reveal-close-side: rem-calc(22) !default;\n$reveal-close-color: $base !default;\n$reveal-close-weight: $font-weight-bold !default;\n\n// We use this to set the default radius used throughout the core.\n$reveal-radius: $global-radius !default;\n$reveal-round: $global-rounded !default;\n\n// We use these to control the modal border\n$reveal-border-style: solid !default;\n$reveal-border-width: 1px !default;\n$reveal-border-color: $steel !default;\n\n$reveal-modal-class: \"reveal-modal\" !default;\n$close-reveal-modal-class: \"close-reveal-modal\" !default;\n\n//\n// @mixins\n//\n\n// We use this to create the reveal background overlay styles\n@mixin reveal-bg( $include-z-index-value: true ) {\n  //position: fixed;\n  position: absolute; // allows modal background to extend beyond window position\n  top: 0;\n  bottom: 0;\n  left: 0;\n  right: 0;\n  background: $reveal-overlay-bg-old; // Autoprefixer should be used to avoid such variables needed when Foundation for Sites can do so in the near future.\n  background: $reveal-overlay-bg;\n  z-index: if( $include-z-index-value, 1004, auto );\n  display: none;\n  #{$default-float}: 0;\n}\n\n// We use this mixin to create the structure of a reveal modal\n//\n// $base-style - Provides reveal base styles, can be set to false to override. Default: true, Options: false\n// $width - Sets reveal width Default: $reveal-default-width || 80%\n//\n@mixin reveal-modal-base( $base-style: true, $width:$reveal-default-width, $max-width:$reveal-max-width, $border-radius: $reveal-radius) {\n  @if $base-style {\n    visibility: hidden;\n    display: none;\n    position: absolute;\n    z-index: 1005;\n    width: 100vw;\n    top:0;\n    border-radius: $border-radius;\n    #{$default-float}: 0;\n\n    @media #{$small-only} {\n      min-height:100vh;\n    }\n\n    // Make sure rows don't have a min-width on them\n    .column, .columns { min-width: 0; }\n\n    // Get rid of margin from first and last element inside modal\n    & > :first-child { margin-top: 0; }\n\n    & > :last-child { margin-bottom: 0; }\n  }\n\n  @if $width {\n    @media #{$medium-up} {\n      width: $width;\n      max-width: $max-width;\n      left: 0;\n      right: 0;\n      margin: 0 auto;\n    }\n  }\n}\n\n// We use this to style the reveal modal defaults\n//\n// $bg - Sets background color of reveal modal. Default: $reveal-modal-bg || $white\n// $padding - Padding to apply to reveal modal. Default: $reveal-modal-padding.\n// $border - Choose whether reveal uses a border. Default: true, Options: false\n// $border-style - Set reveal border style. Default: $reveal-border-style || solid\n// $border-width - Width of border (i.e. 1px). Default: $reveal-border-width.\n// $border-color - Color of border. Default: $reveal-border-color.\n// $box-shadow - Choose whether or not to include the default box-shadow. Default: true, Options: false\n// $radius - If true, set to modal radius which is $global-radius || explicitly set radius amount in px (ex. $radius:10px). Default: false\n// $top-offset - Default: $reveal-position-top || 50px\n@mixin reveal-modal-style(\n  $bg:false,\n  $padding:false,\n  $border:false,\n  $border-style:$reveal-border-style,\n  $border-width:$reveal-border-width,\n  $border-color:$reveal-border-color,\n  $box-shadow:false,\n  $radius:false,\n  $top-offset:false) {\n\n  @if $bg { background-color: $bg; }\n  @if $padding != false { padding: $padding; }\n\n  @if $border { border: $border-style $border-width $border-color; }\n\n  // We can choose whether or not to include the default box-shadow.\n  @if $box-shadow {\n    box-shadow: $reveal-box-shadow;\n  }\n\n  // We can control how much radius is used on the modal\n  @if $radius == true { @include radius($reveal-radius); }\n  @else if $radius { @include radius($radius); }\n\n  @if $top-offset {\n    @media #{$medium-up} {\n      top: $top-offset;\n    }\n  }\n}\n\n// We use this to create a close button for the reveal modal\n//\n// $color - Default: $reveal-close-color || $base\n@mixin reveal-close($color:$reveal-close-color) {\n  font-size: $reveal-close-font-size;\n  line-height: 1;\n  position: absolute;\n  top: $reveal-close-top;\n  #{$opposite-direction}: $reveal-close-side;\n  color: $color;\n  font-weight: $reveal-close-weight;\n  cursor: $cursor-pointer-value;\n}\n\n@include exports(\"reveal\") {\n  @if $include-html-reveal-classes {\n\n    // Reveal Modals\n    .reveal-modal-bg { @include reveal-bg; }\n\n    .#{$reveal-modal-class} {\n      @include reveal-modal-base;\n      @include reveal-modal-style(\n        $bg:$reveal-modal-bg,\n        $padding:$reveal-modal-padding,\n        $border:true,\n        $box-shadow:true,\n        $radius:false,\n        $top-offset:$reveal-position-top\n      );\n      @include reveal-modal-style($padding:$reveal-modal-padding * 1.5);\n\n      &.radius { @include reveal-modal-style($radius:true); }\n      &.round  { @include reveal-modal-style($radius:$reveal-round); }\n      &.collapse { @include reveal-modal-style($padding:0); }\n      &.tiny  { @include reveal-modal-base(false, 30%); }\n      &.small { @include reveal-modal-base(false, 40%); }\n      &.medium  { @include reveal-modal-base(false, 60%); }\n      &.large { @include reveal-modal-base(false, 70%); }\n      &.xlarge { @include reveal-modal-base(false, 95%); }\n      &.full {\n        @include reveal-modal-base(false, 100vw);\n        top:0;\n        left:0;\n        height:100%;\n        height: 100vh;\n        min-height:100vh;\n        max-width: none !important;\n        margin-left: 0 !important;\n      }\n\n      .#{$close-reveal-modal-class} { @include reveal-close; }\n    }\n\n    dialog {\n      @extend .#{$reveal-modal-class};\n      display: none;\n\n      &::backdrop, & + .backdrop {\n        @include reveal-bg(false);\n      }\n\n      &[open]{\n        display: block;\n      }\n    }\n\n    // Reveal Print Styles: It should be invisible, adds no value being printed.\n    @media print {\n      dialog, .#{$reveal-modal-class} { \n        display: none;\n        background: $white !important;\n      }\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n\n$include-html-nav-classes: $include-html-classes !default;\n\n// We use this to control padding.\n$side-nav-padding: rem-calc(14 0) !default;\n\n// We use these to control list styles.\n$side-nav-list-type: none !default;\n$side-nav-list-position: outside !default;\n$side-nav-list-margin: rem-calc(0 0 7 0) !default;\n\n// We use these to control link styles.\n$side-nav-link-color: $primary-color !default;\n$side-nav-link-color-active: scale-color($side-nav-link-color, $lightness: 30%) !default;\n$side-nav-link-color-hover: scale-color($side-nav-link-color, $lightness: 30%) !default;\n$side-nav-link-bg-hover: hsla(0deg, 0%, 0%, 0.025) !default;\n$side-nav-link-margin: 0 !default;\n$side-nav-link-padding: rem-calc(7 14) !default;\n$side-nav-font-size: rem-calc(14) !default;\n$side-nav-font-weight: $font-weight-normal !default;\n$side-nav-font-weight-active: $side-nav-font-weight !default;\n$side-nav-font-family: $body-font-family !default;\n$side-nav-font-family-active: $side-nav-font-family !default;\n\n// We use these to control heading styles.\n$side-nav-heading-color: $side-nav-link-color !default;\n$side-nav-heading-font-size: $side-nav-font-size !default;\n$side-nav-heading-font-weight: bold !default;\n$side-nav-heading-text-transform: uppercase !default;\n\n// We use these to control border styles\n$side-nav-divider-size: 1px !default;\n$side-nav-divider-style: solid !default;\n$side-nav-divider-color: scale-color($white, $lightness: 10%) !default;\n\n\n//\n// @mixins\n//\n\n\n// We use this to style the side-nav\n//\n// $divider-color - Border color of divider. Default: $side-nav-divider-color.\n// $font-size - Font size of nav items. Default: $side-nav-font-size.\n// $link-color - Color of navigation links. Default: $side-nav-link-color.\n// $link-color-hover - Color of navigation links when hovered. Default: $side-nav-link-color-hover.\n@mixin side-nav($divider-color: $side-nav-divider-color,\n  $font-size: $side-nav-font-size,\n  $link-color: $side-nav-link-color,\n  $link-color-hover: $side-nav-link-color-hover,\n  $link-bg-hover: $side-nav-link-bg-hover) {\n  display: block;\n  margin: 0;\n  padding: $side-nav-padding;\n  list-style-type: $side-nav-list-type;\n  list-style-position: $side-nav-list-position;\n  font-family: $side-nav-font-family;\n\n  li {\n    margin: $side-nav-list-margin;\n    font-size: $font-size;\n    font-weight: $side-nav-font-weight;\n\n    a:not(.button) {\n      display: block;\n      color: $link-color;\n      margin: $side-nav-link-margin;\n      padding: $side-nav-link-padding;\n\n      &:hover,\n      &:focus {\n        background: $link-bg-hover;\n        color: $link-color-hover;\n      }\n    }\n\n    &.active>a:first-child:not(.button) {\n      color: $side-nav-link-color-active;\n      font-weight: $side-nav-font-weight-active;\n      font-family: $side-nav-font-family-active;\n    }\n\n    &.divider {\n      border-top: $side-nav-divider-size $side-nav-divider-style;\n      height: 0;\n      padding: 0;\n      list-style: none;\n      border-top-color: $divider-color;\n    }\n\n    &.heading {\n      color: $side-nav-heading-color;\n\n      font: {\n        size: $side-nav-heading-font-size;\n        weight: $side-nav-heading-font-weight;\n      }\n\n      text-transform: $side-nav-heading-text-transform;\n    }\n  }\n}\n\n@include exports(\"side-nav\") {\n  @if $include-html-nav-classes {\n    .side-nav {\n      @include side-nav;\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @name _sub-nav.scss\n// @dependencies _global.scss\n//\n\n//\n// @variables\n//\n\n$include-html-nav-classes: $include-html-classes !default;\n\n// We use these to control margin and padding\n$sub-nav-list-margin: rem-calc(-4 0 18) !default;\n$sub-nav-list-padding-top: rem-calc(4) !default;\n\n// We use this to control the definition\n$sub-nav-font-family: $body-font-family !default;\n$sub-nav-font-size: rem-calc(14) !default;\n$sub-nav-font-color: $aluminum !default;\n$sub-nav-font-weight: $font-weight-normal !default;\n$sub-nav-text-decoration: none !default;\n$sub-nav-padding: rem-calc(3 16) !default;\n$sub-nav-border-radius: 3px !default;\n$sub-nav-font-color-hover: scale-color($sub-nav-font-color, $lightness: -25%) !default;\n\n\n// We use these to control the active item styles\n\n$sub-nav-active-font-weight: $font-weight-normal !default;\n$sub-nav-active-bg: $primary-color !default;\n$sub-nav-active-bg-hover: scale-color($sub-nav-active-bg, $lightness: -14%) !default;\n$sub-nav-active-color: $white !default;\n$sub-nav-active-padding: $sub-nav-padding !default;\n$sub-nav-active-cursor: default !default;\n\n$sub-nav-item-divider: \"\" !default;\n$sub-nav-item-divider-margin: rem-calc(12) !default;\n\n//\n// @mixins\n//\n\n\n// Create a sub-nav item\n//\n// $font-color - Font color. Default: $sub-nav-font-color.\n// $font-size - Font size. Default: $sub-nav-font-size.\n// $active-bg - Background of active nav item. Default: $sub-nav-active-bg.\n// $active-bg-hover - Background of active nav item, when hovered. Default: $sub-nav-active-bg-hover.\n@mixin sub-nav(\n  $font-color: $sub-nav-font-color,\n  $font-size: $sub-nav-font-size,\n  $active-bg: $sub-nav-active-bg,\n  $active-bg-hover: $sub-nav-active-bg-hover) {\n  display: block;\n  width: auto;\n  overflow: hidden;\n  margin: $sub-nav-list-margin;\n  padding-top: $sub-nav-list-padding-top;\n\n  dt {\n    text-transform: uppercase;\n  }\n\n  dt,\n  dd,\n  li {\n    float: $default-float;\n    display: inline;\n    margin-#{$default-float}: rem-calc(16);\n    margin-bottom: 0;\n    font-family: $sub-nav-font-family;\n    font-weight: $sub-nav-font-weight;\n    font-size: $font-size;\n    color: $font-color;\n\n    a {\n      text-decoration: $sub-nav-text-decoration;\n      color: $sub-nav-font-color;\n      padding: $sub-nav-padding;\n      &:hover {\n        color: $sub-nav-font-color-hover;\n      }\n    }\n\n    &.active a {\n      @include radius($sub-nav-border-radius);\n      font-weight: $sub-nav-active-font-weight;\n      background: $active-bg;\n      padding: $sub-nav-active-padding;\n      cursor: $sub-nav-active-cursor;\n      color: $sub-nav-active-color;\n      &:hover {\n        background: $active-bg-hover;\n      }\n    }\n    @if $sub-nav-item-divider != \"\" {\n      margin-#{$default-float}: 0;\n\n      &:before {\n        content: \"#{$sub-nav-item-divider}\";\n        margin: 0 $sub-nav-item-divider-margin;\n      }\n\n      &:first-child:before {\n        content: \"\";\n        margin: 0;\n      }\n    }\n  }\n}\n\n@include exports(\"sub-nav\") {\n  @if $include-html-nav-classes {\n    .sub-nav { @include sub-nav; }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @name _tables.scss\n// @dependencies _global.scss\n//\n\n//\n// @variables\n//\n\n$include-html-table-classes: $include-html-classes !default;\n\n// These control the background color for the table and even rows\n$table-bg: $white !default;\n$table-even-row-bg: $snow !default;\n\n// These control the table cell border style\n$table-border-style: solid !default;\n$table-border-size: 1px !default;\n$table-border-color: $gainsboro !default;\n\n// These control the table head styles\n$table-head-bg: $white-smoke !default;\n$table-head-font-size: rem-calc(14) !default;\n$table-head-font-color: $jet !default;\n$table-head-font-weight: $font-weight-bold !default;\n$table-head-padding: rem-calc(8 10 10) !default;\n\n// These control the table foot styles\n$table-foot-bg: $table-head-bg !default;\n$table-foot-font-size: $table-head-font-size !default;\n$table-foot-font-color: $table-head-font-color !default;\n$table-foot-font-weight: $table-head-font-weight !default;\n$table-foot-padding: $table-head-padding !default;\n\n// These control the caption\n$table-caption-bg: transparent !default;\n$table-caption-font-color: $table-head-font-color !default;\n$table-caption-font-size: rem-calc(16) !default;\n$table-caption-font-weight: bold !default;\n\n// These control the row padding and font styles\n$table-row-padding: rem-calc(9 10) !default;\n$table-row-font-size: rem-calc(14) !default;\n$table-row-font-color: $jet !default;\n$table-line-height: rem-calc(18) !default;\n\n// These are for controlling the layout, display and margin of tables\n$table-layout: auto !default;\n$table-display: table-cell !default;\n$table-margin-bottom: rem-calc(20) !default;\n\n\n//\n// @mixins\n//\n\n@mixin table {\n  background: $table-bg;\n  margin-bottom: $table-margin-bottom;\n  border: $table-border-style $table-border-size $table-border-color;\n  table-layout: $table-layout;\n\n  caption {\n    background: $table-caption-bg;\n    color: $table-caption-font-color;\n    font: {\n      size: $table-caption-font-size;\n      weight: $table-caption-font-weight;\n    }\n  }\n\n  thead {\n    background: $table-head-bg;\n\n    tr {\n      th,\n      td {\n        padding: $table-head-padding;\n        font-size: $table-head-font-size;\n        font-weight: $table-head-font-weight;\n        color: $table-head-font-color;\n      }\n    }\n  }\n\n  tfoot {\n    background: $table-foot-bg;\n\n    tr {\n      th,\n      td {\n        padding: $table-foot-padding;\n        font-size: $table-foot-font-size;\n        font-weight: $table-foot-font-weight;\n        color: $table-foot-font-color;\n      }\n    }\n  }\n\n  tr {\n    th,\n    td {\n      padding: $table-row-padding;\n      font-size: $table-row-font-size;\n      color: $table-row-font-color;\n      text-align: $default-float;\n    }\n\n    &.even,\n    &.alt,\n    &:nth-of-type(even) { background: $table-even-row-bg; }\n  }\n\n  thead tr th,\n  tfoot tr th,\n  tfoot tr td,\n  tbody tr th,\n  tbody tr td,\n  tr td { display: $table-display; line-height: $table-line-height; }\n}\n\n\n@include exports(\"table\") {\n  @if $include-html-table-classes {\n    table {\n      @include table;\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @name _thumbs.scss\n// @dependencies _globals.scss\n//\n\n//\n// @variables\n//\n\n$include-html-media-classes: $include-html-classes !default;\n\n// We use these to control border styles\n$thumb-border-style: solid !default;\n$thumb-border-width: 4px !default;\n$thumb-border-color: $white !default;\n$thumb-box-shadow: 0 0 0 1px rgba($black,.2) !default;\n$thumb-box-shadow-hover: 0 0 6px 1px rgba($primary-color,0.5) !default;\n\n// Radius and transition speed for thumbs\n$thumb-radius: $global-radius !default;\n$thumb-transition-speed: 200ms !default;\n\n//\n// @mixins\n//\n\n// We use this to create image thumbnail styles.\n//\n// $border-width - Width of border around thumbnail. Default: $thumb-border-width.\n// $box-shadow - Box shadow to apply to thumbnail. Default: $thumb-box-shadow.\n// $box-shadow-hover - Box shadow to apply on hover. Default: $thumb-box-shadow-hover.\n@mixin thumb(\n  $border-width:$thumb-border-width, \n  $box-shadow:$thumb-box-shadow, \n  $box-shadow-hover:$thumb-box-shadow-hover) {\n  line-height: 0;\n  display: inline-block;\n  border: $thumb-border-style $border-width $thumb-border-color;\n  max-width: 100%;\n  box-shadow: $box-shadow;\n\n  &:hover,\n  &:focus {\n    box-shadow: $box-shadow-hover;\n  }\n}\n\n\n@include exports(\"thumb\") {\n  @if $include-html-media-classes {\n\n    /* Image Thumbnails */\n    .th {\n      @include thumb;\n      @include single-transition(all,$thumb-transition-speed,ease-out);\n\n      &.radius { @include radius($thumb-radius); }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n$include-html-type-classes: $include-html-classes !default;\n\n// We use these to control header font styles\n$header-font-family: $body-font-family !default;\n$header-font-weight: $font-weight-normal !default;\n$header-font-style: $font-weight-normal !default;\n$header-font-color: $jet !default;\n$header-line-height: 1.4 !default;\n$header-top-margin: .2rem !default;\n$header-bottom-margin: .5rem !default;\n$header-text-rendering: optimizeLegibility !default;\n\n// We use these to control header font sizes\n$h1-font-size: rem-calc(44) !default;\n$h2-font-size: rem-calc(37) !default;\n$h3-font-size: rem-calc(27) !default;\n$h4-font-size: rem-calc(23) !default;\n$h5-font-size: rem-calc(18) !default;\n$h6-font-size: 1rem !default;\n\n// We use these to control header size reduction on small screens\n$h1-font-reduction: rem-calc(10) !default;\n$h2-font-reduction: rem-calc(10) !default;\n$h3-font-reduction: rem-calc(5) !default;\n$h4-font-reduction: rem-calc(5) !default;\n$h5-font-reduction: 0 !default;\n$h6-font-reduction: 0 !default;\n\n// These control how subheaders are styled.\n$subheader-line-height: 1.4 !default;\n$subheader-font-color: scale-color($header-font-color, $lightness: 35%) !default;\n$subheader-font-weight: $font-weight-normal !default;\n$subheader-top-margin: .2rem !default;\n$subheader-bottom-margin: .5rem !default;\n\n// A general <small> styling\n$small-font-size: 60% !default;\n$small-font-color: scale-color($header-font-color, $lightness: 35%) !default;\n\n// We use these to style paragraphs\n$paragraph-font-family: inherit !default;\n$paragraph-font-weight: $font-weight-normal !default;\n$paragraph-font-size: 1rem !default;\n$paragraph-line-height: 1.6 !default;\n$paragraph-margin-bottom: rem-calc(20) !default;\n$paragraph-aside-font-size: rem-calc(14) !default;\n$paragraph-aside-line-height: 1.35 !default;\n$paragraph-aside-font-style: italic !default;\n$paragraph-text-rendering: optimizeLegibility !default;\n\n// We use these to style <code> tags\n$code-color: $oil !default;\n$code-font-family: $font-family-monospace !default;\n$code-font-weight: $font-weight-normal !default;\n$code-background-color: scale-color($secondary-color, $lightness: 70%) !default;\n$code-border-size: 0px !default;\n$code-border-style: solid !default;\n$code-border-color: scale-color($code-background-color, $lightness: -10%) !default;\n$code-padding: rem-calc(2) rem-calc(5) rem-calc(1) !default;\n\n// We use these to style anchors\n$anchor-text-decoration: none !default;\n$anchor-text-decoration-hover: none !default;\n$anchor-font-color: $primary-color !default;\n$anchor-font-color-hover: scale-color($anchor-font-color, $lightness: -14%) !default;\n\n// We use these to style the <hr> element\n$hr-border-width: 1px !default;\n$hr-border-style: solid !default;\n$hr-border-color: $gainsboro !default;\n$hr-margin: rem-calc(20) !default;\n\n// We use these to style lists\n$list-font-family: $paragraph-font-family !default;\n$list-font-size: $paragraph-font-size !default;\n$list-line-height: $paragraph-line-height !default;\n$list-margin-bottom: $paragraph-margin-bottom !default;\n$list-style-position: outside !default;\n$list-side-margin: 1.1rem !default;\n$list-ordered-side-margin: 1.4rem !default;\n$list-side-margin-no-bullet: 0 !default;\n$list-nested-margin: rem-calc(20) !default;\n$definition-list-header-weight: $font-weight-bold !default;\n$definition-list-header-margin-bottom: .3rem !default;\n$definition-list-margin-bottom: rem-calc(12) !default;\n\n// We use these to style blockquotes\n$blockquote-font-color: scale-color($header-font-color, $lightness: 35%) !default;\n$blockquote-padding: rem-calc(9 20 0 19) !default;\n$blockquote-border: 1px solid $gainsboro !default;\n$blockquote-cite-font-size: rem-calc(13) !default;\n$blockquote-cite-font-color: scale-color($header-font-color, $lightness: 23%) !default;\n$blockquote-cite-link-color: $blockquote-cite-font-color !default;\n\n// Acronym styles\n$acronym-underline: 1px dotted $gainsboro !default;\n\n// We use these to control padding and margin\n$microformat-padding: rem-calc(10 12) !default;\n$microformat-margin: rem-calc(0 0 20 0) !default;\n\n// We use these to control the border styles\n$microformat-border-width: 1px !default;\n$microformat-border-style: solid !default;\n$microformat-border-color: $gainsboro !default;\n\n// We use these to control full name font styles\n$microformat-fullname-font-weight: $font-weight-bold !default;\n$microformat-fullname-font-size: rem-calc(15) !default;\n\n// We use this to control the summary font styles\n$microformat-summary-font-weight: $font-weight-bold !default;\n\n// We use this to control abbr padding\n$microformat-abbr-padding: rem-calc(0 1) !default;\n\n// We use this to control abbr font styles\n$microformat-abbr-font-weight: $font-weight-bold !default;\n$microformat-abbr-font-decoration: none !default;\n\n// Text alignment class names\n$align-class-names:\n  small-only,\n  small,\n  medium-only,\n  medium,\n  large-only,\n  large,\n  xlarge-only,\n  xlarge,\n  xxlarge-only,\n  xxlarge;\n\n// Text alignment breakpoints\n$align-class-breakpoints:\n  $small-only,\n  $small-up,\n  $medium-only,\n  $medium-up,\n  $large-only,\n  $large-up,\n  $xlarge-only,\n  $xlarge-up,\n  $xxlarge-only,\n  $xxlarge-up;\n\n// Generates text align and justify classes\n@mixin align-classes{\n  .text-left    { text-align: left !important; }\n  .text-right   { text-align: right !important; }\n  .text-center  { text-align: center !important; }\n  .text-justify { text-align: justify !important; }\n\n  @for $i from 1 through length($align-class-names) {\n    @media #{(nth($align-class-breakpoints, $i))} {\n      .#{(nth($align-class-names, $i))}-text-left { text-align: left !important; }\n      .#{(nth($align-class-names, $i))}-text-right   { text-align: right !important; }\n      .#{(nth($align-class-names, $i))}-text-center  { text-align: center !important; }\n      .#{(nth($align-class-names, $i))}-text-justify { text-align: justify !important; }\n    }\n  }\n}\n\n//\n// Typography Placeholders\n//\n\n// These will throw a deprecation warning if used within a media query.\n@mixin lead {\n  font-size: $paragraph-font-size + rem-calc(3.5);\n  line-height: 1.6;\n}\n\n@mixin subheader {\n  line-height: $subheader-line-height;\n  color: $subheader-font-color;\n  font-weight: $subheader-font-weight;\n  margin-top: $subheader-top-margin;\n  margin-bottom: $subheader-bottom-margin;\n}\n@include exports(\"type\") {\n  @if $include-html-type-classes {\n    // Responsive Text alignment\n    @include align-classes;\n\n    /* Typography resets */\n    div,\n    dl,\n    dt,\n    dd,\n    ul,\n    ol,\n    li,\n    h1,\n    h2,\n    h3,\n    h4,\n    h5,\n    h6,\n    pre,\n    form,\n    p,\n    blockquote,\n    th,\n    td {\n      margin:0;\n      padding:0;\n    }\n\n    /* Default Link Styles */\n    a {\n      color: $anchor-font-color;\n      text-decoration: $anchor-text-decoration;\n      line-height: inherit;\n\n      &:hover,\n      &:focus {\n        color: $anchor-font-color-hover;\n        @if $anchor-text-decoration-hover != $anchor-text-decoration {\n        \ttext-decoration: $anchor-text-decoration-hover;\n        }\n      }\n\n      img { border:none; }\n    }\n\n    /* Default paragraph styles */\n    p {\n      font-family: $paragraph-font-family;\n      font-weight: $paragraph-font-weight;\n      font-size: $paragraph-font-size;\n      line-height: $paragraph-line-height;\n      margin-bottom: $paragraph-margin-bottom;\n      text-rendering: $paragraph-text-rendering;\n\n      &.lead { @include lead; }\n\n      & aside {\n        font-size: $paragraph-aside-font-size;\n        line-height: $paragraph-aside-line-height;\n        font-style: $paragraph-aside-font-style;\n      }\n    }\n\n    /* Default header styles */\n    h1, h2, h3, h4, h5, h6 {\n      font-family: $header-font-family;\n      font-weight: $header-font-weight;\n      font-style: $header-font-style;\n      color: $header-font-color;\n      text-rendering: $header-text-rendering;\n      margin-top: $header-top-margin;\n      margin-bottom: $header-bottom-margin;\n      line-height: $header-line-height;\n\n      small {\n        font-size: $small-font-size;\n        color: $small-font-color;\n        line-height: 0;\n      }\n    }\n\n    h1 { font-size: $h1-font-size - $h1-font-reduction; }\n    h2 { font-size: $h2-font-size - $h2-font-reduction; }\n    h3 { font-size: $h3-font-size - $h3-font-reduction; }\n    h4 { font-size: $h4-font-size - $h4-font-reduction; }\n    h5 { font-size: $h5-font-size - $h5-font-reduction; }\n    h6 { font-size: $h6-font-size - $h6-font-reduction; }\n\n    .subheader { @include subheader; }\n\n    hr {\n      border: $hr-border-style $hr-border-color;\n      border-width: $hr-border-width 0 0;\n      clear: both;\n      margin: $hr-margin 0 ($hr-margin - rem-calc($hr-border-width));\n      height: 0;\n    }\n\n    /* Helpful Typography Defaults */\n    em,\n    i {\n      font-style: italic;\n      line-height: inherit;\n    }\n\n    strong,\n    b {\n      font-weight: $font-weight-bold;\n      line-height: inherit;\n    }\n\n    small {\n      font-size: $small-font-size;\n      line-height: inherit;\n    }\n\n    code {\n      font-family: $code-font-family;\n      font-weight: $code-font-weight;\n      color: $code-color;\n      background-color: $code-background-color;\n      border-width: $code-border-size;\n      border-style: $code-border-style;\n      border-color: $code-border-color;\n      padding: $code-padding;\n    }\n\n    /* Lists */\n    ul,\n    ol,\n    dl {\n      font-size: $list-font-size;\n      line-height: $list-line-height;\n      margin-bottom: $list-margin-bottom;\n      list-style-position: $list-style-position;\n      font-family: $list-font-family;\n    }\n\n    ul {\n      margin-#{$default-float}: $list-side-margin;\n      &.no-bullet {\n        margin-#{$default-float}: $list-side-margin-no-bullet;\n        li {\n          ul,\n          ol {\n            margin-#{$default-float}: $list-nested-margin;\n            margin-bottom: 0;\n            list-style: none;\n          }\n        }\n      }\n    }\n\n    /* Unordered Lists */\n    ul {\n      li {\n        ul,\n        ol {\n          margin-#{$default-float}: $list-nested-margin;\n          margin-bottom: 0;\n        }\n      }\n      &.square,\n      &.circle,\n      &.disc {\n        li ul { list-style: inherit; }\n      }\n\n      &.square { list-style-type: square; margin-#{$default-float}: $list-side-margin;}\n      &.circle { list-style-type: circle; margin-#{$default-float}: $list-side-margin;}\n      &.disc { list-style-type: disc; margin-#{$default-float}: $list-side-margin;}\n      &.no-bullet { list-style: none; }\n    }\n\n    /* Ordered Lists */\n    ol {\n      margin-#{$default-float}: $list-ordered-side-margin;\n      li {\n        ul,\n        ol {\n          margin-#{$default-float}: $list-nested-margin;\n          margin-bottom: 0;\n        }\n      }\n    }\n\n    /* Definition Lists */\n    dl {\n      dt {\n        margin-bottom: $definition-list-header-margin-bottom;\n        font-weight: $definition-list-header-weight;\n      }\n      dd { margin-bottom: $definition-list-margin-bottom; }\n    }\n\n    /* Abbreviations */\n    abbr,\n    acronym {\n      text-transform: uppercase;\n      font-size: 90%;\n      color: $body-font-color;\n      cursor: $cursor-help-value;\n    }\n    abbr {\n      text-transform: none;\n      &[title] {\n        border-bottom: $acronym-underline;\n      }\n    }\n\n    /* Blockquotes */\n    blockquote {\n      margin: 0 0 $paragraph-margin-bottom;\n      padding: $blockquote-padding;\n      border-#{$default-float}: $blockquote-border;\n\n      cite {\n        display: block;\n        font-size: $blockquote-cite-font-size;\n        color: $blockquote-cite-font-color;\n        &:before {\n          content: \"\\2014 \\0020\";\n        }\n\n        a,\n        a:visited {\n          color: $blockquote-cite-link-color;\n        }\n      }\n    }\n    blockquote,\n    blockquote p {\n      line-height: $paragraph-line-height;\n      color: $blockquote-font-color;\n    }\n\n    /* Microformats */\n    .vcard {\n      display: inline-block;\n      margin: $microformat-margin;\n      border: $microformat-border-width $microformat-border-style $microformat-border-color;\n      padding: $microformat-padding;\n\n      li {\n        margin: 0;\n        display: block;\n      }\n      .fn {\n        font-weight: $microformat-fullname-font-weight;\n        font-size: $microformat-fullname-font-size;\n      }\n    }\n\n    .vevent {\n      .summary { font-weight: $microformat-summary-font-weight; }\n\n      abbr {\n        cursor: $cursor-default-value;\n        text-decoration: $microformat-abbr-font-decoration;\n        font-weight: $microformat-abbr-font-weight;\n        border: none;\n        padding: $microformat-abbr-padding;\n      }\n    }\n\n\n    @media #{$medium-up} {\n      h1,h2,h3,h4,h5,h6 { line-height: $header-line-height; }\n      h1 { font-size: $h1-font-size; }\n      h2 { font-size: $h2-font-size; }\n      h3 { font-size: $h3-font-size; }\n      h4 { font-size: $h4-font-size; }\n      h5 { font-size: $h5-font-size; }\n      h6 { font-size: $h6-font-size; }\n    }\n\n    // Only include these styles if you want them.\n    @if $include-print-styles {\n      /*\n       * Print styles.\n       *\n       * Inlined to avoid required HTTP connection: www.phpied.com/delay-loading-your-print-css/\n       * Credit to Paul Irish and HTML5 Boilerplate (html5boilerplate.com)\n      */\n      .print-only { display: none !important; }\n      @media print {\n        * {\n          background: transparent !important;\n          color: $black !important; /* Black prints faster: h5bp.com/s */\n          box-shadow: none !important;\n          text-shadow: none !important;\n        }\n\n        a,\n        a:visited { text-decoration: underline;}\n        a[href]:after { content: \" (\" attr(href) \")\"; }\n\n        abbr[title]:after { content: \" (\" attr(title) \")\"; }\n\n        // Don't show links for images, or javascript/internal links\n        .ir a:after,\n        a[href^=\"javascript:\"]:after,\n        a[href^=\"#\"]:after { content: \"\"; }\n\n        pre,\n        blockquote {\n          border: 1px solid $aluminum;\n          page-break-inside: avoid;\n        }\n\n        thead { display: table-header-group; /* h5bp.com/t */ }\n\n        tr,\n        img { page-break-inside: avoid; }\n\n        img { max-width: 100% !important; }\n\n        @page { margin: 0.5cm; }\n\n        p,\n        h2,\n        h3 {\n          orphans: 3;\n          widows: 3;\n        }\n\n        h2,\n        h3 { page-break-after: avoid; }\n\n        .hide-on-print { display: none !important; }\n        .print-only { display: block !important; }\n        .hide-for-print { display: none !important; }\n        .show-for-print { display: inherit !important; }\n      }\n    }\n\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Foundation Visibility Classes\n//\n$include-html-visibility-classes: $include-html-classes !default;\n$include-accessibility-classes: true !default;\n$include-table-visibility-classes: true !default;\n$include-legacy-visibility-classes: true !default;\n\n//\n// Media Class Names\n//\n// Visibility Breakpoints\n$visibility-breakpoint-sizes:\n  small,\n  medium,\n  large,\n  xlarge,\n  xxlarge;\n\n$visibility-breakpoint-queries:\n  unquote($small-up),\n  unquote($medium-up),\n  unquote($large-up),\n  unquote($xlarge-up),\n  unquote($xxlarge-up);\n\n@mixin visibility-loop {\n  @each $current-visibility-breakpoint in $visibility-breakpoint-sizes {\n    $visibility-inherit-list: ();\n    $visibility-none-list: ();\n\n    $visibility-visible-list: ();\n    $visibility-hidden-list: ();\n\n    $visibility-table-list: ();\n    $visibility-table-header-group-list: ();\n    $visibility-table-row-group-list: ();\n    $visibility-table-row-list: ();\n    $visibility-table-cell-list: ();\n\n    @each $visibility-comparison-breakpoint in $visibility-breakpoint-sizes {\n      @if index($visibility-breakpoint-sizes, $visibility-comparison-breakpoint) < index($visibility-breakpoint-sizes, $current-visibility-breakpoint) {\n        // Smaller than current breakpoint\n\n        $visibility-inherit-list: append($visibility-inherit-list, unquote(\n          '.hide-for-#{$visibility-comparison-breakpoint}-only, .show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-none-list: append($visibility-none-list, unquote(\n          '.show-for-#{$visibility-comparison-breakpoint}-only, .hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-visible-list: append($visibility-visible-list, unquote(\n          '.hidden-for-#{$visibility-comparison-breakpoint}-only, .visible-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-hidden-list: append($visibility-hidden-list, unquote(\n          '.visible-for-#{$visibility-comparison-breakpoint}-only, .hidden-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-list: append($visibility-table-list, unquote(\n          'table.hide-for-#{$visibility-comparison-breakpoint}-only, table.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n          'thead.hide-for-#{$visibility-comparison-breakpoint}-only, thead.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n          'tbody.hide-for-#{$visibility-comparison-breakpoint}-only, tbody.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-list: append($visibility-table-row-list, unquote(\n          'tr.hide-for-#{$visibility-comparison-breakpoint}-only, tr.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n          'th.hide-for-#{$visibility-comparison-breakpoint}-only, td.hide-for-#{$visibility-comparison-breakpoint}-only, th.show-for-#{$visibility-comparison-breakpoint}-up, td.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n\n        // Foundation 4 compatibility:\n        // Include .show/hide-for-[size] and .show/hide-for-[size]-down classes\n        // for small, medium, and large breakpoints only\n        @if $include-legacy-visibility-classes and index((small, medium, large), $visibility-comparison-breakpoint) != false {\n          $visibility-inherit-list: append($visibility-inherit-list, unquote(\n            '.hide-for-#{$visibility-comparison-breakpoint}, .hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-none-list: append($visibility-none-list, unquote(\n            '.show-for-#{$visibility-comparison-breakpoint}, .show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-visible-list: append($visibility-visible-list, unquote(\n            '.hidden-for-#{$visibility-comparison-breakpoint}, .hidden-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-hidden-list: append($visibility-hidden-list, unquote(\n            '.visible-for-#{$visibility-comparison-breakpoint}, .visible-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-list: append($visibility-table-list, unquote(\n            'table.hide-for-#{$visibility-comparison-breakpoint}, table.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n            'thead.hide-for-#{$visibility-comparison-breakpoint}, thead.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n            'tbody.hide-for-#{$visibility-comparison-breakpoint}, tbody.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-list: append($visibility-table-row-list, unquote(\n            'tr.hide-for-#{$visibility-comparison-breakpoint}, tr.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n            'th.hide-for-#{$visibility-comparison-breakpoint}, td.hide-for-#{$visibility-comparison-breakpoint}, th.hide-for-#{$visibility-comparison-breakpoint}-down, td.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n        }\n\n      } @else if index($visibility-breakpoint-sizes, $visibility-comparison-breakpoint) > index($visibility-breakpoint-sizes, $current-visibility-breakpoint) {\n        // Larger than current breakpoint\n\n        $visibility-inherit-list: append($visibility-inherit-list, unquote(\n          '.hide-for-#{$visibility-comparison-breakpoint}-only, .hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-none-list: append($visibility-none-list, unquote(\n          '.show-for-#{$visibility-comparison-breakpoint}-only, .show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-visible-list: append($visibility-visible-list, unquote(\n          '.hidden-for-#{$visibility-comparison-breakpoint}-only, .hidden-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-hidden-list: append($visibility-hidden-list, unquote(\n          '.visible-for-#{$visibility-comparison-breakpoint}-only, .visible-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-list: append($visibility-table-list, unquote(\n          'table.hide-for-#{$visibility-comparison-breakpoint}-only, table.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n          'thead.hide-for-#{$visibility-comparison-breakpoint}-only, thead.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n          'tbody.hide-for-#{$visibility-comparison-breakpoint}-only, tbody.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-list: append($visibility-table-row-list, unquote(\n          'tr.hide-for-#{$visibility-comparison-breakpoint}-only, tr.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n          'th.hide-for-#{$visibility-comparison-breakpoint}-only, td.hide-for-#{$visibility-comparison-breakpoint}-only, th.hide-for-#{$visibility-comparison-breakpoint}-up, td.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n\n        // Foundation 4 compatibility:\n        // Include .show/hide-for-[size] and .show/hide-for-[size]-down classes\n        // for small, medium, and large breakpoints only\n        @if $include-legacy-visibility-classes and index((small, medium, large), $visibility-comparison-breakpoint) != false {\n          $visibility-inherit-list: append($visibility-inherit-list, unquote(\n            '.hide-for-#{$visibility-comparison-breakpoint}, .show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-none-list: append($visibility-none-list, unquote(\n            '.show-for-#{$visibility-comparison-breakpoint}, .hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-visible-list: append($visibility-visible-list, unquote(\n            '.hidden-for-#{$visibility-comparison-breakpoint}, .visible-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-hidden-list: append($visibility-hidden-list, unquote(\n            '.visible-for-#{$visibility-comparison-breakpoint}, .hidden-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-list: append($visibility-table-list, unquote(\n            'table.hide-for-#{$visibility-comparison-breakpoint}, table.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n            'thead.hide-for-#{$visibility-comparison-breakpoint}, thead.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n            'tbody.hide-for-#{$visibility-comparison-breakpoint}, tbody.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-list: append($visibility-table-row-list, unquote(\n            'tr.hide-for-#{$visibility-comparison-breakpoint}, tr.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n            'th.hide-for-#{$visibility-comparison-breakpoint}, td.hide-for-#{$visibility-comparison-breakpoint}, th.show-for-#{$visibility-comparison-breakpoint}-down, td.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n        }\n\n      } @else {\n        // Current breakpoint\n\n        $visibility-inherit-list: append($visibility-inherit-list, unquote(\n          '.show-for-#{$visibility-comparison-breakpoint}-only, .show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-none-list: append($visibility-none-list, unquote(\n          '.hide-for-#{$visibility-comparison-breakpoint}-only, .hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-visible-list: append($visibility-visible-list, unquote(\n          '.visible-for-#{$visibility-comparison-breakpoint}-only, .visible-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-hidden-list: append($visibility-hidden-list, unquote(\n          '.hidden-for-#{$visibility-comparison-breakpoint}-only, .hidden-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-list: append($visibility-table-list, unquote(\n          'table.show-for-#{$visibility-comparison-breakpoint}-only, table.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n          'thead.show-for-#{$visibility-comparison-breakpoint}-only, thead.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n          'tbody.show-for-#{$visibility-comparison-breakpoint}-only, tbody.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-list: append($visibility-table-row-list, unquote(\n          'tr.show-for-#{$visibility-comparison-breakpoint}-only, tr.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n          'th.show-for-#{$visibility-comparison-breakpoint}-only, td.show-for-#{$visibility-comparison-breakpoint}-only, th.show-for-#{$visibility-comparison-breakpoint}-up, td.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n\n        // Foundation 4 compatibility:\n        // Include .show/hide-for-[size] and .show/hide-for-[size]-down classes\n        // for small, medium, and large breakpoints only\n        @if $include-legacy-visibility-classes and index((small, medium, large), $visibility-comparison-breakpoint) != false {\n          $visibility-inherit-list: append($visibility-inherit-list, unquote(\n            '.show-for-#{$visibility-comparison-breakpoint}, .show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-none-list: append($visibility-none-list, unquote(\n            '.hide-for-#{$visibility-comparison-breakpoint}, .hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-visible-list: append($visibility-visible-list, unquote(\n            '.visible-for-#{$visibility-comparison-breakpoint}, .visible-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-hidden-list: append($visibility-hidden-list, unquote(\n            '.hidden-for-#{$visibility-comparison-breakpoint}, .hidden-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-list: append($visibility-table-list, unquote(\n            'table.show-for-#{$visibility-comparison-breakpoint}, table.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n            'thead.show-for-#{$visibility-comparison-breakpoint}, thead.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n            'tbody.show-for-#{$visibility-comparison-breakpoint}, tbody.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-list: append($visibility-table-row-list, unquote(\n            'tr.show-for-#{$visibility-comparison-breakpoint}, tr.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n            'th.show-for-#{$visibility-comparison-breakpoint}, td.show-for-#{$visibility-comparison-breakpoint}, th.show-for-#{$visibility-comparison-breakpoint}-down, td.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n        }\n      }\n    }\n\n    /* #{$current-visibility-breakpoint} displays */\n    @media #{nth($visibility-breakpoint-queries, index($visibility-breakpoint-sizes, $current-visibility-breakpoint))} {\n      #{$visibility-inherit-list} {\n        display: inherit !important;\n      }\n      #{$visibility-none-list} {\n        display: none !important;\n      }\n      @if $include-accessibility-classes != false {\n        #{$visibility-visible-list} {\n          @include element-invisible-off;\n        }\n        #{$visibility-hidden-list} {\n          @include element-invisible;\n        }\n      }\n      @if $include-table-visibility-classes != false {\n        #{$visibility-table-list} {\n          display: table !important;\n        }\n        #{$visibility-table-header-group-list} {\n          display: table-header-group !important;\n        }\n        #{$visibility-table-row-group-list} {\n          display: table-row-group !important;\n        }\n        #{$visibility-table-row-list} {\n          display: table-row !important;\n        }\n        #{$visibility-table-cell-list} {\n          display: table-cell !important;\n        }\n      }\n    }\n  }\n}\n\n\n@if $include-html-visibility-classes != false {\n\n  @include visibility-loop;\n\n  /* Orientation targeting */\n  .show-for-landscape,\n  .hide-for-portrait { display: inherit !important; }\n  .hide-for-landscape,\n  .show-for-portrait { display: none !important; }\n\n  /* Specific visibility for tables */\n  table {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table !important; }\n  }\n  thead {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-header-group !important; }\n  }\n  tbody {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-row-group !important; }\n  }\n  tr {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-row !important; }\n  }\n  td,\n  th {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-cell !important; }\n  }\n\n  @media #{$landscape} {\n    .show-for-landscape,\n    .hide-for-portrait { display: inherit !important; }\n    .hide-for-landscape,\n    .show-for-portrait { display: none !important; }\n\n    /* Specific visibility for tables */\n    table {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table !important; }\n    }\n    thead {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-header-group !important; }\n    }\n    tbody {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-row-group !important; }\n    }\n    tr {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-row !important; }\n    }\n    td,\n    th {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-cell !important; }\n    }\n  }\n\n  @media #{$portrait} {\n    .show-for-portrait,\n    .hide-for-landscape { display: inherit !important; }\n    .hide-for-portrait,\n    .show-for-landscape { display: none !important; }\n\n    /* Specific visibility for tables */\n    table {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table !important; }\n    }\n    thead {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-header-group !important; }\n    }\n    tbody {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-row-group !important; }\n    }\n    tr {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-row !important; }\n    }\n    td,\n    th {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-cell !important; }\n    }\n  }\n\n  /* Touch-enabled device targeting */\n  .show-for-touch { display: none !important; }\n  .hide-for-touch { display: inherit !important; }\n  .touch .show-for-touch { display: inherit !important; }\n  .touch .hide-for-touch { display: none !important; }\n\n  /* Specific visibility for tables */\n  table.hide-for-touch { display: table !important; }\n  .touch table.show-for-touch { display: table !important; }\n  thead.hide-for-touch { display: table-header-group !important; }\n  .touch thead.show-for-touch { display: table-header-group !important; }\n  tbody.hide-for-touch { display: table-row-group !important; }\n  .touch tbody.show-for-touch { display: table-row-group !important; }\n  tr.hide-for-touch { display: table-row !important; }\n  .touch tr.show-for-touch { display: table-row !important; }\n  td.hide-for-touch { display: table-cell !important; }\n  .touch td.show-for-touch { display: table-cell !important; }\n  th.hide-for-touch { display: table-cell !important; }\n  .touch th.show-for-touch { display: table-cell !important; }\n\n\n  /* Print visibility */\n  @media print {\n    .show-for-print { display: block; }\n    .hide-for-print { display: none; }\n\n    table.show-for-print { display: table !important; }\n    thead.show-for-print { display: table-header-group !important; }\n    tbody.show-for-print { display: table-row-group !important; }\n    tr.show-for-print { display: table-row !important; }\n    td.show-for-print { display: table-cell !important; }\n    th.show-for-print { display: table-cell !important; }\n\n  }\n\n}\n","@charset \"utf-8\";\n/* TOC – Typography\n\nCheck typography variables › _3_typography_settings.scss\n\n- Links\n- Customize Foundation Typography\n- Headlines\n- Images\n- Lists\n- Tables\n- Code\n- Quotes\n- Typography for Articles\n- Smaller Fontsize for Bigteaser on small devices\n- Additional typographical elements\n- Footnotes\n- Icon Font\n\n*/\n\n\n\n/* Links\n------------------------------------------------------------------- */\n\na,\na:link {\n    transition: all .4s;\n}\n\na:visited {\n    border-bottom: $grey-2;\n}\n\na:hover {\n    color: darken( $ci-1, 10% );\n}\n\na:focus {\n    color: lighten( $ci-1, 20% );\n}\n\na:active {\n    color: darken( $ci-1, 20% );\n}\n\n\n\n/* Customize Foundation Typography\n------------------------------------------------------------------- */\n\np {\n    -webkit-hyphens: auto;\n    -moz-hyphens: auto;\n    -ms-hyphens: auto;\n    hyphens: auto;\n    -ms-word-break: normal;\n    /* Non standard for webkit */\n    word-break: normal;\n}\np a,\narticle a {\n    font-weight: bold;\n    border-bottom: 1px dotted;\n}\np a:hover,\narticle a:hover {\n    border-bottom: 2px solid;\n}\np a.button,\n.button,\n.button:hover {\n    border: 0;\n    color: #fff;\n}\np.button a {\n    border: 0;\n    color: #fff;\n    text-shadow: 0 1px 3px rgba(0, 0, 0, 0.5);\n}\n\n\n\n/* Headlines\n   The hK::before logic is to accomodate a vert. offset for persistent\n   top of page menu. The logic is copied from\n   https://css-tricks.com/hash-tag-links-padding/\n------------------------------------------------------------------- */\n\nh1, h2, h3, h4, h5, h6 {\n    font-family: $header-font-family;\n    font-weight: normal;\n    padding: 0;\n}\nh1 {\n    font-size: $font-size-h1;\n    margin-top: 0;\n}\nh2 {\n    font-size: $font-size-h2;\n    margin: 1.563em 0 0 0;\n}\n    .blog-index h2 {\n        margin-top: 0;\n    }\nh3 {\n    font-size: $font-size-h3;\n    margin: 1.152em 0 0 0;\n}\nh4 {\n    font-size: $font-size-h4;\n    margin: 1.152em 0 0 0;\n}\nh5 {\n    font-size: $font-size-h5;\n    margin: 1em 0 0 0;\n}\n\n\n\n/* Images\n------------------------------------------------------------------- */\n\nimg { border-radius: $global-radius;}\n    img.alignleft,\n    img.left        { float: left; margin:5px 15px 5px 0; }\n    img.alignright,\n    img.right       { float: right; margin:5px 0 5px 15px; }\n    img.aligncenter,\n    img.center      { display: block; margin:0 auto 10px; }\n\nfigure {\n    margin: 0 0 rem-calc(30) 0;\n}\n#masthead-with-background-color figure,\n#masthead-with-pattern figure {\n    margin: 0;\n}\nfigcaption,\n.masthead-caption {\n    color: $grey-10;\n    font-family: $font-family-sans-serif;\n    font-size: rem-calc(13);\n    padding-top: rem-calc(2);\n}\nfigcaption a,\n.masthead-caption a {\n    border-bottom: 1px dotted $grey-4;\n    color: $grey-10;\n}\nfigcaption a:hover,\n.masthead-caption a:hover {\n    border-bottom: 2px solid $primary-color;\n    color: $primary-color;\n}\n.masthead-caption {\n    padding-right: 10px;\n    text-align: right;\n}\n\n\n\n/* Tables\n------------------------------------------------------------------- */\n\ntd {\n    vertical-align: top;\n}\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\npre {\n    overflow: auto;\n    margin-bottom: rem-calc(20);\n    padding: 5px;\n    background-color: $code-background-color;\n    border-radius: $global-radius;\n}\npre code {\n    padding: rem-calc(2) rem-calc(5) rem-calc(1) rem-calc(0);\n    border: 0;\n}\n\ncode {\n    font-size: rem-calc(14);\n    line-height: 1.5;\n}\n\n\n\n/* Lists\n------------------------------------------------------------------- */\n\nul, ol {\n    margin-left: 20px;\n    padding: 0;\n}\nli {\n    margin-left: 0;\n}\n\n.no-bullet {\n    list-style: none;\n    margin-left: 0;\n}\n\nli {\n    > ul,\n    > ol {\n         margin-bottom: 0;\n    }\n}\n\ndl {\n\n}\ndt:first-child {\n    padding-top: 0px;\n}\ndt {\n    font-weight: bold;\n    padding-top: 30px;\n}\ndd {\n}\narticle dl dt   { line-height: 1.3; }\narticle dl dd   { line-height: 1.6; margin-bottom: rem-calc(12); margin-left: rem-calc(24); }\n\n\n\n/* Quotes\n------------------------------------------------------------------- */\n\nblockquote {\n    font-style: italic;\n    position: relative;\n    border: none;\n    margin: 0 30px 30px 30px;\n    color:  $grey-11;\n}\n\n    blockquote p {font-style: italic; color: $grey-10; }\n\n    blockquote:before {\n        display:block;content:\"\\00BB\";\n        font-size:80px;\n        line-height: 0;\n        position:absolute;\n        left:-25px;\n        top: auto;\n        color: $grey-11;\n    }\n    blockquote:after {\n        display:block;\n        content:\"\\00AB\";\n        font-size:80px;\n        line-height: 0;\n        position:absolute;\n        right:-10px;\n        bottom: 20px;\n        color: $grey-11;\n    }\n    blockquote cite:before {\n        content:\"\\2014 \\0020\"\n    }\n    blockquote cite a,blockquote cite a:visited {\n        color: $grey-10;\n    }\ncite {\n    padding-top: 5px;\n}\n\nbutton, .button {\n    letter-spacing: 1px;\n}\n\nmark {\n    background-color: scale-color($warning-color, $lightness: 60%);\n}\n\n\n\n/* Typography for Articles\n------------------------------------------------------------------- */\n\n.subheadline {\n    font-size: rem-calc(16);\n    margin: 0;\n    text-transform: uppercase;\n}\n.teaser {\n    font-size: rem-calc(20);\n}\n.big-teaser {\n    font-style: italic; font-weight: 300;\n}\n.big-teaser a {\n    font-style: italic; font-weight: 400;\n}\n\n/* Smaller Fontsize for Bigteaser on small devices */\n@media only screen {\n    .big-teaser {\n        font-size: rem-calc(20);\n    }\n}\n@media only screen and (min-width: 40.063em) {\n    .big-teaser         {\n        font-size: rem-calc(29);\n    }\n}\n\n\n\n/* Additional typographical elements\n------------------------------------------------------------------- */\n\n.sans           { font-family: $font-family-sans-serif; }\n.serif          { font-family: $font-family-serif; }\n\n.font-size-h1   { font-size: $font-size-h1; }\n.font-size-h2   { font-size: $font-size-h2; }\n.font-size-h3   { font-size: $font-size-h3; }\n.font-size-h4   { font-size: $font-size-h4; }\n.font-size-h5   { font-size: $font-size-h5; }\n.font-size-p    { font-size: $font-size-p; }\n\n\n\n/* Footnotes\n------------------------------------------------------------------- */\n\n.footnotes:before {\n    content: \"\";\n    position: absolute;\n    height: 1px;\n    width: 60px;\n    margin-top: -10px;\n    border-bottom: 1px solid $grey-2;\n}\n.footnotes {\n    margin-top: 60px;\n}\n.footnotes ol {\n    font-size: $font-size-small;\n}\n.footnotes p {\n    font-size: inherit;\n    margin-bottom: 0;\n}\n\n\n\n\n/* Icon Font\n   See the icon-set/preview in /assets/fonts/iconfont-preview.html\n------------------------------------------------------------------- */\n\n@font-face {\n  font-family: 'iconfont';\n  src: url('../fonts/iconfont.eot'); /* IE9 Compat Modes */\n  src: url('../fonts/iconfont.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */\n       url('../fonts/iconfont.woff') format('woff'), /* Pretty Modern Browsers */\n       url('../fonts/iconfont.ttf')  format('truetype'), /* Safari, Android, iOS */\n       url('../fonts/iconfont.svg#svgFontName') format('svg'); /* Legacy iOS */\n}\n\n.iconfont       { font-family: iconfont; }\n.iconfont-48    { font-size: 48px; }\n\n\n[data-icon]:before { content: attr(data-icon); }\n\n[data-icon]:before,\n.icon-archive:before,\n.icon-browser:before,\n.icon-calendar:before,\n.icon-camera:before,\n.icon-chat:before,\n.icon-check:before,\n.icon-chevron-down:before,\n.icon-chevron-left:before,\n.icon-chevron-right:before,\n.icon-chevron-up:before,\n.icon-circle-with-cross:before,\n.icon-circle-with-minus:before,\n.icon-circle-with-plus:before,\n.icon-cloud:before,\n.icon-code:before,\n.icon-cog:before,\n.icon-dropbox:before,\n.icon-edit:before,\n.icon-export:before,\n.icon-eye:before,\n.icon-facebook:before,\n.icon-feather:before,\n.icon-github:before,\n.icon-globe:before,\n.icon-googleplus:before,\n.icon-heart:before,\n.icon-heart-outlined:before,\n.icon-home:before,\n.icon-instagram:before,\n.icon-lab-flask:before,\n.icon-leaf:before,\n.icon-linkedin:before,\n.icon-mail:before,\n.icon-message:before,\n.icon-mic:before,\n.icon-network:before,\n.icon-paper-plane:before,\n.icon-pinterest:before,\n.icon-price-tag:before,\n.icon-rocket:before,\n.icon-rss:before,\n.icon-soundcloud:before,\n.icon-star:before,\n.icon-star-outlined:before,\n.icon-thumbs-down:before,\n.icon-thumbs-up:before,\n.icon-tree:before,\n.icon-tumblr:before,\n.icon-twitter:before,\n.icon-upload-to-cloud:before,\n.icon-video:before,\n.icon-vimeo:before,\n.icon-warning:before,\n.icon-xing:before,\n.icon-youtube:before {\n  display: inline-block;\nfont-family: \"iconfont\";\nfont-style: normal;\nfont-weight: normal;\nfont-variant: normal;\nline-height: 1;\ntext-decoration: inherit;\ntext-rendering: optimizeLegibility;\ntext-transform: none;\n-moz-osx-font-smoothing: grayscale;\n-webkit-font-smoothing: antialiased;\nfont-smoothing: antialiased;\n}\n\n.icon-archive:before { content: \"\\f100\"; }\n.icon-browser:before { content: \"\\f101\"; }\n.icon-calendar:before { content: \"\\f133\"; }\n.icon-camera:before { content: \"\\f102\"; }\n.icon-chat:before { content: \"\\f103\"; }\n.icon-check:before { content: \"\\f104\"; }\n.icon-chevron-down:before { content: \"\\f105\"; }\n.icon-chevron-left:before { content: \"\\f106\"; }\n.icon-chevron-right:before { content: \"\\f107\"; }\n.icon-chevron-up:before { content: \"\\f108\"; }\n.icon-circle-with-cross:before { content: \"\\f109\"; }\n.icon-circle-with-minus:before { content: \"\\f10a\"; }\n.icon-circle-with-plus:before { content: \"\\f10b\"; }\n.icon-cloud:before { content: \"\\f10c\"; }\n.icon-code:before { content: \"\\f10d\"; }\n.icon-cog:before { content: \"\\f10e\"; }\n.icon-dropbox:before { content: \"\\f10f\"; }\n.icon-edit:before { content: \"\\f110\"; }\n.icon-export:before { content: \"\\f111\"; }\n.icon-eye:before { content: \"\\f112\"; }\n.icon-facebook:before { content: \"\\f113\"; }\n.icon-feather:before { content: \"\\f114\"; }\n.icon-github:before { content: \"\\f115\"; }\n.icon-globe:before { content: \"\\f116\"; }\n.icon-googleplus:before { content: \"\\f136\"; }\n.icon-heart:before { content: \"\\f117\"; }\n.icon-heart-outlined:before { content: \"\\f118\"; }\n.icon-home:before { content: \"\\f119\"; }\n.icon-instagram:before { content: \"\\f11a\"; }\n.icon-lab-flask:before { content: \"\\f11b\"; }\n.icon-leaf:before { content: \"\\f11c\"; }\n.icon-linkedin:before { content: \"\\f11d\"; }\n.icon-mail:before { content: \"\\f11e\"; }\n.icon-message:before { content: \"\\f11f\"; }\n.icon-mic:before { content: \"\\f120\"; }\n.icon-network:before { content: \"\\f121\"; }\n.icon-paper-plane:before { content: \"\\f122\"; }\n.icon-pinterest:before { content: \"\\f123\"; }\n.icon-price-tag:before { content: \"\\f124\"; }\n.icon-rocket:before { content: \"\\f125\"; }\n.icon-rss:before { content: \"\\f126\"; }\n.icon-soundcloud:before { content: \"\\f127\"; }\n.icon-star:before { content: \"\\f128\"; }\n.icon-star-outlined:before { content: \"\\f129\"; }\n.icon-thumbs-down:before { content: \"\\f12a\"; }\n.icon-thumbs-up:before { content: \"\\f12b\"; }\n.icon-tree:before { content: \"\\f134\"; }\n.icon-tumblr:before { content: \"\\f12c\"; }\n.icon-twitter:before { content: \"\\f12d\"; }\n.icon-upload-to-cloud:before { content: \"\\f12e\"; }\n.icon-video:before { content: \"\\f12f\"; }\n.icon-vimeo:before { content: \"\\f130\"; }\n.icon-warning:before { content: \"\\f131\"; }\n.icon-xing:before { content: \"\\f135\"; }\n.icon-youtube:before { content: \"\\f132\"; }\n","@charset \"utf-8\";\n/* TOC\n\n- Adjustments: Video Layout\n- Navigation\n- Search\n- Masthead\n- Masthead › small-only\n- Masthead › medium-only\n- Masthead › large-only\n- Masthead › xlarge-up\n- Breadcrumb\n- Meta\n- Jump to top\n- Footer\n- Subfooter\n- CSS-Classes to add margin at top or bottom\n\n*/\n\n\n\n/* Adjustments: Video Layout\n------------------------------------------------------------------- */\n\nbody.video,\nbody.video #masthead-no-image-header { background: #000; }\nbody.video #masthead-no-image-header { margin-bottom: 60px; }\nbody.video h1,\nbody.video h2,\nbody.video h3,\nbody.video h4,\nbody.video h5,\nbody.video h6,\nbody.video p,\nbody.video a,\nbody.video blockquote:before,\nbody.video blockquote:after,\nbody.video cite a, { color: #fff; }\nbody.video cite a:visited, { color: #fff; }\nbody.video cite { color: #fff; }\n\n\n\n/* Navigation\n------------------------------------------------------------------- */\n\n#navigation {\n    -webkit-box-shadow: 0 2px 2px 0 rgba(0,0,0,.2);\n    box-shadow: 0 2px 3px 0 rgba(0,0,0,.2);\n\n    [class^='icon-']:before, [class*=' icon-']:before {\n        margin-right: rem-calc(8);\n    }\n}\n\n\n\n/* Search\n------------------------------------------------------------------- */\n\n.no-js form#search {\n  display: none;\n}\n\n\n\n/* Masthead\n------------------------------------------------------------------- */\n\n#masthead {\n    background-color: $primary-color;\n}\n#masthead-no-image-header {\n    background-color: $primary-color;\n}\n#masthead-with-text {\n    text-align: center;\n    font-size: rem-calc(54);\n    font-family: $header-font-family;\n    color: #fff;\n    text-transform: uppercase;\n    text-shadow: 0 2px 3px rgba(0,0,0,.4);\n}\n#masthead-no-image-header {\n    height: 175px;\n}\n#masthead-no-image-header #logo img {\n    margin-top: 60px;\n}\n\n/* Masthead › small-only\n------------------------------------------------------------------- */\n\n@media #{$small-only} {\n    #logo img {\n        display: none;\n    }\n    #masthead {\n        height: 200px;\n    }\n    #masthead-with-pattern {\n        padding: 15px 0;\n    }\n    #masthead-with-background-color {\n        padding: 15px 0;\n    }\n    #masthead-with-text {\n        height: 220px;\n        padding: 30px 0;\n        font-size: rem-calc(36);\n    }\n    #masthead-no-image-header {\n        display: none;\n    }\n}\n\n\n/* Masthead › medium-only\n------------------------------------------------------------------- */\n\n@media #{$medium-only} {\n    #logo img {\n        margin-top: 60px;\n    }\n    #masthead {\n        height: 280px;\n    }\n    #masthead-with-pattern {\n        padding: 20px 0;\n    }\n    #masthead-with-background-color {\n        padding: 20px 0;\n    }\n    #masthead-with-text {\n        padding: 60px 0;\n        height: 300px;\n    }\n}\n\n\n/* Masthead › large-only\n------------------------------------------------------------------- */\n\n@media #{$large-only} {\n    #logo img {\n        margin-top: 80px;\n    }\n    #masthead {\n        height: 310px;\n    }\n    #masthead-with-pattern {\n        padding: 30px 0;\n    }\n    #masthead-with-background-color {\n        padding: 30px 0;\n    }\n    #masthead-with-text {\n        height: 330px;\n        padding: 60px 0;\n    }\n}\n\n\n/* Masthead › xlarge-up\n------------------------------------------------------------------- */\n\n@media #{$xlarge-up} {\n    #logo img {\n        margin-top: 110px;\n    }\n    #masthead {\n        height: 380px;\n    }\n    #masthead-with-pattern {\n        padding: 45px 0;\n    }\n    #masthead-with-background-color {\n        padding: 45px 0;\n    }\n    #masthead-with-text {\n        padding: 95px 0;\n        height: 400px;\n    }\n}\n\n\n#title-image-small {\n    height: 240px;\n}\n#title-image-large {\n    height: 520px;\n}\n#title-image-index-small {\n    height: 120px;\n}\n#title-image-index-large {\n    height: 260px;\n}\n\n\n\n/* Breadcrumb\n------------------------------------------------------------------- */\n\n#breadcrumb {\n    background: scale-color($grey-1, $lightness: 55%);\n    border-top: 1px solid scale-color($grey-1, $lightness: 45%);\n    border-bottom: 1px solid scale-color($grey-1, $lightness: 45%);\n}\n.breadcrumbs>.current {\n  font-weight: bold;\n}\n\n\n/* Meta\n------------------------------------------------------------------- */\n\n#page-meta, #page-meta a {\n    color: $grey-5;\n}\n\n#page-meta .button {\n    background: $grey-5;\n    border: 0;\n}\n#page-meta .button {\n    color: #fff;\n}\n#page-meta .button:hover {\n    background: $primary-color;\n}\n.meta-info p {\n  font-size: rem-calc(13);\n  color: scale-color($grey-1, $lightness: 40%);\n}\n  .meta-info a {\n    text-decoration: underline;\n    color: scale-color($grey-1, $lightness: 40%);\n  }\n  .meta-info a:hover {\n    text-decoration: none;\n    color: $secondary-color;\n  }\n\n\n\n/* Jump to top\n------------------------------------------------------------------- */\n\n#up-to-top {\n    padding: 160px 0 10px 0;\n}\n#up-to-top a {\n    font-size: 24px;\n    padding: 5px;\n    border-radius: 3px;\n}\n#up-to-top a:hover {\n    background: $grey-2;\n}\n\n\n\n/* Footer\n------------------------------------------------------------------- */\n\n#footer-content p,\n#footer-content li {\n    font-size: rem-calc(13);\n    font-weight: 300;\n}\n\n#footer {\n    padding-top: 30px;\n    padding-bottom: 20px;\n    background: $footer-bg;\n    color: $footer-color;\n    }\n\n    #footer a {\n        color: $footer-link-color;\n    }\n    #footer h4,\n    #footer h5 {\n        letter-spacing: 1px;\n        color: #fff;\n        text-transform: uppercase;\n    }\n\n\n\n/* Subfooter\n------------------------------------------------------------------- */\n\n#subfooter {\n    background: $subfooter-bg;\n    color: $subfooter-color;\n    padding-top: 30px;\n}\n\n#subfooter-left ul.inline-list {\n    float: left;\n}\n\n.credits a {\n    color: $subfooter-link-color;\n    border: 0;\n    text-transform: uppercase;\n    &:hover {\n        color: #fff;\n  }\n}\n\n.social-icons {\n    margin-bottom: 10px !important;\n\n// Beware of SCSS-Syntax here\n  li {\n    padding: 0 0 20px 0;\n  }\n  a {\n    font-size: rem-calc(23);\n    display: block;\n    width: 36px;\n    border-radius: 50%;\n    color: $subfooter-bg;\n    background: $subfooter-color;\n    text-align: center;\n    &:hover {\n      background: $subfooter-bg;\n      color: #fff;\n    }\n  }\n}\n\n\n\n/* CSS-Classes to add margin at top or bottom\n------------------------------------------------------------------- */\n\n.t10 { margin-top: 10px !important; }\n.t15 { margin-top: 15px !important; }\n.t20 { margin-top: 20px !important; }\n.t30 { margin-top: 30px !important; }\n.t50 { margin-top: 50px !important; }\n.t60 { margin-top: 60px !important; }\n.t70 { margin-top: 70px !important; }\n.t80 { margin-top: 80px !important; }\n.t90 { margin-top: 90px !important; }\n\n.b15 { margin-bottom: 15px !important; }\n.b20 { margin-bottom: 20px !important; }\n.b30 { margin-bottom: 30px !important; }\n.b60 { margin-bottom: 60px !important; }\n\n.l15 { margin-left: 15px !important; }\n.r15 { margin-right: 15px !important; }\n\n.pl20 { padding-left: 20px !important; }\n.pr5  { padding-right: 5px !important; }\n.pr10 { padding-right: 10px !important; }\n.pr20 { padding-right: 20px !important; }\n","@charset \"utf-8\";\n/* TOC\n\n- Table of Contents (Index)\n- Panel\n- Shadows\n- Alerts\n- Breadcrumb\n- Button\n- Side-Nav\n- Accordion\n- Lazy Load XT\n- Frontpage Widget\n\n*/\n\n\n\n/* Table of Contents (Index)\n------------------------------------------------------------------- */\n\n#toc ul,\n#toc ul ul,\n#toc ul ul ul, {\n    list-style: none;\n    margin-left: 30px;\n}\n#toc ul {\n    margin-left: 0;\n    margin-top: $spacing-unit;\n}\n\n\n\n/* Panel\n------------------------------------------------------------------- */\n\n.border-dotted {\n  border: 1px dotted $grey-5;\n  padding: rem-calc(20);\n  border-radius: $global-radius;\n}\n\n\n\n/* Shadows\n------------------------------------------------------------------- */\n\n.shadow-no      {text-shadow: rgba(0, 0, 0, 0) 0 0 0;}\n.shadow-black   {text-shadow: rgba(0, 0, 0, 0.498039) 0px 1px 2px;}\n.shadow-white   {text-shadow: rgba(255, 255, 255, 0.498039) 0px 1px 2px;}\n\n\n\n/* Alerts\n------------------------------------------------------------------- */\n\n.alert-box {\n  font-family: $font-family-sans-serif;\n  text-shadow: 0px 1px 1px rgba(0,0,0,0.9);\n}\n  .alert-box p {\n    margin-bottom: 0;\n  }\n  .alert-box a {\n    text-shadow: 1px 1px 0px rgba(0, 0, 0, 1);\n    color: #fff;\n    border-bottom: 1px dotted #fff;\n  }\n  .alert-box a:hover {\n    border-bottom: 1px solid #fff;\n  }\n  .alert-box.terminal {\n    background: $grey-12; \n    color: #fff; \n    border-color: scale-color($grey-12, $lightness: -14%);\n    font-family: $font-family-monospace;\n  }\n  .alert-box.terminal::before {\n    content: \"$ \";\n    color: $ci-6;\n    float: left;\n    margin: .25em .5em 0 0;\n  }\n  .alert-box.text {\n    background-color: $grey-2;\n    text-shadow: 0px 0px 0px rgba(0,0,0,0.9);\n    border-color: scale-color($grey-2, $lightness: -14%);\n    color: $grey-12;\n  }\n\n\n\n/* Button\n------------------------------------------------------------------- */\n\nbutton, .button         { letter-spacing: 1px; }\n  button.grey, .button.grey { background: $grey-10; }\n  button.grey:hover,\n  button.grey:focus,\n  .button.grey:hover,\n  .button.grey:focus      { background-color: $grey-16; }\n\n\n\n/* Side-Nav\n------------------------------------------------------------------- */\n\n.side-nav li.title { text-transform: uppercase;}\n.side-nav li { border-top: 1px solid $grey-3;}\n.side-nav li a:not(.button) { border-bottom: 0; padding: 0.4375rem 0rem; }\n.side-nav li a:not(.button):hover, .side-nav li a:not(.button):focus { background: $grey-1; }\n\n.homepage p { margin: 0; padding: 0; color: $grey-10; }\n\n\n\n/* Accordion\n------------------------------------------------------------------- */\n\ndl.accordion  { border-top: 1px solid $grey-2;  }\n.accordion dd   { border-bottom: 1px solid $grey-2;  }\ndd.accordion-navigation span { padding-right: 12px; }\ndd.accordion-navigation span:before { content: \"\\F107\" }\ndd.accordion-navigation.active span:before { content: \"\\F105\" }\ndd.accordion-navigation.active span:before { content: \"\\F105\" }\n\n\n\n/* Lazy Load XT\n------------------------------------------------------------------- */\n\n/*! 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defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n  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browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -($width/2);\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n\n\n\n// d. Media Query Ranges\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n$small-range: (\n  0em,\n  40em\n);\n$medium-range: (\n  40.063em,\n  64em\n);\n$large-range: (\n  64.063em,\n  90em\n);\n$xlarge-range: (\n  90.063em,\n  120em\n);\n$xxlarge-range: (\n  120.063em,\n  99999999em\n);\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-small-only {\n    font-family: \"/\" + unquote($small-only) + \"/\";\n    width: lower-bound($small-range);\n  }\n\n  meta.foundation-mq-medium {\n    font-family: \"/\" + unquote($medium-up) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-medium-only {\n    font-family: \"/\" + unquote($medium-only) + \"/\";\n    width: lower-bound($medium-range);\n  }\n\n  meta.foundation-mq-large {\n    font-family: \"/\" + unquote($large-up) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-large-only {\n    font-family: \"/\" + unquote($large-only) + \"/\";\n    width: lower-bound($large-range);\n  }\n\n  meta.foundation-mq-xlarge {\n    font-family: \"/\" + unquote($xlarge-up) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xlarge-only {\n    font-family: \"/\" + unquote($xlarge-only) + \"/\";\n    width: lower-bound($xlarge-range);\n  }\n\n  meta.foundation-mq-xxlarge {\n    font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n    width: lower-bound($xxlarge-range);\n  }\n\n  meta.foundation-data-attribute-namespace {\n    font-family: #{$namespace};\n  }\n\n  @if $include-html-global-classes {\n\n    // Must be 100% for off canvas to work\n    html,\n    body {\n      height: 100%;\n    }\n\n    // Set box-sizing globally to handle padding and border widths\n    *,\n    *:before,\n    *:after {\n      @include box-sizing(border-box);\n    }\n\n    html,\n    body {\n      font-size: $base-font-size;\n    }\n\n    // Default body styles\n    body {\n      background: $body-bg;\n      color: $body-font-color;\n      padding: 0;\n      margin: 0;\n      font-family: $body-font-family;\n      font-weight: $body-font-weight;\n      font-style: $body-font-style;\n      line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n      position: relative;\n      cursor: $cursor-auto-value;\n    }\n\n    a:hover {\n      cursor: $cursor-pointer-value;\n    }\n\n    // Grid Defaults to get images and embeds to work properly\n    img {\n      max-width: 100%;\n      height: auto;\n    }\n\n    img {\n      -ms-interpolation-mode: bicubic;\n    }\n\n    #map_canvas,\n    .map_canvas {\n\n      img,\n      embed,\n      object {\n        max-width: none !important;\n      }\n    }\n\n    // Miscellaneous useful HTML classes\n    .left {\n      float: left !important;\n    }\n\n    .right {\n      float: right !important;\n    }\n\n    .clearfix {\n      @include clearfix;\n    }\n\n    // Hide visually and from screen readers\n    .hide {\n      display: none !important;\n      visibility: hidden;\n    }\n\n    // Hide visually and from screen readers, but maintain layout\n    .invisible {\n      visibility: hidden;\n    }\n\n    // Font smoothing\n    // Antialiased font smoothing works best for light text on a dark background.\n    // Apply to single elements instead of globally to body.\n    // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n    .antialiased {\n      -webkit-font-smoothing: antialiased;\n      -moz-osx-font-smoothing: grayscale;\n    }\n\n    // Get rid of gap under images by making them display: inline-block; by default\n    img {\n      display: inline-block;\n      vertical-align: middle;\n    }\n\n    //\n    // Global resets for forms\n    //\n\n    // Make sure textarea takes on height automatically\n    textarea {\n      height: auto;\n      min-height: 50px;\n    }\n\n    // Make select elements 100% width by default\n    select {\n      width: 100%;\n    }\n  }\n}","/// from https://github.com/Phlow/feeling-responsive/raw/gh-pages/_sass/_01_settings_colors.scss\n@charset \"utf-8\";\n/* TOC – Color Variables\n\n- Basics\n- Corporate Identity Colorpalette\n- Foundation Color Variables\n- Grey Scale\n- Topbar-Navigation\n- Footer\n- Code\n\n*/\n\n\n\n/* Basics\n------------------------------------------------------------------- */\n\n$text-color         : #111;\n$body-font-color    : $text-color;\n$body-bg            : #fdfdfd;\n\n\n\n/* Corporate Identity Colorpalette\n   https://color.adobe.com/de/Flat-Design-Colors-v2-color-theme-4341903/\n------------------------------------------------------------------- */\n\n$ci-1               : #334D5C; // dark turquoise\n$ci-2               : #45B29D; // turquoise\n$ci-3               : #EFC94C; // yellow\n$ci-4               : #E27A3F; // orange\n$ci-5               : #DF4949; // red\n$ci-6               : #A1D044; // green\n\n/// CIL overrides\n$ci-2               : #c92c99;\n$ci-6               : #e50695;\n\n\n/* Foundation Color Variables\n------------------------------------------------------------------- */\n\n$primary-color      : $ci-1;\n$secondary-color    : $ci-6;\n$alert-color        : $ci-5;\n$success-color      : $ci-6;\n$warning-color      : $ci-4;\n$info-color         : $ci-1;\n\n\n\n/* Grey Scale\n------------------------------------------------------------------- */\n\n$grey-1             : #E4E4E4;\n$grey-2             : #D7D7D7;\n$grey-3             : #CBCBCB;\n$grey-4             : #BEBEBE;\n$grey-5             : #A4A4A4;\n$grey-6             : #979797;\n$grey-7             : #8B8B8B;\n$grey-8             : #7E7E7E;\n$grey-9             : #646464;\n$grey-10            : #575757;\n$grey-11            : #4B4B4B;\n$grey-12            : #3E3E3E;\n$grey-13            : #313131;\n$grey-14            : #242424;\n$grey-15            : #171717;\n$grey-16            : #0B0B0B;\n\n/// CIL overrides\n$grey-8             : #043852;\n$grey-13            : #510c76;\n\n\n/* Topbar-Navigation\n------------------------------------------------------------------- */\n\n$topbar-bg-color    : $body-bg;\n$topbar-bg          : $topbar-bg-color;\n\n\n$topbar-dropdown-toggle-color: $ci-1;\n\n$topbar-link-color  : #000;\n$topbar-link-color-hover: #000;\n$topbar-link-color-active: #000;\n$topbar-link-color-active-hover: #000;\n\n$topbar-dropdown-label-color: $ci-2;\n$topbar-dropdown-link-bg-hover: $ci-6;\n\n$topbar-link-bg-active: $ci-6; // Active Navigation Link\n$topbar-link-bg-hover: $ci-6;\n$topbar-link-bg-active-hover: $ci-2;\n\n\n$topbar-dropdown-bg: $ci-6; // Background Mobile Navigation\n$topbar-dropdown-link-color: #000;\n$topbar-dropdown-link-bg: $ci-2;\n\n$topbar-menu-link-color-toggled: $ci-1;\n$topbar-menu-icon-color-toggled: $ci-1;\n$topbar-menu-link-color: #000;\n$topbar-menu-icon-color: #000;\n$topbar-menu-link-color-toggled: $ci-6;\n$topbar-menu-icon-color-toggled: $ci-6;\n\n\n\n/* Footer\n------------------------------------------------------------------- */\n\n$footer-bg          : $grey-8;\n$footer-color       : #fff;\n$footer-link-color  : $ci-6;\n\n\n$subfooter-bg       : $grey-13;\n$subfooter-color    : $grey-8;\n$subfooter-link-color: $grey-8;\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\n$code-background-color: scale-color($secondary-color, $lightness: 70%);\n\n$highlight-background: #ffffff;\n$highlight-comment: #999988;\n$highlight-error: #a61717;\n$highlight-comment-special: #999999;\n$highlight-deleted: #000000;\n$highlight-error-2: #aa0000;\n$highlight-literal-string: #d14;\n$highlight-literal-number: #009999;\n$highlight-name-attribut: #008080;\n$highlight-error-background: #e3d2d2;\n$highlight-generic-deleted: #ffdddd;\n$highlight-generic-deleted-specific: #ffaaaa;\n$highlight-generic-inserted: #ddffdd;\n$highlight-generic-inserted-specific: #aaffaa;\n$highlight-generic-output: #888888;\n$highlight-generic-prompt: #555555;\n$highlight-subheading: #aaaaaa;\n$highlight-keyword-type: #445588;\n$highlight-name-builtin: #0086B3;\n$highlight-name-class: #445588;\n$highlight-name-entity: #800080;\n$highlight-name-exception: #990000;\n$highlight-name-function: #990000;\n$highlight-name-namespace: #555555;\n$highlight-name-tag: #000080;\n$highlight-text-whitespace: #bbbbbb;\n$highlight-literal-string-regex: #009926;\n$highlight-literal-string-symbol: #990073;\n","@charset \"utf-8\";\n/*! normalize.css v3.0.2 | MIT License | git.io/normalize */\n\n/**\n * 1. Set default font family to sans-serif.\n * 2. Prevent iOS text size adjust after orientation change, without disabling\n *    user zoom.\n */\n\nhtml {\n  font-family: sans-serif; /* 1 */\n  -ms-text-size-adjust: 100%; /* 2 */\n  -webkit-text-size-adjust: 100%; /* 2 */\n}\n\n/**\n * Remove default margin.\n */\n\nbody {\n  margin: 0;\n}\n\n/* HTML5 display definitions\n   ========================================================================== */\n\n/**\n * Correct `block` display not defined for any HTML5 element in IE 8/9.\n * Correct `block` display not defined for `details` or `summary` in IE 10/11\n * and Firefox.\n * Correct `block` display not defined for `main` in IE 11.\n */\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\n\n/**\n * 1. Correct `inline-block` display not defined in IE 8/9.\n * 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n */\n\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block; /* 1 */\n  vertical-align: baseline; /* 2 */\n}\n\n/**\n * Prevent modern browsers from displaying `audio` without controls.\n * Remove excess height in iOS 5 devices.\n */\n\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n\n/**\n * Address `[hidden]` styling not present in IE 8/9/10.\n * Hide the `template` element in IE 8/9/11, Safari, and Firefox < 22.\n */\n\n[hidden],\ntemplate {\n  display: none;\n}\n\n/* Links\n   ========================================================================== */\n\n/**\n * Remove the gray background color from active links in IE 10.\n */\n\na {\n  background-color: transparent;\n}\n\n/**\n * Improve readability when focused and also mouse hovered in all browsers.\n */\n\na:active,\na:hover {\n  outline: 0;\n}\n\n/* Text-level semantics\n   ========================================================================== */\n\n/**\n * Address styling not present in IE 8/9/10/11, Safari, and Chrome.\n */\n\nabbr[title] {\n  border-bottom: 1px dotted;\n}\n\n/**\n * Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n */\n\nb,\nstrong {\n  font-weight: bold;\n}\n\n/**\n * Address styling not present in Safari and Chrome.\n */\n\ndfn {\n  font-style: italic;\n}\n\n/**\n * Address variable `h1` font-size and margin within `section` and `article`\n * contexts in Firefox 4+, Safari, and Chrome.\n */\n\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\n\n/**\n * Address styling not present in IE 8/9.\n */\n\nmark {\n  background: #ff0;\n  color: #000;\n}\n\n/**\n * Address inconsistent and variable font size in all browsers.\n */\n\nsmall {\n  font-size: 80%;\n}\n\n/**\n * Prevent `sub` and `sup` affecting `line-height` in all browsers.\n */\n\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\n\nsup {\n  top: -0.5em;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\n/* Embedded content\n   ========================================================================== */\n\n/**\n * Remove border when inside `a` element in IE 8/9/10.\n */\n\nimg {\n  border: 0;\n}\n\n/**\n * Correct overflow not hidden in IE 9/10/11.\n */\n\nsvg:not(:root) {\n  overflow: hidden;\n}\n\n/* Grouping content\n   ========================================================================== */\n\n/**\n * Address margin not present in IE 8/9 and Safari.\n */\n\nfigure {\n  margin: 1em 40px;\n}\n\n/**\n * Address differences between Firefox and other browsers.\n */\n\nhr {\n  -moz-box-sizing: content-box;\n  box-sizing: content-box;\n  height: 0;\n}\n\n/**\n * Contain overflow in all browsers.\n */\n\npre {\n  overflow: auto;\n}\n\n/**\n * Address odd `em`-unit font size rendering in all browsers.\n */\n\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\n\n/* Forms\n   ========================================================================== */\n\n/**\n * Known limitation: by default, Chrome and Safari on OS X allow very limited\n * styling of `select`, unless a `border` property is set.\n */\n\n/**\n * 1. Correct color not being inherited.\n *    Known issue: affects color of disabled elements.\n * 2. Correct font properties not being inherited.\n * 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n */\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit; /* 1 */\n  font: inherit; /* 2 */\n  margin: 0; /* 3 */\n}\n\n/**\n * Address `overflow` set to `hidden` in IE 8/9/10/11.\n */\n\nbutton {\n  overflow: visible;\n}\n\n/**\n * Address inconsistent `text-transform` inheritance for `button` and `select`.\n * All other form control elements do not inherit `text-transform` values.\n * Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n * Correct `select` style inheritance in Firefox.\n */\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n/**\n * 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n *    and `video` controls.\n * 2. Correct inability to style clickable `input` types in iOS.\n * 3. Improve usability and consistency of cursor style between image-type\n *    `input` and others.\n */\n\nbutton,\nhtml input[type=\"button\"], /* 1 */\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button; /* 2 */\n  cursor: pointer; /* 3 */\n}\n\n/**\n * Re-set default cursor for disabled elements.\n */\n\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\n\n/**\n * Remove inner padding and border in Firefox 4+.\n */\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\n\n/**\n * Address Firefox 4+ setting `line-height` on `input` using `!important` in\n * the UA stylesheet.\n */\n\ninput {\n  line-height: normal;\n}\n\n/**\n * It's recommended that you don't attempt to style these elements.\n * Firefox's implementation doesn't respect box-sizing, padding, or width.\n *\n * 1. Address box sizing set to `content-box` in IE 8/9/10.\n * 2. Remove excess padding in IE 8/9/10.\n */\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Fix the cursor style for Chrome's increment/decrement buttons. For certain\n * `font-size` values of the `input`, it causes the cursor style of the\n * decrement button to change from `default` to `text`.\n */\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n * 2. Address `box-sizing` set to `border-box` in Safari and Chrome\n *    (include `-moz` to future-proof).\n */\n\ninput[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  -moz-box-sizing: content-box;\n  -webkit-box-sizing: content-box; /* 2 */\n  box-sizing: content-box;\n}\n\n/**\n * Remove inner padding and search cancel button in Safari and Chrome on OS X.\n * Safari (but not Chrome) clips the cancel button when the search input has\n * padding (and `textfield` appearance).\n */\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * Define consistent border, margin, and padding.\n */\n\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\n\n/**\n * 1. Correct `color` not being inherited in IE 8/9/10/11.\n * 2. Remove padding so people aren't caught out if they zero out fieldsets.\n */\n\nlegend {\n  border: 0; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Remove default vertical scrollbar in IE 8/9/10/11.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * Don't inherit the `font-weight` (applied by a rule above).\n * NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n */\n\noptgroup {\n  font-weight: bold;\n}\n\n/* Tables\n   ========================================================================== */\n\n/**\n * Remove most spacing between table cells.\n */\n\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\n\ntd,\nth {\n  padding: 0;\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-grid-classes: $include-html-classes !default;\n$include-xl-html-grid-classes: false !default;\n\n$row-width: rem-calc(1000) !default;\n$total-columns: 12 !default;\n\n$last-child-float: $opposite-direction !default;\n\n//\n// Grid Functions\n//\n\n// Deprecated: We'll drop support for this in 5.1, use grid-calc()\n@function gridCalc($colNumber, $totalColumns) {\n  @warn \"gridCalc() is deprecated, use grid-calc()\";\n  @return grid-calc($colNumber, $totalColumns);\n}\n\n// @FUNCTION\n// $colNumber - Found in settings file\n// $totalColumns - Found in settings file\n@function grid-calc($colNumber, $totalColumns) {\n  @return percentage(calc($colNumber / $totalColumns));\n}\n\n//\n// @mixins\n//\n\n// For creating container, nested, and collapsed rows.\n//\n//\n// $behavior - Any special behavior for this row? Default: false. Options: nest, collapse, nest-collapse, false.\n@mixin grid-row($behavior: false) {\n\n  // use @include grid-row(nest); to include a nested row\n  @if $behavior ==nest {\n    width: auto;\n    margin-#{$default-float}: - calc($column-gutter/2);\n    margin-#{$opposite-direction}: - calc($column-gutter/2);\n    margin-top: 0;\n    margin-bottom: 0;\n    max-width: none;\n  }\n\n  // use @include grid-row(collapse); to collapsed a container row margins\n  @else if $behavior ==collapse {\n    width: 100%;\n    margin: 0;\n    max-width: $row-width;\n  }\n\n  // use @include grid-row(nest-collapse); to collapse outer margins on a nested row\n  @else if $behavior ==nest-collapse {\n    width: auto;\n    margin: 0;\n    max-width: none;\n  }\n\n  // use @include grid-row; to use a container row\n  @else {\n    width: 100%;\n    margin-#{$default-float}: auto;\n    margin-#{$opposite-direction}: auto;\n    margin-top: 0;\n    margin-bottom: 0;\n    max-width: $row-width;\n  }\n\n  // Clearfix for all rows\n  @include clearfix();\n}\n\n// Creates a column, should be used inside of a media query to control layouts\n//\n// $columns - The number of columns this should be\n// $last-column - Is this the last column? Default: false.\n// $center - Center these columns? Default: false.\n// $offset - # of columns to offset. Default: false.\n// $push - # of columns to push. Default: false.\n// $pull - # of columns to pull. Default: false.\n// $collapse - Get rid of gutter padding on column? Default: false.\n// $float - Should this float? Default: true. Options: true, false, left, right.\n@mixin grid-column($columns: false,\n  $last-column: false,\n  $center: false,\n  $offset: false,\n  $push: false,\n  $pull: false,\n  $collapse: false,\n  $float: true,\n  $position: false) {\n\n  // If positioned for default .column, include relative position\n  // push and pull require position set\n  @if $position or $push or $pull {\n    position: relative;\n  }\n\n  // If collapsed, get rid of gutter padding\n  @if $collapse {\n    padding-left: 0;\n    padding-right: 0;\n  }\n\n  // Gutter padding whenever a column isn't set to collapse\n  // (use $collapse:null to do nothing)\n  @else if $collapse ==false {\n    padding-left: calc($column-gutter / 2);\n    padding-right: calc($column-gutter / 2);\n  }\n\n  // If a column number is given, calculate width\n  @if $columns {\n    width: grid-calc($columns, $total-columns);\n\n    // If last column, float naturally instead of to the right\n    @if $last-column {\n      float: $opposite-direction;\n    }\n  }\n\n  // Source Ordering, adds left/right depending on which you use.\n  @if $push {\n    #{$default-float}: grid-calc($push, $total-columns);\n    #{$opposite-direction}: auto;\n  }\n\n  @if $pull {\n    #{$opposite-direction}: grid-calc($pull, $total-columns);\n    #{$default-float}: auto;\n  }\n\n  @if $float {\n    @if $float ==left or $float ==true {\n      float: $default-float;\n    }\n\n    @else if $float ==right {\n      float: $opposite-direction;\n    }\n\n    @else {\n      float: none;\n    }\n  }\n\n  // If centered, get rid of float and add appropriate margins\n  @if $center {\n    margin-#{$default-float}: auto;\n    margin-#{$opposite-direction}: auto;\n    float: none;\n  }\n\n  // If offset, calculate appropriate margins\n  @if $offset {\n    margin-#{$default-float}: grid-calc($offset, $total-columns) !important;\n  }\n\n}\n\n// Create presentational classes for grid\n//\n// $size - Name of class to use, i.e. \"large\" will generate .large-1, .large-2, etc.\n@mixin grid-html-classes($size) {\n\n  @for $i from 0 through $total-columns - 1 {\n    .#{$size}-push-#{$i} {\n      @include grid-column($push: $i, $collapse: null, $float: false);\n    }\n\n    .#{$size}-pull-#{$i} {\n      @include grid-column($pull: $i, $collapse: null, $float: false);\n    }\n  }\n\n  .column,\n  .columns {\n    @include grid-column($columns: false, $position: true);\n  }\n\n\n  @for $i from 1 through $total-columns {\n    .#{$size}-#{$i} {\n      @include grid-column($columns: $i, $collapse: null, $float: false);\n    }\n  }\n\n  @for $i from 0 through $total-columns - 1 {\n    .#{$size}-offset-#{$i} {\n      @include grid-column($offset: $i, $collapse: null, $float: false);\n    }\n  }\n\n  .#{$size}-reset-order {\n    margin-#{$default-float}: 0;\n    margin-#{$opposite-direction}: 0;\n    left: auto;\n    right: auto;\n    float: $default-float;\n  }\n\n  .column.#{$size}-centered,\n  .columns.#{$size}-centered {\n    @include grid-column($center: true, $collapse: null, $float: false);\n  }\n\n  .column.#{$size}-uncentered,\n  .columns.#{$size}-uncentered {\n    margin-#{$default-float}: 0;\n    margin-#{$opposite-direction}: 0;\n    float: $default-float;\n  }\n\n  // Fighting [class*=\"column\"] + [class*=\"column\"]:last-child\n  .column.#{$size}-centered:last-child,\n  .columns.#{$size}-centered:last-child {\n    float: none;\n  }\n\n  // Fighting .column.<previous-size>-centered:last-child\n  .column.#{$size}-uncentered:last-child,\n  .columns.#{$size}-uncentered:last-child {\n    float: $default-float;\n  }\n\n  .column.#{$size}-uncentered.opposite,\n  .columns.#{$size}-uncentered.opposite {\n    float: $opposite-direction;\n  }\n\n  .row {\n    &.#{$size}-collapse {\n\n      >.column,\n      >.columns {\n        @include grid-column($collapse: true, $float: false);\n      }\n\n      .row {\n        margin-left: 0;\n        margin-right: 0;\n      }\n    }\n\n    &.#{$size}-uncollapse {\n\n      >.column,\n      >.columns {\n        @include grid-column;\n      }\n    }\n  }\n}\n\n@include exports(\"grid\") {\n  @if $include-html-grid-classes {\n    .row {\n      @include grid-row;\n\n      &.collapse {\n\n        >.column,\n        >.columns {\n          @include grid-column($collapse: true, $float: false);\n        }\n\n        .row {\n          margin-left: 0;\n          margin-right: 0;\n        }\n      }\n\n      .row {\n        @include grid-row($behavior: nest);\n\n        &.collapse {\n          @include grid-row($behavior: nest-collapse);\n        }\n      }\n    }\n\n    .column,\n    .columns {\n      @include grid-column($columns: $total-columns);\n    }\n\n    [class*=\"column\"]+[class*=\"column\"]:last-child {\n      float: $last-child-float;\n    }\n\n    [class*=\"column\"]+[class*=\"column\"].end {\n      float: $default-float;\n    }\n\n    @media #{$small-up} {\n      @include grid-html-classes($size: small);\n    }\n\n    @media #{$medium-up} {\n      @include grid-html-classes($size: medium);\n\n      // Old push and pull classes\n      @for $i from 0 through $total-columns - 1 {\n        .push-#{$i} {\n          @include grid-column($push: $i, $collapse: null, $float: false);\n        }\n\n        .pull-#{$i} {\n          @include grid-column($pull: $i, $collapse: null, $float: false);\n        }\n      }\n    }\n\n    @media #{$large-up} {\n      @include grid-html-classes($size: large);\n\n      @for $i from 0 through $total-columns - 1 {\n        .push-#{$i} {\n          @include grid-column($push: $i, $collapse: null, $float: false);\n        }\n\n        .pull-#{$i} {\n          @include grid-column($pull: $i, $collapse: null, $float: false);\n        }\n      }\n    }\n  }\n\n  @if $include-xl-html-grid-classes {\n    @media #{$xlarge-up} {\n      @include grid-html-classes($size: xlarge);\n    }\n\n    @media #{$xxlarge-up} {\n      @include grid-html-classes($size: xxlarge);\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"../functions\";\n//\n// Foundation Variables\n//\n\n// Data attribute namespace\n// styles get applied to [data-mysite-plugin], etc\n$namespace: false !default;\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n$base-font-size: 100% !default;\n\n// $base-line-height is 24px while $base-font-size is 16px\n$base-line-height: 1.5 !default;\n\n//\n// Global Foundation Mixins\n//\n\n// @mixins\n//\n// We use this to control border radius.\n// $radius - Default: $global-radius || 4px\n@mixin radius($radius: $global-radius) {\n  @if $radius {\n    border-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We use this to create equal side border radius on elements.\n// $side - Options: left, right, top, bottom\n@mixin side-radius($side, $radius: $global-radius) {\n  @if ($side ==left or $side ==right) {\n    -webkit-border-bottom-#{$side}-radius: $radius;\n    -webkit-border-top-#{$side}-radius: $radius;\n    border-bottom-#{$side}-radius: $radius;\n    border-top-#{$side}-radius: $radius;\n  }\n\n  @else {\n    -webkit-#{$side}-left-radius: $radius;\n    -webkit-#{$side}-right-radius: $radius;\n    border-#{$side}-left-radius: $radius;\n    border-#{$side}-right-radius: $radius;\n  }\n}\n\n// @mixins\n//\n// We can control whether or not we have inset shadows edges.\n// $active - Default: true, Options: false\n@mixin inset-shadow($active: true) {\n  box-shadow: $shiny-edge-size $shiny-edge-color inset;\n\n  @if $active {\n    &:active {\n      box-shadow: $shiny-edge-size $shiny-edge-active-color inset;\n    }\n  }\n}\n\n// @mixins\n//\n// We use this to add transitions to elements\n// $property - Default: all, Options: http://www.w3.org/TR/css3-transitions/#animatable-properties\n// $speed - Default: 300ms\n// $ease - Default:ease-out, Options: http://css-tricks.com/almanac/properties/t/transition-timing-function/\n@mixin single-transition($property: all, $speed: 300ms, $ease: ease-out) {\n  transition: $property $speed $ease;\n}\n\n// @mixins\n//\n// We use this to add box-sizing across browser prefixes\n@mixin box-sizing($type: border-box) {\n  -webkit-box-sizing: $type; // Android < 2.3, iOS < 4\n  -moz-box-sizing: $type; // Firefox < 29\n  box-sizing: $type; // Chrome, IE 8+, Opera, Safari 5.1\n}\n\n// @mixins\n//\n// We use this to create isosceles triangles\n// $triangle-size - Used to set border-size. No default, set a px or em size.\n// $triangle-color - Used to set border-color which makes up triangle. No default\n// $triangle-direction - Used to determine which direction triangle points. Options: top, bottom, left, right\n@mixin css-triangle($triangle-size, $triangle-color, $triangle-direction) {\n  content: \"\";\n  display: block;\n  width: 0;\n  height: 0;\n  border: inset $triangle-size;\n\n  @if ($triangle-direction ==top) {\n    border-color: $triangle-color transparent transparent transparent;\n    border-top-style: solid;\n  }\n\n  @if ($triangle-direction ==bottom) {\n    border-color: transparent transparent $triangle-color transparent;\n    border-bottom-style: solid;\n  }\n\n  @if ($triangle-direction ==left) {\n    border-color: transparent transparent transparent $triangle-color;\n    border-left-style: solid;\n  }\n\n  @if ($triangle-direction ==right) {\n    border-color: transparent $triangle-color transparent transparent;\n    border-right-style: solid;\n  }\n}\n\n// @mixins\n//\n// We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n// $width - Width of hamburger icon in rem\n// $left - If false, icon will be centered horizontally || explicitly set value in rem\n// $top - If false, icon will be centered vertically || explicitly set value in rem\n// $thickness - thickness of lines in hamburger icon, set value in px\n// $gap - spacing between the lines in hamburger icon, set value in px\n// $color - icon color\n// $hover-color - icon color during hover\n// $offcanvas - Set to true of @include in offcanvas\n@mixin hamburger($width, $left, $top, $thickness, $gap, $color, $hover-color, $offcanvas) {\n  span::after {\n    content: \"\";\n    position: absolute;\n    display: block;\n    height: 0;\n\n    @if $offcanvas {\n      @if $top {\n        top: $top;\n      }\n\n      @else {\n        top: 50%;\n        margin-top: (-$width/2);\n      }\n\n      @if $left {\n        left: $left;\n      }\n\n      @else {\n        left: ($tabbar-menu-icon-width - $width)/2;\n      }\n    }\n\n    @else {\n      top: 50%;\n      margin-top: -(calc($width / 2));\n      #{$opposite-direction}: $topbar-link-padding;\n    }\n\n    box-shadow: 0 0 0 $thickness $color,\n    0 ($gap + $thickness) 0 $thickness $color,\n    0 (2 * $gap + 2*$thickness) 0 $thickness $color;\n    width: $width;\n  }\n\n  span:hover:after {\n    box-shadow:\n      0 0 0 $thickness $hover-color,\n      0 $gap + $thickness 0 $thickness $hover-color,\n      0 (2 * $gap + 2*$thickness) 0 $thickness $hover-color;\n  }\n}\n\n// We use this to do clear floats\n@mixin clearfix {\n\n  &:before,\n  &:after {\n    content: \" \";\n    display: table;\n  }\n\n  &:after {\n    clear: both;\n  }\n}\n\n// @mixins\n//\n// We use this to add a glowing effect to block elements\n// $selector - Used for selector state. Default: focus, Options: hover, active, visited\n// $fade-time - Default: 300ms\n// $glowing-effect-color - Default: fade-out($primary-color, .25)\n@mixin block-glowing-effect($selector: focus, $fade-time: 300ms, $glowing-effect-color: fade-out($primary-color, .25)) {\n  transition: box-shadow $fade-time, border-color $fade-time ease-in-out;\n\n  &:#{$selector} {\n    box-shadow: 0 0 5px $glowing-effect-color;\n    border-color: $glowing-effect-color;\n  }\n}\n\n// @mixins\n//\n// We use this to translate elements in 2D\n// $horizontal: Default: 0\n// $vertical: Default: 0\n@mixin translate2d($horizontal: 0, $vertical: 0) {\n  transform: translate($horizontal, $vertical)\n}\n\n// @mixins\n//\n// Makes an element visually hidden, but accessible.\n// @see http://snook.ca/archives/html_and_css/hiding-content-for-accessibility\n@mixin element-invisible {\n  position: absolute !important;\n  height: 1px;\n  width: 1px;\n  overflow: hidden;\n  clip: rect(1px, 1px, 1px, 1px);\n}\n\n// @mixins\n//\n// Turns off the element-invisible effect.\n@mixin element-invisible-off {\n  position: static !important;\n  height: auto;\n  width: auto;\n  overflow: visible;\n  clip: auto;\n}\n\n$white : #FFFFFF !default;\n$ghost : #FAFAFA !default;\n$snow : #F9F9F9 !default;\n$vapor : #F6F6F6 !default;\n$white-smoke : #F5F5F5 !default;\n$silver : #EFEFEF !default;\n$smoke : #EEEEEE !default;\n$gainsboro : #DDDDDD !default;\n$iron : #CCCCCC !default;\n$base : #AAAAAA !default;\n$aluminum : #999999 !default;\n$jumbo : #888888 !default;\n$monsoon : #777777 !default;\n$steel : #666666 !default;\n$charcoal : #555555 !default;\n$tuatara : #444444 !default;\n$oil : #333333 !default;\n$jet : #222222 !default;\n$black : #000000 !default;\n\n// We use these as default colors throughout\n$primary-color: #008CBA !default; // bondi-blue\n$secondary-color: #e7e7e7 !default; // white-lilac\n$alert-color: #f04124 !default; // cinnabar\n$success-color: #43AC6A !default; // sea-green\n$warning-color: #f08a24 !default; // carrot\n$info-color: #a0d3e8 !default; // cornflower\n\n// We use these to define default font stacks\n$font-family-sans-serif: \"Helvetica Neue\", Helvetica, Roboto, Arial, sans-serif !default;\n$font-family-serif: Georgia, Cambria, \"Times New Roman\", Times, serif !default;\n$font-family-monospace: Consolas, \"Liberation Mono\", Courier, monospace !default;\n\n// We use these to define default font weights\n$font-weight-normal: normal !default;\n$font-weight-bold: bold !default;\n\n// We use these to control various global styles\n$body-bg: #fff !default;\n$body-font-color: #222 !default;\n$body-font-family: $font-family-sans-serif !default;\n$body-font-weight: $font-weight-normal !default;\n$body-font-style: normal !default;\n\n// We use this to control font-smoothing\n$font-smoothing: antialiased !default;\n\n// We use these to control text direction settings\n$text-direction: ltr !default;\n$default-float: left !default;\n$opposite-direction: right !default;\n\n@if $text-direction ==ltr {\n  $default-float: left;\n  $opposite-direction: right;\n}\n\n@else {\n  $default-float: right;\n  $opposite-direction: left;\n}\n\n// We use these to make sure border radius matches unless we want it different.\n$global-radius: 3px !default;\n$global-rounded: 1000px !default;\n\n// We use these to control inset shadow shiny edges and depressions.\n$shiny-edge-size: 0 1px 0 !default;\n$shiny-edge-color: rgba(#fff, .5) !default;\n$shiny-edge-active-color: rgba(#000, .2) !default;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true !default;\n$include-print-styles: true !default;\n$include-html-global-classes: $include-html-classes !default;\n\n$column-gutter: rem-calc(30) !default;\n\n// Media Query Ranges\n$small-range: (\n  0,\n  40em) !default;\n$medium-range: (\n  40.063em,\n  64em) !default;\n$large-range: (\n  64.063em,\n  90em) !default;\n$xlarge-range: (\n  90.063em,\n  120em) !default;\n$xxlarge-range: (\n  120.063em,\n  99999999em) !default;\n\n\n$screen: \"only screen\" !default;\n\n$landscape: \"#{$screen} and (orientation: landscape)\" !default;\n$portrait: \"#{$screen} and (orientation: portrait)\" !default;\n\n$small-up: $screen !default;\n$small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\" !default;\n\n$medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\" !default;\n$medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\" !default;\n\n$large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\" !default;\n$large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\" !default;\n\n$xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\" !default;\n$xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\" !default;\n\n$xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\" !default;\n$xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\" !default;\n\n// Legacy\n$small: $medium-up;\n$medium: $medium-up;\n$large: $large-up;\n\n\n//We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n$cursor-auto-value: auto !default;\n$cursor-crosshair-value: crosshair !default;\n$cursor-default-value: default !default;\n$cursor-pointer-value: pointer !default;\n$cursor-help-value: help !default;\n$cursor-text-value: text !default;\n\n\n@include exports(\"global\") {\n\n  // Meta styles are included in all builds, as they are a dependency of the Javascript.\n  // Used to provide media query values for javascript components.\n  // Forward slash placed around everything to convince PhantomJS to read the value.\n\n  meta.foundation-version {\n    font-family: \"/5.5.0/\";\n  }\n\n  meta.foundation-mq-small {\n    font-family: \"/\" + unquote($small-up) + \"/\";\n    width: lower-bound($small-range\n    );\n}\n\nmeta.foundation-mq-small-only {\n  font-family: \"/\" + unquote($small-only) + \"/\";\n  width: lower-bound($small-range);\n}\n\nmeta.foundation-mq-medium {\n  font-family: \"/\" + unquote($medium-up) + \"/\";\n  width: lower-bound($medium-range);\n}\n\nmeta.foundation-mq-medium-only {\n  font-family: \"/\" + unquote($medium-only) + \"/\";\n  width: lower-bound($medium-range);\n}\n\nmeta.foundation-mq-large {\n  font-family: \"/\" + unquote($large-up) + \"/\";\n  width: lower-bound($large-range);\n}\n\nmeta.foundation-mq-large-only {\n  font-family: \"/\" + unquote($large-only) + \"/\";\n  width: lower-bound($large-range);\n}\n\nmeta.foundation-mq-xlarge {\n  font-family: \"/\" + unquote($xlarge-up) + \"/\";\n  width: lower-bound($xlarge-range);\n}\n\nmeta.foundation-mq-xlarge-only {\n  font-family: \"/\" + unquote($xlarge-only) + \"/\";\n  width: lower-bound($xlarge-range);\n}\n\nmeta.foundation-mq-xxlarge {\n  font-family: \"/\" + unquote($xxlarge-up) + \"/\";\n  width: lower-bound($xxlarge-range);\n}\n\nmeta.foundation-data-attribute-namespace {\n  font-family: #{$namespace};\n}\n\n@if $include-html-global-classes {\n\n  // Must be 100% for off canvas to work\n  html,\n  body {\n    height: 100%;\n  }\n\n  // Set box-sizing globally to handle padding and border widths\n  *,\n  *:before,\n  *:after {\n    @include box-sizing(border-box);\n  }\n\n  html,\n  body {\n    font-size: $base-font-size;\n  }\n\n  // Default body styles\n  body {\n    background: $body-bg;\n    color: $body-font-color;\n    padding: 0;\n    margin: 0;\n    font-family: $body-font-family;\n    font-weight: $body-font-weight;\n    font-style: $body-font-style;\n    line-height: $base-line-height; // Set to $base-line-height to take on browser default of 150%\n    position: relative;\n    cursor: $cursor-auto-value;\n  }\n\n  a:hover {\n    cursor: $cursor-pointer-value;\n  }\n\n  // Grid Defaults to get images and embeds to work properly\n  img {\n    max-width: 100%;\n    height: auto;\n  }\n\n  img {\n    -ms-interpolation-mode: bicubic;\n  }\n\n  #map_canvas,\n  .map_canvas {\n\n    img,\n    embed,\n    object {\n      max-width: none !important;\n    }\n  }\n\n  // Miscellaneous useful HTML classes\n  .left {\n    float: left !important;\n  }\n\n  .right {\n    float: right !important;\n  }\n\n  .clearfix {\n    @include clearfix;\n  }\n\n  // Hide visually and from screen readers\n  .hide {\n    display: none !important;\n    visibility: hidden;\n  }\n\n  // Hide visually and from screen readers, but maintain layout\n  .invisible {\n    visibility: hidden;\n  }\n\n  // Font smoothing\n  // Antialiased font smoothing works best for light text on a dark background.\n  // Apply to single elements instead of globally to body.\n  // Note this only applies to webkit-based desktop browsers and Firefox 25 (and later) on the Mac.\n  .antialiased {\n    -webkit-font-smoothing: antialiased;\n    -moz-osx-font-smoothing: grayscale;\n  }\n\n  // Get rid of gap under images by making them display: inline-block; by default\n  img {\n    display: inline-block;\n    vertical-align: middle;\n  }\n\n  //\n  // Global resets for forms\n  //\n\n  // Make sure textarea takes on height automatically\n  textarea {\n    height: auto;\n    min-height: 50px;\n  }\n\n  // Make select elements 100% width by default\n  select {\n    width: 100%;\n  }\n}\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-button-classes: $include-html-classes !default;\n\n// We use these to build padding for buttons.\n$button-tny: rem-calc(10) !default;\n$button-sml: rem-calc(14) !default;\n$button-med: rem-calc(16) !default;\n$button-lrg: rem-calc(18) !default;\n\n// We use this to control the display property.\n$button-display: inline-block !default;\n$button-margin-bottom: rem-calc(20) !default;\n\n// We use these to control button text styles.\n$button-font-family: $body-font-family !default;\n$button-font-color: $white !default;\n$button-font-color-alt: $oil !default;\n$button-font-tny: rem-calc(11) !default;\n$button-font-sml: rem-calc(13) !default;\n$button-font-med: rem-calc(16) !default;\n$button-font-lrg: rem-calc(20) !default;\n$button-font-weight: $font-weight-normal !default;\n$button-font-align: center !default;\n\n// We use these to control various hover effects.\n$button-function-factor: -20% !default;\n\n// We use these to control button border styles.\n$button-border-width: 0 !default;\n$button-border-style: solid !default;\n$button-bg-color: $primary-color !default;\n$button-bg-hover: scale-color($button-bg-color, $lightness: $button-function-factor) !default;\n$button-border-color: $button-bg-hover !default;\n$secondary-button-bg-hover: scale-color($secondary-color, $lightness: $button-function-factor) !default;\n$secondary-button-border-color: $secondary-button-bg-hover !default;\n$success-button-bg-hover: scale-color($success-color, $lightness: $button-function-factor) !default;\n$success-button-border-color: $success-button-bg-hover !default;\n$alert-button-bg-hover: scale-color($alert-color, $lightness: $button-function-factor) !default;\n$alert-button-border-color: $alert-button-bg-hover !default;\n$warning-button-bg-hover: scale-color($warning-color, $lightness: $button-function-factor) !default;\n$warning-button-border-color: $warning-button-bg-hover !default;\n$info-button-bg-hover: scale-color($info-color, $lightness: $button-function-factor) !default;\n$info-button-border-color: $info-button-bg-hover !default;\n\n// We use this to set the default radius used throughout the core.\n$button-radius: $global-radius !default;\n$button-round: $global-rounded !default;\n\n// We use this to set default opacity and cursor for disabled buttons.\n$button-disabled-opacity: 0.7 !default;\n$button-disabled-cursor: $cursor-default-value !default;\n\n\n//\n// @MIXIN\n//\n// We use this mixin to create a default button base.\n//\n// $style - Sets base styles. Can be set to false. Default: true.\n// $display - Used to control display property. Default: $button-display || inline-block\n\n@mixin button-base($style:true, $display:$button-display) {\n  @if $style {\n    border-style: $button-border-style;\n    border-width: $button-border-width;\n    cursor: $cursor-pointer-value;\n    font-family: $button-font-family;\n    font-weight: $button-font-weight;\n    line-height: normal;\n    margin: 0 0 $button-margin-bottom;\n    position: relative;\n    text-decoration: none;\n    text-align: $button-font-align;\n    -webkit-appearance: none;\n    border-radius:0;\n  }\n  @if $display { display: $display; }\n}\n\n// @MIXIN\n//\n// We use this mixin to add button size styles\n//\n// $padding - Used to build padding for buttons Default: $button-med ||= rem-calc(12)\n// $full-width - We can set $full-width:true to remove side padding extend width - Default: false\n\n@mixin button-size($padding:$button-med, $full-width:false) {\n\n  // We control which padding styles come through,\n  // these can be turned off by setting $padding:false\n  @if $padding {\n    padding-top: $padding;\n    padding-#{$opposite-direction}: $padding * 2;\n    padding-bottom: $padding + rem-calc(1);\n    padding-#{$default-float}: $padding * 2;\n\n    // We control the font-size based on mixin input.\n    @if      $padding == $button-med { font-size: $button-font-med; }\n    @else if $padding == $button-tny { font-size: $button-font-tny; }\n    @else if $padding == $button-sml { font-size: $button-font-sml; }\n    @else if $padding == $button-lrg { font-size: $button-font-lrg; }\n  }\n\n  // We can set $full-width:true to remove side padding extend width.\n  @if $full-width {\n    // We still need to check if $padding is set.\n    @if $padding {\n    padding-top: $padding;\n    padding-bottom: $padding + rem-calc(1);\n    } @else if $padding == false {\n      padding-top:0;\n      padding-bottom:0;\n    }\n    padding-right: 0;\n    padding-left: 0;\n    width: 100%;\n  }\n}\n\n// @MIXIN\n//\n// we use this mixin to create the button hover and border colors\n\n// @MIXIN\n//\n// We use this mixin to add button color styles\n//\n// $bg - Background color. We can set $bg:false for a transparent background. Default: $primary-color.\n// $radius - If true, set to button radius which is $global-radius || explicitly set radius amount in px (ex. $radius:10px). Default: true\n// $disabled - We can set $disabled:true to create a disabled transparent button. Default: false\n// $bg-hover - Button Hover Background Color. Default: $button-bg-hover\n// $border-color - Button Border Color. Default: $button-border-color\n@mixin button-style($bg:$button-bg-color, $radius:false, $disabled:false, $bg-hover:null, $border-color:null) {\n\n  // We control which background styles are used,\n  // these can be removed by setting $bg:false\n  @if $bg {\n\n    @if $bg-hover == null {\n      $bg-hover: if($bg == $button-bg-color, $button-bg-hover, scale-color($bg, $lightness: $button-function-factor));\n    }\n\n    @if $border-color == null {\n      $border-color: if($bg == $button-bg-color, $button-border-color, scale-color($bg, $lightness: $button-function-factor));\n    }\n\n    // This find the lightness percentage of the background color.\n    $bg-lightness: lightness($bg);\n    $bg-hover-lightness: lightness($bg-hover);\n\n    background-color: $bg;\n    border-color: $border-color;\n    &:hover,\n    &:focus { background-color: $bg-hover; }\n\n    // We control the text color for you based on the background color.\n    color: if($bg-lightness > 70%, $button-font-color-alt, $button-font-color);\n\n    &:hover,\n    &:focus {\n      color: if($bg-hover-lightness > 70%, $button-font-color-alt, $button-font-color);\n    }\n  }\n\n  // We can set $disabled:true to create a disabled transparent button.\n  @if $disabled {\n    cursor: $button-disabled-cursor;\n    opacity: $button-disabled-opacity;\n    box-shadow: none;\n    &:hover,\n    &:focus { background-color: $bg; }\n  }\n\n  // We can control how much button radius is used.\n  @if $radius == true { @include radius($button-radius); }\n  @else if $radius { @include radius($radius); }\n\n}\n\n// @MIXIN\n//\n// We use this to quickly create buttons with a single mixin. As @jaredhardy puts it, \"the kitchen sink mixin\"\n//\n// $padding - Used to build padding for buttons Default: $button-med ||= rem-calc(12)\n// $bg - Primary color set in settings file. Default: $button-bg.\n// $radius - If true, set to button radius which is $global-radius || explicitly set radius amount in px (ex. $radius:10px). Default:false.\n// $full-width - We can set $full-width:true to remove side padding extend width. Default:false.\n// $disabled - We can set $disabled:true to create a disabled transparent button. Default:false.\n// $is-prefix - Not used? Default:false.\n// $bg-hover - Button Hover Color - Default null - see button-style mixin\n// $border-color - Button Border Color - Default null - see button-style mixin\n// $transition - We can control whether or not to include the background-color transition property - Default:true.\n@mixin button($padding:$button-med, $bg:$button-bg-color, $radius:false, $full-width:false, $disabled:false, $is-prefix:false, $bg-hover:null, $border-color:null, $transition: true) {\n  @include button-base;\n  @include button-size($padding, $full-width);\n  @include button-style($bg, $radius, $disabled, $bg-hover, $border-color);\n\n  @if $transition {\n    @include single-transition(background-color);\n  }\n}\n\n\n@include exports(\"button\") {\n  @if $include-html-button-classes {\n\n    // Default styles applied outside of media query\n    button, .button {\n      @include button-base;\n      @include button-size;\n      @include button-style;\n\n      @include single-transition(background-color);\n\n      &.secondary { @include button-style($bg:$secondary-color, $bg-hover:$secondary-button-bg-hover, $border-color:$secondary-button-border-color); }\n      &.success   { @include button-style($bg:$success-color, $bg-hover:$success-button-bg-hover, $border-color:$success-button-border-color); }\n      &.alert     { @include button-style($bg:$alert-color, $bg-hover:$alert-button-bg-hover, $border-color:$alert-button-border-color); }\n      &.warning   { @include button-style($bg:$warning-color, $bg-hover:$warning-button-bg-hover, $border-color:$warning-button-border-color); }\n      &.info      { @include button-style($bg:$info-color, $bg-hover:$info-button-bg-hover, $border-color:$info-button-border-color); }\n\n      &.large  { @include button-size($padding:$button-lrg); }\n      &.small  { @include button-size($padding:$button-sml); }\n      &.tiny   { @include button-size($padding:$button-tny); }\n      &.expand { @include button-size($padding:null,$full-width:true); }\n\n      &.left-align  { text-align: left; text-indent: rem-calc(12); }\n      &.right-align { text-align: right; padding-right: rem-calc(12); }\n\n      &.radius { @include button-style($bg:false, $radius:true); }\n      &.round  { @include button-style($bg:false, $radius:$button-round); }\n\n      &.disabled, &[disabled] { @include button-style($bg:$button-bg-color, $disabled:true, $bg-hover:$button-bg-hover, $border-color:$button-border-color);\n        &.secondary { @include button-style($bg:$secondary-color, $disabled:true, $bg-hover:$secondary-button-bg-hover, $border-color:$secondary-button-border-color); }\n        &.success { @include button-style($bg:$success-color, $disabled:true, $bg-hover:$success-button-bg-hover, $border-color:$success-button-border-color); }\n        &.alert { @include button-style($bg:$alert-color, $disabled:true, $bg-hover:$alert-button-bg-hover, $border-color:$alert-button-border-color); }\n        &.warning { @include button-style($bg:$warning-color, $disabled:true, $bg-hover:$warning-button-bg-hover, $border-color:$warning-button-border-color); }\n        &.info { @include button-style($bg:$info-color, $disabled:true, $bg-hover:$info-button-bg-hover, $border-color:$info-button-border-color); }\n      }\n    }\n\n    //firefox 2px fix\n    button::-moz-focus-inner {border:0; padding:0;}\n\n    @media #{$medium-up} {\n      button, .button {\n        @include button-base($style:false, $display:inline-block);\n        @include button-size($padding:false, $full-width:false);\n      }\n    }\n  }\n}\n","@charset \"utf-8\";\n\n$spacing-unit: 30px;\n\n\n// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n//\n\n// Table of Contents\n// Foundation Settings\n//\n// a. Base\n// b. Grid\n// c. Global\n// d. Media Query Ranges\n// e. Typography\n// 01. Accordion\n// 02. Alert Boxes\n// 03. Block Grid\n// 04. Breadcrumbs\n// 05. Buttons\n// 06. Button Groups\n// 07. Clearing\n// 08. Dropdown\n// 09. Dropdown Buttons\n// 10. Flex Video\n// 11. Forms\n// 12. Icon Bar\n// 13. Inline Lists\n// 14. Joyride\n// 15. Keystrokes\n// 16. Labels\n// 17. Magellan\n// 18. Off-canvas\n// 19. Orbit\n// 20. Pagination\n// 21. Panels\n// 22. Pricing Tables\n// 23. Progress Bar\n// 24. Range Slider\n// 25. Reveal\n// 26. Side Nav\n// 27. Split Buttons\n// 28. Sub Nav\n// 29. Switch\n// 30. Tables\n// 31. Tabs\n// 32. Thumbnails\n// 33. Tooltips\n// 34. Top Bar\n// 36. Visibility Classes\n\n// a. Base\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// This is the default html and body font-size for the base rem value.\n// $rem-base: 16px;\n\n// Allows the use of rem-calc() or lower-bound() in your settings\n@import \"functions\";\n\n// The default font-size is set to 100% of the browser style sheet (usually 16px)\n// for compatibility with browser-based text zoom or user-set defaults.\n\n// Since the typical default browser font-size is 16px, that makes the calculation for grid size.\n// If you want your base font-size to be different and not have it affect the grid breakpoints,\n// set $rem-base to $base-font-size and make sure $base-font-size is a px value.\n// $base-font-size: 100%;\n\n$base-font-size: 16px;\n$rem-base: $base-font-size;\n\n\n// The $base-font-size is 100% while $base-line-height is 150%\n// $base-line-height: 150%;\n\n// We use this to control whether or not CSS classes come through in the gem files.\n$include-html-classes: true;\n// $include-print-styles: true;\n$include-html-global-classes: $include-html-classes;\n\n// b. Grid\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-grid-classes: $include-html-classes;\n// $include-xl-html-grid-classes: false;\n\n// $row-width: rem-calc(1000);\n// $total-columns: 12;\n// $column-gutter: rem-calc(30);\n\n// c. Global\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// We use these to define default font stacks\n// $font-family-sans-serif: \"Lato\", \"Helvetica Neue\", \"Helvetica\", Helvetica, Arial, sans-serif;\n// $font-family-serif: \"Volkhov\", Georgia, Times, serif;\n// $font-family-monospace: \"Lucida Console\", Monaco, monospace;\n\n// We use these to define default font weights\n// $font-weight-normal: normal !default;\n// $font-weight-bold: bold !default;\n\n// $white       : #FFFFFF;\n// $ghost       : #FAFAFA;\n// $snow        : #F9F9F9;\n// $vapor       : #F6F6F6;\n// $white-smoke : #F5F5F5;\n// $silver      : #EFEFEF;\n// $smoke       : #EEEEEE;\n// $gainsboro   : #DDDDDD;\n// $iron        : #CCCCCC;\n// $base        : #AAAAAA;\n// $aluminum    : #999999;\n// $jumbo       : #888888;\n// $monsoon     : #777777;\n// $steel       : #666666;\n// $charcoal    : #555555;\n// $tuatara     : #444444;\n// $oil         : #333333;\n// $jet         : #222222;\n// $black       : #000000;\n\n// We use these as default colors throughout\n// $primary-color: #008CBA;\n// $secondary-color: #e7e7e7;\n// $alert-color: #f04124;\n// $success-color: #43AC6A;\n// $warning-color: #f08a24;\n// $info-color: #a0d3e8;\n\n// We use these to control various global styles\n// $body-bg: $white;\n// $body-font-color: $jet;\n// $body-font-family: $font-family-sans-serif;\n// $body-font-weight: $font-weight-normal;\n// $body-font-style: normal;\n\n// We use this to control font-smoothing\n// $font-smoothing: antialiased;\n\n// We use these to control text direction settings\n// $text-direction: ltr;\n// $opposite-direction: right;\n// $default-float: left;\n// $last-child-float: $opposite-direction;\n\n// We use these to make sure border radius matches unless we want it different.\n$global-radius: 3px;\n// $global-rounded: 1000px;\n\n// We use these to control inset shadow shiny edges and depressions.\n// $shiny-edge-size: 0 1px 0;\n// $shiny-edge-color: rgba($white, .5);\n// $shiny-edge-active-color: rgba($black, .2);\n\n// // d. Media Query Ranges\n// // - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $small-range: (0em, 40em);\n// $medium-range: (40.063em, 64em);\n// $large-range: (64.063em, 90em);\n// $xlarge-range: (90.063em, 120em);\n// $xxlarge-range: (120.063em, 99999999em);\n\n// $screen: \"only screen\";\n\n// // $landscape: \"#{$screen} and (orientation: landscape)\";\n// // $portrait: \"#{$screen} and (orientation: portrait)\";\n\n// $small-up: $screen;\n// $small-only: \"#{$screen} and (max-width: #{upper-bound($small-range)})\";\n\n// $medium-up: \"#{$screen} and (min-width:#{lower-bound($medium-range)})\";\n// $medium-only: \"#{$screen} and (min-width:#{lower-bound($medium-range)}) and (max-width:#{upper-bound($medium-range)})\";\n\n// $large-up: \"#{$screen} and (min-width:#{lower-bound($large-range)})\";\n// $large-only: \"#{$screen} and (min-width:#{lower-bound($large-range)}) and (max-width:#{upper-bound($large-range)})\";\n\n// $xlarge-up: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)})\";\n// $xlarge-only: \"#{$screen} and (min-width:#{lower-bound($xlarge-range)}) and (max-width:#{upper-bound($xlarge-range)})\";\n\n// $xxlarge-up: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)})\";\n// $xxlarge-only: \"#{$screen} and (min-width:#{lower-bound($xxlarge-range)}) and (max-width:#{upper-bound($xxlarge-range)})\";\n\n// Legacy\n// $small: $medium-up;\n// $medium: $medium-up;\n// $large: $large-up;\n\n// We use this as cursors values for enabling the option of having custom cursors in the whole site's stylesheet\n// $cursor-crosshair-value: crosshair;\n// $cursor-default-value: default;\n// $cursor-pointer-value: pointer;\n// $cursor-help-value: help;\n// $cursor-text-value: text;\n\n// e. Typography\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-type-classes: $include-html-classes;\n\n// We use these to control header font styles\n// $header-font-family: $font-family-serif;\n// $header-font-weight: $font-weight-normal;\n// $header-font-style: normal;\n// $header-font-color: $jet;\n// $header-line-height: 1.4;\n// $header-top-margin: .2rem;\n// $header-bottom-margin: .5rem;\n// $header-text-rendering: optimizeLegibility;\n\n// We use these to control header font sizes\n// $h1-font-size: rem-calc(54);\n// $h2-font-size: rem-calc(36);\n// $h3-font-size: rem-calc(29);\n// $h4-font-size: rem-calc(24);\n// $h5-font-size: rem-calc(19);\n// $h6-font-size: 1rem;\n\n// We use these to control header size reduction on small screens\n// $h1-font-reduction: rem-calc(10) !default;\n// $h2-font-reduction: rem-calc(10) !default;\n// $h3-font-reduction: rem-calc(5) !default;\n// $h4-font-reduction: rem-calc(5) !default;\n// $h5-font-reduction: 0 !default;\n// $h6-font-reduction: 0 !default;\n\n// These control how subheaders are styled.\n// $subheader-line-height: 1.4;\n// $subheader-font-color: scale-color($header-font-color, $lightness: 35%);\n// $subheader-font-weight: $font-weight-normal;\n// $subheader-top-margin: .2rem;\n// $subheader-bottom-margin: .5rem;\n\n// A general <small> styling\n// $small-font-size: 60%;\n// $small-font-color: scale-color($header-font-color, $lightness: 35%);\n\n// We use these to style paragraphs\n// $paragraph-font-family: inherit;\n// $paragraph-font-weight: $font-weight-normal;\n// $paragraph-font-size: 1rem;\n// $paragraph-line-height: 1.6;\n// $paragraph-margin-bottom: rem-calc(20);\n// $paragraph-aside-font-size: rem-calc(14);\n// $paragraph-aside-line-height: 1.35;\n// $paragraph-aside-font-style: italic;\n// $paragraph-text-rendering: optimizeLegibility;\n\n// We use these to style <code> tags\n// $code-color: $oil;\n// $code-font-family: $font-family-monospace;\n// $code-font-weight: $font-weight-normal;\n// $code-background-color: scale-color($secondary-color, $lightness: 70%);\n// $code-border-size: 1px;\n// $code-border-style: solid;\n// $code-border-color: scale-color($code-background-color, $lightness: -10%);\n// $code-padding: rem-calc(2) rem-calc(5) rem-calc(1);\n\n// We use these to style anchors\n// $anchor-text-decoration: none;\n// $anchor-text-decoration-hover: none;\n// $anchor-font-color: $primary-color;\n// $anchor-font-color-hover: scale-color($primary-color, $lightness: -14%);\n\n// We use these to style the <hr> element\n// $hr-border-width: 1px;\n// $hr-border-style: solid;\n$hr-border-color: $grey-3;\n// $hr-margin: rem-calc(20);\n\n// We use these to style lists\n// $list-font-family: $paragraph-font-family;\n// $list-font-size: $paragraph-font-size;\n// $list-line-height: $paragraph-line-height;\n// $list-margin-bottom: $paragraph-margin-bottom;\n// $list-style-position: outside;\n$list-side-margin: 1.3rem;\n// $list-ordered-side-margin: 1.4rem;\n// $list-side-margin-no-bullet: 0;\n// $list-nested-margin: rem-calc(20);\n// $definition-list-header-weight: $font-weight-bold;\n// $definition-list-header-margin-bottom: .3rem;\n// $definition-list-margin-bottom: rem-calc(12);\n\n// We use these to style blockquotes\n// $blockquote-font-color: scale-color($header-font-color, $lightness: 35%);\n// $blockquote-padding: rem-calc(9 20 0 19);\n// $blockquote-border: 1px solid $gainsboro;\n// $blockquote-cite-font-size: rem-calc(13);\n// $blockquote-cite-font-color: scale-color($header-font-color, $lightness: 23%);\n// $blockquote-cite-link-color: $blockquote-cite-font-color;\n\n// Acronym styles\n// $acronym-underline: 1px dotted $gainsboro;\n\n// We use these to control padding and margin\n// $microformat-padding: rem-calc(10 12);\n// $microformat-margin: rem-calc(0 0 20 0);\n\n// We use these to control the border styles\n// $microformat-border-width: 1px;\n// $microformat-border-style: solid;\n// $microformat-border-color: $gainsboro;\n\n// We use these to control full name font styles\n// $microformat-fullname-font-weight: $font-weight-bold;\n// $microformat-fullname-font-size: rem-calc(15);\n\n// We use this to control the summary font styles\n// $microformat-summary-font-weight: $font-weight-bold;\n\n// We use this to control abbr padding\n// $microformat-abbr-padding: rem-calc(0 1);\n\n// We use this to control abbr font styles\n// $microformat-abbr-font-weight: $font-weight-bold;\n// $microformat-abbr-font-decoration: none;\n\n// 01. Accordion\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-accordion-classes: $include-html-classes;\n\n$accordion-navigation-padding: rem-calc(12);\n// $accordion-navigation-bg-color: #ffffff;\n// $accordion-navigation-hover-bg-color: $grey-1;\n// $accordion-navigation-active-bg-color: $grey-1;\n// $accordion-navigation-font-color: $jet;\n// $accordion-navigation-font-size: rem-calc(16);\n// $accordion-navigation-font-family: $body-font-family;\n\n// $accordion-content-padding: $column-gutter/2;\n$accordion-content-active-bg-color: $body-bg;\n\n// 02. Alert Boxes\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-alert-classes: $include-html-classes;\n\n// We use this to control alert padding.\n// $alert-padding-top: rem-calc(14);\n// $alert-padding-default-float: $alert-padding-top;\n// $alert-padding-opposite-direction: $alert-padding-top + rem-calc(10);\n// $alert-padding-bottom: $alert-padding-top;\n\n// We use these to control text style.\n// $alert-font-weight: $font-weight-normal;\n$alert-font-size: rem-calc(15);\n// $alert-font-color: $white;\n// $alert-font-color-alt: scale-color($secondary-color, $lightness: -66%);\n\n// We use this for close hover effect.\n// $alert-function-factor: -14%;\n\n// We use these to control border styles.\n// $alert-border-style: solid;\n// $alert-border-width: 1px;\n// $alert-border-color: scale-color($primary-color, $lightness: $alert-function-factor);\n// $alert-bottom-margin: rem-calc(20);\n\n// We use these to style the close buttons\n// $alert-close-color: $oil;\n// $alert-close-top: 50%;\n// $alert-close-position: rem-calc(4);\n// $alert-close-font-size: rem-calc(22);\n// $alert-close-opacity: 0.3;\n// $alert-close-opacity-hover: 0.5;\n// $alert-close-padding: 9px 6px 4px;\n\n// We use this to control border radius\n// $alert-radius: $global-radius;\n\n// We use this to control transition effects\n// $alert-transition-speed: 300ms;\n// $alert-transition-ease: ease-out;\n\n// 03. Block Grid\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-block-grid-classes: $include-html-classes;\n// $include-xl-html-block-grid-classes: false;\n\n// We use this to control the maximum number of block grid elements per row\n// $block-grid-elements: 12;\n// $block-grid-default-spacing: rem-calc(20);\n// $align-block-grid-to-grid: false;\n\n// Enables media queries for block-grid classes. Set to false if writing semantic HTML.\n// $block-grid-media-queries: true;\n\n// 04. Breadcrumbs\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-nav-classes: $include-html-classes;\n\n// We use this to set the background color for the breadcrumb container.\n$crumb-bg: $grey-1;\n\n// We use these to set the padding around the breadcrumbs.\n// $crumb-padding: rem-calc(9 9 14 0);\n// $crumb-side-padding: rem-calc(12);\n\n// We use these to control border styles.\n// $crumb-function-factor: -10%;\n$crumb-border-size: 0;\n// $crumb-border-style: solid;\n$crumb-border-color: $grey-1;\n$crumb-radius: 0;\n\n// We use these to set various text styles for breadcrumbs.\n// $crumb-font-size: rem-calc(11);\n// $crumb-font-color: $primary-color;\n// $crumb-font-color-current: $oil;\n// $crumb-font-color-unavailable: $aluminum;\n// $crumb-font-transform: uppercase;\n// $crumb-link-decor: underline;\n\n// We use these to control the slash between breadcrumbs\n// $crumb-slash-color: $base;\n$crumb-slash: \"/\";\n\n// 05. Buttons\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// We use these to build padding for buttons.\n// $button-tny: rem-calc(10);\n// $button-sml: rem-calc(14);\n// $button-med: rem-calc(16);\n// $button-lrg: rem-calc(18);\n\n// We use this to control the display property.\n// $button-display: inline-block;\n// $button-margin-bottom: rem-calc(20);\n\n// We use these to control button text styles.\n// $button-font-family: $body-font-family;\n// $button-font-color: $white;\n// $button-font-color-alt: $oil;\n// $button-font-tny: rem-calc(11);\n// $button-font-sml: rem-calc(13);\n// $button-font-med: rem-calc(16);\n// $button-font-lrg: rem-calc(20);\n// $button-font-weight: $font-weight-normal;\n// $button-font-align: center;\n\n// We use these to control various hover effects.\n// $button-function-factor: -20%;\n\n// We use these to control button border and hover styles.\n// $button-border-width: 0px;\n// $button-border-style: solid;\n// $button-bg-color: $primary-color;\n// $button-bg-hover: scale-color($button-bg-color, $lightness: $button-function-factor);\n// $button-border-color: $button-bg-hover;\n// $secondary-button-bg-hover: scale-color($secondary-color, $lightness: $button-function-factor);\n// $secondary-button-border-color: $secondary-button-bg-hover;\n// $success-button-bg-hover: scale-color($success-color, $lightness: $button-function-factor);\n// $success-button-border-color: $success-button-bg-hover;\n// $alert-button-bg-hover: scale-color($alert-color, $lightness: $button-function-factor);\n// $alert-button-border-color: $alert-button-bg-hover;\n\n// We use this to set the default radius used throughout the core.\n// $button-radius: $global-radius;\n// $button-round: $global-rounded;\n\n// We use this to set default opacity and cursor for disabled buttons.\n// $button-disabled-opacity: 0.7;\n// $button-disabled-cursor: $cursor-default-value;\n\n// 06. Button Groups\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// Sets the margin for the right side by default, and the left margin if right-to-left direction is used\n// $button-bar-margin-opposite: rem-calc(10);\n// $button-group-border-width: 1px;\n\n// 07. Clearing\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-clearing-classes: $include-html-classes;\n\n// We use these to set the background colors for parts of Clearing.\n// $clearing-bg: $oil;\n// $clearing-caption-bg: $clearing-bg;\n// $clearing-carousel-bg: rgba(51,51,51,0.8);\n// $clearing-img-bg: $clearing-bg;\n\n// We use these to style the close button\n// $clearing-close-color: $iron;\n// $clearing-close-size: 30px;\n\n// We use these to style the arrows\n// $clearing-arrow-size: 12px;\n// $clearing-arrow-color: $clearing-close-color;\n\n// We use these to style captions\n// $clearing-caption-font-color: $iron;\n// $clearing-caption-font-size: 0.875em;\n// $clearing-caption-padding: 10px 30px 20px;\n\n// We use these to make the image and carousel height and style\n// $clearing-active-img-height: 85%;\n// $clearing-carousel-height: 120px;\n// $clearing-carousel-thumb-width: 120px;\n// $clearing-carousel-thumb-active-border: 1px solid rgb(255,255,255);\n\n// 08. Dropdown\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-dropdown-classes: $include-html-classes;\n\n// We use these to controls height and width styles.\n// $f-dropdown-max-width: 200px;\n// $f-dropdown-height: auto;\n// $f-dropdown-max-height: none;\n\n// Used for bottom position\n// $f-dropdown-margin-top: 2px;\n\n// Used for right position\n// $f-dropdown-margin-left: $f-dropdown-margin-top;\n\n// Used for left position\n// $f-dropdown-margin-right: $f-dropdown-margin-top;\n\n// Used for top position\n// $f-dropdown-margin-bottom: $f-dropdown-margin-top;\n\n// We use this to control the background color\n// $f-dropdown-bg: $white;\n\n// We use this to set the border styles for dropdowns.\n// $f-dropdown-border-style: solid;\n// $f-dropdown-border-width: 1px;\n// $f-dropdown-border-color: scale-color($white, $lightness: -20%);\n\n// We use these to style the triangle pip.\n// $f-dropdown-triangle-size: 6px;\n// $f-dropdown-triangle-color: $white;\n// $f-dropdown-triangle-side-offset: 10px;\n\n// We use these to control styles for the list elements.\n// $f-dropdown-list-style: none;\n// $f-dropdown-font-color: $charcoal;\n// $f-dropdown-font-size: rem-calc(14);\n// $f-dropdown-list-padding: rem-calc(5, 10);\n// $f-dropdown-line-height: rem-calc(18);\n// $f-dropdown-list-hover-bg: $smoke  ;\n// $dropdown-mobile-default-float: 0;\n\n// We use this to control the styles for when the dropdown has custom content.\n// $f-dropdown-content-padding: rem-calc(20);\n\n// Default radius for dropdown.\n// $f-dropdown-radius: $global-radius;\n\n\n// 09. Dropdown Buttons\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// We use these to set the color of the pip in dropdown buttons\n// $dropdown-button-pip-color: $white;\n// $dropdown-button-pip-color-alt: $oil;\n\n// $button-pip-tny: rem-calc(6);\n// $button-pip-sml: rem-calc(7);\n// $button-pip-med: rem-calc(9);\n// $button-pip-lrg: rem-calc(11);\n\n// We use these to style tiny dropdown buttons\n// $dropdown-button-padding-tny: $button-pip-tny * 7;\n// $dropdown-button-pip-size-tny: $button-pip-tny;\n// $dropdown-button-pip-opposite-tny: $button-pip-tny * 3;\n// $dropdown-button-pip-top-tny: -$button-pip-tny / 2 + rem-calc(1);\n\n// We use these to style small dropdown buttons\n// $dropdown-button-padding-sml: $button-pip-sml * 7;\n// $dropdown-button-pip-size-sml: $button-pip-sml;\n// $dropdown-button-pip-opposite-sml: $button-pip-sml * 3;\n// $dropdown-button-pip-top-sml: -$button-pip-sml / 2 + rem-calc(1);\n\n// We use these to style medium dropdown buttons\n// $dropdown-button-padding-med: $button-pip-med * 6 + rem-calc(3);\n// $dropdown-button-pip-size-med: $button-pip-med - rem-calc(3);\n// $dropdown-button-pip-opposite-med: $button-pip-med * 2.5;\n// $dropdown-button-pip-top-med: -$button-pip-med / 2 + rem-calc(2);\n\n// We use these to style large dropdown buttons\n// $dropdown-button-padding-lrg: $button-pip-lrg * 5 + rem-calc(3);\n// $dropdown-button-pip-size-lrg: $button-pip-lrg - rem-calc(6);\n// $dropdown-button-pip-opposite-lrg: $button-pip-lrg * 2.5;\n// $dropdown-button-pip-top-lrg: -$button-pip-lrg / 2 + rem-calc(3);\n\n// 10. Flex Video\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-media-classes: $include-html-classes;\n\n// We use these to control video container padding and margins\n// $flex-video-padding-top: rem-calc(25);\n// $flex-video-padding-bottom: 67.5%;\n// $flex-video-margin-bottom: rem-calc(16);\n\n// We use this to control widescreen bottom padding\n// $flex-video-widescreen-padding-bottom: 56.34%;\n\n// 11. Forms\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-form-classes: $include-html-classes;\n\n// We use this to set the base for lots of form spacing and positioning styles\n// $form-spacing: rem-calc(16);\n\n// We use these to style the labels in different ways\n// $form-label-pointer: pointer;\n// $form-label-font-size: rem-calc(14);\n// $form-label-font-weight: $font-weight-normal;\n// $form-label-line-height: 1.5;\n// $form-label-font-color: scale-color($black, $lightness: 30%);\n// $form-label-small-transform: capitalize;\n// $form-label-bottom-margin: 0;\n// $input-font-family: inherit;\n// $input-font-color: rgba(0,0,0,0.75);\n// $input-font-size: rem-calc(14);\n// $input-bg-color: $white;\n// $input-focus-bg-color: scale-color($white, $lightness: -2%);\n// $input-border-color: scale-color($white, $lightness: -20%);\n// $input-focus-border-color: scale-color($white, $lightness: -40%);\n// $input-border-style: solid;\n// $input-border-width: 1px;\n// $input-border-radius: $global-radius;\n// $input-disabled-bg: $gainsboro;\n// $input-disabled-cursor: $cursor-default-value;\n// $input-box-shadow: inset 0 1px 2px rgba(0,0,0,0.1);\n\n// We use these to style the fieldset border and spacing.\n// $fieldset-border-style: solid;\n// $fieldset-border-width: 1px;\n// $fieldset-border-color: $gainsboro;\n// $fieldset-padding: rem-calc(20);\n// $fieldset-margin: rem-calc(18 0);\n\n// We use these to style the legends when you use them\n// $legend-bg: $white;\n// $legend-font-weight: $font-weight-bold;\n// $legend-padding: rem-calc(0 3);\n\n// We use these to style the prefix and postfix input elements\n// $input-prefix-bg: scale-color($white, $lightness: -5%);\n// $input-prefix-border-color: scale-color($white, $lightness: -20%);\n// $input-prefix-border-size: 1px;\n// $input-prefix-border-type: solid;\n// $input-prefix-overflow: hidden;\n// $input-prefix-font-color: $oil;\n// $input-prefix-font-color-alt: $white;\n\n// We use this setting to turn on/off HTML5 number spinners (the up/down arrows)\n// $input-number-spinners: true;\n\n// We use these to style the error states for inputs and labels\n// $input-error-message-padding: rem-calc(6 9 9);\n// $input-error-message-top: -1px;\n// $input-error-message-font-size: rem-calc(12);\n// $input-error-message-font-weight: $font-weight-normal;\n// $input-error-message-font-style: italic;\n// $input-error-message-font-color: $white;\n// $input-error-message-font-color-alt: $oil;\n\n// We use this to style the glowing effect of inputs when focused\n// $input-include-glowing-effect: true;\n// $glowing-effect-fade-time: 0.45s;\n// $glowing-effect-color: $input-focus-border-color;\n\n// Select variables\n// $select-bg-color: $ghost;\n// $select-hover-bg-color: scale-color($select-bg-color, $lightness: -3%);\n\n// 12. Icon Bar\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// We use these to style the icon-bar and items\n// $include-html-icon-bar-classes: $include-html-classes;\n// $icon-bar-bg: $oil;\n// $icon-bar-font-color: $white;\n// $icon-bar-font-size: 1rem;\n// $icon-bar-hover-color: $primary-color;\n// $icon-bar-icon-color: $white;\n// $icon-bar-icon-size: 1.875rem;\n// $icon-bar-image-width: 1.875rem;\n// $icon-bar-image-height: 1.875rem;\n// $icon-bar-active-color: $primary-color;\n// $icon-bar-item-padding: 1.25rem;\n\n// 13. Inline Lists\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-inline-list-classes: $include-html-classes;\n\n// We use this to control the margins and padding of the inline list.\n// $inline-list-top-margin: 0;\n// $inline-list-opposite-margin: 0;\n// $inline-list-bottom-margin: rem-calc(17);\n// $inline-list-default-float-margin: rem-calc(-22);\n// $inline-list-default-float-list-margin: rem-calc(22);\n\n// $inline-list-padding: 0;\n\n// We use this to control the overflow of the inline list.\n// $inline-list-overflow: hidden;\n\n// We use this to control the list items\n// $inline-list-display: block;\n\n// We use this to control any elements within list items\n// $inline-list-children-display: block;\n\n// 14. Joyride\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-joyride-classes: $include-html-classes;\n\n// Controlling default Joyride styles\n// $joyride-tip-bg: $oil;\n// $joyride-tip-default-width: 300px;\n// $joyride-tip-padding: rem-calc(18 20 24);\n// $joyride-tip-border: solid 1px $charcoal;\n// $joyride-tip-radius: 4px;\n// $joyride-tip-position-offset: 22px;\n\n// Here, we're setting the tip font styles\n// $joyride-tip-font-color: $white;\n// $joyride-tip-font-size: rem-calc(14);\n// $joyride-tip-header-weight: $font-weight-bold;\n\n// This changes the nub size\n// $joyride-tip-nub-size: 10px;\n\n// This adjusts the styles for the timer when its enabled\n// $joyride-tip-timer-width: 50px;\n// $joyride-tip-timer-height: 3px;\n// $joyride-tip-timer-color: $steel;\n\n// This changes up the styles for the close button\n// $joyride-tip-close-color: $monsoon;\n// $joyride-tip-close-size: 24px;\n// $joyride-tip-close-weight: $font-weight-normal;\n\n// When Joyride is filling the screen, we use this style for the bg\n// $joyride-screenfill: rgba(0,0,0,0.5);\n\n// 15. Keystrokes\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-keystroke-classes: $include-html-classes;\n\n// We use these to control text styles.\n// $keystroke-font: \"Consolas\", \"Menlo\", \"Courier\", monospace;\n// $keystroke-font-size: inherit;\n// $keystroke-font-color: $jet;\n// $keystroke-font-color-alt: $white;\n// $keystroke-function-factor: -7%;\n\n// We use this to control keystroke padding.\n// $keystroke-padding: rem-calc(2 4 0);\n\n// We use these to control background and border styles.\n// $keystroke-bg: scale-color($white, $lightness: $keystroke-function-factor);\n// $keystroke-border-style: solid;\n// $keystroke-border-width: 1px;\n// $keystroke-border-color: scale-color($keystroke-bg, $lightness: $keystroke-function-factor);\n// $keystroke-radius: $global-radius;\n\n// 16. Labels\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-label-classes: $include-html-classes;\n\n// We use these to style the labels\n// $label-padding: rem-calc(4 8 4);\n// $label-radius: $global-radius;\n\n// We use these to style the label text\n// $label-font-sizing: rem-calc(11);\n// $label-font-weight: $font-weight-normal;\n// $label-font-color: $oil;\n// $label-font-color-alt: $white;\n// $label-font-family: $body-font-family;\n\n// 17. Magellan\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-magellan-classes: $include-html-classes;\n\n// $magellan-bg: $white;\n// $magellan-padding: 0 !important;\n\n// 18. Off-canvas\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-off-canvas-classes: $include-html-classes;\n\n// $tabbar-bg: $oil;\n// $tabbar-height: rem-calc(45);\n// $tabbar-icon-width: $tabbar-height;\n// $tabbar-line-height: $tabbar-height;\n// $tabbar-color: $white;\n// $tabbar-middle-padding: 0 rem-calc(10);\n\n// Off Canvas Divider Styles\n// $tabbar-right-section-border: solid 1px scale-color($tabbar-bg, $lightness: 13%);\n// $tabbar-left-section-border: solid 1px scale-color($tabbar-bg, $lightness: -50%);\n\n// Off Canvas Tab Bar Headers\n// $tabbar-header-color: $white;\n// $tabbar-header-weight: $font-weight-bold;\n// $tabbar-header-line-height: $tabbar-height;\n// $tabbar-header-margin: 0;\n\n// Off Canvas Menu Variables\n// $off-canvas-width: rem-calc(250);\n// $off-canvas-bg: $oil;\n// $off-canvas-bg-hover: scale-color($tabbar-bg, $lightness: -30%);\n\n// Off Canvas Menu List Variables\n// $off-canvas-label-padding: 0.3rem rem-calc(15);\n// $off-canvas-label-color: $aluminum;\n// $off-canvas-label-text-transform: uppercase;\n// $off-canvas-label-font-size: rem-calc(12);\n// $off-canvas-label-font-weight: $font-weight-bold;\n// $off-canvas-label-bg: $tuatara;\n// $off-canvas-label-border-top: 1px solid scale-color($tuatara, $lightness: 14%);\n// $off-canvas-label-border-bottom: none;\n// $off-canvas-label-margin:0;\n// $off-canvas-link-padding: rem-calc(10, 15);\n// $off-canvas-link-color: rgba($white, 0.7);\n// $off-canvas-link-border-bottom: 1px solid scale-color($off-canvas-bg, $lightness: -25%);\n// $off-canvas-back-bg: $tuatara;\n// $off-canvas-back-border-top: $off-canvas-label-border-top;\n// $off-canvas-back-border-bottom: $off-canvas-label-border-bottom;\n// $off-canvas-back-hover-bg: scale-color($off-canvas-back-bg, $lightness: -30%);\n// $off-canvas-back-hover-border-top: 1px solid scale-color($off-canvas-label-bg, $lightness: 14%);\n// $off-canvas-back-hover-border-bottom: none;\n\n// Off Canvas Menu Icon Variables\n// $tabbar-menu-icon-color: $white;\n// $tabbar-menu-icon-hover: scale-color($tabbar-menu-icon-color, $lightness: -30%);\n\n// $tabbar-menu-icon-text-indent: rem-calc(35);\n// $tabbar-menu-icon-width: $tabbar-height;\n// $tabbar-menu-icon-height: $tabbar-height;\n// $tabbar-menu-icon-padding: 0;\n\n// $tabbar-hamburger-icon-width: rem-calc(16);\n// $tabbar-hamburger-icon-left: false;\n// $tabbar-hamburger-icon-top: false;\n// $tabbar-hamburger-icon-thickness: 1px;\n// $tabbar-hamburger-icon-gap: 6px;\n\n// Off Canvas Back-Link Overlay\n// $off-canvas-overlay-transition: background 300ms ease;\n// $off-canvas-overlay-cursor: pointer;\n// $off-canvas-overlay-box-shadow: -4px 0 4px rgba($black, 0.5), 4px 0 4px rgba($black, 0.5);\n// $off-canvas-overlay-background: rgba($white, 0.2);\n// $off-canvas-overlay-background-hover: rgba($white, 0.05);\n\n// Transition Variables\n// $menu-slide: \"transform 500ms ease\";\n\n// 19. Orbit\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-orbit-classes: $include-html-classes;\n\n// We use these to control the caption styles\n// $orbit-container-bg: none;\n// $orbit-caption-bg: rgba(51,51,51, 0.8);\n// $orbit-caption-font-color: $white;\n// $orbit-caption-font-size: rem-calc(14);\n// $orbit-caption-position: \"bottom\"; // Supported values: \"bottom\", \"under\"\n// $orbit-caption-padding: rem-calc(10 14);\n// $orbit-caption-height: auto;\n\n// We use these to control the left/right nav styles\n// $orbit-nav-bg: transparent;\n// $orbit-nav-bg-hover: rgba(0,0,0,0.3);\n// $orbit-nav-arrow-color: $white;\n// $orbit-nav-arrow-color-hover: $white;\n\n// We use these to control the timer styles\n// $orbit-timer-bg: rgba(255,255,255,0.3);\n// $orbit-timer-show-progress-bar: true;\n\n// We use these to control the bullet nav styles\n// $orbit-bullet-nav-color: $iron;\n// $orbit-bullet-nav-color-active: $aluminum;\n// $orbit-bullet-radius: rem-calc(9);\n\n// We use these to controls the style of slide numbers\n// $orbit-slide-number-bg: rgba(0,0,0,0);\n// $orbit-slide-number-font-color: $white;\n// $orbit-slide-number-padding: rem-calc(5);\n\n// Hide controls on small\n// $orbit-nav-hide-for-small: true;\n// $orbit-bullet-hide-for-small: true;\n// $orbit-timer-hide-for-small: true;\n\n// Graceful Loading Wrapper and preloader\n// $wrapper-class: \"slideshow-wrapper\";\n// $preloader-class: \"preloader\";\n\n// 20. Pagination\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-pagination-classes: $include-html-classes;\n\n// We use these to control the pagination container\n// $pagination-height: rem-calc(24);\n// $pagination-margin: rem-calc(-5);\n\n// We use these to set the list-item properties\n// $pagination-li-float: $default-float;\n// $pagination-li-height: rem-calc(24);\n// $pagination-li-font-color: $jet;\n// $pagination-li-font-size: rem-calc(14);\n// $pagination-li-margin: rem-calc(5);\n\n// We use these for the pagination anchor links\n// $pagination-link-pad: rem-calc(1 10 1);\n// $pagination-link-font-color: $aluminum;\n// $pagination-link-active-bg: scale-color($white, $lightness: -10%);\n\n// We use these for disabled anchor links\n// $pagination-link-unavailable-cursor: default;\n// $pagination-link-unavailable-font-color: $aluminum;\n// $pagination-link-unavailable-bg-active: transparent;\n\n// We use these for currently selected anchor links\n// $pagination-link-current-background: $primary-color;\n// $pagination-link-current-font-color: $white;\n// $pagination-link-current-font-weight: $font-weight-bold;\n// $pagination-link-current-cursor: default;\n// $pagination-link-current-active-bg: $primary-color;\n\n// 21. Panels\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-panel-classes: $include-html-classes;\n\n// We use these to control the background and border styles\n$panel-bg: $grey-1;\n// $panel-border-style: solid;\n// $panel-border-size: 1px;\n\n// We use this % to control how much we darken things on hover\n// $panel-function-factor: -11%;\n// $panel-border-color: scale-color($panel-bg, $lightness: $panel-function-factor);\n\n// We use these to set default inner padding and bottom margin\n// $panel-margin-bottom: rem-calc(20);\n// $panel-padding: rem-calc(20);\n\n// We use these to set default font colors\n// $panel-font-color: $oil;\n// $panel-font-color-alt: $white;\n\n// $panel-header-adjust: true;\n// $callout-panel-link-color: $primary-color;\n\n// 22. Pricing Tables\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-pricing-classes: $include-html-classes;\n\n// We use this to control the border color\n// $price-table-border: solid 1px $gainsboro;\n\n// We use this to control the bottom margin of the pricing table\n// $price-table-margin-bottom: rem-calc(20);\n\n// We use these to control the title styles\n// $price-title-bg: $oil;\n// $price-title-padding: rem-calc(15 20);\n// $price-title-align: center;\n// $price-title-color: $smoke;\n// $price-title-weight: $font-weight-normal;\n// $price-title-size: rem-calc(16);\n// $price-title-font-family: $body-font-family;\n\n// We use these to control the price styles\n// $price-money-bg: $vapor  ;\n// $price-money-padding: rem-calc(15 20);\n// $price-money-align: center;\n// $price-money-color: $oil;\n// $price-money-weight: $font-weight-normal;\n// $price-money-size: rem-calc(32);\n// $price-money-font-family: $body-font-family;\n\n// We use these to control the description styles\n// $price-bg: $white;\n// $price-desc-color: $monsoon;\n// $price-desc-padding: rem-calc(15);\n// $price-desc-align: center;\n// $price-desc-font-size: rem-calc(12);\n// $price-desc-weight: $font-weight-normal;\n// $price-desc-line-height: 1.4;\n// $price-desc-bottom-border: dotted 1px $gainsboro;\n\n// We use these to control the list item styles\n// $price-item-color: $oil;\n// $price-item-padding: rem-calc(15);\n// $price-item-align: center;\n// $price-item-font-size: rem-calc(14);\n// $price-item-weight: $font-weight-normal;\n// $price-item-bottom-border: dotted 1px $gainsboro;\n\n// We use these to control the CTA area styles\n// $price-cta-bg: $white;\n// $price-cta-align: center;\n// $price-cta-padding: rem-calc(20 20 0);\n\n// 23. Progress Bar\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-media-classes: $include-html-classes;\n\n// We use this to set the progress bar height\n// $progress-bar-height: rem-calc(25);\n// $progress-bar-color: $vapor  ;\n\n// We use these to control the border styles\n// $progress-bar-border-color: scale-color($white, $lightness: 20%);\n// $progress-bar-border-size: 1px;\n// $progress-bar-border-style: solid;\n// $progress-bar-border-radius: $global-radius;\n\n// We use these to control the margin & padding\n// $progress-bar-pad: rem-calc(2);\n// $progress-bar-margin-bottom: rem-calc(10);\n\n// We use these to set the meter colors\n// $progress-meter-color: $primary-color;\n// $progress-meter-secondary-color: $secondary-color;\n// $progress-meter-success-color: $success-color;\n// $progress-meter-alert-color: $alert-color;\n\n// 24. Range Slider\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-range-slider-classes: $include-html-classes;\n\n// These variables define the slider bar styles\n// $range-slider-bar-width: 100%;\n// $range-slider-bar-height: rem-calc(16);\n\n// $range-slider-bar-border-width: 1px;\n// $range-slider-bar-border-style: solid;\n// $range-slider-bar-border-color: $gainsboro;\n// $range-slider-radius: $global-radius;\n// $range-slider-round: $global-rounded;\n// $range-slider-bar-bg-color: $ghost;\n\n// Vertical bar styles\n// $range-slider-vertical-bar-width: rem-calc(16);\n// $range-slider-vertical-bar-height: rem-calc(200);\n\n// These variables define the slider handle styles\n// $range-slider-handle-width: rem-calc(32);\n// $range-slider-handle-height: rem-calc(22);\n// $range-slider-handle-position-top: rem-calc(-5);\n// $range-slider-handle-bg-color: $primary-color;\n// $range-slider-handle-border-width: 1px;\n// $range-slider-handle-border-style: solid;\n// $range-slider-handle-border-color: none;\n// $range-slider-handle-radius: $global-radius;\n// $range-slider-handle-round: $global-rounded;\n// $range-slider-handle-bg-hover-color: scale-color($primary-color, $lightness: -12%);\n// $range-slider-handle-cursor: pointer;\n\n// 25. Reveal\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-reveal-classes: $include-html-classes;\n\n// We use these to control the style of the reveal overlay.\n// $reveal-overlay-bg: rgba($black, .45);\n// $reveal-overlay-bg-old: $black;\n\n// We use these to control the style of the modal itself.\n// $reveal-modal-bg: $white;\n// $reveal-position-top: rem-calc(100);\n// $reveal-default-width: 80%;\n// $reveal-max-width: $row-width;\n// $reveal-modal-padding: rem-calc(20);\n// $reveal-box-shadow: 0 0 10px rgba($black,.4);\n\n// We use these to style the reveal close button\n// $reveal-close-font-size: rem-calc(40);\n// $reveal-close-top: rem-calc(8);\n// $reveal-close-side: rem-calc(11);\n// $reveal-close-color: $base;\n// $reveal-close-weight: $font-weight-bold;\n\n// We use this to set the default radius used throughout the core.\n// $reveal-radius: $global-radius;\n// $reveal-round: $global-rounded;\n\n// We use these to control the modal border\n// $reveal-border-style: solid;\n// $reveal-border-width: 1px;\n// $reveal-border-color: $steel;\n\n// $reveal-modal-class: \"reveal-modal\";\n// $close-reveal-modal-class: \"close-reveal-modal\";\n\n// 26. Side Nav\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-nav-classes: $include-html-classes;\n\n// We use this to control padding.\n$side-nav-padding: rem-calc(0 0 0 0);\n\n// We use these to control list styles.\n// $side-nav-list-type: none;\n// $side-nav-list-position: inside;\n$side-nav-list-margin: rem-calc(0 0 0 0);\n\n// We use these to control link styles.\n$side-nav-link-color: $primary-color;\n$side-nav-link-color-active: scale-color($side-nav-link-color, $lightness: -40%);\n$side-nav-link-color-hover: scale-color($side-nav-link-color, $lightness: -40%);\n$side-nav-font-size: rem-calc(16);\n\n// $side-nav-link-bg-hover: hsla(0, 0, 0, 0.025);\n// $side-nav-link-margin: 0;\n// $side-nav-link-padding: rem-calc(7 14);\n// $side-nav-font-size: rem-calc(14);\n// $side-nav-font-weight: $font-weight-normal;\n// $side-nav-font-weight-active: $side-nav-font-weight;\n// $side-nav-font-family: $body-font-family;\n// $side-nav-font-family-active: $side-nav-font-family;\n\n// We use these to control heading styles.\n// $side-nav-heading-color: $side-nav-link-color;\n// $side-nav-heading-font-size: $side-nav-font-size;\n// $side-nav-heading-font-weight: bold;\n// $side-nav-heading-text-transform: uppercase;\n\n// We use these to control border styles\n$side-nav-divider-size: 1px;\n$side-nav-divider-style: solid;\n$side-nav-divider-color: $grey-1;\n\n\n\n// 27. Split Buttons\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-button-classes: $include-html-classes;\n\n// We use these to control different shared styles for Split Buttons\n// $split-button-function-factor: 10%;\n// $split-button-pip-color: $white;\n// $split-button-pip-color-alt: $oil;\n// $split-button-active-bg-tint: rgba(0,0,0,0.1);\n\n// We use these to control tiny split buttons\n// $split-button-padding-tny: $button-pip-tny * 10;\n// $split-button-span-width-tny: $button-pip-tny * 6;\n// $split-button-pip-size-tny: $button-pip-tny;\n// $split-button-pip-top-tny: $button-pip-tny * 2;\n// $split-button-pip-default-float-tny: rem-calc(-6);\n\n// We use these to control small split buttons\n// $split-button-padding-sml: $button-pip-sml * 10;\n// $split-button-span-width-sml: $button-pip-sml * 6;\n// $split-button-pip-size-sml: $button-pip-sml;\n// $split-button-pip-top-sml: $button-pip-sml * 1.5;\n// $split-button-pip-default-float-sml: rem-calc(-6);\n\n// We use these to control medium split buttons\n// $split-button-padding-med: $button-pip-med * 9;\n// $split-button-span-width-med: $button-pip-med * 5.5;\n// $split-button-pip-size-med: $button-pip-med - rem-calc(3);\n// $split-button-pip-top-med: $button-pip-med * 1.5;\n// $split-button-pip-default-float-med: rem-calc(-6);\n\n// We use these to control large split buttons\n// $split-button-padding-lrg: $button-pip-lrg * 8;\n// $split-button-span-width-lrg: $button-pip-lrg * 5;\n// $split-button-pip-size-lrg: $button-pip-lrg - rem-calc(6);\n// $split-button-pip-top-lrg: $button-pip-lrg + rem-calc(5);\n// $split-button-pip-default-float-lrg: rem-calc(-6);\n\n// 28. Sub Nav\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-nav-classes: $include-html-classes;\n\n// We use these to control margin and padding\n// $sub-nav-list-margin: rem-calc(-4 0 18);\n// $sub-nav-list-padding-top: rem-calc(4);\n\n// We use this to control the definition\n// $sub-nav-font-family: $body-font-family;\n// $sub-nav-font-size: rem-calc(14);\n// $sub-nav-font-color: $aluminum;\n// $sub-nav-font-weight: $font-weight-normal;\n// $sub-nav-text-decoration: none;\n// $sub-nav-padding: rem-calc(3 16);\n// $sub-nav-border-radius: 3px;\n// $sub-nav-font-color-hover: scale-color($sub-nav-font-color, $lightness: -25%);\n\n// We use these to control the active item styles\n// $sub-nav-active-font-weight: $font-weight-normal;\n// $sub-nav-active-bg: $primary-color;\n// $sub-nav-active-bg-hover: scale-color($sub-nav-active-bg, $lightness: -14%);\n// $sub-nav-active-color: $white;\n// $sub-nav-active-padding: $sub-nav-padding;\n// $sub-nav-active-cursor: default;\n\n// $sub-nav-item-divider: \"\";\n// $sub-nav-item-divider-margin: rem-calc(12);\n\n// 29. Switch\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-form-classes: $include-html-classes;\n\n// Controlling border styles and background colors for the switch container\n// $switch-border-color: scale-color($white, $lightness: -20%);\n// $switch-border-style: solid;\n// $switch-border-width: 1px;\n// $switch-bg: $white;\n\n// We use these to control the switch heights for our default classes\n// $switch-height-tny: rem-calc(22);\n// $switch-height-sml: rem-calc(28);\n// $switch-height-med: rem-calc(36);\n// $switch-height-lrg: rem-calc(44);\n// $switch-bottom-margin: rem-calc(20);\n\n// We use these to control default font sizes for our classes.\n// $switch-font-size-tny: 11px;\n// $switch-font-size-sml: 12px;\n// $switch-font-size-med: 14px;\n// $switch-font-size-lrg: 17px;\n// $switch-label-side-padding: 6px;\n\n// We use these to style the switch-paddle\n// $switch-paddle-bg: $white;\n// $switch-paddle-fade-to-color: scale-color($switch-paddle-bg, $lightness: -10%);\n// $switch-paddle-border-color: scale-color($switch-paddle-bg, $lightness: -35%);\n// $switch-paddle-border-width: 1px;\n// $switch-paddle-border-style: solid;\n// $switch-paddle-transition-speed: .1s;\n// $switch-paddle-transition-ease: ease-out;\n// $switch-positive-color: scale-color($success-color, $lightness: 94%);\n// $switch-negative-color: $white-smoke;\n\n// Outline Style for tabbing through switches\n// $switch-label-outline: 1px dotted $jumbo;\n\n// 30. Tables\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-table-classes: $include-html-classes;\n\n// These control the background color for the table and even rows\n// $table-bg: $white;\n$table-even-row-bg: $grey-1;\n\n// These control the table cell border style\n// $table-border-style: solid;\n// $table-border-size: 1px;\n// $table-border-color: $gainsboro;\n\n// These control the table head styles\n$table-head-bg: $grey-2;\n// $table-head-font-size: rem-calc(14);\n// $table-head-font-color: $jet;\n// $table-head-font-weight: $font-weight-bold;\n// $table-head-padding: rem-calc(8 10 10);\n\n// These control the row padding and font styles\n// $table-row-padding: rem-calc(9 10);\n// $table-row-font-size: rem-calc(14);\n// $table-row-font-color: $jet;\n// $table-line-height: rem-calc(18);\n\n// These are for controlling the layout, display and margin of tables\n// $table-layout: auto;\n// $table-display: table-cell;\n// $table-margin-bottom: rem-calc(20);\n\n// 31. Tabs\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-tabs-classes: $include-html-classes;\n\n// $tabs-navigation-padding: rem-calc(16);\n// $tabs-navigation-bg-color: $silver  ;\n// $tabs-navigation-active-bg-color: $white;\n// $tabs-navigation-hover-bg-color: scale-color($tabs-navigation-bg-color, $lightness: -6%);\n// $tabs-navigation-font-color: $jet;\n// $tabs-navigation-active-font-color: $tabs-navigation-font-color;\n// $tabs-navigation-font-size: rem-calc(16);\n// $tabs-navigation-font-family: $body-font-family;\n\n// $tabs-content-margin-bottom: rem-calc(24);\n// $tabs-content-padding: $column-gutter/2;\n\n// $tabs-vertical-navigation-margin-bottom: 1.25rem;\n\n// 32. Thumbnails\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-media-classes: $include-html-classes;\n\n// We use these to control border styles\n// $thumb-border-style: solid;\n// $thumb-border-width: 4px;\n// $thumb-border-color: $white;\n// $thumb-box-shadow: 0 0 0 1px rgba($black,.2);\n// $thumb-box-shadow-hover: 0 0 6px 1px rgba($primary-color,0.5);\n\n// Radius and transition speed for thumbs\n// $thumb-radius: $global-radius;\n// $thumb-transition-speed: 200ms;\n\n// 33. Tooltips\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-tooltip-classes: $include-html-classes;\n\n// $has-tip-border-bottom: dotted 1px $iron;\n// $has-tip-font-weight: $font-weight-bold;\n// $has-tip-font-color: $oil;\n// $has-tip-border-bottom-hover: dotted 1px scale-color($primary-color, $lightness: -55%);\n// $has-tip-font-color-hover: $primary-color;\n// $has-tip-cursor-type: help;\n\n// $tooltip-padding: rem-calc(12);\n// $tooltip-bg: $oil;\n// $tooltip-font-size: rem-calc(14);\n// $tooltip-font-weight: $font-weight-normal;\n// $tooltip-font-color: $white;\n// $tooltip-line-height: 1.3;\n// $tooltip-close-font-size: rem-calc(10);\n// $tooltip-close-font-weight: $font-weight-normal;\n// $tooltip-close-font-color: $monsoon;\n// $tooltip-font-size-sml: rem-calc(14);\n// $tooltip-radius: $global-radius;\n// $tooltip-rounded: $global-rounded;\n// $tooltip-pip-size: 5px;\n// $tooltip-max-width: 300px;\n\n// 34. Top Bar\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-top-bar-classes: $include-html-classes;\n\n// Height and margin\n$topbar-height: rem-calc(50);\n// $topbar-margin-bottom: 0;\n\n// Controlling the styles for the title in the top bar\n$topbar-title-weight: $font-weight-bold;\n$topbar-title-font-size: rem-calc(19);\n\n// Style the top bar dropdown elements\n// $topbar-dropdown-bg: $oil;\n// $topbar-dropdown-link-color: $white;\n// $topbar-dropdown-link-bg: $ci-2;\n// $topbar-dropdown-link-weight: $font-weight-normal;\n// $topbar-dropdown-toggle-size: 5px;\n// $topbar-dropdown-toggle-color: $ci-2;\n// $topbar-dropdown-toggle-alpha: 0.4;\n\n// Set the link colors and styles for top-level nav\n// $topbar-link-color: #000;\n// $topbar-link-color-hover: #000;\n// $topbar-link-color-active: #000;\n// $topbar-link-color-active-hover: #000;\n// $topbar-link-weight: $font-weight-normal;\n$topbar-link-font-size: rem-calc(15);\n// $topbar-link-hover-lightness: -10%; // Darken by 10%\n// $topbar-link-bg: $topbar-bg;\n// $topbar-link-bg-color-hover: #ff0;\n// $topbar-link-bg-hover: #f00;\n// $topbar-link-bg-active: $primary-color;\n// $topbar-link-bg-active-hover: scale-color($primary-color, $lightness: -14%);\n// $topbar-link-font-family: $body-font-family;\n$topbar-link-text-transform: uppercase;\n// $topbar-link-padding: $topbar-height / 3;\n// $topbar-back-link-size: $h5-font-size;\n// $topbar-link-dropdown-padding: 20px;\n\n// $topbar-button-font-size: 0.75rem;\n// $topbar-button-top: 7px;\n\n// $topbar-dropdown-label-color: #f77;\n// $topbar-dropdown-label-text-transform: uppercase;\n// $topbar-dropdown-label-font-weight: $font-weight-bold;\n// $topbar-dropdown-label-font-size: rem-calc(10);\n// $topbar-dropdown-label-bg: $oil;\n\n// Top menu icon styles\n$topbar-menu-link-transform: uppercase;\n// $topbar-menu-link-font-size: rem-calc(13);\n// $topbar-menu-link-weight: $font-weight-bold;\n// $topbar-menu-link-color: $white;\n// $topbar-menu-icon-color: $white;\n// $topbar-menu-link-color-toggled: $ci-6;\n// $topbar-menu-icon-color-toggled: $ci-6;\n\n// Transitions and breakpoint styles\n// $topbar-transition-speed: 300ms;\n// Using rem-calc for the below breakpoint causes issues with top bar\n$topbar-breakpoint: #{lower-bound($large-range)}; // Change to 9999px for always mobile layout\n$topbar-media-query: \"only screen and (min-width: #{$topbar-breakpoint})\" !default;\n\n// Divider Styles\n$topbar-divider-border-bottom: solid 0px scale-color($topbar-bg-color, $lightness: 23%);\n$topbar-divider-border-top: solid 0px scale-color($topbar-bg-color, $lightness: -50%);\n\n// Sticky Class\n// $topbar-sticky-class: \".sticky\";\n// $topbar-arrows: true; //Set false to remove the triangle icon from the menu item\n\n// 36. Visibility Classes\n// - - - - - - - - - - - - - - - - - - - - - - - - -\n\n// $include-html-visibility-classes: $include-html-classes;\n// $include-table-visibility-classes: true;\n// $include-legacy-visibility-classes: true;\n// $include-accessibility-classes: true;\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"buttons\";\n\n//\n// @variables\n//\n$include-html-form-classes: $include-html-classes !default;\n\n// We use this to set the base for lots of form spacing and positioning styles\n$form-spacing: rem-calc(16) !default;\n\n// We use these to style the labels in different ways\n$form-label-pointer: pointer !default;\n$form-label-font-size: rem-calc(14) !default;\n$form-label-font-weight: $font-weight-normal !default;\n$form-label-line-height: 1.5 !default;\n$form-label-font-color: scale-color($black, $lightness: 30%) !default;\n$form-label-small-transform: capitalize !default;\n$form-label-bottom-margin: 0 !default;\n$input-font-family: inherit !default;\n$input-font-color: rgba(0, 0, 0, 0.75) !default;\n$input-font-size: rem-calc(14) !default;\n$input-bg-color: $white !default;\n$input-focus-bg-color: scale-color($white, $lightness: -2%) !default;\n$input-border-color: scale-color($white, $lightness: -20%) !default;\n$input-focus-border-color: scale-color($white, $lightness: -40%) !default;\n$input-border-style: solid !default;\n$input-border-width: 1px !default;\n$input-border-radius: $global-radius !default;\n$input-disabled-bg: $gainsboro !default;\n$input-disabled-cursor: $cursor-default-value !default;\n$input-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1) !default;\n$input-include-glowing-effect: true !default;\n\n// We use these to style the fieldset border and spacing.\n$fieldset-border-style: solid !default;\n$fieldset-border-width: 1px !default;\n$fieldset-border-color: $gainsboro !default;\n$fieldset-padding: rem-calc(20) !default;\n$fieldset-margin: rem-calc(18 0) !default;\n\n// We use these to style the legends when you use them\n$legend-bg: $white !default;\n$legend-font-weight: $font-weight-bold !default;\n$legend-padding: rem-calc(0 3) !default;\n\n// We use these to style the prefix and postfix input elements\n$input-prefix-bg: scale-color($white, $lightness: -5%) !default;\n$input-prefix-border-color: scale-color($white, $lightness: -20%) !default;\n$input-prefix-border-size: 1px !default;\n$input-prefix-border-type: solid !default;\n$input-prefix-overflow: hidden !default;\n$input-prefix-font-color: $oil !default;\n$input-prefix-font-color-alt: $white !default;\n\n// We use this setting to turn on/off HTML5 number spinners (the up/down arrows)\n$input-number-spinners: true !default;\n\n// We use these to style the error states for inputs and labels\n$input-error-message-padding: rem-calc(6 9 9) !default;\n$input-error-message-top: -1px !default;\n$input-error-message-font-size: rem-calc(12) !default;\n$input-error-message-font-weight: $font-weight-normal !default;\n$input-error-message-font-style: italic !default;\n$input-error-message-font-color: $white !default;\n$input-error-message-bg-color: $alert-color !default;\n$input-error-message-font-color-alt: $oil !default;\n\n// We use this to style the glowing effect of inputs when focused\n$glowing-effect-fade-time: 0.45s !default;\n$glowing-effect-color: $input-focus-border-color !default;\n\n// Select variables\n$select-bg-color: $ghost !default;\n$select-hover-bg-color: scale-color($select-bg-color, $lightness: -3%) !default;\n\n//\n// @MIXINS\n//\n\n// We use this mixin to give us form styles for rows inside of forms\n@mixin form-row-base {\n  .row {\n    margin: 0 calc((-1 * $form-spacing) / 2);\n\n    .column,\n    .columns {\n      padding: 0 calc($form-spacing / 2);\n    }\n\n    // Use this to collapse the margins of a form row\n    &.collapse {\n      margin: 0;\n\n      .column,\n      .columns {\n        padding: 0;\n      }\n\n      input {\n        @include side-radius($opposite-direction, 0);\n      }\n\n    }\n  }\n\n  input.column,\n  input.columns,\n  textarea.column,\n  textarea.columns {\n    padding-#{$default-float}: calc($form-spacing / 2);\n  }\n}\n\n// @MIXIN\n//\n// We use this mixin to give all basic form elements their style\n@mixin form-element {\n  background-color: $input-bg-color;\n  font-family: $input-font-family;\n\n  border: {\n    style: $input-border-style;\n    width: $input-border-width;\n    color: $input-border-color;\n  }\n\n  box-shadow: $input-box-shadow;\n  color: $input-font-color;\n  display: block;\n  font-size: $input-font-size;\n  margin: 0 0 $form-spacing 0;\n  padding: calc($form-spacing / 2);\n  height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n  width: 100%;\n  @include box-sizing(border-box);\n\n  @if $input-include-glowing-effect {\n    @include block-glowing-effect(focus, $glowing-effect-fade-time, $glowing-effect-color);\n  }\n\n  // Basic focus styles\n  &:focus {\n    background: $input-focus-bg-color;\n    border-color: $input-focus-border-color;\n    outline: none;\n  }\n\n  // Disabled Styles\n  &:disabled {\n    background-color: $input-disabled-bg;\n    cursor: $input-disabled-cursor;\n  }\n\n  // Disabled background input background color\n  &[disabled],\n  &[readonly],\n  fieldset[disabled] & {\n    background-color: $input-disabled-bg;\n    cursor: $input-disabled-cursor;\n  }\n}\n\n// @MIXIN\n//\n// We use this mixin to create form labels\n//\n// $alignment - Alignment options. Default: false. Options: [right, inline, false]\n// $base-style - Control whether or not the base styles come through. Default: true.\n@mixin form-label($alignment: false, $base-style: true) {\n\n  // Control whether or not the base styles come through.\n  @if $base-style {\n    font-size: $form-label-font-size;\n    color: $form-label-font-color;\n    cursor: $form-label-pointer;\n    display: block;\n    font-weight: $form-label-font-weight;\n    line-height: $form-label-line-height;\n    margin-bottom: $form-label-bottom-margin;\n  }\n\n  // Alignment options\n  @if $alignment ==right {\n    float: none !important;\n    text-align: right;\n  }\n\n  @else if $alignment ==inline {\n    margin: 0 0 $form-spacing 0;\n    padding: calc($form-spacing / 2) + rem-calc($input-border-width) 0;\n  }\n}\n\n// We use this mixin to create postfix/prefix form Labels\n@mixin prefix-postfix-base {\n  display: block;\n  position: relative;\n  z-index: 2;\n  text-align: center;\n  width: 100%;\n  padding-top: 0;\n  padding-bottom: 0;\n  border-style: $input-prefix-border-type;\n  border-width: $input-prefix-border-size;\n  overflow: $input-prefix-overflow;\n  font-size: $form-label-font-size;\n  height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n  line-height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n}\n\n// @MIXIN\n//\n// We use this mixin to create prefix label styles\n// $bg - Default:$input-prefix-bg || scale-color($white, $lightness: -5%) !default;\n// $is-button - Toggle position settings if prefix is a button. Default:false\n//\n@mixin prefix($bg: $input-prefix-bg, $border: $input-prefix-border-color, $is-button: false) {\n\n  @if $bg {\n    $bg-lightness: lightness($bg);\n    background: $bg;\n    border-#{$opposite-direction}: none;\n\n    // Control the font color based on background brightness\n    @if $bg-lightness >70% or $bg ==yellow {\n      color: $input-prefix-font-color;\n    }\n\n    @else {\n      color: $input-prefix-font-color-alt;\n    }\n  }\n\n  @if $border {\n    border-color: $border;\n  }\n\n  @if $is-button {\n    padding-#{$default-float}: 0;\n    padding-#{$opposite-direction}: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    text-align: center;\n    border: none;\n  }\n\n}\n\n// @MIXIN\n//\n// We use this mixin to create postfix label styles\n// $bg - Default:$input-prefix-bg || scale-color($white, $lightness: -5%) !default;\n// $is-button - Toggle position settings if prefix is a button. Default: false\n@mixin postfix($bg: $input-prefix-bg, $border: $input-prefix-border-color, $is-button: false) {\n\n  @if $bg {\n    $bg-lightness: lightness($bg);\n    background: $bg;\n    border-#{$default-float}: none;\n\n    // Control the font color based on background brightness\n    @if $bg-lightness >70% or $bg ==yellow {\n      color: $input-prefix-font-color;\n    }\n\n    @else {\n      color: $input-prefix-font-color-alt;\n    }\n  }\n\n  @if $border {\n    border-color: $border;\n  }\n\n  @if $is-button {\n    padding-#{$default-float}: 0;\n    padding-#{$opposite-direction}: 0;\n    padding-top: 0;\n    padding-bottom: 0;\n    text-align: center;\n    border: none;\n  }\n\n}\n\n// We use this mixin to style fieldsets\n@mixin fieldset {\n  border: $fieldset-border-width $fieldset-border-style $fieldset-border-color;\n  padding: $fieldset-padding;\n  margin: $fieldset-margin;\n\n  // and legend styles\n  legend {\n    font-weight: $legend-font-weight;\n    background: $legend-bg;\n    padding: $legend-padding;\n    margin: 0;\n    margin-#{$default-float}: rem-calc(-3);\n  }\n}\n\n// @MIXIN\n//\n// We use this mixin to control border and background color of error inputs\n// $color - Default: $alert-color (found in settings file)\n@mixin form-error-color($color: $alert-color) {\n  border-color: $color;\n  background-color: rgba($color, 0.1);\n\n  // Go back to normal on focus\n  &:focus {\n    background: $input-focus-bg-color;\n    border-color: $input-focus-border-color;\n  }\n}\n\n// @MIXIN\n//\n// We use this simple mixin to style labels for error inputs\n// $color - Default:$alert-color. Found in settings file\n@mixin form-label-error-color($color: $alert-color) {\n  color: $color;\n}\n\n// @MIXIN\n//\n// We use this mixin to create error message styles\n// $bg - Default: $alert-color (Found in settings file)\n@mixin form-error-message($bg: $input-error-message-bg-color) {\n  display: block;\n  padding: $input-error-message-padding;\n  margin-top: $input-error-message-top;\n  margin-bottom: $form-spacing;\n  font-size: $input-error-message-font-size;\n  font-weight: $input-error-message-font-weight;\n  font-style: $input-error-message-font-style;\n\n  // We can control the text color based on the brightness of the background.\n  $bg-lightness: lightness($bg);\n  background: $bg;\n\n  @if $bg-lightness < 70% or $bg ==yellow {\n    color: $input-error-message-font-color;\n  }\n\n  @else {\n    color: $input-error-message-font-color-alt;\n  }\n}\n\n// We use this mixin to style select elements\n@mixin form-select {\n  -webkit-appearance: none !important;\n  border-radius: 0;\n  background-color: $select-bg-color;\n\n  // Hide the dropdown arrow shown in newer IE versions\n  &::-ms-expand {\n    display: none;\n  }\n\n  // The custom arrow has some fake horizontal padding so we can align it\n  // from the right side of the element without relying on CSS3\n  background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgeD0iMTJweCIgeT0iMHB4IiB3aWR0aD0iMjRweCIgaGVpZ2h0PSIzcHgiIHZpZXdCb3g9IjAgMCA2IDMiIGVuYWJsZS1iYWNrZ3JvdW5kPSJuZXcgMCAwIDYgMyIgeG1sOnNwYWNlPSJwcmVzZXJ2ZSI+PHBvbHlnb24gcG9pbnRzPSI1Ljk5MiwwIDIuOTkyLDMgLTAuMDA4LDAgIi8+PC9zdmc+);\n\n  // We can safely use leftmost and rightmost now\n  background-position: if($text-direction =='rtl', 0%, 100%) center;\n\n  background-repeat: no-repeat;\n\n  border: {\n    style: $input-border-style;\n    width: $input-border-width;\n    color: $input-border-color;\n  }\n\n  padding: calc($form-spacing / 2);\n  font-size: $input-font-size;\n  font-family: $body-font-family;\n  color: $input-font-color;\n  line-height: normal;\n  @include radius(0);\n\n  &.radius {\n    @include radius($global-radius);\n  }\n\n  &:hover {\n    background-color: $select-hover-bg-color;\n    border-color: $input-focus-border-color;\n  }\n\n  // Disabled Styles\n  &:disabled {\n    background-color: $input-disabled-bg;\n    cursor: $input-disabled-cursor;\n  }\n}\n\n// We use this mixin to turn on/off HTML5 number spinners\n@mixin html5number($browser, $on: true) {\n  @if $on==false {\n    @if $browser==webkit {\n      -webkit-appearance: none;\n      margin: 0;\n    }\n\n    @else if $browser==moz {\n      -moz-appearance: textfield;\n    }\n  }\n}\n\n@include exports(\"form\") {\n  @if $include-html-form-classes {\n\n    /* Standard Forms */\n    form {\n      margin: 0 0 $form-spacing;\n    }\n\n    /* Using forms within rows, we need to set some defaults */\n    form .row {\n      @include form-row-base;\n    }\n\n    /* Label Styles */\n    label {\n      @include form-label;\n\n      &.right {\n        @include form-label(right, false);\n      }\n\n      &.inline {\n        @include form-label(inline, false);\n      }\n\n      /* Styles for required inputs */\n      small {\n        text-transform: $form-label-small-transform;\n        color: scale-color($form-label-font-color, $lightness: 15%);\n      }\n    }\n\n    /* Attach elements to the beginning or end of an input */\n    .prefix,\n    .postfix {\n      @include prefix-postfix-base;\n    }\n\n    /* Adjust padding, alignment and radius if pre/post element is a button */\n    .postfix.button {\n      @include button-size(false, false);\n      @include postfix(false, false, true);\n    }\n\n    .prefix.button {\n      @include button-size(false, false);\n      @include prefix(false, false, true);\n    }\n\n    .prefix.button.radius {\n      @include radius(0);\n      @include side-radius($default-float, $button-radius);\n    }\n\n    .postfix.button.radius {\n      @include radius(0);\n      @include side-radius($opposite-direction, $button-radius);\n    }\n\n    .prefix.button.round {\n      @include radius(0);\n      @include side-radius($default-float, $button-round);\n    }\n\n    .postfix.button.round {\n      @include radius(0);\n      @include side-radius($opposite-direction, $button-round);\n    }\n\n    /* Separate prefix and postfix styles when on span or label so buttons keep their own */\n    span.prefix,\n    label.prefix {\n      @include prefix();\n    }\n\n    span.postfix,\n    label.postfix {\n      @include postfix();\n    }\n\n    /* We use this to get basic styling on all basic form elements */\n    #{text-inputs(all, 'input')} {\n      -webkit-appearance: none;\n      border-radius: 0;\n      @include form-element;\n\n      @if $input-include-glowing-effect ==false {\n        @include single-transition(all, 0.15s, linear);\n      }\n\n      &.radius {\n        @include radius($input-border-radius);\n      }\n    }\n\n    form {\n      .row {\n        .prefix-radius.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-radius);\n          }\n\n          .prefix {\n            @include radius(0);\n            @include side-radius($default-float, $button-radius);\n          }\n        }\n\n        .postfix-radius.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($default-float, $button-radius);\n          }\n\n          .postfix {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-radius);\n          }\n        }\n\n        .prefix-round.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-round);\n          }\n\n          .prefix {\n            @include radius(0);\n            @include side-radius($default-float, $button-round);\n          }\n        }\n\n        .postfix-round.row.collapse {\n\n          input,\n          textarea,\n          select {\n            @include radius(0);\n            @include side-radius($default-float, $button-round);\n          }\n\n          .postfix {\n            @include radius(0);\n            @include side-radius($opposite-direction, $button-round);\n          }\n        }\n      }\n    }\n\n    input[type=\"submit\"] {\n      -webkit-appearance: none;\n      border-radius: 0;\n    }\n\n    /* Respect enforced amount of rows for textarea */\n    textarea[rows] {\n      height: auto;\n    }\n\n    /* Not allow resize out of parent */\n    textarea {\n      max-width: 100%;\n    }\n\n    /* Add height value for select elements to match text input height */\n    select {\n      @include form-select;\n      height: ($input-font-size + ($form-spacing * 1.5) - rem-calc(1));\n    }\n\n    /* Adjust margin for form elements below */\n    input[type=\"file\"],\n    input[type=\"checkbox\"],\n    input[type=\"radio\"],\n    select {\n      margin: 0 0 $form-spacing 0;\n    }\n\n    input[type=\"checkbox\"]+label,\n    input[type=\"radio\"]+label {\n      display: inline-block;\n      margin-#{$default-float}: $form-spacing * .5;\n      margin-#{$opposite-direction}: $form-spacing;\n      margin-bottom: 0;\n      vertical-align: baseline;\n    }\n\n    /* Normalize file input width */\n    input[type=\"file\"] {\n      width: 100%;\n    }\n\n    /* HTML5 Number spinners settings */\n    input[type=number] {\n      @include html5number(moz, $input-number-spinners)\n    }\n\n    input[type=\"number\"]::-webkit-inner-spin-button,\n    input[type=\"number\"]::-webkit-outer-spin-button {\n      @include html5number(webkit, $input-number-spinners);\n    }\n\n    /* We add basic fieldset styling */\n    fieldset {\n      @include fieldset;\n    }\n\n    /* Error Handling */\n\n    #{data('abide')} {\n\n      .error small.error,\n      .error span.error,\n      span.error,\n      small.error {\n        @include form-error-message;\n      }\n\n      span.error,\n      small.error {\n        display: none;\n      }\n    }\n\n    span.error,\n    small.error {\n      @include form-error-message;\n    }\n\n    .error {\n\n      input,\n      textarea,\n      select {\n        margin-bottom: 0;\n      }\n\n      input[type=\"checkbox\"],\n      input[type=\"radio\"] {\n        margin-bottom: $form-spacing\n      }\n\n      label,\n      label.error {\n        @include form-label-error-color;\n      }\n\n      small.error {\n        @include form-error-message;\n      }\n\n      >label {\n        >small {\n          color: scale-color($form-label-font-color, $lightness: 15%);\n          background: transparent;\n          padding: 0;\n          text-transform: $form-label-small-transform;\n          font-style: normal;\n          font-size: 60%;\n          margin: 0;\n          display: inline;\n        }\n      }\n\n      span.error-message {\n        display: block;\n      }\n    }\n\n    input.error,\n    textarea.error,\n    select.error {\n      margin-bottom: 0;\n    }\n\n    label.error {\n      @include form-label-error-color;\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"grid\";\n@import \"buttons\";\n@import \"forms\";\n\n//\n// Top Bar Variables\n//\n$include-html-top-bar-classes: $include-html-classes !default;\n\n// Background color for the top bar\n$topbar-bg-color: $oil !default;\n$topbar-bg: $topbar-bg-color !default;\n\n// Height and margin\n$topbar-height: rem-calc(45) !default;\n$topbar-margin-bottom: 0 !default;\n\n// Controlling the styles for the title in the top bar\n$topbar-title-weight: $font-weight-normal !default;\n$topbar-title-font-size: rem-calc(17) !default;\n\n// Set the link colors and styles for top-level nav\n$topbar-link-color: $white !default;\n$topbar-link-color-hover: $white !default;\n$topbar-link-color-active: $white !default;\n$topbar-link-color-active-hover: $white !default;\n$topbar-link-weight: $font-weight-normal !default;\n$topbar-link-font-size: rem-calc(13) !default;\n$topbar-link-hover-lightness: -10% !default; // Darken by 10%\n$topbar-link-bg: $topbar-bg !default;\n$topbar-link-bg-hover: $oil !default;\n$topbar-link-bg-color-hover: $charcoal !default;\n$topbar-link-bg-active: $primary-color !default;\n$topbar-link-bg-active-hover: scale-color($primary-color, $lightness: -14%) !default;\n$topbar-link-font-family: $body-font-family !default;\n$topbar-link-text-transform: none !default;\n$topbar-link-padding: calc($topbar-height / 3) !default;\n$topbar-back-link-size: rem-calc(18) !default;\n$topbar-link-dropdown-padding: rem-calc(20) !default;\n$topbar-button-font-size: 0.75rem !default;\n$topbar-button-top: 7px !default;\n\n// Style the top bar dropdown elements\n$topbar-dropdown-bg: $oil !default;\n$topbar-dropdown-link-color: $white !default;\n$topbar-dropdown-link-color-hover: $topbar-link-color-hover !default;\n$topbar-dropdown-link-bg: $oil !default;\n$topbar-dropdown-link-bg-hover: $oil !default;\n$topbar-dropdown-link-weight: $font-weight-normal !default;\n$topbar-dropdown-toggle-size: 5px !default;\n$topbar-dropdown-toggle-color: $white !default;\n$topbar-dropdown-toggle-alpha: 0.4 !default;\n\n$topbar-dropdown-label-color: $monsoon !default;\n$topbar-dropdown-label-text-transform: uppercase !default;\n$topbar-dropdown-label-font-weight: $font-weight-bold !default;\n$topbar-dropdown-label-font-size: rem-calc(10) !default;\n$topbar-dropdown-label-bg: $oil !default;\n\n// Top menu icon styles\n$topbar-menu-link-transform: uppercase !default;\n$topbar-menu-link-font-size: rem-calc(13) !default;\n$topbar-menu-link-weight: $font-weight-bold !default;\n$topbar-menu-link-color: $white !default;\n$topbar-menu-icon-color: $white !default;\n$topbar-menu-link-color-toggled: $jumbo !default;\n$topbar-menu-icon-color-toggled: $jumbo !default;\n\n// Transitions and breakpoint styles\n$topbar-transition-speed: 300ms !default;\n// Using rem-calc for the below breakpoint causes issues with top bar\n$topbar-breakpoint: #{lower-bound($medium-range)} !default; // Change to 9999px for always mobile layout\n$topbar-media-query: $medium-up !default;\n\n// Top-bar input styles\n$topbar-input-height: rem-calc(28) !default;\n\n// Divider Styles\n$topbar-divider-border-bottom: solid 1px scale-color($topbar-bg-color, $lightness: 13%) !default;\n$topbar-divider-border-top: solid 1px scale-color($topbar-bg-color, $lightness: -50%) !default;\n\n// Sticky Class\n$topbar-sticky-class: \".sticky\" !default;\n$topbar-arrows: true !default; //Set false to remove the triangle icon from the menu item\n$topbar-dropdown-arrows: true !default; //Set false to remove the \\00bb >> text from dropdown subnavigation li\n\n// Accessibility mixins for hiding and showing the menu dropdown items\n@mixin topbar-hide-dropdown {\n  // Makes an element visually hidden by default, but visible when focused.\n  display: block;\n  @include element-invisible();\n}\n\n@mixin topbar-show-dropdown {\n  display: block;\n  @include element-invisible-off();\n  position: absolute !important; // Reset the position from static to absolute\n}\n\n@include exports(\"top-bar\") {\n\n  @if $include-html-top-bar-classes {\n\n    // Used to provide media query values for javascript components.\n    // This class is generated despite the value of $include-html-top-bar-classes\n    // to ensure width calculations work correctly.\n    meta.foundation-mq-topbar {\n      font-family: \"/\" + unquote($topbar-media-query) + \"/\";\n      width: $topbar-breakpoint;\n    }\n\n    /* Wrapped around .top-bar to contain to grid width */\n    .contain-to-grid {\n      width: 100%;\n      background: $topbar-bg;\n\n      .top-bar {\n        margin-bottom: $topbar-margin-bottom;\n      }\n    }\n\n    // Wrapped around .top-bar to make it stick to the top\n    .fixed {\n      width: 100%;\n      #{$default-float}: 0;\n      position: fixed;\n      top: 0;\n      z-index: 99;\n\n      &.expanded:not(.top-bar) {\n        overflow-y: auto;\n        height: auto;\n        width: 100%;\n        max-height: 100%;\n\n        .title-area {\n          position: fixed;\n          width: 100%;\n          z-index: 99;\n        }\n\n        // Ensure you can scroll the menu on small screens\n        .top-bar-section {\n          z-index: 98;\n          margin-top: $topbar-height;\n        }\n      }\n    }\n\n    .top-bar {\n      overflow: hidden;\n      height: $topbar-height;\n      line-height: $topbar-height;\n      position: relative;\n      background: $topbar-bg;\n      margin-bottom: $topbar-margin-bottom;\n\n      // Topbar Global list Styles\n      ul {\n        margin-bottom: 0;\n        list-style: none;\n      }\n\n      .row {\n        max-width: none;\n      }\n\n      form,\n      input {\n        margin-bottom: 0;\n      }\n\n      input {\n        height: $topbar-input-height;\n        padding-top: .35rem;\n        padding-bottom: .35rem;\n        font-size: $topbar-button-font-size;\n      }\n\n      .button,\n      button {\n        padding-top: .35rem + rem-calc(1);\n        padding-bottom: .35rem + rem-calc(1);\n        margin-bottom: 0;\n        font-size: $topbar-button-font-size;\n        // position: relative;\n        // top: -1px;\n\n        // Corrects a slight misalignment when put next to an input field\n        @media #{$small-only} {\n          position: relative;\n          top: -1px;\n        }\n      }\n\n      // Title Area\n      .title-area {\n        position: relative;\n        margin: 0;\n      }\n\n      .name {\n        height: $topbar-height;\n        margin: 0;\n        font-size: $rem-base;\n\n        h1,\n        h2,\n        h3,\n        h4,\n        p,\n        span {\n          line-height: $topbar-height;\n          font-size: $topbar-title-font-size;\n          margin: 0;\n\n          a {\n            font-weight: $topbar-title-weight;\n            color: $topbar-link-color;\n            width: 75%;\n            display: block;\n            padding: 0 $topbar-link-padding;\n          }\n        }\n      }\n\n      // Menu toggle button on small devices\n      .toggle-topbar {\n        position: absolute;\n        #{$opposite-direction}: 0;\n        top: 0;\n\n        a {\n          color: $topbar-link-color;\n          text-transform: $topbar-menu-link-transform;\n          font-size: $topbar-menu-link-font-size;\n          font-weight: $topbar-menu-link-weight;\n          position: relative;\n          display: block;\n          padding: 0 $topbar-link-padding;\n          height: $topbar-height;\n          line-height: $topbar-height;\n        }\n\n        // Adding the class \"menu-icon\" will add the 3-line icon people love and adore.\n        &.menu-icon {\n          top: 50%;\n          margin-top: -16px;\n\n          a {\n            @if $text-direction ==rtl {\n              text-indent: -58px;\n            }\n\n            height: 34px;\n            line-height: 33px;\n            padding: 0 $topbar-link-padding+rem-calc(25) 0 $topbar-link-padding;\n            color: $topbar-menu-link-color;\n            position: relative;\n\n            & {\n              // @include hamburger icon\n              //\n              // We use this to create the icon with three lines aka the hamburger icon, the menu-icon or the navicon\n              // $width - Width of hamburger icon\n              // $left - If false, icon will be centered horizontally || explicitly set value in rem\n              // $top - If false, icon will be centered vertically || explicitly set value in rem\n              // $thickness - thickness of lines in hamburger icon, set value in px\n              // $gap - spacing between the lines in hamburger icon, set value in px\n              // $color - icon color\n              // $hover-color - icon color during hover, here it isn't set b/c it would override $topbar-menu-icon-color-toggled\n              // $offcanvas - Set to false of @include in topbar\n              @include hamburger(16px, false, 0, 1px, 6px, $topbar-menu-icon-color, \"\", false);\n            }\n          }\n        }\n      }\n\n      // Change things up when the top-bar is expanded\n      &.expanded {\n        height: auto;\n        background: transparent;\n\n        .title-area {\n          background: $topbar-bg;\n        }\n\n        .toggle-topbar {\n          a {\n            color: $topbar-menu-link-color-toggled;\n\n            span::after {\n              // Shh, don't tell, but box-shadows create the menu icon :)\n              // Change the color of the bars when the menu is expanded, using given thickness from hamburger() above\n              box-shadow: 0 0 0 1px $topbar-menu-icon-color-toggled,\n                0 7px 0 1px $topbar-menu-icon-color-toggled,\n                0 14px 0 1px $topbar-menu-icon-color-toggled;\n            }\n          }\n        }\n      }\n    }\n\n    // Right and Left Navigation that stacked by default\n    .top-bar-section {\n      #{$default-float}: 0;\n      position: relative;\n      width: auto;\n      @include single-transition($default-float, $topbar-transition-speed);\n\n      ul {\n        padding: 0;\n        width: 100%;\n        height: auto;\n        display: block;\n        font-size: $rem-base;\n        margin: 0;\n      }\n\n      .divider,\n      [role=\"separator\"] {\n        border-top: $topbar-divider-border-top;\n        clear: both;\n        height: 1px;\n        width: 100%;\n      }\n\n      ul li {\n        background: $topbar-dropdown-bg;\n\n        &>a {\n          display: block;\n          width: 100%;\n          color: $topbar-link-color;\n          padding: 12px 0 12px 0;\n          padding-#{$default-float}: $topbar-link-padding;\n          font-family: $topbar-link-font-family;\n          font-size: $topbar-link-font-size;\n          font-weight: $topbar-link-weight;\n          text-transform: $topbar-link-text-transform;\n\n          &.button {\n            font-size: $topbar-link-font-size;\n            padding-#{$opposite-direction}: $topbar-link-padding;\n            padding-#{$default-float}: $topbar-link-padding;\n            @include button-style($bg: $primary-color);\n          }\n\n          &.button.secondary {\n            @include button-style($bg: $secondary-color);\n          }\n\n          &.button.success {\n            @include button-style($bg: $success-color);\n          }\n\n          &.button.alert {\n            @include button-style($bg: $alert-color);\n          }\n\n          &.button.warning {\n            @include button-style($bg: $warning-color);\n          }\n        }\n\n        >button {\n          font-size: $topbar-link-font-size;\n          padding-#{$opposite-direction}: $topbar-link-padding;\n          padding-#{$default-float}: $topbar-link-padding;\n          @include button-style($bg: $primary-color);\n\n          &.secondary {\n            @include button-style($bg: $secondary-color);\n          }\n\n          &.success {\n            @include button-style($bg: $success-color);\n          }\n\n          &.alert {\n            @include button-style($bg: $alert-color);\n          }\n\n          &.warning {\n            @include button-style($bg: $warning-color);\n          }\n        }\n\n        // Apply the hover link color when it has that class\n        &:hover:not(.has-form)>a {\n          background-color: $topbar-link-bg-color-hover;\n\n          @if ($topbar-link-bg-hover) {\n            background: $topbar-link-bg-hover;\n          }\n\n          color: $topbar-link-color-hover;\n        }\n\n        // Apply the active link color when it has that class\n        &.active>a {\n          background: $topbar-link-bg-active;\n          color: $topbar-link-color-active;\n\n          &:hover {\n            background: $topbar-link-bg-active-hover;\n            color: $topbar-link-color-active-hover;\n          }\n        }\n      }\n\n      // Add some extra padding for list items contains buttons\n      .has-form {\n        padding: $topbar-link-padding;\n      }\n\n      // Styling for list items that have a dropdown within them.\n      .has-dropdown {\n        position: relative;\n\n        &>a {\n          &:after {\n            @if ($topbar-arrows) {\n              @include css-triangle($topbar-dropdown-toggle-size, rgba($topbar-dropdown-toggle-color, $topbar-dropdown-toggle-alpha), $default-float);\n            }\n\n            margin-#{$opposite-direction}: $topbar-link-padding;\n            margin-top: -(calc($topbar-dropdown-toggle-size / 2)) - 2;\n            position: absolute;\n            top: 50%;\n            #{$opposite-direction}: 0;\n          }\n        }\n\n        &.moved {\n          position: static;\n\n          &>.dropdown {\n            @include topbar-show-dropdown();\n            width: 100%;\n          }\n\n          &>a:after {\n            display: none;\n          }\n        }\n      }\n\n      // Styling elements inside of dropdowns\n      .dropdown {\n        padding: 0;\n        position: absolute;\n        #{$default-float}: 100%;\n        top: 0;\n        z-index: 99;\n        @include topbar-hide-dropdown();\n\n        li {\n          width: 100%;\n          height: auto;\n\n          a {\n            font-weight: $topbar-dropdown-link-weight;\n            padding: 8px $topbar-link-padding;\n\n            &.parent-link {\n              font-weight: $topbar-link-weight;\n            }\n          }\n\n          &.title h5,\n          &.parent-link {\n            // Back Button\n            margin-bottom: 0;\n            margin-top: 0;\n            font-size: $topbar-back-link-size;\n\n            a {\n              color: $topbar-link-color;\n              // line-height: ($topbar-height / 2);\n              display: block;\n\n              &:hover {\n                background: none;\n              }\n            }\n          }\n\n          &.has-form {\n            padding: 8px $topbar-link-padding;\n          }\n\n          .button,\n          button {\n            top: auto;\n          }\n        }\n\n        label {\n          padding: 8px $topbar-link-padding 2px;\n          margin-bottom: 0;\n          text-transform: $topbar-dropdown-label-text-transform;\n          color: $topbar-dropdown-label-color;\n          font-weight: $topbar-dropdown-label-font-weight;\n          font-size: $topbar-dropdown-label-font-size;\n        }\n      }\n    }\n\n    .js-generated {\n      display: block;\n    }\n\n\n    // Top Bar styles intended for screen sizes above the breakpoint.\n    @media #{$topbar-media-query} {\n      .top-bar {\n        background: $topbar-bg;\n        @include clearfix;\n        overflow: visible;\n\n        .toggle-topbar {\n          display: none;\n        }\n\n        .title-area {\n          float: $default-float;\n        }\n\n        .name h1 a {\n          width: auto;\n        }\n\n        input,\n        .button,\n        button {\n          font-size: rem-calc(14);\n          position: relative;\n          height: $topbar-input-height;\n          top: calc(($topbar-height - $topbar-input-height) / 2);\n        }\n\n        &.expanded {\n          background: $topbar-bg;\n        }\n      }\n\n      .contain-to-grid .top-bar {\n        max-width: $row-width;\n        margin: 0 auto;\n        margin-bottom: $topbar-margin-bottom;\n      }\n\n      .top-bar-section {\n        @include single-transition(none, 0, 0);\n        #{$default-float}: 0 !important;\n\n        ul {\n          width: auto;\n          height: auto !important;\n          display: inline;\n\n          li {\n            float: $default-float;\n\n            .js-generated {\n              display: none;\n            }\n          }\n        }\n\n        li {\n          &.hover {\n            >a:not(.button) {\n              background-color: $topbar-link-bg-color-hover;\n\n              @if ($topbar-link-bg-hover) {\n                background: $topbar-link-bg-hover;\n              }\n\n              color: $topbar-link-color-hover;\n            }\n          }\n\n          &:not(.has-form) {\n            a:not(.button) {\n              padding: 0 $topbar-link-padding;\n              line-height: $topbar-height;\n              background: $topbar-link-bg;\n\n              &:hover {\n                background-color: $topbar-link-bg-color-hover;\n\n                @if ($topbar-link-bg-hover) {\n                  background: $topbar-link-bg-hover;\n                }\n              }\n            }\n          }\n\n          &.active:not(.has-form) {\n            a:not(.button) {\n              padding: 0 $topbar-link-padding;\n              line-height: $topbar-height;\n              color: $topbar-link-color-active;\n              background: $topbar-link-bg-active;\n\n              &:hover {\n                background: $topbar-link-bg-active-hover;\n                color: $topbar-link-color-active-hover;\n              }\n            }\n          }\n        }\n\n        .has-dropdown {\n          @if($topbar-arrows) {\n            &>a {\n              padding-#{$opposite-direction}: $topbar-link-padding + $topbar-link-dropdown-padding !important;\n\n              &:after {\n                @include css-triangle($topbar-dropdown-toggle-size, rgba($topbar-dropdown-toggle-color, $topbar-dropdown-toggle-alpha), top);\n                margin-top: -(calc($topbar-dropdown-toggle-size / 2));\n                top: calc($topbar-height / 2);\n              }\n            }\n          }\n\n          &.moved {\n            position: relative;\n\n            &>.dropdown {\n              @include topbar-hide-dropdown();\n            }\n          }\n\n          &.hover,\n          &.not-click:hover {\n            &>.dropdown {\n              @include topbar-show-dropdown();\n            }\n          }\n\n          >a:focus+.dropdown {\n            @include topbar-show-dropdown();\n          }\n\n          .dropdown li.has-dropdown {\n            &>a {\n              @if ($topbar-dropdown-arrows) {\n                &:after {\n                  border: none;\n                  content: \"\\00bb\";\n                  top: 1rem;\n                  margin-top: -1px;\n                  #{$opposite-direction}: 5px;\n                  line-height: 1.2;\n                }\n              }\n            }\n          }\n        }\n\n        .dropdown {\n          #{$default-float}: 0;\n          top: auto;\n          background: transparent;\n          min-width: 100%;\n\n          li {\n            a {\n              color: $topbar-dropdown-link-color;\n              line-height: $topbar-height;\n              white-space: nowrap;\n              padding: 12px $topbar-link-padding;\n              background: $topbar-dropdown-link-bg;\n            }\n\n            &:not(.has-form):not(.active) {\n              &>a:not(.button) {\n                color: $topbar-dropdown-link-color;\n                background: $topbar-dropdown-link-bg;\n              }\n\n              &:hover>a:not(.button) {\n                color: $topbar-dropdown-link-color-hover;\n                background-color: $topbar-link-bg-color-hover;\n\n                @if ($topbar-dropdown-link-bg-hover) {\n                  background: $topbar-dropdown-link-bg-hover;\n                }\n              }\n            }\n\n            label {\n              white-space: nowrap;\n              background: $topbar-dropdown-label-bg;\n            }\n\n            // Second Level Dropdowns\n            .dropdown {\n              #{$default-float}: 100%;\n              top: 0;\n            }\n          }\n        }\n\n        &>ul>.divider,\n        &>ul>[role=\"separator\"] {\n          border-bottom: none;\n          border-top: none;\n          border-#{$opposite-direction}: $topbar-divider-border-bottom;\n          clear: none;\n          height: $topbar-height;\n          width: 0;\n        }\n\n        .has-form {\n          background: $topbar-link-bg;\n          padding: 0 calc($topbar-height / 3);\n          height: $topbar-height;\n        }\n\n        // Position overrides for ul.right and ul.left\n        .#{$opposite-direction} {\n          li .dropdown {\n            #{$default-float}: auto;\n            #{$opposite-direction}: 0;\n\n            li .dropdown {\n              #{$opposite-direction}: 100%;\n            }\n          }\n        }\n\n        .#{$default-float} {\n          li .dropdown {\n            #{$opposite-direction}: auto;\n            #{$default-float}: 0;\n\n            li .dropdown {\n              #{$default-float}: 100%;\n            }\n          }\n        }\n      }\n\n      // Degrade gracefully when Javascript is disabled. Displays dropdown and changes\n      // background & text color on hover.\n      .no-js .top-bar-section {\n        ul li {\n\n          // Apply the hover link color when it has that class\n          &:hover>a {\n            background-color: $topbar-link-bg-color-hover;\n\n            @if ($topbar-link-bg-hover) {\n              background: $topbar-link-bg-hover;\n            }\n\n            color: $topbar-link-color-hover;\n          }\n\n          // Apply the active link color when it has that class\n          &:active>a {\n            background: $topbar-link-bg-active;\n            color: $topbar-link-color-active;\n          }\n        }\n\n        .has-dropdown {\n          &:hover {\n            &>.dropdown {\n              @include topbar-show-dropdown();\n            }\n          }\n\n          >a:focus+.dropdown {\n            @include topbar-show-dropdown();\n          }\n        }\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n\n$include-html-accordion-classes: $include-html-classes !default;\n\n$accordion-navigation-padding: rem-calc(16) !default;\n$accordion-navigation-bg-color: $silver !default;\n$accordion-navigation-hover-bg-color: scale-color($accordion-navigation-bg-color, $lightness: -5%) !default;\n$accordion-navigation-active-bg-color: scale-color($accordion-navigation-bg-color, $lightness: -3%) !default;\n$accordion-navigation-font-color: $jet !default;\n$accordion-navigation-font-size: rem-calc(16) !default;\n$accordion-navigation-font-family: $body-font-family !default;\n\n$accordion-content-padding: calc($column-gutter / 2) !default;\n$accordion-content-active-bg-color: $white !default;\n\n\n//  Mixin: accordion-container()\n//  Description: Responsible for the container component of accordions, generating styles relating to a margin of zero and a clearfix\n//  Explicit Dependencies: a clearfix mixin *is* defined.\n//  Implicit Dependencies: None\n\n@mixin accordion-container() {\n  @include clearfix;\n  margin-bottom: 0;\n}\n\n//  Mixin: accordion-navigation( $bg, $hover-bg, $active-bg, $padding, $active_class,  $font-color, $font-size, $font-family){\n//    @params $bg-color: [ color or string ]: Specify the background color for the navigation element\n//    @params $hover-bg-color [ color or string ]: Specify the background color for the navigation element when hovered\n//    @params $active-bg [ color or string ]: Specify the background color for the navigation element when clicked and not released.\n//    @params $active_class [ string ]: Specify the class name used to keep track of which accordion tab should be visible\n//    @params $font-color [ color or string ]: Color of the font for accordion\n//    @params $font-size [ number ]: Specify the font-size of the text inside the navigation element\n//    @params $font-family [ string ]: Specify the font family for the text of the navigation of the accordion\n\n@mixin accordion-navigation($bg: $accordion-navigation-bg-color, $hover-bg: $accordion-navigation-hover-bg-color, $active-bg: $accordion-navigation-active-bg-color, $padding: $accordion-navigation-padding, $active_class: 'active', $font-color: $accordion-navigation-font-color, $font-size: $accordion-navigation-font-size, $font-family: $accordion-navigation-font-family ) {\n  display: block;\n  margin-bottom: 0 !important;\n\n  @if type-of($active_class) !=\"string\" {\n    @warn \"`#{$active_class}` isn't a valid string. A valid string is needed to correctly be interpolated as a CSS class. CSS classes cannot start with a number or consist of only numbers. CSS will not be generated for the active state of this navigation component.\"\n  }\n\n  @else {\n    &.#{ $active_class }>a {\n      background: $active-bg;\n    }\n  }\n\n  >a {\n    background: $bg;\n    color: $font-color;\n\n    @if type-of($padding) !=number {\n      @warn \"`#{$padding}` was read as #{type-of($padding)}\";\n\n      @if $accordion-navigation-padding !=null {\n        @warn \"#{$padding} was read as a #{type-of($padding)}\";\n        @warn \"`#{$padding}` isn't a valid number. $accordion-navigation-padding (#{$accordion-navigation-padding}) will be used instead.)\";\n        padding: $accordion-navigation-padding;\n      }\n\n      @else {\n        @warn \"`#{$padding}` isn't a valid number and $accordion-navigation-padding is missing. A value of `null` is returned to not output an invalid value for padding\";\n        padding: null;\n      }\n    }\n\n    @else {\n      padding: $padding;\n    }\n\n    display: block;\n    font-family: $font-family;\n\n    @if type-of($font-size) !=number {\n      @warn \"`#{$font-size}` was read as a #{type-of($font-size)}\";\n\n      @if $accordion-navigation-font-size !=null {\n        @warn \"`#{$font-size}` is not a valid number. The value of $accordion-navigation-font-size will be used instead (#{$accordion-navigation-font-size}).\";\n        font-size: $accordion-navigation-font-size;\n      }\n\n      @else {\n        @warn \"`#{$font-size}` is not a valid number and the default value of $accordion-navigation-font-size is not defined. A value of `null` will be returned to not generate an invalid value for font-size.\";\n        font-size: null;\n\n      }\n    }\n\n    @else {\n      font-size: $font-size;\n    }\n\n    &:hover {\n      background: $hover-bg;\n    }\n  }\n}\n\n//  Mixin: accordion-content($bg, $padding, $active-class)\n//    @params $padding [ number ]: Padding for the content of the container\n//    @params $bg [ color  ]: Background color for the content when it's visible\n//    @params $active_class [ string ]: Class name used to keep track of which accordion tab should be visible.\n\n@mixin accordion-content($bg: $accordion-content-active-bg-color, $padding: $accordion-content-padding, $active_class: 'active') {\n  display: none;\n\n  @if type-of($padding) !=\"number\" {\n    @warn \"#{$padding} was read as a #{type-of($padding)}\";\n\n    @if $accordion-content-padding !=null {\n      @warn \"`#{$padding}` isn't a valid number. $accordion-content-padding used instead\";\n      padding: $accordion-content-padding;\n    }\n\n    @else {\n      @warn \"`#{$padding}` isn't a valid number and the default value of $accordion-content-padding is not defined. A value of `null` is returned to not output an invalid value for padding.\";\n      padding: null;\n    }\n  }\n\n  @else {\n    padding: $padding;\n  }\n\n  @if type-of($active_class) !=\"string\" {\n    @warn \"`#{$active_class}` isn't a valid string. A valid string is needed to correctly be interpolated as a CSS class. CSS classes cannot start with a number or consist of only numbers. CSS will not be generated for the active state of the content. \"\n  }\n\n  @else {\n    &.#{$active_class} {\n      display: block;\n      background: $bg;\n    }\n  }\n}\n\n@include exports(\"accordion\") {\n  @if $include-html-accordion-classes {\n    .accordion {\n      @include clearfix;\n      margin-bottom: 0;\n\n      .accordion-navigation,\n      dd {\n        display: block;\n        margin-bottom: 0 !important;\n\n        &.active>a {\n          background: $accordion-navigation-active-bg-color;\n        }\n\n        >a {\n          background: $accordion-navigation-bg-color;\n          color: $accordion-navigation-font-color;\n          padding: $accordion-navigation-padding;\n          display: block;\n          font-family: $accordion-navigation-font-family;\n          font-size: $accordion-navigation-font-size;\n\n          &:hover {\n            background: $accordion-navigation-hover-bg-color;\n          }\n        }\n\n        >.content {\n          display: none;\n          padding: $accordion-content-padding;\n\n          &.active {\n            display: block;\n            background: $accordion-content-active-bg-color;\n          }\n        }\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Alert Box Variables\n//\n$include-html-alert-classes: $include-html-classes !default;\n\n// We use this to control alert padding.\n$alert-padding-top: rem-calc(14) !default;\n$alert-padding-default-float: $alert-padding-top !default;\n$alert-padding-opposite-direction: $alert-padding-top + rem-calc(10) !default;\n$alert-padding-bottom: $alert-padding-top !default;\n\n// We use these to control text style.\n$alert-font-weight: $font-weight-normal !default;\n$alert-font-size: rem-calc(13) !default;\n$alert-font-color: $white !default;\n$alert-font-color-alt: scale-color($secondary-color, $lightness: -66%) !default;\n\n// We use this for close hover effect.\n$alert-function-factor: -14% !default;\n\n// We use these to control border styles.\n$alert-border-style: solid !default;\n$alert-border-width: 1px !default;\n$alert-border-color: scale-color($primary-color, $lightness: $alert-function-factor) !default;\n$alert-bottom-margin: rem-calc(20) !default;\n\n// We use these to style the close buttons\n$alert-close-color: $oil !default;\n$alert-close-top: 50% !default;\n$alert-close-position: rem-calc(4) !default;\n$alert-close-font-size: rem-calc(22) !default;\n$alert-close-opacity: 0.3 !default;\n$alert-close-opacity-hover: 0.5 !default;\n$alert-close-padding: 9px 6px 4px !default;\n$alert-close-background: inherit !default;\n\n// We use this to control border radius\n$alert-radius: $global-radius !default;\n\n$alert-transition-speed: 300ms !default;\n$alert-transition-ease: ease-out !default;\n\n//\n// Alert Mixins\n//\n\n// We use this mixin to create a default alert base.\n@mixin alert-base {\n  border-style: $alert-border-style;\n  border-width: $alert-border-width;\n  display: block;\n  font-weight: $alert-font-weight;\n  margin-bottom: $alert-bottom-margin;\n  position: relative;\n  padding: $alert-padding-top $alert-padding-opposite-direction $alert-padding-bottom $alert-padding-default-float;\n  font-size: $alert-font-size;\n  @include single-transition(opacity, $alert-transition-speed, $alert-transition-ease)\n}\n\n// We use this mixin to add alert styles\n//\n// $bg - The background of the alert. Default: $primary-color.\n@mixin alert-style($bg: $primary-color) {\n\n  // This finds the lightness percentage of the background color.\n  $bg-lightness: lightness($bg);\n\n  // We control which background color and border come through.\n  background-color: $bg;\n  border-color: scale-color($bg, $lightness: $alert-function-factor);\n\n  // We control the text color for you based on the background color.\n  @if $bg-lightness >70% {\n    color: $alert-font-color-alt;\n  }\n\n  @else {\n    color: $alert-font-color;\n  }\n\n}\n\n// We use this to create the close button.\n@mixin alert-close {\n  font-size: $alert-close-font-size;\n  padding: $alert-close-padding;\n  line-height: 0;\n  position: absolute;\n  top: $alert-close-top;\n  margin-top: -(calc($alert-close-font-size / 2));\n  #{$opposite-direction}: $alert-close-position;\n  color: $alert-close-color;\n  opacity: $alert-close-opacity;\n  background: $alert-close-background;\n\n  &:hover,\n  &:focus {\n    opacity: $alert-close-opacity-hover;\n  }\n}\n\n// We use this to quickly create alerts with a single mixin.\n//\n// $bg - Background of alert. Default: $primary-color.\n// $radius - Radius of alert box. Default: false.\n@mixin alert($bg: $primary-color, $radius: false) {\n  @include alert-base;\n  @include alert-style($bg);\n  @include radius($radius);\n}\n\n@include exports(\"alert-box\") {\n  @if $include-html-alert-classes {\n    .alert-box {\n      @include alert;\n\n      .close {\n        @include alert-close;\n      }\n\n      &.radius {\n        @include radius($alert-radius);\n      }\n\n      &.round {\n        @include radius($global-rounded);\n      }\n\n      &.success {\n        @include alert-style($success-color);\n      }\n\n      &.alert {\n        @include alert-style($alert-color);\n      }\n\n      &.secondary {\n        @include alert-style($secondary-color);\n      }\n\n      &.warning {\n        @include alert-style($warning-color);\n      }\n\n      &.info {\n        @include alert-style($info-color);\n      }\n\n      &.alert-close {\n        opacity: 0\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Breadcrumb Variables\n//\n$include-html-nav-classes: $include-html-classes !default;\n\n// We use this to set the background color for the breadcrumb container.\n$crumb-bg: scale-color($secondary-color, $lightness: 55%) !default;\n\n// We use these to set the padding around the breadcrumbs.\n$crumb-padding: rem-calc(9 14 9) !default;\n$crumb-side-padding: rem-calc(12) !default;\n\n// We use these to control border styles.\n$crumb-function-factor: -10% !default;\n$crumb-border-size: 1px !default;\n$crumb-border-style: solid !default;\n$crumb-border-color: scale-color($crumb-bg, $lightness: $crumb-function-factor) !default;\n$crumb-radius: $global-radius !default;\n\n// We use these to set various text styles for breadcrumbs.\n$crumb-font-size: rem-calc(11) !default;\n$crumb-font-color: $primary-color !default;\n$crumb-font-color-current: $oil !default;\n$crumb-font-color-unavailable: $aluminum !default;\n$crumb-font-transform: uppercase !default;\n$crumb-link-decor: underline !default;\n\n// We use these to control the slash between breadcrumbs\n$crumb-slash-color: $base !default;\n$crumb-slash: \"/\" !default;\n\n//\n// Breadcrumb Mixins\n//\n\n// We use this mixin to create a container around our breadcrumbs\n@mixin crumb-container {\n  display: block;\n  padding: $crumb-padding;\n  overflow: hidden;\n  margin-#{$default-float}: 0;\n  list-style: none;\n  border-style: $crumb-border-style;\n  border-width: $crumb-border-size;\n\n  // We control which background color and border come through.\n  background-color: $crumb-bg;\n  border-color: $crumb-border-color;\n}\n\n// We use this mixin to create breadcrumb styles from list items.\n@mixin crumbs {\n\n  // A normal state will make the links look and act like clickable breadcrumbs.\n  margin: 0;\n  float: $default-float;\n  font-size: $crumb-font-size;\n  line-height: $crumb-font-size;\n  text-transform: $crumb-font-transform;\n  color: $crumb-font-color;\n\n  &:hover a, &:focus a { text-decoration: $crumb-link-decor; }\n\n  a {\n    color: $crumb-font-color;\n  }\n\n  // Current is for the link of the current page\n  &.current {\n    cursor: $cursor-default-value;\n    color: $crumb-font-color-current;\n    a {\n      cursor: $cursor-default-value;\n      color: $crumb-font-color-current;\n    }\n\n    &:hover, &:hover a,\n    &:focus, &:focus a { text-decoration: none; }\n  }\n\n  // Unavailable removed color and link styles so it looks inactive.\n  &.unavailable {\n    color: $crumb-font-color-unavailable;\n    a { color: $crumb-font-color-unavailable; }\n\n    &:hover,\n    &:hover a,\n    &:focus,\n    a:focus {\n      text-decoration: none;\n      color: $crumb-font-color-unavailable;\n      cursor: $cursor-default-value;\n    }\n  }\n\n  &:before {\n    content: \"#{$crumb-slash}\";\n    color: $crumb-slash-color;\n    margin: 0 $crumb-side-padding;\n    position: relative;\n    top: 1px;\n  }\n\n  &:first-child:before {\n    content: \" \";\n    margin: 0;\n  }\n}\n\n@include exports(\"breadcrumbs\") {\n  @if $include-html-nav-classes {\n    .breadcrumbs {\n      @include crumb-container;\n      @include radius($crumb-radius);\n\n      &>* {\n        @include crumbs;\n      }\n    }\n  }\n}\n\n/* Accessibility - hides the forward slash */\n[aria-label=\"breadcrumbs\"] [aria-hidden=\"true\"]:after {\n  content: \"/\";\n  }\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Block Grid Variables\n//\n$include-html-block-grid-classes: $include-html-classes !default;\n$include-xl-html-block-grid-classes: false !default;\n\n// We use this to control the maximum number of block grid elements per row\n$block-grid-elements: 12 !default;\n$block-grid-default-spacing: rem-calc(20) !default;\n\n$align-block-grid-to-grid: false !default;\n\n@if $align-block-grid-to-grid {\n  $block-grid-default-spacing: $column-gutter;\n}\n\n// Enables media queries for block-grid classes. Set to false if writing semantic HTML.\n$block-grid-media-queries: true !default;\n\n//\n// Block Grid Mixins\n//\n\n// Create a custom block grid\n//\n// $per-row - # of items to display per row. Default: false.\n// $spacing - # of ems to use as padding on each block item. Default: rem-calc(20).\n// $base-style - Apply a base style to block grid. Default: true.\n@mixin block-grid($per-row: false,\n  $spacing: $block-grid-default-spacing,\n  $include-spacing: true,\n  $base-style: true) {\n\n  @if $base-style {\n    display: block;\n    padding: 0;\n\n    @if $align-block-grid-to-grid {\n      margin: 0;\n    }\n\n    @else {\n      margin: 0 calc(-1 * $spacing / 2);\n    }\n\n    @include clearfix;\n\n    &>li {\n      display: block;\n      height: auto;\n      float: $default-float;\n\n      @if $include-spacing {\n        padding: 0 calc($spacing / 2) $spacing;\n      }\n    }\n  }\n\n  @if $per-row {\n    &>li {\n      width: calc(100% / $per-row);\n\n      @if $include-spacing {\n        padding: 0 ($spacing/2) $spacing;\n      }\n\n      list-style: none;\n\n      &:nth-of-type(1n) {\n        clear: none;\n      }\n\n      &:nth-of-type(#{$per-row}n+1) {\n        clear: both;\n      }\n\n      @if $align-block-grid-to-grid {\n        @include block-grid-aligned($per-row, $spacing);\n      }\n    }\n  }\n}\n\n@mixin block-grid-aligned($per-row, $spacing) {\n  @for $i from 1 through $block-grid-elements {\n    @if $per-row >=$i {\n      $grid-column: '+'+$i;\n\n      @if $per-row ==$i {\n        $grid-column: '';\n      }\n\n      &:nth-of-type(#{$per-row}n#{unquote($grid-column)}) {\n        padding-left: ($spacing - (($spacing / $per-row) * ($per-row - ($i - 1))));\n        padding-right: ($spacing - (($spacing / $per-row) * $i));\n      }\n    }\n  }\n}\n\n// Generate presentational markup for block grid.\n//\n// $size - Name of class to use, i.e. \"large\" will generate .large-block-grid-1, .large-block-grid-2, etc.\n@mixin block-grid-html-classes($size, $include-spacing) {\n  @for $i from 1 through $block-grid-elements {\n    .#{$size}-block-grid-#{($i)} {\n      @include block-grid($i, $block-grid-default-spacing, $include-spacing, false);\n    }\n  }\n}\n\n@include exports(\"block-grid\") {\n  @if $include-html-block-grid-classes {\n\n    [class*=\"block-grid-\"] {\n      @include block-grid;\n    }\n\n    @if $block-grid-media-queries {\n      @media #{$small-up} {\n        @include block-grid-html-classes($size: small, $include-spacing: false);\n      }\n\n      @media #{$medium-up} {\n        @include block-grid-html-classes($size: medium, $include-spacing: false);\n      }\n\n      @media #{$large-up} {\n        @include block-grid-html-classes($size: large, $include-spacing: false);\n      }\n\n      @if $include-xl-html-block-grid-classes {\n        @media #{$xlarge-up} {\n          @include block-grid-html-classes($size: xlarge, $include-spacing: false);\n        }\n\n        @media #{$xxlarge-up} {\n          @include block-grid-html-classes($size: xxlarge, $include-spacing: false);\n        }\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"buttons\";\n\n//\n// Button Group Variables\n//\n$include-html-button-classes: $include-html-classes !default;\n\n// Sets the margin for the right side by default, and the left margin if right-to-left direction is used\n$button-bar-margin-opposite: rem-calc(10) !default;\n$button-group-border-width: 1px !default;\n\n//\n// Button Group Mixins\n//\n\n// We use this to add styles for a button group container\n@mixin button-group-container($styles: true, $float: false) {\n  @if $styles {\n    list-style: none;\n    margin: 0;\n    #{$default-float}: 0;\n    @include clearfix();\n  }\n\n  @if $float {\n    float: #{$default-float};\n    margin-#{$opposite-direction}: $button-bar-margin-opposite;\n\n    & div {\n      overflow: hidden;\n    }\n  }\n}\n\n// We use this to control styles for button groups\n@mixin button-group-style($radius: false, $even: false, $float: false, $orientation: horizontal) {\n\n  >button,\n  .button {\n    border-#{$default-float}: $button-group-border-width solid;\n    border-color: rgba(255, 255, 255, 0.5);\n  }\n\n  &:first-child {\n\n    button,\n    .button {\n      border-#{$default-float}: 0;\n    }\n  }\n\n  // We use this to control the flow, or remove those styles completely.\n  @if $float {\n    margin: 0;\n    float: $float;\n    display: list-item;\n\n    // Make sure the first child doesn't get the negative margin.\n    &:first-child {\n      margin-#{$default-float}: 0;\n    }\n  }\n\n  @else {\n    margin: 0 -2px;\n    display: inline-block;\n  }\n\n  @if $orientation ==vertical {\n    display: block;\n    margin: 0;\n\n    >button,\n    .button {\n      border-top: $button-group-border-width solid;\n      border-color: rgba(255, 255, 255, 0.5);\n      border-left-width: 0;\n      margin: 0;\n      display: block;\n    }\n\n    &:first-child {\n\n      button,\n      .button {\n        border-top: 0;\n      }\n    }\n  }\n\n  // We use these to control left and right radius on first/last buttons in the group.\n  @if $radius ==true {\n\n    &,\n    &>a,\n    &>button,\n    &>.button {\n      @include radius(0);\n    }\n\n    &:first-child,\n    &:first-child>a,\n    &:first-child>button,\n    &:first-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(top, $button-radius);\n      }\n\n      @else {\n        @include side-radius($default-float, $button-radius);\n      }\n    }\n\n    &:last-child,\n    &:last-child>a,\n    &:last-child>button,\n    &:last-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(bottom, $button-radius);\n      }\n\n      @else {\n        @include side-radius($opposite-direction, $button-radius);\n      }\n    }\n  }\n\n  @else if $radius {\n\n    &,\n    &>a,\n    &>button,\n    &>.button {\n      @include radius(0);\n    }\n\n    &:first-child,\n    &:first-child>a,\n    &:first-child>button,\n    &:first-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(top, $radius);\n      }\n\n      @else {\n        @include side-radius($default-float, $radius);\n      }\n    }\n\n    &:last-child,\n    &:last-child>a,\n    &:last-child>button,\n    &:last-child>.button {\n      @if $orientation ==vertical {\n        @include side-radius(bottom, $radius);\n      }\n\n      @else {\n        @include side-radius($opposite-direction, $radius);\n      }\n    }\n  }\n\n  // We use this to make the buttons even width across their container\n  @if $even {\n    width: percentage(calc((100/$even) / 100));\n\n    button,\n    .button {\n      width: 100%;\n    }\n  }\n}\n\n@include exports(\"button-group\") {\n  @if $include-html-button-classes {\n    .button-group {\n      @include button-group-container;\n\n      &>li {\n        @include button-group-style();\n      }\n\n      &.stack {\n        &>li {\n          @include button-group-style($orientation: vertical);\n          float: none;\n        }\n      }\n\n      &.stack-for-small {\n        &>li {\n          @include button-group-style($orientation: horizontal);\n\n          @media #{$small-only} {\n            @include button-group-style($orientation: vertical);\n          }\n        }\n      }\n\n      &.radius>* {\n        @include button-group-style($radius: $button-radius, $float: null);\n      }\n\n      &.radius.stack>* {\n        @include button-group-style($radius: $button-radius, $float: null, $orientation: vertical);\n      }\n\n      &.radius.stack-for-small>* {\n        @media #{$medium-up} {\n          @include button-group-style($radius: $button-radius, $orientation: horizontal);\n        }\n\n        @media #{$small-only} {\n          @include button-group-style($radius: $button-radius, $orientation: vertical);\n        }\n      }\n\n      &.round>* {\n        @include button-group-style($radius: $button-round, $float: null);\n      }\n\n      &.round.stack>* {\n        @include button-group-style($radius: $button-med, $float: null, $orientation: vertical);\n      }\n\n      &.round.stack-for-small>* {\n        @media #{$medium-up} {\n          @include button-group-style($radius: $button-round, $orientation: horizontal);\n        }\n\n        @media #{$small-only} {\n          @include button-group-style($radius: $button-med, $orientation: vertical);\n        }\n      }\n\n      @for $i from 2 through 8 {\n        &.even-#{$i} li {\n          @include button-group-style($even: $i, $float: null);\n        }\n      }\n    }\n\n    .button-bar {\n      @include clearfix;\n\n      .button-group {\n        @include button-group-container($styles: false, $float: true);\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-clearing-classes: $include-html-classes !default;\n\n// We use these to set the background colors for parts of Clearing.\n$clearing-bg: $oil !default;\n$clearing-caption-bg: $clearing-bg !default;\n$clearing-carousel-bg: rgba(51,51,51,0.8) !default;\n$clearing-img-bg: $clearing-bg !default;\n\n// We use these to style the close button\n$clearing-close-color: $iron !default;\n$clearing-close-size: 30px !default;\n\n// We use these to style the arrows\n$clearing-arrow-size: 12px !default;\n$clearing-arrow-color: $clearing-close-color !default;\n\n// We use these to style captions\n$clearing-caption-font-color: $iron !default;\n$clearing-caption-font-size: 0.875em !default;\n$clearing-caption-padding: 10px 30px 20px !default;\n\n// We use these to make the image and carousel height and style\n$clearing-active-img-height: 85% !default;\n$clearing-carousel-height: 120px !default;\n$clearing-carousel-thumb-width: 120px !default;\n$clearing-carousel-thumb-active-border: 1px solid rgb(255,255,255) !default;\n\n@include exports(\"clearing\") {\n  @if $include-html-clearing-classes {\n    // We decided to not create a mixin for Clearing because it relies\n    // on predefined classes and structure to work properly.\n    // The variables above should give enough control.\n\n    /* Clearing Styles */\n    .clearing-thumbs, #{data('clearing')} {\n      @include clearfix;\n      margin-bottom: 0;\n      margin-#{$default-float}: 0;\n      list-style: none;\n\n      li {\n        float: $default-float;\n        margin-#{$opposite-direction}: 10px;\n      }\n\n      &[class*=\"block-grid-\"] li {\n        margin-#{$opposite-direction}: 0;\n      }\n    }\n\n    .clearing-blackout {\n      background: $clearing-bg;\n      position: fixed;\n      width: 100%;\n      height: 100%;\n      top: 0;\n      #{$default-float}: 0;\n      z-index: 998;\n\n      .clearing-close { display: block; }\n    }\n\n    .clearing-container {\n      position: relative;\n      z-index: 998;\n      height: 100%;\n      overflow: hidden;\n      margin: 0;\n    }\n\n    .clearing-touch-label {\n      position: absolute;\n      top: 50%;\n      left: 50%;\n      color: $base;\n      font-size: 0.6em;\n    }\n\n    .visible-img {\n      height: 95%;\n      position: relative;\n\n      img {\n        position: absolute;\n        #{$default-float}: 50%;\n        top: 50%;\n        margin-#{$default-float}: -50%;\n        max-height: 100%;\n        max-width: 100%;\n      }\n    }\n\n    .clearing-caption {\n      color: $clearing-caption-font-color;\n      font-size: $clearing-caption-font-size;\n      line-height: 1.3;\n      margin-bottom: 0;\n      text-align: center;\n      bottom: 0;\n      background: $clearing-caption-bg;\n      width: 100%;\n      padding: $clearing-caption-padding;\n      position: absolute;\n      #{$default-float}: 0;\n    }\n\n    .clearing-close {\n      z-index: 999;\n      padding-#{$default-float}: 20px;\n      padding-top: 10px;\n      font-size: $clearing-close-size;\n      line-height: 1;\n      color: $clearing-close-color;\n      display: none;\n\n      &:hover,\n      &:focus { color: $iron; }\n    }\n\n    .clearing-assembled .clearing-container { height: 100%;\n      .carousel > ul { display: none; }\n    }\n\n    // If you want to show a lightbox, but only have a single image come through as the thumbnail\n    .clearing-feature li {\n      display: none;\n      &.clearing-featured-img {\n        display: block;\n      }\n    }\n\n    // Large screen overrides\n    @media #{$medium-up} {\n      .clearing-main-prev,\n      .clearing-main-next {\n        position: absolute;\n        height: 100%;\n        width: 40px;\n        top: 0;\n        & > span {\n          position: absolute;\n          top: 50%;\n          display: block;\n          width: 0;\n          height: 0;\n          border: solid $clearing-arrow-size;\n          &:hover { opacity: 0.8; }\n        }\n      }\n      .clearing-main-prev {\n        #{$default-float}: 0;\n        & > span {\n          #{$default-float}: 5px;\n          border-color: transparent;\n          border-#{$opposite-direction}-color: $clearing-arrow-color;\n        }\n      }\n      .clearing-main-next {\n        #{$opposite-direction}: 0;\n        & > span {\n          border-color: transparent;\n          border-#{$default-float}-color: $clearing-arrow-color;\n        }\n      }\n    \n      .clearing-main-prev.disabled,\n      .clearing-main-next.disabled { opacity: 0.3; }\n\n      .clearing-assembled .clearing-container {\n\n        .carousel {\n          background: $clearing-carousel-bg;\n          height: $clearing-carousel-height;\n          margin-top: 10px;\n          text-align: center;\n\n          & > ul {\n            display: inline-block;\n            z-index: 999;\n            height: 100%;\n            position: relative;\n            float: none;\n\n            li {\n              display: block;\n              width: $clearing-carousel-thumb-width;\n              min-height: inherit;\n              float: $default-float;\n              overflow: hidden;\n              margin-#{$opposite-direction}: 0;\n              padding: 0;\n              position: relative;\n              cursor: $cursor-pointer-value;\n              opacity: 0.4;\n              clear: none;\n\n              &.fix-height {\n                img {\n                  height: 100%;\n                  max-width: none;\n                }\n              }\n\n              a.th {\n                border: none;\n                box-shadow: none;\n                display: block;\n              }\n\n              img {\n              cursor: $cursor-pointer-value !important;\n              width: 100% !important;\n              }\n\n              &.visible { opacity: 1; }\n              &:hover { opacity: 0.8; }\n            }\n          }\n        }\n\n        .visible-img {\n          background: $clearing-img-bg;\n          overflow: hidden;\n          height: $clearing-active-img-height;\n        }\n      }\n\n      .clearing-close {\n        position: absolute;\n        top: 10px;\n        #{$opposite-direction}: 20px;\n        padding-#{$default-float}: 0;\n        padding-top: 0;\n      }\n    }\n\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-dropdown-classes: $include-html-classes !default;\n\n// We use these to controls height and width styles.\n$f-dropdown-max-width: 200px !default;\n$f-dropdown-height: auto !default;\n$f-dropdown-max-height: none !default;\n\n// Used for bottom position\n$f-dropdown-margin-top: 2px !default;\n\n// Used for right position\n$f-dropdown-margin-left: $f-dropdown-margin-top !default;\n\n// Used for left position\n$f-dropdown-margin-right: $f-dropdown-margin-top !default;\n\n// Used for top position\n$f-dropdown-margin-bottom: $f-dropdown-margin-top !default;\n\n// We use this to control the background color\n$f-dropdown-bg: $white !default;\n\n// We use this to set the border styles for dropdowns.\n$f-dropdown-border-style: solid !default;\n$f-dropdown-border-width: 1px !default;\n$f-dropdown-border-color: scale-color($white, $lightness: -20%) !default;\n\n// We use these to style the triangle pip.\n$f-dropdown-triangle-size: 6px !default;\n$f-dropdown-triangle-color: $white !default;\n$f-dropdown-triangle-side-offset: 10px !default;\n\n// We use these to control styles for the list elements.\n$f-dropdown-list-style: none !default;\n$f-dropdown-font-color: $charcoal !default;\n$f-dropdown-font-size: rem-calc(14) !default;\n$f-dropdown-list-padding: rem-calc(5, 10) !default;\n$f-dropdown-line-height: rem-calc(18) !default;\n$f-dropdown-list-hover-bg: $smoke !default;\n$dropdown-mobile-default-float: 0 !default;\n\n// We use this to control the styles for when the dropdown has custom content.\n$f-dropdown-content-padding: rem-calc(20) !default;\n\n// Default radius for dropdown.\n$f-dropdown-radius: $global-radius !default;\n\n//\n// @mixins\n//\n//\n// NOTE: Make default max-width change between list and content types. Can add more width with classes, maybe .small, .medium, .large, etc.;\n// We use this to style the dropdown container element.\n// $content-list - Sets list-style. Default: list. Options: [list, content]\n// $triangle - Sets if dropdown has triangle. Default:true.\n// $max-width - Default: $f-dropdown-max-width || 200px.\n@mixin dropdown-container($content:list, $triangle:true, $max-width:$f-dropdown-max-width) {\n  position: absolute;\n  left: -9999px;\n  list-style: $f-dropdown-list-style;\n  margin-#{$default-float}: 0;\n  outline: none;\n\n  > *:first-child { margin-top: 0; }\n  > *:last-child { margin-bottom: 0; }\n\n  @if $content == list {\n    width: 100%;\n    max-height: $f-dropdown-max-height;\n    height: $f-dropdown-height;\n    background: $f-dropdown-bg;\n    border: $f-dropdown-border-style $f-dropdown-border-width $f-dropdown-border-color;\n    font-size: $f-dropdown-font-size;\n    z-index: 89;\n  }\n  @else if $content == content {\n    padding: $f-dropdown-content-padding;\n    width: 100%;\n    height: $f-dropdown-height;\n    max-height: $f-dropdown-max-height;\n    background: $f-dropdown-bg;\n    border: $f-dropdown-border-style $f-dropdown-border-width $f-dropdown-border-color;\n    font-size: $f-dropdown-font-size;\n    z-index: 89;\n  }\n\n  @if $triangle == bottom {\n    margin-top: $f-dropdown-margin-top;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, bottom);\n      position: absolute;\n      top: -($f-dropdown-triangle-size * 2);\n      #{$default-float}: $f-dropdown-triangle-side-offset;\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, bottom);\n      position: absolute;\n      top: -(($f-dropdown-triangle-size + 1) * 2);\n      #{$default-float}: $f-dropdown-triangle-side-offset - 1;\n      z-index: 88;\n    }\n\n    &.right:before {\n      #{$default-float}: auto;\n      #{$opposite-direction}: $f-dropdown-triangle-side-offset;\n    }\n    &.right:after {\n      #{$default-float}: auto;\n      #{$opposite-direction}: $f-dropdown-triangle-side-offset - 1;\n    }\n  }\n\n  @if $triangle == $default-float {\n    margin-top: 0;\n    margin-#{$default-float}: $f-dropdown-margin-right;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, #{$opposite-direction});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset;\n      #{$default-float}: -($f-dropdown-triangle-size * 2);\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, #{$opposite-direction});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset - 1;\n      #{$default-float}: -($f-dropdown-triangle-size * 2) - 2;\n      z-index: 88;\n    }\n\n  }\n\n  @if $triangle == $opposite-direction {\n    margin-top: 0;\n    margin-#{$default-float}: -$f-dropdown-margin-right;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, #{$default-float});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset;\n      #{$opposite-direction}: -($f-dropdown-triangle-size * 2);\n      #{$default-float}: auto;\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, #{$default-float});\n      position: absolute;\n      top: $f-dropdown-triangle-side-offset - 1;\n      #{$opposite-direction}: -($f-dropdown-triangle-size * 2) - 2;\n      #{$default-float}: auto;\n      z-index: 88;\n    }\n\n  }\n\n  @if $triangle == top {\n    margin-top: -$f-dropdown-margin-bottom;\n    margin-left: 0;\n\n    &:before {\n      @include css-triangle($f-dropdown-triangle-size, $f-dropdown-triangle-color, top);\n      position: absolute;\n      top: auto;\n      bottom: -($f-dropdown-triangle-size * 2);\n      #{$default-float}: $f-dropdown-triangle-side-offset;\n      #{$opposite-direction}: auto;\n      z-index: 89;\n    }\n    &:after {\n      @include css-triangle($f-dropdown-triangle-size + 1, $f-dropdown-border-color, top);\n      position: absolute;\n      top: auto;\n      bottom: -($f-dropdown-triangle-size * 2) - 2;\n      #{$default-float}: $f-dropdown-triangle-side-offset - 1;\n      #{$opposite-direction}: auto;\n      z-index: 88;\n    }\n\n  }\n\n  @if $max-width { max-width: $max-width; }\n  @else { max-width: $f-dropdown-max-width; }\n\n}\n\n// @MIXIN\n//\n// We use this to style the list elements or content inside the dropdown.\n\n@mixin dropdown-style {\n  font-size: $f-dropdown-font-size;\n  cursor: $cursor-pointer-value;\n\n  line-height: $f-dropdown-line-height;\n  margin: 0;\n\n  &:hover,\n  &:focus { background: $f-dropdown-list-hover-bg; }\n\n  &.radius { @include radius($f-dropdown-radius); }\n\n  a {\n    display: block;\n    padding: $f-dropdown-list-padding;\n    color: $f-dropdown-font-color;\n  }\n}\n\n@include exports(\"dropdown\") {\n  @if $include-html-dropdown-classes {\n\n    /* Foundation Dropdowns */\n    .f-dropdown {\n      @include dropdown-container(list, bottom);\n\n      &.drop-#{$opposite-direction} {\n        @include dropdown-container(list, #{$default-float});\n      }\n\n      &.drop-#{$default-float} {\n        @include dropdown-container(list, #{$opposite-direction});\n      }\n\n      &.drop-top {\n        @include dropdown-container(list, top);\n      }\n      // max-width: none;\n\n      li { @include dropdown-style; }\n\n      // You can also put custom content in these dropdowns\n      &.content { @include dropdown-container(content, $triangle:false); }\n\n      // Sizes\n      &.tiny    { max-width: 200px; }\n      &.small   { max-width: 300px; }\n      &.medium  { max-width: 500px; }\n      &.large   { max-width: 800px; }\n      &.mega    {\n        width:100%!important;\n        max-width:100%!important;\n\n        &.open{\n          left:0!important;\n        }\n      }\n    }\n\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-button-classes: $include-html-classes !default;\n\n// We use these to set the color of the pip in dropdown buttons\n$dropdown-button-pip-color: $white !default;\n$dropdown-button-pip-color-alt: $oil !default;\n\n$button-pip-tny: rem-calc(6) !default;\n$button-pip-sml: rem-calc(7) !default;\n$button-pip-med: rem-calc(9) !default;\n$button-pip-lrg: rem-calc(11) !default;\n\n// We use these to style tiny dropdown buttons\n$dropdown-button-padding-tny: $button-pip-tny * 7 !default;\n$dropdown-button-pip-size-tny: $button-pip-tny !default;\n$dropdown-button-pip-opposite-tny: $button-pip-tny * 3 !default;\n$dropdown-button-pip-top-tny: calc(-1 * $button-pip-tny / 2) + rem-calc(1) !default;\n\n// We use these to style small dropdown buttons\n$dropdown-button-padding-sml: $button-pip-sml * 7 !default;\n$dropdown-button-pip-size-sml: $button-pip-sml !default;\n$dropdown-button-pip-opposite-sml: $button-pip-sml * 3 !default;\n$dropdown-button-pip-top-sml: calc(-1 * $button-pip-sml / 2) + rem-calc(1) !default;\n\n// We use these to style medium dropdown buttons\n$dropdown-button-padding-med: $button-pip-med * 6 + rem-calc(3) !default;\n$dropdown-button-pip-size-med: $button-pip-med - rem-calc(3) !default;\n$dropdown-button-pip-opposite-med: $button-pip-med * 2.5 !default;\n$dropdown-button-pip-top-med: calc(-1 * $button-pip-med / 2) + rem-calc(2) !default;\n\n// We use these to style large dropdown buttons\n$dropdown-button-padding-lrg: $button-pip-lrg * 5 + rem-calc(3) !default;\n$dropdown-button-pip-size-lrg: $button-pip-lrg - rem-calc(6) !default;\n$dropdown-button-pip-opposite-lrg: $button-pip-lrg * 2.5 !default;\n$dropdown-button-pip-top-lrg: calc(-1 * $button-pip-lrg / 2) + rem-calc(3) !default;\n\n// @mixins\n//\n// Dropdown Button Mixin\n//\n// We use this mixin to build off of the button mixin and add dropdown button styles\n//\n// $padding - Determines the size of button you're working with. Default: medium. Options [tiny, small, medium, large]\n// $pip-color - Color of the little triangle that points to the dropdown. Default: $white.\n// $base-style - Add in base-styles. This can be set to false. Default:true\n\n@mixin dropdown-button($padding: medium, $pip-color: $white, $base-style: true) {\n\n  // We add in base styles, but they can be negated by setting to 'false'.\n  @if $base-style {\n    position: relative;\n    outline: none;\n\n    // This creates the base styles for the triangle pip\n    &::after {\n      position: absolute;\n      content: \"\";\n      width: 0;\n      height: 0;\n      display: block;\n      border-style: solid;\n      border-color: $dropdown-button-pip-color transparent transparent transparent;\n      top: 50%;\n    }\n  }\n\n  // If we're dealing with tiny buttons, use these styles\n  @if $padding ==tiny {\n    padding-#{$opposite-direction}: $dropdown-button-padding-tny;\n\n    &:after {\n      border-width: $dropdown-button-pip-size-tny;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-tny;\n      margin-top: $dropdown-button-pip-top-tny;\n    }\n  }\n\n  // If we're dealing with small buttons, use these styles\n  @if $padding ==small {\n    padding-#{$opposite-direction}: $dropdown-button-padding-sml;\n\n    &::after {\n      border-width: $dropdown-button-pip-size-sml;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-sml;\n      margin-top: $dropdown-button-pip-top-sml;\n    }\n  }\n\n  // If we're dealing with default (medium) buttons, use these styles\n  @if $padding ==medium {\n    padding-#{$opposite-direction}: $dropdown-button-padding-med;\n\n    &::after {\n      border-width: $dropdown-button-pip-size-med;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-med;\n      margin-top: $dropdown-button-pip-top-med;\n    }\n  }\n\n  // If we're dealing with large buttons, use these styles\n  @if $padding ==large {\n    padding-#{$opposite-direction}: $dropdown-button-padding-lrg;\n\n    &::after {\n      border-width: $dropdown-button-pip-size-lrg;\n      #{$opposite-direction}: $dropdown-button-pip-opposite-lrg;\n      margin-top: $dropdown-button-pip-top-lrg;\n    }\n  }\n\n  // We can control the pip color. We didn't use logic in this case, just set it and forget it.\n  @if $pip-color {\n    &::after {\n      border-color: $pip-color transparent transparent transparent;\n    }\n  }\n}\n\n@include exports(\"dropdown-button\") {\n  @if $include-html-button-classes {\n\n    .dropdown.button,\n    button.dropdown {\n      @include dropdown-button;\n\n      &.tiny {\n        @include dropdown-button(tiny, $base-style: false);\n      }\n\n      &.small {\n        @include dropdown-button(small, $base-style: false);\n      }\n\n      &.large {\n        @include dropdown-button(large, $base-style: false);\n      }\n\n      &.secondary:after {\n        border-color: $dropdown-button-pip-color-alt transparent transparent transparent;\n      }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-media-classes: $include-html-classes !default;\n\n// We use these to control video container padding and margins\n$flex-video-padding-top: rem-calc(25) !default;\n$flex-video-padding-bottom: 67.5% !default;\n$flex-video-margin-bottom: rem-calc(16) !default;\n\n// We use this to control widescreen bottom padding\n$flex-video-widescreen-padding-bottom: 56.34% !default;\n\n//\n// @mixins\n//\n\n@mixin flex-video-container {\n  position: relative;\n  padding-top: $flex-video-padding-top;\n  padding-bottom: $flex-video-padding-bottom;\n  height: 0;\n  margin-bottom: $flex-video-margin-bottom;\n  overflow: hidden;\n\n  &.widescreen { padding-bottom: $flex-video-widescreen-padding-bottom; }\n  &.vimeo { padding-top: 0; }\n\n  iframe,\n  object,\n  embed,\n  video {\n    position: absolute;\n    top: 0;\n    #{$default-float}: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@include exports(\"flex-video\") {\n  @if $include-html-media-classes {\n    .flex-video { @include flex-video-container; }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-inline-list-classes: $include-html-classes !default;\n\n// We use this to control the margins and padding of the inline list.\n$inline-list-top-margin: 0 !default;\n$inline-list-opposite-margin: 0 !default;\n$inline-list-bottom-margin: rem-calc(17) !default;\n$inline-list-default-float-margin: rem-calc(-22) !default;\n$inline-list-default-float-list-margin: rem-calc(22) !default;\n\n$inline-list-padding: 0 !default;\n\n// We use this to control the overflow of the inline list.\n$inline-list-overflow: hidden !default;\n\n// We use this to control the list items\n$inline-list-display: block !default;\n\n// We use this to control any elements within list items\n$inline-list-children-display: block !default;\n\n//\n// @mixins\n//\n// We use this mixin to create inline lists\n@mixin inline-list {\n  margin: $inline-list-top-margin auto $inline-list-bottom-margin auto;\n  margin-#{$default-float}: $inline-list-default-float-margin;\n  margin-#{$opposite-direction}: $inline-list-opposite-margin;\n  padding: $inline-list-padding;\n  list-style: none;\n  overflow: $inline-list-overflow;\n\n  & > li {\n    list-style: none;\n    float: $default-float;\n    margin-#{$default-float}: $inline-list-default-float-list-margin;\n    display: $inline-list-display;\n    &>* { display: $inline-list-children-display; }\n  }\n}\n\n@include exports(\"inline-list\") {\n  @if $include-html-inline-list-classes {\n    .inline-list {\n      @include inline-list();\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-keystroke-classes: $include-html-classes !default;\n\n// We use these to control text styles.\n$keystroke-font: \"Consolas\", \"Menlo\", \"Courier\", monospace !default;\n$keystroke-font-size: inherit !default;\n$keystroke-font-color: $jet !default;\n$keystroke-font-color-alt: $white !default;\n$keystroke-function-factor: -7% !default;\n\n// We use this to control keystroke padding.\n$keystroke-padding: rem-calc(2 4 0) !default;\n\n// We use these to control background and border styles.\n$keystroke-bg: scale-color($white, $lightness: $keystroke-function-factor) !default;\n$keystroke-border-style: solid !default;\n$keystroke-border-width: 1px !default;\n$keystroke-border-color: scale-color($keystroke-bg, $lightness: $keystroke-function-factor) !default;\n$keystroke-radius: $global-radius !default;\n\n//\n// @mixins\n//\n// We use this mixin to create keystroke styles.\n// $bg - Default: $keystroke-bg || scale-color($white, $lightness: $keystroke-function-factor) !default;\n@mixin keystroke($bg:$keystroke-bg) {\n  // This find the lightness percentage of the background color.\n  $bg-lightness: lightness($bg);\n\n  background-color: $bg;\n  border-color: scale-color($bg, $lightness: $keystroke-function-factor);\n\n  // We adjust the font color based on the brightness of the background.\n  @if $bg-lightness > 70% { color: $keystroke-font-color; }\n  @else { color: $keystroke-font-color-alt; }\n\n  border-style: $keystroke-border-style;\n  border-width: $keystroke-border-width;\n  margin: 0;\n  font-family: $keystroke-font;\n  font-size: $keystroke-font-size;\n  padding: $keystroke-padding;\n}\n\n@include exports(\"keystroke\") {\n  @if $include-html-keystroke-classes  {\n    .keystroke,\n    kbd {\n      @include keystroke;\n      @include radius($keystroke-radius);\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n$include-html-panel-classes: $include-html-classes !default;\n\n// We use these to control the background and border styles\n$panel-bg: scale-color($white, $lightness: -5%) !default;\n$panel-border-style: solid !default;\n$panel-border-size: 1px !default;\n\n// We use this % to control how much we darken things on hover\n$panel-function-factor: -11% !default;\n$panel-border-color: scale-color($panel-bg, $lightness: $panel-function-factor) !default;\n\n// We use these to set default inner padding and bottom margin\n$panel-margin-bottom: rem-calc(20) !default;\n$panel-padding: rem-calc(20) !default;\n\n// We use these to set default font colors\n$panel-font-color: $oil !default;\n$panel-font-color-alt: $white !default;\n\n$panel-header-adjust: true !default;\n$callout-panel-link-color: $primary-color !default;\n$callout-panel-link-color-hover: scale-color($callout-panel-link-color, $lightness: -14%) !default;\n\n//\n// @mixins\n//\n// We use this mixin to create panels.\n// $bg - Sets the panel background color. Default: $panel-pg || scale-color($white, $lightness: -5%) !default\n// $padding - Sets the panel padding amount. Default: $panel-padding || rem-calc(20)\n// $adjust - Sets the font color based on the darkness of the bg & resets header line-heights for panels. Default: $panel-header-adjust || true\n@mixin panel($bg: $panel-bg, $padding: $panel-padding, $adjust: $panel-header-adjust) {\n\n  @if $bg {\n    $bg-lightness: lightness($bg);\n\n    border-style: $panel-border-style;\n    border-width: $panel-border-size;\n    border-color: scale-color($bg, $lightness: $panel-function-factor);\n    margin-bottom: $panel-margin-bottom;\n    padding: $padding;\n\n    background: $bg;\n\n    @if $bg-lightness >=50% {\n      color: $panel-font-color;\n    }\n\n    @else {\n      color: $panel-font-color-alt;\n    }\n\n    // Respect the padding, fool.\n    &>:first-child {\n      margin-top: 0;\n    }\n\n    &>:last-child {\n      margin-bottom: 0;\n    }\n\n    @if $adjust {\n\n      // We set the font color based on the darkness of the bg.\n      @if $bg-lightness >=50% {\n\n        h1,\n        h2,\n        h3,\n        h4,\n        h5,\n        h6,\n        p,\n        li,\n        dl {\n          color: $panel-font-color;\n        }\n      }\n\n      @else {\n\n        h1,\n        h2,\n        h3,\n        h4,\n        h5,\n        h6,\n        p,\n        li,\n        dl {\n          color: $panel-font-color-alt;\n        }\n      }\n\n      // reset header line-heights for panels\n      h1,\n      h2,\n      h3,\n      h4,\n      h5,\n      h6 {\n        line-height: 1;\n        margin-bottom: calc(rem-calc(20) / 2);\n\n        &.subheader {\n          line-height: 1.4;\n        }\n      }\n    }\n  }\n}\n\n@include exports(\"panel\") {\n  @if $include-html-panel-classes {\n\n    /* Panels */\n    .panel {\n      @include panel;\n\n      &.callout {\n        @include panel(scale-color($primary-color, $lightness: 94%));\n\n        a:not(.button) {\n          color: $callout-panel-link-color;\n\n          &:hover,\n          &:focus {\n            color: $callout-panel-link-color-hover;\n          }\n        }\n      }\n\n      &.radius {\n        @include radius;\n      }\n\n    }\n\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n@import \"grid\";\n\n//\n// @name _reveal.scss\n// @dependencies _global.scss\n//\n\n$include-html-reveal-classes: $include-html-classes !default;\n\n// We use these to control the style of the reveal overlay.\n$reveal-overlay-bg: rgba($black, .45) !default;\n$reveal-overlay-bg-old: $black !default;\n\n// We use these to control the style of the modal itself.\n$reveal-modal-bg: $white !default;\n$reveal-position-top: rem-calc(100) !default;\n$reveal-default-width: 80% !default;\n$reveal-max-width: $row-width !default;\n$reveal-modal-padding: rem-calc(20) !default;\n$reveal-box-shadow: 0 0 10px rgba($black,.4) !default;\n\n// We use these to style the reveal close button\n$reveal-close-font-size: rem-calc(40) !default;\n$reveal-close-top: rem-calc(10) !default;\n$reveal-close-side: rem-calc(22) !default;\n$reveal-close-color: $base !default;\n$reveal-close-weight: $font-weight-bold !default;\n\n// We use this to set the default radius used throughout the core.\n$reveal-radius: $global-radius !default;\n$reveal-round: $global-rounded !default;\n\n// We use these to control the modal border\n$reveal-border-style: solid !default;\n$reveal-border-width: 1px !default;\n$reveal-border-color: $steel !default;\n\n$reveal-modal-class: \"reveal-modal\" !default;\n$close-reveal-modal-class: \"close-reveal-modal\" !default;\n\n//\n// @mixins\n//\n\n// We use this to create the reveal background overlay styles\n@mixin reveal-bg( $include-z-index-value: true ) {\n  //position: fixed;\n  position: absolute; // allows modal background to extend beyond window position\n  top: 0;\n  bottom: 0;\n  left: 0;\n  right: 0;\n  background: $reveal-overlay-bg-old; // Autoprefixer should be used to avoid such variables needed when Foundation for Sites can do so in the near future.\n  background: $reveal-overlay-bg;\n  z-index: if( $include-z-index-value, 1004, auto );\n  display: none;\n  #{$default-float}: 0;\n}\n\n// We use this mixin to create the structure of a reveal modal\n//\n// $base-style - Provides reveal base styles, can be set to false to override. Default: true, Options: false\n// $width - Sets reveal width Default: $reveal-default-width || 80%\n//\n@mixin reveal-modal-base( $base-style: true, $width:$reveal-default-width, $max-width:$reveal-max-width, $border-radius: $reveal-radius) {\n  @if $base-style {\n    visibility: hidden;\n    display: none;\n    position: absolute;\n    z-index: 1005;\n    width: 100vw;\n    top:0;\n    border-radius: $border-radius;\n    #{$default-float}: 0;\n\n    @media #{$small-only} {\n      min-height:100vh;\n    }\n\n    // Make sure rows don't have a min-width on them\n    .column, .columns { min-width: 0; }\n\n    // Get rid of margin from first and last element inside modal\n    & > :first-child { margin-top: 0; }\n\n    & > :last-child { margin-bottom: 0; }\n  }\n\n  @if $width {\n    @media #{$medium-up} {\n      width: $width;\n      max-width: $max-width;\n      left: 0;\n      right: 0;\n      margin: 0 auto;\n    }\n  }\n}\n\n// We use this to style the reveal modal defaults\n//\n// $bg - Sets background color of reveal modal. Default: $reveal-modal-bg || $white\n// $padding - Padding to apply to reveal modal. Default: $reveal-modal-padding.\n// $border - Choose whether reveal uses a border. Default: true, Options: false\n// $border-style - Set reveal border style. Default: $reveal-border-style || solid\n// $border-width - Width of border (i.e. 1px). Default: $reveal-border-width.\n// $border-color - Color of border. Default: $reveal-border-color.\n// $box-shadow - Choose whether or not to include the default box-shadow. Default: true, Options: false\n// $radius - If true, set to modal radius which is $global-radius || explicitly set radius amount in px (ex. $radius:10px). Default: false\n// $top-offset - Default: $reveal-position-top || 50px\n@mixin reveal-modal-style(\n  $bg:false,\n  $padding:false,\n  $border:false,\n  $border-style:$reveal-border-style,\n  $border-width:$reveal-border-width,\n  $border-color:$reveal-border-color,\n  $box-shadow:false,\n  $radius:false,\n  $top-offset:false) {\n\n  @if $bg { background-color: $bg; }\n  @if $padding != false { padding: $padding; }\n\n  @if $border { border: $border-style $border-width $border-color; }\n\n  // We can choose whether or not to include the default box-shadow.\n  @if $box-shadow {\n    box-shadow: $reveal-box-shadow;\n  }\n\n  // We can control how much radius is used on the modal\n  @if $radius == true { @include radius($reveal-radius); }\n  @else if $radius { @include radius($radius); }\n\n  @if $top-offset {\n    @media #{$medium-up} {\n      top: $top-offset;\n    }\n  }\n}\n\n// We use this to create a close button for the reveal modal\n//\n// $color - Default: $reveal-close-color || $base\n@mixin reveal-close($color:$reveal-close-color) {\n  font-size: $reveal-close-font-size;\n  line-height: 1;\n  position: absolute;\n  top: $reveal-close-top;\n  #{$opposite-direction}: $reveal-close-side;\n  color: $color;\n  font-weight: $reveal-close-weight;\n  cursor: $cursor-pointer-value;\n}\n\n@include exports(\"reveal\") {\n  @if $include-html-reveal-classes {\n\n    // Reveal Modals\n    .reveal-modal-bg { @include reveal-bg; }\n\n    .#{$reveal-modal-class} {\n      @include reveal-modal-base;\n      @include reveal-modal-style(\n        $bg:$reveal-modal-bg,\n        $padding:$reveal-modal-padding,\n        $border:true,\n        $box-shadow:true,\n        $radius:false,\n        $top-offset:$reveal-position-top\n      );\n      @include reveal-modal-style($padding:$reveal-modal-padding * 1.5);\n\n      &.radius { @include reveal-modal-style($radius:true); }\n      &.round  { @include reveal-modal-style($radius:$reveal-round); }\n      &.collapse { @include reveal-modal-style($padding:0); }\n      &.tiny  { @include reveal-modal-base(false, 30%); }\n      &.small { @include reveal-modal-base(false, 40%); }\n      &.medium  { @include reveal-modal-base(false, 60%); }\n      &.large { @include reveal-modal-base(false, 70%); }\n      &.xlarge { @include reveal-modal-base(false, 95%); }\n      &.full {\n        @include reveal-modal-base(false, 100vw);\n        top:0;\n        left:0;\n        height:100%;\n        height: 100vh;\n        min-height:100vh;\n        max-width: none !important;\n        margin-left: 0 !important;\n      }\n\n      .#{$close-reveal-modal-class} { @include reveal-close; }\n    }\n\n    dialog {\n      @extend .#{$reveal-modal-class};\n      display: none;\n\n      &::backdrop, & + .backdrop {\n        @include reveal-bg(false);\n      }\n\n      &[open]{\n        display: block;\n      }\n    }\n\n    // Reveal Print Styles: It should be invisible, adds no value being printed.\n    @media print {\n      dialog, .#{$reveal-modal-class} { \n        display: none;\n        background: $white !important;\n      }\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @variables\n//\n\n$include-html-nav-classes: $include-html-classes !default;\n\n// We use this to control padding.\n$side-nav-padding: rem-calc(14 0) !default;\n\n// We use these to control list styles.\n$side-nav-list-type: none !default;\n$side-nav-list-position: outside !default;\n$side-nav-list-margin: rem-calc(0 0 7 0) !default;\n\n// We use these to control link styles.\n$side-nav-link-color: $primary-color !default;\n$side-nav-link-color-active: scale-color($side-nav-link-color, $lightness: 30%) !default;\n$side-nav-link-color-hover: scale-color($side-nav-link-color, $lightness: 30%) !default;\n$side-nav-link-bg-hover: hsla(0deg, 0%, 0%, 0.025) !default;\n$side-nav-link-margin: 0 !default;\n$side-nav-link-padding: rem-calc(7 14) !default;\n$side-nav-font-size: rem-calc(14) !default;\n$side-nav-font-weight: $font-weight-normal !default;\n$side-nav-font-weight-active: $side-nav-font-weight !default;\n$side-nav-font-family: $body-font-family !default;\n$side-nav-font-family-active: $side-nav-font-family !default;\n\n// We use these to control heading styles.\n$side-nav-heading-color: $side-nav-link-color !default;\n$side-nav-heading-font-size: $side-nav-font-size !default;\n$side-nav-heading-font-weight: bold !default;\n$side-nav-heading-text-transform: uppercase !default;\n\n// We use these to control border styles\n$side-nav-divider-size: 1px !default;\n$side-nav-divider-style: solid !default;\n$side-nav-divider-color: scale-color($white, $lightness: 10%) !default;\n\n\n//\n// @mixins\n//\n\n\n// We use this to style the side-nav\n//\n// $divider-color - Border color of divider. Default: $side-nav-divider-color.\n// $font-size - Font size of nav items. Default: $side-nav-font-size.\n// $link-color - Color of navigation links. Default: $side-nav-link-color.\n// $link-color-hover - Color of navigation links when hovered. Default: $side-nav-link-color-hover.\n@mixin side-nav($divider-color: $side-nav-divider-color,\n  $font-size: $side-nav-font-size,\n  $link-color: $side-nav-link-color,\n  $link-color-hover: $side-nav-link-color-hover,\n  $link-bg-hover: $side-nav-link-bg-hover) {\n  display: block;\n  margin: 0;\n  padding: $side-nav-padding;\n  list-style-type: $side-nav-list-type;\n  list-style-position: $side-nav-list-position;\n  font-family: $side-nav-font-family;\n\n  li {\n    margin: $side-nav-list-margin;\n    font-size: $font-size;\n    font-weight: $side-nav-font-weight;\n\n    a:not(.button) {\n      display: block;\n      color: $link-color;\n      margin: $side-nav-link-margin;\n      padding: $side-nav-link-padding;\n\n      &:hover,\n      &:focus {\n        background: $link-bg-hover;\n        color: $link-color-hover;\n      }\n    }\n\n    &.active>a:first-child:not(.button) {\n      color: $side-nav-link-color-active;\n      font-weight: $side-nav-font-weight-active;\n      font-family: $side-nav-font-family-active;\n    }\n\n    &.divider {\n      border-top: $side-nav-divider-size $side-nav-divider-style;\n      height: 0;\n      padding: 0;\n      list-style: none;\n      border-top-color: $divider-color;\n    }\n\n    &.heading {\n      color: $side-nav-heading-color;\n\n      font: {\n        size: $side-nav-heading-font-size;\n        weight: $side-nav-heading-font-weight;\n      }\n\n      text-transform: $side-nav-heading-text-transform;\n    }\n  }\n}\n\n@include exports(\"side-nav\") {\n  @if $include-html-nav-classes {\n    .side-nav {\n      @include side-nav;\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @name _sub-nav.scss\n// @dependencies _global.scss\n//\n\n//\n// @variables\n//\n\n$include-html-nav-classes: $include-html-classes !default;\n\n// We use these to control margin and padding\n$sub-nav-list-margin: rem-calc(-4 0 18) !default;\n$sub-nav-list-padding-top: rem-calc(4) !default;\n\n// We use this to control the definition\n$sub-nav-font-family: $body-font-family !default;\n$sub-nav-font-size: rem-calc(14) !default;\n$sub-nav-font-color: $aluminum !default;\n$sub-nav-font-weight: $font-weight-normal !default;\n$sub-nav-text-decoration: none !default;\n$sub-nav-padding: rem-calc(3 16) !default;\n$sub-nav-border-radius: 3px !default;\n$sub-nav-font-color-hover: scale-color($sub-nav-font-color, $lightness: -25%) !default;\n\n\n// We use these to control the active item styles\n\n$sub-nav-active-font-weight: $font-weight-normal !default;\n$sub-nav-active-bg: $primary-color !default;\n$sub-nav-active-bg-hover: scale-color($sub-nav-active-bg, $lightness: -14%) !default;\n$sub-nav-active-color: $white !default;\n$sub-nav-active-padding: $sub-nav-padding !default;\n$sub-nav-active-cursor: default !default;\n\n$sub-nav-item-divider: \"\" !default;\n$sub-nav-item-divider-margin: rem-calc(12) !default;\n\n//\n// @mixins\n//\n\n\n// Create a sub-nav item\n//\n// $font-color - Font color. Default: $sub-nav-font-color.\n// $font-size - Font size. Default: $sub-nav-font-size.\n// $active-bg - Background of active nav item. Default: $sub-nav-active-bg.\n// $active-bg-hover - Background of active nav item, when hovered. Default: $sub-nav-active-bg-hover.\n@mixin sub-nav(\n  $font-color: $sub-nav-font-color,\n  $font-size: $sub-nav-font-size,\n  $active-bg: $sub-nav-active-bg,\n  $active-bg-hover: $sub-nav-active-bg-hover) {\n  display: block;\n  width: auto;\n  overflow: hidden;\n  margin: $sub-nav-list-margin;\n  padding-top: $sub-nav-list-padding-top;\n\n  dt {\n    text-transform: uppercase;\n  }\n\n  dt,\n  dd,\n  li {\n    float: $default-float;\n    display: inline;\n    margin-#{$default-float}: rem-calc(16);\n    margin-bottom: 0;\n    font-family: $sub-nav-font-family;\n    font-weight: $sub-nav-font-weight;\n    font-size: $font-size;\n    color: $font-color;\n\n    a {\n      text-decoration: $sub-nav-text-decoration;\n      color: $sub-nav-font-color;\n      padding: $sub-nav-padding;\n      &:hover {\n        color: $sub-nav-font-color-hover;\n      }\n    }\n\n    &.active a {\n      @include radius($sub-nav-border-radius);\n      font-weight: $sub-nav-active-font-weight;\n      background: $active-bg;\n      padding: $sub-nav-active-padding;\n      cursor: $sub-nav-active-cursor;\n      color: $sub-nav-active-color;\n      &:hover {\n        background: $active-bg-hover;\n      }\n    }\n    @if $sub-nav-item-divider != \"\" {\n      margin-#{$default-float}: 0;\n\n      &:before {\n        content: \"#{$sub-nav-item-divider}\";\n        margin: 0 $sub-nav-item-divider-margin;\n      }\n\n      &:first-child:before {\n        content: \"\";\n        margin: 0;\n      }\n    }\n  }\n}\n\n@include exports(\"sub-nav\") {\n  @if $include-html-nav-classes {\n    .sub-nav { @include sub-nav; }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @name _tables.scss\n// @dependencies _global.scss\n//\n\n//\n// @variables\n//\n\n$include-html-table-classes: $include-html-classes !default;\n\n// These control the background color for the table and even rows\n$table-bg: $white !default;\n$table-even-row-bg: $snow !default;\n\n// These control the table cell border style\n$table-border-style: solid !default;\n$table-border-size: 1px !default;\n$table-border-color: $gainsboro !default;\n\n// These control the table head styles\n$table-head-bg: $white-smoke !default;\n$table-head-font-size: rem-calc(14) !default;\n$table-head-font-color: $jet !default;\n$table-head-font-weight: $font-weight-bold !default;\n$table-head-padding: rem-calc(8 10 10) !default;\n\n// These control the table foot styles\n$table-foot-bg: $table-head-bg !default;\n$table-foot-font-size: $table-head-font-size !default;\n$table-foot-font-color: $table-head-font-color !default;\n$table-foot-font-weight: $table-head-font-weight !default;\n$table-foot-padding: $table-head-padding !default;\n\n// These control the caption\n$table-caption-bg: transparent !default;\n$table-caption-font-color: $table-head-font-color !default;\n$table-caption-font-size: rem-calc(16) !default;\n$table-caption-font-weight: bold !default;\n\n// These control the row padding and font styles\n$table-row-padding: rem-calc(9 10) !default;\n$table-row-font-size: rem-calc(14) !default;\n$table-row-font-color: $jet !default;\n$table-line-height: rem-calc(18) !default;\n\n// These are for controlling the layout, display and margin of tables\n$table-layout: auto !default;\n$table-display: table-cell !default;\n$table-margin-bottom: rem-calc(20) !default;\n\n\n//\n// @mixins\n//\n\n@mixin table {\n  background: $table-bg;\n  margin-bottom: $table-margin-bottom;\n  border: $table-border-style $table-border-size $table-border-color;\n  table-layout: $table-layout;\n\n  caption {\n    background: $table-caption-bg;\n    color: $table-caption-font-color;\n    font: {\n      size: $table-caption-font-size;\n      weight: $table-caption-font-weight;\n    }\n  }\n\n  thead {\n    background: $table-head-bg;\n\n    tr {\n      th,\n      td {\n        padding: $table-head-padding;\n        font-size: $table-head-font-size;\n        font-weight: $table-head-font-weight;\n        color: $table-head-font-color;\n      }\n    }\n  }\n\n  tfoot {\n    background: $table-foot-bg;\n\n    tr {\n      th,\n      td {\n        padding: $table-foot-padding;\n        font-size: $table-foot-font-size;\n        font-weight: $table-foot-font-weight;\n        color: $table-foot-font-color;\n      }\n    }\n  }\n\n  tr {\n    th,\n    td {\n      padding: $table-row-padding;\n      font-size: $table-row-font-size;\n      color: $table-row-font-color;\n      text-align: $default-float;\n    }\n\n    &.even,\n    &.alt,\n    &:nth-of-type(even) { background: $table-even-row-bg; }\n  }\n\n  thead tr th,\n  tfoot tr th,\n  tfoot tr td,\n  tbody tr th,\n  tbody tr td,\n  tr td { display: $table-display; line-height: $table-line-height; }\n}\n\n\n@include exports(\"table\") {\n  @if $include-html-table-classes {\n    table {\n      @include table;\n    }\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// @name _thumbs.scss\n// @dependencies _globals.scss\n//\n\n//\n// @variables\n//\n\n$include-html-media-classes: $include-html-classes !default;\n\n// We use these to control border styles\n$thumb-border-style: solid !default;\n$thumb-border-width: 4px !default;\n$thumb-border-color: $white !default;\n$thumb-box-shadow: 0 0 0 1px rgba($black,.2) !default;\n$thumb-box-shadow-hover: 0 0 6px 1px rgba($primary-color,0.5) !default;\n\n// Radius and transition speed for thumbs\n$thumb-radius: $global-radius !default;\n$thumb-transition-speed: 200ms !default;\n\n//\n// @mixins\n//\n\n// We use this to create image thumbnail styles.\n//\n// $border-width - Width of border around thumbnail. Default: $thumb-border-width.\n// $box-shadow - Box shadow to apply to thumbnail. Default: $thumb-box-shadow.\n// $box-shadow-hover - Box shadow to apply on hover. Default: $thumb-box-shadow-hover.\n@mixin thumb(\n  $border-width:$thumb-border-width, \n  $box-shadow:$thumb-box-shadow, \n  $box-shadow-hover:$thumb-box-shadow-hover) {\n  line-height: 0;\n  display: inline-block;\n  border: $thumb-border-style $border-width $thumb-border-color;\n  max-width: 100%;\n  box-shadow: $box-shadow;\n\n  &:hover,\n  &:focus {\n    box-shadow: $box-shadow-hover;\n  }\n}\n\n\n@include exports(\"thumb\") {\n  @if $include-html-media-classes {\n\n    /* Image Thumbnails */\n    .th {\n      @include thumb;\n      @include single-transition(all,$thumb-transition-speed,ease-out);\n\n      &.radius { @include radius($thumb-radius); }\n    }\n  }\n}","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n$include-html-type-classes: $include-html-classes !default;\n\n// We use these to control header font styles\n$header-font-family: $body-font-family !default;\n$header-font-weight: $font-weight-normal !default;\n$header-font-style: $font-weight-normal !default;\n$header-font-color: $jet !default;\n$header-line-height: 1.4 !default;\n$header-top-margin: .2rem !default;\n$header-bottom-margin: .5rem !default;\n$header-text-rendering: optimizeLegibility !default;\n\n// We use these to control header font sizes\n$h1-font-size: rem-calc(44) !default;\n$h2-font-size: rem-calc(37) !default;\n$h3-font-size: rem-calc(27) !default;\n$h4-font-size: rem-calc(23) !default;\n$h5-font-size: rem-calc(18) !default;\n$h6-font-size: 1rem !default;\n\n// We use these to control header size reduction on small screens\n$h1-font-reduction: rem-calc(10) !default;\n$h2-font-reduction: rem-calc(10) !default;\n$h3-font-reduction: rem-calc(5) !default;\n$h4-font-reduction: rem-calc(5) !default;\n$h5-font-reduction: 0 !default;\n$h6-font-reduction: 0 !default;\n\n// These control how subheaders are styled.\n$subheader-line-height: 1.4 !default;\n$subheader-font-color: scale-color($header-font-color, $lightness: 35%) !default;\n$subheader-font-weight: $font-weight-normal !default;\n$subheader-top-margin: .2rem !default;\n$subheader-bottom-margin: .5rem !default;\n\n// A general <small> styling\n$small-font-size: 60% !default;\n$small-font-color: scale-color($header-font-color, $lightness: 35%) !default;\n\n// We use these to style paragraphs\n$paragraph-font-family: inherit !default;\n$paragraph-font-weight: $font-weight-normal !default;\n$paragraph-font-size: 1rem !default;\n$paragraph-line-height: 1.6 !default;\n$paragraph-margin-bottom: rem-calc(20) !default;\n$paragraph-aside-font-size: rem-calc(14) !default;\n$paragraph-aside-line-height: 1.35 !default;\n$paragraph-aside-font-style: italic !default;\n$paragraph-text-rendering: optimizeLegibility !default;\n\n// We use these to style <code> tags\n$code-color: $oil !default;\n$code-font-family: $font-family-monospace !default;\n$code-font-weight: $font-weight-normal !default;\n$code-background-color: scale-color($secondary-color, $lightness: 70%) !default;\n$code-border-size: 0px !default;\n$code-border-style: solid !default;\n$code-border-color: scale-color($code-background-color, $lightness: -10%) !default;\n$code-padding: rem-calc(2) rem-calc(5) rem-calc(1) !default;\n\n// We use these to style anchors\n$anchor-text-decoration: none !default;\n$anchor-text-decoration-hover: none !default;\n$anchor-font-color: $primary-color !default;\n$anchor-font-color-hover: scale-color($anchor-font-color, $lightness: -14%) !default;\n\n// We use these to style the <hr> element\n$hr-border-width: 1px !default;\n$hr-border-style: solid !default;\n$hr-border-color: $gainsboro !default;\n$hr-margin: rem-calc(20) !default;\n\n// We use these to style lists\n$list-font-family: $paragraph-font-family !default;\n$list-font-size: $paragraph-font-size !default;\n$list-line-height: $paragraph-line-height !default;\n$list-margin-bottom: $paragraph-margin-bottom !default;\n$list-style-position: outside !default;\n$list-side-margin: 1.1rem !default;\n$list-ordered-side-margin: 1.4rem !default;\n$list-side-margin-no-bullet: 0 !default;\n$list-nested-margin: rem-calc(20) !default;\n$definition-list-header-weight: $font-weight-bold !default;\n$definition-list-header-margin-bottom: .3rem !default;\n$definition-list-margin-bottom: rem-calc(12) !default;\n\n// We use these to style blockquotes\n$blockquote-font-color: scale-color($header-font-color, $lightness: 35%) !default;\n$blockquote-padding: rem-calc(9 20 0 19) !default;\n$blockquote-border: 1px solid $gainsboro !default;\n$blockquote-cite-font-size: rem-calc(13) !default;\n$blockquote-cite-font-color: scale-color($header-font-color, $lightness: 23%) !default;\n$blockquote-cite-link-color: $blockquote-cite-font-color !default;\n\n// Acronym styles\n$acronym-underline: 1px dotted $gainsboro !default;\n\n// We use these to control padding and margin\n$microformat-padding: rem-calc(10 12) !default;\n$microformat-margin: rem-calc(0 0 20 0) !default;\n\n// We use these to control the border styles\n$microformat-border-width: 1px !default;\n$microformat-border-style: solid !default;\n$microformat-border-color: $gainsboro !default;\n\n// We use these to control full name font styles\n$microformat-fullname-font-weight: $font-weight-bold !default;\n$microformat-fullname-font-size: rem-calc(15) !default;\n\n// We use this to control the summary font styles\n$microformat-summary-font-weight: $font-weight-bold !default;\n\n// We use this to control abbr padding\n$microformat-abbr-padding: rem-calc(0 1) !default;\n\n// We use this to control abbr font styles\n$microformat-abbr-font-weight: $font-weight-bold !default;\n$microformat-abbr-font-decoration: none !default;\n\n// Text alignment class names\n$align-class-names:\n  small-only,\n  small,\n  medium-only,\n  medium,\n  large-only,\n  large,\n  xlarge-only,\n  xlarge,\n  xxlarge-only,\n  xxlarge;\n\n// Text alignment breakpoints\n$align-class-breakpoints:\n  $small-only,\n  $small-up,\n  $medium-only,\n  $medium-up,\n  $large-only,\n  $large-up,\n  $xlarge-only,\n  $xlarge-up,\n  $xxlarge-only,\n  $xxlarge-up;\n\n// Generates text align and justify classes\n@mixin align-classes{\n  .text-left    { text-align: left !important; }\n  .text-right   { text-align: right !important; }\n  .text-center  { text-align: center !important; }\n  .text-justify { text-align: justify !important; }\n\n  @for $i from 1 through length($align-class-names) {\n    @media #{(nth($align-class-breakpoints, $i))} {\n      .#{(nth($align-class-names, $i))}-text-left { text-align: left !important; }\n      .#{(nth($align-class-names, $i))}-text-right   { text-align: right !important; }\n      .#{(nth($align-class-names, $i))}-text-center  { text-align: center !important; }\n      .#{(nth($align-class-names, $i))}-text-justify { text-align: justify !important; }\n    }\n  }\n}\n\n//\n// Typography Placeholders\n//\n\n// These will throw a deprecation warning if used within a media query.\n@mixin lead {\n  font-size: $paragraph-font-size + rem-calc(3.5);\n  line-height: 1.6;\n}\n\n@mixin subheader {\n  line-height: $subheader-line-height;\n  color: $subheader-font-color;\n  font-weight: $subheader-font-weight;\n  margin-top: $subheader-top-margin;\n  margin-bottom: $subheader-bottom-margin;\n}\n@include exports(\"type\") {\n  @if $include-html-type-classes {\n    // Responsive Text alignment\n    @include align-classes;\n\n    /* Typography resets */\n    div,\n    dl,\n    dt,\n    dd,\n    ul,\n    ol,\n    li,\n    h1,\n    h2,\n    h3,\n    h4,\n    h5,\n    h6,\n    pre,\n    form,\n    p,\n    blockquote,\n    th,\n    td {\n      margin:0;\n      padding:0;\n    }\n\n    /* Default Link Styles */\n    a {\n      color: $anchor-font-color;\n      text-decoration: $anchor-text-decoration;\n      line-height: inherit;\n\n      &:hover,\n      &:focus {\n        color: $anchor-font-color-hover;\n        @if $anchor-text-decoration-hover != $anchor-text-decoration {\n        \ttext-decoration: $anchor-text-decoration-hover;\n        }\n      }\n\n      img { border:none; }\n    }\n\n    /* Default paragraph styles */\n    p {\n      font-family: $paragraph-font-family;\n      font-weight: $paragraph-font-weight;\n      font-size: $paragraph-font-size;\n      line-height: $paragraph-line-height;\n      margin-bottom: $paragraph-margin-bottom;\n      text-rendering: $paragraph-text-rendering;\n\n      &.lead { @include lead; }\n\n      & aside {\n        font-size: $paragraph-aside-font-size;\n        line-height: $paragraph-aside-line-height;\n        font-style: $paragraph-aside-font-style;\n      }\n    }\n\n    /* Default header styles */\n    h1, h2, h3, h4, h5, h6 {\n      font-family: $header-font-family;\n      font-weight: $header-font-weight;\n      font-style: $header-font-style;\n      color: $header-font-color;\n      text-rendering: $header-text-rendering;\n      margin-top: $header-top-margin;\n      margin-bottom: $header-bottom-margin;\n      line-height: $header-line-height;\n\n      small {\n        font-size: $small-font-size;\n        color: $small-font-color;\n        line-height: 0;\n      }\n    }\n\n    h1 { font-size: $h1-font-size - $h1-font-reduction; }\n    h2 { font-size: $h2-font-size - $h2-font-reduction; }\n    h3 { font-size: $h3-font-size - $h3-font-reduction; }\n    h4 { font-size: $h4-font-size - $h4-font-reduction; }\n    h5 { font-size: $h5-font-size - $h5-font-reduction; }\n    h6 { font-size: $h6-font-size - $h6-font-reduction; }\n\n    .subheader { @include subheader; }\n\n    hr {\n      border: $hr-border-style $hr-border-color;\n      border-width: $hr-border-width 0 0;\n      clear: both;\n      margin: $hr-margin 0 ($hr-margin - rem-calc($hr-border-width));\n      height: 0;\n    }\n\n    /* Helpful Typography Defaults */\n    em,\n    i {\n      font-style: italic;\n      line-height: inherit;\n    }\n\n    strong,\n    b {\n      font-weight: $font-weight-bold;\n      line-height: inherit;\n    }\n\n    small {\n      font-size: $small-font-size;\n      line-height: inherit;\n    }\n\n    code {\n      font-family: $code-font-family;\n      font-weight: $code-font-weight;\n      color: $code-color;\n      background-color: $code-background-color;\n      border-width: $code-border-size;\n      border-style: $code-border-style;\n      border-color: $code-border-color;\n      padding: $code-padding;\n    }\n\n    /* Lists */\n    ul,\n    ol,\n    dl {\n      font-size: $list-font-size;\n      line-height: $list-line-height;\n      margin-bottom: $list-margin-bottom;\n      list-style-position: $list-style-position;\n      font-family: $list-font-family;\n    }\n\n    ul {\n      margin-#{$default-float}: $list-side-margin;\n      &.no-bullet {\n        margin-#{$default-float}: $list-side-margin-no-bullet;\n        li {\n          ul,\n          ol {\n            margin-#{$default-float}: $list-nested-margin;\n            margin-bottom: 0;\n            list-style: none;\n          }\n        }\n      }\n    }\n\n    /* Unordered Lists */\n    ul {\n      li {\n        ul,\n        ol {\n          margin-#{$default-float}: $list-nested-margin;\n          margin-bottom: 0;\n        }\n      }\n      &.square,\n      &.circle,\n      &.disc {\n        li ul { list-style: inherit; }\n      }\n\n      &.square { list-style-type: square; margin-#{$default-float}: $list-side-margin;}\n      &.circle { list-style-type: circle; margin-#{$default-float}: $list-side-margin;}\n      &.disc { list-style-type: disc; margin-#{$default-float}: $list-side-margin;}\n      &.no-bullet { list-style: none; }\n    }\n\n    /* Ordered Lists */\n    ol {\n      margin-#{$default-float}: $list-ordered-side-margin;\n      li {\n        ul,\n        ol {\n          margin-#{$default-float}: $list-nested-margin;\n          margin-bottom: 0;\n        }\n      }\n    }\n\n    /* Definition Lists */\n    dl {\n      dt {\n        margin-bottom: $definition-list-header-margin-bottom;\n        font-weight: $definition-list-header-weight;\n      }\n      dd { margin-bottom: $definition-list-margin-bottom; }\n    }\n\n    /* Abbreviations */\n    abbr,\n    acronym {\n      text-transform: uppercase;\n      font-size: 90%;\n      color: $body-font-color;\n      cursor: $cursor-help-value;\n    }\n    abbr {\n      text-transform: none;\n      &[title] {\n        border-bottom: $acronym-underline;\n      }\n    }\n\n    /* Blockquotes */\n    blockquote {\n      margin: 0 0 $paragraph-margin-bottom;\n      padding: $blockquote-padding;\n      border-#{$default-float}: $blockquote-border;\n\n      cite {\n        display: block;\n        font-size: $blockquote-cite-font-size;\n        color: $blockquote-cite-font-color;\n        &:before {\n          content: \"\\2014 \\0020\";\n        }\n\n        a,\n        a:visited {\n          color: $blockquote-cite-link-color;\n        }\n      }\n    }\n    blockquote,\n    blockquote p {\n      line-height: $paragraph-line-height;\n      color: $blockquote-font-color;\n    }\n\n    /* Microformats */\n    .vcard {\n      display: inline-block;\n      margin: $microformat-margin;\n      border: $microformat-border-width $microformat-border-style $microformat-border-color;\n      padding: $microformat-padding;\n\n      li {\n        margin: 0;\n        display: block;\n      }\n      .fn {\n        font-weight: $microformat-fullname-font-weight;\n        font-size: $microformat-fullname-font-size;\n      }\n    }\n\n    .vevent {\n      .summary { font-weight: $microformat-summary-font-weight; }\n\n      abbr {\n        cursor: $cursor-default-value;\n        text-decoration: $microformat-abbr-font-decoration;\n        font-weight: $microformat-abbr-font-weight;\n        border: none;\n        padding: $microformat-abbr-padding;\n      }\n    }\n\n\n    @media #{$medium-up} {\n      h1,h2,h3,h4,h5,h6 { line-height: $header-line-height; }\n      h1 { font-size: $h1-font-size; }\n      h2 { font-size: $h2-font-size; }\n      h3 { font-size: $h3-font-size; }\n      h4 { font-size: $h4-font-size; }\n      h5 { font-size: $h5-font-size; }\n      h6 { font-size: $h6-font-size; }\n    }\n\n    // Only include these styles if you want them.\n    @if $include-print-styles {\n      /*\n       * Print styles.\n       *\n       * Inlined to avoid required HTTP connection: www.phpied.com/delay-loading-your-print-css/\n       * Credit to Paul Irish and HTML5 Boilerplate (html5boilerplate.com)\n      */\n      .print-only { display: none !important; }\n      @media print {\n        * {\n          background: transparent !important;\n          color: $black !important; /* Black prints faster: h5bp.com/s */\n          box-shadow: none !important;\n          text-shadow: none !important;\n        }\n\n        a,\n        a:visited { text-decoration: underline;}\n        a[href]:after { content: \" (\" attr(href) \")\"; }\n\n        abbr[title]:after { content: \" (\" attr(title) \")\"; }\n\n        // Don't show links for images, or javascript/internal links\n        .ir a:after,\n        a[href^=\"javascript:\"]:after,\n        a[href^=\"#\"]:after { content: \"\"; }\n\n        pre,\n        blockquote {\n          border: 1px solid $aluminum;\n          page-break-inside: avoid;\n        }\n\n        thead { display: table-header-group; /* h5bp.com/t */ }\n\n        tr,\n        img { page-break-inside: avoid; }\n\n        img { max-width: 100% !important; }\n\n        @page { margin: 0.5cm; }\n\n        p,\n        h2,\n        h3 {\n          orphans: 3;\n          widows: 3;\n        }\n\n        h2,\n        h3 { page-break-after: avoid; }\n\n        .hide-on-print { display: none !important; }\n        .print-only { display: block !important; }\n        .hide-for-print { display: none !important; }\n        .show-for-print { display: inherit !important; }\n      }\n    }\n\n  }\n}\n","// Foundation by ZURB\n// foundation.zurb.com\n// Licensed under MIT Open Source\n\n@import \"global\";\n\n//\n// Foundation Visibility Classes\n//\n$include-html-visibility-classes: $include-html-classes !default;\n$include-accessibility-classes: true !default;\n$include-table-visibility-classes: true !default;\n$include-legacy-visibility-classes: true !default;\n\n//\n// Media Class Names\n//\n// Visibility Breakpoints\n$visibility-breakpoint-sizes:\n  small,\n  medium,\n  large,\n  xlarge,\n  xxlarge;\n\n$visibility-breakpoint-queries:\n  unquote($small-up),\n  unquote($medium-up),\n  unquote($large-up),\n  unquote($xlarge-up),\n  unquote($xxlarge-up);\n\n@mixin visibility-loop {\n  @each $current-visibility-breakpoint in $visibility-breakpoint-sizes {\n    $visibility-inherit-list: ();\n    $visibility-none-list: ();\n\n    $visibility-visible-list: ();\n    $visibility-hidden-list: ();\n\n    $visibility-table-list: ();\n    $visibility-table-header-group-list: ();\n    $visibility-table-row-group-list: ();\n    $visibility-table-row-list: ();\n    $visibility-table-cell-list: ();\n\n    @each $visibility-comparison-breakpoint in $visibility-breakpoint-sizes {\n      @if index($visibility-breakpoint-sizes, $visibility-comparison-breakpoint) < index($visibility-breakpoint-sizes, $current-visibility-breakpoint) {\n        // Smaller than current breakpoint\n\n        $visibility-inherit-list: append($visibility-inherit-list, unquote(\n          '.hide-for-#{$visibility-comparison-breakpoint}-only, .show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-none-list: append($visibility-none-list, unquote(\n          '.show-for-#{$visibility-comparison-breakpoint}-only, .hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-visible-list: append($visibility-visible-list, unquote(\n          '.hidden-for-#{$visibility-comparison-breakpoint}-only, .visible-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-hidden-list: append($visibility-hidden-list, unquote(\n          '.visible-for-#{$visibility-comparison-breakpoint}-only, .hidden-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-list: append($visibility-table-list, unquote(\n          'table.hide-for-#{$visibility-comparison-breakpoint}-only, table.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n          'thead.hide-for-#{$visibility-comparison-breakpoint}-only, thead.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n          'tbody.hide-for-#{$visibility-comparison-breakpoint}-only, tbody.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-list: append($visibility-table-row-list, unquote(\n          'tr.hide-for-#{$visibility-comparison-breakpoint}-only, tr.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n          'th.hide-for-#{$visibility-comparison-breakpoint}-only, td.hide-for-#{$visibility-comparison-breakpoint}-only, th.show-for-#{$visibility-comparison-breakpoint}-up, td.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n\n        // Foundation 4 compatibility:\n        // Include .show/hide-for-[size] and .show/hide-for-[size]-down classes\n        // for small, medium, and large breakpoints only\n        @if $include-legacy-visibility-classes and index((small, medium, large), $visibility-comparison-breakpoint) != false {\n          $visibility-inherit-list: append($visibility-inherit-list, unquote(\n            '.hide-for-#{$visibility-comparison-breakpoint}, .hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-none-list: append($visibility-none-list, unquote(\n            '.show-for-#{$visibility-comparison-breakpoint}, .show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-visible-list: append($visibility-visible-list, unquote(\n            '.hidden-for-#{$visibility-comparison-breakpoint}, .hidden-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-hidden-list: append($visibility-hidden-list, unquote(\n            '.visible-for-#{$visibility-comparison-breakpoint}, .visible-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-list: append($visibility-table-list, unquote(\n            'table.hide-for-#{$visibility-comparison-breakpoint}, table.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n            'thead.hide-for-#{$visibility-comparison-breakpoint}, thead.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n            'tbody.hide-for-#{$visibility-comparison-breakpoint}, tbody.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-list: append($visibility-table-row-list, unquote(\n            'tr.hide-for-#{$visibility-comparison-breakpoint}, tr.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n            'th.hide-for-#{$visibility-comparison-breakpoint}, td.hide-for-#{$visibility-comparison-breakpoint}, th.hide-for-#{$visibility-comparison-breakpoint}-down, td.hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n        }\n\n      } @else if index($visibility-breakpoint-sizes, $visibility-comparison-breakpoint) > index($visibility-breakpoint-sizes, $current-visibility-breakpoint) {\n        // Larger than current breakpoint\n\n        $visibility-inherit-list: append($visibility-inherit-list, unquote(\n          '.hide-for-#{$visibility-comparison-breakpoint}-only, .hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-none-list: append($visibility-none-list, unquote(\n          '.show-for-#{$visibility-comparison-breakpoint}-only, .show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-visible-list: append($visibility-visible-list, unquote(\n          '.hidden-for-#{$visibility-comparison-breakpoint}-only, .hidden-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-hidden-list: append($visibility-hidden-list, unquote(\n          '.visible-for-#{$visibility-comparison-breakpoint}-only, .visible-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-list: append($visibility-table-list, unquote(\n          'table.hide-for-#{$visibility-comparison-breakpoint}-only, table.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n          'thead.hide-for-#{$visibility-comparison-breakpoint}-only, thead.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n          'tbody.hide-for-#{$visibility-comparison-breakpoint}-only, tbody.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-list: append($visibility-table-row-list, unquote(\n          'tr.hide-for-#{$visibility-comparison-breakpoint}-only, tr.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n          'th.hide-for-#{$visibility-comparison-breakpoint}-only, td.hide-for-#{$visibility-comparison-breakpoint}-only, th.hide-for-#{$visibility-comparison-breakpoint}-up, td.hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n\n        // Foundation 4 compatibility:\n        // Include .show/hide-for-[size] and .show/hide-for-[size]-down classes\n        // for small, medium, and large breakpoints only\n        @if $include-legacy-visibility-classes and index((small, medium, large), $visibility-comparison-breakpoint) != false {\n          $visibility-inherit-list: append($visibility-inherit-list, unquote(\n            '.hide-for-#{$visibility-comparison-breakpoint}, .show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-none-list: append($visibility-none-list, unquote(\n            '.show-for-#{$visibility-comparison-breakpoint}, .hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-visible-list: append($visibility-visible-list, unquote(\n            '.hidden-for-#{$visibility-comparison-breakpoint}, .visible-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-hidden-list: append($visibility-hidden-list, unquote(\n            '.visible-for-#{$visibility-comparison-breakpoint}, .hidden-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-list: append($visibility-table-list, unquote(\n            'table.hide-for-#{$visibility-comparison-breakpoint}, table.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n            'thead.hide-for-#{$visibility-comparison-breakpoint}, thead.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n            'tbody.hide-for-#{$visibility-comparison-breakpoint}, tbody.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-list: append($visibility-table-row-list, unquote(\n            'tr.hide-for-#{$visibility-comparison-breakpoint}, tr.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n            'th.hide-for-#{$visibility-comparison-breakpoint}, td.hide-for-#{$visibility-comparison-breakpoint}, th.show-for-#{$visibility-comparison-breakpoint}-down, td.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n        }\n\n      } @else {\n        // Current breakpoint\n\n        $visibility-inherit-list: append($visibility-inherit-list, unquote(\n          '.show-for-#{$visibility-comparison-breakpoint}-only, .show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-none-list: append($visibility-none-list, unquote(\n          '.hide-for-#{$visibility-comparison-breakpoint}-only, .hide-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-visible-list: append($visibility-visible-list, unquote(\n          '.visible-for-#{$visibility-comparison-breakpoint}-only, .visible-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-hidden-list: append($visibility-hidden-list, unquote(\n          '.hidden-for-#{$visibility-comparison-breakpoint}-only, .hidden-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-list: append($visibility-table-list, unquote(\n          'table.show-for-#{$visibility-comparison-breakpoint}-only, table.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n          'thead.show-for-#{$visibility-comparison-breakpoint}-only, thead.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n          'tbody.show-for-#{$visibility-comparison-breakpoint}-only, tbody.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-row-list: append($visibility-table-row-list, unquote(\n          'tr.show-for-#{$visibility-comparison-breakpoint}-only, tr.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n        $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n          'th.show-for-#{$visibility-comparison-breakpoint}-only, td.show-for-#{$visibility-comparison-breakpoint}-only, th.show-for-#{$visibility-comparison-breakpoint}-up, td.show-for-#{$visibility-comparison-breakpoint}-up'\n        ), comma);\n\n        // Foundation 4 compatibility:\n        // Include .show/hide-for-[size] and .show/hide-for-[size]-down classes\n        // for small, medium, and large breakpoints only\n        @if $include-legacy-visibility-classes and index((small, medium, large), $visibility-comparison-breakpoint) != false {\n          $visibility-inherit-list: append($visibility-inherit-list, unquote(\n            '.show-for-#{$visibility-comparison-breakpoint}, .show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-none-list: append($visibility-none-list, unquote(\n            '.hide-for-#{$visibility-comparison-breakpoint}, .hide-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-visible-list: append($visibility-visible-list, unquote(\n            '.visible-for-#{$visibility-comparison-breakpoint}, .visible-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-hidden-list: append($visibility-hidden-list, unquote(\n            '.hidden-for-#{$visibility-comparison-breakpoint}, .hidden-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-list: append($visibility-table-list, unquote(\n            'table.show-for-#{$visibility-comparison-breakpoint}, table.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-header-group-list: append($visibility-table-header-group-list, unquote(\n            'thead.show-for-#{$visibility-comparison-breakpoint}, thead.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-group-list: append($visibility-table-row-group-list, unquote(\n            'tbody.show-for-#{$visibility-comparison-breakpoint}, tbody.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-row-list: append($visibility-table-row-list, unquote(\n            'tr.show-for-#{$visibility-comparison-breakpoint}, tr.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n          $visibility-table-cell-list: append($visibility-table-cell-list, unquote(\n            'th.show-for-#{$visibility-comparison-breakpoint}, td.show-for-#{$visibility-comparison-breakpoint}, th.show-for-#{$visibility-comparison-breakpoint}-down, td.show-for-#{$visibility-comparison-breakpoint}-down'\n          ), comma);\n        }\n      }\n    }\n\n    /* #{$current-visibility-breakpoint} displays */\n    @media #{nth($visibility-breakpoint-queries, index($visibility-breakpoint-sizes, $current-visibility-breakpoint))} {\n      #{$visibility-inherit-list} {\n        display: inherit !important;\n      }\n      #{$visibility-none-list} {\n        display: none !important;\n      }\n      @if $include-accessibility-classes != false {\n        #{$visibility-visible-list} {\n          @include element-invisible-off;\n        }\n        #{$visibility-hidden-list} {\n          @include element-invisible;\n        }\n      }\n      @if $include-table-visibility-classes != false {\n        #{$visibility-table-list} {\n          display: table !important;\n        }\n        #{$visibility-table-header-group-list} {\n          display: table-header-group !important;\n        }\n        #{$visibility-table-row-group-list} {\n          display: table-row-group !important;\n        }\n        #{$visibility-table-row-list} {\n          display: table-row !important;\n        }\n        #{$visibility-table-cell-list} {\n          display: table-cell !important;\n        }\n      }\n    }\n  }\n}\n\n\n@if $include-html-visibility-classes != false {\n\n  @include visibility-loop;\n\n  /* Orientation targeting */\n  .show-for-landscape,\n  .hide-for-portrait { display: inherit !important; }\n  .hide-for-landscape,\n  .show-for-portrait { display: none !important; }\n\n  /* Specific visibility for tables */\n  table {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table !important; }\n  }\n  thead {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-header-group !important; }\n  }\n  tbody {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-row-group !important; }\n  }\n  tr {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-row !important; }\n  }\n  td,\n  th {\n    &.hide-for-landscape,\n    &.show-for-portrait { display: table-cell !important; }\n  }\n\n  @media #{$landscape} {\n    .show-for-landscape,\n    .hide-for-portrait { display: inherit !important; }\n    .hide-for-landscape,\n    .show-for-portrait { display: none !important; }\n\n    /* Specific visibility for tables */\n    table {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table !important; }\n    }\n    thead {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-header-group !important; }\n    }\n    tbody {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-row-group !important; }\n    }\n    tr {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-row !important; }\n    }\n    td,\n    th {\n      &.show-for-landscape,\n      &.hide-for-portrait { display: table-cell !important; }\n    }\n  }\n\n  @media #{$portrait} {\n    .show-for-portrait,\n    .hide-for-landscape { display: inherit !important; }\n    .hide-for-portrait,\n    .show-for-landscape { display: none !important; }\n\n    /* Specific visibility for tables */\n    table {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table !important; }\n    }\n    thead {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-header-group !important; }\n    }\n    tbody {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-row-group !important; }\n    }\n    tr {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-row !important; }\n    }\n    td,\n    th {\n      &.show-for-portrait,\n      &.hide-for-landscape { display: table-cell !important; }\n    }\n  }\n\n  /* Touch-enabled device targeting */\n  .show-for-touch { display: none !important; }\n  .hide-for-touch { display: inherit !important; }\n  .touch .show-for-touch { display: inherit !important; }\n  .touch .hide-for-touch { display: none !important; }\n\n  /* Specific visibility for tables */\n  table.hide-for-touch { display: table !important; }\n  .touch table.show-for-touch { display: table !important; }\n  thead.hide-for-touch { display: table-header-group !important; }\n  .touch thead.show-for-touch { display: table-header-group !important; }\n  tbody.hide-for-touch { display: table-row-group !important; }\n  .touch tbody.show-for-touch { display: table-row-group !important; }\n  tr.hide-for-touch { display: table-row !important; }\n  .touch tr.show-for-touch { display: table-row !important; }\n  td.hide-for-touch { display: table-cell !important; }\n  .touch td.show-for-touch { display: table-cell !important; }\n  th.hide-for-touch { display: table-cell !important; }\n  .touch th.show-for-touch { display: table-cell !important; }\n\n\n  /* Print visibility */\n  @media print {\n    .show-for-print { display: block; }\n    .hide-for-print { display: none; }\n\n    table.show-for-print { display: table !important; }\n    thead.show-for-print { display: table-header-group !important; }\n    tbody.show-for-print { display: table-row-group !important; }\n    tr.show-for-print { display: table-row !important; }\n    td.show-for-print { display: table-cell !important; }\n    th.show-for-print { display: table-cell !important; }\n\n  }\n\n}\n","@charset \"utf-8\";\n/* TOC – Typography\n\nCheck typography variables › _3_typography_settings.scss\n\n- Links\n- Customize Foundation Typography\n- Headlines\n- Images\n- Lists\n- Tables\n- Code\n- Quotes\n- Typography for Articles\n- Smaller Fontsize for Bigteaser on small devices\n- Additional typographical elements\n- Footnotes\n- Icon Font\n\n*/\n\n\n\n/* Links\n------------------------------------------------------------------- */\n\na,\na:link {\n    transition: all .4s;\n}\n\na:visited {\n    border-bottom: $grey-2;\n}\n\na:hover {\n    color: darken( $ci-1, 10% );\n}\n\na:focus {\n    color: lighten( $ci-1, 20% );\n}\n\na:active {\n    color: darken( $ci-1, 20% );\n}\n\n\n\n/* Customize Foundation Typography\n------------------------------------------------------------------- */\n\np {\n    -webkit-hyphens: auto;\n    -moz-hyphens: auto;\n    -ms-hyphens: auto;\n    hyphens: auto;\n    -ms-word-break: normal;\n    /* Non standard for webkit */\n    word-break: normal;\n}\np a,\narticle a {\n    font-weight: bold;\n    border-bottom: 1px dotted;\n}\np a:hover,\narticle a:hover {\n    border-bottom: 2px solid;\n}\np a.button,\n.button,\n.button:hover {\n    border: 0;\n    color: #fff;\n}\np.button a {\n    border: 0;\n    color: #fff;\n    text-shadow: 0 1px 3px rgba(0, 0, 0, 0.5);\n}\n\n\n\n/* Headlines\n   The hK::before logic is to accomodate a vert. offset for persistent\n   top of page menu. The logic is copied from\n   https://css-tricks.com/hash-tag-links-padding/\n------------------------------------------------------------------- */\n\nh1, h2, h3, h4, h5, h6 {\n    font-family: $header-font-family;\n    font-weight: normal;\n    padding: 0;\n}\nh1 {\n    font-size: $font-size-h1;\n    margin-top: 0;\n}\nh2 {\n    font-size: $font-size-h2;\n    margin: 1.563em 0 0 0;\n}\n    .blog-index h2 {\n        margin-top: 0;\n    }\nh3 {\n    font-size: $font-size-h3;\n    margin: 1.152em 0 0 0;\n}\nh4 {\n    font-size: $font-size-h4;\n    margin: 1.152em 0 0 0;\n}\nh5 {\n    font-size: $font-size-h5;\n    margin: 1em 0 0 0;\n}\n\n\n\n/* Images\n------------------------------------------------------------------- */\n\nimg { border-radius: $global-radius;}\n    img.alignleft,\n    img.left        { float: left; margin:5px 15px 5px 0; }\n    img.alignright,\n    img.right       { float: right; margin:5px 0 5px 15px; }\n    img.aligncenter,\n    img.center      { display: block; margin:0 auto 10px; }\n\nfigure {\n    margin: 0 0 rem-calc(30) 0;\n}\n#masthead-with-background-color figure,\n#masthead-with-pattern figure {\n    margin: 0;\n}\nfigcaption,\n.masthead-caption {\n    color: $grey-10;\n    font-family: $font-family-sans-serif;\n    font-size: rem-calc(13);\n    padding-top: rem-calc(2);\n}\nfigcaption a,\n.masthead-caption a {\n    border-bottom: 1px dotted $grey-4;\n    color: $grey-10;\n}\nfigcaption a:hover,\n.masthead-caption a:hover {\n    border-bottom: 2px solid $primary-color;\n    color: $primary-color;\n}\n.masthead-caption {\n    padding-right: 10px;\n    text-align: right;\n}\n\n\n\n/* Tables\n------------------------------------------------------------------- */\n\ntd {\n    vertical-align: top;\n}\n\n\n\n/* Code\n------------------------------------------------------------------- */\n\npre {\n    overflow: auto;\n    margin-bottom: rem-calc(20);\n    padding: 5px;\n    background-color: $code-background-color;\n    border-radius: $global-radius;\n}\npre code {\n    padding: rem-calc(2) rem-calc(5) rem-calc(1) rem-calc(0);\n    border: 0;\n}\n\ncode {\n    font-size: rem-calc(14);\n    line-height: 1.5;\n}\n\n\n\n/* Lists\n------------------------------------------------------------------- */\n\nul, ol {\n    margin-left: 20px;\n    padding: 0;\n}\nli {\n    margin-left: 0;\n}\n\n.no-bullet {\n    list-style: none;\n    margin-left: 0;\n}\n\nli {\n    > ul,\n    > ol {\n         margin-bottom: 0;\n    }\n}\n\ndl {\n\n}\ndt:first-child {\n    padding-top: 0px;\n}\ndt {\n    font-weight: bold;\n    padding-top: 30px;\n}\ndd {\n}\narticle dl dt   { line-height: 1.3; }\narticle dl dd   { line-height: 1.6; margin-bottom: rem-calc(12); margin-left: rem-calc(24); }\n\n\n\n/* Quotes\n------------------------------------------------------------------- */\n\nblockquote {\n    font-style: italic;\n    position: relative;\n    border: none;\n    margin: 0 30px 30px 30px;\n    color:  $grey-11;\n}\n\n    blockquote p {font-style: italic; color: $grey-10; }\n\n    blockquote:before {\n        display:block;content:\"\\00BB\";\n        font-size:80px;\n        line-height: 0;\n        position:absolute;\n        left:-25px;\n        top: auto;\n        color: $grey-11;\n    }\n    blockquote:after {\n        display:block;\n        content:\"\\00AB\";\n        font-size:80px;\n        line-height: 0;\n        position:absolute;\n        right:-10px;\n        bottom: 20px;\n        color: $grey-11;\n    }\n    blockquote cite:before {\n        content:\"\\2014 \\0020\"\n    }\n    blockquote cite a,blockquote cite a:visited {\n        color: $grey-10;\n    }\ncite {\n    padding-top: 5px;\n}\n\nbutton, .button {\n    letter-spacing: 1px;\n}\n\nmark {\n    background-color: scale-color($warning-color, $lightness: 60%);\n}\n\n\n\n/* Typography for Articles\n------------------------------------------------------------------- */\n\n.subheadline {\n    font-size: rem-calc(16);\n    margin: 0;\n    text-transform: uppercase;\n}\n.teaser {\n    font-size: rem-calc(20);\n}\n.big-teaser {\n    font-style: italic; font-weight: 300;\n}\n.big-teaser a {\n    font-style: italic; font-weight: 400;\n}\n\n/* Smaller Fontsize for Bigteaser on small devices */\n@media only screen {\n    .big-teaser {\n        font-size: rem-calc(20);\n    }\n}\n@media only screen and (min-width: 40.063em) {\n    .big-teaser         {\n        font-size: rem-calc(29);\n    }\n}\n\n\n\n/* Additional typographical elements\n------------------------------------------------------------------- */\n\n.sans           { font-family: $font-family-sans-serif; }\n.serif          { font-family: $font-family-serif; }\n\n.font-size-h1   { font-size: $font-size-h1; }\n.font-size-h2   { font-size: $font-size-h2; }\n.font-size-h3   { font-size: $font-size-h3; }\n.font-size-h4   { font-size: $font-size-h4; }\n.font-size-h5   { font-size: $font-size-h5; }\n.font-size-p    { font-size: $font-size-p; }\n\n\n\n/* Footnotes\n------------------------------------------------------------------- */\n\n.footnotes:before {\n    content: \"\";\n    position: absolute;\n    height: 1px;\n    width: 60px;\n    margin-top: -10px;\n    border-bottom: 1px solid $grey-2;\n}\n.footnotes {\n    margin-top: 60px;\n}\n.footnotes ol {\n    font-size: $font-size-small;\n}\n.footnotes p {\n    font-size: inherit;\n    margin-bottom: 0;\n}\n\n\n\n\n/* Icon Font\n   See the icon-set/preview in /assets/fonts/iconfont-preview.html\n------------------------------------------------------------------- */\n\n@font-face {\n  font-family: 'iconfont';\n  src: url('../fonts/iconfont.eot'); /* IE9 Compat Modes */\n  src: url('../fonts/iconfont.eot?#iefix') format('embedded-opentype'), /* IE6-IE8 */\n       url('../fonts/iconfont.woff') format('woff'), /* Pretty Modern Browsers */\n       url('../fonts/iconfont.ttf')  format('truetype'), /* Safari, Android, iOS */\n       url('../fonts/iconfont.svg#svgFontName') format('svg'); /* Legacy iOS */\n}\n\n.iconfont       { font-family: iconfont; }\n.iconfont-48    { font-size: 48px; }\n\n\n[data-icon]:before { content: attr(data-icon); }\n\n[data-icon]:before,\n.icon-archive:before,\n.icon-browser:before,\n.icon-calendar:before,\n.icon-camera:before,\n.icon-chat:before,\n.icon-check:before,\n.icon-chevron-down:before,\n.icon-chevron-left:before,\n.icon-chevron-right:before,\n.icon-chevron-up:before,\n.icon-circle-with-cross:before,\n.icon-circle-with-minus:before,\n.icon-circle-with-plus:before,\n.icon-cloud:before,\n.icon-code:before,\n.icon-cog:before,\n.icon-dropbox:before,\n.icon-edit:before,\n.icon-export:before,\n.icon-eye:before,\n.icon-facebook:before,\n.icon-feather:before,\n.icon-github:before,\n.icon-globe:before,\n.icon-googleplus:before,\n.icon-heart:before,\n.icon-heart-outlined:before,\n.icon-home:before,\n.icon-instagram:before,\n.icon-lab-flask:before,\n.icon-leaf:before,\n.icon-linkedin:before,\n.icon-mail:before,\n.icon-message:before,\n.icon-mic:before,\n.icon-network:before,\n.icon-paper-plane:before,\n.icon-pinterest:before,\n.icon-price-tag:before,\n.icon-rocket:before,\n.icon-rss:before,\n.icon-soundcloud:before,\n.icon-star:before,\n.icon-star-outlined:before,\n.icon-thumbs-down:before,\n.icon-thumbs-up:before,\n.icon-tree:before,\n.icon-tumblr:before,\n.icon-twitter:before,\n.icon-upload-to-cloud:before,\n.icon-video:before,\n.icon-vimeo:before,\n.icon-warning:before,\n.icon-xing:before,\n.icon-youtube:before {\n  display: inline-block;\nfont-family: \"iconfont\";\nfont-style: normal;\nfont-weight: normal;\nfont-variant: normal;\nline-height: 1;\ntext-decoration: inherit;\ntext-rendering: optimizeLegibility;\ntext-transform: none;\n-moz-osx-font-smoothing: grayscale;\n-webkit-font-smoothing: antialiased;\nfont-smoothing: antialiased;\n}\n\n.icon-archive:before { content: \"\\f100\"; }\n.icon-browser:before { content: \"\\f101\"; }\n.icon-calendar:before { content: \"\\f133\"; }\n.icon-camera:before { content: \"\\f102\"; }\n.icon-chat:before { content: \"\\f103\"; }\n.icon-check:before { content: \"\\f104\"; }\n.icon-chevron-down:before { content: \"\\f105\"; }\n.icon-chevron-left:before { content: \"\\f106\"; }\n.icon-chevron-right:before { content: \"\\f107\"; }\n.icon-chevron-up:before { content: \"\\f108\"; }\n.icon-circle-with-cross:before { content: \"\\f109\"; }\n.icon-circle-with-minus:before { content: \"\\f10a\"; }\n.icon-circle-with-plus:before { content: \"\\f10b\"; }\n.icon-cloud:before { content: \"\\f10c\"; }\n.icon-code:before { content: \"\\f10d\"; }\n.icon-cog:before { content: \"\\f10e\"; }\n.icon-dropbox:before { content: \"\\f10f\"; }\n.icon-edit:before { content: \"\\f110\"; }\n.icon-export:before { content: \"\\f111\"; }\n.icon-eye:before { content: \"\\f112\"; }\n.icon-facebook:before { content: \"\\f113\"; }\n.icon-feather:before { content: \"\\f114\"; }\n.icon-github:before { content: \"\\f115\"; }\n.icon-globe:before { content: \"\\f116\"; }\n.icon-googleplus:before { content: \"\\f136\"; }\n.icon-heart:before { content: \"\\f117\"; }\n.icon-heart-outlined:before { content: \"\\f118\"; }\n.icon-home:before { content: \"\\f119\"; }\n.icon-instagram:before { content: \"\\f11a\"; }\n.icon-lab-flask:before { content: \"\\f11b\"; }\n.icon-leaf:before { content: \"\\f11c\"; }\n.icon-linkedin:before { content: \"\\f11d\"; }\n.icon-mail:before { content: \"\\f11e\"; }\n.icon-message:before { content: \"\\f11f\"; }\n.icon-mic:before { content: \"\\f120\"; }\n.icon-network:before { content: \"\\f121\"; }\n.icon-paper-plane:before { content: \"\\f122\"; }\n.icon-pinterest:before { content: \"\\f123\"; }\n.icon-price-tag:before { content: \"\\f124\"; }\n.icon-rocket:before { content: \"\\f125\"; }\n.icon-rss:before { content: \"\\f126\"; }\n.icon-soundcloud:before { content: \"\\f127\"; }\n.icon-star:before { content: \"\\f128\"; }\n.icon-star-outlined:before { content: \"\\f129\"; }\n.icon-thumbs-down:before { content: \"\\f12a\"; }\n.icon-thumbs-up:before { content: \"\\f12b\"; }\n.icon-tree:before { content: \"\\f134\"; }\n.icon-tumblr:before { content: \"\\f12c\"; }\n.icon-twitter:before { content: \"\\f12d\"; }\n.icon-upload-to-cloud:before { content: \"\\f12e\"; }\n.icon-video:before { content: \"\\f12f\"; }\n.icon-vimeo:before { content: \"\\f130\"; }\n.icon-warning:before { content: \"\\f131\"; }\n.icon-xing:before { content: \"\\f135\"; }\n.icon-youtube:before { content: \"\\f132\"; }\n","@charset \"utf-8\";\n/* TOC\n\n- Adjustments: Video Layout\n- Navigation\n- Search\n- Masthead\n- Masthead › small-only\n- Masthead › medium-only\n- Masthead › large-only\n- Masthead › xlarge-up\n- Breadcrumb\n- Meta\n- Jump to top\n- Footer\n- Subfooter\n- CSS-Classes to add margin at top or bottom\n\n*/\n\n\n\n/* Adjustments: Video Layout\n------------------------------------------------------------------- */\n\nbody.video,\nbody.video #masthead-no-image-header { background: #000; }\nbody.video #masthead-no-image-header { margin-bottom: 60px; }\nbody.video h1,\nbody.video h2,\nbody.video h3,\nbody.video h4,\nbody.video h5,\nbody.video h6,\nbody.video p,\nbody.video a,\nbody.video blockquote:before,\nbody.video blockquote:after,\nbody.video cite a, { color: #fff; }\nbody.video cite a:visited, { color: #fff; }\nbody.video cite { color: #fff; }\n\n\n\n/* Navigation\n------------------------------------------------------------------- */\n\n#navigation {\n    -webkit-box-shadow: 0 2px 2px 0 rgba(0,0,0,.2);\n    box-shadow: 0 2px 3px 0 rgba(0,0,0,.2);\n\n    [class^='icon-']:before, [class*=' icon-']:before {\n        margin-right: rem-calc(8);\n    }\n}\n\n\n\n/* Search\n------------------------------------------------------------------- */\n\n.no-js form#search {\n  display: none;\n}\n\n\n\n/* Masthead\n------------------------------------------------------------------- */\n\n#masthead {\n    background-color: $primary-color;\n}\n#masthead-no-image-header {\n    background-color: $primary-color;\n}\n#masthead-with-text {\n    text-align: center;\n    font-size: rem-calc(54);\n    font-family: $header-font-family;\n    color: #fff;\n    text-transform: uppercase;\n    text-shadow: 0 2px 3px rgba(0,0,0,.4);\n}\n#masthead-no-image-header {\n    height: 175px;\n}\n#masthead-no-image-header #logo img {\n    margin-top: 60px;\n}\n\n/* Masthead › small-only\n------------------------------------------------------------------- */\n\n@media #{$small-only} {\n    #logo img {\n        display: none;\n    }\n    #masthead {\n        height: 200px;\n    }\n    #masthead-with-pattern {\n        padding: 15px 0;\n    }\n    #masthead-with-background-color {\n        padding: 15px 0;\n    }\n    #masthead-with-text {\n        height: 220px;\n        padding: 30px 0;\n        font-size: rem-calc(36);\n    }\n    #masthead-no-image-header {\n        display: none;\n    }\n}\n\n\n/* Masthead › medium-only\n------------------------------------------------------------------- */\n\n@media #{$medium-only} {\n    #logo img {\n        margin-top: 60px;\n    }\n    #masthead {\n        height: 280px;\n    }\n    #masthead-with-pattern {\n        padding: 20px 0;\n    }\n    #masthead-with-background-color {\n        padding: 20px 0;\n    }\n    #masthead-with-text {\n        padding: 60px 0;\n        height: 300px;\n    }\n}\n\n\n/* Masthead › large-only\n------------------------------------------------------------------- */\n\n@media #{$large-only} {\n    #logo img {\n        margin-top: 80px;\n    }\n    #masthead {\n        height: 310px;\n    }\n    #masthead-with-pattern {\n        padding: 30px 0;\n    }\n    #masthead-with-background-color {\n        padding: 30px 0;\n    }\n    #masthead-with-text {\n        height: 330px;\n        padding: 60px 0;\n    }\n}\n\n\n/* Masthead › xlarge-up\n------------------------------------------------------------------- */\n\n@media #{$xlarge-up} {\n    #logo img {\n        margin-top: 110px;\n    }\n    #masthead {\n        height: 380px;\n    }\n    #masthead-with-pattern {\n        padding: 45px 0;\n    }\n    #masthead-with-background-color {\n        padding: 45px 0;\n    }\n    #masthead-with-text {\n        padding: 95px 0;\n        height: 400px;\n    }\n}\n\n\n#title-image-small {\n    height: 240px;\n}\n#title-image-large {\n    height: 520px;\n}\n#title-image-index-small {\n    height: 120px;\n}\n#title-image-index-large {\n    height: 260px;\n}\n\n\n\n/* Breadcrumb\n------------------------------------------------------------------- */\n\n#breadcrumb {\n    background: scale-color($grey-1, $lightness: 55%);\n    border-top: 1px solid scale-color($grey-1, $lightness: 45%);\n    border-bottom: 1px solid scale-color($grey-1, $lightness: 45%);\n}\n.breadcrumbs>.current {\n  font-weight: bold;\n}\n\n\n/* Meta\n------------------------------------------------------------------- */\n\n#page-meta, #page-meta a {\n    color: $grey-5;\n}\n\n#page-meta .button {\n    background: $grey-5;\n    border: 0;\n}\n#page-meta .button {\n    color: #fff;\n}\n#page-meta .button:hover {\n    background: $primary-color;\n}\n.meta-info p {\n  font-size: rem-calc(13);\n  color: scale-color($grey-1, $lightness: 40%);\n}\n  .meta-info a {\n    text-decoration: underline;\n    color: scale-color($grey-1, $lightness: 40%);\n  }\n  .meta-info a:hover {\n    text-decoration: none;\n    color: $secondary-color;\n  }\n\n\n\n/* Jump to 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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 3be58c54183f71a0ee0545a00867922b
+tags: d77d1c0d9ca2f4c8421862c7c5a0d620
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:38.139092Z",
+     "iopub.status.busy": "2024-10-10T15:02:38.138702Z",
+     "iopub.status.idle": "2024-10-10T15:02:38.141748Z",
+     "shell.execute_reply": "2024-10-10T15:02:38.141395Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2021 - 2022 United Kingdom Research and Innovation\n",
+    "#  Copyright 2021 - 2022 The University of Manchester\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:    Gemma Fardell (UKRI-STFC)\n",
+    "#                   Edoardo Pasca (UKRI-STFC)\n",
+    "#                   Laura Murgatroyd (UKRI-STFC)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A detailed look at CIL geometry\n",
+    "CIL holds your CT data in specialised data-containers, `AcquisitionData` and `ImageData`.\n",
+    "\n",
+    "Each of these has an associated `geometry` which contains the meta-data describing your set-up.\n",
+    "\n",
+    " - `AcquisitionGeometry` describes the acquisition data and parameters\n",
+    "\n",
+    " - `ImageGeometry` describes the image data (i.e., the reconstruction volume)\n",
+    "\n",
+    "The data-readers provided by CIL (Nikon, Zeiss and diamond nexus readers) will read in your data and return you a fully configured acquisition data with the acquisition geometry already configured, however if you read in a stack of tiffs or want to tweak the parameters this is simple to create by hand."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The structure of an AcquisitionGeometry\n",
+    "\n",
+    "An instance of an `AcquisitionGeometry`, `ag`, holds the configuration of the system, in `config` which is subdivided in to:\n",
+    " - `ag.config.system` -  The position and orientations of the `source`/`ray`, `rotation_axis` and `detector`\n",
+    " - `ag.config.panel` - The number of pixels, the size of pixels, and the position of pixel 0\n",
+    " - `ag.config.angles` - The number of angles, the unit of the angles (default is degrees)\n",
+    " - `ag.config.channels` - The number of channels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a simple AcquisitionGeometry\n",
+    "\n",
+    "You can use the `AcquisitionGeometry` methods to describe circular trajectory parallel-beam or cone-beam 2D or 3D data.\n",
+    "\n",
+    " - `ag = AcquisitionGeometry.create_Parallel2D()`\n",
+    " - `ag = AcquisitionGeometry.create_Parallel3D()`\n",
+    " - `ag = AcquisitionGeometry.create_Cone2D(source_position, detector_position)`\n",
+    " - `ag = AcquisitionGeometry.create_Cone3D(source_position, detector_position)`\n",
+    "\n",
+    "This notebook will step though each in turn and show you how to describe both simple and complex geometries with offsets and rotations.\n",
+    "\n",
+    "No matter which type of geometry you create you will also need to describe the panel and projection angles.\n",
+    " - `ag.set_panel(num_pixels, pixel_size)`\n",
+    " - `ag.set_angles(angles, angle_unit)`\n",
+    "\n",
+    "For multi-channel data you need to add the number of channels.\n",
+    " - `ag.set_channels(num_channels)`\n",
+    "\n",
+    "And you will also need to describe the order your data is stored in using the relavent labels from the CIL default labels: `channel`, `angle`, `vertical` and `horizontal`\n",
+    " - `ag.set_labels(['angle','vertical','horizontal'])`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Note on CIL AcquisitionGeometry:\n",
+    " - The geometry is described by a right-handed cooridinate system\n",
+    " - Positive angles describe the object rotating anti-clockwise when viewed from above\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parallel geometry\n",
+    "\n",
+    "Parallel beams of X-rays are emitted onto 1D (single pixel row) or 2D detector array. This geometry is common for synchrotron sources.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data. Note that for 3D geometry we need to describe a 2D panel where `num_pixels=[X,Y]`\n",
+    "\n",
+    "```python\n",
+    "parallel_2D_geometry = AcquisitionGeometry.create_Parallel2D()\\\n",
+    "    \n",
+    "                                          .set_panel(num_pixels=10)\\\n",
+    "        \n",
+    "                                          .set_angles(angles=range(0,180))\n",
+    "\n",
+    "\n",
+    "parallel_3D_geometry = AcquisitionGeometry.create_Parallel3D()\\\n",
+    "    \n",
+    "                                          .set_panel(num_pixels=[10,10])\\\n",
+    "        \n",
+    "                                          .set_angles(angles=range(0,180))\n",
+    "```\n",
+    "Both 2D and 3D parallel-beam geometries are displayed below. Note that the detector position has been set, this is not necessary to describe and reconstruct the data, but it makes the displayed images clearer.\n",
+    "\n",
+    "`show_geometry()` can be used to display the configured geometry and will be used here extensively. You can also print the geometry to obtain a detailed description. If `show_geometry` is not passed an `ImageGeometry` it will show the default geometry associated with the `AcquisitionGeometry` \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example creating a 2D parallel-beam geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:38.143400Z",
+     "iopub.status.busy": "2024-10-10T15:02:38.143145Z",
+     "iopub.status.idle": "2024-10-10T15:02:38.997275Z",
+     "shell.execute_reply": "2024-10-10T15:02:38.996701Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2D Parallel-beam tomography\n",
+      "System configuration:\n",
+      "\tRay direction: [0., 1.]\n",
+      "\tRotation axis position: [0., 0.]\n",
+      "\tDetector position: [ 0., 10.]\n",
+      "\tDetector direction x: [1., 0.]\n",
+      "Panel configuration:\n",
+      "\tNumber of pixels: [10  1]\n",
+      "\tPixel size: [1. 1.]\n",
+      "\tPixel origin: bottom-left\n",
+      "Channel configuration:\n",
+      "\tNumber of channels: 1\n",
+      "Acquisition description:\n",
+      "\tNumber of positions: 180\n",
+      "\tAngles 0-9 in degrees: [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]\n",
+      "\tAngles 170-179 in degrees: [170., 171., 172., 173., 174., 175., 176., 177., 178., 179.]\n",
+      "\tFull angular array can be accessed with acquisition_data.geometry.angles\n",
+      "Distances in units: units distance\n"
+     ]
+    }
+   ],
+   "source": [
+    "from cil.framework import AcquisitionGeometry\n",
+    "from cil.utilities.display import show_geometry\n",
+    "\n",
+    "ag = AcquisitionGeometry.create_Parallel2D(detector_position=[0,10])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "show_geometry(ag)\n",
+    "\n",
+    "print(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example creating a 3D parallel-beam geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.026342Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.026068Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.261809Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.261326Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XX355zfdiXsWtP/ffHVDeg0X9jRMTEzFy5EjUr18fvr6+8PX1RUpKilN8RqMRixcvxtKlS5Genl7k5H49evRA7dq1UbduXQwcOBBLlixBWlpaka+tsHNA9rmjsPNP9vIbaUdFuXr1Ks6cOVNkndmKOg+VN+5obnFxcbBarU6fUQEBAWjQoIH6fNeuXRAR1K9f3+m8t2HDhkLPy8XdLioqCt26dbuumIvbFq/1WZ2tRYsWTt8rOnTogJSUFJw8eRJxcXHIyspyei8aDAa0a9dOfS+OGjUK33//PVq2bIlJkyZhy5Yt1/V6Cnp9xfk89/T0xG233aY+v9a5hoiI3OuGJ1LTaDTw8vIqyVhKXNeuXfHVV19hz5490Gq1aNy4McLCwrBhwwYkJSUhLCxMXVdEnD7Es8vyutZrLmibazGbzdfcZ1GxFVZn3u3MZrPT8+zt8u67OAwGg/r/7O2zr5+fPXs2PvjgA8ydOxfNmjWDl5cXxo8ff82J5xwOB3r37o0ZM2bkW5b9hTqvp556Cj179sQff/yBNWvW4N1338Xs2bMxZswYdfnAgQPxwQcfYP78+XjsscfUL1g1a9ZETEwM1q5di7/++gujR4/GrFmzsGHDBqfXlzfGZ555xuma1Wy1atUq8vgUdczKpFxf6AoUGuqyqvO2s6LaQFHv3x9//BHPP/88Zs+ejQ4dOsDb2xuzZs3Ctm3briue4taf932j0WiK/BsPGTIE58+fx9y5c1G7dm2YTCZ06NAhX1vJ/iJ/6dIlXLp0qdDzkLe3N3bt2oWIiAisWbMGb7zxBqZOnYrIyMgif0wr6BjmLivotWeX3Ug7Ko6i6sxW7tpUEdzR3IrzeeVwOKDT6bBz507odDqnZUVNflec7a712VdYzMVpizf7/USj0RR6jskdw3333Yfjx4/jjz/+wF9//YVu3brh2WefzTefSHEV9/O8oHPNjXz/ICKi0lGhbxnWpUsXJCcnY+7cuQgLC4NGo0FYWBgiIiIQERHhlHQ3btwYmzZtctp+y5YtqF+/fr4vDEVp3Lgxtm7d6lSW93lezZs3x6lTpxAbG1voPouKrXHjxrDZbE6JxMWLFxEbG4tGjRoVWm+jRo1gs9mcJvSKiYm56fuJZ48qGDBgAFq0aIG6des6TQIDKL13uUcQAEDr1q1x4MABhISEIDQ01OlR1BeomjVrYuTIkVi2bBleeOEFfPnll+qyXr16wcvLC5999hn+/PNPDBs2zGlbs9mMBx98EB9++CEiIiLw77//Yt++fdeMMW98oaGhbpkd16Xq1wd69gTyvv91OqW8FKdVbty4MbZs2eL0pXLLli3w9vZG9erVART899q4cSM6duyI0aNHo1WrVggNDc3XO1fQdjdS/43YuHEjxo4di169eqkTJeadODAuLg7PP/88vvzyS7Rv3x6DBg0qMrHU6/Xo3r07Zs6cib179+LYsWP4+++/C12/sHNA9iiTRo0aFXj+yX1uKaod5WUwGIo83j4+PqhWrdo166xo3NHcQkNDYTAYnD6jkpKSnD6LWrVqBbvdjsTExHznvOyZuQtqQ8XZrnnz5kXeKq6g/ZZ0W9yzZw/S09PV51u3boXFYkGNGjXU83ru92JWVhZ27Njh9F6sXLkyhgwZgsWLF2Pu3Ln44osvCn09AIp8/9/o5zkREZVtFTrp9vX1RcuWLbF48WJ07doVgJKI79q1C7GxsWoZALzwwgtYt24dpk+fjtjYWCxcuBAff/wxXnzxxeuqc+TIkYiLi8OECRMQExODb7/9FgsWLChym7CwMHTp0gUPP/ww1q5di/j4ePz555/qbL3Xiq1evXoIDw/HiBEjsGnTJuzZswcDBgxA9erVER4eXmi9DRo0wL333osRI0Zg27Zt2LlzJ5566qkb6n3ILTQ0FGvXrsWWLVsQHR2NZ555BmfPnnVaJyQkBNu2bcOxY8dw4cIFOBwOPPvss7h06RKeeOIJbN++HUePHsWaNWswbNiwQr+kjB8/HqtXr0Z8fDx27dqFv//+2+mLiU6nw5AhQ/DKK68gNDTUaRjlggUL8NVXX2H//v04evQoFi1aBLPZjNq1a6sx/vPPPzh9+rSaCL300kv4999/8eyzzyIqKgqHDx/Gb7/9pvasVzjffQd07+5c1r27Ul6KRo8ejZMnT2LMmDE4dOgQfv31V0yZMgUTJkxQb3tX0HsqNDQUO3bswOrVqxEbG4vJkyc7zTycvd3evXsRExODCxcuICsr64bqvxGhoaFYtGgRoqOjsW3bNvTv39+p/dntdgwcOBD33HMPhg4divnz52P//v0FzjANKJdQfPjhh4iKisLx48fxzTffwOFwOA0XzstgMGDMmDHYtm0bdu3ahaFDh6J9+/Zo164dAGDixIlYsGABPv/8cxw+fBhz5szBsmXL1PPPtdpRXiEhIVi3bh3Onj2LpEKm5Z44cSJmzJiBH374ATExMXj55ZcRFRWFcePGFeu4llel3dwsFguGDx+OiRMnYt26ddi/fz+GDBni9J6uX78++vfvj0GDBmHZsmWIj49HZGQkZsyYgZUrVwJQ/qYpKSlYt24dLly4gLS0tGJt98orryAyMhKjR4/G3r17cejQIXz22Wfq+bagNl3SbdFqtWL48OE4ePAg/vzzT0yZMgXPPfcctFotvLy8MGrUKEycOBGrVq3CwYMHMWLECKSlpWH48OEAgDfeeAO//vorjhw5ggMHDmDFihWFJse1a9eGRqPBihUrcP78eaSkpORb50Y/z4mIqIwrnUvH3eeFF14QALJ//361rEWLFlK5cuV8M0f//PPP0rhxYzEYDFKrVi2ZNWuW0/KCJtbKO1GKiMjvv/8uoaGhYjKZ5M4775Svv/66yInURJTJa4YOHSqVKlUSDw8Padq0qaxYsaLYsV26dEkGDhwovr6+YjabpWfPnuoMsSIFT3Qjokzydf/994vJZJJatWrJN998U+TERwW93qSkJAEg69evV19LeHi4WCwWqVKlirz++usyaNAgp8mqYmJipH379mI2mwWAxMfHi4gyiUzfvn3Fz89PzGazNGzYUMaPH1/oLN/PPfec3HbbbWIymaRy5coycOBAuXDhgtM6cXFxAkCd+Cbb8uXL5Y477hAfHx/x8vKS9u3by19//aUu//fff6V58+ZiMpkkd1PZvn279OjRQywWi3h5eUnz5s2dJn0q6H2S+xgWdhzLtNhYkZUrXTZ5Wm6FTYgWEREhbdu2FaPRKFWrVpWXXnrJaabqgt5TGRkZMmTIEPH19RU/Pz8ZNWqUvPzyy06TdiUmJqp/z+z3cUF/n2vVX1Dc4eHhMnjw4EJf665du6RNmzZiMpmkXr168tNPPzm9f958800JDg52ek//8ssvYjQa1dhy17tx40YJCwsTf39/MZvN0rx5c3Wm9oJknxeWLl0qdevWFaPRKHfffbccO3bMab1PP/1U6tatKwaDQerXry/ffPONuuxa7Shve/jtt98kNDRU9Hq91K5dW0TyT6Rmt9vlzTfflOrVq4vBYJAWLVrIn3/+qS4vznmoPCvF5ibJyckyYMAA8fT0lKCgIJk5c2a+93L2XQBCQkLEYDBI1apVpW/fvrJ37151nZEjR0qlSpUEgDqTeHG2i4iIkI4dO4rJZBI/Pz/p2bOn+llZ2OfEjbTFgmRPovjGG29IpUqVxGKxyFNPPSUZGRnqOunp6TJmzBgJDAwUk8kknTp1ku3bt6vLp0+fLo0aNRKz2SwBAQESHh4uR48eFZGC36fTpk2TqlWrikajUc8NeeO9kc/z5cuXyy3wlY6IqNzSiPAiIKrYNm/ejK5du+LUqVPqBGZEpPRSjx8//qYvKSEqj4YMGYLLly/jl19+cXcoRERUwd3wRGpEZV1mZiZOnjyJyZMno1+/fky4iYiIiIio1FXoa7rp1vbdd9+hQYMGuHLlCmbOnOnucIiIiIiI6BbE4eVERERERERELsKebiIiIiIiIiIXYdJNRERERERE5CJMuomIiIiIiIhchEk3ERERERERkYsw6SYiIiIiIiJyESbdLtS1a1eMHz9efR4SEoK5c+e6JRZ31k1U3hw7dgwajQZRUVHuDuWa8p5nbtaCBQvg5+enPp86dSpatmxZYvu/Hu6s+1ZW0u+p8mTIkCHo06eP+tydx+JW/jsQEVU0TLpLUWRkJJ5++mmX1pH3C3Np1k3kTjf6BTXvl2wAqFmzJhISEtC0adOSCc6Fli1bhunTp7ts/y+++CLWrVvnsv1n02g0+OWXX9xSN92cwj53blZZSDpd3b4AICIiAhqNBpcvXy71uomIqHTo3R1AabJarTAajW6rv3LlykUuz8rKgsFgcEvdRGWVO9qtTqdD1apVS7XOGxUQEODS/VssFlgslkKXu/Lvc626iYrjZt6j12pfrnz/u7ptExFR6anQPd1du3bFc889hwkTJiAwMBA9evQAAMyZMwfNmjWDl5cXatasidGjRyMlJQUAkJqaCh8fH/z8889O+/r999/h5eWF5OTkAutKTU3FoEGDYLFYEBwcjNmzZ+dbJ+8Qb41Gg88//xzh4eHw8vLCW2+9pdZ1++23w8PDA3Xr1sWbb74Jm82mbnf58mU8/fTTCAoKgoeHB5o2bYoVK1YgIiICQ4cOxZUrV6DRaKDRaDB16tQC6z5x4gTCw8NhsVjg4+ODfv364dy5c+ry7GGdixYtQkhICHx9ffH4448X+vqJSkph7XbDhg1o164dTCYTgoOD8fLLL6vtYsiQIdiwYQPmzZunvvePHTsGu92O4cOHo06dOjCbzWjQoAHmzZun1jV16lQsXLgQv/76q7pdREREgcPLi6o/O+6xY8di0qRJCAgIQNWqVdX2V5jIyEj06NEDgYGB8PX1RVhYGHbt2qUuj4iIgNFoxMaNG9Wy2bNnIzAwEAkJCWq9uXsDP/30U9SrVw8eHh4ICgrCI488UmQMCxYsQK1ateDp6Ym+ffvi4sWLTsvzDvHOHhnw7rvvolq1aqhfvz4A4PTp03jsscfg7++PSpUqITw8HMeOHXPa19dff40mTZqox/C5554DoJyfAKBv377QaDTq87x1OxwOTJs2DTVq1IDJZELLli2xatUqdXn2323ZsmW466674OnpiRYtWuDff/8t8hjcyorz2WW1WjFp0iRUr14dXl5euOOOOxAREQEARX7uFLVdts2bNyMsLAyenp7w9/dHz549kZSUVGibBorXFgs6h+Rlt9sxYcIE+Pn5oVKlSpg0aRJExGmdgi4Te+uttzBkyBD4+vpixIgRAIAtW7agS5cuMJvNqFmzJsaOHYvU1FR1u8zMTEyaNAk1a9aEyWRCvXr18NVXX+HYsWO46667AAD+/v7QaDQYMmRIgXUnJSVh0KBB8Pf3h6enJ+677z4cPnxYXZ494mD16tVo1KgRLBYL7r33XvVcQUREbiQ3yWG3Ff5w2Iu/rt12zXWvV1hYmFgsFpk4caIcOnRIoqOjRUTkgw8+kL///luOHj0q69atkwYNGsioUaPU7UaMGCG9evVy2lffvn1l0KBBhdY1atQoqVGjhqxZs0b27t0rDzzwgFgsFhk3bpy6Tu3ateWDDz5QnwOQKlWqyFdffSVxcXFy7NgxWbVqlfj4+MiCBQskLi5O1qxZIyEhITJ16lQREbHb7dK+fXtp0qSJrFmzRuLi4uT333+XlStXSmZmpsydO1d8fHwkISFBEhISJDk5OV/dDodDWrVqJZ07d5YdO3bI1q1bpXXr1hIWFqbGNmXKFLFYLPLQQw/Jvn375J9//pGqVavKq6++et1/BypDHA6RlJTSfzgcxQ6xoHZ76tQp8fT0lNGjR0t0dLQsX75cAgMDZcqUKSIicvnyZenQoYOMGDFCfe/bbDaxWq3yxhtvyPbt2+Xo0aOyePFi8fT0lB9++EFERJKTk6Vfv35y7733qttlZmZKfHy8AJDdu3eLiFyz/uy4fXx8ZOrUqRIbGysLFy4UjUYja9asKfS1rlu3ThYtWiQHDx6UgwcPyvDhwyUoKEiuXr2qrjNx4kSpXbu2XL58WaKiosRkMsmyZcuc6s0+z0RGRopOp5Nvv/1Wjh07Jrt27ZJ58+YVWv/WrVtFo9HIu+++KzExMTJv3jzx8/MTX19fdZ0pU6ZIixYt1OeDBw8Wi8UiAwcOlP3798u+ffskNTVV6tWrJ8OGDZO9e/fKwYMH5cknn5QGDRpIZmamiIh8+umn4uHhIXPnzpWYmBjZvn27ek5KTEwUADJ//nxJSEiQxMTEAuueM2eO+Pj4yHfffSeHDh2SSZMmicFgkNjYWBER9e/WsGFDWbFihcTExMgjjzwitWvXlqysrEKPgyuUg6YmIsX77HryySelY8eO8s8//8iRI0dk1qxZYjKZJDY2tsjPnaK2ExHZvXu3mEwmGTVqlERFRcn+/fvlo48+kvPnzxfapovbFgv67M9rxowZ4uvrKz///LPa/ry9vSU8PNxpX3k/x318fGTWrFly+PBhOXz4sOzdu1csFot88MEHEhsbK5s3b5ZWrVrJkCFD1O369esnNWvWlGXLlklcXJz89ddf8v3334vNZpOlS5cKAImJiZGEhAS5fPlygXU/+OCD0qhRI/nnn38kKipKevbsKaGhoWK1WkVEZP78+WIwGKR79+4SGRkpO3fulEaNGsmTTz55fW8KIiIqcTeddF88taXQx9ULzh90l05vLXzd8/ud1k06E5lvnesVFhYmLVu2vOZ6P/74o1SqVEl9vm3bNtHpdHL69GkRETl//rwYDAaJiIgocPvk5GQxGo3y/fffq2UXL14Us9l8zaR7/PjxTvu688475Z133nEqW7RokQQHB4uIyOrVq0Wr1UpMTEyBscyfP9/pC3NBda9Zs0Z0Op2cOHFCXX7gwAEBINu3bxcR5cuup6dnvi//d9xxR4H1UjmRkiIClP4jJaXYIRbUbl999VVp0KCBOHJlFJ988olYLBax2+3qdrnbW2FGjx4tDz/8sPp88ODBTl+yRSRf0l3c+jt37uy0n7Zt28pLL710zZiy2Ww28fb2lt9//10ty8zMlFatWkm/fv2kSZMm8tRTTzltk/t1L126VHx8fJzabVGeeOIJuffee53KHnvssWsm3UFBQWoyLSLy1Vdf5Ts+mZmZYjabZfXq1SIiUq1aNXnttdcKjQWALF++3Kksb93VqlWTt99+22mdtm3byujRo0Uk5+/2v//9T12efW4rLPFylXLQ1Ir12XXkyBHRaDTq52G2bt26ySuvvCIiBX/uFGe7J554Qjp16lRofAW16eK2xeJ89gcHB8t7772nPs/KypIaNWpcM+nu06eP034GDhwoTz/9tFPZxo0bRavVSnp6usTExAgAWbt2bYFxrF+/XgBIUlKSU3nuumNjYwWAbN68WV1+4cIFMZvN8uOPP4qI8ncAIEeOHFHX+eSTTyQoKOiax4KIiFyrQg8vB4A2bdrkK1u/fj169OiB6tWrw9vbG4MGDcLFixfVoWDt2rVDkyZN8M033wAAFi1ahFq1aqFLly4F1hEXFwer1YoOHTqoZQEBAWjQoMF1x7dz505MmzZNvZbRYrFgxIgRSEhIQFpaGqKiolCjRg11SOeNiI6ORs2aNVGzZk21rHHjxvDz80N0dLRaFhISAm9vb/V5cHAwEhMTb7heouLK2y6io6PRoUMHaDQataxTp05ISUnBqVOnitzX559/jjZt2qBy5cqwWCz48ssvceLEieuKp7j1N2/e3Gm7a7WZxMREjBw5EvXr14evry98fX2RkpLiFJ/RaMTixYuxdOlSpKenF3kXgh49eqB27dqoW7cuBg4ciCVLliAtLe2aryu3vM8L0qxZM6frWHfu3IkjR47A29tbPW8FBAQgIyMDcXFxSExMxJkzZ9CtW7dr7rswV69exZkzZ9CpUyen8k6dOjmdtwDnv0NwcDAA8NxVgOJ8du3atQsigvr16zt9Lm3YsAFxcXGF7rs420VFRV33e6K4bbGgz/7crly5goSEBKfXrtfrr7ldQfveuXMnFixY4PQ6e/bsCYfDgfj4eERFRUGn0yEsLKy4LzOf6Oho6PV63HHHHWpZpUqV0KBBA6f3v6enJ2677Tb1OT+3iYjKhpueSM0/uF3hC3N9KAKAX9Vrf5hl8w1qdaMhOfHy8nJ6fvz4cfTq1QsjR47E9OnTERAQgE2bNmH48OHIyspS13vqqafw8ccf4+WXX8b8+fMxdOhQpw/53CTPNWA3E5/D4cCbb76Jhx56KN+6Hh4eMJvNN1xXNhEp8LXkLc87qZtGo4HD4bjp+smNPD2B/+YvKPV6r0PedlHQeza73RXWLgHgxx9/xPPPP4/Zs2ejQ4cO8Pb2xqxZs7Bt27briqe49V9vmxkyZAjOnz+PuXPnonbt2jCZTOjQoQOsVqvTelu2bAEAXLp0CZcuXcp3fLJ5e3tj165diIiIwJo1a/DGG29g6tSpiIyMLHB26Rs9dxV03rr99tuxZMmSfOtWrlwZWm3J/b5b0N8hb1nuv0P2stI+d5WHplacv7/D4YBOp8POnTuh0+mclhU1yV1xtruRz7PitsXC2khJKOj9/8wzz2Ds2LH51q1VqxaOHDly03UW9rcqzuf2zXxHISKiknHTSbdGq7v2Si5e93rs2LEDNpsNs2fPVr8I/vjjj/nWGzBgACZNmoQPP/wQBw4cwODBgwvdZ2hoKAwGA7Zu3YpatWoBUCY8iY2Nve5ftlu3bo2YmBiEhoYWuLx58+Y4deoUYmNjC+ztNhqNsNvtRdbRuHFjnDhxAidPnlR7uw8ePIgrV66gUaNG1xUvlTMaDeDCL6Ou0rhxYyxdutTpC+aWLVvg7e2N6tWrAyj4vb9x40Z07NgRo0ePVsvy9s4Vt81cq/4bsXHjRnz66afo1asXAODkyZO4cOGC0zpxcXF4/vnn8eWXX+LHH3/EoEGDsG7dukITWb1ej+7du6N79+6YMmUK/Pz88Pfffxf4Q17jxo2xdetWp7K8z4ujdevW+OGHH1ClShX4+PgUuE5ISAjWrVunThqVl8FgKPLv4OPjg2rVqmHTpk1Oo462bNmCdu2K+PHXTcpDUyvOZ1erVq1gt9uRmJiIO++8s8D9FNSGirNd8+bNsW7dOrz55pvF3m9JtUVfX18EBwdj69at6vvJZrNh586daN26dbH3Ayjv/wMHDhT6ud2sWTM4HA5s2LAB3bt3z7c8e9RIUe//xo0bw2azYdu2bejYsSMA4OLFi4iNjeXnNhFROVDhh5fnddttt8Fms+Gjjz7C0aNHsWjRInz++ef51vP398dDDz2EiRMn4p577kGNGjUK3afFYsHw4cMxceJErFu3Dvv378eQIUNuqHfnjTfewDfffIOpU6fiwIEDiI6Oxg8//IDXX38dABAWFoYuXbrg4Ycfxtq1axEfH48///xTncE3JCQEKSkpWLduHS5cuFDg0NLu3bujefPm6N+/P3bt2oXt27dj0KBBCAsLK9bQOqLSNnr0aJw8eRJjxozBoUOH8Ouvv2LKlCmYMGGC2s5CQkKwbds2HDt2DBcuXIDD4UBoaCh27NiB1atXIzY2FpMnT0ZkZKTTvkNCQrB3717ExMTgwoULTiNerqf+GxEaGopFixYhOjoa27ZtQ//+/Z16/+x2OwYOHIh77rkHQ4cOxfz587F///4CZ5gGgBUrVuDDDz9EVFQUjh8/jm+++QYOh6PQS13Gjh2LVatWYebMmYiNjcXHH3/sNBt4cfXv3x+BgYEIDw/Hxo0bER8fjw0bNmDcuHHqkN+pU6di9uzZ+PDDD3H48GHs2rULH330kbqP7KT87NmzSEpKKrCeiRMnYsaMGfjhhx8QExODl19+GVFRURg3btx1x0zF++yqX78++vfvj0GDBmHZsmWIj49HZGQkZsyYgZUrVwIo+HOnONu98soriIyMxOjRo7F3714cOnQIn332mfrDU0FtuiTb4rhx4/Dee+9h+fLlOHToEEaPHp3vXtnF8dJLL+Hff//Fs88+i6ioKBw+fBi//fYbxowZo76OwYMHY9iwYfjll18QHx+PiIgI9Qf/2rVrQ6PRYMWKFTh//rx6N5Xc6tWrh/DwcIwYMQKbNm3Cnj17MGDAAFSvXh3h4eHXHTMREZWy0r2EvHQVNrHSnDlzJDg4WMxms/Ts2VO++eabAicxWbdunQBQJykpSnJysgwYMEA8PT0lKChIZs6cWeAELHknUss7cZCIyKpVq6Rjx45iNpvFx8dH2rVrJ1988YW6/OLFizJ06FCpVKmSeHh4SNOmTWXFihXq8pEjR0qlSpUEgDqja966jx8/Lg8++KB4eXmJt7e3PProo3L27Fl1ed4JjESUWd9r1659zWNBdDMKa7cRERHStm1bMRqNUrVqVXnppZecZqSOiYmR9u3bi9lsFgASHx8vGRkZMmTIEPH19RU/Pz8ZNWqUvPzyy07v7cTEROnRo4dYLBYBIOvXr883kVpx6i8o7vDwcBk8eHChr3XXrl3Spk0bMZlMUq9ePfnpp5+c2uqbb74pwcHBcuHCBXWbX375RYxGoxpb7no3btwoYWFh4u/vL2azWZo3b67O1F6Yr776SmrUqCFms1l69+4t77///jUnUss78ZyISEJCggwaNEgCAwPFZDJJ3bp1ZcSIEXLlyhV1nc8//1waNGggBoNBgoODZcyYMeqy3377TUJDQ0Wv16vnmbx12+12efPNN6V69epiMBikRYsW8ueff6rLC/q7JSUlqX9Xyq84n13ZdwEICQkRg8EgVatWlb59+8revXvVdQr63CnOdhEREdKxY0cxmUzi5+cnPXv2VD+LC2rT2dtcb1ssSFZWlowbN058fHzEz89PJkyYIIMGDbrmRGq5P0uzbd++XT2PeHl5SfPmzZ0m/UtPT5fnn39egoODxWg0SmhoqHz99dfq8mnTpknVqlVFo9Go54y8dV+6dEkGDhwovr6+6veX7JngRQqe0G758uVSwb/qERGVCxoRXuxTmCVLlmDcuHE4c+aM06RBRERERERERMVx09d0V0RpaWmIj4/Hu+++i2eeeYYJNxEREREREd2QW+6a7uKYOXMmWrZsiaCgILzyyivuDoeIiIiIiIjKKQ4vJyIiIiIiInIR9nQTERERERERuQiTbiIiIiIiIiIXYdJNRERERERE5CJMuomIiIiIiIhchEk3ERERERERkYsw6SYiIiIiIiJyESbdRFSmdO3aFePHj3d3GES3LLZBIiKiksWkm4jKlGXLlmH69OnuDuOWdOzYMWg0GkRFRbk7FConFixYAD8/vxLfLxN/IiKqSPTuDoCIKLeAgAB3h0DXYLVaYTQa3R0G0TXxvUpERGUBe7qJqEzJ28MVEhKCt956C4MGDYLFYkHt2rXx66+/4vz58wgPD4fFYkGzZs2wY8cOdZuLFy/iiSeeQI0aNeDp6YlmzZrhu+++c6onOTkZ/fv3h5eXF4KDg/HBBx/kq9tqtWLSpEmoXr06vLy8cMcddyAiIqLI+A8dOoTOnTvDw8MDjRs3xl9//QWNRoNffvlFXef06dN47LHH4O/vj0qVKiE8PBzHjh1TlzscDkybNg01atSAyWRCy5YtsWrVKnV5do/0jz/+iDvvvBNmsxlt27ZFbGwsIiMj0aZNG1gsFtx77704f/68U3zz589Ho0aN4OHhgYYNG+LTTz9Vl9WpUwcA0KpVK2g0GnTt2hUAMGTIEPTp0wfvvvsuqlWrhvr162PatGlo1qxZvtd/++2344033ijyGFHZkZqaqrat4OBgzJ4922l5UW0gIiICQ4cOxZUrV6DRaKDRaDB16tRrbpdt8+bNCAsLg6enJ/z9/dGzZ08kJSVhyJAh2LBhA+bNm6fuN7t9bNiwAe3atYPJZEJwcDBefvll2Gw2dZ9du3bFc889hwkTJiAwMBA9evRw1aEjIiIqPiGiW09KivJwOHLKMjOVsoyMgte123PKrFalLD392utep7CwMBk3bpz6vHbt2hIQECCff/65xMbGyqhRo8Tb21vuvfde+fHHHyUmJkb69OkjjRo1Esd/r+fUqVMya9Ys2b17t8TFxcmHH34oOp1Otm7dqu73qaeektq1a8tff/0l+/btk759+4q3t7dT3U8++aR07NhR/vnnHzly5IjMmjVLTCaTxMbGFhi73W6XBg0aSI8ePSQqKko2btwo7dq1EwCyfPlyERFJTU2VevXqybBhw2Tv3r1y8OBBefLJJ6VBgwaSmZkpIiJz5swRHx8f+e677+TQoUMyadIkMRgMar3x8fECQBo2bCirVq2SgwcPSvv27aV169bStWtX2bRpk+zatUtCQ0Nl5MiRanxffPGFBAcHy9KlS+Xo0aOydOlSCQgIkAULFoiIyPbt2wWA/PXXX5KQkCAXL14UEZHBgweLxWKRgQMHyv79+2Xfvn1y8uRJ0Wq1sn37dnX/e/bsEY1GI3FxcTf41694ynBTExGRUaNGSY0aNWTNmjWyd+9eeeCBB8RisajtoKg2kJmZKXPnzhUfHx9JSEiQhIQESU5OvuZ2IiK7d+8Wk8kko0aNkqioKNm/f7989NFHcv78ebl8+bJ06NBBRowYoe7XZrPJqVOnxNPTU0aPHi3R0dGyfPlyCQwMlClTpqivJywsTCwWi0ycOFEOHTok0dHRN3eAiIiISgCTbqJbEaA8EhNzyt56Syl76inndT09lfL4+JyyDz5Qyp580nndwEClfP/+Gw6toKR7wIAB6vOEhAQBIJMnT1bL/v33XwEgCQkJhe63V69e8sILL4iIyNWrV8VgMMhPP/2kLr98+bJ4enqqdR85ckQ0Go2cPn3aaT/dunWTV155pcA6/vzzT9Hr9U5xrF271inp/uqrr6RBgwbqDwQiIpmZmWI2m2X16tUiIlKtWjV5++23nfbdtm1bGT16tIjkJN3/+9//1OXfffedAJB169apZe+++640aNBAfV6zZk359ttvnfY7ffp06dChg9N+d+/e7bTO4MGDJSgoSP1RINt9990no0aNUp+PHz9eunbtWuCxuVWV4aYmycnJYjQa5fvvv1fLLl68KGazWcaNG1esNjB//nzx9fV1Wl6c7Z544gnp1KlTobHlPQ+IiLz66qv52s4nn3wiFotF7P/9+hAWFiYtW7Ys3gEgIiIqJbymm4jKvObNm6v/DwoKAgCnoc3ZZYmJiahatSrsdjvee+89/PDDDzh9+jQyMzORmZkJLy8vAMDRo0eRlZWFdu3aqfvw9fVFgwYN1Oe7du2CiKB+/fpOsWRmZqJSpUoFxhkTE4OaNWuiatWqalnuOgBg586dOHLkCLy9vZ3KMzIyEBcXh6tXr+LMmTPo1KmT0/JOnTphz549131cEhMTAQDnz5/HyZMnMXz4cIwYMUJdx2azwdfXt8DXk1uzZs3yXRs7YsQIDBs2DHPmzIFOp8OSJUvyDU+msisuLg5WqxUdOnRQywICAtR2cCNtoLjbRUVF4dFHH72ueKOjo9GhQwdoNBq1rFOnTkhJScGpU6dQq1YtAECbNm2ua79ERESuxqSb6FaUkqL86+mZUzZxIjB+PKDPc1r4L2mD2ZxT9uyzwIgRgE7nvG72dcm51y0BBoNB/X/2F+6CyhwOBwBg9uzZ+OCDDzB37lw0a9YMXl5eGD9+PKxWKwBARJy2y5Zdnr0vnU6HnTt3QpfndVoslgLjFJF8+8zL4XDg9ttvx5IlS/Itq1y5cr7XVNS+i3Ncso9J9r9ffvkl7rjjDqf95H19Bcn+wSK33r17w2QyYfny5TCZTMjMzMTDDz98zX3dSspyU8v9fi/IjbSB4m5nvoHAC2oDBbXlgt6rRERE7sSkm+hWVNCXUqNReRRnXYNBeRRnXTfYuHEjwsPDMWDAAABKEnD48GE0atQIAHDbbbfBYDBg+/btqFmzJgDg6tWrOHz4MMLCwgAok4nZ7XYkJibizjvvLFa9DRs2xIkTJ3Du3Dm15zkyMtJpndatW+OHH35AlSpV4OPjU+B+qlWrhk2bNqFLly5q2ZYtW/L1ml+PoKAgVK9eHUePHkX//v0LXCe7J9tutxdrn3q9HoMHD8b8+fNhMpnw+OOPwzN3dklluqmFhobCYDBg69atai9xUlISYmNjERYWVqw2YDQa871firNd8+bNsW7dOrz55pvF3m/jxo2xdOlSp+R7y5Yt8Pb2RvXq1a/rtRMREZUmzl5ORBVOaGgo1q5diy1btiA6OhrPPPMMzp49qy739vbG4MGDMXHiRKxfvx4HDhzAsGHDoNVq1S/z9evXR//+/TFo0CAsW7YM8fHxiIyMxIwZM7By5coC6+3Rowduu+02DB48GHv37sXmzZvx2muvAcjpievfvz8CAwMRHh6OjRs3Ij4+Hhs2bMC4ceNw6tQpAMDEiRMxY8YM/PDDD4iJicHLL7+MqKgojBs37qaOy9SpU/Huu+9i3rx5iI2Nxb59+zB//nzMmTMHAFClShWYzWasWrUK586dw5UrV665z6eeegp///03/vzzTwwbNuym4qPSZbFYMHz4cEycOBHr1q3D/v37MWTIEGi1yleD4rSBkJAQpKSkYN26dbhw4QLS0tKKtd0rr7yCyMhIjB49Gnv37sWhQ4fw2Wef4cKFC+p+t23bhmPHjuHChQtwOBwYPXo0Tp48iTFjxuDQoUP49ddfMWXKFEyYMEGNmYiIqCzipxQRVTiTJ09G69at0bNnT3Tt2hVVq1ZFnz59nNaZM2cOOnTogAceeADdu3dHp06d1FtpZZs/fz4GDRqEF154AQ0aNMCDDz6Ibdu2qb3jeel0Ovzyyy9ISUlB27Zt8dRTT+H1118HAHW/np6e+Oeff1CrVi089NBDaNSoEYYNG4b09HS153vs2LF44YUX8MILL6BZs2ZYtWoVfvvtN9SrV++mjstTTz2F//3vf1iwYAGaNWuGsLAwLFiwQL1VmF6vx4cffoj/+7//Q7Vq1RAeHn7NfdarVw8dO3ZEgwYN8g1bp7Jv1qxZ6NKlCx588EF0794dnTt3xu23364uv1Yb6NixI0aOHInHHnsMlStXxsyZM4u1Xf369bFmzRrs2bMH7dq1Q4cOHfDrr79C/9+Y+xdffBE6nQ6NGzdG5cqVceLECVSvXh0rV67E9u3b0aJFC4wcORLDhw9X2xgREVFZpZFrXdRFRHQLSE1NRfXq1TF79mwMHz68xPa7efNmdO7cGUeOHMFtt91WYvstK0QEDRs2xDPPPIMJEya4OxwiIiKiMofXdBPRLWn37t04dOgQ2rVrhytXrmDatGkAUKze3aIsX74cFosF9erVw5EjRzBu3Dh06tSpQibciYmJWLRoEU6fPo2hQ4e6OxwiIiKiMolJNxHdst5//33ExMTAaDTi9ttvx8aNGxEYGHhT+0xOTsakSZNw8uRJBAYGonv37hX2NlpBQUEIDAzEF198AX9/f3eHQ0RERFQmcXg5ERERERERkYtwIjUiIiIiIiIiF2HSTUREREREROQiTLqJiIiIiIiIXIRJNxEREREREZGLMOkmIiIiIiIichEm3UREREREREQuwqSbiMq8rl27Yvz48e4O47pMnToVLVu2vK5tSup1Xrx4EVWqVMGxY8duel8l5UaOR14rVqxAq1at4HA4SiYoKhDb2/W5kfa2YMEC+Pn53XTd2Y4dOwaNRoOoqKgb3kdmZiZq1aqFnTt3llhcRESkYNJNRBVOREQENBoNLl++7LYYXnzxRaxbt+66tlm2bBmmT59+03W/++676N27N0JCQm56X6XpxIkT6N27N7y8vBAYGIixY8fCarWqyx944AFoNBp8++23boyS8mJ7u/729thjjyE2Nvam674eIoKpU6eiWrVqMJvN6Nq1Kw4cOKAuN5lMePHFF/HSSy+ValxERLcCJt1ElN8PPwBvv130Om+/raxHTkQENpsNFosFlSpVuq5tAwIC4O3tfVP1p6en46uvvsJTTz11U/spbXa7Hffffz9SU1OxadMmfP/991i6dCleeOEFp/WGDh2Kjz76yE1Rugab240rr+3NbDajSpUqN1X39Zo5cybmzJmDjz/+GJGRkahatSp69OiB5ORkdZ3+/ftj48aNiI6OLtXYiIgqOibdRJTfkSPA668DhfUCTZ+uLD9ypMSrTk1NxaBBg2CxWBAcHIzZs2fnW2fx4sVo06YNvL29UbVqVTz55JNITEwEoAyzvOuuuwAA/v7+0Gg0GDJkCABg1apV6Ny5M/z8/FCpUiU88MADiIuLKzKezMxMjB07FlWqVIGHhwc6d+6MyMhIdXl2L9/q1avRpk0bmEwmbNy4Md9wV5vNhrFjx6p1v/TSSxg8eDD69OmjrpN3uGtISAjeeecdDBs2DN7e3qhVqxa++OKLIuP9888/odfr0aFDBwCAw+FAjRo18Pnnnzutt2vXLmg0Ghw9ehSA0sscHh4Oi8UCHx8f9OvXD+fOnQMAHDp0CJ6enk49zMuWLYOHhwf27dsHALhy5QqefvppVKlSBT4+Prj77ruxZ8+eImPNbc2aNTh48CAWL16MVq1aoXv37pg9eza+/PJLXL16VV3vwQcfxPbt29W4KwI3Nje2txJub7lj/OOPP9CiRQt4eHjgjjvuUNsK4Dy8XETQvXt33HvvvRARAMDly5dRq1YtvPbaa+o28+fPR6NGjeDh4YGGDRvi008/LTK23EQEc+fOxWuvvYaHHnoITZs2xcKFC5GWlubUritVqoSOHTviu+++K/a+iYjo2ph0E1F+r70GTJsGvPFG/kxg+nSlfNo0Zb0SNnHiRKxfvx7Lly/HmjVrEBERke8aQ6vViunTp2PPnj345ZdfEB8fr37Rr1mzJpYuXQoAiImJQUJCAubNmwdASTAmTJiAyMhIrFu3DlqtFn379i3yGuFJkyZh6dKlWLhwIXbt2oXQ0FD07NkTly5dyrfeu+++i+joaDRv3jzffmbMmIElS5Zg/vz52Lx5M65evYpffvnlmsdj9uzZaNOmDXbv3o3Ro0dj1KhROHToUKHr//PPP2jTpo36XKvV4vHHH8eSJUuc1vv222/RoUMH1K1bFyKCPn364NKlS9iwYQPWrl2LuLg4PPbYYwCAhg0b4v3338fo0aNx/PhxnDlzBiNGjMB7772HZs2aQURw//334+zZs1i5ciV27tyJ1q1bo1u3bvmOU2H+/fdfNG3aFNWqVVPLevbsiczMTKe/f+3atVGlShVs3LixWPstD9zY3Nje8rjZ9pbbxIkT8f777yMyMhJVqlTBgw8+iKysrHzraTQaLFy4ENu3b8eHH34IABg5ciSCgoIwdepUAMCXX36J1157DW+//Taio6PxzjvvYPLkyVi4cOE1XxMAxMfH4+zZs7jnnnvUMpPJhLCwMGzZssVp3Xbt2lWo9kVEVCYIEVFhpk0TAZR/C3pewpKTk8VoNMr333+vll28eFHMZrOMGzeu0O22b98uACQ5OVlERNavXy8AJCkpqcj6EhMTBYDs27evwOUpKSliMBhkyZIlapnVapVq1arJzJkzner65ZdfnLadMmWKtGjRQn0eFBQks2bNUp/bbDapVauWhIeHq2VhYWFOr7N27doyYMAA9bnD4ZAqVarIZ599VuhrCg8Pl2HDhjmV7dq1SzQajRw7dkxEROx2u1SvXl0++eQTERFZs2aN6HQ6OXHihLrNgQMHBIBs375dLbv//vvlzjvvlG7dukmPHj3E4XCIiMi6devEx8dHMjIynOq97bbb5P/+7/8KPB55jRgxQnr06JGv3Gg0yrfffutU1qpVK5k6dWqh+yqvSrm5sb25qL1lx1jQcf3hhx9ERGT+/Pni6+vrtN2PP/4oJpNJXnnlFfH09JSYmBh1Wc2aNfO1g+nTp0uHDh1ERCQ+Pl4AyO7duwuMc/PmzQJATp8+7VQ+YsQIueeee5zK5s2bJyEhIYW+ZiIiun7s6Saiwk2enNMFZzLldLlNnuyS6uLi4mC1Wp2GagYEBKBBgwZO6+3evRvh4eGoXbs2vL290bVrVwDKEOlr7f/JJ59E3bp14ePjgzp16hS5XVxcHLKystCpUye1zGAwoF27dvmueSystwtQhl6fO3cO7dq1U8t0Oh1uv/32IuMF4NSLp9FoULVqVXVob0HS09Ph4eHhVNaqVSs0bNhQHTK6YcMGJCYmol+/fgCA6Oho1KxZEzVr1lS3ady4Mfz8/Jxe59dff429e/di165dWLBgATQaDQBg586dSElJQaVKlWCxWNRHfHz8NYcT55a9v9xEJF+52WxGWlpasfdbXpRyc2N7K0BJtLdsBR3Xoq6VfvTRR/HQQw/h3XffxezZs1G/fn0AwPnz53Hy5EkMHz7cqX299dZb19W+sl9TbrdS+yIicie9uwMgojJu8mTgrbcAqxUwGl2XAQDq9YxFSU1NxT333IN77rkHixcvRuXKlXHixAn07NnTaabrgvTu3Rs1a9bEl19+iWrVqsHhcKBp06aFbpcdT3G+qHp5eV0z9oL2cy0GgyHfPooanhsYGIikpKR85f3798e3336Ll19+Gd9++y169uyJwMBANY7iJLx79uxBamoqtFotzp49qw4FdzgcCA4ORkRERL59FPe2SFWrVsW2bducypKSkpCVlYWgoCCn8kuXLqFy5crF2m95U4rNje2tACXV3oobU25paWnYuXMndDodDh8+rJZn1//ll1/ijjvucNpGp9MVq96qVasCAM6ePYvg4GC1PDEx8ZZqX0RE7sKebiIq2vTpORmA1Vr4bE8lIDQ0FAaDAVu3blXLkpKSnG6tc+jQIVy4cAHvvfce7rzzTjRs2DBfT5TRaASgzIid7eLFi4iOjsbrr7+Obt26oVGjRtf8shwaGgqj0YhNmzapZVlZWdixYwcaNWpU7Nfl6+uLoKAgbN++XS2z2+3YvXt3sfdRXK1atcLBgwfzlT/55JPYt28fdu7ciZ9//hn9+/dXlzVu3BgnTpzAyZMn1bKDBw/iypUr6uu8dOkShgwZgtdeew1Dhw5F//79kZ6eDgBo3bo1zp49C71ej9DQUKdHdmJ/LR06dMD+/fuRkJCglq1ZswYmk8mphzIjIwNxcXFo1arV9R2YcqIUmxvbWwkorL0BKPC4NmzYsNB9vfDCC9Bqtfjzzz/x4Ycf4u+//wYABAUFoXr16jh69Gi+9pU9euBa6tSpg6pVq2Lt2rVqmdVqxYYNG9CxY0endffv319h2xcRkbsw6SaiwuWexSkzs/DZnkqIxWLB8OHDMXHiRKxbtw779+/HkCFDoNXmnKpq1aoFo9GIjz76CEePHsVvv/2W7167tWvXhkajwYoVK3D+/HmkpKTA398flSpVwhdffIEjR47g77//xoQJE4qMx8vLC6NGjcLEiROxatUqHDx4ECNGjEBaWhqGDx9+Xa9tzJgxePfdd/Hrr78iJiYG48aNQ1JSUpE9XzeiZ8+eOHDgQL4Ep06dOujYsSOGDx8Om82G8PBwdVn37t3RvHlz9O/fH7t27cL27dsxaNAghIWFqcN4R44ciZo1a+L111/HnDlzICJ48cUX1e07dOiAPn36YPXq1Th27Bi2bNmC119/HTt27ChW3Pfccw8aN26MgQMHYvfu3Vi3bh1efPFFjBgxAj4+Pup6W7duhclkchq6W1GUcnNjeysBhbU3AJg2bZrTcQ0MDHSaPT23P/74A19//TWWLFmCHj164OWXX8bgwYPV/U6dOhXvvvsu5s2bh9jYWOzbtw/z58/HnDlzihWnRqPB+PHj8c4772D58uVqTJ6ennjyySed1t24caPThGtERFQC3HIlORGVfYXN4lQKk6kNGDBAPD09JSgoSGbOnJlvwqNvv/1WQkJCxGQySYcOHeS3337LN4nQtGnTpGrVqqLRaGTw4MEiIrJ27Vpp1KiRmEwmad68uURERAgAWb58eaHxpKeny5gxYyQwMFBMJpN06tTJaXKxwiaRyjuxU1ZWljz33HPi4+Mj/v7+8tJLL8mjjz4qjz/+uLpOQRM7ffDBB077bdGihUyZMqXIY9i+fXv5/PPP85V/8sknAkAGDRqUb9nx48flwQcfFC8vL/H29pZHH31Uzp49KyIiCxcuFC8vL4mNjVXX37FjhxiNRvnjjz9EROTq1asyZswYqVatmhgMBqlZs6b0799fnZztWhOpZcdw//33i9lsloCAAHnuuefyTc729NNPyzPPPFPkfsojNzU3tjcXtLfsGH///Xdp0qSJGI1Gadu2rURFRanr5J5ILTExUYKCguSdd95xir9du3bSr18/tWzJkiXSsmVLMRqN4u/vL126dJFly5aJyLUnUhNRJoabMmWKVK1aVUwmk3Tp0iXfpHZbtmwRPz8/SUtLK/I1ExHR9dGIFOMiJyK6tbz9tnJj4MJmccruknvrLdfcx+gW4HA40KhRI/Tr1y9fz+HNWrlyJV588UXs37/fqdeyvDt//jwaNmyIHTt2FHtYbXnA5uZ6pdneIiIicNdddyEpKanYcxqUFY8++ihatWqFV1991d2hEBFVKJxIjYjyCw0t+hv+5MmAVqusR8Vy/PhxrFmzBmFhYcjMzMTHH3+M+Pj4fEM7S0KvXr1w+PBhnD592mlG8vIuPj4en376aYVKuAE2N1dge7t+mZmZaNGiBZ5//nl3h0JEVOGwp5uIqBScPHkSjz/+OPbv3w8RQdOmTfHee++hS5cu7g6NqMJxZ3srzz3dRETkGky6iYiIiIiIiFyk4lzsR0RERERERFTGMOkmIiIiIiIichEm3UREREREREQuwqSbiIiIiIiIyEWYdBMRERERERG5CJNuIiIiIiIiIhdh0k1ERERERETkIky6iYgK0LVrV4wfP97tdbszjtKuf968eahTpw48PT3Rp08fXLlypVTqJSIiInIlvbsDICIqKV27dkXLli0xd+7cm95u2bJlMBgMJRvgDSitOAo7dqVV/6uvvoqffvoJCxcuhMViQd++ffHmm29izpw5Lq+biIiIyJXY001EZZ7Vai31OgMCAuDt7V3q9V5vHK4+NqVxHCIjIzFjxgz88MMP6NKlC1q3bo1nnnkGK1ascGm9RERERKWBSTfRrSg1tfBHRkbx101Pv/a6N6Br16547rnnMGHCBAQGBqJHjx7IzMzE2LFjUaVKFXh4eKBz586IjIxUtxkyZAg2bNiAefPmQaPRQKPR4NixY1i1ahU6d+4MPz8/VKpUCQ888ADi4uKuuV3eYdXXqr9r164YO3YsJk2ahICAAFStWhVTp0695mtNTU3FoEGDYLFYEBwcjNmzZ+c7FrnjKOjYAICIYObMmahbty7MZjNatGiBn3/+2WlfDocDM2bMQGhoKEwmE2rVqoW333670GNQUP2uOA7vv/8+7r77brRu3Votq1y5Mi5cuHDN40dERERU1jHpJroVWSyFPx5+2HndKlUKX/e++5zXDQnJv84NWrhwIfR6PTZv3oz/+7//w6RJk7B06VIsXLgQu3btQmhoKHr27IlLly4BUK4H7tChA0aMGIGEhAQkJCSgZs2aSE1NxYQJExAZGYl169ZBq9Wib9++cDgcRW6X17Xqz47Zy8sL27Ztw8yZMzFt2jSsXbu2yNc5ceJErF+/HsuXL8eaNWsQERGBnTt3XtexAYDXX38d8+fPx2effYYDBw7g+eefx4ABA7BhwwZ1u1deeQUzZszA5MmTcfDgQXz77bcICgoq9jFwxXHIzMzE77//jr59+zqVp6enw9fXt8jjQERERFQuCBHdeoDCH716Oa/r6Vn4umFhzusGBuZf5waEhYVJy5Yt1ecpKSliMBhkyZIlapnVapVq1arJzJkznbYbN25ckftOTEwUALJv374it8tdVpz6w8LCpHPnzk77aNu2rbz00kuFxpKcnCxGo1G+//57tezixYtiNpvVuvPGlvfYZMfn4eEhW7ZscSofPny4PPHEEyIicvXqVTGZTPLll18WGEthx87Vx2HLli0CQDw8PMTLy0t9GI1G6dmzZ4HbEBEREZUnnEiN6FaUklL4Mp3O+XliYuHravMMlvlvSHJJaNOmjfr/uLg4ZGVloVOnTmqZwWBAu3btEB0dXeR+4uLiMHnyZGzduhUXLlxQe7hPnDiBpk2bFiuW4tbfvHlzp+2Cg4ORWMTxi4uLg9VqRYcOHdSygIAANGjQoMh4ch8bADh48CAyMjLUoebZrFYrWrVqBQCIjo5GZmYmunXrVuS+i+KK4xAbGwsPDw/s27fPqfzBBx90qoeIiIiovGLSTXQr8vJy/7rX3FXOvkQEAKDRaJzWEZF8ZXn17t0bNWvWxJdffolq1arB4XCgadOm1zUBWXHrzzvLt0ajUZP8ovZ7vbzyHOfsOv744w9Ur17daZnJZAIAmM3mG6orN1cch6tXr6JKlSoIDQ1Vy06cOIFDhw7h4byXOhARERGVQ7ymm4jKvNDQUBiNRmzatEkty8rKwo4dO9CoUSO1zGg0wm63q88vXryI6OhovP766+jWrRsaNWqEpKSkfPvPu92N1n8jr8tgMGDr1q1qWVJSEmJjY69rP40bN4bJZMKJEycQGhrq9Mi+NrtevXowm81Yt25dgfu41jHIjrekj0NgYCCuXr3q9APE22+/jV69eqFx48Y3tE8iIiKisoQ93URU5nl5eWHUqFGYOHEiAgICUKtWLcycORNpaWkYPny4ul5ISAi2bduGY8eOwWKxICAgAJUqVcIXX3yB4OBgnDhxAi+//HK+/Re03Y3Uf70sFguGDx+OiRMnolKlSggKCsJrr70Gbd5h+9fg7e2NF198Ec8//zwcDgc6d+6Mq1evYsuWLbBYLBg8eDA8PDzw0ksvYdKkSTAajejUqRPOnz+PAwcOYPjw4QUeg7xxuOI43H333cjIyMB7772HJ554At9++y1+++03bN++/Yb2R0RERFTWMOkmonLhvffeg8PhwMCBA5GcnIw2bdpg9erV8Pf3V9d58cUXMXjwYDRu3Bjp6emIj4/H999/j7Fjx6Jp06Zo0KABPvzwQ3Tt2tVp3wVtdyP134hZs2YhJSUFDz74ILy9vfHCCy/gypUr172f6dOno0qVKnj33Xdx9OhR+Pn5oXXr1nj11VfVdSZPngy9Xo833ngDZ86cQXBwMEaOHAmg4GMQEhKSr56SPg5BQUFYsGABJk6ciOnTp+Puu+/Gpk2bCp09nYiIiKi80ciNXlRIREREREREREXiNd1ERERERERELsKkm4iIiIiIiMhFmHQTERERERERuQiTbiIiIiIiIiIXYdJNRERERERE5CJMuomIiIiIiIhchEk3ERERERERkYsw6SYiIiIiIiJyESbdRERERERERC7CpJuIiIiIiIjIRZh0ExEREREREbkIk24iIiIiIiIiF2HSTUREREREROQiTLqJiIiIiIiIXIRJNxEREREREZGLMOkmIiIiIiIichEm3UREREREREQuwqSbiIiIiIiIyEWYdBMRERERERG5CJNuIiIiIiIiIhdh0k1ERERERETkIky6iYiIiIiIiFyESTcRERERERGRizDpJiIiIiIiInIRJt1ERERERERELsKkm4iIiIiIiMhFmHQTERERERERuQiTbiIiIiIiIiIXYdJNRERERERE5CJMuomIiIiIiIhchEk3ERERERERkYsw6SYiIiIiIiJyESbdRERERERERC7CpJuIiIiIiIjIRZh0ExEREREREbkIk24iIiIiIiIiF2HSTUREREREROQiTLqJiIiIiIiIXIRJNxEREREREZGLMOkmIiIiIiIichEm3UREREREREQuwqSbiIiIiIiIyEWYdBMRERERERG5CJNuIiIiIiIiIhdh0k1ERERERETkIky6iYiIiIiIiFyESTcRERERERGRizDpJiIiIiIiInIRJt1ERERERERELsKkm4iIiIiIiMhFmHQTERERERERuQiTbiIiIiIiIiIXYdJNRERERERE5CJMuomIiIiIiIhchEk3ERERERERkYsw6SYiIiIiIiJyESbdRERERERERC7CpJuIiIiIiIjIRZh0ExEREREREbkIk24iIiIiIiIiF2HSTUREREREROQiTLqJiIiIiIiIXIRJNxEREREREZGLMOkmIiIiIiIichEm3UREREREREQuwqSbiIiIiIiIyEWYdBMRERERERG5CJNuIiIiIiIiIhdh0k1ERERERETkIky6iYiIiIiIiFyESTcRERERERGRizDpJiIiIiIiInIRJt1ERERERERELsKkm4iIiIiIiMhFmHQTERERERERuQiTbiIiIiIiIiIXYdJNRERERERE5CJMuomIiIiIiIhchEk3ERERERERkYsw6SYiIiIiIiJyESbdRERERERERC7CpJuIiIiIiIjIRZh0ExEREREREbkIk24iIiIiIiIiF2HSTUREREREROQiTLqJiIiIiIiIXIRJNxEREREREZGLMOkmIiIiIiIichEm3UREREREREQuwqSbiIiIiIiIyEWYdBMRERERERG5CJNuIiIiIiIiIhdh0k1ERERERETkIky6iYiIiIiIiFyESTcRERERERGRi+jdHQARERERUUFEBA5bOmxZqdBotDCaK8FmAzQaQKtV/iUiKuvY001EREREZYLdlglr+kWkXTmO5AsHcTkhElcS9yA16QgyUs4AAIYOBfR6YM4cZZuICGDYMCAy0n1xExEVhT3dRERERFTqHA4bHLYM6I0WtSz5wn447Fan9TQaLXQGLxhMPgAAu10p1+mUfz/9FPjpJ8DTE2jbtlRCJyK6LhoREXcHQUREREQVlzjssNvSYLOmwGZNgT0rBXZbBrRaA/yC26jrpVyKhcOWAZ3RAr3BCzqjN3R6D2g0OYMzU1OBjAzAbFYS7X//Bf7v/4AJE4DmzZV14uOB6dOBp58G2rcv7VdLROSMSTcRERERlRgRgSbXxdapSXGwpp9HQV85dXoPeAc2hVZnKNEYXnsNeOcdoHt3YO3aEt01EdF14/ByIiIiIrphdlsG7Fmpag+2zZoK36qtodUqXzM1Wj1EBFqtAXqj5b9ebOXf7HVKWp8+wOnTQN++OWVpaUpv+JAhwB13cBI2Iio97OkmIiIioutiTb+EzLRE2K0pcDiy8i33DmwMg8kXAOCwZ0IE0OlNJVL3t98CR44ADz4ItGxZ/O0WLlQS7jp1lO21nE6YiEoJe7qJiIiIKB9x2GHLSv2v9zoFZu8a0Bk8AQAOhxVZGUkAAI1GA53BCzqDF/RGb+gNXtDqzep+tLqSSbazLV4M/PknUKvW9SXdLVoAgwcr/2Yn3CLA668DvXuz95uIXIc93UREREQEh11JpJVh4qmwZaUByPma6OVXFyavIADKkPKsjCRluLjBy2miM1f76CPgwAElge7Q4eb2tWUL0KmTMilbQgLg61syMRIR5caebiIiIqJbjN2WAbs1BVqDGXqDl1KWlYbUy0ed1tPqjP9df630YmfT6T2gswSXaszZxowpuX35+yvJu6enc8L98cdAmzbs/SaiksGebiIiIqIKzGHPgi0rBXZryn//pqrXYXtYqsHTt7aynsOG1Eux6kRneqNXiQ8NLw8SEoCaNZX7gcfEAPXruzsiIirv2NNNREREVEGIww4Rm5os222ZuHJuV771sq/D1uqMaplWq4d3YONSi7WsslqB/v2BM2ecE+6ff1aS8Xbt2PtNRNeHPd1ERERE5ZCIA/asNLX32paVAntWOoxmf1gCGqjrXU7YAY3OAL3BK+d2XQbPUr0OuyR16QJs26Ykwb17u64ekZzk2moFatQAzp8HVq8G7rnHdfUSUcXDnm4iIiKicib5wkHYrMkQceRb5rBbnZ77Vm1dbhPsgmRmKkmwq+Xuzb56FbjvPmDzZuDuu3PK//kHMJnY+01ERWNPNxEREVEZ47Bb/5tFPAU2ayoAcRr6ffX8PtisKdBoddAbvKE3ekFnsEBvtDgNGa+IEhOBjAwgMFCZAK002WyAPleXVbt2QGQk8MUXwIgRpRsLEZUf7OkmIiIiKgMyU88hK/MybNaUfL3VgAbisEOj1QEAPH3rQKPVQZfrfti3iipV3Fd37oQ7IwNo1AiIjQXCw3PKo6OVnnH2fhNRNvZ0ExEREZWSvNdhe/rVhea/zCzl0mFY0y+o6+oMnjm36zJk3w+bWVxZk56u3Oc724ABwJIlwBtvAG++6b64iKjsYE83ERERkYvYbRmwWZNzbteVlYrc/R0elmrQGZSMzegZmDNM3OCl9mqTs6+/BpKSgH79lNnE3S13wi0CeHkBHh7Ok7ydOwccO8beb6JbFXu6iYiIiEqAw54JmzUVepMPtFqlXyP96kmkJ59yWk+r1auziBs9q0Cnv/XuhX0zGjRQhnRv2KDMZF4WXb4M+PrmJNhvvglMnQo89RTw5ZfujIyI3IE93URERETXyeGwwZ6V+l8vtnK7ruzrsC2VGsLo4Q8A0Bu9/3v8d6suowU6vYc7Qy/3+vRR7qEdFOTuSArn5+f8PD1d6f2+666csowMYM8e9n4T3QrY001ERERUBBEHIKIO97amX0TKpdgC1tRAb/CEh3cNGM0BpRsklXmXLilDz03/DWz47jvgySeB++8HVqxwb2xE5Frs6SYiIiL6j4jAYUuHTe3FToHdlgazT214WIIBADq953//ekBn8ILe6K38y+uwqQgBeX6HOXlSScDbts0pEwH27gWaN2fvN1FFwp5uIiIiuuXZbZlIuxwHmzUFIvZ8y02eVeDlfxsAJTEXhw1anaG0w6QK5tIlJbn2V65GwI4dShJ+xx3A5s2Ajr/hEFUI7OkmIiKiW4LDYcuZRdyaAp3BC2YfZfprrVaPrMyrAAQajRZ6o0WZRfy/f3NPdqbRaKBhwu029esDFy4AmzYBjRu7O5qbk7f3e+9epfc7NNQ54Y6LA+rWZe83UXnFnm4iIiKqkEQEmalnYbOmwJ6VArstw2m53miBT+Vm6nNr+kVo9Wbo9GbeD7sMCwwELl4E9u8HmjRxdzQl79IlICUFqFVLeZ6YCNSoofzAEBGRf5I2Iir72NNNRERE5ZqIwG5Lh92aDBGHeu21RqNBRkoCHPZMdV3lOmylB1tvtDjtx2iuVKpx043Ztg2wWpWe34ooIMC5BzwyEtBqAaPROeE+e1aZwZ2/DxGVfezpJiIionLFbsuEPStF7cFWrsN2AAC0WgP8gtuo66YnnwYg6u26su+fTVSeXLqk3CataVPluc0G1K4NVK4MLFtWcX+AIKoo+MlDREREZZbDngW7LQ0Gk69alnY5DlmZV5zW02h0/11/7QURBzQaLQDA7F29VOMlcoW8vd979ypD7LOylKHn2S5fBnx92ftNVNYw6SYiIqIyQRx22NIuwy4ZsGWlKrfr+m9ouF9wW7WXWm/0hohdvV2X3mCBVu/B67BvEZ9/rvw7YABgsRS9bkXVurXS833woDLsPFvv3kByMvC//wFt2hS+PRGVLg4vJyIiolIn4gDijkLz999ArVpI79gEGYejYFr4M0x/rEf6xKdh7d0NAKDTm2EJqA+dwdPNUVNZYDAow6tPnQKqcyCD6uxZoE4dpff7xAmgWjWlPD0d8PBg7zeRO7Gnm4iIiFzOHnMQjn83IeuezrAZHLBnpcLnp7XQvToZePhhaO/8BJ6zvoDphxUAAGOqBqbAxtAZvHgdNjl56CElsfTkbzBOqlYFTp9WbqWWnXADwLPPArt2AbNnA926uS8+olsZP8WIiIioZJ07ByQmIqt+bWQkn4Y9KwU+Xe+H4ex5pC/9FLY7WgIA7O1aQte9O9CxIwzmSkCTNsDtCUCjRjCOGAOYfJT9rVkDzJwJvPACcN997ntdVCb88IO7Iyi7AgKABx/MeZ6ZCfz6qzIRm9mcU26zKfcBZ+83Uelg0k1EREQ3TK5egc2WBrtBYMtKgUfELugffgJo2RLYGoGszMsAANsdLaE7mQCjzhsm/1DlOuxqHkC3BwAAWgCYPEV55PXxx8C6dcrUzUy6iYrNZAIOHwZ++QXo0CGnfNYs4McfgalTgfBwd0VHdOtg0k1ERETF43DAAQes6Rdgt6bCMOp5GH5cgcz3X4H1MSV51jUMgV6jARwO6LQe8PSro9yua9mf0Gh10N1IvR9+CDRuDAwbllMWFwdMnw489xxnjCIqQkCAc9MRARYvViZhu3zZuRxg7zeRK2jdHQARERGVPSICuy0dmWnnkRV/SOkmq1YNYrMi7XI8MtMSYff3gUYE+qOnYTQHwOxTC4bbmijf5PfsgdZggodXVeiNFmi0N5RuK0JCgPfeA+rXzyn79FNg4ULgjTdu9qVSOZKWplyvXLOm8n+6fhoNsHEjMG8e8OijOeVLlyqzoi9Z4r7YiCoqzl5OREREcNitsFlTIMt+hu5/C2Ht0BIZzw4AABj1PrDUaatMg3zwIFKCdNDpzdBdSIbeZIG2eq2SCWLaNGDlSmDMGKB//6LXjYxUsoaBA4GePZWylBRl3OxTTylZGVU4V68q96EGcmblppLRqxfw55/Aq68Cb7/t7miIKhb2dBMREd1iHA4b7NY0ZXKyhx6CnDuLK+d2I+VSDGwnj0AfsQWGzZHQaLTQG72h8/QFli9XLg5t2BCWgPow+9SEsW7jkku4AeDIEWDbNiAh4drrtm2rjJHNTrgBpYtu2jTgnntyxspSheLlpczEvWOH8/2p6eYtWgR88AEwYkRO2d69yvQM//uf28IiqhDY001ERFSBiThgjz8M+WUZHGJFxoBw2G3p0Bk84dv1CSA6GlixAlfb1YaIHYYzSTD8EwlN5y7QtWoLjaYUf5/ftUu5+XLjxkBo6PVv/9dfShddeDgwfrxSln0Ba9++gMVSouESVXRjxijzGD7yCPDTT+6Ohqj8YtJNRERUkRw+DPz7L9CpE1L87cjKuATdhm3weXI87LWr4crmnwEAOr0HfH7eAk16utLbXacONBVlBiWRnNmg1q8H7r5buRD4+HFAzzlkiYrr4kWlB7xdO6BjR6Xs0iXg/vuBQYOAp59Wbj1GREXjJw8REVF5JALHiaOwH9iLrM63w25Ngd2WDt8JU6FZsUK53nnAfRARSKvmsHXrDGnbBhb/+tCbfKDVGYBnW6m7qyDptiL3jwfp6coEbN26OSfc27cDt9/OjKGcSU1VelwNhmtf9k83r1KlnEEj2RYtArZuVZrWyJFuCYuo3GFPNxERUXlw+TKQkQGrrwHWtAtwHI2FT7sHIQY9kg79BZiUC1z9Fq+HdsWfwMiRsD/2EAANdPpyMtvUgQPAuXNAvXolOxGaw6FMdZ09vPz4caBuXaB2bSAqCvDxKbm6yKVOnFD+bCYTkJHh7mhuTdm939Wr58x+brcrV3Xcfz8wdCgnuCPKixOpERERlTGSngZb5lVkpCQgNekIHNPeBPz9gXfegT0rDdaMS7AFV4KjSiU46t8Gj2TAy68ufKq0gGbSy8r9gPr3V2YYLy8JN6Bcj92tm3LvopKk1Tpfz33wIODnpyTeuRPuc+dKtl4qcSYTcN99ylx55B7Zvd+5bze2ejXwxx/Aa6+5LSyiMo3Dy4mIiNzMYc9EVsYV2KzJMPV8BLrd+5Cy/js46tQAAJhqV1N+JT9zBgYPf2i0eugMXtCcOAWtyQOebo2+BNWsCTRponyrd6X77lMmbEtMzClLT1cmcGvQAPj5Z+UacCpzgoKUu8pR2dK+vTLzud3u3Mv97LNAs2bAgAGcx5BubRxeTkREVIrstkzYs1Kgi9wL3RtvAkFByJz/MVKTjgAAfB54Cvqog0j9/B04HukLvdECo80EnUPr+mT0VhYRoXSfVqsGxMXlXOudlaVcQExE1yU2VvkNS6sF4uOBWiV4d0Gi8oY93URERC7isGfBnpUC+fxzaP9cg/SRTyCrXTMAgJc9DboNG4DAQOgNXjCYfKAzWCCffgx7lerwqlvPeUIwcq2uXZULho8ezUm4RZQpm0NCgPffVy4mJqJiqVJF6f0+etQ54Z45U7mq48knOZ0C3TrY001ERFQCxGGHXLoI7eJvgePHYZsxHVfP7wMAeD03FaZf1iBt4ghkjh8Gnd4TJp0fTL+tU+7F07gxE+yyaM8eoGVLZbzs6dNAQIC7I7rlHToE9O6tDDPftMnd0dD1Sk5WBpOkpCh38+va1d0REZUOJt1ERETXScQBe8xByLo1sNWuiqx2TWHLSoNHhgGeoW2UdS5ewOWMw9DqTDBu3A19/Glo7rkXumatodHyNlUFmj5dGeY9ZgzQp4+7o1Hs26fMcD5wYE7Z008Dnp7ACy+U7CzrdE27dwOtWyuJ2+nT7o6GrldKCvDll8DffwO//ZbzW+N33wFXrrD3myouJt1ERERFEQGOHQO2b4f0CUfy1SOwZ6XCY+b/wTxvPjL79ULqnNcBAAYPP3i/PEcZjjxqFMTPlwn29ejXT7kJ80cfAc895+5oCnbmjDLM3GZTkvEWLdwd0S0lJUUZgKDTKZN3UfknAjRsqFwD/n//p/ymRVTRMOkmIiLKxXH2DGznTsJepxpsWanQiAaWeu2V+2Tv2oXLwQKHPROGjTtg/mQxHA/cB4wdA73RAq3O5O7wy7fNm5Xrqm+/Hahf393RFMzhANasUXrk33svp/zjj5UbRw8bxmHoRNfBagU+/RT49ltg3TrA21sp/+cf5e5+7P2mioBJNxER3bpSUwGtFhmOK7BlXoX252XwfOYVZLVrgeRlnwEAtFo9/Ea8qdxeas4cZLVtBq3WCK3eAxpeh00AkJmpzBSVmKj01D/yiLsjIir3HnhAuff3pEnAjBnujobo5mjdHQAREVFpEIcdNmsKMlPPIe3qCeCJJ5TukxUrkJV+Cdb0i7DWV2an1mRYYTIHwtO3DiyVGkJ++w3Yvh3o3BkGky90BjMTbnL2zjvAvfcC4eE5ZatXKxerWq3ui6uCOX9e+V1j7Vp3R0Kuds89QKNGwPDhOWWHDytD0K9edV9cRDeCPd1ERFQh2W0ZsFlT4DhyCIanxkCTeB5XNv2oztzjP30BNP/3BTB1KjInjobDboVe7wldhgNaf94P2y1iY4FLl4A6dZTpqcu7O+5QfqyZNQt48UV3R1MhREQAd92lJGMHD7o7GnI1EecbO0yYoNyG7NFHgR9/dF9cRNeLPd1ERFTuOexWWNMvQX74HujZE/jkE2Qkn0Zq0mGkm63Q7doH3fHT0J67CIPJF2bv6pCXJgGnTgFTpsDkWRlm7+owmP2ZcLvTyy8DHToAy5e7O5KbZ7MBvXopk+oNGpRTHh0NbNyoZBN03Xx9gbAwoE0bd0dCpSHvgKLGjZVJ14YMySm7ckXp/U5OLtXQiK4Le7qJiKhcEYcdtowrwOw50ERGImXO63CY9QAA30V/Q/fK68BDDyFz0efITD0LvdECw5pN0DZpAW2DJtDoOJt4mTVqlDIke/p0oH9/d0dTMhwOQJurj2PgQGDxYuDVV4G333ZfXETllIjyyG5Wn30GjB4NtGsHbNvm3tiICqN3dwBERESFEXEAJ05As/JPwGxG5qP3IfXyUQCA7+f/g+7MOWgHhcPRoRV0Bk84et0DnW8A0KkTTJ6VYfKsrOzo0TpufBVUbJ995u4ISl7uhFsE8PcHzGagb9+c8osXgaQkIDS09OMjKmc0GucecH9/pff7ySdzyhwOYNEi4KGHcmZDJ3In9nQTEVGZICJwxByAY/M/sLVvgawqPrBnpcHyzyEYnhgCtGiBrG0bkHzhILQ6E8xf/ACtxgA89hj0oY15P2wqP65edb4H0vTpwJQpwMSJnKaZ6AaIKFd0GAzK87/+Anr0AKpVA44fB/TsZiQ341uQiIjc48wZ4OhR2O5ojfQrx2DLSoFl2CgY/t2FzPdfhe3xBwAAtpaNYOjZE+jQAXqDBX5Vb4dWZwTeaO3mF0B0g/LedPj4cSVraNEipywrC0hJUbrxSPX338pkWq1bA19/7e5oqKzQaHISbkC5i1+DBkC3bs4J9++/A127svebSh+TbiIicjlH0kXY067A5mOC3ZoCw9GzMLXrCnh7Q3PhHLIyrwAAbO1bQWNzwBBYDQb/etAbLdBV9wBWrQIAaABowB7tCuvtt5WLMp97Trlf0K3if/9TJpGrVSunbOlSYNgw4Pnnee13LklJwJ49+X+3IMrt/vuVeQzT03PK4uOVO/pZLMrvXPw9i0oTk24iIipZmZkQgx6Zaedhz0qB7s334DHvK2SOGYT0l0Yq69QKhMnbGwgJgS7pKrz8boPO4And7C+g0Wj54XSr2rZN6Yp68EF3R1L68l7PvWaNkjHk7r4DlDG0t/BY2c6dlbn2fH3dHQmVdRoN4OmZ8/zMGaB+feW3rdwJ9+bNQPPm7P0m1+I13UREdMNEBA5bBmxZKYDNDlOPcGD3bsjxY7gsJyHigGnxL/B6eSasD90H65dzoTdaoDd6Qy9GwGRy90ugsmTtWqUL6s47lbGhtzIRYNMmJUvIvmf5rl3AAw8AY8YAr7zi3viIyiERZbREQIDyPC1Nue7bZgO2b1duSUbkCrfuT6VERHTd7LZM2LNS4Pj7L+jfmwN7rWCkznwJAKAzeMKUkgJkZUGzazdMnZtCo9FCN2Q0HIPHwVi1Goxujp/KuB493B1B2aHRKD8+5LZgAZCQAOzf75aQiMo7jSYn4QaAY8eAqlUBq1WZAT3bgQNKjzh7v6mksKebiIgK5HDY4LBlQP/lQmWo6+TJuFJTD7stHfpNO+Dz+FjYa1XD1X+XQWfwgt7oDc+Yc0DlykBIiPM9XYjo5lmtwLJlSnbQsqVSdv480Ls38NRTyjXguW9RVgGdPAns26d0/t9+u7ujoYpARBl6Xr16zvPmzZVrwH//HbjrLvfGRxUDe7qJiAjisMN+9iRk8SLIuQSkTxoBuy0DGo0OfqtXQ/PfNw/94N5K73WHLsj6eDY07TvBL7gtNJr/vui3q+3eF0Ll27FjQHIyUKMGZzkqiNEIPP64c9nXXyvXwttswPDh7omrFK1Zo/y+8MADSkJEdLM0mpyEG1B+x8rKUu713apVTvmJE8ppib3fdCOYdBMR3WJEBJqjR4F//gEaNUJaoyBkpp6D5sQZ+L30BsSgR8rofoCHCVqdATJkIDR33w3cey+8/G/L2dGzLQqvhOhGjB4N/PknMH8+MGSIu6MpH0aMAHQ64LbbckaX2GxKZvr448os8BWo97tSJaWHu149d0dCFVWVKkB0NHD4MODnl1M+frzyo89XXwGPPeau6Ki8YtJNRFSRicB+9DAc27cg676usNtSYbOmwO+jb6GZ9yHw3HPQvD1JScRr1UTWQ/cDjRrC27sudJWCodUZgIdaXbseopLg76984zWb3R1J+REQALz4onPZ778DCxcCK1cq3XMeHu6JzQX69FEeRK6k0ShzGGazWpUkPDUVaNo0p/zSJeUGA+z9pmth0k1EVJFcvAhcvoys6gHISD0He0oSfJt0hS7TitSNP8JRpwYAwNb+dhiiwoCGDWHyCoLRswp0ehOwdIWbXwDd0pYscXcEFUPLlkq3XJUqzgn3++8rPd/Nm7srMqJyyWgE9u5VbiDQpElO+YwZwCefAO+9Bzz3nPvio7KPE6kREZVTkpIMm2TCrsmCzZoC87J10I0YBdx3HzJ/XojUpCMAAO+HR0ObngnrnLehbd8JeqMFWr0ZGk50RnTr2L8faNZMGYp+5oySkBPRTenSBdi4EVi+PGcERkaGck04e78pt4pzkQ8RUUUmAoc9C5mp55CaFIesXncDfv7I+HUR0q4cgzX9Amz1ainrXr4Mg8kHnr4h8KncFPrNkdDtOQhzt74weVWBzuDJhJvoVqPTAY88Ajz6qHPCvWIFcPas++K6Tt99B3TqBLz1lrsjIQI2bFAeDzyQU7Z4MRAcDEyb5r64qOzh8HIiojLIbsuA3ZoCbdwJ6EePA1JS4Nj2D1IvHwUA6M1GaOx26GPiobmvF3RGL+jaNVEuMPP3hxaAhyXYvS+C6Hq9844yhvPZZ/Pfo5puTqNGwE8/KVMyZ0tKAvr1UyZe27vX+UbFZdTJk8CWLZxIjcoGjUbp7c5t7Vrl2u/cU1OIACkp7P2+lXF4ORGRmznsWbBlpQBLFkP7/U/IfOBuZD7SEwBgyjTC67Y2AAC5eAEpjnPQGS3Qn7oAvcUP2lp13Rk6Ucm65x7lG+uiRcCAAe6OpuKLjlZuM5aSAuzZkzP7+f79ymzoZXBCu8OHlfBq1gTatHF3NET5iShDzhs3BgIDlbJ//gF69QKefhqYM8e98ZF7sKebiKgUicMOR3oqdJ99AezcCcf8r3D54h4AgHl/FIx/bYTdzwLro/dCZ/CCzrcS8OOPQLNm0Pj5w1tbSdlR41pufBVELjJ6tDJOk9lU6WjUSOk2vnw5J+G224HwcKVs1SqgbVt3RphPvXrs5aayraDe719/VXq/k5OdyzMzAZOp9GIj92FPNxGRi4g4YD9yCPLnH7B7mZDZOwz2rHQYjD7wrt9J+VK7cyeuVAOg0cJ46Dj0Ow9C0/Vu6Fq1hUbDaTeIqJTFxwN33w1cuQKcOgV4eirlFy8qt3SrQPf8JiotIkpvd9WqQIMGStmpU8rtxx57TJkBXc+u0AqNf14iopISFwds2wZ0745k3UXYMq/CsPIPWMZPB9o2h/1epcfIIVnA888r9yCpXBk+QdWVBDuoJRAW7t7XQES3tjp1gCNHgNjYnIQbAJ54Ajh+HPjqK6BzZ7eFd/Socl13jRrKCHii8kCjAcLCnMt+/ln5bSsmxjnhttuVeQ+pYmHSTUR0AxynT8AeFwNby4awWVMgYoPPY0OBnTuV4eB3NVF6uts0h61rR6BzB1gCGii369IZgTdaqPviPOJE/zlzRrnfTpUqgMXi7mhuXTqdMvQ826VLwPbtwNWrQPXqOeVuyA6++kqZb2/cOGDu3FKtmqhEjRsHtGrlnHBnZChN7557gJkzAV9f98VHJYtJNxHRtVy9CtjtyDTZkJVxCbJtG7x7DQYqByB51+/qtZByZ2doDAbAYIDZtzY8NVpoq7WHZv0jbn4BROXEgAHA+vXKfaEef9zd0VC2gABlLOyGDUpPeLbx44EDB5T7d3XsWCqhBAYqSUnVqqVSHZHLFNT7/eefwLFjwMqVwKef5pSL5Ey7QOUTk24iolwkywq7WGHLSoHdmgrPaR9DM28e8PbbsI3sB2v6JaBeLYjRAAnwh8lmhq5yMPQGCzDnDuC/67B5ciW6AWYz4OXFixvLIosFuP/+nOeZmcCSJcptx9LTSy2M559XHkQVUZ8+QESEMoVC9iASEaBrV+WOflOmANWquTFAumH8VCOiW5rdlgmb9Srsl8/D1PsxaA8extWoFYDFCwDgUbUydCLA0aMwegZCpzdDF+gFXLwIncUbXm6On6hC+eMPd0dAxWUyKbcZ++EHZeK1bJ9/DmzaBLzwgjJ2loiKraDe76goZRK2bduUSyuofOLs5UR0y3DYrbBZU6CP2Art7DlA8+bIePNFpF09DgDwvaMvdKfPIeXnzyBhYdAbLTCma6GDHggKcnP0RERlnIjSHRcbq0zHPHq0uyMiKveyZz4/cMC5SY0YodxMYNIkTipYHjDpJqIKyeGwwZ6VCpk7F9p/NiHtpWdgu02ZAMh702EYHh8MNG+OrO0bkX71BPRGC/SR+6GrWRe6+o148RQR0Y2IjAS+/BKYPRvw9lbK/v4bWLMGGDkSCAm5qd3Pm6cMiBg0SJkCgOhWdP68MqdhVhawezfQsqW7I6Jr4fByIir3RByQswnQLl0OXL2KrAmjkHzxEADA+7ffoN+yC9q72gK31YDOYIZ0uAP44gugXTsYTD4wVG6q7Kh7iPteBBEBM2YovaSjRgFt2rg7GroRbdsqj9zmzFEy5YyMm55y/OBBYO1at961jMjtAgOVdrB2rXPCPWOGclu9ceOAxo3dFh4VgD3dRFSuiAgch/bD8U8EbI3rIKthbdizUmE+mQKP9j0AiwX2C+dw5cIe6PQeMP22Hrrzl4He4dA3aQmNlje/JCqzunQBNm4EfvoJeISz/lcYv/0GfPSRMuS8fn2lLC5OmaJ58GDAx6fYu9q+Xfldpnlz5UFECpsNqFULSEhQplro18/dEVFuTLqJqOwSUW5TExUFR697kZp0GDZrCswT34bHt78h/dmBSH9lFADAaPSH5elXlJ98J06Ew2SAVmdwb/xEdH2WLAFOnAD69lWuDaaKa8IE4IMPlL/1smXujoao3Mu+9nvJEuDjjwGjUSn/6Sfgr7+AZ5/lD1XuxOHlRFRmOC4kwn7xLGzVAmC3pkCb4YBnSDPA4YDm9CnYkAwRB+ztW8F29BR0oQ1hCagPncELOr0HsHy5ui+tG18HEd2g/v3dHQGVlubNlR9WRozIKUtJUcbL9u7N28YRXafsmc/zzn4+bx6webPSC86k233Y001E7pGeDhgMyMg4D5s1Bbov58P88rvIfOBupH7+FgBAZ/CEb88hyifJggWw1qsBrd4DOr0HNBqm1URE5Vr2V9DsiSs//1y5nv/uu4F16wrc5MgR5dbgtWsDVaqUUpxE5diGDco0Nu+/DwQHK2XbtwNffaXMbcg7+5UOfmslIpcThwO2rDRkpp5DRsoZoFcv5Rq+bduQmXoO1vQLyKqjfBLokq7CaA6Ep28IvPzqAjt2ALt2Ac2bw2gOgN7gyYSbqKK6eBE4e1aZcIsqPo3G+U4RDocyQ1Tv3jllIsr0zP95/XWgXTvg++9LMU6iciwsTBlynp1wA8BnnymJ+Lx57ovrVsNvrkRU4uy2TFjTLyJ98yrYOraFrVMbXE3cg9TLR5F+9STEaFBm/Ni9GyavIJh9asLcsx8ciWeh3xQJS0A9eFiCoTd6AzpOfEZ0y+jdW/lmuGqVuyMhdxg9Gjh5EnjmmZyy9euB1q2Bbt0AEVSqpAyT9fV1X5hE5d2wYcATTyg93SIO2KzJOBZ3FmPGOJCe7u7oKiZeMENEN8Vhz4I9KxWGH35VZusYOhRpXZsgKyMJWn0azP/ugOh10GRkQe8bCJ3BC3j3XeDDj4CaNeHB+2ETUbbsnk+eF25dHh7Ozw8eVGaEatgQ0GjwySfKJOi4eBFAJXdESFSuOexZuKNNMm7/LBk2azIuJ6RARPDm1DpYsFiLXr2A++5zd5QVD5NuIio2cdhhu3Ie+PwzYO8+pM15DXaHFQDgH7Ubmj/+AEJCoL+nPcRuha5+C2T97xNo29wBv5CW0GT3WrOHgogKsnmzuyOgsua554DHHlNGR2U7dky59dgDDyjjzLOnaSYiJyICuy0NOp2HesvUjJQzyqV+uWi1Bhw45IvQ2+wwmTjC0BU4kRoRFUjEARw7Ds1ffwFBQUi/63akXz0JZGXBv2F3aDKtuPzP93DUrQWd3gyv2EvQRx0AunYFmjVzd/hERFRR/d//KeNiu3dXZjvPZrfzkiS6pTkcNtitKbBZk2H7718RO7wDG8NgUno8rOmXkJ58Enqjt/rQ6T2usWe6WezpJiIAgD02GvLvJli73gGbpxb2rFT4/L4JunEvAvfeC233RQAEWg9PZD0zGBo/f3jVbAld8G3QavVAEIA773b3yyAiogrszTeBqKhnMG5+T3RteTlnQVoa0KSJ0vv9zjuAt7fbYiQqbVmZV5F2JR72rLR8yzQaHRx2q/rcaA6A0RxQmuERmHQT3dJs1mSkJ5+CPSMZ3vc9Av3Rk8h4/1XYHn9AWd6mOXTdugFhYTB4+MGvamtodSZg3hdujpyIKqTZs4Hjx5WJtJo0cXc0VAZt2gT89RfwyCMhQMtcC379VRl2vmIFMHeuW2IjciURB+xZqUovdmYyjOZKMHoGAgA0Wp2acOt0JuiM3tCbsnuxPaHhPBlux+HlRBWcOOywZaXCnpUCmzVFOUmblclnbNZkXD2/H97hT8Owcz9ErwccDth+/AbaBx+CVu/BEzURlZ527YDISCVxuv9+d0dDZdDKlcoE53ffDdSrl2uBiHJv75QUoE+fnLLwcKBjR2VmdB8fd4RMdEPEYUdW5mV1mLg9S5nwLJvJszK8/EOVdUWQlXEJeqM3tLobm+OgXz+l+XzyCVCnTom8BMqFSTdRBeNw2JCVflE5SWelwJ6VDiDXSdorSLn/NZRfTTNTE6FPSoMuKRmaFycq98U+fjxnaF5aGuDp6YZXQkS3nM8/B06fBgYMABo0cHc0VN5t2gTceSdgNgOnTgEBHFJLZVP2hGcQh3K7VCjf5y4nRDqtp9Uacq7FNvlAb7SUWAwBAUBSEhAdrdwsgEoWk26icsxuS4fNmgqNVgejhz8A5VYQl8/ucFpPqzNBb/SCzmCBweRb9En6/HmgcuWc5w8+CFy+DHz4IdCyZcm/CCIiIldITwd++AE4dw546aWc8ilTlG7yRx8FTCb3xUe3LOcJz5RJz0TsMJh84B2Yc2lNyqUYaHIl2q6c8OzHH4HUVODhhzkoxBWYdBOVEw67FTZryn/DxFNhy0qGOOwAAIPJD96BjdR1U5OOQKszQWfwgt5oueGhRjh7FqhdG8jKUn76ZM8TERG5UVwckJkJ1KoFWG6kk+/0aeVzzW4H9uwBmjcv8RiJipJ84SCyMq/kK9dodDCYfGCpxG7miogTqRGVQQ6HTbnPtUEZ1i0iuJIYpSbZ2TQarZpY55Z9jU+xLVsGxMYC990HtGiRU161KnD0qDJrTe6Ee+5cZcj54MHsJSCikpOaCjgcynBgPb+iUH6PP65cBXXDl/17eipToO/f75xwf/89EBSk3PaSc5nQTRBxKJ0k//VkO+yZ8KmS817TaLQAAJ3eQ+m9NlqgN/pApzdzHp0KjD3dRG6mzEaZlqsXOwV2Wzp0eg/4BrVS10u+EA2Hwwq9wQK90QKdwQs6g6d68r4p4eHAb78BX3wBjBhR9LqXLytdDMnJnOyIiEpW69bA7t3AqlVAz57ujobKoB49lLfI998rt+kuEZmZQM2ayuVV/FyjG5CVeQVZGZdhs16FPSsVedMr36DW0OmVTgq7LR0aje7GRyG6gN2u/JhlMim/RWlL4KslOePPyERulJp0BNb0C/lOzoDSuy0OOzRaHQDAUqmh634B7dZNmUHDaSrYQhiNwLRpwOrVSs94tl27lC8tua8HJyIiKkFr17pgp6mpwCOPAOvXO//Ys20b4OfHS6tIlT3hmc2aDJO5svodLSvjEjJSzqrrqROe/XfbLq3OoC7T6c2lHve1JCcD7dsr/8/MVL7qUcliTzeRCznsmf9df60MM7JnpcG3amu1dzr18lFkpp6DVqtXhhcZLOq/uU/QZZ7DodxT9/hxpce8xLofiOiWkpmpnE+MRkCnc3c0dKux253fd9m3sFu8GOjf331xkds4T3im/CuiXOrnHdgEBpMy41hWxmVY/7tll6snPHOFixeB228HrFZl2gOOci957OkmKmHW9Euwpp2HLSsFDrs133J7Vqp6OwgPSzV4WKqVu5NzPomJyow2RqPyJSWbzcbrMomo+DhHBLlT7oQ7NVWZ18RsBu65J6f85EnluvBKlUo/PnI5EYfaMZKZdh6pSUfyraPR6NTvcdkMHn4wePiVRoguUakScOyYu6Oo2NjTTXQDlOuwU/+7DjsVHt411MQ5I/kM0q4e/29NDXQGc8512EbLfxNlVMCLZUSUM3adOjllDz+slL/7LofnERHRTXv+eeXS6zfeAOrXL4UKL11yvr/3gAHA0qXAJ58Aw4aVQgDkKnknPLNZk2H2rQ2Tp3KZnM2agqvn93HCMyoRTLqJisFhtyIr8wps1mRlmLgtzek6bC//ejB5BgIA7FlpyMq8DJ3BAr3BS73ep0wbPx74+mvg9deBSZNKZp+nTim3ZREBDhwAGjW69jZEdGubO1e5VeFTTwGh13kXBrol1K0LxMcDW7YAHTqUcuV2O9C5M7B1qzLr1O23K+UpKcpIL14IW+Y57FZkpCQUOuGZybMKvPxvA6Ak5eKwlakJz6j84rhPojzstkzYs1Kg03tCZ1Amu7BZk/MNMdJqDf/96mmB/r9bewGAzuCp3uqr3MjMVGbRSE8vuX3WqKHckuWvv5wT7i++AAIDgT59OD0mETn76ivlvNGjB5NuKtDkycpNNEJC3FC5Tqdk+3v2AC1b5pTPmQN8+qkyqmvoUDcERnnlnvBMqzXAaM6+HECDjJQz6np5JzzTGbzUZRqNFppbJOE+fBh48UWgenXlrUwljz3ddEtTJ8jIyh5elAKHIwsAYPapCbN3DWU9eyZSk44ovddGC3QGi3rrhwrh3Dkl6a5UCfD3d109V/6fvfsOb6u8Hjj+1ZZly9vZe++9E8IMBAhQ9t4zZW9KCC0QNhRKmQUKLfsHCSMUCDuTTCcEyCJ7b29r3fH747UtO84klq8kn8/z6IktXUvHIUj33Pc95xSpcWPFxfDFFzW7nwshxBNPqJXu666rp73DQtSByoZr772nBomDaghos0lHqnpSs+GZanpW2fDM5cnAn9ut6thA8QbslVvGE72nTh2ZNQuGD4f27WFl7TJ2UQck6RYNhmnomKZR1RVcj5RTtP3nWsfZbDYcTh/u1EZ4U5vUd5jJragInnpKjWWZNi260r10qUrGU1P3//NCCCFEvIlEYPJkGDMmusX8vffgkUfg3nvh/POtjS8JGXqkxpSXwi3zMAytxjGVDc9cngy8/mb1HWJC2bpV/RP2+aRRf6xI0i2SktpWFEAPl6BFyipWs8vx+HJJzepQcYxB4ZZ52B3uqhVspzsNh9OXGHXYicw0o1f/DQN69lSr7Z98ourlhBBCiL1YV9GntFkzcMXzZM3jjoPvvoO//Q3++lero0loe2t4ZrM7yGjct+qYkp1LMfRgdGSX2y8Nz0RckZpukVRM06B017KKOYpGrccNPVT1tc1mJ7PJAEmwAebOVbcePeCoo2L/etU/BDdtUjXlkYhKvitVT8yFEA2DUfG+LdtyxT707QsFBbBkSZz35/zoI3jzTTj33Oh9+fkwbhzcfDOMHm1ZaIkiWLqFcGDnXhue2Uwdw9Cw21Uqk5bTOTknw4ikIUm3iAktUsaW5RPxpDYmNasjvvTWdZbcGnoEPaLqr7VIGTZspOWocVQ2mx1DD1fMWXRU1F+n7rMOWxLuCv/7Hzz4IPz5z/WTdFfXsiUsW6bOoDIyovdfdJHa5zR+vNp6LoRIfj16qHKTH3+EI4+0OhoRh1JSVM9PZ7yfwWZmqskg1T3/PHz1lRpBJkk3ULPhmR4uxZfZrip51rUAWrgUqGh4VtHsrLLhWfUkWxLuw1NQAJs3q3+2zZtbHU1yive3LJGg9HApxRX10jvWfI3N7sSX2YG07E6kZXXEk9bkkN4gQ2XbiISK1LiuaqvVoN5oVZKtns+X2Q673YldthUdvJ494ZxzoF8/a17f6YRevaLfr1un6uFMs/ZJixAieUnFmziATZusjuAw3Hefalh69tnR+3bsUHXf119fsyN6ktpfwzMAT2pjnG6/+tqXF02ypeFZTP3vf3DxxWpwxNdfWx1NcpKkW8SE25eHze7CrOgEbhoaZbuXU7Z7OdswsTtTSMvuRGpWJ9KyO+JOycE0jYo67FJ0LYAvo03V84WDBUSCBVXfO5wpVavXTncqEE2uXZ70+vo1k8dZZ6lbvGjdGqZPh6lToXv36P1vv61GkR15pGw9FSIZ/fSTmoWcLu/jIgm1awdPPlnzvtdfh9deg4UL1ezvJKNrQWx2Z9U28HDZdsqL19U4prLhmdOdhs0eLdSvTLhF7DkcapprLAfYNHTSSE3EzJr8FygvXL2fI2yA+udns7txutPIbnkk9oot35lN+mF3qO3g4cAudC1YtV288s1bNCDFxSoZLyxUl2FHjbI6IiGEEOLwzJ0LzzyjOp9Xto3WNDXp45JLVMe4BGEaerR5bcVKtmFESM3qiMeXC0AkVEx54aqKZmdpON3p0vBMNAiSdIuY2bbyc3ZumAp7aWi2VzY7TTr+Cbc3C4c7DW9qE+wOd2yDFIlj5064/341TDI/PzpubO1aVYAU121shRBC1IWrr1YbnZ58smYbkKQyaRKceaZKuNevV8uQcUyLlFNeuGrvDc9sNlL8rWRkl2jwZLlQ1Dl1pbMUm8150Am319+Slt0vxpWSLVc7rfCf/8A998DJJ6ttbvEoNxdefFFtPa1MuE0TTj0VSkrgww9hwABrYxRCHJ4XXoDdu+Gyy1STRSGqMc3oR9SECdbGElNZWXDEEepWPeH+5BO1yys1td5DqiwB1EJqBdvp8eNNbQKA3e486IZnQjRUknSLw1I5lsvQQwSKN1CyaxllBb8TCRZgaIGDeo6cVkfTuP1J8qZspWAQtm6FXbusjuTAqp+ArFmj4g4EoH1762ISQtSNZ5+FlSvhmGMk6RZ79cgj6tprWprVkcTQ0Uermx5tMMYvv8Dpp6sL0GvXxjzxNk0DLVSkJsXspeGZaWrRpNvhJi27Ew5XqjQ8S1Affqiu6YwerRqqibonSbc4aKZpYuhBIsEiAsVrKS9aT6hsK1q4hEhwN6DeeJ2eDHwZbfGmNWP3xhkYenAvz2bHZrfTvNsFZDTqXb+/iKjtzDNhyBA1KyKRtGunOp0vXFiz+8c116iRLLffDnl51sUnhDg0550H27dD48ZWRyLikM0Gf/mL1VHUo+oXmbdtU595/fvXTLh//RW6dYvuAPuDdC2AoUdqNKMt3b2ianEF1JhVp8tfsZJds9mhOyXnsF5fWGvhQnj3XXXKJEl3bEhNt9gv0zQIlGygaOsigmWbiQR2EQkWohqg2XB5M/H6W5Ce24OU9Ja4UnLANKpqsdf9/Dqlu5ZS2TBNUT/XqteVeNOa1v8vJZLb6tXQsSMYBixerMahCSGEEInOMKCoKHqRefduNdGjWTOYMQOaNDmop6nZ8KwYLVyKYURwOFPIaNyn6riygpUAFU3P/NLwLInNmgVz5qipdUcfbXU0yUlWugWg5iZq4VJCpVsJFK0lEi4kEiggULIBo2IutsPtx+3NJjW7M770lqSkt8HlzcDhTNnn8/oyWlck3VFp2Z1p0f0iHK59/5wQf1ibNvDxx2r0UPWE++OPoUMHScKFECJBGYYaa+1wqHHXDS7/s9tr7upavBg8HrXyXX13SGHhPneule7+nUhw114antmx2Z2YplFV7pea1aGOfwERr4YNUzcRO7LS3UCZpkF50XoCRWsIlGwgVL6dSKCgaiu4w+XDl9meFH9LUtJbYXd6VVdxl++Qaq9Ldy9n3aJ/VX2f22YUjdoeL/Xb8WbdOvj+e7WvaMwYq6Ope6Wl0KoVFBTAjz+qOd9CCCESSmFhNOcMhcAtA06grAw2bIAuXQAwg6rHiZnuJ/DuS2jNsklv1KvqvKusYBWh8u3S8EyIeiYr3UnONE0MLUg4VECodBt6pIRA8XoCxRsIB3YCYLO7cHmzSM1qj9ffAl9GG7xpTXG6/Yf9+in+lhWv4aZlj4vw53Y/7OcUMZCfD1dcoS5zJmPSXVwMxx2nVgVGjIjev327utDQ4JZLhIhTXbuqBonTpsGgQVZHI+JM9b5icT5Fq/5EImgZKUSKN6CFS7C//T6pm7fA5i1EdmzEaJSCHinH6Vad57z+5nj9zaXhmahh61Z1ISsnJ8mbFFpIku4kY+ghIqHKxHo9wdLNhAO70EJFgInN7sSb1py0nK44XD48qU3w+lvgcvuxO+p+zrHD5aNlj0vxpDXD48ut8+cXdaRpUzUurGtXqyOJjWbN4P/+T3U5rzxTM004/nj19VtvybZzIeJBMKjO/GQTntiLnBz1T8MwGui1UtNE14No4VJcnkzs99wLTz+NOe4OAtedro457ThSJvwTs0VzPN0G4MxtXqMMUJJtsTc336xOk/7xD7jpJqujSU6SdCcww9DQI+WYhlaVZJfuWkY4sLNqrIPTnY4rJRt/TldSMlqT0ahPTJLr/Ulv1KteX0/8AUOGwOefWx1F7KVU6yPw++9qNBFA8+bWxCOEqGnGDLWcKd3LxX4cZqPuhFHZ8Ewr3YXroquwL1hE8Y/vYWb4ScvuhLt1azBNHBu24PHlVTU8s23fhd1uRzrniIPldILXq24iNqSmO0GYpoEeKSNUtp3yorUESzaqOuxgQVWjM5c3C7c3B6cngxR/C3yZbXCl5OJweqVOR4i92b0bFiyAUaOi991yi0rCr7sO/IdfYiGEEEIcDNM0sf36K/z73+hZaRRfdXJVw7OM4WfjWLeJkrefwTz+OLxpzXAHbRCJQKNGlsQ7cuRIpk+fDoDD4SAvL48hQ4Zw5513Mky6cglRgyTdCaC8aB2hsi0ESjZTsGkmoGqk3SlZuH2NyGjUh9Ss9nVSgy1Eg7ZqFXTqpPYuLlyoZmcIkcAeeeQRxo0bV+v+p59+mttuu82CiIT443btggceUJuWHn/c6mgOj2ka6PlzMX/8nvDRg4k0y8KT2piUaYtgzBjMzp0o+O5N7A43Tnca7h/nYc9rimPgMGzeA6xhL12qSsaaNoWZM2MUv0lGRgbjxo3j0ksvJRgMsnbtWl566SUmTpzIZ599xkknnRST1xYiEUnSnQCCpZspL1oHpkkkXEyKvwXetGY4Y1SHLRqg/Hw45xw17/PHH62OxjrhMLzzjlr9fv756P1Tpqh691atrItNiD+gpKSEsrKyqu8ffPBBvvjiC2bMmEGLFi0sjGwfXn0VSkrgwgtli7moZdUqNfkxLU39M0kohYUYS38j1L0VWrgELVxC2vk34po2j7JH7iB0yRm4vVmk2RrB+PEwfDj62af/sfGqCxbAgAHqM33Dhrr/XYAVK1bQuXNnZs2axdChQ2s8dswxx1BQUMDChQtj8tpCJCKp6U4A7pQ8XN4cHE6P1aGIZBWJqLMZTbM6Emu53XD55epWqaxMJQBFRaredPBg6+IT4hD5/X78FWUSDzzwAF988QVTp06Nz4Qb4MEHYeNGNdZPkm6xh8xMGDcOXPG+3mCa6KUFaA7VX8ezpRg6dsTm9RJc+g2mU5X8RY4cgs3pwdmmM+7cbjhdaWB3wAsvAPCHG7R36QI//RTTFu8LFizA4XDQu3fvWo+NGjWK8ePHYxgG9oZSgJ/gHngANm9WTdS6y6ChmJCkOwHIaraIuW7d1BY0n8/qSOLPzp3Qu7eaZd6/f/T+khKp+RYJ44EHHuCNN95g6tSptG7d2upw9u2MM1SvhZwcqyMRcSgnByZMsDqK2qoanoVLsL/0Mq7Hnyd89kkExt+gJsW07QmZmdhyckgpsWNr2w6H24/jgSHYbLa6PxlPTVUNUmMoPz+fLl264NvLeYPL5cLhcEjCnUAmToRffoGzz5akO1Yk6RZCqORRmp7sXevW8N13UFCg2nuCmllz1FFq2eWll1QduBBxKmESblDzaoSIc4ahYbc54K67YNo0Sl55CC1PXYR1u8GzuxDnz0txuv04Pemq3fratZCeTrI0h16wYAH9+vXb62MrVqygS5cu9RyROBw33QRbtqjyDREbcglKCCEORlZW9OslS2DxYpg9G7KzrYtJiANIqIRbiAPQdbXJKBCov9c0TQNt7e9Enn6E0CPjKNyaT9HWfExQF2TnzsWdvwy7w407JRvnn85Fm/Ytzh9mkp7XA196RS+Q9PT6C3rLFnj/ffjmm5i9xMKFC/eadAcCAT799FPOPPNM8vPzGT16dNVjEydO5Prrr49ZTOKPu+oq1UqgTRurI0lestIthIDSUvjiC/X1OedYG0si6N5dzfhesAByc6P333cftG+vasDdbuviEwKYMGECzz//PJ9//jkej4etW7cCkJWVhccjPUJE4lm0KOb9wdSuptmzCbXKJdw4FS1cgiM/n/Q7xmE0yqHskpPBZsfQgjjuuQc0Dc/RR+Ft0kz9fDbQpkeMgjtIP/8M558P/frVHIlZR1avXk1hYWGtpNswDMaOHYvT6eTGG2/E7/ezZMkSACKRCA8//DBffvllnccjRCKQpFsIATt2wLnnqppuSboPTuvW6lZpzRp47DG1FNO7tzrZEcIipmny5JNPUlxczJA9ajtnz57N4HhtCNi9O2zdClOnQg+LExcRd3TVl4w6KxU2TfTVK9CaZqOFiklJb4X96qth4kTsf7uHyFWnqtft0x3tuCMwBw/Cn9ERZ2o2Nruj6vPSVkfh1JnMTDjmGOjYMSZPv2DBAgCaNm3K1q1bKS4uZsGCBTz33HOsW7eOyZMnk1WxO6xFixZs2LCBjz/+mD/96U80lgaJcWnLFtWgMCsrpv33GjRJuoUQqunKUUep4afij8nNVUn3L7/UTLinT1eJhGxDF/XIZrNRVFRkdRiHbvdudavMroSoZsAANVDijw67NXUNTStHC5egb9+Ib+AJ2ItKKFv6NaT6cHmzcA8bBosXY0/PITWzouGZMwXbN9Pq9peJpSFD1Nb3GMnPzwegU6dOOBwOMjIy6NKlC2PGjGHs2LFkV/u8GzRoEN9//z2vvvoqs2fPjllM4vB07Kj+31q1Ctq1szqa5CRzuoUQIlbKy9VqeCCgku++fa2OSIj49vvvanRh27bgTZaWU8Iqhh4C7Nj/9yU8/DB63x4Ujb+q6vGMwadj31lA2eT/Yh80BLcvD6czBWxxt3adsN59911uvPFGHnnkEa699lqrwxH74PVCKKQmNjZvbnU0yUlWuoUQIlYqP72Ki6Fnz+j94bDUfAuxNzHaDiuSn2ka6JFyzH+9Aj/8QOC2K9HaNMGX0RqvrsPcudjLyrA7/qy6irv9mN9MgXadSfPIBZ5Y6dSpE40aNeKqq6468MHCMsGg2mAkU95iR1a6hRAilkwTNm+OXjo2TTWerWVLePLJmnXhQggh9mnlSnj9dWjWDG68ogymTcPYtIGyP41AC5dgmgb+s67HNXshpU/+hfD5p+JNa4pP98NXX6n33obQnvmTT1Qr6iOPhOeftzSUyy+/nDPPPJMxY8ZYGocQVpPrGUIItf25d29Ve1xebnU0ycVmq7lX6+ef1aixyZOlhl6IPb35Jrz8sqrrFqKSaaL/tpjl01by2GPw6qthlYGfdBK22+5ACxRgmgY2uwPtorOJjLsdz7GnktV0IL6M1qqnxgUXNIyEG2DXLvj1V1i3zrIQVq1aRefOnfH7/ZJwC4GsdAshQBXyVNZPFhZCRoal4SS9xYtV8n3xxdH7Hn1UXfQYM0b2d4mGKy8Pdu5UCUP37lZHI6wSDBKxhVXDs3AJrtvH4/nPRyy68G5eSbmJZs1sjL+/EQwcCL16EX70rzhyG2N3pmCTemy1u2rpUnWxQXqJiAMoKoJ771XrAE89ZXU0yUuSbiEEGIbqdOp0wogRam6EqD/r1qn53roO+flykiQarssuUz0Q/vEPVYIhGgRDD6FrIVz2FBg5EhYsoHjB/9Cy0wDwvPsZvvF/J3zxWehPP4rT7cftzbI4aiGSw/r1qtLN41G13SI2JOkWQgir7dwJTz8NK1bAxInR+/PzoWtX2YYuhEgalQ3P9J9mYH/uefScDMr/egM2u4PMJgOx9eoFv/5K6L1XiRw/UjU90x04vOnYpOGZEHWuoACeeUZ9/eCD1saSzCTpFkKIeBQIROsPv/9ettoKIRLX99/D998TGHMkwVbpmKaBc+Z80s+9Cb15Y4rmfILT5SMtpyv2BQuhcWNo1arW6C7TVDepwDmAFStg2TL1GdKrl9XRCCGQRmpCiEpffaU6npaWWh2JAFi1Cnw+tcrdqVP0fsOwLiYhhNgf00T/9WciH/yHsoJVFG3/GUOPqHKBhx/G8cP0qoZntsFDidx3B+Zr/yKryQDSG/XC7nDBoEFqr+tearM/+ggcDjjqqPr/1RLKRx/BaafBc89ZHYkQooLM6RZCKOeeq2opV6yQWbnxoEcP9d9izZpojb1pwrHHqi3nf/2rWg0SIpn07Km6+vzwg+pzIOJbeTl60S7CaXY1smvVCtIHn4bd5aRkyTeQ4kGLlOI+7TTIzsbRbwgZjXpHG549NOCQXk7X1Z+y0n0ATZvC4MHQrp3VkYgEEImowTVer6rrFrEh28uFEMpxx0FZGbz3XsMZq5Jo5s5VJ1JeL6xdK0m3SD5ZWWqCwrJl0Lmz1dGIPRh6CC1cisOViuMfL8A996BfcQlF91+lDjBNMkacg9m0MZFX/4mjfRecnnTs9rpZ4wmF1LVhh0M15hZCHL4ffoBjjoFu3eC336yOJnnJSrcQQvn2W6sjEAcycCD8+CMsWVIz4f7nP6FfPxg+3LLQRHybM24c5QsWcPRXX1kdyv798INazmzd2upIGrzKhmdauATHNTfgmDaL4vf+gdGuJb6MNjhatQJNw75mPe6UHNXwzJ2GfdVabA5nTE4wPR41VU4IUXfCYfWn221tHMlOVrqFECKRbdigthBqmpr/3bOn1RGJOKOHw5R4vaSbJjt//plG0lhJ7IVpmtjWroXXX8eIBCm6+WxMU/WQ8J9+Ha55iyl9djzG+WfhSWuKR/fC7t17bXgmhEgcpql2kWgapKVZHU3ykqRbCCES2aZN8Le/qeS7+irm8uWqJtYpG5oaulk338yw557DBKb27ctR+flWhyTigL7sN4wfvyXSsxORDk1xebPw/b4DBg7EzMqi4OfPsTmdaib2zEU43Kk4hh2JLT3Dspjz8+GLL6BLFzjrLMvCiH9PPw0ffgiXXw7XXmt1NEIIpHu5EKLSeeeprrGLFlkdiTgUzZvDq6+qM9FKwaBq79uli2rGJhosPRym6csvYwI2YOjChWxfvNjqsPbtvffgrbegpMTqSJJLeTnm7J8IlGyiZNcyCrfMQxt3O66xt2Cb+BG6FkALF0Pv3nDlldgef5yMnB5kNhmIP6crnlPPxzn6VEsTblBtLcaPh3fftTSM+LdmDcyZA5s31+vLrl6tSgDeeKNeX1aIhCBLIEIIZfFiWLpUdQ4Wiad6O98lS1Q70nBYmuI1cHPuvJNhlQV7gANYctllNIrX1e5rrlFjC1euBL/f6mgSllFejGaLYBoaHj0FcnOxRSKEf/sWPcMHgDZ8AI7NO7B36Epadmecbj84XPDaa4D6txJvunSBq6+Gvn2tjiTOXXcdHH98vU8i+dvf1MfOE0+oRXaRGGbPho8/ViPdL7zQ6miSl2wvF0Io33+vZkYMHQo5OVZHIw5XWRn8/jv06aO+N0049VT1/W23qS7RIqnp4TDr/X5ah8M1trWFgKJ4re0+/XT1PvTmm2rskTig6g3PzA//D899jxIZ2peyf9yP3e4is+kAla2WlBD64N+YfXrhdKfhcKVis8mGR1E3Vq2CTp3AUG0AmDYNjjjC2pjEwXnxRbj+ejjzTDXiXcSGJN1CCNEQzJqlupt7PGrroSQ0SW/WTTcx7J//rHW/BsyQ2u6EZRiaGsH1+OPw1VeU3X8DoU7NAHD9OBv/Rbeht2tF2U+Tcbr9pKS3wlZcAhkZ0vBMxMyFF8IHH6jhA3a7mkI6ZYrVUYmDMXMmTJqk+rBedpnV0SQvSbqFEKIh0HX49FNVdHfHHdH733xTzf7u2tWy0ETd08NhNqSl0SoS2Wvzlrhe7RY16Ns2YUz6EHPLRgLXX4SuBclsOgD7mFPhyy8JP/43yi4+UY3sCttxLV6BY+hIy+uvhYUWL4YdO9Ss+xYtYv5yv/6qtibvmVHk50spgBCVJOkWQigLF8KuXepSZ/UZ0CJ5bdoEbduq+u+lS9UWVJEU9rXKXUlWu+NUeTnMm0c4w0OoZSZ6uARWriZz5HmYHjcFS74Gjxt/bndcX0+Fbdswjz0G2rTF1gBWsZ96SjVSu+wyeOklq6OJY2efrfYJP/+82jccY6edpnp5alr0PqdT3S/blYVQpJhHCKHceiuMGqUKsUTDEArBmDHRTueVNm6svWQhEoYeDtPs5Zcx9nOMkzjtZN6nj/q3uGmT1ZHUC33dasJlOygvWoMeCcCdd8JRR2F//Q0iwQIMQ8Ns1xpt1Ei0P19FWkorMpsMwOVJh1NOgauuwta2XYNIuEG9ZQWDNZM7sRctW0L37pCbG/OXmjMHPvus9n8TTVNblpcvj3kI4jBFImoznIgtSbqFEEr79mp/mHQMbjjatVNnRdUL70Ihtd180CBYu9ay0MQfN+eOO2izj23l1VV2Mo8ry5erWxJmVaZpoIVLCZZuoXTHUvQ2LXC0aU/5r9MJlm4lEi6CYcOgWTPs/ix8GW1Iz+tJZrNBOL+eiuvvL+Bu2ha7w2X1r2KZG29Ub0uPPGJ1JHHu739Xe77PPTfmL3XPPeDYR6t7ux0eeyzmIYjDdM89amfCPfdYHUlyk5FhQgjl9detjkBYxe2Ofp2fDwUFquGSNFtLOJWr3JVzufen+mp33NR2f/ONan+cBCUuhh4BwP7THBg/HrNxLsVP31b1uDczHXPjVtyrtkCXvjhdqXD++XDBBdhtNrxWBR7H0tPVTcSH776DH3/c9+O6Dm+9BQ88AK1a1VtY4hCFQupPV8O9nlcvpKZbCCFETTt2wIoVqtt5pQsuUKvfV18NqanWxSb2K7B7N8WNG5Nns2GvtuXYiETANLFXu8BiAmWaxqZ336VzPayIJTs9EsD44F2YMoXQBacQ7tUBb1ozfEs2w7BhmHl5FC36HIfHr5qebdiBs0U7aXgmEpJpwsCBsGjR/rcmOxwwdizsp8WEsFgwqKaMejyQlmZ1NMlLkm4hhBD7VzluzO1W48aaNbM6InGIpvXoQd7q1XQtL7c6lOQQDsOsWRhLl1B2zjHo4RIMQyN17Hg8k7+j/K5rCN50Ge6UHNJSW8N//6v+H+raVcZ2HYZp02DBAujfH0aOtDqaOHbTTfDbb/C3v8VsWPann8Kf/nRwx3o8sH49NGoUk1CESAhS0y2EUCZMgKOPVjW+QlTXrx+88grce2/NhPujjxpMwytRTz7+WL0HBQJWR1KDsXYl4SULKC9aQ3nxejXp4eijsV9/A/rOrRiGhs1mxzhjDJFb/4zrpDPJbDKAtOxOKuO4+mro1k0S7sM0eTLcdptq3CX2Y8EC+P579e80RipXru12tZrtdEZru51OtVXZ5VL3hULwr3/FLBQhEoLUdAshlCVLVHHWwV66Fg2H1wvXXFPzvs2b4cIL1R7DJUugQwdrYhPJ5ZxzVBO1TZsgJcWaGCIRND2AppWhhUtwPP4sKU+8hHHeKQSf+gt2hxtf0/4wYgQ0a4bPlo09rx0Olw/bZYOtibmB6NtXVbr062d1JHHuwQdh+3YYMCBmL/HYYzB3rup8XXlbvBjef19tJ3e5aj4mOxPi1zvvwOrVaiBCnz5WR5O8JOkWQig33KCGavbta3UkIhEUFsKQIaqYr3rCXVgImZkWBSUS3vDh6t9U9eZ+MWboEXQtEB3D9d13BL98i3AHtavD1bUtpsOBozyEN60JTpcf0zSxTZ8OQP1FKi64QN3EARx7bMxfYsCA2jn9hx+qpPvBB+VjIJG88w58+aWaNCdJd+xI0i2EUIYNszoCkUi6dYOpU6GkJHpfOAw9e6r5sK+/Ds2bWxefSEz7a4VcB0zTxNCC6L/mY3v6WUw9QulTdwM2spoOxBYIQCCA++eVmD2643Sn4TzjUjjzWlz+dKS5rxD7VjnpT7pgJ5aTTlIJd5cuVkeS3CTpFkII8cdVn+v+00+wZYtaqczNtS4mIaqbNw+++YbQoB4EujfFMDQcBWvJeOcjzBQvPHY7Dq8fQw/jeOIJSEnB3bkzbru0vREJKj9f7enu1q3me3SMRdSUPEm6E8wNN1gdQcMgnyhCCGXdOpU0rVtndSQiUR15pBo19t//quZRla65Rg1rrTwjEyJG9DUriXzwX8qL1lC8fTF6JKA6OI0bh+N/U6oantl79CJy101ob79BZuP+ZDTug8OVooqFu3ZV3aFE3Ln1VnU975lnrI4kzp17rir/Wby4Xl9Wkm4h9k1WuoUQyt//Ds89pzpUP/yw1dGIRNWunbpV+uknePVVePNN1R2/RQvLQhMJYMAA1Zzvm28gO3v/x0Yi6Du3EfE70MIlaAXbyOh0FA7DoHT+p5hN8tDCxThOPBGKi7EPPYL0vJ6q4ZnNDo//o35+J1FniopUQ+5g0OpI4lyLFmqvt89Xry8biahu5dKkX4jaJOkWQiiNGkH79tL9RNStbt3gkUdUg7XqCfdXX6mVGPn3JqpbsED9WVkcWo2hR9AipTgcXhwfTFQ7KE48nvLn7lYHeJ3oPTtjw4a33IEjqyNOTwaccQaccQZ2ZHtfonv4YbjjDsjLszqSOPfDD5a8bCQiq9yJqFs3WLMGvv1W9bIUsSFJtxBCGTdO3YSoSxkZ8Je/1Lxv61Y1ms7jUdsfW7e2JDQRh778EkwTMz0dIxJACxdju/d+HF9+Q9kTd6MN7EWKvzkprVpBIIB9+e+4vJk43X6cbj+OeYuwudxYNGxMxFjTpuom4pMk3YmpvFztHnFKVhhT8tcrhBCifm3erMaM+f3QqlX0/kDAutnMwnrbt0N+PubWzRT1ycUw1Gp32u8rcPy+Bue8xZhDB2OzO2HwYFiyBFvnzvil/lqIuCBJd2KaO1d9/DZpYnUkyc1mmqZpdRBCCCEaGMOAnTtVWQOos7UuXdSW82eeid4v6sS0Hj3IW72aruXlVocCgL5uNeaP36G1zCPcox0Ol4/UMi+0bInpcFC07DtMnxenOw3X/KU4yjUcRxyNPa+x1aELC339tdoGe8QRakus2IezzlLvqS+/XK9bAyZMgOefV5uZhBA1yUq3EEL58EP4z3/guOPgllusjkYkO7u9ZmL9/fewejWUlUF6unVxiboXicCiRQQ7N0fTStHCJXgefZqUV95Du/h0tEfvVKvazdvDiSdia90af1o77I2bq4Zno7tb/RuIOPHKKzBpErz4oiTd+/XFF2rp8h/12yxQVrqF2DdJuoUQyurV8L//SYcaYY0TTlCzZTdsAK83ev8dd8Axx8CJJ0pL3ARhBMvRCGFoIbwpeWqlbdcutBmfEG6jLrRog/uiz1mMvWNn0rI64nD71Xz3L78EwPHww2CTbeOipkGD1D+T6gMSxF68/DKEw2q+Wj2SpDsxPf44uN2qN2VqqtXRJC/ZXi6EUBYvhvnzoVMnGDHC6miEUIVmgwer7i5r1si4scMQq+3lpmliaAG0cAnGt9/gvmM8evPGlL7zDDabjcymg7AddTT8+ivhf7+AfswI1fTMlYbN7qj5ZJqmsipQ3ZczMuo0ViFEbN15J3z2GSxfbnUk4mDperSB2o4d9X6dpkGRlW4hhNKrl7oJES9atYLbb1dtVasn3DNnQv/+NVfERb0wDV0ly6++Cp98QvDqcwkM7AiAI0UnZeVabNt34rB7cHoz1PGTJkFWFu4DNTxzOtVuByFEQpKV7sRjGHD55RAK1ftY9wZHkm4hhBDxqUkTeOqpmvdt3676DmRmqp0ZzZtbElpDoRcXYH78EebSXwncdiVapJyMxn1wzJoFX3yBq3sHgoM6q9XrAUcQmfgejhFHkdGoWhvcnBzrfgEhGhrDUDvXnE7o2hUcjgP/TB2RpDvxuFzw739bHUXDIEm3EEIpKFD1tKmp0L691dEIsXcrV6r9b82aqVslXa/Xk8ukVNHwLEKYUPs8tHAJ5u5dZF12DQCl5x8Pudno4VIcF10EffviOPYYMpt2Uw3PAM6QYlsRO+edBz/9pDpkn3KK1dHEqfJy6NtXfV1WVq/Ll5J0C7Fv0qVECKF88gn07g033WR1JELs27BhsGoV/N//RRuraZoqjbjpJnXxSBwUY9sWwqU7KC9ejxYuhcceg0GDsD/1DOHALgw9jJmRTuRPo4lcexmpqa3IaNwPty8Xjj0WbroJW/ce0YT7cEUiMHKkupWV1c1ziqSydSusX68ac4t90HV1QbJRo2ixbj2RpFuIfZOVbiGEkpamtvNmZlodiRD753ZD69bR77/4ApYsUV1gHn/curjiWGXP1FDZNrRwKZ4TzsQ572cCk19D79sNGzacQ4dCdjZ2fwYp6S2jDc8+/rJ+gtR1mD49+rUQe/jXv6CoSLqX71dGBmzaZMlLS9KdeFauVBsj8vLUEBsRO5J0CyGUs89WNyESzSmnwDffwM6dkJISvf/BB2H06GhH7AbENHRMU8e+dAXcfTesW4dpGpQVqrMqV0aa+vP3DTiHH4XT7Yejj4YdO7DZ7aTs78ljxeWCjz5SX6dYEoGIc506WR2B2B9JuhNPMAilpfKWWx9kZJgQQojks2ABDBig6rw3bFCzopOYroUwvpwMkycTPm4ooeF98PhySS2wQ/v2TAXyvB5absjH6U7DsWkXztxm2PMaWx26ECJJnHGGSuK++MLqSMTBCofVR6Suy0WtWJOVbiGEEMknOxsuu0zVfVdPuBcvhh494EDjq+KZrsOCBZhz51J2/ii0SCmGHsb3yf/h/feH6Fo5DO+NoYegbTd46SVsTz6JbcsW/Lnd1HOkt7L2dxDiD/j8c7W9/Oija/ZRFNVs3Qo33gh+f723pZaV7sTjdkvv3PoiK91CCGXRInj0UVUr+8QTVkcjRN0wzWjDtR071L/vVq1g6lRonBirvMa2LWg7NqG1agSmjs/TVNVthsOUzJlMpHkOYMPz0y+4v5sFY07BfsLJOJyequeY1qMHeatX07W83Lpf5EB0HWbNUl8PGybd6EUtffrAzz/DV1/BCSdYHU2cWr4cunRR/VnqubHk6NGqPUxllYgQIkpWuoUQyrZtqiN0796SdIvkUZlwA/zyC3g86qywUaPo/dUTc6sZBnq4HE0vQwuXYHvzLXy3PQjHDiP4n6ew2eykpLfGNmoUAF5HFt7c7jhdqdjOGgJnXW3xL3AYAgHVuRzqfdSRSAxDh6qGT7m5VkcSxxo1UjPV6rlzOchKdyJauxYmT4bmzVV5gIgdSbqFEErXrvDcczWTESGSyTHHqHlDmzfXHDc2fDicdBLcfrtKyOuRaehokTJcnnS4+mr46CPCrz9NYHBXABwd1TZwe0k5Hl8j1fDMNNU+WyCpzm9ttmhRYbxcBBFx5aWXrI4gAWRlwfXXW/LSknQnnsWL1bTNwYMl6Y41SbqFEEqrVqoOTIhk5vdD587R7z/7DObOVbNS7rwz5i+vayG0tSuwP/EUbNtKyYsPACYZjfvhKC+HwkKc+UtxjRyiGp4d1QFj+1aceY2T/wM7NVVtjRVCJCRJuhNPkyZwzjnQoYPVkSQ/qekWQgjRcOm6KkAMBFTjtUrPPqtWvw+3neuSJfD114Q7tKB8QFsMPYxtVwFZvU8GoODXr7Dl5JGW3RHnsjXqrLVXrzo/c02Imm4hxOErL4eNG9UMqJYt6/WlBwyA/v3hlVfq9WWFSAgJ3L5VCFGnQiFYuVLdhGgoHA4499yaCffChXDrrarL+Y4dB/1UxrYtRD56l/LdqynZ+RuRUDG89x7ceiuODyaqhNtmw9G0JeG/3IL21mtktBpEZpN+att4r17qjFWWioTYq2OPhW7d1JZYsQ8LFqjdPMcdV+8vLSvdQuxb0u9WE0IcpCVLoF8/NV5p82aroxHCOm43jBkD6emqa1OlNWugTRtVb2wYGDu2EUmzo4VL0IJF+Lschau4lPKv3kDv0RmnpxjXMcfAokXYRhyJv7Lhmd0Bjzxj2a8Xt8rK4Mwz1d/vZ5/J2buoZcUKtYgbiVgdSZzLyFDvX/VMkm4h9k2SbiGE4narJlKpqVZHIoS1undX7Vw1reouc8d26NkTenTHdtvtcO212Hp2p+y9J6uO0fr1wLFpG+6gE3tme5yedDi6BRx9NHZka9kBRSIwZYr6WirfxF5UVoIcbtVHUjviCCgstOSlJelOPC+/DA88oOq6//EPq6NJbpJ0CyGU7t2hpMTqKISIC7oWQguXoJeWqNFdX31DWiSMsXsnDr9fndQuXY7L6ceZko7D7cc55Qfsbi8pVgefqHw++M9/1Ncyo1vsxeDBVkcg9keS7sRTVARbt0JxsdWRJD9JuoUQQogKpqFTtH0Rhh6u+cCxwyie9znuYp2U4aNhwQJsvXrht2AWbtJyu+GSS6yOQgjxB0nSnXiuuAJGj1YVCSK25GxBCCFEg2LoEVWHHSlFCxVjszvx53QBwGZ3YLM5VMMzVypOt7/iloa9uSf6JP36WRS9EA3XZ5+pgQOjRqlqKLEXc+fCiy+qjnN33VWvLy1Jd+LJy6vZukTEjiTdQgilsBBuuw0MA9580+pohKhTofKdaKFCtWVcC9Z4zGazY5oGNpuquk7L6Yzd7lYNz0T90bRoW+q+fVVDNSGquegiVQX1++8yV3ifVq9WZRpHHy1JtxBxRJJuIYQSCsEbb6iv33hDTnhFQjINHS1Sih4px5vWtOr+cGAHkWBh1fcOlw+nK61qJbsy4QZwOPdRlT1/Ppx1FrRtCz/8EKtfoeEqLFQj00AtZ8p7kNjDkCFQWqrK/8U+9O4Njz8OLVrU+0tL0p14ZsxQF7H691dTK0XsSNIthFDS0+HRR8HpVJ2D5YRXJICqhmfhkoot4+WA6nztTsnB7nBXfJ1XkWSn4XD7sdv/wMdfKATr1slZZazY7dFEQd5/xF58/bXVESSArl3VzQKSdCee//4XXn0VHnpIku5Yk6RbCKGkpMA991gdhRD7ZJoGYMNWkZCVF60jWFp7przd4cHp9lccr3h8uYcfQK9eMGcOeL2H/1yituxs2LDB6iiEEH+QJN2Jp3t3OOkkGcNXHyTpFkIIEZf2bHimR8rw53bD6fYD4HCl7r3hmcNzgGf+g/x+GDQoNs8thBB1oaREzX9KS6vXltS6rjbJSdKdWG6+Wd1E7EnSLYRQTBO2bVPNjJo2lTm5whJapJxQ6ea9NjwD0MKlVUm325uFu8lAaXgmRANgGDBggPpo+uYbyMy0OqI49cYbKos691x4//16e9lIRP0pSbcQeydJtxAiqmlF46nt22WGhIipyoZnWrgEp9uPy1OxImPqhMp3VB23Z8Mzhyva5Kzek+1du+CrryA1Ff70p/p97YagsBCuvFLVc3/0kdXRiDij67BwofraNK2NJa6ZpurNUs/ZryTdQuyfJN1CCMVmU5+WNpta7RaiDulaEC1cuteGZ57UxlVJt8OVSoq/xeE1PIuVVavUzKLWrSXpjoVgECZNkiZqYq8cDvjyS5V8y4zu/ajcL1zPVyYk6U5MF1wA8+bBs8/CySdbHU1yi6OzGSGE5cJhqyMQScA0DUxDq+ocbugRirYtrHVcZcOzqlVu1MzslPSW9RbrIUlPh+OOg8aNrY4kOaWnw4svWh2FiFN2O4webXUUCaSeL15J0p2Y1q+HlSvVcA4RW5J0CyGEOCxVDc8qbnqkDKc7HX+uGltjd7hwOFOw2R310/AsVrp0UcWkIjZ8Phg71uoohBB/gCTdienVV2H3bujc2epIkp8k3UIIIf6Q8sI1REKFe214Zug170tv1Ltq1JcQQhyqcBi++05tMx81SqoQ9unTT9VA82OOgTPPrLeXlaQ7MVk00r1BkqRbCBE1bpy65HnffdC8udXRiDhQveGZoYVIzWpf9ZiuBaoS7qqGZ550VY/tTKnxPJJwiwOKRFTdvN0uQ2NFLUVFap4wqLpueUvZh59+UmUaXq8k3ULEEUm6hRBRb7wBW7bAdddJ0t1A6VqoYpt4MXq4tEbDM4CU9FbYHeqsyutvjjetafw1PIuVJUvg7LOhSRO15Cbq1rZtatnF5ZL+EqIWux369VOjw+x2q6OJY8cdBx4PDB1ary8rSXdi+vhj9d9u1CjIyrI6muTWAM6ShBAH7bbboKwMGjWyOhJRD0zTQI+U4XClYrOps9hgyUZC5dtrHFfZ8Mzp9tdYXqreAK1BCAZV4l1UZHUkyclmU2d9brfVkYg4lJMDCxZYHUUCOO44datnknQnpj//GbZuVeP4JOmOLUm6hRBRd9xhdQQihvbW8Mw0DdLzeuJ0qxk8Tk8Gulae2A3PYqVjR/j+e7WKJOpe8+aqvEUIkXDWr1d/bt++/+NEfBk+HHbsgMxMqyNJfjbTrOdBfkIIIepVOFhAoGjtXhue2e0ufJntcKdkWxCZqC/TevQgb/VqupaXWx2KECKWSktB09Q0gHrYNbJ7t5r1PGVK9L4TToD33pOVUyGqk6oYIURUWRkUFEg9ZYyVF61jw6//Zef6qQSKN2Aa+mE/p2noREJFBEo2UrJzKeFAdMXQZnPUaHjm8TUiNasDGY37kNl0gCTcQoi4t2EDjBgBJ59sdSRx7sYbVbb7zDP18nIXXADffltz/e7bb+H88+vl5YVIGLK9XAgRNXw4/PyzGjcyapTV0SStYOkWirf/TPH2nwGwVaw2p2a2JzWrPSn+ltjsjv0+h2FoRIKF+2x45nB5q5JppysVf06XhtPwLFaKi6Pby0880epoks+OHaqvhMcDr71mdTQizpSVwcyZsnp6QJqm/nTG/r3+5/kbmDKlJVCzlbyuq5Xv339XVTlCCEm6hRDVVX5IV35oi5jwZbSp8b1pRCjbvYKy3SsAE5vdSUp6a1KzOpKa2Q6vvwWmoXYfON3+ih/SKSv4vcbzOBweHBW12NWbnNnsDlxeOVM9bOvWwemnq0aD27ZZHU3yKSuDt9+GlBRJukUtzZrBRx/VSy6Z2P7zH/X/Tx21eDdNE10LoIWLMbQQvozWVY+tXLn/CtXHHoOnn5Z64XgVDKqJAG43zJqlKhJE7MhblxAiauZMcDjUTcSMJ7URdocbQ6++jT968mIaGuWFqygvXM2Oivsdbj/Nu56HP6cLoDqKu71Z2J3eqqZndod0fY4pn0+N4cmW7fgxkZUFTz0lWZXYq/T0eh07nbjs9sNq9lg51UILqdGRWrgEw4heiPemNasaG9mle+Z+n+utt+CDD+Cyy+Dmm2XVO94Eg7B0qfpa3nZjT/6KhRBR0pW5XthsdlLS21BWsOIAR1bbLu701toanlaRgIt60r69Wg4QsZGRAbffbnUUQjQopqGDzY6tYhxkeeGaWmMjbTa7urjrSa9xf/ee6Zxwgqrh1qu1JnE41NSyN9+El15StxdfVPX4t94KRx9dY/qksEhqKvzwg2rjI6PeYk+6lwshRD0yDZ1IuIRtKz+nePvCg/gJG43bn0xOq6OqToqEOFTSvVwkupISNUvY54MBA6yOJo69+qoqpj7vPLV3eA+GoaGFiitGRxajR8rw5/aoGhsZKttOoHh9RZLtx+lOx+HyYbPtfbt6QYFqmra/7uXBILz7Ljz7LPzyC/TqBbfcon7O663j31+IOCVJtxAi6vXX1V6j886Ts5o6FCzfQdnuFQSK1xEs3UoksBNDDx3gp+zYHS5a9ryMtOxO9RKnSF4JkXRHIrB1q9oe27y51dGIODNnDgwZAm3bwurVVkcTx044QTVDfestuOgiALRIOaGyrWjhEvRI7feA1Mx2eFIbA2p7OdgO+SLv77/DypXQocO+t5GbpupF+cwz8L//qfYYY8eqW+PGh/RyQiQc2V4uhIiaOBG+/BJ69pSk+w+orIULlm5D18oIFK2jvGgtgZINYBrYbA5cKdmkZnXAk9acnWu/3scz2XCn5NC691W4fbn1+juI/VizBs45R22D/vZbq6NJPuvWqbN1v191iheiGrcbOneGli2tjiS+GaefitGlHXRsU3WSbxoRQmXR5o8OZ0rVdnGnOx2HM1patq8V7QPp2PHANds2Gxx7rLqtWAH/+Ac8+SQ8+qgaPXbLLdC79x96efEHFBWp7eV+v/pvImJLVrqFEFGvvw7Ll6vEQpLuAzL0CJFQkUqsi9YSLN1MOLATPVIGgMubhS+jDe6UPFwpWfgy2uLyZFQ1PFs550lCZVtrPW9adhda9LgYh1P23cWVpUuhWzfVSG3XLqujOSQJsdK9apX6+01PV+PDhBD7ZZqmanoWLlG3UDGGEQHAm9a0alKGaegESjZUNN1Mr2qEFg8KCtSO+H/+EzZuVPXet9wCY8YcfAP2mTNh8mR4+GHpA3so8vOhf3+1sWjjRqujSX6y0i2EiLrySqsjiFvq+qSJoYcIFK2neMcvlBWuIhLcjWlogA2XNxOvvwUp/hakN+pNin//W2RTM9urhjWmUXVfTqtjaNz+xD+82iBiqGVL+Pxz6TgTK+3bQ+hAZRdCCFAXfYu2L1SN0Kqx2ew4XKnYq120tdkdtUZVxousLLjrLtVgbdIkVfd92mlqm/pNN8Hll0Na2r5/3jTh+uvh559VM7cnn6y30BOe16sGcuTlWR1JwyAr3UIIsReGoaGFSwkWb6C8aA2Bko1o4RLC5TsAE7szBZcnE09aE3zpbfBltsOdkoXDmXLQr1G4NZ9NS95B1c/Zad71PDKa1G58I8ThSoiVbiFEDaahVzU808Il2Oxu0rKje7gLty7ANPSKhmdqFdtpurA5XWrJN0Gbb86erZLvjz5SCfdVV8GNN0Lr1rWPnTEDjjgi+v2bb8Kll9ZXpEIcPEm6hRBRhqGaGdntDXI1LxIuoXj7YgJF6wiWbSES2FXV8MzpTiclvSX+vB5qm7g3GzBrjfE6FOHAbn7/6WEcrjRa976KlHQpVhSxIUm3SHTz58O4cdCpk9qKnKzCgd0qyQ4Vo0XKqT460m53kdk0Wvqla0HsDk/Npmd9+qhl3ylT4Pjj6y/wGFi/Hp5/Xm0/Ly6GM85QK+JDh0avJ5x+utpaXjmyzOmEqVNh2DDr4hZib2R7uRAi6oor4D//gSeegDvvtDqamIk2PNtKuHwH4cBOyovWEizZiGnq1RqedSQlvRW+rPZ4fHk4nCl1uu3bnZJNy56XkZLeGtce809FHCovV8WDTqcqPBR1a+tW+Nvf1PDYp5+2OhoRZ7ZvV025E6ydwn7pWgg9UoY7JbvqvmDpZrRwSdX3DoenouGZWsmubq99PzRN/ZkEF85btVKnI/ffD//9r1r9Hj4cBg1Sdd/9+8Onn6ot5pUMA045RdUr721lXAirSNIthIhyVrwlVH5oJ4naDc82EQ7sqtXwLKNJPxwOLykZrWs0PIul9LyeMX8NUUc2b1YrR9JdOzYKC+GVV1SRpyTdYg+9e6vEq3L2cyLSIwG0cDGRijnZlTupMpsOrNo15U7JxeHyqa3ibn+NzuIHZfZstWNtf4XQCSYtDf78Z7juOjVg5ZlnVLfz1NTaxxqG6sp90klqzFwS/TXUuW++gXvvhcGD1Y4CEVuSdAshop59Vp3sphx8XXK8MU0T04hgmjqBovWUFa2hePvP+2h41pK07I6kZnWwOmyRCNxu6NVr72d64vDl5sIDD6juPkLsoXlzuPhiq6P4Y4KlWwiWbKrqLB5lw+lOxdQjUJF0e9OaHN6LJXGWabfDySer208/wciRNVe5K+m6GsRy4YXw8ccH3wW9odm2TZVtJPKFrEQiSbcQIioBP6wNQ0MLlRAs2UhZ0RqCJRuJBHYTCRUCJg5XKi5vNv68XvgyWquGZ95sGcclDl2rVqpWUsRGbq7aRypEAjINHS1SVlGPXYIvozUOlw9QHcUNI4LNZq/YJl4xI9uVhs0uM67+iLlzo3Xce6Pr8NlncN998Mgj9RdXIjn6aFUPn5194GPF4ZNGakKIhBSoSK5Ldq+geFt+jYZnbl8umU0GVHQUzwVMGcElGjRppCYS3e7dsGYNZGSocVJWMw2dSEVXcS1UjB4ppfoptS+zLd5UtWpt6BEMPYjDlRr7z6InnoBAQO3Fbtw4tq9lEV2Htm1hw4aDO/7tt9WqtxBWkpVuIUTUt9/C9OkwZAiceKLV0eyXHgmgRcpwOFNIzepESnrL/TQ8S8yxKUI0KJqmijEdDsjMtDoaEWe++kolTsceqz6q6puhRwCzqteHFi6hdNeyGsfYHe6qhmcuT2a1+13YHfXU2OzJJ2HnTjjrrKRNuidPPviEG+CSS9Rqbpyf1ogkJ0m3ECLq++/h0UdVW9A4/3TypDbGnZKN0+2vl4ZnQrB1K5x3nqo5/uorq6NJPsuWQc+e0KiRKjYUohqvF1q0UP886oOuBatWsbVwMboWxJvWDF+Gaomtmpx5o/OxPenxUbZ0xRWq0WNOjtWRxMzf/17ze7td3SrHiBlGza3nhqH+WrZsqf1cI0eOZPr06QA4HA7y8vIYMmQId955J8OSfO7YqlWwerWqnOrc2epokp8k3UKIqCFDVIvQESOsjuSAZMSWqHfBoBoAm8CNBoVIVGecoW6xZBo6ZYWrKjqLh/fyeLQRms3uIKNx39gG9Ec8/rjVEcRcy5aqp2V6uio38Pujt7S02l/b7dClS+3nMU2TRYsW8dhjj3HppZcSDAZZu3YtL730EiNHjuSzzz7jpJNOqv9fsJ68/baa0njttfDyy1ZHk/wk6RZCRJ16qroJIWrLy4MPPoiO1hN1q3t3tcVcWs2IGDNNAz1ShhZS87C9/maASqS1UHFF0zMbDldatOmZ21811ktY65136uZ5fv/9d0pKShg5ciRNmqj6+zZt2nDUUUdxzDHHMG7cuKROuvPy1MWLVq2sjqRhkHcPIYQQ4mCkpsI551gdRfKy2VQ9txB1THUWL63YKl6CFi7BNA1A1WFXJt2gGqDZ7E7pLN4ALFiwAIfDQe/evWs9NmrUKMaPH49hGNiTdObYn/+sbqJ+JOe/IiGEEEIIkTS+/FJtL3/66QMfaxo1Z0mV7FpKyc4laupFqAjTNLDbnbi9WXjTmlYl4ADulBxcnozETLhNU1248nhUMzWxX/n5+XTp0gWfz1frMZfLhcPhSNqEW9Q/WekWQkT9859w661w7rl1t39LiGQRCsH8+WpFNskb7Fhi0ybVISkjQ+Z1i1pWroSPPwbXXpqAG3q4ahU7Ei7G0AJkNhlYlTg73X4MPVTR8MyPy52O3ZmCzZZkky00TXUNC4elDOYgLFiwgH79+u31sRUrVtBlb4XgQvxBcvlGCBFls6mWn5HIgY8VoqHZuVM1GTzySKsjSU47dqik+5VXrI5ExKGjjoIXX4Qrr1TfR0JFlBWspGjbQgq3LqC04HeCZVvRI+WYpokWKav62ZT0lmQ26U9adke8qU1wuHzJl3CDSrS3bFHztNKl2eiBLFy4cK9JdyAQ4NNPP+XMM88EYPDgwcyfPx+ASy+9lJdeeqle44yVCRPg6KPho4+sjqRhkMtgQoioSy+F009XtatCiJqcTujQQeqOY6VRI7jrLkkWRA2maaJr5XRsW0y3rtk4nB4A9EiAUPmOiqNsOF0+nJ6K8V3u9BpzsW22BrLGZLNBRUMwsX+rV6+msLCwVtJtGAZjx47F6XRy4403AjB+/HgeeeQRhg8fTlpaGmPHjrUi5Dr366/w44/qtE/Ens00pU2oEEIIkcym9ehB3urVdC0vtzoUIfbLNA20cGlVwzMtXFxVo52a2Q5PamMAdE0l3S63H4d0FheH6MMPP+Scc85hxYoV+P1+iouLWbBgAc899xzr1q1j8uTJ9O/fv+r4Pn360KhRI7744gucSbJ1f84cWLsW+vaFTp2sjib5Jce/GiGEEEIIkdC0cAklO5fUaGwGYLM5KCrJZOMOD42bQuPG4HCm4EuXWUc1lJbC88+rwvfbb7c6mriWn58PQKdOnXA4HGRkZNClSxfGjBnD2LFjyc7Orjp27ty5FBYW0qlTp6RJuAEGD1Y3UT9kpVsIEbVsGXz2GbRoARdcYHU0Qog6khAr3ZUNoOx2cLutjkbEiKFHqlawtXAJLk8mKekt1WOGRuGWedjtrmpbxf04XD4mTLBz//1wzTVS9r9PGzdCy5aqFEZ6s9SJTZs2ceKJJ/Lpp59yxhln8O6779K1a1erwxIJKHku1wghDt/PP8Pdd6tGUZJ0C1FTQQFcdJH6+n//szaWZJSfDwMHQqtWsG6d1dGIOmKaBuHA7qokW4/seeHHRgoq6bbbnWQ07ovD6a31PF4vZGVBWlo9BJ2oUlLgiitUbbc4bIFAgLPOOovnn3+etm3bctdddzFhwgTeSZLpLgsXQjAIXbqo/7dEbMlKtxAiat48eOEF9Q58zz1WRyNEfNm2LdqkKME+OhNipXvePBg0SJLuBKdHAhhGGJcnA1CN0Iq2zscwtKpjHC4fTne1leyK5mhCiPozcKCagjl5MowZY3U0yU9WuoUQUQMHwptvWh2FEPEpPR3+/W9ZRYqVfv2gqEj+fhOIaRrokfLodvFQCYYRwe7wkNlEdYW22Wy4fY0AKhJtf43O4kIIa7RoAbt3Q0aG1ZE0DLLSLYQQQiS5hFjpFgmlvGgNobIdmKZe436bzY7T7SctuzM2u4zXE0IIgAYyuFAIIYQQQhwKw9CIBAsoL16vuoobNRNs09Sx2R24vFmkpLciPa8HmU0H4s/tVucJ9zvvwMUXw//9X50+bXJZtAj8fujRw+pIhBB7kKRbCBE1YwZkZsKAAVZHIkT80TR1UrtoUcLVdCeEDRtg/Hh45hmrI0l4LVq04MUXX6xx36xZs/D5fKzbT728oUcIB3ZRXrSG4u2LKdwyn5JdywiWbCISKkKLlFUd60ltSnqjXmQ2GYg/pwsp/uY43X5stticWs6bB2+/rZo/iX0Ih9XYsNJSqyMRQuxBarqFEFGmqWoqi4utjkSI+FNcDH37qq8jETWWR9SdjRthwgRo1w5uvdXqaBLakCFDmDdvXtX3pmlyyy23cMstt9C6deuq+3UtiM3uxG5X/5bDgZ2UF62t8VwOp7eq6ZnDmVLj/vr0pz+paVgDB9bryyaWXr1g5UqroxAJ4qyzIBSCl15S9d0ituSMQQgR1b8/LF+uxo4IIWqy26FpU9XoS1a6616TJnDDDZCTY3UkCW/IkCG8Wa0p5ltvvcX69eu5646bCZZtRQupxmeGHiY1qwMeXx5A1UxsV0VXcacnHbsjPmamH3WUuon98HqhfXuroxAJ4quvoKwM/vEPqyNpGKSRmhBCCJHkpJFawzJjxgyOPPJIioqKMI0wXbp04+7bruSi82vOBbLZbHj9LUjxyzKXEA3Nu+9CIADnngtpaVZHk/xkpVsIIYQQop6Ul5fjcDjweOp2NrVp6GiRUrRwCT27tcHhcJCfn883X08hK9PPBeeeVNVZvHIV2+lKS5gO4zt3qgQhM1P1ChN7sXatGrrcpAmcfbbV0Yg4d8EFVkfQsEjSLYSIKihQlz6dTrj2WqujEUKIpDNy5Eh+/fVXBg0axPDhwxkxYgTDhg0jKyvrkJ7HMDQ1HztUjBYuQY+UUrl50eXJoHfv3kyaNIl//etfTPy//5LZuBcOV2rMGp3F2h13wH/+A489BnffbXU0cerXX+Gmm1ThuyTdQsSVxHznFULExvbtqqZSzmiEqK28XJ3Inn226hIs6tbMmeBwQNeuVkcSU++//z6PPfYYjRo14s0332TMmDFkZ2fTo0cPrrvuOt566y3WrFnDntV/1cd1maZJ0bZ8SnctI1i6GS1cgmma2B1u3Ck5uFNyGTJkCM899xzHHXccJ445K6adxeuD0wluN7hcVkcSx5o0gXPOgeOOszoSEec0DebPh19+kRYl9UVquoUQUVu3wo03quKeN96wOhoh4ktxMWRkqK8DAdW0KEEkRE339OkwciR06qQaOjYApmmyevVqZs6cyYwZM5g5cyZLliwBoGnTJgwa2I+mTXK4/trzadm8CRmN+1b9bMmupRhaEKc7XW0Vd/trdBR//fXXGTt2LL/88gudO3eu999NCBG/tm1T12gADEP1BxWxJUm3EEIIcTDCYXj1VfX1tdcm1MiwhEi6w2HYtUutdjdqZHU0lti68Xc+//wTPvnsC+Yv+IVt23cBcPbpJ/DyP/9GZpP+2B1qqdc0jf2uXB9zzDH06tWLZ599tj5CF0IkkC1bYPBgtcq9YYPV0TQMiXPGIIQQQljJ7Ybrr7c6iuTldquRbA2AaRrokTLWrl7O3PzlTJ8+g+nTp/Pbb78B0LRJHn5/Ktu27+Lss07lzX+/ijc1u2qeNrDXhNswDHbs2MHrr7/O8uXL+fjjj+vtdxJCJI6mTWH9equjaFgSt7hHCCGEECIBmIZOOFjI4vypPP/Mg1xwzim079CZjl0HcuGFF/HDDz8wdOhQ/v36y8z76Rt69+nLmrWbeP755/m/Dz/F529UI+Hel2nTptG0aVPefvttJk2aREZlOUQSePFFuO46mDHD6kji2Lvvqj3Dl19udSRCiD3ISrcQIqq4WDUx0jS138jttjoiIeKHYcCqVerr9u3BLtet69T69SppyMuDK6+0OprDpus6ixcv5vtvv2Dqj98xe+4iduwswG6307N7R0464UhGjBjG0ceOoVmLtgCsXbuWU045hfXr1/PFF19w/PHHH9JrHnXUURiGEYtfx3JffaWmYQ0YACNGWB1NnCopUcW6hYVWRyKE2IMk3UKIKLsdNm9WX2uaJN1CVBcKqSZfoC5QybDgurVqFfzlL9CtW8Il3boWoqxkJ3Nmq4Zos+f9xuw58ykuLsbtdtO/b1cuvuB0jhgxnOEjRpKd2xyHK6XGc8ycOZPTTz8dv9/P7Nmz6ZrkXdwP1YUXqoS7f3+rI4ljZ58NQ4bIe5M4oKVL4d57oXVrkLYP9UOSbiFElM8HCxeqBlEej9XRCBF/0tOtjiB5NW2qtsU2a2Z1JAdkGBq7t69j5oxpzJgxk1mzF7Bg0RJCoTBpaT6GDO7P3XffzRFHHMGAAf1xuxw4nPt+T3377be58sorGTJkCBMnTiQ3N7cef5vEcO65VkeQALKz1U2IA9i2DT75JOknNMYV6V4uhBBCJLmE6F4ep0zTRI+UsWPHDmbPXcS0adOYPn0aCxcuwjAMcnMyGTKoDyOGD2LEiCPo228QXl9WVZfx/TEMg/Hjx/PII49w+eWX8/LLL+OWHUZCiBjbtEmVa6SnwwUXWB1NwyBJtxBCCJHkJOk+eKZpoIVLWb1yCdOmTWXmzNnMmrOQ31euA6BNmzYcccQRDOzXiZFHjKBrt964POnY7I5Dep1QKMSFF17IpEmTePzxx7njjjuwybDcfSosBF1XO6flusQ+LFyobl27wtChVkcjhKhGtpcLIWp66y1Vu3reeZCWZnU0QggRc6ZpsmzZMr7+4gNmzprNT3MWsXHTNgA6d2rLiGH9uefOGxl14jm0bNmyTl5z6dKl/Pjjj3z88cecdtppdfKcyezUU2H6dPjwQzjrLKujiVOTJ8Nf/wrXXCNJtxBxRpJuIURN114LgQAcd5wk3UJUp2lw9dVgmvDCC5CaanVEyeWHH1Rm1bMnzJoVs5cx9AjB8t3kL5jNjJlzmDN/KTNmzGDnzp04HA569ejEn8Ycx/ARwxgxYiRNmrXB4Uqt81XoPn36sGPHDlndPki6rv50HNqGgoalQwc4+WTo1cvqSEScKyiArVshM1O10xCxJ0m3EKKmk06CSEQaqQmxJ9OEN99UXz/zjCTddU3ToLQUysrq9Gl1LURJ0TZm/zSjounZfObO/5WysnK8HjdDhgxl7NixjBw5kgH9euBPz8DhTDnwE9cBSbgP3owZKvGWSX37ccEFUqArDsqkSXDVVeoazeefWx1NwyBJtxCipo8+sjoCIeKTwwGPP66+TqmfpKxBGT4cVq48rIJd0zQxtAAlZSF++mk206ZNY+oP37Bg4WLC4Qjp6WkMGdiLO2+9mhEjhjN48HBS0xsfcj22qH82mxqsIYQ4fA4HZGVBRobVkTQc0khNCCGESHLJ2kjNNA30SBmbNq5m+tQfmTHzJ2bNzue3paswDIMmTZowYvhgBg/oxogRR9C7zwA8vizsdsnehBBC1B/51BFCCCGShabB7t1QXAxeL7RoEX3MMOCnn9T9lbeUFLVNPi1NLSUmANM0WbFsMT989z/V9Gz2Ilat2QBA29bNGTK4DzfeeCNHH3si7du3ly3cSeKJJ9Rs4euug44drY4mTv31r/DOO3DTTeomhIgbknQLIWo6+mhYuxY+/hj69LE6GiHiy+bNqra7SZP67+gUCKia57w89X1xMVx0kcpEdu1St8LC6PGnngqffhr9PhSCYcP2/twjRqjW0JXuvlvtO+zUSWU4HTrEvoZ93Tr47DP1+513HgCGoREOFvHL4nxmz13MrJ/mM336dDZt2oTNZqNbl/YcfeRgxt97C0eMPIpWbTpWND2Twt9k85//wJIlMGaMJN37tG0brFpV831ACBEXJOkWQtS0YYNKupNsG6oQdaJ5c/Xntm3QqFHsXqesTM3bnT8fFixQt2XL4Oyz4YMP1DFpaTBlCoTDtX/e76/dDNFmg3btVPIeDEb/NE1V3FfJNOGf/1SPV9eihboQN3o0XH99nf66gMqobrqJUO+ezM71MG36NGb9NJ/Zc3+msKgEp9PJwIEDufDCCxkxYjgD+nQir0krHM4UWc1uAK66SnVbbtXK6kji2N13w6WXRt+nhNiH99+H//1P9c49/3yro2kYJOkWQtT0wQdqi2q3blZHIkT8cThU8hqrdijhMJxwgmrVrGm1H9+6Nfq13Q5vvKFWoHNzISdH/ZmVtfdVeK9XNSqrzjRVgh8MRu8zDLjnHrVitmKFuu3eDRs3qpvTWTPpfvRRNRN4+HBwuQ7p1zUNnUAwxOzZs5n68SSm5WYx97cllI86A1+KlwH9ezD2GpVkDxtxNJnZkkw0VLfeanUECaBtW3UT4gDmz4e331bjwiTprh/SSE0IIYSwys8/w+LFcPHF0fv69FH3N2sGAwZA//7RW+PGf6j2+rAbqe3apVba589Xq+WnnKLuX7VKbT0HNfB19Gj1u5xwQq3E3zRNdC3A9i3rmDFjKjNmzuKnOT+z6OclaJpGdnY2gwf2ZNiQ/owceQT9BwwhJTUbu+OPdzMXQghR29SpMG8eDBwIRx5pdTQNgyTdQgghRH0KBtW879deU9vGvV7YskUlrQCzZ6ut6+3a1dlLxqx7+e+/w8MPq32KO3dG72/TBq69Fq68klUFG5g29QdmzvyJn+YsYsmyVQA0bZLHsCH9OPb40zjiiCPo1q0bNhtSjy32KhBQ15vcbpnVvU/Tp6u+EwMGQPv2VkcjhKhGkm4hRE3ffQfbt8MRR9TsfCyEODyaBm+9pToMb1DdtnG54LTT4KmnoHXrmL10zEeG6TrmT7PQP3iX1f99h+nFJUwHpjdqxOrt2wFo364VQwf3ZsTwYYwcOZL2HbvjcqfJjGxxUJo3V/lkfj707Wt1NHHqjDNUE9SXXlJt3oUQcUNquoUQNY0fr8YKffKJJN1C7OnGG1XN88MPR1emD8Yvv6jCud9+U983bw633662YufmxiTUWDMMjVB5AYsWzmPGjOnMmDmH2XN/ZntxCXabjd5+P2POPpuhw/ozdEg/mrdoj8Pl2/dK9tdfwyWXQL9+8MUX9fvLiLin6+pPWeXej+7doaBAPrvFAW3bplqI5OSAz2d1NA2DJN1CiJr691eze7OzrY5EiPjz0kvq7H/cuENLul0uNRIrOxv+8hfViCwlJWZhxkooFGL+/Pl8981kZkyfxpz5v1BcXIrb7aJfn25ceN5pjBgxjKOOPZXsnEPs7h4KRcefCbGHVavUZpG0NKsjiWMPPWR1BCJBXHedWluRTRH1R5JuIURN//yn1REIEb/Gj1cr3Yd65t+lC0yeDD17qqWFBKBHAhTs2sysmdOYPmMmc/OXM3fufILBIGlpqQzs352b/nwpI0aMYMjQ4aSlN8Lh9Bz4ifflyCNVA7kEvBghYi/WY+KFaEgcDtUfweu1OpKGQ2q6hRBCiFiYMUOthvfoYXUkfNumDU03bSL1iy9oM2rUXo8x9BCb1v/O9Ok/MnPmT8yak88vv/6OruvkZGcyfPhQjjp6FCNHjqRnz+44HQ7sjkMbESaEEEI0RJJ0CyGEEHXNMFQH71274JtvYNgwS8IoWLWKVYMHo+3aRRA4Cpifk0P7uXNIa57Hxo1bmTV7HtOmTWPatKksW7YcgJYtmjB0UB+GDx/MEUccQfce/XB50qXpmbDMAw+oyo5bb1Wj6MVeXHyxGkH4zDNwzDFWRyOEqEaSbiFETbfeqpKE+++Hc86xOhoh4ktxsUqo09P339Fp9mwYOlQdt3mzZXtj5+fm0mfXrhq1ZBowze3i0rxsNm7aBkDXrl0ZMWIEg/p1ZOTII2nbvgtOd1r9ju9au1ZNT2jcGMaMqb/XFQkhJUVN21u3Dlq1sjqaODVkCMyZA599BqecYnU0QohqpKZbCFHThg2qw7I0MxKitsaN1Zn/2rX7H/H1ww/qz1GjLEu410yZwoC9/H/sBI4JRzi7d1cGPf4gxx5/Onl5efUf4J4WLoSrrlK7AiTpFnv4859Vrz2/3+pI4tjzz0NhIfTubXUkIs799a9qOuwtt0DnzlZH0zBI0i2EqGn8eBg7VjV+EkLUZLMd3HGVnc2DwZiFUp2uhdDCxWihYmx2B76MNuycM4e2exxnAv8ABgHnDz6agRdeUy/xHZQmTVSy3bWr1ZGIOPT001ZHkAAGDLA6ApEgPvgAli9Xkywl6a4fknQLIWqSK+RC7FtBgUq8XQdoINa9u/pz5kzYubPOZ3HrkQBauJhIqBgtXIKhh6oes9td+DLakDt4cO2fA94HbgWGffwxD48cyZFHHontYC8mHC7DgP/+Fy66CJx7nIIMHao6vAshhIipW25RExrb7nllVsSM1HQLIYQQdU3ToF8/+OUXuOACePvtg18l34NpGhhaEIfLV3Vf8Y5f0MKl1Y6y4XSn4nSn43T7cXmzsNlse63pDgPPpqXxXocOLFq0iBEjRjB+/HhGjRoV++T7xhvVFtjjjoOvv/7DfyeiYak8U5V/LgfwzTdqD/7w4dJtTog4U48dUoQQCeHXX9Vq0/LlVkciROJyOuFf/1JZwqZNEIkc9I+ahk4kVESgZCMlO5dSuGU+RdsXYxha1TEuTyYuTwYp/hb4c7uR1XQg6Xk98WW0xp2SXZU8d5g3j0V7zAVfnJPDNYsXk5+fz+TJkwmHw5xwwgkMHTqUzz//nJhci1+7VnVTfv559f3ZZ0sGJQ5aOKz6FjocUFRkdTRx7JprVAO1FSusjkQIsQdJuoUQNb34Ipx6Krz/vtWRCBF/xo+HO+9UW8YPZMgQeOcd+PhjcLvVfftJaMPlOyne8SuFW+dRsnMJgeINREKFmKaOzW7H0KL14SnpLfHndiMlvSUuT8Y+R3lltm3LgJ07Wfv118x74AHWfv01A3buJLNtW2w2G2PGjGH27NlMmTIFl8vFKaecQv/+/Zk0aRKGYRzSX81ebdgA114LHTrAtGkVQWXC1VfXPvbLL6F9e1VkKEQ1uq7+NAyVeIt96NMHBg5UUxOE2I+tW1W1VF28zYuDI9vLhRA1PfkkTJwIV1659xNjIRoyvx9KS2HlSpUgHqoLLsBs15bIrdehOTU8qU1xOL0ABMu2Ul64BgC7w43T7VfbxT3pOJwpMd/6bZomU6dO5cEHH+SHH36gR48ejBs3jrPPPhvHoWY6W7bAww/Dyy+r7yuzJqcTLrsMXn219s98+KEaUzhyJEydeli/i0gupqk6LRuGGiCwv2l9QogDc7lUFdTGjdC8udXRNAySdAshhBAH6777VM3k3XcfdHM0XQuqZmczp5Fy/FkAGFkZhM4bg/2Gm/F0G1hxnOpA7nT7qxJxq8ycOZOHHnqIKVOm0LlzZ+69914uuOACnHs2P9vT9u3wt7+prfWViXZ1Nhv84x+qtntPu3apbbF+P/ToUSe/hxBCiJoMQ13/NE3YsaPO+3yKfZCkWwghhKgjpmmCaVRt944ECynZtbTyQVxfTsU34Xkc6zeru2w2bCefrFZ4x4yJu+ZHc+fOZcKECUyePJl27drxl7/8hUsuuQR35Xb5Sps3w623ql0ye0u2q/v+ezj66NgFLYQQYr9MU610O53SXqO+SNIthBBC/EGmaaBHytBCJUTCxejhEtypjfGltwLAMDSKti7A4UrF6VGdxZ0OH/YvvoIXXlDdhit99RWccIL6urAQUlMPPJqsnixatIgJEyYwceJEWrVqxd13380VV1yB1+uFzz9XzZsO1vbtkJcXu2BF0iktheeeUwnCXXdZHU0cGzlSZVITJ0LTplZHI4SoRpJuIURN//632hp6+ulqC60QIioSwTQNgoFtaJFStHAJplmzE43Lk4E/t1vV96ZpYLPtowh1+XI1Tuy77+CHH8DjUfffcIP6f3HAANWQbdAg6NJFNSTz1vPWc8NQDdEWLeK3L7/k4c8+44MtW2jSrBl33XUXV48ciW/gwAOvcANkZ6tt5Huzbh389JMq2pWVcFHN5s2q7tThUDml2AePR7V637ABWrSwOhohRDUHKM4SQjQ4mzfDnDnQq5fVkQhhOcPQ0ELFmKaBx5cLjRtjKyggMu1DtHaq+4zd7lIr2B7V+Kz6PG1g3wk3QOfO8NBD6lbdb79BIADTp6tb9MmgTRtYtizaEf2776C8HBo1UglrRoZKzL3e/e8bNAy1hLhrl+rGXl4ORx4Zffy66+DHH2HNGnUiD3QH3gX+lprKI8cdx+23384jOTnccfPNjF20iLTvv9/v3+d+31dmzVIzzY85RpJuUUNKiurtKdtgD+Djj9X/q3uMCRSiut274a9/hbQ0ePRRq6NpOGSlWwhR0/LlqplR69aSeIsGx9BDVVvFtXAJeqQcUN3EM5v0Vyu1BQWE5k/F7NIJlzsdeyw6ixuG+v9w9mx1y89X/28WF0PLlrB+ffTYkSNrJubV5eWp7dyVTjxRJdKRSO2V6YwMta290ujRMGWK+trphK5d1cp7tdvqtWt57LHHePPNN0lPT+fWgQO54euvybDZaj+/ywV//jM8++zeY/3+e5gwAfr1g6eeOoi/JCGEEIdq5Uro2FH1rCwutjqahkOSbiGEEAIo3bWccHB3rfsdzhScbj++jDbYiirOUNLT639gcGWr2W3boGfP6P1jx8L8+er+bduqVqUBtfq9bVv0++OOUyvj1Xm9qn1tXp56nsp5TDNmqE7t7durRH8/v+/69et54okneO211/C6XNxst3NzaSnZew6BffVVuOqqP/gXIIQQ4nDt2KGGSDidatiEqB+SdAshhGgQTNNE18rRQmoVWwuXktGod1Wn8fKitQRLt+J0+aJNz9zp2B3x0czsoFS2pA0EIBhUK9rVh7Bu3qzuc7nU9nSfT93qyObNm3nyySd55ZVXcEQi3KBp3AZUtU2bPRsGD66z1xNCVNA01RfC6VS7X+r7oqAQYr8k6RZC1LR5MyxdqmrC+vSxOhohDoseCRAO7kar2C5uGjW3PPtzu+HyZABg6BGw2bDbpd3J4dq+fTtPP/UULzz3HGYoxHXAHUDT4mK1p1GIQ7BsGfTvrzZurFljdTRxqrAwOnIwFIr2fBBCxIX9dHcRQjRIn3+utqDKniORYExDJxIsVMlzhUiokEDxeiLBQkxDx2Zz4PJmkpLeEn9ud5zuaAJod7gOnHA/9ZT6f6N6nbSopVGjRjz+xBOs27SJ2665htfsdtrabNx4771s2LBh7z80ebLaNj92bP0GK+JeJKL6/JWXWx1JnOvVS/VecMqFQ7FvkYjqoRmJHPhYUXck6RZC1JSbCz16QKtWVkcixH4ZeoRwYDflRWsp3r6Ygi3zKNm1lEiwoOoYpycDd0o2vow2pOf1JLPpQPw5XUnxt8DlSd9/Z/G9eeIJeOABSboPUk5ODg+98grrtmxh3F/+wjvvvEP79u259tprWbPnkmVhIfz6qyxlilo6d4bVq1V1gtiHzEz4+WdYsiTal0GIvfj+e7XhaNAgqyNpWGR7uRBCiISiRcop270CXQvUeszh8OD1N8eT2jg2L3733VBSAvfdB82axeY1klhJSQkvvvgiTz/9NLt37+biiy/m3nvvpWPHjrBli0oYsrOhb1+rQxVCiKT02Wdw2mkq6Z4zx+poGg5JuoUQQsQlPRKoqsV2uFLxpjUF1Ap34db5ADhcvqqGZ063H4fTY2XI4iCVlZXxr3/9iyeeeILt27dz3nnnMW7cOLp162Z1aEIIkdQMQ/XZ1HVpsVGfJOkWQghhOdM00SNlqqt4RXdxw4gWnLk8GfhzowlZJFiIw5WaWJ3FRS3BYJDXX3+dxx57jE2bNnHmmWdy33330bt3b6tDE3Fk82b48EO1CeLii62OJk5t2KD+crKy4OOPrY5GCLEHSbqFEDXNmgXjx6siuhdftDoakaRMQ8fQwzhcKep706Rw67wa3cVtNnvFKrYfpye9qsu4SD6hUIj/PPMMjz7zDGu3b+fUU09l/PjxDBgwwOrQRByYPl1NwerYEVassDqaOPXbb6ofS26uGsQshIgr0t5QCFHT7t2qy0ZJidWRiCRiGBp6uIRIxUq2HinFbneT0aQfADabDZcnC9PUcbr9uDzpOFyph97oLNbatIFNm2DePBmpV4c8Hg/XNG7M5du3806vXjyydCkDBw5k9OjRjB8/nmHDhlkdorBQbi6cfz40jlGrhqTQogW8/750LhcHNGuWGhbRty+cc47V0TQccXY2I4SwXL9+8N578MgjVkcikkCwdDPF2xdTuGU+JbuWESzZpOZlmyYmJoahVR2blt0Rf04XUvzNcbr98Zdwg5qxomkgm8TqXl4erkGDuOzYY1m6dCnvvPMO69evZ/jw4Rx77LFMnTrV6giFRbp2hXffhWeesTqSOJaRAeeeC2eeaXUkIs7NnQuPPQaffGJ1JA2LbC8XQghx2HQtWFWP7ctsW5UwlxeuIVi2FQCH0xtteuZJx+H0WhnyH7Nli+pCk5cHbrfV0SQ9wzCYNGkSEyZM4Oeff+aII47gvvvuY9SoUdhsNqvDE0KIhPPjj/Dpp2qNRXok1B9JuoUQQhwS0zTRtYrO4pVNz/Rw1eP+3O64POkAaOFSDD2E0+3H7pAkVfwxpmkyefJkHnroIebPn8/gwYO57777OPnkkyX5FgKgqEjVdaelQa9eVkcjhNhDHO7dE0JYqrQUZs+G+fOtjkTECdM0ME2j6vtQ2VaKt/9MeeEawoFdGHoYm82G0+3Hm9asRkdxpzsNd0qOJNzisNhsNk499VTmzp3Ll19+icPh4JRTTqF///5MmjQJwzAO/CQiYU2ZAo0awUknWR1JHMvPh+HDVfG7ECLuSNIthKhpyRIYOhTOOsvqSIRFTEMnEioiULyBkp1LKNwyj3BgV9XjTncaNpsdlyeDlPSW+HO7kdlkIOl5PfBltMbhTLEw+hh75RV48km1zVzUrU8/hSFD4O6793mIzWZj9OjRzJgxg++++46MjAzOPPNMevfuzQcffICu6/v8WZG4AgHVkLugwOpI4pjbDe3bQ6tWVkci4py0JbGGJN1CiJpSU6FtW/ngbmAMPUx50TqKd/xC4dZ5lOxcQqBkI5FQEaZpoIdLq451uNLIbDoQf243UvwtcHkysNkdFkZfjx55BO66CzZutDqS5LNtG8yZA8uXH/BQm83GMcccww8//MD06dNp1qwZ5513Ht27d+ett95C07QDPodIHMccA7/8Am+9ZXUkcWz4cFi5Er780upIRJy79Vaw2+Gvf7U6koZFkm4hRE3du8Pq1TBtmtWRiBgx9DDh8p1EgtWXjWwESzejhUsxTRO7w407JRdfZlsyGvUmJaNN9EibLT47i9eHs86CSy6BnByrI0k+J5ygVrvvvfeQfmzEiBFMmTKF2bNn07FjRy655BK6dOnC66+/TjgcPvATABMnTuS2225D2tzEp/R0NYK6QwerIxEi8YVC6k+Xa//HiboljdSEECLJ6VoALVSiGp+FS9C1IAAuTyb+3K5Vx5UXr8fhTMHp9idmZ3EhgIULFzJhwgQmTZpEq1atuOeee7jiiivweDz7/JkpU6YwevRonn76aW677bZ6jFYIIepXeTmUlYHXC36/1dE0HJJ0CyFEEivatrAqyY6y4XT5cHoz8aVLGYFITr/88gsPP/ww//d//0fTpk256667uPrqq/H5fHs9/q677uLvf/87P/74IyNGjKjnaMX+/P47TJ0KLVuqDRFiL6ZOVf0m+vaFhx6yOhohxB4k6RZC1LR9O1x1lSr4+eQTq6MRB2CaBnqkjEjF6C7TiJCe17Pq8ZKdS9HCRThcaTg96bjcfhxuP3a708KohdiLjRtVPXejRtCz54GPP0jLli3jkUce4d133yUnJ4c77riDsWPHkpaWVuO4SCTCsccey6pVq1i4cCGNGjWqsxjE4fnPf+Cyy2D0aClZ3qe331ZDl487Dr75xupohBB7kKRbCFHTxo1qOcHlgoOshxT1SwuXEAkWVmwXL60xzgsgs0n/qhFdhh7CZnc13Brsuta/P6xaBV9/DYMGWR1NcnnxRbj+ejjzTPjoozp/+lWrVvHYY4/x5ptvkpGRwa233soNN9xARkZG1TGbN2+mb9++9OzZkylTpuBwNJAGgXHuu+/gueegXz9p/rRPq1apXixNmsCJJ1odjYhjb78N69bBaaepXgmifkjSLYSoqbwc3ntPJd2XXGJ1NA2eoUfQwiW4vJlViXNZ4WpCZduqjrHbXTjdfpyedFWP7UrFZrNZFXJy69BBndzOnAnDhlkdTXJ5/314+GG1UvfMMzF7mfXr1/P444/z2muv4fP5uPnmm7n55pvJysoC4Pvvv2fUqFGMGzeOBx98MGZxCCGEFY49Fr7/Ht55By64wOpoGg5JuoUQIo7oWggtXK3pWaQcgPS8HjjdquNJOLCbSHC3SrTd6ThcSTwXO96sWQORiNoNkiJ/74ls06ZNPPnkk7zyyiu4XC5uuOEGbr31VvLy8nj44YcZP348X3zxBaNHj7Y6VCGEqDN//zssXQrXXAMDB1odTcMhSbcQQsSBcGA35UVrMfRQrcccLh++9Na4vJn1H5gQSW7btm08/fTTvPjii5imydixY7ntttu46qqrmDt3Lvn5+bRqJQ0HRZzbvh22bFHjDFu0sDoaIcQepMhPCFGTYcAvv8DChaDrVkeTVEzTQAuXEizdQunu5YQDu6ses9mdFQm3Dac7DW9aM9KyO5PZZAAZjXpLwi1EjDRu3JgnnniCtWvXcuutt/Lqq6/Svn17mjdvjtfr5Zxzzjnoed8iNt54A9q2hVtusTqSOPb229CnD9xzj9WRCCH2QpJuIURNmga9eqmONSUlVkeT0EzTIBIqJlCykZKdSyncMp/iHb9QXrRWbREPFVYd63Sn4c/pSlbTgaTn9cSX0Rp3SjZ2h8u6X0DU9u678MILsHmz1ZEkn48/VsWGFo07ys3NZcKECaxbt46//OUvTJw4kW3btjFv3jyuvfZaS2ISSkEBrF0LO3daHUkcS0lRTdQqehMIIeKLbC8XQtRkGOqD2+VSK97Z2VZHlDAMQ8M0NBxOr/peD1G4Nb/GMTa7A6dbNTxzeTJwutP29lQiXnXposZaTZ0KI0daHU1yee45uPlmOPdc1VQtRmbMmMG1115LOBzGMAwMw0DX9Rp/GoaBpmkEg0GCQTXnftKkSZx++ukxi0vs29atqttydjZ07Gh1NEIkto4d1XXjqVNhwACro2k4ZFCrEKImu13VhokDUp3Fi9UtVIIWKcflzcSf0wUAu8OD0+3H7nBXdRd3OH3SWTyRjR6tZkjn5lodSfIZPVrtJIhx/XTLli055ZRTME0Th8OB3W7Hbrfv82td11mzZg1HHnlkTOMSNX3wAaxcCePGqevATZrUPubhh9VAgXPPrf/4hEhUZWVqUI1TssB6JSvdQghxiMqL1hAJFqFrgVqPOV2ppDfqZUFUQgiRPB5+GO67Dx58EMaPr/34Qw/B/ffDhAkqMRdCHJwtWyAYhGbNwOOxOpqGQ65xCCHEXpimiaEFiISLMbQQvozWVY/pkUBVwu1w+dRWcXc6Tk86dofbqpCFECJpjBunqp3uv19tLx8xAjp1gv79own3gw9Kwl3l7bdh8mQ45RS46CKroxFxrGlTqyNomCTpFkLUduWVUFSkaiybNbM6mnphmgZ6pEzNyA6pGdmGoVU97k1rVtXUzJvWDE9qE5Vk2+VtVIg6sWWLKtzNzVV7hhPQe++9x+WXX86qVato3rw5QNXosenTp5ORkWFxhImlcoX7/vvhxRfhz3+GL76IJtx7WwFvsBYuhP/7P2jd+sDHCiHqnWwvF0LUlpsLu3bBb79Bt25WRxMTpqGDzV5VX11WsIpQec1adpvNrmqx3X48qU2kk7hQ3bWXLVMFpyNGWB1Ncvn73+H22+HCC9WqXQIyTZM+ffpwxBFH8Pzzz/PAAw/w2muvMXv27KokXByacGAXZ53tZvL//DgcapKlJNx7MXOmSrz79oXhw62ORsQpw4CnnlLbyq+9FrxeqyNqOGSJRghR26OPQjgMjRtbHUmdMQytxiq2HinFn9ujqnu40+0nEtxd1fDM6fbjcKVis8lkRVHN9u2q7WsoZHUkyScjQw1jzsuzOpI/zGaz8fDDD3PWWWfRrFkz/vGPfzB9+nRJuA+SHgkQCRfhScnDZncAoIVLefOVzTRtN4Rw2IbbDaeeCuvXx7znXmIZPlySbXFA4TDcfbf6+vLLJemuT7LSLYRIWnqknFDZNiLhYvRIea3HfZlt8aaqlrimaQA26Swu9m/pUtWBpkMH8PutjkbEqX79+vHbb7/x9ddfS9fz/ahMsrVQMVqoGMOIAODP6YrLmwmAFi7hwQcNHno4A7dbJQ3Nm8O2bfDee3DWWRb+AkIkmGAQrrtOXTf+z3/ALW1o6o2sdAshkoKuBdFCxRWNzdTqtWFECJZtrTrG4fSqGdmVK9nO6CVeWdEWB6VrV6sjEHFuypQpLFu2DF3XaZxEu4XqUiRYQFnBqqoku1JlSQ/VLn4++rifhx6ObikfNw4eeUSNOxo5MvqzmtbARyBt2aJmQeXlqV0jQuyF1wtvvml1FA2TrHQLIWrbuBECAbWc4PNZHU0tpmmia+VqNna4oumZHgbAm9YUX0YbdZyhEyhZrxLtinnZQggRK/n5+Rx11FG88MILvP/++/h8Pj788EOrw7KMHgmghYuJhIpwp+TgTskBQIuUU7z952jfDE96xft0Wo0LoNW7lFev4d7b/Vdcoao/HnsMevSoz98yTlxyCbz1lirYvf12q6MRQuyhIV8TFELsy/HHq220P/4IcbY10tAjFG1fqBqhVWOz2XC40rBXX722O/BltK3vEEUymzxZNRkcNUpdlBJ1Z9IkeOMN1azullusjuaQrV27lpNPPpl77rmHiy++mG7dujFw4EAWLFhA//79rQ6vXuhaAC2kkuzqF0NBrWJXJt0OZwr+3O61kuzqHn54313Kq3c1t9th7Fh49121ZbbBNljzeFTJixTpChGXJOkWQtSWnm7p9jTT0NEipVVNz2x2F2nZHQGwO1zYbA6wmRVbxf3qT1dqVeMdIWLm/vth0SL46itJuuvaypXw+eeQk2N1JIds9+7dnHjiiZx66qnce++9APTv359TTjmFcePG8dVXX1kcYWwYhlY1NtEwNIq2LarxePUJEJU12up+Gy5P+n6fu0MHmDBh33O4x49XCXeHDpCdDb/8Ap9+CoMHR4957z21WeuUU9SxSe3VV9VNiP1YuhSGDlXTYJcssTqahkW2lwsh4kI4WFCRZBejhcuA6FuT3e4io0n/qiZnuhbE7nBLHbaofzfeCKtWqf2tDWT1st4sXgzz5kHHjjWLdUXciK5kq/dqh9OLP7d71ePFO37BZnNUbBnP2O9KdqwFg9CmjWq49sEHcM45loQhRFzJz1cfXc2bq0pCUX8k6RZC1DtdC6Fr5bi9WVX3Fe/4FS1cUvW93eHBVdHwzOlOx+FKsSJUIYRo0ELlO4kEC9DCxTW2iwPYbA4ymw6IywugJSVq+uXnn8OCBeByqfvXrYMmTdRubCEammBQjdszDOjSxepoGhZJuoUQMVfZTKdyTrauqxnHmU0HVm1NDJZuQdcCVU3PHE45IxJCiPqka0G0cAkeX3RWeumuZYSDBYDaFl55IdTpScfpSov7sh7DiG4tN00YNkwlHe+/D0ccYW1sderJJ9Xe4auvVvuHhRBxRWq6hRC1Pfkk/Pab6k5TvUDuEAVLtxAs2VRrLAzYcLp8mHoEKpJub1rTwwhYCJHwtm9Xe4GzsqBFC6ujaRDUqMWiqu3ilSvZ1Ucqun15OFypCZNk76l6Lfe2bbBhg+qF2LGjdTHFxFdfwfffqyaPknQLEXck6RaigTBN4+C3AE6ZAt99p7qYHyDpNk0DLVyqVrJDJfgyWuNwqTFjNpsdw4hUNNNJq1rFdrr9CXfiJgQAZ50FP/8M//oXHH201dEkl9deU12zrrxSfS1iJlS+g0Dx+r1sF1dTIExDq7rPnZIDKYnX3G5vmjRRLRnmz1dfV7rtNnC71Z+NGlkX32G55hqVcPfpY3UkIo6tXq2uz7RoAaeeanU0DYsk3UI0AOFgAStnP4bLk4kvsz2pme3wZbTBlZJT1ZyshquughNO2OuHt2noapt4WDXT0SNlmKZR9XgknFWVdLu82fhzUyxtpiNEnVq/XnXZLiuzOpLkk5qqMp70/Xe1FgdPrWSrVWxPamOcbj9QcUFUD1cl2S5PRsKuZB8qjweGD49+v2kTPP88RCJw2mkJnHSfe67VEYgEsGABXH+9Kq2QpLt+SU23EA2ArgVYNu0+9Y3NDhVJssPlw5fZgdTMtvgy2uJNa1brhMvQI4CJ3eEGIBIspGTX0hrH2O0udcLm9uPyZlVtSxQi6fz8M5SWQteuak6REHGkepIdCRVjVPTPAEhJb0mKX23bNwwNPVLWIJLsAzEM+N//4Ntv4R//iN7/9deq+3mnTpaFJkSdmzkTnnlGfYQ99JDV0TQsknQL0UCsnPMkobKte3mkcqXbxGZ34vW3VCsizlQ8aU0x9CDetGb4Mlqrowyd4h2Lq7aJOz3pOJzSWVwIIepb9bIhLVxK8Y5fajweXclOx+XNxulOsyLMhBMMQrt2sHUrfPMNHHus1REdhK1bVae4nBy1V14IEVdke7kQDURqVkdC5durVrmjotfdTEMjULSGQNEaALJzB+PJaVmj7s9md5DRuG99hCyEEKKayu7ilc3PXJ4MUrPaA2rnkt3uwu70qnGLngxZyf6DCgpgwABYtKhmh/OSEvD7LQtr/0aNgl9/Vf1YjjnG6miEEHuQpFuIBsKX2ZbdG6cf3MGmSe47c8jpmoP99jOqxnoJ0eB9/71qfTxsGDRvbnU0yWXiRPjwQzjuONVXQgCq6Vllkl19uziAFi6p+tpms5PRpJ/0z6gDTZvCZ59BUVHNReOTTgKHA156SW3PjSs2m2rV7pTPayHikbwzC5HETEMnHCykeMevlO5acRA/YcPmcNP622IavzUXpzNFEm4hqrvvPjjnHJg71+pIks9vv8EHH6jW0g2UroWIBAtr3Bcs2USofAeGHqqak+31N8ef05X0vJ41jpWEu25lZES/XrkS5syBn36qeX/cWLwYdD3Jho+Luvb886pz+V13WR1JwyNn00IkEdPQwWYjVLaN8sLVFG1bSLBsC4YWrDjCRvXt5DXZcHkyad3najxHNoZHZWSPELX07Qsul6qbFHVr9GjIzIQePayOpN7oWqhi3GIRWqgYXQ9hs9nJbDqwKoH2pDbCMDRcFSMXZbu4NTp0UOPGfvoJmjWL3v/oo9CqlWoeHheLzHubSCJEhd27Vcf+4mKrI2l4pJGaEAnM0MNEgoWUFa6ivHA1wbItRIKFGFoAbHY8vka4vFmkpLciNbsjuzfOonj7or3UddtIyWhNq15X4HSlWvGrCCFEgxEs3UKodAv6HtvFwYbTnUpqViccTo8lsYmDt2mTargWDsOsWTB0qNURCbF/27apf7fZ2ao7v6g/8XBNTghxCELlOynZ+RvlRWsIlW0lEtiNaerYbA5cKdlkNR1EWk5XfBmtsNldNbYbRsp3Urwtv9ZzZjYZQNMuZ8lWciGEqEPRlexiUtJbVo1eNE2jIuFWSbbTkyEr2QnI74e//U3NPq6ecOfnQ+fOavR8vbnzTtV2/d57VVG6EHvRuLG6ifonK91CxCnTNNG1AKGybURCRQRLNlBeuIZAyQYwDWx2F+6UXLz+Zvgy2pKa2R6XN7PqpG5vwoFd/P7TIzXua9x+DDmtjsJWfUvapEmqA+pxx8Hpp8fqVxRCiKjdu1Xb6IwMyM21Opo/pHqSrYWL0atKe9QECY8vt+K4IIYWwOlOlyQ7yYRC0L69yn+/+UZVpNSL7Gz1/8/SpdClSz29qBDiYMmylhBxwjQN9EgZwZLNlBasJFCynnDZDrRwEQBOTwa+jLakN+qF05NOSnobXJ70Q1qddnmzcbjS0COl2OxOWnS/mPS8vdRPzpoFL76oLtNL0i1E1FVXqUZfTz6pRvSIuvPss/DQQ3D99arbTwIwTbPqgmU4sJvS3cv3OKJiJdudjsPlq7rX4fTicHrrMVJRX9auBa9Xjczu1i16v2nGuNz67ruhtBTy8mL4IiLRTZ8Oq1erkXjdu1sdTcMiSbcQFjJNk3BgJyU7l1C8fTHhwA70SBkADlcaHl8e6Y16k57XA19mu5qr0X+AzWbDn9ed0p1Lad37arz+Zns/cNQoSEtTY5GEEFG//w4//wyFhVZHknzcbvW+443fZNTQQ0RCak62Fi7G7WtEfw1l9QAAYGtJREFUil+NjnO606ieZLs86TjcfinbaWA6d4Zly1Ri46lWln/SSaoZ2333xWh77913x+BJRbJ57TX473/h8ccl6a5vsr1ciHpiGBqRYCHlRWspL1xNJLibUNnWijmrNpyeDDy+RqrpWVYHvGlNcbh8dT4CxjA0bNhkS6MQf8S8eWoLZ69e0KSJ1dGIGDNNg3Bgd1WSXX27OIDLm4k/Jzqw2TR0eW8VteTnQ//+avDB6tVqZJMQVnjqKfj2W7j6ajjzTKujaVgk6RYiRkzTIFS2XXUWL1pDqHQL4cAuTCMCNjvulNyqFWxfRhvAht3hOezVbCGEEH+MoYcx9HDFqrV6Hy/cMg+zauJDdCXb6VGNz2QlWxyIacIPP8Avv8DNN0fv//e/oV8/6NOnDl6goEDNLPP7ZWyYEHFIkm4h6oiuBdBCJUSCBZQVraa8cDXlRevA1LHZnLhScvCmNcGX3gZfdgfc3uz4rekLh6G8XH2Ap6VZHY0QQsSEoYeJhIprrGQ7nClkNO5TdUx54Rqw2SXJFnVqyxY1sikchkWLoHfvw3gyTVPL6KAaEmZl1UGEQoi6JJ8cQvwBpmmiR8oIlW2jrGAl5cXrCJdvJxIsBEwcrlR8me3Ibj4Mb1pTfJntcHkysTtcVod+cJ59VtWHXXopvPmm1dEIET/mzoUdO9TSVPPmVkeTXCZNgv/9T01NOP/8mL5UoHgD4cDOWtvFqSi9MU2jqrTHl9k2prGIhikSgTPOUDOTe/WK3r9pEzRrdoiL1ZoW/dopp/ZCxCP5P1OIQ1RetIZQ+Q7KClZStHUBAA6nD7cvl7ScrmQ3G4onrWlibxOv/NCu/kEuhFAzcL/7Dt55By64wOpoksv8+Wq/rd9fZ0m3oYcrxneVkJLRuiqRNvRwRcJtw+ny4fRkyEq2qFetWsF776nku/J0IRxW874bN4YPPoB27Q7yyTwe9USaVrN7mxB7OOcc1Qv0hRfU9U1Rf+STRYg/wDR0PKlNyWl1NL7MdqT4W+B0p9V50zPL3HQT3HCDXDEXYk+dOkFRkZqJK+rWCSeohHvgwD/8FJVJdqRiVrauBaoec/vyqmq1PamNcXmzcB7i2EUh6pqr2ga4hQth506VOzfbx3CRvbLZ1Oe1fGaLA1i7FlasUHPkRf2Smm4hDpGuBTANHYcrNbFXs4UQIokES7dQXrS21v1OV6qaDpHaOH77aAhRYccOWL4cRoyI3nfFFTB4sKr4iuOJeiIB/Pqr6rnXrRvk5FgdTcMiSbcQQgghEoKhR9BCRVUr2SnpLXGnqDPHSKiYkp2/VSTZ6VUdxmUlWySy2bPVlnOnE1atUtvSaykshIceUhn5ww/Xd4hCiIMgSbcQorYFC1RBWYcOcM01VkcjhGgISkqgrAxSU9U2c1QpTyRUSCRUVGu7OKAmQmSoRmemaWCahiTZIqmUl8Nrr8HWrfDII9H7v/wShgypaFS+bp1qhe71QiCwr6cSQlgoSQpQhRB1aulSePJJ+OgjqyMRIr7cdhsMHw5ffWV1JMnnoYegaVOMv46vusswIpTuXkGobFtVwu10peJNa0padme8/pZVx9psdkm4RdLx+VSbleoJ97ZtqvN569awZg3qItVdd8Ett1gVpkgQH38MEydCcbHVkTQ88ukkhKite3e4/Xbo3NnqSISIL0uWwKxZsH271ZEkBUOPoIWLiYSKcC+arU5KPvsELr8SevbE4fTi8mbicHpxujNku7gQwObN0LGjSsjbtAFs2fD444RCIL3Lxf5cdZUa5b5kCaSnWx1NwyLby4UQQoiDNXOmSrj7999HcaU4GOHxd+L4cBLl9/6ZyLHDAHAsX43/vJuw79gN118Pzz9vcZRCxC/DUJ3OGzVS30ci6nr58OHwxBOQl2dtfCI+nXqqaqT23nvQooXV0TQsknQLIYQQIiaM0mL0zybC4sW4Hnum6v7wxWfhfnsigWsvIPzgXbg86Thd6Tjn/YL9lX+p8pbmzdXBW7aopZnu3S36LYSIc4bBl5/rnHSai0aN1FiolBSrgxJCVCdJtxCiNtNUg0INAzyyWU0IcXCMrZvRdm9Ba5ZNJFyMsXMbmT1PxGYYGBvWYW+hdgdEZnwHGzfhOOZ47I2a7P9Jr78eXnoJJkyAe++th99CiAQzbx4MGsScxqey+aVPOf306EP33w8nnKBWwIUQ1pFGakKI2r79FtxuNRhUCBH166/w449q9VXUEH56AvamzTHvuZtg2Vb0SDlmhp/IaccTueZS0CJVx7pGHIvrvEsOnHCbptoLaZqSNQixL5oGwOC032ok3PPmqf6ERx6p6sCFENaRpFsIUZuzolFRxQe5EKLCX/4CRx8NX3xhdSSWMPQI4cAuInfdhN6tE9rMqdEHe/YEwFFQgje1CWnZnchsMgD3pC9xvfIm9jbtD/0FbTZ4911YsUJlDpX+9S+48EJYvvwwfyMhksCgQWpW97x5Ne5u1AiuvhquuAKaNYve/+uvaiObaFhKSqBHD9WSJBI58PGibkkLUCFEbSNGqBpKl8vqSISILy1bQteukJlpdST1wigpQv/fp5grlxO45hz0SDkAab8sxrH0dyLfToHhKhl2Hnk8xpZNOJs0q/uTi44do19rmtpqvmEDDB0qUxaEcDggI6PW3a1bq+tT1QtJd+xQOXrbtvDDD9FGbCL5BQLw22/qa6dkgPVOarqFEEIIAYCxfSsEA9hbtQUgsnwxri69MR0OCpZMgVQfDpcPz9wlOIqDOI4djb1x0/oPND9fdTd/8UXwetV9S5aospgOHeo/HiESxDffwFlnQadOMHeu2kwiGoZQSA3gCIdh9Giro2l4JOkWQgghGijD0NBCxWjhIuxPPov34eeIXHkRrtfeAsA0dPTjRmK2a4d53704W3bA7ojTHTDHHgtTp8Lrr8Oll1odjRD1Z9kyNQOqVSu48soDHl5UpNpSdOmivtc0VTXzpz/Bn/8snc+FiAXZXCCEqG3bNnj5ZbWCdPfdVkcjhKhDpqGj3X499m9+pOSlBzDaqY7irnbN8AK2TdGOSza7A+f3My2K9BCUl6v3K7sdjjoqer9pylKeSH7LlsGDD6pyi4NIujMyau5GnzgRZsxQTzN2bAzjFKIBk5VuIURtv/2mum3k5cH27VZHI0T8eOABdXZ6yy1w8slWR3NARnEh+lefw7atuG68o+p+bVh/nD/lU/bYXWiXnY/Tk47LcOMMgr1ZCwsjPkzr16vVvkp33KEaTI0frwpchUhGixbBa69Bmzbq3/whCofhrbfUNaqrrore/9prMGYMNDnAkAGRGAoKYPp0dcGlel9KUT8k6RZC1LZ1q0ou0tPh8cetjkaI+HH66fDJJ2onyLXXWh1NLcauHWhlBWgZbrRQMebCfDJGX46ZloqtoLCqe05k4jvYAkHso06ypia7PuzaBc2bq0LGb76B446zOiIhEsaCBTBgAKSlqZ6FDaR3ZFL76ScYNkw10Vu92upoGh7ZXi6EqK1JE3jpJaujECL+3HKL6kI0aJDVkdQSvuPPuP7+MvqNlxC8q+KCQNcOaH17YPbphbO4CFt2DgCuMy+0MNJ6kpOj2jNPnKjqvSvNmKFWw6uviAshatB1GDJE9SWsnnAXFEBWlmVhicOQkgKDB9ccHyfqj6x0CyGEEAnCMDS0QCG2e+7FPm0G9q+/x9ZY7f0MvfAknhvuInz6aCL/fgGXOx2nJx27w21x1HEkHFZtmzdvhi+/rJmMCyFqME01ZsrnU9/v3Ant2qnO16+/Dn6/tfEJkUjsVgcghBBCiL0zigqITHyP0GvPUbx9MYVb5lFa+Du2b77DsXgp+vdTqo51nX8Zxoa1uCd9SWpmO9y+XEm497Rzp1q6y8lR+ywryfqDSGSvvqpKR84+u06f1maLJtwAX30FJSWwapXadi6EOHiyvVwIUduOHdCihZojomnS/VeISqtWwe7damty48Z1/vRGwS5sJlXbwLXp3+A+6wL0Zo0pGz0AbDYcTi/anTdhOlJwHHN81c/as/Mgu85DSi7NmsG336oJDdXnIv3pT9CypWq4FoP/rkLEVDis9oPH2EUXQe/eUFYWPS3QNLj8crj4Yhg1Sk4XhNgX2V4uhKitoACyK87eI5Gq5ktCNHjnnAMffgj//CfccMNhP51haGjhErRQMY4778X9+vvoj/wV5933A6AX7YRBgzGGDMR45ilcGY1k9bquLV6sMgmnE5YvV/tnhUgk5eWqS7/bDbm59frS778P55+vNo+sWwepqfX68uIQfPml6pE7fDg8/bTV0TQ8ciYthKgtI0O1K3U6weGwOhoh4kdurlrlPoxiRqOsBP0vd2D7aQ7F7z0LKR4AvI2ysBkG5rJlVcc6MnJh+Srk/8IY6tVLNVzLz6+ZcH/6KQwcKF2HRPzz+WruA69HI0ao/pLNm9dMuL/9Fo46Sq7Zx5P/b+++w+wqy72Pf9eu02fSAwFC7xACojTpXRQFEbBgOygq+nIUQUVROIj1KMUKoh7FXlFRmnQiVaRDCL2kJ9Nn9t5rr/X+sZKZDAQIZHbWlO/nunKZWXvPzD2Smdm/dT/P/cyfD7fdts7vy2gFO92SJNVI1LGc6nVXE4Qhube/M7lWrcCM9cksXELnby4k3mdPcoUWcl0hefJkNrLTmroFC5IAHsdw992w9dZpVySNGv/5D8yenYxPuPfeoTs5lJ6nn07+20yePHSkhdYN7z9JkjRMovZlhNVewlyVsNxJ5g+X0fSRL1DdbitYEboz2TzlMz5N0NhC075vITN1RSfVY3hGjuXLk9RQrcJWWw1er1Zd/aOR5+abYc4c2HnnEXEe/VNPwZQpyUKRVQO33z7p8qTEdDm9XNLq/e//wle/mkxMkfSKyu85hmDyVMo//yH93c8Tlrup7Dab6sYbEO0yC6Jo4LmFj59G/gMnDQZujSzbbJMEmX/8Y+jEqNmz4dRToaMj3fqkVV19NZx+OvzpT2lXAsCRR8KTT8K3vz14bdky2GwzOPvsZO6bNN7Y6Za0eqefntyWPuEEJ6NIK8T/+03ia64iKuSgo53ctTdDZsX960kTCKpV8g8/SdAwlVyxhdy0FrJPPOOe7NEoCGDCKssP/vY3uO++ZOn5WWelV5f0QjvtBO97H+y2W9qVDHjhNvOf/jTpgP/xj8khAVr35s1L/hvMnJks/de65Z5uSav3wQ8m//uNbwxOMpfGmbijneoN1xBSprz7DhRP/BTFy64mzucJKhWqd91OduddAag+OQ+imOymW6RctWoijpODijs64LjjBq//+Mfw5jcn62klrVa1Cr//fXIf6+CDB6+dfTZ84ANJEFRtnXEGnHsufPzjcMEFaVcz/hi6JUlaIe7sgEKBoC7ZiFj+1jkUPvUFKnvsTNdvv0PuljvJPb2Q3JMLCGbtRPaoY8lMMmyNW7ffDm94Q3Liw5NPQltb2hVJo8ZvfwvHHgvTpiUHpuTzaVc0tp13HvzoR8kCxtNOS7ua8cfl5ZKkcSuOqoTlLirlTvLvP4ncZVdR/e2l5I5a0cnc7wCijX4AW29N44TNyR21M9lcMd2iNXJEEeyyC2y33dDAXamYIKRXMHNmMvdtr72Gfrs8/vjQ0/s0PE45JfmjdDhITZI0rlQXPEvlUydTecvBtC+4g66lD9Hf9RzV+gJBtUp8550Dzy3stBuZp54lf8kvKDZMMXBrqN12gzvugO9+d/Da4sVJmjjjDCiV0qtN48cppySrLb7+9bQreVXe8IZkBtyqe7zvvTcZuPbmNyezC6WxwtAtafW23Raam5PfgNIoFXd1Ev7tj4Q3/nPwWi5D7rzvk//r1QTPLSCbLVJsmELwuc9TffRh8ud+c/ADrJxcvdJzz8EDD8CSJevoK9CIFwTQ1DT49s9/DvPnw5VXQqGQXl0aP7q7obMzWWExCmVWSSM3r5hN2dAAOdfjagxxT7ek1dtoo2ST1R13wOtel3Y10hqJuzoJgzJh1Eul1En+m9+h/us/JHzLoeQu+0fynDim8tlTCDbZnMzb30F20rQ1/wTvfS/87GdJR+nTn67RV6FRLY7hssuSiVH77JNcq1bh/PPh/e8fOhFdGg6LF0N7ezL0dNKktKtZa/PmJfeyNtsseXv5cjjqqGQA2FvfOjSka8196Utw003wiU8kx7pp3fIekqTVu/765MXjBhukXYn0iuI4Ijz8QHL/vIm+332X8HU7JA/sPpvijOnEMwbPww6CgMJXz39tn6i5GSZPhvr6YahaY1IQJMlgVb/+NXzqU8ky9LlzIeshchpGU6aMqen5LzzO6nvfS16SLF364m8trbl774Vrr4Vjjkm7kvHJ0C1p9ZxiohEojqqET8+D730XFi8h/5NfAhAEGchlCSoh+f88Qnbv/ckVWsi9aScyR36IzAuXib9W3/lO8kd6NaZMge23h3e+c2jg7uvzBo70Cj70Iejvh513HuxyRxH84hdJgKyrS7e+0eJTn4K3vx123TXtSsYnl5dLkkasuLuL6k3XEbbWUd5ifaqVboLnFtD2+rcRZ7OwdAlBaxsA4T13EtQ3kt1i6xfvxZbSFkXJZKiV+7zvvhsOOABOPRU++1n/zeq1+9vfkiPr9t8/mccyDvzhD0mA3HZbuO8+l5xr5POfqKTV+93v4MIL4amn0q5E40jc25Nsa1ihctonyB1+JHz/+4TlLuI4hg03pPKR9xNe+M0hQSU363Vkt9zG8KKRKZMZOljtxz9ONqs+8ID/ZrV2Lroo2fA8Z07alawzUZTsfjv66KGBu7c3vZqkl+Pyckmr97WvwV13JZNMZs5MuxqNUXFUJax0E/Z3kH/r8WTn3EH133eR3W7Fnux99yH689/JTJ5GY9um5IqtZHN18L0fp1PwxRcnmwuPPRbe8pZ0atDYcN55sMceyTnfKy1blvwb++hHk/kB0prYay8oFsfVtrBjjkmGga06sP2BB5JvqY9+FM4913tZL3T33VAuw9ZbJyfMad0ydEtavYMOSqaZTHsVk52lNVB9bC7Rxd8j6u2i9zMnsnKXU67cT1CuEN94LawI3fmj301wzHspjJRXT7feCr/8ZbI/19CttZHNwvHHD7327W/DOeckx41de206dWn0Oe20tCtIRaEwdPHIpZcmJ6c9+qiBe3Xe975kmNpVVyUv8bRuGbolrd5XvpJ2BRoD4t4eqjdfR7DhxmS32R6AaMl88l87n7ixgZ5Pvo9MXQO5Qgvx179KddL65LYa3JMYZEfYr6njjksC9557pl2JxqJZs2CLLZKlwitFUTJwrbExvbqkUeDcc5MfzRtvPHitvT25J/HJTyYd3vFsxgzo6ICmprQrGZ8cpCZJGjZxf19yTnapk7DcSeHkz1D8zd+ofOpj5L+ZTP2OKiWqH3g37LY7mfe+n2yT5xZLA8Iw2aS6cqPqb36ThPCzzoKPfCTd2qRR5txz4YwzYIcd4J577IArPQ5SkySttai/h/CNb4AJE+h++Gb6up6lUuqk8oZZRNMmQ3HwTJdMvkj+578j/7FPGrilF8rlhk6GuvRSWLwYlixJryaNbIcfDuuvD1dfnXYlI84hhyR7vz/zmcHAHUVw++3p1qXxx9AtafVOOAGmT4df/zrtSjSCxHFE5aF7qHzuk1S+9LmB60GxHpYuI+gvUbjzQQr1k2ls25T6j5xOZv4i8l/+ZopVD6OlS5Ojedrb065E48Wf/gQ/+Ql84hOD1+6/P9n/3deXXl0aORYuhPnzh04VE5DMKfzzn+Gd7xy89pe/wBvekIzlcL2v1pURtllO0ojR3p78Iu/qSrsSpSju76N68/VUNphMOLmRsNxF9r7bafnKt4nWmwpf/DIEAUGQIf7uhVSnrU/jNjuM3TV8Z5wBP/xhstT3zDPTrkbjQS6XTEBa1Vlnwe9/n4xr/tGPUilLI8hvfgPd3bDJJmlXMirMm5cMYNvhBb+q4njs/uoCeNvbki7/RRc5IzcNdrolrd555yUboI4+Ou1KtA7FL+iUhEe9idxBhxP/8mdUSh3EcUS0685Ujj6C6mdOhWp14Ln5/Q4lu+2OY/tVS6EA9fVJEJLScthhyfFQp5wyeK2nB/r7UytJKdp8c9hpJ8+BWkOnngqPPw6f+tTgtYceSkL4pZemV1etXX550uV3QUQ6HKQmSeNYHEeE5W7CJc+RP/79ZO95AJ6fT9CYjDctnfM58hf8kMrHPginfopcoYVsvj7lqiURRUP3fp91VtLC+ta3knPkJa2xD384+fY58shkOfpY9LOfQakE73oXNDSkXc34Y+iWpHEmfPBe4l/+jGpLPX3vPoI4jiCOadv1rWQWLKZ65d/IHvwmIFleHhTrxnb3Whrtoghmz04O4f3Nb+Ad70i7Iq1Lv/51ssrhiCNg8uS0qxmVOjrg+99Pzq/eZZfkWmcnXHIJnHiix2xp7Rm6Ja3eLbfAI4/Azjsny9Y0KsWlfqpzbiSz3Y5kpk4HoPTzH1I84STCrTej85qfk8nkyRVbKMz5D9mNtiC7w06GbGm0KZXgd79LJkat7IBfcUWyjvaDH4RiMd36VDsbbgjPPgt33jmYGLXWvva1ZOr5HnskL4mkteGmNEmr95OfJLd4zznH0D2KxNWQMOwlLHcSljqpO/IE8nPuIvzBBWQ+/HEAsvsfTOWth8E++9A6ZRbZwop1ZkdsmWLlo8SvfgXXXpt0lI48Mu1qpEHFIrz73YNvRxGcdhrcd1/SxvvsZ9OrTbV1wAGwaBG0taVdyZiyySawxRbwoQ8NXoui5AS/0TaIrFJJfhQUi7DddmlXMz45SE3S6s2aBW96UzKgRSNe+OwThPvuQbzB+nQtuo++zmeolDoId9mOaNIE4u7BKfS5GZuQ/9PfyZ9y+mDg1pq55ZZkWvRdd6VdifTyoihJC9tsAyedNHh96VIol9OrS8Pvpz+Fv/8dNtss7UrGlHe8Ixmw9q53DV67/HKYORNOPz29ul6LBQuSRRA775x2JeOXy8slaRSJ44jqfXcT/+F3BBtvTO79yYvpan8PmclTCXp66brqUoJddiVXaCEf5ck2T3S5+HC58spkCedee8E++6RdjfTKXngO0vHHw7/+BRdfnGxglbTGPvrRZO/3aacly89Hi6eegj33hHwenngi7WrGJ0O3JI1gcblE9dabqGy+IWEhIix3Uvj5H2n8zNcJ99iV3C23Dzy38tufEWy5LdkddybIuJBJ0gt0dcHWW8Pzz8Pdd7t1SHqV4hhuvDH5Nlq5xHzuXDjjjGQHh51kvRT3dEvSSLJKVyqOY6qv24ncfQ/Td8lXqRyyNwDVvd5A+OaDiV/Qpcq/44R1Xq6kUaS5GebNg6uuGhq4L744OXv+Pe/xDPrRaOutk+0EN9wA662XdjVjWhC8eJHTV78Kv/99MkD+r39Npy6NfP5klbR63/oWfPe7cMIJ8MUvpl3NmBXHEdVKD+Ej95P7f6cRLFxE9oFHAAiCgGjnHYmenU++p0q+dWNyxVay6+9G8JejUq58nOruhr6+5JDTxsa0q5Fevfr6oUMAOzuTDarLlyfnIh1zTHq16bWZOze5YatUfPKTyZiEk08evNbdnYwAOfhgd3cp4fpDSavX0ZEcNbNoUdqVjDnhPXdSOftz9P7ie7TPv4POxffTW+ghe+O/yD44l+jxeQPPzZ33fYLFS6n7+Oeoa1qPXL6BwN/g6fnCF2Dq1GSqvzQW5PPJ2ti994ajVrmZ98wzEIbp1aU1969/wc03w6RJaVcyLm2/PVx6Key22+C1H/wADj0UjjsuvbpWdf/9cPTR8OlPp13J+GXolrR6H/wgzJnjT+i1FFfKhDdfS1wqDVyL/vBr8l/8Crlf/I44jshkchQmb0jl4gsI77qNYOYmA8/NtE0kyGbTKF0vx66Sxor6evjUp+D662Hlz5o4Trrh227rpP7R4A1vSKZkFQppV6IVyuVkQdQhhwxei+Pk6K40PP88/PGPcPXV6Xx+OUhNkobVwHLxUheVcgcNrzuY7JPPEl57Jbn9DgagcvM1BF/5GvGbDiM48SSyuXq716PFyl+Z/vfSWPbYY0mQK5WSUceTJ6ddkTTqLFkCLS2D90L+8Y9k+vmZZ8L7379ua3n6afjb32DChOQAA6177umWpGFQ+c/tBJ8+lbivj67fXDBwPdx+S4L2Tnj2mYFr+b0OhMsPTKNMrS3DtsaDzTaDJ59MutyrBu4zz0yGdh177GBXXOkql+EPf0gG4B19NHhyxYjxwntVP/hB8m314IPrvpaNNkoCv9Jjp1vS6s2bl7zgmjEjOZNYwIpO9n/uJL78r2TesDvZgw4HoPL4Q+Q325Y4k6H9wavJT55BrthCrrdKdtJ6LhGXNLrNnQvbbJNMyfa4sZFj6dLBdFetGrpHsN5euOQSePvbB4fMz5uX7Af/xCdg4sR061Nt2emWtHpXXpmM4jzmmHEduuNKmepdtxFutymVchdhuZO6H55H/Q9/Rfje42BF6M5tvCWVC79B8IY9adv89YMhuynF4jX8/vIXuPZa2G+/oROgpbFu+nQ4+2x49NGhgXvuXNh8c8NeWjIZOOCAZOid/w1GtIYG+PjHh177+teTE/vuuy9ZsFAry5fD4sXQ1pbMAtW6Z+iWtHobbAD77psM0hmnonI/bLgBuUVL6b7ul0RbbAxAuN8ehE8tJN5z8GZEkMmSP/nUlCrVOnPTTXD++cnEZ0O3xpOWlmTK+aq6u5MBXtOmJRtGN944ldLGtQkT4Jpr0q5Cr9Ghh8IddyTHjq3U05ME5OH8dvr1r5Pl5UcdVdtwr5dm6Ja0ekceOS5CRRzHVCs9VO+YQ/asc6Ghgdyf/w5AplBHZavNoL9EcX4Hwes2JldoJvuO3QiOPfkVPrLGpP33TwL3G9+YdiVS+u65JxnHXColN2olvSpHHQVve9vQcSEXXwynngqf+czwnU6ZzUJrKzQ3D8/H06vnnm5J40549+3EV1xOedcdKG+3EXFUJfvQY7Qe9B7ihnpY3k6wYtxo9flnyExdjyDnPUpJepH29mTC+ezZydtxDB/4ALz5zfDWt7rkWXqV3vc++L//g4sughNPTLsaDRd/Ekoa0+IwpPrvO4Zci77xFfKfO5vMny4jjqoEmSyZWTtT+dpZVK+9ashU3uz6Gxq4JemltLUNBm5IzkX66U/h3e9OhnyptubNg+23T7aDaUz46U/h9tvhhBMGr111FRx2GMyZk1pZWku+kpS0eldfnaxvmj07+Q0wSqxcLh6WOwmXL6Rh1r5kl3cSPfcMmfWT5Y/xoYcSLm0nu+setEzZgWy+MTkn+7QzU65eI16lkhzRk8tBsZh2NdLIs9tu8PnPJ98jU6YMXv/Pf2DWLI/dG269vfDAA8m+eo0Zu+469O2vfhWuuy45QGCPPdKpSWvHTrek1evuhnvvTe6ijwLh9VcSHnYA5fe9nc7F99Hb8RTlTD/R+tOImxuJHrxv4LnFEz5M7srrKLzvI+QKTUngltbEF78ITU1w+ulpVyKNTBMnwv/8T/K9stJjj8HrXpckia6u9GobizbdFP75T/jNb9KuRDV08cXw4Q/Dpz41eO3JJ5OhaFH0yu//i18ky9YdopYeQ7ek1dtzz6Tbff75aVcyRBzHhP++jcrXzqb6wL0D16O+HnJXXEv+yhsJyJCvm0BD60z4699g6TJyBx6WYtWSNI7dcw/U1SWdbyc5Da+mpmTA4z77pF2JamizzeAHP4AZMwavffWryZnfJ530yu9/663JPvG7765djXp5Li+XtHpTp8KBB6ZdBXG1SvWR+wk3mkpY6iQsd9Lw2VMpXHUzlTAku92OAGTfeACV//k8wf4H0rbe6wgynpOtGvjSl5Jjk9znL625o46CvfeGzs7Ba7298I53JOcYHXaYy86lV2nDDZORCu95z+C1UimZZVhXN/S5b31r8vzdd1+XFWpVTi+XNGKFzz1BZvudCHp6Wf7AldBQD0Dx0ssoXjOH+AMfIH/8+9ItUpL06n3728nhxBtvDHPnJkfx6dVbvBj+9a8kfe29d9rVaB3r7k4WO6x0wQXwla8kf973vtTK0mp4q17S6i1blozJrK+HAw6o2aeJ45hq2Ed0zT/IfOcHxNtuQ/4bFwCQnb4hUUM9QSWk+MxysrtuTa7YTPa0NxCc7u4YSRq13vUueP552HHHwcAdx0mA3H13O99r6p574Mgjk/8f77kn7Wq0jjW9YDXfb38LCxYkHW+NLIZuSav30EPJOatbbJF0IYZRePcdxNdcQfnQvalMbiCKQvLPPELz36+h+sST8I3keUE2R3zdtbDJ5jTmC8Nag/SaXHNNMkJ2t92S7w9Jr83UqfCNbwy9ds01cPDByY3eq67yjO810dyc/DzafPO0K9EIcO21yUy9Y44ZvHb99fCTn8AnPgHbbpv0UrTu+dNM0uq1tiaTZnfcca0+TFytEj3x6NCLJ32I/GlnEl95JVEUEgQZ2Hc/Kl/6DPFFPxzy1NyW2xIYuDVS3HgjnHsuXHll2pVIY8+jjyabUXfYwcC9pt7whmR1wM9/nnYlGgEKhWSPd10dRFFIuW8ZZ58d8bOfJQcI/PrXaVc4ftnpljToN79Jjgg74wzYfnu4/fYXP+fLX07uqB977Go/xMrl4mGpg+rcB6g/8O0E1Sosa4dsMtwsOuQAwsYm8httSd2U7Veck52BLzrhQyPcbrsl7YI3vjHtSqSx56Mfhbe9bej+7qeegve/Pzn7e//906tNGuHiOKJa6aHS306l1EFY7iaOY/7fydsSRa385CfJMDWlw9AtadC8eckLmyiCL3zhxY//z//AmWfCOee86KHK3/5AcNFFVHbelr4T35FcnFSkPgwhrBI9+jCZrbcDoHD2NwF/AGkUOvzw5I+k2lhvvaFvf+UryZaObNbQLa1GtdJHX+fTVModxFF1yGO5fB2HHlrmyKNSKk4DfM0radAZZySB+8wzk7dXDd4rAnd81llU33QI8be+Qu69JxJMmgxA9PijFP96FfGiRQQfOo5coYVcSwvRTdeT3WYHMoXiuv96JEmj2xe+kHS+jztu8Fp/P9xxhytOAP7xj+Qow913h/POS7sa1VgUhYSlDoIgS76uDYAgk6HcvwyATCZHrthKvthKrthKNlf3Mh9N65KhW9JQK4P2mWfCxRfD5psTzZ5F5lvnUfrsJ+g78XCa9zmG3KNPEs7YkNyx7wYge9ibqSxtJzjwINrW2zVZLg4wa0ZKX4gkadSbMQMuvHDotYsvTrZ5vOc98LOfpVPXSLFoUbIVbOLEtCtRDcRxRFjuIix1DCwZB8gX2wZCdyZbpKFtE3L5phXb9Zz8PxIZuiW92Be+AMuXw7e/TfzMM2Suu47eU0+k/2PHQVShsv8exOuvB42NA++S22I7OOurKRYtrQMrt1h8+MPwgx+kXY00Pi1ZknS/99gj7UrSd8AB8Je/wOTJaVeiYda97BEq/e3EcTTkejbfQLbQOORaXeP0dVmaXoMgjuM47SIkpata6aVS7iQsdVKom0ihYTL09hK3tBBUq8RAzwM3kN1w02TZeKFpsJMtjScrQ/eHPgQ//OErP19SbTz1VLL/u7DidIvrrkv2f591VrLUWholomqFSqmDathLQ8tGA9e7ljxEpdROJpNPlozXtZEvtpDJul1vNLLTLY1D1UoflXIHYSkJ2lFUWeXRIAnd//u/SeDO5QjCkKY/3AAf3xEuvyaZLiuNR5/8ZNLl9qBTKV0zZw59+5xzkkOKt9zS0K0RLY6qhOWuZLl4qYOw0jPwWF3jdDLZ5EZSfcuG1Acbkc01uGR8DLDTLY0DURSSyeQG/t4+/44hjwdBhlyhmVyxhXyxjdzXvp10884+O1lqvrK7t+eecMst8IEPwCWXpPGlSJL0Yk8+mXS6zzwz2QcO8Pzz8MwzyVnWY9WTTyYnj8yYAdtsk3Y1egX9Xc/T1/XMi5aM5/KN5Iqt1DWtNxC6NbbY6ZbGoBd2sjO5OlqmbA+smGxZaE6CdrHlxcvFVwbslYEbhg5Xy+Xg6KNT+KokSXoJG2/84i0fX/4yfO978LnPJX8fi373OzjtNHjve+GnP027Gq0QVUtU+pPhZ3XNM8jlGwAIsgXiOCKTLSRNjhWTxjPZ/Ct8RI12hm5pjCj1LqbSv5yw3EVULQ95LA4j4jgaCNYrA/iLfPnLLw7cK60avP/978Gziv/0J7jrLjj9dGhuHs4vSRp55sxJ9o7OmgVHHJF2NZJeShxDtZqc733QQUOvj6WlupMmwfbbwwYbpF3JuBZH1RWzcdqp9HdQDfsGHsvmGwZCd76ujdapO5HNu0VpvHF5uTQKVcM+wnI3xYYpA9e6lz4ycE7jqsvFX9Xgs9/8JlmmdsYZL/2cL38ZNt8cjj0WSqVkOdsTTyTL+j7zmbX90qSR7StfSbpmbrGQRodnnoENNxx8+wc/gL//PRm4Nnt2enVpzAjL3XQtuZ+hkSogV2hMjvaqn0gu3/iS76/xwdAtjQLVsI+w1Eml1ElY7hzoZLdOm002VwdAuW8p1UovuWLrupsuHsdw2WVw3nlw+eWDR4j190NdXe0/v7Su/eMf8Mc/wl57Jcs5JY0e1Spstlky+fyCC+DjH0+7Io0i1bB/xXnZ7WRzDdS3JDdz4jiiff4dyZTxujbyxVZyxdaBWToSGLqlEa3Uu5i+zqdftFx8ZSe7vnXmyLx7+ra3QbkM55+fdMUlSRoJHnkELrwQvvnNwZvD994LUQQ77ZRqaRpZoihc0fBoJyx1UA37Bx7L5uppnbbT4HOrZQeg6WUZuqURYNVOdl3TdHKFZG90uW8p3cvmEgRBsly80LJiyXjzyD0n+4knYKutkhcw994L226bdkWSJL20Aw5Ijhv7/vfhpJPSrubV+d734Pe/h+OPhxNPTLuaMaVj4d1DgnYQBGTzTSvOy24deK0mrQnXPUgpeKnl4gDZXN3AD/JcsZXmyduSyzcRZLJplfvqbLIJ3H8/3HDD0MB9661JF8Fl55KkkaK/H6ZOhWJxcEAojJ6Ba3PnJsMdd9st7UpGpWqlb8V52e2ElV5ap80eOBM7V2gBAvJ1reQKyZTxUfNaTCOOnW5pHYij6sAP6rDcTefi+4Y8vmonO183gVyhKY0ya2fhwmSZ+eTJSRjfaKO0K5Jem/PPh89+Ft71Lrj44rSrkTRcFi1KwvdK//3f8NxzyTGaW22VXl2v5O67kyXz22yTnKqglxVVKyv2ZSd/omppyOMtU7YfaHyseuqLtLbsdEs1sHLYRljuolLqIF9so3HCZgBk841kMnkyuboVwzZaRlcn+7V47DFobYUpUzzWRKNbpQJ9fcnkfkljx6qBu6MjOfO7ry9Zsj2SQ/fs2U5hfxlxHAEMhOdSzwL6up4deDxperQMnJedXWVOjoFbw8lOtzRMSj0LB0L2CwefvXDgxri8e9rbm3S8N9kkebtaTY5e+shHYOONUy1NWmPt7bB8OTQ1JTeRJI1N99+fHKN59tmDy8yvuw6mT0+6yhqxwkpvcl52qYOw1EnjhC0o1E8EoFLqpLfjieQorxX7ssd000MjhqFbeg2qYT9R2Ee+bsLAtY5F91Ct9AKrDNsYL53s1+JHP0o6COutB08+CQWnfkqSRqhyGbbcEp5+Gv76V3jTm9KuKDF3Lixblty8nj497WpSEUUhlf7lA8vGX9j4qGtan4bWmSlVJyVcXi6tgWS5eDL0rFLqJKqWCIIMbevtOtCxLjZMJY4qyTnZhuxXtuuusP/+8OY3Dw3co2V4jSRp/OjogJ13TsL3fvsNXq9WIZvi7/svfQl+9Sv49rfhlFPSq2MdiqMqcRySyRZXvB3Ss3zewONBkCFXbFnR+Ggjl29Iq1RpgKFbehn93fPp757/okEbSSe7kahaIZtLfujXNa2XRomj16xZcM01Sche6e674YQT4Otfh8MOS6826aXcfXcyDHCrrfw3Ko0nU6bAH/+YbDFpWCXEveUtMGkSnHNOOkNCp0xJtm1NmPDKzx2l4jimWulZsVy8g7DcSb44gaZJyV77bK6OQt1EMvn6wSXj420Ln0Y8l5dLrOhkl7sISx3Ut2xEJpt0Xvu6nqOv8+lVlou32MmupaOOgj/9KTlv9Je/TLsa6cXOOy+Zauy/UUkPP5zs787lkgnim26adkVjSql38Ypl451EUWXIY9l8A61Tndau0cNOt8alalgiLHeu2P/TOaSTnSu2UWyYDEChfjK5fKODNtaVSy6BzTaDk08evNbZmQxhG6d71TTCbL11Erj32CPtSiSlbeut4c474dZbhwbu3/4Wdtkl+X2mNRJFIdVKD/li68C1lQNqAYIgu6Lx0Ua+rpVsrj6tUqXXxE63xoU4jglW7BMu9y2le9ncIY+v7GTnii0rgrb7f0aMz3wGvvvdpMP4wQ+mXY0kSS9t5Skd5XKyHWWHHdKuaESK42hgyXilv51qpZs4jmlbb1cymaQnWOpdTBT2JysMC00uGdeoZqdbY9JgJzvpZhcap1HfPANgxV6fwZCdL7TYyR6pogjmzIHubpg2Le1qJEl6ed3dsO++yUTx7bcfvF4u1+aUjs9+Fh54AD79aXjjG4f/4w+zSn87pZ6FVModxFF1yGPZXB1R2E+m0ARAscFjGTV22OlWKko9C1n+/G3UNc+goWUm+fpJA53o1yKOI8p9SwdCdvUFg8/ydW00Txo8VzOOqobs0SKK4Oqr4eCDB6eaX3tt8uJlr73SrU2SpNXp6YHGxuTvYQg77pj8zjr3XJg8efg+zz77wI03Jkvajzlm+D7uMIiikLDUQTbfNDB0ttSzkJ72xwHIZHLkiq0rpoy3ks3VpVmuVFOGbqVi2XO3Mv+R3w28nc01UN+6MQ2tG9PQOpP6lg0HjoJYnWpYIo7K5ArNQBK62+ffQRxHK54RkCs0Jj/M7WSPLaUSbLstPP54ckzKccelXZHGkx//GE4/PZlYfMklaVcjaTT4xz/g8MOTsP3kk4NhfLg+9vPPJ0dwbrLJ8H3c1yCOI8JyN2GpPZk0Xu4BYhpaNx444SWqlij1LiZfbCObb1yrhos0mri8XKlobBv6i6Ea9tK99CG6lz4ExEBAsXEaDW2bDv6wDnJUy12E5U6qYT/ZXD2t03YCkjMZi41TIciSLzSTK7QYsseq/n448ED4+9/hiCPSrkbjTV8fLFmSDPiTpDVx2GFw880wf/7QwP297yW/x9bmqLERcHRhNSzR2/EEYaljleZHIptvGLIXO5MtUt+8wbouUUqdnW6lIo5jHrnpTKph7ys8MyAJ4cnkymlbvHXFXdGkk908eTsHa4xX3d3Q1DT49oknJmd/f/jDkM+nV5fGtqVLYcECaGmBDTdMuxpJo9Udd8DrXw/19fDMM8lZ36NAVK1QKXUQBAGF+qTmOKrSvuAO4jgmk8knqwzr2sgXW1521aI0ntjpViqCIKBx4hZ0LroPiF7mmYP3hIpNM6hvnkG+2EK20Dww3VLj1KqBe84c+NGPIJuFAw5Izk2VamHSpFHz4ljSCJbPw377JTfvVv2Z0teXBPE19dBDyZ7xTTcd3mXrKyRLxruo9LcTljoIKz0A5PKNA6E7yGRpaNucbL6ebK7BJePSatjpViqiaonFT13HkievXoNnB6y31dFMnLF7zevSKBWGSeh+9lk455zB68uWwcSJ6dUlSdLL6e+HuhUDxJYsSW4aH3ccfP3raxa+t9kGHn4Yrr8+Gao2jHqWz6Pct/RFS8Zz+WRmTn3LRgZsaQ3ZKtQ6E0UhfR1P0bHoP/Qse5Ry35JXeI8MmWyODXd4P00Tt1wnNWqUyuXgpJOGXlu4ELbaCo4+Gi64oCYdAI1DDz0EN90EG2+cTNSXpLVRt8rE7t/+Ngnec+YMvf5yJk2CqVNfXXf8BaJqiUqpk7DcRWPbpgPX4zgmjiMy2cKKCeNt5IutZLJu4ZJeLUO3aiKqlin3LaNn+aP0tj9OpdRBf/dzxFFIJltHvn4irdN3oXPxvcTV8mo+QoZcsZmZO32Iusbp67x+jQF//St0dMC9967VixFpiBtvTG7wvPWthm5Jw+sjH4Gtt06OxFzZQa5Wk2PGTjwRpq/m9dDNN7/qTxNHVSrlzhVTxjupVgbn6xQbp5PLNwBQ35xs68uueFvSa2fo1rCIoyql3sX0tM+jd/lj9PfMp9y3FOKIIJOnvnkDpm56OI0TNqfYOA1Izmd85v4KnYvuZdW92xBQ17w+M2f918CRYNKr9l//Bdttl+yby6wYtletwmWXJYEp4wA+vQabbAJHHpkMQJKk4RQEydFfq/rVr+DMM+GHP0yOG8ut3Uv3/p4F9HU8yQt3l+YKTeSLrQTB4Mkvhm1p+Bi69ZpE1TLVsES5bzE9yx+jZ/lcejueHAjZhYbJtE3bmYYJm9PQugn5urbVDj5rnLA5nYvuGXKtecr2bLDtu1y+pLW3+wvmAPzf/8EHP5gMW7v66sFOgrSmDj7YDrekdWfmTNhtN3jzm4cG7s7O5BSFl1AN+wlLHVRKHRQbp5EvtgKQzdYRxzHZbHFgyniu0OJrLqnGDN1aIwPLxdvn0dv+OP3dz1PpW0YcV8nk6mhs25S29XaloXWTlw3ZL9TYttmQtydttD/TNjvMY8BUG2EIzc1w+OEGbknSyPfGNyZ7vKvVwWv/+Q/stRecfHJyhGF/P9E3v044qYlKKZkyXg37B56eyRYHQneu2ELrtNlkc2u4Z1zSsDB06yVFUUj7/NvoWf4Y/d0rl4tXk052/WRap+/CxBl7UNc84zWH5ELDVLL5BqqVPtbf+u1MWH+3Yf4qpFV86EPJ0vLW1sFr99yTdMC/8AWYMCG10iRJWq0gGNrl/uUvoacH7rorGexYKtF58tFEwQarvtPAkvF8/cRVPlTGwC2lwCPDBCSd7Erfcno7nyQsd9GzfB69HU8SRxWCTI5C/RTqmmfQ2LYZDW2brnEne010L3uETLZAQ+smw/LxpDUWx3DQQfDPf8IHPgCXXJJ2RRrpfvtbOO20ZIuC/14krStxTLVzKZVsSNi/nNxfrqLuv/47+T121lm0H7cPQWsL+WIbuWIruWLLsL1Ok7T2/G4cp6JqhUr/crpXTBfv735+oJOdyRZpaNuUqZseQrFhGoWGqcMasl+oaeJWNfm40isKAvj0p5Mp51/4wuD1SiXpKrgEXS/U3Q1PPZUcSSdJNRRVK4TlTqI//Z7C6WcR7rwtvd/7HwDCw/akeOihBM3NcPzxtGy2iSFbGsHsdI8jURTS3/UM7fPvoqf9MSp9S4nj6opO9uQVnexNaZk6i2zOI5Y0jsTx0ID92c/Cv/4F558Ps2alV5dGnsWL4YknoK0Nttwy7WokjSFxHBHM+Rdcfjm88510TosJy93kbr+HlqM+QjR9Mj33Xk+uLjkvO1doSrtkSWvI0D1Grexk9yyfR0/H44SlLvo6nyaOygSZAoX6idQ1zaBxwqY0tG1e0062NKp0d8OGG0J7O/zlL8nEWEmShlscEz76EOH6E5IBaOUu2j5yLsFll8GXv0zfye+m3L+cfKaBwi13k93/UILml55YLmnkMmWNEVEUJkdD9HfQvuB2ejueptK3JOlkBznqWzZk6iYH0zhhM4oN0yEIPB5CWp2mpmS42qWXwhFHDF5/4IHk6JYmOwuSpNcmqlaSgL3gKer3OIzs0uV03n8FNDUCUD3yMHJNTfC611HXvCH1LRsl73jkFilWLWlt2ekeIyqlDrqWPEgUhSx96lqy+QbqmtanccJmdrKltVUuw7bbQm8v/PWvsMsuaVektDzxRHJ8z3rrwf77p12NpBEujqrw4IME//czmDKF/o+eQG/HEwC07nkMmYVL6P3TT8nutTe5Yhu5fEPKFUuqBUP3GBFHVbqWPECu2Eyu0EKu0GInWxouc+fCYYclofvRR+12j2c/+xm8971wyCFwxRVpVyNphImrVap33UplagthU5aw3EXTjQ+RP/79sMUWVB+8h55ljyb7sp9aRG7zbQnqDdrSWPfaDlfWiBNksrRM3ZGG1k0o1E8ycEvDacst4cEH4aqrhgbub3wDHnkkvbq07q2/Phx4IMyenXYlkkaIOKpS6llE97K5hIfuS+4NexH95lIqpQ7iOKK8+07JsZRnn002W5e8XmvZiPwOrzNwS+OEnW5Jei1uugn23huKRXj6aZg6Ne2KJEnrQBxViTqWkv3Sl+HWW4luuoH2xf8BYurO+wn13/8F5U+eRHzaqeSLbWTznggjjXeGbkl6LR59FD71qaTz+YMfDF5/4fFjkqRRLQ4rVG+9iWrXUkqv24ZqpZtspp6WbfeD5cthzhx6tp5KJlsgV86Qa55EUKxLu2xJI4ihW5LWRqUC+RXbORYtgkMPhTPOgKOOMnxL0mgVx5T6llDpX0bwf5fS+MlzqOyyPV2XXQRANldHy59vI5gyFQ46CJqbUy5Y0kjmnm5JWhv5VeYnfPObcPfd8JWvJB1vjT1//3syyf4DH0i7EknDKIpCyn3L4Mtfhs02gz/+kUr/Msp9y6jsuTNRazNsuCGNLRvTOm02rdNmE3z4pOQGq4Fb0ivwDClJGi5nngn19cmgrcyKe5rVKsyfDxtskG5tGh4dHfDQQ8mRYZJGrbhSpnrjtcQ330DfR44nrPQCMW0Lnifz+ONw1VUUDt+PbL6R/JTtCZYsI5/zZbOk18bl5ZJUSz/5CXz0o/DFL8JnPpN2NVpbCxcmobutDXbaKe1qJK2pOIbubiqFmFL381SWL6Rtu4MIyhXab/w10aYbkc3V0/BsH/n5S2GffexgSxo23rKTpFq66iro7x+6DF2j17RpyR9JI15UrRCWOsj+82ayH/sE7LQT8W9+Srl/OdQXqBy8N0GhjoaGGWSn7Uw2VwS/vSXVgJ1uSaqlOIbLL08G7RSLybUHHoDnn0+uSZKGRRxHVK+/mvgvl1E67I2Ut9sYgIbHl1O395tg8mTi55+jv38x+WIr2XwjgQMvJa0DDlKTpFoKAjjiiMHAHcfw//4fHHwwfO1r6damV++55+DPf07OaZeUrjiGuXOJqhW6ljxE+/w7qF74LfLnfZ/s364AIJtvgFk7wZVXwlNPEeQL1DfPIFdoMnBLWmdcXi5J61IYwo47wm23wTvekXY1erXmzEn+u+29N9xwQ9rVSONOVC1TKXVAVxfFnfaEhQsJFi6kGvYQxxHhEQeRKTaS3f9w2qbvQiZbSN7x4PXTLVzSuObycklKQ3t7MoxrpXPPhVwOPvEJqKtLqyq9kuuuS85hnz0bvvvdtKuRxrw4qhLeewdc/COqDTl6T3kvAJlsgbaD3w+PPQaXX055t1lks8Wksy1JI4yhW5LS9uyzsPnmUCrB3/4Gb3pT2hVJUjqiCO6+G2bMoKeul3LvIrI33EbLO08hmjaZ9jsvI1dsJldspX5xmWCDDQe370jSCOXycklK2/rrw0UXJXsODz988HpXl0fWSBrzqmGJsNROpdRJ44c/S/D7P8C3vkXwgbcRxzHRbrtSOeFYgoMOom3qzmQKK1YDtaRbtyStKTvdkjQSlcuw/fawyy5w/vkwdWraFUnSsIiikHD5AoLPfI7MnFvp+MtFUJd0q1t/cS3Zc74Gp55K9XOnATHZXH26BUvSWjJ0S9JIdNVVcOihSdh+9FE73iPFDTfAqacmw/AuuSTtaqTRIQzh9tuhp4fSnrPpWT4P4oi21x1JZuESOn91PhywP/liKwUaydY3Qz6fdtWSNGxcXi5JI9HBB8O//52c571q4L7ySjjggGTomta95cvhzjsNBNLLiWOqlT4qlU7C/g7q/n4TuRNOhB13JHvnHCAmm6+n8qXTyUyYRtOhR5BpnZB21ZJUM3a6JWm0uOUW2Gsv2GEHuOMOhwelYcECuOsumDAB9tgj7WqkESOKQsJSB3z96+QuuZTeT36A8jHJjIpib4bG3d8E++9PfOmlRJmIbM5TGiSNH7ZKJGm0WLAAJk6E3XYzcKdl+nSny0sA1SrcfDPceCPRZ0+nfeG/gZj6JQsoPPM8+ZvuJH73ceSKreSnToBFiyCTIQCyadcuSeuYnW5JGk2WL0+O1Jk0KXl78WI4+2z4/Odh2rR0a5M0dsUx1SXzqdQHVEodBJUKTVvsAd3d8O9/0zkjS0xM4Zkl5J5ZSm7/QwhaWtOuWpJGBDvdkjSaTHjBvsezzoLvfhfuuw+uvz6VksaVxYuTM4RbWpIVB9IYFlUrVEodRFf9neJHP020yQb0/vY7AARBQPz2txOUy5DL0Tx5W4JMFqYCu6RbtySNNIZuSRrNjjsumQr8pS8NXoui5H8zmVRKGtNuuw3e/GbYddfk/3dpDInjiGDOv+Cyy+Atb6Fnm8lU+peTmVSkfv4i6OklHzSQb55Crq4VfvxjCAIAgpRrl6SRzFdkkjSa7bVXEgT33Xfw2s9/DrvvDnPmpFbWmNXaCrNmwRZbpF2JtNbiapXwP3fQ3/U8XUsepH3+HUQ//Ql84xvw+9+TL7aSyzdS2OENhFf8heD5+TSvP4u65vXJ5RsJAqO2JK0J93RL0lgSRbDttvDII/D1r8OnP512RZJGkGTJeDuVjkU07LwfmQWLab/tj0QzpgPQdMfTFC67At7xDjjssJSrlaSxweXlkjSWZDLJ3u5vfxs+/vHB608/nexDbmtLqzJJKYijKvHcR8j84CIoFAjPOp2e5fMAqFt/KkFnN4WnlpLZdjfyxVayb90d3npsylVL0thip1uSxro4hoMPTgaA/eIXcMghaVckqUbiMKR6202EExupTGkkLHfR8OBCige9FSZMIFo4n+72ucnS8YVd5DbajKDomdmSVEvu6ZaksW7ZMnjuOejqci/y2rr99mT//Iknpl2JNCCOI0o9C+leNpfKcW8ht9f+RD/+EZVSJ3EcU9lxC/jYx+Cii8iQoWXKDtS3bER+i+0M3JK0Dri8XJLGukmT4N574Y47YNNNB6//7GfJULBZs9KrbbRZvhxuuAE6OtKuRONYFIVE/d3kTv0cXHcd/GsOvb1PEcdVgl13IH/VjeTiHA1tm5AvtpHN1cF3vpN22ZI0brm8XJLGo6efhq22glIJ7rwTdt457YpGhwUL4MYbk/PSDzoo7Wo0TsTlEtVbbqC66FlK+7+esNxNNlek9Y3HwqOPwp//TN9+syHIkI+LZOtbCfKFtMuWJK1g6Jak8ei55+BTn4KFC+HaawfO2pU0AsQxVKuUyu1U+pbAXy+n6f2nUp25Ph23/B6AbK6O5useJtPQCPvtB83NKRctSXophm5JGs9KJSgWk7+Xy8kRQe99L7z73ckkdEnrRFStEJY7yX/vpwQXXACf+Qw9xx9CqWchdPfQts/xVPd4PdFF3yXXth7ZXDHtkiVJa8g93ZI0nhVXeeH+058mXe/774e3vc3O2eq0t8MDD0BDA8yenXY1GsXisEL1n1cQX3MVfae8j5AyAK09HWSffhquuYbCB95FJlskP6WV4PmF5L0RJkmjkqFbkpQ44YRkUNj66w8N3AsWwPTp6dU1ktx1Fxx4IGy/Pdx3X9rVaDSJY1i2jLC5QF/Xs4R97bQe/x4yyzvo22M7eP0ssvkGonccTfb1e8Dee5MvNpAvtqRduSRpLRm6JUmJujo4/fSh1+bMSY7I+sQn4BvfcO93QwNsvjlstFHalWgUiKplKqUOsrf+m9y73w8bbgg3XUOlvx0CqBx1KNmufuqmb0lu+i5ksgWYCmztiQKSNJYYuiVJL+2yy6BSSY7IGu+BG2D33ZNp0dJqxFGV6g3XEP/xD5TfOJvSnjsBUDetidz8+dDVRbaSoaF1Y3LFVnI/+nW6BUuS1glDtyTppX3ta3DAAbDDDoPXFi9OOuBveYtBXONbFMG99xLvsD3dy+cSlruo/9VPqLv4N1SXvBn23IlcvpHMzMlw882wyy4EdXXU0Zh25ZKkdcjp5ZKkV+fkk+G734WPfQy+8520q5HWqWpYIix1EFdK1O24JzzzDDz0EB0T+qmGfeRvf4C6P1wJR72N7JHHkMnm0y5ZkpQyO92SpFdn0qRk//fRR6ddybp3//1w2mnJ3twf/jDtarQOxFGVyoN3E1z4HaJyHz3nnAJAEGQobrklwfLlMHcuDYfsSyaTJ/u23eFt/5Vu0ZKkEcVOtyTp1Vu0CKZOHXz717+Ghx+GT38aGsfw0tkbb4R99oGttkq+Xo09YQh33glTptA7OUupZwGZBx6l9eATiOvrWH7/leSaJ5AvtlHsjMlMWw/ydrMlSS/NTrck6dVbNXD39cGpp8Jzz0FbG5xySlpV1d5WW8FPfgItHuM0llTDfiqldsJSB42nfZ3gkh/D6acTnPFx4jiGHbaj8rEPwl5707b+68jUNSTv6D8DSdIasNMtSVo7cQx/+AN8//tw+eXJ0nNIwnh9fbq1SasRRSFh33KCj59C5rob6fzTD4inTASg+Yr/kD/1c3DSSUTnnE0cV8nm6lKuWJI0mhm6JUm1ceihUCzCeefBJpukXY3Gs3IZbr0VFi2ifMQBdC99BIhpOeS95B54lJ4Lv0R03NvJFVspZJvJFpsgm027aknSGGHoliQNv4cfhu23h0wGHnoINtss7YqGR09Pck53oQDbbpt2NXopcUy1r4tK1EOl1EHx1vspHHEMTJ9O9MyTtC/8N9lcPXXX3kGm2EzuwMMIWlrTrlqSNEa5p1uSNPy23jqZ9H3rrUMD95w5sMsuSQd8NLrnHthzT9h0U3jssbSr0SqiaoVKqQMuuID8hRdR+sAx9J/0zuTBnbagMHMm7LEHmd4SbdN3IZMtwPE7pVqzJGl8MHRLkmpj662TPys98wwccADMmAE33wzTp6dX22tVLMJ66w0dJKd0RBFcey1ccw3xFz5PR8cDxHFEsdRJ8fmF5G+5k+r/+wj5Yhu5ulZ44gkIAgAyKZcuSRpfXF4uSVo3brgBjjsOttwSrr9+IABJaySOCZ97krCtSKW/gziq0LLLm+DZZ+GKK+jaZSPiqEJ+aR/5x54ju9/BBI1NaVctSZKdbknSOrLPPsl+6KVLBwN3pQKf/Sx84hOw0Ubp1qcRJ6qWqZQ6qN5yA3Xv+jBMbKX3n78YeDw+/jiChYtg8mSaJm1FEGRgKrBNejVLkvRCdrolSem58MIkcM+cCfPmQc57weNZHFXh1tsIfvc72G8/et64LaXexQTLO2jb8XCor6P73mvJT5tJvthKNt+QdsmSJL0iX91IktKz556w997wzncODdxxPDKXn8+bl3Tmp0yB730v7WpGvTgMqd4xh8q2mxBWugjLnbT+7TKC886DhQvJH3gB1bCP/EYzqN70T7K77EZznWe/S5JGFzvdkqR0xXHyJ7NivNW//gUnnQTf/CYcdFC6tb3QHXfA61+fLIV/6qm0qxmVomqFSv8yKv3t1O+8P9mnn6fj7z+mumMydK9xXjvFX/wJjjwS3vSmlKuVJGnt2emWJKUrCIZ2tf/nf+Dee+HXvx55oXvmTPjOd6C5Oe1KRo0oCuHxx8l8+3zo6aH6w/PpaX8cgMI2m5FZ3kFhfifB3pskS8Zn1MM+h6VctSRJw8dOtyRpZFm2DM49Fz75SVh//eTa8uUQhsmybo1ocaVMdc6NhI1ZyjOnEJa7qV/UR/3O+0M+T7x0Cd2lZ8kVmsl3VshO24AgX0i7bEmSasajKiVJI8vEicnS8pWBG+CLX4TNN4dLL02vLq1eHBPHMf09C+he+gilj7yH3L4HwXe/R1juAmLCDafB5z4Hv/0tQaFI8+RtqW/ZkNwGmxq4JUljnsvLJUkjWxjC7bdDZyest166tZRKyV7uXA423TTdWlIURSFRpZfcJz8Ll19OcP319NctI6qWiHebTeGPV5BpbKGxbVNyxTayuSJ8+ctply1JUipcXi5JGvmiCP75z6F7vK+4AtraYLfd1l0d//kPzJ6dhP/nn193nzdlcamf6g3XED39GP1H7k9Y7ibIZGk75pMEt9wCF11E//FHEMcR+Wwj2UITgce/SZIEGLolSaNRby9suSU89xz86U/w1reum897333wxjfCtGnwyCPr5nOmIY6hXKYcdVPqXUz8r1toefN/EbU2037P5ZDLkc030HTnM2SDbHLsW2Nj2lVLkjQieRtakjT6lEpwyCFw/fVw6KHr7vPusAO0t6+7z7cORdUKlVIH+Z/8isy5X4UTT6T6yQ9S6V8OO25FdfONiXbakcbMVPLTNyGTLcDhs9IuW5KkEc9OtyRp9OrpGdphPeEE2H13OPHEZN+1XlJcDan+4y/EV11J38nvIWzMAtDy59vInfzfsO++hFddTlhqJ1dsI5dvSLliSZJGJ0O3JGls+Oc/4cADIZ+Hhx8e14POViuKYP58wqkT6Ot4krDcRcs+x5Kd9xRdF51L5fB9yeUbqesrUnj0GdhrL6irS7tqSZJGPdsAkqSxYe+94cILkzO9Vw3cy5Ylx5ANh2efhc9/Hlpa4IILhudj1lBULVHp7yDzwCPkjzgaGhsJ5j5IpdQBQPmow8g9u4TiVrvQOP11ZLL55B1nbpVi1ZIkjS12uiVJY9ezz8I228A73wnnnQf19Wv38e6/P9nXPWUKLFo0LCUOpziqEt70T/jVr6jM3or+I/YBoFAt0rT5blAowLx5lJoDcoUWsvm1/P9DkiS9IjvdkqSx67LLoLsbHnxweJZKT58OX/saNIyQ/c3VKtx1F/GsWXR3PUZY7qR4xZ9o+OFPiQ/aC47Yl1yhkWzdRLjjjuQGRKFAMe26JUkaR+x0S5LGtptvhuZmmLVi0nalAn/7W3LMWBCkWtprUQ37CUsdVMN+GnY7NNm/fv31dG47ibDcRf7RZ6j7vz/DEUeQfds7yGS8vy5JUpr8TSxJGtv22mvo2xddBCefDEccAX/9azo1vQpRFBLOe4Dgq1+HRQvp+uH/DDxWv9Msgvnz4dlnadh9F4JMjuyM3WHfd6RYsSRJWpWhW5I0vkRRcszY4Ye/+vcNQ1i4MOmQr7/+8NcGSSf+ttugpYW+jSfQ1/UcQfdiJvzkl8RBQGb5p8lMm0G+ro34/PMIfj4Zcjl/oUuSNEK5vFySNP4sWACTJw+e5X3bbfCHP8DnPgdtbS/9fo88AltvnTxn+fLhqSWOqVb6qJQ7CEsdNHztEjLf+Ca8//2ULvwqPe2Pkc3VU3/hzwl22oXc4UcSNDS+8seVJEkjgjfGJUnjz/Tpg3+PY/jv/4Z//Qt6e+E733np9wuCJKjn82v16aNqhbDUQXDKJ8lefhXdP/9fqlslx5wV9tyZwo8nQUsL+fqJtBZbyeaKcM5Oa/U5JUlSOgzdkiR9/vPwpS8lne6VKpUkYK86bG3LLZPrr1apBLfcAk8+SeVdb6dryQMANM99hMzzC8nfeDuZHWeTK7aSPXQHWHQsZDJkAByEJknSqObyckmSIOl4rxqwTzklOZf7/PNhu+1e9ccKu5YRZkpU+jvIz3uOut0Pgvp64iWLaW+/n0yujuLtD5GL82T3O5igqXlYvxxJkjQyePtckiQYGrjb2+GSS5IzvufPX6PQHVXLVEodcPHF5M/9FpW3HULfF04GIN5kCnU77wzbbUfQ1U3r9F2So7yOmFWjL0aSJI0Uhm5Jkl6orQ3uvRd+9zs48MDB69dfD7/8JTQ1Ef/vNwmuvgauvBI+/Wk6eY6oWqaQq1BctJT87fcQ1rWRL7aSK7bBXXcNfJjMuv56JElSalxeLknSGoh7e2HzzQjmLyCur6Pj8VtoPeJDBHfdBT/7GT1H7E610kuuL6Dw0BNk9z2IoK4+7bIlSVLK7HRLkvQSomqJSn87lVIH0b130xhEBM2N9J94HFG1RHTs28nutBNssQUNbZsRrFyiPnOHVOuWJEkjh51uSZJWiKKQIMgQBMkC8N6Op+jvfn7g8aAUUpzfTmbWruTrWsnm7GRLkqSXZ+iWJI1bcRxRrfRQKXVQ6W+nWummaeJW5OsmAFApddDX+cyKfdmt5ApNA4FckiRpTRi6JUnjSlStUO5fStjfQaXcQRxVhzxe37Ih9c0bpFSdJEkaa9zTLUka06IoJI5Csrk6AOIopLf9iYHHM5kcuWLrQDd75fMkSZKGg6FbkjSmxHFEWO4mLCUD0MJyD4X6iTRN3BKAbL6eQv1EsvlG8sVWsvmmwQFokiRJw8zQLUkaE/q7F1AptROWOojjaMhjUbU85O2miVuty9IkSdI4ZuiWJI06UbVCtdJDvq5t4Fq5bzFhuRuATCafLBmvayNfbCGTLaZUqSRJGu8cpCZJGvHiqEpY7kqWi5c6CCs9BEFA2/RdCTJZAEq9i4mrFXJ1rWRzDS4ZlyRJI4KdbknSiFXuW0apZyFhufNFS8azuQaiaplsJjkru9gwJY0SJUmSXpahW5I0IkTVEpVS55Dl4FFUplJqByCTLayYMN5GvthKJptPsVpJkqQ14/JySVIq4qhKpdy5Ysp4J9VKLwANbZtQ1zgdgGpYotK/bMWU8YY0y5UkSXpN7HRLktapathHb/sTK5aMD73vmys0EQSDv5qyuSLZpvXWdYmSJEnDxtAtSaqZathPWOogyOQo1E8CIMjkqZQ6gZhstjgwZTxXbCWT8deSJEkaW3x1I0kaNlEUEpY6B87Lrob9QNLBXhm6M5kcTRO3IJtvJJurS7NcSZKkmjN0S5KGRdfSh6n0twOrLhkPyBWahpynDQwEcEmSpLHO0C1JelWqYR+V/g6qlR4aJ2w2cD0IMkBMNldHvpgsF88VW1wyLkmSxjWnl0uSXlZUrRCWO6n0t1MpdRBVSwOPtU7diWw+OSe7WumDIEM2V0yrVEmSpBHH9oMk6SX1dz1Pb+dTQ64FQUCu0EKu2EqQyQ5cXxm+JUmSNMjQLUkirPQSljqolNqpa5pBvtgCQGZFkM7mG8gXW8kXW8kVWoaEbUmSJL00l5dL0jgUVctUSh0rgnYHUbU88Fhd8wwaWjYCII4j4igkky2kVaokSdKoZqdbksaZaqWXjkX3DLkWBBlyhRbyda3kixOGXA8M3JIkSa+ZoVuSxqA4jqlWega62ZlcHY1tmwKQydWTyeTJZAvk6tpWLBlvXjF9XJIkScPJ0C1JKYrjiEWPX0Gu0ERd8wbUNc14zdO/q2FpYF92WOokiioDj2XCvoG/B0FA67TZ7suWJElaBwzdkpSiKCyx5Kl/DrlWqJ9MfetM6ps3pL4lCeKr21Mdx9GQ7nT3soepVnoH3g6CLPliMmU8X2wb8r4GbkmSpHXD0C1JKcrm6yk2rkepZ/7AtXLfEsr9y+hY8G8gBgIKDZOpb5lJsWEqmVwd2XwzcbWftum7DATofLGNIMiQL7aRK7aSKzS5ZFySJCllhm5JSlnz5G0o9S6EOBq8uOrfiSn3Lqbcu3iV99mepklbE1a6yRdbAWhonbmOKpYkSdKasgUiSSlrnLDlC0L2yys0TGPyxgfSOm32QOCWJEnSyGSnW5JSFMcRubqJEGTWKHhP3GBvpm/xZpeNS5IkjRKGbklax6IopK/jKbqXPkxvxxOUehauUeCetvlbmLzRPuugQkmSJA2XII7jOO0iJGksi6oVqmE/pZ4F9CyfS/fSR+jvfg6AXKGFYuN0CAJ6ls0lGZy2qgCCgA22fSet02av89olSZK0dgzdkjTM4jiiUuqkt/1xepY9Ql/XM5R7lxLHIdl8E00TtyBXN4HGts2ob96AbL6RUs8CHrv9my/4SAGZbJ6NdvwgjRM2T+VrkSRJ0tpxebkkDZPu5fPoXvIgvR1PUupZSFTthyBDoX4yLdN2YtIGb6Suef3V7scuNk4nm2+kWulJLgQZsrkGNp59EnVN663jr0SSJEnDxdAtSa9BVC1T7ltGWGqne9lcupfNHThrO1dspaFtExrbNqNx0tYU6yeTyeZf9uMFQUDTxK3oWHg3EFCom8TGsz9Mvm7COvhqJEmSVCuGbklaA3FUpVLqoKd9Ht3L5tLf9SzlvqUQR+QKLTRN3JK29V5PXfP61DetTzbf8Ko/R9OkrelY+G/qW2Yyc9YHX9PHkCRJ0sjinm5Jehm9HU/SsfA/9HY8Sbl3IVG1TBBkKTRMob5lQ9rWez0NrZsQBMFaf64oCulceDctU3d6xc64JEmSRgdDtyStUA1LlHsXUupdSl/nk3Qve4Ry72IA8nUTKDatT1Pb5jRN2pp83QSDsSRJkl6RoVvSuBVFIZX+dnqWP0rPsrn0dT9HpW8ZEJOvm0jTxC1paNuMfHECdc3Tyebq0y5ZkiRJo4yhW9K409+9gGXP3kxf59OUehcRRxWCTI5Cw1TqWzaiZeosmiZsMSxLxiVJkjS+GboljWnVsJ/+ngX0dz5NqWcR3cvmUulfCgTk6ydS3zSDhgmb0zRxK/J1bWQyzpeUJEnS8DF0SxpTkiXjy+le+jA9y+fR3/08lf5lABQaptA0YUsaJ25JoX4y+bo2srm6lCuWJEnSWGboljRmdC+bS7lvKV1LH6J7yQMEmQLFxqk0tMykcdLWNE/amiDIpF2mJEmSxhFDt6Qxo6f9cUo9CwmCLAQBDW2bUii2EWSyaZcmSZKkccrQLWnMqIYlggAy2WLapUiSJEmAoVuSJEmSpJpxc6MkSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1YihW5IkSZKkGjF0S5IkSZJUI4ZuSZIkSZJqxNAtSZIkSVKNGLolSZIkSaoRQ7ckSZIkSTVi6JYkSZIkqUYM3ZIkSZIk1cj/B26aLDWIRlvoAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647c2e6c0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Parallel3D(detector_position=[0,10,0])\\\n",
+    "    .set_panel(num_pixels=[10,10])\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fan-beam geometry\n",
+    "\n",
+    "A single point-like X-ray source emits a cone-beam onto a single row of detector pixels. The beam is typically collimated to imaging field of view. Collimation greatly reduce amount of scatter radiation reaching the detector. Fan-beam geometry is used when scattering has significant influence on image quality or single-slice reconstruction is sufficient.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data.\n",
+    "\n",
+    "For fan-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin. They are specified as `[x,y]` coordinates.\n",
+    "\n",
+    "```python\n",
+    "cone_2D_geometry = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10])\\\n",
+    "    \n",
+    "                                      .set_panel(num_pixels=10)\\\n",
+    "        \n",
+    "                                      .set_angles(angles=range(0,180))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.263396Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.263217Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.456876Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.456392Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f96474a8b90>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cone-beam geometry\n",
+    "\n",
+    "A single point-like X-ray source emits a cone-beam onto 2D detector array. Cone-beam geometry is mainly used in lab-based CT instruments.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data.\n",
+    "\n",
+    "For cone-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin and aligned in the Z-direction. They are specified as `[X,Y,Z]` coordinates.\n",
+    "\n",
+    "```python\n",
+    "cone_3D_geometry = AcquisitionGeometry.create_Cone3D(source_position=[0,-10,0], detector_position=[0,10,0])\\\n",
+    "    \n",
+    "                                      .set_panel(num_pixels=[10,10])\\\n",
+    "        \n",
+    "                                      .set_angles(angles=range(0,180))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.458382Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.458191Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.676360Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.675872Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647969850>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-10,0],detector_position=[0,10,0])\\\n",
+    "    .set_panel(num_pixels=[10,10])\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an offset AcquisitionGeometry\n",
+    "\n",
+    "It is unusual to have a perfectly aligned CT system. One of the most common offsets is the rotation-axis. If this offset is described by the `AcquisitionGeometry` then it will be accounted for in the reconstruction. This saves having to pad your data to account for this.\n",
+    "\n",
+    "To specify the offset, you could either add an x-component to the `source_position` and `detector_position` or you can offset the rotation axis from the origin using `rotation_axis_position`.\n",
+    "\n",
+    "As with the `source_position` and `detector_position` this is the `rotation_axis_position` is specified in 2D with a 2D vector `[X,Y]` or 3D with a 3D vector `[X,Y,Z]`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we offset the rotation axis by -0.5 in X by setting `rotation_axis_position=[-0.5,0]`. You can see the rotation axis position is no longer a point on the source-to-detector vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.678070Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.677748Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.870076Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.869592Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dhg1VXp/09HRJUteuXSvllV6zeqmaax6ybl3ETl2xnWVmZqp3795h7apv377KycnRDz/8UOOxnn32WfXs2VNNmjSRz+fT888/v8/XYkW1PX/5v7sUfA3W9DfOzs7WVVddpQ4dOig5OVnJycnKyckJq5/b7dbLL7+s6dOnKz8/v8bJ/QYOHKi2bdvqyCOP1NChQ/XKK68oLy+vxudW3T2g9N5R3f2ndPuBtKOa7NmzRz/99FON5yxV033oUBON5rZ+/XoVFRWFvUc1atRIHTt2DD1eunSpzEwdOnQIu+/NnTu32vtybfdbvny5TjvttP2qc23b4r7eq0t179497HNF7969lZOTo++//17r169XcXFx2GvR5XKpV69eodfi1Vdfrddff13HHnusbrnlFs2fP3+/nk9Vz6827+fx8fE66qijQo/3da8BAETXAU+k5nA4lJCQcDDrctD1799fL7zwgr7++mvFxMSoS5cu6tevn+bOnaudO3eqX79+obJmFvYmXppX0b6ec1X77IvX693nMWuqW3XnrLif1+sNe1y6X8Vj14bL5QqlS/cv/f785MmT9dhjj+nxxx9X165dlZCQoOuvv36fE88FAgGdc845evjhhyttK/1AXdEVV1yhQYMG6X//+59mzZqlBx98UJMnT9Z1110X2j506FA99thjmjp1qi666KLQB6zWrVtrzZo1+uijj/Txxx9r9OjRmjRpkubOnRv2/CrW8corrwz7zmqpNm3a1Hh9arpm9VK5D3RVysiI2KkrtrOa2kBNr98333xTN9xwgyZPnqzevXsrMTFRkyZN0oIFC/arPrU9f8XXjcPhqPFvPGLECG3dulWPP/642rZtK4/Ho969e1dqK6Uf5Hfs2KEdO3ZUex9KTEzU0qVLNWfOHM2aNUt33323JkyYoEWLFtX4z7SqrmH5vKqee2negbSj2qjpnKUOuTZVg2g0t9q8XwUCATmdTi1ZskROpzNsW02T39Vmv32991VX59q0xd/6+cThcFR7jylfhzPPPFObNm3S//73P3388cc67bTTdM0111SaT6S2avt+XtW95kA+fwAA6sZhvWTYKaecor179+rxxx9Xv3795HA41K9fP82ZM0dz5swJC7q7dOmizz//PGz/+fPnq0OHDpU+MNSkS5cu+uqrr8LyKj6uqFu3bvrhhx+UlZVV7TFrqluXLl1UUlISFkhs375dWVlZ6ty5c7Xn7dy5s0pKSsIm9FqzZs1vXk+8dFTBpZdequ7du+vII48MmwRGCvbelR9BIEnHHXecvv32W7Vr104ZGRlhPzV9gGrdurWuuuoqzZgxQzfddJOef/750LazzjpLCQkJeuaZZ/T+++/r8ssvD9vX6/Xq3HPP1RNPPKE5c+boyy+/1DfffLPPOlasX0ZGRlRmx42oDh2kQYOkiq9/pzOYX4fTKnfp0kXz588P+1A5f/58JSYmqmXLlpKq/nvNmzdPffr00ejRo9WjRw9lZGRU6p2rar8DOf+BmDdvnsaMGaOzzjorNFFixYkD169frxtuuEHPP/+8TjzxRA0bNqzGwDI2NlYDBgzQI488ohUrVmjjxo365JNPqi1f3T2gdJRJ586dq7z/lL+31NSOKnK5XDVe76SkJLVo0WKf5zzcRKO5ZWRkyOVyhb1H7dy5M+y9qEePHvL7/crOzq50zyudmbuqNlSb/bp161bjUnFVHfdgt8Wvv/5a+fn5ocdfffWVfD6fWrVqFbqvl38tFhcXa/HixWGvxSZNmmjEiBF6+eWX9fjjj+u5556r9vlIqvH1f6Dv5wCA+u2wDrqTk5N17LHH6uWXX1b//v0lBQPxpUuXKisrK5QnSTfddJNmz56tiRMnKisrSy+++KL+9re/6eabb96vc1511VVav369brzxRq1Zs0avvvqqpk2bVuM+/fr10ymnnKI//vGP+uijj7Rhwwa9//77odl691W39u3ba/DgwRo1apQ+//xzff3117r00kvVsmVLDR48uNrzduzYUWeccYZGjRqlBQsWaMmSJbriiisOqPehvIyMDH300UeaP3++MjMzdeWVV+rnn38OK9OuXTstWLBAGzdu1LZt2xQIBHTNNddox44duvjii7Vw4UJ99913mjVrli6//PJqP6Rcf/31+vDDD7VhwwYtXbpUn3zySdgHE6fTqREjRuj2229XRkZG2DDKadOm6YUXXtDKlSv13Xff6aWXXpLX61Xbtm1Ddfzss8/0448/hgKhW2+9VV9++aWuueYaLV++XGvXrtU777wT6lk/7Lz2mjRgQHjegAHB/Do0evRoff/997ruuuu0evVqvf3227rnnnt04403hpa9q+o1lZGRocWLF+vDDz9UVlaWxo8fHzbzcOl+K1as0Jo1a7Rt2zYVFxcf0PkPREZGhl566SVlZmZqwYIFGjJkSFj78/v9Gjp0qE4//XRddtllmjp1qlauXFnlDNNS8CsUTzzxhJYvX65NmzbpX//6lwKBQNhw4YpcLpeuu+46LViwQEuXLtVll12mE088Ub169ZIkjRs3TtOmTdOzzz6rtWvX6tFHH9WMGTNC9599taOK2rVrp9mzZ+vnn3/Wzmqm5R43bpwefvhhvfHGG1qzZo1uu+02LV++XGPHjq3VdT1U1XVz8/l8GjlypMaNG6fZs2dr5cqVGjFiRNhrukOHDhoyZIiGDRumGTNmaMOGDVq0aJEefvhhvffee5KCf9OcnBzNnj1b27ZtU15eXq32u/3227Vo0SKNHj1aK1as0OrVq/XMM8+E7rdVtemD3RaLioo0cuRIrVq1Su+//77uueceXXvttYqJiVFCQoKuvvpqjRs3Th988IFWrVqlUaNGKS8vTyNHjpQk3X333Xr77be1bt06ffvtt3r33XerDY7btm0rh8Ohd999V1u3blVOTk6lMgf6fg4AqOfq5qvj0XPTTTeZJFu5cmUor3v37takSZNKM0f/5z//sS5dupjL5bI2bdrYpEmTwrZXNbFWxYlSzMz++9//WkZGhnk8Hjv55JPtn//8Z40TqZkFJ6+57LLLrHHjxhYXF2fHHHOMvfvuu7Wu244dO2zo0KGWnJxsXq/XBg0aFJoh1qzqiW7MgpN8/eEPfzCPx2Nt2rSxf/3rXzVOfFTV8925c6dJsk8//TT0XAYPHmw+n8+aNm1qd911lw0bNixssqo1a9bYiSeeaF6v1yTZhg0bzCw4icz5559vKSkp5vV6rVOnTnb99ddXO8v3tddea0cddZR5PB5r0qSJDR061LZt2xZWZv369SYpNPFNqZkzZ9oJJ5xgSUlJlpCQYCeeeKJ9/PHHoe1ffvmldevWzTwej5VvKgsXLrSBAweaz+ezhIQE69atW9ikT1W9Tspfw+quY72WlWX23nsRmzytvOomRJszZ44df/zx5na7rVmzZnbrrbeGzVRd1WuqoKDARowYYcnJyZaSkmJXX3213XbbbWGTdmVnZ4f+nqWv46r+Pvs6f1X1Hjx4sA0fPrza57p06VLr2bOneTwea9++vf373/8Oe/3ce++91rx587DX9FtvvWVutztUt/LnnTdvnvXr189SU1PN6/Vat27dQjO1V6X0vjB9+nQ78sgjze122+9//3vbuHFjWLmnn37ajjzySHO5XNahQwf717/+Fdq2r3ZUsT288847lpGRYbGxsda2bVszqzyRmt/vt3vvvddatmxpLpfLunfvbu+//35oe23uQ4eyOmxutnfvXrv00kstPj7e0tPT7ZFHHqn0Wi5dBaBdu3bmcrmsWbNmdv7559uKFStCZa666ipr3LixSQrNJF6b/ebMmWN9+vQxj8djKSkpNmjQoNB7ZXXvEwfSFqtSOoni3XffbY0bNzafz2dXXHGFFRQUhMrk5+fbddddZ2lpaebxeKxv3762cOHC0PaJEyda586dzev1WqNGjWzw4MH23XffmVnVr9P77rvPmjVrZg6HI3RvqFjfA3k/nzlzpjWAj3QAcMhymPElIBzevvjiC/Xv318//PBDaAIzAMFe6uuvv/43f6UEOBSNGDFCu3bt0ltvvRXtqgAADnMHPJEaUN8VFhbq+++/1/jx43XhhRcScAMAAACoc4f1d7rRsL322mvq2LGjdu/erUceeSTa1QEAAADQADG8HAAAAACACKGnGwAAAACACCHoBgAAAAAgQgi6AQAAAACIEIJuAAAAAAAiJCpBt7+kIBqnBQAAAACgTtV50J2/9wetnne38vf+UNenRg3atWunxx9/vMYyEyZM0LHHHlsn9QGiaePGjXI4HFq+fHm0q7JP/fv31/XXX3/Qjjdt2jSlpKSEHkez3XPPiY6D/Zo6lIwYMULnnXde6HE0r0VD/jsAwOGmzoPu3b8sk8yv3b8sr+tTowaLFi3Sn//859Bjh8Oht956K6zMzTffrNmzZ9dxzYDaOdAPqBU/ZEtS69attWXLFh1zzDEHp3IRNGPGDE2cODFix6+rds8959BV8R81B0t9CDoj3b4kac6cOXI4HNq1a1ednxsAUDdi6/JkZqY9vwbbe35ZrvSj/iCHw1GXVYgov98vh8OhmJhD76vyTZo02WcZn88nn89XB7UByhQVFcntdtfpOZ1Op5o1a1an5zxQjRo1iujx99XuI/n34Z6Dg+G3vEb31b4i+fqPdNsGANSdOo0OC3J+VHHhLklSceFOFeT8FNHz/ec//1HXrl3l9XrVuHFjDRgwQLm5uZKkQCCg++67T61atZLH49Gxxx6rDz74ILRvVf95Xr58uRwOhzZu3Cip7L/77777rrp06SKPx6NNmzapsLBQt9xyi1q3bi2Px6P27dvrhRdeCB1n1apVOuuss+Tz+ZSenq6hQ4dq27Zt1T6P0vO89dZb6tChg+Li4jRw4EB9//33YeWeeeYZHXXUUXK73erYsaNeeumlsO0TJkxQmzZt5PF41KJFC40ZMya0rfzw8nbt2kmSzj//fDkcjtDjikM993UNS4fozpgxQ6eeeqri4+PVvXt3ffnll9U+V6B///669tprdeONNyotLU0DBw6UJM2dO1e9evWSx+NR8+bNddttt6mkpERSsLd67ty5mjJlihwOR6id+v1+jRw5UkcccYS8Xq86duyoKVOmhM41YcIEvfjii3r77bdD+82ZM6fK4eU1nb+03mPGjNEtt9yiRo0aqVmzZpowYUKNz3XRokUaOHCg0tLSlJycrH79+mnp0qWh7XPmzJHb7da8efNCeZMnT1ZaWpq2bNkSOm/53sCnn35a7du3V1xcnNLT0/V///d/NdZh2rRpatOmjeLj43X++edr+/btYdsrtvvSkQEPPvigWrRooQ4dOkiSfvzxR1100UVKTU1V48aNNXjw4NC9stQ///lPHX300aFreO2110rinhNNubm5GjZsmHw+n5o3b67JkydXKlNUVKRbbrlFLVu2VEJCgk444QTNmTNHUvA1etlll2n37t2hNlT6uq9pv1JffPGF+vXrp/j4eKWmpmrQoEHauXNntW1aql1brOoeUpHf79eNN96olJQUNW7cWLfccovMLKxMxfbVrl073X///RoxYoSSk5M1atQoSdL8+fN1yimnyOv1qnXr1hozZkzo84akaj8XbNy4UaeeeqokKTU1VQ6HQyNGjKjy3Dt37tSwYcOUmpqq+Ph4nXnmmVq7dm1oe+lnhQ8//FCdO3eWz+fTGWecEbpXAACiyCKkIOdn2/Xz0rCf71e+ZCtn32wrZ99oK2ffbN+vfLlSmYKcnw/K+X/66SeLjY21Rx991DZs2GArVqywp556yvbu3WtmZo8++qglJSXZa6+9ZqtXr7ZbbrnFXC6XZWVlmZnZp59+apJs586doWMuW7bMJNmGDRvMzGzq1KnmcrmsT58+9sUXX9jq1astJyfHLrzwQmvdurXNmDHD1q9fbx9//LG9/vrroXqlpaXZ7bffbpmZmbZ06VIbOHCgnXrqqdU+l9Lz9OzZ0+bPn2+LFy+2Xr16WZ8+fUJlZsyYYS6Xy5566ilbs2aNTZ482ZxOp33yySdmZvbvf//bkpKS7L333rNNmzbZggUL7Lnnngvt37ZtW3vsscfMzCw7O9sk2dSpU23Lli2WnZ1tZmb33HOPde/ePbTPvq7hhg0bTJJ16tTJ3n33XVuzZo393//9n7Vt29aKi4sP4K+K3ywQMMvJqfufQKDWVezXr5/5fD4bN26crV692jIzM+2HH36w+Ph4Gz16tGVmZtrMmTMtLS3N7rnnHjMz27Vrl/Xu3dtGjRplW7ZssS1btlhJSYkVFRXZ3XffbQsXLrTvvvvOXn75ZYuPj7c33njDzMz27t1rF154oZ1xxhmh/QoLC0Ov3WXLlpmZ7fP8pfVOSkqyCRMmWFZWlr344ovmcDhs1qxZ1T7X2bNn20svvWSrVq2yVatW2ciRIy09Pd327NkTKjNu3Dhr27at7dq1y5YvX24ej8dmzJgRdt6xY8eamdmiRYvM6XTaq6++ahs3brSlS5falClTqj3/V199ZQ6Hwx588EFbs2aNTZkyxVJSUiw5OTlUpmK7Hz58uPl8Phs6dKitXLnSvvnmG8vNzbX27dvb5ZdfbitWrLBVq1bZJZdcYh07drTCwkIzM3v66actLi7OHn/8cVuzZo0tXLjwsL7nHAJNzczMrr76amvVqpXNmjXLVqxYYWeffbb5fL7Qa8rM7JJLLrE+ffrYZ599ZuvWrbNJkyaZx+OxrKwsKywstMcff9ySkpJCbaj0fbam/cyC76kej8euvvpqW758ua1cudKefPJJ27p1a7VturZtseI9pCoPP/ywJScn23/+859Q+0tMTLTBgweHHav8tWjbtq0lJSXZpEmTbO3atbZ27VpbsWKF+Xw+e+yxxywrK8u++OIL69Gjh40YMSK0X3WfC0pKSmz69OkmydasWWNbtmyxXbt2VXnuc8891zp37myfffaZLV++3AYNGmQZGRlWVFRkZmWfFQYMGGCLFi2yJUuWWOfOne2SSy7ZvxcFAOCgi1jQvWnF1F+D6/372bRi2kE5/5IlS0ySbdy4scrtLVq0sAceeCAs7/jjj7fRo0ebWe2Dbkm2fPnyUJk1a9aYJPvoo4+qPO/48ePt9NNPD8v7/vvvQ2+4VSk9z1dffRXKy8zMNEm2YMECMzPr06ePjRo1Kmy/Cy64wM466ywzM5s8ebJ16NAh9OZcUfmg28xMks2cOTOsTMUPwPu6hqUfgP/xj3+Etn/77bcmqdoPQYiwnBwzqe5/cnJqXcV+/frZscceG5Z3xx13WMeOHS1QLqJ46qmnzOfzmd/vD+1X/gNqdUaPHm1//OMfQ4+HDx8e9iHbzCoF3bU9/0knnRR2nOOPP95uvfXWfdapVElJiSUmJtp///vfUF5hYaH16NHDLrzwQjv66KPtiiuuCNun/POePn26JSUlhQXtNbn44ovtjDPOCMu76KKL9hl0p6enh4JpM7MXXnih0vUpLCw0r9drH374oZkF7xd33nlntXU53O45h0BTs71795rb7Q79U9jMbPv27eb1ekOvqXXr1pnD4bAff/wxbN/TTjvNbr/9djMLvkeVf83Udr+LL77Y+vbtW239qmrTtW2LFe8hVWnevLk99NBDocfFxcXWqlWrfQbd5513Xthxhg4dan/+85/D8ubNm2cxMTGWn5+/z88FVX3eqHjurKwsk2RffPFFaPu2bdvM6/Xam2++aWZlnxXWrVsXKvPUU09Zenr6Pq8FACCyIja8vGXni5TUtPt+7ZPU9Fi17HzhQTl/9+7dddppp6lr16664IIL9Pzzz2vnzp2SpD179uinn35S3759w/bp27evMjMz9+s8brdb3bp1Cz1evny5nE6n+vXrV2X5JUuW6NNPPw19V9Hn86lTp06SpPXr11d7ntjYWPXs2TP0uFOnTkpJSQnVNzMzs8bnc8EFFyg/P19HHnmkRo0apZkzZ4YNx9tf+3MNy1+f5s2bS5Kys7MP+Nw4/JV/rUvB13fv3r3D5oDo27evcnJy9MMPNa+E8Oyzz6pnz55q0qSJfD6fnn/+eW3evHm/6lPb85d/rUvB13tNr/Xs7GxdddVV6tChg5KTk5WcnKycnJyw+rndbr388suaPn268vPza1xlYODAgWrbtq2OPPJIDR06VK+88ory8vL2+bzKq/i4Kl27dg37HuuSJUu0bt06JSYmhu5rjRo1UkFBgdavX6/s7Gz99NNPOu200/Z57Opwzzn41q9fr6KiorC/eaNGjdSxY8fQ46VLl8rM1KFDh7D3rblz59b4nlWb/ZYvX77fr4natsWK95CKdu/erS1btoQ994rvs9WpWGbJkiWaNm1a2PMcNGiQAoGANmzYsM/PBbWRmZmp2NhYnXDCCaG8xo0bq2PHjmGv//j4eB111FGhx/u6BwEA6kbEJlJzxnrV6uih2tWok7ZkTZcFApIClQs6YuRwxKh5xz8qpdnxB21iNafTqY8++kjz58/XrFmz9OSTT+rOO+/UggUL1Lhx4+CpK5zLzEJ5pZOhWbnvdxUXF1c6j9frDTuO1+utsV6BQEDnnHOOHn744UrbSj8cVqeqa1M+r6bn07p1a61Zs0YfffSRPv74Y40ePVqTJk3S3Llz5XK5ajzv/tSp/DlLlT9+6bZAoIrXAiIvPl7KyYnOefdDQkJC2OOqXlelbbOme8abb76pG264QZMnT1bv3r2VmJioSZMmacGCBftVn9qev2JbcjgcNb7WR4wYoa1bt+rxxx9X27Zt5fF41Lt3bxUVFYWVmz9/viRpx44d2rFjR6XrUyoxMVFLly7VnDlzNGvWLN19992aMGGCFi1aVOXs0uXvb/uj4vkDgYB+97vf6ZVXXqlUtkmTJgd1cslD5Z5zKDS12vz9A4GAnE6nlixZIqfTGbatpknuarPfvt4vq6tzbdpidW3kYKjq9X/llVeGzZNSqk2bNlq3bt1vPmd1f6uK16Oqe9CBtnMAwMET0YnUHA6HUlv00lHH3yRHjLOaMk4ddfxNSm3e66DPZO5wONS3b1/de++9WrZsmdxut2bOnKmkpCS1aNFCn3/+eVj5+fPnq3PnzpLKZvMuPwFJbdbs7dq1qwKBgObOnVvl9uOOO07ffvut2rVrp4yMjLCfmj4klJSUaPHixaHHa9as0a5du0K95J07d67x+UjBDzjnnnuunnjiCc2ZM0dffvmlvvnmmyrP53K55Pf7q61Pba4h6iGHQ0pIqPuf39i2u3Tpovnz54d9eJw/f74SExPVsmVLScEe4Yqv2Xnz5qlPnz4aPXq0evTooYyMjEq9c1XtdyDnPxDz5s3TmDFjdNZZZ4UmGKs4qeL69et1ww036Pnnn9eJJ56oYcOG1RhAxsbGasCAAXrkkUe0YsUKbdy4UZ988km1z+urr74Ky6v4uDaOO+44rV27Vk2bNq10X0tOTlZiYqLatWtX4/Jfh9s951BoahkZGXK5XGF/8507dyorKyv0uEePHvL7/crOzq70ty2d4b+qNlSb/bp161bja6Kq4x6stpicnKzmzZuHPfeSkhItWbKk1scoVfq+XvF5ZmRkyO127/NzQemokZpe/126dFFJSUnYPwy3b9+urKysevn6BwCEq5vZyx0xskDlXmJJskCxHI6DX40FCxboL3/5ixYvXqzNmzdrxowZ2rp1a+jNady4cXr44Yf1xhtvaM2aNbrtttu0fPlyjR07VlLww0jr1q01YcIEZWVl6X//+1+Vs7pW1K5dOw0fPlyXX3653nrrLW3YsEFz5szRm2++KUm65pprtGPHDl188cVauHChvvvuO82aNUuXX355jW+4LpdL1113nRYsWKClS5fqsssu04knnqhevXqFns+0adP07LPPau3atXr00Uc1Y8YM3XzzzZKCs5q+8MILWrlypb777ju99NJL8nq9atu2bbXPY/bs2fr5559Dw/Ir2tc1BA6W0aNH6/vvv9d1112n1atX6+2339Y999yjG2+8MdSL2q5dOy1YsEAbN27Utm3bFAgElJGRocWLF+vDDz9UVlaWxo8fr0WLFoUdu127dlqxYoXWrFmjbdu2VTmipTbnPxAZGRl66aWXlJmZqQULFmjIkCFhvX9+v19Dhw7V6aefrssuu0xTp07VypUrq70Xvfvuu3riiSe0fPlybdq0Sf/6178UCATChguXN2bMGH3wwQd65JFHlJWVpb/97W9hs4HX1pAhQ5SWlqbBgwdr3rx52rBhg+bOnauxY8eGhvxOmDBBkydP1hNPPKG1a9dq6dKlevLJJ0PH4J5T93w+n0aOHKlx48Zp9uzZWrlypUaMGBH2mu7QoYOGDBmiYcOGacaMGdqwYYMWLVqkhx9+WO+9956k4N8uJydHs2fP1rZt25SXl1er/W6//XYtWrRIo0eP1ooVK7R69Wo988wzoX88VdWmD2ZbHDt2rB566CHNnDlTq1ev1ujRoyutlV0bt956q7788ktdc801Wr58udauXat33nlH1113Xeh51PS5oG3btnI4HHr33Xe1detW5VQxRKJ9+/YaPHiwRo0apc8//1xff/21Lr30UrVs2VKDBw/e7zoDAOpYXXxxPHvjbFs5+6bQrOUrZ99oKz8pncX8Jtu6cfZBP+eqVats0KBB1qRJE/N4PNahQwd78sknQ9v9fr/de++91rJlS3O5XNa9e3d7//33w47x+eefW9euXS0uLs5OPvlk+/e//11pIrWKk8eYmeXn59sNN9xgzZs3N7fbbRkZGfbPf/4ztD0rK8vOP/98S0lJMa/Xa506dbLrr78+bGKY8krPM336dDvyyCPN7Xbb73//+0qTxD399NN25JFHmsvlsg4dOti//vWv0LaZM2faCSecYElJSZaQkGAnnniiffzxx6HtFSdSe+eddywjI8NiY2Otbdu2ZlZ5UqN9XcOKk1GZme3cudMk2aefflrlcwWqmxBtzpw5dvzxx5vb7bZmzZrZrbfeGjYj9Zo1a+zEE080r9cbaqcFBQU2YsQIS05OtpSUFLv66qvttttuC3sdZ2dn28CBA83n84Vem1W9dvd1/qrqPXjwYBs+fHi1z3Xp0qXWs2dP83g81r59e/v3v/8d1hbvvfdea968uW3bti20z1tvvWVutztUt/LnnTdvnvXr189SU1PN6/Vat27dQjO1V+eFF16wVq1amdfrtXPOOcf++te/7nMitYoTz5mZbdmyxYYNG2ZpaWnm8XjsyCOPtFGjRtnu3btDZZ599lnr2LGjuVwua968uV133XWhbdxzomPv3r126aWXWnx8vKWnp9sjjzxS6bVcugpAu3btzOVyWbNmzez888+3FStWhMpcddVV1rhxY5MUmkm8NvvNmTPH+vTpYx6Px1JSUmzQoEGhCcWqatOl++xvW6xKcXGxjR071pKSkiwlJcVuvPFGGzZs2D4nUiv/Xllq4cKFoftIQkKCdevWLWzSv319LrjvvvusWbNm5nA4QveMiufesWOHDR061JKTk83r9dqgQYNCM8GbVf2ZZObMmVZHH/UAADVwmEX+yz7rFz6qgpwfJUkJKRlKb3+Ofl77jvJ2BYd5xiW20lHH3xDpahyypk2bpuuvv/6A/gMPAAAAAIieiA8vLyrY+WvA7VD6UX9Q2x5XypvYSu16XKWmR/1BkkMFe39QcUHVwwkBAAAAADhURWz28lIOOeRr1ElNjhyk+KQ2ZfmOGDVp+3slpBylrRtmSTq4k6gBAAAAABBtdTK8HAAAAACAhqhuZi8HAAAAAKABIugGAAAAACBCCLoBAAAAAIgQgm4AAAAAACKEoBsAAAAAgAgh6AYAAAAAIEIIugHUK/3799f1118f7WoADRZtEACAg4ugG0C9MmPGDE2cODHa1WiQNm7cKIfDoeXLl0e7KjhETJs2TSkpKQf9uAT+AIDDSWy0KwAA5TVq1CjaVcA+FBUVye12R7sawD7xWgUA1Af0dAOoVyr2cLVr107333+/hg0bJp/Pp7Zt2+rtt9/W1q1bNXjwYPl8PnXt2lWLFy8O7bN9+3ZdfPHFatWqleLj49W1a1e99tprYefZu3evhgwZooSEBDVv3lyPPfZYpXMXFRXplltuUcuWLZWQkKATTjhBc+bMqbH+q1ev1kknnaS4uDh16dJFH3/8sRwOh956661QmR9//FEXXXSRUlNT1bhxYw0ePFgbN24MbQ8EArrvvvvUqlUreTweHXvssfrggw9C20t7pN98802dfPLJ8nq9Ov7445WVlaVFixapZ8+e8vl8OuOMM7R169aw+k2dOlWdO3dWXFycOnXqpKeffjq07YgjjpAk9ejRQw6HQ/3795ckjRgxQuedd54efPBBtWjRQh06dNB9992nrl27Vnr+v/vd73T33XfXeI1Qf+Tm5obaVvPmzTV58uSw7TW1gTlz5uiyyy7T7t275XA45HA4NGHChH3uV+qLL75Qv379FB8fr9TUVA0aNEg7d+7UiBEjNHfuXE2ZMiV03NL2MXfuXPXq1Usej0fNmzfXbbfdppKSktAx+/fvr2uvvVY33nij0tLSNHDgwEhdOgAAas8ANDw5OcGfQKAsr7AwmFdQUHVZv78sr6gomJefv++y+6lfv342duzY0OO2bdtao0aN7Nlnn7WsrCy7+uqrLTEx0c444wx78803bc2aNXbeeedZ586dLfDr8/nhhx9s0qRJtmzZMlu/fr098cQT5nQ67auvvgod94orrrC2bdvaxx9/bN98842df/75lpiYGHbuSy65xPr06WOfffaZrVu3ziZNmmQej8eysrKqrLvf77eOHTvawIEDbfny5TZv3jzr1auXSbKZM2eamVlubq61b9/eLr/8cluxYoWtWrXKLrnkEuvYsaMVFhaamdmjjz5qSUlJ9tprr9nq1avtlltuMZfLFTrvhg0bTJJ16tTJPvjgA1u1apWdeOKJdtxxx1n//v3t888/t6VLl1pGRoZdddVVofo999xz1rx5c5s+fbp99913Nn36dGvUqJFNmzbNzMwWLlxokuzjjz+2LVu22Pbt283MbPjw4ebz+Wzo0KG2cuVK++abb+z777+3mJgYW7hwYej4X3/9tTkcDlu/fv0B/vUPP/W4qZmZ2dVXX22tWrWyWbNm2YoVK+zss882n88Xagc1tYHCwkJ7/PHHLSkpybZs2WJbtmyxvXv37nM/M7Nly5aZx+Oxq6++2pYvX24rV660J5980rZu3Wq7du2y3r1726hRo0LHLSkpsR9++MHi4+Nt9OjRlpmZaTNnzrS0tDS75557Qs+nX79+5vP5bNy4cbZ69WrLzMz8bRcIAICDgKAbaIik4E92dlne/fcH8664IrxsfHwwf8OGsrzHHgvmXXJJeNm0tGD+ypUHXLWqgu5LL7009HjLli0mycaPHx/K+/LLL02SbdmypdrjnnXWWXbTTTeZmdmePXvM5XLZv//979D2Xbt2WXx8fOjc69atM4fDYT/++GPYcU477TS7/fbbqzzH+++/b7GxsWH1+Oijj8KC7hdeeME6duwY+geBmVlhYaF5vV778MMPzcysRYsW9sADD4Qd+/jjj7fRo0ebWVnQ/Y9//CO0/bXXXjNJNnv27FDegw8+aB07dgw9bt26tb366qthx504caL17t077LjLli0LKzN8+HBLT08P/VOg1JlnnmlXX3116PH1119v/fv3r/LaNFT1uKnZ3r17ze122+uvvx7K2759u3m9Xhs7dmyt2sDUqVMtOTk5bHtt9rv44outb9++1dat4n3AzOyOO+6o1Haeeuop8/l85v/1vw/9+vWzY489tnYXAACAOsJ3ugHUe926dQul09PTJSlsaHNpXnZ2tpo1aya/36+HHnpIb7zxhn788UcVFhaqsLBQCQkJkqTvvvtOxcXF6tWrV+gYycnJ6tixY+jx0qVLZWbq0KFDWF0KCwvVuHHjKuu5Zs0atW7dWs2aNQvllT+HJC1ZskTr1q1TYmJiWH5BQYHWr1+vPXv26KefflLfvn3Dtvft21dff/31fl+X7OxsSdLWrVv1/fffa+TIkRo1alSoTElJiZKTk6t8PuV17dq10ndjR40apcsvv1yPPvqonE6nXnnllUrDk1F/rV+/XkVFRerdu3cor1GjRqF2cCBtoLb7LV++XBdccMF+1TczM1O9e/eWw+EI5fXt21c5OTn64Ycf1KZNG0lSz5499+u4AABEGkE30BDl5AR/x8eX5Y0bJ11/vRRb4bbwa9Amr7cs75prpFGjJKczvGzp95LLlz0IXC5XKF36gbuqvEAgIEmaPHmyHnvsMT3++OPq2rWrEhISdP3116uoqEiSZGZh+5UqzS89ltPp1JIlS+Ss8Dx9Pl+V9TSzSsesKBAI6He/+51eeeWVStuaNGlS6TnVdOzaXJfSa1L6+/nnn9cJJ5wQdpyKz68qpf+wKO+cc86Rx+PRzJkz5fF4VFhYqD/+8Y/7PFZDUp+bWvnXe1UOpA3Udj/vAVS8qjZQVVuu6rUKAEA0EXQDDVFVH0rd7uBPbcq6XMGf2pSNgnnz5mnw4MG69NJLJQWDgLVr16pz586SpKOOOkoul0sLFy5U69atJUl79uzR2rVr1a9fP0nBycT8fr+ys7N18skn1+q8nTp10ubNm/XLL7+Eep4XLVoUVua4447TG2+8oaZNmyopKanK47Ro0UKff/65TjnllFDe/PnzK/Wa74/09HS1bNlS3333nYYMGVJlmdKebL/fX6tjxsbGavjw4Zo6dao8Ho/+9Kc/Kb58dIl63dQyMjLkcrn01VdfhXqJd+7cqaysLPXr169WbcDtdld6vdRmv27dumn27Nm69957a33cLl26aPr06WHB9/z585WYmKiWLVvu13MHAKAuMXs5gMNORkaGPvroI82fP1+ZmZm68sor9fPPP4e2JyYmavjw4Ro3bpw+/fRTffvtt7r88ssVExMT+jDfoUMHDRkyRMOGDdOMGTO0YcMGLVq0SA8//LDee++9Ks87cOBAHXXUURo+fLhWrFihL774Qnfeeaeksp64IUOGKC0tTYMHD9a8efO0YcMGzZ07V2PHjtUPP/wgSRo3bpwefvhhvfHGG1qzZo1uu+02LV++XGPHjv1N12XChAl68MEHNWXKFGVlZembb77R1KlT9eijj0qSmjZtKq/Xqw8++EC//PKLdu/evc9jXnHFFfrkk0/0/vvv6/LLL/9N9UPd8vl8GjlypMaNG6fZs2dr5cqVGjFihGJigh8NatMG2rVrp5ycHM2ePVvbtm1TXl5erfa7/fbbtWjRIo0ePVorVqzQ6tWr9cwzz2jbtm2h4y5YsEAbN27Utm3bFAgENHr0aH3//fe67rrrtHr1ar399tu65557dOONN4bqDABAfcS7FIDDzvjx43Xcccdp0KBB6t+/v5o1a6bzzjsvrMyjjz6q3r176+yzz9aAAQPUt2/f0FJapaZOnaphw4bppptuUseOHXXuuedqwYIFod7xipxOp9566y3l5OTo+OOP1xVXXKG77rpLkkLHjY+P12effaY2bdro//2//6fOnTvr8ssvV35+fqjne8yYMbrpppt00003qWvXrvrggw/0zjvvqH379r/pulxxxRX6xz/+oWnTpqlr167q16+fpk2bFloqLDY2Vk888YT+/ve/q0WLFho8ePA+j9m+fXv16dNHHTt2rDRsHfXfpEmTdMopp+jcc8/VgAEDdNJJJ+l3v/tdaPu+2kCfPn101VVX6aKLLlKTJk30yCOP1Gq/Dh06aNasWfr666/Vq1cv9e7dW2+//bZifx1zf/PNN8vpdKpLly5q0qSJNm/erJYtW+q9997TwoUL1b17d1111VUaOXJkqI0BAFBfOWxfX+oCgAYgNzdXLVu21OTJkzVy5MiDdtwvvvhCJ510ktatW6ejjjrqoB23vjAzderUSVdeeaVuvPHGaFcHAACg3uE73QAapGXLlmn16tXq1auXdu/erfvuu0+SatW7W5OZM2fK5/Opffv2WrduncaOHau+ffselgF3dna2XnrpJf3444+67LLLol0dAACAeomgG0CD9de//lVr1qyR2+3W7373O82bN09paWm/6Zh79+7VLbfcou+//15paWkaMGDAYbuMVnp6utLS0vTcc88pNTU12tUBAAColxheDgAAAABAhDCRGgAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDqPf69++v66+/PtrV2C8TJkzQscceu1/7HKznuX37djVt2lQbN278zcc6WA7kelT07rvvqkePHgoEAgenUqgS7W3/HEh7mzZtmlJSUn7zuUtt3LhRDodDy5cvP+BjFBYWqk2bNlqyZMlBqxcAIIigG8BhZ86cOXI4HNq1a1fU6nDzzTdr9uzZ+7XPjBkzNHHixN987gcffFDnnHOO2rVr95uPVZc2b96sc845RwkJCUpLS9OYMWNUVFQU2n722WfL4XDo1VdfjWItURHtbf/b20UXXaSsrKzffO79YWaaMGGCWrRoIa/Xq/79++vbb78Nbfd4PLr55pt166231mm9AKAhIOgGUNkbb0gPPFBzmQceCJZDGDNTSUmJfD6fGjduvF/7NmrUSImJib/p/Pn5+XrhhRd0xRVX/Kbj1DW/368//OEPys3N1eeff67XX39d06dP10033RRW7rLLLtOTTz4ZpVpGBs3twB2q7c3r9app06a/6dz765FHHtGjjz6qv/3tb1q0aJGaNWumgQMHau/evaEyQ4YM0bx585SZmVmndQOAwx1BN4DK1q2T7rpLqq4XaOLE4PZ16w76qXNzczVs2DD5fD41b95ckydPrlTm5ZdfVs+ePZWYmKhmzZrpkksuUXZ2tqTgMMtTTz1VkpSamiqHw6ERI0ZIkj744AOddNJJSklJUePGjXX22Wdr/fr1NdansLBQY8aMUdOmTRUXF6eTTjpJixYtCm0v7eX78MMP1bNnT3k8Hs2bN6/ScNeSkhKNGTMmdO5bb71Vw4cP13nnnRcqU3G4a7t27fSXv/xFl19+uRITE9WmTRs999xzNdb3/fffV2xsrHr37i1JCgQCatWqlZ599tmwckuXLpXD4dB3330nKdjLPHjwYPl8PiUlJenCCy/UL7/8IklavXq14uPjw3qYZ8yYobi4OH3zzTeSpN27d+vPf/6zmjZtqqSkJP3+97/X119/XWNdy5s1a5ZWrVqll19+WT169NCAAQM0efJkPf/889qzZ0+o3LnnnquFCxeG6n04iGJzo70d5PZWvo7/+9//1L17d8XFxemEE04ItRUpfHi5mWnAgAE644wzZGaSpF27dqlNmza68847Q/tMnTpVnTt3VlxcnDp16qSnn366xrqVZ2Z6/PHHdeedd+r//b//p2OOOUYvvvii8vLywtp148aN1adPH7322mu1PjYAYN8IugFUdued0n33SXffXTkSmDgxmH/ffcFyB9m4ceP06aefaubMmZo1a5bmzJlT6TuGRUVFmjhxor7++mu99dZb2rBhQ+iDfuvWrTV9+nRJ0po1a7RlyxZNmTJFUjDAuPHGG7Vo0SLNnj1bMTExOv/882v8jvAtt9yi6dOn68UXX9TSpUuVkZGhQYMGaceOHZXKPfjgg8rMzFS3bt0qHefhhx/WK6+8oqlTp+qLL77Qnj179NZbb+3zekyePFk9e/bUsmXLNHr0aF199dVavXp1teU/++wz9ezZM/Q4JiZGf/rTn/TKK6+ElXv11VfVu3dvHXnkkTIznXfeedqxY4fmzp2rjz76SOvXr9dFF10kSerUqZP++te/avTo0dq0aZN++uknjRo1Sg899JC6du0qM9Mf/vAH/fzzz3rvvfe0ZMkSHXfccTrttNMqXafqfPnllzrmmGPUokWLUN6gQYNUWFgY9vdv27atmjZtqnnz5tXquIeCKDY32lsFv7W9lTdu3Dj99a9/1aJFi9S0aVOde+65Ki4urlTO4XDoxRdf1MKFC/XEE09Ikq666iqlp6drwoQJkqTnn39ed955px544AFlZmbqL3/5i8aPH68XX3xxn89JkjZs2KCff/5Zp59+eijP4/GoX79+mj9/fljZXr16HVbtCwDqBQOA6tx3n5kU/F3V44Ns79695na77fXXXw/lbd++3bxer40dO7ba/RYuXGiSbO/evWZm9umnn5ok27lzZ43ny87ONkn2zTffVLk9JyfHXC6XvfLKK6G8oqIia9GihT3yyCNh53rrrbfC9r3nnnuse/fuocfp6ek2adKk0OOSkhJr06aNDR48OJTXr1+/sOfZtm1bu/TSS0OPA4GANW3a1J555plqn9PgwYPt8ssvD8tbunSpORwO27hxo5mZ+f1+a9mypT311FNmZjZr1ixzOp22efPm0D7ffvutSbKFCxeG8v7whz/YySefbKeddpoNHDjQAoGAmZnNnj3bkpKSrKCgIOy8Rx11lP3973+v8npUNGrUKBs4cGClfLfbba+++mpYXo8ePWzChAnVHutQVcfNjfYWofZWWseqrusbb7xhZmZTp0615OTksP3efPNN83g8dvvtt1t8fLytWbMmtK1169aV2sHEiROtd+/eZma2YcMGk2TLli2rsp5ffPGFSbIff/wxLH/UqFF2+umnh+VNmTLF2rVrV+1zBgDsP3q6AVRv/PiyLjiPp6zLbfz4iJxu/fr1KioqChuq2ahRI3Xs2DGs3LJlyzR48GC1bdtWiYmJ6t+/v6TgEOl9Hf+SSy7RkUceqaSkJB1xxBE17rd+/XoVFxerb9++oTyXy6VevXpV+s5jdb1dUnDo9S+//KJevXqF8pxOp373u9/VWF9JYb14DodDzZo1Cw3trUp+fr7i4uLC8nr06KFOnTqFhozOnTtX2dnZuvDCCyVJmZmZat26tVq3bh3ap0uXLkpJSQl7nv/85z+1YsUKLV26VNOmTZPD4ZAkLVmyRDk5OWrcuLF8Pl/oZ8OGDfscTlxe6fHKM7NK+V6vV3l5ebU+7qGijpsb7a0KB6O9larqutb0XekLLrhA/+///T89+OCDmjx5sjp06CBJ2rp1q77//nuNHDkyrH3df//9+9W+Sp9TeQ2pfQFANMVGuwIA6rnx46X775eKiiS3O3IRgBT6PmNNcnNzdfrpp+v000/Xyy+/rCZNmmjz5s0aNGhQ2EzXVTnnnHPUunVrPf/882rRooUCgYCOOeaYavcrrU9tPqgmJCTss+5VHWdfXC5XpWPUNDw3LS1NO3furJQ/ZMgQvfrqq7rtttv06quvatCgQUpLSwvVozYB79dff63c3FzFxMTo559/Dg0FDwQCat68uebMmVPpGLVdFqlZs2ZasGBBWN7OnTtVXFys9PT0sPwdO3aoSZMmtTruoaYOmxvtrQoHq73Vtk7l5eXlacmSJXI6nVq7dm0ov/T8zz//vE444YSwfZxOZ63O26xZM0nSzz//rObNm4fys7OzG1T7AoBooacbQM0mTiyLAIqKqp/t6SDIyMiQy+XSV199FcrbuXNn2NI6q1ev1rZt2/TQQw/p5JNPVqdOnSr1RLndbknBGbFLbd++XZmZmbrrrrt02mmnqXPnzvv8sJyRkSG3263PP/88lFdcXKzFixerc+fOtX5eycnJSk9P18KFC0N5fr9fy5Ytq/UxaqtHjx5atWpVpfxLLrlE33zzjZYsWaL//Oc/GjJkSGhbly5dtHnzZn3//fehvFWrVmn37t2h57ljxw6NGDFCd955py677DINGTJE+fn5kqTjjjtOP//8s2JjY5WRkRH2UxrY70vv3r21cuVKbdmyJZQ3a9YseTyesB7KgoICrV+/Xj169Ni/C3OIqMPmRns7CKprb5KqvK6dOnWq9lg33XSTYmJi9P777+uJJ57QJ598IklKT09Xy5Yt9d1331VqX6WjB/bliCOOULNmzfTRRx+F8oqKijR37lz16dMnrOzKlSsP2/YFANFC0A2geuVncSosrH62p4PE5/Np5MiRGjdunGbPnq2VK1dqxIgRiokpu1W1adNGbrdbTz75pL777ju98847ldbabdu2rRwOh959911t3bpVOTk5Sk1NVePGjfXcc89p3bp1+uSTT3TjjTfWWJ+EhARdffXVGjdunD744AOtWrVKo0aNUl5enkaOHLlfz+26667Tgw8+qLfffltr1qzR2LFjtXPnzhp7vg7EoEGD9O2331YKcI444gj16dNHI0eOVElJiQYPHhzaNmDAAHXr1k1DhgzR0qVLtXDhQg0bNkz9+vULDeO96qqr1Lp1a91111169NFHZWa6+eabQ/v37t1b5513nj788ENt3LhR8+fP11133aXFixfXqt6nn366unTpoqFDh2rZsmWaPXu2br75Zo0aNUpJSUmhcl999ZU8Hk/Y0N3DRR03N9rbQVBde5Ok++67L+y6pqWlhc2eXt7//vc//fOf/9Qrr7yigQMH6rbbbtPw4cNDx50wYYIefPBBTZkyRVlZWfrmm280depUPfroo7Wqp8Ph0PXXX6+//OUvmjlzZqhO8fHxuuSSS8LKzps3L2zCNQDAQRCVb5IDqP+qm8WpDiZTu/TSSy0+Pt7S09PtkUceqTTh0auvvmrt2rUzj8djvXv3tnfeeafSJEL33XefNWvWzBwOhw0fPtzMzD766CPr3LmzeTwe69atm82ZM8ck2cyZM6utT35+vl133XWWlpZmHo/H+vbtGza5WHWTSFWc2Km4uNiuvfZaS0pKstTUVLv11lvtggsusD/96U+hMlVN7PTYY4+FHbd79+52zz331HgNTzzxRHv22Wcr5T/11FMmyYYNG1Zp26ZNm+zcc8+1hIQES0xMtAsuuMB+/vlnMzN78cUXLSEhwbKyskLlFy9ebG632/73v/+ZmdmePXvsuuuusxYtWpjL5bLWrVvbkCFDQpOz7WsitdI6/OEPfzCv12uNGjWya6+9ttLkbH/+85/tyiuvrPE4h6IoNTfaWwTaW2kd//vf/9rRRx9tbrfbjj/+eFu+fHmoTPmJ1LKzsy09Pd3+8pe/hNW/V69eduGFF4byXnnlFTv22GPN7XZbamqqnXLKKTZjxgwz2/dEambBieHuuecea9asmXk8HjvllFMqTWo3f/58S0lJsby8vBqfMwBg/zjMavElJwANywMPBBcGrm4Wp9Iuufvvj8w6Rg1AIBBQ586ddeGFF1bqOfyt3nvvPd18881auXJlWK/loW7r1q3q1KmTFi9eXOthtYcCmlvk1WV7mzNnjk499VTt3Lmz1nMa1BcXXHCBevTooTvuuCPaVQGAwwoTqQGoLCOj5k/448dLMTHBcqiVTZs2adasWerXr58KCwv1t7/9TRs2bKg0tPNgOOuss7R27Vr9+OOPYTOSH+o2bNigp59++rAKuCWaWyTQ3vZfYWGhunfvrhtuuCHaVQGAww493QBQB77//nv96U9/0sqVK2VmOuaYY/TQQw/plFNOiXbVgMNONNvbodzTDQCIDIJuAAAAAAAi5PD5sh8AAAAAAPUMQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0A0AV+vfvr+uvvz7q545mPer6/FOmTNERRxyh+Ph4nXfeedq9e3ednBcAACCSYqNdAQA4WPr3769jjz1Wjz/++G/eb8aMGXK5XAe3ggegrupR3bWrq/Pfcccd+ve//60XX3xRPp9P559/vu699149+uijET83AABAJNHTDaDeKyoqqvNzNmrUSImJiXV+3v2tR6SvTV1ch0WLFunhhx/WG2+8oVNOOUXHHXecrrzySr377rsRPS8AAEBdIOgGGqLc3Op/CgpqXzY/f99lD0D//v117bXX6sYbb1RaWpoGDhyowsJCjRkzRk2bNlVcXJxOOukkLVq0KLTPiBEjNHfuXE2ZMkUOh0MOh0MbN27UBx98oJNOOkkpKSlq3Lixzj77bK1fv36f+1UcVr2v8/fv319jxozRLbfcokaNGqlZs2aaMGHCPp9rbm6uhg0bJp/Pp+bNm2vy5MmVrkX5elR1bSTJzPTII4/oyCOPlNfrVffu3fWf//wn7FiBQEAPP/ywMjIy5PF41KZNGz3wwAPVXoOqzh+J6/DXv/5Vv//973XccceF8po0aaJt27bt8/oBAADUdwTdQEPk81X/88c/hpdt2rT6smeeGV62XbvKZQ7Qiy++qNjYWH3xxRf6+9//rltuuUXTp0/Xiy++qKVLlyojI0ODBg3Sjh07JAW/D9y7d2+NGjVKW7Zs0ZYtW9S6dWvl5ubqxhtv1KJFizR79mzFxMTo/PPPVyAQqHG/ivZ1/tI6JyQkaMGCBXrkkUd033336aOPPqrxeY4bN06ffvqpZs6cqVmzZmnOnDlasmTJfl0bSbrrrrs0depUPfPMM/r22291ww036NJLL9XcuXND+91+++16+OGHNX78eK1atUqvvvqq0tPTa30NInEdCgsL9d///lfnn39+WH5+fr6Sk5NrvA4AAACHBAPQ8EjV/5x1VnjZ+Pjqy/brF142La1ymQPQr18/O/bYY0OPc3JyzOVy2SuvvBLKKyoqshYtWtgjjzwStt/YsWNrPHZ2drZJsm+++abG/crn1eb8/fr1s5NOOinsGMcff7zdeuut1dZl79695na77fXXXw/lbd++3bxeb+jcFetW8dqU1i8uLs7mz58flj9y5Ei7+OKLzcxsz5495vF47Pnnn6+yLtVdu0hfh/nz55ski4uLs4SEhNCP2+22QYMGVbkPAADAoYSJ1ICGKCen+m1OZ/jj7Ozqy8ZUGCzz65Dkg6Fnz56h9Pr161VcXKy+ffuG8lwul3r16qXMzMwaj7N+/XqNHz9eX331lbZt2xbq4d68ebOOOeaYWtWltufv1q1b2H7NmzdXdg3Xb/369SoqKlLv3r1DeY0aNVLHjh1rrE/5ayNJq1atUkFBQWioeamioiL16NFDkpSZmanCwkKddtppNR67JpG4DllZWYqLi9M333wTln/uueeGnQcAAOBQRdANNEQJCdEvu89DlR3LzCRJDocjrIyZVcqr6JxzzlHr1q31/PPPq0WLFgoEAjrmmGP2awKy2p6/4izfDocjFOTXdNz9lVDhOpee43//+59atmwZts3j8UiSvF7vAZ2rvEhchz179qhp06bKyMgI5W3evFmrV6/WHyt+1QEAAOAQxHe6AdR7GRkZcrvd+vzzz0N5xcXFWrx4sTp37hzKc7vd8vv9ocfbt29XZmam7rrrLp122mnq3Lmzdu7cWen4Ffc70PMfyPNyuVz66quvQnk7d+5UVlbWfh2nS5cu8ng82rx5szIyMsJ+Sr+b3b59e3m9Xs2ePbvKY+zrGpTW92Bfh7S0NO3ZsyfsHxAPPPCAzjrrLHXp0uWAjgkAAFCf0NMNoN5LSEjQ1VdfrXHjxqlRo0Zq06aNHnnkEeXl5WnkyJGhcu3atdOCBQu0ceNG+Xw+NWrUSI0bN9Zzzz2n5s2ba/PmzbrtttsqHb+q/Q7k/PvL5/Np5MiRGjdunBo3bqz09HTdeeediqk4bH8fEhMTdfPNN+uGG25QIBDQSSedpD179mj+/Pny+XwaPny44uLidOutt+qWW26R2+1W3759tXXrVn377bcaOXJkldegYj0icR1+//vfq6CgQA899JAuvvhivfrqq3rnnXe0cOHCAzoeAABAfUPQDeCQ8NBDDykQCGjo0KHau3evevbsqQ8//FCpqamhMjfffLOGDx+uLl26KD8/Xxs2bNDrr7+uMWPG6JhjjlHHjh31xBNPqH///mHHrmq/Azn/gZg0aZJycnJ07rnnKjExUTfddJN2796938eZOHGimjZtqgcffFDfffedUlJSdNxxx+mOO+4IlRk/frxiY2N1991366efflLz5s111VVXSar6GrRr167SeQ72dUhPT9e0adM0btw4TZw4Ub///e/1+eefVzt7OgAAwKHGYQf6pUIAAAAAAFAjvtMNAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAABAhBN0AAAAAAEQIQTcAAAAAABFC0A0AAAAAQIQQdAMAAAAAECEE3QAAAAAARAhBNwAAAAAAEULQDQAAAABAhBB0AwAAAAAQIQTdAAAAAABECEE3AAAAAAARQtANAAAAAECEEHQDAAAAwGHsjTekBx6oucwDDwTL4eAj6AYAAACAw9i6ddJdd0kTJ1a9feLE4PZ16+q2Xg1FbLQrAAAAAACInDvvlAIB6e67g4/Hjy/bNnFiMP+++4LlcPARdAMAAADAYa400C4feJcPuMsH4ji4HGZm0a4EAAAAACAyLOBXSdFeFRft0UOPJGniAylyu6WiIgLuukDQDQAAAACHkfJBdknhbpUU5UoKhn2OGKeatTteRUUOud1SYWF069oQMJEaAAAAABzCLOAPe5yzY432bs9Uwd4fVVKUI8nkdHrkiW+iyU8eraIih5zOYE93dZOr4eDhO90AAAAAcAip2JPtL85VcrOeiokJhnex7kQFSgoU60lSrCdZse5EOWPjNHGi9MCDwWP4/dKtt1Y9uRoOLoJuAAAAAKjnSopzVZS/vdJw8dD2or1yx6VKkuISW8qb1Dpse+mkabffLj34a+B9661SQgKBd6QRdAMAAABAPVLak+10eRXj9EiS/EU5Ktj7Y6iM0+mp1JNdyuEI/xbxAw+UzVJ+113SLbcE85OTw2c1j4lh2bBIIOgGAAAAgCiqbuKz+OR2ivM1lyTFepLliW9SZZC9LxkZ0v33lwXUKSnh28ePDwbcGRkH5/kgHLOXAwAAAEAU+EsKlLtzbZXDxZ1Ojzy+ForzNTuo5zSTSkqC6dhYyeE4qIdHFejpBgAAAIAIKt+THRPjDgXSMTEu+YuDAXdNw8UPpl27pEaNgukdO6TU1IicBuUQdAMAAADAQVTTOtmxroRQ0O2IcSqhUUc5Y70RC7IrKi6uOo3IIegGAAAAgN/ALBA2ednu7K8V8BeGlSnfk11e6YzjdSUpSbrkkrI0Io/vdAMAAADAfqjYkx3wFyml2e9C23N2rJW/aG+dDBdH/UdPNwAAAADsQ0nRXhUV7Kx2nWx/cb6cLq8kKSH1qErLdqHhIugGAAAAgHJKe7Jj3YlyxDglSUUFOw94nez6pKhI+stfguk77pDc7ujWpyFgeDkAAACABq3icHF/ca7MTImNO8n163euiwv3qCgv+5AfLp6dLaWnB9O//CI1bRrd+jQE9HQDAAAAaJBKivYqb/fGUJBdntPpkVkg9NjlSZLLc+jPPBYbW3UakcNlBgAAAHBYK9+THevyye0NLlTtcDhVUpQjqebh4oeTlBTphx/K0og8hpcDAAAAOKxUN1xcktzexvI16hAqW5i39bAOshF9BN0AAAAADmlmJofDEUwH/Nr18+KwoeFSWU+2Ky5Vbm/jaFQTDRTDywEAAAAcUir2ZDscMUpMO1qS5IhxyumKl/mLG8Rw8f21a5eUGpwbTjt3MsS8LhB0AwAAAKj3igv3qLhwV6Xh4pLkcDhkAX9oeS9f486KiSHUqUpRUdVpRA6vRAAAAAD1igX8KinOkcuTHMorzN2iovwdoccVJz4rDbglEXDXIDFRGjCgLI3I4zvdAAAAAKKquonPktN7hIaFF+ZtVUnhboaL45BD0A0AAAAgKooKdqpg7w8qKcqVVHmd7PjUjMNibWw0bIy7AAAAABBR5Xuy3XGNFOv2/brBGtw62dFWVCRNmRJMjx0rud3RrU9DQE83AAAAgIOq4nDx8j3Z3sRW8ia1liQFAiUqLthJkF2HsrOl9PRg+pdfpKZNo1ufhoCebgAAAAAHjb8kX7t/+VpVDReP9SSV9XIrOOGZJ75JHdewYYuNrTqNyOEyAwAAANgvFXuynbHxSkg9SpIU44xTTIxTDoeT4eL1UEqKlJVVlkbkMbwcAAAAwD4VF+yqcri4JMU4PUppdlzoccBfrBinKwq1BOoferoBAAAAhLGAX/6SPMW6yxZyztuzSf7ivNDjihOflUfADZQh6AYAAAAauKomPnM4HEppfrwcjhhJktvbWAFXAsPFD3G7d5cNK9+1S0pOjmZtGgaCbgAAAKCBKszbqsLcn6tcJzsmxq2Av1DOWK+k4KzjOPQVFladRuQQdAMAAACHufI92XEJ6Ypxen7NL2ad7AYmMVHq3r0sjchjIjUAAADgMFNxuLi/OFelH/sTUjNCy3T5SwpUUrSXIBuIIHq6AQAAgMNIccEu5exYrYp9a6U92aW93JLkjI0j2AYijKAbAAAAOMSE92TvkSsuVd7ElpIkpytBZsZwcVSpuFh67rlg+s9/llxMNB9xDC8HAAAA6jmzgEoK91Q5XFySXJ5kJaZ1CT0O+AvDerSBUlu3Sk2bBtPZ2VKTJtGtT0NATzcAAABQz1jAH5w53BUfysvZkSUzf+hxeE92Utj+BNyoTkxM1WlEDkE3AAAAEGVVrZPtjPUoOb2HJMnhiJHb21iSMVwcv0lKirRsWVkakcfwcgAAACBKCnK2qCh/W6Xh4lKwJzuxaTfFxNBPBhzKaMEAAABAhJX2ZJcU7VFcYis5HMFxvf6SfNbJBg5zBN0AAADAQVbTOtmuuFTFuhMlSZ74Jop1JxJko87s2SMlJwfTu3dLSUk1l8dvR9ANAAAAHESFudnK3fWdpKrXyZbDGcorDbiBulJQEJ4m6I48gm4AAABgP1XsyY7ztfh1ojPJ6fJKYp1s1E8+n9SiRVkakcdEagAAAMA+VFwnu6QoV+V7sj0J6UpIOTJUNuAvIsgGIImebgAAAKASC/gVCJTIGRtc7zrgL9be7ZlhZapbJ9vhiCHgBhBC0A0AAIAGr6p1sl1xKUps3EmS5Iz1yOVJVozTzXBxHNKKi6VXXgmmhwyRXK7o1qchYHg5AAAAGqz8vT+ouGBnpeHikuR0xSu5affoVAyIkK1bpaZNg+nsbKlJk+jWpyGgpxsAAACHvdKebH9JnuJ8LUL5JYV7WCcbDUpMTNVpRA5BNwAAAA47VQ0XL+3JdnubKMYZHFPrSWgm969rZRNkoyFISZE+/7wsjchjeDkAAAAOK/l7f1D+nh9U3TrZcYmtQxOkAUCk0dMNAACAQ07Fnuz4lCMV60qQJMU4PWKdbAD1BT3dAAAAqPcqBtn+4lyV/xgbn9xOcb7mkqRAoEQWKCHIBqqwZ4+UnBxM794tJSXVXB6/HT3dAAAAqHcs4JdZIPTd65LinBrXyXZ5kkP5MTGxUgwfc4GqFBSEpwm6I4+7EQAAAKKuqp5sT0K64pOPkKRfh4d7Fev2MVwc+A0SEqTY2LI0Io/h5QAAAIgKs4Dy9/5Q5XBxSXLFpSqxcaco1Q4ADg56ugEAABBxpT3ZgUCxPPFNJEkOR4yK8rYp4C+UxDrZAA5PBN0AAAA46KpbJzsmJlZub5ocDockyZvYUnLEEGQDdaSkRJo5M5g+//yyoeaIHIaXAwAA4KDK3fWdivKyKw0XL+3J9ia3C052BqDObdsmNQkONtHWrVJaWnTr0xBwtwMAAMB+C+/J3iNf406hQDomJlZmrJMNABJBNwAAAGphX+tklxTtlTsuVZLkSWgmd3xTgmygHkpNlWbNKksj8hheDgAAgEos4JckOWKckqTC3F+Uu+u7sDLh62SnhNbUBgCUoacbAAAAVU58Fp/cTnG+ZpKkWE8Sw8UB4ADQ0w0AANBABfzFKsjdEja7eHmehHQlpBwZncoBiIi9e6WkpGB6zx4pMTG69WkI6OkGAABoAEp7siWT69fvXjscMSrY+5NKg216soHDX35+eJqgO/IIugEAAA5D1U18Fuv2lQXdMU55k1opxukhyAYaiPj4qtOIHIaXAwAAHGZytq9WUcEuVRwuHuzJTlZC6lFRqRcANET0dAMAAByCyvdk+4vzlNi4U9lGR4wk1skGgPqAnm4AAIBDQFWzi5fvyU5ueqycLq8kyV+cLzkcBNkAKikpkT78MJgeNEiKpRs24gi6AQAA6iEL+CWHQw5HjCQpb/cmFeT8FFYmbJ3suFTFxPDpGUDNtm2TmjQJprduldLSolufhoA7MwAAQD1Q1cRnvkad5IpLkfTrOtn52xkuDgCHGIJuAACAKPGXFKow7xeVFO6RvzhHFQcglhTnhoJud1yq3M1So1BLAIeTlBRp5syyNCKP4eUAAAB1oLQn2xHjVKw7uDBuSXGe9mR/HSrDxGcAcPihpxsAACACqpv4zO1Nk69RMOh2xnrlSUhXrDuRIBsADlME3QCAg+uNN6R166Q776y+zAMPSBkZ0kUX1V29gDpiFtDebatUUpSjqtbJjnG6Q48dDocSUo6s4xoCaMhycqTE4P/9tHev5PNFtz4NAUE3AODgWrdOuusuKRCQxo+vvH3iROnuu6X776/7ugEHUfmebAuUhIJnhyNGsoBYJxtAfZSXF54m6I48gm4AwMF1553BgPvuu4OPywfepQH3fffV3BMO1EM1rZPtcDgUn9RWjhinJCk+5Ug5YmIJsgHUO15v1WlEDkE3AODgKw20SwPvG2+UHnoo2Lt9333B7UVFUnGxFBsreTxl++bmBn97vVJMcH1iFRcHyzudUlzcgZXNy5PMgnnOYGCkkhKpsDC4b/lPHvtTNj8/+E8Gjyf4XCTJ75cKCvavrMMhxceXlS0oCG5zuyWXa//LBgLB80lSQkJZ2cLC4HNxuYLl97esWVk3SXx8sC5S2d9zf8rW5m9/MF4nVf09a1HWHAoG0b/+7XN3rlORo1wXUX6hnDEuxSalKTa+UTDv17KxMU7JXe64pX/78tcBAKIgMTF4m0PdiYl2BQAAh6nx44MB9t13B8eu3X+/dOutZQH5pEnB/GuvDd+vadNg/ubNZXlPPRXMGzkyvGy7dsH8zMyyvGnTgnl/+lN42S5dgvlLl5blvfFGMO/cc8PLHn98MH/evLK8d98N5g0YEF72lFOC+R9+WJb3ySfBvN69w8ueeWYwv3StFkn66qtgXvfu4WX/+Mdg/iuvlOV9800wr3378LJDhwbzn3uuLG/9+mBey5bhZa+8Mpg/ZUpZ3pYtwbyKa8fceGMw/y9/KcvbvTuY5/MFA8xSd94ZzCs/gqGkpKzs7t1l+X/5SzDvxhvDz5eSEszfsqUsb8qUYN6VV4aXbdkymL9+fVnec88F84YODS/bvn0w/5tvyvJeeSWY98c/hrIs4Jd16yr5fMp9/xXt+nmR/CUFwb+Xz6f4i6+W0+mRJ76JElIzlPrHsUo+6iQlLNssT3yTYID+4YfB455ySngdBgwI5p98Mp92AaCBIegGAEROxaDqppuiUw+gGmZ+5e3ZrD1bv9HOLYsU8BdJkvwl+TKzXydDC3I43UpudpwSUjOCQfb+9FiXlAT/CXPzzcFRCQAQJX6/NGdO8IfbUd1gnW4AQOTcc0+wtzsmJji09t57y4acM7w8vCzDyyM+vNzcLpX48xQTGyenOaWiIhUV7VZO3qZQUWdhILh8V3JTxXpTgt/J3tfffn9eJ+WvLwBEwbZtUpMmwfTWrVJaWnTr0xAQdAMAIqP8pGnjx1d+DERYdROfeRNbyZvUWpIU8Bcrf88mZhcH0GBs3Rr8JpckZWeXBeCIHCZSAwAcfFUF2BUnVyPwRoQE/EXK2bFG/uJcVexbcDo9cjicoccxTpcSUjMiXym/v2yOgJNPLusZB4A6lpoqvfxyWRqRR9ANADi4Hnig+h7t8oF3TAzLhuE3Kd+T7XA45U0MThrniHGFvpNdb9bJLiiQTj01mM7JYZg5gKiJjZWGDIl2LRoWgm4AwMGVkRGcqfzOO4Pfu/X5gvmlgcb48cGAO6MOehdxWKlpnWyn01MWdDsc8jXqqBinp/4MF3c4gjPol6YBAA0G3+kGAEROVUE3UEtmATkcZQut7M7+Wv7ivLAy5Xuy3d60/ZtRHAAaoNzcstUkf/yRt+a6QE83ACBy4uODs7SUpoEaVOzJ9pfkK6VZz1DgHetOlAL++jFcHAAOUbm50u7dZWmC7sgj6AYARI7DwbSoqFFJUY6KCnZUGi5eyl+cGwy2JcUnt5MjJaaKowAAaqv8iopx/N+yTjC8HAAA1InSnmynK0ExzuB64gV7f1LennLrZNeXic8Otvx86dxzg+l33glfwxsAcFgj6AYARE5RkTRpUjA9bpzkdke3PqhT1U18lpDaXp74NElSSXGuCnO2HH5BdkXMbwAADRZBNwAgcgg0GqSS4jzl7Vpf5XBxp9OjuMRW8iQ0jU7loqWkRHrjjWD6oouCa/YAQBT4/dLSpcH0ccdJTmd069MQcMcHAERObKx0xRVlaRxWyvdkO2O98sQHv78fExOrkqIcSYfxcPH9xcK4AOqJXbukXr2C6W3bpMaNo1qdBoGebgAAUCs1rZPt8qQoMa1zqGxR/nY5XQkNN8gGgHpq61ap6a+DjbKzme+0LtDtAAAAqmRmoXWvzUy7flkiC/jDypT2ZLs8KWH5bi9dJ2EYzwmgnkhJkZ57riyNyKOnGwAASKrck20WUHLT7qHte7dlKlCSz3DxA8H8BgDQYNHTDQCInNzc8DFsBBr1TnHhHhUX7lJJ4W75i3NV8X/xAX9xaHkvX6MOcsTQQ3tAHA6pbduyNACgwSDoBgBEVl5etGuAX5X2ZMd6kuRwxEiSivK2qjAvO1Sm4sRnpQG3JALu3yI+Xtq4Mdq1AADl5Undfx3E9PXXwdsTIougGwAQOV6vtGFDWRp1quJw8dKe7KQmXRXrDg51dsWlSjKGiwNAA5GTI61bV5Ym6I48gm4AQOTExEjt2kW7Fg1OceFu5e/ZXO062YFAceix29tIbm+jOq4hACBaPJ6q04gcgm4AAA5R5XuyXZ5kuTzJv25xsE52fVNQIP3pT8H0669LcfwdAERHcrLEVNp1i9nLAQCRU1wsPfVUMH3NNZLLVXN51KimdbLjEpopPuWIYDkLqCh/O0F2fcLs5QDQYBF0AwAih0DjoAn4i7X7lyWVZhcPrZMdxzDxeq24WJo2LZgeMYJ/QAGImkBAyswMpjt3Dn4TDJHF8HIAQOQ4ndIll5SlUaOKPdkxTrd8jTpKkmKcLsXEuCWJ4eKHIpdLGjUq2rUAAO3cKR1zTDC9bZvUuHF069MQ0NMNAEAUFRfuVnHh7krDxaXgEl0pzY6X49d1nQOBEsXE8P9yAMCBy86W0tOD6V9+kZo2jW59GgLeuQEAqCMW8KukOFcuT1Ior2DvDyou3BN6XHHis9KAWxIB96GM8ZwA6onUVOmxx8rSiDx6ugEAiJDqJj5LadZTMc7gd3oLcn6WvziH4eKHO+Y3AIAGi3+ZAwAiJze3bJ3ujRsbTKBRlL9dBTk/Vb9Otr8wFHTH+ZpFoYaIirS0aNcAABAFBN0AgMjati3aNYiY8j3ZHm8TOV3ecvmsk41yEhKkrVujXQsAUH6+NGBAMP3xx5LXG936NAQE3QCAyPF6pZUry9KHuJrWyY6JcYWCbldcihJSMwiyAQD1zt690vz5ZenD4O253iPoBgBETkyMdPTR0a7FQVFStFd7tn6rqoaLx3qS5HTFh/JinG554pvUcQ0BANg3t7vqNCKHoBsAgF9V7MmOdScpPrmtJMkZGy+HQ4qJYbg4DkBBgTRyZDD9wgtSHK8bANGRkiIxlXbdYvZyAEDkFBdL06YF0yNGSC5XNGtTiZmppHB3lcPFJSnWlaCkpt1CjwP+IsU46RbAAWD2cgBosAi6AQCRU88CDQv45fcXKNZVVo9dPy9RwF8UeszEZ4iI4mLpqaeC6WuuqXf/gALQcAQC0ubNwXSbNsFvgiGyCLoBAJFTUCD96U/B9Ouv1/mQ2orDxf3FuXI4YpXSvGeoTN7uTbJAMUE2AKBB2LFDatw4mN6+XWrUKLr1aQj4TjcAIHLi4qS33qrz0xbm/qLCvGz5i3NV8X/LMTExYcPES7+zDQBAQ1BSUnUakUPQDQA4ZJXvyY7ztVBMTPBtze8vZJ1s1C+M5wRQT6SkSPffX5ZG5DG8HABwyKhquHjp25ivcSe541IlSSXFefIX5xJko/6oZ/MbAADqDj3dAIDIycuTunQJpletkuLjay5fg6L87crZsVbVrZMdE1M2MVWsK16xrgM/FxARv+H1DwA4dBF0AwAix0zatKksva/iFXqyPfFN5UlIlxRcJ1syhovj0JSQEOztBoAoi/Icpw0Sw8sBAJHj90tLlwbTxx0nOZ1hm80CKincU+VwcUlyexvL16hD2eFKCgiyAQD4DbKzpfTg/7P1yy9S06bRrU9DQE83ACBynE7p+ONDDy3gVyBQHAqczQLau321yg8ZD+/JTgo/HAE3AAC/ictVdRqRQ083ACBiqpr4zOnyKanJMaEyOTvWyOFwMlwch7fCQunaa4Ppv/1N8niiWx8AQJ0h6AYAHHT5e39UccEOleTtlvudjyVJReeeJsXGyhkbp6Sm3eVwsGQSGhBmLweABovh5QCAA1bak11SnCNvYqtQvr84J7hOdlGRfGPulSQV/mmEYlOb0ZONhsnlKlsYl/GcAKLITNq2LZhOS5McjujWpyGgpxsAUGsVh4uXFOWq9PvYyenHyhnrlSQVF+5WwF+kWH+snP/vwuDO77wjeb1RqjkAAJCkHTukxo2D6e3bpUaNolufhoCebgBArRTkbFHe7k2qbp3s8lye5LIHH31UB7UDAAC1UVJSdRqRQ9ANAAip2JPtTWoTCqBjnB6xTjZwgBjPCaCeSEmR7rijLI3IY3g5ADRgNQ0XlyRvYit5k1qHypZf7gvAfmAiNQBosOjpBoAGxAJ+mfkV43RLkvz+Au3dnhlWpnxPtqvcsHFHjFPOGOf+nTAvr2yd7kWLpPj431R/AACAQw1BNwAcxqrqyfbEpykhNUOS5IyNV6wrQU5XfGSGi5tJq1aVpYGGKiGBNgCgXigslK69Npj+298kjye69WkIGF4OAIehvD2bqxwuLkmx7kQlNTmmbiri90vz5gXTJ58sOfezpxwAABxU2dlSenow/csvUtOm0a1PQ0BPNwAcwkp7sgP+QnkS0kP5xQU75S/Ok6ToTnzmdEr9+9fd+QAAQI1crqrTiByCbgA4hFQcLu4vzpWZyeGIkTu+iRyOGElSnK+lJGN2caC+KCyUbr01mH74YcZzAoialJTgfI4SU63UFYaXA8AhIm/3JhXmblHF23ZpT7Y3qa1inPXsX9YlJdK77wbTZ58txfK/XjRQzF4OAA0Wn34AoB4J78neo4TU9nLGBnvEYpwumR1i62QXFkrnnx9M5+QQdKPhcrnKFsZlPCeAKDILLi4iBXu6HY7o1qchoKcbAKKouuHipRJSM+SJbyJJCviLZeav30F2Rfn50oABwfTHH0teb3TrAwBAA7dzp9SoUTC9Y4eUmhrd+jQEdDkAQB2ygF8mU0xM8PZbXLhLOTuywsqEr5OdHMoPDh0/xHrIvF7piy+iXYuGY9s26bvvgmujl3ZdvPSS9NlnwaH+aWllP02aBH//7nd8vxgAGpDi4qrTiByCbgCIoKrWyfYmtZI3sZUkKdaddGgNF0f9smlTcEm20p/MzGB++e8Mz5kj/fOf1R/jxx+lFi2C6W+/Df7u3FmKiYlYtRskxnMCqCeSk6UrrihLI/IYXg4AB5kF/MrP+bHK4eKS5PamydeofZRqh8PCgw9Kzz4rbd5ceVvr1tKCBVLz5sHH77wjff118Pv027cHe8O3bZO2bg3+ZGaW9XRffLH0+uvBcYe//700alTw6wEE4L8dE6kBQINFTzcA/AalPdlmfrm9jYOZDocKc36WmV9SlNfJjrb8fOmUU4Lpzz7jO90HqrAw2DPqdgcfJyQEA26nMzg8/OSTg9e5b1+pcePwfc89N/hTG05n8G+0Y4f0n/8Ef9q3l66+Whoxgi/+AQBwAOjpBoD9UN3EZ87YOCWn9wiVK8j5SY4YV8MLsiuid++3+eUXacoU6R//kB55JBj4StLu3dKXX0onnVR2fQ+W4mJpyRLp1VelF1+U9uwJ5h97rLRs2cE9V0PC8HIA9URRkXTvvcH0PfeU/T8XkUPQDQC1lLNjrYryt0uqep3s+JQj5XAwDDdMSYn04YfB9KBBLBlWW36/9Pe/B5eY2r07mHf22dJ//1u39cjJCQbfTz0lXX65NHZsML80gOSfKABwyMnOltLTg+lffpGaNo1ufRoCgm4AKKd8T7a/aK98jTuHAuncXd+pMPeXhj1cHJG3eHFwOPfixcHHxx0n3XlncIh4tP5pYRb8R0Dp+V96KfgPgSlTguuw02sLAIeMHTvKvom0fXvZ8mGIHIJuAA1aVbOLl+/JTmpyjGLdiZIkf0mBJBFkI3ImTZJuvTUY5CYlSQ88EAzAnc5o16yMmdSnj/TVV8HHZ50lPfmkdOSR0a1Xfcd4TgD1hFlwPk0puHIk/zeNPIJuAA2KBYKTmzligkFM/t4flb8nfAbo8HWyU35dHxsHxO+XPvkkmP797+tX8FgfffaZ1K+fdMkl0uTJUrNm0a5R1fLypL/8Jfg98+Li4PfK33gjGICjasxvAAANFkE3gMNaVT3ZvtQMuePTJEklRXuVu2Mtw8UjhUBj3/bulRITyx6vWiV16RK9+uyPNWuCi71+/nlwWbEnnpCuuSbataqfCguDoxgk6eGHy5ZpAwAc9gi6ARx2Av4iFeT+XOVwcUmKS2yp+KQ20alcQ5OfL/XuHUx/+SVLhlX0wgvSbbdJc+ceOoF2RUVFwSHw//xn8PGSJcHvoQMA6qWdO8u+x71jB6tB1gWmkQVwSCvtyZYjRi5PUmmuCvb+GCrDxGdR5PVKy5dHuxb1TyAQnIjs4YeDj//5T+mvf41unQ6U2x1c0qx9++DXCQi4AaBeKy6uOo3IoacbwCGluonPXHEpSmzcOVQub/cmOV3xBNmon8aODQ7FlqS775YmTDj8ZrLJywt+h59h1ABQrxQUSH/6UzD9+utSHB+TIo6gG8AhY++2VSou3KOq1sl2xaUqPuWI6FQM2B9PPimNGRNMT50qjRgR1epExM8/B5c469hR+te/Dr9/KBwI5jcAgAaL4eUA6pXyPdmBkkL5GrWvWILh4oeS/HzpzDOD6fff5zvd//ufdP31wfTDDx+eAbcUnAxu6VJp0aLgsPO77452jQAAiBp6ugFEVcXh4v7iXJW/LaU06xlasqukOE8ORwxB9qGE3r0yZtLpp0sffyyNHCk9//zh3QP83HPSlVcG06+8ElwGrSFjYVwA9URRkTRpUjA9blxwag5EFkE3gDplAb/kiJHj1w+cuTvXqzAvO6xM+Z5sd1yj0JraOASVlEgzZwbT558vxTbwAVb5+dKjjzacTznjxgUniHO7g+u19+0b7RoBQIOXnS2lpwfTv/wiNW0a3fo0BATdACKqqp7sxLRjFOsO9n4W5m1VwZ7vGS4OHI78fun//k966y3pyCOllSv5igEARNmOHVLjxsH09u1ly4chcgi6ARx0/pJ8FeZtrXK4uCTFpxyhuIRmkiQzC/V6A4el996TvvlGuuGGhtG7XdHevVLnztKPP0p33SVNnBjtGkUH4zkB1BOBgLR5czDdpo0UExPd+jQEBN0AfpPSnmyH061YV7wkqbhwt/ZuWxUqw8RnDZjfL331VTB94onBJaQakrw86eijpY0bgxOn3XJLtGsUHdOnS1OmSE8/LR1zTLRrEx3MbwAADRZBN4D9Ut062XG+ZopPDi7ZZRZQ3q4NwUCbILtha+iBxh13SA8+KLVuHZzRu/RaNDSlHzUa8qiWwkLp2muD6b/9jfXLAaABIegGUCuBQIlytmdWOVzc6fTIHd9E3qTWUaod6q28PKl792D666+l+Pjo1qcuZWYGn3txcXAyufPOi3aN6o/c3Ib3DxgAqCd27ZJSU4PpnTullJRo1qZhaODTyAKoqHxPtiTFJ7WRJMXExCrgL5YZ62RjP8THS2vXRrsW0XH77cGA++yzpcGDo12b+iEvT7rzzuDEamvXMps9AERBUVHVaUQOPd1AA1fTOtkxMbFKbtYzNNFZceEexTjdBNnAvmzaFJytOxAIDivv3DnaNaofCguDQ+23bpVmzAguIwcAqFP5+dKAAcH0xx+zqERdYK46oIGxgD/scc6O1dq7PVMFe39USVFOqCfbE99E3uR2ksr+L+fyJBFwA7Xx3HPBgPv3vyfgLs/jka64Iph++uno1qWulQ6pT0gIpgEgSrxe6Ysvgj8E3HWDnm7gMFdVT3Zys56KiQkO68zf872K8rYyXByRUVAg/fGPwfT06VJcA3ltLVokPflk8LkztDzcpk3SEUcEJ1dbvVrq2DHaNaobDX1SQQBowAi6gcNQSXGuivK3h80uXl5i405yxQVn0DALyOFg0AsihEADVTn3XOm//5XGjpUefzzatakbLIwLoJ4oLpaeeiqYvuYayeWKbn0aAoJu4BBX2pPtdHkV4wwuQVOY+4tyd30XKsPEZ4ia4mLplVeC6SFDeGdH0AcfSGeeKSUnSz/+yD9jAKAOZWdL6enB9C+/SE2bRrc+DQFBN3CIqW7is/jkIxTnayZJ8pcUqGDvDwTZQF3Lzg4OK+/Xr2yWGlQWCEhHHSVt3BicyZwh+ABQZ7Zvl9LSgult26TGjaNbn4aAtTqAQ4S/pEC5O9dWOVzc+WsPd+hxbJwSUjPqsHYAJElz50r33y+9+660bFm0a1N/xcQEh5bv2tVwJppjPCeAeiI1VVq5siyNyCPoBuqZ8j3ZMU634hKCvdcxMS75i4MBN8PFccjw+6Vvvgmmu3aVnM7o1ifSPvss+Pvkk6Nbj0PB9ddHuwZ1q6hIuuGGYHrUKIJuAFETEyMdfXS0a9GwEHQDUVZxuHj5nuxYty8UdDtinEpo1FHOWC9BNg4dBQVSjx7BdEOYSG3evOBvgm5U5HRKl1xSlgYANBh8pxuoYxVnC9/18xIF/EVhZcr3ZHvim9R1FYGDJy9Pat8+mF67VoqPj259ImnXLqlRo+BSWFu2SM2aRbtG9d+2bdLnn0udOgV/AAARt3u3lJISTO/aFZzTEpFFTzcQYRV7sgP+IqU0+11oe6w7Uf6iHIaL4/AUHx+cnboh+OKLYMDdvj0Bd21df31wdvvx46X77ot2bQCgQSgsrDqNyCHoBiKgpGivigp2qqRwj/zFOao4oMRfki9nrFeSlJCawTrZwOFgxYrg7xNOiG49DiUnnhgMukuvHQAg4nw+KSOjLI3II+gGfqPSnuxYd6IcMcHv6RXl71BBzk+hMjVNfEbADRwmtm0L/m7ePLr1OJSUjggovXaHs9xcqV27YHrjxsN/fgMA9VZ8fPAbX6g7BN3Afqpu4rPExp3likuRJLniUmWBYoaLAwUF0tChwfRLL0lxh3FbmDBBuuqq+hlMZWVJ69cHuzZKv2NfHzT5dc6KhhB0Sw3neQIAwjCRGlBLJUV7lbd7Y7XrZHuT28rtbRydygH1VW5u2di1hjB7eX2zY0dwxuwPPyzLGzRIeu21+rE467ffSsccIzVufPgHpIGAlJkZTHfuHFyzBwCioLhYmjYtmB4xghUM6wJBN1BB+Z7sWJdPbm8jSVJJcZ72ZH8tqebh4gDKKS6WnnsumP7zn3lnr2tnnCF9/HFwvfRSTqc0YID0wQfRq1epX34JDjF3OIKvFZbSAoCI27pVato0mM7OLht0hMgh6EaDV3G4uL84NzTxmdubJl+jsqGYhXlbCbIBVO2uu4LB4+jR9eN73VlZUseONW+P9lDz4mLJ7Q6mt26V0tKiWx8AaAC2by+73W7bFhxshMjiO91ocMxMDocjmA74tevnRZVmFy/tyXbFhQ+/ZM1sANX6+9+Dn14uuKB+BN3r19e8fd266AfdLpf05JPBRWK93ujWJdIYzwmgnkhJkRYuLEsj8ujpxmGvYk+2w+FUYlqX0PY92StkgRKGiwOREAiUBX9HHXV4f4+1Q4fgdLBz50qnnBLt2hwaPd0NCfMbAECDRU83DkvFhbtVXLg7bHbxUg6HQxbwh5b38qV1UUwMTQGIiPz8YDAqHf6BRlpaMOiuLxOCdegQnDStuu90E3DXLadTGjy4LA0AaDCINHDIs4BfJcU5cnmSQ3mFOT+rqGBH6HHFic9KA25JBNxApCUn77vM4aD8F+Tqi9deky6+OHz28gEDgvmoW3Fx0ltvRbsWAKA9e8remnfvlpKSolufhoBoA4ec6tbJTk7vERoW7vI2kiPGyXBxINoSEqRdu6Jdi7pRH9ecTk0NzlK+dm3wO9z1bZ1uAECdKygITxN0Rx5BNw4ZRQU7VbD3h7DZxUs5nR4F/EWh4NoT34RJzwDUrfrY012qfXuCbQCApOD/w0t7ug/nb33VJwTdqHfK92S74xop1v3rxDNmKinKkcQ62QDqofT04O9ffoluPVA/5eVJXX6dxHPVKik+Prr1AdBgNaRBaPUFs5cj6qobLi5J3sRW8ia1liQFAiUqLthJkA0cSgoLpSuvDKb//nfJ44lufSLphx/+f3v3HmXpWdeJ/rsvde3q6upOd5JOQgi3JKAkkBABkYAxDgIRR0V0ES+oOXKIiIqK64CMwAiMwDgyh4AyuobxCAFRGeUaRWASRoHQAQyQTBIQCLl0+lZdXfd9O3+86bp0V3U6Sb+1u2p/Pmvt1U+9+6mqX6c6Vftbz/M+v6LX9AUXOCiLozm9HKBnCd10Vasxk4P3fSVLTxdPFley+4e2p29wrCu1ASeAoAGFViu56aZifNFFfjEDdE2zmXzgA8X4p386qdv7XDqhm9IduZJdqw9n09bHFM91Ohm/98ZUK3XbxWEjmp9P3v72Yvzrv57093e3HgDocXv2JKeeWozvu2/xHFDKI3RTisbs+IrbxZNiFXvL6RctvN1uNVKt9XWhSoAT7ItfTN7xjuSMM5I3vanb1QDAUfbuXQzae/YsngNKeYRuHrZOu5VWczr1/s0L1w7e95W0GtMLbzv4DOgJn/hE8tznJo9+dPKNb3S7Gk4m9nMCJ4lWK7nhhmL8zGe622UtCN0bVKs5W1qwXengs0qlkrGdl6RSqSZJZg59N+3mrJANva7dTu65pxjv3JlUq92tp2wTE8m2bcUrmptvTr73e7tdEScL5xsA9KwN/uqnN80c+m5uveE/ZObQd0/ox52b3pOJPTfnwD035tC+WzJ76K77W3h1Uq32pd2aW5g7tPmsbNr62AwM7xC4oZfNzCRnnVU8Zma6XU35RkeTH/uxYvyud3W3Fk4u1Wpy+eXFYx388ukLX/hC9u3b1+0yADaEk/+7Pg/awd1fSjqtHNz95Yf0/p12K43Z8UxPfCft1vzi9VZjIWTXagMZGN6RTVsfmy2nPTlbTr8otfrQifkLABtLvd5bW2mvvrr48y/+Ijl0qLu1cPIYGkr+8R+Lx9DJ/fOy0+nkRS96Uc4555y85jWvEb5hgzl0KKlUiocfU2tD6N5gOp1OJu4P2xO7v5zjuXtgacg+ciW7MTexMK9vaNuykG0lG3hAmzYljUbx6JXttJddlpx3XrGF+C//stvVwINWqVRy44035uqrr84f//Efr0n4Puuss/LOd75z2bV//ud/zvDwcL797W+X9nmhFy3deNYLm9BOBkL3BjM7eVcac+NJksbcgcxO3n3M+Y3Z8RW3ix9eya7WFtv71OqDQjbAA6lUFle73/nO5CQ4OuW2227Lxz/+8dx+++3dLoV1YseOHfnDP/zDfOtb31qT8P20pz0tN95448LbnU4nv/Ebv5Hf+I3fyCMf+cgT/vmglw0PL47vPnZU4ARxkNo6Nje1+6hQfWjv13Jw91eStJNUs+W0J2XklPPTbs6m2ZxJuzGd4S2PyuipT0yStFvzGb93l9PFAU6k8fHkWc9Kfu7niv7kfd1pi7h///68+MUvznXXXbdw7TnPeU6uvfbabN26tSs19azp6eSSS4rxjTcuf9W7DuzZsydve9vb8o53vCPVajWveMUr8spXvjKnnHLKCfn4b3vb2/Ke97wnX/3qV5Mkf/EXf5FXvepVuf3227N58+YHeG/geO3fn7z4xcmSHwt5znOSa69N/Fgoj9C9jn3n5vfk0J6bH/T7DY4+Mo968ksX3m635petaAOcMHNzqfz2q5Iknbe9JRkY6HJBveW5z/vR/NM/fSqtVmvhWq1Wyw/90GX5+Mc+3MXKetDUVKpbioDaPrhv3d5usWfPnvzn//zHuead70q1Ws2vvfzq/OZv/vrDDt+f/ez/zg9e9sM5sP++VKvVnP/4J+Y//IfX5Kpf/qUTVDmQJM99Xj3/9E/VtFqVhWu1WnHG4yc+0cXCNjihex1rNWdy960fzMR9X+l2KQArqsw08oQr/t8kydc/8mvpDHVnxbfr2p2kWnngeSfQt+48kCte8t9Xff4xjzwlA/2as66ZTlKdLQ4nbQ/2J2v7z+GEazbb2Tc+nX0HplOtVPL63/rh/PhzH3qLvLn5Zp76o+/In731hfncrm/nU//8jfz1n/5cqmv8/w1sZN+6c3uueMn/s+rzt92WPO5xa1hQD+mh42Q3nlp9KGd9z89lfNv5uee2v0mn3U6xrRzgJFGv5r6ff9rCuOd0Ohm9/vac+p5/zrfe8pNp7li7bbJ33j1+zOfPPH00O08bXZti2FA6nU7u2zeVAxPFCUxjWwZz9lljD+tjDvTXc95jduSTN9yev/7ozXnHH/x7gRtOsDvv3n7M5++4Q+gui5XuDWJu6r5848Y/SqfdOOq5SqWWbWc/O/X+4sVe/+DWjGxb/D9qfmZ/6v2bU6316AoUQFna7VSedVkq//K5dH7yJ9L5wPvW7FPfdtvtefwTnrjq87fe8tU87nGPXbN6WP86nU4+85n/lTe84Q9y/Q2fzcUXX5T/8Nrfy/Of/9xUKg8/IL/i138z73znn+T5z39e/u5//s0JqBhY6rbbKnn8E1a/pdRKd3msdG8UleqKgTtJOp1Wxk6/ONVqX5rzE/cH7OK+ylZzJtMHv5WkWDmvD4ymr3809YFR93kDPFy1JH/yp8lFF6XyN3+byic+mTz/+Wvyqc9//PfmOc95Tj75yU8edU/35ZdfnvPO/541qYP7NZvJRz5SjK+4Yl31ri/C9mfyute9Ltdff30uvvjifPjDH87zn//8ExK2D3vyky9OvV7P2972nxdepwAnzvmPLw5N++QnkyU/Fhbu6Ra4y9ODe/02pok9/5rFG8Tu/7JWDn95K5nc+7X0D23L8JZz0j+0eNhJp91Mva84zKXVnMnc1O5MHrg94/fuysHdX878THk9OYEe0OkUJ3mPj58UrbO64oILihPMk+Rnfza59dY1+9TXXnttLr/88mXXLr/88lx77bVrVgP3m5tLfvzHi8fcXLerOS6dTief/vSn8+xnPzuXXXZZpqam8uEPfzg33nhjrrjiihMauJPkve99b66++uqcd955J/TjAouuvbYI2EtdfnlxnfLYXr5BfOMLf5TZybuSJJvGHpvTHvejuff2v8/0+DeSJIObz8pjLvnNVd+/3W6mOTeR5vxE8WdjKkkysu289A9tS5I05w9lbnqPlXDg+E1NJSMjxXhyct2e2Pywzc4ml12W/Mu/JI9+dPL5zyfbj31v3Yl05ZVX5gtf+EI+9rGP5XGWMrpjZmbxle4nP5kMDXW3ngfw2c9+Nq95zWty/fXX56KLLsrrXve6UoJ2u93Onj178ud//ue55ppr8vWvfz1btmw5oZ8DONqLXpT8678mH/6wFe61sH72NrGq+dkD9wfuSk57zPNyytnPTqVSzTlP/r+z9zufyX3f+FhmD303jdkD6RtcuQFftVpP/9C2hYB9OITXBxYP2WnMjmduanfmpnYnsR0d4LgNDiZ/93fJU5+afPObyb//90XwGhxck0+/ZcuWjI6OCtzdNDSU/O//3e0qjkun08mLXvSi7Ny5M3//939fStg+7Prrr89ll12W888/P3/7t38rcMMamJxMPvjBYrxzZ3dr6RVC9wZQSSUj287Pjkc/J8OjZy9er1Sz45GXZdPYY7Ln3/4hD6Y/yeEQvlR9YEsGO62FlfBWc2ZhS3qSbDn1wtT6hpMknU47lYq7F6DnDQ8n80WbpPV0D2spduxIPvrR5OlPTy6+OOlzeCUnp0qlkjvuuCNDQ0Olhe3Dnv3sZ6fd1nkF1tL09PLx4Q1plKfHXwFtDH2DY3nkk/6vVZ8f3vLIYz5/3J9nYDR99698H7kdvd2aT7W+uFVuevybac5PWgmHXlepCJdLPf7xyVe/mpx1VrcrKc9/+2/F0skVV3S7Eh6G4eHhbpcAlGTp3S0n+Z0uG4bQzUNy5Hb0Tru17LfhjbmJtFtzy1bCF7ajD2xZdpgbQE9ZGrinp5PXvS557WuTzWvXw7sU7Xbyu7+bvO1txb37N9+cPOpR3a7q5DEzk1x6aTG+/nqvdIGu2bw5ueqq4tv0ev/Rs14I3ZwQlWpt2dujp15w1MFsh7ejN+cPLQvdjbmDqdWHrITDRjQ/n7zmNcX4jW9M+v1/vsxLX5r85V8m111XtJN6xCO6XdFDMz2dPO95yf/6X8Xbv/M7yTnndLWkk067nXzxi4tjAHqG0E0pVjuYrTk/sSxcdzrtTO67NZ1O28FssBE1GsXKZ1Ks6Ardy73iFcWBav/6r8n3fV9xjOxTntLtqo5Po5Hs2pV85jPJ//yfxYnslUry//1/yZVXdru6k8/AwGKf7gE9qIHuabWSu+5KDh4sxrXaA78PD4/QzZo4MoQf1m7Np1YfWvFgtlp9KAMjp2dw0+ndKBk4Efr6kt/+7cUxy11ySRFWr7ii2Of3zGcWW81/+7dPvl9QzM8nN95YrGZ/+tPJZz9btEKr1YpXbdu3J3/1V8kP/mC3Kz051evJ85/f7SoAcuBA8vGPL47XsINlzxK66apafTCjp16wYp/wVnMm6SxuwWs15zI7eZeVcFhP+vuTt76121Wc3M4+uwiwL35xcbr5a15TrBb/zd8kT3hC9+qam0u+8IUiZP/TPxU9xufmipDdbiedTjGv1SpWuF/3OoEbAFYgdHNSWG07+uEWZEnSnJ/QJxzYmEZHi63l731v8lu/VSw9nHFG9+r5139Nnva04vCvlUL2kYaGkp//+bWtcb1ptZJPfaoYX3aZ/ZxA14yNFd+G7ryzGFM+jZQ5KR0O4bX64MK1Wn0ogyM7U+/blCQLW9EnD9ye8Xt3ZX72wMLczuEXh0B3dTrFvb+NxmJoY2WVSvKzP5vcemvyd3+3+Eqo2Uze8Ibk3/5t7Wo588xk27akWi3C4rG+drVa8ku/5AjcBzI7m/y7f1c8Zme7XQ3Qw+r15NGPLr7N1y3Brgmhm3Wj3j+S4S3nZPTUCzK285KMbDtvSQivpN43sjB3dvKuHNz95UyNfzPz03vTbs13r3DoZdPTxRbz/v5izAPbujV56lMX3/7wh5Pf//3kMY8p7v3+6EdXXm0+kU45pThRfWio+GXAsbRayctfXm49G0G1mlx4YfGoevkF0Ev8boN1aaXt6NXq4j/n5tzEigezHd6O3je0LZWKFz3AOnDaaclznlOE4I9+tHicc07ykpckz3528vSnn7hD11qt5CtfKQ5Ne9rTkg99KPmRH1l9pbtWK2o477wT8/k3sqGh5Mtf7nYVAJmaSv78z4tv7VNTyaZN3a5o45M62BCWBu4k2bTt3CNWwhe3o0+Nf3PZ3Ob8pJVwKMvwcHF/8oEDxZgH7/u/P/nEJ5Lbbivu9966NfnWt4qDy5797GTfvsW53/xmcvfdRWh+IK1WsmdPcYjbm9+cPPe5xV7Diy9OXv3qYs4P/3Dyrncd+2P8+q8/jL8cAGttamrxd6lTU92tpVdUOm5+pQcsPR096WR4y6MWnju4+0tpNWcdzAasDzMzyQc+UPR7ufvu5IYbFp/7kR8pVsST4nC2HTuS7dtz9T335PMzM9l1332Lc5/4xOSrXz3644+OFmH7gx9c3Fr+W7+V/Jf/cvSK9yMeUdxr7lAwgHVjYiLZsqUYHzxYfNunXLaX0xNW6xPeabdSqRQvFlfajt4/vD1Dm89a83oBVjU0VGwtf8lLll/vdIqV62q1OG18YqJ4fOMbxfNHnpZzyinFn6efnjzjGcmllxZ9wi+44OgQ/Za3JHfckXzkI8XHTorP84pXCNzHa2am2E2QFL8wGRrqbj1AzxodTa66Krn5ZoF7rQjd9LRKtXbMPuFLt513Ou3MHPx26v2brYTD8ZqfT970pmL86lefuHuPOVqlkuzaVYTi8fFk794ihO/dm/zxHxdbz5f6+78vtvwfz9G1tVryvvcV4fxrXytOVO/rK04t5/i020XP88NjAHqG0A1ZvU/40mDdakxldureZOreJPqEw3FpNJLXv74Y/87vCN1roVot7s3eti0599zi2nXXFaveSz3Y5Y1Nm5KPfay45/vee4v2Ztu2PfD7URgYSP7qrxbHAF3SaiW7dyeHDhVjG5bKJ3TDCg6H8KUqlXoGR3YuWwlfuh19eOxRGdx0ejfKhZNXvZ5cffXimPXtjDOKQ92uuqq4z5vjV68nP/VT3a4CIOPjRTfKw+PDdxtRHq+A4DjV+oYyvOWcJFlxO/rhU9KTZH56b2YOfddKOAwMJNdc0+0qOJEuvDC58cZuVwHAQ7T0Dhd3u6wNoRsegpW2oy/t+92Yf4A+4YNbU6naywPQM1qt5HOfK8ZPe5r9nEDXjI0VZ2d+97vFmPIJ3XACHNknfGj07PQNjB11MNvhED52+kWp5P5T0xszqVRrVsIBNrLZ2eQHfqAYT04W98gDdEFfX3Hkx9xcMaZ8QjeUYLWD2ZrzE2k3Z1OtLR6iMz3xrTRmxx3MxsY0NbX4a/TxcUGD3lWpJI997OIYgJ4hdMMaWK1PeFL0Ck9W7hPeNzi2cB85rFvNZrcrgO4bHk5uv73bVQBkerroAtloFOPh4W5XtPEJ3dBlozu+d9U+4dXG8tXumYk7F1bErYSzLgwNFTeNHR4DAF01OVmE7cNjobt8QjecBFbbjr70cLZ2q5GZQ99deNt2dNaFajU588xuVwEA3G9wcOUx5RG64SS0Up/wpHPMPuGDm8/M8OjZa18sAA9sdjb5yZ8sxn/zN17pAl0zOppcdVVy883FmPIJ3bBOVGv9x+wTXqsvbt1tzk9m6sAdVsLpvvn55O1vL8a//utJv3+H9KhWK/nYxxbHAPQMoRvWoRX7hGfxNNzmA/QJrw+OHdXmDErRaCSvelUxvvpqoZve1d+f/Pf/vjgG6JJWK9m3r7ivu9VKarVuV7TxedUNG8CRAbp/+NRUa4Or9gnfvP17Uh0o9hO1W3NJKlbCKUe9nvzCLyyOoVf19SUveUm3qwDI+HjyoQ8tjk85pZvV9AavgGADOlaf8Ob8odT7Rxbmzk7endnJex3MRjkGBpL3vKfbVQAA92u3Vx5THqEbesCx+oS3W40kK/cJrw+MZnjLOctOUQfgIWi1ilOLkuSJT7SfE+iasbHk+78/ufvuYkz5hG7ocSPbzl21T3in005lbDFwz07dm2qlbiUc4MGanU2e/ORiPDmZbNrU3XqAntXXlzzhCcXvAvv6ul1NbxC6gVW3o3c6i3uOOp1OZifuTLvdTKJPOMdpamqxT/dddwka9K5KJTnjjMUxAD1D6AaOsmKf8E47/cM7Vu0T3j+0PSPbHteFajnpHTzY7Qqg+4aHi188AXTZzEzyt39bbMCZmUmGhh74fXh4hG7guFSqtQfoEz64MLfdbubQ3q+l3r/ZSnivGxpKbrttcQwAdNWhQ8n+/YtjP57LJ3QDD9pK29HT6Sw835ybSKsxnVZjeuU+4QNbUq25iagnVKvJ4+yAAICTxcDAymPK40hi4GGrVuvLQnR9YDQj287L4MjO1PuKe3gPb0WfPHB7GrP7F+a22820W/NrXjPAmpqdTX7qp4rH7Gy3qwF62JYtyVVXJU99ajGmfFa6gRPumH3C5yZSHxhdmDs/vTfTB//NwWwbVaORvPvdxfhXfsUxqfSuViv5678uxnrXA/QUoRso3bH7hM8lWb1P+NDmR9iKvp7Nzycvf3kxfslLhG56V39/8o53LI4BuqTdLs44nZ0txlV7n0sndANdNbzlkRncfOaKfcLbrdkMjz5yYe78zL6k07ESvp7UaskLX7g4hl7V15f86q92uwqAjI8nH/zg4njb0WsinGBCN9B1q21Hb7fmUqkuBrXZybvTnJ9Mok/4ujE4uPiTHQDoumZz5THlEbqBk86KfcKT1PtHk05nxT7hfQOj2bz9e9a6VIDj024n3/hGMX7MY+znBLpmbCz5vu9L7r23GFM+oRtYN4a3FFvNV+oTXqkuv1d4Ys9XU+sbthIOnBxmZpJzzy3Gk5PJpk3drQfoWf39yQUXJJWKIybWitANrDsrbUfvtFsLz7caM2nOH0pz/pA+4d02Pb3Yp/v225Ph4e7WA92kNw9ATxK6gXWvWq0n1cVvZ9Vaf0a2nXfUwWyHt6MPjpyxsGre6bTTaTethJel00nuvntxDL1q06bixCKALpuZST72sWLTzcxMMjTU7Yo2PqEb2HAq1dox+4T3LekT3pybyKF9tziYrSyDg8mXvrQ4BgC66tChxd+HHzokdK8FoRvY8I7VJ7zVnFn4c6U+4YMjO1Or+2n0kNVqyZOe1O0qAID7Lb2P2z3da0PoBnra4MjO9A/vWLFPeKs5k8FNpy/MbcxNpNOatxIOPHhzc8lLX1qM//RPk4GB7tYD9KyxseSqq5Kbb3Z6+VoRuoGet1qf8GZjMrW+xYO/5qZ2Z35mbxJ9wo9bo5G8973F+Morkz4H2NGjms3kf/yPYnzNNUI3QA8RugGOsNp29FrfUOrNTSv2Ca/1DWd0xxNTqei9u8z8fPKLv1iMf+qnhG56V19f8pa3LI4BuqTdTqamih/R7XZS9dKldEI3wHEa2nxWhjaftXKf8FSWBe6pA3cklaqV8Foted7zFsfQq/r7k9/5nW5XAZDx8eTaaxfH244+8oYTTOgGeJBW7BPeml94vtNuZX5mbzqdzip9wnsohA8OJh/9aLerAADu12yuPKY8QjfAw3Rkn/BUKtm09dxV+4T3D27LyCnnLUxvtxqp1mw3hQ2t3U7uuacY79xpPyfQNWNjyZOfnNx3n4PU1orQDXCCVSrVY/YJry/pE95qzubg7i/17ko49IqZmeSss4rx5GSyaVN36wF6Vn9/cvHFxenlWoatDaEboGTH7BPemCr+XKVP+MDwqan3j6xpvSfU9HRy4YXF+CtfSYaHjz0fNrK6l10Avch3f4Au6h86JWM7L1m1T3jfwNjC3FZjJq3G1PpaCe90kjvuWBxDr9q0qWihB9Bls7PJpz9dHKI2O1scv0K5hG6ALlu1T/j8ROr9mxfmzc/szcyh7yZZRwezDQ4mn/3s4hgAWFM335zs27f49oEDyTe+UYw//vFk69bF54aGku/7vqRSWdsaNzqhG+Aks9p29EqtL/W+VfqE14eyefvjU60NdKPk1dVqyTOe0e0qAKAnjY8nF1yw+vM/8RNHX7vhhuQHfqC0knqS0A2wTgxuOj2Dm05fsU94uz2fSnVxtXtm4s60242TfyUcesXcXPLKVxbjP/qjZOAk+wUZsCGNjSWXX5586lNFE4UHctZZxUo3J5bQDbDOrLQdvd2cSWXJXrD56T1ptea63ye82Uw+9KFi/OM/7iApelezmbzzncX4LW8RuoE184Y3JJ/85PHN/f3fd6J5Gbz6AVjnqtV6qkvu/U6SoS3nrNonvNY3nC2nXrgwt91uFr3GyzA3l7zoRcV4clLopnf19RWvZg+PAdbI059erHZ/+tNJq7XynEolOfPM5Od/fm1r6xVe/QBsQMfuE74Y0Duddg7euyvV2kA5K+HVavKsZy2OoVf19yeve123qwB61BvekHz/96/+fKdjlbtMQjfABnfkdvTOktZdrcZ0Op32qn3C+4e2p29g9KF/8qGh5DOfeTjlAwAP07FWu61yl8+yA0CPWXrvd71/JGM7L8nItvMyOLIz9b5NSbIQwFvzkwtz261G5qf3pt2aX/OaYd3rdIpjhMfH9awHuuINb1h5e7lV7vJZ6QboccfsEz44tjCvMXcwUwduT7KO+oTDyWJ6erEZ7uRksmlTd+sBes5Kq91WudeG0A3AMqv2CU+O2Sd809bHpt4/svyDzcwUP+WT5F/+pdhuDgB0xZH3dlvlXhtCNwDHpX94e/qHt6/YJ7zVnFm22j03tTvNxlT6GrX0f+UrxcXjaRAKG9XwcDJ//60ZTvEHuuTpT08uu6zo251Y5V4rvusD8KCstB29NT+5LHTPz+xPY248c61W6u/749RqA8ns3emrbLUdnd5UqWgVBpwUXvnKxdD9ylda5V4LQjcAD0u1Wk91yb3fSTI4cnpqfUPFSvil35dmkszuzdzs3lQqtYztfEoqleIsz067lUq1tuZ1A0AvWrq9/Moru1dHLxG6ATjh+ga3pm+wODTqyO3olVrfQuBOkok9NyeJg9nY2Obnk9e8phi/8Y2WloCuGRtLbrstmZhITj2129X0BqEbgPI0m6led136k/Q/5znJlno6ncV7u9utRlrNmSRZuU/4kvAO61qjkbztbcX4da8TuoGuqVSSxz2u21X0FqEbgPLMzSVXXFGMJyeTej2V2bniULWBgVTrfRnbeUma0wfSnNiT5vyhNPvaC6ejZ3omfVv6k4GBdGrVNGb2p17flGqjXbxqGB5e/Fyzs0UPlP7+xXtn2+3iBPVkeYumBzN3bi5pNot5h4NSp1O0gHqwc4eHi7qTYuWz0SgO1RoYWPwYU1MPfu7QUFK9f/dAo1HMr9WSwcHFue12Mf945k5PF3UPDhbPJcXfa26ueN+lp9A/mLkzMwtf+4XDxFqt4uvxYOYez9f+wcx9MF/7h/rvpF5Pfvu3i7F7u4Eu6nSKb1dJ8a3p8I8aylN94CkA8BBVq8lTnlI8Dge9yy9PRkaSj3zk/in19H/xaxk+83sy+vxfytjOSzKy7bwMjuzM0M/9ajH3Ax9IqzGVyQO359A/vi8ZGUn7/HMzP7037db9J0L/zM8Uc9/znsXPf8stxbVzzlle1y//cnH9mmsWr33nO8W1I/favfzlxfW3vnXx2t69xbWRI1qk/e7vFtde//rFa9PTi3MPB7CkmDMyUrzPUofn7t27eO2tby2uvfzly+eeempx/TvfWbx2zTXFtV/+5eVzb765uH7LLYvX3vOe4trP/MzyuU94QnH9ppsWr33gA8W1F7xg+dxLLimu33DD4rWPfKS4dvnly+deemlx/brrFq996lPFtcOt5Q577nOL6x/60OK1z32uuHbhhcvn/uRPFtff+96j/75HLuf83M8V19/97sVr3/hGce3MM5fPfelLi+tvf/vitXvuKa6NjS2f+8pXFtff9KbFawcPLn49q9Xi6/jWt1rlBrpqfLz4NtTfX4wpn5VuAMozNJTceOODepdlp6NXF1cEO5126n2b0ll4u5XJA7cnKbajb241/CYZAB5Ao7HymPJUOp1O54GnAcAJ8mC2Da8wt92YS/PgfWk2JtOsNdJsFFusN488Jn31TUl/fxqtqczP7k9ffST1dl9xMFsPby+/+uqr8/l/+Zfs+uxnbS/v1vbypV9PgC6anV3cDPXnf7782z/lELoBWNcOn47eNzi2cCr61Pg3Fw5lSxYPZuvV09GvvvrqfP7zn8+uXbu6XQoA9BzbywFY1w5vR1+qf+iUVCrVolVZY2rhYLbDQXzs9IsXgnen017WwgwA4EQSugHYcPoGtqRvYEuSo/uEdzqtZSvdk/tvS7s529Mr4QD0jvn5xTMfX/1qZzuuBaEbgA1t2cFsybI+4Z1OJ635Q2m3myv2Ce8b2JL+oVO6UjcAlGF8fLHJxtVXH920gxNP6AagpyzdSl6pVDJ62pOXrYQv3Y7ebs4uC93zswdS79tkJRyAdateX3lMefxnBqCnHbkSvnQ7eq2+eJp2u9XI5L5bkziYDYD1a2ws+e53F8eUT+gGgCWODOGHtduN1Ps2rXgwW60+lMHNZ2ZgeEc3SgaA41atJmee2e0qeovQDQDHod43nNFTLzjqYLbDITxLOnC2GtOZnbrXSjgAIHQDwIOx2nb0ev/mhTmNuYOZm9p99MFsQjgAa+zSSy/NDTfckCSp1WrZvn1Hdu9+WpLfyYED32+L+RrQmBQAHobDIbxa61u4Vu8fyeDIztT7NiXJwlb0yQO3Z/zeXWnMTSzM7SxZIQeAE6nT6eTLX/5y/tN/+k+55557cscdd+Rd77o2SX+SS/Oxj32s2yX2BCvdAHCC1fs3L6x8H7kdvdWcWQjjSTIz8e00ZsethANwwt1+++05dOhQLr300px++ulJktNOOyeXX/7s7Np1Wf7wD1+TF7/4eV2ucuMTugGgREf1CW+3UqnWFp4/HMRX7BPeP5q+oVNSqVS6UjsA69uuXbtSq9Vy4YUXLlwbGkr+8R+TN7/5h/Pa17427XY71aoN0GUSugFgDS0N3Ekysv0Jq/YJb8weyNjw9oW5jbmJ1OqDVsIBOC433XRTzj///AwPDx/1XF9fX2q1msC9BoRuAOiiY/UJr1QWA3qn08nU/tvSbjcczAbAcdm1a1cuuuiiZdfm55O3vz3567++Leedd36XKustfq0BACeRwyF8eMs5GRp9xML1Tru5EK6PPJjt4O4vZ3by7m6VDMBJ6ktf+tJRoXt8PHnVq2byhS/8XZ773J9Mkjz1qU/NF7/4xSTJL/zCL+Rd73rXWpe6oVnpBoB1oFrrO2af8E67uTC33W5mZuI7CyvhAPSeb37zmxkfHz8qdFer7SQvS1LPS1/6a0mS1772tXnTm96UZzzjGRkZGcnLXvaytS94AxO6AWAdWW07eq0+tDCnOX9oWZ/w+em9abfmMj+913Z0gB6xa9euJMnOnTtz7733ZmJiIrt27cp//a//Naee+u38yZ98OOecszVJcsUVV+T3fu/3Mjk5qY1YCYRuAFjHDofwZddqAxkc2bmwEt7uNNNpNzN54PYkyaatj8vAkgPaANh4brrppiTJueeem1qtli1btuT888/PFVdckZe97GXZtm3xZ8cXvvCFjI+P59xzz029LiKeaP6LAsAGU+8bTn3LOUmKlfC+gbFUqn2p921KszG1rE/47NS9mZu818FsABvMm9/85rz5zW9+wHl33XVXrrrqqnz605/OT/zET+SWW27J4x//+DWosHcI3QCwgVWr9YU2Y4fvCa9WF3/8P2Cf8MGtR7U5A2D9OngwGRsrxvfcM5MXvvCFecc73pFHPepRedWrXpU/+IM/yHvf+96u1rjRVDqdTqfbRQAA5bn66qvz+c9/fuH+vqVWOphtUSVjO5+yENKbjelUq3Ur4QDr2H33JaedVox3705OPbW79fQCK90A0MOO1Se8c8Sq+PT4N9Kcn9QnHGAd27w5ufDCxTHlE7oBgAVHhvDDlm6MW2k7ev/QtgyNnr2mtQLw4A0NJV/+crer6C1CNwDwgCqVSkZ3PHHVPuGt5uyy+dMHv5163yYr4QD0PKEbADhuq21HryzZht5qzmR28u6Ft21HBzh5NBrJu99djH/lV5K+vu7W0wuEbgDgIVupT3hSWdYn/Mjt6EOjj8jQ5rPWvlgAMj6evPzlxfhFL0p27OhqOT1B6AYATqhafTDDS/qEH7kdvVYfXpjbmDuY6fF/sxIOsEaq1ZXHlEfoBgBKs9J29Epl8VVec+7gMfuE1wfHlp2gDsDDMzaWfOlLi2PK56cYALBmjgzQAyNnpNY3ssLBbEUIHz31woX3aTVnU6lUrYQDPAy1WvKkJ3W7it4idAMAXXOsPuGtxlRq9aGFubOHvpu56T0OZgNgXRG6AYCTxmp9wpMikCcr9wmvD4xmeMujUqlU1rRegPVmYiLZsqUYHzyYjI52t55eIHQDAOvC5lPOX7VPeKVSXRa4ZyfvTrXabyUc4Aizs8vHQnf5hG4AYN1YbTv6Up12KzMT30mn00miTzjAUiMjyRlnLI4pn9ANAKxbK/UJ73TaGdh0+qp9wgc2nZZNY4/uRrkAXTc8nNx1V7er6C1CNwCwoVRrfQ/QJ3zxcLZWcy6T+26xEg5AaYRuAGDDWmk7+lLN+Ylj9wkf2JJqrW/N6wYoS6ORvPe9xfjKK5M+3+JKJ3QDAD3jyD7hfYNbM7LtvFX7hI9sOzf9Q6ckSdqtRpKOlXBgXRsfT37xF4vx85+f7NjR1XJ6gtANAPSsY/UJb85NpN6/eKzv3PTuzEzc6WA2YF2rVlceUx6hGwDgfsfsE96cS7J6n/Ch0bOPWkkHONmMjSWf/ezimPL5yQAAcBw2bX1MhrY8csU+4e3W/MLhbUkyN70nlVSshAMnnVotecYzul1FbxG6AQCO02rb0TvtRiqVxX2as4e+m1ZzNok+4QC9TugGAHiIVusT3je4NZVV+oT3DW7N5lPO70a5AJmYSLZsKcYHDyajo8eez8MndAMAnECVSvWYfcKXrnR3Ou0c2vPV1PpHrIQDa2J2dvlY6C6f0A0AUJIV+4R32gvPtxpTad7/0CccWAubNiX1+uKY8gndAABr5MjTzWv14WP2CR8aPTtDm89MknTarXQ6LSvhwMOyaVPSaHS7it4idAMAdEmlWjtmn/C+gcV9n/OzBzJ14HYHswGsM0I3AMBJ4ph9wlvFjZir9QkfHDkztfrAmtYLrD/NZvKhDxXjH//xxa3mlMd/YgCAdWBo81kZ2HT6in3CW82ZDG0+a2FuY3Y8nXbTSjhwlPHx5EUvKsZ79iTbt3e1nJ4gdAMArBOr9QlvNaeXhevZqXvSmB1Pok84QLcJ3QAA69Rin/Dl29HrfSPptBor9gmv923K6KkXdKFa4GSwdWvyD/+wOKZ8QjcAwAYzNPqIDI0+YsU+4ZUjTlCf3HdrKrV+K+HQI2q15Id/uNtV9BahGwBgg1ppO3qnvdgrqN1qZH72QJKs0idcCAd4uKrdLgAAgLVRrdZTqw8tvF2pVDOy7bwMjuxMvW9TkixsRZ88cHtmJu5cmNvpdNJuza95zcCJdehQUqkUj0OHul1Nb7DSDQDQox6oT3h9YMvC3FZjKhN7brYSDuvczMzy8ebN3aulVwjdAAAkOXaf8FZzZuHPlfqED2w6PfW+4TWtF3jwhodXHlMeoRsAgAc0MLwjfYNbV+0T3j+02Oy3OT+ZdnPWSjichEZGkk6n21X0FqEbAIDjslqf8Ob8ROr9Iwvz5mf2ZHby3iQOZgMQugEAeEhW245erQ2k3rdpxT7htfpQNu/43lSrXoZCNzSbyXXXFePnPCep+1+xdP4TAwBwQg2OnJHBkTNW7BPe6bSXBe7pg/+WTqdjJRzWyPh4csUVxXjPnmT79mNO5wQQugFgA/nOd76Ts88++7jm3nnnnTnzzDNTreogSjlW2o7ebs4uPN/pdDI/vTftdlOfcGDD8lMWADaIqampPPKRj8z73ve+B5x766235tGPfnSuv/76NagMCtVqfdm930knw2OPWbVP+OT+25a9f7vVWMNqYWMaG0s+9KHiMTbW7Wp6Q6XTcXYdAGwUL3jBC/J//s//yde//vXUarUkydVXX53Pf/7z2bVr18K8K6+8Mtdff33uuOOODAwMdKtcWObI7eh9g1szNPqIhefG7/liavVBK+HAumJ7OQBsIL//+7+fpzzlKXn/+9+fK6+8csU5t956a6699tpcc801AjcnlWP2CW9MJems3id8eEfq/ZvXuGKAB2Z7OQBsIBdffHF+9Ed/NG94wxvSarVWnPMf/+N/zJlnnplf+qVfWuPq4KHrG9iSsZ2XZGTbeStuR281phfmtppzxb3irflulQsnrcnJpFIpHpOT3a6mN1jpBoAN5lir3Va5Wc+O2Sd8YMvCvMbs/kwf/FYSB7PBkaanl49HRlafy4lhpRsANpilq93NZnPZc1a52UgOh/DhLeekVh9cuF6p1lc8mG383l05uPvLaTVmulUydN3Q0MpjymOlGwA2oKWr3YdZ5aZXDAzvyMDwjhX7hLdbs8tWu2cn70mrOWMlnJ6xeXPiKO215fRyANigDp9k/oM/+IO58cYbc/755zuxnJ7WbjfTakynb2B04drEnpvTnF+8sdV2dOBEE7oBYIPatWtXnvKUp+SHfuiHcs899+SWW27JNddck5e97GXdLg1OGvOzB9KcO7iwEr5UtdafsdMvXni7026lUq2tdYlwQrVayQ03FONnPjOp+SddOqEbADawF7zgBbn++utTrVazadMmq9xwDEduR6/WhzKy7XELz4/f88XifnEr4axje/cmO3YU4z17ku3bu1tPL3BPNwBsYIfv7U6SN77xjQI3HMORp6MvXZtqNWfTbjeSdmPFPuH9Q6ekb8kJ6nCyWrrkavl1bVjpBoAN7txzz82dd96Z8fFxoRsehpUOZjtscOSMDG95ZJJiG3pj9oCVcE5KzWbygQ8U45/+6aRuGbZ0QjcAbHD79+/P/v3789jHPrbbpcCGsjSE9w1uWzigrTF7IIf23ZrEwWyA0A0AACfU/OyBzE7cedTBbEkRwofHHr3sBHVgY7OZAAAATqD+wa3pH9y64nb0VnMm1eriS/D5mX1pzB20Es6amZpKzjyzGN91V7JpU3fr6QVCNwAAlODIg9kOh/Ba3/DCnPmZ/Zmf2XvUwWxCOGWZmkoOHlwcC93ls70cAAC6pDE7nsbc+Ip9wpNKxnY+ZWFlvNNpp1Kprn2RbCgTE8mW+w/aP3gwGXWnQ+mEbgAAOAkcuR09lUpGdzxx4fmJPV9Np920Eg7rjNANAAAnoaUr251OO+P3fCFHvnRf2I4+MLawjR04uQjdAACwDhyrT3j/4LaMnHLewtvzM/tS799sJZyjtFrJTTcV44suSmq17tbTC4RuAABYh5aG8HrfSPqHtydJWs3ZHNz9pSQOZuNo+/Yl24t/Ktm7NznllO7W0wucXg4AAOvQkaejH9ZpN1Lv27TQoqzVnFl2OvrQ6CPSPyRp9ap2e+Ux5RG6AQBgA6n3b87oqRes2ic8qSzMbc4fytz0HivhPWRsLHn3uxfHlM/2cgAA6AGHQ3h9YHShDdnMxJ2ZOfTdhTm2o8OJJ3QDAECPasxNpDG7f5U+4cnoqRem3jecRJ9weKhsLwcAgB7VNzCavoHRJEefjt5uzaVWH1qYOz3+zTTnJ62Er3PT08mFFxbjr3wlGR7ubj29wEo3AABwlE67lUp1sZ/UwXtvSqs1t2zO0u3oh09P5+R2333JaacV4927k1NP7W49vcBKNwAAcJSlgTtJNp96wYoHs7WaM2nOH1oWuhtzB1OrD1kJPwkNDKw8pjxuygCADeraa6/N4OBg7rrrroVrV111VS644IIcPHiwi5UB69HhFmXDW87J6KkXZGznJRnZdl4GR3ZmYHjHwrxOp53Jfbdm/N5dObj7y5ka/2bmp/em3ZrvYvUctmVL0ukUjy1bul1Nb7C9HAA2qE6nkyc96Ul55jOfmXe84x15/etfnz/7sz/L5z73uZx55pndLg/YoFrN2Uztv23Fg9lq9aEMjJyewU2nd6Ey6A7bywFgg6pUKnnjG9+YF77whTnjjDPy9re/PTfccIPADZSqVh88dp/wTnthbrs1l5lDdzmYbQ2128kttxTjxz8+qdr7XDor3QCwwV100UX52te+ln/4h3/Is571rG6XA/SowyG81jecWn0wSTI3vSdTB+5YmKNPePn27Uu233/7/d69ySmndLeeXmClGwA2sOuuuy633nprWq1WTjt8XC1AFxy+J3ypWn0ogyM7jzqYbW5qd5Jk5JTz0z+4NUlxy0ylUlnzujeaVmvlMeWx0g0AG9RNN92UZz/72bnmmmvy/ve/P8PDw/ngBz/Y7bIAVnT0dvTpjJ1+caq1viTJzKHvZn56r5Xwh6nRSK65phj/6q8mfX3dracXCN0AsAF961vfytOf/vT82q/9Wl796ldn165dueSSS3LjjTfm4osv7nZ5AA+o3W6mWl3cmHto7y1pzI0vm7N0O3rf0LZUKm5Q5uQjdAPABrN///484xnPyKWXXpo//dM/Xbj+Yz/2Y5mbm8snPvGJLlYH8NCsdDDbYZVKNWM7L1kI3c35yVRr/VbCOSkI3QAAwLqzNISn08nw2KMWnju4+0tpNWcdzLaCmZnk8suL8Sc/mQwNdbeeXiB0AwAAG0an3cqhvV9btU94//D2DG0+qwuVnRzuuy85fK7m7t3Jqad2t55e4PRyAABgw6hUa8fsE95uzS/M7XTamTn47dT7N/fMSnh//8pjymOlGwAA2PAOh/BqrT/1/pEkSXP+UCb2fHVhju3olEHoBgAAetLhnuBHHsx22PCWR2Vw5PQuVMZGYns5AADQk2r1oQxvOSfJyqej1/qGF+bOz+zLzMSd634lvN1OvvOdYnz22UlVl7XSWekGAAA4QrvdTKVSXWhDNjX+zcxN7V42Z1mf8MGtqVRr3Sj1Qdm/PznllGK8b1+ybVt36+kFfq8BAABwhGq1vhC4k2Ro9OyMbDsvgyM7U+/blGRxe/rkgdvTbjcX5rYayw9sO5k0myuPKY/t5QAAAA+gWq2nf2hb+oeKpeGl29HbzdnU6gMLc6cnvp3G7IGT8mC2sbHkD/5gcUz5bC8HAAA4gQ7t/XoacwePul6rD6VvcGzhPnJ6g5VuAACAE2jz9ies2ie82li+2j0zcefCivjJsBLOiSd0AwAAnGCrbUdfep94u9XIzKHvLry9FtvRZ2eTn/mZYvz+9yeDgyf8U3AE28sBAAC6oN2az+zk3av2CR8cOSPDWx55Qj/nffclp51WjHfvTk499YR+eFZgpRsAAKALqrX+4+4T3mxMZWr/7Q97Jbyvb+Ux5bHSDQAAcJJpt5uppLLQ+3t28p5MH/zWsjnLtqMPjqVataZ6MhK6AQAATnIrrYQvtXn7E9I3sKWY25pLUnEw20nCr0IAAABOcsfqE96cP5R638jC3NnJezI7ec+KB7N1OsnevcW87duTSqUbf5veYqUbAABgA5ncf3vmZ/Yedb1WH8rE9FjOfvQ5SZJ9+5Jt29a4uB5kpRsAAGADGdn2uLTbj1qxT/js1OLSdrPZxSJ7iJVuAACADe7wdvROp5WB4R3dLqenCN0AAABQkmq3CwAAAICNSugGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACURugEAAKAkQjcAAACUROgGAACAkgjdAAAAUBKhGwAAAEoidAMAAEBJhG4AAAAoidANAAAAJRG6AQAAoCRCNwAAAJRE6AYAAICSCN0AAABQEqEbAAAASiJ0AwAAQEmEbgAAACiJ0A0AAAAlEboBAACgJEI3AAAAlEToBgAAgJII3QAAAFASoRsAAABKInQDAABASYRuAAAAKInQDQAAACX5/wEdHwQzxww7jgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647896ff0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10],\n",
+    "                                       rotation_axis_position=[-0.5,0])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a more complex AcquisitionGeometry\n",
+    "\n",
+    "We can also set up rotations in the system. These are configured with vectors describing the direction.\n",
+    "\n",
+    "For example a detector yaw can be described by using `detector_direction_x=[X,Y]`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.871906Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.871492Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.100625Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.100144Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f964771ff80>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10],\n",
+    "                                       detector_direction_x=[0.9,0.1]\n",
+    "                                       )\\\n",
+    "     .set_panel(num_pixels=10)\\\n",
+    "     .set_angles(angles=range(0,180))\n",
+    "     \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can set `rotation_axis_direction`, `detector_direction_x` and `detector_direction_y` by specifying a 3D directional vector `[X,Y,Z]`.\n",
+    "\n",
+    "For 3D datasets detector roll is commonly corrected with a dual-slice centre of rotation algorithm. You can specify `detector_direction_x` and `detector_direction_y` - ensuring they are ortogonal vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.102173Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.102009Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.322852Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.322354Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f96478e7f80>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0],\n",
+    "                                       detector_direction_x=[0.9,0.0,-0.1],detector_direction_y=[0.1,0,0.9]\n",
+    "                                      )\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In 3D datasets we can tilt the rotation axis to describe laminograpy geometry by changing `rotation_axis_direction`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.324674Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.324284Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.545305Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.544793Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647778470>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0],rotation_axis_direction=[0,-1,1])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The structure of an ImageGeometry\n",
+    "\n",
+    "ImageGeometry holds the description of the reconstruction volume. It holds:\n",
+    "\n",
+    " - The number of voxels in X, Y, Z: `voxel_num_x`, `voxel_num_y`, `voxel_num_z`\n",
+    " - The size of voxels in X, Y, Z: `voxel_size_x`, `voxel_size_y`, `voxel_size_z`\n",
+    " - The offset of the volume from the rotation axis in voxels: `center_x`, `center_y`, `center_z`\n",
+    " - The number of channels for multi-channel data\n",
+    "\n",
+    "You will also need to describe the order your data is stored in using the relevent labels from the CIL. The default labels are: `channel`, `vertical`, `horizontal_y` and `horizontal_x`\n",
+    " - `ig.set_labels(['vertical','horizontal_y','horizontal_x'])`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a simple ImageGeometry\n",
+    "\n",
+    "To create a default ImageGeometry you can use:\n",
+    " `ig = ag.get_ImageGeometry()`\n",
+    "\n",
+    "This creates an ImageGeometry with:\n",
+    " - `voxel_num_x`, `voxel_num_y` equal to the number of horizontal pixels of the panel\n",
+    " - `voxel_num_z` equal to the number of vertical pixels of the panel\n",
+    " - `voxel_size_x`, `voxel_size_y` is given by the horizontal pixel size divided by magnification\n",
+    " - `voxel_size_z` is given by the vertical pixel size divided by magnification\n",
+    "\n",
+    "\n",
+    " You can pass a resolution argument:\n",
+    " `ig = ag.get_ImageGeometry(resolution)` \n",
+    "\n",
+    " - `resolution=0.5` double the size of your voxels, and half the number of voxels in each dimension\n",
+    " - `resolution=2` half the size of your voxels, and double the number of voxels in each dimension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Note on CIL ImageGeometry:\n",
+    "At 0 degrees `horizontal_y` is aligned with the Y axis, and `horizontal_x` with the X axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.546856Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.546684Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.551654Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.551245Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - default\n",
+      "Number of channels: 1\n",
+      "channel_spacing: 1.0\n",
+      "voxel_num : x2048,y2048,z2048\n",
+      "voxel_size : x0.1,y0.1,z0.1\n",
+      "center : x0,y0,z0\n",
+      "\n",
+      "ImageGeometry - 0.5x resolution\n",
+      "Number of channels: 1\n",
+      "channel_spacing: 1.0\n",
+      "voxel_num : x1024,y1024,z1024\n",
+      "voxel_size : x0.2,y0.2,z0.2\n",
+      "center : x0,y0,z0\n",
+      "\n",
+      "ImageGeometry - 2x resolution\n",
+      "Number of channels: 1\n",
+      "channel_spacing: 1.0\n",
+      "voxel_num : x4096,y4096,z4096\n",
+      "voxel_size : x0.05,y0.05,z0.05\n",
+      "center : x0,y0,z0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "print(\"ImageGeometry - default\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "print(ig)\n",
+    "\n",
+    "print(\"ImageGeometry - 0.5x resolution\")\n",
+    "ig = ag.get_ImageGeometry(resolution=0.5)\n",
+    "print(ig)\n",
+    "\n",
+    "print(\"ImageGeometry - 2x resolution\")\n",
+    "ig = ag.get_ImageGeometry(resolution=2)\n",
+    "print(ig)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a custom ImageGeometry\n",
+    "You can create your own ImageGeometry with:\n",
+    "`ig = ImageGeometry(...)`\n",
+    "\n",
+    "Giving you full control over the parameters.\n",
+    "\n",
+    "You can also change the members directly to reduce the reconstructed volume to exclude empty space.\n",
+    "\n",
+    "Using the previous example, we now can specify a smaller region of interest to reconstruct. We can offset the region of interest from the origin by specifiying the physical distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.553118Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.552816Z",
+     "iopub.status.idle": "2024-10-10T15:02:41.195828Z",
+     "shell.execute_reply": "2024-10-10T15:02:41.195317Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - default\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - RoI\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - Offset RoI\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f96472f8380>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "print(\"ImageGeometry - default\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "show_geometry(ag, ig)\n",
+    "\n",
+    "print(\"ImageGeometry - RoI\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "ig.voxel_num_z = 100\n",
+    "show_geometry(ag, ig)\n",
+    "\n",
+    "print(\"ImageGeometry - Offset RoI\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "ig.voxel_num_z = 200\n",
+    "ig.center_z = -1024 * ig.voxel_size_z\n",
+    "show_geometry(ag, ig)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also create an `ImageGeometry` directly.\n",
+    "\n",
+    "Here we create our ig independently of an `AcquisitionGeometry`, by first importing `ImageGeometry` from `cil.framework`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:41.197491Z",
+     "iopub.status.busy": "2024-10-10T15:02:41.197216Z",
+     "iopub.status.idle": "2024-10-10T15:02:41.199945Z",
+     "shell.execute_reply": "2024-10-10T15:02:41.199478Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from cil.framework import ImageGeometry\n",
+    "\n",
+    "ig = ImageGeometry(voxel_num_x=1000, voxel_num_y=1000, voxel_num_z=500, voxel_size_x=0.1, voxel_size_y=0.1, voxel_size_z=0.2 )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cf07678abc5cc77bc6e1a7d19b1e87ab0c29b83e7ee41c2bc72506d16d80ed44"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/v24.2.0/.doctrees/nbsphinx/demos/callback_demonstration.ipynb b/v24.2.0/.doctrees/nbsphinx/demos/callback_demonstration.ipynb
new file mode 100644
index 0000000000..0fd142943d
--- /dev/null
+++ b/v24.2.0/.doctrees/nbsphinx/demos/callback_demonstration.ipynb
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2024 -  United Kingdom Research and Innovation\n",
+    "#  Copyright 2024 -  The University of Manchester\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:    CIL contributors "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CIL Callback demonstration\n",
+    "\n",
+    "This notebook runs on CIL Master (built on 14/03/2024) and demonstrates the new callback functionality "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cil.utilities import dataexample\n",
+    "from cil.utilities.display import show2D\n",
+    "from cil.recon import FDK\n",
+    "from cil.processors import TransmissionAbsorptionConverter, Slicer\n",
+    "from cil.utilities.quality_measures import psnr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "from cil.plugins.tigre import ProjectionOperator\n",
+    "from cil.optimisation.algorithms import FISTA, Algorithm\n",
+    "from cil.optimisation.functions import LeastSquares, IndicatorBox, ZeroFunction, TotalVariation\n",
+    "from cil.optimisation.operators import GradientOperator\n",
+    "from cil.optimisation.utilities import callbacks\n",
+    "from cil.framework import DataContainer\n",
+    "\n",
+    "from cil.utilities.quality_measures import mse, mae, psnr\n",
+    "\n",
+    "# set up default colour map for visualisation\n",
+    "cmap = \"gray\"\n",
+    "\n",
+    "# set the backend for FBP and the ProjectionOperator\n",
+    "device = 'gpu'\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FDK recon\n",
+      "\n",
+      "Input Data:\n",
+      "\tangle: 60\n",
+      "\thorizontal: 128\n",
+      "\n",
+      "Reconstruction Volume:\n",
+      "\thorizontal_y: 128\n",
+      "\thorizontal_x: 128\n",
+      "\n",
+      "Reconstruction Options:\n",
+      "\tBackend: tigre\n",
+      "\tFilter: ram-lak\n",
+      "\tFilter cut-off frequency: 1.0\n",
+      "\tFFT order: 8\n",
+      "\tFilter_inplace: False\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e75840>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#%% Load data\n",
+    "ground_truth = dataexample.SIMULATED_SPHERE_VOLUME.get()\n",
+    "\n",
+    "data = dataexample.SIMULATED_CONE_BEAM_DATA.get()\n",
+    "twoD = True\n",
+    "if twoD:\n",
+    "    data = data.get_slice(vertical='centre')\n",
+    "    ground_truth = ground_truth.get_slice(vertical='centre')\n",
+    "\n",
+    "absorption = TransmissionAbsorptionConverter()(data)\n",
+    "absorption = Slicer(roi={'angle':(0, -1, 5)})(absorption)\n",
+    "\n",
+    "ig = ground_truth.geometry\n",
+    "\n",
+    "#%%\n",
+    "recon = FDK(absorption, image_geometry=ig).run()\n",
+    "#%%\n",
+    "show2D([ground_truth, recon], title = ['Ground Truth', 'FDK Reconstruction'], origin = 'upper', num_cols = 2)\n",
+    "\n",
+    "# %%\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Default behaviour "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6355b4a0be0c4297a02f41b49e6f7e3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e74b80>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "alpha=0.1\n",
+    "A = ProjectionOperator(image_geometry=ig, \n",
+    "                       acquisition_geometry=absorption.geometry)\n",
+    "\n",
+    "F = LeastSquares(A = A, b = absorption)\n",
+    "G = alpha*TotalVariation(lower=0)\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G)\n",
+    "algo.run(500)\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other provided CIL callbacks "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43acfefb45534d8093aa0174846f19eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    29.53it/s, objective=+8.586e+01\n",
+      "    20/500    29.53it/s, objective=+9.047e+00\n",
+      "    30/500    28.65it/s, objective=+2.640e+00\n",
+      "    40/500    28.53it/s, objective=+1.188e+00\n",
+      "    50/500    18.75it/s, objective=+6.928e-01\n",
+      "    60/500    21.45it/s, objective=+4.585e-01\n",
+      "    70/500    23.55it/s, objective=+3.451e-01\n",
+      "    80/500    25.28it/s, objective=+2.741e-01\n",
+      "    90/500    26.58it/s, objective=+2.337e-01\n",
+      "   100/500    27.57it/s, objective=+2.062e-01\n",
+      "   110/500    28.17it/s, objective=+1.870e-01\n",
+      "   120/500    20.05it/s, objective=+1.729e-01\n",
+      "   130/500    22.35it/s, objective=+1.623e-01\n",
+      "   140/500    24.32it/s, objective=+1.541e-01\n",
+      "   150/500    25.89it/s, objective=+1.476e-01\n",
+      "   160/500    26.91it/s, objective=+1.421e-01\n",
+      "   170/500    27.82it/s, objective=+1.375e-01\n",
+      "   180/500    28.58it/s, objective=+1.336e-01\n",
+      "   190/500    20.24it/s, objective=+1.303e-01\n",
+      "   200/500    22.51it/s, objective=+1.275e-01\n",
+      "   210/500    24.47it/s, objective=+1.250e-01\n",
+      "   220/500    26.06it/s, objective=+1.228e-01\n",
+      "   230/500    27.32it/s, objective=+1.208e-01\n",
+      "   240/500    28.08it/s, objective=+1.190e-01\n",
+      "   250/500    28.55it/s, objective=+1.173e-01\n",
+      "   260/500    28.69it/s, objective=+1.159e-01\n",
+      "   270/500    28.86it/s, objective=+1.145e-01\n",
+      "   280/500    29.03it/s, objective=+1.134e-01\n",
+      "   290/500    29.44it/s, objective=+1.123e-01\n",
+      "   300/500    29.27it/s, objective=+1.113e-01\n",
+      "   310/500    29.51it/s, objective=+1.104e-01\n",
+      "   320/500    21.28it/s, objective=+1.096e-01\n",
+      "   330/500    22.71it/s, objective=+1.088e-01\n",
+      "   340/500    24.61it/s, objective=+1.081e-01\n",
+      "   350/500    26.25it/s, objective=+1.075e-01\n",
+      "   360/500    27.47it/s, objective=+1.068e-01\n",
+      "   370/500    28.42it/s, objective=+1.063e-01\n",
+      "   380/500    29.10it/s, objective=+1.057e-01\n",
+      "   390/500    20.90it/s, objective=+1.052e-01\n",
+      "   400/500    23.14it/s, objective=+1.048e-01\n",
+      "   410/500    24.79it/s, objective=+1.043e-01\n",
+      "   420/500    25.48it/s, objective=+1.039e-01\n",
+      "   430/500    26.67it/s, objective=+1.035e-01\n",
+      "   440/500    27.03it/s, objective=+1.031e-01\n",
+      "   450/500    27.67it/s, objective=+1.028e-01\n",
+      "   460/500    27.81it/s, objective=+1.025e-01\n",
+      "   470/500    28.66it/s, objective=+1.022e-01\n",
+      "   480/500    29.28it/s, objective=+1.019e-01\n",
+      "   490/500    29.76it/s, objective=+1.016e-01\n",
+      "   500/500    30.16it/s, objective=+1.013e-01\n",
+      "   500/500    26.07it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e75030>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)\n",
+    "algo.run(500, callbacks=[callbacks.ProgressCallback(), callbacks.TextProgressCallback()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d4cfa5b60cf48b7acfd008e3c40655f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " 84%|########3 | 501/600 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "   501/600        ?it/s\n",
+      "   511/600    30.76it/s, objective=+1.011e-01\n",
+      "   521/600    30.74it/s, objective=+1.008e-01\n",
+      "   531/600    30.69it/s, objective=+1.006e-01\n",
+      "   541/600    30.26it/s, objective=+1.004e-01\n",
+      "   551/600    28.95it/s, objective=+1.002e-01\n",
+      "   561/600    20.01it/s, objective=+1.000e-01\n",
+      "   571/600    22.57it/s, objective=+9.986e-02\n",
+      "   581/600    24.69it/s, objective=+9.969e-02\n",
+      "   591/600    26.41it/s, objective=+9.953e-02\n",
+      "   600/600    26.40it/s, objective=+9.938e-02\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "algo.run(100, callbacks=[callbacks.ProgressCallback(), callbacks.TextProgressCallback()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Early stopping (a custom callback example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    31.71it/s, objective=+8.586e+01\n",
+      "    20/500    31.18it/s, objective=+9.047e+00\n",
+      "    30/500    29.77it/s, objective=+2.640e+00\n",
+      "    40/500    30.06it/s, objective=+1.188e+00\n",
+      "    50/500    30.28it/s, objective=+6.928e-01\n",
+      "    60/500    30.35it/s, objective=+4.585e-01\n",
+      "    70/500    30.46it/s, objective=+3.451e-01\n",
+      "    80/500    30.57it/s, objective=+2.741e-01\n",
+      "    90/500    22.27it/s, objective=+2.337e-01\n",
+      "   100/500    22.69it/s, objective=+2.062e-01\n",
+      "   110/500    24.61it/s, objective=+1.870e-01\n",
+      "   110/500    26.86it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08d5daaec0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class EarlyStopping(callbacks.Callback):\n",
+    "         def __call__(self, algorithm):\n",
+    "               if algorithm.objective[-1] <= 2e-1:  # arbitrary stopping criterion\n",
+    "                  raise StopIteration\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[callbacks.TextProgressCallback(), EarlyStopping()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    23.79it/s, objective=+8.586e+01\n",
+      "    20/500    26.96it/s, objective=+9.047e+00\n",
+      "    23/500    26.89it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08d5b68d90>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class EarlyStopping(callbacks.Callback):\n",
+    "         def __call__(self, algorithm):\n",
+    "            if np.mean((algorithm.x.array-ground_truth.array)**2) <= 3e-8:  # arbitrary stopping criterion\n",
+    "                  raise StopIteration\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[callbacks.TextProgressCallback(), EarlyStopping()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculating data discrepancy at each iteration (A custom callback example) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08df8487f0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class DataDiscrepancyCallback(callbacks.Callback):\n",
+    "    def __init__(self, A, data):\n",
+    "        self.f = LeastSquares(A, data)\n",
+    "        self.save_values=[]\n",
+    "\n",
+    "    def __call__(self, algorithm):\n",
+    "        self.save_values.append(self.f(algorithm.get_output()))\n",
+    "\n",
+    "mycallback_FISTA_lower_bound= DataDiscrepancyCallback(A, absorption)\n",
+    "algo1=FISTA(initial=ig.allocate(0), f=F, g=alpha*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo1.run(500, callbacks=[mycallback_FISTA_lower_bound])\n",
+    "\n",
+    "        \n",
+    "mycallback_FISTA_no_lower_bound= DataDiscrepancyCallback(A, absorption)\n",
+    "algo2=FISTA(initial=ig.allocate(0), f=F, g=alpha*TotalVariation(), update_objective_interval=10)         \n",
+    "algo2.run(500, callbacks=[mycallback_FISTA_no_lower_bound])\n",
+    "\n",
+    "\n",
+    "show2D([ground_truth, algo1.get_output(), algo2.get_output()], title=['ground_truth', 'FISTA_lower_bound', 'FISTA_no_lower_bound'], num_cols=3)\n",
+    "show2D([absorption, A.direct(algo1.get_output())-absorption, A.direct(algo2.get_output())-absorption], title=['ground_truth', 'Data error FISTA_lower_bound', 'Data error FISTA_no_lower_bound'], fix_range=[[0,3], [-0.02, 0.02], [-0.02, 0.02]], cmap=['gray', 'seismic', 'seismic'], num_cols=3)\n",
+    "plt.plot(range(10,501), mycallback_FISTA_lower_bound.save_values[10:], label='FISTA TV with lower bound ')\n",
+    "plt.plot(range(10, 501), mycallback_FISTA_no_lower_bound.save_values[10:], label='FISTA TV without lower bound ')\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Data discrepancy $\\|Ax-y\\|_2^2$')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the without the lower bound, the reconstruction overfits to the noisy absorption data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculating a noise approximation for each iteration  (A custom callback example) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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whxEMBnHttdfid7/7HW655RYjNF/zmtegp6cH5513Hq644gr4/X7cdNNN2LFjx6zH/qlPfQrf//738cpXvhKf/vSnEY1G8bWvfQ2FQmHG+3rhC18IAPjKV76Cc889F4FAAC94wQuQSCRafsfj8eAf/uEfcMMNN+Cwww7Di170Ivz617/GzTff3JH9K4qiKMuf73znO/j617+Ot771rXjpS1+KVCqFZ599Fv/2b/+G3//+9/j0pz9t61syFT/+8Y/x3HPP4aqrrsKJJ5446fO1a9fimmuuwaZNm/Da1762w2eyh6OPPhrr1q3Dxo0bYVnWjNO+AeCII47AYYcdho9//OOwLAs9PT34wQ9+gM2bN7e9j97eXrzvfe/D9u3bcfjhh+Puu+/GN7/5Tbzvfe+z9SBph2QyieOPPx5f+tKX0NfXh0MOOQRbtmzBpk2b2jaGSK666iqccsopeNOb3oQLLrgAIyMj+PjHP461a9fine98p23bLVu2YPfu3QD2GFSeeeYZE414wgknoL+/f8bHVxRFUeYGlemTWe4y/ZlnnsGDDz4IAHjqqacAwMjpQw45pK3yP4qynNBIdGVZ87d/+7e4/fbbMTY2hrPPPhuXXHIJzjzzTJxxxhltCZETTjgB//3f/41YLIZ3vOMdeMtb3oJMJoPvf//7tgafyWQS99xzDxKJBP7hH/4B559/PtauXYtPfvKTsx772rVrcd999yGZTOLcc8/F//k//wfr1q3DZZddNuN9nXjiibj00kvxgx/8AK94xSvw0pe+FA8//PC03/u///f/4h/+4R/wxS9+EWeccQa2bt3qGsU/2/0riqIoy5vTTz8dZ5xxBu6++268613vwitf+Uq8//3vR6PRwHe+8x189rOfbXtfmzZtQjAYnGSIJX19fTjzzDPxwx/+0DU1ulOcd955sCwLRx11FI455pgZfz8QCOAHP/gBDj/8cLz3ve/F3//932NkZAT33Xdf2/sYHBzEzTffjG9961t4/etfj//v//v/8IlPfAJf/epXZzweYE/E20knnYSPfvSjeMMb3oCHHnoImzdvRiqVmvG+TjzxRNx9990YGhrC6173OnzgAx8w2XXOyLjPfOYzeNOb3oQ3velNKJfLuP/++83fv//972d1LoqiKMrcoDJ9Mstdpv/0pz81cvmee+4BAPP3NddcM6vxKcpSxmNZlrXQg1CU+aRWq+HFL34xDjjgANx7770LPRxFURRFUZS2OfHEEzE6Oorf/e53Cz0URVEURVH2AZXpirK00HIuyrLnvPPOwymnnIKVK1dieHgY119/PZ544gl85StfWeihKYqiKIqiKIqiKIqiKIqyyFEjurLsyeVy+PCHP4zdu3cjEAjgJS95Ce6++2686lWvWuih7TPNZtO1uYvE79d/c0VRFGX/otFoYKpkS4/HA5/Pt2yOOx/U6/UpP/d6vfB6tVKkoiiK0llUpncelemKMju0nIuiLGEuv/zyaWvPbdu2DYcccsj8DEhRFEVRFgEnnngitmzZ0vLzgw8+GE8//fSyOe58wGbqrTj33HNx0003zc9gFEVRlP0GlemdR2W6oswONaIryhLmueeew3PPPTflNuvWrWu7S7qiKIqiLAeefPJJ5HK5lp+HQiG88IUvXDbHnQ8eeuihKT/v6+tTp72iKIrScVSmdx6V6YoyO9SIriiKoiiKoiiKoiiKoiiKoigt0GLJ2FNX+rnnnkMikZg2rUVRFEWZGsuykMvlsGrVKtdaeuVyGdVqdZ+PEwwGEQ6H93k/ytJEZbeiKErnUNmtzAcquxVFUTqHyu75R43o2FMSY/Xq1Qs9DEVRlGXFjh07cOCBB9reK5fLWLNmDYaHh/d5/4ODg9i2bZsK9P0Uld2KoiidR2W3Mpeo7FYURek8KrvnDzWiA0gkEgs9BEVRlGWH29xarVYxPDyMHTt2IJlMznrf2WwWq1evRrVaVWG+n7IcZLfX64XH44FlWWg2mws9HOX/wehIrXi4NNH7t2+o7FbmkuUgu/1+P7xeLxqNBhqNxkIPR/l/6Nyv7M+o7J4/1IiO6TsTK4qiKDNnqrk1kUjskyKlC2RlprJ7sSpXlmUtujEtJuhkUJT5YrHOFfOFym5lLpHP11T/a/yMPxeTo5mOb32eO8v+Pvcqyr6gsnv+UCO6oiiKMu/sq+FQhbnSCrmI5HPi9XpNncBms7lolPHFMo7FzEL8r+v8srTZ1/vnNocoe1DZrXSSVs+Dx+OxvchikZkafT437Ov8oPPL/o06YVqjsruzqBFdURRFmXdUmCuKoiiLFc2AcEdlt6IoiqIsLVR2dxY1oiuKoiiKsmxwW+jJmuOLJZJNUfYHlmJkGJXNxTbmpXgtFWU28Bm3LGtRlnNRlP2FpSR3FusYl9I1VNpDjeiKoijKvKMecWU+WYwGMUVZ7jhLMQCLe+5m9PliHCOv5UKPTWW3Ml9IQ7qiKPOLU3YvBvmzFFHZvTzxLvQAFEVRlP0PGek329dMufbaa7FmzRqEw2GsX78eP//5z6fcfsuWLVi/fj3C4TAOPfRQXH/99ZO2uf3223HUUUchFArhqKOOwp133mn7/LrrrsO6deuQTCaRTCaxYcMG/PjHP550LS6//HKsWrUKkUgEJ554In7/+9/P+PwURVEWI0tF+Vrs41wM41sI2a0oiqIoS5XFIPdUdncWNaIriqIo8858C/PbbrsNF110ET75yU/i0UcfxXHHHYfTTjsN27dvd91+27ZteM1rXoPjjjsOjz76KD7xiU/ggx/8IG6//XazzdatW3H22WfjnHPOwW9+8xucc845ePOb34xf/epXZpsDDzwQ//zP/4yHHnoIDz30EF75ylfijDPOsBnJv/jFL+Lqq6/GNddcgwcffBCDg4M45ZRTkMvlZnhVFUVRFg+qhHWOxXINVRFXFEVZ3jib+urcPXsWi+xT2d1ZPJZeEWSzWaRSqYUehqIoyrIik8kgmUza3uN8u3v37kmfzYRsNov+/n7XY7hxzDHH4CUveQmuu+46896RRx6Jv/u7v8OVV145afuPfexj+P73v48nnnjCvHf++efjN7/5DbZu3QoAOPvss5HNZm2R5X/7t3+L7u5u3HLLLS3H0tPTgy996Us477zzYFkWVq1ahYsuuggf+9jHAACVSgUrVqzAVVddhfe+973TX4z9FJXdM0NrMiqK0g6LSXYryw+V3YqyuFED+tJEZff8oZHoiqIoyrzTKY94Npu1vSqVyqRjVatVPPzwwzj11FNt75966ql44IEHXMe3devWSdu/+tWvxkMPPYRarTblNq322Wg0cOutt6JQKGDDhg0A9kS8Dw8P2/YTCoVwwgkntNyPsrxw1p2cK1QRUhRlX9FoNkVRlOWPztnLC5XdnUWN6IqiKMq80ylhvnr1aqRSKfNyiyofHR1Fo9HAihUrbO+vWLECw8PDruMbHh523b5er2N0dHTKbZz7fPzxxxGPxxEKhXD++efjzjvvxFFHHWX2we+1OzZl+eD1el2bL+7vyFRivTZLm3bvod7vpYEq4orS/ry2v6LXZ2kzk/laZffSQGV3Z/Ev9AAURVGU/Y99Fcj87o4dO2xpZaFQqOV3nAs8y7KmXPS5be98v519vuAFL8Bjjz2GdDqN22+/Heeeey62bNliDOmzGZui7E94PB5dwCvKIqBTsltRlOWPym5FWRyo7O4sakRXFEVRlizJZHLa2mx9fX3w+XyTIrtHRkYmRYCTwcFB1+39fj96e3un3Ma5z2AwiOc973kAgKOPPhoPPvggvvKVr+DrX/86BgcHAeyJSF+5cmVbY1OWD81mU5VMF9wcVsrSpN1ne6H/BxhJpxFXiqJMh84RirKHhf5fkOvEhR6Lsv+g5VwURVGUeWc+08qCwSDWr1+PzZs3297fvHkzjj32WNfvbNiwYdL29957L44++mgEAoEpt2m1T3nurN2+Zs0aDA4O2vZTrVaxZcuWafejLA900d+aVtdGjetKp/F4PPB6vfB6VTWaCk0JVxRlOuT/uZb6UOYSym59xqZGZXdn0Uh0RVEUZd6Z77SySy65BOeccw6OPvpobNiwAd/4xjewfft2nH/++QCASy+9FDt37sS3v/1tAMD555+Pa665Bpdccgne8573YOvWrdi0aRNuueUWs88LL7wQxx9/PK666iqcccYZuOuuu3DffffhF7/4hdnmE5/4BE477TSsXr0auVwOt956K+6//37cc889APYs/i666CJ84QtfwPOf/3w8//nPxxe+8AVEo1G89a1vnfX1UZTljC7mlblAFcXp0ZRwRVEUZbFAmaKyZWpUdncWNaIriqIo8858C/Ozzz4bY2NjuOKKKzA0NIS1a9fi7rvvxsEHHwwAGBoawvbt2832a9aswd13342LL74YX/va17Bq1Sp89atfxRvf+EazzbHHHotbb70Vn/rUp3DZZZfhsMMOw2233YZjjjnGbLNr1y6cc845GBoaQiqVwrp163DPPffglFNOMdt89KMfRalUwgUXXICJiQkcc8wxuPfee5FIJGZ7eRRl2aALd2U+WEyllRZzaRlVxBVFaQf9X1fmg8UoJxcjKrs7i+YsKoqiKPsFF1xwAZ5++mlUKhU8/PDDOP74481nN910E+6//37b9ieccAIeeeQRVCoVbNu2zUStS8466yz87//+L6rVKp544gm84Q1vsH2+adMmc8yRkRHcd999NgM6sMdgcvnll2NoaAjlchlbtmzB2rVrO3fiiqIoyrQsFiVRyx8oiqIoytJDZfderr32WqxZswbhcBjr16/Hz3/+8ym337JlC9avX49wOIxDDz0U119//aRtbr/9dhx11FEIhUI46qijcOedd7bc35VXXmkyviWWZeHyyy/HqlWrEIlEcOKJJ+L3v//9jM5tQY3oP/vZz/C6170Oq1atgsfjwfe+9z3zWa1Ww8c+9jG88IUvRCwWw6pVq/D2t78dzz33nG0flUoFH/jAB9DX14dYLIbXv/71ePbZZ+f5TBRFUZSZoLXZli4quxVFUeYOy7LQbDYXehiuqOxeuqjsVhRFmVsWq4ybb9l922234aKLLsInP/lJPProozjuuONw2mmn2bK+Jdu2bcNrXvMaHHfccXj00UfxiU98Ah/84Adx++23m222bt2Ks88+G+eccw5+85vf4JxzzsGb3/xm/OpXv5q0vwcffBDf+MY3sG7dukmfffGLX8TVV1+Na665Bg8++CAGBwdxyimnIJfLtX1+C2pELxQKeNGLXoRrrrlm0mfFYhGPPPIILrvsMjzyyCO444478Mc//hGvf/3rbdtddNFFuPPOO3HrrbfiF7/4BfL5PF772tei0WjM12koiqIoM0QV8aWLym5FmVu0weX+zWKWcyq7ly4quxVFmQ0aXb30mW/ZffXVV+O8887Du9/9bhx55JHYuHEjVq9ejeuuu851++uvvx4HHXQQNm7ciCOPPBLvfve78a53vQtf/vKXzTYbN27EKaecgksvvRRHHHEELr30Upx88snYuHGjbV/5fB5ve9vb8M1vfhPd3d2TrsPGjRvxyU9+Em94wxuwdu1afOtb30KxWMTNN9/c/glaiwQA1p133jnlNr/+9a8tANYzzzxjWZZlpdNpKxAIWLfeeqvZZufOnZbX67Xuueeeto+dyWQsAPrSl770pa8OvjKZTMv5dvv27VY6nZ71a/v27S2PocwfgMpufemr0y+v12t5vV7L4/Es+Fj0tf+9VHYvfwCV3frSl77ae+laZGm85kN279ixw8pkMuZVLpcnHbNSqVg+n8+64447bO9/8IMftI4//nhXuXDcccdZH/zgB23v3XHHHZbf77eq1aplWZa1evVq6+qrr7Ztc/XVV1sHHXSQ7b23v/3t1kUXXWRZlmWdcMIJ1oUXXmg+e+qppywA1iOPPGL7zutf/3rr7W9/u+vY3FhSYS6ZTAYejwddXV0AgIcffhi1Wg2nnnqq2WbVqlVYu3YtHnjggZb7qVQqyGaztpeiKIqiKJ1HZbeizAxrEUXtukWgac1uRVn+qOxWlOXFbGX3YliLKIuD1atXI5VKmdeVV145aZvR0VE0Gg2sWLHC9v6KFSswPDzsut/h4WHX7ev1OkZHR6fcRu7z1ltvxSOPPOI6Lu6D32t3bG74295ygSmXy/j4xz+Ot771rUgmkwD2XIRgMDgpTH+6i3DllVfis5/97JyOV1EURWnNvhqIdEG3NFgo2e1UEvR5UZYSi+V55f+Rx+NZNGNSFhaV3fsHqncryvLC4/GYMnHNZlPn4v2MTsnuHTt2GJkAAKFQqOV33HSxqZw4rXQ3+f5U+9yxYwcuvPBC3HvvvQiHw1OdzozH5mRJRKLXajW85S1vQbPZxLXXXjvt9tNdhEsvvRSZTMa8duzY0cnhKoqiKNNgtai3NpOXsrhZSNmtz4ii7Dv6P6Q4Udm9/FkqerdmwyjKzND5d/+lU7I7mUzaXm5G9L6+Pvh8vknO1ZGRkUkR4GRwcNB1e7/fj97e3im34T4ffvhhjIyMYP369fD7/fD7/diyZQu++tWvwu/3o9FoYHBwEABmNDY3Fr0RvVar4c1vfjO2bduGzZs32zwfg4ODqFarmJiYsH1nuosQCoUmPQCKoijK/KGK+PJmschufVaUuWa5G3Lc/ofm8/9KS8csLlR2L28Wi+xWJsNIYm06rcwGzr3zMQfzWVXZvXiYT9kdDAaxfv16bN682fb+5s2bceyxx7p+Z8OGDZO2v/fee3H00UcjEAhMuQ33efLJJ+Pxxx/HY489Zl5HH3003va2t+Gxxx6Dz+fDmjVrMDg4aNtPtVrFli1bWo7NjUU9C1OQ/+lPf8J9991nvBBk/fr1CAQCtoswNDSE3/3udzO6CIqiKIqidAaV3dOz0IZBVWw6gyx3onSehf4/UZT9CZXdix+dD5V9Yb6dmfq87r9ccskl+Ld/+zfccMMNeOKJJ3DxxRdj+/btOP/88wHsyVB6+9vfbrY///zz8cwzz+CSSy7BE088gRtuuAGbNm3Chz/8YbMNS7VcddVV+N///V9cddVVuO+++3DRRRcBABKJBNauXWt7xWIx9Pb2Yu3atQD2PJMXXXQRvvCFL+DOO+/E7373O7zjHe9ANBrFW9/61rbPb0Froufzefz5z382f2/btg2PPfYYenp6sGrVKpx11ll45JFH8MMf/hCNRsOE3ff09CAYDCKVSuG8887Dhz70IfT29qKnpwcf/vCH8cIXvhCvetWrFuq0lHnG6/UiHA4jEAjA7/cjFArB6/WiUqmgUqmg0WigUqmgXq8v9FAVRRFoRNrSRGX3vrPQz/5CH9+J3+9HJBKB1+tFtVpFpVJZElGrLGOw2Me5VNHrujjR+7I0WW6ye398DheLXGSUsc/nQzAYhNfrRa1WQ61Wg2VZWnN7P0fv/eJkPu/L2WefjbGxMVxxxRUYGhrC2rVrcffdd+Pggw8GsMcBu337drP9mjVrcPfdd+Piiy/G1772NaxatQpf/epX8cY3vtFsc+yxx+LWW2/Fpz71KVx22WU47LDDcNttt+GYY46Z0dg++tGPolQq4YILLsDExASOOeYY3HvvvUgkEm3vw2Mt4FN+//3346STTpr0/rnnnovLL78ca9ascf3eT3/6U5x44okA9jQ++chHPoKbb74ZpVIJJ598Mq699lqsXr267XFks1mkUqlZnYOy8AQCAfT19SEWiyEWi6G7uxuBQAATExMYHx83qYeFQmGhh6oo+xWZTGZS2i7n27/85S8zElZOcrkcDj30UNdjKHOLyu7lCSOGFmJZGIvF0N/fD7/fj2w2i0wmg0ajgUajocqYoswzKruXJyq7lz/zIcc9Hg/8fr8JYkulUvD7/SgUCsjlcmg2m6jVamg0GnM2BmXxwIyxxeLg2Z9R2T1/LKgRfbGgwnxp4vV64fP5EAqFMDg4iK6uLsRiMfT09Bgj+u7du1GtVrFr1y5kMhlYlqVCXVHmiamE+VNPPbXPwvywww5TYb4fo7K7s8ynEZ31MoPBIPx+P2KxGPr6+owRfWJiAvV6HZVKBbVabd7GpSiKym5lblHZPXfMtRxn9HksFkMoFEI4HEYikYDP5zNG9EajgUKhgEqlMqdjURYHakRfPKjsnj8WtJyLoswWj8eD/v5+rFixAslkEi960Ytw4IEHIhQKIRaLGWGez+eRy+Xw6KOP4plnnkGhUMCuXbtQrVYX+hQWNfuSnk5hKpvfyBpsmuKnKIrSHrKe5FzPm/M1LweDQVMa4G/+5m/wvOc9D/V63USulctllEolFItF/PGPf8Tw8LAxqDebTZUhc4TP5zOyn8EGsjGYXndFUTrJQmY/LVfm8loGg0GEQiF0dXXh+OOPx+GHH45qtYpCoYB6vW50vGKxiIcffhjbt29Ho9FArVYzsrvZbM7Z+PZXpJzm/Zdrx7mW3fPZsFRRFgtqRFeWJB6PB93d3TjssMPQ39+PV7ziFTjiiCPg8/kQCATg8XhQq9VQrVYxPj5u6qGPjo5iYmJCjehTIBt5zUYgyjp5VMrl4kk91Qqw73Ud9RlSZst8Gqb3leVoZAgEAohGo+jv78dJJ52EV7ziFchms9ixY4fpY1Kv15HJZFAqlZDP51GtVtFsNo0s14yyzkKZ7fV6TRkd+T6NI9Ptgyyn51Wxo7Jb6RTLRb4tl/OYjmAwiFgshoGBAZx00kk46aSTMDExgW3btqFUKiEcDiMSiWB8fBwTExMYGxszNdIpU9SI3llk0Jqcm2V0eLuyW+f15Y3K7s6iRnRlSeL1ehGPx9Hf34/e3l5Eo1H4/X6b4ZaTBeu19ff3o1qtwu/Xx55QyAYCAaNA86c0eDNCsFUUoIw+Z4MZn88Hv98Pj8dj+261WjV1btUQsv+iwlxZKNgMcimwlMbaDh6PB+FwGN3d3Ugmk/B4PKhWq6hWqyYa3ev1mjTxeDyOrq4uFItFFItFdcI6oOz1+XyIRCLw+Xyo1Womal9eL3nd+D0q11IR5+8ATMP2RqOBUqk0ZYN2bbK6f6CyW+kky+F5WA7nMB0ejwfRaBR9fX3o6emBz+dDpVIxspv6nN/vRyAQQCKRQG9vL4rFIiqViolUV/YgA85CoRB8Pp8JIGDAQCsd2SlnafuQn0uneLuyW1neqOzuLGpNVJYUVPz8fj8OPPBA/M3f/A1SqRQGBgYQDAZtUdQUIIlEAkceeSR6enrwxBNP4Mknn0Q6nV7YE1kEeL1e+P1++P1+9PT0mMYwkUgEfr/fJsQnJiaQzWZRq9VM2p6EinYgEEAymUQoFDL7lpFt9XrdRBVWq1UUi0WNSthPUWGuLCRL5flZbmmyHo8Hg4OD+Ku/+iukUinUajVs27YNxWIRu3fvRq1WQ1dXF/r6+uDz+XD44YcjkUjgueeeQzqdNoZ0ZQ90VicSCRx66KFIpVIYGRnBzp07Ua1WUavVjFJO57V0cNNBznUVX3Ssh0IhhEIhlMtl7Nq1a0pFHFg+z6nSGpXdSqdwrv81m2Xx4vV6cdBBB2H9+vVIJpNoNpvYtm0bCoUCRkdHUavV4PP5kEgkEIlE8PznPx/hcBg7d+5ENptVfU/g8XgQDAYRCAQQi8Vw4IEHIh6PI5/PI51Oo1qtIpPJIJ/P2yLJpYObTnI60BnARtnOQIRyuYyhoSGV3YrK7g6jRnRlycFoqWQyiVWrViEejyMWixnBQmhQDwaD6O3thd/vx+7duzUS/f8ho8Wj0ShSqZSJHggEAqaOHV+VSgUejwelUsm2Hwp1v9+PYDCIaDSKcDhsFHFpRKdCD+xZAKjnW1GUuWA5pVcvh3MgHo8H8XgcK1asQDQaRbPZNGVb6KBNJpMIBAIAgO7ubjQaDRSLRZMhpexFNljv6+tDb28vGo0GxsbGbH1IgL1KN6PU+H1Z/5wl8SKRCLxeL2KxGMLhsPlMURRFmTlLPUvH4/EgkUjggAMOQDQahWVZyGQyKBaLJtKZTcB9Ph+6u7tRr9dRLBZNSTBlL5S14XAYvb296OrqQigUQr1eN31huI7lsyON6JTvzCYPBoNmn16vF9FoFNFo1Oj5Svss9f9VZX7Q/yplSUIDuYw8n257pk6p4XZvSn1XVxeCwaBJrZdpZXwFAgF0dXXB6/WiXC7bjOCMYGOdvGAwiEgk4mpEr9fr8Pv9JloBAMrlsq1murL/oB5xZS7R52PxwproVPCAybW3qZSXy2WUy2VTV7VdlpMTxUkwGEQ4HIbP50M4HEYoFEI8HgcAUzueToh4PG7S7sfGxlAulwHsdWLLyPNUKmUy+oA9RnUq+Y1GA6FQCMFg0Cav3a7vcrzmyl5UditzxXIuCbUczsnn85nG4CzVSTkC7DnHSqViAqaY/SRf+ysyOpzBa+FwGNFo1BYg4Pf7EQqF0NPTg1gshmq1iomJCVsAG/dFHT0WiyEUCpl7wfvEALdYLGayy8vlss0g307NdGV6lsK8pbK7s6gRXVlySM+srOE51fay1vf+Dh0JiUQCBx54ICKRCJLJJOLxuM3LDeydMOkpz+VyRgDL6HRGsodCIfNTNnml0s16t9VqFR6PB8Vi0aSUqxDfv1BhrihLi04YpqUDl8ok32c/k0ajgUKhgFqthnw+j0KhgFKp1HYPDaezfLnNFZFIBP39/fD7/bZMPI/HY+RzOBw2WXjJZBK5XA6lUsl83mg04PV6EYlETMmWvr4+RCIRVCoV5PN5AEAoFEIikTBR6expUq1WjfItG5kBUKUcsytLsVSeWZXdylyiz8fc0AkjH7OUmJ3E/bH8V7PZNLKbDt1WvbT2JyifY7EY+vv7EQqFEIlEEIlETBS507FNeZxOp/G///u/Rt8G9tanj8ViJoM8HA7bjkV9nmVWw+Ew8vm8KeHGQDnLskzJNwVT2pOm+o6zz8xiRGV3Z1EjurLk4D8xm1RON/lTQMhSIgtFq0h4OTG5TVKtJvXZTGiyrjyj2FibrRVME2NjVjZAkU3NZD1V/i2jDClcWG+d9dKnc4IoyxMV5oqyf+KMSuNPv9+Per1uZAKNvSwpNhPmOirIKbPaORbl4L7OfcwYY5Q4DRjynHkNGbVerVYnBRIwGk1GowcCAXMPqNQzCIGOcZ6LNiPrPBrNpihKp+nUPE0HaaPRmOQApz7H7er1unG6LvT//Gx16On09ZnC7DHKZUaPS+MrryEzuyuVitGX5fEpj9mTjGsAbif7wwWDQQAw9dL5+VKQN/PNbNc1S+FaquzuLGpEV5YUFOD1eh27du3CE088ga6uLrzgBS9AOBy2GallWvgzzzyDHTt2YNu2bSadeT6hAKThmoKMky47lzcaDVQqFZvySkVYLlgAGMMCDQ3tTG70SrMRCeudTlcvTY4jHo+jXq+jVCoZwwbroVOYU6Dze/R6y6ZmfDWbTc0QUBRFWeR0YgFtWRbGxsbw5JNPIhqNYtWqVaZcWDKZNDXTk8kkCoUC/vSnP2FsbAy5XK5tJ7iMhN7XMcs1BeWUbMzJCDtmWjkd+lRspewvl8szarLmzAAIh8Po7u5GIBBAs9lEuVxGKBRCNBpFKBQy+5ZlV2QqOddQHJ8su8YIcyrdAEzkWiwWMxlkjDLUNH13ZnM99DoqijIXdEp2j46O4o9//CPi8ThWr15t5JAsH8YGmU888QSefvppjIyM2Jzg81VqzRmNLSO0qTdTRtbr9Unjod4tdXFG2M9E7sko5VAohGQyacrhNJtNW38SWWKFjghgj8yPxWImA5x6M3VuAEbPdgbrycajzAJ3rpFU7tiZzfVYzBHoytygRnRlyUGBNzo6ij/96U/o6+vDypUrMTAwYDziwF5FulKp4Nlnn8UTTzyB4eFhVCqVeR2vjNSmAKX3mRHdbMxCwzgFJA3R4XDYpGpRODItm4psO6nubEBCozdTuadrGEbjAWvZUlgw5VsaxukskFHmMvrO7/ebnzSiV6vVWV9fZWmiHnFF2f+wLAsTExN46qmnkEqlkEgkjFIZjUbh9/uRTCbR09NjoqInJiaQz+fbLufC43QCqUBLZ7JUgrkmcTPyy8afbJjK/iLtzIFu2WvBYBBdXV0IBALIZrMolUrGGB4KhYxcdZZakVHpHCvlNbMAiJThNIBEo1EEAgFY1p6GckSV8P0Lld2Ksv9hWRbS6TT+8pe/oKurCz09Pejt7TX6pM/nQzKZRCqVgtfrRS6Xw86dO5HJZIwMkZHSc13ixZl17Qxek3XbnYFo1JVDoZCRkSyXJo3vMxk/98n65awd75TvzAKThm46yVmSjTYC1j0HYMvicxrRaT/I5/PmMzl+nZP3jaVy/VR2dxY1oitLEsuyUCgUMDo6imazieeee8402mKkdbVaRaVSQSaTwe7duzExMTGjaLZ9RXrBGQFGJVYKPkaTeTweVKtVUzec6dP8PiPDnOlyFP4zMTA4x7kv21PQO5uNUchLj32rVzvHc/7kcZy/6yS/NFBhrij7H8y8ogN2YmLClCSJx+Pw+/0oFAqYmJhAJpPB6OgoisWiMTrPJ1L2SuWfRnQAppwce30QyilZ3owlUxgtNhM4XzrlIFPrGSFHx7wspcafztIsNCZwTTTV9ZXrDRoRZhKJ59yXsnRR2a0o+x+WZaFcLhsH6ujoqCkrFovF4PP5kM/nsXv3bqTTaYyNjaFQKBijr9zPXEMDM4PGIpHIJCM6HceMQpfR6LLMmTSi873ZlKhxllqTertTf3dmZ/PYsvwpZT/XH7I5qfO4HK+b4X+q8+h0ORtlYVHZ3VnUiK4sSWg4z+fziMViGB0dxcqVK02as9/vRzqdxsTEBAqFgi0Kfb7Kuch6ZQMDA+jq6jLR6BSQFHrJZNKUnqGHWiq88XgckUjEtv9yuYxgMIh6vY5MJmMWAHM9yXH/0nDeaDSMF7xerxslnQJdRstz0SJfboJfRv7J0jByAcIXxzCT0jbKwqLCXFHml/lKo54KRrNVKhWEw2GUy2U888wzCAaDpkFmOp3G+Pg4KpUKRkdHjfN7vjKWqGhTdnd3d9s+pyIN7JHDTLEGYNKlKaeo+IZCIcTjcQQCAZTLZZvSO53x2s1ZTKrVqmnkxojAcrlsi0rn93hMGvIpO3O5nC113S0lnGVjmDUnHfhTwbWAHDvltbI0UdmtKPNLq+Ch+cSy9pRiK5VKCIfDSKfT6O7uNo2ufT4fdu/ejeHhYZTLZQwPDyObzZqeZNzHfEWf9/X1IZVKmZrgTuc1I9ErlQpyuZwpW8Z+X9Fo1JSJlSVXqGcWi8W25JiMKOc+qCsXi0Wzb9lrjMhIdK6BuG6o1WooFApmjcGSOtLZzn2Uy2VzrjLgbKp7QfsDryv3pTq2O4thfT0dKrs7ixrRlSVLNptFLpdDJBKBz+fDxMQEYrEYVqxYgUAggNHRUezatQvFYhE7duzA2NjYvI6PgpcRdqz76mzuBewVlKyXKgUX65mxG7qERuhCoTDrCXxfan9JI3a9Xjdp5FwIyGh06TWX0WytjAiyjpyMBJARdByHdCDs713gFUVRFjOVSgXVatUokMVi0fTbAIBnn30WO3bsWFBDK2VNLBZDV1cXAHskGTPIfD6fcXoHg0GbsZr7oBJPQ7WMiNvXZlR0TANAsVg0TmlZjsWZps4xWZZlst+Avc5uy7ImlXij0s7t25WzXPNI5Y1KvsppRVGU6VksBrpSqYRSqWTk38TEBOLxOKrVKvx+P5566ik8+eSTM24E3kko48LhMBKJhMkAc5YXpdGcZeNoxGaJFQbByYapjUbDBK/NpJeXdCDLXmHUXdnfTPYwcZ6PbB7KsThlt9txqZ+zjFu7z5Bco0inujrAJzPTzEJleaBGdGXJ02g0jDeW9cp8Ph+y2Syy2azxwM73IoQpblSep0rhpmdZpp3RyCwFqPw+lXEqvO1M4jJlrVqtmhT5YDCIUCjU8nsU0LVazUSiVSoV45GuVqsolUpoNpsIBoPmXKjI81z4fR6b3w+FQohEIqb+OxcLjJiThgcZiS6N6DwvNl2h15118dkMRo3siwP1iCvK/gn/dxnNxdTqUqkEj8dj3psNnTDOUvb4/X5Uq1XkcjmbvGF9UWfmEyPBZNYUS9U4U8Pj8ThqtZqJZJ/JfMgINqajc5+stSoN68AepwWz8JxlcRhRTgMCf6e8LRaLJpqQfVuoRDuV+0gkYgwSzBqIxWKIRqO2cjDSKCFryVM28/tcM8xXCT6lPVR2K8r8Ih2QiwFmTlP/o86XTqcXtMEiM7Aoi2k4pmGcBnUANiO2bLBN2cdIdAl1S5/Ph3K5bCK7270vvG7yuFwbMGuMMFq9Vqshn88jn8+jVCrZ9G42K43H4zZDPw3slNeFQsEml6UzndnudIzQMM/rSHnNNYLMQOdY+KrX66Z8j5Tti+W5nSuWSmCAyu7OokZ0ZcnTaDRM2Rafz4fh4WHTDKtaraLRaJhIsfmcAMLhsGn+xSjyqTzXjG7r7u42ht9isQgAtu7nNJbXajVTl5XR7dMZidnE0+PxoFQqmRQ22S3cDQrLcrmMfD5v0vEpQGn0YK13GsK5TwrearWKbDaLSqViW0jEYjHE43GEQiH09PQgGo2ahYHTA++M3qMhgy9G4+XzeUxMTKBWqyGdTiOdTpuFhXrSFx4V5spiQkbazEdZrIVgMZ0TFa5MJoNsNmurOd6qxNd0SPm6L+fKBto+nw+lUsnmgAWARCIBALaSZYx8CwQCtoypVCqFaDQKAEYORqNRdHV1oV6vG7lEOdYOlUoF6XTaGNHZOC2VSiEWixllmWufYrGIQqGAXC6HbDY7aX9UAHndGVUPAGNjY6b3DM9VNokLh8MmU27FihWIxWIolUrIZrOwLAt9fX3o7e01xoNarWZT+guFgjEMUOkul8tIp9MmXb1QKJhxKguPym5lsSGbVS5HFtv/TKPRQC6XM40qh4eHjQxZSP0qFAqZEi4AjOzg2i4cDiMajdrGymh0qWPKDHBZwkSWYmFD75k0GaUOTGc3s9NYv13WPKcNo1QqYWJiwjgoeH0ZzBaNRtHT02OM8DRcZzIZ5PN5Y0+g/kt5zx5tkUgEq1evRjKZRKVSQaFQMHo5S+wxEJBQ76dTnrp8oVDA+Pi4yTSkbWB/CF5bCuensruzqBFdWZJQ4ZZp05wcpKdUlgKRJUWmmkg61QSLx5Vp1e3AscroeX53qmjzdp0E3IaR2lwQ8Hq5nT+Fr6xjLiPw6L2m84Lp2xw7FyryuzxXACYSnco1Fy+tjOjOc+G+mSpHJZ/NZcvlslm0SOP7cl1wLwVUmCvK/kmreVymMjvLibQ7X3fCWU6Htmz+5SxHAuw1iktZ59wPz0Vuy/f4+0yRSrGM0ma0mJTljGSj87jVNeSaifKZf7PeOw0IwN4mZ8BeBz+DBdi7hY6HcDiMUChkrh+dDdFo1FYTXkadMzvN49nTbJ0RctKQrywcKruVxcpSiAZdykhnN5EOYGaV8Xf+nG7e7lTNd+rdzj4cct/OMqPyu/wps547UXaNyEhvmUEuj0W5KPVuynDnucjSqFwL8Psyo0sGJvD+0HBP/Zsymesd6uEyM08Gmsh7Jku1MmiQx6c9Q5aBVRYGld2dRY3oypKCyq3X60UikTAR1Mlk0tRGp0dZGmxzuRxKpRLK5bLxktLLC8Bm6Ob+ZarSbKKXW5VhaRcK0GazaRR67heAUW4pZKdzDhB+XigUMDw8jGAwiFKpZGt8SkWc1yCbzZpUMpbIkQZpjpNCnM4DjpuKMcfO+rfxeBx+vx+JRAKxWAx+vx/RaNQIbJlKJq8hFwl8j/ePin6j0UAkEkEsFkOj0UB/fz+KxSIqlQomJiZMSRqmqS/XyFNFUdpD/v/rXDC3xGIxJBIJW4+QYDBoor/L5bIpD0ak7AYmG0ucpb725R4yi4zGbxm5RSVdwnFSBrF8CmWfbGbOaC02S2Xq80zGWywWMTIyYnPUAzCRcSzBUqvVkM1mTcQam5+6wawAfjedTgPYs06Qyi+j67u7u21ReMFg0DjCWZaO55TNZs1YWbaN14TlXXjfWIs2Ho+bzDI2Tk2n0yZqnRF1iqIoTgenMjckEgmkUimbrsxIahpOZdBUs9lEsVjE+Ph4yzmb8p/yciZ1u50wK0sGsAF79Wa3nmQcg7MECbO8JJRHjERvV+8m+Xwezz33nNF1Q6GQMWYzQ5wOcpZwmU5212o1jI6Omow+AGYdwkakvBaRSASJRMJcB9pMuPZiSTZeE+5LGv/5XqPRsDnTgb1rp2azaTLA6/U6crmcsVewzKqiLHXUiK4sKSi4/X4/enp6sHLlSoTDYfT39xvBwG7aFIbVahUTExOmDAnriRaLRZtHmEZbKnn0/koP60zH2qqRaDuw/AkAIwglFKw8z5ko4lTkGeXFcwwEAkgkEggGg8bzXa/XMTIyYhZB+XzejIvIa82xSgcCxxYIBJBKpRAIBNDb24uDDjrIeMFZe06e51TOB2ekIv+mMOc15PhqtRpKpRJ27txphDsNNftDqtliQz3iymJjf3+m5qNviMfjQSwWQ39/v630VyQSQW9vLwKBADKZDDKZjE3mptNpU35M7svNkL4vChprjNKpzJqqVDQDgYDp6SEjo2kA5tqDZVMod2St1Xw+b85vqujwVjAgwOv1IhqNmuhvHieXy2FsbAzVatWW0j1VbXE6y2U0nMz4k078RCKBNWvWIBAIIJ/PI5fLGSM609MDgQAsy8L4+LgpPdPV1YVIJGJqwUvnA/H5fAiFQiblvlQqmfUa5fRMG6QpnWUhZPe1116LL33pSxgaGsJf/dVfYePGjTjuuONabr9lyxZccskl+P3vf49Vq1bhox/9KM4//3zbNrfffjsuu+wyPPXUUzjssMPwT//0TzjzzDNd93fllVfiE5/4BC688EJs3LhxxuNX5hadC+Yej8eDeDyOFStWGJkdCARMUJTP5zPZvyz/ydJj+Xze1CV33ivq9YzMllHtM4U2ABr2pRFcympnFLpcO9DBy6xqKRNpR5BOgpnAAC6fz4euri4kk0mEw2HEYjGbA7xarZo1T6PRmKRzS2q1GsbGxiYF2nHcjDYPhULo7u7GgQceiGAwaNYedGLwJ68Z9XmuHZy9yHi9mZnG78diMQB7SsHFYjGT8c7yL1OdizK3qN7dWdSIriwJpDCIx+Mm+jyVSiEUCpnmVc463PV63RiEKQCo0FHZBWBT/uiRlTXVZcpxu8Z02XxjJoKWnm0afpkeR0WTxmI5Jl4joP1JTp4P65fxurDWqqxRSqP6dOfC/dJ4zv35fD6z2OKigU1XeX6zidh3IvchU/osyzLRjrwnhUJhUsM1FRLzgwpzRVn+yGwh1tWk05SKOBtbh0IheL1ehEIhU4uU369Wq8ZR7pbaTAc4s9AoO2djcKVMlFFxzqgs/nSWGZElXDjHUaZTOZUp4rNFGgJ4vowaz+fzNpkts8ba2S8Ac+1l8zE2PWegAZ3lvK/yfJ2lX5ghxog6lq2T6yOnAV86ytn8DIAtyl9GDirzw3zL7ttuuw0XXXQRrr32Wrz85S/H17/+dZx22mn4wx/+gIMOOmjS9tu2bcNrXvMavOc978F3v/td/M///A8uuOAC9Pf3441vfCMAYOvWrTj77LPxuc99DmeeeSbuvPNOvPnNb8YvfvELHHPMMbb9Pfjgg/jGN76BdevWzfqcFWWpIeuD08gtM4jZA4QOVBkwRqMrG00nk0njlKYMkNnf1Lu5P8oKyvl25gzqnDI4bqYBbDKoShqjpTG+E7KbcpvXolAomMwr9vWSQXztZJhzzLJkHNddbJJKGS6vi1t5nlbHkHK51bqC7/G6WZZlqgYwq0yWh1XmD9W7O4sa0ZVFDwWxz+dDd3c31qxZg1gshpUrV2LlypXG88kamlIgUCB2d3ejWq0il8shkUigWCxieHgYO3fuNOnJVObpUacRvVqtYnh42KRHF4vFtgzp3LZer0/q8N0KqfSzMSbLyci0LGCvl1iWUKEy2o7RXiqw4+PjyOfztmg5WT+ei5npnAiyNh6vvd/vR29vL3p6ehCJRDAwMGCaiEplvBMG9FbIZ2jlypXo7+9HPp9HT08PyuUyhoaGMDQ0ZCuho8wtKswVZflDRTaVSuGAAw4wihyjunt7exGLxWxGaRrSabz2eDymZFulUsHu3bsxMjJiDNM+nw+JRAKrVq1CKBQyinq1WsXQ0JCRo+3IbWZpDQ0N2QzIsgEYy8E5079laZpIJGKU33K5bGqGy1JnjHybqbyRRniuAVgCh04E1jGXpehmYkjntZc1zA888EAkk0lznkznZuNURvF5PB5jHGDEXaPRMCXUYrEYenp64PP5TJQio+qZaShr1tZqNfj9fqxZswbBYBCZTAbDw8Mol8uYmJjA+Pi4yoN5pFOy29nklo40J1dffTXOO+88vPvd7wYAbNy4ET/5yU9w3XXX4corr5y0/fXXX4+DDjrIRIwfeeSReOihh/DlL3/ZGNE3btyIU045BZdeeikA4NJLL8WWLVuwceNG3HLLLWZf+Xweb3vb2/DNb34Tn//852d9zooyl3Q6i0zqTIw+D4fD6Ovrw8DAgMkY4nxN/ZvlTqTs7u3tRTKZRLlcxsjICIaGhgDsKf1BB3pXV5dpZk25+uyzz2JsbMzmiJ5qvB6Px5R9Y8BdJBKxOXK5f14rymjKSFlHvNlsGvlPvVuWseE8OJv5UAZxeb1eTExMGJnKCHe5tpipE9zj8ZiSptFoFAcccIAJQpDZ2lL3djq15XqJ7zGqnfe6XC7bss6Z6c/vsHnpwMAAAoEAxsfHsWPHDnOfuDZT5gfVuzuLGtGVJYFU5np6epBMJtHX14f+/n4jwCkYnMbYZrOJcDhsotIZdc1JnPXVE4mEqc8tjeiVSsXU83LbfytkOZaZCEAKTkZI0xBNYUcBJhuA8RoBaDtSXkaMM+3O7fOZjF9uK6PR2TE9Fouhr68P8Xh8zg3nEnrO5QKC6Wvlchn5fB6jo6MAoJ5xRVGUDkCnNmV3d3e3iWJjxBkzypwOajln8yedzDSeUrb4/X7E43H09fUhGo2iUqmYkidUTimP2pFl1WrVGHYZbUeZKw3fUtGVTcIoZ2QDUJ6HjOCW0W6zubbA3qbawJ5yLqQTyg4NIXQedHd3o6enx1bXlEaFVk4KGkar1Sqy2SyKxaIxntAYIZuoO6MHuQ7x+/3o7u425eboNCgWix1r+qbML6tXr7b9/ZnPfAaXX3657b1qtYqHH34YH//4x23vn3rqqXjggQdc97t161aceuqptvde/epXY9OmTajVaggEAti6dSsuvvjiSds4S7W8//3vx+mnn45XvepVakRXFi1SjnRqLpSyu6urC7FYDN3d3UilUsaITic253Jgrw7FDCVmh7MEKQ3jzEiLRqPo6uoyRnuv14tKpYLR0VHTl6Rd6Kyt1Wq2LOdWEenSqMifsswJjfOU1bwmgD0ifTbXXGbUFQqFGX9/OpgtwOubSqVs/UTcSt+5XQ/5mXRI8Lrw/vBayD5jvP89PT2m71kulzPOFkVZyqgRXVn0yLqfXV1dRpGKxWI2oQu4G7gp+GhsZzkYpk0BMA0+uI0UEh6PB+Fw2BYZ1w6shdpoNFAoFIzRVjYJJVQUaeCn8T0ej5sapOycLRu40GPOOmNsdsJUMHbGdgpDmTYtr5FEGh1mWjOcXvBUKoVgMIi+vj50dXWZSLzZ1IjvNLIBTW9vr7mGo6OjJpJCI9LnFjV8KMriYa7+HxnVRme1TJmuVqtGmWKUNuupUgmjvKBBu7u728hWypNwOGyczjTyVqtVU4aEsnG6aDZeBxrGZWo3I66cZV4YpcYxM5LMWRKF5ct4rlwbcFzOHh5TIRVdOT46rmfjACeMpPf7/YjFYohEIiaiz9nEnHXdeY8ZTcfxy/UXM89YO9cZxc/rw7R2pvTTqMGgBsuyTMRyd3c3AJgmqlM1YFM6Ryfmih07diCZTJq/3aLQR0dH0Wg0sGLFCtv7K1aswPDwsOt+h4eHXbev1+sYHR3FypUrW24j93nrrbfikUcewYMPPjjjc1OU+aSV4XO2SIMxg6BYilM6cAGYiG3+LuUY5WcoFDJZSQMDA7Asyxi5GcXMfdIxTH1cypzprgEd3c4sLBrF5bayJBq3pTySfb1k2Tk6/xlxTXnE86Re3mqsUtd2ri1o1J5J+TUnLN3CHiTM4JOZXe0inf1yzSZLzMixO8cv72WlUjHrKY6nt7fXBCxmMhlbA3Zl7lC9u3OoEV1Z9FAYdHd3Y3BwEKtWrTJGdCp2U9Uok/XMKMAooMPhsK1xqKztRmFZLpeRSCRQq9Xg9XqRTqfbGjeVP0bdATBGfGlIl+VSyuUy0uk0KpUKQqEQ+vr6THQ8a8bRuy+FGg3AbOJSqVRQLBZtzcs4cVKAU0DKGqREeqkpKNtNCef+U6kUDj74YMRiMQwMDKCvr29eSre0SzAYNF3E+Yzl83ljAJAee6XzaFqZ0ik6ncq8nJCRVAtxfXj8YDCI3t5eM8+yYRbrdzNS3efzoVQqGcMyjeSUg9KxLWU3y7Bks1mjwFHOJxIJY1SfTomUdVW5JuB1oyxnvXHLskx6M2WuNB4DeyPPmdbMknE0xrN5GI/ZbDaNg6DVWOV9lOnbjAp0yq6Z3vdoNIrBwUGzHqKzmVlr0mjAaDoa3EOhkMlsk9fN5/MhmUwaI0WxWDROFK5B2N8mm81iYmLCfAbAOFZoyKdRIxKJoL+/H8ViEU899ZQa0eeBTsnuZDJpM6JPhVvgyVTrSLftne9Ptc8dO3bgwgsvxL333tt2OUZF6SQziS6fC9lOI3I0GkV/fz9SqZTteHSCsu45HaicHxgM5vV6EYvFzPmEQiFb2RLO/1JuU3eOx+NtyW6eP79L5zn1eDqvJTKbrFwum/OhDiu/29XVhVAoZOt1xlJkPL5lWaaBaqv7IZ3uHCtln9/vNwEAsy0Tw1K3wWDQOD5oz3D2hplu/uT9l3XmKfP5uyx5R7uE1J9l/zFe33g8jlgsZkrwFQoF/PGPf1Qj+jygendnUSO6suhhhFMoFLI1IKP3sx2DrEwVlpFr0WjUeI6l552Kr9PQPBMDMBcZgD2yjJ58adzgwkFGsMlO2eFwGOFw2Lwnm3g5vcFUNNlZXEaMyevh9pKfy6ZufG+6CVRep0AgYOqx0XiwGIznRF4zKu8sD0Cnx0wb0into8JcUZY/lAc0QDN7jLKASpcsxyadIlJxljXHqdDSgMvMK+lclsefyTpBylUpZ6kQSsM9G2dJOU6FUp6LdAZQOQ8EAigWi6b0Cx3XsrxJq3G6GVhkNNhsHEtyrMy+o+zm+bjtTxoonTVW5X65npLXkQ57+Uxw7cQId9kg3NnAlde02WxOqlWrzA3zKbv7+vrg8/kmRZ2PjIxMiiQng4ODrtsz63CqbbjPhx9+GCMjI1i/fr35vNFo4Gc/+xmuueYaE1mpKMsRaUSVxmTqqIC9ubWcE6RsJ1J20wEue3o5oTycadCVlN3Sye52HFmWzZk5Rqc0S5nxOkhZzzJlcn9umebO6+rMnJfZapR5M51fnbKb8puZ684xyWNMlRnuFqAox+aU907jv3xGqE/zOtK2I2U30F5PN2V2qN7dWdSIrixaOHnTG51KpZBKpYxwmE5YtdpnMBg0ddJpRJcNJRlxTqWckVaymVg7cLJpNBomzZj1wJwKNhcmNO7TS9vV1WUiAUKh0KR6qoC95jvrwFWrVYTDYZM2ncvlTL1UqXzKOq1TXUvnwqQVTLUPh8NYsWIF+vv7zXVezNDA7/V6sWrVKkQiEdO8TEbUKYqy+ND/z9bMNhq5E/j9fhMN1dXVZeQNHcnMwqL8zWazRjb29fVNUrRrtRo8nr3NNGUtTjcDaqVSQT6fNyXSppPdTsexrK/K6Gq/329quwIwjdVk9Hc8HsfAwMAk4zDHSIWc0XZMmWeE+u7du23R7lIRBWCaqQMw/V3ouKYzgufb7n3n2sPn8yGVSqGvr8+U1+BaRa4TGKEvFXGmbctIPJZkaTabJlJfOhlooABg6tg3Gg1Eo9FJzgo6T2SEm4zmY13dUqmEdDqt/U2WAcFgEOvXr8fmzZtx5plnmvc3b96MM844w/U7GzZswA9+8APbe/feey+OPvpo8/+xYcMGbN682VYX/d5778Wxxx4LADj55JPx+OOP2/bxzne+E0cccQQ+9rGPqQFdmXMWcl3D0itdXV3o7e01gWvAXuOznIPZINPr9SIejxvZTqeyrEvulh1NeUDDPaOanfKkXWT0M2WjW68N2deDmU3JZNLIaY6FshHYG2TG82MWls/nw9DQEDKZjOt4ZZAZsNexLiO5nY77du8Vo+57enowMDBg1liydrnbPmXZWmfGooxAl44GmbHDa1Kr1YyRnHYbadjn+TmDE3j9enp6TFDBxMSEym5lSaBGdGVRQ6Eai8XQ1dVlum3ToDxTpEAJhUKIRqOmDjmFKpU8RoZXq1Xzmqkwp8c6m80agUZhLj3SJBaLYdWqVYhGo0gkEqaci7NxqjMKTdZjC4fDxpgOwKSpMUJdHluWdpnKiC6NE1N5ydn8K5lMor+/H729vS094YsFKvI0QKxcuRLJZBLDw8MYGxubcXNYpT3UI64o88NC/a94vV6kUinTjIyKVTgcNnXKqYTXajXkcjl4vV7jQG42m0in06ZmOhU6Z01yvth0m7KmXC5jaGjI1NduxwEur5VlWSbF2OPxuJZyoxFdytZ4PI4DDjjAyD4Aps43lUZpUKcSPDAwgGg0ikajgZGREZtsl9F+oVDIlMGQZU6YsSdT6mdyrxigkEgk0NPTYyLtZLQdsLd2PQCboV4aVeR71WrVnD9L1UWjUTNmRuCVSiVUq1VjzPB4PMaIQiMH77GsGc/oPa4RWSJHFfG5Yb5l9yWXXIJzzjkHRx99NDZs2IBvfOMb2L59O84//3wAwKWXXoqdO3fi29/+NgDg/PPPxzXXXINLLrkE73nPe7B161Zs2rQJt9xyi9nnhRdeiOOPPx5XXXUVzjjjDNx1112477778Itf/AIAkEgksHbtWts4YrEYent7J72vKMsNlrgcHBw0va1o5KXTksZwYK+Dm1HQUl5JAzEAmzEWgE0vdvbzkMbZmWBZForFoq3ZdivdNRwOo7e315zzgQceaOSbM9qeRm+WXavVagiHw8ZwXalU8Oyzz06SPTIby5n9zXWA0+DdLrSRsCRef38/gsGgzdnPc3EbU6toc3kfKNtlIJ1soC7L6EgDOreT9gZnpgIzhOLxOCYmJlR2zyGqd3cWNaIrs8Y5AbtNwjP1qDr3LyOgZPmWThlkKSiq1aqJ5qIwY/Q5BRGj32Y7ifBYVHBlyRQq0oy2ojLM9+U5u6Vl0cgt661R4QT2KvrO77ntT37ebloZ70sgEDCR9PTcL2YDuoTny5RFGhRo4FGh3llUmCvKwiOjn/alGaUbMptMNraSSpR0zkpFa6oGW1KmMCqZsk/KLEZzt2tAn4rpronX6zUNvZjt5ibrnRFfNCBQ1jPlnc23uPaQUGnluKTy7TxXea2mikyXZXIYpECFmeXuZPMz+awwmo/75rbcr3yPz4K8HnKfUll3eybkseX1AIBIJAIAZi2nzA3zLbvPPvtsjI2N4YorrsDQ0BDWrl2Lu+++GwcffDAAYGhoCNu3bzfbr1mzBnfffTcuvvhifO1rX8OqVavw1a9+FW984xvNNsceeyxuvfVWfOpTn8Jll12Gww47DLfddhuOOeaYWZ+XoswnzshlYN//NyVS33YavoHJ5bt4fDc5I+U8I5LZy4tGaWYzUeYw0nsmGVVOnE5x5/lx3cAyM9J47vyO8zxl41GZocWMMgbeTTUmQpnnPJ7T4OyE15Ol1zgWym6uE5z6/2xw2l5aRbBL2T3VfZNrTq45isWiWWd08llW9qB6d2dRI7oya+hdlnW6JTQ8yoZZM0Ua0J0G5dkaZvk9phmXSiWMjY1hZGTEeFY9Ho8R4rVaDbt37zbe0anKmUwHlVxGTgF7rmM8Hkc0GkU8Hkd3d7eJuGcd9OkM0TJtKhKJ2JwX5XLZLFa4OOG9mM4TLT+bytjOenmpVAorV640ZVxk9MFSgFGQLPMzMDCAfD6PTCZjGtYpnUGFuaIsLIxgCgaDpqEVDZn7IueIx+NBKBRCJBIxdculciSjlJ0p04VCwShZTrnfaDQQi8VQqVQwPDyM8fFxUwuUDnCuPyYmJmyNsDqJHFM8HjeNtD0ej4mel5lfjKaWayIqvpShPp8PiUQCBxxwAMrlMkZGRsx9kc6BsbExY9zgmoXrFWbM8fozOr1UKrV0BgcCAfT29iKVSiGRSBhDRy6Xw9jYGPx+P5LJpIkElxGINHxzHFxH8fxpJGd/FBlQIB38LAsgDfD8jAaWYrFoc1CwjE4gEMCKFSuMAWPHjh22KESlcyyE7L7gggtwwQUXuH520003TXrvhBNOwCOPPDLlPs866yycddZZbY/h/vvvb3tbRZlL6LSVkcCUK26G29lAuQXYjePSAUyjrTToV6tVMzcDdqcv1xzlchnPPvssdu/ebfpn+Xw+o5PX63VkMhnTYLzTdbIZNc8yagMDA4jH4wgEArbodzcHPz8LBoNIJBKmL0q9XkcikcDznvc8VKtVDA0NYXR01DZ+t34tMpJbBg4wO5qlY5zXQGax9fT0mEwsyudyuYx8Pm8r9+I2d/MceV2ItDvwHLm9HL+0D8h9y8+cBnVeL2b+sxQbAAwPD5sshE49y8oeVO/uLBqqocwaWTNMRjCxmQXTdGlonynOSHQ3r/e+QEWsVquhWCwil8sZY2k2m0Umk0E6nUYmk0GhUDCpxvsqzGXEFZVP1j2PRqO2ZpxUMNu5fk5FnbXIGRXOe+JMqZouEr0dfD6fMQIkk0l0dXUhGo0uuWgwevTp0OA9cevsriiKspShA5TKZKczh5yR6FTyZXaPjCxmmRMARoGSUckcH420Xq8XpVIJ4+PjmJiYMPJ6YmICY2NjmJiYQLlcdo1e7iS8jt3d3RgYGEAkEjHZbUwpZzS5jPCm7JfXn/tKpVLo6uoytccl9XodpVLJlKnhdeF6RmbMsRzcdI29qWgnEgkTBW9Zlqkrn8/nzf2QjdB5Hk7jCtchsjE714ky2k9m/8kgA15XqahLYwORkfDxeBw9PT2Ix+Nas1pRlGUL51ip2znrfncCaRx1NhDlOOQ8zTlcGqH5Xc7TLHNWLpeRTqeN3M5ms0Z2j4+Pm/JnnXDou50XM74p9xKJBILBoE2+0UlM5Pk4S602m02EQiFTypQ6sNPI7CYzpT2A45PBg273VTqs4/E4EokEwuGwGYuM6Hcez3kfW0XA82+3agDSeeLch/yeHLu8fvI74XAYyWQS8Xjc9ElZavYDxZ1rr70Wa9asQTgcxvr16/Hzn/98yu23bNmC9evXIxwO49BDD8X1118/aZvbb78dRx11FEKhEI466ijceeedts+vu+46rFu3DslkEslkEhs2bMCPf/xj2zbveMc7bM+yx+PBy172shmdm0aiKzOGkyiN5zSgupULobIoa3TOpDEnBa+MLJOe3Nko+1JosWRLsVhEPp+3ed6lAJ1tJP1USCFEQ8ZMDeet9stUMyrgMkWbNV6dQtQpCKcTtPJ70WjUpvR3uuzOQsD6eMFgEKVSyRiB1BPbGdQjrijzC+djKcOpGFERazabJnvJmZo90/857ou1zqm4SkWREU6y3Asjo2SEFCOXABi5zXRp2dDK4/EY2T0fxnMq4rIkC9dGUvlulaotFedSqWTWUbI0DNPdOWfyfbn4l9HZ8lrIaH+3+8e1GyPiZeYfsKdECqO7eZ7yPkklmvdBZg9Ig75cX/Fv6Vjh8yADMGQpNY6L957HSSaT8Pl8JnKxVCqhp6cHkUgEuVwOuVxO5UUHUdmtKPOL09gjmzVSftNILR2Os5V/jAQul8sIhUKTZBdlNo/pNl46SKUuyX0yO5pZU8ViEV6v15RNnWvZDeyt6U0ZzvKdPH8ZhU7kmOS6yK3Gu9R/+Tllt3zPuT/5t1sZHWCv4z4SiSASiRinscyml0Z42ayVaw7nefE9+Qw5j9lq7m+l73NbGYkv1zxsgFqr1ZDP52FZFnp7exEOhzExMWGyGvYnWjklOsF8y+7bbrsNF110Ea699lq8/OUvx9e//nWcdtpp+MMf/oCDDjpo0vbbtm3Da17zGrznPe/Bd7/7XfzP//wPLrjgAvT395tybFu3bsXZZ5+Nz33uczjzzDNx55134s1vfjN+8YtfmHJsBx54IP75n/8Zz3ve8wAA3/rWt3DGGWfg0UcfxV/91V+Z4/3t3/4tbrzxRvP3TAMm1YiuzAjpjWTta1nTU8JIJCnI3TzZraAAYVPMUqlkmmQy+mqmSEN+rVZDqVRCoVBAOp3G2NjYJEVUCoC5MKJTuIXDYVNGhJEF+1IKhfumcGVkNdO7WhnI3f6eSpADe5vHrV692kTRL+UoMF7zWCyGlStXolKpoFAoYHh4eJ/r8yl7UUVcUeYPmcnFDDL5YiZRs9lELpcDAFvZETqeZwKbalPh9nq9RlGVhmEaA5xGdEZXcyw0yFJuM8q7UqmYcibAvvdjaReWS4nFYojFYsbIHA6HbU1TZc1wGXXfbDZNpBhT2WUj9Xq9bozpMnotFAohlUrZjA5sgk4jAOWYdCa4KcWRSMTIbaZ806lP2c7xx2IxBAIBU3aF58FSPdlsFtVq1QQDUDmuVqvm3vO4NK6wzAzLwQAwayBes3K5bMr98L1CoYB6vY6enh50d3ejWq1ix44dyGazCIfDWLNmDTweD/7yl7+YNY/SGVR2K8r8IQ2bTket/J1lxPx+P+r1OgqFwqR+Gu1CB3g+n7c5vonTIe+GnOMZkFYqlZDL5Yz8ZlBXsVg033MaXucCZyQ6ZXilUpkUKc3zazUeOvidpcycPURYvi2ZTMLr9aJQKNiC2tyO10rnZPBad3e3kbd0WlBOhsNhlMtl45hm2T6nc19meMnxyzWD7Lsir40cl/NZkGs5eT68xlxzNBoNFAoFVCoVeL1eHHTQQWg2m/jzn/+MiYmJ/U5eyMCITp/7fMvuq6++Gueddx7e/e53AwA2btyIn/zkJ7juuutw5ZVXTtr++uuvx0EHHYSNGzcCAI488kg89NBD+PKXv2yM6Bs3bsQpp5yCSy+9FMCexuJbtmzBxo0bTfPw173udbb9/tM//ROuu+46/PKXv7QZ0UOhEAYHB2d0ThLNlVBmjCyxQuVXesVldJvsyjzTyGSnEVemDu/L5OI0pFP5ZLS7M8JtX7z50+GWps6f+xrFzetOoShryrt5v9284O1MuM6SAMshCh2AUfplhJ6ml3UOp6FrNi9FUdpHRrI5S6Q5FfNOlFCTCrRM7+ZY3PY9XZQT98mGm3R6c43gLDHSCaY6f1kXVm5HeeGWzu02LlnjWzoW3Ers8H4507zd9j1dNJvMJJTHcdYzlWs8KngstSNlozMqb7o1m9PA7ywP4HZe8hgM4vD5fKZmeqPRQDQaRSKRMDXclc6hsltR5hdnCQ2p38kGl61kxmxgWRMZXDbT8Tr1JhpRKbs591N2yyj0uXaAO6+llNdyu3aRaxvni5/LY04V2c3tW31Oh4os08rt5JrOWX5lurFLprsHTlnfznk4I9F5zfkMAHuy31jvf3+W3XNx7p2S3Sx7zBeDKiTVahUPP/wwTj31VNv7p556Kh544AHX8W3dunXS9q9+9avx0EMPmfr4rbZptc9Go4Fbb70VhUIBGzZssH12//33Y2BgAIcffjje8573YGRkZIqrNxmNRFdmhNPgS4WKv0sajYZtu9lGjjOajYKCkW2Msp7JREPlu1arIZPJYGxsDLlczkS4TyWw3Ma2L0hHBD3FUpHd1wlULmCkc4Njlyn10ltMpICUSq4U1KwhHo/H0dXVZasPt9ThuTEyMJFIoFqtolAoaLMTRVGWHJzn3ZQqyloANiWW8p3vz1TusbRKsVhEJpNBvV43TlfLskz5DaYlM4qOi3K5vgiFQmg2mxgfH8fQ0JCpOT7XtDpnqcj6fD7TBI3OeH5PNsr0eDzGsUBjeSwWsykpjMyiQ4BZfpQ7NBZ7PB7j8OdxGOHvbCDqtp7weDyIxWLo7e21NVT3+/3muubzebP+orG9UqkgnU7Dsiwkk0mjyDObThrZpZNBriVk81Ngz7NVLBZN81leW0YK8rwLhQIajYZJu5XXgI5v1kYPhULYsWPHfq2IK4qyPHCW6JCGWM730kDJSHA3eTAdlEHcT7lctpXZ4jFl9HKreZZR3z6fD+VyGUNDQ6anhxtzYTx3rnUoX2hXYLaTM1LbbWxyfM6gNBnwx15nlE+U+/l8HsCeBuG1Ws2MRRrwnUZLNwd4LBZDf3+/eRZkICDlIuW5HGulUkGj0TCN3Pk8OY8h33NG1fNcAZgmprVazZSl5fpSlscBYGriE9ZrZ8acz7ensbrH4zH13fc3loKjefXq1ba/P/OZz+Dyyy+3vTc6OopGo4EVK1bY3l+xYgWGh4dd9zs8POy6fb1ex+joKFauXNlyG+c+H3/8cWzYsAHlchnxeBx33nknjjrqKPP5aaedhje96U04+OCDsW3bNlx22WV45StfiYcffthkTU6HhlUqM8IZge4U6PIlU8X3xTter9eRy+UwNjaGdDptUsGopM5ksuFkXyqVjBF9YmICpVLJtvhodd6tvNWzwWnkdl6jTuxfGiBknXUZ/SWjAZzvy2Yn0vAO7K37xs7q7Ay+lEu5SJgOx7JFbDTqLFukzI6FiGZbiAYnV155JV760pcikUhgYGAAf/d3f4cnn3zStk0nGpwoSjs4o6X4npzznUb02Wbh0CDMOqhs1s3yHgBMzfRyuWyUbTcjLI2jdGYODQ1heHgYpVKpcxdnFnBsHo8HpVIJ+XzeNBKlcVtml/HcGI3Hcmt0IPh8PjQaDeTzeRQKBTSbTVsWGbDXiM5yNqwvy6hy6SwHWtcrlUb0VCoFYI+hvlKpmLq1+XwemUzGNBYF9kQYsRE7FXY6A5LJJCKRiM2wI8v9OaMOZTYge9MwrZvXNhqNIhQKGWdMrVYzZXM4nnq9btYk0WgUfX196OvrQzQanatbb7uO+5OhXiPRFWV+4fwiM4ulA5JzqNTXZJbRTOenZrNpSp1SPlcqFVumF0uRTVe3WpZ8LZVKGBoawq5du+ZVdrtFh0u9mOVd2WvDGTAw1RzmNNA7jegsrwLAGNHz+bwxPMtG2vJYzt+d5xOPx9Hf34+uri6bc96ZVS97xsi1BwMd6XyORqO2BuROe48zI1sej+sFOgXonHCuMfkZe6twnLzmPp8P8XgcqVRqXrLIFqPsnks52SnZvWPHDmQyGfNiaRU33AI0p7rmbts7329nny94wQvw2GOP4Ze//CXe97734dxzz8Uf/vAH8/nZZ5+N008/HWvXrsXrXvc6/PjHP8Yf//hH/OhHP2o5NidqDVL2CSq5/OnEORHMZlLgREvjd7FYNNFfkUjEVgZlqn0AMAsNChEpTJxIxZVedIlMPaPxYTHivOZSOFOo8adzu6kUH+nJl06U5YKM0OdilIsOZd/Z10XCTL+7UA1OtmzZgve///146Utfinq9jk9+8pM49dRT8Yc//AGxWMwcb18bnCjKdDgN5zIyWM7flKk0gs/2f5Xfp7G0XC4bhdU5HipscmzA3mgnOVZGUrVKMee+pOzm96WS2wklRWZ6SaXRWVNVvnhdABjZwrUJ74XzurSKUnO+plL+5fWRxhiZPeaMMgP21j3nWkFGl8s0crkP5xjkvZbPF+F1kI4UwF5SwBl4wGtJQ7xskMpnTF5PNd52hvmW3YoyF8j5dLHTyvAk51bOiXJO3peyKJx7K5WKrek1f7oFlbnp+zS6c19TlUmVspsOYafslusH57FawevklINOud0OMriAvU1kA2238bQykPMn5Vsr2S31URmkKKsBOGW3MwCC++bfcp/yu63m91bXmNfRsqy2ytHKYD6uw2TJOL7PtQCAjq3X9nc6JbuTySSSyeSU2/b19cHn802KEB8ZGZkUSU4GBwddt/f7/ejt7Z1yG+c+g8GgaSx69NFH48EHH8RXvvIVfP3rX3c99sqVK3HwwQfjT3/605TnJVEjujIjONkDeyc12SRMQkVVvmbzD9xoNIwnHAASiQTi8TgGBwdNlBYjlaQizvFKQ0CxWMTo6CiKxSJ27dqFkZER42EnFDqJRAKpVArBYBDJZNIW0dRsNpHP55HL5VCtVpFOp03qW7v106VRwK1u6HTeunb2z59SEZW1TImbN3Y6b2gwGEQ8Hjdp+FR8l5ORmdclEomYcjXZbNbca2X2zLcivlANTu655x7bfm+88UYMDAzg4YcfxvHHH2/e39cGJ8rCIA10c9U7oxM4a3dzrFRq2VzL49nTsIqynTJuNv+vzWbTFt3F6OFgMGgWvCztIjN8KLOkrGIke7VaxcjIiC3qzok8zsDAAJLJJOr1uommzmazmJiYmGRgcMrE6WQglTz2A2Ekl2yYxmsvZSyj+Jjt1NPTYxqulUol04sDgDFyW5ZlotQBmChvaaxnVLg8L6kU871gMGjWTF1dXUgmk2Z9xLIo3H84HEZvb69R2vn9np4eNJtNW/N4p2HEqbxLhZ9OQpkeHo/H4fHsadZKpbxcLiOdTsPr9drWeYQRcF6vF/F4HN3d3fD7/SaVuFAo2JqczsX/6GJR7qWTZi7HpEZ0ZalDwx/ny5mWO5lPnA5vzmF0HrIpOOdtGiDZdHu2spulT9PpNJ5++mnE43GsXLkSkUjEyD4ZncxrKXV9OYdXKhXs3r3bVqrMCYPjKLu7urpQq9XMdyYmJjA6OmrkoaynPdU5ul0Dt15eAGw/uX+nk5ryiOVauDaRJdvcMvrc5meWM2GJs1YOENm3huuNcDhs1hPclvKQMpFOFTofWErP6/VOCgCU61hpwJcyl/uWY2VAkMx8kM4cpwPdeU8YrR8IBGxlflKpFKrVKvL5/Kyb5E7Fvsqipeacn0/ZHQwGsX79emzevBlnnnmmeX/z5s0444wzXL+zYcMG/OAHP7C9d++99+Loo482DpUNGzZg8+bNuPjii23bHHvssdOO3a12OxkbG8OOHTuwcuXKac+NqBFdmTHSYyo9pFMpJ9Mpo1PBB79arSIYDGJiYgLVahXRaNQYuZ3pbU4FWKafsSs4U6qY7sTvUdCFQiFT77O/v9943XgOExMTAGDSkEul0owVNDlGKaD31YDutn8Ak5RrIpUv5/dbwdpmVHipHC9H/H6/SVFfrue4VMlms7a/Q6HQpHpmbHDy8Y9/3Pb+bBqcbNq0yZQT2Lp1q02Qcxsa3t3IZDIAgJ6eHtv7bHDS1dWFE044Af/0T/+EgYGBlvtRFh7Om1S0FjvOaG9p+GQUMJW0er1uiwaeLYw683q9yOfzJpLMmSoso5ekLOSxK5UKxsfHUS6XjULd6prTSEsDdX9/P8rlMvx+vymzkslkWkaLyZ9TnbszKkw26HSTr4SGBgCmHJqMtHOuZeT1cYtG599uY3Vbd/l8PmM8YMkyGk2c6wN5TlSKfT6fkYdUxLm+Yo1cZx10rqtktCQAo8TToSCd8TQuMAoyFovZouZ5zvy8q6sLsVjMGF1YmkbW8F0I5ivi1fl8KIrSGjmnLXbkPM+fNJZzfpWy1Ck/ZwPn5lKpZMqwdXV1Gf2UupAzy0qW/2Qfsmw2a/qfTFV3nLI7Eomgr68PAwMDxoHOMmOUOdIg2ypKfypotJ0qk91N3tIGQr3Qee6A3Tnsti/n+KaT3U4HNO0eXKv5/X5bTxvKZspu+SzI0mp8Tx5LOvv5bMnzd56HXJ84S8+5XU9+zjUcbQlcM8iMPNZXX+iyfVOx1Azp88kll1yCc845B0cffTQ2bNiAb3zjG9i+fTvOP/98AHsCz3bu3Ilvf/vbAIDzzz8f11xzDS655BK85z3vwdatW7Fp0yYTlAYAF154IY4//nhcddVVOOOMM3DXXXfhvvvuwy9+8QuzzSc+8QmcdtppWL16NXK5HG699Vbcf//9Jqgtn8/j8ssvxxvf+EasXLkSTz/9ND7xiU+gr6/PZvCfDjWiKzNCCgmmH1P4OGtF07NKJdotasyptMrjuP2sVqumhjkn3EAgYJQnZ90uZ0M0RlqxXhnrq/K8GNXu9/vR19eHlStXGkVcGtEtyzLGY9mUlDU721m4yMgClpbx+Xzmes22GSuRafRMpXd6up3Xe7r3JGz0xpS75RSB7oTRhozSU/adTnnEl0KDEznmSy65BK94xSuwdu1a834nGpwo84/T+bkUoHLlTIVmrU6v14tKpWKrf9oJqIw3Gg0MDw8bYyjlEh1TgUDARBdLZBQSs8OYHi6N8swi6+npQTgcRiKRMMZZyldGYtdqNYyNjSGfz08ySLejGDWbe5phjo+PIxaLGYVaOltpYJcOC8uybA3NJiYmUCwWbWXxaMCQdcNblWrZF2ik5viokAMwxgo6QZzKOK+BvGa8D7y/jP6mEZsGCD5/cr0jSwPK2r40rPN+0wkA7G14xu/IaHNG4vX09KBWq5nrPN/M19zgZpyZq+PMVzSboswVjJheCg5wADZDrXRE1ut1Y1ym7Oy07C6Xy2g2m2Zty34VzJRiaVUZ1c+f5XLZXOtAIGBkt6yhzTVJMpnE4OAgwuEwuru7EYvFEAqFEAgEbLLbmQEOzGxeoeyemJgwEd20HchSa5Tj8lmhDKxUKsjlcq7XmnOkLB3nlN0zQW4vHSU0OAOTnULO4znXD9LZ73Tay3N2BiXK4ER+LqPsGYBB2c5jSQe6W0CANNg3m03TZ8Xn85lmrIuNpSbL5lt2n3322RgbG8MVV1yBoaEhrF27FnfffTcOPvhgAMDQ0BC2b99utl+zZg3uvvtuXHzxxfja176GVatW4atf/arJ/gaAY489Frfeeis+9alP4bLLLsNhhx2G2267zZRQBYBdu3bhnHPOwdDQEFKpFNatW4d77rkHp5xyCoA9/0OPP/44vv3tbyOdTmPlypU46aSTcNtttyGRSLR9fmpEV2aETE2iksvJ3GlEZ1MLChunAVcuAtxqVkpPLr/H6CKfz4dMJoNdu3aZdON4PG5qYtKDGY1GbV5y2fArEomYCDUqrNFoFP39/YhEIjjooINw6KGHGiN6PB63RWkxJbxQKJiFBMfXrhFdNl0rFArwer2oVqtGAd0XIzojy2T0Pc+11XhmAiPootGoLTpsuUEveTwed33OldnRKWG+Y8cOW222qYzObs66qTI+Wjn3nGUf2t3nP/7jP+K3v/2tzWMO7FlokLVr1+Loo4/GwQcfjB/96Ed4wxve0HJ8ysKzVBRwwD7HO0s/sN8I5RKNmJ1SxOv1OrLZLLxeLwqFArZv326c1lIxDoVCNtnPcVYqFaNkUdZXKhWMjY0Z5yYdun19fTj00ENNNhnXBPy/ZDOrYrGIxx57zGZEnsmc1Gg0jGE2mUya1OlGo2EMv4wao5OCJVcikQgAmEakvOaUL1w7VatVm1FkNs+bc20lFVg2WZcKP6PTed1lsIQzFdtZq5S11pnZwGALXi9nuT06UFiqxlmuhVkSwN7ADD4zXq/XGHK8Xq8xyBCv14vu7m5EIhEUi0VUKpUFMaLPF51wqszHcZaa4UFZfkhj31J4HuWczQh0OsRlE2uWJ+1kiRoarL1eryntwswfljTp7+8387Q0IPP7lGexWAwrVqxArVbD7t27bY0ofT4fBgcHceSRRyIcDiMejxtnqRwL9eVHH30Uf/nLX2Y1HzWbTUxMTKBcLqOrqwupVMpkVxHKMkZHO6O2Kbspx/iZNCbTAT5b2S3HC+wtIccSbzLYQPYJkddErqec9eudQWE8ZwDGQO/MMJABgJTpMrqd8p7BGFwHAXt7zfl8PvOcyv9BWbKIjhRmQij7zkLI7gsuuAAXXHCB62c33XTTpPdOOOEEPPLII1Pu86yzzsJZZ53V8vNNmzZN+f1IJIKf/OQnU27TDmoNUtqiVaoUhQqFusRZC537kbWxOKG7NSaRCrycvKmE0nhdrVZN/Uyfz2f7WzY+Yb04ChNGUsuoMBrhaYBnFFs8Hjc1v+T4GKFFTzaVwVbGN7f35IKDUVQUTNMZ+Voh9ynrlXayOYdMx1/u0dn0zjsjOJXZ0ylhvhQanADABz7wAXz/+9/Hz372Mxx44IFTjnc2DU4UpRVStjoj0Ik0Istans7/0VbZY277lH9zvcB9VyoVI6dpHKcxWa4TZGoxoZx2ppTLmqGsn81sKRkJxcg5RjQzFdq5VnE7HycMDgiHw5Mi0qSMlE3IZfqz09ghm25J5Xtfsx3cDOnyOMDUZXvkfXL7TI5dBkg4t5PPlFMx5zPiLH/DiDlnuTvn+lFeI+6Dz4o2Be8cakRXlguL+VmUMtAZtcvfnYFmrSKfWwV+SKOrE3kcOhxkeS3ZC02WM3UacqVcpcyVmeL8ndHtNJ6zNroMhJM9VMLhsC1q2mmHmOre8pzoJJ5qvdNqrdMqk0EamDshu53joT2Dx5KfzXRfrc7X+ZJMV7qXP+WaQR5nKj1a3gNmtMnSgsq+obK7s6gRXWmJTJOll9gptGW5FqeCIhtW0HPKshiMGqOAlIZYpwFYRrIzqoneznQ6DZ/Ph1KphNHRUeNF9fv96O7uRl9fny3ajelgVJppQGetVqaBx2IxdHd3o6urC+FwGKlUyhjROUZO6qFQCCtWrIBlWaa5SKFQMELUsixbJL4UQPy7WCwaJTCbzaLRaJjU8JkYb3lPLGtP6ZpcLmfq0c0kSr4deA/5bCxn+FzR2aIsLRaywYllWfjABz6AO++8E/fffz/WrFkz7Xhn0+BEUYhUtBidzahmKnZOeSsNmlRq5XucA5nlxWZcsmSajICTJc6o3Mpjchz5fN5ElhWLRaMcU/5RYZZpwZQ7lUoFmUwGuVzONMkMhULo7e1FT0+PrUkZ1w7SwMCo9Xq9jkKhgPHxcWPcZ6QXs9fckAYClmSrVComw4xR6VIpZHkXziG8T1LpLpfLKBaLZi3BVPrZRrI5FR/WcqWTgQZ9rs0Y4cZ1DpuquylgNH7IoArWMpfHc64juZ9YLGYzCPD5a7XekWVgeHzZhFUq8QBMXXSZ9q4oirJYoayMRCJG9lEGAPaoZCKDu/g7o4NlmVMpu1kSlWVIuS6g3CoUCqZ8Fr/DqPdarYZcLodyuYx8Po9sNmvkLR3U1LUpa2WgGvXSSqWCYDCIRCJhGlZTjrOMC+WJnL99Ph/6+/vN2MfGxsxYWT5tKtnNa0VDei6XM+OUpcVk9r20icjrT9lNuc+678Vi0ZR37ZTsoT1D9g9xOy/A3sSTY5CGcZ6XXP85sw/5LMra6nIdJ7dxOmecgZL8Po3ibs+xRK4Z1YiuLEbUiK60hEozm0hRmElFb6pmD1JxpBGbBmkpbKlcOZV6KqVMA2eaWiaTMVHbTM2dmJgwEy7HvGLFCjSbTYRCIZNGBsCWcsbtE4kEGo0GUqmUKd3S29uL7u5uo5xTkZTfpeI5MDBgUrQLhYKJSpMGBGfUFWk0GmYBAwC5XM78ztIUU3nDJdKAUSqVzHXL5/M2w34n4LXbHwzLjGhzOjTUKzt75tsjvlANTt7//vfj5ptvxl133YVEImEi15k+2qkGJ4pCqDxRdgeDQVMrVRrRpUxy+75UsgKBAKLRKHw+H1KpFJLJpG2NUKlUjDNappRToZQKkTRm8zOPx4Px8XF4vV6kUil0d3cbZZgym2OLRCKIx+Mol8umnEc4HDZObzrA2fha1lmXUU1erxd9fX0IBoOmcRkAW7NNWY7EDak8l8tlBINB8z06L6QjgiXBZPabrG/K9QAd4CxD0klkxh3vn8/nM6XZisWiKfHCWu8cn4yadwZA8Hq4GdFpiJHZB1wzRSIR1Ot1Y5RhtgCAScYTPrdcx9AwJJ9hZwQlywnMlRF9f1sPaDSboswNMoMmFoshHA7bSnHKzCG3/0NpuKThk/M9+4ClUikzH3O+Hx0dNf0w/H6/0V1rtZqtTCqd5ZZlIZvNmvHyuN3d3ejt7TV6NfVmadCngT0ajaJUKiESiaC7u9uUTqXsZgaZWxY1jeihUAiZTMY0kw4EAkbnZ+CA2zUGYJPvDF5jFjo/l45dnoe0ITid26VSyTi+GRjQyfmODvBwOGyCEHlO0hjObbmGckaCy+8AsDnAneWC+JJrQmBvoCXlLJEZBtLAzn2yPBzXAMQte0I6LZR9R2V3Z1nQJ/NnP/sZXve612HVqlXweDz43ve+Z/vcsixcfvnlWLVqFSKRCE488UT8/ve/t21TqVTwgQ98AH19fYjFYnj961+PZ599dh7PYnkhU6JDoRBisRhisRji8bjtRcWKEzknUvkPRmFDocR9sY42lSfuhx5ypl/ze/wuj8vvUyFzelqd9VynS9Nq5T11CgApCGSaNoU2xypfkUjEeI1lWrkTCnMq4nxR+eOCRqaHyZdsQMbvcB90ROxLJJuyh+mEOReuXADKaApdDNiREbCzfc2Es88+Gxs3bsQVV1yBF7/4xfjZz37WVoOT+++/Hy9+8Yvxuc99rmWDkxtvvBHr1q3DTTfdNKnByXXXXYdMJoMTTzwRK1euNK/bbrsNwN4GJ2eccQYOP/xwnHvuuTj88MOxdevWGTU4mU9Udi9uZF1qRnVTFlEuTSWPZPSR/D7ltDROM/OLSjFldSKRMDLbeUxnXwlnJFSr/21nNBMN0lPtU56TrMcps4t4Phw7z5FKqLMsnJtDm83XGEFOpIxmVLQsWcLzpvOhVCoZue00IHcKjomKrzNLQF4rnpuzQbyM2HMGQDhLtMjj8nPuU37OCEc6eZyl8eQxZTaDPB/ZvHSm5dfk+k8eS7Ez37Jb6Rwquxc31L1DoZCRvdFo1OjedIq3ikKWemk4HLbJXzc9mzKQawNuE4lEEIvFkEgkEIvFzLbO+VDK7XbKljiNvFw/SMO23A/ncM7FMqKdcluOl9eH8lvO4059n3ORlL10/ksbgnTEyiA0Kcsqlcok3X2mPVbaQc6hzrWB06EhZWQ745D7lPM0f3faU5xjkeVyiZTB8vq3KoHjdlznd92QNhm5bpuOdgMUlwsquzvLgkaiFwoFvOhFL8I73/lOm2GCfPGLX8TVV1+Nm266CYcffjg+//nP45RTTsGTTz5pjAsXXXQRfvCDH+DWW29Fb28vPvShD+G1r30tHn744WVfYmIuoJfT7/ejv7/feJQZSc6J0rIspNNpk461e/duFAoF2wSeTCaRSCQQCoXQ09NjDN/stsyJzmnM5j+pFMxUKCuVCtLpNOr1OiYmJjAxMWFSsavVqpnMZaqQ/Od3S2FyMpUhXX5f1mRbsWIFksmkGRsj8liChg1I6fFnqjRfFLg0CjBiv1ar2eq7yugCKWh4nFqthmw2a0rd7N69G9Vq1Yyn05Pg/iJ82jGeU4DTCMOFHwDTtE9GG+7vLIRHfCEanEw3zk41OJlPVHYvTiijqFAGg0Fj6JAp21QKa7UaRkdHUSgUbPvhOiAQCCCZTJqMMcoeKuQSRpFR9vI4uVzOyDdG042PjxsZ6ITy1WmklbJYyjSWU2PEmlRiGdHM/ThrrMoxyQg8rjXolHZGwfF9SblcxtDQkLlmPT09ZkyVSsWUnCuVSibbjc1RmQI+MjJiog7z+bwxvHcaNjllw2xGfdNI7vF4kEwmzdqkUCiYqL56vW4MM4y0dxoc3BzGlHk0dPO8uBbk2iWXyyGVSiGVSiEQCJhMBpZt4TqI6ztG60tnRSQSQTKZNM+K8zlqFVQho/J5TabLuJTrwv2FhZDdSmdQ2b148Xr3NM1mtnZPT4/JAmL0dy6XMw2q0+m0rbEkAJu+2NfXh3g8bvt/YyCazMYKBoNmvgf2ZvB0d3cbeVgqlcy8ncvlbMeUOrN0astngceTZeW4ZuD7MvALgM34Tad9vV5HIBAwspER9R6Px8gnBo7RSSzLwcjeJ5RbxWIRu3btMv2O6DDguJgBX6vVTKkZWdqmXC4b2V4sFk1Ue6czyDgeNjN1Rn8751VnFh3vE9dYTvg+bShOozrhe5SXzWbTlP5hDznef9p4+Her48q1h3TKy8xJRtU79WePx2PKFsrngeV0WskbN7vTckdld2dZUCP6aaedhtNOO831M8uysHHjRnzyk5/EG97wBgDAt771LaxYsQI333wz3vve9yKTyWDTpk34zne+g1e96lUAgO9+97tYvXo17rvvPrz61a+et3NZLlCgskZZf38/AoGAUcilEKKAKxaLpnOy9ITK0i29vb1IJBLGOz6bOt9cSAQCAdvfjLqW28vFgFskl5wI3JQpZ3Q6f0rhIwUEG5B6PB5Ti5UCjMZrLiikIOGxZVOUfD5vOlqHw2FjDOB3eZ+kcJNOBhotcrkcstmsUfjnKgp9ptFeSx23c5VGdFkTUEZYcFHnbD6nKEsNld2LFxoDaehOpVKIx+PG+Em5RMMuZbf8vsywYj8QachmBJiM2GL6tpRRVJRp1GZfjnw+7zp2yldnGi/fY/RdqVRCPp+HZVkmco/KMx3K0rjL/TLqjfvkeodZbSxZl8vlkMvlzOdUqKWD32k4Zdk5Kozd3d0A9kZx8/yLxSISiYQ5PzpWS6USJiYmkM/njSyfK4WFCj7XSjLiHIBZpwGwReCxLi/LvrCxu1yLyLRwt+MCMIYH/i6jAYvFojHQ835znMwa4LG4NuS58L47S+XINRx/ul1bGdxBRZzlf6a7F/ubIV1ZmqjsXrzQWMxAnFQqZSsjSjkLwDhand/n3MUSKV1dXTZnLKO2gb1zKOWcx+Mx8grYm91dKpUwNjaGarVqm5Odx3ZGA7fK2OJYOE/LXmc8vmxa6uZU59pCyi8GCkiDLp3klE3S+CyjqOX1oc7N8dNpX61WbaXDZAYZ+45xbTJX+jadENIGQKRjRGa/yfUUs8h4/vI6OKON3TLKnMZ0OtK5nuS+ncGI0k7gHLtcN0jnu7OMm+y/I8ch16wMAPF4PG0FIEy3JlCUqVi0NdG3bduG4eFhnHrqqea9UCiEE044AQ888ADe+9734uGHH0atVrNts2rVKqxduxYPPPBAS2HO5k8km83O3YksMQKBAOLxuBHk0WjU5lmmwKIixeZZsVgM1WrVtm13dzdSqZQpdcIJbjZGV1mPjHU8U6mUTfjSg8qIbhlBVSgUbGm6VMSZel0qlVAqlRAKhUxEfbFYNB3HZdo7J1oZPS7TiKm8UQiz/huFs/TeuqU0UWFlozNGq0WjUbPYkWORkfrVatXW3JRlYOZCoPPYc7X/xYRMQ3dbTMjyCYwC5UISsEff8aWoV3s5orJ7YaDy7Pf7kUql0Nvba+QQs7SAvU5cWdaEcxUd3IxWk/U3peGVyjpgdwBXKhWjFMsoMBnpzGydrq4um+GbSIMqDZpsBE45I5Vg9vrgsWR0tGx6zv1SRtNgIBuNUp4xcIDrIdlnxK0+qxNmzLH5KRupcZ1SLpcxNjYGj8dj6qfKmu00bMxVCi3vNZuKOp26zig62Tyc1082jpXrDJlJQGOHTN+XpVIIG8Ax2pzrMzpE+D3eHxmtJqPGuUZ1rpGcKe1OBZzPM591Xh+ZhchrIqP/9mdUdi8/VHYvDHR6+/1+dHV1oauryxiYKTP4om7u9e7tJcbv+3w+U0aN5VokUs4BdiMpnYVSdgN7DfOUgwxco4ym7Ja6aD6ft5V5o5HaWXKV5VP4fc6zMpONaxenjHYrUyIDCPx+v8lW45jbkaWVSgUTExPGkUy9nbKoUCiYHmz5fN7WAJzygXJjLvRi2he4tmsVVQ7sLX/mlJcSXg/5jDmN3VIOOgMTuXZgw1M2ZJdyX+rPMouMx3DTpQEY+xL34bzn0qbD/gFcQ0hjPAMA3PRutzXBcmd/Ote5ZtEa0dl8bcWKFbb3V6xYgWeeecZsEwwGTcSP3Ibfd+PKK6/EZz/72Q6PeOnDNPC+vj5Eo1H09fWhq6trUm1ICiIK9EKhYDziTAtj9HlfX5+JSqfSPdMakxTiMuqs2WwiHA4jmUyiXC6bibdcLpvGo4yQp0IlDdwAjMeaaXHpdBqWZSGRSKBSqSAej6Onp8cWkcTz509nuZlGo2EWDTK9mMKg2dzT6FQKZQmFNA0SjIaLxWJG4WUqmVxUUEDw+7K8y1wo4cBegz+j95YzXNC1qi/HkkdsQsuITD5rjIijIjGXUYZLBU0rW56o7F4YgsGgSTVeuXIlVq1aZaKqGSHE+YiKDh1/LOvR399vq6NOxSgUCiGXy6FQKBiZxX1QoZalPPL5PPL5PPx+P3p6esx8SIMt1w7VahUjIyPI5XK2SDEq4rK2qWVZKJVKNgN2s9lEOp1GLpczczAN1xwLa5BTXgF7FVGpsFNO0+jPqH0a5rnOYTT2VORyORSLRZOez6huGpeZJUYnAPcpU5elItnpzKVgMGiyBCm/pfJMAwkAk11F4zQN/PxcGjRoWJelURgUIBvZSZgNwf0z0nF8fBwAzHPKNQ+DNbgm5XqLY+V6kUYG3ne3sjz8DrMQuru7TXkbBi3kcjkTJDI6OjplFsX+gsru5YnK7oUhEolgYGAA4XAYK1aswMDAgJGDNMpybmPd73w+b8qEJpNJrFixwhjYaWR3Zj3LSG9p2KSzk9G7jHTmfMy645yjY7GYKeOay+WModqyLGO4pgE6Go2aEqeyx0e9Xsf4+DgKhYItOI7ztNNI7yzP6jSqykxgOv2pjxcKBWPknm7uyWazZr2USqWM7JO10rkOyWazJuqcY+G94jl2WjemkZgGY6cRWBqpeQ/5u8TNQe/MwJfGc7l+4/dkVoEsESTtH4Q2EBlR7izdwvOjzYVZZrSdOGvMM0CR9iaWcOM5R6NRY88ZGRmZZESfK9vIYkZld2dZtEZ04vSwTeV1a3ebSy+9FJdccon5O5vNYvXq1fs20EWGM31V0ioaB7CXc2HDEVmLlDSbTaN4cHupfDMilx7Bdps8THdOHIdlWcaYToWcHbl5Lkx7AvamJMvFAr3T0qDNBQQNnVQUK5WKa60/CnN+V0ZJcbxcpDjT0fi5c1Ki4KHnnNvKxY6znIs0ojOazS3SvdM4o7OXM1M1aHFGdsraqlKpp2FHUWG+3FHZvW9MJbvdoOGVcpiKJJU8uSaQ8kjOWfK7dNrKpsjScSznfkayUS7JBpo8Jg3hXq/XKOWyxIqMCqdMlZFp0sjNayGPJSOQpUGfihMj7Cxrb/q3VH6dRgUae7kOosLajkGbx6dc4DF5LWX0drFYnFQ7VWbrzTRrrx3kMyCNLFIJdqZXy3PhNvzpFhEo1zjyfWBvrVt+h44ZafiWNdPlGshtzcQxOrMNeV7yOeV7btdDrmlllKd0NDjZH+WQyu7ljcru2dPOdXJCmcjoceq2MvOLUF7Lxtcyapp6N2Wn2/Gdc7cct5veKLOreQxnqTNmhDN6nNlKUj+W9bmlrk17gsxiowOW6wbntXNGpMvrL/UumUXVzrwjy9VyDcT1jRwHZXexWLR9fzH1BJDy1+1/2m37VvuQ5fXcnisp053ylrSS3zwOn3W5/plqfzLbn1kP0sbCsrpcdyoquzvNojWiDw4OAtjj9V65cqV5f2RkxHjJBwcHUa1WMTExYfOKj4yM4Nhjj225bwqr5Qgjtb1er6nNLA3krEVJbyqNxHIbZ8dst4lV1j0LhULo7u42ykcymYTf7zfp4K32sS/I9Fufz2eagbDRCpuW5fN5IwRzuZxNwMr06WKxaNK1R0dHkc1mzWIlHA4jk8kgmUwC2DuJSKWcPxn15DQyOAV+K6EgkYYEAKa2mVMB5fFlNNh8GLZpsJdR+ssV1t3n/44TaVxyK2/De78/lL5R9m9Uds8O6Wx2OuCoXFK2SPnBFxuKMeuL20kHH2UCndzBYBD5fB4ej8d8VzrRaZAG9sjceDyORqNh65Eio7dlxDAjigGY0iwyWollOvr7+5FIJEyJExqXmerdaDSQyWRclSka1hlhz8i90dFRPPPMM7bodGk8YM3Yer1uItml8iYVd+mY5n2aSnY7jbW8Z9J5TjnRyigvjchzIS/YSI2R4T09PWasHA9LzskmoE4jOJ0gNDJL571MxWZkmMy+k3XYuT+uXRmMIK+bfMloOzosOH4Atr4kwN6GqW73jfdArpmY0cd1I3vLTLXOUeVSWeqo7J4dlNnSsCx1TekAlvMV5xyZDeMsgSUNihJGSgMw0eeU3dKpKJ3C0oEMYJLBkt+RZWBkEJg0WFqWhd7eXsTjcRSLRYyMjJj5lxnQu3btQi6Xc3WAszwWZQVLeu3evRvbt28358H1j6yxzmtHBzRltjwfyiNnBvF087R0ErN8GwDbOkhmRTmR8nouZEK1WkU2m0W5XDaZh3S4MHBAylbaEKQh3Ong5nVxRvvLQD/pXOG1lA4HPr8yK8HNGSOvi7w+8pjcjhkM8j5I5PqE45MVA2TJ3vksoSqvq7K8WbRG9DVr1mBwcBCbN2/GX//1XwPYM3ls2bIFV111FQBg/fr1CAQC2Lx5M9785jcDAIaGhvC73/0OX/ziFxds7AuJrEvV09ODrq4u877H4zGps0ylcnq6GS1F760Uxk5kaZTu7m6T9s160LKJ11zAxUowGESj0TDnnclkAMA0qaLB3BkFTk+4VKJKpRJ2795t81wzld1pRJeR6tw3je4y7UkaUKWS2Y4wp0CgAX267ecT1hlb7kZ0LnynM6I7SwPI/ytnCqUKV/WIL1dUds8ORpfJFFUZcVYul02PD6ci3mw2bUZ0yp9ms2mrMUlktDmN6DRyyGhcYK9Bl6VJms2m+a5UMOW6geVQLMsycjIajZo641SgacxvNpsmNZ3zJxVUljdzwv9/6SCgPB4dHTWp3LwmhOXREokEGo0Gdu/ejWw2a8bMa05kLwsaEVrVRHd7j9lugD3SazoH+lw6WxlAEQqFMDAwYO4lM9qk8YLb0rgi14Qyy0rWHWUwgcfjsZWMYekfqdTzGeXaiSn9fA7kmk0agKSxnrKVx3amjcueNU7k/xA/lw6rbDaLbDZrS11320enaaWILwYFXWX38kRl9+zg3MjazNFo1Mx9Xq/X9N2i3OPcwvnKqS87a5I75R+N4iydGg6HjWx1Bllxezcd3i1K2bmtdHLSWMq/GaQ3MTFhSrJRVvKcZZaP02gr9WEagMfGxswahsdhpD0dAdS3WKKFPdLovGDWLw3o0oHa7txDI71zvp1OhrdyjHcKlp8Nh8Oo1+tGdrOHiMzCkv23KIP5rDqN6HLMlINS3srPpRGc+2QkOABbFiKRz5TTiO68N5T3DHBkkKMT3lc6nviSfeVogJ9PG0WrZ0Rl9/JjQY3o+Xwef/7zn83f27Ztw2OPPYaenh4cdNBBuOiii/CFL3wBz3/+8/H85z8fX/jCFxCNRvHWt74VAJBKpXDeeefhQx/6EHp7e9HT04MPf/jDeOELX2i6hu8PyOhkClOpPEolBNirFLoJc5k2Letz8idxpk8x8o0CVk7kc33uPL40qlN4SIHLcUsvJ9+Xk7mzLmutVrPV3ZRGdCqKXADx/KViJq/3VHW1p2OxTV5unv5WURNLHf5PTKeIU/lmHTc+XzQezEWNvKWKCvOli8ruzsPmXVRUZfq0s/yI7LFBR7g0PEejUSPHqKDLLBmpcEjHOdcKMgtKGjulDKQMk1F4RGZgAbApdc7IX6cBwalQSWekhO/L7WUksTTYyjJajFLj+MrlsomIYyQ0jQCU3Z3K7losc5Yz8kxGfxFec2YbAnYDC7/P9adbSn276wE5HhkJ70xHl+OVkZryOFyH8Pnn/XPWU3U7Nh0J8hl1Zg8uBhZ6HCq7ly4quzsLjeXxeNzU9mY5Fcpxp/wEJmen0gAqHbTO0iCUeZyLZONtp5GyldycDc4oZZm5LuU/52E346jTGeA2h3C+Zj8RmY3HtYrU6WWGGGU8j8910r7Kb7cxLiQym0EGZbnJcqdsnMoh4OaokbS6DvL4s3X+y4h3niMwOfjMDR6bJXilY0audRf6vi0WVHZ3lgU1oj/00EM46aSTzN+sl3buuefipptuwkc/+lGUSiVccMEFmJiYwDHHHIN7770XiUTCfOdf/uVf4Pf78eY3vxmlUgknn3wybrrppkVVl2qu8fv9SKVSCIfD6OnpwQEHHGCiyuiZlRGxNJCPj49jYmIC5XIZQ0NDJmWVNVTZTIlKPWA3nnNio6JPRVzWUZ8P6BFnKlFvby8ikQiAPWnLVI5kOhYweWHAc6LiPTY2hmKxCJ/Ph2g0OqnTORUzr9eLrq4u00CsVqvZ0sdrtRrGxsaQTqdRLBaNx3ipT0a1Ws00lZHN1pajIZ1NaovFoms0moy+8/l8tkUgv5/P522Grv0dFeZLF5XdnaerqwuHH344QqGQLYJI1ommMsAmn9VqFePj46hWq8jn8xgaGjJyihHsbBBaKpVM0+16vW4ix3w+n2mmSKc7o8h8Ph+SySTC4TDK5fKk5oyWZZkIPJlCTEciFWDK5t27dwPYkyXm3H8+n7cZL8lUkV/O7fmT87TX68XExMQkmUTDA8fbbDbNdZLG1EajgXQ6bZzpy00Zk84Qec2lYUReF8p5mTbNurEyIMHv95tmsrIxqkwpl1HilInNZtNEU8q6vjSAy5R1/l8w+lOm8jcaDVNGIJ1Om7Wtm+zmubNxaCaTsTU3k+X5FgOLYRwqu5cuKrs7B+fI3t5ePO95zzN9SCh7+DnnOBkdy8aczNQaGxszOjsbg1J2yjKsEgaOSSO6NGRyLpXztcQtyp3fd86njCpnRhZluxwns8ycuEVntzKi53I52zEA2Azq3E4en1no0oAqZTflxr7OW4sBZjNYlmWy3KTs5pqPstH5P+k0Vsu/3TK+nEZybicdzM5ebAyumMqgLsfnNPJznSEbwrZ6ruh4GR8fN+V7GWnP7HHpnJ8PWp33Ynj2VHZ3lgU1op944olT3hCPx4PLL78cl19+ecttwuEw/vVf/xX/+q//OgcjXBpQiESjUXR3d2NwcNAoAK2QEWDZbBajo6MA9pboYDSWsz6m/D6RirJT2M0HPL7Pt6czNxUq1jkFZlZXVCriXDi4NSehUGF9OBrMaYyQ6cb5fN4mzOdzQp8rKMy5yKJAXo5wEdmqnIuMVisUCrZFNLAnfb5UKs27MFeUuUBld+eJRCIYGBhANBo1irbMnJIR4VLpoIG9Wq0il8sZp7rMkopEIsYAzG3ZvCsSidiaiAIw6cH8OxgMGiMAa5DLxpmybJtlWbbmYlR02VeCRlFZY5zGA2Cvc75d3JSCVgZTJ4wi5PgZDSiVQlmbfT6NqYsh9ZdGdNaPB2DqizqN7jJyjUZoGt+dQQN8lqUSzDVns9k0df1phAf2Bi3wueJxGSkv12ksW0hnkFTw3aCRJ5/PA9jjGOAaebk5TpT9G5XdnUE6BGOxGAYHBxGLxYzs5jacQ6hPS+M050BGX0vnLQAjW2XEuowilkZIZ6kUmUUGYFqjJpEZRnJ94XRUy/1T15FjcT5j7ergjCieDto9OPezlJvMkOa9kM7/pT6Xc31F54wMIuD9oC3GGakuz93td3k/+b60d8hMCH7O32Xm/3RI54jzd7k+ZLa/c73hhM6pYrFoKyncySxCRXFj0dZEV9qDikMikUAqlTLp4O0YM4PBIGKxmInCYuQPa0jFYjGjzMh6ZJyQKNhlPTiOab7hMRnBZFmWLUV8tooQv+NWD0wuYmTNOF4HCiJ6RuW4pLd8qSJTEBlNwWdlOUSk8FmnMGfk51TCnNcEsKfVyXJJKsz3oB5xZX+HBu9QKIREImErzSKVCrcyKjQeRqNRW6QQFYrx8XETWU7lHICrsu1Mw5bNxYG9NVqljHereSlLpVWrVRPxzZrkjAriuGlEKBaLAGCyyOh0lmOci/93GtHD4bDJZqPRgsoXI7sY9TVfivh8HYOOAqkQM5hClmSRJc1kk006R1gCh7XupdOFCrab8s0gCFkCptlsmmwuGXQgMwgInULcVpbvkU6hmZRSc+6zXQPMVFkTM2UxyzeV3cr+jt/vR3d3N8LhsGm4LcuLAHudc9IIztKj1Ls5V3GOzOfzmJiYMD06GIkuHY50rstGrVJOusnM2ZTZoE4r52uZBUR5z5rkHA/lyVzDNZCsBS/lCptuejweFIvFZTfvyKbf0lbB4DanYZvPhNMRwnUAI8id5QOJ07YjnSYysMPtOjsdPRyrM6uA4+WYKpWKLaCzFdwnvyOd8u3q3XO1zlxMqOzuLGpEX8JQaQ2HwxgcHMTAwIAtqmoqPB6PMZIHg0GkUikzUY2Ojto85LKBiUwL4z+j3+9HV1fXghpOOUHToRAMBpFOp01aDxczs0EuhCRs/sY67Lz2sns5FdBoNIpms2mipBiZt5SNqjKtLJ/PI5vNmuiz5WBEpyJdq9VQKBSQyWRsDeaAyd58y7KM91wiFwbKHlSYK/s7kUgEa9asQV9fnylBAext/Mlaj9J4SblCA3lXVxfC4bAp18LU1lKphHA4jGq1akvFp+LLqDj5HpUrRqhzHUBZFQwGJ0U2cUw0SAN7oslKpZKRB/F4HACQzWYBALFYDD09PSblOp1Ow+v14tBDD4VlWXj66adRLBYn9SzpNIxmSyQSCIfDiEajpokrja6U8aVSyTQ+XS5YlmWitmVUGHu4APbavbK3B2W/fEYYDebz+VAoFODz+UzpHqeiLBV5rtNoDGF5IllqJhAIoKenB9Fo1Di1nf17+Dsb1vK5YVO/du8doxilUYrXi7itAWRPgaUcIDEdKruV/Z1wOIyDDjoIPT09Rq8rl8s2Q6MMOKORMhAImHKrnCvZf4slzqrVKoLBoMlMk/KW2TV0KFLmOucpWZal1bznlPtSttOYKvupcL53lneNRCJYtWqVmWPHx8fn5qI7xk4HBnVvGTnPcQUCARSLRbP2WC7QyMxnTkaEM6PKmSFGZFQ598VsOzoe2FeE9h5neR3K83bGSYcLKxrw+HRQyyBIrjV4bvl8vmU/E+c58afTRsXf3XCWkpnq/2U5oLK7s6gRfQnDSY2RzozsajcSnBMshTqFJT2bVH7q9botepoTmptncaHLeUjBT4WtEzW6W3lWZbdy2bVcelRl9AGvJQXFUvZ8SucCo9DaTRlcCvDeyahELi7oJHG+gNlFfOyPqDBX9ndoxI3H46ZsCCO1JfxfoWGOMoYR41Juy98tyzIZQlL5lUo29ydxpvVK47qcF6WM42fcngq1zNTisRlBRmWnVqshFAqZWp7TlaPrBDL1mcZ+WY6O5yJLvci1xHKZf5g1wHWTrFXOn1zzSaO2VFplOTO+J59ZGnOmWkcBdqXfmcItDTjO+r7O7Ei3/jfSaCBxG9NsZZOMylvKa7vpUNmt7O/QiMtsbpm55CyHIf9fnLI7GAzaHIF0QPN3Z0CS7CHRau6Sv7ttI+W9LBPn/L6zbJx83+k8ZD14OkTnEo5ZZsw5y+TwXrCsx3KU3VzrOe0c0jDtvIetzr1VoJcs7UPkPuUacarr6hyPXDtyXHL8svyg/N9xPt9u5zFTB/Z0UfTLCZXdnUWN6EsYGs8ZKTXbWuRMC2O0NJUiNiHz+XyYmJgwhkNO0PF4HIlEwuZFXGgjuoyGk176duqjtrt/CuZUKoXe3l4Eg0H09fUhkUggFAqZLu3SoMDGbDRolMtlZDIZjI2NGSPtUo1cajabyGazGBoaQiKRMBkOwMI7VfaFer2OYrGIarWKSCSClStXAtj7nHOR22g0TONQGQ2nKMrC08kSC52m2dzTrJERzjQ4MzUc2JPxFAqFbNFsMqqWjR+l4VwaPUdGRkyksSy30mg0EA6HkUqljIOXEefSYUjFRaaPS3nF92gMZXQ6S7zJaGEZFSQd7zJ9GMCcz5+MOg8Gg+jp6TGZdAxEkGsZynsaCcrlsilfJo0nS5Fms4mJiQk0Gg3E43Eccsghpp6+rKMvI8ll6jwAxONxs3akY4TrIMpJKvosCyjLGzijCAGYe8NniSUPWPdfGsnleLgeLRaLGBkZMSVlBgYGbIZ9qcCXy2VTTmgmuM0lzmMoirJvLDb5LZ1kzghtZ8AW5RodlE7DIWs3W5aFTCZjsn3y+bzJhpLyFdjbiyscDqO3t9fMuW6ZNjyOHEs758efTqcp1wTOe+KMUJ5LYrGYyTbv6elBMpk0fV8YxS9LagJ75D3LlsmMsqXc74JVA+r1OqLRKA466CAkk0mbbGW2OIMFZOAlYC/Xwz4g0jDu9rt8/mV0uQyikNdUZhNKZ7azlj7tBul0GuPj4yiXy7AsC/39/bAsyxbcwPMrFosoFAodMwov1WdBWTjUiL5EoUITCoVsRvTZQOU4FouZSLharYZMJoNsNutar8rr9WLVqlUmgov7WWijKRcrjGajIuaW0jQbWP+dJWxWrlxpjOjJZNIo287u1vF43NQODQaDJm2PRtqlnP7bbDZNGYJyuYzBwUFb5OJShSnd1WoV4XAYBx54oIkcZc1XppqNjIzYDOhL9V7OJ+oRV+YDZwTOYnpu6Ihj/WcaE9nI2O/3G+esVMALhYIxsrMkGw2dNKKz5EaxWJwkl6XRUSpQUhmXKd0ATNScU1FlU1IZyVapVEyEmNxvq+wdGYFE5vI+hcNhdHd3IxQKobe3F6lUyrZ2YKq8ZVlG3kciEQB7ZFwul5vkrFiK0IieTqfR39+PF7zgBejt7TVrlXq9bsrtyDIEsvdLNBpFMpmEx+NBKpUCsLduLp8Fli2gEZ0GDa6neG1l3WCuK5kRyfJqTF/nGPhcsoZwMBjEzp07sWvXLuTzecTjcfT395v987mj82NiYqJjNXyX6nMwU1R2K/OBM9t5ISOJnYZiRjuzL5TUdZxRt9KBzcAbpy4hP2ekeSaTsX2fTkOWVqWe6/V6W9b8lkbL6dZB0jgKwGZolgZ1N11fGlvnCjoeVqxYgVAohO7ubtMHLhgMmjrYvH40vLIkTrlcxvj4uHHMziZqebHQbDaxe/dujI2Noa+vD4cccggSiYStKbpsxO0MDgBgi+KXDeDpfAb23lf5DEpjvDPznmMD3EsMAfYqCsCeQJF4PI5YLIZsNovx8XHkcjmzPpNZcjIoY/fu3SgUCvt8LfcneaSyu7OoEX2J45y09gXnPqTXkJFAzkgyuchZaAM6kQsBZxrSviJTyLiYoRGDk7xzQWVZli2Kn4Z9ltBhtFWnouVng1MwOq+ZcxHl9N6yXi8XMFxYLgbHykxxRkjyxSa7cuHLVEFGs7Fm7HLpBD+XqDBX9lecSoQzxVVuIxU9t/8ZKoRseunEGf3DfbspRc4oI3n8VinfzsZTzhe3l/KDipBUqmQKPGAvHdMpOCYabmkQdh5HOg8o71kvFIBxWCw23OStW/S/83OuQQqFgnGIy8wCtwgyuV8+A7JRmDS+OJ9vOQ5nKSLn8+9cbzjXdDIAgf8DbDDKQBPW93eWoGHkZ7lctj2TKlumRmW3Ml8stmdFykYi57mp5IKc26Te5Kz57Gbolnq2m+x2KyEjZVqriH63/2XneTi3me6eyN4QnYLnSGcunds8lnzJDAAAxgnu9XpNr4vFJr/dor+lLHK7lpS91EGZHQdMrgzgdKIAe8u+yGwFWZ5N4na95Db7cq/5v8CAQq49wuEwksmkue8s5UYjf6lUQrFYnNTsXGmNyu7Ookb0JYqcPPd14pBGXLf0Jgpj5yKBSspiKOMicQrTTo7P7/cjlUqZVLre3l7TzJTpZG6KLBV3v99vIqrYiIsKHCML5xuOjeOLx+O21C9gb3REvV5HPp+3df22rD3p3lRAR0dHYVkWotEouru7l1Q0ujT2+Hw+JJNJAHtS1plpEIlEjBG9WCyiVqtheHgYfX19yOfz+Mtf/oLdu3eb/08VOu6oMFfmg6nqMc830ohLxY7KAQ16AIzhz7Is02BT1jRntG6pVMKuXbuQy+VQqVSmXAtIRZJym6U4wuGwidSSirll7W1kxghiafjnd5rNPY0fo9GokRkympuORUZHOaOHWP6L8y/Hymj6qc5LyqmpFDkqYj6fDz09PVi9ejUCgQBqtRqKxaIZK8+P14rzPa8RS7lIWbGQEZLy/OjolddEpnZTSXWjUCjgD3/4A55++mmsWLEChxxyiFmv8HwZCcnGo7KOL9cwbjVa+buMsuRnLIUG2B0oMiuDyr7MeuBalc3H5D4rlQqSyaSJQu/r65sUNcc17e7du7Fr1y5UKhWMjIzYsgwUd1R2K/PBYnpOOP/JnlvA3vIqhLKd33EzaHu9XtTrdezevRuZTMbogK1gtjjlkWyCDcDMjdTl2RSaTkXOmU7nvZuB1ukkkGPn9s65Uc6nDDTiOKaT3XIfU50/r2sikUB/f78tilruR+r7Mnqf5dh43aVzd6GQDhBG0nOsAExTeZZlafWMlMtlbN++Hel0GslkEgMDA0aGcs0j15mUnTLwYqpADpnJLx08boF1bs+XEx5DRpWPjY3B7/ejUCggHo8jEolgcHAQg4ODtkh0GWlPG0yxWMTQ0BDS6XTHAy+WGyq7O4sa0ZcwMqrLqczNZl9TlaGQEyeVELcJdqFxeubd6tTtCzSiRiIRRKNRkz4cDoenbIYmvcxsLhqLxRCLxYwzYiFh/fhQKIREIoFwOGyrl1YoFFAqlWxp3sDe54LR54FAALlcziwIWCttKUEhw3vt9XrR09ODgYEBY9xhDWI23uXCLZvNYnh42Dgh2ukqrijK3LIY/gelXGL6sWyATQUPgDGuNxoN42ilQiTlWrPZRD6fx9jY2LTHpyIum2DLLCpmD1G2M+qHUT7SwClrYtMAwJrY/IwKLrDX0MB1Co2yNPwGg0FbJDCVSo/HM22GFq/pdIoTlTsq+b29vfB6vRgfH0exWLTV9WSmmc/nQygUMvKQjd5keZ3FAq8ZZQ/vD5XU6a5ltVrF0NCQebYOPvhgs9ar1WqTGq/yfgEwv7P8Gcfj9XoRi8UQjUbNWPgZnw9G0UmkEV2+x/8buealHJYNwKVDoaurC729vTaDgjRs8RyLxSJyuZxxWi20420xOGYURbEjndCAvUEiADNPyv4ITji35HI5jI+PT1vOUx6Tehp1LGBveTb26CCcDzk2GaVNWkUcO+efVgZ353Y0eALt9SJrZ56TkcjhcNj0HXM6MLits7SMHNPu3bvnJEt9Nsg1IYMO6CAB9joBZDNZN+r1ulnHAEB/f78tkFCWIKKDmM+K7KUjxwTAlO9zwm2dDUDd9P2p7EMyI4zjqNfrxonQ09ODlStXmueepQQpo3nN8vk80uk0stnsojGgL/T6QZkf1Ii+hOEkSM9coVAw6avtCAdOfIw0k/W5p/ue/LlYJi3i5intBFLYychtKpXtXHMq8vTYyzqz8600ScGUSqVMBHo8HjcLNOnRD4fDJsKBzdVofOBisV6vI5vNmmMkk0lzrovZmM4FrIyqCwaDiMViJtOACzfWdaXRqVarIZFI2KLWo9EoKpWKuV7KZNQjruxP0Dgto4L9fr/JSrIsa1JklYx+c5PtM3Fgc26jrGHkdalUQi6XszkDJYz+4XgYFe9muJaGAGdWljRsOqOCuX+5vVTQpruu7axB/H4/ksmk6eOSz+eNct3T02OOL8+FUdjSOMHvUDbQucxrMx/IIAHWKafsZskZGTXI583n86FSqUyKIpNlTgCYNSGfv0QigUAgMEk+8l7yfsqMCT7LrGMqAxz4PAMwziJefxkJSUWUUYMcq9NgziaozJaT9yeVSqG7u9tWt1U+xzTKMGKR65SFjkRfzPJNZbeyPyKjZ6l7c26j7HSWPXPWEJfbtqujUv7IuZBys1qtTirJwZrpbuVAplszyHG6BaG5jdd53u3KbnldpxqL3+9HLBYzspu92+jglvuQ18hZko6OezYQd8rB+UA6kGVPu1gsZuQm13nhcNiWWUDZ6yzvJ9cppVLJbO+MtpeyXgZ08G8iZbWE6wE+Q/KeO7P+nY4Mfl+OW24nnVC0SySTSVPORe4jFAqh0WggEokgHo+b97iW6LTdZzb7WqwyTmV3Z1Ej+hKGArxUKiGdTsPr9RpDaDsGS3ohS6USMpkMxsbGjGIyHVIRWUz/VHJczoYo+wIFCiORmQIvjent7ofRhNLjzPfmM7otFAohlUohGAxicHAQfX19trQyjhfYa1Co1WpIp9OmQctzzz1nPOSMMHzuueewe/dulMtlJBIJRKNRRCIRxGKxeTmv2SAdUtlsFuVyGX19fTjggAMQjUYxMDCA/v5+Y0RnJLqMygf2NFnr7+83zefoMVcmo8Jc2d+gbJVlLQqFgjGGUrmj4ZFKH+edRCIBr9drjIUzKVXGMircfzQaRbPZRDqdRqVSQSgUQjweh9frNdHLVKIp36QhmcioOxn17HTEBgIBJJNJo/zLOtqMKqIBmKU/uL+paHceiUQiWLVqlZG5w8PDCIVCOOigg9DX14darYZcLmeuLQ3jlUrF5kCnsZoNOBlJ6DRazCUyk6G/vx89PT0IBoOmWapMk+YaiCnPhULB9C5hEAXHLR0HExMTZo0zMDAAYM/zy2eIzyrXRs1m00SJB4NBU9onm82aOutsui5L/0hDjWweJh3zrHHO8bLMT6PRMOsLZm1Uq1XEYjH09vYiHA7jgAMOwKpVq2BZFvL5vHEi8FxTqZQxWMTjcWQyGXN9VMa4o7JbWW60G8REA7rX60W5XDZZyJR31J3kHAPAZqyUWb7t6NAslcXsMe6fGWrS8ErHpnQ0Smcp9UygdQCcNMjLzHP5PWkQlU55XqN2A/KmOndeq0gkgoGBAVPCJp/PGznc09NjSrLJCGvAnhXA6+z3+9Hd3W0MzVK3nYt5ybk+Yz8WjoON47u7uxEOhyc5tS3LMgGS2WzWZINLx0q9XkculzNyOZ/PG5nJNaJ07EjnhMxmaGXwlt9zGtFbfd+JvNfSkC+fm2azaRqVS9ktnQM01nMfLH+TSCSQyWRsfcn2FelMmE/bzFyisruzqBF9iUMhzDqdoVCorRIa/J7s4DzTpkrzqTTOlk5NfM4yMVxczCQKXe5HplrNdB+dQqYG0jEgSwVIGIXGKACv14t8Pm+LSJQecaZcOdPfeQ0WExy3/D/g/wIXp7xOvGayBAMdIoyKoOHBmY6u2FFhrixH2olccRoIWS4DsEfm0CAtjZVS2Z/J/4BU6uX35PFl5LA8Nud4pyLWKmKY5+GMbnI23JaRVFK54/l3UnGhgzgSiZgeHjw3GiakA8N5fFlihHLAsqy2AhY6jYz2pvymEyYcDqNarU4qKcBoLdl80+3a8rNyuWwMGIxspOFaBgMQKuq8zsFg0IxV3lu3FHP+LrMUmN7tfOYB2Iwi3Ebuh/eHcpvGCdZsl/fVLbOQ5yjPTeXNXlR2K/srMlCLegNLhfBz+VO+zzlF6ksz0bflflvtQ8pjzmVTjasVTh3Qqcu4/e08v04Fr9HxyhJvdGJQDsp1hzyu2+8sDdNoNGy691zM8W76n3TGywbnsVjMRJ47HSt0THPNQRku9y8bZVPvdvY2c94TuYZzZsPLF+HvblHm0rjO77k5ppwOGLc1IPVo6t48Z7k25hrEKbtnkp3ZDu0615YKKrs7ixrRlwG1Wg27d+9GPp9HoVCAZVlmcg6Hw7ZJVDbqmpiYQDqdNo2d2o0ql4sIpqTTA7wQxmDn2BgpQGNou9H10+2XOI0Jzs9nsj/piGg1uUkh3+r6TrcPCYVmNBpFX1+fEeAyHbHV9xjBGAwGUS6XkUwmUS6XjZdYLi4zmQyefvpphMNhrFy50maUXuga8MDeRUej0cDo6CjGxsbMwtPj8ZhFFo0V8nd6z7l45qKIn7WbDaIoyvJhprKPcrhYLCIWi6Fer08qkcE01Vqthp07d5qoNL5YB3Mm0AjJCHemEssaqlSsmC5bKpUwMTFhooUikYjZj9yXz+czPTQ8Hg9SqZQx6FJB5LZUjpw1qGUzzE7BaC1G8HO+ZlaVPF4rWeqM6pPBCDNRTmQ5k3ajpqQDPhaLobu72xj12VgW2BPpxlJrNJzTCM5Sbel02jQId1sbFQoFPPvsswiFQibSXWbjAXvr3cr1kBu83zSuBwIBWJZlmoFynUbjBg0/jGSk0YHf5/G5jh0dHUWpVAJgN/wwDbxQKJhaqYxE57bSkC5lt9OIzuuvCqSiKMCeeWt0dBT5fB6RSASpVMroN+yhwVetVjPRwdSXZiK7pZ7JLCPOVyx9IQ3JMtKYRmdZkq0dfZhzb7u6JeUYde5OZKlzrNwv7Q104MoIdLfyWzITi/uT0fky42m6sToD4HjO011Lt0j0ZDJp1h90CBSLRXPtpJGc9y+ZTCIajSKdTpssRrc1Uj6fx/bt2xEKhTAwMIDe3l7X68rrw0AwYG+2hHROO2v/83ut5KHTcSFtFtLJwizITCYDy9pbyrDZbKK7u9usDWnT4jpRGuG5HpHBbm6ye7Y4nx1FcbLwlixln6lWqxgZGYHX60WpVDLRQz09PZMinavVKvL5PKrVKp577jk899xzJs17JgorhVGlUjGCQE5sCwWFiozwa6UozgYpHGYbie/08k71femJd9YLlfuSkVlTjUnuKxaLGSM6o6yng0Z0YE9ddJYPYpQasLcubiaTQaVSMdEDMkJ7MRjRKZhrtRpGRkawbds2+Hw+9PX1mVIHsgkfxy2jH3geTiO6rAmruKMecWV/p16vI51Ow+PZU3eajjmpIDNLaHR0FDt37jT9T0ql0qwzwRhBx0jdeDxuM4gDeyOLOG8De5pyUYGjEZ29H7gtjej5fB5+v9/Um7Ysyxg+4/G4MaLL6CYpE2Uq/L4gAwhobI7H48bowevYKgvLuQ9n5pVsCNsuzM4C9hpG2jkPKbv7+/vNtc5ms2YdRsdALpeDZVno6uoyZXoSiQQ8nj3NPIeGhlo2K2NABiPZIpGIKSVAIzfXVVT8ZSQ5P6czWjbF4/gKhYLtOlKm8now6p3PHx1K3I7Py+7du/Hss8/C6/UilUqZsbJxbD6ftxnR2ctERtJL+R4IBGz30y0Sfn9HZbeyv0MjOrCnD1KpVEIwGERXV9ckfYHlsSqVCsbGxjAxMdG2MZtIuUgjtXRGOzNzANgalzubjMp9OiOKCQ3zTmOi04DK/XJdIdcR+wKPT6cqnazUrZjx7Bb5Lg2+8vrQGMv1xXROYF4TmS1FvW+m99Dj2VMHPZFImDVVs9k0xmJZJpTXmTacrq4uc9xMJmMrWyPJ5XJGdrPEG0sHyevCsVMeuhm7pa1BBhhMZ1h2swW5GdLT6TS2b98OYE8pVOlY4HqRpeDcAg4Z2Ccj+7kW6RTLTVap7O4sC2/JUjoCJxfZEErWJOXPcrlsFAmmts42UpuTOL/PyN2FRC4y5KKhU//4dB5IT/tMG6hwTHIxJK+/FGAy1YqCTjalkQsFKpUyKs7tvkpvukytbhcaESi8WgktqUiXSiXk83njLZbn50z7n0vk/apUKqaGqqy3xmhJp9fbeb2d91IuymZjAHJbuLil4XXyeV5IVJgryl5FkRFIVI5pLGRZDdZRp+NvXxofOmW3U7HmTzkv03nI91kWQyrLTqO4fF8eg8obf+fnstRIJ5GKoGxi6kw/lttLo77cxjnHu81Dzowup9GB8hzAJOPxdOfBfdMw7ZTnlOnMkmJ0lxwXU6F5TOdxea6MnCwWi5Oyr+Rx3WQX98OfHEMrme/8221NwnvHurAsHUfnhswuk+PjPZPXSJbmkVGardbD7cgbnpc8X7frsdRR2a0oe0usMGALgIk0pzORjlqWXN0X2c15Rcpu5+dOWSNlBuA+r0pZ75T7rcbhNFhLx3en/7/leTvH59Sd5UsaXOWc7IxAd5P9zn3yPRpsnfuZLnhN6vJyfUdjsNyWRm+uM+Q5yaAuPnPOa8XvUHbLbC75u3NcMjuL+5LjaoVb6T63/ViWZeslxv8Hrn+da5VWOO8zsHdNO1vnjduaZLno2hKV3Z1FjehLBGlUc4uK4cSTy+Wwfft2E+nEaBuZFsQ02WKxaAzuMzX4SS+hZVkmPc3ZTGy+YVS9LDHSiUh0TjxMHwP2pDwXCgWTTjdVbXPeK+m8yOfzyOVyRhGkMYXKHaMEZRozP3fbL5VHRnDJ5i7SsBEIBBAIBGydwWdaR4z7YRkApmZLaBSp1+vYuXMnMpkMotEoVq1ahXg8jng8brzkMm1sLpDGDzZnyWQyeO6551AsFpHJZJBOp02TEkatSUWdRiMq42zKywjHXC6HbDZrGqnNpKkoF0WM5uQ94f8SF+k0qsmxLFVUmCvLjdk+k5Sn4+PjNnnt9/uRyWQQCARQLBaRTqf3uWkS5TUV8WKxaGvySAO5rBlKxbW/v984bNPptJnjGc1ULpeN/ItEIkYpYgQ6FTiWkAP2GlVlajY/cypjs0Eq1JSHjHyWCqlUlGXjVbmG4Jwlr5/bHBwIBMz58/ykg5sp/8zQ4vzOlP9WcJ1BOeXz+YyMdUa4kUKhYBRpGbm1YsUKkwnBdZzzWjebTYyOjhojOku5JZNJDA4OTio9xPWEjFTnudHwwOMzaozHkQ4Nef1lYASjDim7aSSQjigaFlgrntH0jKTnGHl/GBHP39PptM2xMJVhRH7ODFBGspfLZZtDDIBxki11VHYrCmzr82w2C49nT2ktZoYzw5cBO9S/ZwtlDzOsWQbOmZ3s1EF9Ph8ikcgkI7vTScxzch5TIh3g3JYl5jjvdRLKTepT1I2cmb78nbJFrk1kwBqdGDJiXl4HZv/JdZi8DtTLAZj+I41Gw4zPLfDK7/cb2RCPx5FIJIwRnOXlmGUlG8fm83mTKUedz+v1Ih6Pmwbira53o9HA0NCQyVLj8Xt6erBq1SpbvXT5rMpSavLaUs479U43Azp/SucC70M6ncbOnTvN/wSDRGSjXdoouG7iOJ02Ahreea0mJiZmtD6W58/rIx0rdHwtJ1R2dxY1oi8hnJ43ZzSyZVnG6Ce3l7+3E+3ULhSYFAZSIC2EEZ3nxomPym8narMRKtVM0atWq/B4PK6pdG7jozBhRCHvl/SwU9mVijINq25GdC4yZF047o9eXm7L70vjwWyN1xS8VHrdzpfPxMTEBLLZLGKxGILBoFmEJRKJeXW6cNFRLBYxPj6OnTt3GmcLnQ+MIuHzI7MFpNGkVqsZ4xCfOfmaSbd3adxgIzdn2iKP1ar+n6IoSxcZoSYVWs5HlLedkGWcQxjhTiO4NGTSCMp67KyPDuwt9yHTeqmgch+UV1IB5DqEBgAakQOBgHHKS8XFLWBgpsi1jzTaSvnnlD90aFIRl4qglAOtIpVYo1NGffE6NJtNU0KN23LdQuP8VOciZYUs+8N0ZhqK2WS+Vqshm82aY9GBkEgkjFM2n8+7ngeVU1maRzbxkkon1ylyPSjXPDJanecgI89k7VWZJcC1UrPZNIao9P/P3psH13ZVZ+LfvZLuPGqW3uT3niewQ5LGHf+eA5hOlU2bdNplTOPuVLsgIRQuEwI4aRoDTkwS45hQrpc0YKDKYSga7Eo5LkI1DTbVjdOASQJhCk4Y7Ge/QdLTcOf56t77+0P1bX1n61zpSrrSk57PqlJJusM5e+9zzl5rfetba+VyWFhYMCXkKJrizvVQcoPemwRKeM/qfdmL2AxzsvvD4bDJGuF9xnXabgDME0882RuiQed2u20Iaaq7uf9onevtCgF0zZrl/mvrS3vPBZx1tLsBauuRqvQ7ysSnH6ulXfsl1MM813qkK2V5K9nILVvYLWNYe7toJhPXmT3EgNWAsOoot8A/SXHUzwSsuZa0E2y9ykAsMQbqMY5vvbKonU4H+Xwe+Xze6G4S54gDaAUBnp9jUttOcYdugLk9X36Xx+K9WiqVcP78edPHBIApN8RsMrcMeT2HZlVwvQnK90ouUzyNz6pdGmm92u+e9C4f+9jH8Od//ueYnZ3FVVddhZMnT+KVr3xl188/9dRTuOuuu/DjH/8Y09PTePe734077rjD8ZnHHnsM99xzD5599lkcP34c9913H2655Rbz/kMPPYSHHnoIzz//PADgqquuwh/+4R/ipptuMp/pdDr4wAc+gE9+8pPIZrO49tpr8dGPfhRXXXVVz3PzQPQ9LKqklSllpyDzYady0LRZ3SSo7BV83yqTleek8qhWq45GVt0Y2TsldGpZI41lOnSN+iF0djudjmHTNRoNh8K1N38Fk7nR8zcBXDpbdkSa9T2ZVWCD6Dy+DfZy8+f9YAM0thG0VbFLAXQTVaSFQsFhbGmHcjU81ent9V7Se5qgjkaUm80mlpaWUK1WDSNdgXH7ftFaa+FwGAAMEFKv11EoFAyDdHFx0bDQtdFqN2GQhAYO2QnxeNywFN1A9EKhgEqlgkaj4VjL/Qise8aJJxej2MwaOrkKLNt6SQPcyojWjBQ6dP0MCrMeJxnM3HupxxV81vmxJrqya2lf8DMatKVuYoCYTOBwOGzKgnHPttOttyOq6zSbR9nkzKCzM/cUtGbQlBlIBHR1bbRUCh1lAtzNZtN8R9dI7wvu+8rgUh1LkJj6gOtJYJtBYtpmwGodd14Lv99v9J6WpetlHTn2UqmEubk5M25gNRhAIIAOKcdrs/Hdjq/zpV5mfX3q3Far5QjgqPB6dTodY5tSvwIw8242m8hkMmi1WlhaWsLCwgIKhQLK5fK69xCF15X3DNc/lUohEok4rjWDHK1WC/l83vQf0uDMftSD+3HMnnjSTZTE5JZl4lZGk5+zWbtuIKqyePvx7BC4LxaLJuvG9pd0XPYYdd76GsfYDUgn4Mu/ycTXjN1+C31n6u5arebw83Q/VpvLJhgySFqtVh12i+pGBqA1O1h1jwYrFJehjtOyM8pMJx5BwpaWNSUDnLah+vFqA/KarJcF5yZqw+XzeczNzSEQCJg1InjOMWkAXsvuMXDRjdygpL5Op2MwDvVR8/n8mkCyjW3xs0NDQ0gkEgBWdTf7+jGYnslkTB34XnQp8RTeM8RbEokEotGoyVqwMxn4NzEmm/Cyn2Q3x/zoo4/ine98Jz72sY/hV3/1V/GJT3wCN910E5555hkcPnx4zedPnTqF1772tXjLW96Cz33uc/jmN7+JO++8E2NjY7j11lsBAE8//TRuu+02/Mmf/AluueUWPP7443jDG96Ab3zjG7j22msBAAcPHsSf/dmf4dJLLwUAfOYzn8HNN9+M733vewYk/9CHPoQHH3wQn/70p3H55ZfjT//0T3HDDTfgJz/5CeLxeE/z80D0PSwamWTkUWtpqsOhThE3a7eamcqeUgdwK2CqKiE2R0skEo405t0A0nWzL5VKWFpaMuyprdSmXk8IYtLxJRDK9dQoNkUVeLVaxeLiImq1GhYWFpDJZByMP6ZKM3U6EomsAdFtxgFBdG7+TCemotA0aFtJ8xhbuU56D220xrznKpUKZmZmDAhD4Hh0dBTpdBrBYBCpVMqsoRoa641RgRINWBSLRTQaDWQyGSwtLaHZbCKXyxnFbpe74bG4VsFgEJFIBPF4HMlkEu32SjMUNm47d+4cyuUyZmZmcO7cOZPKmclkHIwPFYIz4XAYU1NT5vfBgwdNSj7BFN5HdPwZBCgWi8hms3j22WdRKpVMQGc/KfTtggf7aa6evHhEWUcKanJvJ+tGwUTu4XQO1YGjzlfHtl+OOIG9VquFcDiMdDptxs4sIzrHCvJHo1HE43HzfTLH7UAA93C7dAf1G8t20KGjg+K2L7utM8+5ntgANHVPPp83epzjjMfjRs9qEzPqE937uW5ujng4HEYikTAlzwKBgNE5GmigHggEAg4nW8uhELgHVll4HAPtj0QiYQAG1ev8Dq8FA9XLy8umLICmbruBK7qOtDPr9ToWFxfh968086QeZ1A4nU5jdHTU6C+yvRU8toNCtE05PxIhFhYWMD8/7wCctba9iqacVyoVM+fp6Wm02yuladiIdWZmBtVqFS+88AJOnz6Ner2OfD6/7r1EYbB7aGgI6XQaw8PDCAaDGB0dRTKZdJRz4ZgajQZOnz6NxcVFVKtVLC0tOcoL9XIv7xXxdLcnF6N0OquNvYFVPaslPmyykuoXHkODsLTjCfBut58Jhbq33W4jFos5anXbdoKSi9Se0PG67ffqd3Ev134b9EUJ5tMX2ej53myGGRnMBLlZRpQNNEOhEFKplClxQhCdc6TfV6vVkM/nkclkHPgH7ZDBwUFj2zCbbGhoyJT/ZN8sfof3CfUy/XD+JpmK5V78/pVyndVq1diExA+o620QXrPGeT4G9NW/XE938/w+nw/1eh3ZbBYDAwOIRqMIhUIIhUJotVqIRCLGdvH7/WbNiBsoA1yPrefTMqfnzp3D7Oys4z7UkmsqtFGA1eyzaDSKAwcOOHR3qVTCzMyM+X3mzBnUajUsLS31hPUEg0GMjIwgGAwimUwa3GF0dBSxWMy1FNvy8jLOnz+PYrGIXC6HM2fOmPI9+02X7bbufvDBB/HmN78Zv/M7vwMAOHnyJL761a/ioYcewv3337/m8x//+Mdx+PBhnDx5EgDwkpe8BN/5znfw4Q9/2IDoJ0+exA033IC7774bAHD33XfjqaeewsmTJ/GFL3wBAPAbv/EbjuPed999eOihh/Dtb38bV111FTqdDk6ePIn3ve99eN3rXgdgBWifmJjA5z//ebz1rW/taX4eiL5HRYFv3UAVRNdIrCpErSNFJagprcp04t9bSVnRVC4yo2x2zW6x0elUcfOls9fvDU7TkjTtvF6vm+gto7r2d7hGWq+dgQg7TZvKW9lO64HoZAPQUSV4rowBFWX5bQdEVxDITexxEpQBVu9bGkGsRU72Pe9lvZ83Go+yDVkuh5FrAupkiq83bsDZ+NQOXtFQolLn8VnjjQx4t+MrWzEcDiMWiyGVSmF0dNQAOQQ7FIQgAEFDn2vFe2u/pZ15jrgnF6vos0ugUAFbiu6LCsaq7lbA1c5C267Qqa/X6wYspj5SR5tAnzptbvpIRdnWyqa356bBfhuY6KcomKDsaOrLTqfjyABS3abf0wavCj5rFprabNpnQzMK1Dbjfs40buowXSu12TgWG+CwHXCKnpc2gTKu7Ovmdm8pw4+MPh5PgSc6nW62io6JdiKvt8/nM++rLddrgFifE96vPC4DWpwbCRelUgm5XA5LS0tdnXs3IZgTCAQQiUSQSCQMASCRSDiCZwys1Ot1xGIxw5jjNd+p+30nxdPdnlysouQVYLVHg/aOsrPJVP/pPqSv8f9+ZYyqj8P9za4Tbvt59KmoC9xIPr3sszpH7qk76XdTT9DvIoBMnci5aMkT1dn8nuID/L7qRfrZqrtZdo0+oGYl2Kx0rokeV8fP8bAOOq+Hkhs1g9oOxvDYynDX67Le9aOOoU+s9h0AEyBguRcK7xFtRqrHtO8h6m7aCMVisSf9pvcV18Pv95vACNeJurtcLpuACLGVXoSB9VAohFgshmQyiUAgYMgAzWbTkD5Yeof3HOdnB9r2k/RLd7NEIEVLFFIajQa++93v4j3veY/j9RtvvBHf+ta3XI//9NNP48Ybb3S89prXvAYPP/yw6a3z9NNP413veteazxB4t6XVauGv//qvUS6XceLECQArjPe5uTnHuYLBIK6//np861vf8kD0/SrqYPGmJKhqM8sVxKYS4QOuDptGwnWz5nuqaDYr3NTy+bxxyGKxmAFFWQJjJ8B0HXc+nzcOEZs69qsGnQoVc6ezUit0YWHBRKpzuZxxqhip5zjJqmLElEwrGhysC0vmMyPUBJSZEg7AFUTnOqgTqmukUXd+lrXBW62WqaPai3D+9XrdNN/UuqIKFHGcmqKvmRD8LpuXDQ0NmTUl+KCghA0qKABFw0LrlDMiTrBbDRQ3YXQ+lUohEAiYOS4tLZlnjJkOc3Nz+MlPfoJCoYDFxUXMz88bp7+bYtWU79HRUVx++eWIxWIYGRnB8PCw41lXo4LPLu+BWCyGSCQCYKU+8blz59YYsp544smFET7HdMBbrZYJjBHUYwNGMoIBGH2phi73TTKcGYhUp2GrATTulzwns28ILPMzuu9yTGz+SRaXgs3q3Pp8PhPko22jGUy5XM4wxUqlksNBXk+2o9vJOtY5DQ0NmSZcdK7a7Tay2ayp1c4SYFwzXhNmffE3y7fwuvNa08mIRCKIxWImQMy52I64nT6u+p7rxHuJwKyC2OrMazkVDTDbuoL3mrLsaFcokM7/GZDmd/x+v3FyBwYGkM/nHevK+4tzoc5SUaCj1wwr3nMcK22IarWKubk5NJtNnD59GtlsFplMBqdOnTI2ozaOtUUBDK5jMpnE4cOHEY1GEY1GDRNUa/GSbchAud/vx8TEBCKRCGq1GsbGxlCv1zE/P49z585tyf72xBNP+ifUf6x5zf1AgVM2fmRZKep4+k8aFOR+yGOor6KyFWCLmUf8Hn1GloZUXczz87cGX+0yM9QRtv5g0Jz6g+xm6kUSiHrxPbrts93eU6lWq8hkMo7Sp4ODg8hms/D7/Y4sOWIBtVoNuVzO6D36Z8ycInmJf9tCG4d+MoPt9CVJWlM/W4PqOkcGPwAYrIbvKaDPdVC8RwFq6ix+Rs+ltmO3e40YANe00WiYuaVSKfh8K01yi8WiybwiwM5rxbVVf5fB6FarhUKh0NN9rfOm3cnvz8zMoNFo4OzZs8hms1hYWMBPf/pTo7tZHm09/cnnlo1VDx06hFgshlgsZjL/FQDWZ4b3PTMd2BemVqthbm7uRau7Dx065Pj/j/7oj3Dvvfc6XltcXESr1cLExITj9YmJCczNzbked25uzvXzy8vLWFxcxNTUVNfP2Mf80Y9+hBMnTqBWqyEWi+Hxxx/HS1/6UnMefs8+zgsvvLDOzJ3igeh7TNQRCYVCJuWZ5S10A6NwU6by01pXVIJaz43Kkg4Blf5mU8T5Wda5IngYj8cNwLeT9dGpsJiexOaVhUJhzcbeT+GGSQB5YGAAlUrFgN90qCjtdtt8lmC7NmLlRh2JRAyIzkhpJBIxrCfeFwoiA2uj9DpGZegrg15BdLLnewHRleHFlHIC4bzPWONWHXet/a5KvFqtwufzmVQzGp4EmrSsC+8lBRU4FrIMeExlH+g5uV7dZGBgpXne8PAwIpGIAdF9Pp8B5zOZDMrlMk6fPo1nnnnGBG6YWrkeADQ4OIiRkRGMjIxgamoKL3nJS5BIJEzQyY3FotLpdAxglU6nEY/HUa1WMTQ0ZMrU0Nja6+Kx2Ty5GIV7GI1yguk2C5l1tsvlsmFYEERn3UxlH+n7Cn6rft2qI07Hh0F47oM6J5tNx8Apg/2axks7g58lsMz9XEHc8+fPI5/Pm9rsu7F30T5Q1rI6vel02nxmZmbGlOdSG4m6SPtZ0CnXBu903gAYpz8ej2N0dNQ4qmR1M/OM18bvd9ZaVeeYDEQ6v9SZ/I4yD+0SgLxWNoiuziOPSV3NMal0Oh3jSOv3A4EA5ubm4Pf7TcCHpU9YS596ku9rlp8eazP3NI9Lm4pldGZnZ1GtVvHcc8+ZMnrPPfecKT2wnq2oAAWd/FQqhaNHjxqwQZ9t6l/WTWUzN/4eHx835XQajQaeeeYZzM3N7StH3NPdnlyMQhCd9XA1aMw9gr6YlsJS/0nrSOu+oOAg31c/ZrPPf7vdNgF4HQP3GeIBGgQH4MAR7DGpb6afJVDt9/sNK1d1N33B7dRDtxn03YTAPX1NsoapQ0ZHRzE1NYVOp4Pz588jm806/F4en7YL/XaWX+P7Oga/32/uCfpqAIyeZyCCepk6QIMoet34GR6H5+Ca83Mq9PE1S0uzFxWApmi2nM7HZqUDwNLSEjqdjlkTv99vdPvQ0BBGRkYMrkO7IJfLIZ/PO3SofZ5e93oGdNTmzeVyxoZ47rnnkMlkcP78efzLv/yL6YvSiy4i2YHktUsuucRkj9FOdzuOVgHQQMn4+DiazSZ++MMfvmh195kzZ0y9egBrWOgqNp5BXHIzn7df7+WYV1xxBb7//e8jl8vhsccewxvf+EY89dRTBkjfyths8UD0PSi2o60RSsBZmkOdP01LtjcXNfC1PEYvALdG1OkMq9Nus4MJjAaDQZM2RCdku2C6BgeoOFkihbVDd6tOlRo+mh6lLC5+jspKa9fb14brpNkG+qPsblU0Pp/POI6qSLXbtgLaFDLBATi6duu9pnPgbypvbWaqzrrW8+U5aXS6ib2p6ybGsfCeVodWjT0arix5QuNxO6mFvL4EjAg2VSoV05yGRhTXZCM22+DgoKkFz2CJBkg2En1eCUoBMAaCz+dzdD3fy+I54p5crKLPqVvQW7PEuE9ruqy936seAJyNSzcSW4e4GaIKllKfMMNNwVj9rg0o6znUuQPWlv6i/tAU6156a/RLdK4aGKbuYRaT6hPbntL58G/V4W72Dm2wbiVw9HOqx8lY0xraBL+5hm4Nx6kzNUOt0+k4gsucD3/7fKuNUfkde/5u5+Hf/A6zD6iXfT6fo8GoloZRfb2de8DNnmXAQEvoqa24mWOTiUjghRkANnCgc+D62M82ARt7rfdDerinuz25WMXNL9bMb/5PX4R7vepmm/Wt/rH6NBuJnkdLtaj+oZ9CMFQze9zAJ9UL+sMx655o+2X0u7U0KQlau6m7NUBP/aY9REgm4j6/HrnJtl24Nirr+Wa8N2yflcejD059q9eKNpAbOUznSz3J7xBQ1/vJxot4Ts0MtOevept/a3lQ6mbV3Tw+7RHqrH7u6Xqv0cdmGVyS9jZDtiCIzgxu+tz6THZbIxsrUwwsHA6bXgmbtSculPRLdycSCQeI7ibsi2MzxOfn59cwwCmTk5OunycBcb3P2McMBAKmseg111yDf/zHf8Rf/MVf4BOf+AQmJycBrDDSp6amehqbm3gg+h4Tsm61hIey0wGnQqQjzTRe3Qg0tUhBdI02KzBpM4D4vUAggNHRUUSjUSQSCUxMTDjY7vV6HXNzcya9e35+3kQxybBjKpCbI9+LcEwaUc7lcsjlcub8+Xx+TZrTTooqJ9bZtq8Vx67NaDg+Bb3JPOdvMgpYAkDBcTcQhM4cgxd6LmYycG3IqGZDTLLZ6NyzhIyeh0qbDcWYHscu2ZwDG6nZGQhU8lQ0610fVex0vrm2dgBBDUk1UnqNULudm4A5lbbP5zPMsXq9jvPnz6NQKGB2dhaZTAalUmnDRnh0lOPxOA4dOoRLLrnEMBhZsmazwjpu0WgU09PTKJfLhg1SLpfNGu5V8RxxTy5WIZNXhezrTqdjsnXIZCbDB1g12rW0hzrsAEz/DQ1qugnLUxH4S6fTxnkgE35paclkJNVqNfj9fuOsKFDP7Dgyg+wAJUHGTqdjHGt+j3qA+yQbNLH5M53e3XLE7ZIk+rrP5zNZPQDM2FR4jcgGbLdX+lNoWTbqUM2g0jr5bufnefT+Yb1bZtyRMMASKWS/0W6gPicbXZ1vlqOhI0o7hD+00aLRKJLJJHw+HzKZjLFzutlW9l6sQAE/z9Is2o/E1tfbvf62/UU7cXFxEY1GA9ls1pTW61V/cIyBQAAHDx7E+Pg4ksmkYbJxPQE4zq3sfz5Xuj58ZmkTVCoV0zR8r4unuz25GEWDvvq/rZu4bzLzyAYuqR+U8MPv01dS3e32PLCECLObx8bGHMH2Wq2Gs2fPmjIl2Wx2Te80uwcL4Cw3QzBRM9UVDOQ8lT2fy+VMeaxcLmdKgm537+71+woOE8RXQht1HLDKtO62XzFwS/yE4KgGP7p9h6JBE2C1lB/xGJZ2Y8ZhrVZDJpNxNCwPBoNIJBJG1yvoT0IDS8qRxKXkNZKzeD1pO5bLZfNZuxGprqUKs7n5N3+zZI8+I+uVQdus2HYY50xbdHFx0ejHzYLVgUAAR44cwaFDhxxZ39obR59VCm0efpbCZ350dBSXXXYZqtUqZmZmkM1mt70OOy27qbsDgQBe/vKX48knn8Qtt9xiXn/yySdx8803u37nxIkT+NKXvuR47YknnsA111xjiAcnTpzAk08+6aiL/sQTT+C6667bcOzM4D169CgmJyfx5JNP4pd/+ZcBrPgnTz31FB544IGe5+iB6HtM6DwzgqmM3vWANltRanQNWI2kMmqrTp1G2vhd+9jxeBypVAoTExM4duyYcZY7nY6pNQ2sOJzZbNakl5Fpy8CA21h7FY3iUinRqczlcqb+1m5GA3k+KqnNiB2t1gZd/NtmtnUTvY40kLRbO1OS+BkAxikno5lKmIEb/SyNKDrvrHnKFDrOgQAClRS/zyBCL0xpDZjsdmRXmRYMOmjH9Vqthmw2i1wuZxxypoSvp1wYFAuHwxgeHsbk5KRR5jaDsFdhwKPdbiOdTmNsbAyhUAhnz551PPueeOLJ7orNRgPgYGwxCE1HHFh1zuz0az0m91PqVjob3Vg5PH44HEYymcTk5KQj80XrYmutb45V9xDqJoLvgDMzTEFi1cPKwuP8arWaCT5qSvFWpZvt0k02shMYNN3oGMoOt5mI/OFaK7Bij9UNoOHnCGxXKhXk83kzPgVmqSuHh4fNa+qIc83z+fyagAVtC60xS8IEAyoss9Mr+4vX2n5tszbSZkQZeTxfq7VSt5fAOXvlrNcXxRbe336/H8PDwzh48KB5pgYHBx0p80oAUPCMbD4+C3q9I5EIhoeHEQqFTINyTzzxZHelV2KXAuzabFT3ACVJ2eQ1Lf3B47ntRQzAskb1gQMHTBB7cHAQxWIRS0tLyOVyDjAwGo2iWCxiaGgIsVhsjb9NYNDv9xv/UPWSvW/THqHOpx5qNBqmh8luC9fLLbuZWUe9HofXRq8XfTK366K6jd9TW43rqj459SgJBsxc5+fa7ZVecupvK4Beq9VQKBSM/qrX645zMwCuNd55HILA3Wwse45uZAbaDzstfBY4Bq4Z588g/GYDNoODgxgdHcWhQ4cctiLXlwEl22/W4Iy+zusdi8UwNTWFUqlkSgp74pS77roLt99+O6655hqcOHECn/zkJ3H69GnccccdAIC7774b586dw2c/+1kAwB133IGPfOQjuOuuu/CWt7wFTz/9NB5++GF84QtfMMd8xzvegVe96lV44IEHcPPNN+OLX/wivva1r+Eb3/iG+cx73/te3HTTTTh06BCKxSIeeeQRfP3rX8dXvvIVACt73jvf+U588IMfxGWXXYbLLrsMH/zgBxGJRPCbv/mbPc/PA9H3qGyWqb2RKCDK//XHTciMjsfjOHz4MMbHxzEyMoLDhw+bOq/tdtswzmOxGLLZrCmxwvrfNDTq9bqpG+oGDOvfdpqRpmqVSiUTBSfzVuu57SdRsETTAu10wF6Ppce0mdv2sdTBq1QqhsnAenM2g5DKvFwum3qeut62AlTwRN/fy0LFzUDG0tKSaZBG0CmTyZhGtr2C/KxnTLYigxXbfc55bclWabfbBuxaL4VxL4jHZvPkYhJlswCrDb81WKp7vBrz3Dc11dtmttl7LZ1zfc2WwcFBjI2NIZ1OmwbGylaqVqsolUrw+/0oFAqmpnmlUjH1KDkflosicKilzLjf0ymnDtCxa91SPVe/2Oe7uR/YGVGcAzPI1PlS0JT7Mp1pYEU3JJNJo1uZvUdHWO0pdeg0/ZpjYTYer6uuv5Zf47h4LAa4NYBNkH4n0rV3QhjQAGDuX2aT0V5hbxreh70ICQLMDGRQg9eFtqkSGMgi5XOqjFY+C/wZHBxEMpl0XPO9Lp7u9uRiEtXPfI753BLktO9ZJQnxGG6v2362m453k2AwiAMHDmB4eBiJRMIEwDnOaDSKXC5ngm8LCwsm+JvL5UwJTRKoWOdae5aQIKRz13FruU5+L5/Pm2zm3coc66fY/rESDTciK1KUeKhgOY/P4zG4rUx9no94BgPUg4OD5m8t20a91m3N9f7i5wnyUx/Z9+ReE9pDPp8P5XLZ3GOcF20j1sPvVX+o7uY97mab8jqSYU5ioBs5leurJBhm4+8H2W3dfdttt2FpaQl//Md/jNnZWVx99dX48pe/jCNHjgAAZmdncfr0afP5o0eP4stf/jLe9a534aMf/Simp6fxl3/5l7j11lvNZ6677jo88sgjeP/734977rkHx48fx6OPPoprr73WfOb8+fO4/fbbMTs7i2QyiZe97GX4yle+ghtuuMF85t3vfjeq1SruvPNOZLNZXHvttXjiiSdM/4NexAPR95joBm872+t9Z6MbW50jG0B3A9T9fj8SiQSGh4cxOjqKa6+9FseOHUMymcTU1JSjnEu1WsXBgweRy+Xws5/9DIVCwZS5WFpawuDgIHK5HCKRCMLhsElbolOiUXAV7fJM1ho7c9MhoqO4X+pRqajhplkHbnXFNwOk0xjiMYDV+ugK7NDR9/l8OH/+vOl4ziAHP0cFZzcmpSLS0ioKmNvMjI2CNntBlpeXsbS0hEKhgGKxaJq2cT2azSbm5uZMJ3ploXUTn89nGOipVArJZBLxeHzD7IJexefzIRKJYGxszDTIoWJn+Yi9KJ4j7snFJEyp5TNdq9Uc2UXM+GGWFuBMiXXb66nbyJbRgCjThXkcN4lEInjJS16Co0ePYnBw0DQqJxhYrVYRCASwsLCA2dlZU/ZicXERuVzOwbaLxWKmQaQGaMlqCwQCiEajjvRvgrHNZtOUqyAYrGyr7cpu7wVabofX0OfzIRqNIpVKGdazfX30O8Vi0TCkksmkaZpVKpUQj8dNY9elpSUsLS2Ze4HfVwedbHE2AaPtpOuiulsdRDvji3quWCwa+269fh97RUjoIFON96T2oSkUCgaM6PW+Y6O1RCKBeDyOeDyORqOBQqHgeC4VgOGzwCazBO1pL1A3swzQ8PAwyuUyzpw5s5NL1DfxdLcnF5PYfg9BMQacyVjtdDpGj/MetsFnZfJqcA1YC+B2E5/Ph3g8jn/zb/4NrrzySnNO9Q9rtRrS6TQKhQKee+45fOtb3zL+caFQgN+/Ugea5VQJwqsQbKVtQN1N35B+t+pu1sxWH28/CW0x9W/5OsmBCq667VV8T2trDwwMONaT+zybv9olb1qtlmkqPjAwgFwu5wB5bWBcS/8oEM9j8TXNGKN/v5cBdGC11BubcVN3c66NRgNLS0uGuNar7g6FQiarTsum2kEKni8cDptnn2tHUQCduntgYKXJuLL/97pcCN1955134s4773R979Of/vSa166//nr80z/907rHfP3rX4/Xv/71Xd9/+OGHNxyXz+fDvffei3vvvXfDz3YTD0S/iEWVnCoLAI5Nmp+1hfWt4/E4RkZGjLM3PDxsSoMAqx2qA4EAFhcXjWKpVqtGQQQCAbNhse6XRnO1aYoy16g4lFGkip0g4X41yjVqrWxx+2czx9OIuB7ffg1YXWsy3WgIKBCvLDY672oU2tfNTcm7sSn3olBhcw0KhYIJ9JCByfTwzTAx+AwQkNf7fbvCsREY0xTBvWw8eY64JxeTcP9WcNLOtNJ9mKJOt1tQzY3JZrOe3MZCAJz6WwOzzIgZHBw0oCCZ59wDyQzSsmK1Wg2tVstRd1PnpzU0CTyQrUs20XZLt+wFcdN7wKqDzmtvs52UkUxHlw4cnUatdUoww419pcQIHlOZkzy3MubdGHM6B3XcCcLbjvteFq4FGX60Hwmakz2+mfkww0IbANMeYkNXrrl+h3YwnwG1iXhd6YiHQiHzXO0H8XS3JxeTcN/U0iyaDQw4/Wk3vWszkil6r9v3vdtx6AMODQ1heHgYExMTDvBd652Pjo4iGAxicXHR7B3MzKb+4J6ljSF1PAq+2iA6AfNqtWpqcu/3Z1eJZsDqNbGzA7uJrUM3CoqoLraFdhKvFe8hG5fpJWDh9r39FOTQsrgsWwOskhQ2qm/vJrbfrVkl9tqo7dStKoJtI/F57EdW+W6Jp7v7K/vDYnsRCZ0Wn8/nqCO9nuOsbC6Cz3SEyVzS79Ap0nrP9ibv8/kQi8UwPj6OiYkJjIyMYHh42NHZWDfsRCKBgYEBDA8PY2xsDMCKMs9msybiSie6VCoZ0E8ddAX6FUQnw4j/l8tlo+C3siHYjD59ze16uG2e/RJVdt2cZDfDrJdjqvQyZnWe2WyFSiKZTBpmHI21Wq1mmnyQbdXpdIzzqulybMq5nQY0vRg42xWuAcdMdj7n0mq1HLXgN6vMmVqpAZN+CIF0dfb3OojuiScXk9AB8Pv9JqDlps8VNNc9XnULmykp61x1HpnPWjaE4vP5kE6nkUqlMDY2hsnJSYyMjBj9qTozGAxienoaqVQKAwMDOH/+PPL5PIrFoqnNTHuB/U7shqea9s7goKYeE8hcrwHqdsQGiXdS7LkCq2wv6j5N4+b1V2Yj7RwGNCqVCoCVa64ZSly/UCiE4eFhw+bvVpecNpPeW37/So1cluKhw09bTPUxGe3NZtPBrNJyLhutrw3QXCghk14DGbRtNqO3ea2ptzUbgPar1v6nDmbJIgCOJq8E17UuvpZe4zk9R9UTT3ZPuHdqSTZbv6rPqzWx+T5fq9frRndreQglIXHftfdyW3ezprkbuSoQCCCdTiMajSKfz+PQoUPmb/YH495dKBQc4+E+rWAy9RmZzCRXMQhoNxLfrOia2liD2hFuNk0/RIFRm+DgVuID2BhM12vKUnZK/mOAlOW/1mO220FvZv8xQ4DXif1kaG8Sw6HfTX0FwARBevG7u12f3ZZms4l8Pm8CCsBqo/LN2HgMRLECgga5tB8gP0v/Xm1V23/m/sCMBdqBnU7HYdt5+vvFIx6IvsdElTKVSa+1lqiUCVZqWo8Cmtwkady7RUr9fr9JAZuYmMDo6ChGRkZM40hVQD6fD6lUCuFw2IDuPp8PuVzObEp0+rkJcYMjoKgguhoqNAKoKBRo2MpGZTv9HItbLTRdKwU/dmOT3OrxuwHxbq91A9uVKUhQdnJyEgcOHDAlC1gvfGZmBrVazaShM71Z15XGGJXgVgwktyDHTl0DZVO6NVPZ7PXnerBJkF1vvh9CB16b0u51FoIXEffkYhK7KSifQepg1cf6bCqoDqzc15VKxZT7iMfjJpNLg97d9KDfv9IA8dixYxgZGcHk5CRGR0dRKpVMEzJgtXHZoUOHzP5x7tw5ZDIZzMzMGJDVdvrV0aUeJ1jpFti2AwXbETeQVm2bnSzppnaCMvHV8WR5ObUnSFbgvkxdxu+Xy2WUy2UAMOVCtKY5e2iwrvdGILquCR3xkZERk7I/NDSEpaUlnDt3znG8drttGrIFg0Ezt15LudhAyF5wxG3Zypg0AK72UTgcNnY0ARKSQ+r1OvL5vCMAwQwQPssKymnQZTfIAtsVT3d7cjEJdZsNoivYymAXdR11oO6fnU7HZGbr9+gDc/9Qlq2K3+9HOp3G0aNHTS10BjO5NyhTfWRkxARijxw5glgshjNnzqBYLDoCfix/CjiznxnQI3DoFtzn7+08szb7WwOGGqj0+Xw7UprVJi3Z+klBdMpG8yWZQcv8DA4OmkA3M4tY4k8D4G5+ugZjuVbpdBpjY2OOEjTZbBbnz583pDUF0YHV8mME7nspvWNjIBeyNC7Jn7Ye3Mr9pyC6EstIcKHovBkEsUso8v5U24/XZXl52TQm3epYd0s83d1f8UD0PSbc7JRBw824282rTrU6vNwQ6GDaILoCwt2O7RYBB5zObLdSJJwPf+uPvm8rWH5GHQubYbVZ0XHRSFIQ3a0+tR0dtoGLjRTTRmKvvds10bXqVdyOoefj3xsJu7sHg0EkEgkkEglTm49lX1iDlN3geX4KQXR1xDcLQPM4bvfUTgLFOxEs2UkFZANXe13Zecrck4tN3PSdOmhuTCTd9/V/tx+3fX29sbjt9+pIur3WrXwH93EbRKfuV7Zdv0XPo+OxdTfn5KZr+rE/uoH4Nnhsg+gq6izbDr0yBfnZbqnH3USBFjKlw+GwAc8jkYgptRePxw3hwi5xote3FzvRbZ26sbJ2i2ndr3Pofaf2r1tpJpsQYj9jG5XV2y/i6W5PLiZRXa06U/dAAA4AXZ9v1ZvK+uXr+nujAKP60Sr2fmF/Zr1jq97hsain6F/bPTT6KdSH9twUO2BmFsfbTV/3Y6+0AVTV3YoVuNkPqgPssdh2lNpSvdhr1B+hUMhRTleBW/rdwWDQ0XvF1t1bsR84B3vuu0Vg0+PvxDn4/HbDVDbCXtxA8u3gUhdCPN3dX/FA9D0mmkqijSWB1XQrAr+q+BmN5KaqdSABJ1uLP/p+N8eeUXNGXJUVb2/aVMTKcLdFN3SOwc1g4HH1HFt1eskKZOoN026omGwQ3x6DKkSN2JNh1oth5CZqpDH66ff7TRSW9TTdOqe7HcseL5mPHCev4UZgNs81OTmJq6++GrFYDAcPHsTExITD2CH7oVqt4kc/+hF8vpU0/lwuZxjcGrjZLOCt5QH0OeC4OUfb+N1rwuvMeqy8Bv1koxPA0h8aUntVPGXuycUmNLwVUNaGUHzmWUeZ77MMDPc4ZbeRSWTv3+uNoVKpYGFhAZ1OB/l8Hul02tRJ1eMCMCzaxcVFzMzMmAZO9jlslpbaEW4OZb+Ea2WXQwGctaf5vs/nMz1UuPdSR2gpu63WZidDkcxFMsVZY576irYSCRBk2fH6k2GozDvVcQRdtJlbrzo0mUxienoaoVAI09PTGB8fd4C4o6OjOHToEGq1Gv7lX/4Fzz33nCPQzYZoABygUK/ro4EN2iD6umZo7HXR66l9BXh9NbMjEAggGAw69LuWK7RZ+grerBfA2mvi6W5PLiahTrDJayyXYQfK7Axp7nG6X1MnknBEkHqjvZQlS8PhsAP4Vh8PcDaEzufzmJ2dxcLCginlstF8ATiY1DvxTGrWOUtqdAPRqbvddLPiIs1mE/V6fdNj4Xk1MygUChm9zYaRQ0NDZixuDHUNCNikOt43ZDLr7171aDKZxMGDBxEOh3HJJZdgamrKMZbl5WUcP34ctVoNP/jBDxwZg7bupk7hGDdaH10nJS7oby0ruNeF2eTM/lpP3AIiNnEDcAaraJtrv4C9vi6e7u6veCD6HhN1ngh8atdnPtC2sido1mg0TPMv/tgbgUbJ19tYFJizlYn9IOn5tPZrtznyOzspCr4y7Ua7nNORXY+JzjGqseT3Oxuo8bNbAdHVeeJ62w6VXb9tveMpWKDXwk7L7zZWZesnk0kcPXoUyWTSlARQA4jGTLVaxdLSEhYWFkzJAKYRblXUcVUHVue5VZbchRBe260y8jcS+7pvFvjwxBNP+iO6DwMwTqo6InZqMwOoBNbtQLPaBb04yPV6HaVSCaFQyNRXV6YTQQOen45ANptFNpvtyih321N2cp/heBnwDgaDRg9QFxHEVke9XC6bJlGqUzXovRVGtM00o33B86ue1prpai/ovUBRfaZBAo6ZYE6vax0OhzE2NoZoNIqpqSlMTEw4zk9hKbYXXnjBcd9pkGUr60PiAuekDqlev72ssykKLiggznkow5LzZlNefkfL+HCN3VikgOekeuLJbov6EPocavBP93O+T5uee7T6zHze1RfrRXezJCuDpm7BbNuHrlaryOVyyGazPTf/VP92p0TXToPdWgpNdWg3EJ1+txITtrpP6rw1CK/lfNTPdduX1X6zmc28BzgPJT9stFY+n8+UxY3FYpiensb09LSZM+0Dv3+lr8mZM2fMWirrfDsEAdoiXB/+KEayXf9+t0TJhOsx0AH36gJ2sEePC6yW6qvVavuGFOBJf8UD0feYKLhG51obTSkrmQ8yN02N1q4H1vUCOpLNlslkMDg4iEwmg1QqZWptaXmZRqOBXC5nPp/L5ZDP513rSe+GcLOj482IM2uW2crcZjpTyCLgpsrvAHA44Vx/t3q364kNolOJU0ERrGfApFt6OM9pB1RsZnIv7GSyBghYhEIhhMNh1+7TVKwEL+LxODqdjlHqWxV1SN1AdDVq1uu2vZeETeECgYABRPpVG53GfqVSMV3NPSa6J55cOKFjrVljtmPG4LiylNwMeQU2e73nuR+USiXzo/pOx0Enzx7HXhDqo2AwiMHBQYRCIcfYVUd0Oh1jc9RqNTMP6g5dR4IcQPd9pFvqrh28pRPVbreN7kwmkwiFQgCcTeh4LLfSd8rOJojC+0izGXqRQCCARCKBeDxuanYqaK3OYSQSQSqVQqPRQLFY3LIDrvPT9dGgEWvwKvtxK1lquyUELyqViqkxy7WknabOM1mA2kSdrEddf4JlAEwmSrlc3jeOuKe7PblYhX40M4bsYJ/6GwxsKhPYTXe7sVy7PQP1eh2FQgHBYNA0+VZfVo9LvaB2/4XeSzWoyAxwZuh0y/xejySmIC6wmqGsjODNiOp1XutOp2PqZFOH29dIx6t6Dli19xSDsQleG/ndGoSNRqOIRqMGZ1G9of9Ho1GMjo6iVqshl8ttiaHP4+p90y1AzOvAce113d1sNlGtVk2QxLZPKIqn6bzsoLlNfGD2AgNo+0Gvebq7v+KB6HtQ6KjVajWTYqZgOkFKfdi5cdu1zezf9t/rjWFpaQntdhv5fB5HjhwBsJJq1Gq1jMLpdDqoVqs4ffo0MpkMnn32WbzwwgvIZDKmucluirL9gsGgYaZFIhEDngeDQQeriN/TYwBra29T6YZCIUeaEGuUcSPtNdVJI+K1Ws04+nTI6bTZzCd+V3+U7cBMBDIQ6dhVq9UNGcq6VslkEqOjo0gmkwiHw64gOtcynU5jcnISwWAQZ86c6fFqrRU7e4Dp+6rAOGe7NMFeBY0J7uRyOQBAsVhEuVx2BHO2e3xmAxQKBRQKBVPKYC+uB8VT5p5cjELgjUFRZU/zfTv91mbJuIFttnRzxDudDsrlstEj58+fN3Wxh4eHHazY5eVllMtl08iUzKm98mz5/X6HQ0kQlsA/HfVAIIBGo4FCoeBwhmgH0Olmc8h2u70G6LTPq/pF9T+F/1cqFdRqNQwODqLRaBgnOJlMwufzmfR8m+GkwWbeM9Vq1TARqa81k6BXeyoWi5lGczyelmLj9R8aGsLo6CiOHj2KUqmE559/fssgOteKolmQvG6szU7nlrp7O+fcaSmVSqjVaqZ8He08295ot9vms2SmsuGYEl943xB0KRaLyOVyxkbbD+Lpbk8uRul0Oobkorqb+ziwyj5Vv1D3ZtXdqucp3BPd9nP629Qps7OzRnenUimHzmg2m8hms6hWq1hcXDTfudDPlupp6t5AIIBYLOZaltReP9XHBLN5DF1vLZvSi2jAmuelDTA0NGQIYMFg0NFwVcu2UHSsDH4Xi0Xja2vmeq+EJurmWCyGsbExxGIxhEIhR9DGXqOxsTFcccUVKBQK+MlPfrJlEB1YLdumTUw1Y07tBq5JN91t++oXSojLKNGBupfjA1bLqvE9Xj9WL1AMwufzGRuAAa9yubyt7IjdFE9391c8EH2PCpUEwUJlI9vgLoFbboJujtZWbnx29A6FQigUCsjn8xgYGDDODx1KOuCFQgHFYhGlUgmVSuWCpfxoGi0j+Iwud6uDvl4kXNednyfgTfBD2XC9pilrVJSKmCwtKnBloPO4dkBEmeyqCGwGeq8sQ64bwQk7Td1tnVkTlqD3dkQZehoUsRWzvTZ7WVhfUXsM6LO8VeFa0Gln0KTXOngXUjxl7snFKqq/beCVz6utH7vdzxsxmNze535fr9dRqVRQLpdNNpvuqfxMrVbbc4FIzUbSniZ2yRvV45pKb2c0ATBguurrbvuQm05Zb60JhPOaa5bbRsK5uKWA637eixAMCIfDCIfDBpi37ULq10AggGg0uqbnzVbEjazhZo/xGvD9vSy8vgzUqJ2l9jk/y+dbQTUtCcN1oT3GEowkP+wH8XS3Jxer0B9j0NF+drXcyEZBzW56xW0/oNA2iEQiKJfLKJVK8Pl8iEajaz5XrVZNwHwvsNABOIK1tu9t6xfqcYKYtlBvAHD83oru0H2Y/zPQq5lFvPY2C5vv8bs6fjvzWwPvveyVikMQyA8Ggw6fl6LrFAwGEY/HTdbjdoXnsefPMWoAyA5K2HPpFQfZSVE72C4z7IalKLbgtha6HiS40F7bC89eL+Lp7v6KB6LvYdGHmSC5bvb60Pc7DZsOIbCyifz4xz/G/Pw8UqkUDhw44MpEz+VymJubQ7FYvGA1ohj9HhgYMBF87XbtVvNsIyeXAQxV6Kr8tVEkGQq9NmvhZ7jWnU7HsAk45kAggEgkYqLSPDYbVSqriZFw/q5UKoaJ1mtTOi0rQ2d+aGio6+fVEWSEdrtip7rzb9sxd/teP54Bt3tDlWevxhFF08rm5+cRCoWQTCYxNDRkSiRtNvjA69psNrG4uIgzZ84Ylrun6Dzx5MIL91Pdd21gfTvH3khqtRqee+45ZLNZxONxjI2NYXBw0KEzWHptYWFhW0wmSj/24Gg0ilgsZvbFarVqnHEApuY7gVk6ncyU0zVWJhV/k9nearVM5g6w1jnW13oR6oRarYZMJmPGNzIyYjLW6NiVy2UAK/VsFZzl2NUxswMHG0mz2USpVAIADA0NIZFIGLKFgsBafmQzjUs3ErIH7UAx9dVmgvqbPa8b2aAfsry8jPPnz8Pn8yEej+PAgQMIBoPGDgNW2Xq0lxgsYNCchANeBwa/M5mMsZ888cSTCy+qu1V6qW2+0XHX0y0EKKvVKn76059icXERiUQCY2NjGBoacpCkMpmMaSK+3b3DzsDeqpB1ruU/qL+oc9gDhsSrbuthg+8KzpMlTobxRtdFGd36mtZZpy9br9eN7x0KhUzGHrEYfof3B8lR/K6ynnvRcUq6UBBe/UIb72HJvmKxaLLW+iFKCuT/Os5eA0eb1cFa+rDfweRms4nZ2VnD9J+YmEAwGDQBMcUbqKM7nY4jMKFYHK91Pp/H3NycaWTvyYtPPBB9j4sNsgJro687BZjRsSJLhk7q5OSkSX+iwzg7O4tCoWBSl3ebUaMMcXbbjkQiCIfDxslWIHizgKUbmEpHUR1dOokaod5IqISpPIPBINrtNuLxuDEyIpEIotGoKSNDAFX/poFC4LxWq5k6m2QaAuvfL1QSwCojotlsIhAIuH5WQXSmnW/32ttMdBtM57ndPtMvAF0Vqg3Y2+l1vZxTDa9z586h1WphYmICw8PDjvS5zcjy8jJKpRLq9Trm5+dx6tQpY1TtBxD9QkTEP/axj+HP//zPMTs7i6uuugonT57EK1/5yq6ff+qpp3DXXXfhxz/+Maanp/Hud78bd9xxh+Mzjz32GO655x48++yzOH78OO677z7ccsst5v37778ff/M3f4N//dd/RTgcxnXXXYcHHngAV1xxhWMuH/jAB/DJT34S2WwW1157LT760Y/iqquu2vQcPdk7ovvphZBarYaf/vSnBjgeGRnB4OCgMfpZLoSA7XYD33bNyK2Iz+dDLBbD1NQUOp0OstksKpWK6dHh862UuisWixgYGDAOO9nXnDfro9uMImCFwcU64NSfuh9txZHT7zLFnudJp9PGsabOJphMIFWD70xVV9tCmW0bCeubA8Do6KiZK5t+M72Z2Yb8e6dAdP5okF3Xq1+itcd3whEnSeTIkSO48sorkU6nkc1mjc2nZXpob/JaEzQKh8MGMGcQgyDYheojtFnx2GyeXOzSze/e7r3bK7mqXC7jmWeewcDAAOLxOMbHxzE0NGT26mazaQLg/dDdNli61WOwHwcz3whG8pi1Ws0EwFk6RUln6nfZ2eKdTsdgDwqab1QypZs+0GCu3+83/jWJbIlEArFYzJTaUv3MMfCHAXJ+Vkv0bXTNeSwl5XE97VKADEIQaykUCiiVSn0F0Rn80DHbzPqN5rPZczJYshPB9WazibNnzyKfz+PgwYM4ePAg4vE4yuWyowedlqwB4CBN8of2IoPf586dM31T9oN4uru/sr26C55cENkKE3Y752I6DFPH2DiUv7WGWz8dsc0IN35tfEHHejNNTHoRZbRpnSxG3LX5a6+igLSmh/P6uh2LhhMNCCpWvqYlXTbjgHMMVAys96UGB89NZUJQhmBANyC8G4O823q43ee2873dZ4H3DsFsbaiqP5FIxARm+EMGJEFwTfey58N1pRFZLpdNPWI73cxtzjwGjSiyMck+15IM+0HcrvFmfzYjjz76KN75znfife97H773ve/hla98JW666SacPn3a9fOnTp3Ca1/7Wrzyla/E9773Pbz3ve/F7/3e7+Gxxx4zn3n66adx22234fbbb8cPfvAD3H777XjDG96Av//7vzefeeqpp/C2t70N3/72t/Hkk09ieXkZN954o2GiAsCHPvQhPPjgg/jIRz6Cf/zHf8Tk5CRuuOEGA4R54slWRJ0u6nA2IKazx2B5PzLHtmOPaLqw6lFNqe6mS6iTqe90HPZ+rMexdfZ6e3gvQt3IfXy9vZhj5Ge5/ysgsN481hMy0bVGt5Z1U3KErXf6Jbb94hYI78c5VX+7le4LBoNGb7PWrb7fqz3IgBjro9M2InGArHMtFWTbwmqn8Rh6DfaLg7rbuhtYCYAfPXoUoVAIL3/5y/H//t//W/fzTz31FF7+8pcjFArh2LFj+PjHP77mM4899hhe+tKXIhgM4qUvfSkef/xxx/sPPfQQXvaylyGRSCCRSODEiRP43//7f2967J7sf9kNf9s+n5KlWNqF+wb9hn7p7u2IlutSFrOts/Xz9mvq96qusAlsG+nvrehu2/feqHcYv8PP2rp7MwQr+5gE5MvlsqtfSFIc/b1eMsw3OwbVXXYJE/1Mv3Q3rx91ciAQMFkA1N30s/Wn1xI2DIjR5qXvbTcEdbsvNWuPhAfa0bSt9mM5l93U3RezeEx0TzaUTme12QrLUmiarrLZLsRDplFMNhMdGhpCKBQyjS/JDtougE7RVCefz4dQKGSUOJvAkh3eK3CtRpOuuc/nMw3LVFETRNWUbP6ttW6p6HsRNstoNps4f/48fvzjHyMej+OSSy7BgQMHHIGDcrlsAijPP/88nn/+eeMQsnmdMsMAZx1/NydaXyMzoBtornXgt5J6r2lr4XDYZDBEo9E1TWh1HjwnnwXe/2QicA3cxtJsNjE/P296CAAwjWTGx8cxODhoxqHKXMEZAiPZbBanTp1CqVTC7OwsMpmMo17jXpft7hX8LteRwsCGLQ8++CDe/OY343d+53cAACdPnsRXv/pVPPTQQ7j//vvXfP7jH/84Dh8+jJMnTwIAXvKSl+A73/kOPvzhD+PWW281x7jhhhtw9913AwDuvvtuPPXUUzh58iS+8IUvAAC+8pWvOI77qU99CuPj4/jud7+LV73qVeh0Ojh58iTe97734XWvex0A4DOf+QwmJibw+c9/Hm9961u3ukSe7AHpF4PNPuZ6Nb3tvQNYYSdns1nDEHOr2b5d2cozzYCzBr25/3Y6K2XNOp2OCW4y24yl2qhrqtWq2QOVta6iqdHUi1oGRhn5yo7rVYdTd8bjcaPL2DRca86rHcKgKnWHGwC/WWAgk8ngn//5nxGJRIy+AGB0Vy6Xw/z8vLERNINuPVE9uJ7TSDuEthEz2WiHEURW3b0V8fl85lqT6c1UfwJL6XQaw8PDWF5exvz8vOkPQAecgeiN5t5ut03pnZmZGfz93/+9af5GgD4WiyEej6NerxvWPx1+rjsZpM8//7xhErLx7H4LgG/n+5sRBsA/9rGP4Vd/9VfxiU98AjfddBOeeeYZHD58eM3nGQB/y1vegs997nP45je/iTvvvBNjY2NGdzMA/id/8ie45ZZb8Pjjj+MNb3gDvvGNb+Daa68FABw8eBB/9md/hksvvRTAil6++eab8b3vfc/LEnsRST9Y2ls5H89Vq9WwtLQEn2+1ASJ9kH6J7Wf1ItRjzJbWbDC7jBrnxL2a+h5YzapdXl52+O3rkb/o77P0KP1FAp2bmQsDpBrkpC/PADTPyT2afjn7v5VKJUewWoHZzazp0tISfvjDHyIcDuP48eOYnp52BCZyuRzOnj1rsvF6LaPa6z1Mf5Z+McvtKJC/GVKe2zj4o2A4dbdmDMZiMZOBXywWHSWLOp2OIXFuNA71m2l/RaNRxONxJJNJE3wnxmLPk9+vVqsoFos4d+4cKpUKcrmcqbzg6e4Xp3gguicbCoFDACaFaK+JMolZy4yOuM1W6+c5gdUUNmBlrfg307N62XRsYNjn8xmFTKfQ/tHaXCylouVcyEDcjJOq15rKmo4hu8NT6ZVKJSwtLaFYLGJhYQHz8/NmbMoOsOfI+XVzoJkGR8Vtr5+u1Xai/gqih0IhxONxBAIBpFIpE4gJh8MOkEeNLQYxms0mCoWCAV/4PseqwhrELH1Ehnun00EkEjGNWZWVye/xfigWiygWi1hcXMTzzz+PYrGIXC6HUqm0b6Lh/ZRDhw45/v+jP/oj3HvvvY7XGo0Gvvvd7+I973mP4/Ubb7wR3/rWt1yP+/TTT+PGG290vPaa17wGDz/8sAFHnn76abzrXe9a8xkC726Sz+cBAMPDwwBWHP65uTnHuYLBIK6//np861vf8kD0HZJ+lX/q9VxA/wxQ3Y/cjHd9X4N/F5qxZoudQaZMYe6BDGZSn9uNt7i2LFdSq9WQTCbXNLlWdpuuG/U1a6y2Wi2HftqMI8711SAqdYVdX5fp0mSWaQBc9/6tMOuol5SFretarVaxtLRk7AOux3pz1WDyRnqGJd5oG9ll0Xph6vcidMQ5v0gk4qgb7PP5MDExgYMHDzqABt5HAIxO3UjIZuP8lpeXEQgEMDExgampKWNPhMNhYweQUEE7iGxS6u5CofCidkr3egD8N37jNxzHve+++/DQQw/h29/+tgeiv0jEzqABtq/H17M9dJ/kZ9wakfdbtgOIsnwqdTPFTYdpdjj3YK0r7vf7zTFYZqWbvlGyFnUpfbTN+r3KKrd/tLwK58XPMhuAYLp93M1KsVjECy+8gHA4jHg8jkQi4SBXVatVnD9/3pHB5Fa735ZebVBeC4LoxB/43X6wkrV3iBIjaOPRZkin00in01heXsbCwoLp88J7otFo9KRDO52OIRLQ9giFQpiamjJj4HH1WFxTBnlKpZLp/ccsNJIePXlxigeie7LvxU7r0jTdraZlb2UMg4ODWF5edqS10cjYjCNOZ56pw51Ox7DylK3GkiBkRGtd9H6kZxOUaDQaOHfuHABnzflKpYJ8Po96vQ6/3490Ou1Q5G7NMpXJTUeXRo++bjv2qrBtEH2zypxAQjAYNA3mkskkEokEAoEAksmkmSNZE7ahRsYCm5MwhbzZbBpDh+CIG9segGGk1et1AwiRzUYWJZ1vOuXNZhPZbNaUVWJ6Zy8swr0m/YqInzlzBolEwrzu5oQvLi6aOvQqExMTmJubcz3+3Nyc6+eXl5exuLiIqamprp/pdsxOp4O77roLr3jFK3D11Veb8/B79nFeeOEF1+N4sn3ZzedlJ8613vPTD0dnp0UDhVqGTfc8/qZeY/oyg9XU73RebUYy2WTUyyy7RYBcA7kaVGUgV9n+m5kTAEcKMFlr6vjyfaYE21lX27luql8LhQLm5+fN/AcGBpDP5805qbvIGm+328aRVRuKNo7P50O9XncwsFTv24xGXjceg9drO/OjbmZN/Fgs5shgAFbtj0gkYv6PRqNYXl52MOrJNiSzU8vrdBsjHfh2u418Pm/KwPl8PsN4KxQKaDabKJfLBojI5XKoVquGkb5fpV+6ez8FwFutFv76r/8a5XIZJ06c2GiKnlwkwkB0v4+5nfd3QjYT6FedYOtwzYCzm2Lqd/kZ6nn2kFDimwam7WvA/VvHSx1Of7kXcJmi/qqW7hgcHDTl8BgMp1+nGeBu67aV60g95PP5kM1mMTs7a1j3AwMDyGQyRkfpmOPxuLFv7KC12jb1et3R4LxbFjf9ctZ3175e27EtGXBh0DsSiThsNmYzUHfTHoxGo44Gta1WC4lEwgQ6aEeRQd5t/UlqYGYYQfRarYZYLGbmyuPwHiXrXEvk7acSbBSPid5f8UD0F5nYUe6L4YHw+/1mI2QqGB1Dvr/TosAsHXk67j6fb1NlXejQEShlM5lgMGjYasvLy8hms5ifnzeMJyo4uwHIVoXpUwMDA5ibmzNM6UgkYhxGGj6hUAjHjx8316JbbVkFxhkkYKpWsVh0NMtR5pcaC5yXMtV7nSeV9dDQkGmSGwwGMTw8jFQqZUBsOuNkOthz0JrwZLIVCgXU63XTfKxcLmNxcdGk8VOx83epVMLp06fh9/sxNzeHn//85xgcHEQikTD3EO8rGnFcHwLnbBy7H5U50B+FzHqlvYjbtVzPQXL7vP36Zo75u7/7u/jhD3+Ib3zjG9semyf7Q2wnz35tK7JRGQzVA3tR1NFj8JtAJJ0mlvugbiPoTB1DcFQzrlKpFAAYW6DT6aBSqaBUKmFoaMgETbVci9ZhZQBO2Wy9OuHqqHY6HRPUZpkXsqYIptvzA+A4F4H8rewDdBSbzSZOnz6NxcVFB5uN8/f5fEgmk0ilUiaDjY3hRkdHHTVuOT+fz2caarEcDPU1105/tKm5AizbyZoKh8OYmJhAMBg0bD21bcPhsGHwca4DAwOYmJjA2NiYcbpbrRYikQjS6bTRrSzZUiwWuzLlyUxjWTsGKZ577jlzD9MO4vqQAEEwQtdsP0o/xr4fAuA/+tGPcOLECQOyPP7443jpS1+6qXl6sr/FjQSzk+fazSw5wFl7AtWpgAABAABJREFUfCO7QTOS7AA4fW7OQcFM6lk7C4ifY58pDWQTzNZSb8AqE1kzszk2DZRzL99ISGDi8bWsGv1t+lqlUgmFQsEQqWzgVsUOHvRyXQnODgwM4Gc/+xlmZmbM/LUMDsccCoVMGZzl5WWTfaY9XvhZv9+PTCaDs2fPmtre1EOKG3Au5XIZ1Wp1zX1h/96MUHcz6zuZTK6ZE8dPXalscbL+l5eXEY1GMTY2Zkqs5fN5R1k+t3uZet7n86FQKODs2bOGSGeXItIec6yhzuu+l+3rjWS/jnsvigeiv4jkYgRkNL1KgVs3FvROCRX2wMCAcfLV8dwsG53OM0FaGgg0KqjUtYTLeiVEAPdrv9F41NFmzbehoSFT9kTr4MXjcaP46ESqYabnpDGlTG7OTY0lO93bzUnfrCLj8QOBAILBIGKxmHG4Wa6GILqC2FxDPTcBBx0jS7xoWZtu6YhaXoER/8HBQdRqNcOyY5BCWYyFQsGwA9Zjy+112c2I+OjoqAkGqczPz69xpCmTk5Ounx8cHMTIyMi6n3E75tvf/nb87d/+Lf7u7/4OBw8edJwHWHH8p6amehqbJ/tT+pkSvpHs5X1B9aLqbq39ST3h5gypI84gMl9TRjL3Xe7Ftu7Q46q+2moWmw1GcI9mcJwp6wqibxQM4XH5ezN2hIIArMtOnWY7ru122+hFppKTuW4zDev1uqnzzWPb4LnaLf0qM8Z1YEAgFAohGo0iEok49HMoFDIgeqlUMkAKM74ajYYZI4MDZB4qy68biK5sOJbT8/l8qNVqppwg2fFkr/Gz+6V26nrSL929HwLgV1xxBb7//e8jl8vhsccewxvf+EY89dRTHpDuyY7JhWSib6RjVHe7/SjZSUkD1AM2W51Cfa7Cvdh+ZrsFNRTg597e69wVM+B3mS1EH5sBe5Zd20wZlV5tP9Ut1F0aoKe/qiVsCf6zTAnJAtpwlUEClnmjD6tzsIMB/az1zfvD1t2xWGwN2E/WOf1cAA6WvWb8k0UeDAaNbWOXn7HXl9eVdgGPSfIa15M2G+vE75d+Y+uJx0Tvr+x5EH15eRn33nsv/uf//J8GZHjTm96E97///Y7N+gMf+AA++clPIpvN4tprr8VHP/pRr2adJXuZ2dhtXL0oc42Eq2PoBuLupCirrtPpOGph9upEUoG2221UKhVks1lTZ461PgmmlstlExF1A5OV2ce6qPrZarXqAHzdxsg1DIfDiMVihrVNNjcVM2vWKvDsdk25Rp3Oajq+jrHZbCIWixlQOZ/PO+qLco34u9cNXWvpjo6OIhaLIZ1OY2pqCqFQCLFYDIlEwgHA6P3jBqTzM1y3QCBgnPNYLGai4lxn1ix3G7MyB3ltuFYAHMaONqz1FFpvEggE8PKXvxxPPvkkbrnlFvP6k08+iZtvvtn1OydOnMCXvvQlx2tPPPEErrnmGhPcOHHiBJ588klHWvgTTzyB6667zvzf6XTw9re/HY8//ji+/vWv4+jRo45jHj16FJOTk3jyySfxy7/8ywBWjLunnnoKDzzwwPYmfgHF091rZTsMnq1Kv9jvGwkZ4gwAr1d/XVnNwGrqN3tRaACVeyADrwAMo5r6UmugshwJhcFerb9JpjjfV+Ybj8n9drPrxmMXCgWzJgSw6RRzvBsd2+/3I5lMIh6Pm9IsdOp6qW+vuluFjip1Yqez0mwrFouZGuPUdeo4Uud1Oh1Eo1HTlJUN17S2O9l6W9VT9n0bCAQwMjJizhuLxUzqN+fHdWXaOrPdarWaI0jDebfbbZNOXq/XjT1SqVQMiMLv2+I2J71/CP4wcOLp663JhQ6ABwIB01j0mmuuwT/+4z/iL/7iL/CJT3xiW/Pay+Lp7rVyMT+7qo83AoZVzxOotbO/bTtHs4YBGN3NTF76yrbfyONpeTElDtHX57G1hNpmgrcElNvtlUbgPC79MA1Ga3+TbvcE1zORSCAWi2F5ebVXGX3Zje4nn89ngGb+DwCRSMToPma+a9BXe61wPbVkHbASvCQYT71ELIFlWrUsbK/SLQATCAQwPj5uMATaGZFIBPF43PE9xUzcMKuBgQFEo1FHUJTXJB6Pm4arxEjK5fKGa61+tfr9io1cDMFvT/ovex5Ef+CBB/Dxj38cn/nMZ3DVVVfhO9/5Dn7rt34LyWQS73jHOwAAH/rQh/Dggw/i05/+NC6//HL86Z/+KW644Qb85Cc/QTwev8Az2HtyIRz5bqLgZDfmiP23ftctnUwV8m6J1lGl4lcQvdcNmMoPgEkfo9HCuprc7Fkju9t1DAQChjU+NjaGkZERR20z1lajk2crSw1QRKNRpNNpRCIRTExMGNZ5t8at6629GkB0ZGOxmDGC6Ihns1mTdsY0663eswQGIpEIJicnMTo6imQyienpadPMiuli3QIA9tzIFO90OiatnOz0arWKbDZrjCefz4dKpQLAPV1SWf/qsNtAwl56drcrux0Rv+uuu3D77bfjmmuuwYkTJ/DJT34Sp0+fxh133AFgpbHYuXPn8NnPfhYAcMcdd+AjH/kI7rrrLrzlLW/B008/jYcfftg0HQOAd7zjHXjVq16FBx54ADfffDO++MUv4mtf+5qjXMvb3vY2fP7zn8cXv/hFxONx47gzfdDn8+Gd73wnPvjBD+Kyyy7DZZddhg9+8IOIRCL4zd/8zS2vz4UWT3evlQsBnu8W+117SHAP7ybK9gFgGGrRaBSBQMABXFKnE3xutVrGiSTwTceXx2aQy+dbbfqtdbTViWVWFVO16TBtBUTnnsb5M72cDDDqdH52I2FzrenpaVQqFXQ6HdM0tJcSXj6fD9FoFMPDw2i1Wub8wWAQ6XTakfru8/kQj8cNC4zON+0FZfkRRAdg+omwbmi9XkcmkzFNt3pZQ1vP2TqYQfcDBw5gYmLCUTeWjc8BoFqtmvR/BkkqlQoqlYphuZGxRiEbjsECzbBjX5hemoeR4cZxMQ1+L4Dnm8lg6EV2U3dfyAB4t7Hz3rpYxdPdLy5R8tNGoKmWqtJGkNxX7QC6ZiYRFCWITnavlhthFjIAhz+vjcK1xAuw2jeF72lvi16EdgT1DsfJfUp7kW2091FPDg0NYWxsDBMTE6jVapifn0e1WjU6qlcQnZk6DApEIhGTZRUIBAzpgGXkuNY2u55lW3y+lRJuAMx1I4i8vLyMpaUlEzx2wwW6ifr/9tyCwSAOHz6Mqakpg10AK2A+x6JECGXE2764BlSI+dDvrlQqKJfLWFhYMPYj7ab1hDbbeu9fLOIx0fsrex5Ef/rpp3HzzTfj13/91wEAl1xyCb7whS/gO9/5DoCVC3ry5Em8733vw+te9zoAwGc+8xlMTEzg85//PN761reuOSYbRFDsDvEXs2yVje6WArkRyN3tODZwrj/2WN1+7PO5pZJdaOk2p82IljgBVlnqGiHuFlyg865ssUQiYZxPguesQcfXVWgsudWbtxuYbHZt7PPoe1SQWl+eoIrNaOj1fGrokZXPbuAMUPQaBHATBReYGh8KhRCJRAzIziDIekpsLzjcuyW7rcxvu+02LC0t4Y//+I8xOzuLq6++Gl/+8pdx5MgRAMDs7CxOnz5tPn/06FF8+ctfxrve9S589KMfxfT0NP7yL/8St956q/nMddddh0ceeQTvf//7cc899+D48eN49NFHce2115rPPPTQQwCAV7/61Y7xfOpTn8Kb3vQmAMC73/1uVKtV3HnnnYbV9cQTT+xrZ9TT3XtL+gGmux1D9bmWMVtP9NlXcNZ2/PS4BHr12Op02faIG4hvB7N5PKbz2vPaCvhoO7I8xmaafdM5JGOc5UqoT1QXrse4U92nOl6z9bpdPy2Foyn1dNb1O9oUjjVMaVtsVDe2m9hBIGXedQsikIXH73U6nTX1ZPWz/BzvO9oInU7HBFzK5fKm74O9psf7PZYXSwD8ve99L2666SYcOnQIxWIRjzzyCL7+9a/jK1/5ypbnvh/E0917Q3rRo/04lq173T6rvretu5VFbuvo9YB56i7brrDP303Hafm1fgh1FAF1BdF7BeWZAR4MBhGNRk1QmmVLfD6f0V/dWP+qV9kngvpOG37rOjEbzJ4Lr5dmoXO9WA5Gmdass85j8zsbzd3tuvHeYNY6yQo8lt5ralfofW1ng2vAR8vA0M5hWRYlNPYie0lf76R4IHp/Zc+D6K94xSvw8Y9/HD/96U9x+eWX4wc/+AG+8Y1vmA7qp06dwtzcnKMTezAYxPXXX49vfetbrsr8/vvvxwc+8IHdmsKek80C3tyM7DrXuikra8vt+PpdArB6TAVjqbzs41OJMWJpg+e2M77bYhsgusG7jUfX0g4SAHBE7sluUsXYzaggO2BychJXXnklwuEwJicnMTw8bAyE5eVlzM/PIx6Po1wu4/Tp04ZtxTElk0mMjY05Gm8qm49z6PcaEtQmU44lXiKRiGGGkeW1nqiSTSaTpozL5OQkRkZGDMi9XQBdPz84OIhoNOpgPLJ+njYsu9iZTL3IhVDmd955J+68807X9z796U+vee3666/HP/3TP617zNe//vV4/etf3/X9Xsbp8/lw77334t57793ws/tFPN194UV1oTK3yC7ejGgatzq1PAcBSNacVkYX/1anlE2rWcKF5b1U75NpHAwGTTo1WWjqiCrDjWnNrDnO9xuNhkntpf1gB4H1Pa353UsmmQLnCqDbunu9a0V9NTw8bEqNHTp0COPj46jX60in06bM2cLCgtGHxWLRMR+uM3uYkL1NxjjvgVar5aglSluFvT3UzlJ7pt1uG+a22mYEG+LxOFKpFDqdDubn57G0tOTo5+K2dvZa8FqSicgGoolEApVKxZFeX6vVMDAwYMr8kCTQ6XQwNDTkKGdD1p42Dedcg8EgUqkUWq0WUqmUmdvi4uKG1//FJC+WAPj58+dx++23Y3Z2FslkEi972cvwla98BTfccMOW574fxNPde0PUR7SBYvrFvTxLNvDIfY/PMYHObrqdfreek6ClrUOVFMVzMQhOfcK/u5V909JsHCP1TTef2rYFegnm2+ekvlY97eabryejo6M4duyYydoeHh5Go9HAxMQE6vU6crmcYUovLS0hm806vs9gMcuXsU8Sx2CXqlVAmuug9gbXTYPOeh24ZlzreDyOo0ePmibMG+luXSf97fP5kEgkTHYbg+r2mNW2UtuFupsBeo6RGfD8Po9F25O2JEvCdms+/WIVD0Tvr+x5EP2///f/jnw+jyuvvNJE2u677z78l//yXwDAPCBuXdZfeOEF12PefffduOuuu8z/hUIBhw4d2qEZ7F9R50kbTGmJEipDZVi5OUQ8DhUEwV6trWYbCbYDrHWh7Ui3KpULLbpG3QB9e8wKGug6brYOF53/oaEhDA8P4/Dhw4hGo0aZc03JlG42mygWi8hkMmuCEpFIBKOjowiHw0ilUiatbCcDFBo9Zr1TbTBXrVZRLpdNalovx2O99XQ6jXg8jnQ6jVQqZZTtZo2tbufhtQuFQma86XQa4XAY+XzelMCpVqseiO7JRS+e7r5wYu9nqsfVedqMKKOXDqs6q2RPuzHJyTKym03SKaQz7nZO1gX1+XwolUrmPTdwm2NkbVOei59nQJNMMXud6GAoIL0ZHbxdB4W2Qzwex8GDBxGJRDA1NYXR0VE0Gg1EIhHU63XMz8+b+qXVahXFYtF8X9O6qftY+zQUChngmaVdGLzQlHptZM3sMwrXjPeP2oE8bywWw8jIiLnXGECmHbeRcP05JgZoGPjWrAGW89FG4cp8pz3EwAoDPCRzKEOWJYU4hlarhbm5uQtCyvDEKRciAP7www9vaowXi3i6e2+IAqa2ftxMVq762KqfVQfz+FqGVOukK9iq/r7te6sPpwC7zkWPwTre/IwC2IovaHNosrOVLa3rYbOXexVlbW9FCBxfcsklpu9WMplEs9k0fU2WlpaMH8iynyosXcMSoel02owNWG12aYP769lZ9t/A6lrbNksoFEI6nTY2HnVmLz6rHsfn85ljhcNhY28pmUOJFvqbxyFgzmtLu0DLzWnpH+r1cDiM5eVlnD171tPdnuyo7HkQ/dFHH8XnPvc5fP7zn8dVV12F73//+3jnO9+J6elpvPGNbzSfc3OGuj08ugl7slYICDLVh6U1lBkEwME+oqJlvSs7YkonmaVBlDGtzrmKNmmiIUfHXZVrrynkF1KYZkxgWNO1gVVAgEqLjK6tAh2248nINllYZF3R4dQgCZuGMo1ca4heKIY/HWSCAlyb9Rq0EDTiPcdoOO87t0Y2/RI+P9xnwuEwotGogxECvLijul5E/OIWT3dfOFHmsFujZO7rBJvX0zPcJ0OhkGkiSfYvsArcKpucoLc626qzVZfTaVXbQcej9TkVOLfZ0bqnuqV/U7dRD1PnMZjP4LIyq5WRp1lYqje00dh2G2nSQeT41EbSLDumilM36jXQ7D5mdamz3O3Z0sBDs9l0lHzhfDR1ms+hnU3HY9EupM0zMDCASqVirvF6a6RAAOvCEtxmhgH7qNhMNWY5FItFtFot0zjUJnqwVJ6OQ1/ncZX5pqDNi7nRmKe7L27xdPeFE/rY6ncDa8unaXC4G+GKezjLgtEPtZ9fZaKrX2WX/9BsI73OyixXXcwx8bce084Gs0F2FRuYt/WhrpH9w3Wg3626m7qeOmM74LldJ97uzUYMgw01+R3V137/SiPweDy+JnitQQdec7uvjK4rxdbdtug10b+BFd0bj8dNoFltuF7WhH67ZrtpkIaEDI6XtgbtP7d7gWtgBwFsAN/n85n+Jsy83I59drGIp7v7K3seRP9v/+2/4T3veQ/+83/+zwCAX/iFX8ALL7yA+++/H2984xsxOTkJAKaDOGW9zu2edBdVapFIxLBnqYC6gehslkGGU7vddjT6jMVixlmlQ0UHz2a4U/T43ASVTVav101Tkr3CQqeos8bNPBAIIBaLYXp62gDUsVgMPp/PMLXK5TLOnj1rGGalUmlTmxaVcDgcRiwWM+lUsVjMOKF0+CuVClKpFPx+P6LRqKm/Njk5iUgkgmQyiZGREYei301R0CeVSiEajZoO56FQCOVy2TTvdBOWVmETtbGxMYRCIcNW2wkAXY/H6x0OhzEyMoJyuYxisYhCoWDKCniOuKfML1bxdPeFEw2mDg8Pm1JYLNnFHheNRgOzs7OGyex2HDpA6XQahw4dQjAYRDabRSaTAbAKsmvDJ9b/JDDL0iHU6eVy2VFrmjq+XC47AEt+lk0qCfjThlBQVx0uTROnsz00NIREIrGmbjZ7Y/A7BMUJJGg209TUlAEjyG5eXFxEoVBAs9k0+kkd0V6E56CNFIlEjI1Ap5wBcJ/PZwLKtVoNuVwOi4uLhkXNwDFLlQWDQcPi03nbP8xKI4AeDocBrKbJ8/wMJvDaMThvg9H1et0ELiYmJlAul42duFEmBJmHg4ODSKVSOH78OPx+P5aXl7GwsIBYLIYDBw5gcHAQ5XIZpVLJ3EN+vx/FYhEzMzNoNBoYHx93gCy8r+wgULvdNk3btTQRg/B03P1+v7nWWwVd9rt4uvviFk93Xzjh3jM0NIRUKmWyh9jkUVm4LA1J31v3I+osAuijo6MYGhoy/aW4F3KPC4fDjiyzVquFUqlkjksAslqtmkbNwCoIW6/X14Ck1C3KYtd9f2BgwNgXbj6mgusElDWDXdcCcDLSdS3C4TAOHTpkdCIbm5ZKJcMIn5+f33KG8ODgoME4YrGYsSmUJMhzxmIxo39mZ2cNwZC6O5FIYHh42JRi47wYfK7X66hWq0b3Urdp+VfVaWrD2MLrrbYZ7TGfz2fAfG2IzsyvjfZwfn9qasqQAngdSaRk2TXajww08J7TAAmF47MBcSU8cG6xWMyUwisUCqaXTK9lkC5G8XR3f2VLIPqpU6dw9OjRfo/FVXSzpmj35qNHj2JychJPPvkkfvmXfxkA0Gg08NRTT+GBBx7YlTFeTEKlx42fQLcdXQWczTa46VLJA6sMHmVGs2mFNq3UKKyKKm42/tKu23Y0e6+Im2NFZR8IBJBMJg2wTSXJEh+Dg4PIZDLGIVQWWC/ixmbTtVZjQ5t9MIhBY4o1yJUJcSGEClGNhXA4bAzH9UBwvYeVlc/7eKfHTFYCnXIaw8pEfzGLp8x3Xzzd/eIQ6nA6qXSQ6QgxM0dZtusdh/so+z2wfAZZ3TwX9zZ1GoFVRpGWZ9MgprLHyWbjvUNwm+CrzchTMH2jNVFAmudR20PZzLxPleXNYLRdIoQAggIZWwmQKrOeetwGF/heKBQCAIf+5nUKhUKOjLde9spOp2NY/xqkUHtGQQ2tS2rv5RogZnCg1Wo5dO9Gtg2PSWACgAGNNKuMwIed0UCbiuCTrXPt1HYdt2Y7cA1tcOLFrMM93b374unui19sfcvyVQyCaiCTDZy7NbzUbCTaAMwEoK6l7nYD0QncLy8vGz3kprt5PAYZeW6Km34AVgOa+sPz8317LjZjXV9X4pp+l+sViUQQj8eNz8s9nLjF4ODgupnNG103JQbquPi+zpEZcQTQiZFoyTKtB8558VrTjnNjkK+XlU/da+tq/rYz1ogftNttE7zge+utE+cZCAQctdA1aEM96sacX69hul5fG0B3uya0k+wshBereLq7v7IldOzSSy/Fq171Krz5zW/G61//enOT7oT8xm/8Bu677z4cPnwYV111Fb73ve/hwQcfxG//9m8DWHlQ3vnOd+KDH/wgLrvsMlx22WX44Ac/iEgkgt/8zd/csXHththOpv7eKSHzjJsPHTEFH91AdCohrSNKhTI4OOhIT1ImuqbMrgei0wkjS0qPr1HMCwn4ch3slKjBwUEMDw8jkUggmUzi+PHjptmGguiNRgOFQsEwqebm5tBoNAwLvxfWkzICKpWKYftVKhUEAgGTKtVut1EqlVAsFlEul+H3+w37jex17dC9F4TGUCqVMsZHoVAw95sNWvAeZkYFjcjdCrioIcVxsIwO7+deaxp64kk/xNPdvYute/eLcL+JxWIOEJW6nfNh4FtZ41pLmsFrstn8fj9yuZwpfUJgU8ub2DXT7VRiOq9k8TLLjVlSFDKUlGWuDSL9fj8qlQqAleaZyWTSsJd5XDqqWgKm0Wis2XfJzq/VagYIoJM4ODiIsbExJBIJk9ocDAaRSCSQTqfRbrcRjUZNs+tMJoNarYZSqYR8Pt8zI13ZfMvLyyiVSshkMgYwZk1zMqAJEpOtPTIy4mD1MWjL60/Qhd9VhhqdcgVoOH8NWBAM4drRkadNqIw2XXMtOTM1NYVkMolisYj5+Xk0m01zfK4D/1dyBe8xZnYxIK1gPe1FgktjY2NYXl5GNBp12GK8f7kOdkM6O/DAnjJks7Hc3n7bF3qR/brnvRjE090Xr6ivQNIPfWPuy9SdFL7GPYt7ue6dbNqtbGWypu1z6zGoB+j/83PcF6ibCfraoKwGwm2Sm/6vNgPnqp9zA1JtvTQwMGD0iALIAwMDSKfTiEajiEajGBsbMyQA2hvDw8NGX4dCIVQqFRQKBWQymU1lGWk2fqFQMKXEiHdo5jzPwUyoeDxumOhKauN6UDdrNp+ukQYCqMN0rWyiYbe9XXW8XeosEAhgfHwciUQCuVzOZHm5Ce0QzsUunUoA3Qb7eV1pu9GOs+8Jm33uFkTQ0rSJRAKDg4OOfjqeeNIv2RLi+IMf/AB/9Vd/hd///d/H7/7u7+K2227Dm9/8ZvzKr/xKv8eH//E//gfuuece3HnnnZifn8f09DTe+ta34g//8A/NZ9797nejWq3izjvvRDabxbXXXosnnnjCAJT7UZRJxg0CQM9O2VaFGxgjt2SrsU62Mr40dYopW9w86YBSOWtdLJaIIUPJjg4q+4lzp8PTaDQMAGnXDON3ufnvJgCsG72bUzY5OYkDBw4gnU7j8ssvRzKZNIxvACZdvVQqIRwOo1AoYGhoCJlMxqRu9QqiM2WpXC6jUCig0+kYY0pBhWKxiFwuh0qlYhzUWCyGVCqFZDK5pxj+vJZDQ0MYHR019XQXFxcBwKRhq/C+I4hOBuFuMcF57dvttgFgABgGnTaIeTGKFxHfffF09/qizCHKfrvPfD4fwuEw0um0cYaBVTCTfxMEZWYUHTwFQwmi01FeWloy30kkEsYRUscdgGFnK4hOcBdYYRTzvUgkglQq5SjVRhacss4GBwfNOUulEgqFggmqRqNRA2I3m01TixOAKfVB+4GOGO0XHauC6MCKPTQ1NYVDhw45MozGxsZw+PBh+Hw+jI6OolQqoVQqYWZmBuVyGXNzcyiXy8a57+UearVaqNVq8Pl8yOfzmJ+fNw06gVXdznT6QqFgAGOWUHBjgNGeW15eRrFYRLVadWSpKfOe62w73Jy7gs6a+q3gebPZdAAb1Lm8Jp1OBzMzM+ZaqfNugyO0H7VeLD9P/cnAD4PpzDhgyQkFEjgW/Z4SFBis0ZI/sVgMBw8eNGVjGLy5GEu59Aqie7p798XT3RvLftXbys5V8JH7IgFB+tkEObUUmZb80uwpBgWp79RvVtaxnY3F/VazkoAVPUXiFUF07WeiP2of8PuqX9V/pz3Cz+r+qmC6MrF5HDcQfXBwENPT05iamjKBb/5mGVPiCoVCAbFYDMViEWfOnDEB8F6Fa+Lz+ZDL5ZDNZtFoNBCNRs14WUanUChgaWnJ2CLsM0O/UDERrSnPrACuq71WbixrxWc2ykjj96kT1aYLBoM4ePAg/H4/Tp8+jfn5+a4gOjMWmf1Afapl/xRUt0XZ4/ZY7Lkp0dImc5IYmEwmEQgEjO26n/aFnRBPd/dXtgSiX3311XjwwQfxoQ99CF/60pfw6U9/Gq94xStw2WWX4c1vfjNuv/12jI2N9WWA8XgcJ0+exMmTJ7t+xufz4d5778W9997bl3NeKFEnDVhlZ2vUbaM0mn6MQZWwgvn6ugojhnZakILoVOb8rQwzzlk3SQ0e8G86jMqAUuY5jYELyZ62Mwa4fsFg0LCh9YfKgsqq1WqZztIMOug69XJ+Ku16vW7SMsvlsoOJzjT0arVqIuIMgOx0uZPtiLLSmMmg94TbZ3UN10t12wlRA9LtZy8x/XdbPGW+++Lp7vVFU1W3+mwqEL/de3yz59W9Re0FOoRuwWot20G2Gx113T+V6aTMOXve9hqojqe+Zoq52gTa0NM+jw3sKgPZ1vvdgBQ3VpuChvpZ3bcJAOt7yhiLRCLmGGSdaerwZq4/x8GyKgMDA0ZPU1frj50Obae82wEDt7ny2ug62ve+HcR3c2i7zcf+nm1T6nXTZ4+6nfez/XkFbRSI5z3N1xQkX+951HWz18Y+/4td93i6e/fF090by3buKzd9v9u6W/cYezy6N+t+D8AEqAl+8z0FFNfzOxR70HMoWEsSFs9FQFT3TVuHqt/qxjanX6av2TrbLZBr+8P2deL4WRJHfzNLi0EG2juhUAjNZtPonM0K90RmfPl8PqOnWcecQXA2SrfJhnptlGHtRp60r5NdFmW7orYwz2f70256QAPgbmC5BrXt79oBHS1VZNub9jlVbPKFbRO9mMXT3f2VbdW+GBwcxC233ILXvva1+NjHPoa7774bf/AHf4C7774bt912Gx544AFH0xFP3EUdUm3AQIXHVKD16kT1YwwA1oDe9o8qYWUvcZzd5sbIJNnnNiDfzYDRIALLuBBsVsaQ7STupmhKESPydPCovFOpFMbGxpBKpTAyMoJ4PO5IY2fDtKGhIRSLRQSDQSwsLJhUJILivYyF9cLn5ubwox/9COFwGJOTkxgeHnYAJIuLizhz5oxJzR8fHzfMBsD9muwF4X3DZjm1Wg2Li4uOZie879zAmt2eF59rNnhjqma/jZ79Jp4yv3Di6e7uovdVL/eYOqDUjXSMVHdvRhRQ7PU+Z+1oOtZkarMcC7DqgNp1xDudlZJpqVQKAIyTp3Oh86zOOQFf6mUFjxkg1nORecbm1XR0OQael86uMs1ZToMNuEulkpkfa8OyzAcA05Rb+2fw+nCszBKy7SymsodCIcPco+0yMDCARCJhsvQInOfzedRqNQQCAeRyuS054pRyuYzZ2VlTBoVNOWdnZ02tb9oDwWDQ0ZxNAQ3aAywTA6wCF3rdGbhnuRvegwDMumjpPABr5kcH2AYFyKDnvcJrosxKvk4JBoMYHh5GKBRCMplEMBh06MparWYa2PNe8fl8hpnGMTD7oFarmbHwuutYbfKD/q0ARiAQQCQSMfbYxcZG73U+nu6+cOLp7v4LfQUFJrl37sYzTj3CcQBrnxH+T3CSACx1r5ZStZnJwWAQ0WjUEdBW/0OBSzKPlUigx9JMA+oEJTHpOQhi0jfm3s/9WLPcVGyfuhvhyN6jiRnQVgmHw6Y0KcuAKhOdx6TO9vl8xl7SDK3NSDabxU9/+lPTkLNQKKBer2NxcRHVatXYLgCQSCRMOT3FV9bDNGi/2PdCN6KE2mS2bDQ3exzU07QzlYRH0TKqxHw4RmasaaNzzoXn4ef5Ppn0PLbO1bZBOF/ttcfXicWwPO6LVQd5uru/si0Q/Tvf+Q7+6q/+Co888gii0Sj+4A/+AG9+85sxMzODP/zDP8TNN9+Mf/iHf+jXWC9a4cPOB52Ok4LVfOh3moVOx8L+4cZmR7IV7KfSpTLneDVVyXa0NxoPhZshDQa73IudXsxz79YDr8pG65ZRmUejUSSTSUdTUQXRh4aGjIJiyno0GnU01Op1HExty2azBsivVqsoFAqO2mqZTAazs7Pw+XyIRqNIpVKOZq97UVQxBoNBpFIpU9NOPwOs1gykk20z2XZjrAAchp1do9YTTy6EeLp7Y9mM7rBZLwSAGVTdSpNJ3T96kcHBQeMo12o1VCoVo380Y0sBRAr1RCwWMwwqlhGjsL7kwMCAYY2rU6TgKrOvCJIrm5djjcVijqAvHexGo2HsBDr/iUTC1BFlOZNKpYKhoSEDECurq9lsmhItHCuBAw10kH0PwIDIwKpzSgCdjlkkEnEA7EouGBwcxMLCggHzt6NrarWa6f/C0l8MFrPkDQMDyWTSXEP7vJ1Ox9SJV8dTHXVeq25sRdpXGoCgHWaz1WibKetL7VeSBZaXlw3wzddUtIkrQQ2CNTwe684mk0lT95zvcUw8vzbDo1OuBA5+nnakzQZUIgfH0i2d3RNPdlI83d1/oe+tgVbusbsFolNPr6cz1Jfg3/xuJBKB3+83JC5gFSRl2RUGi9ezRzS4TtH9ksFj3S9twptmAtnjZxCc/pm9vrrnKhtbj+V2TZRAxXUhmMseMSQacA6KVUSjUVMKToOwm5VyuYxyuezo+Vav1zE/P2/6mDCIwAbZXHeO38Yu7P9VF3Pd3bLHe2Vf69rZrHG+p6XwNADeDUSn7817Wvvt8P6k/eR2Pg2Kq452myNFS9LpXJSg+mInr10I+djHPoY///M/x+zsLK666iqcPHkSr3zlK7t+/qmnnsJdd92FH//4x5iensa73/1u3HHHHY7PPPbYY7jnnnvw7LPP4vjx47jvvvtwyy23mPfvv/9+/M3f/A3+9V//FeFwGNdddx0eeOABXHHFFeYzb3rTm/CZz3zGcdxrr70W3/72t3ue25ZA9AcffBCf+tSn8JOf/ASvfe1r8dnPfhavfe1rzc189OhRfOITn8CVV165lcNf9MKHmw4aa0jxb3WEuPkUi0Wz+bLG51aipL3Iek67G2PHjvjZadfKfNP5b0UUIFU2mdZL4+fWi1j2S+w6bfpjR8nd0rJ0PfV1O/14s4AOv8uUMYLwCiqUy2UAq2tJtt1+UTAEOYBVdt1uBk56FV1PvX8JythGgkbKLzamm4oXEd998XR3f4R75sDAgAFW+ZoGolmrlDWYbWB6PdnO/U0A2maNcz9Rdg9B/mg06nDMbNBZbRIbONVyK1oiho4QHRf+bjQajswq2j52PxXaPwoqkyVHx1EdNJ5f7QAN9FOUDWWnkrvpZwVZdY/msQjcd0tL36womF2r1ZDP501gQOfKe02dVm1Ix/f9fr+xS+xsRq4vAw269rw2DBjwGqoQbNLrSBBKm4vyvuDnIpGIuffs54JrbTdTY6YAQXgyy5XEwfMpmMI1UZa9XfqG5+U4Nb1eAzG2I642707Z5HtNPN29++Lp7v6I1mhWHaklShgoLpVKZt+tVqs7+mzbAUy3UhgU7uG2T6l7Hn1vYJWEpp/RgGIv4ub72z69G3DOvZ9/61pzLnoOt/JfGqzWxuA2iU7XwwZjqaO0trj2c1MfuB/S6XQMeY33DwP+bnaVrpdNUuxWgYA6ab39eKsAerfPMoNgcHDQ9HCxdXe3EjQ6R/3tNmbbbuP1Wm9Otv0KrNaFp32ga+rGkr/YZbd196OPPop3vvOd+NjHPoZf/dVfxSc+8QncdNNNeOaZZ3D48OE1nz916hRe+9rX4i1veQs+97nP4Zvf/CbuvPNOjI2N4dZbbwUAPP3007jtttvwJ3/yJ7jlllvw+OOP4w1veAO+8Y1v4NprrwWwAsS/7W1vw7/9t/8Wy8vLeN/73ocbb7wRzzzzjCGvAsC///f/Hp/61KfM/yTV9CpbAtEfeugh/PZv/zZ+67d+yzTvseXw4cN4+OGHt3L4i1qUsTY6Oorh4WGEw2FMTU0hHo+bFCSNRjYaDSwtLaFSqSCTyeD06dOm3rUdBdyOqJK0lZQ+ePZvW8mpAw6sMoJ7ZaCvJ2S2dTodA6DakVK+ZoPS/QbSFehm9J/KstlsOtZBMwm0LI8qQTcjYLMbuxpfrVYLpVIJPp8PxWJxjbIlIMHsB23atR8kEAggmUyaBi5M6beNSjcQe7fFjogzKk7mKPeE5eVlZLNZlMtlk0Z/sTrlniO++/Ji1d3dWCxbPRZLaEQiERw8eNCRKq2GPBmztVoN2WwWMzMzxplbT9wc1l7F719pOEbgk8wq3Rs12FsqlVCv1zEyMoLh4WGTFaWAKsFDOh0aJCb4TWCawGYoFEK73TYNNhVsrFQqKJfLGBgYQCqVMsHEWCxmjkcGe6lUMuVAyExW1jjXmOPVdeBYKDy/MqiUjUadTFHWcqfTMU0lo9GoYZtROE8FArZ6zymTP5/PI5fLOcACzQyMRCIYHR11sBlJuPD5VtLT4/G4ceBpUzCIkcvlTP11MhqBVZ3FXi783yYIsMHn0NAQ0uk0QqEQyuUy8vm8aQDPEn5aJpDXvNVqmflReH0IRJAMQECC3yfrTWvb2sGDUChk2PNMpyeQo6w12lssq8eU9UwmY+racr0BmOw37gO8PzjWi1k/ebp79+XFqrv7KWT/xuNxBINBR1kx2uGBQADBYBD1eh25XM6U4njhhRfMs70T49Ksb9oQdrkpwAmgawYWRQOgCqIrcKms8V78Szeyl66ZBkjtQKbODwDC4bAZC0lwnJeK+kq0Y/x+vykhog0lNeCqr6meor2hpXNoD1WrVQeZUee8VWm1WlhaWkKpVEK73TZj1nWjTtR1VqBXe34pFmBjD+r322tIUVBZz2frSwWdVfg/m4Uy+J7JZByfa7fbpim8gtc6DjsArTaTzp/PAOdJwoJmy+v9yGNq9qP2ROE68V4NhUIIh8Pm/mD24sWsn3Zbdz/44IN485vfjN/5nd8BAJw8eRJf/epX8dBDD+H+++9f8/mPf/zjOHz4sOnH8ZKXvATf+c538OEPf9iA6CdPnsQNN9yAu+++GwBw991346mnnsLJkyfxhS98AQDwla98xXHcT33qUxgfH8d3v/tdvOpVrzKvUw9sVbYEov/sZz/b8DOBQAB///d/j1//9V/H6OjoVk5zUYpuDsFgELFYDLFYDKOjo0gkEg4nhjc7nYlwOGzSjTqdTk81sjcrbiws/k1FaW+KNohul3NRUGG7omAkAXUC0VQAbpHRnQRPFezWH47TLj3Dv23mnB202CyrzWYDaDCETchUWLOMBo6yIPaD0OAFnEz0vQ4663oz60TZggzCEJy4WMVzxHdfPN29faG+I0M3kUiYkhoM1lIPEjAPBAIGqCSDeaP7dyv3N79DR8LNubJ1D1nhHJeOH1h1wO1yWDyGZp6ps6IMIpYDodABo2NNx4hgvzKJCPYr8Mk9s91uo1QqmePbAIFtcyj7jc4nAOOouWWKuelpOmYamFAnfjuiTrbP53PUP1dRIILlyhjoUOY0X++WEq81ycnM5PvKluO8uS56HK4/wQ4Gf5WZ7cZWVPBFRW0qZRlSJ9pBHZ2XHp/3v4JACkKprWjbXVw3ggEMyHQ6HXPudrttSAh8lvjZi5nV5unu3RdPd29ffD6fAcljsZgpr8n3AJgANG1vLY2m5Ut2cozKQrezZGyxdY69/6kut8+zGV/Pbc6bIShxHyYwSnELOLuBwZoZZJPMVA/b/rQNqque1r2a69EvJjoAB/Oc57ftMbe/OU/VX90yEvj5br67m86nKEajen494fXTrDM3m4nry9dU1gP77bEzAG7rbLfv6DNjEyq5lgTiuReQ7MEMBU93b/x9YKXnkgqJRSqNRgPf/e538Z73vMfx+o033ohvfetbrsd/+umnceONNzpee81rXoOHH37YNP59+umn8a53vWvNZ9ZrhJ3P5wEAw8PDjte//vWvY3x8HKlUCtdffz3uu+8+jI+Pdz2OLduqib6RfO5zn8Mf/MEfeMpcJBKJIJ1OIxgM4pJLLsH09DRCoRDGxsYMo0Y3pk6nYxxHsn18Ph8qlQrOnTvncC62Ax7aTnWn0zHOtZ0Objumbg8kNy1VBv0WVXZ0pJThTefQ/iywvfIuGjnnpkvmV6PRMKn76tTncjksLCyg2WwinU4bphUNNdaCLRaLWFpaQrFYRC6XQ7FYRKVSWZe9SGYkHTsyrwBnIIYOnrIFWS9OjZr9JHSi6ciqwaJON0EFGmO7IW7sjWAwiKNHj2Jqaso0u6GjTtBtfn4e+XweS0tLePbZZ1EqldaUP/DEk52Ui01398PpZYB7cHAQw8PDhkE9NjaGWCxm9hgABhDmXksdTud8fn4emUymr6yXZrOJUqmEgYEBU/9TgWwKg48MQHOc0WjU1KdWPW8Dj8CKcVypVAyzLxAIGAYvv6M2CfdprW2twDqDhnxdx2yD9mpTcC72a8pwou7TurGlUsmk59Pe0WaRPFaj0cDi4qIhPbDJpQapmaZdKpWQy+VQLpcNY91mf7lda3WAtaa+NmYNBoNotVoOBp6CB1o2hXPhZ2q1GoaGhsy9CKyWXdGAhM/nM9dB05ztQIJeF36XDHhmDWgmg5aYUbuWx7MDLm4OMudHW4VAmJ1FoIAJmeN0nvV1n89njsWAjgaFOp2OyW5gxgIz31hG5vjx4+h0OqbubrvdNkz5XC6H559/3twHnniyW3Kx6e5+CHtrBAIBjI+PmwzwsbExw0Sl3uHeOzAwgGg0isHBQUxOTho9OTc3h4WFhb4C6SzZyvJwbDapupdiM82593IvpSjhTMUus8XMbntftkVJarb/rOArf/Mc7FFBEFyPpWC/6nkFMd3wBuo86nYNbHMe9XodCwsLRu9PTk4akI86gPpO+40UCgVHJv56ovpF+25pIFr9USXX9YKLuAXnlWBgX2PbZlLRa6rXUtfcJhLo2ncLouhcdW6a9WeP077uGuzQfiuAsx9erVYz9f1pv9olXphdSduPQe/Dhw+vCbZTd7daKz3iyuUycrkcTp065enuDeTQoUOO///oj/4I9957r+O1xcVFtFotTExMOF6fmJjA3Nyc63Hn5uZcP7+8vIzFxUVMTU11/Uy3Y3Y6Hdx11114xStegauvvtq8ftNNN+E//af/hCNHjuDUqVO455578Gu/9mv47ne/uyYg0E12FE3y2AZO8fl8iMViOHDgAOLxOC699FIcPnzYpD6ro6HS6aw0m2y320ilUkgmk6hUKuh0OqZmW79qMSrIGgwGjSOs0VBN27I3b/6tEfCdAtE18qpOH1nX6pADTpbgVkWdYaaULy8vo1KpGCBcWX1UEEtLS4hEIqjX60ilUqjX6wiHw8ZBozGQz+dx/vx5FItFLCwsIJ/PG2aym3Cd2aQ0EAggHo87DCM6d0w3573CJiusp7qfWOiA05hgORoGMWgUMPCkzf02W/OqnxIKhTA1NWWa5TGIpiD67OysUeTVahXz8/OmJ8LFtKd6bLa9K97arpVQKITh4WGEQiEcOHAAExMTprmxNn9k4Jv6E1jZ39PpNC655BI0m0384Ac/MCUs+rXW1EUKLNvOKvf4UChk6gLG43GjL8vlMtrttklzBbDGsWm3V5qBFotFkwLN8ykjTFNjaTdwr1NWHQP2CqRrrVbujXxdWesEnBkY5THJiqHj1Ww2jb5Tu4Z16hmg5HgVhD9//jxKpRLS6bQpN6MB+2KxiGKxiFKpZNK3K5XKGlZbt/1O0/YJDDMo7PP5zJpqMJhrzICE2n/MaNIsNGAVPPH7/aYEiYIfdD7J+tfz6/HJpiOhQ1nZhULBkTXA54C17nmNFBhSPa42rGYNsGYtwS2tq6sghQIVvI4kGLB2e7VaNUBZIBAwthGDXGSVsxwNg0VDQ0M4cOCAIcEkEgkHoN9qtVAoFFCtVnHq1CksLCxctI64p7v3rnhru1bC4TDGx8eN7h4fH0cgEDDPMstwUncz8NdqtQzZ5ciRI4ZZubi42Nd1Zq8L6h02+FZAWYFIbeJN/1pLZ9ggqwrtFC1roufpxsJ1yzKj2AF3Cv1g7ZnCY9jBAP64nV9BXw0cs8k4xcYh5ubmUKlUMDo6irGxMUejz06nY0rDVCoVzM3NmUD4RhllijvQ3mI5XupOAMa2oF9ql3OxQWVbdK7UZ4qrKDOfn7Xxlm64kK61BuV1fmoH2c2/9X0eg/efZoaTiKlZh26YkN47tGNpZzCYwPuWJf9oE3HMtH0qlYrR3cQ6LrnkEnMPUHfTTlxeXjZ+9rPPPov5+flN626dz17eg/ulu8+cOYNEImFeXw90drvW62FNbp+3X9/MMX/3d38XP/zhD/GNb3zD8fptt91m/r766qtxzTXX4MiRI/hf/+t/4XWve13X8ansT/rpPhRudoFAwNT1pMFOtk43xcebo9PpGLAQcKZPbAcYts/FTZtsbsBZG9RtXq1WyzVleKdB2W5sd24UqhTo6NHxsufRTezP2mx3Oqy2g8txEHSoVCoIBAIolUrGKKBQ0bKzN+tzuTUC07krG0xZX9r1nEqEIKwba28/gecUPhP8263cgN7LBBB2U8EpKEOwIRaLIZFIIB6PI5FIGGDJ7/ej2WwaACaTySAWi5l7Qed7MYjniHuyn2RwcBDhcNikexOcs9NG7b1aQVI6cdyrCez1617muTX9VWW91F5+zx6L6jvuoUB3g7ab7lVH284IsvcCdcioT9XZt/WVG8NJ37MBWWXeua2TvkfnjT/BYND8bTvifL3XuthcB7sci83etsvhKMt7vcxAnbd9b9rZe91Ev8fPkrRAnaTn0ewDN4ee95Ay4dezP/Sa2yn9PCY/p9eNut6+Brbto2KDUjpWguXcAyKRyBp7ixl+0WjU7A8K7F8s4uluT/aTsPQa/W4C5coIdmNuK2jJzzM4SJ+v37qbe7WC4go6c3+jblSdx71ZRfdFN1FftNe5uJHnuP/puTTYq/W8+bdbHW8ef71j2z9uwuA8dXK1WjWlO/i+6m4Gv3slK/GaqA6gLqfuJilAdRy/10uJWzddbt+rm/Hd7SCLnb1gz88W3m9uQRxbJ+s1Uxtuo3m6jcsei5vNoLaE3mO8Nnz+mZWigf7l5WXE43H4/X7jk/MZ93T32u8DQCKRcIDobsJePTZDfH5+fg2TnDI5Oen6+cHBQYyMjKz7Gbdjvv3tb8ff/u3f4u/+7u9w8ODBdcc7NTWFI0eO9FQ6jeKB6LsgZNAwDfzQoUOIx+MYHh42qcEbbYZ8PxgMIp1Om/Qkbv6MdG5XyNxSZjejewoA8LdGkOlA6EbtZpj0S9SBI1BB8IBpu2QX0BBi2pUaR0D3aC3gTC8jI4wRVqa/k4murC3OmSmA5XIZiUQCtVoNsVjMNLjx+XxGeReLRZw+fRqlUgnZbBbVatXRGENFGXnxeNyUBiFbjpudOqBskEXGhTag24/C54LMPWAVSFFmJVPs6Qi7RTb7LWoI06EOhUI4dOgQRkdHEQgETERcHXefz4d0Og1gJTo6MTGBn/zkJ8hkMn3JNtkr4jninuwHoR5LpVI4evQoIpGIYV8DK7X2qHOog7mvAqt1WBlMbTabiEajmJiYQKPRMI0L+yWdzgr7OJfLAYBhXYfDYbPfsLGnAqSqy/lsqW4j25aObyqVMnpXda7NiiZATJ3JY5Pxq0xi2xljNhHZzWRPKxjLa6HAKIPbtA94LRYXFwGsZAYoo8ttDTkPlihhUDsUCiGdThuDneB5qVTCzMyMCXoqE389ACOdTps6jfwcAXIAxoZRZzUejzsY0Zy7fp+ZBppGD6w6wnTiWfLMfp3/077SwMzAwADi8TgGBgZM9h3tXF4rgk7Koi+Xy2g0Gkin05icnITP58PCwoKDbcd14N+NRsM0fuNx2TuEtp3egzw/sGojavYdQXCCH0wPZ0ZfMBg0DUh5vZnBMDY2hnQ67WDo8TwMkEciERQKBRw8eBChUAi5XA5LS0s9AzMakNir4uluT/aDcP9KJBIO3R0KheDz+cy+1Gw2TTaSPn8KOFKPJpNJHDx40DQcrVarfRsvdSSbFnPf4h5E3WYLS5YoyK5z0Hl0Oqs9HZQh7kYAcBNb1/B7tH90L2cGMLOB7bUNh8PG/+HrPKaK7vc8x0Z7EHW39hSJRqNIJpPGvyoUCsYWImudvvh65DViCel0GvF43FQTUJxBbSBdf5aFI4DbTajzVcfY4HE34oab8Jpo/zo3YpYSEhSU5rMSiUSQSqUAAAsLC0bPEyNpNpsm6522I+83HtvWnW52EufFcdOGY0lFlmKjDaDN6FnKLZFIIJVKIRQKmb6DGrxotVqGoDY6Oorx8XG0Wi0cO3YM0WgUmUwGi4uLPa3vftFpu6m7A4EAXv7yl+PJJ5/ELbfcYl5/8skncfPNN7t+58SJE/jSl77keO2JJ57ANddcY2ziEydO4Mknn3TURX/iiSdw3XXXOcb59re/HY8//ji+/vWv4+jRoxuOd2lpCWfOnMHU1FTPc9yfyNk+E41eJxIJjI+Pm4ai3Uq42N+nMNoZCoUwMjKCQqGAoaGhrrWANitkXtFZBladMNa24mZIBTwwMGBAAHXEdgOoVAPBjtTTqWcElGO2mXGAe0TcjqjbhgKj26xvznPZhkCr1UI2m0WhUDCAC+veMZJH57JUKuHs2bOO8jDrOeCMpobDYXM/RaNRR8RX2QADAwPI5XKmzAnvy35lMlwI4bwYUeZ1ZEAIgGEjAGu7k+/U/akReK5zOBzG6OgoJicnjRFrn5+p781mE5dccgmi0SgWFhb29TXyxJP9KOq4MHBNZ9Xn8xmHl6m63H+oLzRLSEuWBAIBDA8Po1KpoFgs9h1EZ1YT9RSd8UQiAb/fb4Bf1S1DQ0OmqTmPA8AEi+n4Mg2WQAQBVoIR6uTTRtByN+122wCYDCyow2qzmqlbCcYzGMzGpNRjapvQOQJWMwFYLs1uQtpt/6f+pxNOfT84OIhcLmcyhjjncrmM+fl54+z3In6/H/F4HGNjY4614PpQD9hsSTqF2gtFx837TlmK9pjUQdZsCLWF3K4JbUM6s8r8Yjkatcd4fDrVBNHT6bQB+6kn3a4B7Tj+7/P5THCn3W4bIoqdcaZsSQUQyDLjvakNa5ndSRt4eXkZ4XDY1MJPpVKmtCJLNakNyuOkUimMj48buzmbzfbEaLMBKk888WTrwueJQetoNOrYF5g9rFnX6kfyGMCqPopGoxgfH0e5XDblm/opNmDM8WkdaI5Vx8RSV26An2bdKNCrAdPNiK0zNYCpGdkMPBPn0D4b3G/Xy6TSNalUKq4BhG5CMgHtB9pdo6Ojxt7K5XKGgX7+/PmeriX3e9bMHx4eNtlKqqMVGNZMMpIBtAzdenMAnAEdXfvN+LLU0XYpNLfv2FlktEMajYbBrvx+vyHg6bnpd5MAQX2sWRLUhW4+rc6Z36PuZmk4mxjAdSV7nPc7g2bBYBDxeNxh33IcJH8mEglEo1GUSiUDojabTSwtLXVdU082lrvuugu33347rrnmGpw4cQKf/OQncfr0adxxxx0AgLvvvhvnzp3DZz/7WQDAHXfcgY985CO466678Ja3vAVPP/00Hn74YXzhC18wx3zHO96BV73qVXjggQdw880344tf/CK+9rWvOcq1vO1tb8PnP/95fPGLX0Q8HjcYaTKZRDgcRqlUwr333otbb70VU1NTeP755/He974Xo6OjDsB/I/FA9F0QOp4E0mi4u6Ue93o8sm9ZV9tW/NsVOh6MvtMZUOeI89B05AsF9NlOps5BmeRalkabsdjp0oAzKk7DhYYBnXQ78t7NEeSxCJRTIdE55LEInmsN717nrT92upYCDLpO9vf2q9jzBlYDQn6/3xhTBD26lSfqh6gRwPuD57JBiW5zAWDAJ5Z82s/Xx008Npsn+0H47GrGmIJ+dG4JFAKrjUXVCaHzxFqeDMD2+7nm+LQUGPdCNqmmvtHAMsvVUKdzfnRCFOCjzrN1h+0UKiCre7SWPVPQl9/X3h3qPGpgkuvKwLb2bSHozf+Vca17fze2mZuoHVGr1ZDP5x0MbZvJ2Ov+xFJv/K4CwmrD6PvKIlS7T9Pk7bVSG0V1IwAD2KtzS4dXAXZlcFWrVcNE5zH1fLx2eq15X7XbbdO4c3l52dQl1XuWAQUNgLtl5ek9qLYWQSK13fR1va58jyx2BeS5Psqs13tXgzIUAnfMNNmMbFcv7oZ4utuTvS66V9rZ3rqf6d6h+kL3GWb9MmgXDAaN/uy3aIk37lvU3fRZOTbVAdQJnN96epOfoS5crwa47VPxu/ZnuM7c03WPtDONgJXr02g0zPEVYNXG2BpIVZ94M7pbA8WlUsnsySy91i3bu5vo3s9G3LRh+J7qcF0jBv7tOvH2eN1eo63E+04zB9cLhtjXi+veDWOwy+0oOZI6mWNye7Zo19IOYzaCDc4rUA6sLU9jN5nXWuv2XKm7ea80m00TpNHvuOEdvD9oy8RiMaRSKSwuLjo+10+M7ULJbuvu2267DUtLS/jjP/5jzM7O4uqrr8aXv/xlHDlyBAAwOzuL06dPm88fPXoUX/7yl/Gud70LH/3oRzE9PY2//Mu/xK233mo+c9111+GRRx7B+9//ftxzzz04fvw4Hn30UVx77bXmMw899BAA4NWvfrVjPJ/61Kfwpje9CQMDA/jRj36Ez372s8jlcpiamsK/+3f/Do8++iji8XjP89tREP2//tf/umHNnBeDkLUTjUZNPXQ6q5sVtygoGUDdNpfNijKwFITUtCSC+Opc8OEKh8MXDJTV8xLo5gbJSCjTrrTuqN3kBHDW9aKSVWVO0IQKc7111zIvVGDqzNlgh7LH7XW0lXK33/x+p9NZwzywjZ79DtAqQMO5sFFavV5HLpfD4uKieQ41Nbyfc1cF02w2TaM2GoUate/2jPA5ZrMasuH2+zVyk/1ukFys4unuFXFzxAEY54kl2gYHBw0YyuecAB6ZyizfBaw6sHS8+i3KCKcuYZNidYpZCoxNN9l4iSC/3+83DdkqlQqWlpYMM5r7mK4NU2676S8CEUybJluI3wuHw4bJRj3o8/kMw5fH5W+ynqjjCPYXCgXTuFWdOxuQVuB4I6HuZiC2WCya4IPNmNqolqYGcIvFItrttslOCAQCpjyMjo/n4mcZvFFQl9ecdhvf4zE4d4IytIMikYjDjlHbhPcO7xdgxZ6Zn58310bT0mkH8bcGQ1jmaHl5GS+88ALa7bYpc8c1ZWlCjoPHbzQaRndrLWJ9hmg/MVNicHAQ2WwWuVwOPp/PlGtREgvtOp0fwRUCJGQOalCAa87PkVlJu/LAgQNIpVIoFArrAhb2PbaTYoMdHhB+8Ymnu1eEmSFar1qDngCMXmHwm/u3srYBmDrKZDfTB+y37la/m/9zX6bfqIAiS3pxj6R+sOfPzDR95rlXci9123vsIDgBcnvv4N7n8/lQrVbXBMm1bIaWimXjTSXn6f7PvXW7urvVaqFcLsPnWymduri4aHQfs9Q2E0ynTq3X6yiXyw7igQYBtME3yYZs6q7lR3g99Bx6Lr5P1rQGHmzyop7fDvgCcNhVbj6lritBbo6fgHomkzHPgpIReX42c8/n88hkMg4imNoWNvBvk+GYMUk7kcdRG1BtcpZhy2azaDabCIfDZp07nY7R53YvI56L99r09DTi8Tiy2axjL7AD9bspdrBvO7LbuvvOO+/EnXfe6frepz/96TWvXX/99finf/qndY/5+te/Hq9//eu7vr/RHMPhML761a+u+5lepGcN8MMf/rDng77sZS8DsBoJeLELnR8FnbfK2lagV7sL6wPeL+EGTMfBBtFt520jx3EnRTde3RzpIGmJGo1MchOmslCxI+FUTpq6r8bOesL3Cai6jd/+mwrHPo7NmLCvi308G7TVnwuVOdBvcZuLOsdks9EIZKRca8f3C6S2jVSCbawprIp/o/lwv7hYrpOK58Tvjni6e3vSbW+hPmG2iAJsui9r3UYGwwi2uaWlbnesHJ8tCjza9ofaEwQsCRKEQiHE43HHMdU2ALAmTdhmYPG37o0EBVjnXJnoPKatu9S55N/K3KMjrqVj+ikKDBNQt9OybTKDrV9sHcxycGqHUHfQ7qK46Xx1kjU4o+xq6jv9jupABV9od9jOMbAKqmj9f5uBp+vEvzkv3u+tVgvFYtGsE0F4zpUOL9eZQItbxl+3gLSCNfw+Sx642XocK207tWfdyAa65ny+GdTgsw7APBN7RfrhJ3i6e3fE093bEwUZbRBKA+QKFlJ0TyHwBsD4724lQPshbgxrBT4p7LOi+7riAOo/kHHOwCRlI39d18AOwKnw3Hb2neprzVzj/ksdRJ1E3a1ZSG4+drdxdBM7wEyCAMvg6DrY17Sbv8Y5acCac1ZSnOp/ZaPbgVy349vHdPP/3djd9jXQdeJaKAHC7dw8h9px/J/XR7P63MgKtG2AVXtOsaT1fGG+T+Bbx+s2L64hGeW0YfWa6nfs86ptEQ6H0el0TJ81HdOFlM3e927i6e7+Ss8g+i/90i+ta3zpA3GhgNT9JP14GHfrgdZNHHDW57LZYfr53XzYVMm4jYMbPqOnZB9wc+Zc3OZNZaBMcY3WbnaeVHhsakWHi1FWMqjUcNMxKVOdx2PzDM6BDi7HTBCZRonWrtvta7UTotdfX+M1KpfLWFxcNGlaAByNYLbLyLfBIYIES0tLaDQaCAQCyGQySCaTOHz4MEZHRx2NeVUIxJXLZSwtLWFpacmwSLoJ58G/uR8ra2S/X2NPtiae7t66cJ8m0Ais1Qusaa61StXYp6NFdjGwwj4mG7ufa77RM07dbbObqCNyuZxhZvF91p5uNBoIh8MmqEfdz7qnXB878GszjZgNpo6gMuK4ZsoOU/IAsJrZ5fP5jE7z+/2G7UZQut/EAlsYEAmHwyagYpcKUTCca6QgNAMqBOM1MKFsKwIjBH0ILnAc/KFe4TjoCNIG4poyCKCMdTZKZ2aC3iucD7DKcgRgAsWcP7MJNHhEkIoAFMGcdrtt6v+6XSc67MViEcvLy6ZZLrMOeB/SPiOzNBKJOEgrvC6s59rpdFAsFgHAOPmq+6m/GYjJ5XKIxWK46qqrMDo6au5PBm645twnKpUKzp49i2w262hMZoMdev3sMSgA1U+xgzue7F3xdPfWRYOBWt5Kg3r0pWywzRYNylarVZOpspuMVPse0P/J/qVu5ny5B9vZRXoMG1S018AOUroR9mwdwTWmTUQ9wvtUm4zy2Bqg5XWxfbpua7EVoW3H7KhYLAbA2RSe11nnT93t9/sRi8VMDzItf0abSTPY1T7QNXa7hzTYoDaRBiKA1SAI7QElcPA4/O32uq6lHcywA9Mci14vEifsY/K41WoVuVzO9IAjpkE7x+341KMAHM3seV20woBiMLxfmHVRq9UwMzODarWKeDyOq666Cslk0jFWDa5xnygWizh9+jRyuZxpeq5zWm8v1mvH69JPPXshGPCerC89g+inTp3ayXF4sg1xY+H0W+yHV6OPmiLnlj60G2ID5vaPAhx0PnUD7baGCjaoU7PVuakyC4fDSCQSptlJMplEKBTC8PCwyTCwy3gwZYmNVsjm4nWwAX/+zTQnTVN2A9IvdKR1q8K5KgOShke73UahUEC73UYsFjN1iePxuGFe0vjczhrw3mOwIpfLYX5+3tS7BYDh4WFcfvnlmJqaMudWEJ3Xj9f3/PnzmJ+fNw1puwkNNGX7kQWgYMxeEi8ivjvi6e6tCw16BjgBrNEzBEOZbUa9QiYvnUbuA51OxzSZ3myzrO2Irb/osNA5q1arpgYkgV3unUxZJmtcA8qcB/cfBRXVmbCZamygrPqIY2HZtPX2PM5HGzzarG3aJju1V/h8KyVC4vG40d0Ew8nOZp8T6vyBgQGUSiXkcjl0OqulfwhMuwUKCEKoA07Am3pEr6s2y9RyM+Vy2VETX22j5eVl5PN51Go1JJNJ831dP14Pgg8Eo0ulEoLBIKamphCLxUzAiE4u06fZeFSBAdowbvqAjjhBIjrR8XjclCFSUIZNf0OhkLlXVccnk0kkk0kUCgVTmkiD6JRms4lcLodyuYy5uTksLy9jdHQUx48fRyqVMrq13W4jHA4b1jmvz9zcHJ599lnMzMw4moryXGoj632rgR9dj37dv/08jqe7d1483b11oe4mcKlBRPVXbWDRjZnLciDcQwmk72bgwt4LdA+hX8hSVnZwmnsv52I3JgWcYLm9H9GeIQiuILoGVvm/6nG1b9ZjHev3bX2zU8KgL4PGbB49OjoKAMhkMigUCg6Qv1QqmVJrLKlGoT1I3adrxL1/oxKiajep7uf/en+qD88G98wcdAPS9ZgcL6+n6iYbMKfoOnQ6HWMbc9wqtH1arRYikQji8bjpu8Om9Co8l5YI5nFZvjgYDKJSqaBQKBjdrSXmgBW7qFAooFKpIJfL4dSpUxgdHcXll19uyh8zAK4ZAbTDzp07h2effRbnzp3D0tLSGvKom+6ynxteQ33Wtiue7t6b0jOIziLwnmxNVLHQAd/KzaibsgKHux2hUiPfzTnf7QfNPq+d+qTj0c+4KRb+tr+/nTlpwIGOZCKRMDXtRkZGDJt8ZGTEOH7hcHiN0iSIXiqVjBPJ8TJKS8dSmRbauKufc9sLstFcyG5go5NqtWpqBXYzHDdzbq4pnWv+VCoVU0+23V5pSlQqlVAsFtFqtRyMTH6GxnC5XEa5XDbGkT0vDcgw4KJsPwILvBd4jL1yrT1lvjvi6e6tiwLmGpQDnOVJ1tt/3PSPnTW1W8JxKFCn75ExTadPGU92sI8/yvajA6YORze2kc7fZp1tRi/Z31kvKL4doYNHPc55x+NxEwwnsKsg+tDQEOr1ugGetVSKAuNkaSmga9s0+qN1vG1Hzc6w0h4pABzv2anY/G0DG/pdroe9Pt2+q8xz27lXUMvtemsgin0HaPfwPV0j7TNjs8z0vlcAQcsLKCOf5+R5qbv1uiiIz4AOs0xYZ3U90TJICppwLBeyTGI38XT37oinu7cufMbtH2BtaRLb91OxWbvb8d+3Khyjm21hj8stc4jSDbi0z6P/62tu51cdrt/pZgt1m18vn9uqUAcQLOVPIpFAIpFAMBhEIpEwgVb2FCALWsFjrT3Pv2k7ETgmLqPz4fq4ZSx2u+9sfGI9m2a9685j6O9uonZtN9F70b7nVLTsLf1uErzUf+12DrfP6H2u+pyZjMQ9SBJot9tGdzMTlPe02k1sEE69XS6X1/jd9roo4G/vKboGe0nfebq7v7KtrhjPPPMMTp8+bZhYlP/4H//jtgZ1sQlZXgTIuJFoeu9mhBsGI22FQsFR/2m3RA18ZTZrrc1uqUH9EN3EdbO2WdbdNjE1EGzjQY9v/71ZYRpxOBxGLBbDoUOHEI1GMTY2hoMHDyIUCmF0dBSpVMo44t0aXzLSTUevXC6b+4sA8dLSEmq1GhYWFnDu3DkTGc9kMg5gnYrNVib7SVSBKrvO/gxfbzabmJ2dRbFYRDqdNkB2PB5HLBYzgLSW+el2X6jzX6/XDfi9sLBgmIbnzp1zsMFzuRy+973voVAoIJ1O49JLLzWMOQLfc3NzyOfzOH36NH74wx9icXHRkRJOIWuCDekmJycNI52NDvP5POr1OhYXFw2jjuUFLrR4yvzCiae7exMa4CwpwbISbDQGrBr7Wv+RoJgClUNDQ6bWIptPXqgMEXVAODZ1VAj4AjCOhDK+qVt9vpVSYmT4VCoVk+rM43HfUrBQnX7u25o5wzT0zYoNAPRTIpEIDh48iHA4jHQ6jfHxccPOZwk2/uZYFMAlC4vNLufn5x1ZYAyAc4+enZ01LH2bfKHHZsCd56ftQLYX9TzvN5/PZ9LX1VFl7XGfb4XRzaAsx8T0dfYR4ViY0s5zFQoFACv6qdPpGOYmswJUt9Im5nPlZodoLdXz588jm82iVCrB71+pcc6sPV5zDTDwXOl0GgAcAW4lFAAr7PNMJmOcbepOpu/ncjl8+9vfxgsvvODQ3QTayQas1+s4e/as0eN6L2sQjtdxdHQUBw4ccPgCPB9T09l8dreDbt3E090XTjzd3ZvweWRWTKPRMHuxXetYRUuVUWhXM5i8nl+5U6LncgPtqCfcSoACq0xd7ns+32pmkF0+jb+p8xkA5Wv2fqZlS7hfbnW/2u7e4iaRSARTU1OIRCKYnJzE4cOHjY3DNWCAmz1udCxqI3E/5pppuT761qdPnzYNzzXQwd/EftR20N522tTbJgdoRp8enw27iRnYtmU3/MUtoKTX2+16EGivVqvIZrOO/nAqBLZbrRbOnDmD+fl5pNNptNtts/balJznU9xDm31rvXwKsR/qbjYz1TJxrVYL3/zmN/H8889jeHjY6G6Om1l41WoVZ86cwdmzZ5HJZLqS13gdpqenceTIEUd2Xb1eN7pbG9x7uvvilC2B6M899xxuueUW/OhHP3JEpJS15Mmq0FHsdFY7e2sazGZEnRd2hmbkc7dvbmXzKNOZhoY6fDs9Bh0Hx8K/NwIqbCXXT+FmG4lEkEgkTFpROp3GgQMHcPz4cYTDYVPOhUALmW5uNew4p0ajYZzBfD6PUqmEcrmMs2fPolQqYXl5GTMzMwZwZ0kTreler9cdCnM/CkF0Ag5uc9HgysDAgIkyk+1PBpuWIegW+FEASEuv0GBfXFxEPp9HLpfD4uKiuUY06n/2s5+hUqlgamoK0WgUiUTCGPf1eh3PP/88FhYWcObMGTz33HNGmdvzImsiHA7j0KFDuOSSSxxATrVaNTXZ/X6/Uex7kdnmye6Ip7s3J9TdAExWCZ1PgpRkE/F9m72lAXNmnmiN1gslvPZ2MJ9zYokQOho+n88AN9xPGcCLx+Omzrs2sQRWmUp0/BSsp64miN6P/cmNVd0PCYVCmJycRDqdxsGDB3H06FFTKsAOuirLPB6PIxKJYHBw0NRPXVxcxPDwsAE+qItzuZzRIbRpyIS258jAK/WYNplXVjubbKkjNzQ0ZGqU0j4aGBgwWVO0T3kdadMReKAz2+l0jDPM7Lhms2nqlxJ00Jr3WvKHzxdryLqJMu+5DsvLy6Y0G/UnX+d5NMjAEm6sc65ZecDKM8B1X1xcRKPRcDAKObdnnnkGP//5z3H06FETPMrn88hms2g0GlhcXESxWEQ2m0Umk3GUcuO9aAMbyWQSR44ccYAJLJ9UqVRMIEIJH568+MTT3ZsT+j0AUK1WzT7JYK7q324AtepwLdV2IUF0N59ECT1u94G+prqJ5W6oz21g1mYb29l3yvDXYKcdoOx1fjuFFQSDQUxMTCCdTuOKK67AL/3SLxn8RUFRzp9BBoLcWi6TZVSpE0gcoH91/vx5NBoNQ5x0A2Htfmd2KUAlIKhepO6kvaHXhcCzTcy0bVE9Xjd2Om1Wt55a+tl6vW5K17jddxwfm7nyO4lEAtFo1FGujmPVjDXbVlQSoD6/jUYD2WwWuVwOxWIRhULBYTM1Gg388z//M5599lkcPXoUyWQSw8PDqFarJsBG4HxpaQmLi4uGte4mvA7Ec9i8t91uo1Qq4ezZs8Z+0JK7nu6++GRLIPo73vEOHD16FF/72tdw7Ngx/MM//AOWlpbw+7//+/jwhz/c7zHue6Ej6vP5UK1WDVOHjo+Cdt2+T2H0UxkqZCPvtiiYaIPpjNark7ITTHRupgqca03wC8X0I9DChqEHDhzA6OgoRkZGcPjwYaRSKUxMTJj657FYzChrBXPd6owBq5Fgshp5fw0ODqJWqyESiRinnOAGGX/FYtEA6Qpc7DfpFsTZ6HpzjQOBAEKhkLlGNJLU+KbodeA9RaeezjkzQvL5PAqFgmGmayCn1WqhUCgYAy4ejxtghQ39Tp8+bRqS8fg6Jz5LzFpgOYFoNGrSy2hkca8g016ZjhdavIj47ounu7cudAboIHG/Zd8KAn1khem+SqAdgCMAvtt1Vd3mxPkocK4lKuigqT5lphmBUOpcDRgwUEAAV+t0k7nFZ5g6rB+ZMrovbHWPUKb3yMgI4vE4UqkUpqenkUgkkEqlDKtc7TcGCjTdGFgFMTTIq/qG7CtgxeEbGxvDxMSEyTYkE1kbrinbmvrLtrHUNtPvM+tJS8HZgWTWcefrWoJHU9KpS1QHa0M7e121xMxmhWvN+0ufKwAO3Uawgux6ZWtqujedbQIC9Xp9jd7mGtHOKhaLmJmZMfY3bapcLmf6z7jdw7w+Q0NDiEajCAQCJhCuz1goFDKvLy8vO2rZ74WsQU937754unvrQqKL7o922Sw+r+pbKVjH/YDM9n43Bd+sKJHK7kHC991KZWiAwPaRbVBV/U1t/Ek7xw5wksi01f2hH/uCBmlHRkYQi8WQSqVw+PBho8e1UbwbiK6MbF0Lez0Y+OTv5eVlw3RnRhPvE9Xdqh9ZWkbXXkF0vf90bDYoS7tLr7kbGcwG0lXssn0qOj772ejlunEcvDZajxxYzXBgdrwG+PX51Mw6/pD1TSKbPT8+K36/H/l8HmfOnDH9X8hwn5+fN/1d3J5r2+/mb9oiAExGA+1DVgHQTIULLZ7u7q9sCUR/+umn8X/+z//B2NiYUUSveMUrcP/99+P3fu/38L3vfa/f49zX0m6vpIT7/X4sLi7izJkzjk7QdFLXcyp449ZqNZN2MjMzY9KGGOXbTeHGpg4X2UztdttEuf1+/5qUnX4IlZGytbgpEjzcSkR8O6JO4ujoKA4ePIh4PI5f/MVfxNGjR00acCKRMM02VKkAzoanbsK5aHOveDxuFPXU1BQajQamp6cxMTGBQqGARCKBWCyGQqGA559/HoVCwURJNUVqJ4IdOyW8/lRQbrXobBkYGDBldZLJJMbGxhAOh5FMJhGPx9FurzTSI1uNQI+mn2sJo3K5bDIBcrkcms0mstmsGQsVOhVXrVbD888/j5mZGQSDQfzoRz9y1Gvl+Xk/M6NAjVwagKlUytxTIyMjmJiYcLAUyNbjM8LyP3QoLrR4ynz3xdPdWxc+NyyfRaYSM0q0HIk651rOg0BcoVAw5ZXstHxgbfOnnZJWq2Uag0YiEaRSKQwODppGqHQK9DXqdjKVW62WAflox5AVzaAhv8NsKzosnU7HkAl4fIKYW3m++7VWdEjj8The8YpX4Bd+4RcQCARM3VQGYLmGrVbLZBoMDAw4gIZOp+OoBcrf0WjUMK9s5lk8Hkc4HEapVMKpU6cwMzODWq2GbDZrAqvs6zEyMoLh4eE1wDTXQsFX6jUGeKhPeH424ARgmqcxRZxOLYESHoNz02CKz+czWW5cH2XrM6DCdev1mmjqPRuUcc4sJ1Sr1YxNDcBkifF6kknPhqWZTMakpOu9p3obWA1+NRoN44Tr/NQ2oH3gNge/3490Oo3LLrsM8XgcIyMjGBsbA7DaLDYcDmNkZAQAjK9QrVaRyWRQLBYdx7SZfLuhFz3dvfvi6e6tS6VSwdzcnCnFFYvFTBbq0NCQI7OG9rWCl9zTmW3ywgsvdNXdwFoQup/CZ492RKVSMQQa7qXcgzWwTX1AgJKfYzCRn1EQV0FYfod7oIK6tVoNmUzG7J9b0cP9WqtAIGAylF796lcb3R2LxYyOpu7mPLVEioracQxeqp+cSCQcIDx98UQigWKxiJ/+9KcGo1laWnKs3dDQENLptCmbRjuALHUNWDDYS33JMWhvE732bnPQIIHb2tuAvIqNR/Cz62U/2N/XUkrRaNQ8g2Rxs5ycXfJMfV8Sw9gXjuWMiQEpoU6FGByxsoWFBbOOvI+1DLBb5h8xmuHhYVx22WVIJBJIJpMmI5X2RigUMuXjWCK2UqlgYWEB+XzedW10TXdaPN3dX9kSiN5qtYxhNzo6ipmZGVxxxRU4cuQIfvKTn/R1gBeDEGwmkEXnkQ89P7PRzcnjcCOgc3ChmMQcrzKTuOGrM8HPEozs17mVZaWlZPRvnnc3H3wqVEYkyWAjA/3AgQNGgRCI2OzxKXbgpdVqmdRq3ivRaBRnz57F4uIiABinmYpDDaz9BKTrc2VfczdRtmAoFDJGNFP2AoGAuX9pVNPRViNJGWwEuXO5nAHRCcJr9JzCdC9g5dpls1ljBPE60BDtJmpoxWIxEyBhGSCdJ7BiVNJoAbCmZuKFEk+Z7754unvrQofT3hu559hZMVob3E4V3qiEBYCuDk+/58QgG4EDzkkdETLFCSyq00kHk/skAwYExtWJo9NEkBWAg0BA8PFCZ0ZxfsFgEJOTk7j00ksdKdPKBCNjX9lVyvTTMmL8rLLW9Ttcq0qlgvHxcYTDYWQyGeRyOQBwgB8cp5YaANY2jSW4bf/w/gRgzq/XQeuBKhNd9a4yGRV41tT+9djovJd6EY6P9xCdcreUb2ZCAkChUEA+n3d8XgMLpVIJS0tLPQE//Aybj7mNkeL27PLaBwIBYxsS2Nf1IlPd7/cjk8k4ykB1W5tu59wJ8XT37ounu7cuJLx0Oqvlp4DVvg3UYdyPuDdxD9FMZ5JRuu0XbizhnRDaI61Wy7Wu9EY+nPp9NoDK7yqhy95ndc8niKnZZRdKqCMikQgOHDiAyy+/3Ngk1Mm8rnaQoJsu4jz5N7+r5dxIGGSPj0KhgIWFBSwuLq4JXGiWmtt5bT2iOAfnwM+pfcXXNms/6TXj+bplMfAzGlzeSGwGOvW3NqTnHEmK42sU+rY2eW1xcbGnMdCeZTZ+t7l1mxP97lAohOHhYSQSCRPE1yAD7Uafb6VUWyQSMa+7ndPT3ftbtgSiX3311fjhD3+IY8eO4dprr8WHPvQhBAIBfPKTn8SxY8f6PcaLQqjUKpUKzp8/j3w+b5g4wWDQRLS40fA7CpyT5TozM2MiW4zI7SRbbaN5KbjAciJ0BOyUHU2b2s756Lwqw6parZqa82Sh7yaA7vP5TH3zcDiMyy67DL/wC7+AZDKJyy67DIcPH0YkEkEkEjG1+foNVmtKVzKZxIEDB0x6cSQSwcLCApaXl829Uy6XHQx+grNbSbXebaEy5fjdHHZg5brEYjFT+uSlL30pJicnkUwmMT097WhO1mw2TT3xxcVFLCwsGIVNUEGBdoLsdKo1bW+j+05TRNXw3ghY4zWmMcLnTA1eG0gne5KGnicvTvF099alUqlgfn7eBK9YaoF1rDudjmmwSKCvXq9jbm7OZKIRSF1YWOgpaL4bukuBTy0LRkdM9z11vN0ChBTaLGSa85ilUsk4Qsrkpt7eKBC6E6KAAcHo6elpXHLJJYjH45iamjKBRzfn1C3lmvcAAMO0UraUnpsAqYIVoVAI4+PjSCQSqNfrCAaDhsVUKBQc5XAqlQqy2axhWnOsDMQqa41Ov9oeqnP0OxynNjjXciqBQGANsMDf2lhXned0Oo1IJGIyMdj8er3rze9Ho1FcfvnlGB0dddR0p17j/VmtVg1rm2nexWLRsMbz+bwp+9Jutw241i/pBqDp9VUWoTZjtYkfNrjRbZyeY3vxi6e7ty71et00JKZwnySgbtvK9XrdNCjU7JKNQLvdBMNs3c0eGdxjeskA133FLgWiosAtg+oMEDOTebcJaxTOdWBgAEePHsWVV16JeDyOiYkJ8xk7UGDrXDdRv4x7M4PQtIUIAtN2CgaDGB0dRTweR61WQyKRwNLSEprNpgnW8nsM4rLJKOAkMDCowwCqBp01+GOLAv2cr70G9jzt9xXY9vv9pnxduVw2pUaZrdZNaAdHIhEcPXrU9M8ZHx83OAMz8wKBgKltrr3EuNa8FrQRSUbpl2wUeNJMO8VZ7ICDBkmUqb/Rc7EbgTdP+i9bAtHf//73o1wuAwD+9E//FP/hP/wHvPKVr8TIyAgeeeSRvg7wYpJOp4NisWgai3IDjUajpuwH04Q1YsqNpVqt4vz58/j5z3+OWq2GXC5nNrEL+fBpRJxOIRW61tgEVh1VoHu36G5Co0HTm1h3mo4TGfraTGs3hJt8NBrFJZdcgnQ6jV/8xV/EiRMnkEgkcOjQIZOGqUy1fgud906ng5GRESSTSTSbTUQiERw5cgSzs7NoNpuYmZnBuXPn8LOf/cyxflRs+wVEZ10zGrrdQPR0Oo3p6WmMjo7i//v//j8cO3bMMLQ1ktxqtQxwweNnMhnk83lTl1YZeFrnbjNZJfyc1m/V19cTPlMEDzQgo8dR9gP3lWazuWeurRcR333xdPfWpVQqoVqtYnBwEAcOHEAoFDLp3YODg6ZxtDJOyuUynn/+eczNzRknnNksGzniu3V/c8+kU0yQm6AjAOOsEPzbaL+jc02nMhKJmP2U32VjKabZagNmYPfYtRqYDIfDCAQCuOKKK/DqV78a0WgU6XTaZHe51bCn7qc+oEPPWvkcPxlqmqVEx1gZkAzGHzp0CK1WC6FQCBMTE1haWkKr1TKlcKh72OSMAfpQKORI09cMAVs/cH1tfaZOOJvo2tfCZqUpyE6ghfPi2o6OjiKVSqFWq+H06dMmILDeNWbpoOHhYfzKr/wKrrzySlSrVeRyOSwvLxuAvtVqIRwOo1arYW5uDqdPn0apVDJ1TjudDrLZ7Jr7qpeA92alm0NsA+gEvcrlsrGNmWZOUcanm9hA+26Ip7t3XzzdvXWpVCqGIAPA7KsATHkP+gK0qWu1Gs6fP28IRwy+9cK23q37m7qIYKNmEqntbwPFdmkPt32df9uZTUrOIXmI/v+FIvMNDAwgEokgGAziJS95CW666SZEo1HTcw5wLw1i/9hil3PRjIR2u210HH054h8HDx5Eq9VCMpnEpZdeijNnzmBxcdHYRQx0MMhLsJkZVNrHQ8tz2iRE6naKfa1sna/zItDb7drz+Gwgz/JppVIJc3NzKJfLpvG1m9B2iMViGBkZwXXXXYfLLrvMkeWmgSDtZVcsFpHL5UxZUzsAQn93Izt6K+J2PNprGlRQXEvve9qCvGZ6v+wF8XR3f2VLIPprXvMa8/exY8fwzDPPIJPJIJ1O7wgweDEJHRUAJi0MWHG2CYBq3cxOZ6XsS7FYNJ2ECRoyArkZAG6nRCNvOn5u/FrahZE7jfy53TduzoGmEbuVcOH/yggDnE1k9JjdWD+bFQLPoVDINB5LpVKm1jad850Cz21Rp9nv9yMajZoSAqlUyjQso/OpUd29vknqvUAlu1EZF9bE0x865zb7QpvF0GDR+ub6o9kOu6mYlPlop73p8WxDZS9dW0+Z7754unvroqUrbF3DDB4Cl3zmyLCm80QHfC/euxqkJmOeQB+wFtRWENIt/Zf6Vz/HdQBW2WC2Y8pzrWcbuI1jq6KgL0FoZi2x+eN6bDUtraL7rLL7bOdXx673C9eM7G+/349IJGLqhbIsFwEhu56n6gHbsVNARAH7jdaZ47THzmP1egyOifdYr0QHbQIeiUQQjUYBwNjOWg6HrFJmdaqTux4Q3W/pdk/qs8D7ntfD7TP8f6/tF57u3n3xdPfWhXsOALOXAjD9uwhWKqtYM5s1yOvG/L3QonqbosFRtz26W/BN9bYCrUoWcvM/1gPiu513u/sxj8nyV+FwGIlEwvQc6zYWFd1v3dbJ1uv6PTedQpIT8YBOZ6VOejKZRDKZdNxztCW1lJ7iIzaGYa+xHRiwbQ9+3x7jRtnImnHI42gGInuRbVQ2hqVNqLej0agj2019V86PpDASDOzSpnbZun7KesfTNVWci2NSfU2gnfPsZZxeAHx/ypZA9N/+7d/GX/zFXyAej5vXhoeHUS6X8fa3vx1/9Vd/1bcBXmyizhgZRIFAANls1mweTJ2lgmK6JxnDbKTg9/sRi8XMQ8vfW23KtVXRKCI7lysbb2hoCJ3OCvuOqXI0WNycOT2uKm8aCgQzGQVvNpsmyKBp4UyLHhgYMPWrFBRQw6hcLqNWq21p/mw2EY/HcejQIVxzzTWYmJjA8ePHcejQIcNy2C0AXYX3WiqVMoyLYrFoOpUXi0VTm/f8+fMmms9rs9eMc73XmFbGsjTd7nm/34/x8XFceeWVSKVSGBkZMfeCWy26UCiEoaEhU8s+FAoZVgqdXo7FTmffaeFz1Ol0zF7A7IFIJLKmsSjv70KhgEwmg1Kp1NcUuO2Ip8x3XzzdvT3hfsiaiqp7o9Eo5ubmDMOIJcWWl5eRTCaNrlIG914QZVKTEcuyK4FAAKOjo4Y1zrGzliwAB3uce4/OTdnsmUzGOI0sZ0d2tgKqyjZSXaTMMNoE1PdbER6fOvyqq65CKpXCpZdeimQyiWAw6GjYRseOosF8gt9kkzG4Eo1GTc+NSCRiCAYsgce1ZyZAq9UyjdEGBgZMRlkymcTy8jIymQzOnTtnCBYE0JVMoLotGAya0itcJ66/2pkKmChYpPaUzo+6UNnTDGhr3w1eq2q1irNnz2J+fh6zs7MbBr95H8TjcYyOjposD86VdpxmPNLOGhwcNE3HfD6f6UO007JeYEdtl1KphJmZGRSLRYyNjWFyctKUouG+webF3ZoXup13t8TT3bsvnu7eumgAsVQqGf/5/Pnzptkh92bu3bSbqct3u8zYZqTRaCCXy5m5EEsA4MAVNBMGcAZE7cwkfZ/ZMgqs0m+22cIa1CQYyr+VUEedsF7/p/WE9sng4CAmJydxzTXXYHR0FMePHzcsdJ2XzskOXOt1tbEJnstNmLmluEs4HEYsFoPP53OM41WvehWy2Sx+/vOf4/vf/77BG8hKV8DcjSWupDi9fko8UOkG8tugug32ct58FnjNstksCoWCaW6+UQNZv3+lgTZ7wQWDQQdRgDYd58U1n5iYwGWXXYZcLoczZ86YoIPOy64Nv5vCTLdqtYqRkRFMTEwYm5/zo81LIko37OdC7Cee7u6vbAlE/8xnPoM/+7M/cyhzYCWq+9nPftZT5l3EThsql8vGUF5cXFyj4DR9RJnn3OjImmK0jim8W1VK2xF1wqrVqqOMCxm9VDTAqqJi6RE3ppcqOBoxrAXKNWG9agXUuZkxRYpNnOiUMkjBlCE6tFtlYqujx0YmBw4cwPT0NMbHxx1pPxdCGGyJxWIIBoOo1+uYmJhArVbDs88+i3w+b0DWRqOBsbExRCIRtNvtPVP6g8Lngdc/n8+bdEI34X2VTCZx+PBhJBIJUzapW5obmei8pn6/H2fPnnWwKC+UqCPO+5+pdmrUsBQD7+tKpYJCoWAaEXvy4hRPd29d1BGnPmY97+XlZYRCISwtLcHnW+l9Uq1WEQwGMT4+jmg0asCzzTZ92mnhHtnpdIxjRxAhHA4bRjYABwjL4LiCyG5Cvco9iPXQuY+xnIsGKBWwZdBdA+5qG22XHUibKx6P49JLL8X4+Li5Ztprwg3IJyDOfiJ2Rt7Q0JCj1w1L9ZEIYDOTac8QrPf5fEgkEqauaL1ex/DwMACYfh3FYtGRTs/vK8OeTCmOUcvN0Ga0MwSVlFGr1cz4tR4u10DrsNPGo52lDMaFhQW0223k8/meUrF9Ph8ikYgpA8M6srxugBNEj8fjCAaDaLVamJiYQCgUcpRw2Q1Zb05cC9Zsr9VqJmOR9xKDFhqkAjZmD3pycYunu7cv1N30EbnvcZ+m/1qv1w0JSzN+tgtA9UtsVjeJZH6/35Td0OAmAXXqOgVruYfzdc2W5/vMXGYGFMvRkqWvvof6/Moqps+lwWLVF1tZAzKdR0ZGcPXVV+PAgQNIpVIIh8MOQpFNNNRgJ/dXJQIwo6nbtebr9EVZlo6gM0sD8Z4igY2B85/+9Kdmj6fedmPou5XgoW0EYI3dYwPvdjaaHod/0zbQMdB2UIIFbVfb3ljv+sRiMUxMTBifm2uv5WS45rxn0+k0Dhw4gHg8jkKhgLNnz65Z+wv5DDJgxTK5Gmzheijp0w3bUtkL+4knW5dNgeiFQsHcwMVi0dQUA1Y2ky9/+csYHx/v+yD3u6gjY0eENR2EwjWmI0bnhgqDDR64MQNw1AkvlUrGsdtt1imVEoFvGit0sujIEcxWAN0NROfGTgBT65KSvaasP56r0+kgkUggmUwiEAhgeHjYRIfp3DFiTmdwaGgIzWbTKMONhOMmSD8xMYGRkRED2JPtvBfY3FSuDL60222kUimMjY2ZBnha84/10ffK+BVsoJGrQNZ6iohRbmU0dpuTGiEa9d8La0Ahu4A1hmmkJZNJBwOQ17LRaJisi43YA7spXkR898TT3dsXgoX8rWwrijpkfE7JNAKAaDSKTmc1dVwd80ajsSbVdzczXHRfsBlaypTudDprSok0Gg1jn/j9fvMav6u/7XXSUi48H0uq0E5wy1ijjeT3+9c0deb7G8nAwIApuTY2NuYIsiqjiNePY9HsOA3Mq13HALTaZHQ+eW5glbmnIDfXlWtN+5HBjEQigXQ6bWyWarUKYJXVxkCwzdznOvPaENTQtVJ9SX2ogRatWU+dqver6k/9rTYtwYZeUrJZIo+NRDV4oteBa0ZWmGYa7jVdQf3NMZZKJfPc8FppkKlSqaxpSnshxdPduyee7t6+EMTV/VnBPLc9TH80AKp7nq0DNTNpJ8Xt+eFYqaf1c3zdzcdutVqO/hUEuJVVq3utZj7R71adwJIqXHPqLgXp7awolubstfQFsKLb6OMPDw8jHA4b/awYCq+xrePs8yiwTFHgnWsFrOpuO5jBNeU+rk28qZMTiQQmJiZMg20SmyqVijk22evdfDV7Ljrubvq4m7gdy36d9mmz2TTXqZdrxey7cDjsGJfNLNfXlQy6V3xVwBk4IdmEZBmS1vQZUaIn7ZC9IJ7u7q9sCkRPpVLG8L388svXvO/z+fCBD3ygb4Pb78INifW6yIpWcFU3YXWktd4zo8uDg4MYHx/HsWPHDIBO0IzfLxQKWFhYQKPRwLlz5zA7O7urzjg3GG6YjFw3Gg2T0kzW1tDQkHE+tX4lhUqr0+mYjZsgN9Ndy+WyiQLTkSeD7MiRI6aBZCQSWVOTXA2BmZkZ5PN55PN5PP/886be5nrCmtpksL30pS/FwYMHcezYMYyNjRkG2l4BYAm2TkxMYHh4GJVKBfl8HktLS6jVajh37hxqtZphSMXjcQwPDztAogslVKb1eh3nz59HLpczJXzWY7TxevN+20xWAMEylkvZK0IAolAo4NSpU6bcDPeWZDKJSCSCSqViGhJnMhnTqGWvMNE9Zb574unu7UsgEDDOoTKiyY6lTqIhDazoGPaeYIPpoaEhU3ObOrLdbmN+fh6nT582TFQF4nfyXud+AqxtFmkDlMVi0XyezKJCoYBqtWr0xdDQkEn9BWCYaMrKUTag9jChIz88PIyjR4860sE5LgBmX15eXsb58+dNRtLS0pKjLMxG6xYMBnH8+HEcO3YMo6OjOHr0KJLJpIPtThCZJbN8Pp9h4FGnavASWK2L2ul0kMvlsLS0ZMbDNWDAgRlzAwMDhvRAxpemOjNtnQwuNiVrt9um6TXvtUgkYoIQvF/VuScwwjWgHaZ9QBjk0XXgfc5Gocpo5GeURadBeJI8mApOYEWfF1t4Lxw/fhyhUAjBYNA48ixDx/PQTqnX68hms1hYWEA+n+/JltttWV5eNqXV5ufnEYlEjG1CIIvlHufn500pOU93v/jE093bl0AggGQyuaZMpfrdDMTaweRWq4VEIoHJyUmEQiGkUinE43ET4Go2m8jlcpifn0ej0UA+nzfl3nZLFODj/so+UCyjwf2efit930aj4VpqDFgl5xHUJDFHm2PrerH82NGjR42OZoCYOl519/z8PPL5PKrVKubn502GVi/BwkAggGPHjhndPTIyYnpdcVya/W9XArDJVApCE2TXUnHUb8qq537NoC11bKlUMqXcGARn4+/jx4+bEr3f+c53TIk7NqiPxWIYHR1dgx1oQETXh+MmWK92m9ta8p5XkN7ej+3/aWMQNGbwZL193OfzIR6PY3Jy0gQ41JZQ21azJRhULhQKPTXx3S3hmOv1OhYXF42/zUxB2mjNZhPlchnNZhMLCwuYnZ01z9leEE9391c2hY793//7f9HpdPBrv/ZreOyxx0xaKbCyoR05cgTT09N9H+R+Fm7UBNC7geia2kOho0PjemhoCLFYzJTb4PEonU4HoVDIsHWZWr6bN70qVm4aZN8Dq00z1Hni/92Y6GSYMcJOZaVNJfk+wdJgMIhEIoHx8XGz9jYYzHVhyQuWnHGr6+omdNxooI2Pj2NkZMTBmtorADplYGAA4XAYwWDQ1AcnEEAnmeAA695S4V5o4b1VqVRQKpVMMKWXaLVtQPUqW/3eTouCWmw8zE7pvL5q8BI876WR226Jp8x3TzzdvX2h86QBNeoMOiT2fqTMrUQiYZpfDQ8PI5VKGT1Jx3Vubs4Bnu+WqINmiwLdZFJznvy7Xq8bthGdZttp0Pkw2O62B9iZNerU8hjBYBChUMgRVKeDqwzn9fQ4geJkMomxsTHT14TNr8jm4+cAGFuMAQE3NjbgTJkmQGOXuNMyJGTj6bEYXNFsRALX8XgcqVTKlByg7UQGFEuucN15TZQppVmKdkaFDTZooEPHabPy9RrbelNBeAXaewl0xONx46jqubT+e7vdNuCGnX2113QFr2+ns1JeolgsGt+AYAPt0r3IqPd09+6Jp7u3LlouQn1AN18TcAcTO52OKRUVjUYxNjaGkZERLC8vm9IWPp/PZJNUKpVd97t17NyraZvYmWDc6xUcB2ACqMo6535tM9FtcFMBZQKLxCe4Z5PsFAwGHf4Jhbqx13Vjr7Px8XHT80tLl9nAud3PREXvBw2wdMsq0GwzrrPaKdTduubEQBRYZpk/Yja0DXi8XkVLvXCOvdyDbraX2/t63XvtD8A5s88ax6Ygutp0mvnAzI69wt5W0ZJrzNpvtVqmBBBtedp9DLTtFfF0d39lUyD69ddfDwA4deoUDh06tKmH/MUoWguMikOZVbp+ZC0NDQ0hHA47an2Gw2FMTk6aGs00tG2GFplErAmWyWSQz+eNo7mbD7Juhu32SsMyNhYlY5zp31RwuiaqANrttnEiGOVTpr5GW0OhEMbHxxGJREy6czf2Mc/BeqhUholEwrAT1iuHEw6HMTIygpGREUxNTWF6eto0rdyLADqwauz4fCv1yqampgw7fWxszDSLYfo5m9Qwi2A356QKtlQqoVQqmTroZHFtBDTxGPl8HnNzc0gmk0ilUiYt3i5Xo0GgSqWCxcVF05BzrykPBtZYeoAsWLI+1fEOBoNIp9OmAZAGqPbavDzpv3i6e3NCx4sMIgaCbTAVgKOWKh0glpyi800WuzZ5ZAkOPoPMaGo0Gjh//jwWFxdd62XuprC5Gh0D7jFkL5N9T/bQ/Pz/z96bB0l2VWfiX2blvm+VtXV39SZENwKEJUaWwBJgEBZesAxGE/ZgY4NiNMIsaocdyEaBDAMEhlH0ECABMRiZIQA5gtFgAmwQHiO29ngQu9ZWq9VLda1Zua+Vlfn7o37fqfNuvazKqs7a1HkiKro66+V799133z3nfOc758zA7XZLg1Luoxps14xofV+cI4/HI/rYDGLSEdOfcw/k89GOZKd58/l80gBMO3kMIOuMHb/fL2AAbRWWE6P9YNZsp2jmGtnnDFBr24f3RJCHeoh2CHvncM2x3rpufMrABNcLAxoMbnD+NBMeWLaD7NYZP6dTTya+ZrNrp1gTPggasIYobQtdAmA1Fjqvn81m8eyzzyIYDGJ0dFRqo5N5x6xMvU5MBv1OFI6XzvbCwoKsJU2m8Xq9iEajFhAD6Duzl4r0dff6hO8QCUMkVgFYoTu4H7JMKkG7ZrMJj8eDZDIJv9+PaDQqfjfBc4Ks7fZSU/GxsTF5N1lydDtLUiwuLoofp++dtbl12QnqGnP/ZBDSDKCbew/9C5KzdPBb21LAcgmxVqtlyc7n72uBpz6fTxpvs+E0Gd8ALMC3iZMA1gx3BqcBWLAH/qsJE1r/cz3pIE0gEJCMLg1o69I61OdmlpsGlOv1OgqFguhu6unVhPehwWmem+Mm9mRm/tkJP69Wq9LHhmVJug1KMzPu7NmzCAaD2LNnj2Q78D3R9ogmlJZKJQlQ7TTRASM+Lz5L3fi81WrB5/MhkUiIDcdAUl93P3dkQ3UaxsfHkcvl8O///u+YmZlZoST+6I/+qCeD283CTYvgo+7SbFceQ2/gunHTwsICQqEQ9u3bh3Q6LdFLs7wENyBuumSpVqtVqR291dEwXfuStTp13VQ6vrxnrXT17wQI6YSz+ZZOSQaWNv5AICDdoGn4rMYkppJhHXOHw4ELFy7A4XDIJm634ZENNjY2hlQqhf3790tXcBpuO1W41qLRKPbv3y8dtM+fP498Pi/NRpnmx8Yt+hlthejIdDabxdTUFGq1mjRU64atqb9/5swZJBIJjIyMIB6P2zojmjlaKBRw4cIFzM7OSl3KnSSsnccsgnK5LBHyUqlkYTH6/X4MDg4KsK77C2zXffUj4lsvfd3dnVAveL1epFIpBAIBC2uLIB6zw9xutzgcjUYDmUwG9Xodbrdb+nHwhywwlpkgGMh9eHFxEb/85S8tgON2OeLNZhOZTAbz8/MrHDMGndkgnfqZoDGDBYVCQUpvaFDTfH/9fj9GR0ctpUhMh8VkmBGwJTDA4DwBjk7CZpWhUAjhcFgC3wQzq9WqMAojkYgEUnjNarWKUqkk5+J86CbqvL5mQZItx9+B5QbyZJoTsOBc6WCxdrSZks9zM2DBdP7FxUWxr8y68iRrAJDgNLDSGednBOVZHoCl4LQzTjtOZ10CS4BSJpOxMLi63ftbraUyR4899phkb9AW4fpiYLzValkaZ+tAxE4UEzTgnHH9AJDARSqVwsLCAjKZjCUjYLukr7u3Xvq6e23h3kadQFITM1OYpaN1h/Zn6Fdyfx0ZGUEikZBGzAQAyT7neZghzmAzyWtrBQk3U5rNpjRV1j613++XjCYN5FLf0bZhqTZduqWTzxUIBLB3714Eg0FLGTEd/Nb+FoFsljxhs3HO12olOkOhENLpNCKRCEZGRjAyMiJYAgAJXujnynvl37VtYAK5Gi/QJbb07yYozzIsnHedBWYSl5g9r+vI81g+B1YgSCaTXZPXTJa3vj9NBiGGosF2jkNfh0GY2dlZKT9DUuF6dPeTTz6JRCKBZDKJwcFBS5afDmBwXlkeKZPJCFFhJwnXCgMS1WpV7sntdguG0Gq1EAgEMDQ0hEajgdnZWduKE1stfd3dW9kQiP61r30Nf/iHf4hyuYxwOLxis+or8yXhxqA3iLXAVZ0mRMeMwDOjtasBmfw+We2MnG8XK1o7WHz5zLp0VHDcUE3nWbOe9L/aQaIC0MAijaW17l2DAnRwdQS7E4jOSDEbZzBgsluYInquWK+NBhONQabb6XR9bRBtxrrSBgej9kxpNhvZdHs+1qkjAFCtVsUR1+wLAhGMvOt0rJ2iPEymhJlpoedPH0fGOhkJmnmyHdJX5lsvfd3dnWjHU+tkzaCmE2h+z/wO91iymQkI8/3TwDD1jq4lut36xNxrHQ6HOKrUw3SK+Rn3F62713rfdWaN/r523E3n1XQadWmUTrobgAXoZ61tE1AxHWpeUzPH9Nj1jwk0aB2jbRa7cXF+9fF2z0Bn87H0mmbi0+7TbHHTttI2Ke/LLG2g74VrWt+PKTwHgRDqbM3QWo+Qqe31eiW4odmQmi3P9Go6thu53laIdsQpej1r/U27FFgGdfQ63A7p6+6tl77u7l60/rYrFaoBR73P671RZ5FrApH+HkuV6H2U3wFWlo3ZajH3buoDzU7nveixMpCn/exOJW94PO/bbr55DMekg+mmzbSaT6J9GfrcDIBrn9TU23rMpt42n73dNfW/5r3wcz5/M3irbRiNgRBn8Pl8K+ZZl3KjLdMNgc20l8z7MIMZdt81/W8CxBsJSNOP9ng84kub5Vw4j7RxyNjmNXeaaL9b24LaDqHQpmWgRZej6+vu54ZsCET/8z//c/zpn/4pPvShDyEQCPR6TM8Z4WZP4LubEh96s/R6vQLQ8qdbJrDDsVxXVBvh2yWm8+lwOIS1BlijwWYzLYKgpuNu58RSubJmfLfCOSWzkA6ZnXCMbBTHlDLWcN3JLHQtTJsHgOHhYezbtw8ejwenTp2SiPjExATcbjcqlYrU7YzFYmIs9bJsjXa+GQWfn59HvV5HJpNBPp/fUHMtRsSbzSZisRg8Hg9mZmYsNfN57VqthrNnzyKfz+P8+fM4ffo0CoWCRO53gui1znIErNdIhiPvSSt0pk+GQiFLrdXtMlT6ynzrpa+71xYClARzNSuYoBwbB7ndbmnqSwYzdV0wGBSGWjweh8PhEJ2kG4fq2tvUkU6nE8lkUsrC7KS01nZ7uXQUx68dCP5dO4KmaACR39EBB4fDYWH78Zmw/jfnmhlDLPEWjUYRCoWQy+VWzSILBoMYHh5GNBrF+Pg4Dh8+bLm/UChkaX6p9RwdVH7G0jCmg2oCuGRit9ttKfGjj2fwhGuAPS4YSKFNqEEen8+H4eFhCfgSaGbjTdZZZc1O1hYn0A5AmrHzWZHFp4ECXfffJC/oNHE2eOd5FhcXkcvlJDi/XkCb7PLFxaVGqw6HA8888wyi0SiGhobgdrsxPz+PbDaLSqWCU6dOYW5uDsViEVNTU6Lndoqu4FpmiRbWjCUQ1Gw2pfwR3yuyHFk6iSAYa7Fuh/R199ZLX3d3L9TVulEzhf0gCGjpLCcAQo7SBCm9H2o/lt/R2S8+nw9jY2OSGWqWBNXksK0E0zhmMqF5L/yXOkYHxe1qgpv+nhmUBWCZV8AKVusa67rkja4pvVoz6GAwiJGREcRiMellYo5J70/m/Op7tSuva5Z74fc5DzpYoOeD39drTY+DzzwYDEr2wuHDh0VvTk1Nif6krqNeZTP61Uq76MC7CVTbBUH03/gcdMlcli2i/l2vrmm325KRkclk0G63cerUKcRiMezZswder1dshVqthomJCeTzeczOzlrmYqfoCq5fXXaQmXnanuVzJoDOf5klx/Xe193PDdkQsjoxMYF3vvOdfUW+imgQWG/U3X6PGyedSrKlumFW8zzsuG3HmNtq0S+uHauK/2oQnUx6HRm1YzNxzuiE6uj0egBensfv90spk04Gw8DAAEKhEIaGhpBKpRCLxRAKhVaN9O40YYoXACQSCQwPD0stQIfDgVqthkqlIk54rVaTskRcT71aV1qxU9HkcjlcuHBB6u1uFMhut5fqqhYKBWn6WiwWMTg4CKfTaWEClEolPP3005iamsL09LQo852kOLj+mJIfDAYFyCEz0QTRdfokj6Wi78ulI33d3Z0QRHc6nWLoaweRfSNYUoKlNQh8EVwNBoNSgkJnUWnHXYPQGqCNRqNwuVzIZDLbORW2wpR2YNlB1gwbliQhI9oUBva1g6fZ4ZxLimZ76ZRkOnzlclnqz3N82Wy24/iZ6h+PxzEyMoKxsTELMEk9z3vRJTQ0MKAD96bTreeD4AGdU5fLhUgkAmBZ9zGI4HQ6LWnTen6YocDfWWqN+hEAyuWyAMssXwAsgQBmw3OCtARPdG1u/bnJUNP6kIw6NgPjWDKZTE8yuMhgY2kBv9+PvXv3IhqNYmBgQJqMFwoF/PKXv8T58+ct79VOEr4rbrdb9DazIYDl/j+cZzLYuL/4/X4JqnUiefTluSl93d2d6P1XB5+5p7PcFfd3gun8rs785r+dQC9+T9c+9/l8GBwchNfrxdTU1Irv0H432bibLbyO2TSbPzrzmvu+2QSaYmZB6MAupVPWltapBG4BSDkWzfC1EzZmZ3mvaDRq2e81RqJL8OnAr64Tzuvq8ZkgtJ4LBkH1nOhrco/X963nmfhEIBDAnj170Gw2kcvlpHwfA+DtdlvGRltyNTzDHK/OatIZFGaAgT8s9UnyGoNNGyVwtNtt6WXm8XgEP2C5XQDST6dQKOCxxx7DxMQE6vU6isXijmOha93NwDcJa3Y4EQDxD4gz1Ot1wVb68tyQDYHor33ta/GjH/0IBw8e7PV4nlPSi4gPlbfpuHQDDmvg2WRta+BZK43tiJBR8WlHTbOeAFgcuU7n0ArkYubdTBHXoueNYKTf77c0FNktolPYmPVAp44p8Vx7bIwBQKLLdP5Mtp4JzNqltwNWkEGX6mHEls1Du+0GvppwTS0sLCCfz0vfAYIgHEulUsHMzIww29Zad1stpvFqsiDsPtelEHTAarvXaj8ivvXS193dickgMut7er1eATapqwCIPuXeqUuPsTGguRfyeDquAASQ7NSYbLvYbHaiHTLeh/6b3dg0M0ozw3gOHYzmOcz9iw47a9LrIPtqNowmOJCwQHYeHWUNBpCdZdpJZBmRKMEsKc2+p6POOdBZieZ8mGwxE5wgU1kfByyvTzL0dfMv6laC8sViUTIoyPjXQRCdPq6vYTLWdI1hXoOfa6JDL9clgRKHwyGNwv1+P2q1mjjeDIJs9zuxmnBu2SdI9wbi2M310cmG2i7p6+6tl77u7k60X6P1Bf/VpQxNO1nvg2bTZL1H261ffs7yU/QfOo1Rj3Un6G/z/u3Aby1aP5mlYABYbCbze9rvpH6kDl3NL+Hz0z03tI+mQWuTmKDHpzPf+eztdKt5n3b+LefGBKbt5lgHr/W90N/WLPh2uy1ANrDkd7NZPQMNtGEAK25hkgxNHU3AnELbhT96/fdCtL7L5/O4cOECAoGAZGqQlEKbd6frB9M/0Jl9Gs/iO0WsQQdTdsLYN/r9vizLhkD03/zN38Rf/MVf4LHHHsMLX/jCFZG83/md3+nJ4Ha76E18PQuPx3JjY+RYM4S6OQeZVXTsgOWUYTb0CAQCwn7hBstU7a0UrchNp1x/3klBcbxURNy01it01NgAyxQqfqbjDg0NIZlMSimN7d4g1yNU4m63W+6lWq0iHA4L44kgDpvDuVwuZLNZSQGPRCLCAGTnbV0Xz3T+dJScDj9TtUulkoDcvDbX4sUGdrh+SqUSTp48iWeffVYYhGSicGy6oR/T73ea2IHp5mfm8ZrNuVPW6E6c2+eyXIq6226tr7XuqFOob83SG8xe0c0SySRut9soFovCjibIR4Zwq9VCJBJBMBi0OEpkIQNLbOJsNttRDzGluNVqSYmH7RITZNCf638Bq3NLQJT2CEHiZrMpPTqA5UZdOjOMbCAASKVSopeefvppaZS52jPWbKJarSaNU6kHdMNm1uHWf/f5fNIkloziSqUiTVbJcGMqrw7atlotAbBNR58lbMhAbrfblsah1IfU28By1gTB5UwmI8Eajl87sMy2Y2kX2oF0sk3SBZ+hw+GQYEOxWBSmuQ5G0DnfDL3Zai2VdnE6lxq7zs3NWXQ3A/Db+S50K5osEAgEJEOA869L+7AkzU6Tvu7eWrkUdTewUn+vte50YFMHGDW4q/1KXoN7LAk83LPpT3JP0+cy/eRWq4VMJoPZ2dmO763JRNdZXFstei410UhnXpnH6gAAdZtm4zIwaAYw9LX4jKhD8/m87N1r+e0+nw+xWAyRSETK7Zm2h87yI5Nas4O1buY5eH3NTtf3xM90/XUd9NaB5tXmWv/r9XoRCoWkVjjL4PHvbBTudDoxPz8Pp9MpzVtZjlXbkXyGdgx5lnkrFovIZrMrsitZPoUY0MUS17TQZiBYPjU1ZakPTr9bN2HdicJ3lbYrS+wwW1/jbATPGRSwCzhtl/R1d+9kQyD6rbfeCgB4//vfv+Jvmk3Vl41HfbRzpSOU6wG/dE0zHTWj08MUIT4vRtO202A3DQvz807zqSOwGwlc6POYDAQtmolOEJZM9J2wOa5XqHDNe3G5XBZlziCOw+EQh1yDILpsEYM8mqnJ8+j5JYhOhzKXy0kZl3q93nPDktfO5/OWe9d/57h3stixa9Y61u7/2w2k9yPiWy993d29cH3RaTIzbehw6gC3Zk/zczJztXMSCAQsDoyZcUa2kB0TnTqcZU+YqaPHvB2ymgNp7jtaz2vWIOeAekWLyXajHg6FQgKGEwRZzQG0Y6Jrm0fbHnyOdNT5OUtmmeX6mI4NQIAFXRqFZTnsiBC8b15XlxvQGQ7MAuP8aGY7gRvN5tLMfN4nyxnwHpiNxWM5bjtGFQDL+tRMrE49a3oh1N+8frFYlOel36XdIJwrYGnMDNJxznRPhvXa/Vshfd299dLX3d2J1idmBpk+xs6f1CAZ/RStnzv5phQS0kgIsqsnrvdLu14b2yH6HvTe3cnX1nNqknJWY9ua7F2SAWq1msV+Wk1YGoPlbe3WPXWWBjb1eDSgyWvSlgKWS8qY98TvmtfSAfbVnqM5r9TBmg2vsQtdk56Z4Cz1y2A8r8nv2gG1OjuOdigDFk6nU+qS62yyXu7RWt/V63UUCoWenXs7xATM7WxTwJqduVPwob7u7q1sCETfLYbqdgoNfm7KBB7XikTpNBrNDJ+ZmcHi4iLC4TASiYQ4YaaiInDeaDRQKBSQyWRQr9fh8XgQjUYxODiIgwcPwu/3Ix6PSz0xKv3Z2VmcPn1aakxu12anFRP/r/+1O54OdDabRbvdlsZNa21e3OgWFxdRrVZRKpWk/qZ5PSo9ljJhh/Dtrjl/MULAgvfi9XqlUZ6d2BmfBMQBoFQqrXh++nu61AEBDzZBM9MoN1P0PewWxaDnVO8PXq9XUv1ooGvDjuCKbii6k5q2bJXce++9+OhHP4rJyUm84AUvwPHjx/Frv/ZrHY9/+OGHcezYMTz66KMYHR3FX/7lX+K2226zHPOVr3wFd911F06dOoVDhw7hgx/8IG6++Wb5+3e/+1189KMfxSOPPILJyUk8+OCD+N3f/V3LOd7ylrfg7//+7y2fXXPNNfi3f/u3i79pJZei7tZOXzfrnXsUnQxzj9BOW6vVEjaTXeZNs9kUfdRutyXzResM/q711MLCAiqVigDwAER/s2E4a2rzPBcuXMDjjz8uztZOEjsWG/+lw8agA+0VsrKZ3USHWTsw7GNRqVSQy+Xkd7MWLmBlKPr9foRCIWGRE9xm1h57SnAP1bpQp+UCkICvZm9p9pEJwBAQ5xjtys/oADTHR1DG4XBYmpuZGXDdlCBjCZtWqyVZEMxcNPU+51oz98i6p4NIUEqXd1nrXVvPO7ma2H1fAzkbtSV0mZte6EkzoKKbqlWrVUujOoJxTqdTslF5LPeFS3Evv5TlUn3e6333uLcBy+9cJ2BxNZY7GyIyyMXSldQh3OfMvVs3AOYzi8fjGB0dlQymQCAg2SZOpxPnz5/HL37xC1QqlXXda6+lGyKRLm1CXVyr1aSOtdPp7KpuP3UPsYr5+XnR33bkNc47mdjBYNCSIbiefVr7otTxOku/k07hdTTBjHOl50zv5fp7ph2kfTX6vmsFw/T64v0Tw9E4Uyep1WordLcmH/SDcfZCW5TrneQLNimm6DVBJjrr3LMnzqW6lz8XZUMgel/WFh154+92Dk+n79EQIDB54cIFFAoFDA8PSxqT3TmazaakAGUyGUxOTsLpXOoEHQ6HcfToUbz61a9GPB6Xzta6hMVTTz2F733ve5ifn8fp06dRKpW27YVfz3WpiBhwqFarcDgciEQiq843v8s0JjbVyGaztiAjo+dsKkGDyGTN7SYhQ46ggt/vl87ZdvNmGg1cs2xKVigUpAwB07M0EKUNl07lerYK3F1rjen73ymAs65TXy6XBfShYd5qtaTUg07p575QLpcFaNpu9stWRsQfeOABvPvd78a9996Ll73sZfj0pz+Nm266CY899hj27du34vjTp0/jda97HW699VZ84QtfwA9+8APcfvvtGBwcxBve8AYAwIkTJ3DLLbfgAx/4AG6++WY8+OCDeNOb3oTvf//7uOaaawAsleZ48YtfjD/5kz+R79nJb/zGb+Bzn/uc/J81H/ty8bLetaKbSJHdY6buAhCQk4xR00BuNBqYnp5GPp9HLBbD2NiYsIgZ8GJJNe6HNLqLxaK8vw6HA8lkEr/yK7+CSCSCwcFBpFIpeDweJJNJ+P1+/OAHP8D58+d3JIhuit5X6TwSTAQgTTPpNBNEJttcO3/MXJqfnxcg3XxGBD/Y8yMUCkmTaX1+gsgej8fC8KJzqhnkOk1alwDjdyqVigQJCHhrJ51BemYbaN1IG4Nl94LBoJQ445rz+/1S35xrSbPJVxMNmLNcTLPZRLFYlOtoEgHXoJk9YYp+z7oF0bs5di3pBLpw/tcLDPBZb/T7dsIxeTwemf92e6nEXLFYtGQ36ICODi6R5LGVNpKd9NlsfdmJov1n7i92jf8Y4DT1ugY6qbtzuRxSqRT27t0rtbgJorNRIGAth0K7nOdKp9O49tprpcl4IpGA1+tFLBaDz+fDww8/jGeffXbbQXTACgLaidaD3BtZTo2EHh5H0fpCn5/zlMlkMDMzg0KhYAuGM5DudrsRCoUQj8cRCoVWlOPU3zPvQZMpKDoorwl3OjvfDPxr/WpHxNKl3Mya65oprkFZ/l2X0ukkXF8MDnEdZjIZwT30HJjnMte5zsrQ97FTZCf44DooT+IGcbVKpQK3222x7Ti/tKPodzML/lLyu5/rsmEQ/eGHH8bHPvYxPP7443A4HDhy5Aj+4i/+YlVW36UmGig002Qp5gamWbq6jAujXZVKRYB1M+LICJlmmtLJ8nq9UsOaSjyZTCIej0taUKPRQCKRsHy209JI1xLOFWvR6o7dds28NFOXP3RaVktN1wyHnVRjeqOi0wxXuycGEVhX1e/3WwxLl8slTCkdWddGjmbKbUSZrBaA6oVoI8eMMJvj19fkuiCgodNK9TnMFNGNzIEGeGh8ca3r2rtamevyEnwuzwVH3MyWYc8CU+655x689a1vxdve9jYAwPHjx/HNb34T9913Hz784Q+vOP5Tn/oU9u3bh+PHjwMAjhw5gh/96Ef42Mc+JmD48ePH8ZrXvAZ33nknAODOO+/Eww8/jOPHj+NLX/oSAOCmm27CTTfdtOb9eL1eDA8Pd3n3G5e+7l5b9P6kmcTmu8r3Wx+v1zWDXQTjdMqvbmTFf83mjMByM8pAIIBYLIZoNIpoNIpQKASv1yusrEgkIrXE+a7vFuH9NxoNuFwuKYvC/Yz1ak3wlt8h4cBuP9UOkHaIWSaF+o4sb3PP1udYS99rx9jOKdXX0mvFBNF5D53Grsei6//qUkN2wnsjSO9yuWS/JNOca3MzWWkmsaGXekgHTfTcmeCBnWj7hyw/rVv1eNc7N+b39XvPOeecUKdrEL3Xzd4uRvqO+PZIX3evLXY2NrAM6tq9P/p4vW9Td1Mnc+9m0Jz6htfRgVQAsocEg0HEYjHE43HR4cwOZ2ZZOByWYKzOpNpqWc+7qX0iXe6Te5cdI1qX+9L7ml35G4rWfaaPau7xq313NdG62+5v+hp2hC9+z668jT6H/p16Spf/6DT/PK/X67VkNJLA53a7BbfQpYg2Y6/tVRaZeU5zfvScr1ayUNttem3o918Huy7G99W6m+u20/qj7jb3he2Uvu7urWwIRP/CF76AP/mTP8Hv/d7v4Z3vfCfa7TZ++MMf4td//ddx//334w/+4A96Pc5dKRpsKxaLqNVqlrQw7YBTsZDRy1QROonz8/PCEmo0GvB4PMIaBpaZc6VSCZlMBo1GA3Nzc1hYWEAwGMRll12GkZERHDlyBIcOHUI0GpXGWu32Upo5DflarYZsNotarYbz58/LRrATNoC1pFqtYnJyUtg+zWZTGpGwDi0VO+t61ut1TE5OIpfLoVAoSGmbTps1nW9dV3U3g+ha8egfDSbw93A4jIMHDyIajWJ4eBj79+8XhoDb7UatVsPs7CxKpRImJyfxxBNPCAuazCs7ALqbMepxmin1WjFuFJgmAB6LxaS0DRunapbo/Pw8yuWyvKtkMTBYNTY2hkQiIeuOwASwXN6B7LPz588L+NNtQzTNlMnlcpYUMZfLhUAgIExKnrNYLMr+USgUpPTAcyEivnfvXsvn73vf+3D33XdbPms0GnjkkUfwnve8x/L5jTfeiB/+8Ie25z9x4gRuvPFGy2evfe1r8dnPflaCkydOnMAdd9yx4hgC7+uR73znO0in04jFYrjhhhvwwQ9+EOl0et3nWU36urs70aBuNpuV+ssAxCn2+/1SAozvL/Uk08kZ2CYQTOBS71/UH6VSSTKg8vk82u02AoEA9u3bh1gshsOHD+PKK69EKBQSvbW4uCjB9WAwiFe84hXI5/N4/PHH8dRTT6HValkcil4ELTWja7X31+4Yu+P5Wa1WQyaTkSZjbMzK5k2aXVytViWlmVk13AdN0TUqmbLMeeHnZNPFYjG0221L8NTv98tnZJQzaMygMhmIwHKZrVqtBofDIY07dQCa2UBm0MUs6cO1xOZn/KHOHRgYQDwel6w5lqexqwvvdrsRiUTg9XoxODiI8fFxcciZeTY/P49arYaZmRmcPn1aMsp6DabredO672Kcfh3A9vv9iEajwuoGluYzm812ZHu6XC4kEgmxi2OxGFwuF0qlkrC/uaYrlQrm5ubWBXYRlNOkAgCSAUmAToMF3D9ITNgJTFWg74hvh/R1d3fCtUl2bj6ft/gPtO2p32nXa3+bDF8SsXSDcZbXIgjcbrelnJguJxoMBnH48GEkEgkcPnwYR48eRTgctgDPbIKdSCTwG7/xG8jlcvj5z3+ORx991LLnbjTg2Ol73epmOzEJAADkvslQp30cCoUkkEDdRt2uSUDFYnFV/4fzZZILOWbtl9PO0Yx5jbdQb/Jzrg0dzNQBdeoU/quztMy9nOfksXr8+hjOWzweFx3FviR2z8Hj8UjWwujoKC677DKxJd1uN+r1umTgT05O4umnnxYbtNeiG5kzYAIs++gb0Q20qXWPFs4jgwNzc3Mol8uWdUfbzu12i98eCoUwPDwMj8eDbDaLXC4HYDkIUSwWMTExsa7efyaBhrZAtVpFsViUzBTzeNoOCwsLKJVK65qTzZK+7u6tbAhE/+AHP4i//du/tYAH73rXu3DPPffgAx/4QF+Z//+io9vc5Kl8ueFz06HDxHpVOv2HbBS+tOVyGR6Px1LTnJGufD6P6elpS30rt9uNvXv34nnPex4OHTqEsbExhMNhW8YVx10oFPDEE0/A5/MBQM8BNzvHvBdSr9cxNzdniUr6/X4L+EoFWqlUUCwWUa1Wcf78eZk3BjFWE83a7jSPu0k0s97unvj3QCCA8fFxjI2NYe/evXj+859vaUZaq9UwOTmJUqmExx9/HJOTkwAgQNLFjE8zxMwmf2Yt9Y2C9C6XS+oNRyIRUcaM8BMYcTqdqFarYnjz3Q6FQjhw4ADGxsYQjUYxNjYmgSoa5efOncP8/DwmJyeRzWY3HBWn8akBH5fLJTVuuYcASyB6Pp+XJq529f53q5w7d07qQwOwZaHPzc1hcXERQ0NDls+HhoYwNTVle96pqSnb45vNJubm5jAyMtLxmE7n7CQ33XQTfv/3fx/j4+M4ffo07rrrLrzqVa/CI488Yns/G5W+7u5eGITSDQz1HhQIBNBuL5VKIthIp5q6kiAoHXGCr9QvZBO5XC5kMhlcuHDBAsx5PB7s2bMHo6OjOHz4MJ73vOchEAjg3LlzmJiYEGeWmUG/8iu/Io3Nnn76abTbbYsDu1Edrp1uUy90CjZ3+hvPYwpBTofDIVlhDMwSNA6HwxgYGECxWEQul7OUJaENZI6D96+ZbyaI3m63pVY6wUw6h3QYdRBEn5fPVDOH6cw7HEulVzSI7nA4RB/quqTAMoiuHXSy87mO9DN3uVyIRCISzKEu5vf0PA8MDCAcDiMUCmHfvn148YtfLCXAnM6l2tuzs7PS5G1qamoFm7NXQta76XhfrI1JUoPf75cANq9HMLoTED0wMCCZmuFwGMPDw3C73chms5ibm5O173Q6pXTQehmjulEcsNwTgGWMyuUy6vW6hU3HHj0bDYL15bkhfd3dnXDPW1hYkPIJOmDNnmLU7ySOsXya1pd8v7k/695RvFartVRSzNTdPp8P+/fvx759+7B//34cOHAAfr8fuVwO+XwerVZL9qJwOIxrrrkGtVpNfG+7jCrzHtcSu+9ofbMRH8BuH6KdxL2cJcySySRCoZAF0C4Wi+L3UJeRiNBJqA+dTqetv8Rna96r9hOptzX2on+o5zT5UQdmGUDXGUTm8QBWsNlNf1Sfm1mEhUJByFp2z4QB/kgkgsOHD+O6665DMBiUZ0EQvVKpwOPx4OzZs5sGotPXpU1FfXYx64o4De2XUCgkYLrH4xFczNTdtIG8Xq9keQwODuL5z38+AoEAJiYmMDExgcXFRXl+U1NTmJmZWReIznvSz552qhkA51oFlrKkc7lcX28/h2VD7WKfeeYZ/PZv//aKz3/nd34Hp0+fvuhBmTIxMYH/9J/+E5LJJAKBAK688ko88sgj8vd2u427774bo6Oj8Pv9eMUrXoFHH3205+O4GOGGy/qautEf2eU65UOn8+qIHJ0EbmBmdBZY3lg0KMpGVLoJmh3wy2N1nc2NgsTcbP1+PyKRCOLxOJLJJIaGhiw/6XQaqVQKyWRSGOPcTDdyXc1EIHiYyWQwPT2N6elpTE1NYWpqCrOzs8hkMpifnxc2W7ddqbVi3KyUqa0WrSC00mf03u/3S0mBRCIhSp2OOevuR6NRxGIxS/riRksD6fXPhqdMP+/0o9+TboUASSAQkLXKFEuWSgiHw4hEInJfkUhEauMnEgmMjY1hbGxM1rLO9uAP5y+VSmFoaAijo6MYGxsT1tt65kgzWthgVDfGZRScbBfuMTtlvep3aKM/ACTgwZ/VQOfVWLXdHm9+vt5z2sktt9yC3/zN38QVV1yB3/7t38Y//dM/4amnnsLXv/71dZ1nLenr7o2L3u91FpMGZlcL3vE9ZUNHfkZbwC7bi04cwUAG0cmc1llQ1N0MMppj3sg7z/RhlonhPs8fsqzJZjZti43qb9pJBD7L5TJKpRLy+bzUPWcQUzdetDuXJjPoNFzuh7qUmy69Z5btMplk+ns6hd28LutZ63qn5rMwWYK04fjczZJodo48bUGzMTcBWb/fj1QqhdHRUaRSKakJrxuUBgIBBINB0eVr7aerCW1HZmhp3Wz3GT/n/7tdO7QPfD4f4vE4hoaGBLxhEzoGxqPRqNTT5TqNRCIYGxvDnj17kE6nxWbR+j6VSiGVSmFwcBDJZBKDg4NIp9NIp9MIhULrWucm2MCMNvYs0RkHDM7tFJ1N6ZXu7kv30tfdGxdTd5v7tvb7zPWps8V1mUrqEK3/tVB304/WmWp6DwCWg4os3Wb3fqz33SEwSbuYZeDoy/BH9/UyG5yvV6hb2b+hUCiIzs5ms5LVVy6XhXxAHb4a0KifH3UeSUKm/WWK3d5s2gV2BCw+89X0tr6GHbGO5zDHbedn2wl1m9/vx+DgoOhu2ltan9I+i0ajSCaTSKVSXTV4XU2oV/UP/XCuV9qc/Gw9mfnat9elhmmb6HVLTCEYDMo1QqEQRkZGMDQ0JD3+IpGIEBsCgYB8l2s/Ho+L7iYpY6N2KrMS6HvrUsrU3TtN122H7r733ntx4MAB+Hw+XHXVVfje97636vEPP/wwrrrqKvh8Phw8eBCf+tSnVhzzla98BUePHoXX68XRo0fx4IMPWv7+4Q9/GC996UsRDoeRTqfxu7/7u3jyySdXzMXF6rANMdH37t2Lf/mXf8Hhw4ctn//Lv/zLitT6i5VsNouXvexleOUrX4l/+qd/QjqdxqlTpxCLxeSYv/3bv8U999yD+++/H8973vPwX//rf8VrXvMaPPnkkwiHwz0dz0aEjhQAeemAlY039MbebrctJTb8fr9sNvydkTC9UbMRFZlJZKUSCCTTqtOmQWZco9GQDZMM3G6FBgKdMY/Hg1QqJd209SYIQM5PZyKTyUjZCd1opNuXl/ORy+VQrVbhdC51Pycor9nLNGoIMvIZrHVunb6/3bWlL1ZMA1HXVCMgSENs7969OHLkCA4cOIDBwUFptkPji+WDuC7n5+cxPz8vLLf1zJNmxxOs0cxNbYhqw5YBETaKWysKzLXKOsOHDx/G0NDQinIuNMCDwSBKpRJmZ2fFyHrhC1+IF73oRQgGg9L8V6elUVqtFmKxmLBkRkdHUS6X8bOf/QyPPPLIumqn0WBlihpZ8gTXtNLTLNnNqnO7XrlYZ3o9302lUsJC0DIzM7OCSU4ZHh62Pd7lciGZTK56TKdzdisjIyMYHx/HyZMnL+o8pvR198UJ98RCoYBKpSLvVicjU+9RfOepo5mhUigUhA1nvvdOpxM+nw/BYBDNZhMTExPScDYYDFp0v27GqZvSXsz7zhIXJptMO7W8/1qtJtkuOt16NUZ6J6ED4nA4UCgUxCml3cC93g5QNsUskdFuLzHfOFYygZkl6Ha7ZV71v/oe6vW6sKLoyAKw2BYaGKjX6/JMmLZtsrB1thzLBvDvuleLriHLc7NkGn+07qajm0wm8dKXvhT79u1DNBpFOp3GwMAA8vm8pCTrlPJyuYxisYgzZ85IGnW3whRrXT6h3W6LfvP7/ZINSYYZ5wCAJSNwretyrsLhMI4ePYrh4WFLFhad/0ajgWg0inw+j2w2i2eeeQaNRgOHDx/GS17yEgnQ085mORgCbsDy+1wsFjE6OopKpYKTJ0/iqaeesuwDq80LmYpckywNBywHS3QQai0AZztkK3V3X5akr7svTqgjGIAFVvrdpuiyHNy7dLYQM765f2mh7maQjTY6S7Dpd4j7Cv/G81+MeDwejIyMyDPT2WjcGzmGSqUipeQIAG5UmBnmdDqRzWYtrG8AFt3Ne1wr20nbWbrRMuedYG+nJqMa79Bgu+kj8V/dm4Tlc4kf2Nl3xBW0/+VwWMud8Hy6nJ9dgF+vCWIww8PD+A//4T9g//79AjYPDAxIIEg3pOY9MKPhmWeeWfda4v0wmM7xA8u6HVhaYyx3x78zE7Kba1LnhsNhHD58WHpC0Xbx+/2CJ/h8PmSzWWQyGdHd4+PjeMlLXiJZ4LSBmfnH8pjAMpmA4HmlUsHp06dx6tSpjoGwtYQ9DLQdp+dqJ2JDW627H3jgAbz73e/Gvffei5e97GX49Kc/jZtuugmPPfYY9u3bt+L406dP43Wvex1uvfVWfOELX8APfvAD3H777RgcHJReZCdOnMAtt9yCD3zgA7j55pvx4IMP4k1vehO+//3v45prrgGwBMS//e1vx0tf+lI0m0389V//NW688UY89thjCAaDAHqjwzYEov/5n/853vnOd+KnP/0prrvuOjgcDnz/+9/H/fffj//+3//7Rk7ZUT7ykY9g7969+NznPief7d+/X35vt9s4fvw4/vqv/xq/93u/BwD4+7//ewwNDeGLX/wi/vN//s89Hc9GRad36/QXSqeFqWs+cSPXwJyuJUnjm84LlY2Ocq/F8GEUVTPZNxKp4zh8Ph/8fr8wexlx1ONYXFyUNFbWbabzwvvcCBCgG7TYpUxvND3WdNx3GktoI6LvRQMgwPKzZFCEGQVkY+u0bBoijUZDjqMyXK9QkXP9M4WMUe9OILoGV7pdt2TaswFQMpmUgI9pNDebTSnxQgWfSqWwf/9+BAIBxOPxVe+Xf2OZoXK5jHPnzsHlcglw1K1oMIslnzQTVBupO22NbqUy93g8uOqqq/DQQw/h5ptvls8feughvP71r7f9zrXXXouvfe1rls++9a1v4eqrrxYj8tprr8VDDz1kSbH+1re+heuuu249t7JCMpkMzp07h5GRkYs6jyl93X3xoh25bkSnC7PeKvui6Mw0uz4cOv2YwCYD3KzrqrPWGPAz63FuVBi0DAQClqCgzpjjfutwOCxggmZlr1d62dTS3GfoZLJMis54I0histJMx5tZBfzdrI+uwXcNpjebTXmWJogOWMsOcFzUOXrONehg6j6T/UR7LhAIIJ1OY8+ePQgEAuIkkO0PQID+cDiMWCxmG3jsRrTupqPNOTAzJliKh38jE1MDyWsB07QPUqkURkZGUK/XpSwi2fUENNgvh/ZtNBrF+Pi49BFpt9vweDwIhUJSwo7vOm1hAgflchkzMzOW92218ZolBLgWL6bU3XZIH0Tfeunr7ouXbnW33p+1n8r3lXqW4JudrtIZRcByaRj+zbyWLiPTCxkYGBB/DYDFlzZxA/oh1FsbCXxTeqm7tehAOf9dWFiQ+eV9rLU3meC9qeP1D+/FtAm0mCC6fo48h75us9mUQL2+Hzuh/gwGgxgaGsLevXsFVwFgGR/tilgshnQ6DZ/Ph3Pnzq37Wep1T93NueKYOOc8Xs/fenxYYlPsQZJKpSzrk1kS7BtCe0jr7rGxMclqMwM2LpdLAFNtkzBzO5PJYGBgQNb8euVS1t2FQsHyOZ+BKffccw/e+ta34m1vexsA4Pjx4/jmN7+J++67Dx/+8IdXHP+pT30K+/btk75iR44cwY9+9CN87GMfExD9+PHjeM1rXoM777wTAHDnnXfi4YcfxvHjx/GlL30JAPDP//zPlvN+7nOfQzqdxiOPPILrr7++ZzpsQyD6f/kv/wXDw8P4b//tv+Ef/uEf5EYfeOCBjoDERuUf//Ef8drXvha///u/j4cffhhjY2O4/fbbceuttwJYilpMTU1ZGsB5vV7ccMMN+OEPf2g7EZpZAqxcDFslqwHnOgrJGlF0FAi+cUMAlhX8wsKCsMeZSub1elEsFjE9PY1AIIBqtWppIMmxMCLNZiz5fN5S93k14cbFchUejweJRAKpVErqtzNljAACN61WqyWOjW6MReB9YWEBmUxGnKKNKGg91xfj3BM0YKo565Duto1UC9cNn7VOPafockI67dB0CrXhyPW63vRsiq5/zhQyXZcWWDYMddYGwWQAlgZ/qzm2DBIEg0HLtfSYeW80LBKJBA4ePIhms4mRkRFpgNetIcwav263W1L12LBso01I7KLhfYd1SY4dO4Y3v/nNuPrqq3HttdfiM5/5DM6ePYvbbrsNwJIinpiYwOc//3kAwG233YZPfOITOHbsGG699VacOHECn/3sZ0VJA0s1Sa+//np85CMfwetf/3p89atfxbe//W18//vfl2NYn5py+vRp/PSnP0UikcC+fftQKpVw99134w1veANGRkbw7LPP4q/+6q+QSqUsgH8v5Lmsu7sJSm+VcA/hfqHLrxGEIwhOB41p0CwVQpC3UqlIzUzqT5aq4N7dai3VV52cnES5XJZasOsRBhLJlGejJzZs4t4LLGdxAcvzvrCwgHQ6LSwo3YOhmyyvrRSdpqzT2Rns0MCu3kf1+Ml604C8DvqyiRwJCTrdmXYZAysE7/l3bZcRLOCYyMJjdiBTiLl2zLXPMeryA8xa4v1yLNT7ZESaDdK6FTMQz/uh3aDZeJwbOyBDz383Ypcl6Ha7ZY45Jp/Ph6GhISwsLCASich7xFRsvptk95k258LCgtixZEDyPV5tjWvbc7v3qL7sLunr7s0Xh8Nh0d0amON+RSIbAAFDScBhVnO1WoXD4UA+n8fU1BQcDgeGhoYshDbeK4Nxs7OzUrZsvXPAHi0MlgYCAQkqMvOb/owufUYcgVm4zJbTzRCpV3aC7iZRwCxPqwPXdhiBDoroz+wCIFwD/J36XOtu9h0BlvEarf/X0l28JvUNG0ebx1P3a+a+rmxAopi+F10ObCNMaHPsOkig7xeA9BbQc9+tvaDn1iy9yrk1s7AYGNqzZw+azSbS6bSse+p9bV/oc/F8TqcT8XhcSIHdlivu+9NWMbOf3ve+9+Huu++2fNZoNPDII4/gPe95j+XzG2+8ET/84Q9tz3vixAmLXgGA1772tfjsZz8rDYtPnDhhIa7xGALvdkKfKJFIANiYDrOTDYHoAHDzzTf33MG3k2eeeQb33Xcfjh07hr/6q7/Cv//7v+Od73wnvF4v/uiP/khYMnbN3c6cOWN7zg9/+MP4m7/5m00f+0ZEp/SynqPb7RYWNx1dDaJrx4PgbqvVEifR7/cjk8kAWEptLZVKkmbLDZ8bIDsXz8/PY3Z2VpTpWiAxxxyLxXDZZZdJWYuhoSHLRmkqMoreiPfu3YvFxUXMz88jk8lIk0qTZbxe6cUmSIADWGpMWiqVhJW8W6XdbksKlllrtt1uW2qOmrXRNJAOLBs5ACStKhgMWsqvdDv/uowRjVQNRgDW9HndH0DXENZ/7yQs3RKJRBAMBuUd08aDHhMZaqlUCg6HA2NjY0gkEuvK2mCgqdlsYu/evTh8+DAKhYI8i43KTjB2u5GtZrPdcsstyGQyeP/734/JyUlcccUV+MY3voHx8XEAwOTkJM6ePSvHHzhwAN/4xjdwxx134JOf/CRGR0fx8Y9/XKLhAHDdddfhy1/+Mt773vfirrvuwqFDh/DAAw9IShkA/OhHP8IrX/lK+f+xY8cAAH/8x3+M+++/HwMDA/jFL36Bz3/+88jlchgZGcErX/lKPPDAA5uSFv1c1N0ma2w7My+ou5lx5fP54PF4RDcDkD0GgDB+CaI3m03kcjkUi0W43W5xsHkeNk2MRqOWez19+jSeeeYZZLNZzM7Ornsf8Hq9SCQS8Pl8GBkZwdjYmKW+J+tuOhwOAW4ByLjoFLVaLZw/fx5nz55FtVrF5OTkhsqybaYw/ZeMawKpBEzZMAqABFRpf9Dho+PGYDrtM83g0sw0HVBh6RjqJJbio27jnNOJZrk7/st1AUBKuLBUgBbNmieATkC5Xq+LvibbmmA82ZYcy3qFjj6DRwwMEGjWAWoCEu12e0UN8G7WCu1BHdDgvXBe+X7xvH6/X1KJ4/G4fHd+fh7lchl+v19YjpopaDLVCVhxfaz1zu0UMOpipc9E3x7p6+7NFRLBwuGw+B2adUvRGdvcH7lfs9eWw+HA7OyslNs4fPiwBKipR3mfpVIJ58+fl7rh671/BgX9fr/43Xrfph3idDpFFzAASFY996+5uTnMzs6iWq3i3Llz0gTULlNuO8QMFPAZcN1oJrPOPtdrjPsXCWO0XagPqfe0PqbO0nPFZ651O6XTPq/HSZ87n8/bzq/W3brcjD63JjGQDEcffqPkPhNAN3vv2NkELGvSCeOxE23zaEY77Rjt09OGisViUiJpz5494iPlcjnUajWxU/XcAMu61+12I51Oo9VqIRqNdm3f7BTb9WKlV7r73LlziEQi8rkdC31ubg6Li4u2eqJThuPU1JTt8c1mE3NzcxgZGel4TKdzttttHDt2DC9/+ctxxRVXyHX4PfM8nXSYnWwIRP/TP/1T3HDDDfjjP/5jy+eFQgHvfve78Xd/93cbOa2ttFotXH311fjQhz4EAHjJS16CRx99FPfddx/+6I/+SI4zX1pGHu3kzjvvFCCD4+51TbmNik45sWP/6k1dNxjVhgc3Id0clNFORrp1BFGz2XTzLjplaxn9VDBU1IzwUdGtpyQMx9VqtaRUBmCta7kWYK0VZa+lk8PG+d9IStB2i8l2s2vSoteKTik3N2R9jKkQ1ysagNCAtm5epxU7nXVd0qAbpa4j7eZ5Ox2rGSksebDepjw0ToDlgAMZjZeCbIcjfvvtt+P222+3/dv999+/4rMbbrgBP/7xj1c95xvf+Ea88Y1v7Pj3V7ziFauO1e/345vf/Oaq1+iV9HX35gv3EupEgrDMuCKIqJ02/ss9jN+h7iZoXavVpNQZ9SD360qlgmKxiGKxuK6gLvczOqcs30KnW7OAdJM0jt+0SYBlfU3A0e12W5jA2y26GafWGTr1ei3Rc8Pv8BymzrIrsWWXxdWtrtLXM1nfnb7H+15YWJDnZLLCtYOse8ZsRPT+rvU0/9WBd51SvR4nXIseZyc2HedL62zqcE3k0IxGbc9wfWjwjPa5/t5zXfog+tZLX3dvrmg/gLpb94nQYoLo/B79bpLGdMZyuVy2sJqBZWCP2WaFQkH6SXUjvDaDtmzyTRDR1HEALAFNft5ut0UH+v1+CfBrgHOnNElkgJaluUzdqvf+ToQ9fazd8ab+Ns/TSWcTCNbzRIDfHAMBcWIIa80tMQeTjKj/5fUZLN+I723el75/cy64hnQ5lI1iIHaBDjtdqkmZOggEYAUmoedUB7D5DjCjQduDz3Xple4m6bAbWY+e6HS8+fl6zvlnf/Zn+PnPf27JEN/o2EzZEFpz//3344EHHsAjjzyC48ePizFZrVbx93//9z1V5iMjIzh69KjlsyNHjuArX/kKAEgjgqmpKUv92NWau3Wq3bMThI6sy+VCOByWmtMsFcEmSjqFSBv3ZClxU+OGOzc3Jw0eW60WQqGQ1LUmA71er+PcuXP46U9/inw+j2effdYWUDXF4/FgbGxMuuDu3btXGMjrraeuN2vWow6FQqjVakgmk8hmszh9+rSleRaAFcAqI8S9bsrE9Ot2e6khHJtllkolSSUyU5N2qlBJLSwsoFAoYGZmRkqJsDkM545lfoLBIObm5hCNRtFut6VZLI0Onqter2Nubg4XLlzA/Pw8isXiuhgmZjCJP2adfruACb9DRUnlqR14Gjb6valWq5L6TaDBBMX5/JnyzUwRNoHbiDgcS+Ud0um0pPlfCtJ3xLdenqu62zSat2NtUN9qsFnrJILT1NsEYc3SWcCSTiXDplAooFQqoVwuY35+Hj6fD+l0GslkEouLiygUClhYWMDU1BSeeuoplMvlrtlszEhzuVxIpVIYHx8X+4NZPJqNRPYVmU68B6a164Dy8PCwsJ3D4TBKpRJmZmYstWE3Uzg2cx7a7Tay2SyefPJJKVWTSCQsgV+yyjVQrRmHDKASNKXOZ8kCXXZPO6O0ETRAzfPxHLxWu922sAVpu7GUCBncJkivxeFYSk1ntkClUkEul7M0vONceb1exONxYVM+++yzyOVyG2JGcp6BZRYkAFnrdqCEBnd0Sba1xHSO6RRzPllisFarYXZ2FsViUWrMer1eDA4OSrNv2sGcM9oDvI62J/l8YrEYDh06hHK5jImJCWSz2XXP1W6Tvu7eeunr7s0T+hQE6HTpNJZu0b6lJqHpLBWHwyE1mIEldm42m8XJkydRLBYtTHFmnNVqNUxNTeHkyZPSY6GbORgYGEAkEpGyLQcOHEAgELCA/prwxFrV1FnMqtalQYCl55ROpyUrKBQKoVgsYmpq6qIajq5HOhHh2u025ubm8NhjjyEajeLQoUMYHh5eAeza6RczcMs5sDtWB1R1vw5gufmz3gM5v2YAnUEIzrX+Xru9lAFeLBZRLpdtWeMEeoGlZr+Tk5MIh8OS9czxal93YmICU1NTyOVy68po5pj1O8D1bwYC+H+NMfHv6yGSMSPO7/dLSVWSRfR7Rb+b5XOJh+lSLhof4Pcoeq3TvnM6nUgmk7jiiitQLBZx6tQpzM7Odj12YHld8Rq7Qa9tpe5OpVIYGFjZV2c1PTE8PGx7vMvlQjKZXPUYu3O+4x3vwD/+4z/iu9/9Lvbs2WO5DrA+HWYnG6Y8fv3rX8ett96Kxx9/HP/wD/8gzSt6LS972cvw5JNPWj576qmnJAX/wIEDGB4exkMPPYSXvOQlAJYU18MPP4yPfOQjmzKmzRRG13SjQ9ZE54ZMwE6zYXRNdEYGqdSpyGu1GsrlMiqVCvx+P8bGxjA8PIxms4lMJoNqtYozZ87gJz/5CUqlEiqVypqROSqKwcFBDA8PIx6PY3h4WMa4EWCR32PdWG58iUQCExMTmJycFGWugwXczLVysWMnXYxooIBgxcDAUpdsznknh3anCY0E1q2dn58Xo04r/FZrqc6f0+lELpdDLpfD/Pw8PB4ParWasBjIvCiXy6hWq1JWYH5+3rZ7fScxGWrmj2Z/UDSTjOtfs9g1mMHva9YY0/K9Xq+w8c1nqOeDIDoZmxcDfDscDmlI2m63LxkQvS/bI89V3b3dBqwO+tGY1/sO65ZqJ63ZbKJSqaBer4vzQuCTJSiKxaLo8PPnz8PlcmFoaAiDg4NoNBrSL6RQKGB6enpdadcEaX0+HxKJBEZHRxEMBlEsFqVmrWZk0dGjo8N7JMO5VCqh1WohEolIqarFxUUBghnI32zRgIedc5PP53HmzBkUCgUcPHhQ7oP7OxtfMoCsG5QDy+xjDbA3m02Uy+UVbGU9Fg2U64as2pYznRyOxe12i32nsxtWc1oJ0DNQUqvVUCgU0G63EQgELAwflhrgXExOTmJubk7K9mxUOFe8Nzu7kE65ZkV2K5op7nQ6LRmYACTYzWdUKpUQDAYRj8cRDoelQXq9XhfbW9fc5XtqPh/eSzQaxd69e1EsFqUcQ1/6shnS192bI9yDTV+De6/W5ST00A8wWawEBHU/kEKhgGeffRYulwsHDx7Evn37UK/Xcf78edk3JiYm1sVCZ33oYDCIwcFBjI2NIRAIiG7W5DrqG+rqThlX1BehUEjIRNQdc3NzWwKim/pBj7HdbiOXy+Hpp59GIpGQcrFmYNNkptsxx6nvaY+Z2Vz8zCyd0ikDm7aR2e+Ddpwuw0KdxbJx1WrVlnXNTAhgyWaZmZkBsFTXWQcGaF/y3HNzc2IXrscW5POnvaZ9Wz0/nGcdhNAkg27wDx2kYLNV3YBcP1OSBxqNhuBizLqgTaUxIP18tM/OMka0o2KxGA4fPoxcLoeZmZmLAtF7SdZ8rojH48FVV12Fhx56yFKG7KGHHurYx+Paa6/F1772Nctn3/rWt3D11VeLHXnttdfioYcestRF/9a3voXrrrtO/t9ut/GOd7wDDz74IL7zne/gwIEDlnP2SodtGEQ/evQo/u3f/g1veMMb8NKXvhRf+9rXpGB7L+WOO+7Addddhw996EN405vehH//93/HZz7zGXzmM58BsLSI3/3ud+NDH/oQLrvsMlx22WX40Ic+hEAggD/4gz/o+Xg2S7jpmMxbrdj1Z1Tqmh3GzUKnh5usOEa/y+UynE6ngL+sxTw/Py/1sNcC0DkWr9drKeGyVkmM9c4LWVKLi4uSasYNUdf1NNl/wHIkfiNp5J2YXdws2THa5/OhWq0KEKLrfe5koQKr1+tSCqBSqVgY6PrYhYUFaXzJdMFQKCR16VhmYGZmRo7L5XLCTN+IaAfZ/B1YyVjQzBA61NrAoGGjgR8AwvTjXLDWPWAF6AkUsa8AO4X3Kg1Mj/+5Ln022/ZIX3dvjlBXmUwYrcdNx8fpdMo+w/RwYJn1qs+p2TSFQkH0OXU32TvdvBc6oMisMTokOogJwPKZ3uc4Nq1jKdoB0s4Y58FunDrDSO/nG9XdGlzWjhnvQzfRZuCUTGkdiOf3te7X5cz0M2JDSuodPR/6PGuVQuMYtR3HUgH8Dh3zQqGATCaDXC63ooyPdlSpk1k+YHFxUTIe+D2ee35+XpiIGmhYz/zTbrUTfS7zORFsMMsWdhK+Twx45HI5acyryxwxWKUbww8MDEg9eK5fzSzr9D6ZrFQTuF+vdGJe7lTp6+7tkb7u3hzR/rQGA82sV+posr25jvUeBKwsZUXdsrCwIJlA1OMkHXWr5zgO1n6ORCJCWjPfS5NBbPoWdmVqNHjMMjEEVU3dbWcv8Hc7ELobMdm9gHW/aDabqNVq4qvqQAG/T73TzZxqrMKOmW7qaK0XOp3btAv0NfTvJOIVCgVbJjp91Gq1irm5OZlTZnWwNJCe6/n5eVSr1XWXc9Frd7W5Wu2eNYEAgOhF/l1fQ5MHWq0W8vm89BDQPc7ol7MkktfrFdtHZ1WsVsoFQFe2xEZlNwHoW627jx07hje/+c24+uqrce211+Izn/kMzp49i9tuuw3AUpmviYkJfP7znwcA3HbbbfjEJz6BY8eO4dZbb8WJEyfw2c9+Fl/60pfknO9617tw/fXX4yMf+Qhe//rX46tf/Sq+/e1vW8q1vP3tb8cXv/hFfPWrX0U4HBbmejQalX5FvdBhGwLRuQEkk0l8+9vfxm233YZf/dVfxcc+9rGNnG5VeelLX4oHH3wQd955J97//vfjwIEDOH78OP7wD/9QjvnLv/xLVKtV3H777chms7jmmmvwrW99a1Oasm2W6Cig3+8X1hqj2vyMDBm9IejNQzPDqEDIsGm1WpJSzRRkpsKYDbXWUn5kiodCIUSjUYyOjmLPnj2WGmq9koGBAYTDYQQCATSbTYyOjiIUCmF+fh7z8/PC5KUhoVOraZzo9KDVxC5irUVv0Cx5UyqVMDU1hbGxMctz2ulSr9elgd3ExASeeeYZZDIZcbD1vVIpt9tt/OhHP8KpU6ewZ88ezM3NyT273W5pRlMsFnHmzBk8+uijKJfLKBaLXW++GnAgo1yzJ+xqp/JYrluyLrgHhEIh6cTN94apZHwvWEv4/PnzqFarCAQCSCaTwippt5fS786fP49cLoehoSExYllyaSNCw2yt5qfPNek74lsvfd29eUJAnEE3BlXJnNFONfUVgUzNzuZ+S13GOpism9pqLZVfu3DhggT21rt/ENgPBoMYGxtDKpWS8mu0IxgMJvhN55V6lM1EtX6l48jPgKUmqj6fD81mE8Fg0MIK4rzR9tFjoFNWLpdXHLsWuElWP7DsSGugv16vo1AoYGBgAPl8XvZ+nX3HurC6UZldKRLOl9/vRzQaxcDAgLCmdOCXDijnjLpKgza8DueQa4ogBrBUEoV2WrlcxtNPP41f/vKX0u/GHBsD/NTDbrcbsVgMg4OD8Pl8Ul6gXq9jdnZWypIwK43zqe+lm/XFVGsy+s0AAe+V90dgm7WDI5GIzKuufWsyB6vVKiqVCprNJs6dO4dcLicgE1lnvBc2qne5XJiYmIDf75fgBO9N1/3nWO2EQQI2qdXBl/UIbRJeazcE0fu6e+ulr7t7LzpQrJtU6gA3S1FxnydhbHFx0QLkknxUr9ctrHUCfvl8XvTZ2bNnJSjOvbDbJpAETkOhEEZGRpBOp8W/14FP3p/OVqfu7CQ6UO9wOMRvarVaCIfDort5j8zc0eQB7tH0q8xMJtNvM0Xrbm3T8FiWvGH5LV1uC4Bk8fGe2SRbP2+7IIAeH0Xvc9QPWnfpvVufmz/6XvQc8bgLFy5I5r+d7iahsVqtCrFraGgIBw4ckOzBaDRqqTbw7LPPCpFtPf6k1s1cK3od2dkAOkhDmweAlDSKxWKIxWIAlnv36LlkE9SFhQU88cQTOHPmDEKhEFKpFNxut7D0K5UKpqenMTc3h2aziWg0imazKWXpyFTXpYlMsJwl83QDWNqEBOHXK7uR9LbVuvuWW25BJpPB+9//fkxOTuKKK67AN77xDclqmpycxNmzZ+X4AwcO4Bvf+AbuuOMOfPKTn8To6Cg+/vGP4w1veIMcc9111+HLX/4y3vve9+Kuu+7CoUOH8MADD+Caa66RY+677z4ASz3JtHzuc5/DW97yFgC90WEbAtH1JLpcLvyP//E/cPTo0Y4N2y5Wfuu3fgu/9Vu/1fHvDocDd999N+6+++5Nuf5miwYE6byS3WJXE1ozzXUKsnZUNDubzhgd8lwuJ9G+jXZvBpY2JdalCgaDkjLb60gfHXkAAtyznIo2Vrg56hpdNAa6AfXtAHQ7BctNk444a42S2bRbHAQaRKzLpsEE8x6oWMvlMqanp1Eul+FwOKSmLOv1l8tlXLhwAfl8HpOTk1ImaCPrTBsjem3bKQHT2AGsdfLZA4Drk04ym+fSCAEgTnar1ZLADe+/Wq2iUCggm83C7/cL400z9jYimuFwqUjfEd966evuzRM7JjozwwjScR+k095sNiVdWjNqmG5KUI/7Aveaiy2xoRl1oVAIsVhM7A3uY3SctB2h7QvaHqZjpUFOguO6KTrPpZlZejws6UEHx6zNroFUu2fA+7NLs9WOMMEA/kvWkw7Y8jsm08l0njhXJD2YGV2cM+1oa4dS23EU04EFIA65zsTLZrOYnp6WezGFa46l1oAlHadT9c0AOJnoBIbXa9PxeZJQYXdvnFMGnqiTWSqNoBbtS/1s9TPRQZxCoYBGoyHrmgx+ghEE22nzLCwsIB6PS8CDQJkJ1GvR7wftTbMe63pEv0u7Rfq6e+ulr7s3R/R7rEtC6HdbB0Lp9xJM1cFPnUmjS0pqgLuXujsYDCIajVqAcvPdsgN3Own/RhISa2IziEtCEc/LcVBHaf3M+18NpLbT4xr41yAtv8eyaV6v18K21qSv1djkdvPJ85sBev2vth14zbX8Pc6LJn5Rx7TbbZTLZSmZ1omJrrPAgSU97nK54Pf7JYOuXq9jamoK5XIZs7OzQnpbj+h77bRWtM1mAulc+3xHnM6lfnbMlKHtymtwrTJjjITIZDIpWBLnmUGEUqmEQCCAer0u5BMTBDfHrgNlGh/SNtfF+N27TZdth+6+/fbbO+qp+++/f8VnN9xwA3784x+ves43vvGNeOMb39jx792Msxc6bEMg+r/+67+uSCE7duwYXvSiF+EHP/jBhgdzKYs2yjVbVtdWNet+83O9+enPzO/p718sU5xM9GQyKc1NzLSlzRCPx4NYLAaXy4VCobAiqm1Gmte6BypQDbia6XtatDNIJej1ejE3N4fp6WmJvNJ53My52KjQGCCDfn5+XpqK0rHsJEwfbzQaokgJkrAkyuzsLCqVCrLZrCi5jWy8BPlbrZYYTFzDGsgBIMYEFXmz2ZRURKZsa2MJWC73wlqwrC/JRn4+nw+5XE4a9bZaLWlOViqV4Ha7MTMzg3q9jkAggHQ6ve57BJbWEVPtySboS182Q/q6e/PE6/UiGo1KJhIdULO+s+mgAVZd5HA4JF3c6XRamG29EAalw+Gw1ISOx+OWoKIGdslGdjgcwsrj+NvttoX5ph0X3WiboGY6nUYgEBCQ1uFYasSms+uAZYaQ6VjymlpP67nh3wj8U0zHmCA6S9hNT09b7C091wQO6CxqUBaAgL0ck84QNK/L7+hSYpqpph1tfpelABqNBvL5POr1OorFoqUWPsfTrTPIczFAQUZfJpMRNri2c9Y6r9ap/J1Os8PhQL1etxAdtK2rASav1ys2BcF1XR5PB2fIcOc5qUfZIJzri++VLn3E4JbP58PQ0JAtcUIDSnxWph3dbrcxPz+PkydPSjbbeoX3pK/Zl76Y0tfdmyfMJCUwzWAoWc3apwaW9RNF6yEGibWP3iv9TR0ciUTkh6x43dQbsJYH03rNDH5zb9f3pqXdbsPn82F0dBTRaFR8HoKlfr/foq90cFPvZ9o20AFufsY9lWC9/pvWpfTxmEk9MzMjWWyajKDPTz1KYJa/64be5j3b/Wv3PPivHfFOB7DZdLxWqwkgTB2+ntIr1WoVs7OzEqyfnZ3FwsKC2AaFQqHrc5kBDQq/zx43ZNR36lVCe5HBZILoOgBl2rs8jgEpMtKZAUIbmDhALpcTEtvMzAzK5bIw3k1iBcfTCSDnu8CMeZZ7W6/Yre++XFqyIRD9hhtusP381a9+NV796ldf1IAuVTEj35p1Tsa5ro2uI2r8vlaYgNXgN89BVvrFjDccDmN4eBihUMjS4GEzhPfp8/kwODiIcDiMbDZ7UQwe7fDqmpZ02uxY9abDk8vl0G63cf78ecTjcQwNDWF4eFiMr/U0yNoK4fgZ+X3mmWcwPz+PM2fO4Ny5c5Y0eztZWFiQiPHs7CxOnTplMc7IomQEXQMs6xWCFVS2ej7NuvMEzmksNZtNBAIBJBIJeL1eMc60UFm3221EIhF4PB5Uq1WcPXsW2WxWGqyYzLdarSb3GI1GJXCi371uhe9sPp/HuXPnUCgUNqTMd6P02WxbL33dvXni8/kktZql2LSY7GPtJJpBW+5XBHrp9PXKEQ8Gg0ilUohGo0in00in08jn85iYmECj0ZDAKDO+9H5K54b7LQFQPX6yohnA9Hg80oCR+oX6OxaLIRKJoNlsSvk1gtzcc3WgHIDFpqHu1o67bmZJVhwAAUNZuq5cLmNqago+nw/BYBDpdFrqJfJedHOvdrsttWzn5uYwMDCAwcFBxGIxSxahdvz1GAh06H4uLGFD/cHvagCa5Xzm5uZQKpWQy+Wk9AqD3yaws5poptr09LRkINiVvukGQNcAEvUlAXpmrXm9XqRSKYRCIZlXnp/Hstartnv1Oc1avTy/0+m0pPcTIOfnzArgM2F2nM/nw969e20BED5v2iEkjhDg53OamJjAI488YgmsrEd2YwZaX3dvvfR1d+9FB4cTiYQ0IWbpR9P/01msOqBL37rdbotfyQDfRvyC1cbLRqKRSATJZBKJRAKVSgWZTEZASDMQq8fOPU6X7dB7H3UVx9xutxEKhXDo0CHJ2JmdnZX9MBQKWQLwdhldqxEGCKiaAVaOWROj+DtLnMzMzODMmTMIh8PYs2eP6BTzunqPZTk0ziX7e5nSDTPbvAczCMvmrMyCYkkxNpSdmJhANptdlw5giRNNpjR1dzfYjmlz6mfC+aat43K5EI1GhexA/afXGHU1e+Q5ncu97My547UYNGdGXalUkrKFpu7m8+c80R7lO2j33DQRg3YZ5w0A5ubm8NhjjwnTfSOy23RZX3f3VrpG+I4dO4YPfOADCAaDOHbs2KrH3nPPPRc9sL5YI4Nama/GcDaVYadzmOffiFDxkTG0FcIN3DRMuDHoNHEdudai58dk65sgut0c6e9TYZG9ReeUZWR2mlAZ6fr3BG457rWUOQ0UsgHMdaYNN4qeM7t/7UTPPx1oKnh+V7PMzLR7MiVWa/TKsRMw0ulxnCcaclxLBND5zN1utwBd2phaS3hPCwsL0ihnvXXsdrP0lfnWSF93b43Y6RNgeb/U+xWwEkgHuku57oVo1pAGC0zH2/xM78lmqrWpLzWbncezLJsOaJrMNL2Pd+NYameQ19Lzr4F4O+YVU6UJZNs5/LpOu9YxZlq3Di6Y4+P9dXq+ZiafdgapK8hk0z/U2esBYmkHAOjK4TZFO6J6PeiSJrSPeL+cXzMgpMsi8DzAcr1aE1jR96nfN84B55/f0444hUAMHXUdqNHsUf1M9FrgORhUZ9PSS0X6untrpK+7t0ZM3a11ogn4UrpZw52CcxcjJBWR7WuKHWjfSd/Y6QzTTqEvReBU626tz0y9becHmtcx91UT6Ne2g9YF/DtLlDFbWAc2zHsxM6tWeyZrPVvTjrA7F8eh/UaNFWgG+nr2w4sJuur5tguemPehg+l2fcn0szczDeh3r7UGdNDHtJWJSWhhEASAEC14DpM0amaL8L3gOdk3j+SNS0H6uru30jWI/pOf/EQW3o9//ONVQam+rF9MRaTrPJmOm3Zc9UamWcZ23+kl48XhcEhDpUAgsKUgOhuIMnJNxUTnjBsq06e0IiBzjd/nOZh2rzd/velStMLQTu7s7Cyefvpp1Go1jI2NYWFhAdFoFPF4vGcMwouVdnspNZ9NR06ePImf//znyGazmJqaEkWyXme8UyQYWF6jrIc7MDAg9fV0xoU2mjgGzWTn8yRTXjvawHI5F33NbuvW8TwsGaOVvy5bo99RAKhUKjh79izm5uakURtLHJFtt9q8kQVZqVRw+vRpnD17VhzyS0H6ynxrpK+7t0aYXsweGdzzyOBptVqWBlcMtJI1TYOe+qxarQrzuddC3dZqtaRZE8tp6KbYBL65J+ZyuRWAOc9lOlPUp3QUGZTUxywuLkrTR80+1+fSzi9FM7U5R9zDOWYC9myURvY3ANmnGQCdmppCo9FAMpmEx+ORcVJfsTEqa4qTWe90OiUTr9VqIZPJWOwsXS9bB595XtpRBEQ0m82833q9jkwmg/n5eZRKJSnHshnro5PwuYdCIcTjcbhcLrGzGIymTcZnybU1MDBgKRek2ZzU8QRS2LyP7xBBEs32pv3g8XgwODgo7wzLtVB32wVGCH4zC2xubk6uE41G4Xa7xa7lO+FwOKSRXrlcxunTpyW1frcxyS9WtkN333vvvfjoRz+KyclJvOAFL8Dx48fxa7/2ax2Pf/jhh3Hs2DE8+uijGB0dxV/+5V/itttusxzzla98BXfddRdOnTqFQ4cO4YMf/CBuvvlm+fuHP/xh/K//9b/wxBNPwO/347rrrsNHPvIRXH755ese/0akr7s3VwimNhoNlEol2bNYfiocDq8IFmpfmrqQe5wOdGoyT6+AdNoLPp9PspNyuZwECU1wlPuS1pcU04fVY9TAMwOuBDR1hpIuj6V7gJjBYlOfaXCcek9nILGci8765TkCgYDohkwmg2aziXQ6jcHBQXg8Hst+zfPwHnhdzosZLLADhM3npnEBsw68HbhOXzSbzaJQKGB6ehqTk5PSGHwrhBiH07nUqJvrWttk2vfn/Wsi2WrP15y3WCwm9pc55/p3rmdNdONa0j68lmazKT1dcrkcMpkMvF6vpbE7s88ZrOBaGBgYkGzzarWKyclJCxHhUpC+391b6RpE/9d//Vf5/Tvf+c5mjKUvWFlzm6CiucHr4/SxAFYcY4K+vRACo8FgUEDRrRC9QeqNl+CqdvA1A4lGAkF0OtpM+SVjWdfgtAO/9TMgMEDnmRv+zMyMKFrd9GU7hXPAuqe5XA5nz57FU089Janh9Xp9w2vE7nua3UEl53a7EYlEpFSK3++3sL7o/NJhLpVKwgwnAGJntDD1TAdE1nMvdN51SiENr05CJex2uzE8PIw9e/YgHA4jGAxaarXZzVW7vVS/lU1Yz58/L0BOX0n1pZfS191bIywn4XK5UKlU4HK5pOG2Ll1BoVNLh5g6rNFooFgsSnrpZgSoud+2221xhH0+nzRj1EFJgou8v1arZdGXLF1GphL3Lx3gpCNsAqeLi4tyr9qm0UxAO0abZi3RwWYasW54TiHIz72d9eudTqeAr06n05KBxGsS5NaBDR10DwQC8Hq9qFarEhBgSR/aFqyrbtbW1sCx/tH3xzlgXdC5uTlJP6YD2Gu2o51ofR4KhTA6Ogq32418Pi9r1SQ2tNttCX7T/mBQg/aGBm7o6JLgQEDE7XYLwGUyyPncy+UyZmZmZH3Y6W7tqLNxKhupBgIBKRXo8XgERNf1hAn0FAoFTExMIJfLYX5+/pJxwLdLHnjgAbz73e/Gvffei5e97GX49Kc/jZtuugmPPfYY9u3bt+L406dP43Wvex1uvfVWfOELX8APfvAD3H777RgcHMQb3vAGAMCJEydwyy234AMf+ABuvvlmPPjgg3jTm96E73//+7jmmmsALAHxb3/72/HSl74UzWYTf/3Xf40bb7wRjz32mNh4myl93b35wiAsmcEE8egf6qCy3j80EAxAdBx1uO4t0kuhn0JAUI9NA/4maK33Q3PftQOKeZ/Acg8PDWhz79QkI2A5cGxiDiaAbwbhaVMwaK2zxziPrVZLAqzAUi+PXC4HAEJAALCi1rlZHo7H6ew1PTa7udDzr3+3A8/Nvy8uLiKfzyOTyWB2dlaIbLQnNlsIIjNInE6n4XK5JJuN+lA3/qS+1iXw+G5Qd2tshfetm5WbpAmTcKczIjWRbjW/m/XTm82mPP9QKCRlVXX5PZ1twfVVLBalnM7c3Jyl4Wlf+rJeWTfyyTqCP/3pT3HFFVdsxpguSTGZrnTkyOChg8rfNavNBHY1mx1YbgKqmyHRyaGy56akv9etbCULYjVQ0vy7naFj1onXv9Mp1s6i3fVNA4WMPrKIs9msGAOxWEwcwu2qj87nTQeUgLRu/Mb77yYNbzXRQQo21xsYGJCMBY/HI06qrjnOOaeSptGj6+1phhnXKR1p3SxGl1Ho9j7MAFU3QSd+h8bk1NSUNCRlPWCCChRtrLAZLRuZbkawaydLPyK+tdLX3Zsrem/SjS1ZD9MEhsl0pv4wQWjAWiKDwLDOuNFg4HqEzgsdC46V4LlO3aXO495Op4SAuC6bYupbDS5Sb5oZcdrB0p+Zn2umtj4n51UD+7pWKP8l+5v1tJmRRnCbQVs6YeFwWJxxgvC8JsfTKcvMdAQ5dupG7Xzr0jO6dICeQzp6fG7a+d+MfVA3AaXzTeKC0+lEIpFANBoVwoHuVcJa/rQnvF6v2B/aUdesRt6HJpBom4DOvGYscm40M3I9+p7XYz1h1v7P5/Pw+XxyfyzRpwMbc3NzyOVyEoC61HTRVuvue+65B29961vxtre9DQBw/PhxfPOb38R9992HD3/4wyuO/9SnPoV9+/bh+PHjAIAjR47gRz/6ET72sY8JiH78+HG85jWvwZ133gkAuPPOO/Hwww/j+PHj+NKXvgQA+Od//mfLeT/3uc8hnU7jkUcewfXXX7+ue7gY6evuzRX6G+12W+o40w/XDYqpc82gmQ4Kaz3OvZ211glI8tiN6m79Q9HsXhMc1jrL3GPN++DxdmCynodOfr9J8DO/p/Uf9aFph5g2EAAB5qnjAQhJrlarid/N+t36OnqOqUP1XJq6h7/zWpqgqEXrblMfa31B/W2uHf2seqVDaN/ocmgkdNCuicfjQgzgGqQ+5rrXWWT6MwArQHRNUKBNw+dlllTRogMp68GR+IxKpRJmZmaEhJHL5aRCgtPpRKVSQblctmBj8/PzUn+9r7s39v2+LMu6UT2Xy4Xx8fFLpn7QVogG8BjlIyjr8/mE+WQytZhm0+n73ABdLheGh4cltYkbOWswVyoVnDlzBrlcTpptdcus2Y40QrvUKZ2KpCOaNBSo7NhNnE4iQVytwNdT05rXpDICgJ/97GeIx+PI5/PCVEsmkwiHwxbneLNFO4qs+5XP50WJNBoNAWVCoZA4kRupD8b78ng8iMfj0qhnbGxMgglMtQoEApZGoVqR6rVMA7bVWmpux7qj09PTUst9ampKjqvVamIkktmpUypXE7MhTjfrn2NdWFjAM888g0wmg0AggPHxcSSTSXnubJ4CLBlTs7OzKBQKyOVyePbZZ1GpVFAsFi+qCetulUvtfrdT+rp7c4XOiMvlgt/vF6Yimao684YOid4P2ZdC/41/1/qK1yCLenJyEsVisetxkv0ei8WkbAWZ5vl83mJkO51OKWFBZjCw1NyqVCoJCK37iQCw6FPuwbqsC8ehWWwmiG6mFjO9Xpdb0+xl7ajrZqMcg8fjwcjICAYGBqR5HB0x3supU6cQCoXk/7S/aG9xTigmiE6wnMQEBtM5Hm2DcR7IKtTBboLFHB8BfjrBmmSxGcHXUCiEoaEh0eOJRMLCGmPWgsPhENuCz8jpdErzUx1Qqtfr0gyVtoYeP7Bcn51r3OVySa8ZPo9gMCjzRwAFgJT0W000wMHncO7cOczPz4t9yPUxNjYmgQSnc6ksEYPetVoNhULBYm9fatKL9VYoFCz/12WYKI1GA4888gje8573WD6/8cYb8cMf/tD2vCdOnMCNN95o+ey1r30tPvvZz2JhYQFutxsnTpzAHXfcseIYAu92ks/nAQCJRGLV++q19HX35kqj0UA+n5c9x+/3S+YVs5R0CQ/uCdy7mV2jm15q3U59Cyz3UmAmqvkOrCXUeSRmuVwuCQJTz5k9R7SvpbPMAGvvEjPLV+/NOpuLgX9gJXisfS5tF9DX1vOnyWtkD+vSZ7xfHgNA9Gm73ZZ5z2az+OUvf4lgMIjLL78cfr9fMuSoo2kjaBCef2fgwyQg+nw+2Y90405tk9DO0L1JeK5qtYp6vS7riD6pfibs8Wb27dioJBIJHD58WOaJmXSRSEQIfWyezrVAkJl2WqVSsRDqtO7O5/PSu4v3qW0vrnvaTsRFtK1kBnhW61tmiva7n332WWQyGSGsuVwuxGIxwR24bhYWFnDu3Dlks1nUajXk8/kNYx3PBen73b2TDVFj3/ve9+LOO+/EF77whS03Jp6roo16pnlrdi03LP5uOgNm8we9ATocDqnbyc2m3W7Lxs40o2q1KhviWrId4Lmd6E15NUdGM67pKNGxNUF07SR3Ypnp89L5XVxcRLlcxtzcHGq1GuLxuKS/h8NhyzPbqvnTCocMdLK66ahSybKO3EbYEToCHQgEEAwGkUgkMDo6KmByLBaz1EQ3mRRmWqAGCVg/rlKpCGhjsjo0E11HnrudJ/O96fZ7jIAXCgVJQywWiwiHwwKe8Tk0Gg1x2guFAs6fP79lKX07TfoR8a2Xvu7ePNFMdGDJwSKYqpnEBNEZ4NaMHTMYTN2kHUJgOWWa+ma9Yu77LpdLAACOk8fpsdDRJQgKLLPbzLIkOp2aOk87oWZ6tynmZ7rMh2a0MatJz52eK55rYGBA2FhkolO/LCwsoFqtolAoSAYUyQQE/80eG3YBcR0YMIEAPZ8axOCz1M67Pi/XkGZim+y3XgvB+mAwiOHhYaTTaZlrncUAwBIc0lkTtIuYnq8bePFezACA/j/nks6uy+VCIBCA3++Xdcr1pEsFdSu8TrFYXBGEisViaLVask7Ys+DMmTOYm5uzPKtLUXqlu/fu3Wv5/H3vex/uvvtuy2dzc3NYXFzE0NCQ5fOhoSFMTU3Znn9qasr2eGYAjoyMdDym0znb7TaOHTuGl7/85dvCBu/r7s0TzQrXoGKtVrNkL5sscNP/1OQ1goMsP8oSJPy72+3G7OzsusdqjoVj0LWt7fQSQWS7fUv7/SZBje+q9vHs2O5a9HeoLxhw0KXM6IPzvszr6rnW5VY10E7CIclJ6XRaAqs8VvuamjGt75nX1GV4dJaV1usmiK7JARrHqdfrqFQqEuDVoDPnplMJno2Kz+fD4OCglNplSbRIJGIJZFC3ch557VqthmKxaFknutcIMzPMZ62DLbQPOmVEaNH3361Q/xQKhRVBqEQigWaziWAwCL/fD7/fj1qthnPnzmFyclLsj0vVf+z73b2VDYHoH//4x/H0009jdHQU4+PjK2rD/fjHP+7J4C4lodOg2bfValWaXNk5WTplmZ/relW6vlg6nUYkEpEoNrC8MdJxjEajmJ2dFZB1tZeNDpJm/27VPNEIIUvOdNap5HSdbM0E0+VLGAG1qzu71qbO+9bGxeLicqO0s2fPCqDMjtxut1sYgBzHZswPnX8GRli6p9lsSi37wcFBOYZNOUqlEs6dOyfg/2qOogZLYrGYON179uyRxmNDQ0OSXkXnX5fNMefZZPXxh0463wcy230+nxhQdLZ0F3SyxfgemMqfBk+5XEaxWJS6iBcz94VCQYD0arW6orlNJpMRdmG/FltftlL6untrpNFoSNMoh8NhqaFN55FOBh0rlqwgm4xsnsHBQQQCAQvzizqQDOBEIoFyuYzZ2VkL05vHm0I2Dp0Mv98vOpXjJWBq6ldgiTEaDocBwMLWpaNqOoTUj7Q9TCYddSnvXwfFNQCtHW3T5tAOG+eH5yHwrvtt5PN5tFotsX/a7TYymYzF8Q4EApLqb94/HVKCA7wnHSjgfZGNZVd6hM9L3z9tF5YKY/CYZVVisZjoOM0go77rVhwOh7D2vF4vUqmUlB5IJBLifJvgvwZd2LSV9hgACZYTiCYYnUgkxB4g2zOfz8uzoJ6s1WqYm5uDx+OxXFNni/F5k+nHmr29kEajgWw2i3K5LDaLtqf6jmRv5Ny5c4hEIvJ/k4WuxbTH1yKj2B1vfr6ec/7Zn/0Zfv7zn+P73/9+x2tupvR19+YK9x76kCTjcD/XzZ81IE3dzQA6S4b5fD6Mjo4iHA5bdCODicyuGRoakoaT3L9W090M+ALL+ywAAY41SMrzUA9ptjCvY4Lnpg+myU2aIKBZ3WbNcTMrXB+rQXgz0G0KwXM9H7q0p24KyWyhM2fOCJlreHjYknFHMN/cZzSoTUKBtkkAWOwv/k5fk8eZGc3VahWVSgULCwtyfWYm1Go1+X+tVsP8/Lw8w26EuptAuc5+TqfTYpdw7rlOdcagzkrgscSO9DNzuVxih5KIUK/Xkc1mkc1mZV5IJJyenhbdzZ4ttJc06M6sNN30/WKlXq9jbm5OMvc8Ho9klZg12vvSl4uVDSF4v/u7v9vjYfQFsDY4MRnTZK8xdUtHo4HlVCOC6AAwODiIRCKBYDCIQ4cOIR6PW4BbOj+VSgWhUAhzc3M4deoUZmdnRRl0AlF12g+ALQMDOa6FhQVJ79YpbNr5ZD0sh8MhrDsqMDrvdEoBaz3S1UQrdDuG2fT0NICllPf5+XkEg0Fks1ns2bMH0WgU+/fvt4DKvWSlEyAgmDs/P4/FxUUxErQyDwQCGBoaQqPRwMjICGZmZiRliw7pagqHjDOv14vx8XHs27cP4XAYBw8elFrwzH6wi/53Ev5d16J1u92Sxp1MJoWdfuDAAVSrVTz11FM4deoUKpUKZmZmUK1WBVTyeDxIpVKWtH9gWekz8MHGnusxZCg0MhuNBmZmZjA3N2dhbejjtCJfT93255r0I+JbL33dvblCJ4QBPO6z1DncC8mebjablv2WYG00GkU8HkcoFJL9lA5gu91GPp9HoVBAvV5HOp1GtVrFs88+i0KhYAHDgZW1oum8zM7OIhwOY3h4GOFwWIBJOmcEt6hPNUOYwKhO2TbZwHRsNWuaOo/sZQDCmGKgmXsy2Xw68MBraUddg+m8ZzYBpfNGQJfOt66VSZ1EJjqwVLZhZmYGgUAAlUrFUkqFAEkwGLQEhdvttjiKOgBNZ5pAAwEa2gxkQGp9UKlUJBir64FTd/P51Ot1WRelUknS29ezXiORCJLJJOLxOK688kqk02lLIEDX+aVtwYwEh8MhvWBoU/HeCPIxuE6bjax6ZsadPn3awtRrt9solUo4e/YsBgaW+qkEg0EBSXS2wcDAAMrlMqampnrKDmfTcG2zaILCpS690t2RSMQCottJKpXCwMDACob4zMzMCiY5ZXh42PZ4l8uFZDK56jF253zHO96Bf/zHf8R3v/td7NmzZ/Wb2yTp6+7NE50ppfWY9g01I5q9QTR7nX53JBKRcmFHjx5FMpm06MFcLodcLoeFhQWMjY2hVqvh1KlT8hmvo/dgPc56vS6NnFmmw+l0ii9ux5rXfofJJl9NOGad7cMge7vdFj+WpAH63zoYwHmkzaNF+0Z6XzUz7vT4qY/1/BAXcTqdyOfzePLJJxEIBHDVVVdJaRMdwGXTb529pv1//k6bQfuteu/TPjLZzQSE6YMXi0UhmjHgQKA9FAohHA5LiZT1+J5OpxPRaBTJZBKJRAIvfvGLJWOM49f2AIkFAwMDgtdQB9MmpU1CrIQB9sXFRfh8PikbtHfvXtTrdTz55JOiu0nSy+VyYuPQ9tK2Aeew1Wohm83iwoULlky/i5VKpYKJiYmOuvtS9xv7fndvZUMg+vve975ej6Mv/79oVjk3JoKfJmBOhrr5HR5Lx5OsKbK2dd1SOkPBYBD1en0F6LnaOOngUaFuldCBZqSUzjlBCe3QE5SwS8XTAOda92sndmwBKlEAUipnYWFBOkgDkHqr7XbbNi3PjqFtiplCpRU5yweUy2VpWKlrxJKZT4OQrEI6w4xgr6bUOPeMggeDQUSjUTEKQqGQrK9ugPPVxASh+cx5v2TLsZlINpsV8J3PQtca5njIWuc6psGxEWVuGlk0zBjA0M9LK7FLWSH1lfnWS193b41wbzH3PZOJzL1C17imPiCzmcwpBoAByH7lcDgQDAYFWNVOcaf0YDoU9Xpdym6YAU7TwebfdP1LXVeUf6NogNtMe2fwmd+j80Z2E48z2XJ2Y9T3pOdU3zfnk3aDaRtQN2rwhDXImVHEZpPUoX6/X+ZCg+WdgAl9be2003bTzbLpuJNlbq4Lrf+0o0qWWTdCIIX63+/3IxAIIBwOIxKJiLOs1yifMX9MHabn3GQ78nmz/iuBKNqmtBM0KMHm3LqxmWYdcl51bfWLFbvgk3bE+zpnSbZSd3s8Hlx11VV46KGHcPPNN8vnDz30EF7/+tfbfufaa6/F1772Nctn3/rWt3D11VcLkHfttdfioYcestRF/9a3voXrrrvOMs53vOMdePDBB/Gd73wHBw4c6HrcvZa+7t5cMfUxAIuO0zY9/SNN6iLATJ1G34h+NzNSa7Wa9OVaXFwUslG3e7fOttX+TKesCjvfq1t/jHutyUTX+AIxBK27TbCS46Bf1kk4Zr3Hm+OlnuSxdkRCsr8JYjebTQGDiRNwT9djtZtD/svr6O/owC+fR71eR7VatQDLwHI5Ot4D2fEsddbt89d2AJvNBwIBCUhqO4LrVN8H7RT+Xz9XHmcGFNrttmQc8rs6W5L3yevW63XBNvgOEcDX49AZHBcreq13KlnUl77f3Wu5qFoSjzzyCB5//HE4HA4cPXoUL3nJS3o1rktWCOgNDAyIE8cSFnSkWUJFb1AmE93pdCKZTK5gB+nvUKmxlAewxIALBoPiYHZimJMtxPIv6XTaotA3c34YPXW73cIq0XVRCYQy1UkHGTTDn/PQrfJaTfQ56GhXKhVMTk7C7XajWq3i1KlTiEajOH/+vLC/hoeH4fF4LDXrNePOHJupwHmvrBlerVZx4cIFlMtlAQKcTieGhoaQSCREkdOYocHI9MDFxUXE43GJ9pv10TUjMJ1OY//+/QgEAjh48CDGxsbg9XqlgYlmn/dSeE6v14toNIpAIIBDhw4hHA6jWCzC4/FgZmYGzWZTGAtMl9MsDRo5BEo0C3K9Y9ER90AgIKCArj2nnxXn9VJtSgb0lfl2Sl93b45wTyUAyb3WLINB59MuDZhOC2upMhDLPhNk5HLv8vv9lmZR3YyR9b6Z5hqLxYTtrJuMEpx3u92SeUZWkk4jN5nUdKg0SM4SKgRCI5GIsNo5T41Gw5JRRnvI1EGcGwCWvZxM80AggFQqZQF+NZjOkmZkc7HOdjableAH2WRPPPEEzpw5I5lbfr8fw8PDKBaLEoymU0smunbudcYgGYy6Bm+hUJB5pTPKxpztdlscVTreumHYwsICYrGYpbFnNxKNRqXUGvuVMNOrWCxKXdRWqyU1Vbl++YxpP3Ed08bUoDnXhG5UxjXFQASZbLVaDblcTo7ldfj3TsEUjvNixeVyIR6PIxAICBmBc04bvFAoyFxfyrLVuvvYsWN485vfjKuvvhrXXnstPvOZz+Ds2bO47bbbAAB33nknJiYm8PnPfx4AcNttt+ETn/gEjh07hltvvRUnTpzAZz/7WXzpS1+Sc77rXe/C9ddfj4985CN4/etfj69+9av49re/bSnX8va3vx1f/OIX8dWvfhXhcFiY69wztkP6untzhLq7VCpJ1jfJQC6XC5VKxbLX03fivyzPwT3E4/GIziPgqAPRAwNL/S103XQ9Drt3jLp7ZmZG9DHJPzyWukWXTjGBZ30trSO1Dqedos9JbCIQCAi7OhAICCahM+o0OYDYgxZzHATh7chXeh6oZx0OhzDhCZzruuT1eh0nT56UjLJ0Oi0lXsbHxy090RiMXg0LoI7h/VFP87q049hjrt1uCwbBe+Ke4XA4hIkeCoXkeXUj1N0s08v15nA4RHfquv762VJPa3tE2yk6cwBYwn8qlYrlXCz3RvsxEAigXC7jzJkzyOVyFsIFy8g6nU5h5Gvgvle6e2BgqRm47j/GeSbRoVgs9qxkzG6Wvt/dW9kQiD4zM4P/+B//I77zne8gFouh3V5KL37lK1+JL3/5yxgcHOz1OC8Z0Ywlgm90vMjYoZOnFZOui0llTka6jghq1rNm87C+aTgcht/vl5TbTtJuL6Ucz8/PC7DdKZrb6/khgMD64lT2nCc633T0CRIz9dlsNnKx4zW/z3nV6VlTU1NwuZY6R2cyGUQiEezZswflchl+vx/pdHpF7Ta7mulmxJsR95mZGUkTPHnypHSf5voBluu4BoNBqQnH8xH0qdfriEQiEr23uzeCIqlUCpdffjkikQjGxsYwNDQk66obNv3FzjWfebvdltrBTItzuVyYn59HJpORmqm6lMLAwICloz3fuY2MhyB6JBKB1+uVMgxkkxKQ4Ts1NzeHYrEo67gvfdkq6evuzRfuowsLCwJAUi9xn+XepYXHkGlMB6dUKkk5EDr1mknt9XrhdDrXdAK1sCYrnXIC4bQTeH4GeAmwsgYpnU7aHLpmOD8zHUPuw3RkwuGwBWTnPqkz6aib9Ll0/XHaA3RiWfcyEAggHo/D4XAIKEzdwe/ThiKIzOAmQVIGOGdnZ1Gv1xEKhaTfB50/HSwOBoMYHR0VR9lk+GvwhYA5x8xa5mSwsRQbAKTTaQvgQkAbWHJyo9EoKpWKBCC6kVAohH379iEQCCAajSISiYiDywD63Nwc2u024vG45ZrUk7Sf9N9MBhvXtLYlnU4ngsEgUqkUgGU7iWw82q4MatfrdSm9Y1f+rFdO3cDAAOLxOBKJBKrVKrLZLJrNptSBZVP2Poi+9XLLLbcgk8ng/e9/PyYnJ3HFFVfgG9/4BsbHxwEAk5OTOHv2rBx/4MABfOMb38Add9yBT37ykxgdHcXHP/5xvOENb5BjrrvuOnz5y1/Ge9/7Xtx11104dOgQHnjgAVxzzTVyzH333QcAeMUrXmEZz+c+9zm85S1v2bwbtpG+7t58abWW+iMxIxeAANx6b6Nw7yGwyv2dgU7qezLPGSAHlsthMlCnmc6d9jTuz7rsl26yzLEAy31HNDuXIKpmBJvse82q5/2SnEbsgT4qs455HyaITp9H1zW38ynpq1K36HGbQl3odDqlNxbramvS38LCAp599llpMkndXCwWLRlYzPYjwczuuprJrbO9M5mMlOGhDTU7O4tz587B6XRifHwcIyMjEjzmWtIlz4LBoARoupFwOIwDBw4IqSIajcp3OQYGVewyq/gMdLaYGTyh2GV4eTweIXMMDAwgGo0il8uhVCoJ4UFn9OnAhr6W/uxihSB6JBKxBA4YGNDl4/rSl17KhkD0d7zjHSgUCnj00Udx5MgRAMBjjz2GP/7jP8Y73/lOS7S/LxsTrdi4CQDL0VCzHAk3DR0R56ZBZ5Abl3ZsqUR13ctua0fxWvqHCq6X4Kne4OmcE/zUjTP4OwMQBMx5zzoyvxWilSKNhFKphEwmI3PFtD/W0HW73QiHw5Z6+AAsz5lBAtb9bjQamJ2dRS6XQ6FQwNTUFIrFotw3a9bZpWHr37XYRYc5p6FQSEBj1js1medbMcf6GpxLMuGpvAn0aHaHbt6m09PXuhbfuUAgIFkhNCYjkYgEQchSIiBA1gFBk0QiIcABmZkEtVh39lIA1/sR8a2Xvu7eOqHeIihMB5POjZlFphlEBHPdbjdCoZAcS3Y0AW9+XzPNeG39Yzc2soXoADLdlmykdnu5dJZOH+dY9bg7pQGbbDJdmkQzyXW5D4L4DBZQz+seIrQFgGV2P4PpHIN2qJnWTJCD96f1KgDJZuOPmflFADWfz2Nubs7CZKtUKsKS1/YI70+zz1k6jaXeyH6kE8ym77xXkwihn6su/aPJBGZQmIEP6qtAICDZb3o98fnxPCbgosXU81rXcv70eXWQhfMFQIJDBBMIDuln02kM6xE+LwaH+Iz43EZHRxGPx1Gr1YRtp5vnkWnK9UAgi2w9DWY9l2U7dPftt9+O22+/3fZv999//4rPbrjhhjWbbb7xjW/EG9/4xo5/30nPsq+7t0b4zKmPTaKVZvJqv5u6nf4pYNXttAPMEo/mdbsZH+0K6hHu3/q8eoy6jFgnYdDTDBJocJw4BPdRTcijr611qs4A5z7L8ZvzajKiO4kJAGtMRN8zx899mUHZ+fl5TE5OSnYAs7uq1aqA6JqpzVI+bD5eLpelGXkul5PAPXV7LpcT/576z7xXDWaTEU/9bae7CbiTOEjdrTPh9LPXAQu757qW6HdAr0vajCwzDEAY9vF4XGwk9ubRtt9aNulqQrIFbUT2o6FN5Ha7kUqlEA6HV4D1fFcYrNDZn3x/NB7wXJe+391b2RCI/s///M/49re/LYocAI4ePYpPfvKTuPHGG3s2uEtd6CxTeVFR6VIRdKh0KhYj4tPT04hGo2g0GhgbG0MoFLJtzsWNjxF43Rijk1CZMOo8MzMjzR8TicQKlt3FSKvVknEVCgVhFzOCrNnRTNum0mCUW0cm7cqk9EpMEIFAAFPhyPByOp1S0oWNL6PRqDCZdS1cPVZu/M1m08Jgm52dlVTjubk51Go1RCIRDA4OCuuMjAAaVGYAhevMZAXwvpxOJ0KhEA4fPoxUKoWxsTHs379fGP+bNafdiC7Rc/nll2PPnj04c+YMKpUK8vk8isUiisWiGFSAtXb5WoqB6fOhUAjPf/7zMTo6ilQqhcOHDyMYDEpAgQrdNJxoTNPIZkBlenoa1WoVp0+fxqlTp1AqlfDMM89gdnbWMr7novSV+dZLX3dvjVD/NBoN5PN50Ul0oKm/WeqCDjh1FJvgkV3DTDGykwFIiQnqYToDgH1dUlO49+dyOWkQbQKLGhTQPSOY8UaAnQAj9bHpKDO4TXCYjiOdce7fZJfzPqj7TVuENgqwXNOc4DWw5HQWi0WZZ309jplpxmTYt1otCRBXq1UJRHMvX1hYwPT0NJzOpb4bExMTouP5w/r0rFFKtiHL2TCATr3EjDJm8tFW0TV1mU2mbQIyq1hTlynhtPfI3tbrIRAI4MCBA4jFYkgkEhgbG4PL5ZLgbru9XH6IwXyyJM0APNCZkanL6+hzamZapVJBJpOB0+mE3+/Hvn370Gw2EY/HUa1Wpbk9m+XRZrEDn7oVnfI9MjKCK664AuFwWNag1+tFKpWS3jV8hwqFAvL5PADIs6zX69LY96mnnsJTTz0l5fMuBbZbX3dvvfR199YJ92KWS+H+Rxa23++3+GL0nwYGBjA3Nyf9JYLBIPx+v2QcmfsXfa/12Pla387MzACAZF7Rv7M73i7YTdHBXg248t5Y3ot9WBh81fqW9bjpR3KM1LO67wX1Oe0FndFkl01FApcGYZmpRra7zvrl8+K5G42GlGKanZ3F008/LXqaWQMkr9GWoL5gqbf5+XnR4VNTUxY/2c5X8/l8UvqO98m1xWfh8/kQCoVQq9UQjUaFPKF1N7AUZH7BC14g+imRSEhpWhPPMAMaGgcBsMK+M4Ft2m783dyrSfgCICQPYgC1Wg0XLlzAz372MwlaUDYKoPM66XQa4XAYQ0NDuOKKK6QELrM8GFTg7w6HA/Pz82Izk0BQqVQwMzODSqWC06dP46mnnkK9Xkc2mxV747ksfd3dW9kQiE7D2hQa233pjXAuqWjpEDKlipun3pyozF0uF8rlMsrlsnRVZlSdor/DSDqPs0ubNYXpU263W661VuOQjQgNGp1Oy7rvVOYEx7WRop16Rix1lHszQV8zws7x0yBptZY6U2cyGbjdbszOzgqDOR6Pi1IywWkadgTRs9ks6vU65ufnhdVMYL3RaFga0ZnguVbmmpGtHWYd2aahk0gkkE6nLcD/VrHP7YTvBZ9tIpFAKBRCqVRCKBQSQ0vf63qFwALr4e7fvx9jY2O48sorBehijV+OyU70nBeLRalf73a7BViZmpoSY/a5LH1lvvXS191bJzpopj+j80fda6aZktXMElQApE45S7AQUGUQ3KyLymutJQSQy+WyBXCmPiVgrgF0bTPwHHSYCUiaTHTqZdoZdIqBZZCaYLo5f7x3zhX3UDr0OtuMTiWPdTqd4jxx7jl+zWrn2mf5HV1+hONnqj+fKfuN6NqqZC2Hw2HpScNzlstlTE5OipOcy+Vk7kzWezgcRjweB7DcyE4HZrUDzDEQaKfzrnUQ5ykejyOVSklghsENzXrnXBLU0EQNUzTTTK8pM1NBM/x1IIY2XDQalaALCQecV+30X4wQKA8EAkgkEjh48CDi8bjMHefHBIimp6dFJyeTSWG0kaxQKBRw7tw5ALhkSr30dffWS193b63oADH1HZnDbFKpAWOC6OVyGcVi0QJcaqBcZ6FtBFjUfj5Z0ZqRq7OtNIFsreuYmWGaEU1/sl6vC+hs+tJcmzrzivdvAr1249D2QKe9XtsT1Eu0pXRmH20MM4C/uLgoRDZmFRPkZh17MqupD4LBIGq1GqamplCpVDA9PY3z588LnmJmcLFkKseny+fo+yfIr4PjDJqb4na7MTg4iLGxMdHx5nXtWP3aViBpw7TJTJtRg+nmc+J8M0ssEomI3REIBNBuL2WZeb1eSwm9iwHQeT/BYBCxWAzDw8N43vOeh1gsJuWEaHtx/vnZ1NQULly4ILo7FAohn8/j7NmzKJVKqNVqmJychNPpFNLEc136uru3siEQ/VWvehXe9a534Utf+hJGR0cBABMTE7jjjjvw67/+6z0dYF+WhAuXTjNgVTaauUXll8/nMTk5iVqthtHRUbTbS7XZWP+SwGqxWMS5c+cwPz+PmZkZqXvaTY1obpqzs7NotVpIJpPw+XxSa2yjpV20w82NjmlSuh6pLlFDR5fOG4MBOqpqGpubCfyakX2TuaVrqzkcDtRqNXg8HhQKBYky63qj/J4G48vlsqXBiXbIyZZqNpuYm5uTBitkU3AzZU312dlZzMzMIJ/Pi4Ih8BAMBhEIBJBMJjE4OIh0Oi3raCeJTulnrXYC6YVCYQUjYzXx+XwSJNi7dy/27duHcDiM5z//+RgaGkI8HpfAh/mc1hojsMTCiEaj8Pl8OHToEFwuF0qlEmKxGKamppDNZvHss88K0/RSYLj1ZXOlr7u3XjR4S9HMcZNhS/Z1JpMRprjeX8hwZ3BwcnIShUIBc3NzXelsYNl2aLfbyOVyaDabkgpLoJE6gtdkQy8AEuDtpFPpxDWbTUxNTYke1+ngDHBrMJsOsQYBisUiqtWqRYcSXNcOtU651uPQ52K5FF3/k2C8DiwzfZjzSVBAp8YTVCH7jkEIBkn4GbPk6vU6crmcJdPPBDg4dgIkzWYTs7Oz8Hg80rA7HA6LI0uGIxtjs6webUTOAcvh+Hw+SWEna5zgMtPW6WjzWWtCBR1VAh60/9hYjgELvab1euDfuQ7033RAyOlcqnfLZ1AqlbrW2wAk5ZsMSTLQ9+zZg1gshmQyiZGREfj9fssz5zzqxrMEOwhCmIST0dFRXHfddcLCY3ke1tLXmRV96ctGpa+7t1a0HtL7DktR6bIQOuNHN/4cGRmRIB19Ye773C9KpRLOnTu37hKO9PHJiNaAsA6Acly6p4gJbup9WO91mUxG+mMRhAZgCW4C1jKjHI/eJ3VQXQeD+H8GBnh9k1lNMXWALo2jMwPDKPYAAQAASURBVPwYWNKAsPlcgeW+JyQJMHjKn0KhAJ/PJ7qbetnM+OO9aL++3W5jbm4O58+fRyAQAAD5l+sgm80KAY5YgA7w819mQHUi/uk1apZ30bpHf86x0o4xgxd2gWsdGNBzTjuA52Jwmpl3epxrCTPr2EOO5Wv2798vPUv4mdbJJMoxwDUwMCC2HQAL3kJ7aN++ffB4PKhWq5iYmEA2m0WhUMD58+ct4+5LXzrJhkD0T3ziE3j961+P/fv3Y+/evXA4HDhz5gxe9KIX4X/+z//Z6zH2BStrTXZz7OzsLKrVqoB9xWIRkUgEqVRK0osJtD722GOYnp6WpocEULu5TrVaxZkzZzA1NYXR0VGJBLJG9GqR5dWEbK9SqYSTJ09iYmLCwpRj9JNODmBNj+Pmqp1VbShsheior1Y4VNwE0MkmsGPwmeC7GTDhfes1AkCi5n6/H2fPnpXn0mw2EQqF5PsLCws4e/YsJicnkclkMDMzI021WF4gGo0inU4jnU5LsxTdEG6nCB35druNdDqN5z3veQIuTE9PC6OiG2UeDodx6NAhRKNRvPzlL8fLX/5y+P1+xGIxYavpGnrdsOU0U53ge7vdxvDwMF70ohehXq9LQOvJJ5/E17/+dQHJuh33bpF+RHzrpa+7t0dMJ459D/Tn2rlhCapyuYx0Oi3gNhk2dE4LhQKeeuopzM3NWepIryVk7rRaLUlzZoNKn8+HdDptcWY1ywcA/H6/NO3WAT5Td9XrdZw+fRozMzPwer2WlGnqDzrkPJfW2wsLC5iampKSM5FIxFLXmoAunVLWFmcQmns008q5j5rNxe32Ep/Ph1arZWnkajbOMp+tnl9t92gGvKmn7b5Pp57jLBQK0kSdNblp47AuK8FbplHTyWWtVZYYiEQicg066sFgEMAyGEKw3el0WhqkE5whCMLABbOpTBCd5+cPv6+Z79qGYDq70+lEOp0WwGpmZqYrEJ3nCQQCGB0dRTAYxIEDB3Dw4EGEw2Fcfvnl8j7RvimXyyiVSlhYWEAmk0G1WkUwGMTg4CA8Hg+azaasNV1+h0Ghyy67DDfccAMajQYef/xxTE9PY2JiAv/v//0/ZLNZsTmeS9LX3Vsvfd3dOzGD2p3E9LVN3W2+B61WS0DReDyOwcFBIS3F43HJMmN5kB//+MeYmJiw1FHvZuzUdzMzM3A6nSiXy1KeJJFISFNtneVlgs0cL/1ifqaDqWfOnMH58+ctuhuAlOjSdcl1gFoHCrjXczwk13F+OQazh0knX0rfD6/DY3UZEx0c0EQGDdrra5rsbX0trbvNoIm5jqgTa7Uazp49i0ajIQ0vo9GoHMfnl8lkxN5jcIb3wQAMA992rHZ9Xc4dCQa6zre2c/iZGQA3M120PafXCOeANhQz5LmGx8bGJJifyWTWpS+07r788stx+eWXIxQK4cCBA0ilUmKLcY3SJsvn86jValJWhsQFCvU1S/8MDAzghS98Ia699lo0Gg2cPn0as7OzOHXqlJzruSh93d1b2RCIvnfvXvz4xz/Gt7/9bTz++ONot9s4evQoXv3qV/d6fH2xkW4WsY6Ier1eFItFcZR0jU6mo7K+tu783a3QsWANy3K5LGm6ZDtppdhJOWrnkkzqSqUi6c901Oh4kWnucCw3E9VMMSrJ9dac2yrRm9lagRHNHOhWdIphpVJBqVRCu92W2uB8ziabjRFl7QgzxU2XmelVmnUvRQcqaHwAsDj3a5VKoVPP0jo0hoeGhiRVbz3Mc7sx8l8abmS+kaXo8XiQzWYRi8WkZnKpVNqxa3kj0lfmWy993b0zRDOv7PZPOqPUgWQ/EVQlyKr140aCbNz/W62lRtV04qjPdWaXWU7E3PvpCJMdZN5Du90WYFaX1tI/1Ncmk416jCU/dPpup4C4ZkPzh2PSKfV2xASTza6byenz81hz3rvNCFjtuXDemXHmdrtRLBYtADZJDKw1bzIjgeWAic5u41yTVWa3Bu0yHc1gsQbzOZ92jrc+P+eTx5vPiGuN/WA0630t0fZKKBRCKBRCNBpFPB5HOBxGIpFAMplEvV5HsVi0pP3zPnVfGDtbwQx8+Xw+xGIxNBoNxGIxOXcgEBBH3Cy1tNulr7u3Xvq6u7fSLZBuitbddmLqvVKpJPuE0+kUv5sl2wqFwoayVai7geXgIwPX9LvX+j73b/rOevy6cWm7vVS/2w641t/R/rYmtFEXaNb2WmPrdJwmw/H/ACylZM0McJOIZn6X99AL0bYG+2SwXIgGwBcXF6UPXbVatTRu571TZ+ueLmtlf5uEDVNfmX8znyWwMqBg92P6/3zmxH50SbRuROtuZo+ReR4KhRCPxxGLxQSz0n3bTNIk3wv9fLU9yPsjKa7RaCAej0v2RTAYlEzAvu5e+f2+LMuGQHQA+Jd/+Rf8n//zf4Qh8tOf/hRf/OIXAQB/93d/17MB9mVj0m63JVrXbrfx6KOP4ty5c1J/cmBgQCKF1WoVU1NTwkBfrzIhq4cb0OOPPw6fz4fBwUGkUil4PB7EYjEBMsnqAawpUIwwsqZ3JpPB7OyssIhZc53pONqZJqhrggKcAzqMOhK7FWx00znc6Aa2ke/o9PYzZ86gVCrB7/fj3Llzwtbms5uenkY2m0W1WhWwlorS6/VicHAQBw8eRCwWkzTpnQSem+JwLKXAJZNJBAIBAcFZy7RT01yfz4d9+/YhHo/j4MGDePnLX45kMimlXLoxYi5GBgYG5F3hu5HNZvGTn/wE//f//l9pePdcqL3aV+bbI33d3TvRzkQ3WWJ2sto6rtfrOHPmDObm5iy6k85pvV6XjDMz+L1a4NVOJ9brdenRAcDSgLndbktQ0e12C2hLsKDVagko0G63UalUhJlEJhpLatH2YNkylrQi+Gg26xoYGJB+EwSJdbNV2jFkqjscDgksEITWactMrff7/XKf2lHSDCcAFta63Zxu5j7Ubi83cGdGFXUD67zzc2bu6edKljlroY+OjmJ4eBilUknK+Oia/CzrwhIqAORZsnEqswsptL+0biQrkr/rcju0LYLBoDjOuqwde4+wlMvk5GRXtobT6ZS6tqOjo7jyyivF8WYpHKbkV6tVuX/dT4XOv67Dz/kkEMT7TSaT8r5PT09jcXFRUtCBpXXERuEnT560DXDsVunr7u2Rvu7urWjSTa/XZLVaxTPPPIOZmRnJ0mFPM5YWm5+ft9XbWs+Y47IbZ6lUwvnz5+F2u5HL5ZDJZMR3IxDLvU0D2hr8NolwDN6ylFmpVJLxl0oluFwuaSpJJrIud8XAgNardoEL+uLMcjODFObxdnND3WKWhtFZ6GvNYa+FWWKNRgOZTEZKw+jxFwoFyYIqFotic1B3s4loKpXC2NgYhoaGLOWEdEacZokzOKzL6WgiAL8zMDAgwR0C9WutNx7L7/Mz9n8JBAIYGhoSpv3p06flPJ3mnfXpPR4PDh48iOuvvx6JREJqnnu9Xmn6yabptCNol7GOPe0N6my95jWmoZ8RdXc0GsVll10Gr9eLUqmExx57DD/72c8sPeV2u/R1d29lQyD63/zN3+D9738/rr76aoyMjOxoMO1SFkbldBMsMoqZEk6HlTU6N/qC0OHOZrMScc1ms8hmswgEAhgbG0M0GpUSLJr9xIgt65+yjvvU1BQmJyct43M6ndKQipFOAutkGlOZMorOTZ8gOzdXAJbfey1aeV8MgH6x1282m5iYmMD09LQENDSbrdVqiTLXzEPOr8fjQTKZxN69e6U2umaR7VRhx3W/349kMolUKmXJuLATj8eD8fFx7Nu3D0ePHsWrXvUqpFIpC3NxM4W13CORCKLRKEZHR1Gr1TAwMIAzZ84gn8+j0Wg8J0D0vmy99HV3b4VgJlNauyk5sR6p1+s4f/68XMuUTjrFdMRNsXMsWa5E1zOnA91utxGLxaScjK7pTeeiWq1Kc2s2AGcpC56nXC4DWAJmWd6j1WohFApJY2UNaNNmCQaDYstQNxFEZ/CdZWg8Hg9mZmaEQcfvcF7oRBHk1f0/+MP0eoK9TI82536zRQckHA4HZmZmAMDSL4XPrdOYyLZiP5OhoSEpEQNAHG3tSNMRZ3CkUqlIPXaWIDPTx7VNp1lomqnGsTKji7Yb65kCELA+Fouh3W4jFApZwK5OQgYcm3+/4AUvQCqVsoxV6//5+Xk0m01Eo1EplcMsOzLSeT29Hgk8cB5qtRpmZ2flvqPRKAKBgDDcWq2WNB59Ljjhfdke6evu3okOiunPein1eh1nz55dkYHdiQlNoY5ajfFujpVsZofDgXw+j2w2C5fLhXA4LLqOZVwZNCThjMA5Ad9CoSBENp7T1N1kR7NZo65JzV4dACyAJufcbp6ZecRgO7DMDO8EonP+OE8mcN8L8trFCEFyNpmdnp5ekZml54rfoTgcDiGApVIpDA0NYXh4WErS6cbuPJ7PiufXut0kDbbby6XUqK/NcremnUgWN0FozUZnlYBgMIh0Oo1Wq4UnnnhCgvKridO51Dg0GAxi3759uOaaazA0NCQVCKhndQZmu92WZqwkzZFkot85M0OOQDsAIXYAkPclHo/jsssuEzvz5MmTsn76+rsvpmwIRP/Upz6F+++/H29+85t7PZ5LSjo5ur3e7KlkqDR18yszZbtX1wIg6WxsesJaVCYI2263JdLHenFULGQ92THHqbhpELjdblGm+l9d881M79ksNrppKG2XItdjIBOPDEKtGDj/eox0qtmoho7jRmvcb7VQgbZaS81GCMTYpZjxHln7fWxsDKlUSph5W3XP+hq6hmwikcC+ffuQzWalhutGUkB3kvQj4lsvfd29+8QE8rbiegTC+X+CublcTpp2mY0xmVLOGum6JIzpHFLvEADXbHGdJcTjtPOm9/V2u72ikZmp57VDpfuO6HHR0bdLN6ctcTEkg16I3i+py+k02wENnCefzyeNNZm90Gq1xOnV6c/aJtDnpBNupunzOLM+rhazHq9Zp1cHavS5OT6/349IJCLry8wi470yYy4ejyOdTguzjXagfo/oTPO7DBjxMw346PVnd396DBqkYp1/ZgCwWXCxWOx4nt0ifd299dLX3b0Xvreb9S5u9D3ZyPe0bmBNaO5BZNoSKKW+ow7Xeytrvut62voa1AEOh0N0t65TbddMVJd0NX1uO3BXg65r+eh2fhn3bP3vdom2dUwQXeslPTea2e33++H1ei1BDzMrzvzdtLdW8121fub3NflvNdElfLSe5fdYVq1WqwnOYF6bWQhDQ0NIJBIYHBy0zXS3s4P1WtTrTdt85lyYYga5SNxot9tIJBIYHx9HsVjE9PQ0crncqvOxG6Svu3srGwLRG40Grrvuul6P5ZISbXADywY1N9tei1YmOsLb6wibVly5XE5KsExNTVm6XmvWEjdSszan2+1GKpUSJjqVM0HgUqkkTHMd8eZGrh1J7TjrWmm9Fh0Z57OkkrEDFLZKeN+6bhlgNbzMsXm9XkSjUYRCISSTSSQSCWlSthtER6FjsRj27t2LQCCACxcuWNgRTqcTyWQSQ0NDGBkZwSte8Qq88IUvlFSyrSj7YycDAwNiQL3kJS9BLBbDzMwMHnzwQakJpxl8u036ynzrpa+7ey/byXbqJBczlna7jVKpJI0neZ5cLieNzMz7NeeAoLNmqmuQks724uJSQygG2wnwanCbetrn80k2m9PplBI2bKbJ0hxut1vSnTXAy6AkgYVqtSr2CgOr3EtZ45sMKN00bScIHVIN+ALWNG2ytIaHh3HgwAF4vV4p3dduL9Wn93q9ci7aRwRAKCxfwuxFzgPZ+2wUZwIDtDd0GjkZ6zo4reuacq7JKHe5XBgbG8Pll1+OUqmEM2fOrHBkWa4glUrh5S9/OS6//HJEIhEMDQ1JCRdmbunrs0EryRtut1sy7XSQyAzsmPY5nW6Wk2Etd92Eb3R0FNlsFj/84Q/x5JNPSrmgvu7uS7fS1929Fe2Db0YW2UZFj2MjQDoD0k6nE/l8XnxrXYZLZwfx/9SBrNNtNx/U3QTZmXlOQF5jDCaAznKY5t7BsTAwrPdbzRo25whYBtj5DKkP7fzv7QbS7cagAXTaRtRnPp8PiUQCw8PD8Pv9Uv6HYDxtGjOIbl7DrneZvq4OJHUKnncSDaKTQMGGng6HA7FYDIcPH0apVMLp06dXgOgk56VSKbzqVa/C0aNHEQqF4Pf7LYEcHcTmHHG8LDXHEnBa1iI+2PWD0aS5F7/4xRgZGcHc3By+/vWv4yc/+cmOsvM3In3d3VvZEIj+tre9DV/84hdx11139Xo8l5RokE9HAjdLeg2Yr3UdOkQUKlTNAOImzdptLPdCUNzv98sGys2N9TvpqAPLm6G+P6acaUdes8I3i1nMZ2iy3rfTSON11wMEkMXm8/ksTu12gcrrFZ0C7vf7EQ6HJRvCBB0CgYA0EB0fH8dll11miWxvh2jGSDqdRjAYxOTkJL73ve9JOt1uyAjoJH1lvvXS1929lZ0GnvdKGLA2hamvdkLdDtjrGb2fan1Ip59OrwYQCLIShGd9zHq9LqVBdMmYcrkMj8cjAKVOVda9Uwi6a9vCdB6pO3SN9p3ynPVYtJ6j8FmQCRaLxQQEX1hYEODbbPCpCQbUPeZzbbfbwkAEsIIUoZl3erwaUDEbtWoAnmuB4w+Hw0gmk3C5XJicnFwxFwSI/H4/9u7diyNHjsh3eV0Nyug10W63pXG3Zu5r1pwulaNZknr8vC+CAqwJz2N8Pp/0C1pPs7WdKn3dvfXS1929F50Rs5PkYt4PM+jXjegyq9St3PvtGMHc46vVqiWoDiwHAXS2mMPhEAIWx6f1lz6vbkYKrGxy2SkTSZ+L9gWvtRMCJKvtmRoLoe7iMwkGg/B4PBL45/EmAKzPbQYpOq1v/blmn/N3jdXYjZ0gus5AIC5DXZpIJCxZ1Vr4eSgUwoEDB3DFFVdYCBYmUcOcL91cVONp5toyf9efmd/RwR+W0JmensaJEydW4Aa7Ufq6u7fStTV37Ngx+b3VauEzn/kMvv3tb+NFL3rRCmbqPffc07sRPsdEb5YEKIFlxUMm1nZu+qbS0uMDNv4SmZtgIBAQxc3mYma5Fzq3rMFaq9WkIVSpVJKyFnoz1cqcn3Gz18wn3qvdhnsx96ZZ5yYLfSco826FtUkJXJjO8m4SnVbGe9EsxLGxMVx55ZUYHh6W+qg76T7ZZC0cDmP//v144QtfiEwmg5MnT0qtwr70xU76urv3wvqLBFkvZl/vpWG+mUa+Zi3Z1Z/0+XyIRCJwuVwrmnUC1rJmbF6mM7e0ca9rdNJxZhYagXfWynQ4HGI7kE3Nc5jj1nNEO4DBdnMcBHN1BtlO1N12Y9JAMUFrMr0XFxeFWQ4sB8v5u06J1g40GWAaCGfZFZMZqJ+V1+uV+eWcut1uCXzopl0McrB5LQMkvCdTnE4nhoeHsWfPHqRSKanPqzMNdLYWn7nuDcRyBgCkyRsdeQ3mmEF1M+W91VpqJMoSg8ViEQ7HUtNU2q/sz2LeW1/6Yid93d1boe4ikWan7OfcWwBsio+ogwW6jjQ/Y8kvMpt1vW2Oh/q8Wq3KnkrfVo9fB1+pU3XjUn1+u/2U80Edps8LLGMCPI5/Y38VnQHHn52quyna9tA6lkFZAFIijyA7YF03ZlBdiyYh0u+1A905Duo+j8fTVdCXQRLqbmC5XC7tKzvG99jYGA4dOoTBwUEEg0GxBRjE0TYk31diZ8ByU3gSAIifmc3AO73rZuCf60aXy+Pcx2Ix7N+/X8qy9XV3X4B1gOg/+clPLP+/8sorAQC//OUvLZ/vJOBpJwojjey8HA6HLZtaqVSSl3M7AFddVkVvumb9042K3hDD4bCkKg0NDUm6Fx0p7cARjCaIXq/XMTk5iQsXLqDRaFg6W1M0qM70NgC2Tt/FsKt15FYD52TJcVPmxr6TlbkWr9eLWCyGSCSCQCAgIMVue8cdDoc0V1tYWJASKVzPXq8XR44cwW/91m8hHA5jZGRkRdrjdopmcTgcDlx11VWIxWI4efIkpqamdi2I3o+Ib430dffFiQlMkxEdjUbRaDQwPz8vTsdGz29mUW30PLo8XK8zzhwOh+iAaDSKaDRqKZWWSCSwf/9+ST02m3VSTy8sLCCTyWB2dhbNZhPlcnkF4M4eFgygs846WcOlUklASr/fj2AwuEI36YZS/NeuhB5rjxIM4Hjp9AM7f68xx0dnVpfPW1xcRKFQQLVaFcCE9h5LmPBceh6pdwhUEMTQmQRM36awQV2r1ZJrNZtN5PN51Go1uN1uSwkYXicYDApDnmUCVmuiPTAwgMsuuwzXX389gsEg4vG4MClZ7odp4Q6HA/V6XcCJSCQifXRYDziTyVjst3a7bSF7MACv55z/skkZAXk9RgYLotEo9u7di0wmIw3idqP0dffWSF93X7yYOiEcDiMSiaDZbNr6jdshDFAyYNyrkq4ajObeHY/HEY/HLTqCmbgEPOkfMdjHZo6Li4uYm5vD7Oys7PGNRkMCtmbwFYCAnOVy2VKGA0BHkNbMWjIZ0/r/DBBQz9v53TtdiHHQxtKZc6FQCAsLC8hms6jX6+LLauY6sIx3UEwypC7/onW9nR5jOZ/FxUXb8q2cfx10Z6N2kksIeJNwQbuLMjAwgKNHj+Kmm26STHFd1s8uUwGAEC1ZlUDX4md9fo6f5dk0WXK1+desen39arWK0dFRAMDk5KT07tuN0tfdvZWuQfR//dd/3cxxXFLCTY1KAlhOk90ukFJvtHSadBdvh8NhYVdv9EXUDCmWCWHKUigUEodWs6E0iD8wMGBpUOrz+eBwOFCpVGxZeBynZpJpZ1nLehnpdvevr6Ojm/qz3SKMImsG+m411mls8F600eX1ehEOh5FIJBAMBm2j5tstOgAUiUSQSqUwMzNjW5pmt0hfmW+N9HV374U6TPeX2G5mm7YnNCNb656LGZ+2WciQ0k4uwWy/32+xHTQznI2dyQbkeVdjCQHLQQHONx0e2gnagdf3r20timYOa5Y1dYLZM2Y3CudEs/kBrLBJKDyGtpF5Ls0I1Kx02qz8nH8z15u25fQYzOfC9wqAhUVol01A+yQYDCKZTMp6JAiuG+Ppe9K2tvlD+5DfN23HtdYE16h5HN8Hn8+HQCAgZRC2e8/YqPR199ZIX3f3TrRu4N61U3waTV4DVpZ37UWAnddgvW3qYdaTZk1pr9crJa0GBgYElCbA7/P5hAltsqB5rU77vWa4m/Nup49MnW4ea4Lp+u+7UX9zTeoSdLruuZ1uMW0es0yansONkCD5XTOD3y6QwXWmbQG70m20H9hvjVmNun69DuZrO43/1++MvibX2mpEErv5M981cy4DgQCi0ShKpdKKgMBukr7u7q3s/uJ8u0z4gtJh0ang29V8kkwkprgyJZs/TANmimw+n5dUVLu6qXbCjSoQCCCdTsPn82F0dBSjo6PCCmKKsV1nZm6i3KypjD0ejwVA11FMbXjolB/eE8/LjV5vxqsZVvrZ8Dx0uHTaGtOvTIdut4gu50IltxtFMzH4Q6ZjKpVCMBjEnj17ZA3u5MapbrcbIyMjCAaDqNVqGBwcRLlcRq1Wk/T13SJ9Zd6X3SB268xkqno8HmFKr3ddXsx7oJ0UXdfU7/eLI0w2Wa1WQz6f33BzTOoDj8eDeDyO4eFhS0DS6/VKfXNty+j6lgzKRiIRySyr1+uW3inAEpusVCoBgKVeum4eRn3t9Xrh9/stJdMIZhLc93q9aLfbwo4nk91Mc9YA7G7cX7SdxRRpn88n9hKfH4kKwHIdcq4hp9MpzP92e7lx6MLCAmZnZ1Gr1RAMBoUNB2CFU+n1epFIJABAGnSzjAyZi9SzuqZ9u92WhqkEBQg+M5Xd6XQK6zwQCGDPnj0YGRmBw+GQZvZcq7w/3SSU/6czzHtnMJ3liFj6xmRD0n43GZJsCKfXX7vdRiQSQSQSkfkMhUI4f/48JiYmZE13a0fvFOnr7r7sFtHgGH1A7gfc/7ejYTSBRJanIImMZV7Z8JhZNblcbt3j5L0PDAzI3h+LxZBKpaQsiGaRaxYu9TN1IZsyh8Nhyd5hU2/qTt6X3hf5uy4h1k3mtxkAp/9cqVSkuTQbOfN46g/aHLvN5waWdPTw8LCwqPnMGSTWutsMMphgNW2bxcVFlMtlNBoNuN1uiz9vBikYYOHvZlYCg9cUM6DRai33EyG2RN0NAH6/H/F4XPxvZqoxy1AH5vX5+TvXpSaKOBzL2dosV9Rut20zD/VYdZCBtmm73ZY1RduH9kk0GoXD4cAvfvELybTbbWusr7t7K30QfRuE4K4Jopsslq1arCwtQwB9eHhYNiSd7rqwsCCKnA7BekB0OrSDg4MIh8MCotP5WA2k5UZII4NKPRgMolgsSi1KrUD1PHKc2hjQ7DOem4aVXaTbFM30004+r8/GZ/q57iYhQ4GlXIDdmzZKp13/xONxHDp0CNFoFKOjo5LevZPF7XZjdHQUQ0NDmJ+fRzKZxPz8PACsmvbel770pXei6yb6/X4AS04Igbf1yMXqBc1yZQA8lUohHo+j2Wwim81KGbRSqXRRIDqzv9iEWQOMdG6oQ/VnCwsLorv5ObDkEHP/0mLX2FSD6AQyGXQn6MA9kDqZf/f7/ZJ+zhIy5XJZAAxdT5WO2m4U087i8+J8MeVaM8loq9CJ5FzQoXU4HAKiz8/PY35+HqlUSoJHwHKdWtpEDFITvGFpnmAwKMENfqdSqVjSxzW4RYeajjGd4kAggJGREUQiEdGHLBOUz+cRDocRi8XgdrsFpOf64LtbqVQkYEBgYHBwUNLnWbOfa5HgAJumcf1pO5J2sw6ypVIpDA0NyXe5XzB9XQNVfelLXzZPuLdrEJ1Bs63OCtHZQn6/H9FoFB6Px6K7M5kMKpUKstnshnU3M3wIzsdiMWn2yNJZer/X+x2bRHKsABAKhUQnTE9PyzVWK5tiV07DLuvezBzSLGbiJNVqVWwGu+fFMe82cJPC8rbM6qPupp1iBm9NQFhjF1xfJHhUKhXJFOTz1CA6wWT2C2GGgn62zGBwOBwy1yaxkLYbA961Wk0wHJ/Ph6GhIUQiESSTSSlpXC6Xpdwagzo6AEbR9c51kMWO6KexF64Jk53PcerAhC5Rw9JPbrcbsVgMlUpFAg27NVDTl97JxgtB92VDooHdZrMphrUJ/q4mfMmDwSASiQQGBwcxNjaGffv2Ye/evRgbG8Po6KgwdXQEWItO7/J6vVL7kcpWs3b5GUFVpn51U35GpyeR7U52mC4VYpdea/7oc/J8HBuj0nb3qpn+5o9mKZmpQHbH63Ro3ThUn0+nJa03s8BuLrYDvNYOIse1m0UrXTrN0WgU8Xgcfr/fVrnuROH75PV6pTYzwandJHov3OhPX/qy1UJdQmeTP+sJlGrdFYlEpE5pPB5HLBaTwKVdVpYWAoxad+uSVGSlk9VEvW6WP+lGCF6GQiFh3HbSjRrkttNh3H8JOvJffaydQ8QgtU4H10CnmQqsj9FkBe1gArDobdpkG9lfNLN9O8S0L81yKPoY8zvaGeScci70M9UlCM2murQlmS3YaDQk06BaraJSqaBUKkkDMJ7HrGOr3yU67XSSyTDjmnc6l2uj6rR3rgVdFgZYJmTobEpel2MlAN5pDeg5Y5YHSRw8p66tXi6XpYQL7WcdGNht0tfdfdmNwr3BZCuvZ00SZGQ2TCqVkp9kMin61wQ7TSFIGQqFBDAlOMfgNH1v7XdvpNwkg5nU3RStr7mfmYxdM1ios3rpe681JtP/1vvyaqU3TACUQQS7Wum0yS5Gd2+Xr61FPwd9H3Y63G7uGOxlFrwG1c3nacfI1oEmZqSxL435vujsLm2DUbhezIxF+t0kP2i8xW4d2NmCmojKtWQ3V3bzq0mrJmZjjof3rrMe2c/Q7OuyG2Q7dPe9996LAwcOwOfz4aqrrsL3vve9VY9/+OGHcdVVV8Hn8+HgwYP41Kc+teKYr3zlKzh69Ci8Xi+OHj2KBx980PL37373u/jt3/5tjI6OwuFw4H//7/+94hxvectbLGvf4XDgV3/1V9d1b7vPetvlwg2q1WqhVCpJGjMX51rlXOh8ut1uHDp0COPj4wgEAhgbG0M4HBaDvtFo4Omnn8bZs2dRrVYxPT29ogEhnVemD8XjcYRCIWk6otOc6bgyXbxWq2FiYkKYPasBCHQcPB4PEokERkdHEQ6HEQ6H120Q6BQepje53W4MDQ3B6XRifn4epVLJwmLj+Dm3dAQ160mzn8z639oh1s9Gp31TebPkDR1IOmPdRit5P1rJ6fNzDJstZorTdoECvRQqca7reDyOI0eOYHBwUNbPbhC+k5FIBM973vMQCATwxBNPYHZ2dtdFxfvOdF92m5ANxbITlG5YKXRgQqEQQqEQfD4fhoeHEQqFLHplcnISMzMzUnbCLuOLbLB0Oo1IJGIpxQZAdFoqlRIAvNFooFarCRN7PVlSoVAI4+Pjks5dLpctDpnWc2S4kenH8ZKFzHqYLpdLyn7o5mWmY09HWpdgIRiqmzzx+rSTyFqnLq7X61KXPR6Py7EEVUqlkqUJ+HqEbD/WkdXNsbZKeL16vY5isYjFxUVJ3degN3U655Xri3Ndr9cxPz+PdrstgZl2uy3MOJYv4vdoK9CxLBQKmJqaQrPZFABcN95MJBLYs2cPXC4XSqWSXIu2F9cP1y1LszBbkOUA4/E4HA4HJicnhWlOO5rvqG4ux7TvxcWlJqt6fRGM13WDaRuafYBok7XbSwy6QqEgGZ2mY12pVDAzMyOg2fDwMEqlEgYHB2VcLImwXaLH2+04+rq7L7tN2u2lzBc2Qua677b5JP0GEm+ouynNZhPPPvssJiYmJJulU7a2x+PB6OioZMwQQGew0O12I5lMot1ui66s1WqYn59HNptdF0ErGAxi3759CIfDwiDmj0lOM7NrGHCnvtUBwMHBQTgcDtEXLJuq55v7MTN/NFjNUmOaJKeBbO3zEmdgSRlg2c5oNBpSZnYjuptZRAz+mo3Ot1KYJeVwOGRuNINaP3fOCfUabZlSqWQhE3DeGLQ1AXQK7QHiFqVSSa5JDIVBcqfTiWq1KjqXPyR30QbTWRBcMy984QuRTCaRSqUkoE5dDcAWF9IBBHNd8BoUswSQvj+tx3Vght/RGEy9Xkc+n5eMxWg0inQ6jcsuuwyhUAgXLlzAmTNndl3m4lau7QceeADvfve7ce+99+JlL3sZPv3pT+Omm27CY489hn379q04/vTp03jd616HW2+9FV/4whfwgx/8ALfffjsGBwfxhje8AQBw4sQJ3HLLLfjABz6Am2++GQ8++CDe9KY34fvf/z6uueYaAEs21Ytf/GL8yZ/8iXzPTn7jN34Dn/vc5+T/zK7sVvog+jYIX2K7lOW1FrdmjycSCVGMz3ve85BIJFCv16VGMtOXXS4XMpmM5TxUimQOMf2VjG690bbbbUnxbbeX6op6PB5kMhlxxFZT5jQIdBRPOxvrBS/18VQwoVBI6qtqp56ix2fWw9PlXHTUlufRGzEVF89DhU0WFBWwjmx2u2Fxvu2AfL3Zb5VoQ2q3RVtN0Wx63hfrxg4NDUmK4k4Xbfh4vV4kk0k0Gg2cP39+V4xfy8Uy0vpOfF+2SzQw163ovYfs8VAohKGhIcRiMQDL+3ytVkOxWBR91ulcZJgxG4U1GwniErhjTWk6Nwy285rdvEtMZ41EIigWiygWi2i1WrbpxXTeAGuqLN95ZqYRpGXdTDrh1HUamG+1WnJPmsGrnS9ek2nPGgTVTCNdK5OfkXW10frUdBoJ9Nbr9Q2d52KFTiZTqGnraMdRs9c0OKFtU5bmMWvaE1xmmr9+vsw0JEGELDaPx4NGo4FcLodqtWopodBoNFCpVABAnG6OW5dhI8jEcUQiEVnvXIvaubarpc/z64wDvXb5zHiPLM1iOt/a5q3VagI2cD416FStViXwoO1fsk+3u9+MydrrZi/o6+6+7Fah381gLn28btYkgd9oNIpgMIixsTEJxnLfLBaLyGQycDgcq5ZYZNAtlUrJ3mSSt6jHuM8w4yWfz6/rHfJ4PIhGo4hEIrIv8x3m9Uyfk2PgffP/uv9FKBQS0Jb7qsYNKJwbrS/08drvZpCX39P9xngca1ZzzNS3G9W52u/mWLdLCCgTc7ET6jnqNo4bgKWsXiAQsASJTQa/HZDO65tZ+cByaSD+3+xBx79zXfF587oMig8ODiKdTiMQCKxggK+2rs0ggv6ux+OR69rNmx2jWhMItE3NcWt7kYEAVoBoNpvI5XI7xu8237lO0ivdXSgULJ8zsGbKPffcg7e+9a1429veBgA4fvw4vvnNb+K+++7Dhz/84RXHf+pTn8K+fftw/PhxAMCRI0fwox/9CB/72McEDD9+/Dhe85rX4M477wQA3HnnnXj44Ydx/PhxfOlLXwIA3HTTTbjpppvWvB+v14vh4eEu736l9EH0bRStwLpd1NFoFHv37kUoFMILXvACHD16VOpDspYjU1vr9Tr8fj/m5+cl8s4mJQAkLYuMdP6uUxsArNgMuVnp41cDeBklZTo5naJeMH+56Znn50ZvAuDcODXTWwcCuHFyfuw2SJ5XpzlrxtNaTU14baYEMuWeEV6y2fS1eX4yrMh4Z508M52p0zU5D3o+OomOdO92p0enoPG5ezwehMNhAaB2m7hcLsRiMdTrdUlr70tf+rIzhenffr8fhw4dwv79+6WptsfjEd1Np2hoaAi5XA6/+MUvMDMzYzmXnUOkGWRkwXGPI6OMjGDquW6cFupTBr25hxII51g04E2bQIONWieyFAfHR6eQbDK7wDz3b+pe3isZWEzT1/s87QDqZp6TxwHLGQTrraNKEIYZfJp1b9pZuu5sq9US3b1ZelWTADSzSpcO0UCBLiFAhzEcDqPVaklNc65P3jcZO7xX1vtmCjSvpRuksflsMBhEuVyWQEcsFhOdTHuOdVlJDCkWi2J3cnyRSESyGrneGDhiwzLWWtX2qyYn2Nm6ZhambpSmAQtgqba5rg2v55xAGhuhBQIBJJNJzM3NIRKJoFAobLsjvtttu770ZSNCXdIJdNMSDoeRTCbh8/lw5MgRHD58WIBpr9drCcodOXIEqVQKuVwOP/3pT6VuOIVlxDQrWNvu1Ec62wpY9nWB7gLfBPx1LxKttwH7uuQmsMpANP1LDZaxzAyz8kxgXgOWmvHLOTeDzmQy6+vqIDp9bRIAFhYWhIm+3mzvgYEBRCIRBAIBC3jK/Z6AsKm7K5WKBG03Q/ScUWdr20o/d/6duo395aiPSFDQZfD0mjLXkX4u5uf8v850JHbBz0nuou4mU50VCzhestI5FtqZ1O/8PnEs2gaalc/rE7fRjUF1WTf9nPS9c8zEszi3mjjIcxCXCYfDUmqRc75TQPSt1uN79+61/P9973sf7r77bstnjUYDjzzyCN7znvdYPr/xxhvxwx/+0Pa8J06cwI033mj57LWvfS0++9nPYmFhAW63GydOnMAdd9yx4hgC7+uR73znO0in04jFYrjhhhvwwQ9+EOl0uuvv90H0bZb1LHyHw4Hh4WG87GUvQzKZxJVXXokjR47IRqDZXq1WC/v27UOhUMD58+dRLBYBAMViUZjSBJ7p5LDemt3GoJlGBOwI/FLxdAJx6TxEIhFJYaez1AthajiwxPphzVc7p1gDwpqhxO9QEZkMK4oGY7VjrmuHEgSwA6l1xDuZTGLPnj3w+XzyEtO40sqF12q1WqjVapibm0OtVsP09DTOnDkjTK5O80+nVwMd/LfTd6g8tPLVxtFuFH0PjIin02kMDQ3tytpmfr8fIyMj8Pl8eOKJJ3bd+Ptstr5cShKPx/GCF7wA8XgcN9xwA371V38Vi4uLyOVywmbNZrNYXFzEC17wAni9Xpw7dw7T09MrQHRdl9QEQAGgVqshm83C5/NJEzE6JwCQy+W6Ao2dzqXeK3TGNGuNYCTHoMte0CYAlpnOZC0BS2SAVColDjH17NTUlACWdu+3dqgbjYY4jWS1lctlNJtNBINBC5DM81H/k9FnMpO6dYxpG7Bm4/j4uIDHurY6g+VszMl64DMzMwI8dyPrJVvw3mkber1eeQa6fA31P4/jD9lW7XYbiUQC8Xhcsu3orLLB++zsrDARgSXHnc6Ox+PB4OAgUqmUpPTTXmGT+mg0iuHhYQuwxPlttVq4cOECpqamhDBANncqlZIm9Wz+NTAwgGq1imAwKI1v+Y7xPpmZoZusmiUMyOTnv/l8XkAXAhR8JgRiWq0WCoWCMO3JZuf9Op1ORCIR7NmzB8ViEalUSt7T7dbd69Wlfd3dl+eCdJvdm0wm8ZKXvATJZBLXX389XvrSl1rKU7Gk0+LiIp7//OfD6/XizJkzFt1NvUiQkbXTAWs2CIOVul8YAOlDwgD1WrqKZU/oe9M20IFUM1vLFOpl6jaC3MQKIpGIhWXeaUx24+V+z6B2sVhEs9mU/RywNobkT61WQ6VSkflcbfx24vF4BIc4fPgwRkdHLexrrbuJVVB3Lyws4MKFC9IIe7OEvirJC8yUM9erLo9DW4iVBYAl3RSJRKScHb+jAWLznAS0NTCtbSlmqRHwJl5BUoQmL5RKJeRyORQKBUtmGUkUBM451wzQsGxwLpeTYDvXHZ8NwW3+cMx8frpOO8/PwLbuNcR/NclC/zBQEwwGkUwmkclk4PV6N9SbYCdIr3T3uXPn5D0FYMtCn5ubw+LiIoaGhiyfDw0NYWpqyvb8U1NTtsc3m03Mzc1hZGSk4zGdztlJbrrpJvz+7/8+xsfHcfr0adx111141atehUceecT2fuykD6LvEqGS9fl8iMfjSCaT0oTMruQGN3iPx4NqtYpIJCJdkvXmoRnZJgO90zh0BM50VDt9hw4+lXYvSoRoBW+ef7X70I64ZuXpaCsVswmiaxBaNw81G1J02qQIPjCCy5TeaDQqILpdgIHnZp1P1hJlxJbKwtwgzSyCblno5nV3u9NjBjU4J3T0exXQ2UrRhsVuVOh9R7wvl5K43W6EQiFpJJpMJiWgTaeAjJ1wOIxQKIRCoSDGvw56al2tGeH8mw68anCYzpbWdasJHQ46UwAsOlKzdulE22V/2ekljoU/upzIWqLZbfoeOUd2pdRMVpxZ57qbuTDnnPsv2fTUxUwBBpaY2ATRmapO5xzAijmzu67d76uN22Q32t2nnZ1g3iNtIJNYQRuO59DgDudVp+1zjei6rdqB10x5k53J9PRGo2EBC7gmNRPP7kfPgx63Zlpqe5TAu7lu+R1tW9KGYIM0HQDQc6ZZr5xbBix2o+3R1919uZSEzRBjsRji8biUc+D+SCZ1q9WS7Jh8Pi9AoQZ5tc9s+tKmf6bfMztS12pCMNQuw5x7jj7nWv6DaUdoHd7NHma+87pUCPWGJqY5HA7R43pOdBlV+u5r7Uc6QErg1+v1IhgMSsZVrVaTcXAfZwCczHRm91N3d9sHR/++3r3TzmfnmjPvj38zg8IaONe4hp1vr7+nGfr8Vz8Prl/9r17TDE6QUGHqaT03tP/0ucy50+PS60/r2dUIjJ3OYep5HVgwMQNNhLyUdTcDNN2IubesRci0O978fL3ntJNbbrlFfr/iiitw9dVXY3x8HF//+tfxe7/3e12dow+i7wLhxu9yuZBKpXDw4EEMDg4iHo93BKPJEHM6nYhGoxgfH0ej0cCZM2cwMzNjqRdJJaYB4E6LUUcv9XfXUmB2IDfv7WJFGwbrAen1vepouhkgMO+fyks78GYU0xwfHb5wOIzx8XEEg0HEYjEMDg4KuKIbdpjCz7xeLxKJhLCfYrEYarUazp07h9nZWanJx4YxnA8aA3qsuu6ZnbAkANPleP+8p90kTPXnvGgHfLc2TmUGRrvdlnTN3SR9R7wvl5L4/X4MDQ0hHo8jn8/jpz/9qbCwNOjrcDhQLpdRqVRQrVYxPj4Op3OpafbZs2fl+HZ7qdFjPB7HyMgIgGWdxFRzt9uNer2OQqGARqMh/U7IGFrrHaLuiEQiUvKKThjtC7KHdfkWCsGFZrMpjSepkzKZDBYWFizNINcrDCzrmufAsu6yM7SpA7vdf3Q5GzKRWQbM4/FI404NHAPLOpK6B4AwxDwej5TiOn/+vIWtaIp21vQ5V6vbSpDW7XYjn8/j2WeftdhEtDkACEDNczocS43iWKecLMtKpYKpqSkUCgWEQiEkEgnRpcx24PrStmWlUsHExIRlvS0uLorucjgcKJVKAID5+Xm0WkuNQ+mgFYtFYXz6/X4kk0lEIhGZZ7LbCXK020tsRfbsWVhYsPQ8od3Dd0UDCrouP3/I5DQdb12SUDcppVPPkgMAZF4mJiZQr9cxNTUFh8MhbNS+7u5LX3au+P1+pNNpJBIJ5HI5/PznPxeQkDqc+wKJTo1GA+Pj42i1WpibmxPdzf2Hepg6QQPKOvhHvcIyn902vuN3yLrWekmDrMByGSruYdSj2qfVDHYCzmaQfj2iS7ho/7ler0uJK7ugvNbd3bDxnc6lbLrh4WHJQAoGg1IOk3u67sfBe2UgmaQ3MpDZEJPZBp1wEE0+4HU04c1OSFxwOJZ6fUxOTq4AxHlODeJrQh/nqlarie2XzWZRqVRkDnQghf/q6wCQ0jacHzK9dfAcgGRstVotaSwKLOnuXC6HSqUi+AevzUCArknOa7LHCCsm6ECVtpV1cIqZYxrDoe1qzq8OhJtrSAPr/H1xcRGZTEayzcLhMAYHB/u9yNaQVCqFgYGBFQzxmZmZFUxyyvDwsO3xLpcLyWRy1WM6nbNbGRkZwfj4OE6ePNn1d/og+i4QOqoejweJRALj4+NIp9NS67QTiE62DlNIyehxu93S7EiD4d0wjjXr2mSCrXUPOj2t1xuPCaB3C6JTNMNPO0udnHBTia91/0yzisfjeP7zny/19Vgap9Mz5Ll1kIBGVCKRwJ49e6SBWLvdRrlcRrVaFbCYc85rMVjAcTMzwW787BLucrksSn83Kg0CGVSyml1pAhS7RZhiBtinUvWlL33ZOeL3+zE4OIhoNIp8Pi8gHx1cMlOdTqcA6LVaDXv27EE8HsepU6ek3InWwbFYDMPDw6hWq1IORjvn3Pd4LdoF3QhBdDriwDKITn1Ex5IMbGZ1Act6lWU4EokEPB4PCoUC5ufnJU18o6nR7XZb5knrMN20XetxzTbqRrQdFQqFMDIyIqVCRkdHpYwM7SAzoM97Z3mTYDAIj8cjzeg49rm5Ods54LlMJqEOpNgJdb7H40E+n0e5XJagq8vlksxEMsv4DDhn9Xpd5pQ1YEulEqanpzE/Py/PkUA9AR6mdvOem82mNKHl+mPgmsA20/NJAKjVapJhOTAwgFKpJI30fD6f1OGlo1ur1VYQIBYWFqSEC9PH+dx5rxq44Zph41Om/HOuhoaG0G63pRwPy8I4nU6Uy2Wpb08AhvYG1z/LKE1OTmJyclIA9kAg0NfdfenLDhefz4dUKoV4PC66i0Fhvtv/H3tvHmPZXd2Jf96rt+9L7dVVveH2ig3Y/IyNwAYGMyaTIQ4eLGXEkAygWI40YGvExAEUh5AgEoRaiC3MkBgUBawRIiiKNdgkYDBuEtoLXtq9V3dVdW1v3/f3fn+UPqfOvfVebV1VXd19j1Sqqvfuu8v33vc93/M5n/M5BB4Zf9XrdezZsweBQACnT58W380ffvfD4fAKCQrGsrqCRc+d6zHiBlpmAzCCiOYKHCYENDhO2Qz6IHPi/2J8t5ZB5X420iB0veS9YDCI/fv3IxKJGKTptE47pUW6ScMwmQosAYOs6q/X67KOMeMgjDMpFaqTGKslwHnedrtdtNf5rNG/8l5oLXiuu/T94PsE00ulElqtlqw59TnpxI0mFlJuT1dNmVnbzWYTuVxOmtfzegmiNxoN8fler1eIa1xz6ONTzojJI12xx++IHmuC7YVCAaVSSbTLnU6ngOhcJ+oEOc+/G3Pd/F1pt9uShCiVSpIksHqRrW4ulwu33nornn76adx3333y+tNPP40PfOADXT9zxx134J/+6Z8Mrz311FO47bbbJG6544478PTTTxt00Z966inceeedF3W+qVQK09PTQkpaj1kg+mVi+otvLhVd7TM6m2YubaWzoANnxlFnunW2TuuFkbGzXvDdnEHeTOnFambOim8026a31efX7Rw1cL6eYxDsDAQC0sSSwMN6SvTM58D7SpCcwXkkEhEdMYLo+jz12Oh71usa6LT0Qmc9yYndaAxq6YR7Oc3LyTRrYDsSU9ttFpvNsqvV9BzMpocADA0KnU6nBHO9SpZbrZboTjJw14x2bsMAi0HVakGc+TwZSLlcLsO+6IfMOuBkbhEEoJmrn3SDKl2ivVEzlyp3u4Zuf6/HCKJ7vV75IWCsWVhas5SyHhrs0Owwjo/WqQ8EAuKjzIn51ZL6vYz70uALk8aseNPPlv4cx5Lser0Ne+JoUEaPO9eE+lp6MfS1dSMj6MpFvkdwhcAOz1UzJ/X2BDIY/POa+Rm9zuP9IGih98/rqtVqhr47TBjoqkb93Ov7poECSgYQTLB8t2WW7W4zr7MJHlYqFUm0mmNtM+NbG+Xb2K/MLM/B1+hf2J9qvdrfBKmr1So8Hk/XqmMC9PozvWLzbt/ZzcbbwPbHXDbbkv48QXMSEbX8B2D0zfxtxkn0/zoB4fP5EA6HUa/XRTN9LfxgLeP6jAx6c5xHM69reG91bK/vufZtZrC9m6SJ/l+Dyqsl7pm00LJm+rr02JDhbZaH0deij6uvz6yYwP2Ze/pp3InfH34/zdfZbQ2i77W5Es2Mp10uttO++5FHHsGHP/xh3HbbbbjjjjvwrW99C1NTU3jwwQcBAI8++iguXLiA7373uwCABx98EF/96lfxyCOP4OMf/ziOHDmCb3/72/je974n+/zEJz6Bd77znfjiF7+ID3zgA/jRj36En/zkJ3j22Wdlm2KxiNOnT8v/k5OTeOmllxCLxTAxMYFisYjHHnsMH/zgBzEyMoJz587hT/7kT9Df328A/NcyC0S/TMy8CF/vBN3rS68ZOZ1OB5lMBgBk0tYAr85O5/N5aWyZz+elrGu1LxaPVa1WUavVDGW0Zge1GdNMYwK+62ma1mtf3cp7ei0gVjOOu8fjwf79+0XGhSz0iw2euH+3242JiQnEYjGkUilpYqW7jOsGoVrKZ7VrYNOrZrOJYrGIer0u4M7lpAVGNl0mk0Eul5OyL90E73K0Xoury8WsQNyyq8k493L+JROK8lvhcFj6G1BCIpfLYXJyEplMBsVicYVvKpfLeP311zEzMwOfz4dgMCjgLf2rbpBI6ZNsNruu70+z2UQ6nRbJDLJ6dODRrazb4/HA7/cb2ERsygUs6yk2Gg3kcjkUi0UpB96Ica2i2b9rAekbsb6+PtGv9/v9GBkZgdfrRafTEQkPLW9HTftQKCRs6mKxiEqlApvNJuQD3ZSdrBf6W66RNJNeB7nrmTfJbvd4PBgfHxfmONnV+hnQQArPr9PpSJWT3W4XPfKxsTE5XzPArE0nVNjoTPsrvQ7lMZhY6XQ6wiZzOBzS2JaVFqVSSZqGer1eAbb1mOj1LvVv9bPK8W2320JqACD31syMTCQSaDabcnyfz4d4PC7PtQa2zIkD3i+CIl6vF6VSSRin65Vn2AnT4MNqZvluy65Go79hEpVMYUpr6obCnAd6VQ1Vq1VMTk4ilUohGAxicHBQpLA4b9CfMN6u1WpSlbOWNRoN8d3NZhPxeFzOyzxf60Qkq4vMfp7npa+fCXYyh9c7hhtNCm/G+vr6MDg4iIGBASGwsQG0lpExy8gRCNakRT0+bJDebrdlPVAoFDA5OSnNUc0SJYCxF9tqRt/NXmn02Tru1ueymia8xloGBgYMSWd9HprNbibY6WacvO/mzzOhQPkg+ntWVvJ7wIozavUzyWMGxLlPSgXyHHWCvtPpSFLEbrcLy12vl7TsECvWWDFnBuz1sXXyQBMe/H6/JCJ2Gw7CNdVaz9dO++4HHngAqVQKn/vc5zA3N4ebbroJTz75JPbu3QtgqTpvampKtt+/fz+efPJJPPzww/ja176G0dFRfOUrX8EHP/hB2ebOO+/E97//fXzmM5/BZz/7WRw8eBBPPPEEbr/9dtnm6NGjeNe73iX/P/LIIwCAj3zkI3j88cfR19eHV155Bd/97neRzWYxMjKCd73rXXjiiSekinc9ZoHol5HpCXmjzqebw6JjZzktS4Wo39mNKVatVlEqlcRxarB6NbBZZ9TXw1zfqJn3fzHHMH9us/vhmDscDsRiMYyNjQmrbb3l9KvtWx+DoASdSalUEsdlBjv491qTaavVEkCe93q1TPRutkajgXK5LI50rcTP5WK9WC6Xg12KQPzrX/86/vqv/xpzc3O48cYbcfjwYbzjHe/ouf0zzzyDRx55BK+99hpGR0fxqU99SjLotB/84Af47Gc/izNnzuDgwYP4i7/4C0Mm++c//zn++q//Gs8//zzm5ubwwx/+EL/zO7+z4lr+7M/+DN/61reQyWRw++2342tf+xpuvPHGDV+jZbvTOO9q6bB2uy2SHh6PR4IJAnsEwFmKan7mG40GEokEstksotGo6KxqJrQG0MlYN8ufrHbO1Hj1eDyo1+vSKI0+l4EKr4mBhy6VJhBL0IGMbh3sbJaJzsTudhjvBeVPgsGgALpMamhglsAxg0oC52RQczsGlzabDYFAAAMDA5KosNmWy711MGtmj61m1IQnQE72OLW5S6WSgPWaXKErDQjuMtnT19eHUCgEh8OBWq0mawwmEPg3wQleMxu56bVDN7BWg+yUv9MMepvNhkqlglwuh0qlIpV4vF6dyDE3aNfsNPMaCIAE4gQK+B3R97XRaCCfz6NYLKLVagkgY2b+dQMDCOqzmXy73RYA/nJMglsgumVXq/H7TNkJkov0XML5C4DB52tjUo6Jbepz6+8GE3TlclnkMjbiu8vlMmq1Gnw+n6GHma7I4bZ8jSBjN4IR/Q/nLC3btZHv9E7ELIyF4/G4rDecTqehslrL4tHWAvg5Voy7fT4f0uk05ufnDZVS3BewsfmO95t+RcuOctw1a1r7Pa499HZcj7HBOTGcblUJnNfNazf65tXGhT6USWsmnMyVG0wo0zeb17XmdYA+P/M900l4Lfui5YbNILrWvtcJpW7JfQAGEL3bvdhNtp7zuRS++6GHHsJDDz3U9b3HH398xWt33XUXXnjhhVX3ef/99+P+++/v+f7dd9+96rl6vV78+Mc/XvUY6zELRL8MjGwfYKlEIZlMSmaROqXmL4+eVCuVClKpFObm5pDL5VaUFjM40BlanfnUbDZqXNERMWNtLqnWE1in0xG2UKVSQa1WM3QN36xx8iPIUCwWJeu+URCdzoHSKCx75kJIB1Ms29JssW5GNh5/GBRux8RLhgEbnQEQDS+duTUHuqsZAZK+vj6Uy2UUi0VDZni3m36+y+Uy0um0NOzTCylzYH+5GK+NYIgVmK5uTzzxBD75yU/i61//Ot7+9rfjb/7mb3Dvvffi2LFjmJiYWLH95OQk3v/+9+PjH/84/v7v/x6//OUv8dBDD2FgYECy4keOHMEDDzyAP//zP8d9992HH/7wh/jQhz6EZ599VrLipVIJt9xyC/7gD/7AkE3X9ld/9Vf48pe/jMcffxyHDh3C5z//ebz3ve/FiRMnNpQVt2z3mmYAMenZbDYRDocl0PX5fMJy4TxbKBSQSqUEgDbvk0FIpVJBPp+Xz5lZZAzAOe+Z53FzqbqWyKCxPJqgIpnCOlELLDfU0ky8vr4+xGIx2GxLDZ80CLGZQJxGP6aT+eFwGJFIBAAk6U9G33qAaAZOmllFNjN9vtvtln3y3Jlg6HQ6koDm63odx+ZZXDtRW5ONSwuFgrAQ9ZiYffhqY0J2NjW76bsJkLhcLjk3nnc3VjmPy2vVUjB6rDj2NptNgB6uJfX6kKaBJd0AjusYPhu6samWiOG+2PBVkwQY9BI8Jyihq0C4tuP18Px0hZoO9p1Op6yntH6uTnTwszy+OfFBAIbnqcGQ3WK76Vwss2w3GOdTMs6DwSBarRb8fr8kmNkwUfccyWQymJ2dRTabXQGk0ydzvltcXFxR2cLf1GLm/EXAsFvson23WaqE2+qGmTwXbmeWDCEwy7lUM7PJJF6tUWY34xzM4+n5NhwOIxqNotPpSIVaq9UyVM6vdiwmUHX/DPNcrOU8mDTW47PWuWt/wYRzf38/3G43crkcEomEQRpNj+d64m76buqum/0oz5PrLD5LAAw+S9+zblX6en/829zkXisgaEk2zXzWYDvxnk6nI03BCdwT/+F95BqL+9D71s8px47XRMKJXuvq+6cVGHjfNbFD++5uySKei66c6/a92E2+cjedy9VklwcadpUbtdNqtRqSySTOnTuHcrkMp9OJaDRqmMy1cdIsFAo4f/48Tp48iYWFBUPQyYlgYWEBqVRKsqoEuKnLSkdJNhuZSWSTUf6F2UZO6pwwS6USms0mvF4visWiTEqbBdE1UF+v18VxEeDfKKjIiTIQCGB8fBx+vx+xWEwAaTozltdrwL7XcQKBAEZHRxEIBBCLxaQ8ejtKgHS2eXx8HPF4HGfOnJH7rQN6HfStNkb1eh35fF7KAlOplJQj76YS5F6mWRfpdBrT09Py/BJY4Xdgu0sLt8NYKVAqlaR65HKyrcqI5/N5w+s6+aXty1/+Mj760Y/iYx/7GADg8OHD+PGPf4xvfOMb+MIXvrBi+29+85uYmJjA4cOHAQDXX389jh49ii996UsChh8+fBjvfe978eijjwJY0nd75plncPjwYdFwu/fee3Hvvfeueh2HDx/Gpz/9afzu7/4uAOA73/kOhoaG8A//8A/4wz/8w40Mi2W71BgINptNDA4O4uDBgxIocZ7i91hrii8uLmJqakrAWm1McOtgzW5fbkzKxohut1vAVOox8zuigygGJUx4m4M+srE1k4lBKtnPAKSpJQP6Wq2GYDCIkZERuN1uSaqzCo5VQutlWtMITJtB3bGxMdx0003odDqYmppCMplEuVxGMplc11zJijGfz4dYLIbBwUEJnNicnUxkrg0o3cbkQTabFYaUx+MxaNsy6W+z2eD3+xGNRoXRWKvVcOHCBSwsLBjOdSNzJdd+lEAJhULwer0SSJKdzoSG2+2Wxp4MxvU6gestAvuaZaYrH/h3sVgEAAGczfqoDOb1ePP5I9GA66tUKoXFxUVJAplZ5FyTcM3JtSlLu0ulEtLpNIBloEA3BqUMIMdNg+dc43o8HpFi4nny2dMBNp9Fat0TBDCz8bR+/m4Kftd7LpeCzWaZZZfKGEe73W6Mj4/jmmuuQavVEplLYPmZ1j0YZmZmcOzYMYmftfHzrFZijMakMhN9DocD1WoVhUJBqph0o2QNQAMwJF85z+rkoRkMJaOcpuNEnZRkLxCeO+NKJji7se27mZ47NCDM+XZ8fBxvfOMb0el0cP78eSQSCVQqFaTTacETVjuWx+MRabxoNCoMf14n5/9urGOdJDCfs75vPD5Bap/Ph71796Jer+P8+fNIJpPi77sRHFeb/yip5na7USgURCWAvpWJd64tiNFoJjp9KP0pY0WC/5pVzrUmk84a6ObrupoKWH5GNHFBs7u5zkyn01hYWECtVhM5IsoXttvLDVP12OtYXK/tOGas8uD97JaU0OepCQJr9Rgyn4f+X69fNM61W2y9Ptny3Vtrl5UOwBe+8AXYbDZ88pOflNc6nQ4ee+wxjI6Owuv14u6778Zrr7126U5ym4yOg840n89LmbVu8MkJnizpSqUiLOJeuqPtdlsyygyeS6WSsHa5D/7NYzKTSA0xrRNlbrqgy76o1W0G8zcyFvpzBNJ1s9ONfNHpAJxOp5Ruh0IhRKNR9Pf3Y2BgAPF4HLFYTJwzJVl6Aa90YMyGc1y2g4muM699fX3CaPR4PIYS5o3K3fAzBHe0pv3FTsQ7YUyy8Pngc8znsJv+2+VkXCRv5pnfDcZn6GJ+AGB8fBzhcFh+ugHi9Xodzz//PO655x7D6/fccw+ee+65rud35MiRFdu/733vw9GjRyUg6rVNr312s8nJSczPzxv243a7cdddd21oP7vZrmbfTdN+VrO76CPIltIML7JxV2N7MZjhPM3t6a8J2NFX6hJfBvzmH+27e33vABiAT64BtB61LmdmQMMErG6UZtbY3KhpRhgrwDgfBINB+P1+aS62Hi1LnivHh0G3HkMd8OkEhmbq6fJlPabaHwPLwAvHR5caa3/d7R70Gg+dmCGhQYO2+j5pNrnZdFCpr4nvadPyK93Kv81rD70fjjmfIb1W5PdArz00aOByueRHj6FuKKorzzTAYL6vej2qvyvmpmnmMdLPunnc9H3hjy41v9xsq3y3ZbvfLN+9zA7WSUSdINWMWD3nMm4mKGk2Hb9y/4xTCGbqSi0N3prnpNV8t46V9fykAUINrJvNDCRy7tIJwc0a51e32w2v14tgMCjgt/bdlLhbqxJa+1H6AfOY6OvU8bO2XnOV+TWODav0NDFQj/16426z79Z4i3k7M/Bs/jFLu3QDfs3+yuyzVsMrem2nfbfuVbfaOrTbWpRrK/N4m7EU873i3/p69JiYr7vXdZl/A9jQvdyNZvnurbXLhon+61//Gt/61rdw8803G16/Wsrg+eAuLi7i3/7t3xAOhwUY93q90qySE1Wj0cD09DQuXLiAxcVFnDx5EhcuXJCy1m7BTaezxIoDjDqOnU7HEIhzEmNGnMEPWU4sL+KCAljOKObzeczOzhoaqTDw0iVGvUwHk+VyWTK1i4uLWFhY6MrWW8scDgdGRkYQiUQwNDSEm2++GeFwGLFYDPF4HDabTUCMdDqNWCyGfD6PM2fO4OTJk+LkzAsJn8+HwcFBaYy1E9bX1ye6rYFAAC6Xa9MgBZ15vV5HIpHA5OSkLG5YgaC1RneL8btSq9WQzWZRqVSQSCSwuLgobEmWiRcKBRQKBWGbXU5Wr9eRTqeRyWRQKBQuO+d2sQ6Zn52enkYoFJLXu7HQk8kkWq0WhoaGDK8PDQ1hfn6+6/7n5+e7bt9sNpFMJjEyMtJzm1777HUcfs68n/Pnz697P7vVrkbfrf0r591EIoGjR48iEAhgfn4ex48fh9frxcDAAPx+v7CT6/U6FhcXkUwmRWtzPYlmMr5ZEcbzMIPaZE7xO6MlPPid7AYKFgoFnDhxwiBRxmPR/xGc5PGYSAaW/Do1pVnZxIZpOqneLZjtZXa7XRIQ0WgUb3jDGyQIj8ViAJa0Dw8ePIhSqYT5+XlUKhXMzs5iamqqJxPY6/UiFouJn2MlHWVKWILMcyCrm/eBwbPdbpfSZh14McjnOOtkidZQv1hrt9vIZDKw2WyIRqMYHh5GIBCQwJaJHep8U6qObC9KrLjdbsM6gkx9AMjlcigUCnC5XFJ5pwFrzdri+oyfZ3Cv5QdsNpuASmSu6WQQsOTbU6mUkAYCgYDcC65Zea8oJQQsgxpa3z2VSknVAEEQMk/5bJDNp1mMmonPhqQ8T50M0EAAv4/NZhO5XE5YnFer77Zsd9vV7Lv1M764uIh///d/F999+vRpuN1u9Pf3w+fzSdKaPUrouy9cuCDJv17HoukkHt8jWMiKnna7LWAzTc8rjNV1YhdYYqdfuHABmUwGkUgE4XB4BRuZ56IbSBKw5v+dTge5XA65XA7VahXZbFYqsTYaX2rfEo/Hcd111yEUCiEej2NwcBDAUgy9b98+w7HOnz+PU6dO9ZRSZQNt3ZOEiVKzcez0/5pprkFXWrd4l0zni5Gl67bPdDqNyclJaTBK6Txee7PZNCSBzSC6vgZNeCCRgUkep9OJSCQiEnV8JjSGY06+0Pgc0fcy4cRGopQh4piyop8VcGS663vQ7e9upnuv8BlmskD7XDNxQFfHaVDd/DwQ/+B1EU9hVedGGuruFrN899baZQGiF4tF/Nf/+l/xv//3/8bnP/95eb3TubrK4DudpdLucrks5avtdhuBQAAHDx5EJBKRQKxareI3v/kNXn31VWSzWZw+fRqJREL2pbNy5kx1rwCu0+kYGjbRmeuSH5aKa2YcA7VWq4V8Po+FhQUUCgUp5dGlvOsxnivLdEulEpLJpKGEaiPmdDoxNDSE8fFx7NmzB29961sRj8cRCoVEV5UsLi5C2CF9dnZWEhPm43o8HsTjccmi74RpjVq/3y8dsHmPN2JcUDQaDaRSKdjtdlQqFezfvx/hcHjXNtegEWTm85FKpQyMDoJWpVJJAujLyer1OjKZDBKJhDR5uxqNlSPrsW4A3XqZFtze/PpG97lV53Y52NXquzUjmT8E6xwOB86dO4f+/n5EIhG88Y1vxNDQkJRGV6tVvPrqqzhx4oSB6bXW95t+1mazGRLJZhCdwHMsFkO73RbgTzOVeP5Mmnc6S/qWZ8+ehdPpxL59+0SezAwa6+u32WxSBt5ut6Uny8LCAubn56UkXAdZGwHRmRDw+/2YmJjA29/+dgwMDAgT0GazYWJiAh6PR0D0UqmEl156CXNzc13XCjbbksRJf3+/AKUshSYTUQP+HM9OZ1kLVDMAdRBHH8wKBI4fQW2tW79VgXg2m5Vye0q+AZDg1sx8a7Va0ojN4XDA5/MhHA6Lrmm9XpfkSKezpFs7PT0tiRkC7npNSb9L8CUYDCIQCMDhcAgJgedLQIKVk2Rl6ntF32632wXI0oAQEx66akDvv6+vD4FAAG63G/l8XsrcWTXI43M9y/vLe6erEzSI73A4DJIv+j7o54Vr4XQ6fVmC6JZd+Xa1+m4NQhI4TiQSSKVScDqdmJmZwcmTJxGNRnHzzTdjcHAQpVJJCDuvv/46Tpw4gVqthkwmY+hHpY+hj8U5mMekaRCdUh5kPJtNk9jMVi6XMTs7K+BpMBhcAbKaK6vNbF/O5dlsFjMzMyL32a1KzpyA72YE0QOBAPbv34+7774bg4ODBhB7ZGREgHAey263Y3JysquPtNmWZMYCgYBUYuu1kHlNbQaEbTajRIh53dQNPOZrZF5vlURXq9VCKpVCrVbDwMAA9u3bJ76N/pXjrskL+rq0LCCfH2I47faS/Fk6nZZnSsuP6Yo6TaTUCXZ9nXrNSACdVRX6HrAHn9vtRiQSMej7m9dJvcZRP5OUU2IFHDEsMtF53TxHguhMgLO/iSZy6PWCuVqSPda4PrF899VtlwWI/kd/9Ef4rd/6LfyH//AfDM58rTL4Xs7czPQxa+ruZuMEYbPZkMlkMDc3B7/fD6fTiWw2i0ajIZnhhYUFpNNpFAoFAbM5sQLLTkBPXmtlqbo5LZ1F1OCB2UFxQub5M5tHp8+JuVtZGSdVBpytVksaTjHraZbnWI9xPFhGxgw2gW+yxZgg8Hq9CAQCaLVawjDvlnjgfjUjaaeMjkwHe9qJdctWd3NgNDoYOkRK/ZC11Q1YvFSmE0KUPtJN2jTTg6x0Nl273EBLfgeoY3u5OfOdzIj39/ejr69vBUN8cXFxBQOcNjw83HV7h8OBeDy+6ja99tnrOMASI31kZGTT+9mNdrX67m7Ptq6iqlQqKBQKAJaqJMicJbulUCgICKhBWTOQR6ZQL9CW7zNA4Hb0o8Cy9BiBRHMAbN4XAEnUm0twdXBFTXVdwkvAlOejtaG7+e61vuMEMcnUolxLq9UyVNOx2o0Mcr3W0MfVAbO50kpX3OnATIMRfN2ctNYA8Go+RsuhdNtuPeCENjNAns/nkclkBFDv6+uTQNB8ffw8P0ug27xmoL4+G5UywNdEDZa4a8kBPU76uQSWmJO5XE50883Xy4QGm43y8yQ7aD+v15T8DnB9wG21RItOJPWqtNPnw+ea187vkZaE0cE7QYNisSgJDst3W7bb7Gr13TRzXMy5qVwuI5fLod1uS88pNvUmY5oxqfa5NC0von2P9t38IdNY+0ctSQUsA35mH6OPqcFGxnBmCVYC82afzXOhr6ZsqlmiY6NG3x2JRAy+W8fKvEbK5pCwxzHU46fZ0FoeRDONzXO5GaPo9dpqZl6TaQJBN7xEf2Yt09J+xWIRuVxOro1Gv6zXLvrzvVj4PGcCyd3Oi+OpWe7m6+bffA6q1SrS6bQkwM3HJoGtUqkgEAjI+fL5Mo9ft/Uoz4frFj22/NvMxjefOxMmNL1uMYP4eg3Z6SxVY6TTaVnjXk5m+e6ttV0Pon//+9/HCy+8gF//+tcr3ttsGfwXvvAF/Nmf/dnWnugOmS6VeeWVV3D+/HnJvPILTseWzWaliQMbfnHS1IGidpBko/UyMwBrzmZrh2beBlgKjghEsZGVx+PB4OCgsNLJbtdZegYpbAzJJAGbb/G1jUwQnBh9Ph9GR0dx8OBBjIyMiLSLblBIkLWvb6nxVzgcxoULFzA8PCzNNyuVimH/TqdTgPb1suy3wnTg6vP5xOmQIcWFilmflmAGA1EaGYRcBJ89exblchkDAwMYHx/f8STBatZut4XZODc3h9dffx3ZbBbz8/OSSOIzQmZlpVLBoUOHpPz/crFisYizZ89ienoaCwsLm1rIXkrbSWfucrlw66234umnn8Z9990nrz/99NP4wAc+0PUzd9xxB/7pn/7J8NpTTz2F2267TRadd9xxB55++mk8/PDDhm3uvPPOdZ/b/v37MTw8jKeffhpvfvObASwxLZ955hl88YtfXPd+dptdzb7bDA7q11utFnK5nJTRJpNJKaPlPMzEX7fkts1mQyAQENkMsnu17+a2fJ+sWgbF9FlOp1P6fGjdVnNyHYAhgE6lUgCW1xMOhwPRaFRk5ebm5jA7OyvnyoooJvQpw0bGsAY3N/q93rt3Lw4cOIBoNCrycmRb6XWJy+VCPB5HMBhEKBSS0lyOi92+pGuqmUo6MHe5XIhEIrIG0OAz1yt67cL7UCgUBLSgpqsObHm9DocDkUgE7XYbhUJhhZarZsNptt5axuAwlUrhpZdewtmzZzE+Po7rr78eTqcTmUwG2WxWpFHI5mJ5ei6Xk0aylGIBlptwhsNhQ1KC0jdMKvj9foRCIVlj8t6Q+KATKQTZ5ubmcOzYMRSLRRQKhRW+jZI85XIZHo9HZLbm5+eRz+fh9XoRDoeFNed2uwVIYlKA84zT6UR/f7+sHShp6Pf7pZKC90mXzdMoT6RNA1Baqz0cDiMajWJmZganTp3C5OQkSqXSRWkKXwqzAvEr2652393r+eQ8WiqV4HQ6MT09Lb6b8xirW3VlF43zidfrFRIM41YNDGpQkv6LcyoBbPokErw4nzJBqpn0jLU5R9frdcEN3G63SIC6XC7k83lks1nDdWs/nUgkkMvlpIqsW6Vzt/Ezg6H03QcPHpR1A31cN3ZyMBhEo9EQCRhNqNMJYBLdyK42yzuaq8Z19RzHjMnw1dYjOlFM3+nxeODz+SSRz3vIbczA+mqs9U6nIyz/VquFV155BVNTU9i/fz9uvvlmuN1ukVIDYEj4mhNATCrz+HyWyNgHIOdMgNlms4nv5L3W48V9cyzJip+ensYrr7yCQqGAbDa74vrK5TLm5uZQq9UEe2ETUu27dV8Vbfze0LR/5jhQ7kj7fq1SwPuq2eccM72e47PgcrlE4nd6ehovv/wyzp49K1Vsl5NZvntrbVeD6NPT0/jEJz6Bp556alWpBfOXrFvGUdujjz6KRx55RP7P5/MYHx+/+BPeAdNsl/n5eczPz6+YoHVw1i0TqrOluoEVt++WQe1mvbJ8q419s9lEsVg0HNvn84nTZDDG69HlQfxsJpNBtVpFKpVCIpEwLCA2YpodFAwGMTAwgGg0ilAohEAgYGBk0drttmhpsmlZu902NBOhcd+8pp0wc7kZg1V9fGbMycyz2WyGjvBkLNDozFnGnU6n5bO6zHA3sLhZbsXS88XFRanGMC9YqKvqdDoxNjZ2WTkHLozT6TQSiQSKxaIFoq9hjzzyCD784Q/jtttuwx133IFvfetbmJqawoMPPghgyS9cuHAB3/3udwEADz74IL761a/ikUcewcc//nEcOXIE3/72t/G9731P9vmJT3wC73znO/HFL34RH/jAB/CjH/0IP/nJT/Dss8/KNsViEadPn5b/Jycn8dJLLyEWi2FiYgI221LTrr/8y7/ENddcg2uuuQZ/+Zd/CZ/Ph9/7vd/b9PhcSrvaffdqzzbnU4J1ZgBuLWMZaiAQQK1WQ7FYlPe6VRHxONpqtRqazSbcbjdCoZCBvd1rHtGBfblcFrCaFUkMyHw+nwRFep3BEl8G5CyT5c9mrK+vD9FoFCMjI7KO0AQBHYQxuCKrjX6Q6wZeDxlsGkRnkE4JEgCGJnN8bhnIawYXiQydzlIpfjdmFSvHyEAn6868rtLEh/Wud7i+Y0l/KpUS0MTr9YqcGYEAl8tlaNKuG+HqknFeO+8512laY59rEK/XK0kEapRzLavXnwx0c7kc5ubmBCAwG1n1NptNqrA41ul0GqFQSCo0WSGhj0Mt/na7LbJKDM6r1aqQOfg86HtmBlZYOQJAAn/NTg8EAvLceb1ekZ5JJpOYm5tb1z3cbWYF4leuWb67dwzc6XRERxoAFhYWNrx/zi2aeNXt+0SQT5tOoGq/ppPBWo5DV8OwAjyfz6PT6YjP180wCcgzQUy8gHMcpSy4fqAP34hxbPv6+hCJRDAyMiIkQJ4Dr5/nryvedMNvzrU8T7KFdeJSV94DxmokTdbTPp/H59itZdov6zWAxlTMRMP1YC16bbS4uIhCoYD+/n54vV74/X7UajV5hjTpUJ97t2eLz4ju50G/rckaOlFDcp3+juv98/P5fB5TU1M9K00o58I1IddJrD4jJqTHjMb7xnuoWfTmBIDGuvT4cD88Z90HkNtpn8/9uN1ukcqdm5u7bPtVWb57a21Xg+jPP/88FhcXceutt8prrVYLP//5z/HVr34VJ06cALDxMnjNML4SjBNLt+BsI/tY73Y686gbaWjAmxOUbjBl3k+tVhNdTqfTiVKpBJfLJQ3LzBpbZOGTdU6nrkt3N2t6wrzSTDtL6pCxkaZuvtJoNEQjjSAHF1N8xur1umjP9/X1SfM1yuDweDs5jnwmufgjG21ubs4g92N+BqnlWq1WMTExISDSTp//RoyL4UajgWw2K4mkq1kTfb32wAMPIJVK4XOf+xzm5uZw00034cknn8TevXsBLLEfp6amZPv9+/fjySefxMMPP4yvfe1rGB0dxVe+8hV88IMflG3uvPNOfP/738dnPvMZfPazn8XBgwfxxBNP4Pbbb5dtjh49ine9613yPwPJj3zkI3j88ccBAJ/61KdQqVTw0EMPIZPJ4Pbbb8dTTz112Tbpsnz39hl9Z6FQ2DQArVm1tVpNAmOC3GaNU7PRN9jtdjQaDQlqz5w5A7fbjdnZWQE5yaxmRRz9+Hq13te6DkrjMMD2er0rZFg0u4/jxWCc/zMBy/OhRIkO2jWLzRzME2DpdDoCUPP4DNCY7NcNtbSuKc9bl6QzkGTVnAYUdEmyrsjrtiai72i3l5qNUsecMnV6zNiwy8xqJIONQIE5aa8bzBO8oDYw2ZrclgmPSqUiawtKkzHp3cvYD4RNvsgYGxgYEBZiJBKRZnwEjvTzxvf4fQIgVYPm5mN81mi6KoDvMaFAySCdTKDUg9PpRKVSQTKZXAGQWWbZbjDLd28vUMR5cDN9LxhXVyoVSdgR+DYD6b2SAPw852K32y3xtdPplOofnRShv+d828vHrPca+JtjQfCZyU6d2AaM7HEtQ8NrMCeVdc8Ks3UDV/Xnu2EVAAzHN/e7MPtufXyuE+i7SUADlkFyNtvkuJp9H0HmdruNZDKJ8+fPw+/3SyJAjxXjeI4pz9Vc+aavT1cg8hp4fpRkId7DtYtmtXPtUalUxNf3skqlIg3eDxw4AGDJF0ejURkb9lzjWpTnpu+hOU5nYl77Zr320581S8BwTcOx4utMWNXrddGnTyQSPZva7pSZsT7LLp3tahD9Pe95D1555RXDa3/wB3+A6667Dv/rf/0vHDhw4Iosg9+MbdSZaUe2kS8iJ9NWqyVBNCc7BqR05JrNZC716nQ6wvqx2+1Ip9PinMgSA5abpehFArUsdXn6Tk4mevJeD9h6KSc6HdyRvfiGN7xBSvgDgQBsNpswzYvFIs6dOydlYrpRCu/Z2bNn4Xa7USqVpBRwdHRUGPc7DaAzCKYu4fHjxzE3N4dkMomFhQV5XsyWzWbxyiuvIBwO48CBA2g0GoZGMrvRyNIrFAqYnZ3FuXPnMDk5aQjYLxe7FBnxhx56CA899FDX9whoa7vrrrvwwgsvrLrP+++/H/fff3/P9+++++41z9Vms+Gxxx7DY489tup2l4tZvnv7rNNZauaoNcc3arq8l1Iv1N1cS+OU8y3lPDhXptNpzM7Owm63o1qtCjC5uLhoYGABy8Hjxc4BrdZys3I2gGQTTDY41/qumjmny/A5Jlyz2GxLDUsJ/FDehWsO+lRgubKrUqkglUqhWq0iHo/D6/UagmoNskciEUQiEQGnNZtO65QSHCaLfmBgQBpZRqNRWXvRLxw7dgyZTEYCXj22ZFrb7XZMT0+L1MrExARGR0cNgIVOeBAYJgBDwgRBAn0MssK0lEGxWMTi4iIASOKArHdWvrHnyuzsLHK5nMgN9bJyuYyZmRl4vV5cc801aLfb8Hg8OHjwoFQ18jwWFhYMx+c56jJw9uYhQ5+vA8tkEcAIwOi1Au9TOBwWSR7+LCwsIJFIoNPpIJPJwOVyYXJyckUfndVsO4LmbszF9ZrFZrtyzfLd22cEHVndsxnfzViNoB8TsQS510pO8/M2mw3ZbFbmtNOnT69g65q12AEYXrsYoh6Z9pVKRebdQCAg/oB+hsfkfEmMQL+m/Y1OZhJH0KZBYp4Hk7gEpfk+r1EnYLUkLpMPZtCcLH/6c4/Hg4GBAXi9XkQiEQwMDAi7mwD68ePHxXeb+4A0m03k83nY7XacO3dOyIZjY2PSU4ljxSqrVquFYDAo5DZ9rd3+16QD+nVWD+qksNvtlqQ713HlchlTU1NIpVLI5XKr+m7G6D6fD4cOHYLNtiQbMz4+LngFfXg6nZZKS66D9HOhqw01c53kA3NSie+bqxPs9iV9fq/XayBT8DvFyrd2u40zZ850re7cSeO1riY31Mss3721tqtB9GAwiJtuusnwmt/vRzwel9evtDL4nTBOJpv5MmhWMhcBnNQ4YdGZreVsNbjOBirUSjdnErXeG53dVowDf/N8ec50puax0tetA6VeWf+dnnD0Nelz0M48EAggFApJSZiWfbHZbKKPW61WV4DJzIg3Gg0Ui0UUi0VhsmuWmlnLdTuukX/T6ZFJSS02Nunr5dBZVgZAdNS5QNqtRsfO5q7UcL3cAHTAcuZXslm+e3ttK6qvuB9dlq11I9f6nNnIXubi3sx80gwgczC3WSOwq+f5bolQHQhrgKGbJIG5pLlbGTDfN8vGaG1cGoN2HlcHrd2CfP05c5k6Ge7UGWc5O9dd1FynTzQb7wsDbQaI3bYzz69cB+k1nV5naGKBHjOu2QgwmANfneCgb1urUTZJHAQyGo2GlPCToacDaCZG6Nv1ufK50AG4+TngtXIbc+CuA3g+f1wP6eQE/fZaTPvdbpbvvnLN8t3bazpu3IxxvtJzv45d1/purpV4p78h4L9dsQXPg0C0ubpHmxmv6FYxTAB9o3OL9u26or/b+ZqPv9o+6QuoqU0pL8bfJBtwvcBkfa/x5j0m27vRaCAajcqzoK9FYzN6TFb73/zcaKZ5t+uj76ZGf7lcRrFYXDMeJejOWJZxt65u09iLeV/d7vFq6w+drDJXzuntNLhuvo+dzhKBkES93eK7N/O8W757a21Xg+jrsSutDH67TQcUnES0s1xPZotfQmaLCaJqp24uZ13PeemJ3Owg17NAMLPD15oseI7VahWLi4uYmppCo9HA8PAwGo2GZCaZ0ex0lroyLywsoFAoYH5+HouLiwLWmo2OBQB8Pt+qAfNWm762er2OaDSKQCAgWXBKurB5FkEIMswJrqfT6RWBM5+VZDKJ119/HV6vV8BrZtx5vbrsbSvAdP2M1Ot1KS2cnZ0VDdVz584JI3CtknBmzalTHQ6HsW/fvl3XZJTXXKlU8Prrr+PEiRM4e/asaKFfjo7NcuZXt1m+e/PGpChg9J2bqUhjAMtg6WIS7Bo47xbobDbQ7WXt9lLTazbRzmazcDgcqFarKxqzUkaGOvJk83cDm6vVKjKZDDwej7C5bLZlrUxWPpGxrptzkaXFdQyZc51OR7ZjA1Sel5bGsdvtUroNLDPvGITTt2rwu9PpwOFwoL+/H263G9lsFouLiz2lfsrlMhYWFqSRarFYNDDdnU6n+EA2Z2MpP4NmsrsJEmjfrJ8F6tYzic/GpZQjmpmZwYULF1CtVpFIJKRZ3WrPsk4SXLhwAa+++ioCgQDGx8cRiUQM49JoNISVyAq8arVqALI59olEomsfHD631NOnEfyIRqOw2WzyfPFZ63Q6iMfjePvb345CoYB/+Zd/wWuvvYZ0Om1ojraWbZev26wPtnz31W2W7968ETzWidjNfJ9YWaSlVbbie8UEJM/VbOakMm2jx242m5idnRX/t2/fPjidTtRqNUmicp+cTykbwsbsZh/IRGUymRTWt9/vl+sCjJVFWsJDs5K1BIq+T/yMWaebSV0ABulQl8sFt9sNn8+HYDAofWPMiXiXy4XBwUF4vV6RU+3lu0ulEubm5uB2uyUm9Hq9GB0dlf3H43FZb7DijuC21gHXmvGara0T+MQINF7ExrJTU1M4f/48qtUqFhYWUCwW1/TdOnkyPT2No0ePIhAIYGhoCKFQyJB4rtfrwvp3uVzCfidTX1cScjy1jru+1yQiaIk2yuzwGSsWi0IQbLfbGBgYwMGDB5HL5fDaa6/hpZdeQiqVMvQhuhR2Mf7X8t1ba5cdiP6zn/3M8L/NdmWVwW+3mVlYOlu33gCcn9fNKHVp6GZLvfjZzWi8mhlBvKa1QHSyshKJhDTH2rt3rwFcsNlswtTL5/NYXFxELpdDIpGQTu3m8h5+vlwuiwPYKT1AvfAgiO7z+TA8PIxwOIyBgQGEw2G4XC5DSTVB9Gq1Cp/Ph0qlApfLtUI6h/eHzsTlconOXigUEgdGp7cdTHTet3Q6jUqlgjNnzuDkyZMol8uS2FjrGSQTvVKpYHJyEi+//DL6+/sRjUZ3PYj+y1/+EslkUq7TMst2u12uvtvMaN4NxkBBB+RrybB0M87nmp3WLUBer611/ItdwJut1WpJQBMMBpHL5aQxJhtuscyaSW32YiFrqts5ahCdyWDNkqrVaqL5ThAZgAR8ZPLx+JQ647kQjNe9PHSgyoC30+kIA51AOhP7BN51wNvf3y9AVjKZ7DlulUoF1WpVwPhUKoVIJCJsOPY7sdvtImlDPXUA0sCz3W7D7/eL/jtZdbqSMBKJiMapy+WCw+GQarFKpYILFy7g9OnTK7R611q7sTpxdnYWr732GkKhEFqtFgYHB4VVxmeZzfPC4TACgQByuRyy2axUThDIIsuM46mZ50zUBAIBAXXa7baso5rNJlKplDxjbJI2MTGBt73tbVhcXMT//b//F88++6zh+3apbLfMZZbtftttvnu9idiL8WXbZWZm7GbPsd1uS3xpliC5GFtrbjJXlG3Gp9P3zc3NIZvNAgByuZw0y6Sv4Q8bmlJWg3Fbt3OvVCoSzxPA5nsAZF/AUoWFWdpDa6nrhKr5fZ2g5zlSYkYn0Jn8DgaDCAaDBoCe+yH5LBwOA8CqCXBWILNqv1AoIBqNIhKJSD8Q+mNdFaV71VGihTKvwDJRUVdTkUjIxpp9fX2CgZTLZZw5cwbHjx+XasD19LjR4PfMzAxeeOEFhMNh3HDDDSt8t+5NQjxBE/m08dgkHRC3IdM9EAjItfIcudbpdDqG5Dd13ffu3YvrrrsO8/PzmJ2dxc9+9rNNyzBtpe2m+exqt8sORLfs4q3bF3AzX0od9Onynp0yc8bU7Ax16fZqSQIuRrLZLAKBAFKplDgbMpAIMmcyGSSTSdHtZClat/FjcMwJfCeM520GvjlGuvxcl73r1/R25ooAbUx42Gw2lEolZLNZtNttCa69Xi+CwaDsU3c/N1cN9LoWjquW0OGY5/N5JJNJKU2ntMlGShD5bGjd1kKhgHK5bHDgl9K4MCyXy0ilUshms7KIvNTO/GLMyohbdjnZbnre1mJ9X46mwWWdjF8tGU5/R4A8nU4bwGnul6XTBE4LhcKaJcdamkY3DzWfD/0g96dLvfXaQ7PwzT5FAyDdZOLom91utzDZNNObPpVBIjXcVzPuo16vix5+Op0GAKlS08Gslg2gpjm3ZTNN8/kAkDFk4rvdXmo0m8lkUK1WRUqtVxP6ta6BCQ1guXErAENTVj5bBDlarZZoo+t9cY3BxEm3dQrHTW/L+6OZjUxm1Ot1XLhwQZL7myGJ7DazfLdll9LW+/ww9tlNpoHnzX4Pes1LO2XmJIYGPs2Vcb3Oi36TMpypVErmaL7PWFY3nSZLuJsRYK3X69II3EyqM7Pn9Tqj2/74DBFg5uvm7bTEK/2kHh/KjRGE1/eOTVUBGPxOL+PxOHZOpxOZTAYApFpKH9O8liCLndvSj5rxAGAZR2FyIJ/PC3ntYn13pVKRJIpO0us1hE6Ga2KC2Y+aj98LazA/A3a7Xe4tk+WUhGOVO9UHLnVD0a0wy3dvrVkg+lVoZidifm0t0wGbOVgzA5jb+YVjeQ5Bau2EgKXghQGT1tk0n1O9XsfMzAwymQwWFxdRrVYRDocRi8UwMDAAYLkZSzqdxsmTJ5HP5zE3Nye6ot3AzHK5jPn5eQQCAdEw3W5rNBoCFOhu38ByV/FuQDkXHHyd47oakK4B7ZmZGaTTaXi9XrnmaDSKsbExuN1uhMNhAdTJyjNrkHFhppn0XJhUKhX5YTCazWalDDyTySCbzRqazazXWq0Wzp49i0KhgOHhYWn6EgwGMTw8vGMVBGbTCYTz58/j2LFjSCQSePnll3HmzBkBjy5Xs5y5ZVtpWykTom03Pmeadb4ZH65NM3F7AcVbYZrBpo3HYhmyLunWJevdjAlQJhRtNpswo1mGTN+cz+cxMzMjCd/VwMxKpYJkMgmfz4exsTEEg0G0Wi2Uy2XRjqfv0udGH0tpO7LaKf3C6/f7/bId5UFYTkxgQTf1JrsqHo8jFouJdjiwLLdCxhWZ5etJ/nL8arWalMm73W5EIhGMjIzA4/Ggv78f8Xhc5Fy4Ptq/f7+AHjxPnjOBBD5L1Es9ffo0UqmUNJTj/d1sk1zKypXLZcRiMezduxf9/f0CILAKgCXthUIBiUQCbrcb/f39huZorBys1Woy3qxgoMwO7w37CLTbbbmPZN37/X643W6RxDt58iT+9V//FZlMBq+99tqGrm+3muW7Ldsq2262+G571ggem/32WuPA9zXAqSU2zOuBnTAe1+fzie+uVqsipUmQtdfnKH81NTWFn/70pwgGg4hEIojH4wCWCE31eh3FYhFzc3MCtq8GZmrfvWfPHpEiY6zKOBdY7tcBLI+vxjB0I3HGyGZCHsef+6e/ZyKf/pvX1U0uhz3hGo0GUqmUAMer3ct2u418Pi+V9IlEAi6XC/F4HKOjo+K7o9GogT0dCAQMcqv04Vx76Otj4rtUKuHMmTNSaUVAOZ/PG6oGNmLt9lLT7VKphP7+fuzfvx/Dw8MG1rmW1qGP5jVw/PSz1Gg0pLFrN7KgmZygcQ0mHCKRiBAETp8+jZ/+9KdIp9M4ceLEhq5vt5rlu7fWLBD9KraLCbp1qY0ZYDU31touI9hLXSuWHdEBVSoVQ+BDZ2m+7larJezearUKt9uNYDCI/v5+CVRZSpZKpXDmzBnk83kpB+81jsye08HuhLHxSLlcFkfOTHov9jlgrCpYLxNdOzAy81lK7/F4MDQ0JPfGZrPJvdJa6eb9a4YGFxtcjBWLRdGDy2azSCaTmJmZEcBls2Pc6XSE4c39j42NAYAkUS6ldTodZDIZnD59GslkEnNzc0ilUpf6tLbELIds2VbYbmObbbdtVcWXWQZtuxIR5uPR9LEcDgf8fj88Ho/4bq3f2c3IoNL7okZ1f3+/lPS2Wi1kMhmcOXNmXXqWjUbDoFnt8XgEGDD3etGNMzWgquXPzIG6bl5NoJdmbtDOz1FOJBgMytgw+eD1egXsZaJ6vVav1+X8yApjWbUuiddN4FwuF4aGhkT+jQxzJj4Y8OoEeLFYxPnz5zEzM2MAEvRzt5lnj806yWzTbDaOrc/nk/PMZrOIRCJy/ppswc9yrHXwzXPUDXh1hQETImzePjQ0BLvdjqNHj+JnP/uZSNhcKWb5bsu2wrbb5+w265YsXO/6hXOljs3oa7jvnTTNDA4Gg5K4BJaruXoB3qwiAyDxjcvlwtjYmMzDrDDO5XI4d+4cSqXSmrJ1BN0BCGCu2eG6OoHzuR5THYPqyiud9DAzngGI36Mf1mx0u90Or9eLQCAg0if0G7rSi9Jw65X5oU8DIDHhyMiIaJkzmauJEQSKGYvrtQQTxLqvHQH2qakpzMzMSC+yi12Ddjod6U3Ty3cTz+G5N5tNgzyeZv7zXDVJsFtFnHlMNTZis9kQCAQwODiIvr4+vPDCC3juueeQz+eRSCQu6np3k11N8+12mwWiXyW2Vdl+c8DdrZvxdgfjmgnv9XqlmReZZ2bZEAbidA50GNroZGq1GtLptEiD0BlzMVAsFkVKY7VsOB0SGW9kcBH430qJEO3UyZbP5/OGxlm8lr6+Pin/0k6cjr9SqYhebKVSMUjCrHUOACRobrVaSKVSUqZGDXkCJWS6s6RaL2B4PC42yNBj0oINTNjEZCMNbFc7f577qVOnAAB79uyBy+VCOByG3++X52s7TS/UisUiEokESqUSTpw4gdOnTyObzaJQKGzrOVhm2eVmOjC63Kwbm0wbA4Kt0GI0M9gIuDqdTplLL5apsprR5+gAjr7A7/cjEAhIqS6DJz0+qxkTyAyKdVk4GdfrldKgHyI7mSXkBAl0wlZLpGm9VFaBsVJOM9P4Ps+TVV2ZTAbFYlGS7wAkaPV4PPKbwDTBaLL1KW+WyWQuyifqdVCn05G1RLValXVQrVaD0+kUsIDrLAazHHeC9IVCQYLv7ahWrNfrmJqaQqfTQTgcxtjYmDRLJcGC6xGbzSZjpkFxnrvWrtekAz67TBDo6+x0OrL2zOVymJubQ6PRwJkzZ2T9dyVIuVhm2Vba5QzorCWnsl7fvdb6xRxzU/JDa15rcHk742/26NB+j76bfVqYVHY6nSt6rnQz7btTqZSMBZnqutn0Wn5N+26dIDVLxQFGWRyNkZjBXG26koDzPn1vpVIxSMVxW2q5s1eKZqLTR/LzyWRSYuDN3MNqtSr9shqNBnK5nCGuDgQCSCQScm18fiiXoxP4fKaKxaJUtnfDTy7WarUazpw5g06ng1AohNHRUalu0Ox9jjsrw8yVAZoAqJu7azPfX/M6tFQq4eWXX0atVsPJkydlTXY5V31btn1mgehXsJlZX6sF6hvdr9bP1sdgUymdzd1K0wxqn88nzbDC4bA03OI5kRGtZS9Ybmx26Py/UCgI2K111nVmmg061ip5p1Py+/0YGxtDLBaDy+VCIBDYcp1tnkupVML58+exuLiIQqEggALLmOv1upR3eb1eeV8nCZLJJPL5vMjCrNdp0rnlcjnYbDZks1nMzs6ir69PGqI5HA4EAgE4nU6Ddhvvq1kHr1QqSbOPcrlsWGxx2614zuiIM5kMfvazn+Hf//3fceONN8Jms2FoaAgTExPwer0rtOy2w3gvk8kkfvnLXyKRSODFF1/Ev/3bv0mp4JVgVlmZZVtpa5VBr7bNpTIyjMkmM8+11K9kE6nVKp/WMl2JpKXQfD4fnE6nNKzSTc/0b+DipGP4faeP9nq9Un5M8Jw+XLPUdFJ8tbWL2feYG61xfNdjDB4pi7a4uAiPxyM+vFQqrTgfNs7s6+sT5jUAhEIh+Hw+YcjRv3EfwNK9KZfLmJ6eRjqdloDRZrMZwP9EIiEJeoLobJ5VrVYxNzcnzOyL0e9kwtput+P8+fMSkPLH7XYjEAgYdMDZ5IyyeRx7vXbqBSRvxfeyWCzi6NGjePnllzEyMoJbbrkFoVAINpsN8XhcmOL0r9R/1+fJe8OScH5PuOZkybeWgOH6Uvd+mZ6exr/+678ikUhgdnZWGsVtVQXJpTbLd1u2VdbtWdhuiZetMs4L3UBy+m5KRq3lu1dbv+gfspX13KuTqoylzLG+1vNea1x7jb/NZoPP50M0GhX2udvtlriW8x+BZQLfWtqrm7HHlc1mE9k1YDkW2kisR9/tcDhQKpWQz+dlnaMTDr32oyVceG0EujU7XY9luVzG+fPnkUqlxC/oNV2pVMLCwoJU2GlpmFarhXw+j5MnTyKXy0niYLPPPkl+XOdpomOn04HX60UoFDJUhXP8iY9oX8Xxp+/eDlynUCjgueeew/PPP4/R0VG8+c1vRiwWw80334x9+/bJ8TUZElipc8/nT193L8Jkp9MxSNxyHXz69Gn88z//M+bn5zE9PY2pqSmDlMzlbpbv3lqzQPQr1HSpF/8HlplgwNZ8GcyA4k6yARnUMOChtIzWOyPTWTPfVjvHbgD7Zo363A6HA9VqVcrOzeD7ZsdMB6o6410ul0UDnkw+Btkej0cWN3SyGkSnLAoZ4NrWw27QWXYek/tnR/FarSbBqAbRNXiim6aQDUjd1O0MRtk8Np/PY2BgAOl0Gi6XC9FoVO5lt8ZpF2t6MUBQhAmNhYUFJBIJZLPZKyobbjlzy3bKdmvJ+FpziE5YM7C7mOswl4NrNtlqiV0e1/x7M6aBdGpPm6XYNCDJc1uP76Hv3oqAR7PECCgDxiZqGpjQ46hBFQIfmlUPGCV59HHMSVImlPX79BEMLrXfp++/mIrAtdZBBG3MwDI1Ui+FtdttqdLyeDySUCGLkUZwg4CAHifzusz8nTADWjyu3hfPY2FhQaTnCMBcKWb5bsuudutFUtP/X6zv7rYu0POPlt3std1Wf9cYNzJ+IzmKyUYCk6zCulS+mwA2400AK5LB5s/wt/bVxBO0RBuNQDiruMvl8op90j9UKhVZ8xCXIDjLzzLJDmx+zbqW72a1mAabWe1NEH2nG2i2Wi3kcjkAS3J8JEGQMNeLtKjvo3ltux7jfqlYACwl4xcWFnDhwgVpfn6lJL8By3dvtVkg+hVmZEPZbDYJbgCjbhezwyzNATb2xeBkpkFSmi7l3a4vmy7XMTfL1E5PO3P+vZPZRB2Ez8zMoNFoIBwO48CBAwY29mb3rR303Nwc0uk0MpkMMpmMNHThdixHr1arcDqdCAaDotFmsy3LubAUvFwuw263IxqNCtuKQTtLvtZ7ngDks1yI6BKqbqV0BCEYfOomaxt5rrhfsxa8OZGkFx58fmdnZ/HTn/4UoVAIhw4dwuTkJAKBAPbt2ydNdDwezwpJo/WaXrQVCgUUCgWUy2WcOnUKi4uLmJubw4svvoh0Oo2FhYUd6TOwk2Y5c8t2wi6GPb3dxmBPS0IAxqSvlpS42GPpZB3ZUpROMc/rerx6BZybMYLKHo9HGHUADGw96m5TS5TzMxPDO3Uvm80mEokEqtUqIpEIXC4XgsGgjJ25LLzRaBiYbgzOOLaUzuEaptlsYnFxEel0Wtjf2vh8dDod5PN5TE5OYmFhQeTrgKWgj/6+r68PHo9HZEs0yL6VDDKua+jPuabcLWytQqGAU6dOwev1olAoYHZ2Fm63G7FYDD6fDy6XSxp/kojR6SxrwhJYYOVbsVg0PLOsoGi3l5q7zc7OolarYWFhAblcDslkEtPT01LZeKWZ5bst2067HJ4PDcABENkPnUzVcz2tF1O81zH034zVWPHNWIp+UZ9Xt8+bX1vPcbu9xySk1hnn+dE36kT4pQAhm80mLly4gFKphHA4jImJCQSDwa7bamCd6wteG6+P6zFg6R62Wi2k02lpyG1OHpOhTh/SarXg8Xjg9/sRCoWk+ozENafTiXA4LFryWsJEJ2q3YlwY3+ueH7op+KW0fD6PEydOwOfzIZ/P49y5c/B6vRgaGhLdfZIuzEkrrbZAzILrFK6LgCWgnmuqubk51Go1zM7OIpvNYmFhAWfPnpWqgMthLtqIWb57a80C0a8w40Kf5SnsMgwsOXcylsxZ3/V+MbSz7JatpNPZrolYZxvNILouVweW9cw0iG6Wn9luIwB84cIFJBIJDA4OIhKJGCb1zZ4PgWY6gLNnz6JcLiOdThvYbNQ005lur9e7okkZEyA68xqNRuU1XUrVbZG22hhw/wAM59bt2rvtc7MTty6B5Fhr3Voz+1vrwF24cAELCwtwOBw4e/Yszp8/L83qqMOvQaDNGI/PQD+VSuGnP/0pjh07hlwuh/Pnz0uZ5qVe3Fhm2eVqu3XhR8DYbNQq1/PVxUqAabYX/7fZlhpw6+Cj2+e2yui7mYCkpIzdbketVpNkL99vNpvwer0S5OlAcifuaavVwuLiIhKJBAYGBhCNRoV1bm5epUvO9dqIPprl/fpesofJmTNnejZR5fNBxprdbsfAwADGx8el/J3SMVz3mZMy69Gl3YiZ/dFuq/Sg/rrdbsfc3BxOnDiBcDiMN7/5zRgbG0M4HIbP5xMmJZ8xstZ5z1qt5abmJB/w+QyFQgCWmrFeuHAB2WwWR48exblz5wzSTLtpXCyzzLKtM7N2to5LNVNc+3it17ze+UH7PFbVaBDdDLBuZQxlNsZIPC59na601v1MVmPLb6c1m03Mzs5ibm4OAwMDAlzrWJCmk9XEMOjT+beZhFWv15HNZnH+/HlDpbneZ6VSkSq2QqGAvr4+xGIxDA8Pi1Qbe5XpNQ9BdH6eMelW+G8tTWIeg91gXM/Y7XacO3cOzz//POLxON797nfj0KFDhmQ2TT9f7L/CcaxUKvIZ3euk0+lgfn4e586dQyaTwbPPPovTp08bEha7ZUws271mgehXiDGQ0zppZiY6JxkCobVabcPOnKazttp2kiWmWcuaUazLbPX7q8mobOc5Eyix2WyoVqui0RYIBAAsM+Y1+N9tH7wmOvtarSbgNkFyzUgwf15LvhCc4X41c83j8QjAQtMgusvlMjDXNfC83vHo9vdWma5IIBOE2naaHaJBBa3fRzYa7xuTEOl0Gp1OBzMzM9IglU1a9cJMgyRmhj2wzHTXi6W5uTnMzs4ik8kgmUwil8tJed1uYfdttVkZccssWzY9dxDc0/OHw+GA2+02JKo3+x0wA9E7+V0yM760T9PzspkNtpNri27ny+DMXJJNv6JNNyTTrH9eI6+FfpUM9rUYgLp6oNlsyjPB9+nPeHz6cTYSZ6KYDK2tGs+tTrR0kxyiTzfLsehz0JUWfGaq1SrK5TL6+vqQTqdF0o6BdSAQkMavWgqHQJBucM6Gr3y/0+lIJUE+n5cGogDWvR66HM3y3ZZZtmya2MW42yz55PF4DKzti/0OcL7b6QTmWpIZnJs14epS+W7OwfTdjLU9Hg8AiGa9NvP16bgNMFaYM+ZerW+Yjr31moYYDXukaekfPisej0d8D6vNGMNv1TO0laZxqNV8t7mqXH8nuB4keY9rrWQyiVAoBL/fLzgEq8iY8ACWe98wlifxgIkPvT5YWFhAKpVCNpvtWgl4JZrlu7fWLBD9CjAGSX19ffD7/YhEIsKU9fl8BmfeaDSkUQad72oNn1YzOkgzGL3dgDQnYGqEttttYe1xkgaWMuMsldLBqp7cGfxqR7/V509mWrPZRDKZxEsvvQSPx4N4PI6hoSFpWBYKhYSBaJYc0eXsbPiZSqUwPz+PWq0mwGsvPTPNuCaITyfOUik23zJrfgPLcizMvlerVSwuLuLcuXOoVCoGNtylNH4HnE4nAoEAotEo3G43gsGgSNewUkM/S9RcJzOQ/3NMFxYWUCgU4Ha7cerUKQSDQUQiEezZswderxfxeBz9/f3CVqPWO8eSoEWz2UQ2m0W5XEahUMCFCxdQqVSQSCSwuLgo41ooFAzs/SvRLGdumWVLZrMtNbFic6P+/n6R0srn86hWq/D5fAiFQuh0lspQCehtJEDdTobaRo6vmbqstAGWAOlOZ1neqtlsGvp7bFVJ82asVCrh9OnTErz5/X44HA7x3Ux+MOgjI5y9PfQ6q1gsyhxP3c1upARt+l6Tte/xeDA8PIx4PI5arYbFxUVUKhVEIhEMDAygr6/PEHhTn3thYQFTU1O7rscG/aXL5YLP54PD4ZCKSofDgXA4DI/Hg1KphGQyKUlu/nB8CXCQXc7vUbFYlHVxLBaD2+3G4OAghoaG0Gq1kEwmUSwWMTQ0hBtuuAE+nw+5XA4zMzNSndbX14disSgSLsViEfl8Hs1mE5lM5qpgsFm+2zLLlo0yWg6HA9FoFOFwGACkQSSlPJvNJnK5HHK53JZ8h7R02E59pzTg2606rlarIZ/PS0NN+vdL+Z0vFos4efKk6LhT0mt4eBixWMwAnJubcTJ2KxQKqNfryOfzyGQy0si8VCqtKWWm7xXjQIfDgT179iAWi8k6p9FoiOSL7rfCeL9cLotU2G6RXwFgAMl103G/3y8V26FQCG63W6rliSlo6VZK+uXzecN6qVKp4Gc/+xmOHj0qjW1dLpf4bgBSRRYKhTA+Pg6Xy4VEIoGFhQXD88eq72q1ikKhgGw2i0ajgVQqdSmHcMfM8t1baxaIfgUYJzAyb71eL5xOp0xg3AZY1m1jZtPlcgmwvFG7FMGCOVtJkLHRaAjbik6F2XD9Q4BdLwS4X7NO9lYaj0umkt1ulwwpnYzOrJK1prXsCaJTq2thYQHnzp2TbOt6AmI6JmrXkTkRj8exZ8+enjrfmo3HoN/j8SCTyaCvr29XZHC5EOJCKRgMYnBwEF6vF+FwGOFwWJh7DofDwG6kHjnHhuzCUqmEVquFQqGATCYDADh//jzsdjtisRj2798Pv9+PPXv2YHx8HG63G/F43KC3arPZDFUDiUQC+XweyWQSJ0+eRLFYRCqVQiKR2BULop0yy5lbZpmxbwN9N0FQVvywNNrpdErAweDtcjRdmsz1h67m4ZzLoGo3VOPU63Ukk0kAkH4ibrcbbrfboLep+31oTfJ6vY5cLidg9uLiogEEXo9xzmQVU19fH4LBIMbGxoQVx14mY2NjovXNQDyZTAqjbWZmZtvGajOmyQ1MQlAnlgH4wMAA/H4/stksABgq4agTzHUQZeN0Mpqfc7vdCIVCcLlcGB8fR7FYlN4y1DE/cOAAPB4PKpUKstmsAbBKJBI4fvw4SqXSjo/TbjDLd1tm2bLpZpsk7+i53+PxiFwmdZa34jtwKfy/Bky7WaPRkMSlruq9lN/5er2OxcVFAIDP50MgEJAYMRgM9qwE55zfaDRQKBRQqVSQTCYxOzsr67L1Xhfvlfb3kUgE4+PjBpA8HA4jGo1KhTMbbPr9fpG7m5+fl7XDbjFdiUGCQSQSQTgclrg4EAggl8uhr69PQHNeh24AXiwWDRUN1WpVGo96PB7pT7Nv3z75PhEXGRkZQTQahd/vR6FQEMCeGEkqlcLJkydRKpUMckRXi1m+e2vNAtGvENNdsbU8iJlV3Ol0BEjXjTfXAtF1iS2DRc2M2smMKL/EZBvR2WiggePh8XjQbrcRDAYlaNcJBd3UjM5SZz+ZHWZgtRUTEMeNDtntdgtDWWva63vGBVitVkMmkzH8pjNfj5EB7/V6MTo6itHRUWl0QuC427OggR6yrPv7+3HgwAGUSiUJ1jluGvQgy5KlcxqkZzUEQQYuMjfi2Ah68xhDQ0NSkTE6OipONxwOC9PfDKKXSiUp53I4HCgUCuK0KZdTLBbl3pNhyCasvJ9korOagN81BvJkL5TLZeTzeaTTaamYsJyTZZZdXaZLX7XP1vMo3wNgaHLFkl9qZl4uRn9WqVSkIohrEfaY6OvrQ39/PwCs8IXAMuAKwMDY4pyeyWSQSqW2bU2im5ym02lhltG3ad1bJs5JXODvjTII6VvI6orFYqLnzQSE1+tFp9OB0+kUn6KbvHk8HthsNgwMDOANb3gDKpUKMplMV2Yk13xOp9MgVcZ7oSsZOe704RsxAuculwvRaBRer1dkVsg+D4VCcDgckrjgeZHVT3AhEAhIQE7ZlkqlgnK5bLg2nivvH8voM5kMKpUK5ubm8MorryAQCIiurpYmKBQKl9V3zjLLLNta0z6bsQdjY87t3XpHEWRkMvxymkd0FRSBUN1rCoD4bvoGmn6fUlk6Hub+KWm5XRJu9N1MhjL5qtcZ2rcxPmXzUBKrNnJe2nfH43Ehd5FspX0znyF97SRY2Gw2jIyMSIJmfn4eiUSiZxWbbmROnX69dtLkws2sI4mhOJ1O8d1erxfBYBBOp1Oq9NiQnbE3yZulUknGMxAIiD/ns1Uul8V36ypGnmsymZR1FkF4kgM9Hg9SqRRSqZQhoUOZ1N3C4qfpxqgWFnD5mAWiXwGmm2Xwh07d3HwBgDgN/f5qTcs0050LAF0mzOZLOwmiswyc2mKNRgPFYtHQQGJ4eBgjIyPweDwYHBxEKBSCx+ORUiBeEwADSyCZTKJarWJmZgbnzp1DsVjEmTNnMD8/b9Bv3azRUWUyGRSLRUMJmU4C6Em1W5duZlHXM+lyX8FgEDfeeCMGBgYwODiIffv2CQNeO9le+3A4HAiFQhKwjo6OolKp4De/+Q0cDgdKpRLm5uZWNNKJRCKIxWLweDwYGBgwNLzlwomZ5NnZ2Q2V7fO6IpEIAoEA3vCGNyAejyMSiWBsbAwejwehUMggl6PlXBh0k8m3sLAg7PBgMIhSqYQLFy7IM8LfTKywAQrHz9zFndfIH63zq5uZXm2O08qIW3a1m5ZiYxVSX1+fyHYBEJkQMnLa7baAiwwGLqdAHIAkf/V8yeDK4XBgaGgIe/fuNTCYAGMjN/05rgEYSL388svC+t4Oow+w2WwolUqyjjCDzMAyoGImHWx0zvd6vdi3bx/C4TCCwSDC4bAAz1zH6SoosiDJ6Caru91uIxKJYGJiAtVqFS+++CKOHz9uAC203B2Px2BZ653q5mesUtvos+h0OiXIvu666zA4OCgJAYfDgWAwCL/fL2PZbrcxNDQkjT1nZmZw4cIFAJAgPZ/PY3Z2VtYT/N5wvBlM8/7Nzs7K6+12G6lUSirOzPrxvH8X+2xtR+XjxQbi6z0ny3dbdrWb3W6XuZWVSdoXdQM2bTYbfD6fVMxsZL7czkrpjRjjJE0A0PP10NAQRkZGJNlLKS6v1yvxF/t4aKY646CXXnoJv/rVr0SKdavXNgRabbalxp7nz59fEfNqnXkdt/H3RmXlvF4v9u/fj1AoJDKuVAtg3BgKhWRNweMw2UDZuHa7jZGREdx0002oVqt49tlnkcvlurL97Xa7rBM4/iTKMaFB4hhlRMvl8rqvSUsQ+v1+HDp0CPF4HG63WyoqY7GYyOXohABJd/Pz84Kr0HcVCgUsLCygUqmIb9d69OxNwvvHijreH4fDgVdffVV8tx4bjutGqwh2wphM2SweYPnuS2MWiH6FGCcg7dj4pdKBnbnDtP7ptU+yhAhQE0TX5TgETXeyiQgDGTZ/0uAlJ2pK2vT39wsDe2BgQIBUM4heLpfhdrul9Cefz4sup7lz9sVeox43mmY3mEH0iwFa9X0kiy0ajUrGeCP74bhRGsXr9UrDD7Ikua0+LseQ2/KZY2KAY6HlbNZKzJj37/f7BVAn+5zH5CLXDKKTuUf2Xq1Wg8PhQLPZRDAYNGjdApDzvBzBq91kljO3zDKjjAV/tMamZqfTOMd2a0Kt98vf5rWBZoHpYHGnEuG6pFn3Z9GJZPaWIJuJ5wgYQXQmxMlsqtVqMu/rz2gJmYs1zbLbKamZvr4+0cWnTjj9FmAkU2jAXrP3WbFHMkS1WhW/SIk3HdDyXtBnch3I89HjwXPQAfN6zCyFEA6HDbJy7GfS6XSkrw3XHATMKbXG4J3VhzxnrifMQDjQ/f5RLulytY3eA/05wArELbNsLeP8qOMhXRXV6xnX8/BqhCX+5nE4R2s/pn34TvluHffo+Z7AOOdx+m72qCJg7PF44PF4hNWuY9tms4lIJGLo5caExFYRjS6V76asaCAQEAzF7Ltp3fqz8X32MaPvNlc/0PicUWqO94GJHsDYWH61teRq10Vfzbib50fCHdduBK659gAgvcE6nY4B7yiVSnLuutk8z1XfP8q2Xe62Gha33s8Dlu/eabNA9CvAdBaXwTedLSdjzYrSWlO9GFE2mw3RaFQc2r59+xCJRKRJI/dPUJnNENPpNM6dOyfly9sJMnLRwEDP4/FIQ6hwOCxsa5fLhUgkIplYZsbNyYV2u22Qd/H7/RgeHkalUsHBgweRSCSQSqXw+uuvI5/Pi9zLVk4qvJc6kNUB8WaMGqnUCB8dHcXw8LA0/7gY49iPjo7CZrMhlUpJ2RudqdPpxMjICEZGRqThJ7XCec3RaFQahjUaDfh8PpTLZWns2c14z51OJwYHBzExMYFgMIj9+/cLE31kZESy5VyY8d7rBajf7xfmg8vlQrlcht/vR7vdFu1hNqhNpVIGaRfLNmeWM7fMsmVwl4GMDsb5PrAspdXpLOk/spmhWToLgFSZkX1M33jo0CG43W4kEgmRIWHDrGw2i9nZ2R1tNkkWm8PhQH9/P66//nphT0UiEQnSyMryer3iq+kXNEOaMl1vectbMDY2JmXYlUpFtDALhcKOXd9mzAyA8nlgoErwGFiuKqS0CaVLCKaz0TqZ1HzO2DcEAAYGBkRbdGFhAaVSyQDiaI1TlofrYI/vk5EOwFAlt5qRLReLxRAIBCQxQHk2MuhYueZyudBqtYR532w2pSKOTdP1+sNmswkwz0qF3aKBejEgd6/Pb2Sf3YLu9X7e8t2WXe1GSUfNGDYDft2MTSrJkqUxHu3mu0dGRnD99dfD4/FIHEqZUTZFnJ6eRq1W24lLB7AM0jocDgwODuLmm29GKBRCNBpFLBYTH6R7tWniWjfCGADccsst0g+GcpnJZBLHjh1DPp/fseu7WNNrm2AwiP7+fgwMDMg46BhUkwp0r7dujHhiA81mE4ODg3jjG9+IQqGAc+fOIZ/PG5LdvB96jaQleSkx4/F4UCgUBNPRlVerXZ/f78fAwIAA6OxdwsR8KBSSvnxMbuv1bTQalcqNVColaw9iBKFQCMFgcNf57s3YWr67G7FlI2b57ktjFoh+hZhurkSnzr+1g9Lb6M+YvxgEXicmJtDf34877rhDGicGAgFDIFupVDA5OYlMJoPTp08jk8nI/rYTROdihc7a7/djdHQUd955J4aHhxEKhRAOh1c46dUyfdTi7HQ6GBsbE5Z7KpVCPp/HmTNnROqFDn6rJ5WtHjPey+HhYQwMDGBsbAzDw8MXlfUEjJqpY2Nj6O/vx+zsLM6cOYNMJiMNdtxuN0ZHRzE2NtYz2+p2uxGJREQz1u/3C1jdazwcDgf8fr8sMlkut2/fPsTjcYRCIQwNDcmilEF1t2vm96Berxuy/AAE2Gc373K5LCw1y6FYZpllmzUzg4xBqZnl2+l0pHycOs75fH5FU0rOb/TTXq8Xe/fuRSgUwhvf+Ea8//3vRygUwsmTJ3Hq1CnRfy4Wi5iamkIymbwkIDp9xNve9jYMDg6Kb2+32ygWi6jVaoakKRs1a5ae1jXdu3cvgsEgms0mZmZmkEqlcObMGczOzu5qEF37J5IftN45EwyaDchtbDab9O/QjU6pAQssy5UwqCYA0mw2kclkkM1mJUHMc9EgOgkI+jyp71sqlSSxo8v0V7tWu92OUCiEkZERafhGVjoBe4LoNpttBQMTgIDvpVIJ2WwWuVxOvgNOp1PWgZQQ2o2B+FosstXWrubPrHdN0iuot9Y0llm2tnU6HanGdrvdK8BOACt8MwBpkqgTwboajfO8z+fDxMQEIpEIbrnlFvzn//yfEYlEcPz4cfHdCwsLKBQKOHPmDBYXF3ccRGfCc3h4GG9729swNDRkqIYimU7LaPbSSWfF0MjICN7+9rej2WziwoULyGazOHnyJKanpy8bEF37Tq7FqIXO9wEjaY4/GpMh0A4sg6y6co/9v5LJJJLJpFTOezweQ9W5BuNJNtC+vVQqSQNO+sn1guiDg4MIBAIip+rz+aSajP1NgO5M+VgshoGBAelJlsvlYLfbEQgE0Gq1pAqN57RRGZ3dZHrN1K1XwsVWklyu43K5W28hbMsuG9MTsWaZ6x8GD/o1DabrclxmJsnmjsfjoktJSQ79w9JasscoFcJGGNtlZEb5fD7EYjEMDw+jv79fFiBkMmm98W4yNuYfBuM6mPP5fJJtHRgYkA7QdFaracpfatOgCku6ejUQ3ez+uQhyu93yTOhnhMdcrXGpLuvmZ1Y7Ry7MeAyfzyeNTbxerwTR3e6/lk7Q91z3E+B1MIjn38zoXyyL/2o3nXXf7I9lll3uxmeZDCSzjqO5cSOlx8zfAc7BTATTH9Mnu91ulEolFAoFNJtNCYBZpcMfluLuhPX19SEUCqG/vx+hUEiCRjKv6cO7zdec282NRc1rH2BZV35gYADDw8OiE7ob/bY50OQ9YiUdr5uBsW5CqwNoTZzQPVX4wzUgx4b3nxJ49INMQvOY+jx5rnoNwPPciO/mD1+jTBwlXcxSPHq9RtCG6zSuc/TzzH3uVp99KfzZxTDZLd9t2dVumhncrbpbV4ab3zfLnurYh5U5jKNJRKpUKkIqog/gPMkmlazk2W7jvBuJRDA0NCTnqONtxlxmApu5Z5QGFvVPp9ORud3n82FoaAijo6MIh8OGJqC71cxkBs26N6/5tDyOHjMNqpsbsXKdREk3Hadqv8339bjrc9SNTX0+35q+m/30eDxiQlx7krCm+6d0w1j4LGiSgNlvcz3i8/l2re/eqOlk26U6/k777q9//evYv38/PB4Pbr31VvziF79YdftnnnkGt956KzweDw4cOIBvfvObK7b5wQ9+gBtuuAFutxs33HADfvjDHxre//nPf47f/u3fFpWEf/zHf+w6Fo899hhGR0fh9Xpx991347XXXtvQtVlM9CvAOp2OQdM7n8/D6XSiXq+L9hQnxUajIQFXoVCQzsicxL1er+iY3XLLLXjb294Gn8+HwcFBmcgYTGk9uD179khG0m63I5vN4uWXX8arr766LbpjNpsN4XAY8XgcsVgM73rXu3DdddchHA5jz5494kC2wtEyM0pdbLfbjUKhgKNHj+IXv/gFSqWSNAndjUa22TXXXCMOfTuOYbfb4ff7pXs49fO5CFrPvWATUq/Xi1qt1tVx0hH7fD6RpRkbG8Po6ChCoRCGh4cFNOIz263hm96fXrgEg0EpuWMZGX+y2SySyaSUlu1kQ90rzayyMsuudiMIarPZUC6XJThlEAEsM3hYKcOmxmaJL4/Hg3g8DpfLhfHxcezZs8fAZisWi/jxj38sDB9qRofDYfh8PmEjF4tFLC4uIpFIbPt3zOfz4c1vfjOuueYaOBwOVKtVzM/PS/KelWAMqDlmTNwDy+zqer0uzcaTyaRUy5HFFA6H8Vu/9VtotVp4+eWX8etf/xrValVY07vBNCjNZ2FwcBDDw8OIRCJCaOAart1uI5PJIJPJwOFwIB6PY3h4GLVazSBDxmeMfpqyK8ASoE0QhBIBBHzsdjsGBwcRi8XQ6XRkLM3GgHhgYAChUAjJZHLVKjKfz4eBgQGpIhscHITX68XQ0JAQNijnUywWZW2lE9hMzNO/E6jgObLZKQP5QqGAfD6/qysRetl2BN4X438t322ZZcvs0Wq1KnMwQUVgObHLGJ3+yOy7WYnr8Xhw/fXX49ChQzJHsnfFv/zLv6Ddbhsac/r9frhcLoyNjeG6665DoVDA3NwcZmdnt+07xjk3HA7jzjvvxLXXXitAvm4WbvZlHA/O3xq3IJjc6XQMaxv6rsHBQdx3331oNps4evQonn32WYMP3G3G6xwaGsL4+LgQ+zQwzmehXq/L/Xe73VJNwLVPvV6XJIseN5rD4UAgEBDJXYLsfX19hn4yHK9u1disJojH41Ld0Mt3+/1+DA0NwePxYN++fZiYmIDP58PIyAhCoZAk/TW5AViOu80AOivK9+7di8HBQdTrdWkQT4mkfD6PcrkszcAvR/+heyBcyvPfad/9xBNP4JOf/CS+/vWv4+1vfzv+5m/+Bvfeey+OHTuGiYmJFdtPTk7i/e9/Pz7+8Y/j7//+7/HLX/4SDz30EAYGBvDBD34QAHDkyBE88MAD+PM//3Pcd999+OEPf4gPfehDePbZZ3H77bcDWFIQuOWWW/AHf/AH8jmz/dVf/RW+/OUv4/HHH8ehQ4fw+c9/Hu9973tx4sQJBIPBdV2fBaJfAaYDceo283+tPwZAgD+W3HKioiNi+WswGMTExASuvfZaYSF1y2ICyww4ABKgUV91O7PFXq8X0WgUQ0NDuO666/CWt7zFoAW6VUamFrAEVEQiEdGQfe211yTQ261G3dHBwUFDmfdWH4MZ6lAohEqlIs5UPx9rGRehZLT1Yq2ToUcwiIxLasZSN3a9VQI6S07d4Xa7jXA4LJr6sVgMAKTKodPpbOvzfaWbFYhbZtny94AyKgQFte41AUwmvWu12ornn8042ctjYmICfr8f4+PjiEQiOHHiBI4dO4ZisYhDhw7hmmuuEfCRQRw1O3cqWCHg/8Y3vhH5fB4XLlxAvV5HLBYz+FwGkhwv9tZot9sol8uy5qH0FsuSgeUEbzgcxr59+yQJ/uqrr65oar1bjCwzghVDQ0NSKu3xeCSI7nQ6qFQqqNVqEtwGg0HRgWfihQlpvQ6k/6JMEEHwQCAgjTUptxIIBGTNSACApqu8eI7VanVV5hh9qs/nE79NGQMeLx6Pw263G6R7+KOZjmRlApBnplarIZ/Py3Xk83lZM1h28Wb5bsssW36OGVdzTuLcp2VTNSPdDPwy7qEk6bXXXiuxptfrxeTkJI4fP45yuSwJclaNUzudhKJCobCtvpvX5/P5cPDgQdx6662i8272Deb4iD7H4XAIbgFAkr3tdhvVahWVSkUS54xfr7vuOrhcLmQyGfz617+WpMRuBNGBpWsnqYsMbQArQHQmtFmdxbUe13lMBOvqM33NjLvj8bgkMugvyeLmc9crGetwOBAOh9HpdOQ57mVcKwYCAamYIKkhGAwK0dBut3ddW+lkPv14X1+fkCYqlYr0NSmVSqhUKlJpdrnbbvB7W+W7zdJKOrmn7ctf/jI++tGP4mMf+xgA4PDhw/jxj3+Mb3zjG/jCF76wYvtvfvObmJiYwOHDhwEA119/PY4ePYovfelLAoYfPnwY733ve/Hoo48CAB599FE888wzOHz4ML73ve8BAO69917ce++9q17H4cOH8elPfxq/+7u/CwD4zne+g6GhIfzDP/wD/vAP/3Bd42GB6FeQ0SlpZpVuUAlAJlhmfrux2QYGBqSLtJnFu5bRodPxsREYj3WxxgnY4XBgaGgI119/Pfr7+xGJRFY0C90OYwDHZpbXX3890uk0arWadJneTh34jZiuFqDUynZLzxCE1pIAm22K2u0zWoqFIL0uAeNi5WLlVngcLmpYEq7ljHRm2TLLLLNsM6bnEALmDIKAZbYbpVx6BY4sydVzoG505na7pVF2MBhcocvocrnQ398Pn8+HVCoFh8OxLUwvspuHh4cRjUbR398vDSRjsZhIzZTLZbRaLWGLU37GZrMJy5jjx3PV8za31SXhZIIFg0Hs2bMHxWIRs7OzO6olu15jsKP1YindUq1WUa1W0el04Ha7RbOcbLNqtSr+ib6QSWe32y33VZeU6+dLB78ENcwyBPr5oeSKfm81n89ntZvsGnVzmcih5jAAA5OPTHk2QeO97uvrk6bvBGZ0ibnD4djV4Itllll2eZhmlnJOJQDM1zkf8v1uCVvGzWyo2Gg0xKc1m024XC4MDQ2hWq0iGAyuqKr1eDxSCb6wsCCNpjcbe/W6VmqWj42NIR6PIxqNAlgGcikPxiSveY612WyGBDfHiPtgopPXxfmc8Vaz2UQ0GsV1112HfD6PmZkZJJPJLbm+rTKd6NWkPl2VwO0oMeZyucS3a2IjyRTsB+NyuXpqqHOfwPKYktWtK/LMZn5W19Id1+tMLeFjltXjuZklaPhD3844ncfUskc8npbX5XOwGwDpq9nGx8cN///pn/4pHnvsMcNr9Xodzz//PP74j//Y8Po999yD5557rut+jxw5gnvuucfw2vve9z58+9vfRqPRgNPpxJEjR/Dwww+v2IbA+3pscnIS8/PzhmO53W7cddddeO655ywQ/WozPWmymahm6Gh9TLKQujUVDYfDOHToEOLxOIaGhgy6Vusxv9+PPXv2oFKpSAlyuVxGoVDYkkCVwbbH48FNN92E//Sf/pOwrKmhvZ3Aps7+X3vttQiHw0gmkyiVSpifn5fJfTcEaGRpEWxmlng7tcXYkZuOTpeNr9dWy5Tq59rn8yEajSISichvrYG6mWdBb089O2be4/E4ACASiSCbzQIAcrncrmMxXi5msdksu1rNrBMKLMuyARC5Nb2N1oXu9uyzkRRLhxnYkI0cCoVw0003rdBtZfBDBnC9Xkcmk8HZs2cl6N0qf8ag8Oabb8Z73vMe+Hw+hEIhCchCoZAkERYXF0W6plwuS6LUbrcjl8shnU7D6XRiYGAAkUhEtGIBiJQYAz7dVLrVamFoaAhvfetbkc1mUa/XkU6nt+T6ttJ0coPXUiwWBSAuFAqw2+0YGxvD4OCgjFm5XDZUYTFI1yXXhUIBqVRK5P1YnUg9/lqtJsG31sfX0kJc6/CeMgBvNpsCbPcyl8uFcDgslWQE1Fn1SAY52YhM+nB9q/dNuRfq63s8HhQKBWmS22g0pPkpn7Ne1RyWrc8s323Z1WA6HtDPrJam4HtM6pKVze3ou83JR22Mm0OhEHw+n8TKlJUMBAK44YYbDBIg+lwikQj6+/vRaDSQTCZx6tQp1Go1YTRvxTgw9n3Tm96Ee+65B36/H36/3xCTsTKqXC4bKnUJKttsNqkMYuUSWcv03W63W46n8QeuV/bu3Yv3v//9yGQy+PGPf7zrQHStb09dbyaG9bxJ6VCn0ylkAVa08xkhgOzxeKQHm96Ofoy+2Ey8YOUin7tuzx5f0xWPq83PrHhk0seso04/rb87lKYhNkXTzwexCT63XEMQwyCJTfcEsmzjtlW+e3p6WqSCAHRloSeTSVlvaxsaGsL8/HzX/c/Pz3fdvtlsIplMYmRkpOc2vfbZ6zj8nHk/58+fX/d+LBD9CjOdedasaD3B6aZl3cp6/H6/wbltBIjkhA/AwDDaKmCbDpsB9+DgIPx+Pzwez440CeOiqNPpIBAIYGhoSMqmuAjolu29FMZz1Y1St7shC52hy+VakeFej5mz1r2OoRuB0sl2a6CymWs1O3Y6cj53/Hunmu9dqWYF4pZZZjTNxqFpn7PaM6+DTt3QieA7g1b2TimXy3JMXeHF+ZSB31b6bgaYusxZ+ymWPJM1RWCXAaAGwllRp5ls/NHXQFa6ZgO6XC5Eo1GpnNqtOpva1/EctY4ssEws0Ilr3fBca5CyIq1SqRiSN5rVphlpveRuuoFBGkRfK+miGXb8rdcmBKXIcteyhLxPPAZBezIgHQ6HIUnEMdJN7/jM78Z7fjmY5bsts2zJNEgOGNnXupHkanMimdy6kbK54sbv98v3ToPoAKTSilU7G60gX881MoYMhUIYHR01sKL5vh4PJljN50AiHxP8+hgADNIl9As6JvR6vRgcHBQilZl9vRtM+20dj/IZ0NfKyoNumuW6kTrHpduYmmNt/nCs9Tb6+Dqxo/39etaZ3ZrIms9HH6sb5sTx0OsUnXDqhl9s5Zr0arSt8t2UzV2PdXteV7uHvZ7vbgmY9e5zq87NbBYKdAUaHzhz6RAAg7Pv9kXSZbabAQm13IYOyLcK4A6FQrj22msRCoUwPj4uAPpOd26mg2cDrH379uHGG29ENpvF5OQkSqXSjp5PL9tuZn634/F3rVZDOp2WJifraS7aarWQy+WErdCtHFA3FtPANoH0rXrW6Lw7nY6AD+xK7vV6UalUdiRxc6WaFYhbdrWafnbXAvR0gLTWPrXPp2xFKpUyMK01QEpj8ELQmhqZ1WoV+Xx+S9hsbJTm8/kQCARQrVYNMiIEAthXhYx5riXYKNrtdqNarSIQCAhTnswsNgPSQEOtVhM/SP/jdrsxNjYm2tvhcNggdXepjcE1wXBW2dEoz8PXdHKAkmMMcPL5vMi/sCk25XDIznY6nahWqyiXy8J0z2Qy6HQ6on/K5AUAYZp1Oh0p7S6Xy0gkEsjn8yiVSqtWaJmftVAoJCQE3i+/3w8AUvbNcWHCh2PChrpMMLD53tDQkKwnstmslMSzUd9ulPC5XMzy3ZZdDbaWTyaAvNp263nWqQVOYJpzW7lcFmawjuu5b61BTrkvxitkBG+F+Xw+DA0NSd8K+kkmu3XlEyvdzeMALM3lrKwiGGuOCbXkDX/ruJK+oV6vIxwOIx6PyxpgN/hubZqB341UpvEYJg50PxyujWq1msTFrBQDIBXmpVIJqVQKxWIRpVJJ1nt79+4V39itKoHnwR4i5XJZGpKvdU1mAhuvU1cVaFka8zYaKwKW1zAkcpqfba79Niuj2quq5GqznfTd/f396OvrW8EQX1xcXMEApw0PD3fd3uFwiBpAr2167bPXcYAlRvrIyMim92MhQFewaRYRm22ZS4rM5nA4LgpEB5bBR63jtVUgdygUwnXXXYc3velNlxREByAao9FoFAcOHMAb3/hGHDhwAF6vd8fPpZdp4GCnAHU6yFqthlQqhVQqJazHtazZbCKXyyGRSPTsEK67epNlyN8E0bfiWs3sTIIFLCvjotHKiltmmWUbNc3MWUu7dD0LV80o1lU0qVQKU1NTmJ2dRTqdFlBVa1yT8Ub9awKbbNC8Febz+bBnzx7s3bsXgUAAlUoFxWIRiUQCs7OzWFxcRDabNTTF1LqgDL4DgYCUkns8HpG+aTab0uzK7XajXq+jUqkgk8lgcXERqVRKAF6Px4Px8XGMj49jcHBQSpN3S1KU6zAGjpr5RX/EpC5BZoInDKwDgQACgYBo69ZqNSSTSXkOWAbu8XjQ39+PcDgsSYlyuYxMJiNAOvfF8m2v1ytBtJbkyWazuHDhAtLp9KqABsu7G40G3G63jD+DfWBJ4oASdEyoaDa91+uF3+/H4OAg9u/fj/Hxcfh8PrTbbXg8HgwPD2PPnj2IRqMyhuyhstnKxZ0mJVi2bF//+texf/9+eDwe3HrrrfjFL36x6vbPPPMMbr31Vng8Hhw4cADf/OY3V2zzgx/8ADfccAPcbjduuOEG/PCHPzS8//Of/xy//du/jdHRUdhsNvzjP/7jVl6SZZe5reW7e1V8m42VVbqhJqVP0uk00uk0ksmkJEFpjO3p6yifudWEIp/Ph/HxcRw8eFBA9FKphGw2i0wmg1wuJwlYnTztdu1OpxOhUMhQvQ3AAKjymgjqlkolkfmw2Wzi42KxGPr7+7d0nbKVRixEM60BY3NRYjL06ZSAYT85+u5sNotkMilNN+v1uqGHXavVknuyuLgoMjfxeByxWMzQmJw/9KEul0uA+EKhsCaIzvUIe43oWFj7aD7HukKdZAjeQzYW53q1r69PfH84HDZItXLNsVkQ3YrXd9ZcLhduvfVWPP3004bXn376adx5551dP3PHHXes2P6pp57CbbfdJvNFr2167bOb7d+/H8PDw4b91Ot1PPPMMxvaz+6IGCzbNaYZQpvVQdXBvGa89Spx2ohRjysUComTuVSBDY9LlnU4HBbt0vV+ns5VA8PmEqmLuTazFt9WNprpZmRH8BkiA44LRGbDzWVnXChwOzLldkvGWN8n3ejEvDiybP1mfgY282OZZZYtmQYmdfMnLXNCM7OA+BoDHe5vKxs49fX1SdDGxbAu2TWfiy7hJYNJ68KbZWt6JSR08KQlbrjG8Xq9iEajAjivZea53lx9p5tsbdZ4HdqX8rq1z9HXwrWWWftUs970+Gh2oJmpr//XfpzMOC0TqJ87AgNrPTN6Lcg1g94nWeXm6+JzQbCd1Qx6vPTx9XVoMEP/WL5747bTvvuJJ57AJz/5SXz605/Giy++iHe84x249957MTU11XX7yclJvP/978c73vEOvPjii/iTP/kT/I//8T/wgx/8QLY5cuQIHnjgAXz4wx/Gb37zG3z4wx/Ghz70Ifzbv/2bbFMqlXDLLbfgq1/96uYGyjLL1mE6PqOfJDlsLdkD/mgfap7/LtbIDmZjai03oqUzu8215uOb1yOrzQV6n5rJTf/m9/sRi8U2DKLrMdPx3Fb4bp5jL5+kzdwk1Byvm9nra82fZpC+VquhVCoZSAn6vMxNxderN67Py8zw1uTNXutH81rBvI15TaLvleW7L8522nc/8sgj+D//5//gb//2b/H666/j4YcfxtTUFB588EEAwKOPPor/9t/+m2z/4IMP4vz583jkkUfw+uuv42//9m/x7W9/G//zf/5P2eYTn/gEnnrqKXzxi1/E8ePH8cUvfhE/+clP8MlPflK2KRaLeOmll/DSSy8BWFoTvPTSS7JmsNls+OQnP4m//Mu/xA9/+EO8+uqr+P3f/334fD783u/93rqvb/el7iy7pMaSXwDSeXsjxiZmbH5B1pvNZpMmKcwkMrO8HnCXE2YwGMTBgwcxMTGBgYGBXcEcczgc2LNnjzTX8Hq9q5b48VoIWPDvbl2qGehvppkGP8f7US6XDTqp22E6G55Op7G4uCj6d9RqHRwchM/nk890Oh0Dm2FmZga5XE7KzbtdF58XrX2qA/GtMp1F13IuOpOvx9my9dvFAuEWiG6ZZcvWaDSkoZnf78eBAwfQaDRgs9mQzWYNpcUMHFlG3mg04PV6pUGnzWZDKpVa0cTsYszj8WBkZATxeByRSET8XTgcRigUEk106sMCMJyf3W5HsViU/wn2a6CgVCoJsMuAi9sBMDD88vk8ms0mxsbGEAqFMD09jXw+j0ql0vX8NTigQV2tQ88+MuVyGcVicdNzVLPZRKVSETmeUCgkDa4JYnD8qtWqgM5k5APLjZ7q9TqazaZsbwYvNCihS7V9Ph9arRbS6TROnDghr+vKLGCpuTabq9frdYOmby+jni11fNPptDTG9Xq9qNfrSCQSUonAcfZ6vXC73cjn81hcXESz2UQsFkMsFkO73ZbnA4AwMcnQ143nqSPvdDpRr9fX/YxbPmfJdtp3f/nLX8ZHP/pRfOxjHwMAHD58GD/+8Y/xjW98A1/4whdWbP/Nb34TExMTOHz4MADg+uuvx9GjR/GlL30JH/zgB2Uf733ve/Hoo48CWArmn3nmGRw+fBjf+973AAD33nsv7r333s1epmWWrcsYNzebTQSDQYyPj6PVamFxcRGFQkGIRmTqAisZwZTAcDqdknzkNloiZTMWCARw8OBBYT0TsNaMac63ZBRrAJmJT4KuOnnaTZ5Dy5robTudjszXzWYThw4dwsjICM6dO4dUKrUuGdVumuv6/Nm0e7Pzm44HKY2m5W50AoJAtyYcstEoAENiWSe4zcfifWfFFfc9PT2NYrFoaEyqAXSupVh5Rhb6ar6bz6IGwjW5oV6vI5/Pi9/VTd2J+7DigmsArl/MSQPzM6TZ+g6HY0Nx91rSS1eL7bTvfuCBB5BKpfC5z30Oc3NzuOmmm/Dkk09i7969AIC5uTlDMnz//v148skn8fDDD+NrX/saRkdH8ZWvfEX8NgDceeed+P73v4/PfOYz+OxnP4uDBw/iiSeewO233y7bHD16FO9617vk/0ceeQQA8JGPfASPP/44AOBTn/oUKpUKHnroIWQyGdx+++146qmnRBZyPWaB6JYZrNlsolQqwe12d9U2W8s4wbLZE7UnGbgw+NIMJn5urYw0dbEGBwcxPDy8Lo3tnbC+vj5Eo1G4XC4kEglDcL9ahpWOVeuL6Wwfx0Y7vY04AQLNWs7HZrNJ8Lsd1m63hSHGxUi9XheJm0AgIIAAx4HAezKZRKVSEcmB1Uwvqsx/b7WT1CVq/DE3GN3ovbFsyazxssyyrTEGRdSp7O/vR71eRzqdXhFoEPTlvEmQlc3LCDR309HcrLlcLoTDYcRiMfFBBJ7N1Vs8PzNjmqB+p9ORQF4zsqkfq1nquqKL64VKpSKJhUgkIvqIBJ67md4XYGzISj/g8/lkXEul0kUF4rzOcrmMfD4vWvIEgwkQM9lMOZ5msynNyuhf2ZzOfD28Dg1waPkcgvm1Wk3YiA6HA4FAQKocKpWKJL0J3KxlNptNJNg4Vq1WC+FwGC6XC7VaDcViEY1GAx6PR54Rjnez2UQqlRLAnMANQYG+vj7DNZsrGmw2m4Aau01H93KxrfDd5nUeiQra6vU6nn/+efzxH/+x4fV77rkHzz33XNf9HjlyBPfcc4/htfe973349re/Lf0Xjhw5gocffnjFNgTeLbNsp0zHzW63G7FYTGTK6vW6AQDU8yvnN/bPYA8RHS92a8q8UeN6YmRkpKt2t/k4vCZKc+nXNLNcE8P0tek5Widl9TjZbDYMDQ1hfHwcnU5nXXGtPm8m2AmkE5dotVoXlQDXMSmruFwul4HlrhuN6oozcxWV2TeZfasG38264+12G5lMBsViUfw21yb8DOVySLJbT5+Qbgx7vmaz2VCv10VXndV9HHdgCWMql8srnk8zkVKz8TudjqHSj8D8RtUSrHhzyXZ6HB566CE89NBDXd8joK3trrvuwgsvvLDqPu+//37cf//9Pd+/++6717xOm82Gxx57DI899tiq261mFohumcEqlQoWFxdRr9cxPj6OSqUiYOFq7GU+rGRDVSoVhMNhXH/99Wi1WpI5JGBMthaDnkKhIMArg37N9qKzY0C1lTrrF2vMaJOdTJCVzp7baEYzwQM6bjoHwDiWzEo7HA7JWPdiaPcyOst0Om3QM91K4znz/Mg6Z7ZxbGwMg4OD8Pv9GBkZQTAYNIDo4XAYAwMDKJVKCAQCwkrP5XKSkGFShwtFSsCwuU2tVpNFAp+bi0my8Fi6NI0JCbJCzMCKBaRbZplll8J0g81UKoW5uTkBloGVjGMGIZyvyuUy5ufnxe8ODg6i3W6L36afIhMbgLB/OQ/36mMBLAWOrN4xN0EjYEu/r9ls9XodtVpNWEhmBpIOGnUwx8CNr+sSYDMAwPHpZubAHoBB75MJVc3010xuvQ7YDCmhWq0il8vJ+IXDYRQKBQn0WRnFoJ3nSvY+AIOmupbSAZaSL+VyWQLqYrGIer0uQTm18dkDhmscghbUYddBuF6/dbNqtYqFhQWUSiUMDQ3JvqiXyuQAEwEcdz4jdrsd4XBY9NjJwmRyhSA6AEOZOpPh1Fi/GNlCyy7exsfHDf//6Z/+6YqANplMotVqrWj2NTQ0tKK5GG1+fr7r9s1mE8lkEiMjIz236bVPyyzbLmOyu9lsYnFxEfPz8wJCmxPJAAx+rdPpyLzNOW9kZET+Nsut8LVWq4VCoSCkJ8Za3Yy+gH6ZvkWzuml6bUCwVSdogZVa8b0IUFxLdJujGZ/p2LCXmYFzTV7j6zw/p9Mp/oR+YjMELa6NMpkM/H6/+CkysYHl5K45Ma+PpbXGzckINvguFoviv4m3uN1uRKNRhMNheL1ejI6OIhQKGUBwriPK5bIk38mg7+UX6/U6stksWq0W4vE4KpUKPB6PYW3AxEc3iR1WGnLtZ96G56efH67p9PpzPbJxllm23WaB6JYZLJvN4tixYwgEAtKwyev1Cnu4l2lnvrCwgGq1ij179mBkZAQulwtDQ0MIBAIrMpIsJTpz5gySySSmpqbw61//WphNBEYDgQC8Xq80oGJ3793ARCdDXjfIYuM0c2BHx8bAW2vWmk0z0JlwoANbr1PnQiORSGByctLgVLfSNOBBBno0GsUNN9wAr9crjb9cLpeBicgFQ6FQQKFQQKlUwqlTp5DNZjEzM4NXX30VxWJRGs6RicAy/2KxiL6+PvksF3gE2i/mGeH1aL12/UP2pl4IWQH5+m2ny8oss+xKtmq1ikQigXw+j1OnTiEejxsaLXcr6dYyWOl0GmfPnpX584YbboDT6cTAwIA0n/L5fIaAr1Kp4OzZs0ilUkgkEjh16lTPJtKU64hGoxL0O51O6SVSqVRQKBQkeex0OtFsNlEsFlGpVBAIBMR3asAYWAYXdMBllhVhAM01CNczZLz3Mh1g8zfPRbOjyNomm9DhcKDdbgupwMy8W4+1223kcjnxZ8FgEMPDw+h0OlhYWECr1UI0GkV/f79UDdAX6eoDng+Da51wYEO4QqGARCIh6wuCDPv378fBgwfh9XrlWWCinJ8vl8tSAu90OiWx0ssfcp3p8Xhw4403ioyL3+9HNBqF3+8XqRc2lmNigMH5nj17YLfbpdlsu90WSRmd0NFkDSYCtE+3fPbGbat89/T0NEKhkLy+ViWIeR+rre26bW9+faP7tMyy7TCC2R6PB0NDQ4hEIobmjAAMpDE+o2wOncvlMDs7K0nHm2++WeJmgsZs3siYpVwu4/Tp00gkEpifn8drr722KojOBDjBWu7fLA+m1xlMYPK4nGvN4DmwUrqEsZ4ZSNefJaFpLRCdVXZaDkT7bsZ6AIRkRhCcgDKT/uud+1qtliQAh4eHceDAAQwMDCCdTotv0klt3l/z2kZXhnEMOSbVahWFQgH5fB7JZBKpVAp9fX3y/Fx77bU4cOAAPB4PYrGYSM1yfLPZLPL5PPL5PLxer+xjdna2p0xKqVTC9PQ0vF6vNHLvdJakWfr7+w1kCPpe3i8SDDjn837ohIGZtEbSIJut83/dm8Wy9ZsVd2+tWSC6ZQYjK1wHfzabzcBe1otM7QDp9MgMDgaDAn4zC6o/y0wvy8adTifK5TL8fr8EN8y00onostzdZFpXTZ8nYGxASia6x+MRB6LZAdq07rcu9dKA7XomNLLZisWiMOS3OljoNjF7vV4MDg4iEAhgdHQUY2NjsphjsERnTkYZs+I+nw+1Wk20qSqVioGpz0UNF2m6CZlmDmz2WvRxzGx0fT94DTSLib5+s5y5ZZZtnWkJkEKhgEwmI1qpLpfLACizlFZrWtbrdWGz0Ud5PB7E43FJogcCAYPMSrlcFlZSpVIRKQ7Neqfp8mkeWzPcCfQTMNb/8zx1wlnvv5s0iZ7HzaabWq5VsaQBdF1RRj+vk6js/6JLpzWAsFH2FIECJtC1bjz9HINs6obqsdPnrysKeG7cTvtO3heuVUKhEGKxmATJXJ/l83kBtHlOgUDAwGrrdU+47uO16WoBsh2pL1+r1Vbcc3NFH6+fvwm26+oxM0ijWZKWbcy2ynezymE16+/vR19f3wqG+OLi4gomOW14eLjr9g6HA/F4fNVteu3TMsu2y+g/W60W8vk80um0kMY0k5xmZuuSvFSv1xEOhxEOhwWsJGjM1+izSqWSJGjL5TLcbrf01zD7bsbcum+Xnk/5mp4XtL/jj5llzc/38tVMiGszj8Nac5H2fwSt6a80s5m+TyfFtc/cqP/udDoiS8Z1AeVWzExzxvW8Zn5eM9DNzVh5LuaGoDabDX6/Xwh7AwMDXfupaH/rcDgQiUQEv+F6sZvvpmwgAJHXo3SaZvUT6OZ6UN8PXqu5ikHfdz5j3cb9Yv3P1WxW3L21ZoHolhmME2S73caJEyeE/bR//35hgTH7zKCLTpwZ55GREclck3GtwWI6TU66GmSn08/lcnj99dfx+uuvS8k099dtUXEpTZ8Hy4U9Ho+hnNrv98Pr9cLlcslvc5ORXvumXhudHfVJyQZba1IjyxBYAqP37NkjCwlmpi/WuMCKx+O48847Ua/XEY1GMTIyArfbbWgmp9kV+vPMbDscDjnPiYkJlEolnDx5EmfOnEG5XMbc3Bzy+TxqtRpSqRQajQZSqZTIAIXDYUNzNLNW32qmme6UFqrVasLW4AI3m81K2btOcFgOZv1mOXPLLNt6a7fbmJmZAbDUzHNgYACBQAButxvBYBB9fX2oVCoCNFarVWlM9YY3vGFF0MZqKQbb5oB0dHQUkUgE/f39CAaDKBaLmJmZwdTUlAHY5fa6lwQDWAKd7KHSarUEhGWJMAF6rf1NRjmTrrpReTfmGkkBnU5HAjgC+2Rpk4VGkNmsec5rr1arMkb039R0ptxMX18fQqEQgsGgyO1o9tx6jNeUz+cxOzsLm82GXC4nPVRIfGAw3W63ZZ1ms9mkgSoAuR7qrBOk0JUG1CH3+XxwuVwYHR3F0NCQPBMsRWeCOxwOi98cHR0Vfzk1NYVyuSy+mf6agE0sFpPxJqNufn7eMC70EUy6838tU8TKBy11R1CJYD2T84lEAul0WqrWCGBYbPSN2U76bpfLhVtvvRVPP/007rvvPnn96aefxgc+8IGun7njjjvwT//0T4bXnnrqKdx2221CBrrjjjvw9NNPG3TRn3rqKdx5550buRTLLNsyYzNIYKlp59jYGILBIHw+n8Q1pVJJ5jzOyS6XC4cOHQKw7IM0AMvkIrXE+R6lNaPRKLxeLwqFAqampnDu3DmZd3UFk9vtlsaeOlmsG2GbQXP6eQ388j2yq5lEoD8ys9vNUjRMxDORHQwGZY2g9eAZazMW1MxvwCg9o+VSgGWplWAwiFqthlwuJ/3d1tOklSSDdntJmzyRSAhRUFe1mY3XZ36N+6zX6yiXywK8sz8JWdqs5nK73RgZGcHAwIDhurSUXSwWQygUkgbrjUYD8/PzOHnypPjLZDIpPr1cLgse4fP5AEDUBKhAwAS33W43yLF1I1/qBDrXLiRzMKGUy+VEFiedTku1nxVvb86suHtrzQLRLTMYnVmtVsOxY8cwPT2NcDiMt7zlLQJ0Dw0Nwel0Sjksg+t6vY7R0VHceOONArTrRk7dyigJnrI5yJ49e7B3717paM1AnF2cOTnvFgCdphcmdGgMsm02myyCKOGim4z0Mv2+x+ORwJ9lySyZXg+Inkwmkc1mUa1WcfDgQQFUujV124zxesLhMN70pjeJY+dzYL5n5mvXTWGGhoZksVCpVFCpVPCrX/0KR48eRTKZFHkXNiGt1WpIJpNIJpMC3uuFX6/FSjfTrLxisYhUKoVKpYKFhQUB0ZPJJDKZjCELf7Hsd8sss8yyrbBWq4WpqSlcuHABPp8P+/btQzQaFXkXl8slJbya5TM6OopDhw7B7/cbKm/o21meC0ACUpfLhfHxcQnux8bGUC6X8atf/QoXLlwQn8XksgbQKTXDAIpJYa4pGNgyECaziwBqvV6XBD2rmHK5XFcQXYOvwHIZMfdPIJ79VsjsInuMSXyyudi/RTPlyPZyOp2oVCoCNEQiEXg8HgHwtRzYWv6C11yv15HJZDAzMyOBKZMTjUYD+XxewGOy6cLhsByPcij0xdxfq9VCJBIRECIajYq+aSwWk3vM9d7i4iLK5TK8Xq8kZEhuYLLc6/Vibm4Ox48fRz6fx8mTJ3H69Gnk83lUKhUB0ePxuFTkERSYm5tDoVAQQF+TEvicECTRz2SpVDLcawJGbBKXz+dRLBaRTCaxsLAgzweTOJpNadnus0ceeQQf/vCHcdttt+GOO+7At771LUxNTeHBBx8EADz66KO4cOECvvvd7wIAHnzwQXz1q1/FI488go9//OM4cuQIvv3tb+N73/ue7PMTn/gE3vnOd+KLX/wiPvCBD+BHP/oRfvKTn+DZZ5+VbYrFIk6fPi3/T05O4qWXXkIsFsPExMQOXb1lV4u1Wi2cP38e09PTCAQCuPbaazEwMIBYLCbEoGQyiUQiIZU/bPp54MABeL1eg5Y0/ZCWUtGx6ujoKGw2G/bs2SO++xe/+AWmp6fRbrcllvX7/TLPaxCdzOpGoyGJeQ1203cAK1njmhxXrVaFia9jNb2tTu6TtMXzYn8M7bsp9cWmmt1iQY6VZqvTbDabkLoYa7MKeT3GpD0r9Fj1otnt+lyY3NdN1bk20/6pVqtJQ+ZoNIpgMAin04mDBw/C5XJJo3RdEd9oNCT+133pwuGw+O6hoSH4fD7Mzs7i2LFjyOVyOHbsGE6cOCE9ysrlspAJPB4P+vr6RPZldnYW6XQagUAA/f39UlHWLfbmc1CpVARIZwzNKjeSA3K5HEqlkjz33ZqwWmbZpTILRLesq+mMZ19fn5SGk/3jdDoNzpQTs5llthp4aWZwA0uBqN/vh81mE2Z6s9nc8kaYO2G6DFxrZ+tGIpvZn97XepMJLL2jA85kMgCW9d82IpGjFzbAcuacZYM+n0+Ybdz3Wqa34d86Wx+NRjE4OCigRD6fF2kaNmqhg+fChQADFyYayDfLrtCpMzinrioXDsViUZqwkC3JxZrlzDdnVkbcMsu2xxgccm4keJ7NZgVELxaLBjaYWYakV8m12ejnWWXE4DsQCMj8y2CK52EuCzc3g9bl49y/mS1sluUwgwTaP/H8dckzj2cO7M3rFia+GZT28rkavAAgAbmunNOB8UaM516pVKQPCNdY2sfy/DR5gefGcSZ4z23IKuPaSzcH61Y+3qusXd9L7rfZbCIQCCASiQCAgUDAJA1L3lkVwbUeEydmUIH3je+zGo/3lfeWCW6uDZgMslhsF2877bsfeOABpFIpfO5zn8Pc3BxuuukmPPnkk9K4fm5uDlNTU7L9/v378eSTT+Lhhx/G1772NYyOjuIrX/kKPvjBD8o2d955J77//e/jM5/5DD772c/i4MGDeOKJJ3D77bfLNkePHsW73vUu+f+RRx4BAHzkIx/B448/vplLt8yyVY1zKOdFJnvT6TScTqfoWDPByT4fWoakm9QZsPJ7R1+ntal9Ph8CgYDE3donmmNXcwWuJsfp+Nd8bM7hZl/C+dscH+vX+Llu25h/qKOtz0N/luOkJdC0dJt5nPR765nDuE2j0UCpVEI+n5ekMO+feX96vPTYsPJOjwd9Npn23Lc53jUnMPT/fE1LqtF3h8NhxONx2O122b/23VyPEIhnzN2tyo7HIVYEQBIT+lz47BN/YpLcqvreGrPi7q01C0S3rKsRROeE9pvf/EYAVzolOqB4PI67774bBw8eFDmWzbLF2WjM6/Xi0KFDkoVcXFyUZhy78UusHREdjAYomNHf7LjQeTIL7HQ60Wg0DPpqq40LHU82m8VLL72E06dPY3x8HNdeey28Xq+UdZkXD92uUzf34s/g4CAGBwfhdDqFHbFWEmUtI1PB4XDg+uuvx+DgIBKJBBwOByYnJ5FMJjE9PY1sNiusemblS6WSlNBzgaGbuJIByTGjHBEz9uVyGZlMBufPn0exWMS5c+dw9uxZVCoVzM7OipYgx8IC0jduljO37Eqx9QZVO20sz02n05ibm8O5c+cMgY7P58OhQ4dEN5Ngq24USSM4C8CQdCUTiQxyYElv+E1vehMqlQqmpqYEAIjH4xgcHJQmqCx5pryJLhvXzDGdQKa0DAN8ynZon6sT1gz+NIOJbHANOJPBTT1QBuGxWAyDg4OGKiUABtYasOSji8WiMPDIBGdzrGq1KkHhZvxFpVLB9PQ0ksmkrCfcbjcOHDiAcDgsSZNWqyXNV3mtTDCTPQhAmOZ79uzB0NCQsPLJXGMjb12yz0oAMg/5LBHInpubM4DtHo8H+/fvx9DQEBKJBDKZjGi7Z7NZ2O12ZDIZWTdoPXQ+Z1r6honrSCSCsbExOBwOnDt3Dvl8Xsr3nU4n0um0sOYXFxeRSCSEQEAQ3Sz3s9WmwZHdODdcjF0K3/3QQw/hoYce6vpeN0D7rrvuwgsvvLDqPu+//37cf//9Pd+/++67r7h7Z9nlYfV6HRcuXEAymYTb7cbJkydht9uFxBMMBnHLLbcIS10nJVmlQ3/cDUAFID6z0+lIfDQ+Pi6+O5lMShNR+hz2TQFgkGNjMpcxIQCDb9U616wSIjiq/S1BVs105zF4TfybpD59PhpAD4fDGBoaEik1Xq957tc9V4DlymqdjGVVFLGRjVipVMLx48cxNTWFWCyGoaEhaSJL0qAZROd18ViaAMCq9vHxcWmwrbXIM5mMjJ/D4RCZXh2v6mQzq/hYpQYsEeze8IY3YHR0VHwp1wapVAoARMYlGAzi2muvlX4TrDDrlgCPRCKy3piZmUEmkxHwnsz2RCIhuM/i4iKq1SpSqZQhYb6dfvVKrkyz4u6tNQtEt6ynmZlCZmNAOzExgXe/+90YHBxct1RJL6MDdrlcGBkZwTXXXCP602xgtttNl5LrRYHO6m/WGKDrkiyCCqsZJ85SqYTz58+LRl4sFkMwGJTybGB17XDNNtOANGV+dOOyizUN2IyMjGB4eFi0yZnBP3XqFMrlMtLpNEKhEGq1GmKxGICl55egDPfDkj2zIybIUavVkM1mUSqVkEqlZPEwPz+PCxcuyPssP7fA882b5cwtuxKsV3XLbjDqSvayaDSK/fv3G8qumTinRIbuYWFmdwMwNKRkkBeJRLBv3z7k83kBy6nfGQqFJBjVEiQAZF4no4rno1lMZnYym27q4JnvU9bL5/MZQGYCswS5GZRrmRRebyAQQDweR61WQzqdFt9u7rVBP9JutyVxC0CAAkrWbDQApzF4JZHB7/fD5/Oh2WzC5/MJ4K0bhvI6OcYMrNnIm7IqQ0NDhkCc1Wq6ySj3p++bBlEajQYymQyq1Sp8Pp/IBw0MDAhAH41GRXedcjj0A+FwGHv27EEgEECn01nB/NcVB2wQ6XK5kEgk5D7wueE1FAoF8eME+7tJr22G2LCWbYSxeLmZ5bsts2x7rdlsSsVwN4vH4yLVQn9F38YG4/p71u07pzXBOdfGYjHs3bsXxWIRlUpFpMB0Q07O27qqR/tc9jOh0c/q4wEQgFoz0HWVFcFY3XiV10hGtPZ52oeRPR0Oh+U6dJ8TjomZma7JX1oajrHjenuZaKvVapibm0NfXx+q1ar0LInH4ytiZV05x3PQ8mX0/ewrwkbIrPorlUrIZrNot9siT8fEt15LcY1AYF33oiHxIBAISIPaY8eOIZPJIJvNIplMStPQfD6PcDiM4eFhYcVTooX9Zfhs8m+uAROJhGF8+ZzQdycSCczNzQkrfTNjfzF2Jfpvy3dvrVkgumWbNo/HI+Crbsq1FQGJzbakI97f3y9OjZlUc+n3pTYNpjJgpUMyl4BthekFhy5d6za58T1dGs+FUK1Ww/z8vDRMISOM5YP6nHWXbN6DoaEhDAwMwOfzwev1GhYv22EMkoeHh0ULNRKJSBKBjUUjkYhk2FnOR4Bcl78TuCFbj70AFhcXUSwWkU6nJSOeyWSscnDLLLNshRE4vpzmAwbE1Axl0AQYF9n0HUw86oDIzAKjhAeZ7Dq4b7fbqFQqSCQS0jicYDJZSDqpqUFus26p2d/poJef19VGpVIJ1WpVzpH712wsniOPq5PSZGppEFyz/Mj20uXW1JIHIH6j1WpJg1cGoASk1/vs8PoIQjebTczOzq6ocCOQ0Wq1kEqlBHwncM41AP0gWfncD4NVVoNROk3fCz4PmiFI/6rXgUxS9PX14eDBg/D7/dK8TDPLWq0WCoUC0um0NGZ3OBxSIdduLzX7ZsN2ss4XFhYkqc3PLC4uIpVKSTDO8e+W9N5OsPtymhMss8yy3W8ul0uqbgOBgPgUYKVMh056M6bjXGs2+ks22aYfpKxlJpOBx+MRMJrHMxv9q44f9XqC5wLAsEbgOdOn8Fy1v9Wxtmals7KOgDD9GPtqMR7X59jNtC8ClgkCzWZTyGv083p/a8X22j8zGc/1QaFQkIQBx4MEAVbi63NmNTpjbq7D9LjosdZJBvP16yS2mUDA62+3l3rTUHN/cnISqVRK3uNahlKBGkBnU1zeR54HiRnEH3h8m82GRCKBRCKBQqGAfD4v984s57cTZvlvy9YyC0S3bFNmt9sl+zg2NoZ4PC7BzlaAqHa7HQMDA/B6vZifn8eRI0ekS7Mur9oKxvNWGAPFarUqkz+bp5EZt1WmnZ0ulTKbLn/3er3C0OJiKp/P4ze/+Y1k7Fmux+ZiuiyPCxG9wLj99ttx5513SnOTi5VvWe16uV+WMFKG5sSJEwJ6nz59Gh6PR9jo0WgU2WxWGs+wXJxJAg1gsON9rVbDwsKCMNgmJyelM3gqlTLo91p2cWZlxC27UuxyehbJrg6Hw4jFYujv70csFoPX6wUAA9DIxCWlXphAJKsXWA4QKcVmsy01s8zlcgK+k1V37NgxKRHXZeIM4BkEA8tjqpluOlmrQXQypcnAo89tt9tYWFhAqVSSBqTsm6EDdfoCMtY0UJzP54X1xgBRM9i0zjvPiU3XGTyyGTgbfmWzWQlESQ4w3yMNQOgfJpCZhEgmk3jppZcMUmpkmnc6SxIriUQCHo8HIyMj0q+EP2wyqv0s/aLdbhdNc44tjckT/RrHgiA9AElgOxwOvOc974Hb7caRI0cwMzMjuvwcs/n5eZRKJUnwOJ1OjI2NiWwMZf1mZ2dx7tw5AUp4X8g0X1hYwPHjxw1SP3oMzWPN528rK8u2a41gBmx6kSfW2uZizPLdlm2VXcnSCVttNttSr7BYLIZYLIbh4WGR0SRDXFdC62phJjZZXc73NYPbbrdLU2kAwgpOJBI4e/YsisXiCt+n1woARObSDMqaY0MywnneWmqErGrGW4zb6IcJxtJv0t9Wq1VD3FutVpHJZFboxOtnTYP4PH/+T/YzgWuuMzgO+lpW+1v781wuJ/Jjp06dEqJXf3+/yKmRwc0ksD6+3+9Hf38/AoGAyKLpuJ5jzzWZ1+tdQQ4wj4VOoBOr0FI2DocD7373u+HxePDTn/4Up06dksqASqWCTqeDqakpZDIZeL1eacA+PDyMoaEhGTNKFFGaqFwui+9msmRubg7Hjh1DoVCQ817N31xO80e3c+2WgNnOa7F899aaBaJbtmljsOb1eqX0dqv3HwwGxeEwGNJMdDMD61KYzp7rn27M7K1KMOhs82rbMdFAIESfjy7ZZ0Mvj8eDTqdjcOYARMtV77vRaBgam2znveB++/r6BBAneM+mn2RIEPQgWOT1eg1AjBlEJyhRKpUko14oFJDNZpHNZgVYoZO3bGvMcuaWWXZpjIGbBlN1iTNgbIyt5U24jQ42uZ252aUOUJvNJorForCM6cMBGJqZ0p+aQWRzdZQO/jSArc+fIDZ9g9frNbDP9f7JbNPMcJ43AXEG+eZz1BVhgFF6hDrqHGdqmRPk7+Yz9djrcmfNsOcYMplBQNztdhtY/myIzW1Z3q3HTTMH9fgwqCZYwntqBva17ItmPQLLoIrT6UR/fz8ikQhOnjwp2+rxr1Qqkrghm61SqUgFgPbTBEz4+WazKX6ahAu9ZlnLLvU6crO2Goue7201097y3ZZZtvNGYJRV4G63W5LQgDHhqudzXb0MGEFU83uMF7keoDxMpVKROVfPu2bTvlEz4fXxzOB9L39M30lfYJ7HzIxxguu6Copxm/a1WsKlm+mEqvZtZkLcaoC5+X+doNbSK4y37XY7/H6/HIdM9GKxaGi2TZkWxvOszOKx9Pjoxuz6nMxjbb4veny1745Go+jv7zesX3ifiCUwuU7JmnA4LNWJJOJpn837RsA+n88jn8/L/laz7aoi4/dnu6zXvrfjWsxm+e6tNQtEt2xTxkAnEAjA7/dvCyOc+yRACyyVVqdSKXi9XoRCIWG+XUqjlAgz9mS5aSdDW8t5r8c0W8o8IVKupa+vTxgLZKiRaajHkudKkLzVaiGRSIhz4j1gSVUwGMQ111yDaDQq+qU83k4YF282mw3xeBw33XQTBgcH8eKLL2J2dhaVSkXYaplMBoVCQRiIwWBQQHQuUgh0aCZ6MplEuVwWBjrleSznsbVmOXPLLNt5I9jNAJzgLv0VwVJqetdqNWES6+bYeg7lb8qCdDodCfSZwPX5fBgYGMDQ0JCAoWSia5Yc/Y5mRPG7rgNGsp15/pQRYfkx/S79Hs+Dx+I2nOeTyeSKRCmvi2wsyodpGRMAwspiUOjxeBAIBAAsJajJhM5mswCWdNK1xitNJ38jkYjhnlAD1ePxIJvNIpFIiMQM2WdkfbOZKLBULk7AXWups/KMRAjNZiuXy6hUKgCAYrEoDDwzmK+Z/GZGI42gDJlnvM7x8XF4PB5kMhmk02lpZloul+FyueD1euFwOFCv17GwsIBOpyNjyWeR58e1CxufUn5nI9+JS+lPNsKmW+82+t5stVm+27KtMutZ2JhpySwmIs0guLmHCU1re2uwmr9pXq/XQI7zeDyIx+Po7+83gOQavNWgu64m06x4DaprsJm+vlsCnCC2Tsw7nU40m02RO1lYWBB2vb5uJtDZG4Wya1rCTY8DAGFvA8uJae6L/lV/Tp+v9t28NxxvAuTValWanJKwZbPZUCgUpFm37vdSq9Xg9/uln0wwGBQwX5MQOD48V7124ZqIWIpeWzHxYE5867Ek4M/11qFDhxCJRDA3N4eFhQU0Gg0UCgXUajW43W5pZM7Gozrpr9eR2WwWuVzOsJ7K5/Nr+m6doDBXz22VrWdfZlLHWp9Z7f3t9Nfmc7B899aZBaJbtikjiB4MBsU5bPX+dRmaLg1OJBLSBCUUCm3pcTdj7IY9OzuLhYUFVCoVKQEzO9utMjMTjEZtNZfLhcHBQRw4cEACesq56AUGg9Lp6WnMz8+jWq1icXFRAmgdXHY6HezZswejo6N4wxvegH379kmDE2673aYXXAMDA3jTm96EVCqF2dlZCaJrtRpstiV93oWFBUkiUM6FUjWaLUhQp9FoIJvNyrjo8n3LLLPMsivBCKLzhw09dSDpdDoN8yHnUK0BTnYzq68089fn86Ferwtgy+bTIyMjyOfzwq6itjVZVZr9DMBQzquDKwL/BMa1UR+VLC/qjbIknJrh9XodyWQSqVRKAnE912v2ucPhEKkzAtdkTlGTm8Gnx+ORRtssWS6Xy5ienkaxWJTgthsTzG5fatQ9Pj4uDPBOpwOfz4exsTH4/X7Mzc0ZAvJyuSxMf13ODRi1Ulk5RuDc6/XK/7oMnmXUzWbTkHTWIAzXEzRzQMnjcm3Y19cnLHKbzYZ9+/YhEong1KlTAqKT8U8Q3W63Y35+XhI7XG/w+J1OB4uLi1hcXDQAN2ZQaDUzswavFF9/pVyHZZZZtmz0jQS4NduY73O+BlbOAzr5SWDTDHazGTeP4ff7pQE1q4IIchNQ19IsZmCxGxtbA+k6aa63I1jOa9b7pVTL7Ows5ufnV8iN6aSrz+cz+G76eSa9geUmoj6fD36/3yAdU6vVkEqlJEFtjgk1c9nv92PPnj3ipxiTDw4OwuPxIJ/PI5VKoVqt4vz585IoIJisjcegvFl/fz9CoZCswfQ95Wta/1yTHAiS89mx2WzC/uZzY7fbV6xNbDYbWq2WSKO5XC7ccMMNGBkZQavVwtzcnIDffA6I2czOzhqOS7Y9e91NT09jZmZmBTGwm2a/Np2M0ZK2O+3zNB5xsSC+5a8vT7NAdMs2ZTv9haeDq9frKBQK4hQv9cTDCb9cLktWXJe5mzXqtuqYvcB5MvvcbreAB5ptCKwEuynL4vF4DFqw5v1zceP3+xEKhQz6tDtpPJ7T6YTf75cSdp4Lx59BORdhAKS7uq4SYBk5kx+1Wm3VRmSWbY1ZGXHLLLs0plnTAGQ+ZCCggUUGRZxb+VtLm5gDds2WIzhfr9elRwXnYPoafS78PIMjBnba9HH1sRn8dZu3dbDOBAAD7Xw+L8zwbmYGBXgsvR+ety4l55jynM0+Re/XbrdLmTSTGwyKu4EOZGKbx67XvMr9M6mh5VQ0gGIGPTSwrFmPveZfbt9tjcLXCYi3Wi1Zl+j3+Xzy2WESg8CBDlhrtZokTDZjvQgJlvU2y3dbZtmlMT1Hat9n9sFmOZFukindvsdaBpTzfK1WQy6XEzZ3r8pj+jH6br1/+ijtA7vFmmafo80ca7IHmfbden86MU0z+2S9bw3y6/hQr5fMsTw/x6Q+426Cxkw6678py8JKMCble+EEdvtS3zKS4fTaqds97WbdpFxWM/0c8fnhOiQQCEiC3bw24jZ6nLmG47NRLpdht9vFd18MPrIb/PZuOIf1muW7t9YsEN2yTRuZZ2zmtNWmJ2WCm2yiFYvF0NfXh7179xoy2DtpXMzUajWcOXMGL7zwgjQxAyA6X8z4kyFGh7oZ4/Go9WYGQnw+H/bs2YNwOIxoNIpIJLJCn1aD0HT+IyMjCAQCyOVy0iiEpeLMYhNAHx8fxzXXXIN4PL7lFQgbMV5rMBhEPB6H3++X8jeOPZlzLC8jAGHWddVlb2zAcjk5xsvRLoUz//rXv46//uu/xtzcHG688UYcPnwY73jHO3pu/8wzz+CRRx7Ba6+9htHRUXzqU5/Cgw8+aNjmBz/4AT772c/izJkzOHjwIP7iL/4C991334aO+/u///v4zne+Y/jM7bffjl/96lcbvkbLLFvNOp2l0tzFxUV5zePxoFKpSDMn3RCbpcm6SRewsr+H9i8ejwfBYFD+r1armJycxD/+4z8iGAzi+uuvx4033ijSIbVazaCprftzcG4GlsFzym2RqR0MBlGtVqVZJwNOYJmVzuDW5XIJc7vRaGBqagqvv/66SKyYTZfHk3VeqVSkuZiWt6FsSb1ex7lz5wBA/A5Z1gymdV8O6rWTaR4MBqXKrlgsSqk6qwI6nQ4GBgaEwVYsFkWSjOXnZnO73RgfH8e+ffsQCASEfV6r1VCpVGT9QBZjIBAQ9jqvkWsGzRrnM8UxYJJFgxr8TQZcIBDA3r17USqVhK2m2XJMbujPkxlvlozR57EZ4z24lP7+cltnWIG4ZZbtvBHQzufzwqrm3KUrcQCsmK8rlcoKsFInfTn3MoFbr9eFjT09PY0f/ehHCIfDuPHGG3HTTTetkISh39G+Wyc4+T7jSpvNJv202KCS2ueadU7TsabNtiT9xiaUxWJR/AXHg/44GAzKOoHV1+VyWRKzvG7un8l+bWxaythbx5G8jtHRUSGY9ff3w2aziY485VqIlVBeZmRkRPp6nT17tqeMicfjwcTEBK655hqEQiEBrwlu89qY2NdSOWb9epLG+Ld+FsyJb524Jw7j8XgwPj6OWCyGU6dOSRWgjqeJdfDZ0An5crksrHU2cd+ocZ86qbGVRMWNnIc5ebXbzfLdW2sWiG7Zpo1B6MWwgHqZBjd1Jjifz2NqagrZbBaHDh2SgH+nAXR9jo1GAwsLCzhz5gwKhYIsFqjzpYPKi9UO5z41C07rlLrdbsTjccRisTX16sk26HQ6CIfDwjLw+XzS8ITXwkUDdVmHh4e3TQt/veZ2uxGNRuF0OhEOh+F2u2Uxp8EJy3an7bQzf+KJJ/DJT34SX//61/H2t78df/M3f4N7770Xx44dw8TExIrtJycn8f73vx8f//jH8fd///f45S9/iYceeggDAwP44Ac/CAA4cuQIHnjgAfz5n/857rvvPvzwhz/Ehz70ITz77LO4/fbbN3Tc//gf/yP+7u/+Tv7XDE3LLNsqI0DZ6XTg9/vRbrfhcrkk4GNlDwM1v98Pv9+PdDqNxcVFQ3Mxs9wafREltBisNRoNJBIJpNNp6aXyxje+UUBcAuLBYHAFW4v71uxmfsbhcEi/j2q1imw2K6Xa1HRnwpmyLy6Xy1CllEqlMDU1JUGi2cwapcCSpnk+nwcAAzuM5e/ZbBaZTEbOw+12yzpJB7gahHC5XIhGo4hGo1IZ1ul0RD+VyWCWVIdCIUOwC2BVf+x0OhGPxzE6Oirl8fSXlUpFEhgulwudTkdk2giM8z2yyphspjGg15rx3a4XWJLwGRgYEK1XNpbVzxETJ9o2onO+XrOS5Rs3KxC3zLJLY5SZpP9iDMj5VverYAJYN27UUhgkWGmQk5Jf1AVvtVpIpVIolUry3rXXXmsAcDVYqquaGYNpkhIlNzV7myA/2c3cl7mZuDbKqM7NzQkOASwnRZlEYL8PnZCnHwdgIOExaVAsFuU9JsApY6PlUrg28Hq9GBwclDg6EAhI/ExiliYbMvaORCIIBoNIp9OYmZnpec9dLhcGBgYwOjoqBAdddd3pdAxEA31+5ooEEu80AUD7bj03ayCez5jL5UI8HhfZG/pus5wan4lu4HahUFjlCV+f7Rbw+lIff6Nm+e6ttYvrcLgD9oUvfAFvfetbEQwGMTg4iN/5nd/BiRMnDNt0Oh089thjGB0dhdfrxd13343XXnvtEp3x1WEMZLPZLPL5vLB/t1L+gmXWuVxOdMjq9TpyuRwymQwSiQQWFhaQSCQMTnEnjMHt7OwsZmdnkUqlupaVcSFhbkqyUYkXnUjgvnTygkExQQKWgq83uUDgwul0yiKIyQnNsuO+d7KZaC/jefG8yTK81Odl2e60L3/5y/joRz+Kj33sY7j++utx+PBhjI+P4xvf+EbX7b/5zW9iYmIChw8fxvXXX4+Pfexj+O///b/jS1/6kmxz+PBhvPe978Wjjz6K6667Do8++ije85734PDhwxs+rtvtxvDwsPzEYrFtGYedMst3715jQERfpQNu7UP6+vpQqVSQzWZRr9fh9XoRDAaFycw5l/NvKBRCKBRCs9nEwsKCgO6AUas6m81iZmYGCwsLsNlsCIfD8Pv9cm5mfwksB/0ul0vKoPU5kP3OPi1aEoUss0qlgnK5jEwmg/n5eSwuLooEW7e1C9c5ZMtTi5tscb/fv0JblBqoTD7QJ+myZw0wmNlggNGfa8Y/rxOANAMHIE1cKeWmfTcDfbLu6B8Jgus+Klo+RSeh+Rmy+MiMNwfpWg+V++DrupkZAIM2ay95IMsss2znzfLdu9cIyBKI5nyv52L6mGazKT6UEp+M38yNOqmDXqvVMDc3Jz2yuD+S5vL5PObn55FMJiVJzIQ3K8Z1RTAAAcwZW2rJE8aWBGX1OWoQndeUz+cxMzODCxcuIJfLSYVSN9/NniHED7jOYSzLhLCutuZahusfnr/ev17LaB/O8ddyNWZ5HN5Dxu867jbfSy1Hw/WAlt7TppMNmnWuExlmGTz9nv689sX6emm6EqwbaG75ccuuFtv1TPRnnnkGf/RHf4S3vvWtaDab+PSnP4177rkHx44dk6Drr/7qr/DlL38Zjz/+OA4dOoTPf/7zeO9734sTJ05ISbFlW2udTgfZbFayv4VCAY1GQyb7rdh/LpfD3Nwc5ubmpMycpc0ulwvHjx/H8PAwIpEIrr/+esmc74R1Oh3Mzc3htddeQyqVwunTp6W5JcdEOxKWfjOopwNfj2n2ORMLZoCBwTWZXWwAtx4QXQe5BEIICLAJGhub6J9LKeUCGBdm7Fxut9tFTsey3W1blREnI5TGxai2er2O559/Hn/8x39seP2ee+7Bc88913X/R44cwT333GN47X3vex++/e1vo9FowOl04siRI3j44YdXbEMQfSPH/dnPfobBwUFEIhHcdddd+Iu/+AsMDg6uMgK72yzfvXtNN8wClkFbamUzoG02m0in0yiVSvD5fIjH41J+zeCTAWwgEMDAwADsdjtee+01vPzyy8hms1IazaCt0+lgenoa//7v/45IJIK3vOUtmJiYQLlcRjqdFrYXS76B5SQv+3Aw6LTb7QLAMwClv/J4PAYGXLvdRiaTgc1mw/T0NM6ePYtCoYBEImFoXqqt3W6jUCigVCohHA5jcHAQfr8fjUYDoVAI9Xod6XQahUJBEgxkx/t8PgEWNPvLXCWlg1AG+U6nUz5PP8v9+v1+ZLNZpNNptNtthEIhkdyJRCIAlsFuggNaZ50NVVkl4PP5EAqFJHnB82UVmv4M1x5sTEuwnDrnBEZKpZJUhNFHE6Cv1+uiiU5wnyX+HA/LNmZm8GS7zWKzXdlm+e7daZ1ORyRUKb0VCoWElc5tGHvSN7MyjExrLYHR6XTg8XgQjUZht9tx/PhxvP7668hkMuK76btarRZmZmbw4osvIhaL4f/7//4/9Pf3i5wY5U5YrUbgWCdXCcZrJjwAqRzToLmWT9HHP3bsmIDprJo2k9KYrC8UClItxiQ4q7Aof8a1D7ELfXy9DtFAsT4mK8X4eQ2iM4nPMWAin2smJjeCwaAkPSg3w7Fgo232EelW1WZ+Tjhe1BzX52wG07k+cDgchsSATrpzv1yj8N7qpPulkFSxbGNm+e6ttV0Pov+///f/DP//3d/9HQYHB/H888/jne98JzqdDg4fPoxPf/rT+N3f/V0AwHe+8x0MDQ3hH/7hH/CHf/iHK/bJciKaGYSxbG1jqZLWNGUDx40woHvtmw6sUChIOTMXBXTC2WwWqVRKMs46G7xd8i7aOZVKJaRSKSSTSWGhd9ueIDg15uh0dAlVL9OLHe6HWWxdkqWz3XTYG9Fd55hpNqK5zE3v25xZvxSmWXtMTFzss2fZztlWOfPx8XHD63/6p3+Kxx57zPBaMplEq9XC0NCQ4fWhoSHMz8933f/8/HzX7ZvNJpLJJEZGRnpuw32u97j33nsv/st/+S/Yu3cvJicn8dnPfhbvfve78fzzz69ICFwuZvnu3WuaOaSDQd2AWZeBVyoVQyWSlhMjk5iBKANlAuhao5vHqlQqSKVS8hrlTvi/Zsjr8nT6IbKYtU8CIGXkfE1/nmuWTqeDQqGATCYjgf9qwR/BCQbzmiTA89LAhPajOsDsNva9TPtjzfTW+uz8HrCEvlqtGrTsdQJC//A9LQXHc+X48PzMQTqDfI41x9WswapBB543AMOahfdRywJcrbbTIPjFmhWIX9lm+e7da0zAmoFUs6a1ZkrruE1X/pgrmdnPgwC69t30V8ViEZlMRvy8uUG1rsjS/qBbXy4N0tJfa/azlu/i+ZbLZaRSqTWbgfPzjUYDLpdLzovAtrnPivZzlG/T75v33e23nsf1a2a/qJvC6rjd6XR21TTX8b2+x+ZYtxuYbpbc0c+H/jF/rtdreh2mj2fN6ZeHWb57a23Xg+hmy+VyACCl7pOTk5ifnzcwBt1uN+666y4899xzXZ35F77wBfzZn/3ZzpzwFWxkshUKBZw5cwZerxfxeBz79+/fNCtcl5lfuHABL774IlKpFDKZzIrt5ubmcPToUcRiMWmE4vf7MTAwIGXCW2WcOEqlEubn51EqlfDKK6/gxRdfFDB/tc8yOGWigaVZDER1eZQGzrlgqlQqkhWnlMtq5VybvXbtvPU+9CJgO5MUmzUz2GLZ7retcubT09PShA/AqqBzt0Xmas9Lt+3Nr69nn2tt88ADD8jfN910E2677Tbs3bsX//zP/yxB6uVulu/efVapVHD8+HHUajWEw2EMDQ1J483FxUUJ9rxeL/r6+oS5RRAdgATRlUpFqrGmp6cxPz9vaIpJ63SWKrPm5+dRLpfx+uuvS3UZ9cPJfm6320in0ygWi+jr6xOGG9cIZH8xOOzG9i6X///2/jw6rvLO88fftag2qaTSaklehO0YDDF0iJ0QCAdDJ006pBNCk0D2MDOd0w5NxuDvaZaEHNz0hGW6Tw6d0ywdhiGTznTs6QY6JMMkmDQ4EBwgNgTjFWxZkrWrVPtequf3h3+fx0+VSnJJ1lJVer/OuUfSrVv33udW6b7v5/N8lriOVgNOPbeMj4+jv79fR4WVQjKZxNDQEILBoNZwSYN2u93aAJYsMTHwzYl/s+ydILplRqxJkIA8M0h/hGg0qvcj9zlzwlsansq1MSPPZD8SzS+vybEk4q2mpkY3BhOktqtElJulWMwyMOaxGxoatMMCOOWAl9J8LS0t6Ozs1JH0UofddC4sJc5mzMXS+OcbGuJLC2p3+RGPx/HOO+8gkUigsbERy5cvh9frRSKRQCKRyPsfzWazSCQS+j5tllmxWCzIZDKIRCJQSmF4eBi9vb26ibaJUgrhcBg9PT0IhUJoa2tDPB7X/UxM/Z6YmEAkEkE8HofVatUNNkV3zDJi5jmJQ1m0b2JiQmdWpdNpnDx5Ukegy5jk3AqRdVIGVrKoTE0S3ZHnmWQyqZucSlaW6Lo5WQ6c1l7JLJdrbkZ/y3nIc1Mul9N6bk56y3gLJ9fNcZnlaEz/gNmvRD5TiTCX953JNjYz9sRhbzruY7EYstmsfk7MZDLwer1au4uVmCFnhtpd2VSUE10phW3btuHyyy/Hhg0bAEBH8xWL9Ovp6Sm6n7vuugvbtm3Tf4fD4UnRjOTMiLiEQiEcOnQIsVgM69at0zXyZoMIWiqVQk9PD373u98hGo1ifHw8b7tcLofe3l4MDQ2hqakJHo8HmUwGbW1teUbZXDvSI5EIjhw5gvHxcezbtw+vvfYaEonEtA2vJKo+lUppw1tS5uVholh5l3Q6rY1ueaCRyMBiDvT5pJgTvVyc1YUz9uVyXmRhkDrM09HS0gKbzTYp6nxkZGSSdgjt7e1Ft7fb7Whubp52G9nnbI4LAB0dHejq6sK777477bgqBWp3eRKPx7F//3709vbi/PPPR1dXFxoaGrST22Kx6NIkknUGnNZ+m82Guro62O12JBIJDA8PI5lMoq+vD4ODg7oBWiHhcFhHtHk8HgQCAbS3t+Oiiy7S5VmkMenY2BiCwaB2NJuRUHLPlyyvwijobDaLWCymy6kBp5zoIyMj6O3t1U1ISzFkEokETp48qa+JlA8DTqWi22w2bYgGg0GdhWLus1j0uRmFJks6nUYkEtFGrUQJhsPhvBRvs9a5fJ6RSCQvI1AcAWK8Syk2iaAPhUKIRCJwOp1oaWnJq1VrOiEkY0DKuMjkiWwrn1UkEkFjYyNWrFgBh8OhJ/8zmQzGxsYQDodRV1en740+n083kzOzEMjMMSM5CZkLqN3lSTQaxb59+/Dee+/hwgsvRFdXly7LJeVCBDPoytQgKXGSyWR0ffHBwUEcP34cyWRyUmlMpRQCgQBCoZDu9zE8PIwVK1bgkksuQVNTky47Iu8vVl7TzE4SHRItlvImpmM2Ho9jYGAA0WgUPT09OHHiRF5fDnNcxe5/El0vJcekl4nootkoOxQK5QUQyD4L9yuTyaYmynmY11omLiSq37T3zRro5jkUG4OZnSYOe/E9yDOA+BdMh35hhn5hRoCcn+i0aDsAvV8JlozFYqitrUVLSwsA5Gl3scA+Mj2FfpRiGQ2kvKkoJ/ott9yCt99+G6+88sqk12YSYVisZi6ZPTLjPD4+rgVWhMKMCJ/q8zDTjKTmdzweRygUQjwenzJlS5zt0vhsdHQUNpsN4+PjOupKIqfMSO+ZjEuMWIn+Hh8fh9/v1+lk0rX7TLXAzNSwwocZs8ar6aBOp9M6DU3E3vy90DgvXIDT6fmlUBi1Zo5pqlSwcsB8WFjqaeGVxELOiDscDmzcuBG7du3Cddddp9fv2rUL1157bdH3XHrppfj5z3+et+7555/Hpk2bdEmHSy+9FLt27cqri/7888/jsssum/VxAcDv96Ovrw8dHR0lj7GcoXaXJ1JaxWazIRwO6+jndDqtHa1yzU0NMJH1Eu2VSCR0TexikVXyHjEaRevdbneek1ii2cwUYtFIwZzYNSO5zLR1aS5mllZJJBKTGquWcj+R7U1D10yTl+cds8ZpYRPwYhRLrzaNXolUk6VwEqHwsyhMi5fPWiLuzH2YJWLMWuhyTPN5ovBZznR0FIugK3xWMM9XvmNLNfp8PmA0G5lrqN3liWi3TK7KRLPcV83/MTPLR+6/ZmNIKdkmzlKZ9Jwqulu0OxqNIhgMoq6uDqFQSEcti2PetHFFY0wNK2xQKZHVouOxWExPDAeDQZ1VZjbFLkW75dnAPIZkSclPyQqXHiHi2JfrUMwHUUy3CzHHZ+qnOK9N+3U6HZ1Kh+UayPvNZwezXOx0Popi51/oLzE13/RhlJs/gEwPtXtuqRgn+re+9S08++yz+M1vfoMVK1bo9e3t7QBOzYybDoczRfqRuSOZTOLYsWMYGBjQEeNS1mXNmjWToqUKmZiYQDQa1alae/fuRSAQwOHDhzEyMpLXdMpEbuDRaBRvvvkmuru7dSSEz+dDV1cX1qxZoxtPulwuAGeO2pabTDwe13XXjhw5goGBAfj9fhw4cAChUAh+v78kB3rhWCUlXVLA3W43Vq5ciba2Nv23RKOJiA8NDcHv9yMSieDEiROIRCJ5BrFEwsfjcb1Io5BSxisPFqlUCtFoVNd4l2uRyWSQSCR0qro0YylsPLKQmE4BcZRItD4pfxZazLdt24avfvWr2LRpEy699FL88Ic/RG9vL7Zs2QLgVKRUf38/fvzjHwMAtmzZgn/8x3/Etm3b8I1vfAN79uzBE088gZ/+9Kd6n1u3bsUVV1yBBx98ENdeey1+9rOf4YUXXsgzOM903Gg0iu3bt+P6669HR0cHTpw4gW9/+9toaWnJc7xXKtTu8kVql0uqbjweR11dHd73vvfhfe97ny6x4nA4kEwmdQNxszGmOKWHhoawb98+hMNhjI6O6kjq6e7HmUwG/f39GB8fx+DgIPx+P2pra7Fq1SqsWbNGR4vV1taioaEBK1euhMvlwvj4OMbGxmCxWHRT0Xg8jtHRUaTT6bxouBMnTqCnp0c3JpdmqZK+Dcz8XiLGqzTzrKur045/Sbeura1FKpXCgQMH0NPTo7PICp8XxPA1a8/X1NToaDlzu3g8rpuYFS4mEhkmv5uf9cjIiA5wqKmpgc/ng9fr1U1Sk8kkfD4f2tvbYbfbdSCDjFvOpdDJIpHu9fX1urmo9KoRw76+vl5PhPT29iKbzWJkZKTkYAQymam+u/MZzUZDfGlA7S5fJGo6Fovp+399fT3OPfdcnHvuuTqrSbKEQqEQstmsXm9GDw8MDGDv3r0IBoMYGBhALBbL0/hiiL3u9/sxPDyMQCAAr9eLFStWoKurK6+ES11dHZYvXw6Xy4VIJKLr4cuzhdVqRTwe1453CY47cOAA+vr6dA+VVCqFYDA4KdIbmP6eIjasZM9JBHhLSwt8Ph8cDofOqDOD2I4ePYr+/n6dGWZmnItmT0xM6HKyEhUuQTYycWD2GzEj5kVHU6mUztoz68oLmUxGl5kx9y/ZaBLIODExAZfLpZvImg3gzZr55rObrCvU3sLIfrMHyvDwMDKZDPx+v+4rQ7t75kylo8Wi0+f7mDN5PzlN2TvRlVL41re+hWeeeQYvvfQSVq9enff66tWr0d7ejl27duHiiy8GcOrmvnv3bjz44IOLccpLjkwmg+HhYQCnbrwNDQ1obGyEx+PRKb3TOVol0jsWi2FgYABvvvkmhoeHMTo6inA4fMYZ1GQyiZ6eHvT09KC1tVUbgblcDs3NzbpOmzjRp8OM3pJZ+VAohCNHjugyLkePHkUkEpnhVTq9f9O4FYO7paUFq1atgtPpRGNjIxwOhxbOVCoFj8eDuro6+P1+jIyMaKPWbLgqQi3LTJzbItxiyCcSCS36ALQDQB4U5GFksTFT8WiIVx4LKcg33ngj/H4/7r33XgwODmLDhg147rnn0NXVBQAYHBxEb2+v3n716tV47rnncNttt+Hhhx9GZ2cnfvCDH+D666/X21x22WXYsWMH7r77bnz3u9/F2rVrsXPnTlxyySUlH9dms2H//v348Y9/jGAwiI6ODlx11VXYuXMnvF7vAl2duYfaXf5I6jdwqlzJ+Pg4PB4PWltb0dbWpmto19TU6HrcYpyZmjMxMYFgMIju7m49uVxYC70YExMTCAQCegmHw3C5XEin03C5XDp60eVyoaGhAStWrEBdXR1sNpuucy7bST1wSWUXw3Z0dBS9vb1IJpMIBALTll4rFTG07XY7amtr4fP5tJbb7Xa0tLRg+fLlusTJ4OCgbnJW7DMw9TeTyeT1SxGDSrTZjO4zI9EKS9kUGrXyWUciET3xIA4Fm82mJ+jHx8e1o0UalhYeU87XnICXSQWp95pKpSadlzyLAcDo6CiSyaQuI0AjfPZMp6PzVeKFxnT1Qu0uf0ztlgCo2tpaNDU14QMf+ABcLhc8Hg9qamp0UBQAXS5FHK+SYf32229jZGQkr9/JdEhvEQAIBoNau8VBLsdxOBxwuVxob29HXV0dxsbGtCNPAr0Ka5Mrdap0and3t+6ZEg6Hi+pnKfehQsexNEL3er1ob2/Pm/ytra1FfX29dlpLLfViZWlkX5J1NzExkZchJs8JZoS9fHaio6J9EixYzMkpPVUkmFCc/RLEJr3SZNKivr5eZyPIUqycj/lZFyvRam4rfhylFILBoA64k0C/amWxSqvMZ2k2avfcUfZO9L/6q7/Cv/zLv+BnP/sZvF6vrsXW0NAAt9sNi8WCW2+9Fffddx/WrVuHdevW4b777oPH48GXvvSlRT77pUcikdBO3iNHjugUZ2lAYRpUpuE4Njam6535/X4dmV7qP7tsJ7XPMpkMjh8/rmuPt7S0aONb6pfJIrOwhenP8XhcG+UScQacqlcsIisGZrE65cWoqalBXV0dampqdN1jqQ8qBqZED8i5WK1WNDY26msZDofR2NiIQCCA4eFhLYLioJdyOg0NDVr4zDpo5jUTcZUmLdFoVDujC5u0yWcVi8V0XdNSJibmC2mWI5Fy4uSnMU6m4uabb8bNN99c9LUf/ehHk9Zt3rwZ+/btm3afn/vc5/C5z31u1sd1u9341a9+Ne37KxFqd2UhepxMJtHf34+3335bRyxLve9UKqWN93g8nldCa2hoqGjzrVIRR0Aul8PY2Bh6enq0Ftrtdh09Xltbi0AgoJt5R6NR1NTUIBwO60hqsyGZ1WrVfVLMieZitdqnwywLV19fj+XLl8PhcMDn8+k64/LT6XQilUohm82iqakJq1ev1vXZs9msTk83r5Nk5AHQ0eaSxSfPKeLwlu1zuRxCoZDuG1MsY8/cvzjRrVarjlYzn7GkYaiUp5FxA6efL5RSsNvtuu+N6RyQbeXcZSzZbFYb/fJsY26/GCyVuqPVPj4y91C7KwvJBgOA3t5evPXWW3ryWRpFJ5PJvBKl8h6JKJfXZ3O/kH3lcjmMjo7ixIkTOqva4XAgEAggl8vB4/HkNfqWbOZYLIbh4WGkUiltqyaTSR1glkqlYLfb85zFMzlPmfC22WxoamrSUfFid4ujX7aR6yAT98lkEnV1dUin0/o5o9AZLaXwJIOsMAPfrAMPnLovi2M+EolMO3EhpXui0aj2ExRqp2nnmz4N+WmWupNIdvMaFvYWk+OamWTyXCA+gaWiLQs9zqVyXauBsneiP/roowCAK6+8Mm/9k08+iZtuugkAcPvttyORSODmm29GIBDAJZdcgueff76io/gqlWAwiHfeeQd2ux1HjhzBiy++qGehZXZUBEAafiWTSQwODiISiejIJDG8ZnoziUajOHbsGGw2G44fP47f/va3Woile3hzc7OeAZcGpDKTDJw2rsSBAJxKZVq9ejUSiQRaWlqQTqcxPDyMgYEBnWJWbKa6EI/Hg7Vr16KhoQHr1q3DhRdeCJfLpaP12m29AABGyElEQVTpTKGSaDSlFOrq6vSD0MqVK3Vn9j179ug0ODHST548ibGxMXR0dOQ1PpGxyBjF0ZDJZDAyMqInMgKBgK4hK9dfZsuj0SiGh4fR39+vm7iaRvFCkkqlMD4+jmAwqH+K44KUP0wrq26o3fPLXEeqiJM1mUzitddew8GDB/Miib1er9aUgYEBDA8PQymlo69jsZiewJ7NeZkTwPF4HN3d3Xk9Qmpra9HW1gan0wmPxwOPx5Nn8CWTSQSDQWQyGbjdbt0MtaamBitXrkQ6nUZ9fT1SqZTO6JqJs99qtWqnRFdXFz70oQ/B5XJpx4TT6URTU5Oe6A4EApiYmMDatWtx7rnnIpPJIBKJIJVK4fDhw3jzzTfzovVTqVReWnwymYTdbkddXR3cbjdsNhvq6+u100EiuEWvp4qWM6/vyMgInE4nstksGhoa8pzd0sdGHB+FE+8y2Z7JZNDY2IjGxkZddk7S8GUCQ/rRANAlByQQweVy6cmaYtFvC4F8rwHkPedUE/M5Jmp3dUPtnnvms0SDBI+FQiG8/PLLePPNN/OcuPX19ejo6IDdbsfg4CCGhobytC8ejyMYDM7K5pbjj4+Pw2q1IhgM4r333tN6KZPLTU1NOjpeHM1yTWKxmC7FJlnXElHt8/mQTCZ15tLIyAj6+/tnFCwlOup0OnHeeefhkksu0ba1TApLcJ05mb9q1Sp0dHToCflsNouDBw/itddey5uETyQS6Ovrw9DQENra2nQGvtvt1v1kikWGSx85MyihGJItMDQ0hObm5rwJcPm8xDFuTgKYPdTENna73fr9NptNR5GbAY5mj5l0Op03CWHud7EC1syMuPnWksXQqrPV1/ncN7U7n7J3opfygVksFmzfvh3bt2+f/xMi02KmLEciEYyOjsLlciGVSsHn82nHsMViQTgcRigUQjqdxsDAAMLh8Fn/g5vpSZFIRBvRkUgEXq8XtbW1iMfj2mld6EQ3Z2M9Hg+8Xi9sNptOfxZHtKSrSzMXqUt3pvOXFDKfz4fm5mbtFDiTISkCKYKWSCTQ398Ph8Ohz13ETeqCx+PxvG7bZlMSOU9xvps1xc0JBcGs3yaN46Sb+mIhDzZS8kbGwpt8ZUAxr26o3fODqRVz6UiX/8dcLofx8XGdqi00NzfrBlyjo6Po7+8HgLxoN7OZ2GyOL7ojEV8mLpcL8XgcDocDTU1NaG5uznPySpaUGJASdSVGo+iE1OuWppalGmKio5LyLU4BieYT577D4dA6CgA+nw/19fXauZ9OpzE4ODipHJppqEsdW7OuLQBdMiaRSOj0bmnMKvo3FWbZPNHuwiy8wqw1WW9Gn8kxJGrdPH95X2HUnbxmPosUHnsxMJvSktKhdlc31O75Za7vOWbTyVQqhbGxMQDQWUwtLS3aUTw0NISenp68Btxmea65OL40CBcnutPpRCQS0ZlbjY2NeY2rk8mkzqQS3ZSs7bq6Oq2xYsubtcZL1W6Z2PV6vdrulv1arVbU1NTouuGicR6PRzdLlWeb/v7+vIhs4HQAgmR4iZ1uajeQb3vLuGUCfDqHtKndtbW1RccsTnHzGGZ2vVnj3mwqbq4r1ihYmpPKItdnsZzoizXxXi1Qu+eWsneik8pFhEcphdHRUUSjUT07brFY8pyfplE3V5gCJ/VcY7EYYrGYLi0jqeINDQ16Bry9vR1ut1tHs1mt1rzabYlEQqdpt7e3IxaL4dixYxgcHMyLhhOkDIvdbkdTUxPWrl2LZcuW6cZdMxEEm82m69y1t7dj3bp1iEQiOHnyJEZGRvKc58PDw3oGWaLZgNNdtyU1LpPJYGxsDIFAQEeVTUUikcDx48cBnPp8u7q69MPHQgib+f0IhUI4evQoxsbG9NgX2ygnpUMxJ2TmLNb3Xkq1Wa1WPeENQEc6zfe9VxqDig5Ho9E8J7qUbXO5XLoRqTxnZLNZOJ1ObYCuWLECF110ERKJhG4eJoamiRjCNptNN0eT8mtyTmKcxuNxDA0N6WPKtZAG3fIcIBPpDQ0NqKmp0Ya8HA84FVXv9/v1tRYHQGNjI5xOJ0KhEEZHR/UzjdnYa6oyJVKmR55/1q9frycSpHyelL+zWCz6eU1Ky0gAhDjZJbItEonozDX5LojDQc5F6uqLY318fByHDx9GKBTCwMDAoqRLFzoaSOlQuwmZGaIBC/ndF02ORqMYGBiA3W7XZcRE7+YrotcsISL7t9lsSCQSOvhMzlG0VZzcDQ0NuuyIzWbTupPJZNDW1ob169cjkUjgvffew8DAQJ6vQTCd2A0NDVi7dq12oEvvLDN4Tn5ONRktkeSSkSeT15L9JZMIkUhEX2tT92pqanTDUvEhhMNh3ex8ugnweDyOI0eOYHR0FBdeeCHWrFmTF0AnWiv6Kr1rzMA5sdNl8kIi7qWuvnwepo9G/pZgBMlCPHjwIEKhEPr7+xe8B5k49ulInx3U7rmFTnQyb4hxKU7yYje9+TZmRMhlZtxcgFPi4na7sW7dOp1q9r73vU8bqxI1b0YsySI10yORiI7GC4VCuryKIDPybrcbra2tOO+887Bq1SrtwJ8JYswrpbB8+XJccMEFupSMOJJF1BOJBAKBAOx2u47CN9PzpcaaRK+LsT+dKMbjcbz77rsIBoPweDz40Ic+BI/Ho6MaFopcLodAIIB33nkHQ0NDGBwcXFI12gghZCGR7CcgPwJsujrcc4k4bC0WC0Kh0KTniRUrVuDcc89FS0sLmpqa0NLSglwuh6GhIQQCAR1Bbrfb0djYiKamJt2sbKpJWIvFosuhNTc349xzz0VjYyPq6ur09mLIyn4ymYyusSpl6yT7rbm5GQ6HAw0NDbr0i+zDHE8ikdDNy+XZQ2q4ulwuhEIhjI2NacMfOP0sNVXZgEQioWvVNjU1acNaMtByuZw2+C0Wi77WpnEuzzk2m01PFPj9foyPj0MppZ8tnE6nbrImE//yvGO1WjE6OorXXnsNY2NjGBsbW5Rm4GxATkjlUA09DBb63E3HbiwW0w7zhbr3iR0sjmOgeCTxOeecg40bN2LZsmXaiQ4gL0q7vr4eSin4fD6t3b/4xS900JoZaS3HERu+ubkZ559/PlpaWlBTU6NLsZhR4WYGm7kPIN+x7PP5sHLlSp31JvXdxTEdDof1tZZJ85qamrwmr4FAQEf+m+cwFdFoFAcOHNDR+VdeeSUcDocet6m9APRzgdl0XCYUJLIegI6EN8cqZWgKneoej0c3jt2zZw+Gh4dnXBJvruDkNykXrGfehJCzw0wDLlwWKnrYFEoxeM1jSydxt9udV+rFTKcWMZE0ZrO+m9frRX19PWpraydFZouxKvuUn7OtJS6i6HA4UFtbqxuVFhrT0jU8lUrpTuPSiFMaiZrZAHI9pkMi7sLhMCKRiHa+L5QD22wMJ3X8wuHwjBvNkMWncAJtNgshZGEwU4IX639PdNx0BIgOulwurauyiGabWl74u5R7kbrjhUjGmsPh0LXYpbxaMb0snKw3nwXkviWRXW63O6+JeKFRL89JUipHtFwyxmbyDKWU0iVgZD/SGN2sLW9uX7jPwiAEc52Z2Wc2Ji08vjyLiPN+tjX0yeJB7SakcjBt8MVyepoNTeU8JPK8rq5OT9AW2ttic8tP0/6ura1FfX29LrtSiGiSONNN7TYp1OvpEIeyqd0yRgCTdFv0VjQ3lUpp/Ta1e7p7omin7MO0u+X5opjeTrVP8zXT0S5LsYBHGYdot2SvkcpiMbT7kUcewerVq+FyubBx40a8/PLL026/e/dubNy4ES6XC2vWrMFjjz02aZunnnoKF1xwAZxOJy644AI888wzMz7uTTfdNOl5/SMf+ciMxsZIdLJkEVFtaGjAueeei/Xr1+t65TITO13KkBnZtm7dOvh8PvT19WFsbCzPgS0dwdva2tDe3q5T0842Hamurg4rV65EfX09Dhw4MClSw5yhzuVyenZctpMsAYnWL4VkMom+vj6Mjo6itbUV+/fvR0tLC1auXIlly5bNe1mXTCaDoaEhRCIRHD58GPv378fIyAj8fj8NswqDaWWEkNkgk9Iejwfr1q1DZ2cnXC4XfD6fLkEiEVYSRW4ai9KoLJVKYfny5bBarfD7/Th48GBeLXaJJnO5XDoVvLW1VZdCE4eEGP3SRAw4nb4vqdwA9AS2w+HAmjVrdCPXQCAApZSuz2pOEsj+pJG2pLUXi5wTit0bTef8+Pg4jh49Cp/Ph/b2drS2tiKVSunmbWajczkv81gS2SbN3yRNXJwUZgq/PAfJBHwmk9HPScFgkIb4AjBVFO9sy0tQu8lCw+9MdSCO2rq6Olx88cVYs2YNPB6PrpMu2i22qtvt1llSol9SevScc85BXV0dhoaG8Pvf/z5PSyR7S4LcGhsb4fP58srHFkOc37IP6XUmUe5utxsdHR2IRqMYGhrS7zMzwaRMjji/pfyZ1WrN027ZvpTvtkwqSAa2z+dDZ2cn2tratLaazwyFmfNmCR85PyktJ0F50q/EPL9cLodEIgG/3490Oo333nsPQ0NDGB8fX/S+aNWOPD+an9/ZstDavXPnTtx666145JFH8NGPfhT/9E//hE9+8pM4ePAgVq1aNWn77u5uXHPNNfjGN76Bn/zkJ/jtb3+Lm2++Ga2trbj++usBAHv27MGNN96Iv/3bv8V1112HZ555BjfccANeeeUVXHLJJTM67p/+6Z/iySef1H+bfX5KgU50smQRJ7jX60V7eztWr16to9pKKU0iqcp2ux0dHR3wer1QSqGurg6hUEhHTVutVni9XrS2tsLn88HhcMyJo9nj8QCArr1WKMamYE5X53wmZDIZXav15MmT6O3tRTKZRENDA5YtWzYnx5iObDaLQCCAsbEx9Pf3o6enByMjI2woWoHQECeEzAZpcOr1erF27Vqcd955eU21c7mcNlqdTmdeHVBx7IrRKeVZXC4Xjh07lncc6Ski5dgaGhrQ2Nioy7aJMSpOZCl1IhFoAHTmWSaTyas93t7ejng8jhMnTmjtLkxJN5mqbupM7oNiZEuN3Hg8jubmZni9Xtjtdl1nXRzkdrsd6XR6UvaBGHcSUS9O9fr6ejidTl1rVYIJZPH7/YjFYhgbG9P1YMn8YjqCgPxmsfJzplpK7SaEzAbRDrfbjfPOOw8bN27UvT1E/5LJpNZuKQ0mpWjkdQBobW1Fa2srnE4n3nnnnUnHkT4eEjBXV1enJ3KlrnZhBLo4wYHTUdpmJL3D4UBjY6OeyC9md8tPc4I7lUoVvR6l3AvNSPF4PI6enh6EQiH4fD44nU5ks1l9Tcz9mo50s5m3jF0i+80JcFO7ZT+pVAojIyMIh8MYGBhAMBjU5eZI6cxUa83v5VxlcC20dn//+9/Hf/kv/wV/8Rd/AQB46KGH8Ktf/QqPPvoo7r///knbP/bYY1i1ahUeeughAMD555+P3//+9/j7v/977UR/6KGH8Cd/8ie46667AAB33XUXdu/ejYceegg//elPZ3Rcp9OJ9vb2mV0EAzrRyZJEIsykHIrZAGSmDm4xNiVlTBqSTkxM5NWBmyrN+WzGIDVGTUNpvg0UEeJwOIzjx48jHA5r54LUX5fIu7Mdq4xFUs+DwSCOHz+OgYEB9Pf3I5VKsZkoIYQsIcQ5m8lktA6K0WhiGshmGrmZRi0aKpFYpo6akeg1NTW6sbj0Q1FKaSe5vKfQ4SwTvFI2RRqCSYkWALpO6Xyn2st5xGIxDA4OIhaLoampSTdctdlsqK+v188xpgPBHFsmk0EikYDFYskrLSO9VgDoGrIyaRGLxTA8PKwnwUvNfiOnmare/Zk4U6kAQghZCMxJZ3Goi5Pc3EYoLCVhaqQ0uTZta3OSUGx8p9Opg9qkJIlof01NzaR74FRBdIWlT+T5YiFsbnFqh8Nhrd2tra26CasE05nbmu83y8bIc5O8JhPi8lwk11JKx8ik+9jYGEZHR6ndC0hhRmK5YGZsAtATXibpdBp79+7FnXfembf+6quvxquvvlp0v3v27MHVV1+dt+4Tn/gEnnjiCR3gsWfPHtx2222TthHH+0yO+9JLL6GtrQ0+nw+bN2/G9773PbS1tU0/eAM60cmSpba2Fh0dHTrNSxpvzsaJLk54n8+H1tZWLTLS0FQc7Gbt8rNFor+KGf/zecOV/ff09CAej6O+vl7Pgjc0NGDNmjXw+Xz6fGY7XrMszdjYGEZGRjAwMIBf/vKXOHz4MAKBAEKhEKPQKxRGsxFCZoOkREvqpcvl0q9JCTPpTSIGoaQlmwagpHzL5K84xCXqW9LOW1tbUVtbi0QigWAwCL/fj6GhIVgsFrS0tGgnuhincm8yG49KLVVJWY/H44jH49oRLxMD8+VIN43roaEhhMNh3ShsdHQUPp8P559/PpqamrSTQK6lvNfsZxOPx/UY5ZzHx8cBQKeaW61WjI2N6Uao+/btQ19fn54UJzPDdNqUqn/FoiQLf58p1G5CyGwwe2dIf5LCMiSmHW7WUS8sg+J0OnW0udk0Uyas6+vr0dbWhvr6eqTTaUQiEYTDYa1TXq9XZ3SbkejyXGEeyzx/03Ev75vPCXDJrLNYLOjv78f4+DjcbreOGvf5fHj/+9+P5uZmZLNZ/RwhTnGzvJo8Z5iTEjabDel0GjabTfeJAU7peTAYxPDwMF5//XUcO3aM2n2WzGTSxQwQnCvNnCvtXrlyZd76e+65B9u3b89bJ6WNC6sULFu2LK8UksnQ0FDR7bPZLMbGxtDR0THlNrLPUo/7yU9+Ep///OfR1dWF7u5ufPe738Uf//EfY+/evZMmBKaCTnSyJClM0zaj0GfjRJf3iqA7nU4diSXbmAb9XI2h2LJQxONxXZ/crEte6AgwG7Wc6fzMm7vsQyL6g8EgxsfHMTAwgN7eXt0QlQZZZUJDnBAyG8zUZNHfwoZghc0yC+uBmlHqov+ymPolxr7dbtf1UqVRmJn6PdX9TIxaOSeZSBfHgKxfiPtZYWaXw+HAyMgIfD6f3sackBADW+q0yz5Mo9xcxGgXx4TFYtGp5vF4HH6/HyMjI/M+zmpmIZ/xpoLaTQiZDWYpsOkaWRb2cSjUGtOmNhcA+rlAMsSl1rfZ7FO2E+R381nCzCorfGZYaHtbzi+RSCCRSMDpdGJsbAxjY2M6SEAanRZG9puTE2bNc9PBLhov19DMOJMSbMPDwws2XnKKudbKudLuvr4+1NfX6/XTOZ0L/0/OFFxZbPvC9aXs80zb3Hjjjfr3DRs2YNOmTejq6sL//b//F3/+538+5fmZ0IlOlixm9++zbYhpiqvs16xllUwmEYvFUFdXN2XN05kixmkymZzU6bvUcxbBLOaEEIfDVNHtMqNtsVhw6NAhZLNZNDU1we/3o7W1FU1NTejo6NBR+DK7X3itzag9uVYTExMIBoMYGhpCIpHAkSNHcOzYMfj9fvT39+uIQhpjlQsNcVJpFBp2ZHGoq6uD1+tFU1MTmpqa4PV69WtiQIrhLHW4nU4n6urq4HA48qKpxKi02+1obGzExMQEIpEIgsEglFKIxWLw+/0AoFPApRGaRLvV1dXpSLdMJpOXJi5Od7vdjtraWng8HqRSKTidTl3OZbpa6CbF0sfNdcUmr6eLkJMsLwAYHR2F3W5Ha2urbsgmzVIl4g+A/l2MpmQyqScU5LknEong1VdfRSqVQl9fHwYHBxEOh1lH9Swx0/Jn+r65Pg9qNyFkJlgsFtTV1aGlpQWNjY3weDx59mBhEFUymdTZUGJXm40/ZRJaeoxYLBaEQiH4/X7kcjkd4DUxMaFfdzqdqK+v1xnkDocDuVwuLyBL9DQej2utbmlpgdPpRCQSQSKRmHUZUdP5LnopDv6ZRLNPTExgcHAQANDY2IhcLqfrw4t2S6laMzNPap8D0L1KTO0OBAI4fPgw4vE4+vv7MTg4iFAohEAgMOOxknzKQffmSrvr6+vznOjFaGlpgc1mmxR1PjIyMmUPvfb29qLb2+12NDc3T7uN7HM2xwWAjo4OdHV14d133512XCZ0opMli9lYYy6c6Gaal0S2Aaed6OFwGF6vd87Svsz6rNL8ayb7lnM168HK+QLQM/Yi8IU3XklVz2Qy+MMf/oDDhw/D5/Ohv78fy5Ytw/ve9z58+MMfhtfrhc/ny4toK6w5Jw8k0nwtmUyiu7sb+/btw/j4ON566y0cPHhQ11ZlBDohZCExm/ywD8PiYbFYUF9fj87OTjQ3N6OlpQU+ny9PV8QoTKfTugxYW1sbOjs70dDQgEAgoLVN9M10IA8ODiISiUAppX9KhLrdbkddXZ3WMykFF4vFMDo6ilgsBpfLhbq6OiildA1WWdfY2IhkMgmXy6Uj4qTR2XTfKTMCDpgcOWdGhpvfVXPbQsQQHx4eRl1dHQKBgC5v197eDpfLhXPOOQednZ06zV0cIc3NzbBarQgGgwiHw9podzgceOONN/DrX/8ao6OjGB8fRyAQ0NfZHA9QHoZlpTDfpfoIqTamimoki4PX60VnZyd8Pl+ejhailEIikUA6nYbT6YTP59MT4GKPSvCY3W7HihUrUF9fj56eHgSDQUxMTCAQCGi7eN26dbBarbqsKgBdTkZs3Ww2q58hZDI9HA7D5/Nh1apV8Pl8yGazefudCWawndjdUj5OGp3LdnINpiKbzaKnpwcnT56E1+vVGWWtra0455xz4PF4sGLFCt3bREq/mgEAsVgMsVgMwOngthMnTuDll1/G2NgYBgYGMDQ0hImJCT1xQUipOBwObNy4Ebt27cJ1112n1+/atQvXXntt0fdceuml+PnPf5637vnnn8emTZv0/+2ll16KXbt25dVFf/7553HZZZfN+rgA4Pf70dfXh46OjpLHSCc6WdIURlufTf1uM6XZTAMTsZfoN9MhfTaOe3GizyYq24yaN2f55Xzlp9kVfaoHBinhks1mUVNTA7/frxuUDQ0NIRaL6YYuZjq7iTyQpNNpBAIBJBIJjIyMYHR0FMFgEKFQCNFoVNdj5YNw5cNoNlJp8DtXHpj1PAvXmynKZrkXID/iWyLVAeRtL7VXZX9SjiWVSuWVYJHJd/P9wOkyMmYpN9PxXVgSpvBZodTxy74lm07GWmiInwm5JslkEtFoVI/J5XLB5XIhEAjA5XLpeqlyLWWMoVAI4XAYVqsVmUwGDocDwWBQO9djsRhrqFYZ1G5CyGwo1OjCMi2FS+F7AeRpK3A6m6wwGzubzeqyn+JwB5BXurUwq+tM5y7lRePx+Ixs0WKlKMTuFh0vHOeZJtZlzIlEQjd6rKmpwfj4OJLJJDwej9ZsCZiT5xeLxYJoNKp7m8g5SR30cDiMaDSqm7CS6mChtXvbtm346le/ik2bNuHSSy/FD3/4Q/T29mLLli0AgLvuugv9/f348Y9/DADYsmUL/vEf/xHbtm3DN77xDezZswdPPPEEfvrTn+p9bt26FVdccQUefPBBXHvttfjZz36GF154Aa+88krJx41Go9i+fTuuv/56dHR04MSJE/j2t7+NlpaWPMf7maATnSxJJDo8GAzCZrNp57Y4lGe6LzGuxRANh8NafCYmJuD3+5FKpWCz2RAOh1FfX69nhmdLMBjEu+++i1AohGAwWNJ7zPqxkvZVGIkuY5JOyLlcTjdAK+a4AE6LfSQSwdGjR9HX14djx45h//79cDqdaGpqgs/n0w3cZNxmw7dEIoFkMgm/349EIoFQKIShoSGkUimMj48jlUoxEquKoCFOKgnz+8bv3uKhlNINpWOxmJ50lbIi6XQa8XhcG4WNjY06vXl0dBR+vx8ulws+n0+Xa0mlUgiHwzh58iT8fr82kIFTD9upVAoOhwPDw8PIZDJoaGiAz+eDxWLR56CU0v1QJMJMdFQm6MfGxhCJRNDd3Y2jR4/q95aS2VBMe61WKxobG9HU1KSvhzjoJeV8qjqShc6DbDarJ63dbjdGRkZgs9lw6NAhOJ1OuN1udHR0wOv1IhaL6c9ArrlEu1mtVoyOjuLkyZP6M5lqPDNlqvMnCwu1m1QK/K6VD6LdPT09iEQiOO+887R+yOS12XtDgq6AU2XDRGcK7eZ0Oo0TJ05gdHQUkUhE66kEcHk8HkQiEXg8Hp0RZrFY9OR4YW8VySR3uVzI5XJwOp1IJpOwWCw4efIk9u/fj0gkgrGxsZKzEk3Hv4zN7XbrjDWXy6X9CJIlJxMAZyKdTmN4eBjj4+MYHh5GT08P7HY7fD4f6uvrtQ0uzUilZGosFtNOdGF8fBx9fX26aSn/f6qLhdbuG2+8EX6/H/feey8GBwexYcMGPPfcc+jq6gIADA4Oore3V2+/evVqPPfcc7jtttvw8MMPo7OzEz/4wQ9w/fXX620uu+wy7NixA3fffTe++93vYu3atdi5cycuueSSko9rs9mwf/9+/PjHP0YwGERHRweuuuoq7Ny5M6885JmgE50sWTKZDOLxuE6rNiPJZhohLjPCZiMOswO21FFraGhAIpHQNUSLRWWXglIK0WhU1yuLRqMlC7k8KDgcDt3VXBYTmbmWcZnjKRYhIBMT/f39AE7PsttsNjQ3N6OhoUGLucvlymt0IvVSU6kURkZGEIvF8rqIk+qDhjipNPidKw/i8bg28EQ3xBCXyWwAusSIy+VCLBbD8PAw0uk0Ojs7UVtbCwB6Al0mcIeHh/M+Z4kYd7vdCAaDuq6o2+0GAAQCAYyPj8PhcKC+vj4v8mtiYkKna0vUttVqxfDwsO7tEY1GS04LL/z+WSwWeL1etLa26lrsUvKsmHYWSxMXHc5mswiFQgBOab80LJMoPq/Xi/POOw9NTU0YGhrCe++9p2vWLuT/RWH0IFl4qN2EkNkgE71SszyTyWjdnpiYgM1m0xHjZpkX04FuNjGUieqxsTH09/fnZYWJdkejUe0wloAxq9Wa55g3y7FKpLrT6dQl3ERP/X4/Tpw4gWg0qku9lYqZlSZjESe6BKyZDcvN+u/T7VNKzBSul15kHo8HK1euhNfrRTAYxODgoA42YKT50mIxtPvmm2/GzTffXPS1H/3oR5PWbd68Gfv27Zt2n5/73Ofwuc99btbHdbvd+NWvfjXt+0uBTnSyZEmn04hGo6ipqUE0GtW/S9RaqUgkdSwW0ylQ0uwTOB1BJmlXAwMDAIDm5ma0tbXNqLGp1E7LZDIYHx/XkW1TRXsVo7CMi9nhXM4XyG88Kg8YM6kBZ3YBTyaTsNlsugasWYderos8XEl9dzPNnRBCCDGR5mOhUAiJRALA6clbs9zKxMQErFYrvF6vNtRjsZg25OPxeF4Ts2LRZaaT2ev1auM7l8sVLd8i6+SnlD0TPTTLuZSC6LG5f7vdrvcrzggpweZ0OmG325FIJPLK0xR7zigcq2i3WR4mm83qlPFoNJpXBm8h4TMBIYRUJpKdLSVOY7GYzvoSO9S0v0XzREdlYtcsI2ruoxjpdBpjY2NaO+rr6/N03tRUm82mm4KL3SpR21JHXCbtZ6LdYkPL84kZuFZoc8uzS7Fa8cWu53TXWbLFwuEwstmszqyT8jbU0+nhpD2ZDjrRyZJEIrnFgXvy5En4fD54vV7U1NToSOkzIV29pX53b2+v/l1m2wFo43Z0dBRvvPEGvF4v3v/+98PtdsPtdmuD90wkEgn09PQgHA7j8OHDuou2OBDOhFlDVZqqiGibDy6FkWrSgHUmzm0R8YmJCQSDQUQikTzHvYm5X7MmLaleGM1GCJmKUptrjY2Noa+vT2utlB7xeDw6kyqVSqGmpgYrVqyAzWZDKBRCf38/MpkMwuEw4vE4wuGw1rpijbRisRiOHz8Op9MJi8WCZcuW6egxcWjLhLjoq0SxWa1WHfklfT+CweCMnOiS/m1qtozP7/frKDaZMKivr9f3WJm8nq7OrIk815ivp1IpnDx5EjabTaec04E+NYWTKlNl8VUi1G5CyGyQki3JZBKBQACDg4PavqypqdE6ajovbTYbnE6ndmJLBHswGEQsFsPAwMC05T7D4TD2798Pl8uFDRs2oKmpSWeIyfYSge5yueDxeHSPLtG6oaEhpNNpDAwMIBgMIplMlhRUJg500W23243a2tq8ZwSzj4noulyjs0Em1qWEi2h3Npudts8ZOYXZoL3Usj0LgZzTbHSY2j230IlOlixiJDocDi3MMutszgxPhdxUpcGnGYUuddYEs9zJ2NgY4vE4li9fjlQqpcV7uuh30xiORCK6cVcoFCpZzGU8MsNdrAFase3E6S3G4EzKz8h1lJRwQgSKOSGkGKbOTPd/LllgMkErDmtxMgOnjXbTCS0NL6X0iWi2GYleyMTEhNZ3ieaSiWJzUth8dih0nqbTad1gXKLHZ3JNzAg2qQsrzysSPZ7L5bTTQaLuZ0Ox6PTC+qmkdKopoo3aTQiZDXLvEOeuZEpJadNifcnMSWMAOmM5Ho8jEoloG3iq+4o43O12O8LhsM7cNrOpTPvWtHdlO7Nvlzinz0ThPuUZRRp1m5le5nukUXgpkejTIWOThuZk5pSrbpfyfFwMavfcQic6WbKIsEh0dzabRUtLC5RSqK+vh8fj0Q1IRPDEaM7lcrp8SywWw+HDh9Hb24tAIIB4PD6loEtKdDKZxLvvvguLxQKPx4OmpiY0NDTAbrfD6XRqZ77MFktN9XA4jOPHjyMSiWBoaGhG3cEF06ifSqjN16spgoqUDxRzshRgM8SZU+p1ymQy6OvrQzKZRG1tLdra2uByufIMcvk9l8shEAjAYrEgGAxqg3h4eFg3s5aGosWiwyU6O5fLYXBwEPv27UNNTQ2cTqeOoAuFQnmR4plMBkNDQwgEArruejab1ZHjpWA6Fsz65VI7VZqZFpZnM/ufWK1WavgCI44ZuebVdP2p3WQpUDgRSuaOdDqNnp4e3Sts1apV2ub2er155VbM0qCRSEQ7zvv7+zEyMqL/nupzEqd9LpfDwMAAfv/738PpdOpGozIpLbot2Wii3el0GuFwWJeFKTV7TILjzGC1TCajS8OYvgX5aWJ+//gcuTiYjW7LBfOZYjbvpXbPHXSikyWLOKhjsRiOHj2KkydPorOzEzabDU1NTWhtbc2rUSaObamFNjQ0hJMnTyISieAPf/gDuru7dZ31qQxkmRG3WCyIRqM4ceIEXC4XzjnnHCxbtgwulws+n0+nmqVSKWSzWZw8eRJ+vx+xWAyDg4O68eZMZ5dNh3jhjXiqNO9CRzpvooQQUhpzaYjPNvqkEilljKlUCseOHcOJEye0Xjc0NOhSLhLRZrPZkEqlEAwGkclkEIlEdLOxvr4+3ZBsuklppZSOQOvp6UF/fz8cDgc6OzvR3NycFy1nGszd3d0YGRkBgLz1pWq32bNEDHJpSCoOdrNRt4xXnBCyjTkpThaGszF255tSauMTspQx+1mcbYnJpaTdpZBKpfDuu+/i+PHjaG9vh9VqRVNTEwDopt9mtrfY3aFQCMFgEPF4HN3d3ejp6dHl16bTbtHbEydOaO1euXKlPrboq+wjnU6jr68PIyMjuiScOPNL+S5IRLlkjAlSIgY4XULGbrfD7XYXLelqRrNLMAC/QwtDufo7ip2X+f0gCwed6GTJI7VEgVMNs0KhkK5NJrPUsphiLiVVxCBPJBJ6tvtMxwOgjykNy1wul66BJk50SVsLh8MIh8M6pWwmjUQLKRZlXhiRLhMMhVFU5SgopDLhjDgh5GyRGp/JZBLxeFw3Cg+Hw9ooVkohlUohEonoie5YLKa1dCb1vcUgl0U02SwnI5jlW4DTTpnZGjpmyRZxkMt+5XUzcq+wfwnvmWQuoHaTpQS/r/ODlPlMJBKIxWJwOBxwOBxwu9164heA1lkpqRaPxyeVYSv1MyrU7mg0mjdBLRMm0rBUMsunc9KfCdOOFv03tbswylzOgd87MtdQu+cWOtHJkkcppeucDQ4OIpvNwuVyoaGhAY2NjbDZbLoJp3QUlxlxv9+PdDqt08FncoOS2W2JNB8fH4fVatXlXMRQlmh5SQWXJmGzHat05bZarbrZiDRIk22AUw840qhM6rfSkU7mCoo5WQrM5QQkv/NTE41G8d5778HhcMDlcqGuri6v94dkdomeifYGg8FZX9dcLofx8XHE4/GiPUNyuZzOTLNYLJiYmJhxjU3TMS77kUlvqX3u8Xj0RIJE5clYxflAiMnZai+1m1Q7cxnVye/81ITDYRw6dAhOpxMul0sHr0lptkwmg2g0qp3uEs3t9/tnfS+amJjA6OgoYrEYAORNfsvrsVhs1navaWubmBlrtbW18Hq9eU5zCQYQ7TabjlYi5VpTvNoo9TtK7Z5b6EQnSx4ROzE8pdyK1GcTQ1Ui0cWZLbPYYuDOFDGKxTG/EEjKuqSaSY3XQoFW6lQTNIm6l4cBOtHJXMLvEql2+B1fGJLJJAYGBvLWSUaZRKSLQSuR2meL9CuJRCJn3Ha2pT3MbDFJAzcnB6Quu81m03VhJXtNotbn4zvI8gRLG37upNrhd3xhSCQS6Ovry1tns9nQ0NAAt9uNVCqFUCg0o0bcZyKXy+kM7/liqtIvdrtd9zOpq6vTk+0SJCe/m++t5O8iHenlBT+LuYNOdEIMxFg1m3dJbVGpiS610So1MlvOVyLyzBQ283WZ8TebuhBCCCGVgDnBbf5e6XUjTcd6MplEJBLRE/ymbs+nZvN5gBBCyHwgwW1Wq1UHe1UDYmtbLBYkEgnY7XZMTEzkZZrP5+Q3IWTuoBOdkALEQBWD1EzTNmuJSyR5pWHWZBcxl2g9IN/JLg8vdKKTueZsv0/8PpJKwiz1UYm6UakURoOV+32jlKgtef6QUnKhUCgvJZyaTeYTajdZajDzZmHJ5XKIx+M6u6panpnEQS4lUv1+v15fmHFWDd81ZsGVF9TuuYVOdEKmoFKd5KUi0fRitBdzomez2YqMtiflD8WcLBUKy2UxvXXhqKTrPJO6pzKuVCp1Vo3GCZkp1G5CyHwzVTmUSsa0p6ttbKT8oXbPLXSiE7JEMW+GZtSa/JSIe940yXxAMSdLBaVUnuOc3935xYz6ryQNq5TzJEsbajchhJDFgPoxe6jdcwud6IQsYczaqoQQQuYHPnwuXBquNM42S5wQQgghs4EasnCwXAchpBKgE50QQsiCwxlxQsh8wfsDIfMDtZsQQgipLKjdcwud6IQQQhYcijkhS4uF+p+VCHSATVwJmWuo3YSQ+YL3B0LmB2r33EInOiGEkAWHYk4ImQ8qqQ46IZUGtZsQQgipLKjdc4t1sU+AEEIIIYQQQgghhBBCCClXGIlOCCFkweGMOCGEEHIKi8UCi8VS9pkU1G5CCCHkFNTupQmd6IQQQhYcijkhhBBSWVC7CSELjTgpCSlXyv07Su2eW+hEJ4QQsuBQzAkhhJBTVIqmUbsJIQsN7xukkHJyWpfLeUwHtXtuoROdEEIIIYQsScrJECNLG34PCSGEkOmREirA4uvmYh+fLA50ohNCCFlwOCNOCCGEVBbUbkIIIaSyoHbPLXSiE0IIWXAo5oTMjHKJupkLymks5XAOS4FKab5FpofaTcjSpZy0myxd+Bwxc6jdcwud6IQQQhYcijkhpWOmrgKV/f03nalk6VBO6ddk9lC7CVma8B5OSOVC7Z5b6EQnhBBCCCljlFJ5TvRKhg/ipNqho4kQUm3wfrZ0YeADIfnQiU4IIWTB4Yw4ITMjl8st9inMGfz/XXpU0/d3OiwWC6xWKywWC3K5XNWNm9pNSD5LqVRVqeOb68w5OnEXj6WUgWC1WgFUZ7kYavfcQic6IYSQBYdiTgghpBoRp0O1ZI+YULsJyaca/8/ngrlyfPP6Li5L5fpXU9nEYlC75xY60QkhhCw4FHNCyFKK4CNLA6UUcrmcjkSvNqjdhOQj3+ml9t2eLjq5Eq4Jo9tLY6lcI1O7q3HM1O65hU50QgghhBCy4FitVlitVuRyOUxMTCz26ZAKp1yM32p0nhNCilMO95yFRkpWTTcBXs7XZSmVKDlbltL1YUAHKRXrYp/AXPHII49g9erVcLlc2LhxI15++eXFPiVCCCFTIA8qZ7PMlJnqxO7du7Fx40a4XC6sWbMGjz322KRtnnrqKVxwwQVwOp244IIL8Mwzz8z4uEopbN++HZ2dnXC73bjyyitx4MCBGY+vEqF2E0JI5UDtJgCvDVk46NQk5Oyhdudfi7O2u1UVsGPHDlVTU6Mef/xxdfDgQbV161ZVW1urenp6Snp/KBRSALhw4cKFyxwuoVBo2vutxWKZ9TLdMeZCJ44fP648Ho/aunWrOnjwoHr88cdVTU2N+rd/+ze9zauvvqpsNpu677771KFDh9R9992n7Ha7+t3vfjej4z7wwAPK6/Wqp556Su3fv1/deOONqqOjQ4XD4ZLGVqksNe0u/O5yOXVNrFbrvF4XXnMulbKUy3eV2n32+lTNLDXtLvwfLZf/08VeKv1aVPr5cymPpZi+LdZC7V44u7sqnOgf/vCH1ZYtW/LWrV+/Xt15550lvb+SxZwLFy5cynU5k5jP1zHmQiduv/12tX79+rx1f/mXf6k+8pGP6L9vuOEG9ad/+qd523ziE59QX/jCF0o+bi6XU+3t7eqBBx7QryeTSdXQ0KAee+yxksZWqSw17S6Xh+yltJSTccOlspaF/s6U03eV2n32+lTNLDXtNv9H53vSl0t1LvzOVO9STvcFavfC2d0VX84lnU5j7969uPrqq/PWX3311Xj11VeLvieVSiEcDuslFAotxKkSQsiSQi1ACqZ5Lw+Hw0ilUpO2mY1O7NmzZ9L2n/jEJ/D73/8emUxm2m1kn6Uct7u7G0NDQ3nbOJ1ObN68ecpzqwaWonYr1lpccOR687qTmbLQ3xl1FinT83Eu802la/dSZSlqt1BO/6OksuB3pnopp/sCtXvh7O6Kd6KPjY1hYmICy5Yty1u/bNkyDA0NFX3P/fffj4aGBr2sWrVqIU6VEEKWFJFIZNI6h8OB9vb2Odl/XV0dVq5cmXc/v//++ydtNxudGBoaKrp9NpvF2NjYtNvIPks5rvycyblVA9RuslCUg2FDSCVB7Z75cZcKS127qSeEkELK5b5A7V44u9te8pZljnRYFpRSk9YJd911F7Zt26b/DgaD6OrqQm9vLxoaGub1PBeKcDiMlStXoq+vD/X19Yt9OnNGNY6LY6ocqnFc8zEmpRQikQg6OzsnveZyudDd3Y10Oj0nxym8zzudzim3n4lOTLV94fpS9jlX21Qj1O58qvEeA1TnuDimyqEax0XtLg/tXqpQu/OpxnsMUJ3j4pgqh2ocF7W7PLT7bPW94p3oLS0tsNlsk2YORkZGJs0wCE6ns+gH3tDQUDX/oEJ9fX3VjQmoznFxTJVDNY5rrsc0nWHkcrngcrnm7FhnYjY60d7eXnR7u92O5ubmabeRfZZyXIkOGBoaQkdHR0nnVg1Qu6enGu8xQHWOi2OqHKpxXNTufBZKu5cq1O7pqcZ7DFCd4+KYKodqHBe1O59Ks7srvpyLw+HAxo0bsWvXrrz1u3btwmWXXbZIZ0UIIaRcmI1OXHrppZO2f/7557Fp0ybU1NRMu43ss5Tjrl69Gu3t7XnbpNNp7N69u6o1jNpNCCFkOspZu5cqvDaEEEKmo5y1e87s7pJbkJYxO3bsUDU1NeqJJ55QBw8eVLfeequqra1VJ06cKOn90rW21G6zlUA1jkmp6hwXx1Q5VOO4qnFMxTiTTtx5553qq1/9qt7++PHjyuPxqNtuu00dPHhQPfHEE6qmpkb927/9m97mt7/9rbLZbOqBBx5Qhw4dUg888ICy2+3qd7/7XcnHVUqpBx54QDU0NKinn35a7d+/X33xi19UHR0dKhwOL8CVWTyo3ZOpxjEpVZ3j4pgqh2ocVzWOqRjlrN1LFWr3ZKpxTEpV57g4psqhGsdVjWMqRjlr91zY3VXhRFdKqYcfflh1dXUph8OhPvjBD6rdu3eX/N5kMqnuuecelUwm5/EMF5ZqHJNS1TkujqlyqMZxVeOYpmI6nfj617+uNm/enLf9Sy+9pC6++GLlcDjUOeecox599NFJ+/zXf/1Xdd5556mamhq1fv169dRTT83ouEoplcvl1D333KPa29uV0+lUV1xxhdq/f//cDLrMoXbnU41jUqo6x8UxVQ7VOK5qHNNUlKt2L2Wo3flU45iUqs5xcUyVQzWOqxrHNBXlqt1zYXdblCqTdrKEEEIIIYQQQgghhBBCSJlR8TXRCSGEEEIIIYQQQgghhJD5gk50QgghhBBCCCGEEEIIIWQK6EQnhBBCCCGEEEIIIYQQQqaATnRCCCGEEEIIIYQQQgghZAqWvBP9kUcewerVq+FyubBx40a8/PLLi31KJXP//ffjQx/6ELxeL9ra2vDZz34WR44cydtGKYXt27ejs7MTbrcbV155JQ4cOLBIZzxz7r//flgsFtx66616XaWOqb+/H1/5ylfQ3NwMj8eDD3zgA9i7d69+vdLGlc1mcffdd2P16tVwu91Ys2YN7r33XuRyOb1NJYzpN7/5DT796U+js7MTFosF//7v/573eiljSKVS+Na3voWWlhbU1tbiM5/5DE6ePLmAo8hnujFlMhnccccduPDCC1FbW4vOzk587Wtfw8DAQN4+ym1MhJhQu8sbanf5jovafZpy0zlqN6l2qN3lDbW7fMdF7T5NuekctXsJopYwO3bsUDU1Nerxxx9XBw8eVFu3blW1tbWqp6dnsU+tJD7xiU+oJ598Ur3zzjvqrbfeUp/61KfUqlWrVDQa1ds88MADyuv1qqeeekrt379f3Xjjjaqjo0OFw+FFPPPSeP3119U555yjLrroIrV161a9vhLHND4+rrq6utRNN92kXnvtNdXd3a1eeOEF9d577+ltKm1c/+2//TfV3NysfvGLX6ju7m71r//6r6qurk499NBDeptKGNNzzz2nvvOd76innnpKAVDPPPNM3uuljGHLli1q+fLlateuXWrfvn3qqquuUn/0R3+kstnsAo/mFNONKRgMqo9//ONq586d6vDhw2rPnj3qkksuURs3bszbR7mNiRCB2l3eULvLe1zUbmo3IYsBtbu8oXaX97io3dRuUj4saSf6hz/8YbVly5a8devXr1d33nnnIp3R2TEyMqIAqN27dyullMrlcqq9vV098MADeptkMqkaGhrUY489tlinWRKRSEStW7dO7dq1S23evFmLeaWO6Y477lCXX375lK9X4rg+9alPqf/8n/9z3ro///M/V1/5yleUUpU5pkLhK2UMwWBQ1dTUqB07duht+vv7ldVqVb/85S8X7NynotgDSiGvv/66AqANmXIfE1naULvLF2p3+Y+L2k3tJmQxoHaXL9Tu8h8XtZvaTcqHJVvOJZ1OY+/evbj66qvz1l999dV49dVXF+mszo5QKAQAaGpqAgB0d3djaGgob4xOpxObN28u+zH+1V/9FT71qU/h4x//eN76Sh3Ts88+i02bNuHzn/882tracPHFF+Pxxx/Xr1fiuC6//HL8+te/xtGjRwEAf/jDH/DKK6/gmmuuAVCZYyqklDHs3bsXmUwmb5vOzk5s2LChYsYZCoVgsVjg8/kAVMeYSHVC7S7vMVK7y39c1G5qNyELDbW7vMdI7S7/cVG7qd2kfLAv9gksFmNjY5iYmMCyZcvy1i9btgxDQ0OLdFazRymFbdu24fLLL8eGDRsAQI+j2Bh7enoW/BxLZceOHdi3bx/eeOONSa9V6piOHz+ORx99FNu2bcO3v/1tvP766/iv//W/wul04mtf+1pFjuuOO+5AKBTC+vXrYbPZMDExge9973v44he/CKByPyuTUsYwNDQEh8OBxsbGSdtUwr0kmUzizjvvxJe+9CXU19cDqPwxkeqF2l2+905q92nKeVzUbmo3IQsNtbt8753U7tOU87io3dRuUj4sWSe6YLFY8v5WSk1aVwnccsstePvtt/HKK69Meq2SxtjX14etW7fi+eefh8vlmnK7ShoTAORyOWzatAn33XcfAODiiy/GgQMH8Oijj+JrX/ua3q6SxrVz50785Cc/wb/8y7/g/e9/P9566y3ceuut6OzsxNe//nW9XSWNaSpmM4ZKGGcmk8EXvvAF5HI5PPLII2fcvhLGRJYG1XBfAajd5TwmgNptUs5jmgpq9ykqYUxkaVAN9xWA2l3OYwKo3SblPKapoHafohLGtFRZsuVcWlpaYLPZJs3ujIyMTJr9Kne+9a1v4dlnn8WLL76IFStW6PXt7e0AUFFj3Lt3L0ZGRrBx40bY7XbY7Xbs3r0bP/jBD2C32/V5V9KYAKCjowMXXHBB3rrzzz8fvb29ACrzs/rrv/5r3HnnnfjCF76ACy+8EF/96ldx22234f777wdQmWMqpJQxtLe3I51OIxAITLlNOZLJZHDDDTegu7sbu3bt0rPhQOWOiVQ/1O7yHCO1u3LGRe2mdhOy0FC7y3OM1O7KGRe1m9pNyocl60R3OBzYuHEjdu3albd+165duOyyyxbprGaGUgq33HILnn76afzHf/wHVq9enff66tWr0d7enjfGdDqN3bt3l+0YP/axj2H//v1466239LJp0yZ8+ctfxltvvYU1a9ZU3JgA4KMf/SiOHDmSt+7o0aPo6uoCUJmfVTweh9Wafwux2WzI5XIAKnNMhZQyho0bN6KmpiZvm8HBQbzzzjtlO04R8nfffRcvvPACmpub816vxDGRpQG1uzzHSO2unHFRu6ndhCw01O7yHCO1u3LGRe2mdpMyYiG6l5YrO3bsUDU1NeqJJ55QBw8eVLfeequqra1VJ06cWOxTK4lvfvObqqGhQb300ktqcHBQL/F4XG/zwAMPqIaGBvX000+r/fv3qy9+8Yuqo6NDhcPhRTzzmWF2CVeqMsf0+uuvK7vdrr73ve+pd999V/3v//2/lcfjUT/5yU/0NpU2rq9//etq+fLl6he/+IXq7u5WTz/9tGppaVG333673qYSxhSJRNSbb76p3nzzTQVAff/731dvvvmm7phdyhi2bNmiVqxYoV544QW1b98+9cd//Mfqj/7oj1Q2my27MWUyGfWZz3xGrVixQr311lt5945UKlW2YyJEoHZXBtTu8hwXtZvaTchiQO2uDKjd5Tkuaje1m5QPS9qJrpRSDz/8sOrq6lIOh0N98IMfVLt3717sUyoZAEWXJ598Um+Ty+XUPffco9rb25XT6VRXXHGF2r9//+Kd9CwoFPNKHdPPf/5ztWHDBuV0OtX69evVD3/4w7zXK21c4XBYbd26Va1atUq5XC61Zs0a9Z3vfCdPECphTC+++GLR/6Ovf/3rSqnSxpBIJNQtt9yimpqalNvtVn/2Z3+ment7F2E0p5huTN3d3VPeO1588cWyHRMhJtTu8ofaXZ5Qu09TbjpH7SbVDrW7/KF2lyfU7tOUm85Ru5ceFqWUmn0cOyGEEEIIIYQQQgghhBBSvSzZmuiEEEIIIYQQQgghhBBCyJmgE50QQgghhBBCCCGEEEIImQI60QkhhBBCCCGEEEIIIYSQKaATnRBCCCGEEEIIIYQQQgiZAjrRCSGEEEIIIYQQQgghhJApoBOdEEIIIYQQQgghhBBCCJkCOtEJIYQQQgghhBBCCCGEkCmgE50QQgghhBBCCCGEEEIImQI60Qn5/3PllVfi1ltvrZj9zjUnTpyAxWLBW2+9tdinQgghhJQEtZvaTQghpLKgdlO7CalU7It9AoRUO08//TRqamoW7HgvvfQSrrrqKgQCAfh8vgU7LiGEEFItULsJIYSQyoLaTQiZb+hEJ2SeyGQyqKmpQVNT02KfCiGEEEJKgNpNCCGEVBbUbkLIQsFyLoQY5HI53H777WhqakJ7ezu2b9+uX+vt7cW1116Luro61NfX44YbbsDw8LB+ffv27fjABz6A//k//yfWrFkDp9MJpVReWtlLL70Ei8Uyabnpppv0fh599FGsXbsWDocD5513Hv75n/857xwtFgv+x//4H7juuuvg8Xiwbt06PPvsswBOpYZdddVVAIDGxsa8ff/yl7/E5ZdfDp/Ph+bmZvzZn/0Zjh07NqvrdO+996KzsxN+v1+v+8xnPoMrrrgCuVxuVvskhBBCZgO1uzSo3YQQQsoFandpULsJKS/oRCfE4H/9r/+F2tpavPbaa/jv//2/495778WuXbuglMJnP/tZjI+PY/fu3di1axeOHTuGG2+8Me/97733Hv7P//k/eOqpp4rWOLvsssswODiol//4j/+Ay+XCFVdcAQB45plnsHXrVvx//9//h3feeQd/+Zd/if/0n/4TXnzxxbz9/M3f/A1uuOEGvP3227jmmmvw5S9/GePj41i5ciWeeuopAMCRI0cwODiIf/iHfwAAxGIxbNu2DW+88QZ+/etfw2q14rrrrpuV+H7nO9/BOeecg7/4i78AADz22GP4zW9+g3/+53+G1crbCiGEkIWD2l0a1G5CCCHlArW7NKjdhJQZihCilFJq8+bN6vLLL89b96EPfUjdcccd6vnnn1c2m0319vbq1w4cOKAAqNdff10ppdQ999yjampq1MjIyKT9bt26ddLxxsbG1Nq1a9XNN9+s11122WXqG9/4Rt52n//859U111yj/wag7r77bv13NBpVFotF/b//9/+UUkq9+OKLCoAKBALTjndkZEQBUPv371dKKdXd3a0AqDfffHPa9wnHjh1TXq9X3XHHHcrj8aif/OQnJb2PEEIImSuo3dRuQgghlQW1m9pNSKXCqStCDC666KK8vzs6OjAyMoJDhw5h5cqVWLlypX7tggsugM/nw6FDh/S6rq4utLa2nvE4mUwG119/PVatWqVnrAHg0KFD+OhHP5q37Uc/+tG8YxSeZ21tLbxeL0ZGRqY95rFjx/ClL30Ja9asQX19PVavXg3gVLrcbFizZg3+/u//Hg8++CA+/elP48tf/vKs9kMIIYScDdTu0qF2E0IIKQeo3aVD7SakfGBjUUIMCrt5WywW5HI5KKVgsVgmbV+4vra2tqTjfPOb30Rvby/eeOMN2O35/4aFxyl27KnOczo+/elPY+XKlXj88cfR2dmJXC6HDRs2IJ1Ol3TOxfjNb34Dm82GEydOIJvNThoLIYQQMt9Qu2cGtZsQQshiQ+2eGdRuQsoDRqITUgIXXHABent70dfXp9cdPHgQoVAI559//oz29f3vfx87d+7Es88+i+bm5rzXzj//fLzyyit561599dUZHcPhcAAAJiYm9Dq/349Dhw7h7rvvxsc+9jGcf/75CAQCMzrvQnbu3Imnn34aL730Evr6+vC3f/u3Z7U/QgghZC6hdk+G2k0IIaScoXZPhtpNSPnA6StCSuDjH/84LrroInz5y1/GQw89hGw2i5tvvhmbN2/Gpk2bSt7PCy+8gNtvvx0PP/wwWlpaMDQ0BABwu91oaGjAX//1X+OGG27ABz/4QXzsYx/Dz3/+czz99NN44YUXSj5GV1cXLBYLfvGLX+Caa66B2+1GY2Mjmpub8cMf/hAdHR3o7e3FnXfeOePrIJw8eRLf/OY38eCDD+Lyyy/Hj370I3zqU5/CJz/5SXzkIx+Z9X4JIYSQuYLanQ+1mxBCSLlD7c6H2k1IecFIdEJKwGKx4N///d/R2NiIK664Ah//+MexZs0a7Ny5c0b7eeWVVzAxMYEtW7ago6NDL1u3bgUAfPazn8U//MM/4O/+7u/w/ve/H//0T/+EJ598EldeeWXJx1i+fDn+5m/+BnfeeSeWLVuGW265BVarFTt27MDevXuxYcMG3Hbbbfi7v/u7GZ27oJTCTTfdhA9/+MO45ZZbAAB/8id/gltuuQVf+cpXEI1GZ7VfQgghZC6hdp+G2k0IIaQSoHafhtpNSPlhUUqpxT4JQgghhBBCCCGEEEIIIaQcYSQ6IYQQQgghhBBCCCGEEDIFdKITQiaxZcsW1NXVFV22bNmy2KdHCCGEkAKo3YQQQkhlQe0mpLJgORdCyCRGRkYQDoeLvlZfX4+2trYFPiNCCCGETAe1mxBCCKksqN2EVBZ0ohNCCCGEEEIIIYQQQgghU8ByLoQQQgghhBBCCCGEEELIFNCJTgghhBBCCCGEEEIIIYRMAZ3ohBBCCCGEEEIIIYQQQsgU0IlOCCGEEEIIIYQQQgghhEwBneiEEEIIIYQQQgghhBBCyBTQiU4IIYQQQgghhBBCCCGETAGd6IQQQgghhBBCCCGEEELIFPz/AJ4qsPHS3MbRAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08dfcb7670>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import skimage\n",
+    "\n",
+    "class SigmaEstimateCallback(callbacks.Callback):\n",
+    "    def __init__(self):\n",
+    "\n",
+    "        self.save_values=[]\n",
+    "\n",
+    "    def __call__(self, algorithm):\n",
+    "        self.save_values.append(skimage.restoration.estimate_sigma(algorithm.get_output().as_array()))\n",
+    "\n",
+    "mycallback_FISTA_TV_alpha_01= SigmaEstimateCallback()\n",
+    "algo1=FISTA(initial=ig.allocate(0), f=F, g=0.1*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo1.run(500, callbacks=[mycallback_FISTA_TV_alpha_01])\n",
+    "\n",
+    "        \n",
+    "mycallback_FISTA_TV_alpha_1= SigmaEstimateCallback()\n",
+    "algo2=FISTA(initial=ig.allocate(0), f=F, g=1*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo2.run(500, callbacks=[mycallback_FISTA_TV_alpha_1])\n",
+    "\n",
+    "\n",
+    "show2D([ground_truth, algo1.get_output(), algo2.get_output()], title=['ground_truth', 'FISTA_TV_alpha_01', 'FISTA_TV_alpha_1'], num_cols=3)\n",
+    "show2D([absorption, A.direct(algo1.get_output())-absorption, A.direct(algo2.get_output())-absorption], title=['ground_truth', 'Data error FISTA_TV_alpha_01', 'Data error FISTA_TV_alpha_1'], fix_range=[[0,3], [-0.02, 0.02], [-0.02, 0.02]], cmap=['gray', 'seismic', 'seismic'], num_cols=3)\n",
+    "plt.plot(range(10,501), mycallback_FISTA_TV_alpha_01.save_values[10:], label='FISTA TV alpha=0.1 ')\n",
+    "plt.plot(range(10, 501), mycallback_FISTA_TV_alpha_1.save_values[10:], label='FISTA TV alpha=1.0 ')\n",
+    "plt.ylabel('Noise Estimate')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()\n",
+    "                                   \n",
+    "                            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see with a larger regularisation parameter, the resulting image is less noisy. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image metric callbacks (custom callback example) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 MSE                  MAE                 PSNR\n",
+      "         1.07888e-06          5.48145e-04          9.48530e+00\n",
+      "         5.85316e-07          6.22034e-04          1.21411e+01\n",
+      "         5.05844e-07          5.72563e-04          1.27749e+01\n",
+      "         4.31374e-07          5.19819e-04          1.34665e+01\n",
+      "         3.64704e-07          4.67054e-04          1.41956e+01\n",
+      "         3.06416e-07          4.16492e-04          1.49519e+01\n",
+      "         2.56388e-07          3.70092e-04          1.57261e+01\n",
+      "         2.14156e-07          3.28810e-04          1.65077e+01\n",
+      "         1.78987e-07          2.92725e-04          1.72868e+01\n",
+      "         1.50022e-07          2.60981e-04          1.80535e+01\n",
+      "         1.26383e-07          2.33361e-04          1.87981e+01\n",
+      "         1.07187e-07          2.09652e-04          1.95136e+01\n",
+      "         9.16141e-08          1.89309e-04          2.01954e+01\n",
+      "         7.89449e-08          1.72049e-04          2.08418e+01\n",
+      "         6.85910e-08          1.57283e-04          2.14524e+01\n",
+      "         6.00884e-08          1.44610e-04          2.20271e+01\n",
+      "         5.30737e-08          1.33746e-04          2.25662e+01\n",
+      "         4.72670e-08          1.24414e-04          2.30695e+01\n",
+      "         4.24393e-08          1.16364e-04          2.35374e+01\n",
+      "         3.84176e-08          1.09416e-04          2.39697e+01\n",
+      "         3.50543e-08          1.03451e-04          2.43676e+01\n",
+      "         3.22300e-08          9.82989e-05          2.47324e+01\n",
+      "         2.98493e-08          9.39156e-05          2.50657e+01\n",
+      "         2.78304e-08          9.01341e-05          2.53698e+01\n",
+      "         2.61075e-08          8.68679e-05          2.56474e+01\n",
+      "         2.46249e-08          8.40164e-05          2.59013e+01\n",
+      "         2.33423e-08          8.14809e-05          2.61336e+01\n",
+      "         2.22266e-08          7.92211e-05          2.63463e+01\n",
+      "         2.12462e-08          7.72332e-05          2.65422e+01\n",
+      "         2.03792e-08          7.54337e-05          2.67232e+01\n",
+      "         1.96080e-08          7.38151e-05          2.68907e+01\n",
+      "         1.89173e-08          7.23520e-05          2.70464e+01\n",
+      "         1.82934e-08          7.10307e-05          2.71921e+01\n",
+      "         1.77264e-08          6.98001e-05          2.73288e+01\n",
+      "         1.72101e-08          6.86725e-05          2.74572e+01\n",
+      "         1.67384e-08          6.76756e-05          2.75779e+01\n",
+      "         1.63068e-08          6.67997e-05          2.76913e+01\n",
+      "         1.59109e-08          6.59966e-05          2.77981e+01\n",
+      "         1.55498e-08          6.52429e-05          2.78978e+01\n",
+      "         1.52207e-08          6.45565e-05          2.79907e+01\n",
+      "         1.49199e-08          6.39012e-05          2.80774e+01\n",
+      "         1.46448e-08          6.32729e-05          2.81582e+01\n",
+      "         1.43935e-08          6.26837e-05          2.82334e+01\n",
+      "         1.41640e-08          6.21308e-05          2.83032e+01\n",
+      "         1.39533e-08          6.16084e-05          2.83683e+01\n",
+      "         1.37602e-08          6.11234e-05          2.84288e+01\n",
+      "         1.35827e-08          6.06739e-05          2.84852e+01\n",
+      "         1.34200e-08          6.02613e-05          2.85375e+01\n",
+      "         1.32710e-08          5.98831e-05          2.85860e+01\n",
+      "         1.31342e-08          5.95365e-05          2.86310e+01\n",
+      "         1.30086e-08          5.92132e-05          2.86727e+01\n",
+      "         1.28935e-08          5.89066e-05          2.87113e+01\n",
+      "         1.27882e-08          5.86154e-05          2.87469e+01\n",
+      "         1.26929e-08          5.83402e-05          2.87794e+01\n",
+      "         1.26069e-08          5.81077e-05          2.88090e+01\n",
+      "         1.25294e-08          5.79025e-05          2.88357e+01\n",
+      "         1.24593e-08          5.77139e-05          2.88601e+01\n",
+      "         1.23964e-08          5.75408e-05          2.88821e+01\n",
+      "         1.23400e-08          5.73717e-05          2.89019e+01\n",
+      "         1.22899e-08          5.72179e-05          2.89196e+01\n",
+      "         1.22457e-08          5.70800e-05          2.89352e+01\n",
+      "         1.22065e-08          5.69476e-05          2.89491e+01\n",
+      "         1.21716e-08          5.68219e-05          2.89616e+01\n",
+      "         1.21399e-08          5.67079e-05          2.89729e+01\n",
+      "         1.21121e-08          5.66082e-05          2.89828e+01\n",
+      "         1.20881e-08          5.65168e-05          2.89914e+01\n",
+      "         1.20672e-08          5.64386e-05          2.89990e+01\n",
+      "         1.20490e-08          5.63735e-05          2.90055e+01\n",
+      "         1.20338e-08          5.63197e-05          2.90110e+01\n",
+      "         1.20213e-08          5.62742e-05          2.90155e+01\n",
+      "         1.20117e-08          5.62335e-05          2.90190e+01\n",
+      "         1.20049e-08          5.61994e-05          2.90215e+01\n",
+      "         1.20006e-08          5.61720e-05          2.90230e+01\n",
+      "         1.19991e-08          5.61517e-05          2.90236e+01\n",
+      "         1.19998e-08          5.61385e-05          2.90233e+01\n",
+      "         1.20029e-08          5.61309e-05          2.90222e+01\n",
+      "         1.20088e-08          5.61240e-05          2.90201e+01\n",
+      "         1.20170e-08          5.61242e-05          2.90171e+01\n",
+      "         1.20275e-08          5.61325e-05          2.90133e+01\n",
+      "         1.20408e-08          5.61499e-05          2.90085e+01\n",
+      "         1.20565e-08          5.61750e-05          2.90028e+01\n",
+      "         1.20747e-08          5.62071e-05          2.89963e+01\n",
+      "         1.20954e-08          5.62405e-05          2.89888e+01\n",
+      "         1.21182e-08          5.62744e-05          2.89806e+01\n",
+      "         1.21432e-08          5.63137e-05          2.89717e+01\n",
+      "         1.21702e-08          5.63569e-05          2.89620e+01\n",
+      "         1.21990e-08          5.64026e-05          2.89518e+01\n",
+      "         1.22295e-08          5.64532e-05          2.89410e+01\n",
+      "         1.22611e-08          5.65052e-05          2.89297e+01\n",
+      "         1.22934e-08          5.65577e-05          2.89183e+01\n",
+      "         1.23272e-08          5.66137e-05          2.89064e+01\n",
+      "         1.23621e-08          5.66716e-05          2.88941e+01\n",
+      "         1.23983e-08          5.67352e-05          2.88814e+01\n",
+      "         1.24357e-08          5.68040e-05          2.88683e+01\n",
+      "         1.24743e-08          5.68758e-05          2.88549e+01\n",
+      "         1.25140e-08          5.69482e-05          2.88411e+01\n",
+      "         1.25548e-08          5.70229e-05          2.88269e+01\n",
+      "         1.25965e-08          5.71005e-05          2.88125e+01\n",
+      "         1.26388e-08          5.71802e-05          2.87980e+01\n",
+      "         1.26821e-08          5.72615e-05          2.87831e+01\n",
+      "         1.27264e-08          5.73452e-05          2.87680e+01\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "class MetricsDiagnostics(callbacks.Callback):\n",
+    "    \n",
+    "    def __init__(self, reference_image, metrics_dict, print_interval=1):\n",
+    "\n",
+    "        # reference image as numpy (level) array\n",
+    "        self.reference_image = reference_image      \n",
+    "        self.metrics_dict = metrics_dict\n",
+    "        # if data_range is None:\n",
+    "            # self.data_range = np.abs(self.reference_image.max() - self.reference_image.min())\n",
+    "        self.computed_metrics = []    \n",
+    "        self.print_interval=print_interval\n",
+    "\n",
+    "        super(MetricsDiagnostics, self).__init__()  \n",
+    "\n",
+    "    def __call__(self, algo):\n",
+    "\n",
+    "            \n",
+    "        for metric_name, metric_func in self.metrics_dict.items():\n",
+    "\n",
+    "            if not hasattr(algo, metric_name):\n",
+    "                setattr(algo, metric_name, [])   \n",
+    "                \n",
+    "            metric_list = getattr(algo, metric_name)\n",
+    "            metric_value = metric_func(self.reference_image, algo.get_output())\n",
+    "            metric_list.append(metric_value)\n",
+    "            \n",
+    "            self.computed_metrics.append(metric_value)\n",
+    "            \n",
+    "        if algo.iteration == 0:\n",
+    "                    \n",
+    "            print (self.callback_header())\n",
+    "        \n",
+    "        print(self.callback_iteration())  \n",
+    "                        \n",
+    "    \n",
+    "            \n",
+    "               \n",
+    "    def callback_header(self):\n",
+    "        return \" \".join(\"{:>20}\".format(metric_name) for metric_name in self.metrics_dict.keys())\n",
+    "\n",
+    "    def callback_iteration(self):\n",
+    "        if isinstance(self.computed_metrics, list):\n",
+    "            # Handle list of metrics\n",
+    "            return \" \".join(\"{:>20.5e}\".format(metric) for metric in self.computed_metrics[-len(self.metrics_dict):])\n",
+    "        else:\n",
+    "            # Handle single metric\n",
+    "            return \"{:>20.5e}\".format(self.computed_metrics)     \n",
+    "        \n",
+    "\n",
+    "from cil.utilities.quality_measures import mae, psnr, mse \n",
+    "metric_callback= MetricsDiagnostics(ground_truth, {'MSE':mse, 'MAE':mae, 'PSNR':psnr})\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(100, callbacks=[metric_callback])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More complex example, image metric callbacks with region of interests \n",
+    "\n",
+    "Warning - this is a complex example! But the code may be useful to adapt and reuse "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ImageQualityCallback(callbacks.Callback):\n",
+    "    \"\"\"\n",
+    "      Parameters\n",
+    "    ----------\n",
+    "\n",
+    "    reference_image: CIL or STIR ImageData\n",
+    "      containing the reference image used to calculate the metrics\n",
+    "\n",
+    "    roi_mask_dict : dictionary of ImageData objects\n",
+    "      list containing one binary ImageData object for every ROI to be\n",
+    "      evaluated. Voxels with values 1 are considered part of the ROI\n",
+    "      and voxels with value 0 are not.\n",
+    "      Dimension of the ROI mask images must be the same as the dimension of\n",
+    "      the reference image.\n",
+    "      \n",
+    "    metrics_dict : dictionary of lambda functions f(x,y) mapping\n",
+    "      two 1-dimensional numpy arrays x and y to a scalar value or a\n",
+    "      numpy.ndarray.\n",
+    "      x and y can be the voxel values of the whole images or the values of\n",
+    "      voxels in a ROI such that the metric can be computed on the whole\n",
+    "      images and optionally in the ROIs separately.\n",
+    "\n",
+    "      E.g. f(x,y) could be MSE(x,y), PSNR(x,y), MAE(x,y)\n",
+    "\n",
+    "    statistics_dict : dictionary of lambda functions f(x) mapping a \n",
+    "      1-dimensional numpy array x to a scalar value or a numpy.ndarray.\n",
+    "      E.g. mean(x), std_deviation(x) that calculate global and / or\n",
+    "      ROI mean and standard deviations.\n",
+    "\n",
+    "      E.g. f(x) could be x.mean()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "      \"\"\"\n",
+    "      \n",
+    "    def __init__(self, reference_image, \n",
+    "                       roi_mask_dict   = None,\n",
+    "                       metrics_dict    = None,\n",
+    "                       statistics_dict = None,\n",
+    "                       ):\n",
+    "\n",
+    "        # the reference image\n",
+    "        self.reference_image = reference_image\n",
+    "\n",
+    "\n",
+    "        self.roi_indices_dict = {}\n",
+    "        self.roi_store=[]\n",
+    "\n",
+    "\n",
+    "\n",
+    "        self.roi_mask_dict=roi_mask_dict\n",
+    "        \n",
+    "        \n",
+    "        self.metrics_dict = metrics_dict\n",
+    "        self.metrics_store={}\n",
+    "        for key, value in self.metrics_dict.items():\n",
+    "                self.metrics_store['global_'+key] = []\n",
+    "                if roi_mask_dict is not None:\n",
+    "                  for roi_name, value in roi_mask_dict.items():\n",
+    "                    self.metrics_store[roi_name+'_'+key] = []\n",
+    "\n",
+    "        self.statistics_dict = statistics_dict\n",
+    "        self.stat_store={}\n",
+    "        for key, value in self.statistics_dict.items():\n",
+    "                self.stat_store['global_'+key] = []\n",
+    "                if roi_mask_dict is not None:\n",
+    "                  for roi_name, value in roi_mask_dict.items():\n",
+    "                    self.stat_store[roi_name+'_'+key] = []\n",
+    "                \n",
+    "    def __call__(self, algorithm):\n",
+    "            if self.metrics_dict is not None:\n",
+    "                for metric_name, metric in self.metrics_dict.items():\n",
+    "                    ans = metric(self.reference_image, algorithm.x)\n",
+    "                    self.metrics_store['global_'+metric_name].append(ans)\n",
+    "                    \n",
+    "                    \n",
+    "                    for roi_name, roi in self.roi_mask_dict.items():\n",
+    "                      ans = metric(self.reference_image, algorithm.x, mask=roi)\n",
+    "                      self.metrics_store[roi_name+'_'+metric_name].append(ans)\n",
+    "                       \n",
+    "                    \n",
+    "                    \n",
+    "            if self.statistics_dict is not None:\n",
+    "                for statistic_name, stat in self.statistics_dict.items():\n",
+    "                    ans = stat( algorithm.x.array, np._NoValue)\n",
+    "                    self.stat_store['global_'+statistic_name].append(ans)\n",
+    "                    \n",
+    "                    \n",
+    "                    for roi_name, roi in self.roi_mask_dict.items():\n",
+    "                        ans = stat( algorithm.x.array, roi.array.astype('bool'))\n",
+    "                        self.stat_store[roi_name+'_'+statistic_name].append(ans)\n",
+    "                        \n",
+    "        \n",
+    "                    \n",
+    "                    \n",
+    "       \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mse(dc1, dc2, mask=None):\n",
+    "    ''' Calculates the mean squared error of two images\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dc1: `DataContainer`\n",
+    "        One image to be compared\n",
+    "    dc2: `DataContainer`\n",
+    "        Second image to be compared\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number, the mean squared error of the two images\n",
+    "    '''\n",
+    "    dc1 = dc1.as_array()\n",
+    "    dc2 = dc2.as_array()\n",
+    "\n",
+    "    if mask is not None:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "\n",
+    "        mask = mask.astype('bool')\n",
+    "        dc1 = np.extract(mask, dc1)\n",
+    "        dc2 = np.extract(mask, dc2)\n",
+    "    return np.mean(((dc1 - dc2)**2))\n",
+    "\n",
+    "\n",
+    "def mae(dc1, dc2, mask=None):\n",
+    "    ''' Calculates the Mean Absolute error of two images.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dc1: `DataContainer`\n",
+    "        One image to be compared\n",
+    "    dc2: `DataContainer`\n",
+    "        Second image to be compared\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero.\n",
+    "\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number with the mean absolute error between the two images.\n",
+    "    '''\n",
+    "    dc1 = dc1.as_array()\n",
+    "    dc2 = dc2.as_array()\n",
+    "\n",
+    "    if mask is not None:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "\n",
+    "        mask = mask.astype('bool')\n",
+    "        dc1 = np.extract(mask, dc1)\n",
+    "        dc2 = np.extract(mask, dc2)\n",
+    "\n",
+    "    return np.mean(np.abs((dc1-dc2)))\n",
+    "\n",
+    "\n",
+    "def psnr(ground_truth, corrupted, mask=None):\n",
+    "    ''' Calculates the Peak signal to noise ratio (PSNR) between the two images.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ground_truth: `DataContainer`\n",
+    "        The reference image\n",
+    "    corrupted: `DataContainer`\n",
+    "        The image to be evaluated\n",
+    "    data_range: scalar value, default=None\n",
+    "        PSNR scaling factor, the dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values). We take the maximum value in the ground truth array.\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero..\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number, the peak signal to noise ration between the two images.\n",
+    "    '''\n",
+    "    \n",
+    "\n",
+    "    if mask is None:\n",
+    "        data_range = ground_truth.as_array().max()\n",
+    "\n",
+    "\n",
+    "    else:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "        data_range = np.max(ground_truth.as_array(),\n",
+    "                                where=mask.astype('bool'), initial=-1e-8)\n",
+    "\n",
+    "    \n",
+    "    tmp_mse = mse(ground_truth, corrupted, mask=mask)\n",
+    "\n",
+    "    return 10 * np.log10((data_range ** 2) / tmp_mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAAAAAAAABMBGOgAAAAAAAAAAAbCRDgAAAAAAAABAAGykAwAAAAAAAAAQwP8BErYreQJZftMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08dcf79120>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#%% create masks\n",
+    "top = ig.allocate(0)\n",
+    "bottom = ig.allocate(0)\n",
+    "\n",
+    "top.fill(\n",
+    "    np.asarray(ground_truth.array > 0.8 * ground_truth.max(), \n",
+    "               dtype=np.float32)\n",
+    "    )\n",
+    "bottom.fill(\n",
+    "    np.asarray(np.invert(ground_truth.array < 0.4 * ground_truth.max()), \n",
+    "               dtype=np.float32)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "roi_image_dict = {\n",
+    "    'top' : top,\n",
+    "    'bottom' : bottom\n",
+    "}\n",
+    "\n",
+    "show2D([ground_truth, top, bottom], num_cols=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_qual_callback = ImageQualityCallback(ground_truth,\n",
+    "                                              roi_mask_dict = roi_image_dict,\n",
+    "                                              metrics_dict = {'MSE':mse, \n",
+    "                                                              'MAE':mae, \n",
+    "                                                              'PSNR':psnr},\n",
+    "                                              statistics_dict = {'MEAN': (lambda x, y: np.mean(x, where=y)),\n",
+    "                                                                 'STDDEV': (lambda x, y: np.std(x, where=y)),\n",
+    "                                                                 'MAX': (lambda x, y: np.max(x, where=y, initial=0))},\n",
+    "                                              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08dcda0af0>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[img_qual_callback])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f08dec65b10>]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(501), img_qual_callback.metrics_store['global_MSE'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08dec67bb0>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(501), img_qual_callback.metrics_store['top_PSNR'], label='Top')\n",
+    "plt.plot(range(501), img_qual_callback.metrics_store['global_PSNR'], label='Global')\n",
+    "plt.plot(range(501), img_qual_callback.metrics_store['bottom_PSNR'], label='Bottom')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cil_testing2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/v24.2.0/.doctrees/nbsphinx/demos/deriv2_cgls.ipynb b/v24.2.0/.doctrees/nbsphinx/demos/deriv2_cgls.ipynb
new file mode 100644
index 0000000000..3cce6a2e50
--- /dev/null
+++ b/v24.2.0/.doctrees/nbsphinx/demos/deriv2_cgls.ipynb
@@ -0,0 +1,645 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "602dbdfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2023 United Kingdom Research and Innovation\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:  Bill Lionheart (University of Manchester),\n",
+    "#   Edited by:  Margaret Duff (STFC - UKRI)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8acbea",
+   "metadata": {},
+   "source": [
+    "# 1D inverse problem demo using deriv2 from regtools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc422b7",
+   "metadata": {},
+   "source": [
+    "We roughly translated deriv2 (P. C. Hansen, Regularization Tools Version 4.0 for Matlab 7.3, Numerical Algorithms, 46 (2007), pp. 189-194.) to Python. The righthand side vector b is made as Ax so is \"exact\" as a vector. We will look at the singular valued decomposition (SVD) and regularized solution as an example of a mildly ill posed problem and show how to recostruct using the Core Imaging Library (CIL. See Jørgensen, Jakob S., et al. \"Core Imaging Library-Part I: a versatile Python framework for tomographic imaging.\" Philosophical Transactions of the Royal Society A 379.2204 (2021): 20200192. and https://tomographicimaging.github.io/CIL/nightly/index.html). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f0222cb",
+   "metadata": {},
+   "source": [
+    "This notebook was developed as part of the CCPi CIL Hackathon https://ccpi.ac.uk/events/byod-cil-hackathon/ in March 2023 in Cambridge as part of the Rich Nonlinear Tomography programme at the Isaac Newton Institute for Mathematical Sciences https://www.newton.ac.uk/event/rnt/.  The CIL is supported by the CCPi EPSRC grant EP/T026677/1 and the Isaac NewtonInstitute by EP/R014604/1 The author would like to thank the Isaac Newton Institute for support  and hospitality.(c) W.R.B. Lionheart 2023. Apache License"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b2a54694",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from cil.optimisation.algorithms import CGLS\n",
+    "from cil.optimisation.operators import MatrixOperator\n",
+    "from cil.framework import VectorData, BlockDataContainer\n",
+    "from deriv2 import deriv2\n",
+    "from cil.optimisation.operators import BlockOperator,IdentityOperator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d33631d9",
+   "metadata": {},
+   "source": [
+    "### CIL version 23.0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "751f22b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cil\n",
+    "print(cil.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "250103e8",
+   "metadata": {},
+   "source": [
+    "We set up a 1D inverse problem, in which the forward model integrates twice.  The notebook will first set up and solve a basic formulation of the problem using just python/numpy, and after that using CIL.\n",
+    "\n",
+    "Consider a discretization of a first kind Fredholm integral equation whose kernel K is the  Green's function for the second derivative:\n",
+    " $$\n",
+    "    K(s,t) = \\begin{cases} s(t-1)  ,  s <  t \\\\\n",
+    "              t(s-1)  ,  s \\geq t \\end{cases} $$\n",
+    "\n",
+    "and $$ \\int_0^1K(s,t)x(t)dt=b(s). $$\n",
+    "\n",
+    " For this notebook, consider the case\n",
+    "$$ b(s) = (s^3 - s)/6          , \\ \\  x(t) = t$$\n",
+    "\n",
+    " where the integral is disretised using the Galerkin method with orthonormal box functions, with interval size $1/n$.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "27770924",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Test of one dimensional inverse problems using deriv2 from reg tools\n",
+    "n = 100\n",
+    "A,b,x = deriv2(n,1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "113e7dc1",
+   "metadata": {},
+   "source": [
+    "The functions $b$ and $x$ can be plotted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "336dc870",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),x, label='x')\n",
+    "plt.plot(np.linspace(0,1,n),b, label='b')\n",
+    "plt.plot()\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84dfaf27",
+   "metadata": {},
+   "source": [
+    "This has made a A, x, and b where Ax=b. We can check check this is the case up to floating point error: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6d854bd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(A@x-b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554b487c",
+   "metadata": {},
+   "source": [
+    "Now make a version of b with noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "35d30bb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HYQ81lIvg7gNBPfWOVKoyFzje3t54e3tfs19oaCjp6els27aNzp07A7B161bS09Pp2rVrqdsEBQXh5+fHihUrCA4OBiA/P59169Yxc+bMskatMpRazQBobDjNh7tPMaZHEM38Ks5skkIIIRzH6oMpDDNeGhfb6l4wGPUNdAV2G4PTvHlz+vfvz5gxY4iKiiIqKooxY8YwaNCgYndQNWvWjIiICEC7NDVx4kSmT59OREQE+/fvZ/To0bi5uTFy5MiibZKSkoiOjubw4cMA7Nu3j+joaC5cuGCv3anYLhU4HVyTUVWY+YdM+ieEEOLWu5hXyP6EU/Q17NQaKujlKbDzPDiLFi2idevWhIWFERYWRps2bVi4cGGxPnFxcaSnpxd9P2nSJCZOnMi4cePo2LEjp0+fZvny5Xh4eBT1+eSTTwgODmbMmDEA9OjRg+DgYH755Rd77k7FdanA8beewtmgsibuLFuOnNc5lBBCCEez8dBZble346rko3o1BP/2eke6IkVVVVXvEOUtIyMDi8VCeno6np4OcCnHZoXpdaAwhznNF/Pebhtt61pY+mS3mxqcLYQQQvzdcz/sYfDe8fQ07oVek6HXi+X688vy+S1rUTkCgxG8tTupHm2Sh5uzkT2n0lm278ZvrRdCCCH+zmpTiY6N5zbDPq2hAk7u93dS4DiKS5epLJmHGdO9AQBv/3mQgtSTsPt/2lkeIYQQ4gbtPpFKp7wtGBUVm397qNlQ70hXJQWOo/DRChzOxjGmRwO8qzlz5nwaWZ8Php+fhOhF+uYTQghRqa2ITaanYS8AhqYDdU5zbVLgOIpalwucg1QzO/FU78aMc/qZ6lkJWntMFR2ALYQQ4pZYc+A03Qz7tW8aVayVw0tjt8U2RTm7XOCciweblRGBF1Gc/lbUJKyD3AxwcYBB1UIIIcpVwrksLOf34GHOweZaE0PtYL0jXZOcwXEUNQLBaIbCXLiQgOm3p3DCynJrB46q/mDNh8Mr9U4phBCiEloVm0xP4x4ADI3uAEPFLx8qfkJxfQxG8G6iPf9jEpzegersweJaT/GntYPWfvB3/fIJIYSotFbEJNPToBU4NOytb5jrJAWOI6l1aYboI6sAUPpOZeyg21hu7QiANf5PKMzXK50QQohKKDUrn4Tjx2htOKY1NKz4429AChzHcvlOKoCALtDhUUIa1MS7aSgpanWM+ZlwbIN++YQQQlQ6a+NT6Ip29xR+bcDjxhe/Lk9S4DiSWs21r0ZnGPxe0TXSSQNasNKmXaZK2fGTXumEEEJUQitjUorG39CoclyeAilwHEvjMOjwCNzzWbGzOY19PchtOAAAY/wfqDLpnxBCiOuQX2hjfXwy3S/PXtyoj76BykAKHEfi5AyD50LLu0u8dOeQYVxUXalpO8/WTavKP5sQQohKZ2vCeernH8ZbyUB1rgZ1O+sd6bpJgVNF+HpZOOndDYCjG76j0GrTOZEQQoiKbmVMMj0uzV6sBPXU/pCuJKTAqULqdxsGQKfcLXy/45TOaYQQQlRkqqqyMjZFWzkcKtX4G5ACp0pxa9Efq+JEY8Npflqxhqy8Qr0jCSGEqEjys8GmneE/kJhBRtp5Oijx2mtS4IgKy8WinWIEOuRs4fMNCToHEkIIUWEcWQ3Ta8O8DrDpfdZFH6Sr4QBOig1qNtJmzK9EZC2qKsbQ/E44uooBxq2MXH+YB0IC8PFw0TuWEEIIvR1aoX29cBRWvMoYTCQ7VdfaKtHdU5fJGZyqptkgVKMz7QxH6V64lfdWHtI7kRBCiIrg3KXPgxZDyavVGmcKCDCc1doqyfIMfycFTlXj4YvSdQIAr5oWsnT7YQ6nXNQ5lBBCCN2dP6x97TyGr1t9xZC8aax2HwAdRkPD23WNdiOkwKmKuj8LlgDqKucYa1jKzMiDeicSQgihp8J8SDuuPa/ZiD9jUtirNuT0bW9pM+MbTfrmuwFS4FRFzm7QfwYAY42/ER+7h20JF3QOJYQQQjepx0C1gXM1UlQLu06kAtC3hZ++uW6CFDhVVbNB0LA3ZqWQqU5fM/33GFRV1TuVEEIIPVy+PFWzIStjz6Kq0DagOn6WynsTihQ4VZWiwIBZqAYTtxv3UCtxFcv2JemdSgghhB6KCpzGLI/RPgvCWlSOVcOvRAqcqsy7EUrXfwPwmtNC5kbuIb9QlnAQQogq57x2B1WeJYjNh88D0K9l5b08BVLgiB7PYfOsQ4DhLHdlfMP/oo7rnUgIIUR5O38EgAN5PuRbbTSo5U4jn2o6h7o5UuBUdc7uGAbMBLQBx3+sWkl6ToHOoYQQQpSrS5eoVqZ4AJX/7A1IgSMAmg/G1mwwJsXKy9aP+Hh1nN6JhBBClJfcDLiYDMBPx7VBxZV9/A1IgSMuMQx8mwKTB+0MR7FFfcKp1Gy9IwkhhCgPF7TLU3ku3iTlOePraaZt3er6ZroFpMARGs/aOPV7E4CJhu/58te1usYRQghRTi6NvzltrANA3xa+GAyKnoluCSlwRBGl/UNc9OuCm5JHr0PT2XcyTe9IQggh7O3SGlTRWd4ADGhVW880t4wUOOIvBgPV7vuQAsVED+M+1i/5QCb/E0IIR3dpgPHBAh9quJkICfLSOdCtIQWOKM67EdmhzwHwQOonrN97WOdAQggh7OpSgZOg1iashR9ORscoDRxjL8QtZen9LOdcAvFSLnLq97cotMrkf0II4ZBUFfXSGJyjam36t678t4dfJgWOKMlowm3gGwDcnfcrP2/YpXMgIYQQdnExBSU/E6uqkGr2p1tDb70T3TJS4IhSubUeQoqlDW5KHtZ1s8jMlcn/hBDC4Vy6PHVKrUXP5nVxdnKcssBx9kTcWoqC113TAbjbtpLvItfpHEgIIcStpl66g+qoWpsBrRzn8hRIgSOuwqlBd8769cCkWPHd9S5n0nP0jiSEEOIWOnv8AAAnDf70aFJL5zS3lhQ44qq879Im/xts2Mw3P/+ucxohhBC3UvrJWACcfZvgYjLqnObWkgJHXJVSuy2pDYYA0OHw++w/na5zIiGEELeCqqo4pycAENikjc5pbj0pcMQ11Rj0OlaM9DLsISJisUz+J4QQDiD2dCq1bWcAaNOmo85pbj27FjipqamEh4djsViwWCyEh4eTlpZ21W1UVWXq1Kn4+/vj6upKr169OHDgQNHrFy5c4N///jdNmzbFzc2NevXqMWHCBNLT5cyC3Xg1ILt1OAB3p3zE6pgzOgcSQghxs7bs2o2zYiVfMePmXU/vOLecXQuckSNHEh0dTWRkJJGRkURHRxMeHn7VbWbNmsXs2bOZN28e27dvx8/Pj759+5KZmQlAYmIiiYmJvPPOO+zbt4+vvvqKyMhIHnvsMXvuSpXn0f9Vco3VaGU4xp5fPqBAJv8TQohK7UjsHgByPOqDwfEu6Ciqna43xMbG0qJFC6KioggJCQEgKiqK0NBQDh48SNOmTUtso6oq/v7+TJw4kRdeeAGAvLw8fH19mTlzJmPHji31Z/3www88+OCDZGVl4eTkdM1sGRkZWCwW0tPT8fT0vIm9rFpyN36Iy8qXuKBWY3nvPxjRw/Gu2QohRFUQn5zJ4vcn85ppIQVNh2B6YKHeka5LWT6/7VaybdmyBYvFUlTcAHTp0gWLxcLmzZtL3SYhIYGkpCTCwsKK2sxmMz179rziNkDRjl6puMnLyyMjI6PYQ5SdS+i/SKvWEC/lIuqa6WTI5H9CCFEp/b73DEGKNtzA5NNY5zT2YbcCJykpCR8fnxLtPj4+JCUlXXEbAF9f32Ltvr6+V9zm/PnzTJs27YpndwBmzJhRNA7IYrEQEBBwvbsh/s5owmPouwDcb/uT73+P1DmQEEKIslJVld/3/VXgULORvoHspMwFztSpU1EU5aqPHTt2AKAoSontVVUttf3v/vn6lbbJyMjgzjvvpEWLFkyZMuWK7zd58mTS09OLHidPnryeXRWlMDa6nZS6/XBSbLTaM52T57P0jiSEEKIM4pMvcjjlIg0Ml04cOGiBc+0BK/8wfvx4RowYcdU+gYGB7N27l+Tk5BKvnT17tsQZmsv8/LRpopOSkqhdu3ZRe0pKSoltMjMz6d+/P9WqVSMiIgKTyXTFPGazGbPZfNXM4vrVuvdt8t9bQxdDDF8s+YzH/vW03pGEEEJcp9/2JhKgJOOvnNcapMDReHt74+197dVGQ0NDSU9PZ9u2bXTu3BmArVu3kp6eTteuXUvdJigoCD8/P1asWEFwcDAA+fn5rFu3jpkzZxb1y8jIoF+/fpjNZn755RdcXFzKuhviJig16pPW4Ul8ds6h/+kP2H10BMENal97QyGEELpSVZXf957hQeNKraFRH3Dz0jeUndhtDE7z5s3p378/Y8aMISoqiqioKMaMGcOgQYOK3UHVrFkzIiIiAO3S1MSJE5k+fToRERHs37+f0aNH4+bmxsiRIwHtzE1YWBhZWVl88cUXZGRkkJSURFJSElar1V67I/7Bp/8LpJp8qKOcJyriQ5n8TwghKoHYM5kknrvAcONaraHTGD3j2FWZz+CUxaJFi5gwYULRXVFDhgxh3rx5xfrExcUVm6Rv0qRJ5OTkMG7cOFJTUwkJCWH58uV4eHgAsHPnTrZu3QpAo0bFT6slJCQQGBhoxz0SRUyuOHUbD2tfIyz9R37b8wSD29XVO5UQQoir+H1fIoONW6iuZEH1+tC4r96R7MZu8+BUZDIPzi2Sl0nerGaYrRd5wTSZ1yc973CLtQkhhKNQVZXb317Dhxcn0tJwHPq+Ad2e0jtWmVSIeXBEFWD2wNBZm0H6nrwIvtyUoHMgIYQQV3IgMQOv1D20NBxHdXKB4KuvLFDZSYEjboop9P+wKU6EGA6yYU0kZzPz9I4khBCiFL/tPcNDTssBUFrd57CDiy+TAkfcHM/aKG2GATDK9gtzVsbrHEgIIcQ/qarKlr0HGGjQxrDS2XEHF18mBY64aUrXfwMwwLCNTdu2czBJlsIQQoiKZN/pdHpkLMNZsWKt0xH82+kdye6kwBE3z7cFNOqDUVF5xPgH//k9Vm4bF0KICmRZ9ElGOq0GwBhy5aWNHIkUOOLWuHQWZ5hxHfsOJbAmLkXnQEIIIQBsNpWM6KXUVi6QZ/aCFnfpHalcSIEjbo2gnuDXGjclj1HGVbz5eywFVpveqYQQosrbcTyVu/J/A8DYYTQ4VY2li6TAEbeGokCodhYn3LSaY2cz+V/UcZ1DCSGE2L5lDSGGg1gx4tTF8QcXXyYFjrh1WtwFrjXw4xw9DHuYu/IQadn5eqcSQogqq8BqIyD+awDO1x8Anv46Jyo/UuCIW8fkAm0fAGCM+wbScwqYu/KQzqGEEKLq2rrvIP3UTQB43TFB5zTlSwoccWu1fxiAroXbqUUqC6OOczjlos6hhBCiarq48TPMSiGn3FrgVD9E7zjlSgoccWv5NIOAEBTVyou1d2G1qUxfFqt3KiGEqHJyc3PocDYCgPyOVePW8L+TAkfcepfO4gyxrsTZqLL6YArr4s/qHEoIIaqWgyv/Sy0ljbN4Edh9pN5xyp0UOOLWazkUzJ6YMo7zasvzAEz7Leav28bPxkF+ln75hBDC0akq1fd9AUBM3WEYTM46Byp/UuCIW8/ZHVrfD8Bww2q83J05nHKRRVsSYMVr8GFn+KIfFModVkIIYQ9ZRzYRmBdHnmrC7/Yn9I6jCylwhH100C5TOR9axuSetXCmAN+V/4ZN72mvJ++DDe/qGFAIIRzXhVXvA7DauSdNGgTqG0YnUuAI+6jdFmq3A2s+9+b/yo/ubzOATVgxQvCDWp8N70DyAV1jCiGEw8k6h/+ZFQCktn4MRVF0DqQPKXCE/Vw6i2PY+A5trPvJVF0ZXfAC8SEzoOmdYCuEn58Ea6HOQYUQwnGkx63HiI04W126du2pdxzdSIEj7KfVfWBy0557+DO77ntssLZi2u+xqHe+A2YLJO6GqA/1zSmEEA7kRLS2avhRtzYEervrnEY/UuAI+3HxhP5vQYuh8PhKRt8zCJNRYcOhc6w+bYR+/9H6rZkO5w7rGlUIIRyFKXEbANUaddM5ib6kwBH21eFhGPY1WOpQv6Y7j3YLAuDN32PJbz0SGtwOhbnwy7/BJquPCyHEzThyOoWGBdofjK1C++mcRl9S4IhyNf6ORnhXM5NwLouvtxyHwe+ByR1ObIa4ZXrHE0KISm3bppWYFCupxprU8G+kdxxdSYEjypWHi4lJ/ZsC8P6qQ5x18vvrrqqja/ULJoQQlZzNppIRvxGAHL9OUEXvnrpMChxR7u5rX5c2dS1k5hXyzp9xUL+r9sKJLfoGE0KISmz7sQs0ydsPQK2WVffuqcukwBHlzmBQmDK4JQDf7zxJrKmV9kLyAchJ1TGZEEJUXkt3naS94RAApsBQndPoTwocoYsO9WswtJ0/qgqvrEpBrdkIUOHEVr2jCSFEpZNbYOXg/u1YlGysTm7g21rvSLqTAkfo5sUBzXE1Gdl5PJUT1dpqjcc36RtKCCEqodUHU2hREAOAIaATGJ10TqQ/KXCEbvwsLjx5e0MAvjrlrzXKOBwhhCizn3adpoMhHgClXhed01QMUuAIXT3evQEBXq6syNYKHRJ3Q36WvqGEEKISuZCVz9q4FDopcVqDFDiAFDhCZy4mIy8PbMEptRZnVC9tfapTO/SOJYQQlcbvexPxsl0gwHAWFAPU7aR3pApBChyhu34tfeneuBbbbM20BrlMJYQQ123JrtN0NFw6e+PbEswe+gaqIKTAEbpTFIUpg1uwQ9UKnNTYtfoGEkKISuJwSibRJ9PobNTG31BPbg+/TAocUSE08vGgTtveALgm7yQ/L1fnREIIUfH9uPM0AL1cj2oNASE6pqlYpMARFcbIQX1JwwMX8vljRaTecYQQokKz2lQidp/CjVzqFRzRGuUMThEpcESF4elqJstXGxx3ePsKUjLlLI4QQlzJhkNnSc7Io7vrMQyqFSwBYKmjd6wKQwocUaHUbnMHAG1tB5gVGadzGiGEqLh+3HkKgBF+iVqDXJ4qRgocUaEYArWFNzsZ4liy8wS7TsjaVEII8U/p2QUsj0nGgI3QnLVaY325PPV3UuCIisWvLZjcsSjZNFVOMeXnA1htqt6phBCiQvl1byL5hTae8NqFS9phcKkOre/XO1aFIgWOqFiMThDQGYDuzvHsO53O9ztO6hxKCCEqlh93nsKIlbHqD1pD13+Di0XfUBWMXQuc1NRUwsPDsVgsWCwWwsPDSUtLu+o2qqoydepU/P39cXV1pVevXhw4cKBYn7Fjx9KwYUNcXV2pVasWd911FwcPHrTjnohyVb8bAA/4adeXZ0UeJC07X89EQghRYVye++Y+pw1Yck6CW00IeULvWBWOXQuckSNHEh0dTWRkJJGRkURHRxMeHn7VbWbNmsXs2bOZN28e27dvx8/Pj759+5KZmVnUp0OHDixYsIDY2Fj+/PNPVFUlLCwMq9Vqz90R5eXSdeSgc2t5sfoq0rLzeHd5vM6hhBCiYvhx52lMFPK8y89aw21Pg7mavqEqIEVVVbsMcIiNjaVFixZERUUREqKN7I6KiiI0NJSDBw/StGnTEtuoqoq/vz8TJ07khRdeACAvLw9fX19mzpzJ2LFjS/1Ze/fupW3bthw+fJiGDRteM1tGRgYWi4X09HQ8PT1vYi+FXdhs8MNDEPsrAButLZlU+ASfjb+LVnXkFKwQouqy2lS6vrWKPlm/8x/Tl1DNFyZEg7Ob3tHKRVk+v+12BmfLli1YLJai4gagS5cuWCwWNm/eXOo2CQkJJCUlERYWVtRmNpvp2bPnFbfJyspiwYIFBAUFERAQUGqfvLw8MjIyij1EBWYwwLCFMGgOmNy4zXiAP5xfYPn3H2KnelwIISqF9fFnScvIZIJpqdbQ/dkqU9yUld0KnKSkJHx8fEq0+/j4kJSUdMVtAHx9fYu1+/r6ltjmo48+olq1alSrVo3IyEhWrFiBs7Nzqe87Y8aMonFAFovlioWQqEAUBTo+CmM3kO8XjEXJ5pn0mWz/5VO9kwkhhG4Wbz/BA8bV+HIBPOtA+4f1jlRhlbnAmTp1KoqiXPWxY8cOQFtE8Z9UVS21/e/++Xpp24waNYrdu3ezbt06GjduzLBhw8jNLX3m28mTJ5Oenl70OHlS7sqpNLwb4TxmBQf87wPAbffnZOQW6BxKCCHKSWI0RL4Evz5F7nePcfehyUx0WqK91uM5MLnoGq8icyrrBuPHj2fEiBFX7RMYGMjevXtJTk4u8drZs2dLnKG5zM/PD9DO5NSuXbuoPSUlpcQ2l8/GNG7cmC5dulCjRg0iIiJ44IEHSryv2WzGbDZfc99EBWU00Xj4dArnRNCKQ3z0yx+MGzZE71RCCGFfNhv8MBpSEwBwAfpfPi1RIxDaPahTsMqhzAWOt7c33t7e1+wXGhpKeno627Zto3NnbV6TrVu3kp6eTteuXUvdJigoCD8/P1asWEFwcDAA+fn5rFu3jpkzZ17156mqSl5eXhn3RlQWzhZfzgX0xvvkcsx7vyHmtl608JcB4kIIB3Zis1bcOFdD7fpvPt18hlNZCoM7NCDkjrvBqfRhGUJjtzE4zZs3p3///owZM4aoqCiioqIYM2YMgwYNKnYHVbNmzYiIiAC0S1MTJ05k+vTpREREsH//fkaPHo2bmxsjR44E4OjRo8yYMYOdO3dy4sQJtmzZwrBhw3B1dWXgwIH22h1RAXh3fxyAocaNvLF0tww4FkI4tt3/0762uocdgf/irYwwfjL2p+Wd46C6jCW9FrvOg7No0SJat25NWFgYYWFhtGnThoULFxbrExcXR3p6etH3kyZNYuLEiYwbN46OHTty+vRpli9fjoeHBwAuLi5s2LCBgQMH0qhRI4YNG4a7uzubN28udVCzcCANe2N196Wmkkn1U6v5addpvRMJIYR95GbAgaXa8+BwFm/Txo4ObuNPNXOZL75USXabB6cik3lwKrGVr8PG2ay2tmOS+RVWPdsLi6tJ71RCCHFr7fwKfn0KvJuQ8dgmOk9fRW6BjZ/GdaV9vRp6p9NNhZgHRwi7CNYG1fU07sV4MYk5K2SGYyGEA9q9SPsa/CC/7DlDboGNxj7VCA6ormusykQKHFG51GwI9bpixMa9xvX8d8sx9p9Ov/Z2QghRWZyNg1PbQDFCmxFFCw4P7xRwzWlWxF+kwBGVT3ttPbNH3DaiqjZe/Xk/NluVu9IqhHBUlwcXN+nHgUwX9p5Kx2RUuKd9XX1zVTJS4IjKp8Vd4OxBrYJEepkPsftEWtFfOEIIUalZC2DPYu15u1F8v137ty2spR9e7nJbeFlIgSMqH2d3aHUPAC/V3gnAW5EHuZCVr2cqIYS4eYdWQFYKuNciJ7APP+3W7hYd3lFuCy8rKXBE5RSsXaZqdG4lt9e6SFp2ATP/OKhzKCGEuEmXL0+1Gc6vB86SmVtIPS83bmt07Ql2RXFS4IjKqW5HCOyOUpjL++5fomDjux0n2Xn8gt7JhBDixlxMgUN/as+DH2TR1hMAPNC5HgaDDC4uKylwROWkKDDkAzC54ZEUxbsNdgPwcsR+Cq02ncMJIcQNiI8EWyH4t2d/gT97TqZhMirc31EGF98IKXBE5eUVBL2nAHD32U9o7prGwaRMvtp8TN9cQghxI05EaV8b3s4327SzN/1b1ca7miwWfSOkwBGVW+d/QUAXlIIsvqz5P0Bl9op4EtNy9E4mhBBlc6nAyfHrxM+XBhePCqmnZ6JKTQocUbkZDHDXh+DkQu1zm3neZzvZ+Vbe+DVG72RCCHH9Lp6FC0cA+PVCXbLyrTSs5U5IkJfOwSovKXBE5efdCG5/GYAncr/E35BK5IEkVsUm6xxMCCGu08mtAKi1mrNgVxoAI0Pqy8zFN0EKHOEYQp+EOh0w5mfwQ42PMZPPaz8fICffqncyIYS4tpPa5alzXu2IPZOB2cnAve3r6ByqcpMCRzgGgxHu/gxcqlMnaz+fuX3MmbQs3l99SO9kQghxbSe0MzgrLwYBMKiNP9XdZObimyEFjnAc3o3ggW/B6ExP21ZedVrI/PVHiE/O1DuZEEJcWUEunIkG4IvjPgCMlMHFN00KHOFY6neFuz8F4BGnP3lY+Z2XI/bJYpxCiIorcTdY88l2rsnhwlo08/Ogfb3qeqeq9KTAEY6n1T0Q9iYAr5oWUevEH7IYpxCi4ro0wHi7tTGg8FBooAwuvgWkwBGOKXQ8dB4LwBzTx3yzbBVnM/N0DiWEEKW4VOCsz22Ih4sTQ4P9dQ7kGKTAEY5JUaD/DGwNbsesFPCc9Qve/O2A3qmEEKI4VS0qcHbamjKsYwBuzk46h3IMUuAIx2UwYhg0G5vRTA/jPvL3LWV9/Fm9UwkhxF/OH4bs8+SqJg6ogYR3qa93IochBY5wbF4NMNw2EYBXTQv5T8R2mRtHCFFxXFqeYY/akG5NaxPo7a5zIMchBY5wfLc9jc1SD3/lAndnfiNz4wghKozC41sA2GlrwsOhgfqGcTBS4AjHZ3LFMHAWAI8Zl7F6/QYOJmXoHEoIISD78CYAjru3pmeTWjqncSxS4IiqoekAaDIAk2LlNeMCJi/Zi1XmxhFC6EjNOodn1jEAWoX0xWCQW8NvJSlwRNUx4C1UowvdjAeoe/oP/hd1XO9EQogq7NCOVQAcVuswpEsrndM4HilwRNVRIxCl+zMATDV9zZeRUSSm5egcSghRVR2LXg3AhZrtsbiZdE7jeKTAEVXLbRNRfVtRU8lkivoxr0bsQ1XlUpUQonwlpuXgdX43AHXb9NI3jIOSAkdULU5mlHs/x2Y0c4cxGr/D3/L7vjN6pxJCVDE/rN9FO0W7o9O/TW+d0zgmKXBE1ePTHEOfqQC87LSIL39eSXp2gb6ZhBBVRnZ+IVm7fsRJsZHu1Rq8gvSO5JCkwBFVU8gT2AJ74qbk8VrBHN76fZ/eiYQQVcSSnafob1sPgEfHB3RO47ikwBFVk8GA4e6PKXT2pJ3hKL57PmDzkXN6pxJCODibTSVywxbaGw5jw4Ch9X16R3JYUuCIqstSB6fBcwAYb1zKgh8iZBkHIYRdrYlLIThduz1cDewBHr46J3JcUuCIqq31fRQ0H4qTYmNC9jzmrojRO5EQwoF9ufEoQ43a7MXGtsN0TuPYpMARVZ7pzrcpMHnS2nCMwi2fsudkmt6RhBAOKPZMBmlHd9LIkIhqdIHmg/WO5NCkwBGimg+m/tMAeMb4Pe/8sJL8QpvOoYQQjubLjQlFZ2+Upv3BxVPnRI5NChwhAIIfoqBOZ9yVPB5K/ZBP1h7WO5EQwoGcu5jHr9GnGGzUVg+n9f36BqoCpMARAsBgwHTXB9gUJ/oadxG39hsOJWfqnUoI4SD+u+U4weoB/JRUVBcLNO6rdySHJwWOEJf5NEO5bSIArxq/4rXvo2TFcSHETcvOL2ThlmMMNVy6PNViKDiZ9Q1VBUiBI8TfKD2eo7B6EH5KKo+kzOCb1Tv1jiSEqOR+2HGK7OwsBjpt0xrk8lS5kAJHiL8zueI05D1sGAgz7uSuDYM5t/I9sBbqnUwIUQkVWm2sXL+Ot0zz8SAbPOtA/W56x6oS7FrgpKamEh4ejsViwWKxEB4eTlpa2lW3UVWVqVOn4u/vj6urK7169eLAgQNX7DtgwAAURWHp0qW3fgdE1dSgJ8qjkSSYGuGpZOO98TXUT7tDzM+w9wdY/zb88m9YeDds/1zvtEKIikhV4chqLnw2hIW5E7j70t1ThD4JBjm3UB7sepRHjhxJdHQ0kZGRREZGEh0dTXh4+FW3mTVrFrNnz2bevHls374dPz8/+vbtS2ZmyQGfc+fORVEUe8UXVZhSLwTTE2uZanucC2o1lJQY+P4h+OlxWP0m7PovHFkNkS9BYZ7ecYUQFYmqwuKRsPBufJI3YFMVDtfsBY/8AV3G6Z2uynCy1xvHxsYSGRlJVFQUISEhAMyfP5/Q0FDi4uJo2rRpiW1UVWXu3Lm8/PLL3HPPPQB8/fXX+Pr68s033zB27Niivnv27GH27Nls376d2rVr22s3RBVWt6YHDQdO4PalnXneeQnDvQ5hsvhD9XpQoz5s/RRyLkBiNNQL0TuuEKKiSD4AccuwGUx8nX8H3ygD+O7RUeDurHeyKsVuZ3C2bNmCxWIpKm4AunTpgsViYfPmzaVuk5CQQFJSEmFhYUVtZrOZnj17FtsmOzubBx54gHnz5uHn53fNLHl5eWRkZBR7CHE9RnWuR/MG9Xgl/2HC3T7C9vBvcPfH0OtFqN9V63Rii74hhRAVS3wkAHucO/B64cOEduyElxQ35c5uBU5SUhI+Pj4l2n18fEhKSrriNgC+vsUXH/P19S22zdNPP03Xrl256667rivLjBkzisYBWSwWAgICrnc3RBVnMCjMurctriYjUUcv8L+tx/96UQocIURpLhU432e2xKDA47c10DlQ1VTmAmfq1KkoinLVx44dOwBKHR+jquo1x8388/W/b/PLL7+wevVq5s6de92ZJ0+eTHp6etHj5MmT172tEPVquvFCf+2S6oxlBzl+PuvSC120ryeiwCZLOwghgItn4ZT2GbjaGsyA1rWpV9NN51BVU5nH4IwfP54RI0ZctU9gYCB79+4lOTm5xGtnz54tcYbmssuXm5KSkoqNq0lJSSnaZvXq1Rw5coTq1asX2/bee++le/furF27tsT7ms1mzGaZVEncuIdCA4k8kETU0Qs8/8NeFv+rCwa/NmByg9w0OBcHPs31jimE0Nuh5YDKflsQyXgxtoecvdFLmQscb29vvL29r9kvNDSU9PR0tm3bRufOnQHYunUr6enpdO3atdRtgoKC8PPzY8WKFQQHBwOQn5/PunXrmDlzJgAvvvgijz/+eLHtWrduzZw5cxg8WFZmFfZhMCi8fV9b+s1dz7ZjF/hq8zEevS0I6naChHVwfLMUOEKIostTq2zB3NbImzZ1q+ubpwqz2xic5s2b079/f8aMGUNUVBRRUVGMGTOGQYMGFbuDqlmzZkRERADapamJEycyffp0IiIi2L9/P6NHj8bNzY2RI0cC2lmeVq1aFXsA1KtXj6CgIHvtjhAEeLnx0kCtiJn150GOnr0I9UK1F09E6ZhMCFEhFOZhO7wKgFXWYJ68vZHOgao2u86Ds2jRIlq3bk1YWBhhYWG0adOGhQsXFusTFxdHenp60feTJk1i4sSJjBs3jo4dO3L69GmWL1+Oh4eHPaMKcV1GhdTjtkbe5BbYeO6HPVgD/jYORwhRtR3fhKEgixS1Os4B7enSwEvvRFWaoqpqlVtNMCMjA4vFQnp6Op6ennrHEZXM6bQc+s9ZT2ZeIa/2rcdjG3uCaoWnD4Clrt7xhBA6yf3lOVx2zWdxYS98H5zP7c1K3kksbk5ZPr9lvmghyqhOdVdeHdQCgJmrT5HjrV0mlbM4QlRhqkrugd8BOGTpRq+mtXQOJKTAEeIG3N+xLnc08yHfauPPjECtUebDEaLKyjp9gOp5ieSpJjr1vkeWEaoApMAR4gYoisJb97SmupuJPzIvDW4/LgWOEFXV3tXfARDt1Jq+7RrqnEaAFDhC3DAfTxf+M7Q1O23aXYFqSgzkpOqcSghR3nILrLgcXQ6AqcVAjAY5e1MRSIEjxE24s01turVrzlGbHwoqeQlb9Y4khChnERv30kaNA6B1r/t1TiMukwJHiJv0xpBW7HfSBh1vW/ebzmmEEOUpt8DKwY0/YVRUUqs1xlQzUO9I4hIpcIS4SRY3E006hQFgPrONjYfO6ZxICFFevt16nLsLtLunPNoN0TmN+DspcIS4BZp11gqctsoRXvp+O2nZ+TonEkLYW26BlZg139LOcIRCowtOXZ7QO5L4GylwhLgVvBqguvtgVgrxuRjDy0v3UwXn0BSiSvk2KoExBYsAULqMg2oysV9FIgWOELeCoqDU19alGuW0ht/3nmFp9GmdQwkh7CW3wMqxtV/RxHCaPJMnxtue0juS+AcpcIS4Vbo8CYqBu40b6GfYxmtLD3DyQrbeqYQQdvDtliM8XrAYAGP3p8G1ur6BRAlS4Ahxq9QLgW7aX3Fvm7/EJe8cz36/B6tNLlUJ4Uhy8q2krP2UAMNZss21ZOxNBSUFjhC3Uq+XwLc1nmoGb5s/Z9ux83y6/ojeqYQQt9B3mw/yqPUHAJzveAGc3XROJEojBY4Qt5KTM9zzGRjN9FJ28YBxNbOXx7P3VJreyYQQt0B2fiEX18+jlpLORdc6OHV4WO9I4gqkwBHiVvNtAb1fA2CK8yLqqGd4anE0WXmFOgcTQtysResPEG5dCoBL2KvaHzWiQpICRwh76DIOArvjoubyvstnJJy7yOu/HtA7lRDiJqRnF5C48X9YlGwy3QNxajtM70jiKqTAEcIeDAYY+jEYzbRVD9LEcJrvd5zit72JeicTQtygT9cfYYhtNQDuoY+CwahzInE1UuAIYS/VAyCoBwCvNDoOwOSf9nE6LUfPVEKIG5CSmcvGTesJNhzGpjhhaPeA3pHENUiBI4Q9Ne0PwG227bQLqE5mbiFPL46WW8eFqGTmrT7MXap29kZp2l9mLa4EpMARwp6aaAWO4dR2PrgrAHdnI9uOXWDe6sM6BxNCXK+TF7JZsu0Idxs3AKC0f0jnROJ6SIEjhD1Z6oJfa0Al4NxGpg1tBcB7q+LZlnBB32xCiOsyZ0U8PdUdeCkXwaM2NOytdyRxHaTAEcLeLp3FIT6Se9rX5Z72dbCp8NTi3aRm5cPhlbBnMcjinEJUOPHJmUREn2a4ca3W0G4kGJ30jCSukxQ4QthbkwHa18OroTCfaXe1ooG3O2fSc5nx7XLUb0ZAxFiIj9Q3pxCihFmRcfirZ+lu3Kc1BD+obyBx3aTAEcLe/IOhmi/kZ8LxjbibnfhgZDDORgPtj81HsRVo/SJfhIJcfbMKIYpEHT3Pythk7ndajwEVAruDVwO9Y4nrJAWOEPZmMEDjMO15nHaWpqW/hbd6uXGfcT0AVmdPSD0GWz7QKaQQ4u9sNpXpy2JRsPGw6yatUQYXVypS4AhRHppeukwV/0fRWJu7M/6Hk2JjtbUd05XHtNc3zIb0UzqFFEJc9uveRPaeSqeP+SA1CpLAbIHmg/WOJcpAChwhykODXmA0Q9oJSImFlIMo+7TViBe6jOKL9I4cdm0NBdmw/FV9swpRxeUWWJkVGQeovOKlzX1Dm2FgctU1lygbKXCEKA/O7tCgp/Y8/g9YOx1Qoflgxj94P04GAxPSHsCGAQ78BMc26hpXiKrs683HOJ2Ww7Bqe6mfuhkMJgh5Qu9YooykwBGivFy+XXz7lxDzM6BAr5foUL8GLw5oRowayGLrHVqfZZPAKquPC1HeUrPymbfmMK7kMsX0X62x67/Bu5G+wUSZSYEjRHlp0k/7mnFpjE3r+8C3BQCP3RZE3xa+zCq4nwyqQcoB2P65TkGFqLreX32IzNxCplb/A/ecM2AJgB7P6R1L3AApcIQoL0WzGgOKEXq+WPSSoii8c19bqtXwYVbB/QCoK6dCykEdggpRRax/B95vDzG/AJBwLouFW47TUDnN/flLtT7939IuMYtKRwocIcpTy7u1r8GjSpzytriZ+HBke76nD+utrVEKc2DJYzI3jhD2UJgPm96HC0fg+3BYOo63f9lOoc3G+5ZvMNgKtOkdmt2pd1Jxg6TAEaI8dZ0Ao5bAwHdLfbltQHVeGtiSZwv+j/OqJyTvhxWvlXNIIaqAYxsgLx2cXAEFohcx+djjvGD6npa5u7W7HgfMBEXRO6m4QVLgCFGejCZo3AecnK/Y5eGugYS0ac6zBWO1hm2fQtwf5RRQiCri4O/a17bDKXjod84oPgQYzvJ/xp+19u7PyKzFlZwUOEJUMIqiMPPeNpzy7s7nhdoEgerScZBxRudkQjgImw3ilmnPmw3iq1O16ZsznZ+VS3cx1mwE3Z7SL5+4JaTAEaICcjc78cmDHZhnGMV+WyBKzgX4aQzYrHpHE6LyS9wNmWfA2YOz3iG8v+oQF3Ejb+D7MG4rPL5SJvVzAFLgCFFBNfKpxn/u68iEgvFkq2ZtzMDu/+kdS4jK7+Bv2tfGfXl7VQKZeYW0qWvhvg51wacZuNbQN5+4JaTAEaICu7NNbW7v1o13C7VbxwtXvgG56TqnEqKSu1TgHPe5nR92avNSTR3SEoNBBhQ7EilwhKjgXhzQjJi6wzliq41Tzjny18zSO5IQldfZeDgXj2owMXmvH6oK9wTXoX09OWvjaOxa4KSmphIeHo7FYsFisRAeHk5aWtpVt1FVlalTp+Lv74+rqyu9evXiwIEDxfr06tULRVGKPUaMGGHHPRFCPyajgfce7MwHzo8CYNj6Cbazh3VOJUQlFafdPXXGqzObTxdQzezECwOa6RxK2INdC5yRI0cSHR1NZGQkkZGRREdHEx4eftVtZs2axezZs5k3bx7bt2/Hz8+Pvn37kpmZWazfmDFjOHPmTNHj008/teeuCKErHw8XHnpoDGttwThRyPFvJ+odSYjK6dLt4V+caw7As2FN8PV00TORsBO7FTixsbFERkby+eefExoaSmhoKPPnz+e3334jLi6u1G1UVWXu3Lm8/PLL3HPPPbRq1Yqvv/6a7Oxsvvnmm2J93dzc8PPzK3pYLBZ77YoQFUL7ejXI6vU6BaqRoAsb2LNmid6RhKhcMs7Aqe0A/JIbTEt/T8K71Nc5lLAXuxU4W7ZswWKxEBISUtTWpUsXLBYLmzdvLnWbhIQEkpKSCAsLK2ozm8307NmzxDaLFi3C29ubli1b8txzz5U4w/N3eXl5ZGRkFHsIURndeUdPtta6D4Bq617lWHKavoGEqEwuzX2zy9aIc0oN/nN3a5yMMhTVUdntv2xSUhI+Pj4l2n18fEhKSrriNgC+vr7F2n19fYttM2rUKL799lvWrl3Lq6++ypIlS7jnnnuumGXGjBlF44AsFgsBAQE3sktCVAidR88kXfGkIaf5/cs3ycwt0DuSEJWCLVa7PLXc2pGRnevRLqC6voGEXZW5wJk6dWqJAb7/fOzYsQPQZmT9J1VVS23/u3++/s9txowZQ58+fWjVqhUjRozgxx9/ZOXKlezatavU95s8eTLp6elFj5MnT5Z1t4WoMJyr1UDp/SoAD+cu5M2Fy7DaVJ1TCVHB5aajJqwDYLtLKJP6ycBiR+dU1g3Gjx9/zTuWAgMD2bt3L8nJySVeO3v2bIkzNJf5+fkB2pmc2rVrF7WnpKRccRuA9u3bYzKZOHToEO3bty/xutlsxmw2XzWzEJWJZ9fHuLj/R6olbeW+k2/y7p+NmDSgpd6xhKiw0lfPwaIWctjmz6ghfbC4mfSOJOyszGdwvL29adas2VUfLi4uhIaGkp6ezrZt24q23bp1K+np6XTt2rXU9w4KCsLPz48VK1YUteXn57Nu3borbgNw4MABCgoKihVFQjg0g5Fqw+dT4OROJ0M86sb3Wbr7tN6phKiQ1CNr8dg2F4BlXuHcHVxH30CiXNhtDE7z5s3p378/Y8aMISoqiqioKMaMGcOgQYNo2rRpUb9mzZoREREBaJemJk6cyPTp04mIiGD//v2MHj0aNzc3Ro4cCcCRI0d444032LFjB8eOHWPZsmXcf//9BAcH061bN3vtjhAVT436mAa9A8DTTj+wYMnPRJ9M0zeTEBVNZjJ53z+KAZXvbHcw+MGnrjlMQjgGuw4fX7RoEa1btyYsLIywsDDatGnDwoULi/WJi4sjPf2vqecnTZrExIkTGTduHB07duT06dMsX74cDw8PAJydnVm1ahX9+vWjadOmTJgwgbCwMFauXInRaLTn7ghR8bR9ALXZYJwVK28b5jH+680kpefqnUqIisFmJf/7R3DJO0+sLYDMXm8S5O2udypRThRVVavc6MSMjAwsFgvp6el4enrqHUeIm5N1HttHXTBkpfB1YV/21gjjP3fUwCU7UZv3o1FvaNxX75RClL81M2DdW2SpZp6t8T7z/j1Mbguv5Mry+S0FjhQ4whHE/wnfDCv9Nedq8NwhcHYr30xC6OnoWtT/DkVB5ZnCJ3l83Iu08Jd/7yu7snx+SykrhCNo0g+6TsBmNHNK9WarrRn7vPpBNV/IvwjxkXonFKL85Gdji/g/FFS+LbydOj0eluKmCpICRwhHETYNw6sp7Lx7PcPzX2Nw4sPs9x2ivbbvR32zCVGeoj7EkJnIKdWbhdX/j/F3NNI7kdCBFDhCOJi72tXh6T5NAHjuYGOt8dByyEnVMZUQ5eTiWQrXzwHg7cLhTLu/E2YnuQGlKpICRwgHNKF3I4a28+egtS7xaj2wFUDML3rHEsLuclf+B6fCLPbagvDrNooO9b30jiR0IgWOEA5IURTeurcNnQO9iCjUJsnMi/5O51RC2Jd6Nh5T9NcALPQYwzNhshxDVSYFjhAOysVk5LOHOrCn+h0AmE5uJvPsCZ1TCXGTbFb48VFYcCec2lHspcQlL2LExmpbe0aPelAuTVVxUuAI4cCquzkz87HBRCvNMKDy8/8+IL/QpncsIW7crv/C/iVwfCN83gd+fxZy0zl7YA11klZRqBpI6vwSLf0teicVOpMCRwgHF+Dlhk/XBwFok7qCF5fspQpOfyUcQfYFWPW69tw/GFBh++eo8zqTH/FvAFa59mPYgN76ZRQVhhQ4QlQB/l0fwKYYaWNIIDp6O7P+jNM7khBlt/pN7W5Anxbw2Ep4+FfwaohyMYk6hSfJUs00f2CGzFYsAClwhKga3L0xNNTG4gwxbubjtUf4YmOCzqGEKIMze2DnAu35gFlgdIKgHuwf8gfvW+/lrGphX6sXqFc/SN+cosKQAkeIqqL1/QCM9tgBqEz7LYalu0/rm0mI66GqsOx5UG3Q6l4I6g7AxbxCxv8Qw+yCe3ml0U+E3PeMzkFFRSIFjhBVRbOB4ORK9ZwTzGpxAlB57oc9rIlL0TuZEFe39zs4uRVM7tB3WlHza0v3c+x8Nv4WF2be2wZFUXQMKSoaKXCEqCrMHtBCW7ph2NHJrK4xg+7sYtz/drLrhMxyLCqo3AxY/qr2vOfzYKkDwE+7TvHT7tMYFHjvgWCquznrGFJURE56BxBClKMBs7TVxXf/jwY5+1ngvJ8YW32Wf3kb/t1b4uftDc7u4OYFdTtr4xyE0NO2TyErBWo2gi7jAEg4l8UrS/cDMLFPEzoFymzFoiT510uIqsS1OgyaDT0nwZYPUXd8QYv847TgOGz4R9+uEyBsWmnvIkT5ObpO+xr6JDiZyS2w8u9vd5Gdb6VLAy+evF0W0hSlk0tUQlRFHn4QNg1l4n5ye7zKKufbibR2YpvShnzfdlqfrZ9A6jE9U4qqzloIp3dpzwO6APD6rwfYfzqDGm4m5g4PxmiQcTeidFLgCFGVuXnhcsdztJvwHW9Xf4VhOS8SdnEq+fV7gjVfm3dECL2kHICCLDB7Qq1mfL/9JN9uO4miwHsjgvGzuOidUFRgUuAIIahZzcyix7sQ4OXKsfPZPHV+KCoK7PsBEnfrHU9UVSe3aV/rdmL/mUxe+Vkbd/Ns3yb0aFJLx2CiMpACRwgBgJ/FhW8e74Kvp5k/zvmyxrmX9sLyV7V5SIQob5cKnBy/Djzxv53kF9ro09yHcb1k3I24NilwhBBFArzcWPR4F7yrmXk1YygFOMGxDXB4pd7RRFV0cisA8w55cSo1h3pebrw7rB0GGXcjroMUOEKIYhr5VOPbMSHkutfhy8J+AFj/fBVsVp2TiSolMxnSjqOi8PUJb8xOBj55sAMWV5PeyUQlIQWOEKKExr4efDOmC98430+a6o7xXCw5O/6ndyxRlZzSLk8dtNXlIm7MuKc1Lfw9dQ4lKhMpcIQQpWrq58Gn/+rNl4Z7AciKfJ30zEydU4mqIiVmPQC7bE0Y26MB97Svq3MiUdlIgSOEuKJmfp4MeORVkqiJt+08X3w6h9SsfL1jCQeXlJ7Lmf3aBH85fh2Y1L+ZzolEZSQFjhDiqprX88HQ6VEAbs/4mQfmR3HuYl7Jjsc2wdZP5Y4rcVNy8q3839dbaGY7CsCIe+6VyfzEDZECRwhxTT49/4VqMBFsOIxT8h6Gf7qFpPTcvzpknYdvhsMfk+DIav2CikrNZtNWuOfMHsxKAVYXL6rVbqp3LFFJSYEjhLi2aj4oLYcCMNZ1DUfOZjH8sy2cSs3WXt84G/Ivjc+RAkfcoBl/xPL7vjN0cjoEgLF+F1Dk7I24MVLgCCGuT6cxANypbKJFjUKOn8/m/k+2cOxIHGyb/1e/o2v1yScqtc83HGX+hgQAHg5I0RrrdtIxkajspMARQlyfgM7g2xqDNZfFnY7QsJY7Z9Jz2b3wRbDmQe22Wr/k/XAxRd+solL5ZU8ib/4eC8CL/ZtSJ3Of9kJAiI6pRGUnBY4Q4vooCnR+HADPff/lh7FdGFg7kyHqGgCiW78Mfq21vkfX6ZVSVDKbj5zjue/3ADC6ayBj25og8wwYnMA/WOd0ojKTAkcIcf1a3w9mC6Qm4HVmI+/7/o5RUVlh7cD9v1s54tFZ63d0jb45RaUQk5jB2P/uJN9qY2BrP14d1ALl1HbtRb/W4Oymb0BRqUmBI4S4fs7u0G6k9nz5Kzgd/AUVha1BT1JgVXn9wKUVno+skdvFxVUdTrnIQ19uJTOvkM5BXswe1k67HfzyCuJyeUrcJClwhBBl00m7TMVZbcyE0mY4k0ffw4Nd6rHV1ow81QSZiVjPxusYUlRkx89nMerzKM5dzKdFbU/mP9QRF5NRe/HSApsEdNYvoHAIUuAIIcrGuxE0uF17bjDB7ZMxGhSm3dWKpwe0YbutCQA/fP9fcgtkgU5R3Om0HEbO30pyRh6jahzkF/MrWD5tDzODYFotOBOtdawrBY64OU56B6jIrFYrBQUFescQOjOZTBiNRr1jVCw9noNjG+C2p6FGIACKovBEz4bEJIdBzAFqJm/mgflRfP5QR2pWM+ubV1QIKRm5jJofxem0HHp4pfJm/tsoSTklOwZ2B4usPSVujqKqVe9CeUZGBhaLhfT0dDw9S65Oq6oqSUlJpKWllX84USFVr14dPz8/FJl07C/WQjAYS07ElrgbPuvFRVxpl/sptb08+OLhTjTx9dAnp6gQUjJzGTV/K4dSLhJU3Yk/PabhfHafVsz0ngLmauBcTfvqUl0m+BOlutbn99/JGZxSXC5ufHx8cHNzkw+1KkxVVbKzs0lJ0eZ1qV27ts6JKhDjFf758GsLrl5Uy7lAmOUUyy4EcveHm3hvRDB9WviWb0ZRIZxJz2HU/K0cPZeFn6cLPzeLxDl6H7h6wT2fgae/3hGFA5IC5x+sVmtRcVOzZk2944gKwNXVFYCUlBR8fHzkctW1GAzQoCcciODdDqmcT2jP1oQLjFm4g0n9mvFEzwbyR0NVYbNxIjWXkZ9HcSo1hzrVXfmpbxaev36mvT70IyluhN3IION/uDzmxs1N5l8Qf7n8+yBjsq5Tg14AuJ7cwMLHQhgZUg9VhZmRB3nm+z0y+NiR5aZrq8rP64T6Zi1OzxtIl4xIWnnZ+DG8Ib6rJmr9Ov8Lmg7QNapwbHYtcFJTUwkPD8disWCxWAgPD7/muBZVVZk6dSr+/v64urrSq1cvDhw4UKLfli1buOOOO3B3d6d69er06tWLnJxSBqvdIPkLU/yd/D6U0eW7rE5tx7nwIv8Z2oo37mqJ0aAQsfs09368mRPns/XNKG6t5Bj4dSK821xbVf5cPIqtkFB1N++YPuXX3Eeo/d1AyD4HPi2h7zS9EwsHZ9cCZ+TIkURHRxMZGUlkZCTR0dGEh4dfdZtZs2Yxe/Zs5s2bx/bt2/Hz86Nv375kZmYW9dmyZQv9+/cnLCyMbdu2sX37dsaPH4/BICekhKgQatQHrwagWuHYRhRF4aHQQP77aGe83J05kJjBoA82sOagrFnlEE7vhE97wM4FUJBFtqUx/+FRBuTNYKHLKAprNkOxFUDGKXByhfu+BJOL3qmFo1PtJCYmRgXUqKioorYtW7aogHrw4MFSt7HZbKqfn5/61ltvFbXl5uaqFotF/eSTT4raQkJC1FdeeeWGs6Wnp6uAmp6eXuK1nJwcNSYmRs3Jybnh93cE9evXV+fMmWO39z9z5ozap08f1c3NTbVYLHb7OaVJSEhQAXX37t3XvY38XtyAX59W1SmeqvrVYFXdt0RVUw6qamGBejo1Wx0yb6Na/4Xf1Pov/Ka+uzxOLbTa9E4rbsbP47X/1vN7qxuW/6Q2ekn7b3vfx5vUtKx8rU9yjKpumK2qR9frm1VUalf7/P4nu53y2LJlCxaLhZCQv6bb7tKlCxaLhc2bN5e6TUJCAklJSYSFhRW1mc1mevbsWbRNSkoKW7duxcfHh65du+Lr60vPnj3ZuHHjFbPk5eWRkZFR7OGIRo8ejaIovPXWW8Xaly5dWuZLLNu3b+df//rXrYxXzJw5czhz5gzR0dHEx8uMtw6pcV/ta8I6+PER+LAzTK+Nf8S9fD/cn/Au9QF4f9UhRi/YRkpmro5hxQ0rzIeYXwBYWvMxHlzlTIEV7mxdm4WPhWBxM2n9fJpr8yYFddcxrKhK7FbgJCUl4ePjU6Ldx8eHpKSkK24D4Otb/FZSX1/foteOHj0KwNSpUxkzZgyRkZG0b9+e3r17c+jQoVLfd8aMGUXjgCwWCwEBATe8XxWdi4sLM2fOJDU19abep1atWnYdaH3kyBE6dOhA48aNS/09ARnQW+k17gdD5kFwONTpCCZ3sObD8U2Yf3qUaYOaMGd4W1xMBjYcOsfA9zawLv6s3qlFWR1ZDblpZDjV5Jmt1QB4/LYgPngg+K/lF4TQQZkLnKlTp6IoylUfO3bsAEofmKmq6jXPJvzz9b9vY7PZABg7diyPPPIIwcHBzJkzh6ZNm/Lll1+W+n6TJ08mPT296HHy5Mmy7nal0adPH/z8/JgxY8ZV+y1ZsoSWLVtiNpsJDAzk3XffLfZ6YGAgc+fOLfp+6tSp1KtXD7PZjL+/PxMmTCh6LT8/n0mTJlGnTh3c3d0JCQlh7dq1V/zZgYGBLFmyhP/+978oisLo0aMB7b/7J598wl133YW7uztvvvkmAL/++isdOnTAxcWFBg0a8Prrr1NYWFj0funp6fzrX//Cx8cHT09P7rjjDvbs2XPNY3Xw4EG6du2Ki4sLLVu2vGpmcQMMBmgfDnfNgzGrYPIpeGKTNolb4i5Y8Rp3B9fl1/G30czPg3MX83n4y21MXxZLfqFN7/TiOuVFfw/Aj7mdUBUDUwa34JVBLTAYZGC+0FeZ58EZP348I0aMuGqfwMBA9u7dS3JyconXzp49W+IMzWV+fn6Adibn7xOqpaSkFG1zub1FixbFtm3evDknTpwo9X3NZjNm841PFa+qKjk63dbqajKW6fKS0Whk+vTpjBw5kgkTJlC3bsnpznfu3MmwYcOYOnUqw4cPZ/PmzYwbN46aNWsWFRt/9+OPPzJnzhwWL15My5YtSUpKKlZAPPLIIxw7dozFixfj7+9PREQE/fv3Z9++fTRu3LjE+23fvp2HHnoIT09P3nvvvaJ5ZgCmTJnCjBkzmDNnDkajkT///JMHH3yQ999/n+7du3PkyJGiS2dTpkxBVVXuvPNOvLy8WLZsGRaLhU8//ZTevXsTHx+Pl5fXFY/V888/z9y5c2nRogWzZ89myJAhJCQkyPxH9mIwgF8ruPsT+HYEbP0YArvRuPlglj7ZjRnLYvl6yzEubFrAjuh9NA3uRs3goeDbUma1raBiTyRTP/Z3AJYbuvHxiA70b+WncyohNGUucLy9vfH29r5mv9DQUNLT09m2bRudO2uLpm3dupX09HS6du1a6jZBQUH4+fmxYsUKgoODAe3swLp165g5cyagFU/+/v7ExcUV2zY+Pp4BA+wzp0JOgZUWr/1pl/e+lpg3+uHmXLb/THfffTft2rVjypQpfPHFFyVenz17Nr179+bVV18FoEmTJsTExPD222+XWuCcOHECPz8/+vTpg8lkol69ekX/TY8cOcK3337LqVOn8PfXJux67rnniIyMZMGCBUyfPr3E+9WqVQuz2Yyrq2tRUXvZyJEjefTRR4u+Dw8P58UXX+Thhx8GoEGDBkybNo1JkyYxZcoU1qxZw759+0hJSSkqYt955x2WLl3Kjz/+eNVxROPHj+fee+8F4OOPPyYyMpIvvviCSZMmXXEbcQs0HQBd/w2bP4ClT4Jfa1xqBPJ6Twv/TvwY7+SNkA9s3Qxb30atXg+l6UBo/5BW7Igbl5cJihGcb/7y8y97Elm95BPmGnI5o/jw+v+Npmntq0+dL0R5stsYnObNm9O/f3/GjBlDVFQUUVFRjBkzhkGDBtG0adOifs2aNSMiIgLQLlFMnDiR6dOnExERwf79+xk9ejRubm6MHDmyqM/zzz/P+++/z48//sjhw4d59dVXOXjwII899pi9dqfSmTlzJl9//TUxMTElXouNjaVbt27F2rp168ahQ4ewWkueqbr//vvJycmhQYMGjBkzhoiIiKJLRLt27UJVVZo0aUK1atWKHuvWrePIkSNlzt2xY8di3+/cuZM33nij2HuPGTOGM2fOkJ2dzc6dO7l48SI1a9Ys1ichIeGaPz80NLTouZOTEx07diQ2NrbMmcUN6D1FWy06Lx1+eAS2fwEfheKdvBHVaGaZx32ssLYnVzWhpJ2ArZ/A/N5wvuy/U+KSvEyY1xk+7wO2G78EmF9o4z+/xzDh2930V7WbP2p0fkCKG1Hh2HWphkWLFjFhwoSiu6KGDBnCvHnzivWJi4sjPT296PtJkyaRk5PDuHHjSE1NJSQkhOXLl+Ph8ddCfRMnTiQ3N5enn36aCxcu0LZtW1asWEHDhg3tsh+uJiMxb/Szy3tfz8++ET169KBfv3689NJLJc7KlDYOSr3KmqsBAQHExcWxYsUKVq5cybhx43j77bdZt24dNpsNo9HIzp07SyxhUK1atTLndnd3L/a9zWbj9ddf55577inR18XFBZvNRu3atUsdP1O9evUy/3yZ0K+cGE3aXCifdtfG4yTu0toDQlDu+pABNRvx7baTdPt9N8EFe5hg+pk2hYdRl45DeWSZtsinKJtjGyEzUXucPQi+La69zT/f4lwWExbvZu+pdDzIpo/THlDBJXiYHQILcXPsWuB4eXnxv//976p9/vnBqigKU6dOZerUqVfd7sUXX+TFF1+82YjXRVGUMl8mqgjeeust2rVrR5MmTYq1t2jRosRt9Zs3b6ZJkyZXXGfJ1dWVIUOGMGTIEJ588kmaNWvGvn37CA4Oxmq1kpKSQvfut/72z/bt2xMXF0ejRo2u+HpSUhJOTk4EBgaW6b2joqLo0aMHAIWFhezcuZPx48ffbGRxvaoHwNBL43GcXKD3axAyFgxGFGBkSD26N/bm+R99+L+j9fjT/ALVTkaRtOI9/Po9o3f6yidhw1/Pj28qc4Hz065TvLp0P1n5ViyuJhZ2OIfTjnzwbiqXDkWFVPk+tcV1a926NaNGjeKDDz4o1v7ss8/SqVMnpk2bxvDhw9myZQvz5s3jo48+KvV9vvrqK6xWKyEhIbi5ubFw4UJcXV2pX78+NWvWZNSoUTz00EO8++67BAcHc+7cOVavXk3r1q0ZOHDgTe3Da6+9xqBBgwgICOD+++/HYDCwd+9e9u3bx5tvvkmfPn0IDQ1l6NChzJw5k6ZNm5KYmMiyZcsYOnRoiUtef/fhhx/SuHFjmjdvzpw5c0hNTS02/keUg6b94f82g2sN8Cy5UnuAlxvfPN6F/22tzbuR4UxhPpbNM/gkqyUPDepdKf/w0M2x9X89P74JOo+5rs0ycguY8vMBInafBqBzkBdzh7fD/7dPtQ6t75NB4KJCkrUNHNy0adNKnCVr374933//PYsXL6ZVq1a89tprvPHGG6UOMAbtUs/8+fPp1q0bbdq0YdWqVfz6669FdxstWLCAhx56iGeffZamTZsyZMgQtm7dekvmG+rXrx+//fYbK1asoFOnTnTp0oXZs2dTv742SZyiKCxbtowePXrw6KOP0qRJE0aMGMGxY8eueLfeZW+99RYzZ86kbdu2bNiwgZ9//vm6BtCLW8y3RanFzWUGg7bMw9inpxHr2gFXJZ8O0a/Q7901RO5P0n6/rYUQFwmxv8FVLrdWWdkXIGn/X98f33xdx2llTDJ9Z68jYvdpjAaFZ/s24dsxXfA3ZcGRNVqnliUvHwtRESjq1QZfOKiMjAwsFgvp6el4ehYfGJebm0tCQgJBQUG4uMhaKUIjvxcVRNoJCud1wakwizcKwllq7cakWlu51/YnpqxErU9QTxg8V1sLS2hif4XvHoQaQZCRCNY8GL8TvEu/9Hv+Yh6v/xrDL3u0YxpY04137m9Lx8BL0y5s/wJ+fwZqt4Wx60t9DyHs4Wqf3/8k53eFEJVH9Xo49X8Tfnual5wX86LtW5wztTv6so2euCr5KAnr4KOucPtL0GUcGG/inzlVhTPR2pkhv9bQcugt2Y1yd3n8TaPekBKrXaI6vqlEgaOqKj9HJ/LGbzFkZmVzh3EfE2vH0Dp3J8o3WWAr1BZQteZrG7S6r5x3RIjrJwWOEKJy6fAIxPyM09G1oMAxczM+yOzFb7YuNHBO59MaC6mXvgNWvAr7l2h3a9Us4x2W54/Avh+0x/nDf7WfngB9pla+u7iOXSpwArtr452Ob9IuU3V4uKjL3lNpvPFrDDkndvOy0x/0c9lFNbLg3BXe07UGtL7f/tmFuEFS4AghKhdFgfsWwO6FUP82Aut2YMSxCxz6LYa9p5zpkfw0D7tsZLLT/3A5Ew1LHoMxa65/IOy6t2HNm3997+QCdTtpRcLm97WC5575YC77NAi6uHgWUi7NhxXYHVwswNtakQOkZObydmQcP+46hYd6kc3maVRTcrT+1fygxRBoPhg864DBSSvuDE7akhsmuVwrKi4pcIQQlY+bF3R7qujbToFe/PxkN/48kMS7y+P5OqU7y2nBKpfncUvcTda+X3Bvc9e13zf7Amx4R3ve8A5oMxya3QlmD9j7A/z8JMQtgy/7wQOLtVvdK7rLZ298W4F7TQjorBUo6Sf5/Nd1zN2Ry8U87TLfG/WiqZaSAzUbw5APICBEW2JDiEpIfnOFEA5BURT6t6pN5MQevDeiHeaaAXxZqE3QeXrJK0z//QBJ6blXf5M930JhLvi2hgd/grYjtOIGoM398MgycPeB5P0w/3Y4G2/nvboF/n55CsiwOXPGvRkAMVuWcTGvkLYB1fnp/7owtOAPrW/ok1A/VIobUanJb68QwqEYDQp3tavDymd6Uu/OF7iIO02UE5zZ9A3dZ63m2e/3EH0yreTs3aoKO77Unnd6tPRLWnU7wpjVWgGUdRaWv2L/HbpZlwYYp/t1Yc6KeG57azU/pwYB0Nf9CB+Nak/E/3Wlff5uSE0AswXayMzEovKTAkcI4ZCcjAaGhLbE/faJAEx2jcBmLWTJrlMM/XATgz7YyDdbTxRdniFhnTa+xtkDWl/lA756AAz7Wlu08tCfcGKr/XfmRmWcgfOHsKHQ+8cC3lt1iIzcQk56tAOgv8dRBraujcGgwPb52jbtRoKz+5XfU4hKQgocIYRDU7r8H7jVxN96mtV9znBPcB2cnQwcSMzgpYh9hPxnJc//sIdzay7N5N12+LUHENdsCMGjtOerp1W4yQUv5hWyZOcp3vtCOyN1wFafc1Y32gZU54MHgnnj348DCsqFI5CZBKnHIf5PbeNOsmixcAxS4AghHJvZA257GoD6++Yx+97mbJ3cm1fubE4Db3ey8q2s27mP6idWAPBhZk92HLuA1XaNoqXHJDA6a2Ncjq618078Q95FOLO3WGFVYLWx5mAKE77dTcc3V/DsD3vwOb8dgBTvECLGdeXnJ7sxuK0/Rrfq2rw+oN1NteNLQIUGvcC7cfnuixB2IndRiVIFBgYyceJEJk6ceMvfe+3atdx+++2kpqbe0IrfQpRZp8dh8zxIPwG7/kuNzmN4vHsDHrstiG0JF8j88z84JdvYZmvK29FGiN5CDTcTPZrU4vamPvRoUgsvd+fi71k9ADo+Cls/gVVvaMVBeazJlHEGvroTLhwht8tE/vQdw9r4c6yNSyE1u6CoWwNvdwYUHoJc6N3/PqhXo/j7BN4GSXvhyGo4uExr63R961MJURnIGRwHMnr0aBRF4a233irWvnTpUpQy/sO7fft2/vWvf93KeELox+QKPZ7Tnq9/By4cBbQ7r0LqW+iTrd095N71X9wdXAcPFydSswv4OTqRid9F0+HNFdz5/gam/Lyfn6NPczrt0jwx3Z8Fkxsk7tJuH7eztOSTZM0fCBeOAOASNZeEJa8Ssfs0qdkF1HR3ZnTXQH5+shurHm9A9dxT2lih+qEl36x+V+1r9DeQcwE860KT/nbfByHKi5zBcTAuLi7MnDmTsWPHUqNGjWtvcAW1atW6hamEqADaPwybP4C04/BRqFbwdJ0Ah5ZDZiK4edOyTzhznMwUWG3sPpHGmrgU1hxM4WBSJgcSMziQmMHXW44D4OtpppmfJ//yvo9uZ/5Lzp+vk1f3DizuLmX7g0JVIS8DctIgNw1yUsk3uHDcpTlHzmVz5GwWR1Iucuz4MWZefInGhtOcUr352dqVJ51+YaLTT7QP9MZ8x4u0r18Dk/HS363Rlwou/+C/bnX/u3qXChzVpn3t+MjNLWshRAUjv80Opk+fPhw+fJgZM2Ywa9asK/ZbsmQJr732GocPH6Z27dr8+9//5tlnny16/Z+XqKZOncqXX35JcnIyNWvW5L777uP999/njTfe4IcffmDfvn3F3r9Dhw7ceeedvPHGG1fMsGnTJl566SXi4uJo27Ytn3/+Oa1bt765AyDElTg5w0M/w69PaXdMrX4T9n6vzVQMEPwgOJkBMBkNdA7yonOQFy/0b0ZSei7bj11g5/FUdh5PJeZMBskZeSRnnGU3t7HR/COeqXG8MH0ay43d8fV0KXpUMxtxNTnh5myklu0s9dO24pmVgGf2CWpkH6d63imMamGxqM6Am1qTY9au/GLtRopanW+d/0Njw2lSlJr8t9EHBDZuRXpmWywbp9Hj1Gdwyhfq/h/E/AH7foTDK7U3C+pe+vFwrwm1msPZWDCYoP1DdjrwQuhDCpzroapQkK3Pzza5lem6vtFoZPr06YwcOZIJEyZQt27dEn127tzJsGHDmDp1KsOHD2fz5s2MGzeOmjVrMnr06BL9f/zxR+bMmcPixYtp2bIlSUlJ7NmzB4BHH32U119/ne3bt9OpUycA9u7dy+7du/nhhx+umvX555/nvffew8/Pj5deeokhQ4YQHx+PyWS67v0Voky8grQiZ9+P8OdkOHd5oj5FO4NxBX4WFwa39WdwW38AsvMLOZCYQXxyJoeSL/LH4fsZnvE1k0zfEZXXguPna3D8fPF/M9oph1noPAOPy8sg/EOuaiIdd9JVd/yUC9RRzvOE06884fQr+UY3nK3Z2Nx98Xn0D14qWlvrOTAbYNXr2t1c69+Bwr+9f+22Vy9cgrprBU7LoVDN5xoHT4jKRQqc61GQDdP99fnZLyWWeU6Ku+++m3bt2jFlyhS++OKLEq/Pnj2b3r178+qrrwLQpEkTYmJiePvtt0stcE6cOIGfnx99+vTBZDJRr149OnfuDEDdunXp168fCxYsKCpwFixYQM+ePWnQoMFVc06ZMoW+ffsC8PXXX1O3bl0iIiIYNkwmGRN2pCjarMSN+2iDg3cs0JZkqBF43W/h5uxEp0AvOgV6aQ150+HjNdRNO8Emv3fZ32cRJ63VScnIJSffSrW0WEbEvI2rNYcz5iASqgVzwaU+aW6BpLvXw9nTj+qeHnh7mPF2N5PrpuJxZi3Kvh/g0HKcrdng7oPhkd9LLhza/RmwWbX1swpztP1ofb+20rdPs6vvSM8XtJmZOz5apkMoRGUgBY6DmjlzJnfccUexy06XxcbGctddxdfl6datG3PnzsVqtWI0Fl8p+f7772fu3Lk0aNCA/v37M3DgQAYPHoyTk/brM2bMGB599FFmz56N0Whk0aJFvPvuu9fMGBr618BHLy8vmjZtSmxs7I3srhBl51oDBs2BPq9rZ0pvhrkaPPwbfD0IU9pRgleNJHj0b2BpACkH4auJYM2EgC7UfnAJta9noU6vodqZlZxUOLJGW0PKUvKMLAA9n4eATuBcDep0uP6zvu7e2rZCOCApcK6HyU07k6LXz74BPXr0oF+/frz00kslzsqoqlpiEGSJaev/JiAggLi4OFasWMHKlSsZN24cb7/9NuvWrcNkMjF48GDMZjMRERGYzWby8vK49957byh3We/2EuKmuXjemvepUR9G/w5fD9aWPFgwEIa8Dz+Nhezz2mDfUd+XfRVy1xrQ6p5r92vQ64ZiC+GopMC5HopSKacuf+utt2jXrh1NmjQp1t6iRQs2btxYrG3z5s00adKkxNmby1xdXRkyZAhDhgzhySefpFmzZuzbt4/27dvj5OTEww8/zIIFCzCbzYwYMQI3t2sXZlFRUdSrVw+A1NRU4uPjadbsGqfUhajIqtfTipyvBmlFzn8vnSn1baUt3uli0TefEFWIFDgOrHXr1owaNYoPPvigWPuzzz5Lp06dmDZtGsOHD2fLli3MmzePjz76qNT3+eqrr7BarYSEhODm5sbChQtxdXWlfv36RX0ef/xxmjdvDmh3R12PN954g5o1a+Lr68vLL7+Mt7c3Q4cOvbGdFaKisNTVVh3/apA2X03NxhC+FNy89E4mRJUiE/05uGnTppW4/NS+fXu+//57Fi9eTKtWrXjttdd44403Sh1gDFC9enXmz59Pt27daNOmDatWreLXX3+lZs2aRX0aN25M165dadq0KSEhIdeV7a233uKpp56iQ4cOnDlzhl9++QVnZ+drbyhERefpD4/+CQPfgUf+gGoyr5QQ5U1Rrzb4wkFlZGRgsVhIT0/H07P49ffc3FwSEhIICgrCxcVFp4SVj6qqNGvWjLFjx/LMM8/oHeeWk98LIYTQ39U+v/9JLlGJm5aSksLChQs5ffo0jzxy5blEhBBCiPIiBY64ab6+vnh7e/PZZ5/d1PIQQgghxK0iBY64aVXwKqcQQogKTgYZCyGEEMLhSIEjhBBCCIcjBc4V2Gw2vSOICkR+H4QQonKRMTj/4OzsjMFgIDExkVq1auHs7CzLB1RhqqqSn5/P2bNnMRgMMk+PEEJUElLg/IPBYCAoKIgzZ86QmKjT+lOiwnFzc6NevXoYDHLSUwghKgMpcErh7OxMvXr1KCwsxGq16h1H6MxoNOLk5CRn8oQQohKRAucKFEXBZDJhMpn0jiKEEEKIMpLz7UIIIYRwOFLgCCGEEMLhSIEjhBBCCIdTJcfgXF5aICMjQ+ckQgghhLhelz+3r2eJoCpZ4GRmZgIQEBCgcxIhhBBClFVmZiYWi+WqfRS1Cq6UaLPZSExMxMPD45bf+puRkUFAQAAnT57E09Pzlr63+Isc5/Ihx7l8yHEuP3Ksy4e9jrOqqmRmZuLv73/Necmq5Bkcg8FA3bp17fozPD095X+eciDHuXzIcS4fcpzLjxzr8mGP43ytMzeXySBjIYQQQjgcKXCEEEII4XCkwLnFzGYzU6ZMwWw26x3FoclxLh9ynMuHHOfyI8e6fFSE41wlBxkLIYQQwrHJGRwhhBBCOBwpcIQQQgjhcKTAEUIIIYTDkQJHCCGEEA5HCpwb8NFHHxEUFISLiwsdOnRgw4YNV+2/bt06OnTogIuLCw0aNOCTTz4pp6SVW1mO808//UTfvn2pVasWnp6ehIaG8ueff5Zj2sqrrL/Pl23atAknJyfatWtn34AOoqzHOS8vj5dffpn69etjNptp2LAhX375ZTmlrbzKepwXLVpE27ZtcXNzo3bt2jzyyCOcP3++nNJWTuvXr2fw4MH4+/ujKApLly695ja6fA6qokwWL16smkwmdf78+WpMTIz61FNPqe7u7urx48dL7X/06FHVzc1Nfeqpp9SYmBh1/vz5qslkUn/88cdyTl65lPU4P/XUU+rMmTPVbdu2qfHx8erkyZNVk8mk7tq1q5yTVy5lPc6XpaWlqQ0aNFDDwsLUtm3blk/YSuxGjvOQIUPUkJAQdcWKFWpCQoK6detWddOmTeWYuvIp63HesGGDajAY1Pfee089evSoumHDBrVly5bq0KFDyzl55bJs2TL15ZdfVpcsWaICakRExFX76/U5KAVOGXXu3Fl94oknirU1a9ZMffHFF0vtP2nSJLVZs2bF2saOHat26dLFbhkdQVmPc2latGihvv7667c6mkO50eM8fPhw9ZVXXlGnTJkiBc51KOtx/uOPP1SLxaKeP3++POI5jLIe57fffltt0KBBsbb3339frVu3rt0yOprrKXD0+hyUS1RlkJ+fz86dOwkLCyvWHhYWxubNm0vdZsuWLSX69+vXjx07dlBQUGC3rJXZjRznf7LZbGRmZuLl5WWPiA7hRo/zggULOHLkCFOmTLF3RIdwI8f5l19+oWPHjsyaNYs6derQpEkTnnvuOXJycsojcqV0I8e5a9eunDp1imXLlqGqKsnJyfz444/ceeed5RG5ytDrc7BKLrZ5o86dO4fVasXX17dYu6+vL0lJSaVuk5SUVGr/wsJCzp07R+3ate2Wt7K6keP8T++++y5ZWVkMGzbMHhEdwo0c50OHDvHiiy+yYcMGnJzkn4/rcSPH+ejRo2zcuBEXFxciIiI4d+4c48aN48KFCzIO5wpu5Dh37dqVRYsWMXz4cHJzcyksLGTIkCF88MEH5RG5ytDrc1DO4NwARVGKfa+qaom2a/UvrV0UV9bjfNm3337L1KlT+e677/Dx8bFXPIdxvcfZarUycuRIXn/9dZo0aVJe8RxGWX6fbTYbiqKwaNEiOnfuzMCBA5k9ezZfffWVnMW5hrIc55iYGCZMmMBrr73Gzp07iYyMJCEhgSeeeKI8olYpenwOyp9gZeDt7Y3RaCzx10BKSkqJ6vQyPz+/Uvs7OTlRs2ZNu2WtzG7kOF/23Xff8dhjj/HDDz/Qp08fe8as9Mp6nDMzM9mxYwe7d+9m/PjxgPZBrKoqTk5OLF++nDvuuKNcslcmN/L7XLt2berUqYPFYilqa968OaqqcurUKRo3bmzXzJXRjRznGTNm0K1bN55//nkA2rRpg7u7O927d+fNN9+UM+y3iF6fg3IGpwycnZ3p0KEDK1asKNa+YsUKunbtWuo2oaGhJfovX76cjh07YjKZ7Ja1MruR4wzamZvRo0fzzTffyDX061DW4+zp6cm+ffuIjo4uejzxxBM0bdqU6OhoQkJCyit6pXIjv8/dunUjMTGRixcvFrXFx8djMBioW7euXfNWVjdynLOzszEYin8MGo1G4K8zDOLm6fY5aNchzA7o8m2IX3zxhRoTE6NOnDhRdXd3V48dO6aqqqq++OKLanh4eFH/y7fHPf3002pMTIz6xRdfyG3i16Gsx/mbb75RnZyc1A8//FA9c+ZM0SMtLU2vXagUynqc/0nuoro+ZT3OmZmZat26ddX77rtPPXDggLpu3Tq1cePG6uOPP67XLlQKZT3OCxYsUJ2cnNSPPvpIPXLkiLpx40a1Y8eOaufOnfXahUohMzNT3b17t7p7924VUGfPnq3u3r276Hb8ivI5KAXODfjwww/V+vXrq87Ozmr79u3VdevWFb328MMPqz179izWf+3atWpwcLDq7OysBgYGqh9//HE5J66cynKce/bsqQIlHg8//HD5B69kyvr7/HdS4Fy/sh7n2NhYtU+fPqqrq6tat25d9ZlnnlGzs7PLOXXlU9bj/P7776stWrRQXV1d1dq1a6ujRo1ST506Vc6pK5c1a9Zc9d/bivI5qKiqnIcTQgghhGORMThCCCGEcDhS4AghhBDC4UiBI4QQQgiHIwWOEEIIIRyOFDhCCCGEcDhS4AghhBDC4UiBI4QQQgiHIwWOEEIIIRyOFDhCCCGEcDhS4AghhBDC4UiBI4QQQgiHIwWOEEIIIRzO/wNIrlWT9zGyPAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bn= b + 0.001*np.random.randn(n)\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),b, label='Noise free b')\n",
+    "plt.plot(np.linspace(0,1,n),bn, label='Noisy b')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd88fee8",
+   "metadata": {},
+   "source": [
+    "$A$, $x$ and $b$ are stored as Numpy arrays. Just as in Matlab we can look at the singular value decomposition (SVD).   On a log scale we see the singular values decay as a negative power as expected for an operator that approximates the inverse of two derivatives.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "761ac845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
+    "plt.figure()\n",
+    "plt.plot(s)\n",
+    "plt.title('The singular values of the forward operator A')\n",
+    "plt.show()\n",
+    "plt.figure()\n",
+    "plt.loglog(s)\n",
+    "plt.title('A log-log plot of the singular values of the forward operator A')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad79f585",
+   "metadata": {},
+   "source": [
+    "Solving the least squares problem $\\min_x\\|Ax-b\\|_2^2$ demonstrates the ill-posedness of the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cab55e6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xlq=np.linalg.lstsq(A,bn,rcond=None)[0]\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),xlq, label='Least squares solution')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.figure()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44ed725c",
+   "metadata": {},
+   "source": [
+    "We see the solution matches the observed data well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "28589f81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.213659415696782e-15"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(A@xlq-bn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c64567",
+   "metadata": {},
+   "source": [
+    "However this is not a desired solution to our inverse problem. Now, instead, solve using Tikonov regularization: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e14a076f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg_param= 0.005\n",
+    "\n",
+    "Atik=np.vstack((A,reg_param*np.eye(n))) \n",
+    "\n",
+    "btik = np.hstack((bn,np.zeros(n)))\n",
+    "\n",
+    "xtik =  np.linalg.lstsq(Atik,btik,rcond=None)[0]\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),xtik, label='Tikhonov regularised solution- numpy')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.figure()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e35764d",
+   "metadata": {},
+   "source": [
+    "This solution is an improvement, although we still have this odd behaviour at the end of the interval. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c93e53f",
+   "metadata": {},
+   "source": [
+    "Now convert the matrix and operators to CIL types so we can use inbuilt optimisation routines and regularisers in CIL: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7ab3314c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Aop = MatrixOperator(A)\n",
+    "bop= VectorData(bn) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a523f4b",
+   "metadata": {},
+   "source": [
+    "We run CGLS to solve the un-regularised least squares problem $$\\min_x \\|Ax-b\\|_2^2.$$ We choose a small number of iterations to implicitly regularise via early stopping: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a8bd2985",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Iter   Max Iter     Time/Iter            Objective\n",
+      "                               [s]                     \n",
+      "        0          4         0.000          2.16038e-01\n",
+      "-------------------------------------------------------\n",
+      "        4          4         0.081                     \n",
+      "Stop criterion has been reached.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MlClTaN++PQEBATn+q83JyYn+/fuzcOFCVq5cyZo1awxtyJaWlrla4fhB56hZsyahoaHcvHnTUPbeTpNZPyjh4eGGfSdOnMhWZs+ePfTo0YPnn3+eevXqUalSpfv6Q+TnHoH+s9CyZUumT5/O8ePHsbKyYt26dQ88p4eHB0OHDmX58uXMnj2bBQsWAFCzZk1OnDiRrfPrvn37MDMzo1q1nP+IPTw8sr3u+Ph4QkJCspV51Hvg5OREuXLl2Lt3b7b9+/fvJyAg4IHH5UZWv5G///6bNm3aAPp/NPzyyy8P7C+SxcrKqkBWx27YsCEXL17E09PzvvfQ2dkZ0H8+Xn31VTp37kytWrWwtrZ+4JQAj/L8888TGhrKnDlzOHv2LEOGDHlo+Yd9fnK6B7n9nJw8eZKUlBTD9sGDB3FwcMhWy3jw4MFs587qL2Nunr9/8NSoUeO+fklHjhzJ17mEiYoNg2Vd/k1EGg2HkduKRSICJTAZMSVxcXGcOHEi2yM0NJQxY8YQExPD//73P/755x+uXLnCli1bGD58OFqt1jDyYMGCBVy6dIm///6bCRMmZDv3l19+yS+//MK5c+e4cOECq1atomzZsoYhmn5+fmzfvp2IiIgHVj8/7BwdOnSgevXqDB48mJMnT7Jnz577OsRVqVIFHx8fpk2bxoULF9i4cSOff/75fWWy/gUaHBzMqFGj7ht9lZNH3aNDhw7x4YcfcuTIEUJDQ1m7di23b99+4I/4e++9x++//86lS5c4e/YsGzZsMJQdOHAgNjY2DBkyhDNnzrBjxw5eeeUVBg0adN8cOlnatWvHjz/+yJ49ezhz5gxDhgy574ckN+/Bm2++yccff8zKlSs5f/48EydO5MSJE4wbN+6R9+hhnnzySRISEvjjjz8MyUibNm1Yvnw5Hh4e1KxZ84HH+vn5cejQIa5evUpUVNRDa2AeZuDAgbi7u9OjRw/27NlDSEgIu3btYty4cVy/fh3Qfz5+/PFHgoODOXToEAMHDsTW1jZf1ytTpgy9e/fmzTffpFOnTtl+/O/1qM+Pn58fp06d4vz580RFRZGRkZHrz0l6ejojRowwdJieOnUqY8eOzTb9elhYGBMmTOD8+fP8/PPPfP3114/1no8aNYpz587x9ttvc+HCBX799VdDLbYsLFoKnNsI85+AsENg7QTPfg9dvwTL/P0tFYoC761SCPLSgdVUDBkyRAH3PYYMGaKUUurChQuqV69eysXFRdna2qoaNWqo8ePHGzq1bd26VQUEBChra2tVt25dtXPnzmwdIhcsWKDq16+v7O3tlZOTk2rfvr06duyY4frr169XVapUURYWFsrX1zfHGB91jvPnz6snnnhCWVlZqWrVqqnNmzff1ylz7969qk6dOsrGxka1atVKrVq1KlsH1ujoaNWjRw/l4OCgPD091ZQpU9TgwYMf2WHyUfcoKChIPfXUU8rDw0NZW1uratWqqa+//vqB78f777+vAgIClK2trXJ1dVU9evRQV65cMTx/6tQp1bZtW2VjY6NcXV3VCy+8oBIS/u3sdW+Hxri4ONWvXz/l5OSkfHx81LJly+7rwJrTe3BvB1atVqumT5+uypcvrywtLVW9evUMnRKV+rcD6/Hjxw377ty5owC1Y8eOB75epZQKDAxUHh4ehs9UdHS00mg0qm/fvtnK3fvazp8/r5o1a6ZsbW0N72V+OyuHh4erwYMHK3d3d2Vtba0qVaqkXnjhBcPf+rFjx1SjRo2UtbW1qlq1qlq1alWOnTfv7QicUzxKKbV9+3YFqF9//fWh9+ZRn5/IyEjVsWNH5eDgkO1e5/Zz8t577yk3Nzfl4OCgRo4cqVJTUw1lWrdurV5++WU1evRo5eTkpMqUKaMmTpyYrUNrbu6Bs7OzWrp0qWH7999/V1WqVFHW1taqTZs2at68eQ/tYGyq363iPzJSldr01r+dVBe0VSompEhDyG0HVo1Sxb/hMD4+HmdnZ+Li4u6bcyQ1NZWQkBD8/f1lZcliQKPR5GolZyGMYcWKFYwbN46bN29iZWVV5NcfOnQosbGxD539uE2bNtSvX7/Qp3ufOXMm8+fPf+CkkvLdauKiL8PqYRB+Ur/dfCy0nwoWRfu5f9jv93/luQOrEEKYmuTkZEJCQpg1axajRo0ySiJibHPnzqVx48a4ubmxb98+Pv3008eaK0cUY6dXwx/jIT0BbF2h13yoZpyBHbklyYgQosT75JNPmDlzJk8++SSTJk0ydjhGcfHiRT744ANiYmKoWLEir7/+eqm4F5laHWduxhMZn0p0UjoxSelEJabh4WhNt7rl8HEtQdPqpyfD5rfhmH4IOxVbQJ9F4FzeuHHlgjTTCCGEuI+pf7cqpdgadIuPNp/jyu0HT3Me6FuGnvXL0aVuOVztTbjGLDIYVg2D28GABp58E1q/DebGrXOQZhohhBCl0vHQO8zadI5/ruqnMnC0saCyhwPuDla42ltRxt6Kszfi2Xc5iqPX7nD02h2m/xHEiFb+TOhYDWuLgpkzqkgoBceXw6Y3ITMFHLyg9wKo1MbYkeWJJCNCCCFKhNQMLW+vOcXvJ/TzH1lbmDGylT+jWlfGyeb+tdBuxafyx8mb/HbiBmduxPPdrivsvhDF7P71qV72/jWLip20BNjwGpxepd+u1FafiDh4GjeufJBkRAghhMnL0Op4ecUx/j4XiUYDfRtWYEKnang7P3guDS8nG0a2qsTIVpX462wEk9aeJjg8nm7f7OWtp6ozvKU/ZmbFdB6Wmyf0o2ViroDGHNpNgZbjwcw0pw+TZEQIIYRJ0+kUr/96kr/PRWJtYcayYU1oXtktT+d4qlZZGlR04e3Vp9hx/jYfbAxm14XbLBzcCJsCWuqjQCgF/yyALVNAmw5OFaDvYqjYzNiRPRbTTKGEEEII9B1V31t/hvUnb2JhpmH+84F5TkSyeDrasGRoYz7oWRtbS3P2XIxi+h9BBRzxY0i5Ayufhz/f0ici1TvD6D0mn4iAJCNCCCFM2GdbzrP8YCgaDXzRvz5tazxefwmNRsPzzXz5blAgGg38/E8oa45eL6BoH0PYPzC/FZzbAGaW8PRH8NxPYOdq7MgKhCQjIs+mTZtG/fr1jR0Gbdq0yffqsvml0WgeOntmbvn5+RX6DJtClHTL9oXw7Y7LAHzQszbd65UrsHM/Wc2Dce2rAjD5t9Oci4gvsHPniU4He2fDkqchLgzK+MOILdDsJShB6wpJMmJEERERjBs3jipVqmBjY4OXlxdPPPEE8+fPJzk52djh5dvOnTvRaDTExsYWy/MVpWXLlhkWJ/yvw4cP8+KLLxZ9QEKUEMHh8Xy46RwAbz5VnYFNfQv8Gq+0q0qrqu6kZuh4efkxEtMyC/waD5V4G1b0hW1TQWmhVm8YtRvKNyzaOIqAdGA1kitXrtCyZUtcXFz48MMPqVOnDpmZmVy4cIElS5ZQrlw5unfvnuOxGRkZWFreP0zN1KSnp5fKabkBPDw8jB2CECYrLVPLaytPkK7V0SHAi5fbVC6U65ibafjquQZ0mbOHK1FJvL36FN8MaFA0Kx2H7IE1IyExAixs4JlPoOHgElUb8l9SM2IkL7/8MhYWFhw5coR+/foREBBAnTp16NOnDxs3bqRbt26GshqNhvnz59OjRw/s7e354IMPAJg3bx6VK1fGysqK6tWr8+OPPxqOuXr1KhqNhhMnThj2xcbGotFo2LlzJ/BvjcP27dtp1KgRdnZ2tGjRgvPnz2eL9aOPPsLLywtHR0dGjBhBamrqA1/X1atXadu2LaBfsl2j0TB06FBA36wyduxYJkyYgLu7Ox07dnxknA87H4BOp+Ott97C1dWVsmXLMm3atIfe9507d9KkSRPs7e1xcXGhZcuWXLt2zfD8w+5pTue6t8bmxIkTaDQarl69ys6dOxk2bBhxcXFoNBo0Go0hvnubaUJDQ+nRowcODg44OTnRr18/bt26ZXg+q2nsxx9/xM/PD2dnZ5577jkSEhIe+nqFKIm+3HqRcxEJuNlbMat3nUJNDlztrfh2YEMszDRsPB3O8oPXHn3Q49BpYccs+KG7PhHxqAEv7IDAISU2EYGSmIwoBelJxnnkcmb96OhotmzZwpgxY7C3t8+xzL1/XFOnTqVHjx6cPn2a4cOHs27dOsaNG8frr7/OmTNnGDVqFMOGDWPHjh15vmWTJ0/m888/58iRI1hYWDB8+HDDc7/++itTp05l5syZHDlyBG9vb+bOnfvAc/n4+LBmzRoAzp8/T3h4OF999ZXh+e+//x4LCwv27dvHd99998jYcnM+e3t7Dh06xCeffMKMGTPYunVrjufKzMykZ8+etG7dmlOnTnHgwAFefPFFw70uyHsK0KJFC2bPno2TkxPh4eGEh4fzxhtv3FdOKUXPnj2JiYlh165dbN26lcuXL9O/f/9s5S5fvsxvv/3Ghg0b2LBhA7t27eKjjz7KV2xCmKrDV2P4bre+n8iHvevg4Whd6NdsWLEMkzoHAPDpX+eJTU4vnAvF34Tvu8Ouj0DpoMHz8MLf4FWzcK5XjJS8ZpqMZPiw4Dox5ck7N8Eq5+Tivy5duoRSiurVq2fb7+7ubqh1GDNmDB9//LHhuQEDBmRLEgYMGMDQoUN5+eWXAZgwYQIHDx7ks88+M9Qk5NbMmTNp3bo1ABMnTqRLly6kpqZiY2PD7NmzGT58OCNHjgTggw8+YNu2bQ+sHTE3N8fVVd+729PT877+ElWqVOGTTz4xbF+9evWhsT3qfHXr1mXq1KkAVK1alW+++Ybt27fTsWPH+84VHx9PXFwcXbt2pXJlfbVuQECA4fnPPvuswO4pgJWVFc7Ozmg0GsqWLfvActu2bePUqVOEhITg4+MDwI8//kitWrU4fPgwjRs3BvS1QMuWLcPRUT8z5KBBg9i+fTszZ87Mc2xCmKLEtExe//UkSkHfwAo8VevBf1cFbWgLP1YdCeNcRAJzd17mnc4Bjz4oLy5uhXWjIDkarByg65dQt1/BXqMYK3k1Iybk3tqPf/75hxMnTlCrVi3S0tKyPdeoUaNs28HBwbRs2TLbvpYtWxIcHJznOOrWrWv4f29vbwAiIyMN12nevHm28vdu58W9r+Nx/Td20MefFfu9XF1dGTp0KE899RTdunXjq6++Ijw83PB8Qd7TvAgODsbHx8eQiADUrFkTFxeXbNf28/MzJCLw8NcqREk0c2MQoTHJlHex5b1uRVtbYG6m4e2nawCwbP9VbsSmFMyJtRmw5V19R9XkaChbV99JtRQlIlASa0Ys7fQ1FMa6di5UqVIFjUbDuXPnsu2vVKkSALa2909fnFNzzr3JjFLKsM/s7pTA/12UOSMjI+ew/9MZNut4nU73yNeRH/e+jrzEmZN7O/JqNJqHxr506VJeffVVNm/ezMqVK5kyZQpbt26lWbNmhuP/67/39F6PG/ujrnHv/ry+ViFKku3Bt/j5nzA0Gvjs2Xo5rjVT2NpU96BZJVcOXonh8y3n+aJf/cc74Z1rsHo43Dii324yCjq9DxaF3/RU3JS8mhGNRt9UYoxHLjsXubm50bFjR7755huSkh68tPXDBAQEsHfv3mz79u/fb2h2yBqt8d9/+f+3k2hernPw4MFs++7dvlfWCBmtVvvI8+cmzrycLzcaNGjApEmT2L9/P7Vr1+ann34CHn1P8xv7o+KuWbMmoaGhhIWFGfYFBQURFxf3wGsLUZpEJabx9ppTAIxo6Z/vGVYfl0ajYdIz+r/JdcdvEBz+GHOPBP2un8TsxhGwcYZ+P0LnT0plIgIlMRkxEXPnziUzM5NGjRqxcuVKgoODOX/+PMuXL+fcuXOYmz98LYQ333yTZcuWMX/+fC5evMgXX3zB2rVrDR0kbW1tadasGR999BFBQUHs3r2bKVOm5DnOcePGsWTJEpYsWcKFCxeYOnUqZ8+efegxvr6+aDQaNmzYwO3bt0lMTHxg2dzEmZfzPUxISAiTJk3iwIEDXLt2jS1btnDhwgXDD/6j7um9qlSpgo+PD9OmTePChQts3LiRzz//PFsZPz8/EhMT2b59O1FRUTnOH9OhQwfq1q3LwIEDOXbsGP/88w+DBw+mdevWBd6sJYSpUUoxcc0pohLTqVHWkTeeqv7ogwpRPR8XutT1Rin4ePO5Rx9wr4xU2Pg6/DoY0uKgQmMYtQdq5jyVQ6mhTEBcXJwCVFxc3H3PpaSkqKCgIJWSkmKEyB7PzZs31dixY5W/v7+ytLRUDg4OqkmTJurTTz9VSUlJhnKAWrdu3X3Hz507V1WqVElZWlqqatWqqR9++CHb80FBQapZs2bK1tZW1a9fX23ZskUBaseOHUoppXbs2KEAdefOHcMxx48fV4AKCQkx7Js5c6Zyd3dXDg4OasiQIeqtt95S9erVe+hrmzFjhipbtqzSaDRqyJAhSimlWrdurcaNG3df2UfFmZfz9ejRw/D8vSIiIlTPnj2Vt7e3srKyUr6+vuq9995TWq3WUOZR9/Te92Lv3r2qTp06ysbGRrVq1UqtWrXqvvs3evRo5ebmpgA1depUpZRSvr6+6ssvvzSUuXbtmurevbuyt7dXjo6O6tlnn1URERGG56dOnXrfPf/yyy+Vr69vjq9ViMdVXL5bVxy8pnzf3qCqvrNJBYff/xtgDCG3E1XlSRuV79sb1L5Lt3N/4O2LSs1tqdRUJ/1jy7tKZaYXXqDFwMN+v/9Lo1Qux6MaUXx8PM7OzsTFxeHk5JTtudTUVEJCQvD398fGxsZIEQohRMlSHL5bQ6KS6PzVHlIytEzpEsDIVpWMEkdOpv5+hu8PXKNuBWd+e7klZmaPaKY/uRI2vAYZSWDnBr0WQNUORROsET3s9/u/pJlGCCFEsZOh1TF+5QlSMrS0qOzG8Jb+xg4pm1faV8XeypxT1+PYEhTx4ILpSfDbGFj3oj4R8WsFo/eVikQkLyQZEUIIUex88/clTobF4mRjwWfP1nt0zUMRc3ewZvgT+gTp678vkWMjw62zsKAtnFgOGjNo8w4M/h2cvIs42uJPkhEhhBDFypkbcXyz4xIAH/SqQzmX+6c7KA6Gt/TH3sqcszfj+fvcf+b8UQqOLIWF7SDqPDiUhcHroc3bYPbwwQmllSQjQgghio30TB1vrj6FVqfoXKcs3esZaUbtXChjb8XzzfWrBc/Jqh1JjYPVw2DDeMhMhSod4aV94N/KuMEWc5KMCCGEKDbm7bxMcHg8ZewsmdGjtrHDeaQXWlXCxtKMk2GxHDu4A757Es6uAzML6DgDBvwK9u7GDrPYKzEzsJrAoCAhhDAZxvhOPRcRzzc7LgIwrXst3B2K/wRg7g7WDGxSEXVwHnX/+hnIBOeK0HcJ+DQ2dngmw+STkawpspOTk3OcRl0IIUTeZU3Qd+8yBIUlU6vjzVWnyNAqOtb0KtbNM9kkx/BW7HSsLf8CILriU7j97zuwLWPkwEyLyScj5ubmuLi4GBYMs7Oze+BaIkIIIR5OKUVycjKRkZG4uLg8cjbogvLd7iucvhGHs60lM3vWNo3v8dCDsHoE1vHXydRYMi39eS5l9OMXSUTyzOSTEcCwPLusYCqEEAXDxcXF8N1a2C5FJvDVNn3zzHtda+LpVMwnsNTpYN+X8PdMUFpwrUzM0/NZ+X0UGSF3OHw1hsZ+rsaO0qSUiGREo9Hg7e2Np6dnvlZNFUII8S9LS8siqxHR6RTvrD1DulZH2+oe9G5Yvkium2+JkbD2RbiyQ79dpx90/QJPa0f6Bp7m539C+frvS/wwvIlx4zQxJSIZyWJubl5kf0BCCCEe3+qj1/nnagy2lua8X9ybZ67s1CciibfA0g46fwr1BxpWbH+5TWVWHg5l94XbXLiVQDUvR+PGa0JkaK8QQgijiE5M48M/gwF4rWNVKpSxM3JED6DNhL8/gB966hMRz5rwwg5o8LwhEQHwcbWjU01909bSfSFGCtY0STIihBDCKGb9eY7Y5AxqlHVkWDFbe8Yg7gZ83w12fwooaDgEXvgbPGvkWHxEK/3rWHvsBjFJ6UUYqGmTZEQIIUSRO3glmtVHr6PRwIe962BpXgx/js5vhvlPQOh+sHLUzx3SfQ5YPngaiUa+ZahbwZm0TB0rDl4rwmBNWzF894UQQpRkaZlaJq87DcCAJhVpWLGYDYXNTIe/JsPP/SElBrzrw+jdULvPIw/VaDSMuLuA3g8Hr5GWqS3kYEsGSUaEEEIUqQW7rnD5dhLuDta89XTOzR1GExMCS56CA9/ot5u9DCO2gGulXJ+icx1vvJysuZ2QxoaT4YUUaMkiyYgQQogicy06ia/vrsj7btcAnG2LZobXXDmzVr+2zM1jYOMCz/0MT88Ci7xNS29pbsbg5n4ALN4bIsuV5IIkI0IIIYqEUor3fj9LeqaOVlXdi8+U7xkpsOE1/Wq7afHg0wxG74UanfN9yoFNK2JjaUZQeDwHr8QUYLAlkyQjQgghisSfZyLYdeE2VhZmzOhRTOYUuX0BFraHI0sADbR6HYZuBBefxzqti50VfRpWAPS1I+LhJBkRQghR6BLTMpnxRxAAL7WujL+7vZEjAk78BAtaQ+RZsPeA59dA+/fAvGDmAx1+tyPr9nO3uBqVVCDnLKkkGRFCCFHovtx6gYj4VHzd7HipTWXjBpOWCOtGw28vQUYy+LeG0fugSvsCvUxlDwdaV/NAKVh7/EaBnrukkWRECCFEoQq6Gc+y/VcBmNGjNjaWRly2I+I0LGgDJ38GjRm0mwKD1oGjV6FcrmcDfb+YjaduSkfWhyhRa9MIIYQoXnQ6xZTfTqPVKbrU8aZ1NQ/jBKIUHFkMm98BbRo4loM+i8CvZaFetkOAF1YWZly+ncT5WwnUKOtUqNczVVIzIoQQotD8cOAqx0Jjsbcy592uNY0TREos/DoYNr6uT0SqPqUfLVPIiQiAo42lIQHbeErmHHkQSUaEEEIUihNhsczcpF8I762na1DW2abog7h+FL5rBcHrwcwSOs2EASvB3q3IQuha1xvQJyPSVJMzaaYRQghR4O4kpTNmxTEytIpnapdlcHPfog1Ap4OD38K2aaDLBBdf6LsUKgQWbRxA+7tNNVeikggOT6BmOWmquZfUjAghhChQOp1i/MoT3IhNwd/dnk/61i3aOUWSovXrymyZok9EavaEUbuNkogAOFhb0Lb63aaa0zeNEkNxJ8mIEEKIAvXNjkvsunAbG0sz5g5siKNNEU75fnWffqXdi1vA3Bq6fgnPLgNbl6KLIQdd62aNqpGmmpxIM40QQogCs/diFF9uuwDABz3rEOBdRE0SOi3s+Rx2zgKlA/dq+maZsrWL5vqP0K6GJzaWZlyNTubszXhql3c2dkjFitSMCCGEKBCXIhN55edjKAXPNfahb2CForlwQgT82BN2zNQnIvX+By/sKDaJCIC9tQXtangCsEFG1dxHkhEhhBCPLSwmmecXHeJOcgZ1KzgzrXutornwpe36ZpmQ3WBpDz3nQ6/5YO1QNNfPgy517jbVnJYJ0O6Vr2Rk7ty5+Pv7Y2NjQ2BgIHv27Hlo+RUrVlCvXj3s7Ozw9vZm2LBhREdH5ytgIYQQxUtkfCrPLz5ERHwqVTwdWDasSeHPsqrNgG3TYXlvSLoNXrXhxZ1Q/3+Fe93H0LaGB7aW5oTFpHD6RpyxwylW8pyMrFy5kvHjxzN58mSOHz9Oq1ateOaZZwgNDc2x/N69exk8eDAjRozg7NmzrFq1isOHDzNy5MjHDl4IIYRxxSanM2jxP1yLTsbH1ZblI5riam9VyBcNg2VdYO8X+u1GI2DkNvCoVrjXfUx2Vha0C9A31cgEaNnlORn54osvGDFiBCNHjiQgIIDZs2fj4+PDvHnzcix/8OBB/Pz8ePXVV/H39+eJJ55g1KhRHDly5LGDF0IIYTyJaZkMWXqY87cS8HS0ZsWIZoU/sdm5TfpmmbBDYO0Ez34PXb8AS9vCvW4B6XZ3ArRNZyQZ+a88JSPp6ekcPXqUTp06ZdvfqVMn9u/fn+MxLVq04Pr162zatAmlFLdu3WL16tV06dIl/1ELIYQwKqUUr608wcmwWFzsLFk+sikV3ewK74KZafDnRPjlf5AaC+UDYfQeqNWz8K5ZCJ6s5oGluYawmBRCo5ONHU6xkadkJCoqCq1Wi5dX9tUNvby8iIiIyPGYFi1asGLFCvr374+VlRVly5bFxcWFr7/++oHXSUtLIz4+PttDCCFE8bF4bwhbg25hZW7G0qGNqeblWHgXi74MizvCobs18M3HwrDNUMav8K5ZSOysLKjv4wLA/stRxg2mGMlXB9Z7Z9JTSj1wdr2goCBeffVV3nvvPY4ePcrmzZsJCQlh9OjRDzz/rFmzcHZ2Njx8fHzyE6YQQohCcPTaHT768xwA73arSYOKZQrvYqdXw3etIfwk2LrCgF/hqZlgUcj9UgpRi8ruAOy7LAM5suQpGXF3d8fc3Py+WpDIyMj7akuyzJo1i5YtW/Lmm29St25dnnrqKebOncuSJUsID8+5zWzSpEnExcUZHmFhYXkJUwghRCGJSUpn7E/HyNQputb15vmmFQvnQunJsP4VWDMC0hPAt6V+pd1qTxXO9YpQi8r6RfoOXI6SIb535SkZsbKyIjAwkK1bt2bbv3XrVlq0aJHjMcnJyZiZZb+Mubl+yNeD3gRra2ucnJyyPYQQQhiXTqeY8OsJwuNSqeRuz0d9CmnNmchzsLAdHPsB0EDrt2HwenAuX/DXMoL6FV2wsTQjKjGdC7cSjR1OsZDnZpoJEyawaNEilixZQnBwMK+99hqhoaGGZpdJkyYxePBgQ/lu3bqxdu1a5s2bx5UrV9i3bx+vvvoqTZo0oVy5cgX3SoQQQhSq+bsvs/P8bawtzPh2YEMcrAt4RRGl4NiPsKAN3A4GBy8Y/Bu0fQfMS87qJdYW5jT2cwWk30iWPL+7/fv3Jzo6mhkzZhAeHk7t2rXZtGkTvr765aHDw8OzzTkydOhQEhIS+Oabb3j99ddxcXGhXbt2fPzxxwX3KoQQQhSqk2GxfL5Fv+bMjB61Cn7NmbQE2PAanF6l367cDnp9Bw6eBXudYqJFZXf2XIxi/+VohrX0N3Y4RqdRJtBgFR8fj7OzM3FxcdJkI4QQRSwtU0u3r/dy4VYi3eqVY85z9Qu2eSb8JKwaBjGXQWMO7aZAy/FgVnJXLDl1PZbu3+zD0caC4+92xMK8ZL7W3P5+l5x6LyGEEIXi2x2XuXArEXcHK2Z0r1VwiYhS8M9C2DIZtOngVAH6LoaKzQrm/MVYrXLOONpYkJCaydmb8dS7O9y3tCqZqZgQQogCcfZmHHN3XAJgRo/alCmoqd5T7sDK5+HPN/WJSPXO+knMSkEiAmBupqFZJf2omn3Sb0SSESGEEDnL0Op4a/UpMnWKZ2qXpXMd74I5cdg/MP9JOLcBzCzh6Y/guZ/AzrVgzm8iWhqG+Mp8I9JMI4QQIkcLdl/h7M14XOwsmd6j1uOfUKeD/XNg+wxQWijjD32XQPmGj39uE9Siin7ys8NXY0jL1GJtUcgrHRdjUjMihBDiPhdvJfDVtosATO1WE0/Hx1wAL/E2rOgL26bqE5FavWHU7lKbiABU9XTA3cGa1Awdx0NjjR2OUUkyIoQQIhudTjFx7WnStTra1/CkZ/3HnGwsZLd+pd3L28HCBrrN0deI2JTu0ZEajcYwG+v+S6W734gkI0IIIbJZfew6R6/dwc7KnA961c7/6BmdFnZ8CN93h8QI8KgBL+yAwCFQGDO3miBDMlLK+41InxEhhBAGcckZhkXwxneoirezbf5OFH8T1rwA1/bqt+s/D50/ASv7Aoq0ZGh5t9/IibBYktIysS/oWW1NhNSMCCGEMPh0yzliktKp6umQ/5lBL27VN8tc2wtWDtB7EfT8VhKRHPi42lGhjC2ZOsU/V2OMHY7RSDIihBAC0M8KuuKQfjmPGT1qY5nXWUG1GbDlXX1H1eRoKFtX30m17rOFEG3J0fzufCP/hEgyIoQQohTT6hTv/nYGpaBn/XI0v9uXIdfuXIMlT+uH7gI0GQUjt4Fb5YIPtoQJ9C0DwLFrd4wcifGUzsYpIYQQ2aw8HMbJ63E4WlvwTueAvB0c9Dv8/gqkxYGNM3T/Bmp2L5xAS6CGd5ORU9fjyNTqSuw6NQ8jyYgQQpRy0YlpfPKXvtPqax2r4emUyzlFMlJhyxQ4vFC/XaEx9FkMZXwLKdKSqYqHA47WFiSkZXIuIoHa5Z2NHVKRK33plxBCiGym/xFEbHIGNco6Mrh5LhOJqEuwqMO/iUjLcTDsT0lE8sHMTEP9ii4AHA+LNWosxiLJiBBClGLbgm6x/uRNzDTwcZ+6uWsiOLkSvnsSbp0GOzcYuBo6zgBzy8IPuIRqUFHfVHO8lPYbkWYaIYQopeJTM5jy2xkAXmhV6dHL2Kcnwaa34MRy/bZfK+i9EJwKaAG9UqxBKa8ZkWRECCFKqVmbgomIT8XPzY7xHao9vPCtIFg1FKLOg8YMWk+EJ98As9K7uFtBanA3EQyJSiImKR1XeyvjBlTEpJlGCCFKof2Xovj5nzBA3zxja/WApEIpOLoMFrbVJyIOZWHwemjztiQiBcjFzopKHvpJ4Y6Hlr6mGklGhBCilElOz2Ti2tMAPN+sIk0rPWBOkdR4WD0c/hgHmalQpSO8tA/8WxVhtKVHw6x+I6VwBV9JRoQQopT57K8LhMYkU87ZhrefrpFzoZvH9Z1Uz64FMwt9B9UBv4K9e9EGW4pk9Rs5VgprRqTPiBBClCI7zkWyZF8IADN718HR5p4RMErBwXmw9T3QZYBzRei7BHwaGyHa0iWrZuRkWCxancLcrPSsbCzJiBBClBLhcSlM+PUEAENb+NG2umf2Askx8PsYOL9Jvx3QDbp/DbZlijbQUqqalyP2VuYkpWu5cCuBAG8nY4dUZKSZRgghSoFMrY5xv5zgTnIGtcs7ManzPc0zoQdhfit9ImJuBZ0/g34/SiJShMzNNIbh1aWt34gkI0IIUQrM+fsS/4TEYG9lztf/a4i1xd2RMDod7PkclnaG+OvgWlm/wF2TF0BTepoJiouspprS1m9EmmmEEKKE238piq//vgjAh73r4O+uH0JKYiSsfRGu7NBv13kWun4J1o5GilSU1k6skowIIUQJFpWYxriVJ1AK+jfyoUf98vonLu/QJyJJkWBhC10+g/oDpTbEyLKmhb9yO4nY5HRc7ErH5GfSTCOEECWUUopJa09zOyGNqp4OTOteC7SZsP19+LGXPhHxrAkv7oQGz0siUgy42lvh52YHlK6p4SUZEUKIEmrNsRtsDbqFpbmGr55rgG1KBHzfFfZ8BihoOARGbgfPB8w1IoyiNE5+JsmIEEKUQDdiU5i+/iwA4ztUo2bCPpjfEkIPgJUj9FkM3eeAlZ2RIxX3MiyaV4r6jUifESGEKGF0OsVbq0+SkJZJYx97XkpbAj9/q3/Su75+EjO3ykaNUTxYVr+RE6Gx6HQKs1Iw+ZkkI0IIUcL8ePAa+y5FU8XyNj9qPsHs4An9E81ehg7TwMLamOGJR6hR1hErCzMS0jK5FpP87+inEkyaaYQQogS5cjuRWX8G08XsIJusJ2MTeQJsXOC5n+DpWZKImAALczNqlNUPrz57M87I0RQNSUaEEKKE0OoUk379h3fVQr61moNVZiL4NIXRe6FGF2OHJ/KgVjn9VPBnb8YbOZKiIc00QghRQqz562+m3RpHgEUYCg2aJ16Dtu+AueWjDxbFSs1yzkCYJCNCCCFMR+SeJXQ9OBE7szRSrVyx6bcIqrQ3dlgin7JqRoIkGRFCCFHspSWiNk7A89RK0MAZ6wbUGvsLOJY1dmTiMQSUdcJMo59BNzI+FU8nG2OHVKikz4gQQpiqiNOwoDWaUyvRKg1f6frj8uIfaCQRMXm2VuZU8nAASke/EUlGhBDC1CgFhxfBwvYQfYkI5cpz6e/i2nkyFdxkkbuS4t9OrCV/RI0kI0IIYUpSYmHVENj4OmjTOGrdlGfSPsTcvwUDm1Q0dnSiANX0Lj0jaqTPiBBCmIrrR2H1MIi9BmaWHKk2nr4n6mNracEnfeqVipk6S5Na5ZyB0pGMSM2IEEIUdzod7P8alnTSJyIuvkT1X8/Q4MaAhjefqk5FN1ljpqTJaqYJjUkmPjXDyNEULklGhBCiOEuKhp+fgy1TQJcJNXuiRu1m4kFLEtMyaVDRhSEt/IwdpSgEZeytKOesH0UTXMJrRyQZEUKI4urqPpj/BFz8C8ytoeuX8OwyNlxIZltwJJbmGj7uUxdzaZ4psWqWkqYaSUaEEKK40Wlh16fwfVdIuAnu1eCFv6HRcO4kZzBt/VkAxrStQjUvGT1TkpWWaeGlA6sQQhQnCRGw9gUI2a3frjcAOn8K1vo5J97fGER0UjrVvBx4uU0VIwYqikJpGd4ryYgQQhQXl7bDulGQdBss7aHrF1DvOcPTuy/cZu2xG2g08FGfulhZSOV2SVfzbjJyKTKRtEwt1hbmRo6ocMgnWQghjE2bAdumwfLe+kTEqzaM2pUtEUlJ1/LOutMADG3hR8OKZYwUrChK5V1scba1JFOnuHgr0djhFBpJRoQQwphiw2BZF9j7pX670QgYuQ3cq2YrNm/XZa7fSaG8iy1vdKpuhECFMWg0mlLRVCPJiBBCGMu5jfrRMmGHwNoJnl2mb5qxtM1WLDQ6mfm7LgPwbtcA7K2lhb00KQ2dWOUTLYQQRS0zDba+B4fm67fLNYS+S8DVP8fiMzYEkZ6po1VVd56qJYvglTalYSZWSUaEEKIoRV/WT+keflK/3XwstJ8KFlY5Ft9xLpJtwbewMNMwtVstNBqZU6S0yaoZCQ6PR6tTJXJeGUlGhBCiqJxeDX+Mh/QEsHWFnvOg+tMPLJ6WqWX6H/o5RUY84U8VT4ciClQUJ5U8HLCxNCM5XcvV6CQqe5S8z4H0GRFCiMKWngzrX4E1I/SJSMUWMHrvQxMRgEV7QrganYynozWvtK/60LKi5DI301C9rL52JKiENtVIMiKEEIUp8hwsbAfHfgA08ORbMOQPcC7/0MNuxqbwzd+XAJjcJQAH6bRaqpX0Tqzy6RZCiMKgFBxfDpvehMwUcPCC3gugUptHHpqp1fHGqpOkZGhp4udK93rlCj9eUawFlNVP+3/hVoKRIykckowIIURBS0uADa/B6VX67Upt9YmIg2euDv/oz3PsvxyNnZU5M3vVlk6rwrAGkSQjQgghHi38JKwaCjFXQGMO7aZAy/FglrtW8d9P3GDR3hAAPn+2HlVlITzBv8nI9TspJKVllri5ZvLVZ2Tu3Ln4+/tjY2NDYGAge/bseWj5tLQ0Jk+ejK+vL9bW1lSuXJklS5bkK2AhhCiWlIJD38GiDvpExKkCDNsErSbkOhE5cyOOt1afAmBM28o8U8e7MCMWJqSMvRXuDtYAXIwsedPC5zm1WrlyJePHj2fu3Lm0bNmS7777jmeeeYagoCAqVqyY4zH9+vXj1q1bLF68mCpVqhAZGUlmZuZjBy+EEMVCyh34fSyc26Dfrt4ZenwLdq65PkVMUjqjfjxKWqaONtU9mNBRpnwX2VUv60DUpTQu3Eqgvo+LscMpUHlORr744gtGjBjByJEjAZg9ezZ//fUX8+bNY9asWfeV37x5M7t27eLKlSu4uur/MP38/B4vaiGEKC7C/oHVwyEuDMytoOP70HQU5KGfR4ZWx9ifjnEjNgU/Nzu+eq5BiZzYSjyeqp6O7LsUzYWIktdvJE/NNOnp6Rw9epROnTpl29+pUyf279+f4zHr16+nUaNGfPLJJ5QvX55q1arxxhtvkJKS8sDrpKWlER8fn+0hhBDFik4He2fDkqf1iUgZfxixBZqNzlMiAvDBhiD2X47G3sqcBYMb4WxrWTgxC5Nm6MRa2ptpoqKi0Gq1eHl5Zdvv5eVFREREjsdcuXKFvXv3YmNjw7p164iKiuLll18mJibmgf1GZs2axfTp0/MSmhBCFJ3E27BuFFzert+u3Qe6zgYbpzyf6qdDoXx/4BoAn/erb/jBEeJe1cvqZ169WAJH1OSrA+u9w8yUUg8ceqbT6dBoNKxYsYImTZrQuXNnvvjiC5YtW/bA2pFJkyYRFxdneISFheUnTCGEKHghe/Qr7V7eDha20G0O9Fmcr0Tk0JVo3vv9DACvd6zG07VlETzxYFU89YlqeFwqcSkZRo6mYOUpGXF3d8fc3Py+WpDIyMj7akuyeHt7U758eZydnQ37AgICUEpx/fr1HI+xtrbGyckp20MIIYxKp4Uds+CH7pAYAR414IW/IXBInptlAMJiknlpxTEydYqudb0Z265KIQQtShJnW0u8nW0AuBRZsmpH8pSMWFlZERgYyNatW7Pt37p1Ky1atMjxmJYtW3Lz5k0SE/9t47pw4QJmZmZUqFAhHyELIUQRi78J33eHXR+B0kGD5/WJiFfNfJ0uKS2TF344QkxSOrXLO/Fp33oysZnIlax5Z85HlKx+I3luppkwYQKLFi1iyZIlBAcH89prrxEaGsro0aMBfRPL4MGDDeUHDBiAm5sbw4YNIygoiN27d/Pmm28yfPhwbG1tC+6VCCFEYbi4Vd8sc20vWDlA74X6YbtW9vk6nVKKN1ef5FxEAu4O1iwY1AhbK/MCDlqUVNXurtxc0mZizfPQ3v79+xMdHc2MGTMIDw+ndu3abNq0CV9fXwDCw8MJDQ01lHdwcGDr1q288sorNGrUCDc3N/r168cHH3xQcK9CCCEKmjYDts+A/XP022XrwrPLwK3yY5124Z4rbDodgaW5hu8GBVLORf5RJnKv2t01ai6WsGYajVJKGTuIR4mPj8fZ2Zm4uDjpPyKEKHx3rsGaEXD9sH67ySjo9D5YWD/WafdfjuL5RYfQKfigZ22eb+ZbAMGK0uREWCw9v92Hu4M1R6Z0MHY4j5Tb3++SNbm9EEI8rqD1sH4spMaBjbO+SSag22OfNjwuhVd+Oo5OQZ+GFRjYNOcZq4V4mKp3m2miEtO4k5ROGXsrI0dUMPI1tFcIIUqcjFTY+Ab8OkifiFRoDKP3Fkgikpap5aXlx4hOSqemt5OsxCvyzd7aggpl9E17JanfiCQjQggRdQkWd4DDC/XbLcfBsD/BpWBqLz7YEMyJsFicbCyY/3wgNpbSYVXkn2EmVklGhBCihDi5Er57EiJOg50bDFwDHWeAecFMyb7i0DV+PKifYfWr5xpQ0c2uQM4rSq+qXlkjakrO8F7pMyKEKJ3Sk2DTW3BiuX7br5V+2K6Td4FdYsf5SN797d8ZVtvW8Cywc4vSq3oJrBmRZEQIUfrcCoJVQyHqPGjMoPXb8OSbYFZwzSdnb8YxdsUxdAr6BlaQGVZFgflvM83DlmMxJZKMCCFKD6Xg2Pfw59uQmQoOZaHPIvBvVaCXCY9LYfiywySla2lZxY0Pe9UpET8Yonio7OGARgN3kjOISkzHw/HxhpwXB5KMCCFKh9R4+GMcnF2r367SAXp9B/buBXqZhNQMhi09zK34NKp5OTB3YCBWFtI9TxQcWytzfF3tuBqdzMVbCSUiGZG/ECFEyXfzuL6T6tm1YGah76A6YFWBJyLpmTpeXnGMcxH6H4glQxvjbFswHWGF+C/DGjUlpN+IJCNCiJJLKTgwFxZ1hDsh4FwRhm3WD901K9ivP51O8fqqk+y5GIWdlTmLhzSiQhkZOSMKx7+dWEvGiBppphFClEzJMfD7GDi/Sb9doyv0+AZsyxT4pZRSTP/jLH+cvImluYb5zwdSt4JLgV9HiCxZw3svlpCaEUlGhBAlz7UD+rVl4m+AuRU89SE0HgmF1In0678v8f2Ba2g08Nmz9XiymkehXEeILNX+00xTEkbUSDIihCg5dDrY+wXs+BCUFlwrw7NLwbteoV1y+cFrfLH1AgDTutWiR/3yhXYtIbJU8rDH3ExDQmomt+LTKOtsY+yQHoskI0KIkiHhFqx7Ea7s1G/X6QddvwBrx0K75G/Hb/Du7/pJzV5tX5UhLfwK7VpC/Je1hTm+bnZcuZ3ExcgEk09GpAOrEML0Xd4B85/QJyKWdvqVdnsvKNREZPXR67z26wmUguebVeS1DlUL7VpC5KSap/7zfbEEdGKVZEQIYbq0mbD9ffixFyRFgmdNeGEHNHi+0PqHAPx6OIw3V59EKRjYtCIzussqvKLoGTqxRpp+MiLNNEII0xR3Q99JNfSAfrvhEHj6I7Aq3OG0Px0K5Z11pwEY3NyX6d1rSSIijKKKpz4ZuRRp+iNqJBkRQpie85vht9GQcgesHKHbbKjTt9Av++OBq7z7+1kAhrX0472uNSUREUZT1fPfuUZMfUSNJCNCCNORmQ7bp8OBb/Tb3vWh7xJwq1yol9XpFJ/8dZ75uy4DMPIJfyZ3CTDpL39h+ip52GOmgbiUDG4npuHpaLqdWCUZEUKYhpgQWD0cbh7Tbzd9CTpOB4vCXZcjNUPL66tOsvFUOADjO1RlXPuqkogIo7OxNKfi3TVqLt1KlGRECCEK1dl1sP5VSIsHGxfoORdqdCn0y8YkpfPCD0c4eu0OluYaPupdlz6BFQr9ukLkVhVPR/2CeZGJtKhSsGstFSVJRoQQxVdGCvz1DhxZot/2aQp9FoOLT6Ff+srtRIYvO8zV6GQcbSz4blAgLSqb7pe9KJmqeTmwLfgWF028E6skI0KI4un2BVg9DG6dATTwxGvQ9h0wL/xVcHdduM3Yn46RkJpJhTK2LBvWmCqehTdniRD59e8aNaY9vFeSESFE8XPiJ9j4OmQkg70H9PoOqrQv9MsqpVi8N4QPNwWjUxDoW4b5zwfi4Vi4/VKEyK+sETWXTHyuEUlGhBDFR1oibHoDTv6s3/Z/EnovBMeyhX/pTC1T1p1h1dHrADwbWIEPetXG2sK80K8tRH5V9nBAo4HopHSiE9NwczDNxFmSESFE8RBxGlYNg+iLoDGDNu9AqwlgVvjJQFxKBiO/P8zhq3cw08DkLjUZ3tJPRsyIYs/WypwKZWwJi0nhYmSiJCNCCJEvSsGRxbD5HdCmgWM56LMI/FoWyeWjEtMYtPgfgsPjcbSx4NsBDXmymkeRXFuIglDV09GQjDSr5GbscPJFkhEhhPGkxML6VyB4vX67aifoOR/si+YL9WZsCs8vPsSV20m4O1izfGQTapR1KpJrC1FQqno68Pe5SC7dMt0RNZKMCCGM4/pRWD0UYkPBzAI6TIdmL4NZ0azfeTUqiYGLDnEjNoXyLrYsH9kUf3f7Irm2EAUpa40aU14wT5IRIUTR0ung4LewbRroMsHFF/ouhQqBRRbCuYh4Bi3+h9sJafi727N8ZFPKu9gW2fWFKEjVvPQjaiQZEUKUaHeS0jl5PZZT1+MIi0nGydYSV3sr3OytcLW3wtvZlopudjjbPmIOkKRo+O0luPiXfrtmD+g2B2xdCv01ZDl4JZoXfjhCQmomNco68uOIpjJ0V5i0yndrRm4npBGbnI6LnZWRI8o7SUaEEDk6FxHPoj0h/BMSQ2hMcq6OcbGzpKKrHT6udpR1ssHT0Rqvu//1TzpJ2W1j0STcBHNreHoWNBoORThi5c/T4YxbeYL0TB2NfMuwaEgjk/ziFuK/HKwtKO9iy41YfSfWxn6uxg4pzyQZEUJkE3QznjnbL7L5bES2/f7u9tSt4ExlDwcS0zKJTkwnJimNmKR0bsSmEJWYTmxyBrHJcZy6Hmc4zgwdY8x/o6nFGjQaRaimPIs838UsvC6uf1/C0cYCRxtLHKwtsLUyx0wDGjRoNGCm0eDvbk9Z58dfAOz7/VeZ9sdZlIJONb2Y878G2FjKHCKiZKji6aBPRm5JMiKEMGEXbyXw6V/n2RJ0C9BXWHSu403/Rj7Uq+CCs93Dm2CS0jIJjUnmWnQy1+8kE5mQRmrMDfqFvU/ttBMArNG24t2MYSSH2EDI1VzH5uVkTb0KLtTzcaGRbxka+7liZpa7GpXk9Exmb7vIgt1XABjYtCIzetTGPJfHC2EKqno6sOvCbZNdo0aSESFKOaUUKw+HMXX9WdIydWg00LVuOV5tV4WqXrlfj8Xe2oIAbycCvO8Ojb20DdaOgrQosLRD1/kzWlTqzaKoJEKikgiNSSY+JYP41EwSUjNJSM0gJV17NyZQKDK0imvRSdyKT2NL0C1DolTexZZnG1Xg2UY+D+x4mpSWyY8Hr7Fw9xWik9IBeL1jNca2qyKTmYkSJ2uNGlOdFl6SESFKsaS0TCavO81vJ24C8GQ1D97rGvB4i8JpM2DHTNj7pX7bqzb0XYqZRzW8AW9n2zytfpucnsmZG/Gcuh7L8bBYdl+4zY3YFGZvu8hX2y/yRBV3An3L4GhjiaO1BQ42Fly5ncjivSHcSc4AwMfVlrefrkHXuuXy/7qEKMay/mZNdcE8SUaEKKWCw+MZ89MxrtxOwtxMwxudqjPqyUq5bv7IUWworB4B1//RbzcaDk99CJb5HzZrZ2VBE39Xmvjr28FTM7RsPhPBysNhHLgSzZ6LUey5GJXjsf7u9oxpW4Ue9cthaV4085cIYQxZNSMR8anEp2bgZFP4q1sXJElGhChltDrFsv1X+WTzOdIydZR1suHrAQ0ev9PbuY3w28uQGgvWTtB9DtTqVSAx/5eNpTk9G5SnZ4PyXItOYv2Jm4THp5KYmkliWiaJqZmYmcFzjSvSta43FpKEiFLAycaSsk42RMSncikykYYVyxg7pDyRZESIUuTK7UTeWn2KI9fuANC6mgdf9Kv3eItrZabB1vfg0Hz9drmG0HcJuPoXQMQP5+tmzyvtqxb6dYQwBVW9HIiIT+XirQRJRoQQxY9Wp1i6L4RP/zpPWqYOeytz3ukSwIAmFR+vM2f0ZVg9DMJP6rebj4X2U8FC5u4QoqhV8XRgz8Uok+w3IsmIECVcXHIGo5cf5cCVaACeqOLOR33qUKGM3eOd+PRq+GM8pCeAbRn9AnfVn378gIUQ+VL1bifWS7clGRFCFCPX7yQzdOlhLkUmYm9lzpSuNXmusc/j1YakJ8Pmt+HYD/rtii2gzyJwLl8wQQsh8qWSh36hxyu3k4wcSd5JMiJECXXmRhzDlh3mdkIaZZ1sWDqs8b9zgORX5DlYNRRuBwMaePINaD0RzOWrRAhjq3R31enrd5JJy9RibWE6MwzLN4gQJdCuC7d5eflRktK1VPdyZNnwxng7P8aqtErB8eWw6U3ITAF7T+izECq1KbCYhRCPx8PRGgdrCxLTMgmNTs7TpIXGJmPehChh1hy9zvBlh0lK19KishurXmr+eIlIWgKsfRHWj9UnIpXawkv7JBERopjR3F3LCeBKlGk11UjNiBAlyNJ9IUz/IwiAXg3K83GfulhZPMa/OcJPwqphEHMZNObQbgq0HA9m8u8YIYqjSh72nL4RZ3L9RiQZEaIEUErx1faLzN52EYDhLf2Z0iUg/7OpKgX/LIQtk0GbDk4VoO9iqNisAKMWQhS0rJqRkCjTGlEjyYgQJk6nU7y/MYil+64CMKFjNV55nMXgUu7A72Ph3Ab9dvXO0ONbsDO9ZcmFKG0qeeinhZeaESFEkUnP1DFx7SnWHrsBwLRuNRna8jFmPg37R7+2TFwomFlCpw+g6SiQVW6FMAmVDDUjkowIIYpAVGIaLy0/yuGrdzA30/Bp37r0blghfyfT6WD/HNg+A5QWyvhB36VQvmGBxiyEKFxZzTTRSenEJWfgbGcaC+ZJMiKECQoOj2fk90e4EZuCo40FX/+vAW2qe+bvZIm3Yd0ouLxdv12rN3SbDTbOBRavEKJo2Ftb4OVkza34NK5EJdLARNaokWRECBPz19kIXlt5guR0Lf7u9iwa0ojKd9uJ8yxkD6wZCYkRYGEDT38EgUOlWUYIE1bJ3UGfjNxOkmRECFGwlFJ8t/sKH/15DtCvMfPtgIb5q4bVaWHXJ7D7E1A6cK8Ozy4Fr1oFHLUQoqj5e9hz4Eq0SfUbkWRECBOg1Smmrj/D8oOhAAxt4ceULgFYmOdjvo/4m7DmBbi2V7/d4Hl45hOwsi/AiIUQxlLJMPGZ6QzvlWREiGIuJV3LKz8fZ1vwLTQaeK9rTYbld8TMxa36/iHJ0WBpD12/hHr9CzZgIYRRmeKCeZKMCFGMRSWmMeL7I5wMi8XawoyvnqvP07W9834ibYZ+pMz+OfrtsnWg7zJwr1Kg8QohjK+Su74P2dXoJHQ6lf/JD4tQvuZ0njt3Lv7+/tjY2BAYGMiePXtyddy+ffuwsLCgfv36+bmsEKXKzdgU+s7bz8mwWFzsLFkxsmn+EpE712DJ0/8mIo1fgBHbJBERooSqUMYWS3MNqRk6wuNTjR1OruQ5GVm5ciXjx49n8uTJHD9+nFatWvHMM88QGhr60OPi4uIYPHgw7du3z3ewQpQW4XEp/G/hQa5GJ1OhjC1rXmpBI798zIAa9DvMbwU3joC1M/T7Ebp8BpY2BR+0EKJYsDA3o6KrHQBXbptGv5E8JyNffPEFI0aMYOTIkQQEBDB79mx8fHyYN2/eQ48bNWoUAwYMoHnz5vkOVojSICIulQELD3EtOpmKrnb8Oqp53ofuZqTCxtfh18GQFgflG8HoPVCze+EELYQoVvzvNtWYyoiaPCUj6enpHD16lE6dOmXb36lTJ/bv3//A45YuXcrly5eZOnVqrq6TlpZGfHx8tocQpUFkfCoDFh4kJCqJCmVs+fnFZpRzsc3bSaIuwaIOcHiRfrvlOBi+Gcr4FnzAQohiqbKJdWLNUwfWqKgotFotXl5e2fZ7eXkRERGR4zEXL15k4sSJ7NmzBwuL3F1u1qxZTJ8+PS+hCWHyIhNSeW7hQa5EJVHexZafX2hG+bwmIidXwobXICMJ7Nyg13dQtWPhBCyEKLb8DcN7TSMZyVcH1ntXA1VK5bhCqFarZcCAAUyfPp1q1arl+vyTJk0iLi7O8AgLC8tPmEKYjLCYZPrNP8CV2/pE5JcXm+Fzt803V9KT4LcxsO5FfSLi1wpG75VERIhS6t/Ve02jz0ieakbc3d0xNze/rxYkMjLyvtoSgISEBI4cOcLx48cZO3YsADqdDqUUFhYWbNmyhXbt2t13nLW1NdbW1nkJTQiTdeFWAoMWH+JWfBoVytjy08g8JiK3zsKqYRB1HtBAm4nw5JtgZl5oMQshiresmpEbsSmkZmixsSze3wd5SkasrKwIDAxk69at9OrVy7B/69at9OjR477yTk5OnD59Otu+uXPn8vfff7N69Wr8/R9jqXMhSoDjoXcYtuwwsckZVPNy4McRTfFyyuVIF6Xg6DLYPBEyU8GhLPRZCP5PFmrMQojiz93BCkcbCxJSM7kWnUz1so7GDumh8jzp2YQJExg0aBCNGjWiefPmLFiwgNDQUEaPHg3om1hu3LjBDz/8gJmZGbVr1852vKenJzY2NvftF6K02X3hNqOXHyU5XUuDii4sHdoYFzur3B2cGg9/jIOza/XbVTpAz/ng4FF4AQshTIZGo6GSuz0nr8cREpVY8pKR/v37Ex0dzYwZMwgPD6d27dps2rQJX199T/3w8PBHzjkiRGn319kIxv50jAytolVVd74bFIidVS7/HG8cg9XD4M5VMLOA9u9B81fALF9dwIQQJVQlDwdOXo/jsgmMqNEopZSxg3iU+Ph4nJ2diYuLw8nJydjhCPFYNpy6ybhfTqDVKbrU8ebL/vWxsshFIqEUHJoPW94FXQY4V4S+S8CnceEHLYQwOXO2X+SLrRfoG1iBz56tZ5QYcvv7LWvTCFGE1h2/zuu/nkSnoHeD8nzSt27uVt5NjoHfx8D5TfrtGl2hxzdgW6ZwAxZCmKx/F8wr/iNqJBkRooj8ejiMt9eeQino38iHD3vXwTw3C1iFHoTVIyD+OphbwVMfQuORkMNweiGEyJI1osYUZmGVZESIIrD84DWm/HYGgEHNfJnevdajV9LU6WDfl/D3TFBacK0Mzy4Fb+NUtwohTEtWMnInOYM7SemUsc9lB3kjkB5vQhSynw6FGhKREU/4M6NHLhKRxEhY3hu2z9AnInX6wahdkogIIXLNzsoCb2f9VAFXoop3U40kI0IUol/+CeWddfq5dkY+4c+ULgE5zlaczeUdMK8lXNkBFrbQ/RvovQCsi/fQPCFE8ePrpp9A8Vp0spEjeThJRoQoJL8eCWPS3URkWEs/Jj8qEdFmwvb34cdekBQJnjXhxZ3QcJD0DxFC5Iufm76pprgnI9JnRIhCsObodd5eo++sOrSFH+91rfnwRCTuBqwZCaF3V79uOASe/gis8jAtvBBC3KPi3ZqR0BhJRoQoVVYdCeOtu4nIoGa+TO32iETk/Gb47SVIiQErR+g2G+r0LbJ4hRAll69rVs1I8R5RI8mIEAXo+/1Xmbr+LAADmlZkevdaD05EMtNh+3Q48I1+27u+fhIzt8pFE6wQosQzlT4jkowIUUC+3XGJT/86D+hHzTy0s2pMCKweDjeP6bebvgQdp4OFrFYthCg4Wc000UnpJKZl4mBdPH/2i2dUQpgQpRSfbTnPtzsuA/Bq+6q81qHqgxORM2v1i9ylxYONC/ScCzW6FF3AQohSw8nGEld7K2KS0rkWnUStcs7GDilHkowI8RgytTpmbAjihwPXAJj0TA1GtX5AM0tGCmyeBEeX6rd9mkKfxeDiU0TRCiFKo4qudsQkpRManSzJiBAlTXxqBmN/Os7uC7cBeL9nbQY188258O0LsGooROr7k/DEBGj7DphbFk2wQohSy9fNjhNhsVwtxv1GJBkRIh+uRiUx4vvDXL6dhK2lOV/0q8czdbxzLnziJ9j4OmQkg70H9PoOqrQv2oCFEKWW7925RkJjiu+IGklGhMij/ZeieGnFMeJSMvB2tmHh4EbULp9D1Wdaoj4JOfWLftv/Sei9EBzLFm3AQohSzde1+I+okWREiFxSSrFs/1VmbgwmU6eo7+PCgkGBeDrZ3F844rS+WSb6EmjMoM070GoCmJkXedxCiNLNFIb3SjIiRC4kpWUyae1p1p+8CUCP+uX4uE9dbCzvSS6UgiOLYfM7oE0Dx3LQZxH4tTRC1EII8e/w3ptxKaRlarG2KH7/KJJkRIhHuHI7kdHLj3LhViIWZhomdQ5geEu/+4fupsTC+lcgeL1+u+pT0HMe2LsVecxCCJHFw8EaOytzktO1XL+TQmUPB2OHdB9JRoR4iL/ORvD6rydJTMvEw9GauQMb0tjP9f6C14/C6qEQGwpmFtBhOjQfIwvcCSGMTqPRUNHVjnMRCYRGJ0syIoQp2XPxNi+vOIZWp2ji58o3Axrc3z9Ep4OD38K2aaDLBBdf6LsUKgQaJWYhhMiJr5s+GblaTNeokWREiBxcikw0JCI96pfjs2frYWlulr1QUjT8NhoubtFv1+wB3eaArUuRxyuEEA+TNby3uHZilWREiHvcSUpnxPeHSUjNpJFvGT7pW/f+ROTqPlgzEhJugrk1PP0hNBohzTJCiGIpa0RNaIwkI0IUe+mZOkYtP8q16GR8XG35blBg9p7nOi3s+QJ2fghKB25V4dmlULaO8YIWQohH8HXNqhmRZhohijWlFJPXneafkBgcrS1YPKQxbg7/WUU3IQLWvgAhu/Xb9QZA50/Buvh1BhNCiP/KqhkJi0lBq1OYmxWvWlxJRoS4a9n+q6w6eh0zDXw9oAHVvBz/ffLSdlg3CpJug6UddPkc6g8wXrBCCJEH3s42WJhpSNfqiIhPpbyLrbFDysbs0UWEKPmCbsYza9M5ACZ3qUmb6p76J7QZ+pEyy3vrExGv2vDiLklEhBAmxcLcDB/DtPDFr6lGkhFR6qVmaBn3y3HStTo6BHgxvKWf/onYMFjWBfZ+qd9uNBxGbgOPakaLVQgh8qvi3WQktBiOqJFmGlHqzdwYzMXIRDwcrfm4Tx39zKrnNsJvL0NqLFg7Qfc5UKuXsUMVQoh8y+o3clWSESGKl21Bt/jx4DUAPn+2Hm42wJ8T4dA8fYFyDaHvEnD1N16QQghRAAw1IzHFr5lGkhFRakUmpPLWmlMAjHjCnyfd4mFxHwg/qS/QfCy0nwoWVkaMUgghCoZfMZ74TJIRUSrpdIo3Vp0iJimdAG8nJvqcge8mQHoC2JaBnvOh+tPGDlMIIQqMYeKz6GSUUvcv9mlEkoyIUmnp/qvsvnAbZ4sMfim7Ast1P+ufqNgC+iwC5/LGDVAIIQpY1miahLRMYpLSs8+jZGQymkaUOkE34/n4z3NU0Vxnl8sMnIN/BjTw5Jsw5A9JRIQQJZKNpTll7y72ea2YTQsvyYgoVVIztIz7+Rg9+JuNNu/ikngZ7D1h8G/QbgqYS2WhEKLk+m9TTXEi37yiVPl0/RFejv2EXpb7QAGV2kDvheDgaezQhBCi0Pm62XEoJKbYdWKVZESUGof27WDgydFUMo9AaczRtJsMLV8DM6kgFEKUDr5uxXPBPElGRMmnFAm759Jgx3tYmWUSZ+WF8/M/QMVmxo5MCCGKVNZcI8Wtz4gkI6JkS7lDxtoxOF7cCMABy6Y0fOUncHQ3cmBCCFH0spKRsGKWjEj9tCi5wg6jndcKy4sbSVfmfKoZhsfINVhLIiKEKKWyhvdGJqSRmqE1cjT/kmRElDw6HeydjVr6NObxYVzVeTHCYibdR71PFS9HY0cnhBBGU8bOEjsrcwBuxKYYOZp/STIiSpakKPjpWdg2FY0ukz+0zRhm9RlTRw2iellJRIQQpZtGo8GnTPFrqpE+I6LkCNkDa0ZCYgRpWDEtYxA77Tvz04vN8Xe3N3Z0QghRLPi42nL+VgLX7xSfmhFJRoTp02lh1yew+xNQOi6p8oxJf4VE5+qsfKEZFe9O8iOEEAIqZNWM3JGaESEKRnw4rH0Bru4BYGVmG6ZlDqZB5fL8+Fx9PB1tjBygEEIULxXK2AJwPUZqRoR4fBe3wboXITmaFGyYlD6c9eoJxnWoxth2VTA3Kz4rUgohRHGRNaLmutSMCPEYtBnw9/uw7ysAgpSfvlnGwY8VzzWgeWU3IwcohBDFV1bNSJj0GREin2JDYfVwuH4YgGWZnZiVOYAmVcvxZf/6uBejJbGFEKI4yuozEpOUTlJaJvbWxk8FjB+BELkV/Af8PgZS40jAnjfSX2A7TXnjmeq82KoSZtIsI4QQj+Rsa4mTjQXxqZlcv5NSLKY9kGREFH8ZqbBlChxeCMAJXRXGZryCcq7IrwMa0LBiGSMHKIQQpsXH1Y6zN+MJi0mWZESIR4q6BKuHQsRpAOZnduOzzGfpUKsCH/epi7OdpXHjE0IIE+RT5m4yUkw6sUoyIoqvkythw2uQkUQsToxPH80+TQOmdAtgSAs/NBpplhFCiPwwDO8tJp1YJRkRxU96Emx6C04sB+Cgriavpo/B0qUcqwY2pL6Pi3HjE0IIE+dTzFbvlWREFC+3zsKqYRB1Hh0avsrozdfaXrQLKMvnz9aXZhkhhCgAPq5SMyLE/ZSCY9/Dn29DZirRGlfGpr3EIVWLN5+uwejWlaRZRgghCkhxmxJekhFhfKnxsGE8nFkDwD7q82rKKHR27vzwv4Y8UdXduPEJIUQJk9VnJCE1k7jkDKPXOksyIozr5nF9s8ydEHQaCz7OeJYFmV2oVd6F+c8HGrJ3IYQQBcfOygI3eyuik9IJu5OMs52zUeORZEQYh1JwaD5seRd0Gdyx9GJE4kscU9Xo07ACM3vVxsbS3NhRCiFEiVXB1Y7opHSu30mmdnnjJiNm+Tlo7ty5+Pv7Y2NjQ2BgIHv27Hlg2bVr19KxY0c8PDxwcnKiefPm/PXXX/kOWJQAyTHwywDYPBF0Gfxj3YLWCe9zgmpM6RLAZ8/WlURECCEKmU/WGjXFYPXePCcjK1euZPz48UyePJnjx4/TqlUrnnnmGUJDQ3Msv3v3bjp27MimTZs4evQobdu2pVu3bhw/fvyxgxcmKPQgzG8F5zehM7NituUL9Isbg87ahcVDGjOylXRUFUKIopDVDF4cVu/VKKVUXg5o2rQpDRs2ZN68eYZ9AQEB9OzZk1mzZuXqHLVq1aJ///689957uSofHx+Ps7MzcXFxODk55SVcUVzodLDvS/h7JigtqU5+DI5/iX9SffBxtWXxkMZU8zL+lMRCCFFarDh0jcnrztCuhidLhjYulGvk9vc7T31G0tPTOXr0KBMnTsy2v1OnTuzfvz9X59DpdCQkJODq6vrAMmlpaaSlpRm24+Pj8xKmKG4SI2HdKLj8NwC3/XvQ+XIvbqdb0bCiCwsHN8JNVtsVQogi5VOm+Ex8lqdmmqioKLRaLV5eXtn2e3l5ERERkatzfP755yQlJdGvX78Hlpk1axbOzs6Gh4+PT17CFMXJlZ0w/wl9ImJhy/mms3ji4nPcTreiZRU3lo9sKomIEEIYwX+nhM9jI0mBy1cH1nvb9JVSuWrn//nnn5k2bRorV67E09PzgeUmTZpEXFyc4REWFpafMIUxaTPh7w/gh56QeAs8a7K3/Wq67fUnLVPRroYni4c0xs5KBnQJIYQxlC9ji0YDKRlaopPSjRpLnn4J3N3dMTc3v68WJDIy8r7aknutXLmSESNGsGrVKjp06PDQstbW1lhby7+WTVbcDVgzEkLvNt01HMJmn/GMWXUOrU7RuU5ZZvdvgJVFvnJhIYQQBcDawhwvRxsi4lMJi0nG3Yi11Hn6NbCysiIwMJCtW7dm279161ZatGjxwON+/vlnhg4dyk8//USXLl3yF6kwDec365tlQveDlSP0WczuGu/yymp9ItK7QXnmPCeJiBBCFAfFZfXePNeRT5gwgUGDBtGoUSOaN2/OggULCA0NZfTo0YC+ieXGjRv88MMPgD4RGTx4MF999RXNmjUz1KrY2tri7GzcSVZEAcpMh+3T4cA3+m3vetB3KUcTXRm16BAZWkXXut58+mw9zM1k6K4QQhQHPq52HLl2x+hr1OQ5Genfvz/R0dHMmDGD8PBwateuzaZNm/D19QUgPDw825wj3333HZmZmYwZM4YxY8YY9g8ZMoRly5Y9/isQxhcTAquHw81j+u2mL0HH6ZyPSmf4sgOkZGh5spoHX/SrL4mIEEIUIxWKycRneZ5nxBhknpFi7Ow6WP8qpMWDjQv0nAs1uhAanUzf+fuJTEijYUUXlo9sKp1VhRCimPn1cBhvrTlFq6ru/DiiaYGfv1DmGRHCICMF/noHjizRb/s0hT6LwcWHyIRUBi05RGRCGtW9HFkyVEbNCCFEcVTB1UT7jAjB7QuwehjcOqPffmICtH0HzC2JS8lg8OJ/uBadjI+rLT+OaIKLnZVx4xVCCJGjrInPbtxJQadTmBmpKV2SEZE3J36Cja9DRjLYe0Cv76BKewBS0rWM/P4w5yIScHewZvmIpng62Rg5YCGEEA/i7WyDuZmGdK2OyIQ0yjob5ztbkhGRO2mJsOkNOPmzftv/Sei9EBzLApCh1THmp2McvnoHRxsLfhzRBF83eyMGLIQQ4lEszM3wdrbh+p0Uwu4kGy0ZkckexKNFnIYFbfSJiMYM2k6BQb8ZEhGdTvHW6lP8fS4SG0szlgxtTIC3dDQWQghT8O+IGuMN75WaEfFgSuk7qG6eBNo0cCwHfRaBX8v/FFF8sDGYdcdvYGGmYd7AQBr7PXgRRCGEEMVLv0Y+tKnuSZ3yxpv7S5IRkbPUOP2Q3aDf9NtVn4Ke88DeLVuxBbuvsGRfCACfPluXtjUevOaQEEKI4qd3wwrGDkGSEZGDG0dh1TCIvQZmltBhGjQfA/cshrj22HVm/XkOgCldAujVwPgfaCGEEKZHkhHxL6XgwLewbRroMsDFF/ouhQqB9xXdeT6St1afAuDFJysxslWlIg5WCCFESSHJiNBLiobfXoKLf+m3a/aE7nPA5v42xJNhsby84hiZOkXP+uWY+HSNoo1VCCFEiSLJiIBr+2H1CEi4CebW8PQsaDT8vmYZgH9CYhi9/CjJ6VpaVXXnk771jDZJjhBCiJJBkpHSTKeFPV/Azg9B6cCtKjy7FMrWub+oTrFgzxU+/es8Wp2ibgVn5j0fiJWFjA4XQgjxeCQZKa0SImDtixCyS79d73/Q+TOwdrivaFxyBq+vOsG24EgAejcozwe9ast6M0IIIQqE/JqURpe2w7pRkHQbLO2gy+dQf8B9xZRSHLgczVtrTnH9TgpWFmZM716L5xr7oMmhCUcIIYTID0lGShNtJuyYCXu/0G971daPlvGolq1YplbHpjMRLNx9hdM34gDwdbPj2wENqW3ESXGEEEKUTJKMlBaxYbBmJIQd1G83Gg5PfQiWtoYiqRlaVhwKZcneEG7E6peTtrYwo18jH954qjrOtpbGiFwIIUQJJ8lIaXBuk37YbmosWDvph+zW6mV4WinFptMRzPozmOt39EmIm70VQ1r48XwzX1ztrYwUuBBCiNJAkpGSLDMNtk6FQ/P02+Ua6JtlXP0NRU5dj+X9DUEcvnoHgLJONrzaviq9G5bHxtLcGFELIYQoZSQZKamiL8PqYRB+Ur/dfCy0nwoW+lqOuOQMZm4K4tcj1wGwtTRnVOtKvPhkJRklI4QQokjJr05JdHo1/DEe0hPAtgz0nA/Vnwb0TTJ/nongvd/PEpWYBuiH6r75dHW8nW0fclIhhBCicEgyUpKkJ8Pmt+HYD/rtis2hz2JwLg/ArfhU3v3tDFuCbgFQ2cOej/vUpZGfq7EiFkIIISQZKTEiz8GqoXA7GNDAk29A64lgboFOp/j5cCgf/XmOhNRMLMw0vNymMmPaVcHaQvqFCCGEMC5JRkydUnB8OWx6EzJTwN4T+iyESm0AOBcRzztrT3MsNBaAuhWc+bhPXQK8nYwXsxBCCPEfkoyYsrQE2DABTv+q367UFnovAAdPktMz+Wr7RRbtCUGrU9hbmfN6p+oMbu6LhbmsJyOEEKL4kGTEVIWfhFXDIOYyaMyh3WRo+RpKo+GvM+G8vyHYMHHZ07XKMrV7TemgKoQQoliSZMTUKAX/LIQtk0GbDk4VoO9iqNiMS5GJTP/jLHsuRgFQ3sWW6d1r0aGml5GDFkIIIR5MkhFTknIHfh8L5zbot6t3hh7fkmDmyDebglm8N4RMncLKwoxRT1bi5TZVsLWSDqpCCCGKN0lGTEXYYVg9HOJCwcwSOr2PtvEoVh29zmdbjhKVmA5AhwBP3u1aE183eyMHLIQQQuSOJCPFnU4HB76G7TNAlwll/KHvEvanVuT9b/YRHB4PgL+7Pe92DaBdDWmSEUIIYVokGSnOkqJg3Wi4tFW/XbsP11vOYsbWMLYEHQLAycaCcR2qMaiZL1YWMkpGCCGE6ZFkpLgK2QNrX4CEcLCwIaPTLL6Jbcn8b4+RlqnD3EzDwKYVGd+hmqyqK4QQwqRJMlLc6LSw+1PY9TEoHcqjBgcafMpbOzK5fucSAM0ruTG9Ry2qeTkaOVghhBDi8UkyUpzEh+trQ67uASCx5nO8njCAv9YnAODtbMPkLgF0qeONRqMxZqRCCCFEgZFkpLi4uA3WvQjJ0SgrBzb7vc24U1VJz0zE0lzDyFaVGNu2CvbW8pYJIYQoWeSXzdi0GfqRMvvnAJDgEsDotLHsO1UG0PFEFXem96hFZQ8H48YphBBCFBJJRozpzjVYMwKuHwbgL/vuvBrRmzSs8HKy5t2uNaVJRgghRIknyYixBK2H9WMhNY5kMwdeSx3JX6lNsDI348WWfrzavioO0iQjhBCiFJBfu6KWkQpb34V/FgBwQleFsWmvcAMPejUoz4SO1fBxtTNykEIIIUTRkWSkKEVdgtVDIeI0APMzu/FZ5rO0rObNd09Xp1Y5Z+PGJ4QQQhiBJCNF5dSvsOE1SE8kVuPE+LTR7Nc05JN+dejdsIKxoxNCCCGMRpKRwpaeBJveghPLAThCTV5OGYPOoSy/DA6kYcUyRg5QCCGEMC5JRgrTrSBYNRSizqPQ8LW2N7MzelHD24WFQxpR3sXW2BEKIYQQRifJSGFQCo59D3++DZmpxJi5Mib1JQ7oatG5Tlk+e7YedlZy64UQQgiQZKTgpcbDH+Pg7FoA9qj6jE8eRZq1G590q8mzgRVk3hAhhBDiPyQZKUg3jsHqYXDnKpmY82lGPxZou9C0kjuf9q0nQ3aFEEKIHEgyUhCUgkPzYcu7oMvglpkno1PGcNa8OlO61mBYCz/MzKQ2RAghhMiJJCOPKzkGfh8D5zcBsMusKa8kj8DC3pVfhjSS0TJCCCHEI0gy8jhCD8LqERB/HZ2ZFR/pnmdBcnv83R1YNqwxvm72xo5QCCGEKPYkGckPnQ72fQl/zwSlJdHel4Gxozip9SPQtwwLBzfC1d7K2FEKIYQQJkGSkbxKjIS1L8KVHQCElOtCt5DeJCpbOtcpyxf96mNjaW7kIIUQQgjTIclIXlzZCWtegKRIsLDl78pvMfxkNUDDwKYVmdGjNubSUVUIIYTIE0lGckObCbs+gt2fAQrlWZNFXu8y87AC4OU2lXnzqeoyf4gQQgiRD5KMPErcDVgzEkL3A5BZfzBT059nxeEoACY9U4NRrSsbM0IhhBDCpEky8jAX/oJ1oyElBqwcCW05i2FHfLh8OwozDXzYqw7PNalo7CiFEEIIkybJSE4y02H7dDjwDQDKuz7LK0xl+l+pZOqS8HS05rNn6/FkNQ8jByqEEEKYPklG7hUTAquHw81jAMTWG8mo8G4c2pMEQOc6ZZnZsw5lZOiuEEIIUSDM8nPQ3Llz8ff3x8bGhsDAQPbs2fPQ8rt27SIwMBAbGxsqVarE/Pnz8xVsoTv7G3zXGm4eQ9m4sKbapzQ+0p5DoUk4WFvw+bP1+HZAQ0lEhBBCiAKU52Rk5cqVjB8/nsmTJ3P8+HFatWrFM888Q2hoaI7lQ0JC6Ny5M61ateL48eO88847vPrqq6xZs+axgy8wGamwYQKsGgJpccS4NaC37mNeP1WeDK2iTXUP/hzXij6y4q4QQghR4DRKKZWXA5o2bUrDhg2ZN2+eYV9AQAA9e/Zk1qxZ95V/++23Wb9+PcHBwYZ9o0eP5uTJkxw4cCBX14yPj8fZ2Zm4uDicnJzyEu6j3b6gX2n31hkUGv5w6s+EyM5kYkF5F1umdqtJx5pekoQIIYQQeZTb3+889RlJT0/n6NGjTJw4Mdv+Tp06sX///hyPOXDgAJ06dcq276mnnmLx4sVkZGRgaWl53zFpaWmkpaVlezGF4sTPsPF1yEgizdqN8ekv8WdkTawszHj1yUq81KYKtlYym6oQQghRmPKUjERFRaHVavHy8sq238vLi4iIiByPiYiIyLF8ZmYmUVFReHt733fMrFmzmD59el5CyzudDk6thIwkLjkE8r+oEdzGhdrlnZjdvwFVPB0K9/pCCCGEAPLZgfXeJgul1EObMXIqn9P+LJMmTSIuLs7wCAsLy0+YD2dmRnDzz5hvPZROUa8RpXFhVOtKrH2ppSQiQgghRBHKU82Iu7s75ubm99WCREZG3lf7kaVs2bI5lrewsMDNzS3HY6ytrbG2ts5LaHmmlGLCpnCC4zrh5WTNl/3q06KKe6FeUwghhBD3y1PNiJWVFYGBgWzdujXb/q1bt9KiRYscj2nevPl95bds2UKjRo1y7C9SVDQaDZ89W5eudb3ZPO5JSUSEEEIII8lzM82ECRNYtGgRS5YsITg4mNdee43Q0FBGjx4N6JtYBg8ebCg/evRorl27xoQJEwgODmbJkiUsXryYN954o+BeRT7VKufMNzJviBBCCGFUeZ6BtX///kRHRzNjxgzCw8OpXbs2mzZtwtfXF4Dw8PBsc474+/uzadMmXnvtNb799lvKlSvHnDlz6NOnT8G9CiGEEEKYrDzPM2IMhTrPiBBCCCEKRW5/v/M1mkYIIYQQoqBIMiKEEEIIo5JkRAghhBBGJcmIEEIIIYxKkhEhhBBCGJUkI0IIIYQwKklGhBBCCGFUkowIIYQQwqgkGRFCCCGEUUkyIoQQQgijkmRECCGEEEaV54XyjCFr+Zz4+HgjRyKEEEKI3Mr63X7UMngmkYwkJCQA4OPjY+RIhBBCCJFXCQkJODs7P/B5k1i1V6fTcfPmTRwdHdFoNAV23vj4eHx8fAgLC5PVgAuZ3OuiIfe5aMh9Lhpyn4tGYd5npRQJCQmUK1cOM7MH9wwxiZoRMzMzKlSoUGjnd3Jykg96EZF7XTTkPhcNuc9FQ+5z0Sis+/ywGpEs0oFVCCGEEEYlyYgQQgghjKpUJyPW1tZMnToVa2trY4dS4sm9Lhpyn4uG3OeiIfe5aBSH+2wSHViFEEIIUXKV6poRIYQQQhifJCNCCCGEMCpJRoQQQghhVJKMCCGEEMKoSnwyMnfuXPz9/bGxsSEwMJA9e/Y8tPyuXbsIDAzExsaGSpUqMX/+/CKK1LTl5T6vXbuWjh074uHhgZOTE82bN+evv/4qwmhNW14/01n27duHhYUF9evXL9wAS4i83ue0tDQmT56Mr68v1tbWVK5cmSVLlhRRtKYrr/d5xYoV1KtXDzs7O7y9vRk2bBjR0dFFFK1p2r17N926daNcuXJoNBp+++23Rx5T5L+FqgT75ZdflKWlpVq4cKEKCgpS48aNU/b29uratWs5lr9y5Yqys7NT48aNU0FBQWrhwoXK0tJSrV69uogjNy15vc/jxo1TH3/8sfrnn3/UhQsX1KRJk5SlpaU6duxYEUduevJ6r7PExsaqSpUqqU6dOql69eoVTbAmLD/3uXv37qpp06Zq69atKiQkRB06dEjt27evCKM2PXm9z3v27FFmZmbqq6++UleuXFF79uxRtWrVUj179iziyE3Lpk2b1OTJk9WaNWsUoNatW/fQ8sb4LSzRyUiTJk3U6NGjs+2rUaOGmjhxYo7l33rrLVWjRo1s+0aNGqWaNWtWaDGWBHm9zzmpWbOmmj59ekGHVuLk9173799fTZkyRU2dOlWSkVzI633+888/lbOzs4qOji6K8EqMvN7nTz/9VFWqVCnbvjlz5qgKFSoUWowlTW6SEWP8FpbYZpr09HSOHj1Kp06dsu3v1KkT+/fvz/GYAwcO3Ff+qaee4siRI2RkZBRarKYsP/f5XjqdjoSEBFxdXQsjxBIjv/d66dKlXL58malTpxZ2iCVCfu7z+vXradSoEZ988gnly5enWrVqvPHGG6SkpBRFyCYpP/e5RYsWXL9+nU2bNqGU4tatW6xevZouXboURcilhjF+C01iobz8iIqKQqvV4uXllW2/l5cXEREROR4TERGRY/nMzEyioqLw9vYutHhNVX7u870+//xzkpKS6NevX2GEWGLk515fvHiRiRMnsmfPHiwsSuyfe4HKz32+cuUKe/fuxcbGhnXr1hEVFcXLL79MTEyM9Bt5gPzc5xYtWrBixQr69+9PamoqmZmZdO/ena+//rooQi41jPFbWGJrRrJoNJps20qp+/Y9qnxO+0V2eb3PWX7++WemTZvGypUr8fT0LKzwSpTc3mutVsuAAQOYPn061apVK6rwSoy8fKZ1Oh0ajYYVK1bQpEkTOnfuzBdffMGyZcukduQR8nKfg4KCePXVV3nvvfc4evQomzdvJiQkhNGjRxdFqKVKUf8Wlth/Krm7u2Nubn5fhh0ZGXlfxpelbNmyOZa3sLDAzc2t0GI1Zfm5z1lWrlzJiBEjWLVqFR06dCjMMEuEvN7rhIQEjhw5wvHjxxk7diyg/9FUSmFhYcGWLVto165dkcRuSvLzmfb29qZ8+fLZlkoPCAhAKcX169epWrVqocZsivJzn2fNmkXLli158803Aahbty729va0atWKDz74QGqvC4gxfgtLbM2IlZUVgYGBbN26Ndv+rVu30qJFixyPad68+X3lt2zZQqNGjbC0tCy0WE1Zfu4z6GtEhg4dyk8//STtvbmU13vt5OTE6dOnOXHihOExevRoqlevzokTJ2jatGlRhW5S8vOZbtmyJTdv3iQxMdGw78KFC5iZmVGhQoVCjddU5ec+JycnY2aW/WfL3Nwc+Pdf7uLxGeW3sNC6xhYDWcPGFi9erIKCgtT48eOVvb29unr1qlJKqYkTJ6pBgwYZymcNZ3rttddUUFCQWrx4sQztzYW83ueffvpJWVhYqG+//VaFh4cbHrGxscZ6CSYjr/f6XjKaJnfyep8TEhJUhQoVVN++fdXZs2fVrl27VNWqVdXIkSON9RJMQl7v89KlS5WFhYWaO3euunz5stq7d69q1KiRatKkibFegklISEhQx48fV8ePH1eA+uKLL9Tx48cNQ6iLw29hiU5GlFLq22+/Vb6+vsrKyko1bNhQ7dq1y/DckCFDVOvWrbOV37lzp2rQoIGysrJSfn5+at68eUUcsWnKy31u3bq1Au57DBkypOgDN0F5/Uz/lyQjuZfX+xwcHKw6dOigbG1tVYUKFdSECRNUcnJyEUdtevJ6n+fMmaNq1qypbG1tlbe3txo4cKC6fv16EUdtWnbs2PHQ79zi8FuoUUrqtoQQQghhPCW2z4gQQgghTIMkI0IIIYQwKklGhBBCCGFUkowIIYQQwqgkGRFCCCGEUUkyIoQQQgijkmRECCGEEEYlyYgQQgghjEqSESGEEEIYlSQjQgghhDAqSUaEEEIIYVSSjAghhBDCqP4Py/upeFPT1bsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#bop = VectorData(np.reshape(b,[-1]))\n",
+    "op = VectorData(bn)\n",
+    "cgls = CGLS(operator=Aop, data=bop, max_iteration=4, update_objective_interval=10)\n",
+    "cgls.run()\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,100),cgls.solution.as_array(), label='Least squares solution with early stopping')\n",
+    "plt.plot(np.linspace(0,1,100),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90dcba3f",
+   "metadata": {},
+   "source": [
+    "We can also do Tikhonov regularisation using the CIL framework. For example, in the block framework below: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "15ff9d79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Iter   Max Iter     Time/Iter            Objective\n",
+      "                               [s]                     \n",
+      "        0       1000         0.000          2.16038e-01\n",
+      "        1       1000         0.130          3.73899e-03\n",
+      "        2       1000         0.108          9.73406e-04\n",
+      "        3       1000         0.109          8.16896e-04\n",
+      "        4       1000         0.111          8.07526e-04\n",
+      "        5       1000         0.106          8.07019e-04\n",
+      "        6       1000         0.105          8.07002e-04\n",
+      "        7       1000         0.106          8.07002e-04\n",
+      "        8       1000         0.110          8.07002e-04\n",
+      "Tolerance is reached: 1e-06\n",
+      "-------------------------------------------------------\n",
+      "        8       1000         0.110          8.07002e-04\n",
+      "Stop criterion has been reached.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ig = Aop.domain_geometry()\n",
+    "L = IdentityOperator(ig)\n",
+    "operator_block = BlockOperator(Aop, reg_param*L)\n",
+    "\n",
+    "zero_data = L.range.allocate(0)\n",
+    "data_block = BlockDataContainer(bop, zero_data)\n",
+    "\n",
+    "cglsb = CGLS(operator=operator_block, data=data_block, max_iteration=1000, update_objctive_interval=10)\n",
+    "cglsb.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "74107db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n), cglsb.solution.as_array(), label='Tikhonov regularised solution- CIL')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fa3e6b0",
+   "metadata": {},
+   "source": [
+    "We can compare the results of Tikhonov regularisation implemented by numpy and CIL: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7c6e22c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),  cglsb.solution.as_array(),'o', label='Tikhonov regularised solution- CIL')\n",
+    "plt.plot(np.linspace(0,1,n),xtik, '+', label='Tikhonov regularised solution- numpy')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc199d9",
+   "metadata": {},
+   "source": [
+    "You can see that the solutions are identical!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c27a9218",
+   "metadata": {},
+   "source": [
+    "We think the reconstruction error near the boundary at 1 is caused by the discretisation of the integral - for future investigation "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
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+                  
+  <h1>Source code for cil.framework.acquisition_data</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">.labels</span> <span class="kn">import</span> <span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">Backend</span>
+<span class="kn">from</span> <span class="nn">.data_container</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+<span class="kn">from</span> <span class="nn">.partitioner</span> <span class="kn">import</span> <span class="n">Partitioner</span>
+
+
+<div class="viewcode-block" id="AcquisitionData">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionData">[docs]</a>
+<span class="k">class</span> <span class="nc">AcquisitionData</span><span class="p">(</span><span class="n">DataContainer</span><span class="p">,</span> <span class="n">Partitioner</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;DataContainer for holding 2D or 3D sinogram&#39;&#39;&#39;</span>
+    <span class="n">__container_priority__</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span>
+
+    <span class="nd">@geometry</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span>
+
+    <span class="nd">@dimension_labels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unable to set the dimension_labels directly. Use geometry.set_labels() instead&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">array</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">deep_copy</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                 <span class="n">geometry</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span><span class="s2">&quot;AcquisitionData requires a geometry&quot;</span><span class="p">)</span>
+
+        <span class="n">labels</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dimension_labels&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">labels</span> <span class="o">!=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Deprecated: &#39;dimension_labels&#39; cannot be set with &#39;allocate()&#39;. Use &#39;geometry.set_labels()&#39; to modify the geometry before using allocate.&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">array</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="c1"># remove singleton dimensions</span>
+            <span class="n">array</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;array must be a CIL type DataContainer or numpy.ndarray got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)))</span>
+
+        <span class="k">if</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Shape mismatch got </span><span class="si">{}</span><span class="s1"> expected </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">AcquisitionData</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">deep_copy</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">geometry</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Check if two AcquisitionData objects are equal. This is done by checking if the geometry, data and dtype are equal.</span>
+<span class="sd">        Also, if the other object is a numpy.ndarray, it will check if the data and dtype are equal.</span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other: AcquisitionData or numpy.ndarray</span>
+<span class="sd">            The object to compare with.</span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        bool</span>
+<span class="sd">            True if the two objects are equal, False otherwise.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span> \
+                <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">geometry</span> \
+                <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+                <span class="k">return</span> <span class="kc">True</span> 
+        <span class="k">elif</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">other</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="o">==</span><span class="n">other</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="AcquisitionData.get_slice">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionData.get_slice">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">channel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">horizontal</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns a new dataset of a single slice in the requested direction.&#39;&#39;&#39;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">geometry_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="n">angle</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">vertical</span><span class="p">,</span> <span class="n">horizontal</span><span class="o">=</span><span class="n">horizontal</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">force</span><span class="p">:</span>
+                <span class="n">geometry_new</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span> <span class="p">(</span><span class="s2">&quot;Unable to return slice of requested AcquisitionData. Use &#39;force=True&#39; to return DataContainer instead.&quot;</span><span class="p">)</span>
+
+        <span class="c1">#get new data</span>
+        <span class="c1">#if vertical = &#39;centre&#39; slice convert to index and subset, this will interpolate 2 rows to get the center slice value</span>
+        <span class="k">if</span> <span class="n">vertical</span> <span class="o">==</span> <span class="s1">&#39;centre&#39;</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
+
+            <span class="n">centre_slice_pos</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.</span>
+            <span class="n">ind0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">centre_slice_pos</span><span class="p">))</span>
+            <span class="n">w2</span> <span class="o">=</span> <span class="n">centre_slice_pos</span> <span class="o">-</span> <span class="n">ind0</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="n">angle</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">ind0</span><span class="p">,</span> <span class="n">horizontal</span><span class="o">=</span><span class="n">horizontal</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">w2</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">out2</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="n">angle</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">ind0</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">horizontal</span><span class="o">=</span><span class="n">horizontal</span><span class="p">)</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">w2</span><span class="p">)</span> <span class="o">+</span> <span class="n">out2</span> <span class="o">*</span> <span class="n">w2</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="n">angle</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">vertical</span><span class="p">,</span> <span class="n">horizontal</span><span class="o">=</span><span class="n">horizontal</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">geometry_new</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">AcquisitionData</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">geometry_new</span><span class="p">,</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionData.reorder">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionData.reorder">[docs]</a>
+    <span class="k">def</span> <span class="nf">reorder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Reorders the data in memory as requested. This is an in-place operation.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        order: list or str</span>
+<span class="sd">            Ordered list of labels from self.dimension_labels, or string &#39;astra&#39; or &#39;tigre&#39;.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">order</span> <span class="ow">in</span> <span class="n">Backend</span><span class="p">:</span>
+            <span class="n">order</span> <span class="o">=</span> <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">get_order_for_engine</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">order</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/framework/acquisition_geometry/index.html b/v24.2.0/_modules/cil/framework/acquisition_geometry/index.html
new file mode 100644
index 0000000000..e1cdbb6292
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@@ -0,0 +1,2768 @@
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+<!DOCTYPE html>
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+                  
+  <h1>Source code for cil.framework.acquisition_geometry</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">.labels</span> <span class="kn">import</span> <span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">AngleUnit</span><span class="p">,</span> <span class="n">AcquisitionType</span><span class="p">,</span> <span class="n">FillType</span>
+<span class="kn">from</span> <span class="nn">.acquisition_data</span> <span class="kn">import</span> <span class="n">AcquisitionData</span>
+<span class="kn">from</span> <span class="nn">.image_geometry</span> <span class="kn">import</span> <span class="n">ImageGeometry</span>
+
+<span class="k">class</span> <span class="nc">ComponentDescription</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class enables the creation of vectors and unit vectors used to describe the components of a tomography system</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dof</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_dof</span> <span class="o">=</span> <span class="n">dof</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_vector</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">vec</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Can&#39;t convert to numpy array&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">vec</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_unit_vector</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+        <span class="n">vec</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_vector</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+        <span class="n">dot_product</span> <span class="o">=</span> <span class="n">vec</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">dot_product</span><span class="p">)</span><span class="o">&gt;</span><span class="mf">1e-8</span><span class="p">:</span>
+            <span class="n">vec</span> <span class="o">=</span> <span class="p">(</span><span class="n">vec</span><span class="o">/</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">dot_product</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Can&#39;t return a unit vector of a zero magnitude vector&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">vec</span>
+
+    <span class="k">def</span> <span class="nf">length_check</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">val_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Vectors for </span><span class="si">{0}</span><span class="s2">D geometries must have length = </span><span class="si">{0}</span><span class="s2">. Got </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dof</span><span class="p">,</span><span class="n">val</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">val_length</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dof</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Vectors for </span><span class="si">{0}</span><span class="s2">D geometries must have length = </span><span class="si">{0}</span><span class="s2">. Got </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dof</span><span class="p">,</span><span class="n">val</span><span class="p">))</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">test_perpendicular</span><span class="p">(</span><span class="n">vector1</span><span class="p">,</span> <span class="n">vector2</span><span class="p">):</span>
+        <span class="n">dor_prod</span> <span class="o">=</span> <span class="n">vector1</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">vector2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">dor_prod</span><span class="p">)</span> <span class="o">&lt;</span><span class="mf">1e-10</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">test_parallel</span><span class="p">(</span><span class="n">vector1</span><span class="p">,</span> <span class="n">vector2</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;For unit vectors only. Returns true if directions are opposite&#39;&#39;&#39;</span>
+        <span class="n">dor_prod</span> <span class="o">=</span> <span class="n">vector1</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">vector2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="mi">1</span><span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">dor_prod</span><span class="p">)</span> <span class="o">&lt;</span><span class="mf">1e-10</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">return</span> <span class="kc">False</span>
+
+
+<span class="k">class</span> <span class="nc">PositionVector</span><span class="p">(</span><span class="n">ComponentDescription</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates a component of a tomography system with a position attribute</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">position</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_position</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span>
+
+    <span class="nd">@position</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">position</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">length_check</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_position</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_vector</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">DirectionVector</span><span class="p">(</span><span class="n">ComponentDescription</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates a component of a tomography system with a direction attribute</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">direction</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_direction</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span>
+
+    <span class="nd">@direction</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">direction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">length_check</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_direction</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">PositionDirectionVector</span><span class="p">(</span><span class="n">PositionVector</span><span class="p">,</span> <span class="n">DirectionVector</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates a component of a tomography system with position and direction attributes</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+    <span class="k">pass</span>
+
+
+<span class="k">class</span> <span class="nc">Detector1D</span><span class="p">(</span><span class="n">PositionVector</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates a component of a tomography system with position and direction_x attributes used for 1D panels</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">direction_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_direction_x</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span>
+
+    <span class="nd">@direction_x</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">direction_x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">length_check</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_direction_x</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_direction_x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">_direction_x</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span>
+
+
+<span class="k">class</span> <span class="nc">Detector2D</span><span class="p">(</span><span class="n">PositionVector</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates a component of a tomography system with position, direction_x and direction_y attributes used for 2D panels</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">direction_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_direction_x</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">direction_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_direction_y</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_direction_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_direction_y</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span>
+
+    <span class="k">def</span> <span class="nf">set_direction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">length_check</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">length_check</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+
+        <span class="n">dot_product</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">dot_product</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;vectors detector.direction_x and detector.direction_y must be orthogonal&quot;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_direction_y</span> <span class="o">=</span> <span class="n">y</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_direction_x</span> <span class="o">=</span> <span class="n">x</span>
+
+
+<span class="k">class</span> <span class="nc">SystemConfiguration</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;This is a generic class to hold the description of a tomography system&#39;&#39;&#39;</span>
+    <span class="n">SYSTEM_SIMPLE</span> <span class="o">=</span> <span class="s1">&#39;simple&#39;</span>
+    <span class="n">SYSTEM_OFFSET</span> <span class="o">=</span> <span class="s1">&#39;offset&#39;</span>
+    <span class="n">SYSTEM_ADVANCED</span> <span class="o">=</span> <span class="s1">&#39;advanced&#39;</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_type</span><span class="o">.</span><span class="n">dimension</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_type</span><span class="o">.</span><span class="n">geometry</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">acquisition_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_acquisition_type</span>
+
+    <span class="nd">@acquisition_type</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">acquisition_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_acquisition_type</span> <span class="o">=</span> <span class="n">AcquisitionType</span><span class="p">(</span><span class="n">val</span><span class="p">)</span><span class="o">.</span><span class="n">validate</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dof</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">geometry</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units&#39;</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_type</span> <span class="o">=</span> <span class="n">AcquisitionType</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dof</span><span class="si">}</span><span class="s2">D&quot;</span><span class="p">)</span> <span class="o">|</span> <span class="n">AcquisitionType</span><span class="p">(</span><span class="n">geometry</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="n">units</span>
+
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">PARALLEL</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ray</span> <span class="o">=</span> <span class="n">DirectionVector</span><span class="p">(</span><span class="n">dof</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">source</span> <span class="o">=</span> <span class="n">PositionVector</span><span class="p">(</span><span class="n">dof</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">detector</span> <span class="o">=</span> <span class="n">Detector1D</span><span class="p">(</span><span class="n">dof</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span> <span class="o">=</span> <span class="n">PositionVector</span><span class="p">(</span><span class="n">dof</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">detector</span> <span class="o">=</span> <span class="n">Detector2D</span><span class="p">(</span><span class="n">dof</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span> <span class="o">=</span> <span class="n">PositionDirectionVector</span><span class="p">(</span><span class="n">dof</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Implements the string representation of the system configuration</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Implements the equality check of the system configuration</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">rotation_vec_to_y</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39; returns a rotation matrix that will rotate the projection of vec on the x-y plane to the +y direction [0,1, Z]&#39;&#39;&#39;</span>
+
+        <span class="n">vec</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
+
+        <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vec</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">vec</span><span class="p">[:</span><span class="mi">2</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="k">pass</span>
+        <span class="k">elif</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">vec</span><span class="p">[:</span><span class="mi">2</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">theta</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">vec</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+            <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+            <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">axis_rotation</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">rotation_vec_to_z</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39; returns a rotation matrix that will align vec with the z-direction [0,0,1]&#39;&#39;&#39;</span>
+
+        <span class="n">vec</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
+
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">vec</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="k">pass</span>
+            <span class="k">elif</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">vec</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+                <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+                <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">vx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">vec</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="p">[</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vec</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">]])</span>
+                <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">vx</span> <span class="o">+</span> <span class="n">vx</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">vx</span><span class="p">)</span> <span class="o">*</span>  <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">vec</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Vec must have length 3, got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vec</span><span class="p">)))</span>
+
+        <span class="k">return</span> <span class="n">axis_rotation</span>
+
+    <span class="k">def</span> <span class="nf">update_reference_frame</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms the system origin to the rotation_axis position</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_origin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">set_origin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms the system origin to the input origin</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">translation</span> <span class="o">=</span> <span class="n">origin</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="s1">&#39;source&#39;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">-=</span> <span class="n">translation</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-=</span> <span class="n">translation</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-=</span> <span class="n">translation</span>
+
+
+    <span class="k">def</span> <span class="nf">get_centre_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the 2D system configuration corresponding to the centre slice</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+    <span class="k">def</span> <span class="nf">calculate_magnification</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Calculates the magnification of the system using the source to rotate axis,</span>
+<span class="sd">        and source to detector distance along the direction.</span>
+
+<span class="sd">        :return: returns [dist_source_center, dist_center_detector, magnification],  [0] distance from the source to the rotate axis, [1] distance from the rotate axis to the detector, [2] magnification of the system</span>
+<span class="sd">        :rtype: list</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+    <span class="k">def</span> <span class="nf">system_description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns `simple` if the the geometry matches the default definitions with no offsets or rotations,</span>
+<span class="sd">            \nReturns `offset` if the the geometry matches the default definitions with centre-of-rotation or detector offsets</span>
+<span class="sd">            \nReturns `advanced` if the the geometry has rotated or tilted rotation axis or detector, can also have offsets</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a copy of SystemConfiguration&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">Parallel2D</span><span class="p">(</span><span class="n">SystemConfiguration</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates the SystemConfiguration of a parallel beam 2D tomographic system</span>
+
+<span class="sd">    :param ray_direction: A 2D vector describing the x-ray direction (x,y)</span>
+<span class="sd">    :type ray_direction: list, tuple, ndarray</span>
+<span class="sd">    :param detector_pos: A 2D vector describing the position of the centre of the detector (x,y)</span>
+<span class="sd">    :type detector_pos: list, tuple, ndarray</span>
+<span class="sd">    :param detector_direction_x: A 2D vector describing the direction of the detector_x (x,y)</span>
+<span class="sd">    :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">    :param rotation_axis_pos: A 2D vector describing the position of the axis of rotation (x,y)</span>
+<span class="sd">    :type rotation_axis_pos: list, tuple, ndarray</span>
+<span class="sd">    :param units: Label the units of distance used for the configuration</span>
+<span class="sd">    :type units: string</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ray_direction</span><span class="p">,</span> <span class="n">detector_pos</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_pos</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructor method</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Parallel2D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">dof</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">AcquisitionType</span><span class="o">.</span><span class="n">PARALLEL</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="n">units</span><span class="p">)</span>
+
+        <span class="c1">#source</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">ray_direction</span>
+
+        <span class="c1">#detector</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">detector_pos</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">=</span> <span class="n">detector_direction_x</span>
+
+        <span class="c1">#rotate axis</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_axis_pos</span>
+
+
+    <span class="k">def</span> <span class="nf">align_reference_frame</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">definition</span><span class="o">=</span><span class="s1">&#39;cil&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms and rotates the system to backend definitions</span>
+
+<span class="sd">        &#39;cil&#39; sets the origin to the rotation axis and aligns the y axis with the ray-direction</span>
+<span class="sd">        &#39;tigre&#39; sets the origin to the rotation axis and aligns the y axis with the ray-direction</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1">#in this instance definitions are the same</span>
+        <span class="k">if</span> <span class="n">definition</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;cil&#39;</span><span class="p">,</span><span class="s1">&#39;tigre&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Geometry can be configured for definition = &#39;cil&#39; or &#39;tigre&#39;  only. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">definition</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_origin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+        <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_y</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+
+
+    <span class="k">def</span> <span class="nf">system_description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns `simple` if the the geometry matches the default definitions with no offsets or rotations,</span>
+<span class="sd">            \nReturns `offset` if the the geometry matches the default definitions with centre-of-rotation or detector offsets</span>
+<span class="sd">            \nReturns `advanced` if the the geometry has rotated or tilted rotation axis or detector, can also have offsets</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+
+        <span class="n">rays_perpendicular_detector</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+
+        <span class="c1">#rotation axis position + ray direction hits detector position</span>
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">):</span> <span class="c1">#points are equal so on ray path</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">vec_a</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="n">vec_a</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">rays_perpendicular_detector</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_ADVANCED</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="n">rotation_axis_centred</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_OFFSET</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_SIMPLE</span>
+
+        <span class="k">return</span> <span class="n">config</span>
+
+
+    <span class="k">def</span> <span class="nf">rotation_axis_on_detector</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the world coordinate system</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        cil.framework.system_configuration.PositionVector</span>
+<span class="sd">            Position in the 3D system</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">Pv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="n">Pv</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">PositionVector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span> <span class="o">*</span> <span class="n">ratio</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">calculate_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the detector coordinate system</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">         - Origin is in the centre of the detector</span>
+<span class="sd">         - Axes directions are specified by detector.direction_x, detector.direction_y</span>
+<span class="sd">         - Units are the units of distance used to specify the component&#39;s positions</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Float</span>
+<span class="sd">            Offset position along the detector x_axis at y=0</span>
+<span class="sd">        Float</span>
+<span class="sd">            Angle between the y_axis and the rotation axis projection, in radians</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1">#convert to the detector coordinate system</span>
+        <span class="n">dp1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis_on_detector</span><span class="p">()</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Configures the geometry to have the requested centre of rotation offset at the detector</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">offset_current</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculate_centre_of_rotation</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">offset_new</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">-</span> <span class="n">offset_current</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">offset_new</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">csv</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">)</span>
+
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;2D Parallel-beam tomography</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;System configuration:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Ray direction: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Rotation axis position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector direction x: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span> \
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">get_centre_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="nf">calculate_magnification</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Method to calculate magnification and distance from the sample to</span>
+<span class="sd">        the detector using the detector positions and the rotation axis.</span>
+<span class="sd">        For parallel beam geometry magnification = 1</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        list</span>
+<span class="sd">            A list containing the [0] distance from the source to the rotate</span>
+<span class="sd">            axis, [1] distance from the rotate axis to the detector,</span>
+<span class="sd">            [2] magnification of the system</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">ab</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="n">dist_center_detector</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ab</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ab</span><span class="p">)))</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">dist_center_detector</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]</span>
+
+
+<span class="k">class</span> <span class="nc">Parallel3D</span><span class="p">(</span><span class="n">SystemConfiguration</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates the SystemConfiguration of a parallel beam 3D tomographic system</span>
+
+<span class="sd">    :param ray_direction: A 3D vector describing the x-ray direction (x,y,z)</span>
+<span class="sd">    :type ray_direction: list, tuple, ndarray</span>
+<span class="sd">    :param detector_pos: A 3D vector describing the position of the centre of the detector (x,y,z)</span>
+<span class="sd">    :type detector_pos: list, tuple, ndarray</span>
+<span class="sd">    :param detector_direction_x: A 3D vector describing the direction of the detector_x (x,y,z)</span>
+<span class="sd">    :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">    :param detector_direction_y: A 3D vector describing the direction of the detector_y (x,y,z)</span>
+<span class="sd">    :type detector_direction_y: list, tuple, ndarray</span>
+<span class="sd">    :param rotation_axis_pos: A 3D vector describing the position of the axis of rotation (x,y,z)</span>
+<span class="sd">    :type rotation_axis_pos: list, tuple, ndarray</span>
+<span class="sd">    :param rotation_axis_direction: A 3D vector describing the direction of the axis of rotation (x,y,z)</span>
+<span class="sd">    :type rotation_axis_direction: list, tuple, ndarray</span>
+<span class="sd">    :param units: Label the units of distance used for the configuration</span>
+<span class="sd">    :type units: string</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span>  <span class="n">ray_direction</span><span class="p">,</span> <span class="n">detector_pos</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">,</span> <span class="n">rotation_axis_pos</span><span class="p">,</span> <span class="n">rotation_axis_direction</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructor method</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Parallel3D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">dof</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">AcquisitionType</span><span class="o">.</span><span class="n">PARALLEL</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="n">units</span><span class="p">)</span>
+
+        <span class="c1">#source</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">ray_direction</span>
+
+        <span class="c1">#detector</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">detector_pos</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">set_direction</span><span class="p">(</span><span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">)</span>
+
+        <span class="c1">#rotate axis</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_axis_pos</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">rotation_axis_direction</span>
+
+    <span class="k">def</span> <span class="nf">align_z</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms the system origin to the rotate axis with z direction aligned to the rotate axis direction</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_origin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+        <span class="c1">#calculate rotation matrix to align rotation axis direction with z</span>
+        <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_z</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+
+        <span class="c1">#apply transform</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_x</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_y</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">set_direction</span><span class="p">(</span><span class="n">new_x</span><span class="p">,</span> <span class="n">new_y</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">align_reference_frame</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">definition</span><span class="o">=</span><span class="s1">&#39;cil&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms and rotates the system to backend definitions</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1">#in this instance definitions are the same</span>
+        <span class="k">if</span> <span class="n">definition</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;cil&#39;</span><span class="p">,</span><span class="s1">&#39;tigre&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Geometry can be configured for definition = &#39;cil&#39; or &#39;tigre&#39;  only. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">definition</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">align_z</span><span class="p">()</span>
+        <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_y</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_direction_x</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_direction_y</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">set_direction</span><span class="p">(</span><span class="n">new_direction_x</span><span class="p">,</span> <span class="n">new_direction_y</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">system_description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns `simple` if the the geometry matches the default definitions with no offsets or rotations,</span>
+<span class="sd">            \nReturns `offset` if the the geometry matches the default definitions with centre-of-rotation or detector offsets</span>
+<span class="sd">            \nReturns `advanced` if the the geometry has rotated or tilted rotation axis or detector, can also have offsets</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        simple</span>
+<span class="sd">         - rays perpendicular to detector</span>
+<span class="sd">         - rotation axis parallel to detector y</span>
+<span class="sd">         - rotation axis position + ray direction hits detector with no x offset (y offsets allowed)</span>
+<span class="sd">        offset</span>
+<span class="sd">         - rays perpendicular to detector</span>
+<span class="sd">         - rotation axis parallel to detector y</span>
+<span class="sd">        rolled</span>
+<span class="sd">         - rays perpendicular to detector</span>
+<span class="sd">         - rays perpendicular to rotation axis</span>
+<span class="sd">        advanced</span>
+<span class="sd">         - not rays perpendicular to detector (for parallel just equates to an effective pixel size change?)</span>
+<span class="sd">         or</span>
+<span class="sd">         - not rays perpendicular to rotation axis  (tilted, i.e. laminography)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">rays_perpendicular_detector</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">rays_perpendicular_rotation</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_perpendicular</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+        <span class="n">rotation_parallel_detector_y</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">)</span>
+
+        <span class="c1">#rotation axis to detector is parallel with ray</span>
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">):</span> <span class="c1">#points are equal so on ray path</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">vec_a</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="n">vec_a</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">rays_perpendicular_detector</span> <span class="ow">or</span>\
+            <span class="ow">not</span> <span class="n">rays_perpendicular_rotation</span> <span class="ow">or</span>\
+            <span class="ow">not</span> <span class="n">rotation_parallel_detector_y</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_ADVANCED</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="n">rotation_axis_centred</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_OFFSET</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_SIMPLE</span>
+
+        <span class="k">return</span> <span class="n">config</span>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">csv</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">)</span>
+
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;3D Parallel-beam tomography</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;System configuration:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Ray direction: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Rotation axis position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Rotation axis direction: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector direction x: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector direction y: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span> \
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">):</span>
+
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">calculate_magnification</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Method to calculate magnification and distance from the sample to</span>
+<span class="sd">        the detector using the detector positions and the rotation axis.</span>
+<span class="sd">        For parallel beam geometry magnification = 1</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        list</span>
+<span class="sd">            A list containing the [0] distance from the source to the rotate</span>
+<span class="sd">            axis, [1] distance from the rotate axis to the detector,</span>
+<span class="sd">            [2] magnification of the system</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">ab</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="n">dist_center_detector</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ab</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ab</span><span class="p">)))</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">dist_center_detector</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">get_centre_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the 2D system configuration corresponding to the centre slice</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dp1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+        <span class="n">dp2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">dp1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">dp2</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="n">temp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+            <span class="c1">#convert to rotation axis reference frame</span>
+            <span class="n">temp</span><span class="o">.</span><span class="n">align_reference_frame</span><span class="p">()</span>
+
+            <span class="n">ray_direction</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">detector_position</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">detector_direction_x</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">rotation_axis_position</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+
+            <span class="k">return</span> <span class="n">Parallel2D</span><span class="p">(</span><span class="n">ray_direction</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot convert geometry to 2D. Requires axis of rotation to be perpendicular to ray direction and the detector direction x.&#39;</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">rotation_axis_on_detector</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the world coordinate system</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        cil.framework.system_configuration.PositionDirectionVector</span>
+<span class="sd">            Position and direction in the 3D system</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#calculate the rotation axis line with the detector</span>
+        <span class="n">vec_a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span>
+
+        <span class="c1">#calculate the intersection with the detector</span>
+        <span class="n">Pv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="n">Pv</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">vec_a</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">point1</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">+</span> <span class="n">vec_a</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="n">Pv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="n">Pv</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">vec_a</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">point2</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">+</span> <span class="n">vec_a</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="n">out</span> <span class="o">=</span> <span class="n">PositionDirectionVector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">point1</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">point2</span> <span class="o">-</span> <span class="n">point1</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+
+    <span class="k">def</span> <span class="nf">calculate_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the detector coordinate system</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">         - Origin is in the centre of the detector</span>
+<span class="sd">         - Axes directions are specified by detector.direction_x, detector.direction_y</span>
+<span class="sd">         - Units are the units of distance used to specify the component&#39;s positions</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Float</span>
+<span class="sd">            Offset position along the detector x_axis at y=0</span>
+<span class="sd">        Float</span>
+<span class="sd">            Angle between the y_axis and the rotation axis projection, in radians</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">rotate_axis_projection</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis_on_detector</span><span class="p">()</span>
+
+        <span class="n">p1</span> <span class="o">=</span> <span class="n">rotate_axis_projection</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">p2</span> <span class="o">=</span> <span class="n">p1</span> <span class="o">+</span> <span class="n">rotate_axis_projection</span><span class="o">.</span><span class="n">direction</span>
+
+        <span class="c1">#point1 and point2 are on the detector plane. need to return them in the detector coordinate system</span>
+        <span class="n">dp1</span> <span class="o">=</span> <span class="n">p1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp1</span><span class="p">)</span>
+        <span class="n">y1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp1</span><span class="p">)</span>
+        <span class="n">dp2</span> <span class="o">=</span> <span class="n">p2</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">x2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp2</span><span class="p">)</span>
+        <span class="n">y2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp2</span><span class="p">)</span>
+
+        <span class="c1">#y = m * x + c</span>
+        <span class="c1">#c = y1 - m * x1</span>
+        <span class="c1">#when y is 0</span>
+        <span class="c1">#x=-c/m</span>
+        <span class="c1">#x_y0 = -y1/m + x1</span>
+        <span class="n">offset_x_y0</span> <span class="o">=</span> <span class="n">x1</span> <span class="o">-</span><span class="n">y1</span> <span class="o">*</span> <span class="p">(</span><span class="n">x2</span> <span class="o">-</span> <span class="n">x1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">y2</span><span class="o">-</span><span class="n">y1</span><span class="p">)</span>
+
+        <span class="n">angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">x2</span> <span class="o">-</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y2</span> <span class="o">-</span> <span class="n">y1</span><span class="p">)</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="n">offset_x_y0</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">angle</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Configures the geometry to have the requested centre of rotation offset at the detector</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1">#two points on the detector</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="n">offset</span>
+        <span class="n">y1</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">x2</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">tan</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
+        <span class="n">y2</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="c1">#convert to 3d coordinates in system frame</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">x1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">+</span> <span class="n">y1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span>
+        <span class="n">p2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">x2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">+</span> <span class="n">y2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span>
+
+        <span class="c1"># find where vec p1 + t * ray dirn intersects plane defined by rotate axis (pos and dir) and det_x direction</span>
+
+        <span class="n">vector_pos</span><span class="o">=</span><span class="n">p1</span>
+        <span class="n">vec_dirn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span>
+        <span class="n">plane_pos</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">plane_normal</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+
+
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="n">plane_pos</span> <span class="o">-</span> <span class="n">vector_pos</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">vec_dirn</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span>
+        <span class="n">p1_on_plane</span> <span class="o">=</span> <span class="n">vector_pos</span> <span class="o">+</span> <span class="n">vec_dirn</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="n">vector_pos</span><span class="o">=</span><span class="n">p2</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="n">plane_pos</span> <span class="o">-</span> <span class="n">vector_pos</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">vec_dirn</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span>
+        <span class="n">p2_on_plane</span> <span class="o">=</span> <span class="n">vector_pos</span> <span class="o">+</span> <span class="n">vec_dirn</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">p1_on_plane</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">p2_on_plane</span> <span class="o">-</span> <span class="n">p1_on_plane</span>
+
+
+<span class="k">class</span> <span class="nc">Cone2D</span><span class="p">(</span><span class="n">SystemConfiguration</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates the SystemConfiguration of a cone beam 2D tomographic system</span>
+
+<span class="sd">    :param source_pos: A 2D vector describing the position of the source (x,y)</span>
+<span class="sd">    :type source_pos: list, tuple, ndarray</span>
+<span class="sd">    :param detector_pos: A 2D vector describing the position of the centre of the detector (x,y)</span>
+<span class="sd">    :type detector_pos: list, tuple, ndarray</span>
+<span class="sd">    :param detector_direction_x: A 2D vector describing the direction of the detector_x (x,y)</span>
+<span class="sd">    :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">    :param rotation_axis_pos: A 2D vector describing the position of the axis of rotation (x,y)</span>
+<span class="sd">    :type rotation_axis_pos: list, tuple, ndarray</span>
+<span class="sd">    :param units: Label the units of distance used for the configuration</span>
+<span class="sd">    :type units: string</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_pos</span><span class="p">,</span> <span class="n">detector_pos</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_pos</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructor method</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Cone2D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">dof</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">AcquisitionType</span><span class="o">.</span><span class="n">CONE</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="n">units</span><span class="p">)</span>
+
+        <span class="c1">#source</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">source_pos</span>
+
+        <span class="c1">#detector</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">detector_pos</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">=</span> <span class="n">detector_direction_x</span>
+
+        <span class="c1">#rotate axis</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_axis_pos</span>
+
+
+    <span class="k">def</span> <span class="nf">align_reference_frame</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">definition</span><span class="o">=</span><span class="s1">&#39;cil&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms and rotates the system to backend definitions</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_origin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">definition</span><span class="o">==</span><span class="s1">&#39;cil&#39;</span><span class="p">:</span>
+            <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_y</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">definition</span><span class="o">==</span><span class="s1">&#39;tigre&#39;</span><span class="p">:</span>
+            <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_y</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Geometry can be configured for definition = &#39;cil&#39; or &#39;tigre&#39;  only. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">definition</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+
+
+    <span class="k">def</span> <span class="nf">system_description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns `simple` if the the geometry matches the default definitions with no offsets or rotations,</span>
+<span class="sd">            \nReturns `offset` if the the geometry matches the default definitions with centre-of-rotation or detector offsets</span>
+<span class="sd">            \nReturns `advanced` if the the geometry has rotated or tilted rotation axis or detector, can also have offsets</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">vec_src2det</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+        <span class="n">principal_ray_centred</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="n">vec_src2det</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+
+        <span class="c1">#rotation axis to detector is parallel with centre ray</span>
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">):</span> <span class="c1">#points are equal</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">vec_b</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="n">vec_src2det</span><span class="p">,</span> <span class="n">vec_b</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">principal_ray_centred</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_ADVANCED</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="n">rotation_axis_centred</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_OFFSET</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_SIMPLE</span>
+
+        <span class="k">return</span> <span class="n">config</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">csv</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">)</span>
+
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;2D Cone-beam tomography</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;System configuration:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Source position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Rotation axis position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector direction x: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span> \
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">get_centre_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="nf">calculate_magnification</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">ab</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="n">dist_source_center</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ab</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ab</span><span class="p">)))</span>
+
+        <span class="n">ab_unit</span> <span class="o">=</span> <span class="n">ab</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ab</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ab</span><span class="p">))</span>
+
+        <span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span>
+
+        <span class="c1">#perpendicular distance between source and detector centre</span>
+        <span class="n">sd</span> <span class="o">=</span> <span class="nb">float</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ab_unit</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
+
+        <span class="n">source_to_detector</span> <span class="o">=</span> <span class="n">sd</span> <span class="o">/</span> <span class="n">ratio</span>
+        <span class="n">dist_center_detector</span> <span class="o">=</span> <span class="n">source_to_detector</span> <span class="o">-</span> <span class="n">dist_source_center</span>
+        <span class="n">magnification</span> <span class="o">=</span> <span class="p">(</span><span class="n">dist_center_detector</span> <span class="o">+</span> <span class="n">dist_source_center</span><span class="p">)</span> <span class="o">/</span> <span class="n">dist_source_center</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="n">dist_source_center</span><span class="p">,</span> <span class="n">dist_center_detector</span><span class="p">,</span> <span class="n">magnification</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">rotation_axis_on_detector</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the world coordinate system</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        PositionVector</span>
+<span class="sd">            Position in the 3D system</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#calculate the point the rotation axis intersects with the detector</span>
+        <span class="n">vec_a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span>
+
+        <span class="n">Pv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="n">Pv</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">vec_a</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+
+        <span class="n">out</span> <span class="o">=</span> <span class="n">PositionVector</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">+</span> <span class="n">vec_a</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+
+    <span class="k">def</span> <span class="nf">calculate_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the detector coordinate system</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">         - Origin is in the centre of the detector</span>
+<span class="sd">         - Axes directions are specified by detector.direction_x, detector.direction_y</span>
+<span class="sd">         - Units are the units of distance used to specify the component&#39;s positions</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Float</span>
+<span class="sd">            Offset position along the detector x_axis at y=0</span>
+<span class="sd">        Float</span>
+<span class="sd">            Angle between the y_axis and the rotation axis projection, in radians</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#convert to the detector coordinate system</span>
+        <span class="n">dp1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis_on_detector</span><span class="p">()</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Configures the geometry to have the requested centre of rotation offset at the detector</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">offset_current</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculate_centre_of_rotation</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">offset_new</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">-</span> <span class="n">offset_current</span>
+
+        <span class="n">cofr_shift</span> <span class="o">=</span> <span class="n">offset_new</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">calculate_magnification</span><span class="p">()[</span><span class="mi">2</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">cofr_shift</span>
+
+
+<span class="k">class</span> <span class="nc">Cone3D</span><span class="p">(</span><span class="n">SystemConfiguration</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class creates the SystemConfiguration of a cone beam 3D tomographic system</span>
+
+<span class="sd">    :param source_pos: A 3D vector describing the position of the source (x,y,z)</span>
+<span class="sd">    :type source_pos: list, tuple, ndarray</span>
+<span class="sd">    :param detector_pos: A 3D vector describing the position of the centre of the detector (x,y,z)</span>
+<span class="sd">    :type detector_pos: list, tuple, ndarray</span>
+<span class="sd">    :param detector_direction_x: A 3D vector describing the direction of the detector_x (x,y,z)</span>
+<span class="sd">    :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">    :param detector_direction_y: A 3D vector describing the direction of the detector_y (x,y,z)</span>
+<span class="sd">    :type detector_direction_y: list, tuple, ndarray</span>
+<span class="sd">    :param rotation_axis_pos: A 3D vector describing the position of the axis of rotation (x,y,z)</span>
+<span class="sd">    :type rotation_axis_pos: list, tuple, ndarray</span>
+<span class="sd">    :param rotation_axis_direction: A 3D vector describing the direction of the axis of rotation (x,y,z)</span>
+<span class="sd">    :type rotation_axis_direction: list, tuple, ndarray</span>
+<span class="sd">    :param units: Label the units of distance used for the configuration</span>
+<span class="sd">    :type units: string</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_pos</span><span class="p">,</span> <span class="n">detector_pos</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">,</span> <span class="n">rotation_axis_pos</span><span class="p">,</span> <span class="n">rotation_axis_direction</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructor method</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Cone3D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">dof</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">AcquisitionType</span><span class="o">.</span><span class="n">CONE</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="n">units</span><span class="p">)</span>
+
+        <span class="c1">#source</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">source_pos</span>
+
+        <span class="c1">#detector</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">detector_pos</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">set_direction</span><span class="p">(</span><span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">)</span>
+
+        <span class="c1">#rotate axis</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_axis_pos</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">rotation_axis_direction</span>
+
+    <span class="k">def</span> <span class="nf">align_z</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms the system origin to the rotate axis with z direction aligned to the rotate axis direction</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_origin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_z</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+
+        <span class="c1">#apply transform</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_x</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_y</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">set_direction</span><span class="p">(</span><span class="n">new_x</span><span class="p">,</span> <span class="n">new_y</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">align_reference_frame</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">definition</span><span class="o">=</span><span class="s1">&#39;cil&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Transforms and rotates the system to backend definitions</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">align_z</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">definition</span><span class="o">==</span><span class="s1">&#39;cil&#39;</span><span class="p">:</span>
+            <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_y</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">definition</span><span class="o">==</span><span class="s1">&#39;tigre&#39;</span><span class="p">:</span>
+            <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">rotation_vec_to_y</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Geometry can be configured for definition = &#39;cil&#39; or &#39;tigre&#39;  only. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">definition</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_direction_x</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">new_direction_y</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">set_direction</span><span class="p">(</span><span class="n">new_direction_x</span><span class="p">,</span> <span class="n">new_direction_y</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">system_description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns `simple` if the the geometry matches the default definitions with no offsets or rotations,</span>
+<span class="sd">            \nReturns `offset` if the the geometry matches the default definitions with centre-of-rotation or detector offsets</span>
+<span class="sd">            \nReturns `advanced` if the the geometry has rotated or tilted rotation axis or detector, can also have offsets</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">vec_src2det</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+        <span class="n">principal_ray_centred</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="n">vec_src2det</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">centre_ray_perpendicular_rotation</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_perpendicular</span><span class="p">(</span><span class="n">vec_src2det</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+        <span class="n">rotation_parallel_detector_y</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">)</span>
+
+        <span class="c1">#rotation axis to detector is parallel with centre ray</span>
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">):</span> <span class="c1">#points are equal</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">vec_b</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">create_unit_vector</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+            <span class="n">rotation_axis_centred</span> <span class="o">=</span> <span class="n">ComponentDescription</span><span class="o">.</span><span class="n">test_parallel</span><span class="p">(</span><span class="n">vec_src2det</span><span class="p">,</span> <span class="n">vec_b</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">principal_ray_centred</span> <span class="ow">or</span>\
+            <span class="ow">not</span> <span class="n">centre_ray_perpendicular_rotation</span> <span class="ow">or</span>\
+            <span class="ow">not</span> <span class="n">rotation_parallel_detector_y</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_ADVANCED</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="n">rotation_axis_centred</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_OFFSET</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">config</span> <span class="o">=</span>  <span class="n">SystemConfiguration</span><span class="o">.</span><span class="n">SYSTEM_SIMPLE</span>
+
+        <span class="k">return</span> <span class="n">config</span>
+
+    <span class="k">def</span> <span class="nf">get_centre_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the 2D system configuration corresponding to the centre slice</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#requires the rotate axis to be perpendicular to the normal of the detector, and perpendicular to detector_direction_x</span>
+        <span class="n">dp1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">dp2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">dp1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">dp2</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="n">temp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">temp</span><span class="o">.</span><span class="n">align_reference_frame</span><span class="p">()</span>
+            <span class="n">source_position</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">detector_position</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">detector_direction_x</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">rotation_axis_position</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+
+            <span class="k">return</span> <span class="n">Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot convert geometry to 2D. Requires axis of rotation to be perpendicular to the detector.&#39;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">csv</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">)</span>
+
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;3D Cone-beam tomography</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;System configuration:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Source position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Rotation axis position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Rotation axis direction: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector position: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector direction x: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">))</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Detector direction y: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span> \
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>\
+        <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">):</span>
+
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">calculate_magnification</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">ab</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+        <span class="n">dist_source_center</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ab</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ab</span><span class="p">)))</span>
+
+        <span class="n">ab_unit</span> <span class="o">=</span> <span class="n">ab</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ab</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ab</span><span class="p">))</span>
+
+        <span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span>
+
+        <span class="c1">#perpendicular distance between source and detector centre</span>
+        <span class="n">sd</span> <span class="o">=</span> <span class="nb">float</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ab_unit</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
+
+        <span class="n">source_to_detector</span> <span class="o">=</span> <span class="n">sd</span> <span class="o">/</span> <span class="n">ratio</span>
+        <span class="n">dist_center_detector</span> <span class="o">=</span> <span class="n">source_to_detector</span> <span class="o">-</span> <span class="n">dist_source_center</span>
+        <span class="n">magnification</span> <span class="o">=</span> <span class="p">(</span><span class="n">dist_center_detector</span> <span class="o">+</span> <span class="n">dist_source_center</span><span class="p">)</span> <span class="o">/</span> <span class="n">dist_source_center</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="n">dist_source_center</span><span class="p">,</span> <span class="n">dist_center_detector</span><span class="p">,</span> <span class="n">magnification</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">rotation_axis_on_detector</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the world coordinate system</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        PositionDirectionVector</span>
+<span class="sd">            Position and direction in the 3D system</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#calculate the intersection with the detector, of source to pv</span>
+        <span class="n">Pv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">vec_a</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="n">Pv</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">vec_a</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">point1</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">+</span> <span class="n">vec_a</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="c1">#calculate the intersection with the detector, of source to pv</span>
+        <span class="n">Pv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span>
+        <span class="n">vec_a</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="n">Pv</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">vec_a</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">normal</span><span class="p">)</span>
+        <span class="n">point2</span> <span class="o">=</span> <span class="n">Pv</span> <span class="o">+</span> <span class="n">vec_a</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="n">out</span> <span class="o">=</span> <span class="n">PositionDirectionVector</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">point1</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">point2</span> <span class="o">-</span> <span class="n">point1</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">calculate_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Calculates the position, on the detector, of the projection of the rotation axis in the detector coordinate system</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">         - Origin is in the centre of the detector</span>
+<span class="sd">         - Axes directions are specified by detector.direction_x, detector.direction_y</span>
+<span class="sd">         - Units are the units of distance used to specify the component&#39;s positions</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Float</span>
+<span class="sd">            Offset position along the detector x_axis at y=0</span>
+<span class="sd">        Float</span>
+<span class="sd">            Angle between the y_axis and the rotation axis projection, in radians</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">rotate_axis_projection</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis_on_detector</span><span class="p">()</span>
+
+        <span class="n">p1</span> <span class="o">=</span> <span class="n">rotate_axis_projection</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">p2</span> <span class="o">=</span> <span class="n">p1</span> <span class="o">+</span> <span class="n">rotate_axis_projection</span><span class="o">.</span><span class="n">direction</span>
+
+        <span class="c1">#point1 and point2 are on the detector plane. need to return them in the detector coordinate system</span>
+        <span class="n">dp1</span> <span class="o">=</span> <span class="n">p1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp1</span><span class="p">)</span>
+        <span class="n">y1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp1</span><span class="p">)</span>
+        <span class="n">dp2</span> <span class="o">=</span> <span class="n">p2</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">x2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp2</span><span class="p">)</span>
+        <span class="n">y2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">dp2</span><span class="p">)</span>
+
+        <span class="c1">#y = m * x + c</span>
+        <span class="c1">#c = y1 - m * x1</span>
+        <span class="c1">#when y is 0</span>
+        <span class="c1">#x=-c/m</span>
+        <span class="c1">#x_y0 = -y1/m + x1</span>
+        <span class="n">offset_x_y0</span> <span class="o">=</span> <span class="n">x1</span> <span class="o">-</span><span class="n">y1</span> <span class="o">*</span> <span class="p">(</span><span class="n">x2</span> <span class="o">-</span> <span class="n">x1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">y2</span><span class="o">-</span><span class="n">y1</span><span class="p">)</span>
+
+        <span class="n">angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">x2</span> <span class="o">-</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y2</span> <span class="o">-</span> <span class="n">y1</span><span class="p">)</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="n">offset_x_y0</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">set_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">angle</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Configures the geometry to have the requested centre of rotation offset at the detector</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#two points on the detector</span>
+        <span class="n">x1</span> <span class="o">=</span> <span class="n">offset</span>
+        <span class="n">y1</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">x2</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">tan</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
+        <span class="n">y2</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="c1">#convert to 3d coordinates in system frame</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">x1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">+</span> <span class="n">y1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span>
+        <span class="n">p2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">x2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span> <span class="o">+</span> <span class="n">y2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span>
+
+        <span class="c1"># vectors from source define plane</span>
+        <span class="n">sp1</span> <span class="o">=</span> <span class="n">p1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">sp2</span> <span class="o">=</span> <span class="n">p2</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span>
+
+        <span class="c1">#find vector intersection with a plane defined by rotate axis (pos and dir) and det_x direction</span>
+        <span class="n">plane_normal</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>
+
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">sp1</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span>
+        <span class="n">p1_on_plane</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">sp1</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="n">ratio</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span> <span class="o">/</span> <span class="n">sp2</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">plane_normal</span><span class="p">)</span>
+        <span class="n">p2_on_plane</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span> <span class="o">+</span> <span class="n">sp2</span> <span class="o">*</span> <span class="n">ratio</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">p1_on_plane</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">p2_on_plane</span> <span class="o">-</span> <span class="n">p1_on_plane</span>
+
+
+<span class="k">class</span> <span class="nc">Panel</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This is a class describing the panel of the system.</span>
+
+<span class="sd">    :param num_pixels: num_pixels_h or (num_pixels_h, num_pixels_v) containing the number of pixels of the panel</span>
+<span class="sd">    :type num_pixels: int, list, tuple</span>
+<span class="sd">    :param pixel_size: pixel_size_h or (pixel_size_h, pixel_size_v) containing the size of the pixels of the panel</span>
+<span class="sd">    :type pixel_size: int, lust, tuple</span>
+<span class="sd">    :param origin: the position of pixel 0 (the data origin) of the panel `top-left`, `top-right`, `bottom-left`, `bottom-right`</span>
+<span class="sd">    :type origin: string</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">num_pixels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_pixels</span>
+
+    <span class="nd">@num_pixels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">num_pixels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span><span class="nb">int</span><span class="p">):</span>
+            <span class="n">num_pixels_temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">val</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">length_val</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;num_pixels expected int x or [int x, int y]. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">)))</span>
+
+
+            <span class="k">if</span> <span class="n">length_val</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">val0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">val</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="n">val1</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">val</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;num_pixels expected int x or [int x, int y]. Got </span><span class="si">{0}</span><span class="s1">,</span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
+
+                <span class="n">num_pixels_temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">val0</span><span class="p">,</span> <span class="n">val1</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;num_pixels expected int x or [int x, int y]. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">num_pixels_temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dimension</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;2D acquisitions expects a 1D panel. Expected num_pixels[1] = 1. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_pixels_temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="k">if</span> <span class="n">num_pixels_temp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">num_pixels_temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;num_pixels (x,y) must be &gt;= (1,1). Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_pixels_temp</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_num_pixels</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">num_pixels_temp</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int16</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">pixel_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_size</span>
+
+    <span class="nd">@pixel_size</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">pixel_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">pixel_size_temp</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">length_val</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">temp</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+                    <span class="n">pixel_size_temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">temp</span><span class="p">,</span> <span class="n">temp</span><span class="p">]</span>
+
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;pixel_size expected float xy or [float x, float y]. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">length_val</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">temp0</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                        <span class="n">temp1</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">pixel_size_temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">temp0</span><span class="p">,</span> <span class="n">temp1</span><span class="p">]</span>
+                    <span class="k">except</span><span class="p">:</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;pixel_size expected float xy or [float x, float y]. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;pixel_size expected float xy or [float x, float y]. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">pixel_size_temp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">pixel_size_temp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;pixel_size (x,y) at must be &gt; (0.,0.). Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">pixel_size_temp</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_size</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pixel_size_temp</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">origin</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_origin</span>
+
+    <span class="nd">@origin</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">origin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="n">allowed</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;top-left&#39;</span><span class="p">,</span> <span class="s1">&#39;top-right&#39;</span><span class="p">,</span><span class="s1">&#39;bottom-left&#39;</span><span class="p">,</span><span class="s1">&#39;bottom-right&#39;</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">allowed</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_origin</span><span class="o">=</span><span class="n">val</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;origin expected one of </span><span class="si">{0}</span><span class="s1">. Got </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">allowed</span><span class="p">,</span> <span class="n">val</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;Panel configuration:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Number of pixels: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Pixel size: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Pixel origin: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">origin</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">)</span> \
+            <span class="ow">and</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">)</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">origin</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_pixels</span><span class="p">,</span> <span class="n">pixel_size</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">dimension</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructor method</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_dimension</span> <span class="o">=</span> <span class="n">dimension</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_pixels</span> <span class="o">=</span> <span class="n">num_pixels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span> <span class="o">=</span> <span class="n">pixel_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">origin</span> <span class="o">=</span> <span class="n">origin</span>
+
+
+<span class="k">class</span> <span class="nc">Channels</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This is a class describing the channels of the data.</span>
+<span class="sd">    This will be created on initialisation of AcquisitionGeometry.</span>
+
+<span class="sd">    :param num_channels: The number of channels of data</span>
+<span class="sd">    :type num_channels: int</span>
+<span class="sd">    :param channel_labels: A list of channel labels</span>
+<span class="sd">    :type channel_labels: list, optional</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">num_channels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_channels</span>
+
+    <span class="nd">@num_channels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">num_channels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;num_channels expected a positive integer. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">)))</span>
+
+        <span class="k">if</span> <span class="n">val</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_num_channels</span> <span class="o">=</span> <span class="n">val</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;num_channels expected a positive integer. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">channel_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channel_labels</span>
+
+    <span class="nd">@channel_labels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">channel_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_channels</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_channel_labels</span> <span class="o">=</span> <span class="n">val</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;labels expected to have length </span><span class="si">{0}</span><span class="s1">. Got </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_channels</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)))</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;Channel configuration:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Number of channels: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_channels</span><span class="p">)</span>
+
+        <span class="n">num_print</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">num_channels</span><span class="p">)</span>
+        <span class="k">if</span>  <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;channel_labels&#39;</span><span class="p">):</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Channel labels 0-</span><span class="si">{0}</span><span class="s2">: </span><span class="si">{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_print</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">channel_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">num_print</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_channels</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">num_channels</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="s1">&#39;channel_labels&#39;</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">channel_labels</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">channel_labels</span><span class="p">:</span>
+                <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_channels</span><span class="p">,</span> <span class="n">channel_labels</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructor method</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_channels</span> <span class="o">=</span> <span class="n">num_channels</span>
+        <span class="k">if</span> <span class="n">channel_labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">channel_labels</span> <span class="o">=</span> <span class="n">channel_labels</span>
+
+
+<span class="k">class</span> <span class="nc">Angles</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This is a class describing the angles of the data.</span>
+
+<span class="sd">    :param angles: The angular positions of the acquisition data</span>
+<span class="sd">    :type angles: list, ndarray</span>
+<span class="sd">    :param initial_angle: The angular offset of the object from the reference frame</span>
+<span class="sd">    :type initial_angle: float, optional</span>
+<span class="sd">    :param angle_unit: The units of the stored angles &#39;degree&#39; or &#39;radian&#39;</span>
+<span class="sd">    :type angle_unit: string</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">angle_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_angle_data</span>
+
+    <span class="nd">@angle_data</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">angle_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;angle_data expected to be a list of floats&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+            <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span> <span class="o">=</span> <span class="mi">1</span>
+                <span class="n">val</span> <span class="o">=</span> <span class="p">[</span><span class="n">val</span><span class="p">]</span>
+
+            <span class="k">finally</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_angle_data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;angle_data expected to be a list of floats&#39;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">initial_angle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initial_angle</span>
+
+    <span class="nd">@initial_angle</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">initial_angle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">val</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;initial_angle expected a float. Got </span><span class="si">{0}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">)))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_initial_angle</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">angle_unit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_angle_unit</span><span class="o">.</span><span class="n">value</span>
+
+    <span class="nd">@angle_unit</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">angle_unit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_angle_unit</span> <span class="o">=</span> <span class="n">AngleUnit</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;Acquisition description:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Number of positions: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span><span class="p">)</span>
+        <span class="c1"># max_num_print = 30</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span> <span class="o">&lt;</span> <span class="mi">31</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Angles 0-</span><span class="si">{0}</span><span class="s2"> in </span><span class="si">{1}</span><span class="s2">s: </span><span class="si">{2}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">angle_unit</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle_data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span><span class="p">],</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Angles 0-9 in </span><span class="si">{0}</span><span class="s2">s: </span><span class="si">{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle_unit</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle_data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">],</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">))</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Angles </span><span class="si">{0}</span><span class="s2">-</span><span class="si">{1}</span><span class="s2"> in </span><span class="si">{2}</span><span class="s2">s: </span><span class="si">{3}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">angle_unit</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span><span class="o">-</span><span class="mi">10</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">num_positions</span><span class="p">],</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">))</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Full angular array can be accessed with acquisition_data.geometry.angles</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">angle_unit</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">angle_unit</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">initial_angle</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">initial_angle</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle_data</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">angle_data</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">angles</span><span class="p">,</span> <span class="n">initial_angle</span><span class="p">,</span> <span class="n">angle_unit</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructor method</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">angle_data</span> <span class="o">=</span> <span class="n">angles</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initial_angle</span> <span class="o">=</span> <span class="n">initial_angle</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">angle_unit</span> <span class="o">=</span> <span class="n">angle_unit</span>
+
+
+<span class="k">class</span> <span class="nc">Configuration</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This class holds the description of the system components.</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">units_distance</span><span class="o">=</span><span class="s1">&#39;units distance&#39;</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">system</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1">#has distances</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">angles</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1">#has angles</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">panel</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1">#has distances</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">Channels</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="n">units_distance</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">configured</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">system</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Please configure AcquisitionGeometry using one of the following methods:</span><span class="se">\</span>
+<span class="s2">                    </span><span class="se">\n\t</span><span class="s2">AcquisitionGeometry.create_Parallel2D()</span><span class="se">\</span>
+<span class="s2">                    </span><span class="se">\n\t</span><span class="s2">AcquisitionGeometry.create_Cone3D()</span><span class="se">\</span>
+<span class="s2">                    </span><span class="se">\n\t</span><span class="s2">AcquisitionGeometry.create_Parallel2D()</span><span class="se">\</span>
+<span class="s2">                    </span><span class="se">\n\t</span><span class="s2">AcquisitionGeometry.create_Cone3D()&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">angles</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Please configure angular data using the set_angles() method&quot;</span><span class="p">)</span>
+            <span class="n">configured</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">panel</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Please configure the panel using the set_panel() method&quot;</span><span class="p">)</span>
+            <span class="n">configured</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">return</span> <span class="n">configured</span>
+
+    <span class="k">def</span> <span class="nf">shift_detector_in_plane</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                                          <span class="n">pixel_offset</span><span class="p">,</span>
+                                          <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Adjusts the position of the detector in a specified direction within the imaging plane.</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        -----------</span>
+<span class="sd">        pixel_offset : float</span>
+<span class="sd">            The number of pixels to adjust the detector&#39;s position by.</span>
+<span class="sd">        direction : {&#39;horizontal&#39;, &#39;vertical&#39;}, optional</span>
+<span class="sd">            The direction in which to adjust the detector&#39;s position. Defaults to &#39;horizontal&#39;.</span>
+
+<span class="sd">        Notes:</span>
+<span class="sd">        ------</span>
+<span class="sd">        - If `direction` is &#39;horizontal&#39;:</span>
+<span class="sd">            - If the panel&#39;s origin is &#39;left&#39;, positive offsets translate the detector to the right.</span>
+<span class="sd">            - If the panel&#39;s origin is &#39;right&#39;, positive offsets translate the detector to the left.</span>
+
+<span class="sd">        - If `direction` is &#39;vertical&#39;:</span>
+<span class="sd">            - If the panel&#39;s origin is &#39;bottom&#39;, positive offsets translate the detector upward.</span>
+<span class="sd">            - If the panel&#39;s origin is &#39;top&#39;, positive offsets translate the detector downward.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">        --------</span>
+<span class="sd">        None</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">direction</span> <span class="o">==</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">:</span>
+            <span class="n">pixel_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">pixel_direction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span>
+
+        <span class="k">elif</span> <span class="n">direction</span> <span class="o">==</span> <span class="s1">&#39;vertical&#39;</span><span class="p">:</span>
+            <span class="n">pixel_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">pixel_direction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span>
+
+        <span class="k">if</span> <span class="s1">&#39;bottom&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span> <span class="ow">or</span> <span class="s1">&#39;left&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">-=</span> <span class="n">pixel_offset</span> <span class="o">*</span> <span class="n">pixel_direction</span> <span class="o">*</span> <span class="n">pixel_size</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span> <span class="o">+=</span> <span class="n">pixel_offset</span> <span class="o">*</span> <span class="n">pixel_direction</span> <span class="o">*</span> <span class="n">pixel_size</span>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">configured</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">system</span><span class="p">)</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">panel</span><span class="p">)</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">channels</span><span class="p">)</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angles</span><span class="p">)</span>
+
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Distances in units: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">units</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">repres</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">system</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">system</span>\
+        <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">panel</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">panel</span>\
+        <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">channels</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">channels</span>\
+        <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">angles</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">angles</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="kc">False</span>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry">[docs]</a>
+<span class="k">class</span> <span class="nc">AcquisitionGeometry</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;This class holds the AcquisitionGeometry of the system.</span>
+
+<span class="sd">    Please initialise the AcquisitionGeometry using the using the static methods:</span>
+
+<span class="sd">    `AcquisitionGeometry.create_Parallel2D()`</span>
+
+<span class="sd">    `AcquisitionGeometry.create_Cone2D()`</span>
+
+<span class="sd">    `AcquisitionGeometry.create_Parallel3D()`</span>
+
+<span class="sd">    `AcquisitionGeometry.create_Cone3D()`</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+    <span class="c1">#for backwards compatibility</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">ANGLE</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use AcquisitionDimension.Angle instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">ANGLE</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">CHANNEL</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use AcquisitionDimension.Channel instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">CHANNEL</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">DEGREE</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use AngleUnit.DEGREE&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AngleUnit</span><span class="o">.</span><span class="n">DEGREE</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">HORIZONTAL</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use AcquisitionDimension.HORIZONTAL instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">HORIZONTAL</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">RADIAN</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use AngleUnit.RADIAN instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AngleUnit</span><span class="o">.</span><span class="n">RADIAN</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">VERTICAL</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use AcquisitionDimension.VERTICAL instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">VERTICAL</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geom_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">geometry</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">num_projections</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angles</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">pixel_num_h</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@pixel_num_h</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">pixel_num_h</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">pixel_num_v</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="nd">@pixel_num_v</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">pixel_num_v</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">pixel_size_h</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@pixel_size_h</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">pixel_size_h</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">pixel_size_v</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="nd">@pixel_size_v</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">pixel_size_v</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">channels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">num_channels</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">angles</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dist_source_center</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">calculate_magnification</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dist_center_detector</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">calculate_magnification</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">out</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">magnification</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">calculate_magnification</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">out</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">dimension</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">shape_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">num_channels</span><span class="p">,</span>
+                      <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">ANGLE</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">num_positions</span><span class="p">,</span>
+                      <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                      <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">HORIZONTAL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape_dict</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">labels_default</span> <span class="o">=</span> <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">get_order_for_engine</span><span class="p">(</span><span class="s2">&quot;cil&quot;</span><span class="p">)</span>
+
+        <span class="n">shape_default</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">num_channels</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">num_positions</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                            <span class="p">]</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+            <span class="n">labels</span> <span class="o">=</span> <span class="n">labels_default</span>
+        <span class="n">labels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
+
+        <span class="c1">#remove from list labels where len == 1</span>
+        <span class="c1">#</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape_default</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">x</span><span class="o">==</span><span class="mi">1</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">labels</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">labels_default</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                    <span class="k">pass</span> <span class="c1">#if not in custom list carry on</span>
+
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
+
+    <span class="nd">@dimension_labels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">val</span><span class="p">))</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">ndim</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">system_description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">system_description</span><span class="p">()</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span>
+
+    <span class="nd">@dtype</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">val</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span>
+
+
+    <span class="k">def</span> <span class="nf">get_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">distance_units</span><span class="o">=</span><span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="n">angle_units</span><span class="o">=</span><span class="s1">&#39;radian&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the system centre of rotation offset at the detector</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">         - Origin is in the centre of the detector</span>
+<span class="sd">         - Axes directions are specified by detector.direction_x, detector.direction_y</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        distance_units : string, default=&#39;default&#39;</span>
+<span class="sd">            Units of distance used to calculate the return values.</span>
+<span class="sd">            &#39;default&#39; uses the same units the system and panel were specified in.</span>
+<span class="sd">            &#39;pixels&#39; uses pixels sizes in the horizontal and vertical directions as appropriate.</span>
+<span class="sd">        angle_units : string</span>
+<span class="sd">            Units to return the angle in. Can take &#39;radian&#39; or &#39;degree&#39;.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Dictionary</span>
+<span class="sd">            {&#39;offset&#39;: (offset, distance_units), &#39;angle&#39;: (angle, angle_units)}</span>
+<span class="sd">            where,</span>
+<span class="sd">            &#39;offset&#39; gives the position along the detector x_axis at y=0</span>
+<span class="sd">            &#39;angle&#39; gives the angle between the y_axis and the projection of the rotation axis on the detector</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="p">,</span> <span class="s1">&#39;calculate_centre_of_rotation&#39;</span><span class="p">):</span>
+            <span class="n">offset_distance</span><span class="p">,</span> <span class="n">angle_rad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">calculate_centre_of_rotation</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+        <span class="k">if</span> <span class="n">distance_units</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
+            <span class="n">offset</span> <span class="o">=</span> <span class="n">offset_distance</span>
+            <span class="n">offset_units</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">units</span>
+        <span class="k">elif</span> <span class="n">distance_units</span> <span class="o">==</span> <span class="s1">&#39;pixels&#39;</span><span class="p">:</span>
+
+            <span class="n">offset</span> <span class="o">=</span> <span class="n">offset_distance</span><span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">offset_units</span> <span class="o">=</span> <span class="s1">&#39;pixels&#39;</span>
+
+            <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM3</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="c1">#if aspect ratio of pixels isn&#39;t 1:1 need to convert angle by new ratio</span>
+                <span class="n">y_pix</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">x_pix</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">tan</span><span class="p">(</span><span class="n">angle_rad</span><span class="p">)</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">angle_rad</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">x_pix</span><span class="p">,</span><span class="n">y_pix</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;`distance_units` is not recognised. Must be &#39;default&#39; or &#39;pixels&#39;. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">distance_units</span><span class="p">))</span>
+
+        <span class="n">angle_units</span> <span class="o">=</span> <span class="n">AngleUnit</span><span class="p">(</span><span class="n">angle_units</span><span class="p">)</span>
+
+        <span class="n">angle</span> <span class="o">=</span> <span class="n">angle_rad</span>
+        <span class="k">if</span> <span class="n">angle_units</span> <span class="o">==</span> <span class="n">AngleUnit</span><span class="o">.</span><span class="n">DEGREE</span><span class="p">:</span>
+            <span class="n">angle</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">degrees</span><span class="p">(</span><span class="n">angle_rad</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;offset&#39;</span><span class="p">:</span> <span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">offset_units</span><span class="p">),</span> <span class="s1">&#39;angle&#39;</span><span class="p">:</span> <span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="n">angle_units</span><span class="o">.</span><span class="n">value</span><span class="p">)}</span>
+
+
+    <span class="k">def</span> <span class="nf">set_centre_of_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">distance_units</span><span class="o">=</span><span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">angle_units</span><span class="o">=</span><span class="s1">&#39;radian&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Configures the system geometry to have the requested centre of rotation offset at the detector.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">         - Origin is in the centre of the detector</span>
+<span class="sd">         - Axes directions are specified by detector.direction_x, detector.direction_y</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        offset: float, default 0.0</span>
+<span class="sd">            The position of the centre of rotation along the detector x_axis at y=0</span>
+
+<span class="sd">        distance_units : string, default=&#39;default&#39;</span>
+<span class="sd">            Units the offset is specified in. Can be &#39;default&#39;or &#39;pixels&#39;.</span>
+<span class="sd">            &#39;default&#39; interprets the input as same units the system and panel were specified in.</span>
+<span class="sd">            &#39;pixels&#39; interprets the input in horizontal pixels.</span>
+
+<span class="sd">        angle: float, default=0.0</span>
+<span class="sd">            The angle between the detector y_axis and the rotation axis direction on the detector</span>
+
+<span class="sd">            Notes</span>
+<span class="sd">            -----</span>
+<span class="sd">            If aspect ratio of pixels is not 1:1 ensure the angle is calculated from the x and y values in the correct units.</span>
+
+<span class="sd">        angle_units : string, default=&#39;radian&#39;</span>
+<span class="sd">            Units the angle is specified in. Can take &#39;radian&#39; or &#39;degree&#39;.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="p">,</span> <span class="s1">&#39;set_centre_of_rotation&#39;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
+
+
+        <span class="n">angle_units</span> <span class="o">=</span> <span class="n">AngleUnit</span><span class="p">(</span><span class="n">angle_units</span><span class="p">)</span>
+
+        <span class="n">angle_rad</span> <span class="o">=</span> <span class="n">angle</span>
+        <span class="k">if</span> <span class="n">angle_units</span> <span class="o">==</span> <span class="n">AngleUnit</span><span class="o">.</span><span class="n">DEGREE</span><span class="p">:</span>
+            <span class="n">angle_rad</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">distance_units</span> <span class="o">==</span><span class="s1">&#39;default&#39;</span><span class="p">:</span>
+            <span class="n">offset_distance</span> <span class="o">=</span> <span class="n">offset</span>
+        <span class="k">elif</span> <span class="n">distance_units</span> <span class="o">==</span><span class="s1">&#39;pixels&#39;</span><span class="p">:</span>
+            <span class="n">offset_distance</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;`distance_units` is not recognised. Must be &#39;default&#39; or &#39;pixels&#39;. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">distance_units</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">set_centre_of_rotation</span><span class="p">(</span><span class="n">offset_distance</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">set_centre_of_rotation</span><span class="p">(</span><span class="n">offset_distance</span><span class="p">,</span> <span class="n">angle_rad</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">set_centre_of_rotation_by_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset1</span><span class="p">,</span> <span class="n">slice_index1</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset2</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">slice_index2</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Configures the system geometry to have the requested centre of rotation offset at the detector.</span>
+
+<span class="sd">        If two slices are passed the rotation axis will be rotated to pass through both points.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">         - Offset is specified in pixels</span>
+<span class="sd">         - Offset can be sub-pixels</span>
+<span class="sd">         - Offset direction is specified by detector.direction_x</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        offset1: float</span>
+<span class="sd">            The offset from the centre of the detector to the projected rotation position at slice_index_1</span>
+
+<span class="sd">        slice_index1: int, optional</span>
+<span class="sd">            The slice number of offset1</span>
+
+<span class="sd">        offset2: float, optional</span>
+<span class="sd">            The offset from the centre of the detector to the projected rotation position at slice_index_2</span>
+
+<span class="sd">        slice_index2: int, optional</span>
+<span class="sd">            The slice number of offset2</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="p">,</span> <span class="s1">&#39;set_centre_of_rotation&#39;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">offset2</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;2D so offset2 is ingored&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_centre_of_rotation</span><span class="p">(</span><span class="n">offset1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">offset2</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">offset1</span> <span class="o">==</span> <span class="n">offset2</span><span class="p">:</span>
+            <span class="n">offset_x_y0</span> <span class="o">=</span> <span class="n">offset1</span>
+            <span class="n">angle</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">slice_index1</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">slice_index2</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">slice_index1</span> <span class="o">==</span> <span class="n">slice_index2</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot calculate angle. Please specify `slice_index1` and `slice_index2` to define a rotated axis&quot;</span><span class="p">)</span>
+
+            <span class="n">offset_x_y0</span> <span class="o">=</span> <span class="n">offset1</span> <span class="o">-</span><span class="n">slice_index1</span> <span class="o">*</span> <span class="p">(</span><span class="n">offset2</span> <span class="o">-</span> <span class="n">offset1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">slice_index2</span><span class="o">-</span><span class="n">slice_index1</span><span class="p">)</span>
+            <span class="n">angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">offset2</span> <span class="o">-</span> <span class="n">offset1</span><span class="p">,</span> <span class="n">slice_index2</span> <span class="o">-</span> <span class="n">slice_index1</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_centre_of_rotation</span><span class="p">(</span><span class="n">offset_x_y0</span><span class="p">,</span> <span class="s1">&#39;pixels&#39;</span><span class="p">,</span> <span class="n">angle</span><span class="p">,</span> <span class="s1">&#39;radian&#39;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.set_angles">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.set_angles">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_angles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">angles</span><span class="p">,</span> <span class="n">initial_angle</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">angle_unit</span><span class="o">=</span><span class="s1">&#39;degree&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This method configures the angular information of an AcquisitionGeometry object.</span>
+
+<span class="sd">        :param angles: The angular positions of the acquisition data</span>
+<span class="sd">        :type angles: list, ndarray</span>
+<span class="sd">        :param initial_angle: The angular offset of the object from the reference frame</span>
+<span class="sd">        :type initial_angle: float, optional</span>
+<span class="sd">        :param angle_unit: The units of the stored angles &#39;degree&#39; or &#39;radian&#39;</span>
+<span class="sd">        :type angle_unit: string</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span> <span class="o">=</span> <span class="n">Angles</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">initial_angle</span><span class="p">,</span> <span class="n">angle_unit</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.set_panel">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.set_panel">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_panel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_pixels</span><span class="p">,</span> <span class="n">pixel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;bottom-left&#39;</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This method configures the panel information of an AcquisitionGeometry object.</span>
+
+<span class="sd">        :param num_pixels: num_pixels_h or (num_pixels_h, num_pixels_v) containing the number of pixels of the panel</span>
+<span class="sd">        :type num_pixels: int, list, tuple</span>
+<span class="sd">        :param pixel_size: pixel_size_h or (pixel_size_h, pixel_size_v) containing the size of the pixels of the panel</span>
+<span class="sd">        :type pixel_size: int, list, tuple, optional</span>
+<span class="sd">        :param origin: the position of pixel 0 (the data origin) of the panel &#39;top-left&#39;, &#39;top-right&#39;, &#39;bottom-left&#39;, &#39;bottom-right&#39;</span>
+<span class="sd">        :type origin: string, default &#39;bottom-left&#39;</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">dof</span> <span class="o">=</span> <span class="p">{</span><span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM3</span><span class="p">:</span> <span class="mi">3</span><span class="p">}[</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">dimension</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span> <span class="o">=</span> <span class="n">Panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="p">,</span> <span class="n">pixel_size</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">dof</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.set_channels">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.set_channels">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_channels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">channel_labels</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This method configures the channel information of an AcquisitionGeometry object.</span>
+
+<span class="sd">        :param num_channels: The number of channels of data</span>
+<span class="sd">        :type num_channels: int, optional</span>
+<span class="sd">        :param channel_labels: A list of channel labels</span>
+<span class="sd">        :type channel_labels: list, optional</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">Channels</span><span class="p">(</span><span class="n">num_channels</span><span class="p">,</span> <span class="n">channel_labels</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.set_labels">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.set_labels">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This method configures the dimension labels of an AcquisitionGeometry object.</span>
+
+<span class="sd">        :param labels:  The order of the dimensions describing the data.\</span>
+<span class="sd">                        Expects a list containing at least one of the unique labels: &#39;channel&#39; &#39;angle&#39; &#39;vertical&#39; &#39;horizontal&#39;</span>
+<span class="sd">                        default = [&#39;channel&#39;,&#39;angle&#39;,&#39;vertical&#39;,&#39;horizontal&#39;]</span>
+<span class="sd">        :type labels: list, optional</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="n">labels</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.create_Parallel2D">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.create_Parallel2D">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_Parallel2D</span><span class="p">(</span><span class="n">ray_direction</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">detector_direction_x</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">rotation_axis_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units distance&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates the AcquisitionGeometry for a parallel beam 2D tomographic system</span>
+
+<span class="sd">        :param ray_direction: A 2D vector describing the x-ray direction (x,y)</span>
+<span class="sd">        :type ray_direction: list, tuple, ndarray, optional</span>
+<span class="sd">        :param detector_position: A 2D vector describing the position of the centre of the detector (x,y)</span>
+<span class="sd">        :type detector_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param detector_direction_x: A 2D vector describing the direction of the detector_x (x,y)</span>
+<span class="sd">        :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">        :param rotation_axis_position: A 2D vector describing the position of the axis of rotation (x,y)</span>
+<span class="sd">        :type rotation_axis_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param units: Label the units of distance used for the configuration, these should be consistent for the geometry and panel</span>
+<span class="sd">        :type units: string</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">AG</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="p">()</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">Configuration</span><span class="p">(</span><span class="n">units</span><span class="p">)</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span> <span class="o">=</span> <span class="n">Parallel2D</span><span class="p">(</span><span class="n">ray_direction</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">,</span> <span class="n">units</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AG</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.create_Cone2D">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.create_Cone2D">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">rotation_axis_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units distance&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates the AcquisitionGeometry for a cone beam 2D tomographic system</span>
+
+<span class="sd">        :param source_position: A 2D vector describing the position of the source (x,y)</span>
+<span class="sd">        :type source_position: list, tuple, ndarray</span>
+<span class="sd">        :param detector_position: A 2D vector describing the position of the centre of the detector (x,y)</span>
+<span class="sd">        :type detector_position: list, tuple, ndarray</span>
+<span class="sd">        :param detector_direction_x: A 2D vector describing the direction of the detector_x (x,y)</span>
+<span class="sd">        :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">        :param rotation_axis_position: A 2D vector describing the position of the axis of rotation (x,y)</span>
+<span class="sd">        :type rotation_axis_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param units: Label the units of distance used for the configuration, these should be consistent for the geometry and panel</span>
+<span class="sd">        :type units: string</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+        <span class="n">AG</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="p">()</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">Configuration</span><span class="p">(</span><span class="n">units</span><span class="p">)</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span> <span class="o">=</span> <span class="n">Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">,</span> <span class="n">units</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AG</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.create_Parallel3D">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.create_Parallel3D">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_Parallel3D</span><span class="p">(</span><span class="n">ray_direction</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">detector_direction_x</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">detector_direction_y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">rotation_axis_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">rotation_axis_direction</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units distance&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates the AcquisitionGeometry for a parallel beam 3D tomographic system</span>
+
+<span class="sd">        :param ray_direction: A 3D vector describing the x-ray direction (x,y,z)</span>
+<span class="sd">        :type ray_direction: list, tuple, ndarray, optional</span>
+<span class="sd">        :param detector_position: A 3D vector describing the position of the centre of the detector (x,y,z)</span>
+<span class="sd">        :type detector_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param detector_direction_x: A 3D vector describing the direction of the detector_x (x,y,z)</span>
+<span class="sd">        :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">        :param detector_direction_y: A 3D vector describing the direction of the detector_y (x,y,z)</span>
+<span class="sd">        :type detector_direction_y: list, tuple, ndarray</span>
+<span class="sd">        :param rotation_axis_position: A 3D vector describing the position of the axis of rotation (x,y,z)</span>
+<span class="sd">        :type rotation_axis_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param rotation_axis_direction: A 3D vector describing the direction of the axis of rotation (x,y,z)</span>
+<span class="sd">        :type rotation_axis_direction: list, tuple, ndarray, optional</span>
+<span class="sd">        :param units: Label the units of distance used for the configuration, these should be consistent for the geometry and panel</span>
+<span class="sd">        :type units: string</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+        <span class="n">AG</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="p">()</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">Configuration</span><span class="p">(</span><span class="n">units</span><span class="p">)</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span> <span class="o">=</span> <span class="n">Parallel3D</span><span class="p">(</span><span class="n">ray_direction</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">,</span> <span class="n">rotation_axis_direction</span><span class="p">,</span> <span class="n">units</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AG</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.create_Cone3D">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.create_Cone3D">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">detector_direction_y</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">rotation_axis_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">rotation_axis_direction</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">units</span><span class="o">=</span><span class="s1">&#39;units distance&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates the AcquisitionGeometry for a cone beam 3D tomographic system</span>
+
+<span class="sd">        :param source_position: A 3D vector describing the position of the source (x,y,z)</span>
+<span class="sd">        :type source_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param detector_position: A 3D vector describing the position of the centre of the detector (x,y,z)</span>
+<span class="sd">        :type detector_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param detector_direction_x: A 3D vector describing the direction of the detector_x (x,y,z)</span>
+<span class="sd">        :type detector_direction_x: list, tuple, ndarray</span>
+<span class="sd">        :param detector_direction_y: A 3D vector describing the direction of the detector_y (x,y,z)</span>
+<span class="sd">        :type detector_direction_y: list, tuple, ndarray</span>
+<span class="sd">        :param rotation_axis_position: A 3D vector describing the position of the axis of rotation (x,y,z)</span>
+<span class="sd">        :type rotation_axis_position: list, tuple, ndarray, optional</span>
+<span class="sd">        :param rotation_axis_direction: A 3D vector describing the direction of the axis of rotation (x,y,z)</span>
+<span class="sd">        :type rotation_axis_direction: list, tuple, ndarray, optional</span>
+<span class="sd">        :param units: Label the units of distance used for the configuration, these should be consistent for the geometry and panel</span>
+<span class="sd">        :type units: string</span>
+<span class="sd">        :return: returns a configured AcquisitionGeometry object</span>
+<span class="sd">        :rtype: AcquisitionGeometry</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">AG</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="p">()</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">Configuration</span><span class="p">(</span><span class="n">units</span><span class="p">)</span>
+        <span class="n">AG</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span> <span class="o">=</span> <span class="n">Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">,</span> <span class="n">rotation_axis_direction</span><span class="p">,</span> <span class="n">units</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">AG</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_order_by_label</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="p">,</span> <span class="n">default_dimension_labels</span><span class="p">):</span>
+        <span class="n">order</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">default_dimension_labels</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">ek</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dimension_labels</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">el</span> <span class="o">==</span> <span class="n">ek</span><span class="p">:</span>
+                    <span class="n">order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">j</span><span class="p">)</span>
+                    <span class="k">break</span>
+        <span class="k">return</span> <span class="n">order</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">config</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">dtype</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a copy of the AcquisitionGeometry&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;alias of clone&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">get_centre_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a 2D AcquisitionGeometry that corresponds to the centre slice of the input&#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span>
+
+        <span class="n">AG_2D</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">AG_2D</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">get_centre_slice</span><span class="p">()</span>
+        <span class="n">AG_2D</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">AG_2D</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">AG_2D</span>
+
+<div class="viewcode-block" id="AcquisitionGeometry.get_ImageGeometry">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.get_ImageGeometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_ImageGeometry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resolution</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a default configured ImageGeometry object based on the AcquisitionGeomerty&#39;&#39;&#39;</span>
+
+        <span class="n">num_voxel_xy</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">resolution</span><span class="p">))</span>
+        <span class="n">voxel_size_xy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">resolution</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnification</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM3</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="n">num_voxel_z</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">resolution</span><span class="p">))</span>
+            <span class="n">voxel_size_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">resolution</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnification</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">num_voxel_z</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">voxel_size_z</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="k">return</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">num_voxel_xy</span><span class="p">,</span> <span class="n">num_voxel_xy</span><span class="p">,</span> <span class="n">num_voxel_z</span><span class="p">,</span> <span class="n">voxel_size_xy</span><span class="p">,</span> <span class="n">voxel_size_xy</span><span class="p">,</span> <span class="n">voxel_size_z</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">channels</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span> <span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.get_slice">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.get_slice">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">horizontal</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Returns a new AcquisitionGeometry of a single slice of in the requested direction. Will only return reconstructable geometries.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">geometry_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">channel</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">num_channels</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="p">,</span><span class="s1">&#39;channel_labels&#39;</span><span class="p">):</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">channel_labels</span> <span class="o">=</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">channel_labels</span><span class="p">[</span><span class="n">channel</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">angle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span> <span class="o">=</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span><span class="p">[</span><span class="n">angle</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">vertical</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">PARALLEL</span> <span class="o">&amp;</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">geom_type</span> <span class="ow">or</span> <span class="n">vertical</span> <span class="o">==</span> <span class="s1">&#39;centre&#39;</span> <span class="ow">or</span> <span class="nb">abs</span><span class="p">(</span><span class="n">geometry_new</span><span class="o">.</span><span class="n">pixel_num_v</span><span class="o">/</span><span class="mi">2</span> <span class="o">-</span> <span class="n">vertical</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">1e-6</span><span class="p">:</span>
+                <span class="n">geometry_new</span> <span class="o">=</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">get_centre_slice</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Can only subset centre slice geometry on cone-beam data. Expected vertical = &#39;centre&#39;. Got vertical = </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">vertical</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">horizontal</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot calculate system geometry for a horizontal slice&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">geometry_new</span></div>
+
+
+<div class="viewcode-block" id="AcquisitionGeometry.allocate">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.AcquisitionGeometry.allocate">[docs]</a>
+    <span class="k">def</span> <span class="nf">allocate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;allocates an AcquisitionData according to the size expressed in the instance</span>
+
+<span class="sd">        :param value: accepts numbers to allocate an uniform array, or a string as &#39;random&#39; or &#39;random_int&#39; to create a random array or None.</span>
+<span class="sd">        :type value: number or string, default None allocates empty memory block</span>
+<span class="sd">        :param dtype: numerical type to allocate</span>
+<span class="sd">        :type dtype: numpy type, default numpy.float32</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dimension_labels&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Deprecated: &#39;dimension_labels&#39; cannot be set with &#39;allocate()&#39;. Use &#39;geometry.set_labels()&#39; to modify the geometry before using allocate.&quot;</span><span class="p">)</span>
+
+        <span class="n">out</span> <span class="o">=</span> <span class="n">AcquisitionData</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                              <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
+                              <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="c1"># it&#39;s created empty, so we make it 0</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">FillType</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM</span><span class="p">:</span>
+                <span class="n">seed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;seed&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
+                    <span class="n">r</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM_INT</span><span class="p">:</span>
+                <span class="n">seed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;seed&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+                <span class="n">max_value</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;max_value&#39;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
+                    <span class="n">r</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">max_value</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">max_value</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">r</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">max_value</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Value </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s1"> unknown&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
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+    Optimisation framework
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+    <!-- dropdown will be populated by javascript on page load -->
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+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../developer_guide/">Developers’ Guide</a></li>
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+                  
+  <h1>Source code for cil.framework.block</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">functools</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">..utilities.multiprocessing</span> <span class="kn">import</span> <span class="n">NUM_THREADS</span>
+<span class="kn">from</span> <span class="nn">.labels</span> <span class="kn">import</span> <span class="n">FillType</span>
+
+
+<div class="viewcode-block" id="BlockGeometry">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.BlockGeometry">[docs]</a>
+<span class="k">class</span> <span class="nc">BlockGeometry</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">RANDOM</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use FillType.RANDOM instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">RANDOM_INT</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use FillType.RANDOM_INT instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM_INT</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometries</span><span class="p">)</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Class to hold Geometry as column vector&#39;&#39;&#39;</span>
+    <span class="c1">#__array_priority__ = 1</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;&#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">geometries</span> <span class="o">=</span> <span class="n">args</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span>
+
+        <span class="n">n_elements</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">*</span><span class="n">y</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">!=</span> <span class="n">n_elements</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s1">&#39;Dimension and size do not match: expected </span><span class="si">{}</span><span class="s1"> got </span><span class="si">{}</span><span class="s1">&#39;</span>
+                    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)))</span>
+
+<div class="viewcode-block" id="BlockGeometry.get_item">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.BlockGeometry.get_item">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_item</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns the Geometry in the BlockGeometry located at position index&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometries</span><span class="p">[</span><span class="n">index</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="BlockGeometry.allocate">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.BlockGeometry.allocate">[docs]</a>
+    <span class="k">def</span> <span class="nf">allocate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Allocates a BlockDataContainer according to geometries contained in the BlockGeometry&#39;&#39;&#39;</span>
+
+        <span class="n">symmetry</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;symmetry&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">containers</span> <span class="o">=</span> <span class="p">[</span><span class="n">geom</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">geom</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometries</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">symmetry</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+
+            <span class="c1"># for 2x2</span>
+            <span class="c1"># [ ig11, ig12\</span>
+            <span class="c1">#   ig21, ig22]</span>
+
+            <span class="c1"># Row-wise Order</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">containers</span><span class="p">)</span><span class="o">==</span><span class="mi">4</span><span class="p">:</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+            <span class="c1"># for 3x3</span>
+            <span class="c1"># [ ig11, ig12, ig13\</span>
+            <span class="c1">#   ig21, ig22, ig23\</span>
+            <span class="c1">#   ig31, ig32, ig33]</span>
+
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">containers</span><span class="p">)</span><span class="o">==</span><span class="mi">9</span><span class="p">:</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span>
+
+            <span class="c1"># for 4x4</span>
+            <span class="c1"># [ ig11, ig12, ig13, ig14\</span>
+            <span class="c1">#   ig21, ig22, ig23, ig24\ c</span>
+            <span class="c1">#   ig31, ig32, ig33, ig34</span>
+            <span class="c1">#   ig41, ig42, ig43, ig44]</span>
+
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">containers</span><span class="p">)</span> <span class="o">==</span> <span class="mi">16</span><span class="p">:</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">12</span><span class="p">]</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span>
+                <span class="n">containers</span><span class="p">[</span><span class="mi">11</span><span class="p">]</span><span class="o">=</span><span class="n">containers</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">containers</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;BlockGeometry is an iterable&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;BlockGeometry is an iterable&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometries</span><span class="p">):</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometries</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="k">return</span> <span class="n">result</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometries</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">geometries</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+        <span class="k">return</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="ow">and</span> <span class="n">y</span><span class="p">,</span> \
+                                <span class="p">[</span><span class="n">sel</span> <span class="o">==</span> <span class="n">vel</span> <span class="k">for</span> <span class="n">sel</span><span class="p">,</span><span class="n">vel</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometries</span><span class="p">,</span> <span class="n">value</span><span class="o">.</span><span class="n">geometries</span><span class="p">)],</span> <span class="kc">True</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockDataContainer">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer">[docs]</a>
+<span class="k">class</span> <span class="nc">BlockDataContainer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Class to hold DataContainers as column vector</span>
+
+<span class="sd">    Provides basic algebra between BlockDataContainer&#39;s, DataContainer&#39;s and</span>
+<span class="sd">    subclasses and Numbers</span>
+
+<span class="sd">    1) algebra between `BlockDataContainer`s will be element-wise, only if</span>
+<span class="sd">       the shape of the 2 `BlockDataContainer`s is the same, otherwise it</span>
+<span class="sd">       will fail</span>
+<span class="sd">    2) algebra between `BlockDataContainer`s and `list` or `numpy array` will</span>
+<span class="sd">       work as long as the number of `rows` and element of the arrays match,</span>
+<span class="sd">       independently on the fact that the `BlockDataContainer` could be nested</span>
+<span class="sd">    3) algebra between `BlockDataContainer` and one `DataContainer` is possible.</span>
+<span class="sd">       It will require all the `DataContainers` in the block to be</span>
+<span class="sd">       compatible with the `DataContainer` we want to operate with.</span>
+<span class="sd">    4) algebra between `BlockDataContainer` and a `Number` is possible and it</span>
+<span class="sd">       will be done with each element of the `BlockDataContainer` even if nested</span>
+
+<span class="sd">    A = [ [B,C] , D]</span>
+<span class="sd">    A * 3 = [ 3 * [B,C] , 3* D] = [ [ 3*B, 3*C]  , 3*D ]</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">ADD</span>       <span class="o">=</span> <span class="s1">&#39;add&#39;</span>
+    <span class="n">SUBTRACT</span>  <span class="o">=</span> <span class="s1">&#39;subtract&#39;</span>
+    <span class="n">MULTIPLY</span>  <span class="o">=</span> <span class="s1">&#39;multiply&#39;</span>
+    <span class="n">DIVIDE</span>    <span class="o">=</span> <span class="s1">&#39;divide&#39;</span>
+    <span class="n">POWER</span>     <span class="o">=</span> <span class="s1">&#39;power&#39;</span>
+    <span class="n">SAPYB</span>     <span class="o">=</span> <span class="s1">&#39;sapyb&#39;</span>
+    <span class="n">MAXIMUM</span>   <span class="o">=</span> <span class="s1">&#39;maximum&#39;</span>
+    <span class="n">MINIMUM</span>   <span class="o">=</span> <span class="s1">&#39;minimum&#39;</span>
+    <span class="n">ABS</span>       <span class="o">=</span> <span class="s1">&#39;abs&#39;</span>
+    <span class="n">SIGN</span>      <span class="o">=</span> <span class="s1">&#39;sign&#39;</span>
+    <span class="n">SQRT</span>      <span class="o">=</span> <span class="s1">&#39;sqrt&#39;</span>
+    <span class="n">CONJUGATE</span> <span class="o">=</span> <span class="s1">&#39;conjugate&#39;</span>
+    <span class="n">__array_priority__</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">__container_priority__</span> <span class="o">=</span> <span class="mi">2</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;&#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">containers</span> <span class="o">=</span> <span class="n">args</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1">#if len(set([i.shape for i in self.containers])):</span>
+        <span class="c1">#    self.geometry = self.containers[0].geometry</span>
+
+        <span class="n">shape</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;shape&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+           <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1">#        shape = (len(args),1)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span>
+
+        <span class="n">n_elements</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">*</span><span class="n">y</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">!=</span> <span class="n">n_elements</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s1">&#39;Dimension and size do not match: expected </span><span class="si">{}</span><span class="s1"> got </span><span class="si">{}</span><span class="s1">&#39;</span>
+                    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)))</span>
+
+
+<div class="viewcode-block" id="BlockDataContainer.__iter__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__iter__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;BlockDataContainer is Iterable&#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">=</span><span class="mi">0</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+<div class="viewcode-block" id="BlockDataContainer.next">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.next">[docs]</a>
+    <span class="k">def</span> <span class="nf">next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;python2 backwards compatibility&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__next__</span><span class="p">()</span></div>
+
+    <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">except</span> <span class="ne">IndexError</span> <span class="k">as</span> <span class="n">ie</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">+=</span><span class="mi">1</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+<div class="viewcode-block" id="BlockDataContainer.is_compatible">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.is_compatible">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_compatible</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;basic check if the size of the 2 objects fit&#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="p">:</span>
+            <span class="k">for</span> <span class="n">ot</span> <span class="ow">in</span> <span class="n">other</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ot</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;List/ numpy array can only contain numbers </span><span class="si">{}</span><span class="s1">&#39;</span>\
+                                     <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">ot</span><span class="p">)))</span>
+            <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">containers</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># this should work for other as DataContainers and children</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">el</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+                    <span class="n">a</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">is_compatible</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">a</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">shape</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">ret</span> <span class="ow">and</span> <span class="n">a</span>
+            <span class="c1"># probably will raise</span>
+            <span class="k">return</span> <span class="n">ret</span></div>
+
+
+
+    <span class="k">def</span> <span class="nf">get_item</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">row</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Requested row </span><span class="si">{}</span><span class="s1"> &gt; max </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="n">row</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+
+<div class="viewcode-block" id="BlockDataContainer.add">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.add">[docs]</a>
+    <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: add method of BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        :param: other (number, DataContainer or subclasses or BlockDataContainer</span>
+<span class="sd">        :param: out (optional): provides a placehold for the resul.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">ADD</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="BlockDataContainer.subtract">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.subtract">[docs]</a>
+    <span class="k">def</span> <span class="nf">subtract</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: subtract method of BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        :param: other (number, DataContainer or subclasses or BlockDataContainer</span>
+<span class="sd">        :param: out (optional): provides a placeholder for the result.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SUBTRACT</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="BlockDataContainer.multiply">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.multiply">[docs]</a>
+    <span class="k">def</span> <span class="nf">multiply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: multiply method of BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        :param: other (number, DataContainer or subclasses or BlockDataContainer)</span>
+<span class="sd">        :param: out (optional): provides a placeholder for the result.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MULTIPLY</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="BlockDataContainer.divide">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.divide">[docs]</a>
+    <span class="k">def</span> <span class="nf">divide</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: divide method of BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        :param: other (number, DataContainer or subclasses or BlockDataContainer)</span>
+<span class="sd">        :param: out (optional): provides a placeholder for the result.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">DIVIDE</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="BlockDataContainer.power">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.power">[docs]</a>
+    <span class="k">def</span> <span class="nf">power</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        :param: other (number, DataContainer or subclasses or BlockDataContainer</span>
+<span class="sd">        :param: out (optional): provides a placeholder for the result.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">POWER</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="BlockDataContainer.maximum">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.maximum">[docs]</a>
+    <span class="k">def</span> <span class="nf">maximum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        :param: other (number, DataContainer or subclasses or BlockDataContainer)</span>
+<span class="sd">        :param: out (optional): provides a placeholder for the result.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MAXIMUM</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="BlockDataContainer.minimum">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.minimum">[docs]</a>
+    <span class="k">def</span> <span class="nf">minimum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        :param: other (number, DataContainer or subclasses or BlockDataContainer)</span>
+<span class="sd">        :param: out (optional): provides a placeholder for the result.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MINIMUM</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockDataContainer.sapyb">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.sapyb">[docs]</a>
+    <span class="k">def</span> <span class="nf">sapyb</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">num_threads</span> <span class="o">=</span> <span class="n">NUM_THREADS</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;performs axpby element-wise on the BlockDataContainer containers</span>
+
+<span class="sd">        Does the operation .. math:: a*x+b*y and stores the result in out, where x is self</span>
+
+<span class="sd">        :param a: scalar</span>
+<span class="sd">        :param b: scalar</span>
+<span class="sd">        :param y: compatible (Block)DataContainer</span>
+<span class="sd">        :param out: (Block)DataContainer to store the result</span>
+
+
+<span class="sd">        Example:</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        &gt;&gt;&gt; a = 2</span>
+<span class="sd">        &gt;&gt;&gt; b = 3</span>
+<span class="sd">        &gt;&gt;&gt; ig = ImageGeometry(10,11)</span>
+<span class="sd">        &gt;&gt;&gt; x = ig.allocate(1)</span>
+<span class="sd">        &gt;&gt;&gt; y = ig.allocate(2)</span>
+<span class="sd">        &gt;&gt;&gt; bdc1 = BlockDataContainer(2*x, y)</span>
+<span class="sd">        &gt;&gt;&gt; bdc2 = BlockDataContainer(x, 2*y)</span>
+<span class="sd">        &gt;&gt;&gt; out = bdc1.sapyb(a,bdc2,b)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;out container cannot be None&quot;</span><span class="p">)</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;a&#39;</span><span class="p">:</span><span class="n">a</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span><span class="n">b</span><span class="p">,</span> <span class="s1">&#39;out&#39;</span><span class="p">:</span><span class="n">out</span><span class="p">,</span> <span class="s1">&#39;num_threads&#39;</span><span class="p">:</span> <span class="n">NUM_THREADS</span><span class="p">}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">binary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SAPYB</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="BlockDataContainer.axpby">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.axpby">[docs]</a>
+    <span class="k">def</span> <span class="nf">axpby</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">num_threads</span> <span class="o">=</span> <span class="n">NUM_THREADS</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Deprecated method. Alias of sapyb&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">b</span><span class="p">,</span><span class="n">out</span><span class="p">,</span><span class="n">num_threads</span><span class="p">)</span></div>
+
+
+
+
+<div class="viewcode-block" id="BlockDataContainer.binary_operations">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.binary_operations">[docs]</a>
+    <span class="k">def</span> <span class="nf">binary_operations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operation</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algebra: generic method of algebric operation with BlockDataContainer with number/DataContainer or BlockDataContainer</span>
+
+<span class="sd">        Provides commutativity with DataContainer and subclasses, i.e. this</span>
+<span class="sd">        class&#39;s reverse algebraic methods take precedence w.r.t. direct algebraic</span>
+<span class="sd">        methods of DataContainer and subclasses.</span>
+
+<span class="sd">        This method is not to be used directly</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_compatible</span><span class="p">(</span><span class="n">other</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible for operation </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">operation</span><span class="p">))</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;out&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="c1"># try to do algebra with one DataContainer. Will raise error if not compatible</span>
+            <span class="n">kw</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">ADD</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">add</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SUBTRACT</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">subtract</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MULTIPLY</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">multiply</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">DIVIDE</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">divide</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">POWER</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">power</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MAXIMUM</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">maximum</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MINIMUM</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">minimum</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unsupported operation&#39;</span><span class="p">,</span> <span class="n">operation</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                    <span class="n">op</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">op</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">out</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="o">*</span><span class="n">res</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">)):</span>
+            <span class="n">kw</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+                <span class="n">the_other</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">containers</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">the_other</span> <span class="o">=</span> <span class="n">other</span>
+
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">zel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span> <span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">the_other</span><span class="p">)</span> <span class="p">):</span>
+                <span class="n">el</span> <span class="o">=</span> <span class="n">zel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">ot</span> <span class="o">=</span> <span class="n">zel</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">ADD</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">add</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SUBTRACT</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">subtract</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MULTIPLY</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">multiply</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">DIVIDE</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">divide</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">POWER</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">power</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MAXIMUM</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">maximum</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MINIMUM</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">minimum</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SAPYB</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> cannot handle </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">operation</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">sapyb</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unsupported operation&#39;</span><span class="p">,</span> <span class="n">operation</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SAPYB</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kw</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">],</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+                            <span class="n">a</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">a</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span>
+
+                        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kw</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+                            <span class="n">b</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">b</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
+
+                        <span class="n">el</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">ot</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">num_threads</span><span class="o">=</span><span class="n">kw</span><span class="p">[</span><span class="s1">&#39;num_threads&#39;</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                        <span class="n">op</span><span class="p">(</span><span class="n">ot</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">op</span><span class="p">(</span><span class="n">ot</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">out</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="o">*</span><span class="n">res</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># try to do algebra with one DataContainer. Will raise error if not compatible</span>
+            <span class="n">kw</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">operation</span> <span class="o">!=</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SAPYB</span><span class="p">:</span>
+                <span class="c1"># remove keyworded argument related to SAPYB</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;num_threads&#39;</span><span class="p">,</span> <span class="s1">&#39;dtype&#39;</span><span class="p">]:</span>
+                    <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">kw</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                        <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+
+            <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">ADD</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">add</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SUBTRACT</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">subtract</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MULTIPLY</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">multiply</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">DIVIDE</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">divide</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">POWER</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">power</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MAXIMUM</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">maximum</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">MINIMUM</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">minimum</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SAPYB</span><span class="p">:</span>
+
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kw</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">],</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+                        <span class="n">a</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">a</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span>
+
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kw</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+                        <span class="n">b</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">b</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
+
+                    <span class="n">el</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;num_threads&#39;</span><span class="p">])</span>
+
+                    <span class="c1"># As axpyb cannot return anything we `continue` to skip the rest of the code block</span>
+                    <span class="k">continue</span>
+
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unsupported operation&#39;</span><span class="p">,</span> <span class="n">operation</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                    <span class="n">op</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">op</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">out</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="o">*</span><span class="n">res</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span></div>
+
+
+    <span class="c1">## unary operations</span>
+
+<div class="viewcode-block" id="BlockDataContainer.unary_operations">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.unary_operations">[docs]</a>
+    <span class="k">def</span> <span class="nf">unary_operations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operation</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Unary operation on BlockDataContainer:</span>
+
+<span class="sd">        generic method of unary operation with BlockDataContainer: abs, sign, sqrt and conjugate</span>
+
+<span class="sd">        This method is not to be used directly</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;out&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">ABS</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">abs</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SIGN</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">sign</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SQRT</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">sqrt</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">CONJUGATE</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">conjugate</span>
+                <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">op</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">res</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;out&#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">elout</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">ABS</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">abs</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SIGN</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">sign</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SQRT</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">sqrt</span>
+                <span class="k">elif</span> <span class="n">operation</span> <span class="o">==</span> <span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">CONJUGATE</span><span class="p">:</span>
+                    <span class="n">op</span> <span class="o">=</span> <span class="n">el</span><span class="o">.</span><span class="n">conjugate</span>
+                <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">elout</span>
+                <span class="n">op</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">abs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">unary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">ABS</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">sign</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">unary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SIGN</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">unary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">SQRT</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">unary_operations</span><span class="p">(</span><span class="n">BlockDataContainer</span><span class="o">.</span><span class="n">CONJUGATE</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="c1"># def abs(self, *args,  **kwargs):</span>
+    <span class="c1">#     return type(self)(*[ el.abs(*args, **kwargs) for el in self.containers], shape=self.shape)</span>
+    <span class="c1"># def sign(self, *args,  **kwargs):</span>
+    <span class="c1">#     return type(self)(*[ el.sign(*args, **kwargs) for el in self.containers], shape=self.shape)</span>
+    <span class="c1"># def sqrt(self, *args,  **kwargs):</span>
+    <span class="c1">#     return type(self)(*[ el.sqrt(*args, **kwargs) for el in self.containers], shape=self.shape)</span>
+    <span class="c1"># def conjugate(self, out=None):</span>
+    <span class="c1">#     return type(self)(*[el.conjugate() for el in self.containers], shape=self.shape)</span>
+
+    <span class="c1">## reductions</span>
+
+    <span class="k">def</span> <span class="nf">sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span> <span class="n">el</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">])</span>
+
+    <span class="k">def</span> <span class="nf">squared_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">el</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+
+    <span class="k">def</span> <span class="nf">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="nf">pnorm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+        <span class="c1"># See https://github.com/TomographicImaging/CIL/issues/1525#issuecomment-1757413803</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="ow">and</span> <span class="n">y</span><span class="p">,</span> <span class="p">[</span><span class="n">el</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">],</span> <span class="kc">True</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;pnorm: Incompatible shapes - each container in the BlockDataContainer must have the same shape in order to calculate the pnorm&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">p</span><span class="o">==</span><span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abs</span><span class="p">())</span>
+        <span class="k">elif</span> <span class="n">p</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">:</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">*</span><span class="n">b</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">0</span> <span class="p">)</span><span class="o">.</span><span class="n">sqrt</span><span class="p">()</span>
+            <span class="k">return</span> <span class="n">tmp</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="BlockDataContainer.copy">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.copy">[docs]</a>
+    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;alias of clone&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span></div>
+
+    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="o">*</span><span class="p">[</span><span class="n">el</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">fill</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_compatible</span><span class="p">(</span><span class="n">other</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible containers&#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="n">el</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">ot</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot fill with object provided </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span>
+
+    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">add</span><span class="p">(</span> <span class="n">other</span> <span class="p">)</span>
+    <span class="c1"># __radd__</span>
+
+    <span class="k">def</span> <span class="fm">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">other</span> <span class="p">)</span>
+    <span class="c1"># __rsub__</span>
+
+    <span class="k">def</span> <span class="fm">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="c1"># __rmul__</span>
+
+    <span class="k">def</span> <span class="nf">__div__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="c1"># __rdiv__</span>
+    <span class="k">def</span> <span class="fm">__truediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__pow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="c1"># reverse operand</span>
+<div class="viewcode-block" id="BlockDataContainer.__radd__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__radd__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__radd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reverse addition</span>
+
+<span class="sd">        to make sure that this method is called rather than the __mul__ of a numpy array</span>
+<span class="sd">        the class constant __array_priority__ must be set &gt; 0</span>
+<span class="sd">        https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="n">other</span></div>
+
+    <span class="c1"># __radd__</span>
+
+<div class="viewcode-block" id="BlockDataContainer.__rsub__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__rsub__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rsub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reverse subtraction</span>
+
+<span class="sd">        to make sure that this method is called rather than the __mul__ of a numpy array</span>
+<span class="sd">        the class constant __array_priority__ must be set &gt; 0</span>
+<span class="sd">        https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span><span class="p">)</span> <span class="o">+</span> <span class="n">other</span></div>
+
+    <span class="c1"># __rsub__</span>
+
+<div class="viewcode-block" id="BlockDataContainer.__rmul__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__rmul__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reverse multiplication</span>
+
+<span class="sd">        to make sure that this method is called rather than the __mul__ of a numpy array</span>
+<span class="sd">        the class constant __array_priority__ must be set &gt; 0</span>
+<span class="sd">        https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="n">other</span></div>
+
+    <span class="c1"># __rmul__</span>
+
+<div class="viewcode-block" id="BlockDataContainer.__rdiv__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__rdiv__">[docs]</a>
+    <span class="k">def</span> <span class="nf">__rdiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reverse division</span>
+
+<span class="sd">        to make sure that this method is called rather than the __mul__ of a numpy array</span>
+<span class="sd">        the class constant __array_priority__ must be set &gt; 0</span>
+<span class="sd">        https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="nb">pow</span><span class="p">(</span><span class="bp">self</span> <span class="o">/</span> <span class="n">other</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span></div>
+
+    <span class="c1"># __rdiv__</span>
+<div class="viewcode-block" id="BlockDataContainer.__rtruediv__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__rtruediv__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rtruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reverse truedivision</span>
+
+<span class="sd">        to make sure that this method is called rather than the __mul__ of a numpy array</span>
+<span class="sd">        the class constant __array_priority__ must be set &gt; 0</span>
+<span class="sd">        https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__rdiv__</span><span class="p">(</span><span class="n">other</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockDataContainer.__rpow__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__rpow__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rpow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reverse power</span>
+
+<span class="sd">        to make sure that this method is called rather than the __mul__ of a numpy array</span>
+<span class="sd">        the class constant __array_priority__ must be set &gt; 0</span>
+<span class="sd">        https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">other</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockDataContainer.__iadd__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__iadd__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__iadd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Inline addition&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">+=</span> <span class="n">ot</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">:</span>
+                <span class="n">el</span> <span class="o">+=</span> <span class="n">other</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_compatible</span><span class="p">(</span><span class="n">other</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible for __iadd__&#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">+=</span> <span class="n">ot</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+    <span class="c1"># __iadd__</span>
+
+<div class="viewcode-block" id="BlockDataContainer.__isub__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__isub__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__isub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Inline subtraction&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">-=</span> <span class="n">ot</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">:</span>
+                <span class="n">el</span> <span class="o">-=</span> <span class="n">other</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_compatible</span><span class="p">(</span><span class="n">other</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible for __isub__&#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">-=</span> <span class="n">ot</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+    <span class="c1"># __isub__</span>
+
+<div class="viewcode-block" id="BlockDataContainer.__imul__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__imul__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__imul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Inline multiplication&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">*=</span> <span class="n">ot</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">:</span>
+                <span class="n">el</span> <span class="o">*=</span> <span class="n">other</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_compatible</span><span class="p">(</span><span class="n">other</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible for __imul__&#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">*=</span> <span class="n">ot</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+    <span class="c1"># __imul__</span>
+
+<div class="viewcode-block" id="BlockDataContainer.__idiv__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__idiv__">[docs]</a>
+    <span class="k">def</span> <span class="nf">__idiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Inline division&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">containers</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">/=</span> <span class="n">ot</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">:</span>
+                <span class="n">el</span> <span class="o">/=</span> <span class="n">other</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_compatible</span><span class="p">(</span><span class="n">other</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible for __idiv__&#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">el</span><span class="p">,</span><span class="n">ot</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+                <span class="n">el</span> <span class="o">/=</span> <span class="n">ot</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+    <span class="c1"># __rdiv__</span>
+<div class="viewcode-block" id="BlockDataContainer.__itruediv__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__itruediv__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__itruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Inline truedivision&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__idiv__</span><span class="p">(</span><span class="n">other</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockDataContainer.__neg__">
+<a class="viewcode-back" href="../../../../optimisation/#cil.framework.BlockDataContainer.__neg__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__neg__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Return - self &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span></div>
+
+
+    <span class="k">def</span> <span class="nf">dot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="c1">#</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">containers</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">el</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">containers</span><span class="p">])</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">None</span></div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
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+            
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\ No newline at end of file
diff --git a/v24.2.0/_modules/cil/framework/data_container/index.html b/v24.2.0/_modules/cil/framework/data_container/index.html
new file mode 100644
index 0000000000..0315761a9a
--- /dev/null
+++ b/v24.2.0/_modules/cil/framework/data_container/index.html
@@ -0,0 +1,1486 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>cil.framework.data_container &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
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+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.framework.data_container</span></li>
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+                  
+  <h1>Source code for cil.framework.data_container</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">ctypes</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">.cilacc</span> <span class="kn">import</span> <span class="n">cilacc</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.multiprocessing</span> <span class="kn">import</span> <span class="n">NUM_THREADS</span>
+
+
+<div class="viewcode-block" id="DataContainer">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer">[docs]</a>
+<span class="k">class</span> <span class="nc">DataContainer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Generic class to hold data</span>
+
+<span class="sd">    Data is currently held in a numpy arrays&#39;&#39;&#39;</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="nd">@geometry</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;DataContainers cannot hold a geometry, use ImageData or AcquisitionData instead&quot;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">default_labels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_dimensions</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">):</span>
+                <span class="n">default_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;dimension_</span><span class="si">{0:02}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">default_labels</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span>
+
+    <span class="nd">@dimension_labels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_tuple</span> <span class="o">:=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">val</span><span class="p">))</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span> <span class="o">=</span> <span class="n">val_tuple</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dimension_labels expected a list containing </span><span class="si">{0}</span><span class="s2"> strings got </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">,</span> <span class="n">val</span><span class="p">))</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the shape of the DataContainer&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span>
+
+    <span class="nd">@shape</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deprecated - shape will be set automatically&quot;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">ndim</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the ndim of the DataContainer&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">ndim</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">number_of_dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the shape of the  of the DataContainer&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the dtype of the data array.&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the number of elements of the DataContainer&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">size</span>
+
+    <span class="n">__container_priority__</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">deep_copy</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">array</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Array must be NumpyArray, passed </span><span class="si">{0}</span><span class="s1">&#39;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)))</span>
+
+        <span class="c1">#Don&#39;t set for derived classes</span>
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="ow">is</span> <span class="n">DataContainer</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="n">dimension_labels</span>
+
+        <span class="c1"># finally copy the geometry, and force dtype of the geometry of the data = the dype of the data</span>
+        <span class="k">if</span> <span class="s1">&#39;geometry&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;geometry&#39;</span><span class="p">]</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+    <span class="k">def</span> <span class="nf">get_dimension_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dimension_label</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">dimension_label</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dimension_label</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unknown dimension </span><span class="si">{0}</span><span class="s1">. Should be one of </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dimension_label</span><span class="p">,</span>
+                             <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">))</span>
+
+<div class="viewcode-block" id="DataContainer.get_dimension_axis">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.get_dimension_axis">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_dimension_axis</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dimension_label</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the axis index of the DataContainer array if the specified dimension_label(s) match</span>
+<span class="sd">        any dimension_labels of the DataContainer or their indices</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        dimension_label: string or int or tuple of strings or ints</span>
+<span class="sd">            Specify dimension_label(s) or index of the DataContainer from which to check and return the axis index</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        int or tuple of ints</span>
+<span class="sd">            The axis index of the DataContainer matching the specified dimension_label</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension_label</span><span class="p">,(</span><span class="nb">tuple</span><span class="p">,</span><span class="nb">list</span><span class="p">)):</span>
+            <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_dimension_axis</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">dimension_label</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">dimension_label</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dimension_label</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dimension_label</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="n">dimension_label</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">dimension_label</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">dimension_label</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unknown dimension </span><span class="si">{0}</span><span class="s1">. Should be one of </span><span class="si">{1}</span><span class="s1">, or an integer in range </span><span class="si">{2}</span><span class="s1"> - </span><span class="si">{3}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dimension_label</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">))</span></div>
+
+
+
+<div class="viewcode-block" id="DataContainer.as_array">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.as_array">[docs]</a>
+    <span class="k">def</span> <span class="nf">as_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the pointer to the array.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span></div>
+
+
+
+<div class="viewcode-block" id="DataContainer.get_slice">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.get_slice">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Returns a new DataContainer containing a single slice in the requested direction. \</span>
+<span class="sd">        Pass keyword arguments &lt;dimension label&gt;=index</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1"># Force is not relevant for a DataContainer:</span>
+        <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;force&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">new_array</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1">#get ordered list of current dimensions</span>
+        <span class="n">dimension_labels_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+        <span class="c1">#remove axes from array and labels</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">kw</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">axis</span> <span class="o">=</span> <span class="n">dimension_labels_list</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                <span class="n">dimension_labels_list</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">new_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">new_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+                <span class="n">new_array</span> <span class="o">=</span> <span class="n">new_array</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">new_array</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">DataContainer</span><span class="p">(</span><span class="n">new_array</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">dimension_labels_list</span><span class="p">,</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="kn">from</span> <span class="nn">.vector_data</span> <span class="kn">import</span> <span class="n">VectorData</span>
+        <span class="k">return</span> <span class="n">VectorData</span><span class="p">(</span><span class="n">new_array</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="n">dimension_labels_list</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.reorder">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.reorder">[docs]</a>
+    <span class="k">def</span> <span class="nf">reorder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        reorders the data in memory as requested.</span>
+
+<span class="sd">        :param order: ordered list of labels from self.dimension_labels</span>
+<span class="sd">        :type order: list, sting</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">order</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The axes list for resorting must have </span><span class="si">{0}</span><span class="s1"> dimensions. Got </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">order</span><span class="p">)))</span>
+        <span class="k">except</span> <span class="ne">TypeError</span> <span class="k">as</span> <span class="n">ae</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The order must be an iterable with __len__ implemented, like a list or a tuple. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">order</span><span class="p">)))</span>
+
+        <span class="n">correct</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">order</span><span class="p">:</span>
+            <span class="n">correct</span> <span class="o">=</span> <span class="n">correct</span> <span class="ow">and</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">correct</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The axes list for resorting must contain the dimension_labels </span><span class="si">{0}</span><span class="s1"> got </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span> <span class="n">order</span><span class="p">))</span>
+
+        <span class="n">new_order</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">dimension_labels_new</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">new_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
+            <span class="n">dimension_labels_new</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">axis</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ascontiguousarray</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">new_order</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="n">dimension_labels_new</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">set_labels</span><span class="p">(</span><span class="n">dimension_labels_new</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.fill">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.fill">[docs]</a>
+    <span class="k">def</span> <span class="nf">fill</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="o">**</span><span class="n">dimension</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;fills the internal data array with the DataContainer, numpy array or number provided</span>
+
+<span class="sd">        :param array: number, numpy array or DataContainer to copy into the DataContainer</span>
+<span class="sd">        :type array: DataContainer or subclasses, numpy array or number</span>
+<span class="sd">        :param dimension: dictionary, optional</span>
+
+<span class="sd">        if the passed numpy array points to the same array that is contained in the DataContainer,</span>
+<span class="sd">        it just returns</span>
+
+<span class="sd">        In case a DataContainer or subclass is passed, there will be a check of the geometry,</span>
+<span class="sd">        if present, and the array will be resorted if the data is not in the appropriate order.</span>
+
+<span class="sd">        User may pass a named parameter to specify in which axis the fill should happen:</span>
+
+<span class="sd">        dc.fill(some_data, vertical=1, horizontal_x=32)</span>
+<span class="sd">        will copy the data in some_data into the data container.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
+            <span class="k">return</span>
+        <span class="k">if</span> <span class="n">dimension</span> <span class="o">==</span> <span class="p">{}:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">numpy</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="vm">__class__</span> <span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">!=</span> <span class="n">array</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input array is not in the same order as destination array. Use &quot;array.reorder()&quot;&#39;</span><span class="p">)</span>
+                <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+                    <span class="k">pass</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">array</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot fill with the provided array.&#39;</span> <span class="o">+</span> \
+                                     <span class="s1">&#39;Expecting shape </span><span class="si">{0}</span><span class="s1"> got </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                                     <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Can fill only with number, numpy array or DataContainer and subclasses. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)))</span>
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="n">axis</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;:&#39;</span><span class="p">]</span><span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_dimensions</span>
+            <span class="n">dimension_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">dimension</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">i</span> <span class="o">=</span> <span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+                <span class="n">axis</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+
+            <span class="n">command</span> <span class="o">=</span> <span class="s1">&#39;self.array[&#39;</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">axis</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">command</span> <span class="o">+=</span> <span class="s1">&#39;,&#39;</span>
+                <span class="n">command</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">el</span><span class="p">)</span>
+                <span class="n">i</span><span class="o">+=</span><span class="mi">1</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">command</span> <span class="o">=</span> <span class="n">command</span> <span class="o">+</span> <span class="s2">&quot;] = array[:]&quot;</span>
+            <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+                <span class="n">command</span> <span class="o">=</span> <span class="n">command</span> <span class="o">+</span> <span class="s2">&quot;] = array.as_array()[:]&quot;</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="n">command</span> <span class="o">=</span> <span class="n">command</span> <span class="o">+</span> <span class="s2">&quot;] = array&quot;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Can fill only with number, numpy array or DataContainer and subclasses. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)))</span>
+            <span class="n">exec</span><span class="p">(</span><span class="n">command</span><span class="p">)</span></div>
+
+
+
+    <span class="k">def</span> <span class="nf">check_dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">shape</span>
+
+    <span class="c1">## algebra</span>
+
+    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">__div__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__truediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+    <span class="k">def</span> <span class="fm">__pow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+
+
+    <span class="c1"># reverse operand</span>
+    <span class="k">def</span> <span class="fm">__radd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="n">other</span>
+    <span class="c1"># __radd__</span>
+
+    <span class="k">def</span> <span class="fm">__rsub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span><span class="p">)</span> <span class="o">+</span> <span class="n">other</span>
+    <span class="c1"># __rsub__</span>
+
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">*</span> <span class="n">other</span>
+    <span class="c1"># __rmul__</span>
+
+    <span class="k">def</span> <span class="nf">__rdiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">tmp</span> <span class="o">*=</span> <span class="n">other</span>
+        <span class="k">return</span> <span class="n">tmp</span>
+    <span class="c1"># __rdiv__</span>
+    <span class="k">def</span> <span class="fm">__rtruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__rdiv__</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__rpow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span> <span class="p">:</span>
+            <span class="n">fother</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">))</span> <span class="o">*</span> <span class="n">other</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="n">fother</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span> <span class="p">,</span>
+                           <span class="n">dimension_labels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span>
+                           <span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+    <span class="c1"># __rpow__</span>
+
+    <span class="c1"># in-place arithmetic operators:</span>
+    <span class="c1"># (+=, -=, *=, /= , //=,</span>
+    <span class="c1"># must return self</span>
+
+    <span class="k">def</span> <span class="fm">__iadd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;out&#39;</span><span class="p">:</span><span class="bp">self</span><span class="p">}</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__imul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;out&#39;</span><span class="p">:</span><span class="bp">self</span><span class="p">}</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__isub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;out&#39;</span><span class="p">:</span><span class="bp">self</span><span class="p">}</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__idiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;out&#39;</span><span class="p">:</span><span class="bp">self</span><span class="p">}</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__itruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;out&#39;</span><span class="p">:</span><span class="bp">self</span><span class="p">}</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__neg__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;negation operator&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">representation</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Number of dimensions: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Shape: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Axis labels: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">representation</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Representation: </span><span class="se">\n</span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+<div class="viewcode-block" id="DataContainer.get_data_axes_order">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.get_data_axes_order">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_data_axes_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">new_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns the axes label of self as a list</span>
+
+<span class="sd">        If new_order is None returns the labels of the axes as a sorted-by-key list.</span>
+<span class="sd">        If new_order is a list of length number_of_dimensions, returns a list</span>
+<span class="sd">        with the indices of the axes in new_order with respect to those in</span>
+<span class="sd">        self.dimension_labels: i.e.</span>
+<span class="sd">          &gt;&gt;&gt; self.dimension_labels = {0:&#39;horizontal&#39;,1:&#39;vertical&#39;}</span>
+<span class="sd">          &gt;&gt;&gt; new_order = [&#39;vertical&#39;,&#39;horizontal&#39;]</span>
+<span class="sd">          returns [1,0]</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">new_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_order</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">:</span>
+
+                <span class="n">axes_order</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_order</span><span class="p">):</span>
+                    <span class="n">axes_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">axes_order</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Expecting </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2"> axes, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">new_order</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.clone">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.clone">[docs]</a>
+    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a copy of DataContainer&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.copy">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.copy">[docs]</a>
+    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;alias of clone&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span></div>
+
+
+    <span class="c1">## binary operations</span>
+
+    <span class="k">def</span> <span class="nf">pixel_wise_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pwop</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;out&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="p">,</span> <span class="n">x2</span> <span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">x2</span><span class="o">.</span><span class="vm">__class__</span> <span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="p">,</span> <span class="n">x2</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="p">,</span> <span class="n">x2</span> <span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Expected x2 type as number or DataContainer, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x2</span><span class="p">)))</span>
+            <span class="n">geom</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span>
+            <span class="k">if</span> <span class="n">geom</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">geom</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="n">out</span><span class="p">,</span>
+                   <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                   <span class="n">dimension_labels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span>
+                   <span class="n">geometry</span><span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                   <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x2</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_dimensions</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_dimensions</span><span class="p">(</span><span class="n">x2</span><span class="p">):</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+                <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">x2</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span>
+                <span class="c1">#return type(self)(out.as_array(),</span>
+                <span class="c1">#       deep_copy=False,</span>
+                <span class="c1">#       dimension_labels=self.dimension_labels,</span>
+                <span class="c1">#       geometry=self.geometry)</span>
+                <span class="k">return</span> <span class="n">out</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Wrong size for data memory: out </span><span class="si">{</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> x2 </span><span class="si">{</span><span class="n">x2</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="ow">and</span> \
+             <span class="nb">isinstance</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="p">(</span><span class="n">Number</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_dimensions</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">and</span>\
+                    <span class="ow">not</span> <span class="p">(</span><span class="n">x2</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="ow">and</span> <span class="n">x2</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Wrong size for data memory: out </span><span class="si">{</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> x2 </span><span class="si">{</span><span class="n">x2</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span><span class="o">=</span><span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+                <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">x2</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span>
+                <span class="k">return</span> <span class="n">out</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Wrong size for data memory: </span><span class="si">{</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">out</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span>
+                <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">x2</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="c1">#return type(self)(out,</span>
+                <span class="c1">#       deep_copy=False,</span>
+                <span class="c1">#       dimension_labels=self.dimension_labels,</span>
+                <span class="c1">#       geometry=self.geometry)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;incompatible class: </span><span class="si">{</span><span class="n">pwop</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">out</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="s1">&#39;__container_priority__&#39;</span><span class="p">)</span> <span class="ow">and</span> \
+           <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span> <span class="o">&lt;</span> <span class="n">other</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">other</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_binary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">subtract</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="s1">&#39;__container_priority__&#39;</span><span class="p">)</span> <span class="ow">and</span> \
+           <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span> <span class="o">&lt;</span> <span class="n">other</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">other</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_binary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">subtract</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">multiply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="s1">&#39;__container_priority__&#39;</span><span class="p">)</span> <span class="ow">and</span> \
+           <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span> <span class="o">&lt;</span> <span class="n">other</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">other</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_binary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">multiply</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">divide</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="s1">&#39;__container_priority__&#39;</span><span class="p">)</span> <span class="ow">and</span> \
+           <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span> <span class="o">&lt;</span> <span class="n">other</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="n">__container_priority__</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">other</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_binary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">divide</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">power</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_binary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">power</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">maximum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_binary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">minimum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x2</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_binary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">minimum</span><span class="p">,</span> <span class="n">x2</span><span class="o">=</span><span class="n">x2</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="DataContainer.sapyb">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.sapyb">[docs]</a>
+    <span class="k">def</span> <span class="nf">sapyb</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_threads</span><span class="o">=</span><span class="n">NUM_THREADS</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;performs a*self + b * y. Can be done in-place</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        a : multiplier for self, can be a number or a numpy array or a DataContainer</span>
+<span class="sd">        y : DataContainer</span>
+<span class="sd">        b : multiplier for y, can be a number or a numpy array or a DataContainer</span>
+<span class="sd">        out : return DataContainer, if None a new DataContainer is returned, default None.</span>
+<span class="sd">            out can be self or y.</span>
+<span class="sd">        num_threads : number of threads to use during the calculation, using the CIL C library</span>
+<span class="sd">            It will try to use the CIL C library and default to numpy operations, in case the C library does not handle the types.</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        &gt;&gt;&gt; a = 2</span>
+<span class="sd">        &gt;&gt;&gt; b = 3</span>
+<span class="sd">        &gt;&gt;&gt; ig = ImageGeometry(10,11)</span>
+<span class="sd">        &gt;&gt;&gt; x = ig.allocate(1)</span>
+<span class="sd">        &gt;&gt;&gt; y = ig.allocate(2)</span>
+<span class="sd">        &gt;&gt;&gt; out = x.sapyb(a,y,b)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span> <span class="o">*</span> <span class="mf">0.</span>
+
+        <span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float64</span> <span class="p">]:</span>
+            <span class="c1"># handle with C-lib _axpby</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_axpby</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">num_threads</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">out</span>
+            <span class="k">except</span> <span class="ne">RuntimeError</span> <span class="k">as</span> <span class="n">rte</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;sapyb defaulting to Python due to: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rte</span><span class="p">))</span>
+            <span class="k">except</span> <span class="ne">TypeError</span> <span class="k">as</span> <span class="n">te</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;sapyb defaulting to Python due to: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">te</span><span class="p">))</span>
+            <span class="k">finally</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+
+        <span class="c1"># cannot be handled by _axpby</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span> <span class="o">*</span> <span class="n">a</span>
+        <span class="n">y</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_axpby</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">num_threads</span><span class="o">=</span><span class="n">NUM_THREADS</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;performs axpby with cilacc C library, can be done in-place.</span>
+
+<span class="sd">        Does the operation .. math:: a*x+b*y and stores the result in out, where x is self</span>
+
+<span class="sd">        :param a: scalar</span>
+<span class="sd">        :type a: float</span>
+<span class="sd">        :param b: scalar</span>
+<span class="sd">        :type b: float</span>
+<span class="sd">        :param y: DataContainer</span>
+<span class="sd">        :param out: DataContainer instance to store the result</span>
+<span class="sd">        :param dtype: data type of the DataContainers</span>
+<span class="sd">        :type dtype: numpy type, optional, default numpy.float32</span>
+<span class="sd">        :param num_threads: number of threads to run on</span>
+<span class="sd">        :type num_threads: int, optional, default 1/2 CPU of the system</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">c_float_p</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">)</span>
+        <span class="n">c_double_p</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span><span class="p">)</span>
+
+        <span class="c1">#convert a and b to numpy arrays and get the reference to the data (length = 1 or ndx.size)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">nda</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">nda</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">ndb</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">ndb</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
+
+        <span class="n">a_vec</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">nda</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">a_vec</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="n">b_vec</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">ndb</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">b_vec</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="c1"># get the reference to the data</span>
+        <span class="n">ndx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">ndy</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">ndout</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">ndout</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">dtype</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Warning</span><span class="p">(</span><span class="s2">&quot;out array of type </span><span class="si">{0}</span><span class="s2"> does not match requested dtype </span><span class="si">{1}</span><span class="s2">. Using </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndout</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">))</span>
+            <span class="n">dtype</span> <span class="o">=</span> <span class="n">ndout</span><span class="o">.</span><span class="n">dtype</span>
+        <span class="k">if</span> <span class="n">ndx</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">dtype</span><span class="p">:</span>
+            <span class="n">ndx</span> <span class="o">=</span> <span class="n">ndx</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">casting</span><span class="o">=</span><span class="s1">&#39;safe&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ndy</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">dtype</span><span class="p">:</span>
+            <span class="n">ndy</span> <span class="o">=</span> <span class="n">ndy</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">casting</span><span class="o">=</span><span class="s1">&#39;safe&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">nda</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">dtype</span><span class="p">:</span>
+            <span class="n">nda</span> <span class="o">=</span> <span class="n">nda</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">casting</span><span class="o">=</span><span class="s1">&#39;same_kind&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ndb</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">dtype</span><span class="p">:</span>
+            <span class="n">ndb</span> <span class="o">=</span> <span class="n">ndb</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">casting</span><span class="o">=</span><span class="s1">&#39;same_kind&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+            <span class="n">x_p</span> <span class="o">=</span> <span class="n">ndx</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+            <span class="n">y_p</span> <span class="o">=</span> <span class="n">ndy</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+            <span class="n">out_p</span> <span class="o">=</span> <span class="n">ndout</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+            <span class="n">a_p</span> <span class="o">=</span> <span class="n">nda</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+            <span class="n">b_p</span> <span class="o">=</span> <span class="n">ndb</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+            <span class="n">f</span> <span class="o">=</span> <span class="n">cilacc</span><span class="o">.</span><span class="n">saxpby</span>
+
+        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">:</span>
+            <span class="n">x_p</span> <span class="o">=</span> <span class="n">ndx</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_double_p</span><span class="p">)</span>
+            <span class="n">y_p</span> <span class="o">=</span> <span class="n">ndy</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_double_p</span><span class="p">)</span>
+            <span class="n">out_p</span> <span class="o">=</span> <span class="n">ndout</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_double_p</span><span class="p">)</span>
+            <span class="n">a_p</span> <span class="o">=</span> <span class="n">nda</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_double_p</span><span class="p">)</span>
+            <span class="n">b_p</span> <span class="o">=</span> <span class="n">ndb</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_double_p</span><span class="p">)</span>
+            <span class="n">f</span> <span class="o">=</span> <span class="n">cilacc</span><span class="o">.</span><span class="n">daxpby</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Unsupported type </span><span class="si">{}</span><span class="s1">. Expecting numpy.float32 or numpy.float64&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dtype</span><span class="p">))</span>
+
+        <span class="c1">#out = numpy.empty_like(a)</span>
+
+
+        <span class="c1"># int psaxpby(float * x, float * y, float * out, float a, float b, long size)</span>
+        <span class="n">cilacc</span><span class="o">.</span><span class="n">saxpby</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the first array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the second array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the third array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to A</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>                    <span class="c1"># type of type of A selector (int)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to B</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>                    <span class="c1"># type of type of B selector (int)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_longlong</span><span class="p">,</span>               <span class="c1"># type of size of first array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">]</span>                    <span class="c1"># number of threads</span>
+        <span class="n">cilacc</span><span class="o">.</span><span class="n">daxpby</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span><span class="p">),</span> <span class="c1"># pointer to the first array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span><span class="p">),</span> <span class="c1"># pointer to the second array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span><span class="p">),</span> <span class="c1"># pointer to the third array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span><span class="p">),</span> <span class="c1"># type of A (c_double)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>                    <span class="c1"># type of type of A selector (int)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span><span class="p">),</span> <span class="c1"># type of B (c_double)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>                    <span class="c1"># type of type of B selector (int)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_longlong</span><span class="p">,</span>               <span class="c1"># type of size of first array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">]</span>                    <span class="c1"># number of threads</span>
+
+        <span class="k">if</span> <span class="n">f</span><span class="p">(</span><span class="n">x_p</span><span class="p">,</span> <span class="n">y_p</span><span class="p">,</span> <span class="n">out_p</span><span class="p">,</span> <span class="n">a_p</span><span class="p">,</span> <span class="n">a_vec</span><span class="p">,</span> <span class="n">b_p</span><span class="p">,</span> <span class="n">b_vec</span><span class="p">,</span> <span class="n">ndx</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">num_threads</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;axpby execution failed&#39;</span><span class="p">)</span>
+
+
+    <span class="c1">## unary operations</span>
+    <span class="k">def</span> <span class="nf">pixel_wise_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pwop</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;out&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="n">out</span><span class="p">,</span>
+                       <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                       <span class="n">dimension_labels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span>
+                       <span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span>
+                       <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_dimensions</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+                <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Wrong size for data memory: </span><span class="si">{</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">out</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+                <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span>
+                <span class="n">pwop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;incompatible class: </span><span class="si">{pwop.__name__}</span><span class="s2"> {type(out)}&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">abs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">sign</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sign</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">conjugate</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>  <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="DataContainer.exp">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.exp">[docs]</a>
+    <span class="k">def</span> <span class="nf">exp</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Applies exp pixel-wise to the DataContainer&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">exp</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.log">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.log">[docs]</a>
+    <span class="k">def</span> <span class="nf">log</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Applies log pixel-wise to the DataContainer&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_wise_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="c1">## reductions</span>
+<div class="viewcode-block" id="DataContainer.squared_norm">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.squared_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">squared_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;return the squared euclidean norm of the DataContainer viewed as a vector&#39;&#39;&#39;</span>
+        <span class="c1">#shape = self.shape</span>
+        <span class="c1">#size = reduce(lambda x,y:x*y, shape, 1)</span>
+        <span class="c1">#y = numpy.reshape(self.as_array(), (size, ))</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
+
+        <span class="c1">#return self.dot(self)</span>
+<div class="viewcode-block" id="DataContainer.norm">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;return the euclidean norm of the DataContainer viewed as a vector&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.dot">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.dot">[docs]</a>
+    <span class="k">def</span> <span class="nf">dot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns the inner product of 2 DataContainers viewed as vectors. Suitable for real and complex data.</span>
+<span class="sd">          For complex data,  the dot method returns a.dot(b.conjugate())</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="s1">&#39;numpy&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;numpy&#39;</span><span class="p">,</span><span class="s1">&#39;reduce&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;dot: specified method not valid. Expecting numpy or reduce got </span><span class="si">{}</span><span class="s1"> &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">method</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;numpy&#39;</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="o">.</span><span class="n">conjugate</span><span class="p">())</span>
+            <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;reduce&#39;</span><span class="p">:</span>
+                <span class="c1"># see https://github.com/vais-ral/CCPi-Framework/pull/273</span>
+                <span class="c1"># notice that Python seems to be smart enough to use</span>
+                <span class="c1"># the appropriate type to hold the result of the reduction</span>
+                <span class="n">sf</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                            <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span>
+                                <span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()),</span>
+                            <span class="mi">0</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">sf</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Shapes are not aligned: </span><span class="si">{}</span><span class="s1"> != </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_directional_reduction_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reduction_function</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the result of a unary function, considering the direction from an axis argument to the function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        reduction_function : function</span>
+<span class="sd">            The unary function to be evaluated</span>
+<span class="sd">        axis : string or tuple of strings or int or tuple of ints, optional</span>
+<span class="sd">            Specify the axis or axes to calculate &#39;reduction_function&#39; along. Can be specified as</span>
+<span class="sd">            string(s) of dimension_labels or int(s) of indices</span>
+<span class="sd">            Default None calculates the function over the whole array</span>
+<span class="sd">        out: ndarray or DataContainer, optional</span>
+<span class="sd">            Provide an object in which to place the result. The object must have the correct dimensions and</span>
+<span class="sd">            (for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See</span>
+<span class="sd">            `Output type determination &lt;https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type&gt;`_ for more details.</span>
+<span class="sd">            Default is None</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scalar or ndarray</span>
+<span class="sd">            The result of the unary function</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dimension_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">reduction_function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">new_dimensions</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+                <span class="n">new_dimensions</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">new_dimensions</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">DataContainer</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="n">new_dimensions</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">result</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">out</span><span class="p">,</span><span class="s1">&#39;array&#39;</span><span class="p">):</span>
+                <span class="n">out_arr</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">out_arr</span> <span class="o">=</span> <span class="n">out</span>
+
+            <span class="n">reduction_function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">out</span><span class="o">=</span><span class="n">out_arr</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span>  <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="DataContainer.sum">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.sum">[docs]</a>
+    <span class="k">def</span> <span class="nf">sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the sum of values in the DataContainer</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        axis : string or tuple of strings or int or tuple of ints, optional</span>
+<span class="sd">            Specify the axis or axes to calculate &#39;sum&#39; along. Can be specified as</span>
+<span class="sd">            string(s) of dimension_labels or int(s) of indices</span>
+<span class="sd">            Default None calculates the function over the whole array</span>
+<span class="sd">        out : ndarray or DataContainer, optional</span>
+<span class="sd">            Provide an object in which to place the result. The object must have the correct dimensions and</span>
+<span class="sd">            (for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See</span>
+<span class="sd">            `Output type determination &lt;https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type&gt;`_ for more details.</span>
+<span class="sd">            Default is None</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scalar or DataContainer</span>
+<span class="sd">            The sum as a scalar or inside a DataContainer with reduced dimension_labels</span>
+<span class="sd">            Default is to accumulate and return data as float64 or complex128</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;dtype is ignored (auto-using float64 or complex128)&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isrealobj</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;dtype&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float64</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;dtype&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">complex128</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_directional_reduction_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.min">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.min">[docs]</a>
+    <span class="k">def</span> <span class="nf">min</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the minimum pixel value in the DataContainer</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        axis : string or tuple of strings or int or tuple of ints, optional</span>
+<span class="sd">            Specify the axis or axes to calculate &#39;min&#39; along. Can be specified as</span>
+<span class="sd">            string(s) of dimension_labels or int(s) of indices</span>
+<span class="sd">            Default None calculates the function over the whole array</span>
+<span class="sd">        out : ndarray or DataContainer, optional</span>
+<span class="sd">            Provide an object in which to place the result. The object must have the correct dimensions and</span>
+<span class="sd">            (for DataContainers) the correct dimension_labels, but the type will be cast if necessary.  See</span>
+<span class="sd">            `Output type determination &lt;https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type&gt;`_ for more details.</span>
+<span class="sd">            Default is None</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scalar or DataContainer</span>
+<span class="sd">            The min as a scalar or inside a DataContainer with reduced dimension_labels</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_directional_reduction_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.max">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.max">[docs]</a>
+    <span class="k">def</span> <span class="nf">max</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the maximum pixel value in the DataContainer</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        axis : string or tuple of strings or int or tuple of ints, optional</span>
+<span class="sd">            Specify the axis or axes to calculate &#39;max&#39; along. Can be specified as</span>
+<span class="sd">            string(s) of dimension_labels or int(s) of indices</span>
+<span class="sd">            Default None calculates the function over the whole array</span>
+<span class="sd">        out : ndarray or DataContainer, optional</span>
+<span class="sd">            Provide an object in which to place the result. The object must have the correct dimensions and</span>
+<span class="sd">            (for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See</span>
+<span class="sd">            `Output type determination &lt;https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type&gt;`_ for more details.</span>
+<span class="sd">            Default is None</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scalar or DataContainer</span>
+<span class="sd">            The max as a scalar or inside a DataContainer with reduced dimension_labels</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_directional_reduction_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataContainer.mean">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataContainer.mean">[docs]</a>
+    <span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the mean pixel value of the DataContainer</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        axis : string or tuple of strings or int or tuple of ints, optional</span>
+<span class="sd">            Specify the axis or axes to calculate &#39;mean&#39; along. Can be specified as</span>
+<span class="sd">            string(s) of dimension_labels or int(s) of indices</span>
+<span class="sd">            Default None calculates the function over the whole array</span>
+<span class="sd">        out : ndarray or DataContainer, optional</span>
+<span class="sd">            Provide an object in which to place the result. The object must have the correct dimensions and</span>
+<span class="sd">            (for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See</span>
+<span class="sd">            `Output type determination &lt;https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type&gt;`_ for more details.</span>
+<span class="sd">            Default is None</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scalar or DataContainer</span>
+<span class="sd">            The mean as a scalar or inside a DataContainer with reduced dimension_labels</span>
+<span class="sd">            Default is to accumulate and return data as float64 or complex128</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;dtype is ignored (auto-using float64 or complex128)&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isrealobj</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;dtype&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float64</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;dtype&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">complex128</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_directional_reduction_unary</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="c1"># Logic operators between DataContainers and floats</span>
+    <span class="k">def</span> <span class="fm">__le__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns boolean array of DataContainer less or equal than DataContainer/float&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&lt;=</span><span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&lt;=</span><span class="n">other</span>
+
+    <span class="k">def</span> <span class="fm">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns boolean array of DataContainer less than DataContainer/float&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&lt;</span><span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&lt;</span><span class="n">other</span>
+
+    <span class="k">def</span> <span class="fm">__ge__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns boolean array of DataContainer greater or equal than DataContainer/float&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&gt;=</span><span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&gt;=</span><span class="n">other</span>
+
+    <span class="k">def</span> <span class="fm">__gt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns boolean array of DataContainer greater than DataContainer/float&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&gt;</span><span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">&gt;</span><span class="n">other</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns boolean array of DataContainer equal to DataContainer/float&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">==</span><span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">==</span><span class="n">other</span>
+
+    <span class="k">def</span> <span class="fm">__ne__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns boolean array of DataContainer negative to DataContainer/float&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">!=</span><span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">!=</span><span class="n">other</span></div>
+
+</pre></div>
+
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new file mode 100644
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+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
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+    Optimisation framework
+  </a>
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+    Processors
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+    CIL Plugins
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+  >
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+    <!-- dropdown will be populated by javascript on page load -->
+  </div>
+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../demos/">Tutorials</a></li>
+</ul>
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+
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+    <li class="breadcrumb-item"><a href="../../../" class="nav-link">Module code</a></li>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.framework.image_data</span></li>
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+              
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+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <h1>Source code for cil.framework.image_data</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">.data_container</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+<span class="kn">from</span> <span class="nn">.labels</span> <span class="kn">import</span> <span class="n">ImageDimension</span><span class="p">,</span> <span class="n">Backend</span>
+
+<div class="viewcode-block" id="ImageData">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageData">[docs]</a>
+<span class="k">class</span> <span class="nc">ImageData</span><span class="p">(</span><span class="n">DataContainer</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;DataContainer for holding 2D or 3D DataContainer&#39;&#39;&#39;</span>
+    <span class="n">__container_priority__</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span>
+
+    <span class="nd">@geometry</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span>
+
+    <span class="nd">@dimension_labels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unable to set the dimension_labels directly. Use geometry.set_labels() instead&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">array</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                 <span class="n">geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span><span class="s2">&quot;ImageData requires a geometry&quot;</span><span class="p">)</span>
+
+
+        <span class="n">labels</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dimension_labels&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">labels</span> <span class="o">!=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Deprecated: &#39;dimension_labels&#39; cannot be set with &#39;allocate()&#39;. Use &#39;geometry.set_labels()&#39; to modify the geometry before using allocate.&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">array</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">array</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="c1"># remove singleton dimensions</span>
+            <span class="n">array</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;array must be a CIL type DataContainer or numpy.ndarray got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)))</span>
+
+        <span class="k">if</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Shape mismatch </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">array</span><span class="o">.</span><span class="n">ndim</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Number of dimensions are not 2 or 3 or 4 : </span><span class="si">{0}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">ndim</span><span class="p">))</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ImageData</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">deep_copy</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">geometry</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Check if two ImageData objects are equal. This is done by checking if the geometry, data and dtype are equal.</span>
+<span class="sd">        Also, if the other object is a numpy.ndarray, it will check if the data and dtype are equal.</span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other: ImageData or numpy.ndarray</span>
+<span class="sd">            The object to compare with.</span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        bool</span>
+<span class="sd">            True if the two objects are equal, False otherwise.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">other</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span> \
+                <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">geometry</span> \
+                <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+                <span class="k">return</span> <span class="kc">True</span> 
+        <span class="k">elif</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">other</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="o">==</span><span class="n">other</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+    
+<div class="viewcode-block" id="ImageData.get_slice">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageData.get_slice">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">channel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">horizontal_x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">horizontal_y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Returns a new ImageData of a single slice of in the requested direction.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">geometry_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">vertical</span><span class="p">,</span> <span class="n">horizontal_x</span><span class="o">=</span><span class="n">horizontal_x</span><span class="p">,</span> <span class="n">horizontal_y</span><span class="o">=</span><span class="n">horizontal_y</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">force</span><span class="p">:</span>
+                <span class="n">geometry_new</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span> <span class="p">(</span><span class="s2">&quot;Unable to return slice of requested ImageData. Use &#39;force=True&#39; to return DataContainer instead.&quot;</span><span class="p">)</span>
+
+        <span class="c1">#if vertical = &#39;centre&#39; slice convert to index and subset, this will interpolate 2 rows to get the center slice value</span>
+        <span class="k">if</span> <span class="n">vertical</span> <span class="o">==</span> <span class="s1">&#39;centre&#39;</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
+            <span class="n">centre_slice_pos</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.</span>
+            <span class="n">ind0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">centre_slice_pos</span><span class="p">))</span>
+
+            <span class="n">w2</span> <span class="o">=</span> <span class="n">centre_slice_pos</span> <span class="o">-</span> <span class="n">ind0</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">ind0</span><span class="p">,</span> <span class="n">horizontal_x</span><span class="o">=</span><span class="n">horizontal_x</span><span class="p">,</span> <span class="n">horizontal_y</span><span class="o">=</span><span class="n">horizontal_y</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">w2</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">out2</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">ind0</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">horizontal_x</span><span class="o">=</span><span class="n">horizontal_x</span><span class="p">,</span> <span class="n">horizontal_y</span><span class="o">=</span><span class="n">horizontal_y</span><span class="p">)</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">w2</span><span class="p">)</span> <span class="o">+</span> <span class="n">out2</span> <span class="o">*</span> <span class="n">w2</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="n">vertical</span><span class="p">,</span> <span class="n">horizontal_x</span><span class="o">=</span><span class="n">horizontal_x</span><span class="p">,</span> <span class="n">horizontal_y</span><span class="o">=</span><span class="n">horizontal_y</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">geometry_new</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">geometry_new</span><span class="p">,</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="ImageData.apply_circular_mask">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageData.apply_circular_mask">[docs]</a>
+    <span class="k">def</span> <span class="nf">apply_circular_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        Apply a circular mask to the horizontal_x and horizontal_y slices. Values outside this mask will be set to zero.</span>
+
+<span class="sd">        This will most commonly be used to mask edge artefacts from standard CT reconstructions with FBP.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        radius : float, default 0.99</span>
+<span class="sd">            radius of mask by percentage of size of horizontal_x or horizontal_y, whichever is greater</span>
+
+<span class="sd">        in_place : boolean, default True</span>
+<span class="sd">            If `True` masks the current data, if `False` returns a new `ImageData` object.</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            If `in_place = False` returns a new ImageData object with the masked data</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span>
+
+        <span class="c1"># grid</span>
+        <span class="n">y_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">ig</span><span class="o">.</span><span class="n">voxel_num_y</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+        <span class="n">x_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">ig</span><span class="o">.</span><span class="n">voxel_num_x</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+
+        <span class="n">Y</span><span class="p">,</span> <span class="n">X</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ogrid</span><span class="p">[</span><span class="o">-</span><span class="n">y_range</span><span class="p">:</span><span class="n">y_range</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="n">x_range</span><span class="p">:</span><span class="n">x_range</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># use centre from geometry in units distance to account for aspect ratio of pixels</span>
+        <span class="n">dist_from_center</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">X</span><span class="o">*</span><span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_x</span><span class="o">+</span> <span class="n">ig</span><span class="o">.</span><span class="n">center_x</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">Y</span><span class="o">*</span><span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_y</span><span class="o">+</span><span class="n">ig</span><span class="o">.</span><span class="n">center_y</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="n">size_x</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">*</span> <span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_x</span>
+        <span class="n">size_y</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">*</span> <span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_y</span>
+
+        <span class="k">if</span> <span class="n">size_x</span> <span class="o">&gt;</span> <span class="n">size_y</span><span class="p">:</span>
+            <span class="n">radius_applied</span> <span class="o">=</span><span class="n">radius</span> <span class="o">*</span> <span class="n">size_x</span><span class="o">/</span><span class="mi">2</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">radius_applied</span> <span class="o">=</span><span class="n">radius</span> <span class="o">*</span> <span class="n">size_y</span><span class="o">/</span><span class="mi">2</span>
+
+        <span class="c1"># approximate the voxel as a circle and get the radius</span>
+        <span class="c1"># ie voxel area = 1, circle of area=1 has r = 0.56</span>
+        <span class="n">r</span><span class="o">=</span><span class="p">((</span><span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_x</span> <span class="o">*</span> <span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_y</span> <span class="p">)</span><span class="o">/</span><span class="n">numpy</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="c1"># we have the voxel centre distance to mask. voxels with distance greater than |r| are fully inside or outside.</span>
+        <span class="c1"># values on the border region between -r and r are preserved</span>
+        <span class="n">mask</span> <span class="o">=</span><span class="p">(</span><span class="n">radius_applied</span><span class="o">-</span><span class="n">dist_from_center</span><span class="p">)</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="o">-</span><span class="n">r</span><span class="p">,</span><span class="n">r</span><span class="p">)</span>
+
+        <span class="c1">#  rescale to -pi/2-&gt;+pi/2</span>
+        <span class="n">mask</span> <span class="o">*=</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="n">numpy</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">/</span><span class="n">r</span>
+
+        <span class="c1"># the sin of the linear distance gives us an approximation of area of the circle to include in the mask</span>
+        <span class="n">numpy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mask</span><span class="p">)</span>
+
+        <span class="c1"># rescale the data 0 - 1</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">+</span> <span class="n">mask</span> <span class="o">*</span> <span class="mf">0.5</span>
+
+        <span class="c1"># reorder dataset so &#39;horizontal_y&#39; and &#39;horizontal_x&#39; are the final dimensions</span>
+        <span class="n">labels_orig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span>
+        <span class="n">labels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">labels_orig</span><span class="p">)</span>
+
+        <span class="n">labels</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;horizontal_y&#39;</span><span class="p">)</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;horizontal_x&#39;</span><span class="p">)</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;horizontal_y&#39;</span><span class="p">)</span>
+        <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;horizontal_x&#39;</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="n">in_place</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
+            <span class="n">numpy</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">labels_orig</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">image_data_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">image_data_out</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
+            <span class="n">numpy</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">image_data_out</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">image_data_out</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+            <span class="n">image_data_out</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">labels_orig</span><span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">image_data_out</span></div>
+
+
+<div class="viewcode-block" id="ImageData.reorder">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageData.reorder">[docs]</a>
+    <span class="k">def</span> <span class="nf">reorder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Reorders the data in memory as requested. This is an in-place operation.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        order: list or str</span>
+<span class="sd">            Ordered list of labels from self.dimension_labels, or string &#39;astra&#39; or &#39;tigre&#39;.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">order</span> <span class="ow">in</span> <span class="n">Backend</span><span class="p">:</span>
+            <span class="n">order</span> <span class="o">=</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">get_order_for_engine</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="n">order</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/framework/image_geometry/index.html b/v24.2.0/_modules/cil/framework/image_geometry/index.html
new file mode 100644
index 0000000000..f5ed26a94a
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+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
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+  <h1>Source code for cil.framework.image_geometry</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">.image_data</span> <span class="kn">import</span> <span class="n">ImageData</span>
+<span class="kn">from</span> <span class="nn">.labels</span> <span class="kn">import</span> <span class="n">ImageDimension</span><span class="p">,</span> <span class="n">FillType</span>
+
+
+<div class="viewcode-block" id="ImageGeometry">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageGeometry">[docs]</a>
+<span class="k">class</span> <span class="nc">ImageGeometry</span><span class="p">:</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">CHANNEL</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use ImageDimension.CHANNEL instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">CHANNEL</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">HORIZONTAL_X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use ImageDimension.HORIZONTAL_X instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">HORIZONTAL_Y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use ImageDimension.HORIZONTAL_Y instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">RANDOM</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use FillType.RANDOM instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">RANDOM_INT</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use FillType.RANDOM_INT instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM_INT</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">VERTICAL</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use ImageDimension.VERTICAL instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">VERTICAL</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">shape_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">channels</span><span class="p">,</span>
+                      <span class="n">ImageDimension</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span><span class="p">,</span>
+                      <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span><span class="p">,</span>
+                      <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span><span class="p">}</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape_dict</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="nd">@shape</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deprecated - shape will be set automatically&quot;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">spacing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">spacing_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">channel_spacing</span><span class="p">,</span>
+                        <span class="n">ImageDimension</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_z</span><span class="p">,</span>
+                        <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_y</span><span class="p">,</span>
+                        <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_x</span><span class="p">}</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">spacing_dict</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">length</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">ndim</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">labels_default</span> <span class="o">=</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">get_order_for_engine</span><span class="p">(</span><span class="s2">&quot;cil&quot;</span><span class="p">)</span>
+
+        <span class="n">shape_default</span> <span class="o">=</span> <span class="p">[</span>   <span class="bp">self</span><span class="o">.</span><span class="n">channels</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span><span class="p">]</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+            <span class="n">labels</span> <span class="o">=</span> <span class="n">labels_default</span>
+        <span class="n">labels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape_default</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">x</span><span class="o">==</span><span class="mi">1</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">labels</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">labels_default</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                    <span class="k">pass</span> <span class="c1">#if not in custom list carry on</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
+
+    <span class="nd">@dimension_labels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_labels</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">ImageDimension</span><span class="p">,</span> <span class="n">labels</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">voxel_num_x</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">voxel_num_y</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">voxel_num_z</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_x</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">voxel_size_x</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_y</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">voxel_size_y</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_z</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">voxel_size_z</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_x</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">center_x</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_y</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">center_y</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_z</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">center_z</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">channels</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">channels</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">channel_spacing</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">channel_spacing</span> \
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span>
+
+    <span class="nd">@dtype</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_dtype</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">voxel_num_x</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">voxel_num_y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">voxel_num_z</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">voxel_size_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                 <span class="n">voxel_size_y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                 <span class="n">voxel_size_z</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                 <span class="n">center_x</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">center_y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">center_z</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">channels</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">voxel_num_x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">voxel_num_y</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">voxel_num_z</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_x</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">voxel_size_x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_y</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">voxel_size_y</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_z</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">voxel_size_z</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">center_x</span> <span class="o">=</span> <span class="n">center_x</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">center_y</span> <span class="o">=</span> <span class="n">center_y</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">center_z</span> <span class="o">=</span> <span class="n">center_z</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">channels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">channel_labels</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">channel_spacing</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dimension_labels&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="ImageGeometry.get_slice">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageGeometry.get_slice">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">channel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">horizontal_x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">horizontal_y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Returns a new ImageGeometry of a single slice of in the requested direction.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">geometry_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">channel</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geometry_new</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="mi">1</span>
+
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">channel_labels</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">channel_labels</span><span class="p">[</span><span class="n">channel</span><span class="p">]]</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">channel_labels</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="n">vertical</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">horizontal_y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">horizontal_x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">return</span> <span class="n">geometry_new</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_order_by_label</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="p">,</span> <span class="n">default_dimension_labels</span><span class="p">):</span>
+        <span class="n">order</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">default_dimension_labels</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">ek</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dimension_labels</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">el</span> <span class="o">==</span> <span class="n">ek</span><span class="p">:</span>
+                    <span class="n">order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">j</span><span class="p">)</span>
+                    <span class="k">break</span>
+        <span class="k">return</span> <span class="n">order</span>
+
+    <span class="k">def</span> <span class="nf">get_min_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_x</span> <span class="o">-</span> <span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_x</span>
+
+    <span class="k">def</span> <span class="nf">get_max_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_x</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_x</span>
+
+    <span class="k">def</span> <span class="nf">get_min_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_y</span> <span class="o">-</span> <span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_y</span>
+
+    <span class="k">def</span> <span class="nf">get_max_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_y</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_y</span>
+
+    <span class="k">def</span> <span class="nf">get_min_z</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_z</span> <span class="o">-</span> <span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_z</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">0</span>
+
+    <span class="k">def</span> <span class="nf">get_max_z</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_z</span> <span class="o">+</span> <span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_z</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">0</span>
+
+<div class="viewcode-block" id="ImageGeometry.clone">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageGeometry.clone">[docs]</a>
+    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a copy of the ImageGeometry&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ImageGeometry.copy">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageGeometry.copy">[docs]</a>
+    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;alias of clone&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span> <span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Number of channels: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">channels</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;channel_spacing: </span><span class="si">{0}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">channel_spacing</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;voxel_num : x</span><span class="si">{0}</span><span class="s2">,y</span><span class="si">{1}</span><span class="s2">,z</span><span class="si">{2}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_z</span><span class="p">)</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;voxel_size : x</span><span class="si">{0}</span><span class="s2">,y</span><span class="si">{1}</span><span class="s2">,z</span><span class="si">{2}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_z</span><span class="p">)</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;center : x</span><span class="si">{0}</span><span class="s2">,y</span><span class="si">{1}</span><span class="s2">,z</span><span class="si">{2}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_z</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;voxel_num : x</span><span class="si">{0}</span><span class="s2">,y</span><span class="si">{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_num_y</span><span class="p">)</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;voxel_size : x</span><span class="si">{0}</span><span class="s2">,y</span><span class="si">{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_y</span><span class="p">)</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;center : x</span><span class="si">{0}</span><span class="s2">,y</span><span class="si">{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">center_y</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">repres</span>
+<div class="viewcode-block" id="ImageGeometry.allocate">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.ImageGeometry.allocate">[docs]</a>
+    <span class="k">def</span> <span class="nf">allocate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;allocates an ImageData according to the size expressed in the instance</span>
+
+<span class="sd">        :param value: accepts numbers to allocate an uniform array, or a string as &#39;random&#39; or &#39;random_int&#39; to create a random array or None.</span>
+<span class="sd">        :type value: number or string, default None allocates empty memory block, default 0</span>
+<span class="sd">        :param dtype: numerical type to allocate</span>
+<span class="sd">        :type dtype: numpy type, default numpy.float32</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dimension_labels&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Deprecated: &#39;dimension_labels&#39; cannot be set with &#39;allocate()&#39;. Use &#39;geometry.set_labels()&#39; to modify the geometry before using allocate.&quot;</span><span class="p">)</span>
+
+        <span class="n">out</span> <span class="o">=</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                            <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
+                            <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="c1"># it&#39;s created empty, so we make it 0</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">FillType</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM</span><span class="p">:</span>
+                <span class="n">seed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;seed&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
+                    <span class="n">r</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM_INT</span><span class="p">:</span>
+                <span class="n">seed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;seed&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+                <span class="n">max_value</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;max_value&#39;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">):</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">max_value</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.</span><span class="n">j</span><span class="o">*</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">max_value</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">max_value</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Value </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s1"> unknown&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/framework/labels/index.html b/v24.2.0/_modules/cil/framework/labels/index.html
new file mode 100644
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+  </a>
+</li>
+
+
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+    Framework
+  </a>
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+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
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+    Optimisation framework
+  </a>
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+    Processors
+  </a>
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+    Recon
+  </a>
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+    CIL Plugins
+  </a>
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+         placeholder="Search the docs ..."
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+  >
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+    role="listbox" aria-labelledby="pst-version-switcher-button-2">
+    <!-- dropdown will be populated by javascript on page load -->
+  </div>
+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../developer_guide/">Developers’ Guide</a></li>
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+</ul>
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+      
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+        
+        
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+            <div class="bd-article-container">
+              
+              <div class="bd-header-article d-print-none">
+<div class="header-article-items header-article__inner">
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+    <div class="header-article-items__start">
+      
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+
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+  <ul class="bd-breadcrumbs">
+    
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+      <a href="../../../../" class="nav-link" aria-label="Home">
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+      </a>
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+    
+    <li class="breadcrumb-item"><a href="../../../" class="nav-link">Module code</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.framework.labels</span></li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <h1>Source code for cil.framework.labels</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span><span class="p">,</span> <span class="n">Flag</span> <span class="k">as</span> <span class="n">_Flag</span><span class="p">,</span> <span class="n">auto</span><span class="p">,</span> <span class="n">unique</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">EnumType</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> <span class="c1"># Python&lt;3.11</span>
+    <span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">EnumMeta</span> <span class="k">as</span> <span class="n">EnumType</span>
+
+
+<span class="k">class</span> <span class="nc">_StrEnumMeta</span><span class="p">(</span><span class="n">EnumType</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Python&lt;3.12 requires this in a metaclass (rather than directly in StrEnum)&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__contains__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">key</span> <span class="o">=</span> <span class="n">item</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
+        <span class="k">except</span> <span class="p">(</span><span class="ne">AttributeError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+        <span class="k">return</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">__members__</span> <span class="ow">or</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">__members__</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
+
+
+<span class="nd">@unique</span>
+<span class="k">class</span> <span class="nc">StrEnum</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">Enum</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">_StrEnumMeta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Case-insensitive StrEnum&quot;&quot;&quot;</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_missing_</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">__members__</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">upper</span><span class="p">(),</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">[</span><span class="n">value</span><span class="o">.</span><span class="n">upper</span><span class="p">()]</span>
+        <span class="k">except</span> <span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">AttributeError</span><span class="p">):</span>
+            <span class="k">pass</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__eq__</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;consistent hashing for dictionary keys&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="c1"># compatibility with Python&gt;=3.11 `enum.StrEnum`</span>
+    <span class="fm">__str__</span> <span class="o">=</span> <span class="nb">str</span><span class="o">.</span><span class="fm">__str__</span>
+    <span class="fm">__format__</span> <span class="o">=</span> <span class="nb">str</span><span class="o">.</span><span class="fm">__format__</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_generate_next_value_</span><span class="p">(</span><span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">count</span><span class="p">,</span> <span class="n">last_values</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+
+
+<span class="k">class</span> <span class="nc">Backend</span><span class="p">(</span><span class="n">StrEnum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Available backends for CIL.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    ```</span>
+<span class="sd">    FBP(data, backend=Backend.ASTRA)</span>
+<span class="sd">    FBP(data, backend=&quot;astra&quot;)</span>
+<span class="sd">    ```</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ASTRA</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">TIGRE</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">CIL</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+
+
+<span class="k">class</span> <span class="nc">_DimensionBase</span><span class="p">:</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_default_order</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get_order_for_engine</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the order of dimensions for a specific engine and geometry.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        geometry: ImageGeometry | AcquisitionGeometry</span>
+<span class="sd">            If unspecified, the default order is returned.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">order</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_default_order</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">order</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">label</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">order</span> <span class="k">if</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">check_order_for_engine</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">geometry</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns True iff the order of dimensions is correct for a specific engine and geometry.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        geometry: ImageGeometry | AcquisitionGeometry</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError if the order of dimensions is incorrect.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">order_requested</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">get_order_for_engine</span><span class="p">(</span><span class="n">engine</span><span class="p">,</span> <span class="n">geometry</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">order_requested</span> <span class="o">==</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Expected dimension_label order </span><span class="si">{</span><span class="n">order_requested</span><span class="si">}</span><span class="s2">,&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot; got </span><span class="si">{</span><span class="nb">tuple</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot; Try using `data.reorder(&#39;</span><span class="si">{</span><span class="n">engine</span><span class="si">}</span><span class="s2">&#39;)` to permute for </span><span class="si">{</span><span class="n">engine</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="ImageDimension">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.labels.ImageDimension">[docs]</a>
+<span class="k">class</span> <span class="nc">ImageDimension</span><span class="p">(</span><span class="n">_DimensionBase</span><span class="p">,</span> <span class="n">StrEnum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Available dimension labels for image data.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data.reorder([ImageDimension.HORIZONTAL_X, ImageDimension.VERTICAL])</span>
+<span class="sd">    &gt;&gt;&gt; data.reorder([&quot;horizontal_x&quot;, &quot;vertical&quot;])</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">CHANNEL</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">VERTICAL</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">HORIZONTAL_X</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">HORIZONTAL_Y</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_default_order</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">:</span>
+        <span class="n">engine</span> <span class="o">=</span> <span class="n">Backend</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
+        <span class="n">orders</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">Backend</span><span class="o">.</span><span class="n">ASTRA</span><span class="p">:</span> <span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">),</span>
+            <span class="n">Backend</span><span class="o">.</span><span class="n">TIGRE</span><span class="p">:</span> <span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">),</span>
+            <span class="n">Backend</span><span class="o">.</span><span class="n">CIL</span><span class="p">:</span> <span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">)}</span>
+        <span class="k">return</span> <span class="n">orders</span><span class="p">[</span><span class="n">engine</span><span class="p">]</span></div>
+
+
+
+<div class="viewcode-block" id="AcquisitionDimension">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.labels.AcquisitionDimension">[docs]</a>
+<span class="k">class</span> <span class="nc">AcquisitionDimension</span><span class="p">(</span><span class="n">_DimensionBase</span><span class="p">,</span> <span class="n">StrEnum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Available dimension labels for acquisition data.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data.reorder([AcquisitionDimension.CHANNEL,</span>
+<span class="sd">                  AcquisitionDimension.ANGLE,</span>
+<span class="sd">                  AcquisitionDimension.HORIZONTAL])</span>
+<span class="sd">    &gt;&gt;&gt; data.reorder([&quot;channel&quot;, &quot;angle&quot;, &quot;horizontal&quot;])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">CHANNEL</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">ANGLE</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">VERTICAL</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">HORIZONTAL</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_default_order</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">:</span>
+        <span class="n">engine</span> <span class="o">=</span> <span class="n">Backend</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
+        <span class="n">orders</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">Backend</span><span class="o">.</span><span class="n">ASTRA</span><span class="p">:</span> <span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ANGLE</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL</span><span class="p">),</span>
+            <span class="n">Backend</span><span class="o">.</span><span class="n">TIGRE</span><span class="p">:</span> <span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ANGLE</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL</span><span class="p">),</span>
+            <span class="n">Backend</span><span class="o">.</span><span class="n">CIL</span><span class="p">:</span> <span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ANGLE</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">HORIZONTAL</span><span class="p">)}</span>
+        <span class="k">return</span> <span class="n">orders</span><span class="p">[</span><span class="n">engine</span><span class="p">]</span></div>
+
+
+
+<div class="viewcode-block" id="FillType">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.labels.FillType">[docs]</a>
+<span class="k">class</span> <span class="nc">FillType</span><span class="p">(</span><span class="n">StrEnum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Available fill types for image data.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    RANDOM:</span>
+<span class="sd">        Fill with random values.</span>
+<span class="sd">    RANDOM_INT:</span>
+<span class="sd">        Fill with random integers.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data.fill(FillType.RANDOM)</span>
+<span class="sd">    &gt;&gt;&gt; data.fill(&quot;random&quot;)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">RANDOM</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">RANDOM_INT</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="AngleUnit">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.labels.AngleUnit">[docs]</a>
+<span class="k">class</span> <span class="nc">AngleUnit</span><span class="p">(</span><span class="n">StrEnum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Available units for angles.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data.geometry.set_angles(angle_data, angle_units=AngleUnit.DEGREE)</span>
+<span class="sd">    &gt;&gt;&gt; data.geometry.set_angles(angle_data, angle_units=&quot;degree&quot;)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">DEGREE</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">RADIAN</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">_FlagMeta</span><span class="p">(</span><span class="n">EnumType</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Python&lt;3.12 requires this in a metaclass (rather than directly in Flag)&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__contains__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">item</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">__members__</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+
+
+<span class="nd">@unique</span>
+<span class="k">class</span> <span class="nc">Flag</span><span class="p">(</span><span class="n">_Flag</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">_FlagMeta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Case-insensitive Flag&quot;&quot;&quot;</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_missing_</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">__members__</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">upper</span><span class="p">(),</span> <span class="kc">None</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_missing_</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">upper</span><span class="p">())</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">value</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="AcquisitionType">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.labels.AcquisitionType">[docs]</a>
+<span class="k">class</span> <span class="nc">AcquisitionType</span><span class="p">(</span><span class="n">Flag</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Available acquisition types &amp; dimensions.</span>
+
+<span class="sd">    WARNING: It&#39;s best to use strings rather than integers to initialise.</span>
+<span class="sd">    &gt;&gt;&gt; AcquisitionType(3) == AcquisitionType(2 | 1) == AcquisitionType.CONE|PARALLEL != AcquisitionType(&#39;3D&#39;)</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    PARALLEL:</span>
+<span class="sd">        Parallel beam.</span>
+<span class="sd">    CONE:</span>
+<span class="sd">        Cone beam.</span>
+<span class="sd">    DIM2:</span>
+<span class="sd">        2D acquisition.</span>
+<span class="sd">    DIM3:</span>
+<span class="sd">        3D acquisition.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">PARALLEL</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">CONE</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">DIM2</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">DIM3</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+
+<div class="viewcode-block" id="AcquisitionType.validate">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.labels.AcquisitionType.validate">[docs]</a>
+    <span class="k">def</span> <span class="nf">validate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Check if the geometry and dimension types are allowed</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimension</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="si">}</span><span class="s2"> must be 2D xor 3D&quot;</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="si">}</span><span class="s2"> must be parallel xor cone beam&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the label for the dimension type</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">&amp;</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">|</span> <span class="bp">self</span><span class="o">.</span><span class="n">DIM3</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the label for the geometry type</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">&amp;</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">PARALLEL</span> <span class="o">|</span> <span class="bp">self</span><span class="o">.</span><span class="n">CONE</span><span class="p">)</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_missing_</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;2D/3D aliases&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">value</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;2D&#39;</span><span class="p">:</span> <span class="s1">&#39;DIM2&#39;</span><span class="p">,</span> <span class="s1">&#39;3D&#39;</span><span class="p">:</span> <span class="s1">&#39;DIM3&#39;</span><span class="p">}</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">upper</span><span class="p">(),</span> <span class="n">value</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_missing_</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;2D/3D special handling&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="s1">&#39;2D&#39;</span> <span class="k">if</span> <span class="bp">self</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">DIM2</span> <span class="k">else</span> <span class="s1">&#39;3D&#39;</span> <span class="k">if</span> <span class="bp">self</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">DIM3</span> <span class="k">else</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="ow">or</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__str__</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;consistent hashing for dictionary keys&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="c1"># compatibility with Python&gt;=3.11 `enum.Flag`</span>
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">bin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s1">&#39;1&#39;</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+</div>
+                </footer>
+              
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+            
+          </div>
+          <footer class="bd-footer-content">
+            
+          </footer>
+        
+      </main>
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diff --git a/v24.2.0/_modules/cil/framework/partitioner/index.html b/v24.2.0/_modules/cil/framework/partitioner/index.html
new file mode 100644
index 0000000000..66e62e07c3
--- /dev/null
+++ b/v24.2.0/_modules/cil/framework/partitioner/index.html
@@ -0,0 +1,732 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>cil.framework.partitioner &#8212; CIL 24.2.0 documentation</title>
+  
+  
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+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
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+    <script>DOCUMENTATION_OPTIONS.pagename = '_modules/cil/framework/partitioner';</script>
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+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
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+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../introduction/">
+    Introduction
+  </a>
+</li>
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+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
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+<li class="nav-item ">
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+  <h1>Source code for cil.framework.partitioner</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">math</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">.block</span> <span class="kn">import</span> <span class="n">BlockGeometry</span>
+
+
+<div class="viewcode-block" id="Partitioner">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.Partitioner">[docs]</a>
+<span class="k">class</span> <span class="nc">Partitioner</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Interface for AcquisitionData to be able to partition itself in a number of batches.</span>
+
+<span class="sd">    This class, by multiple inheritance with AcquisitionData, allows the user to partition the data,</span>
+<span class="sd">    by using the method ``partition``.</span>
+<span class="sd">    The partitioning will generate a ``BlockDataContainer`` with appropriate ``AcquisitionData``.</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="c1"># modes of partitioning</span>
+    <span class="n">SEQUENTIAL</span> <span class="o">=</span> <span class="s1">&#39;sequential&#39;</span>
+    <span class="n">STAGGERED</span> <span class="o">=</span> <span class="s1">&#39;staggered&#39;</span>
+    <span class="n">RANDOM_PERMUTATION</span> <span class="o">=</span> <span class="s1">&#39;random_permutation&#39;</span>
+
+    <span class="k">def</span> <span class="nf">_partition_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Partition a list of indices into num_batches of indices.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_batches : int</span>
+<span class="sd">            The number of batches to partition the indices into.</span>
+<span class="sd">        indices : list of int, int</span>
+<span class="sd">            The indices to partition. If passed a list, this list will be partitioned in ``num_batches``</span>
+<span class="sd">            partitions. If passed an int the indices will be generated automatically using ``range(indices)``.</span>
+<span class="sd">        stagger : bool, default False</span>
+<span class="sd">            If True, the indices will be staggered across the batches.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        list of list of int</span>
+<span class="sd">            A list of batches of indices.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># Partition the indices into batches.</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+            <span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">indices</span><span class="p">))</span>
+
+        <span class="n">num_indices</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span>
+        <span class="c1"># sanity check</span>
+        <span class="k">if</span> <span class="n">num_indices</span> <span class="o">&lt;</span> <span class="n">num_batches</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;The number of batches must be less than or equal to the number of indices.&#39;</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">stagger</span><span class="p">:</span>
+            <span class="n">batches</span> <span class="o">=</span> <span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">::</span><span class="n">num_batches</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_batches</span><span class="p">)]</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># we split the indices with floor(N/M)+1 indices in N%M groups</span>
+            <span class="c1"># and floor(N/M) indices in the remaining M - N%M groups.</span>
+
+            <span class="c1"># rename num_indices to N for brevity</span>
+            <span class="n">N</span> <span class="o">=</span> <span class="n">num_indices</span>
+            <span class="c1"># rename num_batches to M for brevity</span>
+            <span class="n">M</span> <span class="o">=</span> <span class="n">num_batches</span>
+            <span class="n">batches</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="n">indices</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">j</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">N</span> <span class="o">/</span> <span class="n">M</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">%</span> <span class="n">M</span><span class="p">)</span>
+            <span class="p">]</span>
+            <span class="n">offset</span> <span class="o">=</span> <span class="n">N</span> <span class="o">%</span> <span class="n">M</span> <span class="o">*</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">N</span> <span class="o">/</span> <span class="n">M</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span> <span class="o">-</span> <span class="n">N</span> <span class="o">%</span> <span class="n">M</span><span class="p">):</span>
+                <span class="n">start</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">N</span> <span class="o">/</span> <span class="n">M</span><span class="p">)</span>
+                <span class="n">end</span> <span class="o">=</span> <span class="n">start</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">N</span> <span class="o">/</span> <span class="n">M</span><span class="p">)</span>
+                <span class="n">batches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">indices</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">batches</span>
+
+    <span class="k">def</span> <span class="nf">_construct_BlockGeometry_from_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Convert a list of boolean masks to a list of BlockGeometry.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">          indices : list of lists of indices</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">            BlockGeometry</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">ags</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">mask</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">:</span>
+            <span class="n">ag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">ag</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">ags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="n">ags</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Partitioner.partition">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.Partitioner.partition">[docs]</a>
+    <span class="k">def</span> <span class="nf">partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Partition the data into ``num_batches`` batches using the specified ``mode``.</span>
+
+
+<span class="sd">        The modes are</span>
+
+<span class="sd">        1. ``sequential`` - The data will be partitioned into ``num_batches`` batches of sequential indices.</span>
+
+<span class="sd">        2. ``staggered`` - The data will be partitioned into ``num_batches`` batches of sequential indices, with stride equal to ``num_batches``.</span>
+
+<span class="sd">        3. ``random_permutation`` - The data will be partitioned into ``num_batches`` batches of random indices.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_batches : int</span>
+<span class="sd">            The number of batches to partition the data into.</span>
+<span class="sd">        mode : str</span>
+<span class="sd">            The mode to use for partitioning. Must be one of ``sequential``, ``staggered`` or ``random_permutation``.</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            The seed to use for the random permutation. If not specified, the random number</span>
+<span class="sd">            generator will not be seeded.</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        BlockDataContainer</span>
+<span class="sd">            Block of `AcquisitionData` objects containing the data requested in each batch</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        Partitioning a list of ints [0, 1, 2, 3, 4, 5, 6, 7, 8] into 4 batches will return:</span>
+
+<span class="sd">        1. [[0, 1, 2], [3, 4], [5, 6], [7, 8]] with ``sequential``</span>
+<span class="sd">        2. [[0, 4, 8], [1, 5], [2, 6], [3, 7]] with ``staggered``</span>
+<span class="sd">        3. [[8, 2, 6], [7, 1], [0, 4], [3, 5]] with ``random_permutation`` and seed 1</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="n">Partitioner</span><span class="o">.</span><span class="n">SEQUENTIAL</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_deterministic</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="n">Partitioner</span><span class="o">.</span><span class="n">STAGGERED</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_deterministic</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="n">Partitioner</span><span class="o">.</span><span class="n">RANDOM_PERMUTATION</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_random_permutation</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unknown partition mode </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mode</span><span class="p">))</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_partition_deterministic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Partition the data into ``num_batches`` batches.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_batches : int</span>
+<span class="sd">            The number of batches to partition the data into.</span>
+<span class="sd">        stagger : bool, optional</span>
+<span class="sd">            If ``True``, the batches will be staggered. Default is ``False``.</span>
+<span class="sd">        indices : list of int, optional</span>
+<span class="sd">            The indices to partition. If not specified, the indices will be generated from the number of projections.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">indices</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">num_projections</span>
+        <span class="n">partition_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_indices</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">stagger</span><span class="p">)</span>
+        <span class="n">blk_geo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_construct_BlockGeometry_from_indices</span><span class="p">(</span><span class="n">partition_indices</span><span class="p">)</span>
+
+        <span class="c1"># copy data</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">blk_geo</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+        <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;angle&#39;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_batches</span><span class="p">):</span>
+            <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span>
+                <span class="n">numpy</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span>
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">partition_indices</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">_partition_random_permutation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Partition the data into ``num_batches`` batches using a random permutation.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_batches : int</span>
+<span class="sd">            The number of batches to partition the data into.</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            The seed to use for the random permutation. If not specified, the random number generator</span>
+<span class="sd">            will not be seeded.</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+
+        <span class="n">indices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">num_projections</span><span class="p">)</span>
+        <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span>
+
+        <span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_deterministic</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">)</span></div>
+
+</pre></div>
+
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+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
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+                  
+  <h1>Source code for cil.framework.processors</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">weakref</span>
+
+<span class="kn">from</span> <span class="nn">.data_container</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+
+
+<div class="viewcode-block" id="Processor">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.Processor">[docs]</a>
+<span class="k">class</span> <span class="nc">Processor</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Defines a generic DataContainer processor</span>
+
+<span class="sd">    accepts a DataContainer as input</span>
+<span class="sd">    returns a DataContainer</span>
+<span class="sd">    `__setattr__` allows additional attributes to be defined</span>
+
+<span class="sd">    `store_output` boolian defining whether a copy of the output is stored. Default is False.</span>
+<span class="sd">    If no attributes are modified get_output will return this stored copy bypassing `process`</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">attributes</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;store_output&#39;</span> <span class="ow">in</span> <span class="n">attributes</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;store_output&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;output&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;shouldRun&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;_shape_out&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">attributes</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+
+    <span class="k">def</span> <span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s1">&#39;input&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+
+            <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s1">&#39;shouldRun&#39;</span><span class="p">:</span>
+                <span class="k">pass</span>
+            <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s1">&#39;output&#39;</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;shouldRun&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;shouldRun&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s1">&#39;Attribute </span><span class="si">{0}</span><span class="s1"> not found&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">_set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Configure processor attributes that require the data to setup</span>
+<span class="sd">        Must set _shape_out</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">shape</span>
+
+<div class="viewcode-block" id="Processor.set_input">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.Processor.set_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set the input data to the processor</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        input : DataContainer</span>
+<span class="sd">            The input DataContainer</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_input</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">weakref</span><span class="o">.</span><span class="n">ref</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;shouldRun&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input data not compatible&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Input type mismatch: got </span><span class="si">{0}</span><span class="s2"> expecting </span><span class="si">{1}</span><span class="s2">&quot;</span> \
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">))</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">_set_up</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="Processor.check_input">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.Processor.check_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Checks parameters of the input DataContainer</span>
+
+<span class="sd">        Should raise an Error if the DataContainer does not match expectation, e.g.</span>
+<span class="sd">        if the expected input DataContainer is 3D and the Processor expects 2D.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Implement basic checks for input DataContainer&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Processor.get_output">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.Processor.get_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Runs the configured processor and returns the processed data</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        out : DataContainer, optional</span>
+<span class="sd">           Fills the referenced DataContainer with the processed data</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The processed data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_output</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Output data not compatible with processor&#39;</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">shouldRun</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">process</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_output</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">out</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span></div>
+
+    
+    <span class="k">def</span> <span class="nf">check_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+        
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Input type mismatch: got </span><span class="si">{0}</span><span class="s2"> expecting </span><span class="si">{1}</span><span class="s2">&quot;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
+            
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span> <span class="o">!=</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;out size mismatch: got </span><span class="si">{0}</span><span class="s2"> expecting </span><span class="si">{1}</span><span class="s2">&quot;</span>\
+                                 <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span><span class="p">))</span>
+         
+        <span class="k">return</span> <span class="kc">True</span>
+    
+    <span class="k">def</span> <span class="nf">set_input_processor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">processor</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">processor</span><span class="p">),</span> <span class="n">DataProcessor</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span>  <span class="n">weakref</span><span class="o">.</span><span class="n">ref</span><span class="p">(</span><span class="n">processor</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Input type mismatch: got </span><span class="si">{0}</span><span class="s2"> expecting </span><span class="si">{1}</span><span class="s2">&quot;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">processor</span><span class="p">),</span> <span class="n">DataProcessor</span><span class="p">))</span>
+
+<div class="viewcode-block" id="Processor.get_input">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.Processor.get_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_input</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns the input DataContainer</span>
+
+<span class="sd">        It is useful in the case the user has provided a DataProcessor as</span>
+<span class="sd">        input</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input has been deallocated externally&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="p">()),</span> <span class="n">DataProcessor</span><span class="p">):</span>
+            <span class="n">dsi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="p">()</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">dsi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">dsi</span></div>
+
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;process must be implemented&#39;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DataProcessor">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.DataProcessor">[docs]</a>
+<span class="k">class</span> <span class="nc">DataProcessor</span><span class="p">(</span><span class="n">Processor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Basically an alias of Processor Class&#39;&#39;&#39;</span>
+    <span class="k">pass</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">DataProcessor23D</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Regularizers DataProcessor</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Checks number of dimensions input DataContainer</span>
+
+<span class="sd">        Expected input is 2D or 3D</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">number_of_dimensions</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">or</span> \
+           <span class="n">dataset</span><span class="o">.</span><span class="n">number_of_dimensions</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+               <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Expected input dimensions is 2 or 3, got </span><span class="si">{0}</span><span class="s2">&quot;</span>\
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">))</span>
+
+<span class="c1">###### Example of DataProcessors</span>
+
+<span class="k">class</span> <span class="nc">AX</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Example DataProcessor</span>
+<span class="sd">    The AXPY routines perform a vector multiplication operation defined as</span>
+
+<span class="sd">    y := a*x</span>
+<span class="sd">    where:</span>
+
+<span class="sd">    a is a scalar</span>
+
+<span class="sd">    x a DataContainer.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;scalar&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                  <span class="s1">&#39;input&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                  <span class="p">}</span>
+
+        <span class="c1">#DataProcessor.__init__(self, **kwargs)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">AX</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    
+    <span class="k">def</span> <span class="nf">check_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">dsi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="p">(</span><span class="n">a</span> <span class="o">*</span> <span class="n">dsi</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="kc">True</span><span class="p">,</span>
+                              <span class="n">dimension_labels</span><span class="o">=</span><span class="n">dsi</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">a</span> <span class="o">*</span> <span class="n">dsi</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+
+<span class="c1">###### Example of DataProcessors</span>
+
+<span class="k">class</span> <span class="nc">CastDataContainer</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Example DataProcessor</span>
+<span class="sd">    Cast a DataContainer array to a different type.</span>
+
+<span class="sd">    y := a*x</span>
+<span class="sd">    where:</span>
+
+<span class="sd">    a is a scalar</span>
+
+<span class="sd">    x a DataContainer.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;dtype&#39;</span><span class="p">:</span><span class="n">dtype</span><span class="p">,</span>
+                  <span class="s1">&#39;input&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                  <span class="p">}</span>
+
+        <span class="c1">#DataProcessor.__init__(self, **kwargs)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">CastDataContainer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    
+    <span class="k">def</span> <span class="nf">check_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">dsi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">dsi</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">dsi</span><span class="p">)(</span><span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">dsi</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">),</span>
+                                <span class="n">dimension_labels</span><span class="o">=</span><span class="n">dsi</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">dsi</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">))</span>
+
+<span class="k">class</span> <span class="nc">PixelByPixelDataProcessor</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Example DataProcessor</span>
+
+<span class="sd">    This processor applies a python function to each pixel of the DataContainer</span>
+
+<span class="sd">    f is a python function</span>
+
+<span class="sd">    x a DataSet.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;pyfunc&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                  <span class="s1">&#39;input&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                  <span class="p">}</span>
+        <span class="c1">#DataProcessor.__init__(self, **kwargs)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">PixelByPixelDataProcessor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">pyfunc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pyfunc</span>
+        <span class="n">dsi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+
+        <span class="n">eval_func</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">frompyfunc</span><span class="p">(</span><span class="n">pyfunc</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+
+
+        <span class="n">y</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="p">(</span><span class="n">eval_func</span><span class="p">(</span><span class="n">dsi</span><span class="o">.</span><span class="n">as_array</span><span class="p">()),</span> <span class="kc">True</span><span class="p">,</span>
+                          <span class="n">dimension_labels</span><span class="o">=</span><span class="n">dsi</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">y</span>
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
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diff --git a/v24.2.0/_modules/cil/framework/vector_data/index.html b/v24.2.0/_modules/cil/framework/vector_data/index.html
new file mode 100644
index 0000000000..7c3f59b981
--- /dev/null
+++ b/v24.2.0/_modules/cil/framework/vector_data/index.html
@@ -0,0 +1,598 @@
+
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+  </a>
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+                  
+  <h1>Source code for cil.framework.vector_data</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">.data_container</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+
+
+<div class="viewcode-block" id="VectorData">
+<a class="viewcode-back" href="../../../../framework/#cil.framework.VectorData">[docs]</a>
+<span class="k">class</span> <span class="nc">VectorData</span><span class="p">(</span><span class="n">DataContainer</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;DataContainer to contain 1D array&#39;&#39;&#39;</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span>
+
+    <span class="nd">@geometry</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;geometry&#39;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span>
+
+    <span class="nd">@dimension_labels</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="s1">&#39;geometry&#39;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="n">val</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_dimension_labels</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;geometry&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dtype&#39;</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Please specify either a geometry or an array&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">.vector_geometry</span> <span class="kn">import</span> <span class="n">VectorGeometry</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible size: expected 1D got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">array</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">=</span> <span class="n">VectorGeometry</span><span class="p">(</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">length</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">length</span>
+
+            <span class="k">if</span> <span class="n">array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">==</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                    <span class="n">out</span> <span class="o">=</span> <span class="n">array</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incompatible size: expecting </span><span class="si">{}</span><span class="s1"> got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">,),</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+        <span class="n">deep_copy</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="c1"># need to pass the geometry, othewise None</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">VectorData</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">deep_copy</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span> <span class="n">geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
+              
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.io.NEXUSDataReader</span></li>
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+                  
+  <h1>Source code for cil.io.NEXUSDataReader</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># Kyle Pidgeon (UKRI-STFC)</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">ImageGeometry</span>
+
+<span class="n">h5pyAvailable</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">h5py</span>
+<span class="k">except</span><span class="p">:</span>
+    <span class="n">h5pyAvailable</span> <span class="o">=</span> <span class="kc">False</span>
+
+
+<div class="viewcode-block" id="NEXUSDataReader">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader">[docs]</a>
+<span class="k">class</span> <span class="nc">NEXUSDataReader</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a reader for NeXus files.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    file_name: str</span>
+<span class="sd">        the full path to the NeXus file to read.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)</span>
+
+<div class="viewcode-block" id="NEXUSDataReader.set_up">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+               <span class="n">file_name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Initialise reader.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        file_name : str</span>
+<span class="sd">            Full path to NeXus file</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span>
+
+        <span class="c1"># check that h5py library is installed</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">h5pyAvailable</span> <span class="o">==</span> <span class="kc">False</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;h5py is not available, cannot load NEXUS files.&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Path to nexus file is required.&#39;</span><span class="p">)</span>
+
+        <span class="c1"># check if nexus file exists</span>
+        <span class="k">if</span> <span class="ow">not</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;File</span><span class="se">\n</span><span class="s1"> </span><span class="si">{}</span><span class="se">\n</span><span class="s1"> does not exist.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+    <span class="k">def</span> <span class="nf">read_dimension_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attrs</span><span class="p">):</span>
+        <span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="mi">4</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">attrs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dim0&#39;</span><span class="p">,</span> <span class="s1">&#39;dim1&#39;</span><span class="p">,</span> <span class="s1">&#39;dim2&#39;</span> <span class="p">,</span> <span class="s1">&#39;dim3&#39;</span><span class="p">]:</span>
+                <span class="n">dimension_labels</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">3</span><span class="p">:])]</span> <span class="o">=</span> <span class="n">v</span>
+
+        <span class="c1"># remove Nones</span>
+        <span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dimension_labels</span> <span class="k">if</span> <span class="n">i</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dimension_labels</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">dimension_labels</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">return</span> <span class="n">dimension_labels</span>
+
+<div class="viewcode-block" id="NEXUSDataReader.get_geometry">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader.get_geometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Parse NEXUS file and return acquisition or reconstructed volume</span>
+<span class="sd">        parameters, depending on file type.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        AcquisitionGeometry or ImageGeometry</span>
+<span class="sd">            Acquisition or reconstructed volume parameters. Exact type</span>
+<span class="sd">            depends on file content.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">dfile</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">bytes_</span><span class="p">(</span><span class="n">dfile</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;creator&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">bytes_</span><span class="p">(</span><span class="s1">&#39;NEXUSDataWriter.py&#39;</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;We can parse only files created by NEXUSDataWriter.py&#39;</span><span class="p">)</span>
+
+            <span class="n">ds_data</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/data/data&#39;</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;data_type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ImageData&#39;</span><span class="p">:</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_num_x&#39;</span><span class="p">]),</span>
+                                                <span class="n">voxel_num_y</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_num_y&#39;</span><span class="p">]),</span>
+                                                <span class="n">voxel_num_z</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_num_z&#39;</span><span class="p">]),</span>
+                                                <span class="n">voxel_size_x</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_size_x&#39;</span><span class="p">],</span>
+                                                <span class="n">voxel_size_y</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_size_y&#39;</span><span class="p">],</span>
+                                                <span class="n">voxel_size_z</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_size_z&#39;</span><span class="p">],</span>
+                                                <span class="n">center_x</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;center_x&#39;</span><span class="p">],</span>
+                                                <span class="n">center_y</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;center_y&#39;</span><span class="p">],</span>
+                                                <span class="n">center_z</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;center_z&#39;</span><span class="p">],</span>
+                                                <span class="n">channels</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">])</span>
+
+                <span class="k">if</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;channel_spacing&#39;</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">channel_spacing</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;channel_spacing&#39;</span><span class="p">]</span>
+
+                <span class="c1"># read the dimension_labels from dim{}</span>
+                <span class="n">dimension_labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_dimension_labels</span><span class="p">(</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
+
+            <span class="k">else</span><span class="p">:</span>   <span class="c1"># AcquisitionData</span>
+                <span class="k">if</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;dist_source_center&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry&#39;</span><span class="p">]</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;config/source/position&#39;</span><span class="p">):</span>
+                    <span class="n">geom_type</span> <span class="o">=</span> <span class="s1">&#39;cone&#39;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">geom_type</span> <span class="o">=</span> <span class="s1">&#39;parallel&#39;</span>
+
+                <span class="k">if</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;num_pixels_v&#39;</span><span class="p">):</span>
+                    <span class="n">num_pixels_v</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;num_pixels_v&#39;</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;pixel_num_v&#39;</span><span class="p">):</span>
+                    <span class="n">num_pixels_v</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pixel_num_v&#39;</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">num_pixels_v</span> <span class="o">=</span> <span class="mi">1</span>
+
+                <span class="k">if</span> <span class="n">num_pixels_v</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">dim</span> <span class="o">=</span> <span class="mi">3</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">dim</span> <span class="o">=</span> <span class="mi">2</span>
+
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_old_file_version</span><span class="p">():</span>
+                    <span class="n">num_pixels_h</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pixel_num_h&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+                    <span class="n">num_channels</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">]</span>
+                    <span class="n">ds_angles</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/data/rotation_angle&#39;</span><span class="p">]</span>
+
+                    <span class="k">if</span> <span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span> <span class="ow">and</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;dist_source_center&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">],</span>
+                                                                               <span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;dist_center_detector&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="k">elif</span> <span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span> <span class="ow">and</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;dist_source_center&#39;</span><span class="p">]],</span>
+                                                        <span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;dist_center_detector&#39;</span><span class="p">]])</span>
+                    <span class="k">elif</span> <span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;parallel&#39;</span> <span class="ow">and</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel3D</span><span class="p">()</span>
+                    <span class="k">elif</span> <span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;parallel&#39;</span> <span class="ow">and</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel2D</span><span class="p">()</span>
+
+
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">num_pixels_h</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;num_pixels_h&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+                    <span class="n">num_channels</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;num_channels&#39;</span><span class="p">]</span>
+                    <span class="n">ds_angles</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/angles&#39;</span><span class="p">]</span>
+
+                    <span class="n">rotation_axis_position</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/rotation_axis/position&#39;</span><span class="p">])</span>
+                    <span class="n">detector_position</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/detector/position&#39;</span><span class="p">])</span>
+
+                    <span class="n">ds_detector</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/detector&#39;</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">ds_detector</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;direction_x&#39;</span><span class="p">):</span>
+                        <span class="n">detector_direction_x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/detector/direction_x&#39;</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">detector_direction_x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/detector/direction_row&#39;</span><span class="p">])</span>
+
+                    <span class="k">if</span> <span class="n">ds_detector</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;direction_y&#39;</span><span class="p">):</span>
+                        <span class="n">detector_direction_y</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/detector/direction_y&#39;</span><span class="p">])</span>
+                    <span class="k">elif</span> <span class="n">ds_detector</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;direction_col&#39;</span><span class="p">):</span>
+                        <span class="n">detector_direction_y</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/detector/direction_col&#39;</span><span class="p">])</span>
+
+                    <span class="n">ds_rotate</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/rotation_axis&#39;</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">ds_rotate</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="s1">&#39;direction&#39;</span><span class="p">):</span>
+                        <span class="n">rotation_axis_direction</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/rotation_axis/direction&#39;</span><span class="p">])</span>
+
+                    <span class="k">if</span> <span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+                        <span class="n">source_position</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/source/position&#39;</span><span class="p">])</span>
+
+                        <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="p">,</span>\
+                                                <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">,</span>\
+                                                <span class="n">rotation_axis_position</span><span class="p">,</span> <span class="n">rotation_axis_direction</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">ray_direction</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/config/ray/direction&#39;</span><span class="p">])</span>
+
+                        <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel2D</span><span class="p">(</span><span class="n">ray_direction</span><span class="p">,</span> <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">rotation_axis_position</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel3D</span><span class="p">(</span><span class="n">ray_direction</span><span class="p">,</span>\
+                                                <span class="n">detector_position</span><span class="p">,</span> <span class="n">detector_direction_x</span><span class="p">,</span> <span class="n">detector_direction_y</span><span class="p">,</span>\
+                                                <span class="n">rotation_axis_position</span><span class="p">,</span> <span class="n">rotation_axis_direction</span><span class="p">)</span>
+
+                <span class="c1"># for all Aquisition data</span>
+                <span class="c1">#set angles</span>
+                <span class="n">angles</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ds_angles</span><span class="p">)</span>
+                <span class="n">angle_unit</span> <span class="o">=</span> <span class="n">ds_angles</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;angle_unit&#39;</span><span class="p">,</span><span class="s1">&#39;degree&#39;</span><span class="p">)</span>
+                <span class="n">initial_angle</span> <span class="o">=</span> <span class="n">ds_angles</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;initial_angle&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">initial_angle</span><span class="o">=</span><span class="n">initial_angle</span><span class="p">,</span> <span class="n">angle_unit</span><span class="o">=</span><span class="n">angle_unit</span><span class="p">)</span>
+
+                <span class="c1">#set panel</span>
+                <span class="n">pixel_size_v</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pixel_size_v&#39;</span><span class="p">,</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;pixel_size_h&#39;</span><span class="p">])</span>
+                <span class="n">origin</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;panel_origin&#39;</span><span class="p">,</span><span class="s1">&#39;bottom-left&#39;</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">set_panel</span><span class="p">((</span><span class="n">num_pixels_h</span><span class="p">,</span> <span class="n">num_pixels_v</span><span class="p">),</span>\
+                                        <span class="n">pixel_size</span><span class="o">=</span><span class="p">(</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;pixel_size_h&#39;</span><span class="p">],</span> <span class="n">pixel_size_v</span><span class="p">),</span>\
+                                        <span class="n">origin</span><span class="o">=</span><span class="n">origin</span><span class="p">)</span>
+
+                <span class="c1"># set channels</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">set_channels</span><span class="p">(</span><span class="n">num_channels</span><span class="p">)</span>
+
+                <span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">dimension_labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_dimension_labels</span><span class="p">(</span><span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">)</span>
+
+        <span class="c1">#set labels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">set_labels</span><span class="p">(</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span></div>
+
+
+<div class="viewcode-block" id="NEXUSDataReader.get_data_scale">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader.get_data_scale">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_data_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Parse NEXUS file and return the scale factor applied to compress</span>
+<span class="sd">        the dataset.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scale : float</span>
+<span class="sd">            The scale factor applied to compress the dataset</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">dfile</span><span class="p">:</span>
+            <span class="n">ds_data</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/data/data&#39;</span><span class="p">]</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">scale</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;scale&#39;</span><span class="p">]</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="n">scale</span> <span class="o">=</span> <span class="mf">1.0</span>
+
+        <span class="k">return</span> <span class="n">scale</span></div>
+
+
+<div class="viewcode-block" id="NEXUSDataReader.get_data_offset">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader.get_data_offset">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_data_offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Parse NEXUS file and return the offset factor applied to compress</span>
+<span class="sd">        the dataset.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        offset : float</span>
+<span class="sd">            The offset factor applied to compress the dataset</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">dfile</span><span class="p">:</span>
+            <span class="n">ds_data</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/data/data&#39;</span><span class="p">]</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">offset</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;offset&#39;</span><span class="p">]</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="n">offset</span> <span class="o">=</span> <span class="mf">0.0</span>
+
+        <span class="k">return</span> <span class="n">offset</span></div>
+
+
+    <span class="k">def</span> <span class="nf">__read_as</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Parse NEXUS file and return raw file content.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        dtype : data-type</span>
+<span class="sd">            The data type used for storing the parsed data.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        output : ImageData or AcquisitionData</span>
+<span class="sd">            The parsed raw data. Exact type depends on file content.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">get_geometry</span><span class="p">()</span>
+
+        <span class="c1">#allocate data container as requested type</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">dfile</span><span class="p">:</span>
+
+            <span class="n">ds_data</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/data/data&#39;</span><span class="p">]</span>
+            <span class="n">ds_data</span><span class="o">.</span><span class="n">read_direct</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">output</span>
+
+<div class="viewcode-block" id="NEXUSDataReader.read_as_original">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader.read_as_original">[docs]</a>
+    <span class="k">def</span> <span class="nf">read_as_original</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the compressed data from the file.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        output : ImageData or AcquisitionData</span>
+<span class="sd">            The raw, compressed data. Exact type depends on file content.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">dfile</span><span class="p">:</span>
+            <span class="n">ds_data</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/data/data&#39;</span><span class="p">]</span>
+            <span class="n">dtype</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">dtype</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__read_as</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="NEXUSDataReader.read">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader.read">[docs]</a>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the uncompressed data as numpy.float32.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        output : ImageData or AcquisitionData</span>
+<span class="sd">            The uncompressed data. Exact type depends on file content.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__read_as</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+        <span class="n">scale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_data_scale</span><span class="p">()</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_data_offset</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">offset</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">output</span> <span class="o">-=</span> <span class="n">offset</span>
+        <span class="k">if</span> <span class="n">scale</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">output</span> <span class="o">/=</span> <span class="n">scale</span>
+
+        <span class="k">return</span> <span class="n">output</span></div>
+
+
+
+<div class="viewcode-block" id="NEXUSDataReader.load_data">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataReader.load_data">[docs]</a>
+    <span class="k">def</span> <span class="nf">load_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Alias of `read`.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        read</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">read</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">is_old_file_version</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1">#return ds_data.attrs.__contains__(&#39;geom_type&#39;)</span>
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">dfile</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">bytes_</span><span class="p">(</span><span class="n">dfile</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;creator&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">bytes_</span><span class="p">(</span><span class="s1">&#39;NEXUSDataWriter.py&#39;</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;We can parse only files created by NEXUSDataWriter.py&#39;</span><span class="p">)</span>
+
+            <span class="n">ds_data</span> <span class="o">=</span> <span class="n">dfile</span><span class="p">[</span><span class="s1">&#39;entry1/tomo_entry/data/data&#39;</span><span class="p">]</span>
+
+            <span class="k">return</span> <span class="s1">&#39;geom_type&#39;</span> <span class="ow">in</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span></div>
+
+            <span class="c1"># return True</span>
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
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diff --git a/v24.2.0/_modules/cil/io/NEXUSDataWriter/index.html b/v24.2.0/_modules/cil/io/NEXUSDataWriter/index.html
new file mode 100644
index 0000000000..bb19bb5eaa
--- /dev/null
+++ b/v24.2.0/_modules/cil/io/NEXUSDataWriter/index.html
@@ -0,0 +1,759 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+    <script>DOCUMENTATION_OPTIONS.pagename = '_modules/cil/io/NEXUSDataWriter';</script>
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+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.io.NEXUSDataWriter</span></li>
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+                  
+  <h1>Source code for cil.io.NEXUSDataWriter</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionType</span>
+<span class="kn">from</span> <span class="nn">cil.version</span> <span class="kn">import</span> <span class="n">version</span>
+<span class="kn">import</span> <span class="nn">datetime</span>
+<span class="kn">from</span> <span class="nn">cil.io</span> <span class="kn">import</span> <span class="n">utilities</span>
+
+<span class="n">h5pyAvailable</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">h5py</span>
+<span class="k">except</span><span class="p">:</span>
+    <span class="n">h5pyAvailable</span> <span class="o">=</span> <span class="kc">False</span>
+
+
+<div class="viewcode-block" id="NEXUSDataWriter">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataWriter">[docs]</a>
+<span class="k">class</span> <span class="nc">NEXUSDataWriter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Create a writer for NEXUS files.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data: AcquisitionData, ImageData,</span>
+<span class="sd">        The dataset to write to file</span>
+<span class="sd">    file_name: os.path or string, default None</span>
+<span class="sd">        The file name to write</span>
+<span class="sd">    compression: str, {&#39;uint8&#39;, &#39;uint16&#39;, None}, default None</span>
+<span class="sd">        The lossy compression to apply, default None will not compress data.</span>
+<span class="sd">        uint8 or unit16 will compress to 8 and 16 bit dtypes respectively.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+
+        <span class="k">if</span> <span class="p">((</span><span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">file_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">,</span> <span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="n">compression</span><span class="p">)</span>
+
+<div class="viewcode-block" id="NEXUSDataWriter.set_up">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataWriter.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+               <span class="n">data</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">file_name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">compression</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Set up the writer</span>
+
+<span class="sd">        data: AcquisitionData, ImageData,</span>
+<span class="sd">            The dataset to write to file</span>
+<span class="sd">        file_name: os.path or string, default None</span>
+<span class="sd">            The file name to write</span>
+<span class="sd">        compression: int, default 0</span>
+<span class="sd">            The lossy compression to apply, default 0 will not compress data.</span>
+<span class="sd">            8 or 16 will compress to 8 and 16 bit dtypes respectively.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Path to write file is required.&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Data to write is required.&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;nxs&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;nex&#39;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="o">+=</span><span class="s1">&#39;.nxs&#39;</span>
+
+        <span class="c1"># Deal with compression</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">compress</span>           <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compress</span><span class="p">(</span><span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span>              <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compressed_dtype</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">compression</span>        <span class="o">=</span> <span class="n">compression</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">))</span> <span class="ow">or</span>
+                <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">))):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Writer supports only following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData&#39;</span><span class="p">)</span>
+
+
+
+        <span class="c1"># check that h5py library is installed</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">h5pyAvailable</span> <span class="o">==</span> <span class="kc">False</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;h5py is not available, cannot write NEXUS files.&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="NEXUSDataWriter.write">
+<a class="viewcode-back" href="../../../../io/#cil.io.NEXUSDataWriter.write">[docs]</a>
+    <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        write dataset to disk</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1"># check filename and data have been set:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Path to nexus file to write to is required.&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Data to write out must be set.&#39;</span><span class="p">)</span>
+
+        <span class="c1"># if the folder does not exist, create the folder</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compress</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span> <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compression_scale_offset</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">compression</span><span class="p">)</span>
+
+        <span class="c1"># create the file</span>
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+
+            <span class="c1"># give the file some important attributes</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;file_name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;cil_version&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">version</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;file_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">())</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;creator&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bytes_</span><span class="p">(</span><span class="s1">&#39;NEXUSDataWriter.py&#39;</span><span class="p">)</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;NeXus_version&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;4.3.0&#39;</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;HDF5_Version&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">h5py</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">hdf5_version</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;h5py_version&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">h5py</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span>
+
+            <span class="c1"># create the NXentry group</span>
+            <span class="n">nxentry</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry&#39;</span><span class="p">)</span>
+            <span class="n">nxentry</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;NX_class&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;NXentry&#39;</span>
+
+            <span class="c1">#create empty data entry</span>
+            <span class="n">ds_data</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/data/data&#39;</span><span class="p">,</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compress</span><span class="p">:</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;scale&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">scale</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;offset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">offset</span>
+
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="n">ds_data</span><span class="p">[</span><span class="n">i</span><span class="p">:(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">scale</span> <span class="o">+</span> <span class="n">offset</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">write_direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+
+            <span class="c1"># set up dataset attributes</span>
+            <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;data_type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;ImageData&#39;</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">)</span> <span class="k">else</span> <span class="s1">&#39;AcquisitionData&#39;</span>
+
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">):</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;dim</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">):</span>
+                <span class="c1"># create group to store configuration</span>
+                <span class="n">f</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config&#39;</span><span class="p">)</span>
+                <span class="n">f</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/source&#39;</span><span class="p">)</span>
+                <span class="n">f</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/detector&#39;</span><span class="p">)</span>
+                <span class="n">f</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/rotation_axis&#39;</span><span class="p">)</span>
+
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;geometry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;dimension&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">dimension</span><span class="p">)</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;num_channels&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">num_channels</span>
+
+                <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/detector/direction_x&#39;</span><span class="p">,</span>
+                                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                 <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                 <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">)</span>
+
+                <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/detector/position&#39;</span><span class="p">,</span>
+                                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                 <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                 <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">geometry</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+                    <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/source/position&#39;</span><span class="p">,</span>
+                                     <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                     <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                     <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/ray/direction&#39;</span><span class="p">,</span>
+                                     <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                     <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                     <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+
+                <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/rotation_axis/position&#39;</span><span class="p">,</span>
+                                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                 <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                 <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;num_pixels_h&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;pixel_size_h&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;panel_origin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span>
+
+                <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM3</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+                    <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/detector/direction_y&#39;</span><span class="p">,</span>
+                                     <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                     <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                     <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span><span class="p">)</span>
+
+                    <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/rotation_axis/direction&#39;</span><span class="p">,</span>
+                                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                 <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                 <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+
+                    <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;num_pixels_v&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;pixel_size_v&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+                <span class="n">angles</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;entry1/tomo_entry/config/angles&#39;</span><span class="p">,</span>
+                                                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
+                                                 <span class="n">dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span><span class="p">,</span>
+                                                 <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span><span class="p">)</span>
+                <span class="n">angles</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;angle_unit&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_unit</span>
+                <span class="n">angles</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;initial_angle&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">initial_angle</span>
+
+            <span class="k">else</span><span class="p">:</span>   <span class="c1"># ImageData</span>
+
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_num_x&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_x</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_num_y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_y</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_num_z&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_z</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_size_x&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">voxel_size_x</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_size_y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">voxel_size_y</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;voxel_size_z&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">voxel_size_z</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;center_x&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">center_x</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;center_y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">center_y</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;center_z&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">center_z</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">channels</span>
+                <span class="n">ds_data</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;channel_spacing&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">channel_spacing</span></div>
+</div>
+
+</pre></div>
+
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diff --git a/v24.2.0/_modules/cil/io/NikonDataReader/index.html b/v24.2.0/_modules/cil/io/NikonDataReader/index.html
new file mode 100644
index 0000000000..9b4ed8a9d3
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@@ -0,0 +1,913 @@
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+  </a>
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+    Optimisation framework
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+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../demos/">Tutorials</a></li>
+</ul>
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+  <h1>Source code for cil.io.NikonDataReader</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionType</span>
+<span class="kn">from</span> <span class="nn">cil.io.TIFF</span> <span class="kn">import</span> <span class="n">TIFFStackReader</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+
+<div class="viewcode-block" id="NikonDataReader">
+<a class="viewcode-back" href="../../../../io/#cil.io.NikonDataReader">[docs]</a>
+<span class="k">class</span> <span class="nc">NikonDataReader</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Basic reader for xtekct files</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    file_name: str</span>
+<span class="sd">        full path to .xtekct file</span>
+
+<span class="sd">    roi: dict, default=None</span>
+<span class="sd">        dictionary with roi to load:</span>
+<span class="sd">        {&#39;angle&#39;: (start, end, step),</span>
+<span class="sd">            &#39;horizontal&#39;: (start, end, step),</span>
+<span class="sd">            &#39;vertical&#39;: (start, end, step)}</span>
+
+<span class="sd">    normalise: bool, default=True</span>
+<span class="sd">        normalises loaded projections by detector white level (I_0)</span>
+
+<span class="sd">    fliplr: bool, default = False,</span>
+<span class="sd">        flip projections in the left-right direction (about vertical axis)</span>
+
+<span class="sd">    mode: str: {&#39;bin&#39;, &#39;slice&#39;}, default=&#39;bin&#39;</span>
+<span class="sd">        In bin mode, &#39;step&#39; number of pixels is binned together,</span>
+<span class="sd">        values of resulting binned pixels are calculated as average.</span>
+<span class="sd">        In &#39;slice&#39; mode &#39;step&#39; defines standard numpy slicing.</span>
+<span class="sd">        Note: in general output array size in bin mode != output array size in slice mode</span>
+
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    `roi` behaviour:</span>
+<span class="sd">        Files are stacked along axis_0. axis_1 and axis_2 correspond</span>
+<span class="sd">        to row and column dimensions, respectively.</span>
+
+<span class="sd">        Files are stacked in alphabetic order.</span>
+
+<span class="sd">        To skip projections or to change number of projections to load,</span>
+<span class="sd">        adjust &#39;angle&#39;. For instance, &#39;angle&#39;: (100, 300)</span>
+<span class="sd">        will skip first 100 projections and will load 200 projections.</span>
+
+<span class="sd">        ``&#39;angle&#39;: -1`` is a shortcut to load all elements along axis.</span>
+
+<span class="sd">        ``start`` and ``end`` can be specified as ``None`` which is equivalent</span>
+<span class="sd">        to ``start = 0`` and ``end = load everything to the end``, respectively.</span>
+<span class="sd">        Start and end also can be negative.</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">roi</span><span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">normalise</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;bin&#39;</span><span class="p">,</span> <span class="n">fliplr</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">roi</span> <span class="o">=</span> <span class="n">roi</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">normalise</span> <span class="o">=</span> <span class="n">normalise</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fliplr</span> <span class="o">=</span> <span class="n">fliplr</span>
+
+        <span class="k">if</span> <span class="n">file_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span><span class="p">,</span>
+                        <span class="n">roi</span> <span class="o">=</span> <span class="n">roi</span><span class="p">,</span>
+                        <span class="n">normalise</span> <span class="o">=</span> <span class="n">normalise</span><span class="p">,</span>
+                        <span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span><span class="p">,</span>
+                        <span class="n">fliplr</span> <span class="o">=</span> <span class="n">fliplr</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+               <span class="n">file_name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">roi</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">normalise</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+               <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;bin&#39;</span><span class="p">,</span>
+               <span class="n">fliplr</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">roi</span> <span class="o">=</span> <span class="n">roi</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">normalise</span> <span class="o">=</span> <span class="n">normalise</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fliplr</span> <span class="o">=</span> <span class="n">fliplr</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Path to xtekct file is required.&#39;</span><span class="p">)</span>
+
+        <span class="c1"># check if xtekct file exists</span>
+        <span class="k">if</span> <span class="ow">not</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s1">&#39;File</span><span class="se">\n</span><span class="s1"> </span><span class="si">{}</span><span class="se">\n</span><span class="s1"> does not exist.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">!=</span> <span class="s1">&#39;xtekct&#39;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;This reader can only process xtekct files. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">roi</span><span class="o">=</span> <span class="p">{</span><span class="s1">&#39;angle&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;vertical&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+
+        <span class="c1"># check labels</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="s1">&#39;vertical&#39;</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Wrong label. One of the following is expected: angle, horizontal, vertical&quot;</span><span class="p">)</span>
+
+        <span class="n">roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="s1">&#39;angle&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">roi</span><span class="p">[</span><span class="s1">&#39;angle&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+        <span class="k">if</span> <span class="s1">&#39;horizontal&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">roi</span><span class="p">[</span><span class="s1">&#39;horizontal&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+        <span class="k">if</span> <span class="s1">&#39;vertical&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">roi</span><span class="p">[</span><span class="s1">&#39;vertical&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+        <span class="c1"># parse xtekct file</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s1">&#39;replace&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">content</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+
+        <span class="n">content</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">content</span><span class="p">]</span>
+
+        <span class="c1">#initialise parameters</span>
+        <span class="n">detector_offset_h</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">detector_offset_v</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">object_tilt_deg</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">object_offset_x</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">object_roll_deg</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">centre_of_rotation_top</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">centre_of_rotation_bottom</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">content</span><span class="p">:</span>
+            <span class="c1"># filename of TIFF files</span>
+            <span class="k">if</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;Name&quot;</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_experiment_name</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="c1"># number of projections</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;Projections&quot;</span><span class="p">):</span>
+                <span class="n">num_projections</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># white level - used for normalization</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;WhiteLevel&quot;</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_white_level</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># number of pixels along Y axis</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;DetectorPixelsY&quot;</span><span class="p">):</span>
+                <span class="n">pixel_num_v_0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># number of pixels along X axis</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;DetectorPixelsX&quot;</span><span class="p">):</span>
+                <span class="n">pixel_num_h_0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># pixel size along X axis</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;DetectorPixelSizeX&quot;</span><span class="p">):</span>
+                <span class="n">pixel_size_h_0</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># pixel size along Y axis</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;DetectorPixelSizeY&quot;</span><span class="p">):</span>
+                <span class="n">pixel_size_v_0</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># source to center of rotation distance</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;SrcToObject&quot;</span><span class="p">):</span>
+                <span class="n">source_to_origin</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># source to detector distance</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;SrcToDetector&quot;</span><span class="p">):</span>
+                <span class="n">source_to_det</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># initial angular position of a rotation stage</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;InitialAngle&quot;</span><span class="p">):</span>
+                <span class="n">initial_angle</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># angular increment (in degrees)</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;AngularStep&quot;</span><span class="p">):</span>
+                <span class="n">angular_step</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># detector offset x in units</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;DetectorOffsetX&quot;</span><span class="p">):</span>
+                <span class="n">detector_offset_h</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># detector offset y in units</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;DetectorOffsetY&quot;</span><span class="p">):</span>
+                <span class="n">detector_offset_v</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1">#new file format rotation axis offset and angle</span>
+            <span class="c1"># object offset x in units</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;ObjectOffsetX&quot;</span><span class="p">):</span>
+                <span class="n">object_offset_x</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># object roll in degrees  (around z)</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;ObjectRoll&quot;</span><span class="p">):</span>
+                <span class="n">object_roll_deg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1"># object tilt in degrees  (around x)</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;ObjectTilt&quot;</span><span class="p">):</span>
+                <span class="n">object_tilt_deg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="c1">#old file format rotation axis offset and angle, in mm at the detector</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;CentreOfRotationTop&quot;</span><span class="p">):</span>
+                <span class="n">centre_of_rotation_top</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;CentreOfRotationBottom&quot;</span><span class="p">):</span>
+                <span class="n">centre_of_rotation_bottom</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="c1"># directory where data is stored</span>
+            <span class="k">elif</span> <span class="n">line</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;InputFolderName&quot;</span><span class="p">):</span>
+                <span class="n">input_folder_name</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">input_folder_name</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">tiff_directory_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">tiff_directory_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">),</span> <span class="n">input_folder_name</span><span class="p">)</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_projections</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="p">,[</span><span class="mi">0</span><span class="p">,</span> <span class="n">pixel_num_v_0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">pixel_num_h_0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;angle&#39;</span><span class="p">:</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;vertical&#39;</span><span class="p">:</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">:</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="mi">2</span>
+            <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Negative step is not allowed&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;bin&#39;</span><span class="p">:</span>
+            <span class="c1"># calculate number of pixels and pixel size</span>
+            <span class="n">pixel_num_v</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">pixel_num_h</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">pixel_size_v</span> <span class="o">=</span> <span class="n">pixel_size_v_0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">pixel_size_h</span> <span class="o">=</span> <span class="n">pixel_size_h_0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># slice</span>
+            <span class="n">pixel_num_v</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+            <span class="n">pixel_num_h</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+
+            <span class="n">pixel_size_v</span> <span class="o">=</span> <span class="n">pixel_size_v_0</span>
+            <span class="n">pixel_size_h</span> <span class="o">=</span> <span class="n">pixel_size_h_0</span>
+
+        <span class="n">det_start_0</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">pixel_num_h_0</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">det_start</span> <span class="o">=</span> <span class="n">det_start_0</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">det_end</span> <span class="o">=</span> <span class="n">det_start</span> <span class="o">+</span> <span class="n">pixel_num_h</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+        <span class="n">det_pos_h</span> <span class="o">=</span> <span class="p">(</span><span class="n">det_start</span> <span class="o">+</span> <span class="n">det_end</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">pixel_size_h_0</span> <span class="o">+</span> <span class="n">detector_offset_h</span>
+
+        <span class="n">det_start_0</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">pixel_num_v_0</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">det_start</span> <span class="o">=</span> <span class="n">det_start_0</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">det_end</span> <span class="o">=</span> <span class="n">det_start</span> <span class="o">+</span> <span class="n">pixel_num_v</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+        <span class="n">det_pos_v</span> <span class="o">=</span> <span class="p">(</span><span class="n">det_start</span> <span class="o">+</span> <span class="n">det_end</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">pixel_size_v_0</span> <span class="o">+</span> <span class="n">detector_offset_v</span>
+
+        <span class="c1">#angles from xtekct ignore *.ang and _ctdata.txt as not correct</span>
+        <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span> <span class="p">[</span> <span class="n">angular_step</span> <span class="o">*</span> <span class="n">proj</span> <span class="k">for</span> <span class="n">proj</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_projections</span><span class="p">)</span> <span class="p">]</span> <span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;bin&#39;</span><span class="p">:</span>
+            <span class="n">n_elem</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_elem</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">angles</span> <span class="o">=</span> <span class="n">angles</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">n_elem</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">])]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">angles</span> <span class="o">=</span> <span class="n">angles</span><span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">])]</span>
+
+        <span class="c1">#convert NikonGeometry to CIL geometry</span>
+        <span class="n">angles</span> <span class="o">=</span> <span class="o">-</span><span class="n">angles</span> <span class="o">-</span> <span class="n">initial_angle</span> <span class="o">+</span> <span class="mi">180</span>
+
+        <span class="k">if</span> <span class="n">object_offset_x</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">object_offset_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">centre_of_rotation_bottom</span><span class="o">+</span><span class="n">centre_of_rotation_top</span><span class="p">)</span><span class="o">*</span> <span class="mf">0.5</span> <span class="o">*</span>  <span class="n">source_to_origin</span> <span class="o">/</span><span class="n">source_to_det</span>
+
+        <span class="k">if</span> <span class="n">object_roll_deg</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">centre_of_rotation_top</span><span class="o">-</span><span class="n">centre_of_rotation_bottom</span><span class="p">)</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">pixel_num_v_0</span> <span class="o">*</span> <span class="n">pixel_size_v_0</span>
+            <span class="n">object_roll</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">object_roll</span> <span class="o">=</span> <span class="n">object_roll_deg</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span><span class="mf">180.</span>
+
+        <span class="n">object_tilt</span> <span class="o">=</span> <span class="o">-</span><span class="n">object_tilt_deg</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span><span class="mf">180.</span>
+
+        <span class="n">tilt_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="n">tilt_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">tilt_matrix</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">object_tilt</span><span class="p">)</span>
+        <span class="n">tilt_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">object_tilt</span><span class="p">)</span>
+        <span class="n">tilt_matrix</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">object_tilt</span><span class="p">)</span>
+
+        <span class="n">roll_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="n">roll_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">roll_matrix</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">object_roll</span><span class="p">)</span>
+        <span class="n">roll_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">object_roll</span><span class="p">)</span>
+        <span class="n">roll_matrix</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">object_roll</span><span class="p">)</span>
+
+        <span class="c1">#order of construction may be reversed, but unlikely to have both in a dataset</span>
+        <span class="n">rot_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">tilt_matrix</span><span class="p">,</span><span class="n">roll_matrix</span><span class="p">)</span>
+        <span class="n">rotation_axis_direction</span> <span class="o">=</span> <span class="n">rot_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fliplr</span><span class="p">:</span>
+            <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;top-left&#39;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;top-right&#39;</span>
+
+        <span class="k">if</span> <span class="n">pixel_num_v</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">==</span> <span class="n">pixel_num_v_0</span> <span class="o">//</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">source_to_origin</span><span class="p">],</span>
+                                                     <span class="n">rotation_axis_position</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="n">object_offset_x</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                                                     <span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="n">det_pos_h</span><span class="p">,</span> <span class="n">source_to_det</span><span class="o">-</span><span class="n">source_to_origin</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span>
+                                <span class="n">angle_unit</span><span class="o">=</span><span class="s1">&#39;degree&#39;</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">pixel_num_h</span><span class="p">,</span> <span class="n">pixel_size</span><span class="o">=</span><span class="n">pixel_size_h</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="n">origin</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="o">.</span><span class="n">set_labels</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">source_to_origin</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                                                         <span class="n">rotation_axis_position</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="n">object_offset_x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                                                         <span class="n">rotation_axis_direction</span><span class="o">=</span><span class="n">rotation_axis_direction</span><span class="p">,</span>
+                                                         <span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="n">det_pos_h</span><span class="p">,</span> <span class="n">source_to_det</span><span class="o">-</span><span class="n">source_to_origin</span><span class="p">,</span> <span class="n">det_pos_v</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span>
+                                <span class="n">angle_unit</span><span class="o">=</span><span class="s1">&#39;degree&#39;</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="o">.</span><span class="n">set_panel</span><span class="p">((</span><span class="n">pixel_num_h</span><span class="p">,</span> <span class="n">pixel_num_v</span><span class="p">),</span>
+                               <span class="n">pixel_size</span><span class="o">=</span><span class="p">(</span><span class="n">pixel_size_h</span><span class="p">,</span> <span class="n">pixel_size_v</span><span class="p">),</span>
+                               <span class="n">origin</span><span class="o">=</span><span class="n">origin</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="o">.</span><span class="n">set_labels</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span> <span class="s1">&#39;vertical&#39;</span><span class="p">,</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">])</span>
+
+<div class="viewcode-block" id="NikonDataReader.get_geometry">
+<a class="viewcode-back" href="../../../../io/#cil.io.NikonDataReader.get_geometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Return AcquisitionGeometry object</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span></div>
+
+<div class="viewcode-block" id="NikonDataReader.get_roi">
+<a class="viewcode-back" href="../../../../io/#cil.io.NikonDataReader.get_roi">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_roi</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns the roi&#39;&#39;&#39;</span>
+        <span class="n">roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_par</span><span class="p">[:]</span>
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="n">roi</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">roidict</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">roi</span><span class="p">):</span>
+            <span class="c1"># print (i, el)</span>
+            <span class="n">roidict</span><span class="p">[</span><span class="s1">&#39;axis_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">el</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">roidict</span></div>
+
+
+<div class="viewcode-block" id="NikonDataReader.read">
+<a class="viewcode-back" href="../../../../io/#cil.io.NikonDataReader.read">[docs]</a>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Reads projections and returns AcquisitionData with corresponding geometry,</span>
+<span class="sd">        arranged as [&#39;angle&#39;, horizontal&#39;] if a single slice is loaded</span>
+<span class="sd">        and [&#39;vertical, &#39;angle&#39;, horizontal&#39;] if more than 1 slice is loaded.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">reader</span> <span class="o">=</span> <span class="n">TIFFStackReader</span><span class="p">()</span>
+
+        <span class="n">roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_roi</span><span class="p">()</span>
+
+        <span class="n">reader</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tiff_directory_path</span><span class="p">,</span>
+                      <span class="n">roi</span><span class="o">=</span><span class="n">roi</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">)</span>
+
+        <span class="n">ad</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read_as_AcquisitionData</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_ag</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">normalise</span><span class="p">):</span>
+            <span class="n">ad</span><span class="o">.</span><span class="n">array</span><span class="p">[</span><span class="n">ad</span><span class="o">.</span><span class="n">array</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="c1"># cast the data read to float32</span>
+            <span class="n">ad</span> <span class="o">=</span> <span class="n">ad</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_white_level</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fliplr</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="n">ad</span><span class="o">.</span><span class="n">get_dimension_axis</span><span class="p">(</span><span class="s1">&#39;horizontal&#39;</span><span class="p">)</span>
+            <span class="n">ad</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">ad</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ad</span></div>
+
+
+<div class="viewcode-block" id="NikonDataReader.load_projections">
+<a class="viewcode-back" href="../../../../io/#cil.io.NikonDataReader.load_projections">[docs]</a>
+    <span class="k">def</span> <span class="nf">load_projections</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;alias of read for backward compatibility&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">read</span><span class="p">()</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
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+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
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+                  
+  <h1>Source code for cil.io.RAWFileWriter</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2023 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2023 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">DataContainer</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">cil.io</span> <span class="kn">import</span> <span class="n">utilities</span>
+<span class="kn">import</span> <span class="nn">configparser</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">compress_and_save</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compress</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">fname</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Compress and save numpy array to file</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : numpy array</span>
+<span class="sd">    compress : bool</span>
+<span class="sd">    scale : float</span>
+<span class="sd">    offset : float</span>
+<span class="sd">    dtype : numpy dtype</span>
+<span class="sd">    fname : string</span>
+
+<span class="sd">    Note:</span>
+<span class="sd">    -----</span>
+<span class="sd">    Data is always written in ‘C’ order, independent of the order of d.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">compress</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">compress_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">data</span>
+
+    <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+        <span class="s2">&quot;Data is always written in ‘C’ order, independent of the order of d.&quot;</span><span class="p">)</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">tofile</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+
+    <span class="c1"># return shape, fortran order, dtype</span>
+    <span class="k">return</span> <span class="n">d</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">str</span>
+
+
+<div class="viewcode-block" id="RAWFileWriter">
+<a class="viewcode-back" href="../../../../io/#cil.io.RAWFileWriter">[docs]</a>
+<span class="k">class</span> <span class="nc">RAWFileWriter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Writer to write DataContainer (or subclass AcquisitionData, ImageData) to disk as a binary blob</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : DataContainer, AcquisitionData or ImageData</span>
+<span class="sd">            This represents the data to save to TIFF file(s)</span>
+<span class="sd">        file_name : string</span>
+<span class="sd">            This defines the file name prefix, i.e. the file name without the extension.</span>
+<span class="sd">        compression : str, default None. Accepted values None, &#39;uint8&#39;, &#39;uint16&#39;</span>
+<span class="sd">            The lossy compression to apply. The default None will not compress data.</span>
+<span class="sd">            &#39;uint8&#39; or &#39;unit16&#39; will compress to unsigned int 8 and 16 bit respectively.</span>
+
+
+<span class="sd">        This writer will also write a text file with the minimal information necessary to</span>
+<span class="sd">        read the data back in. This text file will need to reside in the same directory as the raw file.</span>
+
+<span class="sd">        The text file will look something like this::</span>
+
+<span class="sd">            [MINIMAL INFO]</span>
+<span class="sd">            file_name = filename.raw</span>
+<span class="sd">            data_type = &lt;u2</span>
+<span class="sd">            shape = (6, 5, 4)</span>
+<span class="sd">            is_fortran = False</span>
+
+<span class="sd">            [COMPRESSION]</span>
+<span class="sd">            scale = 550.7142857142857</span>
+<span class="sd">            offset = -0.0</span>
+
+<span class="sd">        The ``data_type`` describes the data layout when packing and unpacking data. This can be</span>
+<span class="sd">        read as numpy dtype with ``np.dtype(&#39;&lt;u2&#39;)``.</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        Example of using the writer with compression to ``uint8``:</span>
+
+<span class="sd">        &gt;&gt;&gt; from cil.io import RAWFileWriter</span>
+<span class="sd">        &gt;&gt;&gt; writer = RAWFileWriter(data=data, file_name=fname, compression=&#39;uint8&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; writer.write()</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        Example of reading the data from the ini file:</span>
+
+<span class="sd">        &gt;&gt;&gt; config = configparser.ConfigParser()</span>
+<span class="sd">        &gt;&gt;&gt; inifname = &quot;file_name.ini&quot;</span>
+<span class="sd">        &gt;&gt;&gt; config.read(inifname)</span>
+<span class="sd">        &gt;&gt;&gt; read_dtype = config[&#39;MINIMAL INFO&#39;][&#39;data_type&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; dtype = np.dtype(read_dtype)</span>
+<span class="sd">        &gt;&gt;&gt; fname = config[&#39;MINIMAL INFO&#39;][&#39;file_name&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; read_array = np.fromfile(fname, dtype=read_dtype)</span>
+<span class="sd">        &gt;&gt;&gt; read_shape = eval(config[&#39;MINIMAL INFO&#39;][&#39;shape&#39;])</span>
+<span class="sd">        &gt;&gt;&gt; scale = float(config[&#39;COMPRESSION&#39;][&#39;scale&#39;])</span>
+<span class="sd">        &gt;&gt;&gt; offset = float(config[&#39;COMPRESSION&#39;][&#39;offset&#39;])</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+
+<span class="sd">          If compression ``uint8`` or ``unit16`` are used, the scale and offset used to compress the data are saved</span>
+<span class="sd">          in the ``ini`` file in the same directory as the raw file, in the &quot;COMPRESSION&quot; section .</span>
+
+<span class="sd">          The original data can be obtained by: ``original_data = (compressed_data - offset) / scale``</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+
+<span class="sd">          Data is always written in ‘C’ order independent of the order of the original data,</span>
+<span class="sd">          https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html#numpy.ndarray.tofile,</span>
+
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">file_name</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">DataContainer</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Writer supports only following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39;DataContainer - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="n">file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">file_name</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;dir_name </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">)</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;file_name </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)</span>
+
+        <span class="c1"># Deal with compression</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">compress</span> <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compress</span><span class="p">(</span><span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compressed_dtype</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span> <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compression_scale_offset</span><span class="p">(</span>
+            <span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">compression</span> <span class="o">=</span> <span class="n">compression</span>
+
+<div class="viewcode-block" id="RAWFileWriter.write">
+<a class="viewcode-back" href="../../../../io/#cil.io.RAWFileWriter.write">[docs]</a>
+    <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Write data to disk&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">)</span>
+
+        <span class="n">fname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">+</span> <span class="s1">&#39;.raw&#39;</span><span class="p">)</span>
+
+        <span class="c1"># write to disk</span>
+        <span class="n">header</span> <span class="o">=</span> \
+            <span class="n">compress_and_save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">compress</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">fname</span><span class="p">)</span>
+
+        <span class="n">shape</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">fortran_order</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">read_dtype</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+        <span class="c1"># save information about the file we just saved</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">configparser</span><span class="o">.</span><span class="n">ConfigParser</span><span class="p">()</span>
+        <span class="n">config</span><span class="p">[</span><span class="s1">&#39;MINIMAL INFO&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;file_name&#39;</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">fname</span><span class="p">),</span>
+            <span class="s1">&#39;data_type&#39;</span><span class="p">:</span> <span class="n">read_dtype</span><span class="p">,</span>
+            <span class="s1">&#39;shape&#39;</span><span class="p">:</span> <span class="n">shape</span><span class="p">,</span>
+            <span class="c1"># Data is always written in ‘C’ order, independent of the order of d.</span>
+            <span class="s1">&#39;is_fortran&#39;</span><span class="p">:</span> <span class="n">fortran_order</span>
+        <span class="p">}</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compress</span><span class="p">:</span>
+            <span class="n">config</span><span class="p">[</span><span class="s1">&#39;COMPRESSION&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+                <span class="s1">&#39;scale&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span>
+                <span class="s1">&#39;offset&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span><span class="p">,</span>
+            <span class="p">}</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Saving to </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">config</span><span class="p">))</span>
+        <span class="c1"># write the configuration to an ini file</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">+</span> <span class="s1">&#39;.ini&#39;</span><span class="p">),</span>
+                  <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">configfile</span><span class="p">:</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">configfile</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/io/TIFF/index.html b/v24.2.0/_modules/cil/io/TIFF/index.html
new file mode 100644
index 0000000000..6ec68f05c9
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@@ -0,0 +1,1151 @@
+
+<!DOCTYPE html>
+
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+<html lang="en" data-content_root="../../../../" >
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+    Read/ write AcquisitionData and ImageData
+  </a>
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+
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+    Optimisation framework
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.io.TIFF</span></li>
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+                  
+  <h1>Source code for cil.io.TIFF</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">ImageData</span>
+<span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">re</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">ImageGeometry</span>
+
+<span class="n">pilAvailable</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
+<span class="k">except</span><span class="p">:</span>
+    <span class="n">pilAvailable</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="kn">import</span> <span class="nn">functools</span>
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="kn">import</span> <span class="nn">re</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">cil.io</span> <span class="kn">import</span> <span class="n">utilities</span>
+<span class="kn">import</span> <span class="nn">json</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">save_scale_offset</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Save scale and offset to file</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    fname : string</span>
+<span class="sd">    scale : float</span>
+<span class="sd">    offset : float</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">dirname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+    <span class="n">txt</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dirname</span><span class="p">,</span> <span class="s1">&#39;scaleoffset.json&#39;</span><span class="p">)</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;scale&#39;</span><span class="p">:</span> <span class="n">scale</span><span class="p">,</span> <span class="s1">&#39;offset&#39;</span><span class="p">:</span> <span class="n">offset</span><span class="p">}</span>
+    <span class="n">utilities</span><span class="o">.</span><span class="n">save_dict_to_file</span><span class="p">(</span><span class="n">txt</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
+
+<div class="viewcode-block" id="TIFFWriter">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFWriter">[docs]</a>
+<span class="k">class</span> <span class="nc">TIFFWriter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Write a DataSet to disk as a TIFF file or stack of TIFF files</span>
+
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : DataContainer, AcquisitionData or ImageData</span>
+<span class="sd">            This represents the data to save to TIFF file(s)</span>
+<span class="sd">        file_name : string</span>
+<span class="sd">            This defines the file name prefix, i.e. the file name without the extension.</span>
+<span class="sd">        counter_offset : int, default 0.</span>
+<span class="sd">            counter_offset indicates at which number the ordinal index should start.</span>
+<span class="sd">            For instance, if you have to save 10 files the index would by default go from 0 to 9.</span>
+<span class="sd">            By counter_offset you can offset the index: from `counter_offset` to `9+counter_offset`</span>
+<span class="sd">        compression : str, default None. Accepted values None, &#39;uint8&#39;, &#39;uint16&#39;</span>
+<span class="sd">            The lossy compression to apply. The default None will not compress data.</span>
+<span class="sd">            &#39;uint8&#39; or &#39;unit16&#39; will compress to unsigned int 8 and 16 bit respectively.</span>
+
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+
+<span class="sd">          If compression ``uint8`` or ``unit16`` are used, the scale and offset used to compress the data are saved</span>
+<span class="sd">          in a file called ``scaleoffset.json`` in the same directory as the TIFF file(s).</span>
+
+<span class="sd">          The original data can be obtained by: ``original_data = (compressed_data - offset) / scale``</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+
+<span class="sd">          In the case of 3D or 4D data this writer will save the data as a stack of multiple TIFF files,</span>
+<span class="sd">          not as a single multi-page TIFF file.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">counter_offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">counter_offset</span> <span class="o">=</span> <span class="n">counter_offset</span>
+        <span class="k">if</span> <span class="p">((</span><span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">file_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">,</span> <span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span><span class="p">,</span>
+                        <span class="n">counter_offset</span><span class="o">=</span><span class="n">counter_offset</span><span class="p">,</span>
+                        <span class="n">compression</span><span class="o">=</span><span class="n">compression</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+               <span class="n">data</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">file_name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">counter_offset</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+               <span class="n">compression</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="n">file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span>
+                <span class="n">file_name</span>
+                <span class="p">)</span>
+            <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;dir_name </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">)</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;file_name </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">counter_offset</span> <span class="o">=</span> <span class="n">counter_offset</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">))</span> <span class="ow">or</span>
+                <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">))):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Writer supports only following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData&#39;</span><span class="p">)</span>
+
+        <span class="c1"># Deal with compression</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">compress</span>           <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compress</span><span class="p">(</span><span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span>              <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compressed_dtype</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span> <span class="o">=</span> <span class="n">utilities</span><span class="o">.</span><span class="n">get_compression_scale_offset</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">compression</span>        <span class="o">=</span> <span class="n">compression</span>
+
+
+<div class="viewcode-block" id="TIFFWriter.write">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFWriter.write">[docs]</a>
+    <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Write data to disk&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">):</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">)</span>
+
+        <span class="n">ndim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="c1"># save single slice</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter_offset</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">fname</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">_idx_</span><span class="si">{:04d}</span><span class="s2">.tiff&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter_offset</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">fname</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.tiff&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">))</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span>
+                    <span class="n">utilities</span><span class="o">.</span><span class="n">compress_data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                    <span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;tiff&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">sliceno</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="c1"># save single slice</span>
+                <span class="c1"># pattern = self.file_name.split(&#39;.&#39;)</span>
+                <span class="n">dimension</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">fname</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">_idx_</span><span class="si">{:04d}</span><span class="s2">.tiff&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">),</span>
+                    <span class="n">sliceno</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter_offset</span><span class="p">)</span>
+                <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                    <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span>
+                            <span class="n">utilities</span><span class="o">.</span><span class="n">compress_data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">as_array</span><span class="p">()[</span><span class="n">sliceno</span><span class="p">]</span> <span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                        <span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;tiff&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+            <span class="c1"># find how many decimal places self.data_container.shape[0] and shape[1] have</span>
+            <span class="n">zero_padding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_zero_padding</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">zero_padding</span> <span class="o">+=</span> <span class="s1">&#39;_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_zero_padding</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">format_string</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">_</span><span class="si">{}</span><span class="s2">x</span><span class="si">{}</span><span class="s2">x</span><span class="si">{}</span><span class="s2">x</span><span class="si">{}</span><span class="s2">_&quot;</span><span class="o">+</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">.tiff&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">zero_padding</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">sliceno1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="c1"># save single slice</span>
+                <span class="c1"># pattern = self.file_name.split(&#39;.&#39;)</span>
+                <span class="n">dimension</span> <span class="o">=</span> <span class="p">[</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="p">]</span>
+                <span class="k">for</span> <span class="n">sliceno2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="n">fname</span> <span class="o">=</span> <span class="n">format_string</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dir_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">),</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="p">,</span> <span class="n">sliceno1</span><span class="p">,</span> <span class="n">sliceno2</span><span class="p">)</span>
+                    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                        <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span>
+                            <span class="n">utilities</span><span class="o">.</span><span class="n">compress_data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_container</span><span class="o">.</span><span class="n">as_array</span><span class="p">()[</span><span class="n">sliceno1</span><span class="p">][</span><span class="n">sliceno2</span><span class="p">]</span> <span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                        <span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s1">&#39;tiff&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot handle more than 4 dimensions&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compress</span><span class="p">:</span>
+            <span class="n">save_scale_offset</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_zero_padding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">number</span><span class="p">):</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">while</span> <span class="mi">10</span><span class="o">**</span><span class="n">i</span> <span class="o">&lt;</span> <span class="n">number</span><span class="p">:</span>
+            <span class="n">i</span><span class="o">+=</span><span class="mi">1</span>
+        <span class="n">i</span><span class="o">+=</span><span class="mi">1</span>
+        <span class="n">zero_padding_string</span> <span class="o">=</span> <span class="s1">&#39;{:0&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;d}&#39;</span>
+        <span class="k">return</span> <span class="n">zero_padding_string</span></div>
+
+
+
+<div class="viewcode-block" id="TIFFStackReader">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFStackReader">[docs]</a>
+<span class="k">class</span> <span class="nc">TIFFStackReader</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Basic TIFF reader which loops through all tiff files in a specific</span>
+<span class="sd">        folder and loads them in alphabetic order</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        file_name : str, abspath to folder, list</span>
+<span class="sd">            Path to folder with tiff files, list of paths of tiffs, or single tiff file</span>
+
+<span class="sd">        roi : dictionary, default `None`</span>
+<span class="sd">            dictionary with roi to load:</span>
+<span class="sd">            ``{&#39;axis_0&#39;: (start, end, step),</span>
+<span class="sd">               &#39;axis_1&#39;: (start, end, step),</span>
+<span class="sd">               &#39;axis_2&#39;: (start, end, step)}``</span>
+<span class="sd">            roi is specified for axes before transpose.</span>
+
+<span class="sd">        transpose : bool, default False</span>
+<span class="sd">            Whether to transpose loaded images</span>
+
+<span class="sd">        mode : str, {&#39;bin&#39;, &#39;slice&#39;}, default &#39;bin&#39;.</span>
+<span class="sd">            Defines the &#39;step&#39; in the roi parameter:</span>
+
+<span class="sd">            In bin mode, &#39;step&#39; number of pixels</span>
+<span class="sd">            are binned together, values of resulting binned pixels are calculated as average.</span>
+
+<span class="sd">            In &#39;slice&#39; mode &#39;step&#39; defines standard numpy slicing.</span>
+
+<span class="sd">            Note: in general output array size in bin mode != output array size in slice mode</span>
+
+<span class="sd">        dtype : numpy type, string, default np.float32</span>
+<span class="sd">            Requested type of the read image. If set to None it defaults to the type of the saved file.</span>
+
+
+<span class="sd">        Notes:</span>
+<span class="sd">        ------</span>
+<span class="sd">        roi behaviour:</span>
+<span class="sd">            Files are stacked along ``axis_0``, in alphabetical order.</span>
+
+<span class="sd">            ``axis_1`` and ``axis_2`` correspond</span>
+<span class="sd">            to row and column dimensions, respectively.</span>
+
+<span class="sd">            To skip files or to change number of files to load,</span>
+<span class="sd">            adjust ``axis_0``. For instance, ``&#39;axis_0&#39;: (100, 300)``</span>
+<span class="sd">            will skip first 100 files and will load 200 files.</span>
+
+<span class="sd">            ``&#39;axis_0&#39;: -1`` is a shortcut to load all elements along axis 0.</span>
+
+<span class="sd">            ``start`` and ``end`` can be specified as ``None`` which is equivalent</span>
+<span class="sd">            to ``start = 0`` and ``end = load everything to the end``, respectively.</span>
+
+<span class="sd">            Start and end also can be negative.</span>
+
+<span class="sd">            roi is specified for axes before transpose.</span>
+
+
+<span class="sd">        Example:</span>
+<span class="sd">        --------</span>
+<span class="sd">        You can rescale the read data as `rescaled_data = (read_data - offset)/scale` with the following code:</span>
+
+<span class="sd">        &gt;&gt;&gt; reader = TIFFStackReader(file_name = &#39;/path/to/folder&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; rescaled_data = reader.read_rescaled(scale, offset)</span>
+
+
+<span class="sd">        Alternatively, if TIFFWriter has been used to save data with lossy compression, then you can rescale the</span>
+<span class="sd">        read data to approximately the original data with the following code:</span>
+
+<span class="sd">        &gt;&gt;&gt; writer = TIFFWriter(file_name = &#39;/path/to/folder&#39;, compression=&#39;uint8&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; writer.write(original_data)</span>
+<span class="sd">        &gt;&gt;&gt; reader = TIFFStackReader(file_name = &#39;/path/to/folder&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; about_original_data = reader.read_rescaled()</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">roi</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">transpose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;bin&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span>
+                        <span class="n">roi</span> <span class="o">=</span> <span class="n">roi</span><span class="p">,</span>
+                        <span class="n">transpose</span> <span class="o">=</span> <span class="n">transpose</span><span class="p">,</span>
+                        <span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+               <span class="n">file_name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">roi</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+               <span class="n">transpose</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+               <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;bin&#39;</span><span class="p">,</span>
+               <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Set up method for the TIFFStackReader class</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        file_name : str, abspath to folder, list</span>
+<span class="sd">            Path to folder with tiff files, list of paths of tiffs, or single tiff file</span>
+
+<span class="sd">        roi : dictionary, default `None`</span>
+<span class="sd">            dictionary with roi to load</span>
+<span class="sd">            ``{&#39;axis_0&#39;: (start, end, step), &#39;axis_1&#39;: (start, end, step), &#39;axis_2&#39;: (start, end, step)}``</span>
+<span class="sd">            Files are stacked along axis_0. axis_1 and axis_2 correspond</span>
+<span class="sd">            to row and column dimensions, respectively.</span>
+<span class="sd">            Files are stacked in alphabetic order.</span>
+<span class="sd">            To skip files or to change number of files to load,</span>
+<span class="sd">            adjust axis_0. For instance, &#39;axis_0&#39;: (100, 300)</span>
+<span class="sd">            will skip first 100 files and will load 200 files.</span>
+<span class="sd">            &#39;axis_0&#39;: -1 is a shortcut to load all elements along axis.</span>
+<span class="sd">            Start and end can be specified as None which is equivalent</span>
+<span class="sd">            to start = 0 and end = load everything to the end, respectively.</span>
+<span class="sd">            Start and end also can be negative.</span>
+<span class="sd">            Notes: roi is specified for axes before transpose.</span>
+
+<span class="sd">        transpose : bool, default False</span>
+<span class="sd">            Whether to transpose loaded images</span>
+
+<span class="sd">        mode : str, default &#39;bin&#39;. Accepted values &#39;bin&#39;, &#39;slice&#39;</span>
+<span class="sd">            Referring to the &#39;step&#39; defined in the roi parameter, in bin mode, &#39;step&#39; number of pixels</span>
+<span class="sd">            are binned together, values of resulting binned pixels are calculated as average.</span>
+<span class="sd">            In &#39;slice&#39; mode &#39;step&#39; defines standard numpy slicing.</span>
+<span class="sd">            Note: in general output array size in bin mode != output array size in slice mode</span>
+
+<span class="sd">        dtype : numpy type, string, default np.float32</span>
+<span class="sd">            Requested type of the read image. If set to None it defaults to the type of the saved file.</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">roi</span> <span class="o">=</span> <span class="n">roi</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transpose</span> <span class="o">=</span> <span class="n">transpose</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">dtype</span>
+
+        <span class="k">if</span> <span class="n">file_name</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;file_name to tiff files is required. Can be a tiff, a list of tiffs or a directory containing tiffs&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">roi</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;axis_0&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;axis_1&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;axis_2&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">}</span>
+
+        <span class="c1"># check that PIL library is installed</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">pilAvailable</span> <span class="o">==</span> <span class="kc">False</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;PIL (pillow) is not available, cannot load TIFF files.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># check labels</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;axis_0&#39;</span><span class="p">,</span> <span class="s1">&#39;axis_1&#39;</span><span class="p">,</span> <span class="s1">&#39;axis_2&#39;</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Wrong label. axis_0, axis_1 and axis_2 are expected&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bin&#39;</span><span class="p">,</span> <span class="s1">&#39;slice&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Wrong mode, bin or slice is expected.&quot;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="s1">&#39;axis_0&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="s1">&#39;axis_0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+        <span class="k">if</span> <span class="s1">&#39;axis_1&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="s1">&#39;axis_1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+        <span class="k">if</span> <span class="s1">&#39;axis_2&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="s1">&#39;axis_2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span> <span class="o">=</span> <span class="n">file_name</span>
+        <span class="k">elif</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">file_name</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span> <span class="o">=</span> <span class="p">[</span><span class="n">file_name</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">file_name</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">escape</span><span class="p">(</span><span class="n">file_name</span><span class="p">),</span><span class="s2">&quot;*.tif&quot;</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">escape</span><span class="p">(</span><span class="n">file_name</span><span class="p">),</span><span class="s2">&quot;*.tiff&quot;</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;No tiff files were found in the directory </span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file_name</span><span class="p">))</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;file_name expects a tiff file, a list of tiffs, or a directory containing tiffs.</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file_name</span><span class="p">))</span>
+
+
+        <span class="k">for</span> <span class="n">fn</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s1">&#39;.tif&#39;</span> <span class="ow">in</span> <span class="n">fn</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">fn</span><span class="p">)):</span>
+                    <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;File </span><span class="se">\n</span><span class="s1"> </span><span class="si">{}</span><span class="se">\n</span><span class="s1"> does not exist.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fn</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;file_name expects a tiff file, a list of tiffs, or a directory containing tiffs.</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file_name</span><span class="p">))</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">__natural_keys</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_get_file_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">):</span>
+        <span class="n">mode</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">mode</span>
+        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;1&#39;</span><span class="p">:</span>
+            <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bool_</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;L&#39;</span><span class="p">:</span>
+            <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;F&#39;</span><span class="p">:</span>
+            <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;I&#39;</span><span class="p">:</span>
+            <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;I;16&#39;</span><span class="p">]:</span>
+            <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unsupported type </span><span class="si">{}</span><span class="s2">. Expected any of 1 L I I;16 F.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mode</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">dtype</span>
+
+<div class="viewcode-block" id="TIFFStackReader.read">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFStackReader.read">[docs]</a>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Reads images and return numpy array</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1"># load first image to find out dimensions and type</span>
+        <span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="k">with</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span> <span class="k">as</span> <span class="n">img</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_file_type</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="n">array_shape_0</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">),</span> <span class="n">tmp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">tmp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="n">roi_par</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="n">array_shape_0</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">array_shape_0</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">array_shape_0</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">1</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;axis_0&#39;</span><span class="p">:</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;axis_1&#39;</span><span class="p">:</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;axis_2&#39;</span><span class="p">:</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="mi">2</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">roi_par</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Negative step is not allowed&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;bin&#39;</span><span class="p">:</span>
+            <span class="c1"># calculate number of pixels</span>
+            <span class="n">n_rows</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">n_cols</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">num_to_read</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">transpose</span><span class="p">:</span>
+                <span class="n">im</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_to_read</span><span class="p">,</span> <span class="n">n_rows</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">im</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_to_read</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">n_rows</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">num_to_read</span><span class="p">):</span>
+
+                <span class="n">raw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">array_shape_0</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">array_shape_0</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]):</span>
+
+                    <span class="n">index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">j</span><span class="p">)</span>
+                    <span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">[</span><span class="n">index</span><span class="p">])</span>
+
+                    <span class="n">arr</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
+                    <span class="n">raw</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+
+                <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_rows</span><span class="p">,</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span>
+                         <span class="n">n_cols</span><span class="p">,</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">raw</span><span class="p">[</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]:(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="p">(((</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">])),</span> \
+                          <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]:(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="p">(((</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]))]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transpose</span><span class="p">:</span>
+                    <span class="n">im</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">im</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">tmp</span>
+
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># slice mode</span>
+            <span class="c1"># calculate number of pixels</span>
+            <span class="n">n_rows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+            <span class="n">n_cols</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+            <span class="n">num_to_read</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">transpose</span><span class="p">:</span>
+                <span class="n">im</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_to_read</span><span class="p">,</span> <span class="n">n_rows</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">im</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_to_read</span><span class="p">,</span> <span class="n">n_cols</span><span class="p">,</span> <span class="n">n_rows</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]):</span>
+
+                <span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="c1">#try:</span>
+                <span class="n">raw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="p">),</span> <span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="c1">#except:</span>
+                <span class="c1">#    print(&#39;Error reading\n {}\n file.&#39;.format(filename))</span>
+                <span class="c1">#    raise</span>
+
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">raw</span><span class="p">[(</span><span class="nb">slice</span><span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]),</span>
+                           <span class="nb">slice</span><span class="p">(</span><span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]))]</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transpose</span><span class="p">:</span>
+                    <span class="n">im</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">im</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="n">roi_par</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">tmp</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">im</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">__atoi</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">text</span><span class="p">)</span> <span class="k">if</span> <span class="n">text</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()</span> <span class="k">else</span> <span class="n">text</span>
+
+    <span class="k">def</span> <span class="nf">__natural_keys</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        https://stackoverflow.com/questions/5967500/how-to-correctly-sort-a-string-with-a-number-inside</span>
+<span class="sd">        alist.sort(key=natural_keys) sorts in human order</span>
+<span class="sd">        http://nedbatchelder.com/blog/200712/human_sorting.html</span>
+<span class="sd">        (See Toothy&#39;s implementation in the comments)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">__atoi</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">re</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;(\d+)&#39;</span><span class="p">,</span> <span class="n">text</span><span class="p">)</span> <span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_read_as</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">geometry</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;reads the TIFF stack as an ImageData with the provided geometry&#39;&#39;&#39;</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+            <span class="n">gsize</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">*</span><span class="n">y</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">dsize</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">*</span><span class="n">y</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">gsize</span> <span class="o">!=</span> <span class="n">dsize</span><span class="p">:</span>
+                <span class="n">added_dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">*</span><span class="n">y</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">1</span><span class="o">+</span><span class="n">added_dims</span><span class="p">],</span> <span class="mi">1</span><span class="p">):</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot reshape read data </span><span class="si">{}</span><span class="s2"> to the requested shape </span><span class="si">{}</span><span class="s2">.</span><span class="se">\n</span><span class="s2">&quot;</span>\
+                        <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span>
+                                    <span class="s2">&quot;Geometry requests first dimension of data to be </span><span class="si">{}</span><span class="s2"> but it is </span><span class="si">{}</span><span class="s2">&quot;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="p">))</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;data </span><span class="si">{}</span><span class="s1"> and requested </span><span class="si">{}</span><span class="s1"> shapes are not compatible: data size does not match! Expected </span><span class="si">{}</span><span class="s1">, got </span><span class="si">{}</span><span class="s1">&#39;</span>\
+                    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dsize</span><span class="p">,</span> <span class="n">gsize</span><span class="p">))</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Data should have 3 dimensions, got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
+
+
+            <span class="n">reshaped</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_return_appropriate_data</span><span class="p">(</span><span class="n">reshaped</span><span class="p">,</span> <span class="n">geometry</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Requested </span><span class="si">{}</span><span class="s1"> shape is incompatible with data. Expected </span><span class="si">{}</span><span class="s1">, got </span><span class="si">{}</span><span class="s1">&#39;</span>\
+                <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_return_appropriate_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">geometry</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_return_appropriate_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">geometry</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">AcquisitionData</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Unsupported Geometry type. Expected ImageGeometry or AcquisitionGeometry, got </span><span class="si">{}</span><span class="s2">&quot;</span>\
+                <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">geometry</span><span class="p">)))</span>
+
+<div class="viewcode-block" id="TIFFStackReader.read_as_ImageData">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFStackReader.read_as_ImageData">[docs]</a>
+    <span class="k">def</span> <span class="nf">read_as_ImageData</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;reads the TIFF stack as an ImageData with the provided geometry</span>
+
+<span class="sd">        Notice that the data will be reshaped to what requested in the geometry but there is</span>
+<span class="sd">        no warranty that the data will be read in the right order!</span>
+<span class="sd">        In facts you can reshape a (2,3,4) array as (3,4,2), however we do not check if the reshape</span>
+<span class="sd">        leads to sensible data.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_read_as</span><span class="p">(</span><span class="n">image_geometry</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="TIFFStackReader.read_as_AcquisitionData">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFStackReader.read_as_AcquisitionData">[docs]</a>
+    <span class="k">def</span> <span class="nf">read_as_AcquisitionData</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;reads the TIFF stack as an AcquisitionData with the provided geometry</span>
+
+<span class="sd">        Notice that the data will be reshaped to what requested in the geometry but there is</span>
+<span class="sd">        no warranty that the data will be read in the right order!</span>
+<span class="sd">        In facts you can reshape a (2,3,4) array as (3,4,2), however we do not check if the reshape</span>
+<span class="sd">        leads to sensible data.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_read_as</span><span class="p">(</span><span class="n">acquisition_geometry</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="TIFFStackReader.read_scale_offset">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFStackReader.read_scale_offset">[docs]</a>
+    <span class="k">def</span> <span class="nf">read_scale_offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reads the scale and offset from a json file in the same folder as the tiff stack</span>
+
+<span class="sd">        This is a courtesy method that will work only if the tiff stack is saved with the TIFFWriter</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        tuple: (scale, offset)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1"># load first image to find out dimensions and type</span>
+        <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_tiff_files</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;scaleoffset.json&quot;</span><span class="p">),</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">d</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;scale&#39;</span><span class="p">],</span> <span class="n">d</span><span class="p">[</span><span class="s1">&#39;offset&#39;</span><span class="p">])</span></div>
+
+
+<div class="viewcode-block" id="TIFFStackReader.read_rescaled">
+<a class="viewcode-back" href="../../../../io/#cil.io.TIFFStackReader.read_rescaled">[docs]</a>
+    <span class="k">def</span> <span class="nf">read_rescaled</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Reads the TIFF stack and rescales it with the provided scale and offset, or with the ones in the json file if not provided</span>
+
+<span class="sd">        This is a courtesy method that will work only if the tiff stack is saved with the TIFFWriter</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        -----------</span>
+
+<span class="sd">        scale: float, default None</span>
+<span class="sd">            scale to apply to the data. If None, the scale will be read from the json file saved by TIFFWriter.</span>
+<span class="sd">        offset: float, default None</span>
+<span class="sd">            offset to apply to the data. If None, the offset will be read from the json file saved by TIFFWriter.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        numpy.ndarray in float32</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">scale</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">offset</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_scale_offset</span><span class="p">()</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+        <span class="n">data</span> <span class="o">-=</span> <span class="n">offset</span>
+        <span class="n">data</span> <span class="o">/=</span> <span class="n">scale</span>
+        <span class="k">return</span> <span class="n">data</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/io/ZEISSDataReader/index.html b/v24.2.0/_modules/cil/io/ZEISSDataReader/index.html
new file mode 100644
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+
+<!DOCTYPE html>
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+<html lang="en" data-content_root="../../../../" >
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.io.ZEISSDataReader</span></li>
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+                  
+  <h1>Source code for cil.io.ZEISSDataReader</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2022 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2022 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># Andrew Shartis (UES, Inc.)</span>
+
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">ImageGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AngleUnit</span><span class="p">,</span> <span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">ImageDimension</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">olefile</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="n">dxchange_logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;dxchange&#39;</span><span class="p">)</span>
+<span class="n">dxchange_logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">ERROR</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">dxchange</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+
+<div class="viewcode-block" id="ZEISSDataReader">
+<a class="viewcode-back" href="../../../../io/#cil.io.ZEISSDataReader">[docs]</a>
+<span class="k">class</span> <span class="nc">ZEISSDataReader</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Create a reader for ZEISS files</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    file_name: str</span>
+<span class="sd">        file name to read</span>
+<span class="sd">    roi: dict, default None</span>
+<span class="sd">        dictionary with roi to load for each axis:</span>
+<span class="sd">        ``{&#39;axis_labels_1&#39;: (start, end, step),&#39;axis_labels_2&#39;: (start, end, step)}``.</span>
+<span class="sd">        ``axis_labels`` are defined by ImageGeometry and AcquisitionGeometry dimension labels.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    `roi` behaviour:</span>
+<span class="sd">        For ImageData to skip files or to change number of files to load,</span>
+<span class="sd">        adjust ``vertical``. E.g. ``&#39;vertical&#39;: (100, 300)`` will skip first 100 files</span>
+<span class="sd">        and will load 200 files.</span>
+
+<span class="sd">        ``&#39;axis_label&#39;: -1`` is a shortcut to load all elements along axis.</span>
+
+<span class="sd">        ``start`` and ``end`` can be specified as ``None`` which is equivalent</span>
+<span class="sd">        to ``start = 0`` and ``end = load everything to the end``, respectively.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">roi</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+
+        <span class="c1"># Set logging level for dxchange reader.py</span>
+        <span class="n">logger_dxchange</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;dxchange.reader&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">logger_dxchange</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logger_dxchange</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">ERROR</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">file_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="n">roi</span> <span class="o">=</span> <span class="n">roi</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="ZEISSDataReader.set_up">
+<a class="viewcode-back" href="../../../../io/#cil.io.ZEISSDataReader.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+               <span class="n">file_name</span><span class="p">,</span>
+               <span class="n">roi</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Set up the reader</span>
+
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        file_name: str</span>
+<span class="sd">            file name to read</span>
+<span class="sd">        roi: dict, default None</span>
+<span class="sd">            dictionary with roi to load for each axis:</span>
+<span class="sd">            ``{&#39;axis_labels_1&#39;: (start, end, step),&#39;axis_labels_2&#39;: (start, end, step)}``.</span>
+<span class="sd">            ``axis_labels`` are defined by ImageGeometry and AcquisitionGeometry dimension labels.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        `roi` behaviour:</span>
+<span class="sd">            ``&#39;axis_label&#39;: -1`` is a shortcut to load all elements along axis.</span>
+
+<span class="sd">            ``start`` and ``end`` can be specified as ``None`` which is equivalent</span>
+<span class="sd">            to ``start = 0`` and ``end = load everything to the end``, respectively.</span>
+
+<span class="sd">            **Acquisition Data**</span>
+
+<span class="sd">            The axis labels in the `roi` dict for `AcquisitionData` will be:</span>
+<span class="sd">            ``{&#39;angle&#39;:(...),&#39;vertical&#39;:(...),&#39;horizontal&#39;:(...)}``</span>
+
+<span class="sd">            **Image Data**</span>
+
+<span class="sd">            The axis labels in the `roi` dict for `ImageData` will be:</span>
+<span class="sd">            ``{&#39;angle&#39;:(...),&#39;vertical&#39;:(...),&#39;horizontal&#39;:(...)}``</span>
+
+<span class="sd">            To skip files or to change number of files to load,</span>
+<span class="sd">            adjust ``vertical``. E.g. ``&#39;vertical&#39;: (100, 300)`` will skip first 100 files</span>
+<span class="sd">            and will load 200 files.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># check if file exists</span>
+        <span class="n">file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">file_name</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file_name</span><span class="p">))</span>
+
+        <span class="n">file_type</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">file_name</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">file_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;txrm&#39;</span><span class="p">,</span> <span class="s1">&#39;txm&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;This reader can only process TXRM or TXM files. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">file_name</span><span class="p">)))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">file_name</span> <span class="o">=</span> <span class="n">file_name</span>
+
+
+        <span class="n">metadata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_metadata</span><span class="p">()</span>
+        <span class="n">default_roi</span> <span class="o">=</span> <span class="p">[</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;number_of_images&#39;</span><span class="p">],</span><span class="mi">1</span><span class="p">],</span>
+                        <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;image_height&#39;</span><span class="p">],</span><span class="mi">1</span><span class="p">],</span>
+                        <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;image_width&#39;</span><span class="p">],</span><span class="mi">1</span><span class="p">]]</span>
+
+        <span class="k">if</span> <span class="n">roi</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;data geometry&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;acquisition&#39;</span><span class="p">:</span>
+                <span class="n">zeiss_data_order</span> <span class="o">=</span> <span class="p">{</span><span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">ANGLE</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                                    <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+                                    <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">HORIZONTAL</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">zeiss_data_order</span> <span class="o">=</span> <span class="p">{</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                                    <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+                                    <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
+
+            <span class="c1"># check roi labels and create tuple for slicing</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">roi</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="n">zeiss_data_order</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">roi</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
+                        <span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                            <span class="k">continue</span>
+
+                        <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span> <span class="c1">#start and stop</span>
+                            <span class="n">default_roi</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">default_roi</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span>
+                        <span class="k">else</span><span class="p">:</span> <span class="c1">#step</span>
+                            <span class="n">default_roi</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span>  <span class="n">x</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">1</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span> <span class="o">=</span> <span class="n">default_roi</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slice_metadata</span><span class="p">(</span><span class="n">metadata</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span> <span class="o">=</span> <span class="n">metadata</span>
+
+        <span class="c1">#setup geometry using metadata</span>
+        <span class="k">if</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;data geometry&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;acquisition&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_setup_acq_geometry</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_setup_image_geometry</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">read_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Read one image to get the metadata</span>
+        <span class="n">_</span><span class="p">,</span><span class="n">metadata</span> <span class="o">=</span> <span class="n">dxchange</span><span class="o">.</span><span class="n">read_txrm</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,((</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">),(</span><span class="kc">None</span><span class="p">),(</span><span class="kc">None</span><span class="p">)))</span>
+
+        <span class="k">with</span> <span class="n">olefile</span><span class="o">.</span><span class="n">OleFileIO</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">)</span> <span class="k">as</span> <span class="n">ole</span><span class="p">:</span>
+            <span class="c1">#Configure beam geometry</span>
+            <span class="n">xray_geometry</span> <span class="o">=</span> <span class="n">dxchange</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">_read_ole_value</span><span class="p">(</span><span class="n">ole</span><span class="p">,</span> <span class="s1">&#39;ImageInfo/XrayGeometry&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;i&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">xray_geometry</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;beam geometry&#39;</span><span class="p">]</span> <span class="o">=</span><span class="s1">&#39;cone&#39;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;beam geometry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;parallel&#39;</span>
+
+            <span class="c1">#Configure data geometry</span>
+            <span class="n">file_type</span> <span class="o">=</span> <span class="n">dxchange</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">_read_ole_value</span><span class="p">(</span><span class="n">ole</span><span class="p">,</span> <span class="s1">&#39;ImageInfo/AcquisitionMode&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;i&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">file_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;data geometry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;acquisition&#39;</span>
+
+                <span class="c1"># Read source to center and detector to center distances</span>
+                <span class="n">StoRADistance</span> <span class="o">=</span> <span class="n">dxchange</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">_read_ole_arr</span><span class="p">(</span><span class="n">ole</span><span class="p">,</span> \
+                        <span class="s1">&#39;ImageInfo/StoRADistance&#39;</span><span class="p">,</span> <span class="s2">&quot;&lt;</span><span class="si">{0}</span><span class="s2">f&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;number_of_images&#39;</span><span class="p">]))</span>
+                <span class="n">DtoRADistance</span> <span class="o">=</span> <span class="n">dxchange</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">_read_ole_arr</span><span class="p">(</span><span class="n">ole</span><span class="p">,</span> \
+                        <span class="s1">&#39;ImageInfo/DtoRADistance&#39;</span><span class="p">,</span> <span class="s2">&quot;&lt;</span><span class="si">{0}</span><span class="s2">f&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;number_of_images&#39;</span><span class="p">]))</span>
+
+                <span class="n">dist_source_center</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">StoRADistance</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="n">dist_center_detector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">DtoRADistance</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+                <span class="c1"># Pixelsize loaded in metadata is really the voxel size in um.</span>
+                <span class="c1"># We can compute the effective detector pixel size as the geometric</span>
+                <span class="c1"># magnification times the voxel size.</span>
+                <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;dist_source_center&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dist_source_center</span>
+                <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;dist_center_detector&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dist_center_detector</span>
+                <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;detector_pixel_size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">((</span><span class="n">dist_source_center</span><span class="o">+</span><span class="n">dist_center_detector</span><span class="p">)</span><span class="o">/</span><span class="n">dist_source_center</span><span class="p">)</span><span class="o">*</span><span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;pixel_size&#39;</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;data geometry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;image&#39;</span>
+
+        <span class="k">return</span> <span class="n">metadata</span>
+
+<div class="viewcode-block" id="ZEISSDataReader.slice_metadata">
+<a class="viewcode-back" href="../../../../io/#cil.io.ZEISSDataReader.slice_metadata">[docs]</a>
+    <span class="k">def</span> <span class="nf">slice_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">metadata</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Slices metadata to configure geometry before reading data</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">image_slc</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">height_slc</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">width_slc</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+        <span class="c1">#These values are 0 or do not exist in TXM files and can be skipped</span>
+        <span class="k">if</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;data geometry&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;acquisition&#39;</span><span class="p">:</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;thetas&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;thetas&#39;</span><span class="p">][</span><span class="n">image_slc</span><span class="p">]</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;x_positions&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;x_positions&#39;</span><span class="p">][</span><span class="n">image_slc</span><span class="p">]</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;y_positions&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;y_positions&#39;</span><span class="p">][</span><span class="n">image_slc</span><span class="p">]</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;z_positions&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;z_positions&#39;</span><span class="p">][</span><span class="n">image_slc</span><span class="p">]</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;x-shifts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;x-shifts&#39;</span><span class="p">][</span><span class="n">image_slc</span><span class="p">]</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;y-shifts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;y-shifts&#39;</span><span class="p">][</span><span class="n">image_slc</span><span class="p">]</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;reference&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;reference&#39;</span><span class="p">][</span><span class="n">height_slc</span><span class="o">.</span><span class="n">start</span><span class="p">:</span><span class="n">height_slc</span><span class="o">.</span><span class="n">stop</span><span class="p">:</span><span class="n">height_slc</span><span class="o">.</span><span class="n">step</span><span class="p">,</span>
+                                                          <span class="n">width_slc</span><span class="o">.</span><span class="n">start</span><span class="p">:</span><span class="n">width_slc</span><span class="o">.</span><span class="n">stop</span><span class="p">:</span><span class="n">width_slc</span><span class="o">.</span><span class="n">step</span><span class="p">]</span>
+        <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;number_of_images&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">image_slc</span><span class="p">)</span>
+        <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;image_width&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">width_slc</span><span class="p">)</span>
+        <span class="n">metadata</span><span class="p">[</span><span class="s1">&#39;image_height&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">height_slc</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">metadata</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_setup_acq_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Setup AcquisitionData container</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;beam geometry&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span>
+                <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;dist_source_center&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;dist_center_detector&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">]</span> \
+                <span class="p">)</span> \
+                    <span class="o">.</span><span class="n">set_panel</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;image_width&#39;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;image_height&#39;</span><span class="p">]],</span>\
+                        <span class="n">pixel_size</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;detector_pixel_size&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">1000</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;detector_pixel_size&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">1000</span><span class="p">])</span>\
+                    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;thetas&#39;</span><span class="p">],</span><span class="n">angle_unit</span><span class="o">=</span><span class="n">AngleUnit</span><span class="o">.</span><span class="n">RADIAN</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel3D</span><span class="p">()</span>\
+                    <span class="o">.</span><span class="n">set_panel</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;image_width&#39;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;image_height&#39;</span><span class="p">]])</span>\
+                    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;thetas&#39;</span><span class="p">],</span><span class="n">angle_unit</span><span class="o">=</span><span class="n">AngleUnit</span><span class="o">.</span><span class="n">RADIAN</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span>  <span class="p">[</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span> <span class="s1">&#39;vertical&#39;</span><span class="p">,</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_setup_image_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Setup ImageData container</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">slices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;number_of_images&#39;</span><span class="p">]</span>
+        <span class="n">width</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;image_width&#39;</span><span class="p">]</span>
+        <span class="n">height</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;image_height&#39;</span><span class="p">]</span>
+        <span class="n">voxel_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;pixel_size&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">voxel_num_x</span><span class="o">=</span><span class="n">width</span><span class="p">,</span>
+                                    <span class="n">voxel_size_x</span><span class="o">=</span><span class="n">voxel_size</span><span class="p">,</span>
+                                    <span class="n">voxel_num_y</span><span class="o">=</span><span class="n">height</span><span class="p">,</span>
+                                    <span class="n">voxel_size_y</span><span class="o">=</span><span class="n">voxel_size</span><span class="p">,</span>
+                                    <span class="n">voxel_num_z</span><span class="o">=</span><span class="n">slices</span><span class="p">,</span>
+                                    <span class="n">voxel_size_z</span><span class="o">=</span><span class="n">voxel_size</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ZEISSDataReader.read">
+<a class="viewcode-back" href="../../../../io/#cil.io.ZEISSDataReader.read">[docs]</a>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Reads projections and return Acquisition (TXRM) or Image (TXM) Data container</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1"># Load projections or slices from file</span>
+        <span class="n">slice_range</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">:</span>
+            <span class="n">slice_range</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi</span><span class="p">)</span>
+        <span class="n">data</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">dxchange</span><span class="o">.</span><span class="n">read_txrm</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">file_name</span><span class="p">,</span><span class="n">slice_range</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="p">,</span><span class="n">AcquisitionGeometry</span><span class="p">):</span>
+            <span class="c1"># Normalise data by flatfield</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;reference&#39;</span><span class="p">]</span>
+
+            <span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;number_of_images&#39;</span><span class="p">]):</span>
+                <span class="n">data</span><span class="p">[</span><span class="n">num</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">num</span><span class="p">,:,:],</span> \
+                    <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;x-shifts&#39;</span><span class="p">][</span><span class="n">num</span><span class="p">]),</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span><span class="p">[</span><span class="s1">&#39;y-shifts&#39;</span><span class="p">][</span><span class="n">num</span><span class="p">])),</span> \
+                    <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+
+            <span class="n">acq_data</span> <span class="o">=</span> <span class="n">AcquisitionData</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span><span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">acq_data</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ig_data</span> <span class="o">=</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">array</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>
+            <span class="k">return</span> <span class="n">ig_data</span></div>
+
+
+
+<div class="viewcode-block" id="ZEISSDataReader.get_geometry">
+<a class="viewcode-back" href="../../../../io/#cil.io.ZEISSDataReader.get_geometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Return Acquisition (TXRM) or Image (TXM) Geometry object</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span></div>
+
+
+<div class="viewcode-block" id="ZEISSDataReader.get_metadata">
+<a class="viewcode-back" href="../../../../io/#cil.io.ZEISSDataReader.get_metadata">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;return the metadata of the file&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/io/utilities/index.html b/v24.2.0/_modules/cil/io/utilities/index.html
new file mode 100644
index 0000000000..7ca60e8059
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+++ b/v24.2.0/_modules/cil/io/utilities/index.html
@@ -0,0 +1,859 @@
+
+<!DOCTYPE html>
+
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+<html lang="en" data-content_root="../../../../" >
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+<li class="nav-item ">
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+  </a>
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+    Framework
+  </a>
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+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
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+    Optimisation framework
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+                  
+  <h1>Source code for cil.io.utilities</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2023 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2023 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">json</span>
+<span class="kn">import</span> <span class="nn">h5py</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+
+
+<span class="k">def</span> <span class="nf">get_compress</span><span class="p">(</span><span class="n">compression</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns whether the data needs to be compressed and to which numpy type</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    compression : string, int. Default is None, no compression.</span>
+<span class="sd">        It specifies the number of bits to use for compression, allowed values are None, &#39;uint8&#39;, &#39;uint16&#39; and deprecated 0, 8, 16.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    compress : bool, True if compression is required, False otherwise</span>
+
+<span class="sd">    Note:</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    The use of int is deprecated and will be removed in the future. Use string instead.</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compression</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Use string instead of int&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">compression</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">compression</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">compress</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">elif</span> <span class="n">compression</span> <span class="ow">in</span> <span class="p">[</span> <span class="mi">8</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">]:</span>
+        <span class="n">compress</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="k">elif</span> <span class="n">compression</span> <span class="ow">in</span> <span class="p">[</span> <span class="mi">16</span><span class="p">,</span> <span class="s1">&#39;uint16&#39;</span><span class="p">]:</span>
+        <span class="n">compress</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Compression bits not valid. Got </span><span class="si">{0}</span><span class="s1"> expected value in </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">compression</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">,</span> <span class="s1">&#39;uint16&#39;</span><span class="p">]))</span>
+
+    <span class="k">return</span> <span class="n">compress</span>
+
+<span class="k">def</span> <span class="nf">get_compressed_dtype</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns whether the data needs to be compressed and to which numpy type</span>
+
+<span class="sd">    Given the data and the compression level, returns the numpy type to be used for compression.</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    data : DataContainer, numpy array</span>
+<span class="sd">        the data to be compressed</span>
+<span class="sd">    compression : string, int. Default is None, no compression.</span>
+<span class="sd">        It specifies the number of bits to use for compression, allowed values are None, &#39;uint8&#39;, &#39;uint16&#39; and deprecated 0, 8, 16.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    dtype : numpy type, the numpy type to be used for compression</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compression</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Use string instead of int&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">compression</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">compression</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span>
+    <span class="k">elif</span> <span class="n">compression</span> <span class="ow">in</span> <span class="p">[</span> <span class="mi">8</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">]:</span>
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span>
+    <span class="k">elif</span> <span class="n">compression</span> <span class="ow">in</span> <span class="p">[</span> <span class="mi">16</span><span class="p">,</span> <span class="s1">&#39;uint16&#39;</span><span class="p">]:</span>
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Compression bits not valid. Got </span><span class="si">{0}</span><span class="s1"> expected value in </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">compression</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">16</span><span class="p">]))</span>
+
+    <span class="k">return</span> <span class="n">dtype</span>
+
+<span class="k">def</span> <span class="nf">get_compression_scale_offset</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns the scale and offset to be applied to the data to compress it</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    data : DataContainer, numpy array</span>
+<span class="sd">        The data to be compressed</span>
+<span class="sd">    compression : string, int. Default is None, no compression.</span>
+<span class="sd">        It specifies the number of bits to use for compression, allowed values are None, &#39;uint8&#39;, &#39;uint16&#39; and deprecated 0, 8, 16.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    scale : float, the scale to be applied to the data for compression to the specified number of bits</span>
+<span class="sd">    offset : float, the offset to be applied to the data for compression to the specified number of bits</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compression</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Use string instead of int&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">compression</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">compression</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="c1"># no compression</span>
+        <span class="c1"># return scale 1.0 and offset 0.0</span>
+        <span class="k">return</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span>
+
+    <span class="n">dtype</span> <span class="o">=</span> <span class="n">get_compressed_dtype</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">compression</span><span class="p">)</span>
+    <span class="n">save_range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">iinfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">max</span>
+
+    <span class="n">data_min</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
+    <span class="n">data_range</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">data_min</span>
+
+    <span class="k">if</span> <span class="n">data_range</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">scale</span> <span class="o">=</span> <span class="n">save_range</span> <span class="o">/</span> <span class="n">data_range</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="o">-</span> <span class="n">data_min</span> <span class="o">*</span> <span class="n">scale</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">scale</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">return</span> <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span>
+
+<span class="k">def</span> <span class="nf">save_dict_to_file</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="n">dictionary</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Save scale and offset to file</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    fname : string</span>
+<span class="sd">    dictionary : dictionary</span>
+<span class="sd">        dictionary to write to file</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">configfile</span><span class="p">:</span>
+        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">dictionary</span><span class="p">,</span> <span class="n">configfile</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">compress_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Compress data to dtype using scale and offset</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : numpy array</span>
+<span class="sd">    scale : float</span>
+<span class="sd">    offset : float</span>
+<span class="sd">    dtype : numpy dtype</span>
+
+<span class="sd">    returns compressed casted data&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">data</span>
+    <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="c1"># compress each slice</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="n">tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">compress_data</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">scale</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">data</span> <span class="o">*</span> <span class="n">scale</span> <span class="o">+</span> <span class="n">offset</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">tmp</span>
+
+<div class="viewcode-block" id="HDF5_utilities">
+<a class="viewcode-back" href="../../../../io/#cil.io.utilities.HDF5_utilities">[docs]</a>
+<span class="k">class</span> <span class="nc">HDF5_utilities</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Utility methods to read in from a generic HDF5 file and extract the relevant data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_descend_obj</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">depth</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        obj: str</span>
+<span class="sd">            The initial group to print the metadata for</span>
+<span class="sd">        sep: str, default &#39;\t&#39;</span>
+<span class="sd">            The separator to use for the output</span>
+<span class="sd">        depth: int</span>
+<span class="sd">            depth to print from starting object. Values 1-N, if -1 will print all</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">depth</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="n">h5py</span><span class="o">.</span><span class="n">_hl</span><span class="o">.</span><span class="n">group</span><span class="o">.</span><span class="n">Group</span><span class="p">,</span> <span class="n">h5py</span><span class="o">.</span><span class="n">_hl</span><span class="o">.</span><span class="n">files</span><span class="o">.</span><span class="n">File</span><span class="p">]:</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">obj</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="n">sep</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">obj</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+                    <span class="n">HDF5_utilities</span><span class="o">.</span><span class="n">_descend_obj</span><span class="p">(</span><span class="n">obj</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="n">sep</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="n">depth</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="o">==</span> <span class="n">h5py</span><span class="o">.</span><span class="n">_hl</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">obj</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="n">sep</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">obj</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+
+
+<div class="viewcode-block" id="HDF5_utilities.print_metadata">
+<a class="viewcode-back" href="../../../../io/#cil.io.utilities.HDF5_utilities.print_metadata">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">print_metadata</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="s1">&#39;/&#39;</span><span class="p">,</span> <span class="n">depth</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Prints the file metadata</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        filename: str</span>
+<span class="sd">            The full path to the HDF5 file</span>
+<span class="sd">        group: (str), default: &#39;/&#39;</span>
+<span class="sd">            a specific group to print the metadata for, this defaults to the root group</span>
+<span class="sd">        depth: int, default -1</span>
+<span class="sd">            depth of group to output the metadata for, -1 is fully recursive</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">HDF5_utilities</span><span class="o">.</span><span class="n">_descend_obj</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="n">group</span><span class="p">],</span> <span class="n">depth</span><span class="o">=</span><span class="n">depth</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="HDF5_utilities.get_dataset_metadata">
+<a class="viewcode-back" href="../../../../io/#cil.io.utilities.HDF5_utilities.get_dataset_metadata">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">get_dataset_metadata</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">dset_path</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the dataset metadata as a dictionary</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        filename: str</span>
+<span class="sd">            The full path to the HDF5 file</span>
+<span class="sd">        dset_path: str</span>
+<span class="sd">            The internal path to the requested dataset</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        A dictionary containing keys, values are `None` if attribute can&#39;t be read.:</span>
+<span class="sd">            ndim, shape, size, dtype, nbytes, compression, chunks, is_virtual</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                <span class="n">dset</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dset_path</span><span class="p">,</span> <span class="p">)</span>
+
+                <span class="n">attribs</span> <span class="o">=</span> <span class="p">{</span>
+                    <span class="s1">&#39;ndim&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;shape&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;size&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;dtype&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;compression&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;chunks&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="s1">&#39;is_virtual&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">}</span>
+
+                <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">attribs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">attribs</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">dset</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+                    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+                        <span class="k">pass</span>
+
+                <span class="k">return</span> <span class="n">attribs</span></div>
+
+
+
+
+<div class="viewcode-block" id="HDF5_utilities.read">
+<a class="viewcode-back" href="../../../../io/#cil.io.utilities.HDF5_utilities.read">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">dset_path</span><span class="p">,</span> <span class="n">source_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Reads a dataset entry and returns a numpy array with the requested data</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        filename: str</span>
+<span class="sd">            The full path to the HDF5 file</span>
+<span class="sd">        dset_path: str</span>
+<span class="sd">            The internal path to the requested dataset</span>
+<span class="sd">        source_sel: tuple of slice objects, optional</span>
+<span class="sd">            The selection of slices in each source dimension to return</span>
+<span class="sd">        dtype: numpy type, default np.float32</span>
+<span class="sd">            the numpy data type for the returned array</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        numpy.ndarray</span>
+<span class="sd">            The requested data</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        source_sel takes a tuple of slice objects to defining crop and slicing behaviour</span>
+
+<span class="sd">        This can be constructed using numpy indexing, i.e. the following lines are equivalent.</span>
+
+<span class="sd">        &gt;&gt;&gt; source_sel = (slice(2, 4, None), slice(2, 10, 2))</span>
+
+<span class="sd">        &gt;&gt;&gt; source_sel = np.s_[2:4,2:10:2]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">dset</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dset_path</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">source_sel</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">source_sel</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)]</span><span class="o">*</span><span class="n">dset</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span>
+
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">dset</span><span class="p">[</span><span class="n">source_sel</span><span class="p">],</span><span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">arr</span></div>
+
+
+
+<div class="viewcode-block" id="HDF5_utilities.read_to">
+<a class="viewcode-back" href="../../../../io/#cil.io.utilities.HDF5_utilities.read_to">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">read_to</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">dset_path</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">source_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dest_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Reads a dataset entry and directly fills a numpy array with the requested data</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        filename: str</span>
+<span class="sd">            The full path to the HDF5 file</span>
+<span class="sd">        dset_path: str</span>
+<span class="sd">            The internal path to the requested dataset</span>
+<span class="sd">        out: numpy.ndarray</span>
+<span class="sd">            The output array to be filled</span>
+<span class="sd">        source_sel: tuple of slice objects, optional</span>
+<span class="sd">            The selection of slices in each source dimension to return</span>
+<span class="sd">        dest_sel: tuple of slice objects, optional</span>
+<span class="sd">            The selection of slices in each destination dimension to fill</span>
+
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        source_sel and dest_sel take a tuple of slice objects to defining crop and slicing behaviour</span>
+
+<span class="sd">        This can be constructed using numpy indexing, i.e. the following lines are equivalent.</span>
+
+<span class="sd">        &gt;&gt;&gt; source_sel = (slice(2, 4, None), slice(2, 10, 2))</span>
+
+<span class="sd">        &gt;&gt;&gt; source_sel = np.s_[2:4,2:10:2]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">dset</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dset_path</span><span class="p">)</span>
+            <span class="n">dset</span><span class="o">.</span><span class="n">read_direct</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">source_sel</span><span class="p">,</span> <span class="n">dest_sel</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
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diff --git a/v24.2.0/_modules/cil/optimisation/algorithms/ADMM/index.html b/v24.2.0/_modules/cil/optimisation/algorithms/ADMM/index.html
new file mode 100644
index 0000000000..658f43ac5c
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+  <h1>Source code for cil.optimisation.algorithms.ADMM</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="LADMM">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.LADMM">[docs]</a>
+<span class="k">class</span> <span class="nc">LADMM</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        LADMM is the Linearized Alternating Direction Method of Multipliers (LADMM)</span>
+
+<span class="sd">        General form of ADMM : min_{x} f(x) + g(y), subject to Ax + By = b</span>
+
+<span class="sd">        Case: A = Id, B = -K, b = 0   ==&gt; min_x f(Kx) + g(x)</span>
+
+<span class="sd">        The quadratic term in the augmented Lagrangian is linearized for the x-update.</span>
+
+<span class="sd">        Main algorithmic difference is that in ADMM we compute two proximal subproblems,</span>
+<span class="sd">        where in the PDHG a proximal and proximal conjugate.</span>
+
+<span class="sd">        Reference (Section 8) : https://link.springer.com/content/pdf/10.1007/s10107-018-1321-1.pdf</span>
+
+<span class="sd">            x^{k} = prox_{\tau f } (x^{k-1} - tau/sigma A^{T}(Ax^{k-1} - z^{k-1} + u^{k-1} )</span>
+
+<span class="sd">            z^{k} = prox_{\sigma g} (Ax^{k} + u^{k-1})</span>
+
+<span class="sd">            u^{k} = u^{k-1} + Ax^{k} - z^{k}</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> \
+                       <span class="n">tau</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sigma</span> <span class="o">=</span> <span class="mf">1.</span><span class="p">,</span>
+                       <span class="n">initial</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Initialisation of the algorithm</span>
+
+<span class="sd">        :param operator: a Linear Operator</span>
+<span class="sd">        :param f: Convex function with &quot;simple&quot; proximal</span>
+<span class="sd">        :param g: Convex function with &quot;simple&quot; proximal</span>
+<span class="sd">        :param sigma: Positive step size parameter</span>
+<span class="sd">        :param tau: Positive step size parameter</span>
+<span class="sd">        :param initial: Initial guess ( Default initial_guess = 0)&#39;&#39;&#39;</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">LADMM</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">f</span> <span class="o">=</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span> <span class="o">=</span> <span class="n">g</span><span class="p">,</span> <span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">tau</span><span class="p">,</span>\
+             <span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">)</span>
+
+<div class="viewcode-block" id="LADMM.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.LADMM.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> setting up&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sigma</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">tau</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Need tau &lt;= sigma / ||K||^2&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">f</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span> <span class="o">=</span> <span class="n">g</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="o">=</span> <span class="n">tau</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">normK</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">/</span> <span class="n">normK</span> <span class="o">**</span> <span class="mi">2</span>
+
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="c1"># allocate space for operator direct &amp; adjoint</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tmp_adj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">u</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> configured&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="LADMM.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.LADMM.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">u</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">z</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_adj</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_adj</span><span class="p">,</span> <span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="c1"># apply proximal of f</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span><span class="p">)</span>
+        <span class="c1"># store the result in x</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="k">del</span> <span class="n">tmp</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">u</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span>
+
+        <span class="c1"># apply proximal of g</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">u</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">z</span><span class="p">)</span>
+
+        <span class="c1"># update</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">u</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">z</span></div>
+
+
+<div class="viewcode-block" id="LADMM.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.LADMM.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span>  <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">))</span> <span class="p">)</span></div>
+</div>
+
+</pre></div>
+
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new file mode 100644
index 0000000000..60c0ee2514
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+  <h1>Source code for cil.optimisation.algorithms.Algorithm</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">count</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Integral</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.utilities.callbacks</span> <span class="kn">import</span> <span class="n">Callback</span><span class="p">,</span> <span class="n">LogfileCallback</span><span class="p">,</span> <span class="n">_OldCallback</span><span class="p">,</span> <span class="n">ProgressCallback</span>
+
+
+<div class="viewcode-block" id="Algorithm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm">[docs]</a>
+<span class="k">class</span> <span class="nc">Algorithm</span><span class="p">:</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Base class providing minimal infrastructure for iterative algorithms.</span>
+
+<span class="sd">   An iterative algorithm is designed to solve an optimization problem by repeatedly refining a solution. In CIL, we use iterative algorithms to minimize an objective function, often referred to as a loss. The process begins with an initial guess, and with each iteration, the algorithm updates the current solution based on the results of previous iterations (previous iterates). Iterative algorithms typically continue until a stopping criterion is met, indicating that an optimal or sufficiently good solution has been found. In CIL, stopping criteria can be implemented using a callback function (`cil.optimisation.utilities.callbacks`).</span>
+<span class="sd">     </span>
+<span class="sd">    The user is required to implement the :code:`set_up`, :code:`__init__`, :code:`update` and :code:`update_objective` methods.</span>
+
+<span class="sd">    The method :code:`run` is available to run :code:`n` iterations. The method accepts :code:`callbacks`: a list of callables, each of which receive the current Algorithm object (which in turn contains the iteration number and the actual objective value) and can be used to trigger print to screens and other user interactions. The :code:`run` method will stop when the stopping criterion is met or `StopIteration` is raised.</span>
+<span class="sd">    </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    update_objective_interval: int, optional, default 1</span>
+<span class="sd">        The objective (or loss) is calculated and saved every `update_objective_interval`.  1 means every iteration, 2 every 2 iterations and so forth. This is by default 1 and should be increased when evaluating the objective is computationally expensive.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">log_file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">__max_iteration</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="n">max_iteration</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use `Algorithm.run(iterations)` instead of `Algorithm(max_iteration)`&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__max_iteration</span> <span class="o">=</span> <span class="n">max_iteration</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">__loss</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">memopt</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_iteration</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update_objective_interval</span> <span class="o">=</span> <span class="n">update_objective_interval</span>
+        <span class="c1"># self.x = None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">iter_string</span> <span class="o">=</span> <span class="s1">&#39;Iter&#39;</span>
+        <span class="k">if</span> <span class="n">log_file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use `run(callbacks=[LogfileCallback(log_file)])` instead of `log_file`&quot;</span><span class="p">,</span>
+                 <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__log_file</span> <span class="o">=</span> <span class="n">log_file</span>
+
+<div class="viewcode-block" id="Algorithm.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Set up the algorithm&#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+<div class="viewcode-block" id="Algorithm.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;A single iteration of the algorithm&#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="Algorithm.should_stop">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.should_stop">[docs]</a>
+    <span class="k">def</span> <span class="nf">should_stop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;default stopping criterion: number of iterations</span>
+
+<span class="sd">        The user can change this in concrete implementation of iterative algorithms.&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span></div>
+
+
+    <span class="k">def</span> <span class="nf">__set_up_logger</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">_</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Do not use: this is being deprecated&quot;&quot;&quot;</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use `run(callbacks=[LogfileCallback(log_file)])` instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Algorithm.max_iteration_stop_criterion">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.max_iteration_stop_criterion">[docs]</a>
+    <span class="k">def</span> <span class="nf">max_iteration_stop_criterion</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Do not use: this is being deprecated&quot;&quot;&quot;</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use `should_stop()` instead of `max_iteration_stop_criterion()`&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algorithm is an iterable&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Algorithm is an iterable</span>
+
+<span class="sd">        This method triggers :code:`update()` and :code:`update_objective()`</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">should_stop</span><span class="p">():</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_objective_interval</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_iteration</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iteration</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">update_objective</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">configured</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Algorithm not configured correctly. Please run set_up.&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_update_previous_solution</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_objective_interval</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span>\
+            <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_objective_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_iteration</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iteration</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">update_objective</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span>
+
+    <span class="k">def</span> <span class="nf">_update_previous_solution</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; An optional but common function that can be implemented by child classes to update a stored previous solution with the current one. </span>
+<span class="sd">        Best practice for memory efficiency would be to do this by the swapping of pointers:</span>
+
+<span class="sd">        .. highlight:: python</span>
+<span class="sd">        .. code-block:: python</span>
+
+<span class="sd">            tmp = self.x_old</span>
+<span class="sd">            self.x_old = self.x</span>
+<span class="sd">            self.x = tmp</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span>
+
+<div class="viewcode-block" id="Algorithm.get_output">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.get_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the current solution. </span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The current solution </span>
+<span class="sd">             </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_provable_convergence_condition</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Checks if the algorithm set-up (e.g. chosen step-sizes or other parameters) meets a mathematical convergence criterion.</span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        bool: Outcome of the convergence check  </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot; Convergence criterion is not implemented for this algorithm. &quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Algorithm.is_provably_convergent">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.is_provably_convergent">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_provably_convergent</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Check if the algorithm is convergent based on the provable convergence criterion.</span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Boolean</span>
+<span class="sd">            Outcome of the convergence check  </span>
+<span class="sd">        </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_provable_convergence_condition</span><span class="p">()</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">solution</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="s2">&quot; Returns the current solution. &quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+
+<div class="viewcode-block" id="Algorithm.get_last_loss">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.get_last_loss">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_last_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">return_all</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns the last stored value of the loss function. &quot;Loss&quot; is an alias for &quot;objective value&quot;. If `update_objective_interval` is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        return_all: Boolean, default is False</span>
+<span class="sd">            If True, returns all the stored loss functions </span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Float</span>
+<span class="sd">            Last stored value of the loss function </span>
+<span class="sd">        </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">objective</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loss</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+            <span class="n">objective</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">objective</span> <span class="k">if</span> <span class="n">return_all</span> <span class="k">else</span> <span class="n">objective</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">objective</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span> <span class="k">if</span> <span class="n">return_all</span> <span class="k">else</span> <span class="n">objective</span></div>
+
+
+    <span class="n">get_last_objective</span> <span class="o">=</span> <span class="n">get_last_loss</span> <span class="c1"># alias</span>
+
+<div class="viewcode-block" id="Algorithm.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;calculates the objective with the current solution&#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">iterations</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns the iterations at which the objective has been evaluated&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iteration</span>
+    
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a list of the values of the objective (alias of loss) during the iteration</span>
+
+<span class="sd">        The length of this list may be shorter than the number of iterations run when the `update_objective_interval` &gt; 1</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loss</span>
+
+    <span class="n">objective</span> <span class="o">=</span> <span class="n">loss</span> <span class="c1"># alias</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">max_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;gets the maximum number of iterations&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__max_iteration</span>
+
+    <span class="nd">@max_iteration</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">max_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;sets the maximum number of iterations&#39;&#39;&#39;</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Integral</span><span class="p">)</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">isposinf</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">__max_iteration</span> <span class="o">=</span> <span class="n">value</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">update_objective_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;gets the update_objective_interval&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__update_objective_interval</span>
+
+    <span class="nd">@update_objective_interval</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">update_objective_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;sets the update_objective_interval&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Integral</span><span class="p">)</span> <span class="ow">or</span> <span class="n">value</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;interval must be an integer &gt;= 0&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">__update_objective_interval</span> <span class="o">=</span> <span class="n">value</span>
+
+<div class="viewcode-block" id="Algorithm.run">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.run">[docs]</a>
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">callbacks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Callback</span><span class="p">]]</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;run upto :code:`iterations` with callbacks/logging.</span>
+<span class="sd">        </span>
+<span class="sd">        For a demonstration of callbacks see https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        -----------</span>
+<span class="sd">        iterations: int, default is None</span>
+<span class="sd">            Number of iterations to run. If not set the algorithm will run until :code:`should_stop()` is reached</span>
+<span class="sd">        callbacks: list of callables, default is Defaults to :code:`[ProgressCallback(verbose)]`</span>
+<span class="sd">            List of callables which are passed the current Algorithm object each iteration. Defaults to :code:`[ProgressCallback(verbose)]`.</span>
+<span class="sd">        verbose: 0=quiet, 1=info, 2=debug</span>
+<span class="sd">            Passed to the default callback to determine the verbosity of the printed output. </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        
+        <span class="k">if</span> <span class="s1">&#39;print_interval&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use `TextProgressCallback(miniters)` instead of `run(print_interval)`&quot;</span><span class="p">,</span>
+                 <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">callbacks</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">callbacks</span> <span class="o">=</span> <span class="p">[</span><span class="n">ProgressCallback</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)]</span>
+        <span class="c1"># transform old-style callbacks into new</span>
+        <span class="n">callback</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;callback&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">callback</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">callbacks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_OldCallback</span><span class="p">(</span><span class="n">callback</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">))</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;__log_file&#39;</span><span class="p">):</span>
+            <span class="n">callbacks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LogfileCallback</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__log_file</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">should_stop</span><span class="p">():</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Stop criterion has been reached.&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">iterations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;`run()` missing `iterations`&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">iterations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_objective_interval</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+            <span class="n">iterations</span><span class="o">+=</span><span class="mi">1</span>
+
+        <span class="c1"># call `__next__` upto `iterations` times or until `StopIteration` is raised</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration</span> <span class="o">+</span> <span class="n">iterations</span>
+        <span class="n">iters</span> <span class="o">=</span> <span class="p">(</span><span class="n">count</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iteration</span><span class="p">)</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isposinf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span><span class="p">)</span>
+                 <span class="k">else</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iteration</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">iters</span><span class="p">,</span> <span class="bp">self</span><span class="p">):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">callback</span> <span class="ow">in</span> <span class="n">callbacks</span><span class="p">:</span>
+                    <span class="n">callback</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span>
+                <span class="k">break</span></div>
+
+
+<div class="viewcode-block" id="Algorithm.objective_to_dict">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.objective_to_dict">[docs]</a>
+    <span class="k">def</span> <span class="nf">objective_to_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal function to save and print objective functions&quot;&quot;&quot;</span>
+        <span class="n">obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_last_objective</span><span class="p">(</span><span class="n">return_all</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">obj</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
+                <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;primal&#39;</span><span class="p">:</span> <span class="n">obj</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;dual&#39;</span><span class="p">:</span> <span class="n">obj</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;primal_dual&#39;</span><span class="p">:</span> <span class="n">obj</span><span class="p">[</span><span class="mi">2</span><span class="p">]}</span>
+            <span class="n">obj</span> <span class="o">=</span> <span class="n">obj</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;objective&#39;</span><span class="p">:</span> <span class="n">obj</span><span class="p">}</span></div>
+
+
+<div class="viewcode-block" id="Algorithm.objective_to_string">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.objective_to_string">[docs]</a>
+    <span class="k">def</span> <span class="nf">objective_to_string</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Do not use: this is being deprecated&quot;&quot;&quot;</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;consider using `run(callbacks=[LogfileCallback(log_file)])` instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">objective_to_dict</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="Algorithm.verbose_output">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.verbose_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">verbose_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">_</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Do not use: this is being deprecated&quot;&quot;&quot;</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;use `run(callbacks=[ProgressCallback()])` instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Algorithm.verbose_header">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.Algorithm.verbose_header">[docs]</a>
+    <span class="k">def</span> <span class="nf">verbose_header</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">_</span><span class="p">,</span> <span class="o">**</span><span class="n">__</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Do not use: this is being deprecated&quot;&quot;&quot;</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;consider using `run(callbacks=[LogfileCallback(log_file)])` instead&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
+              
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+              
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+                <footer class="prev-next-footer d-print-none">
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diff --git a/v24.2.0/_modules/cil/optimisation/algorithms/CGLS/index.html b/v24.2.0/_modules/cil/optimisation/algorithms/CGLS/index.html
new file mode 100644
index 0000000000..e1b64c3004
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+
+<!DOCTYPE html>
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+<html lang="en" data-content_root="../../../../../" >
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+  <head>
+    <meta charset="utf-8" />
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+  
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+  
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+  <h1>Source code for cil.optimisation.algorithms.CGLS</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">warnings</span> 
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="CGLS">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.CGLS">[docs]</a>
+<span class="k">class</span> <span class="nc">CGLS</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Conjugate Gradient Least Squares (CGLS) algorithm</span>
+<span class="sd">    </span>
+<span class="sd">    The Conjugate Gradient Least Squares (CGLS) algorithm is commonly used for solving large systems of linear equations, due to its fast convergence.</span>
+
+<span class="sd">    Problem:</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">      \min_x || A x - b ||^2_2</span>
+<span class="sd">      </span>
+<span class="sd">      </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ------------</span>
+<span class="sd">    operator : Operator</span>
+<span class="sd">        Linear operator for the inverse problem</span>
+<span class="sd">    initial : (optional) DataContainer in the domain of the operator, default is a DataContainer filled with zeros. </span>
+<span class="sd">        Initial guess </span>
+<span class="sd">    data : DataContainer in the range of the operator </span>
+<span class="sd">        Acquired data to reconstruct</span>
+<span class="sd">        </span>
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    Passing tolerance directly to CGLS is being deprecated. Instead we recommend using the callback functionality: https://tomographicimaging.github.io/CIL/nightly/optimisation/#callbacks and in particular the CGLSEarlyStopping callback replicated the old behaviour.</span>
+
+<span class="sd">    Reference</span>
+<span class="sd">    ---------</span>
+<span class="sd">    https://web.stanford.edu/group/SOL/software/cgls/</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;initialisation of the algorithm</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1">#We are deprecating tolerance </span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;tolerance&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">message</span><span class="o">=</span><span class="s2">&quot;Passing tolerance directly to CGLS is being deprecated. Instead we recommend using the callback functionality: https://tomographicimaging.github.io/CIL/nightly/optimisation/#callbacks and in particular the CGLSEarlyStopping callback replicated the old behaviour&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="o">=</span> <span class="mi">0</span>
+        
+        <span class="nb">super</span><span class="p">(</span><span class="n">CGLS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">operator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">initial</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">operator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="n">operator</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span> 
+
+<div class="viewcode-block" id="CGLS.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.CGLS.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Initialisation of the algorithm</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ------------</span>
+<span class="sd">        operator : Operator</span>
+<span class="sd">            Linear operator for the inverse problem</span>
+<span class="sd">        initial : (optional) DataContainer in the domain of the operator, default is a DataContainer filled with zeros. </span>
+<span class="sd">            Initial guess </span>
+<span class="sd">        data : DataContainer in the range of the operator </span>
+<span class="sd">            Acquired data to reconstruct</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> setting up&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">r</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">norms0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">norms</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norms0</span><span class="o">**</span><span class="mi">2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">normx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> configured&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="CGLS.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.CGLS.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;single iteration&#39;&#39;&#39;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">)</span>
+        <span class="n">delta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">/</span><span class="n">delta</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+        <span class="c1">#self.x += alpha * self.p</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="o">-</span><span class="n">alpha</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="p">)</span>
+        <span class="c1">#self.r -= alpha * self.q</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">norms</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norms</span><span class="o">**</span><span class="mi">2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma1</span>
+        <span class="c1">#self.p = self.s + self.beta * self.p</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">normx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="c1"># TODO: Deprecated, remove when CGLS tolerance is removed</span></div>
+
+
+
+<div class="viewcode-block" id="CGLS.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.CGLS.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nan</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="CGLS.should_stop">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.CGLS.should_stop">[docs]</a>
+    <span class="k">def</span> <span class="nf">should_stop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># TODO: Deprecated, remove when CGLS tolerance is removed</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">flag</span><span class="p">()</span> <span class="ow">or</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">should_stop</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="CGLS.flag">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.CGLS.flag">[docs]</a>
+    <span class="k">def</span> <span class="nf">flag</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="c1"># TODO: Deprecated, remove when CGLS tolerance is removed</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns whether the tolerance has been reached&#39;&#39;&#39;</span>
+        <span class="n">flag</span>  <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norms</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norms0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">normx</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">flag</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">update_objective</span><span class="p">()</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tolerance is reached: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">flag</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/algorithms/FISTA/index.html b/v24.2.0/_modules/cil/optimisation/algorithms/FISTA/index.html
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+  <h1>Source code for cil.optimisation.algorithms.FISTA</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">ZeroFunction</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.utilities</span> <span class="kn">import</span> <span class="n">ConstantStepSize</span><span class="p">,</span> <span class="n">StepSizeRule</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Real</span><span class="p">,</span> <span class="n">Number</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="ISTA">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.ISTA">[docs]</a>
+<span class="k">class</span> <span class="nc">ISTA</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Iterative Shrinkage-Thresholding Algorithm, see :cite:`BeckTeboulle_b`, :cite:`BeckTeboulle_a`.</span>
+
+<span class="sd">    Iterative Shrinkage-Thresholding Algorithm (ISTA)</span>
+
+<span class="sd">    .. math:: x^{k+1} = \mathrm{prox}_{\alpha^{k} g}(x^{k} - \alpha^{k}\nabla f(x^{k}))</span>
+
+<span class="sd">    is used to solve</span>
+
+<span class="sd">    .. math:: \min_{x} f(x) + g(x)</span>
+
+<span class="sd">    where :math:`f` is differentiable, :math:`g` has a *simple* proximal operator and :math:`\alpha^{k}`</span>
+<span class="sd">    is the :code:`step_size` per iteration.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    For a constant step size, i.e., :math:`a^{k}=a` for :math:`k\geq1`, convergence of ISTA</span>
+<span class="sd">    is guaranteed if</span>
+
+<span class="sd">    .. math:: \alpha\in(0, \frac{2}{L}),</span>
+
+<span class="sd">    where :math:`L` is the Lipschitz constant of :math:`f`, see :cite:`CombettesValerie`.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    initial : DataContainer</span>
+<span class="sd">              Initial guess of ISTA.</span>
+<span class="sd">    f : Function</span>
+<span class="sd">        Differentiable function. If `None` is passed, the algorithm will use the ZeroFunction.</span>
+<span class="sd">    g : Function or `None`</span>
+<span class="sd">        Convex function with *simple* proximal operator. If `None` is passed, the algorithm will use the ZeroFunction.</span>
+<span class="sd">    step_size : positive :obj:`float` or child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39;,  default = None</span>
+<span class="sd">                Step size for the gradient step of ISTA. If a float is passed, this is used as a constant step size.  If a child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39; is passed then it&#39;s method `get_step_size` is called for each update. </span>
+<span class="sd">                The default :code:`step_size` is a constant :math:`\frac{0.99*2}{L}` or 1 if `f=None`.</span>
+<span class="sd">     preconditioner: class with a `apply` method or a function that takes an initialised CIL function as an argument and modifies a provided `gradient`.</span>
+<span class="sd">            This could be a custom `preconditioner` or one provided in :meth:`~cil.optimisation.utilities.preconditoner`. If None is passed  then `self.gradient_update` will remain unmodified. </span>
+
+
+<span class="sd">    kwargs: Keyword arguments</span>
+<span class="sd">        Arguments from the base class :class:`.Algorithm`.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    If the function `g` is set to `None` or to the `ZeroFunction` then the ISTA algorithm is equivalent to Gradient Descent.</span>
+
+<span class="sd">    If the function `f` is set to `None` or to the `ZeroFunction` then the ISTA algorithm is equivalent to a Proximal Point Algorithm.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    .. math:: \underset{x}{\mathrm{argmin}}\|A x - b\|^{2}_{2}</span>
+
+<span class="sd">    &gt;&gt;&gt; f = LeastSquares(A, b=b, c=0.5)</span>
+<span class="sd">    &gt;&gt;&gt; g = ZeroFunction()</span>
+<span class="sd">    &gt;&gt;&gt; ig = Aop.domain</span>
+<span class="sd">    &gt;&gt;&gt; ista = ISTA(initial = ig.allocate(), f = f, g = g, max_iteration=10)</span>
+<span class="sd">    &gt;&gt;&gt; ista.run()</span>
+
+
+<span class="sd">    See also</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    :class:`.FISTA`</span>
+<span class="sd">    :class:`.GD`</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">_provable_convergence_condition</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
+                <span class="s2">&quot;Can&#39;t check convergence criterion if a preconditioner is used &quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="p">,</span> <span class="n">ConstantStepSize</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">step_size</span> <span class="o">&lt;=</span> <span class="mf">0.99</span><span class="o">*</span><span class="mf">2.0</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">L</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Can&#39;t check convergence criterion for non-constant step size&quot;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="p">,</span> <span class="n">ConstantStepSize</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">step_size</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Note the step-size is set by a step-size rule and could change wit each iteration&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">get_step_size</span><span class="p">()</span>
+
+    <span class="c1"># Set default step size</span>
+
+    <span class="k">def</span> <span class="nf">_calculate_default_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Calculates the default step size if a step size rule or a step size is not provided. </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        
+        <span class="k">return</span> <span class="mf">0.99</span><span class="o">*</span><span class="mf">2.0</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">L</span>
+
+        
+
+<div class="viewcode-block" id="ISTA.__init__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.ISTA.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preconditioner</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ISTA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_step_size</span> <span class="o">=</span> <span class="n">step_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="n">f</span><span class="o">=</span><span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">g</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="n">step_size</span><span class="p">,</span>
+                    <span class="n">preconditioner</span><span class="o">=</span><span class="n">preconditioner</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ISTA.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.ISTA.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">step_size</span><span class="p">,</span> <span class="n">preconditioner</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Set up of the algorithm&quot;&quot;&quot;</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> setting up&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+        <span class="c1"># set up ISTA</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initial</span> <span class="o">=</span> <span class="n">initial</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">f</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">f</span> <span class="o">=</span> <span class="n">ZeroFunction</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">f</span>
+
+        <span class="k">if</span> <span class="n">g</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">g</span> <span class="o">=</span> <span class="n">ZeroFunction</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span> <span class="o">=</span> <span class="n">g</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">ZeroFunction</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">ZeroFunction</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;You set both f and g to be the ZeroFunction and thus the iterative method will not update and will remain fixed at the initial value.&#39;</span><span class="p">)</span>
+
+        <span class="c1"># set step_size</span>
+        <span class="k">if</span> <span class="n">step_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span> <span class="o">=</span> <span class="n">ConstantStepSize</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_default_step_size</span><span class="p">())</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">step_size</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span> <span class="o">=</span> <span class="n">ConstantStepSize</span><span class="p">(</span><span class="n">step_size</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">step_size</span><span class="p">,</span> <span class="n">StepSizeRule</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span> <span class="o">=</span> <span class="n">step_size</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span> <span class="o">=</span> <span class="n">preconditioner</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> configured&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ISTA.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.ISTA.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Performs a single iteration of ISTA</span>
+
+<span class="sd">        .. math:: x_{k+1} = \mathrm{prox}_{\alpha g}(x_{k} - \alpha\nabla f(x_{k}))</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># gradient step</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">step_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">get_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NameError</span><span class="p">(</span><span class="n">msg</span><span class="o">=</span><span class="s1">&#39;`step_size` must be `None`, a real float or a child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">,</span> <span class="o">-</span><span class="n">step_size</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">)</span>
+
+        <span class="c1"># proximal step</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="n">step_size</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_update_previous_solution</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Swaps the references to current and previous solution based on the :func:`~Algorithm.update_previous_solution` of the base class :class:`Algorithm`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">tmp</span>
+
+<div class="viewcode-block" id="ISTA.get_output">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.ISTA.get_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="s2">&quot; Returns the current solution. &quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span></div>
+
+
+<div class="viewcode-block" id="ISTA.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.ISTA.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Updates the objective</span>
+
+<span class="sd">        .. math:: f(x) + g(x)</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">calculate_objective_function_at_point</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="ISTA.calculate_objective_function_at_point">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.ISTA.calculate_objective_function_at_point">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_objective_function_at_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Calculates the objective at a given point x</span>
+
+<span class="sd">        .. math:: f(x) + g(x)</span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">        </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
+</div>
+
+
+<div class="viewcode-block" id="FISTA">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.FISTA">[docs]</a>
+<span class="k">class</span> <span class="nc">FISTA</span><span class="p">(</span><span class="n">ISTA</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fast Iterative Shrinkage-Thresholding Algorithm, see :cite:`BeckTeboulle_b`, :cite:`BeckTeboulle_a`.</span>
+
+<span class="sd">    Fast Iterative Shrinkage-Thresholding Algorithm (FISTA)</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">        \begin{cases}</span>
+<span class="sd">            y_{k} = x_{k} - \alpha\nabla f(x_{k})  \\</span>
+<span class="sd">            x_{k+1} = \mathrm{prox}_{\alpha g}(y_{k})\\</span>
+<span class="sd">            t_{k+1} = \frac{1+\sqrt{1+ 4t_{k}^{2}}}{2}\\</span>
+<span class="sd">            y_{k+1} = x_{k} + \frac{t_{k}-1}{t_{k-1}}(x_{k} - x_{k-1})</span>
+<span class="sd">        \end{cases}</span>
+
+<span class="sd">    is used to solve</span>
+
+<span class="sd">    .. math:: \min_{x} f(x) + g(x)</span>
+
+<span class="sd">    where :math:`f` is differentiable, :math:`g` has a *simple* proximal operator and :math:`\alpha^{k}`</span>
+<span class="sd">    is the :code:`step_size` per iteration.</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    initial : DataContainer</span>
+<span class="sd">            Starting point of the algorithm</span>
+<span class="sd">    f : Function</span>
+<span class="sd">        Differentiable function.  If `None` is passed, the algorithm will use the ZeroFunction.</span>
+<span class="sd">    g : Function or `None`</span>
+<span class="sd">        Convex function with *simple* proximal operator. If `None` is passed, the algorithm will use the ZeroFunction.</span>
+<span class="sd">    step_size : positive :obj:`float` or child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39;,  default = None</span>
+<span class="sd">                Step size for the gradient step of ISTA. If a float is passed, this is used as a constant step size. If a child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39; is passed then it&#39;s method `get_step_size` is called for each update. </span>
+<span class="sd">                The default :code:`step_size` is a constant :math:`\frac{1}{L}` or 1 if `f=None`.</span>
+<span class="sd">    preconditioner: class with a `apply` method or a function that takes an initialised CIL function as an argument and modifies a provided `gradient`.</span>
+<span class="sd">            This could be a custom `preconditioner` or one provided in :meth:`~cil.optimisation.utilities.preconditoner`. If None is passed  then `self.gradient_update` will remain unmodified. </span>
+
+<span class="sd">    kwargs: Keyword arguments</span>
+<span class="sd">        Arguments from the base class :class:`.Algorithm`.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    If the function `g` is set to `None` or to the `ZeroFunction` then the FISTA algorithm is equivalent to Accelerated Gradient Descent by Nesterov (:cite:`nesterov2003introductory` algorithm 2.2.9).</span>
+
+<span class="sd">    If the function `f` is set to `None` or to the `ZeroFunction` then the FISTA algorithm is equivalent to Guler&#39;s First Accelerated Proximal Point Method  (:cite:`guler1992new` sec 2).</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    .. math:: \underset{x}{\mathrm{argmin}}\|A x - b\|^{2}_{2}</span>
+
+
+<span class="sd">    &gt;&gt;&gt; f = LeastSquares(A, b=b, c=0.5)</span>
+<span class="sd">    &gt;&gt;&gt; g = ZeroFunction()</span>
+<span class="sd">    &gt;&gt;&gt; ig = Aop.domain</span>
+<span class="sd">    &gt;&gt;&gt; fista = FISTA(initial = ig.allocate(), f = f, g = g, max_iteration=10)</span>
+<span class="sd">    &gt;&gt;&gt; fista.run()</span>
+
+<span class="sd">    See also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :class:`.FISTA`</span>
+<span class="sd">    :class:`.GD`</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">_calculate_default_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate the default step size if a step size rule or step size is not provided </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="mf">1.</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">L</span>
+
+
+
+    <span class="k">def</span> <span class="nf">_provable_convergence_condition</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
+                <span class="s2">&quot;Can&#39;t check convergence criterion if a preconditioner is used &quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="p">,</span> <span class="n">ConstantStepSize</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">step_size</span> <span class="o">&lt;=</span> <span class="mf">1.</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">L</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Can&#39;t check convergence criterion for non-constant step size&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="FISTA.__init__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.FISTA.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="n">preconditioner</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">FISTA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="n">f</span><span class="o">=</span><span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">g</span><span class="p">,</span>
+                                    <span class="n">step_size</span><span class="o">=</span><span class="n">step_size</span><span class="p">,</span>  <span class="n">preconditioner</span><span class="o">=</span><span class="n">preconditioner</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="FISTA.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.FISTA.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Performs a single iteration of FISTA</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">            \begin{cases}</span>
+<span class="sd">                x_{k+1} = \mathrm{prox}_{\alpha g}(y_{k} - \alpha\nabla f(y_{k}))\\</span>
+<span class="sd">                t_{k+1} = \frac{1+\sqrt{1+ 4t_{k}^{2}}}{2}\\</span>
+<span class="sd">                y_{k+1} = x_{k} + \frac{t_{k}-1}{t_{k-1}}(x_{k} - x_{k-1})</span>
+<span class="sd">            \end{cases}</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">t_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">t</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">)</span>
+
+        <span class="n">step_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">get_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">,</span> <span class="o">-</span><span class="n">step_size</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">step_size</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">t_old</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(((</span><span class="bp">self</span><span class="o">.</span><span class="n">t_old</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span></div>
+</div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+
+    <span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">L2NormSquared</span>
+    <span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">GD</span>
+    <span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageGeometry</span>
+    <span class="n">f</span> <span class="o">=</span> <span class="n">L2NormSquared</span><span class="p">()</span>
+    <span class="n">g</span> <span class="o">=</span> <span class="n">L2NormSquared</span><span class="p">()</span>
+    <span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+    <span class="n">initial</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+    <span class="n">fista</span> <span class="o">=</span> <span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">1443432</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">fista</span><span class="o">.</span><span class="n">is_provably_convergent</span><span class="p">())</span>
+
+    <span class="n">gd</span> <span class="o">=</span> <span class="n">GD</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="n">objective</span><span class="o">=</span><span class="n">f</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">1023123</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">gd</span><span class="o">.</span><span class="n">is_provably_convergent</span><span class="p">())</span>
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/algorithms/GD/index.html b/v24.2.0/_modules/cil/optimisation/algorithms/GD/index.html
new file mode 100644
index 0000000000..e96ca7567c
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+  <h1>Source code for cil.optimisation.algorithms.GD</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.utilities</span> <span class="kn">import</span> <span class="n">ConstantStepSize</span><span class="p">,</span> <span class="n">ArmijoStepSizeRule</span><span class="p">,</span> <span class="n">StepSizeRule</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Real</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="GD">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.GD">[docs]</a>
+<span class="k">class</span> <span class="nc">GD</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Gradient Descent algorithm</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    initial: DataContainer (e.g. ImageData)</span>
+<span class="sd">        The initial point for the optimisation </span>
+<span class="sd">    objective_function: CIL function (:meth:`~cil.optimisation.functions.Function`. ) with a defined gradient method </span>
+<span class="sd">        The function to be minimised. </span>
+<span class="sd">    step_size: positive real float or subclass of :meth:`~cil.optimisation.utilities.StepSizeRule`, default = None </span>
+<span class="sd">        If you pass a float this will be used as a constant step size. If left as None and do not pass a step_size_rule then the Armijio rule will be used to perform backtracking to choose a step size at each iteration. If a child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39; is passed then it&#39;s method `get_step_size` is called for each update. </span>
+<span class="sd">    preconditioner: class with a `apply` method or a function that takes an initialised CIL function as an argument and modifies a provided `gradient`.</span>
+<span class="sd">            This could be a custom `preconditioner` or one provided in :meth:`~cil.optimisation.utilities.preconditioner`. If None is passed  then `self.gradient_update` will remain unmodified. </span>
+
+<span class="sd">    rtol: positive float, default 1e-5</span>
+<span class="sd">        optional parameter defining the relative tolerance comparing the current objective function to 0, default 1e-5, see numpy.isclose</span>
+<span class="sd">    atol: positive float, default 1e-8</span>
+<span class="sd">        optional parameter defining the absolute tolerance comparing the current objective function to 0, default 1e-8, see numpy.isclose</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">objective_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span>  <span class="n">preconditioner</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;GD algorithm creator</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;beta&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warn</span><span class="p">(</span><span class="s1">&#39;To modify the parameters for the Armijo rule please use `step_size_rule=ArmijoStepSizeRule(alpha, beta, kmax)`. The arguments `alpha` and `beta` will be deprecated. &#39;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">rtol</span> <span class="o">=</span> <span class="n">rtol</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">atol</span> <span class="o">=</span> <span class="n">atol</span>
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">objective_function</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="n">objective_function</span><span class="o">=</span><span class="n">objective_function</span><span class="p">,</span>
+                        <span class="n">step_size</span><span class="o">=</span><span class="n">step_size</span><span class="p">,</span>  <span class="n">preconditioner</span><span class="o">=</span><span class="n">preconditioner</span><span class="p">)</span>
+
+<div class="viewcode-block" id="GD.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.GD.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">objective_function</span><span class="p">,</span> <span class="n">step_size</span><span class="p">,</span> <span class="n">preconditioner</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;initialisation of the algorithm</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        initial: DataContainer (e.g. ImageData)</span>
+<span class="sd">            The initial point for the optimisation </span>
+<span class="sd">        objective_function: CIL function with a defined gradient </span>
+<span class="sd">            The function to be minimised. </span>
+<span class="sd">        step_size: positive real float or subclass of :meth:`~cil.optimisation.utilities.StepSizeRule`, default = None </span>
+<span class="sd">            If you pass a float this will be used as a constant step size. If left as None and do not pass a step_size_rule then the Armijio rule will be used to perform backtracking to choose a step size at each iteration. If a child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39; is passed then it&#39;s method `get_step_size` is called for each update. </span>
+<span class="sd">        preconditioner: class with a `apply` method or a function that takes an initialised CIL function as an argument and modifies a provided `gradient`.</span>
+<span class="sd">            This could be a custom `preconditioner` or one provided in :meth:`~cil.optimisation.utilities.preconditioner`. If None is passed  then `self.gradient_update` will remain unmodified. </span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> setting up&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_objective_function</span> <span class="o">=</span> <span class="n">objective_function</span>
+
+        <span class="k">if</span> <span class="n">step_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span> <span class="o">=</span> <span class="n">ArmijoStepSizeRule</span><span class="p">(</span>
+                <span class="n">alpha</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">step_size</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span> <span class="o">=</span> <span class="n">ConstantStepSize</span><span class="p">(</span><span class="n">step_size</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">step_size</span><span class="p">,</span> <span class="n">StepSizeRule</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span> <span class="o">=</span> <span class="n">step_size</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s1">&#39;`step_size` must be `None`, a Real float or a child class of :meth:`cil.optimisation.utilities.StepSizeRule`&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span> <span class="o">=</span> <span class="n">preconditioner</span>
+
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> configured&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="GD.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.GD.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Performs a single iteration of the gradient descent algorithm&#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_objective_function</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">preconditioner</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">)</span>
+
+        <span class="n">step_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">get_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">,</span> <span class="o">-</span><span class="n">step_size</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="GD.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.GD.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_objective_function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solution</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="GD.should_stop">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.GD.should_stop">[docs]</a>
+    <span class="k">def</span> <span class="nf">should_stop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Stopping criterion for the gradient descent algorithm &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">should_stop</span><span class="p">()</span> <span class="ow">or</span> \
+            <span class="n">numpy</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_last_objective</span><span class="p">(),</span> <span class="mf">0.</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rtol</span><span class="p">,</span>
+                          <span class="n">atol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="p">,</span> <span class="n">ConstantStepSize</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size_rule</span><span class="o">.</span><span class="n">step_size</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;There is not a constant step size, it is set by a step-size rule&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="GD.calculate_objective_function_at_point">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.GD.calculate_objective_function_at_point">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_objective_function_at_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Calculates the objective at a given point x</span>
+
+<span class="sd">        .. math:: f(x) + g(x)</span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">        </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_objective_function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
+
+    
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">objective_function</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The attribute `objective_function` will be deprecated in the future. Please use `calculate_objective_function_at_point` instead.&#39;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_objective_function</span></div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/algorithms/PD3O/index.html b/v24.2.0/_modules/cil/optimisation/algorithms/PD3O/index.html
new file mode 100644
index 0000000000..cefb27d92b
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+
+<!DOCTYPE html>
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+<html lang="en" data-content_root="../../../../../" >
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+  <h1>Source code for cil.optimisation.algorithms.PD3O</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">ZeroFunction</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<div class="viewcode-block" id="PD3O">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PD3O">[docs]</a>
+<span class="k">class</span> <span class="nc">PD3O</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+<span class="w">    </span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Primal Dual Three Operator Splitting (PD3O) algorithm, see &quot;A New Primal–Dual Algorithm for Minimizing the Sum</span>
+<span class="sd">        of Three Functions with a Linear Operator&quot;.  This is a primal dual algorithm for minimising :math:`f(x)+g(x)+h(Ax)` where all functions are proper, lower semi-continuous and convex, </span>
+<span class="sd">        :math:`f` should be differentiable with a Lipschitz continuous gradient and :math:`A` is a bounded linear operator. </span>
+<span class="sd">    </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        f : Function</span>
+<span class="sd">            A smooth function with Lipschitz continuous gradient.</span>
+<span class="sd">        g : Function</span>
+<span class="sd">            A convex function with a computationally computable proximal.</span>
+<span class="sd">        h : Function</span>
+<span class="sd">            A composite convex function.</span>
+<span class="sd">        operator: Operator</span>
+<span class="sd">            Bounded linear operator</span>
+<span class="sd">        delta: Float, optional, default is  `1./(gamma*operator.norm()**2)`</span>
+<span class="sd">            The dual step-size </span>
+<span class="sd">        gamma: Float, optional, default is `2.0/f.L`</span>
+<span class="sd">            The primal step size </span>
+<span class="sd">        initial : DataContainer, optional default is a container of zeros, in the domain of the operator </span>
+<span class="sd">            Initial point for the  algorithm.             </span>
+
+
+<span class="sd">        Reference</span>
+<span class="sd">        ---------</span>
+<span class="sd">        Yan, M. A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator. J Sci Comput 76, 1698–1717 (2018). https://doi.org/10.1007/s10915-018-0680-3</span>
+<span class="sd">     &quot;&quot;&quot;</span>    
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">PD3O</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+              
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">f</span><span class="o">=</span><span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">g</span><span class="p">,</span> <span class="n">h</span><span class="o">=</span><span class="n">h</span><span class="p">,</span>  <span class="n">operator</span><span class="o">=</span><span class="n">operator</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="n">delta</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+ 
+                  
+<div class="viewcode-block" id="PD3O.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PD3O.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> setting up&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="p">))</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">f</span> <span class="c1"># smooth function</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">,</span> <span class="n">ZeroFunction</span><span class="p">):</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot; If self.f is the ZeroFunction, then PD3O = PDHG. Please use PDHG instead. Otherwise, select a relatively small parameter gamma &quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">gamma</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">gamma</span> <span class="o">=</span> <span class="mf">1.0</span><span class="o">/</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>                      
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span> <span class="o">=</span> <span class="n">g</span> <span class="c1"># proximable</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h</span> <span class="o">=</span> <span class="n">h</span> <span class="c1"># composite</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+        
+        <span class="k">if</span> <span class="n">gamma</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">gamma</span> <span class="o">=</span> <span class="mf">0.99</span><span class="o">*</span><span class="mf">2.0</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">L</span>
+        
+        <span class="k">if</span> <span class="n">delta</span> <span class="ow">is</span> <span class="kc">None</span> <span class="p">:</span>
+            <span class="n">delta</span> <span class="o">=</span> <span class="mf">0.99</span><span class="o">/</span><span class="p">(</span><span class="n">gamma</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">gamma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">delta</span> <span class="o">=</span> <span class="n">delta</span>  
+
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>    
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">s_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                
+        <span class="bp">self</span><span class="o">.</span><span class="n">grad_f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>        
+  
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> configured&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="p">))</span>
+        
+        <span class="c1"># initial proximal conjugate step</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">)</span></div>
+
+        
+
+<div class="viewcode-block" id="PD3O.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PD3O.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Performs a single iteration of the PD3O algorithm        </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># Following equations 4 in https://link.springer.com/article/10.1007/s10915-018-0680-3</span>
+        <span class="c1"># in this case order of proximal steps we recover the (primal) PDHG, when f=0</span>
+
+        
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">tmp</span>
+        
+        
+        <span class="c1"># proximal step        </span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">grad_f</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_f</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_f</span><span class="p">)</span> <span class="c1"># x_old - gamma * grad_f(x_old)        </span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_f</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+    
+        <span class="c1"># update step        </span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">)</span>                    
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">grad_f</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">grad_f</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">,</span>  <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">)</span> <span class="c1"># 2*x - x_old + gamma*(grad_f_x_old) - gamma*(grad_f_x)</span>
+        
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s_old</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">s_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">s</span> <span class="o">=</span> <span class="n">tmp</span>
+        
+        <span class="c1"># proximal conjugate step</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s</span><span class="p">)</span></div>
+
+        
+        
+               
+           
+                                                                        
+<div class="viewcode-block" id="PD3O.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PD3O.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Evaluates the primal objective</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="p">)</span>        
+        <span class="n">fun_h</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">s_old</span><span class="p">)</span>         
+        <span class="n">fun_g</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">fun_f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="n">fun_f</span> <span class="o">+</span> <span class="n">fun_g</span> <span class="o">+</span> <span class="n">fun_h</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span></div>
+</div>
+
+        
+        
+        
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.algorithms.PDHG</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataContainer</span><span class="p">,</span> <span class="n">BlockDataContainer</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="PDHG">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG">[docs]</a>
+<span class="k">class</span> <span class="nc">PDHG</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Primal Dual Hybrid Gradient (PDHG) algorithm, see :cite:`CP2011`, :cite:`EZXC2010`.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    f : Function</span>
+<span class="sd">        A convex function with a &quot;simple&quot; proximal method of its conjugate.</span>
+<span class="sd">    g : Function</span>
+<span class="sd">        A convex function with a &quot;simple&quot; proximal.</span>
+<span class="sd">    operator : LinearOperator</span>
+<span class="sd">        A Linear Operator.</span>
+<span class="sd">    sigma : positive :obj:`float`, or `np.ndarray`, `DataContainer`, `BlockDataContainer`, optional, default=None</span>
+<span class="sd">        Step size for the dual problem.</span>
+<span class="sd">    tau : positive :obj:`float`, or `np.ndarray`, `DataContainer`, `BlockDataContainer`, optional, default=None</span>
+<span class="sd">        Step size for the primal problem.</span>
+<span class="sd">    initial : DataContainer, optional, default=None</span>
+<span class="sd">        Initial point for the PDHG algorithm.</span>
+<span class="sd">    gamma_g : positive :obj:`float`, optional, default=None</span>
+<span class="sd">        Strongly convex constant if the function g is strongly convex. Allows primal acceleration of the PDHG algorithm.</span>
+<span class="sd">    gamma_fconj : positive :obj:`float`, optional, default=None</span>
+<span class="sd">        Strongly convex constant if the convex conjugate of f is strongly convex. Allows dual acceleration of the PDHG algorithm.</span>
+
+<span class="sd">    **kwargs:</span>
+<span class="sd">        Keyward arguments used from the base class :class:`Algorithm`.</span>
+
+<span class="sd">        max_iteration : :obj:`int`, optional, default=0</span>
+<span class="sd">            Maximum number of iterations of the PDHG.</span>
+<span class="sd">        update_objective_interval : :obj:`int`, optional, default=1</span>
+<span class="sd">            Evaluates objectives, e.g., primal/dual/primal-dual gap every ``update_objective_interval``.</span>
+<span class="sd">        check_convergence : :obj:`boolean`, default=True</span>
+<span class="sd">            Checks scalar sigma and tau values satisfy convergence criterion</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    In our `CIL-Demos &lt;https://github.com/TomographicImaging/CIL-Demos/blob/main/binder/TomographyReconstruction.ipynb&gt;`_ repository\</span>
+<span class="sd">    you can find examples using the PDHG algorithm for different imaging problems, such as Total Variation denoising, Total Generalised Variation inpainting\</span>
+<span class="sd">    and Total Variation Tomography reconstruction. More examples can also be found in :cite:`Jorgensen_et_al_2021`, :cite:`Papoutsellis_et_al_2021`.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    Currently, the strongly convex constants are passed as parameters of PDHG.</span>
+<span class="sd">    In the future, these parameters will be properties of the corresponding functions.</span>
+
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    A first-order primal-dual algorithm for convex optimization problems with known saddle-point structure with applications in imaging.</span>
+
+<span class="sd">    The general problem considered in the PDHG algorithm is the generic saddle-point problem</span>
+
+<span class="sd">    .. math:: \min_{x\in X}\max_{y\in Y} \langle Kx, y \rangle + g(x) - f^{*}(x)</span>
+
+<span class="sd">    where :math:`f` and :math:`g` are convex functions with &quot;simple&quot; proximal operators.</span>
+
+<span class="sd">    :math:`X` and :math:`Y` are two two finite-dimensional vector spaces with an inner product and representing the domain of :math:`g` and :math:`f^{*}`, the convex conjugate of :math:`f`, respectively.</span>
+
+<span class="sd">    The operator :math:`K` is a continuous linear operator with operator norm defined as</span>
+
+<span class="sd">    .. math:: \|K\| = \max\{ \|Kx\| : x\in X, \|x\|\leq1\}</span>
+
+
+<span class="sd">    The saddle point problem is decomposed into the primal problem:</span>
+
+<span class="sd">    .. math:: \min_{x\in X} f(Kx) + g(x),</span>
+
+<span class="sd">    and its corresponding dual problem</span>
+
+<span class="sd">    .. math:: \max_{y\in Y} - g^{*}(-K^{*}y) - f^{*}(y).</span>
+
+<span class="sd">    The PDHG algorithm consists of three steps:</span>
+
+<span class="sd">    * gradient ascent step for the dual problem,</span>
+<span class="sd">    * gradient descent step for the primal problem and</span>
+<span class="sd">    * an over-relaxation of the primal variable.</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">        y^{n+1} = \mathrm{prox}_{\sigma f^{*}}(y^{n} + \sigma K \bar{x}^{n})</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">        x^{n+1} = \mathrm{prox}_{\tau g}(x^{n} - \tau K^{*}y^{n+1})</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">        \bar{x}^{n+1} = x^{n+1} + \theta (x^{n+1} - x^{n})</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    - Convergence is guaranteed if :math:`\theta` = 1.0,  the operator norm :math:`\|K\|`, \the dual step size :math:`\sigma` and the primal step size :math:`\tau`, satisfy the following inequality:</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">        \tau \sigma \|K\|^2 &lt; 1</span>
+
+
+<span class="sd">    - By default, the step sizes :math:`\sigma` and :math:`\tau` are positive scalars and defined as below:</span>
+
+<span class="sd">      * If ``sigma`` is ``None`` and ``tau`` is ``None``:</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">        \sigma = \frac{1}{\|K\|},  \tau = \frac{1}{\|K\|}</span>
+
+<span class="sd">      * If ``tau`` is ``None``:</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">        \tau = \frac{1}{\sigma\|K\|^{2}}</span>
+
+<span class="sd">      * If ``sigma`` is ``None``:</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">        \sigma = \frac{1}{\tau\|K\|^{2}}</span>
+
+
+<span class="sd">    - To monitor the convergence of the algorithm, we compute the primal/dual objectives and the primal-dual gap in :meth:`update_objective`.\</span>
+
+<span class="sd">      The primal objective is</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">        f(Kx) + g(x)</span>
+
+<span class="sd">      and the dual objective is</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">        - g^{*}(-K^{*}y) - f^{*}(y)</span>
+
+<span class="sd">      The primal-dual gap (or duality gap) is</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">        f(Kx) + g(x) + g^{*}(-K^{*}y) + f^{*}(y)</span>
+
+<span class="sd">      and measures how close is the primal-dual pair (x,y) to the primal-dual solution. It is always non-negative and is used to monitor convergence of the PDHG algorithm. \</span>
+<span class="sd">      For more information, see `Duality Gap &lt;https://en.wikipedia.org/wiki/Duality_gap&gt;`_.</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">        - The primal objective is printed if `verbose=1`, ``pdhg.run(verbose=1)``.</span>
+<span class="sd">        - All the objectives are printed if `verbose=2`, ``pdhg.run(verbose=2)``.</span>
+
+<span class="sd">        Computing these objectives can be costly, so it is better to compute every some iterations. To do this, use ``update_objective_interval = #number``.</span>
+
+
+<span class="sd">    - PDHG algorithm can be accelerated if the functions :math:`f^{*}` and/or :math:`g` are strongly convex. In these cases, the step-sizes :math:`\sigma` and :math:`\tau` are updated using the :meth:`update_step_sizes` method. A function :math:`f` is strongly convex with constant :math:`\gamma&gt;0` if</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">          f(x) - \frac{\gamma}{2}\|x\|^{2} \quad\mbox{ is convex. }</span>
+
+
+<span class="sd">      * For instance the function :math:`\frac{1}{2}\|x\|^{2}_{2}` is :math:`\gamma` strongly convex for :math:`\gamma\in(-\infty,1]`. We say it is 1-strongly convex because it is the largest constant for which :math:`f - \frac{1}{2}\|\cdot\|^{2}` is convex.</span>
+
+
+<span class="sd">      * The :math:`\|\cdot\|_{1}` norm is not strongly convex. For more information, see `Strongly Convex &lt;https://en.wikipedia.org/wiki/Convex_function#Strongly_convex_functions&gt;`_.</span>
+
+
+<span class="sd">      * If :math:`g` is strongly convex with constant :math:`\gamma` then the step-sizes :math:`\sigma`, :math:`\tau` and :math:`\theta` are updated as:</span>
+
+
+<span class="sd">      .. math::</span>
+<span class="sd">         :nowrap:</span>
+
+<span class="sd">            \begin{aligned}</span>
+
+<span class="sd">                \theta_{n} &amp; = \frac{1}{\sqrt{1 + 2\gamma\tau_{n}}}\\</span>
+<span class="sd">                \tau_{n+1} &amp; = \theta_{n}\tau_{n}\\</span>
+<span class="sd">                \sigma_{n+1} &amp; = \frac{\sigma_{n}}{\theta_{n}}</span>
+
+<span class="sd">            \end{aligned}</span>
+
+<span class="sd">      * If :math:`f^{*}` is strongly convex, we swap :math:`\sigma` with :math:`\tau`.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    The case where both functions are strongly convex is not available at the moment.</span>
+
+
+<span class="sd">    .. todo:: Implement acceleration of PDHG when both functions are strongly convex.</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gamma_g</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gamma_fconj</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># check for gamma_g, gamma_fconj, strongly convex constants</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_gamma_g</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_gamma_fconj</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_gamma_g</span><span class="p">(</span><span class="n">gamma_g</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_gamma_fconj</span><span class="p">(</span><span class="n">gamma_fconj</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">f</span><span class="o">=</span><span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">g</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="n">operator</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="n">tau</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">tau</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">sigma</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">gamma_g</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gamma_g</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">gamma_fconj</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gamma_fconj</span>
+
+<div class="viewcode-block" id="PDHG.set_gamma_g">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.set_gamma_g">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_gamma_g</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Set the value of the strongly convex constant for function `g`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">            value : a positive number or None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_fconj</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The adaptive update of the PDHG stepsizes in the case where both functions are strongly convex is not implemented at the moment.&quot;</span> <span class="o">+</span>\
+                <span class="s2">&quot;Currently the strongly convex constant of the convex conjugate of the function f has been specified as &quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_fconj</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">value</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Strongly convex constant is a positive number, </span><span class="si">{}</span><span class="s2"> is passed for the strongly convex function g.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_gamma_g</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">elif</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Positive float is expected for the strongly convex constant of function g, </span><span class="si">{}</span><span class="s2"> is passed&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="PDHG.set_gamma_fconj">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.set_gamma_fconj">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_gamma_fconj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Set the value of the strongly convex constant for the convex conjugate of function `f`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">            value : a positive number or None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_g</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The adaptive update of the PDHG stepsizes in the case where both functions are strongly convex is not implemented at the moment.&quot;</span> <span class="o">+</span>\
+                <span class="s2">&quot;Currently the strongly convex constant of the function g has been specified as &quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_g</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">value</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Strongly convex constant is positive, </span><span class="si">{}</span><span class="s2"> is passed for the strongly convex conjugate function of f.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_gamma_fconj</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">elif</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Positive float is expected for the strongly convex constant of the convex conjugate of function f, </span><span class="si">{}</span><span class="s2"> is passed&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="PDHG.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Initialisation of the algorithm</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        f : Function</span>
+<span class="sd">            A convex function with a &quot;simple&quot; proximal method of its conjugate.</span>
+<span class="sd">        g : Function</span>
+<span class="sd">            A convex function with a &quot;simple&quot; proximal.</span>
+<span class="sd">        operator : LinearOperator</span>
+<span class="sd">            A Linear Operator.</span>
+<span class="sd">        sigma : positive :obj:`float`, or `np.ndarray`, `DataContainer`, `BlockDataContainer`, optional, default=None</span>
+<span class="sd">            Step size for the dual problem.</span>
+<span class="sd">        tau : positive :obj:`float`, or `np.ndarray`, `DataContainer`, `BlockDataContainer`, optional, default=None</span>
+<span class="sd">            Step size for the primal problem.</span>
+<span class="sd">        initial : DataContainer, optional, default=None</span>
+<span class="sd">            Initial point for the PDHG algorithm.</span>
+<span class="sd">        theta : Relaxation parameter, Number, default 1.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> setting up&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+        <span class="c1"># Triplet (f, g, K)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">f</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span> <span class="o">=</span> <span class="n">g</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_step_sizes</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="n">tau</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;check_convergence&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">check_convergence</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">y_tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="c1"># relaxation parameter, default value is 1.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;theta&#39;</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_g</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Primal Acceleration of PDHG: The function g is assumed to be strongly convex with positive parameter `gamma_g`. You need to be sure that gamma_g = </span><span class="si">{}</span><span class="s2"> is the correct strongly convex constant for g. &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma_g</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_fconj</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Dual Acceleration of PDHG: The convex conjugate of function f is assumed to be strongly convex with positive parameter `gamma_fconj`. You need to be sure that gamma_fconj = </span><span class="si">{}</span><span class="s2"> is the correct strongly convex constant&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma_fconj</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> configured&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_update_previous_solution</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Swaps the references to current and previous solution based on the</span>
+<span class="sd">        :func:`~Algorithm.update_previous_solution` of the base class :class:`Algorithm`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">tmp</span>
+
+<div class="viewcode-block" id="PDHG.get_output">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.get_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="s2">&quot; Returns the current solution. &quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span></div>
+
+
+<div class="viewcode-block" id="PDHG.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Performs a single iteration of the PDHG algorithm&quot;&quot;&quot;</span>
+        <span class="c1">#calculate x-bar and store in self.x_tmp</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="o">.</span><span class="n">sapyb</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+
+        <span class="c1"># Gradient ascent for the dual variable</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_tmp</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">y_tmp</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="mf">1.0</span> <span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_tmp</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_tmp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span>
+
+        <span class="c1"># Gradient descent for the primal variable</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="mf">1.0</span> <span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="c1"># update_previous_solution() called after update by base class</span>
+        <span class="c1">#i.e current solution is now in x_old, previous solution is now in x</span>
+
+        <span class="c1"># update the step sizes for special cases</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update_step_sizes</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="PDHG.check_convergence">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.check_convergence">[docs]</a>
+    <span class="k">def</span> <span class="nf">check_convergence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check whether convergence criterion for PDHG is satisfied with scalar values of tau and sigma</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Boolean</span>
+<span class="sd">            True if convergence criterion is satisfied. False if not satisfied or convergence is unknown.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">**</span><span class="mi">2</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Convergence criterion of PDHG for scalar step-sizes is not satisfied.&quot;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="kc">False</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Convergence criterion can only be checked for scalar values of tau and sigma.&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">False</span></div>
+
+
+<div class="viewcode-block" id="PDHG.set_step_sizes">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.set_step_sizes">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_step_sizes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Sets sigma and tau step-sizes for the PDHG algorithm. The step sizes can be either scalar or array-objects.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">            sigma : positive :obj:`float`, or `np.ndarray`, `DataContainer`, `BlockDataContainer`, optional, default=None</span>
+<span class="sd">                Step size for the dual problem.</span>
+<span class="sd">            tau : positive :obj:`float`, or `np.ndarray`, `DataContainer`, `BlockDataContainer`, optional, default=None</span>
+<span class="sd">                Step size for the primal problem.</span>
+
+<span class="sd">        The user can set either, both or none. Values passed by the user will be accepted as long as they are positive numbers,</span>
+<span class="sd">        or correct shape array like objects.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Check acceptable values of the primal-dual step-sizes</span>
+        <span class="k">if</span> <span class="n">tau</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The step-sizes of PDHG must be positive, passed tau = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="n">tau</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot; The shape of tau = </span><span class="si">{0}</span><span class="s2"> is not the same as the shape of the domain_geometry = </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tau</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">sigma</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sigma</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">sigma</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The step-sizes of PDHG are positive, passed sigma = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sigma</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="n">sigma</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot; The shape of sigma = </span><span class="si">{0}</span><span class="s2"> is not the same as the shape of the range_geometry = </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+        <span class="c1"># Default sigma and tau step-sizes</span>
+        <span class="k">if</span> <span class="n">tau</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">sigma</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span> <span class="o">=</span> <span class="mf">1.0</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span> <span class="o">=</span> <span class="mf">1.0</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">tau</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">sigma</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span> <span class="o">=</span> <span class="n">sigma</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span> <span class="o">=</span> <span class="n">tau</span>
+        <span class="k">elif</span> <span class="n">sigma</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="n">tau</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span> <span class="o">=</span> <span class="n">tau</span>
+        <span class="k">elif</span> <span class="n">tau</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sigma</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span> <span class="o">=</span> <span class="n">sigma</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;If using arrays for sigma or tau both must arrays must be provided.&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="PDHG.update_step_sizes">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.update_step_sizes">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_step_sizes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Updates step sizes in the cases of primal or dual acceleration using the strongly convexity property.</span>
+<span class="sd">        The case where both functions are strongly convex is not available at the moment.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Update sigma and tau based on the strong convexity of G</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_g</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="mf">1.0</span><span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_g</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span> <span class="o">/=</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span>
+
+        <span class="c1"># Update sigma and tau based on the strong convexity of F</span>
+        <span class="c1"># Following operations are reversed due to symmetry, sigma --&gt; tau, tau --&gt;sigma</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_fconj</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma_fconj</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sigma</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_tau</span> <span class="o">/=</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span></div>
+
+
+<div class="viewcode-block" id="PDHG.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.PDHG.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Evaluates the primal objective, the dual objective and the primal-dual gap.&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_tmp</span><span class="p">)</span>
+        <span class="n">f_eval_p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_tmp</span><span class="p">)</span>
+        <span class="n">g_eval_p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_old</span><span class="p">)</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="n">f_eval_p</span> <span class="o">+</span> <span class="n">g_eval_p</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+
+        <span class="n">f_eval_d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span>
+        <span class="n">g_eval_d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+        <span class="n">d1</span> <span class="o">=</span> <span class="n">f_eval_d</span> <span class="o">+</span> <span class="n">g_eval_d</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="o">-</span><span class="n">d1</span><span class="p">,</span> <span class="n">p1</span><span class="o">+</span><span class="n">d1</span><span class="p">])</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dual_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">primal_dual_gap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">]</span></div>
+
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+  <h1>Source code for cil.optimisation.algorithms.SIRT</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">IndicatorBox</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockDataContainer</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.errors</span> <span class="kn">import</span> <span class="n">InPlaceError</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="SIRT">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SIRT">[docs]</a>
+<span class="k">class</span> <span class="nc">SIRT</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Simultaneous Iterative Reconstruction Technique, see :cite:`Kak2001`.</span>
+
+<span class="sd">    Simultaneous Iterative Reconstruction Technique (SIRT) solves</span>
+<span class="sd">    the following problem</span>
+
+<span class="sd">    .. math:: A x = b</span>
+
+<span class="sd">    The SIRT algorithm is</span>
+
+<span class="sd">    .. math:: x^{k+1} =  \mathrm{proj}_{C}( x^{k} + \omega * D ( A^{T} ( M * (b - Ax^{k}) ) ) ),</span>
+
+<span class="sd">    where,</span>
+<span class="sd">    :math:`M = \frac{1}{A*\mathbb{1}}`,</span>
+<span class="sd">    :math:`D = \frac{1}{A^{T}\mathbb{1}}`,</span>
+<span class="sd">    :math:`\mathbb{1}` is a :code:`DataContainer` of ones,</span>
+<span class="sd">    :math:`\mathrm{prox}_{C}` is the projection over a set :math:`C`,</span>
+<span class="sd">    and :math:`\omega` is the relaxation parameter.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    initial : DataContainer, default = None</span>
+<span class="sd">        Starting point of the algorithm, default value =  DataContainer in the domain of the operator allocated with zeros. </span>
+<span class="sd">    operator : LinearOperator</span>
+<span class="sd">        The operator A.</span>
+<span class="sd">    data : DataContainer</span>
+<span class="sd">        The data b.</span>
+<span class="sd">    lower : :obj:`float`, default = None</span>
+<span class="sd">        Lower bound constraint</span>
+<span class="sd">    upper : :obj:`float`, default = None</span>
+<span class="sd">        Upper bound constraint</span>
+<span class="sd">    constraint : Function, default = None</span>
+<span class="sd">        A function with :code:`proximal` method, e.g., :class:`.IndicatorBox` function and :meth:`.IndicatorBox.proximal`,</span>
+<span class="sd">        or :class:`.TotalVariation` function and :meth:`.TotalVariation.proximal`.</span>
+
+<span class="sd">    kwargs:</span>
+<span class="sd">        Keyword arguments used from the base class :class:`.Algorithm`.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    If :code:`constraint` is not passed, :code:`lower` and :code:`upper` are used to create an :class:`.IndicatorBox` and apply its :code:`proximal`.</span>
+
+<span class="sd">    If :code:`constraint` is passed, :code:`proximal` method is required to be implemented.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    The preconditioning arrays (weights) :code:`M` and :code:`D` used in SIRT are defined as</span>
+
+<span class="sd">    .. math:: M = \frac{1}{A*\mathbb{1}} = \frac{1}{\sum_{j}a_{i,j}}</span>
+
+<span class="sd">    .. math:: D = \frac{1}{A*\mathbb{1}} = \frac{1}{\sum_{i}a_{i,j}}</span>
+
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    .. math:: \underset{x}{\mathrm{argmin}} \frac{1}{2}\| x - d\|^{2}</span>
+
+<span class="sd">    &gt;&gt;&gt; sirt = SIRT(initial = ig.allocate(0), operator = A, data = d, max_iteration = 5)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">constraint</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SIRT</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="n">operator</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="n">upper</span><span class="p">,</span> <span class="n">constraint</span><span class="o">=</span><span class="n">constraint</span><span class="p">)</span>
+
+<div class="viewcode-block" id="SIRT.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SIRT.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">constraint</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Initialisation of the algorithm&quot;&quot;&quot;</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> setting up&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+        
+        <span class="n">warning</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">operator</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warning</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;an `operator`&quot;</span>
+        <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warning</span> <span class="o">+=</span> <span class="mi">10</span>
+            <span class="k">if</span> <span class="n">warning</span> <span class="o">&gt;</span> <span class="mi">10</span><span class="p">:</span>
+                <span class="n">msg</span> <span class="o">+=</span> <span class="s2">&quot; and `data`&quot;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;`data`&quot;</span>
+        <span class="k">if</span> <span class="n">warning</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;You must pass </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s1"> to the SIRT algorithm&#39;</span> <span class="p">)</span>
+        
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">initial</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tmp_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">*</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">r</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">constraint</span> <span class="o">=</span> <span class="n">constraint</span>
+        <span class="k">if</span> <span class="n">constraint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">lower</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">upper</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="c1"># IndicatorBox accepts None for lower and/or upper</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">constraint</span><span class="o">=</span><span class="n">IndicatorBox</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="n">lower</span><span class="p">,</span><span class="n">upper</span><span class="o">=</span><span class="n">upper</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_relaxation_parameter</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="c1"># Set up scaling matrices D and M.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_set_up_weights</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> configured&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">relaxation_parameter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_relaxation_parameter</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">D</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_Dscaled</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_relaxation_parameter</span>
+
+<div class="viewcode-block" id="SIRT.set_relaxation_parameter">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SIRT.set_relaxation_parameter">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_relaxation_parameter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the relaxation parameter :math:`\omega`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        value : float</span>
+<span class="sd">            The relaxation parameter to be applied to the update. Must be between 0 and 2 to guarantee asymptotic convergence.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">value</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">value</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Expected relaxation parameter to be in range 0-2. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_relaxation_parameter</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_set_up_weights</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_Dscaled</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_relaxation_parameter</span></div>
+
+
+
+    <span class="k">def</span> <span class="nf">_set_up_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">M</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mf">1.0</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_Dscaled</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mf">1.0</span><span class="p">))</span>
+
+        <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_Dscaled</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_remove_nan_or_inf</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">replace_with</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_remove_nan_or_inf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">datacontainer</span><span class="p">,</span> <span class="n">replace_with</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Replace nan and inf in datacontainer with a given value.</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        -------------</span>
+
+<span class="sd">        datacontainer: DataContainer, BlockDataContainer</span>
+
+<span class="sd">        replace_with: float, default 1.0</span>
+<span class="sd">            Value to replace elements that evaluate to NaN or inf</span>
+
+
+<span class="sd">        In case the input datacontainer is a :code:`BlockDataContainer` the substitution is executed for each container in the :code:`BlockDataContainer`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">datacontainer</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">datacontainer</span><span class="o">.</span><span class="n">containers</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_remove_nan_or_inf</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">replace_with</span><span class="o">=</span><span class="n">replace_with</span><span class="p">)</span>
+            <span class="k">return</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">datacontainer</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">numpy</span><span class="o">.</span><span class="n">nan_to_num</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">nan</span><span class="o">=</span><span class="n">replace_with</span><span class="p">,</span> <span class="n">posinf</span><span class="o">=</span><span class="n">replace_with</span><span class="p">,</span> <span class="n">neginf</span><span class="o">=</span><span class="n">replace_with</span><span class="p">)</span>
+        <span class="n">datacontainer</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="SIRT.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SIRT.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Performs a single iteration of the SIRT algorithm</span>
+
+<span class="sd">        .. math:: x^{k+1} =  \mathrm{proj}_{C}( x^{k} + \omega * D ( A^{T} ( M * (b - Ax) ) ) )</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># self.r = self.data - self.operator.direct(self.x)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="p">)</span>
+
+        <span class="c1"># self.D is prescaled by _relaxation_parameter (default 1)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">r</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_Dscaled</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">constraint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">constraint</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">except</span> <span class="n">InPlaceError</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">constraint</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SIRT.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SIRT.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the objective</span>
+
+<span class="sd">        .. math:: \frac{1}{2}\|A x - b\|^{2}</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">r</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">())</span></div>
+</div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.algorithms.SPDHG</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># Claire Delplancke (University of Bath)</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="SPDHG">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SPDHG">[docs]</a>
+<span class="k">class</span> <span class="nc">SPDHG</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Stochastic Primal Dual Hybrid Gradient</span>
+
+<span class="sd">    Problem:</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">      \min_{x} f(Kx) + g(x) = \min_{x} \sum f_i(K_i x) + g(x)</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    f : BlockFunction</span>
+<span class="sd">        Each must be a convex function with a &quot;simple&quot; proximal method of its conjugate</span>
+<span class="sd">    g : Function</span>
+<span class="sd">        A convex function with a &quot;simple&quot; proximal</span>
+<span class="sd">    operator : BlockOperator</span>
+<span class="sd">        BlockOperator must contain Linear Operators</span>
+<span class="sd">    tau : positive float, optional, default=None</span>
+<span class="sd">        Step size parameter for Primal problem</span>
+<span class="sd">    sigma : list of positive float, optional, default=None</span>
+<span class="sd">        List of Step size parameters for Dual problem</span>
+<span class="sd">    initial : DataContainer, optional, default=None</span>
+<span class="sd">        Initial point for the SPDHG algorithm</span>
+<span class="sd">    prob : list of floats, optional, default=None</span>
+<span class="sd">        List of probabilities. If None each subset will have probability = 1/number of subsets</span>
+<span class="sd">    gamma : float</span>
+<span class="sd">        parameter controlling the trade-off between the primal and dual step sizes</span>
+
+<span class="sd">    **kwargs:</span>
+<span class="sd">    norms : list of floats</span>
+<span class="sd">        precalculated list of norms of the operators</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    Example of usage: See https://github.com/vais-ral/CIL-Demos/blob/master/Tomography/Simulated/Single%20Channel/PDHG_vs_SPDHG.py</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    Convergence is guaranteed provided that [2, eq. (12)]:</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">    \|\sigma[i]^{1/2} * K[i] * tau^{1/2} \|^2  &lt; p_i for all i</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    Notation for primal and dual step-sizes are reversed with comparison</span>
+<span class="sd">        to PDHG.py</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    this code implements serial sampling only, as presented in [2]</span>
+<span class="sd">        (to be extended to more general case of [1] as future work)</span>
+
+<span class="sd">    References</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    [1]&quot;Stochastic primal-dual hybrid gradient algorithm with arbitrary</span>
+<span class="sd">    sampling and imaging applications&quot;,</span>
+<span class="sd">    Chambolle, Antonin, Matthias J. Ehrhardt, Peter Richtárik, and Carola-Bibiane Schonlieb,</span>
+<span class="sd">    SIAM Journal on Optimization 28, no. 4 (2018): 2783-2808.</span>
+
+<span class="sd">    [2]&quot;Faster PET reconstruction with non-smooth priors by randomization and preconditioning&quot;,</span>
+<span class="sd">    Matthias J Ehrhardt, Pawel Markiewicz and Carola-Bibiane Schönlieb,</span>
+<span class="sd">    Physics in Medicine &amp; Biology, Volume 64, Number 22, 2019.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SPDHG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="n">f</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">operator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">g</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">f</span><span class="o">=</span><span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">g</span><span class="p">,</span> <span class="n">operator</span><span class="o">=</span><span class="n">operator</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="n">tau</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+                        <span class="n">initial</span><span class="o">=</span><span class="n">initial</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="n">prob</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span> <span class="n">norms</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;norms&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
+
+
+<div class="viewcode-block" id="SPDHG.set_up">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SPDHG.set_up">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> \
+               <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">norms</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;set-up of the algorithm</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        f : BlockFunction</span>
+<span class="sd">            Each must be a convex function with a &quot;simple&quot; proximal method of its conjugate</span>
+<span class="sd">        g : Function</span>
+<span class="sd">            A convex function with a &quot;simple&quot; proximal</span>
+<span class="sd">        operator : BlockOperator</span>
+<span class="sd">            BlockOperator must contain Linear Operators</span>
+<span class="sd">        tau : positive float, optional, default=None</span>
+<span class="sd">            Step size parameter for Primal problem</span>
+<span class="sd">        sigma : list of positive float, optional, default=None</span>
+<span class="sd">            List of Step size parameters for Dual problem</span>
+<span class="sd">        initial : DataContainer, optional, default=None</span>
+<span class="sd">            Initial point for the SPDHG algorithm</span>
+<span class="sd">        prob : list of floats, optional, default=None</span>
+<span class="sd">            List of probabilities. If None each subset will have probability = 1/number of subsets</span>
+<span class="sd">        gamma : float</span>
+<span class="sd">            parameter controlling the trade-off between the primal and dual step sizes</span>
+
+<span class="sd">        **kwargs:</span>
+<span class="sd">        norms : list of floats</span>
+<span class="sd">            precalculated list of norms of the operators</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> setting up&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+        <span class="c1"># algorithmic parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">f</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span> <span class="o">=</span> <span class="n">g</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="o">=</span> <span class="n">tau</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">prob</span> <span class="o">=</span> <span class="n">prob</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ndual_subsets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">gamma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">=</span> <span class="mf">.99</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">prob</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">ndual_subsets</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndual_subsets</span>
+
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">norms</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="c1"># Compute norm of each sub-operator</span>
+                <span class="n">norms</span> <span class="o">=</span> <span class="p">[</span><span class="n">operator</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndual_subsets</span><span class="p">)]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">norms</span> <span class="o">=</span> <span class="n">norms</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">/</span> <span class="n">ni</span> <span class="k">for</span> <span class="n">ni</span> <span class="ow">in</span> <span class="n">norms</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span> <span class="p">[</span> <span class="n">pi</span> <span class="o">/</span> <span class="p">(</span> <span class="n">si</span> <span class="o">*</span> <span class="n">ni</span><span class="o">**</span><span class="mi">2</span> <span class="p">)</span> <span class="k">for</span> <span class="n">pi</span><span class="p">,</span> <span class="n">ni</span><span class="p">,</span> <span class="n">si</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prob</span><span class="p">,</span> <span class="n">norms</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">)]</span> <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tau</span> <span class="o">*=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="p">)</span>
+
+        <span class="c1"># initialize primal variable</span>
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="c1"># initialize dual variable to 0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">y_old</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="c1"># initialize variable z corresponding to back-projected dual variable</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">zbar</span><span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="c1"># relaxation parameter</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> configured&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SPDHG.update">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SPDHG.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Gradient descent for the primal variable</span>
+        <span class="c1"># x_tmp = x - tau * zbar</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zbar</span><span class="p">,</span>  <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="c1"># Choose subset</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prob</span><span class="p">))</span>
+
+        <span class="c1"># Gradient ascent for the dual variable</span>
+        <span class="c1"># y_k = y_old[i] + sigma[i] * K[i] x</span>
+        <span class="n">y_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="n">y_k</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_old</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">y_k</span><span class="p">)</span>
+
+        <span class="n">y_k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="n">y_k</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+        <span class="c1"># Back-project</span>
+        <span class="c1"># x_tmp = K[i]^*(y_k - y_old[i])</span>
+        <span class="n">y_k</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_old</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_old</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_old</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">)</span>
+        <span class="c1"># Update backprojected dual variable and extrapolate</span>
+        <span class="c1"># zbar = z + (1 + theta/p[i]) x_tmp</span>
+
+        <span class="c1"># z = z + x_tmp</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">z</span><span class="p">)</span>
+        <span class="c1"># zbar = z + (theta/p[i]) * x_tmp</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">z</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_tmp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">zbar</span><span class="p">)</span>
+
+        <span class="c1"># save previous iteration</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">save_previous_iteration</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">y_k</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SPDHG.update_objective">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.algorithms.SPDHG.update_objective">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># p1 = self.f(self.operator.direct(self.x)) + self.g(self.x)</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">op</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">operators</span><span class="p">):</span>
+            <span class="n">p1</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">[</span><span class="n">i</span><span class="p">](</span><span class="n">op</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">))</span>
+        <span class="n">p1</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="n">d1</span> <span class="o">=</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_old</span><span class="p">)</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_old</span><span class="p">)</span>
+        <span class="n">tmp</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">d1</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">d1</span><span class="p">,</span> <span class="n">p1</span><span class="o">-</span><span class="n">d1</span><span class="p">])</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">         </span><span class="sd">&#39;&#39;&#39;alias of loss&#39;&#39;&#39;</span>
+         <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">]</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dual_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">primal_dual_gap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">]</span>
+    <span class="k">def</span> <span class="nf">save_previous_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">y_current</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">y_old</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">y_current</span><span class="p">)</span></div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.functions.ApproximateGradientSumFunction</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># - CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># - Daniel Deidda (National Physical Laboratory, UK)</span>
+<span class="c1"># - Claire Delplancke (Electricite de France, Research and Development)</span>
+<span class="c1"># - Ashley Gillman (Australian e-Health Res. Ctr., CSIRO, Brisbane, Queensland, Australia)</span>
+<span class="c1"># - Zeljko Kereta (Department of Computer Science, University College London, UK)</span>
+<span class="c1"># - Evgueni Ovtchinnikov (STFC - UKRI)</span>
+<span class="c1"># - Georg Schramm (Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Leuven, Belgium)</span>
+
+
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">SumFunction</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.utilities</span> <span class="kn">import</span> <span class="n">Sampler</span>
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="ApproximateGradientSumFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">ApproximateGradientSumFunction</span><span class="p">(</span><span class="n">SumFunction</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;ApproximateGradientSumFunction represents the following sum </span>
+
+<span class="sd">    .. math:: \sum_{i=0}^{n-1} f_{i} = (f_{0} + f_{2} + ... + f_{n-1})</span>
+
+<span class="sd">    where there are :math:`n` functions. This function class has two ways of calling gradient</span>
+<span class="sd">    </span>
+<span class="sd">        - `full_gradient` calculates the gradient of the sum :math:`\sum_{i=0}^{n-1} \nabla f_{i}`</span>
+<span class="sd">        - `gradient` calls an `approximate_gradient` function which may be less computationally expensive to calculate than the full gradient</span>
+<span class="sd">    </span>
+<span class="sd">    </span>
+
+<span class="sd">    This class is an abstract class.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+<span class="sd">    functions : `list`  of functions</span>
+<span class="sd">        A list of functions: :math:`[f_{0}, f_{2}, ..., f_{n-1}]`. Each function is assumed to be smooth with an implemented :func:`~Function.gradient` method. All functions must have the same domain. The number of functions (equivalently the length of the list) must be strictly greater than 1. </span>
+<span class="sd">    sampler: An instance of a CIL Sampler class ( :meth:`~optimisation.utilities.sampler`) or of another class which has a :code:`__next__` function implemented to output integers in :math:`{0,...,n-1}`.</span>
+<span class="sd">        This sampler is called each time :code:`gradient` is called and sets the internal :code:`function_num` passed to the :code:`approximate_gradient` function.  Default is :code:`Sampler.random_with_replacement(len(functions))`. </span>
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    We ensure that the approximate gradient is of a similar order of magnitude to the full gradient calculation. For example, in the :code:`SGFunction` we approximate the full gradient by :math:`n\nabla f_i` for an index :math:`i` given by the sampler. </span>
+<span class="sd">    The multiplication by :math:`n` is a choice to more easily allow comparisons between stochastic and non-stochastic methods and between stochastic methods with varying numbers of subsets. </span>
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    Each time :code:`gradient` is called the class keeps track of which functions have been used to calculate the gradient. This may be useful for debugging or plotting after using this function in an iterative algorithm.  </span>
+<span class="sd">    </span>
+<span class="sd">        - The property :code:`data_passes_indices` is a list of lists holding the indices of the functions that are processed in each call of `gradient`. This list is updated each time `gradient` is called by appending a list of the indices of the functions used to calculate the gradient. </span>
+<span class="sd">        - The property :code:`data_passes` is a list of floats that holds the amount of data that has been processed up until each call of `gradient`. This list is updated each time `gradient` is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning: if your functions do not contain an equal `amount` of data, for example your data was not partitioned into equal batches, then you must first use the `set_data_partition_weights&quot; function for this to be accurate.    </span>
+
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    The :meth:`~ApproximateGradientSumFunction.gradient` returns the approximate gradient depending on an index provided by the  :code:`sampler` method. </span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    This class is an abstract base class, so we give an example using the SGFunction child class. </span>
+<span class="sd">    </span>
+<span class="sd">    Consider the objective is to minimise: </span>
+
+<span class="sd">    .. math:: \sum_{i=0}^{n-1} f_{i}(x) = \sum_{i=0}^{n-1}\|A_{i} x - b_{i}\|^{2}</span>
+
+<span class="sd">    &gt;&gt;&gt; list_of_functions = [LeastSquares(Ai, b=bi)] for Ai,bi in zip(A_subsets, b_subsets))   </span>
+<span class="sd">    &gt;&gt;&gt; f = ApproximateGradientSumFunction(list_of_functions) </span>
+
+<span class="sd">    &gt;&gt;&gt; list_of_functions = [LeastSquares(Ai, b=bi)] for Ai,bi in zip(A_subsets, b_subsets)) </span>
+<span class="sd">    &gt;&gt;&gt; sampler = Sampler.sequential(len(list_of_functions))</span>
+<span class="sd">    &gt;&gt;&gt; f = SGFunction(list_of_functions, sampler=sampler)   </span>
+<span class="sd">    &gt;&gt;&gt; f.full_gradient(x)</span>
+<span class="sd">    This will return :math:`\sum_{i=0}^{n-1} \nabla f_{i}(x)`</span>
+<span class="sd">    &gt;&gt;&gt; f.gradient(x)</span>
+<span class="sd">    As per the approximate gradient implementation in the SGFunction this will return :math:`\nabla f_{0}`. The choice of the `0` index is because we chose a `sequential` sampler and this is the first time we called `gradient`. </span>
+<span class="sd">    &gt;&gt;&gt; f.gradient(x)</span>
+<span class="sd">    This will return :math:`\nabla f_{1}` because we chose  a `sequential` sampler and this is the second time we called `gradient`. </span>
+
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">random_with_replacement</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">functions</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">functions</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Input to functions should be a list of functions&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">sampler</span><span class="p">,</span> <span class="s2">&quot;next&quot;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The provided sampler must have a `next` method&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">sampler</span> <span class="o">=</span> <span class="n">sampler</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_partition_weights</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">functions</span><span class="p">)]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">functions</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_data_passes_indices</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ApproximateGradientSumFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">functions</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the sum of functions at :math:`x`.</span>
+
+<span class="sd">        .. math:: (f_{0} + f_{1} + ... + f_{n-1})(x) = f_{0}(x) + f_{1}(x) + ... + f_{n-1}(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        --------</span>
+<span class="sd">        float</span>
+<span class="sd">            the value of the SumFunction at x</span>
+
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">ApproximateGradientSumFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ApproximateGradientSumFunction.full_gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.full_gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">full_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the  full gradient of the sum of functions at :math:`x`.</span>
+
+<span class="sd">        .. math:: \nabla_x(f_{0} + f_{1} + ... + f_{n-1})(x) = \nabla_xf_{0}(x) + \nabla_xf_{1}(x) + ... + \nabla_xf_{n-1}(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The value of the gradient of the sum function at x or nothing if `out`  </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">ApproximateGradientSumFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ApproximateGradientSumFunction.approximate_gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.approximate_gradient">[docs]</a>
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">approximate_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">function_num</span><span class="p">,</span>   <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns the approximate gradient at a given point :code:`x` given a `function_number` in {0,...,len(functions)-1}.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+<span class="sd">        function_num: `int` </span>
+<span class="sd">            Between 0 and the number of functions in the list  </span>
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            the value of the approximate gradient of the sum function at :code:`x` given a `function_number` in {0,...,len(functions)-1} </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div>
+
+
+<div class="viewcode-block" id="ApproximateGradientSumFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Selects a random function using the `sampler` and then calls the approximate gradient at :code:`x`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            the value of the approximate gradient of the sum function at :code:`x`   </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">_update_data_passes_indices</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="p">])</span>
+        
+
+        
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">approximate_gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+        
+
+    <span class="k">def</span> <span class="nf">_update_data_passes_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Internal function that updates the list of lists containing the function indices used to calculate the approximate gradient. </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        indices: list</span>
+<span class="sd">            List of indices used to calculate the approximate gradient in a given iteration</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_data_passes_indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ApproximateGradientSumFunction.set_data_partition_weights">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.set_data_partition_weights">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_data_partition_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weights</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Setter for the partition weights used to calculate the data passes  </span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        weights: list of positive floats that sum to one. </span>
+<span class="sd">            The proportion of the data held in each function. Equivalent to the proportions that you partitioned your data into. </span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;The provided weights must be a list the same length as the number of functions&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">1e-6</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The provided weights must sum to one&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;The provided weights must be greater than or equal to zero&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_partition_weights</span> <span class="o">=</span> <span class="n">weights</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">data_passes_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; The property :code:`data_passes_indices` is a list of lists holding the indices of the functions that are processed in each call of `gradient`. This list is updated each time `gradient` is called by appending a list of the indices of the functions used to calculate the gradient.  &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_passes_indices</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">data_passes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; The property :code:`data_passes` is a list of floats that holds the amount of data that has been processed up until each call of `gradient`. This list is updated each time `gradient` is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning: if your functions do not contain an equal `amount` of data, for example your data was not partitioned into equal batches, then you must first use the `set_data_partition_weights&quot; function for this to be accurate.   &quot;&quot;&quot;</span>
+        <span class="n">data_passes</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_passes_indices</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">data_passes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_passes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="n">data_passes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">el</span><span class="p">:</span>
+                <span class="n">data_passes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">data_passes</span></div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
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+  <h1>Source code for cil.optimisation.functions.BlockFunction</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockDataContainer</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+
+<div class="viewcode-block" id="BlockFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">BlockFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; BlockFunction represents a *separable sum* function :math:`F` defined as</span>
+
+<span class="sd">    .. math:: F:X_{1}\times X_{2}\cdots\times X_{m} \rightarrow (-\infty, \infty]</span>
+
+<span class="sd">    where :math:`F` is the separable sum of functions :math:`(f_{i})_{i=1}^{m}`,</span>
+
+<span class="sd">    .. math:: F(x_{1}, x_{2}, \cdots, x_{m}) = \overset{m}{\underset{i=1}{\sum}}f_{i}(x_{i}), \mbox{ with } f_{i}: X_{i} \rightarrow (-\infty, \infty].</span>
+
+<span class="sd">    A nice property (due to it&#39;s separability structure) is that the proximal operator</span>
+<span class="sd">    can be decomposed along the proximal operators of each function :math:`f_{i}`.</span>
+
+<span class="sd">    .. math:: \mathrm{prox}_{\tau F}(x) = ( \mathrm{prox}_{\tau f_{i}}(x_{i}) )_{i=1}^{m}</span>
+
+<span class="sd">    In addition, if :math:`\tau := (\tau_{1},\dots,\tau_{m})`, then</span>
+
+<span class="sd">    .. math:: \mathrm{prox}_{\tau F}(x) = ( \mathrm{prox}_{\tau_{i} f_{i}}(x_{i}) )_{i=1}^{m}</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="BlockFunction.__init__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">functions</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">BlockFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">functions</span> <span class="o">=</span> <span class="n">functions</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># compute Lipschitz constant if possible</span>
+        <span class="n">tmp_L</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">func</span><span class="o">.</span><span class="n">L</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tmp_L</span> <span class="o">+=</span> <span class="n">func</span><span class="o">.</span><span class="n">L</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">tmp_L</span> <span class="o">=</span> <span class="kc">None</span>
+                <span class="k">break</span>
+        <span class="k">return</span> <span class="n">tmp_L</span>
+
+<div class="viewcode-block" id="BlockFunction.__call__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction.__call__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the value of the BlockFunction :math:`F`</span>
+
+<span class="sd">        .. math:: F(x) = \overset{m}{\underset{i=1}{\sum}}f_{i}(x_{i}), \mbox{ where } x = (x_{1}, x_{2}, \cdots, x_{m}), \quad i = 1,2,\dots,m</span>
+
+<span class="sd">        Parameter:</span>
+
+<span class="sd">            x : BlockDataContainer and must have as many rows as self.length</span>
+
+<span class="sd">            returns ..math:: \sum(f_i(x_i))</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;BlockFunction and BlockDataContainer have incompatible size&#39;</span><span class="p">)</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="n">t</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">](</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">t</span></div>
+
+
+<div class="viewcode-block" id="BlockFunction.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the BlockFunction at :math:`x^{*}`.</span>
+
+<span class="sd">            .. math:: F^{*}(x^{*}) = \overset{m}{\underset{i=1}{\sum}}f_{i}^{*}(x^{*}_{i})</span>
+
+<span class="sd">            Parameter:</span>
+
+<span class="sd">                x : BlockDataContainer and must have as many rows as self.length</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;BlockFunction and BlockDataContainer have incompatible size&#39;</span><span class="p">)</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="n">t</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">t</span></div>
+
+
+<div class="viewcode-block" id="BlockFunction.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Proximal operator of the BlockFunction at x:</span>
+
+<span class="sd">            .. math:: \mathrm{prox}_{\tau F}(x) =  (\mathrm{prox}_{\tau f_{i}}(x_{i}))_{i=1}^{m}</span>
+
+<span class="sd">            Parameter:</span>
+
+<span class="sd">                x : BlockDataContainer and must have as many rows as self.length</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;BlockFunction and BlockDataContainer have incompatible size&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+            <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="BlockFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the gradient of the BlockFunction function at x.</span>
+
+<span class="sd">        .. math:: F&#39;(x) = [f_{1}&#39;(x_{1}), ... , f_{m}&#39;(x_{m})]</span>
+
+<span class="sd">        Parameter:</span>
+
+<span class="sd">            x : BlockDataContainer and must have as many rows as self.length</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;BlockFunction and BlockDataContainer have incompatible size&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+        <span class="k">return</span>  <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="BlockFunction.proximal_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction.proximal_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Proximal operator of the convex conjugate of BlockFunction at x:</span>
+
+<span class="sd">            .. math:: \mathrm{prox}_{\tau F^{*}}(x) = (\mathrm{prox}_{\tau f^{*}_{i}}(x^{*}_{i}))_{i=1}^{m}</span>
+
+<span class="sd">            Parameter:</span>
+
+<span class="sd">                x : BlockDataContainer and must have as many rows as self.length</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;BlockFunction and BlockDataContainer have incompatible size&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+                    <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">tau</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+            <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">out</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">row</span><span class="p">]</span>
+
+<div class="viewcode-block" id="BlockFunction.__rmul__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.BlockFunction.__rmul__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Define multiplication with a scalar</span>
+
+<span class="sd">        :param other: number</span>
+<span class="sd">        Returns a new `BlockFunction`_ containing the product of the scalar with all the functions in the block</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="bp">NotImplemented</span>
+        <span class="k">return</span> <span class="n">BlockFunction</span><span class="p">(</span> <span class="o">*</span> <span class="p">[</span> <span class="n">other</span> <span class="o">*</span> <span class="n">el</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">]</span> <span class="p">)</span></div>
+</div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.functions.Function</h1><div class="highlight"><pre>
+<span></span>
+<span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.errors</span> <span class="kn">import</span> <span class="n">InPlaceError</span>
+
+
+<div class="viewcode-block" id="Function">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Function">[docs]</a>
+<span class="k">class</span> <span class="nc">Function</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Abstract class representing a function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        L: number, positive, default None</span>
+<span class="sd">            Lipschitz constant of the gradient of the function F(x), when it is differentiable.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+<span class="sd">        The Lipschitz of the gradient of the function is a positive real number, such that :math:`\|f&#39;(x) - f&#39;(y)\| \leq L \|x-y\|`, assuming :math:`f: IG \rightarrow \mathbb{R}`</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">L</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="c1"># overrides the type check to allow None as initial value</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="n">L</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+<div class="viewcode-block" id="Function.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Function.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the gradient of function :math:`F`  evaluated at :math:`x`, if it is differentiable</span>
+
+<span class="sd">        .. math:: F&#39;(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer, the value of the gradient of the function at x.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="Function.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Function.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the proximal operator of function :math:`\tau F`  evaluated at x</span>
+
+<span class="sd">        .. math:: \text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        tau: scalar</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the proximal operator of the function at x with scalar :math:`\tau`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="Function.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Function.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Evaluation of the function F* at x, where F* is the convex conjugate of function F,</span>
+
+<span class="sd">        .. math:: F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        The value of the convex conjugate of the function at x.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="Function.proximal_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Function.proximal_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the proximal operator of the convex conjugate of function :math:`\tau F` evaluated at :math:`x^{*}`</span>
+
+<span class="sd">        .. math:: \text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})</span>
+
+<span class="sd">        Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate :math:`F^{*}`</span>
+
+<span class="sd">        .. math:: \text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        tau: scalar</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the value of the proximal operator of the convex conjugate at point :math:`x` for scalar :math:`\tau` or None if `out`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span><span class="p">(</span>
+                <span class="n">message</span><span class="o">=</span><span class="s2">&quot;The proximal_conjugate of a CIL function cannot be used in place&quot;</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="mf">1.0</span><span class="o">/</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="n">val</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="o">-</span><span class="n">tau</span><span class="p">,</span>  <span class="n">x</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">val</span></div>
+
+
+    <span class="c1"># Algebra for Function Class</span>
+
+        <span class="c1"># Add functions</span>
+        <span class="c1"># Subtract functions</span>
+        <span class="c1"># Add/Substract with Scalar</span>
+        <span class="c1"># Multiply with Scalar</span>
+
+    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns the sum of the functions.</span>
+
+<span class="sd">            Cases: a) the sum of two functions :math:`(F_{1}+F_{2})(x) = F_{1}(x) + F_{2}(x)`</span>
+<span class="sd">                   b) the sum of a function with a scalar :math:`(F_{1}+scalar)(x) = F_{1}(x) + scalar`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span>  <span class="n">Number</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">SumScalarFunction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">SumFunction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__radd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Making addition commutative. &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="n">other</span>
+
+    <span class="k">def</span> <span class="fm">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns the subtraction of the functions.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">other</span>
+
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a function multiplied by a scalar.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">ScaledFunction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__rmul__</span><span class="p">(</span><span class="n">scalar</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__neg__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Return the negative of the function &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span>
+
+
+<div class="viewcode-block" id="Function.centered_at">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Function.centered_at">[docs]</a>
+    <span class="k">def</span> <span class="nf">centered_at</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">center</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns a translated function, namely if we have a function :math:`F(x)` the center is at the origin.</span>
+<span class="sd">            TranslateFunction is :math:`F(x - b)` and the center is at point b.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        center: DataContainer</span>
+<span class="sd">            The point to center the function at.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        The translated function.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">center</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">TranslateFunction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">center</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Lipschitz of the gradient of function f.</span>
+
+<span class="sd">        L is positive real number, such that :math:`\|f&#39;(x) - f&#39;(y)\| \leq L\|x-y\|`, assuming :math:`f: IG \rightarrow \mathbb{R}`&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span>
+        <span class="c1"># return self._L</span>
+
+    <span class="nd">@L</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Setter for Lipschitz constant&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">Number</span><span class="p">,))</span> <span class="ow">and</span> <span class="n">value</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;The Lipschitz constant is a real positive number&#39;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="SumFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SumFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">SumFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;SumFunction represents the sum of :math:`n\geq2` functions</span>
+
+<span class="sd">    .. math:: (F_{1} + F_{2} + ... + F_{n})(\cdot)  = F_{1}(\cdot) + F_{2}(\cdot) + ... + F_{n}(\cdot)</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    *functions : Functions</span>
+<span class="sd">                 Functions to set up a :class:`.SumFunction`</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ValueError</span>
+<span class="sd">            If the number of function is strictly less than 2.</span>
+
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    .. math:: F(x) = \|x\|^{2} + \frac{1}{2}\|x - 1\|^{2}</span>
+
+<span class="sd">    &gt;&gt;&gt; from cil.optimisation.functions import L2NormSquared</span>
+<span class="sd">    &gt;&gt;&gt; from cil.framework import ImageGeometry</span>
+<span class="sd">    &gt;&gt;&gt; f1 = L2NormSquared()</span>
+<span class="sd">    &gt;&gt;&gt; f2 = 0.5 * L2NormSquared(b = ig.allocate(1))</span>
+<span class="sd">    &gt;&gt;&gt; F = SumFunction(f1, f2)</span>
+
+<span class="sd">    .. math:: F(x) = \sum_{i=1}^{50} \|x - i\|^{2}</span>
+
+<span class="sd">    &gt;&gt;&gt; F = SumFunction(*[L2NormSquared(b=i) for i in range(50)])</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">functions</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SumFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">functions</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s1">&#39;At least 1 function needed&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">functions</span> <span class="o">=</span> <span class="n">functions</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the Lipschitz constant for the SumFunction</span>
+
+<span class="sd">        .. math:: L = \sum_{i} L_{i}</span>
+
+<span class="sd">        where :math:`L_{i}` is the Lipschitz constant of the smooth function :math:`F_{i}`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">L</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">f</span><span class="o">.</span><span class="n">L</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">L</span> <span class="o">+=</span> <span class="n">f</span><span class="o">.</span><span class="n">L</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">L</span> <span class="o">=</span> <span class="kc">None</span>
+                <span class="k">break</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="n">L</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span>
+
+    <span class="nd">@L</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="c1"># call base class setter</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SumFunction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span><span class="o">.</span><span class="n">L</span><span class="o">.</span><span class="n">fset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">Lmax</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the maximum Lipschitz constant for the SumFunction</span>
+
+<span class="sd">        .. math:: L = \max_{i}\{L_{i}\}</span>
+
+<span class="sd">        where :math:`L_{i}` is the Lipschitz constant of the smooth function :math:`F_{i}`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">f</span><span class="o">.</span><span class="n">L</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">l</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">L</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">l</span> <span class="o">=</span> <span class="kc">None</span>
+                <span class="k">break</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_Lmax</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_Lmax</span>
+
+    <span class="nd">@Lmax</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">Lmax</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="c1"># call base class setter</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SumFunction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span><span class="o">.</span><span class="n">Lmax</span><span class="o">.</span><span class="n">fset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the sum of functions evaluated at :math:`x`.</span>
+
+<span class="sd">        .. math:: (F_{1} + F_{2} + ... + F_{n})(x) = F_{1}(x) + F_{2}(x) + ... + F_{n}(x)</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span>
+
+<div class="viewcode-block" id="SumFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SumFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the sum of the gradient of functions evaluated at :math:`x`, if all of them are differentiable.</span>
+
+<span class="sd">        .. math:: (F&#39;_{1} + F&#39;_{2} + ... + F&#39;_{n})(x) = F&#39;_{1}(x) + F&#39;_{2}(x) + ... + F&#39;_{n}(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">            Point to evaluate the gradient at.</span>
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the value of the sum of the gradients evaluated at point :math:`x`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">f</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">+=</span> <span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Addition for the SumFunction.</span>
+
+<span class="sd">        *  :code:`SumFunction` + :code:`SumFunction` is a :code:`SumFunction`.</span>
+
+<span class="sd">        *  :code:`SumFunction` + :code:`Function` is a :code:`SumFunction`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">SumFunction</span><span class="p">):</span>
+            <span class="n">functions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">)</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">functions</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">SumFunction</span><span class="p">(</span><span class="o">*</span><span class="n">functions</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Function</span><span class="p">):</span>
+            <span class="n">functions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">)</span>
+            <span class="n">functions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">SumFunction</span><span class="p">(</span><span class="o">*</span><span class="n">functions</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">SumFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__add__</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">num_functions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="ScaledFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ScaledFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">ScaledFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; ScaledFunction represents the scalar multiplication with a Function.</span>
+
+<span class="sd">    Let a function F then and a scalar :math:`\alpha`.</span>
+
+<span class="sd">    If :math:`G(x) = \alpha F(x)` then:</span>
+
+<span class="sd">    1. :math:`G(x) = \alpha  F(x)` ( __call__ method )</span>
+<span class="sd">    2. :math:`G&#39;(x) = \alpha  F&#39;(x)` ( gradient method )</span>
+<span class="sd">    3. :math:`G^{*}(x^{*}) = \alpha  F^{*}(\frac{x^{*}}{\alpha})` ( convex_conjugate method )</span>
+<span class="sd">    4. :math:`\text{prox}_{\tau G}(x) = \text{prox}_{(\tau\alpha) F}(x)` ( proximal method )</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ScaledFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;expected scalar: got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">scalar</span><span class="p">)))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span> <span class="o">=</span> <span class="n">scalar</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">function</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">L</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scalar</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">L</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span>
+
+    <span class="nd">@L</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="c1"># call base class setter</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ScaledFunction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span><span class="o">.</span><span class="n">L</span><span class="o">.</span><span class="n">fset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scalar</span>
+
+    <span class="nd">@scalar</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">Number</span><span class="p">,</span> <span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_scalar</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s1">&#39;Expecting scalar type as a number type. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)))</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the scaled function evaluated at :math:`x`.</span>
+
+<span class="sd">        .. math:: G(x) = \alpha F(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer, the value of the scaled function.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ScaledFunction.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ScaledFunction.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the convex conjugate of the scaled function.</span>
+
+<span class="sd">        .. math:: G^{*}(x^{*}) = \alpha  F^{*}(\frac{x^{*}}{\alpha})</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        The value of the convex conjugate of the scaled function.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scalar</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scalar</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scalar</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span> <span class="o">*</span> <span class="n">val</span></div>
+
+
+<div class="viewcode-block" id="ScaledFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ScaledFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the gradient of the scaled function evaluated at :math:`x`.</span>
+
+<span class="sd">        .. math:: G&#39;(x) = \alpha  F&#39;(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">            Point to evaluate the gradient at.</span>
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the value of the gradient of the scaled function evaluated at :math:`x`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">res</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span>
+        <span class="k">return</span> <span class="n">res</span></div>
+
+
+<div class="viewcode-block" id="ScaledFunction.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ScaledFunction.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the proximal operator of the scaled function, evaluated at :math:`x`.</span>
+
+<span class="sd">        .. math:: \text{prox}_{\tau G}(x) = \text{prox}_{(\tau\alpha) F}(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        tau: scalar</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the proximal operator of the scaled function evaluated at :math:`x` with scalar :math:`\tau`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">scalar</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ScaledFunction.proximal_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ScaledFunction.proximal_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;This returns the proximal  conjugate operator for the function at :math:`x`, :math:`\tau`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        tau: scalar</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the proximal conjugate operator for the function evaluated at :math:`x` and :math:`\tau`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span><span class="o">/</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="n">val</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="o">-</span><span class="n">tau</span><span class="p">,</span>  <span class="n">x</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">val</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="SumScalarFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SumScalarFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">SumScalarFunction</span><span class="p">(</span><span class="n">SumFunction</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; SumScalarFunction represents the sum a function with a scalar.</span>
+
+<span class="sd">        .. math:: (F + scalar)(x)  = F(x) + scalar</span>
+
+<span class="sd">        Although SumFunction has no general expressions for</span>
+
+<span class="sd">        i) convex_conjugate</span>
+<span class="sd">        ii) proximal</span>
+<span class="sd">        iii) proximal_conjugate</span>
+
+<span class="sd">        if the second argument is a ConstantFunction then we can derive the above analytically.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span> <span class="n">constant</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SumScalarFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">function</span><span class="p">,</span> <span class="n">ConstantFunction</span><span class="p">(</span><span class="n">constant</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">constant</span> <span class="o">=</span> <span class="n">constant</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">function</span>
+
+<div class="viewcode-block" id="SumScalarFunction.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SumScalarFunction.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the convex conjugate of a :math:`(F+scalar)`, evaluated at :math:`x`.</span>
+
+<span class="sd">        .. math:: (F+scalar)^{*}(x^{*}) = F^{*}(x^{*}) - scalar</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        The value of the convex conjugate evaluated at :math:`x`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">constant</span></div>
+
+
+<div class="viewcode-block" id="SumScalarFunction.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SumScalarFunction.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns the proximal operator of :math:`F+scalar`</span>
+
+<span class="sd">        .. math:: \text{prox}_{\tau (F+scalar)}(x) = \text{prox}_{\tau F}</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        tau: scalar</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the evaluation of the proximal operator evaluated at :math:`x` and :math:`\tau`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">L</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">L</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span>
+
+    <span class="nd">@L</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="c1"># call base class setter</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SumScalarFunction</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span><span class="o">.</span><span class="n">L</span><span class="o">.</span><span class="n">fset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="ConstantFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ConstantFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">ConstantFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; ConstantFunction: :math:`F(x) = constant, constant\in\mathbb{R}`</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">constant</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">constant</span> <span class="o">=</span> <span class="n">constant</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConstantFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns the value of the function, :math:`F(x) = constant`&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">constant</span>
+
+<div class="viewcode-block" id="ConstantFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ConstantFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns the value of the gradient of the function, :math:`F&#39;(x)=0`</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">            Point to evaluate the gradient at.</span>
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        A DataContainer of zeros, the same size as :math:`x`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="mf">0.</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="ConstantFunction.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ConstantFunction.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; The convex conjugate of constant function :math:`F(x) = c\in\mathbb{R}` is</span>
+
+<span class="sd">        .. math::</span>
+<span class="sd">            F(x^{*})</span>
+<span class="sd">            =</span>
+<span class="sd">            \begin{cases}</span>
+<span class="sd">                -c, &amp; if x^{*} = 0\\</span>
+<span class="sd">                \infty, &amp; \mbox{otherwise}</span>
+<span class="sd">            \end{cases}</span>
+
+
+<span class="sd">        However, :math:`x^{*} = 0` only in the limit of iterations, so in fact this can be infinity.</span>
+<span class="sd">        We do not want to have inf values in the convex conjugate, so we have to penalise this value accordingly.</span>
+<span class="sd">        The following penalisation is useful in the PDHG algorithm, when we compute primal &amp; dual objectives</span>
+<span class="sd">        for convergence purposes.</span>
+
+<span class="sd">        .. math:: F^{*}(x^{*}) = \sum \max\{x^{*}, 0\}</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        The maximum of x and 0, summed over the entries of x.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="ConstantFunction.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ConstantFunction.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the proximal operator of the constant function, which is the same element, i.e.,</span>
+
+<span class="sd">        .. math:: \text{prox}_{\tau F}(x) = x</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        tau: scalar</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, equal to :math:`x`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">constant</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_constant</span>
+
+    <span class="nd">@constant</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">constant</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;expected scalar: got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_constant</span> <span class="o">=</span> <span class="n">value</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mf">1.</span>
+
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;defines the right multiplication with a number&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="bp">NotImplemented</span>
+        <span class="n">constant</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">constant</span> <span class="o">*</span> <span class="n">other</span>
+        <span class="k">return</span> <span class="n">ConstantFunction</span><span class="p">(</span><span class="n">constant</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="ZeroFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.ZeroFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">ZeroFunction</span><span class="p">(</span><span class="n">ConstantFunction</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; ZeroFunction represents the zero function, :math:`F(x) = 0`</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ZeroFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">constant</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="TranslateFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TranslateFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">TranslateFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; TranslateFunction represents the translation of function F with respect to the center b.</span>
+
+<span class="sd">    Let a function F and consider :math:`G(x) = F(x - center)`.</span>
+
+<span class="sd">    Function F is centered at 0, whereas G is centered at point b.</span>
+
+<span class="sd">    If :math:`G(x) = F(x - b)` then:</span>
+
+<span class="sd">    1. :math:`G(x) = F(x - b)` ( __call__ method )</span>
+<span class="sd">    2. :math:`G&#39;(x) = F&#39;(x - b)` ( gradient method )</span>
+<span class="sd">    3. :math:`G^{*}(x^{*}) = F^{*}(x^{*}) + &lt;x^{*}, b &gt;` ( convex_conjugate method )</span>
+<span class="sd">    4. :math:`\text{prox}_{\tau G}(x) = \text{prox}_{\tau F}(x - b)  + b` ( proximal method )</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span> <span class="n">center</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">L</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">L</span>
+        <span class="k">except</span> <span class="ne">NotImplementedError</span> <span class="k">as</span> <span class="n">nie</span><span class="p">:</span>
+            <span class="n">L</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">TranslateFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span><span class="o">=</span><span class="n">L</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">function</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">center</span> <span class="o">=</span> <span class="n">center</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the translated function.</span>
+
+<span class="sd">        .. math:: G(x) = F(x - b)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        The value of the translated function evaluated at :math:`x`.</span>
+
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">val</span>
+
+<div class="viewcode-block" id="TranslateFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TranslateFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the gradient of the translated function.</span>
+
+<span class="sd">        .. math:: G&#39;(x) =  F&#39;(x - b)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">            Point to evaluate the gradient at.</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the gradient of the translated function evaluated at :math:`x`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">val</span></div>
+
+
+<div class="viewcode-block" id="TranslateFunction.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TranslateFunction.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the proximal operator of the translated function.</span>
+
+<span class="sd">        .. math:: \text{prox}_{\tau G}(x) = \text{prox}_{\tau F}(x-b) + b</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        tau: scalar</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer, the proximal operator of the translated function at :math:`x` and :math:`\tau`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">val</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">val</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">val</span></div>
+
+
+<div class="viewcode-block" id="TranslateFunction.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TranslateFunction.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the convex conjugate of the translated function.</span>
+
+<span class="sd">        .. math:: G^{*}(x^{*}) = F^{*}(x^{*}) + &lt;x^{*}, b &gt;</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        The value of the convex conjugate of the translated function at :math:`x`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">center</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/functions/IndicatorBox/index.html b/v24.2.0/_modules/cil/optimisation/functions/IndicatorBox/index.html
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.optimisation.functions.IndicatorBox</span></li>
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+  <h1>Source code for cil.optimisation.functions.IndicatorBox</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numba</span>
+<span class="kn">from</span> <span class="nn">cil.utilities</span> <span class="kn">import</span> <span class="n">multiprocessing</span> <span class="k">as</span> <span class="n">cil_mp</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="IndicatorBox">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.IndicatorBox">[docs]</a>
+<span class="k">class</span> <span class="nc">IndicatorBox</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Indicator function for box constraint</span>
+
+<span class="sd">      .. math::</span>
+
+<span class="sd">         f(x) = \mathbb{I}_{[a, b]} = \begin{cases}</span>
+<span class="sd">                                            0, \text{ if } x \in [a, b] \\</span>
+<span class="sd">                                            \infty, \text{otherwise}</span>
+<span class="sd">                                     \end{cases}</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    lower : float, DataContainer or numpy array, default None</span>
+<span class="sd">        Lower bound. If set to None, it is equivalent to ``-np.inf``.</span>
+<span class="sd">    upper : float, DataContainer or numpy array, default None</span>
+<span class="sd">        Upper bound. If set to None, it is equivalent to ``np.inf``.</span>
+<span class="sd">    accelerated : bool, default True</span>
+<span class="sd">        Specifies whether to use the accelerated version or not, using numba or</span>
+<span class="sd">        numpy backends respectively.</span>
+
+
+<span class="sd">    If ``lower`` or ``upper`` are passed a ``DataContainer`` (or derived class</span>
+<span class="sd">    such as ``ImageData`` or ``AcquisitionData``) or a ``numpy array``, the bounds</span>
+<span class="sd">    can be set to different values for each element.</span>
+
+<span class="sd">    In order to save computing time it is possible to suppress the evaluation of</span>
+<span class="sd">    the function. This is achieved by setting ``suppress_evaluation`` to ``True``.</span>
+<span class="sd">    ``IndicatorBox`` evaluated on any input will then return 0.</span>
+
+<span class="sd">    If ``accelerated`` is set to ``True`` (default), the Numba backend is used.</span>
+<span class="sd">    Otherwise, the Numpy backend is used. An optional parameter to set the number of</span>
+<span class="sd">    threads used by Numba can be set with ``set_num_threads``. Setting the number of</span>
+<span class="sd">    threads when ``accelerate`` is set to ``False`` will not have any effect.</span>
+<span class="sd">    The default number of threads is defined in the ``cil.utilities.multiprocessing``</span>
+<span class="sd">    module, and it is equivalent to half of the CPU cores available.</span>
+
+<span class="sd">    Example:</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    In order to save computing time it is possible to suppress the evaluation of the</span>
+<span class="sd">    function.</span>
+
+<span class="sd">    .. code-block:: python</span>
+
+<span class="sd">        ib = IndicatorBox(lower=0, upper=1)</span>
+<span class="sd">        ib.set_suppress_evaluation(True)</span>
+<span class="sd">        ib.evaluate(x) # returns 0</span>
+
+
+<span class="sd">    Example:</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Set the number of threads used in accelerated mode.</span>
+
+<span class="sd">    .. code-block:: python</span>
+
+
+<span class="sd">        num_threads = 4</span>
+<span class="sd">        ib = IndicatorBox(lower=0, upper=1)</span>
+<span class="sd">        ib.set_num_threads(num_threads)</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">accelerated</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">accelerated</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Numba backend is used.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">IndicatorBox</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">IndicatorBox_numba</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Numpy backend is used.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">IndicatorBox</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">IndicatorBox_numpy</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">accelerated</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;__init__&#39;&#39;&#39;</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">IndicatorBox</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+        <span class="c1"># We set lower and upper to either a float or a numpy array</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">if</span> <span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">_get_as_nparray_or_number</span><span class="p">(</span>
+            <span class="n">lower</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">if</span> <span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">_get_as_nparray_or_number</span><span class="p">(</span>
+            <span class="n">upper</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">orig_lower</span> <span class="o">=</span> <span class="n">lower</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">orig_upper</span> <span class="o">=</span> <span class="n">upper</span>
+        <span class="c1"># default is to evaluate the function</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_suppress_evaluation</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="c1"># optional parameter to track the number of threads used by numba</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_num_threads</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">suppress_evaluation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_suppress_evaluation</span>
+
+<div class="viewcode-block" id="IndicatorBox.set_suppress_evaluation">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.IndicatorBox.set_suppress_evaluation">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_suppress_evaluation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Suppresses the evaluation of the function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        value : bool</span>
+<span class="sd">            If True, the function evaluation on any input will return 0, without calculation.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">bool</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Value must be boolean&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_suppress_evaluation</span> <span class="o">=</span> <span class="n">value</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Evaluates IndicatorBox at x</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">            x : DataContainer</span>
+
+<span class="sd">        Evaluates the IndicatorBox at x. If ``suppress_evaluation`` is ``True``, returns 0.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">suppress_evaluation</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="mf">0.0</span>
+
+<div class="viewcode-block" id="IndicatorBox.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.IndicatorBox.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Proximal operator of IndicatorBox at x</span>
+
+<span class="sd">        .. math:: prox_{\tau * f}(x)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">            Input to the proximal operator</span>
+<span class="sd">        tau : float</span>
+<span class="sd">            Step size. Notice it is ignored in IndicatorBox</span>
+<span class="sd">        out : DataContainer, optional</span>
+<span class="sd">            Output of the proximal operator. If not provided, a new DataContainer is created.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+
+<span class="sd">            ``tau`` is ignored but it is in the signature of the generic Function class</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">outarr</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="c1"># calculate the proximal</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_proximal</span><span class="p">(</span><span class="n">outarr</span><span class="p">)</span>
+
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">outarr</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="IndicatorBox.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.IndicatorBox.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;IndicatorBox is not differentiable, so calling gradient will raise a ``ValueError``&#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;The IndicatorBox is not differentiable&#39;</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">outarr</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Implement this in the derived class&#39;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">num_threads</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Get the optional number of threads parameter to use for the accelerated version.</span>
+
+<span class="sd">        Defaults to the value set in the CIL multiprocessing module.&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">cil_mp</span><span class="o">.</span><span class="n">NUM_THREADS</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_threads</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_threads</span>
+
+<div class="viewcode-block" id="IndicatorBox.set_num_threads">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.IndicatorBox.set_num_threads">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_num_threads</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Set the optional number of threads parameter to use for the accelerated version.</span>
+
+<span class="sd">        This is discarded if ``accelerated=False``.&#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_num_threads</span> <span class="o">=</span> <span class="n">value</span></div>
+</div>
+
+
+
+<span class="k">class</span> <span class="nc">IndicatorBox_numba</span><span class="p">(</span><span class="n">IndicatorBox</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Evaluates IndicatorBox at x&#39;&#39;&#39;</span>
+        <span class="c1"># set the number of threads to the number of threads defined by the user</span>
+        <span class="c1"># or default to what set in the CIL multiprocessing module</span>
+        <span class="n">num_threads</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">get_num_threads</span><span class="p">()</span>
+        <span class="n">numba</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_threads</span><span class="p">)</span>
+        <span class="n">breaking</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">numba</span><span class="o">.</span><span class="n">get_num_threads</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+
+                <span class="n">_array_within_limits_aa</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
+                                        <span class="n">breaking</span><span class="p">)</span>
+
+            <span class="k">else</span><span class="p">:</span>
+
+                <span class="n">_array_within_limits_af</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
+                                        <span class="n">breaking</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+
+                <span class="n">_array_within_limits_fa</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
+                                        <span class="n">breaking</span><span class="p">)</span>
+
+            <span class="k">else</span><span class="p">:</span>
+
+                <span class="n">_array_within_limits_ff</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
+                                        <span class="n">breaking</span><span class="p">)</span>
+
+        <span class="c1"># reset the number of threads to the original value</span>
+        <span class="n">numba</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="n">num_threads</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">if</span> <span class="n">breaking</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mf">0.0</span>
+
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Convex conjugate of IndicatorBox at x&#39;&#39;&#39;</span>
+        <span class="c1"># set the number of threads to the number of threads defined by the user</span>
+        <span class="c1"># or default to what set in the CIL multiprocessing module</span>
+        <span class="n">num_threads</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">get_num_threads</span><span class="p">()</span>
+        <span class="n">numba</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_threads</span><span class="p">)</span>
+
+        <span class="n">acc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">numba</span><span class="o">.</span><span class="n">get_num_threads</span><span class="p">()),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint32</span><span class="p">)</span>
+        <span class="n">_convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">acc</span><span class="p">)</span>
+
+        <span class="c1"># reset the number of threads to the original value</span>
+        <span class="n">numba</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="n">num_threads</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">acc</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">outarr</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">orig_lower</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">orig_upper</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                    <span class="n">_proximal_aa</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_proximal_af</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                    <span class="n">_proximal_fa</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">outarr</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">orig_lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">_proximal_na</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">outarr</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">orig_upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">_proximal_an</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">outarr</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">IndicatorBox_numpy</span><span class="p">(</span><span class="n">IndicatorBox</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Evaluates IndicatorBox at x&#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
+                <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)):</span>
+            <span class="n">val</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+        <span class="k">return</span> <span class="n">val</span>
+
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Convex conjugate of IndicatorBox at x&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">outarr</span><span class="p">):</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">outarr</span><span class="p">,</span>
+                <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">orig_lower</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
+                <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">orig_upper</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
+                <span class="n">out</span><span class="o">=</span><span class="n">outarr</span><span class="p">)</span>
+
+
+<span class="c1">## Utilities</span>
+<span class="k">def</span> <span class="nf">_get_as_nparray_or_number</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns x as a numpy array or a number&#39;&#39;&#39;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+        <span class="c1"># In this case we trust that it will be either a numpy ndarray</span>
+        <span class="c1"># or a number as described in the docstring</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;Assuming that x is a numpy array or a number&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">x</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_array_within_limits_ff</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">,</span> <span class="n">breaking</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns 0 if all elements of x are within [lower, upper]&#39;&#39;&#39;</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="n">j</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">ufunc</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">_get_thread_id</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">lower</span> <span class="ow">or</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">upper</span><span class="p">):</span>
+            <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_array_within_limits_af</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">,</span> <span class="n">breaking</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns 0 if all elements of x are within [lower, upper]&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">lower</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x and lower must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">loarr</span> <span class="o">=</span> <span class="n">lower</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="n">j</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">ufunc</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">_get_thread_id</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">or</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">upper</span><span class="p">):</span>
+            <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_array_within_limits_aa</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">,</span> <span class="n">breaking</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns 0 if all elements of x are within [lower, upper]&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">lower</span><span class="o">.</span><span class="n">size</span> <span class="ow">or</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">upper</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x, lower and upper must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">uparr</span> <span class="o">=</span> <span class="n">upper</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">loarr</span> <span class="o">=</span> <span class="n">lower</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="n">j</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">ufunc</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">_get_thread_id</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">or</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
+            <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_array_within_limits_fa</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">,</span> <span class="n">breaking</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns 0 if all elements of x are within [lower, upper]&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">upper</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x and upper must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">uparr</span> <span class="o">=</span> <span class="n">upper</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="n">j</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">ufunc</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">_get_thread_id</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">lower</span> <span class="ow">or</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
+            <span class="n">breaking</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+
+<span class="c1">##########################################################################</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_proximal_aa</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Slightly faster than using np.clip&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">lower</span><span class="o">.</span><span class="n">size</span> <span class="ow">or</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">upper</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x, lower and upper must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">loarr</span> <span class="o">=</span> <span class="n">lower</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">uparr</span> <span class="o">=</span> <span class="n">upper</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_proximal_af</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Slightly faster than using np.clip&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">lower</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x, lower and upper must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">loarr</span> <span class="o">=</span> <span class="n">lower</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">upper</span><span class="p">:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">upper</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_proximal_fa</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Slightly faster than using np.clip&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">upper</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x, lower and upper must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">uparr</span> <span class="o">=</span> <span class="n">upper</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">lower</span><span class="p">:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">lower</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_proximal_na</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Slightly faster than using np.clip&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">upper</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x and upper must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">uparr</span> <span class="o">=</span> <span class="n">upper</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">uparr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_proximal_an</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lower</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Slightly faster than using np.clip&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">lower</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;x and lower must have the same size&#39;</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">loarr</span> <span class="o">=</span> <span class="n">lower</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">loarr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+
+<span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">acc</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Convex conjugate of IndicatorBox</span>
+
+<span class="sd">    im.maximum(0).sum()</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="n">j</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">ufunc</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">_get_thread_id</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">acc</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
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+  <h1>Source code for cil.optimisation.functions.KullbackLeibler</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">import</span> <span class="nn">scipy.special</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.errors</span> <span class="kn">import</span> <span class="n">InPlaceError</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">njit</span><span class="p">,</span> <span class="n">prange</span><span class="p">,</span> <span class="n">get_thread_id</span><span class="p">,</span> <span class="n">get_num_threads</span>
+    <span class="n">has_numba</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">ie</span><span class="p">:</span>
+    <span class="n">has_numba</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="KullbackLeibler">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.KullbackLeibler">[docs]</a>
+<span class="k">class</span> <span class="nc">KullbackLeibler</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Kullback Leibler</span>
+
+<span class="sd">    .. math:: F(u, v)</span>
+<span class="sd">            = \begin{cases}</span>
+<span class="sd">            u \log(\frac{u}{v}) - u + v &amp; \mbox{ if } u &gt; 0, v &gt; 0\\</span>
+<span class="sd">            v &amp; \mbox{ if } u = 0, v \ge 0 \\</span>
+<span class="sd">            \infty, &amp; \mbox{otherwise}</span>
+<span class="sd">            \end{cases}</span>
+
+<span class="sd">    where the :math:`0\log0 := 0` convention is used.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    b : DataContainer, non-negative</span>
+<span class="sd">        Translates the function at point `b`.</span>
+<span class="sd">    eta : DataContainer, default = 0</span>
+<span class="sd">        Background noise</span>
+<span class="sd">    mask : DataContainer, default = None</span>
+<span class="sd">        Mask for the data `b`</span>
+<span class="sd">    backend : {&#39;numba&#39;,&#39;numpy&#39;}, optional</span>
+<span class="sd">        Backend for the KullbackLeibler methods. Numba is the default backend.</span>
+
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    The Kullback-Leibler function is used in practice as a fidelity term in minimisation problems where the</span>
+<span class="sd">    acquired data follow Poisson distribution. If we denote the acquired data with :code:`b`</span>
+<span class="sd">    then, we write</span>
+
+<span class="sd">    .. math:: \underset{i}{\sum} F(b_{i}, (v + \eta)_{i})</span>
+
+<span class="sd">    where, :math:`\eta` is an additional noise.</span>
+
+<span class="sd">    In the case of Positron Emission Tomography reconstruction :math:`\eta` represents</span>
+<span class="sd">    scatter and random events contribution during the PET acquisition. Hence, in that case the KullbackLeibler</span>
+<span class="sd">    fidelity measures the distance between :math:`\mathcal{A}v + \eta` and acquisition data :math:`b`, where</span>
+<span class="sd">    :math:`\mathcal{A}` is the projection operator. This is related to `PoissonLogLikelihoodWithLinearModelForMean &lt;http://stir.sourceforge.net/documentation/doxy/html/classstir_1_1PoissonLogLikelihoodWithLinearModelForMean.html&gt;`_ ,</span>
+<span class="sd">    definition that is used in PET reconstruction in the `SIRF &lt;https://github.com/SyneRBI/SIRF&gt;`_ software.</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    The default implementation uses the build-in function `kl_div &lt;https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.kl_div.html&gt;`_ from scipy.</span>
+<span class="sd">    The methods of the :class:`.KullbackLeibler` are accelerated provided that `numba &lt;https://numba.pydata.org/&gt;`_ library is installed.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.optimisation.functions import KullbackLeibler</span>
+<span class="sd">    &gt;&gt;&gt; from cil.framework import ImageGeometry</span>
+<span class="sd">    &gt;&gt;&gt; ig = ImageGeometry(3,4)</span>
+<span class="sd">    &gt;&gt;&gt; data = ig.allocate(&#39;random&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; F = KullbackLeibler(b = data)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">mask</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">backend</span> <span class="o">=</span> <span class="s1">&#39;numba&#39;</span><span class="p">):</span>
+
+        <span class="c1"># default backend numba</span>
+        <span class="bp">cls</span><span class="o">.</span><span class="n">backend</span> <span class="o">=</span> <span class="n">backend</span>
+
+        <span class="k">if</span> <span class="bp">cls</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s1">&#39;numba&#39;</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">has_numba</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Numba is not installed.&quot;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Numba backend is used.&quot;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">KullbackLeibler</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">KullbackLeibler_numba</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Numpy backend is used.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">KullbackLeibler</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">KullbackLeibler_numpy</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">mask</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">backend</span> <span class="o">=</span> <span class="s1">&#39;numba&#39;</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">b</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">=</span> <span class="n">eta</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">*</span> <span class="mf">0.0</span>
+
+        <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input data should be non-negative.&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">KullbackLeibler</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s1">&#39;numba&#39;</span><span class="p">:</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">KullbackLeibler</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span></div>
+
+
+<span class="k">class</span> <span class="nc">KullbackLeibler_numpy</span><span class="p">(</span><span class="n">KullbackLeibler</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        To avoid infinity values, we consider only pixels/voxels for :math:`x+\eta\geq0`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">tmp_sum</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="p">)</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">ind</span> <span class="o">=</span> <span class="n">tmp_sum</span> <span class="o">&gt;=</span> <span class="mi">0</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">kl_div</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">()[</span><span class="n">ind</span><span class="p">],</span> <span class="n">tmp_sum</span><span class="p">[</span><span class="n">ind</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the gradient of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`F&#39;(b, x + \eta) = 1 - \frac{b}{x+\eta}`</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        To avoid inf values, we :math:`x+\eta&gt;0` is required.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">arr</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">arr</span><span class="p">[</span><span class="n">arr</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">()[</span><span class="n">arr</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">arr</span><span class="p">[</span><span class="n">arr</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span>
+
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`F^{*}(b, x + \eta) = - b \log(1-x^{*}) - &lt;x^{*}, \eta&gt;`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">ind</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">&gt;</span><span class="mi">0</span>
+        <span class="n">xlogy</span> <span class="o">=</span> <span class="o">-</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">xlogy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">()[</span><span class="n">ind</span><span class="p">],</span> <span class="n">tmp</span><span class="p">[</span><span class="n">ind</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">xlogy</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`\mathrm{prox}_{\tau F}(x) = \frac{1}{2}\bigg( (x - \eta - \tau) + \sqrt{ (x + \eta - \tau)^2 + 4\tau b} \bigg)`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span>  <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">==</span><span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span><span class="p">(</span><span class="n">message</span><span class="o">=</span><span class="s2">&quot;KullbackLeibler.proximal cannot be used in place&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mf">0.5</span> <span class="o">*</span><span class="p">(</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span> <span class="o">+</span> \
+                <span class="p">(</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">tau</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span>   <span class="p">)</span> <span class="o">.</span><span class="n">sqrt</span><span class="p">()</span> \
+                    <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">-=</span> <span class="n">tau</span>
+            <span class="n">out</span> <span class="o">*=</span> <span class="n">out</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">*</span> <span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="n">tau</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">*=</span> <span class="mf">0.5</span>
+            <span class="k">return</span> <span class="n">out</span>
+
+
+    <span class="k">def</span> <span class="nf">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the convex conjugate of KullbackLeibler at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`\mathrm{prox}_{\tau F^{*}}(x) = 0.5*((z + 1) - \sqrt{(z-1)^2 + 4 * \tau b})`, where :math:`z = x + \tau \eta`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">tau</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span>
+            <span class="k">return</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">((</span><span class="n">z</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">((</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">tau</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">sqrt</span><span class="p">())</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">tau</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span>
+            <span class="n">tmp</span> <span class="o">+=</span> <span class="n">x</span>
+            <span class="n">tmp</span> <span class="o">-=</span> <span class="mi">1</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="mi">4</span><span class="o">*</span><span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+            <span class="n">out</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="n">tmp</span> <span class="o">+=</span> <span class="mi">2</span>
+            <span class="n">out</span> <span class="o">+=</span> <span class="n">tmp</span>
+            <span class="n">out</span> <span class="o">*=</span> <span class="mf">0.5</span>
+            <span class="k">return</span> <span class="n">out</span>
+
+<span class="c1">####################################</span>
+<span class="c1">## KullbackLeibler numba routines ##</span>
+<span class="c1">####################################</span>
+
+<span class="k">if</span> <span class="n">has_numba</span><span class="p">:</span>
+
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">E</span> <span class="o">=</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span>  <span class="p">(</span>
+                <span class="p">(</span> <span class="n">X</span> <span class="o">-</span> <span class="n">E</span> <span class="o">-</span> <span class="n">tau</span> <span class="p">)</span> <span class="o">+</span>\
+                <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span> <span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">E</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span><span class="o">**</span><span class="mf">2.</span> <span class="o">+</span> \
+                    <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">tau</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal_arr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="n">t</span> <span class="o">=</span> <span class="n">tau</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">E</span> <span class="o">=</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span>  <span class="p">(</span>
+                <span class="p">(</span> <span class="n">X</span> <span class="o">-</span> <span class="n">E</span> <span class="o">-</span> <span class="n">t</span> <span class="p">)</span> <span class="o">+</span>\
+                <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span> <span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">E</span> <span class="o">-</span> <span class="n">t</span><span class="p">)</span><span class="o">**</span><span class="mf">2.</span> <span class="o">+</span> \
+                    <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">t</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal_mask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mask</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">E</span> <span class="o">=</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span>  <span class="p">(</span>
+                    <span class="p">(</span> <span class="n">X</span> <span class="o">-</span> <span class="n">E</span> <span class="o">-</span> <span class="n">tau</span> <span class="p">)</span> <span class="o">+</span>\
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span> <span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">E</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span><span class="o">**</span><span class="mf">2.</span> <span class="o">+</span> \
+                        <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">tau</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="p">)</span>
+                <span class="p">)</span>
+
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal_arr_mask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mask</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">t</span> <span class="o">=</span> <span class="n">tau</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">E</span> <span class="o">=</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span>  <span class="p">(</span>
+                    <span class="p">(</span> <span class="n">X</span> <span class="o">-</span> <span class="n">E</span> <span class="o">-</span> <span class="n">t</span> <span class="p">)</span> <span class="o">+</span>\
+                    <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span> <span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="n">E</span> <span class="o">-</span> <span class="n">t</span><span class="p">)</span><span class="o">**</span><span class="mf">2.</span> <span class="o">+</span> \
+                        <span class="p">(</span><span class="mf">4.</span> <span class="o">*</span> <span class="n">t</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="p">)</span>
+                <span class="p">)</span>
+
+    <span class="c1"># proximal conjugate</span>
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal_conjugate_arr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+        <span class="c1">#z = x + tau * self.bnoise</span>
+        <span class="c1">#return 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt())</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="n">t</span> <span class="o">=</span> <span class="n">tau</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span> <span class="n">t</span> <span class="o">*</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span>
+                <span class="p">(</span><span class="n">z</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">t</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="p">)</span>
+
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal_conjugate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+        <span class="c1">#z = x + tau * self.bnoise</span>
+        <span class="c1">#return 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt())</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span> <span class="n">tau</span> <span class="o">*</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span>
+                <span class="p">(</span><span class="n">z</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">tau</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="p">)</span>
+
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal_conjugate_arr_mask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">mask</span><span class="p">):</span>
+        <span class="c1">#z = x + tau * self.bnoise</span>
+        <span class="c1">#return 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt())</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">t</span> <span class="o">=</span> <span class="n">tau</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span> <span class="n">t</span> <span class="o">*</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">)</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span>
+                    <span class="p">(</span><span class="n">z</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">t</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="p">)</span>
+
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_proximal_conjugate_mask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">mask</span><span class="p">):</span>
+        <span class="c1">#z = x + tau * self.bnoise</span>
+        <span class="c1">#return 0.5*((z + 1) - ((z-1)**2 + 4 * tau * self.b).sqrt())</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span> <span class="n">tau</span> <span class="o">*</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">)</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span>
+                    <span class="p">(</span><span class="n">z</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">z</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">tau</span> <span class="o">*</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="p">)</span>
+
+    <span class="c1"># gradient</span>
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_gradient_mask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mask</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        
+    <span class="c1"># KL divergence</span>
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_div</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
+        <span class="n">accumulator</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">get_num_threads</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">Y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">Y</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="c1"># out.flat[i] = X * numpy.log(X/Y) - X + Y</span>
+                <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">X</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">X</span><span class="o">/</span><span class="n">Y</span><span class="p">)</span> <span class="o">-</span> <span class="n">X</span> <span class="o">+</span> <span class="n">Y</span>
+            <span class="k">elif</span> <span class="n">X</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">Y</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="c1"># out.flat[i] = Y</span>
+                <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">Y</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># out.flat[i] = numpy.inf</span>
+                <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">inf</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
+    
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_div_mask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mask</span><span class="p">):</span>
+        <span class="n">accumulator</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">get_num_threads</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">Y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">Y</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># out.flat[i] = X * numpy.log(X/Y) - X + Y</span>
+                    <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">X</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">X</span><span class="o">/</span><span class="n">Y</span><span class="p">)</span> <span class="o">-</span> <span class="n">X</span> <span class="o">+</span> <span class="n">Y</span>
+                <span class="k">elif</span> <span class="n">X</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">Y</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># out.flat[i] = Y</span>
+                    <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">Y</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="c1"># out.flat[i] = numpy.inf</span>
+                    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">inf</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
+
+    <span class="c1"># convex conjugate</span>
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
+        <span class="n">accumulator</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">get_num_threads</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">x_f</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">Y</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">x_f</span>
+            <span class="k">if</span> <span class="n">Y</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># out.flat[i] = X * numpy.log(X/Y) - X + Y</span>
+                    <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">X</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
+                <span class="c1"># else xlogy is 0 so it doesn&#39;t add to the accumulator</span>
+                <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">x_f</span>
+        <span class="k">return</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
+    
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">kl_convex_conjugate_mask</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mask</span><span class="p">):</span>
+        <span class="n">accumulator</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">get_num_threads</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">X</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">x_f</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">Y</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">x_f</span>
+                <span class="k">if</span> <span class="n">Y</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="c1"># out.flat[i] = X * numpy.log(X/Y) - X + Y</span>
+                        <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">X</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
+                    <span class="c1"># else xlogy is 0 so it doesn&#39;t add to the accumulator</span>
+                    <span class="n">accumulator</span><span class="p">[</span><span class="n">get_thread_id</span><span class="p">()]</span> <span class="o">+=</span> <span class="n">eta</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">x_f</span>
+        <span class="k">return</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">accumulator</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">KullbackLeibler_numba</span><span class="p">(</span><span class="n">KullbackLeibler</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        To avoid infinity values, we consider only pixels/voxels for :math:`x+\eta\geq0`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">kl_div_mask</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">kl_div</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`F^{*}(b, x + \eta) = - b \log(1-x^{*}) - &lt;x^{*}, \eta&gt;`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">kl_convex_conjugate_mask</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">kl_convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the gradient of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`F&#39;(b, x + \eta) = 1 - \frac{b}{x+\eta}`</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        To avoid inf values, we :math:`x+\eta&gt;0` is required.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">0</span>
+
+        <span class="n">out_np</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">kl_gradient_mask</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">out_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">kl_gradient</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">out_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">out_np</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span>        
+
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the KullbackLeibler function at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`\mathrm{prox}_{\tau F}(x) = \frac{1}{2}\bigg( (x - \eta - \tau) + \sqrt{ (x + \eta - \tau)^2 + 4\tau b} \bigg)`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">0</span>
+
+        <span class="n">out_np</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">kl_proximal_mask</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out_np</span><span class="p">,</span> \
+                    <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">kl_proximal</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># it should be a DataContainer</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">kl_proximal_arr_mask</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="n">tau</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> \
+                    <span class="n">out_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">kl_proximal_arr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="n">tau</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> \
+                    <span class="n">out_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">out_np</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+
+    <span class="k">def</span> <span class="nf">proximal_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the convex conjugate of KullbackLeibler at :math:`(b, x + \eta)`.</span>
+
+<span class="sd">        :math:`\mathrm{prox}_{\tau F^{*}}(x) = 0.5*((z + 1) - \sqrt{(z-1)^2 + 4 * \tau b})`, where :math:`z = x + \tau \eta`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">0</span>
+        <span class="n">out_np</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">kl_proximal_conjugate_mask</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">,</span> \
+                    <span class="n">tau</span><span class="p">,</span> <span class="n">out_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">kl_proximal_conjugate</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">,</span> \
+                    <span class="n">tau</span><span class="p">,</span> <span class="n">out_np</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">kl_proximal_conjugate_arr_mask</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">,</span> \
+                    <span class="n">tau</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">out_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">kl_proximal_conjugate_arr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_np</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta_np</span><span class="p">,</span> \
+                    <span class="n">tau</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">out_np</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">out_np</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span>
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/optimisation/functions/L1Norm/index.html b/v24.2.0/_modules/cil/optimisation/functions/L1Norm/index.html
new file mode 100644
index 0000000000..b651e1fea9
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+  <h1>Source code for cil.optimisation.functions.L1Norm</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockDataContainer</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="k">def</span> <span class="nf">soft_shrinkage</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the soft-shrinkage operator at x. This is used for the calculation of the proximal. </span>
+<span class="sd">    </span>
+<span class="sd">    .. math:: soft_shrinkage (x) = \begin{cases}</span>
+<span class="sd">                x-\tau, \mbox{if } x &gt; \tau \</span>
+<span class="sd">                    x+\tau, \mbox{if } x &lt; -\tau \</span>
+<span class="sd">                0, \mbox{otherwise}</span>
+<span class="sd">                \end{cases}.</span>
+<span class="sd">   </span>
+
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+<span class="sd">    x : DataContainer</span>
+<span class="sd">        where to evaluate the soft-shrinkage operator.</span>
+<span class="sd">    tau : float, non-negative real,  numpy ndarray, DataContainer</span>
+<span class="sd">    out : DataContainer, default None</span>
+<span class="sd">        where to store the result. If None, a new DataContainer is created.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    --------</span>
+<span class="sd">    the value of the soft-shrinkage operator at x: DataContainer.</span>
+<span class="sd">    </span>
+<span class="sd">    Note</span>
+<span class="sd">    ------</span>
+<span class="sd">    Note that this function can deal with complex inputs, defining the `sgn` function as: </span>
+<span class="sd">    .. math:: sgn (z) = \begin{cases}</span>
+<span class="sd">                0, \mbox{if } z = 0 \</span>
+<span class="sd">                \frac{z}{|z|}, \mbox{otherwise}</span>
+<span class="sd">                \end{cases}.</span>
+<span class="sd">                </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;tau should be non-negative!&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;tau should be real!&quot;</span><span class="p">)</span>
+
+    <span class="c1"># get the sign of the input</span>
+    <span class="n">dsign</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()))</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">else</span> <span class="n">x</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">csingle</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cdouble</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">clongdouble</span><span class="p">]:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">0</span>
+            <span class="n">outarr</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="n">outarr</span><span class="o">.</span><span class="n">real</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">outarr</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">csingle</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cdouble</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">clongdouble</span><span class="p">]:</span>
+            <span class="n">outarr</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="n">outarr</span><span class="o">.</span><span class="n">real</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="n">outarr</span><span class="o">.</span><span class="n">imag</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">outarr</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span>
+    <span class="n">out</span> <span class="o">-=</span> <span class="n">tau</span>
+    <span class="n">out</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span>
+    <span class="n">out</span> <span class="o">*=</span> <span class="n">dsign</span>
+    <span class="k">return</span> <span class="n">out</span>
+
+
+<div class="viewcode-block" id="L1Norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L1Norm">[docs]</a>
+<span class="k">class</span> <span class="nc">L1Norm</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;L1Norm function</span>
+
+<span class="sd">    Consider the following cases:</span>
+
+<span class="sd">    a) .. math:: F(x) = ||x||_{1}</span>
+<span class="sd">    b) .. math:: F(x) = ||x - b||_{1}</span>
+
+<span class="sd">    In the weighted case, :math:`w` is an array of non-negative weights.</span>
+
+<span class="sd">    a) .. math:: F(x) = ||x||_{L^1(w)}</span>
+<span class="sd">    b) .. math:: F(x) = ||x - b||_{L^1(w)}</span>
+
+<span class="sd">    with :math:`||x||_{L^1(w)} = || x  w||_1 = \sum_{i=1}^{n} |x_i| w_i`.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+
+<span class="sd">        weight: DataContainer, numpy ndarray, default None</span>
+<span class="sd">            Array of non-negative weights. If :code:`None` returns the L1 Norm.</span>
+<span class="sd">        b: DataContainer, default None</span>
+<span class="sd">            Translation of the function.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">L1Norm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">weight</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">_L1Norm</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">_WeightedL1Norm</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the L1Norm function at x.</span>
+
+<span class="sd">        .. math:: f(x) = ||x - b||_{L^1(w)}</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+<div class="viewcode-block" id="L1Norm.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L1Norm.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the L1 Norm function at x.</span>
+
+
+<span class="sd">    This is the indicator of the unit :math:`L^{\infty}` norm:</span>
+
+
+<span class="sd">    a) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*})</span>
+<span class="sd">    b) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*}) + \langle x^{*},b\rangle</span>
+
+
+<span class="sd">    .. math:: \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*})</span>
+<span class="sd">        = \begin{cases}</span>
+<span class="sd">        0, \mbox{if } \|x^{*}\|_{\infty}\leq1\\</span>
+<span class="sd">        \infty, \mbox{otherwise}</span>
+<span class="sd">        \end{cases}</span>
+
+<span class="sd">    In the weighted case the convex conjugate is the indicator of the unit</span>
+<span class="sd">    :math:`L^{\infty}(w^{-1})` norm.</span>
+
+<span class="sd">    See:</span>
+<span class="sd">    https://math.stackexchange.com/questions/1533217/convex-conjugate-of-l1-norm-function-with-weight</span>
+
+<span class="sd">    a) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(x^{*})</span>
+<span class="sd">    b) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(x^{*}) + \langle x^{*},b\rangle</span>
+
+<span class="sd">    with :math:`\|x\|_{L^\infty(w^{-1})} = \max_{i} \frac{|x_i|}{w_i}` and possible cases of 0/0 are defined to be 1..</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+
+<span class="sd">    x : DataContainer</span>
+<span class="sd">        where to evaluate the convex conjugate of the L1 Norm function.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    --------</span>
+<span class="sd">    the value of the convex conjugate of the WeightedL1Norm function at x: DataContainer.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="L1Norm.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L1Norm.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the L1 Norm function at x with scaling parameter `tau`.</span>
+
+
+<span class="sd">    Consider the following cases:</span>
+
+<span class="sd">    a) .. math:: \mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_\tau(x)</span>
+<span class="sd">    b) .. math:: \mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_\tau(x - b) + b</span>
+
+<span class="sd">    where,</span>
+
+<span class="sd">    .. math :: \mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}(x) = sgn(x) * \max\{ |x| - \tau, 0 \}</span>
+
+<span class="sd">    The weighted case follows from Example 6.23 in Chapter 6 of &quot;First-Order Methods in Optimization&quot;</span>
+<span class="sd">    by Amir Beck, SIAM 2017 https://archive.siam.org/books/mo25/mo25_ch6.pdf</span>
+
+<span class="sd">    a) .. math:: \mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_{\tau*w}(x)</span>
+<span class="sd">    b) .. math:: \mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_{\tau*w}(x - b) + b</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+<span class="sd">    x: DataContainer</span>
+<span class="sd">    tau: float, real,  ndarray, DataContainer</span>
+<span class="sd">    out: DataContainer, default None</span>
+<span class="sd">        If not None, the result will be stored in this object.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    --------</span>
+<span class="sd">    The value of the proximal operator of the L1 norm function at x: DataContainer.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+            
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+</div>
+
+
+
+<span class="k">class</span> <span class="nc">_L1Norm</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;L1Norm function</span>
+
+<span class="sd">        Consider the following cases:</span>
+<span class="sd">            a) .. math:: F(x) = ||x||_{1}</span>
+<span class="sd">            b) .. math:: F(x) = ||x - b||_{1}</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;creator</span>
+
+<span class="sd">        Cases considered (with/without data):</span>
+<span class="sd">        a) :math:`f(x) = ||x||_{1}`</span>
+<span class="sd">        b) :math:`f(x) = ||x - b||_{1}`</span>
+
+<span class="sd">        :param b: translation of the function</span>
+<span class="sd">        :type b: :code:`DataContainer`, optional</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
+        <span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">&lt;=</span><span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="mf">0.</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+
+
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">soft_shrinkage</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">soft_shrinkage</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span>
+
+
+<span class="k">class</span> <span class="nc">_WeightedL1Norm</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">b</span>
+
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Weights should be non-negative!&quot;</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">weight</span><span class="p">))</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Weights should be real!&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span> 
+
+        <span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">):</span> <span class="c1"># This handles weight being zero problems</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="mf">0.</span>
+        
+
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">_L1Norm</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span>
+
+<div class="viewcode-block" id="MixedL11Norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.MixedL11Norm">[docs]</a>
+<span class="k">class</span> <span class="nc">MixedL11Norm</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;MixedL11Norm function</span>
+
+<span class="sd">    .. math:: F(x) = ||x||_{1,1} = \sum |x_{1}| + |x_{2}| + \cdots + |x_{n}|</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    MixedL11Norm is a separable function, therefore it can also be defined using the :class:`BlockFunction`.</span>
+
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    L1Norm, MixedL21Norm</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">MixedL11Norm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the MixedL11Norm function at x.</span>
+
+<span class="sd">        :param x: :code:`BlockDataContainer`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;__call__ expected BlockDataContainer, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
+
+        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+<div class="viewcode-block" id="MixedL11Norm.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.MixedL11Norm.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the MixedL11Norm function at x.</span>
+
+<span class="sd">        .. math:: \mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}(x)</span>
+
+<span class="sd">        where,</span>
+
+<span class="sd">        .. math :: \mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}(x) := sgn(x) * \max\{ |x| - \tau, 0 \}</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;__call__ expected BlockDataContainer, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
+
+        <span class="k">return</span> <span class="n">soft_shrinkage</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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+  <h1>Source code for cil.optimisation.functions.L1Sparsity</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2023 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2023 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span><span class="p">,</span> <span class="n">L1Norm</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<div class="viewcode-block" id="L1Sparsity">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L1Sparsity">[docs]</a>
+<span class="k">class</span> <span class="nc">L1Sparsity</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;L1Sparsity function</span>
+
+<span class="sd">    Calculates the following cases, depending on if the optional parameter `weight`  or data `b` is passed. For `weight=None`:</span>
+
+
+<span class="sd">    a) .. math:: F(x) = ||Qx||_{1}</span>
+<span class="sd">    b) .. math:: F(x) = ||Qx - b||_{1}</span>
+
+<span class="sd">    In the weighted case, `weight` = :math:`w` is an array of non-negative weights.</span>
+
+<span class="sd">    a) .. math:: F(x) = ||Qx||_{L^1(w)}</span>
+<span class="sd">    b) .. math:: F(x) = ||Qx - b||_{L^1(w)}</span>
+
+<span class="sd">    with :math:`||x||_{L^1(w)} = || x \cdot w||_1 = \sum_{i=1}^{n} |x_i| w_i`.</span>
+
+<span class="sd">    In all cases :math:`Q` is an orthogonal operator.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ---------</span>
+<span class="sd">    Q: orthogonal Operator</span>
+<span class="sd">        Note that for the correct calculation of the proximal the provided operator must be orthogonal</span>
+<span class="sd">    b : Data, DataContainer, default is None</span>
+<span class="sd">    weight: array, optional, default=None</span>
+<span class="sd">        non-negative weight array matching the size of the range of operator :math:`Q`.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Q</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;creator</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">Q</span><span class="o">.</span><span class="n">is_orthogonal</span><span class="p">():</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Invalid operator: `</span><span class="si">{</span><span class="n">Q</span><span class="si">}</span><span class="s2">`. L1Sparsity is properly defined only for orthogonal operators!&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">L1Sparsity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Q</span> <span class="o">=</span> <span class="n">Q</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">l1norm</span> <span class="o">=</span> <span class="n">L1Norm</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the L1Sparsity function at x.</span>
+
+<span class="sd">        Consider the following cases:</span>
+
+<span class="sd">        a) .. math:: F(x) = ||Qx||_{1}</span>
+<span class="sd">        b) .. math:: F(x) = ||Qx - b||_{1}</span>
+
+<span class="sd">        In the weighted case, `weight` = :math:`w` is an array of non-negative weights.</span>
+
+<span class="sd">        a) .. math:: F(x) = ||Qx||_{L^1(w)}</span>
+<span class="sd">        b) .. math:: F(x) = ||Qx - b||_{L^1(w)}</span>
+
+<span class="sd">        with :math:`|| y ||_{L^1(w)} = || y w ||_1 = \sum_{i=1}^{n} | y_i | w_i`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Q</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">l1norm</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+
+<div class="viewcode-block" id="L1Sparsity.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L1Sparsity.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the L1Sparsity function at x.</span>
+<span class="sd">        Here, we need to use the convex conjugate of L1Sparsity, which is the Indicator of the unit</span>
+<span class="sd">        :math:`\ell^{\infty}` norm on the range of the (bijective) operator Q.</span>
+
+
+<span class="sd">        Consider the non-weighted case:</span>
+
+
+<span class="sd">        a) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(Qx^{*})</span>
+<span class="sd">        b) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(Qx^{*}) + \langle Qx^{*},b\rangle</span>
+
+
+<span class="sd">        .. math:: \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*})</span>
+<span class="sd">            = \begin{cases}</span>
+<span class="sd">            0, \mbox{if } \|x^{*}\|_{\infty}\leq1\\</span>
+<span class="sd">            \infty, \mbox{otherwise}</span>
+<span class="sd">            \end{cases}</span>
+
+<span class="sd">        In the weighted case the convex conjugate is the indicator of the unit</span>
+<span class="sd">        :math:`L^{\infty}( w^{-1} )` norm.</span>
+
+<span class="sd">        See:</span>
+<span class="sd">        https://math.stackexchange.com/questions/1533217/convex-conjugate-of-l1-norm-function-with-weight</span>
+
+<span class="sd">        a) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(Qx^{*})</span>
+<span class="sd">        b) .. math:: F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(Qx^{*}) + \langle Qx^{*},b\rangle</span>
+
+<span class="sd">        with :math:`\|x\|_{L^\infty(w^{-1})} = \max_{i} \frac{|x_i|}{w_i}` and possible cases of 0 / 0 are defined to be 1.</span>
+
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Q</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">l1norm</span><span class="o">.</span><span class="n">convex_conjugate</span><span class="p">(</span><span class="n">y</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="L1Sparsity.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L1Sparsity.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the L1 Norm function at x with scaling parameter `tau`.</span>
+
+
+<span class="sd">        Consider the following cases:</span>
+
+<span class="sd">        a) .. math:: \mathrm{prox}_{\tau F}(x) = Q^T \mathrm{ShinkOperator}_{\tau}(Qx)</span>
+<span class="sd">        b) .. math:: \mathrm{prox}_{\tau F}(x) = Q^T \left( \mathrm{ShinkOperator}_\tau(Qx- b) + b \right)</span>
+
+<span class="sd">        where,</span>
+
+<span class="sd">        .. math :: \mathrm{prox}_{\tau | \cdot |}(x) = \mathrm{ShinkOperator}(x) = sgn(x) * \max\{ |x| - \tau, 0 \}</span>
+
+<span class="sd">        The weighted case follows from Example 6.23 in Chapter 6 of &quot;First-Order Methods in Optimization&quot;</span>
+<span class="sd">        by Amir Beck, SIAM 2017 https://archive.siam.org/books/mo25/mo25_ch6.pdf</span>
+
+<span class="sd">        a) .. math:: \mathrm{prox}_{\tau F}(x) = Q^T \mathrm{ShinkOperator}_{\tau*w}(Qx)</span>
+<span class="sd">        b) .. math:: \mathrm{prox}_{\tau F}(x) = Q^T \left( \mathrm{ShinkOperator}_{\tau*w}(Qx-b) + b \right)</span>
+
+
+<span class="sd">        Parameters</span>
+<span class="sd">        -----------</span>
+<span class="sd">        x: DataContainer</span>
+<span class="sd">        tau: float, ndarray, DataContainer</span>
+<span class="sd">        out: DataContainer, default None</span>
+<span class="sd">            If not None, the result will be stored in this object.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        The value of the proximal operator of the L1 norm function at x: DataContainer.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Q</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">l1norm</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">Q</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.functions.L2NormSquared</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">DiagonalOperator</span>
+
+
+<div class="viewcode-block" id="L2NormSquared">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L2NormSquared">[docs]</a>
+<span class="k">class</span> <span class="nc">L2NormSquared</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; L2NormSquared function: :math:`F(x) = \| x\|^{2}_{2} = \underset{i}{\sum}x_{i}^{2}`</span>
+
+<span class="sd">    Following cases are considered:</span>
+
+<span class="sd">        a) :math:`F(x) = \|x\|^{2}_{2}`</span>
+<span class="sd">        b) :math:`F(x) = \|x - b\|^{2}_{2}`</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    b:`DataContainer`, optional</span>
+<span class="sd">        Translation of the function</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    For case b) we can use :code:`F = L2NormSquared().centered_at(b)`, see *TranslateFunction*.</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+
+<span class="sd">        &gt;&gt;&gt; F = L2NormSquared()</span>
+<span class="sd">        &gt;&gt;&gt; F = L2NormSquared(b=b)</span>
+<span class="sd">        &gt;&gt;&gt; F = L2NormSquared().centered_at(b)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">L2NormSquared</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span> <span class="k">as</span> <span class="n">ae</span><span class="p">:</span>
+            <span class="c1"># added for compatibility with SIRF</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+<div class="viewcode-block" id="L2NormSquared.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L2NormSquared.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the gradient of the L2NormSquared function at x.</span>
+
+<span class="sd">        Following cases are considered:</span>
+
+<span class="sd">            a) :math:`F&#39;(x) = 2x`</span>
+<span class="sd">            b) :math:`F&#39;(x) = 2(x-b)`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="L2NormSquared.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L2NormSquared.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the L2NormSquared function at x.</span>
+
+<span class="sd">        Consider the following cases:</span>
+
+<span class="sd">                a) .. math:: F^{*}(x^{*}) = \frac{1}{4}\|x^{*}\|^{2}_{2}</span>
+<span class="sd">                b) .. math:: F^{*}(x^{*}) = \frac{1}{4}\|x^{*}\|^{2}_{2} + \langle x^{*}, b\rangle</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="mf">0.25</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span> <span class="o">+</span> <span class="n">tmp</span></div>
+
+
+<div class="viewcode-block" id="L2NormSquared.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.L2NormSquared.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the L2NormSquared function at x.</span>
+
+
+<span class="sd">        Consider the following cases:</span>
+
+<span class="sd">                a) .. math:: \text{prox}_{\tau F}(x) = \frac{x}{1+2\tau}</span>
+<span class="sd">                b) .. math:: \text{prox}_{\tau F}(x) = \frac{x-b}{1+2\tau} + b</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">mult</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">tau</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">mult</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">mult</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">mult</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="WeightedL2NormSquared">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.WeightedL2NormSquared">[docs]</a>
+<span class="k">class</span> <span class="nc">WeightedL2NormSquared</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; WeightedL2NormSquared function: :math:`F(x) = \|x\|_{W,2}^2 = \Sigma_iw_ix_i^2 = \langle x, Wx\rangle = x^TWx`</span>
+<span class="sd">    where :math:`W=\text{diag}(weight)` if `weight` is a `DataContainer` or :math:`W=\text{weight} I` if `weight` is a scalar.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+<span class="sd">    **kwargs</span>
+
+<span class="sd">    weight: a `scalar` or a `DataContainer` with the same shape as the intended domain of this `WeightedL2NormSquared` function</span>
+<span class="sd">    b: a `DataContainer` with the same shape as the intended domain of this `WeightedL2NormSquared` function</span>
+<span class="sd">        A shift so that the function becomes  :math:`F(x) = \| x-b\|_{W,2}^2 = \Sigma_iw_i(x_i-b_i)^2 = \langle x-b, W(x-b) \rangle = (x-b)^TW(x-b)`</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="c1"># Weight can be either a scalar or a DataContainer</span>
+        <span class="c1"># Lispchitz constant L = 2 *||weight||</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;weight&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">tmp_norm</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tmp_space</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">*</span><span class="mf">0.</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator_weight</span> <span class="o">=</span> <span class="n">DiagonalOperator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
+            <span class="n">tmp_norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator_weight</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tmp_space</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator_weight</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Weight contains negative values&#39;</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedL2NormSquared</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <span class="n">tmp_norm</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator_weight</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_space</span><span class="p">)</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_space</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator_weight</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_space</span><span class="p">)</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_space</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">y</span>
+
+<div class="viewcode-block" id="WeightedL2NormSquared.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the value of :math:`F&#39;(x) = 2Wx` or, if `b` is defined,  :math:`F&#39;(x) = 2W(x-b)`</span>
+<span class="sd">        where :math:`W=\text{diag}(weight)` if `weight` is a `DataContainer` or :math:`\text{weight}I` if `weight` is a scalar.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">out</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator_weight</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">*=</span> <span class="mi">2</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">x</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
+            <span class="k">return</span> <span class="mi">2</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">*</span><span class="n">y</span></div>
+
+
+<div class="viewcode-block" id="WeightedL2NormSquared.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the WeightedL2NormSquared function at x.&quot;&quot;&quot;</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">sqrt</span><span class="p">())</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span> <span class="o">+</span> <span class="n">tmp</span></div>
+
+
+<div class="viewcode-block" id="WeightedL2NormSquared.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the WeightedL2NormSquared function at x.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">/=</span> <span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">tau</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">((</span><span class="mi">1</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">tau</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+</div>
+
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+  <h1>Source code for cil.optimisation.functions.LeastSquares</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2018 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2018 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span><span class="p">,</span> <span class="n">DiagonalOperator</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="LeastSquares">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.LeastSquares">[docs]</a>
+<span class="k">class</span> <span class="nc">LeastSquares</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; (Weighted) Least Squares function</span>
+
+<span class="sd">    .. math:: F(x) = c\|Ax-b\|_2^2</span>
+
+<span class="sd">    or if weighted</span>
+
+<span class="sd">    .. math:: F(x) = c\|Ax-b\|_{2,W}^{2}</span>
+
+<span class="sd">    where :math:`W=\text{diag}(weight)`.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+<span class="sd">    A : LinearOperator</span>
+<span class="sd">    b : Data, DataContainer</span>
+<span class="sd">    c : Scaling Constant, float, default 1.0</span>
+<span class="sd">    weight: DataContainer with all positive elements of size of the range of operator A, default None</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    L is the  Lipshitz Constant of the gradient of :math:`F` which is :math:`2 c ||A||_2^2 = 2 c \sigma_1(A)^2`, or :math:`2 c ||W|| ||A||_2^2 = 2c||W|| \sigma_1(A)^2`, where :math:`\sigma_1(A)` is the largest singular value of :math:`A` and :math:`W=\text{diag}(weight)`.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">LeastSquares</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">A</span> <span class="o">=</span> <span class="n">A</span>  <span class="c1"># Should be a LinearOperator</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">b</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">=</span> <span class="n">c</span>  <span class="c1"># Default 1.</span>
+
+        <span class="c1"># weight</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">weight</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_weight_norm</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="n">weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">&lt;</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Weight contains negative values&#39;</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the value of :math:`F(x) = c\|Ax-b\|_2^2` or :math:`c\|Ax-b\|_{2,W}^2`, where :math:`W=\text{diag}(weight)`:</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># c * (A.direct(x)-b).dot((A.direct(x) - b))</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">y</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">wy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">wy</span><span class="p">)</span>
+
+<div class="viewcode-block" id="LeastSquares.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.LeastSquares.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the value of the gradient of :math:`F(x)`:</span>
+
+<span class="sd">        .. math:: F&#39;(x) = 2cA^T(Ax-b)</span>
+
+<span class="sd">        or</span>
+
+<span class="sd">        .. math:: F&#39;(x) = 2cA^T(W(Ax-b))</span>
+
+<span class="sd">        where :math:`W=\text{diag}(weight)`.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mf">0.0</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">tmp</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">*</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">calculate_Lipschitz</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span>
+    <span class="nd">@L</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;You should set the Lipschitz constant with calculate_Lipschitz().&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">Number</span><span class="p">,))</span> <span class="ow">and</span> <span class="n">value</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;The Lipschitz constant is a real positive number&#39;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">calculate_Lipschitz</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Compute the Lipschitz parameter from the operator if possible</span>
+        <span class="c1"># Leave it initialised to None otherwise</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">c</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span> <span class="k">as</span> <span class="n">ae</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">is_linear</span><span class="p">():</span>
+                <span class="n">Anorm</span> <span class="o">=</span> <span class="n">LinearOperator</span><span class="o">.</span><span class="n">PowerMethod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="p">,</span> <span class="mi">10</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">c</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">Anorm</span><span class="o">*</span><span class="n">Anorm</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> could not calculate Lipschitz Constant. </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">ae</span><span class="p">))</span>
+        <span class="k">except</span> <span class="ne">NotImplementedError</span> <span class="k">as</span> <span class="n">noe</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> could not calculate Lipschitz Constant. </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">noe</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight_norm</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">weight_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_weight_norm</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">D</span> <span class="o">=</span> <span class="n">DiagonalOperator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_weight_norm</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_weight_norm</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_weight_norm</span>
+
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;defines the right multiplication with a number&#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="bp">NotImplemented</span>
+        <span class="n">constant</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">*</span> <span class="n">other</span>
+
+        <span class="k">return</span> <span class="n">LeastSquares</span><span class="p">(</span><span class="n">A</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">constant</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/functions/MixedL21Norm/index.html b/v24.2.0/_modules/cil/optimisation/functions/MixedL21Norm/index.html
new file mode 100644
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+  <h1>Source code for cil.optimisation.functions.MixedL21Norm</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockDataContainer</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="n">has_numba</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">numba</span>
+    <span class="nd">@numba</span><span class="o">.</span><span class="n">jit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">_proximal_step_numba</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">abstau</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Numba implementation of a step in the calculation of the proximal of MixedL21Norm</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        -----------</span>
+<span class="sd">        arr : numpy array, best if contiguous memory.</span>
+<span class="sd">        abstau: float &gt;= 0</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">        --------</span>
+<span class="sd">        Stores the output in the input array.</span>
+
+<span class="sd">        Note:</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        Input arr should be contiguous for best performance&#39;&#39;&#39;</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numba</span><span class="o">.</span><span class="n">prange</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">continue</span>
+            <span class="n">a</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/</span> <span class="n">abstau</span>
+            <span class="n">el</span> <span class="o">=</span> <span class="n">a</span> <span class="o">-</span> <span class="mi">1</span>
+            <span class="k">if</span> <span class="n">el</span> <span class="o">&lt;=</span> <span class="mf">0.0</span><span class="p">:</span>
+                <span class="n">el</span> <span class="o">=</span> <span class="mf">0.</span>
+
+            <span class="n">tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">el</span> <span class="o">/</span> <span class="n">a</span>
+        <span class="k">return</span> <span class="mi">0</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="n">has_numba</span> <span class="o">=</span> <span class="kc">False</span>
+
+
+
+<span class="k">def</span> <span class="nf">_proximal_step_numpy</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Numpy implementation of a step in the calculation of the proximal of MixedL21Norm</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    arr : DataContainer, best if contiguous memory.</span>
+<span class="sd">    tau: float, numpy array or DataContainer</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    A DataContainer where we have substituted nan with 0.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="c1"># Note: we divide x by tau so the cases of tau both scalar and</span>
+    <span class="c1"># DataContainers run</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+    <span class="k">except</span> <span class="n">np</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">_exceptions</span><span class="o">.</span><span class="n">_UFuncInputCastingError</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">tau</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span>
+
+
+    <span class="n">arr</span> <span class="o">/=</span> <span class="n">tmp</span>
+    <span class="n">res</span> <span class="o">=</span> <span class="n">arr</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">res</span><span class="p">)</span>
+    <span class="n">res</span> <span class="o">/=</span> <span class="n">arr</span>
+
+    <span class="n">arr</span> <span class="o">*=</span> <span class="n">tmp</span>
+
+    <span class="n">resarray</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+    <span class="n">resarray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">resarray</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">resarray</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">res</span>
+
+
+<div class="viewcode-block" id="MixedL21Norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.MixedL21Norm">[docs]</a>
+<span class="k">class</span> <span class="nc">MixedL21Norm</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; MixedL21Norm function: :math:`F(x) = ||x||_{2,1} = \sum |x|_{2} = \sum \sqrt{ (x^{1})^{2} + (x^{2})^{2} + \dots}`</span>
+
+<span class="sd">        where x is a BlockDataContainer, i.e., :math:`x=(x^{1}, x^{2}, \dots)`</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">MixedL21Norm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the MixedL21Norm function at x.</span>
+
+<span class="sd">        :param x: :code:`BlockDataContainer`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;__call__ expected BlockDataContainer, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
+
+        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">pnorm</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+
+<div class="viewcode-block" id="MixedL21Norm.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.MixedL21Norm.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the convex conjugate of the MixedL21Norm function at x.</span>
+
+<span class="sd">        This is the Indicator function of :math:`\mathbb{I}_{\{\|\cdot\|_{2,\infty}\leq1\}}(x^{*})`,</span>
+
+<span class="sd">        i.e.,</span>
+
+<span class="sd">        .. math:: \mathbb{I}_{\{\|\cdot\|_{2, \infty}\leq1\}}(x^{*})</span>
+<span class="sd">            = \begin{cases}</span>
+<span class="sd">            0, \mbox{if } \|x\|_{2, \infty}\leq1\\</span>
+<span class="sd">            \infty, \mbox{otherwise}</span>
+<span class="sd">            \end{cases}</span>
+
+<span class="sd">        where,</span>
+
+<span class="sd">        .. math:: \|x\|_{2,\infty} = \max\{ \|x\|_{2} \} = \max\{ \sqrt{ (x^{1})^{2} + (x^{2})^{2} + \dots}\}</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;__call__ expected BlockDataContainer, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">pnorm</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">tmp</span><span class="o">&lt;=</span><span class="mf">1e-5</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span></div>
+
+
+<div class="viewcode-block" id="MixedL21Norm.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.MixedL21Norm.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the proximal operator of the MixedL21Norm function at x.</span>
+
+<span class="sd">        .. math :: \mathrm{prox}_{\tau F}(x) = \frac{x}{\|x\|_{2}}\max\{ \|x\|_{2} - \tau, 0 \}</span>
+
+<span class="sd">        where the convention 0 · (0/0) = 0 is used.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">pnorm</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">has_numba</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="c1"># may involve a copy if the data is not contiguous</span>
+                <span class="n">tmparr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tmp</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">_proximal_step_numba</span><span class="p">(</span><span class="n">tmparr</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># if numba silently crashes</span>
+                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;MixedL21Norm.proximal: numba silently crashed.&#39;</span><span class="p">)</span>
+
+                <span class="n">res</span> <span class="o">=</span> <span class="n">tmp</span>
+                <span class="n">res</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmparr</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="n">res</span> <span class="o">=</span> <span class="n">_proximal_step_numpy</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="n">_proximal_step_numpy</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="SmoothMixedL21Norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm">[docs]</a>
+<span class="k">class</span> <span class="nc">SmoothMixedL21Norm</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; SmoothMixedL21Norm function: :math:`F(x) = ||x||_{2,1} = \sum |x|_{2} = \sum \sqrt{ (x^{1})^{2} + (x^{2})^{2} + \epsilon^2 + \dots}`</span>
+
+<span class="sd">        where x is a BlockDataContainer, i.e., :math:`x=(x^{1}, x^{2}, \dots)`</span>
+
+<span class="sd">        Conjugate, proximal and proximal conjugate methods no closed-form solution</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        :param epsilon: smoothing parameter making MixedL21Norm differentiable</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SmoothMixedL21Norm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;We need epsilon&gt;0. Otherwise, call &quot;MixedL21Norm&quot; &#39;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the value of the SmoothMixedL21Norm function at x.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;__call__ expected BlockDataContainer, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">pnorm</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sqrt</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+
+<div class="viewcode-block" id="SmoothMixedL21Norm.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the gradient of the SmoothMixedL21Norm function at x.</span>
+
+<span class="sd">        \frac{x}{|x|}</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;__call__ expected BlockDataContainer, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
+        <span class="n">denom</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">pnorm</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sqrt</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">denom</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/functions/OperatorCompositionFunction/index.html b/v24.2.0/_modules/cil/optimisation/functions/OperatorCompositionFunction/index.html
new file mode 100644
index 0000000000..a7575bb150
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+  <h1>Source code for cil.optimisation.functions.OperatorCompositionFunction</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">Operator</span><span class="p">,</span> <span class="n">ScaledOperator</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<div class="viewcode-block" id="OperatorCompositionFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.OperatorCompositionFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">OperatorCompositionFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; Composition of a function with an operator as : :math:`(F \circ A)(x) = F(Ax)`</span>
+
+<span class="sd">            :parameter function: :code:`Function` F</span>
+<span class="sd">            :parameter operator: :code:`Operator` A</span>
+
+
+<span class="sd">        For general operator, we have no explicit formulas for convex_conjugate,</span>
+<span class="sd">        proximal and proximal_conjugate</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span> <span class="n">operator</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;creator</span>
+
+<span class="sd">    :param A: operator</span>
+<span class="sd">    :type A: :code:`Operator`</span>
+<span class="sd">    :param f: function</span>
+<span class="sd">    :type f: :code:`Function`</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">OperatorCompositionFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">function</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">L</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">L</span>  <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ve</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns :math:`F(Ax)`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+
+<div class="viewcode-block" id="OperatorCompositionFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.OperatorCompositionFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Return the gradient of :math:`F(Ax)`,</span>
+
+<span class="sd">        :math:`(F(Ax))&#39; = A^{T}F&#39;(Ax)`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+</div>
+
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.functions.Rosenbrock</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">VectorData</span><span class="p">,</span> <span class="n">VectorGeometry</span>
+
+<div class="viewcode-block" id="Rosenbrock">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Rosenbrock">[docs]</a>
+<span class="k">class</span> <span class="nc">Rosenbrock</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Rosenbrock function</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">    F(x,y) = (\alpha - x)^2 + \beta(y-x^2)^2</span>
+
+<span class="sd">    The function has a global minimum at .. math:: (x,y)=(\alpha, \alpha^2)</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Rosenbrock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">beta</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">VectorData</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Rosenbrock function works on VectorData only&#39;</span><span class="p">)</span>
+        <span class="n">vec</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">-</span> <span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="n">vec</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">a</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">*</span> <span class="n">b</span> <span class="o">*</span> <span class="n">b</span>
+
+<div class="viewcode-block" id="Rosenbrock.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.Rosenbrock.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Gradient of the Rosenbrock function</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">        \nabla f(x,y) = \left[ 2*((x-\alpha) - 2\beta x(y-x^2)) ; 2\beta (y - x^2)  \right]</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">VectorData</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Rosenbrock function works on VectorData only&#39;</span><span class="p">)</span>
+
+        <span class="n">vec</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+        <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="n">vec</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">vec</span><span class="p">)</span>
+        <span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span> <span class="n">a</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">*</span> <span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">b</span><span class="p">)</span>
+        <span class="n">res</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">*</span> <span class="n">b</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">VectorData</span><span class="p">(</span><span class="n">res</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
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diff --git a/v24.2.0/_modules/cil/optimisation/functions/SAGFunction/index.html b/v24.2.0/_modules/cil/optimisation/functions/SAGFunction/index.html
new file mode 100644
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+  <h1>Source code for cil.optimisation.functions.SAGFunction</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1"># </span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1"># </span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1"># </span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1"># </span>
+<span class="c1"># Authors:</span>
+<span class="c1"># - CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># - Daniel Deidda (National Physical Laboratory, UK)</span>
+<span class="c1"># - Claire Delplancke (Electricite de France, Research and Development)</span>
+<span class="c1"># - Ashley Gillman (Australian e-Health Res. Ctr., CSIRO, Brisbane, Queensland, Australia)</span>
+<span class="c1"># - Zeljko Kereta (Department of Computer Science, University College London, UK)</span>
+<span class="c1"># - Evgueni Ovtchinnikov (STFC - UKRI)</span>
+<span class="c1"># - Georg Schramm (Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Leuven, Belgium)</span>
+
+<span class="kn">from</span> <span class="nn">.ApproximateGradientSumFunction</span> <span class="kn">import</span> <span class="n">ApproximateGradientSumFunction</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="SAGFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SAGFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">SAGFunction</span><span class="p">(</span><span class="n">ApproximateGradientSumFunction</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    The stochastic average gradient (SAG) function takes a index :math:`i_k` and calculates the approximate gradient of :math:`\sum_{i=1}^{n-1}f_i` at iteration :math:`x_k` as</span>
+<span class="sd">    </span>
+<span class="sd">    .. math ::</span>
+<span class="sd">                \sum_{i=1}^{n-1} g_i^k \qquad \text{where} \qquad g_i^k= \begin{cases}</span>
+<span class="sd">                                                                            \nabla f_i(x_k), \text{ if } i=i_k\\</span>
+<span class="sd">                                                                            g_i^{k-1},\text{ otherwise }</span>
+<span class="sd">                                                                            \end{cases}</span>
+
+<span class="sd">        </span>
+<span class="sd">            </span>
+<span class="sd">    </span>
+<span class="sd">    The idea is that by incorporating a memory of previous gradient values the SAG method can achieve a faster convergence rate than black-box stochastic gradient methods. </span>
+<span class="sd">    </span>
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    Compared with the literature, we do not divide by :math:`n`, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</span>
+
+<span class="sd">    Reference</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Schmidt, M., Le Roux, N. and Bach, F., 2017. Minimizing finite sums with the stochastic average gradient. Mathematical Programming, 162, pp.83-112. https://doi.org/10.1007/s10107-016-1030-6. </span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    functions : `list`  of functions</span>
+<span class="sd">        A list of functions: :math:`[f_{0}, f_{1}, ..., f_{n-1}]`. Each function is assumed to be smooth with an implemented :func:`~Function.gradient` method. All functions must have the same domain. The number of functions (equivalently the length of the list `n`) must be strictly greater than 1. </span>
+<span class="sd">    sampler: An instance of a CIL Sampler class ( :meth:`~optimisation.utilities.sampler`) or of another class which has a `next` function implemented to output integers in :math:`{0,...,n-1}`.</span>
+<span class="sd">        This sampler is called each time `gradient` is called and sets the internal `function_num` passed to the `approximate_gradient` function.  Default is `Sampler.random_with_replacement(len(functions))`. </span>
+<span class="sd">    </span>
+<span class="sd">    Note</span>
+<span class="sd">    ------</span>
+<span class="sd">    </span>
+<span class="sd">    The user has the option of calling the class method `warm_start_approximate_gradients` after initialising this class. This will compute and store the gradient for each function at an initial point, equivalently setting :math:`g_i^0=\nabla f_i(x_0)` for initial point :math:`x_0`.  If this method is not called, the gradients are initialised with zeros. </span>
+
+<span class="sd">    Note:  </span>
+<span class="sd">    ------  </span>
+
+<span class="sd">    This function&#39;s memory requirements are `n + 3` times the image space, that is with 100 subsets the memory requirement is 103 images, which is huge.</span>
+<span class="sd">    </span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span>  <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_iterate</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_warm_start_just_done</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_sampled_grad</span> <span class="o">=</span> <span class="kc">None</span>
+        
+        <span class="nb">super</span><span class="p">(</span><span class="n">SAGFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="p">)</span>
+
+
+        
+
+<div class="viewcode-block" id="SAGFunction.approximate_gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SAGFunction.approximate_gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">approximate_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">function_num</span><span class="p">,</span>  <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; SAG approximate gradient, calculated at the point :math:`x` and updated using the function index given by `function_num`.  </span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer (e.g. ImageData object)</span>
+<span class="sd">            Element in the domain of the `functions`</span>
+<span class="sd">        function_num: `int` </span>
+<span class="sd">            Between 0 and the number of functions in the list  </span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># Initialise the stored gradients on the first call of gradient unless using warm start.  </span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="mi">0</span><span class="o">*</span><span class="n">x</span> <span class="k">for</span> <span class="n">fi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_iterate</span> <span class="o">=</span> <span class="mi">0</span><span class="o">*</span><span class="n">x</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sampled_grad</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="o">&lt;</span><span class="mi">0</span> <span class="p">:</span> <span class="c1"># check the sampler and raise an error if needed</span>
+            <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The sampler has produced the index </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="si">}</span><span class="s2"> which does not match the expected range of available functions to sample from. Please ensure your sampler only selects from [0,1,...,len(functions)-1] &quot;</span><span class="p">)</span>
+
+            
+        <span class="c1"># Calculate the gradient of the sampled function at the current iterate </span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">function_num</span><span class="p">]</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_sampled_grad</span><span class="p">)</span>
+
+        
+        <span class="c1"># Calculate the difference between the new gradient of the sampled function and the stored one</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_sampled_grad</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span>
+            <span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">[</span><span class="n">function_num</span><span class="p">],</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span><span class="p">)</span>
+
+        <span class="c1"># Calculate the  approximate gradient</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_approx_gradient</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="c1"># Update the stored gradients </span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">[</span><span class="n">function_num</span><span class="p">]</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sampled_grad</span><span class="p">)</span>
+        
+        <span class="c1"># Calculate the stored full gradient</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_iterate</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span>
+            <span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_iterate</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span></div>
+
+    
+    <span class="k">def</span> <span class="nf">_update_approx_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal function used to differentiate between the SAG and SAGA calculations. This is the SAG approximation: &quot;&quot;&quot;</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span>  
+                <span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_iterate</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>  
+
+        <span class="k">return</span> <span class="n">out</span> 
+    
+<div class="viewcode-block" id="SAGFunction.warm_start_approximate_gradients">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SAGFunction.warm_start_approximate_gradients">[docs]</a>
+    <span class="k">def</span> <span class="nf">warm_start_approximate_gradients</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;A function to warm start SAG or SAGA algorithms by initialising all the gradients at an initial point. Equivalently setting :math:`g_i^0=\nabla f_i(x_0)` for initial point :math:`x_0`. </span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        initial: DataContainer,</span>
+<span class="sd">            The initial point to warmstart the calculation</span>
+<span class="sd">            </span>
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        When using SAG or SAGA with a deterministic algorithm, you should warm start the SAG-SAGA Function with the same initial point that you initialise the algorithm</span>
+<span class="sd">        </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">fi</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">initial</span><span class="p">)</span> <span class="k">for</span> <span class="n">fi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_iterate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_update_data_passes_indices</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span><span class="p">)))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_sampled_grad</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">data_passes_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> 
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; The property :code:`data_passes_indices` is a list of lists holding the indices of the functions that are processed in each call of `gradient`. This list is updated each time `gradient` is called by appending a list of the indices of the functions used to calculate the gradient.  </span>
+<span class="sd">        This is overwritten from the base class to first check to see if the approximate gradient was warm started and, if it was, ensure that the first element of `data_passes_indices` contains each index used to warm start and the index used in the first call to `gradient`. Thus the length of `data_passes_indices` is always equal to the number of calls to `gradient`. </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_passes_indices</span><span class="p">[:]</span>  
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span><span class="p">:</span>  
+            <span class="n">a</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>  
+            <span class="n">ret</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">a</span>  
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    
+<div class="viewcode-block" id="SAGAFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SAGAFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">SAGAFunction</span><span class="p">(</span><span class="n">SAGFunction</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    SAGA (SAG-Ameliore) is an accelerated version of the stochastic average gradient (SAG) function which takes a index :math:`i_k` and calculates the approximate gradient of :math:`\sum_{i=1}^{n-1}f_i` at iteration :math:`x_k` as</span>
+<span class="sd">    </span>
+<span class="sd">    .. math ::</span>
+<span class="sd">                 n\left(g_{i_k}^{k}-g_{i_k}^{k-1}\right)+\sum_{i=1}^{n-1} g_i^{k-1} \qquad \text{where} \qquad g_i^k= \begin{cases}</span>
+<span class="sd">                                                                            \nabla f_i(x_k), \text{ if } i=i_k\\</span>
+<span class="sd">                                                                            g_i^{k-1},\text{ otherwise}</span>
+<span class="sd">                                                                            \end{cases}</span>
+<span class="sd">                                                                        </span>
+<span class="sd">         </span>
+<span class="sd">    SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates. Compared to SAG it is an unbiased estimator. </span>
+<span class="sd">    </span>
+<span class="sd">    Note</span>
+<span class="sd">    ------</span>
+<span class="sd">    Compared with the literature, we do not divide by :math:`n`, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</span>
+
+<span class="sd">    Note:  </span>
+<span class="sd">    ------  </span>
+
+<span class="sd">    This function&#39;s memory requirements are `n + 3` times the image space, that is with 100 subsets the memory requirement is 103 images, which is huge.</span>
+<span class="sd">    </span>
+<span class="sd">    Reference</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Defazio, A., Bach, F. and Lacoste-Julien, S., 2014. SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. Advances in neural information processing systems, 27. https://proceedings.neurips.cc/paper_files/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf</span>
+<span class="sd">   </span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    functions : `list`  of functions</span>
+<span class="sd">                A list of functions: :code:`[f_{0}, f_{1}, ..., f_{n-1}]`. Each function is assumed to be smooth function with an implemented :func:`~Function.gradient` method. Each function must have the same domain. The number of functions must be strictly greater than 1. </span>
+<span class="sd">    sampler: An instance of one of the :meth:`~optimisation.utilities.sampler` classes which has a `next` function implemented and a `num_indices` property.</span>
+<span class="sd">        This sampler is called each time gradient is called and  sets the internal `function_num` passed to the `approximate_gradient` function.  The `num_indices` must match the number of functions provided. Default is `Sampler.random_with_replacement(len(functions))`. </span>
+<span class="sd">    </span>
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    The user has the option of calling the class method `warm_start_approximate_gradients` after initialising this class. This will compute and store the gradient for each function at an initial point, equivalently setting :math:`g_i^0=\nabla f_i(x_0)` for initial point :math:`x_0`. If this method is not called, the gradients are initialised with zeros. </span>
+
+<span class="sd">  </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span>  <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SAGAFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_update_approx_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal function used to differentiate between the SAG and SAGA calculations. This is the SAGA approximation and differs in the constants multiplying the gradients: &quot;&quot;&quot;</span>
+
+        <span class="c1"># Due to the convention that we follow: without the 1/n factor</span>
+        <span class="n">out</span><span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span>  
+            <span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_iterate</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span>  
+
+        <span class="k">return</span> <span class="n">out</span> </div>
+
+</pre></div>
+
+                </article>
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+  <h1>Source code for cil.optimisation.functions.SGFunction</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># - CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># - Daniel Deidda (National Physical Laboratory, UK)</span>
+<span class="c1"># - Claire Delplancke (Electricite de France, Research and Development)</span>
+<span class="c1"># - Ashley Gillman (Australian e-Health Res. Ctr., CSIRO, Brisbane, Queensland, Australia)</span>
+<span class="c1"># - Zeljko Kereta (Department of Computer Science, University College London, UK)</span>
+<span class="c1"># - Evgueni Ovtchinnikov (STFC - UKRI)</span>
+<span class="c1"># - Georg Schramm (Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Leuven, Belgium)</span>
+
+<span class="kn">from</span> <span class="nn">.ApproximateGradientSumFunction</span> <span class="kn">import</span> <span class="n">ApproximateGradientSumFunction</span>
+<span class="kn">from</span> <span class="nn">.Function</span> <span class="kn">import</span> <span class="n">SumFunction</span>
+
+<div class="viewcode-block" id="SGFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SGFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">SGFunction</span><span class="p">(</span><span class="n">ApproximateGradientSumFunction</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Stochastic gradient function, a child class of :code:`ApproximateGradientSumFunction`, which defines from a list of functions, :math:`{f_0,...,f_{n-1}}` a `SumFunction`, :math:`f_0+...+f_{n-1}` where each time the `gradient` is called, the :code:`sampler` provides an index, :math:`i \in {0,...,n-1}` </span>
+<span class="sd">    and the :code:`gradient` method returns the approximate gradient :math:`n \nabla_x f_i(x)`. This can be used with the :code:`cil.optimisation.algorithms` algorithm :code:`GD` to give a stochastic gradient descent algorithm. </span>
+<span class="sd">   </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+<span class="sd">    functions : `list`  of functions</span>
+<span class="sd">        A list of functions: :code:`[f_{0}, f_{1}, ..., f_{n-1}]`. Each function is assumed to be smooth with an implemented :func:`~Function.gradient` method. Although CIL does not define a domain of a :code:`Function`, all functions are supposed to have the same domain. The number of functions must be strictly greater than 1. </span>
+<span class="sd">    sampler: An instance of a CIL Sampler class ( :meth:`~optimisation.utilities.sampler`) or of another class which has a :code:`__next__` function implemented to output integers in {0,...,n-1}.</span>
+<span class="sd">        This sampler is called each time gradient is called and  sets the internal :code:`function_num` passed to the :code:`approximate_gradient` function.  Default is :code:`Sampler.random_with_replacement(len(functions))`. </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+  
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SGFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="p">)</span>    
+        
+
+<div class="viewcode-block" id="SGFunction.approximate_gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SGFunction.approximate_gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">approximate_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">function_num</span><span class="p">,</span>  <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the gradient of the function at index `function_num` at :code:`x`. </span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+<span class="sd">        function_num: `int` </span>
+<span class="sd">            Between 0 and the number of functions in the list  </span>
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            the value of the approximate gradient of the sum function at :code:`x` given a `function_number` in {0,...,len(functions)-1} </span>
+<span class="sd">        &quot;&quot;&quot;</span> 
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="o">&lt;</span><span class="mi">0</span> <span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span>
+                <span class="s1">&#39;The sampler has outputted an index larger than the number of functions to sample from. Please ensure your sampler samples from {0,1,...,len(functions)-1} only.&#39;</span><span class="p">)</span>
+
+        
+
+        <span class="c1"># compute gradient of the function indexed by function_num</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">function_num</span><span class="p">]</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">function_num</span><span class="p">]</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span> 
+
+        <span class="c1"># scale wrt number of functions </span>
+        <span class="n">out</span><span class="o">*=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span> 
+        
+        <span class="k">return</span> <span class="n">out</span>         </div>
+</div>
+
+
+
+
+
+
+           
+</pre></div>
+
+                </article>
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+  <h1>Source code for cil.optimisation.functions.SVRGFunction</h1><div class="highlight"><pre>
+<span></span><span class="c1">#   Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#   Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#   Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#   you may not use this file except in compliance with the License.</span>
+<span class="c1">#   You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#       http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#   Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#   distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#   See the License for the specific language governing permissions and</span>
+<span class="c1">#   limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1">#  Authors:</span>
+<span class="c1">#  - CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1">#  - Daniel Deidda (National Physical Laboratory, UK)</span>
+<span class="c1">#  - Claire Delplancke (Electricite de France, Research and Development)</span>
+<span class="c1">#  - Ashley Gillman (Australian e-Health Res. Ctr., CSIRO, Brisbane, Queensland, Australia)</span>
+<span class="c1">#  - Zeljko Kereta (Department of Computer Science, University College London, UK)</span>
+<span class="c1">#  - Evgueni Ovtchinnikov (STFC - UKRI)</span>
+<span class="c1">#  - Georg Schramm (Department of Imaging and Pathology, Division of Nuclear Medicine, KU Leuven, Leuven, Belgium)</span>
+
+
+<span class="kn">from</span> <span class="nn">.ApproximateGradientSumFunction</span> <span class="kn">import</span> <span class="n">ApproximateGradientSumFunction</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numbers</span>
+
+
+<div class="viewcode-block" id="SVRGFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SVRGFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">SVRGFunction</span><span class="p">(</span><span class="n">ApproximateGradientSumFunction</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    The Stochastic Variance Reduced Gradient (SVRG) function calculates the approximate gradient of :math:`\sum_{i=1}^{n-1}f_i`.  For this approximation, every `snapshot_update_interval` number of iterations, a full gradient calculation is made at this &quot;snapshot&quot; point. Intermediate gradient calculations update this snapshot by taking a index :math:`i_k` and calculating the gradient of :math:`f_{i_k}`s at the current iterate and the snapshot, updating the approximate gradient to be:</span>
+
+<span class="sd">    .. math ::</span>
+<span class="sd">        n*\nabla f_{i_k}(x_k) - n*\nabla f_{i_k}(\tilde{x}) + \nabla \sum_{i=0}^{n-1}f_i(\tilde{x}),</span>
+
+<span class="sd">    where :math:`\tilde{x}` is the latest &quot;snapshot&quot; point and :math:`x_k` is the value at the current iteration. </span>
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    Compared with the literature, we multiply by :math:`n`, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient. </span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    In the case where `store_gradients=False` the memory requirements are 4 times the image size (1 stored full gradient at the &quot;snapshot&quot;, one stored &quot;snapshot&quot; point and two lots of intermediary calculations). Alternatively, if  `store_gradients=True`  the memory requirement is `n+4` (`n` gradients at the snapshot for each function in the sum, one stored full gradient at the &quot;snapshot&quot;, one stored &quot;snapshot&quot; point and two lots of intermediary calculations).</span>
+<span class="sd">    </span>
+<span class="sd">    Reference</span>
+<span class="sd">    ---------</span>
+<span class="sd">    Johnson, R. and Zhang, T., 2013. Accelerating stochastic gradient descent using predictive variance reduction. Advances in neural information processing systems, 26.https://proceedings.neurips.cc/paper_files/paper/2013/file/ac1dd209cbcc5e5d1c6e28598e8cbbe8-Paper.pdf</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    functions : `list`  of functions</span>
+<span class="sd">        A list of functions: :code:`[f_{0}, f_{1}, ..., f_{n-1}]`. Each function is assumed to be smooth with an implemented :func:`~Function.gradient` method. All functions must have the same domain. The number of functions must be strictly greater than 1. </span>
+<span class="sd">    sampler: An instance of a CIL Sampler class ( :meth:`~optimisation.utilities.sampler`) or of another class which has a `next` function implemented to output integers in {0, 1, ..., n-1}.</span>
+<span class="sd">        This sampler is called each time gradient is called and  sets the internal `function_num` passed to the `approximate_gradient` function.  Default is `Sampler.random_with_replacement(len(functions))`. </span>
+<span class="sd">    snapshot_update_interval : positive int or None, optional</span>
+<span class="sd">        The interval for updating the full gradient (taking a snapshot). The default is 2*len(functions) so a &quot;snapshot&quot; is taken every 2*len(functions) iterations. If the user passes `0` then no full gradient snapshots will be taken. </span>
+<span class="sd">    store_gradients : bool, default: `False`</span>
+<span class="sd">        Flag indicating whether to store an update a list of gradients for each function :math:`f_i` or just to store the snapshot point :math:` \tilde{x}` and its gradient :math:`\nabla \sum_{i=0}^{n-1}f_i(\tilde{x})`.</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">snapshot_update_interval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">store_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SVRGFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="p">)</span>
+
+        <span class="c1">#  snapshot_update_interval for SVRG</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_interval</span> <span class="o">=</span> <span class="n">snapshot_update_interval</span>
+    
+        <span class="k">if</span> <span class="n">snapshot_update_interval</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_interval</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_gradients</span> <span class="o">=</span> <span class="n">store_gradients</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_svrg_iter_number</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">stoch_grad_at_iterate</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span> <span class="o">=</span> <span class="kc">None</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">snapshot</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="SVRGFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SVRGFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Selects a random function using the `sampler` and then calls the approximate gradient at :code:`x` or calculates a full gradient depending on the update frequency</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer (e.g. ImageData object)</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer (e.g. ImageData object)</span>
+<span class="sd">            the value of the approximate gradient of the sum function at :code:`x` </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1">#  For SVRG, every `snapshot_update_interval` a full gradient step is calculated, else an approximate gradient is taken.</span>
+        <span class="k">if</span> <span class="p">(</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_interval</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_svrg_iter_number</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_interval</span><span class="p">))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_full_gradient_and_return</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Batch gradient is not yet implemented&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;The sampler has produced the index </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="si">}</span><span class="s2"> which does not match the expected range of available functions to sample from. Please ensure your sampler only selects from [0,1,...,len(functions)-1] &quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">approximate_gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SVRGFunction.approximate_gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.SVRGFunction.approximate_gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">approximate_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">function_num</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Calculates the stochastic gradient at the point :math:`x` by using the gradient of the selected function, indexed by :math:`i_k`, the `function_number` in {0,...,len(functions)-1}, and the full gradient at the snapshot :math:`\tilde{x}`</span>
+<span class="sd">        </span>
+<span class="sd">        .. math ::</span>
+<span class="sd">            n*\nabla f_{i_k}(x_k) - n*\nabla f_{i_k}(\tilde{x}) + \nabla \sum_{i=0}^{n-1}f_i(\tilde{x})</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+<span class="sd">        Compared with the literature, we multiply by :math:`n`, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer (e.g. ImageData object)</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+<span class="sd">        function_num: `int` </span>
+<span class="sd">            Between 0 and n-1, where n is the number of functions in the list  </span>
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer (e.g. ImageData object)</span>
+<span class="sd">            the value of the approximate gradient of the sum function at :code:`x` given a `function_number` in {0,...,len(functions)-1}</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_svrg_iter_number</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">stoch_grad_at_iterate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">function_num</span><span class="p">]</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stoch_grad_at_iterate</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_gradients</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stoch_grad_at_iterate</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span>
+                <span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">[</span><span class="n">function_num</span><span class="p">],</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stoch_grad_at_iterate</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span>
+                <span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">function_num</span><span class="p">]</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">snapshot</span><span class="p">),</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_update_data_passes_indices</span><span class="p">([</span><span class="n">function_num</span><span class="p">])</span>
+
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stochastic_grad_difference</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_update_full_gradient_and_return</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Takes a &quot;snapshot&quot; at the point :math:`x`, saving both the point :math:` \tilde{x}=x` and its gradient :math:`\sum_{i=0}^{n-1}f_i{\tilde{x}}`. The function returns :math:`\sum_{i=0}^{n-1}f_i{\tilde{x}}` as the gradient calculation. If :code:`store_gradients==True`, the gradient of all the :math:`f_i`s is computed and stored at the &quot;snapshot&quot;..</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        Takes a &quot;snapshot&quot; at the point :math:`x`. The function returns :math:`\sum_{i=0}^{n-1}f_i{\tilde{x}}` as the gradient calculation. If :code:`store_gradients==True`, the gradient of all the :math:`f_i`s is stored, otherwise only  the sum of the gradients and the snapshot point :math:` \tilde{x}=x` are stored.</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer (e.g. ImageData object)</span>
+<span class="sd">            the value of the approximate gradient of the sum function at :code:`x` given a `function_number` in {0,...,len(functions)-1}</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_svrg_iter_number</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_gradients</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="c1"># Save the gradient of each individual f_i and the gradient of the full sum at the point x.</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span> <span class="o">=</span> <span class="p">[</span>
+                    <span class="n">fi</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">fi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="o">*</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">fi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">):</span>
+                    <span class="n">fi</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span>
+                    <span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="o">*</span><span class="n">x</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span> <span class="o">*=</span> <span class="mi">0</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_list_stored_gradients</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span> <span class="o">+=</span> <span class="n">el</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Save the snapshot point and the gradient of the full sum at the point x.</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">full_gradient</span><span class="p">(</span>
+                <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">snapshot</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">snapshot</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">snapshot</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="c1"># In this iteration all functions in the sum were used to update the gradient</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_update_data_passes_indices</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span><span class="p">)))</span>
+
+        <span class="c1"># Return the gradient of the full sum at the snapshot.</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_full_gradient_at_snapshot</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+
+<div class="viewcode-block" id="LSVRGFunction">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.LSVRGFunction">[docs]</a>
+<span class="k">class</span> <span class="nc">LSVRGFunction</span><span class="p">(</span><span class="n">SVRGFunction</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;&quot;&quot;</span>
+<span class="sd">    A class representing a function for Loopless Stochastic Variance Reduced Gradient (SVRG) approximation. This is similar to SVRG, except the full gradient at a &quot;snapshot&quot;  is calculated at random intervals rather than at fixed numbers of iterations. </span>
+
+
+<span class="sd">    Reference</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    Kovalev, D., Horváth, S. &amp;; Richtárik, P.. (2020). Don’t Jump Through Hoops and Remove Those Loops:  SVRG and Katyusha are Better Without the Outer Loop. Proceedings of the 31st International Conference  on Algorithmic Learning Theory, in Proceedings of Machine Learning Research 117:451-467 Available from https://proceedings.mlr.press/v117/kovalev20a.html.</span>
+
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">     functions : `list`  of functions</span>
+<span class="sd">        A list of functions: :code:`[f_{0}, f_{1}, ..., f_{n-1}]`. Each function is assumed to be smooth with an implemented :func:`~Function.gradient` method. All functions must have the same domain. The number of functions `n` must be strictly greater than 1. </span>
+<span class="sd">    sampler: An instance of a CIL Sampler class ( :meth:`~optimisation.utilities.sampler`) or of another class which has a `next` function implemented to output integers in {0,...,n-1}.</span>
+<span class="sd">        This sampler is called each time gradient is called and  sets the internal `function_num` passed to the `approximate_gradient` function.  Default is `Sampler.random_with_replacement(len(functions))`. </span>
+<span class="sd">    snapshot_update_probability: positive float, default: 1/n</span>
+<span class="sd">        The probability of updating the full gradient (taking a snapshot) at each iteration. The default is :math:`1./n` so, in expectation, a snapshot will be taken every :math:`n` iterations. </span>
+<span class="sd">    store_gradients : bool, default: `False`</span>
+<span class="sd">        Flag indicating whether to store an update a list of gradients for each function :math:`f_i` or just to store the snapshot point :math:` \tilde{x}` and it&#39;s gradient :math:`\nabla \sum_{i=0}^{n-1}f_i(\tilde{x})`.</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    In the case where `store_gradients=False` the memory requirements are 4 times the image size (1 stored full gradient at the &quot;snapshot&quot;, one stored &quot;snapshot&quot; point and two lots of intermediary calculations). Alternatively, if  `store_gradients=True`  the memory requirement is `n+4` (`n` gradients at the snapshot for each function in the sum, one stored full gradient at the &quot;snapshot&quot;, one stored &quot;snapshot&quot; point and two lots of intermediary calculations).</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">snapshot_update_probability</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">store_gradients</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">LSVRGFunction</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">functions</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">sampler</span><span class="p">,</span> <span class="n">store_gradients</span><span class="o">=</span><span class="n">store_gradients</span><span class="p">)</span>
+
+        <span class="c1"># Update frequency based on probability.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_probability</span> <span class="o">=</span> <span class="n">snapshot_update_probability</span>
+        <span class="c1">#  Default snapshot_update_probability for Loopless SVRG</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_probability</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_probability</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span>
+
+        <span class="c1">#  The random generator used to decide if the gradient calculation is a full gradient or an approximate gradient</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generator</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
+<div class="viewcode-block" id="LSVRGFunction.gradient">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.LSVRGFunction.gradient">[docs]</a>
+    <span class="k">def</span> <span class="nf">gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Selects a random function using the `sampler` and then calls the approximate gradient at :code:`x` or calculates a full gradient depending on the update probability.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer (e.g. ImageData objects)</span>
+<span class="sd">        out: return DataContainer, if `None` a new DataContainer is returned, default `None`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer (e.g. ImageData object)</span>
+<span class="sd">            the value of the approximate gradient of the sum function at :code:`x`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_svrg_iter_number</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="o">.</span><span class="n">uniform</span><span class="p">()</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">snapshot_update_probability</span><span class="p">:</span>
+
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_full_gradient_and_return</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Batch gradient is not yet implemented&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_functions</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;The sampler has produced the index </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="si">}</span><span class="s2"> which does not match the expected range of available functions to sample from. Please ensure your sampler only selects from [0,1,...,len(functions)-1] &quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">approximate_gradient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_num</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
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+  <h1>Source code for cil.optimisation.functions.TotalVariation</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># Claire Delplancke (University of Bath)</span>
+
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span><span class="p">,</span> <span class="n">IndicatorBox</span><span class="p">,</span> <span class="n">MixedL21Norm</span><span class="p">,</span> <span class="n">MixedL11Norm</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">GradientOperator</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.errors</span> <span class="kn">import</span> <span class="n">InPlaceError</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="TotalVariation">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TotalVariation">[docs]</a>
+<span class="k">class</span> <span class="nc">TotalVariation</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Total variation Function</span>
+
+<span class="sd">    .. math:: \mathrm{TV}(u) := \|\nabla u\|_{2,1} = \sum \|\nabla u\|_{2},\, (\mbox{isotropic})</span>
+
+<span class="sd">    .. math:: \mathrm{TV}(u) := \|\nabla u\|_{1,1} = \sum \|\nabla u\|_{1}\, (\mbox{anisotropic})</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    The :code:`TotalVariation` (TV) :code:`Function` acts as a composite function, i.e.,</span>
+<span class="sd">    the composition of the :class:`.MixedL21Norm` function and the :class:`.GradientOperator` operator,</span>
+
+<span class="sd">    .. math:: f(u) = \|u\|_{2,1}, \Rightarrow (f\circ\nabla)(u) = f(\nabla x) = \mathrm{TV}(u)</span>
+
+<span class="sd">    In that case, the proximal operator of TV does not have an exact solution and we use an iterative</span>
+<span class="sd">    algorithm to solve:</span>
+
+<span class="sd">    .. math:: \mathrm{prox}_{\tau \mathrm{TV}}(b) := \underset{u}{\mathrm{argmin}} \frac{1}{2\tau}\|u - b\|^{2} + \mathrm{TV}(u)</span>
+
+<span class="sd">    The algorithm used for the proximal operator of TV is the Fast Gradient Projection algorithm (or FISTA)</span>
+<span class="sd">    applied to the _dual problem_ of the above problem, see :cite:`BeckTeboulle_b`, :cite:`BeckTeboulle_a`, :cite:`Zhu2010`.</span>
+
+<span class="sd">    See also &quot;Multicontrast MRI Reconstruction with Structure-Guided Total Variation&quot;, Ehrhardt, Betcke, 2016.</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    max_iteration : :obj:`int`, default = 5</span>
+<span class="sd">        Maximum number of iterations for the FGP algorithm to solve to solve the dual problem</span>
+<span class="sd">        of the Total Variation Denoising problem (ROF). If warm_start=False, this should be around 100,</span>
+<span class="sd">        or larger, with a set tolerance.</span>
+<span class="sd">    tolerance : :obj:`float`, default = None</span>
+<span class="sd">        Stopping criterion for the FGP algorithm used to to solve the dual problem</span>
+<span class="sd">        of the Total Variation Denoising problem (ROF). If the difference between iterates in the FGP algorithm is less than the tolerance</span>
+<span class="sd">        the iterations end before the max_iteration number.</span>
+
+<span class="sd">        .. math:: \|x^{k+1} - x^{k}\|_{2} &lt; \mathrm{tolerance}</span>
+
+<span class="sd">    correlation : :obj:`str`, default = `Space`</span>
+<span class="sd">        Correlation between `Space` and/or `SpaceChannels` for the :class:`.GradientOperator`.</span>
+<span class="sd">    backend :  :obj:`str`, default = `c`</span>
+<span class="sd">        Backend to compute the :class:`.GradientOperator`</span>
+<span class="sd">    lower : :obj:`&#39;float`, default = None</span>
+<span class="sd">        A constraint is enforced using the :class:`.IndicatorBox` function, e.g., :code:`IndicatorBox(lower, upper)`.</span>
+<span class="sd">    upper : :obj:`&#39;float`, default = None</span>
+<span class="sd">        A constraint is enforced using the :class:`.IndicatorBox` function, e.g., :code:`IndicatorBox(lower, upper)`.</span>
+<span class="sd">    isotropic : :obj:`boolean`, default = True</span>
+<span class="sd">        Use either isotropic or anisotropic definition of TV.</span>
+
+<span class="sd">        .. math:: |x|_{2} = \sqrt{x_{1}^{2} + x_{2}^{2}},\, (\mbox{isotropic})</span>
+
+<span class="sd">        .. math:: |x|_{1} = |x_{1}| + |x_{2}|\, (\mbox{anisotropic})</span>
+
+<span class="sd">    split : :obj:`boolean`, default = False</span>
+<span class="sd">        Splits the Gradient into spatial gradient and spectral or temporal gradient for multichannel data.</span>
+
+<span class="sd">    strong_convexity_constant : :obj:`float`, default = 0</span>
+<span class="sd">        A strongly convex term weighted by the :code:`strong_convexity_constant` (:math:`\gamma`) parameter is added to the Total variation.</span>
+<span class="sd">        Now the :code:`TotalVariation` function is :math:`\gamma` - strongly convex and the proximal operator is</span>
+
+<span class="sd">        .. math:: \underset{u}{\mathrm{argmin}} \frac{1}{2\tau}\|u - b\|^{2} + \mathrm{TV}(u) + \frac{\gamma}{2}\|u\|^{2} \Leftrightarrow</span>
+
+<span class="sd">        .. math:: \underset{u}{\mathrm{argmin}} \frac{1}{2\frac{\tau}{1+\gamma\tau}}\|u - \frac{b}{1+\gamma\tau}\|^{2} + \mathrm{TV}(u)</span>
+
+<span class="sd">    warm_start : :obj:`boolean`, default = True</span>
+<span class="sd">        If set to true, the FGP algorithm used to solve the dual problem of the Total Variation Denoising problem (ROF) is initiated by the final value from the previous iteration and not at zero.</span>
+<span class="sd">        This allows the max_iteration value to be reduced to 5-10 iterations.</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    With warm_start set to the default, True, the TV function will keep in memory the range of the gradient of the image to be denoised, i.e. N times the dimensionality of the image. This increases the memory requirements.</span>
+<span class="sd">    However, during the evaluation of `proximal` the memory requirements will be unchanged as the same amount of memory will need to be allocated and deallocated.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    In the case where the Total variation becomes a :math:`\gamma` - strongly convex function, i.e.,</span>
+
+<span class="sd">    .. math:: \mathrm{TV}(u) + \frac{\gamma}{2}\|u\|^{2}</span>
+
+<span class="sd">    :math:`\gamma` should be relatively small, so as the second term above will not act as an additional regulariser.</span>
+<span class="sd">    For more information, see :cite:`Rasch2020`, :cite:`CP2011`.</span>
+
+
+
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    .. math:: \underset{u}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + \alpha\|\nabla u\|_{2,1}</span>
+
+<span class="sd">    &gt;&gt;&gt; alpha = 2.0</span>
+<span class="sd">    &gt;&gt;&gt; TV = TotalVariation()</span>
+<span class="sd">    &gt;&gt;&gt; sol = TV.proximal(b, tau = alpha)</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    .. math:: \underset{u}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + \alpha\|\nabla u\|_{1,1} + \mathbb{I}_{C}(u)</span>
+
+<span class="sd">    where :math:`C = \{1.0\leq u\leq 2.0\}`.</span>
+
+<span class="sd">    &gt;&gt;&gt; alpha = 2.0</span>
+<span class="sd">    &gt;&gt;&gt; TV = TotalVariation(isotropic=False, lower=1.0, upper=2.0)</span>
+<span class="sd">    &gt;&gt;&gt; sol = TV.proximal(b, tau = alpha)</span>
+
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    .. math:: \underset{u}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + (\alpha\|\nabla u\|_{2,1} + \frac{\gamma}{2}\|u\|^{2})</span>
+
+<span class="sd">    &gt;&gt;&gt; alpha = 2.0</span>
+<span class="sd">    &gt;&gt;&gt; gamma = 1e-3</span>
+<span class="sd">    &gt;&gt;&gt; TV = alpha * TotalVariation(isotropic=False, strong_convexity_constant=gamma)</span>
+<span class="sd">    &gt;&gt;&gt; sol = TV.proximal(b, tau = 1.0)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">max_iteration</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                 <span class="n">tolerance</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">correlation</span><span class="o">=</span><span class="s2">&quot;Space&quot;</span><span class="p">,</span>
+                 <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;c&quot;</span><span class="p">,</span>
+                 <span class="n">lower</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">upper</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">isotropic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                 <span class="n">split</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                 <span class="n">strong_convexity_constant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">TotalVariation</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">L</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="c1"># Regularising parameter = alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">regularisation_parameter</span> <span class="o">=</span> <span class="mf">1.</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">iterations</span> <span class="o">=</span> <span class="n">max_iteration</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="o">=</span> <span class="n">tolerance</span>
+
+        <span class="c1"># Total variation correlation (isotropic=Default)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">isotropic</span> <span class="o">=</span> <span class="n">isotropic</span>
+
+        <span class="c1"># correlation space or spacechannels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="o">=</span> <span class="n">correlation</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">=</span> <span class="n">backend</span>
+
+        <span class="c1"># Define orthogonal projection onto the convex set C</span>
+        <span class="k">if</span> <span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">lower</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+        <span class="k">if</span> <span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">upper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">lower</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">upper</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">projection_C</span> <span class="o">=</span> <span class="n">IndicatorBox</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span><span class="o">.</span><span class="n">proximal</span>
+
+        <span class="c1"># Setup GradientOperator as None. This is to avoid domain argument in the __init__</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_gradient</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_domain</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># splitting Gradient</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="o">=</span> <span class="n">split</span>
+
+        <span class="c1"># For the warm_start functionality</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">warm_start</span> <span class="o">=</span> <span class="n">warm_start</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># Strong convexity for TV</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span> <span class="o">=</span> <span class="n">strong_convexity_constant</span>
+
+        <span class="c1"># Define Total variation norm</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isotropic</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">MixedL21Norm</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">MixedL11Norm</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_get_p2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;The initial value for the dual in the proximal calculation - allocated to zero in the case of warm_start=False</span>
+<span class="sd">          or initialised as the last iterate seen in the proximal calculation in the case warm_start=True .&quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">regularisation_parameter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_regularisation_parameter</span>
+
+    <span class="nd">@regularisation_parameter</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">regularisation_parameter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;regularisation_parameter: expected a number, got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_regularisation_parameter</span> <span class="o">=</span> <span class="n">value</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the value of the TotalVariation function at :code:`x` .&quot;&quot;&quot;</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_domain</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">geometry</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_domain</span> <span class="o">=</span> <span class="n">x</span>
+
+        <span class="c1"># Compute Lipschitz constant provided that domain is not None.</span>
+        <span class="c1"># Lipschitz constant dependes on the GradientOperator, which is configured only if domain is not None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">calculate_Lipschitz</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">strongly_convex_term</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">x</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">strongly_convex_term</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">regularisation_parameter</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="n">strongly_convex_term</span>
+
+<div class="viewcode-block" id="TotalVariation.proximal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TotalVariation.proximal">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the proximal operator of the TotalVariation function at :code:`x` .&quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">==</span><span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span><span class="p">(</span><span class="n">message</span><span class="o">=</span><span class="s2">&quot;TotalVariation.proximal cannot be used in place&quot;</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+
+            <span class="n">strongly_convex_factor</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">tau</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span><span class="p">)</span>
+            <span class="n">x</span> <span class="o">/=</span> <span class="n">strongly_convex_factor</span>
+            <span class="n">tau</span> <span class="o">/=</span> <span class="n">strongly_convex_factor</span>
+        <span class="n">solution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fista_on_dual_rof</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">*=</span> <span class="n">strongly_convex_factor</span>
+            <span class="n">tau</span> <span class="o">*=</span> <span class="n">strongly_convex_factor</span>
+
+        <span class="k">return</span> <span class="n">solution</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_fista_on_dual_rof</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Runs the Fast Gradient Projection (FGP) algorithm to solve the dual problem</span>
+<span class="sd">        of the Total Variation Denoising problem (ROF).</span>
+
+<span class="sd">        .. math: \max_{\|y\|_{\infty}&lt;=1.} \frac{1}{2}\|\nabla^{*} y + x \|^{2} - \frac{1}{2}\|x\|^{2}</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_domain</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">geometry</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_domain</span> <span class="o">=</span> <span class="n">x</span>
+
+        <span class="c1"># Compute Lipschitz constant provided that domain is not None.</span>
+        <span class="c1"># Lipschitz constant depends on the GradientOperator, which is configured only if domain is not None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">calculate_Lipschitz</span><span class="p">()</span>
+
+        <span class="c1"># initialise</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="c1"># dual variable - its content is overwritten during iterations</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+        <span class="n">p2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_p2</span><span class="p">()</span>
+        <span class="n">tmp_q</span> <span class="o">=</span> <span class="n">p2</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="c1"># multiply tau by -1 * regularisation_parameter here so it&#39;s not recomputed every iteration</span>
+        <span class="c1"># when tau is an array this is done inplace so reverted at the end</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="n">tau_reg_neg</span> <span class="o">=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">regularisation_parameter</span> <span class="o">*</span> <span class="n">tau</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tau_reg_neg</span> <span class="o">=</span> <span class="n">tau</span>
+            <span class="n">tau</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">regularisation_parameter</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tau_reg_neg</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iterations</span><span class="p">):</span>
+
+            <span class="n">t0</span> <span class="o">=</span> <span class="n">t</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">gradient_operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">tmp_q</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">tau_reg_neg</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">projection_C</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">gradient_operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">p1</span><span class="p">)</span>
+
+            <span class="n">multip</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">L</span><span class="p">)</span><span class="o">/</span><span class="n">tau_reg_neg</span>
+
+            <span class="n">tmp_q</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">multip</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp_q</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">k</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">p1</span> <span class="o">*=</span> <span class="n">multip</span>
+                <span class="n">error</span> <span class="o">=</span> <span class="n">p1</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+                <span class="n">error</span> <span class="o">/=</span> <span class="n">tmp_q</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+                <span class="k">if</span> <span class="n">error</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">:</span>
+                    <span class="k">break</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="n">proximal_conjugate</span><span class="p">(</span><span class="n">tmp_q</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">p1</span><span class="p">)</span>
+
+            <span class="n">t</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">t0</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span> <span class="o">/</span> <span class="mi">2</span>
+            <span class="n">p1</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">p2</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp_q</span><span class="p">)</span>
+            <span class="n">tmp_q</span> <span class="o">*=</span> <span class="p">(</span><span class="n">t0</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">t</span>
+            <span class="n">tmp_q</span> <span class="o">+=</span> <span class="n">p1</span>
+
+            <span class="c1"># switch p1 and p2 references</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">p1</span>
+            <span class="n">p1</span> <span class="o">=</span> <span class="n">p2</span>
+            <span class="n">p2</span> <span class="o">=</span> <span class="n">tmp</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">warm_start</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span> <span class="o">=</span> <span class="n">p2</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Stop at </span><span class="si">%d</span><span class="s2"> iterations with tolerance </span><span class="si">%r</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Stop at </span><span class="si">%d</span><span class="s2"> iterations.&quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+
+        <span class="c1"># return tau to its original state if it was modified</span>
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">tau_reg_neg</span><span class="p">)</span> <span class="o">==</span> <span class="nb">id</span><span class="p">(</span><span class="n">tau</span><span class="p">):</span>
+            <span class="n">tau_reg_neg</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">regularisation_parameter</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tau</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+<div class="viewcode-block" id="TotalVariation.convex_conjugate">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TotalVariation.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the value of convex conjugate of the TotalVariation function at :code:`x` .&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="mf">0.0</span></div>
+
+
+<div class="viewcode-block" id="TotalVariation.calculate_Lipschitz">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.functions.TotalVariation.calculate_Lipschitz">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_Lipschitz</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Default value for the Lipschitz constant.&quot;&quot;&quot;</span>
+
+        <span class="c1"># Compute the Lipschitz parameter from the operator if possible</span>
+        <span class="c1"># Leave it initialised to None otherwise</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_L</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">gradient_operator</span><span class="o">.</span><span class="n">norm</span><span class="p">())</span><span class="o">**</span><span class="mi">2</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">gradient_operator</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; GradientOperator is created if it is not instantiated yet. The domain of the `_gradient`,</span>
+<span class="sd">        is created in the `__call__` and `proximal` methods.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_domain</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_gradient</span> <span class="o">=</span> <span class="n">GradientOperator</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_domain</span><span class="p">,</span> <span class="n">correlation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">backend</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot; The domain of the TotalVariation is </span><span class="si">{}</span><span class="s2">. Please use the __call__ or proximal methods first before calling gradient.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_domain</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gradient</span>
+
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;scalar: Expected a number, got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">scalar</span><span class="p">)))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">regularisation_parameter</span> <span class="o">*=</span> <span class="n">scalar</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/operators/BlockOperator/index.html b/v24.2.0/_modules/cil/optimisation/operators/BlockOperator/index.html
new file mode 100644
index 0000000000..085009a361
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+  <h1>Source code for cil.optimisation.operators.BlockOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">functools</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">,</span> <span class="n">DataContainer</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">Operator</span><span class="p">,</span> <span class="n">LinearOperator</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockGeometry</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">sirf</span> <span class="kn">import</span> <span class="n">SIRF</span>
+    <span class="kn">from</span> <span class="nn">sirf.SIRF</span> <span class="kn">import</span> <span class="n">DataContainer</span> <span class="k">as</span> <span class="n">SIRFDataContainer</span>
+    <span class="n">has_sirf</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">ie</span><span class="p">:</span>
+    <span class="n">has_sirf</span> <span class="o">=</span> <span class="kc">False</span>
+
+
+<div class="viewcode-block" id="BlockOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">BlockOperator</span><span class="p">(</span><span class="n">Operator</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;A Block matrix containing Operators</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    *args : Operator</span>
+<span class="sd">        Operators in the block.</span>
+<span class="sd">    **kwargs : dict</span>
+<span class="sd">        shape (:obj:`tuple`, optional): If shape is passed the Operators in vararg are considered input in a row-by-row fashion.</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    The Block Framework is a generic strategy to treat variational problems in the</span>
+<span class="sd">    following form:</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">      \min Regulariser + Fidelity</span>
+
+
+<span class="sd">    BlockOperators have a generic shape M x N, and when applied on an</span>
+<span class="sd">    Nx1 BlockDataContainer, will yield and Mx1 BlockDataContainer.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    BlockDatacontainer are only allowed to have the shape of N x 1, with</span>
+<span class="sd">    N rows and 1 column.</span>
+
+<span class="sd">    User may specify the shape of the block, by default is a row vector</span>
+
+<span class="sd">    Operators in a Block are required to have the same domain column-wise and the</span>
+<span class="sd">    same range row-wise.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    BlockOperator(op0,op1) results in a row block</span>
+
+<span class="sd">    BlockOperator(op0,op1,shape=(1,2)) results in a column block</span>
+
+
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">__array_priority__</span> <span class="o">=</span> <span class="mi">1</span>
+
+<div class="viewcode-block" id="BlockOperator.__init__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">operators</span> <span class="o">=</span> <span class="n">args</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;shape&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span>
+        <span class="n">n_elements</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">*</span><span class="n">y</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">!=</span> <span class="n">n_elements</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Dimension and size do not match: expected </span><span class="si">{}</span><span class="s1"> got </span><span class="si">{}</span><span class="s1">&#39;</span>
+                <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)))</span></div>
+
+        <span class="c1"># TODO</span>
+        <span class="c1"># until a decent way to check equality of Acquisition/Image geometries</span>
+        <span class="c1"># required to fullfil &quot;Operators in a Block are required to have the same</span>
+        <span class="c1"># domain column-wise and the same range row-wise.&quot;</span>
+        <span class="c1"># let us just not check if column/row-wise compatible, which is actually</span>
+        <span class="c1"># the same achieved by the column_wise_compatible and row_wise_compatible methods.</span>
+
+        <span class="c1"># # test if operators are compatible</span>
+        <span class="c1"># if not self.column_wise_compatible():</span>
+        <span class="c1">#     raise ValueError(&#39;Operators in each column must have the same domain&#39;)</span>
+        <span class="c1"># if not self.row_wise_compatible():</span>
+        <span class="c1">#     raise ValueError(&#39;Operators in each row must have the same range&#39;)</span>
+
+<div class="viewcode-block" id="BlockOperator.column_wise_compatible">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.column_wise_compatible">[docs]</a>
+    <span class="k">def</span> <span class="nf">column_wise_compatible</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Operators in a Block should have the same domain per column&#39;&#39;&#39;</span>
+        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">compatible</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">cols</span><span class="p">):</span>
+            <span class="n">column_compatible</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">rows</span><span class="p">):</span>
+                <span class="n">dg0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span>
+                <span class="n">dg1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span>
+                <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">dg0</span><span class="p">,</span> <span class="s1">&#39;handle&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">dg1</span><span class="p">,</span> <span class="s1">&#39;handle&#39;</span><span class="p">):</span>
+                    <span class="n">column_compatible</span> <span class="o">=</span> <span class="kc">True</span> <span class="ow">and</span> <span class="n">column_compatible</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">column_compatible</span> <span class="o">=</span> <span class="n">dg0</span><span class="o">.</span><span class="vm">__dict__</span> <span class="o">==</span> <span class="n">dg1</span><span class="o">.</span><span class="vm">__dict__</span> <span class="ow">and</span> <span class="n">column_compatible</span>
+            <span class="n">compatible</span> <span class="o">=</span> <span class="n">compatible</span> <span class="ow">and</span> <span class="n">column_compatible</span>
+        <span class="k">return</span> <span class="n">compatible</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.row_wise_compatible">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.row_wise_compatible">[docs]</a>
+    <span class="k">def</span> <span class="nf">row_wise_compatible</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Operators in a Block should have the same range per row&#39;&#39;&#39;</span>
+        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">compatible</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rows</span><span class="p">):</span>
+            <span class="n">row_compatible</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">cols</span><span class="p">):</span>
+                <span class="n">dg0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span>
+                <span class="n">dg1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span>
+                <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">dg0</span><span class="p">,</span> <span class="s1">&#39;handle&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">dg1</span><span class="p">,</span> <span class="s1">&#39;handle&#39;</span><span class="p">):</span>
+                    <span class="n">row_compatible</span> <span class="o">=</span> <span class="kc">True</span> <span class="ow">and</span> <span class="n">column_compatible</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">row_compatible</span> <span class="o">=</span> <span class="n">dg0</span><span class="o">.</span><span class="vm">__dict__</span> <span class="o">==</span> <span class="n">dg1</span><span class="o">.</span><span class="vm">__dict__</span> <span class="ow">and</span> <span class="n">row_compatible</span>
+
+            <span class="n">compatible</span> <span class="o">=</span> <span class="n">compatible</span> <span class="ow">and</span> <span class="n">row_compatible</span>
+
+        <span class="k">return</span> <span class="n">compatible</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.get_item">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.get_item">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_item</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the Operator at specified row and col</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        row: `int`</span>
+<span class="sd">            The row index required.</span>
+<span class="sd">        col: `int`</span>
+<span class="sd">            The column index required.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">row</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Requested row </span><span class="si">{}</span><span class="s1"> &gt; max </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="k">if</span> <span class="n">col</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Requested col </span><span class="si">{}</span><span class="s1"> &gt; max </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+
+        <span class="n">index</span> <span class="o">=</span> <span class="n">row</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">col</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[</span><span class="n">index</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the Euclidean norm of the norms of the individual operators in the BlockOperators &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_norms_as_list</span><span class="p">())</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.get_norms_as_list">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.get_norms_as_list">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_norms_as_list</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns a list of the individual norms of the Operators in the BlockOperator</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">op</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span> <span class="k">for</span> <span class="n">op</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.set_norms">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.set_norms">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_norms</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norms</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Uses the set_norm() function in Operator to set the norms of the operators in the BlockOperator from a list of custom values.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ------------</span>
+<span class="sd">        norms: list</span>
+<span class="sd">            A list of positive real values the same length as the number of operators in the BlockOperator.</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">norms</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;The length of the list of norms should be equal to the number of operators in the BlockOperator&quot;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">norms</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_norm</span><span class="p">(</span><span class="n">value</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Direct operation for the BlockOperator</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+<span class="sd">        BlockOperators work on BlockDataContainers, but they will also work on DataContainers</span>
+<span class="sd">        and inherited classes by simple wrapping the input in a BlockDataContainer of shape (1,1)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="n">x_b</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x_b</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_output_shape</span><span class="p">(</span><span class="n">x_b</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>        
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="n">col</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">prod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span>
+                            <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">prod</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span>
+                                              <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
+                <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prod</span><span class="p">)</span>
+            <span class="k">if</span> <span class="mi">1</span> <span class="o">==</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="c1"># the output is a single DataContainer, so we can take it out </span>
+                <span class="k">return</span> <span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> 
+            <span class="k">else</span><span class="p">:</span> 
+                <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">res</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> 
+            
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="n">col</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span><span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span>
+                                                      <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">col</span><span class="p">),</span>
+                                                      <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">temp_out_row</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">)</span> <span class="c1"># temp_out_row points to the element in out that we are adding to</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span><span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span>
+                                                      <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">col</span><span class="p">),</span>
+                                                      <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">))</span>
+                        <span class="n">temp_out_row</span> <span class="o">+=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Adjoint operation for the BlockOperator</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+<span class="sd">        BlockOperator may contain both LinearOperator and Operator</span>
+<span class="sd">        This method exists in BlockOperator as it is not known what type of</span>
+<span class="sd">        Operator it will contain.</span>
+
+<span class="sd">        BlockOperators work on BlockDataContainers, but they will also work on DataContainers</span>
+<span class="sd">        and inherited classes by simple wrapping the input in a BlockDataContainer of shape (1,1)</span>
+
+<span class="sd">        Raises: ValueError if the contained Operators are not linear</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_linear</span><span class="p">():</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not all operators in Block are linear.&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">):</span>
+            <span class="n">x_b</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x_b</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_output_shape</span><span class="p">(</span><span class="n">x_b</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">adjoint</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="n">row</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">prod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+                            <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">prod</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span>
+                                              <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">))</span>
+                <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prod</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="c1"># the output is a single DataContainer, so we can take it out</span>
+                <span class="k">return</span> <span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">res</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="n">row</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="ow">or</span> \
+                                <span class="p">(</span><span class="n">has_sirf</span> <span class="ow">and</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> <span class="n">SIRFDataContainer</span><span class="p">)):</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+                                <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">),</span>
+                                <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">op</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+                                <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">),</span>
+                                <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="ow">or</span> \
+                                <span class="p">(</span><span class="n">has_sirf</span> <span class="ow">and</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> <span class="n">SIRFDataContainer</span><span class="p">)):</span>
+                            <span class="n">out</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+                                <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">))</span>
+                        <span class="k">else</span><span class="p">:</span>
+
+                            <span class="n">temp_out_col</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">col</span><span class="p">)</span> <span class="c1"># out_col_operator points to the column in out that we are updating</span>
+                            <span class="n">temp_out_col</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span><span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+                                                        <span class="n">x_b</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">),</span>
+                                                        <span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.is_linear">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.is_linear">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_linear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns whether all the elements of the BlockOperator are linear&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="ow">and</span> <span class="n">y</span><span class="o">.</span><span class="n">is_linear</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.get_output_shape">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.get_output_shape">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xshape</span><span class="p">,</span> <span class="n">adjoint</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the shape of the output BlockDataContainer</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        xshape: BlockDataContainer</span>
+
+<span class="sd">        adjoint: `bool`</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        A(N,M) direct u(M,1) -&gt; N,1</span>
+
+<span class="sd">        A(N,M)^T adjoint u(N,1) -&gt; M,1</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xrows</span><span class="p">,</span> <span class="n">xcols</span> <span class="o">=</span> <span class="n">xshape</span>
+        <span class="k">if</span> <span class="n">xcols</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;BlockDataContainer cannot have more than 1 column&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">adjoint</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">rows</span> <span class="o">!=</span> <span class="n">xrows</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s1">&#39;Incompatible shapes </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">xshape</span><span class="p">))</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">cols</span><span class="p">,</span> <span class="n">xcols</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">cols</span> <span class="o">!=</span> <span class="n">xrows</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Incompatible shapes </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">),</span> <span class="n">xshape</span><span class="p">))</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">xcols</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.__rmul__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.__rmul__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Defines the left multiplication with a scalar. Returns a block operator with Scaled Operators inside.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ------------</span>
+
+<span class="sd">        scalar: number or iterable containing numbers</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span> <span class="ow">or</span> \
+                <span class="nb">isinstance</span><span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">scalar</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s1">&#39;dimensions of scalars and operators do not match&#39;</span><span class="p">)</span>
+            <span class="n">scalars</span> <span class="o">=</span> <span class="n">scalar</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">scalars</span> <span class="o">=</span> <span class="p">[</span><span class="n">scalar</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">]</span>
+        <span class="c1"># create a list of ScaledOperator-s</span>
+        <span class="n">ops</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="o">*</span> <span class="n">op</span> <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">op</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">scalars</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">)]</span>
+        <span class="c1"># return BlockScaledOperator(self, scalars ,shape=self.shape)</span>
+        <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="o">*</span><span class="n">ops</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">T</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the transposed of self.</span>
+
+<span class="sd">        Recall the input list is shaped in a row-by-row fashion&#39;&#39;&#39;</span>
+        <span class="n">newshape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">oplist</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">newshape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">newshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="n">oplist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">row</span><span class="p">))</span>
+        <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="o">*</span><span class="n">oplist</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">newshape</span><span class="p">)</span>
+
+<div class="viewcode-block" id="BlockOperator.domain_geometry">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.domain_geometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">domain_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the domain of the BlockOperator</span>
+
+<span class="sd">        If the shape of the BlockOperator is (N,1) the domain is a ImageGeometry or AcquisitionGeometry.</span>
+<span class="sd">        Otherwise it is a BlockGeometry.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="c1"># column BlockOperator</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># get the geometries column wise</span>
+            <span class="c1"># we need only the geometries from the first row</span>
+            <span class="c1"># since it is compatible from __init__</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">())</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">return</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="n">tmp</span><span class="p">)</span></div>
+
+
+            <span class="c1"># shape = (self.shape[0], 1)</span>
+            <span class="c1"># return BlockGeometry(*[el.domain_geometry() for el in self.operators],</span>
+            <span class="c1">#        shape=self.shape)</span>
+
+<div class="viewcode-block" id="BlockOperator.range_geometry">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.range_geometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">range_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the range of the BlockOperator&#39;&#39;&#39;</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">())</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="n">tmp</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">sum_abs_row</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="n">col</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">prod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">sum_abs_row</span><span class="p">()</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">prod</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">sum_abs_row</span><span class="p">()</span>
+            <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prod</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">sum_abs_col</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="n">col</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">prod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">sum_abs_col</span><span class="p">()</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">prod</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span><span class="o">.</span><span class="n">sum_abs_col</span><span class="p">()</span>
+            <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prod</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">)</span>
+
+<div class="viewcode-block" id="BlockOperator.__getitem__">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.__getitem__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the index-th operator in the block irrespectively of it&#39;s shape&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[</span><span class="n">index</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="BlockOperator.get_as_list">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.BlockOperator.get_as_list">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_as_list</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the list of operators&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span></div>
+</div>
+
+</pre></div>
+
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diff --git a/v24.2.0/_modules/cil/optimisation/operators/ChannelwiseOperator/index.html b/v24.2.0/_modules/cil/optimisation/operators/ChannelwiseOperator/index.html
new file mode 100644
index 0000000000..a97c979733
--- /dev/null
+++ b/v24.2.0/_modules/cil/optimisation/operators/ChannelwiseOperator/index.html
@@ -0,0 +1,641 @@
+
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+                  
+  <h1>Source code for cil.optimisation.operators.ChannelwiseOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">BlockGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+
+
+<div class="viewcode-block" id="ChannelwiseOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ChannelwiseOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">ChannelwiseOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;ChannelwiseOperator:  takes in a single-channel operator op and the</span>
+<span class="sd">    number of channels to be used, and creates a new multi-channel</span>
+<span class="sd">    ChannelwiseOperator, which will apply the operator op independently on</span>
+<span class="sd">    each channel for the number of channels specified.</span>
+
+<span class="sd">    ChannelwiseOperator supports simple operators as input but not</span>
+<span class="sd">    BlockOperators. Typically if such behaviour is desired, it can be achieved</span>
+<span class="sd">    by creating instead a BlockOperator of ChannelwiseOperators.</span>
+
+<span class="sd">        :param op: Single-channel operator</span>
+<span class="sd">        :param channels: Number of channels</span>
+<span class="sd">        :param dimension: &#39;prepend&#39; (default) or &#39;append&#39; channel dimension onto existing dimensions</span>
+
+<span class="sd">     &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">dimension</span><span class="o">=</span><span class="s1">&#39;prepend&#39;</span><span class="p">):</span>
+
+        <span class="n">dom_op</span> <span class="o">=</span> <span class="n">op</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span>
+        <span class="n">ran_op</span> <span class="o">=</span> <span class="n">op</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span>
+
+        <span class="n">geom_mc</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="c1"># Create multi-channel domain and range geometries: Clones of the</span>
+        <span class="c1"># input single-channel geometries but with the specified number of</span>
+        <span class="c1"># channels and additional dimension_label &#39;channel&#39;.</span>
+        <span class="k">for</span> <span class="n">geom</span> <span class="ow">in</span> <span class="p">[</span><span class="n">dom_op</span><span class="p">,</span><span class="n">ran_op</span><span class="p">]:</span>
+            <span class="k">if</span> <span class="n">dimension</span> <span class="o">==</span> <span class="s1">&#39;prepend&#39;</span><span class="p">:</span>
+                <span class="n">new_dimension_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;channel&#39;</span><span class="p">]</span><span class="o">+</span><span class="nb">list</span><span class="p">(</span><span class="n">geom</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">dimension</span> <span class="o">==</span> <span class="s1">&#39;append&#39;</span><span class="p">:</span>
+                <span class="n">new_dimension_labels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">geom</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span><span class="o">+</span><span class="p">[</span><span class="s1">&#39;channel&#39;</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;dimension must be either &#39;prepend&#39; or &#39;append&#39;&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">geom</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">):</span>
+
+                <span class="n">geom_channels</span> <span class="o">=</span> <span class="n">geom</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+                <span class="n">geom_channels</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">channels</span>
+                <span class="n">geom_channels</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="n">new_dimension_labels</span>
+
+                <span class="n">geom_mc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">geom_channels</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">geom</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">):</span>
+                <span class="n">geom_channels</span> <span class="o">=</span> <span class="n">geom</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+                <span class="n">geom_channels</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">num_channels</span> <span class="o">=</span> <span class="n">channels</span>
+                <span class="n">geom_channels</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="n">new_dimension_labels</span>
+
+                <span class="n">geom_mc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">geom_channels</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">geom</span><span class="p">,</span><span class="n">BlockGeometry</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;ChannelwiseOperator does not support BlockOperator as input. Consider making a BlockOperator of ChannelwiseOperators instead.&quot;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ChannelwiseOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">geom_mc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                                           <span class="n">range_geometry</span><span class="o">=</span><span class="n">geom_mc</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="o">=</span> <span class="n">op</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">channels</span>
+
+<div class="viewcode-block" id="ChannelwiseOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ChannelwiseOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns D(x)&#39;&#39;&#39;</span>
+        <span class="c1"># Loop over channels, extract single-channel data, apply single-channel</span>
+        <span class="c1"># operator&#39;s direct method and fill into multi-channel output data set.</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="n">cury</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">channels</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="n">k</span><span class="p">),</span><span class="n">cury</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">cury</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span><span class="n">channel</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="ChannelwiseOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ChannelwiseOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns D^{*}(y)&#39;&#39;&#39;</span>
+        <span class="c1"># Loop over channels, extract single-channel data, apply single-channel</span>
+        <span class="c1"># operator&#39;s adjoint method and fill into multi-channel output data set.</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="n">cury</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">channels</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="n">k</span><span class="p">),</span><span class="n">cury</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">cury</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span><span class="n">channel</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="ChannelwiseOperator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ChannelwiseOperator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Evaluates operator norm of DiagonalOperator&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span></div>
+</div>
+
+</pre></div>
+
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diff --git a/v24.2.0/_modules/cil/optimisation/operators/DiagonalOperator/index.html b/v24.2.0/_modules/cil/optimisation/operators/DiagonalOperator/index.html
new file mode 100644
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+  <h1>Source code for cil.optimisation.operators.DiagonalOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageData</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+
+<div class="viewcode-block" id="DiagonalOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.DiagonalOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">DiagonalOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;DiagonalOperator</span>
+
+<span class="sd">    Performs an element-wise multiplication, i.e., `Hadamard Product &lt;https://en.wikipedia.org/wiki/Hadamard_product_(matrices)#:~:text=In%20mathematics%2C%20the%20Hadamard%20product,elements%20i%2C%20j%20of%20the&gt;`_</span>
+<span class="sd">    of a :class:`DataContainer` `x` and :class:`DataContainer` `diagonal`, `d` .</span>
+
+<span class="sd">    .. math:: (D\circ x) = \sum_{i,j}^{M,N} D_{i,j} x_{i, j}</span>
+
+<span class="sd">    In matrix-vector interpretation, if `D` is a :math:`M\times N` dense matrix and is flattened, we have a :math:`M*N \times M*N` vector.</span>
+<span class="sd">    A sparse diagonal matrix, i.e., :class:`DigaonalOperator` can be created if we add the vector above to the main diagonal.</span>
+<span class="sd">    If the :class:`DataContainer` `x` is also flattened, we have a :math:`M*N` vector.</span>
+<span class="sd">    Now, matrix-vector multiplcation is allowed and results to a :math:`(M*N,1)` vector. After reshaping we recover a :math:`M\times N` :class:`DataContainer`.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    diagonal : DataContainer</span>
+<span class="sd">        DataContainer with the same dimensions as the data to be operated on.</span>
+<span class="sd">    domain_geometry : ImageGeometry</span>
+<span class="sd">        Specifies the geometry of the operator domain. If &#39;None&#39; will use the diagonal geometry directly. default=None .</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">diagonal</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">domain_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">domain_geometry</span> <span class="o">=</span> <span class="n">diagonal</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">DiagonalOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                    <span class="n">range_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">diagonal</span> <span class="o">=</span> <span class="n">diagonal</span>
+
+<div class="viewcode-block" id="DiagonalOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.DiagonalOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="s2">&quot;Returns :math:`D\circ x` &quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagonal</span> <span class="o">*</span> <span class="n">x</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">diagonal</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="DiagonalOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.DiagonalOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="s2">&quot;Returns :math:`D^*\circ x` &quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagonal</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DiagonalOperator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.DiagonalOperator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the operator norm of DiagonalOperator which is the :math:`\infty` norm of `diagonal`</span>
+
+<span class="sd">        .. math:: \|D\|_{\infty} = \max_{i}\{|D_{i}|\}</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagonal</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span></div>
+</div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.operators.FiniteDifferenceOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.errors</span> <span class="kn">import</span> <span class="n">InPlaceError</span>
+
+<div class="viewcode-block" id="FiniteDifferenceOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.FiniteDifferenceOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">FiniteDifferenceOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Computes forward/backward/centered finite differences of a DataContainer</span>
+<span class="sd">        under Neumann/Periodic boundary conditions</span>
+
+<span class="sd">        :param domain_geometry: Domain geometry for the FiniteDifferenceOperator</span>
+<span class="sd">        :param direction: Direction to evaluate finite differences</span>
+<span class="sd">        :type direction: string label from domain geometry or integer number</span>
+<span class="sd">        :param method: Method for finite differences</span>
+<span class="sd">        :type method: &#39;forward&#39;, &#39;backward&#39;, &#39;centered&#39;</span>
+<span class="sd">        :param bnd_cond: &#39;Neumann&#39;, &#39;Periodic&#39;</span>
+
+<span class="sd">     &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span>
+                       <span class="n">range_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                       <span class="n">direction</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;forward&#39;</span><span class="p">,</span>
+                       <span class="n">bnd_cond</span> <span class="o">=</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">direction</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">direction</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="ow">or</span> <span class="n">direction</span><span class="o">&lt;</span><span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Requested direction is not possible. Accepted direction </span><span class="si">{}</span><span class="s1">, </span><span class="se">\n</span><span class="s1">got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)),</span> <span class="n">direction</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">direction</span>
+        <span class="k">else</span><span class="p">:</span>
+           <span class="k">if</span> <span class="n">direction</span> <span class="ow">in</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">direction</span><span class="p">)</span>
+           <span class="k">else</span><span class="p">:</span>
+               <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Requested direction is not possible. Accepted direction is </span><span class="si">{}</span><span class="s1"> or </span><span class="si">{}</span><span class="s1">, </span><span class="se">\n</span><span class="s1">got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)),</span>  <span class="n">direction</span><span class="p">))</span>
+
+        <span class="c1">#get voxel spacing, if not use 1s</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">=</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">spacing</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">direction</span><span class="p">]</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">=</span> <span class="n">bnd_cond</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">method</span>
+
+        <span class="c1"># Domain Geometry = Range Geometry if not stated</span>
+        <span class="k">if</span> <span class="n">range_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">FiniteDifferenceOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span><span class="p">,</span>
+                                         <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">range_geometry</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">size_dom_gm</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39; Need a positive voxel size &#39;</span><span class="p">)</span>
+
+        <span class="c1"># check direction and &quot;length&quot; of geometry</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">direction</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">size_dom_gm</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Finite differences direction </span><span class="si">{}</span><span class="s1"> larger than geometry shape length </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">direction</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size_dom_gm</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">get_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)]</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">size_dom_gm</span>
+        <span class="n">tmp</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">direction</span><span class="p">]</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">tmp</span>
+
+<div class="viewcode-block" id="FiniteDifferenceOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.FiniteDifferenceOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">==</span><span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span><span class="p">(</span><span class="n">message</span><span class="o">=</span><span class="s2">&quot;FiniteDifferenceOperator.direct cannot be used in place&quot;</span><span class="p">)</span>
+
+        <span class="n">x_asarr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="n">outnone</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">outnone</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="n">outa</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">outa</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="n">outa</span><span class="p">[:]</span><span class="o">=</span><span class="mi">0</span>
+
+        <span class="c1">#######################################################################</span>
+        <span class="c1">##################### Forward differences #############################</span>
+        <span class="c1">#######################################################################</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;forward&#39;</span><span class="p">:</span>
+
+            <span class="c1"># interior nodes</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">))],</span> \
+                             <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span> \
+                             <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))])</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+
+
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Periodic&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+
+                <span class="c1"># right boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))])</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+        <span class="c1">#######################################################################</span>
+        <span class="c1">##################### Backward differences ############################</span>
+        <span class="c1">#######################################################################</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;backward&#39;</span><span class="p">:</span>
+
+            <span class="c1"># interior nodes</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))],</span> \
+                             <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">))],</span> \
+                             <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))])</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">:</span>
+
+                    <span class="c1"># right boundary</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">))],</span> \
+                                 <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span> \
+                                 <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">))])</span>
+
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Periodic&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+
+                <span class="c1"># right boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))])</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+        <span class="c1">#######################################################################</span>
+        <span class="c1">##################### Centered differences ############################</span>
+        <span class="c1">#######################################################################</span>
+
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;centered&#39;</span><span class="p">:</span>
+
+            <span class="c1"># interior nodes</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">))],</span> \
+                             <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">))],</span> \
+                             <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))])</span>
+
+            <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))]</span> <span class="o">/=</span> <span class="mf">2.</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))],</span> \
+                                 <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span> \
+                                 <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span> <span class="o">/=</span><span class="mf">2.</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">))],</span> \
+                                 <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span> \
+                                 <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">))])</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">))]</span> <span class="o">/=</span><span class="mf">2.</span>
+
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Periodic&#39;</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+               <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))],</span> \
+                                 <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span> \
+                                 <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span> <span class="o">/=</span> <span class="mf">2.</span>
+
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))],</span> \
+                                 <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span> \
+                                 <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">))])</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">))]</span> <span class="o">/=</span> <span class="mf">2.</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">!=</span> <span class="mf">1.0</span><span class="p">:</span>
+            <span class="n">outa</span> <span class="o">/=</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size</span>
+
+        <span class="k">if</span> <span class="n">outnone</span><span class="p">:</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">outa</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">ret</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">outa</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="FiniteDifferenceOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.FiniteDifferenceOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">==</span><span class="nb">id</span><span class="p">(</span><span class="n">out</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="n">InPlaceError</span>
+
+        <span class="c1"># Adjoint operation defined as</span>
+
+        <span class="n">x_asarr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="n">outnone</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">outnone</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="n">outa</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">outa</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="n">outa</span><span class="p">[:]</span><span class="o">=</span><span class="mi">0</span>
+
+
+        <span class="c1">#######################################################################</span>
+        <span class="c1">##################### Forward differences #############################</span>
+        <span class="c1">#######################################################################</span>
+
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;forward&#39;</span><span class="p">:</span>
+
+            <span class="c1"># interior nodes</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))],</span> \
+                             <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">))],</span> \
+                             <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))])</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> <span class="o">=</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span>
+
+                <span class="c1"># right boundary</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))]</span> <span class="o">=</span> <span class="o">-</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))]</span>
+
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Periodic&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+                <span class="c1"># right boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))])</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+        <span class="c1">#######################################################################</span>
+        <span class="c1">##################### Backward differences ############################</span>
+        <span class="c1">#######################################################################</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;backward&#39;</span><span class="p">:</span>
+
+            <span class="c1"># interior nodes</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">))],</span> \
+                             <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span> \
+                             <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))])</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> <span class="o">=</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))]</span>
+
+                <span class="c1"># right boundary</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))]</span> <span class="o">=</span> <span class="o">-</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))]</span>
+
+
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Periodic&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+
+                <span class="c1"># right boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))])</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+
+        <span class="c1">#######################################################################</span>
+        <span class="c1">##################### Centered differences ############################</span>
+        <span class="c1">#######################################################################</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;centered&#39;</span><span class="p">:</span>
+
+            <span class="c1"># interior nodes</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span> <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">))],</span> \
+                             <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">))],</span> \
+                             <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))])</span>
+            <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))]</span> <span class="o">/=</span> <span class="mf">2.0</span>
+
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> <span class="o">/=</span> <span class="mf">2.0</span>
+
+                <span class="c1"># right boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))])</span>
+
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))]</span> <span class="o">/=</span> <span class="o">-</span><span class="mf">2.0</span>
+
+
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">boundary_condition</span> <span class="o">==</span> <span class="s1">&#39;Periodic&#39;</span><span class="p">:</span>
+
+                <span class="c1"># left boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))]</span> <span class="o">/=</span> <span class="mf">2.0</span>
+
+                <span class="c1"># right boundary</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))],</span>\
+                            <span class="n">x_asarr</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))],</span>
+                            <span class="n">out</span> <span class="o">=</span> <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))])</span>
+                <span class="n">outa</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">))]</span> <span class="o">/=</span> <span class="mf">2.0</span>
+
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not implemented&#39;</span><span class="p">)</span>
+
+        <span class="n">outa</span> <span class="o">*=</span> <span class="o">-</span><span class="mf">1.</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">!=</span> <span class="mf">1.0</span><span class="p">:</span>
+            <span class="n">outa</span> <span class="o">/=</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size</span>
+
+        <span class="k">if</span> <span class="n">outnone</span><span class="p">:</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">outa</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">ret</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">outa</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span>      </div>
+</div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.operators.GradientOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">FiniteDifferenceOperator</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockGeometry</span><span class="p">,</span> <span class="n">ImageGeometry</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.multiprocessing</span> <span class="kn">import</span> <span class="n">NUM_THREADS</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">NEUMANN</span> <span class="o">=</span> <span class="s1">&#39;Neumann&#39;</span>
+<span class="n">PERIODIC</span> <span class="o">=</span> <span class="s1">&#39;Periodic&#39;</span>
+<span class="n">C</span> <span class="o">=</span> <span class="s1">&#39;c&#39;</span>
+<span class="n">NUMPY</span> <span class="o">=</span> <span class="s1">&#39;numpy&#39;</span>
+<span class="n">CORRELATION_SPACE</span> <span class="o">=</span> <span class="s2">&quot;Space&quot;</span>
+<span class="n">CORRELATION_SPACECHANNEL</span> <span class="o">=</span> <span class="s2">&quot;SpaceChannels&quot;</span>
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="GradientOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.GradientOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">GradientOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Gradient Operator: Computes first-order forward/backward differences on</span>
+<span class="sd">    2D, 3D, 4D ImageData under Neumann/Periodic boundary conditions</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    domain_geometry: ImageGeometry</span>
+<span class="sd">        Set up the domain of the function</span>
+<span class="sd">    method: str, default &#39;forward&#39;</span>
+<span class="sd">        Accepts: &#39;forward&#39;, &#39;backward&#39;, &#39;centered&#39;, note C++ optimised routine only works with &#39;forward&#39;</span>
+<span class="sd">    bnd_cond: str, default,  &#39;Neumann&#39;</span>
+<span class="sd">        Set the boundary conditions to use &#39;Neumann&#39; or &#39;Periodic&#39;</span>
+<span class="sd">    **kwargs:</span>
+<span class="sd">        correlation: str, default &#39;Space&#39;</span>
+<span class="sd">            &#39;Space&#39; will compute the gradient on only the spatial dimensions, &#39;SpaceChannels&#39; will include the channel dimension direction</span>
+<span class="sd">        backend: str, default &#39;c&#39;</span>
+<span class="sd">            &#39;c&#39; or &#39;numpy&#39;, defaults to &#39;c&#39; if correlation is &#39;SpaceChannels&#39; or channels = 1</span>
+<span class="sd">        num_threads: int</span>
+<span class="sd">            If backend is &#39;c&#39; specify the number of threads to use. Default is number of cpus/2</span>
+<span class="sd">        split: boolean</span>
+<span class="sd">            If &#39;True&#39;, and backend &#39;c&#39; will return a BlockDataContainer with grouped spatial domains. i.e. [Channel, [Z, Y, X]], otherwise [Channel, Z, Y, X]</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    BlockDataContainer</span>
+<span class="sd">        a BlockDataContainer containing images of the derivatives order given by `dimension_labels`</span>
+<span class="sd">        i.e. [&#39;horizontal_y&#39;,&#39;horizontal_x&#39;] will return [d(&#39;horizontal_y&#39;), d(&#39;horizontal_x&#39;)]</span>
+
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    2D example</span>
+
+<span class="sd">    .. math::</span>
+<span class="sd">       :nowrap:</span>
+
+<span class="sd">        \begin{eqnarray}</span>
+<span class="sd">        \nabla : X \rightarrow Y\\</span>
+<span class="sd">        u \in X, \nabla(u) &amp;=&amp; [\partial_{y} u, \partial_{x} u]\\</span>
+<span class="sd">        u^{*} \in Y, \nabla^{*}(u^{*}) &amp;=&amp; \partial_{y} v1 + \partial_{x} v2</span>
+<span class="sd">        \end{eqnarray}</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1">#kept here for backwards compatbility</span>
+    <span class="n">CORRELATION_SPACE</span> <span class="o">=</span> <span class="n">CORRELATION_SPACE</span>
+    <span class="n">CORRELATION_SPACECHANNEL</span> <span class="o">=</span> <span class="n">CORRELATION_SPACECHANNEL</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;forward&#39;</span><span class="p">,</span> <span class="n">bnd_cond</span> <span class="o">=</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="c1"># Default backend = C</span>
+        <span class="n">backend</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;backend&#39;</span><span class="p">,</span><span class="n">C</span><span class="p">)</span>
+
+        <span class="c1"># Default correlation for the gradient coupling</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;correlation&#39;</span><span class="p">,</span><span class="n">CORRELATION_SPACE</span><span class="p">)</span>
+
+        <span class="c1"># Add assumed attributes if there is no CIL geometry (i.e. SIRF objects)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="s1">&#39;channels&#39;</span><span class="p">):</span>
+            <span class="n">domain_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="s1">&#39;dimension_labels&#39;</span><span class="p">):</span>
+            <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="n">C</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="o">==</span> <span class="n">CORRELATION_SPACE</span> <span class="ow">and</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">backend</span> <span class="o">=</span> <span class="n">NUMPY</span>
+                <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;C backend cannot use correlation=&#39;Space&#39; on multi-channel dataset - defaulting to `numpy` backend&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+                <span class="n">backend</span> <span class="o">=</span> <span class="n">NUMPY</span>
+                <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;C backend is only for arrays of datatype float32 - defaulting to `numpy` backend&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">method</span> <span class="o">!=</span> <span class="s1">&#39;forward&#39;</span><span class="p">:</span>
+                <span class="n">backend</span> <span class="o">=</span> <span class="n">NUMPY</span>
+                <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;C backend is only implemented for forward differences - defaulting to `numpy` backend&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="n">NUMPY</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">Gradient_numpy</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="n">bnd_cond</span><span class="o">=</span><span class="n">bnd_cond</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">Gradient_C</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="n">bnd_cond</span><span class="o">=</span><span class="n">bnd_cond</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">GradientOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                       <span class="n">range_geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">())</span>
+
+
+<div class="viewcode-block" id="GradientOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.GradientOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Computes the first-order forward differences</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : ImageData</span>
+<span class="sd">        out : BlockDataContainer, optional</span>
+<span class="sd">            pre-allocated output memory to store result</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        BlockDataContainer</span>
+<span class="sd">            result data if `out` not specified</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="GradientOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.GradientOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Computes the first-order backward differences</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : BlockDataContainer</span>
+<span class="sd">            Gradient images for each dimension in ImageGeometry domain</span>
+<span class="sd">        out : ImageData, optional</span>
+<span class="sd">            pre-allocated output memory to store result</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            result data if `out` not specified</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="GradientOperator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.GradientOperator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the analytical norm of the GradientOperator.</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">            (\partial_{z}, \partial_{y}, \partial_{x}) &amp;= \sqrt{\|\partial_{z}\|^{2} + \|\partial_{y}\|^{2} + \|\partial_{x}\|^{2} } \\</span>
+<span class="sd">            &amp;=  \sqrt{ \frac{4}{h_{z}^{2}} + \frac{4}{h_{y}^{2}} + \frac{4}{h_{x}^{2}}}</span>
+
+
+<span class="sd">        Where the voxel sizes in each dimension are equal to 1 this simplifies to:</span>
+
+<span class="sd">          - 2D geometries :math:`norm = \sqrt{8}`</span>
+<span class="sd">          - 3D geometries :math:`norm = \sqrt{12}`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="o">==</span><span class="n">CORRELATION_SPACE</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_domain_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">[</span><span class="mi">1</span><span class="p">::])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">)</span>
+
+        <span class="n">norm</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">/</span> <span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="n">norm</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">norm</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span></div>
+</div>
+
+
+
+<span class="k">class</span> <span class="nc">Gradient_numpy</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;forward&#39;</span><span class="p">,</span> <span class="n">bnd_cond</span> <span class="o">=</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;creator</span>
+
+<span class="sd">        :param gm_domain: domain of the operator</span>
+<span class="sd">        :type gm_domain: :code:`AcquisitionGeometry` or :code:`ImageGeometry`</span>
+<span class="sd">        :param bnd_cond: boundary condition, either :code:`Neumann` or :code:`Periodic`.</span>
+<span class="sd">        :type bnd_cond: str, optional, default :code:`Neumann`</span>
+<span class="sd">        :param correlation: optional, :code:`SpaceChannel` or :code:`Space`</span>
+<span class="sd">        :type correlation: str, optional, default :code:`Space`</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># Consider pseudo 2D geometries with one slice, e.g., (1,voxel_num_y,voxel_num_x)</span>
+        <span class="n">domain_shape</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">size</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">domain_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+        <span class="c1"># Dimension of domain geometry</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_shape</span><span class="p">)</span>
+
+        <span class="c1"># Default correlation for the gradient coupling</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;correlation&#39;</span><span class="p">,</span><span class="n">CORRELATION_SPACE</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">=</span> <span class="n">bnd_cond</span>
+
+        <span class="c1"># Call FiniteDifference operator</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">method</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">FD</span> <span class="o">=</span> <span class="n">FiniteDifferenceOperator</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="n">direction</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">,</span> <span class="n">bnd_cond</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="o">==</span><span class="n">CORRELATION_SPACE</span> <span class="ow">and</span> <span class="s1">&#39;channel&#39;</span> <span class="ow">in</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">-=</span> <span class="mi">1</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;channel&#39;</span><span class="p">))</span>
+
+        <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">domain_geometry</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span> <span class="p">]</span> <span class="p">)</span>
+
+        <span class="c1">#get voxel spacing, if not use 1s</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">spacing</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Gradient_numpy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span><span class="p">,</span>
+                                             <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">range_geometry</span><span class="p">)</span>
+
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Initialised GradientOperator with numpy backend&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+         <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="p">):</span>
+                 <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">axis_index</span>
+                 <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">[</span><span class="n">axis_index</span><span class="p">]</span>
+                 <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+             <span class="k">return</span> <span class="n">out</span>
+         <span class="k">else</span><span class="p">:</span>
+             <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="p">):</span>
+                 <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">axis_index</span>
+                 <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">[</span><span class="n">axis_index</span><span class="p">]</span>
+                 <span class="n">tmp</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+             <span class="k">return</span> <span class="n">tmp</span>
+
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">axis_index</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">[</span><span class="n">axis_index</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">out</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">out</span> <span class="o">+=</span> <span class="n">tmp</span>
+            <span class="k">return</span> <span class="n">out</span> 
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">axis_index</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">voxel_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">[</span><span class="n">axis_index</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">tmp</span>
+
+<span class="kn">import</span> <span class="nn">ctypes</span><span class="o">,</span> <span class="nn">platform</span>
+<span class="kn">from</span> <span class="nn">ctypes</span> <span class="kn">import</span> <span class="n">util</span>
+<span class="c1"># check for the extension</span>
+<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Linux&#39;</span><span class="p">:</span>
+    <span class="n">dll</span> <span class="o">=</span> <span class="s1">&#39;libcilacc.so&#39;</span>
+<span class="k">elif</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
+    <span class="n">dll_file</span> <span class="o">=</span> <span class="s1">&#39;cilacc.dll&#39;</span>
+    <span class="n">dll</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">find_library</span><span class="p">(</span><span class="n">dll_file</span><span class="p">)</span>
+<span class="k">elif</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Darwin&#39;</span><span class="p">:</span>
+    <span class="n">dll</span> <span class="o">=</span> <span class="s1">&#39;libcilacc.dylib&#39;</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not supported platform, &#39;</span><span class="p">,</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">())</span>
+
+<span class="n">cilacc</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">cdll</span><span class="o">.</span><span class="n">LoadLibrary</span><span class="p">(</span><span class="n">dll</span><span class="p">)</span>
+
+<span class="n">c_float_p</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">)</span>
+
+<span class="n">cilacc</span><span class="o">.</span><span class="n">openMPtest</span><span class="o">.</span><span class="n">restypes</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span>
+<span class="n">cilacc</span><span class="o">.</span><span class="n">openMPtest</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">]</span>
+
+<span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff4D</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span>
+<span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff4D</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">]</span>
+
+<span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff3D</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span>
+<span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff3D</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">]</span>
+
+<span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff2D</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span>
+<span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff2D</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">,</span>
+                       <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int32</span><span class="p">]</span>
+
+
+<span class="k">class</span> <span class="nc">Gradient_C</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Finite Difference Operator:</span>
+
+<span class="sd">            Computes first-order forward/backward differences</span>
+<span class="sd">                     on 2D, 3D, 4D ImageData</span>
+<span class="sd">                     under Neumann/Periodic boundary conditions&#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span>  <span class="n">bnd_cond</span> <span class="o">=</span> <span class="n">NEUMANN</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="c1"># Number of threads</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_threads</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;num_threads&#39;</span><span class="p">,</span><span class="n">NUM_THREADS</span><span class="p">)</span>
+
+        <span class="c1"># Split gradients, e.g., space and channels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;split&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="c1"># Consider pseudo 2D geometries with one slice, e.g., (1,voxel_num_y,voxel_num_x)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">domain_shape</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">size</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="p">):</span>
+            <span class="k">if</span> <span class="n">size</span><span class="o">!=</span><span class="mi">1</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">domain_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">spacing</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+        <span class="c1"># Dimension of domain geometry</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_shape</span><span class="p">)</span>
+
+        <span class="c1">#default is &#39;Neumann&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">bnd_cond</span> <span class="o">==</span> <span class="n">PERIODIC</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="c1"># Define range geometry</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="s1">&#39;channel&#39;</span> <span class="ow">in</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">domain_geometry</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="o">-</span><span class="mi">1</span><span class="p">)]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">domain_geometry</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">fd</span> <span class="o">=</span> <span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff4D</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">fd</span> <span class="o">=</span> <span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff3D</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">fd</span> <span class="o">=</span> <span class="n">cilacc</span><span class="o">.</span><span class="n">fdiff2D</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Number of dimensions not supported, expected 2, 3 or 4, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">Gradient_C</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                         <span class="n">range_geometry</span><span class="o">=</span><span class="n">range_geometry</span><span class="p">)</span>
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Initialised GradientOperator with C backend running with </span><span class="si">%d</span><span class="s2"> threads&quot;</span><span class="p">,</span> <span class="n">cilacc</span><span class="o">.</span><span class="n">openMPtest</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_threads</span><span class="p">))</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">datacontainer_as_c_pointer</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+        <span class="n">ndx</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">ndx</span><span class="p">,</span> <span class="n">ndx</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">ndarray_as_c_pointer</span><span class="p">(</span><span class="n">ndx</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">ndx</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">ndx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
+        <span class="n">x_p</span> <span class="o">=</span> <span class="n">Gradient_C</span><span class="o">.</span><span class="n">ndarray_as_c_pointer</span><span class="p">(</span><span class="n">ndx</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">ndout</span> <span class="o">=</span> <span class="p">[</span><span class="n">el</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">out</span><span class="o">.</span><span class="n">containers</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;channel&#39;</span><span class="p">)</span>
+            <span class="n">ndout</span> <span class="o">=</span> <span class="p">[</span><span class="n">el</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">containers</span><span class="p">]</span>
+            <span class="n">ndout</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span> <span class="c1">#insert channels dc at correct point for channel data</span>
+
+        <span class="c1">#pass list of all arguments</span>
+        <span class="n">arg1</span> <span class="o">=</span> <span class="p">[</span><span class="n">Gradient_C</span><span class="o">.</span><span class="n">ndarray_as_c_pointer</span><span class="p">(</span><span class="n">ndout</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ndout</span><span class="p">))]</span>
+        <span class="n">arg2</span> <span class="o">=</span> <span class="p">[</span><span class="n">el</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_shape</span><span class="p">]</span>
+        <span class="n">args</span> <span class="o">=</span> <span class="n">arg1</span> <span class="o">+</span> <span class="n">arg2</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_threads</span><span class="p">]</span>
+        <span class="n">status</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fd</span><span class="p">(</span><span class="n">x_p</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;Call to C gradient operator failed&#39;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">el</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ndout</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/=</span><span class="n">el</span>
+
+        <span class="c1">#fill back out in corerct (non-trivial) order</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">):</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">ndout</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;channel&#39;</span><span class="p">)</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">ndout</span><span class="p">[</span><span class="n">ind</span><span class="p">])</span>
+
+            <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">ind</span><span class="p">:</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">j</span><span class="p">)</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">ndout</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="n">j</span> <span class="o">+=</span><span class="mi">1</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">ndout</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
+        <span class="n">out_p</span> <span class="o">=</span> <span class="n">Gradient_C</span><span class="o">.</span><span class="n">ndarray_as_c_pointer</span><span class="p">(</span><span class="n">ndout</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">ndx</span> <span class="o">=</span> <span class="p">[</span><span class="n">el</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">containers</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;channel&#39;</span><span class="p">)</span>
+            <span class="n">ndx</span> <span class="o">=</span> <span class="p">[</span><span class="n">el</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">containers</span><span class="p">]</span>
+            <span class="n">ndx</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">el</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ndx</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/=</span><span class="n">el</span>
+
+        <span class="n">arg1</span> <span class="o">=</span> <span class="p">[</span><span class="n">Gradient_C</span><span class="o">.</span><span class="n">ndarray_as_c_pointer</span><span class="p">(</span><span class="n">ndx</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)]</span>
+        <span class="n">arg2</span> <span class="o">=</span> <span class="p">[</span><span class="n">el</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_shape</span><span class="p">]</span>
+        <span class="n">args</span> <span class="o">=</span> <span class="n">arg1</span> <span class="o">+</span> <span class="n">arg2</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_threads</span><span class="p">]</span>
+
+        <span class="n">status</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fd</span><span class="p">(</span><span class="n">out_p</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">status</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;Call to C gradient operator failed&#39;</span><span class="p">)</span>
+    
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">ndout</span><span class="p">)</span>
+
+        <span class="c1">#reset input data</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">voxel_size_order</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">el</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ndx</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*=</span> <span class="n">el</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.operators.IdentityOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+<span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="k">as</span> <span class="nn">sp</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="IdentityOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.IdentityOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">IdentityOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;IdentityOperator:  Id: X -&gt; Y,  Id(x) = x\in Y</span>
+
+<span class="sd">                   X : gm_domain</span>
+<span class="sd">                   Y : gm_range ( Default: Y = X )</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="n">range_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+
+        <span class="k">if</span> <span class="n">range_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">IdentityOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                       <span class="n">range_geometry</span><span class="o">=</span><span class="n">range_geometry</span><span class="p">)</span>
+
+<div class="viewcode-block" id="IdentityOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.IdentityOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns Id(x)&#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="IdentityOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.IdentityOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns Id(x)&#39;&#39;&#39;</span>
+
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="IdentityOperator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.IdentityOperator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Evaluates operator norm of IdentityOperator&#39;&#39;&#39;</span>
+
+        <span class="k">return</span> <span class="mf">1.0</span></div>
+
+
+
+    <span class="c1">###########################################################################</span>
+    <span class="c1">###############  For preconditioning ######################################</span>
+    <span class="c1">###########################################################################</span>
+    <span class="k">def</span> <span class="nf">matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="k">return</span> <span class="n">sp</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gm_domain</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">sum_abs_row</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gm_range</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">sum_abs_col</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gm_domain</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    
+<div class="viewcode-block" id="IdentityOperator.is_orthogonal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.IdentityOperator.is_orthogonal">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_orthogonal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns if the operator is orthogonal</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        `Bool`</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="kc">True</span> </div>
+</div>
+
+</pre></div>
+
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new file mode 100644
index 0000000000..95df35b9e3
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+  <h1>Source code for cil.optimisation.operators.MaskOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">DiagonalOperator</span>
+
+<div class="viewcode-block" id="MaskOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.MaskOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">MaskOperator</span><span class="p">(</span><span class="n">DiagonalOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; MaskOperator</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mask : DataContainer</span>
+<span class="sd">        Boolean array with the same dimensions as the data to be operated on.</span>
+<span class="sd">    domain_geometry : ImageGeometry</span>
+<span class="sd">        Specifies the geometry of the operator domain. If &#39;None&#39; will use the mask geometry size and spacing as float32. default = None .</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="c1">#if domain_geometry is not specified assume float32 for domain_geometry data type</span>
+        <span class="k">if</span> <span class="n">domain_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">domain_geometry</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">MaskOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagonal</span></div>
+
+</pre></div>
+
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+                  
+  <h1>Source code for cil.optimisation.operators.MatrixOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">from</span> <span class="nn">scipy.sparse.linalg</span> <span class="kn">import</span> <span class="n">svds</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">VectorGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+
+<div class="viewcode-block" id="MatrixOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.MatrixOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">MatrixOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; Matrix wrapped into a LinearOperator</span>
+
+<span class="sd">    :param: a numpy matrix</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">A</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;creator</span>
+
+<span class="sd">        :param A: numpy ndarray representing a matrix</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">A</span> <span class="o">=</span> <span class="n">A</span>
+        <span class="n">M_A</span><span class="p">,</span> <span class="n">N_A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">domain_geometry</span> <span class="o">=</span> <span class="n">VectorGeometry</span><span class="p">(</span><span class="n">N_A</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">A</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">VectorGeometry</span><span class="p">(</span><span class="n">M_A</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">A</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">s1</span> <span class="o">=</span> <span class="kc">None</span>   <span class="c1"># Largest singular value, initially unknown</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">MatrixOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                                   <span class="n">range_geometry</span><span class="o">=</span><span class="n">range_geometry</span><span class="p">)</span>
+
+<div class="viewcode-block" id="MatrixOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.MatrixOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="p">,</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()))</span>
+            <span class="k">return</span> <span class="n">tmp</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Below use of out is not working, see</span>
+            <span class="c1"># https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html</span>
+            <span class="c1"># numpy.dot(self.A, x.as_array(), out = out.as_array())</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()))</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="MatrixOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.MatrixOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(),</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()))</span>
+            <span class="k">return</span> <span class="n">tmp</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(),</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()))</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+    <span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span></div>
+
+</pre></div>
+
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diff --git a/v24.2.0/_modules/cil/optimisation/operators/Operator/index.html b/v24.2.0/_modules/cil/optimisation/operators/Operator/index.html
new file mode 100644
index 0000000000..00bb2f4f37
--- /dev/null
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@@ -0,0 +1,1362 @@
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+  <h1>Source code for cil.optimisation.operators.Operator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">from</span> <span class="nn">textwrap</span> <span class="kn">import</span> <span class="n">dedent</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">functools</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="Operator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator">[docs]</a>
+<span class="k">class</span> <span class="nc">Operator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Operator that maps from a space X -&gt; Y</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    domain_geometry : ImageGeometry or AcquisitionGeometry</span>
+<span class="sd">        domain of the operator</span>
+
+<span class="sd">    range_geometry : ImageGeometry or AcquisitionGeometry, optional, default None</span>
+<span class="sd">        range of the operator</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_norm</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_domain_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_range_geometry</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;range_geometry&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Operator.is_linear">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.is_linear">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_linear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns if the operator is linear</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        `Bool`</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="kc">False</span></div>
+
+    
+<div class="viewcode-block" id="Operator.is_orthogonal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.is_orthogonal">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_orthogonal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns if the operator is orthogonal</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        `Bool`</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="kc">False</span></div>
+
+    
+
+<div class="viewcode-block" id="Operator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Calls the operator</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: DataContainer or BlockDataContainer</span>
+<span class="sd">            Element in the domain of the Operator</span>
+<span class="sd">        out:  DataContainer or BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer or BlockDataContainer containing the result.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="Operator.norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the norm of the Operator. On first call the norm will be calculated using the operator&#39;s calculate_norm</span>
+<span class="sd">        method. Subsequent calls will return the cached norm.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        norm: positive:`float`</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dedent</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
+<span class="s2">                norm: the norm method does not use any parameters.</span>
+<span class="s2">                For LinearOperators you can use PowerMethod to calculate the norm with non-default parameters and use set_norm to set it&quot;&quot;&quot;</span><span class="p">),</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_norm</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculate_norm</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_norm</span></div>
+
+
+<div class="viewcode-block" id="Operator.set_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.set_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Sets the norm of the operator to a custom value.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ---------</span>
+<span class="sd">        norm: float, optional</span>
+<span class="sd">            Positive real valued number or `None`</span>
+
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        The passed values are cached so that when self.norm() is called, the saved value will be returned and not calculated via the power method.</span>
+<span class="sd">        If `None` is passed, the cache is cleared prompting the function to call the power method to calculate the norm the next time self.norm() is called.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="n">norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">norm</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">norm</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="s2">&quot;Norm must be a positive real valued number or None, got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">norm</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Norm must be a number or None, got </span><span class="si">{}</span><span class="s2"> of type </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">norm</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">norm</span><span class="p">)))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_norm</span> <span class="o">=</span> <span class="n">norm</span></div>
+
+
+<div class="viewcode-block" id="Operator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the norm of the Operator. Note that this gives a NotImplementedError if the SumOperator is not linear.</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Scalar: the norm of the Operator</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_linear</span><span class="p">():</span>
+            <span class="k">return</span> <span class="n">LinearOperator</span><span class="o">.</span><span class="n">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="Operator.range_geometry">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.range_geometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">range_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the range of the Operator: Y space&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_range_geometry</span></div>
+
+
+<div class="viewcode-block" id="Operator.domain_geometry">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.Operator.domain_geometry">[docs]</a>
+    <span class="k">def</span> <span class="nf">domain_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the domain of the Operator: X space&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_domain_geometry</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">domain</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">range</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Defines the multiplication by a scalar on the left</span>
+
+<span class="sd">        returns a ScaledOperator&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">ScaledOperator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">compose</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">other</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="c1"># TODO: check equality of domain and range of operators</span>
+        <span class="c1"># if self.operator2.range_geometry != self.operator1.domain_geometry:</span>
+        <span class="c1">#    raise ValueError(&#39;Cannot compose operators, check domain geometry of {} and range geometry of {}&#39;.format(self.operator1,self.operator2))</span>
+
+        <span class="k">return</span> <span class="n">CompositionOperator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">other</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">SumOperator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__rmul__</span><span class="p">(</span><span class="n">scalar</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__neg__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Return -self &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="fm">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; Returns the subtraction of the operators.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">other</span></div>
+
+
+
+<div class="viewcode-block" id="LinearOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.LinearOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">LinearOperator</span><span class="p">(</span><span class="n">Operator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Linear operator that maps from a space X &lt;-&gt; Y</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    domain_geometry : ImageGeometry or AcquisitionGeometry</span>
+<span class="sd">        domain of the operator</span>
+
+<span class="sd">    range_geometry : ImageGeometry or AcquisitionGeometry, optional, default None</span>
+<span class="sd">        range of the operator</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="LinearOperator.is_linear">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.LinearOperator.is_linear">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_linear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns if the operator is linear&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="kc">True</span></div>
+
+
+<div class="viewcode-block" id="LinearOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.LinearOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the adjoint/inverse operation evaluated at the point :math:`x`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: DataContainer or BlockDataContainer</span>
+<span class="sd">            Element in the domain of the Operator</span>
+<span class="sd">        out:  DataContainer or BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer or BlockDataContainer containing the result.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        Only available to linear operators&#39;&#39;&#39;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="LinearOperator.PowerMethod">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.LinearOperator.PowerMethod">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">PowerMethod</span><span class="p">(</span><span class="n">operator</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span>  <span class="n">return_all</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Power method or Power iteration algorithm</span>
+
+<span class="sd">        The Power method computes the largest (dominant) eigenvalue of a matrix in magnitude, e.g.,</span>
+<span class="sd">        absolute value in the real case and modulus in the complex case.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        operator: LinearOperator</span>
+<span class="sd">        max_iteration: positive:`int`, default=10</span>
+<span class="sd">            Number of iterations for the Power method algorithm.</span>
+<span class="sd">        initial: DataContainer, default = None</span>
+<span class="sd">            Starting point for the Power method.</span>
+<span class="sd">        tolerance: positive:`float`, default = 1e-5</span>
+<span class="sd">            Stopping criterion for the Power method. Check if two consecutive eigenvalue evaluations are below the tolerance.</span>
+<span class="sd">        return_all: `boolean`, default = False</span>
+<span class="sd">            Toggles the verbosity of the return</span>
+<span class="sd">        method: `string` one of `&quot;auto&quot;`, `&quot;composed_with_adjoint&quot;` and `&quot;direct_only&quot;`, default = `&quot;auto&quot;`</span>
+<span class="sd">            The default `auto` lets the code choose the method, this can be specified with `&quot;direct_only&quot;` or `&quot;composed_with_adjoint&quot;`</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        dominant eigenvalue: positive:`float`</span>
+<span class="sd">        number of iterations: positive:`int`</span>
+<span class="sd">            Number of iterations run. Only returned if return_all is True.</span>
+<span class="sd">        eigenvector: DataContainer</span>
+<span class="sd">            Corresponding eigenvector of the dominant eigenvalue. Only returned if return_all is True.</span>
+<span class="sd">        list of eigenvalues: :obj:`list`</span>
+<span class="sd">            List of eigenvalues. Only returned if return_all is True.</span>
+<span class="sd">        convergence: `boolean`</span>
+<span class="sd">            Check on wether the difference between the last two iterations is less than tolerance. Only returned if return_all is True.</span>
+
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+<span class="sd">        The power method contains two different algorithms chosen by the `method` flag.</span>
+
+<span class="sd">        In the case `method=&quot;direct_only&quot;`, for operator, :math:`A`, the power method computes the iterations</span>
+<span class="sd">        :math:`x_{k+1} = A (x_k/\|x_{k}\|)` initialised with a random vector :math:`x_0` and returning the largest (dominant) eigenvalue in magnitude given by :math:`\|x_k\|`.</span>
+
+<span class="sd">        In the case `method=&quot;composed_with_adjoint&quot;`, the algorithm computes the largest (dominant) eigenvalue of :math:`A^{T}A`</span>
+<span class="sd">        returning the square root of this value, i.e. the iterations:</span>
+<span class="sd">        :math:`x_{k+1} = A^TA (x_k/\|x_{k}\|)` and returning  :math:`\sqrt{\|x_k\|}`.</span>
+
+<span class="sd">        The default flag is `method=&quot;auto&quot;`, the algorithm checks to see if the `operator.domain_geometry() == operator.range_geometry()` and if so</span>
+<span class="sd">        uses the method &quot;direct_only&quot; and if not the method &quot;composed_with_adjoint&quot;.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        &gt;&gt;&gt; M = np.array([[1.,0],[1.,2.]])</span>
+<span class="sd">        &gt;&gt;&gt; Mop = MatrixOperator(M)</span>
+<span class="sd">        &gt;&gt;&gt; Mop_norm = Mop.PowerMethod(Mop)</span>
+<span class="sd">        &gt;&gt;&gt; Mop_norm</span>
+<span class="sd">        2.0000654846240296</span>
+
+<span class="sd">        `PowerMethod` is called when we compute the norm of a matrix or a `LinearOperator`.</span>
+
+<span class="sd">        &gt;&gt;&gt; Mop_norm = Mop.norm()</span>
+<span class="sd">        2.0005647295658866</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">allowed_methods</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> <span class="s2">&quot;direct_only&quot;</span><span class="p">,</span> <span class="s2">&quot;composed_with_adjoint&quot;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">allowed_methods</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The argument &#39;method&#39; can be set to one of </span><span class="si">{0}</span><span class="s2"> got </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="n">allowed_methods</span><span class="p">,</span> <span class="n">method</span><span class="p">))</span>
+
+        <span class="n">apply_adjoint</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;direct_only&quot;</span><span class="p">:</span>
+            <span class="n">apply_adjoint</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">geometries_match</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span> <span class="o">==</span> <span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span>
+
+            <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
+                <span class="c1"># catch AssertionError for SIRF objects https://github.com/SyneRBI/SIRF-SuperBuild/runs/5110228626?check_suite_focus=true#step:8:972</span>
+                <span class="k">pass</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">geometries_match</span><span class="p">:</span>
+                    <span class="n">apply_adjoint</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">initial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">x0</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="s1">&#39;random&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x0</span> <span class="o">=</span> <span class="n">initial</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="n">y_tmp</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+
+        <span class="c1"># Normalize first eigenvector</span>
+        <span class="n">x0_norm</span> <span class="o">=</span> <span class="n">x0</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+        <span class="n">x0</span> <span class="o">/=</span> <span class="n">x0_norm</span>
+
+        <span class="c1"># initial guess for dominant eigenvalue</span>
+        <span class="n">eig_old</span> <span class="o">=</span> <span class="mf">1.</span>
+        <span class="k">if</span> <span class="n">return_all</span><span class="p">:</span>
+            <span class="n">eig_list</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">convergence_check</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="n">diff</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">max</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">while</span> <span class="p">(</span><span class="n">i</span> <span class="o">&lt;</span> <span class="n">max_iteration</span> <span class="ow">and</span> <span class="n">diff</span> <span class="o">&gt;</span> <span class="n">tolerance</span><span class="p">):</span>
+            <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">y_tmp</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">apply_adjoint</span><span class="p">:</span>
+                <span class="c1"># swap datacontainer references</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">x0</span>
+                <span class="n">x0</span> <span class="o">=</span> <span class="n">y_tmp</span>
+                <span class="n">y_tmp</span> <span class="o">=</span> <span class="n">tmp</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">y_tmp</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">x0</span><span class="p">)</span>
+
+            <span class="c1"># Get eigenvalue using Rayleigh quotient: denominator=1, due to normalization</span>
+            <span class="n">x0_norm</span> <span class="o">=</span> <span class="n">x0</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">x0_norm</span> <span class="o">&lt;</span> <span class="n">tolerance</span><span class="p">:</span>
+                <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                    <span class="s2">&quot;The operator has at least one zero eigenvector and is likely to be nilpotent&quot;</span><span class="p">)</span>
+                <span class="n">eig_new</span> <span class="o">=</span> <span class="mf">0.</span>
+                <span class="k">break</span>
+            <span class="n">x0</span> <span class="o">/=</span> <span class="n">x0_norm</span>
+
+            <span class="n">eig_new</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x0_norm</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">apply_adjoint</span><span class="p">:</span>
+                <span class="n">eig_new</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">eig_new</span><span class="p">)</span>
+            <span class="n">diff</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">eig_new</span> <span class="o">-</span> <span class="n">eig_old</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">return_all</span><span class="p">:</span>
+                <span class="n">eig_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">eig_new</span><span class="p">)</span>
+            <span class="n">eig_old</span> <span class="o">=</span> <span class="n">eig_new</span>
+            <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">return_all</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">==</span> <span class="n">max_iteration</span><span class="p">:</span>
+            <span class="n">convergence_check</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">return_all</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">eig_new</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">eig_list</span><span class="p">,</span> <span class="n">convergence_check</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">eig_new</span></div>
+
+
+<div class="viewcode-block" id="LinearOperator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.LinearOperator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Returns the norm of the LinearOperator calculated by the PowerMethod with default values.</span>
+<span class="sd">                &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">LinearOperator</span><span class="o">.</span><span class="n">PowerMethod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;composed_with_adjoint&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="LinearOperator.dot_test">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.LinearOperator.dot_test">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">dot_test</span><span class="p">(</span><span class="n">operator</span><span class="p">,</span> <span class="n">domain_init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">range_init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Does a dot linearity test on the operator</span>
+<span class="sd">        Evaluates if the following equivalence holds</span>
+<span class="sd">        .. math::</span>
+<span class="sd">          Ax\times y = y \times A^Tx</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        operator:</span>
+<span class="sd">            operator to test the dot_test</span>
+<span class="sd">        range_init:</span>
+<span class="sd">            optional initialisation container in the operator range</span>
+<span class="sd">        domain_init:</span>
+<span class="sd">            optional initialisation container in the operator domain</span>
+<span class="sd">        seed: int, default = 1</span>
+<span class="sd">            Seed random generator</span>
+<span class="sd">        tolerance:float, default 1e-6</span>
+<span class="sd">            Check if the following expression is below the tolerance</span>
+<span class="sd">        .. math::</span>
+
+<span class="sd">            |Ax\times y - y \times A^Tx|/(\|A\|\|x\|\|y\| + 1e-12) &lt; tolerance</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        boolean, True if the test is passed.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">seed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;seed&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">range_init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="s1">&#39;random&#39;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span> <span class="o">+</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">range_init</span>
+        <span class="k">if</span> <span class="n">domain_init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="s1">&#39;random&#39;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">domain_init</span>
+
+        <span class="n">fx</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">by</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+
+        <span class="n">lhs</span> <span class="o">=</span> <span class="n">fx</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="n">rhs</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">by</span><span class="p">)</span>
+
+        <span class="c1"># Check relative tolerance but normalised with respect to</span>
+        <span class="c1"># operator, x and y norms and avoid zero division</span>
+        <span class="n">error</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lhs</span> <span class="o">-</span> <span class="n">rhs</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">*</span><span class="n">x</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span><span class="o">*</span><span class="n">y</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span> <span class="o">+</span> <span class="mf">1e-12</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">error</span> <span class="o">&lt;</span> <span class="n">tolerance</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Left hand side  </span><span class="si">{}</span><span class="s1">, </span><span class="se">\n</span><span class="s1">Right hand side </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">))</span>
+            <span class="k">return</span> <span class="kc">False</span></div>
+</div>
+
+
+<span class="k">class</span> <span class="nc">AdjointOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    The Adjoint operator :math:`A^{*}: Y^{*}\rightarrow X^{*}` of a linear operator :math:`A: X\rightarrow Y` defined as</span>
+
+<span class="sd">    .. math:: &lt;x, A^* y&gt; = &lt;Ax, y&gt;</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    operator : A linear operator</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    This example demonstrates that :math:` LHS:=&lt;Gx, y&gt; =&lt;x, G^* y&gt;=:RHS`, where :math:`G` is the gradient operator.</span>
+<span class="sd">    &gt;&gt;&gt; ig = ImageGeometry(2,3)</span>
+<span class="sd">    &gt;&gt;&gt; G = GradientOperator(ig)</span>
+<span class="sd">    &gt;&gt;&gt; div = AdjointOperator(G)</span>
+<span class="sd">    &gt;&gt;&gt; x = G.domain.allocate(&quot;random_int&quot;)</span>
+<span class="sd">    &gt;&gt;&gt; y = G.range.allocate(&quot;random_int&quot;)</span>
+<span class="sd">    &gt;&gt;&gt; lhs = G.direct(x).dot(y)</span>
+<span class="sd">    &gt;&gt;&gt; rhs = x.dot(div.direct(y))</span>
+<span class="sd">    &gt;&gt;&gt; lhs == rhs # returns True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operator</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">AdjointOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">(),</span>
+                                       <span class="n">range_geometry</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">())</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="ScaledOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ScaledOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">ScaledOperator</span><span class="p">(</span><span class="n">Operator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;ScaledOperator</span>
+
+<span class="sd">    A class to represent the scalar multiplication of an Operator with a scalar.</span>
+<span class="sd">    It holds an operator and a scalar. Basically it returns the multiplication</span>
+<span class="sd">    of the result of direct and adjoint of the operator with the scalar.</span>
+<span class="sd">    For the rest it behaves like the operator it holds.</span>
+
+<span class="sd">    Parameters</span>
+
+<span class="sd">    -----------</span>
+<span class="sd">    operator: a `Operator` or `LinearOperator`</span>
+<span class="sd">    scalar:  Number</span>
+<span class="sd">        a scalar multiplier</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    --------</span>
+<span class="sd">    The scaled operator behaves like the following:</span>
+
+<span class="sd">    .. code-block:: python</span>
+
+<span class="sd">      sop = ScaledOperator(operator, scalar)</span>
+<span class="sd">      sop.direct(x) = scalar * operator.direct(x)</span>
+<span class="sd">      sop.adjoint(x) = scalar * operator.adjoint(x)</span>
+<span class="sd">      sop.norm() = operator.norm()</span>
+<span class="sd">      sop.range_geometry() = operator.range_geometry()</span>
+<span class="sd">      sop.domain_geometry() = operator.domain_geometry()</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">scalar</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ScaledOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">(),</span>
+                                             <span class="n">range_geometry</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">())</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;expected scalar: got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">scalar</span><span class="p">)))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span> <span class="o">=</span> <span class="n">scalar</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+<div class="viewcode-block" id="ScaledOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ScaledOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;direct method&#39;&#39;&#39;</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">tmp</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+
+<div class="viewcode-block" id="ScaledOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ScaledOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;adjoint method&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">is_linear</span><span class="p">():</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Operator is not linear&#39;</span><span class="p">)</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">tmp</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scalar</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+
+<div class="viewcode-block" id="ScaledOperator.norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ScaledOperator.norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;norm of the operator&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scalar</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ScaledOperator.is_linear">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ScaledOperator.is_linear">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_linear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns a `boolean` indicating whether the operator is linear &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">is_linear</span><span class="p">()</span></div>
+</div>
+
+
+
+<span class="c1">###############################################################################</span>
+<span class="c1">################   SumOperator  ###########################################</span>
+<span class="c1">###############################################################################</span>
+
+<div class="viewcode-block" id="SumOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SumOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">SumOperator</span><span class="p">(</span><span class="n">Operator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Sums two operators.</span>
+<span class="sd">    For example, `SumOperator(left, right).direct(x)` is equivalent to  `left.direct(x)+right.direct(x)`</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    operator1: `Operator`</span>
+<span class="sd">        The first `Operator` in the sum</span>
+<span class="sd">    operator2: `Operator`</span>
+<span class="sd">        The second `Operator` in the sum</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    Both operators must have the same domain and range.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operator1</span><span class="p">,</span> <span class="n">operator2</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator1</span> <span class="o">=</span> <span class="n">operator1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator2</span> <span class="o">=</span> <span class="n">operator2</span>
+
+        <span class="c1"># if self.operator1.domain_geometry() != self.operator2.domain_geometry():</span>
+        <span class="c1">#     raise ValueError(&#39;Domain geometry of {} is not equal with domain geometry of {}&#39;.format(self.operator1.__class__.__name__,self.operator2.__class__.__name__))</span>
+
+        <span class="c1"># if self.operator1.range_geometry() != self.operator2.range_geometry():</span>
+        <span class="c1">#     raise ValueError(&#39;Range geometry of {} is not equal with range geometry of {}&#39;.format(self.operator1.__class__.__name__,self.operator2.__class__.__name__))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">linear_flag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator1</span><span class="o">.</span><span class="n">is_linear</span><span class="p">()</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator2</span><span class="o">.</span><span class="n">is_linear</span><span class="p">()</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SumOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">operator1</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">(),</span>
+                                          <span class="n">range_geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">operator1</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">())</span>
+
+<div class="viewcode-block" id="SumOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SumOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Calls the sum operator</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: DataContainer or BlockDataContainer</span>
+<span class="sd">            Element in the domain of the SumOperator</span>
+<span class="sd">        out:  DataContainer or BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of the SumOperator will be filled in out, otherwise a new object is instantiated and returned.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer or BlockDataContainer containing the result.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator1</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator2</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">ret</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="SumOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SumOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Calls the adjoint of the sum operator, evaluated at the point :math:`x`.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: DataContainer or BlockDataContainer</span>
+<span class="sd">            Element in the range of the SumOperator</span>
+<span class="sd">        out: DataContainer or BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of the adjoint of the SumOperator will be filled in out, otherwise a new object is instantiated and returned.</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer or BlockDataContainer containing the result.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear_flag</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No adjoint operation with non-linear operators&#39;</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator1</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operator2</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">ret</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="SumOperator.is_linear">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SumOperator.is_linear">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_linear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear_flag</span></div>
+</div>
+
+
+
+<span class="c1">###############################################################################</span>
+<span class="c1">################   Composition  ###########################################</span>
+<span class="c1">###############################################################################</span>
+
+
+<div class="viewcode-block" id="CompositionOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.CompositionOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">CompositionOperator</span><span class="p">(</span><span class="n">Operator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Composes one or more operators.</span>
+<span class="sd">    For example, `CompositionOperator(left, right).direct(x)` is equivalent to `left.direct(right.direct(x))`</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    args: `Operator` s</span>
+<span class="sd">        Operators to be composed. As in mathematical notation, the operators will be applied right to left</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">operators</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="c1"># get a reference to the operators</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operators</span> <span class="o">=</span> <span class="n">operators</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">linear_flag</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="ow">and</span> <span class="n">y</span><span class="o">.</span><span class="n">is_linear</span><span class="p">(),</span>
+                                            <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="c1"># self.preallocate = kwargs.get(&#39;preallocate&#39;, False)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">preallocate</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preallocate</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tmp_domain</span> <span class="o">=</span> <span class="p">[</span><span class="n">op</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+                               <span class="k">for</span> <span class="n">op</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tmp_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">op</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+                              <span class="k">for</span> <span class="n">op</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[</span><span class="mi">1</span><span class="p">:]]</span>
+            <span class="c1"># pass</span>
+
+        <span class="c1"># TODO address the equality of geometries</span>
+        <span class="c1"># if self.operator2.range_geometry() != self.operator1.domain_geometry():</span>
+        <span class="c1">#     raise ValueError(&#39;Domain geometry of {} is not equal with range geometry of {}&#39;.format(self.operator1.__class__.__name__,self.operator2.__class__.__name__))</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">CompositionOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">domain_geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">(),</span>
+            <span class="n">range_geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">())</span>
+
+<div class="viewcode-block" id="CompositionOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.CompositionOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calls the composition operator at the point :math:`x`.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: DataContainer or BlockDataContainer</span>
+<span class="sd">            Element in the domain of the CompositionOperator</span>
+<span class="sd">        out:  DataContainer or BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of the CompositionOperator will be filled in out, otherwise a new object is instantiated and returned.</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer or BlockDataContainer containing the result.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># return self.operator1.direct(self.operator2.direct(x))</span>
+            <span class="c1"># return functools.reduce(lambda X,operator: operator.direct(X),</span>
+            <span class="c1">#                        self.operators[::-1][1:],</span>
+            <span class="c1">#                        self.operators[-1].direct(x))</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preallocate</span><span class="p">:</span>
+                <span class="k">pass</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">operator</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">step</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># tmp = self.operator2.range_geometry().allocate()</span>
+            <span class="c1"># self.operator2.direct(x, out = tmp)</span>
+            <span class="c1"># self.operator1.direct(tmp, out = out)</span>
+
+            <span class="c1"># out.fill (</span>
+            <span class="c1">#     functools.reduce(lambda X,operator: operator.direct(X),</span>
+            <span class="c1">#                        self.operators[::-1][1:],</span>
+            <span class="c1">#                        self.operators[-1].direct(x))</span>
+            <span class="c1"># )</span>
+
+            <span class="c1"># TODO this is a bit silly but will handle the pre allocation later</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preallocate</span><span class="p">:</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">operator</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_range</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                        <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_range</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">tmp_range</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_range</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">operator</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="CompositionOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.CompositionOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calls the adjoint of the composition operator at the point :math:`x`.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: DataContainer or BlockDataContainer</span>
+<span class="sd">            Element in the range of the CompositionOperator</span>
+<span class="sd">        out: DataContainer or BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of the adjoint of the CompositionOperator will be filled in out, otherwise a new object is instantiated and returned.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer or BlockDataContainer containing the result.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear_flag</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="c1"># return self.operator2.adjoint(self.operator1.adjoint(x))</span>
+                <span class="c1"># return functools.reduce(lambda X,operator: operator.adjoint(X),</span>
+                <span class="c1">#                    self.operators[1:],</span>
+                <span class="c1">#                    self.operators[0].adjoint(x))</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preallocate</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">operator</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_domain</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                        <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                            <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">tmp_domain</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">tmp_domain</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_domain</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="k">return</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">operator</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">out</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preallocate</span><span class="p">:</span>
+                    <span class="k">pass</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">operator</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operators</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">step</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
+
+                    <span class="k">return</span> <span class="n">step</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No adjoint operation with non-linear operators&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="CompositionOperator.is_linear">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.CompositionOperator.is_linear">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_linear</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear_flag</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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+  <h1>Source code for cil.optimisation.operators.ProjectionMap</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockGeometry</span>
+
+
+
+<div class="viewcode-block" id="ProjectionMap">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ProjectionMap">[docs]</a>
+<span class="k">class</span> <span class="nc">ProjectionMap</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Projection Map or Canonical Projection (https://en.wikipedia.org/wiki/Projection_(mathematics))</span>
+
+<span class="sd">    Takes an element :math:`x = (x_{0},\dots,x_{i},\dots,x_{n})` from a Cartesian product space :math:`X_{1}\times\cdots\times X_{n}\rightarrow X_{i}`</span>
+<span class="sd">    and projects it to element :math:`x_{i}` specified by the index :math:`i`.</span>
+
+<span class="sd">    .. math:: \pi_{i}: X_{1}\times\cdots\times X_{n}\rightarrow X_{i}</span>
+
+<span class="sd">    .. math:: \pi_{i}(x_{0},\dots,x_{i},\dots,x_{n}) = x_{i}</span>
+
+<span class="sd">    The adjoint operation, is defined as</span>
+
+<span class="sd">    .. math:: \pi_{i}^{*}(x_{i}) = (0, \cdots, x_{i}, \cdots, 0)</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    </span>
+<span class="sd">    domain_geometry:`BlockGeometry`</span>
+<span class="sd">        The domain of the `ProjectionMap`. A `BlockGeometry` is expected.</span>
+
+<span class="sd">    index: int</span>
+<span class="sd">        Index to project to the corresponding `ImageGeometry`</span>
+<span class="sd">    </span>
+<span class="sd">    </span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">range_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">index</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="n">BlockGeometry</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;BlockGeometry is expected, </span><span class="si">{}</span><span class="s2"> is passed.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">geometries</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Index = </span><span class="si">{}</span><span class="s2"> is larger than the total number of geometries = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">geometries</span><span class="p">)))</span>
+
+        <span class="k">if</span> <span class="n">range_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">geometries</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ProjectionMap</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                            <span class="n">range_geometry</span><span class="o">=</span><span class="n">range_geometry</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ProjectionMap.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ProjectionMap.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the ith (`index`) element of the Block data container, :math:`x`</span>
+<span class="sd">        </span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: `BlockDataContainer`</span>
+<span class="sd">        </span>
+<span class="sd">        out: `DataContainer`, default `None`</span>
+<span class="sd">            If `out` is not `None` the output of the `ProjectionMap` will be filled in `out`, otherwise a new object is instantiated and returned.</span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        `DataContainer`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="ProjectionMap.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ProjectionMap.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a `BlockDataContainer` of zeros with the ith (`index`) filled with the `DataContainer`, :math:`x`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x: `DataContainer`</span>
+<span class="sd">        </span>
+<span class="sd">        out: `BlockDataContainer`, default `None`</span>
+<span class="sd">            If `out` is not `None` the output of the adjoint of the `ProjectionMap` will be filled in `out`, otherwise a new object is instantiated and returned.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        `BlockDataContainer`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">*=</span> <span class="mi">0</span>
+        <span class="n">out</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+
+</pre></div>
+
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diff --git a/v24.2.0/_modules/cil/optimisation/operators/SparseFiniteDifferenceOperator/index.html b/v24.2.0/_modules/cil/optimisation/operators/SparseFiniteDifferenceOperator/index.html
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+  <h1>Source code for cil.optimisation.operators.SparseFiniteDifferenceOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="k">as</span> <span class="nn">sp</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">ImageGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">Operator</span>
+
+<div class="viewcode-block" id="SparseFiniteDifferenceOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SparseFiniteDifferenceOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">SparseFiniteDifferenceOperator</span><span class="p">(</span><span class="n">Operator</span><span class="p">):</span>
+
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Create Sparse Matrices for the Finite Difference Operator&#39;&#39;&#39;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="n">range_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+      <span class="n">direction</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bnd_cond</span> <span class="o">=</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SparseFiniteDifferenceOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                               <span class="n">range_geometry</span><span class="o">=</span><span class="n">range_geometry</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">direction</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">=</span> <span class="n">bnd_cond</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">get_dims</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">direction</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Gradient directions more than geometry domain&#39;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+            <span class="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">direction</span>
+
+            <span class="n">mat</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">spdiags</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">get_dims</span><span class="p">[</span><span class="n">i</span><span class="p">])),</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">get_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]))]),</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dims</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_dims</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="nb">format</span> <span class="o">=</span> <span class="s1">&#39;lil&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">==</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">:</span>
+                <span class="n">mat</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,:]</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">==</span> <span class="s1">&#39;Periodic&#39;</span><span class="p">:</span>
+                <span class="n">mat</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+            <span class="n">tmpGrad</span> <span class="o">=</span> <span class="n">mat</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">sp</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">length</span><span class="p">):</span>
+
+                <span class="n">tmpGrad</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">tmpGrad</span> <span class="p">)</span> <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="n">i</span> <span class="k">else</span> <span class="n">sp</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">sp</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_dims</span><span class="p">[</span><span class="n">j</span><span class="p">]),</span> <span class="n">tmpGrad</span> <span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">tmpGrad</span>
+
+    <span class="k">def</span> <span class="nf">T</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">()</span><span class="o">.</span><span class="n">T</span>
+
+<div class="viewcode-block" id="SparseFiniteDifferenceOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SparseFiniteDifferenceOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+        <span class="n">x_asarr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">()</span> <span class="o">*</span> <span class="n">x_asarr</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="s1">&#39;F&#39;</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)(</span><span class="n">res</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+
+        <span class="n">x_asarr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">()</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="n">x_asarr</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="s1">&#39;F&#39;</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)(</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">sum_abs_row</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">())</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">))</span>
+        <span class="c1">#res[res==0]=0</span>
+        <span class="k">return</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">sum_abs_col</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">())</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="p">)</span>
+        <span class="c1">#res[res==0]=0</span>
+        <span class="k">return</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">res</span><span class="p">)</span></div>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+    <span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span>
+    <span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
+    <span class="n">arr</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="s1">&#39;random_int&#39;</span><span class="p">)</span>
+    <span class="n">sFD_neum1</span> <span class="o">=</span> <span class="n">SparseFiniteDifferenceOperator</span><span class="p">(</span><span class="n">ig</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bnd_cond</span><span class="o">=</span><span class="s1">&#39;Neumann&#39;</span><span class="p">)</span>
+    <span class="n">sFD_neum2</span> <span class="o">=</span> <span class="n">SparseFiniteDifferenceOperator</span><span class="p">(</span><span class="n">ig</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bnd_cond</span><span class="o">=</span><span class="s1">&#39;Neumann&#39;</span><span class="p">)</span>
+    <span class="n">DY</span> <span class="o">=</span> <span class="n">sFD_neum1</span><span class="o">.</span><span class="n">matrix</span><span class="p">()</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
+    <span class="n">DX</span> <span class="o">=</span> <span class="n">sFD_neum2</span><span class="o">.</span><span class="n">matrix</span><span class="p">()</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
+
+
+    <span class="n">rows</span> <span class="o">=</span> <span class="n">sFD_neum1</span><span class="o">.</span><span class="n">sum_abs_row</span><span class="p">()</span>
+    <span class="n">cols</span> <span class="o">=</span> <span class="n">sFD_neum1</span><span class="o">.</span><span class="n">sum_abs_col</span><span class="p">()</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="n">rows</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">cols</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/optimisation/operators/SymmetrisedGradientOperator/index.html b/v24.2.0/_modules/cil/optimisation/operators/SymmetrisedGradientOperator/index.html
new file mode 100644
index 0000000000..7681b98d68
--- /dev/null
+++ b/v24.2.0/_modules/cil/optimisation/operators/SymmetrisedGradientOperator/index.html
@@ -0,0 +1,695 @@
+
+<!DOCTYPE html>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.optimisation.operators.SymmetrisedGradientOperator</span></li>
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+  <h1>Source code for cil.optimisation.operators.SymmetrisedGradientOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockGeometry</span><span class="p">,</span> <span class="n">BlockDataContainer</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">FiniteDifferenceOperator</span>
+
+
+<div class="viewcode-block" id="SymmetrisedGradientOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SymmetrisedGradientOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">SymmetrisedGradientOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39; The symmetrised gradient is the operator, :math:`E`, defined by :math:`E: V \rightarrow W` where `V` is `BlockGeometry` and  `W` is the range of the Symmetrised Gradient and</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">            E(v) = 0.5  ( \nabla v + (\nabla v)^{T} ) \\</span>
+
+
+<span class="sd">    In 2 dimensions, let :math:`v(x,y)=(v_1(x,y),v_2(x,y))` which gives</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">            \nabla v =\left( \begin{matrix}</span>
+<span class="sd">                \partial_{x} v_1 &amp; \partial_x v_2\\</span>
+<span class="sd">                \partial_{y}v_1 &amp; \partial_y v_2</span>
+<span class="sd">            \end{matrix}\right)</span>
+
+<span class="sd">    and thus</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">            E(v) = 0.5  ( \nabla v + (\nabla v)^{T} )</span>
+<span class="sd">            =\left( \begin{matrix}</span>
+<span class="sd">                \partial_{x} v_1 &amp; 0.5  (\partial_{y} v_1 + \partial_{x} v_2) \\</span>
+<span class="sd">                0.5  (\partial_{x} v_1 + \partial_{y} v_2) &amp; \partial_{y} v_2</span>
+<span class="sd">            \end{matrix}\right)</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    domain_geometry: `BlockGeometry` with shape (2,1) or (3,1)</span>
+<span class="sd">        Set up the domain of the function.</span>
+<span class="sd">    bnd_cond: str, optional, default :code:`Neumann`</span>
+<span class="sd">        Boundary condition either :code:`Neumann` or :code:`Periodic`</span>
+<span class="sd">    correlation: str, optional, default :code:`Channel`</span>
+<span class="sd">        Correlation either :code:`SpaceChannel` or :code:`Channel`</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">CORRELATION_SPACE</span> <span class="o">=</span> <span class="s2">&quot;Space&quot;</span>
+    <span class="n">CORRELATION_SPACECHANNEL</span> <span class="o">=</span> <span class="s2">&quot;SpaceChannels&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="n">bnd_cond</span> <span class="o">=</span> <span class="s1">&#39;Neumann&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">=</span> <span class="n">bnd_cond</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;correlation&#39;</span><span class="p">,</span><span class="n">SymmetrisedGradientOperator</span><span class="o">.</span><span class="n">CORRELATION_SPACE</span><span class="p">)</span>
+
+        <span class="n">tmp_gm</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">geometries</span><span class="p">)</span><span class="o">*</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">geometries</span>
+
+
+        <span class="c1"># Define FD operator. We need one geometry from the BlockGeometry of the domain</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">FD</span> <span class="o">=</span> <span class="n">FiniteDifferenceOperator</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">direction</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+                             <span class="n">bnd_cond</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order_ind</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order_ind</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SymmetrisedGradientOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+                                          <span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                          <span class="n">range_geometry</span><span class="o">=</span><span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="n">tmp_gm</span><span class="p">))</span>
+
+
+<div class="viewcode-block" id="SymmetrisedGradientOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SymmetrisedGradientOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns :math:`E(v) = 0.5 * ( \nabla v + (\nabla v)^{T} )`</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        -------------</span>
+
+<span class="sd">        x: BlockDataContainer</span>
+<span class="sd">        out: BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of direct will be filled in out, otherwise a new object is instantiated and returned.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">i</span>
+                    <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">j</span><span class="p">)))</span>
+
+            <span class="n">tmp1</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">order_ind</span><span class="p">]</span>
+
+            <span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">tmp1</span><span class="p">)]</span>
+
+            <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">res</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="n">ind</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">i</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_item</span><span class="p">(</span><span class="n">j</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">[</span><span class="n">ind</span><span class="p">])</span>
+                    <span class="n">ind</span><span class="o">+=</span><span class="mi">1</span>
+            <span class="n">out1</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">out</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">order_ind</span><span class="p">])</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">out</span> <span class="o">+</span> <span class="n">out1</span><span class="p">)</span> <span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="SymmetrisedGradientOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.SymmetrisedGradientOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns the adjoint of the symmetrised gradient operator</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        -------------</span>
+
+<span class="sd">        x: BlockDataContainer</span>
+<span class="sd">        out: BlockDataContainer, default None</span>
+<span class="sd">            If out is not None the output of adjoint will be filled in out, otherwise a new object is instantiated and returned.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="n">tmp1</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">j</span>
+                    <span class="n">tmp1</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                    <span class="n">i</span><span class="o">+=</span><span class="mi">1</span>
+                <span class="n">tmp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp1</span>
+            <span class="k">return</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="o">*</span><span class="n">tmp</span><span class="p">)</span>
+
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="n">tmp1</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direction</span> <span class="o">=</span> <span class="n">j</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">FD</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
+                    <span class="n">i</span><span class="o">+=</span><span class="mi">1</span>
+                    <span class="n">tmp1</span><span class="o">+=</span><span class="n">tmp</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+                <span class="n">out</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp1</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.operators.WaveletOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2023 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2023 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pywt</span>  <span class="c1"># PyWavelets module</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">VectorGeometry</span>
+
+
+<div class="viewcode-block" id="WaveletOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.WaveletOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">WaveletOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Computes forward or inverse (adjoint) discrete wavelet transform (DWT) of the input</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        domain_geometry: cil geometry</span>
+<span class="sd">            Domain geometry for the WaveletOperator</span>
+<span class="sd">        range_geometry: cil geometry, optional</span>
+<span class="sd">            Output geometry for the WaveletOperator. Default = domain_geometry with the right coefficient array size deduced from pywavelets</span>
+<span class="sd">        level: int, optional, default= log_2(min(shape(axes)))</span>
+<span class="sd">            integer for decomposition level. Default = log_2(min(shape(axes))), i.e. the maximum number of accurate downsamplings possible</span>
+<span class="sd">        wname: string, optional, default=&#39;haar&#39;</span>
+<span class="sd">            label for wavelet used.</span>
+<span class="sd">        axes: list of ints, optional, default=`None`</span>
+<span class="sd">            Defines the dimensions to decompose along. Note that channel is the first dimension: for example, spatial DWT is given by axes=range(1,3) and channelwise DWT is axes=range(1)</span>
+<span class="sd">            Default = `None`, meaning all dimensions are transformed. Same as axes = range(ndim)</span>
+
+
+<span class="sd">        **kwargs</span>
+<span class="sd">        ---------</span>
+<span class="sd">        correlation: str, default &#39;All&#39;. Note: Only applied if `axes = None`!</span>
+<span class="sd">            &#39;All&#39; will compute the wavelet decomposition on every possible dimension.</span>
+<span class="sd">            &#39;Space&#39; will compute the wavelet decomposition on only the spatial dimensions. If there are multiple channels, each channel is decomposed independently.</span>
+<span class="sd">            &#39;Channels&#39; will compute the wavelet decomposition on only the channels, independently for every spatial point.</span>
+<span class="sd">        bnd_cond: str, default &#39;symmetric&#39;. More commonly known as the padding or extension method used in discrete convolutions. All options supported by PyWavelets are valid.</span>
+<span class="sd">            Most common examples are &#39;symmetric&#39; (padding by mirroring edge values), &#39;zero&#39; (padding with zeros), &#39;periodic&#39; (wrapping values around as in circular convolution).</span>
+<span class="sd">            Some padding methods can have unexpected effect on the wavelet coefficients at the edges.</span>
+<span class="sd">            See https://pywavelets.readthedocs.io/en/latest/ref/signal-extension-modes.html for more details and all options.</span>
+<span class="sd">        true_adjoint: bool, default `True`. For biorthogonal wavelets the true mathematical adjoint should no longer produce perfect reconstructions, setting `true_adjoint`as `False` the reconstruction (using the adjoint) should be (almost) the original input.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+<span class="sd">        The default decomposition level is the theoretical maximum: log_2(min(input.shape)).  However, this is not always recommended and pywavelets should give a warning if the coarsest scales are too small to be meaningful.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        We currently do not support wavelets that are not orthogonal or bi-orthogonal.</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span>
+                 <span class="n">range_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">level</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">wname</span><span class="o">=</span><span class="s2">&quot;haar&quot;</span><span class="p">,</span>
+                 <span class="n">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="c1"># Correlation is different way of defining decomposition axes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;correlation&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;All&#39;</span><span class="p">]:</span>
+                <span class="n">axes</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;space&quot;</span><span class="p">,</span> <span class="s2">&quot;spatial&quot;</span><span class="p">]:</span>
+                <span class="n">axes</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
+                    <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span> <span class="k">if</span> <span class="n">l</span> <span class="o">!=</span> <span class="s1">&#39;channel&#39;</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;channels&quot;</span><span class="p">,</span> <span class="s2">&quot;channel&quot;</span><span class="p">]:</span>
+                <span class="n">axes</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
+                    <span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span> <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="s1">&#39;channel&#39;</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Unknown correlation type: &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">axes</span> <span class="o">==</span> <span class="p">[]:</span>
+                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Correlation set to &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="si">}</span><span class="s2">&#39; but the data only has &#39;</span><span class="si">{</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="si">}</span><span class="s2">&#39; as possible dimensions&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">axes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Decomposition axes &#39;</span><span class="si">{</span><span class="n">axes</span><span class="si">}</span><span class="s2">&#39; take priority over correlation &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="si">}</span><span class="s2">&#39;. Both should not be used.&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">correlation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Setting correlation &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">correlation</span><span class="si">}</span><span class="s2">&#39; is not valid for 1D data.&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
+
+        <span class="c1"># Convolution boundary condition i.e. padding method</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;bnd_cond&#39;</span><span class="p">,</span> <span class="s1">&#39;symmetric&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_trueAdj</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;true_adjoint&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">wname</span> <span class="o">=</span> <span class="n">wname</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">Wavelet</span><span class="p">(</span><span class="n">wname</span><span class="p">)</span>
+        <span class="c1"># True adjoint for biorthogonal wavelet</span>
+        <span class="k">if</span> <span class="nb">all</span><span class="p">([</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="o">.</span><span class="n">orthogonal</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="o">.</span><span class="n">biorthogonal</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trueAdj</span><span class="p">]):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getBiortFilters</span><span class="p">(</span><span class="n">wname</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">level</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">level</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">dwtn_max_level</span><span class="p">(</span>
+                <span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">wavelet</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="n">axes</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">level</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shapes</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">wavedecn_shapes</span><span class="p">(</span>
+            <span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">wavelet</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="n">axes</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">axes</span> <span class="o">=</span> <span class="n">axes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_slices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape2slice</span><span class="p">()</span>
+
+        <span class="c1"># Compute the correct wavelet domain size</span>
+        <span class="n">range_shape</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axes</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+        <span class="c1"># Name of the diagonal element in unknown dimensional DWT</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="s1">&#39;d&#39;</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">axes</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">axes</span><span class="p">:</span>
+            <span class="n">range_shape</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">k</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">level</span><span class="p">):</span>
+                <span class="n">range_shape</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shapes</span><span class="p">[</span><span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">][</span><span class="n">d</span><span class="p">][</span><span class="n">k</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">range_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+            <span class="c1"># Update new size</span>
+            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">range_geometry</span><span class="p">,</span> <span class="s1">&#39;channels&#39;</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">range_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">range_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">range_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="c1"># Remove channels temporarily</span>
+                    <span class="n">range_shape</span> <span class="o">=</span> <span class="n">range_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">range_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="n">range_geometry</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="n">range_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+                <span class="n">range_geometry</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">=</span> <span class="n">range_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">range_geometry</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">=</span> <span class="n">range_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">range_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="n">range_geometry</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="n">range_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">range_geometry</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">=</span> <span class="n">range_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">range_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># VectorGeometry is bit special</span>
+                <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">VectorGeometry</span><span class="p">(</span><span class="n">range_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Spatial dimension of range_geometry can be at most 3. Now it is </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">range_shape</span><span class="p">)</span><span class="si">}</span><span class="s2">!&quot;</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="p">(</span><span class="n">range_geometry</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">range_shape</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Size of the range geometry is </span><span class="si">{</span><span class="n">range_geometry</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> but the size of the wavelet coefficient array must be </span><span class="si">{</span><span class="nb">tuple</span><span class="p">(</span><span class="n">range_shape</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span> <span class="n">range_geometry</span><span class="o">=</span><span class="n">range_geometry</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_shape2slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function for turning shape of coefficients to slices&quot;&quot;&quot;</span>
+        <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shapes</span>
+        <span class="n">coeff_tmp</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">coeff_tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+        <span class="k">for</span> <span class="n">cd</span> <span class="ow">in</span> <span class="n">shapes</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
+            <span class="n">subbs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">v</span><span class="p">))</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">cd</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
+            <span class="n">coeff_tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subbs</span><span class="p">)</span>
+
+        <span class="n">_</span><span class="p">,</span> <span class="n">slices</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">coeffs_to_array</span><span class="p">(</span><span class="n">coeff_tmp</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">slices</span>
+
+    <span class="k">def</span> <span class="nf">_getBiortFilters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">wname</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function for creating a custom wavelet object.</span>
+<span class="sd">        Using mirrored decomposition filters for reconstruction gives adjoint.</span>
+<span class="sd">        This is only needed for biorthogonal wavelets.&quot;&quot;&quot;</span>
+        <span class="n">fb</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">Wavelet</span><span class="p">(</span><span class="n">wname</span><span class="p">)</span><span class="o">.</span><span class="n">filter_bank</span>
+        <span class="n">ifb</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">Wavelet</span><span class="p">(</span><span class="n">wname</span><span class="p">)</span><span class="o">.</span><span class="n">inverse_filter_bank</span>
+        <span class="n">adj_filter_bank</span> <span class="o">=</span> <span class="n">fb</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">ifb</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
+        <span class="n">wavelet</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">Wavelet</span><span class="p">(</span><span class="n">wname</span><span class="p">,</span> <span class="n">filter_bank</span><span class="o">=</span><span class="n">adj_filter_bank</span><span class="p">)</span>
+        <span class="n">wavelet</span><span class="o">.</span><span class="n">orthogonal</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="n">wavelet</span><span class="o">.</span><span class="n">biorthogonal</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">return</span> <span class="n">wavelet</span>
+
+<div class="viewcode-block" id="WaveletOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.WaveletOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the WaveletOperator applied to :math:`x`</span>
+
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer, the value of the WaveletOperator applied to :math:`x` or `None` if `out`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">x_arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="n">coeffs</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">wavedecn</span><span class="p">(</span>
+            <span class="n">x_arr</span><span class="p">,</span> <span class="n">wavelet</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">level</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span><span class="p">)</span>
+
+        <span class="n">Wx</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">coeffs_to_array</span><span class="p">(</span><span class="n">coeffs</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">Wx</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">ret</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">Wx</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="WaveletOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.WaveletOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Wx</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the value of the adjoint of the WaveletOperator applied to :math:`x`</span>
+
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : DataContainer</span>
+
+<span class="sd">        out: return DataContainer, if None a new DataContainer is returned, default None.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        DataContainer, the value of the adjoint of the WaveletOperator applied to :math:`x` or `None` if `out`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="o">.</span><span class="n">orthogonal</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="o">.</span><span class="n">biorthogonal</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;CIL currently only supports orthogonal and biorthogonal wavelets&#39;</span><span class="p">)</span>
+
+        <span class="n">Wx_arr</span> <span class="o">=</span> <span class="n">Wx</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">coeffs</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">array_to_coeffs</span><span class="p">(</span><span class="n">Wx_arr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slices</span><span class="p">)</span>
+
+        <span class="n">x</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">waverecn</span><span class="p">(</span>
+            <span class="n">coeffs</span><span class="p">,</span> <span class="n">wavelet</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bnd_cond</span><span class="p">)</span>
+
+        <span class="c1"># Need to slice the output in case original size is of odd length</span>
+        <span class="n">org_size</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">Wx</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">org_size</span><span class="p">])</span>
+            <span class="k">return</span> <span class="n">ret</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">org_size</span><span class="p">])</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="WaveletOperator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.WaveletOperator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns the norm of WaveletOperator, which is equal to 1.0 if the wavelet is orthogonal</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        norm: float</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="o">.</span><span class="n">orthogonal</span><span class="p">:</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="n">LinearOperator</span><span class="o">.</span><span class="n">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">norm</span></div>
+
+
+<div class="viewcode-block" id="WaveletOperator.is_orthogonal">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.WaveletOperator.is_orthogonal">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_orthogonal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns if the operator is orthogonal</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        `Bool`</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wavelet</span><span class="o">.</span><span class="n">orthogonal</span></div>
+</div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.operators.ZeroOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageData</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">LinearOperator</span>
+
+<div class="viewcode-block" id="ZeroOperator">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ZeroOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">ZeroOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;ZeroOperator:  O: X -&gt; Y,  maps any element of :math:`x\in X` into the zero element :math:`\in Y,  O(x) = O_{Y}`</span>
+
+<span class="sd">        :param gm_domain: domain of the operator</span>
+<span class="sd">        :param gm_range: range of the operator, default: same as domain</span>
+
+<span class="sd">        Note:</span>
+
+<span class="sd">        .. math::</span>
+<span class="sd">                O^{*}: Y^{*} -&gt; X^{*} \text{(Adjoint)}</span>
+<span class="sd">                &lt; O(x), y &gt; = &lt; x, O^{*}(y) &gt;</span>
+<span class="sd">     &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">domain_geometry</span><span class="p">,</span> <span class="n">range_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">range_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">range_geometry</span> <span class="o">=</span> <span class="n">domain_geometry</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ZeroOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">domain_geometry</span><span class="p">,</span>
+                                           <span class="n">range_geometry</span><span class="o">=</span><span class="n">range_geometry</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ZeroOperator.direct">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ZeroOperator.direct">[docs]</a>
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns O(x)&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="ZeroOperator.adjoint">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ZeroOperator.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Returns O^{*}(y)&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+
+<div class="viewcode-block" id="ZeroOperator.calculate_norm">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.operators.ZeroOperator.calculate_norm">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Evaluates operator norm of ZeroOperator&#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="mi">0</span></div>
+</div>
+
+</pre></div>
+
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+  <h1>Source code for cil.optimisation.utilities.StepSizeMethods</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># - CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<div class="viewcode-block" id="StepSizeRule">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.StepSizeRule">[docs]</a>
+<span class="k">class</span> <span class="nc">StepSizeRule</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Abstract base class for a step size rule. The abstract method, `get_step_size` takes in an algorithm and thus can access all parts of the algorithm (e.g. current iterate, current gradient, objective functions etc) and from this  should return a float as a step size. </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Initialises the step size rule </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">pass</span>
+
+<div class="viewcode-block" id="StepSizeRule.get_step_size">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.StepSizeRule.get_step_size">[docs]</a>
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">get_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        the calculated step size:float </span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="ConstantStepSize">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.ConstantStepSize">[docs]</a>
+<span class="k">class</span> <span class="nc">ConstantStepSize</span><span class="p">(</span><span class="n">StepSizeRule</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Step-size rule that always returns a constant step-size. </span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    step_size: float</span>
+<span class="sd">        The step-size to be returned with each call. </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">step_size</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Initialises the constant step size rule</span>
+<span class="sd">        </span>
+<span class="sd">         Parameters:</span>
+<span class="sd">         -------------</span>
+<span class="sd">         step_size : float, the constant step size </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">step_size</span> <span class="o">=</span> <span class="n">step_size</span>
+
+<div class="viewcode-block" id="ConstantStepSize.get_step_size">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.ConstantStepSize.get_step_size">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        the calculated step size:float</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_size</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="ArmijoStepSizeRule">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule">[docs]</a>
+<span class="k">class</span> <span class="nc">ArmijoStepSizeRule</span><span class="p">(</span><span class="n">StepSizeRule</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Applies the Armijo rule to calculate the step size (step_size).</span>
+
+<span class="sd">    The Armijo rule runs a while loop to find the appropriate step_size by starting from a very large number (`alpha`). The step_size is found by reducing the step size (by a factor `beta`) in an iterative way until a certain criterion is met. To avoid infinite loops, we add a maximum number of times (`max_iterations`) the while loop is run.</span>
+
+<span class="sd">    Reference</span>
+<span class="sd">    ---------</span>
+<span class="sd">    - Algorithm 3.1 in Nocedal, J. and Wright, S.J. eds., 1999. Numerical optimization. New York, NY: Springer New York. https://www.math.uci.edu/~qnie/Publications/NumericalOptimization.pdf)</span>
+<span class="sd">    </span>
+<span class="sd">    - https://projecteuclid.org/download/pdf_1/euclid.pjm/1102995080</span>
+<span class="sd">    </span>
+<span class="sd">    </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    alpha: float, optional, default=1e6</span>
+<span class="sd">        The starting point for the step size iterations </span>
+<span class="sd">    beta: float between 0 and 1, optional, default=0.5</span>
+<span class="sd">        The amount the step_size is reduced if the criterion is not met</span>
+<span class="sd">    max_iterations: integer, optional, default is numpy.ceil (2 * numpy.log10(alpha) / numpy.log10(2))</span>
+<span class="sd">        The maximum number of iterations to find a suitable step size </span>
+<span class="sd">    warmstart: Boolean, default is True</span>
+<span class="sd">        If `warmstart = True` the initial step size at each Armijo iteration is the calculated step size from the last iteration. If `warmstart = False` at each  Armijo iteration, the initial step size is reset to the original, large `alpha`. </span>
+<span class="sd">        In the case of *well-behaved* convex functions, `warmstart = True` is likely to be computationally less expensive. In the case of non-convex functions, or particularly tricky functions, setting `warmstart = False` may be beneficial. </span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">max_iterations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">warmstart</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Initialises the step size rule </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_orig</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha_orig</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># Can be removed when alpha and beta are deprecated in GD</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha_orig</span> <span class="o">=</span> <span class="mf">1e6</span> 
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha_orig</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">beta</span> 
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>  <span class="c1"># Can be removed when alpha and beta are deprecated in GD</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span>
+            
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="n">max_iterations</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_orig</span><span class="p">)</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
+            
+        <span class="bp">self</span><span class="o">.</span><span class="n">warmstart</span><span class="o">=</span><span class="n">warmstart</span>
+
+<div class="viewcode-block" id="ArmijoStepSizeRule.get_step_size">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule.get_step_size">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Applies the Armijo rule to calculate the step size (`step_size`)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        the calculated step size:float</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">warmstart</span><span class="p">:</span>  
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha_orig</span>
+        
+        <span class="n">f_x</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">calculate_objective_function_at_point</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">solution</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_armijo</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">solution</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        
+        <span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Starting Armijo backtracking with initial step size: </span><span class="si">%f</span><span class="s2">&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+        
+        <span class="k">while</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span><span class="p">:</span>
+
+            <span class="n">algorithm</span><span class="o">.</span><span class="n">gradient_update</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_armijo</span><span class="p">)</span>
+            <span class="n">algorithm</span><span class="o">.</span><span class="n">solution</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_armijo</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">x_armijo</span><span class="p">)</span>
+
+            <span class="n">f_x_a</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">calculate_objective_function_at_point</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x_armijo</span><span class="p">)</span>
+            <span class="n">sqnorm</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">gradient_update</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">f_x_a</span> <span class="o">-</span> <span class="n">f_x</span> <span class="o">&lt;=</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">/</span><span class="mf">2.</span><span class="p">)</span> <span class="o">*</span> <span class="n">sqnorm</span><span class="p">:</span>
+                <span class="k">break</span>
+            <span class="n">k</span> <span class="o">+=</span> <span class="mf">1.</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span>
+        
+        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Armijo rule took </span><span class="si">%d</span><span class="s2"> iterations to find step size&quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Could not find a proper step_size in </span><span class="si">{}</span><span class="s1"> loops. Consider increasing alpha or max_iterations.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span><span class="p">))</span>
+        
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="BarzilaiBorweinStepSizeRule">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule">[docs]</a>
+<span class="k">class</span> <span class="nc">BarzilaiBorweinStepSizeRule</span><span class="p">(</span><span class="n">StepSizeRule</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Applies the Barzilai- Borwein rule to calculate the step size (step_size).</span>
+
+<span class="sd">    Let :math:`\Delta x=x_k-x_{k-1}` and :math:`\Delta g=g_k-g_{k-1}`. Where :math:`x_k` is the :math:`k` th iterate (current solution after iteration :math:`k` ) and :math:`g_k` is the gradient calculation in the :math:`k` th iterate, found in :code:`algorithm.gradient_update`.  A Barzilai-Borwein (BB) iteration is :math:`x_{k+1}=x_k-\alpha_kg_k` where the step size :math:`\alpha _k` is either</span>
+
+<span class="sd">    - :math:`\alpha_k^{LONG}=\frac{\Delta x\cdot\Delta x}{\Delta x\cdot\Delta g}`, or</span>
+
+<span class="sd">    - :math:`\alpha_k^{SHORT}=\frac{\Delta x \cdot\Delta g}{\Delta g \cdot\Delta g}`.</span>
+<span class="sd">    </span>
+<span class="sd">    Where the operator :math:`\cdot` is the standard inner product between two vectors. </span>
+<span class="sd">    </span>
+<span class="sd">    This is suitable for use with gradient based iterative methods where the calculated gradient is stored as `algorithm.gradient_update`.</span>
+<span class="sd">    </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    initial: float, greater than zero </span>
+<span class="sd">        The step-size for the first iteration. We recommend something of the order :math:`1/f.L` where :math:`f` is the (differentiable part of) the objective you wish to minimise.</span>
+<span class="sd">    mode: One of &#39;long&#39;, &#39;short&#39; or &#39;alternate&#39;, default is &#39;short&#39;. </span>
+<span class="sd">        This calculates the step-size based on the LONG, SHORT or alternating between the two, starting with short. </span>
+<span class="sd">    stabilisation_param: &#39;auto&#39;, float or &#39;off&#39;, default is &#39;auto&#39;</span>
+<span class="sd">        In order to add stability the step-size has an upper limit of :math:`\Delta/\|g_k\|` where by &#39;default&#39;, the `stabilisation_param`, :math:`\Delta` is  determined automatically to be the minimium of :math:`\Delta x` from the first 3 iterations. The user can also pass a fixed constant or turn &quot;off&quot; the stabilisation, equivalently passing `np.inf`.</span>
+<span class="sd">        </span>
+<span class="sd">    </span>
+<span class="sd">    Reference</span>
+<span class="sd">    ---------</span>
+<span class="sd">    - Barzilai, Jonathan; Borwein, Jonathan M. (1988). &quot;Two-Point Step Size Gradient Methods&quot;. IMA Journal of Numerical Analysis. 8: 141–148, https://doi.org/10.1093/imanum/8.1.141</span>
+<span class="sd">    </span>
+<span class="sd">    - Burdakov, O., Dai, Y. and Huang, N., 2019. STABILIZED BARZILAI-BORWEIN METHOD. Journal of Computational Mathematics, 37(6). https://doi.org/10.4208/jcm.1911-m2019-0171</span>
+
+<span class="sd">    - https://en.wikipedia.org/wiki/Barzilai-Borwein_method</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;short&#39;</span><span class="p">,</span> <span class="n">stabilisation_param</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Initialises the step size rule </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+ 
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">=</span><span class="n">mode</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;short&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">is_short</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;long&#39;</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;alternate&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">is_short</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Mode should be chosen from &quot;long&quot;, &quot;short&quot; or &quot;alternate&quot;. &#39;</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="o">=</span><span class="kc">None</span> 
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="o">=</span><span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initial</span><span class="o">=</span><span class="n">initial</span>
+        <span class="k">if</span> <span class="n">stabilisation_param</span> <span class="o">==</span> <span class="s1">&#39;auto&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">adaptive</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">stabilisation_param</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">inf</span>
+        <span class="k">elif</span> <span class="n">stabilisation_param</span> <span class="o">==</span> <span class="s2">&quot;off&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">adaptive</span> <span class="o">=</span> <span class="kc">False</span> 
+            <span class="n">stabilisation_param</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">inf</span>
+        <span class="k">elif</span> <span class="p">(</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">stabilisation_param</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span> <span class="ow">and</span> <span class="n">stabilisation_param</span> <span class="o">&gt;=</span><span class="mi">0</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">adaptive</span> <span class="o">=</span> <span class="kc">False</span> 
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot; The stabilisation_param should be &#39;auto&#39;, a positive number or &#39;off&#39;&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">stabilisation_param</span><span class="o">=</span><span class="n">stabilisation_param</span>
+        
+    
+
+<div class="viewcode-block" id="BarzilaiBorweinStepSizeRule.get_step_size">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule.get_step_size">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_step_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Applies the B-B rule to calculate the step size (`step_size`)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        the calculated step size:float</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#For the first iteration we use an initial step size because the BB step size requires a previous iterate. </span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_x</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="o">=</span><span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="c1"># We store the last iterate in order to calculate the BB step size </span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="o">=</span><span class="n">algorithm</span><span class="o">.</span><span class="n">gradient_update</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span><span class="c1"># We store the last gradient in order to calculate the BB step size </span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">initial</span>
+        
+        <span class="n">gradient_norm</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">gradient_update</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+        <span class="c1">#If the gradient is zero, gradient based algorithms will not update and te step size calculation will divide by zero so we stop iterations. </span>
+        <span class="k">if</span> <span class="n">gradient_norm</span> <span class="o">&lt;</span> <span class="mf">1e-8</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+
+        <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="p">)</span> 
+        <span class="n">algorithm</span><span class="o">.</span><span class="n">gradient_update</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_short</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="p">))</span><span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="p">))</span><span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="p">))</span>
+
+        
+        <span class="c1">#This computes the default stabilisation parameter, using the first three iterations</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">iteration</span> <span class="o">&lt;=</span><span class="mi">3</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">adaptive</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">stabilisation_param</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stabilisation_param</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span> <span class="p">)</span>
+        
+        <span class="c1"># Computes the step size as the minimum of the ret, above, and :math:`\Delta/\|g_k\|` ignoring any NaN values. </span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">ret</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stabilisation_param</span><span class="o">/</span><span class="n">gradient_norm</span><span class="p">]))</span>
+        
+        <span class="c1"># We store the last iterate and gradient in order to calculate the BB step size </span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_x</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_grad</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">gradient_update</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;alternate&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">is_short</span> <span class="o">=</span>  <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_short</span>       
+        
+        <span class="k">return</span> <span class="n">ret</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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+  <h1>Source code for cil.optimisation.utilities.callbacks</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partialmethod</span>
+
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span> <span class="k">as</span> <span class="n">tqdm_auto</span>
+<span class="kn">from</span> <span class="nn">tqdm.std</span> <span class="kn">import</span> <span class="n">tqdm</span> <span class="k">as</span> <span class="n">tqdm_std</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="Callback">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.callbacks.Callback">[docs]</a>
+<span class="k">class</span> <span class="nc">Callback</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Base Callback to inherit from for use in :code:`Algorithm.run(callbacks: list[Callback])`.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    verbose: int, choice of 0,1,2, default 1</span>
+<span class="sd">        0=quiet, 1=info, 2=debug.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
+
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">_OldCallback</span><span class="p">(</span><span class="n">Callback</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Converts an old-style :code:`def callback` to a new-style :code:`class Callback`.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    callback: :code:`callable(iteration, objective, x)`</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">callback</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">update_objective_interval</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">iteration</span> <span class="o">%</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">update_objective_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">iteration</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">get_last_objective</span><span class="p">(</span><span class="n">return_all</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="o">&gt;=</span><span class="mi">2</span><span class="p">),</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="ProgressCallback">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.callbacks.ProgressCallback">[docs]</a>
+<span class="k">class</span> <span class="nc">ProgressCallback</span><span class="p">(</span><span class="n">Callback</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;:code:`tqdm`-based progress bar.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    tqdm_class: default :code:`tqdm.auto.tqdm`</span>
+<span class="sd">    **tqdm_kwargs:</span>
+<span class="sd">        Passed to :code:`tqdm_class`.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">tqdm_class</span><span class="o">=</span><span class="n">tqdm_auto</span><span class="p">,</span> <span class="o">**</span><span class="n">tqdm_kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tqdm_class</span> <span class="o">=</span> <span class="n">tqdm_class</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tqdm_kwargs</span> <span class="o">=</span> <span class="n">tqdm_kwargs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_obj_len</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># number of objective updates</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;pbar&#39;</span><span class="p">):</span>
+            <span class="n">tqdm_kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tqdm_kwargs</span>
+            <span class="n">tqdm_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s1">&#39;total&#39;</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">max_iteration</span><span class="p">)</span>
+            <span class="n">tqdm_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s1">&#39;disable&#39;</span><span class="p">,</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">)</span>
+            <span class="n">tqdm_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s1">&#39;initial&#39;</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">iteration</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">pbar</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tqdm_class</span><span class="p">(</span><span class="o">**</span><span class="n">tqdm_kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">obj_len</span> <span class="o">:=</span> <span class="nb">len</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">objective</span><span class="p">))</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_obj_len</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">pbar</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">objective_to_dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="o">&gt;=</span><span class="mi">2</span><span class="p">),</span> <span class="n">refresh</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_obj_len</span> <span class="o">=</span> <span class="n">obj_len</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">iteration</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">pbar</span><span class="o">.</span><span class="n">n</span><span class="p">)</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">_TqdmText</span><span class="p">(</span><span class="n">tqdm_std</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;:code:`tqdm`-based progress but text-only updates on separate lines.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    num_format: str</span>
+<span class="sd">        Format spec for postfix numbers (i.e. objective values).</span>
+<span class="sd">    bar_format: str</span>
+<span class="sd">        Passed to :code:`tqdm`.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">num_format</span><span class="o">=</span><span class="s1">&#39;+8.3e&#39;</span><span class="p">,</span> <span class="n">bar_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">{n:&gt;6d}</span><span class="s2">/</span><span class="si">{total_fmt:&lt;6}</span><span class="s2"> </span><span class="si">{rate_fmt:&gt;9}{postfix}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_format</span> <span class="o">=</span> <span class="n">num_format</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">bar_format</span><span class="o">=</span><span class="n">bar_format</span><span class="p">,</span> <span class="n">mininterval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">maxinterval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_instances</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>  <span class="c1"># don&#39;t interfere with external progress bars</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">status_printer</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+        <span class="n">fp_flush</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;flush&#39;</span><span class="p">,</span> <span class="k">lambda</span><span class="p">:</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">def</span> <span class="nf">fp_write</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">s</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="n">fp_flush</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="n">fp_write</span>
+
+    <span class="k">def</span> <span class="nf">format_num</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">n</span><span class="si">:{</span><span class="bp">self</span><span class="o">.</span><span class="n">num_format</span><span class="si">}}</span><span class="s1">&#39;</span>
+
+    <span class="k">def</span> <span class="nf">display</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Clears :code:`postfix` if :code:`super().display()` succeeds</span>
+<span class="sd">        (if display updates are more frequent than objective updates, users should not think the objective has stabilised).</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">updated</span> <span class="o">:=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">display</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_postfix_str</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">refresh</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">updated</span>
+
+
+<div class="viewcode-block" id="TextProgressCallback">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.callbacks.TextProgressCallback">[docs]</a>
+<span class="k">class</span> <span class="nc">TextProgressCallback</span><span class="p">(</span><span class="n">ProgressCallback</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;:code:`ProgressCallback` but printed on separate lines to screen.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    miniters: int, default :code:`Algorithm.update_objective_interval`</span>
+<span class="sd">        Number of algorithm iterations between screen prints.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="fm">__init__</span> <span class="o">=</span> <span class="n">partialmethod</span><span class="p">(</span><span class="n">ProgressCallback</span><span class="o">.</span><span class="fm">__init__</span><span class="p">,</span> <span class="n">tqdm_class</span><span class="o">=</span><span class="n">_TqdmText</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;pbar&#39;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tqdm_kwargs</span><span class="p">[</span><span class="s1">&#39;miniters&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">((</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">tqdm_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;miniters&#39;</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">update_objective_interval</span><span class="p">),</span>
+                <span class="n">algorithm</span><span class="o">.</span><span class="n">update_objective_interval</span><span class="p">))</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">algorithm</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="LogfileCallback">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.callbacks.LogfileCallback">[docs]</a>
+<span class="k">class</span> <span class="nc">LogfileCallback</span><span class="p">(</span><span class="n">TextProgressCallback</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;:code:`TextProgressCallback` but to a file instead of screen.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    log_file: FileDescriptorOrPath</span>
+<span class="sd">        Passed to :code:`open()`.</span>
+<span class="sd">    mode: str</span>
+<span class="sd">        Passed to :code:`open()`.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">log_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fd</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">log_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">file</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fd</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+        
+<span class="k">class</span> <span class="nc">EarlyStoppingObjectiveValue</span><span class="p">(</span><span class="n">Callback</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Callback that stops iterations if the change in the objective value is less than a provided threshold value.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    threshold: float, default 1e-6 </span>
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    This callback only compares the last two calculated objective values. If `update_objective_interval` is greater than 1, the objective value is not calculated at each iteration (which is the default behaviour), only every `update_objective_interval` iterations.</span>
+<span class="sd">    </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span>
+    
+    
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">loss</span><span class="p">)</span><span class="o">&gt;=</span><span class="mi">2</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">loss</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">algorithm</span><span class="o">.</span><span class="n">loss</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">&lt;</span><span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">StopIteration</span>
+                
+<span class="k">class</span> <span class="nc">CGLSEarlyStopping</span><span class="p">(</span><span class="n">Callback</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Callback to work with CGLS. It causes the algorithm to terminate if  :math:`||A^T(Ax-b)||_2 &lt; \epsilon||A^T(Ax_0-b)||_2` where `epsilon` is set to default as &#39;1e-6&#39;, :math:`x` is the current iterate and :math:`x_0` is the initial value. </span>
+<span class="sd">    It will also terminate if the algorithm begins to diverge i.e. if :math:`||x||_2&gt; \omega`, where `omega` is set to default as 1e6. </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    epsilon: float, default 1e-6 </span>
+<span class="sd">        Usually a small number: the algorithm to terminate if :math:`||A^T(Ax-b)||_2 &lt; \epsilon||A^T(Ax_0-b)||_2`</span>
+<span class="sd">    omega: float, default 1e6 </span>
+<span class="sd">        Usually a large number: the algorithm will terminate if  :math:`||x||_2&gt; \omega`</span>
+<span class="sd">        </span>
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    This callback is implemented to replicate the automatic behaviour of CGLS in CIL versions &lt;=24. It also replicates the behaviour of https://web.stanford.edu/group/SOL/software/cgls/. </span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">omega</span><span class="o">=</span><span class="mf">1e6</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">omega</span><span class="o">=</span><span class="n">omega</span>
+    
+    
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        
+        <span class="k">if</span> <span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">norms</span> <span class="o">&lt;=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">norms0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The norm of the residual is less than </span><span class="si">{}</span><span class="s1"> times the norm of the initial residual and so the algorithm is terminated&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">))</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">normx</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">normx</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">omega</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The norm of the solution is greater than </span><span class="si">{}</span><span class="s1"> and so the algorithm is terminated&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">omega</span><span class="p">))</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+            
+        
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/optimisation/utilities/preconditioner/index.html b/v24.2.0/_modules/cil/optimisation/utilities/preconditioner/index.html
new file mode 100644
index 0000000000..5d7f2af118
--- /dev/null
+++ b/v24.2.0/_modules/cil/optimisation/utilities/preconditioner/index.html
@@ -0,0 +1,749 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>cil.optimisation.utilities.preconditioner &#8212; CIL 24.2.0 documentation</title>
+  
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+  <h1>Source code for cil.optimisation.utilities.preconditioner</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># - CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="Preconditioner">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.preconditioner.Preconditioner">[docs]</a>
+<span class="k">class</span> <span class="nc">Preconditioner</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Abstract base class for Preconditioner objects. The `apply` method of this class takes an initialised CIL function as an argument and modifies a provided  `gradient`.</span>
+
+
+<span class="sd">    Methods</span>
+<span class="sd">    -------</span>
+<span class="sd">    apply(x)</span>
+<span class="sd">        Abstract method to call the preconditioner.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Initialises the preconditioner</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">pass</span>
+
+<div class="viewcode-block" id="Preconditioner.apply">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.preconditioner.Preconditioner.apply">[docs]</a>
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">,</span> <span class="n">gradient</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Abstract method to apply the preconditioner.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        algorithm : Algorithm</span>
+<span class="sd">            The algorithm object.</span>
+<span class="sd">        gradient : DataContainer</span>
+<span class="sd">            The calculated gradient to modify</span>
+<span class="sd">        out : DataContainer, </span>
+<span class="sd">            Container to fill with the modified gradient. </span>
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        In CIL algorithms, the preconditioners are used in-place. Make sure this method is safe to use in place. </span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The modified gradient</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Sensitivity">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.preconditioner.Sensitivity">[docs]</a>
+<span class="k">class</span> <span class="nc">Sensitivity</span><span class="p">(</span><span class="n">Preconditioner</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Sensitivity preconditioner class. </span>
+
+<span class="sd">    In each call to the preconditioner the `gradient` is multiplied by :math:`1/(A^T \mathbf{1})` where :math:`A` is an operator, :math:`\mathbf{1}` is an object in the range of the operator filled with ones.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    operator : CIL Operator </span>
+<span class="sd">        The operator used for sensitivity computation.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operator</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">Sensitivity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">compute_preconditioner_matrix</span><span class="p">()</span>
+
+<div class="viewcode-block" id="Sensitivity.compute_preconditioner_matrix">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.preconditioner.Sensitivity.compute_preconditioner_matrix">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_preconditioner_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Compute the sensitivity. :math:`A^T \mathbf{1}` where :math:`A` is the operator and :math:`\mathbf{1}` is an object in the range of the operator filled with ones.        </span>
+<span class="sd">        Then perform safe division by the sensitivity to store the preconditioner array :math:`1/(A^T \mathbf{1})`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mf">1.0</span><span class="p">))</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>  <span class="c1"># Due to CIL/SIRF compatibility and SIRF divide not taking kwargs</span>
+            <span class="n">sensitivity_np</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">pos_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">sensitivity_np</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
+            <span class="n">array_np</span> <span class="o">=</span> <span class="mi">0</span><span class="o">*</span><span class="n">sensitivity_np</span>
+            <span class="n">array_np</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pos_ind</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="n">sensitivity_np</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pos_ind</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">array_np</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Sensitivity.apply">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.preconditioner.Sensitivity.apply">[docs]</a>
+    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">,</span> <span class="n">gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Update the preconditioner.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        algorithm : object</span>
+<span class="sd">            The algorithm object.</span>
+<span class="sd">        gradient : DataContainer</span>
+<span class="sd">            The calculated gradient to modify</span>
+<span class="sd">        out : DataContainer, </span>
+<span class="sd">            Container to fill with the modified gradient </span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The modified gradient</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">gradient</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">gradient</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="AdaptiveSensitivity">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.preconditioner.AdaptiveSensitivity">[docs]</a>
+<span class="k">class</span> <span class="nc">AdaptiveSensitivity</span><span class="p">(</span><span class="n">Sensitivity</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adaptive Sensitivity preconditioner class. </span>
+
+<span class="sd">    In each call to the preconditioner the `gradient` is multiplied by :math:`(x+\delta) /(A^T \mathbf{1})` where :math:`A` is an operator,  :math:`\mathbf{1}` is an object in the range of the operator filled with ones.</span>
+<span class="sd">    The point :math:`x` is the current iteration, or a reference image,  and :math:`\delta` is a small positive float. </span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    operator : CIL object</span>
+<span class="sd">        The operator used for sensitivity computation.</span>
+<span class="sd">    delta : float, optional</span>
+<span class="sd">        The delta value for the preconditioner.</span>
+<span class="sd">    reference : DataContainer e.g. ImageData, default is None</span>
+<span class="sd">        Reference data, an object in the domain of the operator. Recommended to be a best guess reconstruction. If reference data is passed the preconditioner is always fixed.   </span>
+<span class="sd">    max_iterations : int,  default = 100</span>
+<span class="sd">        The maximum number of iterations before the preconditoner is frozen and no-longer updates. Note that if reference data is passed the preconditioner is always frozen and `iterations` is set to -1. </span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    A reference for the freezing of the preconditioner can be found: Twyman R., Arridge S., Kereta Z., Jin B., Brusaferri L., Ahn S., Stearns CW., Hutton B.F., Burger I.A., Kotasidis F., Thielemans K.. An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction. IEEE Trans Med Imaging. 2023 Jan;42(1):29-41. doi: 10.1109/TMI.2022.3203237. Epub 2022 Dec 29. PMID: 36044488.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">operator</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">max_iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">reference</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="n">max_iterations</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">delta</span> <span class="o">=</span> <span class="n">delta</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">AdaptiveSensitivity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">operator</span><span class="o">=</span><span class="n">operator</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">reference</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">reference</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">reference</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="p">)</span>
+            <span class="n">reference</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+<div class="viewcode-block" id="AdaptiveSensitivity.apply">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.preconditioner.AdaptiveSensitivity.apply">[docs]</a>
+    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">,</span> <span class="n">gradient</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Update the preconditioner.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        algorithm : object</span>
+<span class="sd">            The algorithm object.</span>
+<span class="sd">        gradient : DataContainer</span>
+<span class="sd">            The calculated gradient to modify</span>
+<span class="sd">        out : DataContainer, </span>
+<span class="sd">            Container to fill with the modified gradient </span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The modified gradient</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">gradient</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">iteration</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iterations</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">solution</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">delta</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="p">,</span>
+                                <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="p">)</span>
+            <span class="n">gradient</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">gradient</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">freezing_point</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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+  <h1>Source code for cil.optimisation.utilities.sampler</h1><div class="highlight"><pre>
+<span></span><span class="c1">#   This work is part of the Core Imaging Library (CIL) developed by CCPi</span>
+<span class="c1">#   (Collaborative Computational Project in Tomographic Imaging), with</span>
+<span class="c1">#   substantial contributions by UKRI-STFC and University of Manchester.</span>
+
+<span class="c1">#   Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#   you may not use this file except in compliance with the License.</span>
+<span class="c1">#   You may obtain a copy of the License at</span>
+
+<span class="c1">#   http://www.apache.org/licenses/LICENSE-2.0</span>
+
+<span class="c1">#   Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#   distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#   See the License for the specific language governing permissions and</span>
+<span class="c1">#   limitations under the License.</span>
+
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numbers</span>
+
+<div class="viewcode-block" id="Sampler">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler">[docs]</a>
+<span class="k">class</span> <span class="nc">Sampler</span><span class="p">():</span>
+    <span class="c1"># TODO: Work out how to make the examples testable</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Initialises a sampler that returns and then increments indices from a sequence defined by a function.</span>
+
+<span class="sd">    Static methods to easily configure several samplers are provided, such as sequential, staggered, Herman-Mayer, random with and without replacement.</span>
+
+
+
+
+<span class="sd">    Custom deterministic samplers can be created by using the `from_function` static method or by subclassing this sampler class.</span>
+
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    function : Callable[[int], int]</span>
+<span class="sd">        A function that takes an integer iteration number and returns an integer between 0 and num_indices.</span>
+
+<span class="sd">    num_indices: int</span>
+<span class="sd">        The sampler will select from a range of indices 0 to num_indices.</span>
+
+<span class="sd">    sampling_type:str, optional,  default = None</span>
+<span class="sd">        The sampling type used. This is recorded for reference and printed when `print` is called.</span>
+
+<span class="sd">    prob_weights: list of floats of length num_indices that sum to 1.  Default is [1 / num_indices] * num_indices</span>
+<span class="sd">        Consider that the sampler is incremented a large number of times this argument holds the expected number of times each index would be outputted,  normalised to 1.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Sampler</span>
+<span class="sd">        An instance of the Sampler class representing the desired configuration.</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; sampler = Sampler.random_with_replacement(5)</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.get_samples(20))</span>
+<span class="sd">    [3 4 0 0 2 3 3 2 2 1 1 4 4 3 0 2 4 4 2 4]</span>
+<span class="sd">    &gt;&gt;&gt; print(next(sampler))</span>
+<span class="sd">    3</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.next())</span>
+<span class="sd">    4</span>
+
+
+<span class="sd">    &gt;&gt;&gt; sampler = Sampler.staggered(num_indices=21, stride=4)</span>
+<span class="sd">    &gt;&gt;&gt; print(next(sampler))</span>
+<span class="sd">    0</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.next())</span>
+<span class="sd">    4</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.get_samples(5))</span>
+<span class="sd">    [ 0  4  8 12 16]</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; sampler = Sampler.sequential(10)</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.get_samples(5))</span>
+<span class="sd">    &gt;&gt;&gt; print(next(sampler))</span>
+<span class="sd">    0</span>
+<span class="sd">    [0 1 2 3 4]</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.next())</span>
+<span class="sd">    1</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; sampler = Sampler.herman_meyer(12)</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.get_samples(16))</span>
+<span class="sd">    [ 0  6  3  9  1  7  4 10  2  8  5 11  0  6  3  9]</span>
+
+
+
+<span class="sd">    Example</span>
+<span class="sd">    --------</span>
+<span class="sd">    This example creates a sampler that outputs sequential indices, starting from 1.</span>
+
+<span class="sd">    &gt;&gt;&gt; num_indices=10</span>
+<span class="sd">    &gt;&gt;&gt;</span>
+<span class="sd">    &gt;&gt;&gt; def my_sampling_function(iteration_number):</span>
+<span class="sd">    &gt;&gt;&gt;     return (iteration_number+1)%10</span>
+<span class="sd">    &gt;&gt;&gt;</span>
+<span class="sd">    &gt;&gt;&gt; sampler = Sampler.from_function(num_indices=num_indices, function=my_sampling_function)</span>
+<span class="sd">    &gt;&gt;&gt; print(list(sampler.get_samples(25)))</span>
+<span class="sd">    [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5]</span>
+
+
+
+<span class="sd">    Note</span>
+<span class="sd">    -----</span>
+<span class="sd">    The optimal choice of sampler depends on the data and the number of calls to the sampler.  Note that a low number of calls to a random sampler won&#39;t give an even distribution.</span>
+<span class="sd">    For a small number of samples (e.g. `&lt;5*num_indices`) the user may wish to consider another sampling method e.g. random without replacement, which, when calling `num_indices` samples is guaranteed to draw each index exactly once.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span>  <span class="n">sampling_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prob_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_type</span> <span class="o">=</span> <span class="n">sampling_type</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_num_indices</span> <span class="o">=</span> <span class="n">num_indices</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`num_indices` should be an integer. &#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">callable</span><span class="p">(</span><span class="n">function</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_function</span> <span class="o">=</span> <span class="n">function</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`function` should be an callable function. &#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">prob_weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">prob_weights</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="o">/</span> <span class="n">num_indices</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_indices</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">prob_weights</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">1e-6</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The provided prob_weights must sum to one&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">prob_weights</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s1">&#39;The provided prob_weights must be greater than or equal to zero&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_prob_weights</span> <span class="o">=</span> <span class="n">prob_weights</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">prob_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prob_weights</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">num_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_indices</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">current_iter_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span>
+
+<div class="viewcode-block" id="Sampler.next">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.next">[docs]</a>
+    <span class="k">def</span> <span class="nf">next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a sample from the list of indices `{0, 1, …, N-1}, where N is the number of indices and increments the sampler.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
+
+<div class="viewcode-block" id="Sampler.get_samples">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.get_samples">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_samples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>  <span class="n">num_samples</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Generates a list of the first num_samples output by the sampler. Calling this does not increment the sampler index or affect the behaviour of the sampler .</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_samples: int</span>
+<span class="sd">            The number of samples to return.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        --------</span>
+<span class="sd">        List</span>
+<span class="sd">            The first `num_samples&quot; output by the sampler.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">save_last_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">next</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_samples</span><span class="p">)]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span> <span class="o">=</span> <span class="n">save_last_index</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">output</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;Sampler that selects from a list of indices {0, 1, …, N-1}, where N is the number of indices. </span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Type : </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_type</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Current iteration number : </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Number of indices : </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_indices</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Probability weights : </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_prob_weights</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">repres</span>
+
+<div class="viewcode-block" id="Sampler.sequential">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.sequential">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">sequential</span><span class="p">(</span><span class="n">num_indices</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Instantiates a sampler that outputs sequential indices.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The sampler will select from a range of indices 0 to num_indices.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Sampler</span>
+<span class="sd">            An instance of the Sampler class that will generate indices sequentially.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.sequential(10)</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.get_samples(5))</span>
+<span class="sd">        &gt;&gt;&gt; print(next(sampler))</span>
+<span class="sd">        0</span>
+<span class="sd">        [0 1 2 3 4]</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.next())</span>
+<span class="sd">        1</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">def</span> <span class="nf">function</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">x</span> <span class="o">%</span> <span class="n">num_indices</span>
+
+        <span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">function</span><span class="p">,</span> <span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span>
+                          <span class="n">sampling_type</span><span class="o">=</span><span class="s1">&#39;sequential&#39;</span>
+                          <span class="p">)</span>
+        <span class="k">return</span> <span class="n">sampler</span></div>
+
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_staggered_function</span><span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">iter_number</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Function that takes in an iteration number and outputs an index number based on the staggered ordering.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The sampler will select from a range of indices 0 to num_indices.</span>
+
+<span class="sd">        stride: int</span>
+<span class="sd">            The stride between returned indices. The stride should be less than the num_indices.</span>
+
+<span class="sd">        iter_number: int</span>
+<span class="sd">            The current iteration number of the sampler.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        int</span>
+<span class="sd">            The index to be outputted by the sampler corresponding to the `iter_number`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`num_indices` should be an integer. &#39;</span><span class="p">)</span>
+
+        <span class="n">iter_number_mod</span> <span class="o">=</span> <span class="n">iter_number</span> <span class="o">%</span> <span class="n">num_indices</span>
+        <span class="n">floor</span> <span class="o">=</span> <span class="n">num_indices</span> <span class="o">//</span> <span class="n">stride</span>
+        <span class="n">mod</span> <span class="o">=</span> <span class="n">num_indices</span> <span class="o">%</span> <span class="n">stride</span>
+
+        <span class="k">if</span> <span class="n">iter_number_mod</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">floor</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">mod</span><span class="p">:</span>
+            <span class="n">row_number</span> <span class="o">=</span> <span class="n">iter_number_mod</span> <span class="o">//</span> <span class="p">(</span><span class="n">floor</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">column_number</span> <span class="o">=</span> <span class="p">(</span><span class="n">iter_number_mod</span> <span class="o">%</span> <span class="p">(</span><span class="n">floor</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">row_number</span> <span class="o">=</span> <span class="n">mod</span> <span class="o">+</span> <span class="p">(</span><span class="n">iter_number_mod</span> <span class="o">-</span> <span class="p">(</span><span class="n">floor</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">mod</span><span class="p">)</span> <span class="o">//</span> <span class="n">floor</span>
+            <span class="n">column_number</span> <span class="o">=</span> <span class="p">(</span><span class="n">iter_number_mod</span> <span class="o">-</span> <span class="p">(</span><span class="n">floor</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">mod</span><span class="p">)</span> <span class="o">%</span> <span class="n">floor</span>
+
+        <span class="k">return</span> <span class="n">row_number</span> <span class="o">+</span> <span class="n">stride</span><span class="o">*</span><span class="n">column_number</span>
+
+<div class="viewcode-block" id="Sampler.staggered">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.staggered">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">staggered</span><span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">stride</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Instantiates a sampler which outputs in a staggered order.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The sampler will select from a range of indices 0 to num_indices.</span>
+
+<span class="sd">        stride: int</span>
+<span class="sd">            The stride between returned indices. The stride should be less than the num_indices.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Sampler</span>
+<span class="sd">            An instance of the Sampler class that will generate indices in a staggered pattern.</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.staggered(num_indices=21, stride=4)</span>
+<span class="sd">        &gt;&gt;&gt; print(next(sampler))</span>
+<span class="sd">        0</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.next())</span>
+<span class="sd">        4</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.get_samples(5))</span>
+<span class="sd">        [ 0  4  8 12 16]</span>
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.staggered(num_indices=17, stride=8)</span>
+<span class="sd">        &gt;&gt;&gt; print(next(sampler))</span>
+<span class="sd">        0</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.next())</span>
+<span class="sd">        8</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.get_samples(10))</span>
+<span class="sd">        [ 0  8  16 1 9 2 10 3 11 4]</span>
+
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">stride</span> <span class="o">&gt;=</span> <span class="n">num_indices</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="p">(</span><span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The stride should be less than the number of indices&#39;</span><span class="p">))</span>
+
+        <span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="n">Sampler</span><span class="o">.</span><span class="n">_staggered_function</span><span class="p">,</span> <span class="n">num_indices</span><span class="p">,</span> <span class="n">stride</span><span class="p">),</span>
+                          <span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">sampling_type</span><span class="o">=</span><span class="s1">&#39;staggered&#39;</span>
+                          <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">sampler</span></div>
+
+
+<div class="viewcode-block" id="Sampler.random_with_replacement">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.random_with_replacement">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">random_with_replacement</span><span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Instantiates a sampler which outputs an index between 0 - num_indices with a given probability.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The sampler will select from a range of indices 0 to num_indices</span>
+
+<span class="sd">        prob: list of floats, optional</span>
+<span class="sd">            The probability for each index to be selected by the &#39;next&#39; operation. If not provided, the indices will be sampled uniformly. The list should have a length equal to num_indices, and the values should sum to 1</span>
+
+<span class="sd">        seed:int, optional</span>
+<span class="sd">            Used to initialise the random number generator where repeatability is required.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        `RandomSampler`</span>
+<span class="sd">            An instance of the `RandomSampler` class that will generate indices randomly with replacement</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.random_with_replacement(5)</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.get_samples(10))</span>
+<span class="sd">        [3 4 0 0 2 3 3 2 2 1]</span>
+<span class="sd">        &gt;&gt;&gt; print(next(sampler))</span>
+<span class="sd">        3</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.next())</span>
+<span class="sd">        4</span>
+
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.random_with_replacement(num_indices=4, prob=[0.7,0.1,0.1,0.1])</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.get_samples(10))</span>
+<span class="sd">        [0 1 3 0 0 3 0 0 0 0]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">sampler</span> <span class="o">=</span> <span class="n">SamplerRandom</span><span class="p">(</span>
+            <span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span>
+            <span class="n">sampling_type</span><span class="o">=</span><span class="s1">&#39;random_with_replacement&#39;</span><span class="p">,</span>
+            <span class="n">prob</span><span class="o">=</span><span class="n">prob</span><span class="p">,</span>
+            <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">sampler</span></div>
+
+
+<div class="viewcode-block" id="Sampler.random_without_replacement">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.random_without_replacement">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">random_without_replacement</span><span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Instantiates a sampler which outputs an index between 0 - num_indices. Once sampled the index will not be sampled again until all indices have been returned.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The sampler will select from a range of indices 0 to num_indices.</span>
+
+<span class="sd">        seed: int, optional</span>
+<span class="sd">            Used to initialise the random number generator where repeatability is required.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        `RandomSampler`</span>
+<span class="sd">            An instance of the `RandomSampler` class that will generate indices randomly without replacement</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; sampler=Sampler.randomWithoutReplacement(num_indices=7, seed=1)</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.get_samples(16))</span>
+<span class="sd">        [6 2 1 0 4 3 5 1 0 4 2 5 6 3 3 2]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">sampler</span> <span class="o">=</span> <span class="n">SamplerRandom</span><span class="p">(</span>
+            <span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span>
+            <span class="n">sampling_type</span><span class="o">=</span><span class="s1">&#39;random_without_replacement&#39;</span><span class="p">,</span>
+            <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">sampler</span></div>
+
+
+<div class="viewcode-block" id="Sampler.from_function">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.from_function">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span> <span class="n">prob_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Instantiate a sampler that wraps a function for index selection.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The sampler will select from a range of indices 0 to num_indices.</span>
+
+<span class="sd">    function : callable</span>
+<span class="sd">        A deterministic function that takes an integer as an argument, representing the iteration number, and returns an integer between 0 and num_indices. The function signature should be function(iteration_number: int) -&gt; int</span>
+
+<span class="sd">        prob_weights: list of floats of length num_indices that sum to 1. Default is [1 / num_indices] * num_indices</span>
+<span class="sd">            Consider that the sampler is incremented a large number of times this argument holds the expected number of times each index would be outputted,  normalised to 1.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Sampler</span>
+<span class="sd">            An instance of the Sampler class which samples from a function.</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        --------</span>
+<span class="sd">        This example creates a sampler that always outputs 2.  The probability weights are passed to the sampler as they are not uniform.</span>
+
+<span class="sd">        &gt;&gt;&gt; num_indices=3</span>
+<span class="sd">        &gt;&gt;&gt;</span>
+<span class="sd">        &gt;&gt;&gt; def my_sampling_function(iteration_number):</span>
+<span class="sd">        &gt;&gt;&gt;     return 2</span>
+<span class="sd">        &gt;&gt;&gt;</span>
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.from_function(num_indices=num_indices, function=my_sampling_function, prob_weights=[0, 0, 1])</span>
+<span class="sd">        &gt;&gt;&gt; print(list(sampler.get_samples(12)))</span>
+<span class="sd">        [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        --------</span>
+<span class="sd">        This example creates a sampler that outputs sequential indices, starting from 1.  The probability weights are not passed to the sampler as they are uniform.</span>
+
+<span class="sd">        &gt;&gt;&gt; num_indices=10</span>
+<span class="sd">        &gt;&gt;&gt;</span>
+<span class="sd">        &gt;&gt;&gt; def my_sampling_function(iteration_number):</span>
+<span class="sd">        &gt;&gt;&gt;     return (iteration_number+1)%10</span>
+<span class="sd">        &gt;&gt;&gt;</span>
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.from_function(num_indices=num_indices, function=my_sampling_function)</span>
+<span class="sd">        &gt;&gt;&gt; print(list(sampler.get_samples(25)))</span>
+<span class="sd">        [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5]</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        This example creates a sampler that samples in order from a custom list. The num_indices  is 6, although note that the index 5 is never output by the sampler. The number of indices must be at least one greater than any of the elements in the custom_list.</span>
+<span class="sd">        The probability weights are passed to the sampler as they are not uniform.</span>
+
+<span class="sd">        &gt;&gt;&gt; custom_list = [0,0,0,0,0,0,3,2,1,4]</span>
+<span class="sd">        &gt;&gt;&gt; num_indices = 6</span>
+<span class="sd">        &gt;&gt;&gt;</span>
+<span class="sd">        &gt;&gt;&gt; def my_sampling_function(iteration_number, custom_list=custom_list]):</span>
+<span class="sd">        &gt;&gt;&gt;    return(custom_list[iteration_number%len(custom_list)])</span>
+<span class="sd">        &gt;&gt;&gt;</span>
+<span class="sd">        &gt;&gt;&gt; sampler = Sampler.from_function(num_indices=num_indices, function=my_sampling_function, prob_weights=[0.6, 0.1, 0.1, 0.1, 0.1, 0.0])</span>
+<span class="sd">        &gt;&gt;&gt; print(list(sampler.get_samples(25)))</span>
+<span class="sd">        [0, 0, 0, 0, 0, 0, 3, 2, 1, 4, 0, 0, 0, 0, 0, 0, 3, 2, 1, 4, 0, 0, 0, 0, 0]</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler)</span>
+<span class="sd">        Sampler that wraps a function that takes an iteration number and selects from a list of indices {0, 1, …, N-1}, where N is the number of indices.</span>
+<span class="sd">        Type : from_function</span>
+<span class="sd">        Current iteration number : 0</span>
+<span class="sd">        number of indices : 6</span>
+<span class="sd">        Probability weights : [0.6, 0.1, 0.1, 0.1, 0.1, 0.0]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="p">(</span>
+            <span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span>
+            <span class="n">sampling_type</span><span class="o">=</span><span class="s1">&#39;from_function&#39;</span><span class="p">,</span>
+            <span class="n">function</span><span class="o">=</span><span class="n">function</span><span class="p">,</span>
+            <span class="n">prob_weights</span><span class="o">=</span><span class="n">prob_weights</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">sampler</span></div>
+
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_prime_factorisation</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        n: int</span>
+<span class="sd">            The number to be factorised.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        list of ints</span>
+<span class="sd">            The prime factors of n.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">factors</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">while</span> <span class="n">n</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">n</span> <span class="o">//=</span> <span class="mi">2</span>
+            <span class="n">factors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="n">i</span> <span class="o">=</span> <span class="mi">3</span>
+        <span class="k">while</span> <span class="n">i</span><span class="o">*</span><span class="n">i</span> <span class="o">&lt;=</span> <span class="n">n</span><span class="p">:</span>
+            <span class="k">while</span> <span class="n">n</span> <span class="o">%</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">n</span> <span class="o">//=</span> <span class="n">i</span>
+                <span class="n">factors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="n">i</span> <span class="o">+=</span> <span class="mi">2</span>
+
+        <span class="k">if</span> <span class="n">n</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">factors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">factors</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_herman_meyer_function</span><span class="p">(</span><span class="n">num_indices</span><span class="p">,</span>  <span class="n">addition_arr</span><span class="p">,</span> <span class="n">repeat_length_arr</span><span class="p">,</span> <span class="n">iteration_number</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The number of indices to be sampled from.</span>
+
+<span class="sd">        addition_arr: list of ints</span>
+<span class="sd">            The product of all factors at indices greater than the current factor.</span>
+
+<span class="sd">        repeat_length_arr: list of ints</span>
+<span class="sd">            The product of all factors at indices less than the current factor.</span>
+
+<span class="sd">        iteration_number: int</span>
+<span class="sd">            The current iteration number.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        int</span>
+<span class="sd">            The index to be sampled from.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">addition_arr</span><span class="p">)):</span>
+            <span class="n">addition</span> <span class="o">=</span> <span class="n">addition_arr</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
+            <span class="n">repeat_length</span> <span class="o">=</span> <span class="n">repeat_length_arr</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
+
+            <span class="n">length</span> <span class="o">=</span> <span class="n">num_indices</span> <span class="o">//</span> <span class="p">(</span><span class="n">addition</span><span class="o">*</span><span class="n">repeat_length</span><span class="p">)</span>
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">length</span><span class="p">)</span> <span class="o">*</span> <span class="n">addition</span>
+
+            <span class="n">ind</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">iteration_number</span><span class="o">/</span><span class="n">repeat_length</span><span class="p">)</span> <span class="o">%</span> <span class="n">length</span>
+            <span class="n">index</span> <span class="o">+=</span> <span class="n">arr</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">index</span>
+
+<div class="viewcode-block" id="Sampler.herman_meyer">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.Sampler.herman_meyer">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">herman_meyer</span><span class="p">(</span><span class="n">num_indices</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Instantiates a sampler which outputs in a Herman Meyer order.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_indices: int</span>
+<span class="sd">            The sampler will select from a range of indices 0 to num_indices. For Herman-Meyer sampling this number should not be prime.</span>
+<span class="sd">        </span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Sampler</span>
+<span class="sd">            An instance of the Sampler class which outputs in a Herman Meyer order.</span>
+<span class="sd">        </span>
+<span class="sd">        </span>
+<span class="sd">        </span>
+<span class="sd">            </span>
+<span class="sd">        Reference</span>
+<span class="sd">        ----------</span>
+<span class="sd">        With thanks to Imraj Singh and Zeljko Kereta for their help with the initial implementation of the Herman Meyer sampling. Their implementation was used in:</span>
+
+<span class="sd">        Singh I, et al. Deep Image Prior PET Reconstruction using a SIRF-Based Objective - IEEE MIC, NSS &amp; RTSD 2022. https://discovery.ucl.ac.uk/id/eprint/10176077/1/MIC_Conference_Record.pdf</span>
+
+<span class="sd">        The sampling method was introduced in:</span>
+
+<span class="sd">        Herman GT, Meyer LB. Algebraic reconstruction techniques can be made computationally efficient. IEEE Trans Med Imaging.  doi: 10.1109/42.241889.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; sampler=Sampler.herman_meyer(12)</span>
+<span class="sd">        &gt;&gt;&gt; print(sampler.get_samples(16))</span>
+<span class="sd">        [ 0  6  3  9  1  7  4 10  2  8  5 11  0  6  3  9]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Integral</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`num_indices` should be an integer. &#39;</span><span class="p">)</span>
+
+        <span class="n">factors</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">_prime_factorisation</span><span class="p">(</span><span class="n">num_indices</span><span class="p">)</span>
+
+        <span class="n">n_factors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">factors</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">n_factors</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Herman Meyer sampling defaults to sequential ordering if the number of indices is prime. Please use an alternative sampling method or change the number of indices. &#39;</span><span class="p">)</span>
+
+        <span class="n">addition_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">n_factors</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
+        <span class="n">repeat_length_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">n_factors</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
+
+        <span class="n">repeat_length</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">addition</span> <span class="o">=</span> <span class="n">num_indices</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_factors</span><span class="p">):</span>
+            <span class="n">addition</span> <span class="o">//=</span> <span class="n">factors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">addition_arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">addition</span>
+
+            <span class="n">repeat_length_arr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">repeat_length</span>
+            <span class="n">repeat_length</span> <span class="o">*=</span> <span class="n">factors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+        <span class="n">hmf_call</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">Sampler</span><span class="o">.</span><span class="n">_herman_meyer_function</span><span class="p">,</span>
+                           <span class="n">num_indices</span><span class="p">,</span> <span class="n">addition_arr</span><span class="p">,</span> <span class="n">repeat_length_arr</span><span class="p">)</span>
+
+        <span class="c1"># define the sampler</span>
+        <span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">hmf_call</span><span class="p">,</span>
+                          <span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span>
+                          <span class="n">sampling_type</span><span class="o">=</span><span class="s1">&#39;herman_meyer&#39;</span><span class="p">,</span>
+                          <span class="n">prob_weights</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span> <span class="o">/</span> <span class="n">num_indices</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_indices</span>
+                          <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">sampler</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="SamplerRandom">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.SamplerRandom">[docs]</a>
+<span class="k">class</span> <span class="nc">SamplerRandom</span><span class="p">(</span><span class="n">Sampler</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    The user is recommended to not instantiate this class  directly but instead use one of the static methods  in the parent Sampler class that will return instances of different samplers.</span>
+
+<span class="sd">    This class produces Samplers that output random samples with and without replacement from the set {0, 1, …, N-1} where N=num_indices.</span>
+
+<span class="sd">    Custom random samplers can be created by subclassing this sampler class.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    num_indices: int</span>
+<span class="sd">        The sampler will select from a range of indices 0 to num_indices.</span>
+
+<span class="sd">    sampling_type:str, optional,  default = &#39;random_with_replacement&quot;</span>
+<span class="sd">        The sampling type used. This is recorded for reference and printed when `print` is called.</span>
+
+<span class="sd">    prob_weights: list of floats of length num_indices that sum to 1.  Default is [1 / num_indices] * num_indices</span>
+<span class="sd">        Consider that the sampler is incremented a large number of times this argument holds the expected number of times each index would be outputted,  normalised to 1.</span>
+
+<span class="sd">    replace: bool, default is True</span>
+<span class="sd">        If True, sample with replace, otherwise sample without replacement</span>
+
+<span class="sd">    seed:int, optional</span>
+<span class="sd">        Used to initialise the random number generator where repeatability is required.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Sampler</span>
+<span class="sd">        An instance of the Sampler class representing the desired configuration.</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; sampler = Sampler.random_with_replacement(5)</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.get_samples(20))</span>
+<span class="sd">    [3 4 0 0 2 3 3 2 2 1 1 4 4 3 0 2 4 4 2 4]</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; sampler=Sampler.randomWithoutReplacement(num_indices=7, seed=1)</span>
+<span class="sd">    &gt;&gt;&gt; print(sampler.get_samples(16))</span>
+<span class="sd">    [6 2 1 0 4 3 5 1 0 4 2 5 6 3 3 2]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_indices</span><span class="p">,</span>  <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="n">sampling_type</span><span class="o">=</span><span class="s1">&#39;random_with_replacement&#39;</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_seed</span> <span class="o">=</span> <span class="n">seed</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_seed</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">())</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_seed</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_sampling_list</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_replace</span> <span class="o">=</span> <span class="n">replace</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">SamplerRandom</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">num_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_function</span><span class="p">,</span>
+                                            <span class="n">sampling_type</span><span class="o">=</span><span class="n">sampling_type</span><span class="p">,</span> <span class="n">prob_weights</span><span class="o">=</span><span class="n">prob</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">seed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_seed</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">replace</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_replace</span>
+
+    <span class="k">def</span> <span class="nf">_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">iteration_number</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot; For each iteration number this function samples from a randomly generated list in order. Every num_indices the list is re-created. &quot;&quot;&quot;</span>
+        <span class="n">location</span> <span class="o">=</span> <span class="n">iteration_number</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_indices</span>
+        <span class="k">if</span> <span class="n">location</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sampling_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_num_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_indices</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_prob_weights</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_replace</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sampling_list</span><span class="p">[</span><span class="n">location</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+<div class="viewcode-block" id="SamplerRandom.get_samples">
+<a class="viewcode-back" href="../../../../../optimisation/#cil.optimisation.utilities.SamplerRandom.get_samples">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_samples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>  <span class="n">num_samples</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Generates a list of the first num_samples output by the sampler. Calling this does not increment the sampler index or affect the behaviour of the sampler .</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        num_samples: int</span>
+<span class="sd">            The number of samples to return.</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        list</span>
+<span class="sd">            The first `num_samples` produced by the sampler</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">save_last_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="n">save_generator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_seed</span><span class="p">)</span>
+        <span class="n">save_sampling_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sampling_list</span>
+
+        <span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">next</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_samples</span><span class="p">)]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_iteration_number</span> <span class="o">=</span> <span class="n">save_last_index</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span> <span class="o">=</span> <span class="n">save_generator</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_sampling_list</span> <span class="o">=</span> <span class="n">save_sampling_list</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">output</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">repres</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__str__</span><span class="p">()</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;Seed : </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_seed</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">repres</span></div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+</div>
+                </footer>
+              
+            </div>
+            
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+            
+          </div>
+          <footer class="bd-footer-content">
+            
+          </footer>
+        
+      </main>
+    </div>
+  </div>
+  
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script defer src="../../../../../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+<script defer src="../../../../../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+
+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
+      
+        <div class="footer-item">
+
+  <p class="copyright">
+    
+      © Copyright 2017-2024.
+      <br/>
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+</div>
+      
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+  
+  
+  
+</div>
+
+  </footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/v24.2.0/_modules/cil/plugins/TomoPhantom/index.html b/v24.2.0/_modules/cil/plugins/TomoPhantom/index.html
new file mode 100644
index 0000000000..2abd72cb7d
--- /dev/null
+++ b/v24.2.0/_modules/cil/plugins/TomoPhantom/index.html
@@ -0,0 +1,718 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>cil.plugins.TomoPhantom &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
+  </script>
+  <!-- 
+    this give us a css class that will be invisible only if js is disabled 
+  -->
+  <noscript>
+    <style>
+      .pst-js-only { display: none !important; }
+
+    </style>
+  </noscript>
+  
+  <!-- Loaded before other Sphinx assets -->
+  <link href="../../../../_static/styles/theme.css?digest=fd05d3ddc9a6e3fa7c66" rel="stylesheet" />
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+
+    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=a746c00c" />
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+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="../../../../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66" />
+<link rel="preload" as="script" href="../../../../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66" />
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+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>DOCUMENTATION_OPTIONS.pagename = '_modules/cil/plugins/TomoPhantom';</script>
+    <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.16.0rc0';
+        DOCUMENTATION_OPTIONS.theme_switcher_json_url = '/CIL/versions.json';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '24.2.0';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
+        </script>
+    <link rel="index" title="Index" href="../../../../genindex/" />
+    <link rel="search" title="Search" href="../../../../search/" />
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+  <meta name="docsearch:version" content="24.2.0" />
+  </head>
+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
+
+  
+  
+  <div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
+  
+  <div id="pst-scroll-pixel-helper"></div>
+  
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>Back to top</button>
+
+  
+  <dialog id="pst-search-dialog">
+    
+<form class="bd-search d-flex align-items-center"
+      action="../../../../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form>
+  </dialog>
+
+  <div class="pst-async-banner-revealer d-none">
+  <aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
+</div>
+
+  
+    <header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
+<div class="bd-header__inner bd-page-width">
+  <button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
+    <span class="fa-solid fa-bars"></span>
+  </button>
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
+    
+      <div class="navbar-item">
+
+  
+     
+  
+
+<a class="navbar-brand logo" href="/">
+  
+  
+  
+  
+  
+    
+    
+      
+    
+    
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB.svg" class="logo__image only-light" alt="CIL - Home"/>
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB-reversed.svg" class="logo__image only-dark pst-js-only" alt="CIL - Home"/>
+  
+  
+</a></div>
+    
+  </div>
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+            <li class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
+                    More
+                </button>
+                <ul id="pst-nav-more-links" class="dropdown-menu">
+                    
+<li class=" ">
+  <a class="nav-link dropdown-item nav-internal" href="../../../../demos/">
+    Tutorials
+  </a>
+</li>
+
+                </ul>
+            </li>
+            
+  </ul>
+</nav></div>
+      
+    </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+      
+        <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+      
+    </div>
+    
+  </div>
+  
+  
+
+  
+</div>
+
+    </header>
+  
+
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      
+      
+        
+      
+      <dialog id="pst-primary-sidebar-modal"></dialog>
+      <div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
+        
+
+  
+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          
+          
+            <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../demos/">
+    Tutorials
+  </a>
+</li>
+
+  </ul>
+</nav></div>
+          
+        
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+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
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+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+        
+      </div>
+    
+  </div>
+  
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item">
+<form class="bd-search d-flex align-items-center"
+      action="../../../../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+        <div class="sidebar-primary-item">
+<div class="version-switcher__container dropdown pst-js-only">
+  <button id="pst-version-switcher-button-2"
+    type="button"
+    class="version-switcher__button btn btn-sm dropdown-toggle"
+    data-bs-toggle="dropdown"
+    aria-haspopup="listbox"
+    aria-controls="pst-version-switcher-list-2"
+    aria-label="Version switcher list"
+  >
+    Choose version  <!-- this text may get changed later by javascript -->
+    <span class="caret"></span>
+  </button>
+  <div id="pst-version-switcher-list-2"
+    class="version-switcher__menu dropdown-menu list-group-flush py-0"
+    role="listbox" aria-labelledby="pst-version-switcher-button-2">
+    <!-- dropdown will be populated by javascript on page load -->
+  </div>
+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../demos/">Tutorials</a></li>
+</ul>
+</div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main" role="main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article d-print-none">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
+<nav aria-label="Breadcrumb" class="d-print-none">
+  <ul class="bd-breadcrumbs">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../../../" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../../../" class="nav-link">Module code</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.plugins.TomoPhantom</span></li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <h1>Source code for cil.plugins.TomoPhantom</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageData</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">ImageDimension</span>
+<span class="kn">import</span> <span class="nn">tomophantom</span>
+<span class="kn">from</span> <span class="nn">tomophantom</span> <span class="kn">import</span> <span class="n">TomoP2D</span><span class="p">,</span> <span class="n">TomoP3D</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">import</span> <span class="nn">ctypes</span><span class="o">,</span> <span class="nn">platform</span>
+<span class="kn">from</span> <span class="nn">ctypes</span> <span class="kn">import</span> <span class="n">util</span>
+<span class="c1"># check for the extension</span>
+<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Linux&#39;</span><span class="p">:</span>
+    <span class="n">dll</span> <span class="o">=</span> <span class="s1">&#39;libctomophantom.so&#39;</span>
+<span class="k">elif</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
+    <span class="n">dll_file</span> <span class="o">=</span> <span class="s1">&#39;ctomophantom.dll&#39;</span>
+    <span class="n">dll</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">find_library</span><span class="p">(</span><span class="n">dll_file</span><span class="p">)</span>
+<span class="k">elif</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Darwin&#39;</span><span class="p">:</span>
+    <span class="n">dll</span> <span class="o">=</span> <span class="s1">&#39;libctomophantom.dylib&#39;</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Not supported platform, &#39;</span><span class="p">,</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">())</span>
+
+<span class="n">libtomophantom</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">cdll</span><span class="o">.</span><span class="n">LoadLibrary</span><span class="p">(</span><span class="n">dll</span><span class="p">)</span>
+
+
+
+<span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">tomophantom</span><span class="o">.</span><span class="vm">__file__</span><span class="p">)</span>
+<span class="n">path_library2D</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;Phantom2DLibrary.dat&quot;</span><span class="p">)</span>
+<span class="n">path_library3D</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;Phantom3DLibrary.dat&quot;</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">is_model_temporal</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">num_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns whether a model in the TomoPhantom library is temporal</span>
+
+<span class="sd">    This will go to check the installed library files from TomoPhantom</span>
+<span class="sd">    https://github.com/dkazanc/TomoPhantom/tree/master/PhantomLibrary/models</span>
+
+<span class="sd">    :param num_model: model number</span>
+<span class="sd">    :type num_model: int</span>
+<span class="sd">    :param num_dims: dimensionality of the phantom, 2D or 3D</span>
+<span class="sd">    :type num_dims: int, default 2</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">return</span> <span class="n">get_model_num_channels</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">num_dims</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span>
+
+<span class="k">def</span> <span class="nf">get_model_num_channels</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">num_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns number of temporal steps (channels) the model has</span>
+
+<span class="sd">    This will go to check the installed library files from TomoPhantom</span>
+<span class="sd">    https://github.com/dkazanc/TomoPhantom/tree/master/PhantomLibrary/models</span>
+
+<span class="sd">    https://github.com/dkazanc/TomoPhantom/blob/v1.4.9/Core/utils.c#L27</span>
+<span class="sd">    https://github.com/dkazanc/TomoPhantom/blob/v1.4.9/Core/utils.c#L269</span>
+
+<span class="sd">    :param num_model: model number</span>
+<span class="sd">    :type num_model: int</span>
+<span class="sd">    :param num_dims: dimensionality of the phantom, 2D or 3D</span>
+<span class="sd">    :type num_dims: int, default 2</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">return</span> <span class="n">check_model_params</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">num_dims</span><span class="o">=</span><span class="n">num_dims</span><span class="p">)[</span><span class="mi">3</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">check_model_params</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">num_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns params_switch array from the C TomoPhantom library in function checkParams2D or checkParams3D</span>
+
+<span class="sd">    This will go to check the installed library files from TomoPhantom</span>
+<span class="sd">    https://github.com/dkazanc/TomoPhantom/tree/master/PhantomLibrary/models</span>
+
+<span class="sd">    https://github.com/dkazanc/TomoPhantom/blob/v1.4.9/Core/utils.c#L27</span>
+<span class="sd">    https://github.com/dkazanc/TomoPhantom/blob/v1.4.9/Core/utils.c#L269</span>
+
+<span class="sd">    :param num_model: model number</span>
+<span class="sd">    :type num_model: int</span>
+<span class="sd">    :param num_dims: dimensionality of the phantom, 2D or 3D</span>
+<span class="sd">    :type num_dims: int, default 2</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">libtomophantom</span><span class="o">.</span><span class="n">checkParams2D</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">),</span>  <span class="c1"># pointer to the params array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>                                   <span class="c1"># model number selector (int)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">]</span>                  <span class="c1"># string to the library file</span>
+        <span class="n">params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">10</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
+        <span class="n">params_p</span> <span class="o">=</span> <span class="n">params</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">))</span>
+        <span class="n">lib2d_p</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">path_library2D</span><span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
+        <span class="n">libtomophantom</span><span class="o">.</span><span class="n">checkParams2D</span><span class="p">(</span><span class="n">params_p</span><span class="p">,</span> <span class="n">num_model</span><span class="p">,</span> <span class="n">lib2d_p</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">params</span>
+
+    <span class="k">elif</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">libtomophantom</span><span class="o">.</span><span class="n">checkParams3D</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">),</span>  <span class="c1"># pointer to the params array</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span>                                   <span class="c1"># model number selector (int)</span>
+                                  <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">]</span>                  <span class="c1"># string to the library file</span>
+        <span class="n">params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">11</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
+        <span class="n">params_p</span> <span class="o">=</span> <span class="n">params</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">))</span>
+        <span class="n">lib2d_p</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">path_library3D</span><span class="p">)</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
+        <span class="n">libtomophantom</span><span class="o">.</span><span class="n">checkParams3D</span><span class="p">(</span><span class="n">params_p</span><span class="p">,</span> <span class="n">num_model</span><span class="p">,</span> <span class="n">lib2d_p</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">params</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unsupported dimensionality. Expected 2 or 3, got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dims</span><span class="p">))</span>
+
+<div class="viewcode-block" id="get_ImageData">
+<a class="viewcode-back" href="../../../../plugins/#cil.plugins.TomoPhantom.get_ImageData">[docs]</a>
+<span class="k">def</span> <span class="nf">get_ImageData</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Returns an ImageData relative to geometry with the model num_model from tomophantom</span>
+
+<span class="sd">    :param num_model: model number</span>
+<span class="sd">    :type num_model: int</span>
+<span class="sd">    :param geometry: geometrical info that describes the phantom</span>
+<span class="sd">    :type geometry: ImageGeometry</span>
+<span class="sd">    Example usage:</span>
+
+<span class="sd">    .. code-block:: python</span>
+
+<span class="sd">      ndim = 2</span>
+<span class="sd">      N=128</span>
+<span class="sd">      angles = np.linspace(0, 360, 50, True, dtype=np.float32)</span>
+<span class="sd">      offset = 0.4</span>
+<span class="sd">      channels = 3</span>
+
+<span class="sd">      if ndim == 2:</span>
+<span class="sd">          ag = AcquisitionGeometry.create_Cone2D((offset,-100), (offset,100))</span>
+<span class="sd">          ag.set_panel(N)</span>
+
+<span class="sd">      else:</span>
+<span class="sd">          ag = AcquisitionGeometry.create_Cone3D((offset,-100, 0), (offset,100,0))</span>
+<span class="sd">          ag.set_panel((N,N-2))</span>
+
+<span class="sd">      ag.set_channels(channels)</span>
+<span class="sd">      ag.set_angles(angles, angle_unit=AngleUnit.DEGREE)</span>
+
+<span class="sd">      ig = ag.get_ImageGeometry()</span>
+<span class="sd">      num_model = 1</span>
+<span class="sd">      phantom = TomoPhantom.get_ImageData(num_model=num_model, geometry=ig)</span>
+
+
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">ig</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">ig</span><span class="o">.</span><span class="n">set_labels</span><span class="p">(</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">get_order_for_engine</span><span class="p">(</span><span class="s1">&#39;cil&#39;</span><span class="p">))</span>
+    <span class="n">num_dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ig</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">CHANNEL</span> <span class="ow">in</span> <span class="n">ig</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">is_model_temporal</span><span class="p">(</span><span class="n">num_model</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Selected model </span><span class="si">{}</span><span class="s1"> is not a temporal model, please change your selection&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_model</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+            <span class="c1"># 3D+time for tomophantom</span>
+            <span class="c1"># output dimensions channel and then spatial,</span>
+            <span class="c1"># e.g. [ &#39;channel&#39;, &#39;vertical&#39;, &#39;horizontal_y&#39;, &#39;horizontal_x&#39; ]</span>
+            <span class="n">num_model</span> <span class="o">=</span> <span class="n">num_model</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+            <span class="n">phantom_arr</span> <span class="o">=</span> <span class="n">TomoP3D</span><span class="o">.</span><span class="n">ModelTemporal</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">path_library3D</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="c1"># 2D+time for tomophantom</span>
+            <span class="c1"># output dimensions channel and then spatial,</span>
+            <span class="c1"># e.g. [ &#39;channel&#39;, &#39;horizontal_y&#39;, &#39;horizontal_x&#39; ]</span>
+            <span class="n">N</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">num_model</span> <span class="o">=</span> <span class="n">num_model</span>
+            <span class="n">phantom_arr</span> <span class="o">=</span> <span class="n">TomoP2D</span><span class="o">.</span><span class="n">ModelTemporal</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">path_library2D</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Wrong ImageGeometry&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ig</span><span class="o">.</span><span class="n">channels</span> <span class="o">!=</span> <span class="n">phantom_arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The required model </span><span class="si">{}</span><span class="s1"> has </span><span class="si">{}</span><span class="s1"> channels. The ImageGeometry you passed has </span><span class="si">{}</span><span class="s1">. Please update your ImageGeometry.&#39;</span>\
+                <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">ig</span><span class="o">.</span><span class="n">channels</span><span class="p">,</span> <span class="n">phantom_arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="c1"># 3D</span>
+            <span class="n">num_model</span> <span class="o">=</span> <span class="n">num_model</span>
+            <span class="n">phantom_arr</span> <span class="o">=</span> <span class="n">TomoP3D</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">path_library3D</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">num_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="c1"># 2D</span>
+            <span class="k">if</span> <span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Can only handle square ImageData, got shape&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+            <span class="n">N</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">num_model</span> <span class="o">=</span> <span class="n">num_model</span>
+            <span class="n">phantom_arr</span> <span class="o">=</span> <span class="n">TomoP2D</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">num_model</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">path_library2D</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Wrong ImageGeometry&#39;</span><span class="p">)</span>
+
+
+    <span class="n">im_data</span> <span class="o">=</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">phantom_arr</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">ig</span><span class="p">,</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">im_data</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">im_data</span></div>
+
+</pre></div>
+
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new file mode 100644
index 0000000000..84bad2cfea
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+<li class="toctree-l1"><a class="reference internal" href="../../../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../../../recon/">Recon</a></li>
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+                  
+  <h1>Source code for cil.plugins.astra.operators.ProjectionOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">BlockGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">ImageDimension</span><span class="p">,</span> <span class="n">AcquisitionType</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">BlockOperator</span><span class="p">,</span> <span class="n">LinearOperator</span><span class="p">,</span> <span class="n">ChannelwiseOperator</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.astra.operators</span> <span class="kn">import</span> <span class="n">AstraProjector2D</span><span class="p">,</span> <span class="n">AstraProjector3D</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="ProjectionOperator">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.astra.ProjectionOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">ProjectionOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    ProjectionOperator configures and calls appropriate ASTRA Projectors for your dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    image_geometry : ``ImageGeometry``, default used if None</span>
+<span class="sd">        A description of the area/volume to reconstruct</span>
+
+<span class="sd">    acquisition_geometry : ``AcquisitionGeometry``, ``BlockGeometry``</span>
+<span class="sd">        A description of the acquisition data. If passed a BlockGeometry it will return a BlockOperator.</span>
+
+<span class="sd">    device : string, default=&#39;gpu&#39;</span>
+<span class="sd">        &#39;gpu&#39; will run on a compatible CUDA capable device using the ASTRA 3D CUDA Projectors, &#39;cpu&#39; will run on CPU using the ASTRA 2D CPU Projectors</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.plugins.astra import ProjectionOperator</span>
+<span class="sd">    &gt;&gt;&gt; PO = ProjectionOperator(image.geometry, data.geometry)</span>
+<span class="sd">    &gt;&gt;&gt; forward_projection = PO.direct(image)</span>
+<span class="sd">    &gt;&gt;&gt; backward_projection = PO.adjoint(data)</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    For multichannel data the ProjectionOperator will broadcast across all channels.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> \
+        <span class="n">device</span><span class="o">=</span><span class="s1">&#39;gpu&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">acquisition_geometry</span><span class="p">,</span> <span class="n">BlockGeometry</span><span class="p">):</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;BlockOperator is returned.&quot;</span><span class="p">)</span>
+
+            <span class="n">K</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">ag</span> <span class="ow">in</span> <span class="n">acquisition_geometry</span><span class="p">:</span>
+                <span class="n">K</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">ProjectionOperator_ag</span><span class="p">(</span><span class="n">image_geometry</span><span class="o">=</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="o">=</span><span class="n">ag</span><span class="p">,</span> \
+                        <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">return</span> <span class="n">BlockOperator</span><span class="p">(</span><span class="o">*</span><span class="n">K</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Standard Operator is returned.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">ProjectionOperator</span><span class="p">,</span>
+                         <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">ProjectionOperator_ag</span><span class="p">)</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">ProjectionOperator_ag</span><span class="p">(</span><span class="n">ProjectionOperator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    ProjectionOperator configures and calls appropriate ASTRA Projectors for your dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    image_geometry : ImageGeometry, default used if None</span>
+<span class="sd">        A description of the area/volume to reconstruct</span>
+
+<span class="sd">    acquisition_geometry : AcquisitionGeometry</span>
+<span class="sd">        A description of the acquisition data</span>
+
+<span class="sd">    device : string, default=&#39;gpu&#39;</span>
+<span class="sd">        &#39;gpu&#39; will run on a compatible CUDA capable device using the ASTRA 3D CUDA Projectors, &#39;cpu&#39; will run on CPU using the ASTRA 2D CPU Projectors</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.plugins.astra import ProjectionOperator</span>
+<span class="sd">    &gt;&gt;&gt; PO = ProjectionOperator(image.geometry, data.geometry)</span>
+<span class="sd">    &gt;&gt;&gt; forward_projection = PO.direct(image)</span>
+<span class="sd">    &gt;&gt;&gt; backward_projection = PO.adjoint(data)</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    For multichannel data the ProjectionOperator will broadcast across all channels.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">acquisition_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">device</span><span class="o">=</span><span class="s1">&#39;gpu&#39;</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">acquisition_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Please specify an acquisition_geometry to configure this operator&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">image_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">image_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ProjectionOperator_ag</span><span class="p">,</span>
+              <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">image_geometry</span><span class="p">,</span>
+                             <span class="n">range_geometry</span><span class="o">=</span><span class="n">acquisition_geometry</span><span class="p">)</span>
+
+        <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;astra&#39;</span><span class="p">,</span><span class="n">acquisition_geometry</span><span class="p">)</span>
+        <span class="n">ImageDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;astra&#39;</span><span class="p">,</span><span class="n">image_geometry</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">volume_geometry</span> <span class="o">=</span> <span class="n">image_geometry</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sinogram_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span>
+
+        <span class="n">sinogram_geometry_sc</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">volume_geometry_sc</span> <span class="o">=</span> <span class="n">image_geometry</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="s1">&#39;gpu&#39;</span><span class="p">:</span>
+            <span class="n">operator</span> <span class="o">=</span> <span class="n">AstraProjector3D</span><span class="p">(</span><span class="n">volume_geometry_sc</span><span class="p">,</span>
+                                        <span class="n">sinogram_geometry_sc</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">sinogram_geometry</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="n">operator</span> <span class="o">=</span> <span class="n">AstraProjector2D</span><span class="p">(</span><span class="n">volume_geometry_sc</span><span class="p">,</span>
+                                        <span class="n">sinogram_geometry_sc</span><span class="p">,</span>
+                                        <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Cannot process 3D data without a GPU&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">operator_full</span> <span class="o">=</span> <span class="n">ChannelwiseOperator</span><span class="p">(</span>
+                <span class="n">operator</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sinogram_geometry</span><span class="o">.</span><span class="n">channels</span><span class="p">,</span> <span class="n">dimension</span><span class="o">=</span><span class="s1">&#39;prepend&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator_full</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="n">operator</span>
+
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">IM</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Applies the direct of the operator i.e. the forward projection.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        IM : ImageData</span>
+<span class="sd">            The image/volume to be projected.</span>
+
+<span class="sd">        out : DataContainer, optional</span>
+<span class="sd">           Fills the referenced DataContainer with the processed data and suppresses the return</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The processed data. Suppressed if `out` is passed</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">IM</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">DATA</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Applies the adjoint of the operator, i.e. the backward projection.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        DATA : AcquisitionData</span>
+<span class="sd">            The projections/sinograms to be projected.</span>
+
+<span class="sd">        out : DataContainer, optional</span>
+<span class="sd">           Fills the referenced DataContainer with the processed data and suppresses the return</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The processed data. Suppressed if `out` is passed</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">DATA</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">calculate_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">domain_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">volume_geometry</span>
+
+    <span class="k">def</span> <span class="nf">range_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sinogram_geometry</span>
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/plugins/astra/processors/FBP/index.html b/v24.2.0/_modules/cil/plugins/astra/processors/FBP/index.html
new file mode 100644
index 0000000000..53285c2e0f
--- /dev/null
+++ b/v24.2.0/_modules/cil/plugins/astra/processors/FBP/index.html
@@ -0,0 +1,641 @@
+
+<!DOCTYPE html>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.plugins.astra.processors.FBP</span></li>
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+  <h1>Source code for cil.plugins.astra.processors.FBP</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">ImageDimension</span><span class="p">,</span> <span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">AcquisitionType</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.astra.processors.FBP_Flexible</span> <span class="kn">import</span> <span class="n">FBP_Flexible</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.astra.processors.FDK_Flexible</span> <span class="kn">import</span> <span class="n">FDK_Flexible</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.astra.processors.FBP_Flexible</span> <span class="kn">import</span> <span class="n">FBP_CPU</span>
+
+
+<div class="viewcode-block" id="FBP">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.astra.FBP">[docs]</a>
+<span class="k">class</span> <span class="nc">FBP</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    FBP configures and calls an appropriate ASTRA FBP or FDK algorithm for your dataset.</span>
+
+<span class="sd">    The best results will be on data with circular trajectories of a 2PI angular range and equally spaced small angular steps.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    image_geometry : ImageGeometry, default used if None</span>
+<span class="sd">        A description of the area/volume to reconstruct</span>
+
+<span class="sd">    acquisition_geometry : AcquisitionGeometry</span>
+<span class="sd">        A description of the acquisition data</span>
+
+<span class="sd">    device : string, default=&#39;gpu&#39;</span>
+<span class="sd">        &#39;gpu&#39; will run on a compatible CUDA capable device using the ASTRA FDK_CUDA algorithm</span>
+<span class="sd">        &#39;cpu&#39; will run on CPU using the ASTRA FBP algorithm - see Notes for limitations</span>
+
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.plugins.astra import FBP</span>
+<span class="sd">    &gt;&gt;&gt; fbp = FBP(image_geometry, data.geometry)</span>
+<span class="sd">    &gt;&gt;&gt; fbp.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; reconstruction = fbp.get_output()</span>
+
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    A CPU version is provided for simple 2D parallel-beam geometries only, any offsets and rotations in the acquisition geometry will be ignored.</span>
+
+<span class="sd">    This uses the ram-lak filter only.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;gpu&#39;</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">acquisition_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Please specify an acquisition_geometry to configure this processor&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">image_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">image_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+
+        <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;astra&#39;</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">)</span>
+        <span class="n">ImageDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;astra&#39;</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="s1">&#39;gpu&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;parallel&#39;</span><span class="p">:</span>
+                <span class="n">processor</span> <span class="o">=</span> <span class="n">FBP_Flexible</span><span class="p">(</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">processor</span> <span class="o">=</span> <span class="n">FDK_Flexible</span><span class="p">(</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="ne">UserWarning</span><span class="p">(</span><span class="s2">&quot;ASTRA back-projector running on CPU will not make use of enhanced geometry parameters&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Cannot process cone-beam data without a GPU&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+                <span class="n">processor</span> <span class="o">=</span> <span class="n">FBP_CPU</span><span class="p">(</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Cannot process 3D data without a GPU&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Cannot process multi-channel data&quot;</span><span class="p">)</span>
+            <span class="c1">#processor_full = ChannelwiseProcessor(processor, self.acquisition_geometry.channels, dimension=&#39;prepend&#39;)</span>
+            <span class="c1">#self.processor = operator_full</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">FBP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span> <span class="n">image_geometry</span><span class="o">=</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="o">=</span><span class="n">acquisition_geometry</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">processor</span><span class="o">=</span><span class="n">processor</span><span class="p">)</span>
+
+<div class="viewcode-block" id="FBP.set_input">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.astra.FBP.set_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_input</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+
+<div class="viewcode-block" id="FBP.get_output">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.astra.FBP.get_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">check_input</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+    
+    <span class="k">def</span> <span class="nf">check_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">check_output</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">process</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/plugins/ccpi_regularisation/functions/regularisers/index.html b/v24.2.0/_modules/cil/plugins/ccpi_regularisation/functions/regularisers/index.html
new file mode 100644
index 0000000000..4a32b39d7f
--- /dev/null
+++ b/v24.2.0/_modules/cil/plugins/ccpi_regularisation/functions/regularisers/index.html
@@ -0,0 +1,1077 @@
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+  <h1>Source code for cil.plugins.ccpi_regularisation.functions.regularisers</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">ccpi.filters</span> <span class="kn">import</span> <span class="n">regularisers</span>
+    <span class="kn">from</span> <span class="nn">ccpi.filters.TV</span> <span class="kn">import</span> <span class="n">TV_ENERGY</span>
+<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s1">&#39;Please `conda install &quot;ccpi::ccpi-regulariser&gt;=24.0.1&quot;`&#39;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">exc</span>
+
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">ImageDimension</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">Function</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
+
+<span class="k">class</span> <span class="nc">RegulariserFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">proximal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Generic proximal method for a RegulariserFunction</span>
+
+<span class="sd">        .. math:: \mathrm{prox}_{\tau f}(x) := \argmin_{z} f(x) + \frac{1}{2}\|z - x \|^{2}</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        x : DataContainer</span>
+<span class="sd">            Input of the proximal operator</span>
+<span class="sd">        tau : Number</span>
+<span class="sd">            Positive parameter of the proximal operator</span>
+<span class="sd">        out : DataContainer</span>
+<span class="sd">            Output :class:`Datacontainer` in which the result is placed.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+
+<span class="sd">        If the :class:`ImageData` contains complex data, rather than the default `float32`, the regularisation</span>
+<span class="sd">        is run independently on the real and imaginary part.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">check_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">arr</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
+            <span class="c1"># do real and imag part indep</span>
+            <span class="n">in_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
+            <span class="n">res</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">proximal_numpy</span><span class="p">(</span><span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span>
+            <span class="n">arr</span><span class="o">.</span><span class="n">real</span> <span class="o">=</span> <span class="n">res</span><span class="p">[:]</span>
+            <span class="n">in_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
+            <span class="n">res</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">proximal_numpy</span><span class="p">(</span><span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span>
+            <span class="n">arr</span><span class="o">.</span><span class="n">imag</span> <span class="o">=</span> <span class="n">res</span><span class="p">[:]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">info</span> <span class="o">=</span> <span class="n">info</span>
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
+            <span class="n">res</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">proximal_numpy</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">info</span> <span class="o">=</span> <span class="n">info</span>
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">out</span>
+    <span class="k">def</span> <span class="nf">proximal_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xarr</span><span class="p">,</span> <span class="n">tau</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Please implement proximal_numpy&#39;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+<span class="k">class</span> <span class="nc">TV_Base</span><span class="p">(</span><span class="n">RegulariserFunction</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Total Variation regulariser</span>
+
+<span class="sd">    .. math:: TV(u) = \alpha \|\nabla u\|_{2,1}</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    strong_convexity_constant : Number</span>
+<span class="sd">                              Positive parameter that allows Total variation regulariser to be strongly convex. Default = 0.</span>
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+
+<span class="sd">    By definition, Total variation is a convex function. However,</span>
+<span class="sd">    adding a strongly convex term makes it a strongly convex function.</span>
+<span class="sd">    Then, we say that `TV` is a :math:`\gamma&gt;0` strongly convex function i.e.,</span>
+
+<span class="sd">    .. math:: TV(u) = \alpha \|\nabla u\|_{2,1} + \frac{\gamma}{2}\|u\|^{2}</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">strong_convexity_constant</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span> <span class="o">=</span> <span class="n">strong_convexity_constant</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+        <span class="n">in_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span>
+        <span class="n">EnergyValTV</span> <span class="o">=</span> <span class="n">TV_ENERGY</span><span class="p">(</span><span class="n">in_arr</span><span class="p">,</span> <span class="n">in_arr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">EnergyValTV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">x</span><span class="o">.</span><span class="n">squared_norm</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">EnergyValTV</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mf">0.0</span>
+
+
+<div class="viewcode-block" id="FGP_TV">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_TV">[docs]</a>
+<span class="k">class</span> <span class="nc">FGP_TV</span><span class="p">(</span><span class="n">TV_Base</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Fast Gradient Projection Total Variation (FGP_TV)</span>
+
+<span class="sd">        The :class:`FGP_TV` computes the proximal operator of the Total variation regulariser</span>
+
+<span class="sd">        .. math:: \mathrm{prox}_{\tau (\alpha TV)}(x) = \underset{z}{\mathrm{argmin}} \,\alpha\,\mathrm{TV}(z) + \frac{1}{2}\|z - x\|^{2} .</span>
+
+<span class="sd">        The algorithm used for the proximal operator of TV is the Fast Gradient Projection algorithm</span>
+<span class="sd">        applied to the _dual problem_ of the above problem, see :cite:`BeckTeboulle_b`, :cite:`BeckTeboulle_a`.</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        -----</span>
+<span class="sd">        In CIL Version 24.1.0 we change the default value of nonnegativity to False. This means non-negativity is not enforced by default.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        alpha : :obj:`Number` (positive), default = 1.0 .</span>
+<span class="sd">                Total variation regularisation parameter.</span>
+
+<span class="sd">        max_iteration : :obj:`int`. Default = 100 .</span>
+<span class="sd">                Maximum number of iterations for the Fast Gradient Projection algorithm.</span>
+
+<span class="sd">        isotropic : :obj:`boolean`. Default = True .</span>
+<span class="sd">                    Isotropic or Anisotropic definition of the Total variation regulariser.</span>
+
+<span class="sd">                    .. math:: |x|_{2} = \sqrt{x_{1}^{2} + x_{2}^{2}},\, (\mbox{isotropic})</span>
+
+<span class="sd">                    .. math:: |x|_{1} = |x_{1}| + |x_{2}|\, (\mbox{anisotropic})</span>
+
+<span class="sd">        nonnegativity : :obj:`boolean`. Default = False .</span>
+<span class="sd">                        Non-negativity constraint for the solution of the FGP algorithm.</span>
+
+<span class="sd">        tolerance : :obj:`float`, Default = 0 .</span>
+<span class="sd">                    Stopping criterion for the FGP algorithm.</span>
+
+<span class="sd">                    .. math:: \|x^{k+1} - x^{k}\|_{2} &lt; \mathrm{tolerance}</span>
+
+<span class="sd">        device : :obj:`str`, Default = &#39;cpu&#39; .</span>
+<span class="sd">                FGP_TV algorithm runs on `cpu` or `gpu`.</span>
+
+<span class="sd">        strong_convexity_constant : :obj:`float`, default = 0</span>
+<span class="sd">                A strongly convex term weighted by the :code:`strong_convexity_constant` (:math:`\gamma`) parameter is added to the Total variation.</span>
+<span class="sd">                Now the :code:`TotalVariation` function is :math:`\gamma` - strongly convex and the proximal operator is</span>
+
+<span class="sd">                .. math:: \underset{u}{\mathrm{argmin}} \frac{1}{2\tau}\|u - b\|^{2} + \mathrm{TV}(u) + \frac{\gamma}{2}\|u\|^{2} \Leftrightarrow</span>
+
+<span class="sd">                .. math:: \underset{u}{\mathrm{argmin}} \frac{1}{2\frac{\tau}{1+\gamma\tau}}\|u - \frac{b}{1+\gamma\tau}\|^{2} + \mathrm{TV}(u)</span>
+
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        .. math:: \underset{u\qeq0}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + \alpha TV(u)</span>
+
+
+<span class="sd">        &gt;&gt;&gt; G = alpha * FGP_TV(max_iteration=100, device=&#39;gpu&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; sol = G.proximal(b)</span>
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+
+<span class="sd">        The :class:`FGP_TV` regularisation does not incorparate information on the :class:`ImageGeometry`, i.e., pixel/voxel size.</span>
+<span class="sd">        Therefore a rescaled parameter should be used to match the same solution computed using :class:`~cil.optimisation.functions.TotalVariation`.</span>
+
+<span class="sd">        &gt;&gt;&gt; G1 = (alpha/ig.voxel_size_x) * FGP_TV(max_iteration=100, device=&#39;gpu&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; G2 = alpha * TotalVariation(max_iteration=100, lower=0.)</span>
+
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`~cil.optimisation.functions.TotalVariation`</span>
+
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">isotropic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nonnegativity</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span> <span class="n">strong_convexity_constant</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">isotropic</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">methodTV</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">methodTV</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">nonnegativity</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># Deprecate this warning in future versions and allow nonnegativity to be default False in the init.</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Note that the default behaviour now sets the nonnegativity constraint to False &#39;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">nonnegativity</span><span class="o">=</span><span class="kc">False</span>
+        <span class="k">if</span> <span class="n">nonnegativity</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">nonnegativity</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">nonnegativity</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span> <span class="o">=</span> <span class="n">max_iteration</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="o">=</span> <span class="n">tolerance</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nonnegativity</span> <span class="o">=</span> <span class="n">nonnegativity</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">FGP_TV</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">strong_convexity_constant</span><span class="o">=</span><span class="n">strong_convexity_constant</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_fista_on_dual_rof</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot; Implements the Fast Gradient Projection algorithm on the dual problem</span>
+<span class="sd">        of the Total Variation Denoising problem (ROF).</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">info</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">regularisers</span><span class="o">.</span><span class="n">FGP_TV</span><span class="p">(</span>\
+              <span class="n">in_arr</span><span class="p">,</span>\
+              <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">tau</span><span class="p">,</span>\
+              <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span><span class="p">,</span>\
+              <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">,</span>\
+              <span class="bp">self</span><span class="o">.</span><span class="n">methodTV</span><span class="p">,</span>\
+              <span class="bp">self</span><span class="o">.</span><span class="n">nonnegativity</span><span class="p">,</span>\
+              <span class="n">infovector</span> <span class="o">=</span> <span class="n">info</span><span class="p">,</span>
+              <span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">res</span><span class="p">,</span> <span class="n">info</span>
+
+    <span class="k">def</span> <span class="nf">proximal_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+
+            <span class="n">strongly_convex_factor</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">tau</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span><span class="p">)</span>
+            <span class="n">in_arr</span> <span class="o">/=</span> <span class="n">strongly_convex_factor</span>
+            <span class="n">tau</span> <span class="o">/=</span> <span class="n">strongly_convex_factor</span>
+
+        <span class="n">solution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fista_on_dual_rof</span><span class="p">(</span><span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">strong_convexity_constant</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+            <span class="n">in_arr</span> <span class="o">*=</span> <span class="n">strongly_convex_factor</span>
+            <span class="n">tau</span> <span class="o">*=</span> <span class="n">strongly_convex_factor</span>
+
+        <span class="k">return</span> <span class="n">solution</span>
+
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Define the multiplication with a scalar</span>
+
+<span class="sd">        this changes the regularisation parameter in the plugin&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="bp">NotImplemented</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*=</span> <span class="n">scalar</span>
+            <span class="k">return</span> <span class="bp">self</span>
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> cannot work on more than 3D. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">length</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="TGV">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TGV">[docs]</a>
+<span class="k">class</span> <span class="nc">TGV</span><span class="p">(</span><span class="n">RegulariserFunction</span><span class="p">):</span>
+
+<div class="viewcode-block" id="TGV.__init__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span> <span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Creator of Total Generalised Variation Function</span>
+
+<span class="sd">        :param alpha: regularisation parameter</span>
+<span class="sd">        :type alpha: number, default 1</span>
+<span class="sd">        :param gamma: ratio of TGV terms</span>
+<span class="sd">        :type gamma: number, default 1, can range between 1 and 2</span>
+<span class="sd">        :param max_iteration: max number of sub iterations. The algorithm will iterate up to this number of iteration or up to when the tolerance has been reached</span>
+<span class="sd">        :type max_iteration: integer, default 100</span>
+<span class="sd">        :param tolerance: minimum difference between previous iteration of the algorithm that determines the stop of the iteration earlier than max_iteration. If set to 0 only the max_iteration will be used as stop criterion.</span>
+<span class="sd">        :type tolerance: float, default 0</span>
+<span class="sd">        :param device: determines if the code runs on CPU or GPU</span>
+<span class="sd">        :type device: string, default &#39;cpu&#39;, can be &#39;gpu&#39; if GPU is installed</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">gamma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span> <span class="o">=</span> <span class="n">max_iteration</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="o">=</span> <span class="n">tolerance</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;iter_TGV&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># raise ValueError(&#39;iter_TGV parameter has been superseded by num_iter. Use that instead.&#39;)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">num_iter</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;iter_TGV&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="TGV.__call__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.__call__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: the __call__ method is not implemented. Returning NaN.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span></div>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">gamma</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__gamma</span>
+    <span class="nd">@gamma</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">gamma</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">value</span> <span class="o">&lt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">value</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__gamma</span> <span class="o">=</span> <span class="n">value</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">alpha2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span>
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">alpha1</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mf">1.</span>
+
+    <span class="k">def</span> <span class="nf">proximal_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">):</span>
+
+        <span class="n">info</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">regularisers</span><span class="o">.</span><span class="n">TGV</span><span class="p">(</span><span class="n">in_arr</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">tau</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">alpha1</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">alpha2</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">LipshitzConstant</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">,</span>
+              <span class="n">infovector</span> <span class="o">=</span> <span class="n">info</span><span class="p">,</span>
+              <span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="c1"># info: return number of iteration and reached tolerance</span>
+        <span class="c1"># https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168</span>
+        <span class="c1"># Stopping Criteria  || u^k - u^(k-1) ||_{2} / || u^{k} ||_{2}</span>
+        <span class="k">return</span> <span class="n">res</span><span class="p">,</span> <span class="n">info</span>
+
+<div class="viewcode-block" id="TGV.convex_conjugate">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: the convex_conjugate method is not implemented. Returning NaN.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span></div>
+
+
+<div class="viewcode-block" id="TGV.__rmul__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.__rmul__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Define the multiplication with a scalar</span>
+
+<span class="sd">        this changes the regularisation parameter in the plugin&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="bp">NotImplemented</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*=</span> <span class="n">scalar</span>
+            <span class="k">return</span> <span class="bp">self</span></div>
+
+
+        <span class="c1"># f = TGV()</span>
+        <span class="c1"># f = alpha * f</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">LipshitzConstant</span> <span class="o">=</span> <span class="mi">12</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">LipshitzConstant</span> <span class="o">=</span> <span class="mi">16</span> <span class="c1"># Vaggelis to confirm</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> cannot work on more than 3D. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">length</span><span class="p">))</span></div>
+
+
+
+<div class="viewcode-block" id="FGP_dTV">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV">[docs]</a>
+<span class="k">class</span> <span class="nc">FGP_dTV</span><span class="p">(</span><span class="n">RegulariserFunction</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Creator of FGP_dTV Function</span>
+
+<span class="sd">        :param reference: reference image</span>
+<span class="sd">        :type reference: ImageData</span>
+<span class="sd">        :param alpha: regularisation parameter</span>
+<span class="sd">        :type alpha: number, default 1</span>
+<span class="sd">        :param max_iteration: max number of sub iterations. The algorithm will iterate up to this number of iteration or up to when the tolerance has been reached</span>
+<span class="sd">        :type max_iteration: integer, default 100</span>
+<span class="sd">        :param tolerance: minimum difference between previous iteration of the algorithm that determines the stop of the iteration earlier than max_iteration. If set to 0 only the max_iteration will be used as stop criterion.</span>
+<span class="sd">        :type tolerance: float, default 0</span>
+<span class="sd">        :param eta: smoothing constant to calculate gradient of the reference</span>
+<span class="sd">        :type eta: number, default 0.01</span>
+<span class="sd">        :param isotropic: Whether it uses L2 (isotropic) or L1 (anisotropic) norm</span>
+<span class="sd">        :type isotropic: boolean, default True, can range between 1 and 2</span>
+<span class="sd">        :param nonnegativity: Whether to add the non-negativity constraint</span>
+<span class="sd">        :type nonnegativity: boolean, default True</span>
+<span class="sd">        :param device: determines if the code runs on CPU or GPU</span>
+<span class="sd">        :type device: string, default &#39;cpu&#39;, can be &#39;gpu&#39; if GPU is installed</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+<div class="viewcode-block" id="FGP_dTV.__init__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reference</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+                 <span class="n">tolerance</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">isotropic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nonnegativity</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">isotropic</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">methodTV</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">methodTV</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">nonnegativity</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">nonnegativity</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">nonnegativity</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span> <span class="o">=</span> <span class="n">max_iteration</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="o">=</span> <span class="n">tolerance</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span> <span class="c1"># string for &#39;cpu&#39; or &#39;gpu&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">reference</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">=</span> <span class="n">eta</span></div>
+
+
+<div class="viewcode-block" id="FGP_dTV.__call__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__call__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: the __call__ method is not implemented. Returning NaN.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span></div>
+
+
+    <span class="k">def</span> <span class="nf">proximal_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">):</span>
+
+        <span class="n">info</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">regularisers</span><span class="o">.</span><span class="n">FGP_dTV</span><span class="p">(</span>\
+                <span class="n">in_arr</span><span class="p">,</span>\
+                <span class="bp">self</span><span class="o">.</span><span class="n">reference</span><span class="p">,</span>\
+                <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">tau</span><span class="p">,</span>\
+                <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span><span class="p">,</span>\
+                <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">,</span>\
+                <span class="bp">self</span><span class="o">.</span><span class="n">eta</span><span class="p">,</span>\
+                <span class="bp">self</span><span class="o">.</span><span class="n">methodTV</span><span class="p">,</span>\
+                <span class="bp">self</span><span class="o">.</span><span class="n">nonnegativity</span><span class="p">,</span>\
+                <span class="n">infovector</span> <span class="o">=</span> <span class="n">info</span><span class="p">,</span>
+                <span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">res</span><span class="p">,</span> <span class="n">info</span>
+
+<div class="viewcode-block" id="FGP_dTV.convex_conjugate">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: the convex_conjugate method is not implemented. Returning NaN.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span></div>
+
+
+<div class="viewcode-block" id="FGP_dTV.__rmul__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__rmul__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Define the multiplication with a scalar</span>
+
+<span class="sd">        this changes the regularisation parameter in the plugin&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="bp">NotImplemented</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*=</span> <span class="n">scalar</span>
+            <span class="k">return</span> <span class="bp">self</span></div>
+
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> cannot work on more than 3D. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">length</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="TNV">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TNV">[docs]</a>
+<span class="k">class</span> <span class="nc">TNV</span><span class="p">(</span><span class="n">RegulariserFunction</span><span class="p">):</span>
+
+<div class="viewcode-block" id="TNV.__init__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Creator of TNV Function</span>
+
+<span class="sd">        :param alpha: regularisation parameter</span>
+<span class="sd">        :type alpha: number, default 1</span>
+<span class="sd">        :param max_iteration: max number of sub iterations. The algorithm will iterate up to this number of iteration or up to when the tolerance has been reached</span>
+<span class="sd">        :type max_iteration: integer, default 100</span>
+<span class="sd">        :param tolerance: minimum difference between previous iteration of the algorithm that determines the stop of the iteration earlier than max_iteration. If set to 0 only the max_iteration will be used as stop criterion.</span>
+<span class="sd">        :type tolerance: float, default 0</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1"># set parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span> <span class="o">=</span> <span class="n">max_iteration</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span> <span class="o">=</span> <span class="n">tolerance</span></div>
+
+
+<div class="viewcode-block" id="TNV.__call__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.__call__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">x</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: the __call__ method is not implemented. Returning NaN.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span></div>
+
+
+    <span class="k">def</span> <span class="nf">proximal_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_arr</span><span class="p">,</span> <span class="n">tau</span><span class="p">):</span>
+        <span class="c1"># remove any dimension of size 1</span>
+        <span class="n">in_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">in_arr</span><span class="p">)</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">regularisers</span><span class="o">.</span><span class="n">TNV</span><span class="p">(</span><span class="n">in_arr</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">tau</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">max_iteration</span><span class="p">,</span>
+              <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">res</span><span class="p">,</span> <span class="p">[]</span>
+
+<div class="viewcode-block" id="TNV.convex_conjugate">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.convex_conjugate">[docs]</a>
+    <span class="k">def</span> <span class="nf">convex_conjugate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: the convex_conjugate method is not implemented. Returning NaN.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span></div>
+
+
+<div class="viewcode-block" id="TNV.__rmul__">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.__rmul__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scalar</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Define the multiplication with a scalar</span>
+
+<span class="sd">        this changes the regularisation parameter in the plugin&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">scalar</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="bp">NotImplemented</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*=</span> <span class="n">scalar</span>
+            <span class="k">return</span> <span class="bp">self</span></div>
+
+
+<div class="viewcode-block" id="TNV.check_input">
+<a class="viewcode-back" href="../../../../../../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.check_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;TNV requires 2D+channel data with the first dimension as the channel dimension&#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">ImageDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;cil&#39;</span><span class="p">,</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+            <span class="k">if</span> <span class="p">(</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">==</span> <span class="mi">1</span> <span class="p">)</span> <span class="ow">or</span> <span class="p">(</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">)</span> <span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;TNV requires 2D+channel data. Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># if it is not a CIL DataContainer we assume that the data is passed in the correct order</span>
+            <span class="c1"># discard any dimension of size 1</span>
+            <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">input</span><span class="o">.</span><span class="n">shape</span> <span class="k">if</span> <span class="n">i</span><span class="o">!=</span><span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;TNV requires 3D data (with channel as first axis). Got </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
+              
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diff --git a/v24.2.0/_modules/cil/plugins/tigre/FBP/index.html b/v24.2.0/_modules/cil/plugins/tigre/FBP/index.html
new file mode 100644
index 0000000000..483ae38d64
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@@ -0,0 +1,641 @@
+
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+<html lang="en" data-content_root="../../../../../" >
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.plugins.tigre.FBP</span></li>
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+  <h1>Source code for cil.plugins.tigre.FBP</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">contextlib</span>
+<span class="kn">import</span> <span class="nn">io</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span><span class="p">,</span> <span class="n">ImageData</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">ImageDimension</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.tigre</span> <span class="kn">import</span> <span class="n">CIL2TIGREGeometry</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">tigre.algorithms</span> <span class="kn">import</span> <span class="n">fdk</span><span class="p">,</span> <span class="n">fbp</span>
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span><span class="s2">&quot;This plugin requires the additional package TIGRE</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
+            <span class="s2">&quot;Please install it via conda as tigre from the ccpi channel&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="FBP">
+<a class="viewcode-back" href="../../../../../plugins/#cil.plugins.tigre.FBP">[docs]</a>
+<span class="k">class</span> <span class="nc">FBP</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;FBP Filtered Back Projection is a reconstructor for 2D and 3D parallel and cone-beam geometries.</span>
+<span class="sd">    It is able to back-project circular trajectories with 2 PI angular range and equally spaced angular steps.</span>
+
+<span class="sd">    This uses the ram-lak filter</span>
+<span class="sd">    This is provided for simple and offset parallel-beam geometries only</span>
+
+<span class="sd">    acquisition_geometry : AcquisitionGeometry</span>
+<span class="sd">        A description of the acquisition data</span>
+
+<span class="sd">    image_geometry : ImageGeometry, default used if None</span>
+<span class="sd">        A description of the area/volume to reconstruct</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.plugins.tigre import FBP</span>
+<span class="sd">    &gt;&gt;&gt; fbp = FBP(image_geometry, data.geometry)</span>
+<span class="sd">    &gt;&gt;&gt; fbp.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; reconstruction = fbp.get_output()</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">acquisition_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Please specify an acquisition_geometry to configure this processor&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">image_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">image_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+
+        <span class="n">device</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;device&#39;</span><span class="p">,</span> <span class="s1">&#39;gpu&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">device</span> <span class="o">!=</span> <span class="s1">&#39;gpu&#39;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;TIGRE FBP is GPU only. Got device = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
+
+
+        <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;tigre&#39;</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">)</span>
+        <span class="n">ImageDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;tigre&#39;</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">)</span>
+
+
+        <span class="n">tigre_geom</span><span class="p">,</span> <span class="n">tigre_angles</span> <span class="o">=</span> <span class="n">CIL2TIGREGeometry</span><span class="o">.</span><span class="n">getTIGREGeometry</span><span class="p">(</span><span class="n">image_geometry</span><span class="p">,</span><span class="n">acquisition_geometry</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">FBP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>  <span class="n">image_geometry</span> <span class="o">=</span> <span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="p">,</span>\
+                                    <span class="n">tigre_geom</span><span class="o">=</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="n">tigre_angles</span><span class="o">=</span><span class="n">tigre_angles</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Expected input data to be single channel, got </span><span class="si">{0}</span><span class="s2">&quot;</span>\
+                 <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">channels</span><span class="p">))</span>
+
+        <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;tigre&#39;</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    
+    <span class="k">def</span> <span class="nf">_set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Configure processor attributes that require the data to setup</span>
+<span class="sd">        Must set _shape_out</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">shape</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">is2D</span><span class="p">:</span>
+            <span class="n">data_temp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+                <span class="c1"># suppress print statements from TIGRE https://github.com/CERN/TIGRE/issues/532</span>
+                <span class="k">with</span> <span class="n">contextlib</span><span class="o">.</span><span class="n">redirect_stdout</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">()):</span>
+                    <span class="n">arr_out</span> <span class="o">=</span> <span class="n">fdk</span><span class="p">(</span><span class="n">data_temp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_angles</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">arr_out</span> <span class="o">=</span> <span class="n">fbp</span><span class="p">(</span><span class="n">data_temp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_angles</span><span class="p">)</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+                <span class="c1"># suppress print statements from TIGRE https://github.com/CERN/TIGRE/issues/532</span>
+                <span class="k">with</span> <span class="n">contextlib</span><span class="o">.</span><span class="n">redirect_stdout</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">()):</span>
+                    <span class="n">arr_out</span> <span class="o">=</span> <span class="n">fdk</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_angles</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">arr_out</span> <span class="o">=</span> <span class="n">fbp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_angles</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr_out</span><span class="p">)</span></div>
+
+</pre></div>
+
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\ No newline at end of file
diff --git a/v24.2.0/_modules/cil/plugins/tigre/ProjectionOperator/index.html b/v24.2.0/_modules/cil/plugins/tigre/ProjectionOperator/index.html
new file mode 100644
index 0000000000..3373164fec
--- /dev/null
+++ b/v24.2.0/_modules/cil/plugins/tigre/ProjectionOperator/index.html
@@ -0,0 +1,771 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>cil.plugins.tigre.ProjectionOperator &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
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+  <h1>Source code for cil.plugins.tigre.ProjectionOperator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">BlockGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionDimension</span><span class="p">,</span> <span class="n">ImageDimension</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">BlockOperator</span><span class="p">,</span> <span class="n">LinearOperator</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.tigre</span> <span class="kn">import</span> <span class="n">CIL2TIGREGeometry</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">_Atb</span> <span class="kn">import</span> <span class="n">_Atb_ext</span> <span class="k">as</span> <span class="n">Atb</span>
+    <span class="kn">from</span> <span class="nn">_Ax</span> <span class="kn">import</span> <span class="n">_Ax_ext</span> <span class="k">as</span> <span class="n">Ax</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span>
+        <span class="s2">&quot;This plugin requires the additional package TIGRE</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
+        <span class="s2">&quot;Please install it via conda as tigre from the ccpi channel&quot;</span><span class="p">)</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">tigre.utilities.gpu</span> <span class="kn">import</span> <span class="n">GpuIds</span>
+    <span class="n">has_gpu_sel</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="n">has_gpu_sel</span> <span class="o">=</span> <span class="kc">False</span>
+
+
+<div class="viewcode-block" id="ProjectionOperator">
+<a class="viewcode-back" href="../../../../../plugins/#cil.plugins.tigre.ProjectionOperator">[docs]</a>
+<span class="k">class</span> <span class="nc">ProjectionOperator</span><span class="p">(</span><span class="n">LinearOperator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        ProjectionOperator configures and calls TIGRE Projectors for your dataset.</span>
+
+<span class="sd">        Please refer to the TIGRE documentation for further descriptions</span>
+<span class="sd">        https://github.com/CERN/TIGRE</span>
+<span class="sd">        https://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010</span>
+
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        image_geometry : `ImageGeometry`, default used if None</span>
+<span class="sd">            A description of the area/volume to reconstruct</span>
+
+<span class="sd">        acquisition_geometry :`AcquisitionGeometry`, `BlockGeometry`</span>
+<span class="sd">            A description of the acquisition data. If passed a BlockGeometry it will return a BlockOperator.</span>
+
+<span class="sd">        direct_method : str,  default &#39;interpolated&#39;</span>
+<span class="sd">            The method used by the forward projector, &#39;Siddon&#39; for ray-voxel intersection, &#39;interpolated&#39; for interpolated projection</span>
+
+<span class="sd">        adjoint_weights : str, default &#39;matched&#39;</span>
+<span class="sd">            The weighting method used by the cone-beam backward projector, &#39;matched&#39; for weights to approximately match the &#39;interpolated&#39; forward projector, &#39;FDK&#39; for FDK weights</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; from cil.plugins.tigre import ProjectionOperator</span>
+<span class="sd">        &gt;&gt;&gt; PO = ProjectionOperator(image.geometry, data.geometry)</span>
+<span class="sd">        &gt;&gt;&gt; forward_projection = PO.direct(image)</span>
+<span class="sd">        &gt;&gt;&gt; backward_projection = PO.adjoint(data)</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> \
+        <span class="n">direct_method</span><span class="o">=</span><span class="s1">&#39;interpolated&#39;</span><span class="p">,</span><span class="n">adjoint_weights</span><span class="o">=</span><span class="s1">&#39;matched&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">acquisition_geometry</span><span class="p">,</span> <span class="n">BlockGeometry</span><span class="p">):</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;BlockOperator is returned.&quot;</span><span class="p">)</span>
+
+            <span class="n">K</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">ag</span> <span class="ow">in</span> <span class="n">acquisition_geometry</span><span class="p">:</span>
+                <span class="n">K</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">ProjectionOperator_ag</span><span class="p">(</span><span class="n">image_geometry</span><span class="o">=</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="o">=</span><span class="n">ag</span><span class="p">,</span> \
+                        <span class="n">direct_method</span><span class="o">=</span><span class="n">direct_method</span><span class="p">,</span> <span class="n">adjoint_weights</span><span class="o">=</span><span class="n">adjoint_weights</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">return</span> <span class="n">BlockOperator</span><span class="p">(</span><span class="o">*</span><span class="n">K</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Standard Operator is returned.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">ProjectionOperator</span><span class="p">,</span>
+                         <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">ProjectionOperator_ag</span><span class="p">)</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">ProjectionOperator_ag</span><span class="p">(</span><span class="n">ProjectionOperator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;TIGRE Projection Operator&#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">acquisition_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">direct_method</span><span class="o">=</span><span class="s1">&#39;interpolated&#39;</span><span class="p">,</span>
+                 <span class="n">adjoint_weights</span><span class="o">=</span><span class="s1">&#39;matched&#39;</span><span class="p">,</span>
+                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        ProjectionOperator configures and calls TIGRE Projectors for your dataset.</span>
+
+<span class="sd">        Please refer to the TIGRE documentation for further descriptions</span>
+<span class="sd">        https://github.com/CERN/TIGRE</span>
+<span class="sd">        https://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010</span>
+
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        image_geometry : ImageGeometry, default used if None</span>
+<span class="sd">            A description of the area/volume to reconstruct</span>
+
+<span class="sd">        acquisition_geometry : AcquisitionGeometry</span>
+<span class="sd">            A description of the acquisition data</span>
+
+<span class="sd">        direct_method : str,  default &#39;interpolated&#39;</span>
+<span class="sd">            The method used by the forward projector, &#39;Siddon&#39; for ray-voxel intersection, &#39;interpolated&#39; for interpolated projection</span>
+
+<span class="sd">        adjoint_weights : str, default &#39;matched&#39;</span>
+<span class="sd">            The weighting method used by the cone-beam backward projector, &#39;matched&#39; for weights to approximately match the &#39;interpolated&#39; forward projector, &#39;FDK&#39; for FDK weights</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; from cil.plugins.tigre import ProjectionOperator</span>
+<span class="sd">        &gt;&gt;&gt; PO = ProjectionOperator(image.geometry, data.geometry)</span>
+<span class="sd">        &gt;&gt;&gt; forward_projection = PO.direct(image)</span>
+<span class="sd">        &gt;&gt;&gt; backward_projection = PO.adjoint(data)</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">acquisition_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Please specify an acquisition_geometry to configure this operator&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">image_geometry</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">image_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+
+        <span class="n">device</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;device&#39;</span><span class="p">,</span> <span class="s1">&#39;gpu&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">device</span> <span class="o">!=</span> <span class="s1">&#39;gpu&#39;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;TIGRE projectors are GPU only. Got device = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">device</span><span class="p">))</span>
+
+        <span class="n">ImageDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;tigre&#39;</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">)</span>
+        <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">check_order_for_engine</span><span class="p">(</span><span class="s1">&#39;tigre&#39;</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">ProjectionOperator</span><span class="p">,</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">domain_geometry</span><span class="o">=</span><span class="n">image_geometry</span><span class="p">,</span>\
+             <span class="n">range_geometry</span><span class="o">=</span><span class="n">acquisition_geometry</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">direct_method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;interpolated&#39;</span><span class="p">,</span> <span class="s1">&#39;Siddon&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;direct_method expected &#39;interpolated&#39; or &#39;Siddon&#39; got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span>
+                <span class="nb">format</span><span class="p">(</span><span class="n">direct_method</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">adjoint_weights</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;matched&#39;</span><span class="p">,</span> <span class="s1">&#39;FDK&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;adjoint_weights expected &#39;matched&#39; or &#39;FDK&#39; got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">adjoint_weights</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;direct&#39;</span><span class="p">:</span> <span class="n">direct_method</span><span class="p">,</span> <span class="s1">&#39;adjoint&#39;</span><span class="p">:</span> <span class="n">adjoint_weights</span><span class="p">}</span>
+
+        <span class="c1">#set up TIGRE geometry</span>
+        <span class="n">tigre_geom</span><span class="p">,</span> <span class="n">tigre_angles</span> <span class="o">=</span> <span class="n">CIL2TIGREGeometry</span><span class="o">.</span><span class="n">getTIGREGeometry</span><span class="p">(</span>
+            <span class="n">image_geometry</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">)</span>
+
+        <span class="n">tigre_geom</span><span class="o">.</span><span class="n">check_geo</span><span class="p">(</span><span class="n">tigre_angles</span><span class="p">)</span>
+        <span class="n">tigre_geom</span><span class="o">.</span><span class="n">cast_to_single</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span> <span class="o">=</span> <span class="n">tigre_geom</span>
+
+        <span class="c1">#set up TIGRE GPU targets (from 2.2)</span>
+        <span class="k">if</span> <span class="n">has_gpu_sel</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">gpuids</span> <span class="o">=</span> <span class="n">GpuIds</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">__call_Ax</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">has_gpu_sel</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">Ax</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">angles</span><span class="p">,</span>
+                      <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">[</span><span class="s1">&#39;direct&#39;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpuids</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">Ax</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">angles</span><span class="p">,</span>
+                      <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">[</span><span class="s1">&#39;direct&#39;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">mode</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">direct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">is2D</span><span class="p">:</span>
+            <span class="n">data_temp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__call_Ax</span><span class="p">(</span><span class="n">data_temp</span><span class="p">)</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__call_Ax</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="c1">#if single angle projection remove the dimension for CIL</span>
+        <span class="k">if</span> <span class="n">arr_out</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">AcquisitionData</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span>
+                                  <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                  <span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_range_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                                  <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr_out</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__call_Atb</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">has_gpu_sel</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">Atb</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">angles</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">[</span><span class="s1">&#39;adjoint&#39;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">gpuids</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">Atb</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">angles</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">[</span><span class="s1">&#39;adjoint&#39;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">mode</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="c1">#if single angle projection add the dimension in for TIGRE</span>
+        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">ANGLE</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tigre_geom</span><span class="o">.</span><span class="n">is2D</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__call_Atb</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__call_Atb</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span>
+                            <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                            <span class="n">geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_domain_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                            <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr_out</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">domain_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_domain_geometry</span>
+
+    <span class="k">def</span> <span class="nf">range_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_range_geometry</span>
+</pre></div>
+
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+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
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+                  
+  <h1>Source code for cil.processors.AbsorptionTransmissionConverter</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">ImageGeometry</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+
+<div class="viewcode-block" id="AbsorptionTransmissionConverter">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.AbsorptionTransmissionConverter">[docs]</a>
+<span class="k">class</span> <span class="nc">AbsorptionTransmissionConverter</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Processor to convert from absorption measurements to transmission</span>
+
+<span class="sd">    :param white_level: A float defining incidence intensity in the Beer-Lambert law.</span>
+<span class="sd">    :type white_level: float, optional</span>
+<span class="sd">    :return: returns AcquisitionData, ImageData or DataContainer depending on input data type</span>
+<span class="sd">    :rtype: AcquisitionData, ImageData or DataContainer</span>
+
+<span class="sd">    Processor first multiplies data by -1, then calculates exponent</span>
+<span class="sd">    and scales result by white_level (default=1)</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">white_level</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;white_level&#39;</span><span class="p">:</span> <span class="n">white_level</span><span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">AbsorptionTransmissionConverter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Processor supports only following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - DataContainer&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="n">out</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">white_level</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+
+</pre></div>
+
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+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
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+                  
+  <h1>Source code for cil.processors.Binner</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">Slicer</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">cil.processors.cilacc_binner</span> <span class="kn">import</span> <span class="n">Binner_IPP</span>
+    <span class="n">has_ipp</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span><span class="p">:</span>
+    <span class="n">has_ipp</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="c1"># Note to developers: Binner and Slicer share a lot of common code</span>
+<span class="c1"># so Binner has been implemented as a child of Slicer. This makes use</span>
+<span class="c1"># of commonality and redefines only the methods that differ. These methods</span>
+<span class="c1"># dictate the style of slicer</span>
+<div class="viewcode-block" id="Binner">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Binner">[docs]</a>
+<span class="k">class</span> <span class="nc">Binner</span><span class="p">(</span><span class="n">Slicer</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;This creates a Binner processor.</span>
+
+<span class="sd">    The processor will crop the data, and then average together n input pixels along a dimension from the starting index.</span>
+
+<span class="sd">    The output will be a data container with the data, and geometry updated to reflect the operation.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    roi : dict</span>
+<span class="sd">        The region-of-interest to bin {&#39;axis_name1&#39;:(start,stop,step), &#39;axis_name2&#39;:(start,stop,step)}</span>
+<span class="sd">        The `key` being the axis name to apply the processor to, the `value` holding a tuple containing the ROI description</span>
+
+<span class="sd">        Start: Starting index of input data. Must be an integer, or `None` defaults to index 0.</span>
+<span class="sd">        Stop: Stopping index of input data. Must be an integer, or `None` defaults to index N.</span>
+<span class="sd">        Step: Number of pixels to average together. Must be an integer or `None` defaults to 1.</span>
+
+<span class="sd">    accelerated : boolean, default=True</span>
+<span class="sd">        Uses the CIL accelerated backend if `True`, numpy if `False`.</span>
+
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &gt;&gt;&gt; from cil.processors import Binner</span>
+<span class="sd">    &gt;&gt;&gt; roi = {&#39;horizontal&#39;:(10,-10,2),&#39;vertical&#39;:(10,-10,2)}</span>
+<span class="sd">    &gt;&gt;&gt; processor = Binner(roi)</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; data_binned = processor.get_output()</span>
+
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.processors import Binner</span>
+<span class="sd">    &gt;&gt;&gt; roi = {&#39;horizontal&#39;:(None,None,2),&#39;vertical&#39;:(None,None,2)}</span>
+<span class="sd">    &gt;&gt;&gt; processor = Binner(roi)</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data.geometry)</span>
+<span class="sd">    &gt;&gt;&gt; geometry_binned = processor.get_output()</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    The indices provided are start inclusive, stop exclusive.</span>
+
+<span class="sd">    All elements along a dimension will be included if the axis does not appear in the roi dictionary, or if passed as {&#39;axis_name&#39;,-1}</span>
+
+<span class="sd">    If only one number is provided, then it is interpreted as Stop. i.e. {&#39;axis_name1&#39;:(stop)}</span>
+<span class="sd">    If two numbers are provided, then they are interpreted as Start and Stop  i.e. {&#39;axis_name1&#39;:(start, stop)}</span>
+
+<span class="sd">    Negative indexing can be used to specify the index. i.e. {&#39;axis_name1&#39;:(10, -10)} will crop the dimension symmetrically</span>
+
+<span class="sd">    If Stop - Start is not multiple of Step, then</span>
+<span class="sd">    the resulted dimension will have (Stop - Start) // Step</span>
+<span class="sd">    elements, i.e. (Stop - Start) % Step elements will be ignored</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">roi</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">accelerated</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">accelerated</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">has_ipp</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Cannot run accelerated Binner without the IPP libraries.&quot;</span><span class="p">)</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">Binner</span><span class="p">,</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">roi</span> <span class="o">=</span> <span class="n">roi</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_accelerated</span> <span class="o">=</span> <span class="n">accelerated</span>
+
+
+    <span class="k">def</span> <span class="nf">_configure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Once the ROI has been parsed this configures the input specifically for use with Binner</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1">#as binning we only include bins that are inside boundaries</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">stop</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">start</span><span class="p">)</span><span class="o">//</span><span class="n">x</span><span class="o">.</span><span class="n">step</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="c1"># fix roi_ordered for binner based on shape out</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+            <span class="n">start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">start</span>
+            <span class="n">stop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">step</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span>
+                <span class="n">start</span><span class="p">,</span>
+                <span class="n">stop</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">step</span>
+                <span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+
+
+    <span class="k">def</span> <span class="nf">_get_slice_position</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">roi</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the vertical position to extract a single slice for binned geometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">roi</span><span class="o">.</span><span class="n">start</span> <span class="o">+</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span><span class="o">/</span><span class="mi">2</span>
+
+
+    <span class="k">def</span> <span class="nf">_get_angles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">roi</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the binned angles according to the roi</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">n_elements</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_elements</span><span class="p">,</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">[</span><span class="n">roi</span><span class="o">.</span><span class="n">start</span><span class="p">:</span><span class="n">roi</span><span class="o">.</span><span class="n">start</span><span class="o">+</span><span class="n">n_elements</span><span class="o">*</span><span class="n">roi</span><span class="o">.</span><span class="n">step</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_bin_array_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array_in</span><span class="p">,</span> <span class="n">array_binned</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Bins the array using numpy. This method is slower and less memory efficient than self._bin_array_acc</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">shape_object</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">slice_object</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+            <span class="c1"># reshape the data to add each &#39;bin&#39; dimensions</span>
+            <span class="n">shape_object</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="n">shape_object</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">step</span><span class="p">)</span>
+
+        <span class="n">shape_object</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape_object</span><span class="p">)</span>
+        <span class="n">slice_object</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="nb">slice</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">stop</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">])</span>
+
+        <span class="n">data_resized</span> <span class="o">=</span> <span class="n">array_in</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">)[</span><span class="n">slice_object</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape_object</span><span class="p">)</span>
+
+        <span class="n">mean_order</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+            <span class="n">data_resized</span> <span class="o">=</span> <span class="n">data_resized</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">mean_order</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+        <span class="n">np</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">array_binned</span><span class="p">,</span> <span class="n">data_resized</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_bin_array_acc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array_in</span><span class="p">,</span> <span class="n">array_binned</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Bins the array using the accelerated CIL backend</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">indices_start</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">start</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">]</span>
+        <span class="n">bins</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">step</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">]</span>
+
+        <span class="n">binner_ipp</span> <span class="o">=</span> <span class="n">Binner_IPP</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">,</span> <span class="n">indices_start</span><span class="p">,</span> <span class="n">bins</span><span class="p">)</span>
+
+        <span class="n">res</span> <span class="o">=</span> <span class="n">binner_ipp</span><span class="o">.</span><span class="n">bin</span><span class="p">(</span><span class="n">array_in</span><span class="p">,</span> <span class="n">array_binned</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">res</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Call failed&quot;</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_process_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dc_in</span><span class="p">,</span> <span class="n">dc_out</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Bin the data array</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_accelerated</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bin_array_acc</span><span class="p">(</span><span class="n">dc_in</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">dc_out</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bin_array_numpy</span><span class="p">(</span><span class="n">dc_in</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">dc_out</span><span class="o">.</span><span class="n">array</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/processors/CentreOfRotationCorrector/index.html b/v24.2.0/_modules/cil/processors/CentreOfRotationCorrector/index.html
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+  <h1>Source code for cil.processors.CentreOfRotationCorrector</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span>
+<span class="kn">from</span> <span class="nn">cil.processors.CofR_xcorrelation</span> <span class="kn">import</span> <span class="n">CofR_xcorrelation</span>
+<span class="kn">from</span> <span class="nn">cil.processors.CofR_image_sharpness</span> <span class="kn">import</span> <span class="n">CofR_image_sharpness</span>
+
+
+<div class="viewcode-block" id="CentreOfRotationCorrector">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.CentreOfRotationCorrector">[docs]</a>
+<span class="k">class</span> <span class="nc">CentreOfRotationCorrector</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This class contains methods to create a CentreOfRotationCorrector processor using the desired algorithm.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="CentreOfRotationCorrector.xcorrelation">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.CentreOfRotationCorrector.xcorrelation">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">xcorrelation</span><span class="p">(</span><span class="n">slice_index</span><span class="o">=</span><span class="s1">&#39;centre&#39;</span><span class="p">,</span> <span class="n">projection_index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ang_tol</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates a CentreOfRotationCorrector processor using the cross-correlation algorithm.</span>
+
+<span class="sd">        For use on parallel-beam geometry it requires two projections 180 degree apart.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        slice_index: int or str, optional</span>
+<span class="sd">            An integer defining the vertical slice to run the algorithm on or string=&#39;centre&#39; specifying the central slice should be used (default is &#39;centre&#39;)</span>
+
+<span class="sd">        projection_index: int or list/tuple of ints, optional</span>
+<span class="sd">            An integer defining the index of the first projection the cross correlation algorithm will use, where the second projection is chosen as the projection closest to 180 degrees from this.</span>
+<span class="sd">            Or a list/tuple of ints specifying the two indices to be used for cross correlation (default is 0)</span>
+
+<span class="sd">        ang_tol: float, optional</span>
+<span class="sd">            The angular tolerance in degrees between the two input projections 180 degree gap (default is 0.1)</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; from cil.processors import CentreOfRotationCorrector</span>
+<span class="sd">        &gt;&gt;&gt; processor = CentreOfRotationCorrector.xcorrelation(&#39;centre&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_centred = processor.get_ouput()</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; from cil.processors import CentreOfRotationCorrector</span>
+<span class="sd">        &gt;&gt;&gt; processor = CentreOfRotationCorrector.xcorrelation(slice_index=120)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; processor.get_ouput(out=data)</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; from cil.processors import CentreOfRotationCorrector</span>
+<span class="sd">        &gt;&gt;&gt; import logging</span>
+<span class="sd">        &gt;&gt;&gt; logging.basicConfig(level=logging.WARNING)</span>
+<span class="sd">        &gt;&gt;&gt; cil_log_level = logging.getLogger(&#39;cil.processors&#39;)</span>
+<span class="sd">        &gt;&gt;&gt; cil_log_level.setLevel(logging.DEBUG)</span>
+
+<span class="sd">        &gt;&gt;&gt; processor = CentreOfRotationCorrector.xcorrelation(slice_index=120)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_centred = processor.get_output()</span>
+
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        setting logging to &#39;debug&#39; will give you more information about the algorithm progress</span>
+
+
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">CofR_xcorrelation</span><span class="p">(</span><span class="n">slice_index</span><span class="p">,</span> <span class="n">projection_index</span><span class="p">,</span> <span class="n">ang_tol</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+
+<div class="viewcode-block" id="CentreOfRotationCorrector.image_sharpness">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.CentreOfRotationCorrector.image_sharpness">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">image_sharpness</span><span class="p">(</span><span class="n">slice_index</span><span class="o">=</span><span class="s1">&#39;centre&#39;</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;tigre&#39;</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span> <span class="n">search_range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">initial_binning</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;This creates a CentreOfRotationCorrector processor.</span>
+
+<span class="sd">        The processor will find the centre offset by maximising the sharpness of a reconstructed slice.</span>
+
+<span class="sd">        Can be used on single slice parallel-beam, and centre slice cone beam geometry. For use only with datasets that can be reconstructed with FBP/FDK.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        slice_index : int, str, default=&#39;centre&#39;</span>
+<span class="sd">            An integer defining the vertical slice to run the algorithm on. The special case slice &#39;centre&#39; is the default.</span>
+
+<span class="sd">        backend : {&#39;tigre&#39;, &#39;astra&#39;}</span>
+<span class="sd">            The backend to use for the reconstruction</span>
+
+<span class="sd">        tolerance : float, default=0.005</span>
+<span class="sd">            The tolerance of the fit in pixels, the default is 1/200 of a pixel. This is a stopping criteria, not a statement of accuracy of the algorithm.</span>
+
+<span class="sd">        search_range : int</span>
+<span class="sd">            The range in pixels to search either side of the panel centre. If `None` a quarter of the width of the panel is used.</span>
+
+<span class="sd">        initial_binning : int</span>
+<span class="sd">            The size of the bins for the initial search. If `None` will bin the image to a step corresponding to &lt;128 pixels. The fine search will be on unbinned data.</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        from cil.processors import CentreOfRotationCorrector</span>
+
+<span class="sd">        processor = CentreOfRotationCorrector.image_sharpness(&#39;centre&#39;, &#39;tigre&#39;)</span>
+<span class="sd">        processor.set_input(data)</span>
+<span class="sd">        data_centred = processor.get_output()</span>
+
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        from cil.processors import CentreOfRotationCorrector</span>
+
+<span class="sd">        processor = CentreOfRotationCorrector.image_sharpness(slice_index=120, &#39;astra&#39;)</span>
+<span class="sd">        processor.set_input(data)</span>
+<span class="sd">        processor.get_output(out=data)</span>
+
+
+<span class="sd">        Note</span>
+<span class="sd">        ----</span>
+<span class="sd">        For best results data should be 360deg which leads to blurring with incorrect geometry.</span>
+<span class="sd">        This method is unreliable on half-scan data with &#39;tuning-fork&#39; style artifacts.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">CofR_image_sharpness</span><span class="p">(</span><span class="n">slice_index</span><span class="o">=</span><span class="n">slice_index</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="n">tolerance</span><span class="p">,</span> <span class="n">search_range</span><span class="o">=</span><span class="n">search_range</span><span class="p">,</span> <span class="n">initial_binning</span><span class="o">=</span><span class="n">initial_binning</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+</div>
+
+</pre></div>
+
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+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
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+  <h1>Source code for cil.processors.MaskGenerator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">,</span> <span class="n">ImageGeometry</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">special</span><span class="p">,</span> <span class="n">ndimage</span>
+
+<div class="viewcode-block" id="MaskGenerator">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.MaskGenerator">[docs]</a>
+<span class="k">class</span> <span class="nc">MaskGenerator</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Processor to detect outliers and return a mask with 0 where outliers were detected, and 1 for other pixels. Please use the desiried method to configure a processor for your needs.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+<div class="viewcode-block" id="MaskGenerator.special_values">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.MaskGenerator.special_values">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">special_values</span><span class="p">(</span><span class="n">nan</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inf</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates a MaskGenerator processor which generates a mask for inf and/or nan values.</span>
+
+<span class="sd">        :param nan: mask NaN values</span>
+<span class="sd">        :type nan: bool, default=True</span>
+<span class="sd">        :param inf: mask INF values</span>
+<span class="sd">        :type inf: bool, default=True</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">nan</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">inf</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+                <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;special_values&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nan&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">inf</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+                <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;inf&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Please specify at least one type of value to threshold on&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="MaskGenerator.threshold">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.MaskGenerator.threshold">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">threshold</span><span class="p">(</span><span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates a MaskGenerator processor which generates a mask for values outside boundaries</span>
+
+<span class="sd">        :param min_val: lower boundary</span>
+<span class="sd">        :type min_val: float, default=None</span>
+<span class="sd">        :param max_val: upper boundary</span>
+<span class="sd">        :type max_val: float, default=None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;threshold&#39;</span><span class="p">,</span> <span class="n">threshold_value</span><span class="o">=</span><span class="p">(</span><span class="n">min_val</span><span class="p">,</span><span class="n">max_val</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="MaskGenerator.quantile">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.MaskGenerator.quantile">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">quantile</span><span class="p">(</span><span class="n">min_quantile</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_quantile</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates a MaskGenerator processor which generates a mask for values outside boundaries</span>
+
+<span class="sd">        :param min_quantile: lower quantile, 0-1</span>
+<span class="sd">        :type min_quantile: float, default=None</span>
+<span class="sd">        :param max_quantile: upper quantile, 0-1</span>
+<span class="sd">        :type max_quantile: float, default=None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;quantile&#39;</span><span class="p">,</span> <span class="n">quantiles</span><span class="o">=</span><span class="p">(</span><span class="n">min_quantile</span><span class="p">,</span><span class="n">max_quantile</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="MaskGenerator.mean">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.MaskGenerator.mean">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">threshold_factor</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates a MaskGenerator processor which generates a mask for values outside a multiple of standard-devaiations from the mean.</span>
+
+<span class="sd">        abs(A - mean(A)) &lt; threshold_factor * std(A).</span>
+
+<span class="sd">        :param threshold_factor: scale factor of standard-deviations to use as threshold</span>
+<span class="sd">        :type threshold_factor: float, default=3</span>
+<span class="sd">        :param axis: specify axis as int or from &#39;dimension_labels&#39; to calculate mean. If no axis is specified then operates over flattened array.</span>
+<span class="sd">        :type axis: int, string</span>
+<span class="sd">        :param window: specify number of pixels to use in calculation of a rolling mean</span>
+<span class="sd">        :type window: int, default=None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">window</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">threshold_factor</span><span class="o">=</span><span class="n">threshold_factor</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;movmean&#39;</span><span class="p">,</span> <span class="n">threshold_factor</span><span class="o">=</span><span class="n">threshold_factor</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="MaskGenerator.median">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.MaskGenerator.median">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">median</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">threshold_factor</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;This creates a MaskGenerator processor which generates a mask for values outside a multiple of median absolute deviation (MAD) from the mean.</span>
+
+<span class="sd">        abs(A - median(A)) &lt; threshold_factor * MAD(A),</span>
+<span class="sd">        MAD = c*median(abs(A-median(A))) where c=-1/(sqrt(2)*erfcinv(3/2))</span>
+
+<span class="sd">        :param threshold_factor: scale factor of MAD to use as threshold</span>
+<span class="sd">        :type threshold_factor: float, default=3</span>
+<span class="sd">        :param axis: specify axis as int or from &#39;dimension_labels&#39; to calculate mean. If no axis is specified then operates over flattened array.</span>
+<span class="sd">        :type axis: int, string</span>
+<span class="sd">        :param window: specify number of pixels to use in calculation of a rolling median</span>
+<span class="sd">        :type window: int, default=None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="n">window</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">threshold_factor</span><span class="o">=</span><span class="n">threshold_factor</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">processor</span> <span class="o">=</span> <span class="n">MaskGenerator</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;movmedian&#39;</span><span class="p">,</span> <span class="n">threshold_factor</span><span class="o">=</span><span class="n">threshold_factor</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;special_values&#39;</span><span class="p">,</span>
+                 <span class="n">threshold_value</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
+                 <span class="n">quantiles</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
+                 <span class="n">threshold_factor</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+                 <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+                 <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Processor to detect outliers and return mask with 0 where outliers were detected and 1 for other pixels.</span>
+
+<span class="sd">            :param mode: a method for detecting outliers (special_values, nan, inf, threshold, quantile, mean, median, movmean, movmedian)</span>
+<span class="sd">            :type mode: string, default=&#39;special_values&#39;</span>
+<span class="sd">            :param threshold_value: specify lower and upper boundaries if &#39;threshold&#39; mode is selected</span>
+<span class="sd">            :type threshold_value: tuple</span>
+<span class="sd">            :param quantiles: specify lower and upper quantiles if &#39;quantile&#39; mode is selected</span>
+<span class="sd">            :type quantiles: tuple</span>
+<span class="sd">            :param threshold_factor: scales detection threshold (standard deviation in case of &#39;mean&#39;, &#39;movmean&#39; and median absolute deviation in case of &#39;median&#39;, movmedian&#39;)</span>
+<span class="sd">            :type threshold_factor: float, default=3</span>
+<span class="sd">            :param window: specify running window if &#39;movmean&#39; or &#39;movmedian&#39; mode is selected</span>
+<span class="sd">            :type window: int, default=5</span>
+<span class="sd">            :param axis: specify axis to alculate statistics for &#39;mean&#39;, &#39;median&#39;, &#39;movmean&#39;, &#39;movmean&#39; modes</span>
+<span class="sd">            :type axis: int, string</span>
+<span class="sd">            :return: returns a DataContainer with boolean mask with 0 where outliers were detected</span>
+<span class="sd">            :rtype: DataContainer</span>
+
+<span class="sd">        - special_values    test element-wise for both inf and nan</span>
+<span class="sd">        - nan               test element-wise for nan</span>
+<span class="sd">        - inf               test element-wise for inf</span>
+<span class="sd">        - threshold         test element-wise if array values are within boundaries</span>
+<span class="sd">                            given by threshold_values = (float,float).</span>
+<span class="sd">                            You can secify only lower threshold value by setting another to None</span>
+<span class="sd">                            such as threshold_values = (float,None), then</span>
+<span class="sd">                            upper boundary will be amax(data). Similarly, to specify only upper</span>
+<span class="sd">                            boundary, use threshold_values = (None,float). If both threshold_values</span>
+<span class="sd">                            are set to None, then original array will be returned.</span>
+<span class="sd">        - quantile          test element-wise if array values are within boundaries</span>
+<span class="sd">                            given by quantiles = (q1,q2), 0&lt;=q1,q2&lt;=1.</span>
+<span class="sd">                            You can secify only lower quantile value by setting another to None</span>
+<span class="sd">                            such as quantiles = (float,q2), then</span>
+<span class="sd">                            upper boundary will be amax(data). Similarly, to specify only upper</span>
+<span class="sd">                            boundary, use quantiles = (None,q1). If both quantiles</span>
+<span class="sd">                            are set to None, then original array will be returned.</span>
+<span class="sd">        - mean              test element-wise if</span>
+<span class="sd">                            abs(A - mean(A)) &lt; threshold_factor * std(A).</span>
+<span class="sd">                            Default value of threshold_factor is 3. If no axis is specified,</span>
+<span class="sd">                            then operates over flattened array. Alternatively operates along axis specified</span>
+<span class="sd">                            as dimension_label.</span>
+<span class="sd">        - median            test element-wise if</span>
+<span class="sd">                            abs(A - median(A)) &lt; threshold_factor * scaled MAD(A),</span>
+<span class="sd">                            scaled median absolute deviation (MAD) is defined as</span>
+<span class="sd">                            c*median(abs(A-median(A))) where c=-1/(sqrt(2)*erfcinv(3/2))</span>
+<span class="sd">                            Default value of threshold_factor is 3. If no axis is specified,</span>
+<span class="sd">                            then operates over flattened array. Alternatively operates along axis specified</span>
+<span class="sd">                            as dimension_label.</span>
+<span class="sd">        - movmean           the same as mean but uses rolling mean with a specified window,</span>
+<span class="sd">                            default window value is 5</span>
+<span class="sd">        - movmedian         the same as mean but uses rolling median with a specified window,</span>
+<span class="sd">                            default window value is 5</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;mode&#39;</span><span class="p">:</span> <span class="n">mode</span><span class="p">,</span>
+                <span class="s1">&#39;threshold_value&#39;</span><span class="p">:</span> <span class="n">threshold_value</span><span class="p">,</span>
+                <span class="s1">&#39;threshold_factor&#39;</span><span class="p">:</span> <span class="n">threshold_factor</span><span class="p">,</span>
+                <span class="s1">&#39;quantiles&#39;</span><span class="p">:</span> <span class="n">quantiles</span><span class="p">,</span>
+                <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="n">window</span><span class="p">,</span>
+                <span class="s1">&#39;axis&#39;</span><span class="p">:</span> <span class="n">axis</span><span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">MaskGenerator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;special_values&#39;</span><span class="p">,</span> <span class="s1">&#39;nan&#39;</span><span class="p">,</span> <span class="s1">&#39;inf&#39;</span><span class="p">,</span> <span class="s1">&#39;threshold&#39;</span><span class="p">,</span> <span class="s1">&#39;quantile&#39;</span><span class="p">,</span>
+                             <span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="s1">&#39;movmean&#39;</span><span class="p">,</span> <span class="s1">&#39;movmedian&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Wrong mode. One of the following is expected:</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
+                            <span class="s2">&quot;special_values, nan, inf, threshold, </span><span class="se">\n</span><span class="s2"> quantile, mean, median, movmean, movmedian&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="nb">int</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Wrong label is specified for axis. &quot;</span> <span class="o">+</span>
+                                <span class="s2">&quot;Expected </span><span class="si">{}</span><span class="s2">, got </span><span class="si">{}</span><span class="s2">.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="kc">True</span>
+    
+    <span class="k">def</span> <span class="nf">check_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="nb">bool</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Input type mismatch: got </span><span class="si">{0}</span><span class="s2"> expecting </span><span class="si">{1}</span><span class="s2">&quot;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">bool</span><span class="p">))</span>
+        
+        <span class="k">return</span> <span class="kc">True</span>
+
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="c1"># get input DataContainer</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">data</span>
+
+        <span class="n">ndim</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">ndim</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">axis_index</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> <span class="o">==</span> <span class="nb">int</span><span class="p">:</span>
+                <span class="n">axis_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">axis_index</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># intialise mask with all ones</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+
+        <span class="c1"># if NaN or +/-Inf</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;special_values&#39;</span><span class="p">:</span>
+
+            <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">logical_or</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">arr</span><span class="p">))]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;nan&#39;</span><span class="p">:</span>
+
+            <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">arr</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;inf&#39;</span><span class="p">:</span>
+
+            <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">arr</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;threshold&#39;</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="ow">not</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">threshold_value</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Threshold value must be given as a tuple containing two values,</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>\
+                    <span class="s2">&quot;use None if no threshold value is given&quot;</span><span class="p">)</span>
+
+            <span class="n">threshold</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_threshold_value</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+            <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">logical_or</span><span class="p">(</span><span class="n">arr</span> <span class="o">&lt;</span> <span class="n">threshold</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">arr</span> <span class="o">&gt;</span> <span class="n">threshold</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;quantile&#39;</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="ow">not</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quantiles</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Quantiles must be given as a tuple containing two values,</span><span class="se">\n</span><span class="s2"> &quot;</span> <span class="o">+</span> \
+                    <span class="s2">&quot;use None if no quantile value is given&quot;</span><span class="p">)</span>
+
+            <span class="n">quantile</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_threshold_value</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+            <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">logical_or</span><span class="p">(</span><span class="n">arr</span> <span class="o">&lt;</span> <span class="n">quantile</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">arr</span> <span class="o">&gt;</span> <span class="n">quantile</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
+
+            <span class="c1"># if mean along specific axis</span>
+            <span class="k">if</span> <span class="n">axis_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tile_par</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">slice_obj</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">axis_index</span><span class="p">:</span>
+                        <span class="n">tile_par</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">axis_index</span><span class="p">)</span>
+                        <span class="n">slice_obj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">newaxis</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">tile_par</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+                        <span class="n">slice_obj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="n">tile_par</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tile_par</span><span class="p">)</span>
+                <span class="n">slice_obj</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">slice_obj</span><span class="p">)</span>
+
+                <span class="n">tmp_mean</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">tile</span><span class="p">((</span><span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis_index</span><span class="p">))[</span><span class="n">slice_obj</span><span class="p">],</span> <span class="n">tile_par</span><span class="p">)</span>
+                <span class="n">tmp_std</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">tile</span><span class="p">((</span><span class="n">numpy</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis_index</span><span class="p">))[</span><span class="n">slice_obj</span><span class="p">],</span> <span class="n">tile_par</span><span class="p">)</span>
+                <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">tmp_mean</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">tmp_std</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+            <span class="c1"># if global mean</span>
+            <span class="k">else</span><span class="p">:</span>
+
+                 <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">arr</span><span class="p">))</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">arr</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;median&#39;</span><span class="p">:</span>
+
+            <span class="n">c</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">special</span><span class="o">.</span><span class="n">erfcinv</span><span class="p">(</span><span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+
+            <span class="c1"># if median along specific axis</span>
+            <span class="k">if</span> <span class="n">axis_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tile_par</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">slice_obj</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">axis_index</span><span class="p">:</span>
+                        <span class="n">tile_par</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">axis_index</span><span class="p">)</span>
+                        <span class="n">slice_obj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">newaxis</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">tile_par</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+                        <span class="n">slice_obj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="n">tile_par</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tile_par</span><span class="p">)</span>
+                <span class="n">slice_obj</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">slice_obj</span><span class="p">)</span>
+
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">tile</span><span class="p">((</span><span class="n">numpy</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis_index</span><span class="p">))[</span><span class="n">slice_obj</span><span class="p">],</span> <span class="n">tile_par</span><span class="p">))</span>
+                <span class="n">median_absolute_dev</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">tile</span><span class="p">((</span><span class="n">numpy</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis_index</span><span class="p">))[</span><span class="n">slice_obj</span><span class="p">],</span> <span class="n">tile_par</span><span class="p">)</span>
+                <span class="n">mask</span><span class="p">[</span><span class="n">tmp</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">c</span> <span class="o">*</span> <span class="n">median_absolute_dev</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+            <span class="c1"># if global median</span>
+            <span class="k">else</span><span class="p">:</span>
+
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">arr</span><span class="p">))</span>
+                <span class="n">mask</span><span class="p">[</span><span class="n">tmp</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">c</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">tmp</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;movmean&#39;</span><span class="p">:</span>
+
+            <span class="c1"># if movmean along specific axis</span>
+            <span class="k">if</span> <span class="n">axis_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">kernel</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">ndim</span>
+                <span class="n">kernel</span><span class="p">[</span><span class="n">axis_index</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window</span>
+                <span class="n">kernel</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
+
+                <span class="n">mean_array</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">generic_filter</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
+                <span class="n">std_array</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">generic_filter</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">std</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
+
+                <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">mean_array</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">std_array</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+            <span class="c1"># if global movmean</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mean_array</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">generic_filter</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">,)</span><span class="o">*</span><span class="n">ndim</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
+                <span class="n">std_array</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">generic_filter</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">std</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">,)</span><span class="o">*</span><span class="n">ndim</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
+
+                <span class="n">mask</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">mean_array</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">std_array</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;movmedian&#39;</span><span class="p">:</span>
+
+            <span class="n">c</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">special</span><span class="o">.</span><span class="n">erfcinv</span><span class="p">(</span><span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+
+            <span class="c1"># if movmedian along specific axis</span>
+            <span class="k">if</span> <span class="n">axis_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+                <span class="c1"># construct filter kernel</span>
+                <span class="n">kernel_shape</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">axis_index</span><span class="p">:</span>
+                        <span class="n">kernel_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">kernel_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+                <span class="n">kernel_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kernel_shape</span><span class="p">)</span>
+
+                <span class="n">median_array</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">median_filter</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">footprint</span><span class="o">=</span><span class="n">kernel_shape</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
+
+                <span class="n">tmp</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">median_array</span><span class="p">)</span>
+                <span class="n">mask</span><span class="p">[</span><span class="n">tmp</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">c</span> <span class="o">*</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">median_filter</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">footprint</span><span class="o">=</span><span class="n">kernel_shape</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+
+            <span class="c1"># if global movmedian</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># construct filter kernel</span>
+                <span class="n">kernel_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">]</span><span class="o">*</span><span class="n">ndim</span><span class="p">)</span>
+                <span class="n">median_array</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">median_filter</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">kernel_shape</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
+
+                <span class="n">tmp</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">arr</span> <span class="o">-</span> <span class="n">median_array</span><span class="p">)</span>
+                <span class="n">mask</span><span class="p">[</span><span class="n">tmp</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_factor</span> <span class="o">*</span> <span class="n">c</span> <span class="o">*</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">median_filter</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">kernel_shape</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Mode not recognised. One of the following is expected: &#39;</span> <span class="o">+</span> \
+                              <span class="s1">&#39;special_values, nan, inf, threshold, quantile, mean, median, movmean, movmedian&#39;</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)(</span><span class="n">mask</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mask</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">suppress_warning</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
+        
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">_parse_threshold_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+
+        <span class="n">lower_val</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">upper_val</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="n">quantile</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantiles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">lower_val</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantiles</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantiles</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">upper_val</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantiles</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_value</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">lower_val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_value</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_value</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">upper_val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold_value</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">lower_val</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">lower_val</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">amin</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">upper_val</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">upper_val</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">upper_val</span> <span class="o">&lt;=</span> <span class="n">lower_val</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Upper threshold value must be larger than &quot;</span> <span class="o">+</span> \
+                <span class="s2">&quot;lower treshold value or min of data&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">lower_val</span><span class="p">,</span> <span class="n">upper_val</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/processors/Masker/index.html b/v24.2.0/_modules/cil/processors/Masker/index.html
new file mode 100644
index 0000000000..4eb0a93727
--- /dev/null
+++ b/v24.2.0/_modules/cil/processors/Masker/index.html
@@ -0,0 +1,821 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../" >
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+<form class="bd-search d-flex align-items-center"
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+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
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+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
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+    Framework
+  </a>
+</li>
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+    Read/ write AcquisitionData and ImageData
+  </a>
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+    Optimisation framework
+  </a>
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+  </a>
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+    CIL Plugins
+  </a>
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+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.processors.Masker</span></li>
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+  <h1>Source code for cil.processors.Masker</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">DataContainer</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">interpolate</span>
+
+<div class="viewcode-block" id="Masker">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Masker">[docs]</a>
+<span class="k">class</span> <span class="nc">Masker</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Processor to fill missing values provided by mask.</span>
+<span class="sd">    Please use the desired method to configure a processor for your needs.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mask : DataContainer, ImageData, AcquisitionData, numpy.ndarray</span>
+<span class="sd">        A boolean array with the same dimensions as input, where &#39;False&#39; represents masked values.</span>
+<span class="sd">        Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.</span>
+<span class="sd">        Mask can be generated using &#39;MaskGenerator&#39; processor to identify outliers.</span>
+<span class="sd">    mode : {&#39;value&#39;, &#39;mean&#39;, &#39;median&#39;, &#39;interpolate&#39;}, default=&#39;value&#39;</span>
+<span class="sd">        The method to fill in missing values</span>
+<span class="sd">    value : float, default=0</span>
+<span class="sd">        Substitute all outliers with a specific value if method=&#39;value&#39;, otherwise discarded.</span>
+<span class="sd">    axis : str or int</span>
+<span class="sd">        Specify axis as int or from &#39;dimension_labels&#39; to calculate mean, median or interpolation</span>
+<span class="sd">        (depending on mode) along that axis</span>
+<span class="sd">    method : {&#39;linear&#39;, &#39;nearest&#39;, &#39;zeros&#39;, &#39;linear&#39;, &#39;quadratic&#39;, &#39;cubic&#39;, &#39;previous&#39;, &#39;next&#39;}, default=&#39;linear&#39;</span>
+<span class="sd">        Interpolation method to use.</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+<div class="viewcode-block" id="Masker.value">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Masker.value">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">value</span><span class="p">(</span><span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns a Masker that sets the masked values of the input data to the requested value.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        mask : DataContainer, ImageData, AcquisitionData, numpy.ndarray</span>
+<span class="sd">            A boolean array with the same dimensions as input, where &#39;False&#39; represents masked values.</span>
+<span class="sd">            Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.</span>
+<span class="sd">            Mask can be generated using &#39;MaskGenerator&#39; processor to identify outliers.</span>
+<span class="sd">        value : float, default=0</span>
+<span class="sd">            Values to be assigned to missing elements</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Masker processor</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Masker</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;value&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="Masker.mean">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Masker.mean">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns a Masker that sets the masked values of the input data to the mean of the unmasked values across the array or axis.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        mask : DataContainer, ImageData, AcquisitionData, numpy.ndarray</span>
+<span class="sd">            A boolean array with the same dimensions as input, where &#39;False&#39; represents masked values.</span>
+<span class="sd">            Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.</span>
+<span class="sd">            Mask can be generated using &#39;MaskGenerator&#39; processor to identify outliers.</span>
+<span class="sd">        axis : str, int</span>
+<span class="sd">            Specify axis as int or from &#39;dimension_labels&#39; to calculate mean.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Masker processor</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Masker</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="Masker.median">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Masker.median">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">median</span><span class="p">(</span><span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns a Masker that sets the masked values of the input data to the median of the unmasked values across the array or axis.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        mask : DataContainer, ImageData, AcquisitionData, numpy.ndarray</span>
+<span class="sd">            A boolean array with the same dimensions as input, where &#39;False&#39; represents masked values.</span>
+<span class="sd">            Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.</span>
+<span class="sd">            Mask can be generated using &#39;MaskGenerator&#39; processor to identify outliers.</span>
+<span class="sd">        axis : str, int</span>
+<span class="sd">            Specify axis as int or from &#39;dimension_labels&#39; to calculate median.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Masker processor</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Masker</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="Masker.interpolate">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Masker.interpolate">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">interpolate</span><span class="p">(</span><span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Returns a Masker that operates over the specified axis and uses 1D interpolation over remaining flattened array to fill in missing values.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        mask : DataContainer, ImageData, AcquisitionData, numpy.ndarray</span>
+<span class="sd">            A boolean array with the same dimensions as input, where &#39;False&#39; represents masked values.</span>
+<span class="sd">            Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.</span>
+<span class="sd">            Mask can be generated using &#39;MaskGenerator&#39; processor to identify outliers.</span>
+<span class="sd">        axis : str, int</span>
+<span class="sd">            Specify axis as int or from &#39;dimension_labels&#39; to loop over and perform 1D interpolation.</span>
+<span class="sd">        method : {&#39;linear&#39;, &#39;nearest&#39;, &#39;zeros&#39;, &#39;linear&#39;, &#39;quadratic&#39;, &#39;cubic&#39;, &#39;previous&#39;, &#39;next&#39;}, default=&#39;linear&#39;</span>
+<span class="sd">            Interpolation method to use.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Masker processor</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Masker</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;interpolate&#39;</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">mask</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;value&#39;</span><span class="p">,</span>
+                 <span class="n">value</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+                 <span class="n">axis</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;linear&#39;</span><span class="p">):</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;mask&#39;</span><span class="p">:</span> <span class="n">mask</span><span class="p">,</span>
+                  <span class="s1">&#39;mode&#39;</span><span class="p">:</span> <span class="n">mode</span><span class="p">,</span>
+                  <span class="s1">&#39;value&#39;</span><span class="p">:</span> <span class="n">value</span><span class="p">,</span>
+                  <span class="s1">&#39;axis&#39;</span><span class="p">:</span> <span class="n">axis</span><span class="p">,</span>
+                  <span class="s1">&#39;method&#39;</span><span class="p">:</span> <span class="n">method</span><span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">Masker</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Please, provide a mask.&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Mask and Data must have the same shape.&quot;</span> <span class="o">+</span>
+                            <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> != </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="o">.</span><span class="n">mask</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">,</span> <span class="s1">&#39;dimension_labels&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Mask and Data must have the same dimension labels.&quot;</span> <span class="o">+</span>
+                            <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> != </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;value&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="s1">&#39;interpolate&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Wrong mode. One of the following is expected:</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
+                            <span class="s2">&quot;value, mean, median, interpolate&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="c1">#assumes mask has &#39;as_array&#39; method, i.e. is a DataContainer or is a numpy array</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">mask_arr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">mask_arr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span>
+
+        <span class="n">mask_arr</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mask_arr</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+
+        <span class="n">mask_invert</span> <span class="o">=</span> <span class="o">~</span><span class="n">mask_arr</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">axis_index</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span> <span class="o">==</span> <span class="nb">int</span><span class="p">:</span>
+                <span class="n">axis_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">axis_index</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;value&#39;</span><span class="p">:</span>
+
+            <span class="n">arr</span><span class="p">[</span><span class="n">mask_invert</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">value</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;median&#39;</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">axis_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+                <span class="n">ndim</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">number_of_dimensions</span>
+
+                <span class="n">slice_obj</span> <span class="o">=</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span> <span class="o">*</span> <span class="n">ndim</span>
+
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis_index</span><span class="p">]):</span>
+                    <span class="n">current_slice_obj</span> <span class="o">=</span> <span class="n">slice_obj</span><span class="p">[:]</span>
+                    <span class="n">current_slice_obj</span><span class="p">[</span><span class="n">axis_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+                    <span class="n">current_slice_obj</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">current_slice_obj</span><span class="p">)</span>
+                    <span class="n">slice_data</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[</span><span class="n">current_slice_obj</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
+                        <span class="n">slice_data</span><span class="p">[</span><span class="n">mask_invert</span><span class="p">[</span><span class="n">current_slice_obj</span><span class="p">]]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">slice_data</span><span class="p">[</span><span class="n">mask_arr</span><span class="p">[</span><span class="n">current_slice_obj</span><span class="p">]])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">slice_data</span><span class="p">[</span><span class="n">mask_invert</span><span class="p">[</span><span class="n">current_slice_obj</span><span class="p">]]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">slice_data</span><span class="p">[</span><span class="n">mask_arr</span><span class="p">[</span><span class="n">current_slice_obj</span><span class="p">]])</span>
+                    <span class="n">arr</span><span class="p">[</span><span class="n">current_slice_obj</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_data</span>
+
+            <span class="k">else</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
+                    <span class="n">arr</span><span class="p">[</span><span class="n">mask_invert</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">mask_arr</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">arr</span><span class="p">[</span><span class="n">mask_invert</span><span class="p">]</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">mask_arr</span><span class="p">])</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;interpolate&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="s1">&#39;zeros&#39;</span><span class="p">,</span> <span class="s1">&#39;linear&#39;</span><span class="p">,</span> \
+                                        <span class="s1">&#39;quadratic&#39;</span><span class="p">,</span> <span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="s1">&#39;previous&#39;</span><span class="p">,</span> <span class="s1">&#39;next&#39;</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Wrong interpolation method, one of the following is expected:</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
+                                <span class="s2">&quot;linear, nearest, zeros, linear, quadratic, cubic, previous, next&quot;</span><span class="p">)</span>
+
+            <span class="n">ndim</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">number_of_dimensions</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
+
+            <span class="k">if</span> <span class="n">axis_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span> <span class="p">(</span><span class="s1">&#39;Currently Only 1D interpolation is available. Please specify an axis to interpolate over.&#39;</span><span class="p">)</span>
+
+            <span class="n">res_dim</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">axis_index</span><span class="p">:</span>
+                    <span class="n">res_dim</span> <span class="o">*=</span> <span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+            <span class="c1"># get axis for 1D interpolation</span>
+            <span class="n">interp_axis</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="n">axis_index</span><span class="p">])</span>
+
+            <span class="c1"># loop over slice</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">res_dim</span><span class="p">):</span>
+
+                <span class="n">rest_shape</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">j</span> <span class="o">!=</span> <span class="n">axis_index</span><span class="p">:</span>
+                        <span class="n">rest_shape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
+                <span class="n">rest_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">rest_shape</span><span class="p">)</span>
+
+                <span class="n">rest_idx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">unravel_index</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">rest_shape</span><span class="p">)</span>
+
+                <span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="n">axis_index</span><span class="p">:</span>
+                        <span class="n">idx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">idx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rest_idx</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                        <span class="n">k</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">mask_invert</span><span class="p">[</span><span class="n">idx</span><span class="p">]):</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+                    <span class="n">f</span> <span class="o">=</span> <span class="n">interpolate</span><span class="o">.</span><span class="n">interp1d</span><span class="p">(</span><span class="n">interp_axis</span><span class="p">[</span><span class="n">mask_arr</span><span class="p">[</span><span class="n">idx</span><span class="p">]],</span> <span class="n">tmp</span><span class="p">[</span><span class="n">mask_arr</span><span class="p">[</span><span class="n">idx</span><span class="p">]],</span>
+                                            <span class="n">fill_value</span><span class="o">=</span><span class="s1">&#39;extrapolate&#39;</span><span class="p">,</span>
+                                            <span class="n">assume_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                            <span class="n">kind</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">)</span>
+                    <span class="n">tmp</span><span class="p">[</span><span class="n">mask_invert</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask_arr</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="n">arr</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Mode is not recognised. One of the following is expected: &#39;</span> <span class="o">+</span>
+                              <span class="s1">&#39;value, mean, median, interpolate&#39;</span><span class="p">)</span>
+
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span></div>
+
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/processors/Normaliser/index.html b/v24.2.0/_modules/cil/processors/Normaliser/index.html
new file mode 100644
index 0000000000..bac1b665fe
--- /dev/null
+++ b/v24.2.0/_modules/cil/processors/Normaliser/index.html
@@ -0,0 +1,654 @@
+
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+  <h1>Source code for cil.processors.Normaliser</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">Processor</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span>\
+ <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">ImageData</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<div class="viewcode-block" id="Normaliser">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Normaliser">[docs]</a>
+<span class="k">class</span> <span class="nc">Normaliser</span><span class="p">(</span><span class="n">Processor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Normalisation based on flat and dark</span>
+
+<span class="sd">    This processor read in a AcquisitionData and normalises it based on</span>
+<span class="sd">    the instrument reading with and without incident photons or neutrons.</span>
+
+<span class="sd">    Input: AcquisitionData</span>
+<span class="sd">    Parameter: 2D projection with flat field (or stack)</span>
+<span class="sd">               2D projection with dark field (or stack)</span>
+<span class="sd">    Output: AcquisitionDataSet</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flat_field</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dark_field</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">tolerance</span> <span class="o">=</span> <span class="mf">1e-5</span><span class="p">):</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span>
+                  <span class="s1">&#39;flat_field&#39;</span>  <span class="p">:</span> <span class="n">flat_field</span><span class="p">,</span>
+                  <span class="s1">&#39;dark_field&#39;</span>  <span class="p">:</span> <span class="n">dark_field</span><span class="p">,</span>
+                  <span class="c1"># very small number. Used when there is a division by zero</span>
+                  <span class="s1">&#39;tolerance&#39;</span>   <span class="p">:</span> <span class="n">tolerance</span>
+                  <span class="p">}</span>
+
+        <span class="c1">#DataProcessor.__init__(self, **kwargs)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Normaliser</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">flat_field</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_flat_field</span><span class="p">(</span><span class="n">flat_field</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">dark_field</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_dark_field</span><span class="p">(</span><span class="n">dark_field</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">number_of_dimensions</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">or</span>\
+           <span class="n">dataset</span><span class="o">.</span><span class="n">number_of_dimensions</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+               <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Expected input dimensions is 2 or 3, got </span><span class="si">{0}</span><span class="s2">&quot;</span>\
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">number_of_dimensions</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">set_dark_field</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="ow">is</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">df</span><span class="p">))</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Dark Field should be 2D&#39;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">df</span><span class="p">))</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dark_field</span> <span class="o">=</span> <span class="n">df</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">df</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dark_field</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">set_dark_field</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="nf">set_flat_field</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="ow">is</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">df</span><span class="p">))</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Flat Field should be 2D&#39;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">df</span><span class="p">))</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flat_field</span> <span class="o">=</span> <span class="n">df</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">df</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">flat_field</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">set_flat_field</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">as_array</span><span class="p">())</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">Normalise_projection</span><span class="p">(</span><span class="n">projection</span><span class="p">,</span> <span class="n">flat</span><span class="p">,</span> <span class="n">dark</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">):</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="n">projection</span> <span class="o">-</span> <span class="n">dark</span><span class="p">)</span>
+        <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="n">flat</span><span class="o">-</span><span class="n">dark</span><span class="p">)</span>
+        <span class="k">with</span> <span class="n">numpy</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">):</span>
+            <span class="n">c</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">true_divide</span><span class="p">(</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="p">)</span>
+            <span class="n">c</span><span class="p">[</span> <span class="o">~</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span> <span class="n">c</span> <span class="p">)]</span> <span class="o">=</span> <span class="n">tolerance</span> <span class="c1"># set to not zero if 0/0</span>
+        <span class="k">return</span> <span class="n">c</span>
+
+<div class="viewcode-block" id="Normaliser.estimate_normalised_error">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Normaliser.estimate_normalised_error">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">estimate_normalised_error</span><span class="p">(</span><span class="n">projection</span><span class="p">,</span> <span class="n">flat</span><span class="p">,</span> <span class="n">dark</span><span class="p">,</span> <span class="n">delta_flat</span><span class="p">,</span> <span class="n">delta_dark</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;returns the estimated relative error of the normalised projection</span>
+
+<span class="sd">        n = (projection - dark) / (flat - dark)</span>
+<span class="sd">        Dn/n = (flat-dark + projection-dark)/((flat-dark)*(projection-dark))*(Df/f + Dd/d)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="p">(</span><span class="n">projection</span> <span class="o">-</span> <span class="n">dark</span><span class="p">)</span>
+        <span class="n">b</span> <span class="o">=</span> <span class="p">(</span><span class="n">flat</span><span class="o">-</span><span class="n">dark</span><span class="p">)</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">delta_flat</span> <span class="o">/</span> <span class="n">flat</span>
+        <span class="n">dd</span> <span class="o">=</span> <span class="n">delta_dark</span> <span class="o">/</span> <span class="n">dark</span>
+        <span class="n">rel_norm_error</span> <span class="o">=</span> <span class="p">(</span><span class="n">b</span> <span class="o">+</span> <span class="n">a</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">b</span> <span class="o">*</span> <span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">df</span> <span class="o">+</span> <span class="n">dd</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">rel_norm_error</span></div>
+
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">projections</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="n">dark</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dark_field</span>
+        <span class="n">flat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flat_field</span>
+
+        <span class="k">if</span> <span class="n">projections</span><span class="o">.</span><span class="n">number_of_dimensions</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">projections</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">==</span> <span class="n">dark</span><span class="o">.</span><span class="n">shape</span> <span class="ow">and</span> \
+               <span class="n">projections</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">==</span> <span class="n">flat</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Flats/Dark and projections size do not match.&#39;</span><span class="p">)</span>
+
+
+            <span class="n">a</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
+                    <span class="p">[</span> <span class="n">Normaliser</span><span class="o">.</span><span class="n">Normalise_projection</span><span class="p">(</span>
+                            <span class="n">projection</span><span class="p">,</span> <span class="n">flat</span><span class="p">,</span> <span class="n">dark</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">)</span> \
+                     <span class="k">for</span> <span class="n">projection</span> <span class="ow">in</span> <span class="n">projections</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span> <span class="p">]</span>
+                    <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">projections</span><span class="o">.</span><span class="n">number_of_dimensions</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">a</span> <span class="o">=</span> <span class="n">Normaliser</span><span class="o">.</span><span class="n">Normalise_projection</span><span class="p">(</span><span class="n">projections</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span>
+                                                <span class="n">flat</span><span class="p">,</span> <span class="n">dark</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tolerance</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">projections</span><span class="p">)(</span> <span class="n">a</span> <span class="p">,</span> <span class="kc">True</span><span class="p">,</span>
+                        <span class="n">dimension_labels</span><span class="o">=</span><span class="n">projections</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span>
+                        <span class="n">geometry</span><span class="o">=</span><span class="n">projections</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
+        
+        <span class="k">return</span> <span class="n">out</span></div>
+
+</pre></div>
+
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+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../plugins/">CIL Plugins</a></li>
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+  <h1>Source code for cil.processors.Padder</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">weakref</span>
+
+<div class="viewcode-block" id="Padder">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder">[docs]</a>
+<span class="k">class</span> <span class="nc">Padder</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Processor to pad an array with a border, wrapping numpy.pad. See https://numpy.org/doc/stable/reference/generated/numpy.pad.html</span>
+
+
+<span class="sd">    It is recommended to use the static methods to configure your Padder object rather than initialising this class directly. See examples for details.</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mode: str</span>
+<span class="sd">        The method used to populate the border data. Accepts: &#39;constant&#39;, &#39;edge&#39;, &#39;linear_ramp&#39;, &#39;reflect&#39;, &#39;symmetric&#39;, &#39;wrap&#39;</span>
+<span class="sd">    pad_width: int, tuple, dict</span>
+<span class="sd">        The size of the border along each axis, see usage notes</span>
+<span class="sd">    pad_values: float, tuple, dict, default=0.0</span>
+<span class="sd">        The additional values needed by some of the modes</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    `pad_width`  behaviour (number of pixels):</span>
+<span class="sd">        - int: Each axis will be padded with a border of this size</span>
+<span class="sd">        - tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</span>
+<span class="sd">        - dict: Specified axes will be padded: e.g. {&#39;horizontal&#39;:(8, 23), &#39;vertical&#39;: 10}</span>
+
+<span class="sd">    `pad_values`  behaviour:</span>
+<span class="sd">        - float: Each border will use this value</span>
+<span class="sd">        - tuple(float, float): Each value will be used asymmetrically for each axis i.e. (before, after)</span>
+<span class="sd">        - dict: Specified axes and values: e.g. {&#39;horizontal&#39;:(8, 23), &#39;channel&#39;:5}</span>
+
+<span class="sd">    If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,</span>
+<span class="sd">    and the last two angles in geometry.angles. The user should ensure the output is as expected.</span>
+
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; processor = Padder.edge(pad_width=1)</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">    &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">    [[0. 1. 2.]</span>
+<span class="sd">    [3. 4. 5.]</span>
+<span class="sd">    [6. 7. 8.]]</span>
+<span class="sd">    &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">    [[0. 0. 1. 2. 2.]</span>
+<span class="sd">    [0. 0. 1. 2. 2.]</span>
+<span class="sd">    [3. 3. 4. 5. 5.]</span>
+<span class="sd">    [6. 6. 7. 8. 8.]</span>
+<span class="sd">    [6. 6. 7. 8. 8.]]</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; processor = Padder.constant(pad_width={&#39;horizontal_y&#39;:(1,1),&#39;horizontal_x&#39;:(1,2)}, constant_values=(-1.0, 1.0))</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">    &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">    [[0. 1. 2.]</span>
+<span class="sd">    [3. 4. 5.]</span>
+<span class="sd">    [6. 7. 8.]]</span>
+<span class="sd">    &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">    [[-1. -1. -1. -1.  1.  1.]</span>
+<span class="sd">    [-1.  0.  1.  2.  1.  1.]</span>
+<span class="sd">    [-1.  3.  4.  5.  1.  1.]</span>
+<span class="sd">    [-1.  6.  7.  8.  1.  1.]</span>
+<span class="sd">    [-1.  1.  1.  1.  1.  1.]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+<div class="viewcode-block" id="Padder.constant">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder.constant">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">constant</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">constant_values</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Padder processor wrapping numpy.pad with mode `constant`.</span>
+
+<span class="sd">        Pads the data with a constant value border. Pads in all *spatial*</span>
+<span class="sd">        dimensions unless a dictionary is passed to either `pad_width` or `constant_values`</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        pad_width: int, tuple, dict</span>
+<span class="sd">            The size of the border along each axis, see usage notes</span>
+<span class="sd">        constant_values: float, tuple, dict, default=0.0</span>
+<span class="sd">            The value of the border, see usage notes</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        `pad_width`  behaviour (number of pixels):</span>
+<span class="sd">         - int: Each axis will be padded with a border of this size</span>
+<span class="sd">         - tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes will be padded: e.g. {&#39;horizontal&#39;:(8, 23), &#39;vertical&#39;: 10}</span>
+
+<span class="sd">        `constant_values`  behaviour (value of pixels):</span>
+<span class="sd">         - float: Each border will be set to this value</span>
+<span class="sd">         - tuple(float, float): Each border value will be used asymmetrically for each axis i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes and values: e.g. {&#39;horizontal&#39;:(8, 23), &#39;channel&#39;:5}</span>
+
+<span class="sd">        If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,</span>
+<span class="sd">        and the last two angles in geometry.angles. The user should ensure the output is as expected.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = Padder.constant(pad_width=1, constant_values=0.0)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">        &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">        [[0. 1. 2.]</span>
+<span class="sd">        [3. 4. 5.]</span>
+<span class="sd">        [6. 7. 8.]]</span>
+<span class="sd">        &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">        [[0. 0. 0. 0. 0.]</span>
+<span class="sd">        [0. 0. 1. 2. 0.]</span>
+<span class="sd">        [0. 3. 4. 5. 0.]</span>
+<span class="sd">        [0. 6. 7. 8. 0.]</span>
+<span class="sd">        [0. 0. 0. 0. 0.]]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="n">pad_width</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;constant&#39;</span><span class="p">,</span> <span class="n">pad_values</span><span class="o">=</span><span class="n">constant_values</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+
+<div class="viewcode-block" id="Padder.edge">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder.edge">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">edge</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Padder processor wrapping numpy.pad with mode `edge`.</span>
+
+<span class="sd">        Pads the data by extending the edge values in to the border. Pads in all *spatial*</span>
+<span class="sd">        dimensions unless a dictionary is passed to `pad_width`.</span>
+
+<span class="sd">        pad_width: int, tuple, dict</span>
+<span class="sd">            The size of the border along each axis, see usage notes</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        `pad_width` behaviour (number of pixels):</span>
+<span class="sd">         - int: Each axis will be padded with a border of this size</span>
+<span class="sd">         - tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes will be padded: e.g. {&#39;horizontal&#39;:(8, 23), &#39;vertical&#39;: 10}</span>
+
+<span class="sd">        If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,</span>
+<span class="sd">        and the last two angles in geometry.angles. The user should ensure the output is as expected.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = Padder.edge(pad_width=1)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">        &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">        [[0. 1. 2.]</span>
+<span class="sd">        [3. 4. 5.]</span>
+<span class="sd">        [6. 7. 8.]]</span>
+<span class="sd">        &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">        [[0. 0. 1. 2. 2.]</span>
+<span class="sd">        [0. 0. 1. 2. 2.]</span>
+<span class="sd">        [3. 3. 4. 5. 5.]</span>
+<span class="sd">        [6. 6. 7. 8. 8.]</span>
+<span class="sd">        [6. 6. 7. 8. 8.]]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="n">pad_width</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;edge&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+<div class="viewcode-block" id="Padder.linear_ramp">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder.linear_ramp">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">linear_ramp</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_values</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Padder processor wrapping numpy.pad with mode `linear_ramp`</span>
+
+<span class="sd">        Pads the data with values calculated from a linear ramp between the array edge</span>
+<span class="sd">        value and the set end_value. Pads in all *spatial* dimensions unless a dictionary</span>
+<span class="sd">        is passed to either `pad_width` or `constant_values`</span>
+
+<span class="sd">        pad_width: int, tuple, dict</span>
+<span class="sd">            The size of the border along each axis, see usage notes</span>
+<span class="sd">        end_values: float, tuple, dict, default=0.0</span>
+<span class="sd">            The target value of the linear_ramp, see usage notes</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        `pad_width` behaviour (number of pixels):</span>
+<span class="sd">         - int: Each axis will be padded with a border of this size</span>
+<span class="sd">         - tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes will be padded: e.g. {&#39;horizontal&#39;:(8, 23), &#39;vertical&#39;: 10}</span>
+
+<span class="sd">        `end_values` behaviour:</span>
+<span class="sd">         - float: Each border will use this end value</span>
+<span class="sd">         - tuple(float, float): Each border end value will be used asymmetrically for each axis i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes and end values: e.g. {&#39;horizontal&#39;:(8, 23), &#39;channel&#39;:5}</span>
+
+<span class="sd">        If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,</span>
+<span class="sd">        and the last two angles in geometry.angles. The user should ensure the output is as expected.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = Padder.linear_ramp(pad_width=2, end_values=0.0)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">        &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">        [[0. 1. 2.]</span>
+<span class="sd">        [3. 4. 5.]</span>
+<span class="sd">        [6. 7. 8.]]</span>
+<span class="sd">        &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">        [[0.  0.  0.  0.  0.  0.  0. ]</span>
+<span class="sd">        [0.  0.  0.  0.5 1.  0.5 0. ]</span>
+<span class="sd">        [0.  0.  0.  1.  2.  1.  0. ]</span>
+<span class="sd">        [0.  1.5 3.  4.  5.  2.5 0. ]</span>
+<span class="sd">        [0.  3.  6.  7.  8.  4.  0. ]</span>
+<span class="sd">        [0.  1.5 3.  3.5 4.  2.  0. ]</span>
+<span class="sd">        [0.  0.  0.  0.  0.  0.  0. ]]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="n">pad_width</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;linear_ramp&#39;</span><span class="p">,</span> <span class="n">pad_values</span><span class="o">=</span><span class="n">end_values</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+
+<div class="viewcode-block" id="Padder.reflect">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder.reflect">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">reflect</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Padder processor wrapping numpy.pad with mode `reflect`.</span>
+
+<span class="sd">        Pads with the reflection of the data mirrored along first and last values each axis.</span>
+<span class="sd">        Pads in all *spatial* dimensions unless a dictionary is passed to `pad_width`.</span>
+
+<span class="sd">        pad_width: int, tuple, dict</span>
+<span class="sd">            The size of the border along each axis, see usage notes</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        `pad_width` behaviour (number of pixels):</span>
+<span class="sd">         - int: Each axis will be padded with a border of this size</span>
+<span class="sd">         - tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes will be padded: e.g. {&#39;horizontal&#39;:(8, 23), &#39;vertical&#39;: 10}</span>
+
+<span class="sd">        If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,</span>
+<span class="sd">        and the last two angles in geometry.angles. The user should ensure the output is as expected.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = Padder.reflect(pad_width=1)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">        &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">        [[0. 1. 2.]</span>
+<span class="sd">        [3. 4. 5.]</span>
+<span class="sd">        [6. 7. 8.]]</span>
+<span class="sd">        &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">        [[4. 3. 4. 5. 4.]</span>
+<span class="sd">        [1. 0. 1. 2. 1.]</span>
+<span class="sd">        [4. 3. 4. 5. 4.]</span>
+<span class="sd">        [7. 6. 7. 8. 7.]</span>
+<span class="sd">        [4. 3. 4. 5. 4.]]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="n">pad_width</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+
+<div class="viewcode-block" id="Padder.symmetric">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder.symmetric">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">symmetric</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Padder processor wrapping numpy.pad with mode `symmetric`.</span>
+
+<span class="sd">        Pads with the reflection of the data mirrored along the edge of the array.</span>
+<span class="sd">        Pads in all *spatial* dimensions unless a dictionary is passed to `pad_width`.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        pad_width: int, tuple, dict</span>
+<span class="sd">            The size of the border along each axis</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        `pad_width` behaviour (number of pixels):</span>
+<span class="sd">         - int: Each axis will be padded with a border of this size</span>
+<span class="sd">         - tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes will be padded: e.g. {&#39;horizontal&#39;:(8, 23), &#39;vertical&#39;: 10}</span>
+
+<span class="sd">        If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,</span>
+<span class="sd">        and the last two angles in geometry.angles. The user should ensure the output is as expected.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = Padder.symmetric(pad_width=1)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">        &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">        [[0. 1. 2.]</span>
+<span class="sd">        [3. 4. 5.]</span>
+<span class="sd">        [6. 7. 8.]]</span>
+<span class="sd">        &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">        [[0. 0. 1. 2. 2.]</span>
+<span class="sd">        [0. 0. 1. 2. 2.]</span>
+<span class="sd">        [3. 3. 4. 5. 5.]</span>
+<span class="sd">        [6. 6. 7. 8. 8.]</span>
+<span class="sd">        [6. 6. 7. 8. 8.]]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="n">pad_width</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;symmetric&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+
+<div class="viewcode-block" id="Padder.wrap">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder.wrap">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">wrap</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Padder processor wrapping numpy.pad with mode `wrap`.</span>
+
+<span class="sd">        Pads with the wrap of the vector along the axis. The first values are used to pad the</span>
+<span class="sd">        end and the end values are used to pad the beginning. Pads in all *spatial* dimensions</span>
+<span class="sd">        unless a dictionary is passed to `pad_width`.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        pad_width: int, tuple, dict</span>
+<span class="sd">            The size of the border along each axis</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        `pad_width` behaviour (number of pixels):</span>
+<span class="sd">         - int: Each axis will be padded with a border of this size</span>
+<span class="sd">         - tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</span>
+<span class="sd">         - dict: Specified axes will be padded: e.g. {&#39;horizontal&#39;:(8, 23), &#39;vertical&#39;: 10}</span>
+
+<span class="sd">        If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,</span>
+<span class="sd">        and the last two angles in geometry.angles. The user should ensure the output is as expected.</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = Padder.wrap(pad_width=1)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; data_padded = processor.get_output()</span>
+<span class="sd">        &gt;&gt;&gt; print(data.array)</span>
+<span class="sd">        [[0. 1. 2.]</span>
+<span class="sd">        [3. 4. 5.]</span>
+<span class="sd">        [6. 7. 8.]]</span>
+<span class="sd">        &gt;&gt;&gt; print(data_padded.array)</span>
+<span class="sd">        [[8. 6. 7. 8. 6.]</span>
+<span class="sd">        [2. 0. 1. 2. 0.]</span>
+<span class="sd">        [5. 3. 4. 5. 3.]</span>
+<span class="sd">        [8. 6. 7. 8. 6.]</span>
+<span class="sd">        [2. 0. 1. 2. 0.]]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="n">pad_width</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;wrap&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">processor</span></div>
+
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;constant&#39;</span><span class="p">,</span>
+                 <span class="n">pad_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">pad_values</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;mode&#39;</span><span class="p">:</span> <span class="n">mode</span><span class="p">,</span>
+                <span class="s1">&#39;pad_width&#39;</span><span class="p">:</span> <span class="n">pad_width</span><span class="p">,</span>
+                <span class="s1">&#39;pad_values&#39;</span><span class="p">:</span> <span class="n">pad_values</span><span class="p">,</span>
+                <span class="s1">&#39;_data_array&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+                <span class="s1">&#39;_geometry&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+                <span class="s1">&#39;_shape_in&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                <span class="s1">&#39;_shape_out&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                <span class="s1">&#39;_shape_out_full&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                <span class="s1">&#39;_labels_in&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                <span class="s1">&#39;_processed_dims&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                <span class="s1">&#39;_pad_width_param&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+                <span class="s1">&#39;_pad_values_param&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">Padder</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="Padder.set_input">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Padder.set_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set the input data to the processor</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        dataset : DataContainer, Geometry</span>
+<span class="sd">            The input DataContainer</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,(</span><span class="n">AcquisitionGeometry</span><span class="p">,</span><span class="n">ImageGeometry</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_input</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">weakref</span><span class="o">.</span><span class="n">ref</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;shouldRun&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input data not compatible&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Input type mismatch: got </span><span class="si">{0}</span><span class="s2"> expecting </span><span class="si">{1}</span><span class="s2">&quot;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_set_up</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">ImageData</span><span class="p">,</span><span class="n">AcquisitionData</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">data</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Processor supports following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData</span><span class="se">\n</span><span class="s1"> - DataContainer</span><span class="se">\n</span><span class="s1"> - ImageGeometry</span><span class="se">\n</span><span class="s1"> - AcquisitionGeometry&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Expected float32&quot;</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;constant&#39;</span><span class="p">,</span> <span class="s1">&#39;edge&#39;</span><span class="p">,</span> <span class="s1">&#39;linear_ramp&#39;</span><span class="p">,</span> <span class="s1">&#39;reflect&#39;</span><span class="p">,</span> <span class="s1">&#39;symmetric&#39;</span><span class="p">,</span> <span class="s1">&#39;wrap&#39;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Wrong mode. One of the following is expected:</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span>
+                            <span class="s2">&quot;constant, edge, linear_ramp, reflect, symmetric, wrap&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Please, specify pad_width&#39;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">_create_tuple</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="p">(</span><span class="n">dtype</span><span class="p">(</span><span class="n">value</span><span class="p">),</span><span class="n">dtype</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="p">(</span><span class="n">dtype</span><span class="p">(</span><span class="n">value</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span><span class="n">dtype</span><span class="p">(</span><span class="n">value</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">_get_dimensions_from_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+
+        <span class="n">dimensions</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Dimension label not found in data. Expected labels from </span><span class="si">{0}</span><span class="s1">. Got </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span>
+
+            <span class="n">dimensions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">dimensions</span>
+
+
+    <span class="k">def</span> <span class="nf">_set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="mi">4</span><span class="o">-</span><span class="n">data</span><span class="o">.</span><span class="n">ndim</span>
+
+        <span class="c1">#set defaults</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">[</span><span class="n">offset</span><span class="p">::]</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">[</span><span class="n">offset</span><span class="p">::]</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_processed_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)]</span><span class="o">*</span><span class="mi">4</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_pad_values_param</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)]</span><span class="o">*</span><span class="mi">4</span>
+
+
+        <span class="c1"># if pad_width or set_values is passed a dictionary these keys specify the axes to run over</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_values</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_values</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Dictionaries must contain the same axes&#39;</span><span class="p">)</span>
+
+            <span class="n">dimensions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dimensions_from_dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="n">dimensions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dimensions_from_dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_values</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="n">dimensions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_dimensions_from_dict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_values</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">spatial_dimensions</span> <span class="o">=</span><span class="p">[</span>
+                        <span class="s1">&#39;vertical&#39;</span><span class="p">,</span>
+                        <span class="s1">&#39;horizontal&#39;</span><span class="p">,</span>
+                        <span class="s1">&#39;horizontal_y&#39;</span><span class="p">,</span>
+                        <span class="s1">&#39;horizontal_x&#39;</span>
+                    <span class="p">]</span>
+
+            <span class="n">dimensions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">spatial_dimensions</span><span class="p">)</span> <span class="o">&amp;</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">))</span>
+
+
+        <span class="c1"># get pad_widths for these dimensions</span>
+        <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">dimensions</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span>
+            <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                <span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_width</span>
+
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_tuple</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;`pad_width` should be a integer or a tuple of integers. Got </span><span class="si">{0}</span><span class="s2"> for axis </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
+
+        <span class="c1"># get pad_values for these dimensions</span>
+        <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">dimensions</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_values</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span>
+            <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                <span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad_values</span>
+
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_pad_values_param</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_tuple</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;`pad_values` should be a float or a tuple of floats. Got </span><span class="si">{0}</span><span class="s2"> for axis </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
+
+        <span class="c1">#create list of processed axes and new_shape</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+           <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_processed_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">])</span>
+
+
+    <span class="k">def</span> <span class="nf">_process_acquisition_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates the new acquisition geometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">dim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_processed_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+                <span class="k">continue</span>
+
+            <span class="n">offset</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span><span class="o">*</span><span class="mf">0.5</span>
+
+            <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;channel&#39;</span><span class="p">:</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">set_channels</span><span class="p">(</span><span class="n">num_channels</span><span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">num_channels</span> <span class="o">+</span> \
+                <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;angle&#39;</span><span class="p">:</span>
+                <span class="c1"># extrapolate pre-values from a[1]-a[0]</span>
+                <span class="c1"># extrapolate post-values from a[-1]-a[-2]</span>
+                <span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">angles</span>
+                <span class="n">end_values</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="p">)</span><span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+                    <span class="n">a</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">a</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="p">)</span><span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="p">)</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]),</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;linear_ramp&#39;</span><span class="p">,</span><span class="n">end_values</span><span class="o">=</span><span class="n">end_values</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;vertical&#39;</span><span class="p">:</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">shift_detector_in_plane</span><span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">:</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">shift_detector_in_plane</span><span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">geometry</span>
+
+    <span class="k">def</span> <span class="nf">_process_image_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates the new image geometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">dim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_processed_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+                <span class="k">continue</span>
+
+            <span class="n">offset</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span><span class="o">*</span><span class="mf">0.5</span>
+
+            <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;channel&#39;</span><span class="p">:</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;vertical&#39;</span><span class="p">:</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">center_z</span> <span class="o">-=</span> <span class="n">offset</span> <span class="o">*</span> <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_size_z</span>
+            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;horizontal_x&#39;</span><span class="p">:</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">center_x</span> <span class="o">-=</span> <span class="n">offset</span> <span class="o">*</span> <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_size_x</span>
+            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="s1">&#39;horizontal_y&#39;</span><span class="p">:</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">geometry</span><span class="o">.</span><span class="n">center_y</span> <span class="o">-=</span> <span class="n">offset</span> <span class="o">*</span> <span class="n">geometry</span><span class="o">.</span><span class="n">voxel_size_y</span>
+
+        <span class="k">return</span> <span class="n">geometry</span>
+
+
+    <span class="k">def</span> <span class="nf">_process_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dc_in</span><span class="p">):</span>
+        <span class="n">arr_in</span> <span class="o">=</span> <span class="n">dc_in</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;reflect&#39;</span><span class="p">,</span> <span class="s1">&#39;symmetric&#39;</span><span class="p">,</span> <span class="s1">&#39;wrap&#39;</span><span class="p">,</span> <span class="s1">&#39;edge&#39;</span><span class="p">]:</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">arr_in</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">arr_in</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> \
+                <span class="n">constant_values</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_values_param</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;linear_ramp&#39;</span><span class="p">:</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">arr_in</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_width_param</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> \
+                <span class="n">end_values</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_values_param</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="n">arr_out</span>
+
+
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+
+        <span class="c1"># pad geometry</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">):</span>
+            <span class="n">new_geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_image_geometry</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">):</span>
+            <span class="n">new_geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_acquisition_geometry</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">new_geometry</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># return if just acting on geometry</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">new_geometry</span>
+
+        <span class="c1"># pad data</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">arr_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">new_geometry</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">):</span>
+                <span class="k">return</span> <span class="n">ImageData</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span><span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">new_geometry</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">new_geometry</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">):</span>
+                <span class="k">return</span> <span class="n">AcquisitionData</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span><span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">new_geometry</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">DataContainer</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span><span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># check size and shape if passed out</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Array of `out` not compatible. Expected shape: </span><span class="si">{0}</span><span class="s2">, data type: </span><span class="si">{1}</span><span class="s2"> Got shape: </span><span class="si">{2}</span><span class="s2">, data type: </span><span class="si">{3}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">new_geometry</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">geometry</span> <span class="o">!=</span> <span class="n">new_geometry</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Geometry of `out` not as expected. Got </span><span class="si">{0}</span><span class="s2">, expected </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">new_geometry</span><span class="p">))</span>
+
+            <span class="n">out</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span></div>
+
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/processors/PaganinProcessor/index.html b/v24.2.0/_modules/cil/processors/PaganinProcessor/index.html
new file mode 100644
index 0000000000..f9b8ea6f12
--- /dev/null
+++ b/v24.2.0/_modules/cil/processors/PaganinProcessor/index.html
@@ -0,0 +1,1118 @@
+
+<!DOCTYPE html>
+
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+<html lang="en" data-content_root="../../../../" >
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+  <h1>Source code for cil.processors.PaganinProcessor</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2024 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at:</span>
+<span class="c1"># https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">Processor</span><span class="p">,</span> <span class="n">AcquisitionData</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionDimension</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy.fft</span> <span class="kn">import</span> <span class="n">fft2</span>
+<span class="kn">from</span> <span class="nn">scipy.fft</span> <span class="kn">import</span> <span class="n">ifft2</span>
+<span class="kn">from</span> <span class="nn">scipy.fft</span> <span class="kn">import</span> <span class="n">ifftshift</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">constants</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<div class="viewcode-block" id="PaganinProcessor">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.PaganinProcessor">[docs]</a>
+<span class="k">class</span> <span class="nc">PaganinProcessor</span><span class="p">(</span><span class="n">Processor</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Processor to retrieve quantitative information from phase contrast images</span>
+<span class="sd">    using the Paganin phase retrieval algorithm described in [1]</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    delta: float (optional)</span>
+<span class="sd">        Real part of the deviation of the material refractive index from 1,</span>
+<span class="sd">        where refractive index :math:`n = (1 - \delta) + i \beta` energy-</span>
+<span class="sd">        dependent refractive index information for x-ray wavelengths can be</span>
+<span class="sd">        found at [2], default is 1</span>
+
+<span class="sd">    beta: float (optional)</span>
+<span class="sd">        Complex part of the material refractive index, where refractive index</span>
+<span class="sd">        :math:`n = (1 - \delta) + i \beta` energy-dependent refractive index</span>
+<span class="sd">        information for x-ray wavelengths can be found at [2], default is 1e-2</span>
+
+<span class="sd">    energy: float (optional)</span>
+<span class="sd">        Energy of the incident photon, default is 40000</span>
+
+<span class="sd">    energy_units: string (optional)</span>
+<span class="sd">        Energy units, default is &#39;eV&#39;</span>
+
+<span class="sd">    full_retrieval : bool, optional</span>
+<span class="sd">        If True, perform the full phase retrieval and return the thickness. If</span>
+<span class="sd">        False, return a filtered image, default is True</span>
+
+<span class="sd">    filter_type: string (optional)</span>
+<span class="sd">        The form of the Paganin filter to use, either &#39;paganin_method&#39;</span>
+<span class="sd">        (default) or &#39;generalised_paganin_method&#39; as described in [3]</span>
+
+<span class="sd">    pad: int (optional)</span>
+<span class="sd">        Number of pixels to pad the image in Fourier space to reduce aliasing,</span>
+<span class="sd">        default is 0</span>
+
+<span class="sd">    return_units: string (optional)</span>
+<span class="sd">        The distance units to return the sample thickness in, must be one of</span>
+<span class="sd">        &#39;m&#39;, &#39;cm&#39;, &#39;mm&#39; or &#39;um&#39;. Only applies if full_retrieval=True (default</span>
+<span class="sd">        is&#39;cm&#39;)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    AcquisitionData</span>
+<span class="sd">        AcquisitionData corrected for phase effects, retrieved sample thickness</span>
+<span class="sd">        or (if :code:`full_retrieval=False`) filtered data</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; processor = PaganinProcessor(delta=5, beta=0.05, energy=18000)</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; thickness = processor.get_output()</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; processor = PaganinProcessor(delta=1,beta=10e2, full_retrieval=False)</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; filtered_image = processor.get_output()</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; processor = PaganinProcessor()</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; thickness = processor.get_output(override_filter={&#39;alpha&#39;:10})</span>
+<span class="sd">    &gt;&gt;&gt; phase_retrieved_image = thickness*processor.mu</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    This processor will work most efficiently using the cil data order with</span>
+<span class="sd">    `data.reorder(&#39;cil&#39;)`</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    This processor uses the phase retrieval algorithm described by Paganin et</span>
+<span class="sd">    al. [1] to retrieve the sample thickness</span>
+
+<span class="sd">    .. math:: T(x,y) = - \frac{1}{\mu}\ln\left (\mathcal{F}^{-1}\left</span>
+<span class="sd">        (\frac{\mathcal{F}\left ( M^2I_{norm}(x, y,z = \Delta) \right )}{1 +</span>
+<span class="sd">          \alpha\left ( k_x^2 + k_y^2 \right )}  \right )\right ),</span>
+
+<span class="sd">    where</span>
+
+<span class="sd">        - :math:`T`, is the sample thickness,</span>
+<span class="sd">        - :math:`\mu = \frac{4\pi\beta}{\lambda}` is the material linear</span>
+<span class="sd">        attenuation coefficient where :math:`\beta` is the complex part of the</span>
+<span class="sd">        material refractive index and :math:`\lambda=\frac{hc}{E}` is the probe</span>
+<span class="sd">        wavelength,</span>
+<span class="sd">        - :math:`M` is the magnification at the detector,</span>
+<span class="sd">        - :math:`I_{norm}` is the input image which is expected to be the</span>
+<span class="sd">        normalised transmission data,</span>
+<span class="sd">        - :math:`\Delta` is the propagation distance,</span>
+<span class="sd">        - :math:`\alpha = \frac{\Delta\delta}{\mu}` is a parameter determining</span>
+<span class="sd">        the strength of the filter to be applied in Fourier space where</span>
+<span class="sd">        :math:`\delta` is the real part of the deviation of the material</span>
+<span class="sd">        refractive index from 1</span>
+<span class="sd">        - :math:`k_x, k_y = \left ( \frac{2\pi p}{N_xW}, \frac{2\pi q}{N_yW}</span>
+<span class="sd">        \right )` where :math:`p` and :math:`q` are co-ordinates in a Fourier</span>
+<span class="sd">        mesh in the range :math:`-N_x/2` to :math:`N_x/2` for an image with</span>
+<span class="sd">        size :math:`N_x, N_y` and pixel size :math:`W`.</span>
+
+<span class="sd">    A generalised form of the Paganin phase retrieval method can be called</span>
+<span class="sd">    using :code:`filter_type=&#39;generalised_paganin_method&#39;`, which uses the</span>
+<span class="sd">    form of the algorithm described in [2]</span>
+
+<span class="sd">    .. math:: T(x,y) = -\frac{1}{\mu}\ln\left (\mathcal{F}^{-1}\left (\frac{</span>
+<span class="sd">        \mathcal{F}\left ( M^2I_{norm}(x, y,z = \Delta) \right )}{1 - \frac{2</span>
+<span class="sd">        \alpha}{W^2}\left ( \cos(Wk_x) + \cos(Wk_y) -2 \right )}  \right )</span>
+<span class="sd">        \right )</span>
+
+<span class="sd">    The phase retrieval is valid under the following assumptions</span>
+
+<span class="sd">        - used with paraxial propagation-induced phase contrast images which</span>
+<span class="sd">        can be assumed to be single-material locally</span>
+<span class="sd">        - using intensity data which has been flat field corrected</span>
+<span class="sd">        - and under the assumption that the Fresnel number</span>
+<span class="sd">        :math:`F_N = W^2/(\lambda\Delta) &gt;&gt; 1`</span>
+
+<span class="sd">    To apply a filter to images using the Paganin method, call</span>
+<span class="sd">    :code:`full_retrieval=False`. In this case the pre-scaling and conversion</span>
+<span class="sd">    to absorption is not applied so the requirement to supply flat field</span>
+<span class="sd">    corrected intensity data is relaxed,</span>
+
+<span class="sd">    .. math:: I_{filt} = \mathcal{F}^{-1}\left (\frac{\mathcal{F}\left (</span>
+<span class="sd">        I(x, y,z = \Delta) \right )}</span>
+<span class="sd">        {1 - \alpha\left ( k_x^2 + k_y^2 \right )}  \right )</span>
+
+<span class="sd">    References</span>
+<span class="sd">    ---------</span>
+<span class="sd">    - [1] https://doi.org/10.1046/j.1365-2818.2002.01010.x</span>
+<span class="sd">    - [2] https://henke.lbl.gov/optical_constants/getdb2.html</span>
+<span class="sd">    - [3] https://iopscience.iop.org/article/10.1088/2040-8986/abbab9</span>
+<span class="sd">    With thanks to colleagues at DTU for help with the initial implementation</span>
+<span class="sd">    of the phase retrieval algorithm</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">energy</span><span class="o">=</span><span class="mi">40000</span><span class="p">,</span>
+                 <span class="n">energy_units</span><span class="o">=</span><span class="s1">&#39;eV&#39;</span><span class="p">,</span>  <span class="n">full_retrieval</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                 <span class="n">filter_type</span><span class="o">=</span><span class="s1">&#39;paganin_method&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                 <span class="n">return_units</span><span class="o">=</span><span class="s1">&#39;cm&#39;</span><span class="p">):</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;energy&#39;</span> <span class="p">:</span> <span class="n">energy</span><span class="p">,</span>
+            <span class="s1">&#39;wavelength&#39;</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_energy_to_wavelength</span><span class="p">(</span><span class="n">energy</span><span class="p">,</span> <span class="n">energy_units</span><span class="p">,</span>
+                                                      <span class="n">return_units</span><span class="p">),</span>
+            <span class="s1">&#39;delta&#39;</span><span class="p">:</span> <span class="n">delta</span><span class="p">,</span>
+            <span class="s1">&#39;beta&#39;</span><span class="p">:</span> <span class="n">beta</span><span class="p">,</span>
+            <span class="s1">&#39;_delta_user&#39;</span> <span class="p">:</span> <span class="n">delta</span><span class="p">,</span>
+            <span class="s1">&#39;_beta_user&#39;</span> <span class="p">:</span> <span class="n">beta</span><span class="p">,</span>
+            <span class="s1">&#39;filter_Nx&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;filter_Ny&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;filter_type&#39;</span> <span class="p">:</span> <span class="n">filter_type</span><span class="p">,</span>
+            <span class="s1">&#39;mu&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;alpha&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;pixel_size&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;propagation_distance&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;magnification&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;filter&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;full_retrieval&#39;</span> <span class="p">:</span> <span class="n">full_retrieval</span><span class="p">,</span>
+            <span class="s1">&#39;pad&#39;</span> <span class="p">:</span> <span class="n">pad</span><span class="p">,</span>
+            <span class="s1">&#39;override_geometry&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;override_filter&#39;</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;return_units&#39;</span> <span class="p">:</span> <span class="n">return_units</span>
+            <span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">PaganinProcessor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">AcquisitionData</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Processor only supports AcquisitionData&#39;</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="o">!=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Processor only support dtype=float32&#39;</span><span class="p">)</span>
+    
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span>  <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="n">cil_order</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">get_order_for_engine</span><span class="p">(</span><span class="s1">&#39;cil&#39;</span><span class="p">,</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">!=</span> <span class="n">cil_order</span><span class="p">:</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">msg</span><span class="o">=</span><span class="s2">&quot;This processor will work most efficiently using</span><span class="se">\</span>
+<span class="s2">                        </span><span class="se">\n</span><span class="s2">CIL data order, consider using `data.reorder(&#39;cil&#39;)`&quot;</span><span class="p">)</span>
+
+        <span class="c1"># set the geometry parameters to use from data.geometry unless the</span>
+        <span class="c1"># geometry is overridden with an override_geometry</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_set_geometry</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">override_geometry</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="c1"># make slice indices to get the projection</span>
+        <span class="n">slice_proj</span> <span class="o">=</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">angle_axis</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_dimension_axis</span><span class="p">(</span><span class="s1">&#39;angle&#39;</span><span class="p">)</span>
+        <span class="n">slice_proj</span><span class="p">[</span><span class="n">angle_axis</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">channels</span><span class="o">&gt;</span><span class="mi">1</span><span class="p">:</span>
+            <span class="n">channel_axis</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_dimension_axis</span><span class="p">(</span><span class="s1">&#39;channel&#39;</span><span class="p">)</span>
+            <span class="n">slice_proj</span><span class="p">[</span><span class="n">channel_axis</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">channel_axis</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="n">data_proj</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">slice_proj</span><span class="p">)]</span>
+
+        <span class="c1"># create an empty axis if the data is 2D</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+            <span class="n">slice_proj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span>
+            <span class="n">data_proj</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">slice_proj</span><span class="p">)]</span>
+
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_proj</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span><span class="p">(</span><span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Data must be 2D or 3D per channel&#39;</span><span class="p">))</span>
+        
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+        
+        <span class="c1"># create a filter based on the shape of the data</span>
+        <span class="n">filter_shape</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">data_proj</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">filter_Nx</span> <span class="o">=</span> <span class="n">filter_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="o">*</span><span class="mi">2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">filter_Ny</span> <span class="o">=</span> <span class="n">filter_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="o">*</span><span class="mi">2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_create_filter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">override_filter</span><span class="p">)</span>
+
+        <span class="c1"># pre-calculate the scaling factor</span>
+        <span class="n">scaling_factor</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">)</span>
+
+        <span class="c1"># allocate padded buffer</span>
+        <span class="n">padded_buffer</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">x</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="o">*</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data_proj</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        
+        <span class="c1"># make slice indices to unpad the data</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+            <span class="n">slice_pad</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="nb">slice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="p">,</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="p">)]</span>
+                                <span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">padded_buffer</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">slice_pad</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">padded_buffer</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+        <span class="c1"># loop over the channels</span>
+        <span class="n">mag2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">magnification</span><span class="o">**</span><span class="mi">2</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">channels</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">channel_axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">slice_proj</span><span class="p">[</span><span class="n">channel_axis</span><span class="p">]</span> <span class="o">=</span> <span class="n">j</span>
+            <span class="c1"># loop over the projections</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">))):</span>
+                
+                <span class="n">slice_proj</span><span class="p">[</span><span class="n">angle_axis</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+                <span class="n">padded_buffer</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="n">padded_buffer</span><span class="p">[</span><span class="n">slice_pad</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">[(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">slice_proj</span><span class="p">))]</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">full_retrieval</span><span class="o">==</span><span class="kc">True</span><span class="p">:</span>
+                    <span class="c1"># apply the filter in fourier space, apply log and scale</span>
+                    <span class="c1"># by magnification</span>
+                    <span class="n">padded_buffer</span><span class="o">*=</span><span class="n">mag2</span>
+                    <span class="n">fI</span> <span class="o">=</span> <span class="n">fft2</span><span class="p">(</span><span class="n">padded_buffer</span><span class="p">)</span>
+                    <span class="n">iffI</span> <span class="o">=</span> <span class="n">ifft2</span><span class="p">(</span><span class="n">fI</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">filter</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">iffI</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">padded_buffer</span><span class="p">)</span>
+                    <span class="c1"># apply scaling factor</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">scaling_factor</span><span class="p">,</span> <span class="n">padded_buffer</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">padded_buffer</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="c1"># apply the filter in fourier space</span>
+                    <span class="n">fI</span> <span class="o">=</span> <span class="n">fft2</span><span class="p">(</span><span class="n">padded_buffer</span><span class="p">)</span>
+                    <span class="n">padded_buffer</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">ifft2</span><span class="p">(</span><span class="n">fI</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">filter</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+
+                <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">channels</span><span class="o">&gt;</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">padded_buffer</span><span class="p">[</span><span class="n">slice_pad</span><span class="p">],</span> <span class="n">angle</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> 
+                             <span class="n">channel</span><span class="o">=</span><span class="n">j</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">padded_buffer</span><span class="p">[</span><span class="n">slice_pad</span><span class="p">],</span> <span class="n">angle</span> <span class="o">=</span> <span class="n">i</span><span class="p">)</span>
+                    
+        <span class="n">data</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+<div class="viewcode-block" id="PaganinProcessor.set_input">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.PaganinProcessor.set_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set the input data to the processor</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        dataset : AcquisitionData</span>
+<span class="sd">            The input AcquisitionData</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="PaganinProcessor.get_output">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.PaganinProcessor.get_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">override_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                   <span class="n">override_filter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to get output from the PaganinProcessor</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        out : DataContainer, optional</span>
+<span class="sd">           Fills the referenced DataContainer with the processed data</span>
+
+<span class="sd">        override_geometry: dict, optional</span>
+<span class="sd">            Geometry parameters to use in the phase retrieval if you want to</span>
+<span class="sd">            over-ride values found in `data.geometry`. Specify parameters as a</span>
+<span class="sd">            dictionary :code:`{&#39;parameter&#39;:value}` where parameter is</span>
+<span class="sd">            :code:`&#39;magnification&#39;, &#39;propagation_distance&#39;` or</span>
+<span class="sd">            :code:`&#39;pixel_size&#39;` and value is the new value to use. Specify</span>
+<span class="sd">            distance parameters in the same units as :code:`return_units`</span>
+<span class="sd">            (default is cm).</span>
+
+<span class="sd">        override_filter: dict, optional</span>
+<span class="sd">            Over-ride the filter parameters to use in the phase retrieval.</span>
+<span class="sd">            Specify parameters as :code:`{&#39;parameter&#39;:value}` where parameter</span>
+<span class="sd">            is :code:`&#39;delta&#39;, &#39;beta&#39;` or :code:`&#39;alpha&#39;` and value is the new</span>
+<span class="sd">            value to use.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        AcquisitionData</span>
+<span class="sd">            AcquisitionData corrected for phase effects, retrieved sample</span>
+<span class="sd">            thickness or (if :code:`full_retrieval=False`) filtered data</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = PaganinProcessor(delta=5, beta=0.05, energy=18000)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; thickness = processor.get_output()</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = PaganinProcessor(delta=1,beta=10e2,</span>
+<span class="sd">        full_retrieval=False)</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; filtered_image = processor.get_output()</span>
+
+<span class="sd">        Example</span>
+<span class="sd">        -------</span>
+<span class="sd">        &gt;&gt;&gt; processor = PaganinProcessor()</span>
+<span class="sd">        &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">        &gt;&gt;&gt; thickness = processor.get_output(override_filter={&#39;alpha&#39;:10})</span>
+<span class="sd">        &gt;&gt;&gt; phase_retrieved_image = thickness*processor.mu</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        If :code:`&#39;alpha&#39;` is specified in override_filter the new value will</span>
+<span class="sd">        be used and delta will be ignored but beta will still be used to</span>
+<span class="sd">        calculate :math:`\mu = \frac{4\pi\beta}{\lambda}` which is used for</span>
+<span class="sd">        scaling the thickness, therefore it is only recommended to specify</span>
+<span class="sd">        alpha when also using :code:`get_output(full_retrieval=False)`, or</span>
+<span class="sd">        re-scaling the result by :math:`\mu` e.g.</span>
+<span class="sd">        :code:`thickness*processor.mu` If :code:`alpha` is not specified,</span>
+<span class="sd">        it will be calculated :math:`\frac{\Delta\delta\lambda}{4\pi\beta}`</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">override_geometry</span> <span class="o">=</span> <span class="n">override_geometry</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">override_filter</span> <span class="o">=</span> <span class="n">override_filter</span>
+
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="n">out</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">override_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">override_filter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="n">override_geometry</span><span class="o">=</span><span class="n">override_geometry</span><span class="p">,</span>
+                                  <span class="n">override_filter</span><span class="o">=</span><span class="n">override_filter</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">override_geometry</span><span class="o">=</span><span class="n">override_geometry</span><span class="p">,</span>
+                            <span class="n">override_filter</span><span class="o">=</span><span class="n">override_filter</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">_set_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">geometry</span><span class="p">,</span> <span class="n">override_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to set the geometry parameters for the processor. Values are</span>
+<span class="sd">        from the data geometry unless the geometry is overridden with an</span>
+<span class="sd">        override_geometry dictionary.</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;magnification&#39;</span><span class="p">,</span> <span class="s1">&#39;propagation_distance&#39;</span><span class="p">,</span> <span class="s1">&#39;pixel_size&#39;</span><span class="p">]</span>
+        <span class="c1"># specify parameter names as defined in geometry</span>
+        <span class="n">geometry_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;magnification&#39;</span><span class="p">,</span> <span class="s1">&#39;dist_center_detector&#39;</span><span class="p">,</span>
+                               <span class="p">(</span><span class="s1">&#39;pixel_size_h&#39;</span><span class="p">,</span> <span class="s1">&#39;pixel_size_v&#39;</span><span class="p">)]</span>
+        <span class="c1"># specify if parameter requires unit conversion</span>
+        <span class="n">convert_units</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">override_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">override_geometry</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="c1"># get and check parameters from over-ride geometry dictionary</span>
+        <span class="k">for</span> <span class="n">parameter</span> <span class="ow">in</span> <span class="n">override_geometry</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">parameter</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">parameters</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Parameter </span><span class="si">{}</span><span class="s1"> not recognised, expected one of</span><span class="se">\</span>
+<span class="s1">                                 </span><span class="si">{}</span><span class="s1">.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parameter</span><span class="p">,</span> <span class="n">parameters</span><span class="p">))</span>
+            <span class="k">elif</span> <span class="p">(</span><span class="n">override_geometry</span><span class="p">[</span><span class="n">parameter</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> \
+                <span class="o">|</span> <span class="p">(</span><span class="n">override_geometry</span><span class="p">[</span><span class="n">parameter</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Parameter </span><span class="si">{}</span><span class="s2"> cannot be </span><span class="si">{}</span><span class="s2">, please update </span><span class="se">\</span>
+<span class="s2">                                 data.geometry.</span><span class="si">{}</span><span class="s2"> or over-ride with </span><span class="se">\</span>
+<span class="s2">                                 processor.get_output(override_geometry= </span><span class="se">\</span>
+<span class="s2">                                 {{ &#39;</span><span class="si">{}</span><span class="s2">&#39; : value }} )&quot;</span>\
+                    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parameter</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parameter</span><span class="p">)),</span>
+                            <span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">parameter</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="n">parameter</span><span class="p">,</span> <span class="n">override_geometry</span><span class="p">[</span><span class="n">parameter</span><span class="p">])</span>
+
+
+        <span class="c1"># get and check parameters from geometry if they are not in the</span>
+        <span class="c1"># over-ride geometry dictionary</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">parameter</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">parameter</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">override_geometry</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">==</span><span class="nb">tuple</span><span class="p">:</span>
+                    <span class="n">param1</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="n">param2</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="k">if</span> <span class="p">(</span><span class="n">param1</span> <span class="o">-</span> <span class="n">param2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">param1</span> <span class="o">+</span> <span class="n">param2</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mf">1e-5</span><span class="p">:</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Parameter </span><span class="si">{}</span><span class="s2"> is not homogeneous up </span><span class="se">\</span>
+<span class="s2">                                         to 1e-5: got </span><span class="si">{}</span><span class="s2"> and </span><span class="si">{}</span><span class="s2">, please update</span><span class="se">\</span>
+<span class="s2">                                          geometry using data.geometry.</span><span class="si">{}</span><span class="s2"> and </span><span class="se">\</span>
+<span class="s2">                                         data.geometry.</span><span class="si">{}</span><span class="s2"> or over-ride with </span><span class="se">\</span>
+<span class="s2">                                         processor.get_output(</span><span class="se">\</span>
+<span class="s2">                                         override_geometry={{ &#39;</span><span class="si">{}</span><span class="s2">&#39; : value }})&quot;</span>
+                                         <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parameter</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">param1</span><span class="p">),</span>
+                                                 <span class="nb">str</span><span class="p">(</span><span class="n">param2</span><span class="p">),</span>
+                                                 <span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+                                                 <span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+                                                 <span class="n">parameter</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">param1</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+                <span class="k">if</span> <span class="p">(</span><span class="n">param1</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">param1</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Parameter </span><span class="si">{}</span><span class="s2"> cannot be </span><span class="si">{}</span><span class="s2">, please update</span><span class="se">\</span>
+<span class="s2">                                      data.geometry.</span><span class="si">{}</span><span class="s2"> or over-ride with </span><span class="se">\</span>
+<span class="s2">                                     processor.get_output(override_geometry</span><span class="se">\</span>
+<span class="s2">                                     ={{ &#39;</span><span class="si">{}</span><span class="s2">&#39; : value }} )&quot;</span>
+                                     <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">parameter</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">param1</span><span class="p">),</span>
+                                             <span class="nb">str</span><span class="p">(</span><span class="n">geometry_parameters</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span>
+                                             <span class="n">parameter</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">convert_units</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+                        <span class="n">param1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_convert_units</span><span class="p">(</span><span class="n">param1</span><span class="p">,</span> <span class="s1">&#39;distance&#39;</span><span class="p">,</span>
+                                                       <span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">units</span><span class="p">,</span>
+                                                       <span class="bp">self</span><span class="o">.</span><span class="n">return_units</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="n">parameter</span><span class="p">,</span> <span class="n">param1</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_create_filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">override_filter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to create the Paganin filter, either using the paganin [1] or</span>
+<span class="sd">        generalised paganin [2] method</span>
+<span class="sd">        The filter is created on a mesh in Fourier space kx, ky</span>
+<span class="sd">        [1] https://doi.org/10.1046/j.1365-2818.2002.01010.x</span>
+<span class="sd">        [2] https://iopscience.iop.org/article/10.1088/2040-8986/abbab9</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">override_filter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">override_filter</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="c1"># update any parameter which has been over-ridden with override_filter</span>
+        <span class="k">if</span> <span class="p">(</span><span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">override_filter</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="s1">&#39;delta&#39;</span> <span class="ow">in</span> <span class="n">override_filter</span><span class="p">):</span>
+            <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">msg</span><span class="o">=</span><span class="s2">&quot;Because you specified alpha, it will not be </span><span class="se">\</span>
+<span class="s2">                        calculated and therefore delta will be ignored&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="s1">&#39;delta&#39;</span> <span class="ow">in</span> <span class="n">override_filter</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">delta</span> <span class="o">=</span> <span class="n">override_filter</span><span class="p">[</span><span class="s1">&#39;delta&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">delta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_delta_user</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="s1">&#39;beta&#39;</span> <span class="ow">in</span> <span class="n">override_filter</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">override_filter</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_beta_user</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_mu</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">override_filter</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">override_filter</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_alpha</span><span class="p">()</span>
+
+        <span class="c1"># create the Fourier mesh</span>
+        <span class="n">kx</span><span class="p">,</span><span class="n">ky</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_Nx</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_Nx</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+            <span class="o">*</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_Nx</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_Ny</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_Ny</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+            <span class="o">*</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_Ny</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">),</span>
+            <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">indexing</span><span class="o">=</span><span class="s1">&#39;ij&#39;</span>
+            <span class="p">)</span>
+
+        <span class="c1"># create the filter using either paganin or generalised paganin method</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_type</span> <span class="o">==</span> <span class="s1">&#39;paganin_method&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">filter</span> <span class="o">=</span>  <span class="n">ifftshift</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mf">1.</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">*</span><span class="p">(</span><span class="n">kx</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">ky</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_type</span> <span class="o">==</span> <span class="s1">&#39;generalised_paganin_method&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">filter</span> <span class="o">=</span>  <span class="n">ifftshift</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+                                        <span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span><span class="o">*</span><span class="n">kx</span><span class="p">)</span>
+                                          <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pixel_size</span><span class="o">*</span><span class="n">ky</span><span class="p">)</span> <span class="o">-</span><span class="mi">2</span><span class="p">)))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;filter_type not recognised: got </span><span class="si">{0}</span><span class="s2"> expected one</span><span class="se">\</span>
+<span class="s2">                              of &#39;paganin_method&#39; or </span><span class="se">\</span>
+<span class="s2">                             &#39;generalised_paganin_method&#39;&quot;</span>
+                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_type</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">_calculate_mu</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to calculate the linear attenutation coefficient mu</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">=</span> <span class="mf">4.0</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">wavelength</span>
+
+    <span class="k">def</span> <span class="nf">_calculate_alpha</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to calculate alpha, a constant defining the Paganin filter</span>
+<span class="sd">        strength</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">propagation_distance</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">delta</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">mu</span>
+
+    <span class="k">def</span> <span class="nf">_energy_to_wavelength</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">energy</span><span class="p">,</span> <span class="n">energy_units</span><span class="p">,</span> <span class="n">return_units</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to convert photon energy in eV to wavelength in return_units</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        energy: float</span>
+<span class="sd">            Photon energy</span>
+
+<span class="sd">        energy_units</span>
+<span class="sd">            Energy units</span>
+
+<span class="sd">        return_units</span>
+<span class="sd">            Distance units in which to return the wavelength</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        float</span>
+<span class="sd">            Photon wavelength in return_units</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">top</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_convert_units</span><span class="p">(</span><span class="n">constants</span><span class="o">.</span><span class="n">h</span><span class="o">*</span><span class="n">constants</span><span class="o">.</span><span class="n">speed_of_light</span><span class="p">,</span>
+                                    <span class="s1">&#39;distance&#39;</span><span class="p">,</span> <span class="s1">&#39;m&#39;</span><span class="p">,</span> <span class="n">return_units</span><span class="p">)</span>
+        <span class="n">bottom</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_convert_units</span><span class="p">(</span><span class="n">energy</span><span class="p">,</span> <span class="s1">&#39;energy&#39;</span><span class="p">,</span> <span class="n">energy_units</span><span class="p">,</span> <span class="s1">&#39;J&#39;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">top</span><span class="o">/</span><span class="n">bottom</span>
+
+    <span class="k">def</span> <span class="nf">_convert_units</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">unit_type</span><span class="p">,</span> <span class="n">input_unit</span><span class="p">,</span> <span class="n">output_unit</span><span class="p">):</span>
+        <span class="n">unit_types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;distance&#39;</span><span class="p">,</span><span class="s1">&#39;energy&#39;</span><span class="p">,</span><span class="s1">&#39;angle&#39;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">unit_type</span> <span class="o">==</span> <span class="n">unit_types</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="n">unit_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;m&#39;</span><span class="p">,</span><span class="s1">&#39;cm&#39;</span><span class="p">,</span><span class="s1">&#39;mm&#39;</span><span class="p">,</span><span class="s1">&#39;um&#39;</span><span class="p">]</span>
+            <span class="n">unit_multipliers</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">,</span> <span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-6</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">unit_type</span> <span class="o">==</span> <span class="n">unit_types</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="n">unit_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;meV&#39;</span><span class="p">,</span> <span class="s1">&#39;eV&#39;</span><span class="p">,</span> <span class="s1">&#39;keV&#39;</span><span class="p">,</span> <span class="s1">&#39;MeV&#39;</span><span class="p">,</span> <span class="s1">&#39;J&#39;</span><span class="p">]</span>
+            <span class="n">unit_multipliers</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1e3</span><span class="p">,</span> <span class="mf">1e6</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">constants</span><span class="o">.</span><span class="n">eV</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">unit_type</span> <span class="o">==</span> <span class="n">unit_types</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span>
+            <span class="n">unit_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;deg&#39;</span><span class="p">,</span> <span class="s1">&#39;rad&#39;</span><span class="p">]</span>
+            <span class="n">unit_multipliers</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">rad2deg</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unit type &#39;</span><span class="si">{}</span><span class="s2">&#39; not recognised, must be one of </span><span class="si">{}</span><span class="s2">&quot;</span>
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">unit_type</span><span class="p">,</span> <span class="n">unit_types</span><span class="p">))</span>
+
+        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="n">input_unit</span><span class="p">,</span> <span class="n">output_unit</span><span class="p">]:</span>
+            <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">unit_list</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unit &#39;</span><span class="si">{}</span><span class="s2">&#39; not recognised, must be one of </span><span class="si">{}</span><span class="s2">.</span><span class="se">\</span>
+<span class="s2">                                 </span><span class="se">\n</span><span class="s2">Geometry units can be updated using geometry.config.units&quot;</span>
+                                 <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">unit_list</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">value</span><span class="o">*</span><span class="n">unit_multipliers</span><span class="p">[</span><span class="n">unit_list</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">input_unit</span><span class="p">)]</span>\
+            <span class="o">/</span><span class="n">unit_multipliers</span><span class="p">[</span><span class="n">unit_list</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">output_unit</span><span class="p">)]</span></div>
+
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/processors/RingRemover/index.html b/v24.2.0/_modules/cil/processors/RingRemover/index.html
new file mode 100644
index 0000000000..66cbcb7c04
--- /dev/null
+++ b/v24.2.0/_modules/cil/processors/RingRemover/index.html
@@ -0,0 +1,724 @@
+
+<!DOCTYPE html>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.processors.RingRemover</span></li>
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+  <h1>Source code for cil.processors.RingRemover</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">scipy.fftpack</span> <span class="kn">import</span> <span class="n">fftshift</span><span class="p">,</span> <span class="n">ifftshift</span><span class="p">,</span> <span class="n">fft</span><span class="p">,</span> <span class="n">ifft</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pywt</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">Processor</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">AcquisitionData</span>
+
+<div class="viewcode-block" id="RingRemover">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.RingRemover">[docs]</a>
+<span class="k">class</span> <span class="nc">RingRemover</span><span class="p">(</span><span class="n">Processor</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        RingRemover Processor: Removes vertical stripes from a DataContainer(ImageData/AcquisitionData)</span>
+<span class="sd">        using the algorithm in https://doi.org/10.1364/OE.17.008567</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        decNum : int</span>
+<span class="sd">            Number of wavelet decompositions - increasing the number of decompositions, increases the strength of the ring removal</span>
+<span class="sd">            but can alter the profile of the data</span>
+
+<span class="sd">        wname : str</span>
+<span class="sd">            Name of wavelet filter from pywt e.g. &#39;db1&#39; -- &#39;db35&#39;, &#39;haar&#39; - increasing the wavelet filter increases the strength of</span>
+<span class="sd">            the ring removal, but also increases the computational effort</span>
+
+<span class="sd">        sigma : float</span>
+<span class="sd">            Damping parameter in Fourier space - increasing sigma, increases the size of artefacts which can be removed</span>
+
+<span class="sd">        info : boolean</span>
+<span class="sd">            Flag to enable print of ring remover end message</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            Corrected ImageData/AcquisitionData 2D, 3D, multi-spectral 2D, multi-spectral 3D</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decNum</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">wname</span><span class="o">=</span><span class="s1">&#39;db10&#39;</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;decNum&#39;</span><span class="p">:</span> <span class="n">decNum</span><span class="p">,</span>
+                  <span class="s1">&#39;wname&#39;</span><span class="p">:</span> <span class="n">wname</span><span class="p">,</span>
+                  <span class="s1">&#39;sigma&#39;</span><span class="p">:</span> <span class="n">sigma</span><span class="p">,</span>
+                  <span class="s1">&#39;info&#39;</span><span class="p">:</span> <span class="n">info</span><span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">RingRemover</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">))</span> <span class="ow">or</span>
+                <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">))):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Processor supports only following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">geometry</span> <span class="o">==</span> <span class="kc">None</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Geometry is not defined.&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="n">decNum</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decNum</span>
+        <span class="n">wname</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wname</span>
+        <span class="n">sigma</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span>
+        <span class="n">info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">info</span>
+
+        <span class="c1"># acquisition geometry from sinogram</span>
+        <span class="n">geom</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span>
+
+        <span class="c1"># get channels, vertical info</span>
+        <span class="n">channels</span> <span class="o">=</span>  <span class="n">geom</span><span class="o">.</span><span class="n">channels</span>
+        <span class="n">vertical</span> <span class="o">=</span> <span class="n">geom</span><span class="o">.</span><span class="n">pixel_num_v</span>
+
+        <span class="c1"># allocate datacontainer space</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="mf">0.</span><span class="o">*</span><span class="n">data</span>
+
+        <span class="c1"># for non multichannel data</span>
+        <span class="k">if</span> <span class="s1">&#39;channel&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">geom</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+
+            <span class="c1"># for 3D data</span>
+            <span class="k">if</span> <span class="s1">&#39;vertical&#39;</span> <span class="ow">in</span> <span class="n">geom</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">vertical</span><span class="p">):</span>
+                    <span class="n">tmp_corrected</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xRemoveStripesVertical</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">decNum</span><span class="p">,</span> <span class="n">wname</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp_corrected</span><span class="p">,</span> <span class="n">vertical</span> <span class="o">=</span> <span class="n">i</span><span class="p">)</span>
+
+            <span class="c1"># for 2D data</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">tmp_corrected</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xRemoveStripesVertical</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">decNum</span><span class="p">,</span> <span class="n">wname</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp_corrected</span><span class="p">)</span>
+
+        <span class="c1"># for multichannel data</span>
+        <span class="k">else</span><span class="p">:</span>
+
+           <span class="c1"># for 3D data</span>
+            <span class="k">if</span> <span class="s1">&#39;vertical&#39;</span> <span class="ow">in</span> <span class="n">geom</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">channels</span><span class="p">):</span>
+
+                    <span class="n">out_ch_i</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+                    <span class="n">data_ch_i</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+                    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">vertical</span><span class="p">):</span>
+                        <span class="n">tmp_corrected</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xRemoveStripesVertical</span><span class="p">(</span><span class="n">data_ch_i</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">decNum</span><span class="p">,</span> <span class="n">wname</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
+                        <span class="n">out_ch_i</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp_corrected</span><span class="p">,</span> <span class="n">vertical</span> <span class="o">=</span> <span class="n">j</span><span class="p">)</span>
+
+                    <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">out_ch_i</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">channel</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">info</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Finish channel </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+            <span class="c1"># for 2D data</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">channels</span><span class="p">):</span>
+                        <span class="n">tmp_corrected</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_xRemoveStripesVertical</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">decNum</span><span class="p">,</span> <span class="n">wname</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
+                        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp_corrected</span><span class="p">,</span> <span class="n">channel</span> <span class="o">=</span> <span class="n">i</span><span class="p">)</span>
+                        <span class="k">if</span> <span class="n">info</span><span class="p">:</span>
+                            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Finish channel </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">info</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Finish Ring Remover&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+
+    <span class="k">def</span> <span class="nf">_xRemoveStripesVertical</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ima</span><span class="p">,</span> <span class="n">decNum</span><span class="p">,</span> <span class="n">wname</span><span class="p">,</span> <span class="n">sigma</span><span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&#39;&#39;&#39; Ring removal algorithm via combined wavelet and fourier filtering</span>
+<span class="sd">            code from https://doi.org/10.1364/OE.17.008567</span>
+<span class="sd">            translated in Python</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        ima : ndarray</span>
+<span class="sd">            2D image data</span>
+
+<span class="sd">        decNum : int</span>
+<span class="sd">            Number of wavelet decompositions - increasing the number of decompositions, increases the strength of the ring removal</span>
+<span class="sd">            but can alter the profile of the data</span>
+
+<span class="sd">        wname : str</span>
+<span class="sd">            Name of wavelet filter from pywt e.g. &#39;db1&#39; -- &#39;db35&#39;, &#39;haar&#39; - increasing the wavelet filter increases the strength of</span>
+<span class="sd">            the ring removal, but also increases the computational effort</span>
+
+<span class="sd">        sigma : float</span>
+<span class="sd">            Damping parameter in Fourier space - increasing sigma, increase the size of artefacts which can be removed</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Corrected 2D sinogram data (Numpy Array)</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">original_extent</span> <span class="o">=</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">ima</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="kc">None</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">ima</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="kc">None</span><span class="p">)]</span>
+
+        <span class="c1"># allocate cH, cV, cD</span>
+        <span class="n">Ch</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">decNum</span>
+        <span class="n">Cv</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">decNum</span>
+        <span class="n">Cd</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">decNum</span>
+
+        <span class="c1"># wavelets decomposition</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">decNum</span><span class="p">):</span>
+            <span class="n">ima</span><span class="p">,</span> <span class="p">(</span><span class="n">Ch</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">Cv</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">Cd</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">dwt2</span><span class="p">(</span><span class="n">ima</span><span class="p">,</span><span class="n">wname</span><span class="p">)</span>
+
+        <span class="c1"># FFT transform of horizontal frequency bands</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">decNum</span><span class="p">):</span>
+
+            <span class="c1"># use to axis=0, which correspond to the angles direction</span>
+            <span class="n">fCv</span> <span class="o">=</span> <span class="n">fftshift</span><span class="p">(</span><span class="n">fft</span><span class="p">(</span><span class="n">Cv</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+            <span class="n">my</span><span class="p">,</span> <span class="n">mx</span> <span class="o">=</span> <span class="n">fCv</span><span class="o">.</span><span class="n">shape</span>
+
+            <span class="c1"># damping of vertical stripe information</span>
+            <span class="n">damp</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">my</span><span class="o">/</span><span class="mi">2</span><span class="p">)),</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">my</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span><span class="o">+</span><span class="n">my</span><span class="p">)])</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">sigma</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+            <span class="n">fCv</span> <span class="o">*=</span> <span class="n">damp</span><span class="o">.</span><span class="n">T</span>
+
+            <span class="c1"># inverse FFT</span>
+            <span class="n">Cv</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">ifft</span><span class="p">(</span><span class="n">ifftshift</span><span class="p">(</span><span class="n">fCv</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+
+        <span class="c1"># wavelet reconstruction</span>
+        <span class="n">nima</span> <span class="o">=</span> <span class="n">ima</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">decNum</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">nima</span> <span class="o">=</span> <span class="n">nima</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">Ch</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="mi">0</span><span class="p">:</span><span class="n">Ch</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+            <span class="n">nima</span> <span class="o">=</span> <span class="n">pywt</span><span class="o">.</span><span class="n">idwt2</span><span class="p">((</span><span class="n">nima</span><span class="p">,(</span><span class="n">Ch</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">Cv</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">Cd</span><span class="p">[</span><span class="n">i</span><span class="p">])),</span><span class="n">wname</span><span class="p">)</span>
+
+        <span class="c1"># if the original input is odd, the signal reconstructed with idwt2 will have one extra sample, which can be discarded</span>
+        <span class="n">nima</span> <span class="o">=</span> <span class="n">nima</span><span class="p">[</span><span class="n">original_extent</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">original_extent</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+
+        <span class="k">return</span> <span class="n">nima</span></div>
+
+</pre></div>
+
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.processors.Slicer</span></li>
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+  <h1>Source code for cil.processors.Slicer</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="p">(</span><span class="n">DataProcessor</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">VectorGeometry</span><span class="p">,</span>
+                           <span class="n">AcquisitionGeometry</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">weakref</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<span class="c1"># Note to developers: Binner and Slicer share a lot of common code</span>
+<span class="c1"># so Binner has been implemented as a child of Slicer.  This makes use</span>
+<span class="c1"># of commonality and redefines only the methods that differ. These methods</span>
+<span class="c1"># dictate the style of slicer</span>
+<div class="viewcode-block" id="Slicer">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Slicer">[docs]</a>
+<span class="k">class</span> <span class="nc">Slicer</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;This creates a Slicer processor.</span>
+
+<span class="sd">    The processor will crop the data, and then return every n input pixels along a dimension from the starting index.</span>
+
+<span class="sd">    The output will be a data container with the data, and geometry updated to reflect the operation.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    roi : dict</span>
+<span class="sd">        The region-of-interest to slice {&#39;axis_name1&#39;:(start,stop,step), &#39;axis_name2&#39;:(start,stop,step)}</span>
+<span class="sd">        The `key` being the axis name to apply the processor to, the `value` holding a tuple containing the ROI description</span>
+
+<span class="sd">        Start: Starting index of input data. Must be an integer, or `None` defaults to index 0.</span>
+<span class="sd">        Stop: Stopping index of input data. Must be an integer, or `None` defaults to index N.</span>
+<span class="sd">        Step: Number of pixels to average together. Must be an integer or `None` defaults to 1.</span>
+
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    &gt;&gt;&gt; from cil.processors import Slicer</span>
+<span class="sd">    &gt;&gt;&gt; roi = {&#39;horizontal&#39;:(10,-10,2),&#39;vertical&#39;:(10,-10,2)}</span>
+<span class="sd">    &gt;&gt;&gt; processor = Slicer(roi)</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data)</span>
+<span class="sd">    &gt;&gt;&gt; data_sliced= processor.get_output()</span>
+
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.processors import Slicer</span>
+<span class="sd">    &gt;&gt;&gt; roi = {&#39;horizontal&#39;:(None,None,2),&#39;vertical&#39;:(None,None,2)}</span>
+<span class="sd">    &gt;&gt;&gt; processor = Slicer(roi)</span>
+<span class="sd">    &gt;&gt;&gt; processor.set_input(data.geometry)</span>
+<span class="sd">    &gt;&gt;&gt; geometry_sliced = processor.get_output()</span>
+
+
+<span class="sd">    Note</span>
+<span class="sd">    ----</span>
+<span class="sd">    The indices provided are start inclusive, stop exclusive.</span>
+
+<span class="sd">    All elements along a dimension will be included if the axis does not appear in the roi dictionary, or if passed as {&#39;axis_name&#39;,-1}</span>
+
+<span class="sd">    If only one number is provided, then it is interpreted as Stop. i.e. {&#39;axis_name1&#39;:(stop)}</span>
+<span class="sd">    If two numbers are provided, then they are interpreted as Start and Stop  i.e. {&#39;axis_name1&#39;:(start, stop)}</span>
+
+<span class="sd">    Negative indexing can be used to specify the index. i.e. {&#39;axis_name1&#39;:(10, -10)} will crop the dimension symmetrically</span>
+
+<span class="sd">    If Stop - Start is not multiple of Step, then</span>
+<span class="sd">    the resulted dimension will have (Stop - Start) // Step</span>
+<span class="sd">    elements, i.e. (Stop - Start) % Step elements will be ignored</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">roi</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;_roi_input&#39;</span><span class="p">:</span> <span class="n">roi</span><span class="p">,</span>
+            <span class="s1">&#39;_roi_ordered&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_data_array&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+            <span class="s1">&#39;_geometry&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_processed_dims&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_shape_in&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_shape_out_full&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_shape_out&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_labels_out&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_labels_in&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_pixel_indices&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span>
+            <span class="s1">&#39;_accelerated&#39;</span><span class="p">:</span> <span class="kc">True</span>
+            <span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">Slicer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="Slicer.set_input">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Slicer.set_input">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set the input data or geometry to the processor</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        dataset : DataContainer, Geometry</span>
+<span class="sd">            The input DataContainer or Geometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,(</span><span class="n">AcquisitionGeometry</span><span class="p">,</span><span class="n">ImageGeometry</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_input</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">weakref</span><span class="o">.</span><span class="n">ref</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;shouldRun&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input data not compatible&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Input type mismatch: got </span><span class="si">{0}</span><span class="s2"> expecting </span><span class="si">{1}</span><span class="s2">&quot;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dataset</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_set_up</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">ImageData</span><span class="p">,</span><span class="n">AcquisitionData</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">ImageGeometry</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span> <span class="o">=</span> <span class="n">data</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Processor supports following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData</span><span class="se">\n</span><span class="s1"> - DataContainer</span><span class="se">\n</span><span class="s1"> - ImageGeometry</span><span class="se">\n</span><span class="s1"> - AcquisitionGeometry&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Expected float32&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_roi_input</span> <span class="o">==</span> <span class="kc">None</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Please, specify roi&#39;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_input</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Wrong label is specified for roi, expected one of </span><span class="si">{}</span><span class="s1">.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="kc">True</span>
+
+
+    <span class="k">def</span> <span class="nf">_set_up</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        This parses the input roi generically and then configures the processor according to its class.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1">#read input</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_parse_roi</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ndim</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">)</span>
+        <span class="c1">#processor specific configurations</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_configure</span><span class="p">()</span>
+        <span class="c1"># set boolean of dimensions to process</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_processed_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">else</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_labels_out</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_parse_roi</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndim</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Process the input roi</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="mi">4</span><span class="o">-</span><span class="n">ndim</span>
+        <span class="n">labels_in</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span>
+        <span class="n">labels_in</span><span class="p">[</span><span class="n">offset</span><span class="p">::]</span> <span class="o">=</span> <span class="n">dimension_labels</span>
+        <span class="n">shape_in</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span>
+        <span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span><span class="p">::]</span> <span class="o">=</span> <span class="n">shape</span>
+
+        <span class="c1"># defaults</span>
+        <span class="n">range_list</span> <span class="o">=</span> <span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">shape_in</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim</span><span class="p">):</span>
+
+            <span class="n">roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_input</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="kc">None</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">roi</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">roi</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                <span class="k">continue</span>
+
+            <span class="n">start</span> <span class="o">=</span> <span class="n">range_list</span><span class="p">[</span><span class="n">offset</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">start</span>
+            <span class="n">stop</span> <span class="o">=</span> <span class="n">range_list</span><span class="p">[</span><span class="n">offset</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">stop</span>
+            <span class="n">step</span> <span class="o">=</span> <span class="n">range_list</span><span class="p">[</span><span class="n">offset</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">step</span>
+
+            <span class="c1"># accepts a tuple, range or slice</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">roi</span> <span class="o">=</span> <span class="p">[</span><span class="n">roi</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">roi</span><span class="o">.</span><span class="n">stop</span><span class="p">,</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span><span class="p">]</span>
+            <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+                <span class="n">roi</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+
+            <span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">length</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">stop</span> <span class="o">=</span> <span class="n">roi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">length</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="n">roi</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">stop</span> <span class="o">=</span> <span class="n">roi</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">length</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">roi</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">step</span> <span class="o">=</span> <span class="n">roi</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+            <span class="c1"># deal with negative indexing</span>
+            <span class="k">if</span> <span class="n">start</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">start</span> <span class="o">+=</span> <span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">stop</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">stop</span> <span class="o">+=</span> <span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">stop</span> <span class="o">&gt;</span> <span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span><span class="o">+</span><span class="n">i</span><span class="p">]:</span>
+                <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ROI for axis </span><span class="si">{</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2"> has &#39;stop&#39; out of bounds. Using axis length as stop value.&quot;</span>
+                            <span class="sa">f</span><span class="s2">&quot; Got stop index: </span><span class="si">{</span><span class="n">stop</span><span class="si">}</span><span class="s2">, using </span><span class="si">{</span><span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span><span class="o">+</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">stop</span> <span class="o">=</span> <span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span><span class="o">+</span><span class="n">i</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">start</span> <span class="o">&gt;</span> <span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span><span class="o">+</span><span class="n">i</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ROI for axis </span><span class="si">{</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2"> has &#39;start&#39; out of bounds.&quot;</span>
+                                 <span class="sa">f</span><span class="s2">&quot; Got start index: </span><span class="si">{</span><span class="n">start</span><span class="si">}</span><span class="s2"> for axis length </span><span class="si">{</span><span class="n">shape_in</span><span class="p">[</span><span class="n">offset</span><span class="o">+</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">start</span> <span class="o">&gt;=</span> <span class="n">stop</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ROI for axis </span><span class="si">{</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2"> has &#39;start&#39; out of bounds.&quot;</span>
+                                 <span class="sa">f</span><span class="s2">&quot; Got start index: </span><span class="si">{</span><span class="n">start</span><span class="si">}</span><span class="s2">, stop index </span><span class="si">{</span><span class="n">stop</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="c1"># set values</span>
+            <span class="n">range_list</span><span class="p">[</span><span class="n">offset</span><span class="o">+</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">stop</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">step</span><span class="p">))</span>
+
+        <span class="c1"># set values</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span> <span class="o">=</span> <span class="n">shape_in</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span> <span class="o">=</span> <span class="n">labels_in</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span> <span class="o">=</span> <span class="n">range_list</span>
+
+
+    <span class="k">def</span> <span class="nf">_configure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Once the ROI has been parsed this configure the input specifically for use with Slicer</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_shape_out_full</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span> <span class="o">=</span> <span class="p">[(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">]</span>
+
+
+    <span class="k">def</span> <span class="nf">_get_slice_position</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">roi</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the vertical position to extract a single slice for sliced geometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">roi</span><span class="o">.</span><span class="n">start</span>
+
+
+    <span class="k">def</span> <span class="nf">_get_angles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">roi</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the sliced angles according to the roi</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">[</span><span class="n">roi</span><span class="o">.</span><span class="n">start</span><span class="p">:</span><span class="n">roi</span><span class="o">.</span><span class="n">stop</span><span class="p">:</span><span class="n">roi</span><span class="o">.</span><span class="n">step</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_process_acquisition_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates the new acquisition geometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">geometry_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="n">processed_dims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_processed_dims</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="c1"># deal with vertical first as it may change the geometry type</span>
+        <span class="k">if</span> <span class="s1">&#39;vertical&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="n">vert_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">processed_dims</span><span class="p">[</span><span class="n">vert_ind</span><span class="p">]:</span>
+                <span class="n">roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">vert_ind</span><span class="p">]</span>
+                <span class="n">n_elements</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">n_elements</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="c1"># difference in end indices, minus differences in start indices, divided by 2</span>
+                    <span class="n">pixel_offset</span> <span class="o">=</span> <span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">[</span><span class="n">vert_ind</span><span class="p">]</span> <span class="o">-</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span><span class="p">[</span><span class="n">vert_ind</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span><span class="p">[</span><span class="n">vert_ind</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="mf">0.5</span>
+                    <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">shift_detector_in_plane</span><span class="p">(</span><span class="n">pixel_offset</span><span class="p">,</span> <span class="s1">&#39;vertical&#39;</span><span class="p">)</span>
+                    <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">n_elements</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">position</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_slice_position</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+                        <span class="n">geometry_new</span> <span class="o">=</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span> <span class="o">=</span> <span class="n">position</span><span class="p">)</span>
+                    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                        <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Unable to calculate the requested 2D geometry. Returning geometry=`None`&quot;</span><span class="p">)</span>
+                        <span class="k">return</span> <span class="kc">None</span>
+
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*=</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span>
+                <span class="n">processed_dims</span><span class="p">[</span><span class="n">vert_ind</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis</span>  <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">processed_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+                <span class="k">continue</span>
+
+            <span class="n">roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">n_elements</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">axis</span> <span class="o">==</span> <span class="s1">&#39;channel&#39;</span><span class="p">:</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">set_channels</span><span class="p">(</span><span class="n">num_channels</span><span class="o">=</span><span class="n">n_elements</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">axis</span> <span class="o">==</span> <span class="s1">&#39;angle&#39;</span><span class="p">:</span>
+
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span><span class="o">.</span><span class="n">angle_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_angles</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">axis</span> <span class="o">==</span> <span class="s1">&#39;horizontal&#39;</span><span class="p">:</span>
+                <span class="n">pixel_offset</span> <span class="o">=</span> <span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="mf">0.5</span>
+
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">shift_detector_in_plane</span><span class="p">(</span><span class="n">pixel_offset</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">n_elements</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span>
+
+        <span class="k">return</span> <span class="n">geometry_new</span>
+
+
+    <span class="k">def</span> <span class="nf">_process_image_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates the new image geometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">None</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span><span class="p">)</span> <span class="o">==</span><span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">VectorGeometry</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_labels_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="n">geometry_new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">axis</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_labels_in</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_processed_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+                <span class="k">continue</span>
+
+            <span class="n">roi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">n_elements</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+
+            <span class="n">voxel_offset</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pixel_indices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="mf">0.5</span>
+
+            <span class="k">if</span> <span class="n">axis</span> <span class="o">==</span> <span class="s1">&#39;channel&#39;</span><span class="p">:</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">n_elements</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">channel_spacing</span> <span class="o">*=</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span>
+
+            <span class="k">elif</span> <span class="n">axis</span> <span class="o">==</span> <span class="s1">&#39;vertical&#39;</span><span class="p">:</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">center_z</span> <span class="o">-=</span> <span class="n">voxel_offset</span> <span class="o">*</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_size_z</span>
+
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">=</span> <span class="n">n_elements</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_size_z</span> <span class="o">*=</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span>
+
+            <span class="k">elif</span> <span class="n">axis</span> <span class="o">==</span> <span class="s1">&#39;horizontal_x&#39;</span><span class="p">:</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">center_x</span> <span class="o">-=</span> <span class="n">voxel_offset</span> <span class="o">*</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_size_x</span>
+
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="n">n_elements</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_size_x</span> <span class="o">*=</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span>
+
+            <span class="k">elif</span> <span class="n">axis</span> <span class="o">==</span> <span class="s1">&#39;horizontal_y&#39;</span><span class="p">:</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">center_y</span> <span class="o">-=</span> <span class="n">voxel_offset</span> <span class="o">*</span> <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_size_y</span>
+
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_num_y</span> <span class="o">=</span> <span class="n">n_elements</span>
+                <span class="n">geometry_new</span><span class="o">.</span><span class="n">voxel_size_y</span> <span class="o">*=</span> <span class="n">roi</span><span class="o">.</span><span class="n">step</span>
+
+        <span class="k">return</span> <span class="n">geometry_new</span>
+
+
+    <span class="k">def</span> <span class="nf">_process_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dc_in</span><span class="p">,</span> <span class="n">dc_out</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Slice the data array</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">slice_obj</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="nb">slice</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">stop</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">step</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_roi_ordered</span><span class="p">])</span>
+        <span class="n">arr_in</span> <span class="o">=</span> <span class="n">dc_in</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_in</span><span class="p">)</span>
+        <span class="n">dc_out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">arr_in</span><span class="p">[</span><span class="n">slice_obj</span><span class="p">]))</span>
+
+<div class="viewcode-block" id="Slicer.process">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.Slicer.process">[docs]</a>
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Processes the input data</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        out : ImageData, AcquisitionData, DataContainer, optional</span>
+<span class="sd">           Fills the referenced DataContainer with the processed output and suppresses the return</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The downsampled output is returned. Depending on the input type this may be:</span>
+<span class="sd">            ImageData, AcquisitionData, DataContainer, ImageGeometry, AcquisitionGeometry</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="p">,</span> <span class="n">ImageGeometry</span><span class="p">):</span>
+            <span class="n">new_geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_image_geometry</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_geometry</span><span class="p">,</span> <span class="n">AcquisitionGeometry</span><span class="p">):</span>
+            <span class="n">new_geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_acquisition_geometry</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">new_geometry</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># return if just acting on geometry</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_array</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">new_geometry</span>
+
+        <span class="c1"># create output array or check size and shape of passed out</span>
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">new_geometry</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">data_out</span> <span class="o">=</span> <span class="n">new_geometry</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">processed_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+                <span class="n">data_out</span> <span class="o">=</span> <span class="n">DataContainer</span><span class="p">(</span><span class="n">processed_array</span><span class="p">,</span><span class="kc">False</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_labels_out</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s1">&#39;C&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Array of `out` not compatible. Expected shape: </span><span class="si">{0}</span><span class="s2">, data type: </span><span class="si">{1}</span><span class="s2"> Got shape: </span><span class="si">{2}</span><span class="s2">, data type: </span><span class="si">{3}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_shape_out</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">new_geometry</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">geometry</span> <span class="o">!=</span> <span class="n">new_geometry</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Geometry of `out` not as expected. Got </span><span class="si">{0}</span><span class="s2">, expected </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">new_geometry</span><span class="p">))</span>
+
+            <span class="n">data_out</span> <span class="o">=</span> <span class="n">out</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_process_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">data_out</span><span class="p">)</span>
+
+
+        <span class="k">return</span> <span class="n">data_out</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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+                  
+  <h1>Source code for cil.processors.TransmissionAbsorptionConverter</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataProcessor</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">DataContainer</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+
+<div class="viewcode-block" id="TransmissionAbsorptionConverter">
+<a class="viewcode-back" href="../../../../processors/#cil.processors.TransmissionAbsorptionConverter">[docs]</a>
+<span class="k">class</span> <span class="nc">TransmissionAbsorptionConverter</span><span class="p">(</span><span class="n">DataProcessor</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;Processor to convert from transmission measurements to absorption</span>
+<span class="sd">    based on the Beer-Lambert law</span>
+
+<span class="sd">    :param white_level: A float defining incidence intensity in the Beer-Lambert law.</span>
+<span class="sd">    :type white_level: float, optional</span>
+<span class="sd">    :param min_intensity: A float defining some threshold to avoid 0 in log, is applied after normalisation by white_level</span>
+<span class="sd">    :type min_intensity: float, optional</span>
+<span class="sd">    :return: returns AcquisitionData, ImageData or DataContainer depending on input data type, return is suppressed if &#39;out&#39; is passed</span>
+<span class="sd">    :rtype: AcquisitionData, ImageData or DataContainer</span>
+
+<span class="sd">    Processor first divides by white_level (default=1) and then take negative logarithm.</span>
+<span class="sd">    Elements below threshold (after division by white_level) are set to threshold.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">min_intensity</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
+                 <span class="n">white_level</span> <span class="o">=</span> <span class="mf">1.0</span>
+                 <span class="p">):</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;min_intensity&#39;</span><span class="p">:</span> <span class="n">min_intensity</span><span class="p">,</span>
+                  <span class="s1">&#39;white_level&#39;</span><span class="p">:</span> <span class="n">white_level</span><span class="p">}</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">TransmissionAbsorptionConverter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">check_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">DataContainer</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Processor supports only following data types:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - ImageData</span><span class="se">\n</span><span class="s1"> - AcquisitionData</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; - DataContainer&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_intensity</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Zero or negative values found in the dataset. Please use `min_intensity` to provide a clipping value.&#39;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">arr_in</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">arr_out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="c1">#whitelevel</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">white_level</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">numpy</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">arr_in</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">white_level</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">arr_out</span><span class="p">)</span>
+            <span class="n">arr_in</span> <span class="o">=</span> <span class="n">arr_out</span>
+
+        <span class="c1">#threshold</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_intensity</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">numpy</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">arr_in</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_intensity</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">arr_out</span><span class="p">)</span>
+            <span class="n">arr_in</span> <span class="o">=</span> <span class="n">arr_out</span>
+
+        <span class="c1">#beer-lambert</span>
+        <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">arr_in</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="n">arr_out</span><span class="p">)</span>
+        <span class="n">numpy</span><span class="o">.</span><span class="n">negative</span><span class="p">(</span><span class="n">arr_out</span><span class="p">,</span><span class="n">out</span><span class="o">=</span><span class="n">arr_out</span><span class="p">)</span>
+
+        <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr_out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span></div>
+
+</pre></div>
+
+                </article>
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diff --git a/v24.2.0/_modules/cil/recon/FBP/index.html b/v24.2.0/_modules/cil/recon/FBP/index.html
new file mode 100644
index 0000000000..3090213507
--- /dev/null
+++ b/v24.2.0/_modules/cil/recon/FBP/index.html
@@ -0,0 +1,1217 @@
+
+<!DOCTYPE html>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
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+  <h1>Source code for cil.recon.FBP</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2021 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">cilacc</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionType</span>
+<span class="kn">from</span> <span class="nn">cil.recon</span> <span class="kn">import</span> <span class="n">Reconstructor</span>
+<span class="kn">from</span> <span class="nn">scipy.fft</span> <span class="kn">import</span> <span class="n">fftfreq</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">ctypes</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">c_float_p</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">)</span>
+<span class="n">c_double_p</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span><span class="p">)</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="n">cilacc</span><span class="o">.</span><span class="n">filter_projections_avh</span>
+    <span class="n">has_ipp</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+    <span class="n">has_ipp</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="k">if</span> <span class="n">has_ipp</span><span class="p">:</span>
+    <span class="n">cilacc</span><span class="o">.</span><span class="n">filter_projections_avh</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the data array</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the filter array</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the weights array</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int16</span><span class="p">,</span> <span class="c1">#order of the fft</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_long</span><span class="p">,</span> <span class="c1">#num_proj</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_long</span><span class="p">,</span> <span class="c1">#pix_v</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_long</span><span class="p">]</span> <span class="c1">#pix_x</span>
+
+    <span class="n">cilacc</span><span class="o">.</span><span class="n">filter_projections_vah</span><span class="o">.</span><span class="n">argtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the data array</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the filter array</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span><span class="p">),</span>  <span class="c1"># pointer to the weights array</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int16</span><span class="p">,</span> <span class="c1">#order of the fft</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_long</span><span class="p">,</span> <span class="c1">#pix_v</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_long</span><span class="p">,</span> <span class="c1">#num_proj</span>
+                                    <span class="n">ctypes</span><span class="o">.</span><span class="n">c_long</span><span class="p">]</span> <span class="c1">#pix_x</span>
+
+<span class="k">class</span> <span class="nc">GenericFilteredBackProjection</span><span class="p">(</span><span class="n">Reconstructor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Abstract Base Class GenericFilteredBackProjection holding common and virtual methods for FBP and FDK</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">filter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">filter_inplace</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter_inplace</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">fft_order</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fft_order</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="s1">&#39;ram-lak&#39;</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;tigre&#39;</span><span class="p">):</span>
+
+        <span class="c1">#call parent initialiser</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">,</span> <span class="n">backend</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">has_ipp</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;IPP libraries not found. Cannot use CIL FBP&quot;</span><span class="p">)</span>
+
+        <span class="c1">#additional check</span>
+        <span class="k">if</span> <span class="s1">&#39;channel&#39;</span> <span class="ow">in</span> <span class="nb">input</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input data cannot be multi-channel&quot;</span><span class="p">)</span>
+
+
+        <span class="c1">#define defaults</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_fft_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_default_fft_order</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_filter</span><span class="p">(</span><span class="nb">filter</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_filter_inplace</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span> <span class="o">=</span> <span class="kc">None</span>
+
+
+    <span class="k">def</span> <span class="nf">set_filter_inplace</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        False (default) will allocate temporary memory for filtered projections.</span>
+<span class="sd">        True will filter projections in-place.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        inplace: boolean</span>
+<span class="sd">            Sets the inplace filtering of projections</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">inplace</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">bool</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_filter_inplace</span><span class="o">=</span> <span class="n">inplace</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;set_filter_inplace expected a boolean. Got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">inplace</span><span class="p">)))</span>
+
+
+    <span class="k">def</span> <span class="nf">_default_fft_order</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">min_order</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">while</span> <span class="mi">2</span><span class="o">**</span><span class="n">min_order</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">pixel_num_h</span> <span class="o">*</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">min_order</span><span class="o">+=</span><span class="mi">1</span>
+
+        <span class="n">min_order</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="n">min_order</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">min_order</span>
+
+
+    <span class="k">def</span> <span class="nf">set_fft_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The width of the fourier transform N=2^order.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        order: int, optional</span>
+<span class="sd">            The width of the fft N=2^order</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        If `None` the default used is the power-of-2 greater than 2 * detector width, or 8, whichever is greater</span>
+<span class="sd">        Higher orders will yield more accurate results but increase computation time.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">min_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_default_fft_order</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fft_order</span> <span class="o">=</span> <span class="n">min_order</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">fft_order</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;fft order expected type `int`. Got</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">order</span><span class="p">)))</span>
+
+        <span class="k">if</span> <span class="n">fft_order</span> <span class="o">&lt;</span> <span class="n">min_order</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Minimum fft width 2^order is order = </span><span class="si">{0}</span><span class="s2">. Got</span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">min_order</span><span class="p">,</span><span class="n">order</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">fft_order</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_fft_order</span> <span class="o">=</span> <span class="n">fft_order</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter</span><span class="o">==</span><span class="s1">&#39;custom&#39;</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Filter length changed - please update your custom filter&quot;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1">#create default filter type of new length</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">set_filter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">preset_filters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="s1">&#39;ram-lak&#39;</span><span class="p">,</span> <span class="s1">&#39;shepp-logan&#39;</span><span class="p">,</span> <span class="s1">&#39;cosine&#39;</span><span class="p">,</span> <span class="s1">&#39;hamming&#39;</span><span class="p">,</span> <span class="s1">&#39;hann&#39;</span><span class="p">]</span>
+
+
+    <span class="k">def</span> <span class="nf">set_filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="s1">&#39;ram-lak&#39;</span><span class="p">,</span> <span class="n">cutoff</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set the filter used by the reconstruction.</span>
+
+<span class="sd">        Pre-set filters are constructed in the frequency domain.</span>
+<span class="sd">        Pre-set filters are: &#39;ram-lak&#39;, &#39;shepp-logan&#39;, &#39;cosine&#39;, &#39;hamming&#39;, &#39;hann&#39;</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        filter : string, numpy.ndarray, default=&#39;ram-lak&#39;</span>
+<span class="sd">            Pass a string selecting from the list of pre-set filters, or pass a numpy.ndarray with a custom filter.</span>
+<span class="sd">        cutoff : float, default = 1</span>
+<span class="sd">            The cut-off frequency of the filter between 0 - 1 pi rads/pixel. The filter will be 0 outside the range rect(-frequency_cutoff, frequency_cutoff)</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        If passed a numpy array the filter must have length N = 2^self.fft_order</span>
+
+<span class="sd">        The indices of the array are interpreted as:</span>
+
+<span class="sd">        - [0] The DC frequency component</span>
+<span class="sd">        - [1:N/2] positive frequencies</span>
+<span class="sd">        - [N/2:N-1] negative frequencies</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="nb">filter</span><span class="p">)</span><span class="o">==</span><span class="nb">str</span> <span class="ow">and</span> <span class="nb">filter</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">preset_filters</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">=</span> <span class="nb">filter</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_filter_cutoff</span> <span class="o">=</span> <span class="n">cutoff</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_filter_array</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="nb">filter</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">filter_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">filter</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_filter_array</span> <span class="o">=</span> <span class="n">filter_array</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">=</span> <span class="s1">&#39;custom&#39;</span>
+            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Custom filter not compatible with input.&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Filter not recognised&quot;</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">get_filter_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the filter array in the frequency domain.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        numpy.ndarray</span>
+<span class="sd">            An array containing the filter values</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The filter length N is 2^self.fft_order.</span>
+
+<span class="sd">        The indices of the array are interpreted as:</span>
+
+<span class="sd">        - [0] The DC frequency component</span>
+<span class="sd">        - [1:N/2] positive frequencies</span>
+<span class="sd">        - [N/2:N-1] negative frequencies</span>
+
+<span class="sd">        The array can be modified and passed back using set_filter()</span>
+
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        Filter reference in frequency domain:</span>
+<span class="sd">        Eq. 1.12 - 1.15 T. M. Buzug. Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Berlin: Springer, 2008.</span>
+
+<span class="sd">        Plantagie, L. Algebraic filters for filtered backprojection, 2017</span>
+<span class="sd">        https://hdl.handle.net/1887/48289</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">==</span> <span class="s1">&#39;custom&#39;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter_array</span>
+
+        <span class="n">filter_length</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span>
+
+        <span class="c1"># frequency bins in cycles/pixel</span>
+        <span class="n">freq</span> <span class="o">=</span> <span class="n">fftfreq</span><span class="p">(</span><span class="n">filter_length</span><span class="p">)</span>
+        <span class="c1"># in pi rad/pixel</span>
+        <span class="n">freq</span><span class="o">*=</span><span class="mi">2</span>
+
+        <span class="n">ramp</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">freq</span><span class="p">)</span>
+        <span class="n">ramp</span><span class="p">[</span><span class="n">ramp</span><span class="o">&gt;</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter_cutoff</span><span class="p">]</span><span class="o">=</span><span class="mi">0</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">==</span> <span class="s1">&#39;ram-lak&#39;</span><span class="p">:</span>
+            <span class="n">filter_array</span> <span class="o">=</span> <span class="n">ramp</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">==</span> <span class="s1">&#39;shepp-logan&#39;</span><span class="p">:</span>
+            <span class="n">filter_array</span> <span class="o">=</span> <span class="n">ramp</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sinc</span><span class="p">(</span><span class="n">freq</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">==</span> <span class="s1">&#39;cosine&#39;</span><span class="p">:</span>
+            <span class="n">filter_array</span> <span class="o">=</span> <span class="n">ramp</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">freq</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">==</span> <span class="s1">&#39;hamming&#39;</span><span class="p">:</span>
+            <span class="n">filter_array</span> <span class="o">=</span> <span class="n">ramp</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.54</span> <span class="o">+</span> <span class="mf">0.46</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">freq</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">==</span> <span class="s1">&#39;hann&#39;</span><span class="p">:</span>
+            <span class="n">filter_array</span> <span class="o">=</span> <span class="n">ramp</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.5</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">freq</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">filter_array</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">plot_filter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a plot of the filter array.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        matplotlib.pyplot</span>
+<span class="sd">            A plot of the filter</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">filter_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_filter_array</span><span class="p">()</span>
+        <span class="n">filter_length</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span>
+        <span class="n">freq</span> <span class="o">=</span> <span class="n">fftfreq</span><span class="p">(</span><span class="n">filter_length</span><span class="p">)</span>
+        <span class="n">freq</span> <span class="o">*=</span> <span class="mi">2</span>
+        <span class="n">ind_sorted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">freq</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq</span><span class="p">[</span><span class="n">ind_sorted</span><span class="p">],</span> <span class="n">filter_array</span><span class="p">[</span><span class="n">ind_sorted</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;magenta&#39;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (rads/pixel)&#39;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude&#39;</span><span class="p">)</span>
+        <span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;-π&#39;</span><span class="p">,</span> <span class="s1">&#39;-3π/4&#39;</span><span class="p">,</span> <span class="s1">&#39;-π/2&#39;</span><span class="p">,</span> <span class="s1">&#39;-π/4&#39;</span><span class="p">,</span> <span class="s1">&#39;0&#39;</span><span class="p">,</span> <span class="s1">&#39;π/4&#39;</span><span class="p">,</span> <span class="s1">&#39;π/2&#39;</span><span class="p">,</span> <span class="s1">&#39;3π/4&#39;</span><span class="p">,</span> <span class="s1">&#39;π&#39;</span><span class="p">])</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">plt</span>
+
+
+    <span class="k">def</span> <span class="nf">_calculate_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="ne">NotImplementedError</span>
+
+
+    <span class="k">def</span> <span class="nf">_pre_filtering</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">acquistion_data</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Filters and weights the projections inplace</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        acquistion_data : AcquisitionData</span>
+<span class="sd">            The projections to be filtered</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        self.input is not used to allow processing in smaller chunks</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">acquistion_data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">pixel_num_v</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_weights</span><span class="p">(</span><span class="n">acquistion_data</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">acquistion_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span> <span class="c1">#horizontal</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Weights not compatible&quot;</span><span class="p">)</span>
+
+        <span class="n">filter_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_filter_array</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">filter_array</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="mi">2</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Custom filter has length </span><span class="si">{0}</span><span class="s2"> and is not compatible with requested fft_order </span><span class="si">{1}</span><span class="s2">. Expected filter length 2^</span><span class="si">{1}</span><span class="s2">&quot;</span>\
+                            <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filter_array</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">))</span>
+
+        <span class="c1">#call ext function</span>
+        <span class="n">data_ptr</span> <span class="o">=</span> <span class="n">acquistion_data</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+        <span class="n">filter_ptr</span> <span class="o">=</span> <span class="n">filter_array</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+        <span class="n">weights_ptr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">c_float_p</span><span class="p">)</span>
+
+        <span class="n">ag</span> <span class="o">=</span> <span class="n">acquistion_data</span><span class="o">.</span><span class="n">geometry</span>
+        <span class="k">if</span> <span class="n">ag</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">==</span> <span class="p">(</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">):</span>
+            <span class="n">cilacc</span><span class="o">.</span><span class="n">filter_projections_avh</span><span class="p">(</span><span class="n">data_ptr</span><span class="p">,</span> <span class="n">filter_ptr</span><span class="p">,</span> <span class="n">weights_ptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">,</span> <span class="o">*</span><span class="n">acquistion_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">ag</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">==</span> <span class="p">(</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">):</span>
+            <span class="n">cilacc</span><span class="o">.</span><span class="n">filter_projections_vah</span><span class="p">(</span><span class="n">data_ptr</span><span class="p">,</span> <span class="n">filter_ptr</span><span class="p">,</span> <span class="n">weights_ptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">,</span> <span class="o">*</span><span class="n">acquistion_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">ag</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">==</span> <span class="p">(</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">):</span>
+            <span class="n">cilacc</span><span class="o">.</span><span class="n">filter_projections_vah</span><span class="p">(</span><span class="n">data_ptr</span><span class="p">,</span> <span class="n">filter_ptr</span><span class="p">,</span> <span class="n">weights_ptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">acquistion_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">ag</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">==</span> <span class="p">(</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">):</span>
+            <span class="n">cilacc</span><span class="o">.</span><span class="n">filter_projections_avh</span><span class="p">(</span><span class="n">data_ptr</span><span class="p">,</span> <span class="n">filter_ptr</span><span class="p">,</span> <span class="n">weights_ptr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fft_order</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">acquistion_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span> <span class="p">(</span><span class="s2">&quot;Could not determine correct function call from dimension labels&quot;</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Resets all optional configuration parameters to their default values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_filter</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_fft_order</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_filter_inplace</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_image_geometry</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span> <span class="o">=</span> <span class="kc">None</span>
+
+
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="ne">NotImplementedError</span>
+
+
+<div class="viewcode-block" id="FDK">
+<a class="viewcode-back" href="../../../../recon/#cil.recon.FBP.FDK">[docs]</a>
+<span class="k">class</span> <span class="nc">FDK</span><span class="p">(</span><span class="n">GenericFilteredBackProjection</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates an FDK reconstructor based on your cone-beam acquisition data using TIGRE as a backend.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    input : AcquisitionData</span>
+<span class="sd">        The input data to reconstruct. The reconstructor is set-up based on the geometry of the data.</span>
+
+<span class="sd">    image_geometry : ImageGeometry, default used if None</span>
+<span class="sd">        A description of the area/volume to reconstruct</span>
+
+<span class="sd">    filter : string, numpy.ndarray, default=&#39;ram-lak&#39;</span>
+<span class="sd">        The filter to be applied. Can be a string from: {&#39;`ram-lak`&#39;, &#39;`shepp-logan`&#39;, &#39;`cosine`&#39;, &#39;`hamming`&#39;, &#39;`hann`&#39;}, or a numpy array.</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.utilities.dataexample import SIMULATED_CONE_BEAM_DATA</span>
+<span class="sd">    &gt;&gt;&gt; from cil.recon import FDK</span>
+<span class="sd">    &gt;&gt;&gt; data = SIMULATED_CONE_BEAM_DATA.get()</span>
+<span class="sd">    &gt;&gt;&gt; fdk = FDK(data)</span>
+<span class="sd">    &gt;&gt;&gt; out = fdk.run()</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    The reconstructor can be futher customised using additional &#39;set&#39; methods provided.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">supported_backends</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;tigre&#39;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="s1">&#39;ram-lak&#39;</span><span class="p">):</span>
+        <span class="c1">#call parent initialiser</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;tigre&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">CONE</span> <span class="o">&amp;</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">geom_type</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;This reconstructor is for cone-beam data only.&quot;</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_calculate_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">):</span>
+        <span class="n">ag</span> <span class="o">=</span> <span class="n">acquisition_geometry</span>
+        <span class="n">xv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">ag</span><span class="o">.</span><span class="n">pixel_num_h</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">,(</span><span class="n">ag</span><span class="o">.</span><span class="n">pixel_num_h</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">*</span> <span class="n">ag</span><span class="o">.</span><span class="n">pixel_size_h</span>
+        <span class="n">yv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">ag</span><span class="o">.</span><span class="n">pixel_num_v</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">,(</span><span class="n">ag</span><span class="o">.</span><span class="n">pixel_num_v</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">*</span> <span class="n">ag</span><span class="o">.</span><span class="n">pixel_size_v</span>
+        <span class="p">(</span><span class="n">yy</span><span class="p">,</span> <span class="n">xx</span><span class="p">)</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">xv</span><span class="p">,</span> <span class="n">yv</span><span class="p">)</span>
+
+        <span class="n">principal_ray_length</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">dist_source_center</span> <span class="o">+</span> <span class="n">ag</span><span class="o">.</span><span class="n">dist_center_detector</span>
+        <span class="n">scaling</span> <span class="o">=</span> <span class="mf">0.25</span> <span class="o">*</span> <span class="n">ag</span><span class="o">.</span><span class="n">magnification</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span> <span class="n">ag</span><span class="o">.</span><span class="n">num_projections</span><span class="p">)</span> <span class="o">/</span> <span class="n">ag</span><span class="o">.</span><span class="n">pixel_size_h</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span> <span class="o">=</span> <span class="n">scaling</span> <span class="o">*</span> <span class="n">principal_ray_length</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">principal_ray_length</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">xx</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">yy</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
+
+
+<div class="viewcode-block" id="FDK.run">
+<a class="viewcode-back" href="../../../../recon/#cil.recon.FBP.FDK.run">[docs]</a>
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Runs the configured FDK recon and returns the reconstruction.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        out : ImageData, optional</span>
+<span class="sd">           Fills the referenced ImageData with the reconstructed volume and suppresses the return</span>
+<span class="sd">        verbose : int, default=1</span>
+<span class="sd">           Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The reconstructed volume. Suppressed if `out` is passed</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_inplace</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">proj_filtered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">proj_filtered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_pre_filtering</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">)</span>
+        <span class="n">operator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_PO_class</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="p">,</span><span class="n">adjoint_weights</span><span class="o">=</span><span class="s1">&#39;FDK&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span></div>
+
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;FDK recon</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+        <span class="n">repres</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_str_data_size</span><span class="p">()</span>
+
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reconstruction Options:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Backend: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backend</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Filter: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">!=</span> <span class="s1">&#39;custom&#39;</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Filter cut-off frequency: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter_cutoff</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">FFT order: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_fft_order</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Filter_inplace: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter_inplace</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">repres</span></div>
+
+
+<div class="viewcode-block" id="FBP">
+<a class="viewcode-back" href="../../../../recon/#cil.recon.FBP.FBP">[docs]</a>
+<span class="k">class</span> <span class="nc">FBP</span><span class="p">(</span><span class="n">GenericFilteredBackProjection</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates an FBP reconstructor based on your parallel-beam acquisition data.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    input : AcquisitionData</span>
+<span class="sd">        The input data to reconstruct. The reconstructor is set-up based on the geometry of the data.</span>
+
+<span class="sd">    image_geometry : ImageGeometry, default used if None</span>
+<span class="sd">        A description of the area/volume to reconstruct</span>
+
+<span class="sd">    filter : string, numpy.ndarray, default=&#39;ram-lak&#39;</span>
+<span class="sd">        The filter to be applied. Can be a string from: {&#39;`ram-lak`&#39;, &#39;`shepp-logan`&#39;, &#39;`cosine`&#39;, &#39;`hamming`&#39;, &#39;`hann`&#39;}, or a numpy array.</span>
+
+<span class="sd">    backend : string</span>
+<span class="sd">        The backend to use, can be &#39;astra&#39; or &#39;tigre&#39;. Data must be in the correct order for requested backend.</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    -------</span>
+<span class="sd">    &gt;&gt;&gt; from cil.utilities.dataexample import SIMULATED_PARALLEL_BEAM_DATA</span>
+<span class="sd">    &gt;&gt;&gt; from cil.recon import FBP</span>
+<span class="sd">    &gt;&gt;&gt; data = SIMULATED_PARALLEL_BEAM_DATA.get()</span>
+<span class="sd">    &gt;&gt;&gt; fbp = FBP(data)</span>
+<span class="sd">    &gt;&gt;&gt; out = fbp.run()</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    The reconstructor can be further customised using additional &#39;set&#39; methods provided.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">supported_backends</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;tigre&#39;</span><span class="p">,</span> <span class="s1">&#39;astra&#39;</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">slices_per_chunk</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slices_per_chunk</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">filter</span><span class="o">=</span><span class="s1">&#39;ram-lak&#39;</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;tigre&#39;</span><span class="p">):</span>
+
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">backend</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_split_processing</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">PARALLEL</span> <span class="o">&amp;</span> <span class="nb">input</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">geom_type</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;This reconstructor is for parallel-beam data only.&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="FBP.set_split_processing">
+<a class="viewcode-back" href="../../../../recon/#cil.recon.FBP.FBP.set_split_processing">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_split_processing</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">slices_per_chunk</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Splits the processing in to chunks. Default, 0 will process the data in a single call.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        out : slices_per_chunk, optional</span>
+<span class="sd">            Process the data in chunks of n slices. It is recommended to use value of power-of-two.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        This will reduce memory use but may increase computation time.</span>
+<span class="sd">        It is recommended to tune it too your hardware requirements using 8, 16 or 32 slices.</span>
+
+<span class="sd">        This can only be used on simple and offset data-geometries.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">num_slices</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">slices_per_chunk</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">num_slices</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">if</span>  <span class="n">num_slices</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">pixel_num_v</span><span class="p">:</span>
+            <span class="n">num_slices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">pixel_num_v</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_slices_per_chunk</span> <span class="o">=</span> <span class="n">num_slices</span></div>
+
+
+
+    <span class="k">def</span> <span class="nf">_calculate_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">):</span>
+
+        <span class="n">ag</span> <span class="o">=</span> <span class="n">acquisition_geometry</span>
+        <span class="n">scaling</span> <span class="o">=</span> <span class="mf">0.25</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span> <span class="n">ag</span><span class="o">.</span><span class="n">num_projections</span><span class="p">)</span> <span class="o">/</span> <span class="n">ag</span><span class="o">.</span><span class="n">pixel_size_h</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span><span class="o">==</span><span class="s1">&#39;astra&#39;</span><span class="p">:</span>
+            <span class="n">scaling</span> <span class="o">/=</span>  <span class="n">ag</span><span class="o">.</span><span class="n">pixel_size_v</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">ag</span><span class="o">.</span><span class="n">pixel_num_v</span><span class="p">,</span><span class="n">ag</span><span class="o">.</span><span class="n">pixel_num_h</span><span class="p">),</span><span class="n">scaling</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_setup_PO_for_chunks</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_slices</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">num_slices</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">ag_slice</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">ag_slice</span><span class="o">.</span><span class="n">pixel_num_v</span> <span class="o">=</span> <span class="n">num_slices</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ag_slice</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ig_slice</span> <span class="o">=</span> <span class="n">ag_slice</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_slice</span> <span class="o">=</span> <span class="n">ag_slice</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">operator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_PO_class</span><span class="p">(</span><span class="n">ig_slice</span><span class="p">,</span><span class="n">ag_slice</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_process_chunk</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">step</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_slice</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="o">.</span><span class="n">array</span><span class="p">[:,</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">step</span><span class="p">,:]))</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_inplace</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_pre_filtering</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_slice</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_slice</span><span class="p">)</span><span class="o">.</span><span class="n">array</span>
+
+
+<div class="viewcode-block" id="FBP.run">
+<a class="viewcode-back" href="../../../../recon/#cil.recon.FBP.FBP.run">[docs]</a>
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Runs the configured FBP recon and returns the reconstruction</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        out : ImageData, optional</span>
+<span class="sd">           Fills the referenced ImageData with the reconstructed volume and suppresses the return</span>
+
+<span class="sd">        verbose : int, default=1</span>
+<span class="sd">           Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The reconstructed volume. Suppressed if `out` is passed</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only 3D datasets can be processed in chunks with `set_split_processing`&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">system_description</span> <span class="o">==</span> <span class="s1">&#39;advanced&#39;</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only simple and offset geometries can be processed in chunks with `set_split_processing`&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only default image geometries can be processed in chunks `set_split_processing`&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">out</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_inplace</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_pre_filtering</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="p">)</span>
+
+            <span class="n">tot_slices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">pixel_num_v</span>
+            <span class="n">remainder</span> <span class="o">=</span> <span class="n">tot_slices</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span>
+            <span class="n">num_chunks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slices_per_chunk</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                <span class="n">pbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="n">num_chunks</span><span class="p">)</span>
+
+            <span class="c1">#process dataset by requested chunk size</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_setup_PO_for_chunks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tot_slices</span><span class="o">-</span><span class="n">remainder</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">):</span>
+
+                <span class="k">if</span> <span class="s1">&#39;bottom&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="n">i</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="n">tot_slices</span> <span class="o">-</span><span class="n">i</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">tot_slices</span> <span class="o">-</span> <span class="n">i</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">array</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_chunk</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                    <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+            <span class="c1">#process excess rows</span>
+            <span class="k">if</span> <span class="n">remainder</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_setup_PO_for_chunks</span><span class="p">(</span><span class="n">remainder</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="s1">&#39;bottom&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="n">tot_slices</span><span class="o">-</span><span class="n">remainder</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">tot_slices</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">remainder</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">array</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_chunk</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">remainder</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                    <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                <span class="n">pbar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">ret</span>
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_inplace</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+                <span class="n">proj_filtered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">proj_filtered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_pre_filtering</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">)</span>
+
+            <span class="n">operator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_PO_class</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="FBP.reset">
+<a class="viewcode-back" href="../../../../recon/#cil.recon.FBP.FBP.reset">[docs]</a>
+    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Resets all optional configuration parameters to their default values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_split_processing</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
+
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">repres</span> <span class="o">=</span> <span class="s2">&quot;FBP recon</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+        <span class="n">repres</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_str_data_size</span><span class="p">()</span>
+
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reconstruction Options:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Backend: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backend</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Filter: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter</span> <span class="o">!=</span> <span class="s1">&#39;custom&#39;</span><span class="p">:</span>
+            <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Filter cut-off frequency: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter_cutoff</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">FFT order: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_fft_order</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Filter_inplace: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter_inplace</span><span class="p">)</span>
+        <span class="n">repres</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Split processing: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_slices_per_chunk</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slices_per_chunk</span><span class="p">:</span>
+            <span class="n">num_chunks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slices_per_chunk</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">num_chunks</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="n">repres</span> <span class="o">+=</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reconstructing in </span><span class="si">{}</span><span class="s2"> chunk(s):</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_chunks</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">repres</span></div>
+
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
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+                  
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diff --git a/v24.2.0/_modules/cil/utilities/dataexample/index.html b/v24.2.0/_modules/cil/utilities/dataexample/index.html
new file mode 100644
index 0000000000..eddfa6a846
--- /dev/null
+++ b/v24.2.0/_modules/cil/utilities/dataexample/index.html
@@ -0,0 +1,1270 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../" >
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+  <h1>Source code for cil.utilities.dataexample</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">ImageDimension</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">os.path</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">zipfile</span> <span class="kn">import</span> <span class="n">ZipFile</span>
+<span class="kn">from</span> <span class="nn">scipy.io</span> <span class="kn">import</span> <span class="n">loadmat</span>
+<span class="kn">from</span> <span class="nn">cil.io</span> <span class="kn">import</span> <span class="n">NEXUSDataReader</span><span class="p">,</span> <span class="n">NikonDataReader</span><span class="p">,</span> <span class="n">ZEISSDataReader</span>
+<span class="kn">from</span> <span class="nn">zenodo_get</span> <span class="kn">import</span> <span class="n">zenodo_get</span>
+
+<span class="k">class</span> <span class="nc">DATA</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+<span class="k">class</span> <span class="nc">CILDATA</span><span class="p">(</span><span class="n">DATA</span><span class="p">):</span>
+    <span class="n">data_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">prefix</span><span class="p">,</span> <span class="s1">&#39;share&#39;</span><span class="p">,</span><span class="s1">&#39;cil&#39;</span><span class="p">))</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ddir</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_dir&#39;</span><span class="p">,</span> <span class="n">CILDATA</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">TestData</span><span class="p">(</span><span class="n">data_dir</span><span class="o">=</span><span class="n">ddir</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">dfile</span><span class="p">(),</span> <span class="n">size</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="k">class</span> <span class="nc">REMOTEDATA</span><span class="p">(</span><span class="n">DATA</span><span class="p">):</span>
+
+    <span class="n">FOLDER</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+    <span class="n">ZENODO_RECORD</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+    <span class="n">ZIP_FILE</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">download_data</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">,</span> <span class="n">prompt</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Download a dataset from a remote repository</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str, optional</span>
+<span class="sd">           The path to the data directory where the downloaded data should be stored</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">FOLDER</span><span class="p">)):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dataset folder already exists in &quot;</span> <span class="o">+</span> <span class="n">data_dir</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">user_input</span> <span class="o">=</span> <span class="nb">input</span><span class="p">(</span><span class="s2">&quot;Are you sure you want to download </span><span class="si">{cls.ZIP_FILE}</span><span class="s2"> dataset from Zenodo record </span><span class="si">{cls.ZENODO_RECORD}</span><span class="s2">? [Y/n]: &quot;</span><span class="p">)</span> <span class="k">if</span> <span class="n">prompt</span> <span class="k">else</span> <span class="s1">&#39;y&#39;</span>
+            <span class="k">if</span> <span class="n">user_input</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;yes&#39;</span><span class="p">):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Download cancelled&#39;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="kc">False</span>
+
+            <span class="n">zenodo_get</span><span class="p">([</span><span class="bp">cls</span><span class="o">.</span><span class="n">ZENODO_RECORD</span><span class="p">,</span> <span class="s1">&#39;-g&#39;</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ZIP_FILE</span><span class="p">,</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">])</span>
+            <span class="k">with</span> <span class="n">ZipFile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ZIP_FILE</span><span class="p">),</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">zip_ref</span><span class="p">:</span>
+                <span class="n">zip_ref</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">FOLDER</span><span class="p">))</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ZIP_FILE</span><span class="p">))</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+<span class="k">class</span> <span class="nc">BOAT</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">BOAT</span>
+<span class="k">class</span> <span class="nc">CAMERA</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">CAMERA</span>
+<span class="k">class</span> <span class="nc">PEPPERS</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">PEPPERS</span>
+<span class="k">class</span> <span class="nc">RESOLUTION_CHART</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">RESOLUTION_CHART</span>
+<span class="k">class</span> <span class="nc">SIMPLE_PHANTOM_2D</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">SIMPLE_PHANTOM_2D</span>
+<span class="k">class</span> <span class="nc">SHAPES</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">SHAPES</span>
+<span class="k">class</span> <span class="nc">RAINBOW</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">dfile</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">RAINBOW</span>
+<div class="viewcode-block" id="SYNCHROTRON_PARALLEL_BEAM_DATA">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA">[docs]</a>
+<span class="k">class</span> <span class="nc">SYNCHROTRON_PARALLEL_BEAM_DATA</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+<div class="viewcode-block" id="SYNCHROTRON_PARALLEL_BEAM_DATA.get">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA.get">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        A DLS dataset</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str, optional</span>
+<span class="sd">           The path to the data directory</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        AcquisitionData</span>
+<span class="sd">            The DLS dataset</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">ddir</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_dir&#39;</span><span class="p">,</span> <span class="n">CILDATA</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">NEXUSDataReader</span><span class="p">()</span>
+        <span class="n">loader</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">ddir</span><span class="p">),</span> <span class="s1">&#39;24737_fd_normalised.nxs&#39;</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span></div>
+</div>
+
+<div class="viewcode-block" id="SIMULATED_PARALLEL_BEAM_DATA">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA">[docs]</a>
+<span class="k">class</span> <span class="nc">SIMULATED_PARALLEL_BEAM_DATA</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+<div class="viewcode-block" id="SIMULATED_PARALLEL_BEAM_DATA.get">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA.get">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        A simulated parallel-beam dataset generated from SIMULATED_SPHERE_VOLUME</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str, optional</span>
+<span class="sd">           The path to the data directory</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        AcquisitionData</span>
+<span class="sd">            The simulated spheres dataset</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">ddir</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_dir&#39;</span><span class="p">,</span> <span class="n">CILDATA</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">NEXUSDataReader</span><span class="p">()</span>
+        <span class="n">loader</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">ddir</span><span class="p">),</span> <span class="s1">&#39;sim_parallel_beam.nxs&#39;</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span></div>
+</div>
+
+<div class="viewcode-block" id="SIMULATED_CONE_BEAM_DATA">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA">[docs]</a>
+<span class="k">class</span> <span class="nc">SIMULATED_CONE_BEAM_DATA</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+<div class="viewcode-block" id="SIMULATED_CONE_BEAM_DATA.get">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA.get">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        A cone-beam dataset generated from SIMULATED_SPHERE_VOLUME</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str, optional</span>
+<span class="sd">           The path to the data directory</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        AcquisitionData</span>
+<span class="sd">            The simulated spheres dataset</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="n">ddir</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_dir&#39;</span><span class="p">,</span> <span class="n">CILDATA</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">NEXUSDataReader</span><span class="p">()</span>
+        <span class="n">loader</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">ddir</span><span class="p">),</span> <span class="s1">&#39;sim_cone_beam.nxs&#39;</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span></div>
+</div>
+
+<span class="k">class</span> <span class="nc">SIMULATED_SPHERE_VOLUME</span><span class="p">(</span><span class="n">CILDATA</span><span class="p">):</span>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        A simulated volume of spheres</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str, optional</span>
+<span class="sd">           The path to the data directory</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The simulated spheres volume</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">ddir</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_dir&#39;</span><span class="p">,</span> <span class="n">CILDATA</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>
+        <span class="n">loader</span> <span class="o">=</span> <span class="n">NEXUSDataReader</span><span class="p">()</span>
+        <span class="n">loader</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">ddir</span><span class="p">),</span> <span class="s1">&#39;sim_volume.nxs&#39;</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+
+<div class="viewcode-block" id="WALNUT">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.WALNUT">[docs]</a>
+<span class="k">class</span> <span class="nc">WALNUT</span><span class="p">(</span><span class="n">REMOTEDATA</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    A microcomputed tomography dataset of a walnut from https://zenodo.org/records/4822516</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data_dir = &#39;my_PC/data_folder&#39;</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.WALNUT.download_data(data_dir) # download the data</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.WALNUT.get(data_dir) # load the data</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">FOLDER</span> <span class="o">=</span> <span class="s1">&#39;walnut&#39;</span>
+    <span class="n">ZENODO_RECORD</span> <span class="o">=</span> <span class="s1">&#39;4822516&#39;</span>
+    <span class="n">ZIP_FILE</span> <span class="o">=</span> <span class="s1">&#39;walnut.zip&#39;</span>
+
+<div class="viewcode-block" id="WALNUT.get">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.WALNUT.get">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Get the microcomputed tomography dataset of a walnut from https://zenodo.org/records/4822516</span>
+<span class="sd">        This function returns the raw projection data from the .txrm file</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str</span>
+<span class="sd">           The path to the directory where the dataset is stored. Data can be downloaded with dataexample.WALNUT.download_data(data_dir)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The walnut dataset</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">filepath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">FOLDER</span><span class="p">,</span> <span class="s1">&#39;valnut&#39;</span><span class="p">,</span><span class="s1">&#39;valnut_2014-03-21_643_28&#39;</span><span class="p">,</span><span class="s1">&#39;tomo-A&#39;</span><span class="p">,</span><span class="s1">&#39;valnut_tomo-A.txrm&#39;</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">loader</span> <span class="o">=</span> <span class="n">ZEISSDataReader</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="n">filepath</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">(</span><span class="ne">FileNotFoundError</span><span class="p">):</span>
+            <span class="k">raise</span><span class="p">(</span><span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s2">&quot;Dataset .txrm file not found in specifed data_dir: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2"> </span><span class="se">\</span>
+<span class="s2">                                    Specify a different data_dir or download data with dataexample.</span><span class="si">{}</span><span class="s2">.download_data(data_dir)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)))</span></div>
+</div>
+
+
+<div class="viewcode-block" id="USB">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.USB">[docs]</a>
+<span class="k">class</span> <span class="nc">USB</span><span class="p">(</span><span class="n">REMOTEDATA</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    A microcomputed tomography dataset of a usb memory stick from https://zenodo.org/records/4822516</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data_dir = &#39;my_PC/data_folder&#39;</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.USB.download_data(data_dir) # download the data</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.USB.get(data_dir) # load the data</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">FOLDER</span> <span class="o">=</span> <span class="s1">&#39;USB&#39;</span>
+    <span class="n">ZENODO_RECORD</span> <span class="o">=</span> <span class="s1">&#39;4822516&#39;</span>
+    <span class="n">ZIP_FILE</span> <span class="o">=</span> <span class="s1">&#39;usb.zip&#39;</span>
+
+<div class="viewcode-block" id="USB.get">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.USB.get">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Get the microcomputed tomography dataset of a usb memory stick from https://zenodo.org/records/4822516</span>
+<span class="sd">        This function returns the raw projection data from the .txrm file</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str</span>
+<span class="sd">           The path to the directory where the dataset is stored. Data can be downloaded with dataexample.WALNUT.download_data(data_dir)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The usb dataset</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">filepath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">FOLDER</span><span class="p">,</span> <span class="s1">&#39;gruppe 4&#39;</span><span class="p">,</span><span class="s1">&#39;gruppe 4_2014-03-20_1404_12&#39;</span><span class="p">,</span><span class="s1">&#39;tomo-A&#39;</span><span class="p">,</span><span class="s1">&#39;gruppe 4_tomo-A.txrm&#39;</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">loader</span> <span class="o">=</span> <span class="n">ZEISSDataReader</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="n">filepath</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">(</span><span class="ne">FileNotFoundError</span><span class="p">):</span>
+            <span class="k">raise</span><span class="p">(</span><span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s2">&quot;Dataset .txrm file not found in: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2"> </span><span class="se">\</span>
+<span class="s2">                                    Specify a different data_dir or download data with dataexample.</span><span class="si">{}</span><span class="s2">.download_data(data_dir)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)))</span></div>
+</div>
+
+
+<div class="viewcode-block" id="KORN">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.KORN">[docs]</a>
+<span class="k">class</span> <span class="nc">KORN</span><span class="p">(</span><span class="n">REMOTEDATA</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    A microcomputed tomography dataset of a sunflower seeds in a box from https://zenodo.org/records/6874123</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data_dir = &#39;my_PC/data_folder&#39;</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.KORN.download_data(data_dir) # download the data</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.KORN.get(data_dir) # load the data</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">FOLDER</span> <span class="o">=</span> <span class="s1">&#39;korn&#39;</span>
+    <span class="n">ZENODO_RECORD</span> <span class="o">=</span> <span class="s1">&#39;6874123&#39;</span>
+    <span class="n">ZIP_FILE</span> <span class="o">=</span> <span class="s1">&#39;korn.zip&#39;</span>
+
+<div class="viewcode-block" id="KORN.get">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.KORN.get">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Get the microcomputed tomography dataset of a sunflower seeds in a box from https://zenodo.org/records/6874123</span>
+<span class="sd">        This function returns the raw projection data from the .xtekct file</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str</span>
+<span class="sd">           The path to the directory where the dataset is stored. Data can be downloaded with dataexample.KORN.download_data(data_dir)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The korn dataset</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">filepath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">FOLDER</span><span class="p">,</span> <span class="s1">&#39;Korn i kasse&#39;</span><span class="p">,</span><span class="s1">&#39;47209 testscan korn01_recon.xtekct&#39;</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">loader</span> <span class="o">=</span> <span class="n">NikonDataReader</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="n">filepath</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">loader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">(</span><span class="ne">FileNotFoundError</span><span class="p">):</span>
+            <span class="k">raise</span><span class="p">(</span><span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s2">&quot;Dataset .xtekct file not found in: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2"> </span><span class="se">\</span>
+<span class="s2">                                    Specify a different data_dir or download data with dataexample.</span><span class="si">{}</span><span class="s2">.download_data(data_dir)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)))</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="SANDSTONE">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SANDSTONE">[docs]</a>
+<span class="k">class</span> <span class="nc">SANDSTONE</span><span class="p">(</span><span class="n">REMOTEDATA</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    A synchrotron x-ray tomography dataset of sandstone from https://zenodo.org/records/4912435</span>
+<span class="sd">    A small subset of the data containing selected projections and 4 slices of the reconstruction</span>
+
+<span class="sd">    Example</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; data_dir = &#39;my_PC/data_folder&#39;</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.SANDSTONE.download_data(data_dir) # download the data</span>
+<span class="sd">    &gt;&gt;&gt; dataexample.SANDSTONE.get(data_dir) # load the data</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">FOLDER</span> <span class="o">=</span> <span class="s1">&#39;sandstone&#39;</span>
+    <span class="n">ZENODO_RECORD</span> <span class="o">=</span> <span class="s1">&#39;4912435&#39;</span>
+    <span class="n">ZIP_FILE</span> <span class="o">=</span> <span class="s1">&#39;small.zip&#39;</span>
+
+<div class="viewcode-block" id="SANDSTONE.get">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.SANDSTONE.get">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Get the synchrotron x-ray tomography dataset of sandstone from https://zenodo.org/records/4912435</span>
+<span class="sd">        A small subset of the data containing selected projections and 4 slices of the reconstruction</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data_dir: str</span>
+<span class="sd">           The path to the directory where the dataset is stored. Data can be downloaded with dataexample.SANDSTONE.download_data(data_dir)</span>
+
+<span class="sd">        file: str</span>
+<span class="sd">            The slices or projections to return, specify the path to the file within the data_dir</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The selected sandstone dataset</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">extension</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">filename</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">extension</span> <span class="o">==</span> <span class="s1">&#39;.mat&#39;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">loadmat</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span><span class="n">filename</span><span class="p">))</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown extension: </span><span class="si">{</span><span class="n">extension</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+</div>
+
+        
+
+<div class="viewcode-block" id="TestData">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.TestData">[docs]</a>
+<span class="k">class</span> <span class="nc">TestData</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Class to return test data</span>
+
+<span class="sd">    provides 6 dataset:</span>
+<span class="sd">    BOAT = &#39;boat.tiff&#39;</span>
+<span class="sd">    CAMERA = &#39;camera.png&#39;</span>
+<span class="sd">    PEPPERS = &#39;peppers.tiff&#39;</span>
+<span class="sd">    RESOLUTION_CHART = &#39;resolution_chart.tiff&#39;</span>
+<span class="sd">    SIMPLE_PHANTOM_2D = &#39;hotdog&#39;</span>
+<span class="sd">    SHAPES =  &#39;shapes.png&#39;</span>
+<span class="sd">    RAINBOW = &#39;rainbow.png&#39;</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">BOAT</span> <span class="o">=</span> <span class="s1">&#39;boat.tiff&#39;</span>
+    <span class="n">CAMERA</span> <span class="o">=</span> <span class="s1">&#39;camera.png&#39;</span>
+    <span class="n">PEPPERS</span> <span class="o">=</span> <span class="s1">&#39;peppers.tiff&#39;</span>
+    <span class="n">RESOLUTION_CHART</span> <span class="o">=</span> <span class="s1">&#39;resolution_chart.tiff&#39;</span>
+    <span class="n">SIMPLE_PHANTOM_2D</span> <span class="o">=</span> <span class="s1">&#39;hotdog&#39;</span>
+    <span class="n">SHAPES</span> <span class="o">=</span>  <span class="s1">&#39;shapes.png&#39;</span>
+    <span class="n">RAINBOW</span> <span class="o">=</span>  <span class="s1">&#39;rainbow.png&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_dir</span> <span class="o">=</span> <span class="n">data_dir</span>
+
+<div class="viewcode-block" id="TestData.load">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.TestData.load">[docs]</a>
+    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">which</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Return a test data of the requested image</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        which: str</span>
+<span class="sd">           Image selector: BOAT, CAMERA, PEPPERS, RESOLUTION_CHART, SIMPLE_PHANTOM_2D, SHAPES, RAINBOW</span>
+<span class="sd">        size: tuple, optional</span>
+<span class="sd">            The size of the returned ImageData. If None default will be used for each image type</span>
+<span class="sd">        scale: tuple, optional</span>
+<span class="sd">            The scale of the data values</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The simulated spheres volume</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="n">which</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="n">TestData</span><span class="o">.</span><span class="n">BOAT</span><span class="p">,</span> <span class="n">TestData</span><span class="o">.</span><span class="n">CAMERA</span><span class="p">,</span>
+                         <span class="n">TestData</span><span class="o">.</span><span class="n">PEPPERS</span><span class="p">,</span> <span class="n">TestData</span><span class="o">.</span><span class="n">RESOLUTION_CHART</span><span class="p">,</span>
+                         <span class="n">TestData</span><span class="o">.</span><span class="n">SIMPLE_PHANTOM_2D</span><span class="p">,</span> <span class="n">TestData</span><span class="o">.</span><span class="n">SHAPES</span><span class="p">,</span>
+                         <span class="n">TestData</span><span class="o">.</span><span class="n">RAINBOW</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unknown TestData </span><span class="si">{}</span><span class="s1">.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">which</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">which</span> <span class="o">==</span> <span class="n">TestData</span><span class="o">.</span><span class="n">SIMPLE_PHANTOM_2D</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">N</span> <span class="o">=</span> <span class="mi">512</span>
+                <span class="n">M</span> <span class="o">=</span> <span class="mi">512</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">N</span> <span class="o">=</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">M</span> <span class="o">=</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">sdata</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">))</span>
+            <span class="n">sdata</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">N</span><span class="o">/</span><span class="mi">4</span><span class="p">)):</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="mi">3</span><span class="o">*</span><span class="n">N</span><span class="o">/</span><span class="mi">4</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">M</span><span class="o">/</span><span class="mi">4</span><span class="p">)):</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="mi">3</span><span class="o">*</span><span class="n">M</span><span class="o">/</span><span class="mi">4</span><span class="p">))]</span> <span class="o">=</span> <span class="mf">0.5</span>
+            <span class="n">sdata</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">N</span><span class="o">/</span><span class="mi">8</span><span class="p">)):</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="mi">7</span><span class="o">*</span><span class="n">N</span><span class="o">/</span><span class="mi">8</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="mi">3</span><span class="o">*</span><span class="n">M</span><span class="o">/</span><span class="mi">8</span><span class="p">)):</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="mi">5</span><span class="o">*</span><span class="n">M</span><span class="o">/</span><span class="mi">8</span><span class="p">))]</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="n">M</span><span class="p">,</span> <span class="n">voxel_num_y</span> <span class="o">=</span> <span class="n">N</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="p">[</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">])</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">sdata</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="n">which</span> <span class="o">==</span> <span class="n">TestData</span><span class="o">.</span><span class="n">SHAPES</span><span class="p">:</span>
+
+            <span class="k">with</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">which</span><span class="p">))</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">N</span> <span class="o">=</span> <span class="mi">200</span>
+                    <span class="n">M</span> <span class="o">=</span> <span class="mi">300</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">N</span> <span class="o">=</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">M</span> <span class="o">=</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+                <span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="n">M</span><span class="p">,</span> <span class="n">voxel_num_y</span> <span class="o">=</span> <span class="n">N</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="p">[</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">])</span>
+                <span class="n">data</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">&#39;L&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="n">M</span><span class="p">,</span><span class="n">N</span><span class="p">)))</span>
+                <span class="n">data</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">tmp</span><span class="o">/</span><span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">with</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">which</span><span class="p">))</span> <span class="k">as</span> <span class="n">tmp</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">N</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="n">M</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">N</span> <span class="o">=</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">M</span> <span class="o">=</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+                <span class="n">bands</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">getbands</span><span class="p">()</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bands</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bands</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+                        <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
+                        <span class="n">bands</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">getbands</span><span class="p">()</span>
+
+                    <span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">voxel_num_x</span><span class="o">=</span><span class="n">M</span><span class="p">,</span> <span class="n">voxel_num_y</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">bands</span><span class="p">),</span>
+                    <span class="n">dimension_labels</span><span class="o">=</span><span class="p">[</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">,</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">])</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+                    <span class="n">data</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="n">M</span><span class="p">,</span><span class="n">N</span><span class="p">))))</span>
+                    <span class="n">data</span><span class="o">.</span><span class="n">reorder</span><span class="p">([</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">])</span>
+                    <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">channel_labels</span> <span class="o">=</span> <span class="n">bands</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">voxel_num_x</span> <span class="o">=</span> <span class="n">M</span><span class="p">,</span> <span class="n">voxel_num_y</span> <span class="o">=</span> <span class="n">N</span><span class="p">,</span> <span class="n">dimension_labels</span><span class="o">=</span><span class="p">[</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_Y</span><span class="p">,</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">])</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+                    <span class="n">data</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="n">M</span><span class="p">,</span><span class="n">N</span><span class="p">))))</span>
+
+
+            <span class="k">if</span> <span class="n">scale</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">dmax</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+                <span class="n">dmin</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
+                <span class="c1"># scale 0,1</span>
+                <span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">-</span><span class="n">dmin</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">dmax</span> <span class="o">-</span> <span class="n">dmin</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">scale</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">):</span>
+                    <span class="c1">#data = (data-dmin)/(dmax-dmin) * (scale[1]-scale[0]) +scale[0])</span>
+                    <span class="n">data</span> <span class="o">*=</span> <span class="p">(</span><span class="n">scale</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">scale</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="n">data</span> <span class="o">+=</span> <span class="n">scale</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="c1"># print (&quot;data.geometry&quot;, data.geometry)</span>
+        <span class="k">return</span> <span class="n">data</span></div>
+
+
+<div class="viewcode-block" id="TestData.random_noise">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.TestData.random_noise">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">random_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;Function to add noise to input image</span>
+
+<span class="sd">        :param image: input dataset, DataContainer of numpy.ndarray</span>
+<span class="sd">        :param mode: type of noise</span>
+<span class="sd">        :param seed: seed for random number generator</span>
+<span class="sd">        :param clip: should clip the data.</span>
+<span class="sd">        See https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py</span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="s1">&#39;as_array&#39;</span><span class="p">):</span>
+            <span class="n">arr</span> <span class="o">=</span> <span class="n">TestData</span><span class="o">.</span><span class="n">scikit_random_noise</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="n">clip</span><span class="p">,</span>
+                  <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out</span>
+        <span class="k">elif</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">image</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">TestData</span><span class="o">.</span><span class="n">scikit_random_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="n">clip</span><span class="p">,</span>
+                   <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="TestData.scikit_random_noise">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.dataexample.TestData.scikit_random_noise">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">scikit_random_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Function to add random noise of various types to a floating-point image.</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        image : ndarray</span>
+<span class="sd">            Input image data. Will be converted to float.</span>
+<span class="sd">        mode : str, optional</span>
+<span class="sd">            One of the following strings, selecting the type of noise to add:</span>
+<span class="sd">            - &#39;gaussian&#39;  Gaussian-distributed additive noise.</span>
+<span class="sd">            - &#39;localvar&#39;  Gaussian-distributed additive noise, with specified</span>
+<span class="sd">                        local variance at each point of `image`.</span>
+<span class="sd">            - &#39;poisson&#39;   Poisson-distributed noise generated from the data.</span>
+<span class="sd">            - &#39;salt&#39;      Replaces random pixels with 1.</span>
+<span class="sd">            - &#39;pepper&#39;    Replaces random pixels with 0 (for unsigned images) or</span>
+<span class="sd">                        -1 (for signed images).</span>
+<span class="sd">            - &#39;s&amp;p&#39;       Replaces random pixels with either 1 or `low_val`, where</span>
+<span class="sd">                        `low_val` is 0 for unsigned images or -1 for signed</span>
+<span class="sd">                        images.</span>
+<span class="sd">            - &#39;speckle&#39;   Multiplicative noise using out = image + n*image, where</span>
+<span class="sd">                        n is uniform noise with specified mean &amp; variance.</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            If provided, this will set the random seed before generating noise,</span>
+<span class="sd">            for valid pseudo-random comparisons.</span>
+<span class="sd">        clip : bool, optional</span>
+<span class="sd">            If True (default), the output will be clipped after noise applied</span>
+<span class="sd">            for modes `&#39;speckle&#39;`, `&#39;poisson&#39;`, and `&#39;gaussian&#39;`. This is</span>
+<span class="sd">            needed to maintain the proper image data range. If False, clipping</span>
+<span class="sd">            is not applied, and the output may extend beyond the range [-1, 1].</span>
+<span class="sd">        mean : float, optional</span>
+<span class="sd">            Mean of random distribution. Used in &#39;gaussian&#39; and &#39;speckle&#39;.</span>
+<span class="sd">            Default : 0.</span>
+<span class="sd">        var : float, optional</span>
+<span class="sd">            Variance of random distribution. Used in &#39;gaussian&#39; and &#39;speckle&#39;.</span>
+<span class="sd">            Note: variance = (standard deviation) ** 2. Default : 0.01</span>
+<span class="sd">        local_vars : ndarray, optional</span>
+<span class="sd">            Array of positive floats, same shape as `image`, defining the local</span>
+<span class="sd">            variance at every image point. Used in &#39;localvar&#39;.</span>
+<span class="sd">        amount : float, optional</span>
+<span class="sd">            Proportion of image pixels to replace with noise on range [0, 1].</span>
+<span class="sd">            Used in &#39;salt&#39;, &#39;pepper&#39;, and &#39;salt &amp; pepper&#39;. Default : 0.05</span>
+<span class="sd">        salt_vs_pepper : float, optional</span>
+<span class="sd">            Proportion of salt vs. pepper noise for &#39;s&amp;p&#39; on range [0, 1].</span>
+<span class="sd">            Higher values represent more salt. Default : 0.5 (equal amounts)</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        out : ndarray</span>
+<span class="sd">            Output floating-point image data on range [0, 1] or [-1, 1] if the</span>
+<span class="sd">            input `image` was unsigned or signed, respectively.</span>
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside</span>
+<span class="sd">        the valid image range. The default is to clip (not alias) these values,</span>
+<span class="sd">        but they may be preserved by setting `clip=False`. Note that in this case</span>
+<span class="sd">        the output may contain values outside the ranges [0, 1] or [-1, 1].</span>
+<span class="sd">        Use this option with care.</span>
+<span class="sd">        Because of the prevalence of exclusively positive floating-point images in</span>
+<span class="sd">        intermediate calculations, it is not possible to intuit if an input is</span>
+<span class="sd">        signed based on dtype alone. Instead, negative values are explicitly</span>
+<span class="sd">        searched for. Only if found does this function assume signed input.</span>
+<span class="sd">        Unexpected results only occur in rare, poorly exposes cases (e.g. if all</span>
+<span class="sd">        values are above 50 percent gray in a signed `image`). In this event,</span>
+<span class="sd">        manually scaling the input to the positive domain will solve the problem.</span>
+<span class="sd">        The Poisson distribution is only defined for positive integers. To apply</span>
+<span class="sd">        this noise type, the number of unique values in the image is found and</span>
+<span class="sd">        the next round power of two is used to scale up the floating-point result,</span>
+<span class="sd">        after which it is scaled back down to the floating-point image range.</span>
+<span class="sd">        To generate Poisson noise against a signed image, the signed image is</span>
+<span class="sd">        temporarily converted to an unsigned image in the floating point domain,</span>
+<span class="sd">        Poisson noise is generated, then it is returned to the original range.</span>
+
+<span class="sd">        This function is adapted from scikit-image.</span>
+<span class="sd">        https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py</span>
+
+<span class="sd">        Copyright (C) 2019, the scikit-image team</span>
+<span class="sd">        All rights reserved.</span>
+
+<span class="sd">        Redistribution and use in source and binary forms, with or without</span>
+<span class="sd">        modification, are permitted provided that the following conditions are</span>
+<span class="sd">        met:</span>
+
+<span class="sd">        1. Redistributions of source code must retain the above copyright</span>
+<span class="sd">            notice, this list of conditions and the following disclaimer.</span>
+<span class="sd">        2. Redistributions in binary form must reproduce the above copyright</span>
+<span class="sd">            notice, this list of conditions and the following disclaimer in</span>
+<span class="sd">            the documentation and/or other materials provided with the</span>
+<span class="sd">            distribution.</span>
+<span class="sd">        3. Neither the name of skimage nor the names of its contributors may be</span>
+<span class="sd">            used to endorse or promote products derived from this software without</span>
+<span class="sd">            specific prior written permission.</span>
+
+<span class="sd">        THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS&#39;&#39; AND ANY EXPRESS OR</span>
+<span class="sd">        IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED</span>
+<span class="sd">        WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE</span>
+<span class="sd">        DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,</span>
+<span class="sd">        INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES</span>
+<span class="sd">        (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR</span>
+<span class="sd">        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)</span>
+<span class="sd">        HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,</span>
+<span class="sd">        STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING</span>
+<span class="sd">        IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE</span>
+<span class="sd">        POSSIBILITY OF SUCH DAMAGE.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+
+        <span class="c1"># Detect if a signed image was input</span>
+        <span class="k">if</span> <span class="n">image</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">low_clip</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">low_clip</span> <span class="o">=</span> <span class="mf">0.</span>
+
+        <span class="n">image</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
+        <span class="n">allowedtypes</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span> <span class="s1">&#39;gaussian_values&#39;</span><span class="p">,</span>
+            <span class="s1">&#39;localvar&#39;</span><span class="p">:</span> <span class="s1">&#39;localvar_values&#39;</span><span class="p">,</span>
+            <span class="s1">&#39;poisson&#39;</span><span class="p">:</span> <span class="s1">&#39;poisson_values&#39;</span><span class="p">,</span>
+            <span class="s1">&#39;salt&#39;</span><span class="p">:</span> <span class="s1">&#39;sp_values&#39;</span><span class="p">,</span>
+            <span class="s1">&#39;pepper&#39;</span><span class="p">:</span> <span class="s1">&#39;sp_values&#39;</span><span class="p">,</span>
+            <span class="s1">&#39;s&amp;p&#39;</span><span class="p">:</span> <span class="s1">&#39;s&amp;p_values&#39;</span><span class="p">,</span>
+            <span class="s1">&#39;speckle&#39;</span><span class="p">:</span> <span class="s1">&#39;gaussian_values&#39;</span><span class="p">}</span>
+
+        <span class="n">kwdefaults</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;mean&#39;</span><span class="p">:</span> <span class="mf">0.</span><span class="p">,</span>
+            <span class="s1">&#39;var&#39;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
+            <span class="s1">&#39;amount&#39;</span><span class="p">:</span> <span class="mf">0.05</span><span class="p">,</span>
+            <span class="s1">&#39;salt_vs_pepper&#39;</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span>
+            <span class="s1">&#39;local_vars&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">image</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.01</span><span class="p">}</span>
+
+        <span class="n">allowedkwargs</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s1">&#39;gaussian_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="s1">&#39;var&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;localvar_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;local_vars&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;sp_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;amount&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;s&amp;p_values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;amount&#39;</span><span class="p">,</span> <span class="s1">&#39;salt_vs_pepper&#39;</span><span class="p">],</span>
+            <span class="s1">&#39;poisson_values&#39;</span><span class="p">:</span> <span class="p">[]}</span>
+
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">allowedkwargs</span><span class="p">[</span><span class="n">allowedtypes</span><span class="p">[</span><span class="n">mode</span><span class="p">]]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1"> keyword not in allowed keywords </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span>
+                                <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">allowedkwargs</span><span class="p">[</span><span class="n">allowedtypes</span><span class="p">[</span><span class="n">mode</span><span class="p">]]))</span>
+
+        <span class="c1"># Set kwarg defaults</span>
+        <span class="k">for</span> <span class="n">kw</span> <span class="ow">in</span> <span class="n">allowedkwargs</span><span class="p">[</span><span class="n">allowedtypes</span><span class="p">[</span><span class="n">mode</span><span class="p">]]:</span>
+            <span class="n">kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">kw</span><span class="p">,</span> <span class="n">kwdefaults</span><span class="p">[</span><span class="n">kw</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+            <span class="n">noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;mean&#39;</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;var&#39;</span><span class="p">]</span> <span class="o">**</span> <span class="mf">0.5</span><span class="p">,</span>
+                                    <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">image</span> <span class="o">+</span> <span class="n">noise</span>
+
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;localvar&#39;</span><span class="p">:</span>
+            <span class="c1"># Ensure local variance input is correct</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;local_vars&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All values of `local_vars` must be &gt; 0.&#39;</span><span class="p">)</span>
+
+            <span class="c1"># Safe shortcut usage broadcasts kwargs[&#39;local_vars&#39;] as a ufunc</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">image</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;local_vars&#39;</span><span class="p">]</span> <span class="o">**</span> <span class="mf">0.5</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;poisson&#39;</span><span class="p">:</span>
+            <span class="c1"># Determine unique values in image &amp; calculate the next power of two</span>
+            <span class="n">vals</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>
+            <span class="n">vals</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">vals</span><span class="p">))</span>
+
+            <span class="c1"># Ensure image is exclusively positive</span>
+            <span class="k">if</span> <span class="n">low_clip</span> <span class="o">==</span> <span class="o">-</span><span class="mf">1.</span><span class="p">:</span>
+                <span class="n">old_max</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+                <span class="n">image</span> <span class="o">=</span> <span class="p">(</span><span class="n">image</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">old_max</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span>
+
+            <span class="c1"># Generating noise for each unique value in image.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">poisson</span><span class="p">(</span><span class="n">image</span> <span class="o">*</span> <span class="n">vals</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
+
+            <span class="c1"># Return image to original range if input was signed</span>
+            <span class="k">if</span> <span class="n">low_clip</span> <span class="o">==</span> <span class="o">-</span><span class="mf">1.</span><span class="p">:</span>
+                <span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">*</span> <span class="p">(</span><span class="n">old_max</span> <span class="o">+</span> <span class="mf">1.</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.</span>
+
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;salt&#39;</span><span class="p">:</span>
+            <span class="c1"># Re-call function with mode=&#39;s&amp;p&#39; and p=1 (all salt noise)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">TestData</span><span class="o">.</span><span class="n">random_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;s&amp;p&#39;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+                            <span class="n">amount</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;amount&#39;</span><span class="p">],</span> <span class="n">salt_vs_pepper</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;pepper&#39;</span><span class="p">:</span>
+            <span class="c1"># Re-call function with mode=&#39;s&amp;p&#39; and p=1 (all pepper noise)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">TestData</span><span class="o">.</span><span class="n">random_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;s&amp;p&#39;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+                            <span class="n">amount</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;amount&#39;</span><span class="p">],</span> <span class="n">salt_vs_pepper</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;s&amp;p&#39;</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">p</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;amount&#39;</span><span class="p">]</span>
+            <span class="n">q</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;salt_vs_pepper&#39;</span><span class="p">]</span>
+            <span class="n">flipped</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
+                                    <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">p</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">p</span><span class="p">])</span>
+            <span class="n">salted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
+                                    <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="n">q</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">q</span><span class="p">])</span>
+            <span class="n">peppered</span> <span class="o">=</span> <span class="o">~</span><span class="n">salted</span>
+            <span class="n">out</span><span class="p">[</span><span class="n">flipped</span> <span class="o">&amp;</span> <span class="n">salted</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="n">out</span><span class="p">[</span><span class="n">flipped</span> <span class="o">&amp;</span> <span class="n">peppered</span><span class="p">]</span> <span class="o">=</span> <span class="n">low_clip</span>
+
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;speckle&#39;</span><span class="p">:</span>
+            <span class="n">noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;mean&#39;</span><span class="p">],</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;var&#39;</span><span class="p">]</span> <span class="o">**</span> <span class="mf">0.5</span><span class="p">,</span>
+                                    <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">image</span> <span class="o">+</span> <span class="n">image</span> <span class="o">*</span> <span class="n">noise</span>
+
+        <span class="c1"># Clip back to original range, if necessary</span>
+        <span class="k">if</span> <span class="n">clip</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">low_clip</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
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diff --git a/v24.2.0/_modules/cil/utilities/display/index.html b/v24.2.0/_modules/cil/utilities/display/index.html
new file mode 100644
index 0000000000..acc0ba4e4d
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+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
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+  <h1>Source code for cil.utilities.display</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># Kyle Pidgeon (UKRI-STFC)</span>
+
+
+<span class="c1">#%%</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionGeometry</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">,</span> <span class="n">BlockDataContainer</span>
+<span class="kn">from</span> <span class="nn">cil.framework.labels</span> <span class="kn">import</span> <span class="n">AcquisitionType</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">matplotlib.lines</span> <span class="k">as</span> <span class="nn">mlines</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">FancyArrowPatch</span>
+<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">proj3d</span>
+<span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1</span> <span class="kn">import</span> <span class="n">make_axes_locatable</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">cycle</span>
+
+<span class="n">CB_PALETTE</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;#377eb8&#39;</span><span class="p">,</span> <span class="s1">&#39;#ff7f00&#39;</span><span class="p">,</span> <span class="s1">&#39;#4daf4a&#39;</span><span class="p">,</span>
+              <span class="s1">&#39;#f781bf&#39;</span><span class="p">,</span> <span class="s1">&#39;#a65628&#39;</span><span class="p">,</span> <span class="s1">&#39;#984ea3&#39;</span><span class="p">,</span>
+              <span class="s1">&#39;#999999&#39;</span><span class="p">,</span> <span class="s1">&#39;#e41a1c&#39;</span><span class="p">,</span> <span class="s1">&#39;#dede00&#39;</span><span class="p">]</span>
+
+<span class="k">class</span> <span class="nc">_PlotData</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">axis_labels</span><span class="p">,</span> <span class="n">origin</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">title</span> <span class="o">=</span> <span class="n">title</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">axis_labels</span> <span class="o">=</span> <span class="n">axis_labels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">origin</span> <span class="o">=</span> <span class="n">origin</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">range</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="k">def</span> <span class="nf">set_origin</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">origin</span><span class="p">):</span>
+    <span class="n">shape_v</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+    <span class="n">shape_h</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+
+    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+    <span class="n">data_origin</span><span class="o">=</span><span class="s1">&#39;lower&#39;</span>
+
+    <span class="k">if</span> <span class="s1">&#39;upper&#39;</span> <span class="ow">in</span> <span class="n">origin</span><span class="p">:</span>
+        <span class="n">shape_v</span><span class="o">.</span><span class="n">reverse</span><span class="p">()</span>
+        <span class="n">data_origin</span><span class="o">=</span><span class="s1">&#39;upper&#39;</span>
+
+    <span class="k">if</span> <span class="s1">&#39;right&#39;</span> <span class="ow">in</span> <span class="n">origin</span><span class="p">:</span>
+        <span class="n">shape_h</span><span class="o">.</span><span class="n">reverse</span><span class="p">()</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">extent</span> <span class="o">=</span> <span class="p">(</span><span class="o">*</span><span class="n">shape_h</span><span class="p">,</span><span class="o">*</span><span class="n">shape_v</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">data_origin</span><span class="p">,</span> <span class="n">extent</span>
+
+<span class="k">class</span> <span class="nc">show_base</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">filename</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Saves the image as a `.png` using matplotlib.figure.savefig()</span>
+
+<span class="sd">        matplotlib kwargs can be passed, refer to documentation</span>
+<span class="sd">        https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">file</span><span class="p">,</span><span class="n">extension</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">filename</span><span class="p">))</span>
+        <span class="n">extension</span> <span class="o">=</span> <span class="n">extension</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+
+        <span class="n">extensions</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">get_supported_filetypes</span><span class="p">()</span>
+        <span class="n">extensions</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">extensions</span><span class="o">.</span><span class="n">keys</span><span class="p">()]</span>
+
+        <span class="nb">format</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;format&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">format</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">extension</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                <span class="n">extension</span> <span class="o">=</span> <span class="s1">&#39;png&#39;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">extension</span> <span class="o">=</span> <span class="nb">format</span>
+
+        <span class="k">if</span> <span class="n">extension</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">extensions</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Extension not valid. Got </span><span class="si">{0}</span><span class="s2">, backend supports </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">extension</span><span class="p">,</span><span class="n">extensions</span><span class="p">))</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">path_full</span> <span class="o">=</span> <span class="n">file</span><span class="o">+</span><span class="s1">&#39;.&#39;</span><span class="o">+</span><span class="n">extension</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">set_tight_layout</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">set_facecolor</span><span class="p">(</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">path_full</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Saved image as </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">path_full</span><span class="p">))</span>
+        <span class="k">except</span> <span class="ne">PermissionError</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Unable to save image. Permissions denied: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">path_full</span><span class="p">))</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Unable to save image&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="show1D">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.display.show1D">[docs]</a>
+<span class="k">class</span> <span class="nc">show1D</span><span class="p">(</span><span class="n">show_base</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This creates and displays 1D plots of pixel values by slicing</span>
+<span class="sd">    multi-dimensional data.</span>
+
+<span class="sd">    The behaviour is as follows: if provided multiple datasets and a single</span>
+<span class="sd">    slice set (see first example below), one line plot will be generated</span>
+<span class="sd">    per dataset; if provided a single dataset and multiple sets of slices</span>
+<span class="sd">    (see second example below), one line plot will be generated per slice</span>
+<span class="sd">    set;  if provided multiple datasets and multiple slice sets, the</span>
+<span class="sd">    :math:`i`-th set of slices will apply to the :math:`i`-th dataset, with</span>
+<span class="sd">    a line plot generated in each case.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : DataContainer, list of DataContainer, tuple of DataContainer</span>
+<span class="sd">        Multi-dimensional data to be reduced to 1D.</span>
+<span class="sd">    slice_list : tuple, list of tuple or list of list of tuple, default=None</span>
+<span class="sd">        A tuple of (dimension, coordinate) pair, or a list, or nested list, of</span>
+<span class="sd">        such pairs for slicing `data` (default is None, which is only valid when 1D</span>
+<span class="sd">        data is passed)</span>
+<span class="sd">    label : &#39;default&#39;, str, list of str, None, default=&#39;default&#39;</span>
+<span class="sd">        Label(s) to use in the plot&#39;s legend. Use `None` to suppress legend.</span>
+<span class="sd">    title : str, default None</span>
+<span class="sd">        A title for the plot</span>
+<span class="sd">    line_colours : str, list of str, default=None</span>
+<span class="sd">        Colour(s) for each line plot</span>
+<span class="sd">    line_styles : {&quot;-&quot;,&quot;--&quot;,&quot;-.&quot;,&quot;:&quot;}, list of {&quot;-&quot;,&quot;--&quot;,&quot;-.&quot;,&quot;:&quot;}, default=None</span>
+<span class="sd">        Linestyle(s) for each line plot</span>
+<span class="sd">    axis_labels : tuple of str, list of str, default=(&#39;Index&#39;,&#39;Value&#39;)</span>
+<span class="sd">        Axis labels in the form (x_axis_label,y_axis_label)</span>
+<span class="sd">    size : tuple, default=(8,6)</span>
+<span class="sd">        The size of the figure</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    figure : matplotlib.figure.Figure</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    This example creates two 2D datasets (images), and uses the provided</span>
+<span class="sd">    slicing information to generate two plots on the same axis,</span>
+<span class="sd">    corresponding to the two datasets.</span>
+
+<span class="sd">    &gt;&gt;&gt; from cil.utilities.display import show1D</span>
+<span class="sd">    &gt;&gt;&gt; from cil.utilities.dataexample import PEPPERS</span>
+<span class="sd">    &gt;&gt;&gt; data = PEPPERS.get()</span>
+<span class="sd">    &gt;&gt;&gt; data_channel0 = data.get_slice(channel=0)</span>
+<span class="sd">    &gt;&gt;&gt; data_channel1 = data.get_slice(channel=1)</span>
+<span class="sd">    &gt;&gt;&gt; show1D([data_channel0, data_channel1], slice_list=[(&#39;horizontal_x&#39;, 256)],</span>
+<span class="sd">    ...        label=[&#39;Channel 0&#39;, &#39;Channel 1&#39;], line_styles=[&quot;--&quot;, &quot;-&quot;])</span>
+
+<span class="sd">    The following example uses two sets of slicing information applied to a</span>
+<span class="sd">    single dataset, resulting in two separate plots.</span>
+
+<span class="sd">    &gt;&gt;&gt; from cil.utilities.display import show1D</span>
+<span class="sd">    &gt;&gt;&gt; from cil.utilities.dataexample import PEPPERS</span>
+<span class="sd">    &gt;&gt;&gt; data = PEPPERS.get()</span>
+<span class="sd">    &gt;&gt;&gt; slices = [[(&#39;channel&#39;, 0), (&#39;horizontal_x&#39;, 256)], [(&#39;channel&#39;, 1), (&#39;horizontal_y&#39;, 256)]]</span>
+<span class="sd">    &gt;&gt;&gt; show1D(data, slice_list=slices, title=[&#39;Channel 0&#39;, &#39;Channel 1&#39;])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">line_colours</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">line_styles</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;Index&#39;</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">),</span>
+                 <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">)):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_show1d</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">slice_list</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span>
+                                   <span class="n">line_colours</span><span class="o">=</span><span class="n">line_colours</span><span class="p">,</span> <span class="n">line_styles</span><span class="o">=</span><span class="n">line_styles</span><span class="p">,</span>
+                                   <span class="n">axis_labels</span><span class="o">=</span><span class="n">axis_labels</span><span class="p">,</span> <span class="n">plot_size</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_extract_vector</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">coords</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Extracts a 1D vector by slicing multi-dimensional data using the</span>
+<span class="sd">        coordinates provided.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : DataContainer or numpy.ndarray</span>
+<span class="sd">            Multi-dimensional data to be reduced to 1D.</span>
+<span class="sd">        coords : dict</span>
+<span class="sd">            The dimensions and coordinates used for slicing. If `data` is a</span>
+<span class="sd">            DataContainer, this should comprise dimensions from</span>
+<span class="sd">            `data.dimension_labels`. If `data` is a numpy.ndarray, integers</span>
+<span class="sd">            representing the axes should be used instead.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        numpy.ndarray</span>
+<span class="sd">            The 1-dimensional pixel flux data extracted from `data`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">vector</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">possible_dimensions</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">possible_dimensions</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))]</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">possible_dimensions</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">data</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">possible_dimensions</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">possible_dimensions</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">coords</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Must provide slicing coordinates for multi-dimensional data&#39;</span><span class="p">)</span>
+
+        <span class="n">remaining_dimensions</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">possible_dimensions</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">coords</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">remaining_dimensions</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;One remaining dimension required, &#39;</span> \
+                            <span class="sa">f</span><span class="s1">&#39;found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">remaining_dimensions</span><span class="p">)</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">remaining_dimensions</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="n">data</span>
+            <span class="k">for</span> <span class="n">d</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">coords</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">d</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">possible_dimensions</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Unexpected key &quot;</span><span class="si">{</span><span class="n">d</span><span class="si">}</span><span class="s1">&quot;, not in &#39;</span> \
+                                    <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">possible_dimensions</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
+
+            <span class="n">vector</span> <span class="o">=</span> <span class="n">s</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">sliceme</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">coords</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">possible_dimensions</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Unexpected key &quot;</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s1">&quot;, not in &#39;</span> \
+                                    <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">possible_dimensions</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">sliceme</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">AcquisitionData</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ImageData</span><span class="p">):</span>
+                <span class="n">sliceme</span><span class="p">[</span><span class="s1">&#39;force&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+
+            <span class="n">vector</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">**</span><span class="n">sliceme</span><span class="p">)</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="n">vector</span>
+
+    <span class="k">def</span> <span class="nf">_plot_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                   <span class="n">label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">line_colour</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">line_style</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates 1D plots of pixel flux from multi-dimensional data and slicing information.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            The axis to draw on</span>
+<span class="sd">        data : DataContainer</span>
+<span class="sd">            The data to be sliced and plotted</span>
+<span class="sd">        slice_list : tuple or list of tuples, optional</span>
+<span class="sd">            (dimension, coordinate) pairs for slicing `data` (default is</span>
+<span class="sd">            None, which is only valid when 1D data is passed)</span>
+<span class="sd">        label : str, default=None</span>
+<span class="sd">            Label to use in the plot&#39;s legend</span>
+<span class="sd">        line_colour : str, default=None</span>
+<span class="sd">            Colour of the line plot</span>
+<span class="sd">        line_style : {&quot;-&quot;,&quot;--&quot;,&quot;-.&quot;,&quot;:&quot;}, default=None</span>
+<span class="sd">            Linestyle to pass to `matplotlib.axes.Axes.plot`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">is_1d</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">is_1d</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+            <span class="n">slice_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">slice_list</span><span class="p">]</span>
+
+        <span class="n">dims</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">is_1d</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">slice_list</span><span class="p">:</span>
+                    <span class="n">dims</span><span class="p">[</span><span class="n">el</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">el</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Expected tuple or list of tuples for slicing, &#39;</span> \
+                                <span class="sa">f</span><span class="s1">&#39;received </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+        <span class="n">arr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extract_vector</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dims</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">line_colour</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">line_style</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">_show1d</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">line_colours</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                <span class="n">line_styles</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;Pixel&#39;</span><span class="p">,</span> <span class="s1">&#39;Pixel value&#39;</span><span class="p">),</span> <span class="n">plot_size</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">)):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Displays 1D plots of pixel flux from multi-dimensional data and</span>
+<span class="sd">        slicing information.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : DataContainer, list of DataContainer or tuple of</span>
+<span class="sd">        DataContainer</span>
+<span class="sd">            The data to be sliced and plotted</span>
+<span class="sd">        slice_list : tuple, list of tuple, or list of list of tuple, optional</span>
+<span class="sd">            A (dimension, coordinate) pair or a list, or nested list, of such</span>
+<span class="sd">            pairs for slicing `data` (default is None, which is only valid when 1D</span>
+<span class="sd">            data is passed)</span>
+<span class="sd">        labels : &#39;default&#39;, str, list of str, None, default=&#39;default&#39;</span>
+<span class="sd">            Label(s) to use in the plot&#39;s legend. Use `None` to suppress legend.</span>
+<span class="sd">        titles : str, default=None</span>
+<span class="sd">            A title for the plot</span>
+<span class="sd">        line_colours : str, list of str, default=None</span>
+<span class="sd">            Colour(s) for each line plot</span>
+<span class="sd">        line_styles : {&quot;-&quot;,&quot;--&quot;,&quot;-.&quot;,&quot;:&quot;}, list of {&quot;-&quot;,&quot;--&quot;,&quot;-.&quot;,&quot;:&quot;}, default=None</span>
+<span class="sd">            Linestyle(s) for each line plot</span>
+<span class="sd">        axis_labels : tuple of str, list of str, default=(&#39;Index&#39;,&#39;Value&#39;)</span>
+<span class="sd">            Axis labels in the form (x_axis_label,y_axis_label)</span>
+<span class="sd">        num_cols : int, default=3</span>
+<span class="sd">            The number of columns in the grid of subplots produced in the</span>
+<span class="sd">            case of multiple plots</span>
+<span class="sd">        plot_size : tuple, default=(8,6)</span>
+<span class="sd">            The size of the figure</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        matplotlib.figure.Figure</span>
+<span class="sd">            The figure created to plot the 1D data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">plot_size</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">num_data</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">colour_cyc</span> <span class="o">=</span> <span class="n">cycle</span><span class="p">(</span><span class="n">CB_PALETTE</span><span class="p">)</span>
+        <span class="n">ls_cyc</span> <span class="o">=</span> <span class="n">cycle</span><span class="p">([</span><span class="s2">&quot;-&quot;</span><span class="p">,</span><span class="s2">&quot;--&quot;</span><span class="p">,</span><span class="s2">&quot;-.&quot;</span><span class="p">,</span><span class="s2">&quot;:&quot;</span><span class="p">])</span>
+        <span class="n">_lbls</span> <span class="o">=</span> <span class="n">labels</span>
+
+        <span class="k">if</span> <span class="n">slice_list</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">):</span>
+
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_data</span><span class="p">):</span>
+                <span class="n">_data</span> <span class="o">=</span> <span class="n">data</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">)</span> <span class="k">else</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">_cl</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">colour_cyc</span><span class="p">)</span> <span class="k">if</span> <span class="n">line_colours</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">line_colours</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">_ls</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">ls_cyc</span><span class="p">)</span> <span class="k">if</span> <span class="n">line_styles</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">line_styles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">_lbl</span> <span class="o">=</span> <span class="kc">None</span>
+                <span class="k">elif</span> <span class="n">labels</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
+                    <span class="n">_lbl</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;Dataset </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_lbl</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_plot_slice</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">_data</span><span class="p">,</span> <span class="n">slice_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">_lbl</span><span class="p">,</span>
+                                <span class="n">line_colour</span><span class="o">=</span><span class="n">_cl</span><span class="p">,</span> <span class="n">line_style</span><span class="o">=</span><span class="n">_ls</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="n">labels</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span> <span class="ow">or</span> <span class="n">labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">_lbls</span> <span class="o">=</span>  <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">slice_list</span><span class="p">)</span><span class="o">*</span><span class="n">num_data</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">num_data</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sl</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">slice_list</span><span class="p">):</span>
+                    <span class="n">_cl</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">colour_cyc</span><span class="p">)</span> <span class="k">if</span> <span class="n">line_colours</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">line_colours</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">_ls</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">ls_cyc</span><span class="p">)</span> <span class="k">if</span> <span class="n">line_styles</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">line_styles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">labels</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
+                        <span class="n">_lbls</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">=</span><span class="si">{</span><span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">sl</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_plot_slice</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">sl</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">_lbls</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">line_colour</span><span class="o">=</span><span class="n">_cl</span><span class="p">,</span>
+                                     <span class="n">line_style</span><span class="o">=</span><span class="n">_ls</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sl</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">slice_list</span><span class="p">):</span>
+                    <span class="n">_cl</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">colour_cyc</span><span class="p">)</span> <span class="k">if</span> <span class="n">line_colours</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">line_colours</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">_ls</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">ls_cyc</span><span class="p">)</span> <span class="k">if</span> <span class="n">line_styles</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">line_styles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">labels</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
+                        <span class="n">_lbls</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;Dataset </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">, &#39;</span> <span class="o">+</span> \
+                                   <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">=</span><span class="si">{</span><span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">sl</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_plot_slice</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">sl</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">_lbls</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">line_colour</span><span class="o">=</span><span class="n">_cl</span><span class="p">,</span>
+                                     <span class="n">line_style</span><span class="o">=</span><span class="n">_ls</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Unexpected type for slice_list: </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">)</span><span class="si">}</span><span class="s1">, expected: (tuple, list of tuples, list of list of tuples)&#39;</span><span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">axis_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">axis_labels</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">))</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+        <span class="n">fig2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">fig2</span></div>
+
+
+
+<div class="viewcode-block" id="show2D">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.display.show2D">[docs]</a>
+<span class="k">class</span> <span class="nc">show2D</span><span class="p">(</span><span class="n">show_base</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;This plots 2D slices from cil DataContainer types.</span>
+
+<span class="sd">    Plots 1 or more 2D plots in an (n x num_cols) matrix.</span>
+<span class="sd">    Can plot multiple slices from one 3D dataset, or compare multiple datasets</span>
+<span class="sd">    Inputs can be single arguments or list of arguments that will be sequentially applied to subplots</span>
+<span class="sd">    If no slice_list is passed a 3D dataset will display the centre slice of the outer dimension, a 4D dataset will show the centre slices of the two outer dimension.</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    datacontainers: ImageData, AcquisitionData, list of  ImageData / AcquisitionData, BlockDataContainer</span>
+<span class="sd">        The DataContainers to be displayed</span>
+<span class="sd">    title: string, list of strings, optional</span>
+<span class="sd">        The title for each figure</span>
+<span class="sd">    slice_list: tuple, int, list of tuples, list of ints, optional</span>
+<span class="sd">        The slices to show. A list of integers will show slices for the outer dimension. For 3D datacontainers single slice: (direction, index). For 4D datacontainers two slices: [(direction0, index),(direction1, index)].</span>
+<span class="sd">    fix_range: boolean, tuple, list of tuples</span>
+<span class="sd">        Sets the display range of the data. `True` sets all plots to the global (min, max).</span>
+<span class="sd">    axis_labels: tuple, list of tuples, optional</span>
+<span class="sd">        The axis labels for each figure e.g. (&#39;x&#39;,&#39;y&#39;)</span>
+<span class="sd">    origin: string, list of strings</span>
+<span class="sd">        Sets the display origin. &#39;lower/upper-left/right&#39;</span>
+<span class="sd">    cmap: str, list or tuple of strings</span>
+<span class="sd">        Sets the colour map of the plot (see matplotlib.pyplot). If passed a list or tuple of the</span>
+<span class="sd">        length of datacontainers, allows to set a different color map for each datacontainer.</span>
+<span class="sd">    num_cols: int</span>
+<span class="sd">        Sets the number of columns of subplots to display</span>
+<span class="sd">    size: tuple</span>
+<span class="sd">        Figure size in inches</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    matplotlib.figure.Figure</span>
+<span class="sd">        returns a matplotlib.pyplot figure object</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">datacontainers</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fix_range</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;lower-left&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">)):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__show2D</span><span class="p">(</span><span class="n">datacontainers</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="n">slice_list</span><span class="p">,</span> <span class="n">fix_range</span><span class="o">=</span><span class="n">fix_range</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="n">axis_labels</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="n">origin</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">num_cols</span><span class="o">=</span><span class="n">num_cols</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__show2D</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">datacontainers</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fix_range</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;lower-left&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">)):</span>
+
+        <span class="c1">#get number of subplots, number of input datasets, or number of slices requested</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">datacontainers</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">)):</span>
+            <span class="n">num_plots</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">datacontainers</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">datacontainers</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">slice_list</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="n">num_plots</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
+                <span class="n">num_plots</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
+                <span class="n">num_plots</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">num_plots</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">slice_list</span><span class="p">)</span>
+
+        <span class="n">subplots</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="c1">#range needs subsetted data</span>
+        <span class="n">range_min</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">)</span>
+        <span class="n">range_max</span> <span class="o">=</span> <span class="o">-</span><span class="n">range_min</span>
+
+        <span class="c1">#set up, all inputs can be 1 or num_plots</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_plots</span><span class="p">):</span>
+
+            <span class="c1">#get data per subplot, subset where required</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">datacontainers</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">BlockDataContainer</span><span class="p">)):</span>
+                <span class="n">data</span> <span class="o">=</span> <span class="n">datacontainers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">data</span> <span class="o">=</span> <span class="n">datacontainers</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span><span class="mi">4</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">slice_list</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span> <span class="c1">#none, (direction, ind) or [(direction0, ind), (direction1, ind)] apply to all datasets</span>
+                    <span class="n">slice_requested</span> <span class="o">=</span> <span class="n">slice_list</span>
+                <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="nb">int</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">slice_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> <span class="c1"># [ind0, ind1, ind2] of direction0, or [[(direction0, ind), (direction1, ind)],[(direction0, ind), (direction1, ind)]]</span>
+                    <span class="n">slice_requested</span> <span class="o">=</span> <span class="n">slice_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">slice_requested</span> <span class="o">=</span> <span class="n">slice_list</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># [[(direction0, ind)],[(direction0, ind)]]</span>
+
+                <span class="n">cut_axis</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">cut_slices</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+
+                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">int</span><span class="p">:</span>
+                    <span class="c1">#use axis 0, slice val</span>
+                    <span class="n">cut_slices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span>
+                <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
+                    <span class="c1">#get axis ind,</span>
+                    <span class="c1"># if 0 default 1</span>
+                    <span class="c1"># if 1 default 0</span>
+                    <span class="n">axis</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="nb">str</span><span class="p">:</span>
+                        <span class="n">axis</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">axis</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                        <span class="n">cut_slices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                        <span class="n">cut_slices</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+                <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>
+                    <span class="c1">#use full input</span>
+                    <span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">cut_slices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="n">cut_slices</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+
+                    <span class="k">if</span> <span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                        <span class="n">cut_axis</span><span class="o">.</span><span class="n">reverse</span><span class="p">()</span>
+                        <span class="n">cut_slices</span><span class="o">.</span><span class="n">reverse</span><span class="p">()</span>
+
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;get_slice&#39;</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span><span class="p">:</span>
+                            <span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+                        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span><span class="p">:</span>
+                            <span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+
+                        <span class="n">temp_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">cut_slices</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span><span class="n">cut_slices</span><span class="p">[</span><span class="mi">1</span><span class="p">]}</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">**</span><span class="n">temp_dict</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+                    <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="s1">&#39;as_array&#39;</span><span class="p">):</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">cut_slices</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">plot_data</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">cut_slices</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">cut_slices</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">plot_data</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">cut_slices</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">cut_axis</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Unable to slice input data. Could not obtain 2D slice </span><span class="si">{0}</span><span class="s2"> from </span><span class="si">{1}</span><span class="s2"> with shape </span><span class="si">{2}</span><span class="s2">.</span><span class="se">\n\</span>
+<span class="s2">                            Pass either correct slice information or a 2D array&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+                <span class="n">subtitle</span> <span class="o">=</span> <span class="s2">&quot;direction: (</span><span class="si">{0}</span><span class="s2">,</span><span class="si">{1}</span><span class="s2">), slice: (</span><span class="si">{2}</span><span class="s2">,</span><span class="si">{3}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">cut_axis</span><span class="p">,</span> <span class="o">*</span> <span class="n">cut_slices</span><span class="p">)</span>
+
+
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="c1">#get slice list per subplot</span>
+                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_list</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>            <span class="c1">#[(direction, ind), (direction, ind)],  [ind0, ind1, ind2] of direction0</span>
+                    <span class="n">slice_requested</span> <span class="o">=</span> <span class="n">slice_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>                                   <span class="c1">#(direction, ind) single tuple apply to all datasets</span>
+                    <span class="n">slice_requested</span> <span class="o">=</span> <span class="n">slice_list</span>
+
+                <span class="c1">#default axis 0, centre slice</span>
+                <span class="n">cut_slice</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span>
+                <span class="n">cut_axis</span> <span class="o">=</span> <span class="mi">0</span>
+
+                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">int</span><span class="p">:</span>
+                    <span class="c1">#use axis 0, slice val</span>
+                    <span class="n">cut_slice</span> <span class="o">=</span> <span class="n">slice_requested</span>
+                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
+                    <span class="n">cut_slice</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="n">cut_axis</span> <span class="o">=</span> <span class="n">slice_requested</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;get_slice&#39;</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">cut_axis</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">int</span><span class="p">:</span>
+                            <span class="n">cut_axis</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="n">cut_axis</span><span class="p">]</span>
+                        <span class="n">temp_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">cut_axis</span><span class="p">:</span><span class="n">cut_slice</span><span class="p">}</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="o">**</span><span class="n">temp_dict</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+                    <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="s1">&#39;as_array&#39;</span><span class="p">):</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">cut_slice</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">cut_axis</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">plot_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">cut_slice</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">cut_axis</span><span class="p">)</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Unable to slice input data. Could not obtain 2D slice </span><span class="si">{0}</span><span class="s2"> from </span><span class="si">{1}</span><span class="s2"> with shape </span><span class="si">{2}</span><span class="s2">.</span><span class="se">\n\</span>
+<span class="s2">                            Pass either correct slice information or a 2D array&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+                <span class="n">subtitle</span> <span class="o">=</span> <span class="s2">&quot;direction: </span><span class="si">{0}</span><span class="s2">, slice: </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cut_axis</span><span class="p">,</span><span class="n">cut_slice</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">plot_data</span> <span class="o">=</span> <span class="n">data</span>
+                <span class="n">subtitle</span> <span class="o">=</span> <span class="kc">None</span>
+
+
+            <span class="c1">#check dataset is now 2D</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">plot_data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Unable to slice input data. Could not obtain 2D slice </span><span class="si">{0}</span><span class="s2"> from </span><span class="si">{1}</span><span class="s2"> with shape </span><span class="si">{2}</span><span class="s2">.</span><span class="se">\n\</span>
+<span class="s2">                                Pass either correct slice information or a 2D array&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">slice_requested</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+
+            <span class="c1">#get axis labels per subplot</span>
+            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">axis_labels</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>
+                <span class="n">plot_axis_labels</span> <span class="o">=</span> <span class="n">axis_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">plot_axis_labels</span> <span class="o">=</span> <span class="n">axis_labels</span>
+
+            <span class="k">if</span> <span class="n">plot_axis_labels</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">plot_data</span><span class="p">,</span><span class="s1">&#39;dimension_labels&#39;</span><span class="p">):</span>
+                    <span class="n">plot_axis_labels</span> <span class="o">=</span> <span class="p">(</span><span class="n">plot_data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">plot_data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+            <span class="c1">#get min/max of subsetted data</span>
+            <span class="n">range_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">range_min</span><span class="p">,</span> <span class="n">plot_data</span><span class="o">.</span><span class="n">min</span><span class="p">())</span>
+            <span class="n">range_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">range_max</span><span class="p">,</span> <span class="n">plot_data</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+
+            <span class="c1">#get title per subplot</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">title</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">plot_title</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">plot_title</span> <span class="o">=</span> <span class="n">title</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">plot_title</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">plot_title</span> <span class="o">=</span> <span class="n">title</span>
+
+            <span class="k">if</span> <span class="n">subtitle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">plot_title</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="n">subtitle</span>
+
+            <span class="c1">#get origin per subplot</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">origin</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">plot_origin</span> <span class="o">=</span> <span class="n">origin</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">plot_origin</span> <span class="o">=</span> <span class="n">origin</span>
+
+            <span class="n">subplots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_PlotData</span><span class="p">(</span><span class="n">plot_data</span><span class="p">,</span><span class="n">plot_title</span><span class="p">,</span><span class="n">plot_axis_labels</span><span class="p">,</span> <span class="n">plot_origin</span><span class="p">))</span>
+
+        <span class="c1">#set range per subplot</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">subplot</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">subplots</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">fix_range</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+                <span class="k">pass</span>
+            <span class="k">elif</span> <span class="n">fix_range</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+                <span class="n">subplot</span><span class="o">.</span><span class="n">range</span> <span class="o">=</span> <span class="p">(</span><span class="n">range_min</span><span class="p">,</span><span class="n">range_max</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">fix_range</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>
+                <span class="n">subplot</span><span class="o">.</span><span class="n">range</span> <span class="o">=</span> <span class="n">fix_range</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">subplot</span><span class="o">.</span><span class="n">range</span> <span class="o">=</span> <span class="p">(</span><span class="n">fix_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">fix_range</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="c1">#create plots</span>
+        <span class="k">if</span> <span class="n">num_plots</span> <span class="o">&lt;</span> <span class="n">num_cols</span><span class="p">:</span>
+            <span class="n">num_cols</span> <span class="o">=</span> <span class="n">num_plots</span>
+
+        <span class="n">num_rows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">((</span><span class="n">num_plots</span><span class="o">+</span><span class="mf">0.5</span><span class="p">)</span><span class="o">/</span><span class="n">num_cols</span><span class="p">))</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+        <span class="n">axes</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+
+        <span class="c1">#set up plots</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rows</span><span class="o">*</span><span class="n">num_cols</span><span class="p">):</span>
+            <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">subplot</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">subplots</span><span class="p">):</span>
+
+            <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">subplot</span><span class="o">.</span><span class="n">title</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">subplot</span><span class="o">.</span><span class="n">axis_labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">subplot</span><span class="o">.</span><span class="n">axis_labels</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">subplot</span><span class="o">.</span><span class="n">axis_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+            <span class="c1">#set origin</span>
+            <span class="n">data</span><span class="p">,</span> <span class="n">data_origin</span><span class="p">,</span> <span class="n">extent</span> <span class="o">=</span> <span class="n">set_origin</span><span class="p">(</span><span class="n">subplot</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">subplot</span><span class="o">.</span><span class="n">origin</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cmap</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
+                <span class="n">dcmap</span> <span class="o">=</span> <span class="n">cmap</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dcmap</span> <span class="o">=</span> <span class="n">cmap</span>
+            <span class="n">sp</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">dcmap</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="n">data_origin</span><span class="p">,</span> <span class="n">extent</span><span class="o">=</span><span class="n">extent</span><span class="p">)</span>
+
+            <span class="n">im_ratio</span> <span class="o">=</span> <span class="n">subplot</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">subplot</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">y_axes2</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subplot</span><span class="o">.</span><span class="n">data</span><span class="p">,(</span><span class="n">AcquisitionData</span><span class="p">)):</span>
+                <span class="k">if</span> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylabel</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;angle&#39;</span><span class="p">:</span>
+                    <span class="n">locs</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get_yticks</span><span class="p">()</span>
+                    <span class="n">location_new</span> <span class="o">=</span> <span class="n">locs</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+
+                    <span class="n">ang</span> <span class="o">=</span> <span class="n">subplot</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">angles</span>
+
+                    <span class="n">labels_new</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;</span><span class="si">{:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">ang</span><span class="o">.</span><span class="n">angle_data</span><span class="p">,</span> <span class="n">location_new</span><span class="p">)]</span>
+                    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">location_new</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels_new</span><span class="p">)</span>
+
+                    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;angle / &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ang</span><span class="o">.</span><span class="n">angle_unit</span><span class="p">))</span>
+
+                    <span class="n">y_axes2</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">secondary_yaxis</span><span class="p">(</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
+                    <span class="n">y_axes2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;angle / index&#39;</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">subplot</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">subplot</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:</span>
+                        <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">im_ratio</span><span class="p">)</span>
+                        <span class="n">im_ratio</span> <span class="o">=</span> <span class="mi">1</span>
+
+            <span class="k">if</span> <span class="n">y_axes2</span><span class="p">:</span>
+                <span class="n">scale</span> <span class="o">=</span> <span class="mf">0.041</span><span class="o">*</span><span class="n">im_ratio</span>
+                <span class="n">pad</span> <span class="o">=</span> <span class="mf">0.12</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">scale</span> <span class="o">=</span> <span class="mf">0.0467</span><span class="o">*</span><span class="n">im_ratio</span>
+                <span class="n">pad</span> <span class="o">=</span> <span class="mf">0.02</span>
+
+            <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">sp</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">fraction</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="n">pad</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">subplot</span><span class="o">.</span><span class="n">range</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">sp</span><span class="o">.</span><span class="n">set_clim</span><span class="p">(</span><span class="n">subplot</span><span class="o">.</span><span class="n">range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">subplot</span><span class="o">.</span><span class="n">range</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="n">fig</span><span class="o">.</span><span class="n">set_tight_layout</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">set_facecolor</span><span class="p">(</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+
+        <span class="c1">#plt.show() creates a new figure so we save a copy to return</span>
+        <span class="n">fig2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">fig2</span></div>
+
+
+<span class="k">def</span> <span class="nf">plotter2D</span><span class="p">(</span><span class="n">datacontainers</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fix_range</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;lower-left&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">)):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Alias of show2D&#39;&#39;&#39;</span>
+    <span class="k">return</span> <span class="n">show2D</span><span class="p">(</span><span class="n">datacontainers</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="n">slice_list</span><span class="p">,</span> <span class="n">fix_range</span><span class="o">=</span><span class="n">fix_range</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="n">axis_labels</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="n">origin</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">num_cols</span><span class="o">=</span><span class="n">num_cols</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+<span class="k">class</span> <span class="nc">_Arrow3D</span><span class="p">(</span><span class="n">FancyArrowPatch</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">zs</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">FancyArrowPatch</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_verts3d</span> <span class="o">=</span> <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">zs</span>
+
+    <span class="k">def</span> <span class="nf">draw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">renderer</span><span class="p">):</span>
+        <span class="n">xs3d</span><span class="p">,</span> <span class="n">ys3d</span><span class="p">,</span> <span class="n">zs3d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_verts3d</span>
+        <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">zs</span> <span class="o">=</span> <span class="n">proj3d</span><span class="o">.</span><span class="n">proj_transform</span><span class="p">(</span><span class="n">xs3d</span><span class="p">,</span> <span class="n">ys3d</span><span class="p">,</span> <span class="n">zs3d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">M</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_positions</span><span class="p">((</span><span class="n">xs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ys</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">(</span><span class="n">xs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">ys</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="n">FancyArrowPatch</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">renderer</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">do_3d_projection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">renderer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">xs3d</span><span class="p">,</span> <span class="n">ys3d</span><span class="p">,</span> <span class="n">zs3d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_verts3d</span>
+        <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">zs</span> <span class="o">=</span> <span class="n">proj3d</span><span class="o">.</span><span class="n">proj_transform</span><span class="p">(</span><span class="n">xs3d</span><span class="p">,</span> <span class="n">ys3d</span><span class="p">,</span> <span class="n">zs3d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">M</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_positions</span><span class="p">((</span><span class="n">xs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ys</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">xs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">ys</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">zs</span><span class="p">)</span>
+
+<span class="k">class</span> <span class="nc">_ShowGeometry</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">acquisition_geometry</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">=</span> <span class="mi">2</span>
+            <span class="n">sys</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span>
+            <span class="k">if</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+                <span class="n">ag_temp</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">([</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ag_temp</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel3D</span><span class="p">([</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+
+
+            <span class="n">ag_temp</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span>
+            <span class="n">ag_temp</span><span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">)</span>
+            <span class="n">ag_temp</span><span class="o">.</span><span class="n">set_labels</span><span class="p">([</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span> <span class="o">*</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">])</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span> <span class="o">=</span> <span class="n">ag_temp</span>
+
+        <span class="k">elif</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">=</span> <span class="mi">3</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span> <span class="o">=</span> <span class="n">acquisition_geometry</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">=</span> <span class="mi">3</span>
+
+        <span class="k">if</span> <span class="n">image_geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span> <span class="o">=</span> <span class="n">image_geometry</span>
+
+
+        <span class="n">len1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">len2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">len1</span><span class="p">,</span><span class="n">len2</span><span class="p">)</span><span class="o">/</span><span class="mi">5</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">handles</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">def</span> <span class="nf">draw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span> <span class="n">view_distance</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">&#39;3d&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">text_options</span> <span class="o">=</span> <span class="p">{</span>   <span class="s1">&#39;horizontalalignment&#39;</span><span class="p">:</span> <span class="s1">&#39;center&#39;</span><span class="p">,</span>
+                                <span class="s1">&#39;verticalalignment&#39;</span><span class="p">:</span> <span class="s1">&#39;center&#39;</span><span class="p">,</span>
+                                <span class="s1">&#39;fontsize&#39;</span><span class="p">:</span> <span class="n">fontsize</span> <span class="p">}</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">display_world</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">geom_type</span> <span class="o">==</span> <span class="s1">&#39;cone&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">display_source</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">display_ray</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">display_object</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">display_detector</span><span class="p">()</span>
+
+
+        <span class="k">if</span> <span class="n">grid</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">view_init</span><span class="p">(</span><span class="n">elev</span><span class="o">=</span><span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="n">azim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">dist</span> <span class="o">=</span> <span class="n">view_distance</span>
+
+        <span class="c1">#to force aspect ratio 1:1:1</span>
+        <span class="n">world_limits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">get_w_lims</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_box_aspect</span><span class="p">((</span><span class="n">world_limits</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">world_limits</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">world_limits</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">-</span><span class="n">world_limits</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="n">world_limits</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span><span class="o">-</span><span class="n">world_limits</span><span class="p">[</span><span class="mi">4</span><span class="p">]))</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NaN</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="n">l</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+
+        <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+                        <span class="n">borderaxespad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">text_options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fontsize&#39;</span><span class="p">))</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">set_tight_layout</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">set_facecolor</span><span class="p">(</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+
+        <span class="c1">#plt.show() creates a new figure so we save a copy to return</span>
+        <span class="n">fig2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">fig2</span>
+
+    <span class="k">def</span> <span class="nf">display_world</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;X axis&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Y axis&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s1">&#39;Z axis&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_zticks</span><span class="p">([])</span>
+
+        <span class="c1">#origin and coordinate frame</span>
+        <span class="n">Oo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="o">*</span><span class="n">Oo</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">h</span> <span class="o">=</span> <span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;world coordinate system&#39;</span><span class="p">)</span>
+
+        <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;$x$&#39;</span><span class="p">,</span><span class="s1">&#39;$y$&#39;</span><span class="p">,</span><span class="s1">&#39;$z$&#39;</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">):</span>
+            <span class="n">axis</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="n">axis</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span>
+
+            <span class="n">a</span> <span class="o">=</span> <span class="n">_Arrow3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">Oo</span><span class="p">,</span><span class="n">axis</span><span class="o">*</span><span class="mi">2</span><span class="p">),</span> <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;-&gt;&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">axis</span><span class="o">*</span><span class="mf">2.2</span><span class="p">),</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">text_options</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="nf">detector_vertex</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># detector corners</span>
+        <span class="n">det_size</span>  <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">))</span><span class="o">/</span><span class="mi">2</span>
+        <span class="n">det_rows_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">det_v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span> <span class="o">*</span> <span class="n">det_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">det_h</span> <span class="o">=</span> <span class="n">det_rows_dir</span> <span class="o">*</span> <span class="n">det_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">rt</span> <span class="o">=</span> <span class="n">det_h</span> <span class="o">+</span> <span class="n">det_v</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+            <span class="n">lt</span> <span class="o">=</span> <span class="o">-</span><span class="n">det_h</span> <span class="o">+</span> <span class="n">det_v</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+            <span class="n">lb</span> <span class="o">=</span> <span class="o">-</span><span class="n">det_h</span> <span class="o">-</span> <span class="n">det_v</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+            <span class="n">rb</span> <span class="o">=</span> <span class="n">det_h</span> <span class="o">-</span> <span class="n">det_v</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+
+            <span class="k">return</span> <span class="p">[</span><span class="n">rb</span><span class="p">,</span> <span class="n">lb</span><span class="p">,</span> <span class="n">lt</span><span class="p">,</span> <span class="n">rt</span><span class="p">]</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">det_h</span> <span class="o">=</span> <span class="n">det_rows_dir</span> <span class="o">*</span> <span class="n">det_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">r</span> <span class="o">=</span> <span class="n">det_h</span>  <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+            <span class="n">l</span> <span class="o">=</span> <span class="o">-</span><span class="n">det_h</span>  <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+            <span class="k">return</span> <span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span>
+
+
+    <span class="k">def</span> <span class="nf">display_detector</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">do</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">det</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector_vertex</span><span class="p">()</span>
+
+        <span class="c1">#mark data origin</span>
+        <span class="k">if</span> <span class="s1">&#39;right&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span> <span class="ow">or</span> <span class="s1">&#39;bottom&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+                <span class="n">pix0</span> <span class="o">=</span> <span class="n">det</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">pix0</span> <span class="o">=</span> <span class="n">det</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span> <span class="ow">or</span> <span class="s1">&#39;bottom&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+                <span class="n">pix0</span> <span class="o">=</span> <span class="n">det</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">pix0</span> <span class="o">=</span> <span class="n">det</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+        <span class="n">det_rows_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_x</span>
+
+        <span class="n">x</span> <span class="o">=</span> <span class="n">_Arrow3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">do</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">det_rows_dir</span> <span class="o">+</span> <span class="n">do</span><span class="p">),</span> <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;-|&gt;&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="mf">1.2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">det_rows_dir</span> <span class="o">+</span> <span class="n">do</span><span class="p">),</span><span class="sa">r</span><span class="s1">&#39;$D_x$&#39;</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">text_options</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">det_col_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">direction_y</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">_Arrow3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">do</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">det_col_dir</span> <span class="o">+</span> <span class="n">do</span><span class="p">),</span> <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;-|&gt;&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="mf">1.2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">det_col_dir</span> <span class="o">+</span> <span class="n">do</span><span class="p">),</span><span class="sa">r</span><span class="s1">&#39;$D_y$&#39;</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">text_options</span><span class="p">)</span>
+
+        <span class="n">handles</span><span class="o">=</span><span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="o">*</span><span class="n">do</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;detector position&#39;</span><span class="p">),</span>
+            <span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;detector direction&#39;</span><span class="p">),</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">det</span><span class="p">,</span> <span class="n">det</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;detector&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="o">*</span><span class="n">pix0</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;data origin (pixel 0)&#39;</span><span class="p">),</span>
+        <span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">handles</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_label</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="nf">display_object</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">ro</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">h0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="o">*</span><span class="n">ro</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;rotation axis position&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h0</span><span class="o">.</span><span class="n">get_label</span><span class="p">())</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="c1"># rotate axis arrow</span>
+            <span class="n">r1</span> <span class="o">=</span> <span class="n">ro</span> <span class="o">+</span>  <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="mi">2</span>
+            <span class="n">arrow3</span> <span class="o">=</span> <span class="n">_Arrow3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">ro</span><span class="p">,</span><span class="n">r1</span><span class="p">),</span> <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;-|&gt;&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">arrow3</span><span class="p">)</span>
+
+            <span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">rotation_axis</span><span class="o">.</span><span class="n">direction</span>
+
+
+            <span class="c1"># draw reco</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_min_x</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_max_x</span><span class="p">()])</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_min_y</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_max_y</span><span class="p">()])</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_min_z</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_max_z</span><span class="p">()])</span>
+
+            <span class="n">combos</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">0</span><span class="p">]),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span>
+            <span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+                <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+                <span class="n">axis_rotation</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">vx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="p">[</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">]])</span>
+                <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">vx</span> <span class="o">+</span> <span class="n">vx</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">vx</span><span class="p">)</span> <span class="o">*</span>  <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+            <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matrix</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">axis_rotation</span><span class="p">)</span>
+
+            <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">combos</span><span class="p">:</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+                <span class="n">e</span> <span class="o">=</span> <span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+
+                <span class="n">x_data</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">float</span><span class="p">(</span><span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">y_data</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">float</span><span class="p">(</span><span class="n">e</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">z_data</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="nb">float</span><span class="p">(</span><span class="n">e</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="n">x_data</span><span class="p">,</span><span class="n">y_data</span><span class="p">,</span><span class="n">z_data</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">vox0</span><span class="o">=</span><span class="p">(</span><span class="n">x_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">z_data</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="n">count</span><span class="o">+=</span><span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># draw square</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_min_x</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_max_x</span><span class="p">()]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_min_y</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">get_max_y</span><span class="p">()]</span>
+            <span class="n">vertex</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="mi">0</span><span class="p">),(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="mi">0</span><span class="p">),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="mi">0</span><span class="p">),(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="mi">0</span><span class="p">)])</span> <span class="o">+</span> <span class="n">ro</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">vertex</span><span class="p">,</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">vox0</span><span class="o">=</span><span class="n">vertex</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+
+        <span class="c1">#rotation direction</span>
+        <span class="n">points</span> <span class="o">=</span> <span class="mi">36</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">points</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">points</span>
+        <span class="n">z</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">points</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">points</span><span class="p">):</span>
+            <span class="n">theta</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mf">1.8</span><span class="p">)</span> <span class="o">/</span><span class="mi">36</span>
+            <span class="n">point_i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">point_rot</span> <span class="o">=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">*</span><span class="mf">0.5</span><span class="o">*</span><span class="n">rotation_matrix</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">point_i</span><span class="p">)</span>
+
+            <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">point_rot</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">point_rot</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">z</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">point_rot</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">ro</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">arrow4</span> <span class="o">=</span> <span class="n">_Arrow3D</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span><span class="n">y</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span><span class="n">z</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span><span class="n">mutation_scale</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;-|&gt;&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">arrow4</span><span class="p">)</span>
+
+        <span class="n">handles</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;rotation axis direction&#39;</span><span class="p">),</span>
+            <span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;image geometry&#39;</span><span class="p">),</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="o">*</span><span class="n">vox0</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;data origin (voxel 0)&#39;</span><span class="p">),</span>
+            <span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;rotation direction $\theta$&#39;</span><span class="p">)</span>
+        <span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">handles</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">get_label</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="nf">display_source</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">so</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">position</span>
+        <span class="n">det</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector_vertex</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">det</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">so</span><span class="p">,</span><span class="n">det</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#D4BD72&#39;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">so</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#D4BD72&#39;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s2">&quot;solid&quot;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
+
+        <span class="n">h0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span><span class="o">*</span><span class="n">so</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;*&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;#D4BD72&#39;</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;source position&#39;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h0</span><span class="o">.</span><span class="n">get_label</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="nf">display_ray</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="n">det</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">detector_vertex</span><span class="p">()</span>
+        <span class="n">det</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="p">)</span>
+
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">detector</span><span class="o">.</span><span class="n">position</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="mi">2</span>
+
+        <span class="k">if</span> <span class="n">dist</span> <span class="o">&lt;</span> <span class="mf">0.01</span><span class="p">:</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">num_pixels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">pixel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">rays</span> <span class="o">=</span> <span class="n">det</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="o">*</span><span class="n">dist</span>
+
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rays</span><span class="p">)):</span>
+            <span class="n">h0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">rays</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">det</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#D4BD72&#39;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;ray direction&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">arrow</span> <span class="o">=</span> <span class="n">_Arrow3D</span><span class="p">(</span><span class="o">*</span><span class="nb">zip</span><span class="p">(</span><span class="n">rays</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">rays</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">system</span><span class="o">.</span><span class="n">ray</span><span class="o">.</span><span class="n">direction</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="p">),</span><span class="n">mutation_scale</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">arrowstyle</span><span class="o">=</span><span class="s2">&quot;-|&gt;&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#D4BD72&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">arrow</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h0</span><span class="o">.</span><span class="n">get_label</span><span class="p">())</span>
+
+<div class="viewcode-block" id="show_geometry">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.display.show_geometry">[docs]</a>
+<span class="k">class</span> <span class="nc">show_geometry</span><span class="p">(</span><span class="n">show_base</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Displays a schematic of the acquisition geometry</span>
+<span class="sd">    for 2D geometries elevation and azimuthal cannot be changed</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    acquisition_geometry: AcquisitionGeometry</span>
+<span class="sd">        CIL acquisition geometry</span>
+<span class="sd">    image_geometry: ImageGeometry, optional</span>
+<span class="sd">        CIL image geometry</span>
+<span class="sd">    elevation: float</span>
+<span class="sd">        Camera elevation in degrees, 3D geometries only, default=20</span>
+<span class="sd">    azimuthal: float</span>
+<span class="sd">        Camera azimuthal in degrees, 3D geometries only, default=-35</span>
+<span class="sd">    view_distance: float</span>
+<span class="sd">        Camera view distance, default=10</span>
+<span class="sd">    grid: boolean</span>
+<span class="sd">        Show figure axis, default=False</span>
+<span class="sd">    figsize: tuple (x, y)</span>
+<span class="sd">        Set figure size (inches), default (10,10)</span>
+<span class="sd">    fontsize: int</span>
+<span class="sd">        Set fontsize, default 10</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    matplotlib.figure.Figure</span>
+<span class="sd">        returns a matplotlib.pyplot figure object</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">acquisition_geometry</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">elevation</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">azimuthal</span><span class="o">=-</span><span class="mi">35</span><span class="p">,</span> <span class="n">view_distance</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+            <span class="n">elevation</span> <span class="o">=</span> <span class="mi">90</span>
+            <span class="n">azimuthal</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">display</span> <span class="o">=</span> <span class="n">_ShowGeometry</span><span class="p">(</span><span class="n">acquisition_geometry</span><span class="p">,</span> <span class="n">image_geometry</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">elev</span><span class="o">=</span><span class="n">elevation</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="n">azimuthal</span><span class="p">,</span> <span class="n">view_distance</span><span class="o">=</span><span class="n">view_distance</span><span class="p">,</span> <span class="n">grid</span><span class="o">=</span><span class="n">grid</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fontsize</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
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+<div class="version-switcher__container dropdown pst-js-only">
+  <button id="pst-version-switcher-button-2"
+    type="button"
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+    data-bs-toggle="dropdown"
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+  >
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+    <span class="caret"></span>
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+    class="version-switcher__menu dropdown-menu list-group-flush py-0"
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+    <!-- dropdown will be populated by javascript on page load -->
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+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../developer_guide/">Developers’ Guide</a></li>
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+    <li class="breadcrumb-item"><a href="../../../" class="nav-link">Module code</a></li>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">cil.utilities.jupyter</span></li>
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+                  
+  <h1>Source code for cil.utilities.jupyter</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2019 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2019 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+<span class="c1"># Kyle Pidgeon (UKRI-STFC)</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interactive_output</span>
+    <span class="kn">import</span> <span class="nn">ipywidgets</span> <span class="k">as</span> <span class="nn">widgets</span>
+<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">ie</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;please conda/pip install ipywidgets&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">ie</span>
+<span class="kn">import</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">gridspec</span>
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span><span class="p">,</span> <span class="n">display</span>
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.display</span> <span class="kn">import</span> <span class="n">set_origin</span>
+
+
+<span class="k">def</span> <span class="nf">display_slice</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">clim</span><span class="p">,</span> <span class="n">direction</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">axis_labels</span><span class="p">,</span> <span class="n">origin</span><span class="p">):</span>
+
+
+    <span class="k">def</span> <span class="nf">get_slice_3D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">minmax</span><span class="p">,</span> <span class="n">roi_hdir</span><span class="p">,</span> <span class="n">roi_vdir</span><span class="p">,</span> <span class="n">equal_aspect</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">direction</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">container</span><span class="p">[</span><span class="n">x</span><span class="p">]</span>
+            <span class="n">x_label</span> <span class="o">=</span> <span class="n">axis_labels</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">y_label</span> <span class="o">=</span> <span class="n">axis_labels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="k">elif</span> <span class="n">direction</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">container</span><span class="p">[:,</span><span class="n">x</span><span class="p">,:]</span>
+            <span class="n">x_label</span> <span class="o">=</span> <span class="n">axis_labels</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">y_label</span> <span class="o">=</span> <span class="n">axis_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">elif</span> <span class="n">direction</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">container</span><span class="p">[:,:,</span><span class="n">x</span><span class="p">]</span>
+            <span class="n">x_label</span> <span class="o">=</span> <span class="n">axis_labels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">y_label</span> <span class="o">=</span> <span class="n">axis_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+        <span class="n">dtitle</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
+            <span class="n">dtitle</span> <span class="o">=</span> <span class="n">title</span><span class="p">[</span><span class="n">x</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">dtitle</span> <span class="o">=</span> <span class="n">title</span>
+
+        <span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figure</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span> <span class="n">width_ratios</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mf">.05</span><span class="p">),</span> <span class="n">height_ratios</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
+        <span class="c1"># image</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+        <span class="n">img</span><span class="p">,</span> <span class="n">data_origin</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">set_origin</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">origin</span><span class="p">)</span>
+
+        <span class="n">aspect</span> <span class="o">=</span> <span class="s1">&#39;equal&#39;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">equal_aspect</span><span class="p">:</span>
+            <span class="n">aspect</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_hdir</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_hdir</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">roi_vdir</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">roi_vdir</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="s1">&#39;right&#39;</span> <span class="ow">in</span> <span class="n">origin</span><span class="p">:</span>
+            <span class="n">roi_hdir</span> <span class="o">=</span> <span class="n">roi_hdir</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">roi_hdir</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="s1">&#39;upper&#39;</span> <span class="ow">in</span> <span class="n">origin</span><span class="p">:</span>
+            <span class="n">roi_vdir</span> <span class="o">=</span> <span class="n">roi_vdir</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">roi_vdir</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">aximg</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="n">data_origin</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="n">aspect</span><span class="p">)</span>
+        <span class="n">cmin</span> <span class="o">=</span> <span class="n">clim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">minmax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">clim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">clim</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">cmax</span> <span class="o">=</span> <span class="n">clim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">minmax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">clim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">clim</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">aximg</span><span class="o">.</span><span class="n">set_clim</span><span class="p">((</span><span class="n">cmin</span><span class="p">,</span> <span class="n">cmax</span><span class="p">))</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">*</span><span class="n">roi_hdir</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">*</span><span class="n">roi_vdir</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">x_label</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">y_label</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">dtitle</span><span class="si">}</span><span class="s1"> </span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="c1"># colorbar</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">aximg</span><span class="p">,</span> <span class="n">cax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">get_slice_3D</span>
+
+
+<div class="viewcode-block" id="islicer">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.jupyter.islicer">[docs]</a>
+<span class="k">def</span> <span class="nf">islicer</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">slice_number</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span>
+            <span class="n">minmax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis_labels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;lower-left&#39;</span><span class="p">,</span>
+            <span class="n">play_interval</span><span class="o">=</span><span class="mi">500</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates an interactive slider that slices a 3D volume along an axis.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : DataContainer or numpy.ndarray</span>
+<span class="sd">        A 3-dimensional dataset from which 2-dimensional slices will be</span>
+<span class="sd">        shown</span>
+<span class="sd">    direction : int</span>
+<span class="sd">        Axis to slice on. Can be 0,1,2 or the axis label, default 0</span>
+<span class="sd">    title : str, list of str or tuple of str, default=&#39;&#39;</span>
+<span class="sd">        Title for the display</span>
+<span class="sd">    slice_number : int, optional</span>
+<span class="sd">        Start slice number (default is None, which results in the center</span>
+<span class="sd">        slice being shown initially)</span>
+<span class="sd">    cmap : str or matplotlib.colors.Colormap, default=&#39;gray&#39;</span>
+<span class="sd">        Set the colour map</span>
+<span class="sd">    minmax : tuple</span>
+<span class="sd">        Colorbar (min, max) values, default None (uses the min, max of</span>
+<span class="sd">        values in `data`)</span>
+<span class="sd">    size : int or tuple, optional</span>
+<span class="sd">        Specify the figure size in inches. If `int` this specifies the</span>
+<span class="sd">        width, and scales the height in order to keep the standard</span>
+<span class="sd">        `matplotlib` aspect ratio, default None (use the default matplotlib</span>
+<span class="sd">        figure size)</span>
+<span class="sd">    axis_labels : list of str, optional</span>
+<span class="sd">        The axis labels to use for each of the 3 dimensions in the data</span>
+<span class="sd">        (default is None, resulting in labels extracted from the data, or</span>
+<span class="sd">        [&#39;X&#39;,&#39;Y&#39;,&#39;Z&#39;] if no labels are present)</span>
+<span class="sd">    origin : {&#39;lower-left&#39;, &#39;upper-left&#39;, &#39;lower-right&#39;, &#39;upper-right&#39;}</span>
+<span class="sd">        Sets the display origin</span>
+<span class="sd">    play_interval : int, default=500</span>
+<span class="sd">        The interval of time (in ms) a slice is selected for when iterating</span>
+<span class="sd">        through a set of them</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    box : ipywidgets.Box</span>
+<span class="sd">        The top-level widget container.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">axis_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s2">&quot;dimension_labels&quot;</span><span class="p">):</span>
+            <span class="n">axis_labels</span> <span class="o">=</span> <span class="p">[</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">axis_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">,</span> <span class="s1">&#39;Z&#39;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="n">container</span> <span class="o">=</span> <span class="n">data</span>
+    <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s2">&quot;__getitem__&quot;</span><span class="p">):</span>
+        <span class="n">container</span> <span class="o">=</span> <span class="n">data</span>
+    <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s2">&quot;as_array&quot;</span><span class="p">):</span>
+        <span class="n">container</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">direction</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">direction</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">dimension_labels</span><span class="p">:</span>
+            <span class="n">direction</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_dimension_axis</span><span class="p">(</span><span class="n">direction</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">slice_number</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">slice_number</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">direction</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Direction </span><span class="si">{}</span><span class="s2">: Slice&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">axis_labels</span><span class="p">[</span><span class="n">direction</span><span class="p">])</span>
+
+    <span class="n">style</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;slider_width&#39;</span><span class="p">:</span> <span class="s1">&#39;80%&#39;</span><span class="p">}</span>
+    <span class="n">layout</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="s1">&#39;200px&#39;</span><span class="p">)</span>
+
+    <span class="n">slice_slider</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">IntSlider</span><span class="p">(</span>
+        <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="nb">max</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">direction</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">value</span><span class="o">=</span><span class="n">slice_number</span><span class="p">,</span>
+        <span class="n">continuous_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span>
+        <span class="n">style</span><span class="o">=</span><span class="n">style</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">slice_selector_full</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">VBox</span><span class="p">([</span><span class="n">widgets</span><span class="o">.</span><span class="n">Label</span><span class="p">(</span><span class="s1">&#39;Slice index (direction </span><span class="si">{}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">axis_labels</span><span class="p">[</span><span class="n">direction</span><span class="p">])),</span> <span class="n">slice_slider</span><span class="p">])</span>
+
+
+    <span class="n">play_slices</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">Play</span><span class="p">(</span>
+        <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="nb">max</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">direction</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">interval</span><span class="o">=</span><span class="n">play_interval</span><span class="p">,</span>
+        <span class="n">value</span><span class="o">=</span><span class="n">slice_number</span><span class="p">,</span>
+        <span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">widgets</span><span class="o">.</span><span class="n">jslink</span><span class="p">((</span><span class="n">play_slices</span><span class="p">,</span> <span class="s1">&#39;value&#39;</span><span class="p">),</span> <span class="p">(</span><span class="n">slice_slider</span><span class="p">,</span> <span class="s1">&#39;value&#39;</span><span class="p">))</span>
+
+    <span class="n">amax</span> <span class="o">=</span> <span class="n">container</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+    <span class="n">amin</span> <span class="o">=</span> <span class="n">container</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">minmax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">minmax</span> <span class="o">=</span> <span class="p">(</span><span class="n">amin</span><span class="p">,</span> <span class="n">amax</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span> <span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">)):</span>
+        <span class="n">default_ratio</span> <span class="o">=</span> <span class="mf">6.</span><span class="o">/</span><span class="mf">8.</span>
+        <span class="n">size</span> <span class="o">=</span> <span class="p">(</span> <span class="n">size</span> <span class="p">,</span> <span class="n">size</span> <span class="o">*</span> <span class="n">default_ratio</span> <span class="p">)</span>
+
+    <span class="n">min_max</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">IntRangeSlider</span><span class="p">(</span>
+        <span class="n">value</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span>
+        <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="nb">max</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+        <span class="n">step</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+        <span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">continuous_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span>
+        <span class="n">readout</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span>
+        <span class="n">style</span><span class="o">=</span><span class="n">style</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">min_max_full</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">VBox</span><span class="p">([</span><span class="n">widgets</span><span class="o">.</span><span class="n">Label</span><span class="p">(</span><span class="s1">&#39;Display window percent&#39;</span><span class="p">),</span> <span class="n">min_max</span><span class="p">])</span>
+
+    <span class="n">dirs_remaining</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">direction</span><span class="p">]</span>
+    <span class="n">h_dir</span><span class="p">,</span> <span class="n">v_dir</span> <span class="o">=</span> <span class="n">dirs_remaining</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dirs_remaining</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">h_dir_size</span> <span class="o">=</span> <span class="n">container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">h_dir</span><span class="p">]</span>
+    <span class="n">v_dir_size</span> <span class="o">=</span> <span class="n">container</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">v_dir</span><span class="p">]</span>
+
+    <span class="n">roi_select_hdir</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">IntRangeSlider</span><span class="p">(</span>
+        <span class="n">value</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">h_dir_size</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+        <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="nb">max</span><span class="o">=</span><span class="n">h_dir_size</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">continuous_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span>
+        <span class="n">readout</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">readout_format</span><span class="o">=</span><span class="s1">&#39;d&#39;</span><span class="p">,</span>
+        <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span>
+        <span class="n">style</span><span class="o">=</span><span class="n">style</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">roi_select_hdir_full</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">VBox</span><span class="p">([</span><span class="n">widgets</span><span class="o">.</span><span class="n">Label</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Range: </span><span class="si">{</span><span class="n">axis_labels</span><span class="p">[</span><span class="n">h_dir</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">),</span> <span class="n">roi_select_hdir</span><span class="p">])</span>
+
+
+    <span class="n">roi_select_vdir</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">IntRangeSlider</span><span class="p">(</span>
+        <span class="n">value</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">v_dir_size</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+        <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="nb">max</span><span class="o">=</span><span class="n">v_dir_size</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">continuous_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span>
+        <span class="n">readout</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">readout_format</span><span class="o">=</span><span class="s1">&#39;d&#39;</span><span class="p">,</span>
+        <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span>
+        <span class="n">style</span><span class="o">=</span><span class="n">style</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">roi_select_vdir_full</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">VBox</span><span class="p">([</span><span class="n">widgets</span><span class="o">.</span><span class="n">Label</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Range: </span><span class="si">{</span><span class="n">axis_labels</span><span class="p">[</span><span class="n">v_dir</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">),</span> <span class="n">roi_select_vdir</span><span class="p">])</span>
+
+    <span class="n">equal_aspect</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">Checkbox</span><span class="p">(</span>
+        <span class="n">value</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">description</span><span class="o">=</span><span class="s1">&#39;Pixel aspect ratio = 1&#39;</span><span class="p">,</span>
+        <span class="n">disabled</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">indent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">layout</span><span class="o">=</span><span class="n">widgets</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">),</span>
+    <span class="p">)</span>
+
+    <span class="n">box_layout</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span>
+        <span class="n">display</span><span class="o">=</span><span class="s1">&#39;flex&#39;</span><span class="p">,</span>
+        <span class="n">flex_flow</span><span class="o">=</span><span class="s1">&#39;column&#39;</span><span class="p">,</span>
+        <span class="n">align_items</span><span class="o">=</span><span class="s1">&#39;flex-start&#39;</span><span class="p">,</span>
+        <span class="n">justify_content</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">selectors</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">Box</span><span class="p">([</span>
+        <span class="n">play_slices</span><span class="p">,</span>
+        <span class="n">slice_selector_full</span><span class="p">,</span>
+        <span class="n">min_max_full</span><span class="p">,</span>
+        <span class="n">roi_select_hdir_full</span><span class="p">,</span>
+        <span class="n">roi_select_vdir_full</span><span class="p">,</span>
+        <span class="n">equal_aspect</span><span class="p">],</span>
+        <span class="n">layout</span><span class="o">=</span><span class="n">box_layout</span><span class="p">)</span>
+
+    <span class="n">out</span> <span class="o">=</span> <span class="n">interactive_output</span><span class="p">(</span>
+        <span class="n">display_slice</span><span class="p">(</span>
+            <span class="n">container</span><span class="p">,</span>
+            <span class="n">minmax</span><span class="p">,</span>
+            <span class="n">direction</span><span class="p">,</span>
+            <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
+            <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span>
+            <span class="n">axis_labels</span><span class="o">=</span><span class="n">axis_labels</span><span class="p">,</span>
+            <span class="n">origin</span><span class="o">=</span><span class="n">origin</span><span class="p">),</span>
+        <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="n">slice_slider</span><span class="p">,</span>
+        <span class="s1">&#39;minmax&#39;</span><span class="p">:</span> <span class="n">min_max</span><span class="p">,</span>
+        <span class="s1">&#39;roi_hdir&#39;</span><span class="p">:</span> <span class="n">roi_select_hdir</span><span class="p">,</span>
+        <span class="s1">&#39;roi_vdir&#39;</span><span class="p">:</span> <span class="n">roi_select_vdir</span><span class="p">,</span>
+        <span class="s1">&#39;equal_aspect&#39;</span><span class="p">:</span> <span class="n">equal_aspect</span><span class="p">})</span>
+
+    <span class="n">box</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">HBox</span><span class="p">(</span><span class="n">children</span><span class="o">=</span><span class="p">[</span><span class="n">out</span><span class="p">,</span> <span class="n">selectors</span><span class="p">],</span>
+                       <span class="n">layout</span><span class="o">=</span><span class="n">widgets</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span>
+                            <span class="n">display</span><span class="o">=</span><span class="s1">&#39;flex&#39;</span><span class="p">,</span>
+                            <span class="n">justify_content</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">box</span></div>
+
+
+
+<div class="viewcode-block" id="link_islicer">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.jupyter.link_islicer">[docs]</a>
+<span class="k">def</span> <span class="nf">link_islicer</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Links islicer&#39;s slice-selection widgets</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    *args : tuple of ipywidgets.Box</span>
+<span class="sd">        The widget containers returned from `islicer`, from which the slice</span>
+<span class="sd">        selection sliders will be extracted and linked.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">slice_sliders</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">args</span><span class="p">]</span>
+    <span class="n">play_widgets</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">args</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span>
+    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">play_widgets</span><span class="p">:</span>
+        <span class="n">p</span><span class="o">.</span><span class="n">layout</span><span class="o">.</span><span class="n">visibility</span> <span class="o">=</span> <span class="s1">&#39;hidden&#39;</span>
+    <span class="n">linked</span> <span class="o">=</span> <span class="p">[(</span><span class="n">widg</span><span class="p">,</span> <span class="s1">&#39;value&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">widg</span> <span class="ow">in</span> <span class="n">slice_sliders</span><span class="p">]</span>
+    <span class="c1"># link pair-wise</span>
+    <span class="n">pairs</span> <span class="o">=</span> <span class="p">[(</span><span class="n">linked</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span><span class="n">linked</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">linked</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)]</span>
+    <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">pairs</span><span class="p">:</span>
+        <span class="n">widgets</span><span class="o">.</span><span class="n">link</span><span class="p">(</span><span class="o">*</span><span class="n">pair</span><span class="p">)</span>
+
+    <span class="n">display</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span></div>
+
+
+<span class="c1"># https://stackoverflow.com/questions/31517194/how-to-hide-one-specific-cell-input-or-output-in-ipython-notebook/52664156</span>
+
+<span class="k">def</span> <span class="nf">hide_toggle</span><span class="p">(</span><span class="n">for_next</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="n">this_cell</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;$(&#39;div.cell.code_cell.rendered.selected&#39;)&quot;&quot;&quot;</span>
+    <span class="n">next_cell</span> <span class="o">=</span> <span class="n">this_cell</span> <span class="o">+</span> <span class="s1">&#39;.next()&#39;</span>
+
+    <span class="n">toggle_text</span> <span class="o">=</span> <span class="s1">&#39;Toggle show/hide&#39;</span>  <span class="c1"># text shown on toggle link</span>
+    <span class="n">target_cell</span> <span class="o">=</span> <span class="n">this_cell</span>  <span class="c1"># target cell to control with toggle</span>
+    <span class="n">js_hide_current</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>  <span class="c1"># bit of JS to permanently hide code in current cell (only when toggling next cell)</span>
+
+    <span class="k">if</span> <span class="n">for_next</span><span class="p">:</span>
+        <span class="n">target_cell</span> <span class="o">=</span> <span class="n">next_cell</span>
+        <span class="n">toggle_text</span> <span class="o">+=</span> <span class="s1">&#39; next cell&#39;</span>
+        <span class="n">js_hide_current</span> <span class="o">=</span> <span class="n">this_cell</span> <span class="o">+</span> <span class="s1">&#39;.find(&quot;div.input&quot;).hide();&#39;</span>
+
+    <span class="n">js_f_name</span> <span class="o">=</span> <span class="s1">&#39;code_toggle_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="o">**</span><span class="mi">64</span><span class="p">)))</span>
+
+    <span class="n">html</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">        &lt;script&gt;</span>
+<span class="s2">            function </span><span class="si">{f_name}</span><span class="s2">() {{</span>
+<span class="s2">                </span><span class="si">{cell_selector}</span><span class="s2">.find(&#39;div.input&#39;).toggle();</span>
+<span class="s2">            }}</span>
+
+<span class="s2">            </span><span class="si">{js_hide_current}</span>
+<span class="s2">        &lt;/script&gt;</span>
+
+<span class="s2">        &lt;a href=&quot;javascript:</span><span class="si">{f_name}</span><span class="s2">()&quot;&gt;</span><span class="si">{toggle_text}</span><span class="s2">&lt;/a&gt;</span>
+<span class="s2">    &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+        <span class="n">f_name</span><span class="o">=</span><span class="n">js_f_name</span><span class="p">,</span>
+        <span class="n">cell_selector</span><span class="o">=</span><span class="n">target_cell</span><span class="p">,</span>
+        <span class="n">js_hide_current</span><span class="o">=</span><span class="n">js_hide_current</span><span class="p">,</span>
+        <span class="n">toggle_text</span><span class="o">=</span><span class="n">toggle_text</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">HTML</span><span class="p">(</span><span class="n">html</span><span class="p">)</span>
+</pre></div>
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+</div>
+                </footer>
+              
+            </div>
+            
+            
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+            
+          </div>
+          <footer class="bd-footer-content">
+            
+          </footer>
+        
+      </main>
+    </div>
+  </div>
+  
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script defer src="../../../../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+<script defer src="../../../../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+
+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
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+    <div class="footer-items__start">
+      
+        <div class="footer-item">
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+    
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+      <br/>
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+      
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+  
+  
+  
+</div>
+
+  </footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/v24.2.0/_modules/cil/utilities/quality_measures/index.html b/v24.2.0/_modules/cil/utilities/quality_measures/index.html
new file mode 100644
index 0000000000..c8ea8c691e
--- /dev/null
+++ b/v24.2.0/_modules/cil/utilities/quality_measures/index.html
@@ -0,0 +1,649 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>cil.utilities.quality_measures &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
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+  -->
+  <noscript>
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+      .pst-js-only { display: none !important; }
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+    <script>DOCUMENTATION_OPTIONS.pagename = '_modules/cil/utilities/quality_measures';</script>
+    <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.16.0rc0';
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+        DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
+        </script>
+    <link rel="index" title="Index" href="../../../../genindex/" />
+    <link rel="search" title="Search" href="../../../../search/" />
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+  <meta name="docsearch:version" content="24.2.0" />
+  </head>
+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
+
+  
+  
+  <div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
+  
+  <div id="pst-scroll-pixel-helper"></div>
+  
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>Back to top</button>
+
+  
+  <dialog id="pst-search-dialog">
+    
+<form class="bd-search d-flex align-items-center"
+      action="../../../../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form>
+  </dialog>
+
+  <div class="pst-async-banner-revealer d-none">
+  <aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
+</div>
+
+  
+    <header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
+<div class="bd-header__inner bd-page-width">
+  <button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
+    <span class="fa-solid fa-bars"></span>
+  </button>
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
+    
+      <div class="navbar-item">
+
+  
+     
+  
+
+<a class="navbar-brand logo" href="/">
+  
+  
+  
+  
+  
+    
+    
+      
+    
+    
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB.svg" class="logo__image only-light" alt="CIL - Home"/>
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB-reversed.svg" class="logo__image only-dark pst-js-only" alt="CIL - Home"/>
+  
+  
+</a></div>
+    
+  </div>
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../recon/">
+    Recon
+  </a>
+</li>
+
+
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+  <a class="nav-link nav-internal" href="../../../../utilities/">
+    Utilities
+  </a>
+</li>
+
+
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+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+            <li class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
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+                    
+<li class=" ">
+  <a class="nav-link dropdown-item nav-internal" href="../../../../demos/">
+    Tutorials
+  </a>
+</li>
+
+                </ul>
+            </li>
+            
+  </ul>
+</nav></div>
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+    
+    <div class="navbar-header-items__end">
+      
+      
+        <div class="navbar-item">
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+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
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+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+      
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+  
+
+  
+</div>
+
+    </header>
+  
+
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+      
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+        
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+    
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+        
+          
+          
+            <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../../../../demos/">
+    Tutorials
+  </a>
+</li>
+
+  </ul>
+</nav></div>
+          
+        
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+    
+      <div class="sidebar-header-items__end">
+        
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+
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+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+        
+      </div>
+    
+  </div>
+  
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item">
+<form class="bd-search d-flex align-items-center"
+      action="../../../../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+        <div class="sidebar-primary-item">
+<div class="version-switcher__container dropdown pst-js-only">
+  <button id="pst-version-switcher-button-2"
+    type="button"
+    class="version-switcher__button btn btn-sm dropdown-toggle"
+    data-bs-toggle="dropdown"
+    aria-haspopup="listbox"
+    aria-controls="pst-version-switcher-list-2"
+    aria-label="Version switcher list"
+  >
+    Choose version  <!-- this text may get changed later by javascript -->
+    <span class="caret"></span>
+  </button>
+  <div id="pst-version-switcher-list-2"
+    class="version-switcher__menu dropdown-menu list-group-flush py-0"
+    role="listbox" aria-labelledby="pst-version-switcher-button-2">
+    <!-- dropdown will be populated by javascript on page load -->
+  </div>
+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../../../../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../processors/">Processors</a></li>
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+  <h1>Source code for cil.utilities.quality_measures</h1><div class="highlight"><pre>
+<span></span><span class="c1">#  Copyright 2020 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2020 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1"># Authors:</span>
+<span class="c1"># CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt</span>
+
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">L2NormSquared</span><span class="p">,</span> <span class="n">L1Norm</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="mse">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.quality_measures.mse">[docs]</a>
+<span class="k">def</span> <span class="nf">mse</span><span class="p">(</span><span class="n">dc1</span><span class="p">,</span> <span class="n">dc2</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Calculates the mean squared error of two images</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    dc1: `DataContainer`</span>
+<span class="sd">        One image to be compared</span>
+<span class="sd">    dc2: `DataContainer`</span>
+<span class="sd">        Second image to be compared</span>
+<span class="sd">    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`</span>
+<span class="sd">        The pixelwise operation only considers values where the mask is True or NonZero.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    A number, the mean squared error of the two images</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">dc1</span> <span class="o">=</span> <span class="n">dc1</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+    <span class="n">dc2</span> <span class="o">=</span> <span class="n">dc2</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
+        <span class="n">dc1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc1</span><span class="p">)</span>
+        <span class="n">dc2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(((</span><span class="n">dc1</span> <span class="o">-</span> <span class="n">dc2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span></div>
+
+
+
+<div class="viewcode-block" id="mae">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.quality_measures.mae">[docs]</a>
+<span class="k">def</span> <span class="nf">mae</span><span class="p">(</span><span class="n">dc1</span><span class="p">,</span> <span class="n">dc2</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Calculates the Mean Absolute error of two images.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    dc1: `DataContainer`</span>
+<span class="sd">        One image to be compared</span>
+<span class="sd">    dc2: `DataContainer`</span>
+<span class="sd">        Second image to be compared</span>
+<span class="sd">    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`</span>
+<span class="sd">        The pixelwise operation only considers values where the mask is True or NonZero.</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    A number with the mean absolute error between the two images.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">dc1</span> <span class="o">=</span> <span class="n">dc1</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+    <span class="n">dc2</span> <span class="o">=</span> <span class="n">dc2</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
+        <span class="n">dc1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc1</span><span class="p">)</span>
+        <span class="n">dc2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="n">dc1</span><span class="o">-</span><span class="n">dc2</span><span class="p">)))</span></div>
+
+
+
+<div class="viewcode-block" id="psnr">
+<a class="viewcode-back" href="../../../../utilities/#cil.utilities.quality_measures.psnr">[docs]</a>
+<span class="k">def</span> <span class="nf">psnr</span><span class="p">(</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">corrupted</span><span class="p">,</span> <span class="n">data_range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Calculates the Peak signal to noise ratio (PSNR) between the two images.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ground_truth: `DataContainer`</span>
+<span class="sd">        The reference image</span>
+<span class="sd">    corrupted: `DataContainer`</span>
+<span class="sd">        The image to be evaluated</span>
+<span class="sd">    data_range: scalar value, default=None</span>
+<span class="sd">        PSNR scaling factor, the dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values). We take the maximum value in the ground truth array.</span>
+<span class="sd">    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`</span>
+<span class="sd">        The pixelwise operation only considers values where the mask is True or NonZero..</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    A number, the peak signal to noise ration between the two images.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">data_range</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">data_range</span> <span class="o">=</span> <span class="n">ground_truth</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+                <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+            <span class="n">data_range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ground_truth</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span>
+                                 <span class="n">where</span><span class="o">=</span><span class="n">mask</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span> <span class="n">initial</span><span class="o">=-</span><span class="mf">1e-8</span><span class="p">)</span>
+
+    <span class="n">tmp_mse</span> <span class="o">=</span> <span class="n">mse</span><span class="p">(</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">corrupted</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">((</span><span class="n">data_range</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">tmp_mse</span><span class="p">)</span></div>
+
+</pre></div>
+
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+<h3><a href="../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
+</ul>
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+              
+              
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+                <article class="bd-article">
+                  
+  <h1>All modules for which code is available</h1>
+<ul><li><a href="cil/framework/acquisition_data/">cil.framework.acquisition_data</a></li>
+<li><a href="cil/framework/acquisition_geometry/">cil.framework.acquisition_geometry</a></li>
+<li><a href="cil/framework/block/">cil.framework.block</a></li>
+<li><a href="cil/framework/data_container/">cil.framework.data_container</a></li>
+<li><a href="cil/framework/image_data/">cil.framework.image_data</a></li>
+<li><a href="cil/framework/image_geometry/">cil.framework.image_geometry</a></li>
+<li><a href="cil/framework/labels/">cil.framework.labels</a></li>
+<li><a href="cil/framework/partitioner/">cil.framework.partitioner</a></li>
+<li><a href="cil/framework/processors/">cil.framework.processors</a></li>
+<li><a href="cil/framework/vector_data/">cil.framework.vector_data</a></li>
+<li><a href="cil/io/NEXUSDataReader/">cil.io.NEXUSDataReader</a></li>
+<li><a href="cil/io/NEXUSDataWriter/">cil.io.NEXUSDataWriter</a></li>
+<li><a href="cil/io/NikonDataReader/">cil.io.NikonDataReader</a></li>
+<li><a href="cil/io/RAWFileWriter/">cil.io.RAWFileWriter</a></li>
+<li><a href="cil/io/TIFF/">cil.io.TIFF</a></li>
+<li><a href="cil/io/ZEISSDataReader/">cil.io.ZEISSDataReader</a></li>
+<li><a href="cil/io/utilities/">cil.io.utilities</a></li>
+<li><a href="cil/optimisation/algorithms/ADMM/">cil.optimisation.algorithms.ADMM</a></li>
+<li><a href="cil/optimisation/algorithms/Algorithm/">cil.optimisation.algorithms.Algorithm</a></li>
+<li><a href="cil/optimisation/algorithms/CGLS/">cil.optimisation.algorithms.CGLS</a></li>
+<li><a href="cil/optimisation/algorithms/FISTA/">cil.optimisation.algorithms.FISTA</a></li>
+<li><a href="cil/optimisation/algorithms/GD/">cil.optimisation.algorithms.GD</a></li>
+<li><a href="cil/optimisation/algorithms/PD3O/">cil.optimisation.algorithms.PD3O</a></li>
+<li><a href="cil/optimisation/algorithms/PDHG/">cil.optimisation.algorithms.PDHG</a></li>
+<li><a href="cil/optimisation/algorithms/SIRT/">cil.optimisation.algorithms.SIRT</a></li>
+<li><a href="cil/optimisation/algorithms/SPDHG/">cil.optimisation.algorithms.SPDHG</a></li>
+<li><a href="cil/optimisation/functions/ApproximateGradientSumFunction/">cil.optimisation.functions.ApproximateGradientSumFunction</a></li>
+<li><a href="cil/optimisation/functions/BlockFunction/">cil.optimisation.functions.BlockFunction</a></li>
+<li><a href="cil/optimisation/functions/Function/">cil.optimisation.functions.Function</a></li>
+<li><a href="cil/optimisation/functions/IndicatorBox/">cil.optimisation.functions.IndicatorBox</a></li>
+<li><a href="cil/optimisation/functions/KullbackLeibler/">cil.optimisation.functions.KullbackLeibler</a></li>
+<li><a href="cil/optimisation/functions/L1Norm/">cil.optimisation.functions.L1Norm</a></li>
+<li><a href="cil/optimisation/functions/L1Sparsity/">cil.optimisation.functions.L1Sparsity</a></li>
+<li><a href="cil/optimisation/functions/L2NormSquared/">cil.optimisation.functions.L2NormSquared</a></li>
+<li><a href="cil/optimisation/functions/LeastSquares/">cil.optimisation.functions.LeastSquares</a></li>
+<li><a href="cil/optimisation/functions/MixedL21Norm/">cil.optimisation.functions.MixedL21Norm</a></li>
+<li><a href="cil/optimisation/functions/OperatorCompositionFunction/">cil.optimisation.functions.OperatorCompositionFunction</a></li>
+<li><a href="cil/optimisation/functions/Rosenbrock/">cil.optimisation.functions.Rosenbrock</a></li>
+<li><a href="cil/optimisation/functions/SAGFunction/">cil.optimisation.functions.SAGFunction</a></li>
+<li><a href="cil/optimisation/functions/SGFunction/">cil.optimisation.functions.SGFunction</a></li>
+<li><a href="cil/optimisation/functions/SVRGFunction/">cil.optimisation.functions.SVRGFunction</a></li>
+<li><a href="cil/optimisation/functions/TotalVariation/">cil.optimisation.functions.TotalVariation</a></li>
+<li><a href="cil/optimisation/operators/BlockOperator/">cil.optimisation.operators.BlockOperator</a></li>
+<li><a href="cil/optimisation/operators/ChannelwiseOperator/">cil.optimisation.operators.ChannelwiseOperator</a></li>
+<li><a href="cil/optimisation/operators/DiagonalOperator/">cil.optimisation.operators.DiagonalOperator</a></li>
+<li><a href="cil/optimisation/operators/FiniteDifferenceOperator/">cil.optimisation.operators.FiniteDifferenceOperator</a></li>
+<li><a href="cil/optimisation/operators/GradientOperator/">cil.optimisation.operators.GradientOperator</a></li>
+<li><a href="cil/optimisation/operators/IdentityOperator/">cil.optimisation.operators.IdentityOperator</a></li>
+<li><a href="cil/optimisation/operators/MaskOperator/">cil.optimisation.operators.MaskOperator</a></li>
+<li><a href="cil/optimisation/operators/MatrixOperator/">cil.optimisation.operators.MatrixOperator</a></li>
+<li><a href="cil/optimisation/operators/Operator/">cil.optimisation.operators.Operator</a></li>
+<li><a href="cil/optimisation/operators/ProjectionMap/">cil.optimisation.operators.ProjectionMap</a></li>
+<li><a href="cil/optimisation/operators/SparseFiniteDifferenceOperator/">cil.optimisation.operators.SparseFiniteDifferenceOperator</a></li>
+<li><a href="cil/optimisation/operators/SymmetrisedGradientOperator/">cil.optimisation.operators.SymmetrisedGradientOperator</a></li>
+<li><a href="cil/optimisation/operators/WaveletOperator/">cil.optimisation.operators.WaveletOperator</a></li>
+<li><a href="cil/optimisation/operators/ZeroOperator/">cil.optimisation.operators.ZeroOperator</a></li>
+<li><a href="cil/optimisation/utilities/StepSizeMethods/">cil.optimisation.utilities.StepSizeMethods</a></li>
+<li><a href="cil/optimisation/utilities/callbacks/">cil.optimisation.utilities.callbacks</a></li>
+<li><a href="cil/optimisation/utilities/preconditioner/">cil.optimisation.utilities.preconditioner</a></li>
+<li><a href="cil/optimisation/utilities/sampler/">cil.optimisation.utilities.sampler</a></li>
+<li><a href="cil/plugins/TomoPhantom/">cil.plugins.TomoPhantom</a></li>
+<li><a href="cil/plugins/astra/operators/ProjectionOperator/">cil.plugins.astra.operators.ProjectionOperator</a></li>
+<li><a href="cil/plugins/astra/processors/FBP/">cil.plugins.astra.processors.FBP</a></li>
+<li><a href="cil/plugins/ccpi_regularisation/functions/regularisers/">cil.plugins.ccpi_regularisation.functions.regularisers</a></li>
+<li><a href="cil/plugins/tigre/FBP/">cil.plugins.tigre.FBP</a></li>
+<li><a href="cil/plugins/tigre/ProjectionOperator/">cil.plugins.tigre.ProjectionOperator</a></li>
+<li><a href="cil/processors/AbsorptionTransmissionConverter/">cil.processors.AbsorptionTransmissionConverter</a></li>
+<li><a href="cil/processors/Binner/">cil.processors.Binner</a></li>
+<li><a href="cil/processors/CentreOfRotationCorrector/">cil.processors.CentreOfRotationCorrector</a></li>
+<li><a href="cil/processors/MaskGenerator/">cil.processors.MaskGenerator</a></li>
+<li><a href="cil/processors/Masker/">cil.processors.Masker</a></li>
+<li><a href="cil/processors/Normaliser/">cil.processors.Normaliser</a></li>
+<li><a href="cil/processors/Padder/">cil.processors.Padder</a></li>
+<li><a href="cil/processors/PaganinProcessor/">cil.processors.PaganinProcessor</a></li>
+<li><a href="cil/processors/RingRemover/">cil.processors.RingRemover</a></li>
+<li><a href="cil/processors/Slicer/">cil.processors.Slicer</a></li>
+<li><a href="cil/processors/TransmissionAbsorptionConverter/">cil.processors.TransmissionAbsorptionConverter</a></li>
+<li><a href="cil/recon/FBP/">cil.recon.FBP</a></li>
+<li><a href="cil/utilities/dataexample/">cil.utilities.dataexample</a></li>
+<li><a href="cil/utilities/display/">cil.utilities.display</a></li>
+<li><a href="cil/utilities/jupyter/">cil.utilities.jupyter</a></li>
+<li><a href="cil/utilities/quality_measures/">cil.utilities.quality_measures</a></li>
+</ul>
+
+                </article>
+              
+              
+              
+              
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diff --git a/v24.2.0/_sources/demos.rst.txt b/v24.2.0/_sources/demos.rst.txt
new file mode 100644
index 0000000000..7e9975bae5
--- /dev/null
+++ b/v24.2.0/_sources/demos.rst.txt
@@ -0,0 +1,7 @@
+Tutorials
+*********
+
+.. nbgallery::
+    demos/00_CIL_geometry
+    demos/deriv2_cgls
+    demos/callback_demonstration
diff --git a/v24.2.0/_sources/demos/00_CIL_geometry.ipynb.txt b/v24.2.0/_sources/demos/00_CIL_geometry.ipynb.txt
new file mode 100644
index 0000000000..a09178a666
--- /dev/null
+++ b/v24.2.0/_sources/demos/00_CIL_geometry.ipynb.txt
@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2021 - 2022 United Kingdom Research and Innovation\n",
+    "#  Copyright 2021 - 2022 The University of Manchester\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:    Gemma Fardell (UKRI-STFC)\n",
+    "#                   Edoardo Pasca (UKRI-STFC)\n",
+    "#                   Laura Murgatroyd (UKRI-STFC)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A detailed look at CIL geometry\n",
+    "CIL holds your CT data in specialised data-containers, `AcquisitionData` and `ImageData`.\n",
+    "\n",
+    "Each of these has an associated `geometry` which contains the meta-data describing your set-up.\n",
+    "\n",
+    " - `AcquisitionGeometry` describes the acquisition data and parameters\n",
+    "\n",
+    " - `ImageGeometry` describes the image data (i.e., the reconstruction volume)\n",
+    "\n",
+    "The data-readers provided by CIL (Nikon, Zeiss and diamond nexus readers) will read in your data and return you a fully configured acquisition data with the acquisition geometry already configured, however if you read in a stack of tiffs or want to tweak the parameters this is simple to create by hand."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The structure of an AcquisitionGeometry\n",
+    "\n",
+    "An instance of an `AcquisitionGeometry`, `ag`, holds the configuration of the system, in `config` which is subdivided in to:\n",
+    " - `ag.config.system` -  The position and orientations of the `source`/`ray`, `rotation_axis` and `detector`\n",
+    " - `ag.config.panel` - The number of pixels, the size of pixels, and the position of pixel 0\n",
+    " - `ag.config.angles` - The number of angles, the unit of the angles (default is degrees)\n",
+    " - `ag.config.channels` - The number of channels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a simple AcquisitionGeometry\n",
+    "\n",
+    "You can use the `AcquisitionGeometry` methods to describe circular trajectory parallel-beam or cone-beam 2D or 3D data.\n",
+    "\n",
+    " - `ag = AcquisitionGeometry.create_Parallel2D()`\n",
+    " - `ag = AcquisitionGeometry.create_Parallel3D()`\n",
+    " - `ag = AcquisitionGeometry.create_Cone2D(source_position, detector_position)`\n",
+    " - `ag = AcquisitionGeometry.create_Cone3D(source_position, detector_position)`\n",
+    "\n",
+    "This notebook will step though each in turn and show you how to describe both simple and complex geometries with offsets and rotations.\n",
+    "\n",
+    "No matter which type of geometry you create you will also need to describe the panel and projection angles.\n",
+    " - `ag.set_panel(num_pixels, pixel_size)`\n",
+    " - `ag.set_angles(angles, angle_unit)`\n",
+    "\n",
+    "For multi-channel data you need to add the number of channels.\n",
+    " - `ag.set_channels(num_channels)`\n",
+    "\n",
+    "And you will also need to describe the order your data is stored in using the relavent labels from the CIL default labels: `channel`, `angle`, `vertical` and `horizontal`\n",
+    " - `ag.set_labels(['angle','vertical','horizontal'])`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Note on CIL AcquisitionGeometry:\n",
+    " - The geometry is described by a right-handed cooridinate system\n",
+    " - Positive angles describe the object rotating anti-clockwise when viewed from above\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parallel geometry\n",
+    "\n",
+    "Parallel beams of X-rays are emitted onto 1D (single pixel row) or 2D detector array. This geometry is common for synchrotron sources.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data. Note that for 3D geometry we need to describe a 2D panel where `num_pixels=[X,Y]`\n",
+    "\n",
+    "```python\n",
+    "parallel_2D_geometry = AcquisitionGeometry.create_Parallel2D()\\\n",
+    "    \n",
+    "                                          .set_panel(num_pixels=10)\\\n",
+    "        \n",
+    "                                          .set_angles(angles=range(0,180))\n",
+    "\n",
+    "\n",
+    "parallel_3D_geometry = AcquisitionGeometry.create_Parallel3D()\\\n",
+    "    \n",
+    "                                          .set_panel(num_pixels=[10,10])\\\n",
+    "        \n",
+    "                                          .set_angles(angles=range(0,180))\n",
+    "```\n",
+    "Both 2D and 3D parallel-beam geometries are displayed below. Note that the detector position has been set, this is not necessary to describe and reconstruct the data, but it makes the displayed images clearer.\n",
+    "\n",
+    "`show_geometry()` can be used to display the configured geometry and will be used here extensively. You can also print the geometry to obtain a detailed description. If `show_geometry` is not passed an `ImageGeometry` it will show the default geometry associated with the `AcquisitionGeometry` \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example creating a 2D parallel-beam geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cil.framework import AcquisitionGeometry\n",
+    "from cil.utilities.display import show_geometry\n",
+    "\n",
+    "ag = AcquisitionGeometry.create_Parallel2D(detector_position=[0,10])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "show_geometry(ag)\n",
+    "\n",
+    "print(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example creating a 3D parallel-beam geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Parallel3D(detector_position=[0,10,0])\\\n",
+    "    .set_panel(num_pixels=[10,10])\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fan-beam geometry\n",
+    "\n",
+    "A single point-like X-ray source emits a cone-beam onto a single row of detector pixels. The beam is typically collimated to imaging field of view. Collimation greatly reduce amount of scatter radiation reaching the detector. Fan-beam geometry is used when scattering has significant influence on image quality or single-slice reconstruction is sufficient.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data.\n",
+    "\n",
+    "For fan-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin. They are specified as `[x,y]` coordinates.\n",
+    "\n",
+    "```python\n",
+    "cone_2D_geometry = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10])\\\n",
+    "    \n",
+    "                                      .set_panel(num_pixels=10)\\\n",
+    "        \n",
+    "                                      .set_angles(angles=range(0,180))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cone-beam geometry\n",
+    "\n",
+    "A single point-like X-ray source emits a cone-beam onto 2D detector array. Cone-beam geometry is mainly used in lab-based CT instruments.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data.\n",
+    "\n",
+    "For cone-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin and aligned in the Z-direction. They are specified as `[X,Y,Z]` coordinates.\n",
+    "\n",
+    "```python\n",
+    "cone_3D_geometry = AcquisitionGeometry.create_Cone3D(source_position=[0,-10,0], detector_position=[0,10,0])\\\n",
+    "    \n",
+    "                                      .set_panel(num_pixels=[10,10])\\\n",
+    "        \n",
+    "                                      .set_angles(angles=range(0,180))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-10,0],detector_position=[0,10,0])\\\n",
+    "    .set_panel(num_pixels=[10,10])\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an offset AcquisitionGeometry\n",
+    "\n",
+    "It is unusual to have a perfectly aligned CT system. One of the most common offsets is the rotation-axis. If this offset is described by the `AcquisitionGeometry` then it will be accounted for in the reconstruction. This saves having to pad your data to account for this.\n",
+    "\n",
+    "To specify the offset, you could either add an x-component to the `source_position` and `detector_position` or you can offset the rotation axis from the origin using `rotation_axis_position`.\n",
+    "\n",
+    "As with the `source_position` and `detector_position` this is the `rotation_axis_position` is specified in 2D with a 2D vector `[X,Y]` or 3D with a 3D vector `[X,Y,Z]`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we offset the rotation axis by -0.5 in X by setting `rotation_axis_position=[-0.5,0]`. You can see the rotation axis position is no longer a point on the source-to-detector vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10],\n",
+    "                                       rotation_axis_position=[-0.5,0])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a more complex AcquisitionGeometry\n",
+    "\n",
+    "We can also set up rotations in the system. These are configured with vectors describing the direction.\n",
+    "\n",
+    "For example a detector yaw can be described by using `detector_direction_x=[X,Y]`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10],\n",
+    "                                       detector_direction_x=[0.9,0.1]\n",
+    "                                       )\\\n",
+    "     .set_panel(num_pixels=10)\\\n",
+    "     .set_angles(angles=range(0,180))\n",
+    "     \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can set `rotation_axis_direction`, `detector_direction_x` and `detector_direction_y` by specifying a 3D directional vector `[X,Y,Z]`.\n",
+    "\n",
+    "For 3D datasets detector roll is commonly corrected with a dual-slice centre of rotation algorithm. You can specify `detector_direction_x` and `detector_direction_y` - ensuring they are ortogonal vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0],\n",
+    "                                       detector_direction_x=[0.9,0.0,-0.1],detector_direction_y=[0.1,0,0.9]\n",
+    "                                      )\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In 3D datasets we can tilt the rotation axis to describe laminograpy geometry by changing `rotation_axis_direction`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0],rotation_axis_direction=[0,-1,1])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The structure of an ImageGeometry\n",
+    "\n",
+    "ImageGeometry holds the description of the reconstruction volume. It holds:\n",
+    "\n",
+    " - The number of voxels in X, Y, Z: `voxel_num_x`, `voxel_num_y`, `voxel_num_z`\n",
+    " - The size of voxels in X, Y, Z: `voxel_size_x`, `voxel_size_y`, `voxel_size_z`\n",
+    " - The offset of the volume from the rotation axis in voxels: `center_x`, `center_y`, `center_z`\n",
+    " - The number of channels for multi-channel data\n",
+    "\n",
+    "You will also need to describe the order your data is stored in using the relevent labels from the CIL. The default labels are: `channel`, `vertical`, `horizontal_y` and `horizontal_x`\n",
+    " - `ig.set_labels(['vertical','horizontal_y','horizontal_x'])`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a simple ImageGeometry\n",
+    "\n",
+    "To create a default ImageGeometry you can use:\n",
+    " `ig = ag.get_ImageGeometry()`\n",
+    "\n",
+    "This creates an ImageGeometry with:\n",
+    " - `voxel_num_x`, `voxel_num_y` equal to the number of horizontal pixels of the panel\n",
+    " - `voxel_num_z` equal to the number of vertical pixels of the panel\n",
+    " - `voxel_size_x`, `voxel_size_y` is given by the horizontal pixel size divided by magnification\n",
+    " - `voxel_size_z` is given by the vertical pixel size divided by magnification\n",
+    "\n",
+    "\n",
+    " You can pass a resolution argument:\n",
+    " `ig = ag.get_ImageGeometry(resolution)` \n",
+    "\n",
+    " - `resolution=0.5` double the size of your voxels, and half the number of voxels in each dimension\n",
+    " - `resolution=2` half the size of your voxels, and double the number of voxels in each dimension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Note on CIL ImageGeometry:\n",
+    "At 0 degrees `horizontal_y` is aligned with the Y axis, and `horizontal_x` with the X axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "print(\"ImageGeometry - default\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "print(ig)\n",
+    "\n",
+    "print(\"ImageGeometry - 0.5x resolution\")\n",
+    "ig = ag.get_ImageGeometry(resolution=0.5)\n",
+    "print(ig)\n",
+    "\n",
+    "print(\"ImageGeometry - 2x resolution\")\n",
+    "ig = ag.get_ImageGeometry(resolution=2)\n",
+    "print(ig)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a custom ImageGeometry\n",
+    "You can create your own ImageGeometry with:\n",
+    "`ig = ImageGeometry(...)`\n",
+    "\n",
+    "Giving you full control over the parameters.\n",
+    "\n",
+    "You can also change the members directly to reduce the reconstructed volume to exclude empty space.\n",
+    "\n",
+    "Using the previous example, we now can specify a smaller region of interest to reconstruct. We can offset the region of interest from the origin by specifiying the physical distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "print(\"ImageGeometry - default\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "show_geometry(ag, ig)\n",
+    "\n",
+    "print(\"ImageGeometry - RoI\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "ig.voxel_num_z = 100\n",
+    "show_geometry(ag, ig)\n",
+    "\n",
+    "print(\"ImageGeometry - Offset RoI\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "ig.voxel_num_z = 200\n",
+    "ig.center_z = -1024 * ig.voxel_size_z\n",
+    "show_geometry(ag, ig)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also create an `ImageGeometry` directly.\n",
+    "\n",
+    "Here we create our ig independently of an `AcquisitionGeometry`, by first importing `ImageGeometry` from `cil.framework`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cil.framework import ImageGeometry\n",
+    "\n",
+    "ig = ImageGeometry(voxel_num_x=1000, voxel_num_y=1000, voxel_num_z=500, voxel_size_x=0.1, voxel_size_y=0.1, voxel_size_z=0.2 )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cf07678abc5cc77bc6e1a7d19b1e87ab0c29b83e7ee41c2bc72506d16d80ed44"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/v24.2.0/_sources/demos/callback_demonstration.ipynb.txt b/v24.2.0/_sources/demos/callback_demonstration.ipynb.txt
new file mode 100644
index 0000000000..0fd142943d
--- /dev/null
+++ b/v24.2.0/_sources/demos/callback_demonstration.ipynb.txt
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2024 -  United Kingdom Research and Innovation\n",
+    "#  Copyright 2024 -  The University of Manchester\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:    CIL contributors "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CIL Callback demonstration\n",
+    "\n",
+    "This notebook runs on CIL Master (built on 14/03/2024) and demonstrates the new callback functionality "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cil.utilities import dataexample\n",
+    "from cil.utilities.display import show2D\n",
+    "from cil.recon import FDK\n",
+    "from cil.processors import TransmissionAbsorptionConverter, Slicer\n",
+    "from cil.utilities.quality_measures import psnr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "from cil.plugins.tigre import ProjectionOperator\n",
+    "from cil.optimisation.algorithms import FISTA, Algorithm\n",
+    "from cil.optimisation.functions import LeastSquares, IndicatorBox, ZeroFunction, TotalVariation\n",
+    "from cil.optimisation.operators import GradientOperator\n",
+    "from cil.optimisation.utilities import callbacks\n",
+    "from cil.framework import DataContainer\n",
+    "\n",
+    "from cil.utilities.quality_measures import mse, mae, psnr\n",
+    "\n",
+    "# set up default colour map for visualisation\n",
+    "cmap = \"gray\"\n",
+    "\n",
+    "# set the backend for FBP and the ProjectionOperator\n",
+    "device = 'gpu'\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FDK recon\n",
+      "\n",
+      "Input Data:\n",
+      "\tangle: 60\n",
+      "\thorizontal: 128\n",
+      "\n",
+      "Reconstruction Volume:\n",
+      "\thorizontal_y: 128\n",
+      "\thorizontal_x: 128\n",
+      "\n",
+      "Reconstruction Options:\n",
+      "\tBackend: tigre\n",
+      "\tFilter: ram-lak\n",
+      "\tFilter cut-off frequency: 1.0\n",
+      "\tFFT order: 8\n",
+      "\tFilter_inplace: False\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e75840>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#%% Load data\n",
+    "ground_truth = dataexample.SIMULATED_SPHERE_VOLUME.get()\n",
+    "\n",
+    "data = dataexample.SIMULATED_CONE_BEAM_DATA.get()\n",
+    "twoD = True\n",
+    "if twoD:\n",
+    "    data = data.get_slice(vertical='centre')\n",
+    "    ground_truth = ground_truth.get_slice(vertical='centre')\n",
+    "\n",
+    "absorption = TransmissionAbsorptionConverter()(data)\n",
+    "absorption = Slicer(roi={'angle':(0, -1, 5)})(absorption)\n",
+    "\n",
+    "ig = ground_truth.geometry\n",
+    "\n",
+    "#%%\n",
+    "recon = FDK(absorption, image_geometry=ig).run()\n",
+    "#%%\n",
+    "show2D([ground_truth, recon], title = ['Ground Truth', 'FDK Reconstruction'], origin = 'upper', num_cols = 2)\n",
+    "\n",
+    "# %%\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Default behaviour "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6355b4a0be0c4297a02f41b49e6f7e3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e74b80>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "alpha=0.1\n",
+    "A = ProjectionOperator(image_geometry=ig, \n",
+    "                       acquisition_geometry=absorption.geometry)\n",
+    "\n",
+    "F = LeastSquares(A = A, b = absorption)\n",
+    "G = alpha*TotalVariation(lower=0)\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G)\n",
+    "algo.run(500)\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other provided CIL callbacks "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43acfefb45534d8093aa0174846f19eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    29.53it/s, objective=+8.586e+01\n",
+      "    20/500    29.53it/s, objective=+9.047e+00\n",
+      "    30/500    28.65it/s, objective=+2.640e+00\n",
+      "    40/500    28.53it/s, objective=+1.188e+00\n",
+      "    50/500    18.75it/s, objective=+6.928e-01\n",
+      "    60/500    21.45it/s, objective=+4.585e-01\n",
+      "    70/500    23.55it/s, objective=+3.451e-01\n",
+      "    80/500    25.28it/s, objective=+2.741e-01\n",
+      "    90/500    26.58it/s, objective=+2.337e-01\n",
+      "   100/500    27.57it/s, objective=+2.062e-01\n",
+      "   110/500    28.17it/s, objective=+1.870e-01\n",
+      "   120/500    20.05it/s, objective=+1.729e-01\n",
+      "   130/500    22.35it/s, objective=+1.623e-01\n",
+      "   140/500    24.32it/s, objective=+1.541e-01\n",
+      "   150/500    25.89it/s, objective=+1.476e-01\n",
+      "   160/500    26.91it/s, objective=+1.421e-01\n",
+      "   170/500    27.82it/s, objective=+1.375e-01\n",
+      "   180/500    28.58it/s, objective=+1.336e-01\n",
+      "   190/500    20.24it/s, objective=+1.303e-01\n",
+      "   200/500    22.51it/s, objective=+1.275e-01\n",
+      "   210/500    24.47it/s, objective=+1.250e-01\n",
+      "   220/500    26.06it/s, objective=+1.228e-01\n",
+      "   230/500    27.32it/s, objective=+1.208e-01\n",
+      "   240/500    28.08it/s, objective=+1.190e-01\n",
+      "   250/500    28.55it/s, objective=+1.173e-01\n",
+      "   260/500    28.69it/s, objective=+1.159e-01\n",
+      "   270/500    28.86it/s, objective=+1.145e-01\n",
+      "   280/500    29.03it/s, objective=+1.134e-01\n",
+      "   290/500    29.44it/s, objective=+1.123e-01\n",
+      "   300/500    29.27it/s, objective=+1.113e-01\n",
+      "   310/500    29.51it/s, objective=+1.104e-01\n",
+      "   320/500    21.28it/s, objective=+1.096e-01\n",
+      "   330/500    22.71it/s, objective=+1.088e-01\n",
+      "   340/500    24.61it/s, objective=+1.081e-01\n",
+      "   350/500    26.25it/s, objective=+1.075e-01\n",
+      "   360/500    27.47it/s, objective=+1.068e-01\n",
+      "   370/500    28.42it/s, objective=+1.063e-01\n",
+      "   380/500    29.10it/s, objective=+1.057e-01\n",
+      "   390/500    20.90it/s, objective=+1.052e-01\n",
+      "   400/500    23.14it/s, objective=+1.048e-01\n",
+      "   410/500    24.79it/s, objective=+1.043e-01\n",
+      "   420/500    25.48it/s, objective=+1.039e-01\n",
+      "   430/500    26.67it/s, objective=+1.035e-01\n",
+      "   440/500    27.03it/s, objective=+1.031e-01\n",
+      "   450/500    27.67it/s, objective=+1.028e-01\n",
+      "   460/500    27.81it/s, objective=+1.025e-01\n",
+      "   470/500    28.66it/s, objective=+1.022e-01\n",
+      "   480/500    29.28it/s, objective=+1.019e-01\n",
+      "   490/500    29.76it/s, objective=+1.016e-01\n",
+      "   500/500    30.16it/s, objective=+1.013e-01\n",
+      "   500/500    26.07it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e75030>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)\n",
+    "algo.run(500, callbacks=[callbacks.ProgressCallback(), callbacks.TextProgressCallback()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d4cfa5b60cf48b7acfd008e3c40655f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " 84%|########3 | 501/600 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "   501/600        ?it/s\n",
+      "   511/600    30.76it/s, objective=+1.011e-01\n",
+      "   521/600    30.74it/s, objective=+1.008e-01\n",
+      "   531/600    30.69it/s, objective=+1.006e-01\n",
+      "   541/600    30.26it/s, objective=+1.004e-01\n",
+      "   551/600    28.95it/s, objective=+1.002e-01\n",
+      "   561/600    20.01it/s, objective=+1.000e-01\n",
+      "   571/600    22.57it/s, objective=+9.986e-02\n",
+      "   581/600    24.69it/s, objective=+9.969e-02\n",
+      "   591/600    26.41it/s, objective=+9.953e-02\n",
+      "   600/600    26.40it/s, objective=+9.938e-02\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "algo.run(100, callbacks=[callbacks.ProgressCallback(), callbacks.TextProgressCallback()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Early stopping (a custom callback example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    31.71it/s, objective=+8.586e+01\n",
+      "    20/500    31.18it/s, objective=+9.047e+00\n",
+      "    30/500    29.77it/s, objective=+2.640e+00\n",
+      "    40/500    30.06it/s, objective=+1.188e+00\n",
+      "    50/500    30.28it/s, objective=+6.928e-01\n",
+      "    60/500    30.35it/s, objective=+4.585e-01\n",
+      "    70/500    30.46it/s, objective=+3.451e-01\n",
+      "    80/500    30.57it/s, objective=+2.741e-01\n",
+      "    90/500    22.27it/s, objective=+2.337e-01\n",
+      "   100/500    22.69it/s, objective=+2.062e-01\n",
+      "   110/500    24.61it/s, objective=+1.870e-01\n",
+      "   110/500    26.86it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08d5daaec0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class EarlyStopping(callbacks.Callback):\n",
+    "         def __call__(self, algorithm):\n",
+    "               if algorithm.objective[-1] <= 2e-1:  # arbitrary stopping criterion\n",
+    "                  raise StopIteration\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[callbacks.TextProgressCallback(), EarlyStopping()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    23.79it/s, objective=+8.586e+01\n",
+      "    20/500    26.96it/s, objective=+9.047e+00\n",
+      "    23/500    26.89it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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dY59Dc3MzkslkqAG54447sGbNGvznf/5n6LmpVAq5XK7is66uLrS1tY3L8zlr1izU1dWhq6trh8tqN0uJx+MAgKGhIXfvurq6cce1tbXt8D3DMNW0N1PF9urp4eHh4THzcfjhh2PRokUVP1MB7bLuRwJsJUp/8YtfYHBwEEuWLJnyvh9/93d/hzVr1uD3v/89vve97+GAAw7ARRddhIceesgds3HjRvT09CAWi1UE06PRKDZs2OBIa+ZHnyhFCe1+mO2cPXv2uHlBKpVCIpGo+Cwej1fkhj/nnHNw66234sUXX8SZZ56JWbNm4fjjj8fKlSsn3Q47a8s3b948rRuDTrWd7FwB2NpOfq7g4eHhsX+ho6MDb3nLW3DHHXegVCqhs7MTP/7xj/Hud7+7gjgOQyQSwS9/+Uu87W1vw3XXXYfXvva1aGlpwf/4H/8D/f39AKZun3YHpso5eJvp4bFn4El0j30OtbW1ePOb34xHHnkE69evr/juiCOOwKJFi3DUUUeFnmuNFrDVQG3cuHGcgm7Tpk0YGRlx+dLoINuNP3aWZB8ZGRl3jQ0bNuzwNcMQVu9EIjGuLgB2SB3n4eHh4bF/IwgC/OhHP0I6nQ4l34899lj84he/cDlBn3vuuUlfO5/PY9GiRTj++OPxwQ9+EA888ACi0SiWLl3q1GfNzc1oamoap6Tnzw033ABg254eE20sRsfVzjEAYN26dTucR/UjH/kIHn74YfT29uInP/kJgiDA3//9309aVRZmy4GtTnWYPbdzi5aWlu1uqDYV7Kp28vDw8PDY93Heeeehu7sb9913H77//e+jXC7jvPPOm9S5HR0duOWWW7BhwwY899xzuOSSS3DDDTe4jT53xj7tCp+fZZoM5+Dh4bFn4Ul0j30Sl19+OUZHR3HBBRdgeHh4p671lre8BQMDA/iP//iPis+/+93vuu+BrUuoEokEnnjiiYrj7rvvvh2+90knnQQA+Pd///eKz8N2K59uHHDAAfjv//7viglCV1cXHn744YrjvKrcw8PDw2N7WL58OZ5++mlcfPHF41TZxGtf+1r88pe/RKlUwkknnYRnn312h+51yCGH4LLLLsOTTz6Ju+++GwDw93//9+jq6sLo6Og4Nf2iRYtw6KGHAgBOOeUU1NbW4sYbb6x6/cWLFyOZTOL73/9+xecvv/wyfvWrX7l5wY4inU7j1FNPxZVXXolyuYynnnoKwI7b2wMOOGDc3ORXv/oVBgYGKj479dRT8etf/3rCAMZUysDUMrad1qxZg2eeeWan28nDw8PDY9/FP/7jP6KpqQm33norbrvtNrzqVa+q2JR8snjVq16Fz3zmMzjqqKPw2GOPAdg5+zQVn38qNnOynIOHh8eexeQTWXp47EV4wxvegG9+85v4xCc+gde+9rX4p3/6JyxcuBA1NTVYv3497rnnHgAYl7olDB/60IfwzW9+E+eeey5eeOEFHHXUUXjooYfwpS99Caeddhre+ta3AtiqAPvgBz+IW2+9FQcddBBe/epX4//9v/+3U4T3Kaecgje96U247LLLUCgUsGjRIvzud7/D9773vR2+5mRxzjnn4KabbsIHP/hBnH/++ejq6sJ11103rs2y2Sw6Ojpw33334S1veQsaGxvR3NyMAw44YJeX0cPDw8NjZqGnpwe///3vAQCFQgHPPfcc7rrrLvz2t7/Fe97zHixfvnzC84855hj88pe/xFve8hacdNJJ+NWvfrVDObr/5V/+Bd/61rewfPlyvOc978F73/te/Pu//ztOO+00XHzxxXjd616HaDSKl19+Gb/+9a9x+umn44wzzsABBxyAK664Ap///OcxNDSE973vfcjn83j66afR2dmJ5cuXo76+HldddRWuuOIKfOhDH8L73vc+dHV1Yfny5UgkErj66qunXN7zzz8fyWQSb3jDG9De3o4NGzbgmmuuQT6fx3HHHQcAOPLIIwEAN998M7LZLBKJBBYsWBC6pFtxzjnn4KqrrsJnP/tZLFmyBE8//TS+8Y1vIJ/PVxz3uc99Dj/72c/wpje9CVdccQWOOuoo9PT04P7778ell16Kww47DAcddBCSyST+/d//HYcffjgymQxmz549LkUPABx66KH4p3/6J3z9619HTU0NTj31VLzwwgu46qqrMG/ePFxyySVTbicPDw8Pj/0D8XgcH/jAB/D1r38dQRDgy1/+8qTOe+KJJ3DRRRfh3e9+Nw455BDEYjH86le/whNPPIFPf/rTAHbOPk3F5+fq96997Ws499xzEY1Gceihh4ampJks5+Dh4bGHsSd3NfXw2NV4/PHHg4985CPBggULgng8HiQSieDggw8OPvShDwW//OUvK46daJfwrq6u4IILLgja29uDurq6oKOjI7j88suDYrFYcVxvb2/wsY99LGhtbQ3S6XTwzne+M3jhhRfG7dTNHcc3b95ccf5tt902bgfvnp6e4KMf/WhQX18fpFKp4OSTTw6effbZcdfcHnjtNWvWbLccxHe+853g8MMPDxKJRHDEEUcEd99997gdyoMgCH7xi18Er3nNa4J4PB4ACM4999wp19PDw8PDY2YizH6EoaOjIwAQAAgikUiQyWSCQw89NDjnnHOCn//856HnAAguvPDCcZ//6U9/Cpqbm4PW1tbgqaeemvCe73jHO0K/++Y3vxkACL7zne8EQRAEw8PDwVe/+tXg1a9+dZBIJIJMJhMcdthhwcc//vHgz3/+c8W53/3ud4PjjjvOHfea17wmuO222yqO+bd/+7fg6KOPDmKxWJDP54PTTz99XFmrzS1oH4nvfOc7wUknnRS0trYGsVgsmD17dvCe97wneOKJJyrOW7FiRbBgwYKgtrY2AODKtGTJkmDhwoWh7VAqlYLLLrssmDdvXpBMJoMlS5YEjz/+eNDR0eHsNbF27drgox/9aNDW1hZEo1FXjo0bN7pj7rzzzuCwww4LotFoxVzE1ikIgmB0dDS49tprg1e96lVBNBoNmpubgw9+8IPB2rVrK46rVv6wOYeHh4eHx96NifxuxZ/+9KcAQFBbWxusW7duUtfeuHFj8OEPfzg47LDDgnQ6HWQymeDoo48O/vf//t/ByMiIO26y9inMDk3W5w+CILj88suD2bNnBzU1NQGA4Ne//nUQBFvt3pIlSyqOnSznUG3uFGbXPTw8pheRIDBJlzw8PDw8PDw8PDw8PDw8PDw8PDw8PDw8APic6B4eHh4eHh4eHh4eHh4eHh4eHh4eHh5V4Ul0Dw8PDw8PDw8PDw8PDw8PDw8PDw8PjyrwJLqHh4eHh4eHh4eHh4eHh4eHh4eHh4dHFewzJPoNN9yABQsWIJFI4Nhjj8Vvf/vbPV0kDw8PDw8Pj+3A228PDw8PD4+9D95+e3h4eHjsb9gnSPS7774bn/zkJ3HllVfij3/8I974xjfi1FNPxUsvvbSni+bh4eHh4eFRBd5+e3h4eHh47H3w9tvDw8PDY39EJAiCYE8XYmdx/PHH47WvfS1uvPFG99nhhx+Of/zHf8Q111yzB0vm4eHh4eHhUQ3efnt4eHh4eOx98Pbbw8PDw2N/RN2eLsDOolwu49FHH8WnP/3pis9POeUUPPzww6HnlEollEol9//Y2Bi6u7vR1NSESCSyS8vr4eHhsa8iCAL09/dj9uzZqKkJX+hULBZRLpd36j6xWAyJRGKnruGx5+Htt4eHh8fMwEy13zfccAO+8pWvYP369Vi4cCFWrFiBN77xjVWPX7VqFS699FI89dRTmD17Ni677DJccMEFFcfcc889uOqqq/DXv/4VBx10EL74xS/ijDPOCL3eNddcgyuuuAIXX3wxVqxYMely7+vw9tvDw8NjZmCm2u99GXs9id7Z2YnR0VG0trZWfN7a2ooNGzaEnnPNNddg+fLlu6N4Hh4eHvsd1q5di7lz5477vFgsYsGCBVXH5smira0Nzz//vDfkezm8/fbw8PCYWZhJ9pvpQm644Qa84Q1vwE033YRTTz0VTz/9NObPnz/u+Oeffx6nnXYazj//fHz/+9/H7373OyxduhQtLS0488wzAQCrV6/G2Wefjc9//vM444wzcO+99+I973kPHnroIRx//PEV11uzZg1uvvlmHH300TtV530R3n57eHh4zCzMJPu9r2OvJ9EJG8EOgqBqVPvyyy/HpZde6v7v7e0NnYx5eHh4eEwd2Ww29PNyuYwNGzbgpZdeQi6X26Fr9/X1Yf78+SiXy96I7yOYDvt9yimnYGxsDKVSCWNjYxgeHsbo6CgikQhisRhqa2uh2ev4fRAEGBsbc/cNggA1NTWIxWKIRqOora1FPB5HbW0tIpEIIpEIgiBAqVTCyMgISqUSCoUCRkZGEI/HUVdXh7q6OkSjUUSjUYyNjWF0dLTid01NjTu2XC5jaGjIlY3f895avrq6OtTW1ro68HMiGo2irq4OkUgE0WgUNTU1GB4edm0yOjrqyjA8PIyxsTEMDQ1hYGAAQRAgkUggFoshFoshl8shHo8jm82ivr4eNTU1rg3GxsZQLpcrzh8ZGcHQ0BCKxSJGRkbQ39+PkZERV7ba2lqk02nE43FEIhHU1W2dfpZKJRSLRdTW1iKfzyOZTLq6RiIRlEolDA4OuvqyzmwHtjfLV1dX554L+8PIyEhFPxgbG6soW01NDUZHR92xfH5U8/CZj4yMYGxsDHV1dYjFYu7zsH7MvsT2HhsbQ7FYxOjoqHtOLC/bghgZGcHo6GhFnWtqalx5tFzxeBzRaBSjo6PumbKfajsODw+7fspr8DnYd43X5D35vf5mm/PdYJsPDw+jr68P/f3948qqZWb78Scej7vxvFgsumsVCgUMDw8jmUwil8uhtrYWiUQC8Xi84n0vlUoYGhpy92Sb8vnwmerz4bvHtmZd8/k8EokEamtrEY1GXdn5DvX396NYLFb0R7YV+xbHsFgsVvE+K1hu3l/LxX5YW1vr2jkej1e8h+VyGaVSyfU1he0D6XQaqVTKnc92HhgYcH1/aGgIjzzyyIyy39dffz3OO+88fOxjHwMArFixAj//+c9x4403hqYL+da3voX58+c7xfjhhx+ORx55BF/96lcdib5ixQqcfPLJuPzyywFstSmrVq3CihUrcOedd7prDQwM4AMf+AC+/e1v4wtf+MIO1Xd/gPe/px8clznm7CgSiQTS6TRqa2vdWKJzEdrXsbGxcWPIVMHrq93gWDYZ0OaMjo5O+hyP7UPtbCQSqXhGbPORkRE3p2PfmApocznXmOh82jWdU+0K0H6zz7OP0z7vTdDnZuf2FjPJfu/r2OtJ9ObmZtTW1o6LrGzatGlcdJzgZNTDw8PDY/qxvWW5uVxuh424x76D6bTfSlaR5FUikuQ4QZKb3/P3yMhIBenFc0hk0hHh95FIBCMjI6itrXUEtJLkSl7TOaTDwe/r6uoc4aqkJbCNsARQUX4lVZVk5wSbhLEeUy6XxxEco6Oj7v7qtA8PDzsnuFwuVxDVLD/blGVneekAkwxmnVk3/ZvtxjLZNmAQg+RiJBIZRxjzOZKc1nLRYeO9eP8w4pqEpBLNdAxJQmjbK4nOY+iQ0lFj/YMgcPePxWKOEOUzIxHKsnPJLf9XUtRCz2P51GHmeUqgav20/7HN+by1nxH8jvXX/s2gD8ujfVDvSXJan4c+JyWR2F58t/g578PnnEqlKsrH91nfXb4LY2NjjpDX95/twefNcrKv8V58dy0JoH1Lz2dQjs9LgyM6Tmk7su9HIhEX4NI+wHZiO/B67I/armEkPo/htfj9TLHfO5IuZPXq1TjllFMqPnvb296GW265BcPDw4hGo1i9ejUuueSSccfYVC0XXngh3vGOd+Ctb32rJ9FDMN3+t44Tu4pYmyxsQHR3gmMug5M7Q3AzQMYxQOcSatc4Du1Mu2vgmOWdSrlVzOAxPQjrx2ovaKc12L8j7a9ij8key/5ixQjTCZ03hQWb9yaw3fRZhWGm2O/9AXs9iR6LxXDsscdi5cqVFfnsVq5cidNPP30PlszDw8PDIww7M5nZmydBHpWYTvut5Bf/J4lLdbQSgpbcA7aq0y2o1gLGk6okP+mYqnJUSSmea/su/yfhx89I5FKNyuuTYCNJbpVqdEj0/nV1dRXKLlXmAtuIZxKCqoLm5yTys9ksksmkCzLo/ZU4VhLT5mBkwMESqGx/1lnbjOVgGwwPD2NoaKhClU4iWNtPCXl1LJRM5vF0StgHSNhT9QzAqa55rCoE+dx5H5IfxWLRKe1JZFLJblcc8BlZ50jrxf9JArNN9Fh+Z1dZKEnCZ6dErPYjliOMeOVvrshg2anO5nNUMp7nKLGvgQ2Wh/dlWWKxmOunLAtVc6pki0ajSCQSFUEa7cvavtoOVvHJe5CQ1uCSElp2dYIGMGyQSD/T56jKMr4/9hp6vvYTS0Ro2+k1dEzk8ao4tNeZDKbDfvf19VV8Hkau7ki6kA0bNoQePzIygs7OTrS3t1c9Rq9511134bHHHsOaNWumVsH9CNNpv+1KL2DPEulalt0559RxkbZmZ8CxuKamBslk0tkdDZJy/N/Ze+3sNTi/8Zg+VAtm0I7rnHdn+rna1+1hdz9ntZE78z4rGb+7oQEHPrOdscHe/54e7PUkOgBceumlOOecc7Bo0SIsXrwYN998M1566aVxG8l4eHh4eOx5eCPuQUyX/Wa/INFEB1QVsduDTflir23/tgrfMAWIkufV1MRKwIURqNX+tpN6EmNhaUYmqr+WUQlbOh5WWR7WDkpeKqnIYIa2l4UqhPk/yXt7niW9+YzpmCkhqu0edj/+besymTGmGtEdRgKpAl/7gC2XPv+wvmAdz2pBmWqwfdEGkey1WJeJnEdte1VS6zVsfcOeh7UJ+m7Z+tlnr+cogU1oX+L/YWWz7WHfBf1Mj+H1daWDEui2v00WLJ8lF23ZJoL2PQs9f6KVDhOdN1XwvHnz5lV8fvXVV2PZsmWh59hyaZ+c7PH284muuXbtWlx88cV44IEH/JL17WBX+N82ULe/QvvtzhKcNk2Hrqzx2P+wPzz3iez2VDEVu72rMF1l8P739GGfINHPPvtsdHV14XOf+xzWr1+PI488Ej/96U/R0dGxp4vm4eHh4WHgjbgHMV32u1wuV5DZVK5STatKayX+lGCnwnsy4DUsQUUFrqpprbpMiTGWk2UhVHlNKNmsqh++E8wjbVM3aAoWVbGoIlbrpAQd80aTjKNSXFXZqsxnSgxeT/+nCk7LrasD+GxUWauKfn1ubA8uVyd5rmk8SIzZNCfavtq2LL8eq3nmw5S7YaS3qopjsZhT/ekqBu0/WoaJ+oLNMW9TmmibaX/hvWOxWGjAgPsDaJ+bSFVG9TRXBQwPD7u0P2EpTmzfZzvo82B76LOwbR2W/1NXM2ieep5HJSYDaxaWTOLffDbDw8Pu2dq0THb1BVdnMKUSUxVp8EQJLQUDVCyPVaGr0s+OYWHPR99J/qbivVrf350k+tq1ayuWlIel+NiRdCFtbW2hx9fV1aGpqWnCY3jNRx99FJs2bcKxxx7rvh8dHcVvfvMbfOMb33DpMTymz35zFYgGX4HxK2h2F3aWdNuZ+3IM5bjD8WBn3zuO8zpPsjbEw2NPQIO409UX2efZz3fkumEB7D0BXdGn4oEdKZf3v6cP1WUoexmWLl2KF154AaVSCY8++ije9KY37ekieXh4eHjMANxwww1YsGABEokEjj32WPz2t7+d8PhVq1bh2GOPRSKRwIEHHohvfetb44655557cMQRRyAej+OII47AvffeW/H9jTfeiKOPPtrln1u8eDF+9rOfVRwTBAGWLVuG2bNnI5lM4sQTT8RTTz218xXeyzAd9psOIrAtjzhTBCQSCZdT2JK2uqmgnqOpWZSEBcZPQtX5V1Wufh+26RavyVQUJGztfTRPMkEiT/MjDg8Pu00ZNfWE5nhWQprfq2LWqryZK5wbDw4NDWFwcNClKeGmnXTMSdbZ1CXMCU1SWRHWfqrstkRiWDswnYjWn20Tpra1bUrSkeVlHTT/N89TVZ+SHiSStYx8tny+JCuV+LZpVjT9CaH3CUvFYdXZ2r9Yb62XpvFhfnlNU6MKf/2c9+X1WW9bfy0Hy6CbwIapvnU1gb4zSvaTFLfX5/urG6Wyv9j3xN43TKkMYFy9bRvpD7BtTwL2c21vGzDQa9l2VxU7y6X9xT4jfR/s2MI20dzoNnDDZ6Mbr+5q0C7yp9o+F0wXoli5ciVOOOGE0OsuXrx43PEPPPAAFi1a5OpX7Rhe8y1veQuefPJJPP744+5n0aJF+MAHPoDHH3/cE+gG0+1/61isQefdicmu9NhV96ZdU7uws+AYzc28i8WiG/s9OeaxpzHd77l9j3aEQA9LB7gnoKn/wlbbeewZ7DMkuoeHh4fH3gF1UHbkZyq4++678clPfhJXXnkl/vjHP+KNb3wjTj31VLz00kuhxz///PM47bTT8MY3vhF//OMfccUVV+B//I//gXvuuccds3r1apx99tk455xz8Kc//QnnnHMO3vOe9+APf/iDO2bu3Ln48pe/jEceeQSPPPII3vzmN+P000+vIMmvu+46XH/99fjGN76BNWvWoK2tDSeffDL6+/un2KIeJJaUNFNy0uYIVjVWGKlFqIocCE/xoNdVx5+YiKhT8HwlvbaXtsCWxZLEWh9V7VqSNYwY4nW1PS0hqd9pWZTc1OtZUjsM1nGxba3L/fl9teuwLbRsLL8tu5KXGnQII31tnWzf0TKGEUFKfoddU3+rAiksRQxh+xyPCyNT7XmWjFeldTUSWX9bZbZtAy1vWDBJn7OS9GHvokKfka2D9gu9tgYhbLtp2+k9tcx6jPYTfafsqgftb2Hkt71PWLuEEfj2WfMa1RxuG2yZ7Nhk22d32e9LL70U//Zv/4Zbb70VzzzzDC655JKKdCGXX345PvShD7njL7jgArz44ou49NJL8cwzz+DWW2/FLbfcgn/5l39xxzBVy7XXXotnn30W1157LX7xi1/gk5/8JAAgm83iyCOPrPhJp9NoamrCkUceOaXye0wetp+Hvcf7E3YViR/2Tu7P7ewxM7Ar+vvOEuB7KpBGaPntqsMdLdfutN/7OvaJdC4eHh4eHnsPdsYYT/W866+/Hueddx4+9rGPAQBWrFiBn//857jxxhtxzTXXjDv+W9/6FubPn48VK1YAAA4//HA88sgj+OpXv4ozzzzTXePkk0/G5ZdfDmCrI79q1SqsWLECd955JwDgne98Z8V1v/jFL+LGG2/E73//eyxcuBBBEGDFihW48sor8a53vQsA8J3vfAetra2444478PGPf3xK9dzfwQ0OmdLBqthILFHRXFNTg+Hh4QqnnSkyqpF8NpUIyT4qHHktS24qERiWzkQ30mSaDKumnghM86CbCDLVhl6/rq4OqVSqQpnDlBNjY1s3UGX5VfHMpfYkW6litmlOVDmnhJ6myLApTCw5bYMdbD8lp/l8WX62JaFBAUvG1NRs3XCSz17bx+a0VkW0JXRI2pIc1fJrn1P1v32O7Gv8zWszWKHkpgYzqvVP3pfPhPdmahT+UM2t9QDgVImjo6Mol8uhKyj43tTW1lYQ51wFwfta0l77Cz/TNuV92U9tO0UiEadW5jthyWJtI20rtj9TGbG+QRC4Mun7rM9Gr62ktZZVAyWsK9uc74amldF+wLJpQI+rO/ReHJ/4bGwqKT4bvs9cURGPxyvqpCl4qgVxtofdab+3ly5k/fr1FQHxBQsW4Kc//SkuueQSfPOb38Ts2bPxr//6r852A8AJJ5yAu+66C5/5zGdw1VVX4aCDDsLdd9+N448/fofq5DE9sMSQBon3N0ynAn2y198f23lvwvZEA3szrHBgurAz/XtPjz12/qxB9J21wbvLfu/r8CS6h4eHh8duxe4y4uVyGY8++ig+/elPV3x+yimn4OGHHw49Z/Xq1TjllFMqPnvb296GW265xeWbXr16NS655JJxx5B4txgdHcUPfvADFAoFLF68GMBWxfuGDRsq7hWPx7FkyRI8/PDDnkSfIlQRa1MaaI5oJcpJntk84fo9rwFUkqDaD5XItSppe+z21NdKirG8VlFbTeWu11dCn+fwmvxcyV5bNz2f3zHfO48Py93NczWfa7V2qKbu5vc2jYVVFW1Ppa/3pAOihCjbRclqqyjWNgqDLRvJTj1Xr1eNtNR+x//tWGeJfBuMCLu2loHn6UavYep4Pld7f1V723Q2umxaVzXYdrD9XOtZrR+zP2kb6/XtKhHbRjxHyXNe07ZVWPux3vousu+oelzLpEEWbaOwOtr+oc9S66EpfPTd07QvGqTQfmjLou/qVIm63e2EL126FEuXLg397vbbbx/32ZIlS/DYY49NeM2zzjoLZ5111qTL8OCDD076WI8dh7V1+zt25P2c6vU9PPYkdnWwTOc+exvC5vo721aeRJ8+eBLdw8PDw2OvQ19fX8X/zGWt6OzsxOjo6LhNyFpbW8dtLEZs2LAh9PiRkRF0dnaivb296jH2mk8++SQWL16MYrGITCaDe++9F0cccYS7D8+z13nxxRcnqrpHFSi5B1Qqfa0jqqpvnqskq5LxlgTU+4VNSFXFrLDqYlWYkNTTVBuxWMwpTxkgUOLLkmgsd7WcvWwDqmX5GcukOattyg+qaUngheV6VvJSScCwzQ+rQVOw8H+qxan4r6urc20SBIFTTetzsrnftR4anCA5GUaS2xQm+vxYvmg06tS+fCaaIobltOlAlDC36YW4qoKqYV6Pbc0862wLBfu6BgMsMcV+wmeq5dX3IawP66aovJdNP6TKc+1LqvQHKh1E9nMtI+9nN9jUuujqB/3NMoblQteNdmOxWIUqXvtcmALMbsypq0h4jirebcoVfTY6vugqF5uqhd9r2/B+3OdB08ros2DAhOrzsbExlMtlt0qFz0JXJ3h4eOwYwgjvnSGebKBzOq7psWuwLyvFdyV2ZXvt7c/CrhjZ0+p4j0p4Et3Dw8PDY7diOiLh8+bNq/j86quvxrJly0LPsU7I9tQ9YcfbzydzzUMPPRSPP/44enp6cM899+Dcc8/FqlWrHJG+I2XzCIeSXfxfVaJWBaufk7RUlTYJMCWclawOU7YqgatpQmwZgfEEIs8jUczf5XK5Qn1Lgk4JMKY2Ccu3rE64/k0CnMSakqGW8GUZleQjwa/pSrb3XGxqFw0gRCIRt6Er7xEEQUW6jng87tKIAHApaJhSRJ8x02xY0pMgcUpYFXg1ZT6P5TNLpVJIpVIViuhisYhCoeDS8pRKJdTV1SGRSIxTMzOYo32TZeP55XLZ9Se2k6bpYKBIiVENgljiWQMCuhEqny8J1draWiQSiYpy2efHclkS3eaUr7axrvYvvocaVIrH4+OCOXwGANxz1/5p1ddWmc7yMgBSV1fn+hCP0WvwPkwRw2ux7lo/lqXaigP7vNU51rRIrJf2FSX5WQ+2jz531k+DDNxwl/1kdHS0YhNjpuOZDLySzWN/w2TUrGHj2nSQXnbe6Ym0mQcb7NgVz8c/86ljb35XdlXZvf2ePngS3cPDw8Njt2I6jPjatWuRy+Xc51aFDgDNzc2ora0dpxDftGnTOAU40dbWFnp8XV0dmpqaJjzGXjMWi+Hggw8GACxatAhr1qzB1772Ndx0001oa2sDsFWR3t7ePqmyeVSHdV5tWgQiLJ2LKlzt8STSwtJbhN1fyeywFCRh51jnS89R8lIJZHuuqsF5Hf1NsB42qMB0FVYlq+Qd39tq9atWH4UlqAE4Ml6DEZqKRq9jVwRoO7H+VIOHpdiw51py3JY1TFmvx4Upi3m8rlTQHz3X9g8lfLUM9pnZ+1lof7QBAVVqh/XpiZ5n2LF2RUTYCg6tH8/TAIumGrHto2lk9Mem6NFrh9XBtv1k66hlCwtOaduGtY8G16q9I1bxbtO12BQ2tp23d029dlg5NYiyPXgn3MNjPKoJICZDwE/mul5c4eHhsbPw9nv64El0Dw8PD4/diukw4rlcroJED0MsFsOxxx6LlStX4owzznCfr1y5EqeffnroOYsXL8aPfvSjis8eeOABLFq0yKmDFy9ejJUrV1bkRX/ggQdwwgknbLfspVIJwNYN0Nra2rBy5Uq85jWvAbBVdbxq1Spce+21E17HozrYP8I2sbSKX5KtuvGjqpzHxsYqNh5U0pKkGP9WBSlBIkxV0VQLM42EvgdMtaBK6rq6OqTTaYyNbd3Qk5uAUkEOVJJs/FuVrCTvqBa2pCNVvzzWpi7RtiWRqO3E45Qk1OOrkdQ8hgQeN0xUhSzbUcE2HRsbG6dUZhuyHbkJKlX+qvLWfhIE21KbKGxwhAjLe65EB9uQwQltFw2MhJHJ2ofsddnv2D+0DOwTrB/7gm13qpJZL143Fou5uqnCmmXmvfm5vmvcxHJ0dBTxeLxiBYG2s7YBn5VVtcdiMZemhAEVprbhc6JS3gZZbLBCN8S1JLuqOsP6JsvHv1lObt6pz1pXJ7DtNMCgKzb4rmm6GdZpdHQUxWIRw8PDTi1vV51QPW+fD+vPvq9twRQurL/dUFXfie3BO+Ee+xsm02/5Hun4Yu1YtRUqk7mmrlKywVaPPQsb+PbwmKnw9nv64El0Dw8PD499FpdeeinOOeccLFq0CIsXL8bNN9+Ml156CRdccAEA4PLLL8crr7yC7373uwCACy64AN/4xjdw6aWX4vzzz8fq1atxyy234M4773TXvPjii/GmN70J1157LU4//XTcd999+MUvfoGHHnrIHXPFFVfg1FNPxbx589Df34+77roLDz74IO6//34AW4mMT37yk/jSl76EQw45BIcccgi+9KUvIZVK4f3vf/9ubKF9A9VyYCuY4kAdXJJbE6k2SViRQAMQ6ihbEl1JeP6v5bPqdlXWaooIJcpqamoc2aakIL/jZyT0rPKcP6ooJ+EejUYrCEebHkavzZzxti1UUV4N1uFU8o73YFmUfFXi2QY0SK5q2XkOSXT+JmFLcoJ1Yr14Tba5JbqVwNRUNkp22+djoXVhvTXnvN7b1lnbWlOIWGhfZluHBXt4L5tuxdYvLOWMqqLZ1zVwYYMKbHt+xnQyGhjQtEZaF5Lo7P+sk119YculZbDq/bDzLTnOv9l+GsRR8l433rUEeljf0CACn8nIyIgLAvG91ZUoDMDZFFNKiGuAi9A203bW9vU50T08dg4cI6yt4ndTJdHtNe0qKo+ZA/88PDz2L4zfScnDw8PDw2MXwi7Ln+rPVHD22WdjxYoV+NznPodjjjkGv/nNb/DTn/4UHR0dAID169fjpZdecscvWLAAP/3pT/Hggw/imGOOwec//3n867/+K84880x3zAknnIC77roLt912G44++mjcfvvtuPvuu3H88ce7YzZu3IhzzjkHhx56KN7ylrfgD3/4A+6//36cfPLJ7pjLLrsMn/zkJ7F06VIsWrQIr7zyCh544AFks9kdbdr9FiS8lWxSkFwiQcWN9jQfueZ3VtKL51FJroSXEs1h0O9UnazX5I+S7GGbGCrCSFUlyllnKngt8ci62g0JY7GY2zSUZKhV1Nk20bKrAlivSdJP01Vo0MAGJ/i3tpkulw8bCyxpXC29hSUnt5cOQzfR1MCGDdwMDw87UpTX0naw5Leer32F97OpN7QvhaXbmai+tr1sjnLN7021MwMZ2pf4LML6vPY/1tf2PyXTWe6wFQwa1NB3Tt9XPY/Ha15x3kvJYw0ShPUZ7aNhQTXb1tqXbZBB29Qqvm15tZ/bPqKrDyxBr89CgwB2HLSbjto2nwq5tzvtt4fH3oiwsXdn+74NwIfNczw8PKYPOj/cV7C77fcNN9yABQsWIJFI4Nhjj8Vvf/vbCY9ftWoVjj32WCQSCRx44IH41re+Ne6Ye+65B0cccQTi8TiOOOII3HvvvRXf33jjjTj66KPdivXFixfjZz/7WcUxH/7wh8f5T69//eunVDevRPfw8PDw2K3YGYdiR85bunQpli5dGvrd7bffPu6zJUuW4LHHHpvwmmeddRbOOuusqt/fcsst2y1XJBLBsmXLsKzKhqgekwdTFZCsJdGkKRmYKqGmpgaJRMKRhHYDRCWUgiBAsVgcpwTjfbhhpJKmBAk//lbCfHh4uEJ1q2QlyWOW3yrQmE5CSUKbNsQSa6puV9U5j2UaFRKYLEO5XHb1VyJ1cHAQkUgEiUQCyWTSEfOaOobX4HkDAwMVgQHWVYlNVYCzbGwrJZ5JjFqVdTVCWUlQS9rb/1lehRLDJJlZXtZJU3NoH9H2r6mpcRta6jPXdhoZGUGpVKogWbX92c9UqW9XN2jAREl6wm4GW1NT48qkpK8lfC1sGhC2UzQade8OSXPtixpsITRoxb4TtrqAAR4lkIIgcO+5ppthuzIVEp+fpnCy74ol1cMcSFV9B0FQEWxSspopchi0U/W5EufcgJbXZgBLr8MUOTZ4wDJaAp9ltil5NMimbTRZJfrutt8eHnsb7OoW+/eOXpM2R4OUHPc8PDymB3YViZ0T7M3Ynfb77rvvxic/+UnccMMNeMMb3oCbbroJp556Kp5++mnMnz9/3PHPP/88TjvtNJx//vn4/ve/j9/97ndYunQpWlpanJBt9erVOPvss/H5z38eZ5xxBu6991685z3vwUMPPeSEbHPnzsWXv/xltyfZd77zHZx++un44x//iIULF7r7vf3tb8dtt93m/ud8bbLwJLqHh4eHx26Fd8I9phs6wdWJr1Uuk0RlOhK77FqJOTqpJJdIFPN8uxkmz1FUU3Io+WUJTT3Xpkexk/uamhp3LQ0cKAlvVbVKplnluqZT0Q0OrQPBe1pS3JL2JFX5mabH0DJXU/yQyA1rIy2vfhb2HHhPliGsTGHXsdeyyhVeV9XPuimk9ouw1C+WZFGy3I6T2s+qlVf7e7U2smmEgG2kvP62OXzts7PtpHXT8/X94fdso2pKSl2NoWrtailSlAy219F62yBBWBtbUt22oyXv2T/tOGL7Na8RthrDrnawKyU0iKMIa3+9ryXStfxKDEzFJnv77eExOezMuxIGjoH84Zjh3ysPj+mBne/ta9id9vv666/Heeedh4997GMAgBUrVuDnP/85brzxRlxzzTXjjv/Wt76F+fPnY8WKFQCAww8/HI888gi++tWvOhJ9xYoVOPnkk3H55ZcD2JqSddWqVVixYoVLu/rOd76z4rpf/OIXceONN+L3v/99BYkej8fR1tY2pTopPInu4eHh4eHhsVdDU2OQ1NIc6KryBbYpSS0hSALNbvoZRmBSsV0qlSrIa2C8QprEYZgqmJuN6mdUpcbjcQDj82NreZXs5rFabiq5h4aGnOKcajZVsFqSEthGpqtyeHR0FKVSyV2b6n4toyqS6ehTgW1T1YTlr2aQQwlKrVOYclhTWChhattcVedKVNbW1jrSlqphzYdPdbSWlcpyJYxra2udqtuqfbXvhC3J1zZVwkTblH2G9eBvfZ68VrXN7PSd0D6qgQZNvcJztJyEBkv0fdO+xWfOz20qGRuUYB8IS2lkAz/6buv12Sb8bVd82HrY99s605bwZ5/mahKq41UtzvGmVCq594/pafT6dtUC309usMrNVjUIoP3Bpn3R8S0IAreyQdtX+zlTM3l4eEwvwlTpOwK1YxqA2xUEuh0nZyJmQvDApury2Ddg5yK7qp+FiTL03pPd7Hsmolwu49FHH8WnP/3pis9POeUUPPzww6HnrF69GqecckrFZ29729twyy23YHh4GNFoFKtXr8Yll1wy7hgS7xajo6P4wQ9+gEKhgMWLF1d89+CDD2LWrFmor6/HkiVL8MUvfhGzZs2adB09ie7h4eHhsVvhlWwe0w2SnTbdhW5SyDQbwHgHTM/l96r8VDJUVdSax1iva1WnvIaS7HSArYKcaSxGR0eRSCQcqR+LxSpSx/B6JH7ZBiwHy0dyjGlCmCqC5eD1lHDWa/FevN7w8LAj5crlsiOb2eaa0kLV7wwIKIlo01PwnkoyWgKT7abtxWeuhK5VOpNstalxlFTn9RkkCIKgYpNLSxCT6NRnGovFkEqlXOCEfYBtxf+DIHApO5QgKZfLKBQKCIIAyWSyIr+2prPh+dp+lkRXQlnbgVD1vFVSaz9m+5Jo19QCfG4aeFJyh22mqVk07Y/+re0YlgedfYnBHCXQufEon7El0PmcNDWNltEGqCYi0XU1A1CZYoZ1UrKrVCq5Z1oqlVw9lBhn+XhflplBGbvUWAMQYX1f1e5sH302TDGjaaYmA2+/PTwmh+lUoqvd0ZRXuwLVAqYzBTpWE3uinLRFYauXPPZO6CquXUli24C4FcroXkDTiemw3319fRWfx+NxN78nOjs7MTo6itbW1orPW1tbsWHDhtDrb9iwIfT4kZERdHZ2or29veox9ppPPvkkFi9ejGKxiEwmg3vvvRdHHHGE+/7UU0/Fu9/9bnR0dOD555/HVVddhTe/+c149NFHx9WlGjyJ7uHhMSMmIx77D7wT7rErUG3ppSX67OeWrFU1ri6b5jWIMKLN3m+yy0GVmNfrk8RUtZOdWKuqNqycei0qpTXPdVhKB/uO2om+vbdNURGm8FUHQZei2/ayxK2tx2QQVs4w1Q+Ps6pv264kxG06kWoII5PDYMtkFdb6HLRsumrAtulE96pWRlX9a2CFsIGOsGvZvjPROG/fnR2Fvae2C8tNgpntpMdoIEuvR1Qro125wWP1+Wgb6Dumfcim/bH30PfObkRrVymE9XndxDXsuUykxK8Gb789PPYc9neyVu2kDRjv7nLY+cSOXMNif362MwW74xnY/hM2998VmA77PW/evIrPr776aiyrsrdX2Fxxovel2jw1zNea6JqHHnooHn/8cfT09OCee+7Bueeei1WrVjki/eyzz3bHHnnkkVi0aBE6Ojrwk5/8BO9617uqlk/hSXQPj/0UHLTDyIAwIsTDY7rgnXCP6YamQCCUIONYl0wmK85Tkk1Tc9hUEnZZp6Y44fVVKRxGaFNRSyLNXlvzHuv4q+UIU8Zw/K6mmuG5VKJSXU3lLu+lhDJVwywXCUOqy5WU5DWotOa9eF41kp/XoxpXSV2rMNfUIjoGKBkaBqqJIpGIS41BZbJNKZJIJNy1rLpby071uFUraf8ZGBgYVxbdhFIV+PxhuhH2Yd6TqwxisZj7rSst9DpUGNvNRllu9hNLrmpwRjfnZXnCVM+8liqueS32H21/9h19XvxeyXu9vqqjeZw+f12Joe8kn0UqlXIqca4oUMU7+6y2hUL7l1Xka//g6g5VkGnf0XKxn2nf19UJPJYrGsICASSSbB/SPkP1OzdU5jvJ6+lz48qRycDbbw+PPYvtBSmn4/ozDWFprDRoqfOPXQk7BnPVls5hJnsdXfGnc8u9OY3HrkC1QMWueN4qWpluWPW57cdhge/pxnTY77Vr1yKXy7nPw5Tbzc3NqK2tHacQ37Rp0zglOdHW1hZ6fF1dHZqamiY8xl4zFou5jUUXLVqENWvW4Gtf+xpuuumm0Hu3t7ejo6MDf/7zn0O/D4Mn0T089kOEOWLAeFWXfubh4eExk6GqZSXnOIYpOccxzhJRACpITCBc1WxTvygpDGwjYhWqcOc5loSlY8Wc4DwPgCNHWeawcoWN15awBLbmK7QTdV6TJCgdQ1XOspxhamSSmZojXBWwlohkW2ub8bemDFFbpO2ubanpPbSt9Rkp6WzTibBMLI+mttFyBUHlZq/qxOlmmsxpr2S8rb8+Q+072ke1LTRHNq+pThnBXNdaPps2R6+rxCvbiGBbaMBdV2fYa0zk1Op5SrrbfqTzEr4LNohkFd9hxAMDJ/F4vIIkZn3YX7U9rDJb1euq0A9bscJ+EbYMW/sjgyXcI4BlUeeZz5s5121f0WCDTTPDH6YF4vvMMSWRSIzb2NQGID08PGYudrVftiOk8K6Gjntqi4BtwdTdQaIDE6uIp3I+7b71uT2JPh465wF27TuwK6+t8xv2Yw2ghM1FZhpyuVwFiR6GWCyGY489FitXrsQZZ5zhPl+5ciVOP/300HMWL16MH/3oRxWfPfDAA1i0aJETQSxevBgrV66syIv+wAMP4IQTTpiwPEEQuHl5GLq6urB27Vq0t7dPeB2FnzF5eITAGmklO9ThnMmDXBiUJKJqik4asM1wMH/m7p6YeOwf8Eo2j10BS3qqCnYiJ8cSZ8B4VXhYvnQl1ax62qZTASpJaIWOy9yIUwm/ailUlPwmEcfPdcxW1TDLynZRbI8EVXuoP/q9quJJ+qsNIdHMclp1Oh0KdZT4PR1oJf+ts6nkq6q1aes0aKKkMp8pVd/cAFXbRI9VlXY0Gq14FuVy2ZU9zBkOU39rsKGmZmt+bAYkWB8N1PB/rQPbRsup+d6tem57z9r+b4Mo+p0l0Uka671sChT9XgmRsPLbPqvvugah+FvbSB1Wgm2tqnybJ92S/HotVYWHrebTABwDL1o/u6GsBha290y0rXgu78nc+/qb19Wxis+C7+ZU5njefnvMVOwOgm1/we5uQx1nrf9dbQUOy0lxAcfbyZbdjsPA1Dd0nAqJbm1GNWGHVacr9se+rXOBXbkCY1dhoqCLFUvsjlUmu8t+X3rppTjnnHOwaNEiLF68GDfffDNeeuklXHDBBQCAyy+/HK+88gq++93vAgAuuOACfOMb38Cll16K888/H6tXr8Ytt9yCO++8013z4osvxpve9CZce+21OP3003HffffhF7/4BR566CF3zBVXXIFTTz0V8+bNQ39/P+666y48+OCDuP/++wEAAwMDWLZsGc4880y0t7fjhRdewBVXXIHm5uYKwn978CS6h4eAg1ssFkM6nXab0jFSXCqV3OZsg4ODMz5aaMHJSDwed8uE7SZzACo2jhscHKwgBTw8dhbeCffYFdDx2BLJAJyjpU4MSXIluEi4UgVLx4bXBbYpUXlf/VudPQslX21KBZJ+vFaYMtSSkUroA6jYcEyPUdCWqePJ9uH3YSpaVdxSTaspKFTNys0TlaS0ynarfmYKjkgkglKpVEH0aR2U/FMHmoRzNBpFIpFwm7HqUlMlqfWatPv62cjICIrFIorFYsWz077ClCF8fuoMadCd9+S5VAXbfhQEW9OppFIpVw6eS2KYdVJVsj4z5r3n/+q483p8jvrOaM73MNUzgxDaZrW1te5Z6OaUJI7DlPPaxrr5pyWja2pqXD2Hh4fdPIT9Th1OHs9r8YfvsaZzoUBA05zo+drP+JNIJNymvkp86BhjV1zocQy0WBKI53BeaUl5Xs8+O/te6+agAwMDFfNUS+KTWC+XyxgcHAyt+0Tw9ttjJsLaFt/X9i5w3IvFYkgmk87u6cbdhK6E4ooizhP49/ZAX9jOD8rlMoaGhibsPxzLd4RA102oOS7rvNWO/buaVN0bwLkEsG1j+r2pPaxYgH2Bz1XrtDtWmewu+3322Wejq6sLn/vc57B+/XoceeSR+OlPf4qOjg4AwPr16/HSSy+54xcsWICf/vSnuOSSS/DNb34Ts2fPxr/+67/izDPPdMeccMIJuOuuu/CZz3wGV111FQ466CDcfffdOP74490xGzduxDnnnIP169cjn8/j6KOPxv3334+TTz4ZwNb+9OSTT+K73/0uenp60N7ejpNOOgl33303stnspOvnSXQPj/8/rCOfSCScwxqLxRw5QuNMlc9UVTxh97XOvA5UVnW4M/fhb3WI6Zzb+7C+6qR7eEwXfH/y2FVQ54OEMn8DlcrvMOeFx6gSSq8LhG+CaSenEzlXNr2GVXarY6VQW2PLomS3krdh7WPrYsuvynELqwqrprbWzUsJ1issyGAVziyTBhWs4lmJan1eJGWVROexYeozq3KzqVX0b9pQq3hnve3/NqCjTnGYgxyWn9o6YZZwVsdb5xT2t62vnX/oO1LtHHtPrSvLELbSgcdaAtgq0BXV1O96H207qzzXfmbfKSXJdXzQ6ysZp3NEfW95rH2OdizQPqZtyXpoUCus7a0iU6EBK64CUZW5tgPfR1Xgc9XNZOHtt8dMw1QITY+ZBZ3/6L4fSnLbeQbJR1VwT5XUVnKW1+E4bG22PXei/6vdT+9bzRbYoLOt8/7oj+/t77YVXxATiX52JXZn/1m6dCmWLl0a+t3tt98+7rMlS5bgsccem/CaZ511Fs4666yq399yyy0Tnp9MJvHzn/98wmMmA0+ie+z3YFQ4Ho9j9uzZyOfzyGQyaGpqQjwer1CiF4tFjIyMoK+vD5s2bUKxWMTmzZvR1dVVobjbHtTJ5/3q6uocca8ERH9/PwYHBzE8POwURjsCRsA5aWC9NCetRW1trVMsqrLOw8PDYyYhHo9XqJM4bhFhJLWqv61TA2yb7KrCCcA4Ik5B4trmbuZ3On6TuFJVCr+3pCnvq6plgopiVTarwjUsEKtktJLpljTXQIR+Zo+lgow2xuab1t9KZGtqMX6mubBtWbR9ACCdTrtrpFIp54AnEgnU1NQ4BbFdacD/h4aG3AozTTWjyl5dXUDHPplMIhaLIZPJIJvNVjzndDqNZDKJkZERlMtlp6BWQrMaeB/OOZSs1WXmtNlMmWOvQdU1/6+mZmNb6PMlCctysEy6CeVETr5VAto0M3xeuqKAbc7jwwJA9jh9RlTws09xfqNKeq1fqVRyRLPNYa5BFps7n++CdXrtxqr2++HhYdemYStE9LnwXpyXajofJRO0/Xj9crmMYrGIcrns3kF9bzSApqtsdG8AD4+9FXw3qu1b4TGzkEgkEI/HUVdXh3Q67YLetOvxeNyR3ISOXQMDA261NAO3HPuq+ao6rtbX1zvlKcfYYrGIwcFBt1qH4yjnlDYAbAPS1e6r4gHdmFyDtErsW1uie2bsT9AAQzXhwUyEzjuqpQ1SMYjH3gdPonvs9+By7Hw+j4ULF2LOnDloaGjA3LlznXFnnloa0c7OTrz44osYGBjAf/3Xf6FQKDhnZjKDIY1kLBZDW1sb5syZg1gshvr6eiQSCZeTvFQqYd26dejs7EShUHDO/o6ACntOTLjcPSwnOlAZpeckwg/0HtMBvxzcY7rBtA8cg5lOxJLNqlamU2JzWgOVCmRulGlJaQu7ikfvC2AcuciNT5nyQtNhcKUQCVGSmFoOnm8Jg2rqWA0SaNvQuWMb2iCBOodhDp+Wk+RtmK3Q85WcVPJTgx1K6rG86ojW1NQgmUwim82irq4OmUzGBaIZVEkmk45EtwrusbEx9PT0oLe3d1yQRQlaEuwkM0meR6NRZLNZ5HK5iuc4NDSETCaDkZERDA4OupQhAwMDGB4erkoOs115H0uiqwPPtiEZrM8EgCMe+D+JcVWaW3W3OnTlcrniGbHP6XOvFgzQfqzlInHA1W/avrYsJNItsatpY8bGxhx5oqsCqF60RDr7MOdWbDsSJSyH5qFnP+J1WD9dAWHrrM+M/V2DKWGpazTopnXgPM2uiOE5nJOSRGcKonK5PG4+qml3rMJ+IjGFhbffHjMRYUSmTfnkMXMQiUSQSCSQz+cRjUbR3Nzs7DWFZbqSjM+WwenR0VF0dXWhp6enIi1XEGxNVzcRiU773dTU5O7PsVZJ9IGBARdo7+/vH7dht9osqyivBiXRNUjPa3AsVhLdriraX/qzvs8aUNgbAgn6nDVorvVgf9qd8PZ7+uBJdI/9Fhyck8kk8vm8+6mvr0cul0MqlXIkOkkKKtUymQzy+TxqamqQz+eRy+UcwV2NaFbHM51OI5/PIx6Po7m5GQ0NDYhGo6ivr0c8HnckOlVFQRAgkUigWCwiGo2iXC5POEnYXp2tioxGSp06G13fm5dSecwseCPuMd2walOSdCSngUqSmwSdEtskKqnMterlsHGQRFm1NCSqYFfiyqabqfaZVXvztyrqtZxh6SA0FzzLZtU9+re+Y5aQ1zLaMoWpkvUYhbU19hp8BhrUUGKWhDidXxLbVJgxCEElm1VVs3wMkgNwG4tasB50irinCBXvvCd/s81JOtfW1jrFeBgpbNXNE41xtk3D0pyEHV9Nha7XYXkmc65+Hwarsq6muta+b4M11eYlYfdSR1V/+K5r6hLdbFPJCbY9yWRe025opj8TERralva9CXs3WH67QkXbTQkc9in2Nc4bVZ2v5LxtO223MKVcNXj77TETsbeS5ZMhXvcl6LjDVV3xeByZTMap0bknmeZEZztxXjc6OursL7BthVO1AC8/V/udSqWc6j2ZTFYEYznf4BxBlemav3pn+p0l4W07WexMH6lmg2c6dM6s/+8tCLPhe1JN7+339MGT6B77JdQBX7BgAY444ghks1kceuihaGlpccZVCQk6V1QuptNplEolxONx1NfXo6enB0888QQ2b94c6gjX1dU54nzBggU46qijkE6n0dDQgGw2W6EKV4fukEMOQbFYRF9fH/7617+ir68Pa9euxV//+leUy+UpDWrqmFmHSSdyYdF1q/Dw8NhReCPusStAZ4djGFf1UFVE4kxBcpYkKNVIuvEnidAwstmmLOHnJOeUvNbVPJq6IiwHJ1VVdkNCJc95vpLAqkhl+WhPNM+3EoC8L9WrVOPayb8q4XlvYntEOf9XNY5+ZhXttryjo6NuxRdTn8ViMbS0tKC1tRXxeBwNDQ1IpVIV44uq2zUwwTagPacquaamBsPDwxX58pkzOpPJIJVKoaWlBfPnz3fLzul8k0TnxmRUGlEx3N3djWKxiEKhgN7eXqcatsoqXY6uuaqtLa6trXUEhCrNlYznD4PympKFfYD93L4XSu7zOJ7HeoWtzNA2137OwIYqyVVdZze2VRJdy6wguaHkiQofisWiuzb7Lt+PcrmMQqFQsdFobW2t61sMmLB9KFxgmfmeEDbdkx6rAgt9h5VMYhvrfjy8ji7l53OkyGJsbMypJbkZvG7Gq4GSSCRSscGttuNk4e23x66A+iXbCyaGQe1x2LVnUt8LC4KGzU/2RTD1WjQaRWtrK2bPno1EIoH29nbnCzPwrfuDqL3QFUixWAxDQ0MYGRlx41ihUKiY29B+c84wd+5cJBIJl85FSXRuyjw2NoZisYhSqeRStxaLRfT29mLLli0YHh523+tYXw3a/9jXdTN5Xd3E/q/B3p3pH7oKT8fvmZ6ilWXTtG972zti52N7enWMt9/TB0+ie+yXUOPc0tKCgw8+GNls1qVy2Z7qmlFzGltg627Af/nLX6qSzTU1NUilUkilUpgzZw6OPvpo55TbnG8WQRCgp6cHtbW16O7uRqlUwgsvvDCliWE1ZXkYmW4JG69C9/DwmOkgAcu/6+rqXBoFTVWhzlUYKR2WY1qV7kpU6jHqAOlGgUqiK/muG/pZBbaO65pKQnMsskwkvFWNSyW+OmRKJpNkVMePzprmKNe2sOlorA2ZjJJVyWx9BraNba562loNeDCY3dDQgEQi4ZaD6zJZtonWVdOlqNqMQRemAeF3JHkZXM/lcmhpaUEikajIr8qc8MPDw0gmkxUOU7FYdPlWI5EIBgYGXHszzYcSSEo+h9lfPkvOHTRVS1g/C0vnYlXQCqv+IgGuJHjY3EOJMD5rTVFk+5v+P9FcxCrVCX0PNBDDZ85+wNV7Cn6mwQcNELEfMDCi6QL4PvFeNhCmz17ryoAAf2tb6Z41+s7r+aOjoy51gSV7isXiuNy9LKOmx7H5z8PedQ+P3Q0dO3aE6Kl2jgaTZgoJpLZT5xX7AzRdVS6XQ1NTE1KpFFpbW5HL5catpuNcRffmYhCXY3dtbS1SqZQLTNsUXjU1NS7gXF9fj9bWVpcKjkr0VCpVsTqRgdPh4WEMDg4iEok4+63/2znlZKEBX7XZNgir+23saP/lGK8kupZ5prwX1TBRgGxvgM7DdyRA6DEz4Ul0j/0Smj81l8shl8shnU6PU99NBE74UqkUGhoaMDw87AyypmHR4+bOnYvGxka0tbU5tdNkiAc68PX19airq0NnZydmzZqFQqHgcrZtDzRCqsqi8baTS7vkWQ3AZNsGgHPydaMsq27kJIhkwp6O0nrsevhIuMd0g+phoJLUpaKTCmMlqUl8EVQ4qbNhV+FYUk/TuSgpRjLWqo+oEKXjZ8d/dcQ44eaGhOoYKhGmuYzplKuSld9r3mO9FslBva8uV7blsm2s1+P5YQ6P5ofUcy2xqoS/BevHNC2JRMIFovV4lp3kKlCZ1oY/mm81Ho+jXC478pRLyRn8zmazzsYrwaplU4WZ9ocgCJDNZt09+vv7K/JyW/C6YcSSEgpKsocpndiOVkFtVz7YZ8nrUhXNPLIk0ZWc1qCMEh3quNt3U+9pc3GHken6TrKuwPj860o2c17B33Y/GQ0oaN/kc9V21z6q7yKvQ0LbkikqRNBAEa9PBbp9dzTwxt8aDGF97FxNVw2wXiToeS87XkwV3n577ArYfqXvxHRdf6ZAx7GZVK5dDV1tk0gkkE6nkclk3EbdNoe0jsOck2j6PW7gHYlsTc1aLpddihfauLGxMSQSCWSzWaRSKXc/Hsf7ck8XCgk4f+C8sb6+3u030dfX5xTquv/OZJ4l7QXtM1Bpu/VaOt+cTNsC2+Z5GpjVebCSuQxGqPBAV0x6f3zHoXMBbcc93Zbefk8fPInusV8ikUigtbUVmUwGs2fPxty5cxGLxZBMJid9DRr4xsZGNyFoa2vDwMAABgYG3HIzGrSGhgYce+yxWLBgAbLZLPL5/JTyUCaTSXR0dDiD19/fj76+Pvztb3+bFIlOIzk2NubUcmFRaBp4OmpMDzCVKLsqJNk+VPDp8vTR0VH09fW5jVy6u7vdfffmqLPHxPBG3GO6wTGQTgMdIjpjVBdxLCP5p44Pxx4S7jxfFa5hZKCmxiCUfCZJqkpW/lZi0J6vy5b1vJqamgpyU+0Iy0InUsuppK+q8yy5SkdVnVV7f/1hm2gKGTpnhBKzXDHAe9v2UcLPqrIZmE2n00gkEmhsbERjY2NFgMAqihlMUCKUoBM/ODiIvr4+1+bcjBTYtvKsra0N9fX1yOfzToWu7ajtYYPxJOPHxramqaEDzoC7QtWJlmBVwladZOvAk4hlu6gzp0Fu+0yBrfMjkhpMkRONRhGLxSpU2Uwdwo06Y7FYBclr66R90QaztH/Z89i2GrAgga/BdxIHVJxTtW3JFyW/eV0GRTRVkAYfdBWGkj0sS6lUwsDAQEUdlCTnffnO8jf7mSrd9T2yYxOVlvq/DRLYoJnWifVm+YipiCS8/fbYFeA7qmPpdChQZ2Kf0/nBdAcLZio4llMNnkql0NzcjNbWVhektUFFu1m5Cq5o4+LxOAqFAvr7+918qlAoVGyunE6n0dbW5pTvjY2NzufneM7NTJXspC86PDyMVCqFcrmMWCzm1OmlUsnZocmkRuE1y+VyaCBW7wls86W3d10NnKbTaScsqK+vd2O/DSyMjo66NDi0X2NjlanBwubJHpODzm2BmfN+e/s9ffAkusd+CSrZbDR6qqocKsQBuKVi8XjcLfkCtjnEsVgM+Xwezc3N45ywyYBRdyra8vk8giDYbioYhUa6NT+nJW9U4aQGdKIBVJVWnPhwszfmjW1sbKwgpKiwpHEfGhqqcNL90qd9E96Ie0w3OGZxc0hVNFm1Lr+nw67Oj6p0+DkdM82TbWHVqYQu9VUFsFWuTwSbK5pqYJJy6ogTvJemLrFLpFlfVTsTqrq1xLPeS8nRmpqacWVl3bnqSdPthAUdeLxVYCnZS9KT6VNI7lroOarq0vpRFazKctooDRDQCSV5PlEAXFco6DyA/YhBd7ZLtWvoM9N7WaWYXaJtiVx9Rtvrv6paZs53kgskOLi5Ofs23zUS61bpbtvB3tf+HaZGVRKd5Anrreo9fmaV6Drn4XjA460CXecyfEfZrkqIa7srCWK/D6u7HZvCFIn67mldVHWuddN7a/9ln9bAjA36TQXefnvsStigsgaa9zXYcWVfhwqsuNKL9qZamiklkdkXOG4yRRhFD2rHuV8GA+8MDuueFzo26hgJbLOttPWcN/IaY2NjjjcIm/tVgwaG1HaFqZV1njTR9XW+x+AAAxNcOceALe0F52NsO/r+KqqgXdG5ncfkMRPHLW+/pw+eRPfYL1FXV4dsNotcLodUKjXhjt7bgzqd6XQa2WzWqcRqamqQz+dRX1+PtrY2ZLNZpyDc0UlTJBJBLpdDR0cH6uvr8fLLL0/qPBpIKrVYPk4mgG3EAvMI283KqtUfAHK5nEtTM3fuXLS3t7s8tdxxnTnnWA/dBGtgYABdXV0olUp45ZVXsHHjRhQKBbzyyivo7++fFkWKh4fHvokgCJzqw6pew8hM/g1UbgpK2GXEStiFOTWWrNTP6QQqkWpTmNhy6/csl45/utESy6rHaboHQsvOtqKCl/UJI9OtglsVvZaIZB35uaqrqAomsV/NBmpeU3UuSV4nk0n3Y+22Xfqs5CnbXY9VwtTWm4r+VCrlAsF0mqk+UyW4rtjSZ0eyPJ1Oo7a2FrlcDo2NjYjH4+jq6hq3kkGfmwZcdNl1uVx2xDW/50bjSsyoc81yar5+KqLj8Tiy2Syi0Sjq6+udUq+lpcWVm8p7VaqROOeGqbTlhUIBo6Oj6O/vr1AEWtKAZda+p+Vl+1rSXNXuSoBY1X4sFqtYzQCggjyx9+Y92E+VyLbKbSWzmddej1OiXAM6JJDCAgq2P2o6Fztu6P9hqxd0Cb+SQ5Zw57W8g+wxU6A+x57ol2E2YVe9I2Hzj30NHI9I8jKQrGS2rj7QZ2/bxQYfbeBbhQPcKD6dTruAsArP1Ebq2GrnjzU1NW7T8kKhgKamJiQSCXR2drrrcZzfXj9R+6VBWutnK4GvcxW1JWw/pqaNxWJOaU//m0FwkuhcXR4EQcWeGn19fRgdHUVPTw96e3vdZui094ODg5MS1Hl47C/wJLrHfglGaHO5HJLJ5LgI+FRAY84l5rlcDoVCwRnnXC6HOXPmuE1TppIyphry+TwWLFiAnp4e/OlPf5qUUoMktDplXG5sl8HrZnzbS+XCetbX1+Owww5DQ0MDjjnmGBxzzDFIJpNobm5GKpUapwgl6NAVCgX09PRgaGgIjz/+OJ5++ml0dnZiaGjIbd7iVen7Bnwk3GO6oUpQS4rRAeEYzz5UbczXPsYxkOonOjW6osbeCwjPNWzV8KpgpTOlYzGACuKRam4AFaSgkui8drXgpz2/GqHN8qvi2LY1ALepJp1kDcgyUMrzVQlMcpT312uHqX6BbTnVraJM28EqhCcTfLX30efDvK0k7WOxGCKRiAtEq+rcLsMG4EhmKtiZm7Wpqckp26sRNhb67JiyRIMu7CsqCrAKayrKGBiiwj6bzbogeGtrK9ra2hCPxyvsN59ToVDA4OBgBdFRKpVQKBQwPDyMLVu2oKenx63I4+dMW6P9k+2j/VDVhFxlYBXfJPHZ1vzR3OwAKjZeZXtp39O0N6p0VzIl7NmwX+uydxuE03bntXl/pp6xSnz9UTupKwMJq8C3Yw9JFh2v+H6FvRMTzfNs/b399tiV2FNEXViwndgV/sf+QkhqUI+rwGlPLVGssMIFO8ZZAl2J9CAI3H4m9M3T6bTbB4Xl4hjNcVHvoXOWZDLpgsiFQgGJRAKZTMapuBmk3l4/4VgeiURcMJx1tXXi/7r6isdGIhG3Aj6fz6OtrQ3JZBLt7e3u71mzZjklOleS6SbbLGuxWHQB782bN6OrqwuDg4Ooq6tDf38/BgYGKlKt7g99dl+Ft9/TB0+ie3hMM+wgw4kD8+tOB+gcheXmnGwZaTxJhqhDrMRHNXBSxPywbW1taG9vR319PZqbm5HL5Vwu9GQyGTpJstF3LpdrampCa2sr6urq0NbW5kj2LVu2jNvwzmPvgzfiHtMNqwInqagEKce0HVG4WYVrWEoEJcCs4nmiayrCykRHbyIyOEwBpWXQYAIdRf3e1kXJbHtfvYeqmlXpbZ1c/U6vY5+TXR1gn6kSgWE/Wt+JoLZIiUVbTiq1aWtJfmpA2Npfu9pB21aDPOosT0WNyLJq8NzaUlWuaZ1ZXvYpKu1JNNBmM80df5SY4LJ5bSuWqa6uzuXspnqPz41zDRLn2uYsl5LOYWmY9EfPYd30/bTvZrX3cKJ5mb43Ex2nz1yV7jZApap5vV61FRGsk9YNwLjftu/b8mr/sEGqybSDLY+33x77E7zPsXPQIOhk7LcibEy0hLJeX6+j/rdN96JzxrB5HDDeflvbHTaWT6VNwv4OK4cG64loNIpkMuk2S6VIL5vNIpPJOFueTqcr0rGpUp/txWsz2D08PIza2lpks1k3R6OAIGwut7/B8hh7E7z9nj54Et1jvwQVUOrMhTkVk4FVSOkyY6rm8vm8W1I1HYhGo0ilUhgeHnZK8qkotdVxZYoXS75MtGyLZEI+n8cJJ5yAAw88EO3t7Tj66KORzWZRX1+PhoYGpyQLS3NAkEDgZjPZbBaxWAwHHngg+vr6cNBBB6GzsxPPPvssHnroIbcrui6f9ti74I24x3SDRB/HtlKp5PZc0A0gddwL2/wQ2BYgtEE/VWBT2WoVQzZFiCqt1dZw3CPhXs0x0TJo31dirxr5pnVh+fg/lUhsCx7Lemog1X5P8LvBwUEX2GVb0+Fi+eigaWoc3kP3xtAyUH2vqm4qralypi2w+Z5J+GqeU90HRFN1UFE8NDSEgYEBlEolNx9Ip9NoaWlxdjyVSo17vtYRZxsrkVlbW1uxYSTJeSrEmBKFdpzXYv/RFQv6bO3zALblb9e2V/Ke/SGRSGD27NloamqqUKJnMhlnhzOZDFKplGs3VT8rCc76BMFW9X5DQwOGhoYQj8cxMDCA3t5ebN68GaVSyb2bWicuN+ecgQpy3qNcLmNoaKgiVRHroup6YNvGmpYA4f92nwKCiniCKyb0PJaJbc3nrO8vU/YxbYEGfmpra12AQp+ZhQ0E6Dupm4nacmmf1PtaBbv+5hg5WXj77bGvgv0zzBb7vrvjCIJtG0EXi0UUi0XU1dW5Vc9A5QogIDylDsczfkfbQPvA1CM8JplMoqGhwZHLTFnG1VWqhFfwf65aIjh/UdudSCTc/XZkbLTH6xiutks3v+aqtvnz56OxsRH5fB5z5sxxe481NDQgFou5lWS0q8C2dK1qC7m6a2xsDPl8Hn19fRgcHEQikXC2GwBKpRJ6enrQ398/pTruS7CBcQ0O7Q3w9nv64El0j/0SSnpPx8Cng6iS2SQTuORqupTo6mjSiZxKdFgdrx3JM06Dnk6ncfDBB+OYY45BW1sbFi5ciHQ6HbosLwxKFNBhBYBMJgNg67LxZDKJLVu2YHR0FI8//jhKpdI48svDw2P/Bgk8m66kWnoVHftt6gYl0cOCi5aQImFlCTs6VRMR4Ta/MTDeeZwoXYyWudoE16rCSRby+DClOUlOmydUFb+svx2P6XhqahpVrNvz2YZW6cTzWFamGKMTGI1GHaFqCXM+D+uI2tQXvMfIyIi7LtP3AHBp2pjDlelXqkGVanofOvxK3tLmqao7jEhQ+65zF34fhrAVaqpQpjKNm53ncjk0NzdXqM9JEvC5aN/ghm58hpyTBEHgFHLJZBLlctl93tfXV9HnlPgGtpHWnNtQ7MDfdPJVBMCgjfZTq0q0KkOWOUw4EdZW+iztKhSOFZpDXedmFBGw3FT/s000rZJVaGo5dI6pY4Ul9vX56vhiVel6DU2D4OHh4cmiXQEd8/mj8wwNINtVU0D4M9HgPO23zu3of1OxzbRqNs1V2MqlsOAroem6uNF2tTJOFay/XanEuQ2D5Ol0Gk1NTZg1a5bbdy2ZTCKXy7kguKas5ZyAwgPWie2fTqcxNjbmggxM28Ygf3d3NwqFAgqFwk7XcXdiornxjlzLzoN3hpT22LvhSXSP/RIjIyPo7+9HJBLB4OCgi1zT8ZgKqGjnxlqqZAPgIuXTmUfMOtyTVaBPB2pqajBr1izMmTMHLS0tmD9/Ptrb29HQ0DBtSnuitrYW+XwetbW1mD9/Po466ih0dXXh+eefx9q1a3drvT2mDz4S7jHdsES1El02wGhJJEJJpbC9IGzf4zWUxKP9sEHKSCQSGrQN2zBQ66NKclXPq9LUKmptWZXsVEJdVdth7WBzmVunjmWyhLqSclRNUU1tEfaZEpaa55zXYFtS0Ux1mDqaSpCGKb30O+b5VKeex5DAZfDBboxajbxluhUlYdnevA/JepsuRM/RdC9K4jIgEUaij42NVeRs53Pic6cTnkql0NDQgMbGRpdj3i5513ZgvWxe1DCynuQClX/AVhVbsVh0gYqRkRGnLrdEsVW7875h76B9x1k2tqOdI9k203eN5/DaNgUUP7fXZKBHVYo2bYAlbFhGVZlbqJPOe4UF4ux7wndPlfKEBlh4ju4tsD14++2xKzCdZJfHzIMNxNJ+My942Bhng/TW96Ud1Wvp3jhqr/ijG6nrvTStHElxmy9dbaAGsjnGT0T4TxY6j9K5CoPZVNSn02k0NzejpaXFfcZ88wzU0hbZubEKROwKs3g87o5rbGx0K937+/sxODjo+A7d22QmY7oCG8B40cqeJtHVjgOT29fE2+/pgyfRPfZLFItFbN68GYVCwUVXR0dH3RKvySIItqYN4HLlzs5ObNq0yTmGXMJdLBadimo6QONlN/rY1QMcHbNDDz0US5YsQVNTE173uteho6OjInXLzoIGKhaLYfbs2S4fWzKZRHd3N376059i48aNFSpEj70H3oh7TDeUqKVzpekOLHFeTdXE78vlsnOM6LwoYVqNvCZ4bzogYWomXkeX1NrvVdHN8Y5EGe1LGKGpAVYFy6vpMsLO1ba0RLE6FKomVnKXq5X42ypi1TGmw2rV90omsy14fzpzg4ODzglXApFlZX3o/NiluFSwMc0IA96aAoYbYpZKJackJiFKdXxYWwFAKpVybc3PS6WSC96PjY1V9BFNS6PP2bYZ+yptsvat4eFhDA0NVdRTSeWmpiY0Nzcjk8lgwYIFaG1tRU1NjVstx3tqWzLlDX9bAkGV39rWdMjz+TzS6TSKxaK7ZrFYxJYtW1AsFh3Zrf2WZALfjbANeK2K0QZyALj20Xfekshh+e4pgLDknr6zfKaqTOSGeez7vAd/dH8cfa+Y1ohBiGpBBe3bGqzSdE0MYjBFko5XvL8Gg9gHJwNvvz2mG7pyC/A5yPdF6Iq9wcFBjI2NObXz2NgYMpmMCxITOrdRe855Dzf5pIBtYGDA2XAAzrbX1taiVCo5clltm50jqW3TeSUJYwaDi8WiG1N1vrIjq7sVtGU6j6H/G41G0dbWhra2NmQyGRxyyCFobW1FLBZDPp93adGYUoyBcdZT7QaAioAxv2fKMW5IWiwWUV9f71Kzkfcol8vo7e3dp1Orqt1WAt2S6Hu6bLT9mqaxGrz9nj54Et1jvwQdwZqaGmcMampqkEgkpnQdOj7FYtHlYqPKit9r3tfpItGtU8nPwhCmVJvo+GrgQF1XV4dcLofW1lY0NTWhvr4e6XR6wnvtCHg/OpPcfTwejyOXy7mJwXQq/D12D7wR95huKGlG0khVS0ClE7E9KClJpSfvs71xXMkvloXXsbAK+TByXsltS8jrBlEsX5haV++nyucwkl2dSyUllSDW9tCy2/sp8a35vTWdTDW7YRXaqhCnakwVZhM9W32GLCPbUJeX25QrrH/Yqi+rOrPqaX32/J6/WWZ+ZgMI+pzCFNL2eWl5w4In6gTGYjG34RgV6DZtic0Tq2W2ZLSSX2HkdCQSwfDwMDKZDGpqatyyejrs1RT1+qxsvdmn9Ld93jaYMhHsigG9ThiJrv2d9dW62DpZRzysPvzRdyNs7NC/7eoFDTCoCl4DUVapbo/ZHrz99thVmA4lr8fMhI6tYYFnux+ItZ9KomuaLxWUqbCK51u7rrbKjsH8rXMWFWBYO2j7qSVZbSB7MtCxX++hudAzmQwymQzS6TRSqZRLwaYr/3RsDwsgA5UbufM43c8lmUyipqbG7ZMSiUTc/Rgw3x9g5wXETCHSvf3eM/Akusd+CaZzKZVKWLt2LZ5++mlks1ksWLDAbYhpFWv2fBrutWvX4qWXXkJnZyc6OzsxODjojHgQbM0Dum7dOgRB4CLX1QbkyWJgYAAbN25ET08PCoXCOHJIHSrmNtV7ctkbJyGTiZynUim0tLQgk8ng4IMPxmGHHeZ2Ad8dSKfTmDt3LnK5HA4++GCsXbsW/f39eOmll/brTU48PDwwTn0KbCOUqQ5i8BFABdEY5ghwTFUCW69LqCpbCc1IZGvKEbtBaRgpZtNg6bhMtbOWmc4hSVVLeKraSB1FDTIwQMm/tUxBEFTk6eYPHVX+zfZUJ5PPgooY5oUOgsApnfWa+szonFJFq8coSalk7+DgILq6upzilsphm6JGnVqqz0dHR9Hb24u+vj63KZnmVgWAwcFBRKNRjI6OIp/Pj0tNo32E5dP82GGqOqrjhoaGUC6XUSwWK9RvqqjPZDJIJBJuRZtuKqmqY0sOWBKdzjc3G5s7dy4SiYRbfaf5uy2JrnVlfS1RWy2wpH0wEomgWCy6ecvAwAAGBwcrlHa8t60PVe1KZGj706HnyhF9H1lOfU5KsLCPVXtmWvexsTH3HPQefA4sOze+pepbAwoaACIZxDQ3fGdYJ11doO+aXbnAfQhUPck2s0EOLdNEATUPj90F2jO1p0BlcNZjPPQd3l5QekcERztCAle7Dscl/ubcpq+vD6VSye1xwlXNSkjz/lRBDwwMYHh4GJs3b0ZXVxeGhoacXeH8IxKJYGBgwO2vMTg46MZo2prJBHA5L+FKLIrmVDhHe2DnKByHGaxXYUK1+2kQgVxEIpFALpdDIpHArFmzMHfuXKRSKdTX1ztSO5VKVWxYbufDBFcj8W/bvmoXGQDnvKVQKKCrqwv9/f0YGBhAoVBwc719kWC1AQ3+zfdtd4xN+g7Yv+3c2GP3wpPoHvsl6JDU1dVh7dq1SCQSaGhoQC6XQyqVqnBIwsBlZMViES+99BL+67/+C319feju7sbg4KA7bmxsDL29vU4xqMu/doZELxQKWL9+PXp7e93yKgWNZF1dHbLZbMWEAdhKDDCFjTqkEyGVSmHu3LloaGjAwQcfjEMPPRSJRGLCdpouMPrNncdf9apXYcOGDdi8eTM6Ozs9ib6XwUfCPaYbmgcZ2DYG0jnnOGfTRig4ESU5HKasDlPxhDlMhJLovKcqnkj6EXTaSNRZhbcqhnh/rqTSoIBVlNuys67Mm0lyOUwdy/ZiKg6uvrKpxDRPqeYU1dQbVHbZ50XneGxsrCKXNJ03XkdTjZCU7e7udilWuOklbR6vTUeD5/T19aFcLqOzsxNbtmzB8PCwc8CZYxXYaitramqcE82l4NZm0qGhkssGddQZJ4lOAr1YLLp21wBBXV0d0uk0MpmMqxfLp+1rCdCw/Nq1tbXIZDKIx+Nobm7G7NmznSKdbUN7zvqEqQbtOxaWPsc6eXwPYrEYhoeH0dPT4zYs6+npwcjICOLxuFPE2/eE7xaDMQx2sGw28MCcsCQDGEhThR3vw2to2hzt/3Z1Afsj+2Q8HndtwfdYiXkl6G37sC+MjY25nL4sD4MxVq2pxLoS85r/nM+RhIoly1WduD3iLQzefnvsCvA9VVg1rkcldOWJwgYD1UbvCWggVDf1HB4eRn9/P4rFImKxGEZGRsbZb4I2rr+/H1u2bEGpVMKGDRvQ1dWFUqnkguGWRKcggCR6NBpFqVSqGDNVYEBw/NfAu648J4muQVXdTJ22iPXicQyc2j7NeYol0RkAz+fzSKVSbk8yfsbVZOl0usIe2ACLJWLVHth2Zl/Ruc7Y2BiGhoawefNm9ww2bNjgOIh99R2dCUpz7aN8pvozlf38vP2ePngS3WOXgQO2Gir+qEOiOVV3BaziTp0JRrzpbA0ODqK3t9c5e6r00zIPDQ2ht7fXRb/7+vqcA25BR6lYLKK/vx+9vb0uajzVyCGdS96/p6enIp8s65ZMJp1BzWQyzlG2z0TVb+pwhiEajaK+vh6NjY0u4r07o59KWGUyGTQ1NaFcLruIusfeA2/EPaYbmurEOpBW3aYqkjBVuRKi+rlC05RY54REPe+p99+e4lMnxbQ5lrhXtbD9zl47LLUH2wvYpuAHKpXGSjrTPlA5psoqXitMtQ5szb2ujrt16CysSp1OZE3NtlVVuv+GbppFZS+AUKeCtnJoaMjlUieJrfuL2NQZ6lTaNCy2Lqp25v9KDDMgwZVsGoSw5DSJY9afZK8+L6sktivdlJxnrlQu+w5L96FlZlnseK1zDW2DMPJcP+NvPtNoNIpYLIZYLDaOPNOyax9mP9N78pp2nmfLqymI7Bih99Nnb3Of69/b68N63TACS0lyva79zCretG21rasRJXpcWL3D6j4RvP322FXw/WNqUJsNbBvrNJUHxwe1Nzqu7co9tTToTcV0Mpl0YjXdn4FliUS2qp/V9lDYMDo66lYxlcvlCltORbimhaFfy8CzbkKqZdS5oLYDx13Od3gdvR+P1zkZ251zFpLrFHJwHqQ/1UQdfJ5cOcZrci6k8zSdt1UT61WzWzpP1TppkJlzLwZD+KOiE4/pgbXf9jOLyb6/3n5PHzyJ7rFLEIlsWzKaz+fR1NTkHMK6ujoMDw+75Vh9fX3YsmVLaGR2Z8sQiURcLq+6ujq39InGjeqhuro6lMtl/PWvf8W6detczjGWmUR7oVDA8PAwuru78fLLL2NwcBBr167Fhg0b3GZeiiAInIMei8Xw9NNPY8uWLZgzZw4OPvhgt9R2MhgZGXEThxdffBF//OMfMTAwgK6uLgDbyOVoNIpZs2Y5pVk6nXb34b1IHpTLZaxfvx49PT0YGhpCV1dX1U1CGhoa8OpXvxqzZ89GR0fHlCKf04m6ujp0dHQgCAI8//zz+NOf/uTS5fgBfu+AN+Ie0w2urOFEnw4GMJ5sVHUzsM1RUoUmMJ4A1H6rRJYG+JRct9fRNCUkbS3xTQKdhCedPnW0qOgi0cqx2AYF6EBS7ap15LJplkmdrmg0imw26xRhqVQKACqcUCq5GczVTT5ZbyqHrUOnQQK2tzry+pNKpSrKwvraFGU1NTVuw8toNFrhhANbnVeWr1QqOVvOdC48hvMQVffReR4aGnJ9hvMGXbWmZL46tCTRSZzTbnODUT4LqumpytY5i5IdXElmCYZisegCSewHrFMymUR7ezuy2Syamprc9dUZZx+3qwe0b7JNWVcllPmj17NkDVcAMg1cPp9HJBKp2CyOzywIgopct7qJK8ugaesSiYTrW7o6hM8zbDNdDfrY91H7pQ1WKcGhbadl5v0ikW2rPDTYRPD5cWNRXS4etuJB3ye70sGCx/Fd13tqO/D+k4G33x67Avpeaz9Rm8vxZ7r9xb0VtPEcdznO5XK5CsKaY+zY2Jgjn+lT9vf3V4y/ih1tY447+Xwe9fX1iEajyOfzLojLTSsZJGbKPa52ox/KMo2MjLhNLAcHB7FlyxaMjIygu7sbPT09Tqxmg9LFYtHZAB5HhXUsFkMikXCqdw1Ws+605ZzzlEolbNmyBZs3b0axWHQ8BgC3KonKcaaNicfjFUH0YrHoNlOlKp3qeY79bEO2Vy6XQ3t7O3K5nNsUnCnQdDUhz7PCRSsM0f5j52gq9mD9OVcYHR1FJpNBQ0MDAKCxsRFjY2NOgOiJ9OkD7TZQKcYBMG6c5BgwGXj7PX3wJLrHLoFO2pmCg4YlHo+7/GckmHt7e6edBKXhIJHMXGIk1OlwcVn16Ogouru7AQCJRALZbNYZqWQy6Yx4sVjEpk2b8Le//c3lY+3p6aladjqAzGM+OjqKZDI55WV1NL7FYrFiGRWXndP5jsfjqK+vR3t7u5tEMb+clonL0uiYRiIRbNmyper9k8kk2traMH/+fNTX1++WNC4KJaIaGhpcSh0uAfeT6r0H3oh7TDdImlIlbUkvq1CtlqqFP2H9zBLjOsHV30re8R5K5NM5sQpwhaZ4UfUYSTtNbaLlCFOi29Q0lgBUR4nf0clk3k0lqblUmjlCNYUZ1em1tbUVS7M1sG5VNbad9CcajSKXy6Gurg7JZNI53FyBxPqxD1BtZwlG2m/mISd5zbzcLI/tMzyfJHi5XHbKK6AyXY+9p+1TJANInnP+o+3PuUk6nXbzJ93wXAMPqmwn8aCEk6rbGBghKa/9R0l3rb/WnXVRQlmvr+8Dn7US3rwOA13xeBwjIyNIJpNuLqKbr/PadtNXJSO0D2tOdyUT+E7Yd1/7nAaYVA3OfsM2t6luVMVvlYVad21HEidKXACVKXhUqcjPtF4697IBPkuU2LawY40GEya7ItTbb49dhYn6lrXPvi9thSVeGaSkGCyfz7tAbhAEFUHwIAic+Gu6Ur1YEjifzyMWi6G+vt75o7RBansYgGTQEagM3G/ZsgWDg4MYHBx05e/r60NfX19FYEUD2qoeLxaLbu7A1CqarkRtgbUDLANTuVDQRu5AV8WlUimkUilnv23wlmllSPyzjNbW8pq0bdlsFvl8Hul02on7bCBfz7UK5moBKtoNwq7aUvtHJXoymUSpVKrgR3YnH7C/wAZCgMoNcPV5TjaA4e339MGT6B7ThpqaGtTX1yOdTiOdTqO9vd054M3NzVWV6N3d3S6qS1X0yMgIhoaGphTVrK2tRTabdQ5oc3Ozy3Xe0NCAeDzu8oep+k5389a6cMDiRhqlUgkbN25EoVBAb28vent73bKwyQwsw8PD6OzsdClImGaFRlHVk1Z1RuXc2rVrMTg4iJdffrkiZy4V9Q0NDUin024DkjDjyvrxfrlczhErW7ZscYZcyRBOhurr610b7knE43G3O3kqlXIbr1Gx6eHhsX9DVTScbJLAIzlmUyVUW1ljSV69h5KHeg0eawljPc6mnAAq04DY6/GeQKXamfepZi/DnBurdiWxSaeoubnZqcBJopPILJfLyOVy7ncymXSKL7avEqB0jAE4u0ulM0l2YOvm0QAqnGzab1WiU8FGYoCEJctHEheACzxzMzLNmUqHlTlTVcWmbUmCkzafjjNzllN1xrQkaj/5u1gsoqury+Vw5/yH8wddsq3qPCWM2F9rarbmWmU/5jPRZdVKzOvyfvYHSypb0r0aUWXVUJr/m/2I8yf9XskBPt+xsTEX+GdQXO/J87UfsXya7oZ/6/vB87VvWPKf74ANrGldOR/T9tE+Z5f7K1luA0JA5aZu1dqVZeTx/K3Pj5/ZuZ0G2CxJxf6tey1o0KLaKkQPj5kAHZ/8PH8rOAfgSmT6RG1tbUin0xX2m+NQuVxGf38/hoeH0dXVhUwm40hqrujT1WvV7gtUpjDR1XG01c3NzWhubnYkOoV0/F6DexzfNfCv+4fQfusm4GGBP7UfXEE2NjbmlOy0jbSXTFHH1Vlqa2mji8UiNm/ejKGhoVD7bVcCcFWZrsriNbkynMp7XWXF+tDO62asJLE5xldLC6P9QtsjrJ1oqxhAVpGHrh7ToC/tN0n0RCLhNtL2mD7YZ1hN+OOx5+BJdI9pQ11dHebPn4+Ojg60trbi9a9/PVpaWpzBBLYRBGqkent70d3djd7eXqxatQpPPfUUCoUCNm3a5FTWk0EsFsPcuXPR0tKCuXPnYtGiRaivr3dOOI2PjdBapw3YuplYd3c3isUinnzySbz44ovo6+vD3/72N2zZssWp0uyy3IlQLBbxwgsvoLa2Fps2bUJnZyfS6TQOPvhgzJ8/3+Ucj8ViLvo+PDyMdevWoaurC11dXS4dzMDAAAYGBpzx4wRq3rx5aGhoQDabdcS8XQbE50DHetasWWhoaEAqlXKpYWjYOdHgBqVtbW2YM2eOIzv2BGpqatxStq6uLkfs21QCHjMXPhLusSth1ddhClQdE1WBZYl0SzZZhZIquKl4VdDBtQR62EoeJf31fCWlVY2uhKKmRbFkG50fW3bdMKqmpgapVArNzc3I5XI44IADkM1mkc1m0djYOE7VzT1Aurq6sHHjRgwODiKRSGD9+vVuhRaXjTN1GkHCk2k46CBSYUX1GoPNmUymYtynk26V4gMDAxWkMlXydJ67u7tRKBTG9QMqqeiYK5nOOQj3EKEKq6enx6VbYboZ/lYVMjcfKxQKePnll1EoFDA0NIT+/n7npPK5c/Ns3WQT2LakneWiE8vyMSigZDPrp+lnaPfphOsztasjLFmuASGFbrLJlQYaRNC+xmtQyMB5haqwtV+SXKBajyQvAw3Ms8sgiLUrLIembLHKPBukstBctpqahvVje4YRQJqbl6AgQYMtGnzQsijBb9PnWfUp53maa17THrFvkJhSkoT380p0j5kMnypiPDj/iEajaG5uxqxZs5DL5XDggQcil8shl8s5/5d+L1OvkUTfsGEDhoaG8Je//AUbN25EsVhET0/Pdkl0+ockz+vr6x2J39zc7FZF19fXu4C4KtG1TDbdGklqEueDg4Po6elBf3+/s6cca8PGftqRoaEhFItFl4qEm1n39fUhFouhsbHRzTVov3XOx/sXCgU3txkcHHT2W4OdTJ2qKWI0xR/tGlXlY2PbUsSShKYdoS3W+QBtuI7vNsgeJr6YiESnbaUN0R8NbHCOwnlafX09xsbGkM1mXVCDNs2P8TsPqz4PS7VkMdl29/Z7+jDjSfRrrrkGP/zhD/Hss88imUzihBNOwLXXXotDDz3UHRMEAZYvX46bb74ZW7ZswfHHH49vfvObWLhw4R4s+f4DDqyxWAyZTAaNjY1oampCW1sb2traXGQ2zAELgsAZiVQqhcbGRuRyOQBwecq399LSGePSNSrf29vb0dDQ4Ix4mHMYVh46qdxYVB00LgmbytJXgulYIpEICoUCtmzZ4qLrhUIBiUTCKak1+t7X1+fyznZ3d6O7u9vl3AS2OVAkFlKplDOw1ZSVOkBzAsSNQqLRKMrlsvteVeu6hKzatXcHWB792d3pZTx2Dt4Y79uYCbbbqrdVzaFqWR5jCfCJrmeJxTACHBivQCfCxk86QWFEugXvp2p6LV+1tA5KzGlZ6BAzFzsV6Vzpow4vc3hrWjAuISchrYELVb2zHVXFT7UV75VIJJDL5Zz6nKSrqrRZJl5T872qCtyWw6bZYL1sqgt1WPgZSVxViBWLRcTjcedwav3GxsZQKpUq1HNM46LkLp+LKswmsmeq5Of56ghXC5rbpcHV+qZC20v7PM/T56tBGg3Q6G+WPQiCipQyVjXH65JoDtu4jHMTBojse24DYHqevhua6oBl5OdW6a1jiKLa5wo7p7JjhQ362XdVy8Cy6XPl868mFrHtqcSO3n8y8PZ738ZMsN8e24cGRelHczU4A9DZbLZiTGCaNRJzxWLR+e9MTUr7Hvaeq+3QlWG8H/1w5vImOc35AVdwqb2yqaxoa+0+DmrDafMngtodvSZtMP3l0dFRFwDXsZIEOm037Te5CZ0Xqf224zCfFe9PEQT3jeF5ajfVVtDn1vRrYbZe51hKsIdByXe2EdtWVxSqYETrSb+bZfeYHlTjyqbT5nr7PT2Y8ST6qlWrcOGFF+K4447DyMgIrrzySpxyyil4+umnnRr2uuuuw/XXX4/bb78dr3rVq/CFL3wBJ598Mp577jlks9k9XIN9H42NjW7Di+OOOw4LFy506jUqkyYC06zE43Ecf/zxmDdvHtauXYvf/OY3Ls0Ll/kqaEQ6OjqwYMECZLNZHHnkkZgzZw7y+bxLJ8Nl38Dklr6QWG5oaEAmk8GrX/1qtLa2ore3F+3t7diwYQM2b96Mv/71rygWi5MejOh40xByI7BXXnnFkRAktEnWj4yMYNOmTejp6UGhUHB54NTokdhmtJpEw2QJZV2Kl0qlnJNVKBScsolL8HivPW0wldgn0UPi32Pmw0fC933sbtutql11MoBtY5xVhQKV+SOB6ikdNM2Wknb2HnZsVMeHoEOipB8dKSV9dQMspkHTzf80kKvl0O8secljqWJi8Dkej2PWrFmYNWsWUqmUW46saiTNR87yk0hPJpOYO3cuYrGYU7EVCgVHmKqKl+UZGxtzq8Xi8bhTwevGYwpVXinhyPalqpmqYyp8U6mUU8T39/djcHDQqdZJoIapfGwQQklNti83CCOhXldXV6Ewp8PNtDKqcqf9pjpZA9nqiNNRZZ21P+lmaHTCed9IJOLaUecHnGfYNERsX9p/BgFsP7O583lvJUQ0fYu2Jfsc09HwnryX5rXXXOnaZkqi8H1UQoDnK/TZ6TvOclG0wPLZvqfCCb5HGuzQ62vbhK0GZB/mHJCrIHh/BmjCxjD9W/u+3lOfLeeL7H+qRFfwPZgMvP3e9+F9723QQJoSk3uyL3MMYJ7sRCKBefPmYfbs2Uin02hpaUE6na4Igmu6UI6//IwrphKJRIX9LpVK49JkMqVHbW0tmpqaXOq3OXPmuD03mpqaKnxT2jjOZTi/4LjFsoyOjro0IVw5FI1GnZ1NJpNuFTbzlE8m+GeDiLTVPT09bo82zlfUtnDfsWKx6PxvtS8Ucal/zDpzPmg5EPajsbGxilV6rKOmnGFb63U10K5zBP7PNgS2pfBiWWzgVeesSsDrfM3O2xgwKZVKFSvc9BoeOw6rQAemdxWOt9/ThxlPot9///0V/992222YNWsWHn30UbzpTW9CEARYsWIFrrzySrzrXe8CAHznO99Ba2sr7rjjDnz84x/fE8Xer9DQ0IDDDjsMDQ0NeO1rX4tjjjlmnOGYSFFF48Cl40cddRSeeuop/OUvf3GbjlqymoaAKWROOOEENDQ04Oijj8bcuXPHESOTJVd5HMsUBAGampqwcOFC9PT0IJvNYt26dXj22WfxyiuvOEM1mYGFdaUB5OC4ceNGt6EpQaM9NrY1j9vg4KAzjOroKMnNpeBcRjbZ+tL4Ms94EGzNw86B3JLomuNtT4KTQtadZfbw8Njz2N222zq3uuGjOg2anxKoVEXr56r00e/o8KnKWQl0q3AnAapqJFXnqpKX59JB5HVisdg44k/zG7NcJPd4jLVLSv6xTNy3hHnQZ82a5ZxCJeSVRAdQsbEnCdBisYhUKoXOzk709/e7nKMk/tUhY/txw+9UKoV58+ahsbHRrSyjYqxQKADY5hDq33QiGQhWkp1KLy45jkQi6OvrQ09Pj9uIVjetVCcyTHWt8xrWoVAooFgsoqampmLDMLYZiWHNw642PMwJV/JV21znM0o8cD7BdiCJzneA9luXhZN8137K80lks8xclafErirSbDtpHe08jM+JG4tquhRuAEfiRtPUWBJcU8/oe6xtrn3Cnq+kOwNFfH7a91UZyDkbc+dq/Ww/0TbVcUifI58TgwXsR3w29p0mwsgTfT46xyThMzg4iEKhgCAIHPGk746qID08vO+9DWq79d3dU4SSzjXS6TRaW1sdiT137lwkk0k0NjZWrCqrqdmWOg3Y5rMyYDc0NOSC4bTfrK+mVVViNxqNorGxER0dHUin01iwYAGam5uRTCZdihS1o7pShuSx7meRTCbd3CeRSLgxPhaLOTKb5aXIi2m+JvMsaB843jKgwFQkfX19Lm0bVfgMLtA+2D1Hamtrnc1WsptCAzv+6/yEbUPiWzc51euHzRFs8FQDrmH7c6hinG2u8wj2Cc4pNb2arliiTWW76cp0T7DuPKwf4G3yzMaMJ9Etent7AWxVPwPA888/jw0bNuCUU05xx8TjcSxZsgQPP/xwqCHnBJ3o6+vbxaXeN8GXnfnPqNym8ZjKdYBKpTYj2cy72tPTU3FOLBZzm2c2NzejsbHREQE0yDtDqFqnjxOQ+vp6t0FoU1OTM+aDg4NVr6XOkBpYuzEIUBltpFGi0eNnSv7sKuxNZLQ33HsffCR8/8N02G6guv22fcoqxYHKcW2iMc6SXTo+633oHNLZ42+9tpLkqj63xF61fq3HKGFnlSl6vjo96sSq8pf2h3uWKNGqqlc6ZErU2gAD1VvJZBKlUgmpVAqpVMpt6kUnl84jlWjRaBSpVMot86ZzGLYMWgMdrL8GTuxzZZvpJovJZNLlS9dgs80FHmb/bUBFn7FdFs1+wnLoj9pv2wftfVRtbPtcWF21HCR9tSxWsVbt3dBzbZ+3ddZ2UkWb9nmtixU46HXoNJLAtxuaWSVdWPm1DTmHUuKC97HzKPZjbX9tc/uMbH/Qd8Y+F1tXvpsarKimNLP93t7flg9ABRGjOW313lZoMRW76u33/oddbb9nMnQMmQmgr8xgs9pQ2nENila7BgDnl9J+03ZTCV4ul53d4pjDnOYMUOdyOXee3RgbqExTZ+2OgjaEwW+mnWGaOa44JtlvSfRqgQ1rh6yts4FS2rzJpIvR9gyzd7RbWg7+bcunIoMw26u2L8yuEmrrqtntMJGALY+1e/b+HtOPnbGtu+MeM2UMnCnYq0j0IAhw6aWX4u/+7u9w5JFHAgA2bNgAAGhtba04trW1FS+++GLoda655hosX7581xZ2Hwejw1SCL168GI2NjWhpadmp69Kxb2lpwXHHHYcDDzwQa9aswcaNGysick1NTVi0aBGamppw9NFH4zWveQ0SiQSy2ey0D+68XiqVwqGHHoqOjg7k83mMjIxgy5YtePrpp/GXv/wl1NgyasuJBfPEceICYJwBZDswz2o8HnfLwbds2VKxgRgwPu/vdMBGptUJ2x2D/GShqkYlCzxmNrwR378wXbYbqG6/y+WyS2NFp48K2jC1qCXW6FDY9Ayq6lESKmxPDJLJlvRSItB+B1SmotGUHbwnr7u9sZ4qIjsm8v7cfyQajSKXy7mNtebMmYN4PI6WlhbU19ePG/OVsKZ6WB11/s20LNFoFH19fchms9iwYYMLgrOeqVQK7e3tyGQymD9/PhYsWOCUVrwPlcS6rNmC5DeVymwbJRrphNfU1KCtrQ3lchmZTAaRSATFYhFdXV3uGStZQMeXf+sSairDlMS3jqmq/mnDNVd7GImrS+5Vic5crWF9Tvu1VXLX1tY6kjYSiVSsWEskEuPIXSWxuYQ/LFe49i8luPn+caNRBetmCWBNv8PVDAMDAxgcHKwgi/Q5qjBBiWqqLNleLKuSImx/TfsEwOXp1euwv/Nvjg18JtpPwt5FHXvYtmNjY+jv73cbpg4ODla0P8cLJfq1vuwnHB94TfZdPU/TAmo/sGmjtJ0mA2+/9y/sDvs9k6Hjun62p8BNPJPJJGbPnu2U4LNnz0Zra2uFfea4wLFCxw/ahGw26/bkotCLK7ZqamowMDDglNI1NTXI5/OYO3cuMpkMFixYgIMOOgiJRMKlcqEt51hmg60697JkLsdX2v5cLocgCJxKvampye2bNjQ0hI0bN7pxmmOdguOvteEqYNPc7/YZ6/hOgl/rVC3AyvvSVqj94bE6V2R7MEjANFz8n+ID3QNMBQJaFrtRty2nzu1s2RU26MpVZ3qctVUeO49d3Zbefk8f9ioS/aKLLsITTzyBhx56aNx3loQMU/kQl19+OS699FL3f19fH+bNmze9hd3HoUYpn8+7ZdhTVaHba/LcdDqNuXPnIpvN4m9/+9u4wZ1Lv9va2jB//ny0tbWNy2E5naAxbG5uRhAEKBaLWLBgAXK5HF5++eUJzyVhroo/GvFqsBF8OmzM41pNhbgzg+P2Itdh3+1pzCRC32Py8EZ8/8J02W6guv3mhN9uuFjtXmGKIP5vFTdKprGMeo9qk146kXRQtG6a4kWVq6pU52dKFCoBGWYHlAxVElyvQWKX+dDpRFNJNjY25tKJkaQjOcr70CZp4CGVSqGmpgbDw8PO0e3u7q4gY0l85/N55HI5NDY2orGx0aWTsG1qSWYAFQFdVformWhJ8Egkgkwm4+rU19eHaDSKgYGBcYFsnYtY9bY+E95Ty2rVXZrz1Srsq/U7/eE8S5+nnqNg3ezGkZq/VXOq6vl6ff6tgRt9DkpM67PRnPXaJwE4AoBtoeD9WHaSv3oO51E6FwxTx2k7az/S1QZKonPOatvdtivryXvpig6ts9q2sGfNFC5MDUAVZdixSorYeld7X1g/BqKYuoVL9BnMCKufJ9E9wrA77PfegJnSd2tqtqbo4gai+XweqVQK2WzWbdip47sdwzWor4R7Op12xC3J6y1btoxbvZZIJNDQ0IBcLudyoqs6Xe2lBuGtglkDz6yXlktFaJwzMA1YT08PotGoSxsXiVSmnSF0zA5bQaRjrAbrrdghrPxhz8WKMDTgqW3Ba+v1NNjBgKcq5Xk9/V3NbmxvLNfvrd1T8QfLxOdvif99AdXs7p7Crr6/t9/Th72GRP/EJz6B//zP/8RvfvMbzJ07133e1tYGYGtUvL293X2+adOmcRFygkuWPXYcJM9puBkZ3VEC3SIajaK+vt7t6E0HjUa6tbXVbaJSX19fdXnYrgCd8fnz5yOXy+H555/H888/j3K5jKGhIUe20CiRPK/mvG4PmoOW6jHeh+QOVU1UoE12809VMnFzq8HBQbeRF4AKhVixWHQTlbBcnbsLqpijQzrZvHgeHh67D9Npu4Hq9luJbXUi6urqUCqVxpFf9jx+RnKNjsNEK1wsIRimFqYDZW0Uy6GOlKqcNCUD76HKMqqiWA49XzeT0vIzxzTtqebJZpty7LdOlTrR6ryRkFeyNplMuo3luMx8ZGTE3SedTrs9UHQTbF5Tn586kdrOWkd1WK3CWUlu2qxMJoPGxsYKm6YbdquynaQjSVxdpq4kgD67mpoaVy/rpFvQYab6bGhoqEJ1zbKEEQ1KjrIeXKmmhInaSyqSdQk7j9ENPK1zrhtWalCI91DyIKyfs07sY7pBmzp1kcjWdAHJZHKc2k7zzPI3+6g+B7aTVTrqKg9dYm/7lgZlbMCA9+J7ru1I2ABLJBKpWCGiQgi2CculbWhJe6o7w8goHXt0LLTBnLCAnbafh4did9lvj8mDfiUD3/zRVKZhubiV3OZvDU7STtM+B0GAXC6HdDqN0dFRR7bX19ejqanJpXLR/bI0JZbaR7XVNhBsf4DxabYAuDqWSiU0NTW5DcNp+2hjrc2nHVEVN2FX/lHxbcdiBuht+eh/M6/82NhYhVBON4IPG29pB+ljc8WaDYirf6+2k/VVm6H2mz9sA+0TfCZhgS8lz+2KAk25tiN7A2g/mAk2ZyaUwWPvxIwn0YMgwCc+8Qnce++9ePDBB7FgwYKK7xcsWIC2tjasXLkSr3nNawBsXcK4atUqXHvttXuiyPsFYrEY2trakM/n0dLSUrFhyXSAy9TK5bLb5TsIAsyaNQv19fU4+OCDcfTRR2POnDnj1Em7A42NjTjmmGNQKBTwyiuv4IUXXsDAwAA2btzoSBga4nQ6jfr6+oqI/2ShyjcAyOVyKJfLjkinEebGUQMDA4jFYkin0xXkxESgkR4aGkJvby96e3sxNDQEYOv7x8nJ4OCg25yKJMFU6jKd4KRiaGgI/f39rszeGO4d8JHwfR97wnaHLSslcUhnjI6EKoS0zHRelCi1JC6dEqv4odPDsgCVGzbyeEuEhZGCSmjyWlTlWsLWKmx5Dzo7dHTssuBUKuXUbCTzuOEWHWJ1xDjm6waTVPOyLPybG4z19PQgl8thdHQUqVTK7S3S0tKCfD7vUrBZh1XVtDYwwXqxbRhU0DQcmicV2EY88LhYLFaRq52BWEuKMngdjUaRTCZd/fgMlLCmU8tctUoY0NG14xefTbFYRH9/P0ZGRpBMJl2/4HPlnMISI4VCAUNDQxgYGHBpUBgQUEec7wGdcK4qYPtq8AXYFnBg39J0O1Y9x3fCrrbQeQJz0gOoCMpr/+Q5bG8lzJkXV1PqhAUxWG91/FVlSEKfS9P5TlhSmu1mAzF8HvZ9V9g5qV6Pz5ppkXQepeMBf/Md5CpGPY7X5vtiV7HoGGTfL/v8qtUlDN5+7/vwvvfMRW1tLVKpFHK5HHK5HPL5vNtTBEDFGGfHPY6f+h1XOnHPEwDOr+/v73cpzyhimzVrFubNm4dcLofm5mZks1mXzktTi+h4bGH3bOGcjJ/pOK6bbY+Ojrp6FItFd36hUKhI70axl27WrYEGQoUHHCc5V1ACH4DzfXUcpa0NggB9fX1uJZ/OL5lWkPM32jv6sfSrGRAgkR6JRNyqLIrFYrEYMpmMW6nFALYVNug8l5/x/uwjNvivtk7nTRowpw3TgP1Ee3qEQa+p9/fYPfD2e/ow40n0Cy+8EHfccQfuu+8+l18TgDMakUgEn/zkJ/GlL30JhxxyCA455BB86UtfQiqVwvvf//49XPp9FzRsdHbClhRNx/Vp+GkMmL+Ny9aYw3I67z0ZqPI+k8kglUpVLCG36rgdUaATavS4nMou1bNqNu6erWVRqGNvFWqaS5dRbl1mzSj4ngKNPMtcbWMujczrbwtV03kDsXvgjfi+j91tu8OIPQDOaVHyWUk2HmPHjzB1zkSwhLcSqyyfJVt5b/5WVe1E9aQzqTaG9dbrWXKQ52i9rX1SpzZMIRZWHlXH83qxWAzDw8NOoUabrqo1depUqaZtGtYeVuFWbTyxije2MQPakUjE7VeiBAKP1/ZWJZ9VT7Pt9Z623cKU2tpfaL91lQDPq+agatCHtltzZIcRufqjCmkti1VDa9vZfhAmYLDvggaI9B3QVXBhy8u13TQARlJEVyTwmjxfVzLoM1IlZrV8+zawFlY/TZVky8721+80xY62i+YnJ2zba0ohWyfb5mFKT36uY8LO2FFvv/d9eN97ZkNtrQa1w8brMPut4wHHH1VrM9Csq6/o8+uP3cPDqpYJnU/Z8bWaDdeyq3qe5QDgysFUfrSXLIcKJuwcS+cX9HU1SM02UaLaqqhpg9RH5nwCgBNw8Jnxfhq8VFW3VXZrIJg2hMfq3ITtRYT1BSveUDtg28O2jYJ11lVa1fzvsL81wKB21Pvguwfefk8fZjyJfuONNwIATjzxxIrPb7vtNnz4wx8GAFx22WUYGhrC0qVLsWXLFhx//PF44IEH3HJij+mHKoN2Jg/6RFAHn8b84IMPxkEHHYR58+YhlUrtdvJcQYJ/zpw5eM1rXoPNmzdjy5Yt6Ovrq3D0dJKzM9BI8uDg4DiDPDg4iFdeeQU9PT1oaWlxDhrTyLDMAJwRHh4exubNm9HX14fu7m709/dXpKShkRsb27oZ1tq1awEABxxwAOrr63eqPjuDnp4ebN68GevXr0dfXx8GBwcrHGhdWqjqNaovNOJOpT0j/lpnj10Db8T3fexu28133BKI1mlQVTkJJUva6XlKOilpp0tZlbCzTgFtpY7/qhiyv7nqis6bqoL0b32HWGYSxFZRqs6SXoPfqW0JczTV+WVQgk6QqqN12XQikUAQBMhms2hoaEAQBKivr0c6nUZDQ4NTdUciWzfEZNm1rdmuGhiwTqwSs0pgq81T5Rh/+H0ymUQul6uwG/qslPSnTWe/oK0gqKqmIt6ueEilUgC2KbHZtuy3fX19TolGQj2TyTgFvQY3+ByoUqN6jc8mkUi4uRMJ576+PndPPje2B6+pjreFHmPB+QTVeUyhxP4ZBIFrF6rvuHpuYGDA5eCPRCIVqe9ovxmA4d/RaLRCLc57kpTne8j3KKwONi2PEhv2cx1PLHlg+yqP1fFA0x1RIKIpcjT4wWfHumrfDIJt6nl9J8JS2yh0TNKVB/zOrsqZCN5+7/vwvvfMhQbXaG85ZnLcAMaT1SRgrUpZCWPajGQyidHRUWe/AVTY71wu52yTXR2lpLglwlkWDe7p3ETtE22Gjl0ky6kq574qsVgMPT09zpfjdTWnOtsobNUNy8v2ATBuTCVxrwFg2iyO3dFo1NlilpN2q1wuV9jc0dFRDA0NOXuv8zmb/nVkZAR9fX2IRCIVaXSDIHA2XvcP0We7vSA425Yr86gyZ1/jcZw/UxXPH3IGKsjQgIcGrvlsOefROQPnAGGrD/d22HnonoS339OHGU+iT+aBRSIRLFu2DMuWLdv1BfIAMF6JPt1Qo6vR8Y6ODhx11FFoaGhwy6X3BFi+uro6tLe344gjjsArr7yCJ554AgCcMeWPXRq9o/dk1J+/afTHxrZuBLdhwwa3pC+bzTo1G9tSFVt05Ddv3ozNmzc7Z9ZuzsIBv1AoYN26dQC2TqZUeb87MTY2ht7eXqxfvx6bNm1Cf38/isUigG31ZB76WCzmcvZpTnlOgMrlMgYGBpxTb0kwDw+PHcPutt2WALaqG1supsiw56gjyDKqYwhsSzfClBhEGMmuJLMSprqaib9pL4DKXMs2rYRVvurf1VSmSk5b0jBMzRY2/mmZWE51NFk3Bi+BrfnH8/k8AGDWrFnI5XIVjiWvqyowGzzQ6ytBqUp767jrMmzWh/fRNk8mk8hkMs7h1noBGGfHaV9t2hw+c9oX6xCPjY25wEJvb69zFFkW9hWq2FRZx2OURGcbaJ5UlonPknMnkugDAwPu2SWTyYpraZBZCWZdSWHJEts3NH8r0wLpCgX2KaaV0TQ0JMDZ5pxb5nI5VwfWi4ENznv4m89Ay639UvsSyQP2k7CgmJ0HWJKa76b2R0uiq9KfK2JU3anzZz5fkmKaG5/1ZxuzPFof9hFVuiu0zFZpqM/cw8P73jMXSkTqGK97VgDjV3Kp7Vc7p6SxkvLMj57P5xGJRNDS0uJSyGSzWbfXV5jqXedLdk5my2XLau0Px061b8lkEnV1dchms8jn86it3Zo6tVwuu3GQNp1Erqa4sfMo3kvnUxroBOD8ad10nXaGc4va2tqKtHk8huO6zlfohzMtCsujPr7aksHBQQBbfXumdlGFvar1VVih47vaBJ0vaRvTfttnp4H7oaEhl36mWCy6OmrgnkEE/V+P4bVGRkZcqhzdO2RfgnIw1eboHnsfZjyJ7jEzoQOuzRc7XddXh59IJBJuo7KdVXZPF+LxOHK5nNstXCcSuwKqqrTRTaZZKRQK6O3tdXlfGYnnsTSSdjNUqgP1PqoO6O3tRTwer8g/V02ZNt1QYqW/vx+bN29Gd3e3IyI48autrXWb4cRiMdTX11eQ6MC2CY8uvevv73dGvFAoOKfeLq/z2Hn4SLjHdIMqHKuengiqIlLS3Dp3YUG1aqRTGPmsBLGFHcfDHDlOwJVItfciKagKNcKOYUpU839eQwlHa3/1bzqKdA71eFXoKkmnq8o0PYVta5bF3lOdRduelkQP6wN6fSWPbf53/d4q7HTpts5RrM3X+mv76/MJIznoQFKZxo1g6cCq8ph2jOfTyVdiRYM1vK46/1axpopB23Z83qrMrtZXbBCL9hYAhoaGXA5Yu8kmgw2pVMrl7acqT9MC6YoLVdKxP9rct+rAVps7ap9gnbUv6TvGa+p9NIDDwIW2mSoCtdw6BqiSUNV71cpb7VnZgBlRTRwwlfm0t98eHrsfdny2Nt2KAPQ9tbZPA2lKKqufpfODMGHYRCus7ZilNkbH0DAbbus2UTvQ7yuVSuP2atGgps55tK6KanOKsHG6WoAS2BYk5qo0ltWmD7PXpf/KcV8V6fqMdA5i20TLZQUG1YQRnKNMZC+U82EAnHVkO3MVpa7eo2Keq+LVvlGtz3bh3EfLoqul9kaE9fM9DW+/pw+eRPfYIZDIrKurw+Dg4LS/WKr0o6Goq6tDU1MT5s2b5wboPY2amhq3lCwIggqDsSsIZnXGrMEcHd26+SeNdU9PD+rq6pDJZJwCm8aby7HUeEajUcydO3fcEmNGimOxGJ577jm8/PLLmDVrFhYuXOjU3tZh3RXg5KFYLOLPf/4zVq1ahS1btqBQKLjN6mbNmoVEIoFZs2ahqakJ8XgcDQ0NbiJCEp2TkXK5jP7+fgwPD6OnpwednZ0olUpYv349uru7USqV3Pce04fdbcRvuOEGfOUrX8H69euxcOFCrFixAm984xurHr9q1SpceumleOqppzB79mxcdtlluOCCCyqOueeee3DVVVfhr3/9Kw466CB88YtfxBlnnOG+v+aaa/DDH/4Qzz77LJLJJE444QRce+21OPTQQ90xH/7wh/Gd73yn4rrHH388fv/730+5jvs7crkcADgFDZ1ES6YqqaeEJcmvMLtSTSVlSWCShGFOiRKPFprGhEFBJSG5bFfLwuupWllTdJBw5x4ZSubG43G3+oZlVrUrFVJheS/pGNIB0o0ZeSydSG4cSeI/lUohn8+7lUIasLXEvlXoWqKRbaCBfM4VVIWlz0CfgyrVcrkcgiBwql/aPl6D7Uv1OJ1IJbWpkFbCms+K6mHNK0vVHH94DOvGtCx8fiT72R+sUo8BY5LQtPlKDJdKJQRB4BxgKvp4PfafsL7Gd4TtwSAASXDN56qqO20Pbpy6bt06vPTSS+jv73cpbGpqapDL5VwQnCvIGhsbEY/HXZ/R8g0PD6NQKLjAdzQadbaaJDb7n52HKaHA62kqp7BAVRipwrmPKhZ5r+HhYZd2xhInPF8V8kxdw2tpvmEtZ1hAQMvG91aDJduDTXszEbwT7jHd0DHZIxyaq1zHXiXQ1S7oHETHHYqFaFM4P9BNp5keS1e9MPUZ9wGjX6V+Lp8fxxO1QZZE13JxvAIq07EReg0NXqbTadTX1yMS2ZrmpFAoAIBLTaK+OO0T66djuyq6WQ9VmXNepv6xLT/TyHD+VFtb6/xT2n6er/WjYpv+qV0ZCMCtDKBynfNc5QNoK6yCnWD5+EP7yFRsnMvp89FVdqVSCcPDw9i0aRPWrVvnVoEHwdaVDY2NjYhEIm7fhEQi4bIGqP3W8nAO2t/fjy1btqBUKmHLli3O72aq1rCVAzMdOv+ZSWPb/uB/33jjjbjxxhvxwgsvAAAWLlyIz372szj11FMr6rJ8+XLcfPPNLh3ZN7/5TSxcuHDSdfMkuscOgQaDRndXDA46CQAqjbhOFvYkIpGty8Fra2uRzWYrlldZknu6lfNhkXBOeEZGRlze9MHBwQqHOhKJuKVYANyy6UQigXw+X7Gb+djYmFNlB0GA7u5u9PX1ueXomn5gV4MTlZGREXR3d2Pt2rUYGBhwuWOTyaTLtdva2oqWlpYKI87JJyc5nEDQWNPAF4tFDAwMuPbhpMxj+rA7jfjdd9+NT37yk7jhhhvwhje8ATfddBNOPfVUPP3005g/f/64459//nmcdtppOP/88/H9738fv/vd77B06VK0tLTgzDPPBACsXr0aZ599Nj7/+c/jjDPOwL333ov3vOc9eOihh3D88ccD2DoRuPDCC3HcccdhZGQEV155JU455RQ8/fTTSKfT7n5vf/vbcdttt7n/mS7CY2pgygybZgOo3ByJxCqPDQtM2rE6TIUeNp7TcbTEVZi6SgldVblawljLrE6VKly03Gp/tN6sbxAETm0etqGjOodW8aSEujriJFhZP03twXPoGJIQVUdU20fV9BZKttM5oZNH+2dVZ0qg633U2VcyIKxtldgGtm0CyuuELZ1mP7B/kwDge24DtLYP834kPFQRqH2QJCywdUVZMpl0Zea1+Jw0lYwSMGFkiLaVpsNRpZgNGKnzyB/2ieHhYQwMDKCnp8fNLRiY4bwjk8m44H8ul3MOOTeSZ/uw7UgEcCl/IpGoyBuu77fWQ+vJd4x9QttW66rPV4NYOhdSsgqo3FSWZJWq73R1gb7D+nc15SShqx3Y9+05+o7qZ0rCTAaeRPeYbthxx6MSfD+t3bT2W4MRVnXNvxmcVbsFbPO59UfV6LSVNj2Hjm18fjquqJ3ROY2tgw1y8zOtnwYNOO4mEgkUi8UKgptzHB3XOIZzbmNJftuGWiaO51pfG/hRe6Nzy+HhYbdyisF2qrKV+Na0fpwf6L11TqZBa52v6ByR0HkV60jbQxGCztWs7SfJztXrhUIBAwMDGBwcdHNKbftsNotsNotEIoGmpiYkEgkkk0mkUqkKW6b+N+eTsVjMiS9qamrcinDLd+wN0D5v56J7EvuD/z137lx8+ctfxsEHHwwA+M53voPTTz8df/zjHx1Jft111+H666/H7bffjle96lX4whe+gJNPPhnPPffcpPf18CS6xw5hZGQEvb29brML5ueyEdYdRalUwqZNm9zGFapcCzMSexLq1LKcNooLbFMO7gzshKjagKbf23xlkUgEmUwGra2tqKurQz6fd3nL8vl8hfppbGzMnU+DSye1s7MTyWQSLS0trl7TrbzXtuvp6cG6devQ29uLl19+GVu2bMHIyIgz2G1tbZg7dy5SqRRmz56NlpYWxGKxCiW6kug04tlsFuVy2anqWd94PI7+/n6MjW3NRTc8POwCRnvaCHpMHtdffz3OO+88fOxjHwMArFixAj//+c9x44034pprrhl3/Le+9S3Mnz8fK1asAAAcfvjheOSRR/DVr37VGfEVK1bg5JNPxuWXXw4AuPzyy7Fq1SqsWLECd955JwDg/vvvr7jubbfdhlmzZuHRRx/Fm970Jvd5PB5HW1vbtNd7f4M6e7QR6tSEOWXVcggrqRimftHx1TpgqlK339MmaCoOABUktJJ66ixqmVUpxHJPNO7yOzpmdXV1ToFOh0jJaNuWllxTp4Cqc3WOhoaG3IZTHEvpGDLQbG2YrUvYGKsKKtumY2Nj41S/9lxVx9EBZgoQVf+Glck6QKoGJjHBuQCvZYMt6uzyGqocU/LcKtH03Hg8jkwmg5qaGqeeZxAhCIIKJ1xztWrKI+Z11dz0Yeo/rYMlRVgH7RP2HNaB+c+LxSJ6enqcio2kAUnyaDSKfD7vnHBuXkdHXJWC6mizzUmsM7hTKBQcyW7fZZ2j2fpMpHpT0tkGrXhu2LiiajobsNB3zZLgqtK0aRTttfWdsvmRw97tsACKh8fuhp9XVyKMhOY4yLRdmsOaNp3fVWtL9U2tHeUml4ODgygUCm6VjyV7dYzUcS9szNPfai/1vhqYnSjQpyuF6MfpKira3mrBQB1XbfkAVNTPqtL1XL3e9nxwVbHzHvF4HNls1q0Eo1/K+2kdONZzBRttNOctVJBzThlWLy0Tf9t5qZZZ5zrsJ7Tb5XIZvb29KBQKbgUZN/0mR9PY2IhcLodEIuFWkiWTSaTT6Qo7Q797eHjYzQ31mlTbq/p+bxsntC33trJPB/aU//3Od76z4rpf/OIXceONN+L3v/89Fi5ciCAIsGLFClx55ZV417veBWAr0d7a2oo77rgDH//4xydVP0+ie+wQyuUyNm3ahLq6OmzcuBF9fX2oq6tDOp2eMIfjZDEwMIDnn38eW7ZsQU9PDzKZjNso0y6Z3ZOgY05imeVk1JZGlI7xzhLp1SLRFnSoI5EICoWCm4Aw0DFnzhwcfvjhjnBuaGhwdVGnWAd+Vd+lUim89NJLSKfTTvW2qwIbnLRs3LgRv//979HV1YWnnnoKr7zyCpLJJBYsWIB8Po/Zs2fj4IMPRiqVQltbG5qbm11ggESB5pZjnRhN7+npQUtLC4aGhpBKpdDU1ORyrvf29rpN2djme9vSspmE6YiE9/X1VXxOdYeiXC7j0Ucfxac//emKz0855RQ8/PDDoddfvXo1TjnllIrP3va2t+GWW25xqy9Wr16NSy65ZNwxNPxh6O3tBQA0NjZWfP7ggw9i1qxZqK+vx5IlS/DFL34Rs2bNqnodj3CoQkmJSFVgKzgman5rJUjtChtLrNKhsg4XxxhNj6BEF5W0JHt5b+uMElQu8Zqqdic5alW2ek9Vw9LJpMM8NDSELVu2oLu7G8lkclxg1ipo1QFWspKOEcs0MDCAvr4+l/M6kUggFos5WxEWZNb20XLr8+JvfVZUIJOMVYLckpRKbLOfUJFH+xiPx0MJV61fEAShARg68vxh/7LPU/tHNBp119DUOiTHtR3YD1KpFJqbm13O0UQi4ZZbWxJD7ZX23YGBAUfKcE6laji2c9i8RdtY87XbcV2VWL29vdi4cSMGBwexadMmbN682ZWVcyemxmtubkZ9fT2i0SgymYwj2VOpVIUac3h4GIlEwu3xkslknJotlUq51WTFYtGl4FHCWnPEa4pA1kPfq7BnqClw2K78remAOFboChXbv0lUqUJQ217nfkz/o8RL2JJ3TQXDc1U9SeEHn/VkN3ObDvs9FczU5eAe0wc/n94Gji0aWKutrUUqlXJpVKhwLpVKbsNJEuw67vJ6/M13l/abKuMgCBwx2tfXV7FSiPdncFaV8JYcrOaXa5ksactxMWy+oedz3B8b27ZpJ5XxtN1chWTLYW2gFUjo/EqD4GHj6vYIUbUfuiKKdaP9psiLK1PDAri0TwSPIaFcLBbdvIfPUe9ly2XLr6sA7HNRpfrAwAA2b96MwcFBdHd3o6enB6Ojoy4VTTabRUNDA6LRKGbNmoWGhoYKERttPOdptN+9vb0ol8tudVqpVEI6nUYul8PAwAAika0r5/v6+iraYrJ2JMx+7y7Y9p0pY9zust8zxf8eHR3FD37wAxQKBSxevBjAVsX7hg0bKu4Vj8exZMkSPPzww55E99h5WLWUGkGrmmFqFy4ntgZwMtAXm8t8ent7ncpNI+AzgUBXaHtw2ZY1VGFGbSrQCcdkI5uWGKIamwYtk8m4/KPqkIWBzhsdZ6q/yuWyi4TTGd9ZVZM6++VyGaOjW3Pwd3V1obOzE0NDQ65N4/E40um0q08qlUImk3Ebi3KDMiXGNDAAbFuePzQ0hLq6Orf7eblcRiqVcnXkZFUj/vtbZHk6MB1GfN68eRWfX3311Vi2bFnFZ52dnRgdHUVra2vF562trdiwYUPo9Tds2BB6/MjICDo7O9He3l71mGrXDIIAl156Kf7u7/4ORx55pPv81FNPxbvf/W50dHTg+eefx1VXXYU3v/nNePTRR8cFBDymhmrjj9omq8axk179Tn9blWg1wrfa/QmOYSS3wibZ+n2YXQ0jLvVvHfdIFHJc1WAifzTtCK+h7cVy6P15Dh1GzauqpL6q661jr9ezajVbjrCAiLZFNfsYVhd+puT69myXbQfrjAOVJGk1p5VQolX/1+NYNk2LQ3KZJDrLov1Vr81jWA597npPq4C37aRtWE35pkEOAG4Tc6odNQgTiUQcCaK/wzayU0IH2La6gnMjJdZ1dQLrrgEtLWeYOpLls+1arb5aZ/u8FUqOV2tPvaaS8Ko412Mt0WPLae+hwYSw920i7E4SfSYvB/fw2FXgGMyxjkFeBj1J8vJd1vkI33W7l4LCjnu021T96l4dYTbUjlV23LJ/h41DU4XaVhvoDCuXnbfZcStszjVR+Sbrd+vxel36yGrjmKpM7a0lijVoocFx/aEdZ72s3a5WF/07TKRn94FhShfaOBUgaO7zZDJZsXKCvzUIzrRCDIhQ0MAA+OjoKBKJhFtxwOdu0yXOZPCZzSSeYDrs92REbHva/37yySexePFiJ7C49957ccQRR7j78Dx7nRdffLFq/S08ie4RCqrfuJnnnDlzEIvFkMvlkEqlAGzbuG3u3LlYt24d+vr60NHRMc4gTBZBEGBwcBClUgmvvPIKHnvsMWzYsMGpszhpmGlQh0SVXIwKc0LCNt0R0LjRgGmKle0NhpyMNTY24rDDDkM+n8ecOXMwf/58R0BPRkVOMkYd83K5jPXr16O/v9+pt9Xx3ZHnRVKH0e/nnnsOXV1deOaZZ/Dwww+jt7cXg4ODaGxsRH19PebPn4/m5ma0t7dj3rx5SCaTaGpqQn19vVshoEEg3gPYtoELI+qxWMyp+ZLJJPL5PIaHh9HX14cNGzYgEom4iaZuyDKTDOTegOkw4mvXrnWbSQKY8N0KIzwm6pvVCKFqTsP2rnnRRRfhiSeewEMPPVTx+dlnn+3+PvLII7Fo0SJ0dHTgJz/5iVti5jE50FnQ1BgEVb+W1NK0CDqmWUVQtRQPqiYnOMnnDz/TMvKaSl6FOT28r16f9ob2V3Nqa05lEo8MKFqlNZ2U+vp6VxeuoKKiWlXTrH/YUmiOhUzX0d/fj82bNzslsLYNHShNQ8I2oGPGJeq6tJgKJk05QvJV5xtKGCvRrWQ/78XPNUivP+pU2mekfUiDETyXSmGWn/abwVpu2sY0Yfq8ta+x3qlUCrNmzXJ7f1D1xf0+NMe2Btu176h6nu3b39+PcrnsrkWbSUGEptDT/mDnAqqQ1jyrvH5XVxdefvllt6k578UgdyaTceq1+vp65PN5p3KjnSZhznoAcPNUBhb4rOLxuAt6U13J9tD0Lkq46MZqbH/2Nz5DGxxRpbgG1zRQwevoKkptT9u3Nd8996Nhf6EClc87jCjTAAHrx2diVZE61kx2NeHuJNFn6nJwD49dhZqaGpcaI5PJoL6+3m2AnU6n3VhXW1uLxsbGiv1BmOJK5zxhJDJ/qx3q7u524jWmU2V5lKDn/IJl0DGI46RVDdNusKz8ralLdCzm+GYJeb0+x9qw4KO1yWFtTPBYFRBwXsXPR0ZGXACYPuL2/G+O60y/Rvvd3Nzs0o1yE04tv66O1NR/NgUgAKfQ5mbxtJlctaW513XVEvuL1o0pegcHBx3X0NPT41K5kI/h6vZYLOZSsOVyObS0tCAej6OlpQX19fWIxWJurziS7DqX4LMhWZ5MJt2qwmQyib6+PoyNbd2XjanaOH/inGl72JO+ebXgzZ7GdNjvyYjYiD3lfx966KF4/PHH0dPTg3vuuQfnnnsuVq1a5Yj0HSmbhSfRPUJBwpf5eo866iiXR7uxsdE5hHR2Ozs70dfXh8bGRrS0tADYFgWfLMbGxlwO9E2bNuGZZ57BSy+9hPr6ejQ2Nu60unlXwirZAIxT5akTOBWoUoDLkpVg2B5oQPP5PA4//HC0trYin89PuU2VTCFKpRI6OzvR3d3tlm1xQrAzG45yktTb24unnnoKL774Ip599lk89thjGBoaQmtrK5qamtDY2Ii2tjbMmjUL7e3taGtrQyKRQH19PbLZbAVxzjoogiBwz0SXAnJ5dzqdRrlcRl9fHyKRCPr6+lzOtkhkW1qGvSkyvq+AKygmQnNzM2pra8dFqDdt2jQuAk20tbWFHs+A4kTHhF3zE5/4BP7zP/8Tv/nNbzB37twJy9ve3o6Ojg78+c9/nvA4j/GwhFKY86VjqSpaLAnGz/S3Qp20armWdZkyj9XxVpc2awBAc6rzey2nqnHUoQRQka6FDm4mk8Hs2bOdExOLxTAyMuLynWqKKxKgdN5smZRE52ckgRls7O/vx8DAALq7u93KHk3DRgecG3KrM8qxVxVQE7Ud7QwJXdpcOoY2KBH2nHht+2OdWg3Q8BztU0qaMuBsyX2q/BiE1c1XbWCC5CyVW8wvmslkkM/nkc/nK/Kca1/T6+q7oP2f7VRTU+PmJ1z1RwKb9WXKEg3SkMhmO3CewxQDDPZv2LABhUIBW7Zswfr1690ma3SsSRCl02m3sXkul6tI46L3tc8tHo87O86+QwV6IpFw8yYAzvnWVXWcI5Bw0fbTfQ3s2GDnEqoE1D7NzzT3bVg/VgEG/2Y5de6n41RtbW0FSWQDRPqZphWw5Z9orNtV2JvSsYUtB/fw2FXguMYxf+7cuY5EZ0oM2m2SpRpcr6ZADpsXcd8IBjp7enrQ19fnVvxyRZCunmEQ3OYg15UyGrSzxBSvwXqoLbXEo57LY2yguFo9tSy2PVgfS74raU7wGqrSn4z/rSR6Op1GQ0MDstksmpqanOCMe35oGRjUYPns5p/ANhHjwMAAyuUyYrFYxQamwLYNsmkb2QY6P+W1S6WSC3j39/e71D4bN25EoVBwwgjOGTn3yGazSKVSyOVyaG5uRjwedyK2WCzm7DgD7wpdGakkOu15KpVCsVhEKpVyG5Jz7jRZEn1PYyaR59OJyYjY9rT/HYvF3EqyRYsWYc2aNfja176Gm266ye1DtmHDBrS3t0+qbGHwJLpHBehAJpNJtLW1IZPJYM6cOWhpaUEqlUI+n0cmk6lYJksVEyf9fX19buCmA8bBU40hBxc6W+VyGevWrUN3dzfWrVvnNnO0DuFMgxp9a9xpEHXncCXcJ4J1eOmgkmiYLIHO5WLZbNYts1KV4c6CE6X+/n6sXbvWRaW5cQqXnav6gLATnVKphNHRUfT29qK/vx+dnZ14+eWXsX79ehclD4LAkTCpVMrVicvFSB7ppKlaPbU/qnojHo87w51OpzE2NoZUKuXUeSQbdqfTuS9hdynZYrEYjj32WKxcubIi3+nKlStx+umnh56zePFi/OhHP6r47IEHHsCiRYvcOLZ48WKsXLmywhF/4IEHcMIJJ1SU8xOf+ATuvfdePPjgg1iwYMF2y9vV1YW1a9dWGHWPyUHJKPYRdfzUQQLgnAFLiNMB0VzfSngD2whOHT9YBmCbQp02sRrUmdFjq41bVplFqEPKvKXpdNptYEW1L8diktdsAzpbJAsjkYizM5qmS51aa7+pECoUCm5T0VKpBAButQ9/eH8l73hdEog2l7N1gsP+turasGNt+1mnXH/UodWyaoDEEvz2uoQlFaodp0o/ks20b3Q61b6pil9/LIFu24P92tZbyWo+d7Wpdp5DZ7+/v99tPEbCt1Qqoaenx20qSgJb3ytd4s65CR1q9gO7SasSLOyvaov5HpBU0ICAiju0LnwWhLZxWCA+TB2lx7BteT+rYOf9NECl778G6KrNfdmWbIMwQqha2TQ4MtV54O5Sss3k5eAe04/JqAJnog84XeA4RcI8kUg4X4qEJFeC892lXbBpp8JEBDrX4Q83ESV52tfXh4GBAbc5OO20CoasfdGgIH/rOGuDxNVsdxjssZZDsPZbA/GqZrcit7Axn2Oujvs2qD/ZFeA8n+IF/ugczPrDYSsX9Vr2N89RVb7aSNpvBoTph6vPzbSlfX196Orqcv2AhDXtN0l19ZUpFlCbrelcOBe1eea1X7Bv6H4/XDGpwjbOh3STVo8dw3TY78mI2Pak/12t7PRJFixYgLa2NqxcuRKvec1rAGz1ZVatWoVrr712wusoPInuUQEOXm1tbTjppJPQ0dHh1L46IALbll5rNLO/vx/PPPOMi1BGo1Hk83k0NTVVqHronAZBgI0bN+Lll19GX18fHn30Ufz1r3916TOGhobcQErlHDD1JRe7GkqWMyUNje3AwIDb7IoOj6rSw4hYnaDQyJVKJZcjPmyjvDDU1dVh9uzZaGlpcUrtxsbGaU2NQxXkunXr8Le//Q2lUglz587FnDlzkEqlMH/+fBeVVtUEANdmIyMj6O7uxiuvvILBwUE899xzeOGFF9Df34/nn3/ebThC0iefz6O9vR2NjY1obW3FrFmz3GZkVLhZddZEIHFBxWRjYyMSiQTS6bSLgA8NDaGzs9P1Q9abzxTYtyf404ndRaIDwKWXXopzzjkHixYtwuLFi3HzzTfjpZdechuNXX755XjllVfw3e9+FwBwwQUX4Bvf+AYuvfRSnH/++Vi9ejVuueUWt8wbAC6++GK86U1vwrXXXovTTz8d9913H37xi19UpGu58MILcccdd+C+++5DNpt1DjqXbw4MDGDZsmU488wz0d7ejhdeeAFXXHEFmpubKyYcHpOH7slgVcdAZa5jjrEa9NTNk/mddXYsiaZKdP5W1TXHOOtw8lh1aqg64jVIIGrQU/t/JLItnUkkEqlw1ubOnYt0Ou02fFIicmxsDNlstqIMWmeme2EddMNOqwobHBx0DtemTZtcui06ZKqcI7HKQCiD8fa5KYlIhfNEwWd1OlVJrkStHqf2lcFbEr/8rcpikrHJZNIFBNhPwoIQujpAhQC0dVYZx/5HdVUmk3EOd1NTExoaGtz+Jfyc9WE5uGdHmNpe20LLzDZmGbnxpuY0bWtrc3M5biCuKwe2bNmCoaEh9PT0YN26dSiVSigUCs5OUgzBVEG2b9Ppzmazbol7fX296zf6bG3fY91I9LDvMNgNwK2MI9lk3zlVn+v5dNgs6UKQ8FCS3K7UU2gfJDGj/Z7PIBKJuP4eVlcd22weXJZVAwAMAPE7zj1tsCpsvKyG6bDfe0M6tsksB/eYHliRjdpL+5n+7Avge53NZpFOp5FOp7FgwQLkcjk0NDSgtbXVjb9czWID5+pTUVlMP9GSs0o2DwwMOL9yw4YNbjyn/eY7WlNT4/a0qKmpQalUGmdvbVCc3ADHNbuxtwa9Sc7qM7dBQevT0QYVi8VxP6w/STMS2ppfHIAjtNkuahcAuBRlVOzTlk3U9zjniEajaGhoQGNjo+NBuAmnphlVLuX/x96bx1h6Vmfiz7236u731q2lq6t6dbux2QwKMQyxIUBmBmdgNGIcmFjKCIkRQUEGFPBETBzCL86GRSZCrSisI2KCIsB/JIigILAzEmYAJwF7hgDGxpjeu2u9+153+f1Ret56vlPvraW7qruqfY9Uqqq7fN+7fe855znPOa9d31rqhq+pLciDZUm0AxA4gHZ6etqxwam/NXuQh4UWCgWcP3/elT6rVCouY5FrhfPI7LGRkRF3HhnXaTwex+TkJMbGxhAKhVw5mUEBbCVU0P5tNptujZdKJUQiEVSrVSwvLwPAhnr2+SaWWLAVeT7437/3e7+HN73pTTh69CgqlQq+9KUv4Zvf/Ca+/vWvA1h9nt7//vfjIx/5CG655Rbccsst+MhHPoJkMonf+I3f2HL/hitxKE6o5MhiO378OG699VakUilXX9on3AxZP6tQKDgHmYdnWCaZOuu1Wg1LS0soFAr4+c9/jp/85CeO1caNm4rMlxp2vUUVntYD5WtUuBwjGhLWAfRdl8qcIDoBha06POFwGKlUyqVXKZt6p4RzUiqV8LOf/QyVSsXVbM9kMo6RrgfiAHAKk4zFYrGIy5cvo1wu49lnn8WPfvQjNJtNV5uPUW8ajTQ2aTDQ6aehtN01ooYgT7gn2BQKhZBKpZBIJJzz6zMCh7I1uZZK/J577sHy8jL+6I/+CJcvX8Ztt92Gr33tazh+/DgA4PLlyzh37pz7/IkTJ/C1r30NH/jAB/Dxj38chw4dwl/8xV+4eqoAcOedd+JLX/oSfv/3fx8f/vCHcfLkSTz88MPuUDIA+OQnPwkAeMMb3hBoz0MPPYR3vOMdiEQi+OEPf4jPf/7zKBaLmJ2dxa/8yq/g4YcfRiaT2e6wPO+Fzzv3V91nrN4BgiA6f7S++MrKSgB8tGwrXptsJd3H6Rwos4li9Ze2iaJAmdbq9gn3Re5bzKLJZDKuhiprovN+2n8dA2VbUc+MjIwE2ELsFx10DRxXq1W3/zcaDaysrLg0Y8uUVpaYHdPR0dHAYZcWdN4KgKa/7ed1v1Ymtq3nrf0k4E8nVA/GtPOjwIWtE26zyHQO9F7Uc4lEwuk5zbTStWbbroCGD4SmQ8v+EXSpVqtu7pvNJhKJBLLZrCuXQ/2t3ymXy6jVasjn81hcXESz2US1WkW1WnU2jwaqLDNQ65mTcU8Qn+2zYm0nHU/NfrQHktp58gHpfNato673GVRGQD/rW3sETPh9HxuS37e2xSC9p5kzugdpRg5f13IxV8pCB64dk20vp4MPZWfFBkk1wKvPwY1GWNF+8+wL1kEfHx/H2NgYcrlcgMSmIDr/BhDQXQzYKnOcwr2bxLdSqYRms4lCoeBqYBM8jcViTrdYPaa6zM6T+rmafTRov7E+lE9n+8B17tuDfhSM1oxE2jRamozt5HUBBDLAtcTcZnPK/TYej7sMAhIcOI/WZrBZAnotYE0HacCe/Wy326jX6+h2u4FzRxic5G8GQIgpFAoFB1DzHJtSqYRyuexsAc2mJDGE1yJmFI/HXR/pj3MMbJCF48v+qA3OIDYDIKz1ruWDBtnCzzextu5eBNGvl/89Pz+Pt7/97bh8+TLGxsbw8pe/HF//+tfxxje+0X3mgx/8IBqNBu69914UCgW8+tWvxiOPPLIt/3sIog/FSTi8evjk9PQ0Dh8+jFwu51LCNzO0qYzS6TQmJiZQq9Xw9NNPo16vO4NAmS/cIJliefnyZdRqNczPzwdY3AAcw4ps4FarFXACrqeQPUYFxjR2jVTTgWy324H6ZVoSxPZD2f0EKKjAt7OJsRb6wYMHnSG200JFyJQu1sjnYaOlUgmTk5NIp9OYmpoKtKHT6WB5eRmNRgOLi4s4ffo0qtUqzp8/j3K57AAI3ofOMR1t/U1GwdWsCTUA2Sf+UImz/Tal8UYx6m9Euffee3Hvvfd63/vc5z637rXXv/71ePLJJze85tve9ja87W1vG/j+ZushkUjgG9/4xoafGcrWxedcKwBFp8P3rHIP1meZ4DhBXZ9Tx+8CQdBKnTQAAUBY72nbru/rawTl9WAuCzjQ+RgbG3Ol17LZrANeLYuc4Cavo/djfXPqKBtMUOCR6eD8DkuzUX/R+STLm6wpPZxSx5PjT/1O/ckxVoeS7aUDrGOljqx1VtV547xpSrgym3UOCEzYNUUwgeuINgGD6Bwrn/NtQZR+v++cbOodDQ7rPNrr2YDRoHvpWuZYMcsLgGs/Mwm63a5zZpXJ1mw2cenSJVfKhQePKUOac6Wgt9qCtswQn1kNUrA/+oxz3nl9Bbi1v3ovZhEooG7tyFAoFAjE8z0FowbNJUUDYDag77ueHSdbjo7XZJ+VUKJsdn5H9wm790UikcDBpLpefAGL6yl7OR18KDsrg+xo1am+9wcFm/aLPc7ndWRkBJlMBhMTE65uNs92SqfTgcAgEAyO+gKiBMO5T+v7BFqVid7pdFAsFp0upx/O74+MjLgSL+Fw2GUV6R6qe536tWyr7qnWHgHWdLkNgLN//L4ymNkvAskEvRXkV91h2ewayLcly2gnaXDC7tk+Yc14ZtFruVEF0HUMdA3bcfGRB3zBcfaZB4MyK7vVajlyJOeOPxcuXEClUkE+n8eFCxcclsF9juNgM52oY9Q+sSVbOHc2WGt1tNoEei+1EXhPDYizfc9X2S973fXwvz/72c9u2q5QKIQHHngADww4EHUrMgTRh+IkEolgdnYWL3nJS3DgwAHMzMwgl8utiyBaUcM9l8shHo/j5z//Of7pn/4Jp0+fDigLOksskaGKjwpbS7YAcI5rPB5HtVpFrVZzpUH2AohO5lWpVHJpzOrgWZYb08oYqdV0dg0ccExoCFnHcTPhnExNTeGmm24KpDPvpHAOWLOv2+3izJkzrj764cOHnXF49OjRQPmBZrOJixcvunpsFy5cWBcwUOOHoEIikUA6nQ6kP7KG6tWuCY4b1ytrsvFvpsxtpz7eUIJyLSPhQ3l+CEEm+/z7WL8qNohJQI77j4Louh9R7xGItvchMBkOhwN7hTX6CQADa3sKAFfSgUHXcDjsmEx0XPl99juVSuHAgQMuYJnNZtcxUBnEZh/JJOJ1qKd4WLiPIaV1OOmMcQ54XTqy7XYbtVoNoVDI1dpkiQmymhXMZ1s12KqgtY4978mxZZt0jiw4y9cJkpMxrYx03x6jwLCW2OF8KduK+lvBed7XJzbLTx1TZXhZB5IyiKVnP6Ngs65tjiODx2pnhcNh1Go1xGIxpNNpRCIRV2KuXq/jzJkz7jC6hYUFZ6sxcMO0da4tLVGk9WKVYc/1xTUSiUQCulYDKzaYovamj5VOAEbBH/bdl2HG8VFGHvuiYoECBYBsgE5ZkgpG8cfWvec9tewP588HonPe9J4abLLZMfy+ghsbybXU33s1HXwoOys28LwRIM61r75nKBRaZ49boHEvkl3UrxkfH8fs7Cyy2SwOHTqEXC7nSl0RTGSWt54vwWedwDcPhCyVSu48CpZo09JdLLdFHajBOeqsRqPh9kDWTO/3+0in04EsGAKq3Ld8vqbVU9x71a5QhrgGBDnn3AM1mECgv9FouFIu1ocMhUKBwKzaBMx0o51H/W3HZJBtYIWZ1ywJmk6nkU6nB2ZLcxx0nep4WTCd/bZBZC2b1mq1MDo6ina77ciQxExI9qvVajh37hyKxSJKpRLm5ubcGGlgn3aZnm1ndTjJGloeh2OtZw9p+7XtWlqMupiEObUpmAlOXW5tw0Gy1577nZIr6dfQ/945GYLoQwkIT5HmQWSDSrj4hBsrDfhms4lKpRLYPMkYVhCdRv0gofIie50R0kQicXWd3SFh6Rky6wf1RZ19BTSA9WwLq8RtTbvNxBqYerjnbgmdUipMGmy1Wg0jIyOOsaip+Uwb5GF0dM59YJMaXsouU4NoIxBhO+K7jo8V9nyOgl+tDJXxUHZSLEhtQS1lO6no57ReJoE7y2JW2ciAtywcH/Ocn7PX1L8JxtNpsKx1Bct0D7YlLFTn8LNsl4LTypT1pUWzDdynmRZMIF8dXbZTWXArKyuBsR0U/PAB5r73fWOm8zLIYVAHVfdz/bHza9tqGeta29QGgYE1sFydZxvA0TlRJv1mLPTNxI7DIJabAgsW0NbACPU1gyjK2BtkA/n6p32z7LxBfRikO3w624I29n9+jk67ba/vur77E5y3bdX/LcBnnxP7Wb2/BbfYPu0TECwv5LOhAKzb1zbq1yC5Vvp7L6eDD2XnZaN1NWgf178VhPd9Zq+J7kcEopn1qiUsbCYa9b4NEHCPUEC1Xq8HzqfQci8Engf5M/RbGRjXMz7U5lKw19dH+3szspNPV/n0vPrVtl02EGPBaQ0c2tcInvuutZU5VT9VbTFflpFvrGx/dT/f7BnhWIRCqyQM6jXeWwMONvBAXc/5pE6zdopli/v65+vnVm0Q3zXUbtL37HWGvuXmMhyjnZEhiD4UJ8pku9LSH2TFsA5YNptFvV5HpVJxGzuVAZ3wzR5mKoVWq4VLly7h6aefxuTkZODwp+slvV4PCwsL+PnPf46LFy+iWq1u2B/rKKkjydf4OXXIt7vhEUxhlJgHou0mc39kZATpdBoAMD4+junpaUSjURw9ehSTk5OOya0R+Hg8junpaVc6iMzGUqmESqXi7ZeOmf3ZKVEDjZkSanDwZwikX5kMI+FD2WlherKyrnWvUMBUXycL1bKjgKDusQCWijoaug+pY2z3fgXVqDcVsLZpw9SfZKhrMJIsIc3iUjBTx8KC6by2BeG5RzMQqm0hkKoguvZXdRuvMTIygkqlgkKhgHQ67eq0qwNkWUv6W51hZVQr4K3zrnOngWvVIZrOXi6XUa1WXSCXh2ySFc57af8ZMGA/lZGvB0/rfdXB1vct6MOxoC2lc6JjoTaUz/HU8VNGna4dgrRch7Qb7IGpnOdyuexK9/DsgFgshvHxcbfeOCZc0zr+DOxzbPm3lk7T7DwFcqzu8DngKoMcc7Lj2U7aJ5xfX7CIZfr4/Nj9QJ83zg9L4HAceC2Oia/tuv/wNc2EUMYeyy3q+GqWhO4Jug6uRq61/t6r6eBD2VnRfXyr68QCjRZQ1s/sRdtR20o9roFw349mwviCwATJa7VaIEOItbCpnyxJyyf8XjgcdvXTgVUfT0tDsS0U3cM2CmjofqQ2Ct/zkcharRYajQba7bazKQqFgtNLZOkP2qdUj6suVBKAfn+7+51mF9hMMhsktiQG/rb3tvs351/xAQW1lQyh55IACJxdw+yFcDjsDq5Vv1YzrvSwbdonGvRRBrlmeVm94xtXtXk1eOHzsdV+0iCIkhv28jO/FdnOHrhdGfrfOydDEH0oTkKhEJLJJHK5HLLZ7LZPQCabJxKJuNShVCoVqFHGUhjbET7wKysrWFhYwJkzZ7CysuKYKNdTer0e8vk8Tp8+jfn5eTQajQ0/zw3I1gzdaaGjynqjtibubsjIyAiSySQAIJfLudI+MzMzOHDggPfekUgEk5OTzoBkzT1mKljjayMAfaPo/pUKjU0CRfzha8BQqQxlKHtByKziXqB1OvWQKwKq1G/KDlY2J50rBvYG7dcEyXkNfZ3XoOPLvUqDo0xPJTjM71HvabvZP2DNueH1eS1gTWfSOdJ9kyVUVB9oaQxlu/E+CqITnCM4Tf3OPut+rI4cwfhiseiCAHbvpANoHSjrcHEO+bc64fp6KBQKsKs0jZrOMtPda7Wa0z8EIdjfWCwWaKvPcdU5VSeOgKYNmtu/LaOK16UjboPtPodUv6tAhAYk9G+db/6v5VZ0HXCu+/2+m0eyE0OhkHOmOT68V6PRcAC9PpMWMLfgUSwWC4DYSizwsdN8r6lYAN0GvLT9GsxS5iWBb1tixoLefJb1PdYZ5vX5WS3HQtH5oSjjUwNn0Wg0sN8piM7n3D47BKaU6b4T4PpQhnK1sl2Ax+cjaPDJfmYvCvusfpuyz21NaJIFAKzTlQSASf5hkJg+DAOBW33WeS2W8SqXy65MSDQadXuJDwDWvvE9FRsUpu7S9lk7gTqdvhgDBSwVp+XZNhpv2jxbLWG1HVEyAoOzvjNN7He0zdZe1bZrYEHtI2WCW9tLAwM8w4TjSL0Wj8edz6tBll6vF7CT1U7w/WiW/SA7RV/j377X7HfUdrLrQu2jqwGKr7fYZ2koe1eGIPoNLD6HYqONhRv2RnVBNxMbLd1Jpm6320WpVML8/DySyWQANNlNhrVPOE6tVgvlchmLi4soFotXFCTYTbleG/Do6Kirw7aVMjKh0Go9Opbo8WUY+BgXul4VuLha0XtpKqOyyfi5oWxfhpHwoey0WGeITgUdasv2tcxqfl4NV5u2qnrRgqoWrOL3fSxkgopsm7LKN1vfBNcGOaa2fxaEViDVJz42s72uAnJ2LPi6DcITkKRzr3upz+nxMaxtYGFQ2+1rdkzYHi0RR7DWZ0twfpXlbuef17SOMO/LgIy2w9pGmkWh7dQ1p0Cqz9aybGhlkvN/vYY6pcpwptOvzrAy2dk/rmP+73s+9PvU1ZppYcu52GwOO6fKWNWxJECkqfjU21xzBBQYhLLsQwDu+1qeZpDOsvOlwRGdQ83C5G/f3qB/65zzx5YEsm3R8fG12wZZBj0fG8lQfw9lr4jdE20wcj+sN33eKaoTB/34/CH1Wbh3KdB+JUEKXpMZudSX3FuVcODTbb5AJ6+t7+vnFWBXe6Pf7wfaoSXFrhS32C3x6S2fWLtM17KuC9Xt1lbSYD6zEq0eUb1IIRgOrPrdlp2vbfStJ/bT/igrXK+ha1df00C1/ug685URste2f+9n2a0+DPX3zskQRL8BRZkqdiPcrL4XDwHj4SXbFW5sdE5ZF3snHrx2u41nn30Wi4uLqFareNnLXoZkMumY1psBtTspZErXajX87Gc/w/e//30Xpd8LMsjouhb37PV6SKVSmJmZcZH4rUgikcD09DSazSaWl5fXGcBcV4ye03CiQWdTva90PdBYIODDMi48iIVM+aEyuXIZKvGh7LTYLCBlxqRSqQCgTaO93++vY+Fq/Ug6AtFo1DmRZJNaINnWjrQMd7vmlUnGlFoFlX2MYorqZmWT8j1tm5ad0ANFbZkNXsN3xoTPGVQ7wrKmBoHvtVrNtW16etqxZsm0tk4Qr6eHqPJa6jDzfj5AUj9HxjzncmVlBYVCAcvLy46BruxwtoEHYiYSCXemiwK2FAv6a991rFRfcW6oK3UNqr3GPlv2uoImTGNn4JrjyzWgLEQfgK1rhjaVlhriOiVZgOx+ZVP3ej0HaPC67EOr1cLIyIjT2byH1o7VsjXWGecYarq+jjHHi6n+PDSP2QbVajVQCkDtZAZ5dP3pmCrwwMCBgue2XjFFgwYW2NDAjI+9p0xA+8xvFGzgPA8K6ugBcTq+W7WZhvp7KHtF+JxaXbVf1hn3ALU3NgLNuRcpu1jPG1G/hf6Rnte1Xb++1+u5/b5YLLp2VioVR35KJpNOvxDI1fG3NhGvS9F5415sQXTaSZ1OB9VqFcvLy2g2m1hcXMT8/Dyq1arTO3tNfIEACsdB7T+1wfTQXOp7a8dyzKmL1aZTO0hL+QBr5DVelxmTalcoI53Zh1xbiUQC/X4/oI9UL6l+UeCbfryOB+v3d7td1Go1p6/t37quLXDuKyu0H+VaYTVX+t2hrMkQRL+BxW5mQDBqbx8GBQ+VbbtdUafhSpT2RtctlUpoNpuYnZ11jhAdvmspTPduNBooFotYXFx0juFekeux2fGeNKzUMdxMwuGwA1MG1bpXAECj4lT0O9VnvY+NitMQGcqVy1CJD2WnRWuEWqEDoiUMBjF/LBCsDF0fO4q6VMuy+L6vzBsFzQjmWeaq3m+r4mPi+Bxx/bGs9s3uOcgZ3Oh77BvrQzOtnI79Ru3l9xWsHzQPCqArEG7LrDAQy9/c1+3aUXadpjDTEfWx7ig+wMACPPZ+mg6tojabrk87VnTElanGNus4a/q9Zlpo+RZf4ETnjJ+xbGpgLVihosC0At46X/Y6di54HeplHT+1N1VnE1jSH2VP8v4+W1VtDN7b2s5sF4Mfek2dd7XFbdBHA3z8jD6rvnU5SOx6p9j7+4CtrWZ0DvX3UPaSaIDSrmGfr7vXZFA29UZgus8XUva57oNXm12ugCrPhaLO5LW536mdA6y3KWzZEn7GflbnTfd8BgV4YCqJTXsVQNfg76B909pmuhZ0bej5JbSJqOup5zudjtP3vDbbYQMpLDvD99lWtQO0bQxW+bAdG8Ty2RFqf/hsMtXZSpizbHQb1NZrD2VzGervnZMhiH4DSSgUcgc8EJBURlm/33ebkbKFdHPP5/M4d+4cGo0GbrrpJuRyOa9h4pNer+cO51paWkK5XEatVvM6p1cibH+/38fi4iL+3//7f5ifn8eJEydw8uRJ1+/tAg/buT8Vx8LCAn784x+jUCjgwoULgVpze0HU6KGxwfHZKqi9XSFDoF6ve9lPVyt04sPhsDusdnR0FNVq1R3corV5t9tPNTaYJqhRcDLaNqqPPJShDOX6SDqddiAdEATdaIBriRF1AugcrKysuAAe90t1apihZUE8dRiUWUpwju8psEVdRQYy26PMXZ8jSqdJwTX2u16vu1qXExMTgQNNFbizDFfqVTrHxWLR1Rjlftfvr9VsDYVCAfYZ2fkUdZQ59nT6ms0mIpEIFhYWUK/XXfBBy35pmxU41tIkfE8PaOS99RoKOND2qVarKBQK7hBrHpDJ77Of/X4/cBiYzp/W2ecc0J7aCKhkv+x7ergt1+zIyIgbP3VuFYzmnHF+WGvc2kNWD/NevKZlXVugQ7MkFCzRedFSMRx7LZ2ibDfbFo6hDaj4Usj5Y58Lrg2Oj03/5vzqOQk6pvb6CvZrO/m6fl/HhEEzHVPdL9RO1MCBXSe+dce1qffl3uUrlWOfGTtnCrYM7Zqh7GexgaOr8cd8z56K7qc+H0f3XmWY8zd/uGd2u100m03H8K7X6+6MBj1LgjYF9xVmx3U6HRSLRZRKJSwvL6NQKKBYLKLRaAQymrYr/E6323X1xsPhMBYWFtBsNh3uwCwq9kl1ggVXbbkyO76qS8msp3/ZarWQz+dx6dIlNBoNFAoF9/peAdHV/6YOULvOVzoMCGYCanaXT3errlX7RkugWD2qukEBewXfVU/YPlE/WLBb/45EIutqtut3NLhjCQBskwXPFURXvTYEzodyvWUIot8gwk01Ho8jlUphZGQE6XR6XT1qpsJwg2V6Djf1+fl5hEIhVCoVvOxlL3Ob4iB2kEq328Xy8jLm5uZw8eJFLC8vo1wu79hGRxC93W7jwoUL+Na3voVsNotf+ZVfweHDhwHApS7vhigYc+7cOfzv//2/sbi46IIOe2lDpyJaWVlxKc3xeNwZOrsh7XYbpVIJ1WrVpehvF0DfKAjCPgGrp4vn83kAqymGPBSs2Wyi3++7eq7bFSrxRqOBcrmMYrGIYrGIQqGAUqmEUqnkShQN5cplGAkfyk5LJpNxOsIa6gR5o9GoWz9q6FPoUITDYSQSCefEWqYYwWCCwxTqSa0pTeDWx0y1QW46M7yHArJsMx0OBcH5eqVSweLiotv3Y7GYA9d4TQWkeU06oKVSyR3Ulc/nUalUAgBzPB4PHGYIwDnSFrTWtvL+9XrdAcPRaBSZTAa9Xs854Rb0VVCU80CAkk4mmfzsH9tgGe4MjnY6HRQKBae39TBRDRTowbMKRBI8JZDJ+VInlQA822nLdOia4hxrSj8DG2Ttt9ttN+7aPwILjUbDHewdiUTceA6y2zQ4pO1Q29ACvra0DIMXegAp50ZBZTr1uqY1sKQ/lrGnwIA68QoA6JrRcwuUdc5nUJ9PBsu0XA0/R9BG17OCP/oa76vPFpmZZG5aRjvHwoJMui8QLNOa8zZAo+ucovuBBgt8ATmuBd1ftiJD/T2UvSo7sb64R/AZ9K13u3fZ97RMlg3wKZjJZ7Pb7bogeK/XcyQh6hvrG/FajUYDlUoFrVYL8/PzDkBfWFhAqVRyOu9qxoX6vFQqoVKpYGVlBel02tlcWpKNOk9LtNlSH2p/6J6oNgT3Yj2zpFQqoV6vY25uDqdPnw4Q9rjX7wVhcCMSiTjMhXqB86kYTLfbdeXiNBAOBO1AitUBvDb1t5abU13Ktay6yM4N26JiQW6C2nrGDecoFAohFosF7D+1WWmjK4iuGJSWAtYf2vXabksmGcrWZKi/d06GIPoNIKpgmVJKJ0HZSFSEdMS0rhawBlLXarXATzQadQpRDQZ1TqnsarWaU7Q+kOJqhfekQu12uw7gXFlZcYzkQcbNlYiCAox4E1gtFosOuN1rm4s6oc1m05tGt5P36na7Ls2OzDa+t1XZKO1QDQKNVKuRRaa6jahvtA7sOqYzzeuxT1T8V5oWOZQ1GSrxoey0EGwiuOerVa3lXJQhy89oHUkNIGspBAJ1BM4s22wjsUCW77ub6Sy2S/dyOji+eqj2gE+KMpDpgBJQVmdsIyaegn76urKQ9Pscb+qkkZER5yT1eqtnaXAPt2w1C/zp3/qe2jO8N50vOnwsxcZ9XVnEel0fK0vnUD+n/bWOr4KYGhCwOkrXhAWhOa4KNms/eQ0LVtj9Uu+v31fmnL2mr2+9Xi/wvPju4XNw9RrqmCvAQGfesuZ0XNgGG9zSa3H8VGcPeu7UIbf3s9+zov3h2FvA7Er0lgIl/J/tVjvcx771gX6D7rFdGervoTzfxYLo9ocgvOoyDcLqfqSBSfoyCr4yIM/9QPcqBTBVf9N/2Ul/hXsq2zcyMoJ6ve5IZM1m0xEVBpUlG0SU4h5m93DaNARWme3M++5Vv8zqbx72Sd900B5qfdZBvjB/qx615ctUbNUBazPYQKyvfYNAb64z2m56f8VPtISLxQd4LftjM9H20hzvVxnq752TIYh+A0g4HHZKNpVKIZPJBJjo6kyNjo4GDmMk04mbW7lcdsr3n//5n3Hu3DkcOnQIJ0+eDLDQgLXNsVwuY3l5GbVaDU8++SSeeeYZlEqlXT1ks9ls4vLly4hGo/iXf/kXNBoNjI2N4Rd+4Rdw9OjRQE3uq5F+v++UdbFYxJNPPonLly/j7NmzOHPmjFPoe3Vj6XQ6mJubQyKRwNTUFDKZzI7Wj6dy7PVWS/lcunQJxWLRsRTi8bhjwG8mPFC02WwGDqCz9yNjcm5uDq1WCzMzM279MoiSy+WQyWQCzFJgvcOoxihLuLTbbSwsLGB5eRmLi4u4ePEiCoUClpaWUK1WAyy1oVyZDJX4UHZatCyDOlT6f6fTcUFPZePws9RvCrSTGcTXlJWubCG+pywqAp/2HAUFn/UgRWU9K8hJB4IOMgFaOiPcw2q1mvtcLBZDPp9HIpFANpt1Z4fQyeUP99uVlRXMz88jn8+7tHKWfFMQXNvOoEIymQzoAjpAykSj9HqrJUjy+Tyq1aoD7xOJBNrtNiYnJzE6OupYWcpSVjBRmeLKBOY92BaC9K1WC5cvX0atVkOlUsHS0lLA0eQaURDajj9tJjLV2T89+NSmHCsrXbMCKHoty6oGgFqthqWlJcRiMbRaLZd1RTtMU/6ZIWDZ7+q4KoObfep2u6hUKsjn84Hye5xb1ljVzEW2lSUEdZ0r85FjwPFhAKjVamF0dBSVSsUFrTiWsVgMo6OjbjwV2Ob64VrUswVoy5bLZWe3abBEgQOtba97hDr6+kPbgmNqQR89D4djo9kwumYp9lBXXlt/83pst/62n9d1x73FZqPqOtPAgD7nm8lQfw/lRhbuOzZgSbEZI9y/uRcyG4h7ML9Pv5v7n+419KXpc0ajUedL5XI5x+7mvkgbo1KpODLXhQsXsLCw4MpQcr/eyXGhzbCwsIByuexAYurviYkJxONxZLNZZ09paTzqJto9ar9wr2QJUpao4X5+9uxZFItF5PN5XLhwwR0e3Ww2vYHw6yXUkSsrK6hUKi5LemRkBIlEAslkEqlUKpCxwDHherJl+1R/K7BcrVbdfZaWlgLnzQBAIpFwuk51nNoBJIZY4Jr3tiSAVqvl9PbS0pIb+0gkgng8jkajgXQ6PdCO1XIzSsJklnehUEC5XEalUkGlUnEHilodt1fme7/JUH/vnAxB9BtA6OTT+WA5l1QqFTjoiA4iN2vWrqZT0uv1HPt8ZWUFTz31FObm5tBoNHDgwAF0u12k02nnFCrwfunSJZRKJTz99NP4wQ9+4NJYd0va7TaWl5edA1AsFjE1NYXJyUmMj48jkUi4mvBXI1QY5XIZCwsLePLJJ/GTn/wE5XIZly9f3vNlPbrdLorFIi5dugTAf+DXTtyD9XiXlpaQz+ddCmEqlcKBAwe2dJ2VlRVXR9/W2KVw3dVqNadEl5aW3DOQy+WQTCYDNfqsk2qvpyC6ZhosLy8jn89jaWkJy8vLrr7gXqp9P5ShDGVVNLNKmeQ2nVTLbyizBoAL9mlZDa2d7EtHVmassoWVjU2HVw8PVAeGrxPkVgdd6yor4GUBW2C1XAozZ2KxGCqVCjKZDDqdDkZHR5HNZp0twDbxfIl2u42lpSUsLCy41HJlC7FPylpiOxlMUJBYmdaWrc1MMpavaTabSKVSzmZhaRfaKhYQ1jrlCtbzM/xNgJ565dKlSygUCm6v73a7AbCD7bUMcjqcWr6Hfda1wnXGObJALB1U1Um+wzz1/Xq9jlAo5DLsotGos/HYDq5PWzuXc2cZa9pHgs/VahXlchnNZtOtJ2Yz0gn31RcnUKvPmDppFkhi8IcMw1qtFqilS7BISwbZ50zZ+VxDjUYDtVrN2SJ8Dkga4VrWzE2uH90jdO54L36PAQsCYT6AgMLnxRf44Xf0MFcfkM4x1nar6P05PxaU4f6lfdYyUnx22u32wKyVoQzl+SabAXTco7V0GwHScHi1tKr6ysrC1n1YdTh1bqvVQjweR61WQzabdWe1pNNpJBIJ57MzYMjA9/z8PObm5lw5t90i+/DcFNULqVTKBRm1rBj3VtWxuucpuMu9mKx63ocEpnPnzrm/WbZOyRB7RXQ/Zgm7TqeDZDLpbAjiE1wTlkDhy0rjulHmOXUcy48ywExdACBw5g6vR1Fbg/rbx/xWIJ3zMzIygmKx6NqcSCQQi8Vc25SAor62lo3ja7RDGRCoVqvu0FiS2wZllA1lKNdLhhbTDSI+41iNbv1fN+pBAGOn00G5XEYoFMLly5fx7LPPuuhpIpEIOPBLS0u4ePEiarWaO9TsWqRWsU+MYtZqNVy+fBmZTAapVMpF8/mjEV7dgNXJ5EatRsjCwgIWFxexvLyMpaWlABtwN/pk/77aqGGj0UCpVEI6nXZRcYLMVyNcA8ViEbVazYETmqrf76/W6aUzZ1P8OOa93uphb5VKBfV6fVOwn4ZmvV5HuVx2zMVSqYR2u+1AGDLk1JHUeys7sFqtuhqCrIVOFgRZmVT4QxD96mQYCR/KTot1MPj3VlJ9dd+l06tlPsLhtVqRBJp0f97IiVNHQX+AIAjN/Yp/K1CswLRNvbV9YHsYxCY4T9CXICf1Nw9QJqipQQVlQylL2o6XtsfaGhTf35wj1g/lHsx63gRw7f5NNn0oFAqUndE6quxTuVx2TLZareYCocoC5JgqOcD209pUCkbzdYIK1hnm9RUk9x0cZsF79onANutrE2hWcoS950bzwfkn+EzmHxnb2oZWq+XsLAWEeR99zuyPrnvtuwLhtVrNXYfj0ul03BpQ1jxFSwVyHgiakzGvALqWN7CMPhXeh6AYAzb9fj/A6OZz61sjXDv6zAJrGQf6zFhbXD8/6FlT0flQQJ72j9pbvjWh7dyOXh3q76E838Xu+xq04mtaugPYuHwUsJbpRf3FvY12gWbSkclOti73cC3JtVuiNo+WC61UKq4eNlno8XjclfkgS390dNS9poAt+0Sftd1uo1AouMND6R9qhtzV9NPqS72WtQGs7eb7joplWIfDYUdc1Ex5jom9t72fsrapXzn2tGuYcacgOgPcvnaqHag1zvnaoEAS1+HIyIg7CJdZa8QVaC/bc9i4djUDjHYowXIt10MdrgETa2/65tXXV9/fe1XUlthpGervnZMhiH4DiLKQNIVTDWhl1dEJU4VvN51ms4nz58/j8uXLOH/+PH74wx+6EjHJZNIBzf1+3zm+dFhZH203U200cqusuccffxxPPfUUcrkcjh8/jlQqhampKRw4cADRaBS5XM6liWtNWCqRfD7vGNWnT59GrVbD6dOncfbsWTSbTSwuLqJer+9K9Fv7xP8B/wF4WxUGAYrFItrtNmZmZlCtVjE1NYXp6emBDO3NhMZLrVbDT3/6U1y4cAGLi4uBgzfL5bIb62KxiFQq5VL12TeOeaPRwNLSEi5cuLAu9dqKRrJXVlbw85//HIuLi6hUKuj1VtOuq9UqJicnEY1GMTY2hmg0GkhtVmOEhidPtW80Gjhz5gzm5uZQKBTw85//3Blve63u3n6VoRIfyk5LvV53bGHuEa1WK6CLyIhSwNSyXRnAW1lZcXsyy3hoGQ0t5eKrHW5ZZwCc7gXW9nsyi/k3GbgE05mVQzaafl/Zuxo4YGmsSCTiDnuORqOBkhTcyxiMZCBRy2pwnNh+PZCR40VmGdukLFiKD5Tm99m/VquFCxcuoFwuI5lMOl2hY8IfpouzNAj1Mffyer2OQqHgmGy0S+bm5lCtVgNzpGxjZd5rAIG2gtpUXCfULQBcFpQCx9YO49goCK4gswqZhrVaDbFYzAWIuY7UCVcwxwYweB8CMdSdrVbL6U4lQWhbOd6WNc3r0z6hM09gnqUE+KP2JoESjjdty7GxMZdFSSZnIpFw9+aa9KWDM6CugYFms+mC++w359uyz32gmAqfVwXAuVb4vBAEsyVc1C7X7+g9NUCjdiA/z/bbZ0ozEngvjpkCdcp+1SwOe5DsVmSov4fyfBa7V3Cvon4lK5d7gmYxKXDMfUhLVmlZqtHRUeTzecf6VqYvQUWeQ8YyGNVqdUMAdCeEepv7WygUcszkSqWCZDKJcrnsQHSOiZa9SafTTn9ThzPwXa1Wsby8jFar5UprsoQrM8hsxtN2RPdALR2m2Yka+KDPSGKFZYn7gptsH20PBovj8bgD0kdHR5FKpVzAmDpB2flKDuEPr9dqtTA3N+cCKQsLC27tEMRmCThmTADB7DfqFJbwI0Cuh4Gr9Pt9B3aTqJFMJrG8vIxyuYxYLIbx8XFks1mMjIy4sj6cf9rLWvqPc88DYovFoiuVS3yA+tzqMdtGxVF0HjWT7WqDL7stXHO7haMN9ffOyRBEvwHEsqEsK4mfsSmc+jkrvd5qaRcATqGFw2FkMhmXTkaFQudpN5W2r8/KyqMzRsCYCpx1x1SZay1TOhJUHEwjIqBbLpfx3HPP4bnnnnOKe6f7adlNmsrli0Zv19khmERFSwPGGgPbESp3MtEXFxcdUK8Bhl6v52qnhUIhZLNZ1+dQKOSiztVq1f0Q+NpINC2eB8ymUikHArCOK+ebzAgttUBDlHXONR1OmehkQAya+80i4YMYAM9nGSrxoey0qLPK/UlZlgpaUQbpPn6H/6suVUfYpiPT6FX9ooYwHSQLoBFgJBBG1hY/T0DMZlGpWGYU2cvUf9TXdJo4NtyDqUd9LB8fY1b3Qo6F9onfs23kmGjpDLaNjibLrMRiMedw2WAHa61qQJTOH3WK6j0y9RqNhgOf+V2ray0LWJ1un3PD+VamsYKzeg3OJ8EHH+Ch+6MCndFoFL3e6gGsCkDb9tn1YW1EBaI5LiyBwrEmQLKysuLADp+9yHZwnjQd3DdOarMp01IDQhw/CxDrmCj5AUCAiU6bRw8dt8C5HXO1lclE1/7qXmKdXM6jMua5jvTsAC1tY8fT7i8WRFfR/YTf0bISBE0s2KT7jj6Hg4I4g2Sov4fyfBef/lagmK/xGfMFxhTQ457IvaRWq7nv83os80L/u9/vuzM+qP+uRYlRJSAwGMu2qP4eHR11h3mrfxuLxZw9w/53Oh3ni1UqFRf8LBQK7qwOMq6vVlRfKlvb53MrGULtOmvj6bV1jOgTh8Nh1Go1dLurZXE5ZpxP6+f7shc1mEp9x8NWOTbqg6udo2Xf2C9gTZdQR6pu1T6pjlBdylKA4XA4UIaNNhoAx0bXsn+qv5WJThKEMuv1kFy1n9kPa7PpOTnWprPzuxflSnGZrcpQf++cDEH0G0CsQmMEUZ0hboKasqMb00ZCkKDX6zn2kDoi16NGNJlLjP5ns1mnnMnGIQBaKpVw+vRpx9DxHWzCH9YEZc1zHoCmqclXI5yLRCLhDoBNJpOOOaSnsFP05Gut+VmpVLYE7LPN1Wo1wNputVqIxWKujrgyz3zXYJp0t9t19cIrlQrOnz8fYOircEwJVrO0i77PtrBvWx1jNbzo6IfDYSSTSceqi8fjGB8fd2A6I/66dlmHrVAoYH5+3h3Qwz5xzdPpDYfDyGazSCQSGB0dxdjYmKvjy7JByl7gvRhJJ+udBg9T9IcylKFcufD5tGUYLChOXagsKN8eqvug1hb1BR+VkU0HSw/T4v0VsOK+0G630Ww2Xf32lZUVp5u4V/I3g8FkgfHe1BGDQE4CxwQYtc109BT4VUdDgeZB+kHHn84Z66Fqn/X7TPvVNG+OC3Udx8UCr7FYDJlMJhBUIEOL57EwHZzBWfZND1u3YsFzYK2kDRl0dAa51xOwVIdQWcO0SdRm0nVK9p1PVBeyzJiy+UZHR5HJZALrUgEcnWeCEJ1OJ3DwXLlcdn+rnlMwp9VquX7r2lfWNu1NGzhRp10BafaNfaeu1YxH9k8zyTgWDLyobUobhHpWSzJp22gH8hlTMEXtSLab48j1XK1WXVkFzrkG0zTIr+OhmQ4UG6BSe51l6/i37kPc4whUKIBng/i6tylLkvajztNQhjKUjYXPFJ8f7p3c1zUTSwN2Ct6q3aH7Aa+tQUYAgT1fdSEzyfidayFsC/dKBjmpuxuNhtNVNnjY769mRDG7Se0Y6kLNJGM5Nrv/bVc0OMqsPAK/tEUUvLY+me716ncT6LU2E9cD93LqWpZBob8IrOoFDVhrwIW2HXEJ+o/0m/P5PMrlciBYrIF52kSRSATNZjOwBjWwQWBb7TUF9a1wjAh808YYGRlBuVxGNpt1+AL1qZ4pwudCx5FZFNSvHFfaiBTNitMsQbWXbSYk54z4Cdugh6GrXtwLYPFeaMNQNpYhiH4DiALZeiqzbjoUAud08rjpbvSwUmEDa8A8X9fPXAuhgxGNRt0mnclknEOtJV6WlpbQ7XYdo5gKns6NGjnq/FiHaCdLtygIe/ToUcTjcczMzGByctIB6nTqgDXFTcbYwsIC6vU65ufnce7cOQfAMHAySPr91bSyH//4xxgZGcHi4iKq1SrS6TROnjyJgwcPDiztQ8OABk2r1cKzzz6LZ555BvV6HZcuXUK5XPYy6jqdjjsAVh1EFU2x2i7Ln+B3KBRyaY3RaBRLS0uYmppCPB53p8VrCr2C6JoKSRB9YWEBhUIhEDjg3EWjUUxPT2NqagpjY2M4duyYW4O5XC5QWonskE6ng0KhgOXlZdTrdTz33HPugBwq8+eTDCPhQ9lpsUxLLRvlc764H9FpG8RM5b6kepCvKyvJMtIURGfb9DlXp5CHK7JcB/cPtp//U3/ReVAGdrfbDdQ+Z/sJmoZCIdTrdefQKhOcomUsVP+pQ2jBco6Fgv4cf2YBkankGx91hKi7CVBqkJuOFh01spvoFBNQoH2jgCoDB9y/2UYVC6BbMJj3JMhr93oFyTknOqaqA9knAg4Eki34qa/1eqtZXVxLbA/nQtni6rDSflFCQKlUckEGppsrMEAbpNvtunvq/LJ9kUjEHXLKuWBftf0KELMvykDjPQmWZzIZVxpnYmLCgUdk1REkZ9/owNPp1vc0I4DjlMvlXDkB3jMejwcOxNOyONwn+LOwsOCC7zwrRVnfPia+PWPBrke1Sfk9ZlKwj3aPisfjbp+zz5ReR/dGBpvsnjhkog9lKFsX7ivcA6g3ycDVTDKK6gDdL6zw2Q+FQo5oYxm2vr3mWj1brHWtYDD1D8Fc1klnP+l/E0OwzP1+f61MJw+6pu1gz2q50jYnEglEo1FkMhmMj4+7EjlaLg5YO/ycovYb20lbBIDTmzbTiXqQ7xHMBtbsCfrW1EG+0mUEq8nILxaLLsiQz+ddcJwl1Ci8r48cwLXIgLLVFfyMXVP6P0lwAFAsFjE/P+/0K/EZ+t8kHlCXcv3yN9eM4lgcT+o2PlPZbDbg00ciEVdpQEvc6ZwS71pZWXFEQpLoSDTRM1Wup0++28/xUH/vnAxB9BtANPqpzBJ1fLkZajROHZDt3ut6izrkZCipQ25ZdqyZTiWtrOtBqcc7KWwXN/lUKuXqfvJvKnQFItQh5PcikQhqtRpSqZRzKrfSfp/CikQijsFFkMYC6bx+q9VyB2+SHcBo8UYMAbZpt5QSjQRldbMGMBl0jP6zHjDXB5UowQRGwDUwQeUdi8Uca4F19sfGxjA9PY1UKoVsNouxsbGBIDqdctZuJQuCxo8ai3slEr5bMlTiQ9kN4T67HdEyDoOAJGUZUZRVzM8QNNfrWDAWQEBX85q8FvdS1UvaPwX2tO32PryeBe/1foOeJcve189Tz6hdoeOgbeWPL4CqIKV9n3pPHS5lGCvTl4x36jWC0/YQSQ0AqChbzLZffys4raUz1OHV+VJWFG0NTalWe0UBmEFinXTqFDqHyphXUZCZn2WZk41sQY4J76drU4XtUGddbRE+E5p2zXtp0IH3VEYbAAcmEYQA4PSzsqo12KJ90fEneKM2F+v1JpNJx06kXalrl+uu3W4jlUoF2mP/ts+JPnP8rUE6jo3OtTIi9X9dkxRLaFFighIU7N/2WlvVrUP9PZTnu+izpAFpZbVquRLfc7zZs8B7EHjWZ1p9vt3yWweJ6i0bIGWfuKdb/U09ocQD7oX0lbQu/Fay5TdrK9vL81XIhOf+b0F06jvd59QGpD4iuMtMNN3z9d4UzhNtlFBo9RBZLTVKbEJBdAZq6G8T8FUi5CCfcaO92qe3rS6x1xp0bT4H4XDY+dudTgfxeBzdbtcdJgusseupt3VMbBBCS+1Qf6fTaYeV8Hc8HncZ/UoGsSA6/W22k2vM2jk+G/hGkaH+3jkZgug3gNDpCIfD7tAsblgKLjOCSgeEKbxXAtipI0jRzW+3RBWMgppMLdPUYTqUsVjMMbYYqVUnb7cDA1Sy6XQaN998M3K5HDKZDKamptwp3WRIWwBbxzkcDmNmZgYrKyvI5XKYnZ1Fs9l0h2CylpzP0aUyymQyLiJM1tazzz6L8+fPu7Hg5/k/nW2C6Dz4o1AoOIPneguNjXK5jEhk9TCVpaUl5zBzDsjSpOGpILdNB+92V2vXHTlyBOl0GjfddBNuvfVWJBIJHDx40B1YqmPKdQisGSh8vljDjkxAgunnz59HrVbDz372M/z0pz91h6HV6/XrOaS7KkMlPpSdFh66pbWmuc8TUAWCrCI6Z3TkrEOqALn9UVFHQoOGDHJS/yq7nIcVaxkJlvYi2EpHi8JAG4OqZGAPcqTte9a5UBazBeItEMo++QA8zdSx4DPBXUq32w2A0Qq2c2y5L/Nvzp8yaHn9RCIROCxVQdRer+fGV5nWBN8V5FdwQseITrYeUp1MJpFOpx0IoOxAzjf3feqdSCTiStBx7dGpjMfjjpVOUNjOp3XqOSZLS0solUrramLb1GkGv2n7lcvlAEjhcxoVgFCHV+dVgzR0RqnnuAb1ukwdpy1GMFz7SHa3HrLLeVVGo6+tbBvnmOVgEomEswWy2azL+mNGg9qRvmdJ95JMJuPKzyijv1QqOVvCBp0U8FKiB0WDJATh7FpQxrlmBtCWIWNVnzWboq7tUUa8DbZtJEP9PZTdkisJ6lxr0WeZzx7BN5Zj6/V6rnwkpV6vB7JoeK2t3Mt+dhAQ7wsI76ToHkQ9o9nTGkSg30OcgSCmb69W/MACm1fTVtoHsVgMk5OTiMfjyGQy7hBrMtFVtM67+sRsK4FszjX1tx7MrmNlM+44Lt3uamlUnhvG9UKbotcLMvtZzoUkNup1X1mgrYjaszZIv9VAj71er7dWlod1/G0ZONV9antqiTWurXQ67YIeDHzzbD6+TpIbcR69F3+rTp2ZmUG9XketVsPy8jLa7bYrUUumP20xEtuupQzyM3b6+lf63aGsyRBEvwGERncoFHI1r7SGKrC2kWgEk2D6lTxQPhCdbdltEJ1OPxnCjCwrGMDILB0wLW2z26C5FTq1qVQKN910E2ZmZpxDN4g1qSC2Tf8CgImJCczOzrp6bASAybi31+KaYIo06691u13n+KlDq+BRrVZzzACC9Nd6DLcitjwC1woVK4MvCrDRsNPUb416x2IxHDlyBFNTU3jFK16B17zmNUilUi4dfBBzdTPh+BUKBZw9exaVSgWJRAKFQsEdEnMjg+hDGcpOC41u67jyGbdsTAb/CHTaWpLcJ1Ts+/y+BdGVZUqxgC2BQgvEUn/REdcyD8o6UmdDgTLLdlMgjdfjXqj/b7SX6XvKsFNWsQIL1omx4DwABy5bphadOQYylaHEci2DgAXbDraFOpTgLpnGlq2kY8R2EzSPxWIujZigrM6dBmsIxHNtEaSl/uV92De2jfPq61+n03FgMv/n2uZ4EhBm7XwNghM873Q67nwS3/q2wvWqWY7sm44z+w+ssQ/1GgqG0/5kP3hvri0CGAzOjIyMuEwzfp/gil6bup5tHB0dxeTkJJLJJMbGxjA5OYnR0VGXBagletRO1Pbr8802xeNxNJtN1Go1xGIx1956vY5QKOTaqWPK71qQS8eJ99Iyixp84G+uKX2u7HWAIEN+UIBQ6xVfa0brUIZiRffi3QaEr0b0+dY9jEFigur0Q4BgSYntPGv6PG8kqnN3Y9w0K4uBYfZVbR4GwRn4Zoawr3Sl2gd2r90Joe/N+typVMqdo6ZMdL2/ZnbxdWYOUz8lEgmX8Qas6e9WqxVov4K6SjKgvak6lfpHbQqC9SxxQ7Ce9cOvdpx8IPrVXFcDJxwrDbgDa3aarqNYLOZY5fyJx+OYnJxEIpFANpt1JXgymYwDzxkMp/0/aO1rxloul3MgeiqVcsx5BvDpL5DIcj1A9KHsDxmC6DeY0Gi2rC2KpkldyUapSlTTsIAgI223QFYbOVVHju0j+KE/18tBiEQiLurNAzwJYlvWuU9876tyjkQiyGazmJ6eduVIFIggEEzHkfXJ0uk00uk0AARYAgTJ9eAuBl24rrYzt2o42EwBLUXAfuncKtDtA0w2EwUqlGWqjEdlLeqYTk1NuXG99dZbMTk5idnZWaRSqQBT9EpAdAXpyZSLRCI4evQoXvayl6FcLmNkZAQLCwuOfbBRuZz9KMNI+FB2WpT9AwTLqJD9Gw6HAwA0P6s1kHktmxGk19R9TfcB60jyWjbbi8Y8AVsFs/v9vgsKE1RUcM+e60GnXa9PcBsIHhiobCgbCFcdqQ6Vjq0CzwrqUQYFGSiWkcXXeE0y7zkGHA8FztkvC+IrgKAlUvh5DUzwfzsn2nb2lQwogukM1DBYYxlPVuyYWyHo3e/3XZBcdRK/q+uA7VAbQjMOlUHFwANtPwabLePT7qu6bjSQA6wFQBRYYCk4rn27Hn3rkNfu9XrOcaYoI5FBL1t7Vp8/zW7gwaTJZBLj4+PuIHd1uH1ANMdC51PZc/yf80XAhYEstjMcDrsD1/TgcHst3U/UJtFxUltJ1wOvxb3AOvvcdxTg4z7DMbfrcau6dai/h7Ibos+z6p6dBlZ3S3Tv07OUuNdxL7b7wZX060oJPFcq1pfT13VvJzhKHADApniD7rkKOOt7Pjtqo7bSBiBhjX5vIpFwGX/UvdQ71Iu6RwJrB1lSX2i5FQbHw+GwIzGqnaKZ5NSZZL/zddtXtfMURCcJwOpXX//tjzK9dR4ZeKfNYDMJ1KbbLoZiyQAWRFchaD46OoqxsTE3Z9Tf6XQamUwmQAZk4FxJlfa54P+0/UiY4XxqVhzxDmaRUnfr4eE3ggz1987JEES/gUQZLGTDWMdN2bZXWsaFmzEZPHp/IMgG2+kHTh1prX1KoJSOlabu8HNa52q3heM+OjqK48eP45ZbbkE8HsfBgweRTCavGIClUDEnk0mcOHECMzMz7tDRUCjk2OnhcNixpicmJnDy5EnnTKZSqYBzWi6XcfHiRRehXVhYCKwXy3DcyhhQSdPp1oO79EAtgiGcI9Z+63a7jl2v5Rc2EyqJUCjkmHp2zHW9ckxHR0eRTqfxmte8Bi9/+csxNTWFl770pRgfHw+k8G/G3NxsXHjvZDKJI0eOoNPp4MCBA3jVq16FQqGAb33rWzh9+jQuXLiAH/zgB+7wlhtFhkp8KDstui7UcVGgmntIr7dWUoGlnsh+1oDbRkxdBf3oBOkepqUVuN71fe6rup9UKhW3PzLoSiAXCGbbaN1m3oPlXoCNnWwyrLWvBF/VPrDfYfqsBdktU50BAJ0b6kPLQNcMK50TspapyBdKygABAABJREFUDywbWnW9nnNC+8SW6OHY6DUBOGeM16SjRVbU5OSkYzDncjnntGnNblu3VZ1H6hU7Vxw3zi1ru2upNZ1nsrU4d/ybwQYekMsx0gPJCKDzfQ3EU/+rQ88267wymKDAgzrmzWYTpVLJ9UGzHK1tyLXA/lkgW8FosvoZCNAAF58bzlcul0M8HseRI0ccg21iYsKVatko3dsC2BYw4rNHuyuRSKDT6SCTyaDT6WB6ehqHDx9Gs9nEpUuXUCwWUS6XcenSJRe443pUwondI9TGov2k86Dt9z2n+jm2mfudPnMWnN8uQWGov4ey06L7CWUQYWovimaJ8SwH7ZNmuHDf0oD7duVqQPgrEeohO0cE0TU4SMAYWGNd+9qpvprqFO576vP7Mq0HtZNkp8nJSRw4cMD53yzvQuBb26B2IvdZDbbq+Rq8D8vC0J4gC5uMdOpKEtn4N1nT1LftdjtwQCj1cq1Wc8FvArybPQeaIaC2Aw/TJvisY95qtdz96X/3ej3nh2uJna0K9bhdp74AezKZxMTEBBKJBA4dOoSpqSkkEglMTk46ljrnjDaP2rE6pnptxRc4bul02hEHp6enAyVq6/U6zpw5g+XlZSwvL7tAhs7pfpeh/t45GYLoN5jo5mqj3Xz/ah4CjWyqI8lrXy04vJmoU2bZ5myfBUEGASKDxOdc6f23KlSOqVTKOeGMtF6NaPtCoZBjXbVaLZeapoYa09lYhiSbzbrovDLVGFW36WlXsl7UAaXRQMOFoJBG4oE1hUvQSwESy0rZDcaUgkQHDx7EzTffjMnJSRw/fhxjY2MAdo79oUYYGQ2pVArT09MoFAo4c+aMK9FDY/RqFN9ek6ESH8pui2U0+Vjmyt4lG8c6hBZwU4fPgujKurHgVr/fd0FfDWLzWsAaE7vdbjtwlfqCoBhBRbu/KuuN/VG2LtugTDFtt2Wu8vMqCvirLWH3RctG1+/zR8fUN+b837LcfL/V9uD1qV9sppGypXUNWOeLYDEdOJZI4XW5b1NHbWb7+BjPvA/nj6CK1sKnnUWmFv+mXtBzADTYQGCfADp/U7+y/WpPKTNa11gotJrRRhBAU6f1GdOa7hQbhOe1tR3K+rP2HV/TAJKuXf7N8WHNW4LnrGPvK+dm58OnW9Tm5f9au1UZbqOjo2g2m6hWq24OtGQQx8rWi+c69QVZFLQZtL50zHg9G3Ti6wpMXSkgOdTfQ9kt8e2jm63/vSL6XGi5KT7bBCN1D7lS0UCaEnPYjo2+d6XPoOpX+7rvs5StErAUV7BBfgYwtyI+vzORSDggVsv+KT6gOsXaJ1Yncl/nPfr9vjujhvqK7zMoQGBfdRGvqTXyCaSzPIyWZNvKnq2EAr0/M7FYI15tMa5T1Rea5edbZ1uRzdqrdiwPB81msy4gPjY25oLgPARc50HPB+GY+uZOCYG0fWgXqO1cr9cdGaDdbiMWiwWy924E/TXU3zsnQxD9BhZf1G8nrqmsOlVqVB7bBTq3e386mawPxvtoqtXKygpqtRp6vR6q1apLg/KxdrjpMo2IjjJZx/weD9ako6hsMSvxeNyB1WQUUnnuhoTDqylis7OziMfjWFhYQKPRcBHe8fFxd6BpOp12hgUAl97U7XYxNjaGSCSC5eVlB95sFUjXE88zmYw7cJPKkGA+lR3Zh+o4ap1Xgvs8DK1Wq6FYLLqarvV6fR0j8krHLhQK4dixY3jxi1+M8fFxvPzlL8dNN93kaulpO3dD1MGOx+O49dZbMTY2homJCXf4ydzcHObn54dKbChD8YiyZQg0K8MYWHOuCCTzNxAMQDOg59NjdLbUkVKxhjb3KAtaKbimbaATw/2NbGHqKF95D9XHBPV8DpeC2ARhbc10vZ4FQ9kO3/u8vvZL76nODXU4dQ8DtzpOlrGvoLMFfXk/gt0EfPWznM9arRYAm9kWBd5Z81xTvXkdLQuj60rHQufBzi3XHNvG9djr9VxpFJ07zdyifiUgoCC0jiXtHc6VBmt0jjU7g0w11d/K+iI4beuH81p0ZrWWN8eWa0eZhBrM8j1DlqXNPqqwdEs0GsXU1BQOHTqEeDyO6elpTE5OOsdcbUOKJZvY+3PsNOilKfrqqDOYQcZft9t1hIV+f7VWerFYdOfP8L6a9aEAjg0caeBL7exBLFadY17X7hu8ngba9jpIOZQbW+gD+AJXQDAQ7vu9F4XPtwbv2Dc+59v1m/ldux/oXj/oOtsdK9/Y675D5jL7B8D5y5zPje7JfZ4HPmsglMHWfr/v6pCTFUz7yNdeZj1r4NuyyPXz1o7QuVF7RvuhtbwpzF6rVqsuI5DlVAkQ23Jwej0GXdXOUjtlI1Fdx1Jm/Jvv0ScniK7C4K9m7nc6HVdOhhnqypRn+zYL2gDr2ed2zqempjA7O4tkMokDBw44BjoPFmUfdG+wc6f2mLUfBs0tAXa2hwEL4iXEmhYXF7G0tORsmb2cETOUaydDEP0Glt0EsbmxqxJQx3q3jBpeu91uu9rR1jGlI6eHaCngaoXfi8fjmJ2dxcTEBMbGxjAzM+PqYvEQqWeffdal/NgUbpVkMulKt0xMTDiHdDecFCqEVCrl0pj7/T7m5+cRiazWTJ+cnHRAOmuNJRIJF41lP8jeWlxcDBy2slEZFSomHtzCwzgPHz6MRCKBgwcPOkeX6VisR2bXj84vWZulUgnNZhOLi4u4cOECGo0GLl++7JxQRuyvRKhQI5EIbrnlFvyn//SfMDk5iRe84AU4dOhQYE3ttnAc0+k0Xvayl+ElL3kJDh8+jJWVFSwsLOCJJ57A4uLiFfd1L8kwEj6UnRatXUhQCwga1HzWda/h51WHsCQFdYs+cz42lDJAgaDjwD1KM2zUCVbRMhx0phQo5VkWBET1QG06WppSruVZgLXSJbwm2fd6kCP74yt/RnBWRfuk7HwNbtuMNX7PAnj8jpZis2VjLGud/4fDYVdyi8FrrRVP4DwajaJarbogLfvPwC6dXK27yfkk2GlrilvAR4M17BedU64tjomC0clk0hEAOBd0Hqk/R0dHkclkkM1mA+uH61XPMrFjThBdHV+2LZVKOYeWZ7iwpiyZgQQ26NTyu71ez7Wt11srx8NUaNphy8vLLmDi0wHqBOv64m8y/dgnAv60t2666SbE43HMzMwgl8sFrm1BYgs8aVlAPiNMZ9e9Qh13/ZvPaL/fRyaTQa/Xw9LSkgNVzp8/HziIjiALU8S1xAr/1gAXxyAUCrl9Tp9RCybYsdRAAoNFFnjbqp0z1N9D2Q3RbBVftokG3ix4vFdlkP/kC5ZtBjhTqGs142QrgOaVigUkNSAMrGWCUdQvYxByULu438ViMUxMTDgbZ3x8HKOjo+67rVYL8/PzKJfLqFQqzqay40IwVP1c6nXafzYIyr9pR1Hf2UAvbTgNYNAmINnB6l9tA/1gZoZzj+92uw7A1dIwvKeC6IMCJASkWQpwbGzM2QoEzVnORQ9T5TOnILrOY6VSwcrKCiqVCpaXl9Fut12pMn0G2batCMeLmEE6ncbMzAyOHTuGeDyOQ4cOuUNEGZy2dqQVZcvz87Z9yrpXO7/f77vDx9vtNpLJJBqNBqamphAKhRzWpIeOUn/vRxnq752TIYg+lCsSdSrsa9fCqFFAnAdDqGFBEFZr0A4yMDTVKZfLYWJiwgHPCqJzw6eSIdPdd00qKqZv+VLgdkJUaWntXN5To7W+v/m/NVy3ogzVYGHEW51w/p1Op93BbJqOZe+ja0dZdTQwePApQRBGyOkAX0l6MtvNtO+JiQlX/9x3SMluihp1NHDS6TQmJyfR6/WcMUSQbb9HwofKeCg7LaqX7J6vwJd1RHysaRUa5IPup9fhfqr38u2/Pha7j0GjThQdcmVY+9pg9/fNAm9sM6+rfQGwTs/rvSwj3NoFCn5YppyyzlUUILTt4TV0PrXOKPUuGVjaHu6f7XbbvWfHRufOt2aoK22as7bPAkB2DfCaKnoNHzNO3+OPggF0HHmfQbaYrlFldytDjb+1hIwGYNRJ1T4TpOdccN0yO1APIeWcE1QGgllv1l7SfvJ+Oufadh0Xu151THwgPteksjw11Z5rxM6fnbd+v+9S+DXLQNcz9wGuIdtnvTeDYry/j4Gue48dNxVduzYYtVUZ6u+h7JbY4I5vrV1L23y3ZKefIdUvu/F86r7JPYQBe9VreobVRsA+9z8GrukvptNpZLPZQMbX6OgoKpWKA+YJsKvfp22wgLky6XVt2faoftU++3xnfU31ogZINvos7Qiru63+tuNng8z0GQmOM9OcjHx9XTPbgGD2nz3njq/T52w0GhgZGXFMdOqmrZAnLVbBYDznXfW3Zg1Ym8cnPhxqo/917LiOtRRMIpEAAEfK6PV6LhACrJat2+8y1N87I0MQ/Xko1onbDuitn+UhbDYCfS0eTjKECBDwEDe2hxu/phP7JBwOY3p6GocOHUIul8Mv/uIv4tChQ8hkMg5EZ2mRWq2G6elp5PN5nD17Fv/3//5fl7aljiCAAAM7lUpdE4OPpVNWVlZcRBoI1hOlcaMAE40d/uhhVIPmkkowFovh0KFDDoA+duyYC0bwwBUNJFAxWtaTGso0btRxZ6mZAwcOoN1u49ixY1heXkalUsHp06ddmR1Gx7cqY2Nj+IVf+AVMTU3hla98pTt4NZ1OX8VM7JwcOHAAv/RLv4RKpeLY+PV6HfPz86jVate7eVcsw0j4UHZafLUlLbNzs2AmA5GDjHAtwcC0T+uU6b4LwDkvvL5lUvNvMmtZa5qOhAqDxWRLcS+3dad1D1X9R4dXwXF1QulcKeNay1ioY6kMWL7Ow7C0f8pOtyno1N/2wCbVQWyLOpc6l5lMBmNjY0gkEjhy5Aiy2Syy2SympqYcu6vdbjsmeiqVcsAm2Vc8kJtjxZrk1HH2YFc6gKq3COpyjslw4g9f0wwE2ie8L9cqAW0dD1smh9clK5yf4fhyLhhgVjBA9XA2m0UoFMLY2JgrB0cggyA6+6o6m/OjQV3OCdcR20cHnePOkjqtVguFQiFQ93UQqMv1rOOczWZx9OhRZDIZHDhwwLHver3VMn4KWPC7Ou56bX0O1G6kHal2i5IOFPywqf+pVAoHDhxAOp1GpVJBoVAIsNm4Jtg3Dexw/prNZmAP0n2D37FBG5spQaEtqOVjLGizFRnq76HstvA5sP6qruuN1uGg9b8XRINjqr+38/zZ72hQTvexnWir7n98DVg7mNoXROcet1G5rHA4jLGxMYyPjyORSOAFL3iBy9yenp52JU5YViQej6NcLmN5eRnAKgZRqVRQrVYD11egmAC9lrnTwLfu37RTqIPVP97MdgyF1g7LZrkWjr8NJqhdo2W2KOqHW73N9tE+ZImYdDqN6elpRwhjadZMJuPY3HqYKYPfqg80uM6/k8kker2eI8a1221MTk66bIClpSVXYqfRaAxcc2qz0QbL5XIOPzh48CDGx8dd1oC1wdVusYETPeuEQRw+G3xObJBa17T6CZz3WCyGfn+1FBttxEql4g5qJ4FvP5LZhvp752QIoj/PxEaMlY2ynYeDToYFRLmh7PaDxs0fWGUyMTJonfzN2kHn8ejRo5icnMSLXvQiHD9+3NURJwjBFOvR0VGnwJ999lnnoFoQnfXVyR6+FkJWdb/fdynYPiVugRNuqJb9NygKDgRT8A4cOICZmRkcOHAAt956q0ujszXfgI3ZIzZSzbYx+tvv9zE1NYVut4vx8XEUCgUUCgVUKhX3vWq1ui2llkwmcfLkSRw9ehQveMELHIhwvVkuvH82m0U6nUaz2cRPf/pTzM7OolAooFgs7msQfShD2WmxrGnqB61N7Tt8SPc5ZRD5mJmaHaMHHXL/9JWo0uCuAmW619LRZL3tQUJd0+/316Xj2v1aWTZsC0EJfoYprRSCk+pYDXIWFPjn+PocZnXCCSZzXAbZC5oGTufJXptzxQwolk6jE37w4EGMjo6iXq+j0Wi4g5o5vtTneni1zquCpARmOffqwCkozr81HdwC76rb+Jv2A+/J71v97RtXAsR6UDfvO2jueI9IJOJSzBl4YLq5HgLOQDadUw1waLafBkv4N9cp69QzkMG6tgQ19Hnw6V+OXzgcdsw6kh0YtGe5OBIrNJihoDHbrs+mBbYYrGKAR7NE+F0NivlAdAIarEdLhpseKse/OSdKeND9RgEpvRftLN3/+LwoyUX7Z8f1ets7QxmKFd0fKRYI20ws6LuXxOeLbUd8GSeWSKa6/mrbqvuyAru+NtkyVD5R/T0+Po50Oo2DBw+6YKiWU6X/vbKy4va7SqUSqNdtAwrcW1VvWL1tx4z90Qwt1icHEABrbTlbYM1mZDkzBc19fnWv1wsQzCzIa4PffJ8ZWHqOWzabxYEDBxCLxRyRgGVtFDzX+bNECNt/MrD5Ox6Po9PpIJlMIpPJoF6vOzIjsDFDW+2pRCKBdDqNTCbjysyyrIvayL451Xb79gPFfvRvq/85X8p41xIwtHmmpqaQTCYxPz/vSgQuLS15s76G8vyTIYj+PBPLWLGO7FYjVNxkNYoHBJlt2yk5oZu3ZTxZxWPbp5usGg6b9YN9IHOamzhBYD3EjY5ZOp3GysoKMpkMUqmUYwtZ5UHFTebZtRKNJFMJtlotVCoVRCIRx85S5UwGWK1WQ7VadQex+ow7Zf8R4KXhw3roFqQCrjz10q4DrjPWd+v1eg74jsViLmuA9W4HCdl7mUwGMzMzOHLkCHK53LrMir0gNB4mJiZw8803Y2lpCfPz88jn89e7aVcsw0j4UHZarBNi910a1IPO8gCC+kcBaLuXqZNG59KuS2Wr8scy0QnIDWqzbR8DnfyeMtD5vi84yh/2g+0gqM1+WXaqvk5wlsKxssELO446fuyLtkftEe0zP6t1zdXZ4T1YK5x1Runsse64Hgwdj8ed000nifrKzq3qgkE2je2fLzjjIxmwHz6GlI4FwWXqPtaB1XNMCEI3m033Q7tEf7SNyorneOlBbFwbuoa0XRwPnWddO/os2oNEySTv9XqBw0o1KGSz5VQYjNID2nh4mzLVrC631/MBJ9pvdbTtPNr9wwbDlLDAPhPoGBkZQbFYdNeza04deT0TwD6//JtguY6T2koKfvn6zM9tFLyzMtTfQ7leMsj/20ion68WVPb5pvqb72/l+dB940qFz/lG+nY7YnW+3sfuV6rL9HPb8b2Z6URAlT/U49zTyXZPpVLOV43H467Up5bJAoJMbe6N1k7y7ZtWZ+g+aj+rY6YBVV1n1DnUgTxThv1n1lA4HHZlY63+tiXOwuG1g88JkicSCRf4JpOb/ecYKOtd7S8fRqPf4edoSzHQy9cnJiacvaXBe/rfVucqNsIyLlpyRtut7bDrUNf8Zj6775nVOeL/NtgRCoVcWxOJBMbGxpwdSQLG1T7D10OG+nvnZAiiP49EnSfdhMj6UmbRRtfghqpKjgY4N/1Op4N6vR5IqxkkagRwE9XNUVO7B5Vm2e6mQEeOTOqTJ09iYmIChw4dwvT0tHN61CCKxWJotVrIZDIoFouYnZ11h3lVKpXA/UdHR11KtB4ctttCZUE2WavVQqlUQq1WQ71eRyaTQa1Wc6ADsKrYV1ZWkM/ncf78eVSrVeTzeZemrQqQ83P06FHccsstSKfTOHnypEudTyQSAaW3k4C0romxsTGkUimMj48jlUqh0WjgzJkzLlCwuLiIQqHgvQ6DAOl0GkePHsUrXvEKvPjFL3Y15PaaUJG/8IUvRDwex4ULF3D27FlcvHjxqpTh9ZShEh/KTouyjlQIbnEvs0AXEAziElTWPUzZqnyPIKR1lugwKTuZzoAylKh7qeMAuD2U7eM12XYt/UFwWO9F3Wu/r1lbZLvTibOlSfijjimdKQ1C2PIi2pZwOOwYYzrmWmqHfWLKNYFPtp1AqpYysbUyR0ZGkM1mcfDgQVc6I5fLIR6PI5VKOSY1mcmNRsOB5/l8HuFwGJVKxfVPy4Uw0Eq7Q3+zjQShuT5ou+g46nqjY0m7yQYtlMnN8eI9WcpH2diRSMRlBFarVRQKBVfWjAd6kq2njjud71gshqmpKaTTaUcm4Fywn1w7uk44hxrU4NyzHxxnrkm1E9mPRCKBRCLhbCiWdmGKfq8XTL/nvQi+ZzIZ5HI5jI+PO8dWwXsFRZRVrmtN206ghCCGrnMVddy19BHHRdcvx3t8fDzQT5YQUhCb5Qs435pNoCCI2licfxXa+GyPBdtsbXtecyPbX2Wov4ey06K6dJBsZ93p5xTcvto2cg/TQJkG4dmPjZjYGhS72jbRFtkqeO8TG1TjnsX90BeYZ/90D1ObaiPAmfhBNBrF5OSkK6F66NAhTE1NIZVKYWJiIrAfsmQIy6Tooc32EFCytJXEZgkF2l/ajHo4NL/T7a6dQaP7I20G1eNKLOQY0eYiMY0lYqmfOG71et0d3Kn629psIyMjSKfTjrTGcmGpVMplklGXqC0TiUScTWZLtg6y4dQGIzjf6/UciY2lY9vtNgqFAhYWFtBqtZDP51EqlQL6m/YH9T5LzkxOTmJiYsL59Govaxk2nRefbc7/dZ71s5xP33PCMVE7l21mRt7BgwfRbDZRLBaxsLDgsui2ehjwbst29pKh/t452fmTDndRHnzwQYRCIbz//e93r/X7fTzwwAM4dOgQEokE3vCGN+DHP/7x9WvkHhdVkrpZbSWaR1FFyE1Rf9TR3co16ajo4WD6w2tqZPlqwVl1RFhPLJlMusMj7InQGjnl58jg8jF4NNJuWWa7JRqh1TmmI00gXdPbNc290Wg4JjoZbzZKT0c6nU5jYmIikErNgAH7vJMAuu0fa8YyhX9qasqlBJJRN+j+6sBrP9Lp9I63+WpFAwfZbBaHDh3CwYMHXdr6tVpbOy0KMl3Jz1D2n+y2/lY2kP5Yw9qeAaE/usYsK8Ua3dzjVU8oc0ZBq0F6l6KAHp0gftY6AVraQR15ba9tOxB0+vV1bZ/V2fqegonKvlLhNdhHgq62Dwru2xJitq38zqD2ks1GvU27gWNHBpX9jNYvV4DWspy0PRYg9R1+quOjOovXsfVR9fOWdQWsAekrKyuuFicd606n4w5KZY1xy0Ln2rYseNprZINxfBSIts+DDZDrWrFrXYEry0ajTcUD3RnUTqVSzo4YZD/qs0dGujLY7Nir+AAUO/7Wyfb1SedZ17TvjAJ+nxkTekaMbZ9db/Y5ULDLPk/aN2Wt2+vbffJK7NSh/n7+ydD/Xn/wpNUV9r2NZKeehUH703b9ebVNqLt9vrfukRY03+wZ1/2L9yHLl/40f1NPq35SPUVdZRnX1h7TcdB26X7JNlk9YPW5XsOn+3y2TqfTcZnRzJKmzqbepu7mZ1Rn+5jcOi4cO2Vzq21jbTJrn1q9rTaR/bxmgNH/ZlY6M9NZPsZiNTo+ihuxvUrItLiUHX/fmtd22nnyzZ+uWZ/9zM+xTcyYIPlAM9/2imy1LUP9vXOyb5jo3/ve9/CZz3wGL3/5ywOv/9mf/Rk+9rGP4XOf+xxuvfVW/Mmf/Ane+MY34plnnkEmk7lOrb32YjeOrS506zBvJqpgqeSU/UfHLRxePXAECLLjKCyjwt+shUUAVDdHKpZer4dyuewUUaVSCRyUeb1kL28q/X7fMc37/T5Onz4dULoAnPImg7vZbLoac8CaEkqlUpidnUUqlcKxY8dw+PBhd53dAM03EypOpphNT0/j1ltvdYfMEHBoNBrrSibMzs7ixIkTuPnmm130ey8pQyuh0OqJ4ePj46hWq5idncWRI0dQq9VQKBTWPV9DGcpekmuhv5vN5roD84D15SGAYE1IdRp8wKCCYvpdC7L6nCiKGupkrvCaWspC9Z6yvsmIVnBMy1XZ/ZftILuaTi+FJSN4fV+7rfPZ7/cDjhava+vMK2jINvD7Wk+eOp0MIwUKNVhAFvOg/VkdIALMbJd1pmm32PZyPLTvNhjDudI1wbFX543v27kIhUKBuffNnzp6BM1p40QiETdmZM/rmNTrdRSLRbTbbdTrdXcgJb8fi8WQyWQQjUYxMTGBAwcOIBqNulriNvDgWxc+RiL7AyBQe5b2n8160LFlP5WVyZJs1N9qP7JNdGjJEuPzQqa52oQK+qhofXErDA7Rth0EMKvjzfmyoBLfJ/uy0+m4jEAeWE/GIUkPTPvn88vfmkngA3t87VQ2PL9nAQ4NAgxlKFauhf7eDR/OPvNX66tZnQGsgWx2n9SDkul/WQBvs/ZuVXwBWe4Pel8FRZWpTIayZQBrQJH9oO6ivcUfG3zcTHwkIO5BmoGj/VLdpP1mP/m66m0fUGzHyOqAQYChBkY3+ozu4SzPovYe/VK1FRqNBgqFgtPfmoFFFvnY2JjT3zxEdGJiwgHoJPWpTWKz3mwfLUjPeeBnbMYcsIbf0K5QFreWo+EcttvtwJwwkyydTgfarXNqgX0F49n2Qfa277uD1iWvpVUTfEF/trnT6WBsbAzlchmRyGrm+17Qm3sZh7qRZV+A6NVqFf/1v/5X/K//9b/wJ3/yJ+71fr+PU6dO4UMf+hB+7dd+DQDw13/91zh48CC+8IUv4Ld+67euV5Ovqagi2CydatD3N9pkVOhUMBJKBpCWc+GGSieOr/NeAJzzxnRilkZJJpMO3GWf6FR1Oh0sLS2hUqmgXC67FC86L9datmIwXE9gloZFs9l0LPRareaUGA885di2223UarV1TDmur2w2i5MnTyKXy+Hmm2/G8ePH1zmZ17q/4XAYiUQC/X4fMzMzSCQS7iTtcrnsAgiq5EZGRnDs2DG84hWvwOzsLDKZzHVr/1YlFAo5Flur1cKRI0cwNzeHxcVFF0zaT3I1Ee2hsbC/5Frp71qt5j2TwWcgA0FwTY1uigXn1EmjYa16l4C8T/8qiKxGO1NjuQdpLUkV3Z/YDrKR1ZEgM4ufZT1xZYwTVAMQ2Dcsy0yF/yvri+VEqEvU0bEsJ44RHSzWJadDroArsBZ417bqnFD4Nx152goE/LWdBNFpt/jaalnKNmuAc6wHr2o/tU06rgrSa4CA1+X8aH+ouzT4w3qkfF/np9VqoVgsOoCD9hd138jICHK5HJLJJKanp93Bq0yrV4fTArTaLo6DzezQceD8MZhisxjo9BOY6fV6zpYkiFCv1xGJrNbDteuADm0qlXLPu2aT+ERfp31jA2a8h4LVXD++62maPsed19Lr8prJZBK9Xg/ZbBbNZhOlUgnLy8uBA2A5l3oYLYEUPtPMlPSBeVoCQO9Pm11fswGurdrRQ/39/JFrpb93c13s1LXVTuAewb1Ig6vdbtcdhMn9y7KKd6JdviC+/eF9uFeQBUwSG7N/FDi3tgD1CHUSD4VmljP3wK1iCapjVHdqhhr1gtpZyoqn8Bo++8yy6H1kCR9ZwALkauMpkUHBXPabfeBYNRoNV9OdZdV4qLmuASUGqh2o9tb4+DgSiQRmZmYwMzPjDgQnMzoej7t+csx8WU++QK+dBwZGtIQbhQFZrgmWexkdHQ2w6vm3klW45shcZ/a/gugWBLf2kc+2tjaYfpfft+tSSS0+21tt52QyibGxMQDAxMSEOwdvaWlpw/PXdlJ8wZ4rkaH+3jnZFyD6e97zHvzH//gf8e///b8PKPHTp09jbm4Od911l3stFovh9a9/Pb773e8OVOJkMlPK5fLuNX6HRZUPNx4a1eHwWg0r1n/UWp5A8OHh5qmG9Hbbsdn7NnrIDY1pvCypwXrUykRXZlckslr7k05Ip9NxtWMBbKn2uooqD40W08H3MXUINGs6ltZIVVEHcxCLaafFKhZlYPJ/KnP9PKPkBJsVcFbDhXXQMplMoB6+L5J/rUTvy2hxr9dz2Q3hcBilUinAEAuFVlndVOJ7nYVOoaGopWiq1eo1W187KUMl/vyRa6W/lREFrD8E0e7TdBxU/+na4t6t+6cCwoPAcorPaVFgMhaLod/vu7TYSCSCZrOJSCTiwEA9RJRt0X7wupZhr/f36WodI59jwu8wKKAMLy2joaVSrBNjfxTQ5bXYdusUW1H2lzq0ANY53QrA87OaiWBtHtpTyor3zRsdNV53kN7wlfQYtN/ZNelbh/oZBWm4NpSooH3n6xxbzR7UdG+bicH5U/vLtlnbafvnW2s28GFBFN7HlhKgzehjE1oARufLB5AoIOQbZ/28PvM+GRRkUrGBeXXstWYtv2/HUfcKW67AZ1/z87SV9f583fdsWZ9gKzLU388fGfrfq8JnTMFzZpdkMhkXXNUSZXxWfWeODXoOBgVjKaqzBul13V/VbtEyGlouhdnEFrDUPZP7E+9LTEFZ+NwvBwHpbLv6p9RZqsdZC5335p7mK1Gm9/SBjINsEmC9btN+2t/2c3YONdig39XgLvdlu3/yPe2XzrOWP7MHcPI9DTb4SAw6Jz49OMiO1XVFUV2i7WPNdNoY3W53XbkdDUJpmzeyHX1tUNuXdp6di4302iC7n6LPjupt2r5KWLkecjV6dKi/d072PIj+pS99CU8++SS+973vrXtvbm4OAHDw4MHA6wcPHsTZs2cHXvPBBx/EH/7hH+5sQ6+RUAGm02m85CUvwfT0tDtYi4qHjOKf/vSnWFxcRLFYxKVLl9wmzQOqlEVHEHqrKXWbPYS+zSkSibja2ZlMBidOnHCHb2az2UCkUjfMeDzu2pdIJNBut1GpVNyBknNzc7h8+XLAgdxK+2kELC4u4uc//zmKxSImJycD6UbqhNdqNVy6dAnFYhEXL17E/Pw88vk8arXaurHgwarhcNiVqrlWwqBJrVZzh5Pw9Xa7jVAo5AIHAALOvh27SCTiao0fPXoUJ06ccIeADHLKrocwYp5OpxGNRnHTTTchFAqhUCigVqsF2CAjIyOYmZnBC1/4QmSz2UAwZq9LKLR6+O6RI0dcOuDPfvaz692sbctQiT8/5Frqb3vAjw8gtuAd3/fVqaZQP6pTzH2POsQHumkaKV/XOpDj4+OOkUsnnGdTrKysuNJljUbDMZT0kFEb8ATgmOdk0apTRVCegQYLTCrY1u12MTo6GmBdMfjIeqTZbNaxnxKJRMBJY1BWnVj2n8x+jl+r1QoEDth+dcxZooWHqar+Ylow7QjeQ0uGMABeqVRQq9VcKQ0GVwnY6txxvKlbtG9cX/zNOeb12C/bF107Cm6w/6yHaplNurZJlrBgCvvIOeb9WEOWKeCpVMrpPAIUvlRqZTrTDrKMaT4bTNdWh1OfNZ9TbJ83gucAkM1m0e123bPAdqizTuabz/nW4D7/1uCJAjB6cKodB2Xx6z3sOudccn3abBh+F1g79J1n0bBt+jkG2OiwK/DAsWOJHw0G8h56eLEy2Pl5rieucQV8tiJD/f38kKH/HWSfEzCPxWKYmZlxhKJcLueyZvhTLBbdOVPMNuHhmGpTqAxiTVO4b9l91QbsqP91nw6Hw6698XgcU1NTiMVijpCjewX3SPUL6X9T94bDqyVbCYTqfu3zJYG1Z5/lQ0dGRlAoFLC0tIRms4lcLodQKOT0oJYmaTabLvM2n8+7w6e17IvqILWP7Nksqh8s2QKA02m23RxjHRN+h6xszZwnHtNsNgP6iXpMdRfvqbo9Eok4fT0+Po7p6WnHiOZB2rS/OOc26Mq1pGC+luGxB5dutKYsSE9dxfXMzLCJiQl0u11Uq1XXdz2/hARKBgRsXXQb5Ld2vLVBtH36Oa6DQbpYdSFtIX6PzwHHLplMumc2l8u58+R8WWo7Kb5g2tXq0KH+3jnZ0yD6+fPn8du//dt45JFHXBqiTzaKPvrk/vvvx3333ef+L5fLOHr06NU3+BpIJBJxB2HefPPNOHnyJKampnDzzTcjFou5KH8+n0ev13MM9fn5ebdBq/NmFcJmouD4VgF3Sii0msaby+UwPj6OmZkZZLNZt6lSBgGzvV4PiUTCMY37/b5L+11aWgKAbTkB/GylUsHc3Bw6nQ6KxSLGxsbQ6XRcGjIVIWuO5vN55PN5FItFB3T42tpqtRCNRq9pqQ117qjMlaXIPm+1TeFwGKlUCrlczjnhPDX9ejLQfaJBmKmpKfT7fXdojWU9jo2NYXZ21jHq95OMjIxgcnIStVoNS0tLu67EhzKUK5Frrb9p4Npa1SoKJOn+xT1S2SmWAUxR0NQCYPyuOiI+oJBObC6XC6SD12o15xiOjIyg0Wi4GppkMfkyhpSprfdSR8qOrwarrZOhzoRen04320xAnXYGx4KAAUt62PvQKbb31Dqg1tHRNjNI3e/33ZgBa+d78DO8FgHuZrPpgAwteWEdR3XC6ZQqsMBr6hyznXSG6/W6s8EIBtg6s3TS+Jo60nZNKTCujiznWGvIazvpZKfTaYyNjSGVSgXGne1S9ryCNGy7zWZUVr+1K60Dzr8V+FFHTsHvfr8feCZisVggO9Ay23RNK0iiZU44X/ysMgQJwtBZ1nWsNd7tM8K1yR9dn76DOknyIJjAMjr2mjq3zPRgIIdtJMCja5HrkOPIvmuJBh177b8CQUMZCjD0v1X4PPK5TSaTOHjwoKtHPT4+7jLJCKRGo1HU63WMjo4GyoxZRrq9j5aSss+sBsF9+xGwNh+qV6gnqAdSqRTGx8edbkilUu7+oVAokJmsgGU0GnXgNm0U1Xv6HWuPULQfkUgEtVoNlUrF6XKW51QAlfYCS7mSIKZnR1jdQ52k/dLfvhIhyhgn8cqSxdQuUT3OflnGPF9TYdktbZMlAfI9ZgqQfMgSKAS9CT5r4EVL3nA9sX1WZ/Nv1R8+wNmOn64zPhvUVel02pE14/G4WysKpNsDwW22lQZQrM2kNgjtPJ+da/tk7VDFvnz902eHWRvtdhvJZBKpVMplYO6W+OZhP4LYn/jEJ/A//+f/xOXLl/HSl74Up06dwi//8i8P/Pxjjz2G++67Dz/+8Y9x6NAhfPCDH8S73/3uwGf+9m//Fh/+8Ifx3HPP4eTJk/jTP/1T3H333e79Bx98EH/3d3+Hp59+GolEAnfeeSc++tGP4oUvfKH7zDve8Q789V//deC6r371q/FP//RPW+7bngbRn3jiCSwsLOD22293r3W7XXzrW9/CX/7lX+KZZ54BsBoRn52ddZ9ZWFhYFx1XodO33yQcDmNiYgKzs7OYmJjA8ePHcfjwYYyNjbkDo3QjO3HiBFKpFEZHR7GwsIBKpRJgXwHrD3FQNg43EgUmuLEoA0kBeCoQ/tZI8OjoqANkM5mMi6Ruh6XNdrL0SyQSQTabRSaTcYDxdhwBKu7FxUV0u12cPXsW3W4X6XQalUrF1ehkDbgzZ85geXkZ8/Pzgai7FWYDhEKha8Z0pnPFQ9tsWvd2hco4m81iamrKsfz2EgPdJwR71OhIp9OuRptNi9vLffEJDROySvZb+4FhJPz5INdaf1sWijojGmjS9/WgR76nILwa3hY853sKRtuSMrw/P59MJpHNZpFMJjE5OYlcLuccc7aTDhYA93zXajUAcE6q3depu7m38W91QgAEdK3dNzQAq2xdYA2UJIjOOaATNAikJwBpn1m2T8dNmcHKRrJ9tKxcMp76/T7y+bwDYRV4JWsun8+jWq2iXC4HghHqBHOctTbpoHJx6tDpGLL/FgzwfUe/p/Ni2W/sK+dRx1VBUQYuFITVuudq62m7uP4040LBZ3W8FSSgA61OL6+n68D3HClwoY6sBQcYJKC91e/3A6xNuyZ0PXLMfGQRjoOdJwWhdT7sPOv1LLign6UzThCK4BUDUrY0Aa9jM1/4nq+Uns6lrisNHhCsUeCAa2k7MtTfN74M/e9V4b7CWs4TExNIpVKYmprC5OSkKw0ZiUScf03APB6Po9/vo1wuIxQKBc6x0Osrq5cBaV8gjhlSFGvrKLjI55z7GoP3qVQqwAT2nSOje4rVkQCcXgHgcAfuJZr5PMj/VFCTGWK9Xg9LS0tu3JrNptOh/f7qORGLi4uoVquO5a+HWCqIrmVZlYCgGUUK9Ov46bwM2tMtgK7jrXv5oP4P2pf5WWvP8dwZ6lnqRtUDCqIrGUAD3tpGBex1DQ6yXe37FtD2tbvVagUY7rw/dZgF0PV/DVbYoLudJw3c2d/6+UFzYdelTxRIt/pbAye7JTupN6+l/n744Yfx/ve/H5/4xCfwmte8Bp/+9Kfxpje9CU899RSOHTu27vOnT5/Gm9/8ZrzrXe/C3/zN3+A73/kO7r33Xhw4cABvfetbAQCPP/447rnnHvzxH/8x7r77bnz5y1/Gr//6r+Pb3/42Xv3qVwNYBeLf85734FWvehU6nQ4+9KEP4a677sJTTz3lgoYA8B/+w3/AQw895P6PRqPb6t+eBtH/3b/7d/jhD38YeO2//bf/hhe96EX4H//jf+Dmm2/GzMwMHn30UbziFa8AsApePvbYY/joRz96PZq8a0IFdeutt+KXfumXMDU1hVe84hU4fPhwIOWTm/bKygqmp6fRaDTw5JNPolgsYnFxEQsLC1hcXPQ6EuHwaroX66Nx8yODi+laPLCKIDI3FyDofBLIpfKOx+OYnZ3FsWPHnOFBwJ/t2EiUEUQmH1PLmAZPAHmr0uv1XIAhlUqhXq/jwIEDyGaz7uAO9pklcpaWllAqlVAoFAaC6M1mE/Pz86hWq46psBE7Yyek0+mgVCqh2Ww6hvyVHriqzIXDhw/j5MmTrqyLjz21F0SVJ9PdAGB2dha9Xg/1eh2lUgmpVAqZTAZjY2PrDqnZD0ImOgCMj49f17psVypDJ/zGl2utvwn4aXkGIHhwJMWmtpLRxP/p0HJ/4FkLvEav1wvUoeSeSCYaRYGqkZERHDhwAEeOHEEqlcLx48dd+jKvq4yuYrGIZrOJubk5dzA0GdTqcFH/Un+n0+lA/1UIkipIrg6dvkfdRidudHQU2WwWY2NjLhvOHo7Fa3H8+v2+c2g5R3SgMpkMer2eK4fmc+r6/b43m4v3p3M9Pz+PWCzmAuAEO+jU65iSJV6r1QLnnHD8e72eYxH2equHl/NAbmUF6phZ1p3uybStdM9TwNRmOVjmleparlF1NAm2MgtRD/li2R2W0aMzax1wvbcVOt0EI/i8kGlma61rX5VR6eu/gujK7I9Go+49tpnEBNZd1SCOgkQcJ65vTfe3DrNlJOp46riEQqEAw1/Z5/ycElAUROH86jkCfO6bzSYmJiYQj8fRarXWgWwKQLD9JGYouKPPHoAA6GWfHQV+lKnOsdqKDPX3jS9D/3tV6AdFo1HMzMzg5ptvRjqdxote9CIcOHDAlWcieEudMj4+jlqthnQ67fQNDyMEgjXWVX8zc1b1hmYQ60GIuo9anc73k8mkO2ssl8s54H98fDwQcAeCwCj3MrKyqXc064X7EDO9eE97Jon1kRWUJQudGUdzc3OIx+PI5XIOf+C1l5eXXYkc+t8sq0EdQZ3Dg0/JGKYdp0C/ZR2r3rKEAwuKK3iuP9xztTa+FSUosB02SMEARy6XQy6Xc6VHNXuAtp/WR9cgNu/FtcP5YfCBn9OANF/T9an/s+8cQ+0LiRGJRCKw/ogL8fngM6WBAQb7LSFDSZE6Tmor+sSSEdhenw3Az9t765ywMkEoFML4+LgL/kxMTCASiaBarbqMyO2IL/Dt+3sn5Vrq74997GN45zvfid/8zd8EAJw6dQrf+MY38MlPfhIPPvjgus9/6lOfwrFjx3Dq1CkAwItf/GJ8//vfx5//+Z87EP3UqVN44xvfiPvvvx/AanbTY489hlOnTuGLX/wiAODrX/964LoPPfQQpqen8cQTT+B1r3ude52lua5U9jT6kslkcNtttwVeS6VSmJycdK+///3vx0c+8hHccsstuOWWW/CRj3wEyWQSv/Ebv3E9mryrEgqFkMlkMDs7i/HxcUxNTQXAWX0Y6Xx0Oh2cO3cO2WwWjUYDhULBe11uoHrCMxUsnQytE8ZN1Dp69nAMKlJ1clgH9koATN3kCHIwvY5Kfruiqd1LS0suWEBnjkq6Wq26OugMIAxives17Oa9kwC0bmi9Xs85YsrYstHjrQjnLBKJIJlMuuCKjx2x10TZoVTUrGdG4EtBhL0YENhIuC4HpYPvBxk64Te+XE/9raAcxQd+0uHj/k+nyLI/uY8qC53XtAwgOlwqyjQjyMsMGV4fWAP3CVhSv0WjUXeehS31ofe1dS0V3LXPjW2jgoPK0lfQVtNwuZf6GFd6f7Ke+Dp/W/YRsMZ802CFbTPn1bLUer0eqtWq+4yWY9OzYhhctuxfm5mg9UKVke9zeqzDxf5x7nX9bbZ/+RhUSpDQ9ilLW98H1gBYnS8NKFkms86brhl1WvVH729Bfb2eBkSUfacMcTt2vmtyftTp1fcVELGljuxeoH31Mdgso16vxX741rs++/a5o33NwJBmcxCgV+DGt056vV6gNJC2T8fTd/9BbHMNImynHOJQf9/YMvS/V4X7DIFNnr2RyWRcBjgzyWKxmCNy0f8rlUqIxWIO4KX4dCEDktRvFILXFLt/6uu6V2ug3eoC/q2BRPsdvmbvr5lkvLay3rnf+nwTu992Oh00Gg10u13HSCfOMDo66vQ1z4nRkmxaQobXo56grlCGsOIVvoC27k2+ufKJ1f9WRwza7zYCS3XNEWD2lT7RLC/Vf757abuUbMJ5HNRHawtTFw+ye6wtyjarbtH31RahPvfZH9vxc30BZmD9s2KDBhvhJLwm8QMlDtDW3K4Mej52W0fuhP62B0L7Moza7TaeeOIJ/O7v/m7g9bvuugvf/e53vdd//PHHA4dVA8Cv/uqv4rOf/aw7dPjxxx/HBz7wgXWfIfDuk1KpBACYmJgIvP7Nb34T09PTyOVyeP3rX48//dM/xfT09MDrWNnTIPpW5IMf/CAajQbuvfdeFAoFvPrVr8YjjzyCTCZzvZu2Y8I06ng8jsnJSRw5csRFJX0AOv+n05TL5XD06FHE43GUy2Vcvnw5sIGwPtro6ChyuZw7NJLf1/qmy8vL7pAzrS1KJpdNZQLgDnxkHalkMumufTXC7+vBLow4q8O2mfBzrVbLsdITiQTm5uZc7TcaRktLS6jX6wMZ6JRGo+FSzw4ePIh6ve7GdKeZw9zUGo0GLl++jFKphHw+D2CNpahjopHyQRKLxVzpgbGxMVe7d7+wtrk2YrGYq4/O2oSsJ7cfwWdg7XDR/QyiD2UowM7qb2VvUWyaq4plAvnYsnTEtJyZsj1pWCsrlQd6URg8ZjmzbDbrAtWWpaUsKKZ1ZzIZ5HI5x66qVCoBFv3o6GjgYCn21TKI+HmtiwkEWbsWjKXzok63AujKlgPWDjblWFkQmO1mkL7f77uDw9k2LRnCcbaMO8433+dnC4UCqtUqSqUSyuVygI3U7XYdO01BCDseBBPr9boLhrM2PbP0eE9eZ5AzZF9ne9QOYL8IFPC3z8nRa2rGRSi0xpQGVvXe2NiY+53NZl0GFu0By6znOFnH2NZOZX8tKE/wQx1SDT6oKKNbbSkbaCAwpQehako1iR6ayQGskTk0yGTH0Y6nfU3bouuYz5Yy0O13fONE4HxkZMRlUTBTkWOhTEadZwVVNKCg7dc5IsDEMdM9RudM269g2lCGshW5Ef1vuw+wdGg8Hkc2m3XlSJPJpHue9dwB7kdkp2cyGWSzWYRCIUceA9bsD54JxvuQiW59FAaLtVQq9wAFRjVLDVjDD7QsCNuhoCzvoffTfZh7NZnE7HsymXQBgkaj4Uq8DvJLLIhNXccsmFKpFAhMqN6p1WpOP+oBrbwu91wGzNvtNtLp9LrSsdTh1Ju2nI1lJfM1tlkJBLQTlMTG/Zc2wnaFmXaJRMKV6+WP1X0aDCfJQ/d7tRV1fSj4DgQPcNeAMPWP6lENGLCPaqPZoBAzIRiIsix06iCuG18wwwatbZDb6rmN7CcNPijZQa+la18DB51OxxE3ORdcbz4CzUbis5WvVDYKAOyG2HMs/uAP/gAPPPBA4DWSUn2HT/Ngaitzc3Pez5PkOjs7O/Azg67Z7/dx33334bWvfW0gMPymN70J/+W//BccP34cp0+fxoc//GH823/7b/HEE09sueTYvgPRv/nNbwb+D4VCeOCBB9ZN3o0kyrY+cOAAjh075lKUBikqOtSjo6MYHx/HTTfdhHQ6jfPnzwMIRgzT6bQ7ZHFiYgK5XM458gTRqbji8TiWl5fdgZ6angb4a08xDZzsOzqiVwOic7NiWhAPdiHQu1kU2LaZSnpubm5dVFL7s9XrEmiPxWI4cuSIS0PeDSeFm3atVsOFCxewuLiIer0OYC0tjOUI6LDy9PNB/YhGowEAnUGWvc5CB4JrO5lMYnp62oHNpVLJHSZqjYj9IgSgut1uoBzSfpIhk+35Kbupv6nzNhKCjD6WKN/XvZ7OloLolqGurFwGuKhT6FDQYSCLjQ6QgolsBx1xBsnS6TQmJiYQi8Ucq00daDqcWi6CY6GsIACBkhx0dtlvgqDKzNZx1TNX1AG3YkFZZaoxS0pBfnWmyRCnQ61toQNDB5B7OseQ7D+ydZWxy/uz79SLvLZlFpMk0OmsHmDKOvCabqxnjhAYUEaVgss6t6FQKHCYG7PayKpjaSGOpa47DYZzjOjkapZgNBp1Jc0IoieTSYyNjTk2tLbLMrUUPFYnXGuacvw1kMJyJOoYK7jBsVPAVzP6LJgTDoddzXB+VwEGXe8EYvhbD26zATLV/YOAdIquDc4BATT9rILgfF3Zgspcy2azCIfDqFarLhvFghxakoW2rh0/y57j9zQbUcdTA1g2sKj7xGYy1N/PT7nR/W/dD7hHMEidSqUwNjaGyclJRwbT+uX6LDJQNjIyEgDRmc0LrO21sVgMqVTK7WvJZNLpdetv6WGarLuu56QoOUoDfhZAJwjLoLvNmlK9pnqMe5EeuEy9PTIygmq16kgEG/klun/w+wBQrVYDuk73Y/VffTiD/t9qtVxbqtVqIIhAu0sBc9ZMV/2t42HXh44JbSr61fV63e2/g8q5bCQMHJMoST+c64gBEK4PBYTt3FEf6NpQEF1FiQR2/LvdrrM9FFC2VQesLcRzBGi7cc0Rv9KSiT4Q3QL3Gmz3rS9fn+zYMgBEX4Fjp9fXta82CXU8CaHcB9rtdoDRv10dtxM6kffeSjbZTujv8+fPI5vNutc3Ap3tXHGNbOfz9vXtXPO9730v/vVf/xXf/va3A6/fc8897u/bbrsNr3zlK3H8+HH8wz/8A37t135tYPtU9h2I/nwV3bzVQdvsO7qhWeYRr8VooRr5NB7UQKdiJ3NMN59BSg1Y25BtyvvVigLpTLHxAeDbke0qvEGikd9Go4FKpRI4yPJqAVwdb9aMo4HFKLhV+ECQNTVIuF4YhFFAYr8AtmogcX1rDWP7uf0m+xH8Vxk64UPZabEsdF0nCp5blonux8qktnu0BRqVAaXpzvo5Opwqtg1sh/1N4E/TYpV9o46K/cwgkJC6eBBjRx0G3kuZq749Z9BrOo6WNaR9UMCCjop9xqnr6STwf72uOvoWSLB2iu23bZuC1HSG7djrurCApm3PIPBWHV2Wy9uoTNxGe76OszIMaRsp0LPZHso28W/2UcFb+3m7nn3Pjn02BoEg9hnls8lAjmZB2LHUn0Egue4Vti+6h9C+VAfNAgzq5POZtezurdpOvjWp97br165ZghpaetGKfc4UUNrqwWhD/T2UG1m2s775bFu/QgPidp+yn7GlL/S72ibu5776zrym7nXWB7S6WN/na+yTBSz1s6p7rW1gdd5GOse+xu9qYFf3vo2IX/Y69IEJ0pMQoUAtgVteX19X/e27Pn/7DiEfpN82E59dp6VHdW1spk9UbyuYbudnIzvUdz1rv1qd5LOvdR0rcG6zuDYaFyB4iKhdU742WZKHxUMsFqUkBbVLdZ3sZdnqetsJ/Z3NZgMguk+mpqYQiUTWMcQ3Onx6ZmbG+/mRkbWz4AZ9xnfN973vffj7v/97fOtb38KRI0c2bO/s7CyOHz+OZ599dsPPqQxB9H0gujFth2Gt39dDL6ikmGIzNjbmDjdi9BMIGgdkcvGwrlgshuXl5YACGdQmTSdTBvBOCUuWqIOlKd5bFZ8BYkXHfqNNlWPebrdx/vx51Ot15HI5vOhFL8L4+HjgRPPtiq6FlZUVnDt3DgsLCygUClhcXESlUnF9UDBcjQamXQ+aM7L7x8fHkUqldmXeroVEIhFks1mMjIygVCoFQCZg84joXhTOvT2cZj/J0Akfyk4L2ZVAsL61T38Ca4c/ku0KrD9wVEWdEQDuYEWCe9Yh1WeTGSM8BLvX6wWcCctkU0NeHSk1+FkSJpVKuUO46GypEHjm39SPwBpoxuCBjh/HgYFzzaRSB4NjaPciO9ZkiFvAmfYF9WWtVnNjEAqtlcNgaRU6qWS0c/7IBLPzYFlnnHe1D9guddTJvmLmUjKZdIdI0QZi6Qw66QpQarCF7PdsNuscNbLXarUaqtUq6vU6yuWyY7LpOCtDTOdVRdcxmZOs4Uumo7ZPx8WykrWsD8dDMwWsI8//SWTgWuQ88od2ooIZHE8tNaQZE7xuNBp15Wh4qK0G+rUUkHWmeR+2k7aodfopCsKrHewjIajDztd1b9E5I+FBD8tju5XVx/baNmmGhg1s6GF2ms3AdnCctK886I0yBNGH8nwWfe65P9pyGGSdqv2t2TgKxtrnRJ9x7hGJRALpdNqVvWCGue6vfNapp/icKugMIFAChr+1frMl4PkATJ9e0ZrnbIuWE+P+S4a8ljHhXmbxC8t6VtvKZ39tR1ZWVlCtVh32EAqFXCa89QG5f9qAh2YMKFnPgvrlchnlchnVahX5fB71eh3VatVbWmcj4T2ZuUiWM8uwMdtwEClBRe1Y6lSuXR1jGzTQUoCW6Kg2jepYBZk1kMvPUnfz+tTfXJP87euXfQ40yGCDNoOwMdXfg8iEvt9Wx9Mm0Rr9anNcaeBkp2Q7WMC10t/RaBS33347Hn30Udx9993u9UcffRRvectbvN+544478NWvfjXw2iOPPIJXvvKVzqa844478Oijjwbqoj/yyCO48847A+183/vehy9/+cv45je/iRMnTmza3uXlZZw/fx6zs7Nb7uMQRN8nYqNs230AfIqLzjGVuKZ4qaiySSaTbkMmCKDGvU9owGu91p0ELjWSz43SpgVtJr7ova+NNoq90fWpZJeXl52jfOTIEaTTaTf+VyrcrFdWVpDP53H+/HlUq1VUKhU0m03nxFJx0QjSCOtGwnREOt/7tYa4GlFaB3A/9kVlkPO/X2TohA9lp0Uzp+jQ6mGhQHDtKDNLS4twn7QAkzrSAJzOi0QiATDagl8ECFm6QllRCsCpE8728bdlsyooxoOTtXTERjqZ+7+9lwXR2WfLkLPX0fG06bDUk9YpstfgtQlK65xovwgu2wAF7RCtC66iAQm2RdnqOh7qdEYiEVcTXRnJbJO9hwYIeD2f4wqsAdUMrDQajUAquG/eNtJbHINQKOTsOAIorLNL0SDNIFtASw3xGSJQwr5bhhZtDq4ZBUM0MMPngUF+BVmsI65kANqoTMvX9Htf4ExtObaLNgEDL1quSceH4BTXDG0qXWf6DCnwYoMS+h1Nq/eNsc61PpNq/3NsuKdwv2q32+j3V8vqaAkdfc5sP/mdzex43/hciQz191D2uuizpsCYllzS1/lsbQU05fe07Br3NQKKdu8gyMv72D0BCAKO+rpmJClIqSxbvmbFBqRtph5/633t3mj1oQ1w8hoWCL2afYIB9XB4tU47y+6ofaB6mJlmHGclHnCutVysBj0JmtdqNdRqNdTr9U3LpfqEek6D9FqGh3pyIwDdrlvqRNXfqq/VnuT/ahNb3Eh/W7a2vq5tUR3PPtFms2fr2H5Z/c2/1WbgPFj7U8cUQIBgYvW29p1tVn+An9Wx1D1gv/ni11J/33fffXj729+OV77ylbjjjjvwmc98BufOncO73/1uAMD999+Pixcv4vOf/zwA4N3vfjf+8i//Evfddx/e9a534fHHH8dnP/tZfPGLX3TX/O3f/m287nWvw0c/+lG85S1vwVe+8hX84z/+Y6Bcy3ve8x584QtfwFe+8hVkMhnHXB8bG0MikUC1WsUDDzyAt771rZidncWZM2fwe7/3e5iamgoA/pvJEETfB0JmTrvdRqlUwsLCAtLptGMzA/76QHzgq9UqFhcXsbi4GKhhrsrPboaDxDq9W/mOGv27sdGowtjOhuYzAuyY+IwZAAGHx5cipd+ho8ODP1utFtLptGMPUkH62Ee8BgDH1CMjr1qtotlsOvZ5s9kMKBG2kaCNVaiDhM6g1szbz6AzAwgA1o2BMiv3i/T7a/V/6QAPZWP5xCc+gf/5P/8nLl++jJe+9KU4deoUfvmXf3ng5x977DHcd999+PGPf4xDhw7hgx/8oFP6lL/927/Fhz/8YTz33HM4efIk/vRP/zSgfB988EH83d/9HZ5++mkkEgnceeed+OhHP4oXvvCF7jP9fh9/+Id/iM985jPuYK6Pf/zjeOlLX7rzg3CDC/c5BUNtzUYr6jDwu7qv87eCXRZYJ9jG/y2bMxKJoNlsOnCrXq+7+1inUp0WMrropPEAK7aZwU0tVaWsIMsO1+tbEM86FLa/LA2hh0gRvLOMOfujtoVlwlJ888RsKQKTnBe7ZxOE4PskAeiebh001X/KbFKmujpQGlihg2wP1OL46HjzNQKaPCtFnc5isYharebAc8vkssxD9sdXUkQZdbb0iQU5lBCgc6WOqZ1LvZfOrbIJNdCgNpN1jrXN9nnQ6ypwpLaZOumW6ch1ps+2XsfaNdpv21adT7se9HnVYIQPhFMAvdlsOsCFARQ9DFSvo2PI13xBQY457RqfXc/vtdvtAKBg1/5QhjKUVeG+zyBuvV5HKBRybNRwOOx0IffNdruNer3uyFP8YZDKAqZ2b7P35z6ge4j90b1C9zX1AS2gau+lezKAdXs+26jEARv4tACjXkftDLUJGJzkPu4Tq4u2ClJrBgEJZsoqV2Bd/W8fU502iZZvKRQKgQwyPdR6O6LYimYfaikXnV8Fsa0dae2pQRiFxTx0jrT/XEeqd9WG4W/fta3OtSQKnQvFYSwGo6+pvU79xUAI/9c2WLHjxeurDaQguq5xnltDvd1sNtdlfg1lTe655x4sLy/jj/7oj3D58mXcdttt+NrXvobjx48DAC5fvoxz5865z584cQJf+9rX8IEPfAAf//jHcejQIfzFX/wF3vrWt7rP3HnnnfjSl76E3//938eHP/xhnDx5Eg8//DBe/epXu8988pOfBAC84Q1vCLTnoYcewjve8Q5EIhH88Ic/xOc//3kUi0XMzs7iV37lV/Dwww9v62DsocW0D4TRzk6ng4sXL+InP/kJJiYmkMlkkE6nvZHkXq/nDqyan5/HM888g/n5eSwtLa0zmm0dtkGiG54ygDYDIOkEWuWyU8ClVeCDAG0VZRhprWyt/e0bC91QabhQmWqwQIWMxna7jR/84AcYHR3F9PQ0jh49ikQigampKZcmDqyv48X7NZtN5PN5NJtNzM/P4/z582i1WigUCqhWq+ucba2tygPjbIrXIInH4+4gE81M2E9gM7CWcaGGH41ezt9+Y6Z3u6un1BcKBdRqtatKe7xeci0j4Q8//DDe//734xOf+ARe85rX4NOf/jTe9KY34amnnsKxY8fWff706dN485vfjHe96134m7/5G3znO9/BvffeiwMHDjhF/vjjj+Oee+7BH//xH+Puu+/Gl7/8Zfz6r/86vv3tbztF/thjj+E973kPXvWqV6HT6eBDH/oQ7rrrLjz11FPuUME/+7M/w8c+9jF87nOfw6233oo/+ZM/wRvf+EY888wz21LkQ1k9WFlBQR8IboEl7vl6YKR+l3u3ski511PPkAVLUXBPnZ3R0VEUCgV3yPbExIQ7dInf4TWZjtxsNlEoFFyaMFPJR0ZGXDm2VCrlUn1tsMA+K5pyzR9gPcCpY9ZqtVCpVBCNRpFMJpFMJgM6nDaEtl9FdSnTdi0gaQ+94rj3+31XNkXHVVnFiUQC8Xh8nV6zgADBEHVCe72ec1iZXUdGvNad5bVZakaBXLZdx53OlgoBeDp8DIgTQNcAr7ZZDy7jmDBwoPaYArmcK7LYotFogLWszriPdWXXOduiTi3nQ+efr9Nptuta1wRBAQaC7XPKPpEooqw1tXmZLq4OutqzGiDhbwYXdMw1KKJBF+0vP6PAiwJO2i4CDxxHfqfRaKBYLLrnWoMofB4pNoBnn28de51LflfZ52yLBnU0g9OXRTNIrqX+HspQrqcwkN3pdFAsFrG8vOx0IrNZuN9w32w0GlheXkalUsHi4iIWFhYcUxkIZmfb85qsDaPPPgE8/rC8mWaTqH8fCoUCB25rlhODwdw/uV8BfjYw20z9pfslAwpaE9z6+vrccw8Oh8OOmQwEM8wsgY12kYLi1oe1/3Pfpd4tlUpYWVkJlOBje7V9NnCpbWB/Ofblchm1Ws3ZBz4MYCvCPZi6PZlMOtuGpQptuyz7Wu1HnQdrW6hOs8QBYK1EjwWVaRPp9zW7jPfnNVV/a3BAg7ycC9XVqotscFqBfyUChEKhAEGB3/XhOb75pahtp+NG+63RaKBcLiOfz6NQKKBYLKJSqbiDfveDXGv9fe+99+Lee+/1vve5z31u3Wuvf/3r8eSTT254zbe97W1429veNvD9zdqZSCTwjW98Y8PPbEWGIPo+EI1A12o1V9u52Ww6Rok6QgACjjJrbfJBp1wpcGij5ZtdxzpIOw2iq5GhhsRm7deougXRfQEFVVI2RdmyGO33qGDK5bIzIHK5HLrdLtLptJtHa2wAayw9Gmc8qLRQKKDVaqFWq61zvtR5VdaSGmUbCQ0mW6t3P4oqZxtV3o/9opHUarU2PItgL8u1VOIf+9jH8M53vhO/+Zu/CQA4deoUvvGNb+CTn/wkHnzwwXWf/9SnPoVjx47h1KlTAIAXv/jF+P73v48///M/dyD6qVOn8MY3vhH3338/gNWUtMceewynTp1yaWdf//rXA9d96KGHMD09jSeeeAKve93r0O/3cerUKXzoQx9yJ4H/9V//NQ4ePIgvfOEL+K3f+q1t9fP5Lpad6dsDAawDvS2TluCTTw9YHaOOFcFXHxOMrC8a4gRt1cnhZ+n48bOtVsvpenXYFSi0zB3LatN22oCzFTon+nlNd+Z31OFW55/vW7BVP2uff5/+1n2a92f7lL2k6e0atOCPBSJsyRd+Th07q/PUoSL7kOPhY20ryKBrgWNDXU5QlUx/Xbs6/hpoABBwenXOFTi160PHQcffjrneV/uv46UyaE430q2+dalABq9p17rOi64z3p/rQMF2Hytbr+Obb1/7feNl17vNCFB7Q9ez1lbVw2R9QTALUijIb/cxtlGDCQqi6z6lAMxm82VlCKIP5fki3M/JRG42m4hGo87+JiknEokEwGQFuhkg89kddi8a1AbFAQaxvnWP0n2SJbi4/zBjRYOV2gartxSw3Eg36l5u26+iQKiWRtMggB0L9lHbZ9sxaOw0Q4Dl9fhdvsd78LfPl2ffyERmgIW6fDNy2iCxukjHRXWf9tNiHVZ/+/72jdFG62+Q7+/T3z47T+fYZ5MC/koAimn55tgX5LbttPdT0bFQ/T3IJrKsfn3OleSwX2Sov3dOhiD6PpJer4eLFy/in//5nzE+Po5ut4tjx445VhsP91hZWUG9Xsfp06dRKBTw9NNP4+LFiygWi2g0Gu4BYmoSlb0y04H1GyWVEIFcjTxvJCsrK6jVak75tFotF328mrFg28gKYJBAU9tUGOFkPxn9jEajLsKuB2j6Nl9VSAQwR0ZGAqnt3HBtSpc6QqVSCWfPnkU0GsXi4qKracuIs0ZD9fDWarWKdrvt+quMOSsaUFHjQH8PEqvI9qtYo48GcblcxtLSEuLxOMbHxx0TYj9Ip9NBPp/HpUuXkM/nn7dM9HK5HHhdGS2UdruNJ554Ar/7u78beP2uu+7Cd7/7Xe/1H3/8cdx1112B1371V38Vn/3sZ7GysoLR0VE8/vjjgUNN+BkC7z4plUoAgImJCQCrjPe5ubnAvWKxGF7/+tfju9/97hBE36bwOdd9l/ucLQGipSc6nQ4ajca6fU6fK2WJKetWjXkAruwanVN+h7qC+06j0UAqlXIOeSqVckw2AuhLS0suaF4ulx3oRgeQwDwBOXVGgDXmrrJh6dwz+Moa49ofCzhS7wNwoD7Hz4J5OmZ8j7XFAT8YQKCBTB79ng1kU2dTV1vgktfUDDuC7dTPZKLxO5qBwH4pMzAWi7k288Aw9q/X67lSO2obqF7W8WSbaXNper4Ge5Xdzs+yjRwHW6KFwrmwgRYrCkiwzZZ9znlQBxII1tklo5LCsSawoBmIvj2fIIYC3rx2p9NxLHoGlZQgwmdGy7cAQRBdbSmuT80S0H3A2kd83tXBphOtDHYLjtl0cwVy+LxyXvlc6zjrNS073FfGh/die3W/0+wEBcfss25B9Y1k6IQP5fki1FFkoi8sLKBeryOVSqHRaLisXYLo9JEvXbrk9H2hUHBnXlAfcg/i5/UgZvVZdA9tNpvOBySIP2hvJejN/QaAa4PqSBuY03tb+4b6VUk8jUYjUI5sEGCre5P63/ZMDO7jesYNQX/aM7RHFNzUvVb3NO7txAlY2oY2gAVBdb/Wvut7DKb0er0AmWmn9kTrt6pdx8/7fHrVIdbW0td8rG+rKzWDytdO/azqqn6/HziTRfWlYhkkk/A1Yi+KPaio/WFLCWmwmUKbgj98HlTXa9tpg6ntrmPKZ456m+XYqMP3UzmXof7eORmC6NdRBkXIBkm328W5c+cwPz+PXC6HTqeDCxcu4MCBAzh58iRisZiLehcKBXzve9/DxYsXsbCwgPPnz7u6rMDawRv9fj8AomtqlwKPVFZkttPp3coBGgR/lXVF8Nnn3G1HFESvVqsDNzP2h4bD6OioS+EniA4Ena9BiovjoXXceFo7gIADbMeFnyEwYiPPFkQH4JS0AvM2Cr3R+GikfSvC9vgOk9uPoiACx69cLmNhYcGVRNpvIPry8jIuXryIQqGwr5T3TsrRo0cD///BH/wBHnjggcBrS0tL6Ha7OHjwYOD1gwcPuoNGrMzNzXk/3+l0sLS0hNnZ2YGfGXTNfr+P++67D6997Wtx2223ufvwe/Y6Z8+e9V5nKINlo3qaFkSnUa0sT76uoCyvR2dTAXTqLzW0aaCrrgHWnM9KpYKRkRHUajWMjo6iWq0iHo9jbGwMkUjEAWqtVsulgNfrdRSLxXUlJHgfGvaq2xRkJmuGjhFTUvmjwJmCmHoYlx5Y2Gw2A6XHlNWlTqwNRKtz0ul0nBNKEJ06zs4VrwvA2Sc6p8qyVrY2dSn1mOpq2gnWEWX/WQ6FbeYc0mnSdpVKJVQqlYCdxPnxjWkkEnFghjrukcjqwWUcHwIknCe1SbgW+bedQ46LBUo5rpZpxTHkPNugM4kJZDDTVlQQXe9BYIjzS/aj79A8Ovlqe7FUkTrHpVLJXYvX16CI9pfzrGuHfVWn3o6pfpZzwuvps0ewjH+rM89nzwYwuCYI6ND5ZuCN1+h2u259cv+wAJfaqD4wXO1IfZasLad95hgMZShDWRPuYZFIBIVCwZV/Gx0dRblcRiKRcPqbz3Sz2cTFixdRqVRcCRgtAaLgJAFZteM1EEe/u9vtujMUaCMQTPaJBgepa+v1OpLJpPNf9fBIq7NtZrQCjppJQ12mfqrPDqNe5X21hIeSGjTgrBlVHCuC6Rr4pS5ne3X8gLWzXCqViqtpT4Cdc6b+vfZV91AlS1wtcD5ovnQ/Vv3ks4dU99usBL5Pu1E/Sx2iwXDOuV5LbRoNPGhbqLMU+GableihGfOa0aHEB10P2hY7Rpq5pe/ZtcfnR7O0FBPh+Nj1zzWna0FBdD6HCqIPweXnpwxB9Osk+qACa5vioBRrCjcl1lVMJBLo9XqubhbB7VKp5OotMvKsDzk3Bhr1BNF5GJU6RFTENO418rYVJWKdB6a1xWKxAGtqq6KRQ25uDB7YlH6OtQLVdNY0VVijsoOY6Pyfjj77rsCKRtRVUdsx8r2mwIRGebU+7LVycm4kZ8qOM1lyLK2zX5jcahixRJMCMftJdiISfv78eWSzWff6RoEQ+ywrULTVz9vXt3PN9773vfjXf/3XwOnhV9q2ofjFOjsEwXyfUX1DI1v3d/28rc2obFcCnvwegWzLhOL60fqctVrNgYD8PAFvAt0s42Kzm/h3KLTGFFcH1I6DAoiaDq4gLgDnDPn2TH5X2co+ve1z9vi3jjH7wGtqvWbrSPrmzzr9Vs/7WE0EJjlnqlN1jAY5Z/ZARnXCtM36vwLP2n47dnxN7S5tj87roAyxQQ63jreuIc4F1wLnXtlbliVo2zooYOKbN9+eb6/FudNDaNkmtf14PoCPhWjnlf+T/Ul7VEEKnTNrP1rygo6hZf3RcVcAitfQta7sc7sWFeRQIN3qIR0TtTtt+wEE/A3bt+3KTujvoQzleojdl3zPvIra3czkCoVCqFQqCIVCDkBjEJw/tVrN6XAlVvEZ1Ywy7ku6PyvxR31ovdZWxOp+2htkxPP6gwKtVhda8Jp78UYAOq+p+61Pd/D+Pp+cc6DgrG2XnTOrB+jvqW+u52NZW0nbrT+74Yv79Lm2Rd/XsbP2qs2AsgEKtRPt2Ovfvv7rNew99XNq+1rbmmPOQLSC/OyzXXvWFrKZfr7PUwaB7VZ38z0dE/vscJ1r27W9+0WG+nvnZAiiX2NRJy+VSrlUYT7MZB0NWuTKpP3Rj36E5557DtFoFOl02ilEKjYeQuljZ/f7fQecFwoFVwphcnLSRd2ZXkPDgYeU5vN5105uIhsJy4+0220sLi4iFoshlUohGo06Nvggh9yKRsJrtZq75sWLF12E0AdO6yFkZE7pwVzKpPIB6Dpu/K0pxBoJV+aeghZb6RtLAQBrSnWjVOjdEN7LKrf9KlYR9no9nD9/HslkEocOHcLhw4cxNjZ2vZu5qXBeGo0Gzpw5gx/96EdYWlpad4DdfpGrXc/ZbDYAovtkamoKkUhkHUN8YWFhHQOcMjMz4/38yMgIJicnN/yM75rve9/78Pd///f41re+hSNHjgTuA6wy0mdnZ7fUtqEMFjpe0Wh0IBiqjiL3A1/tRSDoqMTj8QCwpc4dg9DdbhfRaBTxeBxAcP+m/qVDPTIy4lLBeUio6hGy3zR9mm0mUEd2sjJ5eBCVBmSpjwi2U5crOED9pCCqMo/4frVaRb/fD9yDn1MHSp9tnQdeW0HEarWKfD6PTqfjGOm8rs6DDUj4MsY49tTpZOXy4FGmD9PesuVGFARguRCKrhEFPhRYVva/1fnhcBiJRMIRFZjdYPvK8bY/XHO0WTjmqqMVkGZ/OOftdtsdgKYOL9OYR0ZGHBOe40oQnUCDZhEyU0FBfzsW2sZBTq4erMdnigei0i7UMizdbhfFYtGtocnJSVfeRq+twBTnWokXHAfLuNS5VsBcRZ8dXUv8m+1WEJyAWrVaRbFYRLFYdAfCq4RCIaRSqXXPoHXstU/8m23imPPgQt2v7B6o47MdnTx0poey30SDdTaTzIKnKnyvUqm4kn5kdY+MjLhybAoss7wo913VNXx+dW9jcJf7MbNJWDpiZWUFlUoF5XJ5W9nFGmSvVqvOD1bsQfcaBV2B4B7H/ZNtarfbKJfL7sBO3kdFMQ8lrmn2EPc4zQjXcqsk3HGMaAMRRLaBbLbVAu4MgKitp/rT7rF27VCf7Zao3mE/qb+1fZq1RV2j9ofOHe0tq7+V+a02FLBmSytobn84TpqJxh+9Jq9F+67X66FQKLi+zczMBA6ZVQa61XW03dgfC3hbHe57jTIo0AKslYvhGHa7XZTLZRQKBUdQ5cHBW8HA9qLsxzbvRRmC6NdYVKHE43HHJFeQT8uuWOFm0u12cfny5StuBzdpAC5dPBaLuXQrghFkx7GWW6lUQqFQCKSgbibdbtf1qVqtolQqodfrYXJy0hn4W1FMei8qckb8WTPWByiqccB0cD0lmn/zs5RBkWHeX8ECGkPKRGL9tq0aO+oIXW/ZyKDcb2Kj1J3Oam3DixcvYmRkZMOUyL0iqvBXVlawvLyMS5cuOVBkv8m1ioRHo1HcfvvtePTRR3H33Xe71x999FG85S1v8X7njjvuwFe/+tXAa4888ghe+cpXun3ijjvuwKOPPhqoi/7II4/gzjvvDLTzfe97H7785S/jm9/8Jk6cOBG45okTJzAzM4NHH30Ur3jFKwCsBhwfe+wxfPSjH91yH4eyKtzLuT5077WsGspGABk/qw6H1srmbzom3Pstc1fBODoydLjZZi1z4QMabUBVGWsUgsaRSMTpH35H2TsKvNl9Xp00XkvbRBa9jq9lSFmwW8eV99Ngvx7SRXBTgxUK+tn50SwwBUSVDUXAV8F3EhV0TvT6BI41k0F1iNaZpT2hIDfBZ64Jjg1BTR0PdZz1NQVmuZaUEKCBBWWQqUOsfSRDkiCNgsc6VgpucK40eMDPK7PZOqwcC65DgrqWcRYKhZwTrgEAtsOCGewL7UmCKpZJSQeb7Vcm5yAQXYWv0dG3dpCCKRZUYL/sc6XnCWlZBttHu89YtqD2hX0F1sAuAhZcL/o8cD3rnOja3ipAdK3091CGspOizzv3UV8A1Sf9ft+B4nzGuPczyK4BLZZgYcBV9yJej89lo9EInOOhz6nWP9dM7q0+R2yP7kEEVjkWLE2qe6jaUYpPUF9qeQsS6ny+iCWn6V5kg3tWbxNEJzGCukeD9j4QdFDAdjNhO3wg+m6Kjg11h+pu4hpW56he0BJi/IzagQAC19EAuCUwWjKEMsCtnWLLhimITp3C96lLWVInGo2uO1hb7WB9LvU50rP4dM5sHzRgZduogWQdL/6trxHDqtfrqNVq7odlkq616Dq4Ehnq752TIYh+jcUqEnV4dLPxbQq7JZ3Oaq1PRtjpxDK6SCa6Rte3m04GrCrjarWKpaUldDodZLNZtNttxONxx8TTjV2/zx8aELVaDYuLi67+ux5qQtEN3TK41Fn0OQ6bOROqICy7AcA6FtZ2WT7XW6g0YrHYuvT6/SRqiKhBsrS0BGCVuVipVNBoNAJrfq9Jv7+afVIulzE3N4dSqeRqzO2ndXU95L777sPb3/52vPKVr8Qdd9yBz3zmMzh37hze/e53AwDuv/9+XLx4EZ///OcBAO9+97vxl3/5l7jvvvvwrne9C48//jg++9nP4otf/KK75m//9m/jda97HT760Y/iLW95C77yla/gH//xHwPlWt7znvfgC1/4Ar7yla8gk8k45vrY2BgSiQRCoRDe//734yMf+QhuueUW3HLLLfjIRz6CZDKJ3/iN37iGI3RjCIE46gBfdpOCxgpOAn5Hgs4y9bE6buoE0slTIF/Ba7ZLwXZ1FphivR1RB8c6XBwDrdOqTFxgrc67NciVDa1tYl+03Eyv13Pngagjp220JUTojGsNVY631uZUAFCBe36W17cgvpbB4DV1/vh9BYJ5X8uat1lg/J8ggoo69hbs1r7ZgIX2g2OvgRqWLFGQRdcOx9vaIhz3lZUVhMPBOuuaIm+fCRt84hohsQIIHmzqs6F07Lg+fUEVBd/tXBGUURYmD3bl+tGMDj10Vp9tZl1o8Ij2gA3QUDTzg9/RdagOvAYcbDCBzzaAwFkEBOM4H7r+lcCi86PzYUF3C4CzDeyz2tUE+/Rz3M9uBNLEUPanXC0wtBWxwSLua3wG9DnRfcu3d+nBi9xLdL+zwLnvGrwPn8d2ux1gyIfDYXcOiQYBtwOCcR8A1g4+HxkZCRyGzX7TjuHYAAjsm7Qj9DBwAqM+H1H1wyD7in8roK7Z4VbPKEhrA646rlciG13D6rSdEh0f1aU2YGGDqTZwa0Fgu044v/1+PwC4+zIxrR7zjYW+b+0A2hAkYDIDi2fjaRCnUqk4BjpJpWyvtcVUf/tsVyAIiuucaWBAr8172YAyv8uswnq97mwQgui+zItrIb57Xov9cyjrZQiiX2NR58063/ytDt+1eCAIQIfDYZTLZQdGKAhMRakHamxHkXDjW1xcdGkx/X4f6XQa4+PjmJqacoqTjpo6itxAy+Uyms0misUizp49i1qthkKhgEqlso75w02d7C+Ot/7ti8RuBGboRqUOLxkN3W43kI5kmX/7wUmhsVSpVBwjT9/biyDzIOGatbUKz549i/Pnz6PVamF+fh6Tk5MuNXMv9q/f72N+fh7PPfccLl68iIsXLyKfz7tnY7/JtYyE33PPPVheXsYf/dEf4fLly7jtttvwta99DcePHwcAXL58GefOnXOfP3HiBL72ta/hAx/4AD7+8Y/j0KFD+Iu/+Au89a1vdZ+588478aUvfQm///u/jw9/+MM4efIkHn74Ybz61a92n/nkJz8JAHjDG94QaM9DDz2Ed7zjHQCAD37wg2g0Grj33ntRKBTw6le/Go888ggymcy2+jgUBA7C5BpRsAtYcyIV5FZQSZ0KILjfUUdTTyuQqAApjXY6nAQy1enR1G46pbzHIAa2FeoSst4saAvAOStk3qrRz6wzssns3q4AHx08ZcGVSiXHwmMZEIKZKtp/jgkD82QFs62WjafABOeTc8XSLQpwUrdrQFTBbZ1LLaeipfCUzcz2qxPHseGcKWOKZAAeXG7tBP08HTr9nzYJ5xOAO2ST/WM/CH5omryyyDnmDARRD2r5ALaHdh6BHx1nZSuSRa/30TXHfZ3ft842bTIFg3gPXp8OLUGa5eVlNBoNLCwsYHFxEZ1Ox5WA6XZXS7uwJBLnmuAO26KOt7ZJx0+fT1vvVJ8BfobjQHuZwJnOsYIblUoFhUIBtVrNOeMaBFdiB/u3srISyJqwAD37wPnT9hGsYNlGBry0/QrKcA62ItdSfw/l+SHcj3fTntVnBljbj3z6W9mxlgTV662eqwSsJ7/ZPcKCmxTdKzUzRnUSsKZrlN2+HdE+2L2g0WggkUg44hxtAu2XnlVCrICgqLZNWcc6DpYcpyCpAufc9zSQSMCctpcNkhMQ5hhrabYr2aM2A441+LhTorpbdREz3ZiloPpWdb7qGF27uubUtlIMSsdfxZIJfGOhoLMGN4C1g1d5dh9JjwsLC66EXzweR7vdxvz8vGs/yxvaewHBzFIFxn12sm8dsM/8Pr+npWgUeCfuxSD+8vIy5ufnUS6Xsby8jGKxeN2Y6D7hfG6lvOtQf++cDEH0PSj6QF8LUeWgrDhu0uoQXQ2jmtemAq/VagiFVlN6MpnMOmaQjYQzjZfpsIwI6iGnKqrQraKyUd3tAqcWTKeCZ3qxKqoruf71FnWkbTrUfhNl16lyZEYDDwBSNsNeEXWYG42Gq8mmtYP3o1xrJX7vvffi3nvv9b73uc99bt1rr3/96/Hkk09ueM23ve1teNvb3jbw/a20MxQK4YEHHsADDzyw6WeHsrGoQa0Olz5DwHrmjrJSCEYpO426mAa8gqvqEKoQvPQ50ha4tw6fBe1VfKC69s86P8r8VtBSx0aDwCrqFPNvy3oH4BhFBGuVMcZxsiU16JSr822/p6xiX1/5v4/5pqyuQYEI/YwC9soq5D3UIbNlSahfrP6woL1eX+fJgqEKHNh1of1hO30sNl0PnBNtM6+n48r1wmdAAyjaLt/9fPaqruvN9kLbR11jLBvA38qS5+vtdtsRUth+tVk5Fur4D7q/AhK+z9i+abt1PPg5nQc9kMw+kxTfXOra84ne137PrjOda03/344MnfCh7LTYtbtb4lu7uu8QmLTPtBXfs2gzPnz309f52+oSvT4BSZuBciV91n2Qfqpe37eP6PvMnmFQVgPt/J7tv46jHctBn9U90Df+9nN6Hdpzu7HP7NbeZcdIAx+cKwuaa5vsvm7baq+vY2t11lb6a21jK6rzSOLQcnK0bev1eiA7a1BpUvbL9pVtph1vP2/7YPW8jo+vfyQWaOkini+jgfa9IlvdP4f6e+dkCKJfY+HG0u/3XWqqKjdGda9mkV9t+7g5qTM5yPnYjugD3mq1sLS05EpUkE3Eg7d0o1cmXblcdrWoCoWCSwm3ykUBbD20StPFNnKwt9MnGl7AGnsgGo06I0XrpO6Wct9paTabKJVKCIVCLpWQfdlP0u12AzXMNP2w1+theXkZ3/ve9zA/P48Xv/jF+MVf/EVX33AvBA20lu2zzz6L73znO8jn88jn89e7aVclQyU+lJ0W3fP7/b5LgaX+4r7PQ6p8bBvr4AJBVo6C5lrHlAxa1ZWaxu0LRFJXqOPe7wcD2myTBWNVLJCpgLV1PHyZNloGx+pQ3lPTp9lmtV2oH+r1eqCcC8dBAU2t5cy+p1KpwLjTgaRjT3actoPvaw1ZdX443nTa2GY6XLQ1dC34HDk9JFTBcFu2hnu1Or/sHwOe9Xrd9d3W2o7FYshkMu4sFdot6hDqmPMAeI6XMtHr9bqr18vPh8NhZDIZN54cU/aPwWX206bTb+Rk6zpge3T+9TngWtW2EaDhwW8EahqNhiu3xvJB1Om89/nz51GtVjExMeECYGTh6z11Tdvnne/xR7MUfUI7SEkZGjRTAKper7s2k1XP8omajaLriESWQba3LQ3EZ1sDX3b8dZ0o6GQzb7YiQ/09lJ0W7mWWsbwb0u/3AwE5C3irHtW1vpVgoK/dui/YfUX3V83aov5QzEB9MAsoDhLVC/qsa1mNer2OcDjsmOpAsA42s3qo31gClvflwd2+vV7tB+ok+sOKMfA3v0u/XgMcbFMoFHIHTzMbjkx52hgaeNjNtXS1ojqRc6+H0TabTddH2gfRaBSJRAJAUAdzPIGgfUlRW9EHpOtaUv3DsVcSpbV3tR0sIWhLoLB2Pg9t73Q6OH36tMN1QqGQqxbgsz82AvzZJj4jPoCf31OCxSAAnbqYeFM+n8fc3Byq1SrK5TIajcZVkUp3Wrazzof6e+dkfyFiN4Dopq41jfWhvZ4b/m7d225U7XYbS0tLgU0zEokglUq5lGUqAUYAmQZEZ4qKXQ0Tjq1N+9JDRKnEd4ohboGRUCjklDuwlqarjvVel1arhVKp5AwrZRvuBXB5q0LnlUqaEWQaesvLy/j+97+P5557DuFwGLfddptLWd8L/ex2u+4wE4Lo9XodpVLpejdtKEPZU0JnUQ1E6lMFl/hc63sWJFdRJ9Z3eFYsFkM2m3Ug2kY6VJmfupfSqRzEOFNd5QtkKmubekZBAk3dJWjOsWi1Wu7+OiYKvCuITeeEYxEOh13qMUXHwResUBkdHXUgOu+rZSwUBGAJHY4VsL6uLe/F930gIUF0fY0lRDgGGozQcVbnUb+rAWc7hwT9ae8xMEownqBAKBRy+oe/ec+VlRV3kDTvofOhGRhMt9dU7UQigXQ6HTjE02Zo8eDYeDweSHPeCltNx4yf5TipIw6sL4GnWYihUMiRJqj7WFeVAIky186fP++ysxgc8IH+egaOL2hmSQI2GKQsUXsQqgZWGKRgoE1B9Hw+7w4o0zIJdm/hWubaV1EwTP0FzrOCdHYfYT+1LJCCLXsZbBrKjS3qwymouhtCoNKea7HV72703maAtt1XVLerDtbgLUX3KAWrNyrxwtIoSh7TewHrWfXUwfyML6tHg+Asrca9TIF39bltuQ7NplIQnfexIDqvz2v1+30XCGcAVcvWqZ2wV0VtBYLozLhnsDwWiwV+eNgqv8PvczyVWKBzqzrCgui6/+sa1uCFPpu0N1Tvc/4ajUagDnqlUnFgNIPHBNOz2Szy+Tza7TZisZgLFqRSqcDaJX6jtojqeLaJa1Lfs0EwxX8G2TQaLKrX68jn85ifn3eBAZZz2isy1N3XR66OhjuUKxZVfvp7tx8EBZc32kR24j7RaBTxeBypVApjY2PI5XIYHx93P7lcDmNjY8hkMkgmk0gkEgFmFQ9iYlRTD2Lq9/uufnosFkMikXC1WVmX1YLlNgq5G322zqYv2ns9hAqGgIiy8y0zgsaKpkpT6e2HAIBlk9Bp9Z1qz+yGUqmE5eVlLC4uYmlpydUIvh791b2hVqthbm4Oly5dcgfosh/7WSyLZrs/QxmKFWtcc89ThjqdUNWBym62B04PMrAt+KhlSuyPOheq5wcBy4OA/I3Wvu8evs9aoJkOkYL5Og7UFVbH8u9UKoV4PO6YQ1q7c9C9lSWu9gidJBvsHmSvqKOjjpktW6Lzo6xfvZfqQ22L/tiMNm2X7edW5odtYdv1IDoFeX3j4mMV6rriuFi7ktdQJrI+K3ovCxBzvO0ask70RmKfSY4pgW912PU+Onc6fmSKlctl56xXq9VAtpm2ye4Lg1hovs/qc+FbA3pNLUVDIF3tWF9fLCjm20Os2OdDMy19bVXQnHOnYzsond7Ktdbfn/jEJ3DixAnE43Hcfvvt+D//5/9s+PnHHnsMt99+O+LxOG6++WZ86lOfWveZv/3bv8VLXvISxGIxvOQlL8GXv/zlwPsPPvggXvWqVyGTyWB6ehr/+T//ZzzzzDPbbvtQtiaDwLEbRXz6zuoPtS10D9zsGRq0j1N3q29M/U3/nHXIN7J9rN5Su8FnL1hdyf1HRftiMRD2z2Ik2nefnvaN7W7iHFsR35zbtqmo/taAvl5L14yyqn36yAZVdXwHrSmLY2ykQ9UW0fnkfaztpT8kBlQqFZRKJZRKJcfypq5UO2Yj/aH2iW2zrgGrq+332S7V3RrIv5JzCfaaDP3vnZMrYqKfPn0aJ06c2Om2PK+Em4svFWanRRk+BJ55f900dkJ4n3g8jsnJSUSjUeRyOUxMTLjoMVkwFHUouYEy8sdyLYwsc3xYR539YZ80es6oLtlrg5zenRQ6KZ1Ox9VH16jw9VDk7PPo6Kg7fEuVHTMigDUAh+VcACCfz2N5edkdcGaNob0oVOKNRgNzc3NYWFjA8vLyukyPZrOJCxcuYH5+Hslk0q3b22+/HbfccsuGDLzdkn6/7xiNzz33HL7xjW9gfn4eTz31FEqlkpcNu9/kapTxUIlfndyo+ltZXnTyyFrWPY/PjjpWZP2qKPCo4K81oMmMVSY4gEDJMGvAs41sx2aioCX7anWodbYsy1T7ro4P2UtkiOmBnGRsp9NpB3oyq0sPctWgnx5CrmVC+v1+oIQJdQnZVZaJzOytQQ49nRm+z/Yzq4390P1S50TvSVY259sy8ZPJZMA5XFlZce3jazqv1LdcH8pq19rYlvFORr4Cy7SZ1KbQoDZZi+yDzYZLJBJurhj8YI1Ptpsp4slk0tkrPLBNAWmOEUkKHIteL1hGRNltlkTAOSeTjs+BpuDzGv1+P0CmYL+55qrVKs6cOYNIJIJyuYxWq4VEIoGDBw9ifHzczYO2Qe1Atbs5jiqW/cZ2kWmuomABn4NSqYSLFy+iXq/j0qVLWFxcdNllWi9Wx09tc31ObXYA76+2pZI37GsaTFGgnow6PstbkWupvx9++GG8//3vxyc+8Qm85jWvwac//Wm86U1vwlNPPYVjx46t+/zp06fx5je/Ge9617vwN3/zN/jOd76De++9FwcOHHCHgz/++OO455578Md//Me4++678eUvfxm//uu/jm9/+9vucPDHHnsM73nPe/CqV70KnU4HH/rQh3DXXXfhqaeecs/p9ZAbWX/fiLad6hjuez59BiDgnwwCOq3doHpegeR4PI5IJIJkMun0txLM+D6FrGdbvox+NT/T7XYdSY561wZmyTLm/q/2CG0O+ucs28LvK2uebGdfn+0hyNx7tRQW91Be51oTwUKhkAtYAOuztnSvp84GECjnwvmgLUL7i6IBVM6J6hLqtEEAs5YK0qA5f6voPOp8cR7ZVuo26m4FobXUWr1ex9mzZzE6OuqC36lUCsePH8f09DSi0Siy2azDeHzBAdVp2l7aYT79raA/v8exXllZQT6fd+XXTp8+jWq1ivPnz2Nubs71az/L0P/eObkiEP0FL3gBXve61+Gd73wn3va2t7lUnqFsT64lm5SbqoLobMOgDfNK70PHJZ1OI5lM4sCBAzh06JBz0rheuLExtZmONg15TXulEqfzMjIyglQq5aLpTO2ik9Vut1Gv150jqQpit8WyrVSJXw8QXdkCVOhqtDFQYRVro9FALBZzQQ2CC/tBqBRXVlZQLpdRKBTcelDpdDooFosIhUI4d+4cfvrTn2JqagonT550z8X1aDvX8NLSEn70ox/hwoULWFxcdAz5/S5DJX795EbV3xbE42865zSY6ayo7vMFBhUgVdBKnTN+jo6bsjm73a4Ddmm0+1jaPmDdij4v6qxqW/mbuoa6UvuuQDCvofucOkkEuVOplKvFqYxzOuHKpKZoAIK16bWtypbjjzpnbLeysm0fbOkbW5ue16TzrCCugqMEHjqdjgOONWjB6yngDcCB2rQ51KnTgI4SJSwTi8Atg+3tdnsdQEvnWEFyXS96f77OudB7AnD2Hx1ndZ55H94/EokE2q6Ov9bl5Zq3DqsFz9kXjjkBYbaZAI4+A5w7PYxTQXSmW3O90+bMZDLIZDJunim6vjU4pgEwG/iyWRC6ZvTZtEGWer2OarWKYrHo2HYsF6SAu4IYOqf6nNCmtsCR7l3KSFSgndfVe3GclUCzHVvnWurvj33sY3jnO9+J3/zN3wQAnDp1Ct/4xjfwyU9+Eg8++OC6z3/qU5/CsWPHcOrUKQDAi1/8Ynz/+9/Hn//5nzsQ/dSpU3jjG9+I+++/HwBw//3347HHHsOpU6fwxS9+EQDw9a9/PXDdhx56CNPT03jiiSfwute9blt92Em50fX3RiDefhTV+9xj+ToQPOdCdZUNkvO32iPW5lFGeCKRcKXSSDhT5jmDzZblTfBRy8ro3sQ54b5EnaFtYh90/uz+orqb11M9omPi25d9Ze2ULGF1LdtwrUF0jjsQzMZTHWwDJtyXOSa0ezSoCgRLdzHwbZnntAf1+tamUluV7eZvG7hRjIfX4G8GTjhvXD+2kgBlZWUFxWIRwFpZv3Q6jbGxMaRSKXS7XVfmTvEHBfstluXrg/1RTEafLYL/LNlSLpeRz+edHq9UKoH1tF9l6H/vnFwRiP6DH/wAf/VXf4X//t//O9773vfinnvuwTvf+U78m3/zb3a6fUO5QlHjP51OY2JiAiMjI0in00in027D63RW64wvLy+7OqTbjbJxU4pEIhgfH0c6nUYikcDhw4eRTCaRzWbXOeH8HoWb8uTkpKuJFYlE0Gg0UCqVkM/n0e/3XRQ9mUw6QJ5MNnUoCQCoE66ym2x027eNXtvN+9MYmZycRCaTQSKRwNTUlBt/Ko3FxUWUy2V3WKvWk2u32ygWi1hYWEC328Xk5KS3Vupekn5/7WCTSqXilCFrzA36TrVaxdmzZ1Eul/HDH/4QAJBOp906puO9G33m88haeM899xyWlpbw9NNPY2Fhwc3PUIZytXKj6m86WsD6siUq1qm17/n+V9YNZZAO0dcJjlpj337egqY2TZbtZNkz7ePKygqazWbACSCgbzOglDVtnVI6fAQeWV6NTDZto36H/QuFQq7mNstosS10BKn/6VgqyMn7E6BU517ZQgSveUAsx0EZcQoeqlPO8eLrtgwGwWoFzNkWPeBcGdHKGqZ9wuA+ABdoYBu4Jgjckl3O8VBHmuzFdDqNXq+HRCIRyNxjtpUSEOhsc40wwEFWGIAAcMrx1rNpdF3oAV+6BhjE4FhoyRB1ojlHfI/vEyRQIJ3vazkTLeHH9UxwgdcCVkH4YrGIVquFTCaDcHi1Vu/ExESACcr55rzyGgpkW/vGBwDpM0ibg3OxvLyMarWKQqGA5eVllxJuS7PYPUjJFvyta9S3X/lAfr5nnx3NDNE6x9x3rqWDXC6XA//Tjldpt9t44okn8Lu/+7uB1++66y5897vf9V738ccfx1133RV47Vd/9Vfx2c9+FisrKxgdHcXjjz+OD3zgA+s+Q+DdJ8zMnJiY2LBfuy03sv7eStmG/SChUMjpEuoC6tRkMgkgmPVbrVYD/ec1gMElN7Q8k2aNESTPZrMu+5j7oZbqUh+ZWUWpVAqjo6MOzNZgJfdQm4mm+5IGpFUPUBQE5T3ZV99epu/x+9QDNnCse6naPxyzzcpVqV2o11I9ZkvKDBIlCTCIwfFR8Lbb7TqdQZ2o+ptgOg/WrtfrgSA07ROrJ3S8NTtR9Zwy4flb7QbVHTpGFF+WIL9LW4kEPQue+6TZbGJ5eRm1Wg3ZbBahUMjVRafNyzWrILqPHKJriHaUtl8DWFzjxAqazSYWFxdRLBaxvLyMpaUlx6S34zGUoVwRiH7bbbfhYx/7GP7sz/4MX/3qV/G5z30Or33ta3HLLbfgne98J97+9rfjwIEDO93WoWxDQqG19M9Dhw7hZS97mQPTx8fHnfO1srKCixcv4plnnkGtVsP8/Py2QXR1wG666SYcO3YMyWQS09PTzhHfCHglAN7v95HL5ZwDfvjwYbRaLfz85z93BgbT0VKpFKamppxTbg/Z6HQ67iAqKiBVkrstvujntQScOeaJRAIveMELcOzYMWQyGRw6dAjJZNIpnFarhbNnz2JpaQmLi4v4yU9+4pQrlfeFCxfc3zMzMwGgY68J1xEd2Lm5OVy+fBmXL1/etJb4/Pw8isUiEokEyuUyfvzjH+Omm27CG9/4RszOzrqMh90C0SuViqvJ/g//8A/40Y9+hGKxiHPnzrm1fKMo8GEk/PrJjaq/6YBYo9qWZFBw0DJs+T6AdQ4mAU+WAVGHkc8nAOeoKYBIUUfCrmPq7GQy6QBA3o9BvFQq5UqLkMnKfaPdbrvsLSCY2q01p5VJxs/QMWa5D5tGS3CLzoQGzjme0WjUpY23Wi2Uy2XHNCJDiUCxpptz7NnneDzugGYLGDebzQDoqv1REFvnm32kLaAOnXXCCXDQ8dNATLPZdGxiAtLKKKNejMViznEOh8Oo1+sOtCRgTsa59k+Z88oMSyaTmJycdI6yruFer+cCxI1Gw5Eh9H0eFhqJRFAqldxBsgoWEBRJJBLrWOZc0/ps8X50/DmHfKa4Nq0jTudWMwlsFgK/X6/X3VhTd2upIZ3HXm+1tMuFCxcQj8fd/7lcDslk0s0NnXLeS8sC6fPJ9mjg3AIIlkFaLpdRLBbRbDZx/vx5FAoFlEolXLhwwTHQ7ZrimtexYf8ZmLHglbaDGSHWptVsCQ2waAkXzc4YFCwYJDuhv48ePRp4/Q/+4A/wwAMPBF5bWlpCt9vFwYMHA68fPHgQc3Nz3uvPzc15P9/pdLC0tITZ2dmBnxl0zX6/j/vuuw+vfe1rcdttt23ax92UG1l/W128H4X7UyqVcrpgYmICo6OjyGQyjsRG4JRMXIKO7L+C6IPuwX2bNsPY2JjT4ePj4w7ATyQSCIVCbs9UEFzBR+o/EpBYasWWQVGbQgFL2izM6lL9pnsQ93zLcNZArO0rSV1WP2rwnBnpWp6Udg4DyYPA3FAo5ILIOmYEgmnn6b4/SLgvR6NRTE1NuflX/Qqs6hEeit1oNNwBm9SD/X4/YM+VSiWsrKw4EqHN5mP7LWFDbViuPRt49wXBVX/bYAb1k9pafJ21zqnDaXdt9FyXSiXU63WXKVcoFDA+Pg4AyOVy6PV6gawHtZU0SMz+cgw10A4E6/ur7c6ytY1GA2fOnEE+n0c+n8eZM2dcKZrdKgfkC0Tspgz9752TKwLR3ZdHRnD33XfjzW9+Mz7xiU/g/vvvx+/8zu/g/vvvxz333IOPfvSjmJ2d3am2DmUboqljqVQKuVwO2WwWU1NTDkRnXcZ6vY5UKhVwUrbzoNDAZ7mWbDbr6qgSdNyqcU4njE4va2TRseYm6vvRtBzdYK/kBPYrFU2dogxiNPki7b7XVHTzs3/rb84/U6RyuRwymQwmJyeRSCSccmy1Wk4xNxoNlw6miolMCa4XTTNWY+96ixoOPBSEjHRmXWy0rgmctNttLC4uOkCrVCohk8kEnFrrWG/H+eRvZRzS6S+VSigUCpifn8elS5dQq9WcAXIjyVCJX3+5EfW3Mm+UgekLnm7l2VVmqAYN1eHTPVDTRFUGOcUKpvG6unfrwWBklaVSqYATrvXDlTWmzo/uO9zHeD91iBU0pxPIdhDo9rF66EgT6Ov3+y6wrSA2nRtbR9XWqfcB5Wy3zof2SwFHK8pKso69jpOW6LCsLb6u19C5VFBaU7gVsGdwgH3mfPnK2fA7WgdVAWCOKRlhdIS1rby/LYWiY6Ljpgx+3p+fsQ6pZZrbH22HtWt8NowKx5fghQXi9Vr8W0uT1Ot11Ot1xGIxVyfdx6obZJdZ4oO+r8A520lbShmDBBGYym77oPYExQIg9nnTfUbbNQhw02dH172uG73eVmUn9Pf58+eRzWbd675zKSg+kGGjfXsQKOGzv7dyzfe+973413/9V3z7298eeM9rLTea/r5RGOjcL+kT049gkJlZRZqxFY1G3fNufbnN7kP9QBBVDwBndoc9h0z1jG+fY0AUWAOpqXf5fQVP9ZoaYAfWbJxBeyyAde0YJApea3so3FfZZupb6kr7ee037SDiJmQ/833qb733oDny2W/ERxQEVnY8cQ9rg1BnE/hmpoBtgx1fBb7VntLx8+ltBdF94tPf9jX2S3XNRkEHAIHPspzKyMgIGo2Gmw8C8Vxran+oTtOx0eeKP5pFxyAJa7fzd7Vadf/vJoB+PWTof++cXBWI/v3vfx9/9Vd/hS996UtIpVL4nd/5Hbzzne/EpUuX8P/9f/8f3vKWt+Bf/uVfdqqtz2uxDg8QNHytsRyLxXDo0CFks1mcOHECJ0+eRCaTQTabDUTCGZVeWVlBtVp1zDE6JFtR5OPj4zhy5AgSiQSOHj3qSoaosthuXxnJj8fjOHz4MAAE2sh6b1SWqnx4P3VebRR1t9joVBQ+sFSVHQ0spsszBS+VSgXq2qkwws7fZMIxSkpWVa/XQzKZdIGTI0eO4NixY0in05iZmXEOJQGYTqfjGFrnzp0DAOcEdrtdFItFp7wvXLiAarWKiYkJTE5OBpzr6y0MmLRaLVy4cAFnzpxBsVh0jMjNlLheZ2FhAc1mE/l8Hs1mE7lcDocPH8ZNN92ERCKB2dlZjI+PO8aBXXs+UXCB5VlKpRLOnz+PWq2Gs2fP4syZMyiVSvjJT37iSuvs9/prPhkq8esvN5r+brVa3pIEZDADCDgTvgCkAlg+oW7i5y2rGVgLBCtAbnW1gvB04Pgaf8bGxhwjKpfLOf3AICiBObKsWD6l0Wi4fcbuR6or2S469yz3RjYZWd3qVLGNGmjVFGLel4BuKLSakqssQ46LHgCm86QlJywTnY4ZsHagogK/2lYFLNWJUiYZnX0LXgNrB2bVajU3thxDsqJ6vZ4bu0QigenpaVf+ZmxsDKFQyLGp2+22O/ySTjH7ZIEGtolMRtoFtBNoH1hWIUtksDQYx7fVaiEcDqNWq7m260FdoVDIBZ7t86GMNF8AQcETfZ9i1zY/pyAB1zNB81qt5phpfI2OOa/P54bsSQ3MsAwdS8akUilMTk6iWq06RihZi1qbVp8JtpU/Wsec64EHwJHlzIyQfD7v7DKuMwtk2KAP7+nbf3SetM1siw2aUXR96TjasgTWbt1MdkJ/Z7PZAIjuk6mpKUQikXUM8YWFhXVMcsrMzIz38yMjq6UNN/qM75rve9/78Pd///f41re+hSNHjmzcuWsoN5r+3m8MdO4P+mxSn0SjUUxOTiKZTGJsbAzT09OuTCmD4GRFj4yMuP2aWV6bjQP3UwKzuVwOo6OjrpzqyMiIq4OuJdPoU24EeHMfJKvZnkFhg7QKvGsmuP6t+5XqDQ3A20AgwWXdMxV09tVd179tyS72yQYlCXBHo1HkcjkXiEin0wDgiFgsfUubz2Yi6Z7I4AlL1E5OTiIWiyGbzQbWDUF+frZSqQBY099cW2TW53I557PT9iPew/aoXWQZ2L5AlQZuqL8VRPcB8fZatB+oF1V/c+y2+nwTc1hZWXHjkU6nceDAAZRKJUSjUYyNjSGZTAbapM+i6jW1N9kOltChD07iHM8dm5+fR6VSQbVade3fKn5wJXKt972h/71zckUg+sc+9jE89NBDeOaZZ/DmN78Zn//85/HmN7/ZLeYTJ07g05/+NF70ohftaGOfr6KbmEaAdcPghkaFFYvFcPDgQczMzOCmm27CTTfd5GqVkyHFjYEnOpfLZVy+fBlnz551imIjAI9tGBsbw4kTJ5BOp3Hw4EFMTExcEXiuYsuFpFIptFotXLp0Cfl8PpAW7oveAmtOtk0PA3YXSGc7fOAMx4UMcSpZ1iyfmJgIRK213QTMO52OSx1m/c1KpRJgWyeTSUxNTSGXy2F2dhZHjhwJlMDRiGy/33eHeExMTLh1QRC9VCqhUqlgdHQUFy9eRL1ex8jICMbHx696nndKuPZpiM7NzeHZZ59Fo9Fwhs9WpdvturIq58+fx3PPPYdoNIqXvOQluP3225HL5QLGD4MhW2kjjcpSqYRisYgLFy7ge9/7HvL5PJ5++mk888wzjhFvmXNDGcpOyI2qv23QV4E9ApbqUAJrgDZ1mY+Bo/sbgTo6wfp5y+LdaF/k3qHlVJQ5E4/Hkc1mHSCr6cAMgnKPIGjI80NsEFkBQs2gof3Amts8mDGRSLjPckyUhcNr0D7Q/U8BVjrwWj6sVqt563dz7fFwSQvYKtCp4Ley8eikW6aapiir3iOwTOa9toNtp2i5Hq4hAhLU3alUCjMzMy5bIJfLIRwOB0qSjI6OOvDEprgr4Ml+kYHFA8EJprPNbBPbks/nHbDKwBGBmkgkglqt5saGfWEwAEAARFegl/NtSyZx3fO3Zblx/DVYoYEOrmNemz+NRsPVJ1XQgmtH16KWFWBblF3WarUQi8VQLpfRbrcRj8cxNTW1DsBVEJ5jrM8PSQqdTse1rV6vY2lpCe12GwsLC5ifn0en0wkAB2ynOvY6ZvocWsaoTzjGCmzxdQ10cZx1naldYYFzH7Pzeks0GsXtt9+ORx99FHfffbd7/dFHH8Vb3vIW73fuuOMOfPWrXw289sgjj+CVr3yle07uuOMOPProo4G66I888gjuvPNO93+/38f73vc+fPnLX8Y3v/lNnDhxYie7dsVyo+rv3QSpdlq4r/GHzxv1QiwWw8TEBDKZDHK5HA4dOuRKpJGJTnZrKBRypKWlpaVtgei0CbLZLOLxuCNOhcNr5eD4ecDv9yqIrgca23PFmLVOX4p7i/rf6ruonlf7gXaYEghU5wwKINpMKu5XCqTqd6jXWM6Mr2nwgPgFyQn/P3tvHmRpWp13Pjf3mzdv7ktVdVdVd7OZZWQzoGgDlpA8EgyyLNwGiwl7ZEtIHdPTsllaY41bhhAGSwyCIWoUEi0xg0EEI2hHMAxWGI8Aj9QaRGsUNNJIogE13Q1dS1Zl5XJv3i33O39k/N587snvZmVmZWVVZ+eJyMjMu3zf+73bOec5zznvxMREqls/MDCgXC6XbBIPzHppLuwV14mQ5Hp6ejQ6OqrR0VH19vZqeHg44S08E4SDZrOZMu5dfzebzRQQx+enj/zweMaa54r4h9tPWZgE+oJ7uh3CdV0/xOth4xBYpvSdg/67kY2NjYQ5lMtlLS0tqa+vT1NTU6rX6+rr69PExISGhoaS/vfMDw8Q+VqiT/G/wQbm5ua0vLysK1eu6PLly1pdXU0kN57pKBLZjuVgZF8g+kMPPaS3vvWt+pmf+RmdOHEi8zNnzpzRxz72setq3HNdXEG4cR+dBt/IYN6QQsThm7C2/TvOEGLD99TrnQxqNi2vH8Z9Dgqc5v4wsKStw4ecsYMi539+PKUIhXCjwXMXj+rTrzhmAwMDSaGOjY1peHg4GUGk4cVal17LEmMMpxBl09vbq5WVlQTAxLH3uqMOrMQ0cVdAKL+VlZUE0gwPD6ter7coLh+zwxKf+0SV6/W6qtVqUoIOlLkxsZOh6qxFFG+pVNKVK1dUr9c1ODio9fV19fb2amRkpCXdXmplT3A9DK+VlRVduXJFi4uLunz5smZmZlQqlVLUm/l6lAH040j4zZOjqr+duRQdAgQwzCV+Pkvivobz53uo75V+Xf+uv4a+dYdHagVpvbwKzrmDns4qBtDmb2mL3cee4iVoeA70k9sH7nTHwETc611HOZPJD/Hy1GoPVqCXY8A5i+nlbYiOsLc3MuX4jjOr3AFkX/bnWV9fTyXifF75waHYJOhaDnADRMcJx9Hr7u7WyspKYrg7oE+7PTDjgQifdz6n/FkIFvmhpz4+MK/oG8adcXGQg2sznvSJz3Ef9/hdF/98nHduYwCSoyO9lAvf8UwTZ1FGVnecEwDbBHG8fIITGwA4pK39hP6lTIs72dgay8vLiXXnwRAAo6izvE9oswNLWbZKu771NQ4Axu8sJiJ9F/esrHa2k8PU3w888IB+6qd+Sq985Sv1qle9Sh/96Ef1zDPP6L777pMkPfjgg7p48aI++clPSpLuu+8+/cZv/IYeeOAB3XvvvXr00Uf1sY99TJ/+9KfTNd/+9rfrB3/wB/WBD3xAb3zjG/X5z39eX/7yl1vKtfz8z/+8fvd3f1ef//znVSwWE3OdutM3S46q/r7VxPfZyF52kNl9Ji8Hgg/npVX47boPnU1Zthj0bSfsHdwPHcM1fG9jL4G5HK8TwVH/zT0ktdQ5j4Arf7v+lrbKdHibvV1+b3/ma/lnHkx0WyM+E+1nz3MyRWdnpwYHB1Nm0sjISMIy0N/41AQPPCOJvd7/BltB7zNPPMuQdnngku/79biftAkC12q1NI7UDvfa9j4n8Hv9XtHuiuMd+9zBdMdO/DseDAdEBzj34In3/7XG2G0CWPj1ej1l8pGF5xmTYEPcAzuL63ltdnACvyaYgdsfe82OycI/bkV/9dj/PjjZF4j+xBNPXPMzPT09+n//3/9Xf+/v/T2Nj4/v5zbPaUEhA7xGBpIzrKStjZYNbGhoSCdOnNDk5GRiNzv47A4orOe1tbV0urekHQ8YhYnc19enyclJTU1NpbIkBy1Ej/v6+hJTik2baDfCxk1KEWwkDuvAUbhRYK8bESgXjKS+vj6dOHFChUJBExMTOnPmTIqqcggMKXgeLEGcteUM65mZGZXLZZXL5XQIBsqaiDoGlvcBisrTwx1w9/tubGwegPPtb3871Sbj+hMTE+n0celwgXSYBuvr65qentZf//Vfq1qt6qmnntKVK1dS2yPIjYPezjn168MCePLJJzU7O6uuri599atfTQzB0dHRtEYHBgZaAhMYARsbmwePcVhPqVRKZQKoSU9a+PUouGeLHCvxmydHVX9Xq9WWMgweMGSNAzRK2rbPOTDp4o5IZKX79SPDxxlBfiAi7fL64XyX92CiF4vFdJYFuh8AkOAe4CtMdPRCpVJpqTOJRHAZNps7fAT9AFt5dvSD2x+uSzxwyTPlcrl02CNOiTPWvF3OPnemPfd3oD8647DfYhYdIAZtATim7+r1egsQ6QAzfcR+PjQ01MI27Ojo0MDAQApYc+YIBIZcLtcCDE9MTCSiA6xYL18D0w1mIuPjoESWTcC5JQsLC1pcXEylRCSlmus8f3d3t4aHh1N2A23wMY2BHcbH5zYZbbE0i68Vxpq28h62rOvvWq2WyqfNz8+rXC63lBLwOvNZjEVnjzOPIIRI0sLCQkqRn5mZSfYwjjfj5qCUg9iLi4tJV5dKpcRyr1QqSc97EATbCvvVGZfMbScvsB84KBfZ//G56TfWa7RpvGyP7wGejeN7wW7Zgoepv9/ylrdobm5O733vezU9Pa2Xvexl+sIXvqCzZ89Kkqanp1MZQmmTif2FL3xB73znO/Wbv/mbOnXqlH79139db3rTm9JnXv3qV+szn/mM3vWud+nd7363nve85+nhhx/W3XffnT7z0EMPSZJ+6Id+qKU9H//4x/XTP/3Te3zqg5Ojqr9vJWGf9L0qBpuj/SApBbvRBYODgxoZGdHExEQqzZXP51OQlsAmrNqBgQEVCoVEmGrHgMVvp/TI4OBgy/VdL7qujLpfai3B5nrfAWHwAb4fz6pwtroHRH3Pj22KDHLfw6StPR6bLisg2Gw2E2Ab+8cBZQhpkMoAybu6ujQ8PKyBgYFEiMJXR9f4WVlgCi7oGsccsEW6urpSkJ3rOgGQfRkdjo/o5eM8MNpsNlOWQ61WS2VcCoVCyloDMyIgHIlziGfv7RSo5b0sm2BjY6MFmL569aqWl5e1sLCQfF2e0+1dB6izgkXckxJ4nZ2dWllZSXXS/cBwxhGsw8vj+LWq1WrKRltYWEg2Eux+6qAzdj6/kbiGXHxeu016K5apOva/D06uqyb6teRTn/qU/of/4X84VuL7EDY/r3uGMgBER4m7cU1NaOpokmrsBnpkMXnEFCc5KqUoKCTuQy3VG8HyxnBBmaPQ2OhcGXvtTN6PKaw3chPwe6CQaDvp3qT3veAFL1A+n9fU1FRiMxMscVYS4sD80tJSCg5MTEyoVCppYWFBHR2bdVG9rI/XPo1AEH87myuyA+ivRqOhq1evqqenJ9UnW1tb0/Dw8A0NTOwkGF6ARjC85+fnVa1WW4xhjAvGZjcpWlxf2nTCFxYWJG0xUgDRqXc3MjKS1lNnZ2cK4qytbZ42Pjc3l9LBdwpSHXU5VuK3vjzb9DfgOOBUljgYGXVV3Puiw5kFrrsj4rpY2qqZ6k44elbaSsV1oAxdh8NFoA4nDB3NfdDDMCMBbwFV4zPDOvZgujOls4Bx7yt3fiPQ586zg344jg4aRuaag7CxdIen7DooEBnx8RrMBwes/Vlog9sH7vTxfBALcNRwsIvForq6turU44T7IbBcH9Chp6dHq6uriZXI+FCCpaenJx2k5e1tB2468ICuwTlcWVlpYbnlcpvp4Nh42JKcd4LT5yxlnwve1z6OPk+c6RwDTKwFXzsAM4wXbG5Sr52Rhy53lqbf29cWfUL7IR6Q2k8Wndtc1MFlvrC2+SmVSpqfn0/BDljpBL8ZDx8b5q9nl9CvZFB6UCqrNE1kffp4sFb8J5I4mMs48+7s+5jGv3eSw9bf999/v+6///7M9z7xiU9se+21r32tvv71r+94zTe/+c1685vf3Pb9Z7ud8WzT37eaAMASMMV/iAFx1puzyl1v4yP7oZ/oTJjOgKGA8Ny/nY/CGnZbAV8vK0AWdaX7zJ61hvj+6np3aWmpRX9HYDtLf/u+3y4I7vflJwZkuQf60EF/B4l9z3VAmVraBBuoIz80NKRisdjyPmMubQUM1tbWEkju9gd7LYAvgRECtuAvfoYb+tCDnuhASpHFPva+JIi9srKSgvWMXyRKOoM8zh/a4X3HfXjdMROfPx4k55ldf3t52SxSy7WAe/oWnU0fd3Z2phIvjBlgOu9ngeiUUF1dXdXc3Fyyk8BLCIJ7AClL2gHpWUGiW1WHHPvfByc3FEQ/7uy9izuIOGZs+n7Sc9yUMJCXlpYSswwl7Owu3/z4gdVcrVbTJnitQ0W7ujYPISPKmgUyHLTwzKS+weTFeJCUDmIERHAmU0fH1kEevtlx7f0K/eRsRAybjo6OVKeuWCzq+c9/voaHhzU+Pq7bbrtNvb29GhoaSs4bxhrP6uLGA22HYdff359q4dEvjUZDXV1dGh0dVT6fV1dXV+ofj+JXq1WVy2UtLi6m06izxt+Vy+zsrJ5++unEvGYcABaio3YQQnucfcAhILVaTefPn9fMzEw6WM9BDmeVSErsQD+s1sGm3baHOoG0h4wHd1xhZJAu5k7xsRzLrSrPNv3toNlOGR3uvPke225NtgPkkaygHOsfpwnWMk6v62/X4x7M9MwjdzjQ6V7+IoKNHoSPz+YsbTf8cVTY+x10889yPd+P0aM8HyABn3WnhAwdB/v435+Fz/t+iv73sXOQlkCF6x0viYKzCbvbMxS4v+vvrq6upJ/dyc7n80m/AGD4OScOgPi4AHLgVNMfjL2kVDqMuRyzBDwI4IAD9h0sRcQzJjw1nPNVKC/A2GUBLlFi2Z4sxha/ndHPHCa1enl5WaVSScvLy1pcXNTVq1eT4+xMQvrJzw7woIkD0/RNnNs+Fl53n++Sih5tF+5VqVQS67xSqbTYUvG5fT04ecVtROZ/lmBDAVCwLp3p6QE77sG4eyDPSTcOkNAmt4efbXv+sbSX47Hcu/jaADAHFMffQv+wRqWt9eblXHy9+17JOvbSF/gi7TLGssTXr5Ou2PsQX988o7eZa9F+1/3uD7lN4TaJ+03sifxADPD2Oojuz+r6y9uP7icIjy/vweW4B4NPwFouFoupJnlvb6/6+/sTcY0SbFF/owexVwDrIUy4Hqav8vl8yiIgQ8gPd2Wfjnacj387oh8gMgHhcrmcAvpra2sJC2AM6Tv0f9SFPsYR0I44Efd34gF6emFhQY1GI5HXYNXTdx4Y4nrMIfeP3YaOwWgna/D8HmihtA0+ADiKj48z0cnWo61OBNlp3V3rvRiIuNGkTZdIDjmWw5EbCqIfy97FWWHUvYb1RBqSl3Nx9luz2UyKGQDXGSoeocZh3NjYUL1e1/z8vBYXF1s2l52YuhyIyeGVWaltBy04ofl8XrVaLdWRJsosKYGZOGVra2stLDtPxZOu/4BRVzAooY2NDQ0MDKTU7uc///k6e/ZsC4hOCp6n80Ylt5OQBSBJo6OjSRndddddSUnMz8+r2WymqCzBBTdyOJV6bm5OMzMzmp2dTUoxbv58P5fL6ZlnntHc3JwKhYIajYZOnDihkZERnT179sBr48f+3tjYSKlX5XJZf/VXf5Xaf/HiRa2vr6tYLGp8fDwdSMJ6cDCBVOjz58+nwMNuU6/4DKy0yB6LDrjUGmR5riu640j4sRy0ONiEAevMTqk1zTdm+rgj4evYS11E4fNenoE93JnjOOFeDoXf0VmVNvUrQUIOEnXQjfdgLuGIO5iMo+LPht6P6dHoH/ZFvuPvsXdKW8A44Kufj0IGTtQzgO1dXV0tQUt3htzu8IACDqjrFvoaIAJ2Fs+Gc+hOHO31lHXPVuN/QPLe3l6dPHlSExMTqQwKbP9isbjNOaZNDn4ydh0dHYmdzneYY/yGNTo7O5sYi3zOSwkwv529xjx1tl+c19hHZGmRmcWBXQQGsKfi2qGtznpmDB0A8b99ntMfBOvr9bouXbqkarWqWq2mxcXFZD+xZgD247V8HdAXDqjH4I876QAiPBf9CwDh6wDxci1eQsDH0YEX5q0HMTxF3wM4zE3GjfYxt7mnz1P2A9rvZxp5P8VgmI8lICFt3a1dcqy/j+UoigdrIbH19PQkQNYze7NAdNYVILqT2NxOgNXMOQ2RuXutNeKANvuW+7QxgJhl69Amf2ZJLfoR0JpnwKZw/Q3pDl0EkEk73ZZysJ/f7LXsUQT7vD0Omjtw7oEK1zmFQiH5fydOnND4+Lh6e3s1NjaWfOfBwcGEJ+AfYlv4Hot9QL/xLHHeuG9XqVRUKpVa+tnF7Q6Y224zZY0/4LHrCLISyXbY2NhI2W7YDh58Z85GXzS2z3W963zmQaVSUb1eV6PR0PT0tGq1mmq1msrlcponzKtCodAS8PDnX19fT8F89FzWs0c7g77o6upK5WsJKLgN4raPEwdYc74mr0en0Ua3GQ47KM3aIoCxkxzr74OTYxD9FhMUAZufM9lcWboy8o3QI30eJaYOpzvTztqGGZTFrmnXTgxwNyZutPjmjqOKOFsHYAGF7lF67yte2w8j3QEFb5+0WRuPWqkjIyMaHx9XsVjU6OiohoaGUome/QLNbpxgPGHc8cyMI33GfCDqjQKn/ioM9FirNj5zs7mZ8cDf5XI5seUajUYLGyuLAZH1LN6fWfeUWoEbDlkhukyNcZxcnG8UKylvbhBwPTIb9sJC97Y5qzMLcPB+82d+LiujYyV+LDdCHATH+fM9tp1DGRlgAJLSFkjmujPe08WBendQI2PMHUP0tV8vspXQ385o81IhkcUU9+Eo/rrbDrEdWc9JHxE4iP1LvzsbyD/j7XF97DrLJQsUzXqmOL6u/3hmBxmcFe9ACAwqdAc1R722KQEDJxBwLwdOstoWg+a8RtCH67tu8b7nuow7jreD5zvVPPVMAVKgsR0kpTGMfez9uhfH0+1UGGw44TjgjUYjOYD+3E40yApkxbT6rPbGfuN/v5c/U1a9ftZadOgjQB3npoP4XCsrkO7Bq7iH+e+4Tn0uO6jG3OaasV1xb/I96FpyrL+P5SiK78W+nnxdeRCX4Fhci7zuwT1/Dd8VANrPaNrt2vKgra/tLKA3S1fGdnNNaaskHnucX8f1KddgP0QXuS/kz5RlU2S9F/V9BGG9zVkBA4gL+Xw+AcyU60KnExghWOJjyr7ZDtSN947PRECBsUcHu13Ae7EUl49h1pihu8E+lpaW0vykHAmBcPQItoHrItd3UbIIFh5cXllZSZiR4wcA4T5P3ObJEuxav098Zm8X9hVCv2URY/z7fli5Z3Xtlji3VzlsPdfOJs6SY/19cHIMot9CQgTbHTYYOdRtpLwLm1NkovvBkJSPQCEAxBNNhnHuBzJevnw5ORA7AemwwmjrYYDoPDO1yhqNhiqVSkvU0dPDfUOnzZGB7k6n1HoKe5bEDZ3fACFjY2Pq7e3Vbbfdphe96EUaGBjQ2bNnU+mWkZGRNA4H3Wcw8XDEYZnBcKBcT71e1+zsbCp9AqucQ8lgte0kOMLNZlNPP/205ubmNDw8rNnZWfX392tsbExjY2MpRQ4jhbZlGX3+txsatKdarabU78uXL2t2dlb1el0XLlxI6dXcY2hoSJOTky0HrEQHUlLLwajUSdsrmM61i8Viqs9WLBZVKBRangfA308Hf67KsRI/loMWAmXuiLCWnQ2aBZplpZHiLLOv5nJbdVCzhL0H5o+X4YBV66yqCOxKW6CopFSzEaMfYIysIvR3vV5PBx6Wy+UWnQ+rLIL0Xku1o6Nj20FP7pB4P/J9gu9uJ7g+dX1MgJJr0w7XvVklQTxDy8EHv5/UWp4GvepOW7RlvHwKY+rv5XI5TU1N6a677lJ/f79Onjyp8fHxFv3hhATG1+0Pr2sa50wEUKPjl8vlUmk0ZzIy1swHnEIfc0r9OYM5BkZon89n0po5DK2/vz8F+X3O0ldcBzvDASKfI4yPpMS6XF1d1dWrV7WwsJDSweNcijZBBHMceHH2v2djMAcAdbzsAP0c5wzfJUDi96PuK+sj2gkOGjkgFsE23nc2ebwGgi0b+5T2egDHwT5szPX19Zb0/ii+hvZikx7r72M5iuLZToCwEKIA0TnLImtduw7D75K2dK6ziMvlspaXl1Uul3XlypW0F9br9Wv63+zFlK1gb/N9z3WgB0SjLgWAlLYIWTxXT09P8vOy2sC+GgFj96P99Vwul/okSz+30zUxmBxLoHE9DgidnJzUHXfcob6+Pp06dUrj4+Mt+yIl2Sj7QoYY1/fMLvqCtnjQNAYVED7vGYMAz15ClZrcMKOzgp1Zwlkcy8vLqUwrNkBPT4+KxWKqX09ZOi+H5uMXbSz3WV0gW1JTHP09Ozub7g2wz1qhj9zG8vGKmQ5Z54TFwIID895fHgTxswGcMMmYMPd3Yv4fZTnW3wcnxyD6LSJsBChvFHehUEiR092A6LBKNjY2WtJkcKT94FDqQ128eFHf+ta3UjptPH06q60AC/l8vqX+5o0WT1WFjXytKK4rXk/rZtPNKj3im26WuLPnjObR0VENDg7qzjvv1Pd93/dpcHBQU1NTmpiYaEmnuhHCmEhqccIXFxcTy4vAw8WLF/XEE0+kciZzc3NaXl5ONT+vJSgfnO+Ojg4Vi0XNzs4qn8/rzJkzWl5eVl9fnyYmJrSx0VpawQGUyGqQWg+IrVarWl5e1tzcnM6fP696va5nnnlG09PTLTXNAYZIvxwbG9sWJPG+4vWRkRFtbGymS1+5cmXP/c4zFYtFnTx5Uvl8XidPntTw8HB6ro2NDV25ckXT09Pp3IFGo5HeP5ZjOZbrE/Sis0k9tdgZMWSzUGbEDWmcSs8Ek5Sc3wh6OtCIk+3OeE9PT3J2PW06gpsOQnoJFM4soU1emgs9T+kyAucOVDvgi/heCegaQWIcLwfRHQT0YCvP5SUlnDGUxbKj33DAos6NwQb26Hb6k+vxA6AM45nPSK2lNxjfzs7OVBt1ampKZ8+eVaFQ0NTUlMbGxrS2tlULGxuLMS8UCi0BX2nrwFLsNPqP35GxR7skpWviWGLjuB7H9kE/bmxspLnnfe8kA34ImjBW2Ifo8sHBwZQO7sxLP+DVWX/MSeasO+T8rlaryfG+dOmSrly5kuYx6yqCJ3HeuoPPe67LWfPeRmwuLx/k7zP2ADzMHWwp7kkpO77vpRki4ODBsFjqkO9EJpyzA/2+EfRwEMHtYWfrQ6bwwEE7YG63wM2xHMtRF3xs9Dg/HhD34HrWvor/zF5K8NP3qfX1dVUqFS0tLalSqWh2djaV4XT93U7Y1whgoktcn/h6jlk1vm/5nub7Jd/Dz+MacZ8AY3BA0vcUQPVcLpdA0iyd5/dmHLBNYqDRmfLsrz09PamE6okTJ1r0N2XSuI7bZIVCQQMDAy26iqApOtKDxJGl7fo92i+ui9CPAOfz8/PJ5+ZcLb63GxAdn5fyeNizZFbzPsECz1ByXQEm5HMikk2azWayM1dWVnT58mVdvXpVq6urySaK44M4GSLOSeYutp33odsR/prPd/73Z+KZ/XwQt0Xoo2sRBZ+NcqzDD19uKIj+3/63/60GBwdv5C2OjLgC9HItKF53wP01vseG4k46AtDorwOir66uqlQqpQjpXli4N2PBZrGR4ntu2HiEFUXmysG/lwW2tpPIIEIpDw0NaWRkRCMjI4mNjGO7G+V4UBLnkx94A5hNOiE/1zpMNkvc8MBY3NjYUKlUSgboxsZGOrAFRqZnBUSGBMochxglXSqVkuHpLDsH5uM6uVZ/0z/R8d5tPxCs6Onp0dTUlE6cOKG+vj6Nj49rcHBwW7BF2sygADSAlXAUFfpOchwJv/Xl2aq/fW5l7e3Otsla674POFvKrx8B5yyHOuom3+diO7K+w+s4X9KmLvcggJdjc8DMnw+ALYv9HB0bdwajjovtchZTll7OYg7x3P53O4nOvrfPJeo61+sONLozxbWijhwcHFRvb286j6a/vz8FQ+IYOpPJ07S5f5Z9FsfF7RNJCbj2Z3PAxkENT0920BaHP5YBifd1hrzPs46OjpQijo7ykh/0qetZAiuul/2ZpK10ai8946xxb1N8bu9zt/N4Xn/dgSqADGmrDqu0FThxYMGBFcr2+FykPTj/gBdx3Lk3fcoZDf4etraDNnE/iGCN/477RtzHfNzj9aNN5ADgTusx6zv7kWP9fTjybNXfN1t8P3K/wv/mc1nfdRAR/RXX6+rqavK/CXx7SZed2iYpsx2uj7mXpEwdHvfdqL/9b9eTXiIqspazCAH43EhWuQ3X015OzNtBn/q1PJBA4BOfe2hoKB0Y2t/fr97e3qSjPGDg7Y1tctumXR+3kzhvYiCdTADX4R6A2I1k+c348NQLJ0vBQXQC//H9rOwot+WySg+5fsmaT+3mmNtx3Nuxqzhn45z0+ejZngMDAykwQiZ+1M2Oc+VyuURGebZLtBmu9dlj/X0wsmsQ/S/+4i92fdHv+77vkyQ99NBDe2/Rc1BcWZNijUEPEEkaEu9LW2mocaPI5XIJhGw2m6rVapqbm0us5Gq1mlhNMMpqtVoLwLyTXMvxv5Hi90ZQptKWY+zGC9FH3ltbW0uMdI/ExuDETvf3OnfFYlEDAwMaHR3VS1/6Ut122206ceKEzpw5k8Zup2veCMEo6ejoSED++vq6RkdHkzNcr9dVqVRUrVZVrVZ3BE12EowlMh86Ozs1Pz+vp556KgET9AOHsXFATwSqnH0JQ40UcOYqzjrzn2dzpoinaF+rnzAmfA64UZglfG5sbEwvf/nLNTo6qlOnTun2229Pa9RPJJeUDpBpNBp64okndOXKFV25ckV/9Vd/pUql8pxTTs+1572Z8lzS3w4mRiM+AqkczCS1AmW5XC6VZZHU4jhG6erqSqWbYP+wl3If9hd3PBxE89/umEhqCU4uLS0lox/9HYMCEYSNTFhJKcXXnRH/Tfuy1ih7KiAj+6eDtzG1m8NRPcPI/3dGO9eH0RfPPkEcWMQx7OjYPLwTRwtGG2BqtVpNtgBtJvMPBtvAwIAmJiZ09uzZlBGYz+cTg4xxjoeZOnAtbQZYYcq5ndZsNluCyACtPDtzwBl3PCd2XaPRSNkHADA8M44xc4Zx9L7wLATagEMvbbIoOXw0rqOOjo5kl1L6pbe3N7Hh4hpDCNpjN+Xz+RbgljYBiMCydJ3s7O5IFgEoiDVmHdDm3tzHg+BDQ0MaHh5uyaiQtta8z11S8Wu1mq5evdoyJ/jOxsZGS8aZM/xoX8yOiXPb78/zOZDvYLmDIk6KcFCL7xEU8iBVrVbLXGft5Fh/H548l/T3rSARQPfzv7ICuVkAlgcxYTVTTtPJQfg1rMWdBKAxi1FMYI99y4FCL+fCbw+MZgUr0cu53GZ5MQ/+oYv4Pwbs/LsRm9ipnznsE1vM/XonWDl4DmCez+d1xx13pFKeZ8+eTQeEkylGCTxsFQ908uwI7eB1/9v9Vg8Y8JpnbXEdz3rkTBAOu6Scy26BdB97Sck2JFMP+8BJmW4LcpgqGZOQLDyTzMdIUjpDDd2VFVzwjEz0t1dHQJzwQLtWV1fV29vbMq/imMR79fX1aXR0VPl8XsPDwwnfwP92/Y/tvLKyoqtXr6bDX7FZDhvLOmhxksBu5Nn8rLeS7BpE/1t/629tYzu48F6MPB7LtSUr6u3AHhuTv46z5P3uikbaAhFgnVO/an5+Pm3ke1l0UW529M6Bg9iHMbXMgQvEN3FJLUpbanXoY1TfnSA27YGBAY2Pj+vkyZMaGxtLrLbDBM+j5HK5BCpT4w9mmR+yca0SPtcS+pb+pfwAABPA8tDQUFJ+1OP1eYqxWavVVK/Xtb6+nuqwudKOLEIUMT+7AdDpH1gPzhzczfdyuZz6+/t12223aWpqSqdOndKZM2faluxhLpL1wcEvXqfuuaLYjiPhhyvPBf3Nvu8SmUPOGnVn2IEp3wccGI7AuAsOozvOPsdpQwS1/LrSdmeNz1AmhKAn4Glk1rKnck9+R8c/sqSdDQYQB2s4q5+j4x6fz5+N62ZlALQLVnqfUEuU1/k+v91Jc+CfNjnY6k44thS6m7M8hoaG0gHgBHu7u7tbGMU8X0dHR8uBk87KAliPuiA+gz+rzwPXdXzHmejMCQf03bF3p50xdzvS9SSf89T5rLWEDAwMJAYb855xaueQemAlEh+YQ67DHWB2sN/7xSULCIKdF9eZZ8H5+UOA6F7iz1mlDnQRgIvBj8j+dkY+9/dSPw7MxX3J7+//s1/R9zGgEAkxMVjHmon9xDq7lhzr78OV54L+vhXE97joF7qv4pIF8vE6+zMB73K5nEpmAqJnAfDtxPVulv7Osll8bkQ/Oe6v0va9wnVo9I+y9rwIPHuG3E7PBJDa1dWV2h7xDNe70femhCdg6vDwcDpjrq+vryVTyseOa8b1xf7oe3nEWyS19LP3EX3mmc4+L6iTnnUw/LX2SB9DHzMP4hIscKyI73DQqvc5WIDvHz7PnPDgQZEIpLerluD2uc9hB9JjAOhaz8/4u/72AIvPT0ipBPPpL9pyLeLcrS57afux/j442TWI/vTTT9/IdjznJW7AzsBhE8xSnG5MZ11HagUcXa5nMbjTtNPBRQctRO3daSVNyZUFkVJ33AB63dHF+XYWT1RQ8f4Izgy1sCcnJzU+Pq6RkZGUVnSrCM7iyMiIenp6dNttt+nOO+9Mh4lWKpUW4GOv10b8ux51d+VLdD4ecuKf8Ug0n3fl7AaJHybmSjwLwIjCnHAAxgH1aBDi+A4PD2tgYEAnTpzQ5OSkJicnNTAwkGlkx37q6urS6OioOjo6tLKyoomJCXV2dqbAwbGSOpaDlueK/m4HrLkejQ4PoGoE0fmOA5v+O0u8dFbcoyK7J4JnEXhzYM4dkHh/B8uzHHKukwXOeWkNB7rdofC/fQ+OzrTbJXzWU4kd3HYHFeYSzxb72ccr6/lwUDc2NtJhzevr69scKXcC6YtcLpdYTKQDeykPd9q9ra53YBu74wib2XUJeo32LC0tpXu4g+4gp6Tk2HLGyPr6emKSAdLw/Fw/1pmPmXDOZnTbkJq1zkr3sXDbyNvY1dWV2HVZjLNox1Kv3gH7GJjxWuwOoNB+5hA2g89TwH139r0vIBQMDg5qfHxchUIhgS2+B9A27snvfD6f+qm7uzvNwSzyhku0131O+32zCBxZwTHe9+ACr8X7+xhm7Qd+zWO5teS5or9vtrD3xHXo+jn61v6es7/xFbzkE/t1LOW0G3Gfn/0sZotxjwhq+96XhQNkYQj87WCn4xDuV2M7ZO191woQOJBNsMEJVvSz+/Lu76O/C4WCisViYqVzVofjJoD1Dpx7FpjfMytg4GMOYO1+oetD75NcbrN8KhlbnG0H9hDH71pzwkujYud4RoOfB5SFGTkJkNJtXr3Anx1GuQtZXJ2dW+epRNzKMx3Q1T6e6H+fd3yO7L6o16VN/UsQYHh4WGNjYxoYGNDg4KAKhUILScAD58wHDxavra2lzEWCGsdyLHuRXYPoZ8+evZHteM5LdE6k7QBAVgSZz0lqUZTtAAHkeoE6UqMWFxdb0p5upOCwkgaFsiHajAGEYkTxukPpNURxfEg9yuVy25RuZCNFh6ejY7Okxwtf+EKNj4/r9OnTOnXqVEtJkZsttKG/v18nT55MLAhSkWdmZjQ7O5vYjXs16tzZywKd3ZFtZ6AgPodRhqRbw2D38XEww8d0t/3uIL+nlntUHCWO4dvV1aUTJ06k8i133XVXAsKvFTghmHHq1ClNTU2pq6tL3/ve9zQwMKDp6Wk1Go3nBIh+HAk/XHku6e+4x7hecAYun3Xw0Z0oByE9eNpu/qFL8vl82rtwPgFCAS+lrXIofl1nbfEdHLS4t/p9PejndUUdPOT5ePYISuJcRdZuZN/GTB93mLMC/wRMeZ9SctIWeNrX16dms5mcwAgwAMi7PvHfjGFHR0fK9EGv00bPumLcJalYLKazLEZHR9Xf3698Pt/Sn/QVziPj1Gw2057d3d2dAqlxztF+dI0HTL0vGWsOtmMeMC6FQiHNTZy+5eXllnIBa2tryUaSlNK1XTeRIRYDIgQhuKYHlz044muFQ/P8oHd/dg5r7e7uVn9/f5ob/f39Wl9fV39/f3o+2P5eBg8n3uc24iUGGCfWFvYf7fCgzdDQkAqFgsbGxnT69GkVi8UW29vXJHNX2gLHsDsBfDY2tsor+dyItne05yOA5Yx8t5d8vvs68/XgayGLfe++AIceRqBptyD6sf4+XHku6e+bJexLHCzqdgL7v68h1qoD1Q4O+h7APuolrXy/upZkYQFRH0qt2UPRXvD9m2eKgXdf/56p4v3j9hFMeievXStrL4oHQhcXF5XL5RKwGbPc8NHY3/EPp6am0n4+NDSkgYGBVG7Mx4y9zol42ByMvT+/970TGyjNGfU3fUEQmPdh1w8MDEhSAoElpWA247cbFnZfX58GBwdbMrfARADTYVvTLtdBhUIh6W36Mo4nOobzzLB98GHRP3yXOc77tMvLIvqY0yaC9b6mGAdsJp+DlPAZHBzU6Oiobr/99kR+ILDgdjzCmmMOYeNdvXpVnZ2dqlQq+zoX7tkox/r74OS6DhZ9/PHH9cwzz2yL3vzET/zEdTXqWG59QaH5QVKHIR7Jd2aNR8idVRiNFN+8aTdKIBoq8fsYFM52YsMvFArpEDKUxq0AoLvQViK5g4ODWl5ebjnJ+lptdoAki7HJuDhA087I20kIajA+biy4gmQsuC5KPMt5jMJcYE5JWww20vjdGMTgpDSOs9fc+LqWYGRISqloAA3PFTlW4jdfjqL+Zj+KAJb/eCkHfrvO4PvSFsvZg6n+PenaKdZS63zndd+XPEibxdrx7yF+X3f4diMe4IzryduZtdbiXhqddP+cP6t/Jmv9Z13X25e170utKeU4gs7EQjxl2nUSQK07fg5w+n3ajSsASxxj15Xci/dphzvp3ibmqdsb/oOT7iADOjgLcI0BDp6TOYQzDyvN+zSOnQesuWZMB/c14+xBb6+kBGxkleLh854x6AF6H39fM/58Pp4EQvz8FMbe52Cc03HcPcjvIHSWxP2C53MAhueMz+335BniPkB7vT+ygmF+X77DuO8WzPP7HevvmytHUX/fbInrHPH9IK5RzyKK9kOWuI6I+tq/55/Luob7Wb4e97q+/Nmyvut+sOu7qJ+zrrmfdkit9Z3dpnP9zRj4Xg7YmxXo51rxNa7L87fb9/3H7acs/Y3Owl/0drn+9jKlu8ELHOPAl0dvO6s964BvFy8Bh80ZQXT0G33jQDg+OQFzxizLVs16rjjX+DtmBLgdgrj+5oca6JERn3VfaRNfWF1dbZk3t1LlgBstx/r74GRfIPpTTz2le+65R3/5l3/ZshiYoMc12fYuKAWcIDY10kO9PElMgXFlxvvtfmK6135ldXVV8/PziYlz6tSpFiDhoAXHbX5+XrOzs5qbm8us4x03znbKyaOm/jebeJaBw/eazWZKGxsYGNDJkyd19uzZxG66VRjoUfz5RkdHdeedd6pYLOrMmTOan59XuVxuqYkapbe3V0NDQ+ru7tbExIQmJydblHi9Xtfc3Fw6uGN2drYtUHOtdsLEQ0HCKtiJie6p8qSVww508N3XydLSkubm5hIz684771RHR4cGBwc1MDDQsl7K5bLm5ubU3d2t06dP6/Tp0xofH9/GKN3tM0pb2QE9PT0ql8uZgZ+jKMdK/ObJUdXfOC5+aDTP5OsfYxm96YCl6wtYKV7Ow5lCfIcgKmAsgg5xNk53d3c63JE2xVJX7jTwHs/EtZrNZstzZpVD8X3XnWAAT5hKpBjTHljh/l1PicVhA/R01lKWDeBlNzhYjfMuEO7nOpd+dpZcf39/6jNn/fJZ9BcMK5xA2gtTGqYafcrhog784uB66r1nKwFie0CZ7zog6szimO3g/egAMhkNbvPBZHM7cXl5WZVKJR2YBdNR2qqPT2o7h9V3d3dreHhYIyMjLU59vV5PZVlKpVIaA2eIO/vSD2jl2fgbBiBrKx74zdpzIIpABH2QVb7IQRxY6LVaLT0zWSA41eh4t307O7fKsRUKhZYshmi3+di5zU3QDUZgZ2enVlZWVKlUtgEOWYB4BAb8fv6ZOLeyPod4MClmdNDnvj4BKbB99mJ3HOvvmydHVX/fLHFdjK6KwTzWIfsZe7TvXTEIHzN22Af7+/sTEzYGnz3Qi66hPQ72coizpHToNe3BFsjyQb2EJu3O0lM8uwdMV1dXValUUhYzjHoAWQds3T9zaRcQiMK94j7HZyFZYa9QExvyk9t4Wez4rGCH2zl+zywMAb0dSXYOBjuxKjLRBwcHNTw8rO7ubhWLxZRB1g5w5pnxZcfHxzUxMZHA9K6uLjUaDfX392tlZUULCwvpebN8es+8pL/8+bmv6xDvK58v3J+5wPvYNVn7kdtkVAro6OhI/cM10d9+/66urnTm3NDQ0LYDTKXWzA23Twii9/f3S9rMQhwdHVVX12ZteA6OP+pyrL8PTvYFor/97W/XnXfeqS9/+cu666679Kd/+qeam5vTL/zCL+hDH/rQQbfxyItHlVnoHnF1dq8rPmlL0fnnI3DuzuZ+QM0sWVtbS6DryMiI1tbWkoI+aACZtq6vbx6sdvXqVS0uLm6rBxdlp3bEzd0Nhxh19u/wgwM2NDSkiYkJ3XbbbYmJfitHNDEehoaGdPvttyufz+vUqVO6dOmSOjs7NTs72/a7PT09Gh4eVn9/v57//OfrBS94QUsUeG5uTufPn1e1Wk2gcwQIdisoZxx/lKrXcXX2B3PaTyev1+stmQHOIgUYWFpaUqlUUr1e19jYmG677Tb19fVpYmJCQ0NDLQby5cuXU9Saci7FYvG6GOS9vb2amJhQd3e3zp8/f0sGX47laMlR1d/OCpVaWS1ZTG0/c8EBVAQQDn2LLvYyYLncZtaKl3BB3LAHqORwIxwQZwBFEB0HjWsAnHtpFBx8XnM7INZi9XYBtLFn4vyw50qtTK3oHLudAdvKrx+D0Hyekh9eNsyfledpx6pjP8dx9ywiHGbPKurs7EwlXDyLjfIqtB0d5s431yE12ucUQCpgiesj13dZjhyv8TmfU/SfB4zRbWtrayoUCqltHIrlmW9ZIDrP1t3dnVKzi8ViOoSL99HbzEWAZcAa2kbbKYuDDuY3Y+ilSegD/gaU533mrgMbWSA6gj27srKier2eMuogMWCLkQ7uwZbOzs4UJO/t7U1968ENB8y9hAEBDED03t7eBGwsLi62ZBognoWQxTz154zMwXj/uKbc1nRgh2fN6ju+x328/NOx3PpyVPX3zRIP+vn6cj3mQHjcz1hz7jtGPxKdhr/Eff1e6GzP5vH1LKlF77LPu37irAkP4vpzup7iOm5f+P7lnyVg22g0EoiOLvWDtfnubvzynT7jgfGs9lPWjMAE5VEInKNHIgDs93Rdw2exCaJOd4lYjAcqPeDv2U3YkgRtBwYGUgkxdBXBzKz+yuU2s5eLxaL6+vo0NjaWQHSymGu1mjo7OxMoDTAP/uPiZe3QZzybB7YdL4p6yW08WPVxDP13fA8bg2BMX19fIqMwll5+hXZ0dXVpeHg41UD3M4hisDoGu5jLBLDAcDo7O7W4uLitncdyLNeSfYHojz76qP7v//v/1sTERJq4f+fv/B29//3v19ve9jb92Z/92UG388iLG9CkyGD4o6iJ7nmE251BV4xcx/+WNp3k4eFhSa21vtjUGo1Gcq53YjSwAeZyOS0tLaVamji5BynOumo0GqpWqy010XmfjRLH2Z//WuJsnfh61v/UFKUWF+CDgwm3shApJ4o/ODioRqORjAd/bozLQqGgiYmJVINucnIygdwYlysrKxoYGFCpVNLc3JyWl5dVrVb3dGBHVNBuPEXjNH6H9eCGaKVSaWHNOXBEuwATYChi2LghQQkcPhsPR92PeLufS3IcCb95clT1t5c2iUFRxBnZ/r7vN+2cBQfnJW1zvCPQzN+0KTq27oxFxk8E1LgG9bhxzNzRbzabqZY1ezjXdBDOn6VdYN732Ahy+/0cPMhix0pbAcvV1VXV6/VUD9zrkvq4OGPMf2P3eHCB9gFocz9py1FbXV1NIAPX4/sROInOYpwPDnjGPgVgdTAi7nP870EY+t/HzB161xGeygwAjB5fWVlJjifCfciAwDnN5/Pq7+9PrG1A4WKxmIBpCAHoSGcq+hz2PomAsTvjnlEZAw1RfJx9jniAyMErwBTa7LYY68TXr/9kARdZASRvG/ekXwHIYv/Tbz5P/DXaw3U9sMJ9fQ9oZ8t6f/pa9f/5DPMUWynud7uRY/198+So6u/rkXbzdqc9pp2NkPV9JK4l1il7tQOOTlZjjQNys5/HfZSzQmKb3Z5wENxZ4gCiiO83/pqv/+hHEeT1Z8Hv5jyyWq2mRqPR1seOgeR2Eglq1xL/DIEPz3iK4xjtCm+bE6ri80eg3YO7/n4Mhvs94z2w39AX6O+1tbX0t2dr+fd5RkBmfg8MDKSMNWwJDi+t1WrJ74VskDUmHjzIsuH8fXAiD7RkBSe8j7EJvC8iTuX6GzKLl9dzjAs8zMvjOFkm2t5x7tA2xsLLwnDNg6jUcKvLYevvj3zkI/rgBz+o6elpvfSlL9W5c+f0Az/wA20//8gjj+iBBx7QN77xDZ06dUq/+Iu/qPvuu6/lM5/97Gf17ne/W08++aSe97zn6Vd+5Vd0zz33pPff//736//4P/4Pfetb31I+n9erX/1qfeADH9CLXvSilmf5N//m3+ijH/2oFhYWdPfdd+s3f/M39dKXvnTXz7YvxM/TUsbHx3Xp0iW96EUv0tmzZ/Xtb397P5d8TotvOCsrK2o0GtrY2EgHPzSbmww0Tyt2x96Nb6LSsL74gV00MTGRNuLJyUkVCoUUXV5eXtaTTz6pCxcuaG1t8/CvdkD6+vp6Yt7MzMzowoUL6u/v14kTJzQyMiJp9wb5tfqm0WioXC6rWq1qenpaly5dSo7xxsZGywEfbKiA7h5A2M29uE6WOLDQ39+vM2fOaHx8XCdOnNDo6OizCkSHDSFJp0+f1uLiojo6OvTEE0+oVqslI5BIeW9vr26//Xa96lWv0vj4uM6ePauzZ8+2sMOr1aruuOMO1et1FQoF5XI5VSoVPfXUU5qfn99Vu6LR4UCVpzZjHKIQ3SiUNtkZtVpNkhK73tO9MAC7u7tTGZ4TJ07ojjvuaAHRpS3DilTMzs5OnTx5UsPDw6ku3X7FwZHnEpB+7ITfPDmq+jufz2/bfx14wggn4OqMMn7Y89wp9H0Dcf3rDo/rZq/R6eVcuJY7wehn2uksbwBzgnfj4+Pq7OxMLBx03fr6uubm5lQqlVqeNbKCHbQjkEjgHFYTgD19gR3gNkdkvfK+P4O0GcQsl8taXl7W9PS0SqVSSuOlv5GsGpgOnhI8h9WPkwnTy4OnPNPS0lIC0d1GAEgmHRhwwQMDjFN0JhlL7s88wHbDWeZzPT09LXYabHAY2a7HGI96vZ6uwxyinAtt6+vrU6PRSGO7uLiYAHHmDs84MDCgyclJ9fb2amxsTGNjY6l9lHGhTX4AW3d3d3ouB4UYI/odkD4CNxFs8aCSrxkHH3D+IUQg9B/AQC63echaPp9Pqdnx2sydlZWVxPbL5/MpEI4z7pIVbHP7kHJzHR0daf1Vq1VVKpVtGZ9ZwJaDJQ5+015YhKwJ+pxxd+agfy6WWHKfgT3KGXn+TFn7Zzs51t83T46q/nbdsZc54sE9vi+1AqJZ4FoE3SIQGAFz/mY9+2/KmUTiGkFDSntQ+gm2LfobvTYzM6OFhYV0cCX6IZapkJQYx9VqNRGV2AslbdvXHGjkOdzWkLSt/evr6yqXy5qfn9fS0pIuXryo+fn5VEbLs+R3Iz4ebofsRNiL3282m2kPdwa6kwpovwf9ec33dO8b8JNms9mSceB2kBMO0OmMPf3X0dGR9BftwV4ZGBhQZ2dnwmnIesammJ+fV0dHR0vNcjKmhoeHdfr0aeXzeU1NTenEiRPq7Nw8oLura/Ng0eHhYa2srKRx5/By5mk8ONPtxCxGN7rW54UHLhg/ru9BbgI7y8vLLX3tdhD92NHRoWKxmErMYfO4HvXsOyoBsIZihjpjSrtc7+HLQxBgTRaLRRUKhZShvlvc6Nkoh6m/H374Yb3jHe/QRz7yEb3mNa/Rb//2b+sNb3iDHn/8cZ05c2bb559++mn92I/9mO6991596lOf0h//8R/r/vvv18TEhN70pjdJ2gwkv+Utb9H73vc+3XPPPfrc5z6nn/zJn9RXvvIV3X333ZI2gfif//mf1/d///drbW1N//pf/2u97nWv0+OPP54yWX7t135NH/7wh/WJT3xCL3zhC/Vv/+2/1Y/+6I/q29/+torF4q6eb1+I38te9jL9xV/8he666y7dfffd+rVf+zX19PToox/9qO666679XHJHuXjxov7H//F/1H/6T/9JjUZDL3zhC/Wxj31Mr3jFKyQdTDThZotvdM46X11dTSnfUivDB1atbzRew5K/PbKWz+c1Pj6ugYGBVMe7Wq1qfn4+1bSemZmR1FqXMgrKI5fLqVaraXFxsUWhHASAjqyurqbyHLVaLaUde3QyMgZdYexWwSPtNgmcE2mT0V8sFlvSglEGzwYhsr2ystLyHFn1PwEBisWiTp48mUqZnDx5sgUAJjJer9d18eJFjY+PJ+d/LxJZnDHK7EYwjqF/zh1bDFyMFAfRSa8bHh5OKYGDg4PpoFAv0+JAjhvA18tE5zkiI+Soy7ETfvPkMPX3YerunQ4HciYOutBZ674G3dHjt7NvImPbnQd0n/9EFnoE9xwwd4kgAAxb9p1isaienp7EHPY6ovQtDoQHIt05cYawO+oO+HsgmuvgaLrjGfW+A56UcAFo5J7cjzZlAfKeds7z8EzYQA7E8h0cZkBpdBni5X9oNyBvtHu4B21xINhtDk+ddvvMQRjew15YXl5uYdX7nJDUUvMTUB3QGhC5v79fy8vLiQkdAzB8LzLZAHT5ja25uLiYUqsJdjjw2o5F6Mxu7zMHLhxE9nXWLqOS/nY7zsvy+HkplFVyIM3FA2hZDEZ/Fg+oOaDmzwB7LpfLpb/pQ98PXLyvs+YYz8U6cN8gK/Dg33Ww3JmS/hm/Fs/m19qNHOvvmydHVX/HQNVegfS4hv377EluZ7suaydx724XjGJfjPsWa4q9DnJOV1eXBgYGWvT32tpa8m/Rc4jbIg6CS0r6DR2Ej4LO8GvE/SNrj3YglMA17HMPFHrgfy/iNtJ+BWB6JyY6bXN94OMXfXX6IRIoYhZcBOGz7uu4g48FwX5KlG5sbNbG96AuZ96gB8gcy+fzCTweHBxsyYpGF0mb82F+fl6FQkEdHR1aWFho6YMorkN8jiCOs3gQIo5fDDSBy8S57H1EMBqcgEAuNosD357lhv3Qbuyz7BO3xfgO/c2ZQNi0R90XP0z9/eEPf1g/+7M/q5/7uZ+TJJ07d06///u/r4ceekjvf//7t33+t37rt3TmzBmdO3dOkvTiF79YX/va1/ShD30ogejnzp3Tj/7oj+rBBx+UJD344IN65JFHdO7cOX3605+WJP1f/9f/1XLdj3/845qcnNRjjz2mH/zBH1Sz2dS5c+f0r//1v9Y//If/UJL0O7/zO5qamtLv/u7v6r/77/67XT3fvkD0d73rXYnl+W//7b/Vj//4j+sHfuAHNDY2ps985jP7uWRbWVhY0Gte8xr98A//sP7Tf/pPmpyc1JNPPplKkkgHE024VcQZWvV6PSk6jG6UtDvG7pB7jSmi07lcLgGaL3jBC3TnnXcqn8/rxIkTiYlerVZTOY/+/n6Vy2U98cQT6YCKLOH1Wq2m6enpxHjCccsCZa8lHtElEn/58mVdunQppShFZRajzpHVdFCC4gZEHRoa0vDwcAJU40Z+KwvKkrI0o6Ojmp2dbak5i6MFy2toaEhDQ0MtgQMHoqi52tm5eXDX+Ph4ci53K+4MukHq5XrckPS53+7HrxfvQ/swRPx5ouD85nKbB/vA2N8LgyvK6uqqyuWySqWSGo3GsYN5LDdcDkt/H7bu9kwg3z8ktewFOD8Y6tGZcvDJAUEEnRLZqg7EehAP3eSMd3eKHUR38JdMKq+P7XUgs5xw2uQs73aBXW8v2VxLS0stdUWz9rXoeLuO9eeALby4uJgy3QDcs9ohbTmcUW9nOUUOuvqY0u5YWkXaAuxJg+YHXdbOuWBOxBIq3rdum0lKrKkYGHAw3zPmyHTCIfbx8bnszyBtAjNkDvq8pc2Au/39/elMEw7W9MAO83J1dVX9/f3pENdKpaKenp6WYFG7ebRT2TaAGV9TkEW4r9dKxXbN0sWuv7FZCoVCYuX5PEF4nTXhNkIWecCDXTEI4O/DGvX15mcURLDb/3egC9uGQJevpRgU8D7xUkYxgOFBDdY01+F+Pj5HmX13VOSo6m8Oama9RUAzS7IC3/zv/oK/F+e8v859c7lcy14GwOYlID3rmbb6HoYPhX/htZzJDOrt7U1ZR+iJjY0N1Wo11Wq1tO87g9j3PvbKarWa6koDvEpbAVj2N9/7HBwFMPfnoGTL/Py85ufnUzv9wNPrkf2Aley96MoIQLPHZV3XdVxkUmfNH2deR4KEB9F5jzmSRaxy8NZLjzWbzXQoaq1WS9dwwpbr76GhIQ0MDCQ/3DPBIY9hG5KtQraapBbWfBRsNicG0Nesp5jZxLNJagmsUP2APvUx8H6mHwGzHVBHX7uudGG9OMnCbZmoC11oP89Mv/Dcfu+dsiS4l183zqmjKrGGPIEhl5WVFT322GP6V//qX7W8/rrXvU5f/epXM6/76KOP6nWve13La69//ev1sY99LGVJPvroo3rnO9+57TMA71lSLpclSaOjo5I2Ge+XL19uuVdvb69e+9rX6qtf/eqNBdFf//rXp7/vuusuPf7445qfn9fIyMiBg4gf+MAHdPr0aX384x9Pr91xxx3p74OKJtwK0mw2Ww51glVEKrIr8QiioxCpibqysqJKpaKlpaV0YGKxWNTdd9+tl7/85YmVhHNEeZTJyUmdPn1aFy9e1NzcnMrl8o5gdLPZTAAg5S9WV1dVKBR02223pbIeeylVwXPMzc1paWlJ3/3ud/XXf/3XKSIeDf0sh4DrHKTgkMICnJiY0OTkpIrF4o7K+1YUlAWHhU5NTWlubk4DAwMpHRCHd2BgQGNjYxofH9fk5KQmJyc1NDSUUrddMXZ0bJZ/mZyc1KlTp9TV1aV8Pp/J9GonDiahuP2MgOjEOvMjKlZ3lCVtU4gdHR0t5VtII4sK25kEklKJgPX19RanYq+yvLys2dlZzc3NqVqtHnnFixxmJPxYWuWw9Pdh626CmdIWMJzF7HVD2Q9/cqcOBzWyid0J99ec4cW+iU4iZdsBbcqlOOAHiO0HZpItMzg4qN7eXk1OTmp8fFzd3d0JcCX9dHV1NbF5cKo9yM5z0Qe0b319PTnM7nzTdhw7Byvc4fb1iEO+urqqxcXFFifcAeOsNezOG4BeFmjIe6S6+/dpv7ePgzHdee7r60uBYc41kbaCD4wJ1+W7OKyAtvQfbfIsQEqH4Owz7vyGaeXOMgdV+1zxgAR6iJRn0pA7OjpUr9dbru/PSubc1NSUisVii5PtYC32IEy37u7uxDzk+Xhm5j7Oa7PZTGfjxDFhTLFd3Tmlv5k3fC7qal97zu5j/CA1xMAP36E8Dvd2QJ+2xrJHXmrBgQU+t7GxkbIkASAAUTxAEIEHbBUP5Pm68CALfe4608s88FlsJOYu98MuZx37fPKgD3vXbuRYf988Oar6mwClE8J2AtGjH4DEfYK2+nvxO66/0TsA6exJrG3WKyUh3P+mfKq/Bumqr69PJ06c0IkTJ9TT05N0OmeKsX90dXWpXC6rXC5rfX29pRwr10Wv53KbmeC+dwGmr6+vJ3IQ2TIO/PKcnkHF8y4tLalcLidW89WrV7W6upqIeQcBoPvv3Yjvu66/KcPhZT2yiAPYMw7eSsrUa07M86wp5orPKydjkcHsQHpkU3tWWGfn5iHXZOtxwCjthdSYz+c1ODioiYmJ9Ht8fLzFNiEbYXV1VcPDwxoeHk5zD4LWTvsDc41n9Of0DD63af35vMQuOFS0nf372CfOQmccwVlcANbZE3heGPusWdYpui5Lp9Em9Huj0Ujj7WvbgwZZwmdjwD2WzbnV5CD09+nTp1te/+Vf/mW95z3vaXltdnZW6+vrmpqaanl9ampKly9fzrz+5cuXMz+/tram2dlZnTx5su1n2l2z2WzqgQce0N/5O39HL3vZy9J9+F68zve+973M62TJvmpPvPWtb1WlUml5bXR0VPV6XW9961v3c8m28h/+w3/QK1/5Sv2jf/SPNDk5qZe//OX6X//X/zW9f61oQpYsLy9rcXGx5edWEXf4ccaI8AGg42zwNwd0xdf5vqS0QaF4YBP39/en1N5isajh4WGNjIxoaGiopVboTrK+vp4OH/GDw7wee6yL6T8OfsJWw7DgWlxvJ6YMG8P1bBDXEndiiZ56BPTZJig0lFZkSvKZGMWP0eoYnffDP/bTL25M+zxxReUAuoMmzjrd7VzY62dhZaB89xp5dkceJb5bB/YoSFyre/05lv3LYenvG6G7pfb6OzJJs8TBSf9MuznmYJS/F/ec+DnWdtTXUTej+6Ku529Y6R7Eo3QYToUfjgRD2oOBDsDFoKK0BUpzb//xtjqQ6v9nPQcZceh/ByPaiTPlGCOe28GPnRhG/n3XTf5DP/qBUjE9eCcmW9bBVn4/2oYdR99k6YgYhIlzUlLLddwhdaDcGXDt2u62S7usCP87q9RJ1trw9sa54W2OAa04d+JzZs0zZ2vG+8d+dCDAfzw44c/k34/9E8kCPI8DT1lAXgz+0E9xP4nzImu+cw3vz3hPH7OsZ8/6TLz+buRYf988Oar6W2plqu5V2s2tuEdl/fb3WWPsNehuDxBn6cWox11/RwYyP66v/bWov+MzRlvDsQD3u6P+bffj3+en0Wio0WgkwpAfULkbydK9cU/b6zi7DkNvR/0d+yzet91P7OcsvzJL57XTuVl7tZMYou72AID3j7cxS3+7fRT3ddd3u+nrLBs3qy92Iq5FAlt8Pev9KO3mDH9HOyHOy/hdns2fMcsO2atE2ynLjrsV5SD09/nz51Owr1wup9IqWRL7w9fCbj8fX9/LNf/5P//n+ou/+ItU6uV62hZlX0z03/md39H/9D/9T9vStRqNhj75yU/q3/27f7efy2bKU089pYceekgPPPCAfumXfkl/+qd/qre97W3q7e3VP/2n/3Rf0YT3v//9+jf/5t8cWBsPUljcklStVrW8vJxqYJLWzQnfHlXmOyjNtbW1dDrz1NSUTp06lQ6TIuWVKKOnr4yOjurMmTOJYVsoFNJhp+2UpyvyS5cuqVKpKJ/Pa25uLtXxGhoaaqnf5YLTvb6+eYgJ4PnMzIyWlpYS03036X3tJBpn+zHmAZxhLsPm8tTrZ5sQ1feDOkgxlLYOHgEU8XJBzhLM5TbZVhhlBEEAmbnWbqTZbKZ0QthxGxsbKd3TlVWz2UwRb+7pQNS1xnl9fV3ValWSUkmjZrPZwhJFKJuwtLSky5cvq1QqpZJIo6OjLWtzJ1lfX09ZItPT03rqqac0MzOjUqm07/ktaZvybuco3ApyPc70rfg8zyY5LP19I3S31F5/sw+4kQwATTAvpnnGoKx/35k27QxsBzSlbJCeQxtjIJIDoDxAiAMrbR2y6WwyLyMSnTNYS8PDw8rlcmkPw7F2MN73CpxnWD2kFRcKhcR4dpujq6urJQXc/8b+WF5e1sLCQnLgYSixL9NX/js6jDGdF8HmoZ8c6OR/vsczE3DgzIuRkRGNj49raGhIo6OjGh0dVbPZbNEfHMpFP9G/AO/o/Y6OjhY2N+ymWq2W+oxsKtLtY5/xd61Wa5lLPmfQiTCm8vl8Yo81m81UE5wx4FqkuvNcS0tLifWJ/nS9TWkYnEyCvN4nPr/pYweNPOuDec7cwQmOLE6fz9gaPva+tjY2tkqwlMvlxKBjzvucILjgIDZzXlJibZINEMEHatc6iELd4oWFBc3NzSVbhxR2+gi7mP3E5z5rmL6Mzj7suixxQgGZDK77I+iP0H76yFnueyECHOvvmydHVX/7GVb83kl8DsJClbJLfyFZAUOf985ABsTu7u5OexhAOH4TupB1CIDtgo/oRDavBe16eHBwMO2xMSvWn8X1PnsL67laraYMIu5DfWz2YGeio4vISENHzM/PJ53DwdI7kdikVp3lJbKiTcS1fP+LIF0cO/Rvb2+vBgcHNTIyotHRUY2MjKQMJM/q8T6iDa6z4z0j6Eu2j7P+2WejrmP+kZ3sr/HjQQgY4rnc5sHf9Xo9Mc7JfOL6Pr+cNBkJHF5ux1ngDlzvJO0CAdzP5z794/rbXwOUjuArfe7lYsmapAwNLHc/OB77xzPVuAe2saT0221qno3+W1/fLDHYaDQ0Nzen2dlZVSoVVavV9L4H63cSnzuRAOLZ87eaHIT+JltxJxkfH1dnZ+c2hvjMzMw2vYGcOHEi8/NdXV0aGxvb8TNZ1/wX/+Jf6D/8h/+gP/qjP9Ltt9/ech9pk5F+8uTJXbUtS/YEoi8uLqbOr1QqKU1Q2jTqvvCFL2hycnIvl7ymbGxs6JWvfKV+9Vd/VZL08pe/XN/4xjf00EMP6Z/+03+aPreXaMKDDz6oBx54IP2/uLi4LTXhMKQd4OWGREdHR0ttVBxiV/LOLnMQvVarpc10fHxcU1NTGh4ezgT6MBaGhoYkbQKKxWKxJc15JxCdNl+9ejXV1i6Xy+rt7dX4+HhKYfNr0gZPZ7t8+bLm5+fVaDQSiL4TiLGXfnaF5o7eXq7jDDaU3l5K1dxqksu11pdzhiPiILozMKJidBCBz++Uvr+TcP2NjY0WxRijvShk5j7O/25ZE+vr6ykFvV6vJzAJg1namt9+wO358+d14cIFNRoNPf/5z0+Ouu+J7QSwZHFxUVevXtWlS5c0MzNz3SlgbqzGcTmWYzls/X0jdLfUXn/HsgduQEfWERINXC+H4E5SdNilVvB3J0OZMmyRUbS+vp4cWvS0O0C0hcPF2ZspWRIDBZLSmOJcAFRSi9wdwcia6ejoSGc9dHV1qVqtJmeP+tg4qjhMzrjf2NhIeyhOuB+aybPwXK5jvA0RPM4SHH3sImd24eQh3m7qZg8ODqaU5+HhYQ0NDSVwcXV1Ndkja2trLeB8b29vKmMW2++gBMQDnpMgMP97+90Z5lwarunjBDiPTvSDyfw1guBLS0vpeZw17aVSHFAGeCcjEMfbGY1e+9vnESA684C1g/PqJWiiHeZrB3uWNrkt7MCMr0PIJl4OJ2YVsA74DsBRR0dHeiZSyWOQyUvAMMYwNDnPpF6vt4xjZOr5M2Fre+3bLDak91MUrs/cJEgTszboA9ZMXHtuC+/HTjuWw5Ojrr9jAG43AFbW37sVX3O0GRvC9130HfsPP6w5B6QdRGctQgRCd0Biw8/yYDsg+/Ly8o6lSdweWV9fT0F69Hd3d7cajUYC0TmfzPcd35fYtynXsrS0lILge9kXPADOOSOuKyS1BITjGDsonBUAoQxKPKOrWCy26Ad0mF/D/TrHW9hHfS+MAU9sLEB6/GBvp5O3HMDn2g5mY0tIStkJrr/Rw3GcPdOBucr90PlegYDv7bS/R50T5xkZ0u5/u63tvnrUf1zDr+dt6ujoSM/a39+vWq3WUlfbsRUPbvCcBJsA3bkmY+p7EW3lDIFKpaJSqaSFhYUUBI/s9Gv5zz5/mCv0oz/rc1Wv9vT06BWveIW+9KUv6Z577kmvf+lLX9Ib3/jGzO+86lWv0u/93u+1vPbFL35Rr3zlK5MN/apXvUpf+tKXWuqif/GLX9SrX/3q9H+z2dS/+Bf/Qp/73Of0h3/4h7rzzjtbrnnnnXfqxIkT+tKXvqSXv/zlkjZtpEceeUQf+MAHdv2MewLRYTjlcjm98IUv3PZ+Lpc7cIb3yZMn9ZKXvKTltRe/+MX67Gc/K2l/0YSsAvg3WnyRufMaWSBsRP46jow7L85WkloPNPCN1g15jw62M3J8A/RNcbcSFTsGH1F1WFDuHPjmT4TQy2Tsh52LkiL6HCPQbMYon+iwZUl0TrJAmWejZIFMPCcAT6PRUK1WU6lUSszEgYGBFkd1aWlJi4uLKXtgfn4+1cXdq/jchxGAcR3BFYAZL4Ow23nL8xERL5VKLUwAPkOWxMLCQjqId2lpSZVKRZcuXdLKyorGxsZaGG0xuIKxury8rEuXLqlUKml2djYBFnuZ5zy/G46eXeJBDWcM7Gct3Qg5iEj4sexNDlt/3wjdLbXX3+wzroMig1RqZdBKrWUWXOe0C7TG/T4LYM/6TlyjMN68fc5A8teiLo4BeA9ke9o54Cj7Ig50ZIhhW7DXARLAmsY5cUASWwKnG7A+BjH5ro+JA9PRiUOnZLGCuSYgINd2wIPvoN/jfuGMPAceo7PnNpnvq26f0XfO9OIZMPgdHHW7KjLfcNR9DvFM3D+y7n3sfQ64rcOcWFpaSudu+LiTWcY4evkC5mhMM/c1kOUsEyTy9Pp2c5ZroOPbASg8R7wW+poMRphpHgiHjBJLKwHo5HK5tFY8Zd5tcvqI2sD8+DkG0Y5n/vsYO0vOwWz62fuSuedAmwNAHmyKwTH/zZjE/QlgiHHarW491t+HL0ddf8d95EbMk3aAIfPZAXV+s0ewR7GOstY6a9t9Rd/joy0SwVz0Pz48vs1u+wJ7X1LKKvKgvzPbnekcM8n2WrqF/nTdTdaVnz1DW9yn873R7a+4V/n1PZgRdUwc1530jo8f93N/2PfRaDe6vuW72ACMo++96F10Y3y2dsL4kN0mKWU9+57tOAp6ivvEAO1+xJ/X10jWOtit4H+DH5AJ6P4tz0CAan1965yAZrOZSATgRh7ccF3L+ysrK1pcXFS5XE4Z4U4+zfIZ2onbp25veD9EXIn/b6Ycpv5+4IEH9FM/9VN65StfqVe96lX66Ec/qmeeeUb33XefpM2g6sWLF/XJT35SknTffffpN37jN/TAAw/o3nvv1aOPPqqPfexjLaVY3v72t+sHf/AH9YEPfEBvfOMb9fnPf15f/vKX9ZWvfCV95ud//uf1u7/7u/r85z+vYrGYmOuc55fL5fSOd7xDv/qrv6oXvOAFesELXqBf/dVfVX9/v/7xP/7Hu36+PYHof/AHf6Bms6m/+3f/rj772c+mU06lzYjD2bNnderUqb1c8prymte8Rt/+9rdbXvvrv/5rnT17VtLBRRNupHgEEwPCjV4M9GazmeqPReeZDZnPefQSp1XaVE6VSiVtTihGSqzADM5iuOAQE5VrNBrJwdorkL66uqpSqaSOjg6VSiVNT09vCwAgbHQeAHCQey/CMxUKBU1OTqqvr09jY2MaHR1tYaqhkFZXVzU9Pa0rV660pDO3E98s+Xk2g+jMw/hM7vTDtMrn83r88cc1MzOTUp+8llulUtHVq1dVq9X0zW9+U9/4xje0uLi4rX7jbsTnfqVS2cZkc+YA8wTQiPm0G1ldXdXCwkILkNLX15cOcPG5OTMzo0uXLmlpaUkzMzPp4J1Go6F8Pq877rhDL3jBC5TP5zU+Pq7BwcEWo43UsWq1qm9+85uanp5WtVrVwsLCnlnozOO+vj4NDQ2ltH1OfMdwADhZW1tLpWhuBSf22Ak/fDls/X3Yujvu3e7c8Hcs0YTjho6LZSRwFjy1NO73HrhzfeWAN9/zOua0y/cIapH6HAf8o11ZziLrfXl5ObFsyuWyFhcXU9aLg5ruUDtDlnJe3jbYbDCQAK7RE2QASWo53Mz7EXCYfvKa4t7PXjd2eHhY3d3dyufzyufzyRHCQazX6y32EdeJP7TZS4qQXs9htLE/uSaMft5zwAHxmrPoUwAESS1ngzhYQF/4dz2wQrCBMmvs9z7mTo5wkIa+lJT6SpK+973vtTDyHUTF/tnY2MyUqlQqia1N9mLWPI/2EvMrpmPHQET8HmPFs/m1uA8sT+YrthzsfmwSzvzxecraWl9fT+A3mYTOmuTgeJxzxrter6fvnT9/PmVMlkqlVBom2qtZARPGRFI6cND3Br8ONitjyjoiWJUFjDgZgvke+xfxey4tLe06q/JYfx++HHX9zR54I+dHO1C0HbmMPc8Zruzj7CuuZ9B70paNjk3hJSicLYuv62Uw6/W6KpVKIiHtpk/whWBh1+v1ln1AamXKIg40OoC4V58EOwnG/cjISDoAnexzD7KzFwP88rfbDjGg7OXl2DsJtnv5GJ6LPvYgPO8hziD3wKQHrmOg3fuJMXVbBz3kutNJFJ4J5r9jf+LD8fnz58+rr69PpVJJc3NzKVMQsgMgdKlUSroKnebg9F4kzhXae62f3cja2mbZ4Vqtlvq0t7dXw8PDSX+zXsFsCJivrKykknaw0SkLA1DqNhJramlpSc8884zm5+dVrVZ15cqVNC+dOLebZ8DW8ywP7x/PiHAbDZxiL/1/kPviYervt7zlLZqbm9N73/teTU9P62Uve5m+8IUvJD0yPT2tZ555Jn3+zjvv1Be+8AW9853v1G/+5m/q1KlT+vVf/3W96U1vSp959atfrc985jN617vepXe/+9163vOep4cfflh33313+sxDDz0kSfqhH/qhlvZ8/OMf10//9E9Lkn7xF39RjUZD999/vxYWFnT33Xfri1/84rZSaTvJnkD01772tZI2DxQ5ffr0oYCH73znO/XqV79av/qrv6qf/Mmf1J/+6Z/qox/9qD760Y9K0oFFE26kOJMHloynOLmhHsFjj6ihpNyR9QinO5QOQnrkLpbhcHFDgDZkRYR3IziH7foj3vcgBfYWNcvHx8c1OTnZwjojsLC8vKxaraaFhQVJatvm2HZn8l1vhPdmi4MyEaBhHNfW1lStVjU/Py9pkxUzNjaWytuQ/o/ynp+fT7VCr9Wn7YT5CFPcDd0sEJ15uxcFQZApl8upUqloYWEhGWYe4FpfX0/seuaMK16CCSMjI+rv72+p/Y9CLpfLunr1qhYXF3XhwgVduHChJctkt+JzD5CJ34AsrFvAEYx5vn+zHdljJ/zw5bD192Hr7pjxEpkz/hpr241dfkcHzhlPSGQMu16NQLrrB5yC1dXVdNYDbUNnuzMntQaZ2+lu7oX9wF7oAHwE0dkfvA67G+wwfbq7u1Pfcm0HST2Fnb0zspK87fztjqoHHGBxQxLg4HPsHNoDWO/sO6+1GfuG63swPytd3tsZnSn6mPnDuGUx0Z1NxX3jdd0J53kAacgEyOfz6f6erp7Vp95WZ6KjQxcXF9XZ2Zkc0q6urlTnnexBnDxPaae+JxkKzkLz/o3z3O3c6IzH7zgL3ee4j4/bAA7Me3DEfzwwwTzHsQWIdyY6gAUAOu1kjjHXCVI5MB8BataYj5F/xu165qGDXXHPcrY48yZKtI/iNWNZG8B7xmQnAkm8z7H+Plw56vpbuvFzgzXB39xzJ/CP11grrFnWdtRzrC32SPYOB9HivsDegN72wOxeGOGuk64l7kPtx8eP4rqbIHVvb2/ywwGEsX1oL74JerBd++M+6TXQYxkrafseFe0zD374GMbrRB/frxkxEnQ2QtaCz6GdgPMovs+jd9yfpl5/X19f0k/ocS/r4mz965GseR5tgL3oBuzMXC6XMrxXV1dTJpn7upA18Nnd5tzY2Eglk9D92FPMKzLVGo2GFhcXEzmQOvL70WkERvjB54/jzZ7Bd/YyDu3mXnx/L20/bP19//336/7778987xOf+MS211772tfq61//+o7XfPOb36w3v/nNbd/fTTtzuZze85736D3vec81P9tO9nWw6NmzZ1UqlfSnf/qnmpmZ2bYBe72065Xv//7v1+c+9zk9+OCDeu9736s777xT586d0z/5J/8kfeYgogk3QpzZQh00P6SLTQ3nhIXG4od1BUDW1dWliYkJjY+PJ7YMETgUQaFQ0MjISGLL1mo1VatVPf300yqVShoZGdHY2Fiqg029N8CD7373u3rqqad04cKFxLTdKxP9WnKjDKXOzs6Wg8Nuu+22VP8Uto9HpSUldn5HR4cajYYuXLigUqnU1rCIhs9uI5a3ssTniUYbz7i4uKjvfOc7GhwcVKVS0dzcXEttP5jo9XpdzzzzTKqvhwPoG/deN3x38NzIcSMoC0jYyz04aISyQ7Ozsy39sri4qMXFxWTo0jeAbjMzM/rWt76l3t5eXbx4UYVCoeV5KQdDuZt25QauJZ2dnSoUCslAhfHhIDprloAdhpdnfByE4Xwszz45LP192LrbnTCp1dB3sNbZSOwrgMw4sHwHx80Z5DCTnX3k4CKGMw4w7GyAZ2wB1rAHztHFBNfW1zcPQbp06ZL6+/uTU4ajyv84C1evXk2HFOMk8DwOHkfDmzZ7W9hL3RnjM+4o8h5ssJ6engR4ZwUw+GwMRHs2TW9vb2JI4zT5Xu/3w3H313Fm6X9nDUYwg/8JFJOBByDJfonDJ22lzQMMeO1Yd+rpnxgk4G8AeYBcB7593vB5/9v1InPNgYy4FmD5MQ9hy1ML3OeD230eDO7t7U2sN9eBvodEcM8B78gI9M8QKOf/KNjScf5wPZjq6+ubB3fTH85k94PJWGMcxAdI0dXVpVKplA4cJWCBw022pmdN8ps5k1Xv39ce85ZD6pmjgEt8LwL5zNcIlLcT70dvE2PHHGw3dsdy68lR1d83UlgrDnQhMUCN+J7tmWQwTzkwFP3g+4DrlXq9nvwiCC/47z09PS1nTszMzOjy5cvJT3B9d9CyH18sSxzUpuZ7oVBQoVBoCWbG4HJfX1+LzRRBdA8Y0s4YkHTdLW0Bul7Ghj006k5+YrZ9DD4iWXut23nYI25nOZM6itt7BE2ygjA8CweFwyonS255eTmdj8JnPSiB3mTuYiu6Tef382fLItbxXhZ4HmUvYDrB7Y2NjYQ9ed95ligBJuwtfPZqtaquri7Nzc1ty8qu1WqJ/DY/P58CEh68343kcrlkB7n+xmZ1WVvbqtXuQfp2WR6xL73ffXwiUedYbp7sC0T/vd/7Pf2Tf/JPVKvVVCwWWxZXLpc7UBBdkn78x39cP/7jP972/YOIJhy0REN5cHAwOeIoFT7nioFomkeu8/m8JiYm1NfXpzvuuENnz55tcbB8QdVqNdXrdZXL5cREK5VK+ou/+AsNDAxoaGgosWUnJyfV39+fUr8ajYb+6q/+Sn/2Z3+m+fl5Xb169ZYp/7Ab6ezs1NjYWCrfcuedd6ZTyaMig+kmSf39/ZqamlK5XE4brTvOLlnK+9kMRjqo4OBCBNElaX5+XvV6PfXz6OhoywEyjUYjlSahH7mOH87FNffDrtiJKXW9kVWi1LlcLpUf8ve9b9xAg9V2/vx5Xbp0aZux6M+BwnYm4V6Ea5OuXiwWNTk5mQwkZ3gQGOjr62sxPmL64c2Qw46EH8uWHKb+PkzdTcAuBgQBwZ11CiDqTofvgblcLh20zPf5wVD2vT+rTAxsIHfeC4WCBgcHW4LgpDZ7YAvHenl5Oe2prON6va6enp4UHGatr66u6sKFCzp//nzKBlpcXEztcYDV+4lxAFAjMycClNGp4tlwLkldLhQKLWxtBwGxW/zgR69zihMOowgGNLoksrrczkK3kBrvta39QDEHnXk2SAvUyfQ+4dlxWt0BZ4+FTcV8cUCUw1Zx5HGye3t70zyMWRTMXYAbJwAANtO32HkEDpyQwfs4i5RrIVjR2dmZMqeYG359AuCuC9fX19OZNcwP2uoZkQ50E8Sg/9y2ygJuPdjgn8Nm8yxO1j0Ho7LOK5XKNmZi/M0z+lzFSY5rmnnFfHS2KHsBY8AY0x88D+PjAFMsqxODMLQVG53+ZGwceGdOOqCPYKsxVm6LILsF0Y/1982To6q/b5S4fsK/Zr0z/7Nse/9+9OVdfzsDmLnNuQmrq6sql8s6f/58KpMG03Z4eFj5fL4FRL1w4YK++93vqlqtpgMXb5RkreFrsV6zxDOAYJ339/enIAH7GUFYB7QBFAGGHQz3rBkHDz1I4cx9aatcjOsP10e+9zFe2Gm+Z2Ovof/5vu/PXB8bxQPF6DrXfbHvHUD3kjYR1PbAb6PRUEdHRyJHMg+dmCkp2STYXMw9t3UiWa7deHvA1j8b10m81k7zx0lwvmYIFEAedfExY0xhpPvYQBKI/e411Tm7LYs0eC1xm6mvr0/FYjH1setiSS0Bi2q1muzmrOzzrIBRJFH4e9ejR4/198HJvkD0X/iFX9Bb3/rWlLp1LNniEc0YnYyfc2cUh9A3dBYsdTwjE0dqZdmxCeG0AAxzmGGhUFBXV1cCAAHfFxYWVCqV9lSL7VYRZ/o5my0rCixtAR8w55aXl5PyjE6t1Lp5RTbbs1mc5bQToxsnvLOzU7VaLYEcq6urKRLsUX3mHvdw5y4LUN/Nxn4j52Mcy+h8X6tNONk3SnyPcFasg3socfrZUycBknDqb6YcK/GbJ0dVf3tgKwa64mdwnh388r0vgsNuoLtz5iCjM5ec3ekgrq9Zaii6w+jMai/fAMuWuszsvZEx7meZOEDuJUVwaHFiYpZQlvB+tDf4P/YR1+X+OHOeXhztnmgrueNGf7bTE34dv160k9wJdwfHy8LAjnMQ1+dGdOoj0837g2eN/eNtov95jjh/shxw16Fui0QnPLKXPHDgfertj/ZMnJs8H30EcOzzLLL4mCs+brHP4nPGz2f1G7/dlsBR9e/Fe/h1vTQQYw+w49/3eRKD0L4Osp6F1xh/yjr6mTK87p+LfRVtfu/beK8o7ZjrPj92K8f6++bJUdXfN0qiXojBOV8Tbhv7+x4YZc1G/Q1gil/p+ptyadRlxj9nLwFEJ1C2l5rJBy17AdLZj1y3efk7twHYp103R/vDMRK3D2IANOox9F4M6EqtdqGz27N+/Ln4ftTrPp/8e/5c8Rm8PyOGkPUT9aTre7ddAfnJMIv95O1iDudyucTyjuPbLqgS+8f/34sv6WOatc54Ts8MiLZWHLN4TfokBqIBr51EsVcim5MC4j7gZ9/4Z6Xt9vFu+yzL7j4IOdbfByf7AtEvXryot73tbccKfAchEslBGxjMOzE9WJzSFiC8vr6u4eFhnT59OpVu2GkROpNocnJS0mZ0lgMe/+RP/kQXL15MB27m8/lUa2p5eVnf+9730uGJHEZ1M8SVWARisz5Lf4+MjOjUqVMpxWY3mw+fKxQKGh0dTc8+Pz/fwgRoNptJ+VBT9EazBW60NJvNlmdhHmSBwRGckLbmKcGL4eHhFgDLx8uBej9ox1ld1Dy73kjrtSRLiV/LoLjZgiKmdMvAwEA6dM5rCEuttWM3NjZS+mm1WlVnZ2divt4sOVbiN0+Oqv6OjoE7tH5gHywQ9jl3TnD2YApTLxrnmfci0O6McBwkPzxwZGREvb29GhkZ0cjISHJoHNQGaO7v70/MKg4ZxWGcm5vT0tJSOjwJNrO0ueanp6dVKpW0traW0s7dmc0C0Eijja8DTMYAAa9FwA9WbATTPV0WpxdAwp1u7CUyatxeQqc4g9zPbXEnGiAk1g/HeYSJNDs7mwLAAKcwuGAQc+9ms7U8jY+9s4/cAab9zA/6LYKkXv6G7zQaDdXr9ZZ7EShhXqA3q9VqOkyW32730GZvB4xMDuGMe4EHuRGcUgesmd9Z4sEd+pS+wklGNwFi03fcy+dPVhDCMwrdsY4liKRW+xrgAYIKAE901OM8iuCEs/ezmHPeT6xt73NKPUbAhT7xbMdYIogf5kQ7go7bAbTH5/Z+wLpj/X3z5Kjq7xsl7DsenEbYf/g7ApXr6+sp66unp0ejo6OpfCIlHbgm4C3ZZZJSJjf70fT0tCqVinp7ezU0NJRKRrHOL1y4oLm5ueQbHbbErCPvlwgysx95ll488NNLufA9AN8s8NNBeWeCIwQaarWaSqWSJKlUKqWMjKg//DVvgwPsWUFs3/fJ+vH3vX8gi3nGo/eXB/CZU/jc1AHnBzIjQZQsXYQ95/YHfp/bKnyG87lcz2Jv+T7u+tPXgRMFsogp8RnjPHGd5pkJWQQHvu/l8bLayN8e0PB2+PgjTqDAjtyLeCCNjIu+vj4NDQ211EWXtuxsbMq1tbWWoJv3YQxOxNecDBP7YL9yrL8PTvYFor/+9a/X1772Nd11110H3Z4jJc428QW2k7iC7+np0dramgYGBjQ1NZXSyLLYKNIWcE+Ee2hoSBsbG5qdndXMzExKd33yySfV09OjkZER9fX1JRCdlBOct5spMQqdtdHEz3d1dWloaEjj4+O77m9pC2xZXV1VsVjU8PCwJCUl7ffFaaHP6LcbES08DEFhNRqNlFIG0JAlMYCDEYWipvZ8ljHmDC5AgtXV1VQmhnqjNxpAp91Z43Wz5/1OgsFA6hhZKfS9p5O5AQKAAUAlHdc+fS7LUdXfDmD6OpFaU2MjiO77jZf/IPsLZ5D3AIedlYLeZn8DVFtfX08pn/l8XsPDwxoZGdmm16itygGP7sAvLy+rWq2q2dw8V6FcLqu7uzs549IWGLawsJDSRr1+Kw5hFvOGUiSuwwg24Bx6H0tKQQYPptLnzhD2tgFYxNRnZ/E4S9/72h0OBx1iloEzhN0Jxnnh/JNaraZyuaxms5mCkOglmMntGNnMJ/5nvnjbpC0QnffcMXUn0vsOQJf2xmw3vo/O5jfAAvabl/lwEJ0xwS6lnMjAwEALCw6WG88agQdPvc/S9V4j2EvNIG4z0S5P5WceuVMPYBAdZubKyspKqmXvTHufD1yPDEz6yEsHME/oA+7pY88949++z0TggTXT09OjQqGQyhbl8/mW+/vcASAHoHNHmj50MIc5T79GwMHXvz8vcivbP8eyKUdVf98IYR2wRh1E9/XsATvWCX8T/EKPDw8Pt4CUrvvZdynz1NHRkYBffPFSqaSurq4EzEtbumtubk7lcjntnb7vHFZfxYB5Fis46i1wBz/YEZvJWduA1s745zf3jpl9vi/Sp2TkdXV1JQA6fi/6qu7z+B5JexC3IeM1om8ar4P+BifwPdb1EvWxwRD44bWs87Kc4BGZ/9iP6FDX17zH99y3j+A09/QSZZTIiX3oNnAWThPtB8hfTpzwYIwf0o2dvt8g740Q9g9spv7+/kRm8wNupa1+ou88kOe20rWwjqw+PdbRt5bsC0T/e3/v7+lf/st/qccff1z/xX/xX2xjYPzET/zEgTTu2Swx6rkXgDWmBcUUp93eP0bH2JDYaOv1egsojOK+mYs0KiVXvv47fic6pPsFtCPT2sX7z1PnHfh9NgHpKHWMPC/H4tFPN0Bh+A8MDKSgDgxon6uuxBGPEqOUKYFCNJ1IuR/WtteaZVG4L4eAdHZ2ppPjHZwhAwGmgJ+CfitJFnNgt9+5VeQ4En7z5Kjq7yz2hgfw/LfUqjcckAOMwiHkf9/fms1mZpAWkBg91dPTo2KxqEKhkAD5CDg64M41CHrxGQKbzup2tjCv4aB4wC0ydeL64XPejw6YZulCZ2q5reKAta9xPofzHO2ZrDXtjjafddY4dgzti/o/2gr+HK7HPc2btkeHyB0/tw/cXnGd6T9+f5+HPF/W9aId4ePm+z5zALstttWf1+cDAdWurq4URACIjkwpbxvjBlDkYxOdaWdPuz3ggYUYTPF7ss4cDHAmm48FwAr2gx8Y5s/j/e+kiJ3A5HZrgM94poePTwTR241tlCywJ97T51zc2/w9B9IdzOJz/owx/X0nOdbfN0+Oqv4+aHGQ24k9HvxEIuDHvrOxsZGIKuyX0T90IND3aD8Akut7UJSazKyl6H9n7RUH4QdFnev7cwxeej/43t4O1/A2R92Lfs3lcingkHWPeI2o79DdsZY494ltcb0Tx8nv2U543uhv+f4fdV3cW73tXnqNMfd66NEmQAhSEGz3Q2qZy+2eg+vxWQ7ddEzFf7gOfZ2lM+J88ueOc6qjo0P5fF6FQqElmEzbcrmcarWapK0DwG9lyVqHWXaf/x8zCw7qvvu9zs1uw1GRfYHo9957ryTpve9977b32CCPJTuSeS3xxZdVg203Rjjfh+XiZU1Q6pTPQHn5Rn8zxDdd3/C9faThZKUSxYDDXhwCxKO6WaVgms2tk7wXFxc1MzMjSTpz5kxKC9/PfW+GuDGysLCgK1euaHZ2NtXk87qwQ0ND6bT1ycnJVL6Fw0wcqHHF70AM/+PYEx3f2NjQyMhIMio5jOfq1auam5tL5V72q1TdiJ6cnNRdd92lQqGgU6dOaWxsTNKWYTs9Pa3vfve7qtVqunjxomZnZ29ZheFrVtoOqEcGxPUozRslt1p7nityVPU3zqrrSGcA+W9pi3UOKAi4iKMCe9wBOwfMPIWbfT+CcbncZpmwqampFkDe1ydBPRf2X1jo7NXLy8sJvISx42uea7nt4Q5ZZG/TD15mI7LE4rVwKHH6HFz2Wu20zYF+ByFcV9KP7rByfa4rbaVOE/SHmcZ30THOapa2Z96sr6+3gK7OKnZbJJfLtTDjAVJin7vjSTuy5iK/ncnNPIyHZmY5rogD6BwMv7y8nILdzGP/rgfBOfiNuQ2AnsvlWg66c/3Z0dGRSkig9xknB+mlzXIz9LOfyQGr2+cU18fGRTwgQP9gzxKYct22uLio+fl5raysaHZ2dttBw/Rbs9ncZv/6OPhYRXGQDcY8tqLfK+vwXsajHTjmYFwEK6IdTN+4foeQ4HPXbTK/rtu3DvLvNnuTNh/L4ctR1d8HKXHf9gOfmeOsN/6WtgJtvhYHBwc1Pj6u3t5eFQqFtEc5iMoaQ/d1d3drZWUlXR9yDmVaOjo6ks/ouoRMGqk16OWg717Xne85Wdldvoc5iO6B2Qgiu58e2+h6kD2SPqN/YGvHfdN9dwecPVicy+XSIeAbGxspg9ntOcT1iu+Lfi0H9F0cUPZAR9zX/b7YNZHc4GA5dhw/ZJFR+odxiJhIb29vOsRycHBQw8PDLTaJl0iTtgdVfc+HTb2xsdESOEdgxUd70Od9nGMxaELfc4j2wMCAhoeHUynCQqHQMk/K5XIqVwiR7lbVMe386ZjFkWUD+rjczOe7Vfv22Sb7AtFvlfSKZ4vsB1SNjlw743un72cxknzx3yrGlj+XK6LIVsPBa/f9/fRTFGcsRPGoLWnUnnqFEfBskeiEE9V3oMVZayhByofsdGirS1afROaWpBZjgFpxEZjfjzCn8vm8xsfHNTAwoNtvv10nT55sMXByuZwWFxfV09Oj2dnZFmPwVpEs5R1Zt/6av369UfCDlMOOhH/kIx/RBz/4QU1PT+ulL32pzp07px/4gR9o+/lHHnlEDzzwgL7xjW/o1KlT+sVf/EXdd999LZ/57Gc/q3e/+9168skn9bznPU+/8iu/onvuuSe9/0d/9Ef64Ac/qMcee0zT09P63Oc+p3/wD/5ByzV++qd/Wr/zO7/T8trdd9+tP/mTP9nzM+5Wjqr+Zn+O6dru/ERDH2AXJ9sZurEOOk6oA23RQaEN7ggNDAwon8+31Af1NRsBZdqG4+mHHLr+c+DZn8sPRHQ7ot26ieAZTr4HC3gf3ejOF7rAAU+c08j0yrJp6Ie4f7kOikFab2O0H7hPuz3Gr5FVVsTHlb6JtTwjaOBAJ9eLuis+n6de89pOY+TvOaARGYy00x1xSYlU0d3drWKxqMHBwZY558/t/3tf0N9kSTAPeI+55+cT+LzwPs0Cenk22uUp8jDZKNtXLBbT83uZOF873q++73m2CMDztaTdZ3xN+FqLYEsWoWYnPZj1uj8DczU+m98rrjfe8znv39mtbj1s/X0sW3JU9fdBiu/nWSCntGUXeJDZs07IroWF7mWbkKx15MHlWIPdA7/Rn3Xg2tfIfn3Z2B8OyEY7KJIEPGDne5oH3KL+juL6kf7hOt7vMUAfbQK34xxQp2QOLO74XL7fYb/QDgfH/bNZQHp83/d2qTVjLuuzkaSYFRhwNr33l+tKP5SeOYnOdV2btT9wzRgA4jf2lNtGXrrPfcedwN8sHIfScejvnp4eDQ0NaXBwsGWera+vJza6B7puJdlJ7/mYIXE+Rx/8ZgHpx/r74GRfIPqxXFvcufFN6Fqbgn+PdCV+YHllsaSjwOytVCq3RESPzcQdqZjq66+5E+gpt1m1Ll0Z0Wd7ZYWzwXHgR7s+Q2E2Gg1dvXpV6+vrmp+fV7VaTUyjmF55KwrlaCqVimZnZzU9PZ0OtCQCcuwAAQAASURBVJGUanb29PRoYmJCxWJxR2PyegRFgsG6vr6usbExdXV1pUP0YBtw0N5upbe3V1NTUyoUCnr+85+vv/E3/oYKhYImJiY0NDQkqRWw6erqUqVSScGElZUVLS4u3hKHxzL3iNJT+7ZQKGxjptNeLznkBwjfKgG0w5CHH35Y73jHO/SRj3xEr3nNa/Tbv/3besMb3qDHH39cZ86c2fb5p59+Wj/2Yz+me++9V5/61Kf0x3/8x7r//vs1MTGhN73pTZKkRx99VG95y1v0vve9T/fcc48+97nP6Sd/8if1la98RXfffbckqVar6W/+zb+pn/mZn0nfy5L/+r/+r/Xxj388/e8HFR7L3oV9KbKB2GO8TIe0VesQsBQmugPogKjoq2azmdimrr+yBN3i5Ufaga3O7OIZcLxhduNUSZv7uOtUdxIdIPaSbln7p4PctINndufKnUfPKiJAQFsisBsdB97nf/QJzxmZYxFo9uci2OH7I4EKnO2lpaU01nzGHVtnrsdxIYDMPOns7NTy8rJqtVpi3GexwACSvd84MIzn9DlC1oHXBvXnxTb0vd6fj309Bi88iArLkvs7iME8o2+cAcf6gC2PHbq+vp5KETmI3tnZqf7+fq2trSmfz6eAC/aFr4UI2vA671EGqVgsanR0NAUB+vv7W0B01gV9wTMSLHGAzINGPre8XdH55XUXHxO/Ht+JoB1jVK1Wtba2ls40iOuM34ztxsZGOogO/Q/wQp96sM/b7u/7GuWZ2wV6juVYnm3iQLH/eDk21kZkpUut7GPXq67HvDxL1N9RB/jhv16yw/VG/D7i19rrunTw1ff4rB+AWde3WQBxBFDZdz1g4YdIsydHIJwfHxva7HuT6zK+Cw5AuU1+YPBn1aYGdPZnRj8gsZ+xn3ycGXv/zT06OjoSw9z1qOM5zkT3H56B9nh/RkzE+ycryECf+XWwWTwTDPsWEoQf9iop+d4ciEkb0UV+Hxe3nbhnPp9Xf3+/RkZGNDU1pd7eXg0PD2tgYKBFf4L3QKBDx2EL3WzxuQ7GlMvlkh0ibc/CwOd2LI994DgYejRk3yD6I488og996EP65je/qVwupxe/+MX6l//yX+7I7nuuCRskTjOOxk7gI4uLDdkPmyCat5uUS4zuSqWiRqNxUxesKwI/oM3ZXc56841cUlKSKCdnFXp0b3V1NR1QieKMTk+WuINaq9W0uLjYts94rVKp6MKFC6rVapqZmVG5XE51tp8NIPry8rIqlYrK5bKmp6f1ve99T1evXk0p0MViUSdOnFBfX18C0SNQc5DCHCHFrK+vT2NjY2o0Gurs7NTi4qIqlcqeT63P5/O68847NT4+rhe96EX6L//L/1L9/f0JFHAZHR3VmTNn0vh7EOpWAdExPonYS0rzjgCTg0MYJCsrK+kAHg9E3Sw5zEj4hz/8Yf3sz/6sfu7nfk6SdO7cOf3+7/++HnroIb3//e/f9vnf+q3f0pkzZ3Tu3DlJ0otf/GJ97Wtf04c+9KEEhp87d04/+qM/qgcffFCS9OCDD+qRRx7RuXPn9OlPf1qS9IY3vEFveMMbrtm+3t5enThxYk/PdL3yXNDfXjIDAXDFkWAPcJ3E2QzOrob140CfS2Tg4IR57eUs1ok7TO7ourOGYwroiX5xQxyD3kFiHHdPGfd0UsRBeZ7B04QjIAfri6Cxlw7hs36wFt+NAAHOkZeT8ZIUDog4MAkgShCDsy54rqWlpeTEc95LtVpNDjXOPmPp88QDkfRDoVDQyMhIC2OvVCq19HFkleP8eWo3wAn39DM3nLFGqrsDLs5ii+A6gD5ZcV5bFfFnZXywTZn7zu4DXGXuU8O0v78/gRA41OhkJz4Ui8UE1hMAr9VqWlhYaNFTPnd9jrBGsQnIIjt16lRaoz09PS39MzAwoEKh0OK4kiofwWKe2fvEM9LcPoiMvAg6M195/gioOzufPs/lclpaWmoJSjj4w9iRJbi6uppsoPX19ZZUewfiYjCuXfDN2bEOqO/WTzhmst1ceS7o7/2I61InZnnJMddT7a7hAqjM/kvg2n3VGJRiP4sgqe97gKa+b7rsBWjz+/M/ZIAYyON9/G/PJON5fU/0vdmxDC+t6uA0pVXcR3f7w/0YxsrbGW0IBF3n+qe3tzeBkxsbG8l28yys3t5eDQwMpHZyLQhZWe30ILiPgYPt6FjXrWRCeVCYcXdbgb/5cVDf+4X5gb3aLmBD22KZOEgAHgRnbjB+YCs+/1y39PX1Jf8xBgCyBJ2HPT04OJiIa6dPn1ZfX58GBwdbguAbGxsaGBhQX19fIvcxrticN1uwdZrNpur1uiSlueikBT7bbDYThrC6uqparZbKOjnB5GbIsf4+ONkXGvapT31KP/IjP6L+/n697W1v0z//5/9c+Xxe/9V/9V/pd3/3dw+6jc9KcYCXzYgN41rfY1PBYQJQ98MWsxaBO92+Od8sANAjkm7IeFqS/wBAOxvQf/wazrhxJY/TlnW6dTuJ9crcEd3pO41GIzmvMHxvBbD1WoJxF08HX15eTkaVp4xllTXYyQjdr8T5wrygHcyJvbDgSQPHweaQIIwKn0+ku8PC7+/vT0bZrSJxX8H4cSfaDWYUP3ObPeVmK8Is43UvP5K0uLjY8rO8vLztPisrK3rsscf0ute9ruX1173udfrqV7+a2bZHH3102+df//rX62tf+1oydNt9pt01d5I//MM/1OTkpF74whfq3nvvTWct3Cg5qvqbvUPa7ghHhy1Lp7RjPbtDFOdg1D/+4wFx1ysOVjnTSWpN+fR77gXc8r/jz16D6VmMO2eQERTPAi8iS8/37Kx2Oes+i4Ud+90D8A4gIhEkj9f0e3lgMc4Dv48zGuN9/MdtN//tDHJvh9sggNJxnnh/+v/txqvd3Ig6hPbGeR6/4/eI68j7xfsJ24HghY9Tlg6KY+2sObcZo/7mB1sBu8Xvu5Psxan0/olztB1z1Ne3tMWyjJkFWfrd54Sz6uP94n4RA+p8Nut+/jy7kYPQ38eyPzmq+vtGSJxvcX22ywDhs3GPdD/er521dn2dul7P+vxBr5Goq6OuZI/O2kfj57KC/E4IiLrE96ioVyJ2EfWUf2enMfXMc/dvsnz+LNslBkKzxiWOTdbeHu21LF0fP5NlJ0Z70sfLdXqWHttpP3eJcyELU+E3+hYbzzGAvfjD4Al+jhoBcNfp/r77+7stGXvY4uMdbSPej3r8qPnfx7Ip+2Ki/8qv/Ip+7dd+Te985zvTa29/+9v14Q9/WO973/v0j//xPz6wBj5bpdncYm01m0319PRobW2tJVU/Ro+JCvrBCisrK1pYWNCTTz6pfD6vU6dO6fbbb29JLXYFUKlUVK1WValUdOXKlcQuPuxInjP5AEI9NddT6GPKGcJzoShJHcah8KgeDujly5e1srKisbGxxFBDOURhQ6hUKonlMzs7m1hmOynyer2uy5cvq1qt6umnn9btt9+ukZGRFE3NcupvBWFTX1hY0NNPP63Z2Vl997vf1fnz57WxsXnIJ2lXExMTSQFKh1ufjL7r6+vT1NSUhoaGND8/nwJKHIC6k8BCOHXqlM6cOaOJiYmWEgXxs0To19fXNTExoTNnzqi3t1cXLly4Yc+5H2Gf6OjoSGnt+Xxeg4ODKhaLWltbU6lUSmntc3NzqtfrKpfLiX2xVzDtoOV6lDHfO336dMvrv/zLv6z3vOc9La/Nzs5qfX1dU1NTLa9PTU3p8uXLmde/fPly5ufX1tY0OzurkydPtv1Mu2u2kze84Q36R//oH+ns2bN6+umn9e53v1t/9+/+XT322GNp3R20HFX9HQ/RRtBFAHKeusz7/h2YpQBYpG9GwNaZnZGhtLGxkfR4b2+vVlZW1N3dnQ5HdFa0f9+BVJgrMLtgOLnDxfdho/KD4c61fb1ngcRxLcYAHf2KswND2J3seK9Y9stZWZGRt7y8rK6uLvX29qZ79/T0JKZ61oGT2FK0Hwee/vMU5Eajka5PGZJyuazV1VWVSqV04BVZSlkZAj7mzt7GFnFxRj3zIqu/m81mam/WnCQIzEHcgDhkGNA3sLIpwUagmLbEOe5MO+4NI837kT7gdQ8gcE8Y2szJOI9oi6+Jer2e5ix97CAI14TVPjg4mNhrbjdubGyk/uTsgZWVFZXLZa2tbR4AX6vVWtYEwIX/7fOqnd3mNjTj6SVs/FoOsjkg4oe9RiCOoEOzuZXFwrP4AXS01bNOpNZDgZlLjAt/e4mhrEDjbv2Eg9Dfx7I/Oar6+yDEfWl0uO9dzrpmT4rfZ277fgS7loOVWTvoRc+Cwu6uVqvprKkY7HLhXu2eZTey05py+8eJBByazv9+X3Q/PjdsW4hi7LvoP77DXttsNhMYmlX6gz7t6OhI+g/sg5IXOz3/ysqKSqWSVldXtbCwoPn5+UR8orwYz+C6yVndnukeQWhvo9tbUY/yPuSancaR6zio6hls2CdOJHOJZAW3BZ0k5dcnqy3qA66/sbGRsqci6M/3mQPob/rLswR8XHkenomz1IaHhzU4OKje3t5EUIt9lsvlVK/XNTExoXq9rsXFRS0sLGQSpA5bPPjgWQJOHEB/e2m9Wq2WSYK9mXKsvw9O9gWiP/XUU/r7f//vb3v9J37iJ/RLv/RL192ooyJsarncZupmNNYRj0ZSwsWZ0bVaTdPT02nzmZyczCxH0WxupigtLCyoUqloYWFBCwsL6b3DFDZQnEAOhMIhJM2Iz2UB6PQJCg/DCKfbN3L6emFhIRktt9122zUjmRsbmyUvqGtOEOJa/bW8vKz5+Xk1Gg3NzMxoenpaq6uruuOOO1ocl1tJPHpaqVR0+fJlXb16Nf2QMt3T06ORkRENDw9vG5fDEjf4hoeH03gABJMuv9P3MXxHR0cTEM+czBLu19vbq6GhIU1MTCRw51YSjG5Sy3p7e1UoFFQsFjU8PJzS2EkRLJfLLYDcUZHz589rcHAw/b8T6BznsINau/18fH2v18ySt7zlLenvl73sZXrlK1+ps2fP6j/+x/+of/gP/+GerrVbOar620FpF5wTjF1Ar8gil1od2sj0zeVyLeVestjjzir2up3U+R4aGtp22Ji05WjhTHoNxfX1rTJwEURH3EmNKch+fX+udkFrv6YD9eyn9GF/f/+2vdFT1HFm3SmPGWKQDdbW1tTV1aVGo5Hal1Vr0sFlB1KzWP8OqHOmDNdYX19XvV7XxsaGqtWqarVaqn3OoZl81oMO7jShLwA7fR7RjhisoARNBGsYP77vDhkBA4AgnNrIkAewhqzhgY2Yrh0ZlgABWSA6zwbQDhDFsziIHoNJzJnu7m6trq6m9rlTTL86+56fjo7NA8GLxaIKhUJLiSG+R18yVisrKxoeHk518PP5fLIZI8jgwIkD09E+53cERHh+5oEDJT5vuX5cXzG4xTgyH1ZXV1M5NvYTzwJgrbH3UO7Ag1fMA+7nAGCUWyFt/lh2lqOqvw9CWKMEl+Jegs/pgXAX/zzELa7HOvIa03Hfw3/PAoTbMTk9+HY9z91OfF/zDG9IB84ujkzw9fX19J4HJFwnug6hhFkul1OhUGhpVyTKeTDRS9Z5ffB2wr64sbGhSqWiSqWSdJiPYezzyMb28WsHovN3u/ZHfe4BAu6ZxVCO+z+60u0zJAaw47V8fHjNX3ddKW2dB4D+ygoso9PQuZR2yeVyyU6LfZQ19zwT3IP82DdebhBbZ3h4WIuLi+m5bwVplx3gBAv6kr5DJ/s5LcdytGRf6NDp06f1n//zf9bzn//8ltf/83/+z9vYgc91YUPLiqyyGH0zjFFTNlUc+3K5rMuXL7coQf/+3NycFhYWUk1I2nBY4sYJCps0HdJtcfC8Jno7EJ1NFGeZ/+kTdxCInMMCuHr1qmq1WmLqOlPP02tmZ2c1Pz+fQMbd9BeOJkyhK1euaGNjQ6VSKTnihULhlgLSYYARvb9y5Yrm5+dTDVlq7juL7WYA6FkCgDUwMJDAllqtlsnsiBKNnJ0+x2//zq0kBJ0AFYaGhtL8Zr1tbGykmndeL/1WiOYjBxEJh524k4yPj6uzs3MbQ3xmZmYbkxw5ceJE5ue7uro0Nja242faXXO3cvLkSZ09e1ZPPPHEdV1nJzmq+rtdgMwdRPRINPyj8+RgU9b+AvvVnfCowyVt0++AYbTTSy8RRHcmtYPCDnpmPR/3ciA07mP+bHzPmVRup3AfHGW/T5Ze8PXsTqADg4DADkJ40Bm9HMkEMLQiiO5sQwKrgBheq9xtCPQ2NT4rlYpKpVLaK539jp2xsrKSxqqrqyuNTUzFpn1edsydb67B/Il7YHTQvW/X17dqYdNmZzdxX77vDC8HDzx45HMiBiwcYKB/s2p5xs/53OH67iT7/HGyhLMaCZBwX//BcY/2IrZhnFNZoIQD1vQT7d9p3SORPeeBpixww8EGzyL1w9qdTOKsN4LDO7HIvS38zgoEeL/zDBGs2Y0chP4+lv3JUdXfByVZQS90GPqU9e/rMq4p9tK4PpaXl1v0t+t99I8f9uy6+2bNfQ8Ax/Itvn/xWYLargvI4KLP2Cs9SAHWsbKyks4VizZHBJ9dZ7vu3qmv1tfXE4mKTHIAddpB0BxiggOcsfSL20L873u665CoH7ydce7RTxAImBucVxcxh2hP0hb/ycq29EBwtG/5cXa6M9ld/8fgutuAbndxjZ0CQ349rhV/svrPgxCOD12PzjkIcb3M+TBklnrJG+wQ/INYgvhW0H/H+vvgZF8g+i/8wi/obW97m/78z/9cr371q5XL5fSVr3xFn/jEJ/S//C//y0G38VkrTLbV1VVVKpUWVo6zRfxH2jL8Yxpss9nUU089pYsXL6qrq0uFQiGxvXBuOFgTh+swJzxAJ+nFQ0NDKeoI+OlMdC+t4Q46fecbMCwpDrkgxR2mPg4Xaa/8Jqo5MjLSwl5bWlrS4uKi1tbWUjoY7MHdCEp8dXVVTz/9tJrNpk6ePKkTJ05oY2OzLMqtVNOr2dw85IISNN/61rf09a9/XeVyWZVKRT09PYmJTkrcrRAAcGdwYGBAt99+ezKyqtVqmhvR+XNjxmvi7lZgF97sA0BcWCf5fF5nzpzR5OSkxsbGdOrUqZZac52dnRoZGVE+n9fS0pJGRkbU1dWVgku3wvMclhLv6enRK17xCn3pS1/SPffck17/0pe+pDe+8Y2Z33nVq16l3/u932t57Ytf/KJe+cpXpuDSq171Kn3pS19qSan+4he/qFe/+tV7eZRtMjc3p/Pnz+vkyZPXdZ2d5Kjqb+Y/EsFj9gIH4Zy17Z+VlBw516+Ut0J/xRqi0Vlm73KnDTAMIA3HM4KJOFiR6Ux7nRGTBRw40Oef5X/2E/ZH17k4allAQjtmlb8vKYEUOH7e9x70pg30udeP95qcXCfaDOztZN0QYCVgLCmVWEPH8xt7DJZVvV7XwMCABgcHNT4+rlxuM72Y50Av1mq1bYdeMSe8xA3sx3gIpn+PMY8gN+IBlrm5uaTHL1y4kLKOcJzz+XzS9dgyzFMCA9yDtnitTgfYmY+A9JHRyaGYCGPg84g5hH2WBUIAhmTZXxyaylqhBIHbzHFuR12P3ecBFeZ1rN3rwHJcW7Sdz9EXTgYBePFggINXzInBwUHl83nl8/lUSgjxcaFMG9mVniXDZyMYEbM0PKDAd7LsGubEbuTYCb95clT190EK+5ekBGB6gMszuyB3ObAota4jrolOdoCPoC97NExqzulxhu9h++MR+MQf55kdKOaZsXPINEI3AsD29PSkw1W9LAn97D6Zn1MhqWVfoj8ozbm8vJwIgFlkAZelpSXNzc2pu7tbhUJBnZ2dKhaL6ujo0NDQkEZHR5Oewsbq7OxMRCM/dwQd5brEdbGTMxg/H9OYaeCfk7b281qtptnZWdXrdU1PT+v8+fNaXl5WpVJJ+zqYAZlT2Dzobp7HgXS+6+Cz60MPDnNN1wsOsvvB53HOeilEB4yzAh5uA3hAiXVCG7yfXQ/T714zHbvwZgn2X29vr4aHhzUwMKChoaGE9TBOPi9g4Xd2dqper6dgwG717I2SY/19cLIvEP2//+//e504cUL/8//8P+vf//t/L0l68YtfrIcffrgtMPFcFncoPELpDGw2RY+ASptKjcjuxsZm6nG1WlVnZ6cajUaqG+onLd9Mximbvm9+fiikH/aEQ+eOFeLGDAra+5EgAxsrThFGDP3sjrw7uY1GQ6VSKdVWK5fLe9pY3EirVquam5tTT09PAqXz+XwCAaSby+h2AIkIeKlU0uzsrKrVajKIfLyiIr6ZQjtgynt5Bim7Dh3COGUZsu2eL0bkbxVhfyCANjw8nGqiO6DXbDYTmEggrl2pi5slh6nEH3jgAf3UT/2UXvnKV+pVr3qVPvrRj+qZZ57RfffdJ0l68MEHdfHiRX3yk5+UJN133336jd/4DT3wwAO699579eijj+pjH/uYPv3pT6drvv3tb9cP/uAP6gMf+IDe+MY36vOf/7y+/OUv6ytf+Ur6TLVa1Xe+8530/9NPP60///M/1+joqM6cOaNqtar3vOc9etOb3qSTJ0/qu9/9rn7pl35J4+PjLYD/QctR1d+Rie76NoLpbsjH16XWtG50TGTI8D1nYcXrRDAOx80Z2V6ayRlBAK1eO9OZOs4EQ1wvOQvPgV7+d4Z4ZK3H60fDPwvkjesSh4HPY+fwLBGgcGcO5rez1d2hdKCZ/onsPwdTHTTxMh6rq6tJJ66vr6ea0ziI2FaMOX0SA6wOWDiIzjN7TXOeJUsiocLnGMQIggSQADw44aAI/exkhchQZp44YOH39znNPPHAi7fT5xH6Nsu2i8/n68xZ4XH9tWOiO/AvbWVTZOluPpfVLvqa+2d91z/nrHtfa7zmmRfMPeYGzjgAkwPcPo7Y0s1ms2W9ZEkEUOLeENvn6zUGDK4lx074zZOjqr8PWrIAzbjHIL4/Zq0L11Mxw4fgHzob3eO+6M2a804WcEDY9VT8vNTad45LOGEJXeCfdx99aWmpJVPeA6q+v8NEBzjeTclJsA50Nnq7Wq0m4N7P/ICJ7jYAui2L5e3zgSyhaN/FPT+rjdKWLoCdz9kW+N4EHqQtckOcY25TxKCx9/9OPjt6Ht2VFRhoF1D2+ePgd9a8jrZ2OzZ6DOBEW93HxckFN3MtMQZeVYHf0eZD36PDYxmYm6kHj/X3wcm+i/3ec889N9TRP4rimwolGSifQb1mjwCz+SwtLaVo5eLiohYXF5MD6Gw5N+wPUzwgwGGelDMB+KS0CWxB/460PZUubjLuELhSQ6k7Wx0ljhPNIVPOrFtbW1Oj0Whh7O9nc9jY2EhZBhsbG/rWt76ler2uM2fOpL4oFAoqFAo3BZReX19XtVrV0tKSZmZm9Pjjj2thYUFPPPGELl++nOqUjo+Pq1gsZhpWt4pg0EibrPTR0dHEXsia983mJjPk0qVLkjbHanx8vK1DirNer9c1Pz+vK1euqFQqSdo6RM2NxcM2jmFS9PX1pRroZA04w9BLJQG2d3Z2an5+/tDaei05TCX+lre8RXNzc3rve9+r6elpvexlL9MXvvAFnT17VpI0PT2tZ555Jn3+zjvv1Be+8AW9853v1G/+5m/q1KlT+vVf/3W96U1vSp959atfrc985jN617vepXe/+9163vOep4cfflh33313+szXvvY1/fAP/3D6/4EHHpAk/bN/9s/0iU98Qp2dnfrLv/xLffKTn1SpVNLJkyf1wz/8w3r44YdVLBb31Te7laOov71OsKQE6GHsR0fDX4vgGc6ys5Xi/o0uI7UzOqboZv9x0BNWDg6WO5bNZjM55oCTiOtGng+dirPq9oOLM61gJXkQ3lnLBCzZXwh883d0bNqB7c7ExfZxRxbgwZ04+g9wnP3ag6cIJIP19c2zPrgefeTZYMwPLzEHOMB5MmTycYgqtTyddez1ztHv3j4Hw71MDAdq0tcbGxupzmgErz1IT93Xixcvql6va2ZmRvPz8+k60SGmLfydNRZe05fgK9dwXZtVwx/7yu0yZ6J7urZfn7bS757x5QEcBPuXQHC9Xk8AtM957GHa0Gg0Uj1iHF4PZtAnPAP2swNEUXx/IRgU+9b3n8g+5/69vb3p/BLmF2xJngVyDGBUd3d3ChD5GPg68r2KPc9BLl8Dfj4AYxv3mZ3k2Am/uXIU9be0PVB10OIMcg9ikSnt+tv1EX+TBc3f6HN8Sf/szWCfs7/jd/teGUlsPK/rbX57sJW9i/fJ3OK3n1Hm+/jS0pJyuVzKCuc63JO9hprRUWdcS5grS0tL6ZDwy5cvp4Mc2ffJyoK1HkuVeoZWLKmFHqTN2BnoMw8OeIDe5wn+9+Lioi5cuKBaraaZmRnNzc212IixTV7CzgF0grAOQkfihD+LH5TuQVY+678ZPw+WeCAF+8fP5MvCNTwAjs7u7+9XpVLZ9lzMN68djg2HfdPf399CJnU7+bAwL9fhxWIx/eCDewAEjGFpaUl9fX1Jp6PXYzmhw5Zj/X1wsi8Q/a1vfate+9rX6p/9s3/W8vri4qLe8Y536N/9u393II07isLk7ejYPDBxdHRUQ0NDuv3225OzReoTSqXRaGhubk7Ly8t65plndP78+ZZ0sXj9wxZXjv39/RoYGFBfX5+KxWIqEwLIkLX57gQuN5vNFoWF84IT4Cx1TwP3FHiAVr9XBFCuZ0PxgxsHBwc1PT2tcrmcTqQ+ceJEqsl+mAA1fVIqlbSwsKDz58/rz//8z3XlyhV95zvf0fnz59XZ2am77rpLJ06cSKDBrcJAj+IlCIaGhlIqO0ZTljQaDX3ve9/T4uKiurq6dMcdd6Tn89IP0lbN+EqloqtXr+rChQvpAJW+vr4Wxe0sx8Nac6TzsbaGhoZUKBRSCiDi7S0Wi6mcy/T09E2PgN8suf/++3X//fdnvveJT3xi22uvfe1r9fWvf33Ha775zW/Wm9/85rbv/9AP/dCOfZ3P5/X7v//7O97jRshR1d8RRJe2H/Dnjm0745t1jpPMtaXWgy3RbQTBfX1KagG3cL7IJIO9FsuXZO0x3lavq8j7ZHrh6OGYxAC7pKRD3WF1VrHXaI7OgYPozhBy0JznyOVy24Ll6PL4TF7Xm1Irq6urKYjgjiHAgAOe1BnFVnImGw4fbUEok4Fj6oAI/wO0cFgz/+O80h+QBjy7zoUSdl7Cxeck77ssLS0lwL1UKqlarapUKum73/2uqtWqFhYWNDc3p1wup9HR0XSIG+PM/KQP3NnHEaQfOzo6UrkD2tTX15fGkfFzkNzXiQeIPB2cvqQNtAkgxkEQ7k/b6R+YirzuWVcxi8FrznJYLAEYytw42MAPbXLndnl5eRvTHvtIUssc9tIFvk/ARMPuo9/7+vo0OjqqsbGxBJY4q4++5Pt+2HCWve9kG9LdfS7ENeqlHFgbPueO5daWo6q/Y6DuoEEm9jdnkg4MDLSUVKLGMfsJv9mLFxcXk67ykl4EXqVsBvxhCIAva5q9kj2HwCp/+yGWZA15ppSzntmf8L+dfAa7mr0aXYr+djY/AL4Dzk4G3Eufuf6Ym5tTrVbTxsaG8vl8Oi+rr68vlYrp7u7W4OBg8pfYA7Hh6D8nBzh7nz0/Ygj04/r6egomAKKvrKxodnZW5XJZCwsLevrpp7W4uKhSqaS5ubkURPdydZ6JFFnoDqCjt+gL198O8Lv95fZHzG5EZ3swiDYwB/ycu53OTUMvUXZV2rQpyuVyS3a0YyJLS0tJZwOmM4bSpm1JhiD62W2QGylu8xMEHx0dTSRJt4lZP9j9+Xxe0hbpJtrjkehyLM8u2ReI/olPfEIPP/ywHnvsMZ07dy4tgkajod/5nd951irxwxBfiADOMJ1wYDHUiWbDGuvq6tLAwIAGBgaSwXuz08Wk7Wku/MTUZmev8b3dXNv/ZjPzUincFyc9sqpudN9gEFBzure3V+VyObGYGeNYk/RGCM/qKe6VSkULCwtaWFhQqVRSqVRKRg+KO6Yb3arihg2HZ+4UmABY6erqUrVaTVkJAwMDLQzTGAln3XV2diaj00GuyJxknu1FIca+3u08dSMvXidr/KKBdSvIcST85slzRX+3Y7XFAGpcF+708dsdCU/RbPfD9xzURnc5e5t9OpbBkFoB//gc8fX4DFnOhTtYWX3letp1uDtwcR9x9ho/7J38dlaXO030AaA+febtjBk/XNPvzee8rIaPH/fycfXn9GtRk7uvr0+1Wi3VgwUkd3DUn4M54iW1on7x54/94W3lu4AR9Xo9gcL8uI6Kz8c1Ijud+RVBVR+f2Hc+L+inLCcwzoH4LHE8pE27zdnc8TusBV8jgO1elgHBDnOwg2AOz8zajFkP/nfU4w7ue7/wTHzebc/ILIzr1temj7+TbHy/8P/92jyns0WjDcL1eM4ocV3sRo71982To6y/bxTJw9ePBxL9DC9+E1QC7GW/8f0dAC+yz2+m8HzYIO6L03b+bueDsq59v/LP+h7hmWWA666H6B//3/fLWD5kv4KO4MDO9fV15fP5lL0/MDCg/v7+lF2GoAsccN7JT4rzMgZLnJwBHgAL3/U3NeCzytZ4P2f9ndWOLP/Xn8OJF3w/6uiok9qJB+R3M488oE4QOJfLJfKI67asMjK5XK4lo47AOs/l+juOQ3yW6DNn+d97wY0iQceDTVm28rH/fTRl3+Vc/uN//I+699579c1vflP//t//e42MjBxku46kwD6fnJxUoVDQC17wAk1MTKivr0/Dw8MtwLO0NdH7+/uTEujv79fIyIiq1aq+/e1v6+rVqynqe7MmN2ysrq6uFBjo6elRoVBIxolH6vazmbjDzSaOwnLmYDzg5TBAdJTF0tKSvve972lmZiYxF4aGhvSSl7xEz3/+85XP5zU5OZn6iqjzQYmnYVHzvFar6a/+6q/0ve99T1euXNH/9//9f1pYWNDS0lIK5nimwLNF+vv7JW2VOGnnQC4vL2t6elpzc3Mp7bK/v1+33XabxsfHW8oWUCt+aWlJHR0dmpqa0tramqamppKxh/HMoXUETvi70WjsCkz3tEHmQBYw4ALrDYYD7AoMCNaErwvYeY1Go6WG782WYyV+c+Wo6u/oHDhY7UCffxa95PufM4GcIcRe2d3drZGRkcRCLhaLLU4rhjRgJsY/+4SkxObCwaA9DhhHkN+dBWdJuYMRyzvAXvJruOPlrCzSU2FoeWDVHR4AZ3caI4OXPQ49kxWc8O/7uHmg0q/ln0GcuQXTjWswfvSjs8/iuRocTErmWrVaTay2kydPtqSDU4YDgAUGMLZOrN/twQVnYfvcdFlYWNClS5e0tLSk6elpzc/Pq1qt6uLFiyk7ij4kdd/HByfVwXgYlR0dHek8GWdY0RbvU64Ps4p57XXHI6DEs7BmGGMfO67bbDaTDnNAhXWxtraWguWsS9hcKysrLSA5afsAXl6qgTZEJxn9yBzkGTic3vcJZ2Y6OzFmpUUA3QM0vO8EC9rvTrgHYXzf4toO6HNfxow2+T7ia4b+lZRYfh5w2I0c6++bK0dRf/vclw5unjgDmsMAe3t7NTo6qmKxmPxvZ3FLarGhAYkBR1nb7LM3c057UBhCXrRJ/DB0bAH2Nd+3Y0DAdXUut1XaJDLX2XP9YOgIjudyuVSn3Pez/QLozBV8/lwup3K5rI6OjqQv0d/1ej2VdqG8LHgCfeHBBg+qRvvNA7uuV/AvV1dXNTc3p+npaTUaDZ0/f16zs7OqVCo6f/58Cs6vra2l+6NjYya4A8LcI/YrgZN2wHH0610PuN6O12Q9+nfQE+AGnZ2d6Zld6BP8Y57VMzVZV6xNMjsgxpF12N3dnXxfxtozB8kC8Uw4z8qiP53ECGbgfcb1sJm8xBt6mDbUarVkP8byPq6vfa44VuW63v32w9pHjvX3wcm+QfSXvOQl+pM/+RO96U1v0vd///fr937v9zQ6OnqQbTtSwuYzMDCgqakpFYtF3XbbbTpx4kTaSHYCVGHOAlSXy2VNT0+rUqm0nHp82OJOmB8m6r/ZvK4XMI6RPZyp3t7eFNXF4cJRO6x6WSiMmZmZdF9ACRROsVjUwMBAMmJi6YGDaANKZHFxUZcvX1a5XNa3v/1tPfHEE5qbm9PTTz+tSqWS0hZ9nG4kQ/4gBWMQI84BlmiQra2taX5+PoEdKysr6u/vTzVTe3t703kEi4uLmp+fT2y34eHhFuYkipyyDBzy1tHRkRxu0vl2YtSwZiLLLDr6UdzIjYrZjRyUuBsWfnjNrSDHSvzmylHU3850iq/HUinxfQ9outEtbWU6AcwBpJJh5DXRs4xj3z/cKXCWjutvB1hhuDogxx4XA3ERRHcgmWfxABvfxckm6yafzyeQMzoatMP3QnfCANHZm2l7ZLS7AHSzb/qYra2ttaQNZ7Gu/D0Hbl0XeD94xkBnZ2fLQXBzc3OqVqtqNBra2NhIqd6MOUAjARX6gmd1kJcxcGfbGcc+Ru7wNZtNVSoVzc7Oql6v68KFC5qbm0u10JeXlxMQwDyIJXb48QAF4+M63pmZzlJ3woMTOwhqOFDrQRSeyZ3wrOA27cZO8mAGdgx/k6bvIJCX1OF+BJaZR16/1QMZLsvLy6nUjgMKzJUYAGC+kpLuY+d61udpFmOtVqul5/b3ncmGxLniQT/Wva9RDxKyt/lzeD/wnFxvtzbpsf6+uXIU9fdObNvrEddl+XxeAwMDCWQeHBxM5REBIz3rhvUjbZV7pFQie/vNZqA7Q97ru2OTeJkp+sH1k//tIB96PIt9zP4EiMj+GTNqYt+4XXS9/RaD7Q5+or8plQJRoKurK+3X7OHsfxsbGy3AaCwLJ7WW8fIgCzoQQLlcLuvq1auq1Wq6dOmSZmZmUi30paWlbXut2yYxkz4e5iq1z36LWVj+OcbY9YmPf9TT6CY+5+uTtqKror/LdQlSb2xs1oanHJKXv4PEBjmMeUfJGAL8TkChz7kW5DKvre9ZEASAuC92O20Fv3HwXNpOyuG6ZK1T9s6Bce9LJxq4XR4D5j4mhyHH+vvgZF8oHhNzbGxMX/7yl3Xffffpb//tv60PfehDB9q4oyK+0fX392twcDCBq1kpolHcgWXTWV1d1eDgoCqVSoq0HrYy903aWXgxbayd43u993ZFHlPL6FdXHochbIaNRkPz8/NaWVnR+fPn1dfXp8HBQXV0dCQGI+CtBxki8y/r+lJruj5KltItq6urunjxop555hktLi7qypUrmpubU6lUSp/FISUKfCPG6EaKO5LO/tsJvKaPurq6Uh0/gjAcOOblduLBbM64Q+nDvFhZWUljSGYIRhgKNovFKm2BdRwghxEGc82dYgwHAP+NjY3EPKE/YNKtrKyoXC6rXC6nYNuxHMtR1d8RbPLXpe2lFdBfscyY7x8ONOOkUmvUM3gczEYiuMX1AM9cbwFMuuxGd3oWC0ChG+oOUHpQ2QFnZwPhtDiw7tfzsl+RZcaz+v3Q0zuVhaHNBCMJjOIYOqBLUMHHyfsJu8MdkyzQnj5zEDiypVdXV9XZ2al6va7FxcXUR7DFcPI8iLu4uJiylNi/PTvBgyfx4FleX1/fPCC1XC6n1G+vG0+feBkCZ9HFeZw1j3weZs1ddA3jyljwHM604lred8wlD2A5MI2+Bfzu6OhoqefNnGLMcbBxksmk8+xNxg8AHV3u70dWNu3u6+trYbf5eQI+n7kW5/44yMBYAeI4uOPj66ntBOK5D/YqznytVks1fZ39T7+4HexzKc511qr/7yAUfXbsIN/6cpT19/WAO+2EvRIgFXsZ1rYH5rJ8Iddh+Gr+W9I2Ju5hCPOAwC163MFH3wPdjnF9Dcteai3fEkFYqbXMF3uQ17eOmXFRHBy+HvF54oxhXiPgsbGxoXK53FIyl70d/9f3eX57oNEDlbzmrGeux1kcKysr6RyyRqOharWqer2e7AJAYp9X8UfafvAnEgPl7ZjrTiqJ4+bBcn892m48s9sU2IFeZ5/+iusAO6qjo6PlYGwHrLGBPUAAwO36FUIF11xfX0/zDp+7s7Mz2VARR8Fe7+rq0uDgYCIhMGcYIzLcsEXd7sJWpJxyb29vyizw7DSy09HfZMi5PRD3u2Pd++yUfYHoPthdXV363/63/00veclL2h7c9lwXwPOenh5NTEzojjvuSCAqm8C1GMB8huv09vbqtttuU2dnp65evZpqPR+WOODrDHQiv85Cd2D4IO/vDhkgIpFBWH9svofFwkXhlUolNRoNdXd3a35+Xt/61rc0PDysl770pRobG9Pk5KTOnDmjvr4+jYyMJCaEB1ay0rBwoojK4nADCl++fFn1el1PPfWU/vqv/1rValVPPPGErly50nIATnd3t4rFYmKjH/T43EjBCHCGAens7VIrUbyU2VlYWEjfldRSziACYfx2w8oPzymVSlpZWdH8/HxiCs7MzGhxcbHlO5OTk7rzzjtVLBb1ohe9SCdOnEjAgiRdunQpHUr7l3/5l7p48WIyTHwe53K5dMr7+Ph4aj/rbW1tLZWlIZjCgam3iqI+joTfPDmq+tvZLzsJe7QHe71khX+f9zC+h4eHNTExoZ6enhQIczbqTm3jegCIHFQYGdPevqg73XF2MJtgnus6Z4H7Z3G+cIIAFjo7O9NhxQQMokMJSMv/0SmAhc17vObOfAR8cWbZU7ke+5k/N33pwWTGyD8XHV53cnAs/VBXZznh1OH0zszMaGVlRX19fclxGh4elrSVDce4LSwsJHCT9kR2I23yQzolpeAogffvfve7Wlpa0vz8fDpwi/6BVUl6NPrPg/HOsvc+i06wByycOQW4ynzp6enRwMBAC8jiaeDMFQexYXgxTvQD2Rgc2AqgXK1WW4DuXC7Xck4J3+U6zmZHOjq2UsF9bWMXOuDSbDY1PDysZrOZAG2CJzDgCYZ7AG1iYiLZCoz1/Py85ufnt42Zs9xpJ/V6AQYIzABu4MzXajXNzc1pdXW1JRDO2BCs8LHw/SQGUNxHYJwj83Q3cqy/b54cVf0dA1zXK6xLWNns25OTk+rr69PExEQiNqGLfD1nBcDYA6RNUC6fz7cAgoclHvyHSY99UiwWWw4W9WeKAXD2C/elI8BHAMIDdtL2YOfKykoqG8l+H0lNBxUkcf1OG/zajUZDV69eTfZDvV5Xf3+/6vW6BgYGNDIykpjElLCLPnAElOkvB1M9ML68vKz5+flUQvR73/teKscGgQ3/00l+nh0HjpHVx/zP2HsGEfotBmt5DvASnpO+AsT2TDpJLTYBZXF9/Ai+oN+6u7tTgNv1DwFvdGqpVEp2LzYF96PtuVxOhUIhgdx+rVhOh34HuAbQ7u3t3XZI9vDwsIaGhtTX16epqSkNDQ2lvmw2m1pYWEjjd/nyZS0uLmppaUnlcrnlWTY2Nsvl0gaCWPTf2tpaCqaUy2XNzc2lsjbo77gmDluO9ffByb5A9D/4gz/Yljr2wAMP6Pu+7/v0x3/8xwfSsKMkbGDUhILF4s7Atb4vKW1cRP+oP16pVG4KCOoOFptuZILfCADd7+/RXJhNDhrsJkBxI2RlZSVFYFE2tVpNw8PDySkbHBxMzl508KPxIW0ZDig2IqW1Wi0pEJy3ubk5Xb16NR2mWalUtkXXvVb9s008C4F5t9Na8qg6oBcOPAo+yzCJ33ewaG1tLRmdMMXq9XoLs1PaYqL39/drdHRUg4ODuu2223T69OkWEB1lztoGGHIQBDC9Vqul/aRer6dng5FPBJwsFQChW0WOlfjNk6OsvyO7Y6e939lVkRkdP+csNJwAfmKdyp2YJa4z22Vqsf9fq93u1Ebg2plFWTqQ+7ruiWVOIlNeaq3FnKWruKf3hetjz1BzQJ69ztmAzpDLksiEp020x+2mOKbuhDu4Gt8HxIXV1tfXl/Z79DggPBlogJE8H44W15VaQXTGh0PIsBXq9XpioDuAznN61p+DKj4G9L+z87MYhnF+uK3ANf0+/MBy4/oeuPHsQC8NwL25Xm9vb4tD77rWQWEPcNHn/M1zeUZCLOeCg8vzZWVuelAGoJ+2UJYPAgIAHPck+6CzszM5zM7wZK4BOGF74JhzLTLJSIXnOg6SO7AT51b829crIL0D9mSxOBPxWnKsv2+eHFX9fSPmhfuH7AkEHr0+s+vIqD8ia9h9XC8Pctjia5Zn47cz0fmsP4t/3+2PCIBKatErvt8xXtyfrCTf510HH7TsBERScgNfCXJdtVqVpLSHx9Jb7LFus7hN4eCz+2UQGSqVSmKkU+MbHU5A220071efY94e16+If8fFg8NRv3Fdx0WyfH/uhw2BHkdvgEOht5lz7dYBz4v+7ujoaDlYNNp47n/HsYHp7SA6eoxnXllZSf65P69jb0NDQxoZGUl2DXOITPW+vr6Ww9u9ZAtgPuVgySSj3WRBQFqk3PKtUPoJOdbfByf7AtFf+9rXZr7+Iz/yI/qRH/mR62rQURRYXfx4GY29im+0lIYpl8vb0pgPQ3yzjYaFK94bAWK7cvYN39uDImzHEPB2ZbVxJzBkN8JYAHJyYOX58+c1NjamZ555Rn19fZqcnNTw8LB6eno0ODjYYgzF63nNN04ih6lFTdeVlRUtLCwkp7ZQKCTQuFKpJGfVa6E/2wRlTjQbJZ4FFtGfIyMjmpqaUl9fn06cOKGxsbFUG9FPtG9noHBfaatsgLMamXsoVZQzCvYFL3iB/tbf+lsqFou68847NT4+3hIJ7+jo0MDAQIqId3V1qVwu68KFCy2lWNbX1xNjEfDezx6Atba0tKTFxcXEiDtMpsy15FiJ3zw5qvobNnYWG9PXsjtHOJk4ABjoLrBkEPYe1iQGvYN+/rf/OGDLHux7RTtDm/cjU52927/jhzDiJLitgV7ge17mwgHzCGK6OOPJxUF7BzQjAOsMYXfOcXa9X3G4aJ+XyYiAYtae7fse4+vBfT9fwoMBtN/TtHO5XHKMcLhgJDorKvaJl3Phnuirvr6+lJEAGO8ZVTh1Xs6DvvD5klWuBP0H+9A/j87CkfU+9XnsYAE6LgZNshx8b38kU8TrMZezdC/XwV5xXd2OpOE15t0ucIc7K9DG3HX7ETtseHhY4+Pj6unp0djYWDqwlJ+hoaEEpiwtLaW+aTQaLWO+traWWOXMZV8LXIM6/bVaLQUNvE6vz22+58Er1gbCmiMDgPnC2vIU+GvJsf6+eXJU9fdBzwv33yLgHAkufM6/4/ugtL18KRlAfo3DkhiMRxx8jGB5lsR920lV0ddpB7oTWEaXsZ8QUPSA52EKY8hh2ktLS+rq6kpnnnAeRn9/v/r7+9vaOxFEl1p1rWcx4pNzZpaf0RH1ogvve5Ac3QipAVvNA/E+zm53evsiYz8SDB1c57cHXqPtiR0DwO7YT1aAhvZ7cKed7o6YWFYgwOdntIWwaWCx0/6Ojg5NTU3pxIkTyf+Hie5YAvME/Z3L5VStVtMz05ew1L0kjWd5kHFABvjNWgPt5Fh/H5zsGkR/4IEH9L73vU+FQkEPPPDAjp/98Ic/fN0NO0oCiM5BZJTQ2Ct46RHyrq4uDQwMaG1tLaVfu4NwoyUrMu914rIiiTeiDVKrk+oOlkfHd/q+KxTEN5nr6dONjY3EKuvo6NDVq1fV2blZU3N4eFi9vb2amJjQyMiI+vr6NDY2lpxqDnhD2JRR1pQLob04RzjjDsxKSrW56Cfus59gzq0g0aDNqvnd0dGR0hrHxsZ0+vTppERHR0e3scZ3MoazjAyYqc1mMx32Rh/zOsyGF7/4xbr77rtVKBRaaqKzrkdGRnT69GktLCyktLcLFy6kEjEI5VpyuZzm5+d15cqVFmYiQRYcdD+Y5liem/Jc0N8cmhhBTAfQWCfOvIa9EtNg0QG+N7DunenE3gvg6IC0O+UE2/gOe7CD6OwX0ZHBQKdtrnv9e3w3Auhxf3M2mesL3ycc9HVxNpY/I044fev6y1lr7hw6GAAgTZ8zHjB13Ymk/7lmfK6oz93RxJnxsfRDUhHaCdgJINDVtXkIdalUamH+OUjtZYU82Eqf8X5HR0cq54Ye6+joSPNRUgubkPnJPI9BG/rH5wzEjbW1tXTIFwF5nPB42C194c/E3MWepQSJ2w+ROeYgvs8d2uosRp+jzmRHIKFEO5Pr+nj7OnRg3A8ZdXDD5wl9h43OfaampnTy5MlU1gYAzfVrV1fXNie8XC6nvgQEpx/dNvSAmNdk97+dGb+xsZEAfmz/rGdCvDQSqfcA86y5Ywf51pTngv6+EeJ6x7PJ2FMdtOR/B+b8N5/Dzs7n82k/PkwikgOUWcCln1uS9b0YJHeA3EHbrCzlnfwjCFv0V39/f0u21n6ek3buR9gza7WalpeXU6C6r69PpVJJpVKpheDodpzrbwe/3Z5DZ1EqiM82m5sHg+Or+57sPx7o5lkdJGcM2eOxedz+Qtej09y+8PIubnt0dHS0EOiy7CcfA9eZsYQN9o9n23mfua7OKvPr9mvW/JRa12gMGGFjeMCru7s72Uis066uLp06dUpnz55NpZyKxWJL/1COmMxtnqNUKrUEnJ0AQb17D0yg5/kcuvZYtx5N2TWI/md/9mdpU/n617/edjM97Ijss0Wic7dTdHgnaRe5O8x+z1II8fXDbIvfu137/D1+u/HQDkRHSUWDai8bojs9KAsUDPXEms2tenQoGmrSIjjBGAYw3D2A4iwrdwBhasdnfjay0KO4Ast6D6AbYwmjJx66s99564xL6h3DUkG6urpSAI2U8Bi8wLCnjQMDA9tq5CHRsIcJAfjkAN+tCKAfR8IPV54L+htnNgv49UwlFwenIhM3vu/OCcayXyf+HVletAPHwR0MdybcoXBGkgPBvrdnsZdwiH2/5zO72Q+yAsc8Mw6l67MsJ9zbnOUwRolAqLclMmzpP9en7SQLWI9BcsY3tsc/w9zK5XKJWcT3ADpxcr1cSxZbjHnU2dnZUjcb1lQWGy7LwcUZp488MyAriODP744wDmm0b7JsBbdn+ExMFc+aw4AJPq/oz2h7OfDNe36gn8/tOGb0c1b/RVss67s8RySJeImErAN2+Qx62LPVvO/iGGCXuTC36KM4JvE5PZASmaJZ6yqyTbn+bnXrsf4+XHku6O8bKe7PeXC4nc6JOjxLHzt5LO4vByVZbfO2xL+9ze2eKf7t3/egu/uVLjuR0nzfvJZPFcHkrOeO77f7eyfxADzBcDK3PVsYve2sZm9n1j3jvs7/7nu1a2e0CfzH7xX1uL/vgdMImsf7uMR52o58wHvcl/ngeq3dHHVyRNTd7fS392XsB1+rPtdie2KwDJ1N+UUy18Fd6Dc+C1HCffTYTtbE2tpaCxkBe8Brt9+KAPqx/j442TWI/gd/8Afp7z/8wz+8EW05lltUXDm4sxwVbhZAfVjtk7aYY/66t1VSiwMWmXB+rRjBdWfZHda9bihsvl5Hq1KppHb19va2RGm9D53JxPN2dHRodHQ0ZSM4MEzbicrCXMJx9jS9Z6v4GPuz0DdEnQcGBnTy5EmdOHFCvb29KhQKLYfuXE8f8F0A797e3sR8gKnQ3d2t22+/XYODg2mMo7CG+vv7NTExkWrsZX0W8Tnhxk4WKHQrybESP1x5Luhvym04kOgsHQeyvaQBZRNwstjjXSeQppvP59N+zTrGUYhApTserGH2aPZ+SoJgjHtpGG8vugphz+N5SRuG5QMDGD2QFfx1Vjg6p9lspnrNcU91Zy+WG+G+BCh9X23n+NImr/lN3zMu7oz4d8jE6+jYqq/J9enzGEyIwnccEHcnGic7Bjl4v9FoqKOjQ41GI/WJp+4CjEemH2AF45PP51vGGGDdbRPAZmwR7BdY3GQ/uXMXwW93rr2eKI6oZy35/Pe5G9nSHsxx2ckWjPrJgQjawXxiTkEq4IBf2PX+fX++rHnrgHIExyS1tAndzb1gWRJ89/Rt5htMR3Q4NVMdaI/2KZkWPrfdsWeuuI3qgT4Povlvv1ace5Aqurq60r6RxbrdSY719+HKc0F/3yjBRu7o6EgHGDebzVRuhPXQDqx0gLyvry/tjYVCQc3mZnYZ/he1r3eSaCdkraWd9Ja0ZRPQdvakLDJV7AvfC9m/YSVD8In7tbczBvDcR45gcFbgj/bjL8X9is/z7G7Dobf8Ga8lrlecRcx+yPXJ8OYzZOhk6TIA187OThWLxZR1R99RgsvLpHmfOLgsqcWecpKCt9P35kgUof9jRmF8Tp/LkE484HGtYKu3Df2T5Ts7eY3DPAcGBjQ0NNRy6G0kiGCTuW7lWR3QjvOk2Wy24B/FYjHZCAMDA+ru7tbIyEgippFJ52PidmR/f39aC1L7agScP5YFsl8riHIz5Vh/H5zsuSb62trmCdV//ud/rpe97GU3ok3HcouJb7AokRit9k36ekHJ/YgznbwdsW2eSsQmjqPi4pu3H2IhqQWY5rO7FT7rNbIqlUrLZ3YCdt3JQxF51BVgh02S58SQ9PplWey2Z6NEthev8eyDg4MaGhrS8PBwArExgK53nvr3vUwEh45Rc723t1cjIyNtWej+HLR5bGxMxWIx87MIxtCzTY6V+M2Ro6y/vbZvBKH531NA+ayDtdS6xtFBNwAY4oQ7q9ZrfqOHIsvHgTDawHc3NjZSymksJ0P7uX5WQJtyI9yL3+wzALBxzUXHCkctMl/9O+7Euo6hzEZ/f/+21GjEnS+pFfTLqiuflSHgfQqgurS0lOpOu3NHnwEwxPZ7kNwZQ/7+2tpaS7DZHXKvo09QZmFhIdWzBqyJwRDajnPnTHTa7U6wj7mPHfbXxsZGZikzhHngDiPPRhCC53AAiHbH0oOMKf3lrC4fnyyH3J3laEcxfsw/5jD600vcZelQL8eDfeMBnwjox9/tUulZm1kAuoMAnolG6rrblW4bezsIgsc5KW2dJ8D32V88SBKB77jO4hp3ezcSQ45B9FtbjrL+vlHiezx+F4FJD856uaMsPSkpgaeS0hoHrGPd7QSi+57j6zdrTVwLRPcfD2RGAN2v7UC3t8lJXAQDs8DtLNvA95uo79utdXwz7JNo8zj460Cpt2Eve5XbFOydjUYjBaYBUcvlcgqEkFXm/YQtQKYxvjbZR/QjBA1vo+tfruWBXim7nAq/Y396Vhz96DaM97XjNogTEnzu8zvqb+aqB9+z/GfGC1yCmvPFYjEFm9CN8T70jZdp8ef2eeDAPraTzxnWZ7FYVE9PjwqFQgLP3Wbhh8+vra21lJ2hXVlyLdvrVpVj/X1wsmcQvaurS2fPns1MmT6WbGk2t079ZYPG+N4JIMu6Dr8BRTk0sF3keb/ikXBPTY2ANMqQTclZRYctMfLtLAM2V5x9d2izFIJHEQE3HEQnmo1i3ini6EwgGE7cn+txHRiRURHG55S21++LzpM/Q1Y0fC9O060sce5joBBUoFa5s/xvVPCAsebekSVyrTXqhjgH0xwrrWM5KDnK+juCU0jWHu97X7uDJbP0gdR6YGfUIe7I8rezjHHe2ev5joOQzvYBzI+sLKnVoY3vO3AfnR/2P8BR36NwoiNDiPvwXdc7fMfthCxQDkc1OvfYRLC/CWR4dgBOtjP/0aPc2/ssBjfRtd42rzvuAXHXjT6u9I87yx0dHYlFLynVRuXaros9cO/BbMbSmVaMC9eFKed9Sf/7wZk+hxx88CASr0Xw2FnRDsx4DWHE66vSjgiiRxabg8P+mQgISVtglZdicwIE8zXaNFyf9Rsdbtoc53UUX09OFvE5Evs6K3vDr+HBkawAvgNRHqDzvvT15MGZCCjw/Rig8OcCOAQA9Dl4LLemHGX9faMkBkajXvL9yXWb75Pxb973NejrvJ3/5t+Jewht8eB93EscVHZd5OUy0DEx2E47aRt7BPtsZCTHvRUdEX1Kz6Dzv2Pw27EDZ3LH/bRdnzmI7u24lk+FrgUkxS8jSwgCBToaQLrdXMoC5QlEup6JOsCDmC583/dxt1d8Drht6G2KTHTv62h/Zkmcq1n6ye0rSduu5//7XKSMqgPTDtyjc/y53H4hWBDtiXhft225RiQN+n18jOM8j8SKYx/8WNrJnkF0SXrXu96lBx98UJ/61Kc0Ojp60G06csKBkF7LGgM2OifXEgfZFhcXtbCwkFKID8qwwiEg0spGGBU7AAJ1nj0l/DDFN3c3EFAcbng4iO7swSzQJBpfsEBgnVGShVOa2xlOKGZKi1Abm3T0er2u1dVVNRoNXblyRY1GI4EIO0VAvV0O5kdwPb7uP0dBObhBh1CyZWhoSGNjYxodHU1pZVkR8IMU1gSGbV9fX/o7shHic7C2q9WqyuVy2jeOohyFufdslKOqv33Pd2dYamWB8roDne30ZwwKoxsBVDkIMoLXfFbaXo4FMJn7+/kX7Nd8BgcCR8NBcWcOO7AYgwYeYEUcCMbZoURKsViUtD0w6zrf9W2hUEi6jPe9LArXkpTOaqDPpa0gBuVQIAagF2Fqs6/ikBEUpRyK76fOJItkhVwul95bX19vsVuy9KKXDxkcHEx9BSORA7t7enpSKZLV1dVUtodng6nP9fxQb5xoxo2yARsbG1paWkpOOgF9+pSyMhxkiXjdbgd3+/r60mdgaNMX9PnGxkZij3V2bpYx6e/vb5m/nrHhDHCfX+78EwTwYAilaLx2KfZVf39/spOGhoZaSt4wzwCl6Rtfw+50u03KGDNfIgjjfcg6p30ekPAgC/YErzu73vcL1o+L34/x8D51sMEDMhEokNRywK0fYOwBDwe0PAglbc9EuJYcpv7+yEc+og9+8IOanp7WS1/6Up07d04/8AM/0PbzjzzyiB544AF94xvf0KlTp/SLv/iLuu+++1o+89nPflbvfve79eSTT+p5z3uefuVXfkX33HNPev+P/uiP9MEPflCPPfaYpqen9bnPfU7/4B/8gxv1iLuWo6q/b5QA/nZ0dCQmumenRckCGj2oxZ7gwKZnobCGHVj1z3KtrAwZ9rXoa7sPC1iIzQBRqKurK+3V7QDbLHAUVrUzeaUt39GZ386w5vk88L20tJT0N3uQkwHwhSjpEYPJ3Nt1hQvXw/d29nWW5HK5dK/e3l4NDw+nNqAL8d87OztTRptn78W5RL/wHXw0z3bzsiS+f3swxgMTjUYj6XZsNnSOj38EnNnfmW9kSRJ8zuVyLYeAM7cBiGlXJBNkkVF8zvIMPicdxO7r61OhUEg+OH8PDAwkm41yKtzbfXifu+3WqOsz1ibj5TqR8XYbz2uZZ4Hr7BPM8cMC0iMZ4kbKsf99MLIvEP3Xf/3X9Z3vfCeddlsoFFre//rXv34gjTsqkqXEnUWD7ATquRPgYBvKKwu83Y/4xugGQ5aDzv/+nm9+NwNMd3FDydPenA0mZR/a5ZsmihoFhiKJtdWk7amAXB8HsFAoqFgsqr+/X0NDQ4lRQH2+UqnUouCu9bzudDnLzt+PoLm//myPssYIso+3O+ekYt9oFjrCnHAwoR1Igzi7AYMUoOKoyfXMu6PYH4cpR1V/O0NcamWSRXEHJMsRy/pOfN/3fge1+JwD664j2AdcX0YdFMGv2Cbfv90Jd1DPQfys++GkOTPI01yzADV3lJxV6/d0JzzqIvraS6Q5oyv+eADYGXoOOLPPR+YX13UQnb50fenjGceZ97L6CBDdGUu8JinZB4yV21P+DFFH+zMwx3wexMAODntkMwJSO4gR2YnuhMea9Fzfs+b8O15+x8c42kBZ5AQHreMa8DF2+9PnmQM+Wexzv663J/7Oes3b62uLOQ3I4QCZP0OcR75XZOmuCKRzfbdVrmVTu32bRaLIkrjv7IWFfpj6++GHH9Y73vEOfeQjH9FrXvMa/fZv/7be8IY36PHHH9eZM2e2ff7pp5/Wj/3Yj+nee+/Vpz71Kf3xH/+x7r//fk1MTOhNb3qTJOnRRx/VW97yFr3vfe/TPffco8997nP6yZ/8SX3lK1/R3XffLUmq1Wr6m3/zb+pnfuZn0vduBTmq+vtGigNk7WzwrPXvEvWbf4f30Y8RPPfvR/Av7h9ZvoMzfN3O8R++6yWkdmq/r/ssYpHvHa6fvHwZdgL9mkXo8ntH5rzrVO97svazcA3umcXKzhK/J+Ayv6XWTPCYTZTlz9MGxtiDjwTLsyQC4t7P6F/aG311t/O8P6OP78FXt0OjnRptM38u9E+WuL3gEgFv5lS0mTxo4viM28Tt7r3TuvTn9Gs6XkX7vZ/cP8ia43EMbqTsNN8OWo7974OTfYHot0I0/tkksJXX1tY0Nzen6enpFPn1qHM7h9+N/EajoUajocXFRV25ckXT09MqlUoHxlb1jY8DEb0WZVTyuVwupet0dHSkA1qyGHwHLVzbNz360llN3m5PW4vRb5csAyj2EQqUz2JY+Fh0dnZqdHQ01eO+8847NTg4qP7+fg0ODiqX26zNtry8rFqtpv7+flUqFc3OzurSpUst9SpdPHWrVqu1ZDV4ra/19XVVKhXV63XV6/WWA9woJ3BQAZibIRgfMBylLfYBrAzYixFouZHCHNvY2Eg1752FMTQ0pP7+fklbwFutVlO1WlWpVNKTTz6p73znO5qenm45TPaoyLESv3lyVPV3Pp9PKcLS9hTVCP7539L28gjO1HYGD8xdWNKdndsPJuN68TWvy4ouJ1jWLkXZa4a6oQ3Q7MA9z5XlkPszwWADbIXV5iBwdPa9Tx2IIGvKgwCUBnHWNH3mTHRnzXoJK2fpkbXV1dWlwcHBlE3kB1N5Cr33He8zPoAN0iaD3NlhERCJaeD5fF7j4+Mth3g3m011d3er0WhobW1N3d3dWl1dVb1eT88NyN7V1ZXqcwL+N5ubrO5qtdpiQ7l+x8nP5XLJ8fcUfJ/fMYCDU+nnoUhb7HoHAgARmF8EARqNxjYn3EsJOejO3OPZfe75nFleXlaj0UhryIkDHgRwID3ayw4sZYHifi1vExIDS3ze5+v6+rrq9XpLlkhnZ2c6oKyzszMB39Tmr9frLWvaxyHL1mKdRXYd7aBfYrkpvuvPx71gxfp4xO/5HuFzfjdymPr7wx/+sH72Z39WP/dzPydJOnfunH7/939fDz30kN7//vdv+/xv/dZv6cyZMzp37pwk6cUvfrG+9rWv6UMf+lACw8+dO6cf/dEf1YMPPihJevDBB/XII4/o3Llz+vSnPy1JesMb3qA3vOEN+3rGGylHVX/fKGEPlpSY0hwGyj4sbWdpR7Ac/5v1TVY5es7BZAfRfd3zuq8fB5qj7pbUshd6uRbX2fztmS4eeHMglvuzr8DK9X0QO8cBZtffXlIFvR3L4tFu6mJ3dnYmTAG96hl0tJW204+RHe86xHWkC/sg94S4NjExkc6ZQpfWarXkg0ub2XJe4opnimA0bcSO6+7ubgHRYz9LarEpvRSg61Z/Bu8H5gJz1ecT40T7/N6Mi4+v20kRUPf5mRVUwZ5wolfMuojlXLy0qZMufZ5GENnvnxUEj30Sn5vP+plBkBlWV1cTO5++LJfLWlxcVLVa1eLiYsqK9HI/hyHRZ7jWfT1A1dHRsasa7cf+98HJvkD0X/7lXz7odhxpWV9fT47I/Py8Lly4oIGBgQSmOqtmJyCdDXthYUGlUklXrlzRpUuXkrK7XmHzY8PjIAY2wrjhs3hJB/OF7JvfjQQu6RffxHG2ANE9eu8KebeM5Mi6A8CITDY3onits7NT4+PjOnv2rAYHB/U3/sbf0OjoqPr6+lQsFpPztbKyokqlov7+fi0uLurpp5/W/Py8JKVnc3GntVqtSlICQNwgWV9fV6lUSkYCAIWnyD8by4W4AcqzALzA7iclnNR0gJTDANJZA5Rdwoj3kkA4zsyp+fl5Xb16VVevXtW3v/1tffOb30ypkcdyLAclR1V/s8Yxit3wdYOafSIr48f1hGevIATENzY205ijQ8S92GeizsHhpBxYvV5P2WSeKot0dHQkZ8XZyP5MHkDmgCsA82az2QI8ehkRd8IB1KNDg86nL3G+0LnN5ma5FGpp0y5AdNeH3ufOWnPAERDdv5fP51UsFlO6uh8ILmlb3zOmgOP+fMyPjY0NVavVZDO4OIjrZXsKhYLGx8dTBhnX6e7uVq1Wa9nbITbQR4DtxWKxBehAP5TL5URUQI9nAaY8OzZaDHR4sMUD/T6faHtn59YBt9gu7pyjd8iScxAh3tPL9zA/sJGySqFQooYxx8aJ7EhfX7zWjtEXA0zuZDvY72vUg2SMe1xXBEQIFsX0fP5fWlpKoBpr2kEx2tJubOkv/nfwXNrKgOR7kdnu+4y3MQIn3p7IbD1MQsXi4mLL/5RocllZWdFjjz2mf/Wv/lXL66973ev01a9+NfO6jz76qF73ute1vPb6179eH/vYx1KZpUcffVTvfOc7t30G4P1WlqOqv2+UsL81m00tLS2pUqmor6+vhdzixChpe2YSQGOj0VClUtHy8rIqlYqq1WoiJ8XyJVzH9WY7cf0dQXZskc7OzkQIwu92EN0Zt0i8VgT0437AZ/ARY6ae62/27KWlpW0gugO62BzYH85EbhcE93b7HuvSaDTSvhnF9XehUNDw8LAKhYJOnDihwcHBBPI2m00tLi4m/xhbhtIqnu3PNV1HuP3S3d2dgtUe8HV9he738z2yMBPGDgGcp1/58f3a9TdtQ49A9vB7uF7x4ATf5X3X+a6/swggDqJ77Xl+mLMOovvvCIJH/MjnsttptI+xcbsHUHl9fT3p75WVlbSO+E65XFapVFK9XlepVNLi4mIqKei69zDE7YSd7uvziyDFs/Gg02ezXFddg8cee0yf+tSn9L//7/+7/uzP/uyg2nQkhc1nZWVF1WpVtVotMU/ZwD3K6xFEHG+i30TL3BE5qAXuDpanwvjG7WVddgIlIyvsIMWj6xEo8Y0lpsQ5MLAXiQEE7xdXmvEzGEAR0IUh7f/7Z7yOdrv+dcfQI8PxB+ajzxOcXHc2n20S06/8OVDkMTXvMMWdVwJp8/Pzmpub0+zsbALM+ZmZmdHMzIzm5uZUqVSeE+Vc9vtzLNcvR01/R50U132cN1HHuc6I10BYy16rm73VHUhnpHnQkj3a64i68+1Oa7QDnP3FZ730iZfiiHuhP6sH7aPz7Nd2Fpbvte5wuQ7Kar/3QXwOfiLI4IFwL53iTCa3RSLYsRNo4e11J82vF517LykSS7HwOX77AW/8nwX4ep8xB5aXl1v0tge9nd0YbQwHQts5nD4X+B3tI3/eGJTAhvA5v9O8YIz9c7Et0cb1611rn8/SBdE2y3pe/36c8xG8cAAMGwv2KbY7tjyv1ev1dKaNAwuRLZcF/sf/I9kj7ku+/ujzuPZj/7SbI3vVqQehv0+fPq2hoaH0k8Uqn52d1fr6uqamplpen5qa0uXLlzPbdvny5czPr62taXZ2dsfPtLvmrShHTX/fKIl6ynVx1o9ni8V9jz0Zv4rX4164H5s1rmfE17wHvNxm4ftSa1nIWDbN9WMWGznL/siyCRynyNIxnkHkZ66g072sh++3/hxIO1899ovbe35/gFwHdL20iNsVsa3u08exytJ50baJQZWIHUQ7M45nnDtZ+EK0AyIo74S7mC3Qbs5Gm67dZ/y6UYdm/ewk7fT3TjZ8OzvA70X7yPZHd1cqFVUqlcQ6JyiGHndiy2HJ9dxrLxjHYfvfH/nIR3TnnXeqr69Pr3jFK/T//D//z46ff+SRR/SKV7xCfX19uuuuu/Rbv/Vb2z7z2c9+Vi95yUvU29url7zkJfrc5z7X8v4f/dEf6e///b+vU6dOKZfL6f/8P//Pbdf46Z/+6W1z9G//7b+9p2fbFxN9ZmZG/81/89/oD//wDzU8PKxmczOK88M//MP6zGc+o4mJif1c9shLs9lMNa/7+vq0trammZkZFYtFTU5Ops3dmUjN5mb0fHFxUSsrK7pw4YKmp6fVaDQ0MzOjWq2WNrjrlRhBJIIcT/xmg3OmT5bQLthIBwliNptbh1y4o48ClZRSWvcDml9LYPJ5xBbjy53Snp4ejY2N6bbbbtPg4KCmpqY0MjKSmG0dHR3JIOvv79fS0pIKhYIWFxc1MjKS0sqy6tOiyGq1WoqiV6vVbVFMAjQOHqysrCSGdFZJg1tdMFhIna/X69tYeJ5CFg22Gyk43y7NZlPPPPOM5ufnlcvlVCwWU/YGLIu5uTldvXpVjUZD58+f18LCQnrOoybXA4Yfg+jXJ0dVf+P8uMBUckfAnQzYSOyB7jhxPQ+GUXoDFlCtVlNvb29iN2WB+O6sYLCvrq6qVColhnkMZnqb/MAo2se+zp4HGOwOIpk3Du4CRnuwwNm2tDmWY3H96c/k//veGtPB+XFwMWbYwKZxJziXy6UDJmGyOZObfuLH28w1/f4w/UjBhuE1MDCg9fX1lJJN4Lunp0cjIyOamJhIQe7e3t4WXeqgBn3hDGuIErlcLtV5dTB6fn5epVJJXV1dqTSbMyP9ntgyjKMDKcwTdMby8nKaE35Nd7yxmWDRMfYO2lN2xfdsrsP8jDpqaWlJzWYzfcZT15kftLdcLqvRaCSHPLYh2rdRvzqwwGFlzA3/23+znjc2NlLZGi+J5/OLMSuXyy19yrqC0eYg3OLiYhp/zwhwOy7aItitzCkvM0GfsSc4yMW4YXtibzvQTj95lqIDY1lA0U5yEPr7/PnzGhwcTK9HFrpLFoi1U3uzPh9f3+s1bxU5qvp7p5JH1yO+R9VqtRbmM9m/S0tLLcFZaWt+sG7JMEJvz8/PJ7CNgy4dSMzac64lXAN/2bOqY9YY+zh2iaSW19z/jkQBBP0Y20rwwP0sSS0BA/S3f9/tL7ex/BBU2og4SO+lM9ApPIN/lrWKzcBex3d7e3s1MDCg7u5ujY6OanJyUv39/YmR/v+z9+9Bsp/1fSf+7rl399zOnLuIBEJAjCHxelGVSmRtQRxw5GyicAnsuopiE0OhUlWC0O6yVkBrQQIU2KWcsgUmVJEI4gX0h4u4XKFiib3IS1BtRVyyGzubNS5ZEtI5Ope59HWu3b8/5vd65v39zNMzPefMmTPnqD9VXT3T/e3n+9y+n8v7c3mibkbf4aedzla2FJ+xPg6W++/5TQTFOUQTvgx2gNzwTDoHqtFVoqPe8YVut5uizeH/DpBLW5Hm/hl7ITpt2BsEb3mf3UlDYAiBneBQnunl7UXHretO/rxF/Z21937yt+vljMnbQvcH//CMzqirS1Kr1VKj0dDKyoouXLiQAlVzmQ5Xg/wZ7OU0yP2GudiLTD5I+/uwn2nyN//m39S//Jf/Mv3vJS37ocsC0f/hP/yHqtVq+pM/+RO98Y1vlCT96Z/+qT74wQ/qH/2jf5Rqyg2oSN1uN6V7ktqyurqq2dnZdII0wKy0xVCbzaYuXbqk5eVlnT17Vs8//3wq/7FfpR7cg+te2l6HivqDHhXP+EDvN4AtbfcEw8BhnjBfBzT2g1zYoYhhuCFwuDdALulk09PTmpmZ0dTUVALRJaU6qkNDQylVfHp6WtVqVRsbG9sES5wHjLdSqZSEON/5PDkhXIaHhw9MSOwXMSYMcQS6gzo5wOAgDaR4r06no0uXLuknP/lJcqLxTLG+RKmvr68ngOdGpQGIfu3oRpXfPOvsLf5GHrijFZkAaApP4XmMDmNpy3igvIWkBIQ6v+F5dlkBkF+r1ZIjnWyTHLnhD39zebqxsXXOCuN1eRxBb+9fBAnon/92fX09OSaZJ++bR274b32ucNz6/LpDF93F58xL0WB4xxI0OD4cGGd+MZYiYOgGuTuW19fX072QHxsbG4XDqCm9Rmk75DXjikYzOgE1N1kv3gE8SRVuNpuq1+upVqeD5DESjve4T30/Msa1tbX0eYxccxDc5SZ6DXtO2gJUWFNJyUHtzh7fd6wJOiS6pRvEXNNut9VsNgvgQC5q0sfvz2ucEw+siAY3e4B+RuArZv6xd9bX11OtVBxxPFeUZHTgyfeHAzY7kc+fj8mfV3cK0X/aBXRh/iKg5eChO99ywMRutB/ye3p6ugCi5+jYsWMaHh7eFiF+/vz5bZHk0KlTp7LXj4yM6OjRozte06vNw0Q3qvx23rCfxDOM/Gk2m8mmxYaIdm8vh/Di4qIWFhYKzlG3P65UN42BaQ6ke6ZQdNJ7zefh4eHEg+AD2KUOGjrvjDiC/94jqldWVpIuFYOvXOZ64JyXxUOOO3m5UtrNUQRSua+XWKGtsbGxBF5PTU1peno6ZX6Xy+XCfHMGydDQUCp70m63ValUUvkPB9zhnzGwzfkqxDz4Ie5eosxB9Ij70CfG6Wvu8tvX0mUrssF5PX+zl+Kzxtrs5Fz19fcMOignQ6LcRo5F3cmdIchpB4nj8+GfeW195os9sba2pnq9rqWlpYKTxnV8AivA1ni24547CNrLvS7HWXeQ9vdhP9NkfHxcp06d2tOYnC4LRP+3//bf6rvf/W4S4JL0sz/7s/riF7+4rRbdgIrE5sUrPjQ0lAwngPWJiYl0Ld7N+fl5ra6uanFxsWCI7jftBjq7EHbGRjRPzhspbU+1v1xyYQuwkEtP8v5eTWI8GCeurLjh6qBDTpHJfXc54D/3RlAjxL3mHPeNwtD7etjJawvHVDLoajwfV0LsVQxyBw0kXZNDTAb0yqMbVX5HI8T/57McP/XoHjdSPeqb9pA7XI+sIzqd37lxgEGKEz2e3+Ayyw2oXCo2Ts8o4+DZMeIYAzoC6JKSQeWRZh4FRVQzRrnLIwf3okLugKFHL8U+M7de3xOHNA7oWGIlt94OKHsELnPJfEfyOtsYlBiPXj7GM5rcqIzGvJPvF+QrYwU4BkQHgHEnD2MCDHDDn7476Bnn3+dofX2rTr7/hnlifh2IYY+7QRvB5ahL+Jq4gwMACucHe851xUjxvlF/dHDa18TX2cfnfY5z5kEhvkdz4DfjYs2Gh4fTXPk6e1kVB7n9M0ACB8n5259TjzjkGYhAlPfXnWP+HveoAxRcd9j0jrGxMb3lLW/Rk08+qXe9613p8yeffFL33HNP9jd33nmn/vAP/7Dw2RNPPKHbb789OQPvvPNOPfnkk4W66E888YTe+ta3XoVR7C/dyPL7au4/5JpHGktbtaRddkrFSHQiWB04j6Wq9qPvPL8eeQyPc9vMAVHXD9xx71HtyG93vO5E3qa/+M7nx//29nsB/y4vnAfze++/tOWQjeTgs/NTnNjxIMuoR7hTwW11B69djrvDcbe19jmYmJhQpVIplLJxx3K0+yG/F31C5jDnvqa+X12eu6x1eZazjz3YwPUnnyvwHs/w9PmI8xQxDpfl/h73lstl2kJnzO0d779f49kUZG8SgOLzRqkmsvgcV7oeKGa6HQa6Hs40+T/+j/9DJ06c0OzsrO666y595jOf0YkTJ/r+/WWB6DDmSCj2A+pNrmC//PLLunjxokZHR/Xcc8+ldC1AdB5wUsh5+EmV3e+5jkLRDebICJ1cyMVoQEmFiCwE2eWQg+Qxddm9rs5cXbHYT3Km70KMFC0XIP7Kgevx89x3/c4PgpJ0NsrJzMzMFGqXEVFBnb92u11IGzzM1OlsHvCztLSUDvmJStZhFHyACpyLQGo45KVbbnQ+epCe8AEV6UaV30RiOhjIu8uEKA8cDPcyGYCsPLe0TWkOFLp2u11wikWDkt8DvmG8YyBIW8++A+cO/tLnCAjHaFPKkAD+UootHujkQCDGRafTSXXafd5cLkUweWhoK7ra++lzzj1cJkrbazy7QUjUeTS2+I23Tb8wMHF0tFqtBIDg5HfZSjkt9BJkOeVcyuWyRkdH0zklnnbN+rnT2vUTj4DiEK5Saevg13q9njIScJ46MDI0NJQOUSWLDfnOukfHBPvJgxtYn5WVleTk5x1djTIaXMf88TcZCRidroOxZgA1MbDBn0v25tDQUOEw3QikOwDkzhsHnKM+Rf+ZR3823NEBCMB94zMb7+egFf3hMFjW1EFoxpJz+uQCFGL6ecxiyenbrCtr73vG++JOIcYAwBLL67Bfe2XGRDpI+f3AAw/oAx/4gG6//Xbdeeed+spXvqLnn39e9957r6TNKLQXX3xRX//61yVJ9957rx599FE98MAD+vCHP6ynn35aX/3qVwsR2h/96Ef1i7/4i/r85z+ve+65R3/wB3+g7373u/re976Xrmk0GvrJT36S/n/22Wf14x//WHNzc9k09IOiG1V+93Ko7SfBc3B6Ic/q9XrBcS5pG1/B7mi1WklWwqf2a95xKEfw1/m1tGVLOE9y+04qAtuA2Mi06HCOjkK3pWPpkl4OW3i8B3G5QyACqV7Sg3Fx8LefE+Oyx0F4Dzakb/BCgGsy/IlKZ05dN/QMHeeNPs+uE3HPnchLz83OzqaD0b0Uj+s/cS6dxyOTojM7AvBRz/TsAWSwr5/Pveup0Qns2A76cDwbJQYUOCbjay5tBWBEXMQBeXcou+6ObsPYIj4S1wc9hZJL4AVLS0tqNpvpd76HJBXGdD3Ymzg7+q1MsR/yu5+Dwa/GmSanT5/etzNN7r77bv29v/f39OpXv1rPPvusHnroIf31v/7X9YMf/GDH8nJOlwWi//W//tf10Y9+VN/85jd10003SZJefPFFfexjH9Mv/dIvXU6Trzhi00tbHjCMQQedS6VSwSDcKd3pWlH0UntKkKTEeKWdo7d2Imdybhw4s44eSunqlJJxcsPOo/1yBq73L9ff6M2Nn+2FSJ0bGxvT5OSkZmZmkvADWIjCdy/rca2JCDfGs9OeutK5vFKK6+3A1SuVBiD6taMbVX5jbDiQmdsrziNcYY9GRc5p7KBUNEyiMRRBdICqnMHtvIFrHehzwzj3ewcZc69eoCAGKvdyED22DcgXI5p8Hv09ji0a37noJJehzF/8jbfra+bz7ynMHqFDvUOPSvP5wUj1fuTmLu6tKHdyugrADWvrQEEcI3PhB6PlQIzYn9365HqUOz742+ufYqQCOjho7XPfa13oixvhnkrthrqkwj34P0fxGXWwy5+THPjMs+vfOWC9k2zxucMwd2ItS6VSmsf4+1iaL2Z4+B6O/c+BETEy0e/lYBigleuqccwOQu1GBym/3//+9+vSpUv69Kc/rbNnz+rNb36zvvOd7+jVr361JKUSl9Ctt96q73znO/rYxz6mL37xi7rpppv027/924W6qG9961v1rW99S5/85Cf10EMP6bbbbtPjjz+e6qlK0jPPPKO3v/3t6f8HHnhAkvTBD35Qjz322OUMfV/oRpXfB0HwHmSxy9MYvORAKs65eL5Ujh9fCbksy4GljMGBTmSJRwg7n+D6oaGtrHf/nDE6ub2ey/KmPQ9Sc8DcZWfkWTuNG/6Z01MA/nPgqQOgUlG+R77Z6/7xmohZ7IXnITeR4V7TPq7vbsQe66XTenusC9dER3Hunjn5He/lf7uDO2Iv3gZ9j33lnnEe6L/rFXxOm75HvP/xvi6rc7quB6PeKPbkXsaxH/L75ptvLnz+G7/xG3r44Yezv8npojvt/9z18fO9tpmj97///envN7/5zbr99tv16le/Wv/m3/wbvfvd7+6rjcsC0R999FHdc889es1rXqObb75ZpVJJzz33nP7qX/2r+lf/6l9dTpOvaHKByN8uyD1t5mpGGnBv99QjEPnbmbRHf6NkREDBAQC8vPFgs5wnNjJt2vbT03EsxKg36GoD6FAEAbyfklSv1zU/P6/19fVUk5EohlKplICLRqOhhYUF1Wq1FGVNlFo/5CDEiRMn9JrXvEblclmnTp3S7OxsUrI6nY5mZ2dVLpfT/F24cCFFvkUF5bCQ77d6va6LFy+mKEOIfUP0gO8XB3muJrlwcoObyMQInLwSaQCiXzu6UeU3h2M6UM3z7jW3I8AaAWi+Q+56hC1t0KZHu2KYU2OV1F1X7B2E9HTsKEPoG4Qs83tCDvqWy+UUAeWRVx7BwxwhW6hp6SCBGziefuz3AijMGfgOMjh46X33z2L0vwMaPn8Al64zOblDwA0oDG/uQXS+g7HR+YJO02g00sGfXiPV55/f+2GinFkTswkkpQj39fX1VAudz0ZGRjQ7O6sjR45ofHxcR48e1ZEjRwqGPNljfk/nqW6wE92HLsbvmRO+d/2gVCql6H7AV/rqc+T7NjeX0Xhn/ZrNZipnwz1dd/TIR3dcRVDEgQ/2JG3EtHe+Jzovlm2IFPdXBJL82fQgEl/nCEjEaFvXrb0mvs8b4/Co/d1sAeczjJM57aUH5bICetFBy+/77rtP9913X/a7HKB911136Yc//OGObb73ve/Ve9/73p7fv+1tbzuUusaNKr8PkiLI5s8F5LW2ed7Q43vxgislB1/5WyqWXsMhRia2B+QQGez80TOMPSrdx+r2s4OO8N8Y4R5tfdeJXJ9yvYD+cZ9c6Q3XubwvUNQ/+B3XUabDHR/NZlOLi4spch25z9wyPjAFghZbrVaK4u6H39G3UqmUAtfGx8d1/Phxzc7OJt0D+Ybc4x6+F30P+NkoMWDRHba+JtLW/vVIdpdVDla749p1RZcVzG+ns1nWqNlsFkoT+l7y/RPXNDqK3akTP4vOJMZJ7Xqu8XsjZ31deacEbC/w/5VC+yG/+zkY/Ho70+T06dN69atfrT/7sz/r+zeXBaLffPPN+uEPf6jvfve7+k//6T+p2+3qZ3/2Z/U3/sbfuJzmXvHkxi0Rtu7VzXkGr0YfXGD2AtF5OYjupx5jFCGkYOLdbjelaMMsMfKkrbqPTghZT6eDCTqITv/d6IDpHxSQHkF0r5lXr9fTgZELCwuSlADsUqmUxtRoNDQ/P69arZbA9Ha7vScQHeDk5MmT+pmf+RlVq1WdPHkygegItKmpKY2NjandbuvcuXMJ5D9y5Mi22q+HidgLgOjsNyeep5gyftDANc+Ig+iAPK9U4T2ga083qvxuNpsJ/HRDwQFtZIIbbCMjIwm89Igej5ql9InXTfWa6S4LKfNACnGMcHJDBrkPr3WZ6YAWxocbjxAlaIaHh9Ph5EQ/xYhYxszhSfBIQHQ3ljEyOQTZQVNPMWeeJRWMpfju/XfHOEa+G4xuWLth6AdIOw9lzTyt3UFnZC2APaVFPNLc65MzV51OR81mMwEQ7XY76TGVSqUQLECpn+Xl5XQIpTtVuA7Dt1KpqNPZPNiM0gKTk5MaHx9PIPrExISOHj2ajAfGvLS0lEAdygl5Cr2X8PG9z/iQj0NDQ2l9WT9pK5ONuScDwcvORMDVozeZbwdWHLznMFUvRRB1T3eI056vdwTToxPBQXbmxPdijP72Zw1DO4LocS97Xxzc8LY8RT7qVYyX5831Yd7hS96fmCrva+B9YJyAL16SJ4L9hzHLdUDb6UaV3wdNDuQ6XwM8jLJIUrLr+P1+EjzFdQsHGN1u8DJgvc7VwA4G3EJmewYddrfLb+cVHsHL9Q64djqd7CGZOce662ZRrjtwuxMf8rlBZ/DgQ3e+MiccJEupKspOIL/9MGhKrzWbzQQSe4mwnciDAKrVqo4ePaqxsbEEorO20mbAR6PRKGSGMw8+TtqMgRXMJbpiDKwAt0G/YJ58vXP718H4KBfp5/r6etI5cvYs+8LxpLW1tXQQua+5v9DVu92tEoIuv33f4IhgLnyfsYfd3sbRRJCIZ68P6PKon4PBr7czTS5duqQXXnhBp0+f7vs3lwWiS9L/+r/+r/rf/rf/TefPn1en09GPf/xjfeMb35Ak/Yt/8S8ut9lXPOU8ei6EDuL+XibFI4EiE3bveKy/5Yo6xjEGcwTm3SvugpexexqOR/TGAy8kbRMmB0kxMkraHAdG9dDQkOr1eqq7ihEEUI7hXavVCnVI+1XWXAkjEnFiYiIpDazfxsZG+s7BH+bWD1w7TOTlBhwcj8+FK1QxMpHvr7aDIAI4uQjJVzLthyd8QJdPN6L8dmDWn78oU3oR17pSnuMvMQKGduNz3Sttt5dD3KNH+dudyzHCK34eHca58bpR4YdDeYRrjj/mQMudyOcw16de4+91bTTMeq1lLpLNo9d6lbqJ0Vh+f5wMyEPeAQ64n8smd5pKW6Cuv2JdcwB+jGbeHexlP3h7rn+xPjGb0UvMuY7n0XM+p+hvHrHFtb3WbX19PUV6ewk/N4JzMjkC1b7OroO6Ie1lF3aTB3HvMtc5Z4wT9+VZ8f7G59qfV4+0Y/5zADtz4/ejXXdQOMjg/YrtOJAfn69cH6LOvRcayO9rSzei/L4W5HpCBHZzMn03nrEf/eGd5xwHbXS+R/ntMtJ5Xo4n9AJSc7zX+Y1HM8d2c/Lbx+Igf+T7XOdyO8ojl2UeUR/5pTsaut1uyrTrdDopewugVlLi71y3vLycwFYAdJczvdY+2t8eaBCz6xwrQMa4TAEs9HVkfXwdaTNSzJCkrRj1HcfDnLvcct2gl00dyfdQLpiC9d9JfvfSMyLOEvd17K/rYvTnlS6DDlJ+H9YzTRqNhh5++GG95z3v0enTp/UXf/EX+sf/+B/r2LFjBcB/N7oslOxTn/qUPv3pT+v222/X6dOnD2XE6vVOCBRXnvn8at6TtHXAVDyI6+vrBWHgAg/hs76+rlarpeXl5QScDw0NqVwup98vLy9rfHy8kG6cA59dgXGPop+M3mw2kyfca6DFg2GuNkXBFCOyXn75ZTUaDU1OTmplZSWVUpmZmdHQ0FAheu3ZZ59VrVbTpUuX1Gg0etbQ7dUPoh+npqY0MzOjarWq6elpTU5OFgTbzMxM8o6vr6+rVqupVCppfn5ey8vLqlarqlarWQF90IRAbDabevnll7W8vKyFhYWkEOWUseXl5eSwaDQaqYRQuVwu1D++WuTACmV5ms1moabiK5kGRvi1oxtVfsPfOETRPwfUc/ngUZ4uzzwax5V9ZFo8MGt9fT3JP0mFCCLKS0jFzBSpGE3qEegOnmNAYXwRHRdB9AgKI5e9/AnyHflJJFF8Fl2G0TeMQQd24WEO8kUjORo4yHI3kONa+ffdbrdgsHlkmN8rgiDePpFhZBzESPT4W9bIswMbjYZGRkZULpc1Njamcrms1dVVDQ0NpT6trKxofn5eKysrWlhY0OLioiRpcnJSlUolrRV9IjKLMh4TExMpQ2xycjIdAidtGvpe9sYj4Tc2NtIhWTEFnN8AfrshTEo0bfs+o3QLe4ZoNw5jzUWieyQg5er8OeJAL7II2+12mhPfP5KSTkmQAc8TgAb94fNojMfnNjqliJpkf/lvNzY2UmBDu93W4uKiVlZWUvkZgkHIJGAPEyjilANrojPfgxUiUMNYfZ48et8JHTjeH8pF3ke9O7bZiwby+9rRjSq/D5Kc1+RklV/TS65cjT65/IMHuc7iYLYfbBrHAlAJT48Bb/CeCN56CRfP6opRv/63Z8dFhyhz6HKBPnqb7riI5OVfy+VyIUgM/Yu2aDcC/61WS6Ojo2o2mylLq1qtamhoKJVDW15e1sWLF9Vut7W0tKSlpaW0/siK6FSB0M1cfk9OTiZ9Udoq1QPPJ7uLLHTaYaxkksGjvQSMO1hWV1cL8huZgazzjMt4GGuce+bOZTL69MrKSjoMvdFoJMdETuYh44eHh9VqtVL/PHuR++QOuHVHkAc5cN34+HhaG39G2ffI7kajoaWlpRT5TwmkfuXcjUoHKb8P65kmw8PD+n/+n/9HX//617W4uKjTp0/r7W9/ux5//HFNTU31Pb7LAtG//OUv67HHHtMHPvCBy/n5nmh9fV0PP/yw/pf/5X/RuXPndPr0af13/91/p09+8pMF8PJTn/qUvvKVr2hhYUF33HGHvvjFL+pNb3rTVe/f1aQr2eiXS+6l84MPvSame6IxjohA5/RyvOeenoMiAPNECOQi6bx9DD7SiYj0giEehILTLwHWuDe0Xq+nGmLValWtVkvVajWl/QOi1+t1vfTSS6rVaglw3ct4EEhEnk9MTCRQnYPJEKjlclmVSiUpSsvLyxobG0slEajPehiIOVhdXU1R+hi0vQhgi6iC5eXlVEroapMDBx7hAIC+l+yCG5UGRvi1o4OS3wctu3nuYqQJwJODvQ6qScVaxtEghWIkuLfHc42cc1A7yqW492kzOn2j7PQILNrh9x6VxXduODMWZKen5uai2Jk3nyevd+oRbjEyPWeg8bnPrX8ef+eRS56R5Q51N5ZzRhH9B0SmFjrODU9z9qgo2iOggNInZHEBlrIPcFbgMF1eXlaz2Uwp2pRoiWONewHjHpAfhwVrGCPxCHJABwMUoZ9x7dz5gP7EHvMyMFEP43cxsi4Horv+w+/8AD/0N97RWXzfAtITyLG6upqACH9Out2tiPRcdghtQXGf+xjcIeR7lfq46Lhc7882/MbbdudUBKj8Nz5fOR7BnnQHhwNTkdhLcdzxmXCKkfP90EB+Xzu6UeX3QZM/h5GXucx2WXoQfXL+6ZHoUtEuJhM7yhJ+L21loPh3jAuA3uU38gQQ3eWv31/aysiLzlK3fZm7XGkYt4Ncl4C8BJrXQUcOITcYmwPMyGwIWYKzk8C+UqlUANEXFxdTQBtBWrkSc5EAxpHhZID7eSQxmt4d1t7XUqmUnO78z/rA91kf5IfPD7KEfro+3Ku2enz33/o+QG5jy/Yi5Ldn5rkOEgNOIsU9hix0Z7nLTJ4TrkNvcYc9+u4gEv3g5fdhPNOkXC7rj/7oj3a8Rz90WSD66urqFdee6Zc+//nP68tf/rK+9rWv6U1vepOeeeYZ/f2///c1MzOjj370o5KkL3zhC3rkkUf02GOP6Q1veIP+6T/9p3rHO96h//yf//OePAoD2nq4YOxuzMZIdAwF6nI6w+V7jwaC4SO8EQIOZnjaEsyO6DmYOMoFHlKiug4LuVEibQmxdrudPN0TExMpUg2hBJDebrcLB9n1Sz63tDc8PJwMQAfRcXYQ2c98Li0tpQiw8fHxQmraQZILcQDzpaUl1Wq1FPm302/ZvwAaOBIQ4p7Gv999Zs/yXDSbzVT/rlckw4AGdFB0UPL7WsjuGLEK+ET2U8xU4vnPfQY5mCypoLi7nOt0trKiMA5i5KhHYntb3MdrTLoxLOXTbvkd1/pBln4tffW0VgckdxtzBLj5ztNjc4Ys8wPfdeDfnQf+igY9xli3W4xwdsPeDSsHj1l/L7NBPxxAcQM/piHTB9bWDVAH0d3I7Ha7CTj3kjw+jwA37gxw4MbnG4MfMD/Kb/Yjf7darST30NdcN8sBAr7WHimIbhXr3cZ6uj73XOtGv697BFriPscxMDKydZirR6tF51YvcuPbQXQPvvAItwg2MQ8OCPhvYj9iJHgEi7wcT5wz359Ovo/jPf36uK60vZvD3gHEQVTz4acbWX4fJO0EivJ55DVXm1wOuA7jukeu3714ADyMZxzHJXwk8l2X5zEDzNunPy4zvC13vnoEtdejdlkXyWV2lN/eV5cx/vJSLMg+ZNLY2Fj63uU30cuewcdYot0W+5wDJRkj/XOdAbuQQC/kCtcQKc8YpaIT1u1jP2CVNUWXZI3cCUsfIujM57w7fhHLme1mw/IbbGB0J/rE3NM/KMpAxu/6QlwTd4Qz16wnMpsgT3+2BjSg/aDLQsY+9KEP6Rvf+IYeeuih/e7PNnr66ad1zz336G/9rb8lSXrNa16jb37zm3rmmWckbT5sZ86c0Sc+8Qm9+93vliR97Wtf08mTJ/WNb3xDH/nIR656H28kcuOBOt4jIyPJGHNvoBufDrgTBeWCm5RcP+zMgY0oJCOIjhAGRHdB4vU+DyPBzAF0UQr8wChXVvoVVJE6nU5yZlBfnahzBCWCuFaraXFxMR1eSnQiRjqe47GxMU1PT29bn4MgDOkLFy6o2Wxqfn5eFy5cKKTa5wiwhShLDnUdGhrS9PR0AtT7jbraC/FcNBoNtVqt1Od6va56vT6IQv//00F7wge0RQclv6+F7PZIbaJzcAhGQ4JnNYLeHq2OEzj+jqwrIoj8npVKpRAd3u12UxSMR3VJW5FitOPGXayJHSkCmJK2pdi6keHAMenCXhva23SA26NfHah1486NQGSXR7zxPS8ynTy63vULdArAaeQiEVAOxrrOQTtRZ4lR+hh53Mvvxz38EE6ivYhMGx4eVr1eTxGD7pxm3Mhdj3hmvdxZwhhpPwK5OIzpR6lU0tLSkhYWFlKGE9dxWCflXoiar1QqSSbGNYpzw71Y46mpqaSzMZc4qnxP+R7CSPdDa7vd7rZa5Dmdgjnhu0ajUZjTqDNJxYNM4z5mHA6iMwcOYkcHiuttfuAc7fFbL6PiaevsDe7F2PlNzqnDvMNvCGJwMAme5c+k8wAn2vD/41xHJ0g/NJDf145uZPl9kLSbHn4tbEqXZWQX5Qgw2EFs3vkb3QVQmXadP8NLc5HmHgznmTXIb9cfuJfzMnQR5Buy1LOkuA9jkrbK7CFzXG/LOWDh07z7Yem+fiMjIynj2kvQ+JkXzWazALbSv5y9GfeGy24H+ZFlRL1L0uLiYpLfBM65HuU6jGMt7pigfV8zx16Qd5SFc5nlEfxRfniAA3IQGdhPJDdrgqyr1+tpz3mJOpfPHkzh+zi3Xxln7APz51gLAWweid7PM90reOVGoYH83j/qG0Snnoy0ySy+8pWv6Lvf/a7+6l/9q+mhgB555JF96+B/9V/9V/ryl7+s/+//+//0hje8Qf/hP/wHfe9739OZM2ckbRaLP3funN75znem34yPj+uuu+7S97///awgJzIXqtVq+9bfG4U8+kkqejNjJHqMnpOKDynfOQCP0eHGQiR+57Ukc5F2hx2UjFFNUjEiK4Iel0tuiKFMEMnlgAAKDgY48wyIPjQ0lL6TVFAo9jt6OzcGqXgwCfVIPWJgN6K/UbCS/eCCXLqyCKxc5IeDMnGeBzQQ4gdN10J+Xw3ZLe0uv12mOKiaM8IcZHMDg2ugaHhIWymrHl0alX2pyP+d18P7HTRFlhLN65RzrLrxSp/cIPbfuXzuFQHrsjQXpe7go/NYlyERUPXoH95j5lmk6KTPRWv7b2MEVXQCsC+8ffrjwGksueFRbow1Gq4xSssBffrkcs3Hx99xnTy631OVGZ8DBy5/vL9E07n+tBsw5G2xB3wv5uY8RxG0djnrn+eIvcVYGCtz7FGUZFTkwOG4T/3Z9e/jvnAng//va+zgDvPuz2t03MTITdej2S9xXWLUoPOm3PzH567XNREgcD7YLw3k98HSK0l+v5Ip8t/dyGVL7rdR7sPHpC1eBJ+N9rvLd2wulwsuo2k78k+X4f5/BGwZS8x0ygHoubG5UzW+O7CNTMS5IG3Jb3c2RF4c+elu6xP75lgK98Qedeetl0Dz/vmZJt52XH/v114zjLzdqFPEKPR+xu9z6gER7gSXtmdj8hmYkct8yPvowLrrbLn/+wXQByD6zr8d0Bb1DaL/6Ec/Kvz/X/wX/4Uk6T/+x/9Y+Hy/Abb/6X/6n7S0tKSf+ZmfSZ6xz3zmM/pv/9v/VpJ07tw5SdLJkycLvzt58qSee+65bJuf+9zn9KlPfWpf+3kjUgTBpeIhUNHI6PVg+jW05572nFHlQjwKSI86in251hQN4BzwwXU54w7y6Ed3Priyk/OEI6hefvll/b//7/+rcrms+fl5zc7OJk92p9PRSy+9pBdeeEGtVit5ir3PtVpNzz//vCYmJtRutzU1NZUOLPWD+faTXOjVarXkQT5//nxKfevXYcK8c9gbJWomJyc1MTGh2dnZNM87RWrtRr52lI9ZXV3VxYsXtbCwoIWFBS0tLaUa94dhjx4GGgjxg6VrIb+vhuyWesvvsbGxVL/Ss56koiyLQLHXMI+UA8Rpg2wq7gXYyPUYRH5myE5RX25wIQ9jhLbLSowQB3lxGEraBi46sIuDgSwuN6xzRqxnTRFZDI/1CCru48+3G0x878bo2trmIeH++crKiprNZjIs3bgEsIyOkVKplKLMPNLNsxM8eg9+TPuA0l4KzPtN330v5MDhqMu4YyE6ywE1+Y0f9IYOQFk11rrb7arZbKrVahWcyqVS8eCuVquV9hJrRl98XBGkj3uzF6gQwRMHAfyAegxoAO9ut1uoSc8z6/VPO51O6vP8/LzGx8cL5WV4tv0sAObL5zwCJzlQn/H7c4ocZ7/RHvfxkkgOiGPwU7N3aGgoRRMODQ2l9fVnc7d6u96u/8YzQfx3sf3IY6Ju6df2C6YP5PfB0itBfu8H3ejgl5TPGHH7LcpX5/seHBXlc5wvl1lRDsLDh4eHtby8nHgrfAkZ6k5d74cfku182bMH4Xf+mdf0RsZ4SRQHayMmga4iFWWB4wtRh/F58Pn3d78eWePBG8vLy4nH069ms6larab19fUUIOb9pZY3Y/fyJ5HQSd0Z4bY8GXe99oyP38vmoRdxIKcfCtuvE4EyrMhy5P76+roqlcq28oexjZzuyJrF9aHE3erqajqHzvu+U/Z6JO9PxKFuBBrI7/2jvkH0//1//9+vZj960uOPP67f+73f0ze+8Q296U1v0o9//GPdf//9uummm/TBD34wXbdbxIXTgw8+WPDs12o13XzzzVdnAIeM9qJkOCPeCwPKtQPz8dShGD3W6/65Bz6Cy26gsfaXE2FzueReeP7fSdjsxsQckPFarK6Y5EB0HBPnz5/X6uqqxsbGtLi4qCNHjiQjd2NjQ+fOndPZs2dTHbgokBYXF1Wv15PAa7fb6YC0GL21X+ROgEuXLun8+fMJBKeP/TJwruX3jUZDw8PDmpycTCe8O+B2JePxe3Gw3MWLF3XhwgXVarV0Oni/DoABDWi/6VrI76shu6Xe8pvDlAG2y+WyJiYmEl8hcgdjIILEDuJJW9FWLgdpi+/hIdzTQXsvQeKHYGMAACy63PBoWzca/X1sbKxg4LrxhcFQKpUSsO9y1kE/XtGpEME1r0nq5ca8zilArpepyEUL5aJ8HAh24JPSZB5FhDFOn/ywMV8PZB19GhsbS7J0aKhYZsQNRk8LZ80w8pgnjz72eQMc8HWR1FN3clmGnuJlb9ijfiiXp5ujAziIPj4+XgBM3OlA37mfG4lEu7H2bjByP++zv/MCLPGoOpwu9JH7O4hOWQAHNQBFaI96qqzl1NRU4fA19itgRXTyRGdF/J206VRpt9spSpB94XMBQOGlmTyjkL7inAJEj3Oee/b61bM5A8idG1EvYlwOkLFnfA9IW4BBqVS6Ij1/QFePXgny+0opZmr0A/Zdb9QLQM+N1e3gmE0DP/DvpGKQnLeTuz/yP/I0+BoO1Rg4ALlO4wenR+efH5iJ7uIOzuXl5XRulmdd5xy/7gyN+ySHMeScE3F/xwhz/x7HPvILncIdtn4GGn3gQGuXTa7HxEymuE6ur7qMyu0Z9oGXsAOM9oO1PRtsN6IvgOjuPAdDkFQorertuk6cy+D2MTMOQHMH0VutVpLl/fIC9G50OddJbxQQfUD7Rwd7WuBl0P/4P/6P+vVf/3X9N//NfyNJ+it/5a/oueee0+c+9zl98IMf1KlTpyQpnR4OnT9/fpuHHOLBHdC1IxdMvZSuXp7gXDvSdmG431kRvSjH/HdyAOxEHmkUI7XcEYFylGsfIw/BQW179zRjLPYqMcI4NjY2CqVhqPmKkPGIAan/SCbvu4NK9CsKbl/Tvcwnc1EqlVKdNJQEotkcrHJv/k5t+p4DzPDDYjj0zQ/C69XvqITG+0fHDH24nmngCb/x6WrIbqm3/HZjrFdkuRN70MFyaTuPif/DSyMYJRV5s0dhecq0GwA5OZGLPOeV64tUrCu6U2RpzgCP3+cMLq/L7fVN3TB3WR5BZu8v0b/83vvozgTvj68DkcOMFeM7GvE5WbHT/27wu8yJUXa9ZBDGbdxHOYM77jfmgfn06DHAeWR6rE9L35i/CELEufexApx6eT13zHh/fL4iUI2cdceKOz8iEBHXzPdddPSw1wC3O52OxsfHk+z2yMo49vhs+fywz7jWDyBzZ0BcX/6OjqHY91wmiMt6f/fnN9d39oHrALTtfXJngQea9Mok8PnvFcySo4H8vvHpoOW3tD1Lyd93o1fivorPYZwz+IGXzoivnfijt+XkDm3nZfA/5zc5Gc5n6GwuW/0MkX7GHu3tXs4TH2evveJ9jTogY+w1P1HvQy7BZ8nYQ556IBw6aHSqRj0u5yiKz4tjA1LxEE9vGx3HZR0gup8NQz+j3OmHfEzI7253M3CBA8vBKVzfcv0jOkR8jV0P5dySWD6mF04SyfVHMsmYq157zef9eqKB/N4/OvQgeqvV2mYIOpO+9dZbderUKT355JP6+Z//eUmbyvBTTz2lz3/+8wfe38NMrvRLh+NhuFxGBEPDCHQjH+MwV2f9alIETqIxvhs5mDs5OanZ2VmNjIyk6Eo82evr66rX61pYWMimoklK0ZZDQ0NqNBqFg8AQYp6WH8kFVK1W0+rqqhqNRhJ+pFQPDw+nA8wQPjlFw9sElEFA035MccNgBkSgDea6H484wpUSKxsbm4euLS8vq1araWJiQkePHtX4+Hgak3v6c+254sG8rK2taX5+XhcvXtTy8rJeeuklLSwsJGWkl2JHVBkH/XFwmkeS8XsOR3EF6HqlgRC/8emgZXelUlGlUknPsFQ0tKLh6MYBRow7sqSiYcS+y9WH9mhXfuMlIShNEvvlQGW3203K+/DwcDqEyc9w8DrbGCHwaZ9jlyWRTyI3ATwlbQPdfHycSYFx5b9z4NLTsJlfN+x83ESREfVMtJSDkF6ug98CcpJ1QBYAEVq+ZoyV+eVevRwMnU4nHUTlcnF0dDRlL3m5FPaRG97MQQSNeTG/7lTxSG9kebvdVr1eT/f3Q76Qh4DIHNCKLGIv4Pgm2pv+SUop8MwX+yk6Nbrdrur1ulqtVmFOfO96vXX2cS59mj3pUdpO6DpcG+Uf14+Ojqa5GB0d1erqajrojsNcacOfsyg36DMylYPIuKdnSNBv5ogUe3+2uD+HC/vzGSk6UZgb+uQHAfpeJiAAAIqsFI82dzCK/Rb1bPabA/A7HWK8U//3SgP5fX3QQcvv6Pz2LIp+dH3pyvblYabosJOUtfty4N5OTlD/LH6eI2SaR6G7w5DSYx4E5jIcvoOtxXWxrj/3cHuPz+N+gO/5a6c55DcRFI59dceo89SoE7ojFrt/ZWUl1fqPmUquF0hKstx1AklpzQikw0YE8/BzWmjTdT4+W11dTc8tbXuJHeSrBxAQhe59dhkcHfI77RdkVqPRkLQlv8ESyJifmJhQtVotOIGjXuG6O3okZevq9XrCD2q1WiGbbad9zVqUy2WVy2UNDw+rWq0WZKu0pTMxPx5Ecr3xnYH83j869CD63/7bf1uf+cxndMstt+hNb3qTfvSjH+mRRx7RP/gH/0DS5gNw//3367Of/axe//rX6/Wvf70++9nPqlKp6Fd/9Vevce8PD+3Fe3g9UQ4kccP+WvTDlZte4GmOHDwdGxvT5ORkEi6A6KOjo0kAYmRHBUMq1u9qtVqXPSaEnafkYSRivHpasBtl0RiPIDqGaq1WSzVeL126VAC3IrDj4ES/Y6DfzWZTkhIAgwNgYmIirZlHRDAO3n1NMXKp3buysqKlpaUEoi8uLmppaWlXwBuwCNAMQMTLDhCFCIglXf+pqgMhfuPTQctunHtEuWGE9Xq5zHDALwIH/hsMFI/ejM5S2iaSxw2SGO0ZjULnB14v0kFi72cuciryTAfSHQT1e8ZIJyc/2BEnrt/P+b4bw26AxM+Yd8DI1dXVZCxiVGI4ukOR9pgbnA6sOX2LBjPjjZHiPo/ICnisG9Ge5ZCbIwfJ43fRCCcqLRrrDoYyJz6/Lj9JgWYOKN3DweBuJDN/EVB1kIJ58vrr7gzya9hHyDY/BI25Rrb7fva587JHnsnA5w4mMBZqyzJeStwAHnS73cJ+8bHS5zgOP+ANB4rrH75/AVicX/hzRN98//r4HCSPYErOcR/5CX8DVrF36Zf3KTpunGdFAC0+B/3QQH7f+HTQ8pvnNpZRgDe8kvdNtEekfBTyTk7D/SJfF+8f/JaALZyIninlwLEHXsWoX2878i76kHMg7MUu8utygDnvMes6OsH9MFJkqsvf3D2RER6N79HoDiD7c+EyiPZiRkC8l8sFL9HmEdz0n3fkt8sRX2fXWXYj+kpkPvoa8tsd0lF+s5703bM5YxCbg+h+XsxuAWfuxEavqFQqhdJ4/ntvL+fIuh5oIL/3jw49iP47v/M7euihh3Tffffp/Pnzuummm/SRj3xE//P//D+naz7+8Y+r3W7rvvvu08LCgu644w498cQTmpqauoY9P1zkhuON8hBgADjA63UqMVr6ja65HIpAjL8c1O+XEJZjY2MpmqlarSaglxrl1FLD6+3C2wGiy6VyuZwO/jh27FgC9AF5XWEhag7hxzhykX4upDG+6/V6qkFLm55m7AaxC1Rvt5859nS1xcVFSUo1kycmJlSpVFL0DWCOtP0wExQP+kzE+cLCghYXF5ODwBUACCWkWq1qfHxc5XJZc3NzaW4nJycL0R2s78bG5mGr9XpdKysrWlxcTOvvh/oNaECHhQ5adrshJ23xG4C+6NDyCCp4lQNkUYGHUO6dv3tqLKA6gHPuntKWIgvgRjRMpVJJ0cPxzAbnJ/S7n9TnGF3uhpjPXzSMpWLUkht5ETRm/C4PvXSNVOShLq98PA64el/cuehR4W6AxnWDHEB3AJMILQeCXW75/nDjOgKyDopLKhjGMboesJgDJ3GybGxsqNVqJTkFeO31OX08PjcOAgCGk8kwNTWl6enpFLVfKpW0uLiooaGhBCKjQ9Xr9W1OmHhPN2jZH9FJE/sWHT45R4TvJZe9ExMTaX59niSl2uAeXOAOD997rif4HkWOItP53Pc6+84zDjx4oVQqqVKpJPk9MTGRotaZJ69R7387D/E9xf09ujwGJ7iDhX2Uc6rxuwhQ5nSsAQ1IOnj5HWWJy4nrfV/2E7UbyZ3g/vzvBpwftrlyvglPhJ8iw31MvubY764/cB3kEei7jT0H/uaclQ5y4xj1mu2u//mB5K7v7dSXXk4I+DDBIKOjo5qentb09LRGR0dVqVQ0PDycyoUiv4iMRn75/V02u6zhf88kc70M2Rd1MZ/HuBY7kctVl9+dTkdjY2Mpe80dC74P+L0HsTmITiCeYyKuq3oQhMtz/p6amtLU1FQKrsMZxFxNTEyk9oeHhwu4i8/ntaIbCdu7nujQg+hTU1M6c+aMzpw50/OaUqmkhx9+WA8//PCB9et6pRvtIYOBATQSuesA+kEA6ZQn4cCQWI+rH3Kv/OTkpI4cOaKxsTHNzMyoXC4n8BkwXVISHtwPg5j+XM56l0olzc7O6vTp05qamtIb3/hGnT59OhmHpVIpAcb1el3/9//9f+vcuXMFxSACP8xTTGF34e3XkuKH8PMIKrz/rG2/41xfX1ez2UwR3pcuXdL4+LguXryoiYkJTU5OampqKkW6cX/2jqe7Ufu83W5rYWEhlTxoNBqF6+K8Dg1tHjR26tQpzc3N6ciRI3rNa16jarWaylG40uwKw+LiohYXF9VsNvVnf/ZnunDhglqtlhYXF7elzl8PdKPxogEV6aBlN5FNbtxg2MRDPN2Aox8R1HWjDIPB66y7oi4pORQ5NwIQPQf+0j4GJYA5PMiVewi+FyPB/XupCAJHuedGpDucuS4HfEtbhhgyLde/HN+LBgb/x/r18HgHxx384z4Az4DEbuSy5hjU0aBxUJS1BKQEQHWgkfmLfYk1X6ODodvtqlwup0h6ZNnw8LDGx8cL0W6S0r1brVYy0ig3xlgBv732uRvKbnhKW6WNxsbGdPLkSU1NTalcLmt2dlbDw8M6d+6cxsfHtbKyovPnzyd5trCwoE6nU2grOj4AbHvJHM+qipF2DgzF6Mo4916GgGcY4xsZPDIyomq1qpGRkZSOjbOA58wdZIzBDwzlGfbSazFFHjABZxeZAP5sV6vVFGTgkXQe8QfgghMcvSFGLdJXdFp37Dug4VkY7Xa7ADxE8J2yR65z0SZ93otMHsjvG5sOWn6jb0Ps/+uVnL/xd7QHewFf7thyp7JHZHs5iWtdUiIC+K5nOQ9lDPAtxjY6OlrgldEmZB+4LcY9kA25yO9ILoNyQDNzC4jt5VTpp8t/dD4OpUb36wdEZ0wRqO52u5qcnNT09LQmJiZ08uRJzc7Oanx8XDMzMxoeHtbS0pIWFxdTmVLs/VqtVgDFc4ENvka5d/rk+o47cLrdbpqDONadgHXPOFtfX9fS0pLGxsZSuVnPxHb5TXvr6+vJ1uewUJwIGxsbarfbqeyg63/MLzojOgP3wOaemppK84vu5ror+tna2pouXryYHBnovbG820HTXoJqpIH83i869CD6gA4P5aKHpGtfh86jdWBoBwWe+/1zCs1e58WVJq8XNz4+XgAhvGwBwEKpVEpGp0dA7bUPpVIplZOZmprSkSNHNDc3tw0AQnBLKhyYgpCNc+/ghqfm+31jxJWDKD6nGxsbPfdjL3Kgw/tDdD+RWaSXAVy44obx3Wg0Uo00nBgoU70cJw6iEbU2PT2t2dlZVSqVBKT72NnPrhCgBFC/1RXr64WuhGdcT+Mc0MGRG6qQK/lxz/mzHSOb+K07BXnGHODyqHV+C5AMSO0GSHQu8jxHg82jkpyfINt2MoB6zY2DlQ4m+1y53PA57XUvnxPGHyNyHID1uXU+HvvpTlPvRwRhcyUwdopI8/aiwd/LCREjAHvpQDGaK655fAF40l8MdNbX9Qj4PO1GY83774Cvlzgi24pIK87fcGeIR0e748T7gUFLuZkIiK+vryd9pV/9y9vg/263WwDzXQ561N3IyEhBH4jnp+RAdD/wO0a3uY7iafzuDMAAdxC9lxM8gtOASO4Qi+P2efNnNbf3XEeK/CJeF/ex85nL0aUuhwbye0A5cgDuWtuUV0r92Ca7fe/PNEBm1AngMYdJ/+/VDweLo33nv3V9gs+ibhf3R5Thve4feaB/5/dCjiCnY+Q8FKOj++WjrnvkwGtsf2Q3ADA1u1dWVtL5Ie5Y9Wh45JuD6v0GW7nNvRPtRWb4eD3AkBrpfl/mk898bJ1OpxB97wGU4A/+fPgzRJDixMREyhbDCT81NbXN8e/YDnOxurqasuPIfHNd9lo9h3sB0Qfye/9oAKIPaFdyD+zk5KTK5bKkrQex3W4XInAP2hvn0cgrKyuJkXk0OobVfoHqbjCTek1qNB7SfplNFJwYaLwQ4kQ1ElVEyhEgzPr6ehJIy8vLBWHUj/D0qL5Tp07pZ37mZ1StVnXixAlNT08XvPhzc3MaHx/X5ORkikKv1+t6+eWXE6CeA2lySpLPgwu8XH1TXp6azzj3Qu6VX1pa0vDwsBqNhpaWlgpRX8yLVHQCsMaeDo+y0suAnZmZ0dzcnKrVqt7whjfo5MmTqlarOnbsWHKW+KGFrmx2u13NzMxofHxc09PT6nQ6OnXqlF5++eXkJWfvXQ80EOID2m/yyDX2VzScckBSBFMdGM9FM3Od11vkvh7x7rwb4wfDKEZXR3Az3ssBwPjuKcSx7AzvXkPbo5k92hV+BvCPEzGOnXmLjj4pH5Xtcx3rpHrtdzeWcveRVEjDjdFGbvD4uhGFJKkAkALCuuOTveFlYqIcdmeoAxnMgzsB6LMfGE2fJiYmtgG909PTyanbaDS27b8YHSYpZTxgUNI+533gnC2Xy8lZMzc3p+HhYbXbbUmb5ds44HtlZSUZ327w+h70qLJoPJZKpXRYGP33NGrWwZ9R32Nc79FuXkLOI8MA0IeHh5P8Q4/y6EfmmH56Fok/79GBxlzjuJ6amkoZI+wp7uFlmWIwgLRZOg5Df2hoSO12W81mM821Z4YA4NMX5xfMn88Z6xAd6j63vmcw/L3OLvpyPzSQ3wO6GhSdh9cD+fMO35K2y2+uzf0+Aqkuq8fGxlLWFfzb+bJnwOYOdD4ociCxVNo6GNSzi+AzQ0NDiddJKugysRxqzDBE98LuypXNc4o2Za48CX3zyHleHBjtv3UnODoCPH10dLTA23Pkcgm57fcfHx/X1NSUJiYmND09rZmZmfQ39vD4+HgaO2NcWlpKc4Ecd7t7L2vJnvVDruN6xTZdn2Ff52SK28lkh5PZHfXU2L5nDroTw4F57wfY1dTUlI4eParR0dFkh5PBhj7G2TKuj0KVSiXpGKXS5oHu9Xo9YUBEpvuzeZDkAYC70UB+7x8NQPQB7UowmNHRUR09elRHjhyRtMVoKSfhXsCDJGecGJGAGAjh/SaYOcY4woxyLrt5xZ1cyLv32Q13DB3AWg4c5TsUDwfb3djtF0Tn/idOnNAb3vAGVSqVVE7GidSyyclJvepVr9LGxoZefvllvfzyy5etxO0EoscoFY/uiqBFP+SeeYRiBDm81AP3jiCCA3C7jW1qakqvetWrND09rdtuu0033XRTyjLo5dzxz8fGxjQ9Pa21tTVNTEyo3W6rUqno4sWLaT6uFxB9QAPab8oBzLkITucz/M/z67wyB2LDZ7k2AsYYHNIW4OupwUQVDQ1t1TwnMgawlwMJ44FVDgBLxajvmHXkkVvwdAxC5CHXuLGHAeAAdy/HhEfxR8eDg7BeBsN5OjINByJtxTI6/O3gqI+Z9pDHHoXF917GgijmZrNZ0FsicE9Kr0cdMyeSCnLOo65j5DDr62PDCPbrSqVSAmcpzZGLbooRfNRO971BSRUAAAB0xjI0tFl+hFIiABWLi4uFLDPfe3HMOWLs/D5meHnUZJTf/nuM6LiezCGHeXMfgAc/aNSd4HyG4wNj2NckggTMI0Z1tVpNJfZItfdn358pvyfrzGGx6GfLy8saHR1NgAB6CHpHfLZjpHm3u1UH3SMno/7tgL5HgvLd5YDoAxrQ1SAPbrlewBqeHZdlbiN4hm4OvI0AuoPoOGBx3sHTaXdjYyMBefDpa1na0UuUMGbs8JwT352mrl/FoAHmhDGurKyo2WwWApp2AtEBRT3rJkZa7wSiV6vVdI07TPmM9uHp3e5m3e+d9jBtMV50DXQDAhap1U3JUfSgarWaSpi43kYgx5WclcXvfD2jbNqJfH57ge2sLaCv64Ruf+faJBLd+9qrH6zj5OSk5ubmNDExoRMnThTm0vemj9MdJ+VyOekc6E/Uco86WL8lVfaT2N8DOlgagOjXAcXIuUjOpPZT8YB5wHzGx8d14sQJHTt2TNKWkk/qLocrNhqNAwXTfewoEcPDw0mZ4O8IVO4lKj2OJabtexT+XhVAZ+AuSJwhe5qWgwpSsZZgrC3rwEVuHE4e5eZKRG6e4rUxze16Ixf2DppL20F0KR+lmiOfJxQigJJ4qE4/bUlFUKhcLmtycjJ55/fqTLhWNPCED+hqEc/lbnIxRsC6UefAeXSWxb2LfHblG4Ma0BXA3EF0/z7WjKYPzv/53w2QOF7e3VHg9wdMdYMRw8RBvtiX6CzgvjvxG9p1A9ZlUQ7Uc6Mtt0a91tsNbwf9fS09kwjw3EFa5+eM3+eJz2OfHHzN9d2vBwR1Yx7wstPpJMDBjchcVHMvYk18LPE58D6jc8S/e+mcXnO71719LXMZA7k9s5N+yz7yPUnggBP6H/pelN9u2PpzFOW+t8e46EfUjRyEj/OSa5sxeLo+DiYABa5x5wPj8HWNkX5Rb+lXv805Nnajgfwe0NWiXsDbtSSX8ZFPuFzLyYf46vd+MRIdR165XE7PPc8+4B66AnzpoMn7leOH8XPP/OHdX5Bn2rkt7KVLetljcd1yfDLKd3iny0LXBdDb0G8IXiPoYDfe63vE28+B+n6998OdArn77dfz43J0Jz14P+7jGew5HQG52G+Qos+V698eWBIDPHJ7ge/R1cgWpD2AffS7fm36/aSB/L42NADRDzE5w9wJEHVPd7/g3m40PDysarWq0dFR3Xrrrfov/8v/UlNTUzp9+rSOHz8uaetBfPnll/XCCy+kQyb//M//PEX4HiSQ7qldpFVjbBHpRxSWGyT9GKbcw4U39+LACU+L75fwOnt9Lg4xIV3Nvfq+vihTrjwxXgRHTMXe6RBOj5r0914gurSVpVCtVlMq1OVSTuF0I3Sn7/eLvM3c3t0JlMsRafWjo6M6deqUXve616lcLqeItpxS3k+bzPfKyope+9rXJscVkYSHXdANhPiA9pvgEV7rOCrcOaPIjT4HrD01mmfUQVo3mgG6OHgZpxmRaR5p7iC2Z77QJ+7RbrdTpC2yIEbNOpAWQWDuPT09rePHj6cDq6nbDJjfbDbTIVUvv/xyOlwSWSepUFfT9Q6p+Dz6XAOekzUVjb04dtpizvmNr2vOucFaIQujoY6sdv2AsywccIj7hP77uxu7/Abw2fvHb3xc7jSgRAztwss5RJLocdclfJ5idDHz4KnNzAGR90NDQyk6zR3tDgp7STH2PVGEPhe+F3mP68g9cQoQ3AA5SOxGagSHXefl3nzO+Hw/OMji5KWUIkDjzzPz5hFlgCWVSqVQnxbdm2c2ljXy/ru+STt8vrKyovn5+YITC/ISOL4uOf3O9bKoW8T9mAPg+6WB/B7QftNOAOy1JOxv3uFRUm+HaYyqjuWX/NoccOq8Zm5uTqdPn04ZMJOTk4Vne3FxUefPn9fy8rJ++tOfJr7AmUkHQe6oRF7k5E90BEpKkcjOy339kaedTqcgvylh44eKxrZdz3J9zdv3aGN3bPuhy/wW+YF97BgDWUXDw8NqtVrb5EyuXznw1uXEToByLE0TAfcrJfQEiHVhDd0299/4tbm+9yJv38F0yP/vd1/7Wlar1VQWZ3Z2VtPT05KKNeWj08f3jGNwGxsbSX4TiY7OXiqVrklG+F4cZwP5vX80ANEPMbmhH6O5pO1MrV/vXD+EwjA+Pq5jx47p1ltv1ezsrF71qlfpxIkTkrYexJmZGY2NjWlpaUkvvPBCUvpJtzkocsOW+2PAETUEU40RA73IAfQYyeZpU9xrLwCmG8Gkh2HEIkgYR2TyMHUMJ4/6wmD1NCWPZurVPxfMMSptp/7HGqT7QVEA50Dzq6lo71fb/hyR0UFK5uVG7pdKpVTyYXJyUkePHk3RKrmIhsNIAyE+oP2mKAcdkPVrchFJEaT1khZu8ESwineyghyQJOMEkHRoaCg5S3vxSe8LMoYIcR+byzGpmGHkxqGnI2OEV6vVBPJ7CvHKykoBuPcyLxG43ukZxPjwOfHSJdG4dGcGMjam1UZHqvfDAXD/nN8A3noEOq8Y2Q8AQD9zaeD0h/66fPXo8eg0iFFRHgEOEdHmae/cq5fD1T/PORh8XtETPPNhpxcAAWsRHb8R2HWj3nUx11NdPkWngH/mDg0HUnLgMUR/crWB/Z7MfW4f93LYO4jmelrOqeT7IwIpzCvlaiqVikZGRtRoNNJe83n1PcRauBPFHYD005+d3J5xHYr+5WrQ9qKB/B7QflN07BwWcrvIzz2Iunbsf3QKROrFe3i+cb5XKpVUzmN2dlZTU1MFXgsfabfbWlxcVL1e18rKSsHheFCUk88O7jm/jrZ1/N7nB34PYI7N7ZHpO5GDodGx7XPpIHoE3l0mIgOkLaAWmxsbfDdbzPdQ7toITjvP9b4eJOXscPoTx3k5fH6/bXpfNz9QFBvc5z63N32e3eGPzry2tpaCUsgsd/vgIGkvczeQ3/tHAxD9EBIPNQ8lRqjXE5VUUJ5RvDG4c0xO2u5p60UjIyM6cuSIJicndeLECZ04cUIzMzOanZ1N9cH4/ezsrI4fP67x8XEdP35cc3Nzqc7jtRDiklLdNBeupVIppbW7F3c3hufRahjeq6urarfb2zzhewHQnbl7apEfZLZTH3OGp9fhBUSnfYCDXsScSSocVsMedELgrK6uqtFoaHFx8YojH1wBRTlyQMOFN5Fmhy1qJVKsTQuI5obx5RLKW7VaVbfbTW1Ti/awGSMDGtDVJJy21CfsxZPhZfCPyNd5duDRTr0iUzzCGCPLD3L0Ei7xcEVpS2nn2V1bW1O73S4cYByBR4+8hR+7YU20LIdKEo1DLUhSxBnjysqKarVaOugJQN3nLxfxjozhOkBgdBbG7ICfUwwW4LMYvcfvkQXuUIjgtv/O5YUD6C5DWItoxDsIzdgA46PD3PeLl2dzsNbBeI+0djDZHdn0w9fdr/M5ZZzdbjcddNXpbGWfOcDBs7KyspIy6fiMCG9fS8ZECSKvPd6rjw4u5VLc3THibTE+3xdcR9/YT4DKUe/wttzxFZ0b7sCJ/eb7XsEE7MUYnRodRL4n2Qvckz3Ps+ttx4j0OEeR4vMS55z7+hjpG46Ja2H8D2hAkrLZI9eKetnfPOtO/nx7ibDLCWxzOeiA3+TkpCYmJjQ1NaWZmZnEd7iejOiLFy+qXq9rZGQkZUfvh32Uww96Xcf9yNDhtwCMO8mMqF9wHTw+OsB3KuPiv48ZfNFujLIBGRzLm8XP+C17Y3R0NJ1Z5jKqV4R8nFcPAHQ9lheyHCcCsrvZbKrdbhdknWeo7XUdfe56Aft+Te6znRwIB0XITJ7lXoEC0laWqffd5aXrDFEnYl9HR81B0n4G0Q6ofxqA6IeMnFm7EU7kKgzemSOe2U6nkw6ZiILDmacz7F4P3fj4uF71qlfp+PHjuu222/Ta175W09PTmpqaSieFc38OP6nVanruued06dIlLS4uqlarHXhaSzSAS6WtA50w/mCogBke3e/tuCGJgueecISWe8L3AqJzX6KISXv3ul2e4szvIDfMnclHcGhiYiIZVCsrKz0V1Y2NDbVaLa2trWlxcVGXLl1K0ZQTExOF+yPQUdzOnj2r+fn5KwbRPR2QfRsNVBS0a3WQ7V5odHQ0HcA6MzOTIkL3EvW1E42Pj+vIkSOamJhIqWo8m4d5Xg7aE/6lL31Jv/mbv6mzZ8/qTW96k86cOaNf+IVf6Hn9U089pQceeEB/8id/optuukkf//jHde+99xau+f3f/3099NBD+vM//3Pddttt+sxnPqN3vetd6fs//uM/1m/+5m/qBz/4gc6ePatvf/vb+rt/9+9uG8unPvUpfeUrX9HCwoLuuOMOffGLX9Sb3vSmPY/xlU7Ly8vqdDqJx/kz4OAyvN7LvgA4xnJcHgnN/yjLfqYBPAq+TRkVB5I9ojlGj8LHOChqbW1N9Xo9GVLIcWSGA7wOirrRz4FUMzMzqS+zs7OamZlJkfIjIyOamppStVpNh3UtLy+r1WqpVqsV5hfANgKo3M+N1NHRUVWr1W0lQqjdCkVj1EEKeLw7KZgPJ86XkPIgv5cdoWSK15BFP/D1Rn5iBAN+OpjuaegeqeTZRxhqgKQuz4mQ94h0r4PrkU2Qg7EY6X7oLAZ7q9XS0tKSVldXNTs7W4hGL5VKaZ8Tvbi4uKhWq5XmgP74eMfHx1PU1crKSvqesUC+n1nXGPXtIIZHQgPkso7ovv7M0UfG5HoY68g8+jp5JL33lT56333/sB7RkR/XwZ07gD6uJ8foRJ4h+sRe8bWMxj77MwZDRJ2e3+ykH0XnwV7ooOX3gG58uhYlEHrR0NBmySV4D/a3l5aK2TDIxlivey/kgCw2w9zcnI4cOaJyuazjx4/ryJEjKYNseHg42RTNZjPZ3I1GQ7VaLVtOZq/kzuF+gDr4N2c0wYOR7w5GSlu8WsrzFa7DPvXsvN34EPzPA66cj7vzGh4LT/dANq5x57hHMUtKMr/T6SRbvpeN6uCtj5s+LS8vq16vJ/lWqVTS58PDw2o2m2q1Wmq325qfn9elS5fUaDRS4CJz7ToBe4t17Jf3x7mK69Rr/g8DnydwhQAz1hU9zANcPPisVwYZMhN9AEeXpPTZfgTIXQ7tRZYP5Pf+0bVZ7QHtSG6Q+cPuirQbXP5yQR+jeWJ60k7eslKplMDdGEXrIDRRzn7NxMREQVj2O+YolK6E3Gh0L66nc8cDSuIhJf65t5FTlPbKlHysbnzHF9f2M2fuIff0tFg7rRcBEmCMeoSazxfzsLy8nOqBcVr2fjDYCBB5NKD/vx8RFleb3BPuKYL75amm/Rj5eNgjylxpvJzXXujxxx/X/fffr0984hP60Y9+pF/4hV/Q3Xffreeffz57/bPPPqtf+ZVf0S/8wi/oRz/6kf7xP/7H+kf/6B/p93//99M1Tz/9tN7//vfrAx/4gP7Df/gP+sAHPqD3ve99+r/+r/8rXdNsNvVzP/dzevTRR3v27Qtf+IIeeeQRPfroo/r3//7f69SpU3rHO96her2+pzEOSNv4d+QTUel3Pp8zNF1u5nh1LzmNfPSyWG5854wR53kut3K8jr6gNLvj2PvuukIsQ4Ec5x3wP/KQnZ61GLkV333sOfnTSz9wh6k7g6OM9lcvuRDnNBcl6MDATgZhlEu5NfS94uCDj62XMcr3UYbnIuJy7UCMl2i1qK8gv1dWVpJTwbMGXZ/opVvG9xgxHcn3qjuQcjLe6+JGZ1OcJwc+vB++L30ud9Mz4z16zXUE0nP6tUexORARX7HvzFMEX7zNyEviWHzuesnPyDcuB1g5CPk9oAEdJLlcj/a18xuu9Xdpd4CqHxs39sHlNi+CrbC5CcDC9t7NDsjxop34U782BXqDy9toP8eMsJxsjzLBr9mL/ZfTsXZyBjiwHv/eSXZEHTHiMLF9n6v4inPljgNe2N8EBMTMS9cleq33Xul64+FxD+e+z8ntXns+ZwdEnfmw297SQH7vJw0i0Q8ZecoYAhIhGhlBZAoICbxlnk7lCj0CxOus5gCEGBHlhrAb2L2E/U6CJgK98dpo5PbjAY/EA483uFQqRn/l6p7FuUSgIbj9EClPk94r5YzUXgZrPwqXVEzxJuJe0jYl0D3xcb7wXL/44ov60Y9+pGq1qvn5eR07diztzVKplKLXGo2Gnn32Wb388sspatCj1pm/fpUeB7h8zRijK0Neg/SwUoxq2G+AO6fsXw9p2VcijPf6u0ceeUS/9mu/pg996EOSpDNnzuiP/uiP9Lu/+7v63Oc+t+36L3/5y7rlllt05swZSdIb3/hGPfPMM/qt3/otvec970ltvOMd79CDDz4oSXrwwQf11FNP6cyZM/rmN78pSbr77rt199137ziOM2fO6BOf+ITe/e53S5K+9rWv6eTJk/rGN76hj3zkI3sa5yudMCI8KjaC4P59DpD1Mho58qwlf/bIAIlOZqKOna8tLy+r2+0m44d2pO3goEfzSkqylSjdHFgMz6S/1IL0AxGR7Q6wE+GeS5nmeaWfY2NjqZazOwjGx8e1sbGRysU4mIn8ZMxukLjh7SVTfA0jgOFj94PF0IG8X8w9Uf7uoPD5dRDRf98L+JWUnMfO65GxvfSWTqeTzjoh4thrj+ey43yuqJ3OejFv0PLyspaWlrS8vKyxsTG1Wi2Nj49renpaIyMjyQhfW1vTxYsXtbS0lMbnc87fDgCzzz1SnTmIJYpcJ+HZcxlO+7nnjDWJ2RquC3Q6xTIosQSKy1v6G41d+uTt8NzyPMMTvAwOzwvXsv7MlfMVPvOIQPR0nvMIzPGbuP7xuomJicKz4+PmXsxjDjjnPnuRqwcpvwc0oKtNOcchMjPqBJ51DP/zjDTnB5G38dw7r4yOLeQT/YKv4Oj2Q8qHhoYSmE7pLeSkn9vgz5zz8ByG4Nf5351OZ1smWY5oA77i0dE5oNEj0V0eu17AfHH/veABLmti+9JWVhk6Ua/M9Pi/7wNkG/IOPS3+1m11t1/jPiBDDPndbrdTdt/Q0FAKcFteXtb58+e1uLiYdCtkDHPKfov9937vNJc5+XCQPNx1937v68+zl9aJjh2fG8865dpoT9Auei96ULTtB/b3K4cGIPohoxjh7Q9pZOxOKOT8DUiO0eDGoBuA0bPr7dGPHIgubYGkbvh5JF4vEN0FjRvZUhFE9dQaF8z9EtfiwUag+z1jFFA00BDenhJ+OYD+TvMQIxkjwN8PQ46OCdrCyOs3wpC9cf78eTWbTZXLZa2srGhxcVEjI5snxkvSuXPn9PLLL6vVaumnP/2pFhYWCoAM4/N9tpd589Tz6Nzw98NOKLQoZ3uN6tiNXPH3/XTYhfh+UCw3gTHhtLq6qh/84Af69V//9cLn73znO/X9738/2+7TTz+td77znYXPfvmXf1lf/epXk3Pq6aef1sc+9rFt1wC890PPPvuszp07V7jX+Pi47rrrLn3/+98fgOh7JAfo4HvRSHTDhXrjbtBiPEfqFU2EnAREz0WhQwCeKysrBYMUAzlGDOUyytAJIqALMM08uPOcMXm0mvMkd9p61HeM2gKwxME/OTmZHLU+T7xHwwJ+Hq+NDg5KjUQQXVLquxvG7tBmHYeGhtLBUT5XAMcRxMhRDD7AAcLcMhbmC53NAdJefNgN17iveul7nsLvY87VZ19dXVWtVkvGY7PZ1Pj4uBqNhkZGRlIE+traWkoFdweAA0eAAT5nADDc241O10NZ/5GRkUIJNh+vA+EO1nuKv+91D8Tw+/D8Ropp8zh5WKMYge171/Vv1pr1o0SBG+F+L/YOwAb382fO9fMIujmw7uC7A/6slY8rgns5ED3OB3/3G4k+oAHdSAQ/8fOpcrav26M8T/7sOg/g+Xb+zvcx08Tt3mjzwvfdGQ6AB48aGxsrgOhui9Nu5K0E6DlFDIDP6GuudGnOPvP/HXCO9pzbwX4veDBzTjs4jPdCbsvTP9pnbsAHHLzvRa5T8HsPYPCMRL5n7p2fu7x0J4q0CaIPDQ2lkjjNZjOt//DwcKqBvrq6qosXL6rRaGyTvcypOyi4V8SDdpvTa2Vv+3rsBT/wtXTHFmNlzZGd0paO7/uafctzznfMaQxQjPt7QDc+DUD0Q0bONGI0kF/jFL+LirQ/3P7yCLFIGPntdjsdcOa1pJzZrK+vp7Si5eXldKioCw4MS4/IjcBivD/Gh9c/c4Nxr2C2KwPMVfQAx2sjcH4lHrxIETTp9f1+3acfiqDH+vq66vV6Wrdms6lSqZRq3scoA3egRKVzr95k6eCFtyuZUZlyZ8pe057j/tmvtY17er/36NWi/fCE33zzzYXPf+M3fkMPP/xw4bOLFy9qY2NDJ0+eLHx+8uRJnTt3Ltv+uXPnstevr6/r4sWLOn36dM9rerXZ6z78Lrbz3HPP9d3OgDbJjUQ3WCI5cB5LWER5C+jlgB2U49tRbkTjJDqt3Wnr7w4++v8uy+O1Th717enRfh94mct5L/3h7fNy2Y1h3wsY93lzZ3s03J2nYqRHYHMn4yQatBiJLnO4zuUR//s8x3HQhvNvDKqoq/G7nPMjt0beL/rpBm0E4qMe59/znYOr/I7oaQDb4eHhQnk7NxR9/+bkVNyn3NMPjnXHuf/Ox+m6T+45Zf4cQPE57qUv5UDsqM9A3tcoRx30jmNgjQDUqT3rcxNfce7iZznK7cGYKcGa0i8HBSIw421G8O5y9LIrld8DGtBhpF4yXVKBP/u1vqfddo/Z2x7IFKOj/X7IQs8KGxsbKzjCAH6xjwkKcN3CA9akLd4JIAu/zI0j9mdjYyPxOgdk90q9gPbcXPv3l8tzcr/J4SG8YgZ4lFHenvct8tToXPH7ugyEfzu4zXfocehmyHtKqLqO12uscS7iNYzvamAbe23P58R1mlKplOT3XmzcuHfYt+g9BAJw7xwm1OuZ8L+vN5k2kN/7RwMQ/ZCRe84iA5bygKgLbZihp3VHYJT3breY2u5CcXV1VS+//LJWV1d15MgRnT9/XisrKwVGL20+UPV6XRcuXNDS0pLOnj2rl156Sc1mU51OJx066oeYkQbu9dsYoysWlJtZXl5Ws9nU+vq6arWa2u221tc3D8WKEVi7EULIPce95nWvRs9+kfflSsHWfttiD1GGB+VqZWVFzz33nH7605+m67rdblLamBuigKenpwtlejqdTjrwBMB9r3N4UEybfTg8PKzZ2VlVq9UCQNFoNLS0tKSNjY1CDdmdyJ1RXj5pvzzWPMNeK2+39LzDQPshxF944QVNT0+nz2MUulPOMNrtecjdc6fnabc296tvA8pTp9NJUVn8Hwknr7Ql84hazkWPw8vgi7l1cUe0R2ktLy8XQDvuD6jpzmM3gpwXe2YR7x5Blcv04UU0U6VSUbvdTvoAPIPfLCwspKwiDpj0w1lz5WpGR0c1NTVVqLVO/6OsjCm0GEReGo13zuDwtdstVdb5sGedObCLToSj3/k6afJOPsc+DtonYtGNPU+5d+PP9Tn64euUA3Ppg4/Vs/f43KPeI+hKMMXS0lKKUmSfu8EYgXR/h4aGtg5oRX5xn3K5nNrxLAs/BNbXyOcMis+W6w/INHRGj7bz/RAj6aNDgQNz2Q/xzAGPRmQvUQIGw5txEAk4Pj6eSgHwHNBWdKI50ILuGUF618Ejb/B183GyZ+BD3e5WiR+/p0fBMnbuy5r3K5MHRviAbnTiOXF713mAOzNdP+Q5JWI8Olnd+ekZ1/5cbGxspHNxqtWqarWaNjY2kv3svPLSpUs6f/68Wq2WGo1Gkqtkm5G5Bt+AD2CTO690nsA4vR758PBw0ms40LmfSGYoB6D7vXzuc3yp3+Cl3e7rYLlnDoJJeLAf37POUafz8nPuxHcdIuosLn9z5LrL4uKipGJ0udeHp0Sd771eNkS0P9kT7ijZC0gdKTfOfttxDIvz9wjMK5U2qwiw53AY9SLHedwpRQk98Knx8XF1u90ku7EhY+BJ7hWv26tzaS9zs980kN/7RwMQ/ZBR9GLH6KOdfidt1XVyIDRGz/EQoIDHNBSEWqvV0vDwsOr1ekonmpycLBj83e5mJFuz2VSz2VS9Xle9Xi8A7hxQOjY2ppmZmZQGXqlUCuVMIO6PMc29Pc0coyiOqR86SEB8r7RfoPleQFr3/sbMgE6ns+2Ud5+3UqmUDpL1g+rYx6wjbR1mcJA+Dw9v1nWfmpqSVARUWq1WAqP6URwjYOVCeb9A9MsV4teS9kOIT09PF0D0HFHLP0aInz9/flsEOHTq1Kns9SMjIzp69OiO1/Rqs9d9pM2I9NOnT192OwPaJAcPMVDd6HLA2cHMXlFjUIwsdt7oz3E09LiP82LAYwfC/DfRGOf+bvABjkrF0hOxHUrH4PBzPkFfOp2O2u226vX6tmh01xMwvuH11GaF77vxwf097Z0xx9IYGDjIeqLqGJuvXa8IZO4Hr3XgMH7vQL4HKrgDLvJmny9eucPbfH38M3/F/uwEoscI7bgHHUCVlEATnyvkb6vV0sjIiJaXlwul4+IcRTBeUnYuXIZ5NCNzHEuLxLb70WcdSO8nEtHLLnhGnM9FBKZjFCHzHL+Pz7aXciGajcjOHCAR59jf499x7ZlnwJhcVCP/M1+Afw5w5cpbxXv0C1INjPAB3UiU25POF/y91/PENc6f/X+343me/XdOyESc/isrKxoZGUkORfrX7W46y3Hq4cSEL0XdxUvV+JkmHnjgNoU7jomMh1e4Xbffcy8VdYmrwTPievBiTnzdooPaMYRod7mTlzWINlkEY6PeyX273W5hzX3vIF9wmOac004uZ3rhBFEPvZK5vZx2GAM6pZctcrt7t7r83Dv34vc8n2RXeDR+Tnb7eFxu838/Okqcn2sFpA/k9/7RAEQ/ZJTzdu3Hpu3FWHsB9HjCV1dX9eKLL+o//af/pOnpaS0sLOjYsWMFL9/58+f1/PPPq16v6+LFiwlAn52dlSRNTU1pdnY2Ra+Vy+UCc8wZmBhhnU4nXQvjm5yc1PLyskZHR1NENIe1XU8PuAtgxuvezxhx1Q+5wdoLXEXBkjYNz2q1mmrNYgjmhDjOEk8z7KWISHkl5TDT+Pi4pqamND4+rpMnT2pubq4ArhHFt7Kyovn5+b4ivonYkDaj19inRHZeKa2urqrRaKjZbKYozlh39pVMY2Njestb3qInn3xS73rXu9LnTz75pO65557sb+6880794R/+YeGzJ554QrfffnuKuL3zzjv15JNPFuqiP/HEE3rrW9/ad99uvfVWnTp1Sk8++aR+/ud/XtLmej711FP6/Oc/33c7A9okN36i4eNpoF7yIBrHbmT0OlsgKs7SFqiGMg5Q6aBhNGx5d3I+GUH0OCY3cvmb3xOtI22eHXDhwoVUIxxZQMTapUuXdOHCBS0vL6dINmkzAq5arRYi0T3itlwup4gtjHCPvsV4BHR38AHQPJZn89Jf6AV+f4+8zhmhgJh+Lc4EAHo3oBiPR6LnQHTazAHfABN+KJnvn7jGsQ0fN//7oVdugDsQ7FkSDphGg5E+RvKyMXGf+972DCqMea8VzN/MFSC0G/4OvBAlmXP8ePQj+4c9EAGhuE4Y3zhFuAdODfb+xsZGymKMzjPa9owUwCKeHwfkHVQDaILf+B7xfeTPN2UQPUAkOv98v8R6xL0I3Tk6B9iPXpc5BtkMaECvRIL/xKwcvnMewXvkP07RYQjPis9zDoQmYKfT2cwUO3fuXLKbY5ms+fl5Xbx4MZX44HsC1xzMhReOjY0l2e4l1txOdCe4y3Xsbvjc8vJy35Hoe6V+QMZc+ZUIckdMhe/gf17bOupnLmuiE1Ta0nnc2ZDj335vl2PuzHfZgPwbGRnZFvjRa0x+P+9nTs/gflEPdSeKdHlOksuxP8F5hoeHValUUpBlPG8olnTJZbe708pL16EjA6TjAOf+yPyIw0TAnO955sgw9Qyw3ebnWgHoA9pfGoDoh5Ai8OlR2nshZ8yuAMBA+TsnfNbW1nTx4sVkyC0vL6tareo1r3mNbrrppkI/X3rpJf3kJz9Ru91OaWXlclknTpxQtVrV1NSUjhw5kpgkQFQEXKEo6FyYz83NaWVlRY1GQxMTE1peXtbi4uK2WrOHnRz0cK8/whhAXeovqjwa5syHp325gY3iNDY2ppMnT2p2djYdFheNTiIZG42Gzp07l8CW6B2PAFAOTD/MVK1WderUKZXLZd166606efJkUnIk6cKFC5qcnExgdaPRkLSzwrC2tqalpSUtLy9raWlJjUYjOUxyh6DthbrdzSwQDobjPtEIP4x0kJ7wBx54QB/4wAd0++23684779RXvvIVPf/887r33nslSQ8++KBefPFFff3rX5ck3XvvvXr00Uf1wAMP6MMf/rCefvppffWrX9U3v/nN1OZHP/pR/eIv/qI+//nP65577tEf/MEf6Lvf/a6+973vpWsajYZ+8pOfpP+fffZZ/fjHP9bc3JxuueUWlUol3X///frsZz+r17/+9Xr961+vz372s6pUKvrVX/3Vy5qbVzL5AUJu6Dov7Ha7CWiNYBifAeBFgE1S1lh0w9uN1fHx8W0AVS5KWdr+PHAtsjJmasGX4fUAfW4ItFqtdBDm+vq6xsbGEu/GANjY2EhG+traWnL0jY+Pa3Z2NqWDc/ApfSQrzY3PCIpLWzXlmWMyygAxARCjM8Db5UBU12dygDt7QNoCtllPeDbReqwPv6lWq9vWlzVx48tBBeT0yMhIcih4GjgZWb0cMVHuu2MDvYzfsicB19kbGJkRJHdjfmJiIpXnabVa6na7hYh/LzXjY6dPjUZDKysrBVDII8XiQfIxEGR4eDj73MR7uk7EeHle6WcsGxSBCdaAwAD6R5+HhoaSPsOYPJLeS8AwJsoXsEcgeAd8ZXh4eNthr/F6xocBv7KyolarlZ7hXMSif+aOh5wc5Hlgnnj+HAQARON7B0v6ifDz+b8cuh708wG9ssidlPDgiYmJxIfj8xf/9s+iQ1cqnsvgNlqUlxBBbPBrSQV7V9riAbVaTRcvXtTa2loqnTk8PKypqamki0Sbm+ApB9GRCzEamff19XWVy+UEHMK/JKXAuWtBOOljkAEvz6xzG5i1BZdAZ/A5i45efpOT3zgU3NkZfw95+5VKpXDIuztb0POQD8jxiHVEPcNBdi+jGvcj93R91+WX63H90JXsAXS90dFRTU9Pa2pqKulmIyOb57GVy+WCnMyVQnLHgDvuKQGDDgCRvefzEh3Q0THOnFJ5gVJxfi7B1ZyrK6WB/N4/GoDoh4x28phCbqz4NZF44Hul+Oz0MMA0JKVaqaurq5qenk4KOEx+fn4+AXikSg8Nbda1KpfLKpfLPWvO5ij2FWPLI5M2NjY0MTEhSUkA5ebqICgXfe0UjUr/PDpMcl7Pfryafq23k/tfUsEQZ53Gx8dTJLqD6AAgHG6Dd9zHmhtbr37uZV79fbfxX849nDC4mYdyuVwwsqvVaqr/yp7zue3VNxfcKysrKpVKqlaryeC9HHIlwiNQduvPYaGDFOLvf//7denSJX3605/W2bNn9eY3v1nf+c539OpXv1qSdPbsWT3//PPp+ltvvVXf+c539LGPfUxf/OIXddNNN+m3f/u39Z73vCdd89a3vlXf+ta39MlPflIPPfSQbrvtNj3++OO644470jXPPPOM3v72t6f/H3jgAUnSBz/4QT322GOSpI9//ONqt9u67777tLCwoDvuuENPPPFEKiU0oP7JgbyoxObkp0cL7xSJC+2kGLsSnvstbfp9dnv2Y+RbjGRzoJM+5ObAjbtWq6VWq5WicfkM2e36Aoall+iCt3gZtghuO/AQr6H+Zi5aO+opfo9eWQG9eGh0yveSgbTtpe96ydPYZlzn6LzxdfPfxb9jxFguEtnH4fPDnMbSAr4HABhWV1c1OjqajPBemVB+HwcIPCADYNz3JH3a2NhIc4pDxHUJN3Jdv4jUb3SjAwMRBPGMR/QXnzvfq+7sj89RBC120tN78Qk3zLkOgAegpNd9+L1nJ/Tak8wFL9+HuXX3Pd9rT+TmfGCED+hGo373da/nJAfo9brHTrwC3R4QlKh0gse8nUajoeXl5QIA7k547BonZIbLZ79vrzFzDxzIHnBwLewO+JsHT+T0JOY65yTfqe2cXigVI/ZdbqOTRdke94Rn2fn5KsjxiAe4M10qOmvcyeN99H720odjvyK25PIkXh+vy81Vr/nLEXLX5wR8AszHD/MG8O+l/7n8jvsg6lquQ/tv4hhj1gHBCMjvgf39yqMBiH7ICEaB8IzpqDHtRto6AIW/3dON4u1C0j2o7nHsBYQ2m0299NJLGh0d1eLiov7sz/6s0Od2u52icqvVqubm5lSpVHTy5MlCyncufb0fcq+pe4ulzVRbNxKXlpbUbrev+oPuAptDNYeHh9M7xDoSbQfYiUEjbUYrU+YDDylKkIO4fB4FmgsEPKQAtn5yOyl4pVJJk5OTOnr0qMbGxnTkyBFNT08XouegoaGhdDANwN7q6mohJR/PbKm0eWCHgyYeBbhbmRE3/Ijwc8UiF7np+9yfnZzisNt6EtXnL6/dz/0bjYYuXryoer2utbW1BErlxsaar62t6fz58/rzP/9zTU5ObgNw9gKmb2xsqNlspsN//+Iv/kJLS0taWFi4LgS4dPBC/L777tN9992X/Q5A2+muu+7SD3/4wx3bfO9736v3vve9Pb9/29vetmtfS6WSHn74YT388MM7Xjeg3SkXRYK88yhTyA1LeCzXw2eQwSjvzvddDnkEK985r/Y0Wigq5/Sdz3IGikevAXyjxDOOCEoDoq+vr+vixYsJPEdWkI7a7XZTNtLY2FiKRHfDEHDRy3nEseT2fIyuYv673W4C6jGUmD8i+Z0/xpIXAInoOawFkcasIb8j4q5cLmtmZkajo6OanZ3V9PR0kskOqHa73VQqi/VlnZDpKysraS/5PqHfrvc4OM18IcNoi7XxzwCxmZ+JiQlVKpUUdU3UpMtk5oQIanQl1hw9yeeSMm2+Lz3yy3UP9ICNjY10IFu5XE4ZbtVqtSCf19bWtjmP0X1i5F7c9y734z7zZ4p9U6lUUqYE8psSL5RQwQgmKwJycIP2GW+73S44dthvXkoK53oEcZjLbreb9JRms6mFhQWtrq5qfn5e9Xq9MGc+dvaj63lRH3QHBjoGWSS9smh8vQcg+oBeyZRzeHpGMJ/nnObR7sjZ0162KRdF69fCl1utlhYWFpK8xNaG3H70zKRqtVrI3JGK+olnEHk5rqgnoU8gX2iPCGGAd9dJrpR20yfcLpyamtLU1FTBPvR2Wq1WsrvJoHbQmzVhLmnfHQSsrdeBZ+34vetZXqIL/SyC58in6enp5BiBvH8cfulOcNYtznmc+6hz5uaZ33jZPPraa2/zmcui2G4OgI8vJzL6xsfHNT09rSNHjmh8fFwzMzMaGRlJFQ7QjUqlUtKXYgafZ5IhJ8n68gwDdywDkPv6+lgpG4wzC7zp7NmzKdOcTIG9YA87kdsbOco5afqhgfzePxqA6IeMYAgwt5gm7saiP+ReizHWjIrCBQbtaUE7gW9Er5VKJZ09e3bb90Tvjo2N6dixYzpx4oQmJiY0NzeXFPjLAc+dYCJEEpG268KDQ1g4cPRqPuwuEMvlchKIABAQShA1s9xh4UIc4U2kMoC2CzSfR1cyXClDcLuH1D2l/K5SqejEiRNJYFUqlZ5jdXBifHw8zfXi4mKh3qy0VZfYIxMBkXsBzfFeREHikCCLwR0KrmQiuDBQLxdI9kh07s/8+EFylUpFs7OzqTY/gtzXBaJ/a2trmp+f1wsvvKDp6WkdPXpU09PTBeO7H2LcPJOXLl3Siy++qFqtplqttm/Ce0ADut7IU1Hj84gR6AaiO+oAo7wWs4PbtOVpr/5b7uGRrDFCK/ImZLmDnrkoVDdCcIa6wRad6N4PeLD/DqAOnow+MDQ0pMnJSU1OThbOhyBVlTHG6HKfn5zxxN/R+Qs/B/gksk3aStH2KPSo3/i8xjWQtgziCEgS3cT4MMKlLWcH40RHwkiLEXsYX+4Y57f0y/+PkcasGSAEa+IgOn975LaklAY+OTmpcrmc9MYYXYWMWVtbS3NMGxj5zCNzxpjiM+C/Y3wY+F5Dn8AJ2nT9xJ9Rj57kGWbPAhbksityzigc/mNjY6pUKqpWqxobG0tp4Tg1lpeXVavVtLy8rFKptA1E933uIARgFfvVgzv8efB192faHRI8u5xXQIlCADJ+5zzKx4ueF6MvuRfXOzCSczywX3cDWwY0oFcKRVnlDqhof0Euf13W+7XwPXjAbmAb3wGQw7vr9XqhLxCAMoFrgOjuKIv6i5eygm8AREKux1ACrdvtJrsMvu5g8n7QTjac6w7ValUzMzOFwDPG5IAzTtBY2gVZJxXLpvGd14uPZVHc8YzNzRq7LU75LtoaHh5O8rtarapSqRTapx+MVVJBR2J/ea1v//1u8+dzzG+wfV1+ezs+T9xrfX1dzWZzWxmwnA3uz0bOoeul+6rVqqanp1UulzU3N6exsTE1m02NjY0VSqCNjIykYADHN1wu0lcO6t3Y2DoY3jPnfHz+DKMDesY3+sP8/HzhPCECN/aLsCViVoSPM9ooAzpYGoDoh4z8wYUBYzgh0D36C2UbBuKArLcJubHuIHq8rlff4jUewTs2NpaYIFFk8eHfL/J5wGgiKpzTlp2p7ud9pS2GPzw8nIAH+hFrqQHOEv2EQQtgjjAExOHgM94dEHEg3Q0h3y8IDAS6C1nWhD71U17HCcHjdegcvMY4diPSgQM3eN1ZEFOwAbCJumMeXKFxDzFRAg6o8+zw7p7mHKFsEjWAUkF9VdqSpNnZWR09ejSlWQJS9aoNSD8bjYZKpZIWFxdVqVRSlIFHjvlec2CKtSRqzeugM+7rhQae8AHtN/Xa/zmD1o0wJ3iWpG2y1n/nkS5uzDqfhie7Y9wJ3u/glxtCbmBHQC6246BZ7FuMtuF/b4vf+HsO9I1AOfItyvgIcrp8QC67/Ib3eu1zN4LghZF20y+iUUU0H/LFy9VIW44YjxjjewfIvV/uhHBQ0ufC5bzv0zi3Li89UjGOI4KqMYqKz6PewH73EjC5CKx4X3dUsF8AeT3a3u9D6Te/ltr53BPj1Wv2+r6LOk+vfRDX3KPJ0QnZX9KmAwIQHRkuFWsWR37izip3Jnj0Yi9QzJ9x189cR3MHgYMptO02QATKc/fyfjvI7tddiQweyO8B3ajk4KrLGOdJUj4S3YFW3+t+3V6eH5edbjfmSqH1kg+85/oUI12js9vlif8Wnk35y+Hh4WSLRUB3v4gxYn878Mrn7lDHLiSC3APvIr91hzgYAp8xfq+lzWdRZnipj6iz5dYo6o/ed//f7UNkKXoUbXhf+5lHt7/9TBdkplPUTd3Wj7qK6xS+j3xPxAwP1/voB/J7fHw86YgEspHRjQ0dx+8vB9I7nU4hMM+z/13njOP07H6C2PxA0avlhO6FJ/g4L6fNgfzeHxqA6IeM/OFttVpaW1srMHRPI+VagDVXzqNh44YfL37D/3sl2q5Wqyn6/NixY5qbm0tMOXrL94uc0c7MzGh4eDilKDMPHOK43/fF43/8+HGNj4/ryJEjmp2dTanDLnyIOoLZXrx4Uc1mU/V6XRcuXCgwdiLF/CAUStgAsjtg4eAqwhtPabPZTKlrpOtjWA4Pbx4qA/AfjbCdxi5tCptyuazZ2dmU4kS0uQNQUPTeSyocWHf06FFVKhWVy+UU9Q1w4aBObJc5cIMTkKLRaKSDPvzk+mazmQWiut2u2u22Ll68mITq1NSUpqendcstt6hSqWhpaUkzMzOpfMvc3JxqtZpeeOEFtVotXbx4US+//HI2wqTb7aZ0r3K5rE6no7Nnz2p2dla33HJLOjugXC5viyIFgFtaWkqHDf3kJz/RxYsXtbi4mPo8ANEH9EqmlZWVbaW0XBF2JzQGl0dWRZAsB9A6X/K6jB7BDK9CFvsz7FGrvHukM05w+oPcd8dp5Kc4j3E4Ur4jAmn8NkZt0w8itAGy4SkR+HWDxaNxHdiTVPgd42m320k/8ayycrmcMmz8wFTmlggfly85ueWGZ3R8SEr8dWZmJmXKzczMaHJycttv4tqOjIyoXq8X+oX8Zhzoat43N6qJ2nIHTgTZiZgi+p/xeqSd64CeQuxguPNJD3ZwIIi9hiHsmQ1uMLuxTj/4nxIuOCVIy47ZbTHiz41TDwRwp3Tcb9GId8Pbxw24QtYYQR6Uoet2N8sRkJXBgbqNRqNgWHuEOevnpWHQyyj7xly708GdIhjhi4uLSS8juMIdTDw7DtZ59ou/Ytk75xHYDzkwL6f37wXYG8jvAd2o1O1203PJ/5Hvufz2ZzxmgsNrI+DaLyH/kBHLy8sFHuQBTR6IlLufv/tz6CAs13tEOXwF/gzfB8gGnB4eHk64RbPZ3OaEvxJy+VetVnXs2DGNjo5qbm5Os7OzSYdxW7HT6aT5wQYfGhpKmIm0lcW+sbGRHL/wdKLSWWcvreYyB9mNfoP8jsAxcp+Xlwyjz7y7buX6qMtnB+tdd9yNfJ+AWUxMTKTSKZVKpVDaxbEj3yfoh67jOA7l+pPruQDPbuuydugPU1NTqlarOnr0aNJrq9Vq0gWr1apqtVrCO+r1egpic70T2dlutwvOB3TdTmczwwKdxXV+ZHan00mZYisrK7pw4YIajYaazaaWlpa26cj7RTw7Pk+Mi/cBiH5taQCiHzJy4QtDdOPbgQGux/Byr6sLHI9gceUZgXilHjRSx2BCHk12NYn28UQODQ2lKHg3YvaL3IPsY56ZmUkgOgalk5eZoY6olw1g3SQlT+vw8HC61sfLuKKH1QWqCzQXbJKSIelA9V7mCWUKoYtCJW0/0XqnecQ5QLmUqakpTU5Oam5urtA37tlPtKFH3S8tLaVatu12O32/UzsoUwBSY2NjqVQP6f7r6+saGxvT0aNHtbGxebhts9nU6OhoMsA98svJo9XPnz+flIojR44UlHQH+nxMrVZLtVpN9Xpd58+f1/nz5wuOo+uJBkJ8QPtNXt5AKkZ+wZu8Zrin/jqQ6CAYYCV8A97pDm0Hu2J/ovFAhhZ8MwJaboR7tK6XNKG/EOCaG9GxXdcZIK+J6jXO3eHuRpDPqUcdScXDCbnODS3+RiYBAvg8Olgbo7iibIm6RS+548apO96JnvPDU31e2R+sGdHygCjOb5G9Xv4lOjBw8gKi5pzNtOXynL54lHeM/HegZyf565FvfpgZDnaPqnRAPuqcEdB3UID+ObDjUZwRpGF+0WMxWH18DsLEZ43nyA19dEGeBYxlQAbK7tGPiYmJQpmFaPg7wOS6FPdGTydqLhq7/iwzbpz68UAyfpOL5ItnOOQcCJH3+V70PuWe4d32j9NAfg/oRqCcni4V5Rf/93p3oNQdfRFEl/JlLnYj2sXpiR6DbuJlPyIv5N3J+0D/YuSz39t/A99AZ/DvVldXC05a6kf3muPLIeQLthnnX5TL5UJgg4/Vy40SkOZ2tDtkHTB3vgnvpwyXz7/LJneGOn7jekUMDPM2uJ//7TrtyMhIAvvjIdl7wVtcbtMW2Ik7oN0WdX3a97if84EzAp3CnyMPDvH59TlhLPQLPY3gB+Ts5ORkaoMADOx85tP3njuxPfNNkiYmJrbZ36yBB6g6jkOQYrvdvupZ4Mxz3C9XKoMH8nt/aACiH2KKSnRM6XGG5nUnYR4eteMCtZdhuhcaGhpKNUsBGqnFthvoud9EXzqdTqpTPTQ0lCLR91OAY3hPTk6mCCc8pgADMRpS2gJfAMbX1ta0tLRUOPQTTzagKfccHx8vgOtOCK8YiQ547EqdVPRwX8kauUHfz7VES1I7nui/8fFxzc3NFcoAebbFXvaSAwwcJkaaH6VU5ufntbGxoVqtplarVVAIXFlBeGLsYiwDEMzMzKjb3TwUr91ua3JyUisrK1pcXEzR+b3KKq2vb9UlxquOp31ycrIQHYrhv7GxocXFRdVqNbXbbc3Pz6eoxoFQG9CAthx8MYLWwSEAZ1fYpa1oYM/MyUWje61IaQso9/vwN99hVEUiEge5DS/PpYZ6P+gzBqNnGAFGS9sNfcbZ6XQKBiBGKW24vsGc5aLXPcIu6hRuUPoYHKwHcHSjiv4ApLuBSl8ieAgP9z5H0Jf++gGY1CRl7G7wudHH77rdzcO1SVsn0syjDN1gQw9x8BQnAnvV9xBRblFmR6AaANWd6cgaX2ePXvKoNt4B/T2yMhrMvleo30r0WrlclrQF6HqJGO+D7xUPBkFnY34wYD2rw8n1XFKp/flCT3JnBr/xTInh4c0SfPSl2WwmfcEPXwdA973tUYhuiLIniBh1WQ+YxPd+Xo0D34AiudIu/gw4QMa4fG5df/AMHAd+IpDm7wMa0I1GEZiU8kASz1sOfIb8eXNeKW1F6EJRh3DH+V4pyjS33wE/o3z0QADvj0dre7uuy7gTgGsdpI5zCv+E4MP7aX/j0MZWIoI4loHztQUglpTOoaKOdgRa0VtWV1fVarUKchoe7s50aSvT2kF2P//M++UBD+5Y8f0Q593n2Hm7r8tONj2f4VAmkr9cLicbGXyCz9CJ3Kb2OaVNAsk6nU7hoHHPtPPsM2xZl98u13wc0fYH5xka2qwEwPW1Wi2Ve6FWuj+HnsXvmQUexc/Y2+124Z5gMrRNpiQAOvrCQdBAXh9OGoDoh5QwDhDoCCdnLO4l7qUURyMqRrxcLgEoj46OampqKqVG4xE+SHLjbXZ2NvVvfn4+Mcn9YDhDQ5sHr5FKdvz4cU1MTKRIalesnKinTYYAwCkpxJcuXUqpb4AIGFsO3LsAddDGvd+A6IC/cW8gnHIA0V7nwo22nYg+T01N6S/9pb+kcrms48eP69ixYwmUdkPbFbx+yAECB5iYo+PHj6vT6aher+vSpUtaXV3Vc889p/Pnz6e5chC9VCqluRwZGdHS0lIyRlGAjh8/rqmpKc3Ozmp4eDilklE6BhAmEsrWxYsXNT8/r9HRUb3wwgsJmKDOG/OL95yUNUrJ+EGx16MwHXjCB7TfhALvKZ2AVG4QUA/RAT83NFzGuixz3olBhYHgkSvOl2NUsUc8RwOTqB7kROTV3McdmBhpHpmak3c8bw7QYuTgCMcwxLDsBZjzG9cpPBIPWYQh6WNwYxeZRv8AUjFSMIgcSPRzPPh9jA52/ccBD09TplQXBrYDum5QswcqlUraX5OTk2o0GoU9BhiNAel1Nj312oMcMMJxuHpqNG16VBaRzm6Is+dykVA5AId7eqScG/nUfvcoeE+hn5ub08jIiGZnZzU7O1vQU/2ektKcRAc1gC6OCy/r4sA+pZa87A37F+cEzzsZYisrK8n5TNushUdw4gjHad1qtdK6eTafz6G/4CteAoZ95PsQYMX1MKLa0A+YGxw6zIfPjT+//kwzFu9b5HeAQa5H7gS29EMD+T2g64Gi3HQ5luOZbh/nngfnjRHw899HsNOdu/DDy7FLPUMFPgivjA5W5/tRz4k8IBcEwP8e0ORyn3fkE7wKfl2r1bY5FZwijrEbDQ8Pb8v+5mDwarVaAMMdIEfnAGitVCqq1WoFx7Y7XKWiM8XtLB8j4/QgNiKxkSOSEvCL3Pbfcq+crRjnKTpNo27m/D8C3kNDm+V3jhw5kkrYTU9PF3QC1ysINnBbNK45fWF+/OBOypS5Lub6Ta1WS0GO4BRRT3F7Hn0VuV8qlTQ5OalKpaK1tTU1Gg11Optl2VZWVtJ7qbR18CZ9wK5mHVx+42zxQApAdDCZ9fX1VNblSgJR90r7aesP5Pf+0QBEP+QUPcLuoXQG5akxHo0SQXSP+PKUnL2SG6YxQuqgKTJf96JG4/9K7xPrmWHcMte58TsYQc1UfoehJuXrexORRzueXuyeVgSUKwRxfXPAyuWSK1k7EYY54BURbLz6BeL7JQfUvQ/SJnBApgJgSjSYXWHwMToYJG2B9KQw4pFHmdztOWB9PNqPv/0ZdjCq3W6n6Pn9cgxdS7re+z+g64scfO7lRHRwM0bG5N5z5NHYLm8dXJe2InSc/JmI5TLidVzLNbk+OSDguoT/Nhr8ubFHfpYzwiNPyxlyMVLW25O2IqQZr+s6ERRwgCIHNDpI4vd3A815uo8lgikOMjsY20t/iuvs8+Iv+hmjp6JcjVFZ9JPfR2fMTvvTnR+57/wZ4d6u95Bi7WXzes1FLx7vxr6vI88epXR8b/O/OxZyB73FccW1dJ2QlHF0Mu7r0eDIfgdmKN/T6xlhLR1E79VHBz8gHDbMj4MMtOE8JY4r7mnGFLNsroQG8ntANzL14us5XcCfpchDczLGHbY73a+fvrl8inpNToeRtpdDi7pB5JcOpMffYrs40LmTjpWbs37G7g5C1xP4W9oqaZWT3djsHkme46vO46Xth03HvnrJzWh37+S0gGIfuD5HUcZGfS63N5GfRKITOBDP32Ku4mc5XS3uA3cscM+o37o+AXbiTozc/o3zEe1vAgvW1tZSZQSXy76evn70jX0b78f3ZCQgyz1gMeeEu55oIL/3hwYg+nVCzpRzHlwY0MzMjGZmZlKEeIw8iYdcErmMN7FfwrNJGQpOUHbw8iAJxk59q+Xl5VTrcj9OTkYwULoGLyjR+DuBpu69pQ4sUcfDw8NaWlra5uRgXRyEdiEjbe0Jj1xzED1mHHS73W0He0jFWpv9EidU43mOhIIzPj6um266KUXrcxhrrnb81SLWZmJiQkeOHEkR/kePHlWz2dRPf/pTNZtNTU1NpdrslUolpQ76qeVEz7Eu3W43RbPx+6GhIS0tLfXVN4Qz64X3nHVmXd2w30+P9LWigSd8QPtNpVIpRf1EoBJeE+s2O2jmEWJS8QwK58+efuoZLw6Sx0h19iwZJP4ZbUlbfHN0dFTT09Mpat6jrv0gKY8a9zYdTHZDyB113Meju71cTTyki3a73a1DJjFEo6PP55gX/XAnZaQIcPtYXKZEIw4eSfS6t4/B5oZ3qVQqRIczjuj4YOy+fwAL+I2DzMhjMoiQuTFlmTlD1ke+7/uJuUSvGRrajFquVCrJse5ygr3LPDJ2SUnmU3IMnSE62tHnkHMjIyOqVquanZ1N5ewqlUqaC+bS656ivzgI7lGcPF+Qr6mDQvTfjeBOZ7OWOinkOLPjvohAcy7YgYyExcXFVP4tHkInbYElQ0ND2w4kZp29LBCfMc8e2MFecmKeuZbfupMIcuPfwXM+Y08zl+44yf3G56sfGsjvAV1rcrncy77L7e2c/dwvuVyFHPhD1g0Pb5aMIjMFvkDpTmQlMtwzudxui33mc5ffHtTFyx3WOXL+6jwsHpzpgXKRb3imX7zWwW2fr52cwb0oguB+uHvOgRH/9iw9gOOVlZVCJhztRL6NPI9r7vdxfcEzESO5zkYAF/OfA5y9jIr/vtvtprOwPHDOHdqu04FRVCqVdBjrzMxMOufL9WTulcNxXI66nHWnkQP29IfrXDdmLYjqXl9fTwF29DeWh/P5QCcjM5D2+CzqAJLS3KMzuQ5bKm1mo8U6/5zTEoNDeunw/jwdZjpo+f2lL31Jv/mbv6mzZ8/qTW96k86cOaNf+IVf6Hn9U089pQceeEB/8id/optuukkf//jHde+99xau+f3f/3099NBD+vM//3Pddttt+sxnPqN3vetd6fs//uM/1m/+5m/qBz/4gc6ePatvf/vb+rt/9+9uG8unPvUpfeUrX9HCwoLuuOMOffGLX9Sb3vSmvsc2ANGvM8ptfvcYTk1N6fTp0xobG9OJEyeSoQXzIU2adBRPWdoLAVhPTk4m7+ZuYPLVJvokqcCIET6XS+6lxWkAsyeNbreoHn6PQUhUtqQEMLiCRgSyAw9uZEvFFP2YXZAjF+JuEEZP807kwmWnQzUQ5OVyWadPn9aJEydSGl4/87WfxL0ABogaP3HihBYWFtRoNCQpnf7ttXK9VjBeb1c0NjY2VC6X1e12ValUVKlUkoDuh1gzTyUc0IAGtHdyIy6C6FIxotadk64cO/Fbj0znOUWJhwfElFHPDIogfASm/W9kCocXV6vVZDxKW05wTx2GUN4ZSxw7Y4nArwPXPj8O3vqcuBzCIEGeLC8vJ2OE8y/go9EwdPDWI41y8ghZR3tuzPu8c8aIz6s7Thgva+nr50B0BF6ZBwdFIlDhMtz5uddBp+9ucDebzQQGk53ljgPmGeBdUjJAAXnpL3PqAC1jcEOQskE+bl9nXzN0HBz/9NMj0dEjI6Dg6ecOorszJUbCxf3qz7Qb5xMTEwn0oRRMLorNAWfu6bV9GVOlUkmRit5Xd+Z0Op1ClKU74HjWJRXaAIxxMArnmO999BPGubGxkQAGgBx3Cvjed6DFn0ufC3eg+X6NTsMBDeh6IGRUP4DV5TiKerWT+70De9ienJnlgCOBR/BunOruEI9lGqN8gcdFUNkBdXecu13pwLPjAg7ir66upnZpD7nHbz3ozuWN61Uxu43fR4e/y/EcIWddFuwW6e7zhgNDUnJ+ItOcl/K38253gvucuZ7V735y+5t190ANnxP0NPqY0y3YR57FxtwSbT4yMpIwGpzgBFhWq1VJW2dweBS9z6vrWq4T+/o5kM7+iIETMfO6XC4nx/Pa2lphL0f9yvVR7jk8vFmWhXEw3vX19ayjhvH1cjL7c+p4CsEOu5GD6FcCUt9o9Pjjj+v+++/Xl770Jf21v/bX9M//+T/X3XffrT/90z/VLbfcsu36Z599Vr/yK7+iD3/4w/q93/s9/bt/9+9033336fjx43rPe94jSXr66af1/ve/X//kn/wTvetd79K3v/1tve9979P3vvc93XHHHZI2yyX/3M/9nP7+3//76XeRvvCFL+iRRx7RY489pje84Q36p//0n+od73iH/vN//s/JybQbDUD0G4DcACBCKJYZgakABK6vr6c63hhxOwGwkZw55rzL14pc4MbUnv2gnYT2XiinXPRSztygzwncvQhz2iJNydOodgPSHXzHCI9R/sxPuVxOghpg6FocOpsjwCJpU5manp5OfY6pgVFZco99BKx6gXED2k4H7Qkf0I1Pzlecl7mS7yAqtBOPlbbXIfWoYj7DmQiIi6GDQh8NMEkFAwGjgTRbyky4QcpvuNYdqDHl2KN+6CvjjCC4/83cuYzJGRC0Fet9ex9dP+g15zzLzGnuXrn5i8BmBMJ9DA4oeFs5Z0YEz92xwNx4VJzPk+sG3i9fBzeI+Z9x5xw+uX75b3h3I7Hb7W5z4sZ+ed88+pFxoye649/X0tfQDWjAbu+f1yf3dY7O9/jcuUEbnUX0AYd47lny/eXPeM5BE+/pn8fnHZDbgQWAASjuY+5PlHkE9bytbnerHItncXiJlwioeztxj++k30WQvd8U9YH8HtC1oshnr3Vf4JcERxERiwyXVOBXUjGrBZAOJ2SvMfnz7XI7gp1xfnZqz+0Wd6Y5v/D7RJ4a7R+fF3+P98297zbPrqNEOzzuB5cdELzfZVWUm57plAPKXW/Zq83t8+e6W7TpfQxxfl3XiqVFfF84iO6BGZ4pQftR5kU5DODseoLLCtfF+F3cmy5DXYeQthwagOjoG1E25TASl4H98oIoN+Pn3G+v7dIG44yfHSY6SPn9yCOP6Nd+7df0oQ99SJJ05swZ/dEf/ZF+93d/V5/73Oe2Xf/lL39Zt9xyi86cOSNJeuMb36hnnnlGv/Vbv5XA8DNnzugd73iHHnzwQUnSgw8+qKeeekpnzpzRN7/5TUnS3XffrbvvvnvHcZw5c0af+MQn9O53v1uS9LWvfU0nT57UN77xDX3kIx/pa3wDEP0GoMnJSd18880ql8s6duyYjh49mgR7BC0pUwG4ubq6qhdffFHtdrtwANJOhOJAWQ5Pr76WAKkrNM6Qiei+EmYWFZReUU97bStGsjm5ERejinLX7TY+F9y1Wk1nz57VxMSETp06lRSKnSKoOcBjZWVFi4uLWlxcTFEUkgrKyenTp3XLLbdoYmJCR48eTaVrrmWmAoRBirH9ute9LjkEiMKQlCJJGaN7mvkuvjh8pB/P9SuZBkb4gPabouLtTlQMB+pCStuBzmgUQA5Mx8+lzdIYRIwCorux5NHVvDugjzymTEa1Wi2UY6Ov0lYJmU6no3K5rE6nk+4vbRo8HNREtJPPgzu/Heh2ABLwNoLzzE2UpZQGwdApl8sJhPVUc6l4FkR0TPj9IrBJVDRz4fIQvh0Ba36TS3d3UNiNOvpA+rM74z3Fmyy86ECAfB3YBw7Eu1HsY42ANGOOe5OoNqKqYvRcjLLiRX/jIWDUy6YkH6XJ/Hlypw7zHvtKBB1ys9vtFmqCe6R0jPryZ8JLDHqpG08X99RvL4vgkW8xSy/ePxrhLuMdWGG8PD+dTifxEecX0fHha+s8xNcWYgz+fHY6Hc3Ozm6LFvQ95/Vd43NKO3yW09PZj5Qg6ocG8ntA14LcuejOo2vVl1KplA5qJIhtenpaIyMjBflNP+FVGxsbqZZzq9VSqVQs1RadbFJRNiDH4oGiEaB0Hcf7jVMT+eHl7DwC3fUEabusph10jVxAVQRDI+22fh4Y505S16cchMZRyXUetNBut5P8rlQqWl1dLYDUzAMBEb1A870Atj739GV1dVXtdrvgqPC5GR8fTwEDLm85kLrdbqvZbKYADiK50Vmmp6c1OzubssbQez2z0QMF6BsR5i5TWF+XkY5ZuO7DWkTHBNfyfaVSSddVKpUE5vvejnLKdSUHudl36Bz9BIO6rIxOKNZtL44SfuP6BevXr2P6IGk/5HetVit8Tiad0+rqqn7wgx/o13/91wufv/Od79T3v//9bPtPP/203vnOdxY+++Vf/mV99atfTfv86aef1sc+9rFt1wC890PPPvuszp07V7jX+Pi47rrrLn3/+98fgOivJPL0HE/TyRHCCCa6sbGher2+TeDsRtGoudYAOhQj0fuJsL5cyikJ/f4u996L9iq4d2ur2+1uOzADIZaL1GJPILAxwL1WGMS8VyoVzc3NaWxsLNXMP0yEYjI2NqaZmRl1Oh01Gg3VarUCcBGBpBgZ4IZtjGYYUG8aGOED2m9CDsXoHoyGoaGt+s7SVhqrP+Movk5uUGF48Htpy7iJIDr398hd+ukKPNFCY2NjKpfLydABtPSoUzeQnd/4uN2ocF7EvXLRbHzvICJ9joZgBHa9FnY0eP3v2CZ/u/GauyZGL0WAwYECByBiNG6M0vX3aIQDTjhAjGx0Z70D+z4nLhegXk6YHD9jrfzaqGv0ckLENiNgnQNj3dkyPDycItl8jh288H7xOf319WA/RkPbjVj/LhecwJii08NLo3BIu5d1cXnt/fJnZTc55Ne4HjQ2NlYo68S4fO57AS+9dGzWWFKhhBygENkt0Rnjv49t06c4r72e+b0ABgP5PaCDJufh/kxeq7448MeBy0Siw5ek7RHG8F8chjjmdgr2cidflGe9gMB4b2/L9R130ubkpsvv2Lbz2egQ7hUYdjlzHdvy/5m3nK4TQWIH312m9RrTlfbf59plCZkIufYpS+KlCV1e47QANEZGxL3oeqRXJ/D75iLRcUJLxZrv7EHu5zLH2/X1cic5FDEa5LLL9tgn15v9PfKBfmRTLydV3Ed7oZzOdVhl3X7I75tvvrnw+W/8xm/o4YcfLnx28eJFbWxs6OTJk4XPT548qXPnzmXbP3fuXPb69fV1Xbx4UadPn+55Ta82e92H38V2nnvuub7bGYDo1zGhGE9MTCTwvF+w0oUMUVVDQ0OpZttOv+PdI9gOE+WUiyslGDVRb7xQMpzp79QGAs+B6AggHAQRVb66uqoLFy5oeXk5eavd4Op2t2rCtdttLS4upsjHCKATdT4+Pq65ublUlmCn6PbDQCgDHEo7NLRZZ7ZWqyVP+erqagIWut1uOnisXq+nuuqLi4taWlpKB74MqDcNjPABXU2CJ6OoE2HrIDoGgEd9uuHkUUkeqRWNRq9THkFrB1zpE/3CyCmXy6kcG2C6R+VGx4Ab2h656iCctD2axkH9CHJH48qdgT4nfBYjWalV7VH2frgVBoVHDjGnngUQjeA4nw6QejtuqDm47c7O6PAkmh1ezb0YQzzQ2w2/TqdTMGTjQVMOUqMjRL7lKdJe35x++zz7XmIuqY/O3kM/Ye9i6EpbWVVcm4t4dECa2u0O1OaAVo/aZkxx3nPPRE4v870Wx+7vfj/fz4AH9Js5bbVayclDu+12W6Ojo0mX8br1nrHC2kbewLhygQeuE9Mev2XPRYc84wLAiI4FeNTY2Fh65t1x77plfI4iSML+vBIgciC/B3RQ5HwvOlOv1V4qlbay2wAseQes5D060QAhiQQn8pZnfyeHlsuh6OTFnodyTlrn5T6WmLkUQU7IgVx33Dov8jb9Fce0F4dddPpih5dKpWSHx3Yd/I881jOy/HBOH9d+7a2oh0kqZP1EkFrazO7zcixk1Lv8dvub9eda5LjrYegC7jj1/rFmOUcs37sOivzzceb2XJTf8Z7oxu7ckLYOQW80Gin7EmI/NJtNLS0tJR38Wssl1w+gnKPkWsvA/ZinF154QdPT0+nznbDHnDNtJ2yul/MtOs720uZ+9S3SAES/TslBgkqloiNHjiQgvF9CeFcqFc3OzqZDjXYC0bl3NOoOC8Vos/2IkHfBu7Kykk7GRnj74R+7teNlPyihkzNorzYB4A8PDyeBPT4+nlLAPPoOI7PdbmthYaGQNgWVSiVVq1W96lWv0tTUlE6cOKGZmZlD6WRx8v2LQixtpiFdvHgxGducMwCIzvrXajWdO3dOjUZD58+f1/z8fIrWH9CABnRwFPmnH47FeQcOaqGIw8MBM+FtzudcKcaYIMV1eXk5lXPx9OIIKDuIPzIykkq4VCoVTU1NFUpUOIjrRjiGdwQyGRP3dAAT0M4N21y6thvhEQD18XsEFbKWtPUYqe1lPtyBHCOVcym/yFUHTgDRu91uckCzTpJSP2JkEuvM9f4bwGfGSf/ZO5ADOaw7xizzzBx4dL7/PoIMcU4djM85K1gjxo8hSckU7lkul5NjfGNj8/Br5h4DPkZpc7YOtXy5lwdL+F6IGQmsuVN0QPkcRco5RPyda4aGhtK8Dw0NFQ7p9LIDzA/glK8ve2x9fb2QRcJe47Bwvmcc7nBZW1vbpv+6oyo6ctxp5M+xAw9cy3dDQ0NJJ2EtiISP2QfuxIvgurfvffX1v9aG/YAGlCN3grsjrVe08EHQ0NBQKhfKgctejg1AMwZXwV94VuFl8cDq3LjgWx4cEGV4BEYjIOqfOc+M8tz1A/+dA7i7ldGIekZ0kvZLHoWNI5hSOPSZuXWHtdv+LnuwwwmEWllZKZxpk8vouRJi3O4cwebvdDqpHIvPz8TERJJryGTvX7vdLpTPYT8SSIkeybPjNr3PlYP7rh96EJg70iPG4fMUo8OhXiA6c0I54NhHL43qugcBDKzhwsJCwlOulB/sF5BOP3MYlMvn65mmp6cLIHqOjh07puHh4W0R4ufPn98WAQ6dOnUqe/3IyIiOHj264zW92ux1H2kzIv306dOX3c7hRbcGtCu5oZ7zGvdLOSE8oO2EAeQnqjvI0ouBO1ONKdSxjtxBjoX+AOpT2xaPd3wBDqNEQR6553VK3QA/zOSOF54DBDXCudFoqNVqFV7NZlONRkPNZjNlcLAnrsWaXk8UozP2+hrQgHqRR7S4EptTZnP7if89uipG1sa/vZ0ol6Ph67I69s9fkDuFc7zU+VfOiez38Hb8b287F00U5ybXN3+5MyFGx+VeuXWIjnDvo1/v7fjvcn33V1zLXLRLjvfkItr9/jnD0vvTy9j0eXBwMzcnse/uAPLoOs+c8zrl3h7vcW/mjN9cn/wzX6dcO7lI0rjnfd/spDtEGet6LN+j33j2oAdCRL0G8j7k5sT3QgT04l7OjcF/16uduC8jn6Bvrrv7GkZ+0GvN6ONe9LSB/B7QQVAOBOXva72XcrIvyqmcDJO0TV7mZHQEtnP3is9tBM93e6Z7BZ314h1RF+olu/fKy3ciHxP3y8neyH9z+kE8PyS247x4PynKdB9DxAPcYZCT325fxj0R5cFe5j/uhV6/cUfNbtRLh/DfR32ZZ9uDLqghj8x2O9wzQS/HSXM1qJf+dljooOT32NiY3vKWt+jJJ58sfP7kk0/qrW99a/Y3d95557brn3jiCd1+++0pSKPXNb3azNGtt96qU6dOFdpZXV3VU089tad2BpHo1ym5Ar0Tw+u3La8bm/t+p8ghTz291hQB6/30+G1sbKSIw5GREU1PTxcOzQJIjnPlxi2pR/Pz81pYWCikIV0LwhNOdNrKykphP3W73UK0nQsqaXPMRFQeOXJEx44d0+TkpCqVynXnkHGPOwK7Xq9rcXExlV8gQq3VamllZUW1Wk0vvPCCWq1WilyPkasD2k5XougcVsVkQNeWut1uQSYSvYZR4fzMZZdHwAKoEWlMRonL1qjkd7tb9aLJ7iqVSqksC9Fq0cDxmp0O0kVQLRfpKmkbeOtGkEdScy/6l0vVpi0ilD0yKFez2kvARDAuOvW73a1oc68ZH8F6N96o5en941rmPhqlfp3PFXJLKh4Ouby8nCK1ck5fZCFtr6ysqF6vp0O9KNvFWtFv5IjLSF5EGbsslbYOQWVsjIv9kHNw+BwS3chhY+g/RPMvLy8X9pyk9F25XJakQhR6zIBwGhraqulLW9Fp4qVw2u12us73ONHYfmis34/7+EHd0ZHEOB2MZw7ZZxy662CJP7v87ee80C+egW63myLdIC//UiqVkuPdnxP2IZkufJbTmWmP62iDa91BQJtxvmLUv+8dn7+4brTpz89ONJDfA7qaFJ1x7rzy5/ZqAJ47US8ncfwsNx6Xzf5cwpc7nU7ivz4+B+DHx8dTmRiP2oVnAbYin6Pc8H7mbNUciI+c8TNf+L/XOsCf0MkITHJ9pV/iHqVSKR0yPTw8rHq9nkA1+u3lvDiwFf6O/KYECBnWBD9FvWK/CZkXZV0u6Mr5uJ/zwW99X7APJicnUxAbZVTjGroDnzn1Z8zlgu8FqSh7XTf1fchvpa2zf6KsQEYzPpffXOcH1dIGDoVOp5Ps79XV1YTJ1Gq1QlnFa03uxMlRdDAcJPh/kPL7gQce0Ac+8AHdfvvtuvPOO/WVr3xFzz//vO69915J0oMPPqgXX3xRX//61yVJ9957rx599FE98MAD+vCHP6ynn35aX/3qV/XNb34ztfnRj35Uv/iLv6jPf/7zuueee/QHf/AH+u53v6vvfe976ZpGo6Gf/OQn6f9nn31WP/7xjzU3N6dbbrlFpVJJ999/vz772c/q9a9/vV7/+tfrs5/9rCqVin71V3+17/ENQPTrlGB8MQ37csgN5n5AdGcOMHs3JK8luXEZQYYrJZg36Xe1Wi0B6ig1btzwG5g/9TdbrZbq9bpqtVrysl5LwuMtSc1mc0+/HRoaSiedT01NaXZ2VpOTk5J2j4I4bMQzhUOhXq8X9jjj7Ha7KYW+Xq/rxRdfVKvVUrvdTob7gHamgRE+oP0mlHpAOTc0MQhyQJo/4xiKgOiArjlHtRs/bpjG0i3j4+MJRPcoUal3qisAGC8v/cE1bvR535DV1E92wz9GKLlRJikZ4V7P3IFd1ztyB2vmoul8vplfJ++7/w45Stp7LhLGgd4o56McZp3p58rKyrZSNm54UnOVeywvL6vdbidej8HG/R148L64oclc4sCJQLuvbXSg+Dj9XdoqFYNhyffMAWd6cOgdv5GUgAhKusWSLBFM9yAFH5uvPXuEsfkY3Qnl8x/Hx37odb4I80QZJeaLvgH2rK6uJjDenUW06+Pj+7jP+Y65dSMdZ4CTz4nzIAi90AEM5pVr6Qft+3ry7OWCZ2jH28iB6bzH56QfGsjvAV0tclmQywCJkdAH3bdcpsdugWwum308/AYQnRJ0zsvcUedyOWaO+3y4TepzlONLOfLx+Vxjy0Y51GueOp3Otn7u9fl3XQeZPDIykkqaoG+5LEGHk7bKXK2srKSsYWQ44zmIQMDocMgFBEDoppJS7fQYWMA6IrMB0L0UW3xu/Pnh/r53c85u39PunHUgPO4j11ddDkX9CPnm+5R9Rhk1L7fWarXU6WyWmOVzriMI8KAdaztRLzkZ9V1JBwr8H6T8fv/7369Lly7p05/+tM6ePas3v/nN+s53vqNXv/rVkqSzZ8/q+eefT9ffeuut+s53vqOPfexj+uIXv6ibbrpJv/3bv633vOc96Zq3vvWt+ta3vqVPfvKTeuihh3Tbbbfp8ccf1x133JGueeaZZ/T2t789/f/AAw9Ikj74wQ/qsccekyR9/OMfV7vd1n333aeFhQXdcccdeuKJJzQ1NdX3+AYg+nVKMF+iT64ExPaoPGeqXquKmm08fBhjVwrg7yc5eM6ceG3I/Wofhk7Ul9e5RWlw8pIo9Xo9CfFoSF+PNDQ0pHK5nLzg0XC+HgkFcGxsTCsrK2q1WimdrNVqpai0a13XfkADGtB28mhfB0EBdL3muQO8blzECLAcP/PoIGSg1wDFuIml0qJB0Ut+5gDimHrskWYOPDow7Ia7R6J7217r2Y3mCI678e6R5hHI9xqocX59LJK2RcT5uCM/zRnvHmnuIALkkeBuVAKuxu8Zk0d9A7wix73eeS+ez3x5HzxCLxruuX0Qo8EcFPV9GnW/6GhgzrwPtMX+5DBRf2a8nWj4+h7wfRDXKxrz8T0a4YBH0bDPjQd9y59j/96fc49kYw1z8x7X3sfFvSLAlRt/dILEZ77XeqFjxr3p8+Jt+3pFik66HM/JgesDGtC1InciO49n3++nTXc5FHmw85sc/+M30tZZCr1sBecnyLAciB7tbnem+32lYlS/O9R5j/2IMiDqA9EhnRuvz9V+kPNLd5LiOObsD5eFnn1GYJSfZ7bfmeq7EXPna7SXe/v1rCV6JhkMngGWuw9zxDtt7XQvqVjCpdd+iZ/l3qNclZSyHZGt6Ea8cHgTxEDGHVUAopP8WvKGK6Gd5Pj1Tvfdd5/uu+++7HcA2k533XWXfvjDH+7Y5nvf+169973v7fn92972tl3ns1Qq6eGHH9bDDz+843U70QBEv07JjUA8qX6w2V5ofX09efaIDhofH9fRo0dVrVY1MTGharWagAkUeNJ+PVrNo+yuBVHf2w8M2S8vM3NeKpW0uLiojY3NA54ajUZKLZuZmUnpeLwAzpeXl3XhwoVCTe3rvfTHyMiIjh07ppMnT6bD8WLU1fVI5XI5KWLnzp3T4uKipGJUBMrc1TiM5kang/SED+iVQR6FyjPqYJ+kpHRLKji//LBLTy8lPdhLKyBPPF3aU1ilTb44OTmpiYmJAphKrUsHtHPjgNwx3O12U8kJ7oXi7UAyBvjk5GQh8ozv3CBi7DEql7nC6ANgdRDeS5DA+zyCiZfX+fQSOtxzaGgzm4n7u6HuzgLaimuMgRUBW49MK5fLhbGjG0jS5OSkVldXC9GADvy7MTc/P5/WYCenqe85L6nDYV8Y/3FNctFgcZ0jYM7+8nX1/cPce7Q9xD6hdIkfzOplZfiMNWNeHMyJGRb0mbFHcJrfeOmB6CCgrRitBTDC5yMjI4V9wJ4m2MGN8E6nUzgYnn4Tvcl9XJ4zDvY//IIybwSzRF0zAhExMjTOL38zDvrk5RC8NFU/EZ5c47p6dKpI2paBsBMN5PeAria5DHBA1J1vB7mPHAR0Bx3An0fTuqMr2gU4YiMg6s5fgtfgKzHy3R3a8AHsUi+d4jwWHuAgKzy3F/ifc0RiW7szwEvY+Zij/LjcNYtORKKT4V/oVMgAMofoB3oeh1AiB/zg6YMg5jL3+V7bIWNhdHRUs7OzqlQqhYxHd5rwG3Qnd0a5kz9eH2Vw7D+6n8vQCJbnHMXeF5fffh0Ba2trayljv9FopNItgOjeR3dkHXYZs5/9i9ko/d5/IL/3hwYg+nVKUcjxupxIEpQAoqtQ9qvVaqpvPTU1lQRWFARuSF9LAJU++IEh+x0dDJOiXurKykryAHsqHv3Z2NhQq9VKTgrKufiJ3NcrIXRxsqD0Xe8AOvsfAI3yOzwn14OQPuw0EOIDulrksshBcXcCO0jqmUvR6PWIHzd2+Q7l340A/h8dHdX4+Hj6zJX93XhkBMdcznuN5BidxW8BueFhuWghL8XmIHicSweEvSSNRyB7v93g8jG7PI5Rw/SvH6M2RhvFNXag2NfPDUVAgFKpVADQcQygD+FkAJQFQN8tg4z7xGt83nK/2el/5pFxxkiyXnsqGsP0g/1LmRcHaNzgzd0fANtLGvla+r1zxrX/xu/r4LCDRhHQduDK95NHsqEL+gFtrB3nHXjEp6fKu94Y93dcI2/DwQf/XQTAfE1yaxVLRfHO2GON9X7In9d47/jc7EYD+T2gq029eNC1oujgi895BPFyAF/MeIuErIIve+mWnAyXVODHtBF5D7/PAfIxOhqKsjpGorsT3B1w0Vl6pfZS1F+YR2qFe5326AQG+Iffk0mGI+CgA5/2ay+7c2VsbEwTExPb6uTnHKw5x7QD6dHhsVN/cejSnus8O+kivqf4LOrkHmhBwINHooOhRLqRo7lzlNMZ+xn/QH7vHw1A9OuYeBDa7XY61HBmZkZTU1O7Ktd4aFdXV9NhG8vLy6m2VrlcTodEjo2NqVqtJuMzKhAY6teSEKwA1UQa+aFUl0s5o5foCKIAYjpS9IRT9sON8OuZUPTwfler1YIRer0TysH4+Liq1WoS4jutG2mWkgrG+GE6Nfyw0ECID2i/yQ/SlIpAGOngOFgBRx1sQ4bwGwc8vcakR0bFzKsY8R1lJRG0bhgODw+r3W4nI4j7e7o1csz7hxGwaI4ZAADKeUlEQVTrcs6jr0nzdXAs8iF3HLhhhlOYzCKPfotRz240OWhAn3AYO3jJ/T2KOuoXOYPO19MzgCJPIIsAfYZMughuxN/B3702PiC67x36Htc+RuFFvcidO27oRyeGg92sJ2268UpfPQiCa9jTnjEBMe/sEzIBIJ6FaJR5O/SH/ewH+Ppz4tGR0UnlffXa6G6Qe2RlnDccLx4FGUHvmLbv/CFG/UcwvxfYz8FtXr6J69AJvQ/SVmZE7hnMUXRGDQ8Pp3laW1srgB7uSHHeQ59y4+0FnvVDA/k9oFcaRZmEfBgZGUnlOf1gUHQGtwH8DCyiu2kH5+HExETKWsKpS1uxDy43nAfDlyII77JcUpZH0C7yhD57JLr3IfKbXs7BvTz3zmup8+16F9e4PPI++98OnKOH5CLCrxblorGvtD0cFwDogOieHej38vVyucK7t+0vZESUJy43mG+3/dEvPLDT5bPrSNyD58TbpX+egdgr0ML3Ru4ZOazUSxfth6L838vvBvJ7f2gAol/HxMPXbDb10ksvqVwuS9pMT97todrY2FCtVlO73dbCwoKWlpa0vr6uEydO6NixY6pUKjp16pSmpqYKUbluAGAkX+voY2e2rVZLtVpNjUbjiqO93ZCNkYYwfI/ix6DzCDsH9r0mG/2+Xgklj3ro09PTPSPsrjdysGhiYkJTU1NJ4DebzW3rxj4ZHR0tpGAODQ1pbW0tKdiuOAxoQAPaXwLYcnkQDQUHogFGo0GBUu+HbfPuEVcOoHNPj+5xY8WjmjEUnBd4JlOlUkmgGeApBrfLFngKTlkisXAmYFh5DXSvG0n/POrV04TdKHMgMAegO+jIHJMuvby8rFarpY2NjZRG7Qaxz2sOKODdjTBkrmcHSUUwoVwua3x8XNPT05qZmVG32y2kcANeeFQWRrbXz0aPcGOOuYAcQM+B+m4UMj9eQshBjJhi7WVGfG4wUj1Cnjl0JwZR5j6f7Gn2CQdmu37ne83H6PuaOfN94I4R5sn1xTgvfjCnO6tYF9+7HqHmDg/PLvA14R7R8M7J8JxTDMcbf8NfmGM/84A2eE69L6wjbbuzyJ0Ruf3E/Hg5FvrgbdJ/nAqRuJfvK5+Lax0IM6BXNl2uQ+cgKMfPAZdLpZLq9XoC0MvlcgLX4R9+yLE7Pf2d3/CqVquFQ8o9cCvyZ9dZYma08zW31XNjciclfSaACFmOE8DBVJehUX7HVz/k/G5yclKTk5OF9nK2FHIBOc2cUkrV62sfZBkX14+u9J6OSaDrESRA9gI4RAzggFx/dJnl5QVdjkcHscsL339SMfrdnTbSFmYiFQ9OjSB+1EPi87ITiB6dTYc9eC3OZ84B0k8bub8HdDB0/SNeA0oCz6OdqQPmpV4c8PX0GISlG+5uSGM4e/SSH0wiFT3YB00OBDC2eHhL9LDuZjBETy/R+f4aHx9PJ2HDwHNe/1wq3vXO7DztMBrNNwL5PuF56JXq7AY4ig0vPwjoRpqfK6Wccr2X14AG1A/FvYLx5YBnLA8SZUQvedEPP3dlPheByzUOCsdo29zL64vHKNv46tXn2BcHlF2e5dKDfWzx5XMbx+HzFKPi4jz2I6dz8+48GxnNy/mx15vO1RTPgQFOveY4UoxujnPF/EfHQZwHB0Fiqnbkjf0YkB4l7uVucvO+k27XD192oNfvz+/j3oj6k69PDpRwUMWfhTiW+FmcU/a875NeJXi8HZ+bXns2jo9316V7PRMRPIqvverefv9efdyJDlp+f+lLX9Ktt96qiYkJveUtb9H/+X/+nzte/9RTT+ktb3mLJiYm9NrXvlZf/vKXt13z+7//+/rZn/1ZjY+P62d/9mf17W9/+4rvO6BXDjmY7UAfL5fRLqvjGSExE8n5gL/gFTk7NgfGOR/p5VTbTXbvJL8jb3V5433pdf+daGhoKNlQZDtT99ttK3diRgdobqz8nev/1aK94A67UVzjXHm9nXSSHO/dTW7vtoa9ZI/rLDv1K+6buHe8vV79jfLbszFyMvWw0uXatnv93cD+3j8aRKLfALSysqJLly6lqLVarabR0VFNT08nzyQMxEuKnD9/PkVGE6EyPj6uqampJLA8nRtyYxtlAO/0+Ph4AuMPinFR64yo+osXLybPuVQ83ZwXXtHIGOh3tVrV0aNH0zxSzgZhQSSZtHkIJVF/U1NTGhsbKxg8eHw5LMNLCFyPVCqV0j6ZnJxMWQrXi6DaC7H+Q0NDarVahT0gbe4XDtidmZnR7OxsQXivrKxocXFRa2tr6VCUgRAapJMNaP8JuZQr3SBtr/fp0TDO/91ZHKM1o8EAP+C5Rn7EiN1e0TP8dnl5WSMjI6rVatrY2EgRwiMjIymCCmc5Tm+P7HWjyiNjJaWo2G63mIaLkYzRj2zESEWm+b1ykcQO6rpsI+qOiD2y1uCXnqqei5rCaUmksfffHbjLy8tpvvm8Wq3q5MmTqlQqOnLkiGZmZtTpdBIPXl1dVbvdVrfbTeniDmzQjkeWDw0NFWQ2/cutrctDDH7WO6Yl07ZHYcW5dbA/l13h/WQu0HMcICE6mjFTygW9zTMmHCCOIL8bpu6YYuxODob7OGnLMxr9YHIi2Im+JoPBs7pclyuVSmkf+3PIYa4cMErbvg9HRkY0NTWVZD1ElD9ZFI1Go8AP2Etx7Rg36+ROquhQ8P9HR0cL2Q7sSa5jnvifPcpzFIF2b78XKAHlojt70UHK78cff1z333+/vvSlL+mv/bW/pn/+z/+57r77bv3pn/6pbrnllm3XP/vss/qVX/kVffjDH9bv/d7v6d/9u3+n++67T8ePH9d73vMeSdLTTz+t97///fon/+Sf6F3vepe+/e1v633ve5++973v6Y477ris+w7oyuh61es46HBlZSU9o5R4cfkpKUVEw2OdN/tzHYE/f5aRAdL2qGJpy4YFZIZyzsoozx3Y9+h55DftR2DY5b5f1+12C+WuXE7sRJVKRSdOnNDExITm5uY0MzMjSYVIc+bUx4mc8uA+1+M8strnZKc+xWhqftfvfu0VbHg5Wcmu57js9gxol685Z3OUOznw2mUD+zHKL36L7utOHg+8QA76oeTcz3UM9hH39HJz0ubaeyYY7aMH4mjxPq6vryd8i9r5h5WulP/t5fcD+3v/6JqGRv7xH/+x/vbf/tu66aabVCqV9K//9b8ufN/tdvXwww/rpptuUrlc1tve9jb9yZ/8SeGalZUV/cN/+A917NgxVatV/Z2/83f005/+9ABHce1pbW1N9Xpdi4uLevnll/X888/rpz/9qS5cuKDFxUUtLS2pVqtpcXFRFy5c0NmzZ/XSSy/p3LlzOnv2rBYXFxNzJZ3cU8FzRpEr5qR5Ef1+uQLicggmDoheq9VSjXcUBhgtkWdeOzcKCzfCjx8/rtOnT+uWW27R6173Ot1222163etep9e97nV67Wtfq9e85jW65ZZbdOrUKc3NzWl2djYdxFoul1Ot8KmpqfRdr3tfb0St2Z32yY1Aw8PDKV0u1o2VtgxfaqfPzc3pyJEjOnLkSNoT0dkwoIEn/Eagwya/HbB2Q86jT1HoY4SVg1yeQeIGlAPP/orppjHizEHGCLQ74EzZk2azqUajoXq9rnq9ng6lJtus1WqlV7PZTM7cGLUGYQgDxPvnXnrCDyx1WRmjtD0aPkbHx4w3P5QTR70fugrA6evgIHqMBHa9w402d5pQygUePDc3p6NHj6a/cXbyQmZXKpVt94vRXrz6yTCij17f1ufdgfBIvjaMk3JhlUqlUOfcnQ/+yoHrDq7Gsj0xqs/7EoF0Hx99cCM8XpOLquR3noXhTgDWn0xAB/4dbPdx8fyVSqVCJqGfD+B7i33I3FI6AEB9amoqyX6AmDgP8dmPjnL2tUeqQswHffFnzvmEj41nCp6Qq9nvr35pp/2Yu/ag5PcjjzyiX/u1X9OHPvQhvfGNb9SZM2d0880363d/93ez13/5y1/WLbfcojNnzuiNb3yjPvShD+kf/IN/oN/6rd9K15w5c0bveMc79OCDD+pnfuZn9OCDD+qXfumXdObMmcu+7/VGh01+X6+E/Ys8XlxcTHY3tner1VKj0Ujv/A347g4455EO3sbvc3oM33s0LtfSV+eVMeI8RtTn6og7zwLAjBkxzsP91S+Nj4/r2LFjOn78uE6cOKGTJ0/q5MmTOn78ePocmT4zM6NqtZrO5cLGZuy9ssP7jVDOXd+vHZeLGL/crGTnn+4kiRnPMXPK10Ta7jjZjS9H+e2UCwqI46Zvfs5OdKhHmYXexO920gO5Bzb45ORkwl2q1WrCY3Jnw7xSaWB/7x9dUxC92Wzq537u5/Too49mv//CF76gRx55RI8++qj+/b//9zp16pTe8Y53qF6vp2vuv/9+ffvb39a3vvUtfe9731Oj0dB//V//1wcG4h4mwiBBwa7X61paWtLCwkIS6vV6vVAjzB8KF8rRUMgRDG9lZSUpBs1mM7VP9NvVeOjc+PI66Aj9brebGGulUklG0czMjKanpzU9PZ0MJg4tGR4eTtdOTU0lI/zIkSPJ4ObvI0eO6OjRozp69KhmZ2dTtDrGXc5bH1+7kSsrXm/PXxiIsezO1aaoRN3I5ApZbqxxXaOydT2lkw1oQP3SYZPfEUh0WRajZaVihGwugtvbzYGIboi6QSr1jlx35T9G65BJhvyOB1JTjzSWcYnj8zHnDsOMIKaPKbbnbfn1Psfx97nyEs4fHfgmSs9B/ggGuAHmpVjiQbLSVlQaYDNGlL8wuHkB5HvbcTzMYzTEe6UOY9j1KgPmhl+urra/fJ59/fxvN0h7Aei+J33fRdnl5UV8n/hv4l6JQIw/i/F5cEeMR+P7POd+62Czg+MOksS9KalgiO+ms8TnxKPholMtB1g738k9o35N/D/2wamX3iFpG3geeac7ytzp53q+z/FBUa1WK7xyUYKrq6v6wQ9+oHe+852Fz9/5znfq+9//frbdp59+etv1v/zLv6xnnnkmOTB6XUObl3Pf640Om/y+nskjsskWwxmOTYwsx/HlpV9y5V+iIz4nq5138uzHtnK8NhcM4PzIr4n39eh4d+xxNksObM+VuIzZ6jg+sXMnJyc1MzOT7PGcDT81NZXs4Ng2FPEM10NiKRQfY9QJ4qvXOPrZJ1dKvexM1ytz94nym/e4j3J6nesROVkWwfp4X59zn8cYpOH3i+uQ01lyuIrrMFfiuBjQgHaja1rO5e6779bdd9+d/a7b7erMmTP6xCc+oXe/+92SpK997Ws6efKkvvGNb+gjH/mIlpaW9NWvflX/6l/9K/2Nv/E3JEm/93u/p5tvvlnf/e539cu//MsHNpbDQtQDHxoaUr1e38bco4EN04IJIoQ9lTnHfPzaRqOhs2fPqtPpFCJ3jh49Kml7CtV+EGm26+vrmp+f17lz57S8vKyFhYWURjc5OamRkRHNzMxoZmYmCfShoSGtrq6m1G6cDGNjYzpx4oSmpqZ04sQJ/eW//JdVrVbT7yPw4BFGgBsYAwiATqdTAAH6FbjeVz+lPZ6+3e12k/JCRH4OxNhvYn7L5fK+r+1hIqIamfu4bjnFgDV2RYEoTAT9K92beyXOtVf63B0WOmzy29M9pe2ptxFkIppHKh7YFCPWacPfva3Ia5GXRHYTOeREtA2GY6VS0dDQ5kHES0tLBTDT++yAeASp3UByoyUCsETkIKd8/Mh0Sen7aHxHIN35WQRJ0THol4OYrkNwTzcI6SdRSfDQ1dVVbWxsHoSF/sGYK5WKJicndeTIEZ08eVKTk5PJyd3pdFSpVJLznTIwDuCj73Q6ncL3lPKiX/B79gxjabfb6nQ62w5l9WhAaatMWC8HDXPt84RMYV0d5B0bGyukuvteQDdgDpFRMZIeGceY/Vna2NgolNBhT9I/1mx5eTmVSOG63DPDuqM7IEMp2eKGNTQ8vHnQd6fTSX+j/0Qw2vckB/Wypr6HuL/rVOhsbrQTbUq5FfYQe5M14zn1PerABtcxNzkQJK6/rwO/dT7i0ffOD4aHhxOo53PIde6U8M/6of2Q3zfffHPh89/4jd/Qww8/XPjs4sWL2tjY0MmTJwufnzx5UufOncu2f+7cuez16+vrunjxok6fPt3zGtq8nPteb3TY5Pf1Sv7sctaY22/+XDqP4j2CfPAf+LE7CXmupS0Q27PIVlZWksyHVzm4GO8d+UmuXnuMMOZ33JNzyGiXtuGdRAEjgygNE516Q0NDKZr8+PHj+kt/6S9pamoqBbJJW3yVe25sbBQOPm21WimIrtVqpT4hI7xf7jhkzmIAHvzd18Z1H58z/72T60k7Adx7IfZXdASwT5D1UW64s91lhuvEyCXkTtTtPFCD8SBrXG77nHEN9x4aGkol5KKTKILx6FHLy8tJr4jPg+MqPjesM9jIgDZpYH/vHx3amujPPvuszp07V4gEGB8f11133aXvf//7+shHPqIf/OAHWltbK1xz00036c1vfrO+//3v9xTieIqhWq129QZywOTMhzTvfilGCOW80H4t31NfFAPNGTOMNgrryyVn6G4MkxYHmI2XOJ5eDfMdHR1Vt7tZp6vZbCahQUqvR6vj+Y79YA7YTxjbzLt72HN/70RuSCLQMU7da+vCr1QqJSF+NQF07ocBdiN7eN0T3q+zIBrN+xV9cCPRQIjf2HQt5Lfz5F7/R7DbFXxp+7Pr7URycNu/B/zi7xxx7xj9i5GIIesgaryP94/7uNHrIL6TjzGOzQ0YDFxvIwLo0ckejSBvO0YLca2fqRIjwxykROZEx4bLVs/aIhp9YmIiga8AxBjGzGs0sH39fCxuqEXQk/5zXZQZLgMYJ+OP68s9WX8Hh3NgaQ4o8cjs3D6I7fnfuXlwUCiC49HZEyMafX/49+iMbpxzf653oN4N41KpVHhWXPf1+zIW5jz3fOZ+50Z6nOc4r/Ql6tBx/egPAL7rx+yLeF3cO/49fY+OjZjtgeOB9vjbQZN+gjt8Ha9Ufr/wwgsFvZratzmK/drNnshdHz/vp8293vdGoYH9vTeCL7hDC2CY7yNFvjw0NJR+LymBknznFCN/4QHOD7l/zv6Iz2/kFTGzpRcw7Ha4P2MO9kOMDx7pfeZ3XuaNTDHKc0ibgKoH+jmPRY+Ar/lcuWxwGdftdrM813WyHIge58DHvhtf3A970PXGOCbeo67l+pf32WVQ1B1yQRMR5I770PsRyeU6cx11ntxv3FnAmvu4c/PjzmmXbwP7cWB/7ycdWhAdb38uEuC5555L14yNjSUvpV+zU7TA5z73OX3qU5/a5x5f3wQjbLfbmp+fTxFdHlXlBr1HXVMmBmHaaDRSZB2pVpOTk9sMy70o7AhzvO4cIrqysqKLFy+qVqsV6q4SKc29p6amCgIRJQ4P5fLyssbHxwvpYkTVI8RdIEXDzY0nPO1832w21Ww21W63UxpfBBikrUgw5o1IeiL78d66IHIQn7lfW1tTu91Ws9ncJtz2g6K390Y3KtzDH8cKKEMUBNkfzA1llYh2vBLhdSPRQIjf2HQt5DdRWLlUcvZbr8gq/sYpLCnL2yIfAAiFvwLiOpDlfeC9VColXu51x2nbo7jdmMk58wBhMVLb7XYqTeZ1n/kdsjTWPy2VNiPpPGI5Vxfe36NxGY3vWAeadiNQz/3J+vEIMDfKAR/jQaVEuyGrvT4q79zLDfwYNODnq7gsZW95uTaPlKdffA8QwJ7AYBwZGUkgCeTZBexdStz43OeIeaYN+hANWwdd2P+9QFM3PNnP7HU30iNQQdSzA0c5B058Hrw/njnhTiZ/972BjkWEox9s52CBA8w+Lgeq2+12GifPkD/rGxsb6TmK4IuD1LQZo0NjdL1Hwsd1oG/uSIlz4PcGROKZYH74Pwem8H+MKuyH9kN+E6iyEx07dkzDw8Pb5MH58+e3yRbo1KlT2etHRkZSdmyva2jzcu57I9HA/t6dckBeBFUjcOvXQc6bsOdwLC4vLydnsMtgeC0R2RxuDP9EB/GsqZ3sNHiFl4LxqG8/xwF555lP6Ex+PpZnTbnzm6ypHICOvY3tTTm2arWqbreb7um/9zM1CKpz+Y3t7Q6wCPK7zOt0OoU5p2wccx9tae5NVDzzyJztZ5nVHGjv2ZM+PoBx1oHP+D7KAP9dBM99nvx3UZ/wLLjYH2lLX2Q/uP7o80jb6EHRmeNj4fPl5eVtuovjMJ6V8Uq3IQf29/7RoQXRocuJBNjtmgcffFAPPPBA+r9Wq21LLXwlUqfTUb1eV6fTSQczIJABkTHIlpeXNT8/n0D3xcVFra+vJxCRFNpyuazZ2VlJSrVK3Uu4G8FMAQoajUYSkGfPnk3AZa1WSwbH8PDmydUzMzPp/tPT0wXvMgAGgm91dVXj4+OFOugcWAKg7szXo+8RqiggXl5lfX29cJgMik8vjytgwPT0tE6cOJGAARfoCEderkwtLCxodXVVi4uLBQVnP0F0+hrTwW9EQhmMyogDAKRvOoDuIPri4mKhfMCABvRKoYOU3ysrK9sMs0gOskXnGJHggNfIE3gABL9zUBrymtoeDRqjUkdGtg6bjKVBkEscGOhGDlHVkSJgOjIyorW1tcJB4bk0cJy6bgTHd15xPj2yx6NwPSUcYxKnNQYOctfBXjLBcIIjWwAnmWeAagDb8fFxTU5OanR0tHCINw78GIlO6jH98Og9z2xbWlpK+2FtbS0Z+fB25ghCP6KGNoArwAIBCR7BxXt0lgDo+n7rteb0j/bZ5+6sd7DdnwH6HaOa/dmgHniMpkYueg10fk+7uZdHvZVKpQQ2RyDDI8C5n5dHcSA7Rsb5eTxedsAdIB41z/xgiHt6vIPtXoef+QXAAhR3AIA19rrA3BOKzjbuPTY2lp7LCIbz7s+8O//Yh4Ab3M/Xh77yu8N48PnY2Jje8pa36Mknn9S73vWu9PmTTz6pe+65J/ubO++8U3/4h39Y+OyJJ57Q7bffnp6PO++8U08++aQ+9rGPFa5561vfetn3vRFpYH/3JudnEViUemeh9WpL2nTkASivr6+nwxIdiPbSnchW5CvPM85aP3wyOvZdP3E7O5YGdfkNP8PO9eA27H70pejwRz7BZ2mH+cNm5+Bvr4U+OTkpScmWRfbn9Jh2u50wgdXV1VSTPgYsuO7noLGklL0+PDycbPIcIV+Qq9h4BLG58zMGUeyVonyNDtUod1lj9A/WIF4TAXP2X5T3Ebdx+e19iiC6v/vf3p8ow1yPdMeO98cz+WgD50oOX2KP+pgHNKArpUMLop86dUrSprf79OnT6XOPBDh16pRWV1e1sLBQ8IafP38+KUM5wsAZ0HaCaSGM3GOI0MLr54eduZELM8Mg4UCVmIbj0Uw5ZY32XLC32+304t54MDGqnIl6anh8YWTEelqR4Uva1k9XmiJYAIhOv6jZhlDo5d1F4BHFRgQdfyM4EIhudNM/r9+N4hQj3/aD9uIIudHJ96nX+RsZGSk4VAYAepEGSsyNS9dCfueiKyIAieLNd67QeztQlAW5692p1kumeVRpr2ggB5QdkM6N08llCkCptGU0IJu9ZAifxbFGg9rvF8HQaAwiC71Oa6zZCsDnoKu3H989mjb2wfsdDXeX6bm+x7Vk3tB9eHltWN87vq7ejjtcAVv9XAyPwvd19/0jKQEg3vcYzZaTvX5dLyO5X74b58gdVL3uCfjNHotghZcnyD13uX66LhhBA9d94rhzzomd5oBrGEd8Zr0PrCslF9gf6Lj+jPBceNkaqJeDhLFwj/hscA3j517eto/Rx+LtR9qLTD4o+f3AAw/oAx/4gG6//Xbdeeed+spXvqLnn39e9957r6RNUPbFF1/U17/+dUnSvffeq0cffVQPPPCAPvzhD+vpp5/WV7/6VX3zm99MbX70ox/VL/7iL+rzn/+87rnnHv3BH/yBvvvd7+p73/te3/e9kWlgf/dPEdy83Db8b2Q/zjoccp7NlLMnc45V52suF+mz80SXeR7djd0PecZZdMjyHnUlbNEoE1zH8rIdOeA28jzG4VngBJX5ga3RiRh1g9hPHLZuk8d1ijKJvrF28Huu2Yvs3Yl2a8N1CdeddvpdDn+J+p3PU5S18Xc8E+48oI2oP8ZXbC+nL0b9QCqWV4x6At8PbM4tGszF/tChBdFvvfVWnTp1Sk8++aR+/ud/XtKmF/Kpp57S5z//eUnSW97yFo2OjurJJ5/U+973PknS2bNn9R//43/UF77whWvW9+uVut1uOqwDjyAgLh5mhOfq6mqK1ML76srE8vKyLly4oOHhYS0uLmp+fl4jIyOanp5WuVxOkXjuFYZoCyUA0Hx9fV1LS0spvbper28T8FEoR2aaM9TjHERmTZ/cOMObvrq6qkuXLmlhYUG1Wk3PP/+8FhYWCuluS0tLajabCWSPzJxogXK5rBMnTqhSqWh6elpHjx4tRDBFxQQhTqo6kYe8j4yMpIyBWq2W+r1fe2W/lILrmXyvtFqtZPCy9zzFbwCib9GV7J1X+p67Huhaye9Y2zwCkA5MA7i6IeoGHkaUR6O6DImGJfeP5VYgN9QcaERWdDqbmWDwEd6JbI739XJiUV7xOzcgiWYD0EXWS0rR7S7jieqBSqVSmgdPEXcQr9lspgi5Vqu1LR3c071p04FvIvKjXHYgF57rJW8YF+VbvOyKpEI6emyLklv1el3nzp3T4uKiWq2WlpaWtjkxOHDTo6l9XzCPRL9jhLMWBCIwLxDgM3PKeHxPuk4To7T9b65HZyGbodvtqlKpFMq9RFDAnwVvD31MUkGHYE+4bkRbzJUf8kWEWwRxHbyOxjX/e3aCAxg8Oz7XvofRET3gwoM+fPzxWWItSqVSAv/oC/V5eY7cOcWc0wfGyB7w8grxsFg+c0eD66XsYZ49L4HjawOw78AH7zwfrLM/T/3QQcrv97///bp06ZI+/elP6+zZs3rzm9+s73znO3r1q18taVNmPP/88+n6W2+9Vd/5znf0sY99TF/84hd100036bd/+7f1nve8J13z1re+Vd/61rf0yU9+Ug899JBuu+02Pf7447rjjjv6vu+NTAP7e3eKIDSf7QdtbGyWh0SWwjfGxsZSNLcHj7lu4DZ41IHgj/A158Nuw3hUtUcBx5JUfEZb8EXeXReCP7nck7aiipF5UUdwh74HBMDbG42G5ufn1Wg09OKLL2pxcVHtdlu1Wq3gbGBe4JFkNTmvpQ+UgR0ZGVG5XNb4+HjqfwR23aFQLpe1sbGRsgzj/fk9utVeeWF0lvjeixmY0cHvfYdcl4j3wQmOrHBZ4/fxthwDQp77PsmVNPNyMPTFwX8cOzhHYmAGxPz2cmb5nHn/X4n25MD+3j+6piB6o9HQT37yk/T/s88+qx//+Meam5vTLbfcovvvv1+f/exn9frXv16vf/3r9dnPflaVSkW/+qu/KkmamZnRr/3ar+m//+//ex09elRzc3P6H/6H/0F/5a/8lXRa+ID2Rl4PbW1tLZVGKZfLSQChbAMMO7myDxDfarXUbDYTqFutVlNqdKzZxu+JCKOEC/XNANEB+qNAiOBJzsPZC0CPY8iB6ZCnf9frdZ0/f17NZlMXLlzQ/Px8wZCq1+sptSue3o0Q5wDUmZmZlMI2PT1dABh2I6IUEEooQs1mc1sE4ID2RjsJHb7ztMpXsoDuhwZC/Pqnwyi/I//3+pzSFnAegUh4tTtgMagAsaPB6QaZ3ztmCjkQKBUNUfgxhtby8rJarZY6nU7KYpGUANkI1AEiR2MaWetlaAAZAVHdmeBlubw/HuUFSAyQ7kA1zgicAIzFo7sBAWI5FNqKQHE0BiEH0Zl/N8QBCmK6uvMNN+QA/JvNpmq1mhYWFpLcBJx18NsNP9cvPHV6dHQ0lWCjTxh+jMX3Dv2n3B3z4PpHnBuPNHOD2V/SVs183wtRt3HAIhdhB7nhSp8ckPXUam/HnVu+v+LedZCANv2Zc4eCP3e+jxwscX3WM8JiWSfu7fotY/HvGSvr7KWfPAqUcgzc2/cg65Y734D7+f6lnw78R0eQzz1zxbuvO7/hHh6ZGVPmd6KDlt/33Xef7rvvvux3jz322LbP7rrrLv3whz/csc33vve9eu9733vZ973e6TDK7+uNonNrP9vFloCvYItHJ2gMrIoOPefx7mB2HgNParVaye72ci3If5fT0WnreoRHlCNHclHC/O8Aa+TvLkt8vr2ESqPR0NLSUgrYW1lZUbvd3oYPeCQ8c+IOfAL8/EwyQHR3vroOkIuUhq/TP+aS8URHxJXsE1/f+L/PswcC+uceFBDbhOivlyvz63JyO+dYyul39Cenx7gj2isS5Jzg8V4+R5Fcpr8S7cmB/b1/dE1B9GeeeUZvf/vb0//USfvgBz+oxx57TB//+MfVbrd13333aWFhQXfccYeeeOIJTU1Npd/8s3/2zzQyMqL3ve99arfb+qVf+iU99thjfYGOA+pN0bhxzyKvGP0Uf++ALdFCzWYzRclRuw2BHu+NMAdYcO+7pJRihZGKkT48PJwOLSIiDIPdI/+ohe7gZ6vV0tjYmFqtllqtVoq0wmvNeNrtdooUIIoNsB8QBEOt10Gi0taBKghqSrd49FYvsD/XFkoLGQTME15x1vRKKUYC3sjkNV/7HeuNPicDGtBhk98eOe6KuLQVoQ6Y6M9nlGnwXgd8c4aGR77konmk7WUUolGDYxUZQYm0nGEefxt5ko85GqLRMUzfeHfAGXLA2GtNYigxdgd8W61WyhpDVruByf0wTgEGqIWem3PvD3IV+R0PnvK0bsrJucFGHzyyiTmn72S64fB2kMLnNoL8DqJ77dWcwezRdTF1PToTmDu/Z1xrL7vnZUVcj4tyzAEAj6ZjD7jBzB7KEX3OPVO9jPpoJPu44lpB6C8+596Wr00OWIiAtvc1jgWwxQEc9p0/L94Xd5IRLVoqbUXK77QPfN7jegOa0R8o52TxEkx8xrji+vnz6HtnQK8MOmzye0B5cmA8p2c473Ee4plq8fOoI8AD/Mwu5B/392sJLPAgA5zs8TPvJ+PxkjFuS7psb7Va6X7RIY68puY5djf972XnRjkDj/UoeN5dn3QnRZxr1w/4jnakrUM0XRfKrdluFEFrl+E5wHqn9r3vUUfKgeixzxETivPpv3c57Ne6vkPfXY/x9l0P3QuG0es679uABnQldE1B9Le97W07buJSqaSHH35YDz/8cM9rJiYm9Du/8zv6nd/5navQw1cuYdxjBHAQCEQ0DsLCU3qlrXRehCMCEWAaQRzBDu6NsMcI9DTjbrer8fFxTU1NpUM4Z2ZmNDQ0VCgVMzs7W4gMI1KOGuUcPLK4uKilpaUEZmN8HzlyJNUyJ4KM/dpsNjU/P6/l5WW99NJL+ou/+Au1Wi2dPXtWtVqtMI7oSYcYe7Va1dzcnKrVajoUzYH7fkF0QIhut5tOM/eyN4zlSqMmGBOnyO93FMZhIgS4R30OBO+V08ATfv3TYZPflFtAlviBjsgQvnPw2uUL5UAAdz2yStoClqUtUM2duNHQwPCNBhf3h4/iXG40Gmo2mz2jvpzXOqjnMjXKVZel3je/jr9dzvphXTiZ44GfTjjJ/dAwKDo0yuWyjh07lrLcOLjcKRpxUjFqHFmOjjA0NJT6TBkzDh2NB3Mj/zmMm5Jn8/PzKZOM/jNX9N0PWnXAe2RkJJVL4boYHcYLmSKpALh7iSHuGecBcJT557DKoaGhQg1Y31/s09XV1VSGxB0SDmawTxxEjw4b/5v9732kpjz7IpYW4TcY1dFI5zc+fs4YQd/0jAPWyY16MikZp2cXeFkYXh6h6KWQcNI44CKpUNpFUsGpVK1WUzCGl2PwPc5zyWGCnvkSSwzEOY8AiUc5uu7t5PprdGjFdnejgfy+/umwye+rSfCG61F/Jyo8Oh3dPnT9BN7pgHguCjxnW0bHp7T1rJMphZ185MiR5ACn3Ks7hGN0PPIUmQMI7vdrtVpJNly4cEETExPJye16UKPR0KVLl7SysqKzZ8/qpZdeUqvVUq1WU6vV6rnO0fns0f1gCuiAjMnLy8DjY5m8qAu5E4NrWAv+9/Xsd086n3fHN/hEdO7m1jJSL13O5S/yGZmJ455rkY8xg8Dvmcv+Qk9yvdwdy8hzgh4IUOyFOVwOuL5XZ8Z+07W4/0B+7x8d2proA7r25A9aTFv3aCuEngvlWIPMa4HRhnu2cwa0exx596gjhF+1Wk2lTwDR/VTzCNAj0AEHPEJpeXlZo6Oj6eBShGys+4lg5xTuRqNRiGLrlzCevI4c0Xh7AdAhB5AA1DH6+03V7YdceN/IILpUjFQd0P7QQIgPaL/Js5qQUTj5YiSoK/ju7IztRLnmPCA6SGM0kkfgRKDQwVQvNwH46OBZL3CZe7vx1CsrLI7VI4fc8IpAG9G/sVSE39NlNeeE8HuMb+bHgUEA9EqlkgxWB2xzkcxuZHmtdZ8Pn9NSqaSVlZVCRBkAKXNNJhp9RxfwaG4oBxI4kOGp4R5hFXmW7zdvI65LvC4af/G+HoUOeYR+nCcA4AjyR91jN54b91yMasPwzv1mJ4PYn1P67EB5nDvXKV2vjMa59yUXvefPUdxXPi6/N84NSenZxWkTHWxOsaxP3C+5CPbcXEWewue5OY6AfC/esdv9LocG8ntAB017taMOE7ksdIdk5D/xN873o0PtcvvhTneypiuVSnKC5/gHDm7nRbFklvMtZDvOeM9OQzdDXmODe/34ncYa54u/vX0PDnSAmfHlnJNR96Mtsu5ddvm9L3cdXKdz/TY6Q/u5R67/OXkBuf7jeyLqkLGPjgn5vb0PritCce/vlx2+V8fx1aK96Fn7RQP5vX80ANEHtCeKRjDCJp62jhHhqcQxZdhB9pwSEA3G4eFhzczMJKF9+vTpdAjn7OxsIUoMkN8FC3XS/MBPopOI1FtcXEzp9HjYZ2dnNTs7WzCo6/W6Ll26pOXlZZ09ezbVYYs14nuRG7+UXimXy4Xowyth7kTddTqdVNImAklXQkRQxejIG42IYGPfDATI/tBAiA9ovykacUTjen1PJz+cCDDRo8+h6ExGjsFjR0dHUzkSj+b1Ule7GbnIRZdbHq3kpSzoD8alpARoE8UqbT+cUNqKPPVILP8+GjUYgET0MgfSVqQ5ssqjmx0A92ivqakpTU5Oanp6WseOHSuA6JJ6ppOznryzpkQmM2aMb2qkctA2gDpZBh7Jtri4qEajkWq4M8ceJc04fY48Iou18Dqz0ThiTDgZ0JmYm14lgeKe8TlhzemPR7N7VDXfeU1RBy08gtGjriOIH/UHjN7oIHLwmkju+LlHTXNPnkPXEyGiFx0Aj2AwffTnx+/pIDpgfJRF7hTze3n5uvX19cLz48A+EZvs/Tjn/gzSL+7ne7vT6RQyXOIY43q4Qw8Ah89xrng2TtzfPKf90EB+D+h6Igf0chSd24eN3HZzEDPKmuiMvZxArEjwObK+JycnNTk5qePHjycb2Z3g7qx1cBx9ybPqHJB2m1LaKkPabrfVaDQK/Nbt7wsXLmhhYWGbU303Yl4iluFy3Ofc9TWXVfFsC/i+A8JRN+X/yIP7JfQsly0R6M5Fosd+5GSLj0PaCoj09l1n5W90IZftsc8ENEhK+EScE5zRrg/tVxnaSNcKQM8563O67tWigfzePxqA6APaE7lApf6Zlx6BPBUHQ2poaCgZkFL/DzLMdXR0NB1gc+TIEb32ta9VtVrV5OSkpqamCsaT3wPBt7KyUjCW3XhGACPsqes6MTGhubk5zc7OFoRPrVbThQsXkhC/ePFiEmz9EP1EgFcqlZQqF+fycggwRlICKjD8cvObo53Whlp0pVJpXyPcDxt1u1v19ygdMKABDejwUYz2RPaQCur1lN0IgQfDM+G/OSPEozwxokgFhhe6k5g02+gUjeCdA1mcZeFAH6XVuBaD1AExd3BHoA7yMiMO0HGdO7T9OzfKHOCkLxhPHsmNIUDE2tjYmGZmZtLrxIkTqlQqKpfLKpfL6nQ6ajQaKTsMINHvyXzw/crKikqlUpJ1q6urqTzG/Py8RkZGEv92xwFGOOVc6vV6OrCcOY6R6DlAgnlk/0QQPUZjEWUHoM+7ZzzkHC8xEsuNWi/F404VZD39Yn9S8oZSQBFI97Gzt3z/+F7u5YTxfe4lZjyaj/GQhQHAEsEY5gWHWHyW/Dni2caB4GsBYO9z5nPrzyFgeKyNjh6L7usOB36HrsqzxrqT6cCeAdj2+eVvP0fH18/7FvkdvIm94OOJTgL67X/v5sQZ0ICuV9pJb3f5HMHCw0AewS0Vo3Zz2SMOnu9HkBM6CfL7yJEjmp6e1qlTp5L8xgkOL6d+dbfbLciaWBolZrj7eWLSJp+u1+sJpGeszWYzye/z58/r0qVLfa1d/B49Cv2Edy+L5+QOAS/phv7DeJxn57KQrtTB4boEc+3ZCb0C5vwa/u/HcR+BdtfJ6IPrk66vuXxCX8uR79moc3LP/aS4HlfjHjvdOzrCXCZfDsh90GMY0CYNQPQB7ZlcMLiREpm2R2556s9eCSOdWmyTk5OqVqvJA46Aj4yXF/3DyIw1J91whYGtra0lozpGiXW73XSIKIeg5Ly1/cxjFBz7ZcS48IrOhRwQEIl56zUeB6F83DeaEcY4iZYb0P7QwBM+oP0mjwLnf4/idgMDijIqUi5C1X/rMhDQzX/rfYkgtEeh5yJqo1yIn8cyLKVSaRuI6xTlTe6aHLAfv4uA426EwUmJMWrXe03rGOUdjcwYfeeyPgeougNjdXW1sEZDQ5s10TlY3B3oMTIqp9v0Y/xGozP3fa92/Ltea+CfMy4vX+TrG/eKRwdyva95TvbH+8U5ye2Z+Dx5lFWuPfrl4G6cIz4DOPDve/XXaSdQKbce8VmKz3EEJXJz5JkhADDOO3L99bI1/7/23j3Msqo8E39PXU6duvYV+oJNCw4TwdbENJGABjAqCiqIKBgN6oxhRIbIJRkBlbE1owSTxxBFvA0jKon0zAMkaojSJIghdLyAGBRj1DQ2YjdFd9e9zq2q9u+P/r2r3/3V2qdOVdflnK7vfZ56quqcvddet72+td7vXd/iGKZ5UQeDVRZaZ2GszLZtZjt/c/vtOFJwuITmYqHePNay4XOBrr/pMFYbTjtOh5+df3Esp32yB4erTeO8yIZY1TYi2U4bPlMIF0WWXc6ad9RaK1s7yL/V2ZG1to49azbg/KatrW3aDsVYvmutXbWvxIhlvS6rP8XsY5YYgNdZcj6WtvaNI9lu2PnQYjzP7ff8wEl0x6xgPYU0AjGPJo2LGsu5DBCdnZ3o7e1FV1cXNm3ahGOOOQZdXV046qijUovxrMUnjXtLS0s4DGVqaiocXMIDQpUg4AFULS0t2L9//7RtrpVKJSjaVeU4G2jd2fo83IHUps32sc+IPc8aLrvoTZKDisORkREACEr+I03NRGfK8PAwBgcHg0rCcfhwI+6YbxSLxdQheSTPLWFMu6QLPxvDmr91oWSJVF0MZsUvB6YvHDimctwkmasxMzl2c9GpIV4ssafko4Z4sWEpuBiOkey8VrcIU1VGcll/tB55v6rhdXHU09ODVatWhcNEV69enXJ+AwhOSktmqz1va2tDd3d3WDT39fWFA8e0XbiQZ/4ZBkTrtlgsYmhoCNVqFSMjI0EBz7R1zqCLZBsCRwlp7TNK+tN+sh3sgaFcENsQdBp6w4bF07phGgzHQYUzVdO8n9unqYoeHR3FxMQEenp6QvgRpptFxMQIWd3OzfzoYWG8nvlivvWAToYLYhxc7U+FQiH17iZJEs6saWk5eJg874/NXWw4AQDhvVK1t+ZJd+xpe+nheLpTwc5tdT6o4XqYlkLHHe0TJJHsjhDtXyoIseNGbMcI3w3rOCHqnd+4/XY0O7Ic1I2G2Hpb17i0OWqL9Odw1yydnZ1YvXo1CoUCjjrqKKxduxbd3d3hEE7OOfh8joXMk84/ent7w/jT29sbDovmOWK6O2l4eDiEXrPzq2KxiOHh4RACph7E1rocI2kfdCy3jmeOu/zc1r/OD+jkBQ7tCmMoMLaHjtGzFeBVq9Vgv1euXJkSCqiTt1Y92OvUIRDb4WChNlPFh5omba5NizsWuVtLHedsDxtybr53iWg/1brhdwsJfWets2Ex7KPb7/mDk+iOWcESsbqoj12nxmeuxpyHh/b09GDNmjU4+uijA7HORVEtjzFwaMDv6elBW1sbxsbG0NvbCwAYHR2dNqBxkNeyKGoZqXqQRWLP9yRO28vuGlCDaQdwXWjFBlyqwPL5/LRTyI8E6ARpfHwcY2NjqVBEjsODG3HHfENDRJDwUnLRTpZ1/FPCVCe4Ol4qkcexzx4Abcdvq8zhAoEkryWqNR/2sCv9m+UF0gs8KsO4oGFZNA561o4nzV9sIa6qMlteXeCo8zyXy6FQKISdY319fVixYkXYNk1Sj4slLpxiDne2Y1dXF8rlMrq7u9HS0pIillk2VaJrKByCIV6444yhNjTsSKyuLEHOsqtyWMuvdaSLcLWtugjnczWMiHWWkHAncWHbX9ub93KepiQFyQfWuXXMZI3Rdi7HMk9OTqYW8rpIjy0a2cf5ndY/64fkSWwRzp2FLLPOyTQNfb49L0D7rs5lNeQASRF9P6ySTqHq71wuF95HwpLYSmzzRw9hozPEOnQAhAPv1enDNHXezbGF+dPf7D+zsatuvx3NDuvwbWRYIp3Qd1fHDrvL7XDA9XdnZ2ew3zzDS537HPc4fhJqVzo7O0MoNjpuKUTTMuj5U7H2oROY5Z0NYkQ6x3d9pnIYwPQDNe2YrjY8SxRn7bXNTz1txbkt7QKFB2pfs+ouNs6z3nXnUowTiJVVr6VgIXaPLaOd72rd6Npb+/RC243Z2sDDhZY35uCZa5qzefZCPmO5wEl0x6xgyVUuJOzWcjXgc/G0EiQreJgJDbcqneodeLiI4kGePT09mJqaSqm/svIYMySHAx1AY2R1jESYbfrAdEMUI+9jJJAlSmIkO73t5XIZY2NjQVHW7EQ6y0Y1mG4bdAPicDQmSqVSalKuC8/YLhklxQklvZVE1TGNCuHYmG0XsExT02caShwr0adkKndZKYmuqiw+0y5a+VuJN9oa7hqyizhLoFt7mOVM1mfyM807F9x65gdJV62TrDmC1rOquZVABabH97SLXLW19vBTVU6pY4TEtCr8lVBVx4Stf+0X/FzTsKF/tI2YF9aLOhe0zWJ1z8+oPOd8TNMjkdHa2hrC2cR2OSh0HmdjebPN+RxLIMTUcfr+KQmvfSFWXtafPlvVdPoeWjJGd6rYvMQIB/ZjOmO48LeEj9YbCQW9jlBhSVY57JxNlf627uhsATBN8WdJATtfVoeQtrHDcaQgNo7pWBsjQ5caMfJS19vqZLbELsfy+SAdOS5zvcyzTWjDNV86xqodsPnk+pth3Th+8qwIHY9ou6ydZjk5RtYLTUPjsXN8jYXis+XT8FuxeZ22nc6XYmFPa4nYZgId4bTfetYGx/0s4Z/aGULnUOoM4XfaDnaeRjvH/mnnPZqGrZ9YfXNupLsS58MZFEMjvO/A/OSjUcqy3OAkumNWoAHiQpvGwKpyrGJHyYJ6wbS7u7tx1FFHhW3hK1euzFTA10JLS0uYBPT19WHt2rXo7OzE4OAgnn766bD4iRHm8z1A0VhQSVetVlPbu2baSlVv+lxIakxvSyplLSDtIkzrgW1ZKpUwNDQUYtWTSG9mTE1NBdJ8bGwMIyMjGBoaivYNx9zgnnDHfGNsbCws0EiGUnGqZKu1QZbg4wRfnbW0RXahpWRzkiQhNIvaPyDtnKTt1DAuJPv4wwVRV1dXOMSbNo+LVI7Ban9Z3iRJUvGxmV8usK0SivdwazXzZBcvqjC2iyVdYNFut7e3h8PICoUC+vr60N3dHfLCxR8Xaeq8jamK29ra0NXVhVKpFA4b011Rdgu25ov2imVThwevZd9RB0RnZ2coCxf9hKq/+Cwlq/X5udxBVT7nQ1Tfaf50HkU7yvArrAPr/LFzryRJwg49OoI13Azrgcqz8fHx4OAoFArTyFbNoxI0WUpmbQsl06l+V2cI60RJYXXosJw6V+F7p/1EHVD8nuGSmD8l0a2zwo4JbP+2trZw6G25XE7VuT2sWJ0HfPe0HwBIOcCYjr5nuuWfdaDjkbaxdYYwZA+JLKuiZJ9SR8BcCUS3345GhxKB+t6qE82+90vdNy2hqnnV8YE235Lo3FFlSd655IOO+66uLqxYsSLsJOvt7Q22SUNa8T4AYS3N7zjm8ND0arUawrlwrQpM3yEEIMUdWPsz2/bSORPHzXK5nAopwrrXv3VO1t7engrXom3EsvM36yEWhk0FGjrHqadMbOPJyckw71W7H+MPOOfSMvFvtpN1zus8SOPUq62nQ0XncMr1WKe6fb46Szhn5lyBNny2DpPZYKnf+VpOh4XKm9vv+cPhMXWOZQk72Ov/ukDRxcDhvLRUszGGqsZ1nQ1osFSNzsWjXQRlqbHmE7EFtBru+UrfLnq1fLP90bSZbxIvR9Lhm5yw6c9c4t474rDjxWx/ZotbbrkFxx13HAqFArZu3Yp/+qd/qnn9Aw88gK1bt6JQKOD444/Hpz/96WnX3HnnnTjppJPQ0dGBk046CXffffesn/v2t7992jv227/927MunwMp0hpIh2nh2A9MH/8sLGnMe2NjoI6xNnZ4zP7Zv3XHlu3fegi2PYxLw3XEtqTrQkjLpM9VBbwSyloW5keV65pu1njPPNIRoWpu5tc+N0Zk2rpmPlSJTpuuttuSEVpOXZTZ8YT3algddV6wzLE6zKoLtb1UNtt467YPMC9cSKlyLlY/tp6USFVnEp9lw/XYuYd9P5T0zerXdku8vjsaR9bWO/PI/GaFB9S82D5v21HLpc+J3cfvYnWrfcFuPbfvS2wMsOOB5t/mx/YB/q99TL9je7A9a4Vw0LmfPnuuYo3Ftt8Ox1xhSUGiUftl1rrT2rGZfg43D7rrSw8QtTuvYuQf77ef2V1kTNOe7aBp61gbG9frhR1b7Zhaz3iVNXbG5ndaN2wzW09Z/8+Eemy4IpbXLM7Gzj+snZppvmTbMDbXtp/ZOlObqnlbblgoDsrt9/zBleiOWYGDWbVaRalUwsTEBNrb28O2Ul1sUWVXLpdTi4t6oIN3W1tbOA2chnauk3/mj/FVJycn0dPTg5UrV6aMnHpFSRLP5wCiC89SqYSxsTEkycE4uVxkq3pytmlToVQul1EsFjE2NhbKwHqoRSbFjF4MExMTGBwcDH+vXr06ejhXs4DtMjQ0hIGBAQwMDATngBuP+cPhvEuzvW/79u248sorccstt+DFL34xPvOZz+Dss8/G448/jmOPPXba9bt27cI555yDSy65BLfffjv++Z//GZdddhmOOuooXHDBBQCAnTt34qKLLsKf/Mmf4Pzzz8fdd9+NCy+8EA8++CBOOeWUWT33Va96FT7/+c+H/1Vx6KgfdIpSTUt7YQk4VZMD08k1Lgg4DgPpxRDv18/4U6lUwjgbW+gp0aakI/PIbc5UBfPQxNjB2XYRAiCVN35mw4CwzExTyXw6Q2OLKZZVP7exlzs6OtDS0hK2fjP2ucaPV9tnVdSq1qLCSRX7rOfW1tagDs/n80FV1tnZGXZD6f1a11kLadaL9okY4W/nHtoPGCpHnTZWlQUgOBj0+ewbmsdY2BT+8Fouorn9Wbf7c77U1tY2zcnEMlH1NTU1lYrDz/xT5cg61bmRrTe2p7aT7p6gio911dp66DBV9nntr0r+M1+EEjWWbGGZ2Hd4ngnfbSrUtU/ZQ3lt+ZS81zbQ8vNv3ZFnHXs6tvBvvj+ap1h4In1feA/TZz/P5XJhDNTxKkYi8nP72UxYTPvtcMwFsfGh0fse7RL/5m+O5xwbOMZaJycdxHr/XED7S/tKsjsmpGK+dFzSNXps3dnW1hbOJ9Ny6lyCn42Pj0+bs80Vlqhl3pWkp1rbCuqsEIPjN3d66fjKMKdMz84/dP6nu+FmM/7qjjaeb0L7yZ2L+g6Q7LZzUzu/Y98iWE7aJH2+tjXnBmxHAMHOsc11d53yLUyPdUnOQs+rmYvjpFmx0OV0+z1/cBLdMSvo4FosFsPCiotnQre7Usk725ePgz4XV4VCIaVCnwvBrEa8UChgaurgKeGrVq3C1NRUIK65HXhiYgLDw8OpU6fnYxCh0QAQYooDB+P60mlAEmC26QIIhogGloeo0UjOhkTnIprGUcs/OTmJwcFBFItFtLS0YNOmTU0f0mVqagqDg4PYs2cPRkdHUS6XjxiF/XLExz72MbzjHe/AH/zBHwAAbrrpJnzjG9/Apz71Kdxwww3Trv/0pz+NY489FjfddBMA4MQTT8T3vvc9/Pmf/3kg0W+66Sa84hWvwHXXXQcAuO666/DAAw/gpptuwpe//OVZPbejowPr169fsPIvF/DQSY4/JMUs8Rg79EqJOlWeMiwDF3Gq9lQCjt8rYccxQ8cOdSaralRJdMYP52GcWeOz/tZnWcJWiXtdGPJ/OsRJbHJBqIcwsh65+NF0WGdMj/aLO8dsHFWrCAemq59tLGpVJk1OHoyd3dvbi8nJybDQ4nZxOk86OjpC+fkMS6Dr/6wzq67SeuWzdQGq9tCGIOG9SZKkdhDwXm6RZh7tDoAslb4S2WwzJdHVwUFwHkawPXnIKuc/JHWVsNYytbS0pIh6JT60HErgc4HN8D0Ml8Z0+Z5xTsY2sTsu1MGo9chn2jLRqcV2037MOqbTSA8x1T6j75yWU/sRyRjWCevS9i2tR1XvaVgDLY+2N7+z/YjP5fkt+t7q2KTP13dNiUaH40iBOpwbhUSfyWFVK486ltAu2nnBfJWPYy1/SKLrWKF5UQGCrrHV0ag2M5/Po7e3N4R3sXZUbSDH87ms92PQcVvJa9pSFSmoQ9zOCZiWJaUBhLHYzlvUttldBbNpO1s3HPM7OzvDfI2hvYC0eDDWdjEbwDmL2ic9rF0dzOpgoT1Uu03eRkO/qACEaXP+PDY2htHRUZRKpXAmma/BHY0ID+fimBPUm6rbrOz2otgCsBGgi3V7+FlXV1dK1UgSYC4hZGo9W1VuVEwxTqzGyZyNd1oJCk3Len5ng1rPp/Hjs2jwmjF+uCoRyuVy8IA3Yv9tdtiFzWx/6kWlUsHDDz+Ms846K/X5WWedhYceeih6z86dO6dd/8pXvhLf+973AvGTdQ3TnM1zv/nNb+Loo4/Gf/7P/xmXXHIJ+vv76y6f4xCsitaqR5VYnMlxaCf4lniNjQn2+xjZZhdQuhDRUGO2DIS9P5YnVVvFnq15ioWkyFJFsS61Pm3d6kIzq57VGW3LEXtWrI2UiNSwMbrg1zA4vNeOI7SJsa3nti/YfGi71wqrkbVotYtX2290HqD9KzYv4N/ap2rlW+vVEvCxOVut9yVrTqT50Pa0RIS2vaZpt/fXel9VPKDzH40xHqsv7QfaJ20/USI8tmXdpmO3o8feQ23DGKkdg31P7HfMS6yvaD+KlVPrZSYslv12OOaChSKX54pa4+RcUOu9smWdj7Jb+2LHUh2T7PxAYe2AhnVR0l7tuDobD7cOY3MYHR91PqTjZ9acKGa3dWcYldWx8Xi+2iW2E80+U8tv5238vFadWRteaw6t96itz5pXsX61DPozWx7EMTPcfs8fXInumBPUa0sPIkFi2E7WZws1bHZA5XPmkibv5WEcK1euDIstkuW6HYse3Wq1iuHhYZTL5Vk/l8+0JE5LS0vwsk5MTKCnpweVSgU9PT0pJX4tAp8DG9thYmICQ0ND2L9/P0qlEoaHhzE6Opo5GM6kjqj1+dTUVCCbBwYG8Ktf/QrDw8NYu3YtVq9e3VSK9FKphNHRURSLRTzzzDPo7+8P/c4xvzgcY8z7hoeHU58zfIRi3759mJycxLp161Kfr1u3Dnv37o2mv3fv3uj1ExMT2LdvHzZs2JB5DdOs97lnn3023vjGN2Lz5s3YtWsXrr/+evzu7/4uHn744WllcdRGd3c3uru7w0GFGnqBCwlLqiVJMm2BoKoaLqg4RtMWAekDmVQVTPugjkRdbPE3Fzzt7e0hbEtvby96e3vDotKqnpSco4JZw3RoCDA+n6RikhxSFjGvvE4PQ2WatFf6ntoFsCVl+T/zzl1MWnckOPVgRuatq6srHErJ9JlGLpcLC2vNO8OytbS0pM44UfWzHhTNMZ3qe92WnAUldqnwZb2wfjUMB53ymqaS/9rvdPHO60qlUgixYhf5VDmrqovhUnhdjKROkiQVW5+2uVgshjLQIUElop1zsO9QmaYLZbsNn32I9/FQWHXmq4hA66itrS2ox7Uv6L3atzgPKRaLqFarGBoawvDwcKpP6446rRe2XblcnuZsswS0Hgxq1eoss+ZP88602Dd1vCFs3H19p9jnNca89k3elySHDhDWd5d51naypEi9mA/77XDEYB1jc7k/5gRfqn6n6725roWzMNv12mzT5vxB5x1c9+v8ijbNhgGjLWJ+NBwICVPg4Nxd20nHZaqTSUrPFRx31abQTjIfo6OjYTegznWYbx2fOe+ieIzjf5IcDEHDuQYPx9R5ls2X2rLZYGpqKoS7qVarqZ1kascJtW/8X9tR+43OI9ROTU1Npey6ltvOGa3TPDYfplCgVCphaGgI1WoVAwMDOHDgQGqnntuN+YPb7/mDk+iOOYMDPg3rfEONqlV38fu5eqY52Le1taG7uzvkn0aWscZ0a3C5XMbo6OhhlSmmZCKpkMvlQvqtrQdPReeiXBfeFmqQONkoFothOxTzrgbT3p+Vpg62WQMvJyDj4+MYHBxEtVpFd3d3uGc+FRgLiYmJiUCij46OYmRkZE4TG8fMmA8jvmnTptTnH/jAB7Bt27boPbE+P5P6IvZM/byeNGe65qKLLgp/b9myBSeffDI2b96Mv/u7v8PrX//6zPw5poPhQ7h4sAoc4FDMZn6v7WqVNnYBaZVElrzSa/msmKpViT8uSLj44U4ou8jjdXYxax3LGuecP6qIJVg24JASmYshkpeWjAUOLaass0G/B5BaOPF5mid1jBO6+FZSm23B8hGMqc1ruGBk/mgz2SdsnTCMB59py0LCUfuQ5qsWYZzL5UJoEkVMBca6oSAhl8uFuQYJc3V8KBGr6Wpd28WwJde1vAxbR4c+cCisTJZ6TBfiMbU4+5e2D4l3ihRYVr4nrBv2Be1nsfbRfsFyckcfQ9gpYqIO+44zpj3zwO9Z5yyDpqHpsr0rlUp4h2LjDd8BhZZR09L+pvNhrX+WRdPW0DAsD8cG7Sv6fb3wRbhjoVCPqKfWvZaEX0oCXfPULOsghY6P+sOxN2YXYkpljjkcY6hA17FW7SmdmpOTk2FeB0w/AHo2UCc45wc6zwOQsq96RobWR2weQyKY86fx8fFgv+kM0Hmk2rrD6RtJkoR6amlpCQ5xche15nH6d8wuqNM3ay4Xc1DZuTTTV2cKocIA2u1KpRKcEPzebcb8wu33/MFJdEdDQgdnksK6+JpJOZaVJgd+jb+lCjBdfKt3nQdzzfaZuVwuLOJbW1ujh93RIPGA1vHx8aBA53M7OjqiJDjLQiM0OjqKSqUS1Ock1XVCaes3VqYY8TMTqtUqRkZGUK1W0dfXh76+vhAv305GGgXqnBkZGcGBAwdCjN2lnnw7auPJJ59EX19f+D+m3F67di1aW1unqc77+/unqcSJ9evXR69va2vDmjVral7DNOfyXADYsGEDNm/ejJ/+9KeZ1zjiYPxtq2C2k37dmhub1MdIcrsQYDq6CFBC1hL4Vhmn8ZkZP5x2xpKrWbBl4nP1h8+z6nkLS0jyM0IdC1omazsssUxic3x8PDgKSBZr2VQdzTStSl0XVPa72E4tu8C39ab3x8hRppnP54PCnQelxtpG1WQax1XLxDjZVMfrFmZVNzMsmq37LJvMa2o527X8dlFM0QDjuOpOCM5dbJvYPq6f2bmN5pf5tIeU6tzLhkGxi3x977SfUfWnKkd9b2NOjSxhglWa27ZmP6gF+36oIpX5s8o+PWMgli8lcLSueX+MhGTfUidKVtoOx1LAiou0P9bbL7W/Nwo4HjfTWoLkL4BAbiZJglKpFN0pBkyf5+jOKP3Mho7jvdxNpXMLO9+YC3T3FWOG65xBQ8bw+RprXMPCMV8UMVBBzZ2KVJrrZzpn4dhtHZ+c38yln9CW6Nk2dCLrAdasR97D56hTROs75nS2dkodtloOnaOwvPoOa7gWzoPGx8fDrva5hLN1OJYCjclsORw4NHEaHx/HgQMHAkm7YsWK1Las2UC3co+NjYUBm8ZRD1CZmpoKxHehUMgknLPAicGKFSuwcuVKtLe3o7e3N6Ws4oKaRnBkZARDQ0MYHx9HsVhEW1sbent7w3Z/3a4PHPJE04AODg6iVCphZGQEAwMDqS1ktcgT6yVWA6YL4VooFovYs2dPqEMqK9euXduwJHqSJOHQ1aeffho/+9nPQt/wBeXCYT484XTU1EI+n8fWrVuxY8cOnH/++eHzHTt24Lzzzovec+qpp+KrX/1q6rN7770XJ598clB/nnrqqdixYweuuuqq1DWnnXbanJ8LAPv378eTTz6JDRs21CyXYzp6enpShxqr0ppEoaqulWSNqaZ0rGcID1VzchutLjaYXtYCUBchXIxynGxtbU2F0LCLGoWWR8dp5lN/9GBvzYcq3W096PNUVWTzY4l0VRZz99Pw8HAoG9tFbQrJdS7MWQbaLVXdsixcuNHJTVWbzZfaPS5kdQHH53d0dIRn6m6FlpaDIWZ6e3vDYWj2wG91VCsxyr+ppFPVHUOPTExMBPWVEg/j4+MYHx9P5cO2q1W06VbuLCihwd8k661zh2n29fUF+23nAywz+xafrUIHdVQTnMeQoCmXy6kt47E0lWDQPkDif3R0NAgHuCCnE4T9Tt8p5k/V8iyj7izgtdrHKWqwDhI9INTuZFCFpo4beoYA64XhbAi1leqgUjEJ284qCm0IHPuOs55j6vgszIf9djgUlvDTMX42/a1RifRmAu0316QHDhwI54NxnFLHvz0HhWFYOP/gOEThml2XaggQjpsk6a3obDbgWN3Z2Yn29nZ0d3cHW8y5nIbd03kHbRjjs7e2tqJcLgcxH+0OD7/U0COqpNY5oz4rtius3vGX0DkHy6trbc4rLYnOe3U+EKtf60hmO2jdKYeiafPH7hikM4bheUZHR1GtVjE4OIiBgYEgmnQF+sLB7ff8oTGZLYfj/4caXxp1GrfZvsw0GroFKxZvi8bAqrpmY8R18atKts7OzqCYpUGhUQYOKQDUC86FMT2/XKixPPTelsvlsCClJ1y9xLzHep31O/v/bCawuo2MTgDg0GFfsS3ZSwWdsDE+b7FYxNjYWCiDY+GwmEb86quvxsUXX4yTTz4Zp556Kj772c9i9+7duPTSSwEA1113HZ566il88YtfBABceumluPnmm3H11Vfjkksuwc6dO3Hrrbfiy1/+ckjziiuuwOmnn44bb7wR5513Hv72b/8W9913Hx588MG6nzs6Oopt27bhggsuwIYNG/DEE0/gve99L9auXZsi3h31QRdzQDpMhr7vJAGzzpmIKcc4nitBblWhtfq0kudciOghoqqWyhojY2mrUk9VQJZstYszje2peYw5FlQhmKV61ZAT+lsXWsAhElzrWZ9jnRlW2Zyl/rf1bOtNF8kxNXJW+UhM2gNLLTGvJLESl+pU4N/AoRA6GpJHVdW0mVYRZtXYVgVYj33N6ke0hVz4cmFslWWqTtN8xVSk1sGkeVXFdUxxqu8h09d2tKpGVfXzOw2pYssec2xp/fE+Sw7o+6u/GVOe7a3KRS231o+OUbxGxxnb/23eNI/6Hllnid6X5WSZLVHpi3DHfELDPwCH+vhc+otd6ywVYs7vZoA6dLk+4hhHu6BjGJB2QupaW22FOrH5nbb5fNcP7Yw9vNTuZNedeio+0PFbnY1cb/Nvlol8ggoXAKTmjFlzlrmuje1aNkmSlKPChvayqnC2j9oQa781r0lySEyYVa6YI1d3g2k/IAmvc6J6RHuOucPt9/zBSXRHQyNJDqqy9u3bh1KphGeeeSZ4lOllnol80G2/g4ODKBaLGBgYwO7du1Eul4NxJbGg24yocGKIj3o9xSTM29vb0dPTg97e3qiSnF59GqW+vj50dHRgYuLg4aCcuHAbGicAzKOS6Dw4TQ9FnYksp9G0xEJs4Vxve01NTWFwcBC7d+9GZ2cnkuTg4a0dHR2hHvjspQA94fSC/+pXv8LY2Bj27dsX6s1V6AuLxTTiF110Efbv348PfehD2LNnD7Zs2YJ77rkHmzdvBgDs2bMHu3fvDtcfd9xxuOeee3DVVVfhk5/8JDZu3IiPf/zjuOCCC8I1p512Gu644w68//3vx/XXX4/nPOc52L59O0455ZS6n9va2orHHnsMX/ziFzE4OIgNGzbgpS99KbZv347e3t451c1yhhJrdhFIpZAeTqWEqBJXlmRSUlQn/DxrgosXtTNAOgQMSXINk0HHKnAodAjHeFX76LZops9yVCqVaXnm+2EV10mSBPW5LoLomOX/rBfmUw8LywIXV5aEbGk5GGd7ZGQkkNCTk5Noa2sLtptljZGwlijlIV5UK01NTaGzszMcBFYoFEKZWG4qxdgH+DnLzPJxoanEeVtbW0hf5xm6uKZqmaQ728ueQ6LbxcfHxzEyMoLJycng+GadxZw8ul3a9nnWly7mbX+w8xb2NX7H+iqVSmHXQD6fx8TEBNrb22uGNtFFutYhy8s+agll9kNN3zoMWC4NH8A25U4QEgVU9jMt29e1X6ljiX1c1Y8aCsa2i+4M0Njt3M2gP7bNeL8+T+d/StArgc7+GiPV7TyK97Nv6lyOdcDn6W4UJ9EdS4ksEnaufc2SvIsF64g+nHdlqaB1VyqVMDw8jFKphK6uLgAIdpY2UudaSZIEW0KlNsfqkZGRsPYaHx8PY6i1qzqnon2MHcqZBZ23dHV1obu7G21tbUE9rzuQFLTlqkQHDs0RKpUKcrlcarcyd5Kpip1EMHBoBxQQ3+03X31DCX2e61UoFMI8Q3c6at9UAQDtNO13S0tLyplgFe28ln0l5oywDvbJyUmMjIwErmJkZCSE/BsfHw/zPcfCwe33/MFJdEfDg4vlYrGIp59+Gm1tbejr6wuHV+pW6Zjnn8alUqlgcHAQQ0NDeOaZZ/CLX/wCpVIJfX19IdxKLpcLi0cSqjRIGj99JjB0Sz6fx4oVK0LYiZgKiAadyvfOzk7s378fAwMDmJo6uK1Ow7lwWxnjn3FSonHasgZJVXJZwx6rN72nHtAoDwwMYGRkJJD/5XIZK1asQGdnZ2oxvRSYmpoKBntgYABPPPEEBgcHU6esO44sXHbZZbjsssui3912223TPjvjjDPwyCOP1EzzDW94A97whjfM+bmdnZ34xje+UfN+x+ygC28SSOpU5CKD0NBaVjlqnYi0QaOjo2GRaLcs8/m6wOTfLS0t6OnpCWMi47croUfFs11sUfWkah3aNCXeWQe8VlVfQJpg4yLSOjW5WOLCkb8tSW4R+04XdkxnYmIitZhlnpQ01sUdf1er1RCmo1QqhYO7WA+st0KhEOwgF+FciNMBogtKEqcs68TEBPL5PLq6utDe3o6urq5A0isZrAc7sh1ZR7TPSqJr3xobG8Pw8HBw8NiD2XURq7Y4plojia3qrtgWcZ0T6M42fqchVDo6OgIp3dbWhmq1moppzzyogl3Lp+fIkHhQEQF/+Bmfb98jna+wXvgeDg8Pp4gLtq+GA1DE5jS6wCehrf3P9mX2Hba3htCJKfe0zpVQ0LCBsZ0edHDEFOjqILJ9Ut9hkiS81ioONYSC9iWHYymg9ovv3+H0x6Ugr+2OECVkmw0cKxkPnTuqp6am0NPTg0KhAABhnOeOajvnYnzuarWK4eHhIEij/ea4S9vNOQLnPOoAr5dEZxjW1tZWFAoF9PT0pM4Zo/2L9RGdU/B/FRkACAeHcn5hw7HF6pJjLnB4Md6zQBuYy+VQLBYDj0H73dHRgSRJUsIFu4uO+dLz2OhYsCJAzk00NjqQnrsxTYIOaoatJYnOtTcdK26Hjizccsst+LM/+zPs2bMHz3ve83DTTTfhd37ndzKvf+CBB3D11VfjRz/6ETZu3Ij3vOc9YQc3ceedd+L666/Hz3/+czznOc/Bhz/84Wk7uGd67tvf/nZ84QtfSN1zyimn4F/+5V/qLpuT6I6Ghy7UisUiRkZG0NLSEowwDZcuRNQ40otNRdzIyEhqMZ7P58OCUVXeVBlyMjCbGF2q6NPFdq3raZD4W++LKe3t1iirgKoHNJiWQNIFYSzu70xpMg1VIrS1tWFsbAwdHR3BSLN8C6lKt+ozqkiHh4dD+BYuvp1AXzx4XTvmE5bw1s+sMtSSm7zW/raKGjsWarpKUKpyJ4uU1udnKUu1TLFQJvWM91l2R59TywmthKK+s1wYZimqtF54DcluAMFhyQO1afcApEhwLrBIyNrDpxhGAzgUJoX38v6Y/SahyfJr/6DNjamvrKrXkiaxNrWwacSU4rF207rU70nWKHFs8619JrbbwhL92vaq2ub8Kuas1za3NhdAKn9WsalOA0ueW5UiSQ4l0a3iT5WGWe9YrXdD+4StK61LLb+9R+ei2te0r+jcS50QuqOFdaH1r1v0Y/nX9PkZHQwsh22/2Ygb3H47HNmw84lmhY7hdPq2t7cH+93W1hZ2C1FZriQ619lcv6uy3IbXAw6NfRriIyt8WxbU0a3O+piSWgllvZfjrK0L/rY/dn2s8wX7tzrh+RzrMLfPtmN1rbpQ+0s+g84JfZa2Lz/jXELnUKwPtR/qlI3ZSq0rzRc5FZ3TaXifZntf7LxiKZx3c8Fi5XH79u248sorccstt+DFL34xPvOZz+Dss8/G448/jmOPPXba9bt27cI555yDSy65BLfffjv++Z//GZdddhmOOuqosBt8586duOiii/Anf/InOP/883H33XfjwgsvxIMPPhh2g9f73Fe96lX4/Oc/H/7nGTr1wkl0R8ODg+7k5CSefPJJDAwMoK+vD8ViEV1dXejr68OKFSuCwkc9o1NTUxgZGcHo6ChKpVK4n+E7qG4bGhoKW7/owaWRHx4eDoR9vYoCbh3jlv16FidUgFHFzklKV1dX2D6nkwCS/twqN5vBW5V3q1evDtvdqHhXFePAwEBQElD9npWmqjqp8Nu3bx+GhobQ3d2NoaEhdHR0YM2aNVi9enVQDCzUwaM6SRseHsaBAwdQLpfx9NNPY2BgAJVKBUNDQ0FJ4FgcHM5Ew9vJEYOOzSTbbF9R9SeV4oSGpLDpatgWnfArVFGt6VPVrKFB1NnL/PK3JT317A49mEsJfZZdiUOWVRVcSh6qIkkXeSw/01RyVhdVWn5LQnPxrApikp3j4+PI5/NhMb5y5cqw3VhDq7Ceh4aGwpbfffv2hW3UTJt54n35fD61HXx4eDhsaWZZtM7YTlNTUygUCkGJroeX2xjyStayrbXe1HmuJKlVCtuFs9ahhV7X0dGROiCtpeVg2BzmI+bwZj9SwoL1oOpte7gfSQ86O6xYQfuVPottxG3bGqbIEsZ8XykiKJfLoS/aOcfU1BQOHDiA4eHhlIBA1ec6D1QSW9+z2HZ+kgJaB5oG65XPzOVyqa37vM86AqyTRdXvHR0dqboncWRVrOxHqv5jnrW91QmkSn+OXSq+INTZVw/cfjvmG9a51yxklMIKkYDm7+8cg6vVKg4cOJDamZ3P57Fy5cogktIQdAznMjw8jOHhYZTL5bCW5LhNEp5rZLuDaXJyEmNjY0ENPxsleuwcE+sI1/kP7ZAVjyl0TNefmHI6yzYCCHNBnYepQ1iJeR3f9ZBW68S2Nkbtp9Ynn2kPSFcnwuTkZKo9ueOKPASfobaN7cd8co5K+60Okf379wf7rU7wWmHjGhHsN1oH2o6NisW03x/72Mfwjne8A3/wB38AALjpppvwjW98A5/61Kdwww03TLv+05/+NI499ljcdNNNAIATTzwR3/ve9/Dnf/7ngUS/6aab8IpXvALXXXcdgIPnmj3wwAO46aabwtll9T63o6MD69evn10lCJxEdzQ81GgMDg5ibGwMpVIJhUIB3d3dgQDVOKscyJIkweDgYIiFvnfv3kCkcjsWPeStra0oFoshRicHQi4iZzN4tLS0pBa69d7DBT5PFAcQiH1Nhwt1IqYKqwUO+jTmfX19KBQK6O3tDdu/qCbQMDaM3VorTU5MuEgdGxtDkiRhUsXJTU9PTzDO6q2fb9A4j4+Ph0lgf38/9u3bl5qsOBYPvgh3zDe4QIipbNln1LmnIbRqxV5VNQ8XVrHrmSbHav1NRyHP3tDFGu/V38y3qok0XAfHNOa9vb192limhCHtijphdeHIz0mSqj1R54SScEraWcJA64ykuqrMaWNZH1xQMj8k/ZIkCaE7KpVKcGirrdEFHJ3LqnBi7HrNL+vSOi+Yf901oG1iHQdKzqq6y6ZtQ+awL1ry3CqwY32R0Dj7JAr4bG0f7fv83kL7jHWqMKa5qs7se6ax4Hkd5wxsR7YNwWdw4a0LcxuihnM5fjc6OhqcKSwv3zHmS50xdndA1k4BfR9tu6vKjPNRvotZakKtdyuC4GfMs4aGoTOEede6V6jKn/m07cN3X+9R5SR/NCTUTHD77Zhv6NjRrFACsxmdADEoUUzBVrVaDeMUgBThquFYSKIPDg6iXC6H9bvWjYZY4fhnw7nYcGe1oGtb/ljnDMdJtd9KhvOz2JjLfCu5Pdt2tvNE2ioN78d0dZ7H9rD5sGXXcmqIV9p3DRemzn2dK/B+nadwnqnOYn1uTOFODoGE+sTERBA2NPv6O8Z56JykUTEf9nt4eDj1eUdHBzo6OlKfVSoVPPzww7j22mtTn5911ll46KGHounv3LkTZ511VuqzV77ylbj11lvD2mHnzp246qqrpl1D4n02z/3mN7+Jo48+GitXrsQZZ5yBD3/4wzj66KNr1EAaTqI7mgo0JMViEQcOHAhhWcbHx1Nx0Gg8uODigSajo6MhfIcd7KmA0sXbXAd56y2eLUhw0PDZmGy6aLaL3nrS7uzsRE9PT1ASMH67HtTKZ6xcuRL5fD4oytRbzuczj4xpp/lj3XKCUKlUcODAgfAZn62H7qmSr9bWa6tQY9upJ5z5HhwcxP79+1OK+iNloutwOA6BY4FuC7aK7NiYqepgjlt2q6m9PrYDh4sj/nBc1XGNz7bEuZLXVv2uf/P5quxVW2AXVPoZ79PnWuhWZyWErWKQ0GdZtTHrU0OjUcVbKBQwOjqaUn2rE5tKdDp1uYBXBZd1OGh7x2wE82jrXcvBz3VxHWt/259i/9vrNQ4sy2IX5DFiXR0BVNCrzdZt26xjfqdiAyVP1V5rmh0dHdOI5xghHatDrW+2p90yr2QTr2W+S6VSCLFXLBanxUpXpXtsjqVqOebNto8NHwAgVWex9rMkON9TdcDo3M/WH6FkDR0uSkJwLqsOLTvnUiWgbYfY51pu6/xpZuLSceRA7V+jzstrrbWsA+1IA8fJXO7g4dhUo6sNamlpSR3mPjQ0hKGhoZT9B6YrtZXUtuvvemGV5myPmMNcr9e/ZxoLlaRnerPJH9f2XCvT+as2UucpBMtgQ3LpDjs9tF53OLHMKsKwO5xs+XjP1NRUIM/5mYod+HzdMTk6OhrsN88QYkhdtd+N/J7XC1Xj23n2kVC+LGzatCn1/wc+8AFs27Yt9dm+ffswOTmJdevWpT5ft24d9u7dG01379690esnJiawb98+bNiwIfMaplnvc88++2y88Y1vxObNm7Fr1y5cf/31+N3f/V08/PDD0xwCWXAS3dE0oCoql8uhUqlgbGwsKL65zYiebC5ukiQJMdlI4Nrtzrrg5MEcOvjNZRBU4z2X++mpBhDCwgDp+KY0iLGty1mgEV+xYgWOOeYY5PP5EM7FqstoILu6ujAxMYH9+/cHhwUNIuufinYe0EqDrgtdnsherVbxy1/+Ek888QTa29uxevVqdHZ2oru7G6tXrw6HuRUKBeRyudCmbCPNn5JdGjt3cHAwOE727duHcrmMcrkcDqlVIsqx+HAlm2OhwHFQSVc9JFHVvxxTVM2sClZO9lX9reOGEuf2N8cwboPVbZ9KzDIdgs/gzh0d1zj+EqqWsukrCU4lly6A+J0u1pRIjhGASiLqYtSqtrioAw7FKefnAMKuL11oaP0xzNrExMFDqLj1l+3Jcmj+1b5rOBdL0ioRTCI1azs25xGlUinkW+tB68KOS7bu9DcX0QyXonFfOf/goll3nHHh29bWFnZydXZ2hvA4wKGwLby/q6sLHR0dKJVKgbRlWCDtK9zZx11iPLhd50lUB2q92r6ghDLTZ73pWSh6HduAqkOGAND3QBV6nM+xTbUN1XGg5LmS7XxXGE+f+eSchs4vXSDHQs7pjhDbZzi22Dj1HJt0DqoET5IkYRejHijMNrBKc6uItGOU1nFsR4S+0/USVm6/HQuB2A6aRkDMWRpz3h3JBDrXWNylXK1WU6E/df7End5qv7MUunYelsvl5kyi2/zymXbOYMc/ncdkzcv0f3Ve0+7OBBVcFAoF9PX1BfvNUGmcp9o6bWlpCTvx6KCgzeLcIZ/Po6enJ6zFu7u7U/nnPEZtlebJ7pSiHS2Xy6G8dNKTcwAOieTK5XKYsw0NDYX2HxsbC22g9uxIWH+rKEed4DoPbsRyzof9fvLJJ9HX1xc+r0U6xxz9tZxVsevt5/WkOdM1F110Ufh7y5YtOPnkk7F582b83d/9HV7/+tdn5k/hJLqjqaALBhpeNThcpKkRJoFaSyUw3x5D9dDy90wea1Uc6ctuyW1+xglMljIsC5xMdHZ2BgdE1gCosWDHx8dTh5BqXqisY0x3qspjajG2SbFYDPdxkdjZ2Rk87UpyxJRl/M12VrKJp6dz9wGJCt2B4Fg6+CLcsVBQ1SYJJf6tSmpeU2tcVlLKOlZ1DLSHV6kt0pifRNZYzfyog9AS+DEFV4zcZRkVdsGo5VToWGuVqtbWzKTmsg5rfqaqNY0rzetIBOshkqwLEpM6HyApa3eAadlUfZ2lNrbpqj22adaa1FuSWGGVbNpv1MmTlT8ldvVHVd/sS7TFrDcu8nQBHUsnpvS38y/m36quWUfq1FE7ryS3lpftS7JGnSF2LqH3ztR/tX9oW+nCl6hF2mgsedtOOr9h/bAd7LyFf9t0tB45/+H7HXuXa/WRmI1lPmNjUb3zx6y0Z3Ovw2Fhx9ZG7Cd8Lxsxb4sBOy/h+qtSqaRsMklTOnR155CFjnm0GeqonW1d61irtkrH7nrTzLpO7fVs8qRjO9fJtM8AwjxR02a4lyRJgv2O7Q60O8jpKFa7pKIzdXTzt/7N33Rq5HK5cICs5oXpcp5Gkl9FIOwP3A13JL0/1oEOpNu5EceL+bDffX19KRI9hrVr16K1tXWa6ry/v3+aSpxYv3599Pq2tjasWbOm5jVMcy7PBYANGzZg8+bN+OlPf1qzXAon0R1Nj+7ubqxatQr5fB69vb0oFAoolUoYGhoKYTxmG9P8cFGpVMJhaPl8Ht3d3alFpQUNPQ9soWMAQMrrq+nbQ+5m8thzQc0YtAznYtNWkHCfmpoKnm7mn0qErq6uoLqkOo7EkRptVXiSSE+SBCMjIxgfH8fIyAgGBweD15sTCm5756KeRik2maOnm3FwSajr9jHH0sMX4Y6FgG4VZagv3brKST+JKT3Qios3jiFWIWzHWEvEkii0Ialms91XlUV6IBSVtxzzc7lc6jm6+LLqGEvgaX7t2K+EfIxU4xhunZqavjqOVf2t7zx3KtE2rlixIjh229raUKlU0N7eHpye1iYSWjYulLPUKFo22hjWIxeGfA7zzeuVzLTtblXY2pZJkgQVnNow7YNc+Nr+ZRdl3N2Qy+WCA1xDpllntaajOwBYTl5H1TPrwS4A6SBiHfDdKhaLKfurfzPP2gcLhQJGRkbC/IP1z+/ZzycnDx4mx5jnnCfogj2mANf3g7C7DhWqGrMLXc4n2I6EdVbxN/ueJeVZ16wPHubGa0lQqMNG88c253jAdPju61yI/Yr5sUp4to3tt7ad6oHbb8d8Q511QGP1k5kUjssNVFNz1xJ3INMmlUqlcLYWgLBDjHMbRWy+oo7S2YDXV6tVjI2NBXvGcVnXszpf4W/aMA0Hp85itrsS0TP1BX7f2toadiTynDONB29D/fFenXtwLGdIO36fJEmw4bp+Zr75fO5E444v65hW+2MduTynjfNmFY4ASMU+HxkZwejoaGo+q/atkd7tw4V1nhN2p1gjOQ8Wy37n83ls3boVO3bswPnnnx8+37FjB84777zoPaeeeiq++tWvpj679957cfLJJ4c+feqpp2LHjh2puOj33nsvTjvttDk/FwD279+PJ598Ehs2bKi7jE6iO5oauVwOXV1dOProo9HR0YGjjz4avb29GBkZwZ49ewKZOjg4uKgDWLVaxcjISJhkkHiIEdYceLmNWbeHUW1Oo0moUk+Vd7XKyEUct5Jz63YtEp154ASgp6cnxFflJKWrqws9PT3o6upCd3d3IHVUxa6EDT3VJGRGRkamxUrVBak9tIZpsrw6GVCyppEVLQ6HY/7BHSmTk5OB6Mt6/2MkOnctUUFDAsyOJVlKJI6xSrbqZJqIpQcgPFdJdI01yQWJLqD08CxLFsYUsAr7fKYVu56LrBhRbZ+rRCfLoApYLsI7OjrQ29sbtjbTCT4+Ph62MFcqFYyOjob7COsgsERojPhQEp3hwmhfdPFMApU2Ww+t1AV5zJmgxKbaJXsdyVY6XmLOBkswMM5poVBIOcBjcwDrYNDyqIqZ6agCXctFwlZ3l6miTN+X8fHx4CDQLc1TU1OpRXhnZ2eICasHq7F/j42NYXx8fJoDTPuhtfMM96NODnXiK3msbaWHuREkoLU/6ZyE9ajEg6bFnZHapvl8PuTDKtqZT7YJ86+xZ3mt9lng0GFvuVwu5dCgWlD7HPsh86nvcq2dGQ7HQiNG5DUSGllZutjgepDCqlWrVqXI4WKxGEKBkNDWHXUKjlc6Ph8uyUcVvN1lpTuu6fjkmKy7/TSUX8zRqA5cYOZ5Fsd0De/HOQXHcgrN7H1qs3X3ms5l+B1tKtf5Ov/kdSybnj/CMqtz3eaD+W5pORTujLaEdaLn0DHsYCORxwsFOxexcw21ycsNV199NS6++GKcfPLJOPXUU/HZz34Wu3fvxqWXXgoAuO666/DUU0/hi1/8IgDg0ksvxc0334yrr74al1xyCXbu3Ilbb70VX/7yl0OaV1xxBU4//XTceOONOO+88/C3f/u3uO+++/Dggw/W/dzR0VFs27YNF1xwATZs2IAnnngC733ve7F27doU8T4TnER3NCVosNvb23HUUUdhw4YNyOfzWLt2LXp6etDZ2QkAIcYYD5ckMbHQ0IlBpVIJk4oYac0FHD25jMvOha5ulSN0sV/vtjdVy8UW//XAqpeYlhIM6v1nXVjiQdOJEd5WIWZj5ukiWheOrjZvDriSzbEQ4BigYwKQjvsbIwuBdAzNrJ09liy10Ofo+GrHOlUAKfkdU+zEiHtVMOszeY+WJ0Yw23tihHPWOzqTzeB9umC2i1Au+AqFQnC+ckHOWKO014zp3dLSEuKoW1tj1T7a9vqZOmn1t82/Eq9ckLN92F5KevK7mF22SjZ9noYbsv1N+0DW7gJtb+ZFCXIljGPpaZ3YNK3KLkbuqopP+5uWhfXARbpVBZI0iTkBrJPKEhf6Pmm/t4vamRAj2OdqZ5hvfe/0nde6Zv/QPmXfzVi59TN1Vmib6HP1eiXj9f2czdzJ7bdjvtGo/SJmHxo1rwsNjl0kpCms4g5k2moe1pzP5zE6OorOzs7gEM86J2wu4/ZMoN2hE59ORELtKklgteFqi6wTheO1XbtmlSuWjoXOkzS9WvWh80KdG1jBge5+Uxti185ZbcP60XV5S0tLyn7p3Hm5vSdqV63tb7R6WEz7fdFFF2H//v340Ic+hD179mDLli245557sHnzZgDAnj17sHv37nD9cccdh3vuuQdXXXUVPvnJT2Ljxo34+Mc/jgsuuCBcc9ppp+GOO+7A+9//flx//fV4znOeg+3bt+OUU06p+7mtra147LHH8MUvfhGDg4PYsGEDXvrSl2L79u3o7e2tu3xOojuaEitXrsRzn/tcrFy5Es973vPwghe8AB0dHWFrMlVRlUoFjz76KB555BGMjIxg165d2L9//4Lnj6R4tVrFM888g/Hx8XAYGD24NGSMH8YDMbnQJMnDsgCHDGOxWAxKLYYfqAfzpTRSEiMWT9WqNKempoJCHTikeMzKk91uZ8keHchjhJOjseGLcMd8g4cmMYyThi/geFQoFNDb2xscmlTWcvLPgztV8amkKlU4dtumKqwt8ajkqYazYpxvXsdFniqhVA1FNTDH2K6urtS4S5AY0wMXdXFmiVDNby0Fbxb0XVblteaf6ZIgX7lyJTZs2IDu7m4861nPwubNm5HP59HX14fOzk6Mj4+Hg6ypGKcjnLG9qYoiAcjn64I4SZLUIdiqSCMxrwsdVf6rMxtAsF0MQ2Lrhduk2fe4i0Gd4JZIn5o6GDaIfdfmP5fLhe/y+XzqgFrrjEmSJOogYjvqgaN6joz2BW3TWOxU1lc+nw/1x51zdtHPfqjhStgOrGcqoq0ziAp1VQfGYAkRdUYpuazXW7JZ33EbCobl1vRVEWjLzB/eo2Vn/1CSg3mwY0iMDNGFue5ssPOoXC4Xdq4obF7UgcJxrd75odtvx3yjURXoQNoJfiSGpKgHudyhncx9fX045phj0N3djWOOOQabN29GR0cHenp6wk6y/fv3o1wuhzXv+Pg4fvnLX04jWhVZ4oW5IkmSEJaP4UV155V9rrXf1nbwtzoos9Tbmgd1uGtf4rN5nZ5rQlurznmS/OqQjYnYaFs0NJ/aFV2jq+q+VjnINdC+2GdrebMcJUcyZqq/RqqPxbbfl112GS677LLod7fddtu0z8444ww88sgjNdN8wxvegDe84Q1zfm5nZye+8Y1v1Ly/HjiJ7mhKFAoFbNy4EUcddRROOOEEbNmyJSzqdKHBrWT9/f0YGBiYdtDAQoHGj1uTK5VKODykUCgAQFhAjo2NBUNPosAuhJVIBpAiew5XWT9XYt0SM3a7s046dGJgf7Kez8E6awHtaF74Itwx36AySJXAJFyBQzGGSeRyV5DGvtTFExdbSnTHCLCY8of/W9WRLqhsCAldMKnS2ZJdXGwxpmaWUs4uVGO7gdReWuWvqmRt2SzsglDrUxduegB1b28vent7sXr1aqxduxYdHR0pEj2XOxheZ3R0FIODg2hpaUltgVYngJaXbaP1qQS1jfEdi61NwhNA2LZOIjpmr/gs7nzTtlWSP9aHSGzH0gMOheCwuxxi7UxCWL8DkCKjtf+pjbbKZ91iz2u47Zz1r4t9LZPNG/PB/sCyqGOdcwimkxWbX4nnrN1nMQWYqgHVOaTvls2/ku76fxbRo8+w74y+C7F3jTH4Wc+WbFESXR0ANmweof2P97NsWbHrZ3rPbf3OBW6/Hc0Gq9xdrqANpxihr68Pa9euDeFUe3t70dnZibGxMbS3t6NUKmFsbCx11pU6JXV8AxZmbKAzf2JiAh0dHcHJqeM9gFQIPXXCx6Bj8kzrU70WmL7bSUl03ZXFvzVkq9pvOy+0YdgInfNxHa72Rx3mM5XDltWKMWyIs+WEZhob3H7PH5xEdzQVaBy6urqwYcMGbNiwAStXroySsTSSK1euxLHHHovOzk7827/926LnWYluHnKpCyQ9kMNuuU6SQwdTKajIm81gSIMMICjt6Jm34WJs/oFDSjt7KIkSVlwoAtMXb3YBn6VGcDgcjtlCiWeqcEiYayxmu5BjPGeOX6rUUbWNJeF0iyx/qHBWklvV37qA0UWMpj0TdOGkimYdiy2pqSG29G9LytnQEjHY8Vrtlt0KbdVPJF8ZxoWh11hXzAOAoBzjtYxhqs9V9RXzRuc5dyQAmEZKapnVFrH/sO60vVUdrvWgNjt2+KmWycZgtXWpz1SbbPsIn2UXy5YEnZo6dLCXPTBNHfQaQ1vnHvyOhDXLoSo45i+m2lNVnNa31pM6P+z9lsRnOTXPSoRoP7bv0kzOei2TnZfY/Ok8h2nHlIoKJTiUkOB9Ov6oU0vzF3NW6bV0qvFdYroW+gx9jgsWHI0O9tWFXjdYpzOwvAkk2iQ9EJw/jIXOuY+ef9Hd3Y2enh4kSRKU7LoWXCzoWprzEf2ODm11Ntdqb0uO15sH2j/dpcfdZfzhPEHnFOQI7Npf+QXd/aXlo03Tdbs6mg+HUGVatQRxDseRCifRHU0FJcaf+9zn4tnPfjZWrlwZDr8iVM2zceNGtLa2Ys+ePTNuEVkIqGeb26EUMeWYKsWocIvdw7/rgarexsfHMTo6GgilLBJdF9KlUgnDw8MYGxsLh5FwO1ilUglebiqq9Lk6GeC2cp0MOJYX3BPumG9wUUYCt729PSzaent7kc/n0dnZGc7LYIzOarUaFMRZalC1LUraKYHV0tKCzs7OcPiyht7gmG/JN6s4t8pe+0wNxcLFF8dTJSd5DZ/NdO2CTQlOXVgBh8g9JRDV5ihxzrFczxzRLdPMNw+n6u7uxurVq7Fy5UqsWLEihKYBEBRgPISrp6cHfX19yOVyoU55HYDgqOBnudzBECijo6NhRwF3f2mdK2Gpi0ElvK0jmCHa+JkSkbpI5f18niVhCdanVV1zFwSfwe3o7GfcWk3ilGS1dZDQ8U1HPe22kszWcaR1pI536yxgfrOIEC0T20SJAW6Dn5iYCLvyWJ+8P0mS8FvzbPOgda1OCmD6OQW2/rUvc+eJllXfRaal8xYlwfmeKVkeU/vbeY++a3rWgY49VmChSk593zVEDuecGt5Gn6nj3GzmYm6/HUsBdZguJAlLW2XXRbWcy0c61H739PRg9erVWLVqVbDjDI/HuUkud/A8kaGhIQwNDYVwpiMjI6lD2xcLXEfbMG2E2u96xqi5jGNq/3i+C+ej3B3J69Q+U7ym4WYsgQ4cCk2mDiDlEThP5O/5cEbpfIlYjkr0ZoLb7/mDk+iOpgMNIJVsjFGadS1jtelCfbFhF2uzvW++8qBKLcY1ozItVoc0+DYum3rCucDVLWdWZRlTSi6GmsTRmHAj7lhIKJGoqnANGaFqW12MKNkVU8cCSG2L5XWqirbnQijxHstnPQu3WkofLYdVnqvqOItIVLtQ63kk7RR2BwAdBUoEkqzTerFnaViFIf8nqagkL/Mdq4dYuBxLRPLaGImuZdX6YnpWSaxpK2HO9NUxrunZMdAqHi2Bb5+h6nGbhrad2udYKBe2u9aF7RNZ32n5FVnEtU0jlmaMXGaasXlK7J2gEk/r3/bnWFli5IqFtgGQPkeA6c1WuWrnUrb9Ytfa/+22/aw5nS2rXlPv/NTtt+NIho6jQDpM03KGznHUkR8L52lDf3H+lTUPWQyok9WOdRx7F2p80jGea2kewKoOc2tLOKeizdf1M6+1NknnKgDCOt/evxDlczQ+3H7PH5xEdzQd+BKrem6m63XL+XIGDe7w8DB+9atfhUNYe3t7A9mk26UnJycxNDSEYrGI4eFhjI6OBjUbrxkbGwtefHrTVcVBrzmV7FTC0xvu7bL84EbcMd/g4q2rqyv8rwdJkjxnGKtSqRTCWnFhYZXnHJtq7dTRBbeSxrqQjKlIOS7aOOiWCAZqx6zWAzxbWlrCGE5i2qqVlFzjM6n+VmJQlc0aLiW2s4iHW1MBqwd/WhIz9sNn2fj0eogW60HVVqqgpsJKY+JzEc/zSGIh07SuWW+WvNW/Nc/WGazpEXrwa5bSWx3Ptu9pmVXNTaJ4YmIiEBT8zT7Bc1bGx8dTW8i1TGx7PoNpsl55AKjCKrHZ9609t04PO2ezux0sCaCLftYD09L3gP2Bn8UIGiWNrTNN0wKQOjzY5pWEhN1VoteoCjBmr+iEi5VFz2GwZLolz234IZaJ/YBtxRAKWXZXia964PbbsZiIOXoWoh/p2K7OKDvOL1fM5PS0Dlu77o457hYz7+rItkKvhW5fKuFV7c4D4rmrjHMmjeM+PDwcwsAWi8UUOa4Oce5AamlpCYeZq4CB7ZEkSSpszXyVOatPOBoPbr/nD06iO5oOMTVTLaji0AeAg/U3OjoaDjvlVq/29nb09vaGRTS3XPf392NoaChskVdCZnJyMhh4Lua5rY8xbWmsS6USRkZGwmSg1sEtDofDMRsooaphLlpbWwOxzB04HI9IoPNHtyKr2gdIE4K6WLAhNCyhTihpqyRxkiRhdw+vs8RuTD3Oe5Vs7ujoCFuCSaJbUldJNwCBRLfOzFg4Go7XJGQnJiZQLpdDuDE6T9VxoaFTYkQ6yQpd1JL4J8HMz3Xxyzwp8aeEPu8joa8LByVslai3JLqqEfm/JRp1Mcq/Nb96+CPLrMoxljVWB8w/79fFL8lttjsJZn7HMD883M2q19vb28O1mm/bfzXEkRL9dCKos0VJdLv7wi7a1VlgiZmYsk6dEUxD1fV8fq3dCvxOSXT+ZpgetpndcUDoe6f9RZ1VSmDHnj81NZUKU8PPOBfT973W7hEl3LVeeRAu02WasXj9xFLt0nQ4ZsJikeh8ls4l9JnLHXbdbZ2mViQQE68tpZqfYyxtF7CwYYHss0ulUpiTqROcYXBoU2iXJyYmMDo6irGxsVQ4FtovnQdyrqXzqZiD24oHnER3OOYOnzU5mgocnBlrbWBgAK2trejp6ckkLYrFIgYGBjA0NBQWSssdqvYrFouB+FZjTKKkWCyG2MHWOKpB1vibnBgAh0gKKj99V4DDPeGO+QYXFpakBdLjlG5rVRIOSBPMVpWmW2ctYmE0+LkqTS2Jrv/rO2EdxcxL7BqbXxvTfaaxVt8nJUktaQocGsuVMLaOBiVVNQSHVWGrWpqLRy70bPgwdTLYRbgSqMyPdT5w0cyFpyXHY4tJS5bHwnxYUmEmRZter8/U+OtZJIPeq+eOaN0xn3ruCH8T1oHBtJluzCGkSm9b97H+k1UGLWNsvhbr/0xXHRgax53lr0flqI4YS8grCa75teQylX461sSeo3UU+9vWSVYa+u5nXVfrGfru1rqf71E9cPvtWEzwvdXxYSFg5w12/rCcYZ3b5XI5nBHCtSHJWz1vRq9pFCHbQqvOs57JPqTCBYZVVfutB4nqmjm2K4Kf0QFrxRdqw+38Y777dCO0rWNmuP2ePziJ7mgqcKJ/4MAB/OAHP8DevXvxvOc9D319feEwLt3aPTk5iSeeeALf+973cODAARw4cGCpi9AQYP1MTEzg6aefxoEDB4J60yrlyuXytG31RJIkqS30XFzqoXIkNqxK0Afj5Qs34o75BrfCkixS4puLPHX0aUgpJZhIjlEdlLX1l2hpaQlKYB7+qAQs1UJ06I6MjExTm1uCS0lqq5piHvVz7iiiEt2SpKVSKaRL6FiuYS5IHOqBlazHcrmcOtyKCzhew/va29tDnajql2UaHx/HgQMHMDk5ia6uLqxcuTJ1qGOlUgm7loaHh8OWZiqSbbgb5oXtq6FNVJ3PhaaWn0SskuHqlNDY7to+TEPPCdEwMjGlsDptdLHLdqLNtGQmlcTq8GHf4A4KBclzhlsrl8upEDu640LbpVwuhzTYHzo7O8OuAvYz/miZc7mDB7+yTmy4Ei2f7lhQ5b+WTXcS8Le2oyro1dFk20iJMNYVDxm2jg8SG7pDQg+t1zAslpy2jiJbdioPtX5Zx7w2ZtvY97Rf8XOWid+rE4Np5/P5lONO6yjmBKsHbr8d8w3uXK3l7FnovqMKdDtuLATp2ExIkiSE8hwcHER/fz8qlQoKhQK6urqC3efYzN3OXHePjo6GuddSkul2DrdY+WDf5vq6Wq2ipaUl2Ga1qxQW8DprS1VNz/okMc/v9Tqd3wHpnV7N3qf1PXXUB7ff8wcn0R1NBxrzvXv3olwuY+PGjZnboavVKgYHB/HUU09hcHAQ4+PjS5z7xoBVtRExxeNMsOpBIL0FXRd/JCscyxtuxB3zDS7gYuQZxyhVPtuY2yQ0SSqRMFQ1tm6H1X5oF96WUNPxUc+CUAW8kmOavi5yuGjSe1Slq0SjJcRmem/sgsuG9dCy69+8V4lHPWhM48JrO5RKJeTz+RBWhzG51dnBcCTcxaQEpyWnrXNW86MkqKrjY3VC8lvrQPuTXsffSrJo/1DCwCrlY04MbQttN+3LuVwupb5Wcl0d1qwvOsHts2w/0/BCfJ4e+sp60bzqDgqS83Zeof3ahlOxam4lNvi3hl0hoa71p8S0feeySGEKBrSt7M4DJaF1dwd32dFRoCrumZTclgC395Bo136p18REDNoP7X12x4UNsaMkPEMD1QO33475hjqoLBarz+hultj8YTn3XZLAFE5xB/Po6CjGx8eDc7K1tTWEEKtWqxgfH0exWAzO96VQgWeVZymeyR86PWlX1fbRMW9hd9bV0zd1Hqehx2Lz2GZEzHHuqA233/MHJ9EdTYlisYg9e/ZgdHQUvb29aGlpCR5xLszHxsZQqVTw+OOP46mnnsLY2FhQ5DmyMdcBlkZZVVNAOuapD8AOh2MhUK1Wg4oHwLRFCYBAyjK+pKqpgYMTcj2kkaQdCTYllTl5p1KVJB//18m9EulKpOpCiHm1saMVJLsYc5zKd/4w73a8jYVVsWnqeK3EsS7UVAlNYjGmbuXzssLBUGk+OTmJQqEQ6ox5rlQqQck2MDCAYrEYtoXrridLCDLPbAs93FrLpKFjtNxMG8C0A0ZJ/jMt3Qmg6Wo/4cJV69y2iSrCtU41XXsIpu2DFkqsxqCLcfZdJf41r2zfjo6OVDltmbQ/23xoH9c60H5qHfi2n+l9WtfaTlnPt3Vj3w2tC1unusuCbckY+2wn9nnmX8l8O3Zo/WuZbFktuWKdFNbpp1BnHn/bXSXqeNFyOhxLgcWKTV0LHAtju1qOBMLxcMFxo1QqYWBgIDjCOTbqwdZjY2OYmJjAvn37MDg4GJzhepD7coed+8WEE/b6LOV1lihA1+X8zeuPlPE+q+z8DkjbN+9/jvmCk+iOpsTIyAh+8pOfoK2tDXv27MHjjz+Ojo4OrFq1Ct3d3RgbG8MzzzyDcrmMvXv3or+/P7XdyRHH4RoW3Y7tHmJHFtwT7phvlEqlQEByscffSt5qCBfbl9rb21EoFNDa2opCoZBSqwIIBBlJNRLXXDyS3NZJPfOgBLBV3Cmha5+n46iGwNAwG0rU0YnJcjLPliTkIkpjQ9t8WZVzjGSmOlfTsESwEq5TUwfD2vT39yOfz6NYLGJ4eDjcT3KSzg4uvqlep0qd4V2UnGxpaQmHWrNugEMkOtuJoU4s+avl404EdQxriBEq/kl2kkgl2UnHgO7K4vWsW3XSKFlu1dX2fiV7LJmt/c5+ZttW82Bj9VOdzXdAD0dVVTjrKAtZ6sMsYorvDttMn6tkO5XTU1OHQvxon4u9S5pffs4dLLpLwTpc2J48N4ZhYSqVSqi7mCLdqsBju0M0RJO2pyXLtcycx+rzWF59F9hu7Ju8n4p37e+zIQndfjvmG42wNuPOEyC+k22hkEWMNho4Vo2NjeGpp54KSvT9+/ennIwMDTY1NYXR0VGMjIygUqlgeHgYxWJxzuNHs9TTTLA22u7sq1U+XjfX0Ftq6/h/s0J3jtQzz+Fcxu7OW25w+z1/cBLd0ZSYmpoKpAkPF83n86hWq4FE7+/vR6lUwtDQUDDcjsWD17cjC27EHfMNVd8qSa7EoB4qqn2QE20bisSqja26WuNl8367KFGy0IYc0e9tOZgvQolwkpj2/ImsOrHpxYi+2ALVLvJsWvoTK0sM6lRIkgTFYhGjo6OpbcZchOsBpOqAUFW/Kr1tnmIK5Zmcu7U+UwLctjXbRon9rHAlmg9LgmctamOfL+QCWEno2G6ymJItVj5+bvu4Kqmt071WH7LPtyFKYnkiuWzLpHWv6Vno+80+x/5nw68oauUrhixiRB1hWWXUv+0By+yLSthrDHXm1Ul0h6P2++VAyj6Pj49jZGQkzENIaPJ8CbuLbD6cEbWUx80C2vqZnN217p/t8+wzj3ToHMX+vZzh9nv+4CS6o6nBRfi+ffvQ2tqK4eFhtLe3h+1kVLL5i+9wNA7ciDvmG8ViES0tLSnCPKZKt3GpqermwYudnZ3I5XIhVApwaNGmhC0PziRBRTJNQzxQ/U4FNeODqmJUoUo4G/4ilzsYp71QKATFO6+1saBVVc18sxyqXNXnaMgWqyTnvXyWhoGwoWgsYaphJBiOguFDJiYmgp1mWtbZoE4R7iTQUDxaj3yWHpwZI9UtlGxUpZYlGWOxxVW9r2F1CoVCdMFq/4+Ry3QcqFI8ludYGdi3LNGvfVMJfyq98/l8UO8zJqvWmfYPPXBXCVotL5+vuyFsWScmJsJBsNVqNRXzXNO24Wy0HllWDXfC/Oi5B9pOjMGvDjH9XuvMEs8AUgcJAwj1RccQ82r7h54To7Cqeet8USKcY1OsL6tS344vuqWfinR70Fy9amC3344jCTp22p0ZC3WOk90Vps72RsfU1FQgxoGDuwB1fOI1SXIwNBtV6fOh/l0sEngxVO8xwUK996lju5460XuOFBJ5JvU+v7fzEu5I5Byh0d65mYQehwu33/OH+vaDLBC+9a1v4bWvfS02btyIXC6Hv/mbvwnfVatVXHPNNXj+85+P7u5ubNy4EW9961vxq1/9KpVGuVzGH/7hH2Lt2rXo7u7Gueeei1/+8peLXBLHUqJYLOLAgQPYt28fnnzySfzHf/wHnnzySRw4cCDEYnM4HA7H/KHR7DfDfJTLZZRKJRSLxdShVlz0kTDS8As8T6OrqwsdHR2pMCmWLGxvbw8hQ/ijhBwn5jwYk/nhD/NDIlh/SJpq+AglLPP5PDo7O0MoF16rJIANvULSTIllC60PvVeJaksuk2CwxKaGPVGCUwlRzev4+DgGBgZw4MAB7N+/H/v378fAwABGRkbCoWVKnlcqlaBqU1W/ht3QUDMaxqaW2hhIbw/m/6wf5tceTKv1pIe7sl9oKJbYopkLGvYZ7atKysbymuUcsOWwoYJsG2metT/b/qJxi+3zlfiN1bl+znywzPYd4XsJIEWiW2W8LS8dAbbutcwkvLWO9UBVu4DlffrMfD6Prq4udHZ2Bscbn0vnm43VrnVmdx8wjzPt+NCdJ+znWX2D7afhjTimdHR0oFAohB+OJY7lg0az30sJtZ96ADjDPsw3cWRtog211uhQccDQ0BD6+/uxd+9e7N27F08//TSefvppPPPMM9i3b1/KljdLCI16HO/zBbvDqx7Qns1W1R/bCdnsyLKbhHU4AAj2M8sZvdTQ+dti9EHH3LGkJPrY2Bh+/dd/HTfffPO078bHx/HII4/g+uuvxyOPPIK77roL//7v/45zzz03dd2VV16Ju+++G3fccQcefPBBjI6O4jWveU3mKd+OIxdqjA7H0+ZwOBYesfe1nh9HY6DR7LcSZbrIsCEVlGAi2aYksxK/djJrY4srqackHQleVajqdubYpF9VsbHQLSTmYgdVxhYKqhzX33pPrXdKyT5LwrJObTgMph1rG3udLuiyVET80bZUEtf+joW7sXVUa/GpZdaya7lmUrPbA0m1HLHFuZbfEsNWIZ9VP7Yts/oW+yj7piWIlTi3z9S2t7sWasVnte+Spqv5oiMhK2xPDLa8Wc+J1Z06jNShFdutYv9mW8ZIMN0JYXdE2DaxnzN929/tu631ap0M/C62ENf86q4NHV/qhdvv5kaj2e+lhh3fFqrP6jvPOUQzvxs699Dx2tqW+SrjYtRTzNm9WM9bjHuX45hsy7uYjpLZwK4zFsrB5vZ7frCk0oOzzz4bZ599dvS7FStWYMeOHanPPvGJT+BFL3oRdu/ejWOPPRZDQ0O49dZb8aUvfQkvf/nLAQC33347Nm3ahPvuuw+vfOUrF7wMDofD4ZgdFnvS6Jh/NJr9LhaLAA4dZBhbyCuppIrvrq4utLa2BjWpXeQqyajkqpLAqvhOknRM0KGhIVSrVRSLRVSr1RDyQ8PFME9ZB5sBCApSXq+hTnitTnaVKOP3NgyLJVEZKqarqyuERrOqeeZPJ/dM1062Y8Qw2wlAimhk3WmZY4pv5tUSieoQ6ejoCKpnzZuG7rDEP8lIq+7XMBe6sOH/VE5riB/mv1qthjpWFT7zoKGGtGzcckwFMduZeWVMaxK6sbbVdlIVPUOysMy8n+VinqwKnemzD7PcGqbEOlpiJC/bSEkr7cOEHrjKduY1SjgnSRIcYlpu7qLQ3RTMZ6VSQUtLS+pwUO0HLL+Wmfnh4cO53KGt4drf1fmjYWCU7Ncyan/XMxw4FthDR/Ue2wf4PfsMfxh6Ru/n38xnvQt1t9/Nj0az30sJdZTad3U++6slxNTWNStJpfnWHUSx7+fjWYuF2aq8FxuHQ/TrXKIZ+9xcwDLHdkfqXHqpwXkwbbTOJebLIeX2e/7QVPv3hoaGkMvlsHLlSgDAww8/jGq1irPOOitcs3HjRmzZsgUPPfRQphHn9lFieHh4QfPtcDgcjkNwI778sND2myRcjIxTUpyknA3JogeKxlSjSZKE74F0CBRVlltlusZEJ7EeyxeAVPpW7Q4gRRTGFLKxPNtFu1VWK1g3/N3e3j7NUaDkqOa9lhpZn0VSU0kK2z6aTy5+lEDXa2OLIlXXkjTmPZq2zZ8Stkq4qvLeKpf0b6vmtQ4Bph+LiW3bQUOq8Lm2b2u+bJlifaKWQlQdA4yLbvOnRK8NpROrU20jJVj0WeqYscRAkiSpfGh4lphCyiqu7aGzsbQ13zbPbHuS6SSbtc74P5+nRJyWl+S47Tu18qUOn1gIGyXD7bvJcnMMSJIk5M+2K5+jZasFt9/LD8th/b3QpKI6wtT+NQJ5Nx9YTqRsM0MdN8sJdi7G342mRtczlnS+Pl+OPbff84emIdFLpRKuvfZavPnNb0ZfXx8AYO/evcjn81i1alXq2nXr1mHv3r2Zad1www344Ac/uKD5dTgcDkccbsSXFxbDflvlrCU3SUxSkcmYwiTarDJFiTDddk1ijfFSgbSSTVW5NoY0yfuWlpZA4jN/qj7WxTbJNCJGgtsFgBKXvMeS4HoPJ+zMB4lgloFq6lqxo/XZSlZbp4QNEWPTUvJSQ24oUaptrUp0G0qDxDPDjijYnrxH6wpAUCiT5I2pCK3zgwQo65/X6E4A/a0OAn22vYZtSRJUY+FrG2aFqrHvhn42MTGROhiUdRxTV/M7/UwXerwnFrJFlc4aaod/x9qZ7U8o+a7Ka+aH7aTXan9mvnQXiaZv23Rqaioo1rVeVPkfCzWk76v2d6Zt+xqv4zX8m/2DBLd1XNn2BA69szrm6TU6fuiYo7sBZoLb7+WFZll/W7sJNFZ/i5F4jjjsLrEjDTF7vFiYDzVzs0Ftc+xspEaqC7ZNbO6YldfZ5N/t9/yhKUj0arWKN73pTZiamsItt9wy4/WxBYPiuuuuw9VXXx3+Hx4exqZNm+Ylrw6Hw+FwOA5isey3VWrogpq/29vbQygGPXxQw4lo2AMSbCSvGNpkamoKY2NjqFarUaIqSZIU8Uyo4r1QKIRn8X49FNMS4QAC6RlTzuhnvI7ksIa4Yd2o4pz3sE5IEuoBaxqjWslSJehV1aOEgW0HYPp2aSWplTzXgx+t4phtY0OfaDuqsyMWX5JEoip+eYBprW32JCr1Rw8Y1TrVvsF7bRl5vfYhJW/peNEya31bZbg6I7Qfq0PIEvXqwGlvb085jSxhbcMeaJ/SPm/byTo/6BhgO1lyXNXiGmZF+wHzwzAzmjadVna3CPsUf2t/1PeQxLy2rb5LWUpwLSf7tCXfdWeDOl60D5XL5RSBrv3XkobMC+ufh4gS1kmjDgYn9RwxNNP6OzZu6Xi31FDHca2dW8sdWePakYJY+y/mjoTlqERXu2sd541WDxy37HoEmL6bUh3zjVaO5YCGJ9Gr1SouvPBC7Nq1C//4j/8YvOAAsH79elQqFQwMDKS84f39/TjttNMy0+zo6EhNLB0Oh8OxeHBP+PLAUthv2z+4YNEwD0qcK4HF6/W3LnZUeUtyUmMuz9Q3LWkGHFIxMw9WuWtJRVWm27zGoCS3qsO1fmx9WeW9qm1j4W6sopafqfp2pnxaWJVzLI+xz+qpk6w+olBSNHadJTP1PpuWqtKtosiS3Fn5tcS/bYdYebU+skibWsrImdSAtRag9nm2z7Mu+P7wPbB9xuY1Vu+aT1v/WlexWPhWnU3CWwlxthXJaRL2TD/WV2wd2npjeaxjwF7HfGu8ea1fdRzyh8R/rG1sm/K62ZB6br+XB5pt/W2dazreWju6lFClaaPkqRERm/8cSVjKsh2pdVoP1LnWaFA7rmMXf8+Hs9vt9/yhoV2hNOA//elPcd9992HNmjWp77du3Yr29vbUASh79uzBD3/4w5pG3OFwOBxLBztRmO2Po/Gx2PabRJmdfLa1taGrqws9PT3o7OwMB3pShUm1KtWnusC1oRt4OGixWAyHbaqqRQl5jc1NRagefGmJbKvQ1R/ml/ngc0nqqVIVOEQmUj2rKmU+T5/DuPBst0qlgmKxiLGxsaDIZjgRrauYwlxV7FTj6zNZ7zEyXt9x61SwsM4RvR+YrvrTtFgnsbAx9hBVuxOAakf+cDeDdbYwbe1nmgdV+ReLxaDEZn3xHptf7ZtW2c80gUPx8zs7O0Pf1zazxK8llLVOta6yHBvqXOL9tk6UsG9vb0+FVOL/fE/0/eFOA+sM07q1h44yPA0P4yV5x8ODtS70Hra5hpYBDhHnPOOAP2w3tp1935hfLQfbf2pqKpyVwB0XTCemdNdr9ZBYbZ+JiQmUSiWMjo5ibGwMpVIJlUolOPy07TVcjO50mAluv498NNP6W+1nVh9rlH5nHfHA9LHS0Thq6ZitnY80VVCx1GVcjmjUOufcuVKpBDvPObheY8eR2ZTH7ff8YUmV6KOjo/jZz34W/t+1axceffRRrF69Ghs3bsQb3vAGPPLII/ja176GycnJEGdt9erVyOfzWLFiBd7xjnfgj/7oj7BmzRqsXr0af/zHf4znP//54bRwh8PhcDQW3BPe/Gg0+x3bts0FUKFQSBG3JNdJ1Cm5pfcqcc3JbblcDuRTLIYwF1sMpTI5ORmIew3rEFMxazgNEoUkA3XCbNXFlkBUQpD51/wpEcm8KvFr46Dzfq0nVbIqGaCqXSptLZFNpazdtq1tEKsfXm/rIItIJ+GsByrytz1kk2XVH96vRCj7jd1ma8G2YB9kWho2Rol0LtTVcaIHaTIfluTm96xzhvHJ5/OhfwMIDqBYfHimoWnzGtYL+7Mu2vRdixFDvN/2Xdaf9iHgUFijXO5QrHbWt6pLYztHNB58jKwn6WxBp4MN98K2UkeaHm7LPsywL1o+VYHrThU7xtgQPPZzVdXbMxrsbgRdVGsbM5yNgu+6VedrWKpacPvd/Gg0+304sCS6fg5MDx3WCLDOYqCx1PJLCXVALjVUwDBfJKK2uQ2L5nDEhDmKw+2Hbr/nD0tKon/ve9/DS1/60vA/46S97W1vw7Zt2/CVr3wFAPAbv/Ebqfvuv/9+nHnmmQCAv/iLv0BbWxsuvPBCFItFvOxlL8Ntt91W9ynzDofD4XA4ZodGs99WjWFVrEoQWUJOJ6mWbFVySslVYOawGjF1nBJjunhW4tyqrDUmeFY4Fksm8npLFtv7lPAjAae/7bU2dIZNs5aqzjoHYqE7am1ZjT1T1dF6bywebtbvWvVjSUvd8aDpW3I3K//2uTbtmEJfwfYiIazEaVZ5YkStOmbsd7VQq87sO5gVQojPj5VNCWfdDWJDMOk9zFdW6B+mp32BxLeGk4m9K9rf1YnB8mkfjjnHkiRJ5ZHp8BpL3Ot7y7a24xRDzKhTztY7cMgBon1L60DHMqbpWB5oNPs931gq5WSM8HLMDUeq8lVt1ZFaRsfhQ53dvmuhMbGkJPqZZ55Zs0PU01kKhQI+8YlP4BOf+MR8Zs3hcDgcCwT3hDc/Gs1+VyqVMMmk+rynpwetra1BlauLF36uBFNMYauqbIZySJIkFZ7DknzAdIJMlc76bA31wXAWNg2rAFbFq5LWzKMq5pXY5MGOvIdqYF5XLpdDWfkZ86rqfYaWYNlYp1TlWih5rmE0SPzqIZ21yDyGrbCLiomJiVA2OkusCp/lVMW9JXeVeNEdCww7oiF/VFHOerKHfarK3ir9tc7YP7ljIRaah3VH4pT9UEnXWMgVbZ98Pj9t8c7rZyJ/6ETKgn5v3x+N+a51y8+0/dVho0pvvieq+mb9qeNE3zmt0yxnTWxXiTq5bDsq6Q4cOsRTdxDwPdH24edU9dtQUDbEilVj5vP5FJGvhL7WmaajOxvy+Tza29tTIVzYjwCE7+pBo9rvgYEBvPvd7w4E8LnnnotPfOITWLlyZc38fPCDH8RnP/tZDAwM4JRTTsEnP/lJPO95zwvXlMtl/PEf/zG+/OUvB7L4lltuwbOe9axwzYc//GH83d/9HR599FHk83kMDg4uVDHnBY1mv+cKdTgDSI17i01Qqi1Wh6k7p2aPRpnn6xg+X3myzuZGKaujscB5ts7Z56MfNqr9bkZ4EC6Hw+FwLCrsxHS2Pw6HhcavVvKTxKcS3ABSBHRMia5QckvJLyXOrELWbjG3hCfzYPOiMdBnUn3HVPRKHih5q/G0VeGrICFtiV4bZiRLtaxEuw27ElN0Mw9K5M4UfzQ2BthFqbaZksp6rw39kTW+aH6VyLXPsOE2svJt01cHjJLGvEev0881hjb7pJaT12ndKsGrbWHza/uazXOtMdi2hRL7ttyxerL1kaVGB9JtE+szqkLXvqvkeCxkjM1fjKTX+rVqfh0PtN9bNT2v036k/UljtMdi+Nv31BKIPJeAjjHtfxwXNMb6bEj0RrTfb37zm/Hoo4/i61//Or7+9a/j0UcfxcUXX1zzno9+9KP42Mc+hptvvhnf/e53sX79erziFa/AyMhIuObKK6/E3XffjTvuuAMPPvggRkdH8ZrXvCblBKlUKnjjG9+Id73rXQtWPkc2rB1cqrmifbdr7Sqa6XtHY2Ah+pGvZxwzgfMLu7t0PtJtRPvdjFhSJbrD4XA4lh/cE+6Yb1DtSVWvxgK2E1Al0SzxTbUHJ6482EcPmowRZEpKWtKLz1aSWRXoejCokrwkwpQoYz75W4m3SqWCSqWSyr+qs/WZSrCqk4DKFyXb7UGgSiaqapp/x4hDdRDoO0xFPO/T/GieVMlvCQolhals1vZmOTRWtO5MUIU72491ZpXFmi/WhYbUoEpZyV5bphg5rvWjxCxj6jPPLS0tKXW9Enk2/ri+G9pvLMluEXsvWFYNLcTvbEgVq8bUOtb+o++jjQ3L90XzZD/X32wnW7fqJLFnByhBr+pyJewt6c12YDpKPluHC8tq0dHREb6rVCopZbr2J7tTwzqhNG4/323b1nqeg9azVe7WCgdk0Yj2+8c//jG+/vWv41/+5V9wyimnAAA+97nP4dRTT8VPfvIT/Nqv/Vo0LzfddBPe97734fWvfz0A4Atf+ALWrVuHv/7rv8Y73/lODA0N4dZbb8WXvvSlEOv79ttvx6ZNm3Dffffhla98JQDggx/8IADgtttuW5DyObLRSES0JZpq9XefyzY+1B7NBzhPcDQPGqHNYkKQw0lrKe49EuEkusPhcDgcjqYGic/u7u5UCBd+Z8k3VaRa9SlJPx78WCwWwwGQShLqQZNW1auhVTT0BK/r6OhIxWvXUCYkvVRNyvzbwzc13IyGY+H99jBMPRDTqlrpMGhtbUVHR0cguBlixpKzAFKHbGoYCQ0ZoyE5NKQHgBThyDSUMC+XyyiVSoHAZTtbta8ql4E0QUzins+25dfwGWwnEpncyaDOFeZP02CbxxTTSuoriW53HyiJroSqHrLZ1taWUigzfa07dZxomyjZFGtLhSVseY+WJ0Ys63VsB/5oiJ1Y/PHYAjFLZZ7lSLGkh5LSzKPuBNEDU9mmNlST1p3Wi77jmh6debF6aGlpQVdXVzjsVMPD2DZNkiQ4xaz6nk4hOr0KhUJKCc9nqlNM82PbVMuxGBgeHk7939HREZwLc8HOnTuxYsWKQKADwG//9m9jxYoVeOihh6Ik+q5du7B3716cddZZqXycccYZeOihh/DOd74TDz/8MKrVauqajRs3YsuWLXjooYcCie5YWjQCkT4XoquZ1J1W7b8cMJ/to3ZsudRfM8PuFFnKNptpvuZYGng4F4fD4XAsKnw7mWMhkBUOxV4TC9VgoWpuq9JU8jALSprG8mjV7HqP/liFbiz0hg3zkFXmWJ6tIl1V87GwFJoe82NJY16j99vD5rQ8mrZVx9u/NX+2fHbRk/W3RUzxHEvPEiSzHYtmS6zUuj6Wv1gaMcxmQVar3mz6sXrMytdc8mevjz3f7g6JfZeF2Pcz9S2r6rfhXayaXd8X64Cz74wdF1g3+pm++7F60z6r39tyzoX4Oxz7vWnTJqxYsSL83HDDDXU9Owt79+7F0UcfPe3zo48+Gnv37s28BwDWrVuX+nzdunXhu7179yKfz2PVqlWZ1ziWDrF+vJRY6ucvNBrBWeFwLCaOtD7v6+/5gyvRHQ6Hw7GoOBxD7EbcEQMVw1R3KxnK31Y5bkk/q/6l4pfKWSpMNS0qSYFDBw/yb0u+x8h9KnRV4UoFaYyQIwnHa6hGtYpqktaqqrVQkk7DPMRiRWt9Ml9UmKt6ulqthnagEp0qWQ3rwb9VgU/1eaVSCXGaNUQNCUElIW3ce1W2sy5UAa3EI+uJamRV4+q1VH/zHuYzSQ4p/fX5+mytH1UKM4SH9p+sONfMhx7cSUeC9gfWuYbtYFtYJ4n2Eyq4LQGrhHSWKl3vYzuoIp75UyW0KtHt+6J54CGcWn98X9SREiPPWSf2Wn2mjdduHWt8z7njQ51LSo4ztMrY2FjqoNi2trawo0N3mbA8VIlPTR081LdSqYR3S1XzVNLrb82z7t7QnQFaDms32QdjfX8mzIf9fvLJJ9HX1xc+z1Khb9u2LYRKycJ3v/tdAHGyw7Z9DDEidqZ76rnGsXCoxwm92DgS56Y65juJdnjg+O11WBu0qbE5yWLnQ228HVua9X3w9ff8wUl0h8PhcCwq3Ig75huWzLSkGYkpxkonOZal7CYhrMS0EmiWELdqbkuiW6LPkpEApoVzsapyndQzXeZR1doss43rDBwif7Ts9hBR/bFKWt6vIVgIkr1azra2NhQKBQAIYVlizgXWhY1BTzLdvvca3kRJe1W9q3NAlbvaTu3t7SH0ho2lTmg7WELb9rkYsabkrtapbX8Ny6Kksead6dn02SbsQxrCpL29PeUMiu2ssOnFPmfeNa9aXyTrSQbr9SwXCWmNT6/tr+S87bOaZ6ZFaIgfJYct4az9X9sztljX9uI7qZ/rd+wTbW1toc/yGnUssLx0Btk+MDExgXw+nwpdpKGD9PlaN5OTkyE9dZjZEAL6ftj2qVQq0+oghvmw3319fSkSPQuXX3453vSmN9W85tnPfjb+9V//FU8//fS075555plpSnNi/fr1AA6qzTds2BA+7+/vD/esX78elUoFAwMDKTV6f38/TjvttBnz71gY6Jiq77O+747Dh9p6HSe9jucGr7eZoWKSpSaqdb7NHx1jmrE9ff09f3AS3eFwOBwOR1NDyUyNMw4gRejZWMeEhkOxE2VFVpgYhQ2voAuBWiElrLrWEu1Wwc40bRgHqxxXstKqd+uBEhWqYLakryXjYweJxkK0ZNVFlgrJOiNiMcWtClfVupqO3qMke1Y+YoSzkvc2XI+mofnQXQ/aLqpW10NZrRo6lj/9PEupqe1g+0OsDWJ1H2sLBUkX7rCIpTnTe6DkNvuQ7W+aB1se+87ptaoot4Q0gEBIZzkatP/acpBsr1QqKSJAVYja1iTY6YCgE4TlVIeQzbdFrJ9q/Wke7TijOzgaCWvXrsXatWtnvO7UU0/F0NAQvvOd7+BFL3oRAODb3/42hoaGMsnu4447DuvXr8eOHTvwwhe+EMBBR8IDDzyAG2+8EQCwdetWtLe3Y8eOHbjwwgsBAHv27MEPf/hDfPSjH52PIjpmAb5vNkQS4ATPQiA251ioerbPWkp4n1o6NAJ5rnmJzav0b0uoN0K+HYuDxpsxORwOh+OIhnvCHfONnp4edHd3h9AheoglySWqji15qiSwVaFTwasKb4IkF0ktEmgk0TQ8A5AdPzxG+Ok1+kyrHrdK9KyY3qpKt99nEcVKUit5p4paLSdV5AxpUSgU0NbWFsrH71m/SZKE77OgBLQuXngwK0OIUI1OVTlDwGhdlUqloBZmP+E9wEHSt1QqpUhKquGpyLOKaCUgGcZD1b4abkfD7/BvbV+tZ+6YKBQKyOfzSJIkKPnZt7V+tO1Yp9rPta9b6E4NbX8AIcyOjd+tZVenBstRrVbR2to6Td3MPhoLO0Lo+0bFuirXtRx0inR0dIRyx8IaaT3p+6RhdaxS3C6eeZ++bxpuhnkplUrh+d3d3dPS4W+2T5IkQX3OQ2T5Hmkb6AGmdmxj/+JuG0IJf6veZ/knJyfR3t4ePU8hhka03yeeeCJe9apX4ZJLLsFnPvMZAMB/+2//Da95zWtSh4o+97nPxQ033IDzzz8fuVwOV155JT7ykY/ghBNOwAknnICPfOQj6Orqwpvf/GYAwIoVK/COd7wDf/RHf4Q1a9Zg9erV+OM//mM8//nPx8tf/vKQ7u7du3HgwAHs3r0bk5OTePTRRwEA/+k//Sf09PQsSJmPZKht0ndZQ4hpyAcnsGZGrE7rvU+dgQsFOydbKiLVChi8Ty0ubJ1nOeIXC3b3mubH9hPa6kbuM41ov5sVTqI7HA6HY1HhRtwx37BxqW1c9CRJphGAdlGpZBOQ7mtWQWphFSlKellltJKOdsGgE3ZLlKliVvMYI0ftRN8SnTPBkvEa4sXWgVVXA4cIDxtP1ar0Z1qUZ6mhSRbHQs8wf5o2n0WSUuuFvzU/JHc1pnaM8FXHiHU4qKJKw6jo39qnrMOD5DyJXo0lbutI02F/qyfcwUx90RL0er32C+ZbnxFTzcf6ZQxKjim5bd9NJds1fcbbr9XfbfgfW+YsZbYl7rQc7GMtLS2hzXQsYj1yPNLPmN9SqRQU6hqWJuZ4qLVbResnC1bVWw8a1X7/1V/9Fd797nfjrLPOAgCce+65uPnmm1PX/OQnP8HQ0FD4/z3veQ+KxSIuu+wyDAwM4JRTTsG9996L3t7ecM1f/MVfoK2tDRdeeCGKxSJe9rKX4bbbbkv1m//5P/8nvvCFL4T/qWy///77ceaZZy5EcY94WDWwjjuWcHXMjEZW7dv5ibVDS5EX/t1odbUcENuxttTtkDX3499qg5c6r7XQqPa7GeEkusPhcDgWFW7EHfONQqEQ1MmWELIkIie8VLiS/KXqmGphDf9hyWQgHQPchjShWpWqYuDgwYsdHR3T8meVvcxnDPY5fH4WIUeFtJLMzLeGONGDKC2pZsushDhV90o8ktwgkcm6UIVxe3t7+G0JXkveKlmpxDLV5zaUDwlKpkeHhlW38W8tv1UbWTJe88PrVKFt68nmX3cv2H5iD3JlXbJMXLCps8DG3bfOCd0JoHlQxwE/t6QxHQ50JKhDQZWg+pn2Aa1H1gOfyTZmXXBHAMupoVvYF1V1bcl07V+xMwDsYabqsNA+pf2PZWY6ViXJ8qlSlv/zvdLdA/bAW6veZ170kM0sx4k6SwibZ9sn7f2al5kcDopGtd+rV6/G7bffPqvn53I5bNu2Ddu2bcu8p1Ao4BOf+AQ+8YlPZF5z22234bbbbptNdh0RxNSedgwBkBoLNATbkYaY43u2ZY2NxbPBQivCVTRgY1AvphLcCgeWE7LKvJTvlDrt7ZxrMfqEzu/t3E7zod/buUkjrlcb1X43I+qbMTkcDofD0aS45ZZbcNxxx6FQKGDr1q34p3/6p5rXP/DAA9i6dSsKhQKOP/54fPrTn552zZ133omTTjoJHR0dOOmkk3D33XfP+rlJkmDbtm3YuHEjOjs7ceaZZ+JHP/rR4RV2maJQKKCzsxP5fD6EbFGCT4laq5zWAyzL5TIqlUr4XxcRWUQp7y+XyyiVShgfHw+hS4CD5HlXV1cg+hl6QheONv615pXfKUHKZ2apjkkMdnR0oFAopJwMJAuZTrlcDgchMjyKXdhycRtzFpRKJZTL5dT9zD/r0YacoUPBEp6q0rYqQzoE+KMhXEhS6i4E1hlJdJLlSqazHu2hkYQl0+0iQp0TMRJAr2ff0WdUq1UUi0WUy+Vpzgy7u0I/05Au2h4sU6lUita7JVQs4c6+yR9V1WtdWSJHy9Xe3o6Ojo7QLuoY0jyS8OZzOzs70dnZmXpPAARCWvu/9l++azZMjl3M6vda5vb29tCPrFNH+w3Lp/WgjgUNIUXnEscDhjHSH+1PDMOi7yvHNI5rtk5jBylrO5G8V+eS9kPbbvWS6A7HQoD9UPuz2jYgfd4I3yPt70ca+J5zjKL9rpfkVfs0F2JY7dhCEGh2vsLy2bnQQkPzMNe6ambQftn5RT31sJB1pXNaxWL0DRVg6Bk2sfeB9psh/dyeHsKRvP72FnY4HA7HosJOzGf7Mxts374dV155Jd73vvfh+9//Pn7nd34HZ599Nnbv3h29fteuXTjnnHPwO7/zO/j+97+P9773vXj3u9+NO++8M1yzc+dOXHTRRbj44ovxgx/8ABdffDEuvPBCfPvb357Vcz/60Y/iYx/7GG6++WZ897vfxfr16/GKV7wCIyMjs6xRhxK/RKy/xPqTknL6eybFl51QW9JXw4YouVtLKT8TNI8WuiDVbaZK3tq6sIpevSdWb/q3qqBJJNp77L2avoa5sO93TDGqTgwtk26rjaHWmMFyqCK7FqmodaZ1Gut7NuQHr7OoV71knST2x+YvVoeaFwsNlcD/tYw27Vi72jzGyhrLl5LQSvrMZiFaS7Won9u8W6Iuqx9nPcu+i3b8iDnuYotxrTtdiGt92DqJjSf23cwqw1yVl4tpvx3LC7F+WI8NP5Jhx9WlUkovRB7s/MSKARYTy1GBTth5xWzbYKHqrRHaox57Zuc9jZDvLPj6+yDmY/2dS45061MHhoeHsWLFiqXOhsPhcBwRGBoaQl9f37TPdayd6ySDJivrGRannHIKfvM3fxOf+tSnwmcnnngiXve61+GGG26Ydv0111yDr3zlK/jxj38cPrv00kvxgx/8ADt37gQAXHTRRRgeHsbf//3fh2te9apXYdWqVfjyl79c13OTJMHGjRtx5ZVX4pprrgEAlMtlrFu3DjfeeCPe+c53zqZali3Yp17zmteEECFAmphkX7NKYQBBiW3V3TyE0kIXGEpmkRSzKth8Po/u7u6gvm5vb0eSJNHwIkzXkl82LMlMoCqmtbUV7e3tKBQKIS2mQ7V8qVTC6OgokiRJhUbhoaB6GCvzMjk5ifHx8aB4LhaLSJIkKGTz+Tx6e3vR3t4+7WBLku1MU8PJDA8PBwX12NgYJicnUalUgsqdh2x2dXVh7dq14fDSzs5O5HK58DxVCQ8ODmJoaCh1wCfTUIV0kiRBDa6wBzryN8va29sb0mpvb0dLS0vq4FRtR/aNYrGIYrEY8jc2NobW1lYUCoXwu7OzE21tbejs7Ez1a+3HSZKgUqmklPbsx1RO9/X1oaOjI/U5d00o+Z7P59HZ2Znq09VqFePj4yiXy6nFIfsE24Rqcn7G961cLqO/vx9jY2Ph3QKQqrOurq5pcex5bZIkoc2UlOYzlJCmCoztoDs9crlcSjXO+/h+tLS0hDqZmpqatjPDOiqoZtc2jS0wC4UC+vr6UgpLS1LwfyrNOfZQNV8sFjE5OYlSqRQOLNX476w75pnjCNPkAbo2XIxuPeeOkWKxiH/+539uKPvtOLKhfSrmuFWoc5jfxxxGCwUlFRda9c7n6I4guwOnnjLrPCXLqV0L+nxC7613TpIFtSV0oqr95A6jhQbtmg3JYedoh4OYo3ax+m4WaB9po3T+OTk5iXK5XPPA6ZjTPEutPVuo/Y458BcTWk4VRthwdLSlukNmsfPaSPb7SF9/uxLd4XA4HIuOxfCCVyoVPPzww+GgMeKss87CQw89FL1n586d065/5Stfie9973thMp91DdOs57m7du3C3r17U9d0dHTgjDPOyMybIxtcXMcUoDaOMRfeSj7aUB+8zsLGYNV0dEFEAouhKQqFQlikWaWKDZFCgsyGwVCilGXWHxsr3BJ3LDcXqTZWuK1Pjbutz7VEp1VGc6GhCm8lLRmWQol05kfDhQDpbcYMZUEHgY2FzgWMVfraOrXOD9a9DZliCRuNT83n28UVr9NyWOW9DZdiQ/TYfsm6YN3p1n5tZ9uHFFyQKiFir7PtaMPj6PulMfVj92pfyHo3NQ9ciDKcjIYt0VBCfBb7RVZ7xtqe5WCoFV3gkkTRUALa1y0xoGOIhoixPxr2SNtX29nmg++J7Ys2zIF9B7QfxsYlbWtto7lsP18M++1YPtDdLvreZvWdmF1fjDzGdn4s5POsIni275Gtw7nUl9oDkoUUBNTaAVYPONYxLWsrF1PRG6tr/e5w07Y7i5Zasax1TVui84l6wrkogR6zRfOd36WCzvdj4aT4ftk6bVT4+nt+1t9+sCg8UL7D4XDMJxZjTB0eHk79rzF0iX379mFychLr1q1Lfb5u3Trs3bs3mu7evXuj109MTGDfvn3YsGFD5jVMs57n8nfsml/84heZ5Xakwb5WrVbR1tYWSD0lcGMLUBJLSiTrhJgT5KyJO4k/XZzycyWzNH2qaPm5VbMCabWzKrXtPVlEKaHEHz/n/ZZgswcyUrnLfNjn2HuVSGW9KPnL31NThw515fXWiWFDXljCVduJqnO72FPVtY1nqferOt4+i/mOkdJaZps+y24PpGS+2I6W8NY+pEpyppWl9qpVf/yeqnW7+NN+o/3MltMq0ZRQV8LZOohIFGuemC7rh7+13ukIib2PfK4q+divNK69VchZFScXZPbdivVp7Q+2XlQVrlAiW1Wk2v+1viqVSnjXGD9dY7jbcapWn+Tn+h6qk4fPURLS9lmHYzFgbUStvqf2W8fLxcJiO4Tss6xzYTbp6O+55IF/qxJ3PurBlk3HysV0vC208y8rnaUca2Nl1HlsPfmL9YX5qresNBvFPmWNR0udV19/L97620l0APv371/qLDgcDscRg5GRkWiIrHw+j/Xr12ca0HrR09ODTZs2pT77wAc+gG3btkWvjxEctVQNsevt5/WkOV/XOLJB+33PPfcscU4cDoej+bHQ9nv9+vXI5/OHlYbjyICuv2cif6yTc7FhHd4L/SwAwZE2H2nNBZOTkygWi4f1/Fool8sol8sLlv5swIPgFwqL1XdmAzpOK5UKxsfHZ32/CgAOJ6xPFhqxzhTM3+jo6BLn5BB8/T2/19SCk+gAVq9eDQDYvXv3ERMbfXh4GJs2bcKTTz55xMQd9DI1B7xMzYGFKFOSJBgZGcHGjRuj3xcKBezateuwJ6sxQ2e94ACwdu1atLa2Tps09Pf3T/NAE7FJRn9/P9ra2rBmzZqa1zDNep67fv16AAc94hs2bKgrb47pcPvdHPAyNQe8TM2BZrbfDHXlcLj9bg54mZoDXqbmQDPbb19/H4KT6Di0NXXFihVHzAtK9PX1eZmaAF6m5oCXaWbMtBBinOjFQD6fx9atW7Fjxw6cf/754fMdO3bgvPPOi95z6qmn4qtf/Wrqs3vvvRcnn3xyOODv1FNPxY4dO3DVVVelrjnttNPqfu5xxx2H9evXY8eOHXjhC18I4KAa5IEHHsCNN944D6VfHnD73VzwMjUHvEzNgSPZfjuOfLj9bi54mZoDXqbmwJFsv5fD+ttJdIfD4XAcsbj66qtx8cUX4+STT8app56Kz372s9i9ezcuvfRSAMB1112Hp556Cl/84hcBHDwJ/Oabb8bVV1+NSy65BDt37sStt94aTv0GgCuuuAKnn346brzxRpx33nn427/9W9x333148MEH635uLpfDlVdeiY985CM44YQTcMIJJ+AjH/kIurq68OY3v3kRa8jhcDgcDofD4XA4HI7Dx5G+/nYS3eFwOBxHLC666CLs378fH/rQh7Bnzx5s2bIF99xzDzZv3gwA2LNnD3bv3h2uP+6443DPPffgqquuwic/+Uls3LgRH//4x3HBBReEa0477TTccccdeP/734/rr78ez3nOc7B9+3accsopdT8XAN7znvegWCzisssuw8DAAE455RTce++96O3tXYSacTgcDofD4XA4HA6HY/5wxK+/E0dSKpWSD3zgA0mpVFrqrMwbvEzNAS9Tc8DL5HA0Jo7Efuxlag54mZoDXiaHozFxJPZjL1NzwMvUHPAyORoVuSQ5jGObHQ6Hw+FwOBwOh8PhcDgcDofD4TiC0bLUGXA4HA6Hw+FwOBwOh8PhcDgcDoejUeEkusPhcDgcDofD4XA4HA6Hw+FwOBwZcBLd4XA4HA6Hw+FwOBwOh8PhcDgcjgwsexL9lltuwXHHHYdCoYCtW7fin/7pn5Y6S3XjhhtuwG/91m+ht7cXRx99NF73utfhJz/5SeqaJEmwbds2bNy4EZ2dnTjzzDPxox/9aIlyPHvccMMNyOVyuPLKK8NnzVimp556Cr//+7+PNWvWoKurC7/xG7+Bhx9+OHzfbGWamJjA+9//fhx33HHo7OzE8ccfjw996EOYmpoK1zR6mb71rW/hta99LTZu3IhcLoe/+Zu/SX1fT/7L5TL+8A//EGvXrkV3dzfOPfdc/PKXv1zEUqRRq0zVahXXXHMNnv/856O7uxsbN27EW9/6VvzqV79KpdFoZXI4suD2u7Hh9rsx4fb7IBrN1rn9diwnuP1ubLj9bky4/T6IRrN1br+XIRb9KNMGwh133JG0t7cnn/vc55LHH388ueKKK5Lu7u7kF7/4xVJnrS688pWvTD7/+c8nP/zhD5NHH300efWrX50ce+yxyejoaLjmT//0T5Pe3t7kzjvvTB577LHkoosuSjZs2JAMDw8vYc7rw3e+853k2c9+dvKCF7wgueKKK8LnzVamAwcOJJs3b07e/va3J9/+9reTXbt2Jffdd1/ys5/9LFzTbGX6X//rfyVr1qxJvva1ryW7du1K/t//+39JT09PctNNN4VrGr1M99xzT/K+970vufPOOxMAyd133536vp78X3rppckxxxyT7NixI3nkkUeSl770pcmv//qvJxMTE4tcmoOoVabBwcHk5S9/ebJ9+/bk3/7t35KdO3cmp5xySrJ169ZUGo1WJocjBrffjQ23341bJrffB9Fots7tt2O5wO13Y8Ptd+OWye33QTSarXP7vfywrEn0F73oRcmll16a+uy5z31ucu211y5Rjg4P/f39CYDkgQceSJIkSaamppL169cnf/qnfxquKZVKyYoVK5JPf/rTS5XNujAyMpKccMIJyY4dO5IzzjgjGPFmLNM111yTvOQlL8n8vhnL9OpXvzr5r//1v6Y+e/3rX5/8/u//fpIkzVcma/Dqyf/g4GDS3t6e3HHHHeGap556KmlpaUm+/vWvL1resxCbmFh85zvfSQCEhUujl8nhINx+Ny7cfjd2mdx+N76tc/vtOJLh9rtx4fa7scvk9rvxbZ3b7+WBZRvOpVKp4OGHH8ZZZ52V+vyss87CQw89tES5OjwMDQ0BAFavXg0A2LVrF/bu3ZsqY0dHB84444yGL+N//+//Ha9+9avx8pe/PPV5M5bpK1/5Ck4++WS88Y1vxNFHH40XvvCF+NznPhe+b8YyveQlL8E//MM/4N///d8BAD/4wQ/w4IMP4pxzzgHQnGVS1JP/hx9+GNVqNXXNxo0bsWXLlqYoI3BwzMjlcli5ciWAI6NMjiMfbr8bu4xuvxu7TG6/jwxb5/bb0Yxw+93YZXT73dhlcvt9ZNg6t9/Nj7alzsBSYd++fZicnMS6detSn69btw579+5dolzNHUmS4Oqrr8ZLXvISbNmyBQBCOWJl/MUvfrHoeawXd9xxBx555BF897vfnfZdM5bpP/7jP/CpT30KV199Nd773vfiO9/5Dt797nejo6MDb33rW5uyTNdccw2Ghobw3Oc+F62trZicnMSHP/xh/N7v/R6A5mwnRT3537t3L/L5PFatWjXtmmYYQ0qlEq699lq8+c1vRl9fH4DmL5NjecDtd+OOoW6/Ef5v1DK5/W5+W+f229GscPvduGOo22+E/xu1TG6/m9/Wuf0+MrBsSXQil8ul/k+SZNpnzYDLL78c//qv/4oHH3xw2nfNVMYnn3wSV1xxBe69914UCoXM65qpTFNTUzj55JPxkY98BADwwhe+ED/60Y/wqU99Cm9961vDdc1Upu3bt+P222/HX//1X+N5z3seHn30UVx55ZXYuHEj3va2t4XrmqlMMcwl/81Qxmq1ije96U2YmprCLbfcMuP1zVAmx/JDs48vhNvvxi2T2+9DaOQyxeD2+yCaoUyO5YdmH18It9+NWya334fQyGWKwe33QTRDmZYrlm04l7Vr16K1tXWad6e/v3+a96vR8Yd/+If4yle+gvvvvx/Petazwufr168HgKYq48MPP4z+/n5s3boVbW1taGtrwwMPPICPf/zjaGtrC/lupjJt2LABJ510UuqzE088Ebt37wbQnO30P/7H/8C1116LN73pTXj+85+Piy++GFdddRVuuOEGAM1ZJkU9+V+/fj0qlQoGBgYyr2lEVKtVXHjhhdi1axd27NgRvOBA85bJsbzg9rsxy+j2+xAauUxuv5vX1rn9djQ73H43Zhndfh9CI5fJ7Xfz2jq330cWli2Jns/nsXXrVuzYsSP1+Y4dO3DaaactUa5mhyRJcPnll+Ouu+7CP/7jP+K4445LfX/cccdh/fr1qTJWKhU88MADDVvGl73sZXjsscfw6KOPhp+TTz4Zb3nLW/Doo4/i+OOPb7oyvfjFL8ZPfvKT1Gf//u//js2bNwNoznYaHx9HS0t6+GhtbcXU1BSA5iyTop78b926Fe3t7alr9uzZgx/+8IcNW0Ya8J/+9Ke47777sGbNmtT3zVgmx/KD2+/GLKPb74No9DK5/W5OW+f223EkwO13Y5bR7fdBNHqZ3H43p61z+30EYjFOL21U3HHHHUl7e3ty6623Jo8//nhy5ZVXJt3d3ckTTzyx1FmrC+9617uSFStWJN/85jeTPXv2hJ/x8fFwzZ/+6Z8mK1asSO66667kscceS37v934v2bBhQzI8PLyEOZ8d9HTwJGm+Mn3nO99J2trakg9/+MPJT3/60+Sv/uqvkq6uruT2228P1zRbmd72trclxxxzTPK1r30t2bVrV3LXXXcla9euTd7znveEaxq9TCMjI8n3v//95Pvf/34CIPnYxz6WfP/73w8nZdeT/0svvTR51rOeldx3333JI488kvzu7/5u8uu//uvJxMREw5WpWq0m5557bvKsZz0refTRR1NjRrlcbtgyORwxuP1uDrj9bjy4/T6IRrN1br8dywVuv5sDbr8bD26/D6LRbJ3b7+WHZU2iJ0mSfPKTn0w2b96c5PP55Dd/8zeTBx54YKmzVDcARH8+//nPh2umpqaSD3zgA8n69euTjo6O5PTTT08ee+yxpcv0HGCNeDOW6atf/WqyZcuWpKOjI3nuc5+bfPazn01932xlGh4eTq644ork2GOPTQqFQnL88ccn73vf+1LGoNHLdP/990ffn7e97W1JktSX/2KxmFx++eXJ6tWrk87OzuQ1r3lNsnv37iUozUHUKtOuXbsyx4z777+/YcvkcGTB7Xfjw+1348Ht90E0mq1z++1YTnD73fhw+914cPt9EI1m69x+Lz/kkiRJ5q5jdzgcDofD4XA4HA6Hw+FwOBwOh+PIxbKNie5wOBwOh8PhcDgcDofD4XA4HA7HTHAS3eFwOBwOh8PhcDgcDofD4XA4HI4MOInucDgcDofD4XA4HA6Hw+FwOBwORwacRHc4HA6Hw+FwOBwOh8PhcDgcDocjA06iOxwOh8PhcDgcDofD4XA4HA6Hw5EBJ9EdDofD4XA4HA6Hw+FwOBwOh8PhyICT6A6Hw+FwOBwOh8PhcDgcDofD4XBkwEl0h8PhcDgcDofD4XA4HA6Hw+FwODLgJLrD8f/jzDPPxJVXXtk06c43nnjiCeRyOTz66KNLnRWHw+FwOOqG22+33w6Hw+FoLrjtdtvtcDQj2pY6Aw7HkY677roL7e3ti/a8b37zm3jpS1+KgYEBrFy5ctGe63A4HA7HkQS33w6Hw+FwNBfcdjscjoWEk+gOxwKhWq2ivb0dq1evXuqsOBwOh8PhqBNuvx0Oh8PhaC647XY4HIsBD+ficAimpqbwnve8B6tXr8b69euxbdu28N3u3btx3nnnoaenB319fbjwwgvx9NNPh++3bduG3/iN38D/+T//B8cffzw6OjqQJElqS9k3v/lN5HK5aT9vf/vbQzqf+tSn8JznPAf5fB6/9mu/hi996UupPOZyOfzv//2/cf7556OrqwsnnHACvvKVrwA4uC3spS99KQBg1apVqbS//vWv4yUveQlWrlyJNWvW4DWveQ1+/vOfz6mePvShD2Hjxo3Yv39/+Ozcc8/F6aefjqmpqTml6XA4HA7HXOH2uz64/XY4HA5Ho8Btd31w2+1wNA6cRHc4BF/4whfQ3d2Nb3/72/joRz+KD33oQ9ixYweSJMHrXvc6HDhwAA888AB27NiBn//857joootS9//sZz/D//2//xd33nlnNL7Zaaedhj179oSff/zHf0ShUMDpp58OALj77rtxxRVX4I/+6I/wwx/+EO985zvxX/7Lf8H999+fSueDH/wgLrzwQvzrv/4rzjnnHLzlLW/BgQMHsGnTJtx5550AgJ/85CfYs2cP/vIv/xIAMDY2hquvvhrf/e538Q//8A9oaWnB+eefPyfD+773vQ/Pfvaz8Qd/8AcAgE9/+tP41re+hS996UtoafFhxeFwOByLC7ff9cHtt8PhcDgaBW6764PbboejgZA4HI4kSZLkjDPOSF7ykpekPvut3/qt5JprrknuvffepLW1Ndm9e3f47kc/+lECIPnOd76TJEmSfOADH0ja29uT/v7+aeleccUV0563b9++5DnPeU5y2WWXhc9OO+205JJLLkld98Y3vjE555xzwv8Akve///3h/9HR0SSXyyV///d/nyRJktx///0JgGRgYKBmefv7+xMAyWOPPZYkSZLs2rUrAZB8//vfr3kf8fOf/zzp7e1NrrnmmqSrqyu5/fbb67rP4XA4HI75hNtvt98Oh8PhaC647Xbb7XA0I9xt5XAIXvCCF6T+37BhA/r7+/HjH/8YmzZtwqZNm8J3J510ElauXIkf//jH4bPNmzfjqKOOmvE51WoVF1xwAY499tjgrQaAH//4x3jxi1+cuvbFL35x6hk2n93d3ejt7UV/f3/NZ/785z/Hm9/8Zhx//PHo6+vDcccdB+DgVrm54Pjjj8ef//mf48Ybb8RrX/tavOUtb5lTOg6Hw+FwHC7cftcPt98Oh8PhaAS47a4fbrsdjsaAHyzqcAjsSd65XA5TU1NIkgS5XG7a9fbz7u7uup7zrne9C7t378Z3v/tdtLWlX0P7nNizs/JZC6997WuxadMmfO5zn8PGjRsxNTWFLVu2oFKp1JXnGL71rW+htbUVTzzxBCYmJqaVxeFwOByOxYDb79nB7bfD4XA4lhpuu2cHt90Ox9LDlegORx046aSTsHv3bjz55JPhs8cffxxDQ0M48cQTZ5XWxz72MWzfvh1f+cpXsGbNmtR3J554Ih588MHUZw899NCsnpHP5wEAk5OT4bP9+/fjxz/+Md7//vfjZS97GU488UQMDAzMKt8W27dvx1133YVvfvObePLJJ/Enf/Inh5Wew+FwOBzzDbff0+H22+FwOByNDLfd0+G22+FoDLjryuGoAy9/+cvxghe8AG95y1tw0003YWJiApdddhnOOOMMnHzyyXWnc9999+E973kPPvnJT2Lt2rXYu3cvAKCzsxMrVqzA//gf/wMXXnghfvM3fxMve9nL8NWvfhV33XUX7rvvvrqfsXnzZuRyOXzta1/DOeecg87OTqxatQpr1qzBZz/7WWzYsAG7d+/GtddeO+t6IH75y1/iXe96F2688Ua85CUvwW233YZXv/rVOPvss/Hbv/3bc07X4XA4HI75hNvvNNx+OxwOh6PR4bY7DbfdDkfjwJXoDkcdyOVy+Ju/+RusWrUKp59+Ol7+8pfj+OOPx/bt22eVzoMPPojJyUlceuml2LBhQ/i54oorAACve93r8Jd/+Zf4sz/7Mzzvec/DZz7zGXz+85/HmWeeWfczjjnmGHzwgx/Etddei3Xr1uHyyy9HS0sL7rjjDjz88MPYsmULrrrqKvzZn/3ZrPJOJEmCt7/97XjRi16Eyy+/HADwile8Apdffjl+//d/H6Ojo3NK1+FwOByO+Ybb70Nw++1wOByOZoDb7kNw2+1wNBZySZIkS50Jh8PhcDgcDofD4XA4HA6Hw+FwOBoRrkR3OBwOh8PhcDgcDofD4XA4HA6HIwNOojscjmm49NJL0dPTE/259NJLlzp7DofD4XA4InD77XA4HA5Hc8Ftt8PRPPBwLg6HYxr6+/sxPDwc/a6vrw9HH330IufI4XA4HA7HTHD77XA4HA5Hc8Ftt8PRPHAS3eFwOBwOh8PhcDgcDofD4XA4HI4MeDgXh8PhcDgcDofD4XA4HA6Hw+FwODLgJLrD4XA4HA6Hw+FwOBwOh8PhcDgcGXAS3eFwOBwOh8PhcDgcDofD4XA4HI4MOInucDgcDofD4XA4HA6Hw+FwOBwORwacRHc4HA6Hw+FwOBwOh8PhcDgcDocjA06iOxwOh8PhcDgcDofD4XA4HA6Hw5EBJ9EdDofD4XA4HA6Hw+FwOBwOh8PhyICT6A6Hw+FwOBwOh8PhcDgcDofD4XBk4P8DKuChCw1jdoYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08d5b68d90>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class EarlyStopping(callbacks.Callback):\n",
+    "         def __call__(self, algorithm):\n",
+    "            if np.mean((algorithm.x.array-ground_truth.array)**2) <= 3e-8:  # arbitrary stopping criterion\n",
+    "                  raise StopIteration\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[callbacks.TextProgressCallback(), EarlyStopping()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculating data discrepancy at each iteration (A custom callback example) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08df8487f0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class DataDiscrepancyCallback(callbacks.Callback):\n",
+    "    def __init__(self, A, data):\n",
+    "        self.f = LeastSquares(A, data)\n",
+    "        self.save_values=[]\n",
+    "\n",
+    "    def __call__(self, algorithm):\n",
+    "        self.save_values.append(self.f(algorithm.get_output()))\n",
+    "\n",
+    "mycallback_FISTA_lower_bound= DataDiscrepancyCallback(A, absorption)\n",
+    "algo1=FISTA(initial=ig.allocate(0), f=F, g=alpha*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo1.run(500, callbacks=[mycallback_FISTA_lower_bound])\n",
+    "\n",
+    "        \n",
+    "mycallback_FISTA_no_lower_bound= DataDiscrepancyCallback(A, absorption)\n",
+    "algo2=FISTA(initial=ig.allocate(0), f=F, g=alpha*TotalVariation(), update_objective_interval=10)         \n",
+    "algo2.run(500, callbacks=[mycallback_FISTA_no_lower_bound])\n",
+    "\n",
+    "\n",
+    "show2D([ground_truth, algo1.get_output(), algo2.get_output()], title=['ground_truth', 'FISTA_lower_bound', 'FISTA_no_lower_bound'], num_cols=3)\n",
+    "show2D([absorption, A.direct(algo1.get_output())-absorption, A.direct(algo2.get_output())-absorption], title=['ground_truth', 'Data error FISTA_lower_bound', 'Data error FISTA_no_lower_bound'], fix_range=[[0,3], [-0.02, 0.02], [-0.02, 0.02]], cmap=['gray', 'seismic', 'seismic'], num_cols=3)\n",
+    "plt.plot(range(10,501), mycallback_FISTA_lower_bound.save_values[10:], label='FISTA TV with lower bound ')\n",
+    "plt.plot(range(10, 501), mycallback_FISTA_no_lower_bound.save_values[10:], label='FISTA TV without lower bound ')\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Data discrepancy $\\|Ax-y\\|_2^2$')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the without the lower bound, the reconstruction overfits to the noisy absorption data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculating a noise approximation for each iteration  (A custom callback example) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08dfcb7670>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import skimage\n",
+    "\n",
+    "class SigmaEstimateCallback(callbacks.Callback):\n",
+    "    def __init__(self):\n",
+    "\n",
+    "        self.save_values=[]\n",
+    "\n",
+    "    def __call__(self, algorithm):\n",
+    "        self.save_values.append(skimage.restoration.estimate_sigma(algorithm.get_output().as_array()))\n",
+    "\n",
+    "mycallback_FISTA_TV_alpha_01= SigmaEstimateCallback()\n",
+    "algo1=FISTA(initial=ig.allocate(0), f=F, g=0.1*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo1.run(500, callbacks=[mycallback_FISTA_TV_alpha_01])\n",
+    "\n",
+    "        \n",
+    "mycallback_FISTA_TV_alpha_1= SigmaEstimateCallback()\n",
+    "algo2=FISTA(initial=ig.allocate(0), f=F, g=1*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo2.run(500, callbacks=[mycallback_FISTA_TV_alpha_1])\n",
+    "\n",
+    "\n",
+    "show2D([ground_truth, algo1.get_output(), algo2.get_output()], title=['ground_truth', 'FISTA_TV_alpha_01', 'FISTA_TV_alpha_1'], num_cols=3)\n",
+    "show2D([absorption, A.direct(algo1.get_output())-absorption, A.direct(algo2.get_output())-absorption], title=['ground_truth', 'Data error FISTA_TV_alpha_01', 'Data error FISTA_TV_alpha_1'], fix_range=[[0,3], [-0.02, 0.02], [-0.02, 0.02]], cmap=['gray', 'seismic', 'seismic'], num_cols=3)\n",
+    "plt.plot(range(10,501), mycallback_FISTA_TV_alpha_01.save_values[10:], label='FISTA TV alpha=0.1 ')\n",
+    "plt.plot(range(10, 501), mycallback_FISTA_TV_alpha_1.save_values[10:], label='FISTA TV alpha=1.0 ')\n",
+    "plt.ylabel('Noise Estimate')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()\n",
+    "                                   \n",
+    "                            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see with a larger regularisation parameter, the resulting image is less noisy. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image metric callbacks (custom callback example) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 MSE                  MAE                 PSNR\n",
+      "         1.07888e-06          5.48145e-04          9.48530e+00\n",
+      "         5.85316e-07          6.22034e-04          1.21411e+01\n",
+      "         5.05844e-07          5.72563e-04          1.27749e+01\n",
+      "         4.31374e-07          5.19819e-04          1.34665e+01\n",
+      "         3.64704e-07          4.67054e-04          1.41956e+01\n",
+      "         3.06416e-07          4.16492e-04          1.49519e+01\n",
+      "         2.56388e-07          3.70092e-04          1.57261e+01\n",
+      "         2.14156e-07          3.28810e-04          1.65077e+01\n",
+      "         1.78987e-07          2.92725e-04          1.72868e+01\n",
+      "         1.50022e-07          2.60981e-04          1.80535e+01\n",
+      "         1.26383e-07          2.33361e-04          1.87981e+01\n",
+      "         1.07187e-07          2.09652e-04          1.95136e+01\n",
+      "         9.16141e-08          1.89309e-04          2.01954e+01\n",
+      "         7.89449e-08          1.72049e-04          2.08418e+01\n",
+      "         6.85910e-08          1.57283e-04          2.14524e+01\n",
+      "         6.00884e-08          1.44610e-04          2.20271e+01\n",
+      "         5.30737e-08          1.33746e-04          2.25662e+01\n",
+      "         4.72670e-08          1.24414e-04          2.30695e+01\n",
+      "         4.24393e-08          1.16364e-04          2.35374e+01\n",
+      "         3.84176e-08          1.09416e-04          2.39697e+01\n",
+      "         3.50543e-08          1.03451e-04          2.43676e+01\n",
+      "         3.22300e-08          9.82989e-05          2.47324e+01\n",
+      "         2.98493e-08          9.39156e-05          2.50657e+01\n",
+      "         2.78304e-08          9.01341e-05          2.53698e+01\n",
+      "         2.61075e-08          8.68679e-05          2.56474e+01\n",
+      "         2.46249e-08          8.40164e-05          2.59013e+01\n",
+      "         2.33423e-08          8.14809e-05          2.61336e+01\n",
+      "         2.22266e-08          7.92211e-05          2.63463e+01\n",
+      "         2.12462e-08          7.72332e-05          2.65422e+01\n",
+      "         2.03792e-08          7.54337e-05          2.67232e+01\n",
+      "         1.96080e-08          7.38151e-05          2.68907e+01\n",
+      "         1.89173e-08          7.23520e-05          2.70464e+01\n",
+      "         1.82934e-08          7.10307e-05          2.71921e+01\n",
+      "         1.77264e-08          6.98001e-05          2.73288e+01\n",
+      "         1.72101e-08          6.86725e-05          2.74572e+01\n",
+      "         1.67384e-08          6.76756e-05          2.75779e+01\n",
+      "         1.63068e-08          6.67997e-05          2.76913e+01\n",
+      "         1.59109e-08          6.59966e-05          2.77981e+01\n",
+      "         1.55498e-08          6.52429e-05          2.78978e+01\n",
+      "         1.52207e-08          6.45565e-05          2.79907e+01\n",
+      "         1.49199e-08          6.39012e-05          2.80774e+01\n",
+      "         1.46448e-08          6.32729e-05          2.81582e+01\n",
+      "         1.43935e-08          6.26837e-05          2.82334e+01\n",
+      "         1.41640e-08          6.21308e-05          2.83032e+01\n",
+      "         1.39533e-08          6.16084e-05          2.83683e+01\n",
+      "         1.37602e-08          6.11234e-05          2.84288e+01\n",
+      "         1.35827e-08          6.06739e-05          2.84852e+01\n",
+      "         1.34200e-08          6.02613e-05          2.85375e+01\n",
+      "         1.32710e-08          5.98831e-05          2.85860e+01\n",
+      "         1.31342e-08          5.95365e-05          2.86310e+01\n",
+      "         1.30086e-08          5.92132e-05          2.86727e+01\n",
+      "         1.28935e-08          5.89066e-05          2.87113e+01\n",
+      "         1.27882e-08          5.86154e-05          2.87469e+01\n",
+      "         1.26929e-08          5.83402e-05          2.87794e+01\n",
+      "         1.26069e-08          5.81077e-05          2.88090e+01\n",
+      "         1.25294e-08          5.79025e-05          2.88357e+01\n",
+      "         1.24593e-08          5.77139e-05          2.88601e+01\n",
+      "         1.23964e-08          5.75408e-05          2.88821e+01\n",
+      "         1.23400e-08          5.73717e-05          2.89019e+01\n",
+      "         1.22899e-08          5.72179e-05          2.89196e+01\n",
+      "         1.22457e-08          5.70800e-05          2.89352e+01\n",
+      "         1.22065e-08          5.69476e-05          2.89491e+01\n",
+      "         1.21716e-08          5.68219e-05          2.89616e+01\n",
+      "         1.21399e-08          5.67079e-05          2.89729e+01\n",
+      "         1.21121e-08          5.66082e-05          2.89828e+01\n",
+      "         1.20881e-08          5.65168e-05          2.89914e+01\n",
+      "         1.20672e-08          5.64386e-05          2.89990e+01\n",
+      "         1.20490e-08          5.63735e-05          2.90055e+01\n",
+      "         1.20338e-08          5.63197e-05          2.90110e+01\n",
+      "         1.20213e-08          5.62742e-05          2.90155e+01\n",
+      "         1.20117e-08          5.62335e-05          2.90190e+01\n",
+      "         1.20049e-08          5.61994e-05          2.90215e+01\n",
+      "         1.20006e-08          5.61720e-05          2.90230e+01\n",
+      "         1.19991e-08          5.61517e-05          2.90236e+01\n",
+      "         1.19998e-08          5.61385e-05          2.90233e+01\n",
+      "         1.20029e-08          5.61309e-05          2.90222e+01\n",
+      "         1.20088e-08          5.61240e-05          2.90201e+01\n",
+      "         1.20170e-08          5.61242e-05          2.90171e+01\n",
+      "         1.20275e-08          5.61325e-05          2.90133e+01\n",
+      "         1.20408e-08          5.61499e-05          2.90085e+01\n",
+      "         1.20565e-08          5.61750e-05          2.90028e+01\n",
+      "         1.20747e-08          5.62071e-05          2.89963e+01\n",
+      "         1.20954e-08          5.62405e-05          2.89888e+01\n",
+      "         1.21182e-08          5.62744e-05          2.89806e+01\n",
+      "         1.21432e-08          5.63137e-05          2.89717e+01\n",
+      "         1.21702e-08          5.63569e-05          2.89620e+01\n",
+      "         1.21990e-08          5.64026e-05          2.89518e+01\n",
+      "         1.22295e-08          5.64532e-05          2.89410e+01\n",
+      "         1.22611e-08          5.65052e-05          2.89297e+01\n",
+      "         1.22934e-08          5.65577e-05          2.89183e+01\n",
+      "         1.23272e-08          5.66137e-05          2.89064e+01\n",
+      "         1.23621e-08          5.66716e-05          2.88941e+01\n",
+      "         1.23983e-08          5.67352e-05          2.88814e+01\n",
+      "         1.24357e-08          5.68040e-05          2.88683e+01\n",
+      "         1.24743e-08          5.68758e-05          2.88549e+01\n",
+      "         1.25140e-08          5.69482e-05          2.88411e+01\n",
+      "         1.25548e-08          5.70229e-05          2.88269e+01\n",
+      "         1.25965e-08          5.71005e-05          2.88125e+01\n",
+      "         1.26388e-08          5.71802e-05          2.87980e+01\n",
+      "         1.26821e-08          5.72615e-05          2.87831e+01\n",
+      "         1.27264e-08          5.73452e-05          2.87680e+01\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "class MetricsDiagnostics(callbacks.Callback):\n",
+    "    \n",
+    "    def __init__(self, reference_image, metrics_dict, print_interval=1):\n",
+    "\n",
+    "        # reference image as numpy (level) array\n",
+    "        self.reference_image = reference_image      \n",
+    "        self.metrics_dict = metrics_dict\n",
+    "        # if data_range is None:\n",
+    "            # self.data_range = np.abs(self.reference_image.max() - self.reference_image.min())\n",
+    "        self.computed_metrics = []    \n",
+    "        self.print_interval=print_interval\n",
+    "\n",
+    "        super(MetricsDiagnostics, self).__init__()  \n",
+    "\n",
+    "    def __call__(self, algo):\n",
+    "\n",
+    "            \n",
+    "        for metric_name, metric_func in self.metrics_dict.items():\n",
+    "\n",
+    "            if not hasattr(algo, metric_name):\n",
+    "                setattr(algo, metric_name, [])   \n",
+    "                \n",
+    "            metric_list = getattr(algo, metric_name)\n",
+    "            metric_value = metric_func(self.reference_image, algo.get_output())\n",
+    "            metric_list.append(metric_value)\n",
+    "            \n",
+    "            self.computed_metrics.append(metric_value)\n",
+    "            \n",
+    "        if algo.iteration == 0:\n",
+    "                    \n",
+    "            print (self.callback_header())\n",
+    "        \n",
+    "        print(self.callback_iteration())  \n",
+    "                        \n",
+    "    \n",
+    "            \n",
+    "               \n",
+    "    def callback_header(self):\n",
+    "        return \" \".join(\"{:>20}\".format(metric_name) for metric_name in self.metrics_dict.keys())\n",
+    "\n",
+    "    def callback_iteration(self):\n",
+    "        if isinstance(self.computed_metrics, list):\n",
+    "            # Handle list of metrics\n",
+    "            return \" \".join(\"{:>20.5e}\".format(metric) for metric in self.computed_metrics[-len(self.metrics_dict):])\n",
+    "        else:\n",
+    "            # Handle single metric\n",
+    "            return \"{:>20.5e}\".format(self.computed_metrics)     \n",
+    "        \n",
+    "\n",
+    "from cil.utilities.quality_measures import mae, psnr, mse \n",
+    "metric_callback= MetricsDiagnostics(ground_truth, {'MSE':mse, 'MAE':mae, 'PSNR':psnr})\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(100, callbacks=[metric_callback])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More complex example, image metric callbacks with region of interests \n",
+    "\n",
+    "Warning - this is a complex example! But the code may be useful to adapt and reuse "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ImageQualityCallback(callbacks.Callback):\n",
+    "    \"\"\"\n",
+    "      Parameters\n",
+    "    ----------\n",
+    "\n",
+    "    reference_image: CIL or STIR ImageData\n",
+    "      containing the reference image used to calculate the metrics\n",
+    "\n",
+    "    roi_mask_dict : dictionary of ImageData objects\n",
+    "      list containing one binary ImageData object for every ROI to be\n",
+    "      evaluated. Voxels with values 1 are considered part of the ROI\n",
+    "      and voxels with value 0 are not.\n",
+    "      Dimension of the ROI mask images must be the same as the dimension of\n",
+    "      the reference image.\n",
+    "      \n",
+    "    metrics_dict : dictionary of lambda functions f(x,y) mapping\n",
+    "      two 1-dimensional numpy arrays x and y to a scalar value or a\n",
+    "      numpy.ndarray.\n",
+    "      x and y can be the voxel values of the whole images or the values of\n",
+    "      voxels in a ROI such that the metric can be computed on the whole\n",
+    "      images and optionally in the ROIs separately.\n",
+    "\n",
+    "      E.g. f(x,y) could be MSE(x,y), PSNR(x,y), MAE(x,y)\n",
+    "\n",
+    "    statistics_dict : dictionary of lambda functions f(x) mapping a \n",
+    "      1-dimensional numpy array x to a scalar value or a numpy.ndarray.\n",
+    "      E.g. mean(x), std_deviation(x) that calculate global and / or\n",
+    "      ROI mean and standard deviations.\n",
+    "\n",
+    "      E.g. f(x) could be x.mean()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "      \"\"\"\n",
+    "      \n",
+    "    def __init__(self, reference_image, \n",
+    "                       roi_mask_dict   = None,\n",
+    "                       metrics_dict    = None,\n",
+    "                       statistics_dict = None,\n",
+    "                       ):\n",
+    "\n",
+    "        # the reference image\n",
+    "        self.reference_image = reference_image\n",
+    "\n",
+    "\n",
+    "        self.roi_indices_dict = {}\n",
+    "        self.roi_store=[]\n",
+    "\n",
+    "\n",
+    "\n",
+    "        self.roi_mask_dict=roi_mask_dict\n",
+    "        \n",
+    "        \n",
+    "        self.metrics_dict = metrics_dict\n",
+    "        self.metrics_store={}\n",
+    "        for key, value in self.metrics_dict.items():\n",
+    "                self.metrics_store['global_'+key] = []\n",
+    "                if roi_mask_dict is not None:\n",
+    "                  for roi_name, value in roi_mask_dict.items():\n",
+    "                    self.metrics_store[roi_name+'_'+key] = []\n",
+    "\n",
+    "        self.statistics_dict = statistics_dict\n",
+    "        self.stat_store={}\n",
+    "        for key, value in self.statistics_dict.items():\n",
+    "                self.stat_store['global_'+key] = []\n",
+    "                if roi_mask_dict is not None:\n",
+    "                  for roi_name, value in roi_mask_dict.items():\n",
+    "                    self.stat_store[roi_name+'_'+key] = []\n",
+    "                \n",
+    "    def __call__(self, algorithm):\n",
+    "            if self.metrics_dict is not None:\n",
+    "                for metric_name, metric in self.metrics_dict.items():\n",
+    "                    ans = metric(self.reference_image, algorithm.x)\n",
+    "                    self.metrics_store['global_'+metric_name].append(ans)\n",
+    "                    \n",
+    "                    \n",
+    "                    for roi_name, roi in self.roi_mask_dict.items():\n",
+    "                      ans = metric(self.reference_image, algorithm.x, mask=roi)\n",
+    "                      self.metrics_store[roi_name+'_'+metric_name].append(ans)\n",
+    "                       \n",
+    "                    \n",
+    "                    \n",
+    "            if self.statistics_dict is not None:\n",
+    "                for statistic_name, stat in self.statistics_dict.items():\n",
+    "                    ans = stat( algorithm.x.array, np._NoValue)\n",
+    "                    self.stat_store['global_'+statistic_name].append(ans)\n",
+    "                    \n",
+    "                    \n",
+    "                    for roi_name, roi in self.roi_mask_dict.items():\n",
+    "                        ans = stat( algorithm.x.array, roi.array.astype('bool'))\n",
+    "                        self.stat_store[roi_name+'_'+statistic_name].append(ans)\n",
+    "                        \n",
+    "        \n",
+    "                    \n",
+    "                    \n",
+    "       \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mse(dc1, dc2, mask=None):\n",
+    "    ''' Calculates the mean squared error of two images\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dc1: `DataContainer`\n",
+    "        One image to be compared\n",
+    "    dc2: `DataContainer`\n",
+    "        Second image to be compared\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number, the mean squared error of the two images\n",
+    "    '''\n",
+    "    dc1 = dc1.as_array()\n",
+    "    dc2 = dc2.as_array()\n",
+    "\n",
+    "    if mask is not None:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "\n",
+    "        mask = mask.astype('bool')\n",
+    "        dc1 = np.extract(mask, dc1)\n",
+    "        dc2 = np.extract(mask, dc2)\n",
+    "    return np.mean(((dc1 - dc2)**2))\n",
+    "\n",
+    "\n",
+    "def mae(dc1, dc2, mask=None):\n",
+    "    ''' Calculates the Mean Absolute error of two images.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dc1: `DataContainer`\n",
+    "        One image to be compared\n",
+    "    dc2: `DataContainer`\n",
+    "        Second image to be compared\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero.\n",
+    "\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number with the mean absolute error between the two images.\n",
+    "    '''\n",
+    "    dc1 = dc1.as_array()\n",
+    "    dc2 = dc2.as_array()\n",
+    "\n",
+    "    if mask is not None:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "\n",
+    "        mask = mask.astype('bool')\n",
+    "        dc1 = np.extract(mask, dc1)\n",
+    "        dc2 = np.extract(mask, dc2)\n",
+    "\n",
+    "    return np.mean(np.abs((dc1-dc2)))\n",
+    "\n",
+    "\n",
+    "def psnr(ground_truth, corrupted, mask=None):\n",
+    "    ''' Calculates the Peak signal to noise ratio (PSNR) between the two images.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ground_truth: `DataContainer`\n",
+    "        The reference image\n",
+    "    corrupted: `DataContainer`\n",
+    "        The image to be evaluated\n",
+    "    data_range: scalar value, default=None\n",
+    "        PSNR scaling factor, the dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values). We take the maximum value in the ground truth array.\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero..\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number, the peak signal to noise ration between the two images.\n",
+    "    '''\n",
+    "    \n",
+    "\n",
+    "    if mask is None:\n",
+    "        data_range = ground_truth.as_array().max()\n",
+    "\n",
+    "\n",
+    "    else:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "        data_range = np.max(ground_truth.as_array(),\n",
+    "                                where=mask.astype('bool'), initial=-1e-8)\n",
+    "\n",
+    "    \n",
+    "    tmp_mse = mse(ground_truth, corrupted, mask=mask)\n",
+    "\n",
+    "    return 10 * np.log10((data_range ** 2) / tmp_mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAAAAAAAABMBGOgAAAAAAAAAAAbCRDgAAAAAAAABAAGykAwAAAAAAAAAQwP8BErYreQJZftMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08dcf79120>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#%% create masks\n",
+    "top = ig.allocate(0)\n",
+    "bottom = ig.allocate(0)\n",
+    "\n",
+    "top.fill(\n",
+    "    np.asarray(ground_truth.array > 0.8 * ground_truth.max(), \n",
+    "               dtype=np.float32)\n",
+    "    )\n",
+    "bottom.fill(\n",
+    "    np.asarray(np.invert(ground_truth.array < 0.4 * ground_truth.max()), \n",
+    "               dtype=np.float32)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "roi_image_dict = {\n",
+    "    'top' : top,\n",
+    "    'bottom' : bottom\n",
+    "}\n",
+    "\n",
+    "show2D([ground_truth, top, bottom], num_cols=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_qual_callback = ImageQualityCallback(ground_truth,\n",
+    "                                              roi_mask_dict = roi_image_dict,\n",
+    "                                              metrics_dict = {'MSE':mse, \n",
+    "                                                              'MAE':mae, \n",
+    "                                                              'PSNR':psnr},\n",
+    "                                              statistics_dict = {'MEAN': (lambda x, y: np.mean(x, where=y)),\n",
+    "                                                                 'STDDEV': (lambda x, y: np.std(x, where=y)),\n",
+    "                                                                 'MAX': (lambda x, y: np.max(x, where=y, initial=0))},\n",
+    "                                              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08dcda0af0>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[img_qual_callback])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f08dec65b10>]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(501), img_qual_callback.metrics_store['global_MSE'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08dec67bb0>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(501), img_qual_callback.metrics_store['top_PSNR'], label='Top')\n",
+    "plt.plot(range(501), img_qual_callback.metrics_store['global_PSNR'], label='Global')\n",
+    "plt.plot(range(501), img_qual_callback.metrics_store['bottom_PSNR'], label='Bottom')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cil_testing2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/v24.2.0/_sources/demos/deriv2_cgls.ipynb.txt b/v24.2.0/_sources/demos/deriv2_cgls.ipynb.txt
new file mode 100644
index 0000000000..3cce6a2e50
--- /dev/null
+++ b/v24.2.0/_sources/demos/deriv2_cgls.ipynb.txt
@@ -0,0 +1,645 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "602dbdfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2023 United Kingdom Research and Innovation\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:  Bill Lionheart (University of Manchester),\n",
+    "#   Edited by:  Margaret Duff (STFC - UKRI)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8acbea",
+   "metadata": {},
+   "source": [
+    "# 1D inverse problem demo using deriv2 from regtools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc422b7",
+   "metadata": {},
+   "source": [
+    "We roughly translated deriv2 (P. C. Hansen, Regularization Tools Version 4.0 for Matlab 7.3, Numerical Algorithms, 46 (2007), pp. 189-194.) to Python. The righthand side vector b is made as Ax so is \"exact\" as a vector. We will look at the singular valued decomposition (SVD) and regularized solution as an example of a mildly ill posed problem and show how to recostruct using the Core Imaging Library (CIL. See Jørgensen, Jakob S., et al. \"Core Imaging Library-Part I: a versatile Python framework for tomographic imaging.\" Philosophical Transactions of the Royal Society A 379.2204 (2021): 20200192. and https://tomographicimaging.github.io/CIL/nightly/index.html). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f0222cb",
+   "metadata": {},
+   "source": [
+    "This notebook was developed as part of the CCPi CIL Hackathon https://ccpi.ac.uk/events/byod-cil-hackathon/ in March 2023 in Cambridge as part of the Rich Nonlinear Tomography programme at the Isaac Newton Institute for Mathematical Sciences https://www.newton.ac.uk/event/rnt/.  The CIL is supported by the CCPi EPSRC grant EP/T026677/1 and the Isaac NewtonInstitute by EP/R014604/1 The author would like to thank the Isaac Newton Institute for support  and hospitality.(c) W.R.B. Lionheart 2023. Apache License"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b2a54694",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from cil.optimisation.algorithms import CGLS\n",
+    "from cil.optimisation.operators import MatrixOperator\n",
+    "from cil.framework import VectorData, BlockDataContainer\n",
+    "from deriv2 import deriv2\n",
+    "from cil.optimisation.operators import BlockOperator,IdentityOperator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d33631d9",
+   "metadata": {},
+   "source": [
+    "### CIL version 23.0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "751f22b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cil\n",
+    "print(cil.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "250103e8",
+   "metadata": {},
+   "source": [
+    "We set up a 1D inverse problem, in which the forward model integrates twice.  The notebook will first set up and solve a basic formulation of the problem using just python/numpy, and after that using CIL.\n",
+    "\n",
+    "Consider a discretization of a first kind Fredholm integral equation whose kernel K is the  Green's function for the second derivative:\n",
+    " $$\n",
+    "    K(s,t) = \\begin{cases} s(t-1)  ,  s <  t \\\\\n",
+    "              t(s-1)  ,  s \\geq t \\end{cases} $$\n",
+    "\n",
+    "and $$ \\int_0^1K(s,t)x(t)dt=b(s). $$\n",
+    "\n",
+    " For this notebook, consider the case\n",
+    "$$ b(s) = (s^3 - s)/6          , \\ \\  x(t) = t$$\n",
+    "\n",
+    " where the integral is disretised using the Galerkin method with orthonormal box functions, with interval size $1/n$.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "27770924",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Test of one dimensional inverse problems using deriv2 from reg tools\n",
+    "n = 100\n",
+    "A,b,x = deriv2(n,1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "113e7dc1",
+   "metadata": {},
+   "source": [
+    "The functions $b$ and $x$ can be plotted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "336dc870",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),x, label='x')\n",
+    "plt.plot(np.linspace(0,1,n),b, label='b')\n",
+    "plt.plot()\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84dfaf27",
+   "metadata": {},
+   "source": [
+    "This has made a A, x, and b where Ax=b. We can check check this is the case up to floating point error: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6d854bd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(A@x-b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554b487c",
+   "metadata": {},
+   "source": [
+    "Now make a version of b with noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "35d30bb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bn= b + 0.001*np.random.randn(n)\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),b, label='Noise free b')\n",
+    "plt.plot(np.linspace(0,1,n),bn, label='Noisy b')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd88fee8",
+   "metadata": {},
+   "source": [
+    "$A$, $x$ and $b$ are stored as Numpy arrays. Just as in Matlab we can look at the singular value decomposition (SVD).   On a log scale we see the singular values decay as a negative power as expected for an operator that approximates the inverse of two derivatives.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "761ac845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
+    "plt.figure()\n",
+    "plt.plot(s)\n",
+    "plt.title('The singular values of the forward operator A')\n",
+    "plt.show()\n",
+    "plt.figure()\n",
+    "plt.loglog(s)\n",
+    "plt.title('A log-log plot of the singular values of the forward operator A')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad79f585",
+   "metadata": {},
+   "source": [
+    "Solving the least squares problem $\\min_x\\|Ax-b\\|_2^2$ demonstrates the ill-posedness of the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cab55e6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xlq=np.linalg.lstsq(A,bn,rcond=None)[0]\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),xlq, label='Least squares solution')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.figure()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44ed725c",
+   "metadata": {},
+   "source": [
+    "We see the solution matches the observed data well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "28589f81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.213659415696782e-15"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(A@xlq-bn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c64567",
+   "metadata": {},
+   "source": [
+    "However this is not a desired solution to our inverse problem. Now, instead, solve using Tikonov regularization: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e14a076f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg_param= 0.005\n",
+    "\n",
+    "Atik=np.vstack((A,reg_param*np.eye(n))) \n",
+    "\n",
+    "btik = np.hstack((bn,np.zeros(n)))\n",
+    "\n",
+    "xtik =  np.linalg.lstsq(Atik,btik,rcond=None)[0]\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),xtik, label='Tikhonov regularised solution- numpy')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.figure()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e35764d",
+   "metadata": {},
+   "source": [
+    "This solution is an improvement, although we still have this odd behaviour at the end of the interval. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c93e53f",
+   "metadata": {},
+   "source": [
+    "Now convert the matrix and operators to CIL types so we can use inbuilt optimisation routines and regularisers in CIL: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7ab3314c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Aop = MatrixOperator(A)\n",
+    "bop= VectorData(bn) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a523f4b",
+   "metadata": {},
+   "source": [
+    "We run CGLS to solve the un-regularised least squares problem $$\\min_x \\|Ax-b\\|_2^2.$$ We choose a small number of iterations to implicitly regularise via early stopping: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a8bd2985",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Iter   Max Iter     Time/Iter            Objective\n",
+      "                               [s]                     \n",
+      "        0          4         0.000          2.16038e-01\n",
+      "-------------------------------------------------------\n",
+      "        4          4         0.081                     \n",
+      "Stop criterion has been reached.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#bop = VectorData(np.reshape(b,[-1]))\n",
+    "op = VectorData(bn)\n",
+    "cgls = CGLS(operator=Aop, data=bop, max_iteration=4, update_objective_interval=10)\n",
+    "cgls.run()\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,100),cgls.solution.as_array(), label='Least squares solution with early stopping')\n",
+    "plt.plot(np.linspace(0,1,100),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90dcba3f",
+   "metadata": {},
+   "source": [
+    "We can also do Tikhonov regularisation using the CIL framework. For example, in the block framework below: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "15ff9d79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Iter   Max Iter     Time/Iter            Objective\n",
+      "                               [s]                     \n",
+      "        0       1000         0.000          2.16038e-01\n",
+      "        1       1000         0.130          3.73899e-03\n",
+      "        2       1000         0.108          9.73406e-04\n",
+      "        3       1000         0.109          8.16896e-04\n",
+      "        4       1000         0.111          8.07526e-04\n",
+      "        5       1000         0.106          8.07019e-04\n",
+      "        6       1000         0.105          8.07002e-04\n",
+      "        7       1000         0.106          8.07002e-04\n",
+      "        8       1000         0.110          8.07002e-04\n",
+      "Tolerance is reached: 1e-06\n",
+      "-------------------------------------------------------\n",
+      "        8       1000         0.110          8.07002e-04\n",
+      "Stop criterion has been reached.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ig = Aop.domain_geometry()\n",
+    "L = IdentityOperator(ig)\n",
+    "operator_block = BlockOperator(Aop, reg_param*L)\n",
+    "\n",
+    "zero_data = L.range.allocate(0)\n",
+    "data_block = BlockDataContainer(bop, zero_data)\n",
+    "\n",
+    "cglsb = CGLS(operator=operator_block, data=data_block, max_iteration=1000, update_objctive_interval=10)\n",
+    "cglsb.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "74107db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n), cglsb.solution.as_array(), label='Tikhonov regularised solution- CIL')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fa3e6b0",
+   "metadata": {},
+   "source": [
+    "We can compare the results of Tikhonov regularisation implemented by numpy and CIL: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7c6e22c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),  cglsb.solution.as_array(),'o', label='Tikhonov regularised solution- CIL')\n",
+    "plt.plot(np.linspace(0,1,n),xtik, '+', label='Tikhonov regularised solution- numpy')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc199d9",
+   "metadata": {},
+   "source": [
+    "You can see that the solutions are identical!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c27a9218",
+   "metadata": {},
+   "source": [
+    "We think the reconstruction error near the boundary at 1 is caused by the discretisation of the integral - for future investigation "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/v24.2.0/_sources/developer_guide.rst.txt b/v24.2.0/_sources/developer_guide.rst.txt
new file mode 100644
index 0000000000..eb434d86b9
--- /dev/null
+++ b/v24.2.0/_sources/developer_guide.rst.txt
@@ -0,0 +1,153 @@
+.. Copyright 2020 United Kingdom Research and Innovation
+   Copyright 2020 The University of Manchester
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+       http://www.apache.org/licenses/LICENSE-2.0
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+   Authors:
+   CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+   Kyle Pidgeon (UKRI-STFC)
+
+Developers' Guide
+*****************
+
+CIL is an Object Orientated software. It has evolved during the years and it currently does not fully adheres to the following conventions. New additions must comply with
+the following.
+
+Conventions on new CIL objects
+==============================
+
+For each class there are **essential**, and **non-essential** parameters. The non-essential can be further be divided in **often configured** and **advanced** parameters:
+
+* essential
+* non-essential
+
+  * often-configured
+  * advanced
+
+The definition of what are the essential, often-configured and advanced parameters depends on the actual class.
+
+Creator
+-------
+
+To create an instance of a class, the creator of a class should require the **essential** and **often-configured** parameters as named parameters.
+
+It should not accept positional arguments `*args` or key-worded arguments `**kwargs` so that the user can clearly understand what parameters are necessary to
+create the instance.
+
+Setter methods and properties
+-----------------------------
+
+Use of `property` is favoured instead of class members to store parameters so that the parameters can be protected.
+
+The class should provide setter methods to change all the parameters at any time. Setter methods to set multiple parameters at the same time is also accepted.
+Setter methods should be named `set_<parameter>`. The use of `set_` helps IDEs and user to find what they should change in an instance of a class.
+
+
+Other methods
+-------------
+
+Methods that are not meant to be used by the user should have a `_` (underscore) at the beginning of the name.
+All methods should follow the convention of small caps underscore separated words.
+
+Documentation
+=============
+
+Docstrings
+----------
+
+The Core Imaging Library (CIL) follows the `NumpyDoc <https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard>`_
+style with the `PyData Sphinx HTML theme <https://pydata-sphinx-theme.readthedocs.io/en/latest/>`_.
+When contributing your code please refer to `this <https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard>`_ link
+for docstring formatting and this rendered `example <https://numpydoc.readthedocs.io/en/latest/example.html#example>`_.
+
+Example from ``cil``
+^^^^^^^^^^^^^^^^^^^^
+
+The following provides an example of the docstring format used within ``cil``, and the rendered documentation generated from it.
+
+Source
+""""""
+
+.. literalinclude:: ../../Wrappers/Python/cil/recon/FBP.py
+   :caption: `FBP.run method from cil.io.recon.FBP`
+   :language: python
+   :pyobject: FBP.run
+
+Rendered
+""""""""
+
+.. automethod:: cil.recon.FBP.FBP.run
+
+
+Building documentation locally
+------------------------------
+
+The easiest way to test documentation changes is to open a pull request and `download the rendered documentation from the CI <https://github.com/TomographicImaging/CIL/blob/master/.github/workflows/README.md>`_.
+
+Alternatively, to build the docs locally, you will need a working ``cil`` installation.
+For development of the documentation embedded within the source code itself (e.g. docstrings), you should build ``cil`` from source.
+
+The following steps can be used to create an environment that is suitable for building ``cil`` and its documentation, and to start
+a HTTP server to view the documentation.
+
+#. Clone ``cil`` repo
+#. Update conda with ``conda update -n base -c defaults conda``
+#. Follow the instructions `here <https://github.com/TomographicImaging/CIL/tree/master#building-cil-from-source-code>`_ to create a conda environment and build ``cil`` from source
+#. Go to ``docs`` folder
+#. Install packages from ``docs_environment.yml``
+#. [Install Ruby version 3.2](https://www.ruby-lang.org/en/documentation/installation/#installers)
+#. Install the web dependencies with ``make web-deps``
+#. Build the documentation with ``make dirhtml web``
+#. Start an HTTP server with ``make serve`` to access the docs via `localhost:8000 <http://localhost:8000>`_.
+
+Example:
+::
+
+  git clone --recurse-submodule git@github.com:TomographicImaging/CIL
+  cd CIL
+  sh scripts/create_local_env_for_cil_development_tests.sh -n NUMPY_VERSION -p PYTHON_VERSION -e ENVIRONMENT_NAME
+  conda activate ENVIRONMENT_NAME
+  cmake -S . -B ./build -DCMAKE_INSTALL_PREFIX=${CONDA_PREFIX}
+  cmake --build ./build --target install
+  cd docs
+  conda update -n base -c defaults conda
+  conda env update -f docs_environment.yml # with the name field set to ENVIRONMENT_NAME
+  make web-deps  # install dependencies (requires ruby 3.2)
+  make dirhtml web serve
+
+Notebooks gallery
+-----------------
+
+The ``mkdemos.py`` script (called by ``make dirhtml``):
+
+- downloads notebooks from external URLs to ``source/demos/*.ipynb``
+- uses the ``demos-template.rst`` file to generate the gallery in ``source/demos.rst``
+
+The ``nbsphinx`` extension will convert the ``*.ipynb`` files to HTML.
+
+Contributions guidelines
+========================
+
+Make sure that each contributed file contains the following text enclosed in the appropriate comment syntax for the file format. Please replace `[yyyy]` and `[name of copyright owner]` with your own identifying information. Optionally you may add author name and email.
+
+::
+
+  Copyright [yyyy] United Kingdom Research and Innovation
+  Copyright [yyyy] The University of Manchester
+  Copyright [yyyy] [name of copyright owner]
+  Author(s): [Author name, Author email (optional)]
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+      http://www.apache.org/licenses/LICENSE-2.0
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
diff --git a/v24.2.0/_sources/framework.rst.txt b/v24.2.0/_sources/framework.rst.txt
new file mode 100644
index 0000000000..5d20634b9e
--- /dev/null
+++ b/v24.2.0/_sources/framework.rst.txt
@@ -0,0 +1,268 @@
+..    Copyright 2019 United Kingdom Research and Innovation
+      Copyright 2019 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+Framework
+*********
+
+
+AcquisitionGeometry
+===================
+
+The :code:`AcquisitionGeometry` class holds the system acquisition parameters.
+
+.. autoclass:: cil.framework.AcquisitionGeometry
+
+We create the appropriate :code:`AcquisitionGeometry` for our data by using the static methods:
+
+Parallel2D Geometry
+-------------------
+.. automethod:: cil.framework.AcquisitionGeometry.create_Parallel2D
+
+Parallel3D Geometry
+-------------------
+.. automethod:: cil.framework.AcquisitionGeometry.create_Parallel3D
+
+Cone2D Geometry (Fanbeam)
+-------------------------
+.. automethod:: cil.framework.AcquisitionGeometry.create_Cone2D
+
+Cone3D Geometry
+---------------
+.. automethod:: cil.framework.AcquisitionGeometry.create_Cone3D
+
+
+Configure the geometry
+----------------------
+This gives us an acquisition geometry object configured with the spatial geometry of the system.
+
+It is then necessary to configure the panel, angular data and dimension labels:
+
+.. automethod:: cil.framework.AcquisitionGeometry.set_panel
+.. automethod:: cil.framework.AcquisitionGeometry.set_angles
+.. automethod:: cil.framework.AcquisitionGeometry.set_labels
+.. automethod:: cil.framework.AcquisitionGeometry.set_channels
+
+
+Use the geometry
+----------------
+We can use this geometry to generate related objects. Including a 2D slice :code:`AcquisitionGeometry`, an :code:`AcquisitionData` container, and a default :code:`ImageGeometry`.
+
+.. automethod:: cil.framework.AcquisitionGeometry.get_slice
+.. automethod:: cil.framework.AcquisitionGeometry.allocate
+.. automethod:: cil.framework.AcquisitionGeometry.get_ImageGeometry
+
+
+
+ImageGeometry
+=============
+
+
+The :code:`ImageGeometry` class holds meta data describing the reconstruction volume of interest. This will be centred on the rotation axis position defined
+in :code:`AcquisitionGeometry`, with the z-direction aligned with the rotation axis direction.
+
+.. autoclass:: cil.framework.ImageGeometry
+   :members:
+
+
+BlockGeometry
+=============
+
+.. autoclass:: cil.framework.BlockGeometry
+   :members:
+   :inherited-members:
+
+
+
+Data Containers
+===============
+
+:code:`AcquisitionData` and :code:`ImageData` inherit from the same parent :code:`DataContainer` class,
+therefore they largely behave the same way.
+
+There are algebraic operations defined for both :code:`AcquisitionData` and :code:`ImageData`.
+Following operations are defined:
+
+* binary operations (between two DataContainers or scalar and DataContainer)
+
+  * :code:`+` addition
+  * :code:`-` subtraction
+  * :code:`/` division
+  * :code:`*` multiplication
+  * :code:`**` power
+  * :code:`maximum`
+  * :code:`minimum`
+
+* in-place operations
+
+  * :code:`+=`
+  * :code:`-=`
+  * :code:`*=`
+  * :code:`**=`
+  * :code:`/=`
+
+* unary operations
+
+  * :code:`abs`
+  * :code:`sqrt`
+  * :code:`sign`
+  * :code:`conjugate`
+
+* reductions
+
+  * :code:`sum`
+  * :code:`norm`
+  * :code:`dot` product
+
+
+DataContainer
+-------------
+
+.. autoclass:: cil.framework.DataContainer
+   :members:
+   :inherited-members:
+
+AcquisitionData
+---------------
+
+.. autoclass:: cil.framework.AcquisitionData
+   :members:
+   :inherited-members:
+
+ImageData
+---------
+
+.. autoclass:: cil.framework.ImageData
+   :members:
+   :inherited-members:
+
+VectorData
+----------
+
+.. autoclass:: cil.framework.VectorData
+   :members:
+   :inherited-members:
+
+
+BlockDataContainer
+------------------
+
+A :code:`BlockDataContainer` can be instantiated from a number of `DataContainer`_ and subclasses
+represents a column vector of :code:`DataContainer` s.
+
+.. code:: python
+
+  bdc = BlockDataContainer(DataContainer0, DataContainer1)
+
+This provide a base class that will behave as normal :code:`DataContainer`.
+
+.. autoclass:: cil.framework.BlockDataContainer
+   :members:
+   :inherited-members:
+
+Partitioner
+===========
+
+This method partitions an instance of tomography :code:`AcquisitionData` into a number of batches. For example, to use with a stochastic optimisation method. 
+
+The partitioning is done by taking batches of angles and the corresponding data collected by taking projections along these angles. The partitioner method chooses what angles go in which batch depending on the `mode` and takes in an `AquisitionData` object and outputs a `BlockDataContainer` where each element in the block is  `AquisitionData` object with the batch of data and corresponding geometry. 
+We consider a **batch** to be a subset of the :code:`AcquisitionData` and the verb, **to partition**, to be the act of splitting into batches. 
+ 
+
+For example: 
+
+.. code-block :: python
+
+   from cil.utilities import dataexample
+   from cil.plugins.astra.operators import ProjectionOperator
+   
+   # get the data  
+   data = dataexample.SIMULATED_PARALLEL_BEAM_DATA.get()
+   data.reorder('astra')
+   data = data.get_slice(vertical='centre')
+
+   # create the geometries 
+   ag = data.geometry 
+   ig = ag.get_ImageGeometry()
+
+   # partition the data into batches contained in the elements of a BlockDataContainer
+   data_partitioned = data.partition(num_batches=10, mode='staggered') # Choose mode from `sequential`, `staggered` or `random_permutation` 
+   # From the partitioned data build a BlockOperator container the projectors for each batch 
+   A_partitioned = ProjectionOperator(ig, data_partitioned.geometry, device = "cpu")
+
+   print('The total number of angles is ', len(data.geometry.angles))
+   print('The first 30 angles are ', data.geometry.angles[:30])
+
+   print('In batch zero the number of angles is ', len(data_partitioned[0].geometry.angles))
+   print('The angles in batch zero are ', data_partitioned[0].geometry.angles)
+   print('The angles in batch one are ', data_partitioned[1].geometry.angles)
+
+.. code-block :: RST
+
+   The total number of angles is  300
+   The first 30 angles are  [ 0.   1.2  2.4  3.6  4.8  6.   7.2  8.4  9.6 10.8 12.  13.2 14.4 15.6
+   16.8 18.  19.2 20.4 21.6 22.8 24.  25.2 26.4 27.6 28.8 30.  31.2 32.4
+   33.6 34.8]
+   In batch zero the number of angles is  30
+   The angles in batch zero are  [  0.  12.  24.  36.  48.  60.  72.  84.  96. 108. 120. 132. 144. 156.
+   168. 180. 192. 204. 216. 228. 240. 252. 264. 276. 288. 300. 312. 324.
+   336. 348.]
+   The angles in batch one are  [  1.2  13.2  25.2  37.2  49.2  61.2  73.2  85.2  97.2 109.2 121.2 133.2
+   145.2 157.2 169.2 181.2 193.2 205.2 217.2 229.2 241.2 253.2 265.2 277.2
+   289.2 301.2 313.2 325.2 337.2 349.2]
+
+
+The :code:`partition` method is defined as part of:
+
+.. autoclass:: cil.framework.Partitioner
+   :members:
+   
+
+Labels
+=========
+Classes which define the accepted labels
+
+.. autoclass:: cil.framework.labels.ImageDimension
+   :members:
+   :undoc-members:
+
+.. autoclass:: cil.framework.labels.AcquisitionDimension
+   :members:
+   :undoc-members:
+
+.. autoclass:: cil.framework.labels.FillType
+   :members:
+
+.. autoclass:: cil.framework.labels.AngleUnit
+   :members:
+   :undoc-members:
+
+.. autoclass:: cil.framework.labels.AcquisitionType
+   :members:
+   
+
+DataProcessor
+=============
+.. autoclass:: cil.framework.DataProcessor
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.framework.Processor
+   :members:
+   :inherited-members:
+
+:ref:`Return Home <mastertoc>`
diff --git a/v24.2.0/_sources/index.rst.txt b/v24.2.0/_sources/index.rst.txt
new file mode 100644
index 0000000000..7f93157df8
--- /dev/null
+++ b/v24.2.0/_sources/index.rst.txt
@@ -0,0 +1,92 @@
+.. Copyright 2019 United Kingdom Research and Innovation
+   Copyright 2019 The University of Manchester
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+       http://www.apache.org/licenses/LICENSE-2.0
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+   Authors:
+   CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+Welcome to CIL's documentation!
+###############################
+
+The aim of this package is to enable rapid prototyping of optimisation-based
+reconstruction problems, i.e. defining and solving different optimization problems to enforce different properties on the reconstructed image, while being
+powerful enough to be employed on real scale problems.
+
+Firstly, it provides a framework to handle acquisition and reconstruction
+data and metadata; it also provides a basic input/output package to read data
+from different sources, e.g. Nikon X-Radia, NeXus.
+
+Secondly, it provides an object-oriented framework for defining mathematical
+operators and functions as well a collection of useful example operators and
+functions. Both smooth and non-smooth functions can be used.
+
+Further, it provides a number of high-level generic implementations of
+optimisation algorithms to solve generically formulated optimisation problems
+constructed from operator and function objects.
+
+Demos and Examples
+==================
+A number of demos can be found in the `CIL-Demos`_ repository.
+
+For detailed information refer to our articles and the repositories
+with the code to reproduce the article's results.
+
+Cite this work
+==============
+
+If you use this software please consider citing one or both of the articles below.
+
+1. Jørgensen JS et al. 2021 Core Imaging Library Part I: a versatile python framework for tomographic imaging
+https://doi.org/10.1098/rsta.2020.0192 . Phil. Trans. R. Soc. A 20200192.
+The code to reproduce the article results. https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-I
+
+2. Papoutsellis E et al. 2021 Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral
+tomography https://doi.org/10.1098/rsta.2020.0193 Phil. Trans. R. Soc. A 20200193.
+The code to reproduce the article results. https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-II
+
+Table of Contents
+=================
+
+.. toctree::
+   :maxdepth: 3
+   :name: mastertoc
+
+   introduction
+   framework
+   io
+   optimisation
+   processors
+   recon
+   utilities
+   plugins
+   developer_guide
+   demos
+
+.. Indices and tables
+.. ==================
+
+.. * :ref:`genindex`
+.. * :ref:`modindex`
+.. * :ref:`search`
+
+Contacts
+========
+
+Please refer to the main `CCPi website`_ for up-to-date information.
+
+The CIL developers may be contacted:
+
+* by joining the `devel mailing list`_
+* on the CIL's GitHub repository page https://github.com/TomographicImaging/CIL or
+* on the CIL Discord channel https://discord.gg/9NTWu9MEGq
+
+.. _devel mailing list: https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=CCPI-DEVEL
+.. _CCPi website: https://www.ccpi.ac.uk
+.. _CIL-Demos: https://github.com/vais-ral/CIL-Demos
diff --git a/v24.2.0/_sources/introduction.rst.txt b/v24.2.0/_sources/introduction.rst.txt
new file mode 100644
index 0000000000..cfb91af958
--- /dev/null
+++ b/v24.2.0/_sources/introduction.rst.txt
@@ -0,0 +1,158 @@
+..    Copyright 2022 United Kingdom Research and Innovation
+      Copyright 2022 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+Introduction
+************
+
+The goal of the Core Imaging Library is to allow the user to simply create iterative reconstruction methods which
+go beyond the standard filter back projection technique and which better suit the data characteristics.
+The framework comprises:
+
+* :code:`cil.framework` module gives the building blocks used to describe and handle the data
+* :code:`cil.io` module which provides a number of loaders for real CT machines, e.g. Nikon. It also provides reader and writer to save to NeXuS file format.
+* :code:`cil.optimisation` module allows the user to create iterative methods to reconstruct acquisition data applying different types of regularisation, which better suit the data characteristics.
+* :code:`cil.plugins` module which allows CIL to use selected functionality from ASTRA, TIGRE, TomoPhantom and the Regularisation Toolkit
+* :code:`cil.processors` module contains tools for data manipulation and common CT pre-processing steps
+* :code:`cil.recon` module contains an optimised FDK/FBP reconstructors, making using the both CIL accelerated libraries and the Tigre/ASTRA back-projectors
+* :code:`cil.utilities` module contains a selection of display tools for 2D and 3D data, as well as real and simulated test datasets
+
+
+CT Geometry
+==========
+
+Please refer to `this <https://github.com/TomographicImaging/CIL-Demos/blob/main/demos/1_Introduction/00_CIL_geometry.ipynb>`_ notebook on the CIL-Demos
+repository for full description.
+
+
+In conventional CT systems, an object is placed between a source emitting X-rays and a detector array
+measuring the X-ray transmission images of the incident X-rays. Typically, either the object is placed
+on a rotating sample stage and rotates with respect to the source-detector assembly, or the
+source-detector gantry rotates with respect to the stationary object.
+This arrangement results in so-called circular scanning trajectory. Depending on source and detector
+types, there are three conventional data acquisition geometries:
+
+* parallel geometry (2D or 3D),
+* fan-beam geometry, and
+* cone-beam geometry.
+
+Parallel geometry
+-----------------
+
+Parallel beams of X-rays are emitted onto 1D (single pixel row) or 2D detector array. This geometry
+is common for synchrotron sources. 2D parallel geometry is illustrated below.
+
+.. figure:: images/parallel.png
+    :align: center
+    :alt: alternate text
+    :figclass: align-center
+
+    2D Parallel geometry
+
+.. figure:: images/parallel3d.png
+    :align: center
+    :alt: alternate text
+    :figclass: align-center
+
+    3D Parallel geometry
+
+Fan-beam geometry
+-----------------
+
+A single point-like X-ray source emits a cone beam onto 1D detector pixel row. Cone-beam is typically
+ collimated to imaging field of view. Collimation allows greatly reduce amount of scatter radiation
+ reaching the detector. Fan-beam geometry is used when scattering has significant influence on image
+ quality or single-slice reconstruction is sufficient.
+
+.. figure:: images/fan.png
+    :align: center
+    :alt: alternate text
+    :figclass: align-center
+
+    Fan beam geometry
+
+Cone-beam geometry
+------------------
+A single point-like X-ray source emits a cone beam onto 2D detector array.
+Cone-beam geometry is mainly used in lab-based CT instruments. Depending on where the sample
+is placed between the source and the detector one can achieve a different magnification factor :math:`F`:
+
+.. math::
+
+  F = \frac{r_1 + r_2}{r_1}
+
+where :math:`r_1` and :math:`r_2` are the distance from the source to the center of the sample and
+the distance from the center of the sample to the detector, respectively.
+
+.. figure:: images/cone.png
+    :align: center
+    :alt: alternate text
+    :figclass: align-center
+
+    Cone beam geometry
+
+
+Multi channel data
+==================
+
+CIL is designed to work with 4D data.
+
+Both :code:`AcquisitionGeometry`, :code:`AcquisitionData` and :code:`ImageGeometry`, :code:`ImageData`
+can be defined for multi-channel (spectral/time) CT data using :code:`channels` attribute.
+
+
+Block Framework
+===============
+
+The block framework allows writing more advanced `optimisation problems`_. Consider the typical
+`Tikhonov regularisation <https://en.wikipedia.org/wiki/Tikhonov_regularization>`_:
+
+.. math::
+
+  \underset{u}{\mathrm{argmin}}\begin{Vmatrix}A u - b \end{Vmatrix}^2_2 + \alpha^2\|Lu\|^2_2
+
+where,
+
+* :math:`A` is the projection operator
+* :math:`b` is the acquired data
+* :math:`u` is the unknown image to be solved for
+* :math:`\alpha` is the regularisation parameter
+* :math:`L` is a regularisation operator
+
+The first term measures the fidelity of the solution to the data. The second term measures the
+fidelity to the prior knowledge we have imposed on the system, operator :math:`L`.
+
+This can be re-written equivalently in the block matrix form:
+
+.. math::
+  \underset{u}{\mathrm{argmin}}\begin{Vmatrix}\binom{A}{\alpha L} u - \binom{b}{0}\end{Vmatrix}^2_2
+
+With the definitions:
+
+* :math:`\tilde{A} = \binom{A}{\alpha L}`
+* :math:`\tilde{b} = \binom{b}{0}`
+
+this can now be recognised as a least squares problem which can be solved by any algorithm in the :code:`cil.optimisation`
+which can solve least squares problem, e.g. CGLS.
+
+.. math::
+
+  \underset{u}{\mathrm{argmin}}\begin{Vmatrix}\tilde{A} u - \tilde{b}\end{Vmatrix}^2_2
+
+To be able to express our optimisation problems in the matrix form above, we developed the so-called,
+Block Framework comprising 4 main actors: :code:`BlockGeometry`, :code:`BlockDataContainer`,
+:code:`BlockFunction` and :code:`BlockOperator`.
diff --git a/v24.2.0/_sources/io.rst.txt b/v24.2.0/_sources/io.rst.txt
new file mode 100644
index 0000000000..b555071d2d
--- /dev/null
+++ b/v24.2.0/_sources/io.rst.txt
@@ -0,0 +1,101 @@
+..    Copyright 2019 United Kingdom Research and Innovation
+      Copyright 2019 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+     Kyle Pidgeon (UKRI-STFC)
+
+Read/ write AcquisitionData and ImageData
+*****************************************
+
+
+NeXuS
+=====
+
+The CCPi Framework provides classes to read and write :code:`AcquisitionData` and :code:`ImageData`
+as NeXuS files.
+
+.. code:: python
+
+  # imports
+  from cil.io import NEXUSDataWriter, NEXUSDataReader
+
+  # initialise NEXUS Writer
+  writer = NEXUSDataWriter()
+  writer.set_up(data=my_data,
+              file_name='tmp_nexus.nxs')
+  # write data
+  writer.write()
+
+  # read data
+  # initialize NEXUS reader
+  reader = NEXUSDataReader()
+  reader.set_up(file_name='tmp_nexus.nxs')
+  # load data
+  ad1 = reader.read()
+  # get AcquisitionGeometry
+  ag1 = reader.get_geometry()
+
+.. autoclass:: cil.io.NEXUSDataReader
+   :members:
+   :inherited-members:
+.. autoclass:: cil.io.NEXUSDataWriter
+   :members:
+   :inherited-members:
+|
+
+Nikon
+=====
+.. autoclass:: cil.io.NikonDataReader
+   :members:
+   :inherited-members:
+
+ZEISS
+=====
+.. autoclass:: cil.io.ZEISSDataReader
+   :members:
+   :inherited-members:
+
+TIFF Reader/Writer
+==================
+
+.. autoclass:: cil.io.TIFFStackReader
+   :members:
+   :exclude-members: set_up
+
+.. autoclass:: cil.io.TIFFWriter
+   :members:
+   :exclude-members: set_up
+
+RAW File Writer
+===============
+
+.. autoclass:: cil.io.RAWFileWriter
+   :members:
+
+:ref:`Return Home <mastertoc>`
+
+HDF5 Utilities
+==================
+
+Utility functions to browse HDF5 files. These allow you to browse groups and read in datasets as numpy.ndarrays.
+
+A CIL geometry and dataset must be constructed manually from the array and metadata.
+
+.. autoclass:: cil.io.utilities.HDF5_utilities
+   :members:
+
+
+:ref:`Return Home <mastertoc>`
diff --git a/v24.2.0/_sources/optimisation.rst.txt b/v24.2.0/_sources/optimisation.rst.txt
new file mode 100644
index 0000000000..72fc5a6885
--- /dev/null
+++ b/v24.2.0/_sources/optimisation.rst.txt
@@ -0,0 +1,901 @@
+..    Copyright 2019 United Kingdom Research and Innovation
+      Copyright 2019 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+Optimisation framework
+**********************
+This package allows rapid prototyping of optimisation-based reconstruction problems, i.e. defining and solving different optimization problems to enforce different properties on the reconstructed image.
+
+Firstly, it provides an object-oriented framework for defining mathematical operators and functions as well a collection of useful example operators and functions. Both smooth and non-smooth functions can be used.
+
+Further, it provides a number of high-level generic implementations of optimisation algorithms to solve generically formulated optimisation problems constructed from operator and function objects.
+
+The fundamental components are:
+
++ :code:`Operator`: A class specifying a (currently linear) operator.
++ :code:`Function`: A class specifying mathematical functions such as a least squares data fidelity.
++ :code:`Algorithm`: Implementation of an iterative optimisation algorithm to solve a particular generic optimisation problem. Algorithms are iterable Python object which can be run in a for loop. Can be stopped and warm restarted.
+
+
+
+Algorithms (Deterministic)
+==========================
+
+A number of generic algorithm implementations are provided including
+Gradient Descent (GD), Conjugate Gradient Least Squares (CGLS),
+Simultaneous Iterative Reconstruction Technique (SIRT), Primal Dual Hybrid
+Gradient (PDHG), Primal dual three-operator (PD3O),  Iterative Shrinkage Thresholding Algorithm (ISTA),
+and Fast Iterative Shrinkage Thresholding Algorithm (FISTA).
+
+An algorithm is designed for a particular generic optimisation problem accepts and number of
+instances of :code:`Function` derived classes and/or :code:`Operator` derived classes as input to
+define a specific instance of the generic optimisation problem to be solved.
+They are iterable objects which can be run in a for loop.
+The user can provide a stopping criterion different than the default max_iteration.
+
+New algorithms can be easily created by extending the :code:`Algorithm` class.
+The user is required to implement only 4 methods: set_up, __init__, update and update_objective.
+
++ :code:`set_up` and :code:`__init__` are used to configure the algorithm
++ :code:`update` is the actual iteration updating the solution
++ :code:`update_objective` defines how the objective is calculated.
+
+For example, the implementation of the update of the Gradient Descent
+algorithm to minimise a Function will only be:
+
+.. code-block :: python
+
+    def update(self):
+        self.x += -self.rate * self.objective_function.gradient(self.x)
+    def update_objective(self):
+        self.loss.append(self.objective_function(self.x))
+
+The :code:`Algorithm` provides the infrastructure to continue iteration, to access the values of the
+objective function in subsequent iterations, the time for each iteration, and to provide a nice
+print to screen of the status of the optimisation.
+
+Base class
+----------
+.. autoclass:: cil.optimisation.algorithms.Algorithm
+   :members:
+   :inherited-members:
+
+GD
+--
+.. autoclass:: cil.optimisation.algorithms.GD
+   :members:
+   :inherited-members: run, update_objective_interval, max_iteration
+
+CGLS
+----
+.. autoclass:: cil.optimisation.algorithms.CGLS
+   :members:
+   :inherited-members: run, update_objective_interval, max_iteration
+
+SIRT
+----
+.. autoclass:: cil.optimisation.algorithms.SIRT
+   :members: update, update_objective
+   :inherited-members: run, update_objective_interval, max_iteration
+
+ISTA
+----
+.. autoclass:: cil.optimisation.algorithms.ISTA
+   :members:
+   :special-members:
+   :inherited-members: run, update_objective_interval, max_iteration
+
+FISTA
+-----
+.. autoclass:: cil.optimisation.algorithms.FISTA
+   :members:
+   :special-members:
+   :inherited-members: run, update_objective_interval, max_iteration
+
+PDHG
+----
+.. autoclass:: cil.optimisation.algorithms.PDHG
+   :members: update, set_step_sizes, update_step_sizes, update_objective
+   :member-order: bysource
+   :inherited-members: run, update_objective_interval, max_iteration
+
+LADMM
+-----
+.. autoclass:: cil.optimisation.algorithms.LADMM
+   :members:
+   :inherited-members: run, update_objective_interval, max_iteration
+
+PD3O
+----
+.. autoclass:: cil.optimisation.algorithms.PD3O
+   :members:
+   :inherited-members: run, update_objective_interval, max_iteration
+
+
+Algorithms (Stochastic)
+========================
+
+Consider optimisation problems that take the form of a separable sum: 
+
+.. math:: \min_{x} f(x)+g(x) = \min_{x} \sum_{i=0}^{n-1} f_{i}(x) + g(x) = \min_{x} (f_{0}(x) + f_{1}(x) + ... + f_{n-1}(x))+g(x)
+
+where :math:`n` is the number of functions.  Where there is a large number of :math:`f_i` or their gradients are expensive to calculate, stochastic optimisation methods could prove more efficient.   
+There is a growing range of Stochastic optimisation algorithms available with potential benefits of faster convergence in number of iterations or in computational cost. 
+This is an area of continued development for CIL and, depending on  the properties of the :math:`f_i` and the regulariser :math:`g`, there is a range of different options for the user. 
+
+
+
+SPDHG
+-----
+Stochastic Primal Dual Hybrid Gradient (SPDHG) is a stochastic version of PDHG and deals with optimisation problems of the form: 
+    
+    .. math::
+    
+      \min_{x} f(Kx) + g(x) = \min_{x} \sum f_i(K_i x) + g(x)
+
+where :math:`f_i` and the regulariser :math:`g` need only be proper, convex and lower semi-continuous ( i.e. do not need to be differentiable). 
+Each iteration considers just one index of the sum, potentially reducing computational cost. For more examples see our [user notebooks]( https://github.com/vais-ral/CIL-Demos/blob/master/Tomography/Simulated/Single%20Channel/PDHG_vs_SPDHG.py).
+
+
+.. autoclass:: cil.optimisation.algorithms.SPDHG
+   :members:
+   :inherited-members: run, update_objective_interval, max_iteration
+
+
+Approximate gradient methods
+----------------------------------
+
+Alternatively, consider that, in addition to the functions :math:`f_i` and the regulariser :math:`g` being proper, convex and lower semi-continuous, the :math:`f_i` are differentiable. In this case we consider stochastic methods that replace a gradient calculation in a deterministic algorithm with a, potentially cheaper to calculate, approximate gradient. 
+For example, when :math:`g(x)=0`, the standard Gradient Descent algorithm utilises iterations of the form
+
+   .. math::
+      x_{k+1}=x_k-\alpha \nabla f(x_k) =x_k-\alpha \sum_{i=0}^{n-1}\nabla f_i(x_k).
+:math:`\nabla f(x_k)=\sum_{i=0}^{n-1}\nabla f_i(x_k)` with :math:`n \nabla f_i(x_k)`, for an index :math:`i` which changes each iteration, leads to the well known stochastic gradient descent algorithm. 
+
+
+
+Replacing, :math:`\nabla f(x_k)=\sum_{i=0}^{n-1}\nabla f_i(x_k)` with :math:`n \nabla f_i(x_k)`, for an index :math:`i` which changes each iteration, leads to the well known stochastic gradient descent algorithm. 
+
+In addition, if :math:`g(x)\neq 0` and has a calculable proximal ( need not be differentiable) one can consider ISTA iterations: 
+
+   .. math::
+         x_{k+1}=prox_{\alpha g}(x_k-\alpha \nabla f(x_k) )=prox_{\alpha g}(x_k-\alpha \sum_{i=0}^{n-1}\nabla f_i(x_k))
+
+and again replacing :math:`\nabla f(x_k)=\sum_{i=0}^{n-1}\nabla f_i(x_k)` with an approximate gradient. 
+
+In a similar way, plugging approximate gradient calculations into deterministic algorithms can lead to a range of stochastic algorithms. In the following table, the left hand column has the approximate gradient function subclass, :ref:`Approximate Gradient base class` the header row has one of CIL's deterministic optimisation algorithm and the body of the table has the resulting stochastic algorithm.
+
++----------------+-------+------------+----------------+
+|                | GD    | ISTA       | FISTA          |
++----------------+-------+------------+----------------+
+| SGFunction     | SGD   | Prox-SGD   | Acc-Prox-SGD   |
++----------------+-------+------------+----------------+
+| SAGFunction\  | SAG   | Prox-SAG   | Acc-Prox-SAG   |
++----------------+-------+------------+----------------+
+| SAGAFunction\ | SAGA  | Prox-SAGA  | Acc-Prox-SAGA  |
++----------------+-------+------------+----------------+
+| SVRGFunction\ | SVRG  | Prox-SVRG  | Acc-Prox-SVRG  |
++----------------+-------+------------+----------------+
+| LSVRGFunction\| LSVRG | Prox-LSVRG | Acc-Prox-LSVRG |
++----------------+-------+------------+----------------+
+
+\*In development 
+
+The stochastic gradient functions can be found listed under functions in the documentation. 
+
+Stochastic Gradient Descent Example
+----------------------------------
+The below is an example of Stochastic Gradient Descent built of the SGFunction and Gradient Descent algorithm:
+
+.. code-block :: python
+
+   from cil.optimisation.utilities import Sampler
+   from cil.optimisation.algorithms import GD 
+   from cil.optimisation.functions import LeastSquares, SGFunction
+   from cil.utilities import dataexample
+   from cil.plugins.astra.operators import ProjectionOperator
+   
+   # get the data  
+   data = dataexample.SIMULATED_PARALLEL_BEAM_DATA.get()
+   data.reorder('astra')
+   data = data.get_slice(vertical='centre')
+
+   # create the geometries 
+   ag = data.geometry 
+   ig = ag.get_ImageGeometry()
+
+   # partition the data and build the projectors
+   n_subsets = 10 
+   partitioned_data = data.partition(n_subsets, 'sequential')
+   A_partitioned = ProjectionOperator(ig, partitioned_data.geometry, device = "cpu")
+
+   # create the list of functions for the stochastic sum 
+   list_of_functions = [LeastSquares(Ai, b=bi) for Ai,bi in zip(A_partitioned, partitioned_data)]
+
+   #define the sampler and the stochastic gradient function 
+   sampler = Sampler.staggered(len(list_of_functions), stride=2)
+   f = SGFunction(list_of_functions, sampler=sampler)  
+   
+   #set up and run the gradient descent algorithm 
+   alg = GD(initial=ig.allocate(0), objective_function=f, step_size=1/f.L)
+   alg.run(300)
+
+
+Note
+----
+ All the approximate gradients written in CIL are of a similar order of magnitude to the full gradient calculation. For example, in the :code:`SGFunction` we approximate the full gradient by :math:`n\nabla f_i` for an index :math:`i` given by the sampler. 
+ The multiplication by :math:`n` is a choice to more easily allow comparisons between stochastic and non-stochastic methods and between stochastic methods with varying numbers of subsets.
+ The multiplication ensures that the (SAGA, SGD, and SVRG  and LSVRG) approximate gradients are an unbiased estimator of the full gradient ie :math:`\mathbb{E}\left[\tilde\nabla f(x)\right] =\nabla f(x)`.
+  This has an implication when choosing step sizes. For example, a suitable step size for GD with a SGFunction could be 
+  :math:`\propto 1/(L_{max}*n)`, where :math:`L_{max}` is the largest Lipschitz constant of the list of functions in the SGFunction and the additional factor of  :math:`n` reflects this multiplication by  :math:`n` in the approximate gradient. 
+
+  
+Memory requirements
+-------------------
+Note that the approximate gradient methods have different memory requirements:
++ The `SGFunction` has the same requirements as a `SumFunction`, so no increased memory usage
++ `SAGFunction` and `SAGAFunction` both store `n+3` times the image size in memory to store the last calculated gradient for each function in the sum and for intermediary calculations. 
++ `SVRGFunction` and `LSVRGFunction` with the default `store_gradients = False` store 4 times the image size in memory, including the "snapshot" point and gradient. If `store_gradients = True`, some computational effort is saved, at the expensive of stored memory `n+4` times the image size.  
+
+
+Operators
+=========
+The two most important methods are :code:`direct` and :code:`adjoint`
+methods that describe the result of applying the operator, and its
+adjoint respectively, onto a compatible :code:`DataContainer` input.
+The output is another :code:`DataContainer` object or subclass
+hereof. An important special case is to represent the tomographic
+forward and backprojection operations.
+
+
+Operator base classes
+---------------------
+
+All operators extend the :code:`Operator` class. A special class is the :code:`LinearOperator`
+which represents an operator for which the :code:`adjoint` operation is defined.
+A :code:`ScaledOperator` represents the multiplication of any operator with a scalar.
+
+.. autoclass:: cil.optimisation.operators.Operator
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.operators.LinearOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.ScaledOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.CompositionOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.DiagonalOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.ChannelwiseOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.SumOperator
+   :members:
+
+
+Trivial operators
+-----------------
+
+Trivial operators are the following.
+
+.. autoclass:: cil.optimisation.operators.IdentityOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.ZeroOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.MatrixOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.MaskOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.ProjectionMap
+   :members:
+
+GradientOperator
+-----------------
+
+.. autoclass:: cil.optimisation.operators.GradientOperator
+   :members:
+
+
+.. autoclass:: cil.optimisation.operators.FiniteDifferenceOperator
+   :members:
+
+.. autoclass:: cil.optimisation.operators.SparseFiniteDifferenceOperator
+   :members:
+
+.. autoclass:: cil.optimisation.operators.SymmetrisedGradientOperator
+   :members:
+
+
+WaveletOperator
+---------------
+We utilise PyWavelets (https://pywavelets.readthedocs.io/en/latest/index.html) to build wavelet operators in CIL:
+
+.. autoclass:: cil.optimisation.operators.WaveletOperator
+   :members:
+
+
+
+
+Functions
+=========
+
+A :code:`Function` represents a mathematical function of one or more inputs
+and is intended to accept :code:`DataContainers` as input as well as any
+additional parameters.
+
+Fixed parameters can be passed in during the creation of the function object.
+The methods of the function reflect the properties of it, for example, if the function
+represented is differentiable the function should contain a method :code:`gradient`
+which should return the gradient of the function evaluated at an input point.
+If the function is not differentiable but allows a simple proximal operator,
+the method :code:`proximal` should return the proximal operator evaluated at an
+input point. The function value is evaluated by calling the function itself,
+e.g. :code:`f(x)` for a :code:`Function f` and input point :code:`x`.
+
+
+Base classes
+------------
+
+.. autoclass:: cil.optimisation.functions.Function
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.functions.SumFunction
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.functions.ScaledFunction
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.functions.SumScalarFunction
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.functions.TranslateFunction
+   :members:
+   :inherited-members:
+
+Simple functions
+----------------
+.. autoclass:: cil.optimisation.functions.ConstantFunction
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.functions.ZeroFunction
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.functions.Rosenbrock
+   :members:
+   :inherited-members:
+
+Composition of operator and a function
+--------------------------------------
+
+This class allows the user to write a function which does the following:
+
+.. math::
+
+  F ( x ) = G ( Ax )
+
+where :math:`A` is an operator. For instance the least squares function can
+be expressed as
+
+.. math::
+
+  F(x) = || Ax - b ||^2_2 \qquad \text{where} \qquad G(y) = || y - b ||^2_2
+
+.. code-block :: python
+
+  F1 = Norm2Sq(A, b)
+  # or equivalently
+  F2 = OperatorCompositionFunction(L2NormSquared(b=b), A)
+
+
+.. autoclass:: cil.optimisation.functions.OperatorCompositionFunction
+  :members:
+  :inherited-members:
+
+Indicator box
+-------------
+
+.. autoclass:: cil.optimisation.functions.IndicatorBox
+   :members:
+   :inherited-members:
+
+
+KullbackLeibler
+---------------
+
+.. autoclass:: cil.optimisation.functions.KullbackLeibler
+   :members:
+   :inherited-members:
+
+L1 Norm
+-------
+
+.. autoclass:: cil.optimisation.functions.L1Norm
+   :members:
+   :inherited-members:
+
+L2 Norm Squared
+-----------------------
+.. l2norm:
+
+.. autoclass:: cil.optimisation.functions.L2NormSquared
+   :members:
+   :inherited-members:
+
+.. autoclass:: cil.optimisation.functions.WeightedL2NormSquared
+   :members:
+   :inherited-members:
+
+
+Least Squares
+-------------
+
+.. autoclass:: cil.optimisation.functions.LeastSquares
+   :members:
+   :inherited-members:
+
+
+L1 Sparsity
+----------
+.. autoclass:: cil.optimisation.functions.L1Sparsity
+   :members:
+   :inherited-members:
+
+
+Mixed L21 norm
+--------------
+
+.. autoclass:: cil.optimisation.functions.MixedL21Norm
+   :members:
+   :inherited-members:
+
+Smooth Mixed L21 norm
+---------------------
+
+.. autoclass:: cil.optimisation.functions.SmoothMixedL21Norm
+   :members:
+   :inherited-members:
+
+Mixed L11 norm
+---------------------
+
+.. autoclass:: cil.optimisation.functions.MixedL11Norm
+   :members:
+   :inherited-members:
+
+Total variation
+---------------
+
+.. autoclass:: cil.optimisation.functions.TotalVariation
+   :members:
+   :inherited-members:
+
+Approximate Gradient base class 
+--------------------------------
+
+.. autoclass:: cil.optimisation.functions.ApproximateGradientSumFunction 
+   :members:
+   :inherited-members:
+   
+
+Stochastic Gradient function 
+-----------------------------
+
+.. autoclass:: cil.optimisation.functions.SGFunction 
+   :members:
+   :inherited-members:
+
+SAG function
+-------------
+
+.. autoclass:: cil.optimisation.functions.SAGFunction 
+   :members:
+   :inherited-members:
+
+SAGA function
+--------------
+
+.. autoclass:: cil.optimisation.functions.SAGAFunction 
+   :members:
+   :inherited-members:
+
+
+
+Stochastic Variance Reduced Gradient Function 
+----------------------------------------------
+.. autoclass:: cil.optimisation.functions.SVRGFunction 
+   :members:
+   :inherited-members:
+
+
+Loopless Stochastic Variance Reduced Gradient Function 
+----------------------------------------------
+.. autoclass:: cil.optimisation.functions.LSVRGFunction 
+   :members:
+   :inherited-members:
+
+
+
+Utilities
+=========
+
+Contains utilities for the CIL optimisation framework.
+
+Samplers
+--------
+
+Here, we define samplers that select from a list of indices {0, 1, …, N-1} either randomly or by some deterministic pattern.
+The :code:`cil.optimisation.utilities.sampler` class defines a function :code:`next()` which gives the next sample. It also has utility to :code:`get_samples` to access which samples have or will be drawn.
+
+For ease of use we provide the following static methods in `cil.optimisation.utilities.sampler` that allow you to configure your sampler object rather than initialising the classes directly:
+
+.. automethod:: cil.optimisation.utilities.Sampler.from_function
+
+.. automethod:: cil.optimisation.utilities.Sampler.sequential
+
+.. automethod:: cil.optimisation.utilities.Sampler.staggered
+
+.. automethod:: cil.optimisation.utilities.Sampler.herman_meyer
+
+.. automethod:: cil.optimisation.utilities.Sampler.random_with_replacement
+
+.. automethod:: cil.optimisation.utilities.Sampler.random_without_replacement
+
+
+They will all instantiate a Sampler defined in the following class:
+
+.. autoclass:: cil.optimisation.utilities.Sampler
+   :members:
+
+
+In addition, we provide a random sampling class which is a child class of  `cil.optimisation.utilities.sampler` and provides options for sampling with and without replacement:
+
+.. autoclass:: cil.optimisation.utilities.SamplerRandom
+   :members:
+
+Callbacks
+---------
+
+A list of :code:`Callback` s to be executed each iteration can be passed to `Algorithms`_ :code:`run` method.
+
+.. code-block :: python
+
+   from cil.optimisation.utilities.callbacks import LogfileCallback
+   ...
+   algorithm.run(..., callbacks=[LogfileCallback("log.txt")])
+
+.. autoclass:: cil.optimisation.utilities.callbacks.Callback
+   :members:
+
+Built-in callbacks include:
+
+.. autoclass:: cil.optimisation.utilities.callbacks.ProgressCallback
+   :members:
+
+.. autoclass:: cil.optimisation.utilities.callbacks.TextProgressCallback
+   :members:
+
+.. autoclass:: cil.optimisation.utilities.callbacks.LogfileCallback
+   :members:
+
+Users can also write custom callbacks.
+
+Below is an example of a custom callback implementing early stopping.
+In each iteration of the :code:`TestAlgo`, the objective :math:`x` is reduced by :math:`5`. The :code:`EarlyStopping` callback terminates the algorithm when :math:`x \le -15`. The algorithm thus terminates after :math:`3` iterations.
+
+.. code:: python
+
+   from cil.optimisation.algorithms import Algorithm
+   from cil.optimisation.utilities import callbacks
+
+   class TestAlgo(Algorithm):
+       def __init__(self, *args, **kwargs):
+           self.x = 0
+           super().__init__(*args, **kwargs)
+           self.configured = True
+
+       def update(self):
+           self.x -= 5
+
+       def update_objective(self):
+           self.loss.append(2 ** self.x)
+
+   class EarlyStopping(callbacks.Callback):
+       def __call__(self, algorithm: Algorithm):
+           if algorithm.x <= -15:  # arbitrary stopping criterion
+               raise StopIteration
+
+   algo = TestAlgo()
+   algo.run(20, callbacks=[callbacks.ProgressCallback(), EarlyStopping()])
+
+
+.. code:: raw
+
+   Output:
+    15%|███                 | 3/20 [00:00<00:00, 11770.73it/s, objective=3.05e-5]
+
+
+Step size methods 
+------------------
+A step size method is a class which acts on an algorithm and can be passed to  `cil.optimisation.algorithm.GD`, `cil.optimisation.algorithm.ISTA`  `cil.optimisation.algorithm.FISTA` and it's method `get_step_size` is called after the calculation of the gradient before the gradient descent step is taken. It outputs a float value to be used as the step-size. 
+
+Currently in CIL we have a base class:
+
+.. autoclass:: cil.optimisation.utilities.StepSizeMethods.StepSizeRule
+   :members:
+
+We also have a number of example classes:
+
+.. autoclass:: cil.optimisation.utilities.StepSizeMethods.ConstantStepSize
+   :members:
+
+.. autoclass:: cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule
+   :members:
+
+.. autoclass:: cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule
+   :members:
+
+
+
+Preconditioners
+----------------
+A preconditioner is a class which acts on an algorithm and can be passed to  `cil.optimisation.algorithm.GD`, `cil.optimisation.algorithm.ISTA` or `cil.optimisation.algorithm.FISTA` and it's method `apply` is called after the calculation of the gradient before the gradient descent step is taken. It modifies and returns a passed `gradient`. 
+
+Currently in CIL we have a base class:
+
+.. autoclass:: cil.optimisation.utilities.preconditioner.Preconditioner
+   :members:
+
+We also have a number of already provided pre-conditioners
+
+.. autoclass:: cil.optimisation.utilities.preconditioner.Sensitivity
+   :members:
+
+.. autoclass:: cil.optimisation.utilities.preconditioner.AdaptiveSensitivity
+   :members:
+
+
+
+Block Framework
+***************
+
+To be able to express more advanced optimisation problems we developed the
+`Block Framework`_, which provides a generic strategy to treat variational
+problems in the following form:
+
+.. math::
+    \min \text{Regulariser} + \text{Fidelity}
+
+The block framework consists of:
+
++ `BlockDataContainer`_
++ `BlockFunction`_
++ `BlockOperator`_
+
+
+
+
+The block framework allows writing more advanced optimisation problems. Consider the typical
+`Tikhonov regularisation <https://en.wikipedia.org/wiki/Tikhonov_regularization>`_:
+
+.. math::
+
+  \underset{u}{\mathrm{argmin}}\begin{Vmatrix}A u - b \end{Vmatrix}^2_2 + \alpha^2\|Lu\|^2_2
+
+where,
+
+* :math:`A` is the projection operator
+* :math:`b` is the acquired data
+* :math:`u` is the unknown image to be solved for
+* :math:`\alpha` is the regularisation parameter
+* :math:`L` is a regularisation operator
+
+The first term measures the fidelity of the solution to the data. The second term measures the
+fidelity to the prior knowledge we have imposed on the system, operator :math:`L`.
+
+This can be re-written equivalently in the block matrix form:
+
+.. math::
+  \underset{u}{\mathrm{argmin}}\begin{Vmatrix}\binom{A}{\alpha L} u - \binom{b}{0}\end{Vmatrix}^2_2
+
+With the definitions:
+
+* :math:`\tilde{A} = \binom{A}{\alpha L}`
+* :math:`\tilde{b} = \binom{b}{0}`
+
+this can now be recognised as a least squares problem which can be solved by any algorithm in the :code:`cil.optimisation`
+which can solve least squares problem, e.g. CGLS.
+
+.. math::
+
+  \underset{u}{\mathrm{argmin}}\begin{Vmatrix}\tilde{A} u - \tilde{b}\end{Vmatrix}^2_2
+
+To be able to express our optimisation problems in the matrix form above, we developed the so-called,
+Block Framework comprising 4 main actors: :code:`BlockGeometry`, :code:`BlockDataContainer`,
+:code:`BlockFunction` and :code:`BlockOperator`.
+
+
+
+BlockDataContainer
+==================
+
+`BlockDataContainer`_ holds `DataContainer`_ as column vector. It is possible to
+do basic algebra between `BlockDataContainer`_ s and with numbers, list or numpy arrays.
+
+.. math::
+
+  x = [x_{1}, x_{2} ]\in (X_{1}\times X_{2})
+
+  y = [y_{1}, y_{2}, y_{3} ]\in(Y_{1}\times Y_{2} \times Y_{3})
+
+
+.. autoclass:: cil.framework.BlockDataContainer
+   :members:
+   :special-members:
+
+
+Block Function
+==============
+
+`BlockFunction`_ acts on `BlockDataContainer`_ as a separable sum function:
+
+      .. math::
+
+          f = [f_1,...,f_n] \newline
+
+          f([x_1,...,x_n]) = f_1(x_1) +  .... + f_n(x_n)
+
+
+.. math::
+
+  Y = \begin{bmatrix}
+  y_{1}\\
+  y_{2}\\
+  y_{3}\\
+  \end{bmatrix}, \quad  F  = [ f_{1}, f_{2}, f_{3} ]
+
+  F(Y) : = f_{1}(y_{1}) + f_{2}(y_{2}) + f_{3}(y_{3})
+
+
+.. autoclass:: cil.optimisation.functions.BlockFunction
+   :members:
+   :special-members:
+
+Block Operator
+==============
+
+`BlockOperator`_ represent a block matrix with operators
+
+.. math::
+  K = \begin{bmatrix}
+      A_{1} & A_{2} \\
+      A_{3} & A_{4} \\
+      A_{5} & A_{6}
+ \end{bmatrix}_{(3,2)} *  \quad \underbrace{\begin{bmatrix}
+ x_{1} \\
+ x_{2}
+ \end{bmatrix}_{(2,1)}}_{\textbf{x}} =  \begin{bmatrix}
+ A_{1}x_{1}  + A_{2}x_{2}\\
+ A_{3}x_{1}  + A_{4}x_{2}\\
+ A_{5}x_{1}  + A_{6}x_{2}\\
+ \end{bmatrix}_{(3,1)} =  \begin{bmatrix}
+ y_{1}\\
+ y_{2}\\
+ y_{3}
+ \end{bmatrix}_{(3,1)} = \textbf{y}
+
+Column: Share the same domains :math:`X_{1}, X_{2}`
+
+Rows: Share the same ranges :math:`Y_{1}, Y_{2}, Y_{3}`
+
+.. math::
+ K : (X_{1}\times X_{2}) \rightarrow (Y_{1}\times Y_{2} \times Y_{3})
+
+:math:`A_{1}, A_{3}, A_{5}`: share the same domain :math:`X_{1}` and
+:math:`A_{2}, A_{4}, A_{6}`: share the same domain :math:`X_{2}`
+
+.. math::
+
+ A_{1}: X_{1} \rightarrow Y_{1} \\
+ A_{3}: X_{1} \rightarrow Y_{2} \\
+ A_{5}: X_{1} \rightarrow Y_{3} \\
+ A_{2}: X_{2} \rightarrow Y_{1} \\
+ A_{4}: X_{2} \rightarrow Y_{2} \\
+ A_{6}: X_{2} \rightarrow Y_{3}
+
+For instance with these ingredients one may write the following objective
+function,
+
+.. math::
+   \alpha ||\nabla u||_{2,1} + ||u - g||_2^2
+
+where :math:`g` represent the measured values, :math:`u` the solution
+:math:`\nabla` is the gradient operator, :math:`|| ~~ ||_{2,1}` is a norm for
+the output of the gradient operator and :math:`|| x-g ||^2_2` is
+least squares fidelity function as
+
+.. math::
+   K = \begin{bmatrix}
+           \nabla \\
+           \mathbb{1}
+         \end{bmatrix}
+
+ F(x) = \Big[ \alpha \lVert ~x~ \rVert_{2,1} ~~ , ~~ || x - g||_2^2 \Big]
+
+ w = [ u ]
+
+Then we have rewritten the problem as
+
+.. math::
+  F(Kw) =   \alpha \left\lVert \nabla u \right\rVert_{2,1} + ||u-g||^2_2
+
+Which in Python would be like
+
+.. code-block:: python
+
+   op1 = GradientOperator(ig, correlation=GradientOperator.CORRELATION_SPACE)
+   op2 = IdentityOperator(ig, ag)
+
+   # Create BlockOperator
+   K = BlockOperator(op1, op2, shape=(2,1) )
+
+   # Create functions
+   F = BlockFunction(alpha * MixedL21Norm(), 0.5 * L2NormSquared(b=noisy_data))
+
+
+.. autoclass:: cil.optimisation.operators.BlockOperator
+   :members:
+   :special-members:
+
+
+:ref:`Return Home <mastertoc>`
+
+.. _BlockDataContainer: #blockdatacontainer
+.. _DataContainer: ../framework/#datacontainer
+.. _BlockFunction: #block-function
+.. _BlockOperator: #block-operator
+
+
+
+
+References
+----------
+
+.. bibliography::
diff --git a/v24.2.0/_sources/plugins.rst.txt b/v24.2.0/_sources/plugins.rst.txt
new file mode 100644
index 0000000000..54e0a17183
--- /dev/null
+++ b/v24.2.0/_sources/plugins.rst.txt
@@ -0,0 +1,112 @@
+..    Copyright 2019 United Kingdom Research and Innovation
+      Copyright 2019 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+CIL Plugins
+************
+
+CCPi Regularisation
+===================
+
+This plugin allows the use of regularisation functions from the `CCPi Regularisation toolkit
+<https://github.com/vais-ral/CCPi-Regularisation-Toolkit>`_
+(`10.1016/j.softx.2019.04.003 <https://www.sciencedirect.com/science/article/pii/S2352711018301912>`_,
+a set of CPU/GPU optimised regularisation modules for iterative image reconstruction and
+other image processing tasks.
+
+Total variation
+---------------
+
+.. autoclass:: cil.plugins.ccpi_regularisation.functions.FGP_TV
+
+
+Other regularisation functions
+------------------------------
+
+.. autoclass:: cil.plugins.ccpi_regularisation.functions.TGV
+   :members:
+   :special-members:
+
+.. autoclass:: cil.plugins.ccpi_regularisation.functions.FGP_dTV
+   :members:
+   :special-members:
+
+.. autoclass:: cil.plugins.ccpi_regularisation.functions.TNV
+   :members:
+   :special-members:
+
+
+TomoPhantom
+===========
+This plugin allows the use of part of `TomoPhantom
+<https://github.com/dkazanc/TomoPhantom>`_
+(`10.1016/j.softx.2018.05.003 <https://doi.org/10.1016/j.softx.2018.05.003>`_,
+a toolbox written in C language to generate customisable 2D-4D phantoms (with a
+temporal capability).
+
+.. autofunction:: cil.plugins.TomoPhantom.get_ImageData
+
+TIGRE
+=====
+This plugin allows the use of `TIGRE
+<https://github.com/CERN/TIGRE>`_
+(`10.1088/2057-1976/2/5/055010 <http://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010>`_
+for forward and back projections and filter back projection reconstruction.
+
+FBP
+---
+This reconstructs with FBP for parallel-beam data, and with FDK weights for cone-beam data
+
+.. autoclass:: cil.plugins.tigre.FBP
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Projection Operator
+-------------------
+
+.. autoclass:: cil.plugins.tigre.ProjectionOperator
+   :members:
+
+
+
+ASTRA
+=====
+This plugin allows the use of `ASTRA-toolbox
+<https://github.com/astra-toolbox/astra-toolbox>`_
+(`10.1364/OE.24.025129 <http://dx.doi.org/10.1364/OE.24.025129>`_)
+for forward and back projections and filter back projection reconstruction.
+
+
+FBP
+---
+This reconstructs with FBP for parallel-beam data, and with FDK weights for cone-beam data
+
+.. autoclass:: cil.plugins.astra.FBP
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Projection Operator
+-------------------
+
+.. autoclass:: cil.plugins.astra.ProjectionOperator
+   :members:
+
+:ref:`Return Home <mastertoc>`
diff --git a/v24.2.0/_sources/processors.rst.txt b/v24.2.0/_sources/processors.rst.txt
new file mode 100644
index 0000000000..1381a065fc
--- /dev/null
+++ b/v24.2.0/_sources/processors.rst.txt
@@ -0,0 +1,151 @@
+..    Copyright 2021 United Kingdom Research and Innovation
+      Copyright 2021 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+Processors
+**********
+
+This module allows the user to manipulate or pre-process their data.
+
+Data Manipulation
+=================
+
+These processors can be used on `ImageData` or `AcquisitionData` objects.
+
+
+Data Slicer
+-----------
+
+.. autoclass:: cil.processors.Slicer
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Data Binner
+-----------
+
+.. autoclass:: cil.processors.Binner
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Data Padder
+-----------
+
+.. autoclass:: cil.processors.Padder
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Mask Generator from Data
+------------------------
+
+.. autoclass:: cil.processors.MaskGenerator
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Data Masking
+------------
+
+.. autoclass:: cil.processors.Masker
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Pre-processors
+==============
+
+These processors can be used with `AcquisitionData` objects
+
+
+Centre Of Rotation Corrector
+----------------------------
+
+In the ideal alignment of a CT instrument, the projection of the axis of rotation onto the
+detector coincides with the vertical midline of the detector. In practice this is hard to achieve
+due to misalignments and/or kinematic errors in positioning of CT instrument components.
+A slight offset of the center of rotation with respect to the theoretical position will contribute
+to the loss of resolution; in more severe cases, it will cause severe artifacts in the reconstructed
+volume (double-borders).
+
+:code:`CentreOfRotationCorrector` can be used to estimate the offset of center of rotation from the data.
+
+:code:`CentreOfRotationCorrector` supports both parallel and cone-beam geometry with 2 different algorithms:
+
+* Cross-correlation, is suitable for single slice parallel-beam geometry. It requires two projections 180 degree apart.
+
+* Image sharpness method, which maximising the sharpness of a reconstructed slice. It can be used on single
+slice parallel-beam, and centre-slice of cone-beam geometry. For use only with datasets that can be reconstructed with FBP/FDK.
+
+
+.. autoclass:: cil.processors.CentreOfRotationCorrector
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Data Normaliser
+---------------
+
+.. autoclass:: cil.processors.Normaliser
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Transmission to Absorption Converter
+-------------------------------------
+
+.. autoclass:: cil.processors.TransmissionAbsorptionConverter
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Absorption to Transmission Converter
+------------------------------------
+
+.. autoclass:: cil.processors.AbsorptionTransmissionConverter
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Ring Remover
+------------
+
+.. autoclass:: cil.processors.RingRemover
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members: set_input, get_output
+
+
+Paganin Processor
+-----------------
+
+.. autoclass:: cil.processors.PaganinProcessor
+   :exclude-members: check_input, get_input
+   :members:
+   :inherited-members:
+
+:ref:`Return Home <mastertoc>`
diff --git a/v24.2.0/_sources/recon.rst.txt b/v24.2.0/_sources/recon.rst.txt
new file mode 100644
index 0000000000..1b94483a43
--- /dev/null
+++ b/v24.2.0/_sources/recon.rst.txt
@@ -0,0 +1,60 @@
+..    Copyright 2021 United Kingdom Research and Innovation
+      Copyright 2021 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+Recon
+*****
+
+This module allows the user to run pre-configured reconstruction algorithms on their data.
+
+
+Analytical Reconstruction
+=========================
+
+The CIL analytical reconstructions use CIL to filter and prepare the data using highly optimised routines. The filtered data is then
+backprojected using projectors from TIGRE or ASTRA-TOOLBOX.
+
+Standard FBP (filtered-backprojection) should be used for parallel-beam data. FDK (Feldkamp, Davis, and Kress) is a filtered-backprojection
+algorithm for reconstruction of cone-beam data measured with a standard circular orbit.
+
+The filter can be set to a predefined function, or a custom filter can be set. The predefined filters take the following forms:
+
+.. figure:: images/FBP_filters1.png
+    :align: center
+    :alt: FBP Filters
+    :figclass: align-center
+
+
+FBP - Reconstructor for parallel-beam geometry
+----------------------------------------------
+
+
+.. autoclass:: cil.recon.FBP
+   :members:
+   :inherited-members:
+
+
+FDK - Reconstructor for cone-beam geometry
+------------------------------------------
+
+
+.. autoclass:: cil.recon.FDK
+   :members:
+   :inherited-members:
+
+
+:ref:`Return Home <mastertoc>`
diff --git a/v24.2.0/_sources/utilities.rst.txt b/v24.2.0/_sources/utilities.rst.txt
new file mode 100644
index 0000000000..816d491b4b
--- /dev/null
+++ b/v24.2.0/_sources/utilities.rst.txt
@@ -0,0 +1,140 @@
+..    Copyright 2021 United Kingdom Research and Innovation
+      Copyright 2021 The University of Manchester
+
+      Licensed under the Apache License, Version 2.0 (the "License");
+      you may not use this file except in compliance with the License.
+      You may obtain a copy of the License at
+
+          http://www.apache.org/licenses/LICENSE-2.0
+
+      Unless required by applicable law or agreed to in writing, software
+      distributed under the License is distributed on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+      See the License for the specific language governing permissions and
+      limitations under the License.
+
+     Authors:
+     CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+
+Utilities
+*********
+
+
+Test datasets
+=============
+
+A range of small test datasets to generate and use
+
+
+A set of simulated volumes and CT data
+--------------------------------------
+
+.. autoclass:: cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA
+   :members:
+
+.. autoclass:: cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA
+   :members:
+
+.. autoclass:: cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA
+   :members:
+
+
+A CT dataset from the Diamond Light Source
+------------------------------------------
+
+.. autoclass:: cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA
+   :members:
+
+
+Simulated image data
+--------------------
+
+.. autoclass:: cil.utilities.dataexample.TestData
+   :members:
+   :inherited-members:
+
+Remote data
+-----------
+Remote data classes can be used to access specific datasets from zenodo. These
+datasets are not packaged as part of CIL, instead the `download_data(data_dir)`
+method can be used to download the dataset to a chosen data directory then loaded
+from that data directory using `get(data_dir)`.
+
+Walnut
+------
+
+.. autoclass:: cil.utilities.dataexample.WALNUT
+      :members:
+      :inherited-members:
+
+USB
+------
+
+.. autoclass:: cil.utilities.dataexample.USB
+      :members:
+      :inherited-members:
+
+KORN
+------
+
+.. autoclass:: cil.utilities.dataexample.KORN
+      :members:
+      :inherited-members:
+
+SANDSTONE
+------
+.. autoclass:: cil.utilities.dataexample.SANDSTONE
+      :members:
+      :inherited-members:
+
+
+
+Image Quality metrics
+=====================
+
+.. automodule:: cil.utilities.quality_measures
+   :members:
+
+
+Visualisation
+============
+
+show2D - Display 2D slices
+--------------------------
+
+.. autoclass:: cil.utilities.display.show2D
+   :members:
+   :inherited-members:
+
+show1D - Display 1D slices
+--------------------------
+
+.. autoclass:: cil.utilities.display.show1D
+   :members:
+   :inherited-members:
+
+show_geometry - Display system geometry
+---------------------------------------
+
+.. autoclass:: cil.utilities.display.show_geometry
+   :members:
+   :inherited-members:
+
+
+islicer - interactive display of 2D slices
+------------------------------------------
+
+.. autoclass:: cil.utilities.jupyter.islicer
+   :members:
+   :inherited-members:
+
+
+link_islicer - link islicer objects by index
+--------------------------------------------
+
+.. autoclass:: cil.utilities.jupyter.link_islicer
+   :members:
+   :inherited-members:
+
+
+:ref:`Return Home <mastertoc>`
diff --git a/v24.2.0/_static/basic.css b/v24.2.0/_static/basic.css
new file mode 100644
index 0000000000..2af6139e6b
--- /dev/null
+++ b/v24.2.0/_static/basic.css
@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
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+
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+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
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+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
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+    float: right;
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+    width: 270px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
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+div.sphinxsidebar ul {
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+    box-sizing: border-box;
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+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
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+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
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+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
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+    margin: 10px 0 0 20px;
+    padding: 0;
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+    font-weight: bold;
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+
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+    margin-left: auto;
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+}
+
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+    font-style: italic;
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+    font-size: 90%;
+}
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+    width: 100%;
+}
+
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+    text-align: left;
+    vertical-align: top;
+}
+
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+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
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+}
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+}
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+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
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+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
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+}
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+}
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+}
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+}
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+}
+
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+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
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+}
+
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+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
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+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
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+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
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+    font-weight: bold;
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+    font-weight: bold;
+}
+
+div.body p.centered {
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+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
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+div.sidebar > :last-child,
+aside.sidebar > :last-child,
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+}
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+}
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+}
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+}
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+}
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+    margin-left: 1px;
+}
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+    border-bottom: none;
+}
+
+th > :first-child,
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+}
+
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+td > :last-child {
+    margin-bottom: 0px;
+}
+
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+
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+    margin: 0.5em;
+    padding: 0.5em;
+}
+
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+    font-style: italic;
+}
+
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+}
+
+/* -- field list styles ----------------------------------------------------- */
+
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+    border: 0 !important;
+}
+
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+    margin: 0;
+    padding-left: 1em;
+}
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+}
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+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
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+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
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+
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+
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+
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+}
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+    border: 3px solid red;
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+}
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+    margin-top: 1em;
+    margin-bottom: 1em;
+}
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+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
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+}
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+}
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+}
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+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
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+abbr, acronym {
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+    cursor: help;
+}
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+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
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+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
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+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
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+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
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+}
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+}
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+}
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+table.highlighttable tr {
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+}
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+}
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+}
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+    overflow: hidden;
+}
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+}
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+div.highlight pre,
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+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
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+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
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+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
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+}
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+}
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+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
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+}
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+}
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+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
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+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/v24.2.0/_static/binder_badge_logo.svg b/v24.2.0/_static/binder_badge_logo.svg
new file mode 100644
index 0000000000..327f6b639a
--- /dev/null
+++ b/v24.2.0/_static/binder_badge_logo.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="109" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="109" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h64v20H0z"/><path fill="#579aca" d="M64 0h45v20H64z"/><path fill="url(#b)" d="M0 0h109v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"><image x="5" y="3" width="14" height="14" xlink:href="data:image/png;base64,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"/> <text x="415" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="370">launch</text><text x="415" y="140" transform="scale(.1)" textLength="370">launch</text><text x="855" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="350">binder</text><text x="855" y="140" transform="scale(.1)" textLength="350">binder</text></g> </svg>
\ No newline at end of file
diff --git a/v24.2.0/_static/broken_example.png b/v24.2.0/_static/broken_example.png
new file mode 100644
index 0000000000..4fea24e7df
Binary files /dev/null and b/v24.2.0/_static/broken_example.png differ
diff --git a/v24.2.0/_static/check-solid.svg b/v24.2.0/_static/check-solid.svg
new file mode 100644
index 0000000000..92fad4b5c0
--- /dev/null
+++ b/v24.2.0/_static/check-solid.svg
@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/v24.2.0/_static/clipboard.min.js b/v24.2.0/_static/clipboard.min.js
new file mode 100644
index 0000000000..54b3c46381
--- /dev/null
+++ b/v24.2.0/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function 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\ No newline at end of file
diff --git a/v24.2.0/_static/copy-button.svg b/v24.2.0/_static/copy-button.svg
new file mode 100644
index 0000000000..9c074dae52
--- /dev/null
+++ b/v24.2.0/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/v24.2.0/_static/copybutton.css b/v24.2.0/_static/copybutton.css
new file mode 100644
index 0000000000..f1916ec7d1
--- /dev/null
+++ b/v24.2.0/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/v24.2.0/_static/copybutton.js b/v24.2.0/_static/copybutton.js
new file mode 100644
index 0000000000..2ea7ff3e21
--- /dev/null
+++ b/v24.2.0/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/v24.2.0/_static/copybutton_funcs.js b/v24.2.0/_static/copybutton_funcs.js
new file mode 100644
index 0000000000..dbe1aaad79
--- /dev/null
+++ b/v24.2.0/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/v24.2.0/_static/doctools.js b/v24.2.0/_static/doctools.js
new file mode 100644
index 0000000000..4d67807d17
--- /dev/null
+++ b/v24.2.0/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/v24.2.0/_static/documentation_options.js b/v24.2.0/_static/documentation_options.js
new file mode 100644
index 0000000000..7676cbb815
--- /dev/null
+++ b/v24.2.0/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '24.2.0',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'dirhtml',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/v24.2.0/_static/file.png b/v24.2.0/_static/file.png
new file mode 100644
index 0000000000..a858a410e4
Binary files /dev/null and b/v24.2.0/_static/file.png differ
diff --git a/v24.2.0/_static/jupyterlite_badge_logo.svg b/v24.2.0/_static/jupyterlite_badge_logo.svg
new file mode 100644
index 0000000000..5de36d7fd5
--- /dev/null
+++ b/v24.2.0/_static/jupyterlite_badge_logo.svg
@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg width="91px" height="20px" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;"><g transform="matrix(1,0,0,1,-17102,-9779)"><g id="lite-badge-launch" transform="matrix(0.0930728,0,0,0.0635416,15477.2,9173.48)"><rect x="17457.1" y="9529.53" width="977.729" height="314.755" style="fill:none;"/><clipPath id="_clip1"><rect x="17457.1" y="9529.53" width="977.729" height="314.755"/></clipPath><g clip-path="url(#_clip1)"><g transform="matrix(1.03361,0,0,1,-586.737,-1.01863e-10)"><g transform="matrix(2.43224,0,0,2.42119,-20084.1,-14912.4)"><path d="M15802.4,10095L15447.7,10095C15440.6,10095 15434.8,10103.8 15434.8,10114.5L15434.8,10205.5C15434.8,10216.3 15440.6,10225 15447.7,10225L15802.4,10225L15802.4,10095Z" style="fill:rgb(85,85,85);"/></g><g transform="matrix(-0.76361,0,0,2.42119,30189.2,-14912.4)"><path 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diff --git a/v24.2.0/_static/language_data.js b/v24.2.0/_static/language_data.js
new file mode 100644
index 0000000000..367b8ed81b
--- /dev/null
+++ b/v24.2.0/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
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new file mode 100644
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+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
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+/* remove conflicting styling from Sphinx themes */
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+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
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+}
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+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
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+}
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+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
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+}
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+div.nboutput.container div.output_area div.highlight {
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+div.nboutput.container div[class*=highlight] pre {
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+}
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+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
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+div.nbinput.container div.prompt pre,
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+div.nbinput.container div.prompt pre > code {
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+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
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+/* all prompts */
+div.nbinput.container div.prompt,
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+div.nbinput.container div.prompt > div,
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+div.nboutput.container div.output_area {
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+}
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+}
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+}
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+div.nboutput.container div.output_area div[class*=highlight] > pre,
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+    margin: 0;
+}
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+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
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+  color: black;
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+}
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+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
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+}
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+    margin-bottom: 1em;
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+html[data-theme="light"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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diff --git a/v24.2.0/_static/scripts/bootstrap.js b/v24.2.0/_static/scripts/bootstrap.js
new file mode 100644
index 0000000000..c8178debbc
--- /dev/null
+++ b/v24.2.0/_static/scripts/bootstrap.js
@@ -0,0 +1,3 @@
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this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const Fe=".bs.carousel",He=".data-api",Be="ArrowLeft",We="ArrowRight",ze="next",Re="prev",qe="left",Ve="right",Ye=`slide${Fe}`,Ke=`slid${Fe}`,Qe=`keydown${Fe}`,Xe=`mouseenter${Fe}`,Ue=`mouseleave${Fe}`,Ge=`dragstart${Fe}`,Je=`load${Fe}${He}`,Ze=`click${Fe}${He}`,ti="carousel",ei="active",ii=".active",ni=".carousel-item",si=ii+ni,oi={[Be]:Ve,[We]:qe},ri={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},ai={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class li extends ve{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=we.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===ti&&this.cycle()}static get Default(){return ri}static get DefaultType(){return ai}static get NAME(){return"carousel"}next(){this._slide(ze)}nextWhenVisible(){!document.hidden&&Bt(this._element)&&this.next()}prev(){this._slide(Re)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?fe.one(this._element,Ke,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void fe.one(this._element,Ke,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?ze:Re;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&fe.on(this._element,Qe,(t=>this._keydown(t))),"hover"===this._config.pause&&(fe.on(this._element,Xe,(()=>this.pause())),fe.on(this._element,Ue,(()=>this._maybeEnableCycle()))),this._config.touch&&je.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of we.find(".carousel-item img",this._element))fe.on(t,Ge,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(qe)),rightCallback:()=>this._slide(this._directionToOrder(Ve)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new je(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=oi[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=we.findOne(ii,this._indicatorsElement);e.classList.remove(ei),e.removeAttribute("aria-current");const i=we.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(ei),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===ze,s=e||Gt(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>fe.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Ye).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(ei),i.classList.remove(ei,c,l),this._isSliding=!1,r(Ke)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return we.findOne(si,this._element)}_getItems(){return we.find(ni,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===qe?Re:ze:t===qe?ze:Re}_orderToDirection(t){return Kt()?t===Re?qe:Ve:t===Re?Ve:qe}static jQueryInterface(t){return this.each((function(){const e=li.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,Ze,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=we.getElementFromSelector(this);if(!e||!e.classList.contains(ti))return;t.preventDefault();const i=li.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===_e.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),fe.on(window,Je,(()=>{const t=we.find('[data-bs-ride="carousel"]');for(const e of t)li.getOrCreateInstance(e)})),Qt(li);const ci=".bs.collapse",hi=`show${ci}`,di=`shown${ci}`,ui=`hide${ci}`,fi=`hidden${ci}`,pi=`click${ci}.data-api`,mi="show",gi="collapse",_i="collapsing",bi=`:scope .${gi} .${gi}`,vi='[data-bs-toggle="collapse"]',yi={parent:null,toggle:!0},wi={parent:"(null|element)",toggle:"boolean"};class Ei extends ve{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=we.find(vi);for(const t of i){const e=we.getSelectorFromElement(t),i=we.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return yi}static get DefaultType(){return wi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>Ei.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,hi).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(gi),this._element.classList.add(_i),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi,mi),this._element.style[e]="",fe.trigger(this._element,di)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,ui).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(_i),this._element.classList.remove(gi,mi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi),fe.trigger(this._element,fi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(mi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(vi);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(bi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=Ei.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,pi,vi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))Ei.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(Ei);const Ai="dropdown",Ti=".bs.dropdown",Ci=".data-api",Oi="ArrowUp",xi="ArrowDown",ki=`hide${Ti}`,Li=`hidden${Ti}`,Si=`show${Ti}`,Di=`shown${Ti}`,$i=`click${Ti}${Ci}`,Ii=`keydown${Ti}${Ci}`,Ni=`keyup${Ti}${Ci}`,Pi="show",Mi='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',ji=`${Mi}.${Pi}`,Fi=".dropdown-menu",Hi=Kt()?"top-end":"top-start",Bi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",zi=Kt()?"bottom-start":"bottom-end",Ri=Kt()?"left-start":"right-start",qi=Kt()?"right-start":"left-start",Vi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Yi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Ki extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Fi)[0]||we.prev(this._element,Fi)[0]||we.findOne(Fi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Vi}static get DefaultType(){return Yi}static get NAME(){return Ai}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,Si,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Pi),this._element.classList.add(Pi),fe.trigger(this._element,Di,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,ki,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Pi),this._element.classList.remove(Pi),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,Li,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ai.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Ft(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Pi)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Ri;if(t.classList.contains("dropstart"))return qi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Bi:Hi:e?zi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===xi,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Ki.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(ji);for(const i of e){const e=Ki.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Oi,xi].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Mi)?this:we.prev(this,Mi)[0]||we.next(this,Mi)[0]||we.findOne(Mi,t.delegateTarget.parentNode),o=Ki.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Ii,Mi,Ki.dataApiKeydownHandler),fe.on(document,Ii,Fi,Ki.dataApiKeydownHandler),fe.on(document,$i,Ki.clearMenus),fe.on(document,Ni,Ki.clearMenus),fe.on(document,$i,Mi,(function(t){t.preventDefault(),Ki.getOrCreateInstance(this).toggle()})),Qt(Ki);const Qi="backdrop",Xi="show",Ui=`mousedown.bs.${Qi}`,Gi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ji={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Zi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Gi}static get DefaultType(){return Ji}static get NAME(){return Qi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Xi),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Xi),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Ui),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Ui,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const tn=".bs.focustrap",en=`focusin${tn}`,nn=`keydown.tab${tn}`,sn="backward",on={autofocus:!0,trapElement:null},rn={autofocus:"boolean",trapElement:"element"};class an extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return on}static get DefaultType(){return rn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,tn),fe.on(document,en,(t=>this._handleFocusin(t))),fe.on(document,nn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,tn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===sn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?sn:"forward")}}const ln=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",cn=".sticky-top",hn="padding-right",dn="margin-right";class un{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,hn,(e=>e+t)),this._setElementAttributes(ln,hn,(e=>e+t)),this._setElementAttributes(cn,dn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,hn),this._resetElementAttributes(ln,hn),this._resetElementAttributes(cn,dn)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const fn=".bs.modal",pn=`hide${fn}`,mn=`hidePrevented${fn}`,gn=`hidden${fn}`,_n=`show${fn}`,bn=`shown${fn}`,vn=`resize${fn}`,yn=`click.dismiss${fn}`,wn=`mousedown.dismiss${fn}`,En=`keydown.dismiss${fn}`,An=`click${fn}.data-api`,Tn="modal-open",Cn="show",On="modal-static",xn={backdrop:!0,focus:!0,keyboard:!0},kn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class Ln extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new un,this._addEventListeners()}static get Default(){return xn}static get DefaultType(){return kn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,_n,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(Tn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,pn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(Cn),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,fn),fe.off(this._dialog,fn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Zi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new an({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(Cn),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,bn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,En,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,vn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,wn,(t=>{fe.one(this._element,yn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Tn),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,gn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,mn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(On)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(On),this._queueCallback((()=>{this._element.classList.remove(On),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Ln.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,An,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,_n,(t=>{t.defaultPrevented||fe.one(e,gn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&Ln.getInstance(i).hide(),Ln.getOrCreateInstance(e).toggle(this)})),Ee(Ln),Qt(Ln);const Sn=".bs.offcanvas",Dn=".data-api",$n=`load${Sn}${Dn}`,In="show",Nn="showing",Pn="hiding",Mn=".offcanvas.show",jn=`show${Sn}`,Fn=`shown${Sn}`,Hn=`hide${Sn}`,Bn=`hidePrevented${Sn}`,Wn=`hidden${Sn}`,zn=`resize${Sn}`,Rn=`click${Sn}${Dn}`,qn=`keydown.dismiss${Sn}`,Vn={backdrop:!0,keyboard:!0,scroll:!1},Yn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Kn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Vn}static get DefaultType(){return Yn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,jn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new un).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Nn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(In),this._element.classList.remove(Nn),fe.trigger(this._element,Fn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,Hn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(Pn),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(In,Pn),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new un).reset(),fe.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Zi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Bn)}:null})}_initializeFocusTrap(){return new an({trapElement:this._element})}_addEventListeners(){fe.on(this._element,qn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Bn))}))}static jQueryInterface(t){return this.each((function(){const e=Kn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Rn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Wn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Mn);i&&i!==e&&Kn.getInstance(i).hide(),Kn.getOrCreateInstance(e).toggle(this)})),fe.on(window,$n,(()=>{for(const t of we.find(Mn))Kn.getOrCreateInstance(t).show()})),fe.on(window,zn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Kn.getOrCreateInstance(t).hide()})),Ee(Kn),Qt(Kn);const Qn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],dd:[],div:[],dl:[],dt:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Xn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Un=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Gn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Xn.has(i)||Boolean(Un.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Jn={allowList:Qn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Zn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},ts={entry:"(string|element|function|null)",selector:"(string|element)"};class es extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Jn}static get DefaultType(){return Zn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},ts)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Gn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const is=new Set(["sanitize","allowList","sanitizeFn"]),ns="fade",ss="show",os=".tooltip-inner",rs=".modal",as="hide.bs.modal",ls="hover",cs="focus",hs={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ds={allowList:Qn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},us={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class fs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return ds}static get DefaultType(){return us}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(rs),as,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[cs]=!1,this._activeTrigger[ls]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ns,ss),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ns),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new es({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{[os]:this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ns)}_isShown(){return this.tip&&this.tip.classList.contains(ss)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=hs[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ls?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ls?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?cs:ls]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?cs:ls]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(rs),as,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=_e.getDataAttributes(this._element);for(const t of Object.keys(e))is.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".popover-header",ms=".popover-body",gs={...fs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},_s={...fs.DefaultType,content:"(null|string|element|function)"};class bs extends fs{static get Default(){return gs}static get DefaultType(){return _s}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{[ps]:this._getTitle(),[ms]:this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=bs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(bs);const vs=".bs.scrollspy",ys=`activate${vs}`,ws=`click${vs}`,Es=`load${vs}.data-api`,As="active",Ts="[href]",Cs=".nav-link",Os=`${Cs}, .nav-item > ${Cs}, .list-group-item`,xs={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ks={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ls extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xs}static get DefaultType(){return ks}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,ws),fe.on(this._config.target,ws,Ts,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(Ts,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(As),this._activateParents(t),fe.trigger(this._element,ys,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(As);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,Os))t.classList.add(As)}_clearActiveClass(t){t.classList.remove(As);const e=we.find(`${Ts}.${As}`,t);for(const t of e)t.classList.remove(As)}static jQueryInterface(t){return this.each((function(){const e=Ls.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,Es,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ls.getOrCreateInstance(t)})),Qt(Ls);const Ss=".bs.tab",Ds=`hide${Ss}`,$s=`hidden${Ss}`,Is=`show${Ss}`,Ns=`shown${Ss}`,Ps=`click${Ss}`,Ms=`keydown${Ss}`,js=`load${Ss}`,Fs="ArrowLeft",Hs="ArrowRight",Bs="ArrowUp",Ws="ArrowDown",zs="Home",Rs="End",qs="active",Vs="fade",Ys="show",Ks=".dropdown-toggle",Qs=`:not(${Ks})`,Xs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Us=`.nav-link${Qs}, .list-group-item${Qs}, [role="tab"]${Qs}, ${Xs}`,Gs=`.${qs}[data-bs-toggle="tab"], .${qs}[data-bs-toggle="pill"], 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+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/v24.2.0/_static/scripts/bootstrap.js.map b/v24.2.0/_static/scripts/bootstrap.js.map
new file mode 100644
index 0000000000..4a3502aeb2
--- /dev/null
+++ b/v24.2.0/_static/scripts/bootstrap.js.map
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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.3.3 (https://getbootstrap.com/)\n  * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n    const instanceMap = elementMap.get(element);\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n    instanceMap.set(key, instance);\n  },\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n    return null;\n  },\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key);\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n  }\n  return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n  return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  }\n\n  // Get transition-duration of the element\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n  return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object));\n  }\n  return null;\n};\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])');\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n    if (summary === null) {\n      return false;\n    }\n  }\n  return elementIsVisible;\n};\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n  if (element instanceof ShadowRoot) {\n    return element;\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null;\n  }\n  return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n  return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement);\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n  index += shouldGetNext ? 1 : -1;\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n    return fn.apply(element, [event]);\n  };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string';\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n  return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n    callable = wrapFunction(callable);\n  }\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n  if (!fn) {\n    return;\n  }\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n    const evt = hydrateObj(new Event(event, {\n      bubbles,\n      cancelable: true\n    }), args);\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n    return evt;\n  }\n};\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value;\n        }\n      });\n    }\n  }\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n  if (value === 'false') {\n    return false;\n  }\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n  if (value === '' || value === 'null') {\n    return null;\n  }\n  if (typeof value !== 'string') {\n    return value;\n  }\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n    return attributes;\n  },\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n  static get DefaultType() {\n    return {};\n  }\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    return config;\n  }\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.3';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n    if (!element) {\n      return;\n    }\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n  static get VERSION() {\n    return VERSION;\n  }\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href');\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n  return selector ? selector.split(',').map(sel => parseSelector(sel)).join(',') : null;\n};\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n    return parents;\n  },\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n      previous = previous.previousElementSibling;\n    }\n    return [];\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n      next = next.nextElementSibling;\n    }\n    return [];\n  },\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  },\n  getSelectorFromElement(element) {\n    const selector = getSelector(element);\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null;\n    }\n    return null;\n  },\n  getElementFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.findOne(selector) : null;\n  },\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.find(selector) : [];\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n    if (isDisabled(this)) {\n      return;\n    }\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target);\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove();\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n    this._initEvents();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$c;\n  }\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n  static get NAME() {\n    return NAME$d;\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n    this._handleSwipe();\n    execute(this._config.endCallback);\n  }\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n    if (!direction) {\n      return;\n    }\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n    this._addEventListeners();\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$b;\n  }\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n  static get NAME() {\n    return NAME$c;\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n    this._clearInterval();\n  }\n  cycle() {\n    this._clearInterval();\n    this._updateInterval();\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n    this.cycle();\n  }\n  to(index) {\n    const items = this._getItems();\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n    const activeIndex = this._getItemIndex(this._getActive());\n    if (activeIndex === index) {\n      return;\n    }\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n    this._slide(order, items[index]);\n  }\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause();\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n    const direction = KEY_TO_DIRECTION[event.key];\n    if (direction) {\n      event.preventDefault();\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n    if (!element) {\n      return;\n    }\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n    const activeElement = this._getActive();\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n    if (nextElement === activeElement) {\n      return;\n    }\n    const nextElementIndex = this._getItemIndex(nextElement);\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return;\n    }\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n    this._setActiveIndicatorElement(nextElementIndex);\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n  if (slideIndex) {\n    carousel.to(slideIndex);\n    carousel._maybeEnableCycle();\n    return;\n  }\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n    carousel._maybeEnableCycle();\n    return;\n  }\n  carousel.prev();\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n    this._initializeChildren();\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$a;\n  }\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n  static get NAME() {\n    return NAME$b;\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n    let activeChildren = [];\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n    const dimension = this._getDimension();\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.style[dimension] = 0;\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n    this._queueCallback(complete, this._element, true);\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    const dimension = this._getDimension();\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger);\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n    this._element.style[dimension] = '';\n    this._queueCallback(complete, this._element, true);\n  }\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n    config.parent = getElement(config.parent);\n    return config;\n  }\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element);\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {};\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$9;\n  }\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n  static get NAME() {\n    return NAME$a;\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._createPopper();\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    this._element.focus();\n    this._element.setAttribute('aria-expanded', true);\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    this._completeHide(relatedTarget);\n  }\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    super.dispose();\n  }\n  update() {\n    this._inNavbar = this._detectNavbar();\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.setAttribute('aria-expanded', 'false');\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n  _getConfig(config) {\n    config = super._getConfig(config);\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n    return config;\n  }\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n    let referenceElement = this._element;\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n    const popperConfig = this._getPopperConfig();\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n  _getPlacement() {\n    const parentDropdown = this._parent;\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    };\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n    if (!items.length) {\n      return;\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n      context._completeHide(relatedTarget);\n    }\n  }\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n    event.preventDefault();\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n      instance._selectMenuItem(event);\n      return;\n    }\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$8;\n  }\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n  static get NAME() {\n    return NAME$9;\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._append();\n    const element = this._getElement();\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n    element.classList.add(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n    this._element.remove();\n    this._isAppended = false;\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n      this._element = backdrop;\n    }\n    return this._element;\n  }\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n    const element = this._getElement();\n    this._config.rootElement.append(element);\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$7;\n  }\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n  static get NAME() {\n    return NAME$8;\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n    const elements = SelectorEngine.focusableChildren(trapElement);\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n  hide() {\n    const width = this.getWidth();\n    this._disableOverFlow();\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n  isOverflowing() {\n    return this.getWidth() > 0;\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n    this._element.style.overflow = 'hidden';\n  }\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n      this._saveInitialAttribute(element, styleProperty);\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty);\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$6;\n  }\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n  static get NAME() {\n    return NAME$7;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._isTransitioning = true;\n    this._scrollBar.hide();\n    document.body.classList.add(CLASS_NAME_OPEN);\n    this._adjustDialog();\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = false;\n    this._isTransitioning = true;\n    this._focustrap.deactivate();\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n  dispose() {\n    EventHandler.off(window, EVENT_KEY$4);\n    EventHandler.off(this._dialog, EVENT_KEY$4);\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n  handleUpdate() {\n    this._adjustDialog();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n    this._element.style.display = 'block';\n    this._element.removeAttribute('aria-hidden');\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n          return;\n        }\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n  _hideModal() {\n    this._element.style.display = 'none';\n    this._element.setAttribute('aria-hidden', true);\n    this._element.removeAttribute('aria-modal');\n    this._element.removeAttribute('role');\n    this._isTransitioning = false;\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n      this._resetAdjustments();\n      this._scrollBar.reset();\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY;\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n    this._element.classList.add(CLASS_NAME_STATIC);\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n    this._element.focus();\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const scrollbarWidth = this._scrollBar.getWidth();\n    const isBodyOverflowing = scrollbarWidth > 0;\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](relatedTarget);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  });\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$5;\n  }\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n  static get NAME() {\n    return NAME$6;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._backdrop.show();\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._focustrap.deactivate();\n    this._element.blur();\n    this._isShown = false;\n    this._element.classList.add(CLASS_NAME_HIDING);\n    this._backdrop.hide();\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n      this._element.removeAttribute('aria-modal');\n      this._element.removeAttribute('role');\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n    this._queueCallback(completeCallback, this._element, true);\n  }\n  dispose() {\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n      this.hide();\n    };\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n    });\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  });\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  dd: [],\n  div: [],\n  dl: [],\n  dt: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n    }\n    return true;\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  }\n\n  // Getters\n  static get Default() {\n    return Default$4;\n  }\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n  static get NAME() {\n    return NAME$5;\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n  changeContent(content) {\n    this._checkContent(content);\n    this._config.content = {\n      ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n    const template = templateWrapper.children[0];\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n    return template;\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n    this._checkContent(config.content);\n  }\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n    if (!templateElement) {\n      return;\n    }\n    content = this._resolvePossibleFunction(content);\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n      return;\n    }\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n    templateElement.textContent = content;\n  }\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this]);\n  }\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n    templateElement.textContent = element.textContent;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n    super(element, config);\n\n    // Private\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null;\n\n    // Protected\n    this.tip = null;\n    this._setListeners();\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$3;\n  }\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n  static get NAME() {\n    return NAME$4;\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true;\n  }\n  disable() {\n    this._isEnabled = false;\n  }\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n    this._activeTrigger.click = !this._activeTrigger.click;\n    if (this._isShown()) {\n      this._leave();\n      return;\n    }\n    this._enter();\n  }\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n    this._disposePopper();\n    super.dispose();\n  }\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper();\n    const tip = this._getTipElement();\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n    const {\n      container\n    } = this._config;\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n      if (this._isHovered === false) {\n        this._leave();\n      }\n      this._isHovered = false;\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const tip = this._getTipElement();\n    tip.classList.remove(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n      this._element.removeAttribute('aria-describedby');\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n    return this.tip;\n  }\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml();\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null;\n    }\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n    return tip;\n  }\n  setContent(content) {\n    this._newContent = content;\n    if (this._isShown()) {\n      this._disposePopper();\n      this.show();\n    }\n  }\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n    return this._templateFactory;\n  }\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element]);\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element]);\n  }\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n          context._leave();\n        });\n      }\n    }\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n    if (!title) {\n      return;\n    }\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title');\n  }\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n    this._isHovered = true;\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n    this._isHovered = false;\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n    return config;\n  }\n  _getDelegateConfig() {\n    const config = {};\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value;\n      }\n    }\n    config.selector = false;\n    config.trigger = 'manual';\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config;\n  }\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n      this._popper = null;\n    }\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n  static get NAME() {\n    return NAME$3;\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default$1;\n  }\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n  static get NAME() {\n    return NAME$2;\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables();\n    this._maybeEnableSmoothScroll();\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n  dispose() {\n    this._observer.disconnect();\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body;\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n    return config;\n  }\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height;\n      }\n    });\n  }\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n      this._process(targetElement(entry));\n    };\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n        this._clearActiveClass(targetElement(entry));\n        continue;\n      }\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry);\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return;\n        }\n        continue;\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor);\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n    this._clearActiveClass(this._config.target);\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n    this._activateParents(target);\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst HOME_KEY = 'Home';\nconst END_KEY = 'End';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = `:not(${SELECTOR_DROPDOWN_TOGGLE})`;\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n    if (!this._parent) {\n      return;\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren());\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME$1;\n  }\n\n  // Public\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n    if (this._elemIsActive(innerElem)) {\n      return;\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem();\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n    this._deactivate(active, innerElem);\n    this._activate(innerElem, active);\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.add(CLASS_NAME_ACTIVE);\n    this._activate(SelectorEngine.getElementFromSelector(element)); // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n      this._toggleDropDown(element, true);\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n    this._deactivate(SelectorEngine.getElementFromSelector(element)); // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n      this._toggleDropDown(element, false);\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key)) {\n      return;\n    }\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault();\n    const children = this._getChildren().filter(element => !isDisabled(element));\n    let nextActiveElement;\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1];\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true);\n    }\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n    const isActive = this._elemIsActive(child);\n    const outerElem = this._getOuterElement(child);\n    child.setAttribute('aria-selected', isActive);\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n    this._setAttributeIfNotExists(child, 'role', 'tab');\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child);\n    if (!target) {\n      return;\n    }\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`);\n    }\n  }\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  Tab.getOrCreateInstance(this).show();\n});\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n    this._setListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default;\n  }\n  static get DefaultType() {\n    return DefaultType;\n  }\n  static get NAME() {\n    return NAME;\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._clearTimeout();\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n      this._maybeScheduleHide();\n    };\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n    this._element.classList.add(CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  dispose() {\n    this._clearTimeout();\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n    super.dispose();\n  }\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n    if (isInteracting) {\n      this._clearTimeout();\n      return;\n    }\n    const nextElement = event.relatedTarget;\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n    this._maybeScheduleHide();\n  }\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config](this);\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// TODO: @trallard might end up moving this to the main JS file\n// Import and setup functions to control Bootstrap's behavior.\nimport \"@popperjs/core\";\n// Import all of Bootstrap's JS\nimport * as bootstrap from \"bootstrap\";\nimport { documentReady } from \"./mixin\";\n\n/*******************************************************************************\n * Trigger tooltips\n */\n\n/**\n * Add tooltip to each element with the \"tooltip\" data-bs-toogle class\n */\nfunction TriggerTooltip() {\n  var tooltipTriggerList = [].slice.call(\n    document.querySelectorAll('[data-bs-toggle=\"tooltip\"]'),\n  );\n  tooltipTriggerList.map(function (tooltipTriggerEl) {\n    return new bootstrap.Tooltip(tooltipTriggerEl, {\n      delay: { show: 500, hide: 100 },\n    });\n  });\n}\n\n/*******************************************************************************\n * back 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diff --git a/v24.2.0/_static/scripts/fontawesome.js b/v24.2.0/_static/scripts/fontawesome.js
new file mode 100644
index 0000000000..71955b4292
--- /dev/null
+++ b/v24.2.0/_static/scripts/fontawesome.js
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define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","/**\n * Compare [semver](https://semver.org/) version strings to find greater, equal or lesser.\n * This library supports the full semver specification, including comparing versions with different number of digits like `1.0.0`, `1.0`, `1`, and pre-release versions like `1.0.0-alpha`.\n * @param v1 - First version to compare\n * @param v2 - Second version to compare\n * @returns Numeric value compatible with the [Array.sort(fn) interface](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort#Parameters).\n */\nexport const compareVersions = (v1, v2) => {\n    // validate input and split into segments\n    const n1 = validateAndParse(v1);\n    const n2 = validateAndParse(v2);\n    // pop off the patch\n    const p1 = n1.pop();\n    const p2 = n2.pop();\n    // validate numbers\n    const r = compareSegments(n1, n2);\n    if (r !== 0)\n        return r;\n    // validate pre-release\n    if (p1 && p2) {\n        return compareSegments(p1.split('.'), p2.split('.'));\n    }\n    else if (p1 || p2) {\n        return p1 ? -1 : 1;\n    }\n    return 0;\n};\n/**\n * Validate [semver](https://semver.org/) version strings.\n *\n * @param version Version number to validate\n * @returns `true` if the version number is a valid semver version number, `false` otherwise.\n *\n * @example\n * ```\n * validate('1.0.0-rc.1'); // return true\n * validate('1.0-rc.1'); // return false\n * validate('foo'); // return false\n * ```\n */\nexport const validate = (version) => typeof version === 'string' && /^[v\\d]/.test(version) && semver.test(version);\n/**\n * Compare [semver](https://semver.org/) version strings using the specified operator.\n *\n * @param v1 First version to compare\n * @param v2 Second version to compare\n * @param operator Allowed arithmetic operator to use\n * @returns `true` if the comparison between the firstVersion and the secondVersion satisfies the operator, `false` otherwise.\n *\n * @example\n * ```\n * compare('10.1.8', '10.0.4', '>'); // return true\n * compare('10.0.1', '10.0.1', '='); // return true\n * compare('10.1.1', '10.2.2', '<'); // return true\n * compare('10.1.1', '10.2.2', '<='); // return true\n * compare('10.1.1', '10.2.2', '>='); // return false\n * ```\n */\nexport const compare = (v1, v2, operator) => {\n    // validate input operator\n    assertValidOperator(operator);\n    // since result of compareVersions can only be -1 or 0 or 1\n    // a simple map can be used to replace switch\n    const res = compareVersions(v1, v2);\n    return operatorResMap[operator].includes(res);\n};\n/**\n * Match [npm semver](https://docs.npmjs.com/cli/v6/using-npm/semver) version range.\n *\n * @param version Version number to match\n * @param range Range pattern for version\n * @returns `true` if the version number is within the range, `false` otherwise.\n *\n * @example\n * ```\n * satisfies('1.1.0', '^1.0.0'); // return true\n * satisfies('1.1.0', '~1.0.0'); // return false\n * ```\n */\nexport const satisfies = (version, range) => {\n    // if no range operator then \"=\"\n    const m = range.match(/^([<>=~^]+)/);\n    const op = m ? m[1] : '=';\n    // if gt/lt/eq then operator compare\n    if (op !== '^' && op !== '~')\n        return compare(version, range, op);\n    // else range of either \"~\" or \"^\" is assumed\n    const [v1, v2, v3, , vp] = validateAndParse(version);\n    const [r1, r2, r3, , rp] = validateAndParse(range);\n    const v = [v1, v2, v3];\n    const r = [r1, r2 !== null && r2 !== void 0 ? r2 : 'x', r3 !== null && r3 !== void 0 ? r3 : 'x'];\n    // validate pre-release\n    if (rp) {\n        if (!vp)\n            return false;\n        if (compareSegments(v, r) !== 0)\n            return false;\n        if (compareSegments(vp.split('.'), rp.split('.')) === -1)\n            return false;\n    }\n    // first non-zero number\n    const nonZero = r.findIndex((v) => v !== '0') + 1;\n    // pointer to where segments can be >=\n    const i = op === '~' ? 2 : nonZero > 1 ? nonZero : 1;\n    // before pointer must be equal\n    if (compareSegments(v.slice(0, i), r.slice(0, i)) !== 0)\n        return false;\n    // after pointer must be >=\n    if (compareSegments(v.slice(i), r.slice(i)) === -1)\n        return false;\n    return true;\n};\nconst semver = /^[v^~<>=]*?(\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+))?(?:-([\\da-z\\-]+(?:\\.[\\da-z\\-]+)*))?(?:\\+[\\da-z\\-]+(?:\\.[\\da-z\\-]+)*)?)?)?$/i;\nconst validateAndParse = (version) => {\n    if (typeof version !== 'string') {\n        throw new TypeError('Invalid argument expected string');\n    }\n    const match = version.match(semver);\n    if (!match) {\n        throw new Error(`Invalid argument not valid semver ('${version}' received)`);\n    }\n    match.shift();\n    return match;\n};\nconst isWildcard = (s) => s === '*' || s === 'x' || s === 'X';\nconst tryParse = (v) => {\n    const n = parseInt(v, 10);\n    return isNaN(n) ? v : n;\n};\nconst forceType = (a, b) => typeof a !== typeof b ? [String(a), String(b)] : [a, b];\nconst compareStrings = (a, b) => {\n    if (isWildcard(a) || isWildcard(b))\n        return 0;\n    const [ap, bp] = forceType(tryParse(a), tryParse(b));\n    if (ap > bp)\n        return 1;\n    if (ap < bp)\n        return -1;\n    return 0;\n};\nconst compareSegments = (a, b) => {\n    for (let i = 0; i < Math.max(a.length, b.length); i++) {\n        const r = compareStrings(a[i] || '0', b[i] || '0');\n        if (r !== 0)\n            return r;\n    }\n    return 0;\n};\nconst operatorResMap = {\n    '>': [1],\n    '>=': [0, 1],\n    '=': [0],\n    '<=': [-1, 0],\n    '<': [-1],\n};\nconst allowedOperators = Object.keys(operatorResMap);\nconst assertValidOperator = (op) => {\n    if (typeof op !== 'string') {\n        throw new TypeError(`Invalid operator type, expected string but got ${typeof op}`);\n    }\n    if (allowedOperators.indexOf(op) === -1) {\n        throw new Error(`Invalid operator, expected one of ${allowedOperators.join('|')}`);\n    }\n};\n//# sourceMappingURL=index.js.map","// Define the custom behavior of the page\nimport { documentReady } from \"./mixin\";\nimport { compare, validate } from \"compare-versions\";\n\nimport \"../styles/pydata-sphinx-theme.scss\";\n\n/*******************************************************************************\n * Theme interaction\n */\n\nvar prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\");\n\n/**\n * set the the body theme to the one specified by the user browser\n *\n * @param {event} e\n */\nfunction autoTheme(e) {\n  document.documentElement.dataset.theme = prefersDark.matches\n    ? \"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n  // TODO: remove this line after Bootstrap upgrade\n  // v5.3 has a colors mode: https://getbootstrap.com/docs/5.3/customize/color-modes/\n  document.querySelectorAll(\".dropdown-menu\").forEach((el) => {\n    if (theme === \"dark\") {\n      el.classList.add(\"dropdown-menu-dark\");\n    } else {\n      el.classList.remove(\"dropdown-menu-dark\");\n    }\n  });\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10,\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \":not(#pst-search-dialog) > form.bd-search\",\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  const input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  const searchDialog = document.getElementById(\"pst-search-dialog\");\n  const hiddenInput = searchDialog.querySelector(\"input\");\n  if (input === hiddenInput) {\n    if (searchDialog.open) {\n      searchDialog.close();\n    } else {\n      // Note: browsers should focus the input field inside the modal dialog\n      // automatically when it is opened.\n      searchDialog.showModal();\n    }\n  } else {\n    // if the input field is not the hidden one, then toggle its focus state\n\n    if (document.activeElement === input) {\n      input.blur();\n    } else {\n      input.focus();\n      input.select();\n      input.scrollIntoView({ block: \"center\" });\n    }\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if (\n        // Ignore if shift or alt are pressed\n        !event.shiftKey &&\n        !event.altKey &&\n        // On Mac use ⌘, all other OS use Ctrl\n        (useCommandKey\n          ? event.metaKey && !event.ctrlKey\n          : !event.metaKey && event.ctrlKey) &&\n        // Case-insensitive so the shortcut still works with caps lock\n        /^k$/i.test(event.key)\n      ) {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && /Escape/i.test(event.key)) {\n        toggleSearchField();\n      }\n    },\n    true,\n  );\n};\n\n/**\n * If the user is on a Mac, use command (⌘) instead of control (ctrl) key\n *\n * Note: `navigator.platform` is deprecated; however MDN still recommends using\n * it for the one specific use case of detecting whether a keyboard shortcut\n * should use control or command:\n * https://developer.mozilla.org/en-US/docs/Web/API/Navigator/platform#examples\n */\nvar useCommandKey =\n  navigator.platform.indexOf(\"Mac\") === 0 || navigator.platform === \"iPhone\";\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\n\nvar changeSearchShortcutKey = () => {\n  let shortcuts = document.querySelectorAll(\".search-button__kbd-shortcut\");\n  if (useCommandKey) {\n    shortcuts.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\"),\n    );\n  }\n};\n\nconst closeDialogOnBackdropClick = ({\n  currentTarget: dialog,\n  clientX,\n  clientY,\n}) => {\n  if (!dialog.open) {\n    return;\n  }\n\n  // Dialog.getBoundingClientRect() does not include ::backdrop. (This is the\n  // trick that allows us to determine if click was inside or outside of the\n  // dialog: click handler includes backdrop, getBoundingClientRect does not.)\n  const { left, right, top, bottom } = dialog.getBoundingClientRect();\n\n  // 0, 0 means top left\n  const clickWasOutsideDialog =\n    clientX < left || right < clientX || clientY < top || bottom < clientY;\n\n  if (clickWasOutsideDialog) {\n    dialog.close();\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // If user clicks outside the search modal dialog, then close it.\n  const searchDialog = document.getElementById(\"pst-search-dialog\");\n  // Dialog click handler includes clicks on dialog ::backdrop.\n  searchDialog.addEventListener(\"click\", closeDialogOnBackdropClick);\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * path component of URL\n */\nvar getCurrentUrlPath = () => {\n  if (DOCUMENTATION_OPTIONS.BUILDER == \"dirhtml\") {\n    return DOCUMENTATION_OPTIONS.pagename == \"index\"\n      ? `/`\n      : `${DOCUMENTATION_OPTIONS.pagename}/`;\n  }\n  return `${DOCUMENTATION_OPTIONS.pagename}.html`;\n};\n\n/**\n * Allow user to dismiss the warning banner about the docs version being dev / old.\n * We store the dismissal date and version, to give us flexibility about making the\n * dismissal last for longer than one browser session, if we decide to do that.\n *\n * @param {event} event the event that trigger the check\n */\nasync function DismissBannerAndStorePref(event) {\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  banner.remove();\n  const version = DOCUMENTATION_OPTIONS.VERSION;\n  const now = new Date();\n  const banner_pref = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  );\n  console.debug(\n    `[PST] Dismissing the version warning banner on ${version} starting ${now}.`,\n  );\n  banner_pref[version] = now;\n  localStorage.setItem(\"pst_banner_pref\", JSON.stringify(banner_pref));\n}\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nasync function checkPageExistsAndRedirect(event) {\n  // ensure we don't follow the initial link\n  event.preventDefault();\n  const currentFilePath = getCurrentUrlPath();\n  let tryUrl = event.currentTarget.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n  try {\n    let head = await fetch(tryUrl, { method: \"HEAD\" });\n    if (head.ok) {\n      location.href = tryUrl; // the page exists, go there\n    } else {\n      location.href = otherDocsHomepage;\n    }\n  } catch (err) {\n    // something went wrong, probably CORS restriction, fallback to other docs homepage\n    location.href = otherDocsHomepage;\n  }\n}\n\n/**\n * Load and parse the version switcher JSON file from an absolute or relative URL.\n *\n * @param {string} url The URL to load version switcher entries from.\n */\nasync function fetchVersionSwitcherJSON(url) {\n  // first check if it's a valid URL\n  try {\n    var result = new URL(url);\n  } catch (err) {\n    if (err instanceof TypeError) {\n      if (!window.location.origin) {\n        // window.location.origin is null for local static sites\n        // (ie. window.location.protocol == 'file:')\n        //\n        // TODO: Fix this to return the static version switcher by working out\n        // how to get the correct path to the switcher JSON file on local static builds\n        return null;\n      }\n      // assume we got a relative path, and fix accordingly. But first, we need to\n      // use `fetch()` to follow redirects so we get the correct final base URL\n      const origin = await fetch(window.location.origin, { method: \"HEAD\" });\n      result = new URL(url, origin.url);\n    } else {\n      // something unexpected happened\n      throw err;\n    }\n  }\n  // load and return the JSON\n  const response = await fetch(result);\n  const data = await response.json();\n  return data;\n}\n\n// Populate the version switcher from the JSON data\nfunction populateVersionSwitcher(data, versionSwitcherBtns) {\n  const currentFilePath = getCurrentUrlPath();\n  versionSwitcherBtns.forEach((btn) => {\n    // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n    btn.dataset[\"activeVersionName\"] = \"\";\n    btn.dataset[\"activeVersion\"] = \"\";\n  });\n  // in case there are multiple entries with the same version string, this helps us\n  // decide which entry's `name` to put on the button itself. Without this, it would\n  // always be the *last* version-matching entry; now it will be either the\n  // version-matching entry that is also marked as `\"preferred\": true`, or if that\n  // doesn't exist: the *first* version-matching entry.\n  data = data.map((entry) => {\n    // does this entry match the version that we're currently building/viewing?\n    entry.match =\n      entry.version == DOCUMENTATION_OPTIONS.theme_switcher_version_match;\n    entry.preferred = entry.preferred || false;\n    // if no custom name specified (e.g., \"latest\"), use version string\n    if (!(\"name\" in entry)) {\n      entry.name = entry.version;\n    }\n    return entry;\n  });\n  const hasMatchingPreferredEntry = data\n    .map((entry) => entry.preferred && entry.match)\n    .some(Boolean);\n  var foundMatch = false;\n  // create links to the corresponding page in the other docs versions\n  data.forEach((entry) => {\n    // create the node\n    const anchor = document.createElement(\"a\");\n    anchor.setAttribute(\n      \"class\",\n      \"dropdown-item list-group-item list-group-item-action py-1\",\n    );\n    anchor.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n    anchor.setAttribute(\"role\", \"option\");\n    const span = document.createElement(\"span\");\n    span.textContent = `${entry.name}`;\n    anchor.appendChild(span);\n    // Add dataset values for the version and name in case people want\n    // to apply CSS styling based on this information.\n    anchor.dataset[\"versionName\"] = entry.name;\n    anchor.dataset[\"version\"] = entry.version;\n    // replace dropdown button text with the preferred display name of the\n    // currently-viewed version, rather than using sphinx's {{ version }} variable.\n    // also highlight the dropdown entry for the currently-viewed version's entry\n    let matchesAndIsPreferred = hasMatchingPreferredEntry && entry.preferred;\n    let matchesAndIsFirst =\n      !hasMatchingPreferredEntry && !foundMatch && entry.match;\n    if (matchesAndIsPreferred || matchesAndIsFirst) {\n      anchor.classList.add(\"active\");\n      versionSwitcherBtns.forEach((btn) => {\n        btn.innerText = entry.name;\n        btn.dataset[\"activeVersionName\"] = entry.name;\n        btn.dataset[\"activeVersion\"] = entry.version;\n      });\n      foundMatch = true;\n    }\n    // There may be multiple version-switcher elements, e.g. one\n    // in a slide-over panel displayed on smaller screens.\n    document.querySelectorAll(\".version-switcher__menu\").forEach((menu) => {\n      // we need to clone the node for each menu, but onclick attributes are not\n      // preserved by `.cloneNode()` so we add onclick here after cloning.\n      let node = anchor.cloneNode(true);\n      node.onclick = checkPageExistsAndRedirect;\n      // on click, AJAX calls will check if the linked page exists before\n      // trying to redirect, and if not, will redirect to the homepage\n      // for that version of the docs.\n      menu.append(node);\n    });\n  });\n}\n\n/*******************************************************************************\n * Warning banner when viewing non-stable version of the docs.\n */\n\n/**\n * Show a warning banner when viewing a non-stable version of the docs.\n *\n * adapted 2023-06 from https://mne.tools/versionwarning.js, which was\n * originally adapted 2020-05 from https://scikit-learn.org/versionwarning.js\n *\n * @param {Array} data The version data used to populate the switcher menu.\n */\nfunction showVersionWarningBanner(data) {\n  var version = DOCUMENTATION_OPTIONS.VERSION;\n  // figure out what latest stable version is\n  var preferredEntries = data.filter((entry) => entry.preferred);\n  if (preferredEntries.length !== 1) {\n    const howMany = preferredEntries.length == 0 ? \"No\" : \"Multiple\";\n    console.log(\n      `[PST] ${howMany} versions marked \"preferred\" found in versions JSON, ignoring.`,\n    );\n    return;\n  }\n  const preferredVersion = preferredEntries[0].version;\n  const preferredURL = preferredEntries[0].url;\n  // if already on preferred version, nothing to do\n  const versionsAreComparable = validate(version) && validate(preferredVersion);\n  if (versionsAreComparable && compare(version, preferredVersion, \"=\")) {\n    console.log(\n      \"[PST]: This is the preferred version of the docs, not showing the warning banner.\",\n    );\n    return;\n  }\n  // check if banner has been dismissed recently\n  const dismiss_date_str = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  )[version];\n  if (dismiss_date_str != null) {\n    const dismiss_date = new Date(dismiss_date_str);\n    const now = new Date();\n    const milliseconds_in_a_day = 24 * 60 * 60 * 1000;\n    const days_passed = (now - dismiss_date) / milliseconds_in_a_day;\n    const timeout_in_days = 14;\n    if (days_passed < timeout_in_days) {\n      console.info(\n        `[PST] Suppressing version warning banner; was dismissed ${Math.floor(\n          days_passed,\n        )} day(s) ago`,\n      );\n      return;\n    }\n  }\n\n  // now construct the warning banner\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  const middle = document.createElement(\"div\");\n  const inner = document.createElement(\"div\");\n  const bold = document.createElement(\"strong\");\n  const button = document.createElement(\"a\");\n  const close_btn = document.createElement(\"a\");\n  // these classes exist since pydata-sphinx-theme v0.10.0\n  // the init class is used for animation\n  middle.classList = \"bd-header-announcement__content  ms-auto me-auto\";\n  inner.classList = \"sidebar-message\";\n  button.classList =\n    \"btn text-wrap font-weight-bold ms-3 my-1 align-baseline pst-button-link-to-stable-version\";\n  button.href = `${preferredURL}${getCurrentUrlPath()}`;\n  button.innerText = \"Switch to stable version\";\n  button.onclick = checkPageExistsAndRedirect;\n  close_btn.classList = \"ms-3 my-1 align-baseline\";\n  const close_x = document.createElement(\"i\");\n  close_btn.append(close_x);\n  close_x.classList = \"fa-solid fa-xmark\";\n  close_btn.onclick = DismissBannerAndStorePref;\n  // add the version-dependent text\n  inner.innerText = \"This is documentation for \";\n  const isDev =\n    version.includes(\"dev\") ||\n    version.includes(\"rc\") ||\n    version.includes(\"pre\");\n  const newerThanPreferred =\n    versionsAreComparable && compare(version, preferredVersion, \">\");\n  if (isDev || newerThanPreferred) {\n    bold.innerText = \"an unstable development version\";\n  } else if (versionsAreComparable && compare(version, preferredVersion, \"<\")) {\n    bold.innerText = `an old version (${version})`;\n  } else if (!version) {\n    bold.innerText = \"an unknown version\"; // e.g., an empty string\n  } else {\n    bold.innerText = `version ${version}`;\n  }\n  banner.appendChild(middle);\n  banner.append(close_btn);\n  middle.appendChild(inner);\n  inner.appendChild(bold);\n  inner.appendChild(document.createTextNode(\".\"));\n  inner.appendChild(button);\n  banner.classList.remove(\"d-none\");\n}\n\n/*******************************************************************************\n * MutationObserver to move the ReadTheDocs button\n */\n\n/**\n * intercept the RTD flyout and place it in the rtd-footer-container if existing\n * if not it stays where on top of the page\n */\nfunction initRTDObserver() {\n  const mutatedCallback = (mutationList, observer) => {\n    mutationList.forEach((mutation) => {\n      // Check whether the mutation is for RTD, which will have a specific structure\n      if (mutation.addedNodes.length === 0) {\n        return;\n      }\n      if (mutation.addedNodes[0].data === undefined) {\n        return;\n      }\n      if (mutation.addedNodes[0].data.search(\"Inserted RTD Footer\") != -1) {\n        mutation.addedNodes.forEach((node) => {\n          document.getElementById(\"rtd-footer-container\").append(node);\n        });\n      }\n    });\n  };\n\n  const observer = new MutationObserver(mutatedCallback);\n  const config = { childList: true };\n  observer.observe(document.body, config);\n}\n\nasync function fetchAndUseVersions() {\n  // fetch the JSON version data (only once), then use it to populate the version\n  // switcher and maybe show the version warning bar\n  var versionSwitcherBtns = document.querySelectorAll(\n    \".version-switcher__button\",\n  );\n  const hasSwitcherMenu = versionSwitcherBtns.length > 0;\n  const hasVersionsJSON = DOCUMENTATION_OPTIONS.hasOwnProperty(\n    \"theme_switcher_json_url\",\n  );\n  const wantsWarningBanner = DOCUMENTATION_OPTIONS.show_version_warning_banner;\n\n  if (hasVersionsJSON && (hasSwitcherMenu || wantsWarningBanner)) {\n    const data = await fetchVersionSwitcherJSON(\n      DOCUMENTATION_OPTIONS.theme_switcher_json_url,\n    );\n    // TODO: remove the `if(data)` once the `return null` is fixed within fetchVersionSwitcherJSON.\n    // We don't really want the switcher and warning bar to silently not work.\n    if (data) {\n      populateVersionSwitcher(data, versionSwitcherBtns);\n      if (wantsWarningBanner) {\n        showVersionWarningBanner(data);\n      }\n    }\n  }\n}\n\n/*******************************************************************************\n * Sidebar modals (for mobile / narrow screens)\n */\nfunction setupMobileSidebarKeyboardHandlers() {\n  // These are the left and right sidebars for wider screens. We cut and paste\n  // the content from these widescreen sidebars into the mobile dialogs, when\n  // the user clicks the hamburger icon button\n  const primarySidebar = document.getElementById(\"pst-primary-sidebar\");\n  const secondarySidebar = document.getElementById(\"pst-secondary-sidebar\");\n\n  // These are the corresponding left/right <dialog> elements, which are empty\n  // until the user clicks the hamburger icon\n  const primaryDialog = document.getElementById(\"pst-primary-sidebar-modal\");\n  const secondaryDialog = document.getElementById(\n    \"pst-secondary-sidebar-modal\",\n  );\n\n  // These are the hamburger-style buttons in the header nav bar. They only\n  // appear at narrow screen width.\n  const primaryToggle = document.querySelector(\".primary-toggle\");\n  const secondaryToggle = document.querySelector(\".secondary-toggle\");\n\n  // Cut nodes and classes from `from`, paste into/onto `to`\n  const cutAndPasteNodesAndClasses = (from, to) => {\n    Array.from(from.childNodes).forEach((node) => to.appendChild(node));\n    Array.from(from.classList).forEach((cls) => {\n      from.classList.remove(cls);\n      to.classList.add(cls);\n    });\n  };\n\n  // Hook up the ways to open and close the dialog\n  [\n    [primaryToggle, primaryDialog, primarySidebar],\n    [secondaryToggle, secondaryDialog, secondarySidebar],\n  ].forEach(([toggleButton, dialog, sidebar]) => {\n    if (!toggleButton || !dialog || !sidebar) {\n      return;\n    }\n\n    // Clicking the button can only open the sidebar, not close it.\n    // Clicking the button is also the *only* way to open the sidebar.\n    toggleButton.addEventListener(\"click\", (event) => {\n      event.preventDefault();\n      event.stopPropagation();\n\n      // When we open the dialog, we cut and paste the nodes and classes from\n      // the widescreen sidebar into the dialog\n      cutAndPasteNodesAndClasses(sidebar, dialog);\n\n      dialog.showModal();\n    });\n\n    // Listen for clicks on the backdrop in order to close the dialog\n    dialog.addEventListener(\"click\", closeDialogOnBackdropClick);\n\n    // We have to manually attach the escape key because there's some code in\n    // Sphinx's Sphinx_highlight.js that prevents the default behavior of the\n    // escape key\n    dialog.addEventListener(\"keydown\", (event) => {\n      if (event.key === \"Escape\") {\n        event.preventDefault();\n        event.stopPropagation();\n        dialog.close();\n      }\n    });\n\n    // When the dialog is closed, move the nodes (and classes) back to their\n    // original place\n    dialog.addEventListener(\"close\", () => {\n      cutAndPasteNodesAndClasses(dialog, sidebar);\n    });\n  });\n}\n\n/**\n * When the page loads, or the window resizes, or descendant nodes are added or\n * removed from the main element, check all code blocks and Jupyter notebook\n * outputs, and for each one that has scrollable overflow, set tabIndex = 0.\n */\nfunction addTabStopsToScrollableElements() {\n  const updateTabStops = () => {\n    document\n      .querySelectorAll(\n        [\n          // code blocks\n          \"pre\",\n          // NBSphinx notebook output\n          \".nboutput > .output_area\",\n          // Myst-NB\n          \".cell_output > .output\",\n          // ipywidgets\n          \".jp-RenderedHTMLCommon\",\n          // [rST table nodes](https://www.docutils.org/docs/ref/doctree.html#table)\n          \".pst-scrollable-table-container\",\n        ].join(\", \"),\n      )\n      .forEach((el) => {\n        el.tabIndex =\n          el.scrollWidth > el.clientWidth || el.scrollHeight > el.clientHeight\n            ? 0\n            : -1;\n      });\n  };\n  const debouncedUpdateTabStops = debounce(updateTabStops, 300);\n\n  // On window resize\n  window.addEventListener(\"resize\", debouncedUpdateTabStops);\n\n  // The following MutationObserver is for ipywidgets, which take some time to\n  // finish loading and rendering on the page (so even after the \"load\" event is\n  // fired, they still have not finished rendering). Would be nice to replace\n  // the MutationObserver if there is a way to hook into the ipywidgets code to\n  // know when it is done.\n  const mainObserver = new MutationObserver(debouncedUpdateTabStops);\n\n  // On descendant nodes added/removed from main element\n  mainObserver.observe(document.getElementById(\"main-content\"), {\n    subtree: true,\n    childList: true,\n  });\n\n  // On page load (when this function gets called)\n  updateTabStops();\n}\nfunction debounce(callback, wait) {\n  let timeoutId = null;\n  return (...args) => {\n    clearTimeout(timeoutId);\n    timeoutId = setTimeout(() => {\n      callback(...args);\n    }, wait);\n  };\n}\n\n/*******************************************************************************\n * Announcement banner - fetch and load remote HTML\n */\nasync function setupAnnouncementBanner() {\n  const banner = document.querySelector(\".bd-header-announcement\");\n  const { pstAnnouncementUrl } = banner ? banner.dataset : null;\n\n  if (!pstAnnouncementUrl) {\n    return;\n  }\n\n  try {\n    const response = await fetch(pstAnnouncementUrl);\n    if (!response.ok) {\n      throw new Error(\n        `[PST]: HTTP response status not ok: ${response.status} ${response.statusText}`,\n      );\n    }\n    const data = await response.text();\n    if (data.length === 0) {\n      console.log(`[PST]: Empty announcement at: ${pstAnnouncementUrl}`);\n      return;\n    }\n    banner.innerHTML = `<div class=\"bd-header-announcement__content\">${data}</div>`;\n    banner.classList.remove(\"d-none\");\n  } catch (_error) {\n    console.log(`[PST]: Failed to load announcement at: ${pstAnnouncementUrl}`);\n    console.error(_error);\n  }\n}\n\n/*******************************************************************************\n * Reveal (and animate) the banners (version warning, announcement) together\n */\nasync function fetchRevealBannersTogether() {\n  // Wait until finished fetching and loading banners\n  await Promise.allSettled([fetchAndUseVersions(), setupAnnouncementBanner()]);\n\n  // The revealer element should have CSS rules that set height to 0, overflow\n  // to hidden, and an animation transition on the height (unless the user has\n  // turned off animations)\n  const revealer = document.querySelector(\".pst-async-banner-revealer\");\n  if (!revealer) {\n    return;\n  }\n\n  // Remove the d-none (display-none) class to calculate the children heights.\n  revealer.classList.remove(\"d-none\");\n\n  // Add together the heights of the element's children\n  const height = Array.from(revealer.children).reduce(\n    (height, el) => height + el.offsetHeight,\n    0,\n  );\n\n  // Use the calculated height to give the revealer a non-zero height (if\n  // animations allowed, the height change will animate)\n  revealer.style.setProperty(\"height\", `${height}px`);\n\n  // Wait for a bit more than 300ms (the transition duration), then set height\n  // to auto so the banner can resize if the window is resized.\n  setTimeout(() => {\n    revealer.style.setProperty(\"height\", \"auto\");\n  }, 320);\n}\n\n/*******************************************************************************\n * Call functions after document loading.\n */\n\n// This one first to kick off the network request for the version warning\n// and announcement banner data as early as possible.\ndocumentReady(fetchRevealBannersTogether);\n\ndocumentReady(addModeListener);\ndocumentReady(scrollToActive);\ndocumentReady(addTOCInteractivity);\ndocumentReady(setupSearchButtons);\ndocumentReady(initRTDObserver);\ndocumentReady(setupMobileSidebarKeyboardHandlers);\n\n// Determining whether an element has scrollable content depends on stylesheets,\n// so we're checking for the \"load\" event rather than \"DOMContentLoaded\"\nif (document.readyState === \"complete\") {\n  addTabStopsToScrollableElements();\n} else {\n  window.addEventListener(\"load\", addTabStopsToScrollableElements);\n}\n"],"names":["documentReady","callback","document","readyState","addEventListener","validate","version","test","semver","compare","v1","v2","operator","assertValidOperator","res","n1","validateAndParse","n2","p1","pop","p2","r","compareSegments","split","compareVersions","operatorResMap","includes","TypeError","match","Error","shift","isWildcard","s","tryParse","v","n","parseInt","isNaN","compareStrings","a","b","ap","bp","String","forceType","i","Math","max","length","allowedOperators","Object","keys","op","indexOf","join","prefersDark","window","matchMedia","autoTheme","e","documentElement","dataset","theme","matches","setTheme","mode","console","error","colorScheme","querySelectorAll","forEach","el","classList","add","remove","localStorage","setItem","log","onchange","cycleMode","defaultMode","currentMode","getItem","arr","nextPosition","findSearchInput","forms","querySelector","toggleSearchField","input","searchDialog","getElementById","open","close","showModal","activeElement","blur","focus","select","scrollIntoView","block","useCommandKey","navigator","platform","closeDialogOnBackdropClick","currentTarget","dialog","clientX","clientY","left","right","top","bottom","getBoundingClientRect","getCurrentUrlPath","DOCUMENTATION_OPTIONS","BUILDER","pagename","async","DismissBannerAndStorePref","event","VERSION","now","Date","banner_pref","JSON","parse","debug","stringify","checkPageExistsAndRedirect","preventDefault","currentFilePath","tryUrl","getAttribute","otherDocsHomepage","replace","fetch","method","ok","location","href","err","fetchAndUseVersions","versionSwitcherBtns","hasSwitcherMenu","hasVersionsJSON","hasOwnProperty","wantsWarningBanner","show_version_warning_banner","data","url","result","URL","origin","response","json","fetchVersionSwitcherJSON","theme_switcher_json_url","btn","hasMatchingPreferredEntry","map","entry","theme_switcher_version_match","preferred","name","some","Boolean","foundMatch","anchor","createElement","setAttribute","span","textContent","appendChild","matchesAndIsPreferred","matchesAndIsFirst","innerText","menu","node","cloneNode","onclick","append","populateVersionSwitcher","preferredEntries","filter","howMany","preferredVersion","preferredURL","versionsAreComparable","dismiss_date_str","dismiss_date","days_passed","info","floor","banner","middle","inner","bold","button","close_btn","close_x","isDev","newerThanPreferred","createTextNode","showVersionWarningBanner","addTabStopsToScrollableElements","updateTabStops","tabIndex","scrollWidth","clientWidth","scrollHeight","clientHeight","debouncedUpdateTabStops","wait","timeoutId","args","clearTimeout","setTimeout","debounce","MutationObserver","observe","subtree","childList","setupAnnouncementBanner","pstAnnouncementUrl","status","statusText","text","innerHTML","_error","Promise","allSettled","revealer","height","Array","from","children","reduce","offsetHeight","style","setProperty","sidebar","storedScrollTop","sessionStorage","active_pages","latest_active","offset","y","innerHeight","buffer","scrollTop","navLink","parentElement","shortcuts","f","changeSearchShortcutKey","shiftKey","altKey","metaKey","ctrlKey","key","mutationList","observer","mutation","addedNodes","undefined","search","body","primarySidebar","secondarySidebar","primaryDialog","secondaryDialog","primaryToggle","secondaryToggle","cutAndPasteNodesAndClasses","to","childNodes","cls","toggleButton","stopPropagation"],"sourceRoot":""}
\ No newline at end of file
diff --git a/v24.2.0/_static/searchtools.js b/v24.2.0/_static/searchtools.js
new file mode 100644
index 0000000000..b08d58c9b9
--- /dev/null
+++ b/v24.2.0/_static/searchtools.js
@@ -0,0 +1,620 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/v24.2.0/_static/sg_gallery-binder.css b/v24.2.0/_static/sg_gallery-binder.css
new file mode 100644
index 0000000000..420005d227
--- /dev/null
+++ b/v24.2.0/_static/sg_gallery-binder.css
@@ -0,0 +1,11 @@
+/* CSS for binder integration */
+
+div.binder-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.lite-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
diff --git a/v24.2.0/_static/sg_gallery-dataframe.css b/v24.2.0/_static/sg_gallery-dataframe.css
new file mode 100644
index 0000000000..fac74c43b7
--- /dev/null
+++ b/v24.2.0/_static/sg_gallery-dataframe.css
@@ -0,0 +1,47 @@
+/* Pandas dataframe css */
+/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-tr-odd-color: #f5f5f5;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-tr-odd-color: #373737;
+  --sg-tr-hover-color: rgba(30, 81, 122, 0.2);
+}
+
+table.dataframe {
+  border: none !important;
+  border-collapse: collapse;
+  border-spacing: 0;
+  border-color: transparent;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+  width: auto;
+}
+table.dataframe thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+table.dataframe tr,
+table.dataframe th,
+table.dataframe td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+table.dataframe th {
+  font-weight: bold;
+}
+table.dataframe tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+table.dataframe tbody tr:hover {
+  background: var(--sg-tr-hover-color);
+}
diff --git a/v24.2.0/_static/sg_gallery-rendered-html.css b/v24.2.0/_static/sg_gallery-rendered-html.css
new file mode 100644
index 0000000000..93dc2ffb02
--- /dev/null
+++ b/v24.2.0/_static/sg_gallery-rendered-html.css
@@ -0,0 +1,224 @@
+/* Adapted from notebook/static/style/style.min.css */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-background-color: #ffffff;
+  --sg-code-background-color: #eff0f1;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #f5f5f5;
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-background-color: #121212;
+  --sg-code-background-color: #2f2f30;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #1f1f1f;
+}
+
+.rendered_html {
+  color: var(--sg-text-color);
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: var(--sg-text-color);
+  background-color: var(--sg-text-color);
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: var(--sg-background-color);
+}
+.rendered_html code {
+  background-color: var(--sg-code-background-color);
+}
+.rendered_html p code {
+  padding: 1px 5px;
+}
+.rendered_html pre code {
+  background-color: var(--sg-background-color);
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: var(--sg-text-color);
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+.rendered_html tbody tr:hover {
+  color: var(--sg-text-color);
+  background: var(--sg-tr-hover-color);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/v24.2.0/_static/sg_gallery.css b/v24.2.0/_static/sg_gallery.css
new file mode 100644
index 0000000000..9bcd33c8a6
--- /dev/null
+++ b/v24.2.0/_static/sg_gallery.css
@@ -0,0 +1,367 @@
+/*
+Sphinx-Gallery has compatible CSS to fix default sphinx themes
+Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
+scrolls, agogo, traditional, nature, haiku, pyramid
+Tested for Read the Docs theme 0.1.7 */
+
+/* Define light colors */
+:root, html[data-theme="light"], body[data-theme="light"]{
+  --sg-tooltip-foreground: black;
+  --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+  --sg-tooltip-border: #ccc transparent;
+  --sg-thumb-box-shadow-color: #6c757d40;
+  --sg-thumb-hover-border: #0069d9;
+  --sg-script-out: #888;
+  --sg-script-pre: #fafae2;
+  --sg-pytb-foreground: #000;
+  --sg-pytb-background: #ffe4e4;
+  --sg-pytb-border-color: #f66;
+  --sg-download-a-background-color: #ffc;
+  --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+  --sg-download-a-border-color: 1px solid #c2c22d;
+  --sg-download-a-color: #000;
+  --sg-download-a-hover-background-color: #d5d57e;
+  --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+}
+@media(prefers-color-scheme: light) {
+  :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: black;
+    --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+    --sg-tooltip-border: #ccc transparent;
+    --sg-thumb-box-shadow-color: #6c757d40;
+    --sg-thumb-hover-border: #0069d9;
+    --sg-script-out: #888;
+    --sg-script-pre: #fafae2;
+    --sg-pytb-foreground: #000;
+    --sg-pytb-background: #ffe4e4;
+    --sg-pytb-border-color: #f66;
+    --sg-download-a-background-color: #ffc;
+    --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+    --sg-download-a-border-color: 1px solid #c2c22d;
+    --sg-download-a-color: #000;
+    --sg-download-a-hover-background-color: #d5d57e;
+    --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+  }
+}
+
+html[data-theme="dark"], body[data-theme="dark"] {
+  --sg-tooltip-foreground: white;
+  --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+  --sg-tooltip-border: #333 transparent;
+  --sg-thumb-box-shadow-color: #79848d40;
+  --sg-thumb-hover-border: #003975;
+  --sg-script-out: rgb(179, 179, 179);
+  --sg-script-pre: #2e2e22;
+  --sg-pytb-foreground: #fff;
+  --sg-pytb-background: #1b1717;
+  --sg-pytb-border-color: #622;
+  --sg-download-a-background-color: #443;
+  --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+  --sg-download-a-border-color: 1px solid #3a3a0d;
+  --sg-download-a-color: #fff;
+  --sg-download-a-hover-background-color: #616135;
+  --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+}
+@media(prefers-color-scheme: dark){
+  html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: white;
+    --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+    --sg-tooltip-border: #333 transparent;
+    --sg-thumb-box-shadow-color: #79848d40;
+    --sg-thumb-hover-border: #003975;
+    --sg-script-out: rgb(179, 179, 179);
+    --sg-script-pre: #2e2e22;
+    --sg-pytb-foreground: #fff;
+    --sg-pytb-background: #1b1717;
+    --sg-pytb-border-color: #622;
+    --sg-download-a-background-color: #443;
+    --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+    --sg-download-a-border-color: 1px solid #3a3a0d;
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: #616135;
+    --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+  }
+}
+
+.sphx-glr-thumbnails {
+  width: 100%;
+  margin: 0px 0px 20px 0px;
+
+  /* align thumbnails on a grid */
+  justify-content: space-between;
+  display: grid;
+  /* each grid column should be at least 160px (this will determine
+  the actual number of columns) and then take as much of the
+  remaining width as possible */
+  grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+  gap: 15px;
+}
+.sphx-glr-thumbnails .toctree-wrapper {
+  /* hide empty toctree divs added to the DOM
+  by sphinx even though the toctree is hidden
+  (they would fill grid places with empty divs) */
+  display: none;
+}
+.sphx-glr-thumbcontainer {
+  background: transparent;
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  box-shadow: 0 0 10px var(--sg-thumb-box-shadow-color);
+
+  /* useful to absolutely position link in div */
+  position: relative;
+
+  /* thumbnail width should include padding and borders
+  and take all available space */
+  box-sizing: border-box;
+  width: 100%;
+  padding: 10px;
+  border: 1px solid transparent;
+
+  /* align content in thumbnail */
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 7px;
+}
+.sphx-glr-thumbcontainer p {
+  position: absolute;
+  top: 0;
+  left: 0;
+}
+.sphx-glr-thumbcontainer p,
+.sphx-glr-thumbcontainer p a {
+  /* link should cover the whole thumbnail div */
+  width: 100%;
+  height: 100%;
+}
+.sphx-glr-thumbcontainer p a span {
+  /* text within link should be masked
+  (we are just interested in the href) */
+  display: none;
+}
+.sphx-glr-thumbcontainer:hover {
+  border: 1px solid;
+  border-color: var(--sg-thumb-hover-border);
+  cursor: pointer;
+}
+.sphx-glr-thumbcontainer a.internal {
+  bottom: 0;
+  display: block;
+  left: 0;
+  box-sizing: border-box;
+  padding: 150px 10px 0;
+  position: absolute;
+  right: 0;
+  top: 0;
+}
+/* Next one is to avoid Sphinx traditional theme to cover all the
+thumbnail with its default link Background color */
+.sphx-glr-thumbcontainer a.internal:hover {
+  background-color: transparent;
+}
+
+.sphx-glr-thumbcontainer p {
+  margin: 0 0 0.1em 0;
+}
+.sphx-glr-thumbcontainer .figure {
+  margin: 10px;
+  width: 160px;
+}
+.sphx-glr-thumbcontainer img {
+  display: inline;
+  max-height: 112px;
+  max-width: 160px;
+}
+
+.sphx-glr-thumbcontainer[tooltip]::before {
+  content: "";
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  z-index: 97;
+  background-color: var(--sg-tooltip-background);
+  backdrop-filter: blur(3px);
+  opacity: 0;
+  transition: opacity 0.3s;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::before {
+  opacity: 1;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::after {
+  -webkit-border-radius: 4px;
+  -moz-border-radius: 4px;
+  border-radius: 4px;
+  color: var(--sg-tooltip-foreground);
+  content: attr(tooltip);
+  padding: 10px 10px 5px;
+  z-index: 98;
+  width: 100%;
+  max-height: 100%;
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  box-sizing: border-box;
+  overflow: hidden;
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-line-clamp: 6;
+}
+
+.sphx-glr-script-out {
+  color: var(--sg-script-out);
+  display: flex;
+  gap: 0.5em;
+}
+.sphx-glr-script-out::before {
+  content: "Out:";
+  /* These numbers come from the pre style in the pydata sphinx theme. This
+   * turns out to match perfectly on the rtd theme, but be a bit too low for
+   * the pydata sphinx theme. As I could not find a dimension to use that was
+   * scaled the same way, I just picked one option that worked pretty close for
+   * both. */
+  line-height: 1.4;
+  padding-top: 10px;
+}
+.sphx-glr-script-out .highlight {
+  background-color: transparent;
+  /* These options make the div expand... */
+  flex-grow: 1;
+  /* ... but also keep it from overflowing its flex container. */
+  overflow: auto;
+}
+.sphx-glr-script-out .highlight pre {
+  background-color: var(--sg-script-pre);
+  border: 0;
+  max-height: 30em;
+  overflow: auto;
+  padding-left: 1ex;
+  /* This margin is necessary in the pydata sphinx theme because pre has a box
+   * shadow which would be clipped by the overflow:auto in the parent div
+   * above. */
+  margin: 2px;
+  word-break: break-word;
+}
+.sphx-glr-script-out + p {
+  margin-top: 1.8em;
+}
+blockquote.sphx-glr-script-out {
+  margin-left: 0pt;
+}
+.sphx-glr-script-out.highlight-pytb .highlight pre {
+  color: var(--sg-pytb-foreground);
+  background-color: var(--sg-pytb-background);
+  border: 1px solid var(--sg-pytb-border-color);
+  margin-top: 10px;
+  padding: 7px;
+}
+
+div.sphx-glr-footer {
+  text-align: center;
+}
+
+div.sphx-glr-download {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.sphx-glr-download a {
+  background-color: var(--sg-download-a-background-color);
+  background-image: var(--sg-download-a-background-image);
+  border-radius: 4px;
+  border: 1px solid var(--sg-download-a-border-color);
+  color: var(--sg-download-a-color);
+  display: inline-block;
+  font-weight: bold;
+  padding: 1ex;
+  text-align: center;
+}
+
+div.sphx-glr-download code.download {
+  display: inline-block;
+  white-space: normal;
+  word-break: normal;
+  overflow-wrap: break-word;
+  /* border and background are given by the enclosing 'a' */
+  border: none;
+  background: none;
+}
+
+div.sphx-glr-download a:hover {
+  box-shadow: inset 0 1px 0 var(--sg-download-a-hover-box-shadow-1), 0 1px 5px var(--sg-download-a-hover-box-shadow-2);
+  text-decoration: none;
+  background-image: none;
+  background-color: var(--sg-download-a-hover-background-color);
+}
+
+div.sphx-glr-sidebar-item img {
+  max-height: 20px;
+}
+
+.sphx-glr-example-title:target::before {
+  display: block;
+  content: "";
+  margin-top: -50px;
+  height: 50px;
+  visibility: hidden;
+}
+
+ul.sphx-glr-horizontal {
+  list-style: none;
+  padding: 0;
+}
+ul.sphx-glr-horizontal li {
+  display: inline;
+}
+ul.sphx-glr-horizontal img {
+  height: auto !important;
+}
+
+.sphx-glr-single-img {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+
+.sphx-glr-multi-img {
+  max-width: 42%;
+  height: auto;
+}
+
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation {
+  display: block;
+}
+
+p.sphx-glr-signature a.reference.external {
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  padding: 3px;
+  font-size: 75%;
+  text-align: right;
+  margin-left: auto;
+  display: table;
+}
+
+.sphx-glr-clear {
+  clear: both;
+}
+
+a.sphx-glr-backref-instance {
+  text-decoration: none;
+}
diff --git a/v24.2.0/_static/sphinx_highlight.js b/v24.2.0/_static/sphinx_highlight.js
new file mode 100644
index 0000000000..8a96c69a19
--- /dev/null
+++ b/v24.2.0/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/v24.2.0/_static/styles/pydata-sphinx-theme.css b/v24.2.0/_static/styles/pydata-sphinx-theme.css
new file mode 100644
index 0000000000..413192f415
--- /dev/null
+++ b/v24.2.0/_static/styles/pydata-sphinx-theme.css
@@ -0,0 +1,32 @@
+@charset "UTF-8";
+/*!
+ * Bootstrap  v5.3.3 (https://getbootstrap.com/)
+ * Copyright 2011-2024 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ 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var(--bs-accordion-border-color);color:var(--bs-accordion-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type>.accordion-header .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-left-radius:var(--bs-accordion-border-radius);border-bottom-right-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type>.accordion-header 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16 16' fill='%236ea8fe'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E");--bs-accordion-btn-active-icon:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3E%3C/svg%3E")}.breadcrumb{--bs-breadcrumb-padding-x:0;--bs-breadcrumb-padding-y:0;--bs-breadcrumb-margin-bottom:1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color:var(--bs-secondary-color);--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:var(--bs-secondary-color);background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius);display:flex;flex-wrap:wrap;font-size:var(--bs-breadcrumb-font-size);list-style:none;margin-bottom:var(--bs-breadcrumb-margin-bottom);padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media (prefers-reduced-motion:reduce){.page-link{transition:none}}.page-link:hover{background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color);color:var(--bs-pagination-hover-color);z-index:2}.page-link:focus{background-color:var(--bs-pagination-focus-bg);box-shadow:var(--bs-pagination-focus-box-shadow);color:var(--bs-pagination-focus-color);outline:0;z-index:3}.active>.page-link,.page-link.active{background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color);color:var(--bs-pagination-active-color);z-index:3}.disabled>.page-link,.page-link.disabled{background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color);color:var(--bs-pagination-disabled-color);pointer-events:none}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-bottom-left-radius:var(--bs-pagination-border-radius);border-top-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-bottom-right-radius:var(--bs-pagination-border-radius);border-top-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);border-radius:var(--bs-badge-border-radius);color:var(--bs-badge-color);display:inline-block;font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);text-align:center;vertical-align:baseline;white-space:nowrap}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius);color:var(--bs-alert-color);margin-bottom:var(--bs-alert-margin-bottom);padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);position:relative}.alert-heading{color:inherit}.alert-link{color:var(--bs-alert-link-color);font-weight:700}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{padding:1.25rem 1rem;position:absolute;right:0;top:0;z-index:2}.alert-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius);display:flex;font-size:var(--bs-progress-font-size);height:var(--bs-progress-height);overflow:hidden}.progress-bar{background-color:var(--bs-progress-bar-bg);color:var(--bs-progress-bar-color);display:flex;flex-direction:column;justify-content:center;overflow:hidden;text-align:center;transition:var(--bs-progress-bar-transition);white-space:nowrap}@media (prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,hsla(0,0%,100%,.15) 25%,transparent 0,transparent 50%,hsla(0,0%,100%,.15) 0,hsla(0,0%,100%,.15) 75%,transparent 0,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:progress-bar-stripes 1s linear infinite}@media (prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;border-radius:var(--bs-list-group-border-radius);display:flex;flex-direction:column;margin-bottom:0;padding-left:0}.list-group-numbered{counter-reset:section;list-style-type:none}.list-group-numbered>.list-group-item:before{content:counters(section,".") ". ";counter-increment:section}.list-group-item-action{color:var(--bs-list-group-action-color);text-align:inherit;width:100%}.list-group-item-action:focus,.list-group-item-action:hover{background-color:var(--bs-list-group-action-hover-bg);color:var(--bs-list-group-action-hover-color);text-decoration:none;z-index:1}.list-group-item-action:active{background-color:var(--bs-list-group-action-active-bg);color:var(--bs-list-group-action-active-color)}.list-group-item{background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);color:var(--bs-list-group-color);display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);position:relative;text-decoration:none}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-left-radius:inherit;border-bottom-right-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{background-color:var(--bs-list-group-disabled-bg);color:var(--bs-list-group-disabled-color);pointer-events:none}.list-group-item.active{background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color);color:var(--bs-list-group-active-color);z-index:2}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{border-top-width:var(--bs-list-group-border-width);margin-top:calc(var(--bs-list-group-border-width)*-1)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}@media (min-width:576px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media (min-width:768px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-md>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media (min-width:992px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media (min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media (min-width:1400px){.list-group-horizontal-xxl{flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color:var(--bs-primary-text-emphasis);--bs-list-group-bg:var(--bs-primary-bg-subtle);--bs-list-group-border-color:var(--bs-primary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-primary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-primary-border-subtle);--bs-list-group-active-color:var(--bs-primary-bg-subtle);--bs-list-group-active-bg:var(--bs-primary-text-emphasis);--bs-list-group-active-border-color:var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color:var(--bs-secondary-text-emphasis);--bs-list-group-bg:var(--bs-secondary-bg-subtle);--bs-list-group-border-color:var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-secondary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-secondary-border-subtle);--bs-list-group-active-color:var(--bs-secondary-bg-subtle);--bs-list-group-active-bg:var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color:var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color:var(--bs-success-text-emphasis);--bs-list-group-bg:var(--bs-success-bg-subtle);--bs-list-group-border-color:var(--bs-success-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-success-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-success-border-subtle);--bs-list-group-active-color:var(--bs-success-bg-subtle);--bs-list-group-active-bg:var(--bs-success-text-emphasis);--bs-list-group-active-border-color:var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color:var(--bs-info-text-emphasis);--bs-list-group-bg:var(--bs-info-bg-subtle);--bs-list-group-border-color:var(--bs-info-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-info-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-info-border-subtle);--bs-list-group-active-color:var(--bs-info-bg-subtle);--bs-list-group-active-bg:var(--bs-info-text-emphasis);--bs-list-group-active-border-color:var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color:var(--bs-warning-text-emphasis);--bs-list-group-bg:var(--bs-warning-bg-subtle);--bs-list-group-border-color:var(--bs-warning-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-warning-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-warning-border-subtle);--bs-list-group-active-color:var(--bs-warning-bg-subtle);--bs-list-group-active-bg:var(--bs-warning-text-emphasis);--bs-list-group-active-border-color:var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color:var(--bs-danger-text-emphasis);--bs-list-group-bg:var(--bs-danger-bg-subtle);--bs-list-group-border-color:var(--bs-danger-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-danger-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-danger-border-subtle);--bs-list-group-active-color:var(--bs-danger-bg-subtle);--bs-list-group-active-bg:var(--bs-danger-text-emphasis);--bs-list-group-active-border-color:var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color:var(--bs-light-text-emphasis);--bs-list-group-bg:var(--bs-light-bg-subtle);--bs-list-group-border-color:var(--bs-light-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-light-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-light-border-subtle);--bs-list-group-active-color:var(--bs-light-bg-subtle);--bs-list-group-active-bg:var(--bs-light-text-emphasis);--bs-list-group-active-border-color:var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color:var(--bs-dark-text-emphasis);--bs-list-group-bg:var(--bs-dark-bg-subtle);--bs-list-group-border-color:var(--bs-dark-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-dark-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-dark-border-subtle);--bs-list-group-active-color:var(--bs-dark-bg-subtle);--bs-list-group-active-bg:var(--bs-dark-text-emphasis);--bs-list-group-active-border-color:var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url("data:image/svg+xml;charset=utf-8,%3Csvg 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no-repeat;border:0;border-radius:.375rem;box-sizing:content-box;height:1em;opacity:var(--bs-btn-close-opacity);padding:.25em;width:1em}.btn-close,.btn-close:hover{color:var(--bs-btn-close-color)}.btn-close:hover{opacity:var(--bs-btn-close-hover-opacity);text-decoration:none}.btn-close:focus{box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity);outline:0}.btn-close.disabled,.btn-close:disabled{opacity:var(--bs-btn-close-disabled-opacity);pointer-events:none;user-select:none}.btn-close-white,[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex:1090;--bs-toast-padding-x:0.75rem;--bs-toast-padding-y:0.5rem;--bs-toast-spacing:1.5rem;--bs-toast-max-width:350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg:rgba(var(--bs-body-bg-rgb),0.85);--bs-toast-border-width:var(--bs-border-width);--bs-toast-border-color:var(--bs-border-color-translucent);--bs-toast-border-radius:var(--bs-border-radius);--bs-toast-box-shadow:var(--bs-box-shadow);--bs-toast-header-color:var(--bs-secondary-color);--bs-toast-header-bg:rgba(var(--bs-body-bg-rgb),0.85);--bs-toast-header-border-color:var(--bs-border-color-translucent);background-clip:padding-box;background-color:var(--bs-toast-bg);border:var(--bs-toast-border-width) solid 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var(--bs-toast-padding-x)}.toast-header .btn-close{margin-left:var(--bs-toast-padding-x);margin-right:calc(var(--bs-toast-padding-x)*-.5)}.toast-body{word-wrap:break-word;padding:var(--bs-toast-padding-x)}.modal{--bs-modal-zindex:1055;--bs-modal-width:500px;--bs-modal-padding:1rem;--bs-modal-margin:0.5rem;--bs-modal-color: ;--bs-modal-bg:var(--bs-body-bg);--bs-modal-border-color:var(--bs-border-color-translucent);--bs-modal-border-width:var(--bs-border-width);--bs-modal-border-radius:var(--bs-border-radius-lg);--bs-modal-box-shadow:var(--bs-box-shadow-sm);--bs-modal-inner-border-radius:calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-modal-header-padding-x:1rem;--bs-modal-header-padding-y:1rem;--bs-modal-header-padding:1rem 1rem;--bs-modal-header-border-color:var(--bs-border-color);--bs-modal-header-border-width:var(--bs-border-width);--bs-modal-title-line-height:1.5;--bs-modal-footer-gap:0.5rem;--bs-modal-footer-bg: 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var(--bs-modal-margin)*2)}.modal-content{background-clip:padding-box;background-color:var(--bs-modal-bg);border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);color:var(--bs-modal-color);display:flex;flex-direction:column;outline:0;pointer-events:auto;position:relative;width:100%}.modal-backdrop{--bs-backdrop-zindex:1050;--bs-backdrop-bg:#000;--bs-backdrop-opacity:0.5;background-color:var(--bs-backdrop-bg);height:100vh;left:0;position:fixed;top:0;width:100vw;z-index:var(--bs-backdrop-zindex)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{align-items:center;border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius);display:flex;flex-shrink:0;padding:var(--bs-modal-header-padding)}.modal-header 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(min-width:576px){.modal{--bs-modal-margin:1.75rem;--bs-modal-box-shadow:var(--bs-box-shadow)}.modal-dialog{margin-left:auto;margin-right:auto;max-width:var(--bs-modal-width)}.modal-sm{--bs-modal-width:300px}}@media (min-width:992px){.modal-lg,.modal-xl{--bs-modal-width:800px}}@media (min-width:1200px){.modal-xl{--bs-modal-width:1140px}}.modal-fullscreen{height:100%;margin:0;max-width:none;width:100vw}.modal-fullscreen .modal-content{border:0;border-radius:0;height:100%}.modal-fullscreen .modal-footer,.modal-fullscreen .modal-header{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width:575.98px){.modal-fullscreen-sm-down{height:100%;margin:0;max-width:none;width:100vw}.modal-fullscreen-sm-down .modal-content{border:0;border-radius:0;height:100%}.modal-fullscreen-sm-down .modal-footer,.modal-fullscreen-sm-down .modal-header{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media 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(max-width:1399.98px){.modal-fullscreen-xxl-down{height:100%;margin:0;max-width:none;width:100vw}.modal-fullscreen-xxl-down .modal-content{border:0;border-radius:0;height:100%}.modal-fullscreen-xxl-down .modal-footer,.modal-fullscreen-xxl-down .modal-header{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex:1080;--bs-tooltip-max-width:200px;--bs-tooltip-padding-x:0.5rem;--bs-tooltip-padding-y:0.25rem;--bs-tooltip-margin: 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`abbr`\n.initialism {\n  @include font-size($initialism-font-size);\n  text-transform: uppercase;\n}\n\n// Blockquotes\n.blockquote {\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-font-size);\n\n  > :last-child {\n    margin-bottom: 0;\n  }\n}\n\n.blockquote-footer {\n  margin-top: -$blockquote-margin-y;\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-footer-font-size);\n  color: $blockquote-footer-color;\n\n  &::before {\n    content: \"\\2014\\00A0\"; // em dash, nbsp\n  }\n}\n","@charset \"UTF-8\";\n/*!\n * Bootstrap  v5.3.3 (https://getbootstrap.com/)\n * Copyright 2011-2024 The Bootstrap Authors\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\n:root,\n[data-bs-theme=light] {\n  --bs-blue: #0d6efd;\n  --bs-indigo: #6610f2;\n  --bs-purple: #6f42c1;\n  --bs-pink: #d63384;\n  --bs-red: #dc3545;\n  --bs-orange: #fd7e14;\n  --bs-yellow: #ffc107;\n  --bs-green: #198754;\n  --bs-teal: #20c997;\n  --bs-cyan: #0dcaf0;\n  --bs-black: #000;\n  --bs-white: #fff;\n  --bs-gray: #6c757d;\n  --bs-gray-dark: #343a40;\n  --bs-gray-100: #f8f9fa;\n  --bs-gray-200: #e9ecef;\n  --bs-gray-300: #dee2e6;\n  --bs-gray-400: #ced4da;\n  --bs-gray-500: #adb5bd;\n  --bs-gray-600: #6c757d;\n  --bs-gray-700: #495057;\n  --bs-gray-800: #343a40;\n  --bs-gray-900: #212529;\n  --bs-primary: #0d6efd;\n  --bs-secondary: #6c757d;\n  --bs-success: #198754;\n  --bs-info: #0dcaf0;\n  --bs-warning: #ffc107;\n  --bs-danger: #dc3545;\n  --bs-light: #f8f9fa;\n  --bs-dark: #212529;\n  --bs-primary-rgb: 13, 110, 253;\n  --bs-secondary-rgb: 108, 117, 125;\n  --bs-success-rgb: 25, 135, 84;\n  --bs-info-rgb: 13, 202, 240;\n  --bs-warning-rgb: 255, 193, 7;\n  --bs-danger-rgb: 220, 53, 69;\n  --bs-light-rgb: 248, 249, 250;\n  --bs-dark-rgb: 33, 37, 41;\n  --bs-primary-text-emphasis: #052c65;\n  --bs-secondary-text-emphasis: #2b2f32;\n  --bs-success-text-emphasis: #0a3622;\n  --bs-info-text-emphasis: #055160;\n  --bs-warning-text-emphasis: #664d03;\n  --bs-danger-text-emphasis: #58151c;\n  --bs-light-text-emphasis: #495057;\n  --bs-dark-text-emphasis: #495057;\n  --bs-primary-bg-subtle: #cfe2ff;\n  --bs-secondary-bg-subtle: #e2e3e5;\n  --bs-success-bg-subtle: #d1e7dd;\n  --bs-info-bg-subtle: #cff4fc;\n  --bs-warning-bg-subtle: #fff3cd;\n  --bs-danger-bg-subtle: #f8d7da;\n  --bs-light-bg-subtle: #fcfcfd;\n  --bs-dark-bg-subtle: #ced4da;\n  --bs-primary-border-subtle: #9ec5fe;\n  --bs-secondary-border-subtle: #c4c8cb;\n  --bs-success-border-subtle: #a3cfbb;\n  --bs-info-border-subtle: #9eeaf9;\n  --bs-warning-border-subtle: #ffe69c;\n  --bs-danger-border-subtle: #f1aeb5;\n  --bs-light-border-subtle: #e9ecef;\n  --bs-dark-border-subtle: #adb5bd;\n  --bs-white-rgb: 255, 255, 255;\n  --bs-black-rgb: 0, 0, 0;\n  --bs-font-sans-serif: system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\";\n  --bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace;\n  --bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0));\n  --bs-body-font-family: var(--bs-font-sans-serif);\n  --bs-body-font-size: 1rem;\n  --bs-body-font-weight: 400;\n  --bs-body-line-height: 1.5;\n  --bs-body-color: #212529;\n  --bs-body-color-rgb: 33, 37, 41;\n  --bs-body-bg: #fff;\n  --bs-body-bg-rgb: 255, 255, 255;\n  --bs-emphasis-color: #000;\n  --bs-emphasis-color-rgb: 0, 0, 0;\n  --bs-secondary-color: rgba(33, 37, 41, 0.75);\n  --bs-secondary-color-rgb: 33, 37, 41;\n  --bs-secondary-bg: #e9ecef;\n  --bs-secondary-bg-rgb: 233, 236, 239;\n  --bs-tertiary-color: rgba(33, 37, 41, 0.5);\n  --bs-tertiary-color-rgb: 33, 37, 41;\n  --bs-tertiary-bg: #f8f9fa;\n  --bs-tertiary-bg-rgb: 248, 249, 250;\n  --bs-heading-color: inherit;\n  --bs-link-color: #0d6efd;\n  --bs-link-color-rgb: 13, 110, 253;\n  --bs-link-decoration: underline;\n  --bs-link-hover-color: #0a58ca;\n  --bs-link-hover-color-rgb: 10, 88, 202;\n  --bs-code-color: #d63384;\n  --bs-highlight-color: #212529;\n  --bs-highlight-bg: #fff3cd;\n  --bs-border-width: 1px;\n  --bs-border-style: solid;\n  --bs-border-color: #dee2e6;\n  --bs-border-color-translucent: rgba(0, 0, 0, 0.175);\n  --bs-border-radius: 0.375rem;\n  --bs-border-radius-sm: 0.25rem;\n  --bs-border-radius-lg: 0.5rem;\n  --bs-border-radius-xl: 1rem;\n  --bs-border-radius-xxl: 2rem;\n  --bs-border-radius-2xl: var(--bs-border-radius-xxl);\n  --bs-border-radius-pill: 50rem;\n  --bs-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);\n  --bs-box-shadow-sm: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);\n  --bs-box-shadow-lg: 0 1rem 3rem rgba(0, 0, 0, 0.175);\n  --bs-box-shadow-inset: inset 0 1px 2px rgba(0, 0, 0, 0.075);\n  --bs-focus-ring-width: 0.25rem;\n  --bs-focus-ring-opacity: 0.25;\n  --bs-focus-ring-color: rgba(13, 110, 253, 0.25);\n  --bs-form-valid-color: #198754;\n  --bs-form-valid-border-color: #198754;\n  --bs-form-invalid-color: #dc3545;\n  --bs-form-invalid-border-color: #dc3545;\n}\n\n[data-bs-theme=dark] {\n  color-scheme: dark;\n  --bs-body-color: #dee2e6;\n  --bs-body-color-rgb: 222, 226, 230;\n  --bs-body-bg: #212529;\n  --bs-body-bg-rgb: 33, 37, 41;\n  --bs-emphasis-color: #fff;\n  --bs-emphasis-color-rgb: 255, 255, 255;\n  --bs-secondary-color: rgba(222, 226, 230, 0.75);\n  --bs-secondary-color-rgb: 222, 226, 230;\n  --bs-secondary-bg: #343a40;\n  --bs-secondary-bg-rgb: 52, 58, 64;\n  --bs-tertiary-color: rgba(222, 226, 230, 0.5);\n  --bs-tertiary-color-rgb: 222, 226, 230;\n  --bs-tertiary-bg: #2b3035;\n  --bs-tertiary-bg-rgb: 43, 48, 53;\n  --bs-primary-text-emphasis: #6ea8fe;\n  --bs-secondary-text-emphasis: #a7acb1;\n  --bs-success-text-emphasis: #75b798;\n  --bs-info-text-emphasis: #6edff6;\n  --bs-warning-text-emphasis: #ffda6a;\n  --bs-danger-text-emphasis: #ea868f;\n  --bs-light-text-emphasis: #f8f9fa;\n  --bs-dark-text-emphasis: #dee2e6;\n  --bs-primary-bg-subtle: #031633;\n  --bs-secondary-bg-subtle: #161719;\n  --bs-success-bg-subtle: #051b11;\n  --bs-info-bg-subtle: #032830;\n  --bs-warning-bg-subtle: #332701;\n  --bs-danger-bg-subtle: #2c0b0e;\n  --bs-light-bg-subtle: #343a40;\n  --bs-dark-bg-subtle: #1a1d20;\n  --bs-primary-border-subtle: #084298;\n  --bs-secondary-border-subtle: #41464b;\n  --bs-success-border-subtle: #0f5132;\n  --bs-info-border-subtle: #087990;\n  --bs-warning-border-subtle: #997404;\n  --bs-danger-border-subtle: #842029;\n  --bs-light-border-subtle: #495057;\n  --bs-dark-border-subtle: #343a40;\n  --bs-heading-color: inherit;\n  --bs-link-color: #6ea8fe;\n  --bs-link-hover-color: #8bb9fe;\n  --bs-link-color-rgb: 110, 168, 254;\n  --bs-link-hover-color-rgb: 139, 185, 254;\n  --bs-code-color: #e685b5;\n  --bs-highlight-color: #dee2e6;\n  --bs-highlight-bg: #664d03;\n  --bs-border-color: #495057;\n  --bs-border-color-translucent: rgba(255, 255, 255, 0.15);\n  --bs-form-valid-color: #75b798;\n  --bs-form-valid-border-color: #75b798;\n  --bs-form-invalid-color: #ea868f;\n  --bs-form-invalid-border-color: #ea868f;\n}\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n@media (prefers-reduced-motion: no-preference) {\n  :root {\n    scroll-behavior: smooth;\n  }\n}\n\nbody {\n  margin: 0;\n  font-family: var(--bs-body-font-family);\n  font-size: var(--bs-body-font-size);\n  font-weight: var(--bs-body-font-weight);\n  line-height: var(--bs-body-line-height);\n  color: var(--bs-body-color);\n  text-align: var(--bs-body-text-align);\n  background-color: var(--bs-body-bg);\n  -webkit-text-size-adjust: 100%;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\n\nhr {\n  margin: 1rem 0;\n  color: inherit;\n  border: 0;\n  border-top: var(--bs-border-width) solid;\n  opacity: 0.25;\n}\n\nh6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {\n  margin-top: 0;\n  margin-bottom: 0.5rem;\n  font-weight: 500;\n  line-height: 1.2;\n  color: var(--bs-heading-color);\n}\n\nh1, .h1 {\n  font-size: calc(1.375rem + 1.5vw);\n}\n@media (min-width: 1200px) {\n  h1, .h1 {\n    font-size: 2.5rem;\n  }\n}\n\nh2, .h2 {\n  font-size: calc(1.325rem + 0.9vw);\n}\n@media (min-width: 1200px) {\n  h2, .h2 {\n    font-size: 2rem;\n  }\n}\n\nh3, .h3 {\n  font-size: calc(1.3rem + 0.6vw);\n}\n@media (min-width: 1200px) {\n  h3, .h3 {\n    font-size: 1.75rem;\n  }\n}\n\nh4, .h4 {\n  font-size: calc(1.275rem + 0.3vw);\n}\n@media (min-width: 1200px) {\n  h4, .h4 {\n    font-size: 1.5rem;\n  }\n}\n\nh5, .h5 {\n  font-size: 1.25rem;\n}\n\nh6, .h6 {\n  font-size: 1rem;\n}\n\np {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nabbr[title] {\n  text-decoration: underline dotted;\n  cursor: help;\n  text-decoration-skip-ink: none;\n}\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: 700;\n}\n\ndd {\n  margin-bottom: 0.5rem;\n  margin-left: 0;\n}\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\nb,\nstrong {\n  font-weight: bolder;\n}\n\nsmall, .small {\n  font-size: 0.875em;\n}\n\nmark, .mark {\n  padding: 0.1875em;\n  color: var(--bs-highlight-color);\n  background-color: var(--bs-highlight-bg);\n}\n\nsub,\nsup {\n  position: relative;\n  font-size: 0.75em;\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\nsup {\n  top: -0.5em;\n}\n\na {\n  color: rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 1));\n  text-decoration: underline;\n}\na:hover {\n  --bs-link-color-rgb: var(--bs-link-hover-color-rgb);\n}\n\na:not([href]):not([class]), a:not([href]):not([class]):hover {\n  color: inherit;\n  text-decoration: none;\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--bs-font-monospace);\n  font-size: 1em;\n}\n\npre {\n  display: block;\n  margin-top: 0;\n  margin-bottom: 1rem;\n  overflow: auto;\n  font-size: 0.875em;\n}\npre code {\n  font-size: inherit;\n  color: inherit;\n  word-break: normal;\n}\n\ncode {\n  font-size: 0.875em;\n  color: var(--bs-code-color);\n  word-wrap: break-word;\n}\na > code {\n  color: inherit;\n}\n\nkbd {\n  padding: 0.1875rem 0.375rem;\n  font-size: 0.875em;\n  color: var(--bs-body-bg);\n  background-color: var(--bs-body-color);\n  border-radius: 0.25rem;\n}\nkbd kbd {\n  padding: 0;\n  font-size: 1em;\n}\n\nfigure {\n  margin: 0 0 1rem;\n}\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  color: var(--bs-secondary-color);\n  text-align: left;\n}\n\nth {\n  text-align: inherit;\n  text-align: -webkit-match-parent;\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\nlabel {\n  display: inline-block;\n}\n\nbutton {\n  border-radius: 0;\n}\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0;\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n[role=button] {\n  cursor: pointer;\n}\n\nselect {\n  word-wrap: normal;\n}\nselect:disabled {\n  opacity: 1;\n}\n\n[list]:not([type=date]):not([type=datetime-local]):not([type=month]):not([type=week]):not([type=time])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\nbutton,\n[type=button],\n[type=reset],\n[type=submit] {\n  -webkit-appearance: button;\n}\nbutton:not(:disabled),\n[type=button]:not(:disabled),\n[type=reset]:not(:disabled),\n[type=submit]:not(:disabled) {\n  cursor: pointer;\n}\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\ntextarea {\n  resize: vertical;\n}\n\nfieldset {\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\n\nlegend {\n  float: left;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 0.5rem;\n  font-size: calc(1.275rem + 0.3vw);\n  line-height: inherit;\n}\n@media (min-width: 1200px) {\n  legend {\n    font-size: 1.5rem;\n  }\n}\nlegend + * {\n  clear: left;\n}\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n[type=search] {\n  -webkit-appearance: textfield;\n  outline-offset: -2px;\n}\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n::file-selector-button {\n  font: inherit;\n  -webkit-appearance: button;\n}\n\noutput {\n  display: inline-block;\n}\n\niframe {\n  border: 0;\n}\n\nsummary {\n  display: list-item;\n  cursor: pointer;\n}\n\nprogress {\n  vertical-align: baseline;\n}\n\n[hidden] {\n  display: none !important;\n}\n\n.lead {\n  font-size: 1.25rem;\n  font-weight: 300;\n}\n\n.display-1 {\n  font-size: calc(1.625rem + 4.5vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-1 {\n    font-size: 5rem;\n  }\n}\n\n.display-2 {\n  font-size: calc(1.575rem + 3.9vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-2 {\n    font-size: 4.5rem;\n  }\n}\n\n.display-3 {\n  font-size: calc(1.525rem + 3.3vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-3 {\n    font-size: 4rem;\n  }\n}\n\n.display-4 {\n  font-size: calc(1.475rem + 2.7vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-4 {\n    font-size: 3.5rem;\n  }\n}\n\n.display-5 {\n  font-size: calc(1.425rem + 2.1vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-5 {\n    font-size: 3rem;\n  }\n}\n\n.display-6 {\n  font-size: calc(1.375rem + 1.5vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-6 {\n    font-size: 2.5rem;\n  }\n}\n\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n\n.list-inline {\n  padding-left: 0;\n  list-style: none;\n}\n\n.list-inline-item {\n  display: inline-block;\n}\n.list-inline-item:not(:last-child) {\n  margin-right: 0.5rem;\n}\n\n.initialism {\n  font-size: 0.875em;\n  text-transform: uppercase;\n}\n\n.blockquote {\n  margin-bottom: 1rem;\n  font-size: 1.25rem;\n}\n.blockquote > :last-child {\n  margin-bottom: 0;\n}\n\n.blockquote-footer {\n  margin-top: -1rem;\n  margin-bottom: 1rem;\n  font-size: 0.875em;\n  color: #6c757d;\n}\n.blockquote-footer::before {\n  content: \"— \";\n}\n\n.img-fluid {\n  max-width: 100%;\n  height: auto;\n}\n\n.img-thumbnail {\n  padding: 0.25rem;\n  background-color: var(--bs-body-bg);\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n  max-width: 100%;\n  height: auto;\n}\n\n.figure {\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: 0.5rem;\n  line-height: 1;\n}\n\n.figure-caption {\n  font-size: 0.875em;\n  color: var(--bs-secondary-color);\n}\n\n.container,\n.container-fluid,\n.container-xxl,\n.container-xl,\n.container-lg,\n.container-md,\n.container-sm {\n  --bs-gutter-x: 1.5rem;\n  --bs-gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--bs-gutter-x) * 0.5);\n  padding-left: calc(var(--bs-gutter-x) * 0.5);\n  margin-right: auto;\n  margin-left: auto;\n}\n\n@media (min-width: 576px) {\n  .container-sm, .container {\n    max-width: 540px;\n  }\n}\n@media (min-width: 768px) {\n  .container-md, .container-sm, .container {\n    max-width: 720px;\n  }\n}\n@media (min-width: 992px) {\n  .container-lg, .container-md, .container-sm, .container {\n    max-width: 960px;\n  }\n}\n@media (min-width: 1200px) {\n  .container-xl, .container-lg, .container-md, .container-sm, .container {\n    max-width: 1140px;\n  }\n}\n@media (min-width: 1400px) {\n  .container-xxl, .container-xl, .container-lg, .container-md, .container-sm, .container {\n    max-width: 1320px;\n  }\n}\n:root {\n  --bs-breakpoint-xs: 0;\n  --bs-breakpoint-sm: 576px;\n  --bs-breakpoint-md: 768px;\n  --bs-breakpoint-lg: 992px;\n  --bs-breakpoint-xl: 1200px;\n  --bs-breakpoint-xxl: 1400px;\n}\n\n.row {\n  --bs-gutter-x: 1.5rem;\n  --bs-gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  margin-top: calc(-1 * var(--bs-gutter-y));\n  margin-right: calc(-0.5 * var(--bs-gutter-x));\n  margin-left: calc(-0.5 * var(--bs-gutter-x));\n}\n.row > * {\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%;\n  padding-right: calc(var(--bs-gutter-x) * 0.5);\n  padding-left: calc(var(--bs-gutter-x) * 0.5);\n  margin-top: var(--bs-gutter-y);\n}\n\n.col {\n  flex: 1 0 0%;\n}\n\n.row-cols-auto > * {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n.row-cols-1 > * {\n  flex: 0 0 auto;\n  width: 100%;\n}\n\n.row-cols-2 > * {\n  flex: 0 0 auto;\n  width: 50%;\n}\n\n.row-cols-3 > * {\n  flex: 0 0 auto;\n  width: 33.33333333%;\n}\n\n.row-cols-4 > * {\n  flex: 0 0 auto;\n  width: 25%;\n}\n\n.row-cols-5 > * {\n  flex: 0 0 auto;\n  width: 20%;\n}\n\n.row-cols-6 > * {\n  flex: 0 0 auto;\n  width: 16.66666667%;\n}\n\n.col-auto {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n.col-1 {\n  flex: 0 0 auto;\n  width: 8.33333333%;\n}\n\n.col-2 {\n  flex: 0 0 auto;\n  width: 16.66666667%;\n}\n\n.col-3 {\n  flex: 0 0 auto;\n  width: 25%;\n}\n\n.col-4 {\n  flex: 0 0 auto;\n  width: 33.33333333%;\n}\n\n.col-5 {\n  flex: 0 0 auto;\n  width: 41.66666667%;\n}\n\n.col-6 {\n  flex: 0 0 auto;\n  width: 50%;\n}\n\n.col-7 {\n  flex: 0 0 auto;\n  width: 58.33333333%;\n}\n\n.col-8 {\n  flex: 0 0 auto;\n  width: 66.66666667%;\n}\n\n.col-9 {\n  flex: 0 0 auto;\n  width: 75%;\n}\n\n.col-10 {\n  flex: 0 0 auto;\n  width: 83.33333333%;\n}\n\n.col-11 {\n  flex: 0 0 auto;\n  width: 91.66666667%;\n}\n\n.col-12 {\n  flex: 0 0 auto;\n  width: 100%;\n}\n\n.offset-1 {\n  margin-left: 8.33333333%;\n}\n\n.offset-2 {\n  margin-left: 16.66666667%;\n}\n\n.offset-3 {\n  margin-left: 25%;\n}\n\n.offset-4 {\n  margin-left: 33.33333333%;\n}\n\n.offset-5 {\n  margin-left: 41.66666667%;\n}\n\n.offset-6 {\n  margin-left: 50%;\n}\n\n.offset-7 {\n  margin-left: 58.33333333%;\n}\n\n.offset-8 {\n  margin-left: 66.66666667%;\n}\n\n.offset-9 {\n  margin-left: 75%;\n}\n\n.offset-10 {\n  margin-left: 83.33333333%;\n}\n\n.offset-11 {\n  margin-left: 91.66666667%;\n}\n\n.g-0,\n.gx-0 {\n  --bs-gutter-x: 0;\n}\n\n.g-0,\n.gy-0 {\n  --bs-gutter-y: 0;\n}\n\n.g-1,\n.gx-1 {\n  --bs-gutter-x: 0.25rem;\n}\n\n.g-1,\n.gy-1 {\n  --bs-gutter-y: 0.25rem;\n}\n\n.g-2,\n.gx-2 {\n  --bs-gutter-x: 0.5rem;\n}\n\n.g-2,\n.gy-2 {\n  --bs-gutter-y: 0.5rem;\n}\n\n.g-3,\n.gx-3 {\n  --bs-gutter-x: 1rem;\n}\n\n.g-3,\n.gy-3 {\n  --bs-gutter-y: 1rem;\n}\n\n.g-4,\n.gx-4 {\n  --bs-gutter-x: 1.5rem;\n}\n\n.g-4,\n.gy-4 {\n  --bs-gutter-y: 1.5rem;\n}\n\n.g-5,\n.gx-5 {\n  --bs-gutter-x: 3rem;\n}\n\n.g-5,\n.gy-5 {\n  --bs-gutter-y: 3rem;\n}\n\n@media (min-width: 576px) {\n  .col-sm {\n    flex: 1 0 0%;\n  }\n  .row-cols-sm-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-sm-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-sm-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-sm-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-sm-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-sm-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-sm-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-sm-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-sm-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-sm-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-sm-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-sm-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-sm-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-sm-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-sm-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-sm-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-sm-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-sm-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-sm-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-sm-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-sm-0 {\n    margin-left: 0;\n  }\n  .offset-sm-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-sm-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-sm-3 {\n    margin-left: 25%;\n  }\n  .offset-sm-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-sm-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-sm-6 {\n    margin-left: 50%;\n  }\n  .offset-sm-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-sm-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-sm-9 {\n    margin-left: 75%;\n  }\n  .offset-sm-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-sm-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-sm-0,\n  .gx-sm-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-sm-0,\n  .gy-sm-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-sm-1,\n  .gx-sm-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-sm-1,\n  .gy-sm-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-sm-2,\n  .gx-sm-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-sm-2,\n  .gy-sm-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-sm-3,\n  .gx-sm-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-sm-3,\n  .gy-sm-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-sm-4,\n  .gx-sm-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-sm-4,\n  .gy-sm-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-sm-5,\n  .gx-sm-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-sm-5,\n  .gy-sm-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n@media (min-width: 768px) {\n  .col-md {\n    flex: 1 0 0%;\n  }\n  .row-cols-md-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-md-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-md-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-md-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-md-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-md-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-md-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-md-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-md-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-md-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-md-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-md-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-md-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-md-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-md-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-md-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-md-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-md-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-md-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-md-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-md-0 {\n    margin-left: 0;\n  }\n  .offset-md-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-md-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-md-3 {\n    margin-left: 25%;\n  }\n  .offset-md-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-md-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-md-6 {\n    margin-left: 50%;\n  }\n  .offset-md-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-md-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-md-9 {\n    margin-left: 75%;\n  }\n  .offset-md-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-md-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-md-0,\n  .gx-md-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-md-0,\n  .gy-md-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-md-1,\n  .gx-md-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-md-1,\n  .gy-md-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-md-2,\n  .gx-md-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-md-2,\n  .gy-md-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-md-3,\n  .gx-md-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-md-3,\n  .gy-md-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-md-4,\n  .gx-md-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-md-4,\n  .gy-md-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-md-5,\n  .gx-md-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-md-5,\n  .gy-md-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n@media (min-width: 992px) {\n  .col-lg {\n    flex: 1 0 0%;\n  }\n  .row-cols-lg-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-lg-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-lg-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-lg-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-lg-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-lg-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-lg-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-lg-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-lg-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-lg-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-lg-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-lg-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-lg-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-lg-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-lg-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-lg-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-lg-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-lg-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-lg-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-lg-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-lg-0 {\n    margin-left: 0;\n  }\n  .offset-lg-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-lg-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-lg-3 {\n    margin-left: 25%;\n  }\n  .offset-lg-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-lg-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-lg-6 {\n    margin-left: 50%;\n  }\n  .offset-lg-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-lg-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-lg-9 {\n    margin-left: 75%;\n  }\n  .offset-lg-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-lg-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-lg-0,\n  .gx-lg-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-lg-0,\n  .gy-lg-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-lg-1,\n  .gx-lg-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-lg-1,\n  .gy-lg-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-lg-2,\n  .gx-lg-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-lg-2,\n  .gy-lg-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-lg-3,\n  .gx-lg-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-lg-3,\n  .gy-lg-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-lg-4,\n  .gx-lg-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-lg-4,\n  .gy-lg-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-lg-5,\n  .gx-lg-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-lg-5,\n  .gy-lg-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n@media (min-width: 1200px) {\n  .col-xl {\n    flex: 1 0 0%;\n  }\n  .row-cols-xl-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-xl-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-xl-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-xl-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-xl-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-xl-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-xl-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-xl-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-xl-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-xl-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-xl-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-xl-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-xl-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-xl-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-xl-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-xl-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-xl-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-xl-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-xl-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-xl-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-xl-0 {\n    margin-left: 0;\n  }\n  .offset-xl-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-xl-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-xl-3 {\n    margin-left: 25%;\n  }\n  .offset-xl-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-xl-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-xl-6 {\n    margin-left: 50%;\n  }\n  .offset-xl-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-xl-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-xl-9 {\n    margin-left: 75%;\n  }\n  .offset-xl-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-xl-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-xl-0,\n  .gx-xl-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-xl-0,\n  .gy-xl-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-xl-1,\n  .gx-xl-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-xl-1,\n  .gy-xl-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-xl-2,\n  .gx-xl-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-xl-2,\n  .gy-xl-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-xl-3,\n  .gx-xl-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-xl-3,\n  .gy-xl-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-xl-4,\n  .gx-xl-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-xl-4,\n  .gy-xl-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-xl-5,\n  .gx-xl-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-xl-5,\n  .gy-xl-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n@media (min-width: 1400px) {\n  .col-xxl {\n    flex: 1 0 0%;\n  }\n  .row-cols-xxl-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-xxl-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-xxl-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-xxl-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-xxl-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-xxl-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-xxl-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-xxl-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-xxl-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-xxl-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-xxl-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-xxl-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-xxl-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-xxl-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-xxl-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-xxl-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-xxl-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-xxl-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-xxl-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-xxl-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-xxl-0 {\n    margin-left: 0;\n  }\n  .offset-xxl-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-xxl-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-xxl-3 {\n    margin-left: 25%;\n  }\n  .offset-xxl-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-xxl-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-xxl-6 {\n    margin-left: 50%;\n  }\n  .offset-xxl-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-xxl-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-xxl-9 {\n    margin-left: 75%;\n  }\n  .offset-xxl-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-xxl-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-xxl-0,\n  .gx-xxl-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-xxl-0,\n  .gy-xxl-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-xxl-1,\n  .gx-xxl-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-xxl-1,\n  .gy-xxl-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-xxl-2,\n  .gx-xxl-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-xxl-2,\n  .gy-xxl-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-xxl-3,\n  .gx-xxl-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-xxl-3,\n  .gy-xxl-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-xxl-4,\n  .gx-xxl-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-xxl-4,\n  .gy-xxl-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-xxl-5,\n  .gx-xxl-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-xxl-5,\n  .gy-xxl-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n.table {\n  --bs-table-color-type: initial;\n  --bs-table-bg-type: initial;\n  --bs-table-color-state: initial;\n  --bs-table-bg-state: initial;\n  --bs-table-color: var(--bs-emphasis-color);\n  --bs-table-bg: var(--bs-body-bg);\n  --bs-table-border-color: var(--bs-border-color);\n  --bs-table-accent-bg: transparent;\n  --bs-table-striped-color: var(--bs-emphasis-color);\n  --bs-table-striped-bg: rgba(var(--bs-emphasis-color-rgb), 0.05);\n  --bs-table-active-color: var(--bs-emphasis-color);\n  --bs-table-active-bg: rgba(var(--bs-emphasis-color-rgb), 0.1);\n  --bs-table-hover-color: var(--bs-emphasis-color);\n  --bs-table-hover-bg: rgba(var(--bs-emphasis-color-rgb), 0.075);\n  width: 100%;\n  margin-bottom: 1rem;\n  vertical-align: top;\n  border-color: var(--bs-table-border-color);\n}\n.table > :not(caption) > * > * {\n  padding: 0.5rem 0.5rem;\n  color: var(--bs-table-color-state, var(--bs-table-color-type, var(--bs-table-color)));\n  background-color: var(--bs-table-bg);\n  border-bottom-width: var(--bs-border-width);\n  box-shadow: inset 0 0 0 9999px var(--bs-table-bg-state, var(--bs-table-bg-type, var(--bs-table-accent-bg)));\n}\n.table > tbody {\n  vertical-align: inherit;\n}\n.table > thead {\n  vertical-align: bottom;\n}\n\n.table-group-divider {\n  border-top: calc(var(--bs-border-width) * 2) solid currentcolor;\n}\n\n.caption-top {\n  caption-side: top;\n}\n\n.table-sm > :not(caption) > * > * {\n  padding: 0.25rem 0.25rem;\n}\n\n.table-bordered > :not(caption) > * {\n  border-width: var(--bs-border-width) 0;\n}\n.table-bordered > :not(caption) > * > * {\n  border-width: 0 var(--bs-border-width);\n}\n\n.table-borderless > :not(caption) > * > * {\n  border-bottom-width: 0;\n}\n.table-borderless > :not(:first-child) {\n  border-top-width: 0;\n}\n\n.table-striped > tbody > tr:nth-of-type(odd) > * {\n  --bs-table-color-type: var(--bs-table-striped-color);\n  --bs-table-bg-type: var(--bs-table-striped-bg);\n}\n\n.table-striped-columns > :not(caption) > tr > :nth-child(even) {\n  --bs-table-color-type: var(--bs-table-striped-color);\n  --bs-table-bg-type: var(--bs-table-striped-bg);\n}\n\n.table-active {\n  --bs-table-color-state: var(--bs-table-active-color);\n  --bs-table-bg-state: var(--bs-table-active-bg);\n}\n\n.table-hover > tbody > tr:hover > * {\n  --bs-table-color-state: var(--bs-table-hover-color);\n  --bs-table-bg-state: var(--bs-table-hover-bg);\n}\n\n.table-primary {\n  --bs-table-color: #000;\n  --bs-table-bg: #cfe2ff;\n  --bs-table-border-color: #a6b5cc;\n  --bs-table-striped-bg: #c5d7f2;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #bacbe6;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #bfd1ec;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-secondary {\n  --bs-table-color: #000;\n  --bs-table-bg: #e2e3e5;\n  --bs-table-border-color: #b5b6b7;\n  --bs-table-striped-bg: #d7d8da;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #cbccce;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #d1d2d4;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-success {\n  --bs-table-color: #000;\n  --bs-table-bg: #d1e7dd;\n  --bs-table-border-color: #a7b9b1;\n  --bs-table-striped-bg: #c7dbd2;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #bcd0c7;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #c1d6cc;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-info {\n  --bs-table-color: #000;\n  --bs-table-bg: #cff4fc;\n  --bs-table-border-color: #a6c3ca;\n  --bs-table-striped-bg: #c5e8ef;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #badce3;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #bfe2e9;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-warning {\n  --bs-table-color: #000;\n  --bs-table-bg: #fff3cd;\n  --bs-table-border-color: #ccc2a4;\n  --bs-table-striped-bg: #f2e7c3;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #e6dbb9;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #ece1be;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-danger {\n  --bs-table-color: #000;\n  --bs-table-bg: #f8d7da;\n  --bs-table-border-color: #c6acae;\n  --bs-table-striped-bg: #eccccf;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #dfc2c4;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #e5c7ca;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-light {\n  --bs-table-color: #000;\n  --bs-table-bg: #f8f9fa;\n  --bs-table-border-color: #c6c7c8;\n  --bs-table-striped-bg: #ecedee;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #dfe0e1;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #e5e6e7;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-dark {\n  --bs-table-color: #fff;\n  --bs-table-bg: #212529;\n  --bs-table-border-color: #4d5154;\n  --bs-table-striped-bg: #2c3034;\n  --bs-table-striped-color: #fff;\n  --bs-table-active-bg: #373b3e;\n  --bs-table-active-color: #fff;\n  --bs-table-hover-bg: #323539;\n  --bs-table-hover-color: #fff;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-responsive {\n  overflow-x: auto;\n  -webkit-overflow-scrolling: touch;\n}\n\n@media (max-width: 575.98px) {\n  .table-responsive-sm {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n@media (max-width: 767.98px) {\n  .table-responsive-md {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n@media (max-width: 991.98px) {\n  .table-responsive-lg {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n@media (max-width: 1199.98px) {\n  .table-responsive-xl {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n@media (max-width: 1399.98px) {\n  .table-responsive-xxl {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n.form-label {\n  margin-bottom: 0.5rem;\n}\n\n.col-form-label {\n  padding-top: calc(0.375rem + var(--bs-border-width));\n  padding-bottom: calc(0.375rem + var(--bs-border-width));\n  margin-bottom: 0;\n  font-size: inherit;\n  line-height: 1.5;\n}\n\n.col-form-label-lg {\n  padding-top: calc(0.5rem + var(--bs-border-width));\n  padding-bottom: calc(0.5rem + var(--bs-border-width));\n  font-size: 1.25rem;\n}\n\n.col-form-label-sm {\n  padding-top: calc(0.25rem + var(--bs-border-width));\n  padding-bottom: calc(0.25rem + var(--bs-border-width));\n  font-size: 0.875rem;\n}\n\n.form-text {\n  margin-top: 0.25rem;\n  font-size: 0.875em;\n  color: var(--bs-secondary-color);\n}\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: 0.375rem 0.75rem;\n  font-size: 1rem;\n  font-weight: 400;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  appearance: none;\n  background-color: var(--bs-body-bg);\n  background-clip: padding-box;\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-control {\n    transition: none;\n  }\n}\n.form-control[type=file] {\n  overflow: hidden;\n}\n.form-control[type=file]:not(:disabled):not([readonly]) {\n  cursor: pointer;\n}\n.form-control:focus {\n  color: var(--bs-body-color);\n  background-color: var(--bs-body-bg);\n  border-color: #86b7fe;\n  outline: 0;\n  box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n}\n.form-control::-webkit-date-and-time-value {\n  min-width: 85px;\n  height: 1.5em;\n  margin: 0;\n}\n.form-control::-webkit-datetime-edit {\n  display: block;\n  padding: 0;\n}\n.form-control::placeholder {\n  color: var(--bs-secondary-color);\n  opacity: 1;\n}\n.form-control:disabled {\n  background-color: var(--bs-secondary-bg);\n  opacity: 1;\n}\n.form-control::file-selector-button {\n  padding: 0.375rem 0.75rem;\n  margin: -0.375rem -0.75rem;\n  margin-inline-end: 0.75rem;\n  color: var(--bs-body-color);\n  background-color: var(--bs-tertiary-bg);\n  pointer-events: none;\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n  border-inline-end-width: var(--bs-border-width);\n  border-radius: 0;\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-control::file-selector-button {\n    transition: none;\n  }\n}\n.form-control:hover:not(:disabled):not([readonly])::file-selector-button {\n  background-color: var(--bs-secondary-bg);\n}\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: 0.375rem 0;\n  margin-bottom: 0;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  background-color: transparent;\n  border: solid transparent;\n  border-width: var(--bs-border-width) 0;\n}\n.form-control-plaintext:focus {\n  outline: 0;\n}\n.form-control-plaintext.form-control-sm, .form-control-plaintext.form-control-lg {\n  padding-right: 0;\n  padding-left: 0;\n}\n\n.form-control-sm {\n  min-height: calc(1.5em + 0.5rem + calc(var(--bs-border-width) * 2));\n  padding: 0.25rem 0.5rem;\n  font-size: 0.875rem;\n  border-radius: var(--bs-border-radius-sm);\n}\n.form-control-sm::file-selector-button {\n  padding: 0.25rem 0.5rem;\n  margin: -0.25rem -0.5rem;\n  margin-inline-end: 0.5rem;\n}\n\n.form-control-lg {\n  min-height: calc(1.5em + 1rem + calc(var(--bs-border-width) * 2));\n  padding: 0.5rem 1rem;\n  font-size: 1.25rem;\n  border-radius: var(--bs-border-radius-lg);\n}\n.form-control-lg::file-selector-button {\n  padding: 0.5rem 1rem;\n  margin: -0.5rem -1rem;\n  margin-inline-end: 1rem;\n}\n\ntextarea.form-control {\n  min-height: calc(1.5em + 0.75rem + calc(var(--bs-border-width) * 2));\n}\ntextarea.form-control-sm {\n  min-height: calc(1.5em + 0.5rem + calc(var(--bs-border-width) * 2));\n}\ntextarea.form-control-lg {\n  min-height: calc(1.5em + 1rem + calc(var(--bs-border-width) * 2));\n}\n\n.form-control-color {\n  width: 3rem;\n  height: calc(1.5em + 0.75rem + calc(var(--bs-border-width) * 2));\n  padding: 0.375rem;\n}\n.form-control-color:not(:disabled):not([readonly]) {\n  cursor: pointer;\n}\n.form-control-color::-moz-color-swatch {\n  border: 0 !important;\n  border-radius: var(--bs-border-radius);\n}\n.form-control-color::-webkit-color-swatch {\n  border: 0 !important;\n  border-radius: var(--bs-border-radius);\n}\n.form-control-color.form-control-sm {\n  height: calc(1.5em + 0.5rem + calc(var(--bs-border-width) * 2));\n}\n.form-control-color.form-control-lg {\n  height: calc(1.5em + 1rem + calc(var(--bs-border-width) * 2));\n}\n\n.form-select {\n  --bs-form-select-bg-img: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e\");\n  display: block;\n  width: 100%;\n  padding: 0.375rem 2.25rem 0.375rem 0.75rem;\n  font-size: 1rem;\n  font-weight: 400;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  appearance: none;\n  background-color: var(--bs-body-bg);\n  background-image: var(--bs-form-select-bg-img), var(--bs-form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: right 0.75rem center;\n  background-size: 16px 12px;\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-select {\n    transition: none;\n  }\n}\n.form-select:focus {\n  border-color: #86b7fe;\n  outline: 0;\n  box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n}\n.form-select[multiple], .form-select[size]:not([size=\"1\"]) {\n  padding-right: 0.75rem;\n  background-image: none;\n}\n.form-select:disabled {\n  background-color: var(--bs-secondary-bg);\n}\n.form-select:-moz-focusring {\n  color: transparent;\n  text-shadow: 0 0 0 var(--bs-body-color);\n}\n\n.form-select-sm {\n  padding-top: 0.25rem;\n  padding-bottom: 0.25rem;\n  padding-left: 0.5rem;\n  font-size: 0.875rem;\n  border-radius: var(--bs-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  padding-left: 1rem;\n  font-size: 1.25rem;\n  border-radius: var(--bs-border-radius-lg);\n}\n\n[data-bs-theme=dark] .form-select {\n  --bs-form-select-bg-img: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23dee2e6' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e\");\n}\n\n.form-check {\n  display: block;\n  min-height: 1.5rem;\n  padding-left: 1.5em;\n  margin-bottom: 0.125rem;\n}\n.form-check .form-check-input {\n  float: left;\n  margin-left: -1.5em;\n}\n\n.form-check-reverse {\n  padding-right: 1.5em;\n  padding-left: 0;\n  text-align: right;\n}\n.form-check-reverse .form-check-input {\n  float: right;\n  margin-right: -1.5em;\n  margin-left: 0;\n}\n\n.form-check-input {\n  --bs-form-check-bg: var(--bs-body-bg);\n  flex-shrink: 0;\n  width: 1em;\n  height: 1em;\n  margin-top: 0.25em;\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--bs-form-check-bg);\n  background-image: var(--bs-form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  print-color-adjust: exact;\n}\n.form-check-input[type=checkbox] {\n  border-radius: 0.25em;\n}\n.form-check-input[type=radio] {\n  border-radius: 50%;\n}\n.form-check-input:active {\n  filter: brightness(90%);\n}\n.form-check-input:focus {\n  border-color: #86b7fe;\n  outline: 0;\n  box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n}\n.form-check-input:checked {\n  background-color: #0d6efd;\n  border-color: #0d6efd;\n}\n.form-check-input:checked[type=checkbox] {\n  --bs-form-check-bg-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/%3e%3c/svg%3e\");\n}\n.form-check-input:checked[type=radio] {\n  --bs-form-check-bg-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='2' fill='%23fff'/%3e%3c/svg%3e\");\n}\n.form-check-input[type=checkbox]:indeterminate {\n  background-color: #0d6efd;\n  border-color: #0d6efd;\n  --bs-form-check-bg-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/%3e%3c/svg%3e\");\n}\n.form-check-input:disabled {\n  pointer-events: none;\n  filter: none;\n  opacity: 0.5;\n}\n.form-check-input[disabled] ~ .form-check-label, .form-check-input:disabled ~ .form-check-label {\n  cursor: default;\n  opacity: 0.5;\n}\n\n.form-switch {\n  padding-left: 2.5em;\n}\n.form-switch .form-check-input {\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%280, 0, 0, 0.25%29'/%3e%3c/svg%3e\");\n  width: 2em;\n  margin-left: -2.5em;\n  background-image: var(--bs-form-switch-bg);\n  background-position: left center;\n  border-radius: 2em;\n  transition: background-position 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-switch .form-check-input {\n    transition: none;\n  }\n}\n.form-switch .form-check-input:focus {\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%2386b7fe'/%3e%3c/svg%3e\");\n}\n.form-switch .form-check-input:checked {\n  background-position: right center;\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23fff'/%3e%3c/svg%3e\");\n}\n.form-switch.form-check-reverse {\n  padding-right: 2.5em;\n  padding-left: 0;\n}\n.form-switch.form-check-reverse .form-check-input {\n  margin-right: -2.5em;\n  margin-left: 0;\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: 1rem;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.btn-check[disabled] + .btn, .btn-check:disabled + .btn {\n  pointer-events: none;\n  filter: none;\n  opacity: 0.65;\n}\n\n[data-bs-theme=dark] .form-switch .form-check-input:not(:checked):not(:focus) {\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%28255, 255, 255, 0.25%29'/%3e%3c/svg%3e\");\n}\n\n.form-range {\n  width: 100%;\n  height: 1.5rem;\n  padding: 0;\n  appearance: none;\n  background-color: transparent;\n}\n.form-range:focus {\n  outline: 0;\n}\n.form-range:focus::-webkit-slider-thumb {\n  box-shadow: 0 0 0 1px #fff, 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n}\n.form-range:focus::-moz-range-thumb {\n  box-shadow: 0 0 0 1px #fff, 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n}\n.form-range::-moz-focus-outer {\n  border: 0;\n}\n.form-range::-webkit-slider-thumb {\n  width: 1rem;\n  height: 1rem;\n  margin-top: -0.25rem;\n  appearance: none;\n  background-color: #0d6efd;\n  border: 0;\n  border-radius: 1rem;\n  transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-range::-webkit-slider-thumb {\n    transition: none;\n  }\n}\n.form-range::-webkit-slider-thumb:active {\n  background-color: #b6d4fe;\n}\n.form-range::-webkit-slider-runnable-track {\n  width: 100%;\n  height: 0.5rem;\n  color: transparent;\n  cursor: pointer;\n  background-color: var(--bs-secondary-bg);\n  border-color: transparent;\n  border-radius: 1rem;\n}\n.form-range::-moz-range-thumb {\n  width: 1rem;\n  height: 1rem;\n  appearance: none;\n  background-color: #0d6efd;\n  border: 0;\n  border-radius: 1rem;\n  transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-range::-moz-range-thumb {\n    transition: none;\n  }\n}\n.form-range::-moz-range-thumb:active {\n  background-color: #b6d4fe;\n}\n.form-range::-moz-range-track {\n  width: 100%;\n  height: 0.5rem;\n  color: transparent;\n  cursor: pointer;\n  background-color: var(--bs-secondary-bg);\n  border-color: transparent;\n  border-radius: 1rem;\n}\n.form-range:disabled {\n  pointer-events: none;\n}\n.form-range:disabled::-webkit-slider-thumb {\n  background-color: var(--bs-secondary-color);\n}\n.form-range:disabled::-moz-range-thumb {\n  background-color: var(--bs-secondary-color);\n}\n\n.form-floating {\n  position: relative;\n}\n.form-floating > .form-control,\n.form-floating > .form-control-plaintext,\n.form-floating > .form-select {\n  height: calc(3.5rem + calc(var(--bs-border-width) * 2));\n  min-height: calc(3.5rem + calc(var(--bs-border-width) * 2));\n  line-height: 1.25;\n}\n.form-floating > label {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 2;\n  height: 100%;\n  padding: 1rem 0.75rem;\n  overflow: hidden;\n  text-align: start;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n  pointer-events: none;\n  border: var(--bs-border-width) solid transparent;\n  transform-origin: 0 0;\n  transition: opacity 0.1s ease-in-out, transform 0.1s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-floating > label {\n    transition: none;\n  }\n}\n.form-floating > .form-control,\n.form-floating > .form-control-plaintext {\n  padding: 1rem 0.75rem;\n}\n.form-floating > .form-control::placeholder,\n.form-floating > .form-control-plaintext::placeholder {\n  color: transparent;\n}\n.form-floating > .form-control:focus, .form-floating > .form-control:not(:placeholder-shown),\n.form-floating > .form-control-plaintext:focus,\n.form-floating > .form-control-plaintext:not(:placeholder-shown) {\n  padding-top: 1.625rem;\n  padding-bottom: 0.625rem;\n}\n.form-floating > .form-control:-webkit-autofill,\n.form-floating > .form-control-plaintext:-webkit-autofill {\n  padding-top: 1.625rem;\n  padding-bottom: 0.625rem;\n}\n.form-floating > .form-select {\n  padding-top: 1.625rem;\n  padding-bottom: 0.625rem;\n}\n.form-floating > .form-control:focus ~ label,\n.form-floating > .form-control:not(:placeholder-shown) ~ label,\n.form-floating > .form-control-plaintext ~ label,\n.form-floating > .form-select ~ label {\n  color: rgba(var(--bs-body-color-rgb), 0.65);\n  transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem);\n}\n.form-floating > .form-control:focus ~ label::after,\n.form-floating > .form-control:not(:placeholder-shown) ~ label::after,\n.form-floating > .form-control-plaintext ~ label::after,\n.form-floating > .form-select ~ label::after {\n  position: absolute;\n  inset: 1rem 0.375rem;\n  z-index: -1;\n  height: 1.5em;\n  content: \"\";\n  background-color: var(--bs-body-bg);\n  border-radius: var(--bs-border-radius);\n}\n.form-floating > .form-control:-webkit-autofill ~ label {\n  color: rgba(var(--bs-body-color-rgb), 0.65);\n  transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem);\n}\n.form-floating > .form-control-plaintext ~ label {\n  border-width: var(--bs-border-width) 0;\n}\n.form-floating > :disabled ~ label,\n.form-floating > .form-control:disabled ~ label {\n  color: #6c757d;\n}\n.form-floating > :disabled ~ label::after,\n.form-floating > .form-control:disabled ~ label::after {\n  background-color: var(--bs-secondary-bg);\n}\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap;\n  align-items: stretch;\n  width: 100%;\n}\n.input-group > .form-control,\n.input-group > .form-select,\n.input-group > .form-floating {\n  position: relative;\n  flex: 1 1 auto;\n  width: 1%;\n  min-width: 0;\n}\n.input-group > .form-control:focus,\n.input-group > .form-select:focus,\n.input-group > .form-floating:focus-within {\n  z-index: 5;\n}\n.input-group .btn {\n  position: relative;\n  z-index: 2;\n}\n.input-group .btn:focus {\n  z-index: 5;\n}\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: 0.375rem 0.75rem;\n  font-size: 1rem;\n  font-weight: 400;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--bs-tertiary-bg);\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n}\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: 0.5rem 1rem;\n  font-size: 1.25rem;\n  border-radius: var(--bs-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: 0.25rem 0.5rem;\n  font-size: 0.875rem;\n  border-radius: var(--bs-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: 3rem;\n}\n\n.input-group:not(.has-validation) > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n.input-group:not(.has-validation) > .dropdown-toggle:nth-last-child(n+3),\n.input-group:not(.has-validation) > .form-floating:not(:last-child) > .form-control,\n.input-group:not(.has-validation) > .form-floating:not(:last-child) > .form-select {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group.has-validation > :nth-last-child(n+3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n.input-group.has-validation > .dropdown-toggle:nth-last-child(n+4),\n.input-group.has-validation > .form-floating:nth-last-child(n+3) > .form-control,\n.input-group.has-validation > .form-floating:nth-last-child(n+3) > .form-select {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group > :not(:first-child):not(.dropdown-menu):not(.valid-tooltip):not(.valid-feedback):not(.invalid-tooltip):not(.invalid-feedback) {\n  margin-left: calc(var(--bs-border-width) * -1);\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.input-group > .form-floating:not(:first-child) > .form-control,\n.input-group > .form-floating:not(:first-child) > .form-select {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n\n.valid-feedback {\n  display: none;\n  width: 100%;\n  margin-top: 0.25rem;\n  font-size: 0.875em;\n  color: var(--bs-form-valid-color);\n}\n\n.valid-tooltip {\n  position: absolute;\n  top: 100%;\n  z-index: 5;\n  display: none;\n  max-width: 100%;\n  padding: 0.25rem 0.5rem;\n  margin-top: 0.1rem;\n  font-size: 0.875rem;\n  color: #fff;\n  background-color: var(--bs-success);\n  border-radius: var(--bs-border-radius);\n}\n\n.was-validated :valid ~ .valid-feedback,\n.was-validated :valid ~ .valid-tooltip,\n.is-valid ~ .valid-feedback,\n.is-valid ~ .valid-tooltip {\n  display: block;\n}\n\n.was-validated .form-control:valid, .form-control.is-valid {\n  border-color: var(--bs-form-valid-border-color);\n  padding-right: calc(1.5em + 0.75rem);\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%23198754' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e\");\n  background-repeat: no-repeat;\n  background-position: right calc(0.375em + 0.1875rem) center;\n  background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-control:valid:focus, .form-control.is-valid:focus {\n  border-color: var(--bs-form-valid-border-color);\n  box-shadow: 0 0 0 0.25rem rgba(var(--bs-success-rgb), 0.25);\n}\n\n.was-validated textarea.form-control:valid, textarea.form-control.is-valid {\n  padding-right: calc(1.5em + 0.75rem);\n  background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem);\n}\n\n.was-validated .form-select:valid, .form-select.is-valid {\n  border-color: var(--bs-form-valid-border-color);\n}\n.was-validated .form-select:valid:not([multiple]):not([size]), .was-validated .form-select:valid:not([multiple])[size=\"1\"], .form-select.is-valid:not([multiple]):not([size]), .form-select.is-valid:not([multiple])[size=\"1\"] {\n  --bs-form-select-bg-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%23198754' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e\");\n  padding-right: 4.125rem;\n  background-position: right 0.75rem center, center right 2.25rem;\n  background-size: 16px 12px, calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-select:valid:focus, .form-select.is-valid:focus {\n  border-color: var(--bs-form-valid-border-color);\n  box-shadow: 0 0 0 0.25rem rgba(var(--bs-success-rgb), 0.25);\n}\n\n.was-validated .form-control-color:valid, .form-control-color.is-valid {\n  width: calc(3rem + calc(1.5em + 0.75rem));\n}\n\n.was-validated .form-check-input:valid, .form-check-input.is-valid {\n  border-color: var(--bs-form-valid-border-color);\n}\n.was-validated .form-check-input:valid:checked, .form-check-input.is-valid:checked {\n  background-color: var(--bs-form-valid-color);\n}\n.was-validated .form-check-input:valid:focus, .form-check-input.is-valid:focus {\n  box-shadow: 0 0 0 0.25rem rgba(var(--bs-success-rgb), 0.25);\n}\n.was-validated .form-check-input:valid ~ .form-check-label, .form-check-input.is-valid ~ .form-check-label {\n  color: var(--bs-form-valid-color);\n}\n\n.form-check-inline .form-check-input ~ .valid-feedback {\n  margin-left: 0.5em;\n}\n\n.was-validated .input-group > .form-control:not(:focus):valid, .input-group > .form-control:not(:focus).is-valid,\n.was-validated .input-group > .form-select:not(:focus):valid,\n.input-group > .form-select:not(:focus).is-valid,\n.was-validated .input-group > .form-floating:not(:focus-within):valid,\n.input-group > .form-floating:not(:focus-within).is-valid {\n  z-index: 3;\n}\n\n.invalid-feedback {\n  display: none;\n  width: 100%;\n  margin-top: 0.25rem;\n  font-size: 0.875em;\n  color: var(--bs-form-invalid-color);\n}\n\n.invalid-tooltip {\n  position: absolute;\n  top: 100%;\n  z-index: 5;\n  display: none;\n  max-width: 100%;\n  padding: 0.25rem 0.5rem;\n  margin-top: 0.1rem;\n  font-size: 0.875rem;\n  color: #fff;\n  background-color: var(--bs-danger);\n  border-radius: var(--bs-border-radius);\n}\n\n.was-validated :invalid ~ .invalid-feedback,\n.was-validated :invalid ~ .invalid-tooltip,\n.is-invalid ~ .invalid-feedback,\n.is-invalid ~ .invalid-tooltip {\n  display: block;\n}\n\n.was-validated .form-control:invalid, .form-control.is-invalid {\n  border-color: var(--bs-form-invalid-border-color);\n  padding-right: calc(1.5em + 0.75rem);\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23dc3545'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23dc3545' stroke='none'/%3e%3c/svg%3e\");\n  background-repeat: no-repeat;\n  background-position: right calc(0.375em + 0.1875rem) center;\n  background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-control:invalid:focus, .form-control.is-invalid:focus {\n  border-color: var(--bs-form-invalid-border-color);\n  box-shadow: 0 0 0 0.25rem rgba(var(--bs-danger-rgb), 0.25);\n}\n\n.was-validated textarea.form-control:invalid, textarea.form-control.is-invalid {\n  padding-right: calc(1.5em + 0.75rem);\n  background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem);\n}\n\n.was-validated .form-select:invalid, .form-select.is-invalid {\n  border-color: var(--bs-form-invalid-border-color);\n}\n.was-validated .form-select:invalid:not([multiple]):not([size]), .was-validated .form-select:invalid:not([multiple])[size=\"1\"], .form-select.is-invalid:not([multiple]):not([size]), .form-select.is-invalid:not([multiple])[size=\"1\"] {\n  --bs-form-select-bg-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23dc3545'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23dc3545' stroke='none'/%3e%3c/svg%3e\");\n  padding-right: 4.125rem;\n  background-position: right 0.75rem center, center right 2.25rem;\n  background-size: 16px 12px, calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-select:invalid:focus, .form-select.is-invalid:focus {\n  border-color: var(--bs-form-invalid-border-color);\n  box-shadow: 0 0 0 0.25rem rgba(var(--bs-danger-rgb), 0.25);\n}\n\n.was-validated .form-control-color:invalid, .form-control-color.is-invalid {\n  width: calc(3rem + calc(1.5em + 0.75rem));\n}\n\n.was-validated .form-check-input:invalid, .form-check-input.is-invalid {\n  border-color: var(--bs-form-invalid-border-color);\n}\n.was-validated .form-check-input:invalid:checked, .form-check-input.is-invalid:checked {\n  background-color: var(--bs-form-invalid-color);\n}\n.was-validated .form-check-input:invalid:focus, .form-check-input.is-invalid:focus {\n  box-shadow: 0 0 0 0.25rem rgba(var(--bs-danger-rgb), 0.25);\n}\n.was-validated .form-check-input:invalid ~ .form-check-label, .form-check-input.is-invalid ~ .form-check-label {\n  color: var(--bs-form-invalid-color);\n}\n\n.form-check-inline .form-check-input ~ .invalid-feedback {\n  margin-left: 0.5em;\n}\n\n.was-validated .input-group > .form-control:not(:focus):invalid, .input-group > .form-control:not(:focus).is-invalid,\n.was-validated .input-group > .form-select:not(:focus):invalid,\n.input-group > .form-select:not(:focus).is-invalid,\n.was-validated .input-group > .form-floating:not(:focus-within):invalid,\n.input-group > .form-floating:not(:focus-within).is-invalid {\n  z-index: 4;\n}\n\n.btn {\n  --bs-btn-padding-x: 0.75rem;\n  --bs-btn-padding-y: 0.375rem;\n  --bs-btn-font-family: ;\n  --bs-btn-font-size: 1rem;\n  --bs-btn-font-weight: 400;\n  --bs-btn-line-height: 1.5;\n  --bs-btn-color: var(--bs-body-color);\n  --bs-btn-bg: transparent;\n  --bs-btn-border-width: var(--bs-border-width);\n  --bs-btn-border-color: transparent;\n  --bs-btn-border-radius: var(--bs-border-radius);\n  --bs-btn-hover-border-color: transparent;\n  --bs-btn-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n  --bs-btn-disabled-opacity: 0.65;\n  --bs-btn-focus-box-shadow: 0 0 0 0.25rem rgba(var(--bs-btn-focus-shadow-rgb), .5);\n  display: inline-block;\n  padding: var(--bs-btn-padding-y) var(--bs-btn-padding-x);\n  font-family: var(--bs-btn-font-family);\n  font-size: var(--bs-btn-font-size);\n  font-weight: var(--bs-btn-font-weight);\n  line-height: var(--bs-btn-line-height);\n  color: var(--bs-btn-color);\n  text-align: center;\n  text-decoration: none;\n  vertical-align: middle;\n  cursor: pointer;\n  user-select: none;\n  border: var(--bs-btn-border-width) solid var(--bs-btn-border-color);\n  border-radius: var(--bs-btn-border-radius);\n  background-color: var(--bs-btn-bg);\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .btn {\n    transition: none;\n  }\n}\n.btn:hover {\n  color: var(--bs-btn-hover-color);\n  background-color: var(--bs-btn-hover-bg);\n  border-color: var(--bs-btn-hover-border-color);\n}\n.btn-check + .btn:hover {\n  color: var(--bs-btn-color);\n  background-color: var(--bs-btn-bg);\n  border-color: var(--bs-btn-border-color);\n}\n.btn:focus-visible {\n  color: var(--bs-btn-hover-color);\n  background-color: var(--bs-btn-hover-bg);\n  border-color: var(--bs-btn-hover-border-color);\n  outline: 0;\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn-check:focus-visible + .btn {\n  border-color: var(--bs-btn-hover-border-color);\n  outline: 0;\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn-check:checked + .btn, :not(.btn-check) + .btn:active, .btn:first-child:active, .btn.active, .btn.show {\n  color: var(--bs-btn-active-color);\n  background-color: var(--bs-btn-active-bg);\n  border-color: var(--bs-btn-active-border-color);\n}\n.btn-check:checked + .btn:focus-visible, :not(.btn-check) + .btn:active:focus-visible, .btn:first-child:active:focus-visible, .btn.active:focus-visible, .btn.show:focus-visible {\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn-check:checked:focus-visible + .btn {\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn:disabled, .btn.disabled, fieldset:disabled .btn {\n  color: var(--bs-btn-disabled-color);\n  pointer-events: none;\n  background-color: var(--bs-btn-disabled-bg);\n  border-color: var(--bs-btn-disabled-border-color);\n  opacity: var(--bs-btn-disabled-opacity);\n}\n\n.btn-primary {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #0d6efd;\n  --bs-btn-border-color: #0d6efd;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #0b5ed7;\n  --bs-btn-hover-border-color: #0a58ca;\n  --bs-btn-focus-shadow-rgb: 49, 132, 253;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #0a58ca;\n  --bs-btn-active-border-color: #0a53be;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #0d6efd;\n  --bs-btn-disabled-border-color: #0d6efd;\n}\n\n.btn-secondary {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #6c757d;\n  --bs-btn-border-color: #6c757d;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #5c636a;\n  --bs-btn-hover-border-color: #565e64;\n  --bs-btn-focus-shadow-rgb: 130, 138, 145;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #565e64;\n  --bs-btn-active-border-color: #51585e;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #6c757d;\n  --bs-btn-disabled-border-color: #6c757d;\n}\n\n.btn-success {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #198754;\n  --bs-btn-border-color: #198754;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #157347;\n  --bs-btn-hover-border-color: #146c43;\n  --bs-btn-focus-shadow-rgb: 60, 153, 110;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #146c43;\n  --bs-btn-active-border-color: #13653f;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #198754;\n  --bs-btn-disabled-border-color: #198754;\n}\n\n.btn-info {\n  --bs-btn-color: #000;\n  --bs-btn-bg: #0dcaf0;\n  --bs-btn-border-color: #0dcaf0;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #31d2f2;\n  --bs-btn-hover-border-color: #25cff2;\n  --bs-btn-focus-shadow-rgb: 11, 172, 204;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #3dd5f3;\n  --bs-btn-active-border-color: #25cff2;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #000;\n  --bs-btn-disabled-bg: #0dcaf0;\n  --bs-btn-disabled-border-color: #0dcaf0;\n}\n\n.btn-warning {\n  --bs-btn-color: #000;\n  --bs-btn-bg: #ffc107;\n  --bs-btn-border-color: #ffc107;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #ffca2c;\n  --bs-btn-hover-border-color: #ffc720;\n  --bs-btn-focus-shadow-rgb: 217, 164, 6;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #ffcd39;\n  --bs-btn-active-border-color: #ffc720;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #000;\n  --bs-btn-disabled-bg: #ffc107;\n  --bs-btn-disabled-border-color: #ffc107;\n}\n\n.btn-danger {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #dc3545;\n  --bs-btn-border-color: #dc3545;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #bb2d3b;\n  --bs-btn-hover-border-color: #b02a37;\n  --bs-btn-focus-shadow-rgb: 225, 83, 97;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #b02a37;\n  --bs-btn-active-border-color: #a52834;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #dc3545;\n  --bs-btn-disabled-border-color: #dc3545;\n}\n\n.btn-light {\n  --bs-btn-color: #000;\n  --bs-btn-bg: #f8f9fa;\n  --bs-btn-border-color: #f8f9fa;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #d3d4d5;\n  --bs-btn-hover-border-color: #c6c7c8;\n  --bs-btn-focus-shadow-rgb: 211, 212, 213;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #c6c7c8;\n  --bs-btn-active-border-color: #babbbc;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #000;\n  --bs-btn-disabled-bg: #f8f9fa;\n  --bs-btn-disabled-border-color: #f8f9fa;\n}\n\n.btn-dark {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #212529;\n  --bs-btn-border-color: #212529;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #424649;\n  --bs-btn-hover-border-color: #373b3e;\n  --bs-btn-focus-shadow-rgb: 66, 70, 73;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #4d5154;\n  --bs-btn-active-border-color: #373b3e;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #212529;\n  --bs-btn-disabled-border-color: #212529;\n}\n\n.btn-outline-primary {\n  --bs-btn-color: #0d6efd;\n  --bs-btn-border-color: #0d6efd;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #0d6efd;\n  --bs-btn-hover-border-color: #0d6efd;\n  --bs-btn-focus-shadow-rgb: 13, 110, 253;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #0d6efd;\n  --bs-btn-active-border-color: #0d6efd;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #0d6efd;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #0d6efd;\n  --bs-gradient: none;\n}\n\n.btn-outline-secondary {\n  --bs-btn-color: #6c757d;\n  --bs-btn-border-color: #6c757d;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #6c757d;\n  --bs-btn-hover-border-color: #6c757d;\n  --bs-btn-focus-shadow-rgb: 108, 117, 125;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #6c757d;\n  --bs-btn-active-border-color: #6c757d;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #6c757d;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #6c757d;\n  --bs-gradient: none;\n}\n\n.btn-outline-success {\n  --bs-btn-color: #198754;\n  --bs-btn-border-color: #198754;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #198754;\n  --bs-btn-hover-border-color: #198754;\n  --bs-btn-focus-shadow-rgb: 25, 135, 84;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #198754;\n  --bs-btn-active-border-color: #198754;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #198754;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #198754;\n  --bs-gradient: none;\n}\n\n.btn-outline-info {\n  --bs-btn-color: #0dcaf0;\n  --bs-btn-border-color: #0dcaf0;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #0dcaf0;\n  --bs-btn-hover-border-color: #0dcaf0;\n  --bs-btn-focus-shadow-rgb: 13, 202, 240;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #0dcaf0;\n  --bs-btn-active-border-color: #0dcaf0;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #0dcaf0;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #0dcaf0;\n  --bs-gradient: none;\n}\n\n.btn-outline-warning {\n  --bs-btn-color: #ffc107;\n  --bs-btn-border-color: #ffc107;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #ffc107;\n  --bs-btn-hover-border-color: #ffc107;\n  --bs-btn-focus-shadow-rgb: 255, 193, 7;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #ffc107;\n  --bs-btn-active-border-color: #ffc107;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #ffc107;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #ffc107;\n  --bs-gradient: none;\n}\n\n.btn-outline-danger {\n  --bs-btn-color: #dc3545;\n  --bs-btn-border-color: #dc3545;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #dc3545;\n  --bs-btn-hover-border-color: #dc3545;\n  --bs-btn-focus-shadow-rgb: 220, 53, 69;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #dc3545;\n  --bs-btn-active-border-color: #dc3545;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #dc3545;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #dc3545;\n  --bs-gradient: none;\n}\n\n.btn-outline-light {\n  --bs-btn-color: #f8f9fa;\n  --bs-btn-border-color: #f8f9fa;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #f8f9fa;\n  --bs-btn-hover-border-color: #f8f9fa;\n  --bs-btn-focus-shadow-rgb: 248, 249, 250;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #f8f9fa;\n  --bs-btn-active-border-color: #f8f9fa;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #f8f9fa;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #f8f9fa;\n  --bs-gradient: none;\n}\n\n.btn-outline-dark {\n  --bs-btn-color: #212529;\n  --bs-btn-border-color: #212529;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #212529;\n  --bs-btn-hover-border-color: #212529;\n  --bs-btn-focus-shadow-rgb: 33, 37, 41;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #212529;\n  --bs-btn-active-border-color: #212529;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #212529;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #212529;\n  --bs-gradient: none;\n}\n\n.btn-link {\n  --bs-btn-font-weight: 400;\n  --bs-btn-color: var(--bs-link-color);\n  --bs-btn-bg: transparent;\n  --bs-btn-border-color: transparent;\n  --bs-btn-hover-color: var(--bs-link-hover-color);\n  --bs-btn-hover-border-color: transparent;\n  --bs-btn-active-color: var(--bs-link-hover-color);\n  --bs-btn-active-border-color: transparent;\n  --bs-btn-disabled-color: #6c757d;\n  --bs-btn-disabled-border-color: transparent;\n  --bs-btn-box-shadow: 0 0 0 #000;\n  --bs-btn-focus-shadow-rgb: 49, 132, 253;\n  text-decoration: underline;\n}\n.btn-link:focus-visible {\n  color: var(--bs-btn-color);\n}\n.btn-link:hover {\n  color: var(--bs-btn-hover-color);\n}\n\n.btn-lg, .btn-group-lg > .btn {\n  --bs-btn-padding-y: 0.5rem;\n  --bs-btn-padding-x: 1rem;\n  --bs-btn-font-size: 1.25rem;\n  --bs-btn-border-radius: var(--bs-border-radius-lg);\n}\n\n.btn-sm, .btn-group-sm > .btn {\n  --bs-btn-padding-y: 0.25rem;\n  --bs-btn-padding-x: 0.5rem;\n  --bs-btn-font-size: 0.875rem;\n  --bs-btn-border-radius: var(--bs-border-radius-sm);\n}\n\n.fade {\n  transition: opacity 0.15s linear;\n}\n@media (prefers-reduced-motion: reduce) {\n  .fade {\n    transition: none;\n  }\n}\n.fade:not(.show) {\n  opacity: 0;\n}\n\n.collapse:not(.show) {\n  display: none;\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  transition: height 0.35s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .collapsing {\n    transition: none;\n  }\n}\n.collapsing.collapse-horizontal {\n  width: 0;\n  height: auto;\n  transition: width 0.35s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .collapsing.collapse-horizontal {\n    transition: none;\n  }\n}\n\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n}\n.dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0.3em solid;\n  border-right: 0.3em solid transparent;\n  border-bottom: 0;\n  border-left: 0.3em solid transparent;\n}\n.dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n\n.dropdown-menu {\n  --bs-dropdown-zindex: 1000;\n  --bs-dropdown-min-width: 10rem;\n  --bs-dropdown-padding-x: 0;\n  --bs-dropdown-padding-y: 0.5rem;\n  --bs-dropdown-spacer: 0.125rem;\n  --bs-dropdown-font-size: 1rem;\n  --bs-dropdown-color: var(--bs-body-color);\n  --bs-dropdown-bg: var(--bs-body-bg);\n  --bs-dropdown-border-color: var(--bs-border-color-translucent);\n  --bs-dropdown-border-radius: var(--bs-border-radius);\n  --bs-dropdown-border-width: var(--bs-border-width);\n  --bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));\n  --bs-dropdown-divider-bg: var(--bs-border-color-translucent);\n  --bs-dropdown-divider-margin-y: 0.5rem;\n  --bs-dropdown-box-shadow: var(--bs-box-shadow);\n  --bs-dropdown-link-color: var(--bs-body-color);\n  --bs-dropdown-link-hover-color: var(--bs-body-color);\n  --bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);\n  --bs-dropdown-link-active-color: #fff;\n  --bs-dropdown-link-active-bg: #0d6efd;\n  --bs-dropdown-link-disabled-color: var(--bs-tertiary-color);\n  --bs-dropdown-item-padding-x: 1rem;\n  --bs-dropdown-item-padding-y: 0.25rem;\n  --bs-dropdown-header-color: #6c757d;\n  --bs-dropdown-header-padding-x: 1rem;\n  --bs-dropdown-header-padding-y: 0.5rem;\n  position: absolute;\n  z-index: var(--bs-dropdown-zindex);\n  display: none;\n  min-width: var(--bs-dropdown-min-width);\n  padding: var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);\n  margin: 0;\n  font-size: var(--bs-dropdown-font-size);\n  color: var(--bs-dropdown-color);\n  text-align: left;\n  list-style: none;\n  background-color: var(--bs-dropdown-bg);\n  background-clip: padding-box;\n  border: var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);\n  border-radius: var(--bs-dropdown-border-radius);\n}\n.dropdown-menu[data-bs-popper] {\n  top: 100%;\n  left: 0;\n  margin-top: var(--bs-dropdown-spacer);\n}\n\n.dropdown-menu-start {\n  --bs-position: start;\n}\n.dropdown-menu-start[data-bs-popper] {\n  right: auto;\n  left: 0;\n}\n\n.dropdown-menu-end {\n  --bs-position: end;\n}\n.dropdown-menu-end[data-bs-popper] {\n  right: 0;\n  left: auto;\n}\n\n@media (min-width: 576px) {\n  .dropdown-menu-sm-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-sm-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-sm-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-sm-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n@media (min-width: 768px) {\n  .dropdown-menu-md-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-md-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-md-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-md-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n@media (min-width: 992px) {\n  .dropdown-menu-lg-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-lg-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-lg-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-lg-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n@media (min-width: 1200px) {\n  .dropdown-menu-xl-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-xl-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-xl-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-xl-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n@media (min-width: 1400px) {\n  .dropdown-menu-xxl-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-xxl-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-xxl-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-xxl-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n.dropup .dropdown-menu[data-bs-popper] {\n  top: auto;\n  bottom: 100%;\n  margin-top: 0;\n  margin-bottom: var(--bs-dropdown-spacer);\n}\n.dropup .dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0;\n  border-right: 0.3em solid transparent;\n  border-bottom: 0.3em solid;\n  border-left: 0.3em solid transparent;\n}\n.dropup .dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n\n.dropend .dropdown-menu[data-bs-popper] {\n  top: 0;\n  right: auto;\n  left: 100%;\n  margin-top: 0;\n  margin-left: var(--bs-dropdown-spacer);\n}\n.dropend .dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0.3em solid transparent;\n  border-right: 0;\n  border-bottom: 0.3em solid transparent;\n  border-left: 0.3em solid;\n}\n.dropend .dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n.dropend .dropdown-toggle::after {\n  vertical-align: 0;\n}\n\n.dropstart .dropdown-menu[data-bs-popper] {\n  top: 0;\n  right: 100%;\n  left: auto;\n  margin-top: 0;\n  margin-right: var(--bs-dropdown-spacer);\n}\n.dropstart .dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n}\n.dropstart .dropdown-toggle::after {\n  display: none;\n}\n.dropstart .dropdown-toggle::before {\n  display: inline-block;\n  margin-right: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0.3em solid transparent;\n  border-right: 0.3em solid;\n  border-bottom: 0.3em solid transparent;\n}\n.dropstart .dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n.dropstart .dropdown-toggle::before {\n  vertical-align: 0;\n}\n\n.dropdown-divider {\n  height: 0;\n  margin: var(--bs-dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--bs-dropdown-divider-bg);\n  opacity: 1;\n}\n\n.dropdown-item {\n  display: block;\n  width: 100%;\n  padding: var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);\n  clear: both;\n  font-weight: 400;\n  color: var(--bs-dropdown-link-color);\n  text-align: inherit;\n  text-decoration: none;\n  white-space: nowrap;\n  background-color: transparent;\n  border: 0;\n  border-radius: var(--bs-dropdown-item-border-radius, 0);\n}\n.dropdown-item:hover, .dropdown-item:focus {\n  color: var(--bs-dropdown-link-hover-color);\n  background-color: var(--bs-dropdown-link-hover-bg);\n}\n.dropdown-item.active, .dropdown-item:active {\n  color: var(--bs-dropdown-link-active-color);\n  text-decoration: none;\n  background-color: var(--bs-dropdown-link-active-bg);\n}\n.dropdown-item.disabled, .dropdown-item:disabled {\n  color: var(--bs-dropdown-link-disabled-color);\n  pointer-events: none;\n  background-color: transparent;\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n.dropdown-header {\n  display: block;\n  padding: var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);\n  margin-bottom: 0;\n  font-size: 0.875rem;\n  color: var(--bs-dropdown-header-color);\n  white-space: nowrap;\n}\n\n.dropdown-item-text {\n  display: block;\n  padding: var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);\n  color: var(--bs-dropdown-link-color);\n}\n\n.dropdown-menu-dark {\n  --bs-dropdown-color: #dee2e6;\n  --bs-dropdown-bg: #343a40;\n  --bs-dropdown-border-color: var(--bs-border-color-translucent);\n  --bs-dropdown-box-shadow: ;\n  --bs-dropdown-link-color: #dee2e6;\n  --bs-dropdown-link-hover-color: #fff;\n  --bs-dropdown-divider-bg: var(--bs-border-color-translucent);\n  --bs-dropdown-link-hover-bg: rgba(255, 255, 255, 0.15);\n  --bs-dropdown-link-active-color: #fff;\n  --bs-dropdown-link-active-bg: #0d6efd;\n  --bs-dropdown-link-disabled-color: #adb5bd;\n  --bs-dropdown-header-color: #adb5bd;\n}\n\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  flex: 1 1 auto;\n}\n.btn-group > .btn-check:checked + .btn,\n.btn-group > .btn-check:focus + .btn,\n.btn-group > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn-check:checked + .btn,\n.btn-group-vertical > .btn-check:focus + .btn,\n.btn-group-vertical > .btn:hover,\n.btn-group-vertical > .btn:focus,\n.btn-group-vertical > .btn:active,\n.btn-group-vertical > .btn.active {\n  z-index: 1;\n}\n\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n}\n.btn-toolbar .input-group {\n  width: auto;\n}\n\n.btn-group {\n  border-radius: var(--bs-border-radius);\n}\n.btn-group > :not(.btn-check:first-child) + .btn,\n.btn-group > .btn-group:not(:first-child) {\n  margin-left: calc(var(--bs-border-width) * -1);\n}\n.btn-group > .btn:not(:last-child):not(.dropdown-toggle),\n.btn-group > .btn.dropdown-toggle-split:first-child,\n.btn-group > .btn-group:not(:last-child) > .btn {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn:nth-child(n+3),\n.btn-group > :not(.btn-check) + .btn,\n.btn-group > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n\n.dropdown-toggle-split {\n  padding-right: 0.5625rem;\n  padding-left: 0.5625rem;\n}\n.dropdown-toggle-split::after, .dropup .dropdown-toggle-split::after, .dropend .dropdown-toggle-split::after {\n  margin-left: 0;\n}\n.dropstart .dropdown-toggle-split::before {\n  margin-right: 0;\n}\n\n.btn-sm + .dropdown-toggle-split, .btn-group-sm > .btn + .dropdown-toggle-split {\n  padding-right: 0.375rem;\n  padding-left: 0.375rem;\n}\n\n.btn-lg + .dropdown-toggle-split, .btn-group-lg > .btn + .dropdown-toggle-split {\n  padding-right: 0.75rem;\n  padding-left: 0.75rem;\n}\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group {\n  width: 100%;\n}\n.btn-group-vertical > .btn:not(:first-child),\n.btn-group-vertical > .btn-group:not(:first-child) {\n  margin-top: calc(var(--bs-border-width) * -1);\n}\n.btn-group-vertical > .btn:not(:last-child):not(.dropdown-toggle),\n.btn-group-vertical > .btn-group:not(:last-child) > .btn {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn ~ .btn,\n.btn-group-vertical > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n\n.nav {\n  --bs-nav-link-padding-x: 1rem;\n  --bs-nav-link-padding-y: 0.5rem;\n  --bs-nav-link-font-weight: ;\n  --bs-nav-link-color: var(--bs-link-color);\n  --bs-nav-link-hover-color: var(--bs-link-hover-color);\n  --bs-nav-link-disabled-color: var(--bs-secondary-color);\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);\n  font-size: var(--bs-nav-link-font-size);\n  font-weight: var(--bs-nav-link-font-weight);\n  color: var(--bs-nav-link-color);\n  text-decoration: none;\n  background: none;\n  border: 0;\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .nav-link {\n    transition: none;\n  }\n}\n.nav-link:hover, .nav-link:focus {\n  color: var(--bs-nav-link-hover-color);\n}\n.nav-link:focus-visible {\n  outline: 0;\n  box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n}\n.nav-link.disabled, .nav-link:disabled {\n  color: var(--bs-nav-link-disabled-color);\n  pointer-events: none;\n  cursor: default;\n}\n\n.nav-tabs {\n  --bs-nav-tabs-border-width: var(--bs-border-width);\n  --bs-nav-tabs-border-color: var(--bs-border-color);\n  --bs-nav-tabs-border-radius: var(--bs-border-radius);\n  --bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);\n  --bs-nav-tabs-link-active-color: var(--bs-emphasis-color);\n  --bs-nav-tabs-link-active-bg: var(--bs-body-bg);\n  --bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);\n  border-bottom: var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color);\n}\n.nav-tabs .nav-link {\n  margin-bottom: calc(-1 * var(--bs-nav-tabs-border-width));\n  border: var(--bs-nav-tabs-border-width) solid transparent;\n  border-top-left-radius: var(--bs-nav-tabs-border-radius);\n  border-top-right-radius: var(--bs-nav-tabs-border-radius);\n}\n.nav-tabs .nav-link:hover, .nav-tabs .nav-link:focus {\n  isolation: isolate;\n  border-color: var(--bs-nav-tabs-link-hover-border-color);\n}\n.nav-tabs .nav-link.active,\n.nav-tabs .nav-item.show .nav-link {\n  color: var(--bs-nav-tabs-link-active-color);\n  background-color: var(--bs-nav-tabs-link-active-bg);\n  border-color: var(--bs-nav-tabs-link-active-border-color);\n}\n.nav-tabs .dropdown-menu {\n  margin-top: calc(-1 * var(--bs-nav-tabs-border-width));\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n\n.nav-pills {\n  --bs-nav-pills-border-radius: var(--bs-border-radius);\n  --bs-nav-pills-link-active-color: #fff;\n  --bs-nav-pills-link-active-bg: #0d6efd;\n}\n.nav-pills .nav-link {\n  border-radius: var(--bs-nav-pills-border-radius);\n}\n.nav-pills .nav-link.active,\n.nav-pills .show > .nav-link {\n  color: var(--bs-nav-pills-link-active-color);\n  background-color: var(--bs-nav-pills-link-active-bg);\n}\n\n.nav-underline {\n  --bs-nav-underline-gap: 1rem;\n  --bs-nav-underline-border-width: 0.125rem;\n  --bs-nav-underline-link-active-color: var(--bs-emphasis-color);\n  gap: var(--bs-nav-underline-gap);\n}\n.nav-underline .nav-link {\n  padding-right: 0;\n  padding-left: 0;\n  border-bottom: var(--bs-nav-underline-border-width) solid transparent;\n}\n.nav-underline .nav-link:hover, .nav-underline .nav-link:focus {\n  border-bottom-color: currentcolor;\n}\n.nav-underline .nav-link.active,\n.nav-underline .show > .nav-link {\n  font-weight: 700;\n  color: var(--bs-nav-underline-link-active-color);\n  border-bottom-color: currentcolor;\n}\n\n.nav-fill > .nav-link,\n.nav-fill .nav-item {\n  flex: 1 1 auto;\n  text-align: center;\n}\n\n.nav-justified > .nav-link,\n.nav-justified .nav-item {\n  flex-basis: 0;\n  flex-grow: 1;\n  text-align: center;\n}\n\n.nav-fill .nav-item .nav-link,\n.nav-justified .nav-item .nav-link {\n  width: 100%;\n}\n\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n\n.navbar {\n  --bs-navbar-padding-x: 0;\n  --bs-navbar-padding-y: 0.5rem;\n  --bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);\n  --bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);\n  --bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);\n  --bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);\n  --bs-navbar-brand-padding-y: 0.3125rem;\n  --bs-navbar-brand-margin-end: 1rem;\n  --bs-navbar-brand-font-size: 1.25rem;\n  --bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);\n  --bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);\n  --bs-navbar-nav-link-padding-x: 0.5rem;\n  --bs-navbar-toggler-padding-y: 0.25rem;\n  --bs-navbar-toggler-padding-x: 0.75rem;\n  --bs-navbar-toggler-font-size: 1.25rem;\n  --bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833, 37, 41, 0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");\n  --bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);\n  --bs-navbar-toggler-border-radius: var(--bs-border-radius);\n  --bs-navbar-toggler-focus-width: 0.25rem;\n  --bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;\n  position: relative;\n  display: flex;\n  flex-wrap: wrap;\n  align-items: center;\n  justify-content: space-between;\n  padding: var(--bs-navbar-padding-y) var(--bs-navbar-padding-x);\n}\n.navbar > .container,\n.navbar > .container-fluid,\n.navbar > .container-sm,\n.navbar > .container-md,\n.navbar > .container-lg,\n.navbar > .container-xl,\n.navbar > .container-xxl {\n  display: flex;\n  flex-wrap: inherit;\n  align-items: center;\n  justify-content: space-between;\n}\n.navbar-brand {\n  padding-top: var(--bs-navbar-brand-padding-y);\n  padding-bottom: var(--bs-navbar-brand-padding-y);\n  margin-right: var(--bs-navbar-brand-margin-end);\n  font-size: var(--bs-navbar-brand-font-size);\n  color: var(--bs-navbar-brand-color);\n  text-decoration: none;\n  white-space: nowrap;\n}\n.navbar-brand:hover, .navbar-brand:focus {\n  color: var(--bs-navbar-brand-hover-color);\n}\n\n.navbar-nav {\n  --bs-nav-link-padding-x: 0;\n  --bs-nav-link-padding-y: 0.5rem;\n  --bs-nav-link-font-weight: ;\n  --bs-nav-link-color: var(--bs-navbar-color);\n  --bs-nav-link-hover-color: var(--bs-navbar-hover-color);\n  --bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);\n  display: flex;\n  flex-direction: column;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n.navbar-nav .nav-link.active, .navbar-nav .nav-link.show {\n  color: var(--bs-navbar-active-color);\n}\n.navbar-nav .dropdown-menu {\n  position: static;\n}\n\n.navbar-text {\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  color: var(--bs-navbar-color);\n}\n.navbar-text a,\n.navbar-text a:hover,\n.navbar-text a:focus {\n  color: var(--bs-navbar-active-color);\n}\n\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  align-items: center;\n}\n\n.navbar-toggler {\n  padding: var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);\n  font-size: var(--bs-navbar-toggler-font-size);\n  line-height: 1;\n  color: var(--bs-navbar-color);\n  background-color: transparent;\n  border: var(--bs-border-width) solid var(--bs-navbar-toggler-border-color);\n  border-radius: var(--bs-navbar-toggler-border-radius);\n  transition: var(--bs-navbar-toggler-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .navbar-toggler {\n    transition: none;\n  }\n}\n.navbar-toggler:hover {\n  text-decoration: none;\n}\n.navbar-toggler:focus {\n  text-decoration: none;\n  outline: 0;\n  box-shadow: 0 0 0 var(--bs-navbar-toggler-focus-width);\n}\n\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--bs-navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--bs-scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n@media (min-width: 576px) {\n  .navbar-expand-sm {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-sm .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-sm .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-sm .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-sm .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-sm .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-sm .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-sm .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-sm .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-sm .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-expand-md {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-md .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-md .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-md .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-md .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-md .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-md .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-md .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-md .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-md .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n@media (min-width: 992px) {\n  .navbar-expand-lg {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-lg .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-lg .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-lg .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-lg .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-lg .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-lg .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-lg .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-lg .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-lg .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n@media (min-width: 1200px) {\n  .navbar-expand-xl {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-xl .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-xl .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-xl .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-xl .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-xl .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-xl .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-xl .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-xl .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-xl .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n@media (min-width: 1400px) {\n  .navbar-expand-xxl {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-xxl .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-xxl .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-xxl .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-xxl .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-xxl .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-xxl .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-xxl .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-xxl .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-xxl .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n.navbar-expand {\n  flex-wrap: nowrap;\n  justify-content: flex-start;\n}\n.navbar-expand .navbar-nav {\n  flex-direction: row;\n}\n.navbar-expand .navbar-nav .dropdown-menu {\n  position: absolute;\n}\n.navbar-expand .navbar-nav .nav-link {\n  padding-right: var(--bs-navbar-nav-link-padding-x);\n  padding-left: var(--bs-navbar-nav-link-padding-x);\n}\n.navbar-expand .navbar-nav-scroll {\n  overflow: visible;\n}\n.navbar-expand .navbar-collapse {\n  display: flex !important;\n  flex-basis: auto;\n}\n.navbar-expand .navbar-toggler {\n  display: none;\n}\n.navbar-expand .offcanvas {\n  position: static;\n  z-index: auto;\n  flex-grow: 1;\n  width: auto !important;\n  height: auto !important;\n  visibility: visible !important;\n  background-color: transparent !important;\n  border: 0 !important;\n  transform: none !important;\n  transition: none;\n}\n.navbar-expand .offcanvas .offcanvas-header {\n  display: none;\n}\n.navbar-expand .offcanvas .offcanvas-body {\n  display: flex;\n  flex-grow: 0;\n  padding: 0;\n  overflow-y: visible;\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=dark] {\n  --bs-navbar-color: rgba(255, 255, 255, 0.55);\n  --bs-navbar-hover-color: rgba(255, 255, 255, 0.75);\n  --bs-navbar-disabled-color: rgba(255, 255, 255, 0.25);\n  --bs-navbar-active-color: #fff;\n  --bs-navbar-brand-color: #fff;\n  --bs-navbar-brand-hover-color: #fff;\n  --bs-navbar-toggler-border-color: rgba(255, 255, 255, 0.1);\n  --bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");\n}\n\n[data-bs-theme=dark] .navbar-toggler-icon {\n  --bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");\n}\n\n.card {\n  --bs-card-spacer-y: 1rem;\n  --bs-card-spacer-x: 1rem;\n  --bs-card-title-spacer-y: 0.5rem;\n  --bs-card-title-color: ;\n  --bs-card-subtitle-color: ;\n  --bs-card-border-width: var(--bs-border-width);\n  --bs-card-border-color: var(--bs-border-color-translucent);\n  --bs-card-border-radius: var(--bs-border-radius);\n  --bs-card-box-shadow: ;\n  --bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));\n  --bs-card-cap-padding-y: 0.5rem;\n  --bs-card-cap-padding-x: 1rem;\n  --bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);\n  --bs-card-cap-color: ;\n  --bs-card-height: ;\n  --bs-card-color: ;\n  --bs-card-bg: var(--bs-body-bg);\n  --bs-card-img-overlay-padding: 1rem;\n  --bs-card-group-margin: 0.75rem;\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0;\n  height: var(--bs-card-height);\n  color: var(--bs-body-color);\n  word-wrap: break-word;\n  background-color: var(--bs-card-bg);\n  background-clip: border-box;\n  border: var(--bs-card-border-width) solid var(--bs-card-border-color);\n  border-radius: var(--bs-card-border-radius);\n}\n.card > hr {\n  margin-right: 0;\n  margin-left: 0;\n}\n.card > .list-group {\n  border-top: inherit;\n  border-bottom: inherit;\n}\n.card > .list-group:first-child {\n  border-top-width: 0;\n  border-top-left-radius: var(--bs-card-inner-border-radius);\n  border-top-right-radius: var(--bs-card-inner-border-radius);\n}\n.card > .list-group:last-child {\n  border-bottom-width: 0;\n  border-bottom-right-radius: var(--bs-card-inner-border-radius);\n  border-bottom-left-radius: var(--bs-card-inner-border-radius);\n}\n.card > .card-header + .list-group,\n.card > .list-group + .card-footer {\n  border-top: 0;\n}\n\n.card-body {\n  flex: 1 1 auto;\n  padding: var(--bs-card-spacer-y) var(--bs-card-spacer-x);\n  color: var(--bs-card-color);\n}\n\n.card-title {\n  margin-bottom: var(--bs-card-title-spacer-y);\n  color: var(--bs-card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-0.5 * var(--bs-card-title-spacer-y));\n  margin-bottom: 0;\n  color: var(--bs-card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link + .card-link {\n  margin-left: var(--bs-card-spacer-x);\n}\n\n.card-header {\n  padding: var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);\n  margin-bottom: 0;\n  color: var(--bs-card-cap-color);\n  background-color: var(--bs-card-cap-bg);\n  border-bottom: var(--bs-card-border-width) solid var(--bs-card-border-color);\n}\n.card-header:first-child {\n  border-radius: var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0;\n}\n\n.card-footer {\n  padding: var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);\n  color: var(--bs-card-cap-color);\n  background-color: var(--bs-card-cap-bg);\n  border-top: var(--bs-card-border-width) solid var(--bs-card-border-color);\n}\n.card-footer:last-child {\n  border-radius: 0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius);\n}\n\n.card-header-tabs {\n  margin-right: calc(-0.5 * var(--bs-card-cap-padding-x));\n  margin-bottom: calc(-1 * var(--bs-card-cap-padding-y));\n  margin-left: calc(-0.5 * var(--bs-card-cap-padding-x));\n  border-bottom: 0;\n}\n.card-header-tabs .nav-link.active {\n  background-color: var(--bs-card-bg);\n  border-bottom-color: var(--bs-card-bg);\n}\n\n.card-header-pills {\n  margin-right: calc(-0.5 * var(--bs-card-cap-padding-x));\n  margin-left: calc(-0.5 * var(--bs-card-cap-padding-x));\n}\n\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--bs-card-img-overlay-padding);\n  border-radius: var(--bs-card-inner-border-radius);\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%;\n}\n\n.card-img,\n.card-img-top {\n  border-top-left-radius: var(--bs-card-inner-border-radius);\n  border-top-right-radius: var(--bs-card-inner-border-radius);\n}\n\n.card-img,\n.card-img-bottom {\n  border-bottom-right-radius: var(--bs-card-inner-border-radius);\n  border-bottom-left-radius: var(--bs-card-inner-border-radius);\n}\n\n.card-group > .card {\n  margin-bottom: var(--bs-card-group-margin);\n}\n@media (min-width: 576px) {\n  .card-group {\n    display: flex;\n    flex-flow: row wrap;\n  }\n  .card-group > .card {\n    flex: 1 0 0%;\n    margin-bottom: 0;\n  }\n  .card-group > .card + .card {\n    margin-left: 0;\n    border-left: 0;\n  }\n  .card-group > .card:not(:last-child) {\n    border-top-right-radius: 0;\n    border-bottom-right-radius: 0;\n  }\n  .card-group > .card:not(:last-child) .card-img-top,\n  .card-group > .card:not(:last-child) .card-header {\n    border-top-right-radius: 0;\n  }\n  .card-group > .card:not(:last-child) .card-img-bottom,\n  .card-group > .card:not(:last-child) .card-footer {\n    border-bottom-right-radius: 0;\n  }\n  .card-group > .card:not(:first-child) {\n    border-top-left-radius: 0;\n    border-bottom-left-radius: 0;\n  }\n  .card-group > .card:not(:first-child) .card-img-top,\n  .card-group > .card:not(:first-child) .card-header {\n    border-top-left-radius: 0;\n  }\n  .card-group > .card:not(:first-child) .card-img-bottom,\n  .card-group > .card:not(:first-child) .card-footer {\n    border-bottom-left-radius: 0;\n  }\n}\n\n.accordion {\n  --bs-accordion-color: var(--bs-body-color);\n  --bs-accordion-bg: var(--bs-body-bg);\n  --bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;\n  --bs-accordion-border-color: var(--bs-border-color);\n  --bs-accordion-border-width: var(--bs-border-width);\n  --bs-accordion-border-radius: var(--bs-border-radius);\n  --bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));\n  --bs-accordion-btn-padding-x: 1.25rem;\n  --bs-accordion-btn-padding-y: 1rem;\n  --bs-accordion-btn-color: var(--bs-body-color);\n  --bs-accordion-btn-bg: var(--bs-accordion-bg);\n  --bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23212529' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");\n  --bs-accordion-btn-icon-width: 1.25rem;\n  --bs-accordion-btn-icon-transform: rotate(-180deg);\n  --bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;\n  --bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23052c65' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");\n  --bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n  --bs-accordion-body-padding-x: 1.25rem;\n  --bs-accordion-body-padding-y: 1rem;\n  --bs-accordion-active-color: var(--bs-primary-text-emphasis);\n  --bs-accordion-active-bg: var(--bs-primary-bg-subtle);\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);\n  font-size: 1rem;\n  color: var(--bs-accordion-btn-color);\n  text-align: left;\n  background-color: var(--bs-accordion-btn-bg);\n  border: 0;\n  border-radius: 0;\n  overflow-anchor: none;\n  transition: var(--bs-accordion-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .accordion-button {\n    transition: none;\n  }\n}\n.accordion-button:not(.collapsed) {\n  color: var(--bs-accordion-active-color);\n  background-color: var(--bs-accordion-active-bg);\n  box-shadow: inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color);\n}\n.accordion-button:not(.collapsed)::after {\n  background-image: var(--bs-accordion-btn-active-icon);\n  transform: var(--bs-accordion-btn-icon-transform);\n}\n.accordion-button::after {\n  flex-shrink: 0;\n  width: var(--bs-accordion-btn-icon-width);\n  height: var(--bs-accordion-btn-icon-width);\n  margin-left: auto;\n  content: \"\";\n  background-image: var(--bs-accordion-btn-icon);\n  background-repeat: no-repeat;\n  background-size: var(--bs-accordion-btn-icon-width);\n  transition: var(--bs-accordion-btn-icon-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .accordion-button::after {\n    transition: none;\n  }\n}\n.accordion-button:hover {\n  z-index: 2;\n}\n.accordion-button:focus {\n  z-index: 3;\n  outline: 0;\n  box-shadow: var(--bs-accordion-btn-focus-box-shadow);\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--bs-accordion-color);\n  background-color: var(--bs-accordion-bg);\n  border: var(--bs-accordion-border-width) solid var(--bs-accordion-border-color);\n}\n.accordion-item:first-of-type {\n  border-top-left-radius: var(--bs-accordion-border-radius);\n  border-top-right-radius: var(--bs-accordion-border-radius);\n}\n.accordion-item:first-of-type > .accordion-header .accordion-button {\n  border-top-left-radius: var(--bs-accordion-inner-border-radius);\n  border-top-right-radius: var(--bs-accordion-inner-border-radius);\n}\n.accordion-item:not(:first-of-type) {\n  border-top: 0;\n}\n.accordion-item:last-of-type {\n  border-bottom-right-radius: var(--bs-accordion-border-radius);\n  border-bottom-left-radius: var(--bs-accordion-border-radius);\n}\n.accordion-item:last-of-type > .accordion-header .accordion-button.collapsed {\n  border-bottom-right-radius: var(--bs-accordion-inner-border-radius);\n  border-bottom-left-radius: var(--bs-accordion-inner-border-radius);\n}\n.accordion-item:last-of-type > .accordion-collapse {\n  border-bottom-right-radius: var(--bs-accordion-border-radius);\n  border-bottom-left-radius: var(--bs-accordion-border-radius);\n}\n\n.accordion-body {\n  padding: var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x);\n}\n\n.accordion-flush > .accordion-item {\n  border-right: 0;\n  border-left: 0;\n  border-radius: 0;\n}\n.accordion-flush > .accordion-item:first-child {\n  border-top: 0;\n}\n.accordion-flush > .accordion-item:last-child {\n  border-bottom: 0;\n}\n.accordion-flush > .accordion-item > .accordion-header .accordion-button, .accordion-flush > .accordion-item > .accordion-header .accordion-button.collapsed {\n  border-radius: 0;\n}\n.accordion-flush > .accordion-item > .accordion-collapse {\n  border-radius: 0;\n}\n\n[data-bs-theme=dark] .accordion-button::after {\n  --bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");\n  --bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");\n}\n\n.breadcrumb {\n  --bs-breadcrumb-padding-x: 0;\n  --bs-breadcrumb-padding-y: 0;\n  --bs-breadcrumb-margin-bottom: 1rem;\n  --bs-breadcrumb-bg: ;\n  --bs-breadcrumb-border-radius: ;\n  --bs-breadcrumb-divider-color: var(--bs-secondary-color);\n  --bs-breadcrumb-item-padding-x: 0.5rem;\n  --bs-breadcrumb-item-active-color: var(--bs-secondary-color);\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);\n  margin-bottom: var(--bs-breadcrumb-margin-bottom);\n  font-size: var(--bs-breadcrumb-font-size);\n  list-style: none;\n  background-color: var(--bs-breadcrumb-bg);\n  border-radius: var(--bs-breadcrumb-border-radius);\n}\n\n.breadcrumb-item + .breadcrumb-item {\n  padding-left: var(--bs-breadcrumb-item-padding-x);\n}\n.breadcrumb-item + .breadcrumb-item::before {\n  float: left;\n  padding-right: var(--bs-breadcrumb-item-padding-x);\n  color: var(--bs-breadcrumb-divider-color);\n  content: var(--bs-breadcrumb-divider, \"/\") /* rtl: var(--bs-breadcrumb-divider, \"/\") */;\n}\n.breadcrumb-item.active {\n  color: var(--bs-breadcrumb-item-active-color);\n}\n\n.pagination {\n  --bs-pagination-padding-x: 0.75rem;\n  --bs-pagination-padding-y: 0.375rem;\n  --bs-pagination-font-size: 1rem;\n  --bs-pagination-color: var(--bs-link-color);\n  --bs-pagination-bg: var(--bs-body-bg);\n  --bs-pagination-border-width: var(--bs-border-width);\n  --bs-pagination-border-color: var(--bs-border-color);\n  --bs-pagination-border-radius: var(--bs-border-radius);\n  --bs-pagination-hover-color: var(--bs-link-hover-color);\n  --bs-pagination-hover-bg: var(--bs-tertiary-bg);\n  --bs-pagination-hover-border-color: var(--bs-border-color);\n  --bs-pagination-focus-color: var(--bs-link-hover-color);\n  --bs-pagination-focus-bg: var(--bs-secondary-bg);\n  --bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n  --bs-pagination-active-color: #fff;\n  --bs-pagination-active-bg: #0d6efd;\n  --bs-pagination-active-border-color: #0d6efd;\n  --bs-pagination-disabled-color: var(--bs-secondary-color);\n  --bs-pagination-disabled-bg: var(--bs-secondary-bg);\n  --bs-pagination-disabled-border-color: var(--bs-border-color);\n  display: flex;\n  padding-left: 0;\n  list-style: none;\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);\n  font-size: var(--bs-pagination-font-size);\n  color: var(--bs-pagination-color);\n  text-decoration: none;\n  background-color: var(--bs-pagination-bg);\n  border: var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .page-link {\n    transition: none;\n  }\n}\n.page-link:hover {\n  z-index: 2;\n  color: var(--bs-pagination-hover-color);\n  background-color: var(--bs-pagination-hover-bg);\n  border-color: var(--bs-pagination-hover-border-color);\n}\n.page-link:focus {\n  z-index: 3;\n  color: var(--bs-pagination-focus-color);\n  background-color: var(--bs-pagination-focus-bg);\n  outline: 0;\n  box-shadow: var(--bs-pagination-focus-box-shadow);\n}\n.page-link.active, .active > .page-link {\n  z-index: 3;\n  color: var(--bs-pagination-active-color);\n  background-color: var(--bs-pagination-active-bg);\n  border-color: var(--bs-pagination-active-border-color);\n}\n.page-link.disabled, .disabled > .page-link {\n  color: var(--bs-pagination-disabled-color);\n  pointer-events: none;\n  background-color: var(--bs-pagination-disabled-bg);\n  border-color: var(--bs-pagination-disabled-border-color);\n}\n\n.page-item:not(:first-child) .page-link {\n  margin-left: calc(var(--bs-border-width) * -1);\n}\n.page-item:first-child .page-link {\n  border-top-left-radius: var(--bs-pagination-border-radius);\n  border-bottom-left-radius: var(--bs-pagination-border-radius);\n}\n.page-item:last-child .page-link {\n  border-top-right-radius: var(--bs-pagination-border-radius);\n  border-bottom-right-radius: var(--bs-pagination-border-radius);\n}\n\n.pagination-lg {\n  --bs-pagination-padding-x: 1.5rem;\n  --bs-pagination-padding-y: 0.75rem;\n  --bs-pagination-font-size: 1.25rem;\n  --bs-pagination-border-radius: var(--bs-border-radius-lg);\n}\n\n.pagination-sm {\n  --bs-pagination-padding-x: 0.5rem;\n  --bs-pagination-padding-y: 0.25rem;\n  --bs-pagination-font-size: 0.875rem;\n  --bs-pagination-border-radius: var(--bs-border-radius-sm);\n}\n\n.badge {\n  --bs-badge-padding-x: 0.65em;\n  --bs-badge-padding-y: 0.35em;\n  --bs-badge-font-size: 0.75em;\n  --bs-badge-font-weight: 700;\n  --bs-badge-color: #fff;\n  --bs-badge-border-radius: var(--bs-border-radius);\n  display: inline-block;\n  padding: var(--bs-badge-padding-y) var(--bs-badge-padding-x);\n  font-size: var(--bs-badge-font-size);\n  font-weight: var(--bs-badge-font-weight);\n  line-height: 1;\n  color: var(--bs-badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: var(--bs-badge-border-radius);\n}\n.badge:empty {\n  display: none;\n}\n\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n\n.alert {\n  --bs-alert-bg: transparent;\n  --bs-alert-padding-x: 1rem;\n  --bs-alert-padding-y: 1rem;\n  --bs-alert-margin-bottom: 1rem;\n  --bs-alert-color: inherit;\n  --bs-alert-border-color: transparent;\n  --bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);\n  --bs-alert-border-radius: var(--bs-border-radius);\n  --bs-alert-link-color: inherit;\n  position: relative;\n  padding: var(--bs-alert-padding-y) var(--bs-alert-padding-x);\n  margin-bottom: var(--bs-alert-margin-bottom);\n  color: var(--bs-alert-color);\n  background-color: var(--bs-alert-bg);\n  border: var(--bs-alert-border);\n  border-radius: var(--bs-alert-border-radius);\n}\n\n.alert-heading {\n  color: inherit;\n}\n\n.alert-link {\n  font-weight: 700;\n  color: var(--bs-alert-link-color);\n}\n\n.alert-dismissible {\n  padding-right: 3rem;\n}\n.alert-dismissible .btn-close {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  padding: 1.25rem 1rem;\n}\n\n.alert-primary {\n  --bs-alert-color: var(--bs-primary-text-emphasis);\n  --bs-alert-bg: var(--bs-primary-bg-subtle);\n  --bs-alert-border-color: var(--bs-primary-border-subtle);\n  --bs-alert-link-color: var(--bs-primary-text-emphasis);\n}\n\n.alert-secondary {\n  --bs-alert-color: var(--bs-secondary-text-emphasis);\n  --bs-alert-bg: var(--bs-secondary-bg-subtle);\n  --bs-alert-border-color: var(--bs-secondary-border-subtle);\n  --bs-alert-link-color: var(--bs-secondary-text-emphasis);\n}\n\n.alert-success {\n  --bs-alert-color: var(--bs-success-text-emphasis);\n  --bs-alert-bg: var(--bs-success-bg-subtle);\n  --bs-alert-border-color: var(--bs-success-border-subtle);\n  --bs-alert-link-color: var(--bs-success-text-emphasis);\n}\n\n.alert-info {\n  --bs-alert-color: var(--bs-info-text-emphasis);\n  --bs-alert-bg: var(--bs-info-bg-subtle);\n  --bs-alert-border-color: var(--bs-info-border-subtle);\n  --bs-alert-link-color: var(--bs-info-text-emphasis);\n}\n\n.alert-warning {\n  --bs-alert-color: var(--bs-warning-text-emphasis);\n  --bs-alert-bg: var(--bs-warning-bg-subtle);\n  --bs-alert-border-color: var(--bs-warning-border-subtle);\n  --bs-alert-link-color: var(--bs-warning-text-emphasis);\n}\n\n.alert-danger {\n  --bs-alert-color: var(--bs-danger-text-emphasis);\n  --bs-alert-bg: var(--bs-danger-bg-subtle);\n  --bs-alert-border-color: var(--bs-danger-border-subtle);\n  --bs-alert-link-color: var(--bs-danger-text-emphasis);\n}\n\n.alert-light {\n  --bs-alert-color: var(--bs-light-text-emphasis);\n  --bs-alert-bg: var(--bs-light-bg-subtle);\n  --bs-alert-border-color: var(--bs-light-border-subtle);\n  --bs-alert-link-color: var(--bs-light-text-emphasis);\n}\n\n.alert-dark {\n  --bs-alert-color: var(--bs-dark-text-emphasis);\n  --bs-alert-bg: var(--bs-dark-bg-subtle);\n  --bs-alert-border-color: var(--bs-dark-border-subtle);\n  --bs-alert-link-color: var(--bs-dark-text-emphasis);\n}\n\n@keyframes progress-bar-stripes {\n  0% {\n    background-position-x: 1rem;\n  }\n}\n.progress,\n.progress-stacked {\n  --bs-progress-height: 1rem;\n  --bs-progress-font-size: 0.75rem;\n  --bs-progress-bg: var(--bs-secondary-bg);\n  --bs-progress-border-radius: var(--bs-border-radius);\n  --bs-progress-box-shadow: var(--bs-box-shadow-inset);\n  --bs-progress-bar-color: #fff;\n  --bs-progress-bar-bg: #0d6efd;\n  --bs-progress-bar-transition: width 0.6s ease;\n  display: flex;\n  height: var(--bs-progress-height);\n  overflow: hidden;\n  font-size: var(--bs-progress-font-size);\n  background-color: var(--bs-progress-bg);\n  border-radius: var(--bs-progress-border-radius);\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--bs-progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--bs-progress-bar-bg);\n  transition: var(--bs-progress-bar-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .progress-bar {\n    transition: none;\n  }\n}\n\n.progress-bar-striped {\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-size: var(--bs-progress-height) var(--bs-progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n.progress-bar-animated {\n  animation: 1s linear infinite progress-bar-stripes;\n}\n@media (prefers-reduced-motion: reduce) {\n  .progress-bar-animated {\n    animation: none;\n  }\n}\n\n.list-group {\n  --bs-list-group-color: var(--bs-body-color);\n  --bs-list-group-bg: var(--bs-body-bg);\n  --bs-list-group-border-color: var(--bs-border-color);\n  --bs-list-group-border-width: var(--bs-border-width);\n  --bs-list-group-border-radius: var(--bs-border-radius);\n  --bs-list-group-item-padding-x: 1rem;\n  --bs-list-group-item-padding-y: 0.5rem;\n  --bs-list-group-action-color: var(--bs-secondary-color);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-tertiary-bg);\n  --bs-list-group-action-active-color: var(--bs-body-color);\n  --bs-list-group-action-active-bg: var(--bs-secondary-bg);\n  --bs-list-group-disabled-color: var(--bs-secondary-color);\n  --bs-list-group-disabled-bg: var(--bs-body-bg);\n  --bs-list-group-active-color: #fff;\n  --bs-list-group-active-bg: #0d6efd;\n  --bs-list-group-active-border-color: #0d6efd;\n  display: flex;\n  flex-direction: column;\n  padding-left: 0;\n  margin-bottom: 0;\n  border-radius: var(--bs-list-group-border-radius);\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n}\n.list-group-numbered > .list-group-item::before {\n  content: counters(section, \".\") \". \";\n  counter-increment: section;\n}\n\n.list-group-item-action {\n  width: 100%;\n  color: var(--bs-list-group-action-color);\n  text-align: inherit;\n}\n.list-group-item-action:hover, .list-group-item-action:focus {\n  z-index: 1;\n  color: var(--bs-list-group-action-hover-color);\n  text-decoration: none;\n  background-color: var(--bs-list-group-action-hover-bg);\n}\n.list-group-item-action:active {\n  color: var(--bs-list-group-action-active-color);\n  background-color: var(--bs-list-group-action-active-bg);\n}\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);\n  color: var(--bs-list-group-color);\n  text-decoration: none;\n  background-color: var(--bs-list-group-bg);\n  border: var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);\n}\n.list-group-item:first-child {\n  border-top-left-radius: inherit;\n  border-top-right-radius: inherit;\n}\n.list-group-item:last-child {\n  border-bottom-right-radius: inherit;\n  border-bottom-left-radius: inherit;\n}\n.list-group-item.disabled, .list-group-item:disabled {\n  color: var(--bs-list-group-disabled-color);\n  pointer-events: none;\n  background-color: var(--bs-list-group-disabled-bg);\n}\n.list-group-item.active {\n  z-index: 2;\n  color: var(--bs-list-group-active-color);\n  background-color: var(--bs-list-group-active-bg);\n  border-color: var(--bs-list-group-active-border-color);\n}\n.list-group-item + .list-group-item {\n  border-top-width: 0;\n}\n.list-group-item + .list-group-item.active {\n  margin-top: calc(-1 * var(--bs-list-group-border-width));\n  border-top-width: var(--bs-list-group-border-width);\n}\n\n.list-group-horizontal {\n  flex-direction: row;\n}\n.list-group-horizontal > .list-group-item:first-child:not(:last-child) {\n  border-bottom-left-radius: var(--bs-list-group-border-radius);\n  border-top-right-radius: 0;\n}\n.list-group-horizontal > .list-group-item:last-child:not(:first-child) {\n  border-top-right-radius: var(--bs-list-group-border-radius);\n  border-bottom-left-radius: 0;\n}\n.list-group-horizontal > .list-group-item.active {\n  margin-top: 0;\n}\n.list-group-horizontal > .list-group-item + .list-group-item {\n  border-top-width: var(--bs-list-group-border-width);\n  border-left-width: 0;\n}\n.list-group-horizontal > .list-group-item + .list-group-item.active {\n  margin-left: calc(-1 * var(--bs-list-group-border-width));\n  border-left-width: var(--bs-list-group-border-width);\n}\n\n@media (min-width: 576px) {\n  .list-group-horizontal-sm {\n    flex-direction: row;\n  }\n  .list-group-horizontal-sm > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n@media (min-width: 768px) {\n  .list-group-horizontal-md {\n    flex-direction: row;\n  }\n  .list-group-horizontal-md > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-md > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-md > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-md > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-md > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n@media (min-width: 992px) {\n  .list-group-horizontal-lg {\n    flex-direction: row;\n  }\n  .list-group-horizontal-lg > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n@media (min-width: 1200px) {\n  .list-group-horizontal-xl {\n    flex-direction: row;\n  }\n  .list-group-horizontal-xl > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n@media (min-width: 1400px) {\n  .list-group-horizontal-xxl {\n    flex-direction: row;\n  }\n  .list-group-horizontal-xxl > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-xxl > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-xxl > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-xxl > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-xxl > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n.list-group-flush {\n  border-radius: 0;\n}\n.list-group-flush > .list-group-item {\n  border-width: 0 0 var(--bs-list-group-border-width);\n}\n.list-group-flush > .list-group-item:last-child {\n  border-bottom-width: 0;\n}\n\n.list-group-item-primary {\n  --bs-list-group-color: var(--bs-primary-text-emphasis);\n  --bs-list-group-bg: var(--bs-primary-bg-subtle);\n  --bs-list-group-border-color: var(--bs-primary-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-primary-border-subtle);\n  --bs-list-group-active-color: var(--bs-primary-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-primary-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-primary-text-emphasis);\n}\n\n.list-group-item-secondary {\n  --bs-list-group-color: var(--bs-secondary-text-emphasis);\n  --bs-list-group-bg: var(--bs-secondary-bg-subtle);\n  --bs-list-group-border-color: var(--bs-secondary-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);\n  --bs-list-group-active-color: var(--bs-secondary-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-secondary-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-secondary-text-emphasis);\n}\n\n.list-group-item-success {\n  --bs-list-group-color: var(--bs-success-text-emphasis);\n  --bs-list-group-bg: var(--bs-success-bg-subtle);\n  --bs-list-group-border-color: var(--bs-success-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-success-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-success-border-subtle);\n  --bs-list-group-active-color: var(--bs-success-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-success-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-success-text-emphasis);\n}\n\n.list-group-item-info {\n  --bs-list-group-color: var(--bs-info-text-emphasis);\n  --bs-list-group-bg: var(--bs-info-bg-subtle);\n  --bs-list-group-border-color: var(--bs-info-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-info-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-info-border-subtle);\n  --bs-list-group-active-color: var(--bs-info-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-info-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-info-text-emphasis);\n}\n\n.list-group-item-warning {\n  --bs-list-group-color: var(--bs-warning-text-emphasis);\n  --bs-list-group-bg: var(--bs-warning-bg-subtle);\n  --bs-list-group-border-color: var(--bs-warning-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-warning-border-subtle);\n  --bs-list-group-active-color: var(--bs-warning-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-warning-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-warning-text-emphasis);\n}\n\n.list-group-item-danger {\n  --bs-list-group-color: var(--bs-danger-text-emphasis);\n  --bs-list-group-bg: var(--bs-danger-bg-subtle);\n  --bs-list-group-border-color: var(--bs-danger-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-danger-border-subtle);\n  --bs-list-group-active-color: var(--bs-danger-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-danger-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-danger-text-emphasis);\n}\n\n.list-group-item-light {\n  --bs-list-group-color: var(--bs-light-text-emphasis);\n  --bs-list-group-bg: var(--bs-light-bg-subtle);\n  --bs-list-group-border-color: var(--bs-light-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-light-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-light-border-subtle);\n  --bs-list-group-active-color: var(--bs-light-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-light-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-light-text-emphasis);\n}\n\n.list-group-item-dark {\n  --bs-list-group-color: var(--bs-dark-text-emphasis);\n  --bs-list-group-bg: var(--bs-dark-bg-subtle);\n  --bs-list-group-border-color: var(--bs-dark-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-dark-border-subtle);\n  --bs-list-group-active-color: var(--bs-dark-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-dark-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-dark-text-emphasis);\n}\n\n.btn-close {\n  --bs-btn-close-color: #000;\n  --bs-btn-close-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e\");\n  --bs-btn-close-opacity: 0.5;\n  --bs-btn-close-hover-opacity: 0.75;\n  --bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);\n  --bs-btn-close-focus-opacity: 1;\n  --bs-btn-close-disabled-opacity: 0.25;\n  --bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);\n  box-sizing: content-box;\n  width: 1em;\n  height: 1em;\n  padding: 0.25em 0.25em;\n  color: var(--bs-btn-close-color);\n  background: transparent var(--bs-btn-close-bg) center/1em auto no-repeat;\n  border: 0;\n  border-radius: 0.375rem;\n  opacity: var(--bs-btn-close-opacity);\n}\n.btn-close:hover {\n  color: var(--bs-btn-close-color);\n  text-decoration: none;\n  opacity: var(--bs-btn-close-hover-opacity);\n}\n.btn-close:focus {\n  outline: 0;\n  box-shadow: var(--bs-btn-close-focus-shadow);\n  opacity: var(--bs-btn-close-focus-opacity);\n}\n.btn-close:disabled, .btn-close.disabled {\n  pointer-events: none;\n  user-select: none;\n  opacity: var(--bs-btn-close-disabled-opacity);\n}\n\n.btn-close-white {\n  filter: var(--bs-btn-close-white-filter);\n}\n\n[data-bs-theme=dark] .btn-close {\n  filter: var(--bs-btn-close-white-filter);\n}\n\n.toast {\n  --bs-toast-zindex: 1090;\n  --bs-toast-padding-x: 0.75rem;\n  --bs-toast-padding-y: 0.5rem;\n  --bs-toast-spacing: 1.5rem;\n  --bs-toast-max-width: 350px;\n  --bs-toast-font-size: 0.875rem;\n  --bs-toast-color: ;\n  --bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);\n  --bs-toast-border-width: var(--bs-border-width);\n  --bs-toast-border-color: var(--bs-border-color-translucent);\n  --bs-toast-border-radius: var(--bs-border-radius);\n  --bs-toast-box-shadow: var(--bs-box-shadow);\n  --bs-toast-header-color: var(--bs-secondary-color);\n  --bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);\n  --bs-toast-header-border-color: var(--bs-border-color-translucent);\n  width: var(--bs-toast-max-width);\n  max-width: 100%;\n  font-size: var(--bs-toast-font-size);\n  color: var(--bs-toast-color);\n  pointer-events: auto;\n  background-color: var(--bs-toast-bg);\n  background-clip: padding-box;\n  border: var(--bs-toast-border-width) solid var(--bs-toast-border-color);\n  box-shadow: var(--bs-toast-box-shadow);\n  border-radius: var(--bs-toast-border-radius);\n}\n.toast.showing {\n  opacity: 0;\n}\n.toast:not(.show) {\n  display: none;\n}\n\n.toast-container {\n  --bs-toast-zindex: 1090;\n  position: absolute;\n  z-index: var(--bs-toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n}\n.toast-container > :not(:last-child) {\n  margin-bottom: var(--bs-toast-spacing);\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--bs-toast-padding-y) var(--bs-toast-padding-x);\n  color: var(--bs-toast-header-color);\n  background-color: var(--bs-toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);\n  border-top-left-radius: calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));\n  border-top-right-radius: calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));\n}\n.toast-header .btn-close {\n  margin-right: calc(-0.5 * var(--bs-toast-padding-x));\n  margin-left: var(--bs-toast-padding-x);\n}\n\n.toast-body {\n  padding: var(--bs-toast-padding-x);\n  word-wrap: break-word;\n}\n\n.modal {\n  --bs-modal-zindex: 1055;\n  --bs-modal-width: 500px;\n  --bs-modal-padding: 1rem;\n  --bs-modal-margin: 0.5rem;\n  --bs-modal-color: ;\n  --bs-modal-bg: var(--bs-body-bg);\n  --bs-modal-border-color: var(--bs-border-color-translucent);\n  --bs-modal-border-width: var(--bs-border-width);\n  --bs-modal-border-radius: var(--bs-border-radius-lg);\n  --bs-modal-box-shadow: var(--bs-box-shadow-sm);\n  --bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));\n  --bs-modal-header-padding-x: 1rem;\n  --bs-modal-header-padding-y: 1rem;\n  --bs-modal-header-padding: 1rem 1rem;\n  --bs-modal-header-border-color: var(--bs-border-color);\n  --bs-modal-header-border-width: var(--bs-border-width);\n  --bs-modal-title-line-height: 1.5;\n  --bs-modal-footer-gap: 0.5rem;\n  --bs-modal-footer-bg: ;\n  --bs-modal-footer-border-color: var(--bs-border-color);\n  --bs-modal-footer-border-width: var(--bs-border-width);\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--bs-modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  outline: 0;\n}\n\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--bs-modal-margin);\n  pointer-events: none;\n}\n.modal.fade .modal-dialog {\n  transition: transform 0.3s ease-out;\n  transform: translate(0, -50px);\n}\n@media (prefers-reduced-motion: reduce) {\n  .modal.fade .modal-dialog {\n    transition: none;\n  }\n}\n.modal.show .modal-dialog {\n  transform: none;\n}\n.modal.modal-static .modal-dialog {\n  transform: scale(1.02);\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--bs-modal-margin) * 2);\n}\n.modal-dialog-scrollable .modal-content {\n  max-height: 100%;\n  overflow: hidden;\n}\n.modal-dialog-scrollable .modal-body {\n  overflow-y: auto;\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--bs-modal-margin) * 2);\n}\n\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%;\n  color: var(--bs-modal-color);\n  pointer-events: auto;\n  background-color: var(--bs-modal-bg);\n  background-clip: padding-box;\n  border: var(--bs-modal-border-width) solid var(--bs-modal-border-color);\n  border-radius: var(--bs-modal-border-radius);\n  outline: 0;\n}\n\n.modal-backdrop {\n  --bs-backdrop-zindex: 1050;\n  --bs-backdrop-bg: #000;\n  --bs-backdrop-opacity: 0.5;\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--bs-backdrop-zindex);\n  width: 100vw;\n  height: 100vh;\n  background-color: var(--bs-backdrop-bg);\n}\n.modal-backdrop.fade {\n  opacity: 0;\n}\n.modal-backdrop.show {\n  opacity: var(--bs-backdrop-opacity);\n}\n\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  padding: var(--bs-modal-header-padding);\n  border-bottom: var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);\n  border-top-left-radius: var(--bs-modal-inner-border-radius);\n  border-top-right-radius: var(--bs-modal-inner-border-radius);\n}\n.modal-header .btn-close {\n  padding: calc(var(--bs-modal-header-padding-y) * 0.5) calc(var(--bs-modal-header-padding-x) * 0.5);\n  margin: calc(-0.5 * var(--bs-modal-header-padding-y)) calc(-0.5 * var(--bs-modal-header-padding-x)) calc(-0.5 * var(--bs-modal-header-padding-y)) auto;\n}\n\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--bs-modal-title-line-height);\n}\n\n.modal-body {\n  position: relative;\n  flex: 1 1 auto;\n  padding: var(--bs-modal-padding);\n}\n\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center;\n  justify-content: flex-end;\n  padding: calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap) * 0.5);\n  background-color: var(--bs-modal-footer-bg);\n  border-top: var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);\n  border-bottom-right-radius: var(--bs-modal-inner-border-radius);\n  border-bottom-left-radius: var(--bs-modal-inner-border-radius);\n}\n.modal-footer > * {\n  margin: calc(var(--bs-modal-footer-gap) * 0.5);\n}\n\n@media (min-width: 576px) {\n  .modal {\n    --bs-modal-margin: 1.75rem;\n    --bs-modal-box-shadow: var(--bs-box-shadow);\n  }\n  .modal-dialog {\n    max-width: var(--bs-modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n  .modal-sm {\n    --bs-modal-width: 300px;\n  }\n}\n@media (min-width: 992px) {\n  .modal-lg,\n  .modal-xl {\n    --bs-modal-width: 800px;\n  }\n}\n@media (min-width: 1200px) {\n  .modal-xl {\n    --bs-modal-width: 1140px;\n  }\n}\n.modal-fullscreen {\n  width: 100vw;\n  max-width: none;\n  height: 100%;\n  margin: 0;\n}\n.modal-fullscreen .modal-content {\n  height: 100%;\n  border: 0;\n  border-radius: 0;\n}\n.modal-fullscreen .modal-header,\n.modal-fullscreen .modal-footer {\n  border-radius: 0;\n}\n.modal-fullscreen .modal-body {\n  overflow-y: auto;\n}\n\n@media (max-width: 575.98px) {\n  .modal-fullscreen-sm-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-sm-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-sm-down .modal-header,\n  .modal-fullscreen-sm-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-sm-down .modal-body {\n    overflow-y: auto;\n  }\n}\n@media (max-width: 767.98px) {\n  .modal-fullscreen-md-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-md-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-md-down .modal-header,\n  .modal-fullscreen-md-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-md-down .modal-body {\n    overflow-y: auto;\n  }\n}\n@media (max-width: 991.98px) {\n  .modal-fullscreen-lg-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-lg-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-lg-down .modal-header,\n  .modal-fullscreen-lg-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-lg-down .modal-body {\n    overflow-y: auto;\n  }\n}\n@media (max-width: 1199.98px) {\n  .modal-fullscreen-xl-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-xl-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-xl-down .modal-header,\n  .modal-fullscreen-xl-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-xl-down .modal-body {\n    overflow-y: auto;\n  }\n}\n@media (max-width: 1399.98px) {\n  .modal-fullscreen-xxl-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-xxl-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-xxl-down .modal-header,\n  .modal-fullscreen-xxl-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-xxl-down .modal-body {\n    overflow-y: auto;\n  }\n}\n.tooltip {\n  --bs-tooltip-zindex: 1080;\n  --bs-tooltip-max-width: 200px;\n  --bs-tooltip-padding-x: 0.5rem;\n  --bs-tooltip-padding-y: 0.25rem;\n  --bs-tooltip-margin: ;\n  --bs-tooltip-font-size: 0.875rem;\n  --bs-tooltip-color: var(--bs-body-bg);\n  --bs-tooltip-bg: var(--bs-emphasis-color);\n  --bs-tooltip-border-radius: var(--bs-border-radius);\n  --bs-tooltip-opacity: 0.9;\n  --bs-tooltip-arrow-width: 0.8rem;\n  --bs-tooltip-arrow-height: 0.4rem;\n  z-index: var(--bs-tooltip-zindex);\n  display: block;\n  margin: var(--bs-tooltip-margin);\n  font-family: var(--bs-font-sans-serif);\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.5;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n  font-size: var(--bs-tooltip-font-size);\n  word-wrap: break-word;\n  opacity: 0;\n}\n.tooltip.show {\n  opacity: var(--bs-tooltip-opacity);\n}\n.tooltip .tooltip-arrow {\n  display: block;\n  width: var(--bs-tooltip-arrow-width);\n  height: var(--bs-tooltip-arrow-height);\n}\n.tooltip .tooltip-arrow::before {\n  position: absolute;\n  content: \"\";\n  border-color: transparent;\n  border-style: solid;\n}\n\n.bs-tooltip-top .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow {\n  bottom: calc(-1 * var(--bs-tooltip-arrow-height));\n}\n.bs-tooltip-top .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before {\n  top: -1px;\n  border-width: var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * 0.5) 0;\n  border-top-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow {\n  left: calc(-1 * var(--bs-tooltip-arrow-height));\n  width: var(--bs-tooltip-arrow-height);\n  height: var(--bs-tooltip-arrow-width);\n}\n.bs-tooltip-end .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before {\n  right: -1px;\n  border-width: calc(var(--bs-tooltip-arrow-width) * 0.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * 0.5) 0;\n  border-right-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:end:ignore */\n.bs-tooltip-bottom .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow {\n  top: calc(-1 * var(--bs-tooltip-arrow-height));\n}\n.bs-tooltip-bottom .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before {\n  bottom: -1px;\n  border-width: 0 calc(var(--bs-tooltip-arrow-width) * 0.5) var(--bs-tooltip-arrow-height);\n  border-bottom-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow {\n  right: calc(-1 * var(--bs-tooltip-arrow-height));\n  width: var(--bs-tooltip-arrow-height);\n  height: var(--bs-tooltip-arrow-width);\n}\n.bs-tooltip-start .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before {\n  left: -1px;\n  border-width: calc(var(--bs-tooltip-arrow-width) * 0.5) 0 calc(var(--bs-tooltip-arrow-width) * 0.5) var(--bs-tooltip-arrow-height);\n  border-left-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:end:ignore */\n.tooltip-inner {\n  max-width: var(--bs-tooltip-max-width);\n  padding: var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);\n  color: var(--bs-tooltip-color);\n  text-align: center;\n  background-color: var(--bs-tooltip-bg);\n  border-radius: var(--bs-tooltip-border-radius);\n}\n\n.popover {\n  --bs-popover-zindex: 1070;\n  --bs-popover-max-width: 276px;\n  --bs-popover-font-size: 0.875rem;\n  --bs-popover-bg: var(--bs-body-bg);\n  --bs-popover-border-width: var(--bs-border-width);\n  --bs-popover-border-color: var(--bs-border-color-translucent);\n  --bs-popover-border-radius: var(--bs-border-radius-lg);\n  --bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));\n  --bs-popover-box-shadow: var(--bs-box-shadow);\n  --bs-popover-header-padding-x: 1rem;\n  --bs-popover-header-padding-y: 0.5rem;\n  --bs-popover-header-font-size: 1rem;\n  --bs-popover-header-color: inherit;\n  --bs-popover-header-bg: var(--bs-secondary-bg);\n  --bs-popover-body-padding-x: 1rem;\n  --bs-popover-body-padding-y: 1rem;\n  --bs-popover-body-color: var(--bs-body-color);\n  --bs-popover-arrow-width: 1rem;\n  --bs-popover-arrow-height: 0.5rem;\n  --bs-popover-arrow-border: var(--bs-popover-border-color);\n  z-index: var(--bs-popover-zindex);\n  display: block;\n  max-width: var(--bs-popover-max-width);\n  font-family: var(--bs-font-sans-serif);\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.5;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n  font-size: var(--bs-popover-font-size);\n  word-wrap: break-word;\n  background-color: var(--bs-popover-bg);\n  background-clip: padding-box;\n  border: var(--bs-popover-border-width) solid var(--bs-popover-border-color);\n  border-radius: var(--bs-popover-border-radius);\n}\n.popover .popover-arrow {\n  display: block;\n  width: var(--bs-popover-arrow-width);\n  height: var(--bs-popover-arrow-height);\n}\n.popover .popover-arrow::before, .popover .popover-arrow::after {\n  position: absolute;\n  display: block;\n  content: \"\";\n  border-color: transparent;\n  border-style: solid;\n  border-width: 0;\n}\n\n.bs-popover-top > .popover-arrow, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow {\n  bottom: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n}\n.bs-popover-top > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::before, .bs-popover-top > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::after {\n  border-width: var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width) * 0.5) 0;\n}\n.bs-popover-top > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::before {\n  bottom: 0;\n  border-top-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-top > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::after {\n  bottom: var(--bs-popover-border-width);\n  border-top-color: var(--bs-popover-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end > .popover-arrow, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow {\n  left: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n  width: var(--bs-popover-arrow-height);\n  height: var(--bs-popover-arrow-width);\n}\n.bs-popover-end > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::before, .bs-popover-end > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::after {\n  border-width: calc(var(--bs-popover-arrow-width) * 0.5) var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width) * 0.5) 0;\n}\n.bs-popover-end > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::before {\n  left: 0;\n  border-right-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-end > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::after {\n  left: var(--bs-popover-border-width);\n  border-right-color: var(--bs-popover-bg);\n}\n\n/* rtl:end:ignore */\n.bs-popover-bottom > .popover-arrow, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow {\n  top: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n}\n.bs-popover-bottom > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::before, .bs-popover-bottom > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::after {\n  border-width: 0 calc(var(--bs-popover-arrow-width) * 0.5) var(--bs-popover-arrow-height);\n}\n.bs-popover-bottom > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::before {\n  top: 0;\n  border-bottom-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-bottom > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::after {\n  top: var(--bs-popover-border-width);\n  border-bottom-color: var(--bs-popover-bg);\n}\n.bs-popover-bottom .popover-header::before, .bs-popover-auto[data-popper-placement^=bottom] .popover-header::before {\n  position: absolute;\n  top: 0;\n  left: 50%;\n  display: block;\n  width: var(--bs-popover-arrow-width);\n  margin-left: calc(-0.5 * var(--bs-popover-arrow-width));\n  content: \"\";\n  border-bottom: var(--bs-popover-border-width) solid var(--bs-popover-header-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start > .popover-arrow, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow {\n  right: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n  width: var(--bs-popover-arrow-height);\n  height: var(--bs-popover-arrow-width);\n}\n.bs-popover-start > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::before, .bs-popover-start > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::after {\n  border-width: calc(var(--bs-popover-arrow-width) * 0.5) 0 calc(var(--bs-popover-arrow-width) * 0.5) var(--bs-popover-arrow-height);\n}\n.bs-popover-start > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::before {\n  right: 0;\n  border-left-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-start > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::after {\n  right: var(--bs-popover-border-width);\n  border-left-color: var(--bs-popover-bg);\n}\n\n/* rtl:end:ignore */\n.popover-header {\n  padding: var(--bs-popover-header-padding-y) var(--bs-popover-header-padding-x);\n  margin-bottom: 0;\n  font-size: var(--bs-popover-header-font-size);\n  color: var(--bs-popover-header-color);\n  background-color: var(--bs-popover-header-bg);\n  border-bottom: var(--bs-popover-border-width) solid var(--bs-popover-border-color);\n  border-top-left-radius: var(--bs-popover-inner-border-radius);\n  border-top-right-radius: var(--bs-popover-inner-border-radius);\n}\n.popover-header:empty {\n  display: none;\n}\n\n.popover-body {\n  padding: var(--bs-popover-body-padding-y) var(--bs-popover-body-padding-x);\n  color: var(--bs-popover-body-color);\n}\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n}\n.carousel-inner::after {\n  display: block;\n  clear: both;\n  content: \"\";\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  transition: transform 0.6s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-item {\n    transition: none;\n  }\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n.carousel-fade .carousel-item {\n  opacity: 0;\n  transition-property: opacity;\n  transform: none;\n}\n.carousel-fade .carousel-item.active,\n.carousel-fade .carousel-item-next.carousel-item-start,\n.carousel-fade .carousel-item-prev.carousel-item-end {\n  z-index: 1;\n  opacity: 1;\n}\n.carousel-fade .active.carousel-item-start,\n.carousel-fade .active.carousel-item-end {\n  z-index: 0;\n  opacity: 0;\n  transition: opacity 0s 0.6s;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-fade .active.carousel-item-start,\n  .carousel-fade .active.carousel-item-end {\n    transition: none;\n  }\n}\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  width: 15%;\n  padding: 0;\n  color: #fff;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: 0.5;\n  transition: opacity 0.15s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-control-prev,\n  .carousel-control-next {\n    transition: none;\n  }\n}\n.carousel-control-prev:hover, .carousel-control-prev:focus,\n.carousel-control-next:hover,\n.carousel-control-next:focus {\n  color: #fff;\n  text-decoration: none;\n  outline: 0;\n  opacity: 0.9;\n}\n\n.carousel-control-prev {\n  left: 0;\n}\n\n.carousel-control-next {\n  right: 0;\n}\n\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: 2rem;\n  height: 2rem;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n.carousel-control-prev-icon {\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e\") /*rtl:url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\")*/;\n}\n\n.carousel-control-next-icon {\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\") /*rtl:url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e\")*/;\n}\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  margin-right: 15%;\n  margin-bottom: 1rem;\n  margin-left: 15%;\n}\n.carousel-indicators [data-bs-target] {\n  box-sizing: content-box;\n  flex: 0 1 auto;\n  width: 30px;\n  height: 3px;\n  padding: 0;\n  margin-right: 3px;\n  margin-left: 3px;\n  text-indent: -999px;\n  cursor: pointer;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 0;\n  border-top: 10px solid transparent;\n  border-bottom: 10px solid transparent;\n  opacity: 0.5;\n  transition: opacity 0.6s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-indicators [data-bs-target] {\n    transition: none;\n  }\n}\n.carousel-indicators .active {\n  opacity: 1;\n}\n\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 1.25rem;\n  left: 15%;\n  padding-top: 1.25rem;\n  padding-bottom: 1.25rem;\n  color: #fff;\n  text-align: center;\n}\n\n.carousel-dark .carousel-control-prev-icon,\n.carousel-dark .carousel-control-next-icon {\n  filter: invert(1) grayscale(100);\n}\n.carousel-dark .carousel-indicators [data-bs-target] {\n  background-color: #000;\n}\n.carousel-dark .carousel-caption {\n  color: #000;\n}\n\n[data-bs-theme=dark] .carousel .carousel-control-prev-icon,\n[data-bs-theme=dark] .carousel .carousel-control-next-icon, [data-bs-theme=dark].carousel .carousel-control-prev-icon,\n[data-bs-theme=dark].carousel .carousel-control-next-icon {\n  filter: invert(1) grayscale(100);\n}\n[data-bs-theme=dark] .carousel .carousel-indicators [data-bs-target], [data-bs-theme=dark].carousel .carousel-indicators [data-bs-target] {\n  background-color: #000;\n}\n[data-bs-theme=dark] .carousel .carousel-caption, [data-bs-theme=dark].carousel .carousel-caption {\n  color: #000;\n}\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--bs-spinner-width);\n  height: var(--bs-spinner-height);\n  vertical-align: var(--bs-spinner-vertical-align);\n  border-radius: 50%;\n  animation: var(--bs-spinner-animation-speed) linear infinite var(--bs-spinner-animation-name);\n}\n\n@keyframes spinner-border {\n  to {\n    transform: rotate(360deg) /* rtl:ignore */;\n  }\n}\n.spinner-border {\n  --bs-spinner-width: 2rem;\n  --bs-spinner-height: 2rem;\n  --bs-spinner-vertical-align: -0.125em;\n  --bs-spinner-border-width: 0.25em;\n  --bs-spinner-animation-speed: 0.75s;\n  --bs-spinner-animation-name: spinner-border;\n  border: var(--bs-spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  --bs-spinner-width: 1rem;\n  --bs-spinner-height: 1rem;\n  --bs-spinner-border-width: 0.2em;\n}\n\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n.spinner-grow {\n  --bs-spinner-width: 2rem;\n  --bs-spinner-height: 2rem;\n  --bs-spinner-vertical-align: -0.125em;\n  --bs-spinner-animation-speed: 0.75s;\n  --bs-spinner-animation-name: spinner-grow;\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --bs-spinner-width: 1rem;\n  --bs-spinner-height: 1rem;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .spinner-border,\n  .spinner-grow {\n    --bs-spinner-animation-speed: 1.5s;\n  }\n}\n.offcanvas, .offcanvas-xxl, .offcanvas-xl, .offcanvas-lg, .offcanvas-md, .offcanvas-sm {\n  --bs-offcanvas-zindex: 1045;\n  --bs-offcanvas-width: 400px;\n  --bs-offcanvas-height: 30vh;\n  --bs-offcanvas-padding-x: 1rem;\n  --bs-offcanvas-padding-y: 1rem;\n  --bs-offcanvas-color: var(--bs-body-color);\n  --bs-offcanvas-bg: var(--bs-body-bg);\n  --bs-offcanvas-border-width: var(--bs-border-width);\n  --bs-offcanvas-border-color: var(--bs-border-color-translucent);\n  --bs-offcanvas-box-shadow: var(--bs-box-shadow-sm);\n  --bs-offcanvas-transition: transform 0.3s ease-in-out;\n  --bs-offcanvas-title-line-height: 1.5;\n}\n\n@media (max-width: 575.98px) {\n  .offcanvas-sm {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 575.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-sm {\n    transition: none;\n  }\n}\n@media (max-width: 575.98px) {\n  .offcanvas-sm.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-sm.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-sm.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-sm.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-sm.showing, .offcanvas-sm.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-sm.showing, .offcanvas-sm.hiding, .offcanvas-sm.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 576px) {\n  .offcanvas-sm {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-sm .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-sm .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n@media (max-width: 767.98px) {\n  .offcanvas-md {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 767.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-md {\n    transition: none;\n  }\n}\n@media (max-width: 767.98px) {\n  .offcanvas-md.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-md.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-md.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-md.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-md.showing, .offcanvas-md.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-md.showing, .offcanvas-md.hiding, .offcanvas-md.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 768px) {\n  .offcanvas-md {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-md .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-md .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n@media (max-width: 991.98px) {\n  .offcanvas-lg {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 991.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-lg {\n    transition: none;\n  }\n}\n@media (max-width: 991.98px) {\n  .offcanvas-lg.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-lg.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-lg.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-lg.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-lg.showing, .offcanvas-lg.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-lg.showing, .offcanvas-lg.hiding, .offcanvas-lg.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 992px) {\n  .offcanvas-lg {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-lg .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-lg .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n@media (max-width: 1199.98px) {\n  .offcanvas-xl {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 1199.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-xl {\n    transition: none;\n  }\n}\n@media (max-width: 1199.98px) {\n  .offcanvas-xl.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-xl.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-xl.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-xl.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-xl.showing, .offcanvas-xl.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-xl.showing, .offcanvas-xl.hiding, .offcanvas-xl.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 1200px) {\n  .offcanvas-xl {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-xl .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-xl .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n@media (max-width: 1399.98px) {\n  .offcanvas-xxl {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 1399.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-xxl {\n    transition: none;\n  }\n}\n@media (max-width: 1399.98px) {\n  .offcanvas-xxl.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-xxl.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-xxl.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-xxl.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-xxl.showing, .offcanvas-xxl.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-xxl.showing, .offcanvas-xxl.hiding, .offcanvas-xxl.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 1400px) {\n  .offcanvas-xxl {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-xxl .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-xxl .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n.offcanvas {\n  position: fixed;\n  bottom: 0;\n  z-index: var(--bs-offcanvas-zindex);\n  display: flex;\n  flex-direction: column;\n  max-width: 100%;\n  color: var(--bs-offcanvas-color);\n  visibility: hidden;\n  background-color: var(--bs-offcanvas-bg);\n  background-clip: padding-box;\n  outline: 0;\n  transition: var(--bs-offcanvas-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .offcanvas {\n    transition: none;\n  }\n}\n.offcanvas.offcanvas-start {\n  top: 0;\n  left: 0;\n  width: var(--bs-offcanvas-width);\n  border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateX(-100%);\n}\n.offcanvas.offcanvas-end {\n  top: 0;\n  right: 0;\n  width: var(--bs-offcanvas-width);\n  border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateX(100%);\n}\n.offcanvas.offcanvas-top {\n  top: 0;\n  right: 0;\n  left: 0;\n  height: var(--bs-offcanvas-height);\n  max-height: 100%;\n  border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateY(-100%);\n}\n.offcanvas.offcanvas-bottom {\n  right: 0;\n  left: 0;\n  height: var(--bs-offcanvas-height);\n  max-height: 100%;\n  border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateY(100%);\n}\n.offcanvas.showing, .offcanvas.show:not(.hiding) {\n  transform: none;\n}\n.offcanvas.showing, .offcanvas.hiding, .offcanvas.show {\n  visibility: visible;\n}\n\n.offcanvas-backdrop {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: 1040;\n  width: 100vw;\n  height: 100vh;\n  background-color: #000;\n}\n.offcanvas-backdrop.fade {\n  opacity: 0;\n}\n.offcanvas-backdrop.show {\n  opacity: 0.5;\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  padding: var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x);\n}\n.offcanvas-header .btn-close {\n  padding: calc(var(--bs-offcanvas-padding-y) * 0.5) calc(var(--bs-offcanvas-padding-x) * 0.5);\n  margin: calc(-0.5 * var(--bs-offcanvas-padding-y)) calc(-0.5 * var(--bs-offcanvas-padding-x)) calc(-0.5 * var(--bs-offcanvas-padding-y)) auto;\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--bs-offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x);\n  overflow-y: auto;\n}\n\n.placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: 0.5;\n}\n.placeholder.btn::before {\n  display: inline-block;\n  content: \"\";\n}\n\n.placeholder-xs {\n  min-height: 0.6em;\n}\n\n.placeholder-sm {\n  min-height: 0.8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n.placeholder-glow .placeholder {\n  animation: placeholder-glow 2s ease-in-out infinite;\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: 0.2;\n  }\n}\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n.clearfix::after {\n  display: block;\n  clear: both;\n  content: \"\";\n}\n\n.text-bg-primary {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-primary-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-secondary {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-secondary-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-success {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-success-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-info {\n  color: #000 !important;\n  background-color: RGBA(var(--bs-info-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-warning {\n  color: #000 !important;\n  background-color: RGBA(var(--bs-warning-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-danger {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-danger-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-light {\n  color: #000 !important;\n  background-color: RGBA(var(--bs-light-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-dark {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-dark-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.link-primary {\n  color: RGBA(var(--bs-primary-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-primary-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-primary:hover, .link-primary:focus {\n  color: RGBA(10, 88, 202, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(10, 88, 202, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-secondary {\n  color: RGBA(var(--bs-secondary-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-secondary-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-secondary:hover, .link-secondary:focus {\n  color: RGBA(86, 94, 100, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(86, 94, 100, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-success {\n  color: RGBA(var(--bs-success-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-success-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-success:hover, .link-success:focus {\n  color: RGBA(20, 108, 67, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(20, 108, 67, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-info {\n  color: RGBA(var(--bs-info-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-info-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-info:hover, .link-info:focus {\n  color: RGBA(61, 213, 243, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(61, 213, 243, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-warning {\n  color: RGBA(var(--bs-warning-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-warning-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-warning:hover, .link-warning:focus {\n  color: RGBA(255, 205, 57, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(255, 205, 57, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-danger {\n  color: RGBA(var(--bs-danger-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-danger-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-danger:hover, .link-danger:focus {\n  color: RGBA(176, 42, 55, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(176, 42, 55, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-light {\n  color: RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-light:hover, .link-light:focus {\n  color: RGBA(249, 250, 251, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(249, 250, 251, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-dark {\n  color: RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-dark:hover, .link-dark:focus {\n  color: RGBA(26, 30, 33, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(26, 30, 33, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-body-emphasis {\n  color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-body-emphasis:hover, .link-body-emphasis:focus {\n  color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 0.75)) !important;\n  text-decoration-color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 0.75)) !important;\n}\n\n.focus-ring:focus {\n  outline: 0;\n  box-shadow: var(--bs-focus-ring-x, 0) var(--bs-focus-ring-y, 0) var(--bs-focus-ring-blur, 0) var(--bs-focus-ring-width) var(--bs-focus-ring-color);\n}\n\n.icon-link {\n  display: inline-flex;\n  gap: 0.375rem;\n  align-items: center;\n  text-decoration-color: rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 0.5));\n  text-underline-offset: 0.25em;\n  backface-visibility: hidden;\n}\n.icon-link > .bi {\n  flex-shrink: 0;\n  width: 1em;\n  height: 1em;\n  fill: currentcolor;\n  transition: 0.2s ease-in-out transform;\n}\n@media (prefers-reduced-motion: reduce) {\n  .icon-link > .bi {\n    transition: none;\n  }\n}\n\n.icon-link-hover:hover > .bi, .icon-link-hover:focus-visible > .bi {\n  transform: var(--bs-icon-link-transform, translate3d(0.25em, 0, 0));\n}\n\n.ratio {\n  position: relative;\n  width: 100%;\n}\n.ratio::before {\n  display: block;\n  padding-top: var(--bs-aspect-ratio);\n  content: \"\";\n}\n.ratio > * {\n  position: absolute;\n  top: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n}\n\n.ratio-1x1 {\n  --bs-aspect-ratio: 100%;\n}\n\n.ratio-4x3 {\n  --bs-aspect-ratio: 75%;\n}\n\n.ratio-16x9 {\n  --bs-aspect-ratio: 56.25%;\n}\n\n.ratio-21x9 {\n  --bs-aspect-ratio: 42.8571428571%;\n}\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1030;\n}\n\n.sticky-top {\n  position: sticky;\n  top: 0;\n  z-index: 1020;\n}\n\n.sticky-bottom {\n  position: sticky;\n  bottom: 0;\n  z-index: 1020;\n}\n\n@media (min-width: 576px) {\n  .sticky-sm-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-sm-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n@media (min-width: 768px) {\n  .sticky-md-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-md-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n@media (min-width: 992px) {\n  .sticky-lg-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-lg-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n@media (min-width: 1200px) {\n  .sticky-xl-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-xl-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n@media (min-width: 1400px) {\n  .sticky-xxl-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-xxl-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important;\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n}\n.visually-hidden:not(caption),\n.visually-hidden-focusable:not(:focus):not(:focus-within):not(caption) {\n  position: absolute !important;\n}\n\n.stretched-link::after {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1;\n  content: \"\";\n}\n\n.text-truncate {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n\n.vr {\n  display: inline-block;\n  align-self: stretch;\n  width: var(--bs-border-width);\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: 0.25;\n}\n\n.align-baseline {\n  vertical-align: baseline !important;\n}\n\n.align-top {\n  vertical-align: top !important;\n}\n\n.align-middle {\n  vertical-align: middle !important;\n}\n\n.align-bottom {\n  vertical-align: bottom !important;\n}\n\n.align-text-bottom {\n  vertical-align: text-bottom !important;\n}\n\n.align-text-top {\n  vertical-align: text-top !important;\n}\n\n.float-start {\n  float: left !important;\n}\n\n.float-end {\n  float: right !important;\n}\n\n.float-none {\n  float: none !important;\n}\n\n.object-fit-contain {\n  object-fit: contain !important;\n}\n\n.object-fit-cover {\n  object-fit: cover !important;\n}\n\n.object-fit-fill {\n  object-fit: fill !important;\n}\n\n.object-fit-scale {\n  object-fit: scale-down !important;\n}\n\n.object-fit-none {\n  object-fit: none !important;\n}\n\n.opacity-0 {\n  opacity: 0 !important;\n}\n\n.opacity-25 {\n  opacity: 0.25 !important;\n}\n\n.opacity-50 {\n  opacity: 0.5 !important;\n}\n\n.opacity-75 {\n  opacity: 0.75 !important;\n}\n\n.opacity-100 {\n  opacity: 1 !important;\n}\n\n.overflow-auto {\n  overflow: auto !important;\n}\n\n.overflow-hidden {\n  overflow: hidden !important;\n}\n\n.overflow-visible {\n  overflow: visible !important;\n}\n\n.overflow-scroll {\n  overflow: scroll !important;\n}\n\n.overflow-x-auto {\n  overflow-x: auto !important;\n}\n\n.overflow-x-hidden {\n  overflow-x: hidden !important;\n}\n\n.overflow-x-visible {\n  overflow-x: visible !important;\n}\n\n.overflow-x-scroll {\n  overflow-x: scroll !important;\n}\n\n.overflow-y-auto {\n  overflow-y: auto !important;\n}\n\n.overflow-y-hidden {\n  overflow-y: hidden !important;\n}\n\n.overflow-y-visible {\n  overflow-y: visible !important;\n}\n\n.overflow-y-scroll {\n  overflow-y: scroll !important;\n}\n\n.d-inline {\n  display: inline !important;\n}\n\n.d-inline-block {\n  display: inline-block !important;\n}\n\n.d-block {\n  display: block !important;\n}\n\n.d-grid {\n  display: grid !important;\n}\n\n.d-inline-grid {\n  display: inline-grid !important;\n}\n\n.d-table {\n  display: table !important;\n}\n\n.d-table-row {\n  display: table-row !important;\n}\n\n.d-table-cell {\n  display: table-cell !important;\n}\n\n.d-flex {\n  display: flex !important;\n}\n\n.d-inline-flex {\n  display: inline-flex !important;\n}\n\n.d-none {\n  display: none !important;\n}\n\n.shadow {\n  box-shadow: var(--bs-box-shadow) !important;\n}\n\n.shadow-sm {\n  box-shadow: var(--bs-box-shadow-sm) !important;\n}\n\n.shadow-lg {\n  box-shadow: var(--bs-box-shadow-lg) !important;\n}\n\n.shadow-none {\n  box-shadow: none !important;\n}\n\n.focus-ring-primary {\n  --bs-focus-ring-color: rgba(var(--bs-primary-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-secondary {\n  --bs-focus-ring-color: rgba(var(--bs-secondary-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-success {\n  --bs-focus-ring-color: rgba(var(--bs-success-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-info {\n  --bs-focus-ring-color: rgba(var(--bs-info-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-warning {\n  --bs-focus-ring-color: rgba(var(--bs-warning-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-danger {\n  --bs-focus-ring-color: rgba(var(--bs-danger-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-light {\n  --bs-focus-ring-color: rgba(var(--bs-light-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-dark {\n  --bs-focus-ring-color: rgba(var(--bs-dark-rgb), var(--bs-focus-ring-opacity));\n}\n\n.position-static {\n  position: static !important;\n}\n\n.position-relative {\n  position: relative !important;\n}\n\n.position-absolute {\n  position: absolute !important;\n}\n\n.position-fixed {\n  position: fixed !important;\n}\n\n.position-sticky {\n  position: sticky !important;\n}\n\n.top-0 {\n  top: 0 !important;\n}\n\n.top-50 {\n  top: 50% !important;\n}\n\n.top-100 {\n  top: 100% !important;\n}\n\n.bottom-0 {\n  bottom: 0 !important;\n}\n\n.bottom-50 {\n  bottom: 50% !important;\n}\n\n.bottom-100 {\n  bottom: 100% !important;\n}\n\n.start-0 {\n  left: 0 !important;\n}\n\n.start-50 {\n  left: 50% !important;\n}\n\n.start-100 {\n  left: 100% !important;\n}\n\n.end-0 {\n  right: 0 !important;\n}\n\n.end-50 {\n  right: 50% !important;\n}\n\n.end-100 {\n  right: 100% !important;\n}\n\n.translate-middle {\n  transform: translate(-50%, -50%) !important;\n}\n\n.translate-middle-x {\n  transform: translateX(-50%) !important;\n}\n\n.translate-middle-y {\n  transform: translateY(-50%) !important;\n}\n\n.border {\n  border: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-0 {\n  border: 0 !important;\n}\n\n.border-top {\n  border-top: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-top-0 {\n  border-top: 0 !important;\n}\n\n.border-end {\n  border-right: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-end-0 {\n  border-right: 0 !important;\n}\n\n.border-bottom {\n  border-bottom: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-bottom-0 {\n  border-bottom: 0 !important;\n}\n\n.border-start {\n  border-left: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-start-0 {\n  border-left: 0 !important;\n}\n\n.border-primary {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-primary-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-secondary {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-secondary-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-success {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-success-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-info {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-info-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-warning {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-warning-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-danger {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-danger-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-light {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-light-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-dark {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-dark-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-black {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-black-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-white {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-white-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-primary-subtle {\n  border-color: var(--bs-primary-border-subtle) !important;\n}\n\n.border-secondary-subtle {\n  border-color: var(--bs-secondary-border-subtle) !important;\n}\n\n.border-success-subtle {\n  border-color: var(--bs-success-border-subtle) !important;\n}\n\n.border-info-subtle {\n  border-color: var(--bs-info-border-subtle) !important;\n}\n\n.border-warning-subtle {\n  border-color: var(--bs-warning-border-subtle) !important;\n}\n\n.border-danger-subtle {\n  border-color: var(--bs-danger-border-subtle) !important;\n}\n\n.border-light-subtle {\n  border-color: var(--bs-light-border-subtle) !important;\n}\n\n.border-dark-subtle {\n  border-color: var(--bs-dark-border-subtle) !important;\n}\n\n.border-1 {\n  border-width: 1px !important;\n}\n\n.border-2 {\n  border-width: 2px !important;\n}\n\n.border-3 {\n  border-width: 3px !important;\n}\n\n.border-4 {\n  border-width: 4px !important;\n}\n\n.border-5 {\n  border-width: 5px !important;\n}\n\n.border-opacity-10 {\n  --bs-border-opacity: 0.1;\n}\n\n.border-opacity-25 {\n  --bs-border-opacity: 0.25;\n}\n\n.border-opacity-50 {\n  --bs-border-opacity: 0.5;\n}\n\n.border-opacity-75 {\n  --bs-border-opacity: 0.75;\n}\n\n.border-opacity-100 {\n  --bs-border-opacity: 1;\n}\n\n.w-25 {\n  width: 25% !important;\n}\n\n.w-50 {\n  width: 50% !important;\n}\n\n.w-75 {\n  width: 75% !important;\n}\n\n.w-100 {\n  width: 100% !important;\n}\n\n.w-auto {\n  width: auto !important;\n}\n\n.mw-100 {\n  max-width: 100% !important;\n}\n\n.vw-100 {\n  width: 100vw !important;\n}\n\n.min-vw-100 {\n  min-width: 100vw !important;\n}\n\n.h-25 {\n  height: 25% !important;\n}\n\n.h-50 {\n  height: 50% !important;\n}\n\n.h-75 {\n  height: 75% !important;\n}\n\n.h-100 {\n  height: 100% !important;\n}\n\n.h-auto {\n  height: auto !important;\n}\n\n.mh-100 {\n  max-height: 100% !important;\n}\n\n.vh-100 {\n  height: 100vh !important;\n}\n\n.min-vh-100 {\n  min-height: 100vh !important;\n}\n\n.flex-fill {\n  flex: 1 1 auto !important;\n}\n\n.flex-row {\n  flex-direction: row !important;\n}\n\n.flex-column {\n  flex-direction: column !important;\n}\n\n.flex-row-reverse {\n  flex-direction: row-reverse !important;\n}\n\n.flex-column-reverse {\n  flex-direction: column-reverse !important;\n}\n\n.flex-grow-0 {\n  flex-grow: 0 !important;\n}\n\n.flex-grow-1 {\n  flex-grow: 1 !important;\n}\n\n.flex-shrink-0 {\n  flex-shrink: 0 !important;\n}\n\n.flex-shrink-1 {\n  flex-shrink: 1 !important;\n}\n\n.flex-wrap {\n  flex-wrap: wrap !important;\n}\n\n.flex-nowrap {\n  flex-wrap: nowrap !important;\n}\n\n.flex-wrap-reverse {\n  flex-wrap: wrap-reverse !important;\n}\n\n.justify-content-start {\n  justify-content: flex-start !important;\n}\n\n.justify-content-end {\n  justify-content: flex-end !important;\n}\n\n.justify-content-center {\n  justify-content: center !important;\n}\n\n.justify-content-between {\n  justify-content: space-between !important;\n}\n\n.justify-content-around {\n  justify-content: space-around !important;\n}\n\n.justify-content-evenly {\n  justify-content: space-evenly !important;\n}\n\n.align-items-start {\n  align-items: flex-start !important;\n}\n\n.align-items-end {\n  align-items: flex-end !important;\n}\n\n.align-items-center {\n  align-items: center !important;\n}\n\n.align-items-baseline {\n  align-items: baseline !important;\n}\n\n.align-items-stretch {\n  align-items: stretch !important;\n}\n\n.align-content-start {\n  align-content: flex-start !important;\n}\n\n.align-content-end {\n  align-content: flex-end !important;\n}\n\n.align-content-center {\n  align-content: center !important;\n}\n\n.align-content-between {\n  align-content: space-between !important;\n}\n\n.align-content-around {\n  align-content: space-around !important;\n}\n\n.align-content-stretch {\n  align-content: stretch !important;\n}\n\n.align-self-auto {\n  align-self: auto !important;\n}\n\n.align-self-start {\n  align-self: flex-start !important;\n}\n\n.align-self-end {\n  align-self: flex-end !important;\n}\n\n.align-self-center {\n  align-self: center !important;\n}\n\n.align-self-baseline {\n  align-self: baseline !important;\n}\n\n.align-self-stretch {\n  align-self: stretch !important;\n}\n\n.order-first {\n  order: -1 !important;\n}\n\n.order-0 {\n  order: 0 !important;\n}\n\n.order-1 {\n  order: 1 !important;\n}\n\n.order-2 {\n  order: 2 !important;\n}\n\n.order-3 {\n  order: 3 !important;\n}\n\n.order-4 {\n  order: 4 !important;\n}\n\n.order-5 {\n  order: 5 !important;\n}\n\n.order-last {\n  order: 6 !important;\n}\n\n.m-0 {\n  margin: 0 !important;\n}\n\n.m-1 {\n  margin: 0.25rem !important;\n}\n\n.m-2 {\n  margin: 0.5rem !important;\n}\n\n.m-3 {\n  margin: 1rem !important;\n}\n\n.m-4 {\n  margin: 1.5rem !important;\n}\n\n.m-5 {\n  margin: 3rem !important;\n}\n\n.m-auto {\n  margin: auto !important;\n}\n\n.mx-0 {\n  margin-right: 0 !important;\n  margin-left: 0 !important;\n}\n\n.mx-1 {\n  margin-right: 0.25rem !important;\n  margin-left: 0.25rem !important;\n}\n\n.mx-2 {\n  margin-right: 0.5rem !important;\n  margin-left: 0.5rem !important;\n}\n\n.mx-3 {\n  margin-right: 1rem !important;\n  margin-left: 1rem !important;\n}\n\n.mx-4 {\n  margin-right: 1.5rem !important;\n  margin-left: 1.5rem !important;\n}\n\n.mx-5 {\n  margin-right: 3rem !important;\n  margin-left: 3rem !important;\n}\n\n.mx-auto {\n  margin-right: auto !important;\n  margin-left: auto !important;\n}\n\n.my-0 {\n  margin-top: 0 !important;\n  margin-bottom: 0 !important;\n}\n\n.my-1 {\n  margin-top: 0.25rem !important;\n  margin-bottom: 0.25rem !important;\n}\n\n.my-2 {\n  margin-top: 0.5rem !important;\n  margin-bottom: 0.5rem !important;\n}\n\n.my-3 {\n  margin-top: 1rem !important;\n  margin-bottom: 1rem !important;\n}\n\n.my-4 {\n  margin-top: 1.5rem !important;\n  margin-bottom: 1.5rem !important;\n}\n\n.my-5 {\n  margin-top: 3rem !important;\n  margin-bottom: 3rem !important;\n}\n\n.my-auto {\n  margin-top: auto !important;\n  margin-bottom: auto !important;\n}\n\n.mt-0 {\n  margin-top: 0 !important;\n}\n\n.mt-1 {\n  margin-top: 0.25rem !important;\n}\n\n.mt-2 {\n  margin-top: 0.5rem !important;\n}\n\n.mt-3 {\n  margin-top: 1rem !important;\n}\n\n.mt-4 {\n  margin-top: 1.5rem !important;\n}\n\n.mt-5 {\n  margin-top: 3rem !important;\n}\n\n.mt-auto {\n  margin-top: auto !important;\n}\n\n.me-0 {\n  margin-right: 0 !important;\n}\n\n.me-1 {\n  margin-right: 0.25rem !important;\n}\n\n.me-2 {\n  margin-right: 0.5rem !important;\n}\n\n.me-3 {\n  margin-right: 1rem !important;\n}\n\n.me-4 {\n  margin-right: 1.5rem !important;\n}\n\n.me-5 {\n  margin-right: 3rem !important;\n}\n\n.me-auto {\n  margin-right: auto !important;\n}\n\n.mb-0 {\n  margin-bottom: 0 !important;\n}\n\n.mb-1 {\n  margin-bottom: 0.25rem !important;\n}\n\n.mb-2 {\n  margin-bottom: 0.5rem !important;\n}\n\n.mb-3 {\n  margin-bottom: 1rem !important;\n}\n\n.mb-4 {\n  margin-bottom: 1.5rem !important;\n}\n\n.mb-5 {\n  margin-bottom: 3rem !important;\n}\n\n.mb-auto {\n  margin-bottom: auto !important;\n}\n\n.ms-0 {\n  margin-left: 0 !important;\n}\n\n.ms-1 {\n  margin-left: 0.25rem !important;\n}\n\n.ms-2 {\n  margin-left: 0.5rem !important;\n}\n\n.ms-3 {\n  margin-left: 1rem !important;\n}\n\n.ms-4 {\n  margin-left: 1.5rem !important;\n}\n\n.ms-5 {\n  margin-left: 3rem !important;\n}\n\n.ms-auto {\n  margin-left: auto !important;\n}\n\n.p-0 {\n  padding: 0 !important;\n}\n\n.p-1 {\n  padding: 0.25rem !important;\n}\n\n.p-2 {\n  padding: 0.5rem !important;\n}\n\n.p-3 {\n  padding: 1rem !important;\n}\n\n.p-4 {\n  padding: 1.5rem !important;\n}\n\n.p-5 {\n  padding: 3rem !important;\n}\n\n.px-0 {\n  padding-right: 0 !important;\n  padding-left: 0 !important;\n}\n\n.px-1 {\n  padding-right: 0.25rem !important;\n  padding-left: 0.25rem !important;\n}\n\n.px-2 {\n  padding-right: 0.5rem !important;\n  padding-left: 0.5rem !important;\n}\n\n.px-3 {\n  padding-right: 1rem !important;\n  padding-left: 1rem !important;\n}\n\n.px-4 {\n  padding-right: 1.5rem !important;\n  padding-left: 1.5rem !important;\n}\n\n.px-5 {\n  padding-right: 3rem !important;\n  padding-left: 3rem !important;\n}\n\n.py-0 {\n  padding-top: 0 !important;\n  padding-bottom: 0 !important;\n}\n\n.py-1 {\n  padding-top: 0.25rem !important;\n  padding-bottom: 0.25rem !important;\n}\n\n.py-2 {\n  padding-top: 0.5rem !important;\n  padding-bottom: 0.5rem !important;\n}\n\n.py-3 {\n  padding-top: 1rem !important;\n  padding-bottom: 1rem !important;\n}\n\n.py-4 {\n  padding-top: 1.5rem !important;\n  padding-bottom: 1.5rem !important;\n}\n\n.py-5 {\n  padding-top: 3rem !important;\n  padding-bottom: 3rem !important;\n}\n\n.pt-0 {\n  padding-top: 0 !important;\n}\n\n.pt-1 {\n  padding-top: 0.25rem !important;\n}\n\n.pt-2 {\n  padding-top: 0.5rem !important;\n}\n\n.pt-3 {\n  padding-top: 1rem !important;\n}\n\n.pt-4 {\n  padding-top: 1.5rem !important;\n}\n\n.pt-5 {\n  padding-top: 3rem !important;\n}\n\n.pe-0 {\n  padding-right: 0 !important;\n}\n\n.pe-1 {\n  padding-right: 0.25rem !important;\n}\n\n.pe-2 {\n  padding-right: 0.5rem !important;\n}\n\n.pe-3 {\n  padding-right: 1rem !important;\n}\n\n.pe-4 {\n  padding-right: 1.5rem !important;\n}\n\n.pe-5 {\n  padding-right: 3rem !important;\n}\n\n.pb-0 {\n  padding-bottom: 0 !important;\n}\n\n.pb-1 {\n  padding-bottom: 0.25rem !important;\n}\n\n.pb-2 {\n  padding-bottom: 0.5rem !important;\n}\n\n.pb-3 {\n  padding-bottom: 1rem !important;\n}\n\n.pb-4 {\n  padding-bottom: 1.5rem !important;\n}\n\n.pb-5 {\n  padding-bottom: 3rem !important;\n}\n\n.ps-0 {\n  padding-left: 0 !important;\n}\n\n.ps-1 {\n  padding-left: 0.25rem !important;\n}\n\n.ps-2 {\n  padding-left: 0.5rem !important;\n}\n\n.ps-3 {\n  padding-left: 1rem !important;\n}\n\n.ps-4 {\n  padding-left: 1.5rem !important;\n}\n\n.ps-5 {\n  padding-left: 3rem !important;\n}\n\n.gap-0 {\n  gap: 0 !important;\n}\n\n.gap-1 {\n  gap: 0.25rem !important;\n}\n\n.gap-2 {\n  gap: 0.5rem !important;\n}\n\n.gap-3 {\n  gap: 1rem !important;\n}\n\n.gap-4 {\n  gap: 1.5rem !important;\n}\n\n.gap-5 {\n  gap: 3rem !important;\n}\n\n.row-gap-0 {\n  row-gap: 0 !important;\n}\n\n.row-gap-1 {\n  row-gap: 0.25rem !important;\n}\n\n.row-gap-2 {\n  row-gap: 0.5rem !important;\n}\n\n.row-gap-3 {\n  row-gap: 1rem !important;\n}\n\n.row-gap-4 {\n  row-gap: 1.5rem !important;\n}\n\n.row-gap-5 {\n  row-gap: 3rem !important;\n}\n\n.column-gap-0 {\n  column-gap: 0 !important;\n}\n\n.column-gap-1 {\n  column-gap: 0.25rem !important;\n}\n\n.column-gap-2 {\n  column-gap: 0.5rem !important;\n}\n\n.column-gap-3 {\n  column-gap: 1rem !important;\n}\n\n.column-gap-4 {\n  column-gap: 1.5rem !important;\n}\n\n.column-gap-5 {\n  column-gap: 3rem !important;\n}\n\n.font-monospace {\n  font-family: var(--bs-font-monospace) !important;\n}\n\n.fs-1 {\n  font-size: calc(1.375rem + 1.5vw) !important;\n}\n\n.fs-2 {\n  font-size: calc(1.325rem + 0.9vw) !important;\n}\n\n.fs-3 {\n  font-size: calc(1.3rem + 0.6vw) !important;\n}\n\n.fs-4 {\n  font-size: calc(1.275rem + 0.3vw) !important;\n}\n\n.fs-5 {\n  font-size: 1.25rem !important;\n}\n\n.fs-6 {\n  font-size: 1rem !important;\n}\n\n.fst-italic {\n  font-style: italic !important;\n}\n\n.fst-normal {\n  font-style: normal !important;\n}\n\n.fw-lighter {\n  font-weight: lighter !important;\n}\n\n.fw-light {\n  font-weight: 300 !important;\n}\n\n.fw-normal {\n  font-weight: 400 !important;\n}\n\n.fw-medium {\n  font-weight: 500 !important;\n}\n\n.fw-semibold {\n  font-weight: 600 !important;\n}\n\n.fw-bold {\n  font-weight: 700 !important;\n}\n\n.fw-bolder {\n  font-weight: bolder !important;\n}\n\n.lh-1 {\n  line-height: 1 !important;\n}\n\n.lh-sm {\n  line-height: 1.25 !important;\n}\n\n.lh-base {\n  line-height: 1.5 !important;\n}\n\n.lh-lg {\n  line-height: 2 !important;\n}\n\n.text-start {\n  text-align: left !important;\n}\n\n.text-end {\n  text-align: right !important;\n}\n\n.text-center {\n  text-align: center !important;\n}\n\n.text-decoration-none {\n  text-decoration: none !important;\n}\n\n.text-decoration-underline {\n  text-decoration: underline !important;\n}\n\n.text-decoration-line-through {\n  text-decoration: line-through !important;\n}\n\n.text-lowercase {\n  text-transform: lowercase !important;\n}\n\n.text-uppercase {\n  text-transform: uppercase !important;\n}\n\n.text-capitalize {\n  text-transform: capitalize !important;\n}\n\n.text-wrap {\n  white-space: normal !important;\n}\n\n.text-nowrap {\n  white-space: nowrap !important;\n}\n\n/* rtl:begin:remove */\n.text-break {\n  word-wrap: break-word !important;\n  word-break: break-word !important;\n}\n\n/* rtl:end:remove */\n.text-primary {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-primary-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-secondary {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-secondary-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-success {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-success-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-info {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-info-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-warning {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-warning-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-danger {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-danger-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-light {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-light-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-dark {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-dark-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-black {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-black-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-white {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-white-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-body {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-body-color-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-muted {\n  --bs-text-opacity: 1;\n  color: var(--bs-secondary-color) !important;\n}\n\n.text-black-50 {\n  --bs-text-opacity: 1;\n  color: rgba(0, 0, 0, 0.5) !important;\n}\n\n.text-white-50 {\n  --bs-text-opacity: 1;\n  color: rgba(255, 255, 255, 0.5) !important;\n}\n\n.text-body-secondary {\n  --bs-text-opacity: 1;\n  color: var(--bs-secondary-color) !important;\n}\n\n.text-body-tertiary {\n  --bs-text-opacity: 1;\n  color: var(--bs-tertiary-color) !important;\n}\n\n.text-body-emphasis {\n  --bs-text-opacity: 1;\n  color: var(--bs-emphasis-color) !important;\n}\n\n.text-reset {\n  --bs-text-opacity: 1;\n  color: inherit !important;\n}\n\n.text-opacity-25 {\n  --bs-text-opacity: 0.25;\n}\n\n.text-opacity-50 {\n  --bs-text-opacity: 0.5;\n}\n\n.text-opacity-75 {\n  --bs-text-opacity: 0.75;\n}\n\n.text-opacity-100 {\n  --bs-text-opacity: 1;\n}\n\n.text-primary-emphasis {\n  color: var(--bs-primary-text-emphasis) !important;\n}\n\n.text-secondary-emphasis {\n  color: var(--bs-secondary-text-emphasis) !important;\n}\n\n.text-success-emphasis {\n  color: var(--bs-success-text-emphasis) !important;\n}\n\n.text-info-emphasis {\n  color: var(--bs-info-text-emphasis) !important;\n}\n\n.text-warning-emphasis {\n  color: var(--bs-warning-text-emphasis) !important;\n}\n\n.text-danger-emphasis {\n  color: var(--bs-danger-text-emphasis) !important;\n}\n\n.text-light-emphasis {\n  color: var(--bs-light-text-emphasis) !important;\n}\n\n.text-dark-emphasis {\n  color: var(--bs-dark-text-emphasis) !important;\n}\n\n.link-opacity-10 {\n  --bs-link-opacity: 0.1;\n}\n\n.link-opacity-10-hover:hover {\n  --bs-link-opacity: 0.1;\n}\n\n.link-opacity-25 {\n  --bs-link-opacity: 0.25;\n}\n\n.link-opacity-25-hover:hover {\n  --bs-link-opacity: 0.25;\n}\n\n.link-opacity-50 {\n  --bs-link-opacity: 0.5;\n}\n\n.link-opacity-50-hover:hover {\n  --bs-link-opacity: 0.5;\n}\n\n.link-opacity-75 {\n  --bs-link-opacity: 0.75;\n}\n\n.link-opacity-75-hover:hover {\n  --bs-link-opacity: 0.75;\n}\n\n.link-opacity-100 {\n  --bs-link-opacity: 1;\n}\n\n.link-opacity-100-hover:hover {\n  --bs-link-opacity: 1;\n}\n\n.link-offset-1 {\n  text-underline-offset: 0.125em !important;\n}\n\n.link-offset-1-hover:hover {\n  text-underline-offset: 0.125em !important;\n}\n\n.link-offset-2 {\n  text-underline-offset: 0.25em !important;\n}\n\n.link-offset-2-hover:hover {\n  text-underline-offset: 0.25em !important;\n}\n\n.link-offset-3 {\n  text-underline-offset: 0.375em !important;\n}\n\n.link-offset-3-hover:hover {\n  text-underline-offset: 0.375em !important;\n}\n\n.link-underline-primary {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-primary-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-secondary {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-secondary-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-success {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-success-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-info {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-info-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-warning {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-warning-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-danger {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-danger-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-light {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-light-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-dark {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-dark-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-link-color-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-underline-opacity-0 {\n  --bs-link-underline-opacity: 0;\n}\n\n.link-underline-opacity-0-hover:hover {\n  --bs-link-underline-opacity: 0;\n}\n\n.link-underline-opacity-10 {\n  --bs-link-underline-opacity: 0.1;\n}\n\n.link-underline-opacity-10-hover:hover {\n  --bs-link-underline-opacity: 0.1;\n}\n\n.link-underline-opacity-25 {\n  --bs-link-underline-opacity: 0.25;\n}\n\n.link-underline-opacity-25-hover:hover {\n  --bs-link-underline-opacity: 0.25;\n}\n\n.link-underline-opacity-50 {\n  --bs-link-underline-opacity: 0.5;\n}\n\n.link-underline-opacity-50-hover:hover {\n  --bs-link-underline-opacity: 0.5;\n}\n\n.link-underline-opacity-75 {\n  --bs-link-underline-opacity: 0.75;\n}\n\n.link-underline-opacity-75-hover:hover {\n  --bs-link-underline-opacity: 0.75;\n}\n\n.link-underline-opacity-100 {\n  --bs-link-underline-opacity: 1;\n}\n\n.link-underline-opacity-100-hover:hover {\n  --bs-link-underline-opacity: 1;\n}\n\n.bg-primary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-primary-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-secondary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-secondary-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-success {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-success-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-info {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-info-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-warning {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-warning-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-danger {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-danger-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-light {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-light-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-dark {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-dark-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-black {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-black-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-white {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-white-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-body {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-body-bg-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-transparent {\n  --bs-bg-opacity: 1;\n  background-color: transparent !important;\n}\n\n.bg-body-secondary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-secondary-bg-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-body-tertiary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-tertiary-bg-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-opacity-10 {\n  --bs-bg-opacity: 0.1;\n}\n\n.bg-opacity-25 {\n  --bs-bg-opacity: 0.25;\n}\n\n.bg-opacity-50 {\n  --bs-bg-opacity: 0.5;\n}\n\n.bg-opacity-75 {\n  --bs-bg-opacity: 0.75;\n}\n\n.bg-opacity-100 {\n  --bs-bg-opacity: 1;\n}\n\n.bg-primary-subtle {\n  background-color: var(--bs-primary-bg-subtle) !important;\n}\n\n.bg-secondary-subtle {\n  background-color: var(--bs-secondary-bg-subtle) !important;\n}\n\n.bg-success-subtle {\n  background-color: var(--bs-success-bg-subtle) !important;\n}\n\n.bg-info-subtle {\n  background-color: var(--bs-info-bg-subtle) !important;\n}\n\n.bg-warning-subtle {\n  background-color: var(--bs-warning-bg-subtle) !important;\n}\n\n.bg-danger-subtle {\n  background-color: var(--bs-danger-bg-subtle) !important;\n}\n\n.bg-light-subtle {\n  background-color: var(--bs-light-bg-subtle) !important;\n}\n\n.bg-dark-subtle {\n  background-color: var(--bs-dark-bg-subtle) !important;\n}\n\n.bg-gradient {\n  background-image: var(--bs-gradient) !important;\n}\n\n.user-select-all {\n  user-select: all !important;\n}\n\n.user-select-auto {\n  user-select: auto !important;\n}\n\n.user-select-none {\n  user-select: none !important;\n}\n\n.pe-none {\n  pointer-events: none !important;\n}\n\n.pe-auto {\n  pointer-events: auto !important;\n}\n\n.rounded {\n  border-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-0 {\n  border-radius: 0 !important;\n}\n\n.rounded-1 {\n  border-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-2 {\n  border-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-3 {\n  border-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-4 {\n  border-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-5 {\n  border-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-circle {\n  border-radius: 50% !important;\n}\n\n.rounded-pill {\n  border-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-top {\n  border-top-left-radius: var(--bs-border-radius) !important;\n  border-top-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-top-0 {\n  border-top-left-radius: 0 !important;\n  border-top-right-radius: 0 !important;\n}\n\n.rounded-top-1 {\n  border-top-left-radius: var(--bs-border-radius-sm) !important;\n  border-top-right-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-top-2 {\n  border-top-left-radius: var(--bs-border-radius) !important;\n  border-top-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-top-3 {\n  border-top-left-radius: var(--bs-border-radius-lg) !important;\n  border-top-right-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-top-4 {\n  border-top-left-radius: var(--bs-border-radius-xl) !important;\n  border-top-right-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-top-5 {\n  border-top-left-radius: var(--bs-border-radius-xxl) !important;\n  border-top-right-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-top-circle {\n  border-top-left-radius: 50% !important;\n  border-top-right-radius: 50% !important;\n}\n\n.rounded-top-pill {\n  border-top-left-radius: var(--bs-border-radius-pill) !important;\n  border-top-right-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-end {\n  border-top-right-radius: var(--bs-border-radius) !important;\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-end-0 {\n  border-top-right-radius: 0 !important;\n  border-bottom-right-radius: 0 !important;\n}\n\n.rounded-end-1 {\n  border-top-right-radius: var(--bs-border-radius-sm) !important;\n  border-bottom-right-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-end-2 {\n  border-top-right-radius: var(--bs-border-radius) !important;\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-end-3 {\n  border-top-right-radius: var(--bs-border-radius-lg) !important;\n  border-bottom-right-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-end-4 {\n  border-top-right-radius: var(--bs-border-radius-xl) !important;\n  border-bottom-right-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-end-5 {\n  border-top-right-radius: var(--bs-border-radius-xxl) !important;\n  border-bottom-right-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-end-circle {\n  border-top-right-radius: 50% !important;\n  border-bottom-right-radius: 50% !important;\n}\n\n.rounded-end-pill {\n  border-top-right-radius: var(--bs-border-radius-pill) !important;\n  border-bottom-right-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-bottom {\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-bottom-0 {\n  border-bottom-right-radius: 0 !important;\n  border-bottom-left-radius: 0 !important;\n}\n\n.rounded-bottom-1 {\n  border-bottom-right-radius: var(--bs-border-radius-sm) !important;\n  border-bottom-left-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-bottom-2 {\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-bottom-3 {\n  border-bottom-right-radius: var(--bs-border-radius-lg) !important;\n  border-bottom-left-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-bottom-4 {\n  border-bottom-right-radius: var(--bs-border-radius-xl) !important;\n  border-bottom-left-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-bottom-5 {\n  border-bottom-right-radius: var(--bs-border-radius-xxl) !important;\n  border-bottom-left-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-bottom-circle {\n  border-bottom-right-radius: 50% !important;\n  border-bottom-left-radius: 50% !important;\n}\n\n.rounded-bottom-pill {\n  border-bottom-right-radius: var(--bs-border-radius-pill) !important;\n  border-bottom-left-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-start {\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n  border-top-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-start-0 {\n  border-bottom-left-radius: 0 !important;\n  border-top-left-radius: 0 !important;\n}\n\n.rounded-start-1 {\n  border-bottom-left-radius: var(--bs-border-radius-sm) !important;\n  border-top-left-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-start-2 {\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n  border-top-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-start-3 {\n  border-bottom-left-radius: var(--bs-border-radius-lg) !important;\n  border-top-left-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-start-4 {\n  border-bottom-left-radius: var(--bs-border-radius-xl) !important;\n  border-top-left-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-start-5 {\n  border-bottom-left-radius: var(--bs-border-radius-xxl) !important;\n  border-top-left-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-start-circle {\n  border-bottom-left-radius: 50% !important;\n  border-top-left-radius: 50% !important;\n}\n\n.rounded-start-pill {\n  border-bottom-left-radius: var(--bs-border-radius-pill) !important;\n  border-top-left-radius: var(--bs-border-radius-pill) !important;\n}\n\n.visible {\n  visibility: visible !important;\n}\n\n.invisible {\n  visibility: hidden !important;\n}\n\n.z-n1 {\n  z-index: -1 !important;\n}\n\n.z-0 {\n  z-index: 0 !important;\n}\n\n.z-1 {\n  z-index: 1 !important;\n}\n\n.z-2 {\n  z-index: 2 !important;\n}\n\n.z-3 {\n  z-index: 3 !important;\n}\n\n@media (min-width: 576px) {\n  .float-sm-start {\n    float: left !important;\n  }\n  .float-sm-end {\n    float: right !important;\n  }\n  .float-sm-none {\n    float: none !important;\n  }\n  .object-fit-sm-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-sm-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-sm-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-sm-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-sm-none {\n    object-fit: none !important;\n  }\n  .d-sm-inline {\n    display: inline !important;\n  }\n  .d-sm-inline-block {\n    display: inline-block !important;\n  }\n  .d-sm-block {\n    display: block !important;\n  }\n  .d-sm-grid {\n    display: grid !important;\n  }\n  .d-sm-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-sm-table {\n    display: table !important;\n  }\n  .d-sm-table-row {\n    display: table-row !important;\n  }\n  .d-sm-table-cell {\n    display: table-cell !important;\n  }\n  .d-sm-flex {\n    display: flex !important;\n  }\n  .d-sm-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-sm-none {\n    display: none !important;\n  }\n  .flex-sm-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-sm-row {\n    flex-direction: row !important;\n  }\n  .flex-sm-column {\n    flex-direction: column !important;\n  }\n  .flex-sm-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-sm-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-sm-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-sm-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-sm-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-sm-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-sm-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-sm-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-sm-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-sm-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-sm-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-sm-center {\n    justify-content: center !important;\n  }\n  .justify-content-sm-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-sm-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-sm-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-sm-start {\n    align-items: flex-start !important;\n  }\n  .align-items-sm-end {\n    align-items: flex-end !important;\n  }\n  .align-items-sm-center {\n    align-items: center !important;\n  }\n  .align-items-sm-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-sm-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-sm-start {\n    align-content: flex-start !important;\n  }\n  .align-content-sm-end {\n    align-content: flex-end !important;\n  }\n  .align-content-sm-center {\n    align-content: center !important;\n  }\n  .align-content-sm-between {\n    align-content: space-between !important;\n  }\n  .align-content-sm-around {\n    align-content: space-around !important;\n  }\n  .align-content-sm-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-sm-auto {\n    align-self: auto !important;\n  }\n  .align-self-sm-start {\n    align-self: flex-start !important;\n  }\n  .align-self-sm-end {\n    align-self: flex-end !important;\n  }\n  .align-self-sm-center {\n    align-self: center !important;\n  }\n  .align-self-sm-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-sm-stretch {\n    align-self: stretch !important;\n  }\n  .order-sm-first {\n    order: -1 !important;\n  }\n  .order-sm-0 {\n    order: 0 !important;\n  }\n  .order-sm-1 {\n    order: 1 !important;\n  }\n  .order-sm-2 {\n    order: 2 !important;\n  }\n  .order-sm-3 {\n    order: 3 !important;\n  }\n  .order-sm-4 {\n    order: 4 !important;\n  }\n  .order-sm-5 {\n    order: 5 !important;\n  }\n  .order-sm-last {\n    order: 6 !important;\n  }\n  .m-sm-0 {\n    margin: 0 !important;\n  }\n  .m-sm-1 {\n    margin: 0.25rem !important;\n  }\n  .m-sm-2 {\n    margin: 0.5rem !important;\n  }\n  .m-sm-3 {\n    margin: 1rem !important;\n  }\n  .m-sm-4 {\n    margin: 1.5rem !important;\n  }\n  .m-sm-5 {\n    margin: 3rem !important;\n  }\n  .m-sm-auto {\n    margin: auto !important;\n  }\n  .mx-sm-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-sm-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-sm-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-sm-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-sm-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-sm-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-sm-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-sm-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-sm-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-sm-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-sm-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-sm-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-sm-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-sm-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-sm-0 {\n    margin-top: 0 !important;\n  }\n  .mt-sm-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-sm-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-sm-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-sm-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-sm-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-sm-auto {\n    margin-top: auto !important;\n  }\n  .me-sm-0 {\n    margin-right: 0 !important;\n  }\n  .me-sm-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-sm-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-sm-3 {\n    margin-right: 1rem !important;\n  }\n  .me-sm-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-sm-5 {\n    margin-right: 3rem !important;\n  }\n  .me-sm-auto {\n    margin-right: auto !important;\n  }\n  .mb-sm-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-sm-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-sm-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-sm-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-sm-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-sm-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-sm-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-sm-0 {\n    margin-left: 0 !important;\n  }\n  .ms-sm-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-sm-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-sm-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-sm-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-sm-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-sm-auto {\n    margin-left: auto !important;\n  }\n  .p-sm-0 {\n    padding: 0 !important;\n  }\n  .p-sm-1 {\n    padding: 0.25rem !important;\n  }\n  .p-sm-2 {\n    padding: 0.5rem !important;\n  }\n  .p-sm-3 {\n    padding: 1rem !important;\n  }\n  .p-sm-4 {\n    padding: 1.5rem !important;\n  }\n  .p-sm-5 {\n    padding: 3rem !important;\n  }\n  .px-sm-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-sm-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-sm-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-sm-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-sm-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-sm-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-sm-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-sm-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-sm-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-sm-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-sm-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-sm-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-sm-0 {\n    padding-top: 0 !important;\n  }\n  .pt-sm-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-sm-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-sm-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-sm-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-sm-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-sm-0 {\n    padding-right: 0 !important;\n  }\n  .pe-sm-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-sm-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-sm-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-sm-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-sm-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-sm-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-sm-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-sm-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-sm-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-sm-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-sm-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-sm-0 {\n    padding-left: 0 !important;\n  }\n  .ps-sm-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-sm-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-sm-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-sm-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-sm-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-sm-0 {\n    gap: 0 !important;\n  }\n  .gap-sm-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-sm-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-sm-3 {\n    gap: 1rem !important;\n  }\n  .gap-sm-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-sm-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-sm-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-sm-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-sm-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-sm-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-sm-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-sm-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-sm-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-sm-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-sm-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-sm-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-sm-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-sm-5 {\n    column-gap: 3rem !important;\n  }\n  .text-sm-start {\n    text-align: left !important;\n  }\n  .text-sm-end {\n    text-align: right !important;\n  }\n  .text-sm-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 768px) {\n  .float-md-start {\n    float: left !important;\n  }\n  .float-md-end {\n    float: right !important;\n  }\n  .float-md-none {\n    float: none !important;\n  }\n  .object-fit-md-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-md-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-md-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-md-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-md-none {\n    object-fit: none !important;\n  }\n  .d-md-inline {\n    display: inline !important;\n  }\n  .d-md-inline-block {\n    display: inline-block !important;\n  }\n  .d-md-block {\n    display: block !important;\n  }\n  .d-md-grid {\n    display: grid !important;\n  }\n  .d-md-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-md-table {\n    display: table !important;\n  }\n  .d-md-table-row {\n    display: table-row !important;\n  }\n  .d-md-table-cell {\n    display: table-cell !important;\n  }\n  .d-md-flex {\n    display: flex !important;\n  }\n  .d-md-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-md-none {\n    display: none !important;\n  }\n  .flex-md-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-md-row {\n    flex-direction: row !important;\n  }\n  .flex-md-column {\n    flex-direction: column !important;\n  }\n  .flex-md-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-md-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-md-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-md-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-md-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-md-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-md-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-md-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-md-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-md-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-md-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-md-center {\n    justify-content: center !important;\n  }\n  .justify-content-md-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-md-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-md-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-md-start {\n    align-items: flex-start !important;\n  }\n  .align-items-md-end {\n    align-items: flex-end !important;\n  }\n  .align-items-md-center {\n    align-items: center !important;\n  }\n  .align-items-md-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-md-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-md-start {\n    align-content: flex-start !important;\n  }\n  .align-content-md-end {\n    align-content: flex-end !important;\n  }\n  .align-content-md-center {\n    align-content: center !important;\n  }\n  .align-content-md-between {\n    align-content: space-between !important;\n  }\n  .align-content-md-around {\n    align-content: space-around !important;\n  }\n  .align-content-md-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-md-auto {\n    align-self: auto !important;\n  }\n  .align-self-md-start {\n    align-self: flex-start !important;\n  }\n  .align-self-md-end {\n    align-self: flex-end !important;\n  }\n  .align-self-md-center {\n    align-self: center !important;\n  }\n  .align-self-md-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-md-stretch {\n    align-self: stretch !important;\n  }\n  .order-md-first {\n    order: -1 !important;\n  }\n  .order-md-0 {\n    order: 0 !important;\n  }\n  .order-md-1 {\n    order: 1 !important;\n  }\n  .order-md-2 {\n    order: 2 !important;\n  }\n  .order-md-3 {\n    order: 3 !important;\n  }\n  .order-md-4 {\n    order: 4 !important;\n  }\n  .order-md-5 {\n    order: 5 !important;\n  }\n  .order-md-last {\n    order: 6 !important;\n  }\n  .m-md-0 {\n    margin: 0 !important;\n  }\n  .m-md-1 {\n    margin: 0.25rem !important;\n  }\n  .m-md-2 {\n    margin: 0.5rem !important;\n  }\n  .m-md-3 {\n    margin: 1rem !important;\n  }\n  .m-md-4 {\n    margin: 1.5rem !important;\n  }\n  .m-md-5 {\n    margin: 3rem !important;\n  }\n  .m-md-auto {\n    margin: auto !important;\n  }\n  .mx-md-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-md-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-md-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-md-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-md-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-md-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-md-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-md-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-md-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-md-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-md-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-md-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-md-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-md-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-md-0 {\n    margin-top: 0 !important;\n  }\n  .mt-md-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-md-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-md-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-md-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-md-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-md-auto {\n    margin-top: auto !important;\n  }\n  .me-md-0 {\n    margin-right: 0 !important;\n  }\n  .me-md-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-md-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-md-3 {\n    margin-right: 1rem !important;\n  }\n  .me-md-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-md-5 {\n    margin-right: 3rem !important;\n  }\n  .me-md-auto {\n    margin-right: auto !important;\n  }\n  .mb-md-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-md-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-md-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-md-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-md-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-md-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-md-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-md-0 {\n    margin-left: 0 !important;\n  }\n  .ms-md-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-md-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-md-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-md-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-md-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-md-auto {\n    margin-left: auto !important;\n  }\n  .p-md-0 {\n    padding: 0 !important;\n  }\n  .p-md-1 {\n    padding: 0.25rem !important;\n  }\n  .p-md-2 {\n    padding: 0.5rem !important;\n  }\n  .p-md-3 {\n    padding: 1rem !important;\n  }\n  .p-md-4 {\n    padding: 1.5rem !important;\n  }\n  .p-md-5 {\n    padding: 3rem !important;\n  }\n  .px-md-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-md-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-md-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-md-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-md-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-md-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-md-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-md-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-md-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-md-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-md-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-md-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-md-0 {\n    padding-top: 0 !important;\n  }\n  .pt-md-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-md-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-md-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-md-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-md-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-md-0 {\n    padding-right: 0 !important;\n  }\n  .pe-md-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-md-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-md-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-md-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-md-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-md-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-md-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-md-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-md-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-md-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-md-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-md-0 {\n    padding-left: 0 !important;\n  }\n  .ps-md-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-md-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-md-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-md-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-md-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-md-0 {\n    gap: 0 !important;\n  }\n  .gap-md-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-md-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-md-3 {\n    gap: 1rem !important;\n  }\n  .gap-md-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-md-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-md-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-md-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-md-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-md-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-md-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-md-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-md-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-md-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-md-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-md-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-md-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-md-5 {\n    column-gap: 3rem !important;\n  }\n  .text-md-start {\n    text-align: left !important;\n  }\n  .text-md-end {\n    text-align: right !important;\n  }\n  .text-md-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 992px) {\n  .float-lg-start {\n    float: left !important;\n  }\n  .float-lg-end {\n    float: right !important;\n  }\n  .float-lg-none {\n    float: none !important;\n  }\n  .object-fit-lg-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-lg-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-lg-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-lg-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-lg-none {\n    object-fit: none !important;\n  }\n  .d-lg-inline {\n    display: inline !important;\n  }\n  .d-lg-inline-block {\n    display: inline-block !important;\n  }\n  .d-lg-block {\n    display: block !important;\n  }\n  .d-lg-grid {\n    display: grid !important;\n  }\n  .d-lg-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-lg-table {\n    display: table !important;\n  }\n  .d-lg-table-row {\n    display: table-row !important;\n  }\n  .d-lg-table-cell {\n    display: table-cell !important;\n  }\n  .d-lg-flex {\n    display: flex !important;\n  }\n  .d-lg-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-lg-none {\n    display: none !important;\n  }\n  .flex-lg-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-lg-row {\n    flex-direction: row !important;\n  }\n  .flex-lg-column {\n    flex-direction: column !important;\n  }\n  .flex-lg-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-lg-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-lg-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-lg-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-lg-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-lg-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-lg-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-lg-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-lg-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-lg-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-lg-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-lg-center {\n    justify-content: center !important;\n  }\n  .justify-content-lg-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-lg-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-lg-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-lg-start {\n    align-items: flex-start !important;\n  }\n  .align-items-lg-end {\n    align-items: flex-end !important;\n  }\n  .align-items-lg-center {\n    align-items: center !important;\n  }\n  .align-items-lg-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-lg-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-lg-start {\n    align-content: flex-start !important;\n  }\n  .align-content-lg-end {\n    align-content: flex-end !important;\n  }\n  .align-content-lg-center {\n    align-content: center !important;\n  }\n  .align-content-lg-between {\n    align-content: space-between !important;\n  }\n  .align-content-lg-around {\n    align-content: space-around !important;\n  }\n  .align-content-lg-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-lg-auto {\n    align-self: auto !important;\n  }\n  .align-self-lg-start {\n    align-self: flex-start !important;\n  }\n  .align-self-lg-end {\n    align-self: flex-end !important;\n  }\n  .align-self-lg-center {\n    align-self: center !important;\n  }\n  .align-self-lg-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-lg-stretch {\n    align-self: stretch !important;\n  }\n  .order-lg-first {\n    order: -1 !important;\n  }\n  .order-lg-0 {\n    order: 0 !important;\n  }\n  .order-lg-1 {\n    order: 1 !important;\n  }\n  .order-lg-2 {\n    order: 2 !important;\n  }\n  .order-lg-3 {\n    order: 3 !important;\n  }\n  .order-lg-4 {\n    order: 4 !important;\n  }\n  .order-lg-5 {\n    order: 5 !important;\n  }\n  .order-lg-last {\n    order: 6 !important;\n  }\n  .m-lg-0 {\n    margin: 0 !important;\n  }\n  .m-lg-1 {\n    margin: 0.25rem !important;\n  }\n  .m-lg-2 {\n    margin: 0.5rem !important;\n  }\n  .m-lg-3 {\n    margin: 1rem !important;\n  }\n  .m-lg-4 {\n    margin: 1.5rem !important;\n  }\n  .m-lg-5 {\n    margin: 3rem !important;\n  }\n  .m-lg-auto {\n    margin: auto !important;\n  }\n  .mx-lg-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-lg-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-lg-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-lg-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-lg-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-lg-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-lg-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-lg-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-lg-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-lg-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-lg-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-lg-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-lg-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-lg-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-lg-0 {\n    margin-top: 0 !important;\n  }\n  .mt-lg-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-lg-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-lg-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-lg-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-lg-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-lg-auto {\n    margin-top: auto !important;\n  }\n  .me-lg-0 {\n    margin-right: 0 !important;\n  }\n  .me-lg-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-lg-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-lg-3 {\n    margin-right: 1rem !important;\n  }\n  .me-lg-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-lg-5 {\n    margin-right: 3rem !important;\n  }\n  .me-lg-auto {\n    margin-right: auto !important;\n  }\n  .mb-lg-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-lg-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-lg-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-lg-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-lg-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-lg-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-lg-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-lg-0 {\n    margin-left: 0 !important;\n  }\n  .ms-lg-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-lg-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-lg-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-lg-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-lg-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-lg-auto {\n    margin-left: auto !important;\n  }\n  .p-lg-0 {\n    padding: 0 !important;\n  }\n  .p-lg-1 {\n    padding: 0.25rem !important;\n  }\n  .p-lg-2 {\n    padding: 0.5rem !important;\n  }\n  .p-lg-3 {\n    padding: 1rem !important;\n  }\n  .p-lg-4 {\n    padding: 1.5rem !important;\n  }\n  .p-lg-5 {\n    padding: 3rem !important;\n  }\n  .px-lg-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-lg-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-lg-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-lg-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-lg-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-lg-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-lg-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-lg-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-lg-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-lg-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-lg-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-lg-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-lg-0 {\n    padding-top: 0 !important;\n  }\n  .pt-lg-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-lg-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-lg-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-lg-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-lg-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-lg-0 {\n    padding-right: 0 !important;\n  }\n  .pe-lg-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-lg-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-lg-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-lg-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-lg-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-lg-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-lg-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-lg-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-lg-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-lg-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-lg-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-lg-0 {\n    padding-left: 0 !important;\n  }\n  .ps-lg-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-lg-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-lg-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-lg-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-lg-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-lg-0 {\n    gap: 0 !important;\n  }\n  .gap-lg-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-lg-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-lg-3 {\n    gap: 1rem !important;\n  }\n  .gap-lg-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-lg-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-lg-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-lg-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-lg-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-lg-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-lg-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-lg-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-lg-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-lg-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-lg-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-lg-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-lg-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-lg-5 {\n    column-gap: 3rem !important;\n  }\n  .text-lg-start {\n    text-align: left !important;\n  }\n  .text-lg-end {\n    text-align: right !important;\n  }\n  .text-lg-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 1200px) {\n  .float-xl-start {\n    float: left !important;\n  }\n  .float-xl-end {\n    float: right !important;\n  }\n  .float-xl-none {\n    float: none !important;\n  }\n  .object-fit-xl-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-xl-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-xl-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-xl-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-xl-none {\n    object-fit: none !important;\n  }\n  .d-xl-inline {\n    display: inline !important;\n  }\n  .d-xl-inline-block {\n    display: inline-block !important;\n  }\n  .d-xl-block {\n    display: block !important;\n  }\n  .d-xl-grid {\n    display: grid !important;\n  }\n  .d-xl-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-xl-table {\n    display: table !important;\n  }\n  .d-xl-table-row {\n    display: table-row !important;\n  }\n  .d-xl-table-cell {\n    display: table-cell !important;\n  }\n  .d-xl-flex {\n    display: flex !important;\n  }\n  .d-xl-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-xl-none {\n    display: none !important;\n  }\n  .flex-xl-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-xl-row {\n    flex-direction: row !important;\n  }\n  .flex-xl-column {\n    flex-direction: column !important;\n  }\n  .flex-xl-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-xl-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-xl-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-xl-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-xl-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-xl-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-xl-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-xl-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-xl-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-xl-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-xl-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-xl-center {\n    justify-content: center !important;\n  }\n  .justify-content-xl-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-xl-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-xl-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-xl-start {\n    align-items: flex-start !important;\n  }\n  .align-items-xl-end {\n    align-items: flex-end !important;\n  }\n  .align-items-xl-center {\n    align-items: center !important;\n  }\n  .align-items-xl-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-xl-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-xl-start {\n    align-content: flex-start !important;\n  }\n  .align-content-xl-end {\n    align-content: flex-end !important;\n  }\n  .align-content-xl-center {\n    align-content: center !important;\n  }\n  .align-content-xl-between {\n    align-content: space-between !important;\n  }\n  .align-content-xl-around {\n    align-content: space-around !important;\n  }\n  .align-content-xl-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-xl-auto {\n    align-self: auto !important;\n  }\n  .align-self-xl-start {\n    align-self: flex-start !important;\n  }\n  .align-self-xl-end {\n    align-self: flex-end !important;\n  }\n  .align-self-xl-center {\n    align-self: center !important;\n  }\n  .align-self-xl-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-xl-stretch {\n    align-self: stretch !important;\n  }\n  .order-xl-first {\n    order: -1 !important;\n  }\n  .order-xl-0 {\n    order: 0 !important;\n  }\n  .order-xl-1 {\n    order: 1 !important;\n  }\n  .order-xl-2 {\n    order: 2 !important;\n  }\n  .order-xl-3 {\n    order: 3 !important;\n  }\n  .order-xl-4 {\n    order: 4 !important;\n  }\n  .order-xl-5 {\n    order: 5 !important;\n  }\n  .order-xl-last {\n    order: 6 !important;\n  }\n  .m-xl-0 {\n    margin: 0 !important;\n  }\n  .m-xl-1 {\n    margin: 0.25rem !important;\n  }\n  .m-xl-2 {\n    margin: 0.5rem !important;\n  }\n  .m-xl-3 {\n    margin: 1rem !important;\n  }\n  .m-xl-4 {\n    margin: 1.5rem !important;\n  }\n  .m-xl-5 {\n    margin: 3rem !important;\n  }\n  .m-xl-auto {\n    margin: auto !important;\n  }\n  .mx-xl-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-xl-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-xl-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-xl-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-xl-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-xl-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-xl-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-xl-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-xl-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-xl-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-xl-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-xl-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-xl-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-xl-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-xl-0 {\n    margin-top: 0 !important;\n  }\n  .mt-xl-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-xl-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-xl-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-xl-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-xl-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-xl-auto {\n    margin-top: auto !important;\n  }\n  .me-xl-0 {\n    margin-right: 0 !important;\n  }\n  .me-xl-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-xl-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-xl-3 {\n    margin-right: 1rem !important;\n  }\n  .me-xl-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-xl-5 {\n    margin-right: 3rem !important;\n  }\n  .me-xl-auto {\n    margin-right: auto !important;\n  }\n  .mb-xl-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-xl-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-xl-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-xl-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-xl-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-xl-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-xl-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-xl-0 {\n    margin-left: 0 !important;\n  }\n  .ms-xl-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-xl-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-xl-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-xl-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-xl-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-xl-auto {\n    margin-left: auto !important;\n  }\n  .p-xl-0 {\n    padding: 0 !important;\n  }\n  .p-xl-1 {\n    padding: 0.25rem !important;\n  }\n  .p-xl-2 {\n    padding: 0.5rem !important;\n  }\n  .p-xl-3 {\n    padding: 1rem !important;\n  }\n  .p-xl-4 {\n    padding: 1.5rem !important;\n  }\n  .p-xl-5 {\n    padding: 3rem !important;\n  }\n  .px-xl-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-xl-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-xl-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-xl-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-xl-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-xl-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-xl-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-xl-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-xl-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-xl-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-xl-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-xl-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-xl-0 {\n    padding-top: 0 !important;\n  }\n  .pt-xl-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-xl-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-xl-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-xl-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-xl-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-xl-0 {\n    padding-right: 0 !important;\n  }\n  .pe-xl-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-xl-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-xl-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-xl-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-xl-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-xl-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-xl-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-xl-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-xl-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-xl-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-xl-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-xl-0 {\n    padding-left: 0 !important;\n  }\n  .ps-xl-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-xl-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-xl-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-xl-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-xl-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-xl-0 {\n    gap: 0 !important;\n  }\n  .gap-xl-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-xl-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-xl-3 {\n    gap: 1rem !important;\n  }\n  .gap-xl-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-xl-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-xl-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-xl-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-xl-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-xl-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-xl-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-xl-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-xl-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-xl-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-xl-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-xl-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-xl-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-xl-5 {\n    column-gap: 3rem !important;\n  }\n  .text-xl-start {\n    text-align: left !important;\n  }\n  .text-xl-end {\n    text-align: right !important;\n  }\n  .text-xl-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 1400px) {\n  .float-xxl-start {\n    float: left !important;\n  }\n  .float-xxl-end {\n    float: right !important;\n  }\n  .float-xxl-none {\n    float: none !important;\n  }\n  .object-fit-xxl-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-xxl-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-xxl-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-xxl-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-xxl-none {\n    object-fit: none !important;\n  }\n  .d-xxl-inline {\n    display: inline !important;\n  }\n  .d-xxl-inline-block {\n    display: inline-block !important;\n  }\n  .d-xxl-block {\n    display: block !important;\n  }\n  .d-xxl-grid {\n    display: grid !important;\n  }\n  .d-xxl-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-xxl-table {\n    display: table !important;\n  }\n  .d-xxl-table-row {\n    display: table-row !important;\n  }\n  .d-xxl-table-cell {\n    display: table-cell !important;\n  }\n  .d-xxl-flex {\n    display: flex !important;\n  }\n  .d-xxl-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-xxl-none {\n    display: none !important;\n  }\n  .flex-xxl-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-xxl-row {\n    flex-direction: row !important;\n  }\n  .flex-xxl-column {\n    flex-direction: column !important;\n  }\n  .flex-xxl-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-xxl-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-xxl-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-xxl-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-xxl-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-xxl-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-xxl-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-xxl-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-xxl-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-xxl-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-xxl-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-xxl-center {\n    justify-content: center !important;\n  }\n  .justify-content-xxl-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-xxl-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-xxl-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-xxl-start {\n    align-items: flex-start !important;\n  }\n  .align-items-xxl-end {\n    align-items: flex-end !important;\n  }\n  .align-items-xxl-center {\n    align-items: center !important;\n  }\n  .align-items-xxl-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-xxl-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-xxl-start {\n    align-content: flex-start !important;\n  }\n  .align-content-xxl-end {\n    align-content: flex-end !important;\n  }\n  .align-content-xxl-center {\n    align-content: center !important;\n  }\n  .align-content-xxl-between {\n    align-content: space-between !important;\n  }\n  .align-content-xxl-around {\n    align-content: space-around !important;\n  }\n  .align-content-xxl-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-xxl-auto {\n    align-self: auto !important;\n  }\n  .align-self-xxl-start {\n    align-self: flex-start !important;\n  }\n  .align-self-xxl-end {\n    align-self: flex-end !important;\n  }\n  .align-self-xxl-center {\n    align-self: center !important;\n  }\n  .align-self-xxl-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-xxl-stretch {\n    align-self: stretch !important;\n  }\n  .order-xxl-first {\n    order: -1 !important;\n  }\n  .order-xxl-0 {\n    order: 0 !important;\n  }\n  .order-xxl-1 {\n    order: 1 !important;\n  }\n  .order-xxl-2 {\n    order: 2 !important;\n  }\n  .order-xxl-3 {\n    order: 3 !important;\n  }\n  .order-xxl-4 {\n    order: 4 !important;\n  }\n  .order-xxl-5 {\n    order: 5 !important;\n  }\n  .order-xxl-last {\n    order: 6 !important;\n  }\n  .m-xxl-0 {\n    margin: 0 !important;\n  }\n  .m-xxl-1 {\n    margin: 0.25rem !important;\n  }\n  .m-xxl-2 {\n    margin: 0.5rem !important;\n  }\n  .m-xxl-3 {\n    margin: 1rem !important;\n  }\n  .m-xxl-4 {\n    margin: 1.5rem !important;\n  }\n  .m-xxl-5 {\n    margin: 3rem !important;\n  }\n  .m-xxl-auto {\n    margin: auto !important;\n  }\n  .mx-xxl-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-xxl-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-xxl-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-xxl-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-xxl-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-xxl-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-xxl-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-xxl-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-xxl-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-xxl-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-xxl-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-xxl-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-xxl-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-xxl-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-xxl-0 {\n    margin-top: 0 !important;\n  }\n  .mt-xxl-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-xxl-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-xxl-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-xxl-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-xxl-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-xxl-auto {\n    margin-top: auto !important;\n  }\n  .me-xxl-0 {\n    margin-right: 0 !important;\n  }\n  .me-xxl-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-xxl-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-xxl-3 {\n    margin-right: 1rem !important;\n  }\n  .me-xxl-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-xxl-5 {\n    margin-right: 3rem !important;\n  }\n  .me-xxl-auto {\n    margin-right: auto !important;\n  }\n  .mb-xxl-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-xxl-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-xxl-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-xxl-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-xxl-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-xxl-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-xxl-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-xxl-0 {\n    margin-left: 0 !important;\n  }\n  .ms-xxl-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-xxl-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-xxl-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-xxl-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-xxl-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-xxl-auto {\n    margin-left: auto !important;\n  }\n  .p-xxl-0 {\n    padding: 0 !important;\n  }\n  .p-xxl-1 {\n    padding: 0.25rem !important;\n  }\n  .p-xxl-2 {\n    padding: 0.5rem !important;\n  }\n  .p-xxl-3 {\n    padding: 1rem !important;\n  }\n  .p-xxl-4 {\n    padding: 1.5rem !important;\n  }\n  .p-xxl-5 {\n    padding: 3rem !important;\n  }\n  .px-xxl-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-xxl-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-xxl-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-xxl-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-xxl-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-xxl-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-xxl-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-xxl-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-xxl-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-xxl-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-xxl-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-xxl-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-xxl-0 {\n    padding-top: 0 !important;\n  }\n  .pt-xxl-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-xxl-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-xxl-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-xxl-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-xxl-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-xxl-0 {\n    padding-right: 0 !important;\n  }\n  .pe-xxl-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-xxl-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-xxl-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-xxl-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-xxl-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-xxl-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-xxl-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-xxl-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-xxl-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-xxl-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-xxl-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-xxl-0 {\n    padding-left: 0 !important;\n  }\n  .ps-xxl-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-xxl-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-xxl-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-xxl-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-xxl-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-xxl-0 {\n    gap: 0 !important;\n  }\n  .gap-xxl-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-xxl-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-xxl-3 {\n    gap: 1rem !important;\n  }\n  .gap-xxl-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-xxl-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-xxl-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-xxl-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-xxl-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-xxl-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-xxl-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-xxl-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-xxl-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-xxl-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-xxl-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-xxl-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-xxl-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-xxl-5 {\n    column-gap: 3rem !important;\n  }\n  .text-xxl-start {\n    text-align: left !important;\n  }\n  .text-xxl-end {\n    text-align: right !important;\n  }\n  .text-xxl-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 1200px) {\n  .fs-1 {\n    font-size: 2.5rem !important;\n  }\n  .fs-2 {\n    font-size: 2rem !important;\n  }\n  .fs-3 {\n    font-size: 1.75rem !important;\n  }\n  .fs-4 {\n    font-size: 1.5rem !important;\n  }\n}\n@media print {\n  .d-print-inline {\n    display: inline !important;\n  }\n  .d-print-inline-block {\n    display: inline-block !important;\n  }\n  .d-print-block {\n    display: block !important;\n  }\n  .d-print-grid {\n    display: grid !important;\n  }\n  .d-print-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-print-table {\n    display: table !important;\n  }\n  .d-print-table-row {\n    display: table-row !important;\n  }\n  .d-print-table-cell {\n    display: table-cell !important;\n  }\n  .d-print-flex {\n    display: flex !important;\n  }\n  .d-print-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-print-none {\n    display: none !important;\n  }\n}\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n.fa {\n  font-family: var(--fa-style-family, \"Font Awesome 6 Free\");\n  font-weight: var(--fa-style, 900);\n}\n\n.fa,\n.fa-classic,\n.fa-sharp,\n.fas,\n.fa-solid,\n.far,\n.fa-regular,\n.fab,\n.fa-brands {\n  -moz-osx-font-smoothing: grayscale;\n  -webkit-font-smoothing: antialiased;\n  display: var(--fa-display, inline-block);\n  font-style: normal;\n  font-variant: normal;\n  line-height: 1;\n  text-rendering: auto;\n}\n\n.fas,\n.fa-classic,\n.fa-solid,\n.far,\n.fa-regular {\n  font-family: \"Font Awesome 6 Free\";\n}\n\n.fab,\n.fa-brands {\n  font-family: \"Font Awesome 6 Brands\";\n}\n\n.fa-1x {\n  font-size: 1em;\n}\n\n.fa-2x {\n  font-size: 2em;\n}\n\n.fa-3x {\n  font-size: 3em;\n}\n\n.fa-4x {\n  font-size: 4em;\n}\n\n.fa-5x {\n  font-size: 5em;\n}\n\n.fa-6x {\n  font-size: 6em;\n}\n\n.fa-7x {\n  font-size: 7em;\n}\n\n.fa-8x {\n  font-size: 8em;\n}\n\n.fa-9x {\n  font-size: 9em;\n}\n\n.fa-10x {\n  font-size: 10em;\n}\n\n.fa-2xs {\n  font-size: 0.625em;\n  line-height: 0.1em;\n  vertical-align: 0.225em;\n}\n\n.fa-xs {\n  font-size: 0.75em;\n  line-height: 0.0833333337em;\n  vertical-align: 0.125em;\n}\n\n.fa-sm {\n  font-size: 0.875em;\n  line-height: 0.0714285718em;\n  vertical-align: 0.0535714295em;\n}\n\n.fa-lg {\n  font-size: 1.25em;\n  line-height: 0.05em;\n  vertical-align: -0.075em;\n}\n\n.fa-xl {\n  font-size: 1.5em;\n  line-height: 0.0416666682em;\n  vertical-align: -0.125em;\n}\n\n.fa-2xl {\n  font-size: 2em;\n  line-height: 0.03125em;\n  vertical-align: -0.1875em;\n}\n\n.fa-fw {\n  text-align: center;\n  width: 1.25em;\n}\n\n.fa-ul {\n  list-style-type: none;\n  margin-left: var(--fa-li-margin, 2.5em);\n  padding-left: 0;\n}\n.fa-ul > li {\n  position: relative;\n}\n\n.fa-li {\n  left: calc(var(--fa-li-width, 2em) * -1);\n  position: absolute;\n  text-align: center;\n  width: var(--fa-li-width, 2em);\n  line-height: inherit;\n}\n\n.fa-border {\n  border-color: var(--fa-border-color, #eee);\n  border-radius: var(--fa-border-radius, 0.1em);\n  border-style: var(--fa-border-style, solid);\n  border-width: var(--fa-border-width, 0.08em);\n  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em);\n}\n\n.fa-pull-left {\n  float: left;\n  margin-right: var(--fa-pull-margin, 0.3em);\n}\n\n.fa-pull-right {\n  float: right;\n  margin-left: var(--fa-pull-margin, 0.3em);\n}\n\n.fa-beat {\n  animation-name: fa-beat;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, ease-in-out);\n}\n\n.fa-bounce {\n  animation-name: fa-bounce;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));\n}\n\n.fa-fade {\n  animation-name: fa-fade;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));\n}\n\n.fa-beat-fade {\n  animation-name: fa-beat-fade;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));\n}\n\n.fa-flip {\n  animation-name: fa-flip;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, ease-in-out);\n}\n\n.fa-shake {\n  animation-name: fa-shake;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, linear);\n}\n\n.fa-spin {\n  animation-name: fa-spin;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 2s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, linear);\n}\n\n.fa-spin-reverse {\n  --fa-animation-direction: reverse;\n}\n\n.fa-pulse,\n.fa-spin-pulse {\n  animation-name: fa-spin;\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, steps(8));\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .fa-beat,\n  .fa-bounce,\n  .fa-fade,\n  .fa-beat-fade,\n  .fa-flip,\n  .fa-pulse,\n  .fa-shake,\n  .fa-spin,\n  .fa-spin-pulse {\n    animation-delay: -1ms;\n    animation-duration: 1ms;\n    animation-iteration-count: 1;\n    transition-delay: 0s;\n    transition-duration: 0s;\n  }\n}\n@keyframes fa-beat {\n  0%, 90% {\n    transform: scale(1);\n  }\n  45% {\n    transform: scale(var(--fa-beat-scale, 1.25));\n  }\n}\n@keyframes fa-bounce {\n  0% {\n    transform: scale(1, 1) translateY(0);\n  }\n  10% {\n    transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);\n  }\n  30% {\n    transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));\n  }\n  50% {\n    transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);\n  }\n  57% {\n    transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));\n  }\n  64% {\n    transform: scale(1, 1) translateY(0);\n  }\n  100% {\n    transform: scale(1, 1) translateY(0);\n  }\n}\n@keyframes fa-fade {\n  50% {\n    opacity: var(--fa-fade-opacity, 0.4);\n  }\n}\n@keyframes fa-beat-fade {\n  0%, 100% {\n    opacity: var(--fa-beat-fade-opacity, 0.4);\n    transform: scale(1);\n  }\n  50% {\n    opacity: 1;\n    transform: scale(var(--fa-beat-fade-scale, 1.125));\n  }\n}\n@keyframes fa-flip {\n  50% {\n    transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));\n  }\n}\n@keyframes fa-shake {\n  0% {\n    transform: rotate(-15deg);\n  }\n  4% {\n    transform: rotate(15deg);\n  }\n  8%, 24% {\n    transform: rotate(-18deg);\n  }\n  12%, 28% {\n    transform: rotate(18deg);\n  }\n  16% {\n    transform: rotate(-22deg);\n  }\n  20% {\n    transform: rotate(22deg);\n  }\n  32% {\n    transform: rotate(-12deg);\n  }\n  36% {\n    transform: rotate(12deg);\n  }\n  40%, 100% {\n    transform: rotate(0deg);\n  }\n}\n@keyframes fa-spin {\n  0% {\n    transform: rotate(0deg);\n  }\n  100% {\n    transform: rotate(360deg);\n  }\n}\n.fa-rotate-90 {\n  transform: rotate(90deg);\n}\n\n.fa-rotate-180 {\n  transform: rotate(180deg);\n}\n\n.fa-rotate-270 {\n  transform: rotate(270deg);\n}\n\n.fa-flip-horizontal {\n  transform: scale(-1, 1);\n}\n\n.fa-flip-vertical {\n  transform: scale(1, -1);\n}\n\n.fa-flip-both,\n.fa-flip-horizontal.fa-flip-vertical {\n  transform: scale(-1, -1);\n}\n\n.fa-rotate-by {\n  transform: rotate(var(--fa-rotate-angle, 0));\n}\n\n.fa-stack {\n  display: inline-block;\n  height: 2em;\n  line-height: 2em;\n  position: relative;\n  vertical-align: middle;\n  width: 2.5em;\n}\n\n.fa-stack-1x,\n.fa-stack-2x {\n  left: 0;\n  position: absolute;\n  text-align: center;\n  width: 100%;\n  z-index: var(--fa-stack-z-index, auto);\n}\n\n.fa-stack-1x {\n  line-height: inherit;\n}\n\n.fa-stack-2x {\n  font-size: 2em;\n}\n\n.fa-inverse {\n  color: var(--fa-inverse, #fff);\n}\n\n/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen\nreaders do not read off random characters that represent icons */\n.fa-0::before {\n  content: \"\\30 \";\n}\n\n.fa-1::before {\n  content: \"\\31 \";\n}\n\n.fa-2::before {\n  content: \"\\32 \";\n}\n\n.fa-3::before {\n  content: \"\\33 \";\n}\n\n.fa-4::before {\n  content: \"\\34 \";\n}\n\n.fa-5::before {\n  content: \"\\35 \";\n}\n\n.fa-6::before {\n  content: \"\\36 \";\n}\n\n.fa-7::before {\n  content: \"\\37 \";\n}\n\n.fa-8::before {\n  content: \"\\38 \";\n}\n\n.fa-9::before {\n  content: \"\\39 \";\n}\n\n.fa-fill-drip::before {\n  content: \"\\f576\";\n}\n\n.fa-arrows-to-circle::before {\n  content: \"\\e4bd\";\n}\n\n.fa-circle-chevron-right::before {\n  content: \"\\f138\";\n}\n\n.fa-chevron-circle-right::before {\n  content: \"\\f138\";\n}\n\n.fa-at::before {\n  content: \"\\@\";\n}\n\n.fa-trash-can::before {\n  content: \"\\f2ed\";\n}\n\n.fa-trash-alt::before {\n  content: \"\\f2ed\";\n}\n\n.fa-text-height::before {\n  content: \"\\f034\";\n}\n\n.fa-user-xmark::before {\n  content: \"\\f235\";\n}\n\n.fa-user-times::before {\n  content: \"\\f235\";\n}\n\n.fa-stethoscope::before {\n  content: \"\\f0f1\";\n}\n\n.fa-message::before {\n  content: \"\\f27a\";\n}\n\n.fa-comment-alt::before {\n  content: \"\\f27a\";\n}\n\n.fa-info::before {\n  content: \"\\f129\";\n}\n\n.fa-down-left-and-up-right-to-center::before {\n  content: \"\\f422\";\n}\n\n.fa-compress-alt::before {\n  content: \"\\f422\";\n}\n\n.fa-explosion::before {\n  content: \"\\e4e9\";\n}\n\n.fa-file-lines::before {\n  content: \"\\f15c\";\n}\n\n.fa-file-alt::before {\n  content: \"\\f15c\";\n}\n\n.fa-file-text::before {\n  content: \"\\f15c\";\n}\n\n.fa-wave-square::before {\n  content: \"\\f83e\";\n}\n\n.fa-ring::before {\n  content: \"\\f70b\";\n}\n\n.fa-building-un::before {\n  content: \"\\e4d9\";\n}\n\n.fa-dice-three::before {\n  content: \"\\f527\";\n}\n\n.fa-calendar-days::before {\n  content: \"\\f073\";\n}\n\n.fa-calendar-alt::before {\n  content: \"\\f073\";\n}\n\n.fa-anchor-circle-check::before {\n  content: \"\\e4aa\";\n}\n\n.fa-building-circle-arrow-right::before {\n  content: \"\\e4d1\";\n}\n\n.fa-volleyball::before {\n  content: \"\\f45f\";\n}\n\n.fa-volleyball-ball::before {\n  content: \"\\f45f\";\n}\n\n.fa-arrows-up-to-line::before {\n  content: \"\\e4c2\";\n}\n\n.fa-sort-down::before {\n  content: \"\\f0dd\";\n}\n\n.fa-sort-desc::before {\n  content: \"\\f0dd\";\n}\n\n.fa-circle-minus::before {\n  content: \"\\f056\";\n}\n\n.fa-minus-circle::before {\n  content: \"\\f056\";\n}\n\n.fa-door-open::before {\n  content: \"\\f52b\";\n}\n\n.fa-right-from-bracket::before {\n  content: \"\\f2f5\";\n}\n\n.fa-sign-out-alt::before {\n  content: \"\\f2f5\";\n}\n\n.fa-atom::before {\n  content: \"\\f5d2\";\n}\n\n.fa-soap::before {\n  content: \"\\e06e\";\n}\n\n.fa-icons::before {\n  content: \"\\f86d\";\n}\n\n.fa-heart-music-camera-bolt::before {\n  content: \"\\f86d\";\n}\n\n.fa-microphone-lines-slash::before {\n  content: \"\\f539\";\n}\n\n.fa-microphone-alt-slash::before {\n  content: \"\\f539\";\n}\n\n.fa-bridge-circle-check::before {\n  content: \"\\e4c9\";\n}\n\n.fa-pump-medical::before {\n  content: \"\\e06a\";\n}\n\n.fa-fingerprint::before {\n  content: \"\\f577\";\n}\n\n.fa-hand-point-right::before {\n  content: \"\\f0a4\";\n}\n\n.fa-magnifying-glass-location::before {\n  content: \"\\f689\";\n}\n\n.fa-search-location::before {\n  content: \"\\f689\";\n}\n\n.fa-forward-step::before {\n  content: \"\\f051\";\n}\n\n.fa-step-forward::before {\n  content: \"\\f051\";\n}\n\n.fa-face-smile-beam::before {\n  content: \"\\f5b8\";\n}\n\n.fa-smile-beam::before {\n  content: \"\\f5b8\";\n}\n\n.fa-flag-checkered::before {\n  content: \"\\f11e\";\n}\n\n.fa-football::before {\n  content: \"\\f44e\";\n}\n\n.fa-football-ball::before {\n  content: \"\\f44e\";\n}\n\n.fa-school-circle-exclamation::before {\n  content: \"\\e56c\";\n}\n\n.fa-crop::before {\n  content: \"\\f125\";\n}\n\n.fa-angles-down::before {\n  content: \"\\f103\";\n}\n\n.fa-angle-double-down::before {\n  content: \"\\f103\";\n}\n\n.fa-users-rectangle::before {\n  content: \"\\e594\";\n}\n\n.fa-people-roof::before {\n  content: \"\\e537\";\n}\n\n.fa-people-line::before {\n  content: \"\\e534\";\n}\n\n.fa-beer-mug-empty::before {\n  content: \"\\f0fc\";\n}\n\n.fa-beer::before {\n  content: \"\\f0fc\";\n}\n\n.fa-diagram-predecessor::before {\n  content: \"\\e477\";\n}\n\n.fa-arrow-up-long::before {\n  content: \"\\f176\";\n}\n\n.fa-long-arrow-up::before {\n  content: \"\\f176\";\n}\n\n.fa-fire-flame-simple::before {\n  content: \"\\f46a\";\n}\n\n.fa-burn::before {\n  content: \"\\f46a\";\n}\n\n.fa-person::before {\n  content: \"\\f183\";\n}\n\n.fa-male::before {\n  content: \"\\f183\";\n}\n\n.fa-laptop::before {\n  content: \"\\f109\";\n}\n\n.fa-file-csv::before {\n  content: \"\\f6dd\";\n}\n\n.fa-menorah::before {\n  content: \"\\f676\";\n}\n\n.fa-truck-plane::before {\n  content: \"\\e58f\";\n}\n\n.fa-record-vinyl::before {\n  content: \"\\f8d9\";\n}\n\n.fa-face-grin-stars::before {\n  content: \"\\f587\";\n}\n\n.fa-grin-stars::before {\n  content: \"\\f587\";\n}\n\n.fa-bong::before {\n  content: \"\\f55c\";\n}\n\n.fa-spaghetti-monster-flying::before {\n  content: \"\\f67b\";\n}\n\n.fa-pastafarianism::before {\n  content: \"\\f67b\";\n}\n\n.fa-arrow-down-up-across-line::before {\n  content: \"\\e4af\";\n}\n\n.fa-spoon::before {\n  content: \"\\f2e5\";\n}\n\n.fa-utensil-spoon::before {\n  content: \"\\f2e5\";\n}\n\n.fa-jar-wheat::before {\n  content: \"\\e517\";\n}\n\n.fa-envelopes-bulk::before {\n  content: \"\\f674\";\n}\n\n.fa-mail-bulk::before {\n  content: \"\\f674\";\n}\n\n.fa-file-circle-exclamation::before {\n  content: \"\\e4eb\";\n}\n\n.fa-circle-h::before {\n  content: \"\\f47e\";\n}\n\n.fa-hospital-symbol::before {\n  content: \"\\f47e\";\n}\n\n.fa-pager::before {\n  content: \"\\f815\";\n}\n\n.fa-address-book::before {\n  content: \"\\f2b9\";\n}\n\n.fa-contact-book::before {\n  content: \"\\f2b9\";\n}\n\n.fa-strikethrough::before {\n  content: \"\\f0cc\";\n}\n\n.fa-k::before {\n  content: \"K\";\n}\n\n.fa-landmark-flag::before {\n  content: \"\\e51c\";\n}\n\n.fa-pencil::before {\n  content: \"\\f303\";\n}\n\n.fa-pencil-alt::before {\n  content: \"\\f303\";\n}\n\n.fa-backward::before {\n  content: \"\\f04a\";\n}\n\n.fa-caret-right::before {\n  content: \"\\f0da\";\n}\n\n.fa-comments::before {\n  content: \"\\f086\";\n}\n\n.fa-paste::before {\n  content: \"\\f0ea\";\n}\n\n.fa-file-clipboard::before {\n  content: \"\\f0ea\";\n}\n\n.fa-code-pull-request::before {\n  content: \"\\e13c\";\n}\n\n.fa-clipboard-list::before {\n  content: \"\\f46d\";\n}\n\n.fa-truck-ramp-box::before {\n  content: \"\\f4de\";\n}\n\n.fa-truck-loading::before {\n  content: \"\\f4de\";\n}\n\n.fa-user-check::before {\n  content: \"\\f4fc\";\n}\n\n.fa-vial-virus::before {\n  content: \"\\e597\";\n}\n\n.fa-sheet-plastic::before {\n  content: \"\\e571\";\n}\n\n.fa-blog::before {\n  content: \"\\f781\";\n}\n\n.fa-user-ninja::before {\n  content: \"\\f504\";\n}\n\n.fa-person-arrow-up-from-line::before {\n  content: \"\\e539\";\n}\n\n.fa-scroll-torah::before {\n  content: \"\\f6a0\";\n}\n\n.fa-torah::before {\n  content: \"\\f6a0\";\n}\n\n.fa-broom-ball::before {\n  content: \"\\f458\";\n}\n\n.fa-quidditch::before {\n  content: \"\\f458\";\n}\n\n.fa-quidditch-broom-ball::before {\n  content: \"\\f458\";\n}\n\n.fa-toggle-off::before {\n  content: \"\\f204\";\n}\n\n.fa-box-archive::before {\n  content: \"\\f187\";\n}\n\n.fa-archive::before {\n  content: \"\\f187\";\n}\n\n.fa-person-drowning::before {\n  content: \"\\e545\";\n}\n\n.fa-arrow-down-9-1::before {\n  content: \"\\f886\";\n}\n\n.fa-sort-numeric-desc::before {\n  content: \"\\f886\";\n}\n\n.fa-sort-numeric-down-alt::before {\n  content: \"\\f886\";\n}\n\n.fa-face-grin-tongue-squint::before {\n  content: \"\\f58a\";\n}\n\n.fa-grin-tongue-squint::before {\n  content: \"\\f58a\";\n}\n\n.fa-spray-can::before {\n  content: \"\\f5bd\";\n}\n\n.fa-truck-monster::before {\n  content: \"\\f63b\";\n}\n\n.fa-w::before {\n  content: \"W\";\n}\n\n.fa-earth-africa::before {\n  content: \"\\f57c\";\n}\n\n.fa-globe-africa::before {\n  content: \"\\f57c\";\n}\n\n.fa-rainbow::before {\n  content: \"\\f75b\";\n}\n\n.fa-circle-notch::before {\n  content: \"\\f1ce\";\n}\n\n.fa-tablet-screen-button::before {\n  content: \"\\f3fa\";\n}\n\n.fa-tablet-alt::before {\n  content: \"\\f3fa\";\n}\n\n.fa-paw::before {\n  content: \"\\f1b0\";\n}\n\n.fa-cloud::before {\n  content: \"\\f0c2\";\n}\n\n.fa-trowel-bricks::before {\n  content: \"\\e58a\";\n}\n\n.fa-face-flushed::before {\n  content: \"\\f579\";\n}\n\n.fa-flushed::before {\n  content: \"\\f579\";\n}\n\n.fa-hospital-user::before {\n  content: \"\\f80d\";\n}\n\n.fa-tent-arrow-left-right::before {\n  content: \"\\e57f\";\n}\n\n.fa-gavel::before {\n  content: \"\\f0e3\";\n}\n\n.fa-legal::before {\n  content: \"\\f0e3\";\n}\n\n.fa-binoculars::before {\n  content: \"\\f1e5\";\n}\n\n.fa-microphone-slash::before {\n  content: \"\\f131\";\n}\n\n.fa-box-tissue::before {\n  content: \"\\e05b\";\n}\n\n.fa-motorcycle::before {\n  content: \"\\f21c\";\n}\n\n.fa-bell-concierge::before {\n  content: \"\\f562\";\n}\n\n.fa-concierge-bell::before {\n  content: \"\\f562\";\n}\n\n.fa-pen-ruler::before {\n  content: \"\\f5ae\";\n}\n\n.fa-pencil-ruler::before {\n  content: \"\\f5ae\";\n}\n\n.fa-people-arrows::before {\n  content: \"\\e068\";\n}\n\n.fa-people-arrows-left-right::before {\n  content: \"\\e068\";\n}\n\n.fa-mars-and-venus-burst::before {\n  content: \"\\e523\";\n}\n\n.fa-square-caret-right::before {\n  content: \"\\f152\";\n}\n\n.fa-caret-square-right::before {\n  content: \"\\f152\";\n}\n\n.fa-scissors::before {\n  content: \"\\f0c4\";\n}\n\n.fa-cut::before {\n  content: \"\\f0c4\";\n}\n\n.fa-sun-plant-wilt::before {\n  content: \"\\e57a\";\n}\n\n.fa-toilets-portable::before {\n  content: \"\\e584\";\n}\n\n.fa-hockey-puck::before {\n  content: \"\\f453\";\n}\n\n.fa-table::before {\n  content: \"\\f0ce\";\n}\n\n.fa-magnifying-glass-arrow-right::before {\n  content: \"\\e521\";\n}\n\n.fa-tachograph-digital::before {\n  content: \"\\f566\";\n}\n\n.fa-digital-tachograph::before {\n  content: \"\\f566\";\n}\n\n.fa-users-slash::before {\n  content: \"\\e073\";\n}\n\n.fa-clover::before {\n  content: \"\\e139\";\n}\n\n.fa-reply::before {\n  content: \"\\f3e5\";\n}\n\n.fa-mail-reply::before {\n  content: \"\\f3e5\";\n}\n\n.fa-star-and-crescent::before {\n  content: \"\\f699\";\n}\n\n.fa-house-fire::before {\n  content: \"\\e50c\";\n}\n\n.fa-square-minus::before {\n  content: \"\\f146\";\n}\n\n.fa-minus-square::before {\n  content: \"\\f146\";\n}\n\n.fa-helicopter::before {\n  content: \"\\f533\";\n}\n\n.fa-compass::before {\n  content: \"\\f14e\";\n}\n\n.fa-square-caret-down::before {\n  content: \"\\f150\";\n}\n\n.fa-caret-square-down::before {\n  content: \"\\f150\";\n}\n\n.fa-file-circle-question::before {\n  content: \"\\e4ef\";\n}\n\n.fa-laptop-code::before {\n  content: \"\\f5fc\";\n}\n\n.fa-swatchbook::before {\n  content: \"\\f5c3\";\n}\n\n.fa-prescription-bottle::before {\n  content: \"\\f485\";\n}\n\n.fa-bars::before {\n  content: \"\\f0c9\";\n}\n\n.fa-navicon::before {\n  content: \"\\f0c9\";\n}\n\n.fa-people-group::before {\n  content: \"\\e533\";\n}\n\n.fa-hourglass-end::before {\n  content: \"\\f253\";\n}\n\n.fa-hourglass-3::before {\n  content: \"\\f253\";\n}\n\n.fa-heart-crack::before {\n  content: \"\\f7a9\";\n}\n\n.fa-heart-broken::before {\n  content: \"\\f7a9\";\n}\n\n.fa-square-up-right::before {\n  content: \"\\f360\";\n}\n\n.fa-external-link-square-alt::before {\n  content: \"\\f360\";\n}\n\n.fa-face-kiss-beam::before {\n  content: \"\\f597\";\n}\n\n.fa-kiss-beam::before {\n  content: \"\\f597\";\n}\n\n.fa-film::before {\n  content: \"\\f008\";\n}\n\n.fa-ruler-horizontal::before {\n  content: \"\\f547\";\n}\n\n.fa-people-robbery::before {\n  content: \"\\e536\";\n}\n\n.fa-lightbulb::before {\n  content: \"\\f0eb\";\n}\n\n.fa-caret-left::before {\n  content: \"\\f0d9\";\n}\n\n.fa-circle-exclamation::before {\n  content: \"\\f06a\";\n}\n\n.fa-exclamation-circle::before {\n  content: \"\\f06a\";\n}\n\n.fa-school-circle-xmark::before {\n  content: \"\\e56d\";\n}\n\n.fa-arrow-right-from-bracket::before {\n  content: \"\\f08b\";\n}\n\n.fa-sign-out::before {\n  content: \"\\f08b\";\n}\n\n.fa-circle-chevron-down::before {\n  content: \"\\f13a\";\n}\n\n.fa-chevron-circle-down::before {\n  content: \"\\f13a\";\n}\n\n.fa-unlock-keyhole::before {\n  content: \"\\f13e\";\n}\n\n.fa-unlock-alt::before {\n  content: \"\\f13e\";\n}\n\n.fa-cloud-showers-heavy::before {\n  content: \"\\f740\";\n}\n\n.fa-headphones-simple::before {\n  content: \"\\f58f\";\n}\n\n.fa-headphones-alt::before {\n  content: \"\\f58f\";\n}\n\n.fa-sitemap::before {\n  content: \"\\f0e8\";\n}\n\n.fa-circle-dollar-to-slot::before {\n  content: \"\\f4b9\";\n}\n\n.fa-donate::before {\n  content: \"\\f4b9\";\n}\n\n.fa-memory::before {\n  content: \"\\f538\";\n}\n\n.fa-road-spikes::before {\n  content: \"\\e568\";\n}\n\n.fa-fire-burner::before {\n  content: \"\\e4f1\";\n}\n\n.fa-flag::before {\n  content: \"\\f024\";\n}\n\n.fa-hanukiah::before {\n  content: \"\\f6e6\";\n}\n\n.fa-feather::before {\n  content: \"\\f52d\";\n}\n\n.fa-volume-low::before {\n  content: \"\\f027\";\n}\n\n.fa-volume-down::before {\n  content: \"\\f027\";\n}\n\n.fa-comment-slash::before {\n  content: \"\\f4b3\";\n}\n\n.fa-cloud-sun-rain::before {\n  content: \"\\f743\";\n}\n\n.fa-compress::before {\n  content: \"\\f066\";\n}\n\n.fa-wheat-awn::before {\n  content: \"\\e2cd\";\n}\n\n.fa-wheat-alt::before {\n  content: \"\\e2cd\";\n}\n\n.fa-ankh::before {\n  content: \"\\f644\";\n}\n\n.fa-hands-holding-child::before {\n  content: \"\\e4fa\";\n}\n\n.fa-asterisk::before {\n  content: \"\\*\";\n}\n\n.fa-square-check::before {\n  content: \"\\f14a\";\n}\n\n.fa-check-square::before {\n  content: \"\\f14a\";\n}\n\n.fa-peseta-sign::before {\n  content: \"\\e221\";\n}\n\n.fa-heading::before {\n  content: \"\\f1dc\";\n}\n\n.fa-header::before {\n  content: \"\\f1dc\";\n}\n\n.fa-ghost::before {\n  content: \"\\f6e2\";\n}\n\n.fa-list::before {\n  content: \"\\f03a\";\n}\n\n.fa-list-squares::before {\n  content: \"\\f03a\";\n}\n\n.fa-square-phone-flip::before {\n  content: \"\\f87b\";\n}\n\n.fa-phone-square-alt::before {\n  content: \"\\f87b\";\n}\n\n.fa-cart-plus::before {\n  content: \"\\f217\";\n}\n\n.fa-gamepad::before {\n  content: \"\\f11b\";\n}\n\n.fa-circle-dot::before {\n  content: \"\\f192\";\n}\n\n.fa-dot-circle::before {\n  content: \"\\f192\";\n}\n\n.fa-face-dizzy::before {\n  content: \"\\f567\";\n}\n\n.fa-dizzy::before {\n  content: \"\\f567\";\n}\n\n.fa-egg::before {\n  content: \"\\f7fb\";\n}\n\n.fa-house-medical-circle-xmark::before {\n  content: \"\\e513\";\n}\n\n.fa-campground::before {\n  content: \"\\f6bb\";\n}\n\n.fa-folder-plus::before {\n  content: \"\\f65e\";\n}\n\n.fa-futbol::before {\n  content: \"\\f1e3\";\n}\n\n.fa-futbol-ball::before {\n  content: \"\\f1e3\";\n}\n\n.fa-soccer-ball::before {\n  content: \"\\f1e3\";\n}\n\n.fa-paintbrush::before {\n  content: \"\\f1fc\";\n}\n\n.fa-paint-brush::before {\n  content: \"\\f1fc\";\n}\n\n.fa-lock::before {\n  content: \"\\f023\";\n}\n\n.fa-gas-pump::before {\n  content: \"\\f52f\";\n}\n\n.fa-hot-tub-person::before {\n  content: \"\\f593\";\n}\n\n.fa-hot-tub::before {\n  content: \"\\f593\";\n}\n\n.fa-map-location::before {\n  content: \"\\f59f\";\n}\n\n.fa-map-marked::before {\n  content: \"\\f59f\";\n}\n\n.fa-house-flood-water::before {\n  content: \"\\e50e\";\n}\n\n.fa-tree::before {\n  content: \"\\f1bb\";\n}\n\n.fa-bridge-lock::before {\n  content: \"\\e4cc\";\n}\n\n.fa-sack-dollar::before {\n  content: \"\\f81d\";\n}\n\n.fa-pen-to-square::before {\n  content: \"\\f044\";\n}\n\n.fa-edit::before {\n  content: \"\\f044\";\n}\n\n.fa-car-side::before {\n  content: \"\\f5e4\";\n}\n\n.fa-share-nodes::before {\n  content: \"\\f1e0\";\n}\n\n.fa-share-alt::before {\n  content: \"\\f1e0\";\n}\n\n.fa-heart-circle-minus::before {\n  content: \"\\e4ff\";\n}\n\n.fa-hourglass-half::before {\n  content: \"\\f252\";\n}\n\n.fa-hourglass-2::before {\n  content: \"\\f252\";\n}\n\n.fa-microscope::before {\n  content: \"\\f610\";\n}\n\n.fa-sink::before {\n  content: \"\\e06d\";\n}\n\n.fa-bag-shopping::before {\n  content: \"\\f290\";\n}\n\n.fa-shopping-bag::before {\n  content: \"\\f290\";\n}\n\n.fa-arrow-down-z-a::before {\n  content: \"\\f881\";\n}\n\n.fa-sort-alpha-desc::before {\n  content: \"\\f881\";\n}\n\n.fa-sort-alpha-down-alt::before {\n  content: \"\\f881\";\n}\n\n.fa-mitten::before {\n  content: \"\\f7b5\";\n}\n\n.fa-person-rays::before {\n  content: \"\\e54d\";\n}\n\n.fa-users::before {\n  content: \"\\f0c0\";\n}\n\n.fa-eye-slash::before {\n  content: \"\\f070\";\n}\n\n.fa-flask-vial::before {\n  content: \"\\e4f3\";\n}\n\n.fa-hand::before {\n  content: \"\\f256\";\n}\n\n.fa-hand-paper::before {\n  content: \"\\f256\";\n}\n\n.fa-om::before {\n  content: \"\\f679\";\n}\n\n.fa-worm::before {\n  content: \"\\e599\";\n}\n\n.fa-house-circle-xmark::before {\n  content: \"\\e50b\";\n}\n\n.fa-plug::before {\n  content: \"\\f1e6\";\n}\n\n.fa-chevron-up::before {\n  content: \"\\f077\";\n}\n\n.fa-hand-spock::before {\n  content: \"\\f259\";\n}\n\n.fa-stopwatch::before {\n  content: \"\\f2f2\";\n}\n\n.fa-face-kiss::before {\n  content: \"\\f596\";\n}\n\n.fa-kiss::before {\n  content: \"\\f596\";\n}\n\n.fa-bridge-circle-xmark::before {\n  content: \"\\e4cb\";\n}\n\n.fa-face-grin-tongue::before {\n  content: \"\\f589\";\n}\n\n.fa-grin-tongue::before {\n  content: \"\\f589\";\n}\n\n.fa-chess-bishop::before {\n  content: \"\\f43a\";\n}\n\n.fa-face-grin-wink::before {\n  content: \"\\f58c\";\n}\n\n.fa-grin-wink::before {\n  content: \"\\f58c\";\n}\n\n.fa-ear-deaf::before {\n  content: \"\\f2a4\";\n}\n\n.fa-deaf::before {\n  content: \"\\f2a4\";\n}\n\n.fa-deafness::before {\n  content: \"\\f2a4\";\n}\n\n.fa-hard-of-hearing::before {\n  content: \"\\f2a4\";\n}\n\n.fa-road-circle-check::before {\n  content: \"\\e564\";\n}\n\n.fa-dice-five::before {\n  content: \"\\f523\";\n}\n\n.fa-square-rss::before {\n  content: \"\\f143\";\n}\n\n.fa-rss-square::before {\n  content: \"\\f143\";\n}\n\n.fa-land-mine-on::before {\n  content: \"\\e51b\";\n}\n\n.fa-i-cursor::before {\n  content: \"\\f246\";\n}\n\n.fa-stamp::before {\n  content: \"\\f5bf\";\n}\n\n.fa-stairs::before {\n  content: \"\\e289\";\n}\n\n.fa-i::before {\n  content: \"I\";\n}\n\n.fa-hryvnia-sign::before {\n  content: \"\\f6f2\";\n}\n\n.fa-hryvnia::before {\n  content: \"\\f6f2\";\n}\n\n.fa-pills::before {\n  content: \"\\f484\";\n}\n\n.fa-face-grin-wide::before {\n  content: \"\\f581\";\n}\n\n.fa-grin-alt::before {\n  content: \"\\f581\";\n}\n\n.fa-tooth::before {\n  content: \"\\f5c9\";\n}\n\n.fa-v::before {\n  content: \"V\";\n}\n\n.fa-bangladeshi-taka-sign::before {\n  content: \"\\e2e6\";\n}\n\n.fa-bicycle::before {\n  content: \"\\f206\";\n}\n\n.fa-staff-snake::before {\n  content: \"\\e579\";\n}\n\n.fa-rod-asclepius::before {\n  content: \"\\e579\";\n}\n\n.fa-rod-snake::before {\n  content: \"\\e579\";\n}\n\n.fa-staff-aesculapius::before {\n  content: \"\\e579\";\n}\n\n.fa-head-side-cough-slash::before {\n  content: \"\\e062\";\n}\n\n.fa-truck-medical::before {\n  content: \"\\f0f9\";\n}\n\n.fa-ambulance::before {\n  content: \"\\f0f9\";\n}\n\n.fa-wheat-awn-circle-exclamation::before {\n  content: \"\\e598\";\n}\n\n.fa-snowman::before {\n  content: \"\\f7d0\";\n}\n\n.fa-mortar-pestle::before {\n  content: \"\\f5a7\";\n}\n\n.fa-road-barrier::before {\n  content: \"\\e562\";\n}\n\n.fa-school::before {\n  content: \"\\f549\";\n}\n\n.fa-igloo::before {\n  content: \"\\f7ae\";\n}\n\n.fa-joint::before {\n  content: \"\\f595\";\n}\n\n.fa-angle-right::before {\n  content: \"\\f105\";\n}\n\n.fa-horse::before {\n  content: \"\\f6f0\";\n}\n\n.fa-q::before {\n  content: \"Q\";\n}\n\n.fa-g::before {\n  content: \"G\";\n}\n\n.fa-notes-medical::before {\n  content: \"\\f481\";\n}\n\n.fa-temperature-half::before {\n  content: \"\\f2c9\";\n}\n\n.fa-temperature-2::before {\n  content: \"\\f2c9\";\n}\n\n.fa-thermometer-2::before {\n  content: \"\\f2c9\";\n}\n\n.fa-thermometer-half::before {\n  content: \"\\f2c9\";\n}\n\n.fa-dong-sign::before {\n  content: \"\\e169\";\n}\n\n.fa-capsules::before {\n  content: \"\\f46b\";\n}\n\n.fa-poo-storm::before {\n  content: \"\\f75a\";\n}\n\n.fa-poo-bolt::before {\n  content: \"\\f75a\";\n}\n\n.fa-face-frown-open::before {\n  content: \"\\f57a\";\n}\n\n.fa-frown-open::before {\n  content: \"\\f57a\";\n}\n\n.fa-hand-point-up::before {\n  content: \"\\f0a6\";\n}\n\n.fa-money-bill::before {\n  content: \"\\f0d6\";\n}\n\n.fa-bookmark::before {\n  content: \"\\f02e\";\n}\n\n.fa-align-justify::before {\n  content: \"\\f039\";\n}\n\n.fa-umbrella-beach::before {\n  content: \"\\f5ca\";\n}\n\n.fa-helmet-un::before {\n  content: \"\\e503\";\n}\n\n.fa-bullseye::before {\n  content: \"\\f140\";\n}\n\n.fa-bacon::before {\n  content: \"\\f7e5\";\n}\n\n.fa-hand-point-down::before {\n  content: \"\\f0a7\";\n}\n\n.fa-arrow-up-from-bracket::before {\n  content: \"\\e09a\";\n}\n\n.fa-folder::before {\n  content: \"\\f07b\";\n}\n\n.fa-folder-blank::before {\n  content: \"\\f07b\";\n}\n\n.fa-file-waveform::before {\n  content: \"\\f478\";\n}\n\n.fa-file-medical-alt::before {\n  content: \"\\f478\";\n}\n\n.fa-radiation::before {\n  content: \"\\f7b9\";\n}\n\n.fa-chart-simple::before {\n  content: \"\\e473\";\n}\n\n.fa-mars-stroke::before {\n  content: \"\\f229\";\n}\n\n.fa-vial::before {\n  content: \"\\f492\";\n}\n\n.fa-gauge::before {\n  content: \"\\f624\";\n}\n\n.fa-dashboard::before {\n  content: \"\\f624\";\n}\n\n.fa-gauge-med::before {\n  content: \"\\f624\";\n}\n\n.fa-tachometer-alt-average::before {\n  content: \"\\f624\";\n}\n\n.fa-wand-magic-sparkles::before {\n  content: \"\\e2ca\";\n}\n\n.fa-magic-wand-sparkles::before {\n  content: \"\\e2ca\";\n}\n\n.fa-e::before {\n  content: \"E\";\n}\n\n.fa-pen-clip::before {\n  content: \"\\f305\";\n}\n\n.fa-pen-alt::before {\n  content: \"\\f305\";\n}\n\n.fa-bridge-circle-exclamation::before {\n  content: \"\\e4ca\";\n}\n\n.fa-user::before {\n  content: \"\\f007\";\n}\n\n.fa-school-circle-check::before {\n  content: \"\\e56b\";\n}\n\n.fa-dumpster::before {\n  content: \"\\f793\";\n}\n\n.fa-van-shuttle::before {\n  content: \"\\f5b6\";\n}\n\n.fa-shuttle-van::before {\n  content: \"\\f5b6\";\n}\n\n.fa-building-user::before {\n  content: \"\\e4da\";\n}\n\n.fa-square-caret-left::before {\n  content: \"\\f191\";\n}\n\n.fa-caret-square-left::before {\n  content: \"\\f191\";\n}\n\n.fa-highlighter::before {\n  content: \"\\f591\";\n}\n\n.fa-key::before {\n  content: \"\\f084\";\n}\n\n.fa-bullhorn::before {\n  content: \"\\f0a1\";\n}\n\n.fa-globe::before {\n  content: \"\\f0ac\";\n}\n\n.fa-synagogue::before {\n  content: \"\\f69b\";\n}\n\n.fa-person-half-dress::before {\n  content: \"\\e548\";\n}\n\n.fa-road-bridge::before {\n  content: \"\\e563\";\n}\n\n.fa-location-arrow::before {\n  content: \"\\f124\";\n}\n\n.fa-c::before {\n  content: \"C\";\n}\n\n.fa-tablet-button::before {\n  content: \"\\f10a\";\n}\n\n.fa-building-lock::before {\n  content: \"\\e4d6\";\n}\n\n.fa-pizza-slice::before {\n  content: \"\\f818\";\n}\n\n.fa-money-bill-wave::before {\n  content: \"\\f53a\";\n}\n\n.fa-chart-area::before {\n  content: \"\\f1fe\";\n}\n\n.fa-area-chart::before {\n  content: \"\\f1fe\";\n}\n\n.fa-house-flag::before {\n  content: \"\\e50d\";\n}\n\n.fa-person-circle-minus::before {\n  content: \"\\e540\";\n}\n\n.fa-ban::before {\n  content: \"\\f05e\";\n}\n\n.fa-cancel::before {\n  content: \"\\f05e\";\n}\n\n.fa-camera-rotate::before {\n  content: \"\\e0d8\";\n}\n\n.fa-spray-can-sparkles::before {\n  content: \"\\f5d0\";\n}\n\n.fa-air-freshener::before {\n  content: \"\\f5d0\";\n}\n\n.fa-star::before {\n  content: \"\\f005\";\n}\n\n.fa-repeat::before {\n  content: \"\\f363\";\n}\n\n.fa-cross::before {\n  content: \"\\f654\";\n}\n\n.fa-box::before {\n  content: \"\\f466\";\n}\n\n.fa-venus-mars::before {\n  content: \"\\f228\";\n}\n\n.fa-arrow-pointer::before {\n  content: \"\\f245\";\n}\n\n.fa-mouse-pointer::before {\n  content: \"\\f245\";\n}\n\n.fa-maximize::before {\n  content: \"\\f31e\";\n}\n\n.fa-expand-arrows-alt::before {\n  content: \"\\f31e\";\n}\n\n.fa-charging-station::before {\n  content: \"\\f5e7\";\n}\n\n.fa-shapes::before {\n  content: \"\\f61f\";\n}\n\n.fa-triangle-circle-square::before {\n  content: \"\\f61f\";\n}\n\n.fa-shuffle::before {\n  content: \"\\f074\";\n}\n\n.fa-random::before {\n  content: \"\\f074\";\n}\n\n.fa-person-running::before {\n  content: \"\\f70c\";\n}\n\n.fa-running::before {\n  content: \"\\f70c\";\n}\n\n.fa-mobile-retro::before {\n  content: \"\\e527\";\n}\n\n.fa-grip-lines-vertical::before {\n  content: \"\\f7a5\";\n}\n\n.fa-spider::before {\n  content: \"\\f717\";\n}\n\n.fa-hands-bound::before {\n  content: \"\\e4f9\";\n}\n\n.fa-file-invoice-dollar::before {\n  content: \"\\f571\";\n}\n\n.fa-plane-circle-exclamation::before {\n  content: \"\\e556\";\n}\n\n.fa-x-ray::before {\n  content: \"\\f497\";\n}\n\n.fa-spell-check::before {\n  content: \"\\f891\";\n}\n\n.fa-slash::before {\n  content: \"\\f715\";\n}\n\n.fa-computer-mouse::before {\n  content: \"\\f8cc\";\n}\n\n.fa-mouse::before {\n  content: \"\\f8cc\";\n}\n\n.fa-arrow-right-to-bracket::before {\n  content: \"\\f090\";\n}\n\n.fa-sign-in::before {\n  content: \"\\f090\";\n}\n\n.fa-shop-slash::before {\n  content: \"\\e070\";\n}\n\n.fa-store-alt-slash::before {\n  content: \"\\e070\";\n}\n\n.fa-server::before {\n  content: \"\\f233\";\n}\n\n.fa-virus-covid-slash::before {\n  content: \"\\e4a9\";\n}\n\n.fa-shop-lock::before {\n  content: \"\\e4a5\";\n}\n\n.fa-hourglass-start::before {\n  content: \"\\f251\";\n}\n\n.fa-hourglass-1::before {\n  content: \"\\f251\";\n}\n\n.fa-blender-phone::before {\n  content: \"\\f6b6\";\n}\n\n.fa-building-wheat::before {\n  content: \"\\e4db\";\n}\n\n.fa-person-breastfeeding::before {\n  content: \"\\e53a\";\n}\n\n.fa-right-to-bracket::before {\n  content: \"\\f2f6\";\n}\n\n.fa-sign-in-alt::before {\n  content: \"\\f2f6\";\n}\n\n.fa-venus::before {\n  content: \"\\f221\";\n}\n\n.fa-passport::before {\n  content: \"\\f5ab\";\n}\n\n.fa-heart-pulse::before {\n  content: \"\\f21e\";\n}\n\n.fa-heartbeat::before {\n  content: \"\\f21e\";\n}\n\n.fa-people-carry-box::before {\n  content: \"\\f4ce\";\n}\n\n.fa-people-carry::before {\n  content: \"\\f4ce\";\n}\n\n.fa-temperature-high::before {\n  content: \"\\f769\";\n}\n\n.fa-microchip::before {\n  content: \"\\f2db\";\n}\n\n.fa-crown::before {\n  content: \"\\f521\";\n}\n\n.fa-weight-hanging::before {\n  content: \"\\f5cd\";\n}\n\n.fa-xmarks-lines::before {\n  content: \"\\e59a\";\n}\n\n.fa-file-prescription::before {\n  content: \"\\f572\";\n}\n\n.fa-weight-scale::before {\n  content: \"\\f496\";\n}\n\n.fa-weight::before {\n  content: \"\\f496\";\n}\n\n.fa-user-group::before {\n  content: \"\\f500\";\n}\n\n.fa-user-friends::before {\n  content: \"\\f500\";\n}\n\n.fa-arrow-up-a-z::before {\n  content: \"\\f15e\";\n}\n\n.fa-sort-alpha-up::before {\n  content: \"\\f15e\";\n}\n\n.fa-chess-knight::before {\n  content: \"\\f441\";\n}\n\n.fa-face-laugh-squint::before {\n  content: \"\\f59b\";\n}\n\n.fa-laugh-squint::before {\n  content: \"\\f59b\";\n}\n\n.fa-wheelchair::before {\n  content: \"\\f193\";\n}\n\n.fa-circle-arrow-up::before {\n  content: \"\\f0aa\";\n}\n\n.fa-arrow-circle-up::before {\n  content: \"\\f0aa\";\n}\n\n.fa-toggle-on::before {\n  content: \"\\f205\";\n}\n\n.fa-person-walking::before {\n  content: \"\\f554\";\n}\n\n.fa-walking::before {\n  content: \"\\f554\";\n}\n\n.fa-l::before {\n  content: \"L\";\n}\n\n.fa-fire::before {\n  content: \"\\f06d\";\n}\n\n.fa-bed-pulse::before {\n  content: \"\\f487\";\n}\n\n.fa-procedures::before {\n  content: \"\\f487\";\n}\n\n.fa-shuttle-space::before {\n  content: \"\\f197\";\n}\n\n.fa-space-shuttle::before {\n  content: \"\\f197\";\n}\n\n.fa-face-laugh::before {\n  content: \"\\f599\";\n}\n\n.fa-laugh::before {\n  content: \"\\f599\";\n}\n\n.fa-folder-open::before {\n  content: \"\\f07c\";\n}\n\n.fa-heart-circle-plus::before {\n  content: \"\\e500\";\n}\n\n.fa-code-fork::before {\n  content: \"\\e13b\";\n}\n\n.fa-city::before {\n  content: \"\\f64f\";\n}\n\n.fa-microphone-lines::before {\n  content: \"\\f3c9\";\n}\n\n.fa-microphone-alt::before {\n  content: \"\\f3c9\";\n}\n\n.fa-pepper-hot::before {\n  content: \"\\f816\";\n}\n\n.fa-unlock::before {\n  content: \"\\f09c\";\n}\n\n.fa-colon-sign::before {\n  content: \"\\e140\";\n}\n\n.fa-headset::before {\n  content: \"\\f590\";\n}\n\n.fa-store-slash::before {\n  content: \"\\e071\";\n}\n\n.fa-road-circle-xmark::before {\n  content: \"\\e566\";\n}\n\n.fa-user-minus::before {\n  content: \"\\f503\";\n}\n\n.fa-mars-stroke-up::before {\n  content: \"\\f22a\";\n}\n\n.fa-mars-stroke-v::before {\n  content: \"\\f22a\";\n}\n\n.fa-champagne-glasses::before {\n  content: \"\\f79f\";\n}\n\n.fa-glass-cheers::before {\n  content: \"\\f79f\";\n}\n\n.fa-clipboard::before {\n  content: \"\\f328\";\n}\n\n.fa-house-circle-exclamation::before {\n  content: \"\\e50a\";\n}\n\n.fa-file-arrow-up::before {\n  content: \"\\f574\";\n}\n\n.fa-file-upload::before {\n  content: \"\\f574\";\n}\n\n.fa-wifi::before {\n  content: \"\\f1eb\";\n}\n\n.fa-wifi-3::before {\n  content: \"\\f1eb\";\n}\n\n.fa-wifi-strong::before {\n  content: \"\\f1eb\";\n}\n\n.fa-bath::before {\n  content: \"\\f2cd\";\n}\n\n.fa-bathtub::before {\n  content: \"\\f2cd\";\n}\n\n.fa-underline::before {\n  content: \"\\f0cd\";\n}\n\n.fa-user-pen::before {\n  content: \"\\f4ff\";\n}\n\n.fa-user-edit::before {\n  content: \"\\f4ff\";\n}\n\n.fa-signature::before {\n  content: \"\\f5b7\";\n}\n\n.fa-stroopwafel::before {\n  content: \"\\f551\";\n}\n\n.fa-bold::before {\n  content: \"\\f032\";\n}\n\n.fa-anchor-lock::before {\n  content: \"\\e4ad\";\n}\n\n.fa-building-ngo::before {\n  content: \"\\e4d7\";\n}\n\n.fa-manat-sign::before {\n  content: \"\\e1d5\";\n}\n\n.fa-not-equal::before {\n  content: \"\\f53e\";\n}\n\n.fa-border-top-left::before {\n  content: \"\\f853\";\n}\n\n.fa-border-style::before {\n  content: \"\\f853\";\n}\n\n.fa-map-location-dot::before {\n  content: \"\\f5a0\";\n}\n\n.fa-map-marked-alt::before {\n  content: \"\\f5a0\";\n}\n\n.fa-jedi::before {\n  content: \"\\f669\";\n}\n\n.fa-square-poll-vertical::before {\n  content: \"\\f681\";\n}\n\n.fa-poll::before {\n  content: \"\\f681\";\n}\n\n.fa-mug-hot::before {\n  content: \"\\f7b6\";\n}\n\n.fa-car-battery::before {\n  content: \"\\f5df\";\n}\n\n.fa-battery-car::before {\n  content: \"\\f5df\";\n}\n\n.fa-gift::before {\n  content: \"\\f06b\";\n}\n\n.fa-dice-two::before {\n  content: \"\\f528\";\n}\n\n.fa-chess-queen::before {\n  content: \"\\f445\";\n}\n\n.fa-glasses::before {\n  content: \"\\f530\";\n}\n\n.fa-chess-board::before {\n  content: \"\\f43c\";\n}\n\n.fa-building-circle-check::before {\n  content: \"\\e4d2\";\n}\n\n.fa-person-chalkboard::before {\n  content: \"\\e53d\";\n}\n\n.fa-mars-stroke-right::before {\n  content: \"\\f22b\";\n}\n\n.fa-mars-stroke-h::before {\n  content: \"\\f22b\";\n}\n\n.fa-hand-back-fist::before {\n  content: \"\\f255\";\n}\n\n.fa-hand-rock::before {\n  content: \"\\f255\";\n}\n\n.fa-square-caret-up::before {\n  content: \"\\f151\";\n}\n\n.fa-caret-square-up::before {\n  content: \"\\f151\";\n}\n\n.fa-cloud-showers-water::before {\n  content: \"\\e4e4\";\n}\n\n.fa-chart-bar::before {\n  content: \"\\f080\";\n}\n\n.fa-bar-chart::before {\n  content: \"\\f080\";\n}\n\n.fa-hands-bubbles::before {\n  content: \"\\e05e\";\n}\n\n.fa-hands-wash::before {\n  content: \"\\e05e\";\n}\n\n.fa-less-than-equal::before {\n  content: \"\\f537\";\n}\n\n.fa-train::before {\n  content: \"\\f238\";\n}\n\n.fa-eye-low-vision::before {\n  content: \"\\f2a8\";\n}\n\n.fa-low-vision::before {\n  content: \"\\f2a8\";\n}\n\n.fa-crow::before {\n  content: \"\\f520\";\n}\n\n.fa-sailboat::before {\n  content: \"\\e445\";\n}\n\n.fa-window-restore::before {\n  content: \"\\f2d2\";\n}\n\n.fa-square-plus::before {\n  content: \"\\f0fe\";\n}\n\n.fa-plus-square::before {\n  content: \"\\f0fe\";\n}\n\n.fa-torii-gate::before {\n  content: \"\\f6a1\";\n}\n\n.fa-frog::before {\n  content: \"\\f52e\";\n}\n\n.fa-bucket::before {\n  content: \"\\e4cf\";\n}\n\n.fa-image::before {\n  content: \"\\f03e\";\n}\n\n.fa-microphone::before {\n  content: \"\\f130\";\n}\n\n.fa-cow::before {\n  content: \"\\f6c8\";\n}\n\n.fa-caret-up::before {\n  content: \"\\f0d8\";\n}\n\n.fa-screwdriver::before {\n  content: \"\\f54a\";\n}\n\n.fa-folder-closed::before {\n  content: \"\\e185\";\n}\n\n.fa-house-tsunami::before {\n  content: \"\\e515\";\n}\n\n.fa-square-nfi::before {\n  content: \"\\e576\";\n}\n\n.fa-arrow-up-from-ground-water::before {\n  content: \"\\e4b5\";\n}\n\n.fa-martini-glass::before {\n  content: \"\\f57b\";\n}\n\n.fa-glass-martini-alt::before {\n  content: \"\\f57b\";\n}\n\n.fa-rotate-left::before {\n  content: \"\\f2ea\";\n}\n\n.fa-rotate-back::before {\n  content: \"\\f2ea\";\n}\n\n.fa-rotate-backward::before {\n  content: \"\\f2ea\";\n}\n\n.fa-undo-alt::before {\n  content: \"\\f2ea\";\n}\n\n.fa-table-columns::before {\n  content: \"\\f0db\";\n}\n\n.fa-columns::before {\n  content: \"\\f0db\";\n}\n\n.fa-lemon::before {\n  content: \"\\f094\";\n}\n\n.fa-head-side-mask::before {\n  content: \"\\e063\";\n}\n\n.fa-handshake::before {\n  content: \"\\f2b5\";\n}\n\n.fa-gem::before {\n  content: \"\\f3a5\";\n}\n\n.fa-dolly::before {\n  content: \"\\f472\";\n}\n\n.fa-dolly-box::before {\n  content: \"\\f472\";\n}\n\n.fa-smoking::before {\n  content: \"\\f48d\";\n}\n\n.fa-minimize::before {\n  content: \"\\f78c\";\n}\n\n.fa-compress-arrows-alt::before {\n  content: \"\\f78c\";\n}\n\n.fa-monument::before {\n  content: \"\\f5a6\";\n}\n\n.fa-snowplow::before {\n  content: \"\\f7d2\";\n}\n\n.fa-angles-right::before {\n  content: \"\\f101\";\n}\n\n.fa-angle-double-right::before {\n  content: \"\\f101\";\n}\n\n.fa-cannabis::before {\n  content: \"\\f55f\";\n}\n\n.fa-circle-play::before {\n  content: \"\\f144\";\n}\n\n.fa-play-circle::before {\n  content: \"\\f144\";\n}\n\n.fa-tablets::before {\n  content: \"\\f490\";\n}\n\n.fa-ethernet::before {\n  content: \"\\f796\";\n}\n\n.fa-euro-sign::before {\n  content: \"\\f153\";\n}\n\n.fa-eur::before {\n  content: \"\\f153\";\n}\n\n.fa-euro::before {\n  content: \"\\f153\";\n}\n\n.fa-chair::before {\n  content: \"\\f6c0\";\n}\n\n.fa-circle-check::before {\n  content: \"\\f058\";\n}\n\n.fa-check-circle::before {\n  content: \"\\f058\";\n}\n\n.fa-circle-stop::before {\n  content: \"\\f28d\";\n}\n\n.fa-stop-circle::before {\n  content: \"\\f28d\";\n}\n\n.fa-compass-drafting::before {\n  content: \"\\f568\";\n}\n\n.fa-drafting-compass::before {\n  content: \"\\f568\";\n}\n\n.fa-plate-wheat::before {\n  content: \"\\e55a\";\n}\n\n.fa-icicles::before {\n  content: \"\\f7ad\";\n}\n\n.fa-person-shelter::before {\n  content: \"\\e54f\";\n}\n\n.fa-neuter::before {\n  content: \"\\f22c\";\n}\n\n.fa-id-badge::before {\n  content: \"\\f2c1\";\n}\n\n.fa-marker::before {\n  content: \"\\f5a1\";\n}\n\n.fa-face-laugh-beam::before {\n  content: \"\\f59a\";\n}\n\n.fa-laugh-beam::before {\n  content: \"\\f59a\";\n}\n\n.fa-helicopter-symbol::before {\n  content: \"\\e502\";\n}\n\n.fa-universal-access::before {\n  content: \"\\f29a\";\n}\n\n.fa-circle-chevron-up::before {\n  content: \"\\f139\";\n}\n\n.fa-chevron-circle-up::before {\n  content: \"\\f139\";\n}\n\n.fa-lari-sign::before {\n  content: \"\\e1c8\";\n}\n\n.fa-volcano::before {\n  content: \"\\f770\";\n}\n\n.fa-person-walking-dashed-line-arrow-right::before {\n  content: \"\\e553\";\n}\n\n.fa-sterling-sign::before {\n  content: \"\\f154\";\n}\n\n.fa-gbp::before {\n  content: \"\\f154\";\n}\n\n.fa-pound-sign::before {\n  content: \"\\f154\";\n}\n\n.fa-viruses::before {\n  content: \"\\e076\";\n}\n\n.fa-square-person-confined::before {\n  content: \"\\e577\";\n}\n\n.fa-user-tie::before {\n  content: \"\\f508\";\n}\n\n.fa-arrow-down-long::before {\n  content: \"\\f175\";\n}\n\n.fa-long-arrow-down::before {\n  content: \"\\f175\";\n}\n\n.fa-tent-arrow-down-to-line::before {\n  content: \"\\e57e\";\n}\n\n.fa-certificate::before {\n  content: \"\\f0a3\";\n}\n\n.fa-reply-all::before {\n  content: \"\\f122\";\n}\n\n.fa-mail-reply-all::before {\n  content: \"\\f122\";\n}\n\n.fa-suitcase::before {\n  content: \"\\f0f2\";\n}\n\n.fa-person-skating::before {\n  content: \"\\f7c5\";\n}\n\n.fa-skating::before {\n  content: \"\\f7c5\";\n}\n\n.fa-filter-circle-dollar::before {\n  content: \"\\f662\";\n}\n\n.fa-funnel-dollar::before {\n  content: \"\\f662\";\n}\n\n.fa-camera-retro::before {\n  content: \"\\f083\";\n}\n\n.fa-circle-arrow-down::before {\n  content: \"\\f0ab\";\n}\n\n.fa-arrow-circle-down::before {\n  content: \"\\f0ab\";\n}\n\n.fa-file-import::before {\n  content: \"\\f56f\";\n}\n\n.fa-arrow-right-to-file::before {\n  content: \"\\f56f\";\n}\n\n.fa-square-arrow-up-right::before {\n  content: \"\\f14c\";\n}\n\n.fa-external-link-square::before {\n  content: \"\\f14c\";\n}\n\n.fa-box-open::before {\n  content: \"\\f49e\";\n}\n\n.fa-scroll::before {\n  content: \"\\f70e\";\n}\n\n.fa-spa::before {\n  content: \"\\f5bb\";\n}\n\n.fa-location-pin-lock::before {\n  content: \"\\e51f\";\n}\n\n.fa-pause::before {\n  content: \"\\f04c\";\n}\n\n.fa-hill-avalanche::before {\n  content: \"\\e507\";\n}\n\n.fa-temperature-empty::before {\n  content: \"\\f2cb\";\n}\n\n.fa-temperature-0::before {\n  content: \"\\f2cb\";\n}\n\n.fa-thermometer-0::before {\n  content: \"\\f2cb\";\n}\n\n.fa-thermometer-empty::before {\n  content: \"\\f2cb\";\n}\n\n.fa-bomb::before {\n  content: \"\\f1e2\";\n}\n\n.fa-registered::before {\n  content: \"\\f25d\";\n}\n\n.fa-address-card::before {\n  content: \"\\f2bb\";\n}\n\n.fa-contact-card::before {\n  content: \"\\f2bb\";\n}\n\n.fa-vcard::before {\n  content: \"\\f2bb\";\n}\n\n.fa-scale-unbalanced-flip::before {\n  content: \"\\f516\";\n}\n\n.fa-balance-scale-right::before {\n  content: \"\\f516\";\n}\n\n.fa-subscript::before {\n  content: \"\\f12c\";\n}\n\n.fa-diamond-turn-right::before {\n  content: \"\\f5eb\";\n}\n\n.fa-directions::before {\n  content: \"\\f5eb\";\n}\n\n.fa-burst::before {\n  content: \"\\e4dc\";\n}\n\n.fa-house-laptop::before {\n  content: \"\\e066\";\n}\n\n.fa-laptop-house::before {\n  content: \"\\e066\";\n}\n\n.fa-face-tired::before {\n  content: \"\\f5c8\";\n}\n\n.fa-tired::before {\n  content: \"\\f5c8\";\n}\n\n.fa-money-bills::before {\n  content: \"\\e1f3\";\n}\n\n.fa-smog::before {\n  content: \"\\f75f\";\n}\n\n.fa-crutch::before {\n  content: \"\\f7f7\";\n}\n\n.fa-cloud-arrow-up::before {\n  content: \"\\f0ee\";\n}\n\n.fa-cloud-upload::before {\n  content: \"\\f0ee\";\n}\n\n.fa-cloud-upload-alt::before {\n  content: \"\\f0ee\";\n}\n\n.fa-palette::before {\n  content: \"\\f53f\";\n}\n\n.fa-arrows-turn-right::before {\n  content: \"\\e4c0\";\n}\n\n.fa-vest::before {\n  content: \"\\e085\";\n}\n\n.fa-ferry::before {\n  content: \"\\e4ea\";\n}\n\n.fa-arrows-down-to-people::before {\n  content: \"\\e4b9\";\n}\n\n.fa-seedling::before {\n  content: \"\\f4d8\";\n}\n\n.fa-sprout::before {\n  content: \"\\f4d8\";\n}\n\n.fa-left-right::before {\n  content: \"\\f337\";\n}\n\n.fa-arrows-alt-h::before {\n  content: \"\\f337\";\n}\n\n.fa-boxes-packing::before {\n  content: \"\\e4c7\";\n}\n\n.fa-circle-arrow-left::before {\n  content: \"\\f0a8\";\n}\n\n.fa-arrow-circle-left::before {\n  content: \"\\f0a8\";\n}\n\n.fa-group-arrows-rotate::before {\n  content: \"\\e4f6\";\n}\n\n.fa-bowl-food::before {\n  content: \"\\e4c6\";\n}\n\n.fa-candy-cane::before {\n  content: \"\\f786\";\n}\n\n.fa-arrow-down-wide-short::before {\n  content: \"\\f160\";\n}\n\n.fa-sort-amount-asc::before {\n  content: \"\\f160\";\n}\n\n.fa-sort-amount-down::before {\n  content: \"\\f160\";\n}\n\n.fa-cloud-bolt::before {\n  content: \"\\f76c\";\n}\n\n.fa-thunderstorm::before {\n  content: \"\\f76c\";\n}\n\n.fa-text-slash::before {\n  content: \"\\f87d\";\n}\n\n.fa-remove-format::before {\n  content: \"\\f87d\";\n}\n\n.fa-face-smile-wink::before {\n  content: \"\\f4da\";\n}\n\n.fa-smile-wink::before {\n  content: \"\\f4da\";\n}\n\n.fa-file-word::before {\n  content: \"\\f1c2\";\n}\n\n.fa-file-powerpoint::before {\n  content: \"\\f1c4\";\n}\n\n.fa-arrows-left-right::before {\n  content: \"\\f07e\";\n}\n\n.fa-arrows-h::before {\n  content: \"\\f07e\";\n}\n\n.fa-house-lock::before {\n  content: \"\\e510\";\n}\n\n.fa-cloud-arrow-down::before {\n  content: \"\\f0ed\";\n}\n\n.fa-cloud-download::before {\n  content: \"\\f0ed\";\n}\n\n.fa-cloud-download-alt::before {\n  content: \"\\f0ed\";\n}\n\n.fa-children::before {\n  content: \"\\e4e1\";\n}\n\n.fa-chalkboard::before {\n  content: \"\\f51b\";\n}\n\n.fa-blackboard::before {\n  content: \"\\f51b\";\n}\n\n.fa-user-large-slash::before {\n  content: \"\\f4fa\";\n}\n\n.fa-user-alt-slash::before {\n  content: \"\\f4fa\";\n}\n\n.fa-envelope-open::before {\n  content: \"\\f2b6\";\n}\n\n.fa-handshake-simple-slash::before {\n  content: \"\\e05f\";\n}\n\n.fa-handshake-alt-slash::before {\n  content: \"\\e05f\";\n}\n\n.fa-mattress-pillow::before {\n  content: \"\\e525\";\n}\n\n.fa-guarani-sign::before {\n  content: \"\\e19a\";\n}\n\n.fa-arrows-rotate::before {\n  content: \"\\f021\";\n}\n\n.fa-refresh::before {\n  content: \"\\f021\";\n}\n\n.fa-sync::before {\n  content: \"\\f021\";\n}\n\n.fa-fire-extinguisher::before {\n  content: \"\\f134\";\n}\n\n.fa-cruzeiro-sign::before {\n  content: \"\\e152\";\n}\n\n.fa-greater-than-equal::before {\n  content: \"\\f532\";\n}\n\n.fa-shield-halved::before {\n  content: \"\\f3ed\";\n}\n\n.fa-shield-alt::before {\n  content: \"\\f3ed\";\n}\n\n.fa-book-atlas::before {\n  content: \"\\f558\";\n}\n\n.fa-atlas::before {\n  content: \"\\f558\";\n}\n\n.fa-virus::before {\n  content: \"\\e074\";\n}\n\n.fa-envelope-circle-check::before {\n  content: \"\\e4e8\";\n}\n\n.fa-layer-group::before {\n  content: \"\\f5fd\";\n}\n\n.fa-arrows-to-dot::before {\n  content: \"\\e4be\";\n}\n\n.fa-archway::before {\n  content: \"\\f557\";\n}\n\n.fa-heart-circle-check::before {\n  content: \"\\e4fd\";\n}\n\n.fa-house-chimney-crack::before {\n  content: \"\\f6f1\";\n}\n\n.fa-house-damage::before {\n  content: \"\\f6f1\";\n}\n\n.fa-file-zipper::before {\n  content: \"\\f1c6\";\n}\n\n.fa-file-archive::before {\n  content: \"\\f1c6\";\n}\n\n.fa-square::before {\n  content: \"\\f0c8\";\n}\n\n.fa-martini-glass-empty::before {\n  content: \"\\f000\";\n}\n\n.fa-glass-martini::before {\n  content: \"\\f000\";\n}\n\n.fa-couch::before {\n  content: \"\\f4b8\";\n}\n\n.fa-cedi-sign::before {\n  content: \"\\e0df\";\n}\n\n.fa-italic::before {\n  content: \"\\f033\";\n}\n\n.fa-table-cells-column-lock::before {\n  content: \"\\e678\";\n}\n\n.fa-church::before {\n  content: \"\\f51d\";\n}\n\n.fa-comments-dollar::before {\n  content: \"\\f653\";\n}\n\n.fa-democrat::before {\n  content: \"\\f747\";\n}\n\n.fa-z::before {\n  content: \"Z\";\n}\n\n.fa-person-skiing::before {\n  content: \"\\f7c9\";\n}\n\n.fa-skiing::before {\n  content: \"\\f7c9\";\n}\n\n.fa-road-lock::before {\n  content: \"\\e567\";\n}\n\n.fa-a::before {\n  content: \"A\";\n}\n\n.fa-temperature-arrow-down::before {\n  content: \"\\e03f\";\n}\n\n.fa-temperature-down::before {\n  content: \"\\e03f\";\n}\n\n.fa-feather-pointed::before {\n  content: \"\\f56b\";\n}\n\n.fa-feather-alt::before {\n  content: \"\\f56b\";\n}\n\n.fa-p::before {\n  content: \"P\";\n}\n\n.fa-snowflake::before {\n  content: \"\\f2dc\";\n}\n\n.fa-newspaper::before {\n  content: \"\\f1ea\";\n}\n\n.fa-rectangle-ad::before {\n  content: \"\\f641\";\n}\n\n.fa-ad::before {\n  content: \"\\f641\";\n}\n\n.fa-circle-arrow-right::before {\n  content: \"\\f0a9\";\n}\n\n.fa-arrow-circle-right::before {\n  content: \"\\f0a9\";\n}\n\n.fa-filter-circle-xmark::before {\n  content: \"\\e17b\";\n}\n\n.fa-locust::before {\n  content: \"\\e520\";\n}\n\n.fa-sort::before {\n  content: \"\\f0dc\";\n}\n\n.fa-unsorted::before {\n  content: \"\\f0dc\";\n}\n\n.fa-list-ol::before {\n  content: \"\\f0cb\";\n}\n\n.fa-list-1-2::before {\n  content: \"\\f0cb\";\n}\n\n.fa-list-numeric::before {\n  content: \"\\f0cb\";\n}\n\n.fa-person-dress-burst::before {\n  content: \"\\e544\";\n}\n\n.fa-money-check-dollar::before {\n  content: \"\\f53d\";\n}\n\n.fa-money-check-alt::before {\n  content: \"\\f53d\";\n}\n\n.fa-vector-square::before {\n  content: \"\\f5cb\";\n}\n\n.fa-bread-slice::before {\n  content: \"\\f7ec\";\n}\n\n.fa-language::before {\n  content: \"\\f1ab\";\n}\n\n.fa-face-kiss-wink-heart::before {\n  content: \"\\f598\";\n}\n\n.fa-kiss-wink-heart::before {\n  content: \"\\f598\";\n}\n\n.fa-filter::before {\n  content: \"\\f0b0\";\n}\n\n.fa-question::before {\n  content: \"\\?\";\n}\n\n.fa-file-signature::before {\n  content: \"\\f573\";\n}\n\n.fa-up-down-left-right::before {\n  content: \"\\f0b2\";\n}\n\n.fa-arrows-alt::before {\n  content: \"\\f0b2\";\n}\n\n.fa-house-chimney-user::before {\n  content: \"\\e065\";\n}\n\n.fa-hand-holding-heart::before {\n  content: \"\\f4be\";\n}\n\n.fa-puzzle-piece::before {\n  content: \"\\f12e\";\n}\n\n.fa-money-check::before {\n  content: \"\\f53c\";\n}\n\n.fa-star-half-stroke::before {\n  content: \"\\f5c0\";\n}\n\n.fa-star-half-alt::before {\n  content: \"\\f5c0\";\n}\n\n.fa-code::before {\n  content: \"\\f121\";\n}\n\n.fa-whiskey-glass::before {\n  content: \"\\f7a0\";\n}\n\n.fa-glass-whiskey::before {\n  content: \"\\f7a0\";\n}\n\n.fa-building-circle-exclamation::before {\n  content: \"\\e4d3\";\n}\n\n.fa-magnifying-glass-chart::before {\n  content: \"\\e522\";\n}\n\n.fa-arrow-up-right-from-square::before {\n  content: \"\\f08e\";\n}\n\n.fa-external-link::before {\n  content: \"\\f08e\";\n}\n\n.fa-cubes-stacked::before {\n  content: \"\\e4e6\";\n}\n\n.fa-won-sign::before {\n  content: \"\\f159\";\n}\n\n.fa-krw::before {\n  content: \"\\f159\";\n}\n\n.fa-won::before {\n  content: \"\\f159\";\n}\n\n.fa-virus-covid::before {\n  content: \"\\e4a8\";\n}\n\n.fa-austral-sign::before {\n  content: \"\\e0a9\";\n}\n\n.fa-f::before {\n  content: \"F\";\n}\n\n.fa-leaf::before {\n  content: \"\\f06c\";\n}\n\n.fa-road::before {\n  content: \"\\f018\";\n}\n\n.fa-taxi::before {\n  content: \"\\f1ba\";\n}\n\n.fa-cab::before {\n  content: \"\\f1ba\";\n}\n\n.fa-person-circle-plus::before {\n  content: \"\\e541\";\n}\n\n.fa-chart-pie::before {\n  content: \"\\f200\";\n}\n\n.fa-pie-chart::before {\n  content: \"\\f200\";\n}\n\n.fa-bolt-lightning::before {\n  content: \"\\e0b7\";\n}\n\n.fa-sack-xmark::before {\n  content: \"\\e56a\";\n}\n\n.fa-file-excel::before {\n  content: \"\\f1c3\";\n}\n\n.fa-file-contract::before {\n  content: \"\\f56c\";\n}\n\n.fa-fish-fins::before {\n  content: \"\\e4f2\";\n}\n\n.fa-building-flag::before {\n  content: \"\\e4d5\";\n}\n\n.fa-face-grin-beam::before {\n  content: \"\\f582\";\n}\n\n.fa-grin-beam::before {\n  content: \"\\f582\";\n}\n\n.fa-object-ungroup::before {\n  content: \"\\f248\";\n}\n\n.fa-poop::before {\n  content: \"\\f619\";\n}\n\n.fa-location-pin::before {\n  content: \"\\f041\";\n}\n\n.fa-map-marker::before {\n  content: \"\\f041\";\n}\n\n.fa-kaaba::before {\n  content: \"\\f66b\";\n}\n\n.fa-toilet-paper::before {\n  content: \"\\f71e\";\n}\n\n.fa-helmet-safety::before {\n  content: \"\\f807\";\n}\n\n.fa-hard-hat::before {\n  content: \"\\f807\";\n}\n\n.fa-hat-hard::before {\n  content: \"\\f807\";\n}\n\n.fa-eject::before {\n  content: \"\\f052\";\n}\n\n.fa-circle-right::before {\n  content: \"\\f35a\";\n}\n\n.fa-arrow-alt-circle-right::before {\n  content: \"\\f35a\";\n}\n\n.fa-plane-circle-check::before {\n  content: \"\\e555\";\n}\n\n.fa-face-rolling-eyes::before {\n  content: \"\\f5a5\";\n}\n\n.fa-meh-rolling-eyes::before {\n  content: \"\\f5a5\";\n}\n\n.fa-object-group::before {\n  content: \"\\f247\";\n}\n\n.fa-chart-line::before {\n  content: \"\\f201\";\n}\n\n.fa-line-chart::before {\n  content: \"\\f201\";\n}\n\n.fa-mask-ventilator::before {\n  content: \"\\e524\";\n}\n\n.fa-arrow-right::before {\n  content: \"\\f061\";\n}\n\n.fa-signs-post::before {\n  content: \"\\f277\";\n}\n\n.fa-map-signs::before {\n  content: \"\\f277\";\n}\n\n.fa-cash-register::before {\n  content: \"\\f788\";\n}\n\n.fa-person-circle-question::before {\n  content: \"\\e542\";\n}\n\n.fa-h::before {\n  content: \"H\";\n}\n\n.fa-tarp::before {\n  content: \"\\e57b\";\n}\n\n.fa-screwdriver-wrench::before {\n  content: \"\\f7d9\";\n}\n\n.fa-tools::before {\n  content: \"\\f7d9\";\n}\n\n.fa-arrows-to-eye::before {\n  content: \"\\e4bf\";\n}\n\n.fa-plug-circle-bolt::before {\n  content: \"\\e55b\";\n}\n\n.fa-heart::before {\n  content: \"\\f004\";\n}\n\n.fa-mars-and-venus::before {\n  content: \"\\f224\";\n}\n\n.fa-house-user::before {\n  content: \"\\e1b0\";\n}\n\n.fa-home-user::before {\n  content: \"\\e1b0\";\n}\n\n.fa-dumpster-fire::before {\n  content: \"\\f794\";\n}\n\n.fa-house-crack::before {\n  content: \"\\e3b1\";\n}\n\n.fa-martini-glass-citrus::before {\n  content: \"\\f561\";\n}\n\n.fa-cocktail::before {\n  content: \"\\f561\";\n}\n\n.fa-face-surprise::before {\n  content: \"\\f5c2\";\n}\n\n.fa-surprise::before {\n  content: \"\\f5c2\";\n}\n\n.fa-bottle-water::before {\n  content: \"\\e4c5\";\n}\n\n.fa-circle-pause::before {\n  content: \"\\f28b\";\n}\n\n.fa-pause-circle::before {\n  content: \"\\f28b\";\n}\n\n.fa-toilet-paper-slash::before {\n  content: \"\\e072\";\n}\n\n.fa-apple-whole::before {\n  content: \"\\f5d1\";\n}\n\n.fa-apple-alt::before {\n  content: \"\\f5d1\";\n}\n\n.fa-kitchen-set::before {\n  content: \"\\e51a\";\n}\n\n.fa-r::before {\n  content: \"R\";\n}\n\n.fa-temperature-quarter::before {\n  content: \"\\f2ca\";\n}\n\n.fa-temperature-1::before {\n  content: \"\\f2ca\";\n}\n\n.fa-thermometer-1::before {\n  content: \"\\f2ca\";\n}\n\n.fa-thermometer-quarter::before {\n  content: \"\\f2ca\";\n}\n\n.fa-cube::before {\n  content: \"\\f1b2\";\n}\n\n.fa-bitcoin-sign::before {\n  content: \"\\e0b4\";\n}\n\n.fa-shield-dog::before {\n  content: \"\\e573\";\n}\n\n.fa-solar-panel::before {\n  content: \"\\f5ba\";\n}\n\n.fa-lock-open::before {\n  content: \"\\f3c1\";\n}\n\n.fa-elevator::before {\n  content: \"\\e16d\";\n}\n\n.fa-money-bill-transfer::before {\n  content: \"\\e528\";\n}\n\n.fa-money-bill-trend-up::before {\n  content: \"\\e529\";\n}\n\n.fa-house-flood-water-circle-arrow-right::before {\n  content: \"\\e50f\";\n}\n\n.fa-square-poll-horizontal::before {\n  content: \"\\f682\";\n}\n\n.fa-poll-h::before {\n  content: \"\\f682\";\n}\n\n.fa-circle::before {\n  content: \"\\f111\";\n}\n\n.fa-backward-fast::before {\n  content: \"\\f049\";\n}\n\n.fa-fast-backward::before {\n  content: \"\\f049\";\n}\n\n.fa-recycle::before {\n  content: \"\\f1b8\";\n}\n\n.fa-user-astronaut::before {\n  content: \"\\f4fb\";\n}\n\n.fa-plane-slash::before {\n  content: \"\\e069\";\n}\n\n.fa-trademark::before {\n  content: \"\\f25c\";\n}\n\n.fa-basketball::before {\n  content: \"\\f434\";\n}\n\n.fa-basketball-ball::before {\n  content: \"\\f434\";\n}\n\n.fa-satellite-dish::before {\n  content: \"\\f7c0\";\n}\n\n.fa-circle-up::before {\n  content: \"\\f35b\";\n}\n\n.fa-arrow-alt-circle-up::before {\n  content: \"\\f35b\";\n}\n\n.fa-mobile-screen-button::before {\n  content: \"\\f3cd\";\n}\n\n.fa-mobile-alt::before {\n  content: \"\\f3cd\";\n}\n\n.fa-volume-high::before {\n  content: \"\\f028\";\n}\n\n.fa-volume-up::before {\n  content: \"\\f028\";\n}\n\n.fa-users-rays::before {\n  content: \"\\e593\";\n}\n\n.fa-wallet::before {\n  content: \"\\f555\";\n}\n\n.fa-clipboard-check::before {\n  content: \"\\f46c\";\n}\n\n.fa-file-audio::before {\n  content: \"\\f1c7\";\n}\n\n.fa-burger::before {\n  content: \"\\f805\";\n}\n\n.fa-hamburger::before {\n  content: \"\\f805\";\n}\n\n.fa-wrench::before {\n  content: \"\\f0ad\";\n}\n\n.fa-bugs::before {\n  content: \"\\e4d0\";\n}\n\n.fa-rupee-sign::before {\n  content: \"\\f156\";\n}\n\n.fa-rupee::before {\n  content: \"\\f156\";\n}\n\n.fa-file-image::before {\n  content: \"\\f1c5\";\n}\n\n.fa-circle-question::before {\n  content: \"\\f059\";\n}\n\n.fa-question-circle::before {\n  content: \"\\f059\";\n}\n\n.fa-plane-departure::before {\n  content: \"\\f5b0\";\n}\n\n.fa-handshake-slash::before {\n  content: \"\\e060\";\n}\n\n.fa-book-bookmark::before {\n  content: \"\\e0bb\";\n}\n\n.fa-code-branch::before {\n  content: \"\\f126\";\n}\n\n.fa-hat-cowboy::before {\n  content: \"\\f8c0\";\n}\n\n.fa-bridge::before {\n  content: \"\\e4c8\";\n}\n\n.fa-phone-flip::before {\n  content: \"\\f879\";\n}\n\n.fa-phone-alt::before {\n  content: \"\\f879\";\n}\n\n.fa-truck-front::before {\n  content: \"\\e2b7\";\n}\n\n.fa-cat::before {\n  content: \"\\f6be\";\n}\n\n.fa-anchor-circle-exclamation::before {\n  content: \"\\e4ab\";\n}\n\n.fa-truck-field::before {\n  content: \"\\e58d\";\n}\n\n.fa-route::before {\n  content: \"\\f4d7\";\n}\n\n.fa-clipboard-question::before {\n  content: \"\\e4e3\";\n}\n\n.fa-panorama::before {\n  content: \"\\e209\";\n}\n\n.fa-comment-medical::before {\n  content: \"\\f7f5\";\n}\n\n.fa-teeth-open::before {\n  content: \"\\f62f\";\n}\n\n.fa-file-circle-minus::before {\n  content: \"\\e4ed\";\n}\n\n.fa-tags::before {\n  content: \"\\f02c\";\n}\n\n.fa-wine-glass::before {\n  content: \"\\f4e3\";\n}\n\n.fa-forward-fast::before {\n  content: \"\\f050\";\n}\n\n.fa-fast-forward::before {\n  content: \"\\f050\";\n}\n\n.fa-face-meh-blank::before {\n  content: \"\\f5a4\";\n}\n\n.fa-meh-blank::before {\n  content: \"\\f5a4\";\n}\n\n.fa-square-parking::before {\n  content: \"\\f540\";\n}\n\n.fa-parking::before {\n  content: \"\\f540\";\n}\n\n.fa-house-signal::before {\n  content: \"\\e012\";\n}\n\n.fa-bars-progress::before {\n  content: \"\\f828\";\n}\n\n.fa-tasks-alt::before {\n  content: \"\\f828\";\n}\n\n.fa-faucet-drip::before {\n  content: \"\\e006\";\n}\n\n.fa-cart-flatbed::before {\n  content: \"\\f474\";\n}\n\n.fa-dolly-flatbed::before {\n  content: \"\\f474\";\n}\n\n.fa-ban-smoking::before {\n  content: \"\\f54d\";\n}\n\n.fa-smoking-ban::before {\n  content: \"\\f54d\";\n}\n\n.fa-terminal::before {\n  content: \"\\f120\";\n}\n\n.fa-mobile-button::before {\n  content: \"\\f10b\";\n}\n\n.fa-house-medical-flag::before {\n  content: \"\\e514\";\n}\n\n.fa-basket-shopping::before {\n  content: \"\\f291\";\n}\n\n.fa-shopping-basket::before {\n  content: \"\\f291\";\n}\n\n.fa-tape::before {\n  content: \"\\f4db\";\n}\n\n.fa-bus-simple::before {\n  content: \"\\f55e\";\n}\n\n.fa-bus-alt::before {\n  content: \"\\f55e\";\n}\n\n.fa-eye::before {\n  content: \"\\f06e\";\n}\n\n.fa-face-sad-cry::before {\n  content: \"\\f5b3\";\n}\n\n.fa-sad-cry::before {\n  content: \"\\f5b3\";\n}\n\n.fa-audio-description::before {\n  content: \"\\f29e\";\n}\n\n.fa-person-military-to-person::before {\n  content: \"\\e54c\";\n}\n\n.fa-file-shield::before {\n  content: \"\\e4f0\";\n}\n\n.fa-user-slash::before {\n  content: \"\\f506\";\n}\n\n.fa-pen::before {\n  content: \"\\f304\";\n}\n\n.fa-tower-observation::before {\n  content: \"\\e586\";\n}\n\n.fa-file-code::before {\n  content: \"\\f1c9\";\n}\n\n.fa-signal::before {\n  content: \"\\f012\";\n}\n\n.fa-signal-5::before {\n  content: \"\\f012\";\n}\n\n.fa-signal-perfect::before {\n  content: \"\\f012\";\n}\n\n.fa-bus::before {\n  content: \"\\f207\";\n}\n\n.fa-heart-circle-xmark::before {\n  content: \"\\e501\";\n}\n\n.fa-house-chimney::before {\n  content: \"\\e3af\";\n}\n\n.fa-home-lg::before {\n  content: \"\\e3af\";\n}\n\n.fa-window-maximize::before {\n  content: \"\\f2d0\";\n}\n\n.fa-face-frown::before {\n  content: \"\\f119\";\n}\n\n.fa-frown::before {\n  content: \"\\f119\";\n}\n\n.fa-prescription::before {\n  content: \"\\f5b1\";\n}\n\n.fa-shop::before {\n  content: \"\\f54f\";\n}\n\n.fa-store-alt::before {\n  content: \"\\f54f\";\n}\n\n.fa-floppy-disk::before {\n  content: \"\\f0c7\";\n}\n\n.fa-save::before {\n  content: \"\\f0c7\";\n}\n\n.fa-vihara::before {\n  content: \"\\f6a7\";\n}\n\n.fa-scale-unbalanced::before {\n  content: \"\\f515\";\n}\n\n.fa-balance-scale-left::before {\n  content: \"\\f515\";\n}\n\n.fa-sort-up::before {\n  content: \"\\f0de\";\n}\n\n.fa-sort-asc::before {\n  content: \"\\f0de\";\n}\n\n.fa-comment-dots::before {\n  content: \"\\f4ad\";\n}\n\n.fa-commenting::before {\n  content: \"\\f4ad\";\n}\n\n.fa-plant-wilt::before {\n  content: \"\\e5aa\";\n}\n\n.fa-diamond::before {\n  content: \"\\f219\";\n}\n\n.fa-face-grin-squint::before {\n  content: \"\\f585\";\n}\n\n.fa-grin-squint::before {\n  content: \"\\f585\";\n}\n\n.fa-hand-holding-dollar::before {\n  content: \"\\f4c0\";\n}\n\n.fa-hand-holding-usd::before {\n  content: \"\\f4c0\";\n}\n\n.fa-bacterium::before {\n  content: \"\\e05a\";\n}\n\n.fa-hand-pointer::before {\n  content: \"\\f25a\";\n}\n\n.fa-drum-steelpan::before {\n  content: \"\\f56a\";\n}\n\n.fa-hand-scissors::before {\n  content: \"\\f257\";\n}\n\n.fa-hands-praying::before {\n  content: \"\\f684\";\n}\n\n.fa-praying-hands::before {\n  content: \"\\f684\";\n}\n\n.fa-arrow-rotate-right::before {\n  content: \"\\f01e\";\n}\n\n.fa-arrow-right-rotate::before {\n  content: \"\\f01e\";\n}\n\n.fa-arrow-rotate-forward::before {\n  content: \"\\f01e\";\n}\n\n.fa-redo::before {\n  content: \"\\f01e\";\n}\n\n.fa-biohazard::before {\n  content: \"\\f780\";\n}\n\n.fa-location-crosshairs::before {\n  content: \"\\f601\";\n}\n\n.fa-location::before {\n  content: \"\\f601\";\n}\n\n.fa-mars-double::before {\n  content: \"\\f227\";\n}\n\n.fa-child-dress::before {\n  content: \"\\e59c\";\n}\n\n.fa-users-between-lines::before {\n  content: \"\\e591\";\n}\n\n.fa-lungs-virus::before {\n  content: \"\\e067\";\n}\n\n.fa-face-grin-tears::before {\n  content: \"\\f588\";\n}\n\n.fa-grin-tears::before {\n  content: \"\\f588\";\n}\n\n.fa-phone::before {\n  content: \"\\f095\";\n}\n\n.fa-calendar-xmark::before {\n  content: \"\\f273\";\n}\n\n.fa-calendar-times::before {\n  content: \"\\f273\";\n}\n\n.fa-child-reaching::before {\n  content: \"\\e59d\";\n}\n\n.fa-head-side-virus::before {\n  content: \"\\e064\";\n}\n\n.fa-user-gear::before {\n  content: \"\\f4fe\";\n}\n\n.fa-user-cog::before {\n  content: \"\\f4fe\";\n}\n\n.fa-arrow-up-1-9::before {\n  content: \"\\f163\";\n}\n\n.fa-sort-numeric-up::before {\n  content: \"\\f163\";\n}\n\n.fa-door-closed::before {\n  content: \"\\f52a\";\n}\n\n.fa-shield-virus::before {\n  content: \"\\e06c\";\n}\n\n.fa-dice-six::before {\n  content: \"\\f526\";\n}\n\n.fa-mosquito-net::before {\n  content: \"\\e52c\";\n}\n\n.fa-bridge-water::before {\n  content: \"\\e4ce\";\n}\n\n.fa-person-booth::before {\n  content: \"\\f756\";\n}\n\n.fa-text-width::before {\n  content: \"\\f035\";\n}\n\n.fa-hat-wizard::before {\n  content: \"\\f6e8\";\n}\n\n.fa-pen-fancy::before {\n  content: \"\\f5ac\";\n}\n\n.fa-person-digging::before {\n  content: \"\\f85e\";\n}\n\n.fa-digging::before {\n  content: \"\\f85e\";\n}\n\n.fa-trash::before {\n  content: \"\\f1f8\";\n}\n\n.fa-gauge-simple::before {\n  content: \"\\f629\";\n}\n\n.fa-gauge-simple-med::before {\n  content: \"\\f629\";\n}\n\n.fa-tachometer-average::before {\n  content: \"\\f629\";\n}\n\n.fa-book-medical::before {\n  content: \"\\f7e6\";\n}\n\n.fa-poo::before {\n  content: \"\\f2fe\";\n}\n\n.fa-quote-right::before {\n  content: \"\\f10e\";\n}\n\n.fa-quote-right-alt::before {\n  content: \"\\f10e\";\n}\n\n.fa-shirt::before {\n  content: \"\\f553\";\n}\n\n.fa-t-shirt::before {\n  content: \"\\f553\";\n}\n\n.fa-tshirt::before {\n  content: \"\\f553\";\n}\n\n.fa-cubes::before {\n  content: \"\\f1b3\";\n}\n\n.fa-divide::before {\n  content: \"\\f529\";\n}\n\n.fa-tenge-sign::before {\n  content: \"\\f7d7\";\n}\n\n.fa-tenge::before {\n  content: \"\\f7d7\";\n}\n\n.fa-headphones::before {\n  content: \"\\f025\";\n}\n\n.fa-hands-holding::before {\n  content: \"\\f4c2\";\n}\n\n.fa-hands-clapping::before {\n  content: \"\\e1a8\";\n}\n\n.fa-republican::before {\n  content: \"\\f75e\";\n}\n\n.fa-arrow-left::before {\n  content: \"\\f060\";\n}\n\n.fa-person-circle-xmark::before {\n  content: \"\\e543\";\n}\n\n.fa-ruler::before {\n  content: \"\\f545\";\n}\n\n.fa-align-left::before {\n  content: \"\\f036\";\n}\n\n.fa-dice-d6::before {\n  content: \"\\f6d1\";\n}\n\n.fa-restroom::before {\n  content: \"\\f7bd\";\n}\n\n.fa-j::before {\n  content: \"J\";\n}\n\n.fa-users-viewfinder::before {\n  content: \"\\e595\";\n}\n\n.fa-file-video::before {\n  content: \"\\f1c8\";\n}\n\n.fa-up-right-from-square::before {\n  content: \"\\f35d\";\n}\n\n.fa-external-link-alt::before {\n  content: \"\\f35d\";\n}\n\n.fa-table-cells::before {\n  content: \"\\f00a\";\n}\n\n.fa-th::before {\n  content: \"\\f00a\";\n}\n\n.fa-file-pdf::before {\n  content: \"\\f1c1\";\n}\n\n.fa-book-bible::before {\n  content: \"\\f647\";\n}\n\n.fa-bible::before {\n  content: \"\\f647\";\n}\n\n.fa-o::before {\n  content: \"O\";\n}\n\n.fa-suitcase-medical::before {\n  content: \"\\f0fa\";\n}\n\n.fa-medkit::before {\n  content: \"\\f0fa\";\n}\n\n.fa-user-secret::before {\n  content: \"\\f21b\";\n}\n\n.fa-otter::before {\n  content: \"\\f700\";\n}\n\n.fa-person-dress::before {\n  content: \"\\f182\";\n}\n\n.fa-female::before {\n  content: \"\\f182\";\n}\n\n.fa-comment-dollar::before {\n  content: \"\\f651\";\n}\n\n.fa-business-time::before {\n  content: \"\\f64a\";\n}\n\n.fa-briefcase-clock::before {\n  content: \"\\f64a\";\n}\n\n.fa-table-cells-large::before {\n  content: \"\\f009\";\n}\n\n.fa-th-large::before {\n  content: \"\\f009\";\n}\n\n.fa-book-tanakh::before {\n  content: \"\\f827\";\n}\n\n.fa-tanakh::before {\n  content: \"\\f827\";\n}\n\n.fa-phone-volume::before {\n  content: \"\\f2a0\";\n}\n\n.fa-volume-control-phone::before {\n  content: \"\\f2a0\";\n}\n\n.fa-hat-cowboy-side::before {\n  content: \"\\f8c1\";\n}\n\n.fa-clipboard-user::before {\n  content: \"\\f7f3\";\n}\n\n.fa-child::before {\n  content: \"\\f1ae\";\n}\n\n.fa-lira-sign::before {\n  content: \"\\f195\";\n}\n\n.fa-satellite::before {\n  content: \"\\f7bf\";\n}\n\n.fa-plane-lock::before {\n  content: \"\\e558\";\n}\n\n.fa-tag::before {\n  content: \"\\f02b\";\n}\n\n.fa-comment::before {\n  content: \"\\f075\";\n}\n\n.fa-cake-candles::before {\n  content: \"\\f1fd\";\n}\n\n.fa-birthday-cake::before {\n  content: \"\\f1fd\";\n}\n\n.fa-cake::before {\n  content: \"\\f1fd\";\n}\n\n.fa-envelope::before {\n  content: \"\\f0e0\";\n}\n\n.fa-angles-up::before {\n  content: \"\\f102\";\n}\n\n.fa-angle-double-up::before {\n  content: \"\\f102\";\n}\n\n.fa-paperclip::before {\n  content: \"\\f0c6\";\n}\n\n.fa-arrow-right-to-city::before {\n  content: \"\\e4b3\";\n}\n\n.fa-ribbon::before {\n  content: \"\\f4d6\";\n}\n\n.fa-lungs::before {\n  content: \"\\f604\";\n}\n\n.fa-arrow-up-9-1::before {\n  content: \"\\f887\";\n}\n\n.fa-sort-numeric-up-alt::before {\n  content: \"\\f887\";\n}\n\n.fa-litecoin-sign::before {\n  content: \"\\e1d3\";\n}\n\n.fa-border-none::before {\n  content: \"\\f850\";\n}\n\n.fa-circle-nodes::before {\n  content: \"\\e4e2\";\n}\n\n.fa-parachute-box::before {\n  content: \"\\f4cd\";\n}\n\n.fa-indent::before {\n  content: \"\\f03c\";\n}\n\n.fa-truck-field-un::before {\n  content: \"\\e58e\";\n}\n\n.fa-hourglass::before {\n  content: \"\\f254\";\n}\n\n.fa-hourglass-empty::before {\n  content: \"\\f254\";\n}\n\n.fa-mountain::before {\n  content: \"\\f6fc\";\n}\n\n.fa-user-doctor::before {\n  content: \"\\f0f0\";\n}\n\n.fa-user-md::before {\n  content: \"\\f0f0\";\n}\n\n.fa-circle-info::before {\n  content: \"\\f05a\";\n}\n\n.fa-info-circle::before {\n  content: \"\\f05a\";\n}\n\n.fa-cloud-meatball::before {\n  content: \"\\f73b\";\n}\n\n.fa-camera::before {\n  content: \"\\f030\";\n}\n\n.fa-camera-alt::before {\n  content: \"\\f030\";\n}\n\n.fa-square-virus::before {\n  content: \"\\e578\";\n}\n\n.fa-meteor::before {\n  content: \"\\f753\";\n}\n\n.fa-car-on::before {\n  content: \"\\e4dd\";\n}\n\n.fa-sleigh::before {\n  content: \"\\f7cc\";\n}\n\n.fa-arrow-down-1-9::before {\n  content: \"\\f162\";\n}\n\n.fa-sort-numeric-asc::before {\n  content: \"\\f162\";\n}\n\n.fa-sort-numeric-down::before {\n  content: \"\\f162\";\n}\n\n.fa-hand-holding-droplet::before {\n  content: \"\\f4c1\";\n}\n\n.fa-hand-holding-water::before {\n  content: \"\\f4c1\";\n}\n\n.fa-water::before {\n  content: \"\\f773\";\n}\n\n.fa-calendar-check::before {\n  content: \"\\f274\";\n}\n\n.fa-braille::before {\n  content: \"\\f2a1\";\n}\n\n.fa-prescription-bottle-medical::before {\n  content: \"\\f486\";\n}\n\n.fa-prescription-bottle-alt::before {\n  content: \"\\f486\";\n}\n\n.fa-landmark::before {\n  content: \"\\f66f\";\n}\n\n.fa-truck::before {\n  content: \"\\f0d1\";\n}\n\n.fa-crosshairs::before {\n  content: \"\\f05b\";\n}\n\n.fa-person-cane::before {\n  content: \"\\e53c\";\n}\n\n.fa-tent::before {\n  content: \"\\e57d\";\n}\n\n.fa-vest-patches::before {\n  content: \"\\e086\";\n}\n\n.fa-check-double::before {\n  content: \"\\f560\";\n}\n\n.fa-arrow-down-a-z::before {\n  content: \"\\f15d\";\n}\n\n.fa-sort-alpha-asc::before {\n  content: \"\\f15d\";\n}\n\n.fa-sort-alpha-down::before {\n  content: \"\\f15d\";\n}\n\n.fa-money-bill-wheat::before {\n  content: \"\\e52a\";\n}\n\n.fa-cookie::before {\n  content: \"\\f563\";\n}\n\n.fa-arrow-rotate-left::before {\n  content: \"\\f0e2\";\n}\n\n.fa-arrow-left-rotate::before {\n  content: \"\\f0e2\";\n}\n\n.fa-arrow-rotate-back::before {\n  content: \"\\f0e2\";\n}\n\n.fa-arrow-rotate-backward::before {\n  content: \"\\f0e2\";\n}\n\n.fa-undo::before {\n  content: \"\\f0e2\";\n}\n\n.fa-hard-drive::before {\n  content: \"\\f0a0\";\n}\n\n.fa-hdd::before {\n  content: \"\\f0a0\";\n}\n\n.fa-face-grin-squint-tears::before {\n  content: \"\\f586\";\n}\n\n.fa-grin-squint-tears::before {\n  content: \"\\f586\";\n}\n\n.fa-dumbbell::before {\n  content: \"\\f44b\";\n}\n\n.fa-rectangle-list::before {\n  content: \"\\f022\";\n}\n\n.fa-list-alt::before {\n  content: \"\\f022\";\n}\n\n.fa-tarp-droplet::before {\n  content: \"\\e57c\";\n}\n\n.fa-house-medical-circle-check::before {\n  content: \"\\e511\";\n}\n\n.fa-person-skiing-nordic::before {\n  content: \"\\f7ca\";\n}\n\n.fa-skiing-nordic::before {\n  content: \"\\f7ca\";\n}\n\n.fa-calendar-plus::before {\n  content: \"\\f271\";\n}\n\n.fa-plane-arrival::before {\n  content: \"\\f5af\";\n}\n\n.fa-circle-left::before {\n  content: \"\\f359\";\n}\n\n.fa-arrow-alt-circle-left::before {\n  content: \"\\f359\";\n}\n\n.fa-train-subway::before {\n  content: \"\\f239\";\n}\n\n.fa-subway::before {\n  content: \"\\f239\";\n}\n\n.fa-chart-gantt::before {\n  content: \"\\e0e4\";\n}\n\n.fa-indian-rupee-sign::before {\n  content: \"\\e1bc\";\n}\n\n.fa-indian-rupee::before {\n  content: \"\\e1bc\";\n}\n\n.fa-inr::before {\n  content: \"\\e1bc\";\n}\n\n.fa-crop-simple::before {\n  content: \"\\f565\";\n}\n\n.fa-crop-alt::before {\n  content: \"\\f565\";\n}\n\n.fa-money-bill-1::before {\n  content: \"\\f3d1\";\n}\n\n.fa-money-bill-alt::before {\n  content: \"\\f3d1\";\n}\n\n.fa-left-long::before {\n  content: \"\\f30a\";\n}\n\n.fa-long-arrow-alt-left::before {\n  content: \"\\f30a\";\n}\n\n.fa-dna::before {\n  content: \"\\f471\";\n}\n\n.fa-virus-slash::before {\n  content: \"\\e075\";\n}\n\n.fa-minus::before {\n  content: \"\\f068\";\n}\n\n.fa-subtract::before {\n  content: \"\\f068\";\n}\n\n.fa-chess::before {\n  content: \"\\f439\";\n}\n\n.fa-arrow-left-long::before {\n  content: \"\\f177\";\n}\n\n.fa-long-arrow-left::before {\n  content: \"\\f177\";\n}\n\n.fa-plug-circle-check::before {\n  content: \"\\e55c\";\n}\n\n.fa-street-view::before {\n  content: \"\\f21d\";\n}\n\n.fa-franc-sign::before {\n  content: \"\\e18f\";\n}\n\n.fa-volume-off::before {\n  content: \"\\f026\";\n}\n\n.fa-hands-asl-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-american-sign-language-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-asl-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-hands-american-sign-language-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-gear::before {\n  content: \"\\f013\";\n}\n\n.fa-cog::before {\n  content: \"\\f013\";\n}\n\n.fa-droplet-slash::before {\n  content: \"\\f5c7\";\n}\n\n.fa-tint-slash::before {\n  content: \"\\f5c7\";\n}\n\n.fa-mosque::before {\n  content: \"\\f678\";\n}\n\n.fa-mosquito::before {\n  content: \"\\e52b\";\n}\n\n.fa-star-of-david::before {\n  content: \"\\f69a\";\n}\n\n.fa-person-military-rifle::before {\n  content: \"\\e54b\";\n}\n\n.fa-cart-shopping::before {\n  content: \"\\f07a\";\n}\n\n.fa-shopping-cart::before {\n  content: \"\\f07a\";\n}\n\n.fa-vials::before {\n  content: \"\\f493\";\n}\n\n.fa-plug-circle-plus::before {\n  content: \"\\e55f\";\n}\n\n.fa-place-of-worship::before {\n  content: \"\\f67f\";\n}\n\n.fa-grip-vertical::before {\n  content: \"\\f58e\";\n}\n\n.fa-arrow-turn-up::before {\n  content: \"\\f148\";\n}\n\n.fa-level-up::before {\n  content: \"\\f148\";\n}\n\n.fa-u::before {\n  content: \"U\";\n}\n\n.fa-square-root-variable::before {\n  content: \"\\f698\";\n}\n\n.fa-square-root-alt::before {\n  content: \"\\f698\";\n}\n\n.fa-clock::before {\n  content: \"\\f017\";\n}\n\n.fa-clock-four::before {\n  content: \"\\f017\";\n}\n\n.fa-backward-step::before {\n  content: \"\\f048\";\n}\n\n.fa-step-backward::before {\n  content: \"\\f048\";\n}\n\n.fa-pallet::before {\n  content: \"\\f482\";\n}\n\n.fa-faucet::before {\n  content: \"\\e005\";\n}\n\n.fa-baseball-bat-ball::before {\n  content: \"\\f432\";\n}\n\n.fa-s::before {\n  content: \"S\";\n}\n\n.fa-timeline::before {\n  content: \"\\e29c\";\n}\n\n.fa-keyboard::before {\n  content: \"\\f11c\";\n}\n\n.fa-caret-down::before {\n  content: \"\\f0d7\";\n}\n\n.fa-house-chimney-medical::before {\n  content: \"\\f7f2\";\n}\n\n.fa-clinic-medical::before {\n  content: \"\\f7f2\";\n}\n\n.fa-temperature-three-quarters::before {\n  content: \"\\f2c8\";\n}\n\n.fa-temperature-3::before {\n  content: \"\\f2c8\";\n}\n\n.fa-thermometer-3::before {\n  content: \"\\f2c8\";\n}\n\n.fa-thermometer-three-quarters::before {\n  content: \"\\f2c8\";\n}\n\n.fa-mobile-screen::before {\n  content: \"\\f3cf\";\n}\n\n.fa-mobile-android-alt::before {\n  content: \"\\f3cf\";\n}\n\n.fa-plane-up::before {\n  content: \"\\e22d\";\n}\n\n.fa-piggy-bank::before {\n  content: \"\\f4d3\";\n}\n\n.fa-battery-half::before {\n  content: \"\\f242\";\n}\n\n.fa-battery-3::before {\n  content: \"\\f242\";\n}\n\n.fa-mountain-city::before {\n  content: \"\\e52e\";\n}\n\n.fa-coins::before {\n  content: \"\\f51e\";\n}\n\n.fa-khanda::before {\n  content: \"\\f66d\";\n}\n\n.fa-sliders::before {\n  content: \"\\f1de\";\n}\n\n.fa-sliders-h::before {\n  content: \"\\f1de\";\n}\n\n.fa-folder-tree::before {\n  content: \"\\f802\";\n}\n\n.fa-network-wired::before {\n  content: \"\\f6ff\";\n}\n\n.fa-map-pin::before {\n  content: \"\\f276\";\n}\n\n.fa-hamsa::before {\n  content: \"\\f665\";\n}\n\n.fa-cent-sign::before {\n  content: \"\\e3f5\";\n}\n\n.fa-flask::before {\n  content: \"\\f0c3\";\n}\n\n.fa-person-pregnant::before {\n  content: \"\\e31e\";\n}\n\n.fa-wand-sparkles::before {\n  content: \"\\f72b\";\n}\n\n.fa-ellipsis-vertical::before {\n  content: \"\\f142\";\n}\n\n.fa-ellipsis-v::before {\n  content: \"\\f142\";\n}\n\n.fa-ticket::before {\n  content: \"\\f145\";\n}\n\n.fa-power-off::before {\n  content: \"\\f011\";\n}\n\n.fa-right-long::before {\n  content: \"\\f30b\";\n}\n\n.fa-long-arrow-alt-right::before {\n  content: \"\\f30b\";\n}\n\n.fa-flag-usa::before {\n  content: \"\\f74d\";\n}\n\n.fa-laptop-file::before {\n  content: \"\\e51d\";\n}\n\n.fa-tty::before {\n  content: \"\\f1e4\";\n}\n\n.fa-teletype::before {\n  content: \"\\f1e4\";\n}\n\n.fa-diagram-next::before {\n  content: \"\\e476\";\n}\n\n.fa-person-rifle::before {\n  content: \"\\e54e\";\n}\n\n.fa-house-medical-circle-exclamation::before {\n  content: \"\\e512\";\n}\n\n.fa-closed-captioning::before {\n  content: \"\\f20a\";\n}\n\n.fa-person-hiking::before {\n  content: \"\\f6ec\";\n}\n\n.fa-hiking::before {\n  content: \"\\f6ec\";\n}\n\n.fa-venus-double::before {\n  content: \"\\f226\";\n}\n\n.fa-images::before {\n  content: \"\\f302\";\n}\n\n.fa-calculator::before {\n  content: \"\\f1ec\";\n}\n\n.fa-people-pulling::before {\n  content: \"\\e535\";\n}\n\n.fa-n::before {\n  content: \"N\";\n}\n\n.fa-cable-car::before {\n  content: \"\\f7da\";\n}\n\n.fa-tram::before {\n  content: \"\\f7da\";\n}\n\n.fa-cloud-rain::before {\n  content: \"\\f73d\";\n}\n\n.fa-building-circle-xmark::before {\n  content: \"\\e4d4\";\n}\n\n.fa-ship::before {\n  content: \"\\f21a\";\n}\n\n.fa-arrows-down-to-line::before {\n  content: \"\\e4b8\";\n}\n\n.fa-download::before {\n  content: \"\\f019\";\n}\n\n.fa-face-grin::before {\n  content: \"\\f580\";\n}\n\n.fa-grin::before {\n  content: \"\\f580\";\n}\n\n.fa-delete-left::before {\n  content: \"\\f55a\";\n}\n\n.fa-backspace::before {\n  content: \"\\f55a\";\n}\n\n.fa-eye-dropper::before {\n  content: \"\\f1fb\";\n}\n\n.fa-eye-dropper-empty::before {\n  content: \"\\f1fb\";\n}\n\n.fa-eyedropper::before {\n  content: \"\\f1fb\";\n}\n\n.fa-file-circle-check::before {\n  content: \"\\e5a0\";\n}\n\n.fa-forward::before {\n  content: \"\\f04e\";\n}\n\n.fa-mobile::before {\n  content: \"\\f3ce\";\n}\n\n.fa-mobile-android::before {\n  content: \"\\f3ce\";\n}\n\n.fa-mobile-phone::before {\n  content: \"\\f3ce\";\n}\n\n.fa-face-meh::before {\n  content: \"\\f11a\";\n}\n\n.fa-meh::before {\n  content: \"\\f11a\";\n}\n\n.fa-align-center::before {\n  content: \"\\f037\";\n}\n\n.fa-book-skull::before {\n  content: \"\\f6b7\";\n}\n\n.fa-book-dead::before {\n  content: \"\\f6b7\";\n}\n\n.fa-id-card::before {\n  content: \"\\f2c2\";\n}\n\n.fa-drivers-license::before {\n  content: \"\\f2c2\";\n}\n\n.fa-outdent::before {\n  content: \"\\f03b\";\n}\n\n.fa-dedent::before {\n  content: \"\\f03b\";\n}\n\n.fa-heart-circle-exclamation::before {\n  content: \"\\e4fe\";\n}\n\n.fa-house::before {\n  content: \"\\f015\";\n}\n\n.fa-home::before {\n  content: \"\\f015\";\n}\n\n.fa-home-alt::before {\n  content: \"\\f015\";\n}\n\n.fa-home-lg-alt::before {\n  content: \"\\f015\";\n}\n\n.fa-calendar-week::before {\n  content: \"\\f784\";\n}\n\n.fa-laptop-medical::before {\n  content: \"\\f812\";\n}\n\n.fa-b::before {\n  content: \"B\";\n}\n\n.fa-file-medical::before {\n  content: \"\\f477\";\n}\n\n.fa-dice-one::before {\n  content: \"\\f525\";\n}\n\n.fa-kiwi-bird::before {\n  content: \"\\f535\";\n}\n\n.fa-arrow-right-arrow-left::before {\n  content: \"\\f0ec\";\n}\n\n.fa-exchange::before {\n  content: \"\\f0ec\";\n}\n\n.fa-rotate-right::before {\n  content: \"\\f2f9\";\n}\n\n.fa-redo-alt::before {\n  content: \"\\f2f9\";\n}\n\n.fa-rotate-forward::before {\n  content: \"\\f2f9\";\n}\n\n.fa-utensils::before {\n  content: \"\\f2e7\";\n}\n\n.fa-cutlery::before {\n  content: \"\\f2e7\";\n}\n\n.fa-arrow-up-wide-short::before {\n  content: \"\\f161\";\n}\n\n.fa-sort-amount-up::before {\n  content: \"\\f161\";\n}\n\n.fa-mill-sign::before {\n  content: \"\\e1ed\";\n}\n\n.fa-bowl-rice::before {\n  content: \"\\e2eb\";\n}\n\n.fa-skull::before {\n  content: \"\\f54c\";\n}\n\n.fa-tower-broadcast::before {\n  content: \"\\f519\";\n}\n\n.fa-broadcast-tower::before {\n  content: \"\\f519\";\n}\n\n.fa-truck-pickup::before {\n  content: \"\\f63c\";\n}\n\n.fa-up-long::before {\n  content: \"\\f30c\";\n}\n\n.fa-long-arrow-alt-up::before {\n  content: \"\\f30c\";\n}\n\n.fa-stop::before {\n  content: \"\\f04d\";\n}\n\n.fa-code-merge::before {\n  content: \"\\f387\";\n}\n\n.fa-upload::before {\n  content: \"\\f093\";\n}\n\n.fa-hurricane::before {\n  content: \"\\f751\";\n}\n\n.fa-mound::before {\n  content: \"\\e52d\";\n}\n\n.fa-toilet-portable::before {\n  content: \"\\e583\";\n}\n\n.fa-compact-disc::before {\n  content: \"\\f51f\";\n}\n\n.fa-file-arrow-down::before {\n  content: \"\\f56d\";\n}\n\n.fa-file-download::before {\n  content: \"\\f56d\";\n}\n\n.fa-caravan::before {\n  content: \"\\f8ff\";\n}\n\n.fa-shield-cat::before {\n  content: \"\\e572\";\n}\n\n.fa-bolt::before {\n  content: \"\\f0e7\";\n}\n\n.fa-zap::before {\n  content: \"\\f0e7\";\n}\n\n.fa-glass-water::before {\n  content: \"\\e4f4\";\n}\n\n.fa-oil-well::before {\n  content: \"\\e532\";\n}\n\n.fa-vault::before {\n  content: \"\\e2c5\";\n}\n\n.fa-mars::before {\n  content: \"\\f222\";\n}\n\n.fa-toilet::before {\n  content: \"\\f7d8\";\n}\n\n.fa-plane-circle-xmark::before {\n  content: \"\\e557\";\n}\n\n.fa-yen-sign::before {\n  content: \"\\f157\";\n}\n\n.fa-cny::before {\n  content: \"\\f157\";\n}\n\n.fa-jpy::before {\n  content: \"\\f157\";\n}\n\n.fa-rmb::before {\n  content: \"\\f157\";\n}\n\n.fa-yen::before {\n  content: \"\\f157\";\n}\n\n.fa-ruble-sign::before {\n  content: \"\\f158\";\n}\n\n.fa-rouble::before {\n  content: \"\\f158\";\n}\n\n.fa-rub::before {\n  content: \"\\f158\";\n}\n\n.fa-ruble::before {\n  content: \"\\f158\";\n}\n\n.fa-sun::before {\n  content: \"\\f185\";\n}\n\n.fa-guitar::before {\n  content: \"\\f7a6\";\n}\n\n.fa-face-laugh-wink::before {\n  content: \"\\f59c\";\n}\n\n.fa-laugh-wink::before {\n  content: \"\\f59c\";\n}\n\n.fa-horse-head::before {\n  content: \"\\f7ab\";\n}\n\n.fa-bore-hole::before {\n  content: \"\\e4c3\";\n}\n\n.fa-industry::before {\n  content: \"\\f275\";\n}\n\n.fa-circle-down::before {\n  content: \"\\f358\";\n}\n\n.fa-arrow-alt-circle-down::before {\n  content: \"\\f358\";\n}\n\n.fa-arrows-turn-to-dots::before {\n  content: \"\\e4c1\";\n}\n\n.fa-florin-sign::before {\n  content: \"\\e184\";\n}\n\n.fa-arrow-down-short-wide::before {\n  content: \"\\f884\";\n}\n\n.fa-sort-amount-desc::before {\n  content: \"\\f884\";\n}\n\n.fa-sort-amount-down-alt::before {\n  content: \"\\f884\";\n}\n\n.fa-less-than::before {\n  content: \"\\<\";\n}\n\n.fa-angle-down::before {\n  content: \"\\f107\";\n}\n\n.fa-car-tunnel::before {\n  content: \"\\e4de\";\n}\n\n.fa-head-side-cough::before {\n  content: \"\\e061\";\n}\n\n.fa-grip-lines::before {\n  content: \"\\f7a4\";\n}\n\n.fa-thumbs-down::before {\n  content: \"\\f165\";\n}\n\n.fa-user-lock::before {\n  content: \"\\f502\";\n}\n\n.fa-arrow-right-long::before {\n  content: \"\\f178\";\n}\n\n.fa-long-arrow-right::before {\n  content: \"\\f178\";\n}\n\n.fa-anchor-circle-xmark::before {\n  content: \"\\e4ac\";\n}\n\n.fa-ellipsis::before {\n  content: \"\\f141\";\n}\n\n.fa-ellipsis-h::before {\n  content: \"\\f141\";\n}\n\n.fa-chess-pawn::before {\n  content: \"\\f443\";\n}\n\n.fa-kit-medical::before {\n  content: \"\\f479\";\n}\n\n.fa-first-aid::before {\n  content: \"\\f479\";\n}\n\n.fa-person-through-window::before {\n  content: \"\\e5a9\";\n}\n\n.fa-toolbox::before {\n  content: \"\\f552\";\n}\n\n.fa-hands-holding-circle::before {\n  content: \"\\e4fb\";\n}\n\n.fa-bug::before {\n  content: \"\\f188\";\n}\n\n.fa-credit-card::before {\n  content: \"\\f09d\";\n}\n\n.fa-credit-card-alt::before {\n  content: \"\\f09d\";\n}\n\n.fa-car::before {\n  content: \"\\f1b9\";\n}\n\n.fa-automobile::before {\n  content: \"\\f1b9\";\n}\n\n.fa-hand-holding-hand::before {\n  content: \"\\e4f7\";\n}\n\n.fa-book-open-reader::before {\n  content: \"\\f5da\";\n}\n\n.fa-book-reader::before {\n  content: \"\\f5da\";\n}\n\n.fa-mountain-sun::before {\n  content: \"\\e52f\";\n}\n\n.fa-arrows-left-right-to-line::before {\n  content: \"\\e4ba\";\n}\n\n.fa-dice-d20::before {\n  content: \"\\f6cf\";\n}\n\n.fa-truck-droplet::before {\n  content: \"\\e58c\";\n}\n\n.fa-file-circle-xmark::before {\n  content: \"\\e5a1\";\n}\n\n.fa-temperature-arrow-up::before {\n  content: \"\\e040\";\n}\n\n.fa-temperature-up::before {\n  content: \"\\e040\";\n}\n\n.fa-medal::before {\n  content: \"\\f5a2\";\n}\n\n.fa-bed::before {\n  content: \"\\f236\";\n}\n\n.fa-square-h::before {\n  content: \"\\f0fd\";\n}\n\n.fa-h-square::before {\n  content: \"\\f0fd\";\n}\n\n.fa-podcast::before {\n  content: \"\\f2ce\";\n}\n\n.fa-temperature-full::before {\n  content: \"\\f2c7\";\n}\n\n.fa-temperature-4::before {\n  content: \"\\f2c7\";\n}\n\n.fa-thermometer-4::before {\n  content: \"\\f2c7\";\n}\n\n.fa-thermometer-full::before {\n  content: \"\\f2c7\";\n}\n\n.fa-bell::before {\n  content: \"\\f0f3\";\n}\n\n.fa-superscript::before {\n  content: \"\\f12b\";\n}\n\n.fa-plug-circle-xmark::before {\n  content: \"\\e560\";\n}\n\n.fa-star-of-life::before {\n  content: \"\\f621\";\n}\n\n.fa-phone-slash::before {\n  content: \"\\f3dd\";\n}\n\n.fa-paint-roller::before {\n  content: \"\\f5aa\";\n}\n\n.fa-handshake-angle::before {\n  content: \"\\f4c4\";\n}\n\n.fa-hands-helping::before {\n  content: \"\\f4c4\";\n}\n\n.fa-location-dot::before {\n  content: \"\\f3c5\";\n}\n\n.fa-map-marker-alt::before {\n  content: \"\\f3c5\";\n}\n\n.fa-file::before {\n  content: \"\\f15b\";\n}\n\n.fa-greater-than::before {\n  content: \"\\>\";\n}\n\n.fa-person-swimming::before {\n  content: \"\\f5c4\";\n}\n\n.fa-swimmer::before {\n  content: \"\\f5c4\";\n}\n\n.fa-arrow-down::before {\n  content: \"\\f063\";\n}\n\n.fa-droplet::before {\n  content: \"\\f043\";\n}\n\n.fa-tint::before {\n  content: \"\\f043\";\n}\n\n.fa-eraser::before {\n  content: \"\\f12d\";\n}\n\n.fa-earth-americas::before {\n  content: \"\\f57d\";\n}\n\n.fa-earth::before {\n  content: \"\\f57d\";\n}\n\n.fa-earth-america::before {\n  content: \"\\f57d\";\n}\n\n.fa-globe-americas::before {\n  content: \"\\f57d\";\n}\n\n.fa-person-burst::before {\n  content: \"\\e53b\";\n}\n\n.fa-dove::before {\n  content: \"\\f4ba\";\n}\n\n.fa-battery-empty::before {\n  content: \"\\f244\";\n}\n\n.fa-battery-0::before {\n  content: \"\\f244\";\n}\n\n.fa-socks::before {\n  content: \"\\f696\";\n}\n\n.fa-inbox::before {\n  content: \"\\f01c\";\n}\n\n.fa-section::before {\n  content: \"\\e447\";\n}\n\n.fa-gauge-high::before {\n  content: \"\\f625\";\n}\n\n.fa-tachometer-alt::before {\n  content: \"\\f625\";\n}\n\n.fa-tachometer-alt-fast::before {\n  content: \"\\f625\";\n}\n\n.fa-envelope-open-text::before {\n  content: \"\\f658\";\n}\n\n.fa-hospital::before {\n  content: \"\\f0f8\";\n}\n\n.fa-hospital-alt::before {\n  content: \"\\f0f8\";\n}\n\n.fa-hospital-wide::before {\n  content: \"\\f0f8\";\n}\n\n.fa-wine-bottle::before {\n  content: \"\\f72f\";\n}\n\n.fa-chess-rook::before {\n  content: \"\\f447\";\n}\n\n.fa-bars-staggered::before {\n  content: \"\\f550\";\n}\n\n.fa-reorder::before {\n  content: \"\\f550\";\n}\n\n.fa-stream::before {\n  content: \"\\f550\";\n}\n\n.fa-dharmachakra::before {\n  content: \"\\f655\";\n}\n\n.fa-hotdog::before {\n  content: \"\\f80f\";\n}\n\n.fa-person-walking-with-cane::before {\n  content: \"\\f29d\";\n}\n\n.fa-blind::before {\n  content: \"\\f29d\";\n}\n\n.fa-drum::before {\n  content: \"\\f569\";\n}\n\n.fa-ice-cream::before {\n  content: \"\\f810\";\n}\n\n.fa-heart-circle-bolt::before {\n  content: \"\\e4fc\";\n}\n\n.fa-fax::before {\n  content: \"\\f1ac\";\n}\n\n.fa-paragraph::before {\n  content: \"\\f1dd\";\n}\n\n.fa-check-to-slot::before {\n  content: \"\\f772\";\n}\n\n.fa-vote-yea::before {\n  content: \"\\f772\";\n}\n\n.fa-star-half::before {\n  content: \"\\f089\";\n}\n\n.fa-boxes-stacked::before {\n  content: \"\\f468\";\n}\n\n.fa-boxes::before {\n  content: \"\\f468\";\n}\n\n.fa-boxes-alt::before {\n  content: \"\\f468\";\n}\n\n.fa-link::before {\n  content: \"\\f0c1\";\n}\n\n.fa-chain::before {\n  content: \"\\f0c1\";\n}\n\n.fa-ear-listen::before {\n  content: \"\\f2a2\";\n}\n\n.fa-assistive-listening-systems::before {\n  content: \"\\f2a2\";\n}\n\n.fa-tree-city::before {\n  content: \"\\e587\";\n}\n\n.fa-play::before {\n  content: \"\\f04b\";\n}\n\n.fa-font::before {\n  content: \"\\f031\";\n}\n\n.fa-table-cells-row-lock::before {\n  content: \"\\e67a\";\n}\n\n.fa-rupiah-sign::before {\n  content: \"\\e23d\";\n}\n\n.fa-magnifying-glass::before {\n  content: \"\\f002\";\n}\n\n.fa-search::before {\n  content: \"\\f002\";\n}\n\n.fa-table-tennis-paddle-ball::before {\n  content: \"\\f45d\";\n}\n\n.fa-ping-pong-paddle-ball::before {\n  content: \"\\f45d\";\n}\n\n.fa-table-tennis::before {\n  content: \"\\f45d\";\n}\n\n.fa-person-dots-from-line::before {\n  content: \"\\f470\";\n}\n\n.fa-diagnoses::before {\n  content: \"\\f470\";\n}\n\n.fa-trash-can-arrow-up::before {\n  content: \"\\f82a\";\n}\n\n.fa-trash-restore-alt::before {\n  content: \"\\f82a\";\n}\n\n.fa-naira-sign::before {\n  content: \"\\e1f6\";\n}\n\n.fa-cart-arrow-down::before {\n  content: \"\\f218\";\n}\n\n.fa-walkie-talkie::before {\n  content: \"\\f8ef\";\n}\n\n.fa-file-pen::before {\n  content: \"\\f31c\";\n}\n\n.fa-file-edit::before {\n  content: \"\\f31c\";\n}\n\n.fa-receipt::before {\n  content: \"\\f543\";\n}\n\n.fa-square-pen::before {\n  content: \"\\f14b\";\n}\n\n.fa-pen-square::before {\n  content: \"\\f14b\";\n}\n\n.fa-pencil-square::before {\n  content: \"\\f14b\";\n}\n\n.fa-suitcase-rolling::before {\n  content: \"\\f5c1\";\n}\n\n.fa-person-circle-exclamation::before {\n  content: \"\\e53f\";\n}\n\n.fa-chevron-down::before {\n  content: \"\\f078\";\n}\n\n.fa-battery-full::before {\n  content: \"\\f240\";\n}\n\n.fa-battery::before {\n  content: \"\\f240\";\n}\n\n.fa-battery-5::before {\n  content: \"\\f240\";\n}\n\n.fa-skull-crossbones::before {\n  content: \"\\f714\";\n}\n\n.fa-code-compare::before {\n  content: \"\\e13a\";\n}\n\n.fa-list-ul::before {\n  content: \"\\f0ca\";\n}\n\n.fa-list-dots::before {\n  content: \"\\f0ca\";\n}\n\n.fa-school-lock::before {\n  content: \"\\e56f\";\n}\n\n.fa-tower-cell::before {\n  content: \"\\e585\";\n}\n\n.fa-down-long::before {\n  content: \"\\f309\";\n}\n\n.fa-long-arrow-alt-down::before {\n  content: \"\\f309\";\n}\n\n.fa-ranking-star::before {\n  content: \"\\e561\";\n}\n\n.fa-chess-king::before {\n  content: \"\\f43f\";\n}\n\n.fa-person-harassing::before {\n  content: \"\\e549\";\n}\n\n.fa-brazilian-real-sign::before {\n  content: \"\\e46c\";\n}\n\n.fa-landmark-dome::before {\n  content: \"\\f752\";\n}\n\n.fa-landmark-alt::before {\n  content: \"\\f752\";\n}\n\n.fa-arrow-up::before {\n  content: \"\\f062\";\n}\n\n.fa-tv::before {\n  content: \"\\f26c\";\n}\n\n.fa-television::before {\n  content: \"\\f26c\";\n}\n\n.fa-tv-alt::before {\n  content: \"\\f26c\";\n}\n\n.fa-shrimp::before {\n  content: \"\\e448\";\n}\n\n.fa-list-check::before {\n  content: \"\\f0ae\";\n}\n\n.fa-tasks::before {\n  content: \"\\f0ae\";\n}\n\n.fa-jug-detergent::before {\n  content: \"\\e519\";\n}\n\n.fa-circle-user::before {\n  content: \"\\f2bd\";\n}\n\n.fa-user-circle::before {\n  content: \"\\f2bd\";\n}\n\n.fa-user-shield::before {\n  content: \"\\f505\";\n}\n\n.fa-wind::before {\n  content: \"\\f72e\";\n}\n\n.fa-car-burst::before {\n  content: \"\\f5e1\";\n}\n\n.fa-car-crash::before {\n  content: \"\\f5e1\";\n}\n\n.fa-y::before {\n  content: \"Y\";\n}\n\n.fa-person-snowboarding::before {\n  content: \"\\f7ce\";\n}\n\n.fa-snowboarding::before {\n  content: \"\\f7ce\";\n}\n\n.fa-truck-fast::before {\n  content: \"\\f48b\";\n}\n\n.fa-shipping-fast::before {\n  content: \"\\f48b\";\n}\n\n.fa-fish::before {\n  content: \"\\f578\";\n}\n\n.fa-user-graduate::before {\n  content: \"\\f501\";\n}\n\n.fa-circle-half-stroke::before {\n  content: \"\\f042\";\n}\n\n.fa-adjust::before {\n  content: \"\\f042\";\n}\n\n.fa-clapperboard::before {\n  content: \"\\e131\";\n}\n\n.fa-circle-radiation::before {\n  content: \"\\f7ba\";\n}\n\n.fa-radiation-alt::before {\n  content: \"\\f7ba\";\n}\n\n.fa-baseball::before {\n  content: \"\\f433\";\n}\n\n.fa-baseball-ball::before {\n  content: \"\\f433\";\n}\n\n.fa-jet-fighter-up::before {\n  content: \"\\e518\";\n}\n\n.fa-diagram-project::before {\n  content: \"\\f542\";\n}\n\n.fa-project-diagram::before {\n  content: \"\\f542\";\n}\n\n.fa-copy::before {\n  content: \"\\f0c5\";\n}\n\n.fa-volume-xmark::before {\n  content: \"\\f6a9\";\n}\n\n.fa-volume-mute::before {\n  content: \"\\f6a9\";\n}\n\n.fa-volume-times::before {\n  content: \"\\f6a9\";\n}\n\n.fa-hand-sparkles::before {\n  content: \"\\e05d\";\n}\n\n.fa-grip::before {\n  content: \"\\f58d\";\n}\n\n.fa-grip-horizontal::before {\n  content: \"\\f58d\";\n}\n\n.fa-share-from-square::before {\n  content: \"\\f14d\";\n}\n\n.fa-share-square::before {\n  content: \"\\f14d\";\n}\n\n.fa-child-combatant::before {\n  content: \"\\e4e0\";\n}\n\n.fa-child-rifle::before {\n  content: \"\\e4e0\";\n}\n\n.fa-gun::before {\n  content: \"\\e19b\";\n}\n\n.fa-square-phone::before {\n  content: \"\\f098\";\n}\n\n.fa-phone-square::before {\n  content: \"\\f098\";\n}\n\n.fa-plus::before {\n  content: \"\\+\";\n}\n\n.fa-add::before {\n  content: \"\\+\";\n}\n\n.fa-expand::before {\n  content: \"\\f065\";\n}\n\n.fa-computer::before {\n  content: \"\\e4e5\";\n}\n\n.fa-xmark::before {\n  content: \"\\f00d\";\n}\n\n.fa-close::before {\n  content: \"\\f00d\";\n}\n\n.fa-multiply::before {\n  content: \"\\f00d\";\n}\n\n.fa-remove::before {\n  content: \"\\f00d\";\n}\n\n.fa-times::before {\n  content: \"\\f00d\";\n}\n\n.fa-arrows-up-down-left-right::before {\n  content: \"\\f047\";\n}\n\n.fa-arrows::before {\n  content: \"\\f047\";\n}\n\n.fa-chalkboard-user::before {\n  content: \"\\f51c\";\n}\n\n.fa-chalkboard-teacher::before {\n  content: \"\\f51c\";\n}\n\n.fa-peso-sign::before {\n  content: \"\\e222\";\n}\n\n.fa-building-shield::before {\n  content: \"\\e4d8\";\n}\n\n.fa-baby::before {\n  content: \"\\f77c\";\n}\n\n.fa-users-line::before {\n  content: \"\\e592\";\n}\n\n.fa-quote-left::before {\n  content: \"\\f10d\";\n}\n\n.fa-quote-left-alt::before {\n  content: \"\\f10d\";\n}\n\n.fa-tractor::before {\n  content: \"\\f722\";\n}\n\n.fa-trash-arrow-up::before {\n  content: \"\\f829\";\n}\n\n.fa-trash-restore::before {\n  content: \"\\f829\";\n}\n\n.fa-arrow-down-up-lock::before {\n  content: \"\\e4b0\";\n}\n\n.fa-lines-leaning::before {\n  content: \"\\e51e\";\n}\n\n.fa-ruler-combined::before {\n  content: \"\\f546\";\n}\n\n.fa-copyright::before {\n  content: \"\\f1f9\";\n}\n\n.fa-equals::before {\n  content: \"\\=\";\n}\n\n.fa-blender::before {\n  content: \"\\f517\";\n}\n\n.fa-teeth::before {\n  content: \"\\f62e\";\n}\n\n.fa-shekel-sign::before {\n  content: \"\\f20b\";\n}\n\n.fa-ils::before {\n  content: \"\\f20b\";\n}\n\n.fa-shekel::before {\n  content: \"\\f20b\";\n}\n\n.fa-sheqel::before {\n  content: \"\\f20b\";\n}\n\n.fa-sheqel-sign::before {\n  content: \"\\f20b\";\n}\n\n.fa-map::before {\n  content: \"\\f279\";\n}\n\n.fa-rocket::before {\n  content: \"\\f135\";\n}\n\n.fa-photo-film::before {\n  content: \"\\f87c\";\n}\n\n.fa-photo-video::before {\n  content: \"\\f87c\";\n}\n\n.fa-folder-minus::before {\n  content: \"\\f65d\";\n}\n\n.fa-store::before {\n  content: \"\\f54e\";\n}\n\n.fa-arrow-trend-up::before {\n  content: \"\\e098\";\n}\n\n.fa-plug-circle-minus::before {\n  content: \"\\e55e\";\n}\n\n.fa-sign-hanging::before {\n  content: \"\\f4d9\";\n}\n\n.fa-sign::before {\n  content: \"\\f4d9\";\n}\n\n.fa-bezier-curve::before {\n  content: \"\\f55b\";\n}\n\n.fa-bell-slash::before {\n  content: \"\\f1f6\";\n}\n\n.fa-tablet::before {\n  content: \"\\f3fb\";\n}\n\n.fa-tablet-android::before {\n  content: \"\\f3fb\";\n}\n\n.fa-school-flag::before {\n  content: \"\\e56e\";\n}\n\n.fa-fill::before {\n  content: \"\\f575\";\n}\n\n.fa-angle-up::before {\n  content: \"\\f106\";\n}\n\n.fa-drumstick-bite::before {\n  content: \"\\f6d7\";\n}\n\n.fa-holly-berry::before {\n  content: \"\\f7aa\";\n}\n\n.fa-chevron-left::before {\n  content: \"\\f053\";\n}\n\n.fa-bacteria::before {\n  content: \"\\e059\";\n}\n\n.fa-hand-lizard::before {\n  content: \"\\f258\";\n}\n\n.fa-notdef::before {\n  content: \"\\e1fe\";\n}\n\n.fa-disease::before {\n  content: \"\\f7fa\";\n}\n\n.fa-briefcase-medical::before {\n  content: \"\\f469\";\n}\n\n.fa-genderless::before {\n  content: \"\\f22d\";\n}\n\n.fa-chevron-right::before {\n  content: \"\\f054\";\n}\n\n.fa-retweet::before {\n  content: \"\\f079\";\n}\n\n.fa-car-rear::before {\n  content: \"\\f5de\";\n}\n\n.fa-car-alt::before {\n  content: \"\\f5de\";\n}\n\n.fa-pump-soap::before {\n  content: \"\\e06b\";\n}\n\n.fa-video-slash::before {\n  content: \"\\f4e2\";\n}\n\n.fa-battery-quarter::before {\n  content: \"\\f243\";\n}\n\n.fa-battery-2::before {\n  content: \"\\f243\";\n}\n\n.fa-radio::before {\n  content: \"\\f8d7\";\n}\n\n.fa-baby-carriage::before {\n  content: \"\\f77d\";\n}\n\n.fa-carriage-baby::before {\n  content: \"\\f77d\";\n}\n\n.fa-traffic-light::before {\n  content: \"\\f637\";\n}\n\n.fa-thermometer::before {\n  content: \"\\f491\";\n}\n\n.fa-vr-cardboard::before {\n  content: \"\\f729\";\n}\n\n.fa-hand-middle-finger::before {\n  content: \"\\f806\";\n}\n\n.fa-percent::before {\n  content: \"\\%\";\n}\n\n.fa-percentage::before {\n  content: \"\\%\";\n}\n\n.fa-truck-moving::before {\n  content: \"\\f4df\";\n}\n\n.fa-glass-water-droplet::before {\n  content: \"\\e4f5\";\n}\n\n.fa-display::before {\n  content: \"\\e163\";\n}\n\n.fa-face-smile::before {\n  content: \"\\f118\";\n}\n\n.fa-smile::before {\n  content: \"\\f118\";\n}\n\n.fa-thumbtack::before {\n  content: \"\\f08d\";\n}\n\n.fa-thumb-tack::before {\n  content: \"\\f08d\";\n}\n\n.fa-trophy::before {\n  content: \"\\f091\";\n}\n\n.fa-person-praying::before {\n  content: \"\\f683\";\n}\n\n.fa-pray::before {\n  content: \"\\f683\";\n}\n\n.fa-hammer::before {\n  content: \"\\f6e3\";\n}\n\n.fa-hand-peace::before {\n  content: \"\\f25b\";\n}\n\n.fa-rotate::before {\n  content: \"\\f2f1\";\n}\n\n.fa-sync-alt::before {\n  content: \"\\f2f1\";\n}\n\n.fa-spinner::before {\n  content: \"\\f110\";\n}\n\n.fa-robot::before {\n  content: \"\\f544\";\n}\n\n.fa-peace::before {\n  content: \"\\f67c\";\n}\n\n.fa-gears::before {\n  content: \"\\f085\";\n}\n\n.fa-cogs::before {\n  content: \"\\f085\";\n}\n\n.fa-warehouse::before {\n  content: \"\\f494\";\n}\n\n.fa-arrow-up-right-dots::before {\n  content: \"\\e4b7\";\n}\n\n.fa-splotch::before {\n  content: \"\\f5bc\";\n}\n\n.fa-face-grin-hearts::before {\n  content: \"\\f584\";\n}\n\n.fa-grin-hearts::before {\n  content: \"\\f584\";\n}\n\n.fa-dice-four::before {\n  content: \"\\f524\";\n}\n\n.fa-sim-card::before {\n  content: \"\\f7c4\";\n}\n\n.fa-transgender::before {\n  content: \"\\f225\";\n}\n\n.fa-transgender-alt::before {\n  content: \"\\f225\";\n}\n\n.fa-mercury::before {\n  content: \"\\f223\";\n}\n\n.fa-arrow-turn-down::before {\n  content: \"\\f149\";\n}\n\n.fa-level-down::before {\n  content: \"\\f149\";\n}\n\n.fa-person-falling-burst::before {\n  content: \"\\e547\";\n}\n\n.fa-award::before {\n  content: \"\\f559\";\n}\n\n.fa-ticket-simple::before {\n  content: \"\\f3ff\";\n}\n\n.fa-ticket-alt::before {\n  content: \"\\f3ff\";\n}\n\n.fa-building::before {\n  content: \"\\f1ad\";\n}\n\n.fa-angles-left::before {\n  content: \"\\f100\";\n}\n\n.fa-angle-double-left::before {\n  content: \"\\f100\";\n}\n\n.fa-qrcode::before {\n  content: \"\\f029\";\n}\n\n.fa-clock-rotate-left::before {\n  content: \"\\f1da\";\n}\n\n.fa-history::before {\n  content: \"\\f1da\";\n}\n\n.fa-face-grin-beam-sweat::before {\n  content: \"\\f583\";\n}\n\n.fa-grin-beam-sweat::before {\n  content: \"\\f583\";\n}\n\n.fa-file-export::before {\n  content: \"\\f56e\";\n}\n\n.fa-arrow-right-from-file::before {\n  content: \"\\f56e\";\n}\n\n.fa-shield::before {\n  content: \"\\f132\";\n}\n\n.fa-shield-blank::before {\n  content: \"\\f132\";\n}\n\n.fa-arrow-up-short-wide::before {\n  content: \"\\f885\";\n}\n\n.fa-sort-amount-up-alt::before {\n  content: \"\\f885\";\n}\n\n.fa-house-medical::before {\n  content: \"\\e3b2\";\n}\n\n.fa-golf-ball-tee::before {\n  content: \"\\f450\";\n}\n\n.fa-golf-ball::before {\n  content: \"\\f450\";\n}\n\n.fa-circle-chevron-left::before {\n  content: \"\\f137\";\n}\n\n.fa-chevron-circle-left::before {\n  content: \"\\f137\";\n}\n\n.fa-house-chimney-window::before {\n  content: \"\\e00d\";\n}\n\n.fa-pen-nib::before {\n  content: \"\\f5ad\";\n}\n\n.fa-tent-arrow-turn-left::before {\n  content: \"\\e580\";\n}\n\n.fa-tents::before {\n  content: \"\\e582\";\n}\n\n.fa-wand-magic::before {\n  content: \"\\f0d0\";\n}\n\n.fa-magic::before {\n  content: \"\\f0d0\";\n}\n\n.fa-dog::before {\n  content: \"\\f6d3\";\n}\n\n.fa-carrot::before {\n  content: \"\\f787\";\n}\n\n.fa-moon::before {\n  content: \"\\f186\";\n}\n\n.fa-wine-glass-empty::before {\n  content: \"\\f5ce\";\n}\n\n.fa-wine-glass-alt::before {\n  content: \"\\f5ce\";\n}\n\n.fa-cheese::before {\n  content: \"\\f7ef\";\n}\n\n.fa-yin-yang::before {\n  content: \"\\f6ad\";\n}\n\n.fa-music::before {\n  content: \"\\f001\";\n}\n\n.fa-code-commit::before {\n  content: \"\\f386\";\n}\n\n.fa-temperature-low::before {\n  content: \"\\f76b\";\n}\n\n.fa-person-biking::before {\n  content: \"\\f84a\";\n}\n\n.fa-biking::before {\n  content: \"\\f84a\";\n}\n\n.fa-broom::before {\n  content: \"\\f51a\";\n}\n\n.fa-shield-heart::before {\n  content: \"\\e574\";\n}\n\n.fa-gopuram::before {\n  content: \"\\f664\";\n}\n\n.fa-earth-oceania::before {\n  content: \"\\e47b\";\n}\n\n.fa-globe-oceania::before {\n  content: \"\\e47b\";\n}\n\n.fa-square-xmark::before {\n  content: \"\\f2d3\";\n}\n\n.fa-times-square::before {\n  content: \"\\f2d3\";\n}\n\n.fa-xmark-square::before {\n  content: \"\\f2d3\";\n}\n\n.fa-hashtag::before {\n  content: \"\\#\";\n}\n\n.fa-up-right-and-down-left-from-center::before {\n  content: \"\\f424\";\n}\n\n.fa-expand-alt::before {\n  content: \"\\f424\";\n}\n\n.fa-oil-can::before {\n  content: \"\\f613\";\n}\n\n.fa-t::before {\n  content: \"T\";\n}\n\n.fa-hippo::before {\n  content: \"\\f6ed\";\n}\n\n.fa-chart-column::before {\n  content: \"\\e0e3\";\n}\n\n.fa-infinity::before {\n  content: \"\\f534\";\n}\n\n.fa-vial-circle-check::before {\n  content: \"\\e596\";\n}\n\n.fa-person-arrow-down-to-line::before {\n  content: \"\\e538\";\n}\n\n.fa-voicemail::before {\n  content: \"\\f897\";\n}\n\n.fa-fan::before {\n  content: \"\\f863\";\n}\n\n.fa-person-walking-luggage::before {\n  content: \"\\e554\";\n}\n\n.fa-up-down::before {\n  content: \"\\f338\";\n}\n\n.fa-arrows-alt-v::before {\n  content: \"\\f338\";\n}\n\n.fa-cloud-moon-rain::before {\n  content: \"\\f73c\";\n}\n\n.fa-calendar::before {\n  content: \"\\f133\";\n}\n\n.fa-trailer::before {\n  content: \"\\e041\";\n}\n\n.fa-bahai::before {\n  content: \"\\f666\";\n}\n\n.fa-haykal::before {\n  content: \"\\f666\";\n}\n\n.fa-sd-card::before {\n  content: \"\\f7c2\";\n}\n\n.fa-dragon::before {\n  content: \"\\f6d5\";\n}\n\n.fa-shoe-prints::before {\n  content: \"\\f54b\";\n}\n\n.fa-circle-plus::before {\n  content: \"\\f055\";\n}\n\n.fa-plus-circle::before {\n  content: \"\\f055\";\n}\n\n.fa-face-grin-tongue-wink::before {\n  content: \"\\f58b\";\n}\n\n.fa-grin-tongue-wink::before {\n  content: \"\\f58b\";\n}\n\n.fa-hand-holding::before {\n  content: \"\\f4bd\";\n}\n\n.fa-plug-circle-exclamation::before {\n  content: \"\\e55d\";\n}\n\n.fa-link-slash::before {\n  content: \"\\f127\";\n}\n\n.fa-chain-broken::before {\n  content: \"\\f127\";\n}\n\n.fa-chain-slash::before {\n  content: \"\\f127\";\n}\n\n.fa-unlink::before {\n  content: \"\\f127\";\n}\n\n.fa-clone::before {\n  content: \"\\f24d\";\n}\n\n.fa-person-walking-arrow-loop-left::before {\n  content: \"\\e551\";\n}\n\n.fa-arrow-up-z-a::before {\n  content: \"\\f882\";\n}\n\n.fa-sort-alpha-up-alt::before {\n  content: \"\\f882\";\n}\n\n.fa-fire-flame-curved::before {\n  content: \"\\f7e4\";\n}\n\n.fa-fire-alt::before {\n  content: \"\\f7e4\";\n}\n\n.fa-tornado::before {\n  content: \"\\f76f\";\n}\n\n.fa-file-circle-plus::before {\n  content: \"\\e494\";\n}\n\n.fa-book-quran::before {\n  content: \"\\f687\";\n}\n\n.fa-quran::before {\n  content: \"\\f687\";\n}\n\n.fa-anchor::before {\n  content: \"\\f13d\";\n}\n\n.fa-border-all::before {\n  content: \"\\f84c\";\n}\n\n.fa-face-angry::before {\n  content: \"\\f556\";\n}\n\n.fa-angry::before {\n  content: \"\\f556\";\n}\n\n.fa-cookie-bite::before {\n  content: \"\\f564\";\n}\n\n.fa-arrow-trend-down::before {\n  content: \"\\e097\";\n}\n\n.fa-rss::before {\n  content: \"\\f09e\";\n}\n\n.fa-feed::before {\n  content: \"\\f09e\";\n}\n\n.fa-draw-polygon::before {\n  content: \"\\f5ee\";\n}\n\n.fa-scale-balanced::before {\n  content: \"\\f24e\";\n}\n\n.fa-balance-scale::before {\n  content: \"\\f24e\";\n}\n\n.fa-gauge-simple-high::before {\n  content: \"\\f62a\";\n}\n\n.fa-tachometer::before {\n  content: \"\\f62a\";\n}\n\n.fa-tachometer-fast::before {\n  content: \"\\f62a\";\n}\n\n.fa-shower::before {\n  content: \"\\f2cc\";\n}\n\n.fa-desktop::before {\n  content: \"\\f390\";\n}\n\n.fa-desktop-alt::before {\n  content: \"\\f390\";\n}\n\n.fa-m::before {\n  content: \"M\";\n}\n\n.fa-table-list::before {\n  content: \"\\f00b\";\n}\n\n.fa-th-list::before {\n  content: \"\\f00b\";\n}\n\n.fa-comment-sms::before {\n  content: \"\\f7cd\";\n}\n\n.fa-sms::before {\n  content: \"\\f7cd\";\n}\n\n.fa-book::before {\n  content: \"\\f02d\";\n}\n\n.fa-user-plus::before {\n  content: \"\\f234\";\n}\n\n.fa-check::before {\n  content: \"\\f00c\";\n}\n\n.fa-battery-three-quarters::before {\n  content: \"\\f241\";\n}\n\n.fa-battery-4::before {\n  content: \"\\f241\";\n}\n\n.fa-house-circle-check::before {\n  content: \"\\e509\";\n}\n\n.fa-angle-left::before {\n  content: \"\\f104\";\n}\n\n.fa-diagram-successor::before {\n  content: \"\\e47a\";\n}\n\n.fa-truck-arrow-right::before {\n  content: \"\\e58b\";\n}\n\n.fa-arrows-split-up-and-left::before {\n  content: \"\\e4bc\";\n}\n\n.fa-hand-fist::before {\n  content: \"\\f6de\";\n}\n\n.fa-fist-raised::before {\n  content: \"\\f6de\";\n}\n\n.fa-cloud-moon::before {\n  content: \"\\f6c3\";\n}\n\n.fa-briefcase::before {\n  content: \"\\f0b1\";\n}\n\n.fa-person-falling::before {\n  content: \"\\e546\";\n}\n\n.fa-image-portrait::before {\n  content: \"\\f3e0\";\n}\n\n.fa-portrait::before {\n  content: \"\\f3e0\";\n}\n\n.fa-user-tag::before {\n  content: \"\\f507\";\n}\n\n.fa-rug::before {\n  content: \"\\e569\";\n}\n\n.fa-earth-europe::before {\n  content: \"\\f7a2\";\n}\n\n.fa-globe-europe::before {\n  content: \"\\f7a2\";\n}\n\n.fa-cart-flatbed-suitcase::before {\n  content: \"\\f59d\";\n}\n\n.fa-luggage-cart::before {\n  content: \"\\f59d\";\n}\n\n.fa-rectangle-xmark::before {\n  content: \"\\f410\";\n}\n\n.fa-rectangle-times::before {\n  content: \"\\f410\";\n}\n\n.fa-times-rectangle::before {\n  content: \"\\f410\";\n}\n\n.fa-window-close::before {\n  content: \"\\f410\";\n}\n\n.fa-baht-sign::before {\n  content: \"\\e0ac\";\n}\n\n.fa-book-open::before {\n  content: \"\\f518\";\n}\n\n.fa-book-journal-whills::before {\n  content: \"\\f66a\";\n}\n\n.fa-journal-whills::before {\n  content: \"\\f66a\";\n}\n\n.fa-handcuffs::before {\n  content: \"\\e4f8\";\n}\n\n.fa-triangle-exclamation::before {\n  content: \"\\f071\";\n}\n\n.fa-exclamation-triangle::before {\n  content: \"\\f071\";\n}\n\n.fa-warning::before {\n  content: \"\\f071\";\n}\n\n.fa-database::before {\n  content: \"\\f1c0\";\n}\n\n.fa-share::before {\n  content: \"\\f064\";\n}\n\n.fa-mail-forward::before {\n  content: \"\\f064\";\n}\n\n.fa-bottle-droplet::before {\n  content: \"\\e4c4\";\n}\n\n.fa-mask-face::before {\n  content: \"\\e1d7\";\n}\n\n.fa-hill-rockslide::before {\n  content: \"\\e508\";\n}\n\n.fa-right-left::before {\n  content: \"\\f362\";\n}\n\n.fa-exchange-alt::before {\n  content: \"\\f362\";\n}\n\n.fa-paper-plane::before {\n  content: \"\\f1d8\";\n}\n\n.fa-road-circle-exclamation::before {\n  content: \"\\e565\";\n}\n\n.fa-dungeon::before {\n  content: \"\\f6d9\";\n}\n\n.fa-align-right::before {\n  content: \"\\f038\";\n}\n\n.fa-money-bill-1-wave::before {\n  content: \"\\f53b\";\n}\n\n.fa-money-bill-wave-alt::before {\n  content: \"\\f53b\";\n}\n\n.fa-life-ring::before {\n  content: \"\\f1cd\";\n}\n\n.fa-hands::before {\n  content: \"\\f2a7\";\n}\n\n.fa-sign-language::before {\n  content: \"\\f2a7\";\n}\n\n.fa-signing::before {\n  content: \"\\f2a7\";\n}\n\n.fa-calendar-day::before {\n  content: \"\\f783\";\n}\n\n.fa-water-ladder::before {\n  content: \"\\f5c5\";\n}\n\n.fa-ladder-water::before {\n  content: \"\\f5c5\";\n}\n\n.fa-swimming-pool::before {\n  content: \"\\f5c5\";\n}\n\n.fa-arrows-up-down::before {\n  content: \"\\f07d\";\n}\n\n.fa-arrows-v::before {\n  content: \"\\f07d\";\n}\n\n.fa-face-grimace::before {\n  content: \"\\f57f\";\n}\n\n.fa-grimace::before {\n  content: \"\\f57f\";\n}\n\n.fa-wheelchair-move::before {\n  content: \"\\e2ce\";\n}\n\n.fa-wheelchair-alt::before {\n  content: \"\\e2ce\";\n}\n\n.fa-turn-down::before {\n  content: \"\\f3be\";\n}\n\n.fa-level-down-alt::before {\n  content: \"\\f3be\";\n}\n\n.fa-person-walking-arrow-right::before {\n  content: \"\\e552\";\n}\n\n.fa-square-envelope::before {\n  content: \"\\f199\";\n}\n\n.fa-envelope-square::before {\n  content: \"\\f199\";\n}\n\n.fa-dice::before {\n  content: \"\\f522\";\n}\n\n.fa-bowling-ball::before {\n  content: \"\\f436\";\n}\n\n.fa-brain::before {\n  content: \"\\f5dc\";\n}\n\n.fa-bandage::before {\n  content: \"\\f462\";\n}\n\n.fa-band-aid::before {\n  content: \"\\f462\";\n}\n\n.fa-calendar-minus::before {\n  content: \"\\f272\";\n}\n\n.fa-circle-xmark::before {\n  content: \"\\f057\";\n}\n\n.fa-times-circle::before {\n  content: \"\\f057\";\n}\n\n.fa-xmark-circle::before {\n  content: \"\\f057\";\n}\n\n.fa-gifts::before {\n  content: \"\\f79c\";\n}\n\n.fa-hotel::before {\n  content: \"\\f594\";\n}\n\n.fa-earth-asia::before {\n  content: \"\\f57e\";\n}\n\n.fa-globe-asia::before {\n  content: \"\\f57e\";\n}\n\n.fa-id-card-clip::before {\n  content: \"\\f47f\";\n}\n\n.fa-id-card-alt::before {\n  content: \"\\f47f\";\n}\n\n.fa-magnifying-glass-plus::before {\n  content: \"\\f00e\";\n}\n\n.fa-search-plus::before {\n  content: \"\\f00e\";\n}\n\n.fa-thumbs-up::before {\n  content: \"\\f164\";\n}\n\n.fa-user-clock::before {\n  content: \"\\f4fd\";\n}\n\n.fa-hand-dots::before {\n  content: \"\\f461\";\n}\n\n.fa-allergies::before {\n  content: \"\\f461\";\n}\n\n.fa-file-invoice::before {\n  content: \"\\f570\";\n}\n\n.fa-window-minimize::before {\n  content: \"\\f2d1\";\n}\n\n.fa-mug-saucer::before {\n  content: \"\\f0f4\";\n}\n\n.fa-coffee::before {\n  content: \"\\f0f4\";\n}\n\n.fa-brush::before {\n  content: \"\\f55d\";\n}\n\n.fa-mask::before {\n  content: \"\\f6fa\";\n}\n\n.fa-magnifying-glass-minus::before {\n  content: \"\\f010\";\n}\n\n.fa-search-minus::before {\n  content: \"\\f010\";\n}\n\n.fa-ruler-vertical::before {\n  content: \"\\f548\";\n}\n\n.fa-user-large::before {\n  content: \"\\f406\";\n}\n\n.fa-user-alt::before {\n  content: \"\\f406\";\n}\n\n.fa-train-tram::before {\n  content: \"\\e5b4\";\n}\n\n.fa-user-nurse::before {\n  content: \"\\f82f\";\n}\n\n.fa-syringe::before {\n  content: \"\\f48e\";\n}\n\n.fa-cloud-sun::before {\n  content: \"\\f6c4\";\n}\n\n.fa-stopwatch-20::before {\n  content: \"\\e06f\";\n}\n\n.fa-square-full::before {\n  content: \"\\f45c\";\n}\n\n.fa-magnet::before {\n  content: \"\\f076\";\n}\n\n.fa-jar::before {\n  content: \"\\e516\";\n}\n\n.fa-note-sticky::before {\n  content: \"\\f249\";\n}\n\n.fa-sticky-note::before {\n  content: \"\\f249\";\n}\n\n.fa-bug-slash::before {\n  content: \"\\e490\";\n}\n\n.fa-arrow-up-from-water-pump::before {\n  content: \"\\e4b6\";\n}\n\n.fa-bone::before {\n  content: \"\\f5d7\";\n}\n\n.fa-user-injured::before {\n  content: \"\\f728\";\n}\n\n.fa-face-sad-tear::before {\n  content: \"\\f5b4\";\n}\n\n.fa-sad-tear::before {\n  content: \"\\f5b4\";\n}\n\n.fa-plane::before {\n  content: \"\\f072\";\n}\n\n.fa-tent-arrows-down::before {\n  content: \"\\e581\";\n}\n\n.fa-exclamation::before {\n  content: \"\\!\";\n}\n\n.fa-arrows-spin::before {\n  content: \"\\e4bb\";\n}\n\n.fa-print::before {\n  content: \"\\f02f\";\n}\n\n.fa-turkish-lira-sign::before {\n  content: \"\\e2bb\";\n}\n\n.fa-try::before {\n  content: \"\\e2bb\";\n}\n\n.fa-turkish-lira::before {\n  content: \"\\e2bb\";\n}\n\n.fa-dollar-sign::before {\n  content: \"\\$\";\n}\n\n.fa-dollar::before {\n  content: \"\\$\";\n}\n\n.fa-usd::before {\n  content: \"\\$\";\n}\n\n.fa-x::before {\n  content: \"X\";\n}\n\n.fa-magnifying-glass-dollar::before {\n  content: \"\\f688\";\n}\n\n.fa-search-dollar::before {\n  content: \"\\f688\";\n}\n\n.fa-users-gear::before {\n  content: \"\\f509\";\n}\n\n.fa-users-cog::before {\n  content: \"\\f509\";\n}\n\n.fa-person-military-pointing::before {\n  content: \"\\e54a\";\n}\n\n.fa-building-columns::before {\n  content: \"\\f19c\";\n}\n\n.fa-bank::before {\n  content: \"\\f19c\";\n}\n\n.fa-institution::before {\n  content: \"\\f19c\";\n}\n\n.fa-museum::before {\n  content: \"\\f19c\";\n}\n\n.fa-university::before {\n  content: \"\\f19c\";\n}\n\n.fa-umbrella::before {\n  content: \"\\f0e9\";\n}\n\n.fa-trowel::before {\n  content: \"\\e589\";\n}\n\n.fa-d::before {\n  content: \"D\";\n}\n\n.fa-stapler::before {\n  content: \"\\e5af\";\n}\n\n.fa-masks-theater::before {\n  content: \"\\f630\";\n}\n\n.fa-theater-masks::before {\n  content: \"\\f630\";\n}\n\n.fa-kip-sign::before {\n  content: \"\\e1c4\";\n}\n\n.fa-hand-point-left::before {\n  content: \"\\f0a5\";\n}\n\n.fa-handshake-simple::before {\n  content: \"\\f4c6\";\n}\n\n.fa-handshake-alt::before {\n  content: \"\\f4c6\";\n}\n\n.fa-jet-fighter::before {\n  content: \"\\f0fb\";\n}\n\n.fa-fighter-jet::before {\n  content: \"\\f0fb\";\n}\n\n.fa-square-share-nodes::before {\n  content: \"\\f1e1\";\n}\n\n.fa-share-alt-square::before {\n  content: \"\\f1e1\";\n}\n\n.fa-barcode::before {\n  content: \"\\f02a\";\n}\n\n.fa-plus-minus::before {\n  content: \"\\e43c\";\n}\n\n.fa-video::before {\n  content: \"\\f03d\";\n}\n\n.fa-video-camera::before {\n  content: \"\\f03d\";\n}\n\n.fa-graduation-cap::before {\n  content: \"\\f19d\";\n}\n\n.fa-mortar-board::before {\n  content: \"\\f19d\";\n}\n\n.fa-hand-holding-medical::before {\n  content: \"\\e05c\";\n}\n\n.fa-person-circle-check::before {\n  content: \"\\e53e\";\n}\n\n.fa-turn-up::before {\n  content: \"\\f3bf\";\n}\n\n.fa-level-up-alt::before {\n  content: \"\\f3bf\";\n}\n\n.sr-only,\n.fa-sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  white-space: nowrap;\n  border-width: 0;\n}\n\n.sr-only-focusable:not(:focus),\n.fa-sr-only-focusable:not(:focus) {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  white-space: nowrap;\n  border-width: 0;\n}\n\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n:root, :host {\n  --fa-style-family-classic: \"Font Awesome 6 Free\";\n  --fa-font-solid: normal 900 1em/1 \"Font Awesome 6 Free\";\n}\n\n@font-face {\n  font-family: \"Font Awesome 6 Free\";\n  font-style: normal;\n  font-weight: 900;\n  font-display: block;\n  src: url(\"../webfonts/fa-solid-900.woff2\") format(\"woff2\"), url(\"../webfonts/fa-solid-900.ttf\") format(\"truetype\");\n}\n.fas,\n.fa-solid {\n  font-weight: 900;\n}\n\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n:root, :host {\n  --fa-style-family-classic: \"Font Awesome 6 Free\";\n  --fa-font-regular: normal 400 1em/1 \"Font Awesome 6 Free\";\n}\n\n@font-face {\n  font-family: \"Font Awesome 6 Free\";\n  font-style: normal;\n  font-weight: 400;\n  font-display: block;\n  src: url(\"../webfonts/fa-regular-400.woff2\") format(\"woff2\"), url(\"../webfonts/fa-regular-400.ttf\") format(\"truetype\");\n}\n.far,\n.fa-regular {\n  font-weight: 400;\n}\n\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n:root, :host {\n  --fa-style-family-brands: \"Font Awesome 6 Brands\";\n  --fa-font-brands: normal 400 1em/1 \"Font Awesome 6 Brands\";\n}\n\n@font-face {\n  font-family: \"Font Awesome 6 Brands\";\n  font-style: normal;\n  font-weight: 400;\n  font-display: block;\n  src: url(\"../webfonts/fa-brands-400.woff2\") format(\"woff2\"), url(\"../webfonts/fa-brands-400.ttf\") format(\"truetype\");\n}\n.fab,\n.fa-brands {\n  font-weight: 400;\n}\n\n.fa-monero:before {\n  content: \"\\f3d0\";\n}\n\n.fa-hooli:before {\n  content: \"\\f427\";\n}\n\n.fa-yelp:before {\n  content: \"\\f1e9\";\n}\n\n.fa-cc-visa:before {\n  content: \"\\f1f0\";\n}\n\n.fa-lastfm:before {\n  content: \"\\f202\";\n}\n\n.fa-shopware:before {\n  content: \"\\f5b5\";\n}\n\n.fa-creative-commons-nc:before {\n  content: \"\\f4e8\";\n}\n\n.fa-aws:before {\n  content: \"\\f375\";\n}\n\n.fa-redhat:before {\n  content: \"\\f7bc\";\n}\n\n.fa-yoast:before {\n  content: \"\\f2b1\";\n}\n\n.fa-cloudflare:before {\n  content: \"\\e07d\";\n}\n\n.fa-ups:before {\n  content: \"\\f7e0\";\n}\n\n.fa-pixiv:before {\n  content: \"\\e640\";\n}\n\n.fa-wpexplorer:before {\n  content: \"\\f2de\";\n}\n\n.fa-dyalog:before {\n  content: \"\\f399\";\n}\n\n.fa-bity:before {\n  content: \"\\f37a\";\n}\n\n.fa-stackpath:before {\n  content: \"\\f842\";\n}\n\n.fa-buysellads:before {\n  content: \"\\f20d\";\n}\n\n.fa-first-order:before {\n  content: \"\\f2b0\";\n}\n\n.fa-modx:before {\n  content: \"\\f285\";\n}\n\n.fa-guilded:before {\n  content: \"\\e07e\";\n}\n\n.fa-vnv:before {\n  content: \"\\f40b\";\n}\n\n.fa-square-js:before {\n  content: \"\\f3b9\";\n}\n\n.fa-js-square:before {\n  content: \"\\f3b9\";\n}\n\n.fa-microsoft:before {\n  content: \"\\f3ca\";\n}\n\n.fa-qq:before {\n  content: \"\\f1d6\";\n}\n\n.fa-orcid:before {\n  content: \"\\f8d2\";\n}\n\n.fa-java:before {\n  content: \"\\f4e4\";\n}\n\n.fa-invision:before {\n  content: \"\\f7b0\";\n}\n\n.fa-creative-commons-pd-alt:before {\n  content: \"\\f4ed\";\n}\n\n.fa-centercode:before {\n  content: \"\\f380\";\n}\n\n.fa-glide-g:before {\n  content: \"\\f2a6\";\n}\n\n.fa-drupal:before {\n  content: \"\\f1a9\";\n}\n\n.fa-jxl:before {\n  content: \"\\e67b\";\n}\n\n.fa-hire-a-helper:before {\n  content: \"\\f3b0\";\n}\n\n.fa-creative-commons-by:before {\n  content: \"\\f4e7\";\n}\n\n.fa-unity:before {\n  content: \"\\e049\";\n}\n\n.fa-whmcs:before {\n  content: \"\\f40d\";\n}\n\n.fa-rocketchat:before {\n  content: \"\\f3e8\";\n}\n\n.fa-vk:before {\n  content: \"\\f189\";\n}\n\n.fa-untappd:before {\n  content: \"\\f405\";\n}\n\n.fa-mailchimp:before {\n  content: \"\\f59e\";\n}\n\n.fa-css3-alt:before {\n  content: \"\\f38b\";\n}\n\n.fa-square-reddit:before {\n  content: \"\\f1a2\";\n}\n\n.fa-reddit-square:before {\n  content: \"\\f1a2\";\n}\n\n.fa-vimeo-v:before {\n  content: \"\\f27d\";\n}\n\n.fa-contao:before {\n  content: \"\\f26d\";\n}\n\n.fa-square-font-awesome:before {\n  content: \"\\e5ad\";\n}\n\n.fa-deskpro:before {\n  content: \"\\f38f\";\n}\n\n.fa-brave:before {\n  content: \"\\e63c\";\n}\n\n.fa-sistrix:before {\n  content: \"\\f3ee\";\n}\n\n.fa-square-instagram:before {\n  content: \"\\e055\";\n}\n\n.fa-instagram-square:before {\n  content: \"\\e055\";\n}\n\n.fa-battle-net:before {\n  content: \"\\f835\";\n}\n\n.fa-the-red-yeti:before {\n  content: \"\\f69d\";\n}\n\n.fa-square-hacker-news:before {\n  content: \"\\f3af\";\n}\n\n.fa-hacker-news-square:before {\n  content: \"\\f3af\";\n}\n\n.fa-edge:before {\n  content: \"\\f282\";\n}\n\n.fa-threads:before {\n  content: \"\\e618\";\n}\n\n.fa-napster:before {\n  content: \"\\f3d2\";\n}\n\n.fa-square-snapchat:before {\n  content: \"\\f2ad\";\n}\n\n.fa-snapchat-square:before {\n  content: \"\\f2ad\";\n}\n\n.fa-google-plus-g:before {\n  content: \"\\f0d5\";\n}\n\n.fa-artstation:before {\n  content: \"\\f77a\";\n}\n\n.fa-markdown:before {\n  content: \"\\f60f\";\n}\n\n.fa-sourcetree:before {\n  content: \"\\f7d3\";\n}\n\n.fa-google-plus:before {\n  content: \"\\f2b3\";\n}\n\n.fa-diaspora:before {\n  content: \"\\f791\";\n}\n\n.fa-foursquare:before {\n  content: \"\\f180\";\n}\n\n.fa-stack-overflow:before {\n  content: \"\\f16c\";\n}\n\n.fa-github-alt:before {\n  content: \"\\f113\";\n}\n\n.fa-phoenix-squadron:before {\n  content: \"\\f511\";\n}\n\n.fa-pagelines:before {\n  content: \"\\f18c\";\n}\n\n.fa-algolia:before {\n  content: \"\\f36c\";\n}\n\n.fa-red-river:before {\n  content: \"\\f3e3\";\n}\n\n.fa-creative-commons-sa:before {\n  content: \"\\f4ef\";\n}\n\n.fa-safari:before {\n  content: \"\\f267\";\n}\n\n.fa-google:before {\n  content: \"\\f1a0\";\n}\n\n.fa-square-font-awesome-stroke:before {\n  content: \"\\f35c\";\n}\n\n.fa-font-awesome-alt:before {\n  content: \"\\f35c\";\n}\n\n.fa-atlassian:before {\n  content: \"\\f77b\";\n}\n\n.fa-linkedin-in:before {\n  content: \"\\f0e1\";\n}\n\n.fa-digital-ocean:before {\n  content: \"\\f391\";\n}\n\n.fa-nimblr:before {\n  content: \"\\f5a8\";\n}\n\n.fa-chromecast:before {\n  content: \"\\f838\";\n}\n\n.fa-evernote:before {\n  content: \"\\f839\";\n}\n\n.fa-hacker-news:before {\n  content: \"\\f1d4\";\n}\n\n.fa-creative-commons-sampling:before {\n  content: \"\\f4f0\";\n}\n\n.fa-adversal:before {\n  content: \"\\f36a\";\n}\n\n.fa-creative-commons:before {\n  content: \"\\f25e\";\n}\n\n.fa-watchman-monitoring:before {\n  content: \"\\e087\";\n}\n\n.fa-fonticons:before {\n  content: \"\\f280\";\n}\n\n.fa-weixin:before {\n  content: \"\\f1d7\";\n}\n\n.fa-shirtsinbulk:before {\n  content: \"\\f214\";\n}\n\n.fa-codepen:before {\n  content: \"\\f1cb\";\n}\n\n.fa-git-alt:before {\n  content: \"\\f841\";\n}\n\n.fa-lyft:before {\n  content: \"\\f3c3\";\n}\n\n.fa-rev:before {\n  content: \"\\f5b2\";\n}\n\n.fa-windows:before {\n  content: \"\\f17a\";\n}\n\n.fa-wizards-of-the-coast:before {\n  content: \"\\f730\";\n}\n\n.fa-square-viadeo:before {\n  content: \"\\f2aa\";\n}\n\n.fa-viadeo-square:before {\n  content: \"\\f2aa\";\n}\n\n.fa-meetup:before {\n  content: \"\\f2e0\";\n}\n\n.fa-centos:before {\n  content: \"\\f789\";\n}\n\n.fa-adn:before {\n  content: \"\\f170\";\n}\n\n.fa-cloudsmith:before {\n  content: \"\\f384\";\n}\n\n.fa-opensuse:before {\n  content: \"\\e62b\";\n}\n\n.fa-pied-piper-alt:before {\n  content: \"\\f1a8\";\n}\n\n.fa-square-dribbble:before {\n  content: \"\\f397\";\n}\n\n.fa-dribbble-square:before {\n  content: \"\\f397\";\n}\n\n.fa-codiepie:before {\n  content: \"\\f284\";\n}\n\n.fa-node:before {\n  content: \"\\f419\";\n}\n\n.fa-mix:before {\n  content: \"\\f3cb\";\n}\n\n.fa-steam:before {\n  content: \"\\f1b6\";\n}\n\n.fa-cc-apple-pay:before {\n  content: \"\\f416\";\n}\n\n.fa-scribd:before {\n  content: \"\\f28a\";\n}\n\n.fa-debian:before {\n  content: \"\\e60b\";\n}\n\n.fa-openid:before {\n  content: \"\\f19b\";\n}\n\n.fa-instalod:before {\n  content: \"\\e081\";\n}\n\n.fa-expeditedssl:before {\n  content: \"\\f23e\";\n}\n\n.fa-sellcast:before {\n  content: \"\\f2da\";\n}\n\n.fa-square-twitter:before {\n  content: \"\\f081\";\n}\n\n.fa-twitter-square:before {\n  content: \"\\f081\";\n}\n\n.fa-r-project:before {\n  content: \"\\f4f7\";\n}\n\n.fa-delicious:before {\n  content: \"\\f1a5\";\n}\n\n.fa-freebsd:before {\n  content: \"\\f3a4\";\n}\n\n.fa-vuejs:before {\n  content: \"\\f41f\";\n}\n\n.fa-accusoft:before {\n  content: \"\\f369\";\n}\n\n.fa-ioxhost:before {\n  content: \"\\f208\";\n}\n\n.fa-fonticons-fi:before {\n  content: \"\\f3a2\";\n}\n\n.fa-app-store:before {\n  content: \"\\f36f\";\n}\n\n.fa-cc-mastercard:before {\n  content: \"\\f1f1\";\n}\n\n.fa-itunes-note:before {\n  content: \"\\f3b5\";\n}\n\n.fa-golang:before {\n  content: \"\\e40f\";\n}\n\n.fa-kickstarter:before {\n  content: \"\\f3bb\";\n}\n\n.fa-square-kickstarter:before {\n  content: \"\\f3bb\";\n}\n\n.fa-grav:before {\n  content: \"\\f2d6\";\n}\n\n.fa-weibo:before {\n  content: \"\\f18a\";\n}\n\n.fa-uncharted:before {\n  content: \"\\e084\";\n}\n\n.fa-firstdraft:before {\n  content: \"\\f3a1\";\n}\n\n.fa-square-youtube:before {\n  content: \"\\f431\";\n}\n\n.fa-youtube-square:before {\n  content: \"\\f431\";\n}\n\n.fa-wikipedia-w:before {\n  content: \"\\f266\";\n}\n\n.fa-wpressr:before {\n  content: \"\\f3e4\";\n}\n\n.fa-rendact:before {\n  content: \"\\f3e4\";\n}\n\n.fa-angellist:before {\n  content: \"\\f209\";\n}\n\n.fa-galactic-republic:before {\n  content: \"\\f50c\";\n}\n\n.fa-nfc-directional:before {\n  content: \"\\e530\";\n}\n\n.fa-skype:before {\n  content: \"\\f17e\";\n}\n\n.fa-joget:before {\n  content: \"\\f3b7\";\n}\n\n.fa-fedora:before {\n  content: \"\\f798\";\n}\n\n.fa-stripe-s:before {\n  content: \"\\f42a\";\n}\n\n.fa-meta:before {\n  content: \"\\e49b\";\n}\n\n.fa-laravel:before {\n  content: \"\\f3bd\";\n}\n\n.fa-hotjar:before {\n  content: \"\\f3b1\";\n}\n\n.fa-bluetooth-b:before {\n  content: \"\\f294\";\n}\n\n.fa-square-letterboxd:before {\n  content: \"\\e62e\";\n}\n\n.fa-sticker-mule:before {\n  content: \"\\f3f7\";\n}\n\n.fa-creative-commons-zero:before {\n  content: \"\\f4f3\";\n}\n\n.fa-hips:before {\n  content: \"\\f452\";\n}\n\n.fa-behance:before {\n  content: \"\\f1b4\";\n}\n\n.fa-reddit:before {\n  content: \"\\f1a1\";\n}\n\n.fa-discord:before {\n  content: \"\\f392\";\n}\n\n.fa-chrome:before {\n  content: \"\\f268\";\n}\n\n.fa-app-store-ios:before {\n  content: \"\\f370\";\n}\n\n.fa-cc-discover:before {\n  content: \"\\f1f2\";\n}\n\n.fa-wpbeginner:before {\n  content: \"\\f297\";\n}\n\n.fa-confluence:before {\n  content: \"\\f78d\";\n}\n\n.fa-shoelace:before {\n  content: \"\\e60c\";\n}\n\n.fa-mdb:before {\n  content: \"\\f8ca\";\n}\n\n.fa-dochub:before {\n  content: \"\\f394\";\n}\n\n.fa-accessible-icon:before {\n  content: \"\\f368\";\n}\n\n.fa-ebay:before {\n  content: \"\\f4f4\";\n}\n\n.fa-amazon:before {\n  content: \"\\f270\";\n}\n\n.fa-unsplash:before {\n  content: \"\\e07c\";\n}\n\n.fa-yarn:before {\n  content: \"\\f7e3\";\n}\n\n.fa-square-steam:before {\n  content: \"\\f1b7\";\n}\n\n.fa-steam-square:before {\n  content: \"\\f1b7\";\n}\n\n.fa-500px:before {\n  content: \"\\f26e\";\n}\n\n.fa-square-vimeo:before {\n  content: \"\\f194\";\n}\n\n.fa-vimeo-square:before {\n  content: \"\\f194\";\n}\n\n.fa-asymmetrik:before {\n  content: \"\\f372\";\n}\n\n.fa-font-awesome:before {\n  content: \"\\f2b4\";\n}\n\n.fa-font-awesome-flag:before {\n  content: \"\\f2b4\";\n}\n\n.fa-font-awesome-logo-full:before {\n  content: \"\\f2b4\";\n}\n\n.fa-gratipay:before {\n  content: \"\\f184\";\n}\n\n.fa-apple:before {\n  content: \"\\f179\";\n}\n\n.fa-hive:before {\n  content: \"\\e07f\";\n}\n\n.fa-gitkraken:before {\n  content: \"\\f3a6\";\n}\n\n.fa-keybase:before {\n  content: \"\\f4f5\";\n}\n\n.fa-apple-pay:before {\n  content: \"\\f415\";\n}\n\n.fa-padlet:before {\n  content: \"\\e4a0\";\n}\n\n.fa-amazon-pay:before {\n  content: \"\\f42c\";\n}\n\n.fa-square-github:before {\n  content: \"\\f092\";\n}\n\n.fa-github-square:before {\n  content: \"\\f092\";\n}\n\n.fa-stumbleupon:before {\n  content: \"\\f1a4\";\n}\n\n.fa-fedex:before {\n  content: \"\\f797\";\n}\n\n.fa-phoenix-framework:before {\n  content: \"\\f3dc\";\n}\n\n.fa-shopify:before {\n  content: \"\\e057\";\n}\n\n.fa-neos:before {\n  content: \"\\f612\";\n}\n\n.fa-square-threads:before {\n  content: \"\\e619\";\n}\n\n.fa-hackerrank:before {\n  content: \"\\f5f7\";\n}\n\n.fa-researchgate:before {\n  content: \"\\f4f8\";\n}\n\n.fa-swift:before {\n  content: \"\\f8e1\";\n}\n\n.fa-angular:before {\n  content: \"\\f420\";\n}\n\n.fa-speakap:before {\n  content: \"\\f3f3\";\n}\n\n.fa-angrycreative:before {\n  content: \"\\f36e\";\n}\n\n.fa-y-combinator:before {\n  content: \"\\f23b\";\n}\n\n.fa-empire:before {\n  content: \"\\f1d1\";\n}\n\n.fa-envira:before {\n  content: \"\\f299\";\n}\n\n.fa-google-scholar:before {\n  content: \"\\e63b\";\n}\n\n.fa-square-gitlab:before {\n  content: \"\\e5ae\";\n}\n\n.fa-gitlab-square:before {\n  content: \"\\e5ae\";\n}\n\n.fa-studiovinari:before {\n  content: \"\\f3f8\";\n}\n\n.fa-pied-piper:before {\n  content: \"\\f2ae\";\n}\n\n.fa-wordpress:before {\n  content: \"\\f19a\";\n}\n\n.fa-product-hunt:before {\n  content: \"\\f288\";\n}\n\n.fa-firefox:before {\n  content: \"\\f269\";\n}\n\n.fa-linode:before {\n  content: \"\\f2b8\";\n}\n\n.fa-goodreads:before {\n  content: \"\\f3a8\";\n}\n\n.fa-square-odnoklassniki:before {\n  content: \"\\f264\";\n}\n\n.fa-odnoklassniki-square:before {\n  content: \"\\f264\";\n}\n\n.fa-jsfiddle:before {\n  content: \"\\f1cc\";\n}\n\n.fa-sith:before {\n  content: \"\\f512\";\n}\n\n.fa-themeisle:before {\n  content: \"\\f2b2\";\n}\n\n.fa-page4:before {\n  content: \"\\f3d7\";\n}\n\n.fa-hashnode:before {\n  content: \"\\e499\";\n}\n\n.fa-react:before {\n  content: \"\\f41b\";\n}\n\n.fa-cc-paypal:before {\n  content: \"\\f1f4\";\n}\n\n.fa-squarespace:before {\n  content: \"\\f5be\";\n}\n\n.fa-cc-stripe:before {\n  content: \"\\f1f5\";\n}\n\n.fa-creative-commons-share:before {\n  content: \"\\f4f2\";\n}\n\n.fa-bitcoin:before {\n  content: \"\\f379\";\n}\n\n.fa-keycdn:before {\n  content: \"\\f3ba\";\n}\n\n.fa-opera:before {\n  content: \"\\f26a\";\n}\n\n.fa-itch-io:before {\n  content: \"\\f83a\";\n}\n\n.fa-umbraco:before {\n  content: \"\\f8e8\";\n}\n\n.fa-galactic-senate:before {\n  content: \"\\f50d\";\n}\n\n.fa-ubuntu:before {\n  content: \"\\f7df\";\n}\n\n.fa-draft2digital:before {\n  content: \"\\f396\";\n}\n\n.fa-stripe:before {\n  content: \"\\f429\";\n}\n\n.fa-houzz:before {\n  content: \"\\f27c\";\n}\n\n.fa-gg:before {\n  content: \"\\f260\";\n}\n\n.fa-dhl:before {\n  content: \"\\f790\";\n}\n\n.fa-square-pinterest:before {\n  content: \"\\f0d3\";\n}\n\n.fa-pinterest-square:before {\n  content: \"\\f0d3\";\n}\n\n.fa-xing:before {\n  content: \"\\f168\";\n}\n\n.fa-blackberry:before {\n  content: \"\\f37b\";\n}\n\n.fa-creative-commons-pd:before {\n  content: \"\\f4ec\";\n}\n\n.fa-playstation:before {\n  content: \"\\f3df\";\n}\n\n.fa-quinscape:before {\n  content: \"\\f459\";\n}\n\n.fa-less:before {\n  content: \"\\f41d\";\n}\n\n.fa-blogger-b:before {\n  content: \"\\f37d\";\n}\n\n.fa-opencart:before {\n  content: \"\\f23d\";\n}\n\n.fa-vine:before {\n  content: \"\\f1ca\";\n}\n\n.fa-signal-messenger:before {\n  content: \"\\e663\";\n}\n\n.fa-paypal:before {\n  content: \"\\f1ed\";\n}\n\n.fa-gitlab:before {\n  content: \"\\f296\";\n}\n\n.fa-typo3:before {\n  content: \"\\f42b\";\n}\n\n.fa-reddit-alien:before {\n  content: \"\\f281\";\n}\n\n.fa-yahoo:before {\n  content: \"\\f19e\";\n}\n\n.fa-dailymotion:before {\n  content: \"\\e052\";\n}\n\n.fa-affiliatetheme:before {\n  content: \"\\f36b\";\n}\n\n.fa-pied-piper-pp:before {\n  content: \"\\f1a7\";\n}\n\n.fa-bootstrap:before {\n  content: \"\\f836\";\n}\n\n.fa-odnoklassniki:before {\n  content: \"\\f263\";\n}\n\n.fa-nfc-symbol:before {\n  content: \"\\e531\";\n}\n\n.fa-mintbit:before {\n  content: \"\\e62f\";\n}\n\n.fa-ethereum:before {\n  content: \"\\f42e\";\n}\n\n.fa-speaker-deck:before {\n  content: \"\\f83c\";\n}\n\n.fa-creative-commons-nc-eu:before {\n  content: \"\\f4e9\";\n}\n\n.fa-patreon:before {\n  content: \"\\f3d9\";\n}\n\n.fa-avianex:before {\n  content: \"\\f374\";\n}\n\n.fa-ello:before {\n  content: \"\\f5f1\";\n}\n\n.fa-gofore:before {\n  content: \"\\f3a7\";\n}\n\n.fa-bimobject:before {\n  content: \"\\f378\";\n}\n\n.fa-brave-reverse:before {\n  content: \"\\e63d\";\n}\n\n.fa-facebook-f:before {\n  content: \"\\f39e\";\n}\n\n.fa-square-google-plus:before {\n  content: \"\\f0d4\";\n}\n\n.fa-google-plus-square:before {\n  content: \"\\f0d4\";\n}\n\n.fa-web-awesome:before {\n  content: \"\\e682\";\n}\n\n.fa-mandalorian:before {\n  content: \"\\f50f\";\n}\n\n.fa-first-order-alt:before {\n  content: \"\\f50a\";\n}\n\n.fa-osi:before {\n  content: \"\\f41a\";\n}\n\n.fa-google-wallet:before {\n  content: \"\\f1ee\";\n}\n\n.fa-d-and-d-beyond:before {\n  content: \"\\f6ca\";\n}\n\n.fa-periscope:before {\n  content: \"\\f3da\";\n}\n\n.fa-fulcrum:before {\n  content: \"\\f50b\";\n}\n\n.fa-cloudscale:before {\n  content: \"\\f383\";\n}\n\n.fa-forumbee:before {\n  content: \"\\f211\";\n}\n\n.fa-mizuni:before {\n  content: \"\\f3cc\";\n}\n\n.fa-schlix:before {\n  content: \"\\f3ea\";\n}\n\n.fa-square-xing:before {\n  content: \"\\f169\";\n}\n\n.fa-xing-square:before {\n  content: \"\\f169\";\n}\n\n.fa-bandcamp:before {\n  content: \"\\f2d5\";\n}\n\n.fa-wpforms:before {\n  content: \"\\f298\";\n}\n\n.fa-cloudversify:before {\n  content: \"\\f385\";\n}\n\n.fa-usps:before {\n  content: \"\\f7e1\";\n}\n\n.fa-megaport:before {\n  content: \"\\f5a3\";\n}\n\n.fa-magento:before {\n  content: \"\\f3c4\";\n}\n\n.fa-spotify:before {\n  content: \"\\f1bc\";\n}\n\n.fa-optin-monster:before {\n  content: \"\\f23c\";\n}\n\n.fa-fly:before {\n  content: \"\\f417\";\n}\n\n.fa-aviato:before {\n  content: \"\\f421\";\n}\n\n.fa-itunes:before {\n  content: \"\\f3b4\";\n}\n\n.fa-cuttlefish:before {\n  content: \"\\f38c\";\n}\n\n.fa-blogger:before {\n  content: \"\\f37c\";\n}\n\n.fa-flickr:before {\n  content: \"\\f16e\";\n}\n\n.fa-viber:before {\n  content: \"\\f409\";\n}\n\n.fa-soundcloud:before {\n  content: \"\\f1be\";\n}\n\n.fa-digg:before {\n  content: \"\\f1a6\";\n}\n\n.fa-tencent-weibo:before {\n  content: \"\\f1d5\";\n}\n\n.fa-letterboxd:before {\n  content: \"\\e62d\";\n}\n\n.fa-symfony:before {\n  content: \"\\f83d\";\n}\n\n.fa-maxcdn:before {\n  content: \"\\f136\";\n}\n\n.fa-etsy:before {\n  content: \"\\f2d7\";\n}\n\n.fa-facebook-messenger:before {\n  content: \"\\f39f\";\n}\n\n.fa-audible:before {\n  content: \"\\f373\";\n}\n\n.fa-think-peaks:before {\n  content: \"\\f731\";\n}\n\n.fa-bilibili:before {\n  content: \"\\e3d9\";\n}\n\n.fa-erlang:before {\n  content: \"\\f39d\";\n}\n\n.fa-x-twitter:before {\n  content: \"\\e61b\";\n}\n\n.fa-cotton-bureau:before {\n  content: \"\\f89e\";\n}\n\n.fa-dashcube:before {\n  content: \"\\f210\";\n}\n\n.fa-42-group:before {\n  content: \"\\e080\";\n}\n\n.fa-innosoft:before {\n  content: \"\\e080\";\n}\n\n.fa-stack-exchange:before {\n  content: \"\\f18d\";\n}\n\n.fa-elementor:before {\n  content: \"\\f430\";\n}\n\n.fa-square-pied-piper:before {\n  content: \"\\e01e\";\n}\n\n.fa-pied-piper-square:before {\n  content: \"\\e01e\";\n}\n\n.fa-creative-commons-nd:before {\n  content: \"\\f4eb\";\n}\n\n.fa-palfed:before {\n  content: \"\\f3d8\";\n}\n\n.fa-superpowers:before {\n  content: \"\\f2dd\";\n}\n\n.fa-resolving:before {\n  content: \"\\f3e7\";\n}\n\n.fa-xbox:before {\n  content: \"\\f412\";\n}\n\n.fa-square-web-awesome-stroke:before {\n  content: \"\\e684\";\n}\n\n.fa-searchengin:before {\n  content: \"\\f3eb\";\n}\n\n.fa-tiktok:before {\n  content: \"\\e07b\";\n}\n\n.fa-square-facebook:before {\n  content: \"\\f082\";\n}\n\n.fa-facebook-square:before {\n  content: \"\\f082\";\n}\n\n.fa-renren:before {\n  content: \"\\f18b\";\n}\n\n.fa-linux:before {\n  content: \"\\f17c\";\n}\n\n.fa-glide:before {\n  content: \"\\f2a5\";\n}\n\n.fa-linkedin:before {\n  content: \"\\f08c\";\n}\n\n.fa-hubspot:before {\n  content: \"\\f3b2\";\n}\n\n.fa-deploydog:before {\n  content: \"\\f38e\";\n}\n\n.fa-twitch:before {\n  content: \"\\f1e8\";\n}\n\n.fa-ravelry:before {\n  content: \"\\f2d9\";\n}\n\n.fa-mixer:before {\n  content: \"\\e056\";\n}\n\n.fa-square-lastfm:before {\n  content: \"\\f203\";\n}\n\n.fa-lastfm-square:before {\n  content: \"\\f203\";\n}\n\n.fa-vimeo:before {\n  content: \"\\f40a\";\n}\n\n.fa-mendeley:before {\n  content: \"\\f7b3\";\n}\n\n.fa-uniregistry:before {\n  content: \"\\f404\";\n}\n\n.fa-figma:before {\n  content: \"\\f799\";\n}\n\n.fa-creative-commons-remix:before {\n  content: \"\\f4ee\";\n}\n\n.fa-cc-amazon-pay:before {\n  content: \"\\f42d\";\n}\n\n.fa-dropbox:before {\n  content: \"\\f16b\";\n}\n\n.fa-instagram:before {\n  content: \"\\f16d\";\n}\n\n.fa-cmplid:before {\n  content: \"\\e360\";\n}\n\n.fa-upwork:before {\n  content: \"\\e641\";\n}\n\n.fa-facebook:before {\n  content: \"\\f09a\";\n}\n\n.fa-gripfire:before {\n  content: \"\\f3ac\";\n}\n\n.fa-jedi-order:before {\n  content: \"\\f50e\";\n}\n\n.fa-uikit:before {\n  content: \"\\f403\";\n}\n\n.fa-fort-awesome-alt:before {\n  content: \"\\f3a3\";\n}\n\n.fa-phabricator:before {\n  content: \"\\f3db\";\n}\n\n.fa-ussunnah:before {\n  content: \"\\f407\";\n}\n\n.fa-earlybirds:before {\n  content: \"\\f39a\";\n}\n\n.fa-trade-federation:before {\n  content: \"\\f513\";\n}\n\n.fa-autoprefixer:before {\n  content: \"\\f41c\";\n}\n\n.fa-whatsapp:before {\n  content: \"\\f232\";\n}\n\n.fa-square-upwork:before {\n  content: \"\\e67c\";\n}\n\n.fa-slideshare:before {\n  content: \"\\f1e7\";\n}\n\n.fa-google-play:before {\n  content: \"\\f3ab\";\n}\n\n.fa-viadeo:before {\n  content: \"\\f2a9\";\n}\n\n.fa-line:before {\n  content: \"\\f3c0\";\n}\n\n.fa-google-drive:before {\n  content: \"\\f3aa\";\n}\n\n.fa-servicestack:before {\n  content: \"\\f3ec\";\n}\n\n.fa-simplybuilt:before {\n  content: \"\\f215\";\n}\n\n.fa-bitbucket:before {\n  content: \"\\f171\";\n}\n\n.fa-imdb:before {\n  content: \"\\f2d8\";\n}\n\n.fa-deezer:before {\n  content: \"\\e077\";\n}\n\n.fa-raspberry-pi:before {\n  content: \"\\f7bb\";\n}\n\n.fa-jira:before {\n  content: \"\\f7b1\";\n}\n\n.fa-docker:before {\n  content: \"\\f395\";\n}\n\n.fa-screenpal:before {\n  content: \"\\e570\";\n}\n\n.fa-bluetooth:before {\n  content: \"\\f293\";\n}\n\n.fa-gitter:before {\n  content: \"\\f426\";\n}\n\n.fa-d-and-d:before {\n  content: \"\\f38d\";\n}\n\n.fa-microblog:before {\n  content: \"\\e01a\";\n}\n\n.fa-cc-diners-club:before {\n  content: \"\\f24c\";\n}\n\n.fa-gg-circle:before {\n  content: \"\\f261\";\n}\n\n.fa-pied-piper-hat:before {\n  content: \"\\f4e5\";\n}\n\n.fa-kickstarter-k:before {\n  content: \"\\f3bc\";\n}\n\n.fa-yandex:before {\n  content: \"\\f413\";\n}\n\n.fa-readme:before {\n  content: \"\\f4d5\";\n}\n\n.fa-html5:before {\n  content: \"\\f13b\";\n}\n\n.fa-sellsy:before {\n  content: \"\\f213\";\n}\n\n.fa-square-web-awesome:before {\n  content: \"\\e683\";\n}\n\n.fa-sass:before {\n  content: \"\\f41e\";\n}\n\n.fa-wirsindhandwerk:before {\n  content: \"\\e2d0\";\n}\n\n.fa-wsh:before {\n  content: \"\\e2d0\";\n}\n\n.fa-buromobelexperte:before {\n  content: \"\\f37f\";\n}\n\n.fa-salesforce:before {\n  content: \"\\f83b\";\n}\n\n.fa-octopus-deploy:before {\n  content: \"\\e082\";\n}\n\n.fa-medapps:before {\n  content: \"\\f3c6\";\n}\n\n.fa-ns8:before {\n  content: \"\\f3d5\";\n}\n\n.fa-pinterest-p:before {\n  content: \"\\f231\";\n}\n\n.fa-apper:before {\n  content: \"\\f371\";\n}\n\n.fa-fort-awesome:before {\n  content: \"\\f286\";\n}\n\n.fa-waze:before {\n  content: \"\\f83f\";\n}\n\n.fa-bluesky:before {\n  content: \"\\e671\";\n}\n\n.fa-cc-jcb:before {\n  content: \"\\f24b\";\n}\n\n.fa-snapchat:before {\n  content: \"\\f2ab\";\n}\n\n.fa-snapchat-ghost:before {\n  content: \"\\f2ab\";\n}\n\n.fa-fantasy-flight-games:before {\n  content: \"\\f6dc\";\n}\n\n.fa-rust:before {\n  content: \"\\e07a\";\n}\n\n.fa-wix:before {\n  content: \"\\f5cf\";\n}\n\n.fa-square-behance:before {\n  content: \"\\f1b5\";\n}\n\n.fa-behance-square:before {\n  content: \"\\f1b5\";\n}\n\n.fa-supple:before {\n  content: \"\\f3f9\";\n}\n\n.fa-webflow:before {\n  content: \"\\e65c\";\n}\n\n.fa-rebel:before {\n  content: \"\\f1d0\";\n}\n\n.fa-css3:before {\n  content: \"\\f13c\";\n}\n\n.fa-staylinked:before {\n  content: \"\\f3f5\";\n}\n\n.fa-kaggle:before {\n  content: \"\\f5fa\";\n}\n\n.fa-space-awesome:before {\n  content: \"\\e5ac\";\n}\n\n.fa-deviantart:before {\n  content: \"\\f1bd\";\n}\n\n.fa-cpanel:before {\n  content: \"\\f388\";\n}\n\n.fa-goodreads-g:before {\n  content: \"\\f3a9\";\n}\n\n.fa-square-git:before {\n  content: \"\\f1d2\";\n}\n\n.fa-git-square:before {\n  content: \"\\f1d2\";\n}\n\n.fa-square-tumblr:before {\n  content: \"\\f174\";\n}\n\n.fa-tumblr-square:before {\n  content: \"\\f174\";\n}\n\n.fa-trello:before {\n  content: \"\\f181\";\n}\n\n.fa-creative-commons-nc-jp:before {\n  content: \"\\f4ea\";\n}\n\n.fa-get-pocket:before {\n  content: \"\\f265\";\n}\n\n.fa-perbyte:before {\n  content: \"\\e083\";\n}\n\n.fa-grunt:before {\n  content: \"\\f3ad\";\n}\n\n.fa-weebly:before {\n  content: \"\\f5cc\";\n}\n\n.fa-connectdevelop:before {\n  content: \"\\f20e\";\n}\n\n.fa-leanpub:before {\n  content: \"\\f212\";\n}\n\n.fa-black-tie:before {\n  content: \"\\f27e\";\n}\n\n.fa-themeco:before {\n  content: \"\\f5c6\";\n}\n\n.fa-python:before {\n  content: \"\\f3e2\";\n}\n\n.fa-android:before {\n  content: \"\\f17b\";\n}\n\n.fa-bots:before {\n  content: \"\\e340\";\n}\n\n.fa-free-code-camp:before {\n  content: \"\\f2c5\";\n}\n\n.fa-hornbill:before {\n  content: \"\\f592\";\n}\n\n.fa-js:before {\n  content: \"\\f3b8\";\n}\n\n.fa-ideal:before {\n  content: \"\\e013\";\n}\n\n.fa-git:before {\n  content: \"\\f1d3\";\n}\n\n.fa-dev:before {\n  content: \"\\f6cc\";\n}\n\n.fa-sketch:before {\n  content: \"\\f7c6\";\n}\n\n.fa-yandex-international:before {\n  content: \"\\f414\";\n}\n\n.fa-cc-amex:before {\n  content: \"\\f1f3\";\n}\n\n.fa-uber:before {\n  content: \"\\f402\";\n}\n\n.fa-github:before {\n  content: \"\\f09b\";\n}\n\n.fa-php:before {\n  content: \"\\f457\";\n}\n\n.fa-alipay:before {\n  content: \"\\f642\";\n}\n\n.fa-youtube:before {\n  content: \"\\f167\";\n}\n\n.fa-skyatlas:before {\n  content: \"\\f216\";\n}\n\n.fa-firefox-browser:before {\n  content: \"\\e007\";\n}\n\n.fa-replyd:before {\n  content: \"\\f3e6\";\n}\n\n.fa-suse:before {\n  content: \"\\f7d6\";\n}\n\n.fa-jenkins:before {\n  content: \"\\f3b6\";\n}\n\n.fa-twitter:before {\n  content: \"\\f099\";\n}\n\n.fa-rockrms:before {\n  content: \"\\f3e9\";\n}\n\n.fa-pinterest:before {\n  content: \"\\f0d2\";\n}\n\n.fa-buffer:before {\n  content: \"\\f837\";\n}\n\n.fa-npm:before {\n  content: \"\\f3d4\";\n}\n\n.fa-yammer:before {\n  content: \"\\f840\";\n}\n\n.fa-btc:before {\n  content: \"\\f15a\";\n}\n\n.fa-dribbble:before {\n  content: \"\\f17d\";\n}\n\n.fa-stumbleupon-circle:before {\n  content: \"\\f1a3\";\n}\n\n.fa-internet-explorer:before {\n  content: \"\\f26b\";\n}\n\n.fa-stubber:before {\n  content: \"\\e5c7\";\n}\n\n.fa-telegram:before {\n  content: \"\\f2c6\";\n}\n\n.fa-telegram-plane:before {\n  content: \"\\f2c6\";\n}\n\n.fa-old-republic:before {\n  content: \"\\f510\";\n}\n\n.fa-odysee:before {\n  content: \"\\e5c6\";\n}\n\n.fa-square-whatsapp:before {\n  content: \"\\f40c\";\n}\n\n.fa-whatsapp-square:before {\n  content: \"\\f40c\";\n}\n\n.fa-node-js:before {\n  content: \"\\f3d3\";\n}\n\n.fa-edge-legacy:before {\n  content: \"\\e078\";\n}\n\n.fa-slack:before {\n  content: \"\\f198\";\n}\n\n.fa-slack-hash:before {\n  content: \"\\f198\";\n}\n\n.fa-medrt:before {\n  content: \"\\f3c8\";\n}\n\n.fa-usb:before {\n  content: \"\\f287\";\n}\n\n.fa-tumblr:before {\n  content: \"\\f173\";\n}\n\n.fa-vaadin:before {\n  content: \"\\f408\";\n}\n\n.fa-quora:before {\n  content: \"\\f2c4\";\n}\n\n.fa-square-x-twitter:before {\n  content: \"\\e61a\";\n}\n\n.fa-reacteurope:before {\n  content: \"\\f75d\";\n}\n\n.fa-medium:before {\n  content: \"\\f23a\";\n}\n\n.fa-medium-m:before {\n  content: \"\\f23a\";\n}\n\n.fa-amilia:before {\n  content: \"\\f36d\";\n}\n\n.fa-mixcloud:before {\n  content: \"\\f289\";\n}\n\n.fa-flipboard:before {\n  content: \"\\f44d\";\n}\n\n.fa-viacoin:before {\n  content: \"\\f237\";\n}\n\n.fa-critical-role:before {\n  content: \"\\f6c9\";\n}\n\n.fa-sitrox:before {\n  content: \"\\e44a\";\n}\n\n.fa-discourse:before {\n  content: \"\\f393\";\n}\n\n.fa-joomla:before {\n  content: \"\\f1aa\";\n}\n\n.fa-mastodon:before {\n  content: \"\\f4f6\";\n}\n\n.fa-airbnb:before {\n  content: \"\\f834\";\n}\n\n.fa-wolf-pack-battalion:before {\n  content: \"\\f514\";\n}\n\n.fa-buy-n-large:before {\n  content: \"\\f8a6\";\n}\n\n.fa-gulp:before {\n  content: \"\\f3ae\";\n}\n\n.fa-creative-commons-sampling-plus:before {\n  content: \"\\f4f1\";\n}\n\n.fa-strava:before {\n  content: \"\\f428\";\n}\n\n.fa-ember:before {\n  content: \"\\f423\";\n}\n\n.fa-canadian-maple-leaf:before {\n  content: \"\\f785\";\n}\n\n.fa-teamspeak:before {\n  content: \"\\f4f9\";\n}\n\n.fa-pushed:before {\n  content: \"\\f3e1\";\n}\n\n.fa-wordpress-simple:before {\n  content: \"\\f411\";\n}\n\n.fa-nutritionix:before {\n  content: \"\\f3d6\";\n}\n\n.fa-wodu:before {\n  content: \"\\e088\";\n}\n\n.fa-google-pay:before {\n  content: \"\\e079\";\n}\n\n.fa-intercom:before {\n  content: \"\\f7af\";\n}\n\n.fa-zhihu:before {\n  content: \"\\f63f\";\n}\n\n.fa-korvue:before {\n  content: \"\\f42f\";\n}\n\n.fa-pix:before {\n  content: \"\\e43a\";\n}\n\n.fa-steam-symbol:before {\n  content: \"\\f3f6\";\n}\n\nhtml {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n/*******************************************************************************\n* Define the animation behaviour\n*/\n/*******************************************************************************\n* UI shaping and padding\n*/\nhtml {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n  --pst-font-size-base: 1rem;\n  --pst-font-size-h1: 2.625rem;\n  --pst-font-size-h2: 2.125rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1rem;\n  --pst-font-size-milli: 0.9rem;\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n  --pst-admonition-font-weight-heading: 600;\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 600;\n  --pst-font-family-base-system: -apple-system, \"BlinkMacSystemFont\", \"Segoe UI\",\n    \"Helvetica Neue\", \"Arial\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\",\n    \"Segoe UI Symbol\";\n  --pst-font-family-monospace-system: \"SFMono-Regular\", \"Menlo\", \"Consolas\",\n    \"Monaco\", \"Liberation Mono\", \"Lucida Console\", monospace;\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n\nhtml {\n  /*****************************************************************************\n  * Icon\n  */\n  --pst-font-size-icon: 1.5rem;\n  --pst-icon-check-circle: \"\";\n  --pst-icon-info-circle: \"\";\n  --pst-icon-exclamation-triangle: \"\";\n  --pst-icon-exclamation-circle: \"\";\n  --pst-icon-times-circle: \"\";\n  --pst-icon-lightbulb: \"\";\n  --pst-icon-download: \"\";\n  --pst-icon-angle-left: \"\";\n  --pst-icon-angle-right: \"\";\n  --pst-icon-external-link: \"\";\n  --pst-icon-search-minus: \"\";\n  --pst-icon-github: \"\";\n  --pst-icon-gitlab: \"\";\n  --pst-icon-share: \"\";\n  --pst-icon-bell: \"\";\n  --pst-icon-pencil: \"\";\n  --pst-breadcrumb-divider: \"\";\n}\n\nhtml {\n  /*****************************************************************************\n  * Admonitions\n  **/\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n\nhtml {\n  /*****************************************************************************\n  * versionmodified\n  **/\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n\n/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n/**\n* Function to get items from nested maps\n*/\n/* Assign base colors for the PyData theme */\n:root {\n  --pst-teal-50: #f4fbfc;\n  --pst-teal-100: #e9f6f8;\n  --pst-teal-200: #d0ecf1;\n  --pst-teal-300: #abdde6;\n  --pst-teal-400: #3fb1c5;\n  --pst-teal-500: #0a7d91;\n  --pst-teal-600: #085d6c;\n  --pst-teal-700: #064752;\n  --pst-teal-800: #042c33;\n  --pst-teal-900: #021b1f;\n  --pst-violet-50: #f4eefb;\n  --pst-violet-100: #e0c7ff;\n  --pst-violet-200: #d5b4fd;\n  --pst-violet-300: #b780ff;\n  --pst-violet-400: #9c5ffd;\n  --pst-violet-500: #8045e5;\n  --pst-violet-600: #6432bd;\n  --pst-violet-700: #4b258f;\n  --pst-violet-800: #341a61;\n  --pst-violet-900: #1e0e39;\n  --pst-gray-50: #f9f9fa;\n  --pst-gray-100: #f3f4f5;\n  --pst-gray-200: #e5e7ea;\n  --pst-gray-300: #d1d5da;\n  --pst-gray-400: #9ca4af;\n  --pst-gray-500: #677384;\n  --pst-gray-600: #48566b;\n  --pst-gray-700: #29313d;\n  --pst-gray-800: #222832;\n  --pst-gray-900: #14181e;\n  --pst-pink-50: #fcf8fd;\n  --pst-pink-100: #fcf0fa;\n  --pst-pink-200: #f8dff5;\n  --pst-pink-300: #f3c7ee;\n  --pst-pink-400: #e47fd7;\n  --pst-pink-500: #c132af;\n  --pst-pink-600: #912583;\n  --pst-pink-700: #6e1c64;\n  --pst-pink-800: #46123f;\n  --pst-pink-900: #2b0b27;\n  --pst-foundation-white: #ffffff;\n  --pst-foundation-black: #14181e;\n}\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * something inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-heading-color: #fff;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #b780ff;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n}\nhtml:not([data-theme]) {\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\nhtml:not([data-theme]) .only-dark,\nhtml:not([data-theme]) .only-dark ~ figcaption {\n  display: none !important;\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\nhtml[data-theme=light] {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-heading-color: #fff;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #b780ff;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n  color-scheme: light;\n}\nhtml[data-theme=light] {\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\nhtml[data-theme=light] .only-dark,\nhtml[data-theme=light] .only-dark ~ figcaption {\n  display: none !important;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-primary: #3fb1c5;\n  --pst-color-primary-bg: #042c33;\n  --pst-color-secondary: #9c5ffd;\n  --pst-color-secondary-bg: #341a61;\n  --pst-color-accent: #e47fd7;\n  --pst-color-accent-bg: #46123f;\n  --pst-color-info: #79a3f2;\n  --pst-color-info-bg: #06245d;\n  --pst-color-warning: #ff9245;\n  --pst-color-warning-bg: #652a02;\n  --pst-color-success: #5fb488;\n  --pst-color-success-bg: #002f17;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #e78894;\n  --pst-color-danger-bg: #4e111b;\n  --pst-color-text-base: #ced6dd;\n  --pst-color-text-muted: #9ca4af;\n  --pst-color-heading-color: #14181e;\n  --pst-color-shadow: rgba(0, 0, 0, 0.2);\n  --pst-color-border: #48566b;\n  --pst-color-border-muted: #29313d;\n  --pst-color-blockquote-notch: #9ca4af;\n  --pst-color-inline-code: #f3c7ee;\n  --pst-color-inline-code-links: #3fb1c5;\n  --pst-color-target: #675c04;\n  --pst-color-table: #ffffff;\n  --pst-color-table-row-hover-bg: #6432bd;\n  --pst-color-table-inner-border: #364150;\n  --pst-color-background: #14181e;\n  --pst-color-on-background: #222832;\n  --pst-color-surface: #29313d;\n  --pst-color-on-surface: #f3f4f5;\n  /* Adjust images in dark mode (unless they have class .only-dark or\n   * .dark-light, in which case assume they're already optimized for dark\n   * mode).\n   */\n  /* Give images a light background in dark mode in case they have\n  *  transparency and black text (unless they have class .only-dark or .dark-light, in\n  *  which case assume they're already optimized for dark mode).\n  */\n  color-scheme: dark;\n}\nhtml[data-theme=dark] {\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\nhtml[data-theme=dark] .only-light,\nhtml[data-theme=dark] .only-light ~ figcaption {\n  display: none !important;\n}\nhtml[data-theme=dark] img:not(.only-dark, .dark-light) {\n  filter: brightness(0.8) contrast(1.2);\n}\nhtml[data-theme=dark] .bd-content img:not(.only-dark, .dark-light) {\n  background-color: rgb(255, 255, 255);\n  border-radius: 0.25rem;\n}\nhtml[data-theme=dark] .MathJax_SVG * {\n  fill: var(--pst-color-text-base);\n}\n\n.pst-color-primary {\n  color: var(--pst-color-primary);\n}\n\n.pst-color-secondary {\n  color: var(--pst-color-secondary);\n}\n\n.pst-color-accent {\n  color: var(--pst-color-accent);\n}\n\n.pst-color-info {\n  color: var(--pst-color-info);\n}\n\n.pst-color-warning {\n  color: var(--pst-color-warning);\n}\n\n.pst-color-success {\n  color: var(--pst-color-success);\n}\n\n.pst-color-attention {\n  color: var(--pst-color-attention);\n}\n\n.pst-color-danger {\n  color: var(--pst-color-danger);\n}\n\n.pst-color-text-base {\n  color: var(--pst-color-text-base);\n}\n\n.pst-color-text-muted {\n  color: var(--pst-color-text-muted);\n}\n\n.pst-color-heading-color {\n  color: var(--pst-color-heading-color);\n}\n\n.pst-color-shadow {\n  color: var(--pst-color-shadow);\n}\n\n.pst-color-border {\n  color: var(--pst-color-border);\n}\n\n.pst-color-border-muted {\n  color: var(--pst-color-border-muted);\n}\n\n.pst-color-blockquote-notch {\n  color: var(--pst-color-blockquote-notch);\n}\n\n.pst-color-inline-code {\n  color: var(--pst-color-inline-code);\n}\n\n.pst-color-inline-code-links {\n  color: var(--pst-color-inline-code-links);\n}\n\n.pst-color-target {\n  color: var(--pst-color-target);\n}\n\n.pst-color-table {\n  color: var(--pst-color-table);\n}\n\n.pst-color-table-row-hover {\n  color: var(--pst-color-table-row-hover);\n}\n\n.pst-color-table-inner-border {\n  color: var(--pst-color-table-inner-border);\n}\n\n.pst-color-background {\n  color: var(--pst-color-background);\n}\n\n.pst-color-on-background {\n  color: var(--pst-color-on-background);\n}\n\n.pst-color-surface {\n  color: var(--pst-color-surface);\n}\n\n.pst-color-on-surface {\n  color: var(--pst-color-on-surface);\n}\n\n/**\n * Miscellaneous color functions and mixins\n**/\n/**\n* Function to get items from nested maps\n*/\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n/*********************************************\n* SASS Mixins\n*********************************************/\n/**\n * A consistent box shadow style we apply across elements.\n */\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\nhtml {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: 1.65;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n}\nbody::-webkit-scrollbar-track {\n  background-color: var(--pst-color-background);\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n  /* section header in docstring pages */\n}\np.rubric {\n  border-bottom: 1px solid var(--pst-color-border);\n}\np.centered {\n  text-align: center;\n}\n\na {\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n}\na:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\na:active {\n  color: var(--pst-color-link);\n}\na:visited {\n  color: var(--pst-color-link);\n}\na:visited:hover {\n  color: var(--pst-color-link-hover);\n}\na.headerlink {\n  color: var(--pst-color-secondary);\n  font-size: 0.8em;\n  padding: 0 4px;\n  margin-left: 0.2em;\n  text-decoration: none;\n  user-select: none;\n}\na.github::before, a.gitlab::before {\n  color: var(--pst-color-text-muted);\n  font: var(--fa-font-brands);\n  margin-right: 0.25rem;\n}\na.github::before {\n  content: var(--pst-icon-github);\n}\na.gitlab::before {\n  content: var(--pst-icon-gitlab);\n}\n\nh6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1, .h1 {\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-heading-color);\n}\n\nh2, .h2 {\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-heading-color);\n}\n\nh3, .h3 {\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-heading-color);\n}\n\nh4, .h4 {\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-heading-color);\n}\n\nh5, .h5 {\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6, .h6 {\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall, .small,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n}\nkbd:not(.compound) {\n  border: 1px solid var(--pst-color-border);\n  margin: 0 0.1rem;\n  padding: 0.1rem 0.4rem;\n  box-shadow: 1px 1px 1px var(--pst-color-shadow);\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\npre .linenos {\n  opacity: 0.8;\n  padding-right: 10px;\n}\n\n#pst-back-to-top {\n  z-index: 1080;\n  position: fixed;\n  display: none;\n  top: 90vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n}\n#pst-back-to-top .fa-arrow-up {\n  margin-inline-end: 0.5em;\n}\n#pst-back-to-top:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: 1px;\n  background-color: var(--pst-color-secondary-highlight);\n  color: var(--pst-color-secondary-highlight-text);\n}\n#pst-back-to-top:focus-visible {\n  box-shadow: none;\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-color: var(--pst-color-secondary);\n  outline-offset: 0.1875rem;\n}\n\n:focus-visible {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  box-shadow: none;\n}\n\n/***\n * Rules for the UX/UI of skip navigation link btn.\n * It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  padding: 0.5rem;\n  z-index: 1055;\n  border-bottom: 1px solid var(--pst-color-border);\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n}\n.skip-link:focus-within {\n  transform: translateY(0%);\n}\n.skip-link a {\n  color: var(--pst-color-warning-text) !important;\n}\n.skip-link a:focus-visible {\n  outline-color: #14181e;\n}\n\n.bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n}\n.bd-container .bd-container__inner {\n  display: flex;\n}\n\n.bd-page-width {\n  width: 100%;\n}\n@media (min-width: 960px) {\n  .bd-page-width {\n    max-width: 88rem;\n  }\n}\n\n.pst-async-banner-revealer {\n  height: 0;\n  overflow: hidden;\n  transition: height 300ms ease-in-out;\n}\n@media (prefers-reduced-motion) {\n  .pst-async-banner-revealer {\n    transition: none;\n  }\n}\n\n#bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%;\n}\n@media (max-width: 959.98px) {\n  #bd-header-version-warning,\n  .bd-header-announcement {\n    padding: 0.5rem 2%;\n  }\n}\n#bd-header-version-warning p,\n.bd-header-announcement p {\n  font-weight: bold;\n  margin: 0;\n}\n#bd-header-version-warning a,\n.bd-header-announcement a {\n  color: var(--pst-color-inline-code-links);\n}\n#bd-header-version-warning .pst-button-link-to-stable-version,\n.bd-header-announcement .pst-button-link-to-stable-version {\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n  background-color: var(--pst-color-danger);\n  border-color: var(--pst-color-danger);\n  color: var(--pst-color-danger-text);\n  border-radius: 0.25rem;\n}\n#bd-header-version-warning .pst-button-link-to-stable-version:hover,\n.bd-header-announcement .pst-button-link-to-stable-version:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: 1px;\n  background-color: var(--pst-color-danger-highlight);\n  border-color: var(--pst-color-danger-highlight);\n  color: var(--pst-color-danger-highlight-text);\n}\n#bd-header-version-warning .pst-button-link-to-stable-version:focus-visible,\n.bd-header-announcement .pst-button-link-to-stable-version:focus-visible {\n  outline: 0.1875rem solid var(--pst-color-danger);\n  outline-offset: 0.1875rem;\n}\n\n.bd-header-announcement {\n  background-color: var(--pst-color-secondary-bg);\n}\n\n#bd-header-version-warning {\n  background-color: var(--pst-color-danger-bg);\n}\n\n/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n}\n.bd-main .bd-content {\n  display: flex;\n  justify-content: center;\n  height: 100%;\n}\n.bd-main .bd-content .bd-article-container {\n  justify-content: start;\n  display: flex;\n  flex-direction: column;\n  width: 100%;\n  max-width: 60em;\n  overflow-x: auto;\n  padding: 1rem;\n}\n@media (min-width: 1200px) {\n  .bd-main .bd-content .bd-article-container .bd-article {\n    padding-top: 1.5rem;\n    padding-left: 2rem;\n  }\n}\n\n.bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n}\n.bd-footer .bd-footer__inner {\n  display: flex;\n  flex-grow: 1;\n  padding: 1rem;\n  margin: auto;\n}\n.bd-footer .footer-items__start,\n.bd-footer .footer-items__center,\n.bd-footer .footer-items__end {\n  display: flex;\n  flex-direction: column;\n  gap: 0.5rem;\n  justify-content: center;\n  flex-grow: 1;\n}\n.bd-footer .footer-items__center {\n  text-align: center;\n}\n.bd-footer .footer-items__end {\n  text-align: end;\n}\n.bd-footer .footer-item p {\n  margin-bottom: 0;\n}\n\n.bd-footer-article {\n  margin-top: auto;\n}\n.bd-footer-article .footer-article-items {\n  display: flex;\n  flex-direction: column;\n}\n\n.bd-footer-content .footer-content-items {\n  display: flex;\n  flex-direction: column;\n  margin-top: auto;\n}\n\n/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: 1030;\n  background-color: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n}\n.bd-header .bd-header__inner {\n  display: flex;\n  align-items: center;\n  height: fit-content;\n  padding-left: 1rem;\n  padding-right: 1rem;\n}\n.bd-header :focus-visible {\n  border-radius: 0.125rem;\n}\n.bd-header .navbar-item {\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  display: flex;\n  align-items: center;\n}\n.bd-header .navbar-header-items {\n  display: none;\n  flex-shrink: 1;\n}\n@media (min-width: 960px) {\n  .bd-header .navbar-header-items {\n    display: inherit;\n    flex-grow: 1;\n    padding: 0 0 0 0.5rem;\n  }\n}\n.bd-header .navbar-header-items__end,\n.bd-header .navbar-header-items__center,\n.bd-header .navbar-header-items__start {\n  display: flex;\n  align-items: center;\n  flex-flow: wrap;\n  row-gap: 0;\n}\n.bd-header .navbar-header-items__end,\n.bd-header .navbar-header-items__center {\n  column-gap: 1.12rem;\n}\n.bd-header .navbar-header-items__start {\n  flex-shrink: 0;\n  margin-right: auto;\n  gap: 0.5rem;\n}\n.bd-header .navbar-header-items__end {\n  justify-content: end;\n}\n.bd-header ul.navbar-nav {\n  display: flex;\n}\n@media (min-width: 960px) {\n  .bd-header ul.navbar-nav {\n    align-items: baseline;\n  }\n}\n.bd-header ul.navbar-nav > li.nav-item {\n  margin-inline: 2px;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link {\n  color: var(--pst-color-text-muted);\n  position: relative;\n  padding-inline: 6px;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link::before {\n  content: \"\";\n  display: block;\n  position: absolute;\n  inset: 0;\n  background-color: transparent;\n  bottom: calc(-1 * max(3px, .1875rem, .12em));\n  margin: max(3px, .1875rem, .12em) 0;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link:hover {\n  color: var(--pst-color-secondary);\n  text-decoration: none;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link:hover::before {\n  border-bottom: max(3px, .1875rem, .12em) solid var(--pst-color-secondary);\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link:focus-visible {\n  box-shadow: none;\n  outline: 3px solid var(--pst-color-accent);\n  outline-offset: 3px;\n}\n.bd-header ul.navbar-nav > li.nav-item.current > .nav-link {\n  color: var(--pst-color-primary);\n}\n.bd-header ul.navbar-nav > li.nav-item.current > .nav-link::before {\n  border-bottom: 3px solid var(--pst-color-primary);\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown {\n  margin-inline: 4px;\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown button {\n  padding-inline: 8px;\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown > .dropdown-toggle {\n  border-radius: 0.125rem;\n  color: var(--pst-color-text-muted);\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown > .dropdown-toggle:focus-visible {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown > .dropdown-toggle:hover {\n  text-decoration: none;\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-link-hover);\n  color: var(--pst-color-text-base);\n}\n.bd-header ul.navbar-nav li a.nav-link.dropdown-item {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\n.bd-header ul.navbar-nav li a.nav-link.dropdown-item:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n.bd-header ul.navbar-nav .dropdown button {\n  display: unset;\n  border: none;\n}\n.bd-header ul.navbar-nav .dropdown button:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu {\n  z-index: 1070;\n  border: 1px solid var(--pst-color-border);\n  box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n  background-color: var(--pst-color-on-background);\n  padding: 0.5rem 0;\n  margin: 0.5rem 0;\n  min-width: 20rem;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu .dropdown-item {\n  padding: 0.25rem 1.5rem;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu .dropdown-item:focus:not(:hover, :active) {\n  background-color: inherit;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu .dropdown-item:focus-visible {\n  z-index: 10;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu:not(.show) {\n  display: none;\n}\n.bd-header button.sidebar-toggle {\n  font-size: var(--pst-font-size-icon);\n  color: var(--pst-color-muted);\n  margin-bottom: 0;\n  background-color: inherit;\n  padding: 0.5rem;\n}\n.bd-header button.primary-toggle {\n  margin-right: 1rem;\n}\n@media (min-width: 960px) {\n  .bd-header button.primary-toggle {\n    display: none;\n  }\n}\n.bd-header button.secondary-toggle {\n  margin-left: 1rem;\n}\n@media (min-width: 1200px) {\n  .bd-header button.secondary-toggle {\n    display: none;\n  }\n}\n\n@media (min-width: 960px) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n.nav-link {\n  transition: none;\n}\n.nav-link:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n}\n.nav-link.nav-external::after {\n  font: var(--fa-font-solid);\n  content: var(--pst-icon-external-link);\n  font-size: 0.75em;\n  margin-left: 0.3em;\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n.navbar-persistent--mobile {\n  margin-left: auto;\n}\n@media (min-width: 960px) {\n  .navbar-persistent--mobile {\n    display: none;\n  }\n}\n\n.navbar-persistent--container {\n  display: none;\n}\n@media (min-width: 960px) {\n  .navbar-persistent--container {\n    display: flex;\n  }\n}\n\n.header-article__inner {\n  padding: 0 0.5rem;\n}\n.header-article__inner .header-article-item {\n  min-height: var(--pst-header-article-height);\n}\n.header-article__inner .header-article-items__end {\n  margin-left: auto;\n}\n\n/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n  flex: 0 0 auto;\n  width: 25%;\n  padding: 2rem 1rem 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n.bd-sidebar-primary :focus-visible {\n  border-radius: 0.125rem;\n}\n.bd-sidebar-primary .nav-link {\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\n.bd-sidebar-primary.no-sidebar {\n  border-right: 0;\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary.hide-on-wide {\n    display: none;\n  }\n}\n.bd-sidebar-primary h1, .bd-sidebar-primary .h1,\n.bd-sidebar-primary h2,\n.bd-sidebar-primary .h2,\n.bd-sidebar-primary h3,\n.bd-sidebar-primary .h3,\n.bd-sidebar-primary h4,\n.bd-sidebar-primary .h4 {\n  color: var(--pst-color-text-base);\n}\n.bd-sidebar-primary .sidebar-primary-items__start .sidebar-primary-item,\n.bd-sidebar-primary .sidebar-primary-items__end .sidebar-primary-item {\n  padding: 0.5rem 0;\n}\n.bd-sidebar-primary .sidebar-header-items {\n  display: flex;\n  flex-direction: column;\n}\n.bd-sidebar-primary .sidebar-header-items .sidebar-header-items__title {\n  font-weight: var(--pst-sidebar-header-font-weight);\n  font-size: var(--pst-sidebar-header-font-size);\n  color: var(--pst-color-text-base);\n  margin-bottom: 0.5rem;\n}\n.bd-sidebar-primary .sidebar-header-items .bd-navbar-elements .nav-link:focus-visible {\n  box-shadow: none;\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\n.bd-sidebar-primary .sidebar-header-items .sidebar-header-items__center {\n  display: flex;\n  flex-direction: column;\n}\n.bd-sidebar-primary .sidebar-header-items .sidebar-header-items__end {\n  display: flex;\n  align-items: center;\n  gap: 1rem;\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary .sidebar-header-items {\n    display: none;\n  }\n}\n.bd-sidebar-primary .sidebar-primary-items__start {\n  border-top: 1px solid var(--pst-color-border);\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary .sidebar-primary-items__start {\n    border-top: none;\n  }\n}\n.bd-sidebar-primary .sidebar-primary-items__end {\n  margin-top: auto;\n  margin-bottom: 1em;\n}\n.bd-sidebar-primary .list-caption {\n  list-style: none;\n  padding-left: 0;\n}\n.bd-sidebar-primary .list-caption li.toctree-l0.has-children > details > summary {\n  position: relative;\n  height: auto;\n  width: auto;\n  display: flex;\n  justify-content: space-between;\n  align-items: baseline;\n}\n.bd-sidebar-primary .list-caption li.toctree-l0.has-children > details > summary .toctree-toggle {\n  flex: 0 0 auto;\n}\n.bd-sidebar-primary .list-caption li.toctree-l0.has-children > details > summary .toctree-toggle .fa-chevron-down {\n  font-size: 1rem;\n}\n.bd-sidebar-primary li.has-children {\n  position: relative;\n}\n.bd-sidebar-primary li.has-children > .reference,\n.bd-sidebar-primary li.has-children .caption {\n  margin-right: calc(30px + 0.1875rem);\n  padding-top: 0.25rem;\n}\n.bd-sidebar-primary li.has-children > details > summary {\n  list-style: none;\n}\n.bd-sidebar-primary li.has-children > details > summary::-webkit-details-marker {\n  display: none;\n}\n.bd-sidebar-primary li.has-children > details > summary:focus-visible {\n  outline: none;\n}\n.bd-sidebar-primary li.has-children > details > summary:focus-visible > .toctree-toggle {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-offset: -0.1875rem;\n}\n.bd-sidebar-primary li.has-children > details > summary .toctree-toggle {\n  cursor: pointer;\n  position: absolute;\n  top: 0;\n  right: 0;\n  width: 30px;\n  height: 30px;\n  display: inline-flex;\n  justify-content: center;\n  align-items: center;\n}\n.bd-sidebar-primary li.has-children > details > summary .toctree-toggle .fa-chevron-down {\n  font-size: 0.75rem;\n}\n.bd-sidebar-primary li.has-children > details[open] > summary .fa-chevron-down {\n  transform: rotate(180deg);\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -1rem;\n}\n@media (min-width: 960px) {\n  nav.bd-links {\n    display: block;\n  }\n}\nnav.bd-links ul {\n  display: block;\n  list-style: none;\n}\nnav.bd-links ul ul {\n  padding: 0 0 0 1rem;\n}\nnav.bd-links li > a {\n  display: block;\n  padding: 0.25rem 0.65rem;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n  box-shadow: none;\n  margin-right: 0.1875rem;\n}\nnav.bd-links li > a:hover {\n  text-decoration: underline;\n  background-color: transparent;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\nnav.bd-links li > a:active {\n  color: var(--pst-color-link-hover);\n}\nnav.bd-links li > a:focus-visible {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n  z-index: 10;\n}\nnav.bd-links li > a.reference.external::after {\n  font: var(--fa-font-solid);\n  content: var(--pst-icon-external-link);\n  font-size: 0.75em;\n  margin-left: 0.3em;\n}\nnav.bd-links .current > a {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary);\n  background-color: transparent;\n}\nnav.bd-links .current > a:focus-visible {\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary), 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n}\nnav.bd-links p.bd-links__title {\n  font-size: var(--pst-sidebar-header-font-size);\n  font-weight: var(--pst-sidebar-header-font-weight);\n  margin-bottom: 0.5rem;\n}\nnav.bd-links p.caption {\n  font-weight: var(--pst-sidebar-header-font-weight);\n  position: relative;\n  margin-top: 1.25rem;\n  margin-bottom: 0.5rem;\n  color: var(--pst-color-text-base);\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\nnav.bd-links p.caption:first-child {\n  margin-top: 0;\n}\n@media (min-width: 960px) {\n  nav.bd-links p.caption {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n\n/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n@media (min-width: 1200px) {\n  .bd-sidebar-secondary {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem;\n}\n@media (min-width: 1200px) {\n  .sidebar-secondary-item {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n}\n.sidebar-secondary-item i {\n  padding-right: 0.5rem;\n}\n\n/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n/**\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n.bd-sidebar::backdrop {\n  background-color: black;\n  opacity: 0.5;\n}\n\n@media (max-width: 959.98px) {\n  .bd-sidebar-primary {\n    position: fixed;\n    top: 0;\n    z-index: 1055;\n    height: 100vh;\n    max-height: 100vh;\n    width: 75%;\n    flex-grow: 0.75;\n    max-width: 350px;\n    transition: visibility 200ms ease-out, margin 200ms ease-out;\n    visibility: hidden;\n    border: 0;\n    margin-left: -75%;\n    left: 0;\n  }\n}\n.bd-sidebar-primary[open] {\n  margin-left: 0;\n  visibility: visible;\n}\n\n@media (max-width: 1199.98px) {\n  .bd-sidebar-secondary {\n    position: fixed;\n    top: 0;\n    z-index: 1055;\n    height: 100vh;\n    max-height: 100vh;\n    width: 75%;\n    flex-grow: 0.75;\n    max-width: 350px;\n    transition: visibility 200ms ease-out, margin 200ms ease-out;\n    visibility: hidden;\n    border: 0;\n    margin-right: -75%;\n    right: 0;\n  }\n}\n.bd-sidebar-secondary[open] {\n  margin-right: 0;\n  visibility: visible;\n}\n\n/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n  font-size: 0.8rem;\n}\nul.bd-breadcrumbs li.breadcrumb-item {\n  display: flex;\n  align-items: center;\n  white-space: nowrap;\n  overflow-x: hidden;\n  font-weight: bold;\n}\nul.bd-breadcrumbs li.breadcrumb-item a,\nul.bd-breadcrumbs li.breadcrumb-item .ellipsis {\n  overflow-x: hidden;\n  text-overflow: ellipsis;\n  margin: 0.1875rem;\n}\nul.bd-breadcrumbs li.breadcrumb-item a {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\nul.bd-breadcrumbs li.breadcrumb-item a:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\nul.bd-breadcrumbs li.breadcrumb-item:not(.breadcrumb-home)::before {\n  font: var(--fa-font-solid);\n  font-size: 0.8rem;\n  content: var(--pst-breadcrumb-divider);\n  color: var(--pst-color-text-muted);\n  padding: 0 0.5rem;\n}\n\n/**\n * Icon links in the navbar\n */\nhtml .pst-navbar-icon {\n  box-sizing: border-box;\n  min-width: 24px;\n  min-height: 24px;\n  color: var(--pst-color-text-muted);\n  position: relative;\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  border-radius: 0;\n  border: none;\n  font-size: 1rem;\n  line-height: 1.65;\n  padding: 0.25rem 0;\n  height: 2.15rem;\n}\nhtml .pst-navbar-icon::before {\n  content: \"\";\n  display: block;\n  position: absolute;\n  inset: 0;\n  background-color: transparent;\n  bottom: calc(-1 * max(3px, .1875rem, .12em));\n  margin: max(3px, .1875rem, .12em) 0;\n}\nhtml .pst-navbar-icon:hover {\n  color: var(--pst-color-secondary);\n  text-decoration: none;\n}\nhtml .pst-navbar-icon:hover::before {\n  border-bottom: max(3px, .1875rem, .12em) solid var(--pst-color-secondary);\n}\nhtml .pst-navbar-icon:focus-visible {\n  box-shadow: none;\n  outline: 3px solid var(--pst-color-accent);\n  outline-offset: 3px;\n}\n\nul.navbar-icon-links {\n  display: flex;\n  flex-flow: row wrap;\n  column-gap: 1.12rem;\n  justify-content: space-evenly;\n  align-items: center;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\nul.navbar-icon-links i {\n  /* Social media buttons hard-code the brand color */\n}\nul.navbar-icon-links i.fa-brands, ul.navbar-icon-links i.fa-regular, ul.navbar-icon-links i.fa-solid {\n  vertical-align: middle;\n  font-style: normal;\n  font-size: var(--pst-font-size-icon);\n}\nul.navbar-icon-links i.fa-square-twitter::before {\n  color: #55acee;\n}\nul.navbar-icon-links i.fa-square-gitlab::before {\n  color: #548;\n}\nul.navbar-icon-links i.fa-bitbucket::before {\n  color: #0052cc;\n}\nul.navbar-icon-links img.icon-link-image {\n  height: 1.5em;\n  border-radius: 0.2rem;\n}\nul.navbar-icon-links .fa-pydata {\n  stroke: var(--pst-color-background);\n  stroke-linejoin: round;\n  stroke-width: 0.35;\n}\n\n/**\n * Logo in the navbar\n */\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n}\n.navbar-brand p {\n  color: var(--pst-color-text-base);\n  margin-bottom: 0;\n}\n.navbar-brand img {\n  max-width: 100%;\n  height: 100%;\n  width: auto;\n}\n.navbar-brand:hover, .navbar-brand:visited:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  color: var(--pst-color-text-base);\n}\n\n/**\n * Navigation links in the navbar and icon links\n */\nul.navbar-nav ul {\n  padding: 0 0 0 1rem;\n}\nul.navbar-nav li {\n  display: flex;\n  flex-direction: column;\n}\nul.navbar-nav li a {\n  display: flex;\n  align-items: center;\n  height: 100%;\n  padding-top: 0.25rem;\n  padding-bottom: 0.25rem;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\nul.navbar-nav li a:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n\n/**\n * The list of in-page TOC links\n */\n.page-toc .section-nav {\n  padding-left: 0;\n  border-bottom: none;\n}\n.page-toc .section-nav ul {\n  padding-left: 1rem;\n}\n.page-toc .nav-link {\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\n@media (min-width: 1200px) {\n  .page-toc .nav-link {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n.page-toc .onthispage {\n  color: var(--pst-color-text-base);\n  font-weight: var(--pst-sidebar-header-font-weight);\n  margin-bottom: 0.5rem;\n}\n\n/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n}\n.prev-next-area p {\n  color: var(--pst-color-text-muted);\n  margin: 0 0.3em;\n  line-height: 1.3em;\n}\n.prev-next-area i {\n  font-size: 1.2em;\n}\n.prev-next-area a {\n  display: flex;\n  align-items: center;\n  border: none;\n  padding: 10px;\n  max-width: 45%;\n  overflow-x: hidden;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\n.prev-next-area a p.prev-next-title {\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  font-weight: var(--pst-admonition-font-weight-heading);\n  font-size: 1.1em;\n}\n.prev-next-area a p.prev-next-title:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n.prev-next-area a p.prev-next-title:active {\n  color: var(--pst-color-link);\n}\n.prev-next-area a p.prev-next-title:visited {\n  color: var(--pst-color-link);\n}\n.prev-next-area a p.prev-next-title:visited:hover {\n  color: var(--pst-color-link-hover);\n}\n.prev-next-area a:hover p.prev-next-title, .prev-next-area a:visited:hover p.prev-next-title {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n}\n.prev-next-area a .prev-next-info {\n  flex-direction: column;\n  margin: 0 0.5em;\n}\n.prev-next-area a .prev-next-info .prev-next-subtitle {\n  text-transform: capitalize;\n}\n.prev-next-area a.left-prev {\n  float: left;\n}\n.prev-next-area a.right-next {\n  float: right;\n}\n.prev-next-area a.right-next div.prev-next-info {\n  text-align: right;\n}\n\n/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: 0.25rem;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n}\n.bd-search:active {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-muted);\n}\n.bd-search:focus-within {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.bd-search:focus-within input:focus {\n  box-shadow: none;\n}\n.bd-search .icon {\n  position: absolute;\n  color: var(--pst-color-border);\n  left: 25px;\n}\n.bd-search .fa-solid.fa-magnifying-glass {\n  position: absolute;\n  left: calc((2.5rem - 0.7em) / 2);\n  color: var(--pst-color-text-muted);\n}\n.bd-search input.form-control {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-base);\n  border: none;\n}\n.bd-search input.form-control::placeholder {\n  color: var(--pst-color-text-muted);\n}\n.bd-search input.form-control::-webkit-search-cancel-button, .bd-search input.form-control::-webkit-search-decoration {\n  appearance: none;\n}\n.bd-search input.form-control:focus, .bd-search input.form-control:focus-visible {\n  color: var(--pst-color-text-muted);\n}\n.bd-search .search-button__kbd-shortcut {\n  display: none;\n  margin-inline-end: 0.5rem;\n  color: var(--pst-color-border);\n}\n@media (min-width: 720px) {\n  .bd-search .search-button__kbd-shortcut {\n    display: flex;\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n.search-button i {\n  font-size: 1.3rem;\n}\n\n/**\n * The search modal <dialog>\n */\n#pst-search-dialog {\n  display: none;\n}\n#pst-search-dialog[open] {\n  display: flex;\n  position: fixed;\n  z-index: 1055;\n  top: 30%;\n  left: 50%;\n  transform: translate(-50%, -50%);\n  right: 1rem;\n  margin-top: 0.5rem;\n  width: 90%;\n  max-width: 800px;\n  background-color: transparent;\n  padding: 0.1875rem;\n  border: none;\n}\n#pst-search-dialog[open]::backdrop {\n  background-color: black;\n  opacity: 0.5;\n}\n#pst-search-dialog[open] form.bd-search {\n  flex-grow: 1;\n}\n#pst-search-dialog[open] form.bd-search svg,\n#pst-search-dialog[open] form.bd-search input {\n  font-size: var(--pst-font-size-icon);\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: 1.5em;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n}\n.search-button-field:hover {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-link-hover);\n}\n.search-button-field:focus-visible {\n  border-radius: 1.5em;\n}\n.search-button-field .search-button__default-text {\n  font-size: var(--bs-nav-link-font-size);\n  font-weight: var(--bs-nav-link-font-weight);\n  margin-right: 0.5em;\n  margin-left: 0.5em;\n}\n.search-button-field .kbd-shortcut__modifier {\n  font-size: 0.75em;\n}\n.search-button-field > * {\n  align-items: center;\n}\n.search-button-field > :not(svg) {\n  display: none;\n}\n@media (min-width: 960px) {\n  .search-button-field > :not(svg) {\n    display: flex;\n  }\n}\n\n/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox p.highlight-link {\n  margin: 1rem 0;\n  width: fit-content;\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n@media (min-width: 1200px) {\n  div#searchbox p.highlight-link {\n    margin-left: 2rem;\n  }\n}\ndiv#searchbox p.highlight-link a {\n  border-radius: 0.25rem;\n  font-size: 1.25rem;\n  padding: 0.75rem;\n  background-color: var(--pst-color-primary);\n  color: var(--pst-color-primary-text);\n  text-decoration: none;\n  transition: box-shadow 0.25s ease-out;\n}\ndiv#searchbox p.highlight-link a:hover {\n  box-shadow: inset 0 0 50px 50px rgba(0, 0, 0, 0.25);\n}\ndiv#searchbox p.highlight-link a::before {\n  content: var(--pst-icon-search-minus);\n  color: unset;\n  font: var(--fa-font-solid);\n  margin-right: 0.5rem;\n}\n\n/**\n * Light/dark theme switcher\n */\n.theme-switch-button .theme-switch {\n  display: none;\n}\n.theme-switch-button .theme-switch:active {\n  text-decoration: none;\n  color: var(--pst-color-link-hover);\n}\n.theme-switch-button .theme-switch .fa-lg {\n  aspect-ratio: 1/1;\n}\n\nhtml[data-mode=auto] .theme-switch-button .theme-switch[data-mode=auto] {\n  display: inline;\n}\n\nhtml[data-mode=light] .theme-switch-button .theme-switch[data-mode=light] {\n  display: inline;\n}\n\nhtml[data-mode=dark] .theme-switch-button .theme-switch[data-mode=dark] {\n  display: inline;\n}\n\nbutton.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n  margin-bottom: 1em;\n}\n@media (min-width: 960px) {\n  button.btn.version-switcher__button {\n    margin-bottom: unset;\n  }\n}\nbutton.btn.version-switcher__button:hover {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-secondary);\n  border-color: transparent;\n}\nbutton.btn.version-switcher__button:active {\n  color: var(--pst-color-text-base);\n  border-color: var(--pst-color-border);\n}\nbutton.btn.version-switcher__button:focus-visible {\n  border-color: transparent;\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n}\n.version-switcher__menu a.list-group-item {\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-base);\n  padding: 0.75rem 1.25rem;\n}\n.version-switcher__menu a.list-group-item:not(:last-child) {\n  border-bottom: 1px solid var(--pst-color-border);\n}\n.version-switcher__menu a.list-group-item:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  background-color: var(--pst-color-surface);\n}\n.version-switcher__menu a.list-group-item.active {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary);\n  position: relative;\n  z-index: 1;\n}\n.version-switcher__menu a.list-group-item.active:focus-visible {\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary), 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n}\n.version-switcher__menu a.list-group-item.active span::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  z-index: -1;\n  left: 0;\n  top: 0;\n}\n.version-switcher__menu a.list-group-item:focus-visible {\n  z-index: 10;\n}\n\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em;\n  z-index: 1055;\n  box-sizing: border-box;\n  min-width: 24px;\n  min-height: 24px;\n}\n@media (min-width: 960px) {\n  button.version-switcher__button,\n  .version-switcher__menu {\n    font-size: unset;\n  }\n}\n\n/* Collapsing of the TOC sidebar while scrolling */\n/* Nav: hide second level (shown on .active) */\nnav.page-toc {\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav .nav {\n  display: none;\n}\n.bd-toc .nav .nav.visible {\n  display: block;\n}\n.bd-toc .nav > .active > ul {\n  display: block;\n}\n\n.toc-entry {\n  display: block;\n}\n.toc-entry a > code {\n  color: var(--pst-color-text-muted);\n}\n.toc-entry a.nav-link {\n  display: block;\n  padding: 0.25rem 0;\n  padding-left: 1rem;\n  margin-left: -1rem;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\n.toc-entry a.nav-link:hover {\n  text-decoration: underline;\n  background-color: transparent;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n.toc-entry a.nav-link:active {\n  color: var(--pst-color-link-hover);\n}\n.toc-entry a.nav-link:focus-visible {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n  z-index: 10;\n}\n.toc-entry a.nav-link.active {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary);\n  background-color: transparent;\n}\n.toc-entry a.nav-link.active:focus-visible {\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary), 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n}\n.toc-entry a.nav-link.active:hover {\n  color: var(--pst-color-link-hover);\n}\n.toc-entry a.nav-link:focus-visible {\n  border-radius: 0.125rem;\n}\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem;\n  overflow: hidden;\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: 0.25rem;\n  background-color: var(--pst-color-on-background);\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n  position: relative;\n}\ndiv.versionadded > p,\ndiv.versionchanged > p,\ndiv.deprecated > p {\n  margin-bottom: 0.6rem;\n  margin-top: 0.6rem;\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n}\nspan.versionmodified::before {\n  margin-right: 0.6rem;\n  color: var(--pst-color-info);\n  font: var(--fa-font-solid);\n  content: var(--pst-icon-versionmodified-default);\n}\n\nspan.versionmodified.added::before {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-versionmodified-added);\n}\n\nspan.versionmodified.changed::before {\n  color: var(--pst-color-warning);\n  content: var(--pst-icon-versionmodified-changed);\n}\n\nspan.versionmodified.deprecated::before {\n  color: var(--pst-color-danger);\n  content: var(--pst-icon-versionmodified-deprecated);\n}\n\n.sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n}\n@media (min-width: 960px) {\n  .sidebar-indices-items {\n    border-top: none;\n  }\n}\n.sidebar-indices-items .sidebar-indices-items__title {\n  font-weight: var(--pst-sidebar-header-font-weight);\n  font-size: var(--pst-sidebar-header-font-size);\n  color: var(--pst-color-text-base);\n  margin-bottom: 0.5rem;\n}\n.sidebar-indices-items ul.indices-link {\n  margin-right: -1rem;\n  list-style: none;\n  padding: 0;\n}\n.sidebar-indices-items ul.indices-link li > a {\n  display: block;\n  padding: 0.25rem 0;\n  color: var(--pst-color-text-muted);\n}\n.sidebar-indices-items ul.indices-link li > a:hover {\n  color: var(--pst-color-primary);\n  text-decoration: none;\n  background-color: transparent;\n}\n\n.bd-sidebar-primary div#rtd-footer-container {\n  position: sticky;\n  bottom: -1rem;\n  margin: -1rem;\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-versions.rst-badge {\n  position: unset;\n  font-size: 0.9em;\n  font-family: var(--pst-font-family-base);\n  max-width: unset;\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-versions.rst-badge .rst-current-version {\n  display: flex;\n  align-items: center;\n  gap: 0.2rem;\n  height: 2.5rem;\n  transition: background-color 0.2s ease-out;\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-success);\n  border-top: 1px solid var(--pst-color-border);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-versions.rst-badge .fa.fa-book {\n  color: var(--pst-color-text-muted);\n  margin-right: auto;\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-versions.rst-badge .fa.fa-book::after {\n  color: var(--pst-color-text-base);\n  content: \"Read The Docs\";\n  font-family: var(--pst-font-family-base);\n  font-weight: var(--pst-admonition-font-weight-heading);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-versions.rst-badge .fa.fa-caret-down {\n  color: var(--pst-color-text-muted);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-versions.rst-badge.shift-up .rst-current-version {\n  border-bottom: 1px solid var(--pst-color-border);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-other-versions {\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-other-versions dl dd a {\n  color: var(--pst-color-text-muted);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-other-versions hr {\n  background-color: var(--pst-color-border);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-other-versions small a, .bd-sidebar-primary div#rtd-footer-container .rst-other-versions .small a {\n  color: var(--pst-color-link);\n}\n.bd-sidebar-primary div#rtd-footer-container .rst-other-versions input {\n  padding-left: 0.5rem;\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n}\n\n/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem;\n  overflow: hidden;\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: 0.25rem;\n  background-color: var(--pst-color-on-background);\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n}\ndiv.admonition *:last-child,\n.admonition *:last-child {\n  margin-bottom: 0;\n}\ndiv.admonition p.admonition-title ~ *,\n.admonition p.admonition-title ~ * {\n  margin-left: 1.4rem;\n  margin-right: 1.4rem;\n}\ndiv.admonition > ol,\ndiv.admonition > ul,\n.admonition > ol,\n.admonition > ul {\n  margin-left: 1em;\n}\ndiv.admonition > .admonition-title,\n.admonition > .admonition-title {\n  margin: 0 -0.6rem;\n  padding: 0.4rem 0.6rem 0.4rem 2rem;\n  font-weight: var(--pst-admonition-font-weight-heading);\n  position: relative;\n  background-color: var(--pst-color-info-bg);\n  z-index: 1;\n}\ndiv.admonition > .admonition-title::before,\n.admonition > .admonition-title::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\ndiv.admonition > .admonition-title::after,\n.admonition > .admonition-title::after {\n  position: absolute;\n  left: 0.5rem;\n  width: 1rem;\n  height: 1rem;\n  color: var(--pst-color-info);\n  font: var(--fa-font-solid);\n  line-height: inherit;\n  content: var(--pst-icon-admonition-default);\n  opacity: 1;\n}\ndiv.admonition > .admonition-title + *,\n.admonition > .admonition-title + * {\n  margin-top: 0.4em;\n}\ndiv.admonition.attention,\n.admonition.attention {\n  border-color: var(--pst-color-attention);\n}\ndiv.admonition.attention > .admonition-title,\n.admonition.attention > .admonition-title {\n  background-color: var(--pst-color-attention-bg);\n}\ndiv.admonition.attention > .admonition-title::after,\n.admonition.attention > .admonition-title::after {\n  color: var(--pst-color-attention);\n  content: var(--pst-icon-admonition-attention);\n}\ndiv.admonition.caution,\n.admonition.caution {\n  border-color: var(--pst-color-warning);\n}\ndiv.admonition.caution > .admonition-title,\n.admonition.caution > .admonition-title {\n  background-color: var(--pst-color-warning-bg);\n}\ndiv.admonition.caution > .admonition-title::after,\n.admonition.caution > .admonition-title::after {\n  color: var(--pst-color-warning);\n  content: var(--pst-icon-admonition-caution);\n}\ndiv.admonition.warning,\n.admonition.warning {\n  border-color: var(--pst-color-warning);\n}\ndiv.admonition.warning > .admonition-title,\n.admonition.warning > .admonition-title {\n  background-color: var(--pst-color-warning-bg);\n}\ndiv.admonition.warning > .admonition-title::after,\n.admonition.warning > .admonition-title::after {\n  color: var(--pst-color-warning);\n  content: var(--pst-icon-admonition-warning);\n}\ndiv.admonition.danger,\n.admonition.danger {\n  border-color: var(--pst-color-danger);\n}\ndiv.admonition.danger > .admonition-title,\n.admonition.danger > .admonition-title {\n  background-color: var(--pst-color-danger-bg);\n}\ndiv.admonition.danger > .admonition-title::after,\n.admonition.danger > .admonition-title::after {\n  color: var(--pst-color-danger);\n  content: var(--pst-icon-admonition-danger);\n}\ndiv.admonition.error,\n.admonition.error {\n  border-color: var(--pst-color-danger);\n}\ndiv.admonition.error > .admonition-title,\n.admonition.error > .admonition-title {\n  background-color: var(--pst-color-danger-bg);\n}\ndiv.admonition.error > .admonition-title::after,\n.admonition.error > .admonition-title::after {\n  color: var(--pst-color-danger);\n  content: var(--pst-icon-admonition-error);\n}\ndiv.admonition.hint,\n.admonition.hint {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.hint > .admonition-title,\n.admonition.hint > .admonition-title {\n  background-color: var(--pst-color-success-bg);\n}\ndiv.admonition.hint > .admonition-title::after,\n.admonition.hint > .admonition-title::after {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-admonition-hint);\n}\ndiv.admonition.tip,\n.admonition.tip {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.tip > .admonition-title,\n.admonition.tip > .admonition-title {\n  background-color: var(--pst-color-success-bg);\n}\ndiv.admonition.tip > .admonition-title::after,\n.admonition.tip > .admonition-title::after {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-admonition-tip);\n}\ndiv.admonition.important,\n.admonition.important {\n  border-color: var(--pst-color-attention);\n}\ndiv.admonition.important > .admonition-title,\n.admonition.important > .admonition-title {\n  background-color: var(--pst-color-attention-bg);\n}\ndiv.admonition.important > .admonition-title::after,\n.admonition.important > .admonition-title::after {\n  color: var(--pst-color-attention);\n  content: var(--pst-icon-admonition-important);\n}\ndiv.admonition.note,\n.admonition.note {\n  border-color: var(--pst-color-info);\n}\ndiv.admonition.note > .admonition-title,\n.admonition.note > .admonition-title {\n  background-color: var(--pst-color-info-bg);\n}\ndiv.admonition.note > .admonition-title::after,\n.admonition.note > .admonition-title::after {\n  color: var(--pst-color-info);\n  content: var(--pst-icon-admonition-note);\n}\ndiv.admonition.seealso,\n.admonition.seealso {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.seealso > .admonition-title,\n.admonition.seealso > .admonition-title {\n  background-color: var(--pst-color-success-bg);\n}\ndiv.admonition.seealso > .admonition-title::after,\n.admonition.seealso > .admonition-title::after {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-admonition-seealso);\n}\ndiv.admonition.admonition-todo,\n.admonition.admonition-todo {\n  border-color: var(--pst-color-secondary);\n}\ndiv.admonition.admonition-todo > .admonition-title,\n.admonition.admonition-todo > .admonition-title {\n  background-color: var(--pst-color-secondary-bg);\n}\ndiv.admonition.admonition-todo > .admonition-title::after,\n.admonition.admonition-todo > .admonition-title::after {\n  color: var(--pst-color-secondary);\n  content: var(--pst-icon-admonition-todo);\n}\ndiv.admonition.sidebar,\n.admonition.sidebar {\n  max-width: 40%;\n  float: right;\n  clear: both;\n  margin-left: 0.5rem;\n  margin-top: 0;\n  border-width: 0 0 0 0.2rem;\n}\ndiv.admonition.sidebar.attention, div.admonition.sidebar.important,\n.admonition.sidebar.attention,\n.admonition.sidebar.important {\n  border-color: var(--pst-color-attention);\n}\ndiv.admonition.sidebar.caution, div.admonition.sidebar.warning,\n.admonition.sidebar.caution,\n.admonition.sidebar.warning {\n  border-color: var(--pst-color-warning);\n}\ndiv.admonition.sidebar.danger, div.admonition.sidebar.error,\n.admonition.sidebar.danger,\n.admonition.sidebar.error {\n  border-color: var(--pst-color-danger);\n}\ndiv.admonition.sidebar.hint, div.admonition.sidebar.tip, div.admonition.sidebar.seealso,\n.admonition.sidebar.hint,\n.admonition.sidebar.tip,\n.admonition.sidebar.seealso {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.sidebar.note, div.admonition.sidebar.todo,\n.admonition.sidebar.note,\n.admonition.sidebar.todo {\n  border-color: var(--pst-color-info);\n}\ndiv.admonition.sidebar p.admonition-title ~ *,\n.admonition.sidebar p.admonition-title ~ * {\n  margin-left: 0;\n  margin-right: 0;\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n/**\n * Topics and the {contents} directive\n */\ndiv.topic,\ndiv.topic.contents,\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: 0.25rem;\n  padding: 1rem 1.25rem;\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\ndiv.topic .topic-title,\ndiv.topic.contents .topic-title,\nnav.contents .topic-title,\naside.topic .topic-title {\n  margin: 0 0 0.5rem;\n}\ndiv.topic p,\ndiv.topic.contents p,\nnav.contents p,\naside.topic p {\n  color: var(--pst-color-on-surface) !important;\n}\ndiv.topic ul.simple,\ndiv.topic.contents ul.simple,\nnav.contents ul.simple,\naside.topic ul.simple {\n  padding-left: 1rem;\n}\ndiv.topic ul.simple ul,\ndiv.topic.contents ul.simple ul,\nnav.contents ul.simple ul,\naside.topic ul.simple ul {\n  padding-left: 2em;\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: 0.25rem;\n  margin-left: 0.5rem;\n  padding: 0;\n}\naside.sidebar > *:last-child {\n  padding-bottom: 1rem;\n}\naside.sidebar p.sidebar-title {\n  position: relative;\n  margin-bottom: 0;\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  border-bottom: 1px solid var(--pst-color-border);\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-admonition-font-weight-heading);\n}\naside.sidebar > *:not(.sidebar-title):first-child,\naside.sidebar > p.sidebar-title + * {\n  margin-top: 1rem;\n}\naside.sidebar > * {\n  padding-left: 1rem;\n  padding-right: 1rem;\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n\n/*******************************************************************************\n* Styling for field lists\n*/\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n  /* italic font for parameter types */\n}\ntable.field-list th.field-name {\n  padding: 1px 8px 1px 5px;\n  white-space: nowrap;\n  background-color: var(--pst-color-surface);\n}\ntable.field-list td.field-body {\n  /* reduced space around parameter description */\n}\ntable.field-list td.field-body p {\n  font-style: italic;\n}\ntable.field-list td.field-body p > strong {\n  font-style: normal;\n}\ntable.field-list td.field-body blockquote {\n  border-left: none;\n  margin: 0 0 0.3em;\n  padding-left: 30px;\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n.table.autosummary td:first-child {\n  white-space: nowrap;\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors */\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\ndl > dt > a:has(.viewcode-link) {\n  float: right;\n}\ndl > dt > a:has(.viewcode-link) .viewcode-link {\n  float: none;\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) {\n  margin-bottom: 3rem;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dd {\n  margin-left: 2rem;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dd > dl.simple > dt {\n  display: flex;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dl.field-list {\n  display: grid;\n  grid-template-columns: unset;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dt.field-odd,\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dt.field-even {\n  margin-top: 0.2rem;\n  margin-bottom: 0.1rem;\n  background-color: var(--pst-color-surface);\n}\n\n/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\ndiv[class*=highlight-],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: 0.25rem;\n  break-inside: avoid;\n}\n\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\ndiv.literal-block-wrapper div.code-block-caption {\n  margin: 0;\n  border-bottom: 1px solid var(--pst-color-border);\n  padding: 0.5rem;\n  font-size: 1rem;\n  font-weight: var(--pst-font-weight-caption);\n}\ndiv.literal-block-wrapper div.code-block-caption a.headerlink {\n  font-size: inherit;\n}\ndiv.literal-block-wrapper div[class*=highlight-] {\n  margin: 0;\n  border-radius: 0;\n}\ndiv.literal-block-wrapper div[class*=highlight-] pre {\n  border: none;\n  box-shadow: none;\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n}\n\nspan.linenos {\n  opacity: 0.8 !important;\n}\n\nfigure > a,\nfigure > a > img,\nfigure > img,\nfigure > video {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n\nfigure a.headerlink {\n  position: absolute;\n  font-size: inherit;\n}\nfigure:hover a.headerlink {\n  visibility: visible;\n}\nfigure figcaption {\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-caption);\n  color: var(--pst-color-text-muted);\n  margin-left: auto;\n  margin-right: auto;\n  margin-top: 0.3rem;\n  text-align: center;\n}\nfigure figcaption > p:last-child {\n  margin-bottom: 0;\n}\nfigure figcaption p {\n  text-align: start;\n  display: inline-block;\n}\nfigure figcaption table.table {\n  width: fit-content;\n  margin-left: auto;\n  margin-right: auto;\n}\n\ndt.label > span.brackets:not(:only-child)::before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child)::after {\n  content: \"]\";\n}\n\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\naside.footnote {\n  margin-bottom: 0.5rem;\n}\naside.footnote:last-child {\n  margin-bottom: 1rem;\n}\naside.footnote span.label,\naside.footnote span.backrefs {\n  font-weight: bold;\n}\naside.footnote:target {\n  background-color: var(--pst-color-target);\n}\n\n/**\n * Hacky fixes that don't fit cleanly into other sections\n */\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n}\n\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n}\nol li > p:first-child,\nul li > p:first-child {\n  margin-bottom: 0.25rem;\n  margin-top: 0.25rem;\n}\n\nblockquote {\n  padding: 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-blockquote-notch);\n  border-radius: 0.25rem;\n  position: relative;\n  background-color: var(--pst-color-surface);\n}\nblockquote p {\n  color: var(--pst-color-text-base);\n}\nblockquote .line-block {\n  margin: 0;\n}\nblockquote p:last-child {\n  margin-bottom: 0;\n}\nblockquote::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\nblockquote a {\n  color: var(--pst-color-inline-code-links);\n}\nblockquote::before {\n  z-index: -1;\n}\n\n/**\n * Span-level styling within content\n */\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n  background-color: var(--pst-color-info-bg);\n}\nspan.guilabel::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\n\na.reference.download::before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n\n/**\n * Tables\n */\ntable {\n  margin-left: auto;\n  margin-right: auto;\n}\ntable.table-right {\n  margin-right: 0;\n}\ntable.table-left {\n  margin-left: 0;\n}\n\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\nth.text-left,\ntd.text-left {\n  text-align: left;\n}\nth.text-right,\ntd.text-right {\n  text-align: right;\n}\nth.text-center,\ntd.text-center {\n  text-align: center;\n}\n\n.table {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n  --bs-table-bg: transparent;\n  --bs-table-color: var(\n    --pst-color-text-base\n  );\n}\n.table th ~ th,\n.table th ~ td,\n.table td ~ th,\n.table td ~ td {\n  border-left: 1px solid var(--pst-color-table-inner-border);\n}\n.table thead tr {\n  background-color: var(--pst-color-table-heading-bg);\n  border-bottom: 2px solid var(--pst-color-primary);\n}\n.table tbody tr:nth-child(odd) {\n  background-color: var(--pst-color-table-row-zebra-low-bg);\n}\n.table tbody tr:nth-child(even) {\n  background-color: var(--pst-color-table-row-zebra-high-bg);\n}\n.table tbody tr:hover {\n  background-color: var(--pst-color-table-row-hover-bg);\n}\n\n.pst-scrollable-table-container {\n  overflow-x: auto;\n}\n\n/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper p.caption {\n  font-size: 1.5em;\n  margin-bottom: 0;\n}\n.toctree-wrapper > ul {\n  padding-left: 0;\n}\n.toctree-wrapper li[class^=toctree-l] {\n  list-style: none;\n  margin-bottom: 0.2em;\n}\n.toctree-wrapper li[class^=toctree-l] > a {\n  list-style: none;\n  font-size: 1.1em;\n}\n.toctree-wrapper li[class^=toctree-l] > ul {\n  list-style: none;\n  padding-inline-start: 1.5em;\n}\n.toctree-wrapper .toctree-l1 > a {\n  font-size: 1.3em;\n}\n\ndiv.topic.contents ul.simple,\nnav.contents ul.simple {\n  list-style: none;\n  padding-left: 0;\n}\n\n/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n  overflow: hidden;\n}\n\nspan.math {\n  display: inline-flex;\n}\n\ndiv.math {\n  gap: 0.5em;\n  flex-direction: row-reverse;\n}\ndiv.math span.eqno a.headerlink {\n  position: relative;\n  font-size: 1em;\n}\ndiv.math mjx-container {\n  flex-grow: 1;\n  padding-bottom: 0.2rem;\n  overflow: auto;\n}\ndiv.math mjx-container mjx-assistive-mml {\n  height: 0;\n}\n\n/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item h2, .ablog-sidebar-item .h2,\n.ablog-sidebar-item h3,\n.ablog-sidebar-item .h3 {\n  font-size: var(--pst-sidebar-header-font-size);\n  margin-top: 0.5rem;\n}\n.ablog-sidebar-item h2 a, .ablog-sidebar-item .h2 a,\n.ablog-sidebar-item h3 a,\n.ablog-sidebar-item .h3 a {\n  color: var(--pst-color-text-base);\n}\n.ablog-sidebar-item ul {\n  list-style: none;\n  padding-left: 0;\n  overflow-y: hidden;\n  display: flex;\n  flex-direction: column;\n  gap: 0.5em;\n  margin-bottom: 0;\n}\n.ablog-sidebar-item ul.ablog-cloud {\n  flex-flow: row wrap;\n  gap: 0.5rem;\n}\n.ablog-sidebar-item ul.ablog-cloud li {\n  display: flex;\n  align-items: center;\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n}\n.ablog__prev-next > span {\n  display: flex;\n  max-width: 45%;\n}\n.ablog__prev-next > span a {\n  display: flex;\n  align-items: center;\n  margin-left: auto;\n  gap: 1rem;\n  line-height: 1.5rem;\n}\n.ablog__prev-next > span a i::before {\n  color: var(--pst-color-text-base);\n}\n.ablog__prev-next span.ablog__prev i.fa-arrow-circle-left::before {\n  content: var(--pst-icon-angle-left);\n}\n.ablog__prev-next span.ablog__spacer {\n  display: none;\n}\n.ablog__prev-next span.ablog__next {\n  margin-left: auto;\n  text-align: right;\n}\n.ablog__prev-next span.ablog__next i.fa-arrow-circle-right::before {\n  content: var(--pst-icon-angle-right);\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n}\n.ablog__collection .ablog-post,\n.postlist .ablog-post {\n  list-style: none;\n}\n.ablog__collection .ablog-post .ablog-archive,\n.postlist .ablog-post .ablog-archive {\n  display: flex;\n  flex-flow: row wrap;\n  gap: 1rem;\n  list-style: none;\n  font-size: 0.75rem;\n  padding-left: 0;\n}\n.ablog__collection .ablog-post .ablog-post-title,\n.postlist .ablog-post .ablog-post-title {\n  margin-top: 0;\n  font-size: 1.25rem;\n}\n.ablog__collection .ablog-post .ablog-post-title a,\n.postlist .ablog-post .ablog-post-title a {\n  font-weight: bold;\n}\n.ablog__collection .ablog-post .ablog-post-expand,\n.postlist .ablog-post .ablog-post-expand {\n  margin-bottom: 0.5rem;\n}\n\n/**\n * Special cases for Bootstrap functionality\n */\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n\n.btn {\n  --bs-btn-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n\n/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\ndiv.highlight button.copybtn {\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  border: none;\n  background-color: var(--pst-color-surface);\n}\ndiv.highlight button.copybtn:not(.success) {\n  color: var(--pst-color-muted);\n}\ndiv.highlight button.copybtn:hover:not(.success) {\n  color: var(--pst-color-text);\n  background-color: var(--pst-color-shadow);\n}\ndiv.highlight button.copybtn.o-tooltip--left::after {\n  color: var(--pst-color-text);\n  background-color: var(--pst-color-surface);\n}\ndiv.highlight button.copybtn:focus {\n  opacity: 1;\n}\ndiv.highlight button.copybtn:focus-visible {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n}\n\ndiv.highlight:has(button.copybtn) {\n  min-height: 2em;\n}\n\n#ethical-ad-placement .ethical-sidebar a,\n#ethical-ad-placement .ethical-sidebar a:visited,\n#ethical-ad-placement .ethical-sidebar a:hover,\n#ethical-ad-placement .ethical-sidebar a:active,\n#ethical-ad-placement .ethical-footer a,\n#ethical-ad-placement .ethical-footer a:visited,\n#ethical-ad-placement .ethical-footer a:hover,\n#ethical-ad-placement .ethical-footer a:active {\n  color: var(--pst-color-text-base);\n}\n#ethical-ad-placement .ethical-sidebar,\n#ethical-ad-placement .ethical-footer {\n  background-color: var(--pst-color-background);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 5px;\n  color: var(--pst-color-text-base);\n  font-size: 14px;\n  line-height: 20px;\n}\n\n/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n/******************************************************************************\n * Jupyter Sphinx\n */\n.bd-content div.jupyter_container {\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n}\n.bd-content div.jupyter_container div.output,\n.bd-content div.jupyter_container div.highlight {\n  border-radius: 0.25rem;\n}\n.bd-content div.jupyter_container div.highlight {\n  background-color: var(--pst-color-surface);\n}\n.bd-content div.jupyter_container .cell_input,\n.bd-content div.jupyter_container .cell_output {\n  border-radius: 0.25rem;\n}\n.bd-content div.jupyter_container .cell_input pre,\n.bd-content div.jupyter_container .cell_output pre {\n  padding: 1rem;\n}\n\n/* Styles for graphviz generated output from Sphinx */\n/* Style the inheritance diagram such that it has a dark mode */\nhtml[data-theme=dark] div.graphviz > object.inheritance {\n  filter: brightness(0.8) invert(0.82) contrast(1.2);\n  color-scheme: normal;\n}\n\n/**\n * Special-cases for packages in the PyData ecosystem\n */\n.xr-wrap[hidden] {\n  display: block !important;\n}\n\n.jp-OutputArea-output.lm-Widget {\n  overflow: auto;\n}\n\n/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n/**\n* Function to get items from nested maps\n*/\n/* Assign base colors for the PyData theme */\n:root {\n  --pst-teal-50: #f4fbfc;\n  --pst-teal-100: #e9f6f8;\n  --pst-teal-200: #d0ecf1;\n  --pst-teal-300: #abdde6;\n  --pst-teal-400: #3fb1c5;\n  --pst-teal-500: #0a7d91;\n  --pst-teal-600: #085d6c;\n  --pst-teal-700: #064752;\n  --pst-teal-800: #042c33;\n  --pst-teal-900: #021b1f;\n  --pst-violet-50: #f4eefb;\n  --pst-violet-100: #e0c7ff;\n  --pst-violet-200: #d5b4fd;\n  --pst-violet-300: #b780ff;\n  --pst-violet-400: #9c5ffd;\n  --pst-violet-500: #8045e5;\n  --pst-violet-600: #6432bd;\n  --pst-violet-700: #4b258f;\n  --pst-violet-800: #341a61;\n  --pst-violet-900: #1e0e39;\n  --pst-gray-50: #f9f9fa;\n  --pst-gray-100: #f3f4f5;\n  --pst-gray-200: #e5e7ea;\n  --pst-gray-300: #d1d5da;\n  --pst-gray-400: #9ca4af;\n  --pst-gray-500: #677384;\n  --pst-gray-600: #48566b;\n  --pst-gray-700: #29313d;\n  --pst-gray-800: #222832;\n  --pst-gray-900: #14181e;\n  --pst-pink-50: #fcf8fd;\n  --pst-pink-100: #fcf0fa;\n  --pst-pink-200: #f8dff5;\n  --pst-pink-300: #f3c7ee;\n  --pst-pink-400: #e47fd7;\n  --pst-pink-500: #c132af;\n  --pst-pink-600: #912583;\n  --pst-pink-700: #6e1c64;\n  --pst-pink-800: #46123f;\n  --pst-pink-900: #2b0b27;\n  --pst-foundation-white: #ffffff;\n  --pst-foundation-black: #14181e;\n}\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * something inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-heading-color: #fff;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #b780ff;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n}\n\nhtml:not([data-theme]) {\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\n\nhtml:not([data-theme]) .only-dark,\nhtml:not([data-theme]) .only-dark ~ figcaption {\n  display: none !important;\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\nhtml[data-theme=light] {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-heading-color: #fff;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #b780ff;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n  color-scheme: light;\n}\n\nhtml[data-theme=light] {\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\n\nhtml[data-theme=light] .only-dark,\nhtml[data-theme=light] .only-dark ~ figcaption {\n  display: none !important;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-primary: #3fb1c5;\n  --pst-color-primary-bg: #042c33;\n  --pst-color-secondary: #9c5ffd;\n  --pst-color-secondary-bg: #341a61;\n  --pst-color-accent: #e47fd7;\n  --pst-color-accent-bg: #46123f;\n  --pst-color-info: #79a3f2;\n  --pst-color-info-bg: #06245d;\n  --pst-color-warning: #ff9245;\n  --pst-color-warning-bg: #652a02;\n  --pst-color-success: #5fb488;\n  --pst-color-success-bg: #002f17;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #e78894;\n  --pst-color-danger-bg: #4e111b;\n  --pst-color-text-base: #ced6dd;\n  --pst-color-text-muted: #9ca4af;\n  --pst-color-heading-color: #14181e;\n  --pst-color-shadow: rgba(0, 0, 0, 0.2);\n  --pst-color-border: #48566b;\n  --pst-color-border-muted: #29313d;\n  --pst-color-blockquote-notch: #9ca4af;\n  --pst-color-inline-code: #f3c7ee;\n  --pst-color-inline-code-links: #3fb1c5;\n  --pst-color-target: #675c04;\n  --pst-color-table: #ffffff;\n  --pst-color-table-row-hover-bg: #6432bd;\n  --pst-color-table-inner-border: #364150;\n  --pst-color-background: #14181e;\n  --pst-color-on-background: #222832;\n  --pst-color-surface: #29313d;\n  --pst-color-on-surface: #f3f4f5;\n  /* Adjust images in dark mode (unless they have class .only-dark or\n   * .dark-light, in which case assume they're already optimized for dark\n   * mode).\n   */\n  /* Give images a light background in dark mode in case they have\n  *  transparency and black text (unless they have class .only-dark or .dark-light, in\n  *  which case assume they're already optimized for dark mode).\n  */\n  color-scheme: dark;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\n\nhtml[data-theme=dark] .only-light,\nhtml[data-theme=dark] .only-light ~ figcaption {\n  display: none !important;\n}\n\nhtml[data-theme=dark] img:not(.only-dark, .dark-light) {\n  filter: brightness(0.8) contrast(1.2);\n}\n\nhtml[data-theme=dark] .bd-content img:not(.only-dark, .dark-light) {\n  background-color: rgb(255, 255, 255);\n  border-radius: 0.25rem;\n}\n\nhtml[data-theme=dark] .MathJax_SVG * {\n  fill: var(--pst-color-text-base);\n}\n\n.pst-color-primary {\n  color: var(--pst-color-primary);\n}\n\n.pst-color-secondary {\n  color: var(--pst-color-secondary);\n}\n\n.pst-color-accent {\n  color: var(--pst-color-accent);\n}\n\n.pst-color-info {\n  color: var(--pst-color-info);\n}\n\n.pst-color-warning {\n  color: var(--pst-color-warning);\n}\n\n.pst-color-success {\n  color: var(--pst-color-success);\n}\n\n.pst-color-attention {\n  color: var(--pst-color-attention);\n}\n\n.pst-color-danger {\n  color: var(--pst-color-danger);\n}\n\n.pst-color-text-base {\n  color: var(--pst-color-text-base);\n}\n\n.pst-color-text-muted {\n  color: var(--pst-color-text-muted);\n}\n\n.pst-color-heading-color {\n  color: var(--pst-color-heading-color);\n}\n\n.pst-color-shadow {\n  color: var(--pst-color-shadow);\n}\n\n.pst-color-border {\n  color: var(--pst-color-border);\n}\n\n.pst-color-border-muted {\n  color: var(--pst-color-border-muted);\n}\n\n.pst-color-blockquote-notch {\n  color: var(--pst-color-blockquote-notch);\n}\n\n.pst-color-inline-code {\n  color: var(--pst-color-inline-code);\n}\n\n.pst-color-inline-code-links {\n  color: var(--pst-color-inline-code-links);\n}\n\n.pst-color-target {\n  color: var(--pst-color-target);\n}\n\n.pst-color-table {\n  color: var(--pst-color-table);\n}\n\n.pst-color-table-row-hover {\n  color: var(--pst-color-table-row-hover);\n}\n\n.pst-color-table-inner-border {\n  color: var(--pst-color-table-inner-border);\n}\n\n.pst-color-background {\n  color: var(--pst-color-background);\n}\n\n.pst-color-on-background {\n  color: var(--pst-color-on-background);\n}\n\n.pst-color-surface {\n  color: var(--pst-color-surface);\n}\n\n.pst-color-on-surface {\n  color: var(--pst-color-on-surface);\n}\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\nhtml[data-theme=light] {\n  --pst-color-primary: rgb(10, 125, 145);\n  --pst-color-primary-text: #fff;\n  --pst-color-primary-highlight: #086b7c;\n  --sd-color-primary: var(--pst-color-primary);\n  --sd-color-primary-text: var(--pst-color-primary-text);\n  --sd-color-primary-highlight: var(--pst-color-primary-highlight);\n  --pst-color-primary-highlight-text: #fff;\n  --sd-color-primary-bg: #d0ecf1;\n  --sd-color-primary-bg-text: #14181e;\n  --pst-color-secondary: rgb(128, 69, 229);\n  --pst-color-secondary-text: #fff;\n  --pst-color-secondary-highlight: #651ee0;\n  --sd-color-secondary: var(--pst-color-secondary);\n  --sd-color-secondary-text: var(--pst-color-secondary-text);\n  --sd-color-secondary-highlight: var(--pst-color-secondary-highlight);\n  --pst-color-secondary-highlight-text: #fff;\n  --sd-color-secondary-bg: #e0c7ff;\n  --sd-color-secondary-bg-text: #14181e;\n  --pst-color-success: #00843f;\n  --pst-color-success-text: #fff;\n  --pst-color-success-highlight: #007036;\n  --sd-color-success: var(--pst-color-success);\n  --sd-color-success-text: var(--pst-color-success-text);\n  --sd-color-success-highlight: var(--pst-color-success-highlight);\n  --pst-color-success-highlight-text: #fff;\n  --sd-color-success-bg: #d6ece1;\n  --sd-color-success-bg-text: #14181e;\n  --pst-color-info: #276be9;\n  --pst-color-info-text: #fff;\n  --pst-color-info-highlight: #1457d3;\n  --sd-color-info: var(--pst-color-info);\n  --sd-color-info-text: var(--pst-color-info-text);\n  --sd-color-info-highlight: var(--pst-color-info-highlight);\n  --pst-color-info-highlight-text: #fff;\n  --sd-color-info-bg: #dce7fc;\n  --sd-color-info-bg-text: #14181e;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-text: #14181e;\n  --pst-color-warning-highlight: #d25a07;\n  --sd-color-warning: var(--pst-color-warning);\n  --sd-color-warning-text: var(--pst-color-warning-text);\n  --sd-color-warning-highlight: var(--pst-color-warning-highlight);\n  --pst-color-warning-highlight-text: #14181e;\n  --sd-color-warning-bg: #f8e3d0;\n  --sd-color-warning-bg-text: #14181e;\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-text: #fff;\n  --pst-color-danger-highlight: #bb2239;\n  --sd-color-danger: var(--pst-color-danger);\n  --sd-color-danger-text: var(--pst-color-danger-text);\n  --sd-color-danger-highlight: var(--pst-color-danger-highlight);\n  --pst-color-danger-highlight-text: #fff;\n  --sd-color-danger-bg: #f9e1e4;\n  --sd-color-danger-bg-text: #14181e;\n  --pst-color-light: #f3f4f5;\n  --pst-color-light-text: #14181e;\n  --pst-color-light-highlight: #c9cfd6;\n  --sd-color-light: var(--pst-color-light);\n  --sd-color-light-text: var(--pst-color-light-text);\n  --sd-color-light-highlight: var(--pst-color-light-highlight);\n  --pst-color-light-highlight-text: #14181e;\n  --sd-color-light-bg: #f7f7f8;\n  --sd-color-light-bg-text: #14181e;\n  --pst-color-muted: #29313d;\n  --pst-color-muted-text: #fff;\n  --pst-color-muted-highlight: #212936;\n  --sd-color-muted: var(--pst-color-muted);\n  --sd-color-muted-text: var(--pst-color-muted-text);\n  --sd-color-muted-highlight: var(--pst-color-muted-highlight);\n  --pst-color-muted-highlight-text: #fff;\n  --sd-color-muted-bg: #5a6c86;\n  --sd-color-muted-bg-text: #fff;\n  --pst-color-dark: #222832;\n  --pst-color-dark-text: #fff;\n  --pst-color-dark-highlight: #1b222c;\n  --sd-color-dark: var(--pst-color-dark);\n  --sd-color-dark-text: var(--pst-color-dark-text);\n  --sd-color-dark-highlight: var(--pst-color-dark-highlight);\n  --pst-color-dark-highlight-text: #fff;\n  --pst-color-black: #14181e;\n  --pst-color-black-text: #fff;\n  --pst-color-black-highlight: #10141a;\n  --sd-color-black: var(--pst-color-black);\n  --sd-color-black-text: var(--pst-color-black-text);\n  --sd-color-black-highlight: var(--pst-color-black-highlight);\n  --pst-color-black-highlight-text: #fff;\n  --pst-color-white: #fff;\n  --pst-color-white-text: #14181e;\n  --pst-color-white-highlight: #dbd7d7;\n  --sd-color-white: var(--pst-color-white);\n  --sd-color-white-text: var(--pst-color-white-text);\n  --sd-color-white-highlight: var(--pst-color-white-highlight);\n  --pst-color-white-highlight-text: #14181e;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-primary: rgb(63, 177, 197);\n  --pst-color-primary-text: #14181e;\n  --pst-color-primary-highlight: #319aac;\n  --sd-color-primary: var(--pst-color-primary);\n  --sd-color-primary-text: var(--pst-color-primary-text);\n  --sd-color-primary-highlight: var(--pst-color-primary-highlight);\n  --pst-color-primary-highlight-text: #14181e;\n  --sd-color-primary-bg: #042c33;\n  --sd-color-primary-bg-text: #fff;\n  --pst-color-secondary: rgb(156, 95, 253);\n  --pst-color-secondary-text: #14181e;\n  --pst-color-secondary-highlight: #7c2bfc;\n  --sd-color-secondary: var(--pst-color-secondary);\n  --sd-color-secondary-text: var(--pst-color-secondary-text);\n  --sd-color-secondary-highlight: var(--pst-color-secondary-highlight);\n  --pst-color-secondary-highlight-text: #fff;\n  --sd-color-secondary-bg: #341a61;\n  --sd-color-secondary-bg-text: #fff;\n  --pst-color-success: #5fb488;\n  --pst-color-success-text: #14181e;\n  --pst-color-success-highlight: #47a373;\n  --sd-color-success: var(--pst-color-success);\n  --sd-color-success-text: var(--pst-color-success-text);\n  --sd-color-success-highlight: var(--pst-color-success-highlight);\n  --pst-color-success-highlight-text: #14181e;\n  --sd-color-success-bg: #002f17;\n  --sd-color-success-bg-text: #fff;\n  --pst-color-info: #79a3f2;\n  --pst-color-info-text: #14181e;\n  --pst-color-info-highlight: #4681ee;\n  --sd-color-info: var(--pst-color-info);\n  --sd-color-info-text: var(--pst-color-info-text);\n  --sd-color-info-highlight: var(--pst-color-info-highlight);\n  --pst-color-info-highlight-text: #14181e;\n  --sd-color-info-bg: #06245d;\n  --sd-color-info-bg-text: #fff;\n  --pst-color-warning: #ff9245;\n  --pst-color-warning-text: #14181e;\n  --pst-color-warning-highlight: #ff7614;\n  --sd-color-warning: var(--pst-color-warning);\n  --sd-color-warning-text: var(--pst-color-warning-text);\n  --sd-color-warning-highlight: var(--pst-color-warning-highlight);\n  --pst-color-warning-highlight-text: #14181e;\n  --sd-color-warning-bg: #652a02;\n  --sd-color-warning-bg-text: #fff;\n  --pst-color-danger: #e78894;\n  --pst-color-danger-text: #14181e;\n  --pst-color-danger-highlight: #df596a;\n  --sd-color-danger: var(--pst-color-danger);\n  --sd-color-danger-text: var(--pst-color-danger-text);\n  --sd-color-danger-highlight: var(--pst-color-danger-highlight);\n  --pst-color-danger-highlight-text: #14181e;\n  --sd-color-danger-bg: #4e111b;\n  --sd-color-danger-bg-text: #fff;\n  --pst-color-light: #f3f4f5;\n  --pst-color-light-text: #14181e;\n  --pst-color-light-highlight: #c9cfd6;\n  --sd-color-light: var(--pst-color-light);\n  --sd-color-light-text: var(--pst-color-light-text);\n  --sd-color-light-highlight: var(--pst-color-light-highlight);\n  --pst-color-light-highlight-text: #14181e;\n  --sd-color-light-bg: #a3abb2;\n  --sd-color-light-bg-text: #14181e;\n  --pst-color-muted: #f3f4f5;\n  --pst-color-muted-text: #14181e;\n  --pst-color-muted-highlight: #c9cfd6;\n  --sd-color-muted: var(--pst-color-muted);\n  --sd-color-muted-text: var(--pst-color-muted-text);\n  --sd-color-muted-highlight: var(--pst-color-muted-highlight);\n  --pst-color-muted-highlight-text: #14181e;\n  --sd-color-muted-bg: #1d222b;\n  --sd-color-muted-bg-text: #fff;\n  --pst-color-dark: #222832;\n  --pst-color-dark-text: #fff;\n  --pst-color-dark-highlight: #1b222c;\n  --sd-color-dark: var(--pst-color-dark);\n  --sd-color-dark-text: var(--pst-color-dark-text);\n  --sd-color-dark-highlight: var(--pst-color-dark-highlight);\n  --pst-color-dark-highlight-text: #fff;\n  --pst-color-black: #14181e;\n  --pst-color-black-text: #fff;\n  --pst-color-black-highlight: #10141a;\n  --sd-color-black: var(--pst-color-black);\n  --sd-color-black-text: var(--pst-color-black-text);\n  --sd-color-black-highlight: var(--pst-color-black-highlight);\n  --pst-color-black-highlight-text: #fff;\n  --pst-color-white: #fff;\n  --pst-color-white-text: #14181e;\n  --pst-color-white-highlight: #dbd7d7;\n  --sd-color-white: var(--pst-color-white);\n  --sd-color-white-text: var(--pst-color-white-text);\n  --sd-color-white-highlight: var(--pst-color-white-highlight);\n  --pst-color-white-highlight-text: #14181e;\n}\n\nhtml[data-theme=light] {\n  --sd-color-card-border: var(--pst-color-border);\n}\n\nhtml[data-theme=dark] {\n  --sd-color-card-border: var(--pst-color-border);\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=light] .sd-shadow-xs,\nhtml[data-theme=light] .sd-shadow-sm,\nhtml[data-theme=light] .sd-shadow-md,\nhtml[data-theme=light] .sd-shadow-lg {\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/*******************************************************************************\n  * cards\n  */\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n}\n.bd-content .sd-card .sd-card-header {\n  background-color: var(--pst-color-panel-background);\n  border-bottom: 1px solid var(--pst-color-border);\n}\n.bd-content .sd-card .sd-card-footer {\n  background-color: var(--pst-color-panel-background);\n  border-top: 1px solid var(--pst-color-border);\n}\n.bd-content .sd-card .sd-card-body {\n  background-color: var(--pst-color-panel-background);\n}\n.bd-content .sd-card .sd-stretched-link:focus-visible {\n  outline: none;\n}\n.bd-content .sd-card .sd-stretched-link:focus-visible::after {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  border-radius: 0.25rem;\n}\n.bd-content .sd-card.sd-card-hover:hover {\n  border-color: var(--pst-color-link-hover);\n}\n\n/*******************************************************************************\n  * tabs\n  */\n.bd-content .sd-tab-set > input:checked + label {\n  border-style: solid solid none;\n  border-color: var(--pst-color-primary) var(--pst-color-primary) transparent;\n  border-width: 0.125rem 0.125rem 0;\n  border-radius: 0.125rem 0.125rem 0 0;\n  background-color: var(--pst-color-on-background);\n  transform: translateY(0.125rem);\n  color: var(--pst-color-primary);\n}\n.bd-content .sd-tab-set > input:focus-visible + label {\n  border: 0.125rem solid var(--pst-color-accent);\n  border-radius: 0.125rem;\n  background-color: var(--pst-color-accent-bg);\n  color: var(--pst-color-on-surface);\n}\n.bd-content .sd-tab-set > input:not(:checked, :focus-visible) + label:hover {\n  border-color: transparent;\n  color: var(--pst-color-secondary);\n  text-decoration-line: underline;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n}\n.bd-content .sd-tab-set > label {\n  color: var(--pst-color-on-surface);\n  border: 0.125rem solid transparent;\n  border-radius: 0.125rem 0.125rem 0 0;\n  background-color: var(--pst-color-surface);\n  padding: 0 0.75em;\n  margin-inline-end: 0.25rem;\n  line-height: 1.95;\n}\nhtml[data-theme=dark] .bd-content .sd-tab-set > label {\n  background-color: var(--pst-color-on-background);\n}\n.bd-content .sd-tab-set .sd-tab-content {\n  border: 0.125rem solid var(--pst-color-primary);\n  border-radius: 0.1875rem;\n  box-shadow: unset;\n  padding: 0.625rem;\n  background-color: var(--pst-color-on-background);\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\ndetails.sd-dropdown {\n  border: 0 !important;\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\ndetails.sd-dropdown summary.sd-card-header {\n  border: 0 !important;\n  display: flex;\n  align-items: center;\n  position: relative;\n  font-weight: 600;\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  --pst-sd-dropdown-color: var(--pst-gray-500);\n  --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n  background-color: var(--pst-sd-dropdown-bg-color) !important;\n  border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n}\ndetails.sd-dropdown summary.sd-card-header + div.sd-summary-content {\n  border: 0;\n  --pst-sd-dropdown-color: var(--sd-color-card-border);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-primary {\n  --pst-sd-dropdown-color: var(--sd-color-primary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-primary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-primary + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-primary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-primary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-primary {\n  color: var(--sd-color-primary-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-secondary {\n  --pst-sd-dropdown-color: var(--sd-color-secondary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-secondary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-secondary + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-secondary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-secondary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-secondary {\n  color: var(--sd-color-secondary-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-success {\n  --pst-sd-dropdown-color: var(--sd-color-success);\n  --pst-sd-dropdown-bg-color: var(--sd-color-success-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-success + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-success);\n  --pst-sd-dropdown-bg-color: var(--sd-color-success-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-success {\n  color: var(--sd-color-success-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-info {\n  --pst-sd-dropdown-color: var(--sd-color-info);\n  --pst-sd-dropdown-bg-color: var(--sd-color-info-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-info + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-info);\n  --pst-sd-dropdown-bg-color: var(--sd-color-info-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-info {\n  color: var(--sd-color-info-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-warning {\n  --pst-sd-dropdown-color: var(--sd-color-warning);\n  --pst-sd-dropdown-bg-color: var(--sd-color-warning-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-warning + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-warning);\n  --pst-sd-dropdown-bg-color: var(--sd-color-warning-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-warning {\n  color: var(--sd-color-warning-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-danger {\n  --pst-sd-dropdown-color: var(--sd-color-danger);\n  --pst-sd-dropdown-bg-color: var(--sd-color-danger-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-danger + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-danger);\n  --pst-sd-dropdown-bg-color: var(--sd-color-danger-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-danger {\n  color: var(--sd-color-danger-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-light {\n  --pst-sd-dropdown-color: var(--sd-color-light);\n  --pst-sd-dropdown-bg-color: var(--sd-color-light-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-light + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-light);\n  --pst-sd-dropdown-bg-color: var(--sd-color-light-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-light {\n  color: var(--sd-color-light-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-muted {\n  --pst-sd-dropdown-color: var(--sd-color-muted);\n  --pst-sd-dropdown-bg-color: var(--sd-color-muted-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-muted + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-muted);\n  --pst-sd-dropdown-bg-color: var(--sd-color-muted-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-muted {\n  color: var(--sd-color-muted-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-dark {\n  --pst-sd-dropdown-color: var(--sd-color-dark);\n  --pst-sd-dropdown-bg-color: var(--sd-color-dark-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-dark + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-dark);\n  --pst-sd-dropdown-bg-color: var(--sd-color-dark-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-dark {\n  color: var(--sd-color-dark-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-black {\n  --pst-sd-dropdown-color: var(--sd-color-black);\n  --pst-sd-dropdown-bg-color: var(--sd-color-black-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-black + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-black);\n  --pst-sd-dropdown-bg-color: var(--sd-color-black-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-black {\n  color: var(--sd-color-black-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-white {\n  --pst-sd-dropdown-color: var(--sd-color-white);\n  --pst-sd-dropdown-bg-color: var(--sd-color-white-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-white + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-white);\n  --pst-sd-dropdown-bg-color: var(--sd-color-white-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-white {\n  color: var(--sd-color-white-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\ndetails.sd-dropdown summary.sd-card-header + div.sd-summary-content {\n  border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n  border-bottom-left-radius: calc(0.25rem - 1px);\n  background-color: var(--pst-color-on-background);\n}\ndetails.sd-dropdown summary.sd-card-header span.sd-summary-icon {\n  display: inline-flex;\n  align-items: center;\n  color: var(--pst-sd-dropdown-color) !important;\n}\ndetails.sd-dropdown summary.sd-card-header span.sd-summary-icon svg {\n  opacity: 1;\n}\ndetails.sd-dropdown summary.sd-card-header .sd-summary-up,\ndetails.sd-dropdown summary.sd-card-header .sd-summary-down {\n  top: 0.7rem;\n}\ndetails.sd-dropdown summary.sd-card-header:focus-visible {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-offset: -0.1875rem;\n}\n\n/*******************************************************************************\n* Buttons (which in Sphinx Design are actually links that look like buttons)\n* ref: https://sphinx-design.readthedocs.io/en/pydata-theme/badges_buttons.html#buttons\n*/\nhtml .sd-btn {\n  min-width: 2.25rem;\n  padding: 0.3125rem 0.75rem 0.4375rem;\n}\nhtml .sd-btn:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: 1px;\n}\nhtml .sd-btn-primary:hover {\n  color: var(--pst-color-primary-highlight-text) !important;\n}\nhtml .sd-btn-secondary:hover {\n  color: var(--pst-color-secondary-highlight-text) !important;\n}\nhtml .sd-btn-success:hover {\n  color: var(--pst-color-success-highlight-text) !important;\n}\nhtml .sd-btn-info:hover {\n  color: var(--pst-color-info-highlight-text) !important;\n}\nhtml .sd-btn-warning:hover {\n  color: var(--pst-color-warning-highlight-text) !important;\n}\nhtml .sd-btn-danger:hover {\n  color: var(--pst-color-danger-highlight-text) !important;\n}\nhtml .sd-btn-light:hover {\n  color: var(--pst-color-light-highlight-text) !important;\n}\nhtml .sd-btn-muted:hover {\n  color: var(--pst-color-muted-highlight-text) !important;\n}\nhtml .sd-btn-dark:hover {\n  color: var(--pst-color-dark-highlight-text) !important;\n}\nhtml .sd-btn-black:hover {\n  color: var(--pst-color-black-highlight-text) !important;\n}\nhtml .sd-btn-white:hover {\n  color: var(--pst-color-white-highlight-text) !important;\n}\nhtml .sd-btn-primary:focus-visible,\nhtml .sd-btn-outline-primary:focus-visible {\n  background-color: var(--sd-color-primary) !important;\n  border-color: var(--sd-color-primary) !important;\n  outline: var(--sd-color-primary) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-secondary:focus-visible,\nhtml .sd-btn-outline-secondary:focus-visible {\n  background-color: var(--sd-color-secondary) !important;\n  border-color: var(--sd-color-secondary) !important;\n  outline: var(--sd-color-secondary) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-success:focus-visible,\nhtml .sd-btn-outline-success:focus-visible {\n  background-color: var(--sd-color-success) !important;\n  border-color: var(--sd-color-success) !important;\n  outline: var(--sd-color-success) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-info:focus-visible,\nhtml .sd-btn-outline-info:focus-visible {\n  background-color: var(--sd-color-info) !important;\n  border-color: var(--sd-color-info) !important;\n  outline: var(--sd-color-info) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-warning:focus-visible,\nhtml .sd-btn-outline-warning:focus-visible {\n  background-color: var(--sd-color-warning) !important;\n  border-color: var(--sd-color-warning) !important;\n  outline: var(--sd-color-warning) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-danger:focus-visible,\nhtml .sd-btn-outline-danger:focus-visible {\n  background-color: var(--sd-color-danger) !important;\n  border-color: var(--sd-color-danger) !important;\n  outline: var(--sd-color-danger) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-light:focus-visible,\nhtml .sd-btn-outline-light:focus-visible {\n  background-color: var(--sd-color-light) !important;\n  border-color: var(--sd-color-light) !important;\n  outline: var(--sd-color-light) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-muted:focus-visible,\nhtml .sd-btn-outline-muted:focus-visible {\n  background-color: var(--sd-color-muted) !important;\n  border-color: var(--sd-color-muted) !important;\n  outline: var(--sd-color-muted) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-dark:focus-visible,\nhtml .sd-btn-outline-dark:focus-visible {\n  background-color: var(--sd-color-dark) !important;\n  border-color: var(--sd-color-dark) !important;\n  outline: var(--sd-color-dark) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-black:focus-visible,\nhtml .sd-btn-outline-black:focus-visible {\n  background-color: var(--sd-color-black) !important;\n  border-color: var(--sd-color-black) !important;\n  outline: var(--sd-color-black) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\nhtml .sd-btn-white:focus-visible,\nhtml .sd-btn-outline-white:focus-visible {\n  background-color: var(--sd-color-white) !important;\n  border-color: var(--sd-color-white) !important;\n  outline: var(--sd-color-white) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\n\n/**\n * Sphinx togglebutton\n */\n.bd-content .admonition button.toggle-button {\n  color: inherit;\n}\n.bd-content .admonition button.toggle-button.toggle-button-hidden .toggle-chevron-right {\n  transform: rotate(90deg);\n  transition: none;\n}\n.bd-content .admonition button.toggle-button .toggle-chevron-right {\n  transform: rotate(-90deg);\n  transition: none;\n}\n.bd-content .admonition:focus-within {\n  overflow: visible;\n}\n.bd-content .admonition:focus-within .admonition-title:focus-within::before {\n  content: \"\";\n  transform: translateX(-0.2rem);\n  width: calc(100% + 0.2rem);\n  height: 100%;\n  border: var(--pst-color-accent) solid 0.1875rem;\n  border-radius: 0.1875rem;\n}\n.bd-content .admonition:focus-within:not(.toggle-hidden) .admonition-title:focus-within::before {\n  border-bottom-left-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.bd-content details.toggle-details summary {\n  border-left: 3px solid var(--pst-color-primary);\n}\n.bd-content details.toggle-details summary .toggle-chevron-right {\n  transform: rotate(90deg);\n  transition: none;\n}\n.bd-content details.toggle-details[open] .toggle-chevron-right {\n  transform: rotate(-90deg);\n  transition: none;\n}\n.bd-content details.toggle-details[open] :focus-visible {\n  border-bottom-left-radius: 0;\n  border-bottom-right-radius: 0;\n}\n\n/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html,\nhtml .jp-RenderedHTMLCommon {\n  margin: 0.1875rem;\n}\nhtml div.rendered_html table,\nhtml .jp-RenderedHTMLCommon table {\n  table-layout: auto;\n}\n\n.bd-content .nboutput .output_area.rendered_html table.dataframe,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe th ~ th,\n.bd-content .nboutput .output_area.rendered_html table.dataframe th ~ td,\n.bd-content .nboutput .output_area.rendered_html table.dataframe td ~ th,\n.bd-content .nboutput .output_area.rendered_html table.dataframe td ~ td,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe th ~ th,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe th ~ td,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe td ~ th,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe td ~ td {\n  border-left: 1px solid var(--pst-color-table-inner-border);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe thead tr,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe thead tr {\n  background-color: var(--pst-color-table-heading-bg);\n  border-bottom: 2px solid var(--pst-color-primary);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe tbody tr:nth-child(odd),\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe tbody tr:nth-child(odd) {\n  background-color: var(--pst-color-table-row-zebra-low-bg);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe tbody tr:nth-child(even),\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe tbody tr:nth-child(even) {\n  background-color: var(--pst-color-table-row-zebra-high-bg);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe tbody tr:hover,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe tbody tr:hover {\n  background-color: var(--pst-color-table-row-hover-bg);\n}\nhtml[data-theme=dark] .bd-content .nboutput .output_area.rendered_html:not(:has(table.dataframe)),\nhtml[data-theme=dark] .bd-content .nboutput .output_area .widget-subarea {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\nhtml[data-theme=dark] .bd-content .nboutput .output_area.stderr {\n  background-color: var(--pst-color-danger);\n}\n\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\nhtml[data-theme=light] .nbinput.container .prompt pre {\n  color: #005b82;\n}\nhtml[data-theme=dark] .nbinput.container .prompt pre {\n  color: #00e0e0;\n}\n\nhtml[data-theme=light] .nboutput.container .prompt pre {\n  color: #a12236;\n}\nhtml[data-theme=dark] .nboutput.container .prompt pre {\n  color: #ffa07a;\n}\n\n/*******************************************************************************\n * myst NB\n */\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n.bd-content div.cell_output table.dataframe {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n}\n.bd-content div.cell_output table.dataframe th ~ th,\n.bd-content div.cell_output table.dataframe th ~ td,\n.bd-content div.cell_output table.dataframe td ~ th,\n.bd-content div.cell_output table.dataframe td ~ td {\n  border-left: 1px solid var(--pst-color-table-inner-border);\n}\n.bd-content div.cell_output table.dataframe thead tr {\n  background-color: var(--pst-color-table-heading-bg);\n  border-bottom: 2px solid var(--pst-color-primary);\n}\n.bd-content div.cell_output table.dataframe tbody tr:nth-child(odd) {\n  background-color: var(--pst-color-table-row-zebra-low-bg);\n}\n.bd-content div.cell_output table.dataframe tbody tr:nth-child(even) {\n  background-color: var(--pst-color-table-row-zebra-high-bg);\n}\n.bd-content div.cell_output table.dataframe tbody tr:hover {\n  background-color: var(--pst-color-table-row-hover-bg);\n}\nhtml[data-theme=dark] .bd-content div.cell_output img,\nhtml[data-theme=dark] .bd-content div.cell_output .text_html:not(:has(table.dataframe)),\nhtml[data-theme=dark] .bd-content div.cell_output .widget-subarea {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n\n.bd-content div.cell_input {\n  display: flex;\n  flex-direction: column;\n  justify-content: stretch;\n}\n.bd-content div.cell_input,\n.bd-content div.output {\n  border-radius: 0.25rem;\n}\n.bd-content div.output table {\n  table-layout: auto;\n}\n\n/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n\n/**\n * /search.html page special-cases\n */\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n}\n.bd-search-container div#search-results > h2, .bd-search-container div#search-results > .h2 {\n  font-size: var(--pst-font-size-icon);\n  margin-top: 1rem;\n}\n.bd-search-container div#search-results p.search-summary {\n  color: var(--pst-color-text-muted);\n}\n.bd-search-container ul.search {\n  margin: 0;\n  list-style: none;\n}\n.bd-search-container ul.search li {\n  background-image: none;\n  padding: 1rem 0;\n  margin: 1rem 0;\n  border-top: 1px solid var(--pst-color-text-muted);\n}\n.bd-search-container ul.search li > a {\n  font-size: 1.2em;\n}\n.bd-search-container ul.search li div.context,\n.bd-search-container ul.search li p.context {\n  color: var(--pst-color-text-base);\n  margin: 0.5em 0 0;\n}\n.bd-search-container ul.search li div.context a::before,\n.bd-search-container ul.search li p.context a::before {\n  content: \"#\";\n  padding-right: 0.2em;\n  color: var(--pst-color-text-muted);\n}","@mixin bsBanner($file) {\n  /*!\n   * Bootstrap #{$file} v5.3.3 (https://getbootstrap.com/)\n   * Copyright 2011-2024 The Bootstrap Authors\n   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n   */\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb {\n    --#{$prefix}#{$color}-rgb: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-text {\n    --#{$prefix}#{$color}-text-emphasis: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-bg-subtle {\n    --#{$prefix}#{$color}-bg-subtle: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-border-subtle {\n    --#{$prefix}#{$color}-border-subtle: #{$value};\n  }\n\n  --#{$prefix}white-rgb: #{to-rgb($white)};\n  --#{$prefix}black-rgb: #{to-rgb($black)};\n\n  // Fonts\n\n  // Note: Use `inspect` for lists so that quoted items keep the quotes.\n  // See https://github.com/sass/sass/issues/2383#issuecomment-336349172\n  --#{$prefix}font-sans-serif: #{inspect($font-family-sans-serif)};\n  --#{$prefix}font-monospace: #{inspect($font-family-monospace)};\n  --#{$prefix}gradient: #{$gradient};\n\n  // Root and body\n  // scss-docs-start root-body-variables\n  @if $font-size-root != null {\n    --#{$prefix}root-font-size: #{$font-size-root};\n  }\n  --#{$prefix}body-font-family: #{inspect($font-family-base)};\n  @include rfs($font-size-base, --#{$prefix}body-font-size);\n  --#{$prefix}body-font-weight: #{$font-weight-base};\n  --#{$prefix}body-line-height: #{$line-height-base};\n  @if $body-text-align != null {\n    --#{$prefix}body-text-align: #{$body-text-align};\n  }\n\n  --#{$prefix}body-color: #{$body-color};\n  --#{$prefix}body-color-rgb: #{to-rgb($body-color)};\n  --#{$prefix}body-bg: #{$body-bg};\n  --#{$prefix}body-bg-rgb: #{to-rgb($body-bg)};\n\n  --#{$prefix}emphasis-color: #{$body-emphasis-color};\n  --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color)};\n\n  --#{$prefix}secondary-color: #{$body-secondary-color};\n  --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color)};\n  --#{$prefix}secondary-bg: #{$body-secondary-bg};\n  --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg)};\n\n  --#{$prefix}tertiary-color: #{$body-tertiary-color};\n  --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color)};\n  --#{$prefix}tertiary-bg: #{$body-tertiary-bg};\n  --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg)};\n  // scss-docs-end root-body-variables\n\n  --#{$prefix}heading-color: #{$headings-color};\n\n  --#{$prefix}link-color: #{$link-color};\n  --#{$prefix}link-color-rgb: #{to-rgb($link-color)};\n  --#{$prefix}link-decoration: #{$link-decoration};\n\n  --#{$prefix}link-hover-color: #{$link-hover-color};\n  --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color)};\n\n  @if $link-hover-decoration != null {\n    --#{$prefix}link-hover-decoration: #{$link-hover-decoration};\n  }\n\n  --#{$prefix}code-color: #{$code-color};\n  --#{$prefix}highlight-color: #{$mark-color};\n  --#{$prefix}highlight-bg: #{$mark-bg};\n\n  // scss-docs-start root-border-var\n  --#{$prefix}border-width: #{$border-width};\n  --#{$prefix}border-style: #{$border-style};\n  --#{$prefix}border-color: #{$border-color};\n  --#{$prefix}border-color-translucent: #{$border-color-translucent};\n\n  --#{$prefix}border-radius: #{$border-radius};\n  --#{$prefix}border-radius-sm: #{$border-radius-sm};\n  --#{$prefix}border-radius-lg: #{$border-radius-lg};\n  --#{$prefix}border-radius-xl: #{$border-radius-xl};\n  --#{$prefix}border-radius-xxl: #{$border-radius-xxl};\n  --#{$prefix}border-radius-2xl: var(--#{$prefix}border-radius-xxl); // Deprecated in v5.3.0 for consistency\n  --#{$prefix}border-radius-pill: #{$border-radius-pill};\n  // scss-docs-end root-border-var\n\n  --#{$prefix}box-shadow: #{$box-shadow};\n  --#{$prefix}box-shadow-sm: #{$box-shadow-sm};\n  --#{$prefix}box-shadow-lg: #{$box-shadow-lg};\n  --#{$prefix}box-shadow-inset: #{$box-shadow-inset};\n\n  // Focus styles\n  // scss-docs-start root-focus-variables\n  --#{$prefix}focus-ring-width: #{$focus-ring-width};\n  --#{$prefix}focus-ring-opacity: #{$focus-ring-opacity};\n  --#{$prefix}focus-ring-color: #{$focus-ring-color};\n  // scss-docs-end root-focus-variables\n\n  // scss-docs-start root-form-validation-variables\n  --#{$prefix}form-valid-color: #{$form-valid-color};\n  --#{$prefix}form-valid-border-color: #{$form-valid-border-color};\n  --#{$prefix}form-invalid-color: #{$form-invalid-color};\n  --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color};\n  // scss-docs-end root-form-validation-variables\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark, true) {\n    color-scheme: dark;\n\n    // scss-docs-start root-dark-mode-vars\n    --#{$prefix}body-color: #{$body-color-dark};\n    --#{$prefix}body-color-rgb: #{to-rgb($body-color-dark)};\n    --#{$prefix}body-bg: #{$body-bg-dark};\n    --#{$prefix}body-bg-rgb: #{to-rgb($body-bg-dark)};\n\n    --#{$prefix}emphasis-color: #{$body-emphasis-color-dark};\n    --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color-dark)};\n\n    --#{$prefix}secondary-color: #{$body-secondary-color-dark};\n    --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color-dark)};\n    --#{$prefix}secondary-bg: #{$body-secondary-bg-dark};\n    --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg-dark)};\n\n    --#{$prefix}tertiary-color: #{$body-tertiary-color-dark};\n    --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color-dark)};\n    --#{$prefix}tertiary-bg: #{$body-tertiary-bg-dark};\n    --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg-dark)};\n\n    @each $color, $value in $theme-colors-text-dark {\n      --#{$prefix}#{$color}-text-emphasis: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-bg-subtle-dark {\n      --#{$prefix}#{$color}-bg-subtle: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-border-subtle-dark {\n      --#{$prefix}#{$color}-border-subtle: #{$value};\n    }\n\n    --#{$prefix}heading-color: #{$headings-color-dark};\n\n    --#{$prefix}link-color: #{$link-color-dark};\n    --#{$prefix}link-hover-color: #{$link-hover-color-dark};\n    --#{$prefix}link-color-rgb: #{to-rgb($link-color-dark)};\n    --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color-dark)};\n\n    --#{$prefix}code-color: #{$code-color-dark};\n    --#{$prefix}highlight-color: #{$mark-color-dark};\n    --#{$prefix}highlight-bg: #{$mark-bg-dark};\n\n    --#{$prefix}border-color: #{$border-color-dark};\n    --#{$prefix}border-color-translucent: #{$border-color-translucent-dark};\n\n    --#{$prefix}form-valid-color: #{$form-valid-color-dark};\n    --#{$prefix}form-valid-border-color: #{$form-valid-border-color-dark};\n    --#{$prefix}form-invalid-color: #{$form-invalid-color-dark};\n    --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color-dark};\n    // scss-docs-end root-dark-mode-vars\n  }\n}\n","// stylelint-disable scss/dimension-no-non-numeric-values\n\n// SCSS RFS mixin\n//\n// Automated responsive values for font sizes, paddings, margins and much more\n//\n// Licensed under MIT (https://github.com/twbs/rfs/blob/main/LICENSE)\n\n// Configuration\n\n// Base value\n$rfs-base-value: 1.25rem !default;\n$rfs-unit: rem !default;\n\n@if $rfs-unit != rem and $rfs-unit != px {\n  @error \"`#{$rfs-unit}` is not a valid unit for $rfs-unit. Use `px` or `rem`.\";\n}\n\n// Breakpoint at where values start decreasing if screen width is smaller\n$rfs-breakpoint: 1200px !default;\n$rfs-breakpoint-unit: px !default;\n\n@if $rfs-breakpoint-unit != px and $rfs-breakpoint-unit != em and $rfs-breakpoint-unit != rem {\n  @error \"`#{$rfs-breakpoint-unit}` is not a valid unit for $rfs-breakpoint-unit. Use `px`, `em` or `rem`.\";\n}\n\n// Resize values based on screen height and width\n$rfs-two-dimensional: false !default;\n\n// Factor of decrease\n$rfs-factor: 10 !default;\n\n@if type-of($rfs-factor) != number or $rfs-factor <= 1 {\n  @error \"`#{$rfs-factor}` is not a valid  $rfs-factor, it must be greater than 1.\";\n}\n\n// Mode. Possibilities: \"min-media-query\", \"max-media-query\"\n$rfs-mode: min-media-query !default;\n\n// Generate enable or disable classes. Possibilities: false, \"enable\" or \"disable\"\n$rfs-class: false !default;\n\n// 1 rem = $rfs-rem-value px\n$rfs-rem-value: 16 !default;\n\n// Safari iframe resize bug: https://github.com/twbs/rfs/issues/14\n$rfs-safari-iframe-resize-bug-fix: false !default;\n\n// Disable RFS by setting $enable-rfs to false\n$enable-rfs: true !default;\n\n// Cache $rfs-base-value unit\n$rfs-base-value-unit: unit($rfs-base-value);\n\n@function divide($dividend, $divisor, $precision: 10) {\n  $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n  $dividend: abs($dividend);\n  $divisor: abs($divisor);\n  @if $dividend == 0 {\n    @return 0;\n  }\n  @if $divisor == 0 {\n    @error \"Cannot divide by 0\";\n  }\n  $remainder: $dividend;\n  $result: 0;\n  $factor: 10;\n  @while ($remainder > 0 and $precision >= 0) {\n    $quotient: 0;\n    @while ($remainder >= $divisor) {\n      $remainder: $remainder - $divisor;\n      $quotient: $quotient + 1;\n    }\n    $result: $result * 10 + $quotient;\n    $factor: $factor * .1;\n    $remainder: $remainder * 10;\n    $precision: $precision - 1;\n    @if ($precision < 0 and $remainder >= $divisor * 5) {\n      $result: $result + 1;\n    }\n  }\n  $result: $result * $factor * $sign;\n  $dividend-unit: unit($dividend);\n  $divisor-unit: unit($divisor);\n  $unit-map: (\n    \"px\": 1px,\n    \"rem\": 1rem,\n    \"em\": 1em,\n    \"%\": 1%\n  );\n  @if ($dividend-unit != $divisor-unit and map-has-key($unit-map, $dividend-unit)) {\n    $result: $result * map-get($unit-map, $dividend-unit);\n  }\n  @return $result;\n}\n\n// Remove px-unit from $rfs-base-value for calculations\n@if $rfs-base-value-unit == px {\n  $rfs-base-value: divide($rfs-base-value, $rfs-base-value * 0 + 1);\n}\n@else if $rfs-base-value-unit == rem {\n  $rfs-base-value: divide($rfs-base-value, divide($rfs-base-value * 0 + 1, $rfs-rem-value));\n}\n\n// Cache $rfs-breakpoint unit to prevent multiple calls\n$rfs-breakpoint-unit-cache: unit($rfs-breakpoint);\n\n// Remove unit from $rfs-breakpoint for calculations\n@if $rfs-breakpoint-unit-cache == px {\n  $rfs-breakpoint: divide($rfs-breakpoint, $rfs-breakpoint * 0 + 1);\n}\n@else if $rfs-breakpoint-unit-cache == rem or $rfs-breakpoint-unit-cache == \"em\" {\n  $rfs-breakpoint: divide($rfs-breakpoint, divide($rfs-breakpoint * 0 + 1, $rfs-rem-value));\n}\n\n// Calculate the media query value\n$rfs-mq-value: if($rfs-breakpoint-unit == px, #{$rfs-breakpoint}px, #{divide($rfs-breakpoint, $rfs-rem-value)}#{$rfs-breakpoint-unit});\n$rfs-mq-property-width: if($rfs-mode == max-media-query, max-width, min-width);\n$rfs-mq-property-height: if($rfs-mode == max-media-query, max-height, min-height);\n\n// Internal mixin used to determine which media query needs to be used\n@mixin _rfs-media-query {\n  @if $rfs-two-dimensional {\n    @if $rfs-mode == max-media-query {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}), (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n    @else {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) and (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n  }\n  @else {\n    @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) {\n      @content;\n    }\n  }\n}\n\n// Internal mixin that adds disable classes to the selector if needed.\n@mixin _rfs-rule {\n  @if $rfs-class == disable and $rfs-mode == max-media-query {\n    // Adding an extra class increases specificity, which prevents the media query to override the property\n    &,\n    .disable-rfs &,\n    &.disable-rfs {\n      @content;\n    }\n  }\n  @else if $rfs-class == enable and $rfs-mode == min-media-query {\n    .enable-rfs &,\n    &.enable-rfs {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Internal mixin that adds enable classes to the selector if needed.\n@mixin _rfs-media-query-rule {\n\n  @if $rfs-class == enable {\n    @if $rfs-mode == min-media-query {\n      @content;\n    }\n\n    @include _rfs-media-query () {\n      .enable-rfs &,\n      &.enable-rfs {\n        @content;\n      }\n    }\n  }\n  @else {\n    @if $rfs-class == disable and $rfs-mode == min-media-query {\n      .disable-rfs &,\n      &.disable-rfs {\n        @content;\n      }\n    }\n    @include _rfs-media-query () {\n      @content;\n    }\n  }\n}\n\n// Helper function to get the formatted non-responsive value\n@function rfs-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    }\n    @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      @if $unit == px {\n        // Convert to rem if needed\n        $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $value * 0 + $rfs-rem-value)}rem, $value);\n      }\n      @else if $unit == rem {\n        // Convert to px if needed\n        $val: $val + \" \" + if($rfs-unit == px, #{divide($value, $value * 0 + 1) * $rfs-rem-value}px, $value);\n      } @else {\n        // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n        $val: $val + \" \" + $value;\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// Helper function to get the responsive value calculated by RFS\n@function rfs-fluid-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    } @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n      @if not $unit or $unit != px and $unit != rem {\n        $val: $val + \" \" + $value;\n      } @else {\n        // Remove unit from $value for calculations\n        $value: divide($value, $value * 0 + if($unit == px, 1, divide(1, $rfs-rem-value)));\n\n        // Only add the media query if the value is greater than the minimum value\n        @if abs($value) <= $rfs-base-value or not $enable-rfs {\n          $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $rfs-rem-value)}rem, #{$value}px);\n        }\n        @else {\n          // Calculate the minimum value\n          $value-min: $rfs-base-value + divide(abs($value) - $rfs-base-value, $rfs-factor);\n\n          // Calculate difference between $value and the minimum value\n          $value-diff: abs($value) - $value-min;\n\n          // Base value formatting\n          $min-width: if($rfs-unit == rem, #{divide($value-min, $rfs-rem-value)}rem, #{$value-min}px);\n\n          // Use negative value if needed\n          $min-width: if($value < 0, -$min-width, $min-width);\n\n          // Use `vmin` if two-dimensional is enabled\n          $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n          // Calculate the variable width between 0 and $rfs-breakpoint\n          $variable-width: #{divide($value-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n          // Return the calculated value\n          $val: $val + \" calc(\" + $min-width + if($value < 0, \" - \", \" + \") + $variable-width + \")\";\n        }\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// RFS mixin\n@mixin rfs($values, $property: font-size) {\n  @if $values != null {\n    $val: rfs-value($values);\n    $fluid-val: rfs-fluid-value($values);\n\n    // Do not print the media query if responsive & non-responsive values are the same\n    @if $val == $fluid-val {\n      #{$property}: $val;\n    }\n    @else {\n      @include _rfs-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $val, $fluid-val);\n\n        // Include safari iframe resize fix if needed\n        min-width: if($rfs-safari-iframe-resize-bug-fix, (0 * 1vw), null);\n      }\n\n      @include _rfs-media-query-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $fluid-val, $val);\n      }\n    }\n  }\n}\n\n// Shorthand helper mixins\n@mixin font-size($value) {\n  @include rfs($value);\n}\n\n@mixin padding($value) {\n  @include rfs($value, padding);\n}\n\n@mixin padding-top($value) {\n  @include rfs($value, padding-top);\n}\n\n@mixin padding-right($value) {\n  @include rfs($value, padding-right);\n}\n\n@mixin padding-bottom($value) {\n  @include rfs($value, padding-bottom);\n}\n\n@mixin padding-left($value) {\n  @include rfs($value, padding-left);\n}\n\n@mixin margin($value) {\n  @include rfs($value, margin);\n}\n\n@mixin margin-top($value) {\n  @include rfs($value, margin-top);\n}\n\n@mixin margin-right($value) {\n  @include rfs($value, margin-right);\n}\n\n@mixin margin-bottom($value) {\n  @include rfs($value, margin-bottom);\n}\n\n@mixin margin-left($value) {\n  @include rfs($value, margin-left);\n}\n","// scss-docs-start color-mode-mixin\n@mixin color-mode($mode: light, $root: false) {\n  @if $color-mode-type == \"media-query\" {\n    @if $root == true {\n      @media (prefers-color-scheme: $mode) {\n        :root {\n          @content;\n        }\n      }\n    } @else {\n      @media (prefers-color-scheme: $mode) {\n        @content;\n      }\n    }\n  } @else {\n    [data-bs-theme=\"#{$mode}\"] {\n      @content;\n    }\n  }\n}\n// scss-docs-end color-mode-mixin\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type, property-no-vendor-prefix\n\n\n// Reboot\n//\n// Normalization of HTML elements, manually forked from Normalize.css to remove\n// styles targeting irrelevant browsers while applying new styles.\n//\n// Normalize is licensed MIT. https://github.com/necolas/normalize.css\n\n\n// Document\n//\n// Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n\n// Root\n//\n// Ability to the value of the root font sizes, affecting the value of `rem`.\n// null by default, thus nothing is generated.\n\n:root {\n  @if $font-size-root != null {\n    @include font-size(var(--#{$prefix}root-font-size));\n  }\n\n  @if $enable-smooth-scroll {\n    @media (prefers-reduced-motion: no-preference) {\n      scroll-behavior: smooth;\n    }\n  }\n}\n\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Prevent adjustments of font size after orientation changes in iOS.\n// 4. Change the default tap highlight to be completely transparent in iOS.\n\n// scss-docs-start reboot-body-rules\nbody {\n  margin: 0; // 1\n  font-family: var(--#{$prefix}body-font-family);\n  @include font-size(var(--#{$prefix}body-font-size));\n  font-weight: var(--#{$prefix}body-font-weight);\n  line-height: var(--#{$prefix}body-line-height);\n  color: var(--#{$prefix}body-color);\n  text-align: var(--#{$prefix}body-text-align);\n  background-color: var(--#{$prefix}body-bg); // 2\n  -webkit-text-size-adjust: 100%; // 3\n  -webkit-tap-highlight-color: rgba($black, 0); // 4\n}\n// scss-docs-end reboot-body-rules\n\n\n// Content grouping\n//\n// 1. Reset Firefox's gray color\n\nhr {\n  margin: $hr-margin-y 0;\n  color: $hr-color; // 1\n  border: 0;\n  border-top: $hr-border-width solid $hr-border-color;\n  opacity: $hr-opacity;\n}\n\n\n// Typography\n//\n// 1. Remove top margins from headings\n//    By default, `<h1>`-`<h6>` all receive top and bottom margins. We nuke the top\n//    margin for easier control within type scales as it avoids margin collapsing.\n\n%heading {\n  margin-top: 0; // 1\n  margin-bottom: $headings-margin-bottom;\n  font-family: $headings-font-family;\n  font-style: $headings-font-style;\n  font-weight: $headings-font-weight;\n  line-height: $headings-line-height;\n  color: var(--#{$prefix}heading-color);\n}\n\nh1 {\n  @extend %heading;\n  @include font-size($h1-font-size);\n}\n\nh2 {\n  @extend %heading;\n  @include font-size($h2-font-size);\n}\n\nh3 {\n  @extend %heading;\n  @include font-size($h3-font-size);\n}\n\nh4 {\n  @extend %heading;\n  @include font-size($h4-font-size);\n}\n\nh5 {\n  @extend %heading;\n  @include font-size($h5-font-size);\n}\n\nh6 {\n  @extend %heading;\n  @include font-size($h6-font-size);\n}\n\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `<p>`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\n\np {\n  margin-top: 0;\n  margin-bottom: $paragraph-margin-bottom;\n}\n\n\n// Abbreviations\n//\n// 1. Add the correct text decoration in Chrome, Edge, Opera, and Safari.\n// 2. Add explicit cursor to indicate changed behavior.\n// 3. Prevent the text-decoration to be skipped.\n\nabbr[title] {\n  text-decoration: underline dotted; // 1\n  cursor: help; // 2\n  text-decoration-skip-ink: none; // 3\n}\n\n\n// Address\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\n\n// Lists\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: $dt-font-weight;\n}\n\n// 1. Undo browser default\n\ndd {\n  margin-bottom: .5rem;\n  margin-left: 0; // 1\n}\n\n\n// Blockquote\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\n\n// Strong\n//\n// Add the correct font weight in Chrome, Edge, and Safari\n\nb,\nstrong {\n  font-weight: $font-weight-bolder;\n}\n\n\n// Small\n//\n// Add the correct font size in all browsers\n\nsmall {\n  @include font-size($small-font-size);\n}\n\n\n// Mark\n\nmark {\n  padding: $mark-padding;\n  color: var(--#{$prefix}highlight-color);\n  background-color: var(--#{$prefix}highlight-bg);\n}\n\n\n// Sub and Sup\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n\nsub,\nsup {\n  position: relative;\n  @include font-size($sub-sup-font-size);\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n// Links\n\na {\n  color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, 1));\n  text-decoration: $link-decoration;\n\n  &:hover {\n    --#{$prefix}link-color-rgb: var(--#{$prefix}link-hover-color-rgb);\n    text-decoration: $link-hover-decoration;\n  }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n  &,\n  &:hover {\n    color: inherit;\n    text-decoration: none;\n  }\n}\n\n\n// Code\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: $font-family-code;\n  @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\n// 1. Remove browser default top margin\n// 2. Reset browser default of `1em` to use `rem`s\n// 3. Don't allow content to break outside\n\npre {\n  display: block;\n  margin-top: 0; // 1\n  margin-bottom: 1rem; // 2\n  overflow: auto; // 3\n  @include font-size($code-font-size);\n  color: $pre-color;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    @include font-size(inherit);\n    color: inherit;\n    word-break: normal;\n  }\n}\n\ncode {\n  @include font-size($code-font-size);\n  color: var(--#{$prefix}code-color);\n  word-wrap: break-word;\n\n  // Streamline the style when inside anchors to avoid broken underline and more\n  a > & {\n    color: inherit;\n  }\n}\n\nkbd {\n  padding: $kbd-padding-y $kbd-padding-x;\n  @include font-size($kbd-font-size);\n  color: $kbd-color;\n  background-color: $kbd-bg;\n  @include border-radius($border-radius-sm);\n\n  kbd {\n    padding: 0;\n    @include font-size(1em);\n    font-weight: $nested-kbd-font-weight;\n  }\n}\n\n\n// Figures\n//\n// Apply a consistent margin strategy (matches our type styles).\n\nfigure {\n  margin: 0 0 1rem;\n}\n\n\n// Images and content\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\n\n// Tables\n//\n// Prevent double borders\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: $table-cell-padding-y;\n  padding-bottom: $table-cell-padding-y;\n  color: $table-caption-color;\n  text-align: left;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `<td>` alignment by inheriting `text-align`.\n// 3. Fix alignment for Safari\n\nth {\n  font-weight: $table-th-font-weight; // 1\n  text-align: inherit; // 2\n  text-align: -webkit-match-parent; // 3\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\n\n// Forms\n//\n// 1. Allow labels to use `margin` for spacing.\n\nlabel {\n  display: inline-block; // 1\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n// See https://github.com/twbs/bootstrap/issues/24093\n\nbutton {\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 0;\n}\n\n// Explicitly remove focus outline in Chromium when it shouldn't be\n// visible (e.g. as result of mouse click or touch tap). It already\n// should be doing this automatically, but seems to currently be\n// confused and applies its very visible two-tone outline anyway.\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\n// 1. Remove the margin in Firefox and Safari\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0; // 1\n  font-family: inherit;\n  @include font-size(inherit);\n  line-height: inherit;\n}\n\n// Remove the inheritance of text transform in Firefox\nbutton,\nselect {\n  text-transform: none;\n}\n// Set the cursor for non-`<button>` buttons\n//\n// Details at https://github.com/twbs/bootstrap/pull/30562\n[role=\"button\"] {\n  cursor: pointer;\n}\n\nselect {\n  // Remove the inheritance of word-wrap in Safari.\n  // See https://github.com/twbs/bootstrap/issues/24990\n  word-wrap: normal;\n\n  // Undo the opacity change from Chrome\n  &:disabled {\n    opacity: 1;\n  }\n}\n\n// Remove the dropdown arrow only from text type inputs built with datalists in Chrome.\n// See https://stackoverflow.com/a/54997118\n\n[list]:not([type=\"date\"]):not([type=\"datetime-local\"]):not([type=\"month\"]):not([type=\"week\"]):not([type=\"time\"])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\n// 1. Prevent a WebKit bug where (2) destroys native `audio` and `video`\n//    controls in Android 4.\n// 2. Correct the inability to style clickable types in iOS and Safari.\n// 3. Opinionated: add \"hand\" cursor to non-disabled button elements.\n\nbutton,\n[type=\"button\"], // 1\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n\n  @if $enable-button-pointers {\n    &:not(:disabled) {\n      cursor: pointer; // 3\n    }\n  }\n}\n\n// Remove inner border and padding from Firefox, but don't restore the outline like Normalize.\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\n// 1. Textareas should really only resize vertically so they don't break their (horizontal) containers.\n\ntextarea {\n  resize: vertical; // 1\n}\n\n// 1. Browsers set a default `min-width: min-content;` on fieldsets,\n//    unlike e.g. `<div>`s, which have `min-width: 0;` by default.\n//    So we reset that to ensure fieldsets behave more like a standard block element.\n//    See https://github.com/twbs/bootstrap/issues/12359\n//    and https://html.spec.whatwg.org/multipage/#the-fieldset-and-legend-elements\n// 2. Reset the default outline behavior of fieldsets so they don't affect page layout.\n\nfieldset {\n  min-width: 0; // 1\n  padding: 0; // 2\n  margin: 0; // 2\n  border: 0; // 2\n}\n\n// 1. By using `float: left`, the legend will behave like a block element.\n//    This way the border of a fieldset wraps around the legend if present.\n// 2. Fix wrapping bug.\n//    See https://github.com/twbs/bootstrap/issues/29712\n\nlegend {\n  float: left; // 1\n  width: 100%;\n  padding: 0;\n  margin-bottom: $legend-margin-bottom;\n  @include font-size($legend-font-size);\n  font-weight: $legend-font-weight;\n  line-height: inherit;\n\n  + * {\n    clear: left; // 2\n  }\n}\n\n// Fix height of inputs with a type of datetime-local, date, month, week, or time\n// See https://github.com/twbs/bootstrap/issues/18842\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n// 1. This overrides the extra rounded corners on search inputs in iOS so that our\n//    `.form-control` class can properly style them. Note that this cannot simply\n//    be added to `.form-control` as it's not specific enough. For details, see\n//    https://github.com/twbs/bootstrap/issues/11586.\n// 2. Correct the outline style in Safari.\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  outline-offset: -2px; // 2\n}\n\n// 1. A few input types should stay LTR\n// See https://rtlstyling.com/posts/rtl-styling#form-inputs\n// 2. RTL only output\n// See https://rtlcss.com/learn/usage-guide/control-directives/#raw\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n\n// Remove the inner padding in Chrome and Safari on macOS.\n\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n// Remove padding around color pickers in webkit browsers\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n\n// 1. Inherit font family and line height for file input buttons\n// 2. Correct the inability to style clickable types in iOS and Safari.\n\n::file-selector-button {\n  font: inherit; // 1\n  -webkit-appearance: button; // 2\n}\n\n// Correct element displays\n\noutput {\n  display: inline-block;\n}\n\n// Remove border from iframe\n\niframe {\n  border: 0;\n}\n\n// Summary\n//\n// 1. Add the correct display in all browsers\n\nsummary {\n  display: list-item; // 1\n  cursor: pointer;\n}\n\n\n// Progress\n//\n// Add the correct vertical alignment in Chrome, Firefox, and Opera.\n\nprogress {\n  vertical-align: baseline;\n}\n\n\n// Hidden attribute\n//\n// Always hide an element with the `hidden` HTML attribute.\n\n[hidden] {\n  display: none !important;\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n// fusv-disable\n$accordion-button-focus-border-color:     $input-focus-border-color !default; // Deprecated in v5.3.3\n// fusv-enable\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-active-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n\n@import \"variables-dark\"; // TODO: can be removed safely in v6, only here to avoid breaking changes in v5.3\n","// stylelint-disable property-disallowed-list\n// Single side border-radius\n\n// Helper function to replace negative values with 0\n@function valid-radius($radius) {\n  $return: ();\n  @each $value in $radius {\n    @if type-of($value) == number {\n      $return: append($return, max($value, 0));\n    } @else {\n      $return: append($return, $value);\n    }\n  }\n  @return $return;\n}\n\n// scss-docs-start border-radius-mixins\n@mixin border-radius($radius: $border-radius, $fallback-border-radius: false) {\n  @if $enable-rounded {\n    border-radius: valid-radius($radius);\n  }\n  @else if $fallback-border-radius != false {\n    border-radius: $fallback-border-radius;\n  }\n}\n\n@mixin border-top-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n// scss-docs-end border-radius-mixins\n","// Lists\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n@mixin list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n","// Responsive images (ensure images don't scale beyond their parents)\n//\n// This is purposefully opt-in via an explicit class rather than being the default for all `<img>`s.\n// We previously tried the \"images are responsive by default\" approach in Bootstrap v2,\n// and abandoned it in Bootstrap v3 because it breaks lots of third-party widgets (including Google Maps)\n// which weren't expecting the images within themselves to be involuntarily resized.\n// See also https://github.com/twbs/bootstrap/issues/18178\n.img-fluid {\n  @include img-fluid();\n}\n\n\n// Image thumbnails\n.img-thumbnail {\n  padding: $thumbnail-padding;\n  background-color: $thumbnail-bg;\n  border: $thumbnail-border-width solid $thumbnail-border-color;\n  @include border-radius($thumbnail-border-radius);\n  @include box-shadow($thumbnail-box-shadow);\n\n  // Keep them at most 100% wide\n  @include img-fluid();\n}\n\n//\n// Figures\n//\n\n.figure {\n  // Ensures the caption's text aligns with the image.\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: $spacer * .5;\n  line-height: 1;\n}\n\n.figure-caption {\n  @include font-size($figure-caption-font-size);\n  color: $figure-caption-color;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n\n@mixin img-fluid {\n  // Part 1: Set a maximum relative to the parent\n  max-width: 100%;\n  // Part 2: Override the height to auto, otherwise images will be stretched\n  // when setting a width and height attribute on the img element.\n  height: auto;\n}\n","// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n@if $enable-container-classes {\n  // Single container class with breakpoint max-widths\n  .container,\n  // 100% wide container at all breakpoints\n  .container-fluid {\n    @include make-container();\n  }\n\n  // Responsive containers that are 100% wide until a breakpoint\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    .container-#{$breakpoint} {\n      @extend .container-fluid;\n    }\n\n    @include media-breakpoint-up($breakpoint, $grid-breakpoints) {\n      %responsive-container-#{$breakpoint} {\n        max-width: $container-max-width;\n      }\n\n      // Extend each breakpoint which is smaller or equal to the current breakpoint\n      $extend-breakpoint: true;\n\n      @each $name, $width in $grid-breakpoints {\n        @if ($extend-breakpoint) {\n          .container#{breakpoint-infix($name, $grid-breakpoints)} {\n            @extend %responsive-container-#{$breakpoint};\n          }\n\n          // Once the current breakpoint is reached, stop extending\n          @if ($breakpoint == $name) {\n            $extend-breakpoint: false;\n          }\n        }\n      }\n    }\n  }\n}\n","// Container mixins\n\n@mixin make-container($gutter: $container-padding-x) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-right: auto;\n  margin-left: auto;\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n","// Row\n//\n// Rows contain your columns.\n\n:root {\n  @each $name, $value in $grid-breakpoints {\n    --#{$prefix}breakpoint-#{$name}: #{$value};\n  }\n}\n\n@if $enable-grid-classes {\n  .row {\n    @include make-row();\n\n    > * {\n      @include make-col-ready();\n    }\n  }\n}\n\n@if $enable-cssgrid {\n  .grid {\n    display: grid;\n    grid-template-rows: repeat(var(--#{$prefix}rows, 1), 1fr);\n    grid-template-columns: repeat(var(--#{$prefix}columns, #{$grid-columns}), 1fr);\n    gap: var(--#{$prefix}gap, #{$grid-gutter-width});\n\n    @include make-cssgrid();\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n@if $enable-grid-classes {\n  @include make-grid-columns();\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is an invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Basic Bootstrap table\n//\n\n.table {\n  // Reset needed for nesting tables\n  --#{$prefix}table-color-type: initial;\n  --#{$prefix}table-bg-type: initial;\n  --#{$prefix}table-color-state: initial;\n  --#{$prefix}table-bg-state: initial;\n  // End of reset\n  --#{$prefix}table-color: #{$table-color};\n  --#{$prefix}table-bg: #{$table-bg};\n  --#{$prefix}table-border-color: #{$table-border-color};\n  --#{$prefix}table-accent-bg: #{$table-accent-bg};\n  --#{$prefix}table-striped-color: #{$table-striped-color};\n  --#{$prefix}table-striped-bg: #{$table-striped-bg};\n  --#{$prefix}table-active-color: #{$table-active-color};\n  --#{$prefix}table-active-bg: #{$table-active-bg};\n  --#{$prefix}table-hover-color: #{$table-hover-color};\n  --#{$prefix}table-hover-bg: #{$table-hover-bg};\n\n  width: 100%;\n  margin-bottom: $spacer;\n  vertical-align: $table-cell-vertical-align;\n  border-color: var(--#{$prefix}table-border-color);\n\n  // Target th & td\n  // We need the child combinator to prevent styles leaking to nested tables which doesn't have a `.table` class.\n  // We use the universal selectors here to simplify the selector (else we would need 6 different selectors).\n  // Another advantage is that this generates less code and makes the selector less specific making it easier to override.\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y $table-cell-padding-x;\n    // Following the precept of cascades: https://codepen.io/miriamsuzanne/full/vYNgodb\n    color: var(--#{$prefix}table-color-state, var(--#{$prefix}table-color-type, var(--#{$prefix}table-color)));\n    background-color: var(--#{$prefix}table-bg);\n    border-bottom-width: $table-border-width;\n    box-shadow: inset 0 0 0 9999px var(--#{$prefix}table-bg-state, var(--#{$prefix}table-bg-type, var(--#{$prefix}table-accent-bg)));\n  }\n\n  > tbody {\n    vertical-align: inherit;\n  }\n\n  > thead {\n    vertical-align: bottom;\n  }\n}\n\n.table-group-divider {\n  border-top: calc(#{$table-border-width} * 2) solid $table-group-separator-color; // stylelint-disable-line function-disallowed-list\n}\n\n//\n// Change placement of captions with a class\n//\n\n.caption-top {\n  caption-side: top;\n}\n\n\n//\n// Condensed table w/ half padding\n//\n\n.table-sm {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y-sm $table-cell-padding-x-sm;\n  }\n}\n\n\n// Border versions\n//\n// Add or remove borders all around the table and between all the columns.\n//\n// When borders are added on all sides of the cells, the corners can render odd when\n// these borders do not have the same color or if they are semi-transparent.\n// Therefore we add top and border bottoms to the `tr`s and left and right borders\n// to the `td`s or `th`s\n\n.table-bordered {\n  > :not(caption) > * {\n    border-width: $table-border-width 0;\n\n    // stylelint-disable-next-line selector-max-universal\n    > * {\n      border-width: 0 $table-border-width;\n    }\n  }\n}\n\n.table-borderless {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    border-bottom-width: 0;\n  }\n\n  > :not(:first-child) {\n    border-top-width: 0;\n  }\n}\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n// For rows\n.table-striped {\n  > tbody > tr:nth-of-type(#{$table-striped-order}) > * {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// For columns\n.table-striped-columns {\n  > :not(caption) > tr > :nth-child(#{$table-striped-columns-order}) {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// Active table\n//\n// The `.table-active` class can be added to highlight rows or cells\n\n.table-active {\n  --#{$prefix}table-color-state: var(--#{$prefix}table-active-color);\n  --#{$prefix}table-bg-state: var(--#{$prefix}table-active-bg);\n}\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover > * {\n    --#{$prefix}table-color-state: var(--#{$prefix}table-hover-color);\n    --#{$prefix}table-bg-state: var(--#{$prefix}table-hover-bg);\n  }\n}\n\n\n// Table variants\n//\n// Table variants set the table cell backgrounds, border colors\n// and the colors of the striped, hovered & active tables\n\n@each $color, $value in $table-variants {\n  @include table-variant($color, $value);\n}\n\n// Responsive tables\n//\n// Generate series of `.table-responsive-*` classes for configuring the screen\n// size of where your table will overflow.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n  @include media-breakpoint-down($breakpoint) {\n    .table-responsive#{$infix} {\n      overflow-x: auto;\n      -webkit-overflow-scrolling: touch;\n    }\n  }\n}\n","// scss-docs-start table-variant\n@mixin table-variant($state, $background) {\n  .table-#{$state} {\n    $color: color-contrast(opaque($body-bg, $background));\n    $hover-bg: mix($color, $background, percentage($table-hover-bg-factor));\n    $striped-bg: mix($color, $background, percentage($table-striped-bg-factor));\n    $active-bg: mix($color, $background, percentage($table-active-bg-factor));\n    $table-border-color: mix($color, $background, percentage($table-border-factor));\n\n    --#{$prefix}table-color: #{$color};\n    --#{$prefix}table-bg: #{$background};\n    --#{$prefix}table-border-color: #{$table-border-color};\n    --#{$prefix}table-striped-bg: #{$striped-bg};\n    --#{$prefix}table-striped-color: #{color-contrast($striped-bg)};\n    --#{$prefix}table-active-bg: #{$active-bg};\n    --#{$prefix}table-active-color: #{color-contrast($active-bg)};\n    --#{$prefix}table-hover-bg: #{$hover-bg};\n    --#{$prefix}table-hover-color: #{color-contrast($hover-bg)};\n\n    color: var(--#{$prefix}table-color);\n    border-color: var(--#{$prefix}table-border-color);\n  }\n}\n// scss-docs-end table-variant\n","//\n// Labels\n//\n\n.form-label {\n  margin-bottom: $form-label-margin-bottom;\n  @include font-size($form-label-font-size);\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  color: $form-label-color;\n}\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align with the form controls.\n.col-form-label {\n  padding-top: add($input-padding-y, $input-border-width);\n  padding-bottom: add($input-padding-y, $input-border-width);\n  margin-bottom: 0; // Override the `<legend>` default\n  @include font-size(inherit); // Override the `<legend>` default\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  line-height: $input-line-height;\n  color: $form-label-color;\n}\n\n.col-form-label-lg {\n  padding-top: add($input-padding-y-lg, $input-border-width);\n  padding-bottom: add($input-padding-y-lg, $input-border-width);\n  @include font-size($input-font-size-lg);\n}\n\n.col-form-label-sm {\n  padding-top: add($input-padding-y-sm, $input-border-width);\n  padding-bottom: add($input-padding-y-sm, $input-border-width);\n  @include font-size($input-font-size-sm);\n}\n","//\n// Form text\n//\n\n.form-text {\n  margin-top: $form-text-margin-top;\n  @include font-size($form-text-font-size);\n  font-style: $form-text-font-style;\n  font-weight: $form-text-font-weight;\n  color: $form-text-color;\n}\n","//\n// General form controls (plus a few specific high-level interventions)\n//\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y $input-padding-x;\n  font-family: $input-font-family;\n  @include font-size($input-font-size);\n  font-weight: $input-font-weight;\n  line-height: $input-line-height;\n  color: $input-color;\n  appearance: none; // Fix appearance for date inputs in Safari\n  background-color: $input-bg;\n  background-clip: padding-box;\n  border: $input-border-width solid $input-border-color;\n\n  // Note: This has no effect on <select>s in some browsers, due to the limited stylability of `<select>`s in CSS.\n  @include border-radius($input-border-radius, 0);\n\n  @include box-shadow($input-box-shadow);\n  @include transition($input-transition);\n\n  &[type=\"file\"] {\n    overflow: hidden; // prevent pseudo element button overlap\n\n    &:not(:disabled):not([readonly]) {\n      cursor: pointer;\n    }\n  }\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  &:focus {\n    color: $input-focus-color;\n    background-color: $input-focus-bg;\n    border-color: $input-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($input-box-shadow, $input-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $input-focus-box-shadow;\n    }\n  }\n\n  &::-webkit-date-and-time-value {\n    // On Android Chrome, form-control's \"width: 100%\" makes the input width too small\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    //\n    // On iOS Safari, form-control's \"appearance: none\" + \"width: 100%\" makes the input width too small\n    // Tested under iOS 16.2 / Safari 16.2\n    min-width: 85px; // Seems to be a good minimum safe width\n\n    // Add some height to date inputs on iOS\n    // https://github.com/twbs/bootstrap/issues/23307\n    // TODO: we can remove this workaround once https://bugs.webkit.org/show_bug.cgi?id=198959 is resolved\n    // Multiply line-height by 1em if it has no unit\n    height: if(unit($input-line-height) == \"\", $input-line-height * 1em, $input-line-height);\n\n    // Android Chrome type=\"date\" is taller than the other inputs\n    // because of \"margin: 1px 24px 1px 4px\" inside the shadow DOM\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    margin: 0;\n  }\n\n  // Prevent excessive date input height in Webkit\n  // https://github.com/twbs/bootstrap/issues/34433\n  &::-webkit-datetime-edit {\n    display: block;\n    padding: 0;\n  }\n\n  // Placeholder\n  &::placeholder {\n    color: $input-placeholder-color;\n    // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526.\n    opacity: 1;\n  }\n\n  // Disabled inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &:disabled {\n    color: $input-disabled-color;\n    background-color: $input-disabled-bg;\n    border-color: $input-disabled-border-color;\n    // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655.\n    opacity: 1;\n  }\n\n  // File input buttons theming\n  &::file-selector-button {\n    padding: $input-padding-y $input-padding-x;\n    margin: (-$input-padding-y) (-$input-padding-x);\n    margin-inline-end: $input-padding-x;\n    color: $form-file-button-color;\n    @include gradient-bg($form-file-button-bg);\n    pointer-events: none;\n    border-color: inherit;\n    border-style: solid;\n    border-width: 0;\n    border-inline-end-width: $input-border-width;\n    border-radius: 0; // stylelint-disable-line property-disallowed-list\n    @include transition($btn-transition);\n  }\n\n  &:hover:not(:disabled):not([readonly])::file-selector-button {\n    background-color: $form-file-button-hover-bg;\n  }\n}\n\n// Readonly controls as plain text\n//\n// Apply class to a readonly input to make it appear like regular plain\n// text (without any border, background color, focus indicator)\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y 0;\n  margin-bottom: 0; // match inputs if this class comes on inputs with default margins\n  line-height: $input-line-height;\n  color: $input-plaintext-color;\n  background-color: transparent;\n  border: solid transparent;\n  border-width: $input-border-width 0;\n\n  &:focus {\n    outline: 0;\n  }\n\n  &.form-control-sm,\n  &.form-control-lg {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// Repeated in `_input_group.scss` to avoid Sass extend issues.\n\n.form-control-sm {\n  min-height: $input-height-sm;\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n\n  &::file-selector-button {\n    padding: $input-padding-y-sm $input-padding-x-sm;\n    margin: (-$input-padding-y-sm) (-$input-padding-x-sm);\n    margin-inline-end: $input-padding-x-sm;\n  }\n}\n\n.form-control-lg {\n  min-height: $input-height-lg;\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n\n  &::file-selector-button {\n    padding: $input-padding-y-lg $input-padding-x-lg;\n    margin: (-$input-padding-y-lg) (-$input-padding-x-lg);\n    margin-inline-end: $input-padding-x-lg;\n  }\n}\n\n// Make sure textareas don't shrink too much when resized\n// https://github.com/twbs/bootstrap/pull/29124\n// stylelint-disable selector-no-qualifying-type\ntextarea {\n  &.form-control {\n    min-height: $input-height;\n  }\n\n  &.form-control-sm {\n    min-height: $input-height-sm;\n  }\n\n  &.form-control-lg {\n    min-height: $input-height-lg;\n  }\n}\n// stylelint-enable selector-no-qualifying-type\n\n.form-control-color {\n  width: $form-color-width;\n  height: $input-height;\n  padding: $input-padding-y;\n\n  &:not(:disabled):not([readonly]) {\n    cursor: pointer;\n  }\n\n  &::-moz-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &::-webkit-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &.form-control-sm { height: $input-height-sm; }\n  &.form-control-lg { height: $input-height-lg; }\n}\n","// stylelint-disable property-disallowed-list\n@mixin transition($transition...) {\n  @if length($transition) == 0 {\n    $transition: $transition-base;\n  }\n\n  @if length($transition) > 1 {\n    @each $value in $transition {\n      @if $value == null or $value == none {\n        @warn \"The keyword 'none' or 'null' must be used as a single argument.\";\n      }\n    }\n  }\n\n  @if $enable-transitions {\n    @if nth($transition, 1) != null {\n      transition: $transition;\n    }\n\n    @if $enable-reduced-motion and nth($transition, 1) != null and nth($transition, 1) != none {\n      @media (prefers-reduced-motion: reduce) {\n        transition: none;\n      }\n    }\n  }\n}\n","// Gradients\n\n// scss-docs-start gradient-bg-mixin\n@mixin gradient-bg($color: null) {\n  background-color: $color;\n\n  @if $enable-gradients {\n    background-image: var(--#{$prefix}gradient);\n  }\n}\n// scss-docs-end gradient-bg-mixin\n\n// scss-docs-start gradient-mixins\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n  background-image: linear-gradient(to right, $start-color $start-percent, $end-color $end-percent);\n}\n\n// Vertical gradient, from top to bottom\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-y($start-color: $gray-700, $end-color: $gray-800, $start-percent: null, $end-percent: null) {\n  background-image: linear-gradient(to bottom, $start-color $start-percent, $end-color $end-percent);\n}\n\n@mixin gradient-directional($start-color: $gray-700, $end-color: $gray-800, $deg: 45deg) {\n  background-image: linear-gradient($deg, $start-color, $end-color);\n}\n\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-radial($inner-color: $gray-700, $outer-color: $gray-800) {\n  background-image: radial-gradient(circle, $inner-color, $outer-color);\n}\n\n@mixin gradient-striped($color: rgba($white, .15), $angle: 45deg) {\n  background-image: linear-gradient($angle, $color 25%, transparent 25%, transparent 50%, $color 50%, $color 75%, transparent 75%, transparent);\n}\n// scss-docs-end gradient-mixins\n","// Select\n//\n// Replaces the browser default select with a custom one, mostly pulled from\n// https://primer.github.io/.\n\n.form-select {\n  --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator)};\n\n  display: block;\n  width: 100%;\n  padding: $form-select-padding-y $form-select-indicator-padding $form-select-padding-y $form-select-padding-x;\n  font-family: $form-select-font-family;\n  @include font-size($form-select-font-size);\n  font-weight: $form-select-font-weight;\n  line-height: $form-select-line-height;\n  color: $form-select-color;\n  appearance: none;\n  background-color: $form-select-bg;\n  background-image: var(--#{$prefix}form-select-bg-img), var(--#{$prefix}form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: $form-select-bg-position;\n  background-size: $form-select-bg-size;\n  border: $form-select-border-width solid $form-select-border-color;\n  @include border-radius($form-select-border-radius, 0);\n  @include box-shadow($form-select-box-shadow);\n  @include transition($form-select-transition);\n\n  &:focus {\n    border-color: $form-select-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($form-select-box-shadow, $form-select-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $form-select-focus-box-shadow;\n    }\n  }\n\n  &[multiple],\n  &[size]:not([size=\"1\"]) {\n    padding-right: $form-select-padding-x;\n    background-image: none;\n  }\n\n  &:disabled {\n    color: $form-select-disabled-color;\n    background-color: $form-select-disabled-bg;\n    border-color: $form-select-disabled-border-color;\n  }\n\n  // Remove outline from select box in FF\n  &:-moz-focusring {\n    color: transparent;\n    text-shadow: 0 0 0 $form-select-color;\n  }\n}\n\n.form-select-sm {\n  padding-top: $form-select-padding-y-sm;\n  padding-bottom: $form-select-padding-y-sm;\n  padding-left: $form-select-padding-x-sm;\n  @include font-size($form-select-font-size-sm);\n  @include border-radius($form-select-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: $form-select-padding-y-lg;\n  padding-bottom: $form-select-padding-y-lg;\n  padding-left: $form-select-padding-x-lg;\n  @include font-size($form-select-font-size-lg);\n  @include border-radius($form-select-border-radius-lg);\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-select {\n      --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator-dark)};\n    }\n  }\n}\n","//\n// Check/radio\n//\n\n.form-check {\n  display: block;\n  min-height: $form-check-min-height;\n  padding-left: $form-check-padding-start;\n  margin-bottom: $form-check-margin-bottom;\n\n  .form-check-input {\n    float: left;\n    margin-left: $form-check-padding-start * -1;\n  }\n}\n\n.form-check-reverse {\n  padding-right: $form-check-padding-start;\n  padding-left: 0;\n  text-align: right;\n\n  .form-check-input {\n    float: right;\n    margin-right: $form-check-padding-start * -1;\n    margin-left: 0;\n  }\n}\n\n.form-check-input {\n  --#{$prefix}form-check-bg: #{$form-check-input-bg};\n\n  flex-shrink: 0;\n  width: $form-check-input-width;\n  height: $form-check-input-width;\n  margin-top: ($line-height-base - $form-check-input-width) * .5; // line-height minus check height\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--#{$prefix}form-check-bg);\n  background-image: var(--#{$prefix}form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: $form-check-input-border;\n  print-color-adjust: exact; // Keep themed appearance for print\n  @include transition($form-check-transition);\n\n  &[type=\"checkbox\"] {\n    @include border-radius($form-check-input-border-radius);\n  }\n\n  &[type=\"radio\"] {\n    // stylelint-disable-next-line property-disallowed-list\n    border-radius: $form-check-radio-border-radius;\n  }\n\n  &:active {\n    filter: $form-check-input-active-filter;\n  }\n\n  &:focus {\n    border-color: $form-check-input-focus-border;\n    outline: 0;\n    box-shadow: $form-check-input-focus-box-shadow;\n  }\n\n  &:checked {\n    background-color: $form-check-input-checked-bg-color;\n    border-color: $form-check-input-checked-border-color;\n\n    &[type=\"checkbox\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)};\n      }\n    }\n\n    &[type=\"radio\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)};\n      }\n    }\n  }\n\n  &[type=\"checkbox\"]:indeterminate {\n    background-color: $form-check-input-indeterminate-bg-color;\n    border-color: $form-check-input-indeterminate-border-color;\n\n    @if $enable-gradients {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)}, var(--#{$prefix}gradient);\n    } @else {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)};\n    }\n  }\n\n  &:disabled {\n    pointer-events: none;\n    filter: none;\n    opacity: $form-check-input-disabled-opacity;\n  }\n\n  // Use disabled attribute in addition of :disabled pseudo-class\n  // See: https://github.com/twbs/bootstrap/issues/28247\n  &[disabled],\n  &:disabled {\n    ~ .form-check-label {\n      cursor: default;\n      opacity: $form-check-label-disabled-opacity;\n    }\n  }\n}\n\n.form-check-label {\n  color: $form-check-label-color;\n  cursor: $form-check-label-cursor;\n}\n\n//\n// Switch\n//\n\n.form-switch {\n  padding-left: $form-switch-padding-start;\n\n  .form-check-input {\n    --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image)};\n\n    width: $form-switch-width;\n    margin-left: $form-switch-padding-start * -1;\n    background-image: var(--#{$prefix}form-switch-bg);\n    background-position: left center;\n    @include border-radius($form-switch-border-radius, 0);\n    @include transition($form-switch-transition);\n\n    &:focus {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-focus-bg-image)};\n    }\n\n    &:checked {\n      background-position: $form-switch-checked-bg-position;\n\n      @if $enable-gradients {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)};\n      }\n    }\n  }\n\n  &.form-check-reverse {\n    padding-right: $form-switch-padding-start;\n    padding-left: 0;\n\n    .form-check-input {\n      margin-right: $form-switch-padding-start * -1;\n      margin-left: 0;\n    }\n  }\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: $form-check-inline-margin-end;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n\n  &[disabled],\n  &:disabled {\n    + .btn {\n      pointer-events: none;\n      filter: none;\n      opacity: $form-check-btn-check-disabled-opacity;\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-switch .form-check-input:not(:checked):not(:focus) {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image-dark)};\n    }\n  }\n}\n","// Range\n//\n// Style range inputs the same across browsers. Vendor-specific rules for pseudo\n// elements cannot be mixed. As such, there are no shared styles for focus or\n// active states on prefixed selectors.\n\n.form-range {\n  width: 100%;\n  height: add($form-range-thumb-height, $form-range-thumb-focus-box-shadow-width * 2);\n  padding: 0; // Need to reset padding\n  appearance: none;\n  background-color: transparent;\n\n  &:focus {\n    outline: 0;\n\n    // Pseudo-elements must be split across multiple rulesets to have an effect.\n    // No box-shadow() mixin for focus accessibility.\n    &::-webkit-slider-thumb { box-shadow: $form-range-thumb-focus-box-shadow; }\n    &::-moz-range-thumb     { box-shadow: $form-range-thumb-focus-box-shadow; }\n  }\n\n  &::-moz-focus-outer {\n    border: 0;\n  }\n\n  &::-webkit-slider-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    margin-top: ($form-range-track-height - $form-range-thumb-height) * .5; // Webkit specific\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-webkit-slider-runnable-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent; // Why?\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent;\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &::-moz-range-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-moz-range-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent;\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent; // Firefox specific?\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &:disabled {\n    pointer-events: none;\n\n    &::-webkit-slider-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n\n    &::-moz-range-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n  }\n}\n",".form-floating {\n  position: relative;\n\n  > .form-control,\n  > .form-control-plaintext,\n  > .form-select {\n    height: $form-floating-height;\n    min-height: $form-floating-height;\n    line-height: $form-floating-line-height;\n  }\n\n  > label {\n    position: absolute;\n    top: 0;\n    left: 0;\n    z-index: 2;\n    height: 100%; // allow textareas\n    padding: $form-floating-padding-y $form-floating-padding-x;\n    overflow: hidden;\n    text-align: start;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    pointer-events: none;\n    border: $input-border-width solid transparent; // Required for aligning label's text with the input as it affects inner box model\n    transform-origin: 0 0;\n    @include transition($form-floating-transition);\n  }\n\n  > .form-control,\n  > .form-control-plaintext {\n    padding: $form-floating-padding-y $form-floating-padding-x;\n\n    &::placeholder {\n      color: transparent;\n    }\n\n    &:focus,\n    &:not(:placeholder-shown) {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n    // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n    &:-webkit-autofill {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n  }\n\n  > .form-select {\n    padding-top: $form-floating-input-padding-t;\n    padding-bottom: $form-floating-input-padding-b;\n  }\n\n  > .form-control:focus,\n  > .form-control:not(:placeholder-shown),\n  > .form-control-plaintext,\n  > .form-select {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n\n      &::after {\n        position: absolute;\n        inset: $form-floating-padding-y ($form-floating-padding-x * .5);\n        z-index: -1;\n        height: $form-floating-label-height;\n        content: \"\";\n        background-color: $input-bg;\n        @include border-radius($input-border-radius);\n      }\n    }\n  }\n  // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n  > .form-control:-webkit-autofill {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n    }\n  }\n\n  > .form-control-plaintext {\n    ~ label {\n      border-width: $input-border-width 0; // Required to properly position label text - as explained above\n    }\n  }\n\n  > :disabled ~ label,\n  > .form-control:disabled ~ label { // Required for `.form-control`s because of specificity\n    color: $form-floating-label-disabled-color;\n\n    &::after {\n      background-color: $input-disabled-bg;\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // For form validation feedback\n  align-items: stretch;\n  width: 100%;\n\n  > .form-control,\n  > .form-select,\n  > .form-floating {\n    position: relative; // For focus state's z-index\n    flex: 1 1 auto;\n    width: 1%;\n    min-width: 0; // https://stackoverflow.com/questions/36247140/why-dont-flex-items-shrink-past-content-size\n  }\n\n  // Bring the \"active\" form control to the top of surrounding elements\n  > .form-control:focus,\n  > .form-select:focus,\n  > .form-floating:focus-within {\n    z-index: 5;\n  }\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text` since it shares the same border-color\n  // as our inputs.\n  .btn {\n    position: relative;\n    z-index: 2;\n\n    &:focus {\n      z-index: 5;\n    }\n  }\n}\n\n\n// Textual addons\n//\n// Serves as a catch-all element for any text or radio/checkbox input you wish\n// to prepend or append to an input.\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: $input-group-addon-padding-y $input-group-addon-padding-x;\n  @include font-size($input-font-size); // Match inputs\n  font-weight: $input-group-addon-font-weight;\n  line-height: $input-line-height;\n  color: $input-group-addon-color;\n  text-align: center;\n  white-space: nowrap;\n  background-color: $input-group-addon-bg;\n  border: $input-border-width solid $input-group-addon-border-color;\n  @include border-radius($input-border-radius);\n}\n\n\n// Sizing\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: $form-select-padding-x + $form-select-indicator-padding;\n}\n\n\n// Rounded corners\n//\n// These rulesets must come after the sizing ones to properly override sm and lg\n// border-radius values when extending. They're more specific than we'd like\n// with the `.input-group >` part, but without it, we cannot override the sizing.\n\n// stylelint-disable-next-line no-duplicate-selectors\n.input-group {\n  &:not(.has-validation) {\n    > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 3),\n    > .form-floating:not(:last-child) > .form-control,\n    > .form-floating:not(:last-child) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  &.has-validation {\n    > :nth-last-child(n + 3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 4),\n    > .form-floating:nth-last-child(n + 3) > .form-control,\n    > .form-floating:nth-last-child(n + 3) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  $validation-messages: \"\";\n  @each $state in map-keys($form-validation-states) {\n    $validation-messages: $validation-messages + \":not(.\" + unquote($state) + \"-tooltip)\" + \":not(.\" + unquote($state) + \"-feedback)\";\n  }\n\n  > :not(:first-child):not(.dropdown-menu)#{$validation-messages} {\n    margin-left: calc(#{$input-border-width} * -1); // stylelint-disable-line function-disallowed-list\n    @include border-start-radius(0);\n  }\n\n  > .form-floating:not(:first-child) > .form-control,\n  > .form-floating:not(:first-child) > .form-select {\n    @include border-start-radius(0);\n  }\n}\n","// This mixin uses an `if()` technique to be compatible with Dart Sass\n// See https://github.com/sass/sass/issues/1873#issuecomment-152293725 for more details\n\n// scss-docs-start form-validation-mixins\n@mixin form-validation-state-selector($state) {\n  @if ($state == \"valid\" or $state == \"invalid\") {\n    .was-validated #{if(&, \"&\", \"\")}:#{$state},\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  } @else {\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  }\n}\n\n@mixin form-validation-state(\n  $state,\n  $color,\n  $icon,\n  $tooltip-color: color-contrast($color),\n  $tooltip-bg-color: rgba($color, $form-feedback-tooltip-opacity),\n  $focus-box-shadow: 0 0 $input-btn-focus-blur $input-focus-width rgba($color, $input-btn-focus-color-opacity),\n  $border-color: $color\n) {\n  .#{$state}-feedback {\n    display: none;\n    width: 100%;\n    margin-top: $form-feedback-margin-top;\n    @include font-size($form-feedback-font-size);\n    font-style: $form-feedback-font-style;\n    color: $color;\n  }\n\n  .#{$state}-tooltip {\n    position: absolute;\n    top: 100%;\n    z-index: 5;\n    display: none;\n    max-width: 100%; // Contain to parent when possible\n    padding: $form-feedback-tooltip-padding-y $form-feedback-tooltip-padding-x;\n    margin-top: .1rem;\n    @include font-size($form-feedback-tooltip-font-size);\n    line-height: $form-feedback-tooltip-line-height;\n    color: $tooltip-color;\n    background-color: $tooltip-bg-color;\n    @include border-radius($form-feedback-tooltip-border-radius);\n  }\n\n  @include form-validation-state-selector($state) {\n    ~ .#{$state}-feedback,\n    ~ .#{$state}-tooltip {\n      display: block;\n    }\n  }\n\n  .form-control {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-image: escape-svg($icon);\n        background-repeat: no-repeat;\n        background-position: right $input-height-inner-quarter center;\n        background-size: $input-height-inner-half $input-height-inner-half;\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($input-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  // stylelint-disable-next-line selector-no-qualifying-type\n  textarea.form-control {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-position: top $input-height-inner-quarter right $input-height-inner-quarter;\n      }\n    }\n  }\n\n  .form-select {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        &:not([multiple]):not([size]),\n        &:not([multiple])[size=\"1\"] {\n          --#{$prefix}form-select-bg-icon: #{escape-svg($icon)};\n          padding-right: $form-select-feedback-icon-padding-end;\n          background-position: $form-select-bg-position, $form-select-feedback-icon-position;\n          background-size: $form-select-bg-size, $form-select-feedback-icon-size;\n        }\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($form-select-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  .form-control-color {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        width: add($form-color-width, $input-height-inner);\n      }\n    }\n  }\n\n  .form-check-input {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      &:checked {\n        background-color: $color;\n      }\n\n      &:focus {\n        box-shadow: $focus-box-shadow;\n      }\n\n      ~ .form-check-label {\n        color: $color;\n      }\n    }\n  }\n  .form-check-inline .form-check-input {\n    ~ .#{$state}-feedback {\n      margin-left: .5em;\n    }\n  }\n\n  .input-group {\n    > .form-control:not(:focus),\n    > .form-select:not(:focus),\n    > .form-floating:not(:focus-within) {\n      @include form-validation-state-selector($state) {\n        @if $state == \"valid\" {\n          z-index: 3;\n        } @else if $state == \"invalid\" {\n          z-index: 4;\n        }\n      }\n    }\n  }\n}\n// scss-docs-end form-validation-mixins\n","//\n// Base styles\n//\n\n.btn {\n  // scss-docs-start btn-css-vars\n  --#{$prefix}btn-padding-x: #{$btn-padding-x};\n  --#{$prefix}btn-padding-y: #{$btn-padding-y};\n  --#{$prefix}btn-font-family: #{$btn-font-family};\n  @include rfs($btn-font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-font-weight: #{$btn-font-weight};\n  --#{$prefix}btn-line-height: #{$btn-line-height};\n  --#{$prefix}btn-color: #{$btn-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-width: #{$btn-border-width};\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-border-radius: #{$btn-border-radius};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-box-shadow: #{$btn-box-shadow};\n  --#{$prefix}btn-disabled-opacity: #{$btn-disabled-opacity};\n  --#{$prefix}btn-focus-box-shadow: 0 0 0 #{$btn-focus-width} rgba(var(--#{$prefix}btn-focus-shadow-rgb), .5);\n  // scss-docs-end btn-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}btn-padding-y) var(--#{$prefix}btn-padding-x);\n  font-family: var(--#{$prefix}btn-font-family);\n  @include font-size(var(--#{$prefix}btn-font-size));\n  font-weight: var(--#{$prefix}btn-font-weight);\n  line-height: var(--#{$prefix}btn-line-height);\n  color: var(--#{$prefix}btn-color);\n  text-align: center;\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: $btn-white-space;\n  vertical-align: middle;\n  cursor: if($enable-button-pointers, pointer, null);\n  user-select: none;\n  border: var(--#{$prefix}btn-border-width) solid var(--#{$prefix}btn-border-color);\n  @include border-radius(var(--#{$prefix}btn-border-radius));\n  @include gradient-bg(var(--#{$prefix}btn-bg));\n  @include box-shadow(var(--#{$prefix}btn-box-shadow));\n  @include transition($btn-transition);\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}btn-hover-bg);\n    border-color: var(--#{$prefix}btn-hover-border-color);\n  }\n\n  .btn-check + &:hover {\n    // override for the checkbox/radio buttons\n    color: var(--#{$prefix}btn-color);\n    background-color: var(--#{$prefix}btn-bg);\n    border-color: var(--#{$prefix}btn-border-color);\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-hover-color);\n    @include gradient-bg(var(--#{$prefix}btn-hover-bg));\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:focus-visible + & {\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:checked + &,\n  :not(.btn-check) + &:active,\n  &:first-child:active,\n  &.active,\n  &.show {\n    color: var(--#{$prefix}btn-active-color);\n    background-color: var(--#{$prefix}btn-active-bg);\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-active-border-color);\n    @include box-shadow(var(--#{$prefix}btn-active-shadow));\n\n    &:focus-visible {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      @if $enable-shadows {\n        box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n      } @else {\n        box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n      }\n    }\n  }\n\n  .btn-check:checked:focus-visible + & {\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  &:disabled,\n  &.disabled,\n  fieldset:disabled & {\n    color: var(--#{$prefix}btn-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}btn-disabled-bg);\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-disabled-border-color);\n    opacity: var(--#{$prefix}btn-disabled-opacity);\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Alternate buttons\n//\n\n// scss-docs-start btn-variant-loops\n@each $color, $value in $theme-colors {\n  .btn-#{$color} {\n    @if $color == \"light\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: shade-color($value, $btn-hover-bg-shade-amount),\n        $hover-border: shade-color($value, $btn-hover-border-shade-amount),\n        $active-background: shade-color($value, $btn-active-bg-shade-amount),\n        $active-border: shade-color($value, $btn-active-border-shade-amount)\n      );\n    } @else if $color == \"dark\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: tint-color($value, $btn-hover-bg-tint-amount),\n        $hover-border: tint-color($value, $btn-hover-border-tint-amount),\n        $active-background: tint-color($value, $btn-active-bg-tint-amount),\n        $active-border: tint-color($value, $btn-active-border-tint-amount)\n      );\n    } @else {\n      @include button-variant($value, $value);\n    }\n  }\n}\n\n@each $color, $value in $theme-colors {\n  .btn-outline-#{$color} {\n    @include button-outline-variant($value);\n  }\n}\n// scss-docs-end btn-variant-loops\n\n\n//\n// Link buttons\n//\n\n// Make a button look and behave like a link\n.btn-link {\n  --#{$prefix}btn-font-weight: #{$font-weight-normal};\n  --#{$prefix}btn-color: #{$btn-link-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-hover-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-active-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-active-border-color: transparent;\n  --#{$prefix}btn-disabled-color: #{$btn-link-disabled-color};\n  --#{$prefix}btn-disabled-border-color: transparent;\n  --#{$prefix}btn-box-shadow: 0 0 0 #000; // Can't use `none` as keyword negates all values when used with multiple shadows\n  --#{$prefix}btn-focus-shadow-rgb: #{$btn-link-focus-shadow-rgb};\n\n  text-decoration: $link-decoration;\n  @if $enable-gradients {\n    background-image: none;\n  }\n\n  &:hover,\n  &:focus-visible {\n    text-decoration: $link-hover-decoration;\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-color);\n  }\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n  }\n\n  // No need for an active state here\n}\n\n\n//\n// Button Sizes\n//\n\n.btn-lg {\n  @include button-size($btn-padding-y-lg, $btn-padding-x-lg, $btn-font-size-lg, $btn-border-radius-lg);\n}\n\n.btn-sm {\n  @include button-size($btn-padding-y-sm, $btn-padding-x-sm, $btn-font-size-sm, $btn-border-radius-sm);\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n// scss-docs-start btn-variant-mixin\n@mixin button-variant(\n  $background,\n  $border,\n  $color: color-contrast($background),\n  $hover-background: if($color == $color-contrast-light, shade-color($background, $btn-hover-bg-shade-amount), tint-color($background, $btn-hover-bg-tint-amount)),\n  $hover-border: if($color == $color-contrast-light, shade-color($border, $btn-hover-border-shade-amount), tint-color($border, $btn-hover-border-tint-amount)),\n  $hover-color: color-contrast($hover-background),\n  $active-background: if($color == $color-contrast-light, shade-color($background, $btn-active-bg-shade-amount), tint-color($background, $btn-active-bg-tint-amount)),\n  $active-border: if($color == $color-contrast-light, shade-color($border, $btn-active-border-shade-amount), tint-color($border, $btn-active-border-tint-amount)),\n  $active-color: color-contrast($active-background),\n  $disabled-background: $background,\n  $disabled-border: $border,\n  $disabled-color: color-contrast($disabled-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-bg: #{$background};\n  --#{$prefix}btn-border-color: #{$border};\n  --#{$prefix}btn-hover-color: #{$hover-color};\n  --#{$prefix}btn-hover-bg: #{$hover-background};\n  --#{$prefix}btn-hover-border-color: #{$hover-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb(mix($color, $border, 15%))};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$disabled-color};\n  --#{$prefix}btn-disabled-bg: #{$disabled-background};\n  --#{$prefix}btn-disabled-border-color: #{$disabled-border};\n}\n// scss-docs-end btn-variant-mixin\n\n// scss-docs-start btn-outline-variant-mixin\n@mixin button-outline-variant(\n  $color,\n  $color-hover: color-contrast($color),\n  $active-background: $color,\n  $active-border: $color,\n  $active-color: color-contrast($active-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-border-color: #{$color};\n  --#{$prefix}btn-hover-color: #{$color-hover};\n  --#{$prefix}btn-hover-bg: #{$active-background};\n  --#{$prefix}btn-hover-border-color: #{$active-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb($color)};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$color};\n  --#{$prefix}btn-disabled-bg: transparent;\n  --#{$prefix}btn-disabled-border-color: #{$color};\n  --#{$prefix}gradient: none;\n}\n// scss-docs-end btn-outline-variant-mixin\n\n// scss-docs-start btn-size-mixin\n@mixin button-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}btn-padding-y: #{$padding-y};\n  --#{$prefix}btn-padding-x: #{$padding-x};\n  @include rfs($font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-border-radius: #{$border-radius};\n}\n// scss-docs-end btn-size-mixin\n",".fade {\n  @include transition($transition-fade);\n\n  &:not(.show) {\n    opacity: 0;\n  }\n}\n\n// scss-docs-start collapse-classes\n.collapse {\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  @include transition($transition-collapse);\n\n  &.collapse-horizontal {\n    width: 0;\n    height: auto;\n    @include transition($transition-collapse-width);\n  }\n}\n// scss-docs-end collapse-classes\n","// The dropdown wrapper (`<div>`)\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n\n  // Generate the caret automatically\n  @include caret();\n}\n\n// The dropdown menu\n.dropdown-menu {\n  // scss-docs-start dropdown-css-vars\n  --#{$prefix}dropdown-zindex: #{$zindex-dropdown};\n  --#{$prefix}dropdown-min-width: #{$dropdown-min-width};\n  --#{$prefix}dropdown-padding-x: #{$dropdown-padding-x};\n  --#{$prefix}dropdown-padding-y: #{$dropdown-padding-y};\n  --#{$prefix}dropdown-spacer: #{$dropdown-spacer};\n  @include rfs($dropdown-font-size, --#{$prefix}dropdown-font-size);\n  --#{$prefix}dropdown-color: #{$dropdown-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-border-color};\n  --#{$prefix}dropdown-border-radius: #{$dropdown-border-radius};\n  --#{$prefix}dropdown-border-width: #{$dropdown-border-width};\n  --#{$prefix}dropdown-inner-border-radius: #{$dropdown-inner-border-radius};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-divider-bg};\n  --#{$prefix}dropdown-divider-margin-y: #{$dropdown-divider-margin-y};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-link-hover-color};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-link-disabled-color};\n  --#{$prefix}dropdown-item-padding-x: #{$dropdown-item-padding-x};\n  --#{$prefix}dropdown-item-padding-y: #{$dropdown-item-padding-y};\n  --#{$prefix}dropdown-header-color: #{$dropdown-header-color};\n  --#{$prefix}dropdown-header-padding-x: #{$dropdown-header-padding-x};\n  --#{$prefix}dropdown-header-padding-y: #{$dropdown-header-padding-y};\n  // scss-docs-end dropdown-css-vars\n\n  position: absolute;\n  z-index: var(--#{$prefix}dropdown-zindex);\n  display: none; // none by default, but block on \"open\" of the menu\n  min-width: var(--#{$prefix}dropdown-min-width);\n  padding: var(--#{$prefix}dropdown-padding-y) var(--#{$prefix}dropdown-padding-x);\n  margin: 0; // Override default margin of ul\n  @include font-size(var(--#{$prefix}dropdown-font-size));\n  color: var(--#{$prefix}dropdown-color);\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: var(--#{$prefix}dropdown-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}dropdown-border-width) solid var(--#{$prefix}dropdown-border-color);\n  @include border-radius(var(--#{$prefix}dropdown-border-radius));\n  @include box-shadow(var(--#{$prefix}dropdown-box-shadow));\n\n  &[data-bs-popper] {\n    top: 100%;\n    left: 0;\n    margin-top: var(--#{$prefix}dropdown-spacer);\n  }\n\n  @if $dropdown-padding-y == 0 {\n    > .dropdown-item:first-child,\n    > li:first-child .dropdown-item {\n      @include border-top-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n    > .dropdown-item:last-child,\n    > li:last-child .dropdown-item {\n      @include border-bottom-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n\n  }\n}\n\n// scss-docs-start responsive-breakpoints\n// We deliberately hardcode the `bs-` prefix because we check\n// this custom property in JS to determine Popper's positioning\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .dropdown-menu#{$infix}-start {\n      --bs-position: start;\n\n      &[data-bs-popper] {\n        right: auto;\n        left: 0;\n      }\n    }\n\n    .dropdown-menu#{$infix}-end {\n      --bs-position: end;\n\n      &[data-bs-popper] {\n        right: 0;\n        left: auto;\n      }\n    }\n  }\n}\n// scss-docs-end responsive-breakpoints\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n// Just add .dropup after the standard .dropdown class and you're set.\n.dropup {\n  .dropdown-menu[data-bs-popper] {\n    top: auto;\n    bottom: 100%;\n    margin-top: 0;\n    margin-bottom: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(up);\n  }\n}\n\n.dropend {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: auto;\n    left: 100%;\n    margin-top: 0;\n    margin-left: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(end);\n    &::after {\n      vertical-align: 0;\n    }\n  }\n}\n\n.dropstart {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: 100%;\n    left: auto;\n    margin-top: 0;\n    margin-right: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(start);\n    &::before {\n      vertical-align: 0;\n    }\n  }\n}\n\n\n// Dividers (basically an `<hr>`) within the dropdown\n.dropdown-divider {\n  height: 0;\n  margin: var(--#{$prefix}dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--#{$prefix}dropdown-divider-bg);\n  opacity: 1; // Revisit in v6 to de-dupe styles that conflict with <hr> element\n}\n\n// Links, buttons, and more within the dropdown menu\n//\n// `<button>`-specific styles are denoted with `// For <button>s`\n.dropdown-item {\n  display: block;\n  width: 100%; // For `<button>`s\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  clear: both;\n  font-weight: $font-weight-normal;\n  color: var(--#{$prefix}dropdown-link-color);\n  text-align: inherit; // For `<button>`s\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap; // prevent links from randomly breaking onto new lines\n  background-color: transparent; // For `<button>`s\n  border: 0; // For `<button>`s\n  @include border-radius(var(--#{$prefix}dropdown-item-border-radius, 0));\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}dropdown-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    @include gradient-bg(var(--#{$prefix}dropdown-link-hover-bg));\n  }\n\n  &.active,\n  &:active {\n    color: var(--#{$prefix}dropdown-link-active-color);\n    text-decoration: none;\n    @include gradient-bg(var(--#{$prefix}dropdown-link-active-bg));\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}dropdown-link-disabled-color);\n    pointer-events: none;\n    background-color: transparent;\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n  }\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: var(--#{$prefix}dropdown-header-padding-y) var(--#{$prefix}dropdown-header-padding-x);\n  margin-bottom: 0; // for use with heading elements\n  @include font-size($font-size-sm);\n  color: var(--#{$prefix}dropdown-header-color);\n  white-space: nowrap; // as with > li > a\n}\n\n// Dropdown text\n.dropdown-item-text {\n  display: block;\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  color: var(--#{$prefix}dropdown-link-color);\n}\n\n// Dark dropdowns\n.dropdown-menu-dark {\n  // scss-docs-start dropdown-dark-css-vars\n  --#{$prefix}dropdown-color: #{$dropdown-dark-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-dark-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-dark-border-color};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-dark-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-dark-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-dark-link-hover-color};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-dark-divider-bg};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-dark-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-dark-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-dark-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-dark-link-disabled-color};\n  --#{$prefix}dropdown-header-color: #{$dropdown-dark-header-color};\n  // scss-docs-end dropdown-dark-css-vars\n}\n","// scss-docs-start caret-mixins\n@mixin caret-down($width: $caret-width) {\n  border-top: $width solid;\n  border-right: $width solid transparent;\n  border-bottom: 0;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-up($width: $caret-width) {\n  border-top: 0;\n  border-right: $width solid transparent;\n  border-bottom: $width solid;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-end($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: 0;\n  border-bottom: $width solid transparent;\n  border-left: $width solid;\n}\n\n@mixin caret-start($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: $width solid;\n  border-bottom: $width solid transparent;\n}\n\n@mixin caret(\n  $direction: down,\n  $width: $caret-width,\n  $spacing: $caret-spacing,\n  $vertical-align: $caret-vertical-align\n) {\n  @if $enable-caret {\n    &::after {\n      display: inline-block;\n      margin-left: $spacing;\n      vertical-align: $vertical-align;\n      content: \"\";\n      @if $direction == down {\n        @include caret-down($width);\n      } @else if $direction == up {\n        @include caret-up($width);\n      } @else if $direction == end {\n        @include caret-end($width);\n      }\n    }\n\n    @if $direction == start {\n      &::after {\n        display: none;\n      }\n\n      &::before {\n        display: inline-block;\n        margin-right: $spacing;\n        vertical-align: $vertical-align;\n        content: \"\";\n        @include caret-start($width);\n      }\n    }\n\n    &:empty::after {\n      margin-left: 0;\n    }\n  }\n}\n// scss-docs-end caret-mixins\n","// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n\n  > .btn {\n    position: relative;\n    flex: 1 1 auto;\n  }\n\n  // Bring the hover, focused, and \"active\" buttons to the front to overlay\n  // the borders properly\n  > .btn-check:checked + .btn,\n  > .btn-check:focus + .btn,\n  > .btn:hover,\n  > .btn:focus,\n  > .btn:active,\n  > .btn.active {\n    z-index: 1;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n\n  .input-group {\n    width: auto;\n  }\n}\n\n.btn-group {\n  @include border-radius($btn-border-radius);\n\n  // Prevent double borders when buttons are next to each other\n  > :not(.btn-check:first-child) + .btn,\n  > .btn-group:not(:first-child) {\n    margin-left: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn.dropdown-toggle-split:first-child,\n  > .btn-group:not(:last-child) > .btn {\n    @include border-end-radius(0);\n  }\n\n  // The left radius should be 0 if the button is:\n  // - the \"third or more\" child\n  // - the second child and the previous element isn't `.btn-check` (making it the first child visually)\n  // - part of a btn-group which isn't the first child\n  > .btn:nth-child(n + 3),\n  > :not(.btn-check) + .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-start-radius(0);\n  }\n}\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-sm > .btn { @extend .btn-sm; }\n.btn-group-lg > .btn { @extend .btn-lg; }\n\n\n//\n// Split button dropdowns\n//\n\n.dropdown-toggle-split {\n  padding-right: $btn-padding-x * .75;\n  padding-left: $btn-padding-x * .75;\n\n  &::after,\n  .dropup &::after,\n  .dropend &::after {\n    margin-left: 0;\n  }\n\n  .dropstart &::before {\n    margin-right: 0;\n  }\n}\n\n.btn-sm + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-sm * .75;\n  padding-left: $btn-padding-x-sm * .75;\n}\n\n.btn-lg + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-lg * .75;\n  padding-left: $btn-padding-x-lg * .75;\n}\n\n\n// The clickable button for toggling the menu\n// Set the same inset shadow as the :active state\n.btn-group.show .dropdown-toggle {\n  @include box-shadow($btn-active-box-shadow);\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Vertical button groups\n//\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n\n  > .btn,\n  > .btn-group {\n    width: 100%;\n  }\n\n  > .btn:not(:first-child),\n  > .btn-group:not(:first-child) {\n    margin-top: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn-group:not(:last-child) > .btn {\n    @include border-bottom-radius(0);\n  }\n\n  > .btn ~ .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-top-radius(0);\n  }\n}\n","// Base class\n//\n// Kickstart any navigation component with a set of style resets. Works with\n// `<nav>`s, `<ul>`s or `<ol>`s.\n\n.nav {\n  // scss-docs-start nav-css-vars\n  --#{$prefix}nav-link-padding-x: #{$nav-link-padding-x};\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: #{$nav-link-color};\n  --#{$prefix}nav-link-hover-color: #{$nav-link-hover-color};\n  --#{$prefix}nav-link-disabled-color: #{$nav-link-disabled-color};\n  // scss-docs-end nav-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--#{$prefix}nav-link-padding-y) var(--#{$prefix}nav-link-padding-x);\n  @include font-size(var(--#{$prefix}nav-link-font-size));\n  font-weight: var(--#{$prefix}nav-link-font-weight);\n  color: var(--#{$prefix}nav-link-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background: none;\n  border: 0;\n  @include transition($nav-link-transition);\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}nav-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  &:focus-visible {\n    outline: 0;\n    box-shadow: $nav-link-focus-box-shadow;\n  }\n\n  // Disabled state lightens text\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}nav-link-disabled-color);\n    pointer-events: none;\n    cursor: default;\n  }\n}\n\n//\n// Tabs\n//\n\n.nav-tabs {\n  // scss-docs-start nav-tabs-css-vars\n  --#{$prefix}nav-tabs-border-width: #{$nav-tabs-border-width};\n  --#{$prefix}nav-tabs-border-color: #{$nav-tabs-border-color};\n  --#{$prefix}nav-tabs-border-radius: #{$nav-tabs-border-radius};\n  --#{$prefix}nav-tabs-link-hover-border-color: #{$nav-tabs-link-hover-border-color};\n  --#{$prefix}nav-tabs-link-active-color: #{$nav-tabs-link-active-color};\n  --#{$prefix}nav-tabs-link-active-bg: #{$nav-tabs-link-active-bg};\n  --#{$prefix}nav-tabs-link-active-border-color: #{$nav-tabs-link-active-border-color};\n  // scss-docs-end nav-tabs-css-vars\n\n  border-bottom: var(--#{$prefix}nav-tabs-border-width) solid var(--#{$prefix}nav-tabs-border-color);\n\n  .nav-link {\n    margin-bottom: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    border: var(--#{$prefix}nav-tabs-border-width) solid transparent;\n    @include border-top-radius(var(--#{$prefix}nav-tabs-border-radius));\n\n    &:hover,\n    &:focus {\n      // Prevents active .nav-link tab overlapping focus outline of previous/next .nav-link\n      isolation: isolate;\n      border-color: var(--#{$prefix}nav-tabs-link-hover-border-color);\n    }\n  }\n\n  .nav-link.active,\n  .nav-item.show .nav-link {\n    color: var(--#{$prefix}nav-tabs-link-active-color);\n    background-color: var(--#{$prefix}nav-tabs-link-active-bg);\n    border-color: var(--#{$prefix}nav-tabs-link-active-border-color);\n  }\n\n  .dropdown-menu {\n    // Make dropdown border overlap tab border\n    margin-top: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    // Remove the top rounded corners here since there is a hard edge above the menu\n    @include border-top-radius(0);\n  }\n}\n\n\n//\n// Pills\n//\n\n.nav-pills {\n  // scss-docs-start nav-pills-css-vars\n  --#{$prefix}nav-pills-border-radius: #{$nav-pills-border-radius};\n  --#{$prefix}nav-pills-link-active-color: #{$nav-pills-link-active-color};\n  --#{$prefix}nav-pills-link-active-bg: #{$nav-pills-link-active-bg};\n  // scss-docs-end nav-pills-css-vars\n\n  .nav-link {\n    @include border-radius(var(--#{$prefix}nav-pills-border-radius));\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    color: var(--#{$prefix}nav-pills-link-active-color);\n    @include gradient-bg(var(--#{$prefix}nav-pills-link-active-bg));\n  }\n}\n\n\n//\n// Underline\n//\n\n.nav-underline {\n  // scss-docs-start nav-underline-css-vars\n  --#{$prefix}nav-underline-gap: #{$nav-underline-gap};\n  --#{$prefix}nav-underline-border-width: #{$nav-underline-border-width};\n  --#{$prefix}nav-underline-link-active-color: #{$nav-underline-link-active-color};\n  // scss-docs-end nav-underline-css-vars\n\n  gap: var(--#{$prefix}nav-underline-gap);\n\n  .nav-link {\n    padding-right: 0;\n    padding-left: 0;\n    border-bottom: var(--#{$prefix}nav-underline-border-width) solid transparent;\n\n    &:hover,\n    &:focus {\n      border-bottom-color: currentcolor;\n    }\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    font-weight: $font-weight-bold;\n    color: var(--#{$prefix}nav-underline-link-active-color);\n    border-bottom-color: currentcolor;\n  }\n}\n\n\n//\n// Justified variants\n//\n\n.nav-fill {\n  > .nav-link,\n  .nav-item {\n    flex: 1 1 auto;\n    text-align: center;\n  }\n}\n\n.nav-justified {\n  > .nav-link,\n  .nav-item {\n    flex-basis: 0;\n    flex-grow: 1;\n    text-align: center;\n  }\n}\n\n.nav-fill,\n.nav-justified {\n  .nav-item .nav-link {\n    width: 100%; // Make sure button will grow\n  }\n}\n\n\n// Tabbable tabs\n//\n// Hide tabbable panes to start, show them when `.active`\n\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n","// Navbar\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  // scss-docs-start navbar-css-vars\n  --#{$prefix}navbar-padding-x: #{if($navbar-padding-x == null, 0, $navbar-padding-x)};\n  --#{$prefix}navbar-padding-y: #{$navbar-padding-y};\n  --#{$prefix}navbar-color: #{$navbar-light-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-light-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-light-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-light-active-color};\n  --#{$prefix}navbar-brand-padding-y: #{$navbar-brand-padding-y};\n  --#{$prefix}navbar-brand-margin-end: #{$navbar-brand-margin-end};\n  --#{$prefix}navbar-brand-font-size: #{$navbar-brand-font-size};\n  --#{$prefix}navbar-brand-color: #{$navbar-light-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-light-brand-hover-color};\n  --#{$prefix}navbar-nav-link-padding-x: #{$navbar-nav-link-padding-x};\n  --#{$prefix}navbar-toggler-padding-y: #{$navbar-toggler-padding-y};\n  --#{$prefix}navbar-toggler-padding-x: #{$navbar-toggler-padding-x};\n  --#{$prefix}navbar-toggler-font-size: #{$navbar-toggler-font-size};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-light-toggler-icon-bg)};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-light-toggler-border-color};\n  --#{$prefix}navbar-toggler-border-radius: #{$navbar-toggler-border-radius};\n  --#{$prefix}navbar-toggler-focus-width: #{$navbar-toggler-focus-width};\n  --#{$prefix}navbar-toggler-transition: #{$navbar-toggler-transition};\n  // scss-docs-end navbar-css-vars\n\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // allow us to do the line break for collapsing content\n  align-items: center;\n  justify-content: space-between; // space out brand from logo\n  padding: var(--#{$prefix}navbar-padding-y) var(--#{$prefix}navbar-padding-x);\n  @include gradient-bg();\n\n  // Because flex properties aren't inherited, we need to redeclare these first\n  // few properties so that content nested within behave properly.\n  // The `flex-wrap` property is inherited to simplify the expanded navbars\n  %container-flex-properties {\n    display: flex;\n    flex-wrap: inherit;\n    align-items: center;\n    justify-content: space-between;\n  }\n\n  > .container,\n  > .container-fluid {\n    @extend %container-flex-properties;\n  }\n\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    > .container#{breakpoint-infix($breakpoint, $container-max-widths)} {\n      @extend %container-flex-properties;\n    }\n  }\n}\n\n\n// Navbar brand\n//\n// Used for brand, project, or site names.\n\n.navbar-brand {\n  padding-top: var(--#{$prefix}navbar-brand-padding-y);\n  padding-bottom: var(--#{$prefix}navbar-brand-padding-y);\n  margin-right: var(--#{$prefix}navbar-brand-margin-end);\n  @include font-size(var(--#{$prefix}navbar-brand-font-size));\n  color: var(--#{$prefix}navbar-brand-color);\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap;\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}navbar-brand-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n}\n\n\n// Navbar nav\n//\n// Custom navbar navigation (doesn't require `.nav`, but does make use of `.nav-link`).\n\n.navbar-nav {\n  // scss-docs-start navbar-nav-css-vars\n  --#{$prefix}nav-link-padding-x: 0;\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: var(--#{$prefix}navbar-color);\n  --#{$prefix}nav-link-hover-color: var(--#{$prefix}navbar-hover-color);\n  --#{$prefix}nav-link-disabled-color: var(--#{$prefix}navbar-disabled-color);\n  // scss-docs-end navbar-nav-css-vars\n\n  display: flex;\n  flex-direction: column; // cannot use `inherit` to get the `.navbar`s value\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  .nav-link {\n    &.active,\n    &.show {\n      color: var(--#{$prefix}navbar-active-color);\n    }\n  }\n\n  .dropdown-menu {\n    position: static;\n  }\n}\n\n\n// Navbar text\n//\n//\n\n.navbar-text {\n  padding-top: $nav-link-padding-y;\n  padding-bottom: $nav-link-padding-y;\n  color: var(--#{$prefix}navbar-color);\n\n  a,\n  a:hover,\n  a:focus  {\n    color: var(--#{$prefix}navbar-active-color);\n  }\n}\n\n\n// Responsive navbar\n//\n// Custom styles for responsive collapsing and toggling of navbar contents.\n// Powered by the collapse Bootstrap JavaScript plugin.\n\n// When collapsed, prevent the toggleable navbar contents from appearing in\n// the default flexbox row orientation. Requires the use of `flex-wrap: wrap`\n// on the `.navbar` parent.\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  // For always expanded or extra full navbars, ensure content aligns itself\n  // properly vertically. Can be easily overridden with flex utilities.\n  align-items: center;\n}\n\n// Button for toggling the navbar when in its collapsed state\n.navbar-toggler {\n  padding: var(--#{$prefix}navbar-toggler-padding-y) var(--#{$prefix}navbar-toggler-padding-x);\n  @include font-size(var(--#{$prefix}navbar-toggler-font-size));\n  line-height: 1;\n  color: var(--#{$prefix}navbar-color);\n  background-color: transparent; // remove default button style\n  border: var(--#{$prefix}border-width) solid var(--#{$prefix}navbar-toggler-border-color); // remove default button style\n  @include border-radius(var(--#{$prefix}navbar-toggler-border-radius));\n  @include transition(var(--#{$prefix}navbar-toggler-transition));\n\n  &:hover {\n    text-decoration: none;\n  }\n\n  &:focus {\n    text-decoration: none;\n    outline: 0;\n    box-shadow: 0 0 0 var(--#{$prefix}navbar-toggler-focus-width);\n  }\n}\n\n// Keep as a separate element so folks can easily override it with another icon\n// or image file as needed.\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--#{$prefix}navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--#{$prefix}scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n// scss-docs-start navbar-expand-loop\n// Generate series of `.navbar-expand-*` responsive classes for configuring\n// where your navbar collapses.\n.navbar-expand {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $next: breakpoint-next($breakpoint, $grid-breakpoints);\n    $infix: breakpoint-infix($next, $grid-breakpoints);\n\n    // stylelint-disable-next-line scss/selector-no-union-class-name\n    &#{$infix} {\n      @include media-breakpoint-up($next) {\n        flex-wrap: nowrap;\n        justify-content: flex-start;\n\n        .navbar-nav {\n          flex-direction: row;\n\n          .dropdown-menu {\n            position: absolute;\n          }\n\n          .nav-link {\n            padding-right: var(--#{$prefix}navbar-nav-link-padding-x);\n            padding-left: var(--#{$prefix}navbar-nav-link-padding-x);\n          }\n        }\n\n        .navbar-nav-scroll {\n          overflow: visible;\n        }\n\n        .navbar-collapse {\n          display: flex !important; // stylelint-disable-line declaration-no-important\n          flex-basis: auto;\n        }\n\n        .navbar-toggler {\n          display: none;\n        }\n\n        .offcanvas {\n          // stylelint-disable declaration-no-important\n          position: static;\n          z-index: auto;\n          flex-grow: 1;\n          width: auto !important;\n          height: auto !important;\n          visibility: visible !important;\n          background-color: transparent !important;\n          border: 0 !important;\n          transform: none !important;\n          @include box-shadow(none);\n          @include transition(none);\n          // stylelint-enable declaration-no-important\n\n          .offcanvas-header {\n            display: none;\n          }\n\n          .offcanvas-body {\n            display: flex;\n            flex-grow: 0;\n            padding: 0;\n            overflow-y: visible;\n          }\n        }\n      }\n    }\n  }\n}\n// scss-docs-end navbar-expand-loop\n\n// Navbar themes\n//\n// Styles for switching between navbars with light or dark background.\n\n.navbar-light {\n  @include deprecate(\"`.navbar-light`\", \"v5.2.0\", \"v6.0.0\", true);\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=\"dark\"] {\n  // scss-docs-start navbar-dark-css-vars\n  --#{$prefix}navbar-color: #{$navbar-dark-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-dark-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-dark-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-dark-active-color};\n  --#{$prefix}navbar-brand-color: #{$navbar-dark-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-dark-brand-hover-color};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-dark-toggler-border-color};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n  // scss-docs-end navbar-dark-css-vars\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .navbar-toggler-icon {\n      --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.card {\n  // scss-docs-start card-css-vars\n  --#{$prefix}card-spacer-y: #{$card-spacer-y};\n  --#{$prefix}card-spacer-x: #{$card-spacer-x};\n  --#{$prefix}card-title-spacer-y: #{$card-title-spacer-y};\n  --#{$prefix}card-title-color: #{$card-title-color};\n  --#{$prefix}card-subtitle-color: #{$card-subtitle-color};\n  --#{$prefix}card-border-width: #{$card-border-width};\n  --#{$prefix}card-border-color: #{$card-border-color};\n  --#{$prefix}card-border-radius: #{$card-border-radius};\n  --#{$prefix}card-box-shadow: #{$card-box-shadow};\n  --#{$prefix}card-inner-border-radius: #{$card-inner-border-radius};\n  --#{$prefix}card-cap-padding-y: #{$card-cap-padding-y};\n  --#{$prefix}card-cap-padding-x: #{$card-cap-padding-x};\n  --#{$prefix}card-cap-bg: #{$card-cap-bg};\n  --#{$prefix}card-cap-color: #{$card-cap-color};\n  --#{$prefix}card-height: #{$card-height};\n  --#{$prefix}card-color: #{$card-color};\n  --#{$prefix}card-bg: #{$card-bg};\n  --#{$prefix}card-img-overlay-padding: #{$card-img-overlay-padding};\n  --#{$prefix}card-group-margin: #{$card-group-margin};\n  // scss-docs-end card-css-vars\n\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0; // See https://github.com/twbs/bootstrap/pull/22740#issuecomment-305868106\n  height: var(--#{$prefix}card-height);\n  color: var(--#{$prefix}body-color);\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}card-bg);\n  background-clip: border-box;\n  border: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n  @include border-radius(var(--#{$prefix}card-border-radius));\n  @include box-shadow(var(--#{$prefix}card-box-shadow));\n\n  > hr {\n    margin-right: 0;\n    margin-left: 0;\n  }\n\n  > .list-group {\n    border-top: inherit;\n    border-bottom: inherit;\n\n    &:first-child {\n      border-top-width: 0;\n      @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n\n    &:last-child  {\n      border-bottom-width: 0;\n      @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n  }\n\n  // Due to specificity of the above selector (`.card > .list-group`), we must\n  // use a child selector here to prevent double borders.\n  > .card-header + .list-group,\n  > .list-group + .card-footer {\n    border-top: 0;\n  }\n}\n\n.card-body {\n  // Enable `flex-grow: 1` for decks and groups so that card blocks take up\n  // as much space as possible, ensuring footers are aligned to the bottom.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}card-spacer-y) var(--#{$prefix}card-spacer-x);\n  color: var(--#{$prefix}card-color);\n}\n\n.card-title {\n  margin-bottom: var(--#{$prefix}card-title-spacer-y);\n  color: var(--#{$prefix}card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-.5 * var(--#{$prefix}card-title-spacer-y)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: 0;\n  color: var(--#{$prefix}card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link {\n  &:hover {\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  + .card-link {\n    margin-left: var(--#{$prefix}card-spacer-x);\n  }\n}\n\n//\n// Optional textual caps\n//\n\n.card-header {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  margin-bottom: 0; // Removes the default margin-bottom of <hN>\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-bottom: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:first-child {\n    @include border-radius(var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius) 0 0);\n  }\n}\n\n.card-footer {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-top: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:last-child {\n    @include border-radius(0 0 var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius));\n  }\n}\n\n\n//\n// Header navs\n//\n\n.card-header-tabs {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: calc(-1 * var(--#{$prefix}card-cap-padding-y)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  border-bottom: 0;\n\n  .nav-link.active {\n    background-color: var(--#{$prefix}card-bg);\n    border-bottom-color: var(--#{$prefix}card-bg);\n  }\n}\n\n.card-header-pills {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n}\n\n// Card image\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--#{$prefix}card-img-overlay-padding);\n  @include border-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%; // Required because we use flexbox and this inherently applies align-self: stretch\n}\n\n.card-img,\n.card-img-top {\n  @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-bottom {\n  @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n\n//\n// Card groups\n//\n\n.card-group {\n  // The child selector allows nested `.card` within `.card-group`\n  // to display properly.\n  > .card {\n    margin-bottom: var(--#{$prefix}card-group-margin);\n  }\n\n  @include media-breakpoint-up(sm) {\n    display: flex;\n    flex-flow: row wrap;\n    // The child selector allows nested `.card` within `.card-group`\n    // to display properly.\n    > .card {\n      // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      flex: 1 0 0%;\n      margin-bottom: 0;\n\n      + .card {\n        margin-left: 0;\n        border-left: 0;\n      }\n\n      // Handle rounded corners\n      @if $enable-rounded {\n        &:not(:last-child) {\n          @include border-end-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-right-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-right-radius: 0;\n          }\n        }\n\n        &:not(:first-child) {\n          @include border-start-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-left-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-left-radius: 0;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.accordion {\n  // scss-docs-start accordion-css-vars\n  --#{$prefix}accordion-color: #{$accordion-color};\n  --#{$prefix}accordion-bg: #{$accordion-bg};\n  --#{$prefix}accordion-transition: #{$accordion-transition};\n  --#{$prefix}accordion-border-color: #{$accordion-border-color};\n  --#{$prefix}accordion-border-width: #{$accordion-border-width};\n  --#{$prefix}accordion-border-radius: #{$accordion-border-radius};\n  --#{$prefix}accordion-inner-border-radius: #{$accordion-inner-border-radius};\n  --#{$prefix}accordion-btn-padding-x: #{$accordion-button-padding-x};\n  --#{$prefix}accordion-btn-padding-y: #{$accordion-button-padding-y};\n  --#{$prefix}accordion-btn-color: #{$accordion-button-color};\n  --#{$prefix}accordion-btn-bg: #{$accordion-button-bg};\n  --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon)};\n  --#{$prefix}accordion-btn-icon-width: #{$accordion-icon-width};\n  --#{$prefix}accordion-btn-icon-transform: #{$accordion-icon-transform};\n  --#{$prefix}accordion-btn-icon-transition: #{$accordion-icon-transition};\n  --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon)};\n  --#{$prefix}accordion-btn-focus-box-shadow: #{$accordion-button-focus-box-shadow};\n  --#{$prefix}accordion-body-padding-x: #{$accordion-body-padding-x};\n  --#{$prefix}accordion-body-padding-y: #{$accordion-body-padding-y};\n  --#{$prefix}accordion-active-color: #{$accordion-button-active-color};\n  --#{$prefix}accordion-active-bg: #{$accordion-button-active-bg};\n  // scss-docs-end accordion-css-vars\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--#{$prefix}accordion-btn-padding-y) var(--#{$prefix}accordion-btn-padding-x);\n  @include font-size($font-size-base);\n  color: var(--#{$prefix}accordion-btn-color);\n  text-align: left; // Reset button style\n  background-color: var(--#{$prefix}accordion-btn-bg);\n  border: 0;\n  @include border-radius(0);\n  overflow-anchor: none;\n  @include transition(var(--#{$prefix}accordion-transition));\n\n  &:not(.collapsed) {\n    color: var(--#{$prefix}accordion-active-color);\n    background-color: var(--#{$prefix}accordion-active-bg);\n    box-shadow: inset 0 calc(-1 * var(--#{$prefix}accordion-border-width)) 0 var(--#{$prefix}accordion-border-color); // stylelint-disable-line function-disallowed-list\n\n    &::after {\n      background-image: var(--#{$prefix}accordion-btn-active-icon);\n      transform: var(--#{$prefix}accordion-btn-icon-transform);\n    }\n  }\n\n  // Accordion icon\n  &::after {\n    flex-shrink: 0;\n    width: var(--#{$prefix}accordion-btn-icon-width);\n    height: var(--#{$prefix}accordion-btn-icon-width);\n    margin-left: auto;\n    content: \"\";\n    background-image: var(--#{$prefix}accordion-btn-icon);\n    background-repeat: no-repeat;\n    background-size: var(--#{$prefix}accordion-btn-icon-width);\n    @include transition(var(--#{$prefix}accordion-btn-icon-transition));\n  }\n\n  &:hover {\n    z-index: 2;\n  }\n\n  &:focus {\n    z-index: 3;\n    outline: 0;\n    box-shadow: var(--#{$prefix}accordion-btn-focus-box-shadow);\n  }\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--#{$prefix}accordion-color);\n  background-color: var(--#{$prefix}accordion-bg);\n  border: var(--#{$prefix}accordion-border-width) solid var(--#{$prefix}accordion-border-color);\n\n  &:first-of-type {\n    @include border-top-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      @include border-top-radius(var(--#{$prefix}accordion-inner-border-radius));\n    }\n  }\n\n  &:not(:first-of-type) {\n    border-top: 0;\n  }\n\n  // Only set a border-radius on the last item if the accordion is collapsed\n  &:last-of-type {\n    @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      &.collapsed {\n        @include border-bottom-radius(var(--#{$prefix}accordion-inner-border-radius));\n      }\n    }\n\n    > .accordion-collapse {\n      @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n    }\n  }\n}\n\n.accordion-body {\n  padding: var(--#{$prefix}accordion-body-padding-y) var(--#{$prefix}accordion-body-padding-x);\n}\n\n\n// Flush accordion items\n//\n// Remove borders and border-radius to keep accordion items edge-to-edge.\n\n.accordion-flush {\n  > .accordion-item {\n    border-right: 0;\n    border-left: 0;\n    @include border-radius(0);\n\n    &:first-child { border-top: 0; }\n    &:last-child { border-bottom: 0; }\n\n    // stylelint-disable selector-max-class\n    > .accordion-header .accordion-button {\n      &,\n      &.collapsed {\n        @include border-radius(0);\n      }\n    }\n    // stylelint-enable selector-max-class\n\n    > .accordion-collapse {\n      @include border-radius(0);\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .accordion-button::after {\n      --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon-dark)};\n      --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon-dark)};\n    }\n  }\n}\n",".breadcrumb {\n  // scss-docs-start breadcrumb-css-vars\n  --#{$prefix}breadcrumb-padding-x: #{$breadcrumb-padding-x};\n  --#{$prefix}breadcrumb-padding-y: #{$breadcrumb-padding-y};\n  --#{$prefix}breadcrumb-margin-bottom: #{$breadcrumb-margin-bottom};\n  @include rfs($breadcrumb-font-size, --#{$prefix}breadcrumb-font-size);\n  --#{$prefix}breadcrumb-bg: #{$breadcrumb-bg};\n  --#{$prefix}breadcrumb-border-radius: #{$breadcrumb-border-radius};\n  --#{$prefix}breadcrumb-divider-color: #{$breadcrumb-divider-color};\n  --#{$prefix}breadcrumb-item-padding-x: #{$breadcrumb-item-padding-x};\n  --#{$prefix}breadcrumb-item-active-color: #{$breadcrumb-active-color};\n  // scss-docs-end breadcrumb-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--#{$prefix}breadcrumb-padding-y) var(--#{$prefix}breadcrumb-padding-x);\n  margin-bottom: var(--#{$prefix}breadcrumb-margin-bottom);\n  @include font-size(var(--#{$prefix}breadcrumb-font-size));\n  list-style: none;\n  background-color: var(--#{$prefix}breadcrumb-bg);\n  @include border-radius(var(--#{$prefix}breadcrumb-border-radius));\n}\n\n.breadcrumb-item {\n  // The separator between breadcrumbs (by default, a forward-slash: \"/\")\n  + .breadcrumb-item {\n    padding-left: var(--#{$prefix}breadcrumb-item-padding-x);\n\n    &::before {\n      float: left; // Suppress inline spacings and underlining of the separator\n      padding-right: var(--#{$prefix}breadcrumb-item-padding-x);\n      color: var(--#{$prefix}breadcrumb-divider-color);\n      content: var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider)) #{\"/* rtl:\"} var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider-flipped)) #{\"*/\"};\n    }\n  }\n\n  &.active {\n    color: var(--#{$prefix}breadcrumb-item-active-color);\n  }\n}\n",".pagination {\n  // scss-docs-start pagination-css-vars\n  --#{$prefix}pagination-padding-x: #{$pagination-padding-x};\n  --#{$prefix}pagination-padding-y: #{$pagination-padding-y};\n  @include rfs($pagination-font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-color: #{$pagination-color};\n  --#{$prefix}pagination-bg: #{$pagination-bg};\n  --#{$prefix}pagination-border-width: #{$pagination-border-width};\n  --#{$prefix}pagination-border-color: #{$pagination-border-color};\n  --#{$prefix}pagination-border-radius: #{$pagination-border-radius};\n  --#{$prefix}pagination-hover-color: #{$pagination-hover-color};\n  --#{$prefix}pagination-hover-bg: #{$pagination-hover-bg};\n  --#{$prefix}pagination-hover-border-color: #{$pagination-hover-border-color};\n  --#{$prefix}pagination-focus-color: #{$pagination-focus-color};\n  --#{$prefix}pagination-focus-bg: #{$pagination-focus-bg};\n  --#{$prefix}pagination-focus-box-shadow: #{$pagination-focus-box-shadow};\n  --#{$prefix}pagination-active-color: #{$pagination-active-color};\n  --#{$prefix}pagination-active-bg: #{$pagination-active-bg};\n  --#{$prefix}pagination-active-border-color: #{$pagination-active-border-color};\n  --#{$prefix}pagination-disabled-color: #{$pagination-disabled-color};\n  --#{$prefix}pagination-disabled-bg: #{$pagination-disabled-bg};\n  --#{$prefix}pagination-disabled-border-color: #{$pagination-disabled-border-color};\n  // scss-docs-end pagination-css-vars\n\n  display: flex;\n  @include list-unstyled();\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}pagination-padding-y) var(--#{$prefix}pagination-padding-x);\n  @include font-size(var(--#{$prefix}pagination-font-size));\n  color: var(--#{$prefix}pagination-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}pagination-bg);\n  border: var(--#{$prefix}pagination-border-width) solid var(--#{$prefix}pagination-border-color);\n  @include transition($pagination-transition);\n\n  &:hover {\n    z-index: 2;\n    color: var(--#{$prefix}pagination-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}pagination-hover-bg);\n    border-color: var(--#{$prefix}pagination-hover-border-color);\n  }\n\n  &:focus {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-focus-color);\n    background-color: var(--#{$prefix}pagination-focus-bg);\n    outline: $pagination-focus-outline;\n    box-shadow: var(--#{$prefix}pagination-focus-box-shadow);\n  }\n\n  &.active,\n  .active > & {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-active-color);\n    @include gradient-bg(var(--#{$prefix}pagination-active-bg));\n    border-color: var(--#{$prefix}pagination-active-border-color);\n  }\n\n  &.disabled,\n  .disabled > & {\n    color: var(--#{$prefix}pagination-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}pagination-disabled-bg);\n    border-color: var(--#{$prefix}pagination-disabled-border-color);\n  }\n}\n\n.page-item {\n  &:not(:first-child) .page-link {\n    margin-left: $pagination-margin-start;\n  }\n\n  @if $pagination-margin-start == calc(#{$pagination-border-width} * -1) {\n    &:first-child {\n      .page-link {\n        @include border-start-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n\n    &:last-child {\n      .page-link {\n        @include border-end-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n  } @else {\n    // Add border-radius to all pageLinks in case they have left margin\n    .page-link {\n      @include border-radius(var(--#{$prefix}pagination-border-radius));\n    }\n  }\n}\n\n\n//\n// Sizing\n//\n\n.pagination-lg {\n  @include pagination-size($pagination-padding-y-lg, $pagination-padding-x-lg, $font-size-lg, $pagination-border-radius-lg);\n}\n\n.pagination-sm {\n  @include pagination-size($pagination-padding-y-sm, $pagination-padding-x-sm, $font-size-sm, $pagination-border-radius-sm);\n}\n","// Pagination\n\n// scss-docs-start pagination-mixin\n@mixin pagination-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}pagination-padding-x: #{$padding-x};\n  --#{$prefix}pagination-padding-y: #{$padding-y};\n  @include rfs($font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-border-radius: #{$border-radius};\n}\n// scss-docs-end pagination-mixin\n","// Base class\n//\n// Requires one of the contextual, color modifier classes for `color` and\n// `background-color`.\n\n.badge {\n  // scss-docs-start badge-css-vars\n  --#{$prefix}badge-padding-x: #{$badge-padding-x};\n  --#{$prefix}badge-padding-y: #{$badge-padding-y};\n  @include rfs($badge-font-size, --#{$prefix}badge-font-size);\n  --#{$prefix}badge-font-weight: #{$badge-font-weight};\n  --#{$prefix}badge-color: #{$badge-color};\n  --#{$prefix}badge-border-radius: #{$badge-border-radius};\n  // scss-docs-end badge-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}badge-padding-y) var(--#{$prefix}badge-padding-x);\n  @include font-size(var(--#{$prefix}badge-font-size));\n  font-weight: var(--#{$prefix}badge-font-weight);\n  line-height: 1;\n  color: var(--#{$prefix}badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  @include border-radius(var(--#{$prefix}badge-border-radius));\n  @include gradient-bg();\n\n  // Empty badges collapse automatically\n  &:empty {\n    display: none;\n  }\n}\n\n// Quick fix for badges in buttons\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n","//\n// Base styles\n//\n\n.alert {\n  // scss-docs-start alert-css-vars\n  --#{$prefix}alert-bg: transparent;\n  --#{$prefix}alert-padding-x: #{$alert-padding-x};\n  --#{$prefix}alert-padding-y: #{$alert-padding-y};\n  --#{$prefix}alert-margin-bottom: #{$alert-margin-bottom};\n  --#{$prefix}alert-color: inherit;\n  --#{$prefix}alert-border-color: transparent;\n  --#{$prefix}alert-border: #{$alert-border-width} solid var(--#{$prefix}alert-border-color);\n  --#{$prefix}alert-border-radius: #{$alert-border-radius};\n  --#{$prefix}alert-link-color: inherit;\n  // scss-docs-end alert-css-vars\n\n  position: relative;\n  padding: var(--#{$prefix}alert-padding-y) var(--#{$prefix}alert-padding-x);\n  margin-bottom: var(--#{$prefix}alert-margin-bottom);\n  color: var(--#{$prefix}alert-color);\n  background-color: var(--#{$prefix}alert-bg);\n  border: var(--#{$prefix}alert-border);\n  @include border-radius(var(--#{$prefix}alert-border-radius));\n}\n\n// Headings for larger alerts\n.alert-heading {\n  // Specified to prevent conflicts of changing $headings-color\n  color: inherit;\n}\n\n// Provide class for links that match alerts\n.alert-link {\n  font-weight: $alert-link-font-weight;\n  color: var(--#{$prefix}alert-link-color);\n}\n\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissible {\n  padding-right: $alert-dismissible-padding-r;\n\n  // Adjust close link position\n  .btn-close {\n    position: absolute;\n    top: 0;\n    right: 0;\n    z-index: $stretched-link-z-index + 1;\n    padding: $alert-padding-y * 1.25 $alert-padding-x;\n  }\n}\n\n\n// scss-docs-start alert-modifiers\n// Generate contextual modifier classes for colorizing the alert\n@each $state in map-keys($theme-colors) {\n  .alert-#{$state} {\n    --#{$prefix}alert-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}alert-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}alert-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}alert-link-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end alert-modifiers\n","// Disable animation if transitions are disabled\n\n// scss-docs-start progress-keyframes\n@if $enable-transitions {\n  @keyframes progress-bar-stripes {\n    0% { background-position-x: $progress-height; }\n  }\n}\n// scss-docs-end progress-keyframes\n\n.progress,\n.progress-stacked {\n  // scss-docs-start progress-css-vars\n  --#{$prefix}progress-height: #{$progress-height};\n  @include rfs($progress-font-size, --#{$prefix}progress-font-size);\n  --#{$prefix}progress-bg: #{$progress-bg};\n  --#{$prefix}progress-border-radius: #{$progress-border-radius};\n  --#{$prefix}progress-box-shadow: #{$progress-box-shadow};\n  --#{$prefix}progress-bar-color: #{$progress-bar-color};\n  --#{$prefix}progress-bar-bg: #{$progress-bar-bg};\n  --#{$prefix}progress-bar-transition: #{$progress-bar-transition};\n  // scss-docs-end progress-css-vars\n\n  display: flex;\n  height: var(--#{$prefix}progress-height);\n  overflow: hidden; // force rounded corners by cropping it\n  @include font-size(var(--#{$prefix}progress-font-size));\n  background-color: var(--#{$prefix}progress-bg);\n  @include border-radius(var(--#{$prefix}progress-border-radius));\n  @include box-shadow(var(--#{$prefix}progress-box-shadow));\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--#{$prefix}progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--#{$prefix}progress-bar-bg);\n  @include transition(var(--#{$prefix}progress-bar-transition));\n}\n\n.progress-bar-striped {\n  @include gradient-striped();\n  background-size: var(--#{$prefix}progress-height) var(--#{$prefix}progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n@if $enable-transitions {\n  .progress-bar-animated {\n    animation: $progress-bar-animation-timing progress-bar-stripes;\n\n    @if $enable-reduced-motion {\n      @media (prefers-reduced-motion: reduce) {\n        animation: none;\n      }\n    }\n  }\n}\n","// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // scss-docs-start list-group-css-vars\n  --#{$prefix}list-group-color: #{$list-group-color};\n  --#{$prefix}list-group-bg: #{$list-group-bg};\n  --#{$prefix}list-group-border-color: #{$list-group-border-color};\n  --#{$prefix}list-group-border-width: #{$list-group-border-width};\n  --#{$prefix}list-group-border-radius: #{$list-group-border-radius};\n  --#{$prefix}list-group-item-padding-x: #{$list-group-item-padding-x};\n  --#{$prefix}list-group-item-padding-y: #{$list-group-item-padding-y};\n  --#{$prefix}list-group-action-color: #{$list-group-action-color};\n  --#{$prefix}list-group-action-hover-color: #{$list-group-action-hover-color};\n  --#{$prefix}list-group-action-hover-bg: #{$list-group-hover-bg};\n  --#{$prefix}list-group-action-active-color: #{$list-group-action-active-color};\n  --#{$prefix}list-group-action-active-bg: #{$list-group-action-active-bg};\n  --#{$prefix}list-group-disabled-color: #{$list-group-disabled-color};\n  --#{$prefix}list-group-disabled-bg: #{$list-group-disabled-bg};\n  --#{$prefix}list-group-active-color: #{$list-group-active-color};\n  --#{$prefix}list-group-active-bg: #{$list-group-active-bg};\n  --#{$prefix}list-group-active-border-color: #{$list-group-active-border-color};\n  // scss-docs-end list-group-css-vars\n\n  display: flex;\n  flex-direction: column;\n\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 0;\n  @include border-radius(var(--#{$prefix}list-group-border-radius));\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n\n  > .list-group-item::before {\n    // Increments only this instance of the section counter\n    content: counters(section, \".\") \". \";\n    counter-increment: section;\n  }\n}\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive\n// list items. Includes an extra `.active` modifier class for selected items.\n\n.list-group-item-action {\n  width: 100%; // For `<button>`s (anchors become 100% by default though)\n  color: var(--#{$prefix}list-group-action-color);\n  text-align: inherit; // For `<button>`s (anchors inherit)\n\n  // Hover state\n  &:hover,\n  &:focus {\n    z-index: 1; // Place hover/focus items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-action-hover-color);\n    text-decoration: none;\n    background-color: var(--#{$prefix}list-group-action-hover-bg);\n  }\n\n  &:active {\n    color: var(--#{$prefix}list-group-action-active-color);\n    background-color: var(--#{$prefix}list-group-action-active-bg);\n  }\n}\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}list-group-item-padding-y) var(--#{$prefix}list-group-item-padding-x);\n  color: var(--#{$prefix}list-group-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}list-group-bg);\n  border: var(--#{$prefix}list-group-border-width) solid var(--#{$prefix}list-group-border-color);\n\n  &:first-child {\n    @include border-top-radius(inherit);\n  }\n\n  &:last-child {\n    @include border-bottom-radius(inherit);\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}list-group-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}list-group-disabled-bg);\n  }\n\n  // Include both here for `<a>`s and `<button>`s\n  &.active {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-active-color);\n    background-color: var(--#{$prefix}list-group-active-bg);\n    border-color: var(--#{$prefix}list-group-active-border-color);\n  }\n\n  // stylelint-disable-next-line scss/selector-no-redundant-nesting-selector\n  & + .list-group-item {\n    border-top-width: 0;\n\n    &.active {\n      margin-top: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n      border-top-width: var(--#{$prefix}list-group-border-width);\n    }\n  }\n}\n\n// Horizontal\n//\n// Change the layout of list group items from vertical (default) to horizontal.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .list-group-horizontal#{$infix} {\n      flex-direction: row;\n\n      > .list-group-item {\n        &:first-child:not(:last-child) {\n          @include border-bottom-start-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-top-end-radius(0);\n        }\n\n        &:last-child:not(:first-child) {\n          @include border-top-end-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-bottom-start-radius(0);\n        }\n\n        &.active {\n          margin-top: 0;\n        }\n\n        + .list-group-item {\n          border-top-width: var(--#{$prefix}list-group-border-width);\n          border-left-width: 0;\n\n          &.active {\n            margin-left: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n            border-left-width: var(--#{$prefix}list-group-border-width);\n          }\n        }\n      }\n    }\n  }\n}\n\n\n// Flush list items\n//\n// Remove borders and border-radius to keep list group items edge-to-edge. Most\n// useful within other components (e.g., cards).\n\n.list-group-flush {\n  @include border-radius(0);\n\n  > .list-group-item {\n    border-width: 0 0 var(--#{$prefix}list-group-border-width);\n\n    &:last-child {\n      border-bottom-width: 0;\n    }\n  }\n}\n\n\n// scss-docs-start list-group-modifiers\n// List group contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n@each $state in map-keys($theme-colors) {\n  .list-group-item-#{$state} {\n    --#{$prefix}list-group-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-hover-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-hover-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-active-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-active-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-active-color: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-active-bg: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-active-border-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end list-group-modifiers\n","// Transparent background and border properties included for button version.\n// iOS requires the button element instead of an anchor tag.\n// If you want the anchor version, it requires `href=\"#\"`.\n// See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n.btn-close {\n  // scss-docs-start close-css-vars\n  --#{$prefix}btn-close-color: #{$btn-close-color};\n  --#{$prefix}btn-close-bg: #{ escape-svg($btn-close-bg) };\n  --#{$prefix}btn-close-opacity: #{$btn-close-opacity};\n  --#{$prefix}btn-close-hover-opacity: #{$btn-close-hover-opacity};\n  --#{$prefix}btn-close-focus-shadow: #{$btn-close-focus-shadow};\n  --#{$prefix}btn-close-focus-opacity: #{$btn-close-focus-opacity};\n  --#{$prefix}btn-close-disabled-opacity: #{$btn-close-disabled-opacity};\n  --#{$prefix}btn-close-white-filter: #{$btn-close-white-filter};\n  // scss-docs-end close-css-vars\n\n  box-sizing: content-box;\n  width: $btn-close-width;\n  height: $btn-close-height;\n  padding: $btn-close-padding-y $btn-close-padding-x;\n  color: var(--#{$prefix}btn-close-color);\n  background: transparent var(--#{$prefix}btn-close-bg) center / $btn-close-width auto no-repeat; // include transparent for button elements\n  border: 0; // for button elements\n  @include border-radius();\n  opacity: var(--#{$prefix}btn-close-opacity);\n\n  // Override <a>'s hover style\n  &:hover {\n    color: var(--#{$prefix}btn-close-color);\n    text-decoration: none;\n    opacity: var(--#{$prefix}btn-close-hover-opacity);\n  }\n\n  &:focus {\n    outline: 0;\n    box-shadow: var(--#{$prefix}btn-close-focus-shadow);\n    opacity: var(--#{$prefix}btn-close-focus-opacity);\n  }\n\n  &:disabled,\n  &.disabled {\n    pointer-events: none;\n    user-select: none;\n    opacity: var(--#{$prefix}btn-close-disabled-opacity);\n  }\n}\n\n@mixin btn-close-white() {\n  filter: var(--#{$prefix}btn-close-white-filter);\n}\n\n.btn-close-white {\n  @include btn-close-white();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .btn-close {\n      @include btn-close-white();\n    }\n  }\n}\n",".toast {\n  // scss-docs-start toast-css-vars\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n  --#{$prefix}toast-padding-x: #{$toast-padding-x};\n  --#{$prefix}toast-padding-y: #{$toast-padding-y};\n  --#{$prefix}toast-spacing: #{$toast-spacing};\n  --#{$prefix}toast-max-width: #{$toast-max-width};\n  @include rfs($toast-font-size, --#{$prefix}toast-font-size);\n  --#{$prefix}toast-color: #{$toast-color};\n  --#{$prefix}toast-bg: #{$toast-background-color};\n  --#{$prefix}toast-border-width: #{$toast-border-width};\n  --#{$prefix}toast-border-color: #{$toast-border-color};\n  --#{$prefix}toast-border-radius: #{$toast-border-radius};\n  --#{$prefix}toast-box-shadow: #{$toast-box-shadow};\n  --#{$prefix}toast-header-color: #{$toast-header-color};\n  --#{$prefix}toast-header-bg: #{$toast-header-background-color};\n  --#{$prefix}toast-header-border-color: #{$toast-header-border-color};\n  // scss-docs-end toast-css-vars\n\n  width: var(--#{$prefix}toast-max-width);\n  max-width: 100%;\n  @include font-size(var(--#{$prefix}toast-font-size));\n  color: var(--#{$prefix}toast-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}toast-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-border-color);\n  box-shadow: var(--#{$prefix}toast-box-shadow);\n  @include border-radius(var(--#{$prefix}toast-border-radius));\n\n  &.showing {\n    opacity: 0;\n  }\n\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.toast-container {\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n\n  position: absolute;\n  z-index: var(--#{$prefix}toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n\n  > :not(:last-child) {\n    margin-bottom: var(--#{$prefix}toast-spacing);\n  }\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}toast-padding-y) var(--#{$prefix}toast-padding-x);\n  color: var(--#{$prefix}toast-header-color);\n  background-color: var(--#{$prefix}toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-header-border-color);\n  @include border-top-radius(calc(var(--#{$prefix}toast-border-radius) - var(--#{$prefix}toast-border-width)));\n\n  .btn-close {\n    margin-right: calc(-.5 * var(--#{$prefix}toast-padding-x)); // stylelint-disable-line function-disallowed-list\n    margin-left: var(--#{$prefix}toast-padding-x);\n  }\n}\n\n.toast-body {\n  padding: var(--#{$prefix}toast-padding-x);\n  word-wrap: break-word;\n}\n","// stylelint-disable function-disallowed-list\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and stuff\n\n\n// Container that the modal scrolls within\n.modal {\n  // scss-docs-start modal-css-vars\n  --#{$prefix}modal-zindex: #{$zindex-modal};\n  --#{$prefix}modal-width: #{$modal-md};\n  --#{$prefix}modal-padding: #{$modal-inner-padding};\n  --#{$prefix}modal-margin: #{$modal-dialog-margin};\n  --#{$prefix}modal-color: #{$modal-content-color};\n  --#{$prefix}modal-bg: #{$modal-content-bg};\n  --#{$prefix}modal-border-color: #{$modal-content-border-color};\n  --#{$prefix}modal-border-width: #{$modal-content-border-width};\n  --#{$prefix}modal-border-radius: #{$modal-content-border-radius};\n  --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-xs};\n  --#{$prefix}modal-inner-border-radius: #{$modal-content-inner-border-radius};\n  --#{$prefix}modal-header-padding-x: #{$modal-header-padding-x};\n  --#{$prefix}modal-header-padding-y: #{$modal-header-padding-y};\n  --#{$prefix}modal-header-padding: #{$modal-header-padding}; // Todo in v6: Split this padding into x and y\n  --#{$prefix}modal-header-border-color: #{$modal-header-border-color};\n  --#{$prefix}modal-header-border-width: #{$modal-header-border-width};\n  --#{$prefix}modal-title-line-height: #{$modal-title-line-height};\n  --#{$prefix}modal-footer-gap: #{$modal-footer-margin-between};\n  --#{$prefix}modal-footer-bg: #{$modal-footer-bg};\n  --#{$prefix}modal-footer-border-color: #{$modal-footer-border-color};\n  --#{$prefix}modal-footer-border-width: #{$modal-footer-border-width};\n  // scss-docs-end modal-css-vars\n\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--#{$prefix}modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n  // We deliberately don't use `-webkit-overflow-scrolling: touch;` due to a\n  // gnarly iOS Safari bug: https://bugs.webkit.org/show_bug.cgi?id=158342\n  // See also https://github.com/twbs/bootstrap/issues/17695\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--#{$prefix}modal-margin);\n  // allow clicks to pass through for custom click handling to close modal\n  pointer-events: none;\n\n  // When fading in the modal, animate it to slide down\n  .modal.fade & {\n    @include transition($modal-transition);\n    transform: $modal-fade-transform;\n  }\n  .modal.show & {\n    transform: $modal-show-transform;\n  }\n\n  // When trying to close, animate focus to scale\n  .modal.modal-static & {\n    transform: $modal-scale-transform;\n  }\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n\n  .modal-content {\n    max-height: 100%;\n    overflow: hidden;\n  }\n\n  .modal-body {\n    overflow-y: auto;\n  }\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%; // Ensure `.modal-content` extends the full width of the parent `.modal-dialog`\n  // counteract the pointer-events: none; in the .modal-dialog\n  color: var(--#{$prefix}modal-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}modal-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}modal-border-width) solid var(--#{$prefix}modal-border-color);\n  @include border-radius(var(--#{$prefix}modal-border-radius));\n  @include box-shadow(var(--#{$prefix}modal-box-shadow));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  // scss-docs-start modal-backdrop-css-vars\n  --#{$prefix}backdrop-zindex: #{$zindex-modal-backdrop};\n  --#{$prefix}backdrop-bg: #{$modal-backdrop-bg};\n  --#{$prefix}backdrop-opacity: #{$modal-backdrop-opacity};\n  // scss-docs-end modal-backdrop-css-vars\n\n  @include overlay-backdrop(var(--#{$prefix}backdrop-zindex), var(--#{$prefix}backdrop-bg), var(--#{$prefix}backdrop-opacity));\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  padding: var(--#{$prefix}modal-header-padding);\n  border-bottom: var(--#{$prefix}modal-header-border-width) solid var(--#{$prefix}modal-header-border-color);\n  @include border-top-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}modal-header-padding-y) * .5) calc(var(--#{$prefix}modal-header-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}modal-header-padding-y)) calc(-.5 * var(--#{$prefix}modal-header-padding-x)) calc(-.5 * var(--#{$prefix}modal-header-padding-y)) auto;\n  }\n}\n\n// Title text within header\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}modal-title-line-height);\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  // Enable `flex-grow: 1` so that the body take up as much space as possible\n  // when there should be a fixed height on `.modal-dialog`.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}modal-padding);\n}\n\n// Footer (for actions)\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center; // vertically center\n  justify-content: flex-end; // Right align buttons with flex property because text-align doesn't work on flex items\n  padding: calc(var(--#{$prefix}modal-padding) - var(--#{$prefix}modal-footer-gap) * .5);\n  background-color: var(--#{$prefix}modal-footer-bg);\n  border-top: var(--#{$prefix}modal-footer-border-width) solid var(--#{$prefix}modal-footer-border-color);\n  @include border-bottom-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  // Place margin between footer elements\n  // This solution is far from ideal because of the universal selector usage,\n  // but is needed to fix https://github.com/twbs/bootstrap/issues/24800\n  > * {\n    margin: calc(var(--#{$prefix}modal-footer-gap) * .5); // Todo in v6: replace with gap on parent class\n  }\n}\n\n// Scale up the modal\n@include media-breakpoint-up(sm) {\n  .modal {\n    --#{$prefix}modal-margin: #{$modal-dialog-margin-y-sm-up};\n    --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-sm-up};\n  }\n\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    max-width: var(--#{$prefix}modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  .modal-sm {\n    --#{$prefix}modal-width: #{$modal-sm};\n  }\n}\n\n@include media-breakpoint-up(lg) {\n  .modal-lg,\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-lg};\n  }\n}\n\n@include media-breakpoint-up(xl) {\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-xl};\n  }\n}\n\n// scss-docs-start modal-fullscreen-loop\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n  $postfix: if($infix != \"\", $infix + \"-down\", \"\");\n\n  @include media-breakpoint-down($breakpoint) {\n    .modal-fullscreen#{$postfix} {\n      width: 100vw;\n      max-width: none;\n      height: 100%;\n      margin: 0;\n\n      .modal-content {\n        height: 100%;\n        border: 0;\n        @include border-radius(0);\n      }\n\n      .modal-header,\n      .modal-footer {\n        @include border-radius(0);\n      }\n\n      .modal-body {\n        overflow-y: auto;\n      }\n    }\n  }\n}\n// scss-docs-end modal-fullscreen-loop\n","// Shared between modals and offcanvases\n@mixin overlay-backdrop($zindex, $backdrop-bg, $backdrop-opacity) {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex;\n  width: 100vw;\n  height: 100vh;\n  background-color: $backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $backdrop-opacity; }\n}\n","// Base class\n.tooltip {\n  // scss-docs-start tooltip-css-vars\n  --#{$prefix}tooltip-zindex: #{$zindex-tooltip};\n  --#{$prefix}tooltip-max-width: #{$tooltip-max-width};\n  --#{$prefix}tooltip-padding-x: #{$tooltip-padding-x};\n  --#{$prefix}tooltip-padding-y: #{$tooltip-padding-y};\n  --#{$prefix}tooltip-margin: #{$tooltip-margin};\n  @include rfs($tooltip-font-size, --#{$prefix}tooltip-font-size);\n  --#{$prefix}tooltip-color: #{$tooltip-color};\n  --#{$prefix}tooltip-bg: #{$tooltip-bg};\n  --#{$prefix}tooltip-border-radius: #{$tooltip-border-radius};\n  --#{$prefix}tooltip-opacity: #{$tooltip-opacity};\n  --#{$prefix}tooltip-arrow-width: #{$tooltip-arrow-width};\n  --#{$prefix}tooltip-arrow-height: #{$tooltip-arrow-height};\n  // scss-docs-end tooltip-css-vars\n\n  z-index: var(--#{$prefix}tooltip-zindex);\n  display: block;\n  margin: var(--#{$prefix}tooltip-margin);\n  @include deprecate(\"`$tooltip-margin`\", \"v5\", \"v5.x\", true);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}tooltip-font-size));\n  // Allow breaking very long words so they don't overflow the tooltip's bounds\n  word-wrap: break-word;\n  opacity: 0;\n\n  &.show { opacity: var(--#{$prefix}tooltip-opacity); }\n\n  .tooltip-arrow {\n    display: block;\n    width: var(--#{$prefix}tooltip-arrow-width);\n    height: var(--#{$prefix}tooltip-arrow-height);\n\n    &::before {\n      position: absolute;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n    }\n  }\n}\n\n.bs-tooltip-top .tooltip-arrow {\n  bottom: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    top: -1px;\n    border-width: var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-top-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow {\n  left: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    right: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-right-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-bottom .tooltip-arrow {\n  top: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    bottom: -1px;\n    border-width: 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-bottom-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow {\n  right: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    left: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-left-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-tooltip-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-tooltip-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-tooltip-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-tooltip-start;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: var(--#{$prefix}tooltip-max-width);\n  padding: var(--#{$prefix}tooltip-padding-y) var(--#{$prefix}tooltip-padding-x);\n  color: var(--#{$prefix}tooltip-color);\n  text-align: center;\n  background-color: var(--#{$prefix}tooltip-bg);\n  @include border-radius(var(--#{$prefix}tooltip-border-radius));\n}\n","@mixin reset-text {\n  font-family: $font-family-base;\n  // We deliberately do NOT reset font-size or overflow-wrap / word-wrap.\n  font-style: normal;\n  font-weight: $font-weight-normal;\n  line-height: $line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n}\n",".popover {\n  // scss-docs-start popover-css-vars\n  --#{$prefix}popover-zindex: #{$zindex-popover};\n  --#{$prefix}popover-max-width: #{$popover-max-width};\n  @include rfs($popover-font-size, --#{$prefix}popover-font-size);\n  --#{$prefix}popover-bg: #{$popover-bg};\n  --#{$prefix}popover-border-width: #{$popover-border-width};\n  --#{$prefix}popover-border-color: #{$popover-border-color};\n  --#{$prefix}popover-border-radius: #{$popover-border-radius};\n  --#{$prefix}popover-inner-border-radius: #{$popover-inner-border-radius};\n  --#{$prefix}popover-box-shadow: #{$popover-box-shadow};\n  --#{$prefix}popover-header-padding-x: #{$popover-header-padding-x};\n  --#{$prefix}popover-header-padding-y: #{$popover-header-padding-y};\n  @include rfs($popover-header-font-size, --#{$prefix}popover-header-font-size);\n  --#{$prefix}popover-header-color: #{$popover-header-color};\n  --#{$prefix}popover-header-bg: #{$popover-header-bg};\n  --#{$prefix}popover-body-padding-x: #{$popover-body-padding-x};\n  --#{$prefix}popover-body-padding-y: #{$popover-body-padding-y};\n  --#{$prefix}popover-body-color: #{$popover-body-color};\n  --#{$prefix}popover-arrow-width: #{$popover-arrow-width};\n  --#{$prefix}popover-arrow-height: #{$popover-arrow-height};\n  --#{$prefix}popover-arrow-border: var(--#{$prefix}popover-border-color);\n  // scss-docs-end popover-css-vars\n\n  z-index: var(--#{$prefix}popover-zindex);\n  display: block;\n  max-width: var(--#{$prefix}popover-max-width);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}popover-font-size));\n  // Allow breaking very long words so they don't overflow the popover's bounds\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}popover-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-radius(var(--#{$prefix}popover-border-radius));\n  @include box-shadow(var(--#{$prefix}popover-box-shadow));\n\n  .popover-arrow {\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    height: var(--#{$prefix}popover-arrow-height);\n\n    &::before,\n    &::after {\n      position: absolute;\n      display: block;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n      border-width: 0;\n    }\n  }\n}\n\n.bs-popover-top {\n  > .popover-arrow {\n    bottom: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      bottom: 0;\n      border-top-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      bottom: var(--#{$prefix}popover-border-width);\n      border-top-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end {\n  > .popover-arrow {\n    left: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      left: 0;\n      border-right-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      left: var(--#{$prefix}popover-border-width);\n      border-right-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-bottom {\n  > .popover-arrow {\n    top: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      top: 0;\n      border-bottom-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      top: var(--#{$prefix}popover-border-width);\n      border-bottom-color: var(--#{$prefix}popover-bg);\n    }\n  }\n\n  // This will remove the popover-header's border just below the arrow\n  .popover-header::before {\n    position: absolute;\n    top: 0;\n    left: 50%;\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    margin-left: calc(-.5 * var(--#{$prefix}popover-arrow-width)); // stylelint-disable-line function-disallowed-list\n    content: \"\";\n    border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-header-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start {\n  > .popover-arrow {\n    right: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      right: 0;\n      border-left-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      right: var(--#{$prefix}popover-border-width);\n      border-left-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-popover-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-popover-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-popover-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-popover-start;\n  }\n}\n\n// Offset the popover to account for the popover arrow\n.popover-header {\n  padding: var(--#{$prefix}popover-header-padding-y) var(--#{$prefix}popover-header-padding-x);\n  margin-bottom: 0; // Reset the default from Reboot\n  @include font-size(var(--#{$prefix}popover-header-font-size));\n  color: var(--#{$prefix}popover-header-color);\n  background-color: var(--#{$prefix}popover-header-bg);\n  border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-top-radius(var(--#{$prefix}popover-inner-border-radius));\n\n  &:empty {\n    display: none;\n  }\n}\n\n.popover-body {\n  padding: var(--#{$prefix}popover-body-padding-y) var(--#{$prefix}popover-body-padding-x);\n  color: var(--#{$prefix}popover-body-color);\n}\n","// Notes on the classes:\n//\n// 1. .carousel.pointer-event should ideally be pan-y (to allow for users to scroll vertically)\n//    even when their scroll action started on a carousel, but for compatibility (with Firefox)\n//    we're preventing all actions instead\n// 2. The .carousel-item-start and .carousel-item-end is used to indicate where\n//    the active slide is heading.\n// 3. .active.carousel-item is the current slide.\n// 4. .active.carousel-item-start and .active.carousel-item-end is the current\n//    slide in its in-transition state. Only one of these occurs at a time.\n// 5. .carousel-item-next.carousel-item-start and .carousel-item-prev.carousel-item-end\n//    is the upcoming slide in transition.\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n  @include clearfix();\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  @include transition($carousel-transition);\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n\n//\n// Alternate transitions\n//\n\n.carousel-fade {\n  .carousel-item {\n    opacity: 0;\n    transition-property: opacity;\n    transform: none;\n  }\n\n  .carousel-item.active,\n  .carousel-item-next.carousel-item-start,\n  .carousel-item-prev.carousel-item-end {\n    z-index: 1;\n    opacity: 1;\n  }\n\n  .active.carousel-item-start,\n  .active.carousel-item-end {\n    z-index: 0;\n    opacity: 0;\n    @include transition(opacity 0s $carousel-transition-duration);\n  }\n}\n\n\n//\n// Left/right controls for nav\n//\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  // Use flex for alignment (1-3)\n  display: flex; // 1. allow flex styles\n  align-items: center; // 2. vertically center contents\n  justify-content: center; // 3. horizontally center contents\n  width: $carousel-control-width;\n  padding: 0;\n  color: $carousel-control-color;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: $carousel-control-opacity;\n  @include transition($carousel-control-transition);\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: $carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    opacity: $carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  background-image: if($enable-gradients, linear-gradient(90deg, rgba($black, .25), rgba($black, .001)), null);\n}\n.carousel-control-next {\n  right: 0;\n  background-image: if($enable-gradients, linear-gradient(270deg, rgba($black, .25), rgba($black, .001)), null);\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height: $carousel-control-icon-width;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n.carousel-control-prev-icon {\n  background-image: escape-svg($carousel-control-prev-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-next-icon-bg) + \"*/\"};\n}\n.carousel-control-next-icon {\n  background-image: escape-svg($carousel-control-next-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-prev-icon-bg) + \"*/\"};\n}\n\n// Optional indicator pips/controls\n//\n// Add a container (such as a list) with the following class and add an item (ideally a focusable control,\n// like a button) with data-bs-target for each slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  // Use the .carousel-control's width as margin so we don't overlay those\n  margin-right: $carousel-control-width;\n  margin-bottom: 1rem;\n  margin-left: $carousel-control-width;\n\n  [data-bs-target] {\n    box-sizing: content-box;\n    flex: 0 1 auto;\n    width: $carousel-indicator-width;\n    height: $carousel-indicator-height;\n    padding: 0;\n    margin-right: $carousel-indicator-spacer;\n    margin-left: $carousel-indicator-spacer;\n    text-indent: -999px;\n    cursor: pointer;\n    background-color: $carousel-indicator-active-bg;\n    background-clip: padding-box;\n    border: 0;\n    // Use transparent borders to increase the hit area by 10px on top and bottom.\n    border-top: $carousel-indicator-hit-area-height solid transparent;\n    border-bottom: $carousel-indicator-hit-area-height solid transparent;\n    opacity: $carousel-indicator-opacity;\n    @include transition($carousel-indicator-transition);\n  }\n\n  .active {\n    opacity: $carousel-indicator-active-opacity;\n  }\n}\n\n\n// Optional captions\n//\n//\n\n.carousel-caption {\n  position: absolute;\n  right: (100% - $carousel-caption-width) * .5;\n  bottom: $carousel-caption-spacer;\n  left: (100% - $carousel-caption-width) * .5;\n  padding-top: $carousel-caption-padding-y;\n  padding-bottom: $carousel-caption-padding-y;\n  color: $carousel-caption-color;\n  text-align: center;\n}\n\n// Dark mode carousel\n\n@mixin carousel-dark() {\n  .carousel-control-prev-icon,\n  .carousel-control-next-icon {\n    filter: $carousel-dark-control-icon-filter;\n  }\n\n  .carousel-indicators [data-bs-target] {\n    background-color: $carousel-dark-indicator-active-bg;\n  }\n\n  .carousel-caption {\n    color: $carousel-dark-caption-color;\n  }\n}\n\n.carousel-dark {\n  @include carousel-dark();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    @if $color-mode-type == \"media-query\" {\n      .carousel {\n        @include carousel-dark();\n      }\n    } @else {\n      .carousel,\n      &.carousel {\n        @include carousel-dark();\n      }\n    }\n  }\n}\n","// scss-docs-start clearfix\n@mixin clearfix() {\n  &::after {\n    display: block;\n    clear: both;\n    content: \"\";\n  }\n}\n// scss-docs-end clearfix\n","//\n// Rotating border\n//\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--#{$prefix}spinner-width);\n  height: var(--#{$prefix}spinner-height);\n  vertical-align: var(--#{$prefix}spinner-vertical-align);\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 50%;\n  animation: var(--#{$prefix}spinner-animation-speed) linear infinite var(--#{$prefix}spinner-animation-name);\n}\n\n// scss-docs-start spinner-border-keyframes\n@keyframes spinner-border {\n  to { transform: rotate(360deg) #{\"/* rtl:ignore */\"}; }\n}\n// scss-docs-end spinner-border-keyframes\n\n.spinner-border {\n  // scss-docs-start spinner-border-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-border;\n  // scss-docs-end spinner-border-css-vars\n\n  border: var(--#{$prefix}spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  // scss-docs-start spinner-border-sm-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width-sm};\n  // scss-docs-end spinner-border-sm-css-vars\n}\n\n//\n// Growing circle\n//\n\n// scss-docs-start spinner-grow-keyframes\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n// scss-docs-end spinner-grow-keyframes\n\n.spinner-grow {\n  // scss-docs-start spinner-grow-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-grow;\n  // scss-docs-end spinner-grow-css-vars\n\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n}\n\n@if $enable-reduced-motion {\n  @media (prefers-reduced-motion: reduce) {\n    .spinner-border,\n    .spinner-grow {\n      --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed * 2};\n    }\n  }\n}\n","// stylelint-disable function-disallowed-list\n\n%offcanvas-css-vars {\n  // scss-docs-start offcanvas-css-vars\n  --#{$prefix}offcanvas-zindex: #{$zindex-offcanvas};\n  --#{$prefix}offcanvas-width: #{$offcanvas-horizontal-width};\n  --#{$prefix}offcanvas-height: #{$offcanvas-vertical-height};\n  --#{$prefix}offcanvas-padding-x: #{$offcanvas-padding-x};\n  --#{$prefix}offcanvas-padding-y: #{$offcanvas-padding-y};\n  --#{$prefix}offcanvas-color: #{$offcanvas-color};\n  --#{$prefix}offcanvas-bg: #{$offcanvas-bg-color};\n  --#{$prefix}offcanvas-border-width: #{$offcanvas-border-width};\n  --#{$prefix}offcanvas-border-color: #{$offcanvas-border-color};\n  --#{$prefix}offcanvas-box-shadow: #{$offcanvas-box-shadow};\n  --#{$prefix}offcanvas-transition: #{transform $offcanvas-transition-duration ease-in-out};\n  --#{$prefix}offcanvas-title-line-height: #{$offcanvas-title-line-height};\n  // scss-docs-end offcanvas-css-vars\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @extend %offcanvas-css-vars;\n  }\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @include media-breakpoint-down($next) {\n      position: fixed;\n      bottom: 0;\n      z-index: var(--#{$prefix}offcanvas-zindex);\n      display: flex;\n      flex-direction: column;\n      max-width: 100%;\n      color: var(--#{$prefix}offcanvas-color);\n      visibility: hidden;\n      background-color: var(--#{$prefix}offcanvas-bg);\n      background-clip: padding-box;\n      outline: 0;\n      @include box-shadow(var(--#{$prefix}offcanvas-box-shadow));\n      @include transition(var(--#{$prefix}offcanvas-transition));\n\n      &.offcanvas-start {\n        top: 0;\n        left: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-right: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(-100%);\n      }\n\n      &.offcanvas-end {\n        top: 0;\n        right: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-left: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(100%);\n      }\n\n      &.offcanvas-top {\n        top: 0;\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-bottom: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(-100%);\n      }\n\n      &.offcanvas-bottom {\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-top: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(100%);\n      }\n\n      &.showing,\n      &.show:not(.hiding) {\n        transform: none;\n      }\n\n      &.showing,\n      &.hiding,\n      &.show {\n        visibility: visible;\n      }\n    }\n\n    @if not ($infix == \"\") {\n      @include media-breakpoint-up($next) {\n        --#{$prefix}offcanvas-height: auto;\n        --#{$prefix}offcanvas-border-width: 0;\n        background-color: transparent !important; // stylelint-disable-line declaration-no-important\n\n        .offcanvas-header {\n          display: none;\n        }\n\n        .offcanvas-body {\n          display: flex;\n          flex-grow: 0;\n          padding: 0;\n          overflow-y: visible;\n          // Reset `background-color` in case `.bg-*` classes are used in offcanvas\n          background-color: transparent !important; // stylelint-disable-line declaration-no-important\n        }\n      }\n    }\n  }\n}\n\n.offcanvas-backdrop {\n  @include overlay-backdrop($zindex-offcanvas-backdrop, $offcanvas-backdrop-bg, $offcanvas-backdrop-opacity);\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}offcanvas-padding-y) * .5) calc(var(--#{$prefix}offcanvas-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) calc(-.5 * var(--#{$prefix}offcanvas-padding-x)) calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) auto;\n  }\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n  overflow-y: auto;\n}\n",".placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: $placeholder-opacity-max;\n\n  &.btn::before {\n    display: inline-block;\n    content: \"\";\n  }\n}\n\n// Sizing\n.placeholder-xs {\n  min-height: .6em;\n}\n\n.placeholder-sm {\n  min-height: .8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n// Animation\n.placeholder-glow {\n  .placeholder {\n    animation: placeholder-glow 2s ease-in-out infinite;\n  }\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: $placeholder-opacity-min;\n  }\n}\n\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, $black 55%, rgba(0, 0, 0, (1 - $placeholder-opacity-min)) 75%, $black 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .text-bg-#{$color} {\n    color: color-contrast($value) if($enable-important-utilities, !important, null);\n    background-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}bg-opacity, 1)) if($enable-important-utilities, !important, null);\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .link-#{$color} {\n    color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n    text-decoration-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n    @if $link-shade-percentage != 0 {\n      &:hover,\n      &:focus {\n        $hover-color: if(color-contrast($value) == $color-contrast-light, shade-color($value, $link-shade-percentage), tint-color($value, $link-shade-percentage));\n        color: RGBA(#{to-rgb($hover-color)}, var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n        text-decoration-color: RGBA(to-rgb($hover-color), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n      }\n    }\n  }\n}\n\n// One-off special link helper as a bridge until v6\n.link-body-emphasis {\n  color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n  text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n  @if $link-shade-percentage != 0 {\n    &:hover,\n    &:focus {\n      color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, .75)) if($enable-important-utilities, !important, null);\n      text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, .75)) if($enable-important-utilities, !important, null);\n    }\n  }\n}\n",".focus-ring:focus {\n  outline: 0;\n  // By default, there is no `--bs-focus-ring-x`, `--bs-focus-ring-y`, or `--bs-focus-ring-blur`, but we provide CSS variables with fallbacks to initial `0` values\n  box-shadow: var(--#{$prefix}focus-ring-x, 0) var(--#{$prefix}focus-ring-y, 0) var(--#{$prefix}focus-ring-blur, 0) var(--#{$prefix}focus-ring-width) var(--#{$prefix}focus-ring-color);\n}\n",".icon-link {\n  display: inline-flex;\n  gap: $icon-link-gap;\n  align-items: center;\n  text-decoration-color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, .5));\n  text-underline-offset: $icon-link-underline-offset;\n  backface-visibility: hidden;\n\n  > .bi {\n    flex-shrink: 0;\n    width: $icon-link-icon-size;\n    height: $icon-link-icon-size;\n    fill: currentcolor;\n    @include transition($icon-link-icon-transition);\n  }\n}\n\n.icon-link-hover {\n  &:hover,\n  &:focus-visible {\n    > .bi {\n      transform: var(--#{$prefix}icon-link-transform, $icon-link-icon-transform);\n    }\n  }\n}\n","// Credit: Nicolas Gallagher and SUIT CSS.\n\n.ratio {\n  position: relative;\n  width: 100%;\n\n  &::before {\n    display: block;\n    padding-top: var(--#{$prefix}aspect-ratio);\n    content: \"\";\n  }\n\n  > * {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@each $key, $ratio in $aspect-ratios {\n  .ratio-#{$key} {\n    --#{$prefix}aspect-ratio: #{$ratio};\n  }\n}\n","// Shorthand\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n// Responsive sticky top and bottom\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .sticky#{$infix}-top {\n      position: sticky;\n      top: 0;\n      z-index: $zindex-sticky;\n    }\n\n    .sticky#{$infix}-bottom {\n      position: sticky;\n      bottom: 0;\n      z-index: $zindex-sticky;\n    }\n  }\n}\n","// scss-docs-start stacks\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n// scss-docs-end stacks\n","//\n// Visually hidden\n//\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  @include visually-hidden();\n}\n","// stylelint-disable declaration-no-important\n\n// Hide content visually while keeping it accessible to assistive technologies\n//\n// See: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// See: https://kittygiraudel.com/2016/10/13/css-hide-and-seek/\n\n@mixin visually-hidden() {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important; // Fix for https://github.com/twbs/bootstrap/issues/25686\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n\n  // Fix for positioned table caption that could become anonymous cells\n  &:not(caption) {\n    position: absolute !important;\n  }\n}\n\n// Use to only display content when it's focused, or one of its child elements is focused\n// (i.e. when focus is within the element/container that the class was applied to)\n//\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n\n@mixin visually-hidden-focusable() {\n  &:not(:focus):not(:focus-within) {\n    @include visually-hidden();\n  }\n}\n","//\n// Stretched link\n//\n\n.stretched-link {\n  &::#{$stretched-link-pseudo-element} {\n    position: absolute;\n    top: 0;\n    right: 0;\n    bottom: 0;\n    left: 0;\n    z-index: $stretched-link-z-index;\n    content: \"\";\n  }\n}\n","//\n// Text truncation\n//\n\n.text-truncate {\n  @include text-truncate();\n}\n","// Text truncate\n// Requires inline-block or block for proper styling\n\n@mixin text-truncate() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n",".vr {\n  display: inline-block;\n  align-self: stretch;\n  width: $vr-border-width;\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: $hr-opacity;\n}\n","// Utility generator\n// Used to generate utilities & print utilities\n@mixin generate-utility($utility, $infix: \"\", $is-rfs-media-query: false) {\n  $values: map-get($utility, values);\n\n  // If the values are a list or string, convert it into a map\n  @if type-of($values) == \"string\" or type-of(nth($values, 1)) != \"list\" {\n    $values: zip($values, $values);\n  }\n\n  @each $key, $value in $values {\n    $properties: map-get($utility, property);\n\n    // Multiple properties are possible, for example with vertical or horizontal margins or paddings\n    @if type-of($properties) == \"string\" {\n      $properties: append((), $properties);\n    }\n\n    // Use custom class if present\n    $property-class: if(map-has-key($utility, class), map-get($utility, class), nth($properties, 1));\n    $property-class: if($property-class == null, \"\", $property-class);\n\n    // Use custom CSS variable name if present, otherwise default to `class`\n    $css-variable-name: if(map-has-key($utility, css-variable-name), map-get($utility, css-variable-name), map-get($utility, class));\n\n    // State params to generate pseudo-classes\n    $state: if(map-has-key($utility, state), map-get($utility, state), ());\n\n    $infix: if($property-class == \"\" and str-slice($infix, 1, 1) == \"-\", str-slice($infix, 2), $infix);\n\n    // Don't prefix if value key is null (e.g. with shadow class)\n    $property-class-modifier: if($key, if($property-class == \"\" and $infix == \"\", \"\", \"-\") + $key, \"\");\n\n    @if map-get($utility, rfs) {\n      // Inside the media query\n      @if $is-rfs-media-query {\n        $val: rfs-value($value);\n\n        // Do not render anything if fluid and non fluid values are the same\n        $value: if($val == rfs-fluid-value($value), null, $val);\n      }\n      @else {\n        $value: rfs-fluid-value($value);\n      }\n    }\n\n    $is-css-var: map-get($utility, css-var);\n    $is-local-vars: map-get($utility, local-vars);\n    $is-rtl: map-get($utility, rtl);\n\n    @if $value != null {\n      @if $is-rtl == false {\n        /* rtl:begin:remove */\n      }\n\n      @if $is-css-var {\n        .#{$property-class + $infix + $property-class-modifier} {\n          --#{$prefix}#{$css-variable-name}: #{$value};\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            --#{$prefix}#{$css-variable-name}: #{$value};\n          }\n        }\n      } @else {\n        .#{$property-class + $infix + $property-class-modifier} {\n          @each $property in $properties {\n            @if $is-local-vars {\n              @each $local-var, $variable in $is-local-vars {\n                --#{$prefix}#{$local-var}: #{$variable};\n              }\n            }\n            #{$property}: $value if($enable-important-utilities, !important, null);\n          }\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            @each $property in $properties {\n              @if $is-local-vars {\n                @each $local-var, $variable in $is-local-vars {\n                  --#{$prefix}#{$local-var}: #{$variable};\n                }\n              }\n              #{$property}: $value if($enable-important-utilities, !important, null);\n            }\n          }\n        }\n      }\n\n      @if $is-rtl == false {\n        /* rtl:end:remove */\n      }\n    }\n  }\n}\n","// Loop over each breakpoint\n@each $breakpoint in map-keys($grid-breakpoints) {\n\n  // Generate media query if needed\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    // Loop over each utility property\n    @each $key, $utility in $utilities {\n      // The utility can be disabled with `false`, thus check if the utility is a map first\n      // Only proceed if responsive media queries are enabled or if it's the base media query\n      @if type-of($utility) == \"map\" and (map-get($utility, responsive) or $infix == \"\") {\n        @include generate-utility($utility, $infix);\n      }\n    }\n  }\n}\n\n// RFS rescaling\n@media (min-width: $rfs-mq-value) {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    @if (map-get($grid-breakpoints, $breakpoint) < $rfs-breakpoint) {\n      // Loop over each utility property\n      @each $key, $utility in $utilities {\n        // The utility can be disabled with `false`, thus check if the utility is a map first\n        // Only proceed if responsive media queries are enabled or if it's the base media query\n        @if type-of($utility) == \"map\" and map-get($utility, rfs) and (map-get($utility, responsive) or $infix == \"\") {\n          @include generate-utility($utility, $infix, true);\n        }\n      }\n    }\n  }\n}\n\n\n// Print utilities\n@media print {\n  @each $key, $utility in $utilities {\n    // The utility can be disabled with `false`, thus check if the utility is a map first\n    // Then check if the utility needs print styles\n    @if type-of($utility) == \"map\" and map-get($utility, print) == true {\n      @include generate-utility($utility, \"-print\");\n    }\n  }\n}\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n// Font Awesome core compile (Web Fonts-based)\n// -------------------------\n\n@import 'functions';\n@import 'variables';\n@import 'mixins';\n@import 'core';\n@import 'sizing';\n@import 'fixed-width';\n@import 'list';\n@import 'bordered-pulled';\n@import 'animated';\n@import 'rotated-flipped';\n@import 'stacked';\n@import 'icons';\n@import 'screen-reader';\n","// base icon class definition\n// -------------------------\n\n.#{$fa-css-prefix} {\n  font-family: var(--#{$fa-css-prefix}-style-family, '#{$fa-style-family}');\n  font-weight: var(--#{$fa-css-prefix}-style, #{$fa-style});\n}\n\n.#{$fa-css-prefix},\n.#{$fa-css-prefix}-classic,\n.#{$fa-css-prefix}-sharp,\n.fas,\n.#{$fa-css-prefix}-solid,\n.far,\n.#{$fa-css-prefix}-regular,\n.fab,\n.#{$fa-css-prefix}-brands {\n  -moz-osx-font-smoothing: grayscale;\n  -webkit-font-smoothing: antialiased;\n  display: var(--#{$fa-css-prefix}-display, #{$fa-display});\n  font-style: normal;\n  font-variant: normal;\n  line-height: 1;\n  text-rendering: auto;\n}\n\n.fas,\n.#{$fa-css-prefix}-classic,\n.#{$fa-css-prefix}-solid,\n.far,\n.#{$fa-css-prefix}-regular {\n  font-family: 'Font Awesome 6 Free';\n}\n\n.fab,\n.#{$fa-css-prefix}-brands {\n  font-family: 'Font Awesome 6 Brands';\n}\n\n\n%fa-icon {\n  @include fa-icon;\n}\n","// sizing icons\n// -------------------------\n\n// literal magnification scale\n@for $i from 1 through 10 {\n  .#{$fa-css-prefix}-#{$i}x {\n    font-size: $i * 1em;\n  }\n}\n\n// step-based scale (with alignment)\n@each $size, $value in $fa-sizes {\n  .#{$fa-css-prefix}-#{$size} {\n     @include fa-size($value);\n  }\n}\n","// mixins\n// --------------------------\n\n// base rendering for an icon\n@mixin fa-icon {\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n  display: inline-block;\n  font-style: normal;\n  font-variant: normal;\n  font-weight: normal;\n  line-height: 1;\n}\n\n// sets relative font-sizing and alignment (in _sizing)\n@mixin fa-size ($font-size) {\n  font-size: fa-divide($font-size, $fa-size-scale-base) * 1em; // converts step in sizing scale into an em-based value that's relative to the scale's base\n  line-height: fa-divide(1, $font-size) * 1em; // sets the line-height of the icon back to that of it's parent\n  vertical-align: (fa-divide(6, $font-size) - fa-divide(3, 8)) * 1em; // vertically centers the icon taking into account the surrounding text's descender\n}\n\n// only display content to screen readers\n// see: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// see: https://hugogiraudel.com/2016/10/13/css-hide-and-seek/\n@mixin fa-sr-only() {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  white-space: nowrap;\n  border-width: 0;\n}\n\n// use in conjunction with .sr-only to only display content when it's focused\n@mixin fa-sr-only-focusable() {\n  &:not(:focus) {\n    @include fa-sr-only();\n  }\n}\n\n// sets a specific icon family to use alongside style + icon mixins\n\n// convenience mixins for declaring pseudo-elements by CSS variable,\n// including all style-specific font properties, and both the ::before\n// and ::after elements in the duotone case.\n@mixin fa-icon-solid($fa-var) {\n  @extend %fa-icon;\n  @extend .fa-solid;\n\n  &::before {\n    content: unquote(\"\\\"#{ $fa-var }\\\"\");\n  }\n}\n@mixin fa-icon-regular($fa-var) {\n  @extend %fa-icon;\n  @extend .fa-regular;\n\n  &::before {\n    content: unquote(\"\\\"#{ $fa-var }\\\"\");\n  }\n}\n@mixin fa-icon-brands($fa-var) {\n  @extend %fa-icon;\n  @extend .fa-brands;\n\n  &::before {\n    content: unquote(\"\\\"#{ $fa-var }\\\"\");\n  }\n}\n","// fixed-width icons\n// -------------------------\n\n.#{$fa-css-prefix}-fw {\n  text-align: center;\n  width: $fa-fw-width;\n}\n","// icons in a list\n// -------------------------\n\n.#{$fa-css-prefix}-ul {\n  list-style-type: none;\n  margin-left: var(--#{$fa-css-prefix}-li-margin, #{$fa-li-margin});\n  padding-left: 0;\n\n  > li { position: relative; }\n}\n\n.#{$fa-css-prefix}-li {\n  left: calc(var(--#{$fa-css-prefix}-li-width, #{$fa-li-width}) * -1);\n  position: absolute;\n  text-align: center;\n  width: var(--#{$fa-css-prefix}-li-width, #{$fa-li-width});\n  line-height: inherit;\n}\n","// bordered + pulled icons\n// -------------------------\n\n.#{$fa-css-prefix}-border {\n  border-color: var(--#{$fa-css-prefix}-border-color, #{$fa-border-color});\n  border-radius: var(--#{$fa-css-prefix}-border-radius, #{$fa-border-radius});\n  border-style: var(--#{$fa-css-prefix}-border-style, #{$fa-border-style});\n  border-width: var(--#{$fa-css-prefix}-border-width, #{$fa-border-width});\n  padding: var(--#{$fa-css-prefix}-border-padding, #{$fa-border-padding});\n}\n\n.#{$fa-css-prefix}-pull-left {\n  float: left;\n  margin-right: var(--#{$fa-css-prefix}-pull-margin, #{$fa-pull-margin}); \n}\n\n.#{$fa-css-prefix}-pull-right {\n  float: right;\n  margin-left: var(--#{$fa-css-prefix}-pull-margin, #{$fa-pull-margin}); \n}\n","// animating icons\n// --------------------------\n\n.#{$fa-css-prefix}-beat {\n  animation-name: #{$fa-css-prefix}-beat;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, ease-in-out);\n}\n\n.#{$fa-css-prefix}-bounce {\n  animation-name: #{$fa-css-prefix}-bounce;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, cubic-bezier(0.280, 0.840, 0.420, 1));\n}\n\n.#{$fa-css-prefix}-fade {\n  animation-name: #{$fa-css-prefix}-fade;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, cubic-bezier(.4,0,.6,1));\n}\n\n.#{$fa-css-prefix}-beat-fade {\n  animation-name: #{$fa-css-prefix}-beat-fade;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, cubic-bezier(.4,0,.6,1));\n}\n\n.#{$fa-css-prefix}-flip {\n  animation-name: #{$fa-css-prefix}-flip;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, ease-in-out);\n}\n\n.#{$fa-css-prefix}-shake {\n  animation-name: #{$fa-css-prefix}-shake;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, linear);\n}\n\n.#{$fa-css-prefix}-spin {\n  animation-name: #{$fa-css-prefix}-spin;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 2s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, linear);\n}\n\n.#{$fa-css-prefix}-spin-reverse {\n  --#{$fa-css-prefix}-animation-direction: reverse;\n}\n\n.#{$fa-css-prefix}-pulse,\n.#{$fa-css-prefix}-spin-pulse {\n  animation-name: #{$fa-css-prefix}-spin;\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, steps(8));\n}\n\n// if agent or operating system prefers reduced motion, disable animations\n// see: https://www.smashingmagazine.com/2020/09/design-reduced-motion-sensitivities/\n// see: https://developer.mozilla.org/en-US/docs/Web/CSS/@media/prefers-reduced-motion\n@media (prefers-reduced-motion: reduce) {\n  .#{$fa-css-prefix}-beat,\n  .#{$fa-css-prefix}-bounce,\n  .#{$fa-css-prefix}-fade,\n  .#{$fa-css-prefix}-beat-fade,\n  .#{$fa-css-prefix}-flip,\n  .#{$fa-css-prefix}-pulse,\n  .#{$fa-css-prefix}-shake,\n  .#{$fa-css-prefix}-spin,\n  .#{$fa-css-prefix}-spin-pulse {\n    animation-delay: -1ms;\n    animation-duration: 1ms;\n    animation-iteration-count: 1;\n    transition-delay: 0s;\n    transition-duration: 0s;\n  }\n}\n\n@keyframes #{$fa-css-prefix}-beat {\n  0%, 90% { transform: scale(1); }\n  45% { transform: scale(var(--#{$fa-css-prefix}-beat-scale, 1.25)); }\n}\n\n@keyframes #{$fa-css-prefix}-bounce {\n  0%   { transform: scale(1,1) translateY(0); }\n  10%  { transform: scale(var(--#{$fa-css-prefix}-bounce-start-scale-x, 1.1),var(--#{$fa-css-prefix}-bounce-start-scale-y, 0.9)) translateY(0); }\n  30%  { transform: scale(var(--#{$fa-css-prefix}-bounce-jump-scale-x, 0.9),var(--#{$fa-css-prefix}-bounce-jump-scale-y, 1.1)) translateY(var(--#{$fa-css-prefix}-bounce-height, -0.5em)); }\n  50%  { transform: scale(var(--#{$fa-css-prefix}-bounce-land-scale-x, 1.05),var(--#{$fa-css-prefix}-bounce-land-scale-y, 0.95)) translateY(0); }\n  57%  { transform: scale(1,1) translateY(var(--#{$fa-css-prefix}-bounce-rebound, -0.125em)); }\n  64%  { transform: scale(1,1) translateY(0); }\n  100% { transform: scale(1,1) translateY(0); }\n}\n\n@keyframes #{$fa-css-prefix}-fade {\n  50% { opacity: var(--#{$fa-css-prefix}-fade-opacity, 0.4); }\n}\n\n@keyframes #{$fa-css-prefix}-beat-fade {\n  0%, 100% {\n    opacity: var(--#{$fa-css-prefix}-beat-fade-opacity, 0.4);\n    transform: scale(1);\n  }\n  50% {\n    opacity: 1;\n    transform: scale(var(--#{$fa-css-prefix}-beat-fade-scale, 1.125));\n  }\n}\n\n@keyframes #{$fa-css-prefix}-flip {\n  50% {\n    transform: rotate3d(var(--#{$fa-css-prefix}-flip-x, 0), var(--#{$fa-css-prefix}-flip-y, 1), var(--#{$fa-css-prefix}-flip-z, 0), var(--#{$fa-css-prefix}-flip-angle, -180deg));\n  }\n}\n\n@keyframes #{$fa-css-prefix}-shake {\n  0% { transform: rotate(-15deg); }\n  4% { transform: rotate(15deg); }\n  8%, 24% { transform: rotate(-18deg); }\n  12%, 28% { transform: rotate(18deg); }\n  16% { transform: rotate(-22deg); }\n  20% { transform: rotate(22deg); }\n  32% { transform: rotate(-12deg); }\n  36% { transform: rotate(12deg); }\n  40%, 100% { transform: rotate(0deg); }\n}\n\n@keyframes #{$fa-css-prefix}-spin {\n  0% { transform: rotate(0deg); }\n  100% { transform: rotate(360deg); }\n}\n","// rotating + flipping icons\n// -------------------------\n\n.#{$fa-css-prefix}-rotate-90 {\n  transform: rotate(90deg);\n}\n\n.#{$fa-css-prefix}-rotate-180 {\n  transform: rotate(180deg);\n}\n\n.#{$fa-css-prefix}-rotate-270 {\n  transform: rotate(270deg);\n}\n\n.#{$fa-css-prefix}-flip-horizontal {\n  transform: scale(-1, 1);\n}\n\n.#{$fa-css-prefix}-flip-vertical {\n  transform: scale(1, -1);\n}\n\n.#{$fa-css-prefix}-flip-both,\n.#{$fa-css-prefix}-flip-horizontal.#{$fa-css-prefix}-flip-vertical {\n  transform: scale(-1, -1);\n}\n\n.#{$fa-css-prefix}-rotate-by {\n  transform: rotate(var(--#{$fa-css-prefix}-rotate-angle, 0));\n}\n","// stacking icons\n// -------------------------\n\n.#{$fa-css-prefix}-stack {\n  display: inline-block;\n  height: 2em;\n  line-height: 2em;\n  position: relative;\n  vertical-align: $fa-stack-vertical-align;\n  width: $fa-stack-width;\n}\n\n.#{$fa-css-prefix}-stack-1x,\n.#{$fa-css-prefix}-stack-2x {\n  left: 0;\n  position: absolute;\n  text-align: center;\n  width: 100%;\n  z-index: var(--#{$fa-css-prefix}-stack-z-index, #{$fa-stack-z-index});\n}\n\n.#{$fa-css-prefix}-stack-1x {\n  line-height: inherit;\n}\n\n.#{$fa-css-prefix}-stack-2x {\n  font-size: 2em;\n}\n\n.#{$fa-css-prefix}-inverse {\n  color: var(--#{$fa-css-prefix}-inverse, #{$fa-inverse});\n}\n","// variables\n// --------------------------\n\n$fa-css-prefix          : fa !default;\n$fa-style               : 900 !default;\n$fa-style-family        : \"Font Awesome 6 Free\" !default;\n\n$fa-display             : inline-block !default;\n\n$fa-fw-width            : fa-divide(20em, 16) !default;\n$fa-inverse             : #fff !default;\n\n$fa-border-color        : #eee !default;\n$fa-border-padding      : .2em .25em .15em !default;\n$fa-border-radius       : .1em !default;\n$fa-border-style        : solid !default;\n$fa-border-width        : .08em !default;\n\n$fa-size-scale-2xs      : 10 !default;\n$fa-size-scale-xs       : 12 !default;\n$fa-size-scale-sm       : 14 !default;\n$fa-size-scale-base     : 16 !default;\n$fa-size-scale-lg       : 20 !default;\n$fa-size-scale-xl       : 24 !default;\n$fa-size-scale-2xl      : 32 !default;\n\n$fa-sizes: (\n  \"2xs\"                 : $fa-size-scale-2xs,\n  \"xs\"                  : $fa-size-scale-xs,\n  \"sm\"                  : $fa-size-scale-sm,\n  \"lg\"                  : $fa-size-scale-lg,\n  \"xl\"                  : $fa-size-scale-xl,\n  \"2xl\"                 : $fa-size-scale-2xl\n) !default;\n\n$fa-li-width            : 2em !default;\n$fa-li-margin           : $fa-li-width * fa-divide(5, 4) !default;\n\n$fa-pull-margin         : .3em !default;\n\n$fa-primary-opacity     : 1 !default;\n$fa-secondary-opacity   : .4 !default;\n\n$fa-stack-vertical-align: middle !default;\n$fa-stack-width         : ($fa-fw-width * 2) !default;\n$fa-stack-z-index       : auto !default;\n\n$fa-font-display        : block !default;\n$fa-font-path           : \"../webfonts\" !default;\n\n$fa-var-0: \\30;\n$fa-var-1: \\31;\n$fa-var-2: \\32;\n$fa-var-3: \\33;\n$fa-var-4: \\34;\n$fa-var-5: \\35;\n$fa-var-6: \\36;\n$fa-var-7: \\37;\n$fa-var-8: \\38;\n$fa-var-9: \\39;\n$fa-var-fill-drip: \\f576;\n$fa-var-arrows-to-circle: \\e4bd;\n$fa-var-circle-chevron-right: \\f138;\n$fa-var-chevron-circle-right: \\f138;\n$fa-var-at: \\40;\n$fa-var-trash-can: \\f2ed;\n$fa-var-trash-alt: \\f2ed;\n$fa-var-text-height: \\f034;\n$fa-var-user-xmark: \\f235;\n$fa-var-user-times: \\f235;\n$fa-var-stethoscope: \\f0f1;\n$fa-var-message: \\f27a;\n$fa-var-comment-alt: \\f27a;\n$fa-var-info: \\f129;\n$fa-var-down-left-and-up-right-to-center: \\f422;\n$fa-var-compress-alt: \\f422;\n$fa-var-explosion: \\e4e9;\n$fa-var-file-lines: \\f15c;\n$fa-var-file-alt: \\f15c;\n$fa-var-file-text: \\f15c;\n$fa-var-wave-square: \\f83e;\n$fa-var-ring: \\f70b;\n$fa-var-building-un: \\e4d9;\n$fa-var-dice-three: \\f527;\n$fa-var-calendar-days: \\f073;\n$fa-var-calendar-alt: \\f073;\n$fa-var-anchor-circle-check: \\e4aa;\n$fa-var-building-circle-arrow-right: \\e4d1;\n$fa-var-volleyball: \\f45f;\n$fa-var-volleyball-ball: \\f45f;\n$fa-var-arrows-up-to-line: \\e4c2;\n$fa-var-sort-down: \\f0dd;\n$fa-var-sort-desc: \\f0dd;\n$fa-var-circle-minus: \\f056;\n$fa-var-minus-circle: \\f056;\n$fa-var-door-open: \\f52b;\n$fa-var-right-from-bracket: \\f2f5;\n$fa-var-sign-out-alt: \\f2f5;\n$fa-var-atom: \\f5d2;\n$fa-var-soap: \\e06e;\n$fa-var-icons: \\f86d;\n$fa-var-heart-music-camera-bolt: \\f86d;\n$fa-var-microphone-lines-slash: \\f539;\n$fa-var-microphone-alt-slash: \\f539;\n$fa-var-bridge-circle-check: \\e4c9;\n$fa-var-pump-medical: \\e06a;\n$fa-var-fingerprint: \\f577;\n$fa-var-hand-point-right: \\f0a4;\n$fa-var-magnifying-glass-location: \\f689;\n$fa-var-search-location: \\f689;\n$fa-var-forward-step: \\f051;\n$fa-var-step-forward: \\f051;\n$fa-var-face-smile-beam: \\f5b8;\n$fa-var-smile-beam: \\f5b8;\n$fa-var-flag-checkered: \\f11e;\n$fa-var-football: \\f44e;\n$fa-var-football-ball: \\f44e;\n$fa-var-school-circle-exclamation: \\e56c;\n$fa-var-crop: \\f125;\n$fa-var-angles-down: \\f103;\n$fa-var-angle-double-down: \\f103;\n$fa-var-users-rectangle: \\e594;\n$fa-var-people-roof: \\e537;\n$fa-var-people-line: \\e534;\n$fa-var-beer-mug-empty: \\f0fc;\n$fa-var-beer: \\f0fc;\n$fa-var-diagram-predecessor: \\e477;\n$fa-var-arrow-up-long: \\f176;\n$fa-var-long-arrow-up: \\f176;\n$fa-var-fire-flame-simple: \\f46a;\n$fa-var-burn: \\f46a;\n$fa-var-person: \\f183;\n$fa-var-male: \\f183;\n$fa-var-laptop: \\f109;\n$fa-var-file-csv: \\f6dd;\n$fa-var-menorah: \\f676;\n$fa-var-truck-plane: \\e58f;\n$fa-var-record-vinyl: \\f8d9;\n$fa-var-face-grin-stars: \\f587;\n$fa-var-grin-stars: \\f587;\n$fa-var-bong: \\f55c;\n$fa-var-spaghetti-monster-flying: \\f67b;\n$fa-var-pastafarianism: \\f67b;\n$fa-var-arrow-down-up-across-line: \\e4af;\n$fa-var-spoon: \\f2e5;\n$fa-var-utensil-spoon: \\f2e5;\n$fa-var-jar-wheat: \\e517;\n$fa-var-envelopes-bulk: \\f674;\n$fa-var-mail-bulk: \\f674;\n$fa-var-file-circle-exclamation: \\e4eb;\n$fa-var-circle-h: \\f47e;\n$fa-var-hospital-symbol: \\f47e;\n$fa-var-pager: \\f815;\n$fa-var-address-book: \\f2b9;\n$fa-var-contact-book: \\f2b9;\n$fa-var-strikethrough: \\f0cc;\n$fa-var-k: \\4b;\n$fa-var-landmark-flag: \\e51c;\n$fa-var-pencil: \\f303;\n$fa-var-pencil-alt: \\f303;\n$fa-var-backward: \\f04a;\n$fa-var-caret-right: \\f0da;\n$fa-var-comments: \\f086;\n$fa-var-paste: \\f0ea;\n$fa-var-file-clipboard: \\f0ea;\n$fa-var-code-pull-request: \\e13c;\n$fa-var-clipboard-list: \\f46d;\n$fa-var-truck-ramp-box: \\f4de;\n$fa-var-truck-loading: \\f4de;\n$fa-var-user-check: \\f4fc;\n$fa-var-vial-virus: \\e597;\n$fa-var-sheet-plastic: \\e571;\n$fa-var-blog: \\f781;\n$fa-var-user-ninja: \\f504;\n$fa-var-person-arrow-up-from-line: \\e539;\n$fa-var-scroll-torah: \\f6a0;\n$fa-var-torah: \\f6a0;\n$fa-var-broom-ball: \\f458;\n$fa-var-quidditch: \\f458;\n$fa-var-quidditch-broom-ball: \\f458;\n$fa-var-toggle-off: \\f204;\n$fa-var-box-archive: \\f187;\n$fa-var-archive: \\f187;\n$fa-var-person-drowning: \\e545;\n$fa-var-arrow-down-9-1: \\f886;\n$fa-var-sort-numeric-desc: \\f886;\n$fa-var-sort-numeric-down-alt: \\f886;\n$fa-var-face-grin-tongue-squint: \\f58a;\n$fa-var-grin-tongue-squint: \\f58a;\n$fa-var-spray-can: \\f5bd;\n$fa-var-truck-monster: \\f63b;\n$fa-var-w: \\57;\n$fa-var-earth-africa: \\f57c;\n$fa-var-globe-africa: \\f57c;\n$fa-var-rainbow: \\f75b;\n$fa-var-circle-notch: \\f1ce;\n$fa-var-tablet-screen-button: \\f3fa;\n$fa-var-tablet-alt: \\f3fa;\n$fa-var-paw: \\f1b0;\n$fa-var-cloud: \\f0c2;\n$fa-var-trowel-bricks: \\e58a;\n$fa-var-face-flushed: \\f579;\n$fa-var-flushed: \\f579;\n$fa-var-hospital-user: \\f80d;\n$fa-var-tent-arrow-left-right: \\e57f;\n$fa-var-gavel: \\f0e3;\n$fa-var-legal: \\f0e3;\n$fa-var-binoculars: \\f1e5;\n$fa-var-microphone-slash: \\f131;\n$fa-var-box-tissue: \\e05b;\n$fa-var-motorcycle: \\f21c;\n$fa-var-bell-concierge: \\f562;\n$fa-var-concierge-bell: \\f562;\n$fa-var-pen-ruler: \\f5ae;\n$fa-var-pencil-ruler: \\f5ae;\n$fa-var-people-arrows: \\e068;\n$fa-var-people-arrows-left-right: \\e068;\n$fa-var-mars-and-venus-burst: \\e523;\n$fa-var-square-caret-right: \\f152;\n$fa-var-caret-square-right: \\f152;\n$fa-var-scissors: \\f0c4;\n$fa-var-cut: \\f0c4;\n$fa-var-sun-plant-wilt: \\e57a;\n$fa-var-toilets-portable: \\e584;\n$fa-var-hockey-puck: \\f453;\n$fa-var-table: \\f0ce;\n$fa-var-magnifying-glass-arrow-right: \\e521;\n$fa-var-tachograph-digital: \\f566;\n$fa-var-digital-tachograph: \\f566;\n$fa-var-users-slash: \\e073;\n$fa-var-clover: \\e139;\n$fa-var-reply: \\f3e5;\n$fa-var-mail-reply: \\f3e5;\n$fa-var-star-and-crescent: \\f699;\n$fa-var-house-fire: \\e50c;\n$fa-var-square-minus: \\f146;\n$fa-var-minus-square: \\f146;\n$fa-var-helicopter: \\f533;\n$fa-var-compass: \\f14e;\n$fa-var-square-caret-down: \\f150;\n$fa-var-caret-square-down: \\f150;\n$fa-var-file-circle-question: \\e4ef;\n$fa-var-laptop-code: \\f5fc;\n$fa-var-swatchbook: \\f5c3;\n$fa-var-prescription-bottle: \\f485;\n$fa-var-bars: \\f0c9;\n$fa-var-navicon: \\f0c9;\n$fa-var-people-group: \\e533;\n$fa-var-hourglass-end: \\f253;\n$fa-var-hourglass-3: \\f253;\n$fa-var-heart-crack: \\f7a9;\n$fa-var-heart-broken: \\f7a9;\n$fa-var-square-up-right: \\f360;\n$fa-var-external-link-square-alt: \\f360;\n$fa-var-face-kiss-beam: \\f597;\n$fa-var-kiss-beam: \\f597;\n$fa-var-film: \\f008;\n$fa-var-ruler-horizontal: \\f547;\n$fa-var-people-robbery: \\e536;\n$fa-var-lightbulb: \\f0eb;\n$fa-var-caret-left: \\f0d9;\n$fa-var-circle-exclamation: \\f06a;\n$fa-var-exclamation-circle: \\f06a;\n$fa-var-school-circle-xmark: \\e56d;\n$fa-var-arrow-right-from-bracket: \\f08b;\n$fa-var-sign-out: \\f08b;\n$fa-var-circle-chevron-down: \\f13a;\n$fa-var-chevron-circle-down: \\f13a;\n$fa-var-unlock-keyhole: \\f13e;\n$fa-var-unlock-alt: \\f13e;\n$fa-var-cloud-showers-heavy: \\f740;\n$fa-var-headphones-simple: \\f58f;\n$fa-var-headphones-alt: \\f58f;\n$fa-var-sitemap: \\f0e8;\n$fa-var-circle-dollar-to-slot: \\f4b9;\n$fa-var-donate: \\f4b9;\n$fa-var-memory: \\f538;\n$fa-var-road-spikes: \\e568;\n$fa-var-fire-burner: \\e4f1;\n$fa-var-flag: \\f024;\n$fa-var-hanukiah: \\f6e6;\n$fa-var-feather: \\f52d;\n$fa-var-volume-low: \\f027;\n$fa-var-volume-down: \\f027;\n$fa-var-comment-slash: \\f4b3;\n$fa-var-cloud-sun-rain: \\f743;\n$fa-var-compress: \\f066;\n$fa-var-wheat-awn: \\e2cd;\n$fa-var-wheat-alt: \\e2cd;\n$fa-var-ankh: \\f644;\n$fa-var-hands-holding-child: \\e4fa;\n$fa-var-asterisk: \\2a;\n$fa-var-square-check: \\f14a;\n$fa-var-check-square: \\f14a;\n$fa-var-peseta-sign: \\e221;\n$fa-var-heading: \\f1dc;\n$fa-var-header: \\f1dc;\n$fa-var-ghost: \\f6e2;\n$fa-var-list: \\f03a;\n$fa-var-list-squares: \\f03a;\n$fa-var-square-phone-flip: \\f87b;\n$fa-var-phone-square-alt: \\f87b;\n$fa-var-cart-plus: \\f217;\n$fa-var-gamepad: \\f11b;\n$fa-var-circle-dot: \\f192;\n$fa-var-dot-circle: \\f192;\n$fa-var-face-dizzy: \\f567;\n$fa-var-dizzy: \\f567;\n$fa-var-egg: \\f7fb;\n$fa-var-house-medical-circle-xmark: \\e513;\n$fa-var-campground: \\f6bb;\n$fa-var-folder-plus: \\f65e;\n$fa-var-futbol: \\f1e3;\n$fa-var-futbol-ball: \\f1e3;\n$fa-var-soccer-ball: \\f1e3;\n$fa-var-paintbrush: \\f1fc;\n$fa-var-paint-brush: \\f1fc;\n$fa-var-lock: \\f023;\n$fa-var-gas-pump: \\f52f;\n$fa-var-hot-tub-person: \\f593;\n$fa-var-hot-tub: \\f593;\n$fa-var-map-location: \\f59f;\n$fa-var-map-marked: \\f59f;\n$fa-var-house-flood-water: \\e50e;\n$fa-var-tree: \\f1bb;\n$fa-var-bridge-lock: \\e4cc;\n$fa-var-sack-dollar: \\f81d;\n$fa-var-pen-to-square: \\f044;\n$fa-var-edit: \\f044;\n$fa-var-car-side: \\f5e4;\n$fa-var-share-nodes: \\f1e0;\n$fa-var-share-alt: \\f1e0;\n$fa-var-heart-circle-minus: \\e4ff;\n$fa-var-hourglass-half: \\f252;\n$fa-var-hourglass-2: \\f252;\n$fa-var-microscope: \\f610;\n$fa-var-sink: \\e06d;\n$fa-var-bag-shopping: \\f290;\n$fa-var-shopping-bag: \\f290;\n$fa-var-arrow-down-z-a: \\f881;\n$fa-var-sort-alpha-desc: \\f881;\n$fa-var-sort-alpha-down-alt: \\f881;\n$fa-var-mitten: \\f7b5;\n$fa-var-person-rays: \\e54d;\n$fa-var-users: \\f0c0;\n$fa-var-eye-slash: \\f070;\n$fa-var-flask-vial: \\e4f3;\n$fa-var-hand: \\f256;\n$fa-var-hand-paper: \\f256;\n$fa-var-om: \\f679;\n$fa-var-worm: \\e599;\n$fa-var-house-circle-xmark: \\e50b;\n$fa-var-plug: \\f1e6;\n$fa-var-chevron-up: \\f077;\n$fa-var-hand-spock: \\f259;\n$fa-var-stopwatch: \\f2f2;\n$fa-var-face-kiss: \\f596;\n$fa-var-kiss: \\f596;\n$fa-var-bridge-circle-xmark: \\e4cb;\n$fa-var-face-grin-tongue: \\f589;\n$fa-var-grin-tongue: \\f589;\n$fa-var-chess-bishop: \\f43a;\n$fa-var-face-grin-wink: \\f58c;\n$fa-var-grin-wink: \\f58c;\n$fa-var-ear-deaf: \\f2a4;\n$fa-var-deaf: \\f2a4;\n$fa-var-deafness: \\f2a4;\n$fa-var-hard-of-hearing: \\f2a4;\n$fa-var-road-circle-check: \\e564;\n$fa-var-dice-five: \\f523;\n$fa-var-square-rss: \\f143;\n$fa-var-rss-square: \\f143;\n$fa-var-land-mine-on: \\e51b;\n$fa-var-i-cursor: \\f246;\n$fa-var-stamp: \\f5bf;\n$fa-var-stairs: \\e289;\n$fa-var-i: \\49;\n$fa-var-hryvnia-sign: \\f6f2;\n$fa-var-hryvnia: \\f6f2;\n$fa-var-pills: \\f484;\n$fa-var-face-grin-wide: \\f581;\n$fa-var-grin-alt: \\f581;\n$fa-var-tooth: \\f5c9;\n$fa-var-v: \\56;\n$fa-var-bangladeshi-taka-sign: \\e2e6;\n$fa-var-bicycle: \\f206;\n$fa-var-staff-snake: \\e579;\n$fa-var-rod-asclepius: \\e579;\n$fa-var-rod-snake: \\e579;\n$fa-var-staff-aesculapius: \\e579;\n$fa-var-head-side-cough-slash: \\e062;\n$fa-var-truck-medical: \\f0f9;\n$fa-var-ambulance: \\f0f9;\n$fa-var-wheat-awn-circle-exclamation: \\e598;\n$fa-var-snowman: \\f7d0;\n$fa-var-mortar-pestle: \\f5a7;\n$fa-var-road-barrier: \\e562;\n$fa-var-school: \\f549;\n$fa-var-igloo: \\f7ae;\n$fa-var-joint: \\f595;\n$fa-var-angle-right: \\f105;\n$fa-var-horse: \\f6f0;\n$fa-var-q: \\51;\n$fa-var-g: \\47;\n$fa-var-notes-medical: \\f481;\n$fa-var-temperature-half: \\f2c9;\n$fa-var-temperature-2: \\f2c9;\n$fa-var-thermometer-2: \\f2c9;\n$fa-var-thermometer-half: \\f2c9;\n$fa-var-dong-sign: \\e169;\n$fa-var-capsules: \\f46b;\n$fa-var-poo-storm: \\f75a;\n$fa-var-poo-bolt: \\f75a;\n$fa-var-face-frown-open: \\f57a;\n$fa-var-frown-open: \\f57a;\n$fa-var-hand-point-up: \\f0a6;\n$fa-var-money-bill: \\f0d6;\n$fa-var-bookmark: \\f02e;\n$fa-var-align-justify: \\f039;\n$fa-var-umbrella-beach: \\f5ca;\n$fa-var-helmet-un: \\e503;\n$fa-var-bullseye: \\f140;\n$fa-var-bacon: \\f7e5;\n$fa-var-hand-point-down: \\f0a7;\n$fa-var-arrow-up-from-bracket: \\e09a;\n$fa-var-folder: \\f07b;\n$fa-var-folder-blank: \\f07b;\n$fa-var-file-waveform: \\f478;\n$fa-var-file-medical-alt: \\f478;\n$fa-var-radiation: \\f7b9;\n$fa-var-chart-simple: \\e473;\n$fa-var-mars-stroke: \\f229;\n$fa-var-vial: \\f492;\n$fa-var-gauge: \\f624;\n$fa-var-dashboard: \\f624;\n$fa-var-gauge-med: \\f624;\n$fa-var-tachometer-alt-average: \\f624;\n$fa-var-wand-magic-sparkles: \\e2ca;\n$fa-var-magic-wand-sparkles: \\e2ca;\n$fa-var-e: \\45;\n$fa-var-pen-clip: \\f305;\n$fa-var-pen-alt: \\f305;\n$fa-var-bridge-circle-exclamation: \\e4ca;\n$fa-var-user: \\f007;\n$fa-var-school-circle-check: \\e56b;\n$fa-var-dumpster: \\f793;\n$fa-var-van-shuttle: \\f5b6;\n$fa-var-shuttle-van: \\f5b6;\n$fa-var-building-user: \\e4da;\n$fa-var-square-caret-left: \\f191;\n$fa-var-caret-square-left: \\f191;\n$fa-var-highlighter: \\f591;\n$fa-var-key: \\f084;\n$fa-var-bullhorn: \\f0a1;\n$fa-var-globe: \\f0ac;\n$fa-var-synagogue: \\f69b;\n$fa-var-person-half-dress: \\e548;\n$fa-var-road-bridge: \\e563;\n$fa-var-location-arrow: \\f124;\n$fa-var-c: \\43;\n$fa-var-tablet-button: \\f10a;\n$fa-var-building-lock: \\e4d6;\n$fa-var-pizza-slice: \\f818;\n$fa-var-money-bill-wave: \\f53a;\n$fa-var-chart-area: \\f1fe;\n$fa-var-area-chart: \\f1fe;\n$fa-var-house-flag: \\e50d;\n$fa-var-person-circle-minus: \\e540;\n$fa-var-ban: \\f05e;\n$fa-var-cancel: \\f05e;\n$fa-var-camera-rotate: \\e0d8;\n$fa-var-spray-can-sparkles: \\f5d0;\n$fa-var-air-freshener: \\f5d0;\n$fa-var-star: \\f005;\n$fa-var-repeat: \\f363;\n$fa-var-cross: \\f654;\n$fa-var-box: \\f466;\n$fa-var-venus-mars: \\f228;\n$fa-var-arrow-pointer: \\f245;\n$fa-var-mouse-pointer: \\f245;\n$fa-var-maximize: \\f31e;\n$fa-var-expand-arrows-alt: \\f31e;\n$fa-var-charging-station: \\f5e7;\n$fa-var-shapes: \\f61f;\n$fa-var-triangle-circle-square: \\f61f;\n$fa-var-shuffle: \\f074;\n$fa-var-random: \\f074;\n$fa-var-person-running: \\f70c;\n$fa-var-running: \\f70c;\n$fa-var-mobile-retro: \\e527;\n$fa-var-grip-lines-vertical: \\f7a5;\n$fa-var-spider: \\f717;\n$fa-var-hands-bound: \\e4f9;\n$fa-var-file-invoice-dollar: \\f571;\n$fa-var-plane-circle-exclamation: \\e556;\n$fa-var-x-ray: \\f497;\n$fa-var-spell-check: \\f891;\n$fa-var-slash: \\f715;\n$fa-var-computer-mouse: \\f8cc;\n$fa-var-mouse: \\f8cc;\n$fa-var-arrow-right-to-bracket: \\f090;\n$fa-var-sign-in: \\f090;\n$fa-var-shop-slash: \\e070;\n$fa-var-store-alt-slash: \\e070;\n$fa-var-server: \\f233;\n$fa-var-virus-covid-slash: \\e4a9;\n$fa-var-shop-lock: \\e4a5;\n$fa-var-hourglass-start: \\f251;\n$fa-var-hourglass-1: \\f251;\n$fa-var-blender-phone: \\f6b6;\n$fa-var-building-wheat: \\e4db;\n$fa-var-person-breastfeeding: \\e53a;\n$fa-var-right-to-bracket: \\f2f6;\n$fa-var-sign-in-alt: \\f2f6;\n$fa-var-venus: \\f221;\n$fa-var-passport: \\f5ab;\n$fa-var-heart-pulse: \\f21e;\n$fa-var-heartbeat: \\f21e;\n$fa-var-people-carry-box: \\f4ce;\n$fa-var-people-carry: \\f4ce;\n$fa-var-temperature-high: \\f769;\n$fa-var-microchip: \\f2db;\n$fa-var-crown: \\f521;\n$fa-var-weight-hanging: \\f5cd;\n$fa-var-xmarks-lines: \\e59a;\n$fa-var-file-prescription: \\f572;\n$fa-var-weight-scale: \\f496;\n$fa-var-weight: \\f496;\n$fa-var-user-group: \\f500;\n$fa-var-user-friends: \\f500;\n$fa-var-arrow-up-a-z: \\f15e;\n$fa-var-sort-alpha-up: \\f15e;\n$fa-var-chess-knight: \\f441;\n$fa-var-face-laugh-squint: \\f59b;\n$fa-var-laugh-squint: \\f59b;\n$fa-var-wheelchair: \\f193;\n$fa-var-circle-arrow-up: \\f0aa;\n$fa-var-arrow-circle-up: \\f0aa;\n$fa-var-toggle-on: \\f205;\n$fa-var-person-walking: \\f554;\n$fa-var-walking: \\f554;\n$fa-var-l: \\4c;\n$fa-var-fire: \\f06d;\n$fa-var-bed-pulse: \\f487;\n$fa-var-procedures: \\f487;\n$fa-var-shuttle-space: \\f197;\n$fa-var-space-shuttle: \\f197;\n$fa-var-face-laugh: \\f599;\n$fa-var-laugh: \\f599;\n$fa-var-folder-open: \\f07c;\n$fa-var-heart-circle-plus: \\e500;\n$fa-var-code-fork: \\e13b;\n$fa-var-city: \\f64f;\n$fa-var-microphone-lines: \\f3c9;\n$fa-var-microphone-alt: \\f3c9;\n$fa-var-pepper-hot: \\f816;\n$fa-var-unlock: \\f09c;\n$fa-var-colon-sign: \\e140;\n$fa-var-headset: \\f590;\n$fa-var-store-slash: \\e071;\n$fa-var-road-circle-xmark: \\e566;\n$fa-var-user-minus: \\f503;\n$fa-var-mars-stroke-up: \\f22a;\n$fa-var-mars-stroke-v: \\f22a;\n$fa-var-champagne-glasses: \\f79f;\n$fa-var-glass-cheers: \\f79f;\n$fa-var-clipboard: \\f328;\n$fa-var-house-circle-exclamation: \\e50a;\n$fa-var-file-arrow-up: \\f574;\n$fa-var-file-upload: \\f574;\n$fa-var-wifi: \\f1eb;\n$fa-var-wifi-3: \\f1eb;\n$fa-var-wifi-strong: \\f1eb;\n$fa-var-bath: \\f2cd;\n$fa-var-bathtub: \\f2cd;\n$fa-var-underline: \\f0cd;\n$fa-var-user-pen: \\f4ff;\n$fa-var-user-edit: \\f4ff;\n$fa-var-signature: \\f5b7;\n$fa-var-stroopwafel: \\f551;\n$fa-var-bold: \\f032;\n$fa-var-anchor-lock: \\e4ad;\n$fa-var-building-ngo: \\e4d7;\n$fa-var-manat-sign: \\e1d5;\n$fa-var-not-equal: \\f53e;\n$fa-var-border-top-left: \\f853;\n$fa-var-border-style: \\f853;\n$fa-var-map-location-dot: \\f5a0;\n$fa-var-map-marked-alt: \\f5a0;\n$fa-var-jedi: \\f669;\n$fa-var-square-poll-vertical: \\f681;\n$fa-var-poll: \\f681;\n$fa-var-mug-hot: \\f7b6;\n$fa-var-car-battery: \\f5df;\n$fa-var-battery-car: \\f5df;\n$fa-var-gift: \\f06b;\n$fa-var-dice-two: \\f528;\n$fa-var-chess-queen: \\f445;\n$fa-var-glasses: \\f530;\n$fa-var-chess-board: \\f43c;\n$fa-var-building-circle-check: \\e4d2;\n$fa-var-person-chalkboard: \\e53d;\n$fa-var-mars-stroke-right: \\f22b;\n$fa-var-mars-stroke-h: \\f22b;\n$fa-var-hand-back-fist: \\f255;\n$fa-var-hand-rock: \\f255;\n$fa-var-square-caret-up: \\f151;\n$fa-var-caret-square-up: \\f151;\n$fa-var-cloud-showers-water: \\e4e4;\n$fa-var-chart-bar: \\f080;\n$fa-var-bar-chart: \\f080;\n$fa-var-hands-bubbles: \\e05e;\n$fa-var-hands-wash: \\e05e;\n$fa-var-less-than-equal: \\f537;\n$fa-var-train: \\f238;\n$fa-var-eye-low-vision: \\f2a8;\n$fa-var-low-vision: \\f2a8;\n$fa-var-crow: \\f520;\n$fa-var-sailboat: \\e445;\n$fa-var-window-restore: \\f2d2;\n$fa-var-square-plus: \\f0fe;\n$fa-var-plus-square: \\f0fe;\n$fa-var-torii-gate: \\f6a1;\n$fa-var-frog: \\f52e;\n$fa-var-bucket: \\e4cf;\n$fa-var-image: \\f03e;\n$fa-var-microphone: \\f130;\n$fa-var-cow: \\f6c8;\n$fa-var-caret-up: \\f0d8;\n$fa-var-screwdriver: \\f54a;\n$fa-var-folder-closed: \\e185;\n$fa-var-house-tsunami: \\e515;\n$fa-var-square-nfi: \\e576;\n$fa-var-arrow-up-from-ground-water: \\e4b5;\n$fa-var-martini-glass: \\f57b;\n$fa-var-glass-martini-alt: \\f57b;\n$fa-var-rotate-left: \\f2ea;\n$fa-var-rotate-back: \\f2ea;\n$fa-var-rotate-backward: \\f2ea;\n$fa-var-undo-alt: \\f2ea;\n$fa-var-table-columns: \\f0db;\n$fa-var-columns: \\f0db;\n$fa-var-lemon: \\f094;\n$fa-var-head-side-mask: \\e063;\n$fa-var-handshake: \\f2b5;\n$fa-var-gem: \\f3a5;\n$fa-var-dolly: \\f472;\n$fa-var-dolly-box: \\f472;\n$fa-var-smoking: \\f48d;\n$fa-var-minimize: \\f78c;\n$fa-var-compress-arrows-alt: \\f78c;\n$fa-var-monument: \\f5a6;\n$fa-var-snowplow: \\f7d2;\n$fa-var-angles-right: \\f101;\n$fa-var-angle-double-right: \\f101;\n$fa-var-cannabis: \\f55f;\n$fa-var-circle-play: \\f144;\n$fa-var-play-circle: \\f144;\n$fa-var-tablets: \\f490;\n$fa-var-ethernet: \\f796;\n$fa-var-euro-sign: \\f153;\n$fa-var-eur: \\f153;\n$fa-var-euro: \\f153;\n$fa-var-chair: \\f6c0;\n$fa-var-circle-check: \\f058;\n$fa-var-check-circle: \\f058;\n$fa-var-circle-stop: \\f28d;\n$fa-var-stop-circle: \\f28d;\n$fa-var-compass-drafting: \\f568;\n$fa-var-drafting-compass: \\f568;\n$fa-var-plate-wheat: \\e55a;\n$fa-var-icicles: \\f7ad;\n$fa-var-person-shelter: \\e54f;\n$fa-var-neuter: \\f22c;\n$fa-var-id-badge: \\f2c1;\n$fa-var-marker: \\f5a1;\n$fa-var-face-laugh-beam: \\f59a;\n$fa-var-laugh-beam: \\f59a;\n$fa-var-helicopter-symbol: \\e502;\n$fa-var-universal-access: \\f29a;\n$fa-var-circle-chevron-up: \\f139;\n$fa-var-chevron-circle-up: \\f139;\n$fa-var-lari-sign: \\e1c8;\n$fa-var-volcano: \\f770;\n$fa-var-person-walking-dashed-line-arrow-right: \\e553;\n$fa-var-sterling-sign: \\f154;\n$fa-var-gbp: \\f154;\n$fa-var-pound-sign: \\f154;\n$fa-var-viruses: \\e076;\n$fa-var-square-person-confined: \\e577;\n$fa-var-user-tie: \\f508;\n$fa-var-arrow-down-long: \\f175;\n$fa-var-long-arrow-down: \\f175;\n$fa-var-tent-arrow-down-to-line: \\e57e;\n$fa-var-certificate: \\f0a3;\n$fa-var-reply-all: \\f122;\n$fa-var-mail-reply-all: \\f122;\n$fa-var-suitcase: \\f0f2;\n$fa-var-person-skating: \\f7c5;\n$fa-var-skating: \\f7c5;\n$fa-var-filter-circle-dollar: \\f662;\n$fa-var-funnel-dollar: \\f662;\n$fa-var-camera-retro: \\f083;\n$fa-var-circle-arrow-down: \\f0ab;\n$fa-var-arrow-circle-down: \\f0ab;\n$fa-var-file-import: \\f56f;\n$fa-var-arrow-right-to-file: \\f56f;\n$fa-var-square-arrow-up-right: \\f14c;\n$fa-var-external-link-square: \\f14c;\n$fa-var-box-open: \\f49e;\n$fa-var-scroll: \\f70e;\n$fa-var-spa: \\f5bb;\n$fa-var-location-pin-lock: \\e51f;\n$fa-var-pause: \\f04c;\n$fa-var-hill-avalanche: \\e507;\n$fa-var-temperature-empty: \\f2cb;\n$fa-var-temperature-0: \\f2cb;\n$fa-var-thermometer-0: \\f2cb;\n$fa-var-thermometer-empty: \\f2cb;\n$fa-var-bomb: \\f1e2;\n$fa-var-registered: \\f25d;\n$fa-var-address-card: \\f2bb;\n$fa-var-contact-card: \\f2bb;\n$fa-var-vcard: \\f2bb;\n$fa-var-scale-unbalanced-flip: \\f516;\n$fa-var-balance-scale-right: \\f516;\n$fa-var-subscript: \\f12c;\n$fa-var-diamond-turn-right: \\f5eb;\n$fa-var-directions: \\f5eb;\n$fa-var-burst: \\e4dc;\n$fa-var-house-laptop: \\e066;\n$fa-var-laptop-house: \\e066;\n$fa-var-face-tired: \\f5c8;\n$fa-var-tired: \\f5c8;\n$fa-var-money-bills: \\e1f3;\n$fa-var-smog: \\f75f;\n$fa-var-crutch: \\f7f7;\n$fa-var-cloud-arrow-up: \\f0ee;\n$fa-var-cloud-upload: \\f0ee;\n$fa-var-cloud-upload-alt: \\f0ee;\n$fa-var-palette: \\f53f;\n$fa-var-arrows-turn-right: \\e4c0;\n$fa-var-vest: \\e085;\n$fa-var-ferry: \\e4ea;\n$fa-var-arrows-down-to-people: \\e4b9;\n$fa-var-seedling: \\f4d8;\n$fa-var-sprout: \\f4d8;\n$fa-var-left-right: \\f337;\n$fa-var-arrows-alt-h: \\f337;\n$fa-var-boxes-packing: \\e4c7;\n$fa-var-circle-arrow-left: \\f0a8;\n$fa-var-arrow-circle-left: \\f0a8;\n$fa-var-group-arrows-rotate: \\e4f6;\n$fa-var-bowl-food: \\e4c6;\n$fa-var-candy-cane: \\f786;\n$fa-var-arrow-down-wide-short: \\f160;\n$fa-var-sort-amount-asc: \\f160;\n$fa-var-sort-amount-down: \\f160;\n$fa-var-cloud-bolt: \\f76c;\n$fa-var-thunderstorm: \\f76c;\n$fa-var-text-slash: \\f87d;\n$fa-var-remove-format: \\f87d;\n$fa-var-face-smile-wink: \\f4da;\n$fa-var-smile-wink: \\f4da;\n$fa-var-file-word: \\f1c2;\n$fa-var-file-powerpoint: \\f1c4;\n$fa-var-arrows-left-right: \\f07e;\n$fa-var-arrows-h: \\f07e;\n$fa-var-house-lock: \\e510;\n$fa-var-cloud-arrow-down: \\f0ed;\n$fa-var-cloud-download: \\f0ed;\n$fa-var-cloud-download-alt: \\f0ed;\n$fa-var-children: \\e4e1;\n$fa-var-chalkboard: \\f51b;\n$fa-var-blackboard: \\f51b;\n$fa-var-user-large-slash: \\f4fa;\n$fa-var-user-alt-slash: \\f4fa;\n$fa-var-envelope-open: \\f2b6;\n$fa-var-handshake-simple-slash: \\e05f;\n$fa-var-handshake-alt-slash: \\e05f;\n$fa-var-mattress-pillow: \\e525;\n$fa-var-guarani-sign: \\e19a;\n$fa-var-arrows-rotate: \\f021;\n$fa-var-refresh: \\f021;\n$fa-var-sync: \\f021;\n$fa-var-fire-extinguisher: \\f134;\n$fa-var-cruzeiro-sign: \\e152;\n$fa-var-greater-than-equal: \\f532;\n$fa-var-shield-halved: \\f3ed;\n$fa-var-shield-alt: \\f3ed;\n$fa-var-book-atlas: \\f558;\n$fa-var-atlas: \\f558;\n$fa-var-virus: \\e074;\n$fa-var-envelope-circle-check: \\e4e8;\n$fa-var-layer-group: \\f5fd;\n$fa-var-arrows-to-dot: \\e4be;\n$fa-var-archway: \\f557;\n$fa-var-heart-circle-check: \\e4fd;\n$fa-var-house-chimney-crack: \\f6f1;\n$fa-var-house-damage: \\f6f1;\n$fa-var-file-zipper: \\f1c6;\n$fa-var-file-archive: \\f1c6;\n$fa-var-square: \\f0c8;\n$fa-var-martini-glass-empty: \\f000;\n$fa-var-glass-martini: \\f000;\n$fa-var-couch: \\f4b8;\n$fa-var-cedi-sign: \\e0df;\n$fa-var-italic: \\f033;\n$fa-var-table-cells-column-lock: \\e678;\n$fa-var-church: \\f51d;\n$fa-var-comments-dollar: \\f653;\n$fa-var-democrat: \\f747;\n$fa-var-z: \\5a;\n$fa-var-person-skiing: \\f7c9;\n$fa-var-skiing: \\f7c9;\n$fa-var-road-lock: \\e567;\n$fa-var-a: \\41;\n$fa-var-temperature-arrow-down: \\e03f;\n$fa-var-temperature-down: \\e03f;\n$fa-var-feather-pointed: \\f56b;\n$fa-var-feather-alt: \\f56b;\n$fa-var-p: \\50;\n$fa-var-snowflake: \\f2dc;\n$fa-var-newspaper: \\f1ea;\n$fa-var-rectangle-ad: \\f641;\n$fa-var-ad: \\f641;\n$fa-var-circle-arrow-right: \\f0a9;\n$fa-var-arrow-circle-right: \\f0a9;\n$fa-var-filter-circle-xmark: \\e17b;\n$fa-var-locust: \\e520;\n$fa-var-sort: \\f0dc;\n$fa-var-unsorted: \\f0dc;\n$fa-var-list-ol: \\f0cb;\n$fa-var-list-1-2: \\f0cb;\n$fa-var-list-numeric: \\f0cb;\n$fa-var-person-dress-burst: \\e544;\n$fa-var-money-check-dollar: \\f53d;\n$fa-var-money-check-alt: \\f53d;\n$fa-var-vector-square: \\f5cb;\n$fa-var-bread-slice: \\f7ec;\n$fa-var-language: \\f1ab;\n$fa-var-face-kiss-wink-heart: \\f598;\n$fa-var-kiss-wink-heart: \\f598;\n$fa-var-filter: \\f0b0;\n$fa-var-question: \\3f;\n$fa-var-file-signature: \\f573;\n$fa-var-up-down-left-right: \\f0b2;\n$fa-var-arrows-alt: \\f0b2;\n$fa-var-house-chimney-user: \\e065;\n$fa-var-hand-holding-heart: \\f4be;\n$fa-var-puzzle-piece: \\f12e;\n$fa-var-money-check: \\f53c;\n$fa-var-star-half-stroke: \\f5c0;\n$fa-var-star-half-alt: \\f5c0;\n$fa-var-code: \\f121;\n$fa-var-whiskey-glass: \\f7a0;\n$fa-var-glass-whiskey: \\f7a0;\n$fa-var-building-circle-exclamation: \\e4d3;\n$fa-var-magnifying-glass-chart: \\e522;\n$fa-var-arrow-up-right-from-square: \\f08e;\n$fa-var-external-link: \\f08e;\n$fa-var-cubes-stacked: \\e4e6;\n$fa-var-won-sign: \\f159;\n$fa-var-krw: \\f159;\n$fa-var-won: \\f159;\n$fa-var-virus-covid: \\e4a8;\n$fa-var-austral-sign: \\e0a9;\n$fa-var-f: \\46;\n$fa-var-leaf: \\f06c;\n$fa-var-road: \\f018;\n$fa-var-taxi: \\f1ba;\n$fa-var-cab: \\f1ba;\n$fa-var-person-circle-plus: \\e541;\n$fa-var-chart-pie: \\f200;\n$fa-var-pie-chart: \\f200;\n$fa-var-bolt-lightning: \\e0b7;\n$fa-var-sack-xmark: \\e56a;\n$fa-var-file-excel: \\f1c3;\n$fa-var-file-contract: \\f56c;\n$fa-var-fish-fins: \\e4f2;\n$fa-var-building-flag: \\e4d5;\n$fa-var-face-grin-beam: \\f582;\n$fa-var-grin-beam: \\f582;\n$fa-var-object-ungroup: \\f248;\n$fa-var-poop: \\f619;\n$fa-var-location-pin: \\f041;\n$fa-var-map-marker: \\f041;\n$fa-var-kaaba: \\f66b;\n$fa-var-toilet-paper: \\f71e;\n$fa-var-helmet-safety: \\f807;\n$fa-var-hard-hat: \\f807;\n$fa-var-hat-hard: \\f807;\n$fa-var-eject: \\f052;\n$fa-var-circle-right: \\f35a;\n$fa-var-arrow-alt-circle-right: \\f35a;\n$fa-var-plane-circle-check: \\e555;\n$fa-var-face-rolling-eyes: \\f5a5;\n$fa-var-meh-rolling-eyes: \\f5a5;\n$fa-var-object-group: \\f247;\n$fa-var-chart-line: \\f201;\n$fa-var-line-chart: \\f201;\n$fa-var-mask-ventilator: \\e524;\n$fa-var-arrow-right: \\f061;\n$fa-var-signs-post: \\f277;\n$fa-var-map-signs: \\f277;\n$fa-var-cash-register: \\f788;\n$fa-var-person-circle-question: \\e542;\n$fa-var-h: \\48;\n$fa-var-tarp: \\e57b;\n$fa-var-screwdriver-wrench: \\f7d9;\n$fa-var-tools: \\f7d9;\n$fa-var-arrows-to-eye: \\e4bf;\n$fa-var-plug-circle-bolt: \\e55b;\n$fa-var-heart: \\f004;\n$fa-var-mars-and-venus: \\f224;\n$fa-var-house-user: \\e1b0;\n$fa-var-home-user: \\e1b0;\n$fa-var-dumpster-fire: \\f794;\n$fa-var-house-crack: \\e3b1;\n$fa-var-martini-glass-citrus: \\f561;\n$fa-var-cocktail: \\f561;\n$fa-var-face-surprise: \\f5c2;\n$fa-var-surprise: \\f5c2;\n$fa-var-bottle-water: \\e4c5;\n$fa-var-circle-pause: \\f28b;\n$fa-var-pause-circle: \\f28b;\n$fa-var-toilet-paper-slash: \\e072;\n$fa-var-apple-whole: \\f5d1;\n$fa-var-apple-alt: \\f5d1;\n$fa-var-kitchen-set: \\e51a;\n$fa-var-r: \\52;\n$fa-var-temperature-quarter: \\f2ca;\n$fa-var-temperature-1: \\f2ca;\n$fa-var-thermometer-1: \\f2ca;\n$fa-var-thermometer-quarter: \\f2ca;\n$fa-var-cube: \\f1b2;\n$fa-var-bitcoin-sign: \\e0b4;\n$fa-var-shield-dog: \\e573;\n$fa-var-solar-panel: \\f5ba;\n$fa-var-lock-open: \\f3c1;\n$fa-var-elevator: \\e16d;\n$fa-var-money-bill-transfer: \\e528;\n$fa-var-money-bill-trend-up: \\e529;\n$fa-var-house-flood-water-circle-arrow-right: \\e50f;\n$fa-var-square-poll-horizontal: \\f682;\n$fa-var-poll-h: \\f682;\n$fa-var-circle: \\f111;\n$fa-var-backward-fast: \\f049;\n$fa-var-fast-backward: \\f049;\n$fa-var-recycle: \\f1b8;\n$fa-var-user-astronaut: \\f4fb;\n$fa-var-plane-slash: \\e069;\n$fa-var-trademark: \\f25c;\n$fa-var-basketball: \\f434;\n$fa-var-basketball-ball: \\f434;\n$fa-var-satellite-dish: \\f7c0;\n$fa-var-circle-up: \\f35b;\n$fa-var-arrow-alt-circle-up: \\f35b;\n$fa-var-mobile-screen-button: \\f3cd;\n$fa-var-mobile-alt: \\f3cd;\n$fa-var-volume-high: \\f028;\n$fa-var-volume-up: \\f028;\n$fa-var-users-rays: \\e593;\n$fa-var-wallet: \\f555;\n$fa-var-clipboard-check: \\f46c;\n$fa-var-file-audio: \\f1c7;\n$fa-var-burger: \\f805;\n$fa-var-hamburger: \\f805;\n$fa-var-wrench: \\f0ad;\n$fa-var-bugs: \\e4d0;\n$fa-var-rupee-sign: \\f156;\n$fa-var-rupee: \\f156;\n$fa-var-file-image: \\f1c5;\n$fa-var-circle-question: \\f059;\n$fa-var-question-circle: \\f059;\n$fa-var-plane-departure: \\f5b0;\n$fa-var-handshake-slash: \\e060;\n$fa-var-book-bookmark: \\e0bb;\n$fa-var-code-branch: \\f126;\n$fa-var-hat-cowboy: \\f8c0;\n$fa-var-bridge: \\e4c8;\n$fa-var-phone-flip: \\f879;\n$fa-var-phone-alt: \\f879;\n$fa-var-truck-front: \\e2b7;\n$fa-var-cat: \\f6be;\n$fa-var-anchor-circle-exclamation: \\e4ab;\n$fa-var-truck-field: \\e58d;\n$fa-var-route: \\f4d7;\n$fa-var-clipboard-question: \\e4e3;\n$fa-var-panorama: \\e209;\n$fa-var-comment-medical: \\f7f5;\n$fa-var-teeth-open: \\f62f;\n$fa-var-file-circle-minus: \\e4ed;\n$fa-var-tags: \\f02c;\n$fa-var-wine-glass: \\f4e3;\n$fa-var-forward-fast: \\f050;\n$fa-var-fast-forward: \\f050;\n$fa-var-face-meh-blank: \\f5a4;\n$fa-var-meh-blank: \\f5a4;\n$fa-var-square-parking: \\f540;\n$fa-var-parking: \\f540;\n$fa-var-house-signal: \\e012;\n$fa-var-bars-progress: \\f828;\n$fa-var-tasks-alt: \\f828;\n$fa-var-faucet-drip: \\e006;\n$fa-var-cart-flatbed: \\f474;\n$fa-var-dolly-flatbed: \\f474;\n$fa-var-ban-smoking: \\f54d;\n$fa-var-smoking-ban: \\f54d;\n$fa-var-terminal: \\f120;\n$fa-var-mobile-button: \\f10b;\n$fa-var-house-medical-flag: \\e514;\n$fa-var-basket-shopping: \\f291;\n$fa-var-shopping-basket: \\f291;\n$fa-var-tape: \\f4db;\n$fa-var-bus-simple: \\f55e;\n$fa-var-bus-alt: \\f55e;\n$fa-var-eye: \\f06e;\n$fa-var-face-sad-cry: \\f5b3;\n$fa-var-sad-cry: \\f5b3;\n$fa-var-audio-description: \\f29e;\n$fa-var-person-military-to-person: \\e54c;\n$fa-var-file-shield: \\e4f0;\n$fa-var-user-slash: \\f506;\n$fa-var-pen: \\f304;\n$fa-var-tower-observation: \\e586;\n$fa-var-file-code: \\f1c9;\n$fa-var-signal: \\f012;\n$fa-var-signal-5: \\f012;\n$fa-var-signal-perfect: \\f012;\n$fa-var-bus: \\f207;\n$fa-var-heart-circle-xmark: \\e501;\n$fa-var-house-chimney: \\e3af;\n$fa-var-home-lg: \\e3af;\n$fa-var-window-maximize: \\f2d0;\n$fa-var-face-frown: \\f119;\n$fa-var-frown: \\f119;\n$fa-var-prescription: \\f5b1;\n$fa-var-shop: \\f54f;\n$fa-var-store-alt: \\f54f;\n$fa-var-floppy-disk: \\f0c7;\n$fa-var-save: \\f0c7;\n$fa-var-vihara: \\f6a7;\n$fa-var-scale-unbalanced: \\f515;\n$fa-var-balance-scale-left: \\f515;\n$fa-var-sort-up: \\f0de;\n$fa-var-sort-asc: \\f0de;\n$fa-var-comment-dots: \\f4ad;\n$fa-var-commenting: \\f4ad;\n$fa-var-plant-wilt: \\e5aa;\n$fa-var-diamond: \\f219;\n$fa-var-face-grin-squint: \\f585;\n$fa-var-grin-squint: \\f585;\n$fa-var-hand-holding-dollar: \\f4c0;\n$fa-var-hand-holding-usd: \\f4c0;\n$fa-var-bacterium: \\e05a;\n$fa-var-hand-pointer: \\f25a;\n$fa-var-drum-steelpan: \\f56a;\n$fa-var-hand-scissors: \\f257;\n$fa-var-hands-praying: \\f684;\n$fa-var-praying-hands: \\f684;\n$fa-var-arrow-rotate-right: \\f01e;\n$fa-var-arrow-right-rotate: \\f01e;\n$fa-var-arrow-rotate-forward: \\f01e;\n$fa-var-redo: \\f01e;\n$fa-var-biohazard: \\f780;\n$fa-var-location-crosshairs: \\f601;\n$fa-var-location: \\f601;\n$fa-var-mars-double: \\f227;\n$fa-var-child-dress: \\e59c;\n$fa-var-users-between-lines: \\e591;\n$fa-var-lungs-virus: \\e067;\n$fa-var-face-grin-tears: \\f588;\n$fa-var-grin-tears: \\f588;\n$fa-var-phone: \\f095;\n$fa-var-calendar-xmark: \\f273;\n$fa-var-calendar-times: \\f273;\n$fa-var-child-reaching: \\e59d;\n$fa-var-head-side-virus: \\e064;\n$fa-var-user-gear: \\f4fe;\n$fa-var-user-cog: \\f4fe;\n$fa-var-arrow-up-1-9: \\f163;\n$fa-var-sort-numeric-up: \\f163;\n$fa-var-door-closed: \\f52a;\n$fa-var-shield-virus: \\e06c;\n$fa-var-dice-six: \\f526;\n$fa-var-mosquito-net: \\e52c;\n$fa-var-bridge-water: \\e4ce;\n$fa-var-person-booth: \\f756;\n$fa-var-text-width: \\f035;\n$fa-var-hat-wizard: \\f6e8;\n$fa-var-pen-fancy: \\f5ac;\n$fa-var-person-digging: \\f85e;\n$fa-var-digging: \\f85e;\n$fa-var-trash: \\f1f8;\n$fa-var-gauge-simple: \\f629;\n$fa-var-gauge-simple-med: \\f629;\n$fa-var-tachometer-average: \\f629;\n$fa-var-book-medical: \\f7e6;\n$fa-var-poo: \\f2fe;\n$fa-var-quote-right: \\f10e;\n$fa-var-quote-right-alt: \\f10e;\n$fa-var-shirt: \\f553;\n$fa-var-t-shirt: \\f553;\n$fa-var-tshirt: \\f553;\n$fa-var-cubes: \\f1b3;\n$fa-var-divide: \\f529;\n$fa-var-tenge-sign: \\f7d7;\n$fa-var-tenge: \\f7d7;\n$fa-var-headphones: \\f025;\n$fa-var-hands-holding: \\f4c2;\n$fa-var-hands-clapping: \\e1a8;\n$fa-var-republican: \\f75e;\n$fa-var-arrow-left: \\f060;\n$fa-var-person-circle-xmark: \\e543;\n$fa-var-ruler: \\f545;\n$fa-var-align-left: \\f036;\n$fa-var-dice-d6: \\f6d1;\n$fa-var-restroom: \\f7bd;\n$fa-var-j: \\4a;\n$fa-var-users-viewfinder: \\e595;\n$fa-var-file-video: \\f1c8;\n$fa-var-up-right-from-square: \\f35d;\n$fa-var-external-link-alt: \\f35d;\n$fa-var-table-cells: \\f00a;\n$fa-var-th: \\f00a;\n$fa-var-file-pdf: \\f1c1;\n$fa-var-book-bible: \\f647;\n$fa-var-bible: \\f647;\n$fa-var-o: \\4f;\n$fa-var-suitcase-medical: \\f0fa;\n$fa-var-medkit: \\f0fa;\n$fa-var-user-secret: \\f21b;\n$fa-var-otter: \\f700;\n$fa-var-person-dress: \\f182;\n$fa-var-female: \\f182;\n$fa-var-comment-dollar: \\f651;\n$fa-var-business-time: \\f64a;\n$fa-var-briefcase-clock: \\f64a;\n$fa-var-table-cells-large: \\f009;\n$fa-var-th-large: \\f009;\n$fa-var-book-tanakh: \\f827;\n$fa-var-tanakh: \\f827;\n$fa-var-phone-volume: \\f2a0;\n$fa-var-volume-control-phone: \\f2a0;\n$fa-var-hat-cowboy-side: \\f8c1;\n$fa-var-clipboard-user: \\f7f3;\n$fa-var-child: \\f1ae;\n$fa-var-lira-sign: \\f195;\n$fa-var-satellite: \\f7bf;\n$fa-var-plane-lock: \\e558;\n$fa-var-tag: \\f02b;\n$fa-var-comment: \\f075;\n$fa-var-cake-candles: \\f1fd;\n$fa-var-birthday-cake: \\f1fd;\n$fa-var-cake: \\f1fd;\n$fa-var-envelope: \\f0e0;\n$fa-var-angles-up: \\f102;\n$fa-var-angle-double-up: \\f102;\n$fa-var-paperclip: \\f0c6;\n$fa-var-arrow-right-to-city: \\e4b3;\n$fa-var-ribbon: \\f4d6;\n$fa-var-lungs: \\f604;\n$fa-var-arrow-up-9-1: \\f887;\n$fa-var-sort-numeric-up-alt: \\f887;\n$fa-var-litecoin-sign: \\e1d3;\n$fa-var-border-none: \\f850;\n$fa-var-circle-nodes: \\e4e2;\n$fa-var-parachute-box: \\f4cd;\n$fa-var-indent: \\f03c;\n$fa-var-truck-field-un: \\e58e;\n$fa-var-hourglass: \\f254;\n$fa-var-hourglass-empty: \\f254;\n$fa-var-mountain: \\f6fc;\n$fa-var-user-doctor: \\f0f0;\n$fa-var-user-md: \\f0f0;\n$fa-var-circle-info: \\f05a;\n$fa-var-info-circle: \\f05a;\n$fa-var-cloud-meatball: \\f73b;\n$fa-var-camera: \\f030;\n$fa-var-camera-alt: \\f030;\n$fa-var-square-virus: \\e578;\n$fa-var-meteor: \\f753;\n$fa-var-car-on: \\e4dd;\n$fa-var-sleigh: \\f7cc;\n$fa-var-arrow-down-1-9: \\f162;\n$fa-var-sort-numeric-asc: \\f162;\n$fa-var-sort-numeric-down: \\f162;\n$fa-var-hand-holding-droplet: \\f4c1;\n$fa-var-hand-holding-water: \\f4c1;\n$fa-var-water: \\f773;\n$fa-var-calendar-check: \\f274;\n$fa-var-braille: \\f2a1;\n$fa-var-prescription-bottle-medical: \\f486;\n$fa-var-prescription-bottle-alt: \\f486;\n$fa-var-landmark: \\f66f;\n$fa-var-truck: \\f0d1;\n$fa-var-crosshairs: \\f05b;\n$fa-var-person-cane: \\e53c;\n$fa-var-tent: \\e57d;\n$fa-var-vest-patches: \\e086;\n$fa-var-check-double: \\f560;\n$fa-var-arrow-down-a-z: \\f15d;\n$fa-var-sort-alpha-asc: \\f15d;\n$fa-var-sort-alpha-down: \\f15d;\n$fa-var-money-bill-wheat: \\e52a;\n$fa-var-cookie: \\f563;\n$fa-var-arrow-rotate-left: \\f0e2;\n$fa-var-arrow-left-rotate: \\f0e2;\n$fa-var-arrow-rotate-back: \\f0e2;\n$fa-var-arrow-rotate-backward: \\f0e2;\n$fa-var-undo: \\f0e2;\n$fa-var-hard-drive: \\f0a0;\n$fa-var-hdd: \\f0a0;\n$fa-var-face-grin-squint-tears: \\f586;\n$fa-var-grin-squint-tears: \\f586;\n$fa-var-dumbbell: \\f44b;\n$fa-var-rectangle-list: \\f022;\n$fa-var-list-alt: \\f022;\n$fa-var-tarp-droplet: \\e57c;\n$fa-var-house-medical-circle-check: \\e511;\n$fa-var-person-skiing-nordic: \\f7ca;\n$fa-var-skiing-nordic: \\f7ca;\n$fa-var-calendar-plus: \\f271;\n$fa-var-plane-arrival: \\f5af;\n$fa-var-circle-left: \\f359;\n$fa-var-arrow-alt-circle-left: \\f359;\n$fa-var-train-subway: \\f239;\n$fa-var-subway: \\f239;\n$fa-var-chart-gantt: \\e0e4;\n$fa-var-indian-rupee-sign: \\e1bc;\n$fa-var-indian-rupee: \\e1bc;\n$fa-var-inr: \\e1bc;\n$fa-var-crop-simple: \\f565;\n$fa-var-crop-alt: \\f565;\n$fa-var-money-bill-1: \\f3d1;\n$fa-var-money-bill-alt: \\f3d1;\n$fa-var-left-long: \\f30a;\n$fa-var-long-arrow-alt-left: \\f30a;\n$fa-var-dna: \\f471;\n$fa-var-virus-slash: \\e075;\n$fa-var-minus: \\f068;\n$fa-var-subtract: \\f068;\n$fa-var-chess: \\f439;\n$fa-var-arrow-left-long: \\f177;\n$fa-var-long-arrow-left: \\f177;\n$fa-var-plug-circle-check: \\e55c;\n$fa-var-street-view: \\f21d;\n$fa-var-franc-sign: \\e18f;\n$fa-var-volume-off: \\f026;\n$fa-var-hands-asl-interpreting: \\f2a3;\n$fa-var-american-sign-language-interpreting: \\f2a3;\n$fa-var-asl-interpreting: \\f2a3;\n$fa-var-hands-american-sign-language-interpreting: \\f2a3;\n$fa-var-gear: \\f013;\n$fa-var-cog: \\f013;\n$fa-var-droplet-slash: \\f5c7;\n$fa-var-tint-slash: \\f5c7;\n$fa-var-mosque: \\f678;\n$fa-var-mosquito: \\e52b;\n$fa-var-star-of-david: \\f69a;\n$fa-var-person-military-rifle: \\e54b;\n$fa-var-cart-shopping: \\f07a;\n$fa-var-shopping-cart: \\f07a;\n$fa-var-vials: \\f493;\n$fa-var-plug-circle-plus: \\e55f;\n$fa-var-place-of-worship: \\f67f;\n$fa-var-grip-vertical: \\f58e;\n$fa-var-arrow-turn-up: \\f148;\n$fa-var-level-up: \\f148;\n$fa-var-u: \\55;\n$fa-var-square-root-variable: \\f698;\n$fa-var-square-root-alt: \\f698;\n$fa-var-clock: \\f017;\n$fa-var-clock-four: \\f017;\n$fa-var-backward-step: \\f048;\n$fa-var-step-backward: \\f048;\n$fa-var-pallet: \\f482;\n$fa-var-faucet: \\e005;\n$fa-var-baseball-bat-ball: \\f432;\n$fa-var-s: \\53;\n$fa-var-timeline: \\e29c;\n$fa-var-keyboard: \\f11c;\n$fa-var-caret-down: \\f0d7;\n$fa-var-house-chimney-medical: \\f7f2;\n$fa-var-clinic-medical: \\f7f2;\n$fa-var-temperature-three-quarters: \\f2c8;\n$fa-var-temperature-3: \\f2c8;\n$fa-var-thermometer-3: \\f2c8;\n$fa-var-thermometer-three-quarters: \\f2c8;\n$fa-var-mobile-screen: \\f3cf;\n$fa-var-mobile-android-alt: \\f3cf;\n$fa-var-plane-up: \\e22d;\n$fa-var-piggy-bank: \\f4d3;\n$fa-var-battery-half: \\f242;\n$fa-var-battery-3: \\f242;\n$fa-var-mountain-city: \\e52e;\n$fa-var-coins: \\f51e;\n$fa-var-khanda: \\f66d;\n$fa-var-sliders: \\f1de;\n$fa-var-sliders-h: \\f1de;\n$fa-var-folder-tree: \\f802;\n$fa-var-network-wired: \\f6ff;\n$fa-var-map-pin: \\f276;\n$fa-var-hamsa: \\f665;\n$fa-var-cent-sign: \\e3f5;\n$fa-var-flask: \\f0c3;\n$fa-var-person-pregnant: \\e31e;\n$fa-var-wand-sparkles: \\f72b;\n$fa-var-ellipsis-vertical: \\f142;\n$fa-var-ellipsis-v: \\f142;\n$fa-var-ticket: \\f145;\n$fa-var-power-off: \\f011;\n$fa-var-right-long: \\f30b;\n$fa-var-long-arrow-alt-right: \\f30b;\n$fa-var-flag-usa: \\f74d;\n$fa-var-laptop-file: \\e51d;\n$fa-var-tty: \\f1e4;\n$fa-var-teletype: \\f1e4;\n$fa-var-diagram-next: \\e476;\n$fa-var-person-rifle: \\e54e;\n$fa-var-house-medical-circle-exclamation: \\e512;\n$fa-var-closed-captioning: \\f20a;\n$fa-var-person-hiking: \\f6ec;\n$fa-var-hiking: \\f6ec;\n$fa-var-venus-double: \\f226;\n$fa-var-images: \\f302;\n$fa-var-calculator: \\f1ec;\n$fa-var-people-pulling: \\e535;\n$fa-var-n: \\4e;\n$fa-var-cable-car: \\f7da;\n$fa-var-tram: \\f7da;\n$fa-var-cloud-rain: \\f73d;\n$fa-var-building-circle-xmark: \\e4d4;\n$fa-var-ship: \\f21a;\n$fa-var-arrows-down-to-line: \\e4b8;\n$fa-var-download: \\f019;\n$fa-var-face-grin: \\f580;\n$fa-var-grin: \\f580;\n$fa-var-delete-left: \\f55a;\n$fa-var-backspace: \\f55a;\n$fa-var-eye-dropper: \\f1fb;\n$fa-var-eye-dropper-empty: \\f1fb;\n$fa-var-eyedropper: \\f1fb;\n$fa-var-file-circle-check: \\e5a0;\n$fa-var-forward: \\f04e;\n$fa-var-mobile: \\f3ce;\n$fa-var-mobile-android: \\f3ce;\n$fa-var-mobile-phone: \\f3ce;\n$fa-var-face-meh: \\f11a;\n$fa-var-meh: \\f11a;\n$fa-var-align-center: \\f037;\n$fa-var-book-skull: \\f6b7;\n$fa-var-book-dead: \\f6b7;\n$fa-var-id-card: \\f2c2;\n$fa-var-drivers-license: \\f2c2;\n$fa-var-outdent: \\f03b;\n$fa-var-dedent: \\f03b;\n$fa-var-heart-circle-exclamation: \\e4fe;\n$fa-var-house: \\f015;\n$fa-var-home: \\f015;\n$fa-var-home-alt: \\f015;\n$fa-var-home-lg-alt: \\f015;\n$fa-var-calendar-week: \\f784;\n$fa-var-laptop-medical: \\f812;\n$fa-var-b: \\42;\n$fa-var-file-medical: \\f477;\n$fa-var-dice-one: \\f525;\n$fa-var-kiwi-bird: \\f535;\n$fa-var-arrow-right-arrow-left: \\f0ec;\n$fa-var-exchange: \\f0ec;\n$fa-var-rotate-right: \\f2f9;\n$fa-var-redo-alt: \\f2f9;\n$fa-var-rotate-forward: \\f2f9;\n$fa-var-utensils: \\f2e7;\n$fa-var-cutlery: \\f2e7;\n$fa-var-arrow-up-wide-short: \\f161;\n$fa-var-sort-amount-up: \\f161;\n$fa-var-mill-sign: \\e1ed;\n$fa-var-bowl-rice: \\e2eb;\n$fa-var-skull: \\f54c;\n$fa-var-tower-broadcast: \\f519;\n$fa-var-broadcast-tower: \\f519;\n$fa-var-truck-pickup: \\f63c;\n$fa-var-up-long: \\f30c;\n$fa-var-long-arrow-alt-up: \\f30c;\n$fa-var-stop: \\f04d;\n$fa-var-code-merge: \\f387;\n$fa-var-upload: \\f093;\n$fa-var-hurricane: \\f751;\n$fa-var-mound: \\e52d;\n$fa-var-toilet-portable: \\e583;\n$fa-var-compact-disc: \\f51f;\n$fa-var-file-arrow-down: \\f56d;\n$fa-var-file-download: \\f56d;\n$fa-var-caravan: \\f8ff;\n$fa-var-shield-cat: \\e572;\n$fa-var-bolt: \\f0e7;\n$fa-var-zap: \\f0e7;\n$fa-var-glass-water: \\e4f4;\n$fa-var-oil-well: \\e532;\n$fa-var-vault: \\e2c5;\n$fa-var-mars: \\f222;\n$fa-var-toilet: \\f7d8;\n$fa-var-plane-circle-xmark: \\e557;\n$fa-var-yen-sign: \\f157;\n$fa-var-cny: \\f157;\n$fa-var-jpy: \\f157;\n$fa-var-rmb: \\f157;\n$fa-var-yen: \\f157;\n$fa-var-ruble-sign: \\f158;\n$fa-var-rouble: \\f158;\n$fa-var-rub: \\f158;\n$fa-var-ruble: \\f158;\n$fa-var-sun: \\f185;\n$fa-var-guitar: \\f7a6;\n$fa-var-face-laugh-wink: \\f59c;\n$fa-var-laugh-wink: \\f59c;\n$fa-var-horse-head: \\f7ab;\n$fa-var-bore-hole: \\e4c3;\n$fa-var-industry: \\f275;\n$fa-var-circle-down: \\f358;\n$fa-var-arrow-alt-circle-down: \\f358;\n$fa-var-arrows-turn-to-dots: \\e4c1;\n$fa-var-florin-sign: \\e184;\n$fa-var-arrow-down-short-wide: \\f884;\n$fa-var-sort-amount-desc: \\f884;\n$fa-var-sort-amount-down-alt: \\f884;\n$fa-var-less-than: \\3c;\n$fa-var-angle-down: \\f107;\n$fa-var-car-tunnel: \\e4de;\n$fa-var-head-side-cough: \\e061;\n$fa-var-grip-lines: \\f7a4;\n$fa-var-thumbs-down: \\f165;\n$fa-var-user-lock: \\f502;\n$fa-var-arrow-right-long: \\f178;\n$fa-var-long-arrow-right: \\f178;\n$fa-var-anchor-circle-xmark: \\e4ac;\n$fa-var-ellipsis: \\f141;\n$fa-var-ellipsis-h: \\f141;\n$fa-var-chess-pawn: \\f443;\n$fa-var-kit-medical: \\f479;\n$fa-var-first-aid: \\f479;\n$fa-var-person-through-window: \\e5a9;\n$fa-var-toolbox: \\f552;\n$fa-var-hands-holding-circle: \\e4fb;\n$fa-var-bug: \\f188;\n$fa-var-credit-card: \\f09d;\n$fa-var-credit-card-alt: \\f09d;\n$fa-var-car: \\f1b9;\n$fa-var-automobile: \\f1b9;\n$fa-var-hand-holding-hand: \\e4f7;\n$fa-var-book-open-reader: \\f5da;\n$fa-var-book-reader: \\f5da;\n$fa-var-mountain-sun: \\e52f;\n$fa-var-arrows-left-right-to-line: \\e4ba;\n$fa-var-dice-d20: \\f6cf;\n$fa-var-truck-droplet: \\e58c;\n$fa-var-file-circle-xmark: \\e5a1;\n$fa-var-temperature-arrow-up: \\e040;\n$fa-var-temperature-up: \\e040;\n$fa-var-medal: \\f5a2;\n$fa-var-bed: \\f236;\n$fa-var-square-h: \\f0fd;\n$fa-var-h-square: \\f0fd;\n$fa-var-podcast: \\f2ce;\n$fa-var-temperature-full: \\f2c7;\n$fa-var-temperature-4: \\f2c7;\n$fa-var-thermometer-4: \\f2c7;\n$fa-var-thermometer-full: \\f2c7;\n$fa-var-bell: \\f0f3;\n$fa-var-superscript: \\f12b;\n$fa-var-plug-circle-xmark: \\e560;\n$fa-var-star-of-life: \\f621;\n$fa-var-phone-slash: \\f3dd;\n$fa-var-paint-roller: \\f5aa;\n$fa-var-handshake-angle: \\f4c4;\n$fa-var-hands-helping: \\f4c4;\n$fa-var-location-dot: \\f3c5;\n$fa-var-map-marker-alt: \\f3c5;\n$fa-var-file: \\f15b;\n$fa-var-greater-than: \\3e;\n$fa-var-person-swimming: \\f5c4;\n$fa-var-swimmer: \\f5c4;\n$fa-var-arrow-down: \\f063;\n$fa-var-droplet: \\f043;\n$fa-var-tint: \\f043;\n$fa-var-eraser: \\f12d;\n$fa-var-earth-americas: \\f57d;\n$fa-var-earth: \\f57d;\n$fa-var-earth-america: \\f57d;\n$fa-var-globe-americas: \\f57d;\n$fa-var-person-burst: \\e53b;\n$fa-var-dove: \\f4ba;\n$fa-var-battery-empty: \\f244;\n$fa-var-battery-0: \\f244;\n$fa-var-socks: \\f696;\n$fa-var-inbox: \\f01c;\n$fa-var-section: \\e447;\n$fa-var-gauge-high: \\f625;\n$fa-var-tachometer-alt: \\f625;\n$fa-var-tachometer-alt-fast: \\f625;\n$fa-var-envelope-open-text: \\f658;\n$fa-var-hospital: \\f0f8;\n$fa-var-hospital-alt: \\f0f8;\n$fa-var-hospital-wide: \\f0f8;\n$fa-var-wine-bottle: \\f72f;\n$fa-var-chess-rook: \\f447;\n$fa-var-bars-staggered: \\f550;\n$fa-var-reorder: \\f550;\n$fa-var-stream: \\f550;\n$fa-var-dharmachakra: \\f655;\n$fa-var-hotdog: \\f80f;\n$fa-var-person-walking-with-cane: \\f29d;\n$fa-var-blind: \\f29d;\n$fa-var-drum: \\f569;\n$fa-var-ice-cream: \\f810;\n$fa-var-heart-circle-bolt: \\e4fc;\n$fa-var-fax: \\f1ac;\n$fa-var-paragraph: \\f1dd;\n$fa-var-check-to-slot: \\f772;\n$fa-var-vote-yea: \\f772;\n$fa-var-star-half: \\f089;\n$fa-var-boxes-stacked: \\f468;\n$fa-var-boxes: \\f468;\n$fa-var-boxes-alt: \\f468;\n$fa-var-link: \\f0c1;\n$fa-var-chain: \\f0c1;\n$fa-var-ear-listen: \\f2a2;\n$fa-var-assistive-listening-systems: \\f2a2;\n$fa-var-tree-city: \\e587;\n$fa-var-play: \\f04b;\n$fa-var-font: \\f031;\n$fa-var-table-cells-row-lock: \\e67a;\n$fa-var-rupiah-sign: \\e23d;\n$fa-var-magnifying-glass: \\f002;\n$fa-var-search: \\f002;\n$fa-var-table-tennis-paddle-ball: \\f45d;\n$fa-var-ping-pong-paddle-ball: \\f45d;\n$fa-var-table-tennis: \\f45d;\n$fa-var-person-dots-from-line: \\f470;\n$fa-var-diagnoses: \\f470;\n$fa-var-trash-can-arrow-up: \\f82a;\n$fa-var-trash-restore-alt: \\f82a;\n$fa-var-naira-sign: \\e1f6;\n$fa-var-cart-arrow-down: \\f218;\n$fa-var-walkie-talkie: \\f8ef;\n$fa-var-file-pen: \\f31c;\n$fa-var-file-edit: \\f31c;\n$fa-var-receipt: \\f543;\n$fa-var-square-pen: \\f14b;\n$fa-var-pen-square: \\f14b;\n$fa-var-pencil-square: \\f14b;\n$fa-var-suitcase-rolling: \\f5c1;\n$fa-var-person-circle-exclamation: \\e53f;\n$fa-var-chevron-down: \\f078;\n$fa-var-battery-full: \\f240;\n$fa-var-battery: \\f240;\n$fa-var-battery-5: \\f240;\n$fa-var-skull-crossbones: \\f714;\n$fa-var-code-compare: \\e13a;\n$fa-var-list-ul: \\f0ca;\n$fa-var-list-dots: \\f0ca;\n$fa-var-school-lock: \\e56f;\n$fa-var-tower-cell: \\e585;\n$fa-var-down-long: \\f309;\n$fa-var-long-arrow-alt-down: \\f309;\n$fa-var-ranking-star: \\e561;\n$fa-var-chess-king: \\f43f;\n$fa-var-person-harassing: \\e549;\n$fa-var-brazilian-real-sign: \\e46c;\n$fa-var-landmark-dome: \\f752;\n$fa-var-landmark-alt: \\f752;\n$fa-var-arrow-up: \\f062;\n$fa-var-tv: \\f26c;\n$fa-var-television: \\f26c;\n$fa-var-tv-alt: \\f26c;\n$fa-var-shrimp: \\e448;\n$fa-var-list-check: \\f0ae;\n$fa-var-tasks: \\f0ae;\n$fa-var-jug-detergent: \\e519;\n$fa-var-circle-user: \\f2bd;\n$fa-var-user-circle: \\f2bd;\n$fa-var-user-shield: \\f505;\n$fa-var-wind: \\f72e;\n$fa-var-car-burst: \\f5e1;\n$fa-var-car-crash: \\f5e1;\n$fa-var-y: \\59;\n$fa-var-person-snowboarding: \\f7ce;\n$fa-var-snowboarding: \\f7ce;\n$fa-var-truck-fast: \\f48b;\n$fa-var-shipping-fast: \\f48b;\n$fa-var-fish: \\f578;\n$fa-var-user-graduate: \\f501;\n$fa-var-circle-half-stroke: \\f042;\n$fa-var-adjust: \\f042;\n$fa-var-clapperboard: \\e131;\n$fa-var-circle-radiation: \\f7ba;\n$fa-var-radiation-alt: \\f7ba;\n$fa-var-baseball: \\f433;\n$fa-var-baseball-ball: \\f433;\n$fa-var-jet-fighter-up: \\e518;\n$fa-var-diagram-project: \\f542;\n$fa-var-project-diagram: \\f542;\n$fa-var-copy: \\f0c5;\n$fa-var-volume-xmark: \\f6a9;\n$fa-var-volume-mute: \\f6a9;\n$fa-var-volume-times: \\f6a9;\n$fa-var-hand-sparkles: \\e05d;\n$fa-var-grip: \\f58d;\n$fa-var-grip-horizontal: \\f58d;\n$fa-var-share-from-square: \\f14d;\n$fa-var-share-square: \\f14d;\n$fa-var-child-combatant: \\e4e0;\n$fa-var-child-rifle: \\e4e0;\n$fa-var-gun: \\e19b;\n$fa-var-square-phone: \\f098;\n$fa-var-phone-square: \\f098;\n$fa-var-plus: \\2b;\n$fa-var-add: \\2b;\n$fa-var-expand: \\f065;\n$fa-var-computer: \\e4e5;\n$fa-var-xmark: \\f00d;\n$fa-var-close: \\f00d;\n$fa-var-multiply: \\f00d;\n$fa-var-remove: \\f00d;\n$fa-var-times: \\f00d;\n$fa-var-arrows-up-down-left-right: \\f047;\n$fa-var-arrows: \\f047;\n$fa-var-chalkboard-user: \\f51c;\n$fa-var-chalkboard-teacher: \\f51c;\n$fa-var-peso-sign: \\e222;\n$fa-var-building-shield: \\e4d8;\n$fa-var-baby: \\f77c;\n$fa-var-users-line: \\e592;\n$fa-var-quote-left: \\f10d;\n$fa-var-quote-left-alt: \\f10d;\n$fa-var-tractor: \\f722;\n$fa-var-trash-arrow-up: \\f829;\n$fa-var-trash-restore: \\f829;\n$fa-var-arrow-down-up-lock: \\e4b0;\n$fa-var-lines-leaning: \\e51e;\n$fa-var-ruler-combined: \\f546;\n$fa-var-copyright: \\f1f9;\n$fa-var-equals: \\3d;\n$fa-var-blender: \\f517;\n$fa-var-teeth: \\f62e;\n$fa-var-shekel-sign: \\f20b;\n$fa-var-ils: \\f20b;\n$fa-var-shekel: \\f20b;\n$fa-var-sheqel: \\f20b;\n$fa-var-sheqel-sign: \\f20b;\n$fa-var-map: \\f279;\n$fa-var-rocket: \\f135;\n$fa-var-photo-film: \\f87c;\n$fa-var-photo-video: \\f87c;\n$fa-var-folder-minus: \\f65d;\n$fa-var-store: \\f54e;\n$fa-var-arrow-trend-up: \\e098;\n$fa-var-plug-circle-minus: \\e55e;\n$fa-var-sign-hanging: \\f4d9;\n$fa-var-sign: \\f4d9;\n$fa-var-bezier-curve: \\f55b;\n$fa-var-bell-slash: \\f1f6;\n$fa-var-tablet: \\f3fb;\n$fa-var-tablet-android: \\f3fb;\n$fa-var-school-flag: \\e56e;\n$fa-var-fill: \\f575;\n$fa-var-angle-up: \\f106;\n$fa-var-drumstick-bite: \\f6d7;\n$fa-var-holly-berry: \\f7aa;\n$fa-var-chevron-left: \\f053;\n$fa-var-bacteria: \\e059;\n$fa-var-hand-lizard: \\f258;\n$fa-var-notdef: \\e1fe;\n$fa-var-disease: \\f7fa;\n$fa-var-briefcase-medical: \\f469;\n$fa-var-genderless: \\f22d;\n$fa-var-chevron-right: \\f054;\n$fa-var-retweet: \\f079;\n$fa-var-car-rear: \\f5de;\n$fa-var-car-alt: \\f5de;\n$fa-var-pump-soap: \\e06b;\n$fa-var-video-slash: \\f4e2;\n$fa-var-battery-quarter: \\f243;\n$fa-var-battery-2: \\f243;\n$fa-var-radio: \\f8d7;\n$fa-var-baby-carriage: \\f77d;\n$fa-var-carriage-baby: \\f77d;\n$fa-var-traffic-light: \\f637;\n$fa-var-thermometer: \\f491;\n$fa-var-vr-cardboard: \\f729;\n$fa-var-hand-middle-finger: \\f806;\n$fa-var-percent: \\25;\n$fa-var-percentage: \\25;\n$fa-var-truck-moving: \\f4df;\n$fa-var-glass-water-droplet: \\e4f5;\n$fa-var-display: \\e163;\n$fa-var-face-smile: \\f118;\n$fa-var-smile: \\f118;\n$fa-var-thumbtack: \\f08d;\n$fa-var-thumb-tack: \\f08d;\n$fa-var-trophy: \\f091;\n$fa-var-person-praying: \\f683;\n$fa-var-pray: \\f683;\n$fa-var-hammer: \\f6e3;\n$fa-var-hand-peace: \\f25b;\n$fa-var-rotate: \\f2f1;\n$fa-var-sync-alt: \\f2f1;\n$fa-var-spinner: \\f110;\n$fa-var-robot: \\f544;\n$fa-var-peace: \\f67c;\n$fa-var-gears: \\f085;\n$fa-var-cogs: \\f085;\n$fa-var-warehouse: \\f494;\n$fa-var-arrow-up-right-dots: \\e4b7;\n$fa-var-splotch: \\f5bc;\n$fa-var-face-grin-hearts: \\f584;\n$fa-var-grin-hearts: \\f584;\n$fa-var-dice-four: \\f524;\n$fa-var-sim-card: \\f7c4;\n$fa-var-transgender: \\f225;\n$fa-var-transgender-alt: \\f225;\n$fa-var-mercury: \\f223;\n$fa-var-arrow-turn-down: \\f149;\n$fa-var-level-down: \\f149;\n$fa-var-person-falling-burst: \\e547;\n$fa-var-award: \\f559;\n$fa-var-ticket-simple: \\f3ff;\n$fa-var-ticket-alt: \\f3ff;\n$fa-var-building: \\f1ad;\n$fa-var-angles-left: \\f100;\n$fa-var-angle-double-left: \\f100;\n$fa-var-qrcode: \\f029;\n$fa-var-clock-rotate-left: \\f1da;\n$fa-var-history: \\f1da;\n$fa-var-face-grin-beam-sweat: \\f583;\n$fa-var-grin-beam-sweat: \\f583;\n$fa-var-file-export: \\f56e;\n$fa-var-arrow-right-from-file: \\f56e;\n$fa-var-shield: 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\\f7cd;\n$fa-var-book: \\f02d;\n$fa-var-user-plus: \\f234;\n$fa-var-check: \\f00c;\n$fa-var-battery-three-quarters: \\f241;\n$fa-var-battery-4: \\f241;\n$fa-var-house-circle-check: \\e509;\n$fa-var-angle-left: \\f104;\n$fa-var-diagram-successor: \\e47a;\n$fa-var-truck-arrow-right: \\e58b;\n$fa-var-arrows-split-up-and-left: \\e4bc;\n$fa-var-hand-fist: \\f6de;\n$fa-var-fist-raised: \\f6de;\n$fa-var-cloud-moon: \\f6c3;\n$fa-var-briefcase: \\f0b1;\n$fa-var-person-falling: \\e546;\n$fa-var-image-portrait: \\f3e0;\n$fa-var-portrait: \\f3e0;\n$fa-var-user-tag: \\f507;\n$fa-var-rug: \\e569;\n$fa-var-earth-europe: \\f7a2;\n$fa-var-globe-europe: \\f7a2;\n$fa-var-cart-flatbed-suitcase: \\f59d;\n$fa-var-luggage-cart: \\f59d;\n$fa-var-rectangle-xmark: \\f410;\n$fa-var-rectangle-times: \\f410;\n$fa-var-times-rectangle: \\f410;\n$fa-var-window-close: \\f410;\n$fa-var-baht-sign: \\e0ac;\n$fa-var-book-open: \\f518;\n$fa-var-book-journal-whills: \\f66a;\n$fa-var-journal-whills: \\f66a;\n$fa-var-handcuffs: \\e4f8;\n$fa-var-triangle-exclamation: \\f071;\n$fa-var-exclamation-triangle: \\f071;\n$fa-var-warning: \\f071;\n$fa-var-database: \\f1c0;\n$fa-var-share: \\f064;\n$fa-var-mail-forward: \\f064;\n$fa-var-bottle-droplet: \\e4c4;\n$fa-var-mask-face: \\e1d7;\n$fa-var-hill-rockslide: \\e508;\n$fa-var-right-left: \\f362;\n$fa-var-exchange-alt: \\f362;\n$fa-var-paper-plane: \\f1d8;\n$fa-var-road-circle-exclamation: \\e565;\n$fa-var-dungeon: \\f6d9;\n$fa-var-align-right: \\f038;\n$fa-var-money-bill-1-wave: \\f53b;\n$fa-var-money-bill-wave-alt: \\f53b;\n$fa-var-life-ring: \\f1cd;\n$fa-var-hands: \\f2a7;\n$fa-var-sign-language: \\f2a7;\n$fa-var-signing: \\f2a7;\n$fa-var-calendar-day: \\f783;\n$fa-var-water-ladder: \\f5c5;\n$fa-var-ladder-water: \\f5c5;\n$fa-var-swimming-pool: \\f5c5;\n$fa-var-arrows-up-down: \\f07d;\n$fa-var-arrows-v: \\f07d;\n$fa-var-face-grimace: \\f57f;\n$fa-var-grimace: \\f57f;\n$fa-var-wheelchair-move: \\e2ce;\n$fa-var-wheelchair-alt: \\e2ce;\n$fa-var-turn-down: \\f3be;\n$fa-var-level-down-alt: \\f3be;\n$fa-var-person-walking-arrow-right: \\e552;\n$fa-var-square-envelope: \\f199;\n$fa-var-envelope-square: \\f199;\n$fa-var-dice: \\f522;\n$fa-var-bowling-ball: \\f436;\n$fa-var-brain: \\f5dc;\n$fa-var-bandage: \\f462;\n$fa-var-band-aid: \\f462;\n$fa-var-calendar-minus: \\f272;\n$fa-var-circle-xmark: \\f057;\n$fa-var-times-circle: \\f057;\n$fa-var-xmark-circle: \\f057;\n$fa-var-gifts: \\f79c;\n$fa-var-hotel: \\f594;\n$fa-var-earth-asia: \\f57e;\n$fa-var-globe-asia: \\f57e;\n$fa-var-id-card-clip: \\f47f;\n$fa-var-id-card-alt: \\f47f;\n$fa-var-magnifying-glass-plus: \\f00e;\n$fa-var-search-plus: \\f00e;\n$fa-var-thumbs-up: \\f164;\n$fa-var-user-clock: \\f4fd;\n$fa-var-hand-dots: \\f461;\n$fa-var-allergies: \\f461;\n$fa-var-file-invoice: \\f570;\n$fa-var-window-minimize: \\f2d1;\n$fa-var-mug-saucer: \\f0f4;\n$fa-var-coffee: \\f0f4;\n$fa-var-brush: \\f55d;\n$fa-var-mask: \\f6fa;\n$fa-var-magnifying-glass-minus: \\f010;\n$fa-var-search-minus: \\f010;\n$fa-var-ruler-vertical: \\f548;\n$fa-var-user-large: \\f406;\n$fa-var-user-alt: \\f406;\n$fa-var-train-tram: \\e5b4;\n$fa-var-user-nurse: \\f82f;\n$fa-var-syringe: \\f48e;\n$fa-var-cloud-sun: \\f6c4;\n$fa-var-stopwatch-20: \\e06f;\n$fa-var-square-full: \\f45c;\n$fa-var-magnet: \\f076;\n$fa-var-jar: \\e516;\n$fa-var-note-sticky: \\f249;\n$fa-var-sticky-note: \\f249;\n$fa-var-bug-slash: \\e490;\n$fa-var-arrow-up-from-water-pump: \\e4b6;\n$fa-var-bone: \\f5d7;\n$fa-var-user-injured: \\f728;\n$fa-var-face-sad-tear: \\f5b4;\n$fa-var-sad-tear: \\f5b4;\n$fa-var-plane: \\f072;\n$fa-var-tent-arrows-down: \\e581;\n$fa-var-exclamation: \\21;\n$fa-var-arrows-spin: \\e4bb;\n$fa-var-print: \\f02f;\n$fa-var-turkish-lira-sign: \\e2bb;\n$fa-var-try: \\e2bb;\n$fa-var-turkish-lira: \\e2bb;\n$fa-var-dollar-sign: \\24;\n$fa-var-dollar: \\24;\n$fa-var-usd: \\24;\n$fa-var-x: \\58;\n$fa-var-magnifying-glass-dollar: \\f688;\n$fa-var-search-dollar: \\f688;\n$fa-var-users-gear: \\f509;\n$fa-var-users-cog: \\f509;\n$fa-var-person-military-pointing: \\e54a;\n$fa-var-building-columns: \\f19c;\n$fa-var-bank: \\f19c;\n$fa-var-institution: \\f19c;\n$fa-var-museum: \\f19c;\n$fa-var-university: \\f19c;\n$fa-var-umbrella: \\f0e9;\n$fa-var-trowel: \\e589;\n$fa-var-d: \\44;\n$fa-var-stapler: \\e5af;\n$fa-var-masks-theater: \\f630;\n$fa-var-theater-masks: \\f630;\n$fa-var-kip-sign: \\e1c4;\n$fa-var-hand-point-left: \\f0a5;\n$fa-var-handshake-simple: \\f4c6;\n$fa-var-handshake-alt: \\f4c6;\n$fa-var-jet-fighter: \\f0fb;\n$fa-var-fighter-jet: \\f0fb;\n$fa-var-square-share-nodes: \\f1e1;\n$fa-var-share-alt-square: \\f1e1;\n$fa-var-barcode: \\f02a;\n$fa-var-plus-minus: \\e43c;\n$fa-var-video: \\f03d;\n$fa-var-video-camera: \\f03d;\n$fa-var-graduation-cap: \\f19d;\n$fa-var-mortar-board: \\f19d;\n$fa-var-hand-holding-medical: \\e05c;\n$fa-var-person-circle-check: \\e53e;\n$fa-var-turn-up: \\f3bf;\n$fa-var-level-up-alt: \\f3bf;\n\n$fa-var-monero: \\f3d0;\n$fa-var-hooli: \\f427;\n$fa-var-yelp: \\f1e9;\n$fa-var-cc-visa: \\f1f0;\n$fa-var-lastfm: \\f202;\n$fa-var-shopware: \\f5b5;\n$fa-var-creative-commons-nc: \\f4e8;\n$fa-var-aws: \\f375;\n$fa-var-redhat: \\f7bc;\n$fa-var-yoast: \\f2b1;\n$fa-var-cloudflare: \\e07d;\n$fa-var-ups: \\f7e0;\n$fa-var-pixiv: \\e640;\n$fa-var-wpexplorer: \\f2de;\n$fa-var-dyalog: \\f399;\n$fa-var-bity: \\f37a;\n$fa-var-stackpath: \\f842;\n$fa-var-buysellads: \\f20d;\n$fa-var-first-order: \\f2b0;\n$fa-var-modx: \\f285;\n$fa-var-guilded: \\e07e;\n$fa-var-vnv: \\f40b;\n$fa-var-square-js: \\f3b9;\n$fa-var-js-square: \\f3b9;\n$fa-var-microsoft: \\f3ca;\n$fa-var-qq: \\f1d6;\n$fa-var-orcid: \\f8d2;\n$fa-var-java: \\f4e4;\n$fa-var-invision: \\f7b0;\n$fa-var-creative-commons-pd-alt: \\f4ed;\n$fa-var-centercode: \\f380;\n$fa-var-glide-g: \\f2a6;\n$fa-var-drupal: \\f1a9;\n$fa-var-jxl: \\e67b;\n$fa-var-hire-a-helper: \\f3b0;\n$fa-var-creative-commons-by: \\f4e7;\n$fa-var-unity: \\e049;\n$fa-var-whmcs: \\f40d;\n$fa-var-rocketchat: \\f3e8;\n$fa-var-vk: \\f189;\n$fa-var-untappd: \\f405;\n$fa-var-mailchimp: \\f59e;\n$fa-var-css3-alt: \\f38b;\n$fa-var-square-reddit: \\f1a2;\n$fa-var-reddit-square: \\f1a2;\n$fa-var-vimeo-v: \\f27d;\n$fa-var-contao: \\f26d;\n$fa-var-square-font-awesome: \\e5ad;\n$fa-var-deskpro: \\f38f;\n$fa-var-brave: \\e63c;\n$fa-var-sistrix: \\f3ee;\n$fa-var-square-instagram: \\e055;\n$fa-var-instagram-square: \\e055;\n$fa-var-battle-net: \\f835;\n$fa-var-the-red-yeti: \\f69d;\n$fa-var-square-hacker-news: \\f3af;\n$fa-var-hacker-news-square: \\f3af;\n$fa-var-edge: \\f282;\n$fa-var-threads: \\e618;\n$fa-var-napster: \\f3d2;\n$fa-var-square-snapchat: \\f2ad;\n$fa-var-snapchat-square: \\f2ad;\n$fa-var-google-plus-g: \\f0d5;\n$fa-var-artstation: \\f77a;\n$fa-var-markdown: \\f60f;\n$fa-var-sourcetree: \\f7d3;\n$fa-var-google-plus: \\f2b3;\n$fa-var-diaspora: \\f791;\n$fa-var-foursquare: \\f180;\n$fa-var-stack-overflow: \\f16c;\n$fa-var-github-alt: \\f113;\n$fa-var-phoenix-squadron: \\f511;\n$fa-var-pagelines: \\f18c;\n$fa-var-algolia: \\f36c;\n$fa-var-red-river: \\f3e3;\n$fa-var-creative-commons-sa: \\f4ef;\n$fa-var-safari: \\f267;\n$fa-var-google: \\f1a0;\n$fa-var-square-font-awesome-stroke: \\f35c;\n$fa-var-font-awesome-alt: \\f35c;\n$fa-var-atlassian: \\f77b;\n$fa-var-linkedin-in: \\f0e1;\n$fa-var-digital-ocean: \\f391;\n$fa-var-nimblr: \\f5a8;\n$fa-var-chromecast: \\f838;\n$fa-var-evernote: \\f839;\n$fa-var-hacker-news: \\f1d4;\n$fa-var-creative-commons-sampling: \\f4f0;\n$fa-var-adversal: \\f36a;\n$fa-var-creative-commons: \\f25e;\n$fa-var-watchman-monitoring: \\e087;\n$fa-var-fonticons: \\f280;\n$fa-var-weixin: \\f1d7;\n$fa-var-shirtsinbulk: \\f214;\n$fa-var-codepen: \\f1cb;\n$fa-var-git-alt: \\f841;\n$fa-var-lyft: \\f3c3;\n$fa-var-rev: \\f5b2;\n$fa-var-windows: \\f17a;\n$fa-var-wizards-of-the-coast: \\f730;\n$fa-var-square-viadeo: \\f2aa;\n$fa-var-viadeo-square: \\f2aa;\n$fa-var-meetup: \\f2e0;\n$fa-var-centos: \\f789;\n$fa-var-adn: \\f170;\n$fa-var-cloudsmith: \\f384;\n$fa-var-opensuse: \\e62b;\n$fa-var-pied-piper-alt: \\f1a8;\n$fa-var-square-dribbble: \\f397;\n$fa-var-dribbble-square: \\f397;\n$fa-var-codiepie: \\f284;\n$fa-var-node: \\f419;\n$fa-var-mix: \\f3cb;\n$fa-var-steam: \\f1b6;\n$fa-var-cc-apple-pay: \\f416;\n$fa-var-scribd: \\f28a;\n$fa-var-debian: \\e60b;\n$fa-var-openid: \\f19b;\n$fa-var-instalod: \\e081;\n$fa-var-expeditedssl: \\f23e;\n$fa-var-sellcast: \\f2da;\n$fa-var-square-twitter: \\f081;\n$fa-var-twitter-square: \\f081;\n$fa-var-r-project: \\f4f7;\n$fa-var-delicious: \\f1a5;\n$fa-var-freebsd: \\f3a4;\n$fa-var-vuejs: \\f41f;\n$fa-var-accusoft: \\f369;\n$fa-var-ioxhost: \\f208;\n$fa-var-fonticons-fi: \\f3a2;\n$fa-var-app-store: \\f36f;\n$fa-var-cc-mastercard: \\f1f1;\n$fa-var-itunes-note: \\f3b5;\n$fa-var-golang: \\e40f;\n$fa-var-kickstarter: \\f3bb;\n$fa-var-square-kickstarter: \\f3bb;\n$fa-var-grav: \\f2d6;\n$fa-var-weibo: \\f18a;\n$fa-var-uncharted: \\e084;\n$fa-var-firstdraft: \\f3a1;\n$fa-var-square-youtube: \\f431;\n$fa-var-youtube-square: \\f431;\n$fa-var-wikipedia-w: \\f266;\n$fa-var-wpressr: \\f3e4;\n$fa-var-rendact: \\f3e4;\n$fa-var-angellist: \\f209;\n$fa-var-galactic-republic: \\f50c;\n$fa-var-nfc-directional: \\e530;\n$fa-var-skype: \\f17e;\n$fa-var-joget: \\f3b7;\n$fa-var-fedora: \\f798;\n$fa-var-stripe-s: \\f42a;\n$fa-var-meta: \\e49b;\n$fa-var-laravel: \\f3bd;\n$fa-var-hotjar: \\f3b1;\n$fa-var-bluetooth-b: \\f294;\n$fa-var-square-letterboxd: \\e62e;\n$fa-var-sticker-mule: \\f3f7;\n$fa-var-creative-commons-zero: \\f4f3;\n$fa-var-hips: \\f452;\n$fa-var-behance: \\f1b4;\n$fa-var-reddit: \\f1a1;\n$fa-var-discord: \\f392;\n$fa-var-chrome: \\f268;\n$fa-var-app-store-ios: \\f370;\n$fa-var-cc-discover: \\f1f2;\n$fa-var-wpbeginner: \\f297;\n$fa-var-confluence: \\f78d;\n$fa-var-shoelace: \\e60c;\n$fa-var-mdb: \\f8ca;\n$fa-var-dochub: \\f394;\n$fa-var-accessible-icon: \\f368;\n$fa-var-ebay: \\f4f4;\n$fa-var-amazon: \\f270;\n$fa-var-unsplash: \\e07c;\n$fa-var-yarn: \\f7e3;\n$fa-var-square-steam: \\f1b7;\n$fa-var-steam-square: \\f1b7;\n$fa-var-500px: \\f26e;\n$fa-var-square-vimeo: \\f194;\n$fa-var-vimeo-square: \\f194;\n$fa-var-asymmetrik: \\f372;\n$fa-var-font-awesome: \\f2b4;\n$fa-var-font-awesome-flag: \\f2b4;\n$fa-var-font-awesome-logo-full: \\f2b4;\n$fa-var-gratipay: \\f184;\n$fa-var-apple: \\f179;\n$fa-var-hive: \\e07f;\n$fa-var-gitkraken: \\f3a6;\n$fa-var-keybase: \\f4f5;\n$fa-var-apple-pay: \\f415;\n$fa-var-padlet: \\e4a0;\n$fa-var-amazon-pay: \\f42c;\n$fa-var-square-github: \\f092;\n$fa-var-github-square: \\f092;\n$fa-var-stumbleupon: \\f1a4;\n$fa-var-fedex: \\f797;\n$fa-var-phoenix-framework: \\f3dc;\n$fa-var-shopify: \\e057;\n$fa-var-neos: \\f612;\n$fa-var-square-threads: \\e619;\n$fa-var-hackerrank: \\f5f7;\n$fa-var-researchgate: \\f4f8;\n$fa-var-swift: \\f8e1;\n$fa-var-angular: \\f420;\n$fa-var-speakap: \\f3f3;\n$fa-var-angrycreative: \\f36e;\n$fa-var-y-combinator: \\f23b;\n$fa-var-empire: \\f1d1;\n$fa-var-envira: \\f299;\n$fa-var-google-scholar: \\e63b;\n$fa-var-square-gitlab: \\e5ae;\n$fa-var-gitlab-square: \\e5ae;\n$fa-var-studiovinari: \\f3f8;\n$fa-var-pied-piper: \\f2ae;\n$fa-var-wordpress: \\f19a;\n$fa-var-product-hunt: \\f288;\n$fa-var-firefox: \\f269;\n$fa-var-linode: \\f2b8;\n$fa-var-goodreads: \\f3a8;\n$fa-var-square-odnoklassniki: \\f264;\n$fa-var-odnoklassniki-square: \\f264;\n$fa-var-jsfiddle: \\f1cc;\n$fa-var-sith: \\f512;\n$fa-var-themeisle: \\f2b2;\n$fa-var-page4: \\f3d7;\n$fa-var-hashnode: \\e499;\n$fa-var-react: \\f41b;\n$fa-var-cc-paypal: \\f1f4;\n$fa-var-squarespace: \\f5be;\n$fa-var-cc-stripe: \\f1f5;\n$fa-var-creative-commons-share: \\f4f2;\n$fa-var-bitcoin: \\f379;\n$fa-var-keycdn: \\f3ba;\n$fa-var-opera: \\f26a;\n$fa-var-itch-io: \\f83a;\n$fa-var-umbraco: \\f8e8;\n$fa-var-galactic-senate: \\f50d;\n$fa-var-ubuntu: \\f7df;\n$fa-var-draft2digital: \\f396;\n$fa-var-stripe: \\f429;\n$fa-var-houzz: \\f27c;\n$fa-var-gg: \\f260;\n$fa-var-dhl: \\f790;\n$fa-var-square-pinterest: \\f0d3;\n$fa-var-pinterest-square: \\f0d3;\n$fa-var-xing: \\f168;\n$fa-var-blackberry: \\f37b;\n$fa-var-creative-commons-pd: \\f4ec;\n$fa-var-playstation: \\f3df;\n$fa-var-quinscape: \\f459;\n$fa-var-less: \\f41d;\n$fa-var-blogger-b: \\f37d;\n$fa-var-opencart: \\f23d;\n$fa-var-vine: \\f1ca;\n$fa-var-signal-messenger: \\e663;\n$fa-var-paypal: \\f1ed;\n$fa-var-gitlab: \\f296;\n$fa-var-typo3: \\f42b;\n$fa-var-reddit-alien: \\f281;\n$fa-var-yahoo: \\f19e;\n$fa-var-dailymotion: \\e052;\n$fa-var-affiliatetheme: \\f36b;\n$fa-var-pied-piper-pp: \\f1a7;\n$fa-var-bootstrap: \\f836;\n$fa-var-odnoklassniki: \\f263;\n$fa-var-nfc-symbol: \\e531;\n$fa-var-mintbit: \\e62f;\n$fa-var-ethereum: \\f42e;\n$fa-var-speaker-deck: \\f83c;\n$fa-var-creative-commons-nc-eu: \\f4e9;\n$fa-var-patreon: \\f3d9;\n$fa-var-avianex: \\f374;\n$fa-var-ello: \\f5f1;\n$fa-var-gofore: \\f3a7;\n$fa-var-bimobject: \\f378;\n$fa-var-brave-reverse: \\e63d;\n$fa-var-facebook-f: \\f39e;\n$fa-var-square-google-plus: \\f0d4;\n$fa-var-google-plus-square: \\f0d4;\n$fa-var-web-awesome: \\e682;\n$fa-var-mandalorian: \\f50f;\n$fa-var-first-order-alt: \\f50a;\n$fa-var-osi: \\f41a;\n$fa-var-google-wallet: \\f1ee;\n$fa-var-d-and-d-beyond: \\f6ca;\n$fa-var-periscope: \\f3da;\n$fa-var-fulcrum: \\f50b;\n$fa-var-cloudscale: \\f383;\n$fa-var-forumbee: \\f211;\n$fa-var-mizuni: \\f3cc;\n$fa-var-schlix: \\f3ea;\n$fa-var-square-xing: \\f169;\n$fa-var-xing-square: \\f169;\n$fa-var-bandcamp: \\f2d5;\n$fa-var-wpforms: \\f298;\n$fa-var-cloudversify: \\f385;\n$fa-var-usps: \\f7e1;\n$fa-var-megaport: \\f5a3;\n$fa-var-magento: \\f3c4;\n$fa-var-spotify: \\f1bc;\n$fa-var-optin-monster: \\f23c;\n$fa-var-fly: \\f417;\n$fa-var-aviato: \\f421;\n$fa-var-itunes: \\f3b4;\n$fa-var-cuttlefish: \\f38c;\n$fa-var-blogger: \\f37c;\n$fa-var-flickr: \\f16e;\n$fa-var-viber: \\f409;\n$fa-var-soundcloud: \\f1be;\n$fa-var-digg: \\f1a6;\n$fa-var-tencent-weibo: \\f1d5;\n$fa-var-letterboxd: \\e62d;\n$fa-var-symfony: \\f83d;\n$fa-var-maxcdn: \\f136;\n$fa-var-etsy: \\f2d7;\n$fa-var-facebook-messenger: \\f39f;\n$fa-var-audible: \\f373;\n$fa-var-think-peaks: \\f731;\n$fa-var-bilibili: \\e3d9;\n$fa-var-erlang: \\f39d;\n$fa-var-x-twitter: \\e61b;\n$fa-var-cotton-bureau: \\f89e;\n$fa-var-dashcube: \\f210;\n$fa-var-42-group: \\e080;\n$fa-var-innosoft: \\e080;\n$fa-var-stack-exchange: \\f18d;\n$fa-var-elementor: \\f430;\n$fa-var-square-pied-piper: \\e01e;\n$fa-var-pied-piper-square: \\e01e;\n$fa-var-creative-commons-nd: \\f4eb;\n$fa-var-palfed: \\f3d8;\n$fa-var-superpowers: \\f2dd;\n$fa-var-resolving: \\f3e7;\n$fa-var-xbox: \\f412;\n$fa-var-square-web-awesome-stroke: \\e684;\n$fa-var-searchengin: \\f3eb;\n$fa-var-tiktok: \\e07b;\n$fa-var-square-facebook: \\f082;\n$fa-var-facebook-square: \\f082;\n$fa-var-renren: \\f18b;\n$fa-var-linux: \\f17c;\n$fa-var-glide: \\f2a5;\n$fa-var-linkedin: \\f08c;\n$fa-var-hubspot: \\f3b2;\n$fa-var-deploydog: \\f38e;\n$fa-var-twitch: \\f1e8;\n$fa-var-ravelry: \\f2d9;\n$fa-var-mixer: \\e056;\n$fa-var-square-lastfm: \\f203;\n$fa-var-lastfm-square: \\f203;\n$fa-var-vimeo: \\f40a;\n$fa-var-mendeley: \\f7b3;\n$fa-var-uniregistry: \\f404;\n$fa-var-figma: \\f799;\n$fa-var-creative-commons-remix: \\f4ee;\n$fa-var-cc-amazon-pay: \\f42d;\n$fa-var-dropbox: \\f16b;\n$fa-var-instagram: \\f16d;\n$fa-var-cmplid: \\e360;\n$fa-var-upwork: \\e641;\n$fa-var-facebook: \\f09a;\n$fa-var-gripfire: \\f3ac;\n$fa-var-jedi-order: \\f50e;\n$fa-var-uikit: \\f403;\n$fa-var-fort-awesome-alt: \\f3a3;\n$fa-var-phabricator: \\f3db;\n$fa-var-ussunnah: \\f407;\n$fa-var-earlybirds: \\f39a;\n$fa-var-trade-federation: \\f513;\n$fa-var-autoprefixer: \\f41c;\n$fa-var-whatsapp: \\f232;\n$fa-var-square-upwork: \\e67c;\n$fa-var-slideshare: \\f1e7;\n$fa-var-google-play: \\f3ab;\n$fa-var-viadeo: \\f2a9;\n$fa-var-line: \\f3c0;\n$fa-var-google-drive: \\f3aa;\n$fa-var-servicestack: \\f3ec;\n$fa-var-simplybuilt: \\f215;\n$fa-var-bitbucket: \\f171;\n$fa-var-imdb: \\f2d8;\n$fa-var-deezer: \\e077;\n$fa-var-raspberry-pi: \\f7bb;\n$fa-var-jira: \\f7b1;\n$fa-var-docker: \\f395;\n$fa-var-screenpal: \\e570;\n$fa-var-bluetooth: \\f293;\n$fa-var-gitter: \\f426;\n$fa-var-d-and-d: \\f38d;\n$fa-var-microblog: \\e01a;\n$fa-var-cc-diners-club: \\f24c;\n$fa-var-gg-circle: \\f261;\n$fa-var-pied-piper-hat: \\f4e5;\n$fa-var-kickstarter-k: \\f3bc;\n$fa-var-yandex: \\f413;\n$fa-var-readme: \\f4d5;\n$fa-var-html5: \\f13b;\n$fa-var-sellsy: \\f213;\n$fa-var-square-web-awesome: \\e683;\n$fa-var-sass: \\f41e;\n$fa-var-wirsindhandwerk: \\e2d0;\n$fa-var-wsh: \\e2d0;\n$fa-var-buromobelexperte: \\f37f;\n$fa-var-salesforce: \\f83b;\n$fa-var-octopus-deploy: \\e082;\n$fa-var-medapps: \\f3c6;\n$fa-var-ns8: \\f3d5;\n$fa-var-pinterest-p: \\f231;\n$fa-var-apper: \\f371;\n$fa-var-fort-awesome: \\f286;\n$fa-var-waze: \\f83f;\n$fa-var-bluesky: \\e671;\n$fa-var-cc-jcb: \\f24b;\n$fa-var-snapchat: \\f2ab;\n$fa-var-snapchat-ghost: \\f2ab;\n$fa-var-fantasy-flight-games: \\f6dc;\n$fa-var-rust: \\e07a;\n$fa-var-wix: \\f5cf;\n$fa-var-square-behance: \\f1b5;\n$fa-var-behance-square: \\f1b5;\n$fa-var-supple: \\f3f9;\n$fa-var-webflow: \\e65c;\n$fa-var-rebel: \\f1d0;\n$fa-var-css3: \\f13c;\n$fa-var-staylinked: \\f3f5;\n$fa-var-kaggle: \\f5fa;\n$fa-var-space-awesome: \\e5ac;\n$fa-var-deviantart: \\f1bd;\n$fa-var-cpanel: \\f388;\n$fa-var-goodreads-g: \\f3a9;\n$fa-var-square-git: \\f1d2;\n$fa-var-git-square: \\f1d2;\n$fa-var-square-tumblr: \\f174;\n$fa-var-tumblr-square: \\f174;\n$fa-var-trello: \\f181;\n$fa-var-creative-commons-nc-jp: \\f4ea;\n$fa-var-get-pocket: \\f265;\n$fa-var-perbyte: \\e083;\n$fa-var-grunt: \\f3ad;\n$fa-var-weebly: \\f5cc;\n$fa-var-connectdevelop: \\f20e;\n$fa-var-leanpub: \\f212;\n$fa-var-black-tie: \\f27e;\n$fa-var-themeco: \\f5c6;\n$fa-var-python: \\f3e2;\n$fa-var-android: \\f17b;\n$fa-var-bots: \\e340;\n$fa-var-free-code-camp: \\f2c5;\n$fa-var-hornbill: \\f592;\n$fa-var-js: \\f3b8;\n$fa-var-ideal: \\e013;\n$fa-var-git: \\f1d3;\n$fa-var-dev: \\f6cc;\n$fa-var-sketch: \\f7c6;\n$fa-var-yandex-international: \\f414;\n$fa-var-cc-amex: \\f1f3;\n$fa-var-uber: \\f402;\n$fa-var-github: \\f09b;\n$fa-var-php: \\f457;\n$fa-var-alipay: \\f642;\n$fa-var-youtube: \\f167;\n$fa-var-skyatlas: \\f216;\n$fa-var-firefox-browser: \\e007;\n$fa-var-replyd: \\f3e6;\n$fa-var-suse: \\f7d6;\n$fa-var-jenkins: \\f3b6;\n$fa-var-twitter: \\f099;\n$fa-var-rockrms: \\f3e9;\n$fa-var-pinterest: \\f0d2;\n$fa-var-buffer: \\f837;\n$fa-var-npm: \\f3d4;\n$fa-var-yammer: \\f840;\n$fa-var-btc: \\f15a;\n$fa-var-dribbble: \\f17d;\n$fa-var-stumbleupon-circle: \\f1a3;\n$fa-var-internet-explorer: \\f26b;\n$fa-var-stubber: \\e5c7;\n$fa-var-telegram: \\f2c6;\n$fa-var-telegram-plane: \\f2c6;\n$fa-var-old-republic: \\f510;\n$fa-var-odysee: \\e5c6;\n$fa-var-square-whatsapp: \\f40c;\n$fa-var-whatsapp-square: \\f40c;\n$fa-var-node-js: \\f3d3;\n$fa-var-edge-legacy: \\e078;\n$fa-var-slack: \\f198;\n$fa-var-slack-hash: \\f198;\n$fa-var-medrt: \\f3c8;\n$fa-var-usb: \\f287;\n$fa-var-tumblr: \\f173;\n$fa-var-vaadin: \\f408;\n$fa-var-quora: \\f2c4;\n$fa-var-square-x-twitter: \\e61a;\n$fa-var-reacteurope: \\f75d;\n$fa-var-medium: \\f23a;\n$fa-var-medium-m: \\f23a;\n$fa-var-amilia: \\f36d;\n$fa-var-mixcloud: \\f289;\n$fa-var-flipboard: \\f44d;\n$fa-var-viacoin: \\f237;\n$fa-var-critical-role: \\f6c9;\n$fa-var-sitrox: \\e44a;\n$fa-var-discourse: \\f393;\n$fa-var-joomla: \\f1aa;\n$fa-var-mastodon: \\f4f6;\n$fa-var-airbnb: \\f834;\n$fa-var-wolf-pack-battalion: \\f514;\n$fa-var-buy-n-large: \\f8a6;\n$fa-var-gulp: \\f3ae;\n$fa-var-creative-commons-sampling-plus: \\f4f1;\n$fa-var-strava: \\f428;\n$fa-var-ember: \\f423;\n$fa-var-canadian-maple-leaf: \\f785;\n$fa-var-teamspeak: \\f4f9;\n$fa-var-pushed: \\f3e1;\n$fa-var-wordpress-simple: \\f411;\n$fa-var-nutritionix: \\f3d6;\n$fa-var-wodu: \\e088;\n$fa-var-google-pay: \\e079;\n$fa-var-intercom: \\f7af;\n$fa-var-zhihu: \\f63f;\n$fa-var-korvue: \\f42f;\n$fa-var-pix: \\e43a;\n$fa-var-steam-symbol: \\f3f6;\n\n$fa-icons: (\n  \"0\": $fa-var-0,\n  \"1\": $fa-var-1,\n  \"2\": $fa-var-2,\n  \"3\": $fa-var-3,\n  \"4\": $fa-var-4,\n  \"5\": $fa-var-5,\n  \"6\": $fa-var-6,\n  \"7\": $fa-var-7,\n  \"8\": $fa-var-8,\n  \"9\": $fa-var-9,\n  \"fill-drip\": $fa-var-fill-drip,\n  \"arrows-to-circle\": $fa-var-arrows-to-circle,\n  \"circle-chevron-right\": $fa-var-circle-chevron-right,\n  \"chevron-circle-right\": $fa-var-chevron-circle-right,\n  \"at\": $fa-var-at,\n  \"trash-can\": $fa-var-trash-can,\n  \"trash-alt\": $fa-var-trash-alt,\n  \"text-height\": $fa-var-text-height,\n  \"user-xmark\": $fa-var-user-xmark,\n  \"user-times\": $fa-var-user-times,\n  \"stethoscope\": $fa-var-stethoscope,\n  \"message\": $fa-var-message,\n  \"comment-alt\": $fa-var-comment-alt,\n  \"info\": $fa-var-info,\n  \"down-left-and-up-right-to-center\": $fa-var-down-left-and-up-right-to-center,\n  \"compress-alt\": $fa-var-compress-alt,\n  \"explosion\": $fa-var-explosion,\n  \"file-lines\": $fa-var-file-lines,\n  \"file-alt\": $fa-var-file-alt,\n  \"file-text\": $fa-var-file-text,\n  \"wave-square\": $fa-var-wave-square,\n  \"ring\": $fa-var-ring,\n  \"building-un\": $fa-var-building-un,\n  \"dice-three\": $fa-var-dice-three,\n  \"calendar-days\": $fa-var-calendar-days,\n  \"calendar-alt\": $fa-var-calendar-alt,\n  \"anchor-circle-check\": $fa-var-anchor-circle-check,\n  \"building-circle-arrow-right\": $fa-var-building-circle-arrow-right,\n  \"volleyball\": $fa-var-volleyball,\n  \"volleyball-ball\": $fa-var-volleyball-ball,\n  \"arrows-up-to-line\": $fa-var-arrows-up-to-line,\n  \"sort-down\": $fa-var-sort-down,\n  \"sort-desc\": $fa-var-sort-desc,\n  \"circle-minus\": $fa-var-circle-minus,\n  \"minus-circle\": $fa-var-minus-circle,\n  \"door-open\": $fa-var-door-open,\n  \"right-from-bracket\": $fa-var-right-from-bracket,\n  \"sign-out-alt\": $fa-var-sign-out-alt,\n  \"atom\": $fa-var-atom,\n  \"soap\": $fa-var-soap,\n  \"icons\": $fa-var-icons,\n  \"heart-music-camera-bolt\": $fa-var-heart-music-camera-bolt,\n  \"microphone-lines-slash\": $fa-var-microphone-lines-slash,\n  \"microphone-alt-slash\": $fa-var-microphone-alt-slash,\n  \"bridge-circle-check\": $fa-var-bridge-circle-check,\n  \"pump-medical\": $fa-var-pump-medical,\n  \"fingerprint\": $fa-var-fingerprint,\n  \"hand-point-right\": $fa-var-hand-point-right,\n  \"magnifying-glass-location\": $fa-var-magnifying-glass-location,\n  \"search-location\": $fa-var-search-location,\n  \"forward-step\": $fa-var-forward-step,\n  \"step-forward\": $fa-var-step-forward,\n  \"face-smile-beam\": $fa-var-face-smile-beam,\n  \"smile-beam\": $fa-var-smile-beam,\n  \"flag-checkered\": $fa-var-flag-checkered,\n  \"football\": $fa-var-football,\n  \"football-ball\": $fa-var-football-ball,\n  \"school-circle-exclamation\": $fa-var-school-circle-exclamation,\n  \"crop\": $fa-var-crop,\n  \"angles-down\": $fa-var-angles-down,\n  \"angle-double-down\": $fa-var-angle-double-down,\n  \"users-rectangle\": $fa-var-users-rectangle,\n  \"people-roof\": $fa-var-people-roof,\n  \"people-line\": $fa-var-people-line,\n  \"beer-mug-empty\": $fa-var-beer-mug-empty,\n  \"beer\": $fa-var-beer,\n  \"diagram-predecessor\": $fa-var-diagram-predecessor,\n  \"arrow-up-long\": $fa-var-arrow-up-long,\n  \"long-arrow-up\": $fa-var-long-arrow-up,\n  \"fire-flame-simple\": $fa-var-fire-flame-simple,\n  \"burn\": $fa-var-burn,\n  \"person\": $fa-var-person,\n  \"male\": $fa-var-male,\n  \"laptop\": $fa-var-laptop,\n  \"file-csv\": $fa-var-file-csv,\n  \"menorah\": $fa-var-menorah,\n  \"truck-plane\": $fa-var-truck-plane,\n  \"record-vinyl\": $fa-var-record-vinyl,\n  \"face-grin-stars\": $fa-var-face-grin-stars,\n  \"grin-stars\": $fa-var-grin-stars,\n  \"bong\": $fa-var-bong,\n  \"spaghetti-monster-flying\": $fa-var-spaghetti-monster-flying,\n  \"pastafarianism\": $fa-var-pastafarianism,\n  \"arrow-down-up-across-line\": $fa-var-arrow-down-up-across-line,\n  \"spoon\": $fa-var-spoon,\n  \"utensil-spoon\": $fa-var-utensil-spoon,\n  \"jar-wheat\": $fa-var-jar-wheat,\n  \"envelopes-bulk\": $fa-var-envelopes-bulk,\n  \"mail-bulk\": $fa-var-mail-bulk,\n  \"file-circle-exclamation\": $fa-var-file-circle-exclamation,\n  \"circle-h\": $fa-var-circle-h,\n  \"hospital-symbol\": $fa-var-hospital-symbol,\n  \"pager\": $fa-var-pager,\n  \"address-book\": $fa-var-address-book,\n  \"contact-book\": $fa-var-contact-book,\n  \"strikethrough\": $fa-var-strikethrough,\n  \"k\": $fa-var-k,\n  \"landmark-flag\": $fa-var-landmark-flag,\n  \"pencil\": $fa-var-pencil,\n  \"pencil-alt\": $fa-var-pencil-alt,\n  \"backward\": $fa-var-backward,\n  \"caret-right\": $fa-var-caret-right,\n  \"comments\": $fa-var-comments,\n  \"paste\": $fa-var-paste,\n  \"file-clipboard\": $fa-var-file-clipboard,\n  \"code-pull-request\": $fa-var-code-pull-request,\n  \"clipboard-list\": $fa-var-clipboard-list,\n  \"truck-ramp-box\": $fa-var-truck-ramp-box,\n  \"truck-loading\": $fa-var-truck-loading,\n  \"user-check\": $fa-var-user-check,\n  \"vial-virus\": $fa-var-vial-virus,\n  \"sheet-plastic\": $fa-var-sheet-plastic,\n  \"blog\": $fa-var-blog,\n  \"user-ninja\": $fa-var-user-ninja,\n  \"person-arrow-up-from-line\": $fa-var-person-arrow-up-from-line,\n  \"scroll-torah\": $fa-var-scroll-torah,\n  \"torah\": $fa-var-torah,\n  \"broom-ball\": $fa-var-broom-ball,\n  \"quidditch\": $fa-var-quidditch,\n  \"quidditch-broom-ball\": $fa-var-quidditch-broom-ball,\n  \"toggle-off\": $fa-var-toggle-off,\n  \"box-archive\": $fa-var-box-archive,\n  \"archive\": $fa-var-archive,\n  \"person-drowning\": $fa-var-person-drowning,\n  \"arrow-down-9-1\": $fa-var-arrow-down-9-1,\n  \"sort-numeric-desc\": $fa-var-sort-numeric-desc,\n  \"sort-numeric-down-alt\": $fa-var-sort-numeric-down-alt,\n  \"face-grin-tongue-squint\": $fa-var-face-grin-tongue-squint,\n  \"grin-tongue-squint\": $fa-var-grin-tongue-squint,\n  \"spray-can\": $fa-var-spray-can,\n  \"truck-monster\": $fa-var-truck-monster,\n  \"w\": $fa-var-w,\n  \"earth-africa\": $fa-var-earth-africa,\n  \"globe-africa\": $fa-var-globe-africa,\n  \"rainbow\": $fa-var-rainbow,\n  \"circle-notch\": $fa-var-circle-notch,\n  \"tablet-screen-button\": $fa-var-tablet-screen-button,\n  \"tablet-alt\": $fa-var-tablet-alt,\n  \"paw\": $fa-var-paw,\n  \"cloud\": $fa-var-cloud,\n  \"trowel-bricks\": $fa-var-trowel-bricks,\n  \"face-flushed\": $fa-var-face-flushed,\n  \"flushed\": $fa-var-flushed,\n  \"hospital-user\": $fa-var-hospital-user,\n  \"tent-arrow-left-right\": $fa-var-tent-arrow-left-right,\n  \"gavel\": $fa-var-gavel,\n  \"legal\": $fa-var-legal,\n  \"binoculars\": $fa-var-binoculars,\n  \"microphone-slash\": $fa-var-microphone-slash,\n  \"box-tissue\": $fa-var-box-tissue,\n  \"motorcycle\": $fa-var-motorcycle,\n  \"bell-concierge\": $fa-var-bell-concierge,\n  \"concierge-bell\": $fa-var-concierge-bell,\n  \"pen-ruler\": $fa-var-pen-ruler,\n  \"pencil-ruler\": $fa-var-pencil-ruler,\n  \"people-arrows\": $fa-var-people-arrows,\n  \"people-arrows-left-right\": $fa-var-people-arrows-left-right,\n  \"mars-and-venus-burst\": $fa-var-mars-and-venus-burst,\n  \"square-caret-right\": $fa-var-square-caret-right,\n  \"caret-square-right\": $fa-var-caret-square-right,\n  \"scissors\": $fa-var-scissors,\n  \"cut\": $fa-var-cut,\n  \"sun-plant-wilt\": $fa-var-sun-plant-wilt,\n  \"toilets-portable\": $fa-var-toilets-portable,\n  \"hockey-puck\": $fa-var-hockey-puck,\n  \"table\": $fa-var-table,\n  \"magnifying-glass-arrow-right\": $fa-var-magnifying-glass-arrow-right,\n  \"tachograph-digital\": $fa-var-tachograph-digital,\n  \"digital-tachograph\": $fa-var-digital-tachograph,\n  \"users-slash\": $fa-var-users-slash,\n  \"clover\": $fa-var-clover,\n  \"reply\": $fa-var-reply,\n  \"mail-reply\": $fa-var-mail-reply,\n  \"star-and-crescent\": $fa-var-star-and-crescent,\n  \"house-fire\": $fa-var-house-fire,\n  \"square-minus\": $fa-var-square-minus,\n  \"minus-square\": $fa-var-minus-square,\n  \"helicopter\": $fa-var-helicopter,\n  \"compass\": $fa-var-compass,\n  \"square-caret-down\": $fa-var-square-caret-down,\n  \"caret-square-down\": $fa-var-caret-square-down,\n  \"file-circle-question\": $fa-var-file-circle-question,\n  \"laptop-code\": $fa-var-laptop-code,\n  \"swatchbook\": $fa-var-swatchbook,\n  \"prescription-bottle\": $fa-var-prescription-bottle,\n  \"bars\": $fa-var-bars,\n  \"navicon\": $fa-var-navicon,\n  \"people-group\": $fa-var-people-group,\n  \"hourglass-end\": $fa-var-hourglass-end,\n  \"hourglass-3\": $fa-var-hourglass-3,\n  \"heart-crack\": $fa-var-heart-crack,\n  \"heart-broken\": $fa-var-heart-broken,\n  \"square-up-right\": $fa-var-square-up-right,\n  \"external-link-square-alt\": $fa-var-external-link-square-alt,\n  \"face-kiss-beam\": $fa-var-face-kiss-beam,\n  \"kiss-beam\": $fa-var-kiss-beam,\n  \"film\": $fa-var-film,\n  \"ruler-horizontal\": $fa-var-ruler-horizontal,\n  \"people-robbery\": $fa-var-people-robbery,\n  \"lightbulb\": $fa-var-lightbulb,\n  \"caret-left\": $fa-var-caret-left,\n  \"circle-exclamation\": $fa-var-circle-exclamation,\n  \"exclamation-circle\": $fa-var-exclamation-circle,\n  \"school-circle-xmark\": $fa-var-school-circle-xmark,\n  \"arrow-right-from-bracket\": $fa-var-arrow-right-from-bracket,\n  \"sign-out\": $fa-var-sign-out,\n  \"circle-chevron-down\": $fa-var-circle-chevron-down,\n  \"chevron-circle-down\": $fa-var-chevron-circle-down,\n  \"unlock-keyhole\": $fa-var-unlock-keyhole,\n  \"unlock-alt\": $fa-var-unlock-alt,\n  \"cloud-showers-heavy\": $fa-var-cloud-showers-heavy,\n  \"headphones-simple\": $fa-var-headphones-simple,\n  \"headphones-alt\": $fa-var-headphones-alt,\n  \"sitemap\": $fa-var-sitemap,\n  \"circle-dollar-to-slot\": $fa-var-circle-dollar-to-slot,\n  \"donate\": $fa-var-donate,\n  \"memory\": $fa-var-memory,\n  \"road-spikes\": $fa-var-road-spikes,\n  \"fire-burner\": $fa-var-fire-burner,\n  \"flag\": $fa-var-flag,\n  \"hanukiah\": $fa-var-hanukiah,\n  \"feather\": $fa-var-feather,\n  \"volume-low\": $fa-var-volume-low,\n  \"volume-down\": $fa-var-volume-down,\n  \"comment-slash\": $fa-var-comment-slash,\n  \"cloud-sun-rain\": $fa-var-cloud-sun-rain,\n  \"compress\": $fa-var-compress,\n  \"wheat-awn\": $fa-var-wheat-awn,\n  \"wheat-alt\": $fa-var-wheat-alt,\n  \"ankh\": $fa-var-ankh,\n  \"hands-holding-child\": $fa-var-hands-holding-child,\n  \"asterisk\": $fa-var-asterisk,\n  \"square-check\": $fa-var-square-check,\n  \"check-square\": $fa-var-check-square,\n  \"peseta-sign\": $fa-var-peseta-sign,\n  \"heading\": $fa-var-heading,\n  \"header\": $fa-var-header,\n  \"ghost\": $fa-var-ghost,\n  \"list\": $fa-var-list,\n  \"list-squares\": $fa-var-list-squares,\n  \"square-phone-flip\": $fa-var-square-phone-flip,\n  \"phone-square-alt\": $fa-var-phone-square-alt,\n  \"cart-plus\": $fa-var-cart-plus,\n  \"gamepad\": $fa-var-gamepad,\n  \"circle-dot\": $fa-var-circle-dot,\n  \"dot-circle\": $fa-var-dot-circle,\n  \"face-dizzy\": $fa-var-face-dizzy,\n  \"dizzy\": $fa-var-dizzy,\n  \"egg\": $fa-var-egg,\n  \"house-medical-circle-xmark\": $fa-var-house-medical-circle-xmark,\n  \"campground\": $fa-var-campground,\n  \"folder-plus\": $fa-var-folder-plus,\n  \"futbol\": $fa-var-futbol,\n  \"futbol-ball\": $fa-var-futbol-ball,\n  \"soccer-ball\": $fa-var-soccer-ball,\n  \"paintbrush\": $fa-var-paintbrush,\n  \"paint-brush\": $fa-var-paint-brush,\n  \"lock\": $fa-var-lock,\n  \"gas-pump\": $fa-var-gas-pump,\n  \"hot-tub-person\": $fa-var-hot-tub-person,\n  \"hot-tub\": $fa-var-hot-tub,\n  \"map-location\": $fa-var-map-location,\n  \"map-marked\": $fa-var-map-marked,\n  \"house-flood-water\": $fa-var-house-flood-water,\n  \"tree\": $fa-var-tree,\n  \"bridge-lock\": $fa-var-bridge-lock,\n  \"sack-dollar\": $fa-var-sack-dollar,\n  \"pen-to-square\": $fa-var-pen-to-square,\n  \"edit\": $fa-var-edit,\n  \"car-side\": $fa-var-car-side,\n  \"share-nodes\": $fa-var-share-nodes,\n  \"share-alt\": $fa-var-share-alt,\n  \"heart-circle-minus\": $fa-var-heart-circle-minus,\n  \"hourglass-half\": $fa-var-hourglass-half,\n  \"hourglass-2\": $fa-var-hourglass-2,\n  \"microscope\": $fa-var-microscope,\n  \"sink\": $fa-var-sink,\n  \"bag-shopping\": $fa-var-bag-shopping,\n  \"shopping-bag\": $fa-var-shopping-bag,\n  \"arrow-down-z-a\": $fa-var-arrow-down-z-a,\n  \"sort-alpha-desc\": $fa-var-sort-alpha-desc,\n  \"sort-alpha-down-alt\": $fa-var-sort-alpha-down-alt,\n  \"mitten\": $fa-var-mitten,\n  \"person-rays\": $fa-var-person-rays,\n  \"users\": $fa-var-users,\n  \"eye-slash\": $fa-var-eye-slash,\n  \"flask-vial\": $fa-var-flask-vial,\n  \"hand\": $fa-var-hand,\n  \"hand-paper\": $fa-var-hand-paper,\n  \"om\": $fa-var-om,\n  \"worm\": $fa-var-worm,\n  \"house-circle-xmark\": $fa-var-house-circle-xmark,\n  \"plug\": $fa-var-plug,\n  \"chevron-up\": $fa-var-chevron-up,\n  \"hand-spock\": $fa-var-hand-spock,\n  \"stopwatch\": $fa-var-stopwatch,\n  \"face-kiss\": $fa-var-face-kiss,\n  \"kiss\": $fa-var-kiss,\n  \"bridge-circle-xmark\": $fa-var-bridge-circle-xmark,\n  \"face-grin-tongue\": $fa-var-face-grin-tongue,\n  \"grin-tongue\": $fa-var-grin-tongue,\n  \"chess-bishop\": $fa-var-chess-bishop,\n  \"face-grin-wink\": $fa-var-face-grin-wink,\n  \"grin-wink\": $fa-var-grin-wink,\n  \"ear-deaf\": $fa-var-ear-deaf,\n  \"deaf\": $fa-var-deaf,\n  \"deafness\": $fa-var-deafness,\n  \"hard-of-hearing\": $fa-var-hard-of-hearing,\n  \"road-circle-check\": $fa-var-road-circle-check,\n  \"dice-five\": $fa-var-dice-five,\n  \"square-rss\": $fa-var-square-rss,\n  \"rss-square\": $fa-var-rss-square,\n  \"land-mine-on\": $fa-var-land-mine-on,\n  \"i-cursor\": $fa-var-i-cursor,\n  \"stamp\": $fa-var-stamp,\n  \"stairs\": $fa-var-stairs,\n  \"i\": $fa-var-i,\n  \"hryvnia-sign\": $fa-var-hryvnia-sign,\n  \"hryvnia\": $fa-var-hryvnia,\n  \"pills\": $fa-var-pills,\n  \"face-grin-wide\": $fa-var-face-grin-wide,\n  \"grin-alt\": $fa-var-grin-alt,\n  \"tooth\": $fa-var-tooth,\n  \"v\": $fa-var-v,\n  \"bangladeshi-taka-sign\": $fa-var-bangladeshi-taka-sign,\n  \"bicycle\": $fa-var-bicycle,\n  \"staff-snake\": $fa-var-staff-snake,\n  \"rod-asclepius\": $fa-var-rod-asclepius,\n  \"rod-snake\": $fa-var-rod-snake,\n  \"staff-aesculapius\": $fa-var-staff-aesculapius,\n  \"head-side-cough-slash\": $fa-var-head-side-cough-slash,\n  \"truck-medical\": $fa-var-truck-medical,\n  \"ambulance\": $fa-var-ambulance,\n  \"wheat-awn-circle-exclamation\": $fa-var-wheat-awn-circle-exclamation,\n  \"snowman\": $fa-var-snowman,\n  \"mortar-pestle\": $fa-var-mortar-pestle,\n  \"road-barrier\": $fa-var-road-barrier,\n  \"school\": $fa-var-school,\n  \"igloo\": $fa-var-igloo,\n  \"joint\": $fa-var-joint,\n  \"angle-right\": $fa-var-angle-right,\n  \"horse\": $fa-var-horse,\n  \"q\": $fa-var-q,\n  \"g\": $fa-var-g,\n  \"notes-medical\": $fa-var-notes-medical,\n  \"temperature-half\": $fa-var-temperature-half,\n  \"temperature-2\": $fa-var-temperature-2,\n  \"thermometer-2\": $fa-var-thermometer-2,\n  \"thermometer-half\": $fa-var-thermometer-half,\n  \"dong-sign\": $fa-var-dong-sign,\n  \"capsules\": $fa-var-capsules,\n  \"poo-storm\": $fa-var-poo-storm,\n  \"poo-bolt\": $fa-var-poo-bolt,\n  \"face-frown-open\": $fa-var-face-frown-open,\n  \"frown-open\": $fa-var-frown-open,\n  \"hand-point-up\": $fa-var-hand-point-up,\n  \"money-bill\": $fa-var-money-bill,\n  \"bookmark\": $fa-var-bookmark,\n  \"align-justify\": $fa-var-align-justify,\n  \"umbrella-beach\": $fa-var-umbrella-beach,\n  \"helmet-un\": $fa-var-helmet-un,\n  \"bullseye\": $fa-var-bullseye,\n  \"bacon\": $fa-var-bacon,\n  \"hand-point-down\": $fa-var-hand-point-down,\n  \"arrow-up-from-bracket\": $fa-var-arrow-up-from-bracket,\n  \"folder\": $fa-var-folder,\n  \"folder-blank\": $fa-var-folder-blank,\n  \"file-waveform\": $fa-var-file-waveform,\n  \"file-medical-alt\": $fa-var-file-medical-alt,\n  \"radiation\": $fa-var-radiation,\n  \"chart-simple\": $fa-var-chart-simple,\n  \"mars-stroke\": $fa-var-mars-stroke,\n  \"vial\": $fa-var-vial,\n  \"gauge\": $fa-var-gauge,\n  \"dashboard\": $fa-var-dashboard,\n  \"gauge-med\": $fa-var-gauge-med,\n  \"tachometer-alt-average\": $fa-var-tachometer-alt-average,\n  \"wand-magic-sparkles\": $fa-var-wand-magic-sparkles,\n  \"magic-wand-sparkles\": $fa-var-magic-wand-sparkles,\n  \"e\": $fa-var-e,\n  \"pen-clip\": $fa-var-pen-clip,\n  \"pen-alt\": $fa-var-pen-alt,\n  \"bridge-circle-exclamation\": $fa-var-bridge-circle-exclamation,\n  \"user\": $fa-var-user,\n  \"school-circle-check\": $fa-var-school-circle-check,\n  \"dumpster\": $fa-var-dumpster,\n  \"van-shuttle\": $fa-var-van-shuttle,\n  \"shuttle-van\": $fa-var-shuttle-van,\n  \"building-user\": $fa-var-building-user,\n  \"square-caret-left\": $fa-var-square-caret-left,\n  \"caret-square-left\": $fa-var-caret-square-left,\n  \"highlighter\": $fa-var-highlighter,\n  \"key\": $fa-var-key,\n  \"bullhorn\": $fa-var-bullhorn,\n  \"globe\": $fa-var-globe,\n  \"synagogue\": $fa-var-synagogue,\n  \"person-half-dress\": $fa-var-person-half-dress,\n  \"road-bridge\": $fa-var-road-bridge,\n  \"location-arrow\": $fa-var-location-arrow,\n  \"c\": $fa-var-c,\n  \"tablet-button\": $fa-var-tablet-button,\n  \"building-lock\": $fa-var-building-lock,\n  \"pizza-slice\": $fa-var-pizza-slice,\n  \"money-bill-wave\": $fa-var-money-bill-wave,\n  \"chart-area\": $fa-var-chart-area,\n  \"area-chart\": $fa-var-area-chart,\n  \"house-flag\": $fa-var-house-flag,\n  \"person-circle-minus\": $fa-var-person-circle-minus,\n  \"ban\": $fa-var-ban,\n  \"cancel\": $fa-var-cancel,\n  \"camera-rotate\": $fa-var-camera-rotate,\n  \"spray-can-sparkles\": $fa-var-spray-can-sparkles,\n  \"air-freshener\": $fa-var-air-freshener,\n  \"star\": $fa-var-star,\n  \"repeat\": $fa-var-repeat,\n  \"cross\": $fa-var-cross,\n  \"box\": $fa-var-box,\n  \"venus-mars\": $fa-var-venus-mars,\n  \"arrow-pointer\": $fa-var-arrow-pointer,\n  \"mouse-pointer\": $fa-var-mouse-pointer,\n  \"maximize\": $fa-var-maximize,\n  \"expand-arrows-alt\": $fa-var-expand-arrows-alt,\n  \"charging-station\": $fa-var-charging-station,\n  \"shapes\": $fa-var-shapes,\n  \"triangle-circle-square\": $fa-var-triangle-circle-square,\n  \"shuffle\": $fa-var-shuffle,\n  \"random\": $fa-var-random,\n  \"person-running\": $fa-var-person-running,\n  \"running\": $fa-var-running,\n  \"mobile-retro\": $fa-var-mobile-retro,\n  \"grip-lines-vertical\": $fa-var-grip-lines-vertical,\n  \"spider\": $fa-var-spider,\n  \"hands-bound\": $fa-var-hands-bound,\n  \"file-invoice-dollar\": $fa-var-file-invoice-dollar,\n  \"plane-circle-exclamation\": $fa-var-plane-circle-exclamation,\n  \"x-ray\": $fa-var-x-ray,\n  \"spell-check\": $fa-var-spell-check,\n  \"slash\": $fa-var-slash,\n  \"computer-mouse\": $fa-var-computer-mouse,\n  \"mouse\": $fa-var-mouse,\n  \"arrow-right-to-bracket\": $fa-var-arrow-right-to-bracket,\n  \"sign-in\": $fa-var-sign-in,\n  \"shop-slash\": $fa-var-shop-slash,\n  \"store-alt-slash\": $fa-var-store-alt-slash,\n  \"server\": $fa-var-server,\n  \"virus-covid-slash\": $fa-var-virus-covid-slash,\n  \"shop-lock\": $fa-var-shop-lock,\n  \"hourglass-start\": $fa-var-hourglass-start,\n  \"hourglass-1\": $fa-var-hourglass-1,\n  \"blender-phone\": $fa-var-blender-phone,\n  \"building-wheat\": $fa-var-building-wheat,\n  \"person-breastfeeding\": $fa-var-person-breastfeeding,\n  \"right-to-bracket\": $fa-var-right-to-bracket,\n  \"sign-in-alt\": $fa-var-sign-in-alt,\n  \"venus\": $fa-var-venus,\n  \"passport\": $fa-var-passport,\n  \"heart-pulse\": $fa-var-heart-pulse,\n  \"heartbeat\": $fa-var-heartbeat,\n  \"people-carry-box\": $fa-var-people-carry-box,\n  \"people-carry\": $fa-var-people-carry,\n  \"temperature-high\": $fa-var-temperature-high,\n  \"microchip\": $fa-var-microchip,\n  \"crown\": $fa-var-crown,\n  \"weight-hanging\": $fa-var-weight-hanging,\n  \"xmarks-lines\": $fa-var-xmarks-lines,\n  \"file-prescription\": $fa-var-file-prescription,\n  \"weight-scale\": $fa-var-weight-scale,\n  \"weight\": $fa-var-weight,\n  \"user-group\": $fa-var-user-group,\n  \"user-friends\": $fa-var-user-friends,\n  \"arrow-up-a-z\": $fa-var-arrow-up-a-z,\n  \"sort-alpha-up\": $fa-var-sort-alpha-up,\n  \"chess-knight\": $fa-var-chess-knight,\n  \"face-laugh-squint\": $fa-var-face-laugh-squint,\n  \"laugh-squint\": $fa-var-laugh-squint,\n  \"wheelchair\": $fa-var-wheelchair,\n  \"circle-arrow-up\": $fa-var-circle-arrow-up,\n  \"arrow-circle-up\": $fa-var-arrow-circle-up,\n  \"toggle-on\": $fa-var-toggle-on,\n  \"person-walking\": $fa-var-person-walking,\n  \"walking\": $fa-var-walking,\n  \"l\": $fa-var-l,\n  \"fire\": $fa-var-fire,\n  \"bed-pulse\": $fa-var-bed-pulse,\n  \"procedures\": $fa-var-procedures,\n  \"shuttle-space\": $fa-var-shuttle-space,\n  \"space-shuttle\": $fa-var-space-shuttle,\n  \"face-laugh\": $fa-var-face-laugh,\n  \"laugh\": $fa-var-laugh,\n  \"folder-open\": $fa-var-folder-open,\n  \"heart-circle-plus\": $fa-var-heart-circle-plus,\n  \"code-fork\": $fa-var-code-fork,\n  \"city\": $fa-var-city,\n  \"microphone-lines\": $fa-var-microphone-lines,\n  \"microphone-alt\": $fa-var-microphone-alt,\n  \"pepper-hot\": $fa-var-pepper-hot,\n  \"unlock\": $fa-var-unlock,\n  \"colon-sign\": $fa-var-colon-sign,\n  \"headset\": $fa-var-headset,\n  \"store-slash\": $fa-var-store-slash,\n  \"road-circle-xmark\": $fa-var-road-circle-xmark,\n  \"user-minus\": $fa-var-user-minus,\n  \"mars-stroke-up\": $fa-var-mars-stroke-up,\n  \"mars-stroke-v\": $fa-var-mars-stroke-v,\n  \"champagne-glasses\": $fa-var-champagne-glasses,\n  \"glass-cheers\": $fa-var-glass-cheers,\n  \"clipboard\": $fa-var-clipboard,\n  \"house-circle-exclamation\": $fa-var-house-circle-exclamation,\n  \"file-arrow-up\": $fa-var-file-arrow-up,\n  \"file-upload\": $fa-var-file-upload,\n  \"wifi\": $fa-var-wifi,\n  \"wifi-3\": $fa-var-wifi-3,\n  \"wifi-strong\": $fa-var-wifi-strong,\n  \"bath\": $fa-var-bath,\n  \"bathtub\": $fa-var-bathtub,\n  \"underline\": $fa-var-underline,\n  \"user-pen\": $fa-var-user-pen,\n  \"user-edit\": $fa-var-user-edit,\n  \"signature\": $fa-var-signature,\n  \"stroopwafel\": $fa-var-stroopwafel,\n  \"bold\": $fa-var-bold,\n  \"anchor-lock\": $fa-var-anchor-lock,\n  \"building-ngo\": $fa-var-building-ngo,\n  \"manat-sign\": $fa-var-manat-sign,\n  \"not-equal\": $fa-var-not-equal,\n  \"border-top-left\": $fa-var-border-top-left,\n  \"border-style\": $fa-var-border-style,\n  \"map-location-dot\": $fa-var-map-location-dot,\n  \"map-marked-alt\": $fa-var-map-marked-alt,\n  \"jedi\": $fa-var-jedi,\n  \"square-poll-vertical\": $fa-var-square-poll-vertical,\n  \"poll\": $fa-var-poll,\n  \"mug-hot\": $fa-var-mug-hot,\n  \"car-battery\": $fa-var-car-battery,\n  \"battery-car\": $fa-var-battery-car,\n  \"gift\": $fa-var-gift,\n  \"dice-two\": $fa-var-dice-two,\n  \"chess-queen\": $fa-var-chess-queen,\n  \"glasses\": $fa-var-glasses,\n  \"chess-board\": $fa-var-chess-board,\n  \"building-circle-check\": $fa-var-building-circle-check,\n  \"person-chalkboard\": $fa-var-person-chalkboard,\n  \"mars-stroke-right\": $fa-var-mars-stroke-right,\n  \"mars-stroke-h\": $fa-var-mars-stroke-h,\n  \"hand-back-fist\": $fa-var-hand-back-fist,\n  \"hand-rock\": $fa-var-hand-rock,\n  \"square-caret-up\": $fa-var-square-caret-up,\n  \"caret-square-up\": $fa-var-caret-square-up,\n  \"cloud-showers-water\": $fa-var-cloud-showers-water,\n  \"chart-bar\": $fa-var-chart-bar,\n  \"bar-chart\": $fa-var-bar-chart,\n  \"hands-bubbles\": $fa-var-hands-bubbles,\n  \"hands-wash\": $fa-var-hands-wash,\n  \"less-than-equal\": $fa-var-less-than-equal,\n  \"train\": $fa-var-train,\n  \"eye-low-vision\": $fa-var-eye-low-vision,\n  \"low-vision\": $fa-var-low-vision,\n  \"crow\": $fa-var-crow,\n  \"sailboat\": $fa-var-sailboat,\n  \"window-restore\": $fa-var-window-restore,\n  \"square-plus\": $fa-var-square-plus,\n  \"plus-square\": $fa-var-plus-square,\n  \"torii-gate\": $fa-var-torii-gate,\n  \"frog\": $fa-var-frog,\n  \"bucket\": $fa-var-bucket,\n  \"image\": $fa-var-image,\n  \"microphone\": $fa-var-microphone,\n  \"cow\": $fa-var-cow,\n  \"caret-up\": $fa-var-caret-up,\n  \"screwdriver\": $fa-var-screwdriver,\n  \"folder-closed\": $fa-var-folder-closed,\n  \"house-tsunami\": $fa-var-house-tsunami,\n  \"square-nfi\": $fa-var-square-nfi,\n  \"arrow-up-from-ground-water\": $fa-var-arrow-up-from-ground-water,\n  \"martini-glass\": $fa-var-martini-glass,\n  \"glass-martini-alt\": $fa-var-glass-martini-alt,\n  \"rotate-left\": $fa-var-rotate-left,\n  \"rotate-back\": $fa-var-rotate-back,\n  \"rotate-backward\": $fa-var-rotate-backward,\n  \"undo-alt\": $fa-var-undo-alt,\n  \"table-columns\": $fa-var-table-columns,\n  \"columns\": $fa-var-columns,\n  \"lemon\": $fa-var-lemon,\n  \"head-side-mask\": $fa-var-head-side-mask,\n  \"handshake\": $fa-var-handshake,\n  \"gem\": $fa-var-gem,\n  \"dolly\": $fa-var-dolly,\n  \"dolly-box\": $fa-var-dolly-box,\n  \"smoking\": $fa-var-smoking,\n  \"minimize\": $fa-var-minimize,\n  \"compress-arrows-alt\": $fa-var-compress-arrows-alt,\n  \"monument\": $fa-var-monument,\n  \"snowplow\": $fa-var-snowplow,\n  \"angles-right\": $fa-var-angles-right,\n  \"angle-double-right\": $fa-var-angle-double-right,\n  \"cannabis\": $fa-var-cannabis,\n  \"circle-play\": $fa-var-circle-play,\n  \"play-circle\": $fa-var-play-circle,\n  \"tablets\": $fa-var-tablets,\n  \"ethernet\": $fa-var-ethernet,\n  \"euro-sign\": $fa-var-euro-sign,\n  \"eur\": $fa-var-eur,\n  \"euro\": $fa-var-euro,\n  \"chair\": $fa-var-chair,\n  \"circle-check\": $fa-var-circle-check,\n  \"check-circle\": $fa-var-check-circle,\n  \"circle-stop\": $fa-var-circle-stop,\n  \"stop-circle\": $fa-var-stop-circle,\n  \"compass-drafting\": $fa-var-compass-drafting,\n  \"drafting-compass\": $fa-var-drafting-compass,\n  \"plate-wheat\": $fa-var-plate-wheat,\n  \"icicles\": $fa-var-icicles,\n  \"person-shelter\": $fa-var-person-shelter,\n  \"neuter\": $fa-var-neuter,\n  \"id-badge\": $fa-var-id-badge,\n  \"marker\": $fa-var-marker,\n  \"face-laugh-beam\": $fa-var-face-laugh-beam,\n  \"laugh-beam\": $fa-var-laugh-beam,\n  \"helicopter-symbol\": $fa-var-helicopter-symbol,\n  \"universal-access\": $fa-var-universal-access,\n  \"circle-chevron-up\": $fa-var-circle-chevron-up,\n  \"chevron-circle-up\": $fa-var-chevron-circle-up,\n  \"lari-sign\": $fa-var-lari-sign,\n  \"volcano\": $fa-var-volcano,\n  \"person-walking-dashed-line-arrow-right\": $fa-var-person-walking-dashed-line-arrow-right,\n  \"sterling-sign\": $fa-var-sterling-sign,\n  \"gbp\": $fa-var-gbp,\n  \"pound-sign\": $fa-var-pound-sign,\n  \"viruses\": $fa-var-viruses,\n  \"square-person-confined\": $fa-var-square-person-confined,\n  \"user-tie\": $fa-var-user-tie,\n  \"arrow-down-long\": $fa-var-arrow-down-long,\n  \"long-arrow-down\": $fa-var-long-arrow-down,\n  \"tent-arrow-down-to-line\": $fa-var-tent-arrow-down-to-line,\n  \"certificate\": $fa-var-certificate,\n  \"reply-all\": $fa-var-reply-all,\n  \"mail-reply-all\": $fa-var-mail-reply-all,\n  \"suitcase\": $fa-var-suitcase,\n  \"person-skating\": $fa-var-person-skating,\n  \"skating\": $fa-var-skating,\n  \"filter-circle-dollar\": $fa-var-filter-circle-dollar,\n  \"funnel-dollar\": $fa-var-funnel-dollar,\n  \"camera-retro\": $fa-var-camera-retro,\n  \"circle-arrow-down\": $fa-var-circle-arrow-down,\n  \"arrow-circle-down\": $fa-var-arrow-circle-down,\n  \"file-import\": $fa-var-file-import,\n  \"arrow-right-to-file\": $fa-var-arrow-right-to-file,\n  \"square-arrow-up-right\": $fa-var-square-arrow-up-right,\n  \"external-link-square\": $fa-var-external-link-square,\n  \"box-open\": $fa-var-box-open,\n  \"scroll\": $fa-var-scroll,\n  \"spa\": $fa-var-spa,\n  \"location-pin-lock\": $fa-var-location-pin-lock,\n  \"pause\": $fa-var-pause,\n  \"hill-avalanche\": $fa-var-hill-avalanche,\n  \"temperature-empty\": $fa-var-temperature-empty,\n  \"temperature-0\": $fa-var-temperature-0,\n  \"thermometer-0\": $fa-var-thermometer-0,\n  \"thermometer-empty\": $fa-var-thermometer-empty,\n  \"bomb\": $fa-var-bomb,\n  \"registered\": $fa-var-registered,\n  \"address-card\": $fa-var-address-card,\n  \"contact-card\": $fa-var-contact-card,\n  \"vcard\": $fa-var-vcard,\n  \"scale-unbalanced-flip\": $fa-var-scale-unbalanced-flip,\n  \"balance-scale-right\": $fa-var-balance-scale-right,\n  \"subscript\": $fa-var-subscript,\n  \"diamond-turn-right\": $fa-var-diamond-turn-right,\n  \"directions\": $fa-var-directions,\n  \"burst\": $fa-var-burst,\n  \"house-laptop\": $fa-var-house-laptop,\n  \"laptop-house\": $fa-var-laptop-house,\n  \"face-tired\": $fa-var-face-tired,\n  \"tired\": $fa-var-tired,\n  \"money-bills\": $fa-var-money-bills,\n  \"smog\": $fa-var-smog,\n  \"crutch\": $fa-var-crutch,\n  \"cloud-arrow-up\": $fa-var-cloud-arrow-up,\n  \"cloud-upload\": $fa-var-cloud-upload,\n  \"cloud-upload-alt\": $fa-var-cloud-upload-alt,\n  \"palette\": $fa-var-palette,\n  \"arrows-turn-right\": $fa-var-arrows-turn-right,\n  \"vest\": $fa-var-vest,\n  \"ferry\": $fa-var-ferry,\n  \"arrows-down-to-people\": $fa-var-arrows-down-to-people,\n  \"seedling\": $fa-var-seedling,\n  \"sprout\": $fa-var-sprout,\n  \"left-right\": $fa-var-left-right,\n  \"arrows-alt-h\": $fa-var-arrows-alt-h,\n  \"boxes-packing\": $fa-var-boxes-packing,\n  \"circle-arrow-left\": $fa-var-circle-arrow-left,\n  \"arrow-circle-left\": $fa-var-arrow-circle-left,\n  \"group-arrows-rotate\": $fa-var-group-arrows-rotate,\n  \"bowl-food\": $fa-var-bowl-food,\n  \"candy-cane\": $fa-var-candy-cane,\n  \"arrow-down-wide-short\": $fa-var-arrow-down-wide-short,\n  \"sort-amount-asc\": $fa-var-sort-amount-asc,\n  \"sort-amount-down\": $fa-var-sort-amount-down,\n  \"cloud-bolt\": $fa-var-cloud-bolt,\n  \"thunderstorm\": $fa-var-thunderstorm,\n  \"text-slash\": $fa-var-text-slash,\n  \"remove-format\": $fa-var-remove-format,\n  \"face-smile-wink\": $fa-var-face-smile-wink,\n  \"smile-wink\": $fa-var-smile-wink,\n  \"file-word\": $fa-var-file-word,\n  \"file-powerpoint\": $fa-var-file-powerpoint,\n  \"arrows-left-right\": $fa-var-arrows-left-right,\n  \"arrows-h\": $fa-var-arrows-h,\n  \"house-lock\": $fa-var-house-lock,\n  \"cloud-arrow-down\": $fa-var-cloud-arrow-down,\n  \"cloud-download\": $fa-var-cloud-download,\n  \"cloud-download-alt\": $fa-var-cloud-download-alt,\n  \"children\": $fa-var-children,\n  \"chalkboard\": $fa-var-chalkboard,\n  \"blackboard\": $fa-var-blackboard,\n  \"user-large-slash\": $fa-var-user-large-slash,\n  \"user-alt-slash\": $fa-var-user-alt-slash,\n  \"envelope-open\": $fa-var-envelope-open,\n  \"handshake-simple-slash\": $fa-var-handshake-simple-slash,\n  \"handshake-alt-slash\": $fa-var-handshake-alt-slash,\n  \"mattress-pillow\": $fa-var-mattress-pillow,\n  \"guarani-sign\": $fa-var-guarani-sign,\n  \"arrows-rotate\": $fa-var-arrows-rotate,\n  \"refresh\": $fa-var-refresh,\n  \"sync\": $fa-var-sync,\n  \"fire-extinguisher\": $fa-var-fire-extinguisher,\n  \"cruzeiro-sign\": $fa-var-cruzeiro-sign,\n  \"greater-than-equal\": $fa-var-greater-than-equal,\n  \"shield-halved\": $fa-var-shield-halved,\n  \"shield-alt\": $fa-var-shield-alt,\n  \"book-atlas\": $fa-var-book-atlas,\n  \"atlas\": $fa-var-atlas,\n  \"virus\": $fa-var-virus,\n  \"envelope-circle-check\": $fa-var-envelope-circle-check,\n  \"layer-group\": $fa-var-layer-group,\n  \"arrows-to-dot\": $fa-var-arrows-to-dot,\n  \"archway\": $fa-var-archway,\n  \"heart-circle-check\": $fa-var-heart-circle-check,\n  \"house-chimney-crack\": $fa-var-house-chimney-crack,\n  \"house-damage\": $fa-var-house-damage,\n  \"file-zipper\": $fa-var-file-zipper,\n  \"file-archive\": $fa-var-file-archive,\n  \"square\": $fa-var-square,\n  \"martini-glass-empty\": $fa-var-martini-glass-empty,\n  \"glass-martini\": $fa-var-glass-martini,\n  \"couch\": $fa-var-couch,\n  \"cedi-sign\": $fa-var-cedi-sign,\n  \"italic\": $fa-var-italic,\n  \"table-cells-column-lock\": $fa-var-table-cells-column-lock,\n  \"church\": $fa-var-church,\n  \"comments-dollar\": $fa-var-comments-dollar,\n  \"democrat\": $fa-var-democrat,\n  \"z\": $fa-var-z,\n  \"person-skiing\": $fa-var-person-skiing,\n  \"skiing\": $fa-var-skiing,\n  \"road-lock\": $fa-var-road-lock,\n  \"a\": $fa-var-a,\n  \"temperature-arrow-down\": $fa-var-temperature-arrow-down,\n  \"temperature-down\": $fa-var-temperature-down,\n  \"feather-pointed\": $fa-var-feather-pointed,\n  \"feather-alt\": $fa-var-feather-alt,\n  \"p\": $fa-var-p,\n  \"snowflake\": $fa-var-snowflake,\n  \"newspaper\": $fa-var-newspaper,\n  \"rectangle-ad\": $fa-var-rectangle-ad,\n  \"ad\": $fa-var-ad,\n  \"circle-arrow-right\": $fa-var-circle-arrow-right,\n  \"arrow-circle-right\": $fa-var-arrow-circle-right,\n  \"filter-circle-xmark\": $fa-var-filter-circle-xmark,\n  \"locust\": $fa-var-locust,\n  \"sort\": $fa-var-sort,\n  \"unsorted\": $fa-var-unsorted,\n  \"list-ol\": $fa-var-list-ol,\n  \"list-1-2\": $fa-var-list-1-2,\n  \"list-numeric\": $fa-var-list-numeric,\n  \"person-dress-burst\": $fa-var-person-dress-burst,\n  \"money-check-dollar\": $fa-var-money-check-dollar,\n  \"money-check-alt\": $fa-var-money-check-alt,\n  \"vector-square\": $fa-var-vector-square,\n  \"bread-slice\": $fa-var-bread-slice,\n  \"language\": $fa-var-language,\n  \"face-kiss-wink-heart\": $fa-var-face-kiss-wink-heart,\n  \"kiss-wink-heart\": $fa-var-kiss-wink-heart,\n  \"filter\": $fa-var-filter,\n  \"question\": $fa-var-question,\n  \"file-signature\": $fa-var-file-signature,\n  \"up-down-left-right\": $fa-var-up-down-left-right,\n  \"arrows-alt\": $fa-var-arrows-alt,\n  \"house-chimney-user\": $fa-var-house-chimney-user,\n  \"hand-holding-heart\": $fa-var-hand-holding-heart,\n  \"puzzle-piece\": $fa-var-puzzle-piece,\n  \"money-check\": $fa-var-money-check,\n  \"star-half-stroke\": $fa-var-star-half-stroke,\n  \"star-half-alt\": $fa-var-star-half-alt,\n  \"code\": $fa-var-code,\n  \"whiskey-glass\": $fa-var-whiskey-glass,\n  \"glass-whiskey\": $fa-var-glass-whiskey,\n  \"building-circle-exclamation\": $fa-var-building-circle-exclamation,\n  \"magnifying-glass-chart\": $fa-var-magnifying-glass-chart,\n  \"arrow-up-right-from-square\": $fa-var-arrow-up-right-from-square,\n  \"external-link\": $fa-var-external-link,\n  \"cubes-stacked\": $fa-var-cubes-stacked,\n  \"won-sign\": $fa-var-won-sign,\n  \"krw\": $fa-var-krw,\n  \"won\": $fa-var-won,\n  \"virus-covid\": $fa-var-virus-covid,\n  \"austral-sign\": $fa-var-austral-sign,\n  \"f\": $fa-var-f,\n  \"leaf\": $fa-var-leaf,\n  \"road\": $fa-var-road,\n  \"taxi\": $fa-var-taxi,\n  \"cab\": $fa-var-cab,\n  \"person-circle-plus\": $fa-var-person-circle-plus,\n  \"chart-pie\": $fa-var-chart-pie,\n  \"pie-chart\": $fa-var-pie-chart,\n  \"bolt-lightning\": $fa-var-bolt-lightning,\n  \"sack-xmark\": $fa-var-sack-xmark,\n  \"file-excel\": $fa-var-file-excel,\n  \"file-contract\": $fa-var-file-contract,\n  \"fish-fins\": $fa-var-fish-fins,\n  \"building-flag\": $fa-var-building-flag,\n  \"face-grin-beam\": $fa-var-face-grin-beam,\n  \"grin-beam\": $fa-var-grin-beam,\n  \"object-ungroup\": $fa-var-object-ungroup,\n  \"poop\": $fa-var-poop,\n  \"location-pin\": $fa-var-location-pin,\n  \"map-marker\": $fa-var-map-marker,\n  \"kaaba\": $fa-var-kaaba,\n  \"toilet-paper\": $fa-var-toilet-paper,\n  \"helmet-safety\": $fa-var-helmet-safety,\n  \"hard-hat\": $fa-var-hard-hat,\n  \"hat-hard\": $fa-var-hat-hard,\n  \"eject\": $fa-var-eject,\n  \"circle-right\": $fa-var-circle-right,\n  \"arrow-alt-circle-right\": $fa-var-arrow-alt-circle-right,\n  \"plane-circle-check\": $fa-var-plane-circle-check,\n  \"face-rolling-eyes\": $fa-var-face-rolling-eyes,\n  \"meh-rolling-eyes\": $fa-var-meh-rolling-eyes,\n  \"object-group\": $fa-var-object-group,\n  \"chart-line\": $fa-var-chart-line,\n  \"line-chart\": $fa-var-line-chart,\n  \"mask-ventilator\": $fa-var-mask-ventilator,\n  \"arrow-right\": $fa-var-arrow-right,\n  \"signs-post\": $fa-var-signs-post,\n  \"map-signs\": $fa-var-map-signs,\n  \"cash-register\": $fa-var-cash-register,\n  \"person-circle-question\": $fa-var-person-circle-question,\n  \"h\": $fa-var-h,\n  \"tarp\": $fa-var-tarp,\n  \"screwdriver-wrench\": $fa-var-screwdriver-wrench,\n  \"tools\": $fa-var-tools,\n  \"arrows-to-eye\": $fa-var-arrows-to-eye,\n  \"plug-circle-bolt\": $fa-var-plug-circle-bolt,\n  \"heart\": $fa-var-heart,\n  \"mars-and-venus\": $fa-var-mars-and-venus,\n  \"house-user\": $fa-var-house-user,\n  \"home-user\": $fa-var-home-user,\n  \"dumpster-fire\": $fa-var-dumpster-fire,\n  \"house-crack\": $fa-var-house-crack,\n  \"martini-glass-citrus\": $fa-var-martini-glass-citrus,\n  \"cocktail\": $fa-var-cocktail,\n  \"face-surprise\": $fa-var-face-surprise,\n  \"surprise\": $fa-var-surprise,\n  \"bottle-water\": $fa-var-bottle-water,\n  \"circle-pause\": $fa-var-circle-pause,\n  \"pause-circle\": $fa-var-pause-circle,\n  \"toilet-paper-slash\": $fa-var-toilet-paper-slash,\n  \"apple-whole\": $fa-var-apple-whole,\n  \"apple-alt\": $fa-var-apple-alt,\n  \"kitchen-set\": $fa-var-kitchen-set,\n  \"r\": $fa-var-r,\n  \"temperature-quarter\": $fa-var-temperature-quarter,\n  \"temperature-1\": $fa-var-temperature-1,\n  \"thermometer-1\": $fa-var-thermometer-1,\n  \"thermometer-quarter\": $fa-var-thermometer-quarter,\n  \"cube\": $fa-var-cube,\n  \"bitcoin-sign\": $fa-var-bitcoin-sign,\n  \"shield-dog\": $fa-var-shield-dog,\n  \"solar-panel\": $fa-var-solar-panel,\n  \"lock-open\": $fa-var-lock-open,\n  \"elevator\": $fa-var-elevator,\n  \"money-bill-transfer\": $fa-var-money-bill-transfer,\n  \"money-bill-trend-up\": $fa-var-money-bill-trend-up,\n  \"house-flood-water-circle-arrow-right\": $fa-var-house-flood-water-circle-arrow-right,\n  \"square-poll-horizontal\": $fa-var-square-poll-horizontal,\n  \"poll-h\": $fa-var-poll-h,\n  \"circle\": $fa-var-circle,\n  \"backward-fast\": $fa-var-backward-fast,\n  \"fast-backward\": $fa-var-fast-backward,\n  \"recycle\": $fa-var-recycle,\n  \"user-astronaut\": $fa-var-user-astronaut,\n  \"plane-slash\": $fa-var-plane-slash,\n  \"trademark\": $fa-var-trademark,\n  \"basketball\": $fa-var-basketball,\n  \"basketball-ball\": $fa-var-basketball-ball,\n  \"satellite-dish\": $fa-var-satellite-dish,\n  \"circle-up\": $fa-var-circle-up,\n  \"arrow-alt-circle-up\": $fa-var-arrow-alt-circle-up,\n  \"mobile-screen-button\": $fa-var-mobile-screen-button,\n  \"mobile-alt\": $fa-var-mobile-alt,\n  \"volume-high\": $fa-var-volume-high,\n  \"volume-up\": $fa-var-volume-up,\n  \"users-rays\": $fa-var-users-rays,\n  \"wallet\": $fa-var-wallet,\n  \"clipboard-check\": $fa-var-clipboard-check,\n  \"file-audio\": $fa-var-file-audio,\n  \"burger\": $fa-var-burger,\n  \"hamburger\": $fa-var-hamburger,\n  \"wrench\": $fa-var-wrench,\n  \"bugs\": $fa-var-bugs,\n  \"rupee-sign\": $fa-var-rupee-sign,\n  \"rupee\": $fa-var-rupee,\n  \"file-image\": $fa-var-file-image,\n  \"circle-question\": $fa-var-circle-question,\n  \"question-circle\": $fa-var-question-circle,\n  \"plane-departure\": $fa-var-plane-departure,\n  \"handshake-slash\": $fa-var-handshake-slash,\n  \"book-bookmark\": $fa-var-book-bookmark,\n  \"code-branch\": $fa-var-code-branch,\n  \"hat-cowboy\": $fa-var-hat-cowboy,\n  \"bridge\": $fa-var-bridge,\n  \"phone-flip\": $fa-var-phone-flip,\n  \"phone-alt\": $fa-var-phone-alt,\n  \"truck-front\": $fa-var-truck-front,\n  \"cat\": $fa-var-cat,\n  \"anchor-circle-exclamation\": $fa-var-anchor-circle-exclamation,\n  \"truck-field\": $fa-var-truck-field,\n  \"route\": $fa-var-route,\n  \"clipboard-question\": $fa-var-clipboard-question,\n  \"panorama\": $fa-var-panorama,\n  \"comment-medical\": $fa-var-comment-medical,\n  \"teeth-open\": $fa-var-teeth-open,\n  \"file-circle-minus\": $fa-var-file-circle-minus,\n  \"tags\": $fa-var-tags,\n  \"wine-glass\": $fa-var-wine-glass,\n  \"forward-fast\": $fa-var-forward-fast,\n  \"fast-forward\": $fa-var-fast-forward,\n  \"face-meh-blank\": $fa-var-face-meh-blank,\n  \"meh-blank\": $fa-var-meh-blank,\n  \"square-parking\": $fa-var-square-parking,\n  \"parking\": $fa-var-parking,\n  \"house-signal\": $fa-var-house-signal,\n  \"bars-progress\": $fa-var-bars-progress,\n  \"tasks-alt\": $fa-var-tasks-alt,\n  \"faucet-drip\": $fa-var-faucet-drip,\n  \"cart-flatbed\": $fa-var-cart-flatbed,\n  \"dolly-flatbed\": $fa-var-dolly-flatbed,\n  \"ban-smoking\": $fa-var-ban-smoking,\n  \"smoking-ban\": $fa-var-smoking-ban,\n  \"terminal\": $fa-var-terminal,\n  \"mobile-button\": $fa-var-mobile-button,\n  \"house-medical-flag\": $fa-var-house-medical-flag,\n  \"basket-shopping\": $fa-var-basket-shopping,\n  \"shopping-basket\": $fa-var-shopping-basket,\n  \"tape\": $fa-var-tape,\n  \"bus-simple\": $fa-var-bus-simple,\n  \"bus-alt\": $fa-var-bus-alt,\n  \"eye\": $fa-var-eye,\n  \"face-sad-cry\": $fa-var-face-sad-cry,\n  \"sad-cry\": $fa-var-sad-cry,\n  \"audio-description\": $fa-var-audio-description,\n  \"person-military-to-person\": $fa-var-person-military-to-person,\n  \"file-shield\": $fa-var-file-shield,\n  \"user-slash\": $fa-var-user-slash,\n  \"pen\": $fa-var-pen,\n  \"tower-observation\": $fa-var-tower-observation,\n  \"file-code\": $fa-var-file-code,\n  \"signal\": $fa-var-signal,\n  \"signal-5\": $fa-var-signal-5,\n  \"signal-perfect\": $fa-var-signal-perfect,\n  \"bus\": $fa-var-bus,\n  \"heart-circle-xmark\": $fa-var-heart-circle-xmark,\n  \"house-chimney\": $fa-var-house-chimney,\n  \"home-lg\": $fa-var-home-lg,\n  \"window-maximize\": $fa-var-window-maximize,\n  \"face-frown\": $fa-var-face-frown,\n  \"frown\": $fa-var-frown,\n  \"prescription\": $fa-var-prescription,\n  \"shop\": $fa-var-shop,\n  \"store-alt\": $fa-var-store-alt,\n  \"floppy-disk\": $fa-var-floppy-disk,\n  \"save\": $fa-var-save,\n  \"vihara\": $fa-var-vihara,\n  \"scale-unbalanced\": $fa-var-scale-unbalanced,\n  \"balance-scale-left\": $fa-var-balance-scale-left,\n  \"sort-up\": $fa-var-sort-up,\n  \"sort-asc\": $fa-var-sort-asc,\n  \"comment-dots\": $fa-var-comment-dots,\n  \"commenting\": $fa-var-commenting,\n  \"plant-wilt\": $fa-var-plant-wilt,\n  \"diamond\": $fa-var-diamond,\n  \"face-grin-squint\": $fa-var-face-grin-squint,\n  \"grin-squint\": $fa-var-grin-squint,\n  \"hand-holding-dollar\": $fa-var-hand-holding-dollar,\n  \"hand-holding-usd\": $fa-var-hand-holding-usd,\n  \"bacterium\": $fa-var-bacterium,\n  \"hand-pointer\": $fa-var-hand-pointer,\n  \"drum-steelpan\": $fa-var-drum-steelpan,\n  \"hand-scissors\": $fa-var-hand-scissors,\n  \"hands-praying\": $fa-var-hands-praying,\n  \"praying-hands\": $fa-var-praying-hands,\n  \"arrow-rotate-right\": $fa-var-arrow-rotate-right,\n  \"arrow-right-rotate\": $fa-var-arrow-right-rotate,\n  \"arrow-rotate-forward\": $fa-var-arrow-rotate-forward,\n  \"redo\": $fa-var-redo,\n  \"biohazard\": $fa-var-biohazard,\n  \"location-crosshairs\": $fa-var-location-crosshairs,\n  \"location\": $fa-var-location,\n  \"mars-double\": $fa-var-mars-double,\n  \"child-dress\": $fa-var-child-dress,\n  \"users-between-lines\": $fa-var-users-between-lines,\n  \"lungs-virus\": $fa-var-lungs-virus,\n  \"face-grin-tears\": $fa-var-face-grin-tears,\n  \"grin-tears\": $fa-var-grin-tears,\n  \"phone\": $fa-var-phone,\n  \"calendar-xmark\": $fa-var-calendar-xmark,\n  \"calendar-times\": $fa-var-calendar-times,\n  \"child-reaching\": $fa-var-child-reaching,\n  \"head-side-virus\": $fa-var-head-side-virus,\n  \"user-gear\": $fa-var-user-gear,\n  \"user-cog\": $fa-var-user-cog,\n  \"arrow-up-1-9\": $fa-var-arrow-up-1-9,\n  \"sort-numeric-up\": $fa-var-sort-numeric-up,\n  \"door-closed\": $fa-var-door-closed,\n  \"shield-virus\": $fa-var-shield-virus,\n  \"dice-six\": $fa-var-dice-six,\n  \"mosquito-net\": $fa-var-mosquito-net,\n  \"bridge-water\": $fa-var-bridge-water,\n  \"person-booth\": $fa-var-person-booth,\n  \"text-width\": $fa-var-text-width,\n  \"hat-wizard\": $fa-var-hat-wizard,\n  \"pen-fancy\": $fa-var-pen-fancy,\n  \"person-digging\": $fa-var-person-digging,\n  \"digging\": $fa-var-digging,\n  \"trash\": $fa-var-trash,\n  \"gauge-simple\": $fa-var-gauge-simple,\n  \"gauge-simple-med\": $fa-var-gauge-simple-med,\n  \"tachometer-average\": $fa-var-tachometer-average,\n  \"book-medical\": $fa-var-book-medical,\n  \"poo\": $fa-var-poo,\n  \"quote-right\": $fa-var-quote-right,\n  \"quote-right-alt\": $fa-var-quote-right-alt,\n  \"shirt\": $fa-var-shirt,\n  \"t-shirt\": $fa-var-t-shirt,\n  \"tshirt\": $fa-var-tshirt,\n  \"cubes\": $fa-var-cubes,\n  \"divide\": $fa-var-divide,\n  \"tenge-sign\": $fa-var-tenge-sign,\n  \"tenge\": $fa-var-tenge,\n  \"headphones\": $fa-var-headphones,\n  \"hands-holding\": $fa-var-hands-holding,\n  \"hands-clapping\": $fa-var-hands-clapping,\n  \"republican\": $fa-var-republican,\n  \"arrow-left\": $fa-var-arrow-left,\n  \"person-circle-xmark\": $fa-var-person-circle-xmark,\n  \"ruler\": $fa-var-ruler,\n  \"align-left\": $fa-var-align-left,\n  \"dice-d6\": $fa-var-dice-d6,\n  \"restroom\": $fa-var-restroom,\n  \"j\": $fa-var-j,\n  \"users-viewfinder\": $fa-var-users-viewfinder,\n  \"file-video\": $fa-var-file-video,\n  \"up-right-from-square\": $fa-var-up-right-from-square,\n  \"external-link-alt\": $fa-var-external-link-alt,\n  \"table-cells\": $fa-var-table-cells,\n  \"th\": $fa-var-th,\n  \"file-pdf\": $fa-var-file-pdf,\n  \"book-bible\": $fa-var-book-bible,\n  \"bible\": $fa-var-bible,\n  \"o\": $fa-var-o,\n  \"suitcase-medical\": $fa-var-suitcase-medical,\n  \"medkit\": $fa-var-medkit,\n  \"user-secret\": $fa-var-user-secret,\n  \"otter\": $fa-var-otter,\n  \"person-dress\": $fa-var-person-dress,\n  \"female\": $fa-var-female,\n  \"comment-dollar\": $fa-var-comment-dollar,\n  \"business-time\": $fa-var-business-time,\n  \"briefcase-clock\": $fa-var-briefcase-clock,\n  \"table-cells-large\": $fa-var-table-cells-large,\n  \"th-large\": $fa-var-th-large,\n  \"book-tanakh\": $fa-var-book-tanakh,\n  \"tanakh\": $fa-var-tanakh,\n  \"phone-volume\": $fa-var-phone-volume,\n  \"volume-control-phone\": $fa-var-volume-control-phone,\n  \"hat-cowboy-side\": $fa-var-hat-cowboy-side,\n  \"clipboard-user\": $fa-var-clipboard-user,\n  \"child\": $fa-var-child,\n  \"lira-sign\": $fa-var-lira-sign,\n  \"satellite\": $fa-var-satellite,\n  \"plane-lock\": $fa-var-plane-lock,\n  \"tag\": $fa-var-tag,\n  \"comment\": $fa-var-comment,\n  \"cake-candles\": $fa-var-cake-candles,\n  \"birthday-cake\": $fa-var-birthday-cake,\n  \"cake\": $fa-var-cake,\n  \"envelope\": $fa-var-envelope,\n  \"angles-up\": $fa-var-angles-up,\n  \"angle-double-up\": $fa-var-angle-double-up,\n  \"paperclip\": $fa-var-paperclip,\n  \"arrow-right-to-city\": $fa-var-arrow-right-to-city,\n  \"ribbon\": $fa-var-ribbon,\n  \"lungs\": $fa-var-lungs,\n  \"arrow-up-9-1\": $fa-var-arrow-up-9-1,\n  \"sort-numeric-up-alt\": $fa-var-sort-numeric-up-alt,\n  \"litecoin-sign\": $fa-var-litecoin-sign,\n  \"border-none\": $fa-var-border-none,\n  \"circle-nodes\": $fa-var-circle-nodes,\n  \"parachute-box\": $fa-var-parachute-box,\n  \"indent\": $fa-var-indent,\n  \"truck-field-un\": $fa-var-truck-field-un,\n  \"hourglass\": $fa-var-hourglass,\n  \"hourglass-empty\": $fa-var-hourglass-empty,\n  \"mountain\": $fa-var-mountain,\n  \"user-doctor\": $fa-var-user-doctor,\n  \"user-md\": $fa-var-user-md,\n  \"circle-info\": $fa-var-circle-info,\n  \"info-circle\": $fa-var-info-circle,\n  \"cloud-meatball\": $fa-var-cloud-meatball,\n  \"camera\": $fa-var-camera,\n  \"camera-alt\": $fa-var-camera-alt,\n  \"square-virus\": $fa-var-square-virus,\n  \"meteor\": $fa-var-meteor,\n  \"car-on\": $fa-var-car-on,\n  \"sleigh\": $fa-var-sleigh,\n  \"arrow-down-1-9\": $fa-var-arrow-down-1-9,\n  \"sort-numeric-asc\": $fa-var-sort-numeric-asc,\n  \"sort-numeric-down\": $fa-var-sort-numeric-down,\n  \"hand-holding-droplet\": $fa-var-hand-holding-droplet,\n  \"hand-holding-water\": $fa-var-hand-holding-water,\n  \"water\": $fa-var-water,\n  \"calendar-check\": $fa-var-calendar-check,\n  \"braille\": $fa-var-braille,\n  \"prescription-bottle-medical\": $fa-var-prescription-bottle-medical,\n  \"prescription-bottle-alt\": $fa-var-prescription-bottle-alt,\n  \"landmark\": $fa-var-landmark,\n  \"truck\": $fa-var-truck,\n  \"crosshairs\": $fa-var-crosshairs,\n  \"person-cane\": $fa-var-person-cane,\n  \"tent\": $fa-var-tent,\n  \"vest-patches\": $fa-var-vest-patches,\n  \"check-double\": $fa-var-check-double,\n  \"arrow-down-a-z\": $fa-var-arrow-down-a-z,\n  \"sort-alpha-asc\": $fa-var-sort-alpha-asc,\n  \"sort-alpha-down\": $fa-var-sort-alpha-down,\n  \"money-bill-wheat\": $fa-var-money-bill-wheat,\n  \"cookie\": $fa-var-cookie,\n  \"arrow-rotate-left\": $fa-var-arrow-rotate-left,\n  \"arrow-left-rotate\": $fa-var-arrow-left-rotate,\n  \"arrow-rotate-back\": $fa-var-arrow-rotate-back,\n  \"arrow-rotate-backward\": $fa-var-arrow-rotate-backward,\n  \"undo\": $fa-var-undo,\n  \"hard-drive\": $fa-var-hard-drive,\n  \"hdd\": $fa-var-hdd,\n  \"face-grin-squint-tears\": $fa-var-face-grin-squint-tears,\n  \"grin-squint-tears\": $fa-var-grin-squint-tears,\n  \"dumbbell\": $fa-var-dumbbell,\n  \"rectangle-list\": $fa-var-rectangle-list,\n  \"list-alt\": $fa-var-list-alt,\n  \"tarp-droplet\": $fa-var-tarp-droplet,\n  \"house-medical-circle-check\": $fa-var-house-medical-circle-check,\n  \"person-skiing-nordic\": $fa-var-person-skiing-nordic,\n  \"skiing-nordic\": $fa-var-skiing-nordic,\n  \"calendar-plus\": $fa-var-calendar-plus,\n  \"plane-arrival\": $fa-var-plane-arrival,\n  \"circle-left\": $fa-var-circle-left,\n  \"arrow-alt-circle-left\": $fa-var-arrow-alt-circle-left,\n  \"train-subway\": $fa-var-train-subway,\n  \"subway\": $fa-var-subway,\n  \"chart-gantt\": $fa-var-chart-gantt,\n  \"indian-rupee-sign\": $fa-var-indian-rupee-sign,\n  \"indian-rupee\": $fa-var-indian-rupee,\n  \"inr\": $fa-var-inr,\n  \"crop-simple\": $fa-var-crop-simple,\n  \"crop-alt\": $fa-var-crop-alt,\n  \"money-bill-1\": $fa-var-money-bill-1,\n  \"money-bill-alt\": $fa-var-money-bill-alt,\n  \"left-long\": $fa-var-left-long,\n  \"long-arrow-alt-left\": $fa-var-long-arrow-alt-left,\n  \"dna\": $fa-var-dna,\n  \"virus-slash\": $fa-var-virus-slash,\n  \"minus\": $fa-var-minus,\n  \"subtract\": $fa-var-subtract,\n  \"chess\": $fa-var-chess,\n  \"arrow-left-long\": $fa-var-arrow-left-long,\n  \"long-arrow-left\": $fa-var-long-arrow-left,\n  \"plug-circle-check\": $fa-var-plug-circle-check,\n  \"street-view\": $fa-var-street-view,\n  \"franc-sign\": $fa-var-franc-sign,\n  \"volume-off\": $fa-var-volume-off,\n  \"hands-asl-interpreting\": $fa-var-hands-asl-interpreting,\n  \"american-sign-language-interpreting\": $fa-var-american-sign-language-interpreting,\n  \"asl-interpreting\": $fa-var-asl-interpreting,\n  \"hands-american-sign-language-interpreting\": $fa-var-hands-american-sign-language-interpreting,\n  \"gear\": $fa-var-gear,\n  \"cog\": $fa-var-cog,\n  \"droplet-slash\": $fa-var-droplet-slash,\n  \"tint-slash\": $fa-var-tint-slash,\n  \"mosque\": $fa-var-mosque,\n  \"mosquito\": $fa-var-mosquito,\n  \"star-of-david\": $fa-var-star-of-david,\n  \"person-military-rifle\": $fa-var-person-military-rifle,\n  \"cart-shopping\": $fa-var-cart-shopping,\n  \"shopping-cart\": $fa-var-shopping-cart,\n  \"vials\": $fa-var-vials,\n  \"plug-circle-plus\": $fa-var-plug-circle-plus,\n  \"place-of-worship\": $fa-var-place-of-worship,\n  \"grip-vertical\": $fa-var-grip-vertical,\n  \"arrow-turn-up\": $fa-var-arrow-turn-up,\n  \"level-up\": $fa-var-level-up,\n  \"u\": $fa-var-u,\n  \"square-root-variable\": $fa-var-square-root-variable,\n  \"square-root-alt\": $fa-var-square-root-alt,\n  \"clock\": $fa-var-clock,\n  \"clock-four\": $fa-var-clock-four,\n  \"backward-step\": $fa-var-backward-step,\n  \"step-backward\": $fa-var-step-backward,\n  \"pallet\": $fa-var-pallet,\n  \"faucet\": $fa-var-faucet,\n  \"baseball-bat-ball\": $fa-var-baseball-bat-ball,\n  \"s\": $fa-var-s,\n  \"timeline\": $fa-var-timeline,\n  \"keyboard\": $fa-var-keyboard,\n  \"caret-down\": $fa-var-caret-down,\n  \"house-chimney-medical\": $fa-var-house-chimney-medical,\n  \"clinic-medical\": $fa-var-clinic-medical,\n  \"temperature-three-quarters\": $fa-var-temperature-three-quarters,\n  \"temperature-3\": $fa-var-temperature-3,\n  \"thermometer-3\": $fa-var-thermometer-3,\n  \"thermometer-three-quarters\": $fa-var-thermometer-three-quarters,\n  \"mobile-screen\": $fa-var-mobile-screen,\n  \"mobile-android-alt\": $fa-var-mobile-android-alt,\n  \"plane-up\": $fa-var-plane-up,\n  \"piggy-bank\": $fa-var-piggy-bank,\n  \"battery-half\": $fa-var-battery-half,\n  \"battery-3\": $fa-var-battery-3,\n  \"mountain-city\": $fa-var-mountain-city,\n  \"coins\": $fa-var-coins,\n  \"khanda\": $fa-var-khanda,\n  \"sliders\": $fa-var-sliders,\n  \"sliders-h\": $fa-var-sliders-h,\n  \"folder-tree\": $fa-var-folder-tree,\n  \"network-wired\": $fa-var-network-wired,\n  \"map-pin\": $fa-var-map-pin,\n  \"hamsa\": $fa-var-hamsa,\n  \"cent-sign\": $fa-var-cent-sign,\n  \"flask\": $fa-var-flask,\n  \"person-pregnant\": $fa-var-person-pregnant,\n  \"wand-sparkles\": $fa-var-wand-sparkles,\n  \"ellipsis-vertical\": $fa-var-ellipsis-vertical,\n  \"ellipsis-v\": $fa-var-ellipsis-v,\n  \"ticket\": $fa-var-ticket,\n  \"power-off\": $fa-var-power-off,\n  \"right-long\": $fa-var-right-long,\n  \"long-arrow-alt-right\": $fa-var-long-arrow-alt-right,\n  \"flag-usa\": $fa-var-flag-usa,\n  \"laptop-file\": $fa-var-laptop-file,\n  \"tty\": $fa-var-tty,\n  \"teletype\": $fa-var-teletype,\n  \"diagram-next\": $fa-var-diagram-next,\n  \"person-rifle\": $fa-var-person-rifle,\n  \"house-medical-circle-exclamation\": $fa-var-house-medical-circle-exclamation,\n  \"closed-captioning\": $fa-var-closed-captioning,\n  \"person-hiking\": $fa-var-person-hiking,\n  \"hiking\": $fa-var-hiking,\n  \"venus-double\": $fa-var-venus-double,\n  \"images\": $fa-var-images,\n  \"calculator\": $fa-var-calculator,\n  \"people-pulling\": $fa-var-people-pulling,\n  \"n\": $fa-var-n,\n  \"cable-car\": $fa-var-cable-car,\n  \"tram\": $fa-var-tram,\n  \"cloud-rain\": $fa-var-cloud-rain,\n  \"building-circle-xmark\": $fa-var-building-circle-xmark,\n  \"ship\": $fa-var-ship,\n  \"arrows-down-to-line\": $fa-var-arrows-down-to-line,\n  \"download\": $fa-var-download,\n  \"face-grin\": $fa-var-face-grin,\n  \"grin\": $fa-var-grin,\n  \"delete-left\": $fa-var-delete-left,\n  \"backspace\": $fa-var-backspace,\n  \"eye-dropper\": $fa-var-eye-dropper,\n  \"eye-dropper-empty\": $fa-var-eye-dropper-empty,\n  \"eyedropper\": $fa-var-eyedropper,\n  \"file-circle-check\": $fa-var-file-circle-check,\n  \"forward\": $fa-var-forward,\n  \"mobile\": $fa-var-mobile,\n  \"mobile-android\": $fa-var-mobile-android,\n  \"mobile-phone\": $fa-var-mobile-phone,\n  \"face-meh\": $fa-var-face-meh,\n  \"meh\": $fa-var-meh,\n  \"align-center\": $fa-var-align-center,\n  \"book-skull\": $fa-var-book-skull,\n  \"book-dead\": $fa-var-book-dead,\n  \"id-card\": $fa-var-id-card,\n  \"drivers-license\": $fa-var-drivers-license,\n  \"outdent\": $fa-var-outdent,\n  \"dedent\": $fa-var-dedent,\n  \"heart-circle-exclamation\": $fa-var-heart-circle-exclamation,\n  \"house\": $fa-var-house,\n  \"home\": $fa-var-home,\n  \"home-alt\": $fa-var-home-alt,\n  \"home-lg-alt\": $fa-var-home-lg-alt,\n  \"calendar-week\": $fa-var-calendar-week,\n  \"laptop-medical\": $fa-var-laptop-medical,\n  \"b\": $fa-var-b,\n  \"file-medical\": $fa-var-file-medical,\n  \"dice-one\": $fa-var-dice-one,\n  \"kiwi-bird\": $fa-var-kiwi-bird,\n  \"arrow-right-arrow-left\": $fa-var-arrow-right-arrow-left,\n  \"exchange\": $fa-var-exchange,\n  \"rotate-right\": $fa-var-rotate-right,\n  \"redo-alt\": $fa-var-redo-alt,\n  \"rotate-forward\": $fa-var-rotate-forward,\n  \"utensils\": $fa-var-utensils,\n  \"cutlery\": $fa-var-cutlery,\n  \"arrow-up-wide-short\": $fa-var-arrow-up-wide-short,\n  \"sort-amount-up\": $fa-var-sort-amount-up,\n  \"mill-sign\": $fa-var-mill-sign,\n  \"bowl-rice\": $fa-var-bowl-rice,\n  \"skull\": $fa-var-skull,\n  \"tower-broadcast\": $fa-var-tower-broadcast,\n  \"broadcast-tower\": $fa-var-broadcast-tower,\n  \"truck-pickup\": $fa-var-truck-pickup,\n  \"up-long\": $fa-var-up-long,\n  \"long-arrow-alt-up\": $fa-var-long-arrow-alt-up,\n  \"stop\": $fa-var-stop,\n  \"code-merge\": $fa-var-code-merge,\n  \"upload\": $fa-var-upload,\n  \"hurricane\": $fa-var-hurricane,\n  \"mound\": $fa-var-mound,\n  \"toilet-portable\": $fa-var-toilet-portable,\n  \"compact-disc\": $fa-var-compact-disc,\n  \"file-arrow-down\": $fa-var-file-arrow-down,\n  \"file-download\": $fa-var-file-download,\n  \"caravan\": $fa-var-caravan,\n  \"shield-cat\": $fa-var-shield-cat,\n  \"bolt\": $fa-var-bolt,\n  \"zap\": $fa-var-zap,\n  \"glass-water\": $fa-var-glass-water,\n  \"oil-well\": $fa-var-oil-well,\n  \"vault\": $fa-var-vault,\n  \"mars\": $fa-var-mars,\n  \"toilet\": $fa-var-toilet,\n  \"plane-circle-xmark\": $fa-var-plane-circle-xmark,\n  \"yen-sign\": $fa-var-yen-sign,\n  \"cny\": $fa-var-cny,\n  \"jpy\": $fa-var-jpy,\n  \"rmb\": $fa-var-rmb,\n  \"yen\": $fa-var-yen,\n  \"ruble-sign\": $fa-var-ruble-sign,\n  \"rouble\": $fa-var-rouble,\n  \"rub\": $fa-var-rub,\n  \"ruble\": $fa-var-ruble,\n  \"sun\": $fa-var-sun,\n  \"guitar\": $fa-var-guitar,\n  \"face-laugh-wink\": $fa-var-face-laugh-wink,\n  \"laugh-wink\": $fa-var-laugh-wink,\n  \"horse-head\": $fa-var-horse-head,\n  \"bore-hole\": $fa-var-bore-hole,\n  \"industry\": $fa-var-industry,\n  \"circle-down\": $fa-var-circle-down,\n  \"arrow-alt-circle-down\": $fa-var-arrow-alt-circle-down,\n  \"arrows-turn-to-dots\": $fa-var-arrows-turn-to-dots,\n  \"florin-sign\": $fa-var-florin-sign,\n  \"arrow-down-short-wide\": $fa-var-arrow-down-short-wide,\n  \"sort-amount-desc\": $fa-var-sort-amount-desc,\n  \"sort-amount-down-alt\": $fa-var-sort-amount-down-alt,\n  \"less-than\": $fa-var-less-than,\n  \"angle-down\": $fa-var-angle-down,\n  \"car-tunnel\": $fa-var-car-tunnel,\n  \"head-side-cough\": $fa-var-head-side-cough,\n  \"grip-lines\": $fa-var-grip-lines,\n  \"thumbs-down\": $fa-var-thumbs-down,\n  \"user-lock\": $fa-var-user-lock,\n  \"arrow-right-long\": $fa-var-arrow-right-long,\n  \"long-arrow-right\": $fa-var-long-arrow-right,\n  \"anchor-circle-xmark\": $fa-var-anchor-circle-xmark,\n  \"ellipsis\": $fa-var-ellipsis,\n  \"ellipsis-h\": $fa-var-ellipsis-h,\n  \"chess-pawn\": $fa-var-chess-pawn,\n  \"kit-medical\": $fa-var-kit-medical,\n  \"first-aid\": $fa-var-first-aid,\n  \"person-through-window\": $fa-var-person-through-window,\n  \"toolbox\": $fa-var-toolbox,\n  \"hands-holding-circle\": $fa-var-hands-holding-circle,\n  \"bug\": $fa-var-bug,\n  \"credit-card\": $fa-var-credit-card,\n  \"credit-card-alt\": $fa-var-credit-card-alt,\n  \"car\": $fa-var-car,\n  \"automobile\": $fa-var-automobile,\n  \"hand-holding-hand\": $fa-var-hand-holding-hand,\n  \"book-open-reader\": $fa-var-book-open-reader,\n  \"book-reader\": $fa-var-book-reader,\n  \"mountain-sun\": $fa-var-mountain-sun,\n  \"arrows-left-right-to-line\": $fa-var-arrows-left-right-to-line,\n  \"dice-d20\": $fa-var-dice-d20,\n  \"truck-droplet\": $fa-var-truck-droplet,\n  \"file-circle-xmark\": $fa-var-file-circle-xmark,\n  \"temperature-arrow-up\": $fa-var-temperature-arrow-up,\n  \"temperature-up\": $fa-var-temperature-up,\n  \"medal\": $fa-var-medal,\n  \"bed\": $fa-var-bed,\n  \"square-h\": $fa-var-square-h,\n  \"h-square\": $fa-var-h-square,\n  \"podcast\": $fa-var-podcast,\n  \"temperature-full\": $fa-var-temperature-full,\n  \"temperature-4\": $fa-var-temperature-4,\n  \"thermometer-4\": $fa-var-thermometer-4,\n  \"thermometer-full\": $fa-var-thermometer-full,\n  \"bell\": $fa-var-bell,\n  \"superscript\": $fa-var-superscript,\n  \"plug-circle-xmark\": $fa-var-plug-circle-xmark,\n  \"star-of-life\": $fa-var-star-of-life,\n  \"phone-slash\": $fa-var-phone-slash,\n  \"paint-roller\": $fa-var-paint-roller,\n  \"handshake-angle\": $fa-var-handshake-angle,\n  \"hands-helping\": $fa-var-hands-helping,\n  \"location-dot\": $fa-var-location-dot,\n  \"map-marker-alt\": $fa-var-map-marker-alt,\n  \"file\": $fa-var-file,\n  \"greater-than\": $fa-var-greater-than,\n  \"person-swimming\": $fa-var-person-swimming,\n  \"swimmer\": $fa-var-swimmer,\n  \"arrow-down\": $fa-var-arrow-down,\n  \"droplet\": $fa-var-droplet,\n  \"tint\": $fa-var-tint,\n  \"eraser\": $fa-var-eraser,\n  \"earth-americas\": $fa-var-earth-americas,\n  \"earth\": $fa-var-earth,\n  \"earth-america\": $fa-var-earth-america,\n  \"globe-americas\": $fa-var-globe-americas,\n  \"person-burst\": $fa-var-person-burst,\n  \"dove\": $fa-var-dove,\n  \"battery-empty\": $fa-var-battery-empty,\n  \"battery-0\": $fa-var-battery-0,\n  \"socks\": $fa-var-socks,\n  \"inbox\": $fa-var-inbox,\n  \"section\": $fa-var-section,\n  \"gauge-high\": $fa-var-gauge-high,\n  \"tachometer-alt\": $fa-var-tachometer-alt,\n  \"tachometer-alt-fast\": $fa-var-tachometer-alt-fast,\n  \"envelope-open-text\": $fa-var-envelope-open-text,\n  \"hospital\": $fa-var-hospital,\n  \"hospital-alt\": $fa-var-hospital-alt,\n  \"hospital-wide\": $fa-var-hospital-wide,\n  \"wine-bottle\": $fa-var-wine-bottle,\n  \"chess-rook\": $fa-var-chess-rook,\n  \"bars-staggered\": $fa-var-bars-staggered,\n  \"reorder\": $fa-var-reorder,\n  \"stream\": $fa-var-stream,\n  \"dharmachakra\": $fa-var-dharmachakra,\n  \"hotdog\": $fa-var-hotdog,\n  \"person-walking-with-cane\": $fa-var-person-walking-with-cane,\n  \"blind\": $fa-var-blind,\n  \"drum\": $fa-var-drum,\n  \"ice-cream\": $fa-var-ice-cream,\n  \"heart-circle-bolt\": $fa-var-heart-circle-bolt,\n  \"fax\": $fa-var-fax,\n  \"paragraph\": $fa-var-paragraph,\n  \"check-to-slot\": $fa-var-check-to-slot,\n  \"vote-yea\": $fa-var-vote-yea,\n  \"star-half\": $fa-var-star-half,\n  \"boxes-stacked\": $fa-var-boxes-stacked,\n  \"boxes\": $fa-var-boxes,\n  \"boxes-alt\": $fa-var-boxes-alt,\n  \"link\": $fa-var-link,\n  \"chain\": $fa-var-chain,\n  \"ear-listen\": $fa-var-ear-listen,\n  \"assistive-listening-systems\": $fa-var-assistive-listening-systems,\n  \"tree-city\": $fa-var-tree-city,\n  \"play\": $fa-var-play,\n  \"font\": $fa-var-font,\n  \"table-cells-row-lock\": $fa-var-table-cells-row-lock,\n  \"rupiah-sign\": $fa-var-rupiah-sign,\n  \"magnifying-glass\": $fa-var-magnifying-glass,\n  \"search\": $fa-var-search,\n  \"table-tennis-paddle-ball\": $fa-var-table-tennis-paddle-ball,\n  \"ping-pong-paddle-ball\": $fa-var-ping-pong-paddle-ball,\n  \"table-tennis\": $fa-var-table-tennis,\n  \"person-dots-from-line\": $fa-var-person-dots-from-line,\n  \"diagnoses\": $fa-var-diagnoses,\n  \"trash-can-arrow-up\": $fa-var-trash-can-arrow-up,\n  \"trash-restore-alt\": $fa-var-trash-restore-alt,\n  \"naira-sign\": $fa-var-naira-sign,\n  \"cart-arrow-down\": $fa-var-cart-arrow-down,\n  \"walkie-talkie\": $fa-var-walkie-talkie,\n  \"file-pen\": $fa-var-file-pen,\n  \"file-edit\": $fa-var-file-edit,\n  \"receipt\": $fa-var-receipt,\n  \"square-pen\": $fa-var-square-pen,\n  \"pen-square\": $fa-var-pen-square,\n  \"pencil-square\": $fa-var-pencil-square,\n  \"suitcase-rolling\": $fa-var-suitcase-rolling,\n  \"person-circle-exclamation\": $fa-var-person-circle-exclamation,\n  \"chevron-down\": $fa-var-chevron-down,\n  \"battery-full\": $fa-var-battery-full,\n  \"battery\": $fa-var-battery,\n  \"battery-5\": $fa-var-battery-5,\n  \"skull-crossbones\": $fa-var-skull-crossbones,\n  \"code-compare\": $fa-var-code-compare,\n  \"list-ul\": $fa-var-list-ul,\n  \"list-dots\": $fa-var-list-dots,\n  \"school-lock\": $fa-var-school-lock,\n  \"tower-cell\": $fa-var-tower-cell,\n  \"down-long\": $fa-var-down-long,\n  \"long-arrow-alt-down\": $fa-var-long-arrow-alt-down,\n  \"ranking-star\": $fa-var-ranking-star,\n  \"chess-king\": $fa-var-chess-king,\n  \"person-harassing\": $fa-var-person-harassing,\n  \"brazilian-real-sign\": $fa-var-brazilian-real-sign,\n  \"landmark-dome\": $fa-var-landmark-dome,\n  \"landmark-alt\": $fa-var-landmark-alt,\n  \"arrow-up\": $fa-var-arrow-up,\n  \"tv\": $fa-var-tv,\n  \"television\": $fa-var-television,\n  \"tv-alt\": $fa-var-tv-alt,\n  \"shrimp\": $fa-var-shrimp,\n  \"list-check\": $fa-var-list-check,\n  \"tasks\": $fa-var-tasks,\n  \"jug-detergent\": $fa-var-jug-detergent,\n  \"circle-user\": $fa-var-circle-user,\n  \"user-circle\": $fa-var-user-circle,\n  \"user-shield\": $fa-var-user-shield,\n  \"wind\": $fa-var-wind,\n  \"car-burst\": $fa-var-car-burst,\n  \"car-crash\": $fa-var-car-crash,\n  \"y\": $fa-var-y,\n  \"person-snowboarding\": $fa-var-person-snowboarding,\n  \"snowboarding\": $fa-var-snowboarding,\n  \"truck-fast\": $fa-var-truck-fast,\n  \"shipping-fast\": $fa-var-shipping-fast,\n  \"fish\": $fa-var-fish,\n  \"user-graduate\": $fa-var-user-graduate,\n  \"circle-half-stroke\": $fa-var-circle-half-stroke,\n  \"adjust\": $fa-var-adjust,\n  \"clapperboard\": $fa-var-clapperboard,\n  \"circle-radiation\": $fa-var-circle-radiation,\n  \"radiation-alt\": $fa-var-radiation-alt,\n  \"baseball\": $fa-var-baseball,\n  \"baseball-ball\": $fa-var-baseball-ball,\n  \"jet-fighter-up\": $fa-var-jet-fighter-up,\n  \"diagram-project\": $fa-var-diagram-project,\n  \"project-diagram\": $fa-var-project-diagram,\n  \"copy\": $fa-var-copy,\n  \"volume-xmark\": $fa-var-volume-xmark,\n  \"volume-mute\": $fa-var-volume-mute,\n  \"volume-times\": $fa-var-volume-times,\n  \"hand-sparkles\": $fa-var-hand-sparkles,\n  \"grip\": $fa-var-grip,\n  \"grip-horizontal\": $fa-var-grip-horizontal,\n  \"share-from-square\": $fa-var-share-from-square,\n  \"share-square\": $fa-var-share-square,\n  \"child-combatant\": $fa-var-child-combatant,\n  \"child-rifle\": $fa-var-child-rifle,\n  \"gun\": $fa-var-gun,\n  \"square-phone\": $fa-var-square-phone,\n  \"phone-square\": $fa-var-phone-square,\n  \"plus\": $fa-var-plus,\n  \"add\": $fa-var-add,\n  \"expand\": $fa-var-expand,\n  \"computer\": $fa-var-computer,\n  \"xmark\": $fa-var-xmark,\n  \"close\": $fa-var-close,\n  \"multiply\": $fa-var-multiply,\n  \"remove\": $fa-var-remove,\n  \"times\": $fa-var-times,\n  \"arrows-up-down-left-right\": $fa-var-arrows-up-down-left-right,\n  \"arrows\": $fa-var-arrows,\n  \"chalkboard-user\": $fa-var-chalkboard-user,\n  \"chalkboard-teacher\": $fa-var-chalkboard-teacher,\n  \"peso-sign\": $fa-var-peso-sign,\n  \"building-shield\": $fa-var-building-shield,\n  \"baby\": $fa-var-baby,\n  \"users-line\": $fa-var-users-line,\n  \"quote-left\": $fa-var-quote-left,\n  \"quote-left-alt\": $fa-var-quote-left-alt,\n  \"tractor\": $fa-var-tractor,\n  \"trash-arrow-up\": $fa-var-trash-arrow-up,\n  \"trash-restore\": $fa-var-trash-restore,\n  \"arrow-down-up-lock\": $fa-var-arrow-down-up-lock,\n  \"lines-leaning\": $fa-var-lines-leaning,\n  \"ruler-combined\": $fa-var-ruler-combined,\n  \"copyright\": $fa-var-copyright,\n  \"equals\": $fa-var-equals,\n  \"blender\": $fa-var-blender,\n  \"teeth\": $fa-var-teeth,\n  \"shekel-sign\": $fa-var-shekel-sign,\n  \"ils\": $fa-var-ils,\n  \"shekel\": $fa-var-shekel,\n  \"sheqel\": $fa-var-sheqel,\n  \"sheqel-sign\": $fa-var-sheqel-sign,\n  \"map\": $fa-var-map,\n  \"rocket\": $fa-var-rocket,\n  \"photo-film\": $fa-var-photo-film,\n  \"photo-video\": $fa-var-photo-video,\n  \"folder-minus\": $fa-var-folder-minus,\n  \"store\": $fa-var-store,\n  \"arrow-trend-up\": $fa-var-arrow-trend-up,\n  \"plug-circle-minus\": $fa-var-plug-circle-minus,\n  \"sign-hanging\": $fa-var-sign-hanging,\n  \"sign\": $fa-var-sign,\n  \"bezier-curve\": $fa-var-bezier-curve,\n  \"bell-slash\": $fa-var-bell-slash,\n  \"tablet\": $fa-var-tablet,\n  \"tablet-android\": $fa-var-tablet-android,\n  \"school-flag\": $fa-var-school-flag,\n  \"fill\": $fa-var-fill,\n  \"angle-up\": $fa-var-angle-up,\n  \"drumstick-bite\": $fa-var-drumstick-bite,\n  \"holly-berry\": $fa-var-holly-berry,\n  \"chevron-left\": $fa-var-chevron-left,\n  \"bacteria\": $fa-var-bacteria,\n  \"hand-lizard\": $fa-var-hand-lizard,\n  \"notdef\": $fa-var-notdef,\n  \"disease\": $fa-var-disease,\n  \"briefcase-medical\": $fa-var-briefcase-medical,\n  \"genderless\": $fa-var-genderless,\n  \"chevron-right\": $fa-var-chevron-right,\n  \"retweet\": $fa-var-retweet,\n  \"car-rear\": $fa-var-car-rear,\n  \"car-alt\": $fa-var-car-alt,\n  \"pump-soap\": $fa-var-pump-soap,\n  \"video-slash\": $fa-var-video-slash,\n  \"battery-quarter\": $fa-var-battery-quarter,\n  \"battery-2\": $fa-var-battery-2,\n  \"radio\": $fa-var-radio,\n  \"baby-carriage\": $fa-var-baby-carriage,\n  \"carriage-baby\": $fa-var-carriage-baby,\n  \"traffic-light\": $fa-var-traffic-light,\n  \"thermometer\": $fa-var-thermometer,\n  \"vr-cardboard\": $fa-var-vr-cardboard,\n  \"hand-middle-finger\": $fa-var-hand-middle-finger,\n  \"percent\": $fa-var-percent,\n  \"percentage\": $fa-var-percentage,\n  \"truck-moving\": $fa-var-truck-moving,\n  \"glass-water-droplet\": $fa-var-glass-water-droplet,\n  \"display\": $fa-var-display,\n  \"face-smile\": $fa-var-face-smile,\n  \"smile\": $fa-var-smile,\n  \"thumbtack\": $fa-var-thumbtack,\n  \"thumb-tack\": $fa-var-thumb-tack,\n  \"trophy\": $fa-var-trophy,\n  \"person-praying\": $fa-var-person-praying,\n  \"pray\": $fa-var-pray,\n  \"hammer\": $fa-var-hammer,\n  \"hand-peace\": $fa-var-hand-peace,\n  \"rotate\": $fa-var-rotate,\n  \"sync-alt\": $fa-var-sync-alt,\n  \"spinner\": $fa-var-spinner,\n  \"robot\": $fa-var-robot,\n  \"peace\": $fa-var-peace,\n  \"gears\": $fa-var-gears,\n  \"cogs\": $fa-var-cogs,\n  \"warehouse\": $fa-var-warehouse,\n  \"arrow-up-right-dots\": $fa-var-arrow-up-right-dots,\n  \"splotch\": $fa-var-splotch,\n  \"face-grin-hearts\": $fa-var-face-grin-hearts,\n  \"grin-hearts\": $fa-var-grin-hearts,\n  \"dice-four\": $fa-var-dice-four,\n  \"sim-card\": $fa-var-sim-card,\n  \"transgender\": $fa-var-transgender,\n  \"transgender-alt\": $fa-var-transgender-alt,\n  \"mercury\": $fa-var-mercury,\n  \"arrow-turn-down\": $fa-var-arrow-turn-down,\n  \"level-down\": $fa-var-level-down,\n  \"person-falling-burst\": $fa-var-person-falling-burst,\n  \"award\": $fa-var-award,\n  \"ticket-simple\": $fa-var-ticket-simple,\n  \"ticket-alt\": $fa-var-ticket-alt,\n  \"building\": $fa-var-building,\n  \"angles-left\": $fa-var-angles-left,\n  \"angle-double-left\": $fa-var-angle-double-left,\n  \"qrcode\": $fa-var-qrcode,\n  \"clock-rotate-left\": $fa-var-clock-rotate-left,\n  \"history\": $fa-var-history,\n  \"face-grin-beam-sweat\": $fa-var-face-grin-beam-sweat,\n  \"grin-beam-sweat\": $fa-var-grin-beam-sweat,\n  \"file-export\": $fa-var-file-export,\n  \"arrow-right-from-file\": $fa-var-arrow-right-from-file,\n  \"shield\": $fa-var-shield,\n  \"shield-blank\": $fa-var-shield-blank,\n  \"arrow-up-short-wide\": $fa-var-arrow-up-short-wide,\n  \"sort-amount-up-alt\": $fa-var-sort-amount-up-alt,\n  \"house-medical\": $fa-var-house-medical,\n  \"golf-ball-tee\": $fa-var-golf-ball-tee,\n  \"golf-ball\": $fa-var-golf-ball,\n  \"circle-chevron-left\": $fa-var-circle-chevron-left,\n  \"chevron-circle-left\": $fa-var-chevron-circle-left,\n  \"house-chimney-window\": $fa-var-house-chimney-window,\n  \"pen-nib\": $fa-var-pen-nib,\n  \"tent-arrow-turn-left\": $fa-var-tent-arrow-turn-left,\n  \"tents\": $fa-var-tents,\n  \"wand-magic\": $fa-var-wand-magic,\n  \"magic\": $fa-var-magic,\n  \"dog\": $fa-var-dog,\n  \"carrot\": $fa-var-carrot,\n  \"moon\": $fa-var-moon,\n  \"wine-glass-empty\": $fa-var-wine-glass-empty,\n  \"wine-glass-alt\": $fa-var-wine-glass-alt,\n  \"cheese\": $fa-var-cheese,\n  \"yin-yang\": $fa-var-yin-yang,\n  \"music\": $fa-var-music,\n  \"code-commit\": $fa-var-code-commit,\n  \"temperature-low\": $fa-var-temperature-low,\n  \"person-biking\": $fa-var-person-biking,\n  \"biking\": $fa-var-biking,\n  \"broom\": $fa-var-broom,\n  \"shield-heart\": $fa-var-shield-heart,\n  \"gopuram\": $fa-var-gopuram,\n  \"earth-oceania\": $fa-var-earth-oceania,\n  \"globe-oceania\": $fa-var-globe-oceania,\n  \"square-xmark\": $fa-var-square-xmark,\n  \"times-square\": $fa-var-times-square,\n  \"xmark-square\": $fa-var-xmark-square,\n  \"hashtag\": $fa-var-hashtag,\n  \"up-right-and-down-left-from-center\": $fa-var-up-right-and-down-left-from-center,\n  \"expand-alt\": $fa-var-expand-alt,\n  \"oil-can\": $fa-var-oil-can,\n  \"t\": $fa-var-t,\n  \"hippo\": $fa-var-hippo,\n  \"chart-column\": $fa-var-chart-column,\n  \"infinity\": $fa-var-infinity,\n  \"vial-circle-check\": $fa-var-vial-circle-check,\n  \"person-arrow-down-to-line\": $fa-var-person-arrow-down-to-line,\n  \"voicemail\": $fa-var-voicemail,\n  \"fan\": $fa-var-fan,\n  \"person-walking-luggage\": $fa-var-person-walking-luggage,\n  \"up-down\": $fa-var-up-down,\n  \"arrows-alt-v\": $fa-var-arrows-alt-v,\n  \"cloud-moon-rain\": $fa-var-cloud-moon-rain,\n  \"calendar\": $fa-var-calendar,\n  \"trailer\": $fa-var-trailer,\n  \"bahai\": $fa-var-bahai,\n  \"haykal\": $fa-var-haykal,\n  \"sd-card\": $fa-var-sd-card,\n  \"dragon\": $fa-var-dragon,\n  \"shoe-prints\": $fa-var-shoe-prints,\n  \"circle-plus\": $fa-var-circle-plus,\n  \"plus-circle\": $fa-var-plus-circle,\n  \"face-grin-tongue-wink\": $fa-var-face-grin-tongue-wink,\n  \"grin-tongue-wink\": $fa-var-grin-tongue-wink,\n  \"hand-holding\": $fa-var-hand-holding,\n  \"plug-circle-exclamation\": $fa-var-plug-circle-exclamation,\n  \"link-slash\": $fa-var-link-slash,\n  \"chain-broken\": $fa-var-chain-broken,\n  \"chain-slash\": $fa-var-chain-slash,\n  \"unlink\": $fa-var-unlink,\n  \"clone\": $fa-var-clone,\n  \"person-walking-arrow-loop-left\": $fa-var-person-walking-arrow-loop-left,\n  \"arrow-up-z-a\": $fa-var-arrow-up-z-a,\n  \"sort-alpha-up-alt\": $fa-var-sort-alpha-up-alt,\n  \"fire-flame-curved\": $fa-var-fire-flame-curved,\n  \"fire-alt\": $fa-var-fire-alt,\n  \"tornado\": $fa-var-tornado,\n  \"file-circle-plus\": $fa-var-file-circle-plus,\n  \"book-quran\": $fa-var-book-quran,\n  \"quran\": $fa-var-quran,\n  \"anchor\": $fa-var-anchor,\n  \"border-all\": $fa-var-border-all,\n  \"face-angry\": $fa-var-face-angry,\n  \"angry\": $fa-var-angry,\n  \"cookie-bite\": $fa-var-cookie-bite,\n  \"arrow-trend-down\": $fa-var-arrow-trend-down,\n  \"rss\": $fa-var-rss,\n  \"feed\": $fa-var-feed,\n  \"draw-polygon\": $fa-var-draw-polygon,\n  \"scale-balanced\": $fa-var-scale-balanced,\n  \"balance-scale\": $fa-var-balance-scale,\n  \"gauge-simple-high\": $fa-var-gauge-simple-high,\n  \"tachometer\": $fa-var-tachometer,\n  \"tachometer-fast\": $fa-var-tachometer-fast,\n  \"shower\": $fa-var-shower,\n  \"desktop\": $fa-var-desktop,\n  \"desktop-alt\": $fa-var-desktop-alt,\n  \"m\": $fa-var-m,\n  \"table-list\": $fa-var-table-list,\n  \"th-list\": $fa-var-th-list,\n  \"comment-sms\": $fa-var-comment-sms,\n  \"sms\": $fa-var-sms,\n  \"book\": $fa-var-book,\n  \"user-plus\": $fa-var-user-plus,\n  \"check\": $fa-var-check,\n  \"battery-three-quarters\": $fa-var-battery-three-quarters,\n  \"battery-4\": $fa-var-battery-4,\n  \"house-circle-check\": $fa-var-house-circle-check,\n  \"angle-left\": $fa-var-angle-left,\n  \"diagram-successor\": $fa-var-diagram-successor,\n  \"truck-arrow-right\": $fa-var-truck-arrow-right,\n  \"arrows-split-up-and-left\": $fa-var-arrows-split-up-and-left,\n  \"hand-fist\": $fa-var-hand-fist,\n  \"fist-raised\": $fa-var-fist-raised,\n  \"cloud-moon\": $fa-var-cloud-moon,\n  \"briefcase\": $fa-var-briefcase,\n  \"person-falling\": $fa-var-person-falling,\n  \"image-portrait\": $fa-var-image-portrait,\n  \"portrait\": $fa-var-portrait,\n  \"user-tag\": $fa-var-user-tag,\n  \"rug\": $fa-var-rug,\n  \"earth-europe\": $fa-var-earth-europe,\n  \"globe-europe\": $fa-var-globe-europe,\n  \"cart-flatbed-suitcase\": $fa-var-cart-flatbed-suitcase,\n  \"luggage-cart\": $fa-var-luggage-cart,\n  \"rectangle-xmark\": $fa-var-rectangle-xmark,\n  \"rectangle-times\": $fa-var-rectangle-times,\n  \"times-rectangle\": $fa-var-times-rectangle,\n  \"window-close\": $fa-var-window-close,\n  \"baht-sign\": $fa-var-baht-sign,\n  \"book-open\": $fa-var-book-open,\n  \"book-journal-whills\": $fa-var-book-journal-whills,\n  \"journal-whills\": $fa-var-journal-whills,\n  \"handcuffs\": $fa-var-handcuffs,\n  \"triangle-exclamation\": $fa-var-triangle-exclamation,\n  \"exclamation-triangle\": $fa-var-exclamation-triangle,\n  \"warning\": $fa-var-warning,\n  \"database\": $fa-var-database,\n  \"share\": $fa-var-share,\n  \"mail-forward\": $fa-var-mail-forward,\n  \"bottle-droplet\": $fa-var-bottle-droplet,\n  \"mask-face\": $fa-var-mask-face,\n  \"hill-rockslide\": $fa-var-hill-rockslide,\n  \"right-left\": $fa-var-right-left,\n  \"exchange-alt\": $fa-var-exchange-alt,\n  \"paper-plane\": $fa-var-paper-plane,\n  \"road-circle-exclamation\": $fa-var-road-circle-exclamation,\n  \"dungeon\": $fa-var-dungeon,\n  \"align-right\": $fa-var-align-right,\n  \"money-bill-1-wave\": $fa-var-money-bill-1-wave,\n  \"money-bill-wave-alt\": $fa-var-money-bill-wave-alt,\n  \"life-ring\": $fa-var-life-ring,\n  \"hands\": $fa-var-hands,\n  \"sign-language\": $fa-var-sign-language,\n  \"signing\": $fa-var-signing,\n  \"calendar-day\": $fa-var-calendar-day,\n  \"water-ladder\": $fa-var-water-ladder,\n  \"ladder-water\": $fa-var-ladder-water,\n  \"swimming-pool\": $fa-var-swimming-pool,\n  \"arrows-up-down\": $fa-var-arrows-up-down,\n  \"arrows-v\": $fa-var-arrows-v,\n  \"face-grimace\": $fa-var-face-grimace,\n  \"grimace\": $fa-var-grimace,\n  \"wheelchair-move\": $fa-var-wheelchair-move,\n  \"wheelchair-alt\": $fa-var-wheelchair-alt,\n  \"turn-down\": $fa-var-turn-down,\n  \"level-down-alt\": $fa-var-level-down-alt,\n  \"person-walking-arrow-right\": $fa-var-person-walking-arrow-right,\n  \"square-envelope\": $fa-var-square-envelope,\n  \"envelope-square\": $fa-var-envelope-square,\n  \"dice\": $fa-var-dice,\n  \"bowling-ball\": $fa-var-bowling-ball,\n  \"brain\": $fa-var-brain,\n  \"bandage\": $fa-var-bandage,\n  \"band-aid\": $fa-var-band-aid,\n  \"calendar-minus\": $fa-var-calendar-minus,\n  \"circle-xmark\": $fa-var-circle-xmark,\n  \"times-circle\": $fa-var-times-circle,\n  \"xmark-circle\": $fa-var-xmark-circle,\n  \"gifts\": $fa-var-gifts,\n  \"hotel\": $fa-var-hotel,\n  \"earth-asia\": $fa-var-earth-asia,\n  \"globe-asia\": $fa-var-globe-asia,\n  \"id-card-clip\": $fa-var-id-card-clip,\n  \"id-card-alt\": $fa-var-id-card-alt,\n  \"magnifying-glass-plus\": $fa-var-magnifying-glass-plus,\n  \"search-plus\": $fa-var-search-plus,\n  \"thumbs-up\": $fa-var-thumbs-up,\n  \"user-clock\": $fa-var-user-clock,\n  \"hand-dots\": $fa-var-hand-dots,\n  \"allergies\": $fa-var-allergies,\n  \"file-invoice\": $fa-var-file-invoice,\n  \"window-minimize\": $fa-var-window-minimize,\n  \"mug-saucer\": $fa-var-mug-saucer,\n  \"coffee\": $fa-var-coffee,\n  \"brush\": $fa-var-brush,\n  \"mask\": $fa-var-mask,\n  \"magnifying-glass-minus\": $fa-var-magnifying-glass-minus,\n  \"search-minus\": $fa-var-search-minus,\n  \"ruler-vertical\": $fa-var-ruler-vertical,\n  \"user-large\": $fa-var-user-large,\n  \"user-alt\": $fa-var-user-alt,\n  \"train-tram\": $fa-var-train-tram,\n  \"user-nurse\": $fa-var-user-nurse,\n  \"syringe\": $fa-var-syringe,\n  \"cloud-sun\": $fa-var-cloud-sun,\n  \"stopwatch-20\": $fa-var-stopwatch-20,\n  \"square-full\": $fa-var-square-full,\n  \"magnet\": $fa-var-magnet,\n  \"jar\": $fa-var-jar,\n  \"note-sticky\": $fa-var-note-sticky,\n  \"sticky-note\": $fa-var-sticky-note,\n  \"bug-slash\": $fa-var-bug-slash,\n  \"arrow-up-from-water-pump\": $fa-var-arrow-up-from-water-pump,\n  \"bone\": $fa-var-bone,\n  \"user-injured\": $fa-var-user-injured,\n  \"face-sad-tear\": $fa-var-face-sad-tear,\n  \"sad-tear\": $fa-var-sad-tear,\n  \"plane\": $fa-var-plane,\n  \"tent-arrows-down\": $fa-var-tent-arrows-down,\n  \"exclamation\": $fa-var-exclamation,\n  \"arrows-spin\": $fa-var-arrows-spin,\n  \"print\": $fa-var-print,\n  \"turkish-lira-sign\": $fa-var-turkish-lira-sign,\n  \"try\": $fa-var-try,\n  \"turkish-lira\": $fa-var-turkish-lira,\n  \"dollar-sign\": $fa-var-dollar-sign,\n  \"dollar\": $fa-var-dollar,\n  \"usd\": $fa-var-usd,\n  \"x\": $fa-var-x,\n  \"magnifying-glass-dollar\": $fa-var-magnifying-glass-dollar,\n  \"search-dollar\": $fa-var-search-dollar,\n  \"users-gear\": $fa-var-users-gear,\n  \"users-cog\": $fa-var-users-cog,\n  \"person-military-pointing\": $fa-var-person-military-pointing,\n  \"building-columns\": $fa-var-building-columns,\n  \"bank\": $fa-var-bank,\n  \"institution\": $fa-var-institution,\n  \"museum\": $fa-var-museum,\n  \"university\": $fa-var-university,\n  \"umbrella\": $fa-var-umbrella,\n  \"trowel\": $fa-var-trowel,\n  \"d\": $fa-var-d,\n  \"stapler\": $fa-var-stapler,\n  \"masks-theater\": $fa-var-masks-theater,\n  \"theater-masks\": $fa-var-theater-masks,\n  \"kip-sign\": $fa-var-kip-sign,\n  \"hand-point-left\": $fa-var-hand-point-left,\n  \"handshake-simple\": $fa-var-handshake-simple,\n  \"handshake-alt\": $fa-var-handshake-alt,\n  \"jet-fighter\": $fa-var-jet-fighter,\n  \"fighter-jet\": $fa-var-fighter-jet,\n  \"square-share-nodes\": $fa-var-square-share-nodes,\n  \"share-alt-square\": $fa-var-share-alt-square,\n  \"barcode\": $fa-var-barcode,\n  \"plus-minus\": $fa-var-plus-minus,\n  \"video\": $fa-var-video,\n  \"video-camera\": $fa-var-video-camera,\n  \"graduation-cap\": $fa-var-graduation-cap,\n  \"mortar-board\": $fa-var-mortar-board,\n  \"hand-holding-medical\": $fa-var-hand-holding-medical,\n  \"person-circle-check\": $fa-var-person-circle-check,\n  \"turn-up\": $fa-var-turn-up,\n  \"level-up-alt\": $fa-var-level-up-alt,\n);\n\n$fa-brand-icons: (\n  \"monero\": $fa-var-monero,\n  \"hooli\": $fa-var-hooli,\n  \"yelp\": $fa-var-yelp,\n  \"cc-visa\": $fa-var-cc-visa,\n  \"lastfm\": $fa-var-lastfm,\n  \"shopware\": $fa-var-shopware,\n  \"creative-commons-nc\": $fa-var-creative-commons-nc,\n  \"aws\": $fa-var-aws,\n  \"redhat\": $fa-var-redhat,\n  \"yoast\": $fa-var-yoast,\n  \"cloudflare\": $fa-var-cloudflare,\n  \"ups\": $fa-var-ups,\n  \"pixiv\": $fa-var-pixiv,\n  \"wpexplorer\": $fa-var-wpexplorer,\n  \"dyalog\": $fa-var-dyalog,\n  \"bity\": $fa-var-bity,\n  \"stackpath\": $fa-var-stackpath,\n  \"buysellads\": $fa-var-buysellads,\n  \"first-order\": $fa-var-first-order,\n  \"modx\": $fa-var-modx,\n  \"guilded\": $fa-var-guilded,\n  \"vnv\": $fa-var-vnv,\n  \"square-js\": $fa-var-square-js,\n  \"js-square\": $fa-var-js-square,\n  \"microsoft\": $fa-var-microsoft,\n  \"qq\": $fa-var-qq,\n  \"orcid\": $fa-var-orcid,\n  \"java\": $fa-var-java,\n  \"invision\": $fa-var-invision,\n  \"creative-commons-pd-alt\": $fa-var-creative-commons-pd-alt,\n  \"centercode\": $fa-var-centercode,\n  \"glide-g\": $fa-var-glide-g,\n  \"drupal\": $fa-var-drupal,\n  \"jxl\": $fa-var-jxl,\n  \"hire-a-helper\": $fa-var-hire-a-helper,\n  \"creative-commons-by\": $fa-var-creative-commons-by,\n  \"unity\": $fa-var-unity,\n  \"whmcs\": $fa-var-whmcs,\n  \"rocketchat\": $fa-var-rocketchat,\n  \"vk\": $fa-var-vk,\n  \"untappd\": $fa-var-untappd,\n  \"mailchimp\": $fa-var-mailchimp,\n  \"css3-alt\": $fa-var-css3-alt,\n  \"square-reddit\": $fa-var-square-reddit,\n  \"reddit-square\": $fa-var-reddit-square,\n  \"vimeo-v\": $fa-var-vimeo-v,\n  \"contao\": $fa-var-contao,\n  \"square-font-awesome\": $fa-var-square-font-awesome,\n  \"deskpro\": $fa-var-deskpro,\n  \"brave\": $fa-var-brave,\n  \"sistrix\": $fa-var-sistrix,\n  \"square-instagram\": $fa-var-square-instagram,\n  \"instagram-square\": $fa-var-instagram-square,\n  \"battle-net\": $fa-var-battle-net,\n  \"the-red-yeti\": $fa-var-the-red-yeti,\n  \"square-hacker-news\": $fa-var-square-hacker-news,\n  \"hacker-news-square\": $fa-var-hacker-news-square,\n  \"edge\": $fa-var-edge,\n  \"threads\": $fa-var-threads,\n  \"napster\": $fa-var-napster,\n  \"square-snapchat\": $fa-var-square-snapchat,\n  \"snapchat-square\": $fa-var-snapchat-square,\n  \"google-plus-g\": $fa-var-google-plus-g,\n  \"artstation\": $fa-var-artstation,\n  \"markdown\": $fa-var-markdown,\n  \"sourcetree\": $fa-var-sourcetree,\n  \"google-plus\": $fa-var-google-plus,\n  \"diaspora\": $fa-var-diaspora,\n  \"foursquare\": $fa-var-foursquare,\n  \"stack-overflow\": $fa-var-stack-overflow,\n  \"github-alt\": $fa-var-github-alt,\n  \"phoenix-squadron\": $fa-var-phoenix-squadron,\n  \"pagelines\": $fa-var-pagelines,\n  \"algolia\": $fa-var-algolia,\n  \"red-river\": $fa-var-red-river,\n  \"creative-commons-sa\": $fa-var-creative-commons-sa,\n  \"safari\": $fa-var-safari,\n  \"google\": $fa-var-google,\n  \"square-font-awesome-stroke\": $fa-var-square-font-awesome-stroke,\n  \"font-awesome-alt\": $fa-var-font-awesome-alt,\n  \"atlassian\": $fa-var-atlassian,\n  \"linkedin-in\": $fa-var-linkedin-in,\n  \"digital-ocean\": $fa-var-digital-ocean,\n  \"nimblr\": $fa-var-nimblr,\n  \"chromecast\": $fa-var-chromecast,\n  \"evernote\": $fa-var-evernote,\n  \"hacker-news\": $fa-var-hacker-news,\n  \"creative-commons-sampling\": $fa-var-creative-commons-sampling,\n  \"adversal\": $fa-var-adversal,\n  \"creative-commons\": $fa-var-creative-commons,\n  \"watchman-monitoring\": $fa-var-watchman-monitoring,\n  \"fonticons\": $fa-var-fonticons,\n  \"weixin\": $fa-var-weixin,\n  \"shirtsinbulk\": $fa-var-shirtsinbulk,\n  \"codepen\": $fa-var-codepen,\n  \"git-alt\": $fa-var-git-alt,\n  \"lyft\": $fa-var-lyft,\n  \"rev\": $fa-var-rev,\n  \"windows\": $fa-var-windows,\n  \"wizards-of-the-coast\": $fa-var-wizards-of-the-coast,\n  \"square-viadeo\": $fa-var-square-viadeo,\n  \"viadeo-square\": $fa-var-viadeo-square,\n  \"meetup\": $fa-var-meetup,\n  \"centos\": $fa-var-centos,\n  \"adn\": $fa-var-adn,\n  \"cloudsmith\": $fa-var-cloudsmith,\n  \"opensuse\": $fa-var-opensuse,\n  \"pied-piper-alt\": $fa-var-pied-piper-alt,\n  \"square-dribbble\": $fa-var-square-dribbble,\n  \"dribbble-square\": $fa-var-dribbble-square,\n  \"codiepie\": $fa-var-codiepie,\n  \"node\": $fa-var-node,\n  \"mix\": $fa-var-mix,\n  \"steam\": $fa-var-steam,\n  \"cc-apple-pay\": $fa-var-cc-apple-pay,\n  \"scribd\": $fa-var-scribd,\n  \"debian\": $fa-var-debian,\n  \"openid\": $fa-var-openid,\n  \"instalod\": $fa-var-instalod,\n  \"expeditedssl\": $fa-var-expeditedssl,\n  \"sellcast\": $fa-var-sellcast,\n  \"square-twitter\": $fa-var-square-twitter,\n  \"twitter-square\": $fa-var-twitter-square,\n  \"r-project\": $fa-var-r-project,\n  \"delicious\": $fa-var-delicious,\n  \"freebsd\": $fa-var-freebsd,\n  \"vuejs\": $fa-var-vuejs,\n  \"accusoft\": $fa-var-accusoft,\n  \"ioxhost\": $fa-var-ioxhost,\n  \"fonticons-fi\": $fa-var-fonticons-fi,\n  \"app-store\": $fa-var-app-store,\n  \"cc-mastercard\": $fa-var-cc-mastercard,\n  \"itunes-note\": $fa-var-itunes-note,\n  \"golang\": $fa-var-golang,\n  \"kickstarter\": $fa-var-kickstarter,\n  \"square-kickstarter\": $fa-var-square-kickstarter,\n  \"grav\": $fa-var-grav,\n  \"weibo\": $fa-var-weibo,\n  \"uncharted\": $fa-var-uncharted,\n  \"firstdraft\": $fa-var-firstdraft,\n  \"square-youtube\": $fa-var-square-youtube,\n  \"youtube-square\": $fa-var-youtube-square,\n  \"wikipedia-w\": $fa-var-wikipedia-w,\n  \"wpressr\": $fa-var-wpressr,\n  \"rendact\": $fa-var-rendact,\n  \"angellist\": $fa-var-angellist,\n  \"galactic-republic\": $fa-var-galactic-republic,\n  \"nfc-directional\": $fa-var-nfc-directional,\n  \"skype\": $fa-var-skype,\n  \"joget\": $fa-var-joget,\n  \"fedora\": $fa-var-fedora,\n  \"stripe-s\": $fa-var-stripe-s,\n  \"meta\": $fa-var-meta,\n  \"laravel\": $fa-var-laravel,\n  \"hotjar\": $fa-var-hotjar,\n  \"bluetooth-b\": $fa-var-bluetooth-b,\n  \"square-letterboxd\": $fa-var-square-letterboxd,\n  \"sticker-mule\": $fa-var-sticker-mule,\n  \"creative-commons-zero\": $fa-var-creative-commons-zero,\n  \"hips\": $fa-var-hips,\n  \"behance\": $fa-var-behance,\n  \"reddit\": $fa-var-reddit,\n  \"discord\": $fa-var-discord,\n  \"chrome\": $fa-var-chrome,\n  \"app-store-ios\": $fa-var-app-store-ios,\n  \"cc-discover\": $fa-var-cc-discover,\n  \"wpbeginner\": $fa-var-wpbeginner,\n  \"confluence\": $fa-var-confluence,\n  \"shoelace\": $fa-var-shoelace,\n  \"mdb\": $fa-var-mdb,\n  \"dochub\": $fa-var-dochub,\n  \"accessible-icon\": $fa-var-accessible-icon,\n  \"ebay\": $fa-var-ebay,\n  \"amazon\": $fa-var-amazon,\n  \"unsplash\": $fa-var-unsplash,\n  \"yarn\": $fa-var-yarn,\n  \"square-steam\": $fa-var-square-steam,\n  \"steam-square\": $fa-var-steam-square,\n  \"500px\": $fa-var-500px,\n  \"square-vimeo\": $fa-var-square-vimeo,\n  \"vimeo-square\": $fa-var-vimeo-square,\n  \"asymmetrik\": $fa-var-asymmetrik,\n  \"font-awesome\": $fa-var-font-awesome,\n  \"font-awesome-flag\": $fa-var-font-awesome-flag,\n  \"font-awesome-logo-full\": $fa-var-font-awesome-logo-full,\n  \"gratipay\": $fa-var-gratipay,\n  \"apple\": $fa-var-apple,\n  \"hive\": $fa-var-hive,\n  \"gitkraken\": $fa-var-gitkraken,\n  \"keybase\": $fa-var-keybase,\n  \"apple-pay\": $fa-var-apple-pay,\n  \"padlet\": $fa-var-padlet,\n  \"amazon-pay\": $fa-var-amazon-pay,\n  \"square-github\": $fa-var-square-github,\n  \"github-square\": $fa-var-github-square,\n  \"stumbleupon\": $fa-var-stumbleupon,\n  \"fedex\": $fa-var-fedex,\n  \"phoenix-framework\": $fa-var-phoenix-framework,\n  \"shopify\": $fa-var-shopify,\n  \"neos\": $fa-var-neos,\n  \"square-threads\": $fa-var-square-threads,\n  \"hackerrank\": $fa-var-hackerrank,\n  \"researchgate\": $fa-var-researchgate,\n  \"swift\": $fa-var-swift,\n  \"angular\": $fa-var-angular,\n  \"speakap\": $fa-var-speakap,\n  \"angrycreative\": $fa-var-angrycreative,\n  \"y-combinator\": $fa-var-y-combinator,\n  \"empire\": $fa-var-empire,\n  \"envira\": $fa-var-envira,\n  \"google-scholar\": $fa-var-google-scholar,\n  \"square-gitlab\": $fa-var-square-gitlab,\n  \"gitlab-square\": $fa-var-gitlab-square,\n  \"studiovinari\": $fa-var-studiovinari,\n  \"pied-piper\": $fa-var-pied-piper,\n  \"wordpress\": $fa-var-wordpress,\n  \"product-hunt\": $fa-var-product-hunt,\n  \"firefox\": $fa-var-firefox,\n  \"linode\": $fa-var-linode,\n  \"goodreads\": $fa-var-goodreads,\n  \"square-odnoklassniki\": $fa-var-square-odnoklassniki,\n  \"odnoklassniki-square\": $fa-var-odnoklassniki-square,\n  \"jsfiddle\": $fa-var-jsfiddle,\n  \"sith\": $fa-var-sith,\n  \"themeisle\": $fa-var-themeisle,\n  \"page4\": $fa-var-page4,\n  \"hashnode\": $fa-var-hashnode,\n  \"react\": $fa-var-react,\n  \"cc-paypal\": $fa-var-cc-paypal,\n  \"squarespace\": $fa-var-squarespace,\n  \"cc-stripe\": $fa-var-cc-stripe,\n  \"creative-commons-share\": $fa-var-creative-commons-share,\n  \"bitcoin\": $fa-var-bitcoin,\n  \"keycdn\": $fa-var-keycdn,\n  \"opera\": $fa-var-opera,\n  \"itch-io\": $fa-var-itch-io,\n  \"umbraco\": $fa-var-umbraco,\n  \"galactic-senate\": $fa-var-galactic-senate,\n  \"ubuntu\": $fa-var-ubuntu,\n  \"draft2digital\": $fa-var-draft2digital,\n  \"stripe\": $fa-var-stripe,\n  \"houzz\": $fa-var-houzz,\n  \"gg\": $fa-var-gg,\n  \"dhl\": $fa-var-dhl,\n  \"square-pinterest\": $fa-var-square-pinterest,\n  \"pinterest-square\": $fa-var-pinterest-square,\n  \"xing\": $fa-var-xing,\n  \"blackberry\": $fa-var-blackberry,\n  \"creative-commons-pd\": $fa-var-creative-commons-pd,\n  \"playstation\": $fa-var-playstation,\n  \"quinscape\": $fa-var-quinscape,\n  \"less\": $fa-var-less,\n  \"blogger-b\": $fa-var-blogger-b,\n  \"opencart\": $fa-var-opencart,\n  \"vine\": $fa-var-vine,\n  \"signal-messenger\": $fa-var-signal-messenger,\n  \"paypal\": $fa-var-paypal,\n  \"gitlab\": $fa-var-gitlab,\n  \"typo3\": $fa-var-typo3,\n  \"reddit-alien\": $fa-var-reddit-alien,\n  \"yahoo\": $fa-var-yahoo,\n  \"dailymotion\": $fa-var-dailymotion,\n  \"affiliatetheme\": $fa-var-affiliatetheme,\n  \"pied-piper-pp\": $fa-var-pied-piper-pp,\n  \"bootstrap\": $fa-var-bootstrap,\n  \"odnoklassniki\": $fa-var-odnoklassniki,\n  \"nfc-symbol\": $fa-var-nfc-symbol,\n  \"mintbit\": $fa-var-mintbit,\n  \"ethereum\": $fa-var-ethereum,\n  \"speaker-deck\": $fa-var-speaker-deck,\n  \"creative-commons-nc-eu\": $fa-var-creative-commons-nc-eu,\n  \"patreon\": $fa-var-patreon,\n  \"avianex\": $fa-var-avianex,\n  \"ello\": $fa-var-ello,\n  \"gofore\": $fa-var-gofore,\n  \"bimobject\": $fa-var-bimobject,\n  \"brave-reverse\": $fa-var-brave-reverse,\n  \"facebook-f\": $fa-var-facebook-f,\n  \"square-google-plus\": $fa-var-square-google-plus,\n  \"google-plus-square\": $fa-var-google-plus-square,\n  \"web-awesome\": $fa-var-web-awesome,\n  \"mandalorian\": $fa-var-mandalorian,\n  \"first-order-alt\": $fa-var-first-order-alt,\n  \"osi\": $fa-var-osi,\n  \"google-wallet\": $fa-var-google-wallet,\n  \"d-and-d-beyond\": $fa-var-d-and-d-beyond,\n  \"periscope\": $fa-var-periscope,\n  \"fulcrum\": $fa-var-fulcrum,\n  \"cloudscale\": $fa-var-cloudscale,\n  \"forumbee\": $fa-var-forumbee,\n  \"mizuni\": $fa-var-mizuni,\n  \"schlix\": $fa-var-schlix,\n  \"square-xing\": $fa-var-square-xing,\n  \"xing-square\": $fa-var-xing-square,\n  \"bandcamp\": $fa-var-bandcamp,\n  \"wpforms\": $fa-var-wpforms,\n  \"cloudversify\": $fa-var-cloudversify,\n  \"usps\": $fa-var-usps,\n  \"megaport\": $fa-var-megaport,\n  \"magento\": $fa-var-magento,\n  \"spotify\": $fa-var-spotify,\n  \"optin-monster\": $fa-var-optin-monster,\n  \"fly\": $fa-var-fly,\n  \"aviato\": $fa-var-aviato,\n  \"itunes\": $fa-var-itunes,\n  \"cuttlefish\": $fa-var-cuttlefish,\n  \"blogger\": $fa-var-blogger,\n  \"flickr\": $fa-var-flickr,\n  \"viber\": $fa-var-viber,\n  \"soundcloud\": $fa-var-soundcloud,\n  \"digg\": $fa-var-digg,\n  \"tencent-weibo\": $fa-var-tencent-weibo,\n  \"letterboxd\": $fa-var-letterboxd,\n  \"symfony\": $fa-var-symfony,\n  \"maxcdn\": $fa-var-maxcdn,\n  \"etsy\": $fa-var-etsy,\n  \"facebook-messenger\": $fa-var-facebook-messenger,\n  \"audible\": $fa-var-audible,\n  \"think-peaks\": $fa-var-think-peaks,\n  \"bilibili\": $fa-var-bilibili,\n  \"erlang\": $fa-var-erlang,\n  \"x-twitter\": $fa-var-x-twitter,\n  \"cotton-bureau\": $fa-var-cotton-bureau,\n  \"dashcube\": $fa-var-dashcube,\n  \"42-group\": $fa-var-42-group,\n  \"innosoft\": $fa-var-innosoft,\n  \"stack-exchange\": $fa-var-stack-exchange,\n  \"elementor\": $fa-var-elementor,\n  \"square-pied-piper\": $fa-var-square-pied-piper,\n  \"pied-piper-square\": $fa-var-pied-piper-square,\n  \"creative-commons-nd\": $fa-var-creative-commons-nd,\n  \"palfed\": $fa-var-palfed,\n  \"superpowers\": $fa-var-superpowers,\n  \"resolving\": $fa-var-resolving,\n  \"xbox\": $fa-var-xbox,\n  \"square-web-awesome-stroke\": $fa-var-square-web-awesome-stroke,\n  \"searchengin\": $fa-var-searchengin,\n  \"tiktok\": $fa-var-tiktok,\n  \"square-facebook\": $fa-var-square-facebook,\n  \"facebook-square\": $fa-var-facebook-square,\n  \"renren\": $fa-var-renren,\n  \"linux\": $fa-var-linux,\n  \"glide\": $fa-var-glide,\n  \"linkedin\": $fa-var-linkedin,\n  \"hubspot\": $fa-var-hubspot,\n  \"deploydog\": $fa-var-deploydog,\n  \"twitch\": $fa-var-twitch,\n  \"ravelry\": $fa-var-ravelry,\n  \"mixer\": $fa-var-mixer,\n  \"square-lastfm\": $fa-var-square-lastfm,\n  \"lastfm-square\": $fa-var-lastfm-square,\n  \"vimeo\": $fa-var-vimeo,\n  \"mendeley\": $fa-var-mendeley,\n  \"uniregistry\": $fa-var-uniregistry,\n  \"figma\": $fa-var-figma,\n  \"creative-commons-remix\": $fa-var-creative-commons-remix,\n  \"cc-amazon-pay\": $fa-var-cc-amazon-pay,\n  \"dropbox\": $fa-var-dropbox,\n  \"instagram\": $fa-var-instagram,\n  \"cmplid\": $fa-var-cmplid,\n  \"upwork\": $fa-var-upwork,\n  \"facebook\": $fa-var-facebook,\n  \"gripfire\": $fa-var-gripfire,\n  \"jedi-order\": $fa-var-jedi-order,\n  \"uikit\": $fa-var-uikit,\n  \"fort-awesome-alt\": $fa-var-fort-awesome-alt,\n  \"phabricator\": $fa-var-phabricator,\n  \"ussunnah\": $fa-var-ussunnah,\n  \"earlybirds\": $fa-var-earlybirds,\n  \"trade-federation\": $fa-var-trade-federation,\n  \"autoprefixer\": $fa-var-autoprefixer,\n  \"whatsapp\": $fa-var-whatsapp,\n  \"square-upwork\": $fa-var-square-upwork,\n  \"slideshare\": $fa-var-slideshare,\n  \"google-play\": $fa-var-google-play,\n  \"viadeo\": $fa-var-viadeo,\n  \"line\": $fa-var-line,\n  \"google-drive\": $fa-var-google-drive,\n  \"servicestack\": $fa-var-servicestack,\n  \"simplybuilt\": $fa-var-simplybuilt,\n  \"bitbucket\": $fa-var-bitbucket,\n  \"imdb\": $fa-var-imdb,\n  \"deezer\": $fa-var-deezer,\n  \"raspberry-pi\": $fa-var-raspberry-pi,\n  \"jira\": $fa-var-jira,\n  \"docker\": $fa-var-docker,\n  \"screenpal\": $fa-var-screenpal,\n  \"bluetooth\": $fa-var-bluetooth,\n  \"gitter\": $fa-var-gitter,\n  \"d-and-d\": $fa-var-d-and-d,\n  \"microblog\": $fa-var-microblog,\n  \"cc-diners-club\": $fa-var-cc-diners-club,\n  \"gg-circle\": $fa-var-gg-circle,\n  \"pied-piper-hat\": $fa-var-pied-piper-hat,\n  \"kickstarter-k\": $fa-var-kickstarter-k,\n  \"yandex\": $fa-var-yandex,\n  \"readme\": $fa-var-readme,\n  \"html5\": $fa-var-html5,\n  \"sellsy\": $fa-var-sellsy,\n  \"square-web-awesome\": $fa-var-square-web-awesome,\n  \"sass\": $fa-var-sass,\n  \"wirsindhandwerk\": $fa-var-wirsindhandwerk,\n  \"wsh\": $fa-var-wsh,\n  \"buromobelexperte\": $fa-var-buromobelexperte,\n  \"salesforce\": $fa-var-salesforce,\n  \"octopus-deploy\": $fa-var-octopus-deploy,\n  \"medapps\": $fa-var-medapps,\n  \"ns8\": $fa-var-ns8,\n  \"pinterest-p\": $fa-var-pinterest-p,\n  \"apper\": $fa-var-apper,\n  \"fort-awesome\": $fa-var-fort-awesome,\n  \"waze\": $fa-var-waze,\n  \"bluesky\": $fa-var-bluesky,\n  \"cc-jcb\": $fa-var-cc-jcb,\n  \"snapchat\": $fa-var-snapchat,\n  \"snapchat-ghost\": $fa-var-snapchat-ghost,\n  \"fantasy-flight-games\": $fa-var-fantasy-flight-games,\n  \"rust\": $fa-var-rust,\n  \"wix\": $fa-var-wix,\n  \"square-behance\": $fa-var-square-behance,\n  \"behance-square\": $fa-var-behance-square,\n  \"supple\": $fa-var-supple,\n  \"webflow\": $fa-var-webflow,\n  \"rebel\": $fa-var-rebel,\n  \"css3\": $fa-var-css3,\n  \"staylinked\": $fa-var-staylinked,\n  \"kaggle\": $fa-var-kaggle,\n  \"space-awesome\": $fa-var-space-awesome,\n  \"deviantart\": $fa-var-deviantart,\n  \"cpanel\": $fa-var-cpanel,\n  \"goodreads-g\": $fa-var-goodreads-g,\n  \"square-git\": $fa-var-square-git,\n  \"git-square\": $fa-var-git-square,\n  \"square-tumblr\": $fa-var-square-tumblr,\n  \"tumblr-square\": $fa-var-tumblr-square,\n  \"trello\": $fa-var-trello,\n  \"creative-commons-nc-jp\": $fa-var-creative-commons-nc-jp,\n  \"get-pocket\": $fa-var-get-pocket,\n  \"perbyte\": $fa-var-perbyte,\n  \"grunt\": $fa-var-grunt,\n  \"weebly\": $fa-var-weebly,\n  \"connectdevelop\": $fa-var-connectdevelop,\n  \"leanpub\": $fa-var-leanpub,\n  \"black-tie\": $fa-var-black-tie,\n  \"themeco\": $fa-var-themeco,\n  \"python\": $fa-var-python,\n  \"android\": $fa-var-android,\n  \"bots\": $fa-var-bots,\n  \"free-code-camp\": $fa-var-free-code-camp,\n  \"hornbill\": $fa-var-hornbill,\n  \"js\": $fa-var-js,\n  \"ideal\": $fa-var-ideal,\n  \"git\": $fa-var-git,\n  \"dev\": $fa-var-dev,\n  \"sketch\": $fa-var-sketch,\n  \"yandex-international\": $fa-var-yandex-international,\n  \"cc-amex\": $fa-var-cc-amex,\n  \"uber\": $fa-var-uber,\n  \"github\": $fa-var-github,\n  \"php\": $fa-var-php,\n  \"alipay\": $fa-var-alipay,\n  \"youtube\": $fa-var-youtube,\n  \"skyatlas\": $fa-var-skyatlas,\n  \"firefox-browser\": $fa-var-firefox-browser,\n  \"replyd\": $fa-var-replyd,\n  \"suse\": $fa-var-suse,\n  \"jenkins\": $fa-var-jenkins,\n  \"twitter\": $fa-var-twitter,\n  \"rockrms\": $fa-var-rockrms,\n  \"pinterest\": $fa-var-pinterest,\n  \"buffer\": $fa-var-buffer,\n  \"npm\": $fa-var-npm,\n  \"yammer\": $fa-var-yammer,\n  \"btc\": $fa-var-btc,\n  \"dribbble\": $fa-var-dribbble,\n  \"stumbleupon-circle\": $fa-var-stumbleupon-circle,\n  \"internet-explorer\": $fa-var-internet-explorer,\n  \"stubber\": $fa-var-stubber,\n  \"telegram\": $fa-var-telegram,\n  \"telegram-plane\": $fa-var-telegram-plane,\n  \"old-republic\": $fa-var-old-republic,\n  \"odysee\": $fa-var-odysee,\n  \"square-whatsapp\": $fa-var-square-whatsapp,\n  \"whatsapp-square\": $fa-var-whatsapp-square,\n  \"node-js\": $fa-var-node-js,\n  \"edge-legacy\": $fa-var-edge-legacy,\n  \"slack\": $fa-var-slack,\n  \"slack-hash\": $fa-var-slack-hash,\n  \"medrt\": $fa-var-medrt,\n  \"usb\": $fa-var-usb,\n  \"tumblr\": $fa-var-tumblr,\n  \"vaadin\": $fa-var-vaadin,\n  \"quora\": $fa-var-quora,\n  \"square-x-twitter\": $fa-var-square-x-twitter,\n  \"reacteurope\": $fa-var-reacteurope,\n  \"medium\": $fa-var-medium,\n  \"medium-m\": $fa-var-medium-m,\n  \"amilia\": $fa-var-amilia,\n  \"mixcloud\": $fa-var-mixcloud,\n  \"flipboard\": $fa-var-flipboard,\n  \"viacoin\": $fa-var-viacoin,\n  \"critical-role\": $fa-var-critical-role,\n  \"sitrox\": $fa-var-sitrox,\n  \"discourse\": $fa-var-discourse,\n  \"joomla\": $fa-var-joomla,\n  \"mastodon\": $fa-var-mastodon,\n  \"airbnb\": $fa-var-airbnb,\n  \"wolf-pack-battalion\": $fa-var-wolf-pack-battalion,\n  \"buy-n-large\": $fa-var-buy-n-large,\n  \"gulp\": $fa-var-gulp,\n  \"creative-commons-sampling-plus\": $fa-var-creative-commons-sampling-plus,\n  \"strava\": $fa-var-strava,\n  \"ember\": $fa-var-ember,\n  \"canadian-maple-leaf\": $fa-var-canadian-maple-leaf,\n  \"teamspeak\": $fa-var-teamspeak,\n  \"pushed\": $fa-var-pushed,\n  \"wordpress-simple\": $fa-var-wordpress-simple,\n  \"nutritionix\": $fa-var-nutritionix,\n  \"wodu\": $fa-var-wodu,\n  \"google-pay\": $fa-var-google-pay,\n  \"intercom\": $fa-var-intercom,\n  \"zhihu\": $fa-var-zhihu,\n  \"korvue\": $fa-var-korvue,\n  \"pix\": $fa-var-pix,\n  \"steam-symbol\": $fa-var-steam-symbol,\n);\n","// specific icon class definition\n// -------------------------\n\n/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen\nreaders do not read off random characters that represent icons */\n\n@each $name, $icon in $fa-icons {\n  .#{$fa-css-prefix}-#{$name}::before { content: unquote(\"\\\"#{ $icon }\\\"\"); }\n}\n\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n@import 'functions';\n@import 'variables';\n\n:root, :host {\n  --#{$fa-css-prefix}-style-family-classic: '#{ $fa-style-family }';\n  --#{$fa-css-prefix}-font-solid: normal 900 1em/1 '#{ $fa-style-family }';\n}\n\n@font-face {\n  font-family: 'Font Awesome 6 Free';\n  font-style: normal;\n  font-weight: 900;\n  font-display: $fa-font-display;\n  src: url('#{$fa-font-path}/fa-solid-900.woff2') format('woff2'),\n    url('#{$fa-font-path}/fa-solid-900.ttf') format('truetype');\n}\n\n.fas,\n.#{$fa-css-prefix}-solid {\n  font-weight: 900;\n}\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n@import 'functions';\n@import 'variables';\n\n:root, :host {\n  --#{$fa-css-prefix}-style-family-classic: '#{ $fa-style-family }';\n  --#{$fa-css-prefix}-font-regular: normal 400 1em/1 '#{ $fa-style-family }';\n}\n\n@font-face {\n  font-family: 'Font Awesome 6 Free';\n  font-style: normal;\n  font-weight: 400;\n  font-display: $fa-font-display;\n  src: url('#{$fa-font-path}/fa-regular-400.woff2') format('woff2'),\n    url('#{$fa-font-path}/fa-regular-400.ttf') format('truetype');\n}\n\n.far,\n.#{$fa-css-prefix}-regular {\n  font-weight: 400;\n}\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n@import 'functions';\n@import 'variables';\n\n:root, :host {\n  --#{$fa-css-prefix}-style-family-brands: 'Font Awesome 6 Brands';\n  --#{$fa-css-prefix}-font-brands: normal 400 1em/1 'Font Awesome 6 Brands';\n}\n\n@font-face {\n  font-family: 'Font Awesome 6 Brands';\n  font-style: normal;\n  font-weight: 400;\n  font-display: $fa-font-display;\n  src: url('#{$fa-font-path}/fa-brands-400.woff2') format('woff2'),\n    url('#{$fa-font-path}/fa-brands-400.ttf') format('truetype');\n}\n\n.fab,\n.#{$fa-css-prefix}-brands {\n  font-weight: 400;\n}\n\n@each $name, $icon in $fa-brand-icons {\n  .#{$fa-css-prefix}-#{$name}:before { content: unquote(\"\\\"#{ $icon }\\\"\"); }\n}\n","html {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n\n  // Header height will impact the top offset for many sections\n  // Article header is 66% of Header\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n$breakpoint-sidebar-primary: lg; // When we collapse the primary sidebar\n$breakpoint-sidebar-secondary: xl; // When we collapse the secondary sidebar\n$breakpoint-page-width: 88rem; // taken from sphinx-basic-ng, which we are ultimately going to inherit\n\n/*******************************************************************************\n* Define the animation behaviour\n*/\n$animation-time: 200ms;\n\n/*******************************************************************************\n* UI shaping and padding\n*/\n$admonition-border-radius: 0.25rem;\n\n// In this theme, some focus rings have rounded corners while others do not.\n// This variable sets the border radius for the rounded focus rings.\n$focus-ring-radius: 0.125rem; // 2px at 100% zoom and 16px base font.\n\n$navbar-link-padding-y: 0.25rem;\n\n// Ensure that there is no overlap of bumper disks (smallest circle that\n// contains the bounding box of the interactive element).\n// - https://github.com/Quansight-Labs/czi-scientific-python-mgmt/issues/81#issuecomment-2251325783\n$nav-icon-column-gap: 1.12rem;\n\n// Determines vertical space between entries in both the section (left/primary\n// sidebar) and page (right/secondary sidebar) table of contents\n$toc-item-padding-y: 0.25rem;\n","html {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n\n  // base font size - applied at body/html level\n  --pst-font-size-base: 1rem;\n\n  // heading font sizes based on a medium contrast type scale\n  // - see: https://github.com/Quansight-Labs/czi-scientific-python-mgmt/issues/97#issuecomment-2310531483\n  --pst-font-size-h1: 2.625rem;\n  --pst-font-size-h2: 2.125rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1rem;\n\n  // smaller than heading font sizes\n  --pst-font-size-milli: 0.9rem;\n\n  // Sidebar styles\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n\n  // Admonition styles\n  --pst-admonition-font-weight-heading: 600;\n\n  // Font weights\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 600;\n\n  // Font family\n  // These are adapted from https://systemfontstack.com/ */\n  --pst-font-family-base-system: -apple-system, \"BlinkMacSystemFont\", \"Segoe UI\",\n    \"Helvetica Neue\", \"Arial\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\",\n    \"Segoe UI Symbol\";\n  --pst-font-family-monospace-system: \"SFMono-Regular\", \"Menlo\", \"Consolas\",\n    \"Monaco\", \"Liberation Mono\", \"Lucida Console\", monospace;\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n\n$line-height-body: 1.65;\n$fa-font-path: \"vendor/fontawesome/webfonts/\";\n","html {\n  /*****************************************************************************\n  * Icon\n  */\n\n  // Font size across all icons\n  --pst-font-size-icon: 1.5rem;\n\n  // Font Awesome default icons\n  --pst-icon-check-circle: \"\\f058\"; // fa-solid fa-circle-check\n  --pst-icon-info-circle: \"\\f05a\"; // fa-solid fa-circle-info\n  --pst-icon-exclamation-triangle: \"\\f071\"; // fa-solid fa-triangle-exclamation\n  --pst-icon-exclamation-circle: \"\\f06a\"; // fa-solid fa-circle-exclamation\n  --pst-icon-times-circle: \"\\f057\"; // fa-solid fa-circle-xmark\n  --pst-icon-lightbulb: \"\\f0eb\"; // fa-solid fa-lightbulb\n  --pst-icon-download: \"\\f019\"; // fa-solid fa-download\n  --pst-icon-angle-left: \"\\f104\"; // fa-solid fa-angle-left\n  --pst-icon-angle-right: \"\\f105\"; // fa-solid fa-angle-right\n  --pst-icon-external-link: \"\\f35d\"; // fa-solid fa-up-right-from-square\n  --pst-icon-search-minus: \"\\f010\"; // fa-solid fa-magnifying-glass-minus\n  --pst-icon-github: \"\\f09b\"; // fa-brands fa-github\n  --pst-icon-gitlab: \"\\f296\"; // fa-brands fa-gitlab\n  --pst-icon-share: \"\\f064\"; // fa-solid fa-share\n  --pst-icon-bell: \"\\f0f3\"; // fa-solid fa-bell\n  --pst-icon-pencil: \"\\f303\"; // fa-solid fa-pencil\n\n  // Bootstrap icons\n  --pst-breadcrumb-divider: \"\\f105\";\n}\n","html {\n  /*****************************************************************************\n  * Admonitions\n  **/\n\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n","html {\n  /*****************************************************************************\n  * versionmodified\n  **/\n\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n","html {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: $line-height-body;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n\n  // hack to avoid the black background on some browser including Safari\n  &::-webkit-scrollbar-track {\n    background-color: var(--pst-color-background);\n  }\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n\n  /* section header in docstring pages */\n  &.rubric {\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  &.centered {\n    text-align: center;\n  }\n}\n\na {\n  @include link-style-default;\n\n  // The # anchor that appears on hover over headings\n  &.headerlink {\n    color: var(--pst-color-secondary);\n    font-size: 0.8em;\n    padding: 0 4px;\n    margin-left: 0.2em;\n    text-decoration: none;\n    user-select: none;\n  }\n\n  // set up a icon next to the shorten links from github and gitlab\n  &.github,\n  &.gitlab {\n    &::before {\n      color: var(--pst-color-text-muted);\n      font: var(--fa-font-brands);\n      margin-right: 0.25rem;\n    }\n  }\n\n  &.github::before {\n    content: var(--pst-icon-github);\n  }\n\n  &.gitlab::before {\n    content: var(--pst-icon-gitlab);\n  }\n}\n\n%heading-style {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1 {\n  @extend %heading-style;\n\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-heading-color);\n}\n\nh2 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-heading-color);\n}\n\nh3 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-heading-color);\n}\n\nh4 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-heading-color);\n}\n\nh5 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  // use theme negative\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n\n  // Compound keyboard elements will have nested kbd in them, so this prevents double lines\n  &:not(.compound) {\n    border: 1px solid var(--pst-color-border);\n    margin: 0 0.1rem;\n    padding: 0.1rem 0.4rem;\n    box-shadow: 1px 1px 1px var(--pst-color-shadow);\n  }\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  .linenos {\n    // minimum opacity to make the line numbers WCAG AA conformant\n    opacity: 0.8;\n    padding-right: 10px;\n  }\n}\n\n// the back to top btn\n#pst-back-to-top {\n  z-index: $zindex-tooltip;\n  position: fixed;\n  display: none;\n  top: 90vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n\n  .fa-arrow-up {\n    // Using margin instead of a space character prevents the space between the\n    // icon and the text from being underlined when the button is hovered.\n    margin-inline-end: 0.5em;\n  }\n\n  &:hover {\n    @include link-style-hover;\n\n    text-decoration-thickness: 1px;\n    background-color: var(--pst-color-secondary-highlight);\n    color: var(--pst-color-secondary-highlight-text);\n  }\n\n  &:focus-visible {\n    box-shadow: none;\n    outline: $focus-ring-outline;\n    outline-color: var(--pst-color-secondary);\n    outline-offset: $focus-ring-width;\n  }\n}\n\n// Focus ring\n// ----------\n// Note: The Bootstrap stylesheet provides the focus ring (customized by this\n// theme via Sass variables in _bootstrap.scss) in some cases. This rule covers\n// all other cases.\n:focus-visible {\n  outline: $focus-ring-outline;\n  box-shadow: none; // override Bootstrap\n}\n","// Consistent styling for links\n// ============================\n\n@use \"sass:string\";\n\n// Define some useful variables for links styling consistency\n\n// The default thickness of the underline for links will be either:\n//  - 1px\n//  - 0.0625rem if it's thicker than 1px because the user has changed the text\n//    size in their browser\n$link-underline-thickness: string.unquote(\"max(1px, .0625rem)\") !default;\n\n// Offset of link underlines from text baseline\n// The default is 3px expressed as ems, as calculated against the default body\n// font size (on desktop).\n$link-underline-offset: 0.1578em !default;\n\n// Thickness of link underlines in hover state\n// The default for each link will be the thickest of the following:\n//  - 3px\n//  - 0.1875rem, if it's thicker than 3px because the user has changed the text\n//    size in their browser\n//  - 0.12em (relative to the link's text size)\n$link-hover-decoration-thickness: string.unquote(\n  \"max(3px, .1875rem, .12em)\"\n) !default;\n\n// Ensures links have an underline decoration by default - needed to meet\n// WCAG SC 1.4.1\n@mixin link-decoration {\n  text-decoration: underline;\n\n  @if $link-underline-thickness {\n    text-decoration-thickness: $link-underline-thickness;\n  }\n\n  @if $link-underline-offset {\n    text-underline-offset: $link-underline-offset;\n  }\n}\n\n// Ensures links have an underline decoration on hover - distinct from the\n// default behaviour\n@mixin link-decoration-hover {\n  @if $link-hover-decoration-thickness {\n    text-decoration-thickness: $link-hover-decoration-thickness;\n\n    // Disable ink skipping on underlines on hover.\n    text-decoration-skip-ink: none;\n  }\n}\n\n// Simple hover style - can be used alone or in conjunction with other mixins\n// Add the text underline and change in thickness on hover.\n// Intended for use with the `:hover` pseudo-class.\n@mixin link-style-hover {\n  @include link-decoration;\n  @include link-decoration-hover;\n\n  color: var(--pst-color-link-hover);\n}\n\n// Default link styles\n// -------------------\n// Defines: default unvisited, visited, hover, and active.\n// TODO: @trallard to improve focus styles in subsequent PR\n@mixin link-style-default {\n  // So that really long links don't spill out of their container\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n\n  @include link-decoration;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to add active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link);\n  }\n\n  // Visited should still be hoverable\n  &:visited {\n    color: var(--pst-color-link);\n\n    &:hover {\n      color: var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Text link styles\n// ----------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation bar links.\n@mixin link-style-text {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration;\n    @include link-decoration-hover;\n  }\n}\n\n// Sidebar and TOC links\n// ---------------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation the primary sidebar and table of contents.\n// Active and hover should work together rather than one overriding the other.\n@mixin link-sidebar {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    text-decoration: underline;\n    background-color: transparent;\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to update active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    box-shadow: $focus-ring-box-shadow;\n    outline: none;\n    z-index: 10; // keep focus ring on top (prevent the link-sidebar-current notch from lying on top of the ring)\n  }\n}\n\n// Sidebar current page link styles\n// --------------------------------\n// Adds a vertical line on the left hand side of the link to indicate that\n// it's the current page. Note this is distinct from an active state.\n// Used on the primary sidebar and the TOC.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-sidebar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n\n  @if $link-hover-decoration-thickness {\n    $notch-shadow: inset\n      $link-hover-decoration-thickness\n      0\n      0\n      var(--pst-color-primary);\n\n    box-shadow: $notch-shadow;\n\n    &:focus-visible {\n      box-shadow: $notch-shadow, $focus-ring-box-shadow;\n      outline: none;\n    }\n  }\n}\n\n// Header navbar text and icon links\n// ---------------------------------\n// (includes light/dark mode button)\n\n// This mixin makes it possible to show hover/underline and focus/ring styles at\n// the same time. The trick is to use:\n//    - a pseudo-element with bottom border for the hover underline\n//    - a CSS outline for the focus ring.\n\n// Normally we use box-shadow for underline and outline for focus ring. But we\n// cannot apply box-shadow and outline together on the same element because the\n// border-radius value that we use to round the outline will also round the\n// box-shadow used for the underline. We also cannot use text-underline because\n// it does not work on non-text links, nor do we want to use it on text links\n// that we want to treat as blocks, such as the header nav links because the\n// underline will wrap across two lines if the link text also wraps across two\n// lines.\n@mixin link-style-block {\n  color: var(--pst-color-text-muted);\n\n  // Set position relative so that the child ::before pseudo-element's absolute\n  // position is relative to this element.\n  position: relative;\n\n  // Set up pseudo-element used for hover underline styles\n  &::before {\n    content: \"\";\n    display: block;\n    position: absolute;\n    inset: 0;\n    background-color: transparent;\n\n    @if $link-hover-decoration-thickness {\n      bottom: calc(-1 * $link-hover-decoration-thickness);\n      margin: $link-hover-decoration-thickness 0;\n    }\n  }\n\n  &:hover {\n    color: var(--pst-color-secondary);\n    text-decoration: none; // override the link-style-hover mixin\n    &::before {\n      @if $link-hover-decoration-thickness {\n        border-bottom: $link-hover-decoration-thickness\n          solid\n          var(--pst-color-secondary);\n      }\n    }\n  }\n\n  &:focus-visible {\n    box-shadow: none; // override Bootstrap\n    outline: 3px solid var(--pst-color-accent);\n    outline-offset: 3px;\n  }\n}\n","// Override bootstrap variables\n$spacer: 1rem;\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n$dropdown-link-hover-bg: var(--pst-color-surface);\n\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n$focus-ring-width: 0.1875rem; // 3px at 100% zoom (0.1875 * 16px base font = 3px)\n$focus-ring-opacity: 1;\n$focus-ring-color: var(--pst-color-accent);\n$focus-ring-blur: 0;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color;\n\n// outline creates the same style of focus ring, it just uses CSS outline instead of box shadow\n$focus-ring-outline: $focus-ring-color solid $focus-ring-width;\n$btn-focus-box-shadow: $focus-ring-box-shadow;\n","/***\n * Rules for the UX/UI of skip navigation link btn.\n * It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  padding: 0.5rem;\n  z-index: $zindex-modal;\n  border-bottom: 1px solid var(--pst-color-border);\n\n  // This shows / hides the button\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n\n  &:focus-within {\n    transform: translateY(0%);\n  }\n\n  a {\n    // Ensure we are using a WCAG conformant colour\n    color: var(--pst-color-warning-text) !important;\n\n    &:focus-visible {\n      // use color with sufficient contrast\n      outline-color: $foundation-black;\n    }\n  }\n}\n",".bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n\n  .bd-container__inner {\n    display: flex;\n  }\n}\n\n.bd-page-width {\n  width: 100%;\n\n  @include media-breakpoint-up(lg) {\n    max-width: $breakpoint-page-width;\n  }\n}\n",".pst-async-banner-revealer {\n  // Setting height to 0 and overflow to hidden allows us to add up the heights\n  // of this element's children before revealing them.\n  height: 0;\n  overflow: hidden;\n\n  // Height to be set by JavaScript, which should trigger the following\n  // transition rule (unless the user has set their system to reduce motion).\n  transition: height 300ms ease-in-out;\n\n  @media (prefers-reduced-motion) {\n    transition: none;\n  }\n}\n\n#bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%; // Horizontal padding so the width is 75%\n  // One breakpoint less than $breakpoint-sidebar-primary. See variables/_layout.scss for more info.\n  @include media-breakpoint-down(lg) {\n    // Announcements can take a bit more width on mobile\n    padding: 0.5rem 2%;\n  }\n\n  p {\n    font-weight: bold;\n    margin: 0;\n  }\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // The \"Switch to stable version\" link (styled like a button)\n  .pst-button-link-to-stable-version {\n    @include box-shadow;\n\n    $background-color: var(--pst-color-danger);\n    $hover-background-color: var(--pst-color-danger-highlight);\n    $color: var(--pst-color-danger-text);\n\n    background-color: $background-color;\n    border-color: $background-color;\n    color: $color;\n    border-radius: 0.25rem;\n\n    &:hover {\n      // applying the same style as the SD \"buttons\"\n      @include link-style-hover;\n\n      text-decoration-thickness: 1px;\n      background-color: $hover-background-color;\n      border-color: $hover-background-color;\n      color: var(--pst-color-danger-highlight-text);\n    }\n\n    &:focus-visible {\n      outline: $focus-ring-width solid $background-color;\n      outline-offset: $focus-ring-width;\n    }\n  }\n}\n\n// Bg color is now defined in the theme color palette - using our secondary color\n.bd-header-announcement {\n  background-color: var(--pst-color-secondary-bg);\n}\n\n#bd-header-version-warning {\n  background-color: var(--pst-color-danger-bg);\n}\n","/*********************************************\n* SASS Mixins\n*********************************************/\n\n/**\n * A consistent box shadow style we apply across elements.\n */\n@mixin box-shadow() {\n  box-shadow:\n    0 0.2rem 0.5rem var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\n@mixin cell-output-background {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n\n@mixin table-colors {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n\n  th,\n  td {\n    ~ th,\n    ~ td {\n      border-left: 1px solid var(--pst-color-table-inner-border);\n    }\n  }\n\n  thead {\n    tr {\n      background-color: var(--pst-color-table-heading-bg);\n      border-bottom: 2px solid var(--pst-color-primary);\n    }\n  }\n\n  tbody {\n    tr {\n      &:nth-child(odd) {\n        background-color: var(--pst-color-table-row-zebra-low-bg);\n      }\n\n      &:nth-child(even) {\n        background-color: var(--pst-color-table-row-zebra-high-bg);\n      }\n\n      &:hover {\n        background-color: var(--pst-color-table-row-hover-bg);\n      }\n    }\n  }\n}\n\n// Minimum mouse hit area\n// ----------------------\n// Ensures that the element has a minimum hit area that conforms to\n// accessibility guidelines. For WCAG AA, we need 24px x 24px, see:\n// https://www.w3.org/WAI/WCAG22/Understanding/target-size-minimum.html\n@mixin min-hit-area() {\n  box-sizing: border-box;\n  min-width: 24px;\n  min-height: 24px;\n}\n","/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n\n  .bd-content {\n    display: flex;\n    justify-content: center;\n    height: 100%;\n\n    .bd-article-container {\n      justify-content: start;\n      display: flex;\n      flex-direction: column;\n\n      // Max-width is slightly more than the W3 since our docs often have images.\n      // We shoot for about 100 characters per line instead of 80.\n      // ref: https://www.w3.org/WAI/tutorials/page-structure/styling/#line-length\n      width: 100%;\n      max-width: 60em;\n      overflow-x: auto; // Prevent wide content from pushing off the secondary sidebar\n      padding: 1rem;\n\n      .bd-article {\n        // Give a bit more verticle spacing on wide screens\n        @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n          padding-top: 1.5rem;\n          padding-left: 2rem;\n        }\n      }\n    }\n  }\n}\n",".bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n\n  .bd-footer__inner {\n    display: flex;\n    flex-grow: 1;\n    padding: 1rem;\n    margin: auto;\n  }\n\n  .footer-items__start,\n  .footer-items__center,\n  .footer-items__end {\n    display: flex;\n    flex-direction: column;\n    gap: 0.5rem;\n    justify-content: center;\n    flex-grow: 1;\n  }\n\n  .footer-items__center {\n    text-align: center;\n  }\n\n  .footer-items__end {\n    text-align: end;\n  }\n\n  // So that paragraphs don't take up extra room\n  .footer-item p {\n    margin-bottom: 0;\n  }\n}\n",".bd-footer-article {\n  margin-top: auto;\n\n  .footer-article-items {\n    display: flex;\n    flex-direction: column;\n  }\n}\n",".bd-footer-content {\n  .footer-content-items {\n    display: flex;\n    flex-direction: column;\n    margin-top: auto;\n  }\n}\n","/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n\n// Styling for the Icon links can be found in components/_icon-links.scss\n\n// If we want the shadow to only point downward in the future, set\n// box-shadow to: 0 0.125rem 0.25rem -0.125rem rgba(0, 0, 0, 0.11);\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: $zindex-fixed;\n\n  // Overrides bootstrap\n  background-color: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n\n  .bd-header__inner {\n    display: flex;\n    align-items: center;\n    height: fit-content;\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // These items will define the height of the header\n  .navbar-item {\n    height: var(--pst-header-height);\n    max-height: var(--pst-header-height);\n    display: flex;\n    align-items: center;\n  }\n\n  // Hide the navbar header items on mobile because they're in the sidebar\n  .navbar-header-items {\n    display: none;\n    flex-shrink: 1;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: inherit;\n      flex-grow: 1;\n      padding: 0 0 0 0.5rem;\n    }\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center,\n  .navbar-header-items__start {\n    display: flex;\n    align-items: center;\n    flex-flow: wrap;\n\n    // In case we wrap our items to multiple rows on small screens\n    row-gap: 0;\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center {\n    column-gap: $nav-icon-column-gap;\n  }\n\n  // A little smaller because this is displayed by default on mobile\n  .navbar-header-items__start {\n    flex-shrink: 0;\n    margin-right: auto;\n    gap: 0.5rem;\n  }\n\n  .navbar-header-items__end {\n    // End navbar items should snap to the right\n    justify-content: end;\n  }\n\n  // Contains the navigation links within the navbar\n  ul.navbar-nav {\n    display: flex;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      // Align on wide screens so the dropdown button is centered properly\n      align-items: baseline;\n    }\n\n    > li.nav-item {\n      margin-inline: 2px; // breathing room so hover and focus styles do not overlap\n\n      > .nav-link {\n        @include link-style-block;\n\n        padding-inline: 6px;\n      }\n\n      &.current {\n        > .nav-link {\n          color: var(--pst-color-primary);\n\n          // Underline the current navbar item\n          &::before {\n            border-bottom: 3px solid var(--pst-color-primary);\n          }\n        }\n      }\n\n      &.dropdown {\n        margin-inline: 4px;\n\n        button {\n          padding-inline: 8px;\n        }\n\n        > .dropdown-toggle {\n          border-radius: $focus-ring-radius; // make border radius the same for both hover ring and focus ring\n          color: var(--pst-color-text-muted);\n\n          &:focus-visible {\n            box-shadow: $focus-ring-box-shadow;\n          }\n\n          &:hover {\n            text-decoration: none;\n            box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover); // purple focus ring\n            // Brighten the text on hover (muted -> base)\n            color: var(--pst-color-text-base);\n          }\n        }\n      }\n    }\n\n    li a.nav-link.dropdown-item {\n      @include link-style-text;\n    }\n\n    // Dropdowns for the extra links\n    .dropdown {\n      button {\n        display: unset;\n        border: none;\n\n        &:hover {\n          @include link-style-hover;\n        }\n      }\n\n      .dropdown-menu {\n        z-index: $zindex-popover;\n        border: 1px solid var(--pst-color-border);\n        box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n        background-color: var(--pst-color-on-background);\n        padding: 0.5rem 0;\n        margin: 0.5rem 0;\n        min-width: 20rem;\n\n        .dropdown-item {\n          // Give the items in the dropdown some breathing room but let the hit\n          // and hover area of the items extend to the edges of the menu\n          padding: 0.25rem 1.5rem;\n\n          // Override Bootstrap\n          &:focus:not(:hover, :active) {\n            background-color: inherit;\n          }\n\n          &:focus-visible {\n            z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n          }\n        }\n\n        // Hide the menu unless show has been clicked\n        &:not(.show) {\n          display: none;\n        }\n      }\n    }\n  }\n\n  // **************************************************************\n  // Showing and hiding the sidebar toggle buttons and header items\n  // **************************************************************\n\n  // Toggle buttons\n  button.sidebar-toggle {\n    font-size: var(--pst-font-size-icon);\n    color: var(--pst-color-muted);\n    margin-bottom: 0;\n    background-color: inherit;\n    padding: 0.5rem;\n  }\n\n  button.primary-toggle {\n    margin-right: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  button.secondary-toggle {\n    margin-left: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      display: none;\n    }\n  }\n}\n\n// inline the element in the navbar as long as they fit and use display block when collapsing\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n\n.nav-link {\n  &:hover {\n    @include link-style-hover;\n  }\n\n  // Override Bootstrap\n  transition: none;\n\n  &.nav-external::after {\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-external-link);\n    font-size: 0.75em;\n    margin-left: 0.3em;\n  }\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n// THe elements next to the hamburger menu only show on narrow screens\n.navbar-persistent--mobile {\n  margin-left: auto;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: none;\n  }\n}\n\n// The navbar-persistent content should only show on wide screens\n.navbar-persistent--container {\n  display: none;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: flex;\n  }\n}\n",".header-article__inner {\n  padding: 0 0.5rem;\n\n  // The items define the height so that it disappears if there are no items\n  .header-article-item {\n    min-height: var(--pst-header-article-height);\n  }\n\n  .header-article-items__end {\n    margin-left: auto;\n  }\n}\n","/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n\n$sidebar-padding-right: 1rem;\n\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n\n  @include make-col(3);\n\n  // Borders padding and whitespace\n  padding: 2rem $sidebar-padding-right 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // override bootstrap when navlink are displayed in the sidebar\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n  }\n\n  &.no-sidebar {\n    border-right: 0;\n  }\n\n  &.hide-on-wide {\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  // Headers shouldn't be colored in the sidebars and some extensions add headers\n  h1,\n  h2,\n  h3,\n  h4 {\n    color: var(--pst-color-text-base);\n  }\n\n  .sidebar-primary-items__start,\n  .sidebar-primary-items__end {\n    .sidebar-primary-item {\n      padding: 0.5rem 0;\n    }\n  }\n\n  // Hide the sidebar header items on widescreen since they are visible in the header\n  .sidebar-header-items {\n    display: flex;\n    flex-direction: column;\n\n    .sidebar-header-items__title {\n      font-weight: var(--pst-sidebar-header-font-weight);\n      font-size: var(--pst-sidebar-header-font-size);\n      color: var(--pst-color-text-base);\n      margin-bottom: 0.5rem;\n    }\n\n    .bd-navbar-elements {\n      .nav-link {\n        &:focus-visible {\n          box-shadow: none; // Override Bootstrap\n          outline: $focus-ring-outline;\n          outline-offset: $focus-ring-width;\n        }\n      }\n    }\n\n    // Center header items get displayed vertically, end items are displayed horizontally\n    .sidebar-header-items__center {\n      display: flex;\n      flex-direction: column;\n    }\n\n    // Positioning end items\n    .sidebar-header-items__end {\n      display: flex;\n      align-items: center;\n      gap: 1rem;\n    }\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  .sidebar-primary-items__start {\n    // Add a border on mobile to separate it from the header sidebar area\n    border-top: 1px solid var(--pst-color-border);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      border-top: none;\n    }\n  }\n\n  .sidebar-primary-items__end {\n    margin-top: auto;\n    margin-bottom: 1em;\n  }\n\n  .list-caption {\n    list-style: none;\n    padding-left: 0;\n\n    // Level 0 TOC heading is put inside the <summary> tag\n    // so let the <summary> tag take up more space\n    li.toctree-l0.has-children {\n      > details {\n        > summary {\n          position: relative;\n          height: auto;\n          width: auto;\n          display: flex;\n          justify-content: space-between;\n          align-items: baseline;\n\n          .toctree-toggle {\n            // Prevent toggle icon from getting squished by summary being a\n            // flexbox\n            flex: 0 0 auto;\n\n            // Make the level 0 chevron icon slightly bigger than descendant\n            // levels\n            .fa-chevron-down {\n              font-size: 1rem;\n            }\n          }\n        }\n      }\n    }\n  }\n\n  li.has-children {\n    $toctree-toggle-width: 30px;\n\n    position: relative;\n\n    > .reference,\n    .caption {\n      margin-right: calc(\n        $toctree-toggle-width + $focus-ring-width\n      ); // keep clear of the toggle icon\n\n      padding-top: 0.25rem; // align caption text with toggle chevron\n    }\n\n    > details {\n      > summary {\n        // Remove browser default toggle icon\n        list-style: none;\n\n        &::-webkit-details-marker {\n          display: none;\n        }\n\n        // The summary element is natively focusable, but delegate the focus state to the toggle icon\n        &:focus-visible {\n          outline: none;\n\n          > .toctree-toggle {\n            outline: $focus-ring-outline;\n            outline-offset: -$focus-ring-width; // Prevent right side of focus ring from disappearing underneath the sidebar's right edge\n          }\n        }\n\n        // Container for expand/collapse chevron icon\n        .toctree-toggle {\n          cursor: pointer;\n\n          // Position it so that it's aligned with the top right corner of the\n          // last positioned element, in this case the li.has-children\n          position: absolute;\n          top: 0;\n          right: 0;\n\n          // Give it dimensions\n          width: $toctree-toggle-width;\n          height: $toctree-toggle-width; // make it square\n\n          // Vertically and horizontally center the icon within the container\n          display: inline-flex;\n          justify-content: center;\n          align-items: center;\n\n          .fa-chevron-down {\n            font-size: 0.75rem;\n          }\n        }\n      }\n\n      // The section is open/expanded, rotate the toggle icon (chevron) so it\n      // points up instead of down\n      &[open] {\n        > summary {\n          .fa-chevron-down {\n            transform: rotate(180deg);\n          }\n        }\n      }\n    }\n  }\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -$sidebar-padding-right; // align toctree toggle chevrons with right edge of sidebar and allow text to flow closer to the right edge\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: block;\n  }\n\n  ul {\n    display: block;\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  li > a {\n    display: block;\n    padding: $toc-item-padding-y 0.65rem;\n\n    @include link-sidebar;\n\n    box-shadow: none;\n    margin-right: $focus-ring-width; // prevent the right side focus ring from disappearing under the sidebar right edge\n\n    &.reference.external {\n      &::after {\n        font: var(--fa-font-solid);\n        content: var(--pst-icon-external-link);\n        font-size: 0.75em;\n        margin-left: 0.3em;\n      }\n    }\n  }\n\n  .current > a {\n    @include link-sidebar-current;\n\n    background-color: transparent;\n  }\n\n  // Title\n  p.bd-links__title {\n    font-size: var(--pst-sidebar-header-font-size);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n\n  // Toctree captions\n  p.caption {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    position: relative;\n    margin-top: 1.25rem;\n    margin-bottom: 0.5rem;\n    color: var(--pst-color-text-base);\n\n    &:first-child {\n      margin-top: 0;\n    }\n\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n}\n","/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // Color and border\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem;\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n\n  i {\n    padding-right: 0.5rem;\n  }\n}\n","/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n\n/**\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n@mixin sliding-drawer($side: \"left\") {\n  position: fixed;\n  top: 0;\n  z-index: $zindex-modal;\n  height: 100vh;\n  max-height: 100vh;\n  width: 75%;\n  flex-grow: 0.75;\n  max-width: 350px;\n  transition:\n    visibility $animation-time ease-out,\n    margin $animation-time ease-out;\n  visibility: hidden;\n  border: 0;\n\n  @if $side == \"right\" {\n    margin-right: -75%;\n    right: 0;\n  } @else {\n    margin-left: -75%;\n    left: 0;\n  }\n}\n\n.bd-sidebar::backdrop {\n  background-color: black;\n  opacity: 0.5;\n}\n\n.bd-sidebar-primary {\n  @include media-breakpoint-down($breakpoint-sidebar-primary) {\n    @include sliding-drawer(\"left\");\n  }\n\n  &[open] {\n    margin-left: 0;\n    visibility: visible;\n  }\n}\n\n.bd-sidebar-secondary {\n  @include media-breakpoint-down($breakpoint-sidebar-secondary) {\n    @include sliding-drawer(\"right\");\n  }\n\n  &[open] {\n    margin-right: 0;\n    visibility: visible;\n  }\n}\n","/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n\n  // Font size slightly smaller to avoid cluttering up space too much\n  font-size: 0.8rem;\n\n  li.breadcrumb-item {\n    display: flex;\n    align-items: center;\n    white-space: nowrap;\n    overflow-x: hidden;\n\n    a,\n    .ellipsis {\n      overflow-x: hidden;\n      text-overflow: ellipsis;\n\n      // Need to add margin, otherwise the overflow: hidden rule on the parent\n      // will hide the focus ring\n      margin: $focus-ring-width;\n    }\n\n    // Style should look like heavier in-page links\n    // keeping visited in the default link colour\n    font-weight: bold;\n\n    a {\n      @include link-style-text;\n    }\n\n    // Items that aren't the home have a caret to the left\n    &:not(.breadcrumb-home)::before {\n      font: var(--fa-font-solid);\n      font-size: 0.8rem;\n      content: var(--pst-breadcrumb-divider);\n      color: var(--pst-color-text-muted);\n      padding: 0 0.5rem;\n    }\n  }\n}\n","/**\n * Icon links in the navbar\n */\n\n.pst-navbar-icon {\n  // Extra specificity needed for overrides\n  html & {\n    @include min-hit-area;\n    @include link-style-block;\n\n    display: flex;\n    align-items: center;\n    justify-content: center;\n\n    // Bootstrap overrides\n    border-radius: 0;\n    border: none;\n    font-size: 1rem;\n    line-height: $line-height-body; // Override Boostrap, which defines a separate line-height for buttons\n    padding: $navbar-link-padding-y 0; // Horizontal white space in nav bar between items is controlled via column gap rule on the container.\n\n    // Make the navbar icon links have the same size as the navbar text links\n    height: calc(2 * $navbar-link-padding-y + $line-height-body * 1rem);\n  }\n}\n\nul.navbar-icon-links {\n  display: flex;\n  flex-flow: row wrap;\n  column-gap: $nav-icon-column-gap;\n  justify-content: space-evenly;\n  align-items: center;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  // Icons styling\n  i {\n    &.fa-brands,\n    &.fa-regular,\n    &.fa-solid {\n      vertical-align: middle;\n      font-style: normal;\n      font-size: var(--pst-font-size-icon);\n    }\n\n    /* Social media buttons hard-code the brand color */\n    &.fa-square-twitter::before {\n      color: #55acee;\n    }\n\n    &.fa-square-gitlab::before {\n      color: #548;\n    }\n\n    &.fa-bitbucket::before {\n      color: #0052cc;\n    }\n  }\n\n  // Force images to be icon-sized\n  img.icon-link-image {\n    height: 1.5em;\n    border-radius: 0.2rem;\n  }\n\n  .fa-pydata {\n    stroke: var(--pst-color-background);\n    stroke-linejoin: round;\n    stroke-width: 0.35;\n  }\n}\n","/**\n * Logo in the navbar\n */\n\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n\n  // Ensure that the logo stays the same length while other content shrinks\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n\n  // If there's no logo image, we use a p element w/ the site title\n  p {\n    color: var(--pst-color-text-base);\n    margin-bottom: 0;\n  }\n\n  // If there's a logo, it'll be in an img block\n  img {\n    max-width: 100%;\n    height: 100%;\n    width: auto;\n  }\n\n  &:hover,\n  &:visited:hover {\n    @include link-style-hover;\n\n    color: var(--pst-color-text-base);\n  }\n}\n","/**\n * Navigation links in the navbar and icon links\n */\nul.navbar-nav {\n  // Reduce padding of nested `ul` items a bit\n  ul {\n    padding: 0 0 0 1rem;\n  }\n\n  // Navbar links - do not have an underline by default\n  li {\n    display: flex;\n    flex-direction: column;\n\n    a {\n      display: flex;\n      align-items: center;\n      height: 100%;\n      padding-top: $navbar-link-padding-y;\n      padding-bottom: $navbar-link-padding-y;\n\n      @include link-style-text;\n    }\n  }\n}\n","/**\n * The list of in-page TOC links\n */\n.page-toc {\n  .section-nav {\n    padding-left: 0;\n    border-bottom: none;\n\n    ul {\n      padding-left: 1rem;\n    }\n  }\n\n  // override bootstrap settings\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n\n  .onthispage {\n    color: var(--pst-color-text-base);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n}\n","/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n\n  p {\n    color: var(--pst-color-text-muted);\n    margin: 0 0.3em;\n    line-height: 1.3em;\n  }\n\n  i {\n    font-size: 1.2em;\n  }\n\n  a {\n    // So that buttons align with icons\n    display: flex;\n    align-items: center;\n    border: none;\n    padding: 10px;\n    max-width: 45%;\n    overflow-x: hidden;\n    color: var(--pst-color-text-muted);\n    text-decoration: none;\n\n    p.prev-next-title {\n      @include link-style-default;\n\n      font-weight: var(--pst-admonition-font-weight-heading);\n      font-size: 1.1em;\n    }\n\n    &:hover,\n    &:visited:hover {\n      p.prev-next-title {\n        @include link-style-hover;\n      }\n    }\n\n    .prev-next-info {\n      flex-direction: column;\n      margin: 0 0.5em;\n\n      .prev-next-subtitle {\n        text-transform: capitalize;\n      }\n    }\n\n    &.left-prev {\n      float: left;\n    }\n\n    &.right-next {\n      float: right;\n\n      div.prev-next-info {\n        text-align: right;\n      }\n    }\n  }\n}\n","/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: $admonition-border-radius;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Background should always be same color regardless of active or not\n  &:active {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n\n  // Hoist the focus ring from the input field to its parent\n  &:focus-within {\n    box-shadow: $focus-ring-box-shadow;\n\n    input:focus {\n      box-shadow: none;\n    }\n  }\n\n  .icon {\n    position: absolute;\n    color: var(--pst-color-border);\n    left: 25px;\n  }\n\n  .fa-solid.fa-magnifying-glass {\n    position: absolute;\n    left: calc((2.5rem - 0.7em) / 2);\n    color: var(--pst-color-text-muted);\n  }\n\n  input.form-control {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-base);\n    border: none;\n\n    // Inner-text of the search bar\n    &::placeholder {\n      color: var(--pst-color-text-muted);\n    }\n\n    // Remove the little \"x\" that pops up when you start typing\n    &::-webkit-search-cancel-button,\n    &::-webkit-search-decoration {\n      appearance: none;\n    }\n\n    &:focus,\n    &:focus-visible {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  // Shows off the keyboard shortcuts for the button\n  .search-button__kbd-shortcut {\n    display: none;\n    margin-inline-end: 0.5rem;\n    color: var(--pst-color-border);\n\n    @include media-breakpoint-up(md) {\n      display: flex;\n    }\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n.search-button i {\n  // Search link icon should be a bit bigger since it is separate from icon links\n  font-size: 1.3rem;\n}\n\n/**\n * The search modal <dialog>\n */\n#pst-search-dialog {\n  display: none;\n\n  &[open] {\n    display: flex;\n\n    // Center in middle of screen just underneath header\n    position: fixed;\n    z-index: $zindex-modal;\n    top: 30%;\n    left: 50%;\n    transform: translate(-50%, -50%);\n    right: 1rem;\n    margin-top: 0.5rem;\n    width: 90%;\n    max-width: 800px;\n    background-color: transparent;\n    padding: $focus-ring-width;\n    border: none;\n\n    &::backdrop {\n      background-color: black;\n      opacity: 0.5;\n    }\n\n    form.bd-search {\n      flex-grow: 1;\n\n      // Font and input text a bit bigger\n      svg,\n      input {\n        font-size: var(--pst-font-size-icon);\n      }\n    }\n  }\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  $search-button-border-radius: 1.5em;\n\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: $search-button-border-radius;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    border-radius: $search-button-border-radius;\n  }\n\n  // The keyboard shortcut text\n  .search-button__default-text {\n    font-size: var(--bs-nav-link-font-size);\n    font-weight: var(--bs-nav-link-font-weight);\n    margin-right: 0.5em;\n    margin-left: 0.5em;\n  }\n\n  .kbd-shortcut__modifier {\n    font-size: 0.75em;\n  }\n\n  // Ensures that all the text lines up in the middle\n  > * {\n    align-items: center;\n  }\n\n  // Only the icon should be visible on narrow screens\n  > :not(svg) {\n    display: none;\n\n    @include media-breakpoint-up(lg) {\n      display: flex;\n    }\n  }\n}\n","/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox {\n  // Leave `#searchbox` rules empty so that it doesn't show at all when it is empty\n  p.highlight-link {\n    margin: 1rem 0;\n    width: fit-content;\n\n    // A bit more margin on wide screens to mimic article behavior\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      margin-left: 2rem;\n    }\n\n    // Put outer shadow on this one so that we can darken the link w/ an inner shadow\n    @include box-shadow;\n\n    // Style the button to look like a Sphinx Design button\n    a {\n      border-radius: 0.25rem;\n      font-size: 1.25rem;\n      padding: 0.75rem;\n      background-color: var(--pst-color-primary);\n      color: var(--pst-color-primary-text);\n      text-decoration: none;\n\n      // The box shadow is inset so that it darkens the button on hover\n      transition: box-shadow 0.25s ease-out;\n\n      &:hover {\n        box-shadow: inset 0 0 50px 50px rgb(0 0 0 / 25%);\n      }\n\n      &::before {\n        content: var(--pst-icon-search-minus);\n        color: unset;\n        font: var(--fa-font-solid);\n        margin-right: 0.5rem;\n      }\n    }\n  }\n}\n","/**\n * Light/dark theme switcher\n */\n\n.theme-switch-button {\n  .theme-switch {\n    display: none;\n\n    &:active {\n      text-decoration: none;\n      color: var(--pst-color-link-hover);\n    }\n\n    .fa-lg {\n      aspect-ratio: 1 / 1;\n    }\n  }\n}\n\n@each $mode in auto, light, dark {\n  html[data-mode=\"#{$mode}\"]\n    .theme-switch-button\n    .theme-switch[data-mode=\"#{$mode}\"] {\n    display: inline; // inline needed for span height to be calculated using inherited font size and line height\n  }\n}\n","button.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Add a margin on narrow screens to avoid feeling cramped\n  margin-bottom: 1em;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    margin-bottom: unset;\n  }\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-secondary);\n    border-color: transparent;\n  }\n\n  &:active {\n    color: var(--pst-color-text-base);\n    border-color: var(--pst-color-border);\n  }\n\n  &:focus-visible {\n    border-color: transparent;\n  }\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n\n  a.list-group-item {\n    background-color: var(--pst-color-on-background);\n    color: var(--pst-color-text-base);\n    padding: 0.75rem 1.25rem;\n\n    &:not(:last-child) {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n\n    &:hover {\n      @include link-style-hover;\n\n      background-color: var(--pst-color-surface);\n    }\n\n    &.active {\n      @include link-sidebar-current;\n\n      position: relative;\n      z-index: 1;\n\n      span::before {\n        content: \"\";\n        width: 100%;\n        height: 100%;\n        position: absolute;\n        z-index: -1;\n        left: 0;\n        top: 0;\n      }\n    }\n\n    &:focus-visible {\n      z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n    }\n  }\n}\n\n// Font behavior on mobile\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em; // A bit smaller than other menu font\n  z-index: $zindex-modal; // higher than the sidebars\n\n  // Make sure it meets WCAG target size requirement no matter the version\n  // string displayed in the button\n  @include min-hit-area;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: unset;\n  }\n}\n","/* Collapsing of the TOC sidebar while scrolling */\n\n/* Nav: hide second level (shown on .active) */\n\nnav.page-toc {\n  // A little extra space before the buttons\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav {\n  .nav {\n    display: none;\n\n    // So we can manually specify a level as visible in the config\n    &.visible {\n      display: block;\n    }\n  }\n\n  > .active > ul {\n    display: block;\n  }\n}\n\n// Each entry of the in-page TOC\n.toc-entry {\n  display: block;\n\n  a > code {\n    color: var(--pst-color-text-muted);\n  }\n\n  a.nav-link {\n    display: block;\n    padding: $toc-item-padding-y 0;\n\n    // Padding w/ negative margin so the top TOC item highlight overlaps w/ the TOC border\n    padding-left: 1rem;\n    margin-left: -1rem;\n\n    @include link-sidebar;\n\n    &.active {\n      @include link-sidebar-current;\n\n      background-color: transparent;\n\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n\n    &:focus-visible {\n      border-radius: $focus-ring-radius;\n    }\n  }\n}\n","div.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  position: relative;\n\n  > p {\n    margin-bottom: 0.6rem;\n    margin-top: 0.6rem;\n  }\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n\n  &::before {\n    margin-right: 0.6rem;\n    color: var(--pst-color-info);\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-versionmodified-default);\n  }\n}\n\nspan.versionmodified.added {\n  &::before {\n    color: var(--pst-color-success);\n    content: var(--pst-icon-versionmodified-added);\n  }\n}\n\nspan.versionmodified.changed {\n  &::before {\n    color: var(--pst-color-warning);\n    content: var(--pst-icon-versionmodified-changed);\n  }\n}\n\nspan.versionmodified.deprecated {\n  &::before {\n    color: var(--pst-color-danger);\n    content: var(--pst-icon-versionmodified-deprecated);\n  }\n}\n",".sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    border-top: none;\n  }\n\n  .sidebar-indices-items__title {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    font-size: var(--pst-sidebar-header-font-size);\n    color: var(--pst-color-text-base);\n    margin-bottom: 0.5rem;\n  }\n\n  ul.indices-link {\n    margin-right: -1rem;\n    list-style: none;\n    padding: 0;\n\n    li > a {\n      display: block;\n      padding: 0.25rem 0;\n      color: var(--pst-color-text-muted);\n\n      &:hover {\n        color: var(--pst-color-primary);\n        text-decoration: none;\n        background-color: transparent;\n      }\n    }\n  }\n}\n",".bd-sidebar-primary div#rtd-footer-container {\n  position: sticky;\n  bottom: -1rem;\n  margin: -1rem; // ignore sidebar padding\n\n  .rst-versions.rst-badge {\n    position: unset;\n    font-size: 0.9em;\n    font-family: var(--pst-font-family-base);\n    max-width: unset;\n\n    .rst-current-version {\n      display: flex;\n      align-items: center;\n      gap: 0.2rem;\n      height: 2.5rem;\n      transition: background-color 0.2s ease-out;\n      background-color: var(--pst-color-background);\n      color: var(--pst-color-success);\n      border-top: 1px solid var(--pst-color-border);\n    }\n\n    .fa.fa-book {\n      color: var(--pst-color-text-muted);\n      margin-right: auto;\n\n      &::after {\n        color: var(--pst-color-text-base);\n        content: \"Read The Docs\";\n        font-family: var(--pst-font-family-base);\n        font-weight: var(--pst-admonition-font-weight-heading);\n      }\n    }\n\n    .fa.fa-caret-down {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  .rst-versions.rst-badge.shift-up {\n    .rst-current-version {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n  }\n\n  .rst-other-versions {\n    background-color: var(--pst-color-surface);\n    color: var(--pst-color-text-base);\n\n    dl {\n      dd a {\n        color: var(--pst-color-text-muted);\n      }\n    }\n\n    hr {\n      background-color: var(--pst-color-border);\n    }\n\n    small a {\n      color: var(--pst-color-link);\n    }\n\n    input {\n      padding-left: 0.5rem;\n      border: 1px solid var(--pst-color-border);\n      background-color: var(--pst-color-surface);\n    }\n  }\n}\n","/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\n$admonition-border-radius: 0.25rem;\n$admonition-left-border-width: 0.2rem;\n\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: $admonition-left-border-width solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  // Last item should have no spacing since we'll control that w/ padding\n  *:last-child {\n    margin-bottom: 0;\n  }\n\n  // Items after the title should be indented\n  p.admonition-title ~ * {\n    margin-left: 1.4rem;\n    margin-right: 1.4rem;\n  }\n\n  // Lists need to have left margin so they don't spill into it\n  > ol,\n  > ul {\n    margin-left: 1em;\n  }\n\n  // Defaults for all admonitions\n  > .admonition-title {\n    margin: 0 -0.6rem;\n    padding: 0.4rem 0.6rem 0.4rem 2rem;\n    font-weight: var(--pst-admonition-font-weight-heading);\n    position: relative;\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-color-info-bg);\n\n    // now that we use solid colors we want the title on top\n    z-index: 1;\n\n    &::after {\n      position: absolute;\n      left: 0.5rem;\n      width: 1rem;\n      height: 1rem;\n      color: var(--pst-color-info);\n      font: var(--fa-font-solid);\n      line-height: inherit;\n      content: var(--pst-icon-admonition-default);\n      opacity: 1;\n    }\n\n    // Next element after title needs some extra upper-space\n    + * {\n      margin-top: 0.4em;\n    }\n  }\n\n  &.attention {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-attention);\n      }\n    }\n  }\n\n  &.caution {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-caution);\n      }\n    }\n  }\n\n  &.warning {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-warning);\n      }\n    }\n  }\n\n  &.danger {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-danger);\n      }\n    }\n  }\n\n  &.error {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-error);\n      }\n    }\n  }\n\n  &.hint {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-hint);\n      }\n    }\n  }\n\n  &.tip {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-tip);\n      }\n    }\n  }\n\n  &.important {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-important);\n      }\n    }\n  }\n\n  &.note {\n    border-color: var(--pst-color-info);\n\n    > .admonition-title {\n      background-color: var(--pst-color-info-bg);\n\n      &::after {\n        color: var(--pst-color-info);\n        content: var(--pst-icon-admonition-note);\n      }\n    }\n  }\n\n  &.seealso {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-seealso);\n      }\n    }\n  }\n\n  &.admonition-todo {\n    border-color: var(--pst-color-secondary);\n\n    > .admonition-title {\n      background-color: var(--pst-color-secondary-bg);\n\n      &::after {\n        color: var(--pst-color-secondary);\n        content: var(--pst-icon-admonition-todo);\n      }\n    }\n  }\n\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n  &.sidebar {\n    max-width: 40%;\n    float: right;\n    clear: both;\n    margin-left: 0.5rem;\n    margin-top: 0;\n\n    // Undo the .sidebar directive border\n    border-width: 0 0 0 $admonition-left-border-width;\n\n    // TODO: these semantic-color-names border-color rules might no longer be\n    //       needed when we drop support for Sphinx 4 / docutils 0.17\n    &.attention,\n    &.important {\n      border-color: var(--pst-color-attention);\n    }\n\n    &.caution,\n    &.warning {\n      border-color: var(--pst-color-warning);\n    }\n\n    &.danger,\n    &.error {\n      border-color: var(--pst-color-danger);\n    }\n\n    &.hint,\n    &.tip,\n    &.seealso {\n      border-color: var(--pst-color-success);\n    }\n\n    &.note,\n    &.todo {\n      border-color: var(--pst-color-info);\n    }\n\n    // No inner margin since we have less horizontal space w/ the sidebar\n    p.admonition-title ~ * {\n      margin-left: 0;\n      margin-right: 0;\n    }\n  }\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n\n/**\n * Topics and the {contents} directive\n */\n// Docutils <= 0.17\ndiv.topic,\ndiv.topic.contents,\n// Docutils >= 0.18\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  padding: 1rem 1.25rem;\n\n  @include box-shadow;\n\n  .topic-title {\n    margin: 0 0 0.5rem;\n  }\n\n  // Over-ride text color to ensure enough contrast\n  p {\n    color: var(--pst-color-on-surface) !important;\n  }\n\n  // Over-ride large default padding\n  ul.simple {\n    padding-left: 1rem;\n\n    ul {\n      // So that sub-lists will have a bit less padding\n      padding-left: 2em;\n    }\n  }\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: $admonition-border-radius;\n\n  // to match the admonition-styled sidebars:\n  margin-left: 0.5rem;\n  padding: 0;\n\n  > *:last-child {\n    padding-bottom: 1rem;\n  }\n\n  p.sidebar-title {\n    position: relative;\n    margin-bottom: 0;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n    border-bottom: 1px solid var(--pst-color-border);\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-admonition-font-weight-heading);\n  }\n\n  // Add margin to the first non-`.sidebar-title` item\n  > *:not(.sidebar-title):first-child,\n  > p.sidebar-title + * {\n    margin-top: 1rem;\n  }\n\n  > * {\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n","/**\n * Miscellaneous color functions and mixins\n**/\n\n@use \"sass:list\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:math\";\n@use \"sass:string\";\n\n// We must add ::before pseudo-element to some theme components (such as admonitions)\n// because users were instructed to customize the background color this way.\n@mixin legacy-backdrop-placeholder {\n  &::before {\n    content: \"\";\n    width: 100%;\n    height: 100%;\n    position: absolute;\n    left: 0;\n    top: 0;\n    z-index: -1;\n\n    // So that hovering over the text within background is still possible.\n    // Otherwise the background overlays the text and you cannot click or select easily.\n    // ref: https://developer.mozilla.org/en-US/docs/Web/CSS/pointer-events\n    pointer-events: none;\n  }\n}\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetc\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n// @param {String/Color} $color - Color definition from map\n// @return {Color} - Color type (in hex)\n@function check-color($color) {\n  @if meta.type-of($color) == string {\n    $color: from-hex($color);\n  }\n\n  @return $color;\n}\n\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n// @param {String} $string - String representation of a color\n@function from-hex($string) {\n  $string-lower: string.to-lower-case($string);\n  $r: \"\";\n  $g: \"\";\n  $b: \"\";\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $length: string.length($string);\n  $max: if($length == 4, 1, 2);\n\n  // Check for length accuracy\n  @if $length != 4 and $length != 7 {\n    @return $string;\n  }\n\n  // Loop from the second character (omitting #)\n  @for $i from 2 through $length {\n    $c: string.slice($string-lower, $i, $i);\n\n    // If wrong character, return\n    @if not list.index($hex, $c) {\n      @return $string;\n    }\n\n    @if string.length($r) < $max {\n      $r: $r + $c;\n    } @else if string.length($g) < $max {\n      $g: $g + $c;\n    } @else if string.length($b) < $max {\n      $b: $b + $c;\n    }\n  }\n\n  @if $length == 4 {\n    $r: $r + $r;\n    $g: $g + $g;\n    $b: $b + $b;\n  }\n\n  @return rgb(hex-to-dec($r), hex-to-dec($g), hex-to-dec($b));\n}\n\n@function hex-to-dec($string) {\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $string: string.to-lower-case($string);\n  $length: string.length($string);\n  $dec: 0;\n\n  @for $i from 1 through $length {\n    $factor: 1 + (15 * ($length - $i));\n    $index: list.index($hex, string.slice($string, $i, $i));\n    $dec: $dec + $factor * ($index - 1);\n  }\n\n  @return $dec;\n}\n","// Style API docs from sphinx' autodoc / autosummary\n\n/*******************************************************************************\n* Styling for field lists\n*/\n\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n\n  th.field-name {\n    padding: 1px 8px 1px 5px;\n    white-space: nowrap;\n    background-color: var(--pst-color-surface);\n  }\n\n  /* italic font for parameter types */\n  td.field-body {\n    p {\n      font-style: italic;\n\n      > strong {\n        font-style: normal;\n      }\n    }\n\n    /* reduced space around parameter description */\n    blockquote {\n      border-left: none;\n      margin: 0 0 0.3em;\n      padding-left: 30px;\n    }\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n\n.table.autosummary {\n  // The first column (with the signature) should not wrap\n  td:first-child {\n    white-space: nowrap;\n  }\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors */\n\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n// addition\n\n// .sig.c .sig-name .n,\n// .sig.cpp .sig-name .n {\n//   color: var(--pst-color-inline-code);\n// }\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\n// change target color for dark theme\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\ndl > dt > a:has(.viewcode-link) {\n  // Sphinx applies a `float:right` rule to the .viewcode-line span, which\n  // exposes a browser glitch in the focus ring. It seems the browser creates\n  // two separate boxes, an empty box where the anchor element gets laid out and\n  // then another box around the anchor's contents that have been floated right.\n  // Firefox draws the focus ring around the empty anchor element box. Chrome\n  // draws two focus rings: one around the empty anchor and one around the\n  // floated-right element. To fix the glitch, we apply the float rule on the\n  // parent rather than the child.\n  float: right;\n\n  .viewcode-link {\n    float: none;\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\n\n// the API selector\n// from https://github.com/pradyunsg/furo/blob/main/src/furo/assets/styles/content/_api.sass#L6)\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) {\n  // increase margin bottom after the dl elements\n  margin-bottom: 3rem;\n\n  dd {\n    margin-left: 2rem;\n\n    // Fix until this will be solved to Sphinx https://github.com/sphinx-doc/sphinx/issues/10815\n    & > dl.simple > dt {\n      display: flex;\n    }\n  }\n\n  dl.field-list {\n    display: grid;\n    grid-template-columns: unset;\n  }\n\n  dt.field-odd,\n  dt.field-even {\n    margin-top: 0.2rem;\n    margin-bottom: 0.1rem;\n    background-color: var(--pst-color-surface);\n  }\n}\n","/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\n\n// General code block behavior\n// Unset bootstrap behavior\ndiv[class*=\"highlight-\"],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: $admonition-border-radius;\n  break-inside: avoid;\n}\n\n// Code blocks with captions\n// There's a wrapper when the code block has a title\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  // This is where the title goes\n  div.code-block-caption {\n    margin: 0;\n    border-bottom: 1px solid var(--pst-color-border);\n    padding: 0.5rem;\n    font-size: 1rem;\n    font-weight: var(--pst-font-weight-caption);\n\n    a.headerlink {\n      font-size: inherit;\n    }\n  }\n\n  // Remove the upper border radius since we want it to connect with the title\n  // Remove the box shadow so the wrapper gets the shadow instead\n  div[class*=\"highlight-\"] {\n    margin: 0;\n    border-radius: 0;\n\n    pre {\n      border: none;\n      box-shadow: none;\n    }\n  }\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n}\n\n// Minimum opacity needed for linenos to be WCAG AA conformant\nspan.linenos {\n  opacity: 0.8 !important;\n}\n","figure > a,\nfigure > a > img,\nfigure > img,\nfigure > video {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n\nfigure {\n  a.headerlink {\n    // So that header link doesn't push caption to be off-center.\n    position: absolute;\n    font-size: inherit;\n  }\n\n  // Default headerlink hover doesn't trigger on figures\n  &:hover a.headerlink {\n    visibility: visible;\n  }\n\n  figcaption {\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-font-weight-caption);\n    color: var(--pst-color-text-muted);\n    margin-left: auto;\n    margin-right: auto;\n    margin-top: 0.3rem;\n    text-align: center;\n\n    & > p:last-child {\n      // Don't add extra margin to already existing figure bottom margin\n      margin-bottom: 0;\n    }\n\n    p {\n      text-align: start;\n      display: inline-block;\n    }\n\n    table.table {\n      width: fit-content;\n      margin-left: auto;\n      margin-right: auto;\n    }\n  }\n}\n","// For consistency, add bracket around footnotes/citations which are\n// cited more than once. E.g. [Newton](1,2) instead of Newton(1,2)\ndt.label > span.brackets:not(:only-child)::before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child)::after {\n  content: \"]\";\n}\n\n// Make footnote as a superscript\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\n// Docutils 0.18 uses an `aside.footnote` container with different internal structure\naside.footnote {\n  margin-bottom: 0.5rem;\n\n  &:last-child {\n    margin-bottom: 1rem;\n  }\n\n  span.label,\n  span.backrefs {\n    font-weight: bold;\n  }\n\n  &:target {\n    background-color: var(--pst-color-target);\n  }\n}\n","/**\n * Hacky fixes that don't fit cleanly into other sections\n */\n\n// Ensure user highlighting/selecting behaves properly\n// From https://stackoverflow.com/a/34372191\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n}\n","// Override bootstrap by restoring the basic theme default.\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n\n  li > p:first-child {\n    margin-bottom: 0.25rem;\n    margin-top: 0.25rem;\n  }\n}\n","// GitHub blockquote style\nblockquote {\n  padding: 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-blockquote-notch);\n  border-radius: $admonition-border-radius;\n  position: relative;\n\n  p {\n    color: var(--pst-color-text-base);\n  }\n\n  // remove padding from included line-block to avoid duplication\n  .line-block {\n    margin: 0;\n  }\n\n  // remove margin bottom for the last p\n  p:last-child {\n    margin-bottom: 0;\n  }\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-surface);\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // hack to make the text in the blockquote selectable\n  &::before {\n    z-index: -1;\n  }\n}\n","/**\n * Span-level styling within content\n */\n\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-info-bg);\n}\n\na.reference.download::before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n","/**\n * Tables\n */\n\ntable {\n  // default to table-center\n  margin-left: auto;\n  margin-right: auto;\n\n  &.table-right {\n    margin-right: 0;\n  }\n\n  &.table-left {\n    margin-left: 0;\n  }\n}\n\n// customize table caption from bootstrap\n// to display them on top and centered\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\n// MyST Markdown tables use these classes to control alignment\nth,\ntd {\n  &.text-left {\n    text-align: left;\n  }\n\n  &.text-right {\n    text-align: right;\n  }\n\n  &.text-center {\n    text-align: center;\n  }\n}\n\n// override bootstrap table colors\n.table {\n  @include table-colors;\n\n  --bs-table-bg: transparent; // background\n  --bs-table-color: var(\n    --pst-color-text-base\n  ); // ensure text and bullets are visible\n}\n\n.pst-scrollable-table-container {\n  // Put a horizontal scrollbar just below tables that are too wide to fit\n  // within the main column\n  overflow-x: auto;\n}\n","/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper {\n  p.caption {\n    font-size: 1.5em;\n    margin-bottom: 0;\n  }\n\n  & > ul {\n    padding-left: 0;\n  }\n\n  li[class^=\"toctree-l\"] {\n    list-style: none;\n    margin-bottom: 0.2em;\n\n    & > a {\n      list-style: none;\n      font-size: 1.1em;\n    }\n\n    & > ul {\n      list-style: none;\n      padding-inline-start: 1.5em;\n    }\n  }\n\n  // slightly bigger font for l1\n  .toctree-l1 > a {\n    font-size: 1.3em;\n  }\n}\n\ndiv.topic.contents,  // Docutils <= 0.17\nnav.contents  // Docutils >= 0.18\n{\n  // Style similarly to toctree\n  ul.simple {\n    list-style: none;\n    padding-left: 0;\n  }\n}\n","/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\n\n// Applies to all math elements\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n\n  // This will be over-ridden for the y-direction and divs\n  overflow: hidden;\n}\n\n// Inline-only\nspan.math {\n  display: inline-flex;\n}\n\n// Block-level only\ndiv.math {\n  gap: 0.5em;\n\n  // So that the eqno shows up after the equation\n  flex-direction: row-reverse;\n\n  // The equation number / link\n  span.eqno a.headerlink {\n    position: relative;\n    font-size: 1em;\n  }\n\n  // The math container\n  mjx-container {\n    flex-grow: 1;\n    padding-bottom: 0.2rem;\n    overflow: auto;\n\n    // Set height to 0 so that it does not cause scrollbars to appear\n    // ref: https://github.com/mathjax/MathJax/issues/2521\n    mjx-assistive-mml {\n      height: 0;\n    }\n  }\n}\n","/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item {\n  h2,\n  h3 {\n    font-size: var(--pst-sidebar-header-font-size);\n\n    // Remove unnecessary vertical whitespace\n    margin-top: 0.5rem;\n\n    // The headers are all links, but this makes them hard to parse\n    // So we change the colors to make them look like headers\n    a {\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  ul {\n    // No bullet points for the primary sidebar items\n    list-style: none;\n    padding-left: 0;\n\n    // Otherwise a scrollbar randomly shows up\n    overflow-y: hidden;\n\n    // List of recent post items\n    display: flex;\n    flex-direction: column;\n    gap: 0.5em;\n    margin-bottom: 0;\n\n    // The ablog cloud should move horizontally\n    &.ablog-cloud {\n      flex-flow: row wrap;\n      gap: 0.5rem;\n\n      // Vertical-align tag clouds\n      li {\n        // Center the tag cloud items\n        display: flex;\n        align-items: center;\n      }\n    }\n  }\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n\n  // The bottom previous / next arrows\n  > span {\n    // To ensure that the whole thing fits on one line even if there are long titles\n    display: flex;\n    max-width: 45%;\n\n    // Links within each span have the collection of icon + text\n    a {\n      display: flex;\n      align-items: center;\n      margin-left: auto;\n      gap: 1rem;\n      line-height: 1.5rem;\n\n      i::before {\n        color: var(--pst-color-text-base);\n      }\n    }\n  }\n\n  // The first span is for the previous page and aligns to the left\n  span.ablog__prev {\n    i.fa-arrow-circle-left::before {\n      content: var(--pst-icon-angle-left);\n    }\n  }\n\n  // The second span is just an empty space so we remove it because we're\n  // positioning with flex\n  span.ablog__spacer {\n    display: none;\n  }\n\n  // The third span is aligned to the right\n  span.ablog__next {\n    margin-left: auto;\n    text-align: right;\n\n    i.fa-arrow-circle-right::before {\n      content: var(--pst-icon-angle-right);\n    }\n  }\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n\n  .ablog-post {\n    list-style: none;\n\n    // Post metadata tags (author, links ,etc) should be a bit smaller\n    .ablog-archive {\n      display: flex;\n      flex-flow: row wrap;\n      gap: 1rem;\n      list-style: none;\n      font-size: 0.75rem;\n      padding-left: 0;\n    }\n\n    // Title line should be a bit bigger and bold to stand out\n    .ablog-post-title {\n      margin-top: 0;\n      font-size: 1.25rem;\n\n      a {\n        font-weight: bold;\n      }\n    }\n\n    // Read more button should be a bit bigger\n    .ablog-post-expand {\n      margin-bottom: 0.5rem;\n    }\n  }\n}\n","/**\n * Special cases for Bootstrap functionality\n */\n\n// Bootstrap adds margin to their general container class. However, sphinx/docutils\n// can also generate output with the container class, but in those cases we should\n// not add the margin from bootstrap. Same for max-width.\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n\n.btn {\n  --bs-btn-focus-box-shadow: #{$btn-focus-box-shadow};\n}\n","/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\n\ndiv.highlight button.copybtn {\n  // Nicer spacing\n  display: flex;\n  align-items: center;\n  justify-content: center;\n\n  // Don't over-ride the success color\n  &:not(.success) {\n    color: var(--pst-color-muted);\n  }\n\n  border: none;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    &:not(.success) {\n      color: var(--pst-color-text);\n      background-color: var(--pst-color-shadow);\n    }\n  }\n\n  // Tooltip styling\n  &.o-tooltip--left::after {\n    color: var(--pst-color-text);\n    background-color: var(--pst-color-surface);\n  }\n\n  &:focus {\n    // For keyboard users, make the copy button visible when focussed.\n    opacity: 1;\n  }\n\n  &:focus-visible {\n    outline: $focus-ring-outline;\n  }\n}\n\ndiv.highlight:has(button.copybtn) {\n  // Make sure the code block has enough height for the copy button.\n  // Sphinx-copybutton sets 0.3em top offset plus 1.7em height:\n  // https://github.com/executablebooks/sphinx-copybutton/blob/master/sphinx_copybutton/_static/copybutton.css\n  min-height: 2em;\n}\n","// adapt ethical ad to the theme\n#ethical-ad-placement {\n  .ethical-sidebar a,\n  .ethical-sidebar a:visited,\n  .ethical-sidebar a:hover,\n  .ethical-sidebar a:active,\n  .ethical-footer a,\n  .ethical-footer a:visited,\n  .ethical-footer a:hover,\n  .ethical-footer a:active {\n    color: var(--pst-color-text-base);\n  }\n\n  .ethical-sidebar,\n  .ethical-footer {\n    background-color: var(--pst-color-background);\n    border: 1px solid var(--pst-color-border);\n    border-radius: 5px;\n    color: var(--pst-color-text-base);\n    font-size: 14px;\n    line-height: 20px;\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/******************************************************************************\n * Jupyter Sphinx\n */\n\n.bd-content div.jupyter_container {\n  // We don't want borders around the whole container, just around code cells\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n\n  // Code cells should have the same style as our other code objects\n  div.output,\n  div.highlight {\n    border-radius: 0.25rem;\n  }\n\n  div.highlight {\n    background-color: var(--pst-color-surface);\n  }\n\n  // Ensure the style is the same as our code cells. Jupyter Sphinx makes it tiny.\n  .cell_input,\n  .cell_output {\n    border-radius: 0.25rem;\n\n    pre {\n      padding: 1rem;\n    }\n  }\n}\n","/* Styles for graphviz generated output from Sphinx */\n\n/* Style the inheritance diagram such that it has a dark mode */\nhtml[data-theme=\"dark\"] div.graphviz > object.inheritance {\n  filter: brightness(0.8) invert(0.82) contrast(1.2);\n  color-scheme: normal;\n}\n","/**\n * Special-cases for packages in the PyData ecosystem\n */\n\n// xarray output display in bootstrap\n.xr-wrap[hidden] {\n  display: block !important;\n}\n\n// ipywidgets\n.jp-OutputArea-output.lm-Widget {\n  // override overflow:hidden rule from Lumino (.lm-Widget) to allow scrolling\n  overflow: auto;\n}\n","/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetch\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/* Assign base colors for the PyData theme */\n$color-palette: (\n  // Primary color\n  \"teal\":\n    (\n      \"50\": #f4fbfc,\n      \"100\": #e9f6f8,\n      \"200\": #d0ecf1,\n      \"300\": #abdde6,\n      \"400\": #3fb1c5,\n      \"500\": #0a7d91,\n      \"600\": #085d6c,\n      \"700\": #064752,\n      \"800\": #042c33,\n      \"900\": #021b1f,\n    ),\n  // Secondary color\n  \"violet\":\n    (\n      \"50\": #f4eefb,\n      \"100\": #e0c7ff,\n      \"200\": #d5b4fd,\n      \"300\": #b780ff,\n      \"400\": #9c5ffd,\n      \"500\": #8045e5,\n      \"600\": #6432bd,\n      \"700\": #4b258f,\n      \"800\": #341a61,\n      \"900\": #1e0e39,\n    ),\n  // Neutrals\n  \"gray\":\n    (\n      \"50\": #f9f9fa,\n      \"100\": #f3f4f5,\n      \"200\": #e5e7ea,\n      \"300\": #d1d5da,\n      \"400\": #9ca4af,\n      \"500\": #677384,\n      \"600\": #48566b,\n      \"700\": #29313d,\n      \"800\": #222832,\n      \"900\": #14181e,\n    ),\n  // Accent color\n  \"pink\":\n    (\n      \"50\": #fcf8fd,\n      \"100\": #fcf0fa,\n      \"200\": #f8dff5,\n      \"300\": #f3c7ee,\n      \"400\": #e47fd7,\n      \"500\": #c132af,\n      \"600\": #912583,\n      \"700\": #6e1c64,\n      \"800\": #46123f,\n      \"900\": #2b0b27,\n    ),\n  \"foundation\": (\n    \"white\": #ffffff,\n    // gray-900\n    \"black\": #14181e,\n  )\n);\n\n:root {\n  // Add theme colours to the html root element\n  @each $group-color, $color in $color-palette {\n    @each $color-name, $definition in $color {\n      --pst-#{$group-color}-#{$color-name}: #{$definition};\n    }\n  }\n}\n\n// Static SCSS variables used thoroughout the theme\n// Minimum contrast ratio used for the theme.\n// Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n// 4.5 - is for text that is 14pt or less\n$min-contrast-ratio-4: 4.5;\n\n// 3 is for text that is 18pt or bold, or for non-text elements\n$min-contrast-ratio-3: 3;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$foundation-black: #14181e;\n$foundation-white: #fff;\n\n// This is a custom - calculated  color between gray 100 and 200 - to reduce\n// the contrast ratio (avoid a jarring effect)\n$base-light-text: #ced6dd;\n\n// used in sphinx_design - gray 100\n$foundation-light-gray: #f3f4f5;\n\n// used in sphinx_design - gray 700\n$foundation-muted-gray: #29313d;\n\n// used in sphinx_design - gray 800\n$foundation-dark-gray: #222832;\n$pst-semantic-colors: (\n  \"primary\": (\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"teal\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"teal\", \"800\")},\n  ),\n  \"secondary\": (\n    \"light\": #{map-deep-get($color-palette, \"violet\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"violet\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"800\")},\n  ),\n  \"accent\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"pink\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"pink\", \"800\")},\n  ),\n  \"info\": (\n    \"light\": #276be9,\n    \"bg-light\": #dce7fc,\n    \"dark\": #79a3f2,\n    \"bg-dark\": #06245d,\n  ),\n  \"warning\": (\n    \"light\": #f66a0a,\n    \"bg-light\": #f8e3d0,\n    \"dark\": #ff9245,\n    \"bg-dark\": #652a02,\n  ),\n  \"success\": (\n    \"light\": #00843f,\n    \"bg-light\": #d6ece1,\n    \"dark\": #5fb488,\n    \"bg-dark\": #002f17,\n  ),\n  // This is based on the warning color\n  \"attention\":\n    (\n      \"light\": var(--pst-color-warning),\n      \"bg-light\": var(--pst-color-warning-bg),\n      \"dark\": var(--pst-color-warning),\n      \"bg-dark\": var(--pst-color-warning-bg),\n    ),\n  \"danger\": (\n    \"light\": #d72d47,\n    \"bg-light\": #f9e1e4,\n    \"dark\": #e78894,\n    \"bg-dark\": #4e111b,\n  ),\n  \"text-base\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    \"dark\": $base-light-text,\n  ),\n  \"text-muted\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"heading-color\": (\n    \"light\": #{$foundation-white},\n    \"dark\": #{$foundation-black},\n  ),\n  \"shadow\": (\n    \"light\": rgba(0, 0, 0, 0.1),\n    \"dark\": rgba(0, 0, 0, 0.2),\n  ),\n  \"border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"300\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n  ),\n  \"border-muted\": (\n    \"light\": rgba(23, 23, 26, 0.2),\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  \"blockquote-notch\": (\n    // These colors have a contrast ratio > 3.0 against both the background and\n    // surface colors that the notch is sandwiched between\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"500\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"inline-code\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"300\")},\n  ),\n  \"inline-code-links\": (\n    // need to make sure there is enough contrast against the code bg\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"600\")},\n    // keep primary color for dark mode\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n  ),\n  \"target\": (\n    \"light\": #f3cf95,\n    \"dark\": #675c04,\n  ),\n  \"table\": (\n    \"light\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    \"dark\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n  ),\n  \"table-row-hover\": (\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"300\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"600\")},\n  ),\n  \"table-inner-border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"200\")},\n    \"dark\": #364150,\n  ),\n  // DEPTH COLORS - you can see the elevation colours and shades\n  // in the Figma file https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1492%3A922&t=sQeQZehkOzposYEg-1\n  // background: color of the canvas / the furthest back layer\n  \"background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    ),\n  // on-background: provides slight contrast against background\n  // (by use of shadows in light theme)\n  \"on-background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    ),\n  \"surface\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  // on_surface: object on top of surface object (without shadows)\n  \"on-surface\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n      \"dark\": $foundation-light-gray,\n    ),\n);\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * #{something} inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n@mixin theme-colors($mode) {\n  // check if this color is defined differently for light/dark\n  @each $col-name, $definition in $pst-semantic-colors {\n    @if meta.type-of($definition) == map {\n      @each $key, $val in $definition {\n        @if string.index($key, $mode) {\n          // since now we define the bg colours in the semantic colours and not\n          // by changing opacity, we need to check if the key contains bg and the\n          // correct mode (light/dark)\n          @if string.index($key, \"bg\") {\n            --pst-color-#{$col-name}-bg: #{$val};\n          } @else {\n            --pst-color-#{$col-name}: #{$val};\n          }\n        }\n      }\n    } @else {\n      --pst-color-#{$col-name}: #{$definition};\n    }\n  }\n\n  // assign the \"duplicate\" colors (ones that just reference other variables)\n  & {\n    --pst-color-link: var(--pst-color-primary);\n    --pst-color-link-hover: var(--pst-color-secondary);\n    --pst-color-table-outer-border: var(--pst-color-surface);\n    --pst-color-table-heading-bg: var(--pst-color-surface);\n    --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n    --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n  }\n\n  // adapt to light/dark-specific content\n  @if $mode == \"light\" {\n    .only-dark,\n    .only-dark ~ figcaption {\n      display: none !important;\n    }\n  } @else {\n    .only-light,\n    .only-light ~ figcaption {\n      display: none !important;\n    }\n\n    /* Adjust images in dark mode (unless they have class .only-dark or\n     * .dark-light, in which case assume they're already optimized for dark\n     * mode).\n     */\n    img:not(.only-dark, .dark-light) {\n      filter: brightness(0.8) contrast(1.2);\n    }\n\n    /* Give images a light background in dark mode in case they have\n    *  transparency and black text (unless they have class .only-dark or .dark-light, in\n    *  which case assume they're already optimized for dark mode).\n    */\n    .bd-content img:not(.only-dark, .dark-light) {\n      background-color: rgb(255 255 255);\n      border-radius: 0.25rem;\n    }\n\n    // MathJax SVG outputs should be filled to same color as text.\n    .MathJax_SVG * {\n      fill: var(--pst-color-text-base);\n    }\n  }\n}\n\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  @include theme-colors(\"light\");\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    @include theme-colors($mode);\n\n    color-scheme: $mode;\n  }\n}\n\n// assign classes too, for runtime use of theme colors\n@each $col-name, $definition in $pst-semantic-colors {\n  .pst-color-#{$col-name} {\n    color: var(--pst-color-#{$col-name});\n  }\n}\n","/*******************************************************************************\n * Special-cases for the sphinx-design library, mainly to make it compatible\n * with the dark/light themes of pydata-sphinx-theme.\n *\n * NOTE: sphinx-design uses !important quite liberally, so here we must do the\n * same for our overrides to have any effect.\n */\n@use \"../variables/color\" as pst-color;\n@use \"sass:color\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n$sd-semantic-color-names: (\n  \"primary\",\n  \"secondary\",\n  \"success\",\n  \"info\",\n  \"warning\",\n  \"danger\",\n  \"light\",\n  \"muted\",\n  \"dark\",\n  \"black\",\n  \"white\"\n);\n\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\n$extra-semantic-colors: (\n  \"white\": $foundation-white,\n  \"light\": (\n    light: $foundation-light-gray,\n    bg-light: color.scale($foundation-light-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-light-gray, $lightness: -30%),\n  ),\n  \"muted\": (\n    light: $foundation-muted-gray,\n    bg-light: color.scale($foundation-muted-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-muted-gray, $lightness: -30%),\n  ),\n  \"dark\": $foundation-dark-gray,\n  \"black\": $foundation-black,\n);\n$all-colors: map.merge($pst-semantic-colors, $extra-semantic-colors);\n\n@mixin create-sd-colors($value, $name) {\n  // define the pst variables, so that downstream user overrides will work\n  --pst-color-#{$name}: #{$value};\n\n  // we are now using a11y-combination to calculate the text color - this is based\n  // on the WCAG color contrast guidelines\n  --pst-color-#{$name}-text: #{a11y-combination($value)};\n\n  // highlight is used for hover effects on buttons, here we make some fluid scaling\n  // to avoid jarring effects -- we create a local variable that we can access\n  // later to calculate the text color\n  $highlight-color: color.scale($value, $lightness: -15%, $saturation: 5%);\n\n  --pst-color-#{$name}-highlight: #{$highlight-color};\n\n  // override the sphinx-design variables\n  --sd-color-#{$name}: var(--pst-color-#{$name});\n  --sd-color-#{$name}-text: var(--pst-color-#{$name}-text);\n  --sd-color-#{$name}-highlight: var(--pst-color-#{$name}-highlight);\n\n  // calculate the text color for the highlight color\n  --pst-color-#{$name}-highlight-text: #{a11y-combination($highlight-color)};\n}\n\n// Now we override the --sd-color-* variables.\n@each $mode in (light, dark) {\n  @debug \"Creating color variables for mode: #{$mode}\";\n  html[data-theme=\"#{$mode}\"] {\n    // check if this color is defined differently for light/dark\n    @each $name in $sd-semantic-color-names {\n      $definition: map.get($all-colors, $name);\n\n      @if meta.type-of($definition) == map {\n        @each $key, $value in $definition {\n          @if string.index($key, $mode) {\n            // since now we define the bg colours in the semantic colours and not\n            // by changing opacity, we need to check if the key contains bg and the\n            // correct mode (light/dark)\n            @if string.index($key, \"bg\") {\n              --sd-color-#{$name}-bg: #{$value};\n\n              // create local variable -- needed to calculate the text color\n              $value: check-color($value);\n\n              --sd-color-#{$name}-bg-text: #{a11y-combination($value)};\n            } @else {\n              $value: check-color($value);\n\n              @include create-sd-colors($value, $name);\n            }\n          }\n        }\n      } @else {\n        $value: map.get($all-colors, $name);\n\n        @include create-sd-colors($value, $name);\n      }\n    }\n  }\n}\n\n// Make sure the color border variables are set using our variables\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    --sd-color-card-border: var(--pst-color-border);\n  }\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=\"light\"] {\n  .sd-shadow-xs,\n  .sd-shadow-sm,\n  .sd-shadow-md,\n  .sd-shadow-lg {\n    @include box-shadow;\n  }\n}\n\n/*******************************************************************************\n  * cards\n  */\n\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n\n  // TODO - --pst-color-panel-background is not defined... where is this coming from?\n  .sd-card-header {\n    background-color: var(--pst-color-panel-background);\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-footer {\n    background-color: var(--pst-color-panel-background);\n    border-top: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-body {\n    background-color: var(--pst-color-panel-background);\n  }\n\n  // Focus ring for link-cards\n  .sd-stretched-link:focus-visible {\n    // Don't put the focus ring on the <a> element (it has zero height in Sphinx Design cards)\n    outline: none;\n\n    // Put the focus ring on the <a> element's ::after pseudo-element\n    &::after {\n      outline: $focus-ring-outline;\n      border-radius: 0.25rem; // copied from Sphinx Design CSS for .sd-card\n    }\n  }\n\n  &.sd-card-hover:hover {\n    border-color: var(--pst-color-link-hover);\n  }\n}\n\n/*******************************************************************************\n  * tabs\n  */\n\n.bd-content .sd-tab-set {\n  > input {\n    // Active tab label\n    &:checked + label {\n      border-style: solid solid none;\n      border-color: var(--pst-color-primary) var(--pst-color-primary)\n        transparent; // top LR bottom\n\n      border-width: 0.125rem 0.125rem 0;\n      border-radius: 0.125rem 0.125rem 0 0;\n      background-color: var(--pst-color-on-background);\n      transform: translateY(0.125rem);\n      color: var(--pst-color-primary);\n    }\n\n    &:focus-visible + label {\n      border: 0.125rem solid var(--pst-color-accent);\n      border-radius: 0.125rem;\n      background-color: var(--pst-color-accent-bg);\n      color: var(--pst-color-on-surface);\n    }\n\n    // Hover label\n    &:not(:checked, :focus-visible) + label:hover {\n      border-color: transparent;\n      color: var(--pst-color-secondary);\n      text-decoration-line: underline;\n      text-decoration-thickness: $link-hover-decoration-thickness;\n    }\n  }\n\n  // Tab label\n  > label {\n    color: var(--pst-color-on-surface);\n    border: 0.125rem solid transparent;\n    border-radius: 0.125rem 0.125rem 0 0;\n    background-color: var(--pst-color-surface);\n    padding: 0 0.75em;\n    margin-inline-end: 0.25rem;\n    line-height: 1.95;\n\n    html[data-theme=\"dark\"] & {\n      background-color: var(--pst-color-on-background);\n    }\n  }\n\n  // panel\n  .sd-tab-content {\n    border: 0.125rem solid var(--pst-color-primary);\n    border-radius: 0.1875rem;\n    box-shadow: unset;\n    padding: 0.625rem;\n    background-color: var(--pst-color-on-background);\n  }\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\n\ndetails.sd-dropdown {\n  // Remove all borders to over-ride SD behavior, and we'll add our own later\n  border: 0 !important;\n\n  // Drop shadow should behave same as admonitions\n  @include box-shadow;\n\n  // Header is where the \"clickable\" box goes\n  summary.sd-card-header {\n    border: 0 !important;\n    display: flex;\n    align-items: center;\n    position: relative; // So background color works\n    font-weight: 600;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n\n    // Set a variable that we can re-use for colors later\n    // We must set this in the current and content sibling container\n    // so that it is defined in both places\n    --pst-sd-dropdown-color: var(--pst-gray-500);\n    --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n\n    & + div.sd-summary-content {\n      border: 0;\n\n      --pst-sd-dropdown-color: var(--sd-color-card-border);\n    }\n\n    @each $name in $sd-semantic-color-names {\n      &.sd-bg-#{$name} {\n        --pst-sd-dropdown-color: var(--sd-color-#{$name});\n        --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n\n        // Otherwise it won't be defined in the sibling element\n        & + div.sd-summary-content {\n          --pst-sd-dropdown-color: var(--sd-color-#{$name});\n          --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        }\n      }\n      &.sd-bg-text-#{$name} {\n        // Use the WCAG conformant text color\n        color: var(--sd-color-#{$name}-bg-text) !important;\n      }\n    }\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-sd-dropdown-bg-color) !important;\n\n    // Add a left border with the same structure as our admonitions\n    border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n\n    // stylelint-disable-next-line no-duplicate-selectors\n    & + div.sd-summary-content {\n      border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n      border-bottom-left-radius: calc(0.25rem - 1px);\n      background-color: var(--pst-color-on-background);\n    }\n\n    span.sd-summary-icon {\n      display: inline-flex;\n      align-items: center;\n      color: var(--pst-sd-dropdown-color) !important;\n\n      svg {\n        opacity: 1;\n      }\n    }\n\n    // Positioning of the caret\n    .sd-summary-up,\n    .sd-summary-down {\n      top: 0.7rem;\n    }\n\n    // Focus ring\n    &:focus-visible {\n      outline: $focus-ring-outline;\n      outline-offset: -$focus-ring-width;\n    }\n  }\n}\n\n/*******************************************************************************\n* Buttons (which in Sphinx Design are actually links that look like buttons)\n* ref: https://sphinx-design.readthedocs.io/en/pydata-theme/badges_buttons.html#buttons\n*/\nhtml {\n  .sd-btn {\n    min-width: 2.25rem;\n    padding: 0.3125rem 0.75rem 0.4375rem; // 5px 12px 7px\n\n    &:hover {\n      @include link-style-hover; // override Sphinx Design\n\n      text-decoration-thickness: 1px;\n    }\n  }\n\n  @each $name in $sd-semantic-color-names {\n    .sd-btn-#{$name} {\n      &:hover {\n        color: var(--pst-color-#{$name}-highlight-text) !important;\n      }\n    }\n  }\n\n  @each $name in $sd-semantic-color-names {\n    .sd-btn-#{$name},\n    .sd-btn-outline-#{$name} {\n      &:focus-visible {\n        // Override Sphinx Design's use of -highlight colors. The -highlight\n        // colors are 15% darker, so this would create the effect of darkening\n        // the button when focused but we just want the button to have a focus\n        // ring of the same (non-highlight) color.\n        background-color: var(--sd-color-#{$name}) !important;\n        border-color: var(--sd-color-#{$name}) !important;\n        outline: var(--sd-color-#{$name}) solid $focus-ring-width;\n        outline-offset: $focus-ring-width;\n      }\n    }\n  }\n}\n","/**\n * Sphinx togglebutton\n */\n\n.bd-content {\n  @mixin chevron-down {\n    .toggle-chevron-right {\n      transform: rotate(90deg); // point chevron down\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  @mixin chevron-up {\n    .toggle-chevron-right {\n      transform: rotate(-90deg); // point chevron up\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  // Admonition toggles\n  .admonition {\n    button.toggle-button {\n      color: inherit;\n\n      // When disclosure widget is closed\n      &.toggle-button-hidden {\n        @include chevron-down;\n      }\n\n      // When open\n      @include chevron-up;\n    }\n\n    // Focus ring\n    // ----------\n    // Sphinx-togglebutton makes the entire admonition header clickable, but\n    // only the button within the header is focusable. We want the entire\n    // clickable area to be surrounded with a focus ring, so that's why we use\n    // the :focus-within selector, rather than a :focus-visible selector on the\n    // button.\n    &:focus-within {\n      overflow: visible;\n\n      // The complicated focus ring styles here are a consequence of the markup\n      // and border styles for this particular admonition class. (For the other\n      // type of admonition on this site, the focus ring style is achieved with\n      // simple `outline` and `outline-offset` rules on the admonition's\n      // header.) The problem is that Sphinx-togglebutton puts the admonition's\n      // left border on the outermost container (rather than separately setting\n      // the left border on the container's children). This makes it complicated\n      // to get the focus ring to simultaneously cover the left border in the\n      // header and align perfectly on the right with the body.\n      .admonition-title:focus-within::before {\n        content: \"\";\n        transform: translateX(\n          -$admonition-left-border-width\n        ); // align left edges of admonition and ring\n\n        width: calc(100% + $admonition-left-border-width); // align right edges\n        height: 100%;\n        border: $focus-ring-outline;\n        border-radius: $focus-ring-width;\n      }\n\n      // When expanded, sharpen the bottom left and right corners of the focus ring\n      &:not(.toggle-hidden) .admonition-title:focus-within::before {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n\n  // Details buttons\n  details.toggle-details {\n    // Over-ride border color to re-use our primary color\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n\n      @include chevron-down;\n    }\n\n    // When expanded, sharpen the bottom left and right corners of the focus ring\n    &[open] {\n      @include chevron-up;\n\n      :focus-visible {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html,\n// NBsphinx ipywidgets output selector\nhtml .jp-RenderedHTMLCommon {\n  // Add some margin around the element box for the focus ring. Otherwise the\n  // focus ring gets clipped because the containing elements have `overflow:\n  // hidden` applied to them (via the `.lm-Widget` selector)\n  margin: $focus-ring-width;\n\n  table {\n    table-layout: auto;\n  }\n}\n\n.bd-content .nboutput {\n  .output_area {\n    &.rendered_html,\n    .jp-RenderedHTMLCommon {\n      // pandas\n      table.dataframe {\n        @include table-colors;\n      }\n    }\n\n    // Dark theme special-cases\n    html[data-theme=\"dark\"] & {\n      &.rendered_html:not(:has(table.dataframe)),\n      // ipywidgets\n      .widget-subarea {\n        @include cell-output-background;\n      }\n\n      &.stderr {\n        background-color: var(--pst-color-danger);\n      }\n    }\n  }\n}\n\n// Add extra padding to the final item in an nbsphinx container\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\n// Override nbsphinx's colors for notebook cell prompts because they do not have\n// sufficient contrast. Colors chosen from accessible-pygments\n// a11y-high-contrast-{light,dark} themes.\n\n// Notebook cell input line number. Replace nbsphinx's low contrast blue with\n// higher contrast blues.\n.nbinput.container .prompt pre {\n  html[data-theme=\"light\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-light](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_light)\n    color: #005b82;\n  }\n\n  html[data-theme=\"dark\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-dark](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_dark)\n    color: #00e0e0;\n  }\n}\n\n// Notebook cell output line number. Replace nbsphinx's low contrast red with\n// higher contrast red / orange.\n.nboutput.container .prompt pre {\n  html[data-theme=\"light\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-light](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_light)\n    color: #a12236;\n  }\n\n  html[data-theme=\"dark\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-dark](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_dark)\n    color: #ffa07a;\n  }\n}\n\n/*******************************************************************************\n * myst NB\n */\n\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n.bd-content div.cell_output {\n  // pandas\n  table.dataframe {\n    @include table-colors;\n  }\n\n  html[data-theme=\"dark\"] & {\n    img,\n    .text_html:not(:has(table.dataframe)),\n    // ipywidgets\n    .widget-subarea {\n      @include cell-output-background;\n    }\n  }\n}\n\n// Prevent tables from scrunching together\n.bd-content {\n  div.cell_input {\n    display: flex;\n    flex-direction: column;\n    justify-content: stretch;\n  }\n\n  div.cell_input,\n  div.output {\n    border-radius: $admonition-border-radius;\n  }\n\n  div.output {\n    table {\n      table-layout: auto;\n    }\n  }\n}\n","/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=\"dark\"] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n","/**\n * /search.html page special-cases\n */\n\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n  // Whitespace\n  div#search-results {\n    > h2 {\n      font-size: var(--pst-font-size-icon);\n      margin-top: 1rem;\n    }\n\n    p.search-summary {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  ul.search {\n    margin: 0;\n    list-style: none;\n\n    li {\n      background-image: none;\n      padding: 1rem 0;\n      margin: 1rem 0;\n      border-top: 1px solid var(--pst-color-text-muted);\n\n      // First link is the page title, it should be a bit bigger\n      > a {\n        font-size: 1.2em;\n      }\n\n      div.context,\n      p.context {\n        color: var(--pst-color-text-base);\n        margin: 0.5em 0 0;\n\n        // Add a # before page section titles to make it clear they are sections\n        a::before {\n          content: \"#\";\n          padding-right: 0.2em;\n          color: var(--pst-color-text-muted);\n        }\n      }\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/v24.2.0/_static/styles/theme.css b/v24.2.0/_static/styles/theme.css
new file mode 100644
index 0000000000..4519dd9120
--- /dev/null
+++ b/v24.2.0/_static/styles/theme.css
@@ -0,0 +1,2 @@
+/* Provided by Sphinx's 'basic' theme, and included in the final set of assets */
+@import "../basic.css";
diff --git a/v24.2.0/_static/vendor/fontawesome/webfonts/fa-brands-400.ttf b/v24.2.0/_static/vendor/fontawesome/webfonts/fa-brands-400.ttf
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diff --git a/v24.2.0/_static/vendor/fontawesome/webfonts/fa-regular-400.ttf b/v24.2.0/_static/vendor/fontawesome/webfonts/fa-regular-400.ttf
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diff --git a/v24.2.0/_static/vendor/fontawesome/webfonts/fa-solid-900.ttf b/v24.2.0/_static/vendor/fontawesome/webfonts/fa-solid-900.ttf
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diff --git a/v24.2.0/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2 b/v24.2.0/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2
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diff --git a/v24.2.0/_static/webpack-macros.html b/v24.2.0/_static/webpack-macros.html
new file mode 100644
index 0000000000..ab5005b8e9
--- /dev/null
+++ b/v24.2.0/_static/webpack-macros.html
@@ -0,0 +1,24 @@
+<!--
+  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
+  These are re-used in layout.html
+-->
+
+{% macro head_pre_assets() %}
+  <!-- Loaded before other Sphinx assets -->
+  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=fd05d3ddc9a6e3fa7c66" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=fd05d3ddc9a6e3fa7c66" rel="stylesheet" />
+{% endmacro %}
+
+{% macro head_js_preload() %}
+  <!-- So that users can add custom icons -->
+  <script src="{{ pathto('_static/scripts/fontawesome.js', 1) }}?digest=fd05d3ddc9a6e3fa7c66"></script>
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=fd05d3ddc9a6e3fa7c66" />
+<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=fd05d3ddc9a6e3fa7c66" />
+{% endmacro %}
+
+{% macro body_post() %}
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script defer src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=fd05d3ddc9a6e3fa7c66"></script>
+<script defer src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=fd05d3ddc9a6e3fa7c66"></script>
+{% endmacro %}
\ No newline at end of file
diff --git a/v24.2.0/demos/00_CIL_geometry.ipynb b/v24.2.0/demos/00_CIL_geometry.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:38.139092Z",
+     "iopub.status.busy": "2024-10-10T15:02:38.138702Z",
+     "iopub.status.idle": "2024-10-10T15:02:38.141748Z",
+     "shell.execute_reply": "2024-10-10T15:02:38.141395Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2021 - 2022 United Kingdom Research and Innovation\n",
+    "#  Copyright 2021 - 2022 The University of Manchester\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:    Gemma Fardell (UKRI-STFC)\n",
+    "#                   Edoardo Pasca (UKRI-STFC)\n",
+    "#                   Laura Murgatroyd (UKRI-STFC)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A detailed look at CIL geometry\n",
+    "CIL holds your CT data in specialised data-containers, `AcquisitionData` and `ImageData`.\n",
+    "\n",
+    "Each of these has an associated `geometry` which contains the meta-data describing your set-up.\n",
+    "\n",
+    " - `AcquisitionGeometry` describes the acquisition data and parameters\n",
+    "\n",
+    " - `ImageGeometry` describes the image data (i.e., the reconstruction volume)\n",
+    "\n",
+    "The data-readers provided by CIL (Nikon, Zeiss and diamond nexus readers) will read in your data and return you a fully configured acquisition data with the acquisition geometry already configured, however if you read in a stack of tiffs or want to tweak the parameters this is simple to create by hand."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The structure of an AcquisitionGeometry\n",
+    "\n",
+    "An instance of an `AcquisitionGeometry`, `ag`, holds the configuration of the system, in `config` which is subdivided in to:\n",
+    " - `ag.config.system` -  The position and orientations of the `source`/`ray`, `rotation_axis` and `detector`\n",
+    " - `ag.config.panel` - The number of pixels, the size of pixels, and the position of pixel 0\n",
+    " - `ag.config.angles` - The number of angles, the unit of the angles (default is degrees)\n",
+    " - `ag.config.channels` - The number of channels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a simple AcquisitionGeometry\n",
+    "\n",
+    "You can use the `AcquisitionGeometry` methods to describe circular trajectory parallel-beam or cone-beam 2D or 3D data.\n",
+    "\n",
+    " - `ag = AcquisitionGeometry.create_Parallel2D()`\n",
+    " - `ag = AcquisitionGeometry.create_Parallel3D()`\n",
+    " - `ag = AcquisitionGeometry.create_Cone2D(source_position, detector_position)`\n",
+    " - `ag = AcquisitionGeometry.create_Cone3D(source_position, detector_position)`\n",
+    "\n",
+    "This notebook will step though each in turn and show you how to describe both simple and complex geometries with offsets and rotations.\n",
+    "\n",
+    "No matter which type of geometry you create you will also need to describe the panel and projection angles.\n",
+    " - `ag.set_panel(num_pixels, pixel_size)`\n",
+    " - `ag.set_angles(angles, angle_unit)`\n",
+    "\n",
+    "For multi-channel data you need to add the number of channels.\n",
+    " - `ag.set_channels(num_channels)`\n",
+    "\n",
+    "And you will also need to describe the order your data is stored in using the relavent labels from the CIL default labels: `channel`, `angle`, `vertical` and `horizontal`\n",
+    " - `ag.set_labels(['angle','vertical','horizontal'])`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Note on CIL AcquisitionGeometry:\n",
+    " - The geometry is described by a right-handed cooridinate system\n",
+    " - Positive angles describe the object rotating anti-clockwise when viewed from above\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parallel geometry\n",
+    "\n",
+    "Parallel beams of X-rays are emitted onto 1D (single pixel row) or 2D detector array. This geometry is common for synchrotron sources.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data. Note that for 3D geometry we need to describe a 2D panel where `num_pixels=[X,Y]`\n",
+    "\n",
+    "```python\n",
+    "parallel_2D_geometry = AcquisitionGeometry.create_Parallel2D()\\\n",
+    "    \n",
+    "                                          .set_panel(num_pixels=10)\\\n",
+    "        \n",
+    "                                          .set_angles(angles=range(0,180))\n",
+    "\n",
+    "\n",
+    "parallel_3D_geometry = AcquisitionGeometry.create_Parallel3D()\\\n",
+    "    \n",
+    "                                          .set_panel(num_pixels=[10,10])\\\n",
+    "        \n",
+    "                                          .set_angles(angles=range(0,180))\n",
+    "```\n",
+    "Both 2D and 3D parallel-beam geometries are displayed below. Note that the detector position has been set, this is not necessary to describe and reconstruct the data, but it makes the displayed images clearer.\n",
+    "\n",
+    "`show_geometry()` can be used to display the configured geometry and will be used here extensively. You can also print the geometry to obtain a detailed description. If `show_geometry` is not passed an `ImageGeometry` it will show the default geometry associated with the `AcquisitionGeometry` \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example creating a 2D parallel-beam geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:38.143400Z",
+     "iopub.status.busy": "2024-10-10T15:02:38.143145Z",
+     "iopub.status.idle": "2024-10-10T15:02:38.997275Z",
+     "shell.execute_reply": "2024-10-10T15:02:38.996701Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2D Parallel-beam tomography\n",
+      "System configuration:\n",
+      "\tRay direction: [0., 1.]\n",
+      "\tRotation axis position: [0., 0.]\n",
+      "\tDetector position: [ 0., 10.]\n",
+      "\tDetector direction x: [1., 0.]\n",
+      "Panel configuration:\n",
+      "\tNumber of pixels: [10  1]\n",
+      "\tPixel size: [1. 1.]\n",
+      "\tPixel origin: bottom-left\n",
+      "Channel configuration:\n",
+      "\tNumber of channels: 1\n",
+      "Acquisition description:\n",
+      "\tNumber of positions: 180\n",
+      "\tAngles 0-9 in degrees: [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]\n",
+      "\tAngles 170-179 in degrees: [170., 171., 172., 173., 174., 175., 176., 177., 178., 179.]\n",
+      "\tFull angular array can be accessed with acquisition_data.geometry.angles\n",
+      "Distances in units: units distance\n"
+     ]
+    }
+   ],
+   "source": [
+    "from cil.framework import AcquisitionGeometry\n",
+    "from cil.utilities.display import show_geometry\n",
+    "\n",
+    "ag = AcquisitionGeometry.create_Parallel2D(detector_position=[0,10])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "show_geometry(ag)\n",
+    "\n",
+    "print(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example creating a 3D parallel-beam geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.026342Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.026068Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.261809Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.261326Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647c2e6c0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Parallel3D(detector_position=[0,10,0])\\\n",
+    "    .set_panel(num_pixels=[10,10])\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fan-beam geometry\n",
+    "\n",
+    "A single point-like X-ray source emits a cone-beam onto a single row of detector pixels. The beam is typically collimated to imaging field of view. Collimation greatly reduce amount of scatter radiation reaching the detector. Fan-beam geometry is used when scattering has significant influence on image quality or single-slice reconstruction is sufficient.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data.\n",
+    "\n",
+    "For fan-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin. They are specified as `[x,y]` coordinates.\n",
+    "\n",
+    "```python\n",
+    "cone_2D_geometry = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10])\\\n",
+    "    \n",
+    "                                      .set_panel(num_pixels=10)\\\n",
+    "        \n",
+    "                                      .set_angles(angles=range(0,180))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.263396Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.263217Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.456876Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.456392Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f96474a8b90>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cone-beam geometry\n",
+    "\n",
+    "A single point-like X-ray source emits a cone-beam onto 2D detector array. Cone-beam geometry is mainly used in lab-based CT instruments.\n",
+    "\n",
+    "We describe the system, and then set the panel and angle data.\n",
+    "\n",
+    "For cone-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin and aligned in the Z-direction. They are specified as `[X,Y,Z]` coordinates.\n",
+    "\n",
+    "```python\n",
+    "cone_3D_geometry = AcquisitionGeometry.create_Cone3D(source_position=[0,-10,0], detector_position=[0,10,0])\\\n",
+    "    \n",
+    "                                      .set_panel(num_pixels=[10,10])\\\n",
+    "        \n",
+    "                                      .set_angles(angles=range(0,180))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.458382Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.458191Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.676360Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.675872Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647969850>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-10,0],detector_position=[0,10,0])\\\n",
+    "    .set_panel(num_pixels=[10,10])\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an offset AcquisitionGeometry\n",
+    "\n",
+    "It is unusual to have a perfectly aligned CT system. One of the most common offsets is the rotation-axis. If this offset is described by the `AcquisitionGeometry` then it will be accounted for in the reconstruction. This saves having to pad your data to account for this.\n",
+    "\n",
+    "To specify the offset, you could either add an x-component to the `source_position` and `detector_position` or you can offset the rotation axis from the origin using `rotation_axis_position`.\n",
+    "\n",
+    "As with the `source_position` and `detector_position` this is the `rotation_axis_position` is specified in 2D with a 2D vector `[X,Y]` or 3D with a 3D vector `[X,Y,Z]`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we offset the rotation axis by -0.5 in X by setting `rotation_axis_position=[-0.5,0]`. You can see the rotation axis position is no longer a point on the source-to-detector vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.678070Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.677748Z",
+     "iopub.status.idle": "2024-10-10T15:02:39.870076Z",
+     "shell.execute_reply": "2024-10-10T15:02:39.869592Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647896ff0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10],\n",
+    "                                       rotation_axis_position=[-0.5,0])\\\n",
+    "    .set_panel(num_pixels=10)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a more complex AcquisitionGeometry\n",
+    "\n",
+    "We can also set up rotations in the system. These are configured with vectors describing the direction.\n",
+    "\n",
+    "For example a detector yaw can be described by using `detector_direction_x=[X,Y]`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:39.871906Z",
+     "iopub.status.busy": "2024-10-10T15:02:39.871492Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.100625Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.100144Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f964771ff80>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone2D(source_position=[0,-10],detector_position=[0,10],\n",
+    "                                       detector_direction_x=[0.9,0.1]\n",
+    "                                       )\\\n",
+    "     .set_panel(num_pixels=10)\\\n",
+    "     .set_angles(angles=range(0,180))\n",
+    "     \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can set `rotation_axis_direction`, `detector_direction_x` and `detector_direction_y` by specifying a 3D directional vector `[X,Y,Z]`.\n",
+    "\n",
+    "For 3D datasets detector roll is commonly corrected with a dual-slice centre of rotation algorithm. You can specify `detector_direction_x` and `detector_direction_y` - ensuring they are ortogonal vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.102173Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.102009Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.322852Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.322354Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f96478e7f80>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0],\n",
+    "                                       detector_direction_x=[0.9,0.0,-0.1],detector_direction_y=[0.1,0,0.9]\n",
+    "                                      )\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In 3D datasets we can tilt the rotation axis to describe laminograpy geometry by changing `rotation_axis_direction`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.324674Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.324284Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.545305Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.544793Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f9647778470>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0],rotation_axis_direction=[0,-1,1])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "    \n",
+    "show_geometry(ag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The structure of an ImageGeometry\n",
+    "\n",
+    "ImageGeometry holds the description of the reconstruction volume. It holds:\n",
+    "\n",
+    " - The number of voxels in X, Y, Z: `voxel_num_x`, `voxel_num_y`, `voxel_num_z`\n",
+    " - The size of voxels in X, Y, Z: `voxel_size_x`, `voxel_size_y`, `voxel_size_z`\n",
+    " - The offset of the volume from the rotation axis in voxels: `center_x`, `center_y`, `center_z`\n",
+    " - The number of channels for multi-channel data\n",
+    "\n",
+    "You will also need to describe the order your data is stored in using the relevent labels from the CIL. The default labels are: `channel`, `vertical`, `horizontal_y` and `horizontal_x`\n",
+    " - `ig.set_labels(['vertical','horizontal_y','horizontal_x'])`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a simple ImageGeometry\n",
+    "\n",
+    "To create a default ImageGeometry you can use:\n",
+    " `ig = ag.get_ImageGeometry()`\n",
+    "\n",
+    "This creates an ImageGeometry with:\n",
+    " - `voxel_num_x`, `voxel_num_y` equal to the number of horizontal pixels of the panel\n",
+    " - `voxel_num_z` equal to the number of vertical pixels of the panel\n",
+    " - `voxel_size_x`, `voxel_size_y` is given by the horizontal pixel size divided by magnification\n",
+    " - `voxel_size_z` is given by the vertical pixel size divided by magnification\n",
+    "\n",
+    "\n",
+    " You can pass a resolution argument:\n",
+    " `ig = ag.get_ImageGeometry(resolution)` \n",
+    "\n",
+    " - `resolution=0.5` double the size of your voxels, and half the number of voxels in each dimension\n",
+    " - `resolution=2` half the size of your voxels, and double the number of voxels in each dimension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Note on CIL ImageGeometry:\n",
+    "At 0 degrees `horizontal_y` is aligned with the Y axis, and `horizontal_x` with the X axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.546856Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.546684Z",
+     "iopub.status.idle": "2024-10-10T15:02:40.551654Z",
+     "shell.execute_reply": "2024-10-10T15:02:40.551245Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - default\n",
+      "Number of channels: 1\n",
+      "channel_spacing: 1.0\n",
+      "voxel_num : x2048,y2048,z2048\n",
+      "voxel_size : x0.1,y0.1,z0.1\n",
+      "center : x0,y0,z0\n",
+      "\n",
+      "ImageGeometry - 0.5x resolution\n",
+      "Number of channels: 1\n",
+      "channel_spacing: 1.0\n",
+      "voxel_num : x1024,y1024,z1024\n",
+      "voxel_size : x0.2,y0.2,z0.2\n",
+      "center : x0,y0,z0\n",
+      "\n",
+      "ImageGeometry - 2x resolution\n",
+      "Number of channels: 1\n",
+      "channel_spacing: 1.0\n",
+      "voxel_num : x4096,y4096,z4096\n",
+      "voxel_size : x0.05,y0.05,z0.05\n",
+      "center : x0,y0,z0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "print(\"ImageGeometry - default\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "print(ig)\n",
+    "\n",
+    "print(\"ImageGeometry - 0.5x resolution\")\n",
+    "ig = ag.get_ImageGeometry(resolution=0.5)\n",
+    "print(ig)\n",
+    "\n",
+    "print(\"ImageGeometry - 2x resolution\")\n",
+    "ig = ag.get_ImageGeometry(resolution=2)\n",
+    "print(ig)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a custom ImageGeometry\n",
+    "You can create your own ImageGeometry with:\n",
+    "`ig = ImageGeometry(...)`\n",
+    "\n",
+    "Giving you full control over the parameters.\n",
+    "\n",
+    "You can also change the members directly to reduce the reconstructed volume to exclude empty space.\n",
+    "\n",
+    "Using the previous example, we now can specify a smaller region of interest to reconstruct. We can offset the region of interest from the origin by specifiying the physical distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:40.553118Z",
+     "iopub.status.busy": "2024-10-10T15:02:40.552816Z",
+     "iopub.status.idle": "2024-10-10T15:02:41.195828Z",
+     "shell.execute_reply": "2024-10-10T15:02:41.195317Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - default\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - RoI\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageGeometry - Offset RoI\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show_geometry at 0x7f96472f8380>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0])\\\n",
+    "    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\\\n",
+    "    .set_angles(angles=range(0,180))\n",
+    "\n",
+    "print(\"ImageGeometry - default\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "show_geometry(ag, ig)\n",
+    "\n",
+    "print(\"ImageGeometry - RoI\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "ig.voxel_num_z = 100\n",
+    "show_geometry(ag, ig)\n",
+    "\n",
+    "print(\"ImageGeometry - Offset RoI\")\n",
+    "ig = ag.get_ImageGeometry()\n",
+    "ig.voxel_num_z = 200\n",
+    "ig.center_z = -1024 * ig.voxel_size_z\n",
+    "show_geometry(ag, ig)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also create an `ImageGeometry` directly.\n",
+    "\n",
+    "Here we create our ig independently of an `AcquisitionGeometry`, by first importing `ImageGeometry` from `cil.framework`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-10T15:02:41.197491Z",
+     "iopub.status.busy": "2024-10-10T15:02:41.197216Z",
+     "iopub.status.idle": "2024-10-10T15:02:41.199945Z",
+     "shell.execute_reply": "2024-10-10T15:02:41.199478Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from cil.framework import ImageGeometry\n",
+    "\n",
+    "ig = ImageGeometry(voxel_num_x=1000, voxel_num_y=1000, voxel_num_z=500, voxel_size_x=0.1, voxel_size_y=0.1, voxel_size_z=0.2 )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cf07678abc5cc77bc6e1a7d19b1e87ab0c29b83e7ee41c2bc72506d16d80ed44"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/v24.2.0/demos/00_CIL_geometry/index.html b/v24.2.0/demos/00_CIL_geometry/index.html
new file mode 100644
index 0000000000..cec6294ce8
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@@ -0,0 +1,1119 @@
+
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">A detailed look at CIL geometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#The-structure-of-an-AcquisitionGeometry">The structure of an AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Create-a-simple-AcquisitionGeometry">Create a simple AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Create-an-offset-AcquisitionGeometry">Create an offset AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Create-a-more-complex-AcquisitionGeometry">Create a more complex AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#The-structure-of-an-ImageGeometry">The structure of an ImageGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Create-a-simple-ImageGeometry">Create a simple ImageGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Create-a-custom-ImageGeometry">Create a custom ImageGeometry</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../deriv2_cgls/">1D inverse problem demo using deriv2 from regtools</a></li>
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+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">#  Copyright 2021 - 2022 United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2021 - 2022 The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1">#   Authored by:    Gemma Fardell (UKRI-STFC)</span>
+<span class="c1">#                   Edoardo Pasca (UKRI-STFC)</span>
+<span class="c1">#                   Laura Murgatroyd (UKRI-STFC)</span>
+</pre></div>
+</div>
+</div>
+<section id="A-detailed-look-at-CIL-geometry">
+<h1>A detailed look at CIL geometry<a class="headerlink" href="#A-detailed-look-at-CIL-geometry" title="Link to this heading">#</a></h1>
+<p>CIL holds your CT data in specialised data-containers, <code class="docutils literal notranslate"><span class="pre">AcquisitionData</span></code> and <code class="docutils literal notranslate"><span class="pre">ImageData</span></code>.</p>
+<p>Each of these has an associated <code class="docutils literal notranslate"><span class="pre">geometry</span></code> which contains the meta-data describing your set-up.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code> describes the acquisition data and parameters</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ImageGeometry</span></code> describes the image data (i.e., the reconstruction volume)</p></li>
+</ul>
+<p>The data-readers provided by CIL (Nikon, Zeiss and diamond nexus readers) will read in your data and return you a fully configured acquisition data with the acquisition geometry already configured, however if you read in a stack of tiffs or want to tweak the parameters this is simple to create by hand.</p>
+<section id="The-structure-of-an-AcquisitionGeometry">
+<h2>The structure of an AcquisitionGeometry<a class="headerlink" href="#The-structure-of-an-AcquisitionGeometry" title="Link to this heading">#</a></h2>
+<p>An instance of an <code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code>, <code class="docutils literal notranslate"><span class="pre">ag</span></code>, holds the configuration of the system, in <code class="docutils literal notranslate"><span class="pre">config</span></code> which is subdivided in to: - <code class="docutils literal notranslate"><span class="pre">ag.config.system</span></code> - The position and orientations of the <code class="docutils literal notranslate"><span class="pre">source</span></code>/<code class="docutils literal notranslate"><span class="pre">ray</span></code>, <code class="docutils literal notranslate"><span class="pre">rotation_axis</span></code> and <code class="docutils literal notranslate"><span class="pre">detector</span></code> - <code class="docutils literal notranslate"><span class="pre">ag.config.panel</span></code> - The number of pixels, the size of pixels, and the position of pixel 0 - <code class="docutils literal notranslate"><span class="pre">ag.config.angles</span></code> - The number of angles, the unit of the angles (default is degrees) - <code class="docutils literal notranslate"><span class="pre">ag.config.channels</span></code> - The number of channels</p>
+</section>
+<section id="Create-a-simple-AcquisitionGeometry">
+<h2>Create a simple AcquisitionGeometry<a class="headerlink" href="#Create-a-simple-AcquisitionGeometry" title="Link to this heading">#</a></h2>
+<p>You can use the <code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code> methods to describe circular trajectory parallel-beam or cone-beam 2D or 3D data.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">ag</span> <span class="pre">=</span> <span class="pre">AcquisitionGeometry.create_Parallel2D()</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ag</span> <span class="pre">=</span> <span class="pre">AcquisitionGeometry.create_Parallel3D()</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ag</span> <span class="pre">=</span> <span class="pre">AcquisitionGeometry.create_Cone2D(source_position,</span> <span class="pre">detector_position)</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ag</span> <span class="pre">=</span> <span class="pre">AcquisitionGeometry.create_Cone3D(source_position,</span> <span class="pre">detector_position)</span></code></p></li>
+</ul>
+<p>This notebook will step though each in turn and show you how to describe both simple and complex geometries with offsets and rotations.</p>
+<p>No matter which type of geometry you create you will also need to describe the panel and projection angles. - <code class="docutils literal notranslate"><span class="pre">ag.set_panel(num_pixels,</span> <span class="pre">pixel_size)</span></code> - <code class="docutils literal notranslate"><span class="pre">ag.set_angles(angles,</span> <span class="pre">angle_unit)</span></code></p>
+<p>For multi-channel data you need to add the number of channels. - <code class="docutils literal notranslate"><span class="pre">ag.set_channels(num_channels)</span></code></p>
+<p>And you will also need to describe the order your data is stored in using the relavent labels from the CIL default labels: <code class="docutils literal notranslate"><span class="pre">channel</span></code>, <code class="docutils literal notranslate"><span class="pre">angle</span></code>, <code class="docutils literal notranslate"><span class="pre">vertical</span></code> and <code class="docutils literal notranslate"><span class="pre">horizontal</span></code> - <code class="docutils literal notranslate"><span class="pre">ag.set_labels(['angle','vertical','horizontal'])</span></code></p>
+<section id="A-Note-on-CIL-AcquisitionGeometry:">
+<h3>A Note on CIL AcquisitionGeometry:<a class="headerlink" href="#A-Note-on-CIL-AcquisitionGeometry:" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>The geometry is described by a right-handed cooridinate system</p></li>
+<li><p>Positive angles describe the object rotating anti-clockwise when viewed from above</p></li>
+</ul>
+</section>
+<section id="Parallel-geometry">
+<h3>Parallel geometry<a class="headerlink" href="#Parallel-geometry" title="Link to this heading">#</a></h3>
+<p>Parallel beams of X-rays are emitted onto 1D (single pixel row) or 2D detector array. This geometry is common for synchrotron sources.</p>
+<p>We describe the system, and then set the panel and angle data. Note that for 3D geometry we need to describe a 2D panel where <code class="docutils literal notranslate"><span class="pre">num_pixels=[X,Y]</span></code></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">parallel_2D_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel2D</span><span class="p">()</span>\
+
+                                          <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>\
+
+                                          <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+
+<span class="n">parallel_3D_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel3D</span><span class="p">()</span>\
+
+                                          <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>\
+
+                                          <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>Both 2D and 3D parallel-beam geometries are displayed below. Note that the detector position has been set, this is not necessary to describe and reconstruct the data, but it makes the displayed images clearer.</p>
+<p><code class="docutils literal notranslate"><span class="pre">show_geometry()</span></code> can be used to display the configured geometry and will be used here extensively. You can also print the geometry to obtain a detailed description. If <code class="docutils literal notranslate"><span class="pre">show_geometry</span></code> is not passed an <code class="docutils literal notranslate"><span class="pre">ImageGeometry</span></code> it will show the default geometry associated with the <code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code></p>
+<p>An example creating a 2D parallel-beam geometry:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">AcquisitionGeometry</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.display</span> <span class="kn">import</span> <span class="n">show_geometry</span>
+
+<span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel2D</span><span class="p">(</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_7_0.png" src="../../_images/demos_00_CIL_geometry_7_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+2D Parallel-beam tomography
+System configuration:
+        Ray direction: [0., 1.]
+        Rotation axis position: [0., 0.]
+        Detector position: [ 0., 10.]
+        Detector direction x: [1., 0.]
+Panel configuration:
+        Number of pixels: [10  1]
+        Pixel size: [1. 1.]
+        Pixel origin: bottom-left
+Channel configuration:
+        Number of channels: 1
+Acquisition description:
+        Number of positions: 180
+        Angles 0-9 in degrees: [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]
+        Angles 170-179 in degrees: [170., 171., 172., 173., 174., 175., 176., 177., 178., 179.]
+        Full angular array can be accessed with acquisition_data.geometry.angles
+Distances in units: units distance
+</pre></div></div>
+</div>
+<p>An example creating a 3D parallel-beam geometry:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Parallel3D</span><span class="p">(</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_9_0.png" src="../../_images/demos_00_CIL_geometry_9_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f9647c2e6c0&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Fan-beam-geometry">
+<h3>Fan-beam geometry<a class="headerlink" href="#Fan-beam-geometry" title="Link to this heading">#</a></h3>
+<p>A single point-like X-ray source emits a cone-beam onto a single row of detector pixels. The beam is typically collimated to imaging field of view. Collimation greatly reduce amount of scatter radiation reaching the detector. Fan-beam geometry is used when scattering has significant influence on image quality or single-slice reconstruction is sufficient.</p>
+<p>We describe the system, and then set the panel and angle data.</p>
+<p>For fan-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin. They are specified as <code class="docutils literal notranslate"><span class="pre">[x,y]</span></code> coordinates.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cone_2D_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>\
+
+                                      <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>\
+
+                                      <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_11_0.png" src="../../_images/demos_00_CIL_geometry_11_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f96474a8b90&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Cone-beam-geometry">
+<h3>Cone-beam geometry<a class="headerlink" href="#Cone-beam-geometry" title="Link to this heading">#</a></h3>
+<p>A single point-like X-ray source emits a cone-beam onto 2D detector array. Cone-beam geometry is mainly used in lab-based CT instruments.</p>
+<p>We describe the system, and then set the panel and angle data.</p>
+<p>For cone-beam data the source and detector positions are required. As default we place them along the Y-axis where the rotation-axis is on the origin and aligned in the Z-direction. They are specified as <code class="docutils literal notranslate"><span class="pre">[X,Y,Z]</span></code> coordinates.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">cone_3D_geometry</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>\
+
+                                      <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>\
+
+                                      <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_13_0.png" src="../../_images/demos_00_CIL_geometry_13_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f9647969850&gt;
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Create-an-offset-AcquisitionGeometry">
+<h2>Create an offset AcquisitionGeometry<a class="headerlink" href="#Create-an-offset-AcquisitionGeometry" title="Link to this heading">#</a></h2>
+<p>It is unusual to have a perfectly aligned CT system. One of the most common offsets is the rotation-axis. If this offset is described by the <code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code> then it will be accounted for in the reconstruction. This saves having to pad your data to account for this.</p>
+<p>To specify the offset, you could either add an x-component to the <code class="docutils literal notranslate"><span class="pre">source_position</span></code> and <code class="docutils literal notranslate"><span class="pre">detector_position</span></code> or you can offset the rotation axis from the origin using <code class="docutils literal notranslate"><span class="pre">rotation_axis_position</span></code>.</p>
+<p>As with the <code class="docutils literal notranslate"><span class="pre">source_position</span></code> and <code class="docutils literal notranslate"><span class="pre">detector_position</span></code> this is the <code class="docutils literal notranslate"><span class="pre">rotation_axis_position</span></code> is specified in 2D with a 2D vector <code class="docutils literal notranslate"><span class="pre">[X,Y]</span></code> or 3D with a 3D vector <code class="docutils literal notranslate"><span class="pre">[X,Y,Z]</span></code></p>
+<p>Below we offset the rotation axis by -0.5 in X by setting <code class="docutils literal notranslate"><span class="pre">rotation_axis_position=[-0.5,0]</span></code>. You can see the rotation axis position is no longer a point on the source-to-detector vector.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">],</span>
+                                       <span class="n">rotation_axis_position</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_16_0.png" src="../../_images/demos_00_CIL_geometry_16_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f9647896ff0&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Create-a-more-complex-AcquisitionGeometry">
+<h2>Create a more complex AcquisitionGeometry<a class="headerlink" href="#Create-a-more-complex-AcquisitionGeometry" title="Link to this heading">#</a></h2>
+<p>We can also set up rotations in the system. These are configured with vectors describing the direction.</p>
+<p>For example a detector yaw can be described by using <code class="docutils literal notranslate"><span class="pre">detector_direction_x=[X,Y]</span></code>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">],</span>
+                                       <span class="n">detector_direction_x</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.1</span><span class="p">]</span>
+                                       <span class="p">)</span>\
+     <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>\
+     <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_18_0.png" src="../../_images/demos_00_CIL_geometry_18_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f964771ff80&gt;
+</pre></div></div>
+</div>
+<p>You can set <code class="docutils literal notranslate"><span class="pre">rotation_axis_direction</span></code>, <code class="docutils literal notranslate"><span class="pre">detector_direction_x</span></code> and <code class="docutils literal notranslate"><span class="pre">detector_direction_y</span></code> by specifying a 3D directional vector <code class="docutils literal notranslate"><span class="pre">[X,Y,Z]</span></code>.</p>
+<p>For 3D datasets detector roll is commonly corrected with a dual-slice centre of rotation algorithm. You can specify <code class="docutils literal notranslate"><span class="pre">detector_direction_x</span></code> and <code class="docutils literal notranslate"><span class="pre">detector_direction_y</span></code> - ensuring they are ortogonal vectors.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                                       <span class="n">detector_direction_x</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="o">-</span><span class="mf">0.1</span><span class="p">],</span><span class="n">detector_direction_y</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.9</span><span class="p">]</span>
+                                      <span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">2048</span><span class="p">,</span><span class="mi">2048</span><span class="p">],</span> <span class="n">pixel_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_20_0.png" src="../../_images/demos_00_CIL_geometry_20_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f96478e7f80&gt;
+</pre></div></div>
+</div>
+<p>In 3D datasets we can tilt the rotation axis to describe laminograpy geometry by changing <code class="docutils literal notranslate"><span class="pre">rotation_axis_direction</span></code></p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">rotation_axis_direction</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">2048</span><span class="p">,</span><span class="mi">2048</span><span class="p">],</span> <span class="n">pixel_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_22_0.png" src="../../_images/demos_00_CIL_geometry_22_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f9647778470&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="The-structure-of-an-ImageGeometry">
+<h2>The structure of an ImageGeometry<a class="headerlink" href="#The-structure-of-an-ImageGeometry" title="Link to this heading">#</a></h2>
+<p>ImageGeometry holds the description of the reconstruction volume. It holds:</p>
+<ul class="simple">
+<li><p>The number of voxels in X, Y, Z: <code class="docutils literal notranslate"><span class="pre">voxel_num_x</span></code>, <code class="docutils literal notranslate"><span class="pre">voxel_num_y</span></code>, <code class="docutils literal notranslate"><span class="pre">voxel_num_z</span></code></p></li>
+<li><p>The size of voxels in X, Y, Z: <code class="docutils literal notranslate"><span class="pre">voxel_size_x</span></code>, <code class="docutils literal notranslate"><span class="pre">voxel_size_y</span></code>, <code class="docutils literal notranslate"><span class="pre">voxel_size_z</span></code></p></li>
+<li><p>The offset of the volume from the rotation axis in voxels: <code class="docutils literal notranslate"><span class="pre">center_x</span></code>, <code class="docutils literal notranslate"><span class="pre">center_y</span></code>, <code class="docutils literal notranslate"><span class="pre">center_z</span></code></p></li>
+<li><p>The number of channels for multi-channel data</p></li>
+</ul>
+<p>You will also need to describe the order your data is stored in using the relevent labels from the CIL. The default labels are: <code class="docutils literal notranslate"><span class="pre">channel</span></code>, <code class="docutils literal notranslate"><span class="pre">vertical</span></code>, <code class="docutils literal notranslate"><span class="pre">horizontal_y</span></code> and <code class="docutils literal notranslate"><span class="pre">horizontal_x</span></code> - <code class="docutils literal notranslate"><span class="pre">ig.set_labels(['vertical','horizontal_y','horizontal_x'])</span></code></p>
+</section>
+<section id="Create-a-simple-ImageGeometry">
+<h2>Create a simple ImageGeometry<a class="headerlink" href="#Create-a-simple-ImageGeometry" title="Link to this heading">#</a></h2>
+<p>To create a default ImageGeometry you can use: <code class="docutils literal notranslate"><span class="pre">ig</span> <span class="pre">=</span> <span class="pre">ag.get_ImageGeometry()</span></code></p>
+<p>This creates an ImageGeometry with: - <code class="docutils literal notranslate"><span class="pre">voxel_num_x</span></code>, <code class="docutils literal notranslate"><span class="pre">voxel_num_y</span></code> equal to the number of horizontal pixels of the panel - <code class="docutils literal notranslate"><span class="pre">voxel_num_z</span></code> equal to the number of vertical pixels of the panel - <code class="docutils literal notranslate"><span class="pre">voxel_size_x</span></code>, <code class="docutils literal notranslate"><span class="pre">voxel_size_y</span></code> is given by the horizontal pixel size divided by magnification - <code class="docutils literal notranslate"><span class="pre">voxel_size_z</span></code> is given by the vertical pixel size divided by magnification</p>
+<p>You can pass a resolution argument: <code class="docutils literal notranslate"><span class="pre">ig</span> <span class="pre">=</span> <span class="pre">ag.get_ImageGeometry(resolution)</span></code></p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">resolution=0.5</span></code> double the size of your voxels, and half the number of voxels in each dimension</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">resolution=2</span></code> half the size of your voxels, and double the number of voxels in each dimension</p></li>
+</ul>
+<section id="A-Note-on-CIL-ImageGeometry:">
+<h3>A Note on CIL ImageGeometry:<a class="headerlink" href="#A-Note-on-CIL-ImageGeometry:" title="Link to this heading">#</a></h3>
+<p>At 0 degrees <code class="docutils literal notranslate"><span class="pre">horizontal_y</span></code> is aligned with the Y axis, and <code class="docutils literal notranslate"><span class="pre">horizontal_x</span></code> with the X axis.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">2048</span><span class="p">,</span><span class="mi">2048</span><span class="p">],</span> <span class="n">pixel_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ImageGeometry - default&quot;</span><span class="p">)</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">ig</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ImageGeometry - 0.5x resolution&quot;</span><span class="p">)</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">(</span><span class="n">resolution</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">ig</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ImageGeometry - 2x resolution&quot;</span><span class="p">)</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">(</span><span class="n">resolution</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">ig</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ImageGeometry - default
+Number of channels: 1
+channel_spacing: 1.0
+voxel_num : x2048,y2048,z2048
+voxel_size : x0.1,y0.1,z0.1
+center : x0,y0,z0
+
+ImageGeometry - 0.5x resolution
+Number of channels: 1
+channel_spacing: 1.0
+voxel_num : x1024,y1024,z1024
+voxel_size : x0.2,y0.2,z0.2
+center : x0,y0,z0
+
+ImageGeometry - 2x resolution
+Number of channels: 1
+channel_spacing: 1.0
+voxel_num : x4096,y4096,z4096
+voxel_size : x0.05,y0.05,z0.05
+center : x0,y0,z0
+
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Create-a-custom-ImageGeometry">
+<h2>Create a custom ImageGeometry<a class="headerlink" href="#Create-a-custom-ImageGeometry" title="Link to this heading">#</a></h2>
+<p>You can create your own ImageGeometry with: <code class="docutils literal notranslate"><span class="pre">ig</span> <span class="pre">=</span> <span class="pre">ImageGeometry(...)</span></code></p>
+<p>Giving you full control over the parameters.</p>
+<p>You can also change the members directly to reduce the reconstructed volume to exclude empty space.</p>
+<p>Using the previous example, we now can specify a smaller region of interest to reconstruct. We can offset the region of interest from the origin by specifiying the physical distance.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">(</span><span class="n">source_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">detector_position</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">500</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>\
+    <span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">num_pixels</span><span class="o">=</span><span class="p">[</span><span class="mi">2048</span><span class="p">,</span><span class="mi">2048</span><span class="p">],</span> <span class="n">pixel_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">)</span>\
+    <span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">180</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ImageGeometry - default&quot;</span><span class="p">)</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">,</span> <span class="n">ig</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ImageGeometry - RoI&quot;</span><span class="p">)</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+<span class="n">ig</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">,</span> <span class="n">ig</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;ImageGeometry - Offset RoI&quot;</span><span class="p">)</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+<span class="n">ig</span><span class="o">.</span><span class="n">voxel_num_z</span> <span class="o">=</span> <span class="mi">200</span>
+<span class="n">ig</span><span class="o">.</span><span class="n">center_z</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1024</span> <span class="o">*</span> <span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_z</span>
+<span class="n">show_geometry</span><span class="p">(</span><span class="n">ag</span><span class="p">,</span> <span class="n">ig</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ImageGeometry - default
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_28_1.png" src="../../_images/demos_00_CIL_geometry_28_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ImageGeometry - RoI
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_28_3.png" src="../../_images/demos_00_CIL_geometry_28_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ImageGeometry - Offset RoI
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_00_CIL_geometry_28_5.png" src="../../_images/demos_00_CIL_geometry_28_5.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show_geometry at 0x7f96472f8380&gt;
+</pre></div></div>
+</div>
+<p>We can also create an <code class="docutils literal notranslate"><span class="pre">ImageGeometry</span></code> directly.</p>
+<p>Here we create our ig independently of an <code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code>, by first importing <code class="docutils literal notranslate"><span class="pre">ImageGeometry</span></code> from <code class="docutils literal notranslate"><span class="pre">cil.framework</span></code></p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageGeometry</span>
+
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="n">voxel_num_x</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">voxel_num_y</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">voxel_num_z</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">voxel_size_x</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">voxel_size_y</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">voxel_size_z</span><span class="o">=</span><span class="mf">0.2</span> <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+
+
+                </article>
+              
+              
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+              
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\ No newline at end of file
diff --git a/v24.2.0/demos/callback_demonstration.ipynb b/v24.2.0/demos/callback_demonstration.ipynb
new file mode 100644
index 0000000000..0fd142943d
--- /dev/null
+++ b/v24.2.0/demos/callback_demonstration.ipynb
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2024 -  United Kingdom Research and Innovation\n",
+    "#  Copyright 2024 -  The University of Manchester\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:    CIL contributors "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CIL Callback demonstration\n",
+    "\n",
+    "This notebook runs on CIL Master (built on 14/03/2024) and demonstrates the new callback functionality "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cil.utilities import dataexample\n",
+    "from cil.utilities.display import show2D\n",
+    "from cil.recon import FDK\n",
+    "from cil.processors import TransmissionAbsorptionConverter, Slicer\n",
+    "from cil.utilities.quality_measures import psnr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "from cil.plugins.tigre import ProjectionOperator\n",
+    "from cil.optimisation.algorithms import FISTA, Algorithm\n",
+    "from cil.optimisation.functions import LeastSquares, IndicatorBox, ZeroFunction, TotalVariation\n",
+    "from cil.optimisation.operators import GradientOperator\n",
+    "from cil.optimisation.utilities import callbacks\n",
+    "from cil.framework import DataContainer\n",
+    "\n",
+    "from cil.utilities.quality_measures import mse, mae, psnr\n",
+    "\n",
+    "# set up default colour map for visualisation\n",
+    "cmap = \"gray\"\n",
+    "\n",
+    "# set the backend for FBP and the ProjectionOperator\n",
+    "device = 'gpu'\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FDK recon\n",
+      "\n",
+      "Input Data:\n",
+      "\tangle: 60\n",
+      "\thorizontal: 128\n",
+      "\n",
+      "Reconstruction Volume:\n",
+      "\thorizontal_y: 128\n",
+      "\thorizontal_x: 128\n",
+      "\n",
+      "Reconstruction Options:\n",
+      "\tBackend: tigre\n",
+      "\tFilter: ram-lak\n",
+      "\tFilter cut-off frequency: 1.0\n",
+      "\tFFT order: 8\n",
+      "\tFilter_inplace: False\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e75840>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#%% Load data\n",
+    "ground_truth = dataexample.SIMULATED_SPHERE_VOLUME.get()\n",
+    "\n",
+    "data = dataexample.SIMULATED_CONE_BEAM_DATA.get()\n",
+    "twoD = True\n",
+    "if twoD:\n",
+    "    data = data.get_slice(vertical='centre')\n",
+    "    ground_truth = ground_truth.get_slice(vertical='centre')\n",
+    "\n",
+    "absorption = TransmissionAbsorptionConverter()(data)\n",
+    "absorption = Slicer(roi={'angle':(0, -1, 5)})(absorption)\n",
+    "\n",
+    "ig = ground_truth.geometry\n",
+    "\n",
+    "#%%\n",
+    "recon = FDK(absorption, image_geometry=ig).run()\n",
+    "#%%\n",
+    "show2D([ground_truth, recon], title = ['Ground Truth', 'FDK Reconstruction'], origin = 'upper', num_cols = 2)\n",
+    "\n",
+    "# %%\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Default behaviour "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6355b4a0be0c4297a02f41b49e6f7e3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e74b80>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "alpha=0.1\n",
+    "A = ProjectionOperator(image_geometry=ig, \n",
+    "                       acquisition_geometry=absorption.geometry)\n",
+    "\n",
+    "F = LeastSquares(A = A, b = absorption)\n",
+    "G = alpha*TotalVariation(lower=0)\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G)\n",
+    "algo.run(500)\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other provided CIL callbacks "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43acfefb45534d8093aa0174846f19eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    29.53it/s, objective=+8.586e+01\n",
+      "    20/500    29.53it/s, objective=+9.047e+00\n",
+      "    30/500    28.65it/s, objective=+2.640e+00\n",
+      "    40/500    28.53it/s, objective=+1.188e+00\n",
+      "    50/500    18.75it/s, objective=+6.928e-01\n",
+      "    60/500    21.45it/s, objective=+4.585e-01\n",
+      "    70/500    23.55it/s, objective=+3.451e-01\n",
+      "    80/500    25.28it/s, objective=+2.741e-01\n",
+      "    90/500    26.58it/s, objective=+2.337e-01\n",
+      "   100/500    27.57it/s, objective=+2.062e-01\n",
+      "   110/500    28.17it/s, objective=+1.870e-01\n",
+      "   120/500    20.05it/s, objective=+1.729e-01\n",
+      "   130/500    22.35it/s, objective=+1.623e-01\n",
+      "   140/500    24.32it/s, objective=+1.541e-01\n",
+      "   150/500    25.89it/s, objective=+1.476e-01\n",
+      "   160/500    26.91it/s, objective=+1.421e-01\n",
+      "   170/500    27.82it/s, objective=+1.375e-01\n",
+      "   180/500    28.58it/s, objective=+1.336e-01\n",
+      "   190/500    20.24it/s, objective=+1.303e-01\n",
+      "   200/500    22.51it/s, objective=+1.275e-01\n",
+      "   210/500    24.47it/s, objective=+1.250e-01\n",
+      "   220/500    26.06it/s, objective=+1.228e-01\n",
+      "   230/500    27.32it/s, objective=+1.208e-01\n",
+      "   240/500    28.08it/s, objective=+1.190e-01\n",
+      "   250/500    28.55it/s, objective=+1.173e-01\n",
+      "   260/500    28.69it/s, objective=+1.159e-01\n",
+      "   270/500    28.86it/s, objective=+1.145e-01\n",
+      "   280/500    29.03it/s, objective=+1.134e-01\n",
+      "   290/500    29.44it/s, objective=+1.123e-01\n",
+      "   300/500    29.27it/s, objective=+1.113e-01\n",
+      "   310/500    29.51it/s, objective=+1.104e-01\n",
+      "   320/500    21.28it/s, objective=+1.096e-01\n",
+      "   330/500    22.71it/s, objective=+1.088e-01\n",
+      "   340/500    24.61it/s, objective=+1.081e-01\n",
+      "   350/500    26.25it/s, objective=+1.075e-01\n",
+      "   360/500    27.47it/s, objective=+1.068e-01\n",
+      "   370/500    28.42it/s, objective=+1.063e-01\n",
+      "   380/500    29.10it/s, objective=+1.057e-01\n",
+      "   390/500    20.90it/s, objective=+1.052e-01\n",
+      "   400/500    23.14it/s, objective=+1.048e-01\n",
+      "   410/500    24.79it/s, objective=+1.043e-01\n",
+      "   420/500    25.48it/s, objective=+1.039e-01\n",
+      "   430/500    26.67it/s, objective=+1.035e-01\n",
+      "   440/500    27.03it/s, objective=+1.031e-01\n",
+      "   450/500    27.67it/s, objective=+1.028e-01\n",
+      "   460/500    27.81it/s, objective=+1.025e-01\n",
+      "   470/500    28.66it/s, objective=+1.022e-01\n",
+      "   480/500    29.28it/s, objective=+1.019e-01\n",
+      "   490/500    29.76it/s, objective=+1.016e-01\n",
+      "   500/500    30.16it/s, objective=+1.013e-01\n",
+      "   500/500    26.07it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f0908e75030>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)\n",
+    "algo.run(500, callbacks=[callbacks.ProgressCallback(), callbacks.TextProgressCallback()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d4cfa5b60cf48b7acfd008e3c40655f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       " 84%|########3 | 501/600 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "   501/600        ?it/s\n",
+      "   511/600    30.76it/s, objective=+1.011e-01\n",
+      "   521/600    30.74it/s, objective=+1.008e-01\n",
+      "   531/600    30.69it/s, objective=+1.006e-01\n",
+      "   541/600    30.26it/s, objective=+1.004e-01\n",
+      "   551/600    28.95it/s, objective=+1.002e-01\n",
+      "   561/600    20.01it/s, objective=+1.000e-01\n",
+      "   571/600    22.57it/s, objective=+9.986e-02\n",
+      "   581/600    24.69it/s, objective=+9.969e-02\n",
+      "   591/600    26.41it/s, objective=+9.953e-02\n",
+      "   600/600    26.40it/s, objective=+9.938e-02\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "algo.run(100, callbacks=[callbacks.ProgressCallback(), callbacks.TextProgressCallback()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Early stopping (a custom callback example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    31.71it/s, objective=+8.586e+01\n",
+      "    20/500    31.18it/s, objective=+9.047e+00\n",
+      "    30/500    29.77it/s, objective=+2.640e+00\n",
+      "    40/500    30.06it/s, objective=+1.188e+00\n",
+      "    50/500    30.28it/s, objective=+6.928e-01\n",
+      "    60/500    30.35it/s, objective=+4.585e-01\n",
+      "    70/500    30.46it/s, objective=+3.451e-01\n",
+      "    80/500    30.57it/s, objective=+2.741e-01\n",
+      "    90/500    22.27it/s, objective=+2.337e-01\n",
+      "   100/500    22.69it/s, objective=+2.062e-01\n",
+      "   110/500    24.61it/s, objective=+1.870e-01\n",
+      "   110/500    26.86it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08d5daaec0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class EarlyStopping(callbacks.Callback):\n",
+    "         def __call__(self, algorithm):\n",
+    "               if algorithm.objective[-1] <= 2e-1:  # arbitrary stopping criterion\n",
+    "                  raise StopIteration\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[callbacks.TextProgressCallback(), EarlyStopping()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     0/500        ?it/s\n",
+      "    10/500    23.79it/s, objective=+8.586e+01\n",
+      "    20/500    26.96it/s, objective=+9.047e+00\n",
+      "    23/500    26.89it/s\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08d5b68d90>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class EarlyStopping(callbacks.Callback):\n",
+    "         def __call__(self, algorithm):\n",
+    "            if np.mean((algorithm.x.array-ground_truth.array)**2) <= 3e-8:  # arbitrary stopping criterion\n",
+    "                  raise StopIteration\n",
+    "\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[callbacks.TextProgressCallback(), EarlyStopping()])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculating data discrepancy at each iteration (A custom callback example) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P43e/+x2KxSL6+/vnlNb09DS+/OUv49BDD8UPf/jDGT/veMc7sGnTJnz3u9/d5fxyQawHnwHAzTffjMnJydiC+U/+5E/Q29uLq666Cps3b3aL7Be96EX46U9/in/8x3/EEUcc4VR0APCyl70M//Vf/4WhoaEZKsMNGza4+cNCYGJiAt/61rdin33ta19DOp3G85//fFfeSqWCb3zjG7Hrrr/+evc9ML85wFzxwhe+EADwf//v/52Rx4CAgICA+WG52dsknHzyyfj1r3+Nhx9+OPb59ddfj1Qq5WzHrqYNBBsfbHxAwMIjOehWQMAyx+WXX46XvvSlOO200/D2t78d09PT+MQnPoGenh5s37591vuvuOIKnHLKKXjhC1+ISy65BNlsFp/73Ofwb//2b7jhhhucV/klL3kJBgcHce655+Lyyy9Hd3c3rrvuOjz++OO7nPe/+Zu/wbe+9S2cdNJJ+Nu//VsUi0V89rOfxeTk5LzTOuqoowAAn/rUp3DOOecgk8ngGc94BsrlcuI9qVQKf/7nf44vfvGLOPTQQ/EHf/AH+Nd//VevMdyV9AMCAgIC9j185Stfwec//3m87nWvwx//8R+jr68PTzzxBP7hH/4Bv/rVr/C3f/u3sXNLOuG73/0unnzySVx55ZV4wQteMOP7I488Ep/5zGdw7bXX4mUve9ku5feUU07Baaedhne/+90YHx/Hc5/7XPziF7/ABz7wAfzhH/4hzj77bHdtV1cXTjzxRHz729/GIYccgkMPPRQA8NznPhe5XA7f//738ba3vS2W/oUXXoibb74Zz3/+83HRRRfh6KOPRrvdxmOPPYbbb78d73jHO3DsscfuUt4thoaG8OY3vxmPPfYYDj/8cNx66634P//n/+DNb36zO6fkL/7iL/DZz34W55xzDh555BEcddRRuPfee/HRj34UL3nJS9yW9fnMAeaKU089Fc9//vPxrne9C5OTk9iwYQP+5V/+BV/5ylcWpPwBAQEB+xOWm71NwkUXXYTrr78eL33pS3H55Zdj/fr1+M53voPPfe5zePOb37xb53gFGx9sfEDAHsOiHHEaELCXcMstt0RHHXVUlM1mo4MPPjj62Mc+Fr3tbW+LBgYG3DUAore85S3e+3/0ox9FJ510UlQqlaJCoRAdd9xx0be//e0Z1/3rv/5rdMIJJ0SlUik64IADog984APRP/zDP0QAoo0bN7rrfCeuR9GO07hPPPHE2Gf/8i//Eh133HFRLpeL1qxZE73zne+MvvCFL8xIcy649NJLo3Xr1kXpdDoCEP3whz/smJ8oiqKxsbHojW98Y7R69eqoVCpFL3/5y6NHHnnEe/L2fNP3lTcgICAgYOngS1/6UgQgeuCBBxKvsWP8r3/96+gd73hHtGHDhmjlypVRd3d3NDAwEJ144onRV77ylcR0zjnnnKhUKsU+e+UrXxlls9lo69atiff92Z/9WdTd3R1t3rx5TmU655xzovXr18c+q1ar0bvf/e5o/fr1USaTidauXRu9+c1vjkZGRmbc/6lPfSoCEJ133nmxz0855ZQIQPStb31rxj2VSiX6m7/5m+gZz3hGlM1mo76+vuioo46KLrrooli+O81FZsOJJ54YPfvZz47uuuuuaMOGDVEul4vWrl0bvfe9742azWbs2uHh4ej888+P1q5dG3V3d0fr16+PLr300qhWq8Wum+sc4AMf+EAEIHrqqadi97P/6HxldHQ0+qu/+quov78/KhaL0SmnnBL95je/8c4rAgICAvYX7Iv2lnbJ93xrhx999NHoda97XTQ0NBRlMpnoGc94RvSJT3wimp6entOzOqUdbHyw8QEBewKpKDJBlAIC9mE0m0085znPwQEHHIDbb799sbMTEBAQEBAQEBAQEBAQEBAQEBAQsAwQQrsE7NM499xzccopp2Dt2rXYvHkzrrnmGvz7v/87PvWpTy121gICAgICAgICAgICAgICAgICAgKWCQKRHrBPY2JiApdccgmeeuopZDIZ/NEf/RFuvfVWFyNsOaPdbnsPmVF0d4dXPCAgICBg38b09DQ6bbBMpVLo6uraizlaGOyr5QoICAgIWJ5YDLu0r9rCfbVcAQH7A9KLnYGAgD2Jf/zHf8QTTzyBer2OSqWCe+65By9+8YsXO1sLgssvvxyZTKbjzyOPPLLY2QwICAgICNijOPnkkzvaQh4attywr5YrICAgIGB5YjHs0r5qC/fVcgUE7A8IMdIDApYpnnzySTz55JMdrzn66KPnfGJ7QEBAQEDAcsRvf/tbTExMJH6fy+Vw1FFH7cUcLQz21XIFBAQEBCxPLIZd2ldt4b5aroCA/QGBSA8ICAgICAgICAgICAgICAgICAgICAjogBDaJSAgICAgICAgICAgICAgICAgICAgIKADwkmE2HFo45NPPolyuYxUKrXY2QkICAhYdoiiCBMTE1i3bh3S6Zk+2lqthkajsVvPyGazyOfzu5VGwPJFsNUBAQEBu4dgqwP2NIKtDggICNg9BFu99BGIdOyINX3QQQctdjYCAgIClj0ef/xxHHjggbHParUaDjnkEGzevHm30l6zZg02btwYjP5+imCrAwICAhYGwVYH7CkEWx0QEBCwMAi2eukiEOkAyuXyYmchICAgYJ+AbzxtNBrYvHkzHnvsMfT29u5SuuPj4zj44IPRaDSCwd9PsdxtdSqVcqqS6enpRc5NAACnlgzHBS0/hLbbPQRbHbCnwL512GGHeZWU6XQa6XQaqVQKqVQKXV1diKII7XYbURRhenoazWYTURTF3u+urq6Oz+Wz2u22+8ymkUql0N3djUwm4/KRTqcxPT2Ner0eu5dpMn8+sAxMM5VKOftuy8Q0MpmMKwt/c37QbrdRr9fRbDbdPUyHaWm5mN+uri43JnZ1dbn6Zdr8XO/RMvFvlsXe02q1XB6Yj3a7jVarhe7ubpRKJWSzWfcZ607H6Xa7HesPjUbD3Z/NZt13URTF5ktEu91GrVZDvV53eWO5tfw+MN+K7u7uGfe1Wi1Xt6z/TCaDTCYTu7fVaqFaraLdbqNYLCKfzyOKIlQqFTQaDeRyOfT09AAARkdHMTExgWKxiAMPPBA9PT3Yvn07tmzZgna7jb6+PhSLRdRqNWzfvh31eh09PT3o6+ub8Uy+F0QqlUImk0F3d7frq+12G9VqFVNTU+ju7kZfXx8KhQIqlQpGRkYwPT3tyqTvAH+AHURts9mM9R9C3910Ou36i6/Npqen0Wq1Eue8Wpbu7m50d3djenoajUYj1v9TqRSy2azrY7Zd2V7sY2xX3p/JZNDX14dcLoepqSlUKhWXN97Pts9kMsjlct7+Nxc0m020Wi2XZ9YP3/VGo+HSYl599Tw9PY1f//rXwVYvYQQiHTM7bkBAQEDArqHTeNrb27vLBj8gYD62mpN6u4hebCylvAQE7M/Y34n4YKsD9hTYt0hUKklLkOhKIltTqVSMILPknY+g94HkGNMH4kR+V1dXjDi2hKvmr1NZSV6z3Jx78CedTrvnk/DjvdYBwOv1MyUpLZFq/+7u3knv8FrNH9PUsvJvrV8S1fxcyeUoitBoNGL1qPMu5od50fIyX2xvTbNT+bq6upDJZGbUCcvF5/vai3m0bcu8JTkYeJ/WH8vI/1lXLAfTIbnN94B9gMSwOlPoiMlms46A5T1Ml2X1OXu0n9p3RduG+c9kMsjn87F3kO1lHT9aV1ovtp6T6p5l1LL7yHnWF8to+xIdUPZz/d/2R/v+kdDXtmT78xptB+2X6sjzkeqaJstB5wC/b7fbyGQymJ6enlH3Cn1usNVLF4FIDwgICAjYK9gdUnN/JTsCZoddnPEzVX8vhf6z1Ej9gDCuLGfsTtvZhWvoB3EEWx2wEFDyUWFJPd/fJKJarVYsDSW1LKHmgyUFrbJb80r41MtJUHLbKrFJEqsaN4oipzbv6upyhKkq0m0elTjW3yRgma5Vp1MdrsphLbeWk8pcJeFJXirBrYQ686vkLlW+URTFHClKUvP/RqOBRqMRI5xte1pHhpK9Wg7uBEgiJ339g+3SarW8KnreR5LVOnKUgGe5G40G6vW6U7Bbsr/VaqFWq8Wex3oB4HY1sE60vumA0bYjga/1xXbX/qc/URQ5p4SWWevW7hqxUBvBsvjed+3H+h5o+TTv3d3drs926gfap7SeddeD9iXu9GBbW2cM+z7rxj7fpsfxQsukjix1qtl3QPtRJ+fDXBBs9eIiEOkBAQEBAXsFweAHBAQEBAQsbQRbHbAQ2JW+YIlKVXInXU/4SDwlyiz5OldV+1xBotlH5vFvJV55rc37bCS+VWDb7/QzVUfrNcyjkuBK4uszADjClr99+dXyzRU+JXqSIl2hThQL3dXQ6fqk+3WHRKc0bf4tlCjVNPXHd48S0qxPe62KRWzZNS1Nx6Zh29gH9ulO8F1jnUDWGWMxn10muwr7ztnnc7zYHWLbprmnEWz14iIQ6QEBAQEBewXB4AfsCfj6hm7hDH0nIGDPYrmpuy25tVTgI3AWA8FWBywEqCxVtbePzPT1e5KQJNgsSUv77gsRQ5CQJDnM2No2jncnaDgKCyXNNW8aRsLGSLdKXpJ3DO1h059LHpUY17pQpbXGcPcpq5kX3kNFOfOhOwD4u16vxwhT1oGSp1bZ63t+Ut3qb9smvvAmwM4diKlUCrlcLlYfqoy2YxyvYbxx7hxgeVqtluvHbE/G/mbcbqtYJinOumf9axm4k6HRaMxQvttY+8DOECmab6t6VkW5bV+WwZLsBPsL+0ESwe3bDaLhe+wuBL3P5wiic6nTbhDtX1oe7SvWaabjj96n+bF90+6A8fU33xkHSdCdDwodL5KcLbMh2OrFxZ53lQQEBAQEBCwCPve5z+GQQw5BPp/HMcccgx/96Ecdr7/77rtxzDHHIJ/P4+lPfzquueaaGdfcfPPNOOKII5DL5XDEEUfglltuiX1/9dVX4+ijj3Zx644//nh897vfjV0TRREuu+wyrFu3DoVCAS94wQvwq1/9avcLHOAwn4loQEDA7iEpPMNShF2sLhVY5WJAwHKHJa2AZGLUZ7M1JInGT+Y9hE99rKSYHvgHwEscz0beWSJc82rJP71eoaE1mHaz2USj0XAHFPryoeWxY4SSfjZkBNtA48Qn1S3zreVVok8JWxsPXcnGpHbU/CY5P+x9Nja5tocPSrKyzDYki+8e5iuV2hGbPJfLuYM49bnWMcIfPbDStoOS6b7wQTYMC8sMYEY4Fm03TdvGQOe1SiDbtrQH9/raUcs7H/hIdFvPvrawz7e7HqyzppMN94VU8hHpiqQ+qdfru8t71OFl8+xzNPnGj6U0FwmYOwKRHhAQEBCwV+BbhMznZz646aabcOGFF+J973sffvrTn+J5z3seTj/9dDz22GPe6zdu3IiXvOQleN7znoef/vSneO9734u3ve1tuPnmm901999/P8466yycffbZ+PnPf46zzz4br33ta/GTn/zEXXPggQfiYx/7GB588EE8+OCDOOmkk3DGGWfEiPKPf/zj+OQnP4nPfOYzeOCBB7BmzRqccsopmJiYmGeNBgQEBCw+dIxeagS1xVLN21Jy/u1NWx2w74KEF4lASz5S0avxnzspUgHMIAyV+OX3naBkV6ewIb5rbD53pa8rge0jOm2oF5+SWz/XvPiU1gr9ztaDTx1O4rHRaCS2jU9Rbh0Zvnzoc20MaV9+Ne2k2Ps+0jzJkaPPtuSnkuP6mQ19w0Mp2ZbMm21XfQ+0ryohrmVO6lu2PmwdKMmsZdQ69z3PF0ddD07V/mHr0tfWSe/VfEKdME22T9K96gziDhH7rNkc1Fa9b50KrKfZ3nnf+2HHi6Q0rBPLOgKSEGz14iIVhVrE+Pg4+vr6FjsbAQEBAcseY2NjM04Q5xj71FNP7fLp4uPj41i5cqU3fR+OPfZY/NEf/RGuvvpq99mznvUsvPKVr8QVV1wx4/p3v/vd+Na3voV///d/d5+df/75+PnPf477778fAHDWWWdhfHw8pjB/8YtfjIGBAdxwww2JeRkcHMQnPvEJnHvuuYiiCOvWrcOFF16Id7/73QCAer2O1atX48orr8Sb3vSm2StjP0Ww1fODbr0NCAgIUCwVWx2w74H9aO3atSgUCigUCgDi6k6SS0ooK9mlMb3VSWeVrqoG1dAZBAkye8hjFO0M/6EhTBQkGaluVpWzT1Xt2+1iywvsDEfTbDZRqVQwPT2NXC6HfD6PVCrlDdVBUtESzhrShIrmfD6Prq4u1Go1TE1NIYoi5HI5ZLNZV7eWPK5UKmg2m7EwLkmKaj5/amoKU1NTMdW/lrNUKqFQKKDd3nGwqB4c2263nQrfksOqgNfDVK2iV8luTZf1pfer6t6SnTYEDgnVZrPp0mK9sB40HYZumZ6eRqVSQaPRQKFQQLlcRjqdxtjYGCqVCjKZDHp6epDNZlGv1zE5OQkAyOfzLjwMD6Jl3tVBov1eHR8km1utlsszYb9nOam6Zz0y7j3njbVazR0Ey/5kVdgaNkk/Z53ad8Gqt327K/h8bWcbxonvJOsun8+j3W5jampqxvutjg3t74SOM76+4SPUtV/5CHqf6t3uCugUEoZ9oN1u47//+7+DrV7CCIr0gICAgIC9goXwnI+Pj8d+GPNS0Wg08NBDD+HUU0+NfX7qqafivvvu8+bt/vvvn3H9aaedhgcffBDNZrPjNUlpTk9P48Ybb8Tk5CSOP/54ADuU75s3b46lk8vlcOKJJyamExAwX4TwEAEBAbuKoHILWAhY5baSUap41b87qdGBuNLUpxy18BHrvs873euLeW6J3aTya1pKfs4W1qSTWt4+w6pZrZPBhvzwQWNN81oNPZIUv1rLYQnx2erYpx63hLHmR+9LCudjYdOzoVGs0lkV5LZf2b5GAt3mRVXg2l+sKtmnDtf8JimXfX3d52xKqj/Nn1Wfa0gfCx/p7XMkLRTsbgyF1puvLWdToPt++2BJdOsYSILdjeAb33yOO+0rs42F9p5gq/c+ApEeEBAQELBscNBBB6Gvr8/9+NTl27Ztw/T0NFavXh37fPXq1di8ebM33c2bN3uvb7Va2LZtW8drbJq//OUv0dPTg1wuh/PPPx+33HILjjjiCJcG75tr3gL2Hcy2xXShECbJAQEBAQGLCR7cSHVxLpdzSlgNhwHMDMHhI1TVfpLwa7VaqNfrjuztZPd8pK9V/PI6JdlsjHCqeKmmni3kg7XHvCedTqNQKKBUKiGbzc4gsvW3quA1Vrk9UFVDQwCIxZe3z7dEH6H3+0K0WGeEkrB6CKc6UFhfVq2r9UyVrr1f82vVvFovqoZm2ySRnlpXPtgQPDZGvyXPtVzsU/V6HY1GA8DOd4GKa0vo+/Jl88960Hq0xDnzzLRI2GsIG9tHfNBdBiyT7etJIXy03bXtNGSOjyS2YZu0b/qe7QuNpPnX3RRsL9+1cyGsNY9Ml2kzfe4ISCqbvq++dYDdXaNq+aWEpXr+2Bve8IYZ4YaOO+643S9wByzNFgoICAgI2OewO+Qe73v88cdjW9ByuVziPT5vfycS03e9/XwuaT7jGc/Az372M4yOjuLmm2/GOeecg7vvvtuR6buSt4DlD7tICNiBpPcuYPlgrv3aRxQFLD0shK0OCODBjSSJNTQDsDP8A6EhImw/SiJCVUVL4knDRujcymeD2+22I9hsnHAbfoHvBcOUKLFtYVWsCn6WSqVQLBaRSqViYTd8KnslXjVcjNYX86QEKcO52LJr+fW31o2tO0sia5tY0pNkMtFsNtFsNp2K25aLYD1oaBdfX2AYGSWlmY9Wq+WusenbNrJOBC2vtr+2sVU++3YFpFIpNBoNl14mk3Hp8Xm+8EB6v4/wJQHOXRLst8wX88k+qqFttH0I+7f2fbtLQfsK64d50e+ZVlJ4E5LJ+jzbRr5wNlon2q46jvjU5iw7dxdrfnScmAuhru+hth/rW8cF373Mk/YF+57rODYb9rat5vljn/vc5/Dc5z4Xn//853H66afj17/+NQ4++OAZ1/P8sfPOOw9f/epX8S//8i+44IILsHLlSrzmNa8BsPP8sQ996EN41atehVtuuQWvfe1rce+99+LYY48FsPP8sf/xP/4HAODLX/4yzjjjDPz0pz/Fs5/9bPe8F7/4xfjSl77k/uf4t6cQiPSAgICAgL2ChTD49EZ3wooVK9DV1TVD4b1169YZSnBizZo13uu7u7sxNDTU8RqbZjabdcZ+w4YNeOCBB/CpT30Kn//857FmzRoAO5Tpa9eunVPeAgICAgIC9hYCkR6wEPApLy2Z1InA6iQwIIHlCwdCss53D59tiW4lMjVWs+9+zZ/mP2nHmS8tJRTtvXMVVWg5lJC176/PCaH3Mi1bL/qdJW01r/ZZPseB/T9JoGLrUJXIqs6frW3s83ZFrOIjeX2hWFhnncKi2BAoszmfO+XV914lvUdJ9aQOD9/zkp6fVI9JO0psmr7wK0mE8a7YkrnkbT7P8O02Uadg0jU++Mruu28uYZGIvW2rP/nJT+Lcc8/FG9/4RgDAVVddhe9973u4+uqrvTvEr7nmGhx88MG46qqrAOw4q+zBBx/E3/3d3zki/aqrrsIpp5yCSy+9FABw6aWX4u6778ZVV13lzh97+ctfHkv3Ix/5CK6++mr8+Mc/jhHpuVzOrbP3BkJol4CAgICAvQKduO3Kz1yRzWZxzDHH4I477oh9fscdd+CEE07w3nP88cfPuP7222/Hhg0bnHom6ZqkNLXcjOV+yCGHYM2aNbF0Go0G7r777lnTCVje0K2qATuxOwuBgKWBubbhroznCwmrOA3wY2/Z6oB9G4wf7YsNnc1mkc/nZ6i5faSafV+7urqQyWRi8ampRKYy1JKaDMOQy+VihDPvUUUpVb9KkFHZqiEXqHJlCA8Na6J596m3+Z0qr/UQQ19sblWta30xDYbi0HwkkXy+UBi8X9tKwf81r3oAp4bQsCSjEv2+kCokJ7WOVbVbr9fdYbE2xIf+1rAbrDO2Ua1Wm9E29mBVW17Nu4aQabd3HKDKtmf/sQ4IdZaw7rRcdszU2OM2LI/2e63/KIqfOZB02KWWW9vT9gPfD6/nc1h36XR6Rj/TfPB917alOt/nWNA+M9t82b4jGoqHn7H9eYCnrXM+i/1Cv/c5lZhfDanEQ3PZRlq/Gu7F1652JwfbwO7cSMJC2Oq5nD0GLO3zx4i77roLq1atwuGHH47zzjsPW7duTai5hUFQpAcEBAQE7BXsTc/5xRdfjLPPPhsbNmzA8ccfjy984Qt47LHHcP755wPY4fH+/e9/j+uvvx4AcP755+Mzn/kMLr74Ypx33nm4//77ce211zpvOAC8/e1vx/Of/3xceeWVOOOMM/DNb34Td955J+699153zXvf+16cfvrpOOiggzAxMYEbb7wRd911F2677TYAOyZgF154IT760Y/isMMOw2GHHYaPfvSjKBaLeN3rXrdLdRMQsC9BFW0hFE7AQkEJrbkqyPZX7G2VW8C+CRuXWcM5kDDuFKc5Cbw/iqJYuAYSZUoGq8JcCUnex+d3dXXFQqMowa/Eoe3fNha4JQW1/J2IS0uu+ojQTqp1zS9JdA2BYpXkFqwzH4nOMdMqsy1JbEN8aDmUiCb4PPud/T09Pe2IUA1fYp9rCXGWQZ0q+r1vrtFpB4T9joS1VXnrc3zpWvW/PkO/t6FgNNyMJWw7Eb+2rjREi3Ug+Mqv6dkyalgUn1qf746GjLFl9dXfXO2IzY9PGZ90dgLz5qs3fQ9sPenfdBiwbyaFs2EdqHNCxw2tB237+ZR/vuB9Bx10UOzzD3zgA7jssstmXL8nzh9bu3btvM4fO/7441Gr1dDT0xM7fwwATj/9dPzpn/4p1q9fj40bN+L9738/TjrpJDz00EMdw8DuDgKRHhAQEBCwz+Gss87C8PAwLr/8cmzatAlHHnkkbr31Vqxfvx4AsGnTJjz22GPu+kMOOQS33norLrroInz2s5/FunXr8Pd///du6xkAnHDCCbjxxhvxN3/zN3j/+9+PQw89FDfddJOL4QYAW7Zswdlnn41Nmzahr68PRx99NG677Taccsop7pp3vetdqFaruOCCCzAyMoJjjz0Wt99+O8rl8l6omYCApQkuauxiJiBgIRBU0wEBexedwk/MJf6vj5RW4k5VzoQv7rWmZ4m9JILZEnpKevE+JVHtd6rItlAVNK9Rot7GStf82/wpbKiXpHz76l7J2mw268g9q+61pCvzbOtPyWyfg8GWx35myXEbFiQJJNp9anqSpr4+qGpy9hN1snQC82adNpZYpio7m83GDkOdzR5p+kzLfs/yaJ+0oX58z7G7Nnyktl5n1daqRteQQUocK4k+Wx9QZb32q9kcHHoP82TJ/dmgZdM61zjz6gTpNNaoE0F3SficYbqLg2nPdhjsQmI+Z48BS/f8sbPOOstde+SRR2LDhg1Yv349vvOd7+DVr351xzLtKgKRHhAQEBCwV7C3VW4XXHABLrjgAu9311133YzPTjzxRDz88MMd0zzzzDNx5plnJn5/7bXXzpqvVCqFyy67zOvxDwjYnxEIzoA9iaUSVkkX/0uxzwdFesCeAIkhVWNSkawqWUtcWhWrEk4kJxUk01KplAv/wrSV5LSkoyU+eY9N2+aB7zIJcCXGfCEsfGS2XpcUwsUHG7KDpB2JRSXjlJy2RLuqevP5fKxObPiKXC7n6j6TycRIZ6sY5+dKuvvCjOhv3q/Eo/YJ68BQkp2hPRjexqq1lfjnYaSWCObfLC+vS2oL1gMAp5y3YXp4AGqhUEA+n3ehgHzhS2ydaP/v1C987xC/V4Ja77XP5n2+8DC8h/mw6fn6gdaDL99ar1of/JmrMpvlJgGtNmw+aWiebL/K5XLuXW80Gt6+rEQ4P2P/1bAzhG+HAbCzH82GhbDVczl7DFja54/5sHbtWqxfvx6/+93vZi3brmJRY6Tfc889ePnLX45169YhlUrhG9/4hvuu2Wzi3e9+N4466iiUSiWsW7cOf/EXf4Enn3wylka9Xsdb3/pWrFixAqVSCa94xSvwxBNP7OWSBAQEBATMBqsInO9PwOIg2OqAgICAhUcnFddiItjq5YulaK996lZL4HaCT0GunyeFlFD4yMrZQlr4ymHT8H2vMY81L5ag8xGys+VFnQJJjkGrXLf5SkJS+BMA3rjxvFbrcjZl6lzHvKS2sWFb7HXMjw070ylPnRTv81GM82+qj5PKpaE9OoX88f0mLIHrU3rb3Rr2+6TPfd/5FP5JcfTpqNA6mKsy3JfmXPqMOkQ0f/NB0m4NbVs6VfTd9jmGfPmbrf/bfjGX/O9NW72Uzx/zYXh4GI8//jjWrl3bMZ3dwaIS6ZOTk/iDP/gDfOYzn5nx3dTUFB5++GG8//3vx8MPP4yvf/3r+I//+A+84hWviF134YUX4pZbbsGNN96Ie++9F5VKBS972cv22naIgICAgIC5ISzOlyeCrQ4I2DOYD7EQsO9gqdu1YKuXL5aSvdaDEoGZB/mpatPGtrZqamAmqUhiK5vNIpvNzgipEkU7D+ikKtgeJqrwjcc+UtkesGlDYti07Huhz9DwED71OGEP87TQZ+hvlplK80wm43UA8DpVPGsIGtZfs9lMPNCVZWNe9TlKbCtRqKpxW3Zf6AerWNaQIz4ymflnP0mlUt5+xTzbA2XZLo1GA81mM0ac2ufzORrChTHqa7Wa+5mamnKK9KQxU5+RRFrzHeF70NXVFbvO11eS4tprGdRZ4+u7vvdV3ymtEz1Y0+4q8LV/J8Jdr/MpvFk2daZonn0/fKaWWUOx6LvdaDRQrVZjuwk0z6wHrS8eqqwHrKozhWD9zTeky9621RdffDH+4R/+AV/84hfx7//+77joootmnD/2F3/xF+76888/H48++iguvvhi/Pu//zu++MUv4tprr8Ull1zirnn729+O22+/HVdeeSV+85vf4Morr8Sdd96JCy+80F3z3ve+Fz/60Y/wyCOP4Je//CXe97734a677sLrX/96AEClUsEll1yC+++/H4888gjuuusuvPzlL8eKFSvwqle9at7lnCsWNbTL6aefjtNPP937XV9f3wzvxKc//Wn8yZ/8CR577DEcfPDBGBsbw7XXXouvfOUreNGLXgQA+OpXv4qDDjoId955J0477bQ9XoaAgICAgIB9GcFWBwQsPHzb6wP2H4Q2D9gTWEr22hcqxQcdB5WUsip0nzKZJJ0lVgG4EBuW3NL/fWStfZ6SuPyfcbg1BIaPwNSQIhaaVxtfmmmos4Hkrh6wmpQe88OQIul02sU+t0paErLNZtOFarEkOsnkdDrtJb8VPsU97Z0lhpUYnc2pbNXovrbz3WOJUv7WGNqsWx8hb0P26GG2WgZVppPo5TVss1qthijaERokSXGsjhDWmSWoNf+5XM71c73PFxJJifBOTiOtqyR1PdPS/E5PT7u2tMS2EsQaukXzrenaPGrMet+OA61/S3JrGe09Nn37OfPGcC5at/oO+MYB60iw9aXvoa9fLzUs1fPHurq68Mtf/hLXX389RkdHsXbtWrzwhS/ETTfdtEfPH1tWMdLHxsaQSqXQ398PAHjooYfQbDZx6qmnumvWrVuHI488Evfdd1+isa/X67GtAOPj43s03wEBAQEBIe7q/oJgqwMC5o4wtgUsNQRbvf9gIex1kq2ei7JSFbQ2fvNcoQS4ErKWeNVnWPU2sDMWtubDF25FY0uT7FNCTGGdCUnp+N45H8nGvPjiW+v3NryLlj0J1nGQ9LmPALRpW6La5sWmrXHPfSpz3Y0wV6Ix6Tob19uWUUO9+GJgd9oVYElvX/l1FwLzk7RjwQe9j3ns5NRIqgPGi/elrZ/7dlnYtIi57pjw9WtbB9ahoI6mhSSbtY9pP9R8q9NkPv1PnQIEy24PRZ1P2jZ/u4JdvW8pnj9WKBTwve99r+M1ewLLhkiv1Wp4z3veg9e97nUuIP7mzZuRzWYxMDAQu3b16tUzgtYrrrjiCnzwgx/co/kNCAgICIgjLM73fSy2rQ4K34DlAt+CdbHge2/Cu7T/Itjq/QMLZa+TbHW1Wo2FNLCwxJolX224hSRoOJFOilNL/OrhhFQkU22s9+pn/JzP4WGmrVYL9Xp9Bvmnql1V1BJK1trQLs1mE5OTk5ienkY6nUahUIh9b1W+tt40TIvvAFPNk4YHsfXH+mU7qqLfF+ZEiU9ex+f4VL/ML9PlNQzXA8AdgKrPsep6n0Ldtj/robu7O1Yf2tYMkcKyU1Ws4V/YJprnKNoRs5npMs9af8yDrS/+zb5mwwVZ4tmGRkkKw9IJ1gHC94HlYR1Yp5Fvh4gqta0ynO2pB9aq0yTp3fapya2625bD50Cy6Vkinu8gy6e7Dmq1GprNZqxsvrTtc/Tdt89jH+SOEc3/fBxFzEew1YuHRY2RPlc0m0382Z/9GdrtNj73uc/Nev1sHp1LL70UY2Nj7ufxxx9fyOwGBAQEBHhAg7+rPwFLG4tpqxdaoRIQsDew1Me18F7tnwi2et/HQtrrJFutxBkwezgrq+btpLQllBwjwTabml2JdMb91jjqSTHUFXyextRWNbOS8aqMV1WrJZJtvTCcCmOS87vZnAq8xiry50L+JaVt205V1Urkkji1IW98qnWFEu4ay56fs57ZvnNRm2tYF+1TPqcCr7exsfkcG99av2PZNe/aXlq/VoVu68SmrfXju1dJfPu8pDq2ThD9Xsl0+xybTx865V0dV5pn+94npc+617axhLivvjrB5xzQXSh8B2cbD3z1kNQGJNDZR2zoKS3rbAi2enGx5BXpzWYTr33ta7Fx40b84Ac/cB5zAFizZg0ajQZGRkZinvOtW7d2POk1l8shl8vt0XwHBAQEBATsL1hsWx0mhAEBC4u5kC8BAQHLDwttr5NsNQlJS8KrStsHVRgr6ecLj+AjhJQMU0KK6fEgSCXwfOSfku36LBu2g+XL5XKx+zSvJMw6heJIItNIvFWr1RiRq9dbYl7/T3Je2FjxbCuWlzG+mV9Vp1OFb+td885nWMyFJGS5dbeAzxHjI6PZTiwfyVemq3H1WQZ7UKdNj7Hmeb3WCevb9ne2t6bDw0H5TP7WMCtaRt5j61Gfp8/QMlsHjm/XgD7P1j/rXZ/Jsuo7w7pkfviZwr4DTEPzr+S4fX+SHD3aXiTq1QEw224WHznOOO92B0lSOjoe6N/qmAHgnEt03LFdfOnaMSRgaWJJK9Jp6H/3u9/hzjvvxNDQUOz7Y445BplMJnZwyqZNm/Bv//ZvHRfnAQEBAQF7H8Fzvm8i2OrZEVS9yx/7sjo7aYzdW+Puvly3yxHBVu+72Jv2WmOO+whVVfQCcUWs/lhiTAlu/dHwDhruRUNzpNNpZDIZFItFFAqFWOgZTVtJTFVIJymvu7q6UCgUUCwWkc/nkc1mkclkXFlVEc3fPqLMqoqZ91arhfHxcUxMTKBer3sVsqrIt2ElkhTBStLyukajgVqtFvthGVWBT6LVEvqW3NXvSSzSmcG60us1LIi2q01P61BtiO0XURTN6At0BFDtr31ICXXWEclP1oceFqphP/gc1qVVGyt5n8lkXD/RPkrilW1jy8L0WA4lpnWHBWH7MPuO9gPbj7q7u5HL5ZDNZl3erJ3mfdpPtX6tw8SGjWFZdHeHvqcK285qb5iW7z21sE6KJLuldejboeBT0bMu9JBV9ocoima8U81mMxZCyNbrXBBs9eJiURXplUoF//mf/+n+37hxI372s59hcHAQ69atw5lnnomHH34Y//zP/4zp6WkXm21wcBDZbBZ9fX0499xz8Y53vANDQ0MYHBzEJZdcgqOOOsqdNB6w78MXg6qTUiEgIGDxEN7H5Ydgq3cPgSCMw2erA/ZfzKZODVgchPZYnlhK9tqnHrXhTJJUnj7FMcks/Uz/1pjcqrbVQwT1byWxrJ2mklXzzOfxmSQ8+Uz+z3v1t5bfvltWra+/LRGpeUi6Z3fAurChLvhMGwN7Nmg7JuXXKtltfek8QUl0m2/fs1U5bqHk7FzaxhK39hq7E8D3XFVI+2J2+3ZGaFpWSe57x+YSQsf3LPtZUp3Od/5m20/V6PbZ+g7PB7ORxDYc0GztrHmeq6Pf1z6annXUWbX8roSm8ZUlYO9hUYn0Bx98EC984Qvd/xdffDEA4JxzzsFll12Gb33rWwCA5zznObH7fvjDH+IFL3gBAOB//+//je7ubrz2ta9FtVrFySefjOuuu27GyxmwbyKbzaJcLqO7u9spAaanp1GpVNwp8pOTk2GxHhCwBLA7jq0wUVg8BFu9e1iqfXexyMtisYhSqYR2u43JyUk0Go1ZF39LAUu1HZc7Qr0uPQRbvXyxlOy1jywjlExkCBEfiUhVKK/XEBh8hhKvQJyQ07Ae+p0qrEloacgPXpukHFfVNK9l2AotE0OCqI1TBXg2m51BmvPa7u5uZyuVEGZ4CCX4qBjWukun0y7kjq8d+Bymw/KXSiWkUimnSI6i+CGaWgdJxC//tjsRkuKb+2KIsw3b7TYajQa6urq8saVV2cyyan/SdvBBw6Go0t6GNrF51z5sn5kU61zvsaplLZe2D8usymgbK96S+0yX5eI1BN8D3b2h+dUQRBoiRRXkPmdP0i4Ln11gCBUbfz7pfWPdqGJdD/ltNpve/mbbkeVn3nz51fvnMzdVdT3zx10P3MWg6VGBr+ODhjKaDcFWLy4WlUh/wQte0LER59LA+Xwen/70p/HpT396IbMWsEyQyWTQ19eHfD6P/v5+DAwMoNVqYcuWLahUKqhUKqhWq0t+gR4QEBCwVBFs9b6HxVLJp1IpFAoFDA4OzthmHOz0/gGfAycs6AICFgZL0V7bWM16YKBP4QsgRjrbWMpaFp8TVgm/TCbjVR6TIGPYBRLJmrb9bYku5tN+roSgkn9KkDabzVg6rA+tL4bVAHaGK+H9JIaTVMmsZw0vY6F5z2QySKfTyGazKJVKTqBGIn9qasoRgtVqNUbQ2jrT/y1hqSpcu0PBF/4G2BmahOXm39aBwnoEdrR1q9WaEavdEv3Mg4YDsbGxffXGZzDvti46qcz5mY2jzX5h+54NB2ND9/jeIb3GtztC86jkvN1loSFgtJx0uihYd0kxv1VVb9te68KWI6kO0+m0OwzUhlrRkDdJ+WG6vl0T+n+nPPjA9256ehq1Ws2FDGo0GjOuoxPFhmraH0RG+wKW/GGjAQEWXIjncjn09fVh/fr1KBaL6O3tRV9fH1qtForFIiqVCkZGRtxAVq/XZwxiAQsL33Ym/R2wfyN4zgMC9i9wMVMsFrFixQpks1n09PSgp6cHrVYL+XweU1NTqFarGB0djcUoBcJ7vyfgU2J1UmctNEKbLn0EWx2wUPCRUEpsKdGlMbetstSGQvD1USX/NMzKbI47JfU0X0mwa5ukUBdJ74KqfDvlQaEkoy/evNaHT1Grim/fc/P5vIsbzzA/hUIBhUIB7XYblUoFjUYD1WoVqVTKqX/VEeFzbCTVVZIy3RK/eo+qz5kGv09ad6qSmp9reB+bJy3DXB1SSQ4dH2xoF7uLgKS13S1BklvjbyfZcj0cU4ll+3u2Pp6kLvftoODvTup0Jf+TYA9WteFPkvKp+VXC39c3OsG2vQ3X0gl6TVLseSXwbRtou801nEyw1YuLQKQHLDtks1k87WlPw+rVq3HQQQfh+OOPx+DgoDs1Xj3n//Vf/4Uf/ehHGBkZwe9//3ts3rw5DBwJ2J2FtBoEGjCd8PgOxQnY/xAMfsBiYbHCmMwHqi7a03ndG3WhTu/DDz8cZ5xxBlavXo2JiQmMj4/H1DqPPfYYHnzwQYyPjzvHt25nD1gYcMux2mVVpM3FVu9N0j1gcRBsdcBCQMOWADMJLg2loWMRSSiqYTU0hQ0vomOWjzSkc9anGraEtg3NQXBsVCSNk74wGvZ/Gy/ZFz7Czlk0XAyV4Uqass4sAadqX6ZJURnrN5fLYeXKlVizZg2GhoZw6KGHolQquXX19PQ0RkdHMTU1he3bt2Pjxo2YmprC+Pg4xsfHnZ2mYpyhNnyEelKoDauUVtLYV28aisfuELCKdfYBDYmSVOdsZz1I1pLW1tGvinkLzb/Pec3f7MdU3Hd3dyOfz8fKm8lkUCgUXIgb5tWGCuFv7fNsa5s3nz3n/IDlV6cE085ms8hms7GdEgqfI0zbyjpD7HuoCvak91bzGUU74/drmCINeaRtZ8OpJPXXJEW7HR/0N98x7Yu640WvZxtoH2a4F1+9WgRbvbgIRHrAskM6nUZvby9Wr16NAw44AM94xjOwcuVKNxmLosidaA4Av/3tbwEA27dvX8xsL2nYidd8B1f1MnOSooZurp76gH0bweAHLAZ00roc+tFyyeds4OIgm81iaGgIRx55JA4++GBs2bIFTz75ZCysy/T0NH7zm9+g0Wi4rbrzjU0ZMDu4cFObrIu5UN8BQLDVAQsDVRcD8TjnhJJ5lmQjSIIq0a3XWWJTyWn9zrfWsWpW5ltVqFYZ6lOoqnAo6Xn2uTZvncZfq0JWWEW8hpjpVAb9XSqV0N/fjxUrVuDAAw9EuVx2ZOn09DSKxSKmpqaQTqexdetWRFGEWq3mQlhoWnb3t64DGWpGQ90klUdV+D6nupLp/F+f6SOBlRhVpbq9z4ac0x0Oep0tn5ahk5Jay2mfrWFcSMqS49CY2r76Yz65Ftdya56S+qX2Cy2PpkVSXOuE76Zv/LcOId7jg74bnZTgvnec5dK4652Q9P1sNsxXh9pn6eTw9Vf92z7f7hYIivSlj0CkBywrpFI7DmZZu3YtnvGMZ2DNmjXOO+vbHtPf349DDz0U/f39GBkZwaOPPppouPcXqPezWCwik8mgu7sbmUzGTeRoyKemppy32U6MVMWWy+WQz+fdpEaNPB0bVBj6DgMJCAgI2FPgZHy5TBqXSz5nQ3d3N1auXIlVq1ZhaGgIExMT2Lp1K4aHhzE+Pg4Abuv4wMAADj74YJTLZfz+979HpVIJpK6AtrZUKmFgYADpdBrj4+OoVCoxW+vb3qwLblVoUS3a1dXl/p6cnHRpJmFf6Z8BAQF7Fs1mM0aaU2nLkCA27IYqrJW0s4cSEkrA6zjI++di97lescSnLwQJx1hLiPF6pqEq+SRCzxLZOk/RcZxrMhLGfH4nQtQS9MynJY7T6TTy+byzwatXr8bg4CB6enpQKpViZeYar7e3F0NDQ8hms6jVahgdHZ3xfBKcJMup9NZyWRW3zacSokqsJrWnkpBKJut6U9X5vvqy+UiqX36u+WS67IuFQgH9/f1Ip3cc+NrT0xOzxa1Wyx0Yq+VjGnRiMH9cpxeLRaTTaXf+m+1HWr88yN0XH56wTnXfvEvrgu3pqxvtu/zbvq+dyGVb/z6Hmt1dou88217fRd19oG2W5FCz9WR3d/CzpPdOiX11JCYp2OdClgcsXQQiPWDZgMYqn8/jsMMOw4YNG1ysVT1MJZVKOXJ45cqVeM5znoOxsTE88cQT+NnPfrZ4BVgi4PawXC6H1atXo6enB/l8Hj09PUilUo44r1ar2Lx5M6rVKqrVqtvipelks1l0dXWhXC6jt7fXGX89rT6KInfwa6vVQqVSCUT6forgOQ/YU5htwbwc+s/uvB9LEZlMBk972tNw2GGHoa+vDyMjIy4W+ujoKDKZDEqlEkqlElatWoVnPetZGB8fR71exxNPPBHCuvz/Qcd3V1cXVq5ciWc961no7u7Gxo0b8cQTT2B6etrtwtNFpYZM4EKxq6sLmUzGzaVos4vFIgBg69atmJycDPW+nyPY6oCFQLVadSIdEoStVitGXivJpDGClRxnGBMfCUb1sCUMSfgpsebrmxr6wXfAnxLszIdeq2Q+leAaNsv3TFU7WxUwv/ep1hW+vKpC1xLUJKQV2WzWkeNr1qzBwQcfjL6+PgwODiKfz6NWq6FWqwEAisUiisUi2u0d8dLL5bJziGv+tJ30kFXmi+1J5S77Aw/RZDk07A9JZ5/C3NdWbG8+R/uMz/Fh01KHhQ3t4tthQTtKIpd1umrVKnR3d7t6bDabGBsbQ71eR61Wc0S3ErBsV8aop/2fnp52cybWD++36nEAiSFXmGd9/9ShoYpxW3dJoUbU0WQdJkByX9V7bb1aBT3TZ9qaJyXX+T7re6/p290vym0k2S7Nf9JYkRQKyOe48ZVfMVt+FMFWLy4CkR6wbKCDEk8Vz+Vy3lhrvJ4xxprNppvM7e/ghCWbzbrDZagoVyIdAPL5PKIocpMRHbBpqOglz+VyTtnGCTAnUJlMxjk7rLojYP9BMPgBAfsPuFupVCq57eGM7WrJFNp1Omf3NafCrkAVVSS/GbOWNpyLRi4uGRqH91vygDabSj+7XZzpLacdHAELj2CrAxYKStipAl3Vqr4QEhyHlPzyKTh9ZJntv3Ppk0nEliX1rLrUl4ZCr/eRtr7r9Tf/1jLNlo4l5zme23FdnbRqU1QVbuuBaz4S3za0hlW8a/k1X1omnyo9Sa1rbZott4J9bba1v83nXJTCtl+QY6AN1fpUgjibzTpbre1B4l7bhP+zrrPZrPtbHeU8R8D2eVuvPgW5/d+nztcyJ/V9wH+wry/92dT+s93PvGg+9Uf7bqf5jK0vOw75xhr7nTqukuouqc5s+nZnwFx2ZQZbvbgIRHrAsoJvu85s16sx2p/BehsYGMCKFSuQy+WwYsUKFItFN4kCdhoTqtUajQaeeuoppwhoNptotVrIZDIuhl5vby/K5bKbYKlx19PHG42GUxXQexuw/yAY/IA9hdA/lh7S6TSKxSL6+vqco5WfU71Vr9cxMTHhdi1VKhXU6/U5P0MXSftSH0ilUujp6XF2tVgsIpvNolwuu/ibrN90Oo2enh50d3dj27Zt+P3vf++UUXSC87pyuYy+vj4AOxVr3d3d7v6xsTEUi0X3nd3urE7wfam+dwW7IghYLiKCYKsDFgKq5AbgwjtSdKMOPWDmwaEk3TTEhQXXeapW5RojiZBKWg92InJJXtq0LHFMpbWG07Lkbzqddmuh2cDy6bM0XAXHaVWcqyKb9WLzQ4KWgip9hirANQ+WYG80Gi4UmFUlA3DtTDukoUOBuPKe6SohrApnrY/ZDtLUXVk+noB9g+3JfGrdse9wDWvFZOl02q2T+/r6UC6XY4RtuVxGqVSKKZfZ36enp7F9+3bUajVHoPM6EvE8qF3rvLu7G7lcLiZ6a7VamJycdOp0X0x5bQ9tJ7tzwfZ3n1Kf9cR6t23k69P6He+xbW0dRzoWJDm3AMREknRe6D2+8EAA3Luh4kH+tuFhfLsZCPIaeq0ekjubLdW+zvLq+zEbgq1eXAQiPWBZwg5uC3Xtvg4uuFevXo1sNou+vj7k83nvtSTTadCGh4djk1Iu7jkJ43YzEiQAZhjCWq2GbDbriBLfYRwBAQEBAYuLhSD8UqkU8vk8ent7ZyxGuFBpNpsufFi1WkWtVottM57LM5YLOTlfMG5tJpNBb2+vc0ao3eTOMp4X0263sXnz5hhBwTAuTGdwcBDtdhsTExOOwCgUCm4Hnzo9lIjROVQqldqvQ7Ttqmp/X+2rAQE+sL9b0YzG+/apf5VUIjFFZbpv3LEiKxuuwd6rJKzvXewUjsGnwtZyJd0/m/BLr9Myat3wmWo/VdntW+dq2BtVTzOdfD7vnBrATvJa65D3qTCNZG6tVouF1tB8a5sobNv4SHRV0VulsI/Q9MVC99W7OqK1/ylhbfsty65OAj1nrFwuo7+/3+Wj3W679bE6Vui4AHYQsHQyqGOAxC93nulOb1W+1+t1FItFNBoNVKtVb3/Rurbf6/pc6yjJNrGutI9bBbpPBa95UAeXOqWsI0bv037gK4fGw7c7AHgd25XzGdv/2K76XrOcdt6jaU5PT6PZbKJWq8WcHHS6AUicz9ryangfrauApY1ApAcsK3CwGx8fx1NPPYVyuew86TrYcWCq1+vYvn07RkdHMTU1tdcXL+rZVs+4NWTWo6sGwjcB8W1dnC0fqvzgtrxOzgUaAwDOQAA7VWxqLJhPtoPdJqff+7YMBuwfCJ7zgID9B+32jliqw8PDyOVyTl3NxXtXV5cL0TYxMYFGo+GI9PlgIdXRulDSH6sass+ztp6kTbPZ3OV8ZDIZR3BzEUg7DviJGVVN6nxCFW8+ckQd5AyHZ2OshjF4J/Z1OxZsdcBCQMM8WgKLBJMeCOkLNWFJZaZpCTfAr6LV53KNogSdhmXguoXkJtGJBOe9mUwmprTtdI8lgK0zoRPxZr9PUg6zvLxenavMJwnzRqOByclJjI+PI4oi9PX1uefpbgGex1GtVjE1NYVGo+HqTdeUWg9se0vA2nJYm2thSUVdf/rQyfGi69BOO9y17+hn+gw+h+3vGztZ/2qHdT2clDdffVhVvHVe6O+kd8FXvzbkiiXI9Tq+Q75n83rfM1gWLftc+QDbV3Q3gr6rSbs8rPqc/dKmDexUvCunYUUbGp9enTHaH1nf6sTyvcc2NMx8bGiw1YuLQKQHLBtwoKlWq9i4cSOKxSLWrFmD3t5eFzNMjUe73cbw8DB+/vOfY3h4GJs2bdrrg4Ye7MUwKGoAqtUq6vU6Wq0Wpqam3PY3Dt5UhylINtAozGVroHq3S6USyuVyLJyLDzphLRQK6O3tdfFXm81m7GBRjZenigUaGH7PyRYX+rui6ApYvggGPyBgeWAh3rdms4nHHnsM7XYbg4ODOPzww93h1uVyGZlMBgMDAyiVSqhUKhgbG8NTTz01L6f3XOzfXKB2WbdR02arw7tWq81YnJPgzmQyLkwKiQkf8e57Pu0h7WZPTw9WrlwJAJicnES1WnVEN/PLhSHTp0quq6vLbTkGEHOeMzarKuqYRjabxeDgIOr1OprNZmyxmLTo3F+xK/WwXOou2OqAhQDXEMCOftFsNmOHIts1gIqM+DnDjKRSKTdeaWhIe7+GOOGYTSILiB+2yGdZO6KkONPzjX+6DlOlM8N+qNIVgNshxHQBuDWgHlaaRIwTPoW2qrv5o+nxwEslHVutFiqVCtrtNjZt2gQAGBoacuvEQqHgdjpNTEygXq9jZGQEW7ZswdjYGMbGxtBoNNyzcrlcjKzU9WyxWHTtxl3JlhRWp4r2B5aBpCmFaQx/4qsbkv4+klbV1ZZIZ57YphSO2RAtrD+WnQexKlmteWGfph2mo7zZbMbsvp5bokQ65yVaP0pIaz1qOe3f6rTyzZ+s6tqnjNZwTMoVaEgjfqd5p+NMnSBat/qe2jZV55AKFrQcnQj5drvtdj3yXDdtI4L1ybmfj0iv1WqYmpqKqdf1rBnWL/s7+4mGxbEk+q7OsYKtXlwEIj1gWYED09jYGLZt2+a2NdFA6dYrDpojIyMYHh7G5OTkXh80ODHgBIPbvHTSY7dWqcfWF+9LJ6NzVXVbRboasNnu4725XM7VsRoU/d/nPVdywioAAvYvBIMfELD/oN1uY3JyEsPDw8hkMs5u8VBRLmYYB3RXFekLAbVdurjjotfa6qTFojqtSVjPNx+6oGZ4tcnJSbfgUpWUD7T1uiBWu+wjYbjw52LPqs0Cib5/IdjqgIWAJZWUOOf/SUQ3wXtt3GIbagPwH6rItZbdkcNxT8d2VWrTBvgIf4Vvl7AlPbVsvrFVVelK6Or/SbZEFd6qNtZnJJGrJItVkZ7P59FoNNBoNJxt5nX1eh31eh1TU1OoVqsz4qj7FMaaH62TJBLXqpxVecy8s20o5LLtoddZxb62QZIaXZXE2l99ymTd7ZCUllWC67U2XJtVo7Mcvmf6VM62r/rU8bsD9lebju2fGrLH9km7g30u8yR9h5MU+53SIdFPpxyJcluGKIpi6du/mZdGowEAMWeMjUHPa+07n1S+XXXOB1u9eAhEesCyQ7PZxJNPPolms4nt27cjnU6jr68PhULBebzpOX/sscfwm9/8BqOjoxgdHd0rgwYNfDqdRm9vL/r6+tDd3e085zpw53I5N2Hp7u523nPeXyqVHPlOkATnASNzGXwXQk1mjQJ/6LSwk1md4KoqRO9Jqj9rtOwkgr99ntyAgICAgMVHu93GyMiIW6hTrV0sFlEsFgHsDBW2adMmPPLII5iYmNirTm/alnK5jN7e3hmEOndaNRoNtwgDdhJEvJZ2mQ7zTCaDarXqtV1JSFr8RlHknAw8EJRkfi6Xiy0IdUHH80p40LfGdk0i4xmznop0XTAn5d+mo3OFgICA/Rc6NlDZbdWtJLgsiay/+TfHJVW02rRIWGo4GV8YCRJm/J0U8kJJOqtWVsK1Xq+7tRt367Lcmi+WUwk4dXLacVNJUx/xxzrg7qHp6enYrl/mm8SzEsvMV61Ww8jIiLM1unu5t7cXrVbLCdK2b9+OLVu2ODKdZaCdt2tSJYhJhjJ/VDDzN4l2dV7wHiq+GdO9u7sbpVLJic3serHVajmFPHdSa/upo1zDqNI5wH7qO5iU5VIHENtZ29aqjgHEdnGr7dbnAYjtONf7bdtZx4F9H1g3luxV0ledSyyHihR970O73XbzoSTniJLpNg1Lous7b9f3Pj5AnSS+uQnT5c46nQOxjVQkYR1PdjzScUvToSjEOu9Yd6xT61SwebVjn5YhYOkiEOkByw6NRgP//d//jUcffRSDg4PYsmULSqUSBgcHMTg46BblExMTGB4exsaNG1GtVvfawZY6iRocHMTatWuRTqddiBcFDUC9Xkd3dzempqZcGun0joNBS6USgJ0Daj6fx+TkpIthStXb3li0qoHj5If5UOWFEu2cJNufpPagsdFJn51oAnDPZ/qBSF/6CJ7zgIC9AzupXwxw8T0yMoLt27ejUqkgm82iVCqhWCyiXq/j0Ucfxfbt291C10ck7Cmogm5gYADr1q2LLWY0Hnm1WkWj0UC9Xo9tz6dtIqnD8mWzWUxNTXVc6Ck6fc9FrW4lJnFB0l7bWxduVJczHFw6nXaHnekWfz6n2WyiUqnMWHh2gi5K1bEdHNzLF8FWBywElPBWFaeChJySlbzXXletVhFFO8JN8pwNHfuUDNf1iubHQok5n3rXqpo15AfXHxwnJycnnc1Q0pppME8MhaE2RMPV+Eg9EuQKdSbQ2ct6VIKX5bSxpPnT1dWFbdu2oVKpYPv27U6ZTjFYq9XC1q1bMTExgVqthtHRUed0Zf5I/Capp2lrSTIqkd7V1eWIbrWXrCN1anMXG4l0hnZRp3Z3d7cTm1FJX61WY32B9lp3OqizhvMRht1hXrRv6O45EtC6I8yS3gBiO/HoBNDxVmPZ67M1LdYh68/OmXS9rDvRleTXA2b1LBcq5PWgcR/Y3pqvpPlC0i4FH1FseQZLone6Xwluflev192BuGwvfUesM8Q6Vux7b9PJZrPOycM6Yxuyz+h7bp12Gqtdn5dUP0n1tSsItnr3EYj0gGUJTmKq1SrGx8dd/Kl0Oo1ms4mxsTFMTEygUqmgXq97J297CmrIGQ+chjVpaw+v1fhZVnHBAZWf6TazuQyG6r32GZuke4Cd8d30cFRVmfOH+bRkusYJ4/2sJy7ulZAgEUCCQCdgdhKhkzGdhFkDH7D4CAY/IGDvYrHfG9qMer3uFrW0CbVaDePj45iYmFi0/PkWaVpn9mAw2nLdmp1Kpdwin7+BnTYcQMz2zRVKNvgWV0rY6PVWpaYLO2sTVYUHwJHnNq+qKtUFZhRFM+KN6ud8ln2uVZwtdj8NiCPY6oCFghKQHEP5uSUALVkJ+MO0cDzzxVq3JBuJK7v+smunTjuHNE1V96pa3CppfQ5hXqdrL9+6UOshSdXrg60r62C15KBeS6V/V1cXpqamYvHeSUpPTU05ollV5EzDl1dfm6pam/lR22HT0fUwf2t51I5bMRbJbWvn7bpc21PzZW0Uy6NrTM23Oma0DuaqNFa771vLss9bEl37JgB3xgudD+pcT6VSjsifnp52Z5jp+l0dREqQ+96P2exFkhLbpmnLOxt4X9IuDp0/aT35iHhtN9aBvi/aP/Ud9vV3iyTuR5+9qwi2enERiPSAZY16vY6tW7cim81i+/bt2LRpE9rttlNU0QOtJOyeRjabRV9fH7LZLAqFgiOGfeDnmUwG/f39KJVKTglGTycXo5yAcUsYsHNRmzRpI0hkTE9PY2pqChMTE86Lag9qIWioqWYbGxtDrVZzirxGo+FC6NA7q84DTnbb7R3xXSuVCprNprs/k8mgr68PXV1dTnnPEDicKJFIt2oOALFJBOPq1mo1VCoVN+mrVCoxwj1gcREMfsBSwVJQbO9JLJUy8Z2v1+sYHh6OndVBezRfqHpsd8rZ1dXl7E2r1cK2bdsA7Fw05vN5Z6OUsFElJBeupVIJhULBpQsAxWIRg4ODaDabTsk3m60moihy2+25eOOBZgMDA+ju7nZkBkmQRqOBSqWC8fHxmMJMn0nVIgAXbq5Wq2Hbtm1u7sFruLBmrPbu7m709fWhp6fH2fVms4menh4MDg6iq6vLkSxdXV0uLB3D0ig5oI4VHoK3VPpsQLDVAQuDer3uyDwA7m+rTrckJxAPYWFVnK1WCxMTE45gtUptG35DHYH8XJXiPkdfq9WKxUjXnT7MqxL6BMdjXqMhPfW5tCEkNhmyU4lS1k273Z6hilZ1t5ab31PBrtfbtagSkXrANLBjXToxMeF2jFUqFbfWsqIopqUOWl/+eJ1V6dLeqXCMinGeOUJbTZDstLHLWb9c+3OuwQNkrXDLJ0azTmm12fpZo9FAV1cXJicnXWhWJVk1vyo4Yzq6jqWqn7wFlfoqyGs2m86WVqtVdy3trR6QXi6XnROBdadO72KxiHw+78ISKUncarUwOjrqdsHRbnN+oO2tuwm1Hll2PTiV92j/V+cA+QRVfWu/VtKa75LuDlHnk3U4aJ64Q8ZC32/ea3fQa7/itSoWVIea5tO3y0Sv4/uS5OxKQrDVi4tApAcsazSbTYyPj8+YTOlAvLcJVBotbj30TV589xSLRTeZqFQqAOLb9tSLzomBqiI6KdPVQOoWN27x9kGVHzxcplarue1309PT7rAZGnFOBDnhUyKdi2a2SXd3N3p6epDNZl1Ynkwmg3K57LacKZFu1SJKkDOm69TUFJ566iln6Dmxmcv29ICAgP0LnRRoAQuLVqvl7NpCYCHajuosEunj4+MA4uoy2mVVM+lijAvCnp4eRzBT1c0wL3pI23xAYhyAW1QzTR5mSjKdjmqS0kqkK1TBRhs7OTnpFs1KRrBuSaTT4b9q1So0m02MjIygVqu5EHYMT1er1ZDNZtHb2+tC3ExMTLgFN8lzlpEkDhDexYCAfQkko1UlSyeg3SncaZ1EEo3rnna77UJnFYtFR9TZUCq8V9W2+ryk9aGPnJotbVUu6zqHP5om88L1nCW/Ge+cn9kxOYm8tuFDbd1qHVsSXEOrMF+sYwCxg8BZb5YYts+wql7+tnWopKU6A7i2p/CLtpohbLhG5bVKTloHiIZeoQ1X0tQ6U3wkutY/7VgqlYrF7uf3PtLXHj5rP6O4gOtozj9arRYymYzbycfftJvcDZfNZmO8A+uPc4ZMJuPW6jyrhmt5PcNM5xu023wm82kdDZYU1uezTe3Oda1Xrul944LWp+377A96vcbrV2gYH+2TKopgP9FY9exvvh0etvy2H/lg1e9J402YDy19BCI9YNlAB7pCoeDU2lQy+4h0xhW1qif1Xqpn3G5Zns8WI5vPXd2qo0oEGjCdmGiccR3A55p2tVrF6Oio817TYKpRYB5IflPhr44JrUeS/+o9VyKdkyMexgbsUMP19/e7BXepVIrFrPNtxdO/aaDb7bbzwHNy02w20d3djVwu52K+chKonveAvYvgOQ9Yagj9as+BajwN0QUgZiO4QFPQVndKV7fm7mobqlJLQftNW+WzsyR1GFZOw5hQvaSk9mzxRi3o9KZQgHZXCahqterS5+4wGwfWotlsYmpqyhE1tVrN2XkFd4fRFvOMFy7C0+kdZ7jk83l3aLruTKOKX+P2KtGTy+XQ39+PVquFbDbrFux0zvuUjAF7D3vbVn/uc5/DJz7xCWzatAnPfvazcdVVV+F5z3te4vV33303Lr74YvzqV7/CunXr8K53vQvnn39+7Jqbb74Z73//+/Ff//VfOPTQQ/GRj3wEr3rVq7zpXXHFFXjve9+Lt7/97bjqqqvmnf8AP5RctvN4kqK6dvORj7ON8ZzzM129l3/rb/t3EtRmWVh1KbBz3WgJQs2LHZvVjpFYV1WqvYfENElkhSr2lQz2Pd9H+PL5uVwO5XJ5hhOAtlpFahRJAZhhQ5iejQGtebVCM1Vyq2JZ25BrP9s+vnr3QeuD9/nKxvzoj698XHfSEa2ENL+n0xsAJiYmnJPcOr051+Aan+tVzocYu53OFe7w4JyKTger7CeJTjuuu9l9XEgURcjlcm4uQ8W5b8c5bTbbX99b7We+vsn60nByvnfEV/++eY7t1/Y7jRNvz1ewThX77trr9G+tD3tfp50gvnFqvgjr6sVFINIDlg1IjGYyGaxduxb9/f0oFosYGhpyqjIufGlopqamMDIygkajgS1btmDbtm1uIUmCWmOIcesXPZlUeM1nsOEkwHei/FxA8jyKIje51MGeB2eoQQVmHxA5YI+MjKBarSKfz6NWq7nFcLFYRDqdjnmet27diqmpKTdZ0u1NVKpzEkLlAsutBiKfzzuF/po1a1Aul5HP5x15rhMjrbNOChUlF9hu7XYbAwMDjoRgPW3evBmjo6OoVCrYunVrINMXCcHgBywV7E5f3BewN9T4XV1dGBwcRG9vb4yMLZfL6O3tRbvdxujoaMzGTk9PY8uWLdiyZcuMvKkajIu93dlxlsvlMDg4iEKhEFMdFYtFp/6yW3WpaAN22OKxsTFnb6ge5wKtUqlgbGzMEcrzIYajKMLY2Jg7vI55yufz7uDTkZERjI2NuXxQFdepThjjVheVWjZ1Iqxfvx4DAwNoNBqYnJxEu91GuVxGsVhEKpVCb28vUqkUqtUqJiYmEEURSqWSU+arnVVFKh0YK1asQDqddnOMWq2GLVu2YGxszIkf9vaOwoAd2Ju2+qabbsKFF16Iz33uc3juc5+Lz3/+8zj99NPx61//GgcffPCM6zdu3IiXvOQlOO+88/DVr34V//Iv/4ILLrgAK1euxGte8xoAwP3334+zzjoLH/rQh/CqV70Kt9xyC1772tfi3nvvxbHHHhtL74EHHsAXvvAFHH300btU3oBkKKmoik8A7sBIPZxRVeV0UmqsaAuu03itho6xxG4SrJKd45WGAAEQG8cUSizyOksSWoIdwIz1IcdkXquhKXzEuI+gZv7tYYdsC/7PsV9/GOozn8+jv7/fKZz17C4Azh60Wi1s3rw5tjuYz1BCnE5mWw7Wu+8AVbajKvHpRNC1dSeFs5Ka+qOhVNieFN1pfXNHAcVa5BcY6oNzGmCHU3vr1q3u+3a77XajpdNpTExMOKJ9amoKXV1dGB0dxcjIiHMmc/1N0n1qaiq2g0PV4VEUoVgsolwue3dys4753tEBbnd66w41zgW4Du/t7UVPT4+rTwrxJicnY/XI3WzqALdEvta1JY7Zf7Qfse/4dm3QceETJ1jCnu8Jn8+yq/NOd4toNAMdP5huUqheXxQE9lfriLE7HJRX2RWbG9bVi4tApAcsG9gtXlQxDwwMOBI8l8vFBsFcLhdb5HLrtm4VIpGr9wOIGVtg1wacuarSrcHXCZSdZHBitiuDr3qW6Ulm+jwkjQqyWq0WW+DqJEjzzXR1gsYJHj/jhDmbzaJcLqO/vx+5XM4p3nYVvns52eGEsNlsxsLKUAUDYF7ERsDuIxj8gIB9H7R73GHELdl0ePf19aG/v9/ZMS5kgB1j8ujoqIthaW2c2m0uVHZV0ZNKpWJnjvAzHsJFW6h54N9K1pA0VmJAF8W7srMN2Ln7TBeSdKDzmSTVGd5sNgexxmnVeRDT14VrqVRCX19fbNu3EjBcuOsBpUyXi1iN86uKL25b1wV3d3c3RkdHHXGjirmAvYu9aas/+clP4txzz8Ub3/hGAMBVV12F733ve7j66qtxxRVXzLj+mmuuwcEHH+yU48961rPw4IMP4u/+7u8ckX7VVVfhlFNOwaWXXgoAuPTSS3H33Xfjqquuwg033ODSqlQqeP3rX4//83/+Dz784Q/vSnEDOqBTP1KSTcce3XVqQy/45uwa/3g2W6B2JAlzXbclKW71WSS2aavmkiZ/c1ePpquK9CTngiUSNUY8QRuqQiYVPDEUCAlkLV+j0UA6nUa9Xnc7m4GdBLM6RPTAT65tdfezr058CnDbnjbWvd5riUqF2nLtd6qKtmp11pG2jT6PZD/DrNA+d3d3O6e1Krb5u1arufkBOQjNgzrydU2tzgPuzGdIFw1bQxJf20HrVXd68G8to0/Ip3XOvFJE53sHtC9YKNeiZw1o26gzq5N63ELv17Lo7nvf7hc7f7L9h/fZa9gHfHO9pPHEcj5zGR86pbOr9wbsHgKRHrCkoUZscHAQq1atQqFQwEEHHYQVK1Ygl8uhr69vxhY0Dmb1eh3FYtF5fAuFAmq1GjZv3oxKpYJ8Po+enh63qLMHb4yPjzuPsR6q2QlUcwNwZPFcQCX45OQkJiYmYoZUB37dKg4gFs9rLoMinQONRgOjo6OOTKdHnJMhbhe3ZIIPfD4NRrvddge1MQb6wMAAstks+vv73eRrVw3HXKDqlKGhIRSLRfT19TklPtUA1ikRsOcQDH5AwL4JXfTQWcrxvlQqub91+/H09LQbk4Gdi+Ao2rGtuF6vO8U65wJdXV0YGBhAT0+Pc5IzDNno6GhMiTcbGo0GhoeHnfOVCy4SuRqSxadQ4jZ4LpwajYZT4HHhyvBpu0Kk6wJMFe1cdE1OTjqSm9/PZ5xkPWlItv7+fgwMDDj1O59py0lSiwvwUqnkiPPR0VEXGqZUKjkVGwDnuLACAe4GWLduHdasWYOpqSl3AGqlUlnQ+PoBs2MhbDWFK0Qul5txuH2j0cBDDz2E97znPbHPTz31VNx3333e9O+//36ceuqpsc9OO+00XHvttWg2m8hkMrj//vtx0UUXzbjGhm15y1vegpe+9KV40YteFIj0PQSOE1aVqaRTs9mMKUW17+kOV5KeQJxM4w/HJh+hp33aKmR1l5Pe02nnkzqMlQjTsmqavjO79NDD2Qg4Sy5bp4Fer8p2Xst6oYObKnSGR+3r60OxWHTncHCXN+0zbTNFarSNFGDxzAyK0rq6ulAqlVAsFmNO50aj4XZPcY2ZBN6XyWRiJKc6W3RuoPepI5cqb60TJWi1jXX3FBXMarPYj7SfcWcEBWoMN8R+zTBsTLerq8vtuOJan2Ss9k0Nl8K8MQZ6b2+vOwxdHRq2H1lVtCWQbZ3aXe6sXwDuWQBi8xyK7niwuNajkv/6ztsdBPbd67SzX3dv2HNfdB5Jh4ZvB4bWcScluq8Okwh3+46qWME6xLQebJgZW65OCOvqxUUg0gOWNGjAurq6MDQ0hKc//ekolUo48MADsWLFCudFt4MQ0Wq1MDQ0hFar5bZBUZ3Mwy55yGWpVEKhUHAEfLPZRD6fj8Upmw+RHkU7tl7NBVSKc3sZt4Mrkc7FPScENLy6UJ/LoKgD9Pbt22d4elUdP1elnxpKTlR6enqwatUqlEolrFq1CqtWrZphwOaq/NgVcGKlxMjU1BT6+vpQr9excePGGdvRAgICAgLmD92NNDg4iEMOOcTZZ6qbV61a5QhyLiJVdUe7wK3lk5OT+O///m+3GOLOswMPPBBr1qyJxdTevHmzI2vnSig3Gg1s377dEclUzPPMDd0+TFJdtxzTOU/Hc71ed3bfbqNWJfh8wDqh2q1arWL79u0Adj++pt7DLfyrVq3C+vXrnfqdZSYBStJewflTFEUuhFqxWMTAwIATMnBxrbFclcii8n7NmjXo7e3F2NgYCoUCJicnsWXLFredPGD54KCDDor9/4EPfACXXXZZ7LNt27Zhenoaq1evjn2+evVqbN682Zvu5s2bvde3Wi1s27YNa9euTbxG07zxxhvx8MMP44EHHphv0QLmCV2f2DAISiRaR4uOESSw7LpBlaSq4FVFLZ9tSTwlCRWWXEwitLh+YvgNzTfT1THa7gbijiw7jlvFtu4csust/q3rYCV9dVeQrokymQx6e3sxODiIbDbrHNS0W+n0jvPIeOCl7jwql8vOLhcKBVSrVXR1dWFiYiJGpPf39zunN9u7Wq2iUqm4eUAS1NliHeRKcJLU9hGjGlpIiX/tS9o/WI9KplMtns1mY31N7wd2iuF4ptjU1JRzAJNUJxHe1dUVE+cp0ax9Ut+TKIrcDj8KE/r7+2cQsPquWQW2EunqgNI65XM5Z2FedBcDQ/TxEHI6y1UIqCI8FQHYd4nPItTZozvlmAdC3289n4WCQJZV53SEpqMHk7KPWxW7Omvo8GB9K9SB1WlcsU4h3ZnDPC1lLMaZJldffTWuvvpqPPLIIwCAZz/72fjbv/1bnH766e6aKIrwwQ9+EF/4whcwMjKCY489Fp/97Gfx7Gc/e2ErQBCI9IAlDRowqqOKxaLbekZDb7ddKXRLNLerRVGEnp4eNJtNFxOOA202m3VGi88mUT1X0pcTBnrpraJcoVub9DBPDTdDcsJ6ZnVCpip6a5Rmy6v+Zp6sgZ0rSC5wcsVTwbkF3OeZ3dNQg8YJXiqVckoM3XofFup7HqGOAwL2bXD8p422qi9dOOjB33o/1dCFQgH1et2pwzSuN6Fkhm9rbRIs8WLTJHQrtf7vUxDqLjrOW2wsT99iai555W8+Z3dtFucUnPvQdtt5iuZdF+H6fN6jZI69htdxTqZb3e01FFFQxVwsFt08aVfU/QHzx+7a6scffxy9vb3uf0uSKuyc0I4Jc7neft4pzccffxxvf/vbcfvttzuFZcDCY7Y25FiuxDZhlZ1WrQvMDHVid8Za0Y7+n5Q3HZ+5vkoSa821rBY233OFvdZ3b9JOXx3HrdDIdxClrq31R9tMd5fRqaznoejB4iy3EpNJdaN5pq2j6p82XuvO2lclu22MeqtiJ/HKXVcaj1tDgSbZHWvPtc4U8+kfWtfaVrTXnFv52tq+SzYvlnjXv9V54etbmi+mwzzRWUEHvJLmSjz7xmq2qy/fc6k3ay+S3gHrGKOjQkP76BxOn285EdaXfZatn6T32/ZHX7vMd364N7BYZ5oceOCB+NjHPob/8T/+BwDgy1/+Ms444wz89Kc/dUT5xz/+cXzyk5/Eddddh8MPPxwf/vCHccopp+C3v/0tyuXyHqmPQKQHLGlks1msWLECxWIRa9euxQEHHOBChnB7UydiVslobivv6+tDFEUYHx+P3a+hXeiV5GGc3d3dqFarmJqamjXPzWYT4+PjsdhzVOTZQ1todBhmhUqvFStWIJVKoa+vD4VCIRZ6RT3rjLHWaDQwPj7uDiihest6PTnA63YjCyXS57NNnnW9du1aDAwMoLe3FwcccAAKhYJTJzAfi4VMJoO+vj5Xrp6eHlQqFTzyyCMuzMtS9wQvZ4QtaAELhfkuQPcHJC2QfNgTdUeb0tPTg5UrVyKTybjtvs1mE2NjY25rMA/impycjB1UzcU51cztdhsrVqyY4ewdHh52SnHaLDrIK5UKqtVqx3yy/1BFxsUgsHNLN+0zF/D8jCp4VYQrqZDP5x35OzAwgHa77Q4OVZVcrVbrmE+f4lwPE9OwZPNtz+7ubgwNDaGnpweZTMbNcwqFAhqNhlNL0tlAtSXFDAxDx2s432Fs3XQ67Xb2UXXIOi6VSqjVau4geFWk1mo1VCoVR8ywLYaGhlCtVvHEE0/MCBkSsPBYCFvd29sbI9J9WLFiBbq6umaoz7du3TpDUU6sWbPGez37dKdrmOZDDz2ErVu34phjjnHfT09P45577sFnPvMZ1Ov13Tq/J2AHdO4PzAyRAOwkszqRq4QlnHSNZc9AIiGo4WLoMLXOPoUlZK16WcvhU6RqOkqq0c4owcv0WRfWeUBiljZORVL22eqgJnQnlIq8aKfK5bIL5aXObzpUfWSgqqS5szuXy7kxn2MHBWw8qyyTybiyc9e2treGK2V9M++1Wi1mY1gv/ExJW4rSlBwFEAvTQnKaPILuPmNdMo8qtmJabBPeo+HhuFZXIR933gGIreXVycDf/Izx6bu7u1Eulx2H0NfX55zMts/QUcHPrXNfCWJVunMeRXW27buqbNf49HznuJuhUCi4OcT27dtdmBvOAbQOVVFudxQkkej6PqoDQ50sPiEA+z2dEc1mMxYWjyGh2O+5AxLYudNARYoay342J5vvOx0ndBxkfudjf/f2unqxzjR5+ctfHkv3Ix/5CK6++mr8+Mc/xrOf/WxEUYSrrroK73vf+/DqV78awA6yffXq1fja176GN73pTfMu61wQiPSAJY2uri53sGhfX58j0KlInw00AFEUuYMtGSO7UCg4owHAHTDGwYweao3fOheQgE+nd8Qzr9VqbtJit5LReDOuWLVaRaFQQG9vrzuJu1QqxWLaAYhNRnjQWBTtiBfL7e5Jajk1hEkTJVUO0qB2GnDVe9/b24uVK1e6OLlLSe3DyRMNTy6Xw/j4OLZu3eoW5yFe+p5DINIDFgKL6Yxb6lhMBwNtDA+V5vZlOqa5/ZshWoCdu7G4eO7q6nKLsUwmg0aj4YhbLvx0y7Sqp2gjO5HTChITVsVuF+icC1DxTnutTmm1rdwGzt+6mGd6XORzsT5bPrWOuXDzEe1zBQmJgYEBR6BrTHfWLdNVVR937vEaYOcWcobyabfbztlAEp73ZrNZd1Dq1NSUW7xyIc95DAmPXC6HcrmMiYkJPPXUU/MqZ8CuYW/Z6mw2i2OOOQZ33HFHbBv3HXfcgTPOOMN7z/HHH49vf/vbsc9uv/12bNiwwY0rxx9/PO64445YnPTbb78dJ5xwAgDg5JNPxi9/+ctYGn/5l3+JZz7zmXj3u98dSPQFglXN2vESiCt9kwQ+vNeO03Rg6vjMNJX01GclrXn0t49c96naO5VVSTYlU1VhrXm2RKBPpZqkjk3Kk0LrhmtKnuNFRbpPiW7V3qpc5visu4x0Xa3rVr5TStKzvdWOqYNB24POLVXPax3pjmwNyUbCXneKMR3aK+tw4HNtSFVb33Z3Gvsj+4ISpiyrCgZsn/ZdS9Kc3EehUHD1anc1KKlrdxFYB5b2K1+McCW6fekoT8AdY0S9XnchbjS8izoq2J+Vp2Hd2jzq+2t3LFjVu96jOxO0btmfVEGvOylY78DOQ3Ttu21jyWv6s0G5F+v48o1LnbAQtnou55kAi3+mCTE9PY1/+qd/wuTkJI4//ngAO5Tvmzdvjj0rl8vhxBNPxH333ReI9ID9DxxcGdIln88nbj2eS1o0XNPT07GFIL2vHBh1wU6jMtthmxZKdI+PjzuVhE+Rbj37pVLJEen08KqnlOlzcsA6IZEO7FysUzmnhtD3wzoirApjtrJTfVcoFNDX14fe3l4XB26pgpNvVQ1OTk66w2UDAgKWJnRBGRDHYpHo3HXFCTgXXepIVfXT1NQU2u22+96Swro9WEncdDrt1G96vR7uNZfQZraeaC9brZZTUlEdTVKYinRVofX29s6ITUtiQWOt8ppcLoeenh4AO2wuVVGaJ13E07lPclkX4ruiRucikbaPDg9d3LMsar/1b1vHrH+q+Kz9VOVfo9FwB8xlMpnYOTK6gFU1ohIr3MlAAUHSdvuA5YOLL74YZ599NjZs2IDjjz8eX/jCF/DYY4+5GKqXXnopfv/73+P6668HAJx//vn4zGc+g4svvhjnnXce7r//flx77bVOuQYAb3/72/H85z8fV155Jc444wx885vfxJ133ol7770XAFAul3HkkUfG8lEqlTA0NDTj84DdB8dvkq9W4QzMjQT2feZTl2v6tCPWCWnzp45DJfz0t90B7SPRfGnr55aYttdZVa0qg5OeZYlnS7Jr/dFWc73JtZoSsxY+tTs/o4PAOletbVJVvR682mlntNpQXl+v12NrepbbZwd5L50GtDEk1GmXbP6mp6edjaQ90u+VnPe1K//XOrGELutQnSC075xf8PkMRcod9lYUx/RtX7HrepaD/ysxzfuZpu0Ttt/bdwbYGdYvnU672PoA3HzP9h86kjRUrZ3fW8GfnXvwh2WzZzDY81g4t+T3qVQq5lzh3E0dMyyrz8nlU84nOQrnCluvexpzOc8EWNwzTQDgl7/8JY4//ngXLeKWW27BEUcc4Z7D+2w6jz76aELJdx+BSA9YkuDAyQNQBgcH3cEWjLc9X+hgWiwW3d9TU1MzDB4XijwMhYq6uYID+8TEhNu2buOE63W5XA4rV65EoVBAuVzG0NCQixNL8t03geMA32g00NPT4w5VpeLrqaeechNIu0XLGlyreqPRUPVEEsrlMlavXo1SqYS1a9di5cqVM7zxSwk0nPw5+OCDMTQ05A41oxELKuiFRVCkBywUQn+YicWsE4Zh6+npQX9/v1uAlctl9PT0oF6vY2JiAq1WC1NTU2g0Gm5BPzg4iFqtFjtkm2Q6f1S51N3dPeMg71qt5tJV5ZgPPqKjVqu5BfrY2Jhb0NHucYGlKJfLOOSQQ2KOAz2vREPEaIi3VatWufkAy6wLbdrsYrGIvr6+GLmvjgRux55vO/Fg9ZUrV2LNmjVOZW9Vl5wz8Tn84UGxqrKnE39qasrtPKDaj4o61vPk5CTS6R0H2ZVKJRfejgR/Pp93czAbXmfFihXo6+vDyMgINm3aFIj0PYS9aavPOussDA8P4/LLL8emTZtw5JFH4tZbb8X69esBAJs2bcJjjz3mrj/kkENw66234qKLLsJnP/tZrFu3Dn//93/vtokDwAknnIAbb7wRf/M3f4P3v//9OPTQQ3HTTTe5eKsBewd0sAE7+gXHSAufkMe3NiF0bOaajZ9bxSqdkBxffGsfjmd6RpYla5UwU/JXScUkWLLVhhzRa5Sgs8/V0DC2XvQzS0RqfvP5PAYHB91hleVy2bWL7prWvKuTNZVKuTUxd16x3vg5nc0kJVX1zF1qqlpXYpPpWHW8Ct14sLgtP7Az/BjX3SR1+/v7Z+wQ4E51fhZFEarVqjuXhc+1O+hUwWz7NdvAKtzpROLcgO1kyWX+FAoF9Pf3I5fLubmVQp+vNtK37rZ5AmYeysp2JueghPpsRDrvpWqe7UtV8/DwcMyZBSAWkknz5asT1tv09PSM+Z0KCthvtA8qoc15Tr1ed/M0hh9iP9HdcfzNg2Jt/9H8WQ7FB8vj2OtmG0d8WAhbPZ/zTIC9f6YJ8YxnPAM/+9nPMDo6iptvvhnnnHMO7r77bkem70redhdLk+UKWFZI6qC7u6hX76xu5UrymM8FPjKZkxodbDmwWyMzX9DwW2La/nCiRjUft75zksL68JWH+eKhZiTfaaz1OUTSZ/zNgcenrvDlgR5zxkOnId6Tg9fugkZQVXGqzNjbHuH9AYFIDwhYXHQiJ3Y3XZKgNv5jKrXzYElgZkxcLsR1MWqdmaq8U5Uav1Ml2a6UTRcvSTuSuCizcTS5CEun07G5g3VY0wnAhRvnNZoHS4BovFjWs08Bxe9mK7uGntED1klq+KDOf+bTLoS0fTjn0XJwNyAXuyTXaWvtVn+rqNOFend3NyYnJ5f0/GK5Y2/b6gsuuAAXXHCB97vrrrtuxmcnnngiHn744Y5pnnnmmTjzzDPnnIe77rprztcGzB1WYW2xu7ZoNuJJiXINp+ITZKlaN2mtOduO6KRxOCmfVu3KNPR5zG8n4slXz0n553keJLq5Rk0i9jSkh3UoKOFtQ3BofWsYNQ2l4rNp2mbWxvgUyarIzmazMfvE/JG4JVh2FbixrCRGaR/Vkc5y+shAC+1PvnbwgXlRO832Uoe2tZE27U79jb+tIl3b2PIVWqZOux/IaeRyOXcuQFdXl5sTaR1aJ89sNl0daHZOwvt154B+x7/1R1X8nNdZZ5Xv/dS8qkp9trGsE8lr22uuHMRC2Oq5nGcCLN6ZJkQ2m3WHjW7YsAEPPPAAPvWpT+Hzn/881qxZA2CHMn3t2rVzyttCIBDpAbsE9cwxrppCvYHqeZ8rdEHFQdjnFZ1vmhw0uHCsVCrYunUrGo2G81xTNdVqtTA6OorR0VGnmNpV0Btvt3AVi0VHQA8MDKBcLrswNlrmTmWi0WIcWSry6vU6pqamvB7npMmYTgxmmyTQ28+DRtauXevity2nBW5XVxd6enqQzWZRrVYxNDTkYvDO5XDZgLkjEOkBAYsDkpCZTMapeuzW3t0BF35U/NldXFQF0tlLG0eFEO2rXahz6/XY2JhTNXGBqaqhkZER94w9oVKmzc7n81i9erWz01Sv2QUhlWLMHxVyjEebSu04THzNmjVoNBoYHh52h4QDcMo4XeySdKdynoQ/nfA8RIt16isDlehsAwDusPNms+lsuJIKNrydhqnh80lKkRxn+1G5x0Uq46ayjKw3fs+48Xwey99sNtHV1YUVK1a4PvHkk0/u1rwsIBnBVgcsJJTossSoqjCV1FVS1kKJT17jWx9akozP4/oK2KkIV5vIe4GdoWFIZtr8K9nIsvgEU8wvnaoA3JinKl1ey/WoEv+aJ13b2XpWoZjWhaahpLYlM/Ua7gjjvWp3CI4XVPtu377dhTMlAc28TUxMoFKpxOJxk0ug41jz7dvBoKS6jlVK4nLnms5LtB59jghgZ4gx7Yc9PT3Ovo6NjbndYSo8U/iIZ81fd3e3i9+uZWWeGeq1WCw6Z706JOyOCv1RWKeDhoLRH+aFYgiWS9OzpK8vD/xf19Xt9o6wqdzhr6F5bH2xbVhHVl2v75eGfuM8Q98te+adLTffez5HxxOtN54DwDwr0a750j6kn2k5fU4AJe2tgGQu2Ju2erHONOmUf+6GPOSQQ7BmzRrccccd+MM//EMAO+a2d999N6688sp5lXM+CER6wC6BqiYuLO1gx209u7NI10HLniC+q6BxIJE+OTmJ4eFh1Go1N5i2220Xz2t8fDy23Xx3YOuCdcgDRRlbnLHcZlM9sDzATs+6emGnpqYwOjrqiHUuyvVen/HXiUHSJAGAa/tMJoO+vj6sXr3aGfvlhK6uLufQqNVqGBwcRC6XcyEIAgICApY7SOIWCgU0m01MTk7OiFW6O+Dih0SDLpKjaGeYFi7mSOKS1NcFhqqiaAu3bduGzZs3o16vu51PSrpyzrEnSTzmZ926dc7hOj4+HlvYMnwL48bq9nX+cE7T09ODtWvXOsftxMRELP+M006SgbZVz0JhnXEnWrVa7UguFwoFDA0NxRbMzWYTIyMjqNVq6O/vd3M7zgEs4cH2IfGhhBjnBFzgcP5ARbpu22d6TIOLY265VpKMqsAVK1ZgYGAAk5OTy26uERCwv8GuY5KU2kqMcuyZjZTi2KE2TENmcFzhuk5JPx3HdH2phyPyWl370b5pOUjcKclniTabJz5fn6HqcCUElWQkuc4xdDZHos2nJT6tItmu9XSHl+7WBRDLv6ZXq9Xc2lN3f/P5lUrFnT3Ctmc7AHBzBU1b60H7CttK1cGchzCMmR5MynQsn8A0NC+0R7qTjI5qErc+5XcST2HFgXye9q9MJoNCoYB8Pu9i2FM4oAfTan5VKKfQ+tU8aD1aQp1zCdp9q+ruJLBjW/G9KpVKKBaLaLfbqFQqrl9wN6KPg9B3ku+DPtfHKen7T5GCPXhY56JMi/1Nn+MTl6hIwu621Lqxda/XJTlttO7UKZTkGFkKWIwzTQDgve99L04//XQcdNBBmJiYwI033oi77roLt912G4AdffLCCy/ERz/6URx22GE47LDD8NGPfhTFYhGve93r9lh9hFlowLxgjZVumVJYT7cdhOcCNcwaQ3R3YCcPzWYTtVrNEelc/DIGKBUKCz2Y0YDQWOgBF7vjMFDHA7eN20mkwk6cksrp+5xeWm5Tt9vtlxPUSBYKBeetthPmgN1DULkFBOxdqBpOt2GrWkft8+68nyS0VdnFMB+0TTbsR1JatNFczPFvDUNCG70Qju7ZoIs7XeQlKfn0M6u81MW/bn/mXInX684/XXj7VEtciCWpN5k+SQX+z781HJwlXHS7M9uZ8wydU2ldcVHOxaHG39Xn8xp+R8Wg3kPFfXd3dyzWfqlUAgB3jk3AwiHY6oCFAMc2q2TtBN81nQi8JFjSXclrH9Q+KjGpY5uPKLdrNh3zNUyGHdttmZRcpI3Rd0nzZu16J2e4HZuBnWFHOylg7fpHyT3lAvi3nmnRaDRQr9djNlLJaR/Jq4r9JKWz5kvth5ZNoTZO20KvtU4WbQvWtxKtWmZfvTNt2jHraFFexDqL6DRnWNckTkDrxTd/S+oPeo1t86R3K+l6VW9TJGHnD+zLPCNFd1z45g6+vM7GH7G+dY6r76kS3uqU0vs7OQb4dydh4UJA+3KSo8GHvW2rF+tMky1btuDss8/Gpk2b0NfXh6OPPhq33XYbTjnlFHfNu971LlSrVVxwwQUYGRnBsccei9tvvx3lcnlXqmdOCER6wJyhi7l8Po9isegWYJZIp0dYF4zqFZwNuuCs1WpOZcXwKLOFPElKk4szHkw2MTHhDvHSCQX/tqq6hYAuJPP5PHp7e2PxxXelbEwX2Bnzm9vRGOKFh6rqj2+yqluMOjkRcrkc1qxZg3K5jMHBQRcaZzmS6ay7UqmEAw44ANVqFVNTUxgeHt6tXRUBcYTFeUDA3gEXCiQ8uQOKv0lCcps17a2O//MBVc2NRsOpkmnn9N3lolTT58JKt+pyUc4DRKempty2YB78ZO3VnhojSHhTYcX45rlczimulPTV+uNCnARDs9l085h8Po+BgQHkcjkUCoVYHHFgx6FpbCc6FHi/kjhRtCMcXTqdjqkpie7ubpTLZdfudLIzvEtPTw8GBwdRr9ddGaMocuF5OM+LosjZRl3gt1otp5JnuzNvAJxKkaSCnqWSyWTQaDRc/nm/7prgHKPVauHJJ59EtVpFKpXCoYceilarhccffxxPPvlksBELiGCrAxYCutuX44V1pnJdprBEJ6HqUd5r79HdLkqA8TP+tqEpuGuLO4m4o0Ydfeo81PQsdEcPQ1HpQdhKhCtpy3G1p6fHOVlJUuo9DLei5KHvvdN1t67RKCKjyprto3Vny8e1kHIBbA+u1ScmJjA6Oopt27ahVqvF1OAavssqqLVs1nGh5LWPANd6UCcs7Q3rX523lgjXtDlnYp+0oTzUrtEWU7SmTmvt57TfqVTK2XANcaZpl8tllMvlGCegfd6qyLW8ej6Llo2Iosj1J+uQsfWqAgXrUNHreID49PQ0SqVS7Gw2AG6ew0NmKVQkx6J9VIlw3amosEpwfR7HGtaDpmnBfqX8lXJAfLblrfSd9dUL61yv812jsM40/j0Xgchi2OrFONPk2muvnTVfqVQKl112GS677LJZr10oBCI9YF7g4MCFFb2nSVts1ajM54XVgZoGZ3cJbSWQuahVknlvqNqA+ISBi3HWIQ3C7qavivRcLhc75Vo92Ul16VMhWHALOw0++8VyBkPtUBHAftuprgLmjrA4DwjY81A1CxdherAWFUKpVMotbFTNvCuOQ5KlwM642JlMJqZU0gWxqoP0ty5iNGSLKs+5AFzoMSEpTdaj2gXd+aXzBlVa8V4lD5RwJ5EdRZELp6LP5zZvEuWsWyU0VA2oiz9FOp12IeMYWoY/LFOhUHC7zDKZTGw3AOcUAJxjQ+O16hZ7XfTrYlNFFVbRr2QVEUWRC/HCeKvVahWVSgXDw8Po7+/HunXrkEqlsG3btt1t+gCDYKsDFgJ6NpR1/vHzJAKY0DVL0vUWVtHpC9XA71UBr8Qw79Md1pb0J+nnW/uo/aVDVYl0zYMSxRSice3mI3HpjLYqda0z/tY2IKnNMdvGGGfe9Le2AduN6ShJyTU1idV6ve7WtiyXJcqV4PSR5Ultq9fb+mS7cd7BdTXjMWu/43U2HRUiaP5sPHklszVfvE9DjAA7w7HRLmq906Zns9nYTm/lBazgz/ZTS/pbsD/Y+uR9OgfR/PneORUn0lYzZJ+2E50Z5I18YVQsOa/f2V0Xdp6mPFPSDg0dC/i9VbL7RBmaL213dWzpdTrv83E5c523sh/O5dpgqxcXgUgPmDPUwFhvtB0waDQ5MM+XkOQ1rVYLExMTSKfT7vBHhhPJZrNzzjsH+0ajgWq1iomJCYyNjTkCvZOazZZtIQYencjRUPq8l7uTti72dbCnobATTUINm2/bH9Nm/LlyuewOHVvuILHQbrdd3HQqN/aGkyUgICBgIeBbGOt3ul1WF4LzdXprmiRVqUxT4plE+/T0tDuoE4gTw7qQzmazmJqawuTkJEZHR10scl/5Fgqd0uQikGo+EsoM/wbElUpKVnBBbwkC1hXV2nYrP20PSWXWlRLQvgWjhR5cls1mHaFDJwqJD8bdZZiXarWKarXqdhik0ztCrJTL5Rg5RgLdLsDVgc/PqUrTRTYd/lon09PTjvhgXTC2KuPV9vX1IZVKxRbvAQEBSwf20MAkR+VcoYcKEiSD2+12TFntIyutEledhPyeYyxJYCrT+b0+W9W7SYSWktx6rz6fa2YbVsvCEql27Wrv0fKR8MxkMqhWqwB2qNR5uCUPtbT51LwqgZhK7Tw/g+G19JBxJX81PVW9cw2cRKCTLFZOgfeSX9A+RvulCnEL5SJov3XdbB0crDuCu7mUzFaHgtpm/VGHhs8hAsDxG7S5rAPdGa39TAl/5VuUa5mtf9g20ed0ghULWD6Bf9NeawSDVCrl5gfatr53S+tHxQL2+TaeuhWTJMHndGCausuwUx0kORrsd9YhZvNh5+27ywcF7HksvxgMAYsG611XRZGNHcrvrIGbD6Jox9bi7du3Y8uWLRgeHsb4+DgmJia8p13PBm4Pn5iYwPDwMLZs2eIOREk6cM2qBebiLZ8NOuhrfPSFItKT0lfDrso4VcrZH99EjWqJfD6P/v5+DA4OulAyyx3cdl4qlVAqldDb24uenp5wqNkCQVUtu/IzX3zuc5/DIYccgnw+j2OOOQY/+tGPOl5/991345hjjkE+n8fTn/50XHPNNTOuufnmm3HEEUcgl8vhiCOOwC233BL7/oorrsAf//Efo1wuY9WqVXjlK1+J3/72t7Fr3vCGN8QWI6lUCscdd9y8yxcQMBdYu8JFncYfV6WZKrvmCt09RtUwQ71wsTg+Po7R0VE0m013GBgVUiRjGZqsr68PuVwO27ZtwyOPPIJt27YtqjOTdq+7uxvVahWjo6OoVCou1Azzr/VHUrher8fIbNrkZrOJiYkJTE5OztgezvkPDyGdnJzE1NQUWq1W7EByHRuT2iyTyWBwcBCrVq1yzuF6vY5qtYrJyUkX4m5kZMQRLNPT0xgfH8fw8DAqlYorX6lUwtDQEHp7e5FOp90Br5xrcOHLOQSdDdwFODk5ibGxMVSrVbcrj6EC9SBchgjiwWesZy7Ky+UyVq9ejTVr1qBUKu3xBef+tqDd27Y6YN+EHpRoD9NMCrlAKCkIxEN+qgOW5LgSq7outYSohtnSUFnqVOaOW4bcUjLO9nWmqT8avkTTBeLEI9dprCPuJAZmEmuWnCU6EZ9K0LdaLberZ2xsDOPj4xgbG8PIyAjGxsYcCd5pR5oVYHG85s5uPexalcVKrmu76k4pJcBtW5Gkpx1gXnVnl9oRKro79S86FnioN22Vigp0TsT6ZZg3Opztzjmq8nleDPuIHlTK+tD1NMVbDO1CsaBdtyuJzv5iVezWIWD7oG1TFUjqe2XnF74xPpVKxXgMX/8rlUro6elBoVCIkem8huXQ/GpYI8LuEGE9Mk11KPh4Kd94Yn807zqfs/O7pPt1LNHxSscs/Yx9R8cNfddmQ7DVi4vlz3wFLAp83vVduWY2tNvt2MElVDPpxKfTYGAnO2romJZv0qBENI27NfS7Gst8T8PnESbsAGrrcLaB1pL0dnK83KGTJz2sdV8o21LA3jT4N910Ey688EK8733vw09/+lM873nPw+mnnx47BEWxceNGvOQlL8Hznvc8/PSnP8V73/tevO1tb8PNN9/srrn//vtx1lln4eyzz8bPf/5znH322Xjta1+Ln/zkJ+6au+++G295y1vw4x//GHfccQdarRZOPfVUTE5Oxp734he/GJs2bXI/t95667zKFxAwF9ixy5LRSTuTdgVc3FBJze2+unBRwkMVaxZqs5Ws9pWP47Y6jrngWSiosk/tpi5urXOMTgKqmjRerJZRtyxrW+j31qk9l/ZSRZYeXsa0lfThZ75Fq50DJAkKfHmyDhxgpiLRpsfnsU2BnaFrtJ3tDoCAhUNYnAcsBHxKX36e9P7PBl2/+HbNEp3S9o1tSfcq+Z9kq3x5snlNglUpd7KLTHO+UHKSRJ4S1CSmZ3t/1Tb5CGJVeNsxWXecJZGclrjU8vJ3Utupsl3jZtv7bZq6o0Htq7UrPjto85U0Htq61efYNbWS2rYf2TLbOUcnOzjXfqPPmavAz7adXq+RDPRvC1/fSnq/fTvxkvKY9J1vrpP0/XzFJba9kgj3JP5pV58VbPXeR5BZBswZOiGwhsc3yFnl8668sK1WC+Pj46hWq+7gLSqqVJ1kt4b5Bq/x8XFs374dY2NjeOqpp7Bt2zanBAN2DrbZbBa9vb3Ow9vT0xPbttxoNJwqfnJy0h3UNp/yzTbx2p1FYaeBm8ZJJ7MWNi+KVGrnQbP0yO9rRDP7UaFQQH9/v1PQBew+dsdwz/e+T37ykzj33HPxxje+EQBw1VVX4Xvf+x6uvvpqXHHFFTOuv+aaa3DwwQfjqquuAgA861nPwoMPPoi/+7u/c6eLX3XVVTjllFNw6aWXAgAuvfRS3H333bjqqqtwww03AABuu+22WLpf+tKXsGrVKjz00EN4/vOf7z7nYb0Bywd28bRUJ6FchOp2WACo1+uONM3n8+ju7nZEqh7suStlm56edodQkrQtFAro6+tziycqrGhPaY+oSGc+R0ZGUKvVMDY2hsnJSe8BmgBcqJGuri53IGUqlXKquHq9jrGxMS8Jn+RkTqpPbn3XAzvpLNDrtI/odu5CoYCBgQEXrqbRaDjnvJLDJCFUuaRtSOcC/2belXBnXhgCjwq33t5eF86FYP7K5TKmp6fdlvVUKoXe3l50d3ejUCg4UYNVWuqcK4qiGCGi7dxut92cCtgx/nGnHNsa2BkXXuuy0Wg4RyTDyWWzWTz11FNot9vuULt02n/Y6kJgKbzrSaTOnsDetNUB+z6UmM5kMjPGBY55eh4W+7tVg1vyiWsWhhhheI+k2NLsnxoyhNA1EZ3AOlYB8MbJttBxfK5rYF1b0+bY8to0NP86/uuuKC0L/65Wq+76RqMRO6vLJyDykY7tdtsp27lLa3JyEs1m080vWB7aEY7vPFeLcxUN6aXpsz9YclHtDvuLkucUwSksEc460Z316vTR9bnuYrCKc6tw9sWr13akXdSwrqx/5TOS1tVMS8lo1rNPQBBFO0PYaN3xfdR3Q88yYXtpDHSdk6hYslAoAIgfaGuvYbvw/Wf76vU2OgD7sO4IUFGD1qfv3dDna1sAcce+zrksr6U7E7TO+Gyt06R3XO9Jgr7Ltv46IdjqxUUg0gPmBQ4ESsjyb3ud3d4y2yDiQ6vVQqVScQtRbn2iseFvGhBCDReJgkqlgtHRUfczMjISMy46yevv73fby1esWBEj0qemprBlyxbUajW3aGeZ5zIoad4skb67JLrvGdaraydE1qtu/1bQ0cDtc50Oml3OIAnR29sLAF7vecDiYHx8PPa/xnUkGo0GHnroIbznPe+JfX7qqafivvvu86Z7//3349RTT419dtppp+Haa69Fs9lEJpPB/fffj4suumjGNSTffSBBNDg4GPv8rrvuwqpVq9Df348TTzwRH/nIR7Bq1arEdAIWH+qsXepnJuiCiraARKiqwHK5nFvQkLTeFZDQBHYe2FYqlVCv12PEqlVFK6HOxeDIyIgLKTI5OZlY17oAHRoawvr165FOpzEyMoKpqSl3rorvcC3792z2mwtAbp9mrHEls+1in3XOeu7t7XWLVdZLPp+PKfQYB1ydIHbxl0Tc2DJyC3pPT48TIaTT6RgRzuf19PQgiiIXViCVSrmY6lzEMt/NZnMGWa7zKCts0Hoiuc/7o2hnCBsAGBoaio3nJAHGx8eRzWaxcuVKlEolNJtNF5qv0Wi4beG69X1vQtWDexK2XwQELGVY56IeOKlENg9lpGPVkn28X0k1tQvqaPQp4IG4yAiY+S75VMZc8/B5dGImkdt2/FciUNfPvjFcHaIkem0oCruGU8JdP1dykrZJSfVarebIYZ6DMTAwgJ6enpjQyo4zdm05OTmJ7du3u3NRNPxYd3e3I8JJoPOMjYGBgVj+SKhq2FZdu/pIdLY3P+N9ep6JPkP7hkKdPKqaV8Gg9k2S6XoGiNa/b76i7UruRBXaGpolqd/6oByM1pO18/pe8Tpd11qOgHO1UqkUC3nDdICdOwB4Lfuind/Z959zJzsGMB9M3+5cY5raVqwrn5OAv217W/GiXq9zFbaX5k93FPKZev6Db/5h3ydbN5pPLdNcdgIELD72PQYsYI/Bt+gF4l40wsZf7TTpn23LDQ1ZvV5HpVLB9PQ0RkZGHJE+PT09I9wKfzSvJBEAOO94KpVyZLqqzXp7e1Eul9HX14f+/v6YYeBJ6pyI0APPw8DmU5caW4/1NN9tPRYa41zjcCV5Le1nsy3OND7+vjzI06myK/H9A/xYCM/5QQcdFPv8Ax/4AC677LLYZ4ynvHr16tjnq1evxubNm73pb9682Xt9q9XCtm3bsHbt2sRrktKMoggXX3wx/r//7//DkUce6T4//fTT8ad/+qdYv349Nm7ciPe///046aST8NBDD+0zB/fui9id/rtUQEK0Vqu5g9TsoZm7CxKuXV1dGB0dxebNmx1RPz097Q7UiqLILUhps1WVR6K8UCi4BSywc2GVyWTQ09Pjdo6RlC6Xyy40F5XpVIIn2btO7cqFfrVaRRRFTpHOBSd/+8hvks08oFQV7LxWF+qWON8d6ELMxu8FdjpbdMFtCSrOo6xKjItmVV0C8YP1lEgnYe/bpahOCN/i2qo/bTicbDaLnp4eN8cj2bUvYi79daGes7u2OiCgE3wqTa4vZoNVhibtrtXPlTAldMxTAkvfM46POi7yWjs2WnJOiWf9nnm3hCnzzHFUr9GxU9eM+jxbF7oepopZSUklLXmGGA+DtgIiWyeaD47hdIjw3mw2i66urtghmhTEMZ/p9M6zNvhs2nueJTIXqHOYh6oqEW/7CElse7+2OdtEv7N8hpLvFmxnn0pb28hHGuv3Selr3q0S3ldWdUxpLHVLequzyvZdvc46IHwOALUl6qCy6n9bF0rUc541F87B904BO+cO7PO+/Nm87wpsHn1p2brSd2G+vEqw1YuLQKQHzBk0UKlUyqmpaPCpYgLih45xS5A9HFQHYZ8KQA0VJxJjY2POOI6NjaGvrw/FYtEpmHSbNA/04rPa7XZs23p/fz+y2SzGx8fdRGBwcBDlchn9/f047LDD0N/fj97eXgwMDMQU6dVqFStXrkStVnMHbk1NTWF4eBgTExNzrkcAqNVqbnHP7cm6BX5XwC1ytVrNkQfcluXLy3xAlRsPTduXCeZ8Pu+Ub0GRvjBYCIP/+OOPu50CADoSz74JTadJStIESD+fT5p//dd/jV/84he49957Y5+fddZZ7u8jjzwSGzZswPr16/Gd73wHr371qxPzF7C48C2IlyqYV6tMB3bYMBK66sid64J1NtTrdYyOjjqFGrdxc7s3DyXjgpk2nsQD88FDMsvlsjsQUxX1vb29WL9+fczxnU6nMTAwAGDHQWhPf/rT0Wq18F//9V/4zW9+48hWJWK1bpLqkurnnp6eWCgCbicnUcCQb5zz0GE/Pj7u1HpRFDmyiI4M2mw6NBbCqcE6bbVamJiYcG1NRTkdxbpbwRLcQFwFr84Bqg651Z3XqcOD7UBnSavVcuFdaFfpXCGR0mq13A7Erq4u5/RJp9POCaRbq1esWIFSqYRKpeIU6nsbe2M82JuOvLA4D1goKEEMxMkitU10ijGcBENn2HsBzCAn1SFpQzQQlozkGMIxWclGTYNjEpW3lgAkwU4npT5TyUAd0zWshyqVeQ13imk5uK7m39PT07F42vo8Ip1Ou/Ukx3q1uxyra7Uafv/732NiYgL5fB79/f2xcyhoL9Rx2m63Y7vYGGZNQ9KwngqFgjvsure3F4ODgzFRFg/ebjabGB4eRnd3t7OJPvg4A/4wZCt3P6ljxlc3zCvLpLsOrILYOkZYp0rKW0e0Ooes48Oq0lW0p0Q1ldx0NPB+zpnUrhNKjrOcyreo/beEP//XnQvqyGF7s0/wtyWntf40bAvzxPrSOQPfC6rc2ce1jW07+oh8bT9yVQwfxLbT9lKBqNav3dlgnWcKrUd1tNnrNb9JTpa52uBgqxcXgUgPmBfU4JNg9CmTfYp0QgcPnzpAn8XPuMCih7qrqwuNRgPFYhHAzu3CXKhTYa6eVT1YlAs5quaUIOa2MyXWlUhnvNB6vY7x8XEUi0VH9M8Favg5WaTnnAPwXD2vvrRpsDjp4qn0CzFg0sgxZuC+rEjXWL/7cjn3JhbC4Pf29saIdB8Yjskqxbdu3TpDUU6sWbPGe313dzeGhoY6XuNL861vfSu+9a1v4Z577sGBBx7YMb9r167F+vXr8bvf/a7jdQGLj+Uy8VQbYifnNi65bmVdCOg2Zg1hwsU5F4U+hZjmJ5XaEWKLynIlX5gGbTTVbvyOdpTzkG3btrmYrbpYmutCRQ9OVdJZF0RdXV3ueZwfKWFer9fdGMg5he4g0/A2uwJVUupnAFx+bPl1gUhixqds0wWwlleV5D4nky7yuXimIEOVYmx7tj9JBY25znu0f5CkoXBiXww1txgIi/OAhYAdC3xQO5AkWrHKcyXQeY8d/yxByPHbKuGt8lbHIgrDSBZbIl2fr8plzUMn4s0SwrTbvId5Uyej2jVbZ2ojLbHoG9O17EpAk8i0cbxJaKoinWXmmlDV/bT3erYWw4xx1xhDwNC2VqtVZLPZWN3OFawbhl613AN/q+PBkqNqh2272WuTlMR2Da+ch4+sTRIVKk9iFfRapiTClrD5U4Kfdp/Xad9WAlz7t/I4ygNZ55XmTR0R5BI0z9Zhpor5+Si19f0l7E59VbirM4z53Z01v+/d0++AmST97ogSg61eXIQZZ4AXOnBpaAt96ZQotwognXiQ+NbFMw0oB3M1BjpJoKKdBpuLMXoVGU6FadP7zDiaNOBMn/HHGo2GI+NLpRLa7bZTn/f396Ovrw+9vb2OWFcinVvNarUaBgcHsXr1akxNTaFWq8WMrC4grUHm/7VaDZVKBVEUYXJyMjZZ6TQYW+hkqFqtYnJyEtVqFdVq1W0nX6gBc38K7cJy7svK+30R2WwWxxxzDO644w686lWvcp/fcccdOOOMM7z3HH/88fj2t78d++z222/Hhg0bHElz/PHH44477ojFSb/99ttxwgknuP+jKMJb3/pW3HLLLbjrrrtwyCGHzJrf4eFhPP7441i7du28yhkQoAswX6xZqwoC4jEiaYOZFpV5tPu0q3YhSOW0qpQsucxn0mE8MTHh4oDXajUXno0Lds4JSHjQaUsFHd9DLsRpp/v6+lwIGF6rYdZSqR0xwNeuXYtarYbR0VF3MCqJ19mUzDoHaTabjtQgKayLGS4CGVfd2knWEw8s08PLdodIt+pLjemuyjclfqamphBFkZuTJaXlU9Tp3EYPbFNVHQAnUmBauohkfSlYXyT0OS9SBZ7mU0UDC+UMCggIWBhw7FT1LGFJbXWa+VTfNl2bjj2osd1uO4W0hpgC4vZQx0e9X8c+PQ+CNo6OR+bBN0ZxvNdy0VbrGpg21DoZrRBK78lms87uad5V7W6VuVSiM23GJ6f6mEpwu65m+DSFEu6qSNYdWfl8HoVCAf39/ejp6Ykp0pV4LRQK7jm06dy5pmtcnduwjmj3Va3c1dXlzjDjWUq0NdoWnPMoJ8F0lePg7jHaGorUrDNFD+xU4pR1z/5k5w12va99jemqTUwi4a0I0cKS777rtL/4iHHNp6rELf+hnAhDtukOCZLaNn/qONC0VHBIp5YeEqtzW5s/S+bzGutIsHmx9Wrr1jfnsPXWiUOwiveA5YVApAd4wcGOah96h7nIo6FN8typ6kpP6u7t7XWLO2731oUm72M4GMZFbzQaLn4bF380KFu3bnUTh0wm44zvwMAACoWCC81CVRxjm1JVxtAqQ0NDWLVqFXp7ezE0NOSMfl9f3wxFOgBnWLmVm+Q8jTw9/PTyAzMHYhr5RqOBcrmM6elplMvlmBprLmQ1DT1jtpMoGB8fd3W2EAtMTjKo1N5XCWYacJILuk0rYNexNz3nF198Mc4++2xs2LABxx9/PL7whS/gsccew/nnnw8AuPTSS/H73/8e119/PQDg/PPPx2c+8xlcfPHFOO+883D//ffj2muvxQ033ODSfPvb347nP//5uPLKK3HGGWfgm9/8Ju68885Y6Ja3vOUt+NrXvoZvfvObKJfLTsHe19eHQqGASqWCyy67DK95zWuwdu1aPPLII3jve9+LFStWxEj/gIC5gGNyV1cXisUicrkc6vW62yZNdRgQD5nG31SE0Za32223OO/u7kZfXx96enpicwI6gJUMbrVaqFar7nlcSNDZTcd3KpXC6OioG18HBwfdoZalUilGTPD53NJMope7xgYGBjA0NITBwUFn+6Mowvj4+IxY5H19fTjkkENQrVbx6KOPot1uu3lJOp3G+Pi4W/z7QIcBw7CwrFwI6rZlJdHphCDBrOe1VCoVF7JucnLSzakWql/kcjl3OLuGjSMZwnAzPJSUW+xtPXCeBuwUSSixoUQCnwXs7G9s2yiKUKlUHDnC+Y09W0aJA51HKnQxzDbRXYcLjf3N/geVW8BCgO+xhYZJUaeuhuoj4aj3+95vdcxZUk/to+68sn1Uw6T4dgXrgZ6qqmVZmK6S7Zo2oWIyPXQZQMwRqHGc1bmqhB/V4z09PY70pp3hGpLraI6j6XTa2WpVbk9PT7vPNP+MZ57P52MCNXWu0+76FP2cPxSLRaxevdqdQTY4OIh0Ou1CjzJvugOAIjqGTOO6Xw8QV8U/HQKKSqUSc3irSI19R/PMz1jvtNXq7OZv/nAuwfbi86zgzKqrlXjuBHIvTF+dOFbVrOS8jwD3pWsd0zpHnI07UJKZ80cl0u27q07xTCYz4xqmqU55Oizo9GHd8nsVu/nyyz6l9aj5AeD6hwpHNR3rnLBjiI4H6ggDks9v0O913Jiv/Qy2enGxqCzYPffcg5e//OVYt24dUqkUvvGNb8S+j6IIl112GdatW4dCoYAXvOAF+NWvfhW7pl6v461vfauLkfiKV7wCTzzxxF4sxb4FHcD0cJBCoeB+qHBS5ZH19nGQ13SoGGN6xWLRpUlDrc+xnzMN3RKuaiT1GOvBWjowqcFRY6YKMh2cdZDW/3W7mq0bm1deawlxVUiookrDsWj+fT+qCNR0SPDbLVkL1Uf2ZSW6wpbTV26dqLP/0BjvT3U1FyT147n+zAdnnXUWrrrqKlx++eV4znOeg3vuuQe33nor1q9fDwDYtGkTHnvsMXf9IYccgltvvRV33XUXnvOc5+BDH/oQ/v7v/x6vec1r3DUnnHACbrzxRnzpS1/C0Ucfjeuuuw433XQTjj32WHfN1VdfjbGxMbzgBS/A2rVr3c9NN90EYMdC6Je//CXOOOMMHH744TjnnHNw+OGH4/7770e5XN6d6t1jCLZ66UJtE8lkxq5U+6MqYfsuqeOQCjT7w/BpfIbab84T9Nl2N4/aK1V2a1xuXqOLQ82jb/Fkt/9apRY/owNff5hXksxJCi8F1Xx6uHiSTeZcSBfuvJ/2mbZ6IW20BfOhcx0AM/LGazU0T9K2YzsvAeLblpkWF6tKUPnqRkkYVQr6FvZJqv251J+vb8zlHv29P2Bv2uqAhcVSttf6DtvfHBt2Z1cOEA9X1Smd2ebnOk7qby0L07d2wze26Bohm83G1oi0QdZm2meok5Y2Wc8I49+atv7md3Z+oPGi1WHM9TTtlbXXWpdqf+2a2dpsu0ZS+83yca7BOYZdT+vztE0smav2VnfQ+WyYrXe937emVtuURLB26ndJinDtbz5b2CnNpDJZaJ6T5h/62Wy20j7PV7+2rTUsDJ+hz9G21XmEvptJ8zSbF85FeE/SrvpOY8JcxiVb93NxRMwlndmuC7Z6cbCoivTJyUn8wR/8Af7yL/8yRlYQH//4x/HJT34S1113HQ4//HB8+MMfximnnILf/va3jnC48MIL8e1vfxs33ngjhoaG8I53vAMve9nL8NBDD807ttb+Dm6z7urqQn9/P1auXOkUW/l8PrbAGhsbw/j4OBqNBrZv347JycnYooghUbLZLAYGBpxSjqonDe0CxOPd8Te9iENDQ045PjY2hlarhe3bt6NSqTgvtXql7UKLv3WLFK9V+Ay6fsZ06O1PpVJO7c7DxQqFQozEn5iYwOjoqDtAhduomRaVX9PT09i6dSsqlQp6e3vdNnYq9exClOUiEcFt881mE9u3b8e2bducin+h4qPbutpfkFRWdTpREaJKTIbUoaohGKy97zm/4IILcMEFF3i/u+6662Z8duKJJ+Lhhx/umOaZZ56JM888M/H72fJZKBTwve99r+M1Sw3BVi9NUMHX09MTO5iz2WxiaGgo5oylbZicnIylwQV5d3c3yuVy7MCwdDqNUqk0Qw2cz+fdgZ669VkPtKRze3h4eEbIFEu+cmHsWxyPjY0hinaojgcHB536i7vi6DynwglA7LBu2nyGY+vu7saBBx7o5hUMp8I867xAQ7MRjUYDmzdvxsjICEqlkju4nKR4tVrF8PCwC/3GAzO5CJ+amsJTTz3lDgOvVCouHwuJKNqx44070gYGBlz9UK1PZ0K73Xa2Srf6M/a8kt8sh6oxdY6lyi9VWPKws4mJCYyMjKCnp8cd3KqHoZMw0bka61XndlRiplIp1/7WMWDHYvYRJXNYT7MdtBsU6fO7N2DxsBTtNfuETxXO9RjV0p3e49n6ls7XVWGsil57HbDTTnBnsYaqUqW5EqzcfaUhWqjO1uezLIVCAeVy2e20UrvKsZHrOK4fdB1KoVZ3d7dbG3L3mOZZ6886dfmscrnsdk1PTEy4MZ/jKdfVOp7THgI7Y8X7oCHTuB5SpbnWna6ZmLdsNou+vj60Wi1ks1m3A2xkZCS2M432yEdUMt+jo6Po7u52IVzJc6gq2jpKmF6tVsPY2JirDx6sSuW+VTf7yGjtA1o3qVRqhoPCRzhzh4Z1FvA3f7Qfahgl3qv/W3vN/hxFUSzkkFVhsxzaZvY8FU2bzgdVXKfTO0O78Gy46elpJ5DQMEG6+4JREXgQsRLuvnbQ/HBOAyAmdCOPo/dbUl/LZNtUr2M61qFClbuFFR1YqNNnNgRbvbhYVCL99NNPx+mnn+79LooiXHXVVXjf+96HV7/61QCAL3/5y1i9ejW+9rWv4U1vehPGxsZw7bXX4itf+Qpe9KIXAQC++tWv4qCDDsKdd96J0047ba+VZV8AB5ZsNov+/n6sXbsWmUwGPT09bnLARTOJccbjJpHOwb9UKmFwcBD5fB6rVq1CqVRyqjbdjtQJHBxoKCcnJ1EqldyiK4oit5D2Dfi+Aa0TfGo0nzKN6rZUKuWcBT09PY6g4MKPEwLGaZuYmHChXHRSyTodHx93sd+p5Gf+7TZIfQ4JgrGxMdRqNYyMjGBkZCS2NW4hsT+R6J2gSkf2bxppjQ2nW+EDApYjgq1eeqBdUmc3z/jQ80NoHxiOxRLpdAzncjkMDg6iVCrFyGwq21Q9mM/n3XXAjrGw0WhgbGzMHRbGkGzj4+Pe/NsFoyrCudjkApYhYRhnm7u2WEZu9dWFiyqWSMQz7AgXY7SbJGpJ9mre7NjdarUwMjKCVCqFFStWoK+vL0Yc8xDyqakpR6KTIKrVapiYmMBTTz2Fqakpt3tsT4BlnpqacvMVkkJU0pO0Jqmvu+Joz/L5vOtPXCgy1mk6nZ6x0GParN92e0eoIM512DdoKzkn1HA+nPtw/sM+ZQmsTrsRfPaW9lp3X+iCPyBgX8BStNeWDOe7rJ+RjFIb4FOB+2DVpnotxyu7FrJrULte5Pig9zJtjYtuxx9bJl0X9/b2ujV2uVyOkZgAnFOQpK06tSlGy2azbn2tIWJ8ZVey1bcW5L0aqoRjrDridacVY5t3EhlpHHDmw6rBtR30f66paDsYPpUOX13b0kazD+iz2u02KpWKs1PkONRxo+E9OGegnWPINYaYZTg7n3PCPl/rnvnkHEfnBCSaLZGu+dH+pH9bglvzr3WvpK3eY+uL5dHn+q5Xstl+x8+S6kmdU5yHcb7Gz5R4jqIo5iDSz5k2+5Pt38w7+7I60DkHYHln40l85bRiS2CnA4fPZX0pZuNQNM9zIdIDFhdLNkb6xo0bsXnzZpx66qnus1wuhxNPPBH33Xcf3vSmN+Ghhx5Cs9mMXbNu3ToceeSRuO+++xKNfb1ejy1ekhZ6+xvS6bRbkGtcTS546E3kQrpYLDovORdDJI7L5bJTt3EhPt9wFxz0OdCp553edMZO5UCo6rhareYmCByMObDrIV9UofFwTi4sqV5j/DYf+a1/k9RQxTu3olljYQdtLuYAuIUmF6F8PheAwE5DxcNhGBOXhIB6uBcavvzvq9CJNf8nqADRsAdKpGv77QmHxnJFcCjsWwi2eu+DC8Kuri6niqZ9tbG6bexXLmS4gC6VSiiVSrEQbCSFmYYe1kYilgo2XWhzAU2VO9PXOKO6ANe0VAHF72zMWNpqXaCqLaYSS+vA7nYDdo7rdBRw7sMdZRrXPWm8opN/YmLC2X06LZg2DzYlMaKENdP25W+hwK3xelCoPkcV+SSZ9V5VEepch2B720WjLixJVGjbs875bpMw4pyPZJouUpkfzrOoJtO+5FMEMp/MK3df0HnEvkMSj/PFgGCr90XsKXudZKt13QPExyArgPLtKrHo5CSz4LhmbYsiidDlWVq++T/XpEoQ6neaVxVBUVGuZy8BO3f1cEwiiU/yj7vEaKN0reEjGOcCriV1Xa2q7VqthmazGbMdSkj6wpExH6po17qlDdF0ktbTans5Z2Hd6DxF5x/anlRMs+35w7mGlkNV/EwjinbsVFIHc5KNUZKcz7Z9QR0ZOgfyXaMkOfPFe3QepvG8bRvo5z5BoK8MvndO86n8gy89+27yvde64XxSd4hover96kizYZY0Zj77AeGbUyWJI5McTD7oeGXryjpRmA99nj5L20fLrs6B+eQt2OrFw5Il0nlA2+rVq2Ofr169Go8++qi7hqFD7DW834crrrgCH/zgBxc4x8sbNNyDg4PusM2BgQG3KNZtbcBOw0aVWxRFyOVybtvaypUrsWLFCreNSrduz0fRrIs7TkRarRZyuRxWrFiB8fFxpFIpRz5zW9z4+LhbuCvB39W141CwSqWCer2O0dFRjI6OIooiDA8Pu8MvRkZGAOxUV+mgp4ZMyXhOdNrttlug0chzAcfJkV2gT0/vOECFi+9KpYKuri7n0NAy6OBKh0Cz2XSHquhhYQu9OGfZNVbcvgpVZFgDz/eFDiUebKuTBIb8mZycdAfT7e/GztbjfO8NWHoItnrvg1vDc7kcVq1ahYMOOgjp9I6Du6ampmJqXY2pyoV6sVjEihUrXPgwbhen2q1SqWBkZCRGjtPeqVotinZsR5+amkI+n3cqOR0H2+02yuWyC2lCMpwLIIY7YzgzKtF1QUG7ODw8jOnpaUcmFAqFmJqYcwuO26p014WwKsEGBgZihHatVsOWLVucAjpp3EmlUm4eQYdBoVBw93R1dWFiYsLNj6hsU7JWFXJJW9R3Fel0GsViEYODg24rtVWqcQec3easJAcXq7oFnOQ4hQKqnFKSm6BKn4vq3t5edHV1OWU/lWh8frPZjIXQo2NGy0YyCdh52LqSOz6nNw+f1zj5URShWCw60mTbtm1BnY5gq/dV7Cl7nWSraQ+A+NrJOkQVVulpCbHZYFW0GgbCpyDW/9Vxax3IzJtCCTlNW89L4gGdfX19GBwcjB1CqU5t2kWOZSThaZ97e3vR19fnxkYtlxKvSXWh9auhKLmuZkiVSqXiwqdpGuo8tyE9WOZUKoXx8XFs27bNCdR6enrQbDYxODjoDkOfnJyMOeLVUa4hZJhuLpdDf3+/G+N5vcZt9/Up5pEiM86TVKSnIgOWlyKo0dFRFxbERz7TBjEd7T8k85Uk5bO0z9gQIhrSg3aRv3noqc6DrOKb1yrp7yO9laDnO2odAipo1DrT720/43UUUipBrHWmZeX8gqLD7u7umNNJ3xFgR/gqzr/Iw3Bdzvmq1inbhz98dhKU/FZiWwUDHFt8pLw6V7Qefe8j68bu1pgrhxNs9eJiyRLphM8ozGVbRKdrLr30Ulx88cXu//HxcRx00EG7l9ElCF8ddHppaFCpTFMVuZ08kEQEELuHcdt40rca7N0thxosKpharRby+bxTZwPxw8AymYxTSSiRzsGX25g5SaASnSFjeL9OBPkMYGcMVU4qrAeSRoDqg05KC+uhpRFmWWcj0rng3dOKcfUg76tQo5lkpNi39VAcfkZSiLsb5uM82pcRDP6+i2Cr9x5IJNIOl0olRzb7lMG6YNTFuRKKtDG6CLOLVB0X6aylyppQp/v09DSKxSIAOIeybmPXtHQ3l8a0JBibVBX2qpTmohyAs8lKnvhUb7ro1N1jQOdFluap2WzGVNFsG6rd6BRgiBrbjj5l2kKB5ePcSd831j0X/NxBYNVaGiOXZSR86lHaQCUI1LlPJwvJb36mC3jWI59h+zEX43ahm+Ss5v0UdvCHwgeq5elcCQi2el/HQtvrJFutxJVVa2o8dB9JqdcCM2MTJ+XRrtNsCI+k+zUPlvRS0mwuY7V1YqsjW9fUHKN1PdFu7wxporuKSEzqGNupHjqB5dADvBmzvd1uo1AouB3dHLvpoAZ2zi9YR6oA584sit64c9y3e0xtsrXRfA5/U1TH+tQ2tSS4hdoHCsFUcOAj0knQ0i50sg1qo7hmt/2J+eD/luS2pK0vfVuWTvkAENvdpdd0Egjwt96nv33kvJaRYB9RUYRV/yc519g3dde3Qvke7U+WRGeeLDej+e30HiXVlZ1L2TSSnHesB82Hzq9tu87FlgZbvbhYskT6mjVrAOzwjK9du9Z9vnXrVudJX7NmDRqNBkZGRmKe861bt+KEE05ITJvGaF+DeuO4UNa4oySb6fHm4U26UFHCdrbtdfSk9/X1AYBTpDO+marBFhK61W16ehorVqxwsce5rZoLaVWEMz/T09PucCn+jqLIbUWs1Wool8uYmJhAb2+v85wTTJ8GgD+67dxODkge+AykBQmMVCrl8qcGX0kIlpPPm0v6uwM6HKamppzqfl8Fw7MkhWXRdrZEw2wL+/0VweDvewi2etegTmo9rIxjvFVlAzvfHy6qS6VS7PAlTYPXk3BvNpvo7e1Fu73zjBNd5FOBDOxUM0dR5FRPauN4HwB3TggXMPV63R3ATJKc+V+9erU7lHRsbAwAYod9shw6nvK9p0OSBHq1WkV3dzeefPJJFwdcSXvmk/Og8fFxjI6OujkPy8lFkCrhlBhIGnesAkvvozqK9l+VTArrPF9ItNttTExMIIp2HCzHHQwkEXSByfpV6PxNw/voTjslQ0jW02mgC0kSCwBcX+EC3ypGNVyBOm5I4NDBwp0UrD+NTWrbTOde/I79cnp62u2qUEeUvV9/7w8ItnrfxJ6y10m22r5P2jdUGGTJLR2Xk+Ajwmw6wM5QGkqs+0hzBQVXdIzSXnJ3jxLtvrGHY5p1aCeVgw5FhpShQIyHifOAUYY32VXYtYmu//WQbsZLp/KXdpmf014oOcl0GN+dinRidHTUrb9pz3V3s66hmTbJVCVcAcQcNECcjGQ5mQbbk2t5PkMds2qj2VZJO6/ZrkkEK3/7+iX7nhW9aTx6Fd212+0Yj8M69vUnX79I+oxltmp4Ww57X5Jt5ZzCtovtYwwBRW6D5WbZKUywDhLCF/qHokUAsfkvoe2nefOR3/o3+4pvLW+d/qwH9gv2Eb1W287Wq90Vwfqcy7sebPXiYskS6YcccgjWrFmDO+64A3/4h38IYMfAfvfdd+PKK68EABxzzDHIZDK444478NrXvhYAsGnTJvzbv/0bPv7xjy9a3hcL6fSOOJ80vENDQ7ETkJvNpjuQkgeB2UFOQ7lYz6F9FhdODGvBLeP0cKtRXkjQYUDVVxRFLqYpTx9nfDedBOjAyC1humAfGRlBpVLB+Pi4237c19eHsbGxWB1MT0/HtkTzhxMtDqjqKbWKvk7gxAzYGVqG5fZd6/t7T4GEAU9R35eJdDqbuB3fQkl07UdKoKhzIxisgH0RwVbPH1ShUS1MlRnVWww/RpLZbv0kacxQLPyO6RIk0ovFotu1BOzcUUY7TVutqtxyuQxg544zqqlJSuihjbR5XBj19fU50p6EZT6fd2eb/P73v3dneXDxPDU1hbGxsUSVExcbQDxe+hNPPIFt27Yhn8+7w0hpr3O5HHp7e9Hd3Y3t27fjqaeeAgBXb/osOk41NryO6748KbjLLQm+e3QhvdBot9sYGRnB2NgYVq1ahac97Wmx3Xt0yGQyGScq4E4qtinrnAILYCfBBOxU/nPOx+9Vpa9qUCrfSehzrqPEgBIErB8lNFTZrtd2CjfHz7Qt9aDasbGxGPnjw56w3z7Sz/dZQMBCYW/ba0tiK5QcVELd57jqREL7CEVNQ5+tilVNw35Gxy6dsdyl3dPT49T0tM0kBdXhp7HR1TmY9K6TfKNTXc8g6+vrc+FB6azgWGUVtkmEqC2f7spi/lhG7tLiXEDXyja0G8luYKfdVRW71s22bdtQr9eRy+UwMTEBYKfd1LFd479zXa32n+3Iutb1tbWpJKHpANDPlLjkLnamTdGdOp21zazKO6kf8RoVFWqb8MfubrfXqy3WEDK2vWdTVyddo/nWOZd1SNg+xnebbci5LO+184BGo+HOgCMPw/kcBSTsU/bdJv+gdQXEne9KpNt3zjpO1LFm21PnNOw3Oj5pfenanz/sQ8w371XnoeXfbLtQqBCwtLGoRHqlUsF//ud/uv83btyIn/3sZxgcHMTBBx+MCy+8EB/96Edx2GGH4bDDDsNHP/pRFItFvO51rwMA9PX14dxzz8U73vEODA0NYXBwEJdccgmOOuood9L4/gAaA6rQ+UNSW1VvjLvGkCg6GeAAaZXNNLY+qDJdSXg1eHsCapy46NMFoB74pdsHOZhrnHKWkwaMXnjeo4fCADsXkpxk6SGgQFz9pDHLO4UJScLeJsrnAlUZsjz74mDPfmInUoQqO3goj5Lntt0Dgud8uSLY6oUFF8jcWUUbQ1K7q6srtojV8daqdDW0iS42Vc2u15C8V3uoCwGr1gPiC6tOyjpCFUq6YMhmszH1MtPWcSGJSNdFr5bLhkvRBSiV0F1dXahWq7GDxkio+MiCfWWMssSCklYE21TV53bxqgtOhe6U07bxtaXtu3q/JYM0/7xOFetKjLM9NRyAD+rsoa2mfV8Mh/dSnzMFW718sa/Y6ySbQLul44ZvjapKd72G93cCx0Kub0kyMwQWSUBVw6rt9DkE1NapklXfNRJtfLaOr3NVIHd6/+yYrPnSeqKN1vWt7rBWBTKdoaxTnavoGM3dasovkEgnSdput70hcLRcNuTcfMG5kG0rJaeTyGRLuKoTZFfX9vxf52Kd1tVJcyTf3/Yd6iRU8BHX1i7PpYxaT0xX+71vF739Pilsn4oTtTxWwe4jx2396ThiYfOkfcPnLLHvj8LXr/Q5+nymlcS5+RBs9eJiUYn0Bx98EC984Qvd/4yvds455+C6667Du971LlSrVVxwwQUYGRnBsccei9tvv90ppQDgf//v/43u7m689rWvRbVaxcknn4zrrrtujxO5SwmlUgk9PT0oFAo48MADY95rHUza7TYGBwcxPT2N8fFxDA4Ool6vY8uWLdi+fXts61Mul0Oj0Yh55azHkgNAPp93i3MSAntrkdDV1eW2uw0MDLjtufQ+0zgD8UWkb9HEBfTk5CSefPJJF5uOsd51MKTB7+3tRW9vLzKZDPr7+10cLw68w8PD2L59u4sjyzwt18ErinYoDNjuum1/X3rnoijC1NQURkdH3SGu9ntVi5IE09AMExMTTuXXaYG/PyEY/OWJYKsXFrlcDgcffDBWrlzpHHG0p1TiDAwMOKV2pVJBs9nE6Oio2w20detWF3tU46/29PSgXq9jbGzMKdsrlQoAOKW5Ht44PT2NsbExdHV1oVwuI5/Po1arYXJyMmbzqTayC2vGQiWhkEql3IGd7XYbk5OTaDQayGaz6O3tdYt0deATvkW+/s9xmOMId9mRkLVxYzkup1I7dwkpcaux2EdGRjAxMREjCfaVMSdpoU2iolAooFwuOxWihq6jo0HjnpNA4Zk4dJxwUaqkN+ee6vBgH2KYAnW8UCmuz+zq6kKpVAKwk/hvt9vuUPmRkRGMj487csuC+WK/1F2T+uy92d5JRMlS6XPBVi9fLEV7bQ/40/7lW1vq5xxz1NFnVd6664Xp6NkQJKd4P5WtfI51NjJ0Wi6Xw7p169w6j3aT4UloQ+jU5W4fDRPGNagloFlO3cHDsZEOZy2DKt51DT4bcc6y+whAdUCyHqJoR2iZ6elpdxAzd3rznBJ1ZlqOQL+n85KHjY+NjcV2rCthShtSLBZRLpfdmRa8lmWZnJzE5OSks1WdCHW1F4SGUKGzQglVVQmrMlqdzCR3uaZXBbLmlW1jd1qxzzLNer2OKIpcGD62iVU1M21+brkM2mxtW67XkwjeJFgy2fYzTY9rXw37p+mwndmHGN6FZ8fwOUqSj4+Po1arufkty8f1t77Pqvi2gg/2R0tY61zFOrR0zsKxQq/ns5m+9gvtR0nzC617n2OQwpO5cAfBVi8uFpVIf8ELXjCrAbjssstw2WWXJV6Tz+fx6U9/Gp/+9Kf3QA6XPviy9fb2olQqYfXq1RgaGpqVyOYp4NVqFePj426g1AM2OXDoNiJgppdTCfQ9ERO9E7gYa7fbKJVKKJfLse3tPu9hEjhRYSxYpk9ygFCvMcObUJGvz2SsUIab4WRrOYOTQg150mnHwnIFDTW3oPnaTVUROiHj+zI5Oeniq++KamJfRDD4yxPBVi8suru7sWLFChx44IGo1+vOWafqK/5wCzTHIy5Mx8fH3XbsoaEhpNNp9PT0uINHx8bG3OK00Wi4w8SogidJSSdvJpNBX1+fOyBZt7ZzYWaJai4SqtWqW+x0dXW5RZIujvV7qtJ8izPA/677xg7a2LmAz89kMujp6XGLIy6WOF7rbrW9AZ8Se0/AV3+c32UyGTd3mpqacgeX644qu+jk3LNYLHoJbA29omQSF41UdzK0DLDTpnKRzOsZ/ojOmq6uLufoZgg2huVJsrXqeEmlUigUCo6c35Xdgvs6gq1evlhq9tqn4gX84SR83/lCMNCOADsVxgBiu52skprzczpRORbpOlHT4TkMAwMD6O/vj5GEHN+mp6fdwZccm1qtFqamptwamvlTglmfpeMk02XoUnVe+tazPke0tSlWgesjdfWH46yGflOOQNtEbblV7rLu+T9JdCWdNWwdCUfWl3WEMK+Tk5OufpPCcamjUm0X80ihoLVpti61jXSdy+81xI2P+1CHgs2DpsO61UNZ9XrLu+h3eg3JW+aN/R6Ik/Jar7ZMeg3/1nkAofNVdbDrO8i8cT7BA9j5o2e28DlsHwrROAdV2Fjq2gb2+bb+bf59anObF+5uVEFB0nul+WJadn5k59EE867Cz9kQbPXiYsnGSA+YHXxZC4UC+vv7HTk+FyJbFeuM/9bV1eUOEMnn8xgfH3ceeDWk6jXkYmoxVYU6eNKLzgkNB8P5Qg2NehsJ3QZttzZbbzY9qXrS+HIm1JWc4cEhe3snwp4C+zjLx8NGOxkbLtBVEaPvSFigBwQEAPHdYwCc6pv2yypXOB5RFZXP590hoFyM1Wo1d/YJJ/R6/gmhizBdaJNI5YKYE35dFKhaCNhJIvB6XWQwxjufx8M+uRhqtVrI5XJYtWoVGo0GxsfHZ4Rn2RPg/EBj0+v2cKoQGVZnb43be+MZ7BN0kBBcIE5PTzvihm1E8onhABkWhwtgVa9zQaxzG86b2Ne4QNRdWyS/1XlBpbvNv27t5rPZdwHEFuSzgfM6CibUKT4bFtrxEeYGAfsy+L7OJrZRAt2S6hxHlCzk5wSJcR3fNB0lpbk2I9Ftlb60BQy/pmSdjwjn+ldDfVKRzr85xlarVTduqR3V0CC6brXk3lzHC991Wj4dm7XOrCpW43Kz3nziIBs2xzqjeY+qwX3QNrIhbZg265n586ntNV86j9H2961ZKQj0pWnbgYS/79kUVM1HRGXJZLWhrLf57J6y8z/bf+08M8khYEl1bQ/lPTQt7WMsC22uzhdsXi1JrXWixDLTZB3TYcA8+fqnLZs6IXwOIN394asH69jQ9K2zZC7ciI3vrvUSsHQRiPRlCr64XV1d6O/vx/r1690BZHMBDxprNBoYHh7GxMSE2+I9MjLiFuJUKuXzebeQVs91d3c3+vr6HDGwGC+9OhR4sBjJf43BtSvwLa643YwHcFHpRk87jXW73UahUEBPT49bxLIOq9XqslxAkTSm4mJ8fBzd3d3uVPn/H3tfGmTrVV237jzf29PrN+pJQggwCCcOIpQwFiRVyAGXIwsUK3GFWDGmrBIQQCEYGbDlASvCLvJigwRUhDBFGCohKkyFgMQPZGPkxMjYYIQBg6Q39uvxzlPfIT+61un17T63+/bweni6u6qru+/9hvOdc76z91577X0OepkGAgdkiRaLxUBaF7BWmWpQCQim9I1A9KCMIucjebZKOBzG4cOH8ZznPMex0+bn5xGPx5FKpZyzxCwW/iY7jbqWoGapVEK73XZrFJnkY2NjgXsCq7XC6XgCQUZZKpVyxxJcpM7XNU7T0hkUbjQaDhxIJpNIpVLOSaJ9ks/n0Ww2HYM4m83ihS98Ier1On7wgx+4TUAv5TueSCQwNjaGWCzmUvbb7bZjtkWjUeTzecemv5yyiDqdlY3YbRCFDHwG+6PRaIAVx++BVfJAtVp1fcagTqvVQq1Wc8dZ1icJFwwYEcQqFosBRihL5GUyGbdXjTLjmY1BUCqVSrl9cThmw46bsuht8IriY3by2YYF3teT/azTRrp6JDshunmzivpJCuTZsiA8l++eMj/5m8CcZREruUWZ5toW2u4EvUkMy2azzvdVUpbObZaG4rOoXgTg1kAya/lOUVeyTAzbrOCjDWKzrQQylYylQD/FF2zQ6xK001rveg4DlCxxyue1m3xqkIREPt/Gzxqw1nYxgKugOfuSP/SrCaZqWRgGhy3rV0Fhjp+SFrTPFaDnfLPEAr0ubTMF/fWenGMkGVjRcbQBBbZTmfoMcAOr2RVWX2lAxs5/gtmDgGHqVc7jQYRMG5ji+8KAOr/jfGKwgvYsSx/V6/U1+4cxkGSDJ2y3BdJtBgP/Jral9ipFSY76vS8DUYF0fq/jpmMzaBz1HbUb8lrgnu3TcnY2MDhIRrp6b2UEpB9g4cubSCScQzEskKmKktFdBUlZI5U7ZgNrQUOr5Pcycsa+4KJORbYTi4S9Bhc5OoS6cZum4PE4GiG6GSqBlIMoHH+ytgfVJT2oQkNFDRZNvfSxQy4n0OVSykjhj+TZLPF4HLlcDv1+35X8simgPkYS1x8GbdWhYaktOuxk0Gl9amvU2xRl1V0KXlhgQ51RDRRq2q/dnJslPJTlzJIg6tReKqGtQjuHYICW4eLzKmttp5nHeylkpHOzN9p+dBaVha/kA4IfyjS3nyszjH1n7w2sAmla/k+dVR5LMMpmAuo89tU8VbvLim8M7TsxrDxbGGIjXT2SnRCbleljAOt7aG1s/rbvur2Wvv8+faWiACvb4ANwqSusjuY5+gzaRupfZSWToc4ya1quQgE+XkNLitr3yfbLIEa0FQXSfQxdbb/+TVCP/TJond2IRetjCRPstUC22iX2OC0fwjYNWssHtUXvYdui/TKIca5EBB6n4PiwLGR7TQXS2U7LIrfg67DrrS9YrN+tlyngE18/6Xuoz682repvDZD5Al/sFyuDsjTsfQetO2q/Kig+TF/ZdvjeNZ894suu0PHTecM+GAbTG+nqvZURkH5AhaAxwVm+cJtduEOhUGBDTdZq5eZkBIITiUQgIpfNZpHP5wGspofvtYNBw4cbmxG03oyiWU9oGKVSKUxNTSGZTGJsbAxTU1Ou7ipBcy5sykiPRqNu4zcNWhxEQL3f77tsBjIYuZHcXs+D7Qg3xWm320in0zh+/DiAVWOJxnC320WlUkG9XncgxUghbSwjhT+SSy0+I3q/CDN5lI0VCq3UKadDkc1mA86G/qZ+AVYdN2UOz8/Po1qtBoxxgpWsgc7yZ7p/gzowBEN5L9VR/Eyd20aj4cAG6jUFXS2Iomye7WSLDSPRaBSZTAaxWAwTExOYnJwMlKtjSRmtn0s2Npn1XOM5JgdRWq0WZmZmUCwWceTIERw6dAih0EqdcN1QixuLJpPJQMA8mUw6/c6/mYlGBme73Uavt1r7nOARmeYEhjj3lWmoWV/Ly8suQ1KDMwRzuClqrVbDhQsXXPbD9PT0GueYc5sbmm1WfHPTMuMuVxnp6pHshNjylz4SFv1XBddUFOwCVnWSAsm2JIKCkRQybzV4x3bZmuRaAkwBYi3jwmtSz5IVT1BYNzNeXl5GrVZzWTrFYtEB7mS98xwlalnWrga4tfSHD7jkOcru5XFav131sw1sx2IxlzHHzcZ9ov1PX1zXYR1HzTjgTzwedxlj+XzeZd8NIghSb0SjUdTrddRqNczPz69hwuu84b30M3ttG/ixQR1LSND+tf3MY9YjWun1u92VzThZFSCdTqNQKKy5Fm0tDYrrc6uO5W9bcojX8wHPOk8ssEvRZ9Vr8V3n35rBRRJFoVAI9LOWelH2uRLY2O96X22rZsHp/ND26fOpja3zUfuBY853TgkqGoTxMc+1rdo/PrHvLZnowxIuR7p6b2UEpB9QoeJnxNyywIaVUGh1wyi+jEwDpkOudZtouBw5ciRQO24/AKhU3lT2NGJ2ol00MOLxOLLZLA4fPoxsNouxsTFMTEy4zbCskUEWGIH0er2OhYUF1Ot1V+rloDrnBNJrtRrS6XQAkNnrubBV6XQ6bjf4TCaDEydOuJqtsVjMZWosLy/j/Pnz7n05qAGRkYzkchN11IH9ZSjSmaaTzDRwAuncLBRYdRRarZYrywEgUD5MHWoA7joqdGzT6bQD47lRNJ0vrtt0AmhbECAlwEpnTB2PZDLpWHZahkP7fxCQ7kup3UnR4AGBdNoJDBbQOSVwQiczlUq5EmxkEh7UNb7ZbOLChQuIRCIOpKA+46Ze8/Pz6Ha7ruwN5xh1YS6Xc+VvaAdq6jbBb5YsYJCFjC1mINCJ1frmzWYTzWbTBaU5j2l/6kaxmUwG8Xgcp0+fxszMDJrNprPJLNimgaidqMO/HYd1JCN5NorW1ub/lmlNPTDo3dJ3mUAZ9SDXCN8Ghxa4VaEO5t8aTGY7lagGBGsha/CYgFcksrLPWLfbdVk11IcsbcHNsZnJm0wm3SbfXDsTiQQymcwacFADDgQdtc0+38uyjK1dpFnmBD71ewajtRyNjpP+7esbBSu1RKYF06PRKMbHx90+MrlcLgBy87rUC2T0R6NRZ1NVKpVAAJX3t5ubKyN6UKaeii8gwDZZIFqxEg2A+K6tRD/ObQLpjUYDU1NTrh8sYM8+0PGw9pSyu7X0LIlfSp7wPSf7XwNIOh70f3ktnQOa2cF+5v44fKfUxuR7U6vVnC3A51H/WvvBB2AzWKblXjSgoH2upV80uGfXC8XBLEtc55vNTNFxsMEWu9ZZBr4G6Paj3H///fiDP/gDXLhwAS960Ytw6tQp/MzP/MzA4x977DHcdddd+O53v4tjx47hXe96F+64447AMZ///Ofxvve9Dz/60Y9wzTXX4P3vfz9uueUW9/29996L//W//hf+/u//HqlUCi9/+ctx33334fnPf7475vbbb8ef/MmfBK77spe9DH/5l3+5Q0++VkZA+gGWnTbqrXLUBcE6w5Y5sF+AUxtB3cl2aWo4AXs6dzTk7AJLY6zf7zvnkufRyNpL0Yi0Kk0gmGI5iN3BTcdoJPb7/U2VGNovonObbaeTH4lEHKux2WwiFou5+q2sp07DZORory+jyPlIdlP2w5wZpI90feX3NO5V1FHXTZ594IBl0QAIGP90RHXd1/8tUwdY6/DSLlDdr8wh/aHzrHVY6VjxWurM7dR4adAhlUohlUoFGHJ6HzrZyu5KJBKOubVfbBuKz3kHggCJrx/prHOD10QiERgPBaHVuQb89ULpHCvQY51RdbC1rq7alzr2GiBSxpfaH9S1usFtMpl0AShlH3IuNxoNx07j7/2wNuxnGenqkeykKICln1F8YCRFfRMLaHIdsv6LXl/9VZ6jTFQVBf15Dd7T51NafahtsPoQWFtvWdcprn8+m2Gj9X09UTB9GH1m7+HTOT7ff1CZCz4Lg+06vloilTpYS6ZaMJrXISjKGu5KKtR72CDCMH2nto4PXLWANu9j/ybYDSCwwTmDz9S5tg9JXCCJod/vr+mT9Z7D4jf6uWVd+2w6/V7fmUEgsL0vsLaGuYLR/E2sgAErxRiot+16Ye1SfU6+1zYAY20Jbatdl3zjrBvT+t4jzRTwvRe+vh203unzDFqjrOy2rv7c5z6Ht7/97bj//vvx0z/90/joRz+K17zmNXjyySdx8uTJNcc/9dRTeO1rX4s3velN+NSnPoW/+Iu/wJ133olDhw7h9a9/PQDg8ccfx2233Ybf/d3fxS233IKHH34Yv/iLv4ivf/3reNnLXgZgBYx/85vfjJe+9KXodDp4z3veg5tuuglPPvmks/8A4F/8i3+Bhx56yP3PPRUulYyA9AMqGt3djlOgzpVNieL3fJlVSahjvhUm/KUQG33VaOhOXDuTyaBQKCCfz2NqasqxBzOZjOsPXyRS2fGMHlerVdRqNfT7fcdM320Jh8NuYw7LgKDBw9q7ZGKrccHMBbLsZ2dnkUwmHavgoIgaE9Fo1G3WVygU3CaqqVTKbcTWaDTQbrcxOTmJ+fl5FItF/PCHP0S5XA6kzY1krYyc85FcaqGu2uv5ogFKMm+SyaRj45Blw03CQ6GQq3euTgcN9F6vh6WlJczNzWF5eRnNZnPgvaPRKNLpdIAVRECZG5zphlLsL7KTycxWh7Df7zvHhhs9AsF6sAQE9KdcLrvyX2ovcC2Ix+PIZDJOzwwaN+vIrTe+dDpzuRyOHTuGQqHg+ow2k4LmoVDIse1006p+v4+5ubmAs8n775UQOLZjw3aTPe+Tfr+P8+fPo91uI5lM4rnPfS6mp6ed095qtZDNZgNsSjLFCAA0m03H7ragD2utW5YfNxhTMIi2kQZwQqGV8gEkHRBcajQaqFQqgWdpNpsu7X16ehqTk5NrAgOc0+Pj41hcXEStVsPs7Kxjyu8122s/rFODZKSrR7ITQl/VllhQ4TvL95drrZZR4Rqs5RoUaGJgTd9/Kxos5DnaJpYx0c0i6RfwOAXiLUhOXU/d3mq1Au3VgKT2j65FCiZTR9myFOuBa4P8XQaLrZ+q12P/+IB0BRB9xCsfw1vHU1nvlHA4jHQ6jUQigWw2i0Kh4GwUC37RzlAgnRuwp1IplxXFUnMsoecDNtkeZXprsGTQJqE8FwgSFdRe0/bS1mP2G59J9Vq9XnfEBd67XC47G4bnMYuMtuAgcJYEN7aR7dTNbi0gbd83BZXZLgtO+46z16ad0Gq1HFg+NjaGsbGxNe3n3FtYWECpVEK9Xg/oapaOUyKGZjOo2LI9Oja6HumY8zu2RfEtW6aJ46vvhM244XrE7+26p4C/Xau0H/djaZcPfvCDeOMb34hf/dVfBQCcOnUKX/nKV/DAAw/g3nvvXXP8Rz7yEZw8eRKnTp0CAPzET/wEvvnNb+IP//APHZB+6tQpvPrVr8bdd98NALj77rvx2GOP4dSpU/jMZz4DAPjyl78cuO5DDz2E6elpPPHEE7jxxhvd54lEAkeOHNn0c21VRkD6ARZdSAZFIIe9znqlKazzo4rEt5jvpQyKEO/EdVkCR3+o8AcFEzQqTue30Wg4EMI6h7spXPwJirDuKZUmFQGZ5gRRVHk2m02Ew2HUajVUq1V0u13kcrk9e6atCp+L4xSJRFwpAALp7AumwzNlMBKJ4OzZs4EyACPxy8g5H8luyH6YKwowKsuKxrPWLieg2Gq1UKvVAmmvaoTX63UsLi5ueG/eU7OlCOLHYjG3ttNmiEajznkkAwpYrWNNkJwlYQA4QN4GkS0wQCCWbSDQwHVSAYONgMVhgGwFdbmPydjYGIrFokur134lkK6borImLNulmQN7PbeUyUVnToMtGwWSGPzOZrM4fvz4mvmodh7Zcbpu12o114+cl9wLhkAKSR60J7rdrjtHn4MlgSgcF2bs0bnmRrpajigcDjvQq1AoYHx8PFALlm0mkMXvisXivtrTZD/MKZ+MdPVIdlI0UwQIbkxJ4MpmaFkmKdcDLaNA4d88R/0UeyyPZ2kUZZwq81fJahbA9oHZqnv4zLbMmY/xqvpSiWpKDANW97FQ9vpGov3iA/TUT1bd4WP82nMU4LfrmGXk0obg51zrGThlSRsCx77sbmU0M7hCMlgmk3E2iS+YPAjfsBiGjrMGin3n2UAyx8kGVqirGJxXzIZlTNS2IbGx0Wg4IDmTyQyFZ+j1beBivZrgfCZtv37vK8mkv21QSQFrZoyEw2FHRNTrc55rJmQ4HMbCwoK7vq8MoPajTzSIovaRDUhZlj77jnON5Wgsw57zX8tMse2c7/p8eg+2Rz+zfT+sDt4JXV0ulwOf81200m638cQTT+Dd73534PObbroJ3/jGN7z3ePzxx3HTTTcFPvvZn/1ZPPjgg1heXkYsFsPjjz+Od7zjHWuOIfjuk1KpBACYmJgIfP61r30N09PTGBsbwytf+Uq8//3vx/T09MDrbFdGQPoBFX2ZyRbmJlrDlNVQJ4lRUTq8G52ninY7L/ClEG3Ldpj6KgqGsKwLgZFhGe800Pr91VQ0ghm7zXIjmMLNXdLptKv9rqn/ZEty81mC69Vq1bEhOf6ssZpOp10NUzqu+ynQoqIpltysL5lMIpfLuc1jWRdWmftkaxYKBbTbbbTbbWSz2UD0fT+9EyMZyUh2X9QgJpuGtaDpVHCd1PWCTnwikXDOIq8z7FrKe7JmtdaiLpVKATaW3ptrPHU8WchWzxGE0DYrK4iOLR0GOpvqDHEdJTg6jO0xzLoaCoXc2p1Op9FoNNx31uBmmxjEUJuGDhQztRqNhmPM75RtsZEoqJJKpRxjknVTFexgH9brdTfGquMsY6zb7TrbsdPpuH1AWLOUTK9UKhUACTg/GGhQRh/nC3+0pj6dZCVgWKYlhbapguDpdDpQJohjE4/HMT4+jsnJSXctfc5QKIR0Ou3O4zu3Hqtzt2RkJ4zk2SKDdJd9BxTU5W8NGPr8Wwv2cn2xTGpdFyy4xXsxcNhut90eJnpPux8Z1z2t58xAMUtz6Pdsh5KsKMwCov+lazXv5esv37X0eB/Q7etz3/mDggiWLWvboIETZf0S3NXyHizLogA99Y0y5BUg1nbRFmEmFf101YsW5Nf2aDCan+l84ve+/lMyAgly1HEkCTCoT6Bf+5I+JP1QnWvtdhvVahXtdjsQKLD3t2NJm9LiC/oO6FzSoLeOteIBqud99+QP578NYtO+yGazLujuA9Lr9XogW1Pr4vtE7z1IFIhXW4hin32Qra3z0xJHOH/4XPre+WwNbYMvcKHrym7IFVdcEfj/t37rt3DPPfesOY576Rw+fDjw+eHDhzEzM+O99szMjPf4TqeD+fl5HD16dOAxg67Z7/dx11134RWveAWuu+469/lrXvMa/Kt/9a9w5ZVX4qmnnsL73vc+/PN//s/xxBNPeAMDOyEjIP2AChV+t9tFtVrFwsICEokEJicn3WK+0fnc4KFSqWBhYcGbdjZIlBmwX5wBy1jYCaBfjTEy0lOplIvWaXr8ekLDIBRaSSFPpVLo9/suWr1b/RgKhRz7PJlM4sSJE84pJ+itipftqtVqjsl17ty5NTXBi8Uims2m6x8aMgTn96Mo2FQul9FqtXDo0CEXXJiensbU1JQD0gkwNJtNLC8vo9PpuGDKoUOH3HWr1eq+eSf2m+xE5HwkIzkIYkHLUCiEer2OarXqypno+g+sOjQszcKAq24ONYxw02TqrnQ6DWCFwdFut5FIJNy6zzXQst90UzcVOlOsH07dqPU7GaS12W68JvUDnRsFsdeTYcDPSCSCqakpHDlyBN1uF8ViEcViEcePH3fOQqVSQbPZRKfTcfdutVoBgJgldyYnJ9FsNlEsFgMpwbuxHjFjLJFI4OjRo66c3NjYmCujohvG9no9LCwsuHFVIIcBXh7XbrdRKpWwsLCAcDjsSppR35MZriV8OLbLy8vOOY7H46jValhaWkKv13NzQQPQCpjze7XR6HwzsEHQm2w9ZgB2Oh3U63UXvJ6amkIikcCJEydw9OhRdLtdVCoVB0zQER0bG3PBJLuJ7Eiv+GWkq0eyEzJIZylwaY8nm5OsYq4Zyma2oBfnnAZu1X+zBDALfBEEbDabgTVL9WEikVhTwgOAW2NV35GgxjVYfWsLJgNw6xHXJl7HAu1WB2pAwAY1db3XPrJ/azDfkuS4jtpyGFyjlcHPNZYBAQvC096hXiPATLY2+4OkJQY02BYLKqoPB8BlffN+JH7ZQC2fW0Fi26c+cHYQKxpYYfDm83lHguD4KiPdAnkE/rnPFvufwrIvtNfYR7Z8kZ6j42D9eQVndWzY33zv+G7QTtPz7Qaddg5RRysjPZ/PY3x8HIlEAocOHQr4y7RrmQHS6/VcsCqZTLpSPXo/PY/PrFkgNmiigShbwpjzTd/zQYE2y/BXzEltaG0jwXSdu9o+n03La/rKLftkJ3T1mTNnkM/n3ecbgc723bDBzGGOt59v5ppvectb8O1vfxtf//rXA5/fdttt7u/rrrsO119/Pa688kr87//9v/G6171unSfauuxPhGskQwlfHq2X6lvkfOdp/TYqRJ+SWO++/Hs/iUb2dsrZtcaYRmc3w7amI2rT4tTg223nnEaMKiIrVIjKTKOxwjbTAA2Hw451yXM3mo+7LWwPFRXZI7qpCw0IZdarogawZsPZzWQoPFtl5JyP5HKSYddsddSZTq5GohrLVq+oob6Zd0AZfXodlpPhvcgE4t/qTPE8dVg2aoM6G5adYx0YBTd2KpjM+zMwTFCVDhoQLEtn26bBBHWylIW0G6LMPLL2qG8ssKRBbc1AWI8cQTJGs9l01w6Hww4AUcY52wMEa+Iz4KBgkLWVLKilQIAGkHxOlAI3dm5o32h5JGubKXBDfe4by+3aX7tlv+2WjHT1SHZKFFyyzGbLggUGl5hQ4I6/1RfR75TFrt8puKVi1xtdO7UEjGWr8lg9hz/8zAJ7/K1+oAWuVf9qe/WZtB3r6ScbtLDtseepTaDHaxvXWx9Unypb1/adz4+2Y22fQ9th/X19Hu2PQf2vx1hAT/vbXkPBeg3K2A1TqSMJblMvU3gc55dtoxIWtKQP2+7rf/sdj98I6NTn0nHhtXi+7xo6Hjr3NfhiN5K1wRzeU/W5+tPa54PGy9cfNkDks0EBBEqx2Hd0PSxNf/S91LYMCr5sdM1hZSd0dT6fDwDpg2RqagqRSGQNU3x2dnYNo5xy5MgR7/HRaNRlEg46xnfNt771rfjTP/1T/Nmf/RlOnDixbnuPHj2KK6+8Ej/84Q83fLatyghIP+DS7/dRKpVw+vRpJJNJdLtd5PP5NZtHMiJG8LxSqWBubg7NZhNLS0uBjSg2uh+AAHDKxXKzwPJOiy7iBEc1sLCd61rjYSuLnTU+rHFir2UNLZ8hpAbEeouzgiS5XA7T09MOSN+oFBAj07lcDolEAoVCwUXQa7VagInRbDZx7tw51Go1FAoF9Hq9AIN/r0FmNXTn5+cxOzsLYDVdk9F5/dE6wf3+Smke/lbDydb1G8laGTnnI7ncZFgArd/vu80OY7EY2u020ul0ALhNJpOOeTs7O4vl5WVXN73T6aBYLG66fQTQyZQiE10ZawQ4ub+FMoyz2axrE3U92cYA3AZfBHdZsoXBVK6NZBjSIaNTs5UgwUbPW6/XsbS0hFBoNb262+1icXHROaU8Vn9TFMzgmmVZecMIHWnaJsOk6WqwnkwultwplUoO6A6Hw64kn9bxDIfDmJiYwPLyMmZmZgay/TnHarUastksjh07hmQy6foMWK3xq/1BO0RL4rDWubLYOA58dgIC1JnKJuT84xzVjbs6nQ7m5ubw1FNPuX6hns3n8wiFQi5jrtdb2XCO91IAgXqc81H3MrFA21bkctNPI109kp0QC54O+l7BRF99bP6tdrr6o9RxGsjlOqQ+kgXa7f24RrPUKfdGisViyGQyAcYvj+feIVoqq1arBdZqMpMtSOkDctlW3p/H85n7/b5jMPuYs7we/X5m+7A/7P3Ud+dvLQenfrT1W5VcpcFR+ku2bdSlqlP4N/uR67Oux6qP6W9StzAo3Gg00Gq1XDYAbRbqGD4T+5G6xALNPkKUltlQm43/M4ONf+u46DhoH4bDYbe5d61WczaTBppJBKA9xvtQX2rGmI6LbbO2iW1n3zO4zb7h3xxD9i/7SPeOYRuZPVYul10WAJ+D77aS0XwgtQbaqa+VyMB76vG+3xsFDdg2Bt8VROd1GHxX4gDtGLVXLa6j7HfbTr2/fu/DbnQd20h2U1fH43G85CUvwaOPPopbbrnFff7oo4/i5ptv9p5zww034Itf/GLgs0ceeQTXX3+9m0s33HADHn300UCd9EceeQQvf/nLA21961vfiocffhhf+9rXcPXVV2/Y3oWFBZw5cwZHjx7d1HNuRkZA+gGXfr+PcrmMWq3mNl5aXl52aePAatSNKTetVgtzc3N4+umnndLZbB0mGg3qkAN7yzpmOpn+7HR9qa0A6IPOX+86VDDKnrLGJRDczGY9J1CBdKZEs1a7zwizQpCEQAwdeBqbNI6azSYuXryIxcVFHDp0CLlczinNS1WfajPCCP/y8rJzzqPRKA4dOoR0Ou2MCgui82/7vUbZlbU+kpGM5PKWrei6er2ORqPhjEc6izTYGdys1+uYn593JbOq1eqWGdt0OgkgZjIZpze03nU4HHZ1LAl6c+3mJpzcMJK6gA5wrVZDr9dDLpcLOIA8l+fzeOoqTS/eCeF16/W6SxsfGxtzjnmxWAw4RoDf4SHgovqV/bWZMWBqN685LJBORzKXy+HQoUPodDpuk1AtE1CtVt3+OJOTky4wPjY2huXlZczPz6/ZdI3S7XYxNzeH+fl5TE5OOjYS2emcNwQiGARRRhkdZOpFYHUPFgsmAXBznP2p9c5tWj8BqH6/j/n5eZw9exbhcBjj4+OOda+bnXOOEcjSvuf8Vga7TY/fDog+kpGMxC/KcB0kBMG5vnANHPRu2uvpusrvFOjUTZSVGa2ZUxSuTfQneS3qA/oJPLbX6znQtlqtBkqAKRtXwTu9p11zLNmKvp2C/r4SLzzeErD4/Own66vbY3Wt1HO1rUosY5AACLLJNWChomC4/pA0QNtB92rR59AAgwXSm80mWq2W8001gE0f1sp6QLoveKB9pM9Le4NzzGZysf/1N20u1lcnOYHjrnuCcF4lEgl0u10HqOu4WaGOpe/Pd4PjxXlOoFqDNnbM2M/MwAbgxkt1fblcRqlUcrZfNBp177cC6RpUUvGB6bac0jD2oo6nT6/bgJjFY7T8kLZRCSXDis4nzn1fu/idJQzsN7nrrrvwhje8Addffz1uuOEGfOxjH8Pp06dxxx13AADuvvtunDt3Dp/85CcBAHfccQc+9KEP4a677sKb3vQmPP7443jwwQfxmc98xl3zbW97G2688Ubcd999uPnmm/GFL3wBX/3qVwOlW9785jfj05/+NL7whS8gl8s5BnuhUEAqlUK1WsU999yD17/+9Th69Ciefvpp/MZv/AampqYCoP9OywhIv0xEGcF0um1KMhX98vKyW/Q2uyBQbAqbsgH2AkzXKDXboxHs7VyXv7UECP+mMt/omdk+9htL6tiUNAABhablQiyQrpF9jdAPAiZ4Xa3Nt5mxUkBfnWkrylSrVCrOaOA9FXDejbmixhbbxE1cyIAg44RzR41gTc9UAMpmPmw2cKMReR8zRSPd253H+0VGLLeRXC4yzLrvO0cZPs1m0+nmaDSKWq2GSCTiHELV0Vud/wQE6NBY9rc661z36GxRjxBUt+naVnfZtYqBS57De1OP0hHbKeF6qkx3rq/rOW0K7LOt+iyWdWTP1z7gcwNwwG2/3w/UGl2PgW91grLD1CGm4w4EN3jTvqeDvl69TY4HgyEEpC2gwkCy3ViWwnmjwJk+B0EXFe07BQ8AoNFooFarOca7ghB8Th1XPjdtMvY9AAes82/LKhtW9HnUzvKxAQ+6jHT1SHZCbIkNHxOTawvBdGsLb+Sr6DqgrHPLdrXzUv1W333YJh5Hna3gcre7Ug+90+msWav0mdWX0NIbuiZbhraueVx7faLX8BGjdH3SQLbtE984qU+pvrUe72Pc8nNtjw8j0HVUvxtkX9k2WntD9atlGfuY5jrmvlrcHGsfuKyAp26Kqc85qG/4W8+nzcUfCx5TL3O+kZjhA+31uQcByr4+0THzER1ouwJw9mm73XZzn3ZrOBwOlDEc1D61PfUdsX64r30a+NAMFR0ba5tS9/vwFI6His6hQfN/kNh3zILogwB+fdZhfIzd1tW33XYbFhYW8Du/8zu4cOECrrvuOnzpS1/ClVdeCQC4cOECTp8+7Y6/+uqr8aUvfQnveMc78OEPfxjHjh3DH/3RH+H1r3+9O+blL385PvvZz+K9730v3ve+9+Gaa67B5z73ObzsZS9zxzzwwAMAgFe96lWB9jz00EO4/fbbEYlE8J3vfAef/OQnUSwWcfToUfyzf/bP8LnPfc5l214KGQHpB0RUYdlFkS9Rq9XChQsXMDc3F0hL4Tn9ft+Bt+12G41GY0tsMF6HgH2j0UCz2XSpQ3sFpLdaLZTLZadcht3AbJhrk21dLpcdA4rPqbU5fefy/Far5dL0yS7jBmDA6s7RsVjMMcsY/bUKTwMHXNjJDqcy08VVU795za1EO0OhFWZ5NpsF4N/lnW1aXFzE8vIy4vE4pqenceTIEcTjcUxNTSGTyawBkHda1AAsl8tuw7gzZ864sSyVSojH40gmk+j1eqhWq2g2m0gkEm6zNjJAGBygwVCpVFAul1GpVFAqldw9hplvGiSJxWJIpVIBgITvKMeTaXMHHVAfOecjuZxkO+B2qVRCtVoNOFyLi4tIpVKujMdO6DANFh46dMilp1Of0BnSdOJIJILDhw+7ey8sLDi9zzWp0WgAWNEtmUzG1frk83Ed02Aqgc1Go+F0NIMJ2+lPIGgbdTodt0mV6lGrQxWMpv6kE97v9wMbS/vsJE3n5g91IMFzpl6z9B51x6Da5exT6mzqJqZ9p9Npt9noxMQEwuGw2zi00WgE2Fu5XM4xt+fn59Fqtbz9XK/X8fTTTyMSWdlAlFkEJ06cQCaTcY4xgXaWCOB84PwKhUJOl5G9xvPoVOoc46brykoko7xSqeDs2bMBdiH7lo48SyEpizWZTKLf7yORSLjrh0Ir5V/6/T4qlQoWFxe9ASXfnNLvotGos10Y7AqFQm5uEVS4HHTVSFePZCeE2SV8L7TEFecJ/RJgFTRWVq+yMy0AzYAaA5YaDPb5FjxPAVKWk1JAXYOq/M1Slhp4JUGHxBotz8E2q+7Q8h4+MJ3ncO+KSCTi1l/d04p9YgOLFoBW5i3XbH7O3zaQEAqtbo6oxC/qfdoLakdQ7LvPYKz6sLb/tP06zhqoZHBUAxOKgSjBiUF6Oz98ulqDFPTB2Q/Ly8uBEjHUa+wb6mfdY4TjbgPRNpiivjkAZLNZ9x1Z6LRBdNyAlXJ64XDYgdaxWAz5fN7ZMr6+1+CJnWs6jgpKc15roIP2pM77Wq2Gubk511/1ej0Q9FHg2QYYdJyVDMqsd96DwnNtH1tiH/1qndO0K3RzYAXwgdUNcQG4PueaxfbRvvW93xrY5+c6B2z2jJ6/XkBkPdkLXX3nnXfizjvv9H73iU98Ys1nr3zlK/HXf/3X617z1ltvxa233jrw+43amkql8JWvfGXdYy6FjID0AyJW0VkHgC9orVZzn+tLqS/2Thi5dJxYSoZR9r0QPg+Zd1x4t8I4Wu8edJK4C/vy8rJLU/YpKd/5apCosqRCoMJnqjMVNIFXFRqn+luzA9TQpOKxjPTNjhmvYzc7s+0CVsASsvap1NLptKudvlvzhSBEvV5HpVLB/Py8C2Iw4MC5zLHV+aPzSOcAf3TT3s0w0jkOFkhn/VhG9GlE2ej2QZWD3v6RjGS7wjUbCL4PGuCmwb5dUWa2Zs2oLcF1hs4CAdV+v++Ci6pP6FgCcEAimXt8PuoGguWaHcdAvJaP2Yl1QYFatlEDtgoeaNsBuBIkCvbob1/7qAu5blPv8remQWsdzvUcJWW10all+jUzF3hd1iNvNBool8sBQALAmnrjg/p5eXkZpVIJwAqAk8lknAOtziLBjHg8HigLxD4DgvaGOuiW/ak2De0pOucMNrC0kYqSP7TsGseVz07biW1WMglBFopPt1ogjoEXjjdtAQ2KX2667XJ7npHsvvC9oH7R9ZTzS9cCZZYrWKbrBtc4BaCoc6xPNYgVTH/Ygs+qIyyYTt9A78M1hSCoBf1ULLNX28HPtKyM+iHhcHhNjXNeX/16Po/qOAWntWSKfWZ937VNykjns1pAVPvWBgbUv9WxVBBT2+8LCKxHIrQgtY6D6n6u09RNfA4C6QSiNWObgX7Vy9peG6S3+lvLmWi7bbCIfj/1rt5b9SuwGoxhsFgBfe2nQeNjAW37oyxyi6MQ+yHgzyyNRqPhSF+6wbsla9pxZP9zPmhpXs0Qp+g7rYEPO798+wTyevTZ1bawOIwGPIjV8B1geyyr3QYHLDakc1XXFxVfUGsYGenqvZMRkL5PRY0ATRtSJWONE42u2ugvz1HFpY7xZoWLaCQScQxrLuRkUO+G6MJWr9cdI50bY+wkkM6a8OFw2DmtVIYERG0qkZZyKZfLaDQaKJVKqFQqgchmOBx2Ne6pAMhAUOVvF2o6tGQuMJWKfUMFwP9tJH+rfTFsQIZzrFKpYHZ21tWqzWaza55Tmf3DRmbZBraH85JAFMH8hYUF1Ot1VKtV95kv/TIUCjkHPJVKIZPJOKC92+2iXq9jcXERzWYTMzMzmJ2dxeLiIkqlkktDH9QvodAKWy+VSiEej2N8fNyx/7gBIOeCsgHq9brbZK5YLKJarQaecyQjGcneCQ16goUEhhls9K2XdIyoq1XX+xz5ndBj/X7fAa7xeNxtWBUKhVxtz35/tRQL70nGuTKQbJuoA3VDNdaXpV4jIywUWmEHMxC5kwFv9jPXxng87uq6837hcDhQk9syscg6ov4gg0kZYRwvMs51E0sGCfj8PF4DCWR5KyNKgRnNgNK69HQM6Qi2Wi1EIhHXj7wP2ZlkdW2mPBB1SygUwvnz51EulwP2RzKZdPclUM/x5LywLDYdH/3hOFUqFVSr1UCpNLI/ragNzJJsk5OTmJiYQK/Xc/qdtlan08GFCxewsLCA+fl5l0lhxfaNbiDOMU6n05iYmEAkEnEb2tH2i0QiqFarLhOPgAKvPXJ0R/JsFX1HuEZprWGuBQpqWqKNDYJaEFqP088UIAMQKFmmNr+CckAwYGfvwaAa/+Yxyra39yaQZq+lQLcCzO12G/V6PQDSKrhH/9MG/9h+2iRaWs32qQX8tA0cG+pC+kC+frZgtu0zLd1jn59BTQLHPJ7+tOpNZYRT59Hfp/7gfRRU5v/qTzPwT5IA97risTyG7eP40kZSvW0DKz7RMbMAswbvNSOO9gn7xPZfo9HA0tKS2+CdfjTtOetP6yajHEP2PeeABrqow2gTaRkXzQDgHOEzWFBegwpsn/rV3Kuv3W7j4sWLmJ2dRbFYxOLiostoVOxK5wNtEmYEsA+YOW/3SiBJgM/B7M9qtRp49zleFjfTIInadxoYsUG+9QBx3kffK/UFdgtLG8nWZQSk71MJhVZZXolEIpA2ZIF0Oojq6KoC8hkHjHhrhHQzQkXCFPVoNIp8Po9sNrurC4Ay98rlMhYWFtyivpMgI4F6KpVsNuvqzANwykE3rwAQKAUyNzeHer2OpaUlLC4uujGjAie4nEwmHcCcSCScQWBLsVChU6Exms15o0Yqj1fHXZXSZvtiGMa/BnkINkciEczOziKRSCCfz2N6ehrJZBKFQgH5fN6BMYC/1p+9Ptuj6eXlchntdhuLi4uYnZ11AHS9XncRbzV4tF+oiFOpFHK5HAqFggMsyNo7e/Ys6vU6nnnmGVy8eBGlUgnz8/MBRqAVvg+FQgHT09PIZDJ4znOeg6mpKQek6/vKdnJez8/Po9ls4kc/+hHOnj2LdrvtnvMgyXYAhREQMZL9KArMkpETDoeRyWSQTqedrrXrLpnn4XDYBRHtnhibCVpuJFxLer2eS3Gmc8nyIQQhuaYCKxv5EPhdWlpa4xArCM1+UNuCOpHgfSi0UsKGjtMgtrcKHb31RNcW2kKhUAilUgntdjvAXmYatHW2CfBUq1UXlF9aWloDDPM6XLv5bKlUKhCoVfAnnU47BjkA9+w8lo6oOurMoiKIToeZ9czVxtG5ROYig/e6KbgSK3x9yrI4LH9G4H9iYsI5qNy09uTJkygUCohGo27TetqkvDb7V9ln1NUscTM7O4uZmZkAiKBzUOcB7eBer+f2FEin0zh69Cja7TZmZ2fR6/WwtLSEc+fOoV6v4x/+4R9w7tw513fDCG2URCKB6elp5HI5ZLNZHD58GLFYzNndwCrIMTs7i6effhrNZhMLCwsolUoBR/kg6bCRrh7JTgl1pNraBIq53rVaLUco4lpBm5j2vfq1QHCeKajF9c1mX9msXmWvKmAJrG4qyL/tnFafjIFLWwpDiVLUmRZ8Vv2jRCmWFCHxhhlN9DnZJgX5bLCC6y7PoVj/R/uD7eQaXq/XXaBUs9AUV2A/a0Ya13LNNFDbgYArCUgEghWA5NpuwUUF9+nbEYxVIF3JSdls1gGrtLGUZUzdzHtSzzFozOdi220wWIkRvvFUO9Cnu3XfNI4p9TnnpfWJSQwLhUKYn593baeupo3DILCCw773Rj+zjPNisejA50ajEbgWj7Eguupr2ojENwjAsyzd7OwsGo0Gnn76aczMzKBareLChQuuTIxmC3CMeU2S3mKxGMbGxpDJZJBIJBxBzb4nAALs+dOnT7t+t9mZGuTQ9UHBcX0ffCC7rhe0/7i+qL0HrAY7lAi7kYx09d7KCEjfp+JbgPQltZFfLu5cZJRBpOA7r81FeqtOujo6NIKoaHUhuNSibaAi0kVpp+9Fx5dgCRX6oPR0prVz0daaXwx4qELQHypR9qeWUtFACj/j97oZmC7oqqCp8Dc7RmoI+5gV+lsXd2sIs3wAFbKmx5GRouyKQW3hD8EIMvi0VhvZaWQ7+AxiG3DS63PcOYbVahW1Ws398B42BY2ixj+j5NlsFrlczjnqVPgWSFelH4vFHDgXDgfr9B8UZThS+CO53ITrqL6/qrsJHloH0vdjr6sB8O2KOnPU1QQReH8F+y1LTddJBUZ5bd+6qr/5HAqm7lSQwPeswOqm6FxLaZ8wmw5YrYlp7Qhb5kufn864Zcupk67gjbLfqZ8t68nqHnXs1BlTZqPqYR0bte1s0FvBG9tfem/2H9umG8NSR9OO4Q/7kPamtkEz48LhcKCfaSdtZp7r8+jfaqNRV1erVbenz0blktg3BCIYWM9kMshkMkilUgHACoAjL1A/Ayv1OpVxtxNlmnZTRrp6JDsllqEJBBm2XNN07aHYADTXj0HzcyPgyYJclmHKHy2Rqe3XZ9J7WWDNJ741y9du9TssK3bQdZT56gNzLVDq8x3Ur6d+pg7U4PgwokFt6nx7D7ZLS6ipHuF6rm1Xn19LbWrpE2B1zzH9rSQ/4ij8rTab2nIamFDfVfWZ/ijWYsdK+8D2kSW56Vjpb9vHzHzTIDkzo9VWYz9q3+s8sJkUJCMQ3yHGw31jNDjM+UIMSt8pH6lS50Cn03F+O7O8+DfHlWOk19ExjcVijkBAnU0CKjEFfX857rTPeLwlkWgwzo6HD5vgvOIxiq/47Gde315T16Vh3rmRrt5bGQHp+0x00eYmSspI12g7FyOm8vZ6q6U+rDJVh5yLr0aerRLaSFTRF4tFt8iyVAUdjmEjapsVLecyNzfnNtMql8sBp20nhYZFo9FwZUqq1WqAMcZoI4WKgWw+ploz8syxJcOLCzpTuLVUjo1w9/t9xGIxZ1TQ+KMQFFCwmSyuWq0W2IxjI1GHuFaroVQqOceXgD/bqYaorZXW7wc3cGO0n8qPf9NoWe/ZNcJPNh/ZdIx2k4W+3gZgkUgEuVwOY2NjSCaTrvzBxYsXXR8yJfypp57C3/3d36FarWJubs5F6dkXvnskk0kcOnQIyWQSV111FZ7znOe4z7hJn2+jXpaFYAo9N5U5dOgQSqUS/v7v/z5w/4OgEEcKfySXm5D9xKAngWnNJCLLjhts9vv9wEaSynCi/qb+5AbVGggHNv8+cO3lPQA45jLXGl5fQcLl5WVUKhUAcM+ojDNtd7fbdY4rnQjaF2Qg6no6KPjoa/tWhGm7LI8CrIwX06GTySQmJycRjUaxsLCAYrHo0sSVLU+QlHtZsKRJOBx2Kebsm26363RZJpNBPp9HKBTyso7UiVUAWnU2x4zziYFnjg11vzqwBEDooCpLkv1JsFiZjmpX6lrdbrextLQUCAjE43FUKhWkUilXfqzf7yOXy7kNVjnP2BYLiNE+IDN1mHmgwSjaTtVq1bHPn376aZRKJVy8eNGxwzmug/SPBdTC4TCmp6dx9dVXO3tMswloc7C0C9/liYkJN9bcMK1cLuMf/uEfXA36gyIjXT2SnRKuPda+ZYkHArUEttbzGXk8/7aBQV1b1bfpdruBUlzqVyiQpWuBlk9Uv4NiA91cC3SN4/c8V9tOfUl9QcBQj9MSL/SLNEBK/4s2h7LuNXihQCQxBg1qKyDK9Yu6v1QqBYBSvZYtcaPAPv9mO2nbMIja7/dduTWywukfc3+tSqUS0DvASqlQllwrl8sB3QKsZBPRp6KOsxgK5xo3+WSQ2wZElBFO/a4YgwYc+Eyqq9VO8s1lDQzwfD1WiXQM+FufWkvCsO/pS3NOUFfpHOZ+J2oTKjOeQLqWdtHzFRRWW442iO7/RfIhM9Pb7TYWFhYcnnL69GnU63XMzMxgYWHBZXvpvNf5G4/HMTEx4QLdzDYoFAquOoASHexv2uydTgfHjh1DKpVCrVZzLHj2KUUJM4qtqVi8S/sJgAtmWKxGcTrFAYYlFox09d7KCEjfZ8IFUNNymE5rX16+ZGRZURlYJ8iykqkEbfR5K455t9t19b4BYHx83LWHqTg7DaSrocEaYaxl6kt53sn7EozggkigXEuzKNBRq9UccMyNs9g+BVlo4NEho2HlA9LVYKGDbFPPGMVluRcCGQS3CWIkEomhn5/XJdOLjikNEt1NXoEDTfcjOECAgKnjygagEuQ7wOsry44GGBU+o9c0aDajWAh4jI2NuVQ+toeGBBX+2bNn8cMf/hD1eh3FYtEBXOvNt0QigcnJSeRyOZw8eRLXXnutMzBsKaBB5+dyOTdeExMTmJubw8WLF52xsV5t9pGMZCSXTuhsca1WZ0ZZTQBcNhKB8mg06jJm1OnSDSX7/b4DGpWRvFnp91drUtIh4n2y2WzAeVSdTd1BXaXrIp0nZSSFw6vlsfhM3Dzy/Pnzrvb4bqxZDPxSdyjgEQ6vpHuzHy5evIiZmZmAjlGwIJVKYWxsDOFw2JEcCKZ2Oh0kk0nnjBJoz+VyGB8fBwBXZkVZcsAqY88Gi7XvqetVr6tNp8EPtY+0nqkPSOf401lWEF9FN9nj95FIBEtLS47hz1qy4+PjyGazAFZBBt1El9faqqhNQWe5Xq9jdnYWlUoFp0+fxtLSEi5evIinnnpqqPJn2n+85tTUFK666qrAnj8cJwIf9XrdjUUsFkM+n0c6nQ6AELOzszh37tyBA9JHMpKdEPonlpXe6/UckK77SWgpEK5Ddu3ib/VngdWNTS2LV4PEDL4pwUzXewWGLYjOoKquj/o31wE+k22/j13Pdus+K1xjQqGQW1t5rgZbFUinXmFwnGuvDRRwDdMSoGwbg8etVssFkwlaazDEByL6RO0IJQnwmfg81Cu6AbnaO7wf/dVKpeJIcwzOq15VANeWeLHPYAM3SlYEVkvTkfDEIAMxDw0SKDvczlHLRtf5o2C67TdlTnNu+pjKdr+ZaDTq9hAhS53EO+pPYg7MmiK+QSCZ9qrOWQ3Ys332GSnErhiUCodXMqnZtmKxiEajgbm5OczMzKBerztCgz6/ZvqxbxKJBAqFgiNv0iZjWRcNgmggSbEE9gcAZDIZFItFLC0tBTYhVfuK2JydL3qsjivnBo/j++2z4QfZ35cCyxrJzsoISN9nYkFFjZhbMJWLgRoEjFbrgspFQBd3VRb6AvtebjqFCpbyOkAw/ZcbRFGhc+Gxkf+tiEZh6YjXajU0Gg1Xw207kbnNtgVYZVppNJdC4ELZ0mr4qfOmJXxsepmOPwDvIm7TzpTNTEXH8aWRRDa8RkP1mqr4+RwEwAmS6/05zgoIUOmsNyZqFAJra/8pIKUGrU3Z11IBm5kDGtFXtgHBIwYOuFmpbryz3r2o+Knw8/m8q+PGvhpW1PjlhqX5fN4x0avV6tDX2ksZRc5HcrmJruXWYWLAlQ6QMtZ1bWYgXIF3rqPUoRutN7y+MobVcQRWgU3rQNtgJ+8bCoXWsP+AYLkqdf7YZmXZs6Ym9YbWi98N8aXG83na7bars62sNrWVqL+oEzhW6ogDQcKCBcZVrGOkeoL9zbbQmSUDut/vOyCI7dPrURdqiRoLwKiNSUdamWfrzTH9zjIZ+bmyzzV4rky67Yg68WwP78d6u8PW3rfPRkYbWehabo7vjc4jbYftOwX30uk0stlsgHW432Wkq0eyE+IDw+13VgatnT6xxw26pgWqNrq+D2j3vRNcF/Q5da3gtXgdDUAMeAABAABJREFUDRgw6Dw2NuYAeF/7uWZwnfbZGcpoHwTS2X5SEFdZ1VxDyQzXUiAbiYL27A/VO2oPaVsZcKeusqV2aLOEQiHHWtcSNOq/qh/q+1+vO+iZbHsJilq9z3t3u8GNSxWAtcI+0bJoGqy2PjlBYSVkWDyA802fh6At+0xrtLMdnE+04zj22j7tV30GPvegfuP8ok0AwBEPid+wjAsxHQWa9fkVuGd/8EczCSxWRnuU17HHAKuZmcwg5L2bzeZA7MU+M9vms8m0DYNEbU19f4exl0a6em9lBKTvI2G0jI4SWW4ajdNjgVVmlVX4dMCAYPqNAo1csJTJpC8V2zM1NYWxsTHk83lcccUVjmlGJ/ns2bNYXFxEr9fDzMwMALgNt7j5A2tV2ecYRtgu3fSRZVy4IYWWS7nUCwOfvdfruU1OAP9mINbZpPLhmLKmF5noBLcZxdUoNBBkWHCBVydZ0+7JXONCTiXPzddSqRR6vR7y+bzbLE2ZEHTKe70eisUi5ubmAiVqCOzy3FwuFwBgyART1sCgsVFlo8rfMggsEKIGwaCI/0ZCRc/sgkaj4drNzVzOnz/v5hozC2y6vAqBkXg8jqmpKVxzzTWYmJjA5OSkY0ps9j0Ih8Ou/E8kEsE111yD8fFxPPPMM24TwP2uFEcKfySXm1BPKrOJ2VJkAfF7ZpnpWmWD2vxRdhU3LaX+8wnXh1QqhWQyienpaRfMJuA9MzODSqXirsXSH8rAAeCyokKhkCuVpUBCOLy6KRwdLxvo594hpVIJ5XLZBSS3AySq/htGuLZb3aHjRGdJAQMrfJ5+v+9K1ynJAECglI+C7QpYWAdL9zNhmR+Wq6NOIjGh0+m4oCxLv5FFqNflRqkE5PlM6oCy3ePj48hkMi6Tj/PLN8dsnzMQQb3N75mFp+dY23Orov1Lp5VZByxxppvPbWae0JG+9tprUSgUnK4GVu1ZvpcUfbesPqfdF4/HceTIEaTTaSwuLuLixYsD59l+kpGuHslOCNcSvjcWBFbgGQgGJG1Qk2KBKwr9LD1GgVTLplXwm3pNA9paeoZt9QGjFK7F6utZX4b3oY4tFAo4efIk0ul0YP8SliSjTgAQKKHJ67FtFoTlWqngqgVZ6eNRT1QqFWcPlMvlAHGKY2j7zgaPLQue9wqHVzdl1/VP/ftyuYxIZGXvrGq1uoaAxzmkxDTVq1oSlMCzMtItq9mKAuWcX+zvSCTi+lqzz/ibujoSiaBer7ss81wuFyidx7Fi4IDYBffzYFY2n4f2I0uecq819fcV3FfMgGMLwJXno1iwWftWN1BlhQHNgqMoGUMD9Hx/tKQfs9e4yXir1cLc3Bzq9Trm5+exsLDgMB47jznvwuGww8S4ZwlxCxt8U0CfNop9N9hPCqT3eivlb8+cOePsVYux6T1ox+m8tgEGO985twatQzxHSS7ryUhX762MgPR9JspO1gVpowi6Ve6hUGhNahfPV0PBArL2GuHwShpzoVDA+Pg4jh49imw265wEllRhOhCVCTdUZLqSr9b1MKLOu9YRIzuYTGFNfdsN4X22Wo+dfU6nUJmEylIf1F9qvLB/NO1LmY52wda65c1m0ylIa4Cyz6nw2c8EkbXNlpHO8zVNc9g+3Qsnk0x0/mbknunbBCcIjm0EBnFsWZ6GNdiZfrZZEJ1Co4qBi263G9jVfr8rxZHCH8nlJj6GC9dfMsUoCsJpqQ5grYGuDCtlfw96DwiSJpNJZLNZt+cDdQodGDph1OFc79gOriPqdNp76npjmVN8FmUi8Wc9sPpSiW/NUcdlUGDCHkugguCNZUspIUEZinp/qwdVv+sY8zPqXurqXq/nAhhsv63BSpa9zYBTp1RLp6VSKafPN2vP+ALJtrboTou1ZckEJJhOIGoz9iCPi0ajGBsbw9jYmNu/hNexzFNgdfwsAUCvSRuagNhWdf9uy0hXj2QnZL0Aml0/1C/aaA2h/rH6yQYOfYC2nqM+pq6ryqgmQOwjNenfugZYu0CBPYLK3MR4bGwM2WzW+VcshUW/lqQkssUZYGd7bEaZPvOgMWEfKGCnbHRmWtMvUjByvTFRvagBVP3eh2cocKv9boF6fU4dL2Dwhqv6M6xoUIW6UXU9wXENlLMOOEH3cHi1fJwGj2zmszLT9dmVTMcsfw2Y2H5huym8D7C6r5yv7+mrst08VoMVGiixQRTL+Nb3hNdpNpuBALgy0hm8YaBCbV+KBsboB9OOGSS+d1GvZYNtAJDP5xGNRrG4uDhw3nDdsfeyADqP1b9tXw06hu/kCEjf/zIC0veZ+F76rYrPedTF3GdAUKjg0+k0nve85+Gqq65CoVDAlVde6Vg6VBypVApzc3OYm5vDk08+6dJ0ZmZmEIvFHPs5Fos5JpWteeUzhqhw6PjXajUXIVxYWEClUtmS07TXYh1v9oMC55sJOKjBQABEGRg2GKOKbWFhwUXPbWSXhlW320WtVnMsNwIPvjmkinq9+bWfpNPpuGfr9/uBjQPD4ZX0rnPnzqFaraJYLA5VYiEUCrkNULLZrGOKbgdE12tHIhGXAcDa/BzXg8B2G8lIDrrQ8dANn2ncE9Qmm5iijDmu1WRraeBSnVZ16n2GNyUajeL48eM4duwY0uk0xsfHA7Xb6ZQnk0mUSiW3rwQ3iSTICsAFwSORiEs5pu2gwV9g1VlTJh7LuXQ6HQfe62abW5Xd1iNWF3PtZ7CC427Td5Wo0Gg0EAqFXNowM+mazaZjJfZ6PVSrVddvOkd4T/ahBtzJ/lZQiNlf6vyy7cBqzVdeE4Abq/1uR9Hm7Ha7KBaLmJmZQTwed6AT664y8D/Ms4RCq5v0pVIppNNpB3yXy2X0+/3A3OW7wuMsqYHjxbII/X4f4+PjyOVyDmgZyUiezaLANAk3NoCnAWYfeKd/q15UVjR1FDNwtNSknqtgsYLzVt+qD+3LKiXwqdfU75hdFo/HcfToUUxNTaFQKOA5z3kO0um0W/9brRYSiYTbh4ls3UajgYWFBfds6ufxnsyWUlYwfRvqZ2AVjGZpVDLStRSpgq98fmXd+sBE+9sHnHOtVRBfx8sCtFZsOwAE5g+Z2MzK1rauZ0v5AFcL4LL9Wm7PBgE4d7j+c05wblLHE0jXcSG5gfckKYNENfqoyvi29qINrGzkr/Keyu7nXGb7idlo3XQFmfW9Y59xXxSC/3zPWR51cXHRkTC1LdaOAuBsaQagdC81JSAyE5wsfr4XGoDwrSFsu9rMut8AxQZttP9U/68HqPN/n6/OsdJytSPZ3zIC0veR+ED07Rrd6oQPisD7Fth0Oo0jR46gUCjgp37qp/DiF78Y2WwWR48edelujCoePXoUS0tL+OEPf4iLFy9ifn4etVoNs7OziEajqFQqzkEZHx93SlRrxVJB8IfOO3cNZ9rX0tKS+0w37tzPzp9P+MxUUPZnoxQ0K+ow2xrpykpXIJ1sQbYjmUwGgHym/xPc8W3iqUYugRcCQ8qO2M/j0+l0sLi4iFKp5GqsapZAq9XC7Oysi5qvxwqlkIU2MTGBsbEx5HI5x3DbCWHJJKaGskzPIOW8X2QUOR/J5SAE3+ik0Qnh/gV0drgGK3OMjGMNoAJwes9uOMU1fKOsk1gshquvvhr/6B/9o8AeJXQ8uNFlPp/HuXPncP78eReUXlpack4Eg3RkKeu96bQRSNZnYgmvTqeDYrGIhYWFQNBxvwdUfaLjRL0JwGXpkVVlGXJ0uLvdLqrVqluvx8fHEQ6Hcfr0adRqNTeH1L5RR5K/lVnGEjk+sAZAINitG2up0895xNr1Wgt3P4+RsrrZTmXS0za04MR6wvnMfUcY+K5UKqhWqwFyAtsQiURcajmDIOw//iazMxaLYWpqCvF43JUuOAgy0tUjuRSivq2PIatAp9ahtmuiT6yvQd3Lcplcywl00l9hZpL9zsc6pijQzvZSh5KAo+0GVghqLKf1ghe8ACdPnkQ+n8fJkyeRTCYdkN5oNJBKpVzJC+qYWq3mNmLks2j5D81y8/nVmk3L4GClUkGxWAywo1UswU39SPUXlWmrAKgF4pkpncvl3HquZbOsnaNBZasblUSm+8wQ7E0kEm6cLKivbdJjfML5wLVb97liO1imBYADhpmlzzkYj8cdOVBBd0ug0P09eG8G0YGVMnDUMepns6/Ihrf2ge8Z1T7Q8dP3j3YK28FyMdyrhdch8M/xoc/MOackDCXy6fPy+TVDkp/TrqbuJaBO4FszNVhah/OBbdbx9o0zcSluYgpgYElFS1KweBT7m59bdrtth56vgayNZKSr91ZGQPo+k+1Mal2INCVEX0S+qPrC+pxcAqvpdBqZTAa5XA7pdNoB4DyeqUuMhCcSCedEUlFr/VUueGyjsqZ9QDpT3PibCojfb6fPhgWpd3qhsVH7jY7b6vUHXUMDKozSckG346GGEo1Krf/H69hIto2o7vfFWllkjUYjEHxg9JzO+TBCI0Svs5ksg2Gub4MkB4HpNlL4I7kcZZjgtB5r/7cGuL0G12jfdbkOEAzMZrOBADWdvn6/70pCZTKZgC5Wtg5BhWaz6VKJfQFAfkb9rGU1CMwedDaNJTf4yAgqg1Kn7fEcM8uGWi9QosCCstB4rgIrVn9ru1VX0+EeJstqvwjbSdYh7QxNl98sk0vtno0IDFaXK8BjHWj+KHv0oMhIV49kJ4X+HuDfkNIHMPkAzvXWyEGyHkCqbVrvvR8E5K+3bttzNABP/5o+dSKRALD67jArvF6vBwJ5ti619hX1LoFIC9LZMiL8zG7cuZ4wkGhFwXT7uW2LjrWWQbHrqg1QK2ubv1UP6rHW99Tx9Y31Rn65HV8F520NbCU1kXilgRkFqdkHutko2x+NRgMA+6DMDJ+sN47a/7ZPfUD7VoXXJkGEfUMMx24Krux23/hokEaxCeITmq1ngyZ2fq4XPLH30mcZ1C/WNhzG198pPGCkq/dWRkD6PhJdZDRKqIoFCL581oin80onwzpTepxujGJfxGw2i2PHjmFiYgKHDx/G1NSUU/50voEVBcAU8mKxiKNHjyISiTimG9lpTIFfWlpy0UKCgKpktQ/4Q1Y067bZiO2wYhdVX3SaogbQTjOr1wNc9P9hF2Pf9fU+wxzP6DZToyKRCFKpFA4dOuTqejMNc2lpyW0Wwo1La7VaAIwPhUKBzdK2usGcneuXQvj8LGHD90+BJT7HsHOAhhKj5evVu9+K8Pq9Xi+wc/l+d9RHCn8kl4PQaaWzk0qlEAqFAqU+fOni6uwRWCDDWN/lXq8X2CiS9/SxYsbHx1322MmTJzE9PY1Op+PStYFVB/7KK6/E0aNHkclksLCwgMXFRSwtLWFxcRHAah1wrudc83kNW46N53BtVOd8uyC6z+7RNflSrgfKrmI5AAUZuBEaEHQWmZXFNV+fhTqz2WxifHwc2WzWjTOJCGRqlcvlgUFbBdJ57VgshvHxcVfCgA57sVgMsNeBlTq75XIZ4XDYbWDGtlsGpU907PcyUNJqtVwmhdrBmw3gsP/Yd7SNyTonaE8AiWPLuuw63zlfALhjGdRg/24UMNkvMtLVI9kJoV6gaJkLDTQpGKxkLx+Qvl5gWUX9SZ7Dd1LXSa6Duo7QN7WBR/qganNTV3BttmQvSjKZxOTkJHK5HCYmJlw2KZ9PN5U8duwYCoUCYrEYzp0759Yh6zuq70a9b3Wl+rNqf/T7fVenejP+5kbHKrDJ7DiOne84iuoVLTVi11Y7p/gZdalmfKsuUPvF6m87n9hXPJ7jqSxh3/Fqr3A+9Ho91weJRAITExOO+cxMgvn5eSwtLaHT6QRKvwBwZAbiQmoHct7pe6IkN/uZLZfC4wYBxex/tUdsCR4ren/axHazU5Is6W/rvm4aWOEz8n2Lx+NIJpOBEi7MugyHwwHbS98Zva+PHU7RvtCghq/0lPYRz7Mlh2yf8Lo6B+1x6h8MIyNdvbcyAtL3mfBF1MVLHdhB5/BHa4YzAsgFQ8FgVQjWKQ2FVlJdJyYmMDk5ibGxMeTzeVczSh3EbrfrHEBu0tRut3HhwoWAc83r8jm4KPIzZaQDCDh2tj7lVkWNN00hs32r/WRB+0tRNkOBb/1sO6Crr5/WW2zVaKWxEovFcOTIEYyPj7s0qW63i7Nnz2Jubg7VahWlUimwOZoacExhG8Y594kaBtofl2LhZ/u4I/pOiG6GstNsNPaNstJVse9XGSn8kVwuorXDNUtLayRqmSvruNGx54bG3W4XuVzOpZMqKL+e3slkMjh27BjGxsZczVWuY9b5mZqaQiwWQ6fTweHDhxEOh9FutwN7PwBwDg7XcT6HgsrDlLjaqgxiCSl7cDeAdB+brt9fqS2uzjqAQJ/E43F3DoMR3FQrFAq5rIFqtYpKpYJut+vqpYfDYVSr1YFjTvvNsrMymQyy2SySySQymYwbN62hDqxucquBXbU5N+pX3pc6ea/WZa1Jux2hHtVUfabRs349bUG+A+Fw2NUWVsebDj37SEEiDcSMgPSRPFtEfVoFzgmaAVizjvrYtzYryr5DChZbsUAj76llKOy7r2u/ApokqOl1WC9bgVMFvSmxWAzZbBa5XM6VkFKfmnZ8NBp1wdZ6vY5MJuN0jgU9aWvwOa3/ovaDj5nLTG8ALsigYv1S7S8rCtZyvGx5Hr031157H8UmFLjWUnrWN+TYaUk6nmuD8eorKXCpz6nzS4M8m8120gwCYGUOTExMOIIa20mwlrabtolzkMdpMEVxDe1XK5ZEqG3S59Z3QQFxG5BY7z72erRZAATar7pVAwX6PNbXJQ6l2dhaypDYEvvT2jSKdQDBTAobpNK+su/yRhgNgz+D+sn3ub7fzNQYRka6em9lBKTvM6HSUyeBqUM2sqiGg9ax5mKrQLpGermorcew5fX0GN+iy9/KuFnP6beANBWgBUl5b10gtvLCK/NcwUbLJGBbeB8+s/aBKrHtpkLzHoN+VElu5Xra7s04yJFIBGNjY5icnEQ+n3dMRzK2ODeTyaRjuzGSrGOqDL5h7q2iTBVVYvqc2+n73RI7Z7YbHFFRg5Z9fxD6ZCQjuRxFdaAav1bHaGCZ64EC2D4m1kbvNGuTx+Nxt4cDGUvqJOr9arWaqwFtgQG2jXpZ114NDFyKtUbTda2Dbcuh0HawDDH7s9ngt3UEFYRRAgDby3aqc6/kAf7mtagvLXmAwIvOh43ayQ0vU6kUpqamkMvlnK4msSIej7tMq0ajEegn2olbKcGmIIgCJJthjO4XUQIAsPa91TlhS6kpGYNjakXtFv4/kpE8G8QClfQ/fSUJFRgEgsQl9RN5rA/QVP3EgKF+R93YbrcD16JocJH3VZa5buSs9yPwZX04HsNrq45gG6hX9XsNQAxaU3iu/m19DNWRvqxVXzBC2+EDaHXvFF0nKUog8vnVdh20uk/7i/NnGLCY+lVrcet9lECn5EI75jxG9aT94fho9p3qdv5PwJf7yqTTaeTzeQekMwjDDMJ6ve4y13TsOf8UILZkM+tjqo5WNr6Ol74vvgCJxUHse+gLqNj3Uv/31X+3bdYgB4WEPWaMKcnUzg0bhFE8ySd6vC0P53sn+P9GPv1WfH4NjKxHoB3J/pERkL6PhIqfi1qj0XBp3lTUfLnUiNA0Mm6WqA67Lky6YK4HvtF44CYLuhGLjcopgK91Un3Pp5Fzn6LWY7fjdCjzgVHseDzuIu5kjOkGIoOAdI4J08a03Mxmo9O2Hzh2/Jvt0UXfV49Or8X2sp0a3ebY+VICbX9xQ5Srr74a1113HbLZLK644grkcjn3fa/Xw8mTJ9FsNl3WwezsbGBzOV5vvfv5RMEKKkcFU/icNGQtu2E/Cd9lvj+cJ1bhb0c4F2lUHISaxKPI+UguJ1FHa5DDyXWMAXGCmdTnLHvV7/edntV1fCNpNBqYn5/H8vIySqUSKpWKc3aYrk6HvdFooN/vY2FhAefPn8fc3BwajYY3SK7Ahz7rpRLVy9xrRfuUAHE4HHbfdzqdwEaPlijA/t7sukhd1OutpB+HQiG3uRWZT7SrCKyrzubazDRkAAHQhNfWID2DIL7Ahq99kUgE09PTOHr0KHK5nNPVlF6vh2PHjqHT6WBmZgaNRiOQ4dDvBzefG3Z8FXQmWKIbkGlJhK2U4NsL0eCNblzG91afjWw4ZhcAq0xOghI2gOIjSux3GenqkeyE6PqgQCrXemWlA6v+jrXx7XujPrACTlwbNahNn4LA+vLysmMCKxjH8wG4NlOHEMCr1+tubWN5DmWuK3hHv12zRvksLOmWSqWQTqfdc9C/p22vdr2uH6qf1U7gM/NeqlPUt6IvorqRvqiWpSIAzzHi81IP6nOxD6mrbeCDos/AfVioA7XUBwMMem8L3GufKOiZTqcDm36zHIiWq9O+Uxa9YgH0rbQcrvpezCZkn+izcn5Ho1EUCgUcOXIEmUwGJ06cQDabdexqtm98fBylUgndbheVSsVlC9A2JC7DOWg3fWc71P4kmGvH0L4XOne0b3W8tG/stQb5tWojq23G8rFkj9s+Y/vZLvZ3rVZDLBZz2Xt8BrXZeI5mxA+yAYkp8F3j5qiafcB3WMW+O+s9/6A+8gHt/J9zfBgfYKSr91ZGQPo+EwW5qUSUBU3lwmOt02gVOSO5uiDy96CFRYFZG5njokLRttrUo42e81K+wFxUqXSZ8sNFVxdf30Zq+qw+Bh7Z/oNYEeuJRsHZf0x1tIv+eiC6vaZ12DhnfAwJX3+xr3K5HKamppDJZDA2NuYUNZ3CeDzuFE4mk0E6nXa1QrcL5LIdlumg0XZlV+5nsZHwnZzz1jjZaHz3i4wU/kguN/GtvcoqsYw71d9Wt6gDPuxaSochkUi4wJ11eqw+aDabaDQaaDQaA9NHfe/bpdbZuu77gHQ679ygTdn3NgA9DGNoI1FdDQQdR95Hx9myw9gOvZ4NMtug/bCMdGCl5m6hUEA2m0U+n0c2m10DrAArART2nQU0tipWV/O5LFv7IK7bOp6D2HIUHyvTPrOuDwdFRrp6JDshSuKyALO+D/q3+qzA2vKX671HuuYosYe/lWCma5iK2tfq26p/zRIiPJ7+3KBgmdUNBGjJSCbRhscoOcr6mYOC28ps1+wxzW5nOwex020gUMdC+0xJAgwmK4Crm17bvuAz6feqT3kvy5y2bVBbi9e1ekn7i5/pPLLBT72f6mo7fy1Ww8Cy9iMDKFrbO5VKueAJiX7d7kqJXJIQk8mkA3Z1bIHVTAYGIYYJumuQSp9T9z7zMctVeH8NnGwk1q7VPrNzzOJVlpXO/qfNZ99t3k/Hy2JXSgJQ0XG0WQcUX1klFZ+dqc+xEdA+CFAfRka6em9lBKTvM1ElS4YSnSuNnPNlZqSSkVKypi24Zp2/jV6eWq2Gc+fOoV6v4+zZsxgbG0M6nXabjvL8druNmZkZlMtlnD171rHcqtXqrr+gukgy8sza3mRbM2JMBaJKxjLSgaACoDJkxJxpXYz2D+sA6/U5dgAc60mj2OsZecBqPUG2pdvturQwKlydE4OE4EQqlUI+n0ehUHAby2rfsG2M+E9MTKDX6wXYWVsRNXz02TlG7C81OH31z/aLsE/IRKjX6wFGxHal0+mgUqmgVquhWq26KPp+d9RHCn8kl5tQ75IdYwHUUCiEer0OAI7Zxs/p8Oq1NEg7jHDzyH6/j8XFRczPzyMWizm9Z9nVzWYT9Xo9UON9P0g0GnX7rZAVDKw6UGQCh8NhFwigvqNOYEBa2WwbMa7V2bbgtv6QGUbwJB6PY2JiAtls1tkDtAUsKERR2446grq60Wg4Hb7R2NPhLBQKOHbsmGN3ch6qYxYKrex5MzU1hUgkgmKxiKWlpS2Pu56nc5d2Ae0s9oeCUftRer0eqtUqOp0OYrGYC0pZEACAy4DgHGQWIbDqaKsNTxCrWq2i3W67TYUPgox09Uh2Quhz2ZJlXKt9tcSBtXW4NQhpfbZBzGd7Pv1lH3CrvpQCdgr6EhRl29QPU+CP3ynoC8DtR9JqtZDNZhGNRtFqtQKZLvQx5+fnUa1WMTc3h2KxiHK57JjIg4gzg0pBaNBC286/tWynDRbyfrRv6J8pKU2Z2P1+P9AOvY8GpPmZto16g/6d3p/34vMoMMv7+MBaZZRz7DUDwbce83wtpaJYjAZVVOw8UInH48hkMk63cMNw6pZEIoFsNotut4uxsTHXL8RSdFwt4Kt9r2Ouc1zL2mjAg8epr+97n3yAtc/O8YHCOj7st36/7+yD9YTjz/WDlRrS6bTzsfks2l4NeimQrhsb894aJGm1WgFCCs/jeAxaa7Qf7Hz39YcP19H+02zHjWSkq/dWRkD6PhNdBPjSMs2Ei4CN4mr6l88x3sqLUqlUcObMGRSLRVx11VXI5XLI5XIIh8NIpVIB5/LcuXOYn5/HM888g3PnzmFhYcHtAr6boqzqZDLpSrkw8sv/gcEGh0/UkefizHQopo0rq2wjUSer3W47p5obvjCyTYVvFYSNjlM5ETAnWGL/Xs+Bi0ajSKVSjtk2MTHhGPy2n2hIMbASDoexsLCwLSBdx47Gmo+RrgZgv792R+79IgRJ+v0+yuUyarVaYFfx7bLpl5eXUS6X3U+z2XSA0khGMpLdEwLpmi1D4VqvDvl665U6Rio2EK7Cd7/dbmN+fh5zc3NIpVKBet48v16vuw0uB5Vg2yuJxWJu8zX+UEcya4uf1et11Go1AEFGONPRqe/C4bBLvR7EEtR0e/a9bwzYz9T9sVjM2UWRSMSt97oG02ZTR5hBUAbRuSksgfRh9VkoFEKhUMAVV1yBfn9lM9N2u+1ADu2bVCqFY8eOuU1OS6XSlnWFAhZaFo/lUFKpFJLJpAMylEG3X3V1uVxGpVJBIpFAo9FAMpl0th7fH457pVIBsLohLxAEz3QOkciwuLiIRqNxoID0kYxkJ0Q3iNTsTL4vmkENDC7TYrNbNBPGt177gCoFRxXo1uAr26MkJ15P2c7K+gZWAVklylg7v9lsYnFx0flaJDCxn6j3lpeXcfHiRZRKJVy8eBELCwuoVCprMpao4xTEHJQxw7ZY8JNrN4OIJIrZc6jLGKAnkK4ENVv/WoV97GOEs8+oQ5SQoEA8n01BbA1cKqisDGYADifh3BsEZOq1NMte7Tg7h2zgwQeiJhIJZDIZV653eXk5UA6G7wkATE5Ouj1v9Jpsn82OsIF/XzYVmeQ+IJ19r/Nb+1btJOpz22/KELdkMbUZNPOO7bJ+Md9Jfbc4L5vNJsLhcABI5/kA1pR+ZX/xXVVMQdukQLqSGoDgJroaWLIBDoqv//W7YcS+gyPZvzLciI5k10UjfhYwtVFRdRJ3ylGhE9BsNlEsFjE/P4+FhQX3e2FhAXNzc+6z+fl5lEolx6LfC2dBwViCvfqji6FGxtf74TFas9t3/c0A88DalCcbqVXnTdtDY9LOC1VOg5TsZto1bH/vlPhSrvQz+3OpZSfuR8Or3W6j0Wg4oGM77ykNik6n40AxW3tyP4sae1v52azcf//9uPrqq5FMJvGSl7wEf/7nf77u8Y899hhe8pKXIJlM4jnPeQ4+8pGPrDnm85//PF74whcikUjghS98IR5++OHA9/feey9e+tKXIpfLYXp6Gr/wC7+A73//+2v64Z577sGxY8eQSqXwqle9Ct/97nc3/Xwj2T+iDp0y2/TvS/V+8r61Wg1LS0soFosolUoolUqOgby4uOi+I7i8H4QAOQPGtB3UiVfwwOpi6kb+rc4gHdVEIjGQ3aMO9Ebis8s0zViDwDxebTTV03aObHWN87HO+LneQ/t0J8WnK21AYb8L27u8vOx0NYM3CjaocJ7ZPvWBULSlNVV/v8tu6+qRXL7is+spup4quGVBcz130Dq23rzTNduWLFWfieu5fs/ztS1bEeoLZg1zrWFmaaVSceSYSqXisk4JAm8n8D3M2m9BeXu+T9/6zhk0Tvq97xo+X1s/s231tR8IguE6t6yetTpSs4j0R6+lv+18U/vAx5gfdLyvn32ld2z/AoPLHPHeNrixEUvajqklBljxseP1eMUy+Fv3DVDmtW9tsL/1ndU9+dYb50HXtuOlmQsavNkoWGWfWY8bZr3Yrr4c6eq9lREjfR8Lo8BU7LoY6MLiWzi2K0wXbzab+Na3voUzZ84gm83i+PHjSCaT7n6tVgvnzp1zjvvi4uJQ6To7KVy4WIOMbGn92y6GwOYMIkbt6VhpxJSsKwqV+EbC/uv3V9INWe+MJUCY6pVOpwPMvHq97tLaaVyRHd/pdJxRpuniG80NXj8WiznlBMBbioTXUjB3JxxEn0PuM8R8x2x33lvlZ69tle5GwgyD5eVlLCws4Omnn8bS0hKuuOIKNy99htJ60u/3XV3jxcVFPP3005ibm8P8/Py+KtGwnmxnjdrseZ/73Ofw9re/Hffffz9++qd/Gh/96Efxmte8Bk8++SROnjy55vinnnoKr33ta/GmN70Jn/rUp/AXf/EXuPPOO3Ho0CG8/vWvBwA8/vjjuO222/C7v/u7uOWWW/Dwww/jF3/xF/H1r38dL3vZywCsgPFvfvOb8dKXvhSdTgfvec97cNNNN+HJJ59EJpMBAHzgAx/ABz/4QXziE5/A8573PPze7/0eXv3qV+P73/9+YMPAkRwsUafF6ujtyDDnt9tt/OAHP8CFCxeQSqVw6NAhV0ec2UosA1Wv1125ma2KgqZblVgshsnJSWSzWQditNtt51xpm6kLI5GIqweuelF1gjrhiUQCvV4PpVIJS0tLAQeYunqzOoRjWq/XMTc359Lbp6en0el0UC6XnR2kDGY6fVpnVzOJyF4f1n5oNBooFouIx+PIZrOIx+NotVquBADtg0ql4uyGncriIkOR7EFeUzcwvRQkD0so2Emp1Wr48Y9/jFQqhWuuuQbPfe5z0ev1UKlUHBOO9hCzHoFVfZ9IJBzDVIMoCwsLKBaLbi+ZgyC7qauBlaD3H/zBH+DChQt40YtehFOnTuFnfuZnBh7/2GOP4a677sJ3v/tdHDt2DO9617twxx13BI75/Oc/j/e973340Y9+hGuuuQbvf//7ccstt7jvH3jgATzwwAN4+umnAQAvetGL8Ju/+Zt4zWtes+n2j8QvupkhsLY0JYA1flm/H2T66jvj80fsbwW9FRglM56fk6VK3aFMZwXR1VZXUJDHsTyG9c2ttNttLC0tBTYsjsVimJubc2s/jyMLvVKpYH5+Hq1WyzHWeR/tH+vjsp+Vretjo6tfwzVdA9K6UaqSx8hI1xKpykq2hDV+zjHUbCY9hu1g25SFr3pRAWqeo4z1RqPh2t7r9RCLxQL7qbA2PVn42g/U0yRB0dfVrDOdZ2wrr6/zhngBs8MBIJPJBNj5fDYy5rmZphIMNEtb55vONf7PTCraUyyVx3ngC3zQ3mL72ff84bzTftf3hb+5CaidA7p5JudrMpl0mSA2mMZjdb6ybcy2X1paclgFM759wL+SLGywv9frOZup0Wi4EkqtVstlSCjDndUhfO83j2V/6maplEHrlI6nfY6NZLd19UiCMgLS97lsBrzbSWF0sd1u48yZM5idnUUmk8Hi4uIaIJ0paLoz+24LjRsqnmQy6QBLLU+yWTbBelFvjV5HIhFXV2vY56fCIJjO8gC9Xs+1O5FIuDRpXfDYz8pUYIq4lnMZlsWgTA0tX+NbZBVIZ/R2J0usbMRmsH/vlChLwgLpajgNC7qwb1jnsN1uo1AoOCNns7XS+/2+27G9Wq1iYWEBs7OzqFar+4ZdupHspsL/4Ac/iDe+8Y341V/9VQDAqVOn8JWvfAUPPPAA7r333jXHf+QjH8HJkydx6tQpAMBP/MRP4Jvf/Cb+8A//0AHpp06dwqtf/WrcfffdAIC7774bjz32GE6dOoXPfOYzAIAvf/nLges+9NBDmJ6exhNPPIEbb7wR/X4fp06dwnve8x687nWvAwD8yZ/8CQ4fPoxPf/rT+LVf+7VNPedI9o9wXdwL6Xa7mJ2dxezsLFKpFEqlEuLxeIBhu5OsWB9zaLMSDoeRzWYxNjaGer2OhYUFp4fYlwT+E4kEgFUnWAEaTfNm28LhcGBT8Xa7HShpst1AB9djlgThxp9kGlrWI3WytlWflf2xGfuB+gCAA3F5P24Izt+0FXZSV6hTr06wvcdOOmvbyRLbSFqtFubm5pBMJnHFFVe4oCbL+vDeNkVcSzfR3iTwzoBLuVzeVOmevZZnQ9D7xIkT+M//+T/juc99LoAVPXzzzTfjW9/6Fl70ohdt6dlHsrEQSNP/rRAAtSC7gqy+eea7ls3+0Wxje5wlpeiarGDXes/GY61o2ZhSqeSyXmq1mtNnBFQXFhZQq9XQbDadjW99BuuL0K9QMFKfUY/X51BAWJm0Ck5b1rgC7np9vY7tP+1TBmAZ2GDb+JxcO7WNPt3lC7IQHNXrUy/ZQAP9WPXxqDeJaWgmgU/ot1sgWNvF+xBYt+3Q8zWAYe9j/WMdT8tSV1Kc9r2PqMZxZftVl9t3QPW9Hsf+JGDPNhFQBhAoO0cgXff500CXFQXTl5eXUavV0O/3kcvlvJiFfc5BQLqW2aO9rPNSgXQ+60Y4AMeQwvN95ykp065Nw+jSEZC+tzIC0keyoRBQD4fDWFxcdJtlcNHjBmq+VKdLLTQcuGgpa3wrJVc2e1+C96yZqqlgwy6AukDbVDL90Sixguf8m847GWHKqBimHTQUFhcXcf78ecfkT6fTboHv9/vOKV9aWnKlfRqNxhoQ2l6f/eFrE7/X/mA/axRcFdp2wBsgmIYfj8cDrAtrIGrQROvkKbNwUHuWl5dRLBaxvLyMfD6PfD6PRCKBQqHgouhadsCy4MmM6HQ6mJ2ddSUayuXypuvqPluk3W7jiSeewLvf/e7A5zfddBO+8Y1veM95/PHHcdNNNwU++9mf/Vk8+OCDWF5eRiwWw+OPP453vOMda44h+O6TUqkEAJiYmACwAgLMzMwE7pVIJPDKV74S3/jGN0ZA+kjWyEbsb/t9t9t1eln3UNnJIPd6zLtBok6gAt1aJ7Pb7bq1udvtBj6jg8WgNTA4tZltU2YeS7yoPqXTO+xzcE22DjOD4lr/k88MIBDsJoteN77brJ1Clv3Zs2eRy+UcK519w+w0sqnZtmHmwDDZBrQZ2LfKMFQm6E7MO00D13nAfmZddjIHGazmc+h8WU8UgJmbm8NTTz2FcDjsxo3BikgkEgApaA8yG6HX62Fubg5LS0suG2CvbOSDIHsV9P75n//5wHXf//7344EHHsBf/uVfjoD0HRLOd/oGBJNUrL29URkPDTwCwfXKd227BvmAXb3uIBm0RitAu946rt+R7awBYc0ytnWa+b36Qmwz/Sbbj/b5bN8PIkppe7W8CpnNlomux7MNykC2+oS6gaIMawUf9XjV1wri+gBs9UMJfhMYB+CY4bYEqvXz1PfSAPh6+lGfQftJN5uu1+sIhULO7+z3+24PjVarhVqt5vx4DWBQp3EvmEQi4YBp1e2+4IaPlEndbXEGizkoYdAGyi37OplMujnC5/NhA6rLda849jftLM4fmwVCG7ff77usPCVZqF1pfXn2F3UyyyoRSFciovYt+8EGSvTd1xKFtA9U76/3fvrG56CQ5J7NMgLSR7Ku9Pt9t6A3m01UKpVApNin2HZT1BlPJpNuY1GmRW2Vib6R+HaI1k1NfIutT7hoEgBnf3MjEip/Glh01llOhfXz6EDSSee1hnVgqSi63S5Onz4NABgbG0M4HMbExEQgFY718GdmZvDUU0+5tENbs1b7RyPW+twUjYLTYNExUyNA27rZOUfQmkxIbv6Sz+edAcDUNGB1d3jek33PdPlarYbl5WXnvOszU2q1Gs6cOYNoNOrOz2azuOqqqzAxMeE2xNWNYNgPNLqKxSKazSb+4R/+AWfOnEG9XseFCxdcqvhBcc53InJeLpcDn7MMksr8/Dy63S4OHz4c+Pzw4cOYmZnxXn9mZsZ7fKfTwfz8PI4ePTrwmEHX7Pf7uOuuu/CKV7wC1113nbsPz7PXeeaZZ7zXGcnBkWFAyM1eT5lWdu1UZ5ZrAYN3BC4UUN4p2ey16GTwN1ONddMyMqpZ3iwUCrmArm7GxZrvsVgMhULBMaB4H/3NdTkajSKfzwecNerTzQRnqQdCoZADknnNcDgcYLepHqT9pIA6v/MxxDaSbreLs2fPYmlpCWNjY+h2u5iYmAg898WLFzE3N+cCv5YB7xPV3+sBv2TucWzoyFIYmGcfbVVCoRASiYSz7Xgv2kShUAhHjx7F9PQ0ms0mTp8+jWq16myWTqeDxcXFoYB0lhIKh8P4wQ9+gPn5eSSTSUxPT7t0fNoMi4uLqNfrCIfDbnNflvpptVr40Y9+hHPnzgXG+6DoaWD3WG77Jejd7XbxP/7H/0CtVsMNN9wwdPtHsr7o+jsMSKSA4SBwmmurHktAknazspx5XepGyzhX/cnrD1qTtfyWZa3ye98z6bX6/b5jm+tz0s/WvuF6zWexDGPbjwTL1W6gaBaNL6Ch7y37hXuM0MdWwHIQI14BTGt7WIBU+8wCpVwz+b9vI0kLvPI5WaqEgYpoNIpMJuPwCpLNtCY+v2u1Wk530cfTjAb2j89mICahQYVwOIxqteoy+1lyL5vNus1Hi8UiFhYW3L50zHbjNbWvkskkACCdTiOTybisMy2bYuevJanxWhYgJpahvno4HHbBBN97yzZxXtC+Y7m5WCzmSGPaJgoBbGZNMguAAQefsJ+i0SiazSbK5TIikQgKhYKrSJDP5wN+NcebY0wi4uLiIkqlElqtForF4hr8ROuwsy9tVgHtWtqtzWbTMeZ1bigupaLvCvtdyQLryW7p6pH4ZQSkj2RDsSDmfhNNN7N/8/udvh8Q3HG83+8H7r9ZdhmV0zA/VGg2Yq6O8lacNo5vrVZDqVRCJBJBtVp1JXJ4baY+MXJLIIIgiU3LU6OXCofKwRrW/G2j5/q//t7sM3K8GC1PpVKIRqPIZrMOSKfCV0OQfavRfwY5QqFQQPHaNingwM2EGAUncERwX/uJCr/RaKBWq7n6bdzUlwzDgyQ7ofCvuOKKwOe/9Vu/hXvuucd7js9RGJYxpPf0MTyGueZb3vIWfPvb38bXv/71bbdtJNuTjVIxD7LYZ1Oncr+IgiOWMcTv9W91yjVjh8/GNVdBBn5vnUdgNYhqg7DKkh92fqg+4vVV9ypzEEDAWdWsJj6zffeHaQvBeabCV6tVB3hEo9HApna+4Iv+qNjaresFGHx62DIet/POUR/qhrS0R3q9nmtrNpt1TnM6nXYAitbztcF9X7vUDiNInkwmHXhOO4D20PLysnOOGUSp1+uuHEOpVNoTgslOyLMl6P2d73wHN9xwg9uH4eGHH8YLX/jC4R92JOuKsmQtkGsB7PVEQVb9TAFLIAjE2/v6ahVrO4YVXeP0fK7b69lx+swKegMI1NHmsfZ69p20BCUF9IexX+1z2bVc/RIbvLBjYtnp9re1Uegb8hgF/n1kKwXR9Rpct6mPCJqSba1Maks8UIDcx8xWf3q9rAZ9bmUmUxi0J6GK+5FZYFfLymgwXucxqwIQpGbfK3Nbn1OzMnQMqEO1/waBt9bOolDPUgdqoIU1/Vn6zrLSfZkGGiDR9vveUQL7DEjxfuwv9qElqFF3k3nOftfNS+2882Uucg5oSRm16ThvNkvoVJxjmLVxBKTvrYyA9GeZ7DRbbq+F0V+tKX6pyrn47k0nT6ORBNU3ckJV6GSHQiEHmBIo5cZhjNiynIqWdtE08e0wnxgZ73a7WFxcRKPRcBF0bqJF5d9sNjE1NYVsNhvoc1//aySb6YqtVss9ExkAPJapkmp4azBnWAUDwCnYaDSKVCqFsbExJBIJjI2NYWxsDPF43JVbITPSGlv8YTrm8vIyxsfHUavVUK/Xsbi46Gr5Mn3PFxCgYx2Px1Gr1ZDL5ZBIJBwIQGBAWZO1Ws0xFObm5rC4uBhINTxost2158yZM8jn8+5/65gDwNTUFCKRyBqneXZ2do1zTTly5Ij3+Gg0isnJyXWP8V3zrW99K/70T/8Uf/Znf4YTJ04E7gOsgAFHjx4dqm0j2b7sts7bKV1rA4qb/X6vRVlZ1JXU1dFo1NX51mAw10BgtQQXWT5cPycnJ10QNBaLOfASgGNsAUHdw/7hNTVtezN9p44d9TD/Xl5eRqVSCZTV6Xa7LnVYbQKO3VbtFba7UqngqaeecpvW0Q4plUpus7FsNuvAFbKzs9ksUqlUwNGj3mWdXo6NlhCzQW8y9xRcscGEzQoZZWw77Qzd/I7OeTKZRL+/Up/80KFDKBQKAZAkEom4DW01c09BKytMxeeYzc3NIZ1OY2ZmxrEdNSMBgEvPZ0bIQQXRKdtt+0EIej//+c/H3/zN36BYLOLzn/88fvmXfxmPPfbYCEzfIWG2qgW6NZipoDLgr2kMBMFvFdZTHyQaiFVQjEQYgq4W2B00/weBXPT7BoGuvBevoSAf+0bt2VAo5PxCfm9FdSUZtwroa9mNQWKZwhaQVWDWgukEm3meDVjqde3/bJ8Cj3wOHWtbTkSvw7FVRq8G6nWD1maz6YLMnDNKQCMpjDaBZkfTbtGxU99Qn12P4d9ktlNPplIpZDIZLC0tod/vY3Fx0ZUFow3EMmIAAhlf7AuOLQO6WmK13++7oC77kfYT5wbtIH3e9dZ8/U6zu9PptMsMjMVi7od+Ne09zn8Fz/k7m806ghmBbb6X6vfbdgBwpL5QKIRqtYp4PI5kMomJiYlAqRfaKsRTmCFYrVYdzqIBA18AxdcXnIvsYwABEJ9BBi2HpH2tgShdH21GyXpykO2Mgy77HkjvdDq455578N//+393wMPtt9+O9773vYEF+7d/+7fxsY99DEtLS3jZy16GD3/4w6MadyLWQAEO/otHZc9Fmwu2ZbpdSiGQTwOIiy+V9LCOFCOl/f5KrbRSqYREIoFWq4VEIoFut4tarYZOp4NisejAVK27uRPjSee7VCohGo3i/PnzDoCmI8s0ZoIZGsgYxMjXaDOB6FqthsXFRbTbbVdDnIYNDVpfeuBmnzcUCjkmVKFQwIkTJ5BKpTA5OekULYMFCvbwXFWkBEc6nQ5KpRKq1SpqtRoSiYR7Hq2Tq4ZUt9tFqVRCuVxGOBzG7OysU/j5fN4ZRTQqdbd4bky00xu7HkRhnfn1JB6P4yUveQkeffRR3HLLLe7zRx99FDfffLP3nBtuuAFf/OIXA5898sgjuP76650BfcMNN+DRRx8NpIw/8sgjePnLX+7+7/f7eOtb34qHH34YX/va13D11VcHrnn11VfjyJEjePTRR/FTP/VTAFZAm8ceewz33XffED2wP2Wkq1dEAZ2dAtM3AiS3A1healHnW+tc07klEK3AJB0jAuV09hRIJzOZpV/ofKkjrWW5dD3WNV6Z7cOk0dLu0HR8nk9GU6VScQGCSqXiZYNReO5WMtmUQUdwgOwvnX/xeBy5XM5tXN5qtRAOh3HkyBEUCoWA3iM40Wg0EAqFnE6zrCzVwVp/dqckHo9jamoKqVQKuVwO+Xw+wIjLZDKYnJxEOBx2Ae1IJIJDhw4hFAq5QH2n03GB81arhfn5ebTbbVeHdtC7qRu1k1nNjDUy31OplKuLzqCJzodns54GDkbQOx6Pu81Gr7/+evzVX/0V/ut//a/46Ec/OuRTHjzZTV3dbDZdOSauL1ovfVAgcdB6qKCxssFtNpN+Bqxm2mgJRoLO2iYFKxXosm0ZROZZbw0nQKrn6719zFsGD6mfFJT39Y2vzbbuuA2IKlBugXPbn9bHo471sbB5bR+xTBnUyhhXXamscN96ynvajANel34p7Xeu0XwG6k3qRG7crXOL/cDr8Zk5Hnx22zYLwNZqNdRqNcTjcfR6PaenM5kMgBUwmEEn6uJ0Oo1sNusyo+hj6xxV3U8wme3R/URYykaBdLV71iNnMRht+55s82w2i6mpqQBoTiDd7gOmPqyC43yeWCzmst75PCyhBMDrA9NeBFbK/hHcr9VqgRKCirOwZCrHXktQcZ3gZ3yX9J42C4NzWPen04AF56D2n30PeX/tj90ghY5ke3LpkcZtyn333YePfOQj+NCHPoTvfe97+MAHPoA/+IM/wB//8R+7Yz7wgQ/ggx/8ID70oQ/hr/7qr3DkyBG8+tWvdvWlRnL5iQKdVrFbFsNutslnjAwrVLxkfxEY0DIqWj9MU9l8keJYLOYUMeuBp9NpFx0d1DZ1AtXI1Egzo/z6GQF1ZQNoar6mWRMIYZuy2SxyuRyy2axTwgSUVSltxkHVdvNe+sMNyuwGOjS49Tf/5jOw/alUytWpy2azSKfTrj6bL5qsz6Op9/V6HfV63QECjJDTmKDxsJ1sg/0gdgw3+7MZueuuu/Df/tt/w8c//nF873vfwzve8Q6cPn0ad9xxB4CVzcf+3b/7d+74O+64A8888wzuuusufO9738PHP/5xPPjgg3jnO9/pjnnb296GRx55BPfddx/+/u//Hvfddx+++tWv4u1vf7s75s1vfjM+9alP4dOf/jRyuRxmZmYwMzODRqMBYGVevv3tb8fv//7v4+GHH8bf/d3f4fbbb0c6ncYv/dIvbaN391ZGuvrZIz69u9HxepxmknGd1+N0nQQQcJR9TGddp+0Pr6VOuwY4tgN60nFiRhWBdE0RXg9E1/azDjh1x2aBdXVWeT/V27we9ReztDQoYB076msFs1V3bsR03KyEQiEkk0nkcjlkMhnXRrZXNwXnvLE2go4z259MJp1+Zh8zuK5lX1TstdU245jTHmMgQefnQdbTwM7oaga9dZN1Kxr0Vnn00UcDAWoVBrRVBgW97TGDrqnPzezIy1V2U1crKKoBQ679vgzWzax9tgyC3ssHdFtwmOJ7XwcRz3yArg8EV59OdcFG7fMB1oMY4eqr8Djrfw5ai3xMcV0/LYvWPpcyi+2aN0ivqo9sxbees08oltFv+0YD3fr8Og70vbiOc+22zGcbjKAOoo/HrDrOZe1z39haXWuDI9bn1HKx9sfqOILaBOipo9lO1fnaJ4PGyd5Px4/35r1Ul9p70saw+IzOXf3h+alUKvDDa9l3Usvw2NK3Vk8zkKL6Wuvu2/ln55u2376H2ibtq63KZuzS3fSrKffffz+uvvpqJJNJvOQlL8Gf//mfr3v8Y489hpe85CVIJpN4znOeg4985CNrjvn85z+PF77whUgkEnjhC1+Ihx9+OPD9vffei5e+9KXI5XKYnp7GL/zCL+D73//+mr645557cOzYMaRSKbzqVa/Cd7/73S0947Cy7xnpjz/+OG6++Wb83M/9HADgqquuwmc+8xl885vfBLDSaadOncJ73vMevO51rwMA/Mmf/AkOHz6MT3/60/i1X/u1PWv7fhI6GPy9X0QXVxVfpM6ex8VMQVoqtN2UQe1QI2Mj6ff7LrJdrVadE0zloVFTOu2DGO/cdDUWi2FychLpdDpgICwuLmJpaSkQPbXPw2hzoVBAKpVCoVBw7HMFQHwGxKD+18htv9936WBkGjJYMDc35zbiYZkU9pH+3kgYGU8kEjh27BjGx8cxNjaG48ePuz5Kp9POqLfGp85J/q3sEEbcG40Gksmkq7HJzU/m5+cdk832MRkCNOhYj1WBJMt68F3noMl2FPdmz7vtttuwsLCA3/md38GFCxdw3XXX4Utf+hKuvPJKAMCFCxfcxrrAClP8S1/6Et7xjnfgwx/+MI4dO4Y/+qM/wutf/3p3zMtf/nJ89rOfxXvf+168733vwzXXXIPPfe5zeNnLXuaOeeCBBwAAr3rVqwLteeihh3D77bcDAN71rneh0WjgzjvvdGyvRx55BLlcblPPuJ9kpKuDsp25vhUZBhjYKWH5EADOER3UJmVy0UlhOS2eT8YRWUPUccwSisVizhEiaykejwecHm5uRd3CjC6mCNMB7vf7a0qiWWbURsKxbTabjgFFh5GpwrpB2UZjEY1GcejQIYyNjaFarWJhYcEBtsMwvaPRKMbGxpBOpwN7dzA4TYe82+06pjcA14fK+qNzq0zueDyOTCaDUCgUCPDOzs66lOit6CbVd/3+SqbByZMn3QbntC9yuRzGxsbc3KCtRBYa2X60vcgCo9CxJ0OtVqu5ObK8vIxSqeQCnSp23Hq91Y1mWfqFtttW+2C/ym7q6rvuugtveMMbcP311+OGG27Axz72sTVB73PnzuGTn/wkgJWg94c+9CHcddddeNOb3oTHH38cDz74ID7zmc+4a77tbW/DjTfeiPvuuw8333wzvvCFL+CrX/1qYL+S3/iN38BrXvMaXHHFFahUKvjsZz+Lr33ta/jyl7+8pec+KLKbupqkEQDOp0kmk47luh7rUsscAGs31qYoAEZhENEXdNVgLUVB1438Ul5HgwI+gI+/SX7is9vjCDRqGRZlrCtD2v7Qt9N+sML+8wHYyozVNiSTSacLdINEHQtglSFMHWj7SftZsweU7U1RBrrqZF6X84jfKWgeDoedPlMfVcv4aHsUhOWPPptmKEQiK5tM5/P5gJ/WarVcyRb6r9QHnEdshwaCSdzSsSChi8fq2NoAis4X9ls4HHbMdNYJp5+r2evMnNbAgYLQatv5sAvt64mJCeTzeaTTaZfdrfXQiReonWYDPTpXGKxg9lg6nUa73Ua5XHblTfVZmFmnLHXOGS1/o7XTeT8SE/nsipHxfHtNDSpoUITjoGuLziPfe2kBd74L2n4+50aym7oaAD73uc/h7W9/O+6//3789E//ND760Y/iNa95DZ588kmcPHlyzfFPPfUUXvva1+JNb3oTPvWpT+Ev/uIvcOedd+LQoUPOv3788cdx22234Xd/93dxyy234OGHH8Yv/uIv4utf/7rzrx977DG8+c1vxktf+lJ0Oh285z3vwU033YQnn3zSZXYwAPyJT3wCz3ve8/B7v/d7ePWrX43vf//7l8y/3vdA+ite8Qp85CMfwQ9+8AM873nPw9/+7d/i61//utt5/amnnsLMzExgB/dEIoFXvvKV+MY3vnHZOefbEbtQ7LXYCKWVjdprI5qbYcjtlHDhUzDdZyAMI1xAuSu7svbUGNuoPjaNoGQy6eqA29rkZJX4+kpZCYwM05FV5vZmRI/n31TWdI6ZCs+66VovbyuibPR8Po/JyUn3HATYbT3CQaLfUTEyWELjjeBPpVJBNBp1JVwGtV8VJMf8cpfdVvh33nkn7rzzTu93n/jEJ9Z89spXvhJ//dd/ve41b731Vtx6660Dvx+mnaFQCPfccw/uGVAv9iDKSFfvnVjH/1LreK6toVBoQ6CXulDffXX2uPZZp4NOIT8HVp0bAjDKZqe+BFaZ1FqKRAF9LecyDGN8kHDzbQUgdBOrYSUcXtkwc3x8HOFw2JWDGfYaZIex3jmBZmalqdNq2emaIUUgnQADbYlMJuNKcJRKJcTjcdTrdSwtLQ2036z45qY9LxKJYGxsDEeOHHG2CoBAEERLG7DNDDpof+rfuVzOkQvK5bLrCwbxa7XaUP2sxIj9tEfJpXjvnw1B74sXL+INb3gDLly4gEKhgJ/8yZ/El7/8Zbz61a/e0nMfFNlNXU2gmIC5ZaErqG0BVyXn6HvPdQpYW2JF/7cAt+oYtb99TPJhRcFc2xauMQQAueayDfqMFOtbqX+pfcbr2FKXPn9DAfn1/FJlO2t2sQKISmhSMNr2m689PiBdj9d5wOeijmf7bdCE/WVBTs4xC5gDWFO2Q9uvc0QBzng87srIabCW85slYzSAwrZxvDSjyupNHqsZ3LopubZFfXTOO/qkBLOB1RI0JG4pKUufXYMLFr+wASedH8QGlLynpeU0mOELWunffCbOOz4DyX4MdOvaoXNF9TLnnr4jNmBHm0HfHf7N4J0FsW0wS9uitqjO9Y108yD7SasObCS77Vd/8IMfxBvf+Eb86q/+KgDg1KlT+MpXvoIHHngA995775rjP/KRj+DkyZNOv/zET/wEvvnNb+IP//APnc4+deoUXv3qV+Puu+8GsBI8f+yxx3Dq1CkXILcB7oceegjT09N44okncOONN+4ZWWvfA+m//uu/jlKphBe84AXuhXj/+9+Pf/Nv/g0AuBp4vt3Zn3nmGe81dWNDYO3O8pezbAZM1wVCFSCvowptvRdZlYCmiakCtAuJXlOVgY2s2x9t926Jgun835fyo2INCBU60OrsKevJ18+qtCYmJlxay9GjR5HL5QKKlGOg7G9tO1OgU6kUJiYmXCmUS8n2V9B7fHwcyWTSbcxJYGLYdFv2O6+VSqUcG50sNAYEtiucx4zA5/N5jI+PIx6Pu83l6KxfTmy1kYzEykhX7w9RhwsIbq62mWtwfVTdrnqcoLVN++U5FNXZXHd9ekTBdbJxGES2uk/bp5uF0bFqNpsOvLDOobVXtPzLsE6LOp1qUw3r/LA9mUwGuVwOqVQKx44dw+TkJDKZDMLhlZrvlUrFbYjFTT99dgVryY+NjbmaxPxcGW3KsNO+G8TOZL/ymQhmEGQni75er6NcLq/LorJ2C8dDbSVN5WZaNu2hdrvtwAutdwqs1EvX+rF0eGnrdLtdp4vZN5zDBNIPcnmp/UKO2Y7sRdD7wQcf3FQbLxfZTV2taz2DXj4/UtdnBTZ1bluQmqJ6h+uZAoYKXvt8LwvgW1FQTH0lBpPpG9HPUv1Lhq+WvrTX0b+1bRbo9PnKGkDWZ7JMWv7WQIJl+ltfXX8G+bN8fn4/KINYgXw77vq3BtMJfirIqmxyC/oyUO5rq/5PMNzOLR+oy7GjXlI7huVLqFdKpRKWl5dRr9fXzHNei/u6UF9p3XUKbSS2UZ/R1/++ecPvqCup8zudDrLZrMta42d2/HTesU30q1mCtVAoIJfLrSlvo31q3+H1nkPnXa/Xc/uxsdRtLBZDtVpFo9EIBFtsJgrngrWD2T/EFTRzQNvm06c612xgS6+v75q1xfmcvjG11/PNi0sp1reyRENKu93GE088gXe/+92Bz2+66SZ84xvf8F778ccfDwRlAeBnf/Zn8eCDD2J5eRmxWAyPP/54YO8xHkPw3SelUgkAMDExAWDvyFr7Hkj/3Oc+5+rNvuhFL8Lf/M3f4O1vfzuOHTuGX/7lX3bH2UXTKkyVe++9F7/92799Sdu9n2VYEJ0vNqOo6vToIkaA1i5ovA4XYKZjM7rLhVeBeopGkHl93chJr6/32G0QXUWde99CS7HRfWt4aCokjTJ+P2jsCObGYjEcP34cL37xi5HJZHD48GHk83nnjHY6HZeOVS6XXUkVNRwOHTqEw4cPI5lMYnJyMpDix2fbSVFDlMyzbreLpaUlJBIJB/jbsR90Lc7VfD6PI0eOIJPJ4OjRo5iamnKsAsuI2UqbKbFYzAUr+B3T38lYJLPg2S67HTkfye7JSFfvraj+03Rs6s/NiJ6vbB61CWhkc4MuAGv0mjosdDoJKPA4/o7FYm7jSwCOed7vr5bz4DUVyOf9lWXIdGseZ4PAeiwd2s2s0Zp+q3NXQYFBos7ikSNHcNVVVyGdTuPEiRMYGxtDo9HAoUOH0G63cfHiRczMzKDZbGJmZgbVajUAXPBeiUQCk5OTmJycDADQDECTqcb7kknH/Tg0w0B1I0vJESzgszMTizUy5+bm8PTTT6PVajlHdyPhc5DdxvIx+XwehUIB1WoVlUrFZdPV63VXTo0lWRigpk7XEnjh8Erd+VAo5Eqo9ft9RxLgPKjVapifn8fc3NxQY/9skZGuvnxlN3W1BbmZRWJ9JHscgDVAlxUNhhKgtaxbAG5vKNsmS8xS9i8/t8+iBLBerxco1aGBSR5DXToIrLd+oq7rPjDd7ksRDocdA1j9yUFMa4KUzWZzjU/nA9J1M0/td2X92u80eK/XJhBOlrJ+T72uQQu2lQQw7UfqXsuYt30z6H/LIKato/8T9E4kEk4vsR3aJ71eD/Pz80gmk2i1WlhYWEC9Xl9jZ3AeUofp2LKMG59J9xDwPYvtI9/7ozpQx5FEA85XBu71edjHtvRQoVDA9PQ0kskkpqen3SbgChrbMbb978MS9HwGERKJRCDY0mg0UC6XUavVHEFNn1WvQ3DfEkLIcOfzkxChc5Ni26l4lQZBdDNcLfOidq4Pt/LtMaNBIRJJNpKd0NVXXHFF4PPf+q3fwj2ejOn5+Xl0u11vkNVu7k2ZmZnxHt/pdDA/P4+jR48OPGbQNfv9Pu666y684hWvwHXXXefuw/PsdQYFgHdC9j2Q/p/+03/Cu9/9bvzrf/2vAQAvfvGL8cwzz+Dee+/FL//yL+PIkSMA4HYep6y34/vdd9+Nu+66y/1fLpfXTKJnu+iCZhd1KhwaHsro4oLHa9jr0GnVDbDUcaNwYbLpXTaNbS+B80FijUMFN9gftg4en229FLn1hNelkiernJuMKpDOjTEJNLDNHB+miCvwcan7mQYBARXdtARYBTt8wZpBfaHX0M1RBxldW2kzhcYc+6/b7Tomg0b+n+0ycs4vXxnp6r0R6/irvlWnTMHtja7HoCZBSP1cnWw6Duo8AGvZP8o4Y1stA1xtBftcPF4/8wEOvCevrdfitX1sR7s+K5uI4gt6r/f3ev2rOorALnVuv993GWDcMEwZZb4gvTqNZKARgPb1sz4/bTkFrNYjAmi/2Y3FaA+qHbhRX7D91NHa9wQJgBWn3scyU5AoFAoFWLE+cMHapcx88IGGz2YZ6erLV3ZTV9v3SoOywCogbeeMru2AP4t3EMjOaxIktD6DZTIPEt899XP16RRMpM+qOmvQvZSgZoMKti2+a2j5FeuPK6iuehtYzVZiG/S33m8j38W3bqqu1GtwvGypEAuoEoT1fa7nKHPbtt+KT3frdbW9eh/VrRpYUF3EYDb1N4/l/FMchUFtgqQawNE2rTcXNlpffbqc7aYfzD7hvbXvfDaSBbgtKULbrteg2Lk16LfFTNTG5DNo4Ev7w9pstk/4vL5MD5/otTZ6F+w6NeiaG40d26OklI1kJ3T1mTNn3L5FALxsdBXfe79e/wyyr+y8H/aab3nLW/Dtb387sOfJVtu2XdkSkP7UU0/h6quv3um2eIWb8KmocX311VfjyJEjePTRR/FTP/VTAFZYvI899hjuu+8+7zUHpSyMZHWx0A0jyHRWZ0UZ42Tckn1LZc6fVCrlHCVe0zLSNbqrCx4ZRr1ez6UOavqwjQzvB1GjUGupTU1NuYh0Op0GALcxGKPYjBQ3Go1NLYxkJSSTSeRyOVcTNJvNIpvNBlK0c7mc2+STY5vJZNymYocOHcL4+HjAWNgtUYWeSqVw+PDhAKOSG3wNqsvL+UZ2IzdbzWQyjll/KZ6H1yRzIRaLYWJiAu12G8Vi0aW9DwNkXc4ycs53X3ZLX4909d4ImS3hcNiVCdGMMYKynU4HS0tLAQa5vQ71aT6fx+HDhxGJRFAul1GtVgEEA4ZkwSUSCUxMTLhMKtXdvV7PpTnT8VGGMbCaVtzpdFx5jUajgVarFah5btPi7X3UGctms2uAeerHTqezZuMqy3QfHx8PbEQNAJVKBbVazZUJsUH9YYVBCk0X5xgx8EqbiGyvSqWChYUF1Go1xONxpFIp11bqb/ahZWMqUKN9y3Zns1kAwbWZ45xOpx2gD6w6x7wW09cjkQgmJiZcv9BmW0/f9ft996xTU1M4ceKE229kfn4e0WgUhw8fdvYmx4mB6UqlgtnZWfR6PUxMTKBQKAScd9qM2m7es9fruUA3syvI3ON8YQbks1VGunr35XLU1VxLdL8Ku4GiipaDUBBawVi7DhHgI4ANwPlVek+W4+B9GfzTzSs1uOdjW/vAfbvWKRBrQVdl5a4H+nEtHLSes0yIbgTtu45lh1NPxOPxANvVgu8UJXjtBAGJ96Lommt9eQWiiSvo2NHmoT6lTaK2ATODm82ms2OsaGCF/jr7VkF03l/BXIK92WzWMf25Abden365xT+oz7QNSlTg+Cs4T7Y77YZBorqf7aFvyk22WTrOvosMPpORrgQ3Auk6Xhv51QSIbZBAg+42CKMBfj4/923Tsn/2XP4mLqXCd90G9NgH7GsfOcTOf7239pvOQZ6rz6/H8nsdMwV+NyIO6rNvRXhePp8PAOmDhHvlWKb4ekHWI0eOeI+PRqOYnJxc9xjfNd/61rfiT//0T/Fnf/ZnOHHiROA+wOYCwDshWwLSn/vc5+LGG2/EG9/4Rtx6662BXah3Wn7+538e73//+3Hy5Em86EUvwre+9S188IMfxK/8yq8AWHnR3v72t+P3f//3ce211+Laa6/F7//+7yOdTuOXfumXLlm7LkdRRU/lHIlEHPhro2RcxKLRqEsnJsDNRTcajToQ0+cAWhCdolFDBdK5EWWj0UC73XYA/26CveuJXcy4IKfTaRw9etQ56EwR407fVPClUgm1Ws3t/j2s0LBKpVIOOE6n047tRmOx2+26uqztdts5k5lMxqVrTU5OugV1L/qV90yn0w74YLCm0Wi4v31CRZtIJJDNZlEoFFyf6M7zl6K9ANw9mALHuUujaTsKbyQj2Yrslr4e6eq9EepQlr0oFAoOqGY9zGw26wK06wHp1MuFQgHHjh1DLBbD7OysO0adSupe1sjsdDqoVqsBR4U6R5lXBNItoMDzAbg1XoEU3lcdDOv801Hhmq92BPUCsFqr11dqIB6PY3JyEhMTE87p7ff7mJ2dRSgUCtRe32xglPdgPycSCafn7EZkkUgEmUwmABKw3+lIZjIZpFKpNdkDvBd/69+tVgvFYhHAivOUyWRcCRc6ewSleG3NoFOnl+cRKEgmk65UjGUeWlFALZPJ4Pjx4wiHw6hWqygWixgbG8Px48eRSCSwuLiIYrHo7JhwOOxKsnQ6Hcfs53UJvtsawErC0Hr9HAsNnADPnk3AR7I/5HLU1VxLfExlCyZvxNpWsFuvpcCX1gvXzGYLumu7SGBS0Wvq9WwtayC4mad+x/WEa7eCseoTMChL4M9mU/F7ZepSh2gg1ucLq47j2k0dzGdhu+1eThqAHMSQHsb/1iAIf6ve9AGMPl+JfaL1xcfGxtxmlwSqOU7tdhuLi4uOmFatVtcETHRe6Zgp41qZ0AoGKyZCnVyr1RwpTlnc3GuM+oagOOcx768BJgWZ+b4wQMRgyEZiAVnad9TdtVptTfkYzQLT8wigM/is4zaMb60ZazzHNzdU7POTiNhqtQLvrI9pThtS1wq1Fy2Qr+8dx81+btcxn+jc1Xb5Aod6jH5nx3+/SDwex0te8hI8+uijuOWWW9znjz76KG6++WbvOTfccAO++MUvBj575JFHcP3117v1+oYbbsCjjz4aqJP+yCOP4OUvf7n7v9/v461vfSsefvhhfO1rX1sTdN5KAHgnZEtA+t/+7d/i4x//OP7jf/yPeMtb3oLbbrsNb3zjG/FP/+k/3en24Y//+I/xvve9D3feeSdmZ2dx7Ngx/Nqv/Rp+8zd/0x3zrne9C41GA3feeSeWlpbwspe9DI888ghyudyOt2c3xRe5utT3UyWvysrWM9donjqVLGuhQLqW1uDiq06jb3FRxrv+r7+BIFNtr5npdnENhUKOBccNLycmJpBKpRyQnkwm0Ww2EY/HUSqV3MJOEGRYR10DG2TZ8TeNKH6nn1OpM1sgmUwGNmzZD8IgQSaTAQC3+YjP0FIwiPOP83g3nkdBH2uM0WgeJsJ8ucqI5bb7slv6+tmsq/dSNF1XHSLqbhvoY2DVpu+qcw4gUH/Sgoy2nIt1dtQRVIYNr6+6Wmtc0g7QTb56vZ5zmBQkUUBGnRz+ppOq2Uy8lk0bB+D0NPUg9SIZ28pgJyDNTbA2s6YrsNNqtdBsNgMgjOpy6mkSFrTkmtpmytZX9rZlSvlYk751VYEMC2qo08fr6dwgUWCj7DFgbU1e2iPAKgBnWZN8DmafseSLPhf7wYJCBCwsy4yBd+27y1VPD+ucj3T17suzTVf7mN38XIFOioKeKuqPWlaprls+kG7Qu661oVWv6Pqna5/1YfmdZV+rfmT7mA2g4JkCglomRNm5vI5lSmvf8r56Ta6PPgZyv98PkNwsQ9gHBFowVPWBD2i1vr1lNvtAdrYFwBp8gcFo7pVBID0ajbr9OljCaxCDGVhlJSvbmZ9pBpoyyH02B20y9Qftc2lf8fl8QLP2uwba1Z/UH/aZnYe+MWMAmqS+Qes222dL0wxqq8qg++v3PjKEPgfHhPZrq9VCKBRCtVoNjKm21/Yz1w6fqN2sWY+81nrnDiM6XwY9v/6/Waxit3X1XXfdhTe84Q24/vrrccMNN+BjH/sYTp8+jTvuuAPASpmvc+fO4ZOf/CQA4I477sCHPvQh3HXXXXjTm96Exx9/HA8++CA+85nPuGu+7W1vw4033oj77rsPN998M77whS/gq1/9aqB0y5vf/GZ8+tOfxhe+8AXkcjnHYCdhcq/IWqH+NiyeTqeDL37xi/jEJz6B//N//g+uvfZavPGNb8Qb3vAGHDp0aCfbeUmlXC6jUCjsdTMCotFRG027VMKoaTQaRTabdUwz3WHaOilMc6bDR6dSHbxsNuscU2WkW4dbhcpNHZtms+muz83EVLFzgWf/DWI37LSwrbVazTnWpVIJvV4Px48fx4kTJ5DNZnHttddiamrKlV+JRCJuM7BarYYf//jHKJfLeOaZZ/C9730vsCHIRsINLxOJBF784hfjn/yTf+I2G+VGmDQKTp8+jdOnT6NareJHP/oRFhYWcOjQIVx55ZWBWuL7QTj3yUQvlUr4wQ9+4HZHtyySbDaLY8eOIZ1O4+qrr8bznvc8xONx1zfApQ0Q0CBYXl7GuXPnsLS0hNnZWXz3u99FpVJxwMnlLKVSaU2KGNfYP/qjP3JlAjYrjUYD/+E//Afv9UeysVwO+vpS6Go1mLdjkO6VsISV1pnmek+njhspkemtuprM3Wg06tKkNR1dM9IU+CQIQD2lDHS2pd1u4+zZs1hYWHBrczKZDGwcTv1O8JzX5vXJvqKjHAqtlPWo1+vOvohGoy5jqdfrORa1OsmaMUebhdlOAHDy5ElcddVVrh1ku5GZv7S0hFKphEajgQsXLqBer2NxcREXLlxYw/hbTxRMueKKK3Dy5Enkcjk8//nPx/T0NMrlMs6fP49ms4lareZK45TLZedA0onV4DfHqVwuY2lpyYHvdL4TiYRzmPnMZJwTtAfg2O/se44N2bGVSgXlctnNF93EFICzWcrlMn74wx+iXC4HHFMFZXjeC17wArz4xS8O2B28J9nntVoNAJwtyfmmQSBlejIDw4IMFGYodDodXLhwAcViEaVSCWfOnHGZj8Ns9HWQxI7DSFfvT7mcdPW11167BoylKGBGsIoBT+oQBZ2BIACqABqP4zl6HMtZcaNH6ki11202j8+HVH+UwnWYQKgNDtCv1tIYXK/YVvr6LF/KfuC6zFIeqju4DiaTSXd93avCZg8BcDpP107ev9froVqtotlsuqAtA7wsv2YD88qaZ/9ov6hNxfb0+33nJ4fDYbeZp16Hvmqv13N6W8eFZSiY+UvbgAQ13q/VamF+fh6NRgPz8/M4e/as0wssE6t9xeCCkv9oE01NTeHw4cMBX1vJfcz2YzlP+nm0m/icBNnZTv5wXvH+kUgE7XbblcXLZDJIJBJoNpsoFotYXl4O7KHSaDSczqcdxT607xifl/ZQrVbD2bNnUavVAjYh50smk8HRo0eRSqVw5MgRHD9+PGAn+eYE54MGiNSG1PFWX96W0OFzsC1LS0uo1WoolUp4+umnnU3LcnUWmFYCqCVSaAaCLzvGx0L33UPFR+jU421QTo+xfcQA2t/93d/tO119//334wMf+AAuXLiA6667Dv/lv/wX3HjjjQCA22+/HU8//TS+9rWvueMfe+wxvOMd78B3v/tdHDt2DL/+67/ugHfK//yf/xPvfe978eMf/xjXXHMN3v/+9+N1r3ud+34QdvPQQw/h9ttvB7DSj7/927+Nj370oy4A/OEPf9htSHopZFubjUajUdxyyy147Wtfi/vvvx9333033vnOd+Luu+/Gbbfdhvvuuy9Qp2Ykw4k6q9bJH5ZNstX7KiuNiz7/14VHI+Q0ZtheOtL8obNG1jNZR8po8zEMgNWUOX1mLvTWyFGGwG6JXRxt2lAymcT4+Djy+TympqZw6NAhJBIJ5HI5hMNhZzilUilUq1UkEgmUSqU1zu1G0u+vpkmzzE40GkWr1XIKn8YJwVwy0lnbkLXFdysAMYxQsVBhM52NjAEf44MGKgMCu81I57uh7fAp6mejjFhueycjfb1WBrFoDpIQcCSASP1Dg57OMIDABsgAAk41A9GsndpoNJwOY+1RBT7ofGu9cN6XIAiPpcPOvVfoTCsgojYObQUC3SxDRgeUDp8+v+p+ZbEDq6VcarWaW4NsWnI6ncbk5GTAwWUmGe+bSCRQq9Vc/XaCAZuxy6irQ6GQA8vJomPbNdjBeqtqM3EsNPWcDi3HRB1oZTdagMjXbnUY7YZ91pnUcadtp+QGirVl2TZ1HBUI5704R+34KrhPx5+guo4tn4HPrtfnNZmlQRCLgZHLTdjPwzzbSFfvnVxOutrHIKeo32TfV8tEH8RG13N4Lb7/9BOAYJkKnqMAqr2eb/7rOqO+n2Y4WX9ERVnjyurVNdMydLnWaWCU66RlolOHs03KYtd2KdingXaCjNQltuSH6g5dz3l9jpGWxbEseNUrXN9pKyjIqH2sc0HLe5BRzWwy7unB65ORTjuGz6lt0TFlf9Emol7m8cqe12ANzyVwTfvHh+HYuaXEBM5dnT/W5lJgmPelTcH261hbvanX1tJR8XjckRY4hroZLeeWZgOoT2t9bPusg95b/lYdb6/JAFkkEnFkCgbGtNSe2hV8Xvap2jB6H/arLeNkRd/vYQmtFpjX+9u1iKLr17CyF7r6zjvvxJ133un97hOf+MSaz175ylfir//6r9e95q233opbb7114PfDElXuuece3HPPPRseu1OyLSD9m9/8Jj7+8Y/js5/9LDKZDN75znfijW98I86fP4/f/M3fxM0334z/9//+30619bIVjUKpE2CjcT6Fv5Nt4GKuLCY6HYPY46FQyIGcGulUthGZPzYCb6OTKqroWaNUlYEqnEvJ0h9GFAzQjUFCoZVNz8bGxlwEnZFsboamfcJoYC6Xc2VMOp3OUHU6lckxPz+Pp556CqlUCrVaDYVCIZCmdvbsWZw7d84p/PHxcVe7bbcA582IGm9klAArzDiCMRQaqHaTmN1+Jr5Lvhp7lzIYNpKRDJKRvl4rW30PbbBbdbVNMx32eptpj7KWIpEI6vW6A3qpL9VRU+eP6z7Zv2Q9UVfTCaSOSqfTzjkjAK9p1gQCeH0A7hrRaBRTU1MOGKdz2e12HYOIepAbNdOBY9YZgWsyoOnk8foE2+kE0qmkY6QlT+gkqtNJ/cvr6Vqt+7sQcK1Wq44dzXJsW5V6ve7qqZ45cwbNZhOVSgUXLlxAs9kM1LRnu3zlyggCcw4OGm/aafxO7S/aVQrqcJwtSUHTy6nPWPee1+GYJZPJNQxJYDWLjnONgW/eQ+u283i20wLxtJPUdiZgYtPq7XgpaMN5QKb6Vt7l/SwKXIxk/8rlpKu5vvp8V30nlVjFd9GuMwp2cY3j2kLAk38r0EfGNtcjBpP1vfeBUQr0838F+ZToZTNfrG/LvxV0t78JWPZ6PQccWhCZn1FfUbeqr+MDIjWQan9T2D9qM9j2+3771lTtMwueW5/d978PDOZcSqfTLrMgn887Mhi/t5kKtFE0w0nBb4LknEMKetMeqVQqAayD2U60KWhHUV/SDuO817+1Dr6PuKbjRgzG6jCdTzxHAWI7TvxOgW3tm3Q6jX6/7/Zs43NY31WBfPXN1xMdf99cod2mc4H31+dV/UUbkIQLH8Bt9fcgG8DXLt8zsJ81iGFFg4C+Z1K7eT0sQIM8I9nfsiUg/YMf/CAeeughfP/738drX/tafPKTn8RrX/taN3muvvpqfPSjH8ULXvCCHW3s5Sq6WBJw1gWKzoTPGdkJsUpcI4+Wka6LJ5WiOi12UY1EIs6I4bUGGRhWdCHlwsmUXrZVI5nKONgtR0GVsaaj8XlzuRwOHTqEfD7vNjCj4xgOh12dVTLd4vE45ufnUSgUEA6HHWNtI6Hj2m63cf78eTQaDSSTSczNzTkgnfPo4sWLuHjxIhKJBK688kqMjY257IP9BqJTNJ1xcnLSpTGxhI46+HSE94IJrswMpr7R+LK1gZ+NsheR82e7jPT1xrKZuaVgsTq14XDYlYTYzPWsPhzm3Gg06hzI5eVlVCoVF4y1OljZynz/WGYtHA6jXq8Hyk3xeVh+zDK3lL1OcIPOhQbeaRfE43GXtkynlQA5QQ7qRG7wOTc359KXWRKLpdC05Iyyr215EjqqPJ7p4wTeyS4jCKE6g2w3bsZJhz0cDqNcLqNWqyEcDqNYLG5Zv/T7fVQqFaere70e5ubmUK1WMTs760qksL8VMLAMRvZpv98PlH/h92SfMdONtpM6qAzEU1/R5lKwSHWbZcbp2HY6Hbf3Cze6tbXkY7FYYPN1jgPtuHa77UrU5HK5NSXqdJy5ua4yIoEgIYPBFLWh1W7UfuK8uhyB9GHXppGu3n25HHU1GaT6Lmnwqt/vB3SUHmPLj+jaw/VKy1cwK9cez1IX6oMCWFMyRO9tQTn7PijYbFnvei6BadXLugYp8Kl+rYLfzLhScht1AOtG6/UV6GQ7ub4p45j9x+907Vc7RttndR0/12e2zGd+RjtAr21Bdx5rwUcNztOfpp1C3WFZ8MBqoD+ZTCKbzbrSKKqvqEO4Xwmfn0x5zfZiJhrHxGIyOkdtPXaraxRbsf2ltpXtYxscUV1uAW9fQENBYfrM+XwesVgMxWLRbfpu7Tp9t6zdY+eFHVO+Iwrsa4aCjr0GMTiGNsMyHA47HEAD7nr/QfrbBor6/X6gVLAGx/QYjolWZPAROTcC9H2BQdsn+nsjGenqvZUtAekPPPAAfuVXfgX//t//exw5csR7zMmTJ/Hggw9uq3GXsyhThmwsAoXK7tIFU6OZ23lxBrVHAXXf5/q/LtpUysog4GJoU898ini9Nunfg9LRuOCxn4DdXRxUAdhFT5WOry983+n/W2kHa8V2u11UKhVnPNFgYB03NcgOAsCrhjDZJb65NGyg5lKJZWqw3RxfGqoUnyF5ucpI4e++jPT19kUDdVx3fEC6OvabBdQ32x6uJ+qs+UTXFtVPmlZuDX0tiQUEHQF1jJUB7GNXKThAtpfPAVPG06Bn5T3VmeRz+VhuFDpAQHAzKb2+tWnsZzzX6ujt6hbOE5ZxIdtda7gro03H3bKetK98DrTtI+1X+7f+1vuoM6mfW3Ca92HAxecMalu1n9kn6lDruAwz33k9naM+wEftSmXqW/tLAw7PFj000tW7L5ejrtZ1xMfW1vcVWA1+DcvE9AHImrlj/SkCl1rCRMuTDpq7Pp/Crr18HivW3vd9r+uhrqXaJtU7g3zqQeu4Xp//8zMFOQkSbtaHGgSk+p7bp2d9QK/qlkF6bj3bwfa11TXrPRvvzb5g1hODzvYe7Dtrn/iu78MrLHahepvH+tZkC+LbuTnof22zloez+pDH2X7z9ZdP7Dlqj+kzDWtL2XdDwX69jhJPrS5XbG2YZ/HZIRQfmL6e/uM917u3DWBtJCNdvbeyJSD9hz/84YbHxONx/N//+3/xcz/3c5iamtrKbS5L4YsYj8cxOTmJZDLpoqtkQmm9sH6/j2q1ilKphGazidnZWRdNtQyf7YiNCOpnuojYxT4UCgWi/ZquwgWMUeTNAsNW9HwGHrhxmCp+tl8do50WZUAwpZyboVoj0WYTUDnS0dPvlNGwmbG1jma1WkUkEnG1XO39aWSy/u1WgPu9ELL8E4kEKpWKqweofQpgXSW5F0JjJZ/PI5vNus+AlWh6pVIJsPkuVxkp/N2Xkb7enoTDYZexUygUMDU1FQjiKbher9ddKZKFhQVUKpUNr7+Rk72RKOhN1rEyzfiburLdbrtSYzxXS7aEQqFAWrky1piurDqKTG5enwF0OpXVahXtdtulDzNLrV6vO/uBQfFSqeScRjKQU6mUY/+ofiXLjn2mNgZ1IQPHtr99QW+eByCQKcYUbjK9WXJmJ7IDyQYLh8NYXFxEuVwObASrz5rL5dyYkeXIAHk0urrpaq1WC2TIaRkbMqCUJWmdSi3TR1G2fyqVQi6Xc3X0m82mC8jwHhpo6na7WFhYQLVaDQSXeD4ANy/Zv+xrsllTqZRzlC1LNRQKuech65GlimxmAse92+26vWqWl5fdhnSs18u+Zf+T4KJ29+Wuj0a6evflctTVy8vLAfBXgSGuM7SFrc2uusayebUchAaFs9msyyaamJhYUxqDP9x/QjcwpA3OtthAHHWbDfIRhNRn0mfQEp1ch1iCi2sZ1y8faKm1rCma/WX7VPtS1yrbbl+wmN+xbQDc2jyMWIxAy1Mo6EncgBlWLOemNeiZGbS8vOxssH5/ZWNN6mGu1ySF8Z7W99Y5BASDqFo2jf9zvDlWZKzr5ugU6gjddFZtER1jAIExY3t8G3NyrxTaXiQk+LABHR9fgMeOH/uLPjTn2DAZ8Pa+Gjz3BTxUfLpB56AGeCi8Nv1jzf7v9/vOrgTgyhNyTxy1f4EgEXUQxmIDaoov2ICfrw8U27Gi80JtGq1swOdIJBIjRvoBkG3VSN9IPvWpT+Gd73zngVD2uyV8EePxOCYmJlzpjyuuuMKlaCUSiUAktlgsYnFxEfV63TkJ3KRyJ8RGCPW3ZRrZl45GBBUYfyvQvlOMZ43CAwgoGn0WAGs2p9pJ0cVRFz+OjUbJlTGvxgHban/08804awqks03AaukTPS6Xyzmnm9kQ/G6/SzgcdpvX0rmmobYeA2AvRY1HNSBpvFUqlUA92BGQPvjckVw6uVz0tdVl2xUGg+PxOMbHx3HFFVc4Pcfvqd9qtRqq1apz9IYB0rfbVqY8c41RB0R1Cet81uv1QP1wTZtVMNQCCNTlAAJ6lw4ZnRWCmFybq9Uqms0mMpkMCoWC28OCabkE7Qm683rZbBb9ft8B6SwH0+/319gZluXDdmtJM7VTrJNjz2PZgGaz6ewsHk/AVXX0VsdPwd5KpbJmc3Hd1CudTrtNyqmz6SiyRA37iploHA86gZFIBKlUKlAKgH1DQEKzKdQOZD+wzArL7jSbzTXl/3httpPBDO2nXq/nABqWsWH/1ut1B3RrSr8NLvBenP8MQjBwwz5W9p+WJEylUg6cabVa6Ha7gZJFnBPcJLfVagU2Ir2cddJIV+9fOUi6mgFJC6YPWrspCkwpOGx9PGAVmOT+U4lEAhMTEzhy5EigdAYDbdQLqVQKrVYrsM4rQE8dqMCnDSRTX2oZLPs81F8KvlJfWkDV+jHUEfacQcEHitVvPn/SZglQVO9bdvp677b6trwf+1v9YiXdUS/zHK1dTx1B3UAdxTHr9/suu5rXo96zPrf1s/nsagtptrAFuDWLmzpXAxHUy1Y32GNs8IJAqdokek+W3WNb1aazAReK1um280PbQZ+UhAgAa2xIPc/3t227zg+1Bey88QHug+7BeUjbxFZloH4mSSMej6PZbLrSh7QLgNX69FrWxz6nvsODnksDgBoo0jVL3y1LXFCigcWR+H4nk8kRkH4A5JIC6aMBCgqZNclkErlcDpOTk8jn826zRzqYZKRrtI1MmYmJCXS7XVeeQ+unb1e4WGl0jBFQG/XmQq4AuU2fGbTI75T4lJJlMFiFsF2Wsl0kufAp80tZE9w8LBxe2eAklUo54yscDrsxrNVqDoRhzVoaIIOEizENEzqTaghwF3YFaDUSvZ9Y28MKn1HBGjWUNFoN7E2ZHwqZCkeOHEGz2UQqlXIbu9KYrFQqyOfzaDabmJ+fx/z8vBuvnXivRzKSYeRy0dc79RwEMQmgJxIJjI2NuSCkBmvprNMRDIVCmJycRCQSQbvddsGynWob13YAbk8InyjgSJYOgcfl5WUHHvp0KP8mG4trGR0v6n51coBgaQySBmhDqFPL4+06Z9lbAFwJG60fy8/VIaWOI8tNbSTqU/2bfUR2dLFYDGxWrUwj6vlGo+F+lLW8UQDH51ArI4x2Add+dZgtcGNBAraPjhnvp6Aw7QP2Fe0jy56yAQIFrjlefHaOgQUyLACmz+5jy6nNFIkE69XTxrHzko4wgXBen/pfn5nzWOcox1znAjcvA+Bqt4dCK5vGJ5NJV35neXkZxWIRpVLJO9YjGcmllIOoq32+oAWegLU1lW3wTQF5XoPrKDcwZj1sBsr0fGWa81y+58wOteWjbNuVrW2fUYNwGmTXNUz1ynrMVQu+sRzNILFrNq9jv7f3UV3ua4ueZ/tC9Z6eb+/P5+CPAulW31OXU6/RbiGg3mq1UK/XnR1EzET1mQKTvAZBedUDti/0WQiO0/YhkO/rbwVmB4HXWyEU+gDcQXsKsC3DiuI9GmjhHON3dl7YZ7RgubZjPXvIvov8XH9bnc93n/XxiY1R19NG5X46y8vLKJfLa7L0bJ/yb1//sW80u8IG+nw2rN7DZyfasdVgnK4RI9nfckmB9JEEJRKJYGpqCocPH0ahUMALXvACjI2NBcA1fZH5kmUyGUxOTmJ5eRnZbBalUgkXL150CwSjs9sRZZNxoWZaiS4sunj7Fhx1htQQ2Emh8uVCZBd9LZGhinqnQHQqZzpiZIUxzVmNqYWFBZw/fx7VahWZTMalFGezWUQiEceeqtVqOH/+PEqlEmZmZlwGwqCsA/ZrJpNxCp4bmFLIQGSaOtl8DOQweHMpgx07LWqU6SY7HHP+ptHEzYd22/HQ+01MTODEiROIRqOYnJzEoUOHAmATx7xWq+GJJ57At771LbRaLfduX04yipyP5CBIOBx2pTQymQxOnDiBbDaLXC6H8fFxhMNhx06l8a5GfK/Xw9TUFPr9Pi5evIjvfOc7jp2+E/O40+m4MigE97W2pTo6qVQK6XQaAFxJDgCoVquoVqsOdGbbut1uoNZno9FAuVxGPB53pedo5NPQ1zJzBCe4aSPXYM0eUmYvAw/AqgNHwIDXIiBvhWy9aDTqgtDhcBiFQgH5fN5lBahOUPCCerTb7WJ2dhadTgepVApXX3010ul0YOO1arWKSqXiNgMtl8tuTAkK2CCBCsFmDXprEEQ3uSyVSq6v2Ed0AHUNpc3TbrdRrVYDmYpkXNuMM27MCayWBSLxgNdWBiGB8lQqhX5/Ja2em5ERrGYacigUcnpYA/q0CTWQYh1LMjbT6bTre5ZsUTahOrMULQ+wvLyMer3u5ms4HHaZGMpeb7fbmJ+fd4Eilsc5cuSIex6C+Szt0mg0HAv/m9/8Jr797W9fto7uSFePZCdEgVPAz7jWtc3uLcJ1VdcL6glmO+fzeQegHzp0COl02pWg0sCjXp9rXrfbRS6Xc6Sn8+fPBzZ61sAmsDZrWwOjDAISSFOwmOsYr2mBY9s3XHupO+11CH7a6zC4yEwvbet646PCdlq2NHW2BjJIatIANXWGHXdl41Kv26w4Xof7fJE0qKz0paUlAHDZVbwf+6PRaDi/lxlx1N/83Aa1dV6xf+PxODKZjLs+g958TtVFLFPGIIvWHQcQON/OcZ3rKpyj+j0D/RaQ1XJz6wVn7DmaTc+5k8lksLy87PpOj7PZiGwXn1vfNdX1PvxFs8os8GzJHPrekIE+MTHh7L2xsbEAPlCv17G4uIhWq4VnnnkGrVYL/X7fsfxpC7Dt2ve6ZrANbI+WX9WgnwYP+Dx8XzVoQNF1SNvNttAuHTHS97+MgPRdEr5MrInOF398fDywMNqFtN/vu/Iby8vLbrFg2orWN92uaMRPy2Uoe4lO8SCGke+ZL4XogmWjfGog6eJOg2wrgLqPLeGrQ2qj8c1mE7VazS3s9XrdtYtgDKPrZKQTXKfTv14fMOCRSCSQSqUCNcN1g1FlQdDYO4iMdFVINFIYMAFW57CtkbeXyiKZTGJqagqpVArT09M4cuRIwEgulUpIJBKoVqt4+umnHSPuoI3NMDJS+CM5KEIwOJVKuZri6XTageZcm7meEuwl4EsQjqUv1tOVmxU6QcDqRqCWwUMh2EyHgc42U6MJhgJBFqCCtWSE6TXYBz5gWgOFWqfax3qmqH1B8IIOrDq6lh2nm2TxugQSCCpTH9KZoqh90mw2US6XnW63NkW73XbBcjLSOQYWFLaijDSOB/tG2Yu0XXxMM3XqLCikbHytaU+HU89hgBlYrderOlOPtenrPK7RaLixUaKCdRh1PgzqH2VecT4quKL3tc/O6xO40mP5fNSjZKlzDttaqxwPkgwYSOP7zXe5XC6jXq+jUCiscY4vJxnp6pHstNj3X4NkupYpS5VrmQXkFIBLJBJON5OJToIRASnLQlawD4DTgbS/VZ+yXbbtvufhj9r4KlYH6pqr9x20dq635qivq/1rj9Hr+NZm6j/tK6vnKcoCVyDXttn2j+oLxRIUONUfrtkMgjYaDYRCIafPuPYDfv9cQWBrA6gdwflC0Jprv5YI0/nE37Sr2H7LdNb5vB2flAAtr2WBczs37LxS0f5SAoaWLrR+tA+o1/mmup/HD9L7/K1zTeeMDe7rGsA9d6LRKMbHxzE1NRXoc+4VxExwLberNpZtm76vlmXOc+0xbB/H2LLsbdkhe321m/Taw86Rka7eWxkB6bsgdOqYIn706FFks1mk02kXUV/PAePiEY1GXXS02Wxienoa8XjcbZS03RdCo5rcQEo3RgFWGWMaaWc02udUXWowXSOhoVAokA7WarXWRPVt+4fpEy6gdFDJPqdDyc3IfKyFUqmEM2fOuHFbWFhwjPBoNOoi57VaDc888wzK5TIuXryIZrM5sLSHOuN0+MhI53gBq8oxGl3ZgIxt1jTISzU2l1LUwCRIo7XKGG1miZx+v+82yeP5l0rUSOL7XSgUcPz4caRSKRQKBZd9wvmbz+fR6/WQz+dx5ZVXYm5uDsVi0c2ty0lGCn8k+12oK8bHx3Hy5Ekkk0mXvdPpdFAulx2wquU06OjpWkQHj6BbvV53bN6dEjKHFDBQJ4ObYQNY4wzRGeN3PFaZZ9Fo1G1yqY4IQWkLanNda7fbDuDk+mtZSsDqe60pt2QDa21LMrosOEGGEBlkANwmcizr4mPd8d5sE3XF8vIynnnmGRSLReTzeRSLRcd4Z/37CxcuOAY8y6dZwFuFrKlsNhuwiRjQ7vf7jvnFvgyFVkqKsOavbjjHe2h/2Mw79jHvpWx8X4o59Sr7kkxs1unUuc4+VZuKpYZYbziRSGByctIFidUZtkGBTmd1E08t2caNz5ShxfapDafXTiQS6PV6DgBhAIMMTWaNcM+hbrfr7HESXJh5YftHNzOcmprC0aNHHbg+bPaYnff7VUa6eiQ7IdSJPoDIBn8tAKy+jPp4XCOYsUIAnZkkXBe5Rli/zN6LaxizawaB3NR9Vo8RONaAJtckW3OaaxzXPfrPCrZyrWOglc/J83muAtu8vwW6+fegcjR2TCgK/unz8/m43mkw2NoDg3SuXoeYg5ZdoQ/PID6PpQ2m9aUXFhZcNhP7SRnWXJu5ibUC6to+xS9IbCSZQu0ato39SluJ/rQNgOg8VrBYQWEGuHldBcUVuFVMxQYqVC9rP1N8mIwGpHVzS9pr9p3luNFO0f3vFDT2sah1Tur7boF0vZ+1qUKhkCMNJhIJ5PN5RKNRl5Wp9yDmxuxuls8lXqPlcfQ8+x7o+6VrivarkkjssfosNoBi31P7jtqAxSAZ6eq9lRGQvgvCtNhUKoXDhw/j5MmTgU1RKOsBfHzJGG3vdDo4evQoMpkM2u02FhYWhkoBWU+ozLQdBAfoxKmzpnUx6bxTQfgWqJ0UVSBsC5+BDrQ64AACNc5UiW3kYFOJa7kQMssJdqojR6GSr1arSCaTjsWUSqUwNjaGUGilhjp3jz9z5oxLG+Zi7xMyMAiej42NOQdPGXtUkGRLsvafbmq7lZpt+0XIxleFz1TAXq+HWq3m/mZpAyBYv20nRedLKBRyO4lPTU3h5MmTzgHXaD+wAqYwlW5hYQHNZhMXL17E2bNnsbCwsOPtHMlIRuIXZbtMT0/jmmuucUBcKBRCvV5HpVIJMJ6YFUN96ANGx8fHkUqlMD8/70ps7ZS0222Uy2UAcI4iQYVwOOzVJ1oLGlhduxh8JJhAAJIltBTMJIDMtZeOLGuxq/6lg0awVBnq1FcsP9br9ZDJZNxxDCpns1lXVofgB8F/PhvbzRRuynqBc55LXUyAnEFQEhbo6DYaDVeujXp2I4lEIhgfH8fhw4edndXr9RCPxxGLxRyLWjP+wuGVsj2HDx92favOOQCn24Ggk27HRUvDWKfPF2AmUJ1Op93+HbR5FBSgI63ADvXu2NgYJicnMTY2hrm5uUBfc87xhwGTXq/nnlPnA0vgEFjQjAE+A+0vZZbSXk0kEu4dZYCJmX8AnF2Wy+UwNTXlgAQFy0KhkHsXlpeXceTIEZw8eRKVSsXV4t9IFDDYDitxJCM5KEJwUIFIG9y074ECkLSp6c+QIEUfJhwOO5+KwCftbFvaRPUORdcxBg77/b4LFFsGqd2gkOsf10YF0vkclqnKoCQ/5zqkPjWD9/T1FPzkfTqdjtMf7FvLmF3PDx/U/2yrLRfHNV73HlG/2gZzdRw1aK8BVS3JqXqQvrYG9lmei9fnXKhUKojFYkilUoE5try8jFKphFar5bK9iQ8wy079QYKruk8KN7FU5jFxEdof/X7fgfg+YJbrPvtTwWfOH7WnqKNtMEd1HtvL4xXct8z3QQC8jk06nXaBKtqoOj8UV+AzUyfbrAadi/yfOAzfH7VBfGuBzh39jEHudDrtdDXLwWkber0exsbGHPGx3W6jVCqhVCqtIYbqWGlWpgLZfDe1vDHbZd83nUtqG9mggD3HynYA8pHsnlxSIP3f/tt/i3w+fylvcSCEi4gu0GR3DQPo6UurTiSdC7sYbEd0sdSadVykuShrGrX+7MVLrwoaWFX6NC7YdkagNQJqo7YqahzRePCVclnvuTU4QSNA0+bovDNlnPdYrx+tQlTWvSphzheNolpld9BlUIRdDTBGyH0KbKdEGS+aFqjvqxrJKgwE0TChg3A5jI+VUeR8/8pIX69NzbYptKo79Md3HepIOsMEphW83AlR5516iesfwV+taWkNfW0PQW+umbatus6pI2T7Ttdj6lh1qpSh6NOh1nGz5bpUd+tmkVq6xj7zRn2uTj6B7kaj4Vjn7Fuy9jdLXFCnUOeNgtMUnYMsT2CdNx0H9iOv57unOtmWiGDPo3BuWd1pgQK1AVXUjqQtog6lTcHX4Mqga6rNxmfRecB+GwQg6fnq0BM4YTuVBcv5qwAIHXjNQrmcZKSr968cRF2tugAIslJ1vqgPw//1GsDqWsX1y1dmSn09ZZVadjiwGmhlIJzvtG/90PXUp/t1DWNbFRjlM1On6ZrC/tEyJj6msb2/6mX+tgCk728rm/E5dBy1fXxODW6yPTb4qQEB61OrntBrsH+5JjP73NpXBOn12nZNs3OC/6u/pvNoUL/5AkE+cNnqT7VNtA0+Brreh/3A4LDPfvK1cb3xHTSfrQ2nNqYtcaN9v5n7W1DZPgvH285jvvc6ThS1NywOQrG2hbVNBr1Dw4j2m15X15z1xAafhrnXVmSkq7cvQyOw3/72t4e+6E/+5E8CAB544IHNt+gyFKZZZ7NZZDIZV8vat2htJHyxEokExsbGHMNGge3tCF8qTe1utVrO4VZgUMuD8N4ald0tUWXI+zJizmggsJouTuBSNwGxwn5UFhdBCWXWqSLxLUiaBnjx4kUsLi4655jf84fp6Gr0UXwABp+DSkKNRwo/1+8VIDrIYK0GEfgc7Md2u41isYi5uTmXts3Ubo0o74To2LRaLVQqFWfscW8DtnfQc3BcWP6lWq16N9e7HGSkuC+9jPT11kTXFP7f7/cdsBqJRHD48GH0+33HcgmFVrN+6BiGwyulPBKJBMrlMpaXl1Gr1dwmWTsp1EdqtDebTczNzQXsgnA47FhuZPPEYrFASvGRI0cCmyoquEnnnn2STqcDTq6CstRNLM9Bh09T8RXs103OuT5YFg/7kPozFouh1Wo5xpmOlwLdXHc3ClBT1AEncK66VUumaYDAJ6rHWRKI2Ygsu6MlZXiOppiTdakOGf9W4IC2htoc2i+8lvYD57pu7kYGdygUQrFYdGWDNH2fx2kqOvshGo26jc/PnTuHubk5lEolZLNZxONxt4l6p9NBtVp14BWzJKir1Zbl/TknOp2OK3cTCq1k27EMDFO6NdVeg00cT5agoS3Ljds1eKN9znFhUCyfz+Oqq67C/Pw8zpw5s+G8Yt+tZy9uVyzgs917jHT1pZdng66mDvWxwa2vo/6YAllkbVNoL3MzbfrB6vMw+5PrNtvia1uns7LR9Pj4ONLptPMh2A4fO1h/qPuYzaIZRiw3addRvQ6fnX4KdQVLbWi5GmXRs33aTupVDWBSLJhtn4F6XnWoBfs12Mj7RSIRF4Tv91fY46qLuX5qIJ12FNnttANIKlNAU3WfstRrtRqWl5cDe7Hw+ZjpxEw96kYNUOv+NsQFmJnAtrJvLXhswVH1r6l/iPUo5qDAuAZsNTOC/UO9FQqFAvrPYh4K/Nr2KQBv3zHOS56vmARxFOpYZrQrlsJgkA3423eNNoMNZPjOUR1s28E5xEwBLVULrNp5DLLYkrt8N2ywy64LWqaJouQTfYd0TbPBLLbJii9gouUN+f2whI2Rrt47GRpI/8f/+B9vGMnkYrDdEiOXm2jKGBeg9VKNB4m+vATQ+/1+YAHbbNRskHABozLSFBwu+PxhYGAvWDnWCFMFpY66NWx0DAYB6RqBpVOqaWg2CuwTVQilUmmo5wHWbjRJZ1ANnkE/qjip2G2EfFD0+SCJjdYDCDAGuYErgECd4J3M4LDCWq+8jyrC9QJMHA+mjtOQutxkFDnfHRnp662LBhyBYJYLNwun3mg2m4G1lQZ6OBx2ZTFCoRAWFxfR7XZ3dF8KXscH5rI2qDrH3GOFYCD31iiXy2g0Gu6zfD6PpaUlVKvVAADJ+yhIocCyL4irADg3bqXdQAdWnUXLarPODsEYSrvddnXLd1LYn0wLBuDADPbFRmwh1U8EDLrdLjKZjKvnST2l/ecjLHBusa8sW5vjRBuBc1drmRLk0BqmZHPpc3D+93orjHzOe7ZZnXJ12PWaTPkulUro9VZKBtHx1bIAfH9qtRqy2axrs48Npu3ivQhSdDodVCoVB0TZ7C9tH21A2ofsL83utP3Lcxm8AIBUKoWpqSn0er1NBb0vtR7TZ96OjHT17sizQVcTbNMgH9fPQf6MMlC73a4Lsqo9zaCZ+tXq7/h8UtVRlj3OcplcLzU4bMFc9Yl53UGBTR+AqQAfnykejwfKxLGNGmTl57acqYK61J+sHe7zkbUPqHe0VJdeV7EHnw/JdZTtY41y6mx+r8EABqKZja22htVxtu91HrRaLedj67rPflPGu/aPgtvUtdo2LacDrJYksaCrXSfZB/qjZDa9pj4Tz9O9YBggsIC0L8NQRXW0BdH13bLnaJBeAxbU28DK/jO0ZeinKtFS35n12qPzj8f42sQ+J2mFc1ptUf5Y28Q3XorpKOiu/aP3tm3VYA3H2/YfxQY0fHiVfWbNttF5vp6MdPXeytCI0lNPPXUp2zGSLcpOOeWDxCpnNXwYNdfF0UZAL7XYaKuvHfxcnXVlcyn7kOfa6DeNEjVYNvuMvL6CEFrmh4agKiaKTWvnMcq01mgyz2HaGyPp/PEpiYMmg8aBz8VNwHq9nqtTrxkJPiB+s/fn/ThHWEOZ842GfqPRcLUbrdOt86xer6NcLqNarXqj2Co2MMK2XErG20gOhoz09dZEQWdrIBMUrtfrDgTV9VgdaGBl/SG7e9iyXZsR6mLf9XRdtM6RgttkgwOrZciU4WXBCLLANHivDpAGcAE4gLLf7weAEG2Lj/mj9oYCxGQTK+tOj71UQkebLDUt+aO2gi0tooQErv8MIChAo/q43+8Hrq2bovHa/X4/YL+wf3k9vRbHjPOaNgGAAPvb2nrAajAYWN1cLhwOB5jb/M32cPM5bYMCSHacOMbca6ZWqwVq7vK52UbqT91MVEsnkrQCIMCKtOQB3rPRaGBmZgb1eh3j4+PI5XJuHyLOSyVdcD7zvIWFhUB99EFA9nqAx07KSO8fLHk26Or1wCkgWGLBB1L51r+NiCa6dlowzdrLWqpCN6NUEHYQcYrrJdcYrllsp13b7fPrNS2Y3G63A0Cq9cX5mYKjPjCSOsgCwPrbnufzhwms8reu+wACGzhT71AHatDCgpkW8PWBqoo7DPrOAqvhcDiwWXcqlXLjTduNARP6avSrU6nUmn5gP6o+saxwxRnszzA6wPq1FvjW7zWgofPKN9f5t50j+m7a4xhQUAJIt9tdA6TrMdYnt8+tbdaxtHpT5w4z93hep9Nx2ZT0qW37afvo+8sxJxCv/ajf2f5kezSb34LkCnj7AgkafLNjTVE7DNi4/MtI9ocMDaRfeeWVl7Idz0rZLgh+qUF0ChcMuxhyUVDFNMjYuJSixom2Q40kdXK17da50msqAAGsBeo3K7wnWY2xWAzj4+OuBMDY2JgDey0jmSlqTOMn05oOpD6rLRfDGuyM3FOxaBr9QRRVtD7lWyqVEAqFHMO70+m4lHpN4/MZbcPcm787nY4DnxYWFnDu3LnAXGu1Wnj+85/v0sctK5UGf7PZxOLiIs6cOYO5uTnHbPeJL/DS7/dd1oRV7PtFthNkO8hzdbdlpK+3JgR+NUVZwV+ycZTBBAQdfDJnuAkhNziq1WpDbUi4GVnvnbBgOoFdBuxmZ2cDIGSv10O5XEatVkMoFEIul3PnEmTnZtgEKpVppXqY141EIg6YVMBCmUDtdnsNo9znbHQ6HZfeq9lXDIqrrr4UwrJb8Xjc6WwtA0Qdq6nZtVoN5XIZ/X7fsczo+LH9yvqjA8hrEuxl6jTLmxCsIWhNAJv6ho4uASGObyQSQb1eR7VaRSgUwtTUlAMGdC4RICHI0Gq1XFkibiDa7Xad3lNbRZndHB8tPeSTRqOBxcVFxyztdDrIZDKu/BDnHwBXxiGdTrt+5jubTCYxPj6ObDaLcrmMhYUFp4utE9toNDA/P49Op4Pz58+j3+/jBS94Aa699lpMTEw45nw4HA6UYyTTcXFxEd/61rdQrVZRqVQArL4P7EN9HxQUVDBkOzrRyn651khXDy+Xu64eBCAqs1MBYh8rnfqE51lWqG++UbcAwWxu2xauLbTBWXqKJUEIGtuApwq/V0a07j/CDJyN7HJLyiEb2PqzKgT3LFintgvP8YHQFsBWUVYsdRH7RMvd8ZrU47re6vXVl1bCF9thwU3LVtbyKPY46lb6dwy45vN5d2/2rQLpWmaX1+Jm7Uq0ow7VsjIM3PIZaZ+ofUj7SJnaGsjRvreBFxtMpliwnmPL/tRgxHqEMQvOW/stmUy6LEaOJTcdZ3be8vJyQBdr230BBMVe1Ha0QR6+l+12G/V6HcViEf3+SqCdY3H8+HFHTtBggwLpvAbnK6so6HvNe0ajUaRSKRfIVxvN4kocB7Vr1R4cFESztquuCWobsw3DYBMjXb23sq0aB08++SROnz69pnbZv/yX/3JbjXq2yFYmsF1sdvMlsJEzGz27FI7BMGIZuBbw9rXJKhifccXzt+ucW6WRSCQcE6xQKGBiYgKJRAJTU1NuEecO1BSC4qwrSudSv6dSUKNBGQR2zC418HCpxRofFP7NMiuhUMixQnUjISozy9QA1gapfPNZ+5EKm0ELBYq4W3yr1XLsP3UCeD7bW61WnbHiE3UsaKypk6Fju9+U5Ejh752M9PVwos6JXf/5XikoZpkmCvxqvVGtObobYoO++u71ej0H+urzsp0M0un5XCet8W/XUQta0Fm1OooyrB7y6W/ef6fF2gYMXDLQnc1mkUqlnPNOp7rdbrvavXTubdq5Olt8LmUTah8Cq04zn5/9zb/JkCIgQPaf1fPsYzLSOTf1OHtvBYb1GlrrVue0JSf0+/2A4zho/afTHAqFXGBFnVl97/TebBt1IFnp1IfMAvHVEyYYRL3dbrddBhufRUEx3VeGQZ1isYhms+lS7XmenUsKKCizT8Gu/SYjXb13cjnpah8By8fOtHaxFQusU6ztr3rYBtHs+mZ1ooJiSkiyOsq202dr+9bHjcTqRXtvBY/tvQb5soP6x3eMBf/0M/aF2jJqAwCruspm9ekzkVGsWWgK/A/S87yP6iTVN6pbtWY213D2J20zzg0NarN/GJjl+QCcDtF5asdY9ZRd7/Vvn79q+8kGXtcbV72HFfv++SQUWls2SvtV9bmW+qP+1Ky2Qc/ku7ZPdB7ofNNgFveXYQBMsxZ5DY637nejBDdrqwBwwQMNmgPBvfJIhvHZrNZWts/kG0OLW2lgaVgZ6eq9lS0B6T/+8Y9xyy234Dvf+U5gwqiBOJJVYSQ3HA4HAL3N1mrWl2V5eRnVahXlctnVF9tNAFsXKgsULC8vB1JthomobeX+QHBzFdsOjcwOusYgw0LvsVUJhUIunSyRSODw4cPIZrMYGxvD8ePHkUwmMTU1hYmJCcTjceTzecdyYzScQnYF06qq1apLeeJGYaVSCe12GwsLC5ibm3MgLlmEakQQYD6Ii6gFg8i0t8LyBZ1OBxcvXkSr1UKhUAAAt0ERN0Wj4hwUAbbzjfcmi4UM8tnZWczMzAQMxUQigb/5m7/BoUOHcPToURw/fjwAYFWrVczNzaFer+PJJ5/ED37wA1QqFdTr9UAbQqGQ2zg1mUzi0KFDTukTzKlUKmg0GqjVaq5NvtIJeyUjhb/7MtLXwwuzSxiw5GaIWi+Tfcg1mU6E1uYEVt77VCrlSlbsto6mqGNC4FXXOZar4PPz2ZgRow7z2NiYYyMrG8c6RVp7lM+t6eBk+5Kpt93n20mJxWKYnp5GJpNBOp3G5OSkyybKZDKIRCIus8myzAi6cl1eWlrC7OzsGoCENkqj0QgEUq0zRTCA12S/kimujDhNlQdWHHBts5abUcCf2QW8r5aw4byNxWIYGxtz161Wq+7vZDIZyCqwDisAl0VHYNuKOo/z8/MumMznU3tZAWjq7Egkgnw+j0gk4jYB5nusQYf/z96XBkl2VWd+mZWV+1579abu1oIWsDU0ISQMMmCExXgsC2E0YYeNMSiskYetHWYQRoEAA8MyijYBAhQjIxgCkCMYBSaMjYRtxAi1h5Awga0FtHSrl1pz37OqMnN+1Hy3vnfrZW1dXVXqzhNRUVW5vHfffffdc853vnNOs9nE7Ows5ubmjM4lKL6wsICpqSk88sgjSKVSGBoawsjICDweD7LZrAEOyMp87rnnTCNhm1XJvwEgGAxibGwM4XDYjKPdbpsAO+v8E4zfKXqup6u3Xs5FXa31i7vZ1yyVoYCn6lKtF05xy8TVoJcC0Db4CjifVb7ebi9mz2hwTPd4HYMdaNZj2kECZTW7jYP7rDLatRSYXr9dDkNf0zIwNlkHcO7N9hgVYKZfo+Qg+pmaXWSvT73P3fYA+kYMEmn5MAU3ybJnhhP9HLKGmSWUSqUQCoUQiUSQTqcN41j1mn2f9F7S99NAsmZ/Mbur0WiYkiC65jQjmnOqjHtdJ/yuzWTm+3YQ2g5w23uCruuVcA7qyLUEqtwCJhyH+sjaC6XdbhsSIH9oa/DctGUo9hzaJLBms4lKpWJ+V6vVZUGM5557DrFYDIODgxgeHnZcX6VSQSaTQb1ex+nTpzE5OWnsEOpsPqu838RkSITgPkLAnhmmboQQLTNnB0w4h3ZQT7/PedX9Ya16tKert1c2BKS/5z3vwf79+/GDH/wABw4cwE9+8hNks1n82Z/9GT772c9u9hhf9MJUagAORcSNfj3CB4bsGQLpmka6FaIGgw1ikxXlFjHd7DG4gfl2Xbtu33X7e7OEm2I4HEYqlUI8Hsdll12GkZERDA8P48ILL0QkEsHAwABSqZSjaaudBgYsKZ1Op2NStxcWFlAsFk1d7tOnTxtlo0B6Pp83c+TxLHUyZ3T9xSicD03ZsoXKj+nZ5XIZ5XLZcV8AGMPMjkIDTqYEsMT8b7VaBuyuVquYmJhAtVpFJpPB5OSkwwhhSZlUKoWLL74YnU7HGIR9fX3IZDJ4/vnnUS6X8e///u946qmnjNGq4vV6EYvFkE6nkUgkcOGFFyIejzsMGjYJzOVyJnKujVZ7cv5JT1+vXZT9QiBdm2QSFGi32yiXy44gnjqBZDXRuQDcWWtbIepIujHFmCVFfaqAd6ezVGcyEAhgeHgY4XAYxWLR6Bg3hh8bwKlovw4C6QpCrldsZ2Wz5tbv92PXrl0YHh7G0NAQLrzwQgNiK/NNnSGmg4fDYVNj1efzIZPJYGJiwhF4aDQaKBaLJgBLnUKwQoXObV9fn3HkI5GIAVfIkmdwXO8bA6+0MxV0ajabxnEMBoMOh5YAO7CkO/r7+xGLxeDxeIy9QX3k9/uNLtYgAZ1tj8djMq2YEm8Lx0bbhOfx+/2IRCKIx+MmRV9BCK5pv99vAjzlctmsMRvUbjQaOHXqlAk4M/OLY56amsIPf/hDBAIB/Oqv/ioOHToEj2exWTBL4mSzWTQaDbzwwgummaquQXs9B4NB7Nu3z5TRARbtiOnpaRQKBVNKQgMEPTk/5VzU1drQWAE9BQoBZ+YN9yGb0cvj8bftAxKwCwQCBviy9R6PMz8/78qYJnismT020OsmGrBW0FGBbpYd0evh9dIvoB9Cm0ObnuqxbNDZZhXbIJ7+ze90E/qY7AnDgCPxCzsQ3431rHuZsuqVaGT7XICzNjTtLc02CoVCiMViCAaDGBkZMTpiaGjIZIyFw2Fjv/B+aF8RLYmmWU96DSz/RTCXmXUAHCCzgtu8D7ZtpMCxZi2TSW3bZqqvOU+cP83OcCslZgs/p+UKbT2jID5LwtnZbHofuZapj0kYtLPpARigmq9z/JwDLWlCG4GEQTtLm9fJZzsUCmF8fNyUsKOdVigUcOrUKdRqNUxMTGBiYsKsZwZxNIBF0sHQ0BBisZgj25DfIbDP+89jKPhtPw/c97ifcI65Z7llLqj07IGdLxsC0o8ePYp/+qd/wtDQkFkYv/Zrv4ZPfvKTePe7341//dd/3exxvqiFipKOJJlerHXZ7QHS7+tx6LSwnpvtfG2F2EaDWyqcGkNn8/w8H3/0ve3YhKhg+vr6kEwmMTw8jFgshqGhIQwPD2NwcBCpVArhcBixWMwwxtRZd2MLqIOsyppzzCZdqVQKyWQSfX19iEQijgYwer+UrfViEY3QuzEC3D5vB3TsunUaOednbPYpjQdtQlStVh0/NDIVRGdknQ737OwsEomEMQi9Xi9yuRxmZ2dN9gBZe3a0mmskGo0atiTr11Hh00BoNBoIhUIG3Gg2mzviXvci51svPX29frFBZU2xV6a1re8AZ31QHosBv+1kFNJhYU1rdaTtseueZwcIbKdRQWUN2PF17kF0ANWJOhMdtFn7gQa9maWUTqeNHlXg2gZGqIfoQCngwddYj5PrhKng/f39SCQSiMfjhlzBHwa9lbHmlk2gc8E1yLklyYH3Ue+nglv8jg1m6DFJjrBBAtsO5D3RDA2bebmR+6Pj1zTuubk5s560yZ0C6TbDjWXW1CZSoIkgGmusAzCAd61WQ7FYdGRTuF0TWY39/f0G1AmFQg6bNRwOO7LWNEiwE6Snq7dezkVdbesL27YG3MFUtzVks8GBpdKIWhqK+xT3Cd3HbIKSApbUj/ocuvkFbuL2ngKHek67XIfbd20/1g7YdRsL53UtgBzHpSC3nltBVP1fgwTdxqH7mO6vfM/WS4CTvc3jUpdpbyvW7g4Gg46eGSQusNcGAOMb2Wxq+oBa/9vez9vttsnOYlk3zRygnnYTG4NQ/aUMbP5Qj9nPgc1MVrur27nWiinp8W0bwg1EV9uG88ffahvZ4D4xKw1Iqe1hA+TMEmP2F3EzxUGazSZqtRra7cXsrmKx6PDtlWhKkoGtX7XcLkkFzPhmYIP3ifebwQObcMf5WWm+uR+p0FbSOebv1fYcPX5PV2+fbAhIb7VaiEajAIDBwUFMTEzgkksuwb59+/CLX/xiUwd4LghLTMzPz2NychKRSASxWMxEQrXGcTfhBkImzczMDE6ePIlCoYBCobDlxrc6kEwTJmuKkXUqHa1PuVnn5obIgAIZWUxHo5O0lSxAbnyxWAy7du1CNBrFxRdfjEsuuQSxWAwXX3wxBgcHDRtam6IoINHNUeYGSwZyp9NBJBIxDLPh4WHMzc1haGgIo6OjKBaLiEajZp2cPHnSADrlctkR/QW2rnntmYg6m1SkqzGumX4ejUaRSCQwODiISCRiQIx2u23AcDrgGrDgPAFw1EAn+7vRaCCbzToaFKmRVCqV8Itf/AJ+vx8vvPACHn/8cYcBV6/XUSgUTNqYgioAHCyb0dFRHDx4EJFIBOPj46Y0DddGKBRCs9lEOBxGuVxGoVDA9PT0ijXXt1J6Cn/rpaev1y801KvVqglYe71eJJNJDA0NAYBpgqRMuFar5WCQAYt7RiaTMVkiKzm0ZzP42263jWMRDAaRSCTg8/kMG9rj8RiHgnqV+jscDqOvr8+wlOhQdDod47TOz8+jVCphYWEBoVAIqVQK7XYb09PTpvEzdV61WjXlyTaSLaNzdCbzxX24v78fv/qrv4qXv/zlpmElGVYMViqIQGDdTs1uNBoGwCb4HIlEHI58p9PB8PAw2u22yVqo1Wo4duyYYTsXi0UD6gAwDh5LyqhuUnIFAWKt48nAibKmIpEIgMU1XCgUzHiZRUAdyONxrQNLWRu0Qzwej2MOOKd8JtZi31IIQDMAEQwGHc1LyehnRh2brjOIzWwA1qunri4UCoYRTz3PeeNxARi2YLPZxNNPP43p6WkAMDqd9kG73TYlm+zx83r37t2L0dFRo6sjkYi5N51Ox5R6yeVyAIByuYxSqWSaqtnrlPO7VXqwp6u3Xs5FXU1gW5smEjSkKBtWhWtQv6/kLPqg5XIZ09PT6O/vR7PZRCAQWNbUWdnI/K6SYmq1GkqlksnyJfOa+5ntJ9nAJcdJwJ7sWPtZcGNiK4DNvZhZKl6v17Dn3ZjO+n2dTxsQtudaa34ryUp9LBtYV7EDyzoOZRsrU5r7rQY5eN6+vqXm1nqP6d/TZgkEAhgbG8P4+DgCgYDR1eFwGMlk0rD6lb3OnmI8vw3+csw6D51OxwDzOv9kJ3c6HWM/KQCugLSuF63VbpcD02CxAqjUp2o/8FhcQyR5qM/J8+qPgtC8Hzomgs3MstasAV1HBJzZ4412CUvT8l60Wi2TIc9nS/1q4jc6D+122+hq2qC0TyuVipkHPh9koReLRUxNTTn2EDLZFxYWUC6XTTk6YKn0IHX14OCgwWbGxsaMzcZxcb0Wi0VUq1WHjcd7pkECXeN2QEJ/23uGnb1CG3I16enq7ZUNAelXXHEFfv7zn+PAgQO46qqr8OlPfxp+vx/33HMPDhw4sNljfNELNwxGznK5nAEC+ZBQ4a4EZqpzT5BMgbetFFth2qVd+Pd6nKj1nl8ZyXRwFETfauCQSpl1thKJBPbv34/LLrsM8XgcBw4cMDXcaDCs59gUrTfGFGwa4NyQ+/r6DIhKp3pqasrMDev0d2Oj7WSx2SPaedtNPB6PmXMC6mQwBINBcwyC4KxbqwY/DSfWH5+fn0c2mzWpXqxPb0fwAZi6rAAwMzOD/v5+B5uDQaBu16BsjHg8bhQ+jRg1lGjYtVotpFIpeDwelEqlzZv8M5Sewt966enr9Qv1C0EAllliGQsauHQ0uD/wWVWnamFhwfQzcRObtXu2hDYHwdlYLAZgqemigp8ahNYa6soc0lqudvCADiw/S31D4JVzeyYM3M2YKwVKdu3ahSuvvNKAMLZDqzYNwV3+r6xIdXwBmEA5gQuek/aclumjU8y1Qt1DUIAAuM6B2kLUiQT0mf3o9XodzTs1g8lOHefYeDwbVLFLBehaURanAuprzUwk0MW50lIHOh8s10ZdVygUUC6XTX37cDjscMQzmQwymcyq5+d9BhZ19czMjOsY+Vm393jdqVTK9MIhAKR6ng3nfT4fZmZmzHroxkDsds6zJT1dvfVyLupq3Tu6+Rk2oKnftdnP/FszpxqNBsrlsnmeWGLCZu3agLcdhCRRhgE4ZZraekr3M7f3AZhgK/c1tRncSqIo+5vXpwxmDeYqEM3vrCSKL3A8nB+bda6/u+1z9j3R89igPK/ZjVXfarXMXCjASfCbY/P7/cZnY6a39hZjuRfVOeztwfJnWurHDfikbcTz0lcjKFwqlUxmMO0k2gHK7LZ1Ie8tf9v4hL2/q0+uYLg93wrY2/fKJuW5jU0z62i/0C7r5ldrDxiC6rQB+UPWNsfIQDxtZ9sG5DhbrRZyuRzy+byDnU4yC7DU9JXvkcBJoFyDNvTJeV20w3SNMWuM/nQ8HjfZY90yFGgv20x0e3/TZ4n3X++7Bj1oU/O467Gberp6e2VDCOeHPvQhwwT5y7/8S/zWb/0WXv3qV2NgYADf+ta3NnWA54pwoVcqFczMzGBubg7JZBKNRsOU+LAjsfyebnCTk5MoFAqmUdJ21j/mNakjTkeQgLamrawnVaXb+ag4CH5yXng+/qhyOdvCTS+ZTJqI5sUXX4xUKoULLrgAo6OjBrhVZ3qzx0CHMxaLYXh4GKFQCAcPHkQgEEAsFjNRWZ0zzptb7dydKDSaafiulHXAyHg4HMaBAwcwOjrqaPRKxhuZE7VaDblcztTE0xpxyoJn8xnWSHcDG1YaPw0HKt2VStMATqOX98ktxV8NJ/3cSumXPTn3paev1y/U1ZlMxgCYPp8PzWbTOFMej8e8zlJepVLJZPvw2ZuZmTHBzG7n2ipjVtkwdlqtx7NUhoU2hYLIChao461lSDT7jKwmYMn5UZaaXVZjK0T3R6/Xi0Qigd27dyMajWJ8fNzcR45R50CBBXWGlClJ59+u/c5jahAVWGSaDwwMIBgMolgsor+/H7lczoDhGihmQ3GCBjbQQrFBc0qr1TL3AHCuAU3RVrBfG4TxHHxNHT3qGD4r0WgU8/PzJsDM0mbdhOcNBAK44IILzJwkk0nzfLHucbvdht/vdzQwZdkVn8+HXC5nmGh0trU009kWnQ8+C6w3bJebAJaXcLCFr/d0+Lkv56KuXimYpmQVYDmIbgN+9nG5p5GQQgZ6IBBwANjaxJPPpGbwkGnKrBU3AM0WmzRjs7dtPeH2fT7XCprqvsDxaiabDaryWBpc0DEx0Mvv2veEoL19LW7ArI1R6Fg4DtoCqi/tubFFAy3UO9Qz1KnRaBTDw8OGqBaPx00TcC3novebPjHnUnW1BmL0/Bq8oR0HLPqTyWQSgUAAlUoFPp8PlUrFQbTSYI3iI6qHuW4473xPy40q+GqDrd38OfUH3d63dQjPxUAAs6WICWgAR20mv99vsvXYu4TELt4HPn9KEiNeowEs6kQF+5mBoH63HYBpt9vL1jBtbH3uaOsoyVNtIG3mzmvSc9jPL58nt4yGtQQJ7eCN2/vKRN/ucpA9WZtsCEh/4xvfaP4+cOAAnnzySeRyOcOA7Im7tFotZDIZlEolRKNRLCwsmI7D4+PjhrHFlGk+yNVqFYVCAfV6Hc8995wB0XO5nNnAt0tsZn2lUnGw07T+mLLT17tO1Pkmo5oMAk3Z1ZSkrQLRPZ7FlPjdu3djdHQU+/fvx2te8xpzX3fv3u1olMLvbaZQwXU6HYyMjCCVSqHZbCIajSKXy+G5554DAGSzWRw/fhwnT56E1+tFtVpFOBx2pNTtZFlYWGywylQvG0SgeDweA5onk0n82q/9Gg4ePIhgMGgaiWhkPB6Pm3X885//HMVi0QQebNCJhraCQWsB0YHuDWFWA9K5fmioKJjOz/AZA2Ca0JH1tlOkFznfeunp6/VLu91GNptFoVBAJBLBnj17EI1GUS6XMT8/j/7+fqTTadNAkM9foVDAv//7vzvY1kyXXe18W3VddFCYrsx9X4Fj6m9el+51gPNZpOPl9S423PR4PIYFTKG9wyAkCQB00pRZd7ZEr4sp2xdccAFe//rXO/qW0CnT3jM20EEHkCw5dZ6ZtaQlurxer6npqnPLHiksx1UoFDAxMYFOp4NyuYx8Pm9Kr+RyOVSrVSSTSQOk0xaivaMscGApBZwOuDIwKQT4GRRSUAFYqjGrr9ugip43nU5jZGQEs7OzePrppzE7O7uqQ0iWWyqVwitf+UpcdtllJmhFsIwsN87X5OQkJiYmTPPTSqUCj2exMSjBCAUzNnMd6dzZ76nty/lkWruCPCpumWwq26H7erp66+Vc1NXsFUDfx7aB7etS1qy9BpWVzT2Dfgxt5EajgUAgYIJ5ZKhrDwsAJrOb5a2q1aohymipKlvn6djcgDaybnnNNrBt70lugC8BRfoZyvLlfqhgsPqVWsILcJaQ0UAyg62cV3uO7WAy/RANUCjAys/pNajPrteuOpDn5P5IP4d1qunDJJNJ7N6925RwYZCVvcWIn3CP1dK5nGctm0Gbh36fzo2uTerOVCqFQCCAZrMJv9+PUqmEfD4Pn89ndH6lUnEEFWjf6P+8J9QVvA92QELXHP/W8bg9PzZZ0W2N6r1jwILZWySH0T4joM5nh4AzWdvj4+MYGBhAKBRy9P7SIJeWZ6GurlarZmxqAwJwgPi0s+xeL/ws77WuMQXn+VwwmG4D2lyLHD8/z5K3bgE1PjucE13P9n6l57ODHHqvdF3S3iqXy6Zfy2rS09XbKxsC0v/4j/8Yf/VXf2VSgwEgnU6jWq3iXe96F/76r/960wZ4Lgkfcj6MTEUJBoOo1WoOAJ2bTKfTMRs0wcNyuWw2/zN5gDZLFFRkqo2dzmWnoQFrA5Nt44U/PJemH2uasZ7Djv65GUYbFW6CTA+igh8YGMDAwICJ1qoxc7aE16rKLJlMwuPxoFAoIJVKodVqmeYpnFMqyp0uHC8j56uVBSA4EIvFzD1hOprN1AuFQg5GISPpBNL1mSRosdFnb73fsdew23G6HbPbd7dLegp/66WnrzcmZAOTscO9h06wpn5rCRM6ItyjduK6VX2qjhL1N+BsRKaOh309tk4ng077b9AmUL3tBkKuNuYzEXVW6ajHYjEMDAxgcHDQ6IBu7EOge3M2dYIJniijyWaY0SnWbIZYLIZOZzETgr1PWI+TjEv+tsdlg9w2GG6DFvodN5vMvi7bOXRjvtkAOwBTinC1QLOCH7wnbJLOZ5DgB4M2ai+71XPdbuHYgCVmnD2/9t+rHW8r95Kert56OVd19Wr2azexyyAo+AYsNaLUz5K97ff7HSQv3dOApX5HWtbRDjC66To7k8TtmnSfdwsI6OdZHoR61sYA1Nawdafb8W1/1/6tPoH6QbaOcgNzbeDPZuXSz3djTbv53raojqY/xtIh9OWYZUwwU/1dzapy87n1+lfzjew5I4AKwNQAn5ubM70uqKfsc9prwhZlpK9GKNAghT12BWbd9Ix+3x6bBj26jVMzoknoIlmLLHTticIMEQa9eA6C9jbIr8/xatiWfW+AJR3L79uZLrq+3Y6na1afXXue3Na1LfY53J452zbjeuW414Mx9HT19sqG0LOvfvWr+O///b87lD2wmL74ta997UWr7M+26CbHWlCVSsXUTWdKmm46CqTPz88bVhKwCMDbym+1etGbLWrY0GliLSoARvFwk1W2ukbOV2Il8NqYitdutw3jSxnp2gSEmzmZCapgW62WYR1o5+eNCMHzeDyOl770pbjwwgsxPj6OPXv2IJFIIBKJdE3FOlvCc7FOZygUQrvdNhF0j8djGmWxNEkikTAsNz3GThHNRMjn84Yx3k0JeL1epNNpXHTRRUgmk0in08sAdIrH4zGshmQyidHRURPcshuHdjMwz6bwugGYWsuhUMisawU7yE6sVCqmETGj/ztBegp/66WnrzcudF4LhYJhiDEQns/nDdOaLJhKpWJKeKmjsFPWLnVtu902+5umU6fTaYTDYQNOErAElspS0Ulxc8LJBOL+Ayw1dlJdzTRcZbQpw1ltAu6/WuN0I8JrDIVCuPDCC03DMjLRCWjzs3p9vDbWsNb0W64HHoPNWRmYUHuI64fXEYlETC3fwcFBkzrOvht00t2IA8rqo+1o3wcyzux0cqZDa0kybdjJ97XxNteBMr7IGlRbbXZ21mRR0rZa7Z6Fw2EMDQ0hmUwa5iXnVcfMeeKay+fzKJfLxibciudsJTCIwTZgMfuPQMPIyAjC4bBJb2fQjXbzauPejv2jp6u3Xs5FXa1lwej7UmxAGHBnqOtv1Rk8NvfAhYUFwyynXtb3bX+SjE9mkHHNKwjnZvsDzvrUFNVhmvljk8r0evg+r8uNEc1SIkpWI5BMnMAuYaPjtOdWM3l1HrScGH1D7lcAHEApy+hoGRZe09zcnAl0snkzj8/x8t7p/eXeyQaWfr8fIyMjiMfjJsCqJTu9Xq/x3RQM5fGBpYa0em+oW+zAgc2c5z3Ue93X12fqslPnNhoNs9+320u11dWO4BrjfbDBU2U128Fr6hWC9VxrHLt9ffo9HkdLqFBIdmBpNPqZoVDIsWYZ0GDN+ZGREYRCIQwNDSGdTjuyQ9Sei0ajCIVCaDQaplwbme9KSlP2uD6rGmDS69bqBjyOssj1+vUztEX57PAz9XodhULBgVfpPSf+QIII7T5dO3zmVW9y7Hy29RnUtcF5IGtebcQekL7zZV1AOtMTO53FtINgMGjea7Va+N73vofh4eFNH+S5JKrwC4UCPJ7FVFQ7TUqBdKab6cNCtq2yv90Y2VshygTQlDOv12ucSEYnqSQINKhCUdENihsXFT6BQgLpBO9ZO5sOHlniIyMjZuNkM0YCI2wWuZGNyONZrJE7PDyMoaEhvOQlL8FLX/pSpNNp7N692wDT2wFKUzElk0l0Oh2jkIrFIorFIiYnJ03NVa7DwcFBR5reThE+C2SOFAoFU0e2W3TZ4/EglUrhwIEDiMViJi2P76sQhOjv70c8Hsfo6CgCgQBOnjzpMCK3S7j2PR6PaXhDpU7DmcE3zhE/t13NiHuy/dLT12cmGvQuFoumv0Sj0YDH48Hs7CxCoRDm5+dRqVTQarVM5gsbC9br9VVZRlspynjh2Gg7MDinQDq/Q/3hljZLUee8UCggn88DgCmjwkA7wUTqf+5fdHDUwVQ7iE7+mQDpTE3ev38/XvKSlyCRSJhmZOrY6Pn5P+0w2i+cO5b6IQmi3W6b49l7Lx0krhcC8FqHPxgMmqAMA+CsM06AiMCL1qRnI1e9R6z7qeV06IhSr7HsCkEMsvWVmEGghM9Do9FY5szyXmUyGVPGh427VxKPx4NwOGwCCf39/QZI5z1TNlwsFjNNf3O5HEqlEnK53IbWxEZkpfVHAKfTWSzzxID+/v37MTg4aO6lPku0P3ea3dWTrZNzWVezhJi9DwLLWeKUbmA64GRRa3CQoDL1GnU1sASEabkIBZjV/1ZWsQLluo+pv2hfl5Z80J4gbj6mDfpyLOo3UhdoVpc2fNS9MhgMupZydAtG8Jz0F2wgXUvL8Np5bayTTdY49QCPv7CwYPQYr0drZCtYrXsf9Tt963A4jOHhYaTTaUQiEQwMDDjqcDOgoOXHON8rAenU08QYbCCda4PrxQ5ER6NRAxKzd47aBNzjFQBWm8oNSNd1YwdoGHDVEm66DjUIw2NroNxec3yf4LlmZwAwAQGCzwTS2dx1aGgI4XAYAwMDSKVSAJzl73gdvC+NRgPpdBp+vx+ZTMaMm+OzbUmdG45f17VNdCApks8PgyWcO2Wpc6617HCj0UCxWDS907g++MNyM9qLT0kyur+5lWLR51ltTDL33So47BSfoSery7qAdJaJ8Hg8uPjii5e97/F48JGPfGTTBneuiG7gjN5q9FSBZG6ifHj5PzdKAn5+v9+wnbUxA0FllqbYyodRFQBZZ1QAFAVqqWjcwGZuJKrImC5PYFE3NW0ewYhxMBhENBo1TUnokFEZ0RDgMezUvpWE46YySSaTSCQSJvXMNoa2S3h+MvHa7TYSiQRSqRT8fr9x6GnkECTYKaVeaNTMzc0Zx1wN4ZWEz52CAG5CJaisN5sZsp2iRh4VPpkeNHJ4DbqO+dtNsW+X9CLnWyc9fX1mok61/QMsBbg0iE1jG4AJ5NpMKT7PDAoDW1u2Qdky/K3OI3/UsFfHZ25uzjixBG71OmzAgXaMzpUC1Gz6rLVibVCRThn3QQXt1zpvbF4Zj8eRSCQQjUZNmTMyingsMqF1XmxdokxHAKYOKMfGedDPqV7heTl3dBT7+voQiUTg8SzWUE8kEqhWqybjDsAyO4UOvLLDOSa9dwqkqFNpzyHnVp1jvRfqwLs5tXqf1xJI8vv9pl68zXC30821fKAGZXaS0HalnUrbhQCLst0IZGit+50gPV29dXKu62oFrVV0X7H3fGUM6x5if4fZMto40A6K8hiAswwWP8MgqOogBbTU51Bw0tZ36vfxfPZvZTavNF/8jD1u1dEEBxkgVd1KUbCYf+tc04/mtfFaNWhNf5D+NbOu1FfSe8RAL49DHUeA0taP/CE+wkyecDiMUChkgvFKHOJ9UmZ+N3vKDsSo7WAD6qp73AIf+puYDIPBkUjEUdfbBnTt8ej6VvayLVxPuq70dXvNrRbste053iPOH589297lNeg5dT7c5tu2HXgsew7UVugWOLPPwfuo94rHsV93w5jsYBIJJjp2xaHoXzPgpPuavXbsgJ8+//ybc673xN5b1qJLt0NX33333fjMZz6DyclJXH755Thy5Ahe/epXd/38ww8/jMOHD+OJJ57A+Pg43v/+9+PWW291fObb3/427rjjDjz33HM4ePAgPv7xj+PGG2807//oRz/CZz7zGTz++OOYnJzEAw88gN/5nd9xHOOP/uiP8NWvftXx2lVXXYV/+Zd/2dB1rkXWhZb98z//MzqdDl73utfh29/+NtLptHnP7/dj3759GB8f3/RBvliFDwsbUvX19SEYDBpmLBWpPjAEv8joabVaBjQPBALYv38/hoaGDLOKkT8C70yrzeVyOHXq1JrSRjdLCPQpqEcGOJulKPhHg8dORwKcxgIDAupskJHOFDTOE5tgXHjhhYaJzrnXSCyVXaFQwNTUFOr1Ok6fPo3p6elV50sDGnv27MGVV16JwcFBXHzxxdi3bx/8fr+jkct2C5X8rl270Gw2kcvl0Gw2kclkkMvlkMlkUK1WMTs7i0AgYMCG7R6/AsjZbNY02SUzdCUgnYEnDaCsRdSY2+7rp6hTPjk5iVqthnQ6jf7+fqRSKUQiESQSCbTbbRQKBZRKJdMokU2Kd4pj23POt056+nrjwj2TjSTpNNCRpBFeqVQcxi91UX9/PwYGBgxYG4/HTVkxGuMvvPACCoWCI8X1bDNR1IhXp8v+aTabpmQGsOhAz8/Po1QqodFoIJlMmgZ4tVoNpVIJAIzeU8eeTaGVHKDH3b17t2naqiwhjpElV4rFIqampkxzOLI41zJnXq8X4+PjuPzyyxGPx3HxxRdj165djtRadRxDoZCD1ejxeEzwQOdOgfFCoYBsNuvY42jz8fvURbRdFhYWUCqVDCgAwDQuV5C4WCya4ChL/Pl8PpNBoIC2riey2pl1pWwsnot1TJWNR2YamfZcJ/o5zoMN9jCgQCaqAmJu4vEsZo8dPHgQ4XAYwWDQXCvLC9jp3M1mE4VCAdPT06ZB2k4SBro5L6dOnTI9kbS8IDPspqenTdmjnaLnerp66+R80NUEk5SZqWCT/q2BRwWNqSOUQU5draUlqEOUSU2dTXuafhozd9VHV2Cy3W6bfg3M4p2fn0e1WnXoqk5neRNNvTY7gKZBWN0bdcw8Lj+v4KFey/z8vGNOVFQfuAWgtWQq9QuvtdVqIRaLIRaLmf4VZIQTf7ADCcASkY5BT2Z0sdQO/+b9JL7Bkimjo6PYu3cvwuEwRkdHEY/HTalWMu9Z2pJ6VTO93OwCrg0F3HUe3MgDem1uwDHL+DFLIBwOo1ar4fjx40bHU4drkFTPzzWjWSiKDXE8NtFAf3jfeT9strSKBoO04S6z85kZyM/yGaDPrWXldIxu4Lmuby3B4sbC5npwC67x3ul91fuo65rH0/vLeeAzpNmWCwsLmJqaQj6fRyQSwdzcHGKxmFnjAEy1A7U5iEfpOPU+61pRDMIOdtjguVvAbDXZal19//33473vfS/uvvtuvOpVr8KXv/xlXH/99XjyySexd+/eZZ8/duwY3vSmN+GWW27B17/+dfz4xz/GbbfdhqGhIdx0000AgKNHj+Lmm2/Gxz72Mdx444144IEH8Na3vhWPPPIIrrrqKgCLDaJ/5Vd+BW9/+9vN99zkN3/zN/GVr3zF/O+WpbOZsi4g/dprrwWwOCl79uzpGinqyZIok5zgN2uL2Sx0u84TNwdG2wOBAAYGBjA+Pu7oUM0HgfU7S6WSUWBbKdxEPB6PYaETNCdjD1hqPKZdte21pKnCZNUqkK5/c4OnU8h0sLGxMaN83SKmnU7HpMZVq1Vks9k1RXS5MWopkIGBAaTTaSQSCVdG3XYLjaBgMIiBgQGMjY0BgHGi6bS2Wi1EIhEA7kp4K0WNGYI1VGgrsaxVgW3kXthMw50gfBZoKAJAuVw2wF44HDbBplqtZpx0GrM7RXrO+dZJT19vXLjHq3NDoVNAh1OFDgr1EetvDw4Owu/3GwezVqshk8mgUqkYR8Vm3p0tcXN8+D9/tOSEMu6pd9vttgG9GezWkm56Dk2xt8/Z19eHaDRq6l7bKcsEtWkrcF+r1WquAQE3oQMVjUYxNjZmMrJisZixL5RpBcABNmvfFV4bz6XXy9I1diaDliWhLaQAkTqonFemVqfTaQwMDJiAgj3fBIN0XMpyImDB8djN0DhGXXskcui4uSbUydO51XtAW1ad1dXWdCAQQCKRMPVu1fGm487XyDxlOjpB+52mH5huXq/XUS6XAcA457w31NXVatX0Ptop0tPVWyfnsq5WdijgZFnbfrD6G1peYaVSTwQhGfzkPq37IM/FfbzT6Zh9ltkwBGuZHaWBSZJ3+vv7TV8R6n/6KCv5G3zPthdswJHzoN+xQUGey2ZVa6aW2hF8nWClW/kSLTfFH9WL1DMsZ8LSLpwf+95yjlqtluln1m63jf7TEi4EiLWxaDQaNRlK/NF7S11KHci10o0cYM+52/6koKUNpLuxwHnftOwd15c2HHdjydvgveI49vns9aDguf2/fs9meevcqA2rAXrNHLDHouzvlRjo/F/P6waA20Cy2/5g3zfaP/Y94/rmc2EHImwgXZ8NjofBskQiYY5NW0PZ6OxZqOA99yY3okE30aAY758Nqu9UIP2uu+7CO97xDrzzne8EABw5cgTf//738cUvfhGf/OQnl33+S1/6Evbu3YsjR44AAC699FI89thj+OxnP2sA8SNHjuANb3gDbr/9dgDA7bffjocffhhHjhzBN7/5TQDA9ddfj+uvv37V8TEYt1WyofoN+/btQ6FQwE9+8hPMzMwsu9F/+Id/uCmDezEL2WtMPSKQrmUzNFrHB5+RE0ZtPR4P4vE4RkZGEIlETAMOt6g3nVKvd7E2OetNauRsK8QGwKlYuFGr4uymCHSjs8Fzvqb11tRRZm01ZXm5icfjMenejIZHIhFz/G4bGBtuKGDP/7sxEnaC0BAh+N/pdDA8PIxcLodOZ7Gmp90FnUblVl4PFQjTrdh4jcbsaqVKqFSq1Sqmp6fNs2ArLf08jcFarYZsNot8Po9arbajHELePzI0CVApK5Kgj9e7WMcPgDHgqeTXoph7cu5IT1+vLtQ9LIHF4DfgdPYZqG232/D7/UY3kb3FlORgMIhIJGJ0tZZ0oBM/NjaGeDyOcrlsmNZb3SxcpdVqmSA8QT5gsdEdG2DS6SqVSpiZmYHX6zV7EAEHgrEsw+ZWgoVp0EzbBpzzzDnQvZp2A9lfCkZ326eVvBCJRIzzrc3UqOtZd1WdGqbx0o7plrrPPZf3W3/bx1RQXB1MllbjtXLu1H5kCjt7ZnDf53XqXHU6HQNMcJw6Xwpw8bVOp2POx3umYBHnmeAFbYV2u22YbdVq1TwPdHBX06PlchmnTp1COBzG+Pi4IYnQVuZ12CxOG+DfSaK2bKPRME653ZiPdd+p0zfas6cnL345F3W1zUR3Y466AYEqBCyBpSCnMpSZQWoDbQTPFUCjX8ieICSlcQwKFPI3j89+GEpes8epe7gCrh6Px5XUouCZioKZWnJDGa/c/wnkUWepX009wr2cezJf0z3abqzJeSdJjX67lnPRIC6Pw3vi9S5mQrHnBvUn93beF+0zQpuANeC5ZroRnDgfWru6GwDptr7ciAWqR/U1N6CegQu3oDn9VQXk7UA855r6UudWr0OfEZ0L1ScKkGvQxfZ3lZxYrVaX1VLXc2qmA31JZivW63WDe2iJUSUJ0Bcvl8sGgKZ9wzXeDWPQebMDShwfABO40Lmxg3J6n+z7wPtlr219zoiHJBIJ+Hw+Q+jQ4IsbSUH/VvCcx9Ux6bXZpYF2iszNzeHxxx/HBz7wAcfr1113HR599FHX7xw9ehTXXXed47U3vvGNuPfee82zf/ToUbzvfe9b9hmC7+uRH/7whxgeHkYymcS1116Lj3/842e1x8iGgPTvfve7+P3f/31Uq1XEYrFl0boXo7LfbKGzxFpibCTlxr7WzYDOFxtYeL1eDA0N4ZJLLjEp4jbDWllUqVQKyWQSXq8X+XwelUoFmUxmS0u8AHBExpnOpYYP50ZZW6ok1VniZkXmjrL1KV7vYlOngwcPIhQKIZlMmqBEt+gmsOjQsxFaJpNBoVAwLN5u9dIDgQCGh4eRSqWwb98+HDx40NRd3Sm1xW3RTZsMt1AohH379qFWq2FmZgbHjx93RM+DwSDS6bRhO28FmK5KuFKp4PTp02g0GpidnUU+n19ThJbHKBQKOHHiBMrlsmlY4xZYIZtgYWEBxWIRp06dQi6XMyyynSLcU8jaY/dzOt/A0nPn8/kwODiIaDRq9hIFjbZLeiy3rZeevl5dqIei0agpnUbHjLqLhjQzdorFIur1umGyMJtnZGTEBGYJbjKQTf3v9/tx8cUXw+/34+TJk6ZkFR3b7RCWhqPBrwxAvs/0X5amUFb0/Pw88vm8SRNXZo3tpCYSCQOYsteL7SSqE+jxODME1lquiyV2GDBngJ3BWaY0t9ttw4LjcXmNzALSEnG0Y7QWqgY5tREb7TU61GRQ0tHmfDEVnHNGvRQKhdBoNAx7slarGUeWoDqBdtuG4nl472z2mwIgtKfIAGy1WmaNA86SA3Q6WeJuYWEBuVwO9Xrd0WtmLft9p9PB7OwsnnjiCVNWbmBgwKSY9/X1maaw7Xbb9AHiPdrJoDPXMeew0+mY9cD1rSXaGMhaLUC0FdLT1Vsv56KuXsneVIDYLn2g104giwFulmCh7rDPoUAg4GT18rmKxWJIp9PmWAoE2qQbAsHUAzynNpVU8E79eAW1VWfp3ksh6K3+Gq9HgTqbDct5LJfLRmfTT1DQV/uUaMPEbmPiddBPZmla2kN63dRhBL3ZEDoUCjl6mBE40wyrZrNpcJJ4PG5K4jGgy6wqW+cri56sagXS3YIzNmDptlepLaLZCZx/m5jE+8XrIr5BHIFzoutRWdK8h7S1ODcapFdbiMeiHuaYuH44PjegluuWQW/anTwP1yHvv56Lwd5sNotGo2H0llZI4L1utVqmrwvL3tF+pD3nBqSr/uNcKWFS12w3PM3GlTSzTT+vNg3tK8XpuDZ4nEgkguHhYTQaDZRKJbMPaNamZrzYQXPNBtF7ymfJDgLaOFc32QxdzfKMFBJLbMlkMmi1WhgZGXG8PjIygqmpKddzTE1NuX5+YWEBmUwGY2NjXT/T7Zjd5Prrr8fv/u7vYt++fTh27BjuuOMOvO51r8Pjjz/uej2bIRtC/f7sz/4Mf/zHf4xPfOITCIfDmz2mc0LsDY8P4kpgpH6e32F6SSgUMgqsG8uaDyQAx4a+1SVeKBqxVcWsEUFep73JaURW02/cnAzb0VancjWhguA8k/HWbY45NjIPyapjXb2dLnTcGPUPhUIIh8OmezS7tbMuLOdc2Q9nA1DnvaRhxNICHAsNwPUcjyBJf3+/cfC1zpyd9UCWIhu77aSSKLqfqEFJpcznRyPoZM7QYNmqYMhK0nPOt156+nplsR0UPi92Sq6yrQA4nBR1NqiDtJ667jX8LlnE/OxKun2rRBnkwFK5NpvtRdY6ARBNsdXmSd1EWW7K/AGcLCSOQRnTwNL86efchA49Gc08p7LoVNS55jXZtoaywdTZ68aq1Huq+zNJBhQ3MEjXo/b74DnoNGu5PHWgaRfZbCllXdqAAq+tG9DAY/F+8zq08ft6grVkE9ZqNfh8PlO+R9P/dW2wXAqzQdZ7vq0SBWGA5TVPlVVHEE91+3ZfU09Xb72ci7p6pf3Z3m+B5bXS+Vv3NGU2c99drdyjMla5RyoDdSXda5/XDZDj/zaL1AYyef712OLKelVfiefi67wu/tbPAkv63Saj6R5FUT2nc6Svq/2j86zXbf/N/91wCT3XasFyWz+pjeCm0/S3PdZue53ec/7d6XQc/hd1pU0GUFazvZcrNmIHO1a6Xnu83ewVBZ65Hii899oUlXNvz63Om9occ3NzhqBFEpcy63lcvk8fXjPKbTt6LdLtHunfapfzPrmtI5vcwTVnP9v2Z/x+PzqdjrHHaAPZNqE+82vVh25BwbXIZujqPXv2OF7/8Ic/jDvvvLPr9+x7thq+4PZ5+/X1HtNNbr75ZvP3FVdcgUOHDmHfvn34u7/7O7z5zW9e17HWKhsC0k+fPo13v/vd54yiPxtCwFKZ12vZMOg4KfOUgCcB4rUIa2J7vV4UCoVNuKKNCxU+o6Qej8ekMauC1TnS1G1+T+u76UOoaTfhcNjRFGyt4vUupvqRkeY2ZxwvG3cODw9jZGQEyWTSpI/vdPF4Ftl0sVgMjUYD4+PjBmR+7rnnMDc3Z5h6wWAQjUYDoVDINLSkkbNZwRneR5Y1YDPUer2OUqmETCZjwP31HjebzeLpp582jUPYTGR4eNjUhieLgsz148eP48SJE6hUKiiXyzvCIbTXOJmuWq+Qae/K/KDBU61WTRMVzvN2Sc8533rp6euVhbqawVc2CdQgbqlUQrvdRjgcNkHGer1uAnzUzYlEAqOjow7Qk44LnR0ekw2L5ufnEY/HDQi605oDM6hIwFOvo9PpmHlwY7gBS6whfo/7mTa6pDNGHcv3eHyWjSmXy1hYWEAwGMTQ0BCazSZmZ2e7giiBQACDg4NIJBLYs2cPDh48aFhCwKJtEovFADjroisYoix0shFtx1IBUjYQJeuRwD2PSwCH52eWkM1so21EMsT4+Di8Xi+KxSICgYBpCMeSZ319i01Mw+GwsWPUydeeOvZ8q7NvkxVsp5SlfdrtNiYmJsxadksRX6uwDFuxWITP58OJEycQi8VMdoeWW3v22WeRy+VQLBYxOTmJubk5w/jeblHAjexKLS/EfQNYYhB6PB5T3oi2DstF9XT1+SXnoq5mVhZtUw1kaq1kFQLBBLXoE3Mf070acJZOsP1DwFlORvdY+thaokoD5jaQSL3V6XQcOoo+qo5T9187e1p9XWUp6xgV6HUDxfW7fF9tDWaO6fvdnktb11HodyhLV8FzziMBRTLSeT20E2xmLQFJj8fjmC+eg/Nol9vg3yRK6RyxjAjF/tsNFLYDHirEIZhdpmuL/2uZEo6H3yPuo2A1x8sfO6jPMZAYpT3gaCuqPibGxPngufgdrR9fr9exsLCASqWCQqFgGubaJA+uY/5tB0MqlQq8Xq95dgqFAiKRiMki4/fm5uYwNTVlMt5nZmaMrajH5vPhFlRzwxl4Dvu5su+b3lO9t0qQ4zqmP01Wuj67+tx4vV5DPKT9paWSdE1pzXkeww6k67hsEF3nZzXZDF198uRJxONx83o39vbg4CD6+vqWMcVnZmaWMcopo6Ojrp/3+XwYGBhY8TPdjrlWGRsbw759+/DMM8+c0XFWkg0B6W984xvx2GOP4cCBA5s9nnNK7AjXWiIr/BwZW9zINfVsreemMbbd5Ub0Ibc3Pt3o1PigctZNlsZKt0gimdbrZeGrwxqNRo0R1e1zdM7HxsYwMDBgNuDtnue1Cuen0WhgaGgI1WrVbGrAYnpgsVg0Dc/C4TDS6TRCodBZYU3S+GPNsenpaZRKJZPCvpbUJjcpFAqoVCqmt0CtVkM6nTbBEBp3lUoFv/zlLzEzM4OpqSlMTk5uewkUFRtIj0QiprkS17umi1MBMx2Sn+vGAunJuS09fb2yUN8yEEqgnLppfn4epVLJUS+bwADZKHRoEokEBgYGjHOl7BxgqQ4mj00mMXuf7LRyUgAcrCPaJwo4qANh75ncc5SZz/lmFpc6IfyOpkYzK0kzhbgPVqtVFAqFroHW/v5+JBIJDA0NYXR0FLt27TK6T51SYCld3nZ4eM20L+xr1b+5Lggq+3w+45woKML9mkETdbRsMIaAw9DQENrtthnD3NwcJicnTWkhn89nCAokUeh56VSTHQYslchR51UBEAqz8ACYMkQsh6fXsFFh01Cy3FgrPRqNoq+vD7VaDbVaDcViET//+c8xMTFhyBU7DbTlfWPTPLvMAkv58Nnp6+sz95T9FdgEbjuB9J5svZyLupplUfR/v99v9nY3II2iQLqWBbWJaRoQ1BrECqQrGAbAQXQjQEv97Aak87skqbD2t2ZhaXYJxdaJ9LNs8F/FDdRVdqsd/OT7GuDmsfVYq+2V1Ic2s1rnW4F0Xm8gEHCU8+L+Rp1q60jqeJ0HBecJEishQYF2Hlvnt1uvDFuPu4GXbvOvgRO15/QzHBv9SP0e14H2CrHHxGNoQIDvE/NgwJVEKI4bgHmO+Fkt7aL3fH5+HsVi0dhRhULBUY5EhXqKoiXpSGwgwQJYLAkSi8UMoYTSaDQwMTGBUqmEXC6HXC7nqN+voveT86JBGzuorwEJO4tCMxm63WvqXb3HJGHaGd/6m/gPew/SjlOiiNpRbtiFHeTTcemzo7bpVkg8HncA6d3E7/fj5S9/OR566CHceOON5vWHHnoIN9xwg+t3rr76anz3u991vPbggw/i0KFDhlRy9dVX46GHHnLUSX/wwQdxzTXXbORyjGSzWZw8eRJjY2NndJyVZEPI33/8j/8Rf/7nf44nn3wSL33pSx0MHwD47d/+7U0Z3PkoduRKf2yluNpx1IGnaARbU0j1nFvpmKhRYF+77dR2G5f9Xf5e7ybk5kCqaKScJVFo1K01ULITRA0kbY6nTjXXDwEKsjf4m9etdVltpQUsNyK7GVlsUMYf1vQ+k7XIc83Pz6NQKBhjj/eQ66pWq5ka7NVqtatBtl3SbV2pUWgbiPq/Znxs9xrdjMh5T9YnPX29NqEuUocRWNr7tFwSsAQSq8GsNU61VjQ/z8+SqQTApNl2a6SsAbDt3pvcWEOUbjpa93zdg2wWnLKy7AC7ZuMw8LqW5qwEwLnmuf8T/LBLbxCYVduDOk5L9ZBtpEwzPZZmIdpzwePympU1zs+TkaagkK4/Mpd1nAwM1Ot1lMtlAz4pu53XqQFX+95yfqn/yZDmPFM327VMN0N4HY1GA5VKBTMzM6hUKmg0Gmg2myaLg3bDTtUJan+r3Uj70l4XW+kwr1V6unrr5XzQ1XaQkvuQGzCqoK2CcMoeVztXfUEbcOa+Rr+CAcVOp+Pwh/U4tj7WAJeWMFOA2S5Jwj2tG4nM/tttrnge6gS16XXO1P/l+Bis51yrf63fU0aszoUbqG+PUYPQbtfgRn5zmw9lv68kbvYHj7GRvYffswMzNtjZzcfi//ytgQeda13b+j2C0Bo8twFWXWscC0FzBmjt83B96Ov6s5qordtuL5UL4rPKZ9Lj8aBQKDhsDbsEmw0gu80z17iC6G6BIPs+dXtNnxP7u7redN3ZgSN7zHaQyS79w3F2C2KtdP16jm7vdbtPW6mrDx8+jD/4gz/AoUOHcPXVV+Oee+7BiRMncOuttwIAbr/9dpw+fRpf+9rXAAC33norPv/5z+Pw4cO45ZZbcPToUdx777345je/aY75nve8B695zWvwqU99CjfccAO+853v4Ac/+AEeeeQR85lKpYJnn33W/H/s2DH87Gc/Qzqdxt69e1GpVHDnnXfipptuwtjYGI4fP44PfvCDGBwcdID+my0bAtJvueUWAMBHP/rRZe/x4e7JctbSeoxlOkusM0Wm0VqbbNEJYuoJsORUaiqLOvwLCwvbUhtaHTg1ftRIUGfc/i4Vlf6sl3nL+eY8uDHrWHOeDelGRkaQSCQMw3u7Qcq1CjfoQCCAdDqNubk5DA4OIhKJGOXHSHg+n4fXu1geaHZ21jDsyLRKJBIGqCBzzk5R0nvUbreNAcuGHWR8cu1putSZOOlcL/V6Hc8//zxOnTplgiDKkGy1WqaBGa97pzmCbkwQNxDdTQErOLgTZKfN7bku55u+dlvnK60520nR4Cj3snA47AD5gKU6381m0zCQa7WaaXqVy+VQqVQQDAZNI+p2u22aLVEf1+t10+SaDD0VZpUQJO3WBHsrxA5a6+v6W4UMfLLX6HiwSaadIg8sOfIej8cEedlovdVq4dixY5iYmHCkkrsJ2b7hcBitVgv5fN6x92vDcwK1Cl74/X7TKJ79UMjqqlarDgdMU9y5bsjittlKmpZOoJ52BPURx6dAPhu45XI51Go1UyaEjDiv12sazPf19SEWi5mygNFo1DRaVWYb1yWFZWLYJKxcLhunk+8TTN/svUOD6rVazVyHBt/ZNHWnS6PRQKFQQDQaxfDwsGHtcY3retlpPVkoPV29tXIu6moN9gFw9D7g3qsBUX0OFLDjHgk4We7cw4AlUFf1kZJ25ufnzR6vPbU0UMq9m/6g+p7lctnoapYco6/KYFkkEnH4nyw91Ww2l5WkAZYDa7wOJZNpzWs3li1FwTrOHe0UgqEaVHAj6XF8BHZ53G6kKNo7qg9sQJu+HzNrqSNVvF6vKUvG8ep5FFTm8WzWrrKVdT5WEhss1XXJDGy+Zh9Px8j/VVfTzuEcaWk8Bjm47liWjY3RyTbX0iE2YEqcp9VqmTVmN3LlWqDOtu+jvYZUbMxFCSOc92q1inw+bxrVa48hzeTk/dfz8vN6Dg1q2UC0Ath6j3UPsDNVeB08P599Nrpn9QYtk6rNXu354P5Cgo3X60W1WnWQVPlZN1H/XIk4XBf299fqt2+lrr755puRzWbx0Y9+FJOTk7jiiivwve99D/v27QMATE5O4sSJE+bz+/fvx/e+9z28733vwxe+8AWMj4/jc5/7HG666SbzmWuuuQbf+ta38KEPfQh33HEHDh48iPvvvx9XXXWV+cxjjz2G1772teb/w4cPAwDe9ra34b777kNfXx/+7d/+DV/72tdQKBQwNjaG1772tbj//vtNCcezIRsC0jeTgXIuizqd6xEFjm3lsVaAmMaDbpx8cLmJhMNhB/vOVkpbLXYUTwF2/d3te6pgNyor1fjkZs0NVBnpOwWkXKtwLQQCAVM6iAqQ10IjCYABvtVgpRGq0W9l1wHObtX8YakApmoTSGdN8s1kmvE5YEdqe43ba2yny0rskBfDGtzqyPndd9+Nz3zmM5icnMTll1+OI0eO4NWvfnXXzz/88MM4fPgwnnjiCYyPj+P973+/ibJTvv3tb+OOO+7Ac889h4MHD+LjH/+4I9r9ox/9CJ/5zGfw+OOPY3JyEg888AB+53d+x3GMP/qjP8JXv/pVx2tXXXUV/uVf/mXd17iavFjW9naK6h51oAA49sRqtWrAbhr0ZIkpq1wD2TwOHTwy7dSh5Z7oxtIiQwnAMibcdokbW49iO6b23NrOOfWQ6l7do2nzkJHearVMGZOVGOm0aajXyNqynTm1HVhblD9ujCUCP81m0wGE07ZikFrtNb1XymzjOtLvK8ihDqI6XnROtQYn/7bTv/ke+2jw+ADM+XQ9aXNZXcMcp4ImZ2MN6nVUKhXHveT87VThODnnJGbo2uCzwGwB2lY7TbZaV/dkZ6/tjYodeFWmOZ8J3cfcvqPzwr1HAUDulfb39H/qaO5fzLbR8wJw7HMKZLfbbVNeg3pewXzqGgKj9n7l9jypv2szaRWYVl2r9oQdpOD7+pqy2N1Yybw+fd9mBOu4bXCT3++mD3Qs/K4Cocoa1vmzz6X3SW0KG/Bcrx/UjQ3sxghey7GVqUx9q2tP516zBdRWIMCr98htDjTgwqwtAvT6fNiMdPs+rQSmq0+uwTyv1+uwQVjqRQP+LDvXLUhsk8J0/vVageX12vmbdpEbWcwG1O3v0ra2Swe5BXncnoOVSG1uOAPPCTh7B/H69PMr3RNbtkNX33bbbbjttttc37vvvvuWvXbttdfipz/96YrHfMtb3oK3vOUtXd//9V//9RXHGwqF8P3vf3/Fc5wNeXEUdX4RCg1pOm7cSFZjhlJ5amNANlUKBoMYHBxENBoF4J5ixY26UqkY1lKn00EikUA4HMaFF16IVCqFWCyGgYEBeL1e1Go1ww5+5plnUC6XTeR9Owy7bsbQSp8liyubzSIUCiGdTpt5WkkUyGATDkZuVWgkkZXOutN26uWLTRRI10YbbmIHhgi0t9uLjUzUqLUZATS0+B3OOZlYPI5+52wI15WtFHeyKPhGJgywVMuZ16PgHJ1zgiE0srab0bSVCv/+++/He9/7Xtx999141atehS9/+cu4/vrr8eSTT2Lv3r3LPn/s2DG86U1vwi233IKvf/3r+PGPf4zbbrsNQ0NDJnJ+9OhR3HzzzfjYxz6GG2+8EQ888ADe+ta34pFHHjGR82q1il/5lV/B29/+dkfE3Zbf/M3fxFe+8hXz/4uhWfGLQdyCsat9nvu93YjSZgF6vV7DMnZjrNTrdUxPTxsgPpFIOOou+v1+07tEy7UwBZZs1WAwiNHRUUSjUUQiEaRSKUcZrWw2iyeeeAL5fH6j03TWROcfcNoptE+4P3U6HcOABmCYQPysAvZzc3PI5/OmVBezAOw0aepqli5jjwzWwKQDSEfWBtJV9B6TnahlxzhGghVsRKegN4+pgQLVoVwr2t+FIA77X7DeOp22bg6xfR80g4JONuuN0060xWaUsfkuHUPbKV9NNkPXun1fx7SR4LuyDDcrMMW1x32jXq+jv78ftVrNsb9zHRFcIdhA9upK4MNWSQ9I78lmiBtQpn/zmWHWlwaKu7Gl+RpBVw1885lWUhXPQYB6YWFhma5ngFZ1EEF+7pd6bvaM6u/vRzKZND0R2COFUiqV8OyzzzrYuarX7OCyzg/BtG7Ass6h/T6PSeIXsKSDeK06v16v1/TRsMdkM8iJNej5eGx733AbswKLzGiORCJIJBJIJpPmR0vAaO+QbnuTDbTr/Nh6yPZR9XXVJ3bw3W2eta77/Pz8MoC1GxlQMxoUFCeBTV+jTuD/fFZoKzCrmj1MODYem/YLMwQ1mKH2hA3oUrS3D4/N123hObtllvN/BqSUIKLH1PJEquv5uvYmoO3sVvfdZqZzPlgCT/eRVqvlyFTU+81702otNVinLcuqETYIr3uZvq/19dU21N+816tJT1dvr2wYSH/44Yfx2c9+Fk899RQ8Hg8uvfRS/Pmf//mKbL/zSfigkpWk5SK6NaXUjZHpQHNzc8hms+jr60MkEjHpKMpsodBpm5ubQ7FYxPT0NGq1GiKRCNLpNIaHh/H6178eBw4cQDKZxPDwMPr6+gzLbnJyEj/84Q8xMTGBEydOmBS47ZD1nJefLZVKmJiYQCwWQzgcNvO0kpCZUK/Xkc/nkcvlHOm3FBpiZKJHIhFzP14MTGA3IdBAkIEMczcgXaPfquwI/lQqFdMY1K2+udY01RS1tQRLNltWO5f9TG236LPAsgLtdhuRSATAkjFmA+m1Ws38rlarrnUaz2W566678I53vAPvfOc7AQBHjhzB97//fXzxi1/EJz/5yWWf/9KXvoS9e/fiyJEjAIBLL70Ujz32GD772c8aQPzIkSN4wxvegNtvvx3AYi24hx9+GEeOHDH13q6//npcf/31q44vEAhgdHR0My51VTnf9PV6nlsa5AQuVwoIer1e89zxOwral0olnDx5EoFAALt27TIAOA1z1lrXfZD7J5s4eTyLzZAvvfRSjI+PI5VKYXR0FIFAAMlkEolEAr/4xS9MX4edKGqbqBNrswHb7bZhSrPMi9frXdbgFIApZdJsNpHP51EqlRz3iM6hx+Mx/Tyi0ahpohSNRk1TR3V8GDBRva9ZCAQZ2AxbmWUsRcbvE3AgIN5utw1womw03nsGsLV0DNn2ZJGHQiHj3GnwZaXsOYqWJgiFQgYUKpfLDv1tC++XPb+8t3qfVxI3VtZGxf4+bTJg6Vlcj2ha+Zn2Y6Fo9gjXMG1sAkYE8JiST5up3W4bEGSzxtOTF5+ca7raZnlyD6ReoF5U8I360X4OuAexprQGqRWg4vfpd7N2sz7vfE4VXKPuIDDq8/lM9hGzlTgOlreMx+O45JJLMDAwgHg8jsHBQUe5ipmZGfT19WF6ehqZTAYTExOOvUrHDixncHcjNqmfouxzCv8mWYp6lXoPWGK6cwwE0qlHqcO0XJnOmYrbaxrc4LXZQp+ajdqHhoYwNDSEwcFBo3fn5+cdtcPddJ8GRHUO7R4hboFTe42pX1qv1x2MZT2WBk5JsuNYuwGqtt9LIgGviXpZgW3aD7Qh+L7WINcm4jy+3XhTs9/se6c61M2u0DWpz6HaCsBSHwHA+ey7lRdys710jVEvEveygwgK4vOZV/yB95XlA3UN0xZSQJtBbz77NlbHPYUkBNpRxNBsZrzeP9rEGhTkPkHf3A5MdbsXPdlZsiEg/etf/zre/va3481vfjPe/e53o9Pp4NFHH8XrX/963Hffffi93/u9zR7ni1KU0caHRDczwJnuDSw5SMr6mZ+fN84amzG6Rb6YSkNgmE6rz+dDNBpFIpFAOp3G4OAg4vE4UqmUUZ6MlKdSKdRqNWSzWdd0lp0qNI64mamj7JYFoCw3zqld580WvX/djvtiEr2WbqlsFAYRuJbi8bjDsFEFprXY1BhQhbIR9lg32SwHmPPBvzVar4EAG1jwer2OCLabAUXgRJX/esE+NRQYnGPtSK2rRuOMhpeWm9juZ3kzIucs00OhQ6UyNzeHxx9/HB/4wAccr1933XV49NFHXY9/9OhRXHfddY7X3vjGN+Lee+/F/Pw8+vv7cfToUUdXcX6G4Pt65Ic//CGGh4eRTCZx7bXX4uMf/ziGh4fXfZzVpKevVxebmaPsKptBp4a+/VyR1cw9kc8s91VNxaa+oRHdbrdN3WSys9LptAkMs/4qGdaxWAyxWMxRZoRj3O7n3BY7IKHsbzYABZbqsiqbUB00AvDdmrfZ+kt1tZ06b+/V6iDbjjPf1+Nq7W57HHzf1n0266sbc87tWAzIKDO9G0OR10agPhAImOACQSSCFGvVCxtZUxyj2zydiVDnEcRQdvlK16PBFqagU1/azLq1ssFU1GFW4gAdcBU++7oH7BQ9DfRYbtsh56Kutvc7e++jnww4GbQUNyYxn00Fm7TspK2b3YKA+hnayR6PxwBl+jxq/yzqJrLoCQInk0nEYjHjG/Fc9XodsVjMEFpUL9hjcps7zoH+5lzRptDX3cqJrSa2HuGccf/i3Nh10PlZvZdq46j+06xkta24J/PeE9jUDDxlyeteb4/XniebUazX5TY/bq+rDdctc8AmDeg49bf+8DwaxGC5LwY0yGLm/Nn9QhRQVpvVDXzld7WcL4XnsQMF9tzZoufh34p38Xi0XezPKjiv49BmwiQckBipIL4y9NWvto+v9r19LTp/nCfbLtNro652Y9t3C850EzuwAsBhg67V/ujp6u2VDQHpH//4x/HpT3/aASi85z3vwV133YWPfexjL0plv9nCjU43YDKAGZlTRcgIGpuV0bhm5IvOT7vdxuTkJAKBAGKxmEnB5eez2SwqlQqq1aqpWbV7925cfvnlGB4exiWXXIK9e/eaGukezyL7jcc/dOgQ9u/fj76+Phw/fvxFw2TtdDooFototVqmMVupVEI4HEYqlTJAJzfFarWKVmuxadfExARqtRqmp6cdNVJt0RQumwXxYhXbSFEmHrAEMI+OjuLAgQOIRCLYv38/RkZGHKl2bDZSrVbx5JNP4vTp0yZ1ShnowNo3bjU6NLKuzBYFqzayRulQp1IpU+JGUwoZFc9kMoYNyXJJZJfFYjHs3bsXkUgE8XgcyWTSYXzU63XMzs5ibm4OU1NTmJycdBgBaxEq20qlYgJmAEw5AD7LnA+yNrUMghob2yWbofD37NnjeP3DH/4w7rzzTsdrmUwGrVYLIyMjjtdHRkYwNTXlevypqSnXzy8sLCCTyWBsbKzrZ7ods5tcf/31+N3f/V3s27cPx44dwx133IHXve51ePzxx5cFBc5Uevp6daEzX6/XkclkHPs6n3Gm7ZINRBBMncv5+XmT7pnJZJalP3Mfo65mY0WymMbHx7Fr1y4MDg7iP/yH/4Ddu3ebsmP1eh3FYtEwtV/5ylfioosuwjPPPIOf//znjtRSDc6vR7o51Px7te+t5Ejo/k8mDxlALLtSKpWMPuJ80yZidg1fcxsDdbyWyFO9xuuJxWKIRqOO+QoEAojH4/B4PIbpyD1egXeysJj9x9IpDHLQKfZ6vcYO473g/qc6jKxJBkVJAqDtSLtvaGgI/f39yOfzCIVCJmvRvs+hUMiUFKKuZnp/X18fisUiCoUCGo0GnnvuOczMzDjGsVnCe6jpywqwbETUFgiHw4jH44bVxTnMZrNda44HAgEMDAwgGAwiFoshkUgY27FarQJYcqRzuRyy2ey6xtpqtcy5FeSrVqvG9rT1PjNSuK53imPbc863Xs5FXU1yk7Kmaau6MWfVprfLvABLjZqZvUG2q808tktGEpDUH/YvaDabprQl9YKCyDMzM44ylAAwOjqKPXv2IJlM4uDBg2Z/ZoaVAswXX3wxhoeHEQwGUSqVDNmNez6vT4O97XbbsJXtEqLd9lbqTu6FZJLzR/1bZR/z3OofqL/caDRMyRuSSshyVzuIgVnd5+wSGFp7nnutHdjWYHA4HHaA9Db4qoQ/Ho8+uu13uvlbCrJqEACAY3waJOV61UCBzpuWf2W/FA0eaC8VZqENDg4iFos5GtZyzlhKhOuG2Ys2CMx50cCEZhfw3DbTWlnZ9jpTIN/e1xVMVqIZbS7aIQw2qd2zsLBgeqSRVe/1LpZDZNlcBv+TySQGBgbM/kBAm7ZzNpvF7OysmQctAaXX5yYej8c0muf3lCCozwjXuK3DiYXweAr421kZGkjh+uFa1QamLP3WK+2y82VDQPrzzz+P//Sf/tOy13/7t38bH/zgB894ULacPn0a/+2//Tf8/d//Per1Oi6++GLce++9ePnLXw5gcSF85CMfwT333IN8Po+rrroKX/jCF3D55Zdv+ljWI6r4AWekSzc2On3KKOdD2+l0DAuLDjg7DA8ODsLv9zsaoExOTpqSLEx/GxgYwEUXXYTh4WHs3r0bY2NjrmwmMtNHRkZw8uRJ46xtFKTsJm7O+mYIU7/Jwpqbm0MikUAwGDSRWG6OdM5nZ2fxwgsvoF6vG0NppXFrxHklVtiLQfQ6GCDg6/zNdZpKpXDhhRcikUjg0ksvxd69ex2M9Ewmg+npaRQKBeRyOVNyYKOADoVzTFBDDWU7KmwzV9Zy/Tw2nep4PI7R0VED4mjQhfPDGuVs2huPx7F//35TPmlsbMzhOBQKBZw4ccKA4Nls1hioawXSgcX9hIZDs9mEz+dDo9FAMBh0pMXR8C0UCpibm3M0SNxupbkZCv/kyZOIx+Pm9ZWA55WAwbV+3n59vcd0k5tvvtn8fcUVV+DQoUPYt28f/u7v/g5vfvOb13Ws1WQr9fWLVVdzbNSjwNL+qOXUmMZJQ17BQWAJSFtYWDDNk/V9Zk8wqFYsFs35fT4fBgYGcODAAQwNDeHgwYPYvXs3ZmZmDNBWqVSMI3/JJZcYB+fJJ590ZGCpY7Nesdlb+rrb8WyGldu82kKbhjUpa7UaAoGA2dfI9GMgsF6vm+wxZuW4jUPBGNpAamvxmui8aqquz+cz4AAdOQXn/X6/YXS3Wi3jnPHesgwLHWmC4kqosJ0rjhWAqa2qtVABp57p6+tDPB43wJFbWZZAIIBEIoFoNIqDBw/i4MGDZl0Aixk9uVwO5XLZlAeiM76Z+kGddgWLzkQH6DFDoRCSyaSjPnKtVjNAlZswaM5axsPDw+h0OpienkapVDL3myBWLpdb11j5PbfXuOar1aoD9Go2m0ZXAzvHse0551sv56Ku1h4YAMy6V/1kM8pXAtLpo3m9XgOkE7BUYpD24aCoLuBzyWeQpTkqlYoDaGR5VWZCEsQOhUIYGRkxpdfsbEId865du5BIJFAul3Hs2DFjS9iBdhUFjRnM5fzwOrUfA/cukut4fgbpeU43cJm/bSCd881eDwwUM+jAYzKgq0C51u6mb2033LQDy27Mbt5PJTGorlcg3QbiFXzX63abc8VcOD4Foe01pIESPS7XB+0FYjT9/f3G/mCgPhwOI5lMwu/3Y2hoyOizcDiMvr4+17KcGpTXgAXHoAEJzV7Ta3ErL6zPZKvVcgQ3FFDW+dI549rR+xYKhUz2pJY8Iq7VbrcN0YDjC4VCSKVSCAaDJug9MDCA0dFRQ3ZZWFgwRD3qzVKpZAIeNsHRDafRoJWucfYT0LESA2M5GNqVSkK0z6n4kJ5Lz8l55XcDgQCi0ajjOz0gfefLhoD0PXv24B//8R9x4YUXOl7/x3/8x2VswTOVfD6PV73qVXjta1+Lv//7v8fw8DCee+45JJNJ85lPf/rTuOuuu3Dffffh4osvxl/+5V/iDW94A37xi18gFott6ng2InwQ6agDToWugKBbSrkCnZraq5/ndxgZ4+bGTZ2O10rlSBSwpJO40fIlVPIEIvi3XXtUFR03R2X7rech52fpmDPCynlTIL1SqWBubs40F6VyX+34atSdCbNqJ4h9HaqgARgDlUDz4OAgkskkUqmUcerVyCEQMDAwgMHBQcN8c3P2VxKuD2W8c/1yXDTguP7JlOgWOXcTsg3D4TDS6bRpwptIJAzI0mot1k0bGBgw6XYEcUZGRpBOp5FOpzE6OmrqFxNM0fU4ODiISCRiAmWNRgNTU1MolUrrUoLqYLDBqzISeD+V/WEbzy92Yc3jlWRwcBB9fX3LmOIzMzPLGOWU0dFR188T4FzpM92OuVYZGxvDvn378Mwzz5zRcdxkq/T1uaCrbeEeSaNfgdHVQEE6fm5B826lSaivvV6vAQW12ZU6imQqu9Vk3IhuooOs5TJ4TD22G8jBMa33nNT1tGcAOJhYCwsLJnjA9Hpl06moDlMnj8xtTZlWUGKl+eL4+Dfvu56Hf3PMPL6m99vBCf6vOlQdNps5qY6cMgCVnUe9mEgksHv3bqOz4/G441isHQ8AyWTSAM/ryZCi8JzKfON5tBwca7V3Oh1HerqCRaudh4H7gYEB09PFtkOCwSAqlQoCgYABrT0eD9LpNOLxOMLhMEZGRgwIT0Z6u902TDDeC9WlxWLRBNBXE/u5abVaRlczCA7AMPDU9jxXdHRPNibnoq62dYS91/JZs4FCJZq47RH6zHCPVrawW+kFFQVHeX4FtzhOkskoBK2VDUwd43bd6vPb79tjs8tKKnGIv+nLMtjLvYufoY9LQN0GTtUf4/HpP1Cnq82iY+N+qgxinQdmjKkeIzPezkiydRzn1gbZ7Xutv1cjxSjzV7+nwKb9HZ0XmzCn98MNSNfvaDk5xVLo1zLTMZ1Ow+/3Y3Bw0FFuV9ey1h3nvLtdqw3s6vjt89s2nuorrnfbbtV7wevlc8TfxH6CwaDBCjR7jOcj2VH7hni9XqOXw+GwAdLj8bgZM593nm9ubg4DAwMmw6NQKJiMRR2rHVDTa1CMzQ5QcE74mu4F6ne7ib3G3MB8HQPnmsd22yN6svNkQ0D6n/3Zn+Hd7343fvazn+Gaa66Bx+PBI488gvvuuw9/9Vd/takD/NSnPoU9e/bgK1/5inntggsuMH93Oh0cOXIEf/EXf2GYfF/96lcxMjKCb3zjG/iTP/mTTR3PRoQPIcE+txQPfWCpyPhgsb4lI5VsZGWD7wCMM8A0aKYYxeNxk57eTfnQEWF5FDqebuyvbsJrYh3XYDCIoaEhRKNRw+xTJUYng2k+2WzWbIZk9K1nI6HhwsYuPp8Pzz33nMPho0PD3yuVc1FRY8uuSfZiFft6NHhB9lY4HMaBAwdw+eWXI5lMYs+ePRgaGnJs/rFYDKlUCqVSCdlsFv39/Th16hSmpqbW7IACzrQ0NkKl4teSSBy7ssZp3Gkq/UrnYR3ieDxuMjbUOeeaaDabiEajqNVqplGQx+PBK17xClx22WUIh8MYHR01z6gGugAgkUhgeHgYCwsL2L17Ny688ELk83n8n//zfxxg+FrWkQI53EvUSNNAj8382AmyVZFzv9+Pl7/85XjooYdw4403mtcfeugh3HDDDa7fufrqq/Hd737X8dqDDz6IQ4cOGUPv6quvxkMPPeRIvX7wwQdxzTXXrOdSlkk2m8XJkycxNjZ2Rsdxk63S1+eCrlZRRziXyzkCv/q+Lfwc9ySWYfN4PCaQRufbFupIn89nSlR4PIsN0BRoZZk4vqf6dKPPu8/nQzqdRjQaddQo1T2EjkStVjOl1M7knJwn9oBRPa3gsjrf3WwCG5Slw1UqlRCJRAzY4ff7kUwmEQ6Hl12b7bAzo4cAtNZtVVYjj8EUeLLMqCt4TJIb+EPbjnPBsi5q29FGYdYcWWq1Ws0BpDPYfMEFF+A1r3kN4vE4BgYGkEwm0Ww2kcvl0Gw2kUwmEY/HUa/XUS6XEQqFTFm29QS9PR6Po4kuszaY1eH3+02qOsfcbi+VL+DcrkUncJ7i8The9rKXYWRkxOjATqdjgPVGo4F4PI5qtYrJyUk8//zz8Hq9eOlLX4qXvvSlhlTh9XqNjerxeDA2Nmaunc70nj17sH//fpRKJfzsZz/D6dOnlwEybnOiBBnqYfYcUjtT18Vag/9bKT2W29bLuair+czzuLr++QyoDay2u9tnlTlL0EmBP7eSm3zu+F1lK6s/Qd3KYK6yq4GlespkX5PFWiqVjI1oB9Joh9sALH0vCnWCNipVdi/3Lu7zzLZWNqyCoJwjkpxarRZKpZJh0/I7GsjTIKgNtrbbbczMzJi9jCQnBeMZ9Nb5V53K67VJabYfyutwu+c2kEu8ROeeJDrFV3TvpS5X4TySnNDpdByEQrvON+8hdZCSG5jdQKxGiZD9/f2Ix+MIBoOmZGooFML4+DjS6bS5Z7QHmI3n9/tRLBbh9XqXkXkURNfsbV4XfzMYTRyG39X7bDcLZeYzbR2uMz2n2otsmk7cIJVKmfKjGmBoNpsIh8Oo1+uoVCooFArweBbLyKbTaUQiEYyNjZlSvbzXxKjUdksmkxgdHUWtVsMzzzyD2dlZo1N1bdnC9aulet2C+zZBxfYBupEQ7H3Ifk/XDT9LvGS1nn32+Hq6evtkQ0D6f/kv/wWjo6P4H//jf+Bv/uZvAACXXnop7r///q4gxUblb//2b/HGN74Rv/u7v4uHH34Yu3btwm233YZbbrkFAHDs2DFMTU05msQFAgFce+21ePTRR10VfrPZdIDDdvO6syGqNCi2wrVFmT7ckN2AOhUqYSoDBdq6RWH1fOokrpeNrt9n6lswGEQikUAsFjNpPsp4Y6o87wcZXWSU25HptUin03HcX1UUqljXu4G4RdNfzJuQrkk1dtSI8/v9xknWhjqsya3GISWRSCCRSCCfz3etS7aS8D5x3TO6bRt2NAip4JVNsRbhcbkutZ6bMsr7+/vRaDSMwRwKhdDX14fBwUGMj4+buuorlRgJhUJmbjmvZPuvVE7ITWymixrS6ujvxEj2Vir8w4cP4w/+4A9w6NAhXH311bjnnntw4sQJ3HrrrQCA22+/HadPn8bXvvY1AMCtt96Kz3/+8zh8+DBuueUWHD16FPfeey+++c1vmmO+5z3vwWte8xp86lOfwg033IDvfOc7+MEPfoBHHnnEfKZSqeDZZ581/x87dgw/+9nPkE6nsXfvXlQqFdx555246aabMDY2huPHj+ODH/wgBgcHHaD/ZslW6etzRVfbog7EaqJ6hga5ssl5LA2Y636reyrrp7KOpuqulfTzep8T3W+DwSDC4bDDGVOAQx3SWq3mOD/Htd7zb2bpOPvcdHIIJiggwnugNoYCHsASI10dKAURdD40uKnAqBszTh1+LUfjxkjX4KmCDPY5eKxIJIKhoSFDngiFQgBgdI3agdFoFNFoFNVqdUWnz010vWr2GK9TQR/WFtUgAtfLanYwsNTLhWVrBgcHTe8AAulct8woI+jg8/mQTCZNkJLnobNPfaxgnK4Lnnct47RBCX5+rfvHTpKec771cj7oatqlug+5gVMq+jwpaKoENN1j+Qza5WK4p/K7buxiHktJanaAlvuxBstYu91+9u1j6Ln0OvW8vD6bVax9O7SUGP171XEU1pJeWFhwZGPZ86nz4/ZZ1dMKonKPpS/PcjU2k95NbF9a9Z0NtrvtKba+tsW2T+xAuX7GBu1tPISf6WZz6feVxa7BBgAOQJ2+J/trJRIJE6TR8iosk8JAvds10x7U9aDCZ4EYkpZyVXtSfV+3SgL2/GqQgLZRKBRCJBIxGd60LRWP8vl8jn4EXDcMcPP7oVBoGdnMths6ncVMN/rvDGgQI9Agjh1A4N98nfde14naf7pPuQX9KG73yMYn9Bln8EftPXvv6SY9Xb29siEgHQBuvPHGs+L02/L888/ji1/8Ig4fPowPfvCD+MlPfoJ3v/vdCAQC+MM//EMTmXNrAPfCCy+4HvOTn/wkPvKRj5z1sa8mbguYDzdLTpCFTpYYmeKaUqPKiAqTgHRf32IjlcnJSczPz+OCCy5ALBYzYLd+n00RyQwnU2w1J5eKQlNxWN6D7CyydbmZUtrttokwxmIxJJNJk6rDFJ2ZmRmjWDbicCu4uBZnqNsxyCJsNBqGGcaU5RejtFot0zyzXq87mpwBS01TCGJreSBbSWgknOwxZSysdY7o1HKtcN3w3GqgaFYAFaYa8yspIZ6HQAJrLGotQgU7yPocGRkxRuTo6Cji8bgru6HbOUOhENLpNIDFJsCVSgWVSgXT09PrLoFDsUHznbwet1Lh33zzzchms/joRz+KyclJXHHFFfje976Hffv2AQAmJydx4sQJ8/n9+/fje9/7Ht73vvfhC1/4AsbHx/G5z30ON910k/nMNddcg29961v40Ic+hDvuuAMHDx7E/fffj6uuusp85rHHHsNrX/ta8//hw4cBAG9729tw3333oa+vD//2b/+Gr33taygUChgbG8NrX/ta3H///WetvMlW6Out1tW2w7pdQieHzgSbDlFf0eHl/hiJRNBut01dUbKNmWXGJsEDAwOmYVUikTDgPBnhU1NTqNVqprHueoTjoDPDfYxMKW2aRmYesKTrtXRULpdDqVTC3NwcSqWSYWjthEAe2dLhcNjh1Pl8PqM3tCEdgQwtXUMHk2U5+L+yy4ClhlZcA+oAU9cy1V6ZaryvWiKg2Wyi0+k4mtKxyZgy4CnqKBLUIeObbETqMb7Pe6pM+/UIHUkNQCgzTQM9ZN8raNINUOp2LgValKlP0N7n8znYleFwGLt374bP50MkEjG2AnscsAa/Br30fO12G9FoFPPz844gwUrPmq77nayH1yI953x75FzT1bZdbZekAmBKkVBsG9zNxlagW7+nAJn6yHyf+7QGi3VPZ6NHZUdHIhETKCXjmk0kNaCpQTcNeGoWEY/J86moTuC42B+DuppktGg06ijDyrnVWtUAjE5ZWFgwQVMFve2mpQTRV/JpFHRUwDwYDJp5UeBS9QDvN/8niKq+KP1rXht9SLfMHZvVz3HZukZ9VrXTNPjC/+3KAFo+zAbXae9QH7F0J/W46lr6tnYQmqRBuyyOAtVc7xrMoFD/036xAwH6o+Cw3jsF6BXE5bq3S7vofdDgC/3qSCTiIIC6BWw0A4JkEcUOyITX+0Smto6h3W6be8o5Zmk3LUHoFpDROQKcPRx0PdjZFDYBQ9cg8QmbLKBj5nkVpyB2p+uxB6TvfNkQkP7Hf/zHuPbaa/G2t73N8XqpVMJ73/te/PVf//WmDA5YXESHDh3CJz7xCQDAlVdeiSeeeAJf/OIX8Yd/+Ifmc7YTYEd+VG6//XYDbnDcm13bfaOiDHSmxVJ50rnV+lkAHM4MnVvWBAUWU0VOnDhhaj0y1cZOZanX65iensbs7CxmZ2dRqVTWVCaDm1A8HsfBgwcRjUYxOjqK0dHRZQpIRQFWbgRUFJlMBoVCwZR6USWzEdHzbPT7rHlbr9dRrVZNGtuLdSPqdDomrYplCBiwAJZSt9kgj2C6GnoUBma4RsPhsGn+sx6hgcqotgZguJ6VwaflS7jGNOV9JSXEbIl4PG7OpQqVQiXPoNbo6Ch8Ph/27t1rAK61ghB6TXv37sXCwgKmpqaQy+XOCEh/sa7Bsy233XYbbrvtNtf37rvvvmWvXXvttfjpT3+64jHf8pa34C1veUvX93/91399xfsRCoXw/e9/f8VzbKZslb7eSl1tMz+3MzvI5/OZBpAEzTudjnGaNZWWzjuB9Ha7jUqlglwuZ9jqmUzG7Gcsy5ZKpdDf32909czMDCYnJzE1NYXp6el168VAIIB0Oo1QKIRdu3ZhfHzcwcphKq7H4zFBVo5HGUXz8/N4/vnnMTExYRqi0vHYCUB6X1+fA0RPp9MOJ4cNXBcWFozeUTCdxwBgyobxHmrmnQIfys5ilpI6xwqkq66h3cYfsv77+/tNsIXBbnut03bqdJYa5hI8UZuSzGt+hjp+I5ljtvPOayeQotdHHc6AAtfJenSX2ogaLNBz00bsdDqIxWKmP080GjWBCZa4iUajDudYHWMAhiXYarXM/VrtOVsNaO9JT1aSc1FX02dltrRmj9pZIPZ+RJ1Ef0D3WC3LybG5jdsGFAnScT9U0EwDzArEMuhNHxCACZBqdhP3GpJ6uG+Xy2WUSiWjaxigJZhrM2MJSnM8g4ODCAQCSCaTSCaTy3QIsQCdD1439Qr1l/Zooj5hTyjeG5vxu9qeRntFy3vVajVXpjawVP+Z+APHRN+a/rUSt9Q+sdckr1f9Pr1/fJ/HIuiqoK4CzSSUKYNax0FgWIHU+fl51Go1NJtNB5hOIJ12geIh+hptQjtgQLY/MQgbSNf7w+8wA0rZ55phoPeF80pdyftDUJf61mb02/YdwXTWfme5NZsAp/eHwmC31+s13/N6vYZEwDXOZ9GuH04An4Eh9khhLzy9dgXWdewAlt0Prh8F7+1rsIF425bS9c91AiwFGHm/eP/tUoI7wY7uycqyPpTr/8t9992H2267De9+97sdN7ler+OrX/3qpg0OWGzCdtlllzleu/TSSw2TcHR0FADW1QCOEV792Smi0U9tjsWHTjtBE7jke/zhe9xEGREvl8sGnC4UCiiVSkbB5/N55PN5ZLNZZLNZVCoV1+iv23jpdDOdh3/rOKiE9MdOa1Fnj2BsJBIxqT7dgFkFVdYDaq5XlBGljuqLEcjU6C4dW7cURGV/KUPDjihTiaqTu9a63yq20asGDP+337f/dlsHbuuiW4S623wBMFkhmhmynvWmDAeWbCB4cz6IGxNxPT89Wb9slb7eLl29nevCjfWjzj91IB0BZdqpE6y6mhlPpVIJhUIBxWLR6Gk2cM7n8ygUCiZra61zwL2HGW/cf5Q1ZO9FqhNsNhaBD+6JZKRthOF8NoTALTPrVD/onm6PV+dTnVBlvGmwQJ38bnuW7Uy6EQvcbCQdkw32qigQoDpb75/ef4JRZ5JZp2NR3eYWbNfr26idRgDA/r7aCbRbGERhUF/L6Nj3Re+Xskx5PUoO2Anreiukp6u3Xs5VXe0WXLWBUTtTxRbdy7T8B4+vvoiywd1KQug+SF9b/VR7n7b3ZYJi9APpX5N8ZgPC1N8EWgmIumXo8H8NvKstoeCovZ/ar+n4NWNYcQLd/2zwuZvo53Te9d7qvbfvr36Gx1DfUZnZGoTutte4+aNuutLGCdwY5qoHdD25rSEVey3a2Qr2Nat/7Vayzf6xgzYqOhab4Gb70N2CTLpeaPOo3WP/aP142z+350SvuZttRKKeEve0tIwSOIh12VmASmYhGN/t2bCvRZ8pnUe3+XLbE2y7R++1fbxux+ZvxQHXku3e09XbKxsu7fJ3f/d3uOWWW/DUU0/hb/7mb5BKpTZzXEZe9apX4Re/+IXjtV/+8pcmPX///v0YHR3FQw89hCuvvBLAYnTn4Ycfxqc+9amzMqazJYxCcyNIJBKGkR6LxQyIx82FD60qDr/fb1hwHs8i86pcLqNcLmNmZgatVgtDQ0NIJBIYHR2F1+s1jORsNovHHnsMs7OzJlq9klL1eBbLY+zevRvpdBoDAwO44IILTESxm2PeTfi5vr4+wxIOh8NotVqoVqs4deoUnn/+eUfNUo0OcwOiI7mS07le6XSWGoCUy2XMzs4iEAhgZGTEMLt2CniwmnCtkJ01NTWFbDZrDEBGXtkAdn5+HplMBjMzM5ibmzOKjsoJAKrVqgF6JicncerUKczOzq7LQec9VMNWFaibkrIBAlVANEYBmPdosPHZqNfr8Pv9xnizAROegwYea8VTaW9UfD4fUqmUMcLXoizPBTmT57Gn8DcuW6Gvt1JXq9Oiv7dSlN3EPUeNdc1w4X6ljDbuMWSxcU+enJw0we5wOGwaKXm9XpTLZbPXPvvss4blthYWLNk+fr8fQ0NDOHDgACKRCACgVqs5xsRaqx7PEiOd6cWsec293e/3Y9euXahWq+jr60O1WkUul9sQU34josC4vQ5qtRqeffZZ5PN5vOQlL8HIyIijBIjPt1hTG1ieFs8ggbIGyZwkgEIdoA6b1+s1tdK1/BjXitbt5fF4b8ikojNORhe/301oG3KshUIB8/Pzhhmm65Jp18ViEblcDk899ZSjT816pdPpGLCZ7DmbvcWx02ml3bpWe0nBMwahWSbH611kQqbTaZTLZZw+fRqlUgmpVMo0AR8ZGUEqlUKlUkGxWESn0zHgOABDitD1yvXU39+P3bt3AwDy+TyOHz++4bl6MUlPV5WVLnoAAQAASURBVG+PnGu6mlnSgLPMB7C0pzLoRxtdCTMej8c8b3bwjJ9x8/UIPHo8Hgcr3i6doq8Bzr1GwWIemz5muVzG3NwcCoUCqtWq6ZU0PDxs9GC9XkepVMIvf/lLFAoF5HI50wgRgMOHpdCPTyaTxvdn/yZmhKnfT1+Gx+NYFXwm+Kps/1gsBp/PZ9jj3Ld1TFrypZtwPFrSy41spX6VBjtpZ9RqNZOZFw6HMTg4aABT7vNKOuL9WI2wpffNBlJ5rTZ4zmwnzR4A4LhP9vXo+Ri8t9n9nAvaB7xm1rq3j0ud1Gw2US6XUSwWTeaflmYj5sE55Xwpu9vOXuD12A1RVY9zfTD4Y2dN2/qb80QWuQL/nGt+TwNGwWDQVGAYGhpCMplcdv/sfYPzwueFtlOtVkMymcTExATy+TwqlYoDkNZyUIoPdAsA6DpVQozNqKfYxEK+zx/aaBqQ4/cVU2F9+IWFBZw8eRIrSU9Xb69sGL257LLL8C//8i+46aab8IpXvALf/e53Te3fzZT3ve99uOaaa/CJT3wCb33rW/GTn/wE99xzD+655x4Aiwvvve99Lz7xiU/goosuwkUXXYRPfOITCIfD+L3f+71NH8/ZFCojRuPI7rJLXCi4qAqVypn11xjhLZVKxuH2er2YnJzE0NCQaeiZz+dRKpWQyWTw1FNPYXZ21qT0rPaQ9fX1IZVKYWxsDMlk0tR13SigTKWjrF8CntVq1bD2dGNWg4rOvs1W2AzhBqxKjUw3BfZ3umh0m9dRrVYNmKzpcfV63QQPSqUS+vr6TJ1WNUQICFerVWMwlsvldQMpajzb7HM1wnluNXC0QY5+D1gCvXgOAI4Udyp8G6xQQ5SGvmZbbFRo5CQSCUcH9XNdegp/e2Qr9PVW6+q1rAc6SWdD7OCdDaZSf6szzv0GWDKu+dl2u20YbQCQy+Xg9XqRTqfN39ls1jDbpqenUa/X1zxeBr5DoRBisRiGhoYQjUaNfcBropNCPcs0ZS1Lw/qTHo/HNKD2+/1oNpsIBoNoNBpbpgvd2D+UZrOJmZkZNJtN7Nq1y3wegHFgGBTWsmZ6bILl2o9Dm6ppLxCb4aXzSPtOGYDqJCpoz1ICdmq7jt8epwZ32QiWjUbtz5FwUalUMDk56XD6NiJ0MLXurD1OBcmA5c7rSqJzZQetOp2OaTLGlPxarYaBgQEkk0lT2od10hk4J0hDW5l2M8+n404mk2ZtnDp1qgekr+G7PdmYnGu62o345JaxYoO/uo9qkItri+xq9YH5PoFFtya/6htyLFq6ieCcXX+d80KbgvWw+/r6UC6X0dfXh3Q6bXRluVxGrVZDsVjE8ePHjZ9VLpeN76I+itfrNfqC/g+JdQwEaK15HZsCurQ11JfTz/E6A4GAmWf6QDr3Coi7zaHON++jBknt7ADVkXrPOdfs6aE+Ju8T9Yb6cXqfu90n+9q7MacVTNfxKLFKA+o22UqFY1qpHAjXupbyiEQiBvRWsJbHoo/NoC+F94n3TDPotESffl5Ji5qppc8nwV1ep/q7umZ17nhc6le3LBT+1teZzaLlizhfdiBLs+mIT9D28fv9GB4eht/vR6PRMD1htA69igaBSL6znxsFyWkP6nzqutJ9g+uAx9A55jqx74fiHCzpvBa7rKert1c2BKTzpg8MDOAHP/gBbr31Vrzyla/EZz/72U0dHAC84hWvwAMPPIDbb78dH/3oR7F//34cOXIEv//7v28+8/73vx/1eh233XYb8vk8rrrqKjz44INnrXHb2RBu/HygNf2KDoo6Kwo0Uknpg0wnDIBjk2D0vFaroVKpGCOAkXPWlFoNAKUTws7KjJ5vNiBIZ9Dr9SIWiyEej5uoY7PZdERUOQcaqVbQfa3nc/tb/5+fn0exWEQwGESxWDTAB53UnS5UymQ2EvRWBQDAAD+dTge5XA7Hjx9HPp+H1+vF3NycMUA9Hg9yuRwymYxhpM/MzKBQKKzbObcZFWrc8G+uc31Nx81nRZUS1wfrHJKdQeOkVCoZpr12Eie7IJfLoV6vw+PxIB6PG0bbmQjH7Wao9qQnmyVbpa93oq4+W0aiBm7VAQawrIyEBr01uMvv83Pa5EsN+kKhYL7P7DECresZq9/vNyn3zHCjKDuef+vx9Tq5/9JmodPOawfg6Glhp0XzeOqgqANrf3ale6h2kO6jqhe0vjhBEq01zjHz8zbQrfNAG40NY9UJUmdSX1PAQV/T6yKQzjRl1hkHYHQ1+9e4NZelnmq328hmszh+/LixTxKJBILBINLptKmRvrCwYEoEKYi8nsCTBga4ltzuAYX1PllKhmCOpm/zuDpfej4+D4FAwJSl8Xg8KJVK8Hq9xh7je8ViEXNzcyaopQ3wdLxu18znRksibDTrUOe257j2pJuci7qaa1+BJA04Akt7g4qClW7PndtrqjsUBCUYzeeYRLVgMGgyXAj8EWAmgKlCHa1ANUF16gOC3VojndejwVKK2gfUAX6/H4lEAolEwhB36OfadoT+b/tJClza4DKzvqlfGBggSYq+nZsfpoxctzrc9nkVsNT7q79p82jvCzvTQPdRtSUoHJMGYfQ314xiBXotbmVkdPw6D9SjtCu0zjvxFdb4pm4mLsPMPmI9XIcMsHCc2mR8dnYW+Xze0Z9NA+C6BmifqY5UjKnVapnMPJ6X91rnkutBA1LUh3pM9WNZi7/dbiOXy5lnhs3r9V4TxyHw3eks9m2jTab3gWPk3OlYleTIRrXsB0BbmWtPj0V7lnOomAeF91sDFWoj6mcU+Nf9TTM9bFHMTLM3GMw5E4JDT7ZGNgSk28bx//yf/xOXXXZZ16ZuZyq/9Vu/hd/6rd/q+r7H48Gdd96JO++886yc/2wLH1BuqpFIxJQ1IdOGkUrtaEwFog8xo/f83dfXh0ajgUAggGaziRdeeGEZW4yKgxHStdT9ZqPGaDSK8fFx7NmzxwHyb5Ywxa3dbqNYLGJ8fNw0RWUTKHYxp6KkQUMFsJZa76oU3KLZwNK6bzQamJiYQK1Ww549e5DNZh3A8k6WTmeRYcha+SdPnsTzzz+P6enpZeAMS+p4PB48//zzaLVaiEajmJmZwd69ew1jwuv1YmpqChMTEyiXy/j3f/93nDx5cpkCXsvY1Ejhb1tRU/Rz/NvrXUxdB2ACL8qA4RpvtVqm/nCz2cSpU6dQqVQQjUYxNDRkotecgxMnTqBcLqPRaBh2m832W+99ULbG+eJc9yLnWy9bqa/PdV1NoUNC55ZGfCwWMyA1n2+mrXK/ZDNS/tBw7+vrMw7B3NwcSqWSCXDPzs4CwLL9bi1CxyccDmN8fBwjIyMOx01BTIINBABoU1CvqaNJPbmwsGCaZMXjcbTbbZRKJYTDYeNEkr1Np5a1q4GlPaHZbJoyNeqE2KAz51+dYzu4qs3sGCRmTxifz2fAXI5D06M1YKDOD++R1+tFIpEwac8sgadsPo7RTovnOG1HzudbbGjNIAznmsHubDaLJ598EseOHTMlBVTILvd4PKYMXn9/P0ZGRkzGwN69exEOh5HL5TAzM4NarYYTJ06YADHt0LUGd5mdRXCKc6SgBO8Dm5DTOSwWi6akEZ8LMufU9uJYGEBaWFjAiRMnkMlkjL1MGzeTyaBeryOXy5lShazfT7Cn2Ww6AB0CZbbjrAAKQYBoNLqhbEuej/d7K8odbYb0dPXWy7moq7m/2QxXfdYJFANLJbZsBrNdSlHfIzmGrwMwe45mkQYCAVPSJBqNIh6Po6+vz5Q8U6CWJS30nszPzxsGMUFS7meNRgPlchmZTMYBvnIPZI8vLZdC0Iw+CvuBhUIh7N69G4ODg2b/UfCuW8BT7QI7U5vzwvlPJBJot9tmD5+bm8Ps7KzJPOZ3FcykraJ7GIOUwFLwnddPAJNjpt0BONc69/larWaCEHapH3tN6TogOKwlhLQcjJ0FpZlhCpITL7DZ+TbYyeAz7TNmqbP03uzsLHK5HHK5nHmfYDvXHrMWGBiuVqsYHh52AP58v16v4/jx46ZaAEuu6nxooIX3gqC76jy7RBB/WI5Fr7NarTrsG2JQJDeqDci69hwfiWrEslKplFkTtCFZUojjYtaFAtQ24YDlfvU9nd9sNmt6/rFvAY/NuVL7UXEz2t42eK2EAf6tpA/NllF/Xm1YDeLb9hZFgyHaeHY16enq7ZUNAen//M//vCzV7PDhw3jZy16GH//4x5sysPNJ7Ai9Rr30b00FV7atPoiqQFRBc3NlJ2NVOhsRgsaaeqaR6M0SRvHb7bZxtjqdjlE06uzwerV25loY8m4MCbfvKdDL2tp0yLVb+k4WggVUOmyIQ2VpCzfxWq2GbDaLZrOJTCZjjC/edzLSK5UKCoUCKpXKhtnWOs/2/1zvNjtAjR4yIVnCQNfCwsKCKRfEMbJ0ENdQrVYzn+G9pkFCNijX5JmIAg3ni/QU/tZLT1+fHbH1q+pczYwClhjYZKSpI6Asby1Zwf2B+/VGhU4UwVoNPOseZpen0euk2OwgvmanFjNjTdnqOkfKSOfx1Hmx7QgbOLAZb8pq0/e5v3MOCQRo7XK3a1PdQmKCOkd0+GwdoA4gz2+Pyd7HVE8qe46v00lkeTUGOGyhrqY+433gNSQSCSwsLBggnew5ZVt1IxG4id5PnpfXo/Oh2ZF8Llh3VNnetCX4Oc4dnXPeAzrfdIxJnmBQg4EbZYNq+TYen2NdbW3pfJyJDlrP3O4E6enqrZdzWVfb4Ke+rgC2G4i+Vl9ORctWcJ+hHtRGhfoav6d7tb2Wqb/ZGwuA0S8MFKq/bgOyyqTntSrAy71Q90QNPtg6SsXWzbZvq//znLQJyARWjEHvCwFt7t2qD1X/EnC1gUq7jAjg1BXUd9ynbWa4vXZ0bfCaOVb7Om3Q2MYq9H6vdE7V6wyMsnY4f1gilb9pa2gZUTuwND8/j0qlYvqZKJDOErKsuU87Ruuv2zYGhTaOzgsxEvrSSoqw74fWRdf51KCTrktd1/SdeUwGtDVQwevgfHk8HkdJHPs+0C7WgA2w5Etzju2SrXyfwLXqf/v5USIpP2uvKY5lNYzDJnjoHKv962Yb6rOwmvR09fbKhoD0a6+91vX13/iN38Bv/MZvnNGAzlfRDV4BdAXR1SiwHR/+8OHkZxlxVQddU3k28hAxKjg0NGQaIrg54JspPOfw8DAqlQqy2ax5fSPHckv3soEE+9jcDMloa7fbmJ2dxcmTJ5FMJk3kdSNG39kWKoVWq2VSv9kUdHJyEsViccUNu16vm/q8bH7BIIfX6zUKn9HotWQ1uImmVnm9XhPB53vAUiqfrZCZoUDGCaPuajRxzH19fY4mc5lMBrOzswiFQpicnDQGY7u91JS10WjA7/cjnU4jHo8jkUhgYGBgQ2twfn4e2WwWp0+fRiaTOW/St3oKf+ulp683X7xerylppqxXMlbtdW4D0NTB7Xbb1LHkfkNHfLMkFAqZ0muDg4MYHBw0zDoC+hwPy5SRpadgOw17N+eD9UTJEE6lUti7dy9qtRomJiYwNzdn0tU1CE7HRRk7PCaF+70CDpxL3c9tVhDHTAetUCjg5MmThpFlpyczw0CPwUZsnU7HZAaqc875Ud3J+eh0Og5HVEEKvX7eA2a1VSoV5HI5zM3NGWZVLpczZfmUfbmStNuLvU3m5+cdzKxKpWJY+gSa7cC1m9iglsezVFuXQBLZ5Sx7ZrP92u3FtGyuA86J1v611xOwaH9wrkulEjqdxSa3LE+oQQ/OEZ+naDSKXbt2OcZtzxMdedseBBbZbsePH8eJEydQKBTWlWXH61FQ6MUiPV299XIu6mrNEgKcQKgCrfpcuoFT3cAvYAm402dMASsbkFZxC5hyLNxbtNazMsMJ3rGcB8+reknnQIFajs1mT7MnGvdE7ofdjqN7lvq2bqCw6k0t48ZeLpxLJevpMRSw5d+qt9VW4FzxHLw2Xpfq/4WFBQSDQTPXDByz9AgBUAU39T6ouAHktl/vBojqulRinjLobT9NgWAlprEcC0u6ADAZdyR0Ub+xYkB/f79paMlzkpHO+vG0M7i2OB+0Pe11ztIsvC6+rvfeLRhjB/dZ/ohZE5FIxAQSaL8wwMQAPtcBbQyekwEG/a7H4zHEvFQqZewwt6CG7iOKDfB45XIZ+XzeZNppOR9dCyr23qFrl8+yTZCxQXSbmKJljNwAd7dx6DE00LGa9HT19sqagfTDhw/jYx/7GCKRCA4fPrziZ++6664zHtj5JDaArsrVZqfrZ+2NwQ2Ip9K0j3WmgDeB9Gg06kjXOhvCsfKcoVAIx48fX2Z46Sa30uagoDmdZ5173XRVuKl5vV5UKhU0Gg0DpNdqNYyOjjoUw9kKKmxEFBjI5XJ44YUXTDmWycnJVRn1rDHr8XgwMzOzzDBRpXgmm7qC4l6v1zjavAZgyaCmAWYbY/F43DVNnN/lOonFYujv70etVsOzzz6LXC63rIGRzhuZdclkEpVKBXv37t3QNXK8uVwOp0+fNnVqe9KTzZKevj674vF4TMNOLS+mwKjbD78LLM8eowO5mUFpj8djnBMC6cPDwygUCshkMsaJoxNUr9cxPz+PWCyGVCplGEQaVLRLr7AxmLKVkskk+vr6TGmSfD4Pv9+PVCplnEcC1AyIKstIgw6cHy0dxzFpMJfzpU4x9QkA5PN5nDp1CpFIBKOjo44ydDp2BQiYpt1utzE8PGwcVTqL1CUcC8dGfUqWtNoXPB8/SxCITjSzv+r1OjKZDLLZrClDxgD+WvRrp9MxALrH48HExIR53c1OWkn/2wEHZYHze8ViEV6vF8PDw0aH8r5xbdPh5vvKCqVwTgg06D1mUIBZYTwOnXO9NjLbyFJ3A9L4ed4nBrUUQGk2mzh27Bj+7d/+zRFwWI+cafZaT85dOdd1NUEuBajdAE4bKNeAqH08+3lSIEzLehGgs8F6ZYDqZzWABiwH0ulP83sMwLEms9astjPIlenudu0MKBIQpS+q5CK3EhLqs2qQXvcqfpZ6k7pOgXR+huA1500DxW7zrtes4KziDwwSRCIRRw8rYBHsnZ+fXwa+MtjLkmfKntZ7aP+2/1bSnBuQbvv7XDO8Bn6POlrXDbOfqGe0Pne9XncwqwmkezweE3QmeMygfqPRMOsOgLGfyHxXIF3vp8/nQzgcRiQScfi4wBLwzPWnOJIGRCh2UIrzEY/Hzfpk+VS7SS31u9/vx/z8vClro+AwsQRbWH6QhDhdt3aAjPNnB41IEKGtpBknbsJj2iX4KBpQsQl9drDBXn/2d3RfU4zIxkv0GTsTLKUnWydrBtL/9V//1WwiP/3pT7s6eTsJPHwxyVoZRt0+3+2BO1sGvKZvbwX7mgpAU/KA5caWKhdlEfAYVCDK7rejz92UjALpvOa5uTlUKhVT5oVd3G2jabuF6dGM9OfzeRQKBWPErKVOtwJCanjyPXut2caLbcjwM3psVfQ0XuymP8pIp/OsynwtQJQar2rAa1qpMiCULUpjqFKpGGWtRulKQsOrUqmYUjFssnI+SC9yvjXS09dnX2h8E+CzgQLuU2roA87yFvzNPdhm7W2GKBitKei613JcatwrIAE4G3npvq2iTrftoCpYzDHo3trt+Va9o460na2n+lqviXZKp9MxLCU7Y4p6g8EQvQ+8j5qWzvutNVnVZtDzK9ChP6oT9TrplGuDVALWG9k/N7rncu4UmAGcbDjA6ewyOMCa+BRd2zbZQ20xXXcKnFDH8p5oAEYDKyp8n83V6fSr7cp1YTeAI/hP9j4DSOeTbLWuvvvuu/GZz3wGk5OTuPzyy3HkyBG8+tWv7vr5hx9+GIcPH8YTTzyB8fFxvP/978ett97q+My3v/1t3HHHHXjuuedw8OBBfPzjH8eNN95o3v/kJz+J//2//zeefvpphEIhXHPNNfjUpz6FSy65ZN3j36ic67p6NbtYATL7dbd15KZX1IanflVfTr+rwWu77IeOR8dl/9hgmJ5DdZWt8zkG/la9AGCZT6rgvoLi/J4Gm/V4tq7kuNxAOjtgbc/DSqxbe97t+6SAov7wWnmfNBBN34vBTwbZ1Z7SH9t3VF1uX5OKXr/bvVQ9bt9POzDBMRJUt3V1tznlfdUyfrx+AA7fXMemNhAJXorHcI6ZEcc5AeCoTmDfX14bv6O4QLe1bouuJc4ZMzc8Ho9r7zQ2VQVgytjY9qg+l5wPzhf7GWhzVDu4Q7H3C16zXfZN7XY3kN1+vpVAo+O0AwE6Z90AdD1+r7TLzpc1A+n//M//bP7+4Q9/eDbGct6KDVAqgKc/qiC5SdmAnz58uplvZmqpx+NBIBAwzF9Gks+2MOo6Pz9vnFM6O+qgdjqLDTXthiGaQsyIv7Lc1CF3M9bsTdHjWUwX/+Uvf4l0Om2YWNFo1NTC3W7jl/ebDciq1SqeeOIJPP7448jn85idnUW9Xl/XRqwKwQ1QUQOK5VXYoMTj8Zi5B5aUlhoj2tyEtYG13AEVrALsvG927bSVhOMiA4OKk+fU61TW3TPPPINoNIrh4WHDAhgdHV2xERmPkc1mMTExgVwuh+eeew7Hjx83bLrzQXoKf2ukp6/PrrTbiw0QqUNKpRJ8Ph/i8ThSqZTRz3Rg6BB5vV7D/tIGSUzbPRuZKdRl3H/oxLAEh9oUbGYOALlcDoCzpAodMHWyyFgCsKyGp9oczWYTMzMz6Ovrc7DYAGdtUhUCq9zb+bc25mRZHQKmWnOzv78f6XTaNIaemppCOBw2DTh1jCz5wvRgsp5TqRQAGHtnYWHBzKMCMgRhaWsR/GV2lN/vd2Qb8t7Yjl2tVsPU1JRpXkZWudZEPZuiDMLR0VHEYjE0m03TZE+BZm3OpsGFYrFoAifKxKJd4PV6EYlEEIvFDCOPWREMHgBLoH0wGMTIyIijViwD25w3Cuen1WqZrId8Po/p6WlzP8gEZONfv99vSBDU45OTkzh9+rRpUnu+yVbq6vvvvx/vfe97cffdd+NVr3oVvvzlL+P666/Hk08+6Zr5d+zYMbzpTW/CLbfcgq9//ev48Y9/jNtuuw1DQ0O46aabAABHjx7FzTffjI997GO48cYb8cADD+Ctb30rHnnkEVx11VUAFsH4P/3TP8UrXvEKLCws4C/+4i9w3XXX4cknn0QkEtnQta9XznVdrU1C1WegcG9fKShLsckvPKba6PyfPbyUeAXA+HzsuWUHQe2MMu4zCpaqD6jgN/c/6i0CwLbvwuu2QV42Q2WpD+pW6lc785fH1KCBlvywfTQ7YEBdpYB9IBBY9n17zDYgr/eD79PH4x6v/qCKsqp5r7QXF1nsOka7/rw9n2RF24EEXo/Oi15nOBx24CcK4ura4D3WYDd7mLBvGr+vAWAGQfi/Buq1pwdF65PrHPAZ4Nh9Pp8pL6u9ywKBgLmfnAPNvOf1Eyymz81xaK8bvfe03RTo1+dFdTDtQR6Tdq8KgXA+k41GA8Fg0JRO1GdY7xeZ7YVCAfl8HtVq1ZTBYwac2xrQgBgAR0Ykn6VuADq/o0EhEmr0O/qs6j6mGfZugRaOTW3o1aTnV2+vrLtGOssn/OxnP8MVV1xxNsZ03opGK+1oqdsPv+P2f7f3VKgk1ivcUJkSfjbLutjnVYCamx2dKQXStWEFhQqEDq4C6fyuRsvtDViZedpMI5vNotVqmfRrjU5y3Nspnc5i6nKpVEKpVML09LRxEKvV6hmBN27rRw06znM4HDblDyKRiDESafRpY5ZKpWKiytoMzQ5wKJDOEjDrWc9ct8r+sJWeLYx4szt4LpdDq9XC4ODgqvPU6XSWdXTP5/NnpARfbNJT+FsrPX19doR7Vq1Wg9frNaCqlv5QFhn3Fa0fSSCSIKWysjZT1FnjeeiwE9TmPso9lHtvp9NxODK8Njswzz2Zx3FLBWepFM4Hv6tBTLf9wXbSlFnF8h86p6ojCIxGIhEDkCsArNdAEIGseTqd6pTSqWQtVB6btoOCHwzQuvVgob2h95zXx/Il1M92rdWzLRwf07lTqZRxgrVRGueH4BLvFe0iOtGavca5UduANh3njiAU1ybXIOvTqyO6kq7mXPb19aFer5tmbolEwpRPUOBEbRZgKfBDRvr5Jlupq++66y684x3vwDvf+U4AwJEjR/D9738fX/ziF/HJT35y2ee/9KUvYe/evThy5AgA4NJLL8Vjjz2Gz372swZIP3LkCN7whjfg9ttvBwDcfvvtePjhh3HkyBF885vfBAD8wz/8g+O4X/nKVzA8PIzHH38cr3nNa9Z1DWcq56qu1gxS1W22rlNGqL6u9j/fdwNjbeapMqDV19VMJrs3g+oZBU8Z+NVMZ+5/GkzlfsVx6zW4MZ11X+TY1K/mXsxrsDN9dC55zSsB6TbBTp9T6ihlSfP7NoPXBtP5ff7WwAFLuvBvm6CmJWeVJMXALbN41e/X4IGOiedn+TA7MKL32WY6azkbDe7YuAz9f9oR2mjUzh7T9arn43Xwfigr3V7Xeo8Vv9B7oiWBAoGAqWHuBnTbwRReDz/LbDjOpRIlFXzX7/O3LbqOeC1ujHR+v9lsIpvNmjXDci+8Rv2sfrdWq6FcLqNarZrKABqE0DHr2Dh2Pte6x9jPr+4NALoSJfm+3ktlx3Nd6POl5+Rnuef0gPSdL+sG0n0+H/bt23fepTmebbE3U27Qyt6i4lYQmb81qqhKH4Bh4CwsLCASiRggkKxtlpiwGUQriZ1itBViR70p3TZ1blKcMzpsmtqr4Lo6afY5gKXAg+34srzH5OQkgsEgqtWqachB1t9WMMlUuLEyqlmr1Yzz3+l0HJ3hyZDcCKDOa6Lh4vP5TAPaYDCIRCJhlCE7kqvTrKA412ytVjNGSa1WQ6vVMmDC/Py8oxahKks64GsFHFSBqqJcy7zOz89jcnLS1ExvNBqGSaLMdH6WDdBOnDiB48ePG5ZhT4n15GxKT1+fHeHeWq1WEQgEEA6H4ff7sbCwgEKhYPSKZpEBMHuNOi7K9iFo7fF4kEwmHbWmCeCuF9Sjk0UWHo/J4yjzT8FysnVpj1DvaSkPG9RQJ8EtC85tv3Nz6Aly8H/AWVNeM8rUCefnyWKLRqOGvU6mmMfjQS6XMyxkMpOj0agj+KEgh62/lfnHuVHwQ+ue6jXZNgVtER5HQYNgMOjIztpsXUFWOAME1M902AOBgGGkNxoNw9bnmJkNSHYZx6mObDemqf6mjUsAwufzmXvBz9ls0JVEQRJ+d3p62qybmZkZ05Q8mUzC41mqsa7g/+nTpzE9Pe1o2NaT9UmpVHL8T3tTZW5uDo8//jg+8IEPOF6/7rrr8Oijj7oe9+jRo7juuuscr73xjW/Evffea+otHz16FO973/uWfYbgu5sUi0UAQDqdXvG6zoacq7raJiRRqD9sIEn9OX6/G6nJrcyE6ib6zc1m0xyHfSa0rIvt09mgus1I57l8vsVGpgMDA4hGoya4TlCQe6OC2xw3fRUFyHkeZrFxPybIp/Ojc8v33Qhk9tzy+twAO/rLCjBqEMDWgfa9tBsycm41KKGiPjqPQ3+Pe/H09DTm5+dNjW4GQCm8Lwqy6/l1jEoYcMtqsO0Qe6yquxRjoO3m9/vRbrcRCoXMfHLtMRuKxyYmoTYf55XvKzZBv1mZ/UoA1EwGzf6ORCLLAgq8DgYAbPazBkLsAITOEe+bG5DOZ8wGpWkr8TMKJrfbi5memUzGZOHRvmb2iNrSJEQUi0Xk83mjqzUAZY/Lftb1fur7GuTQtavPvhvxxe1ZUYKm2stuwUPuFcRTtopA0ZONy7qBdAD40Ic+hNtvvx1f//rXt8XgONfE3hjo4PI3nUqNwKoDyw2ByluddAAmjVmBy0ajYZyg559/HqdOncLc3BzK5fKaH9ytBof1vBRV9ioaDdb0YDZGVUdca6lRbIcXWM4wAGCAk0ajgaeffhq5XA67du0yaUkDAwOG9XA22IZuwvXQarVQrVbRbDZN6hMbl1C5RqNR48TSAFyPcE1Fo1EMDAwgFAph3759GBoaQjgcxuDgoHGeGFDQVEdVoGq0ttuLje3K5TLm5uYwNTWF2dlZVKtVnDhxwihMRs4ZCKABo8q5m2ja2VpLH3G98X4fO3YMsVgMF1xwAWKxGNLpNIaGhowy7HQWG75NTEyYlP2JiQnT5f18BNLPx2veTunp680XsmDq9Tri8bjZ77ShVDQaNU2yuL+Ew2GzR2kJFACG9RwKhUwgksxcMn9PnTq1biCdZdi4B/v9fjSbTZRKJVMuBFg04NlokXqy0+mgUCiY8mkEUhkoaLfbhllsM5M0OKvONOeP4uboENhWkIPZYyoEdbXkDACkUikTNKADziZgjUYDJ0+eRKlUwuDgoHFCyXriOFVHuTnonI96vY6+vj5T0g2Ao1kW54DjVMIC7zntCKaK0xFmxth6gr1rFZ/Ph6GhIaTTacRiMYyPjy8LULBsjtoHvP9zc3OmKSrXTLvdxvT0tGlkS+BCHVVNfea5WIuczw3L7pCJT7BnrbXKdZ4ajQaOHTuGU6dOOYgVw8PDGB8fd1xvoVDAyZMnTeBez38+ypmutT179jj+//CHP4w777zT8Vomk0Gr1cLIyIjj9ZGREUxNTbked2pqyvXzCwsLyGQyGBsb6/qZbsfsdDo4fPgwfu3Xfm3bGOHnoq6m7qIoE5P7pO61SgLjayqaxaOgu/rBqoO4f9N2536mjaDVv1bmOI/BJty6nzPYSF+PQDY/WygUUCqVTE8kN7a6Dehp424G79TPtgPGNqua41a9qfOkhD0GDJXAR5Cz0+m47rWaqa3lORUYVlxCSQB63fZYCSTz3rNRZ39/PxqNBiKRCAYHB42vmU6nja4lGMy5cWPLk9BEn8sOdOvnOCa3AA6vQct2aCZTJBKBz+dDs9k018R631wnNgtZ7Skl/fE7DHAr4zwWixngXrO+Gcznc0WWNgNAHDfve7FYRL1eN0Q73m/eWyWE8XvMRqMu1SARbRoeX8vA8X0F0fksaXmbTCaDQqGA/v5+TExMwOfzGb9acZlGo4FisWiIaiTdacNWPRdtF3uNakBPn0UNMPHadR1oaRe3oJ+uJfuc9vq3QXQAJutgrYHVnl+9fbIhIP1zn/scnn32WYyPj2Pfvn3Lasn99Kc/3ZTBnU+ihgAfXv6oYUFHVh9cZYhRIWoUmVFJbiJerxeNRsMoqWg0akBOpl6vZ8zbIWrw2E4uRRW5pt+5/ahi4zFt6QauM3DBbtGxWAzlctmUMdEU77MNptvrgGNTZ5yBBKaSsn6sXZ9tNaEBqsBHKBRCIpFAMplEOBxGKpUyyl7ZlcrU47iV7d1ut00NXDI/aQQFAgFHwzxlO6ihaAdH7HmyjYr1rGVl3rVaLWSz2WV1XTmuUqmEbDaLWq1mjOuzwTB8MciZ7Bnn43xthvT09dkRddYBZ1YMnUKmCqv+VuOcx6BBTiZyp7NY95JAOg1qAuH8/lqfCRrrqu/UeXbbJ7mH2c6kDSYr0EwWlq3juu33/Nv+jJZEUXaR1gbluZRFyO8TdOe82mABwe9gMGiCBNTV9pj0GvWH43BjamnatV6bzqMNbjCgTRtPA/1qq+hx1yMcE9cTmV7RaBTRaBTxeNxRU9jj8RgdrHOuWQBcq36/3zDW6fADMHXPlcnpBnrRVlGHUq9RMy3Xe+2dTse1LiuZewTEfD6facTOoMH5zAbbDF198uRJxONx87rNRldxe0ZWspXdPm+/vp5j/tf/+l/x85//HI888kjXc55tOV91ta0/KG5Zwbof6+v8vAZBeb/tvYaMcAKw/IzqPN2n3HwElmHhXspjMvubAK+O2c1HVV3mpk9snbvSGlYSkT0/etxuz7btn3UTZSyrXlGmt/08dgsEq07l+5qFzz4X4XAY1WrV+IQakOU8asDEnj++bo9Fx6f3xc32UYayPdckPHIdaV1vtWlsO4Hzzd+q67XOP4FygusE0kl6YH10ngeAA0tiUFhtUAZTlLChtonOhT0HbmvMtons+bI/awdZmIlhV2Egm1/Z3CzXxoAYy8BqwKrbnqK2pq5l/axto+nY3X67rWtdQ25BGVvscbrhWm7S86u3VzYEpP/O7/zOJg+jJ3xomQbU6XQQCoXMpkAQmA4JN1zbSGC5Fr/fbxo1jI2NYWRkxDijfX19JnLOBg+pVAr5fB5PPPEE8vn8ik5Ep7NUH9bj8SAWi23JHPHaaayQscbNVYFRpkjTSaLRw7+ZIqWb6kogOl+3Nx3d2HO5nAFXvV4vYrEY9u7di3379iEYDGJoaMgou81uRKpzw4ZcZL5xThhM2b9/PxKJBCqVCgYHB1EoFDA1NYVnnnnGrJ+VoqB07iORCMbGxhCJRDAyMmIYbQMDAyZiTuWnIMBqQAtZgVyvrVYLfr8fw8PDqNVqGBoaQqVSwezsLE6ePOkA2mu1mmmgGolEzBqxI+udTgfFYhG5XM6UOVqv8JltNBqYnp5GPp9HJpPByZMnHdfH7A+WpTlfQXSgp/C3Q3r6+uwJAWmyeZTxQv1C3UNdzeCblvFIJBLw+XwYGRnBwMCAI7hLR2Fubg6xWAxjY2OoVCo4fvy4Yb+7OYMU9vFgPW+CM8ViER6PxzhmdkCav8lSV6aUvq96ke9pmrNmghFwYBkTGwSn86DnAZw1cFVXtNttB5OKLDWWrGu325idnTW2VaPRMPcsn8+jWCxibm4O4XAYe/fuRa1WcwD2DDQTmKduIlBMfUInjM6cW5BDnUuCyhwTy6TMzc0Z0IABbgIIAEx2APuyrFW4xvx+v9HVtKGYDk6HnSAQ55kMOwIavJd03hmQiEaj5nzBYBBzc3NIJpOGJTwzM2PAJdopp0+fNmuC5yJzUMkg9Xod+XzepKFvhlQqFUxOTppnlAQTAg7nu77ZDF0dj8cdQLqbDA4Ooq+vbxlTfGZmZhmjnDI6Our6eZ/Ph4GBgRU/43bMd73rXfjbv/1b/OhHP8Lu3btXvrizKOeirmbpK2B500Q3gFmzjlYKigDdbUIb6FQ/zwb4CNwSRKSus9nt9EcYoB0aGjK6mn4lfZD5+XnDrKWuZnaR2/5FAI/+DgCzp7oBw8Dy3iIco33NbvPGICmw5DdzH280Gg5QWGta8zzaW0z9O84D93mOk6JkLjuwq9epwDezqrk2IpGIYawTZNVAO0kMlUrFzC11LscEwOg4/ui9tvf/TqdjmNt2MJjnZOYbs8o6nY4ppaqZEQre8n8bLCazPR6PI5lMOuxKlqyze9doQIjzpUQMzrGSNZmFyEbb9Xod5XIZhULB3CvOU7VaxdzcHPx+f9da/QAMqM3eP8QUlH3OdWvjWwqwMzBFm4D2Kudufn7eZFVqpoh+hveT94AYmpbw5XOjpAx+VzPR7CCBnaVmg956LN1zeM2cX31G1eZVUuBq0vOrt1c2BKR/+MMf3uxx9ARLzBk+rHQQNUrZ6XQMw1nZSW5AOpsqjY+PY/fu3Q7nnsDr3Nwc+vv7EYvFMDExYeo3r8T84SZCVtdWpb0qMKxpVGwGxrraCwsLKJfLAJYceiogVUYaiVwrqK2MKRtIKJVKyOfzpjlVOBw2cxmLxRz14zRavVlzw2svFovIZrMmmkvggHVyw+EwRkdHTVmCYrGIQCCAiYkJs/a6bd409lhjdP/+/Uin04ZBo7XSVwpIdHudYwaW6g8CQCKRQKezmG5IQP355583mQAsS1Sv102pHQCmrpptRBO4mJ2dPSPnnM/s7Oysa+SZn3H7uyc92Qrp6euzIwpes28DgWo6dvxNIJ3lYFi2guWokskkQqEQ9u7di927dy9jyWazWQNMVioVzMzMYGZmxjiL6kC7OYC5XM4EvFnOi/tiMBhEPB43e5k6sx6PB6FQyADtdMrolPJcBD2pH9rttilrwwA2U5J9Pp/p10Gdzu/rMaif3Uq3cf7paDO7iuPk3DabTRSLReNQ0oEiyE6gg8AvwQDaTmRa026g3icIoY6OHSThNTFVnnW4FTBgSROW9aNtw0awtOMIJtBuoaO7Vunv70cqlUI4HMYll1yCX/3VXzUOKp1Qnp9AtgZWGMzpdDomWMFAAwPmsVjMsaba7TaGhoaMM8z607QXGYDu6+tDPB43Op5zxGcHWATSM5nMpjLE+Syq9PTz1ovf78fLX/5yPPTQQ7jxxhvN6w899BBuuOEG1+9cffXV+O53v+t47cEHH8ShQ4fM+rz66qvx0EMPOeqkP/jgg7jmmmvM/51OB+9617vwwAMP4Ic//CH279+/mZe2bjkXdTX3XmApi1XBLgr3S7t+tR1AVZBc90Al6jBYS+BUfQAFEnk8BXip57R8B49BHeb3+7Fr1y7s27fP+EME0svlMubn55FMJk1JzXK5DK/X6+gnYYNmeg0ATNNI1ZHcq92CA/be1S0bl+dWIp6WdWGTTmYbKTGLPwqkMxBL3cnAq1t2uwYl7ECAzrcClrVazawjBgDoQ1M/EgOgMFirx1KWs5YHciPQ2bqVthGBcWXia+YYSQOs122X/tA1qveIc8eMQ9Y7Z3lYXRfEa/Q12mW0ibSXiB1QUfsjEAggGo2i2Wyiv78fzWYTmUzGBE9YagiAKVlkZ4/p/Wy3F2ucs6SRZpLbpXB4T9T247Uou93r9RrAXM9FYppmhHINK7bDdcdjKQhu3w/Ojw2C62f4Hp8dnsfNNlG8geuKpWs5LpsUax9vM22enpwd2RCQTnn88cfx1FNPwePx4LLLLsOVV165WeM6L0UfOk07YxkWTWvmZqLf0xqS3Og0GqnKi9/jZkoFwGi7W8RNhefi5kmn+2zPj12eRIF0Orq60fM1nQNbAWxUFLTQYzIavrCwgFwuh4mJCdOAc25uzgAnBNPVsNSotX3t/K3GgTbIKhQKmJubQ6FQQKFQgNfrNU3XaPxwPniPw+Ew5ufnDWjAhnlu18r5jMVipmZdKpVCIpEwaWfK8t/IeujGOuHxtE5uIpHA4OAgQqGQqffPtEqywMl202g954zMkY2kiwNLzUZo/NvX7BaFXinT43yQXuR8+6SnrzdXuC8pa0cbiLJGNIFlj8djmOXcEzR9lc47WcnKzOPaJ3NYnYXVhPsh90aeU4FSTUHX7wHOWpBaCksBZHWGyCRiDXg6vxrI1vIhmk7Lv9WeUMeKY9CUZXVkbf1BncX5JDjAbABeJ9lNU1NTBlQPh8Mm0EGgRAEWN3aZ22/NktN7r06nbT9xjqgrFhYWTP38tWazEdAm85x6m0AE2YYErvU+6HhU9+pn1FaxdZvaGe1225xbG8iqvuQaJSOT95x2Cp+JMxV1tHUNc91qCZrzXbZSVx8+fBh/8Ad/gEOHDuHqq6/GPffcgxMnTuDWW28FANx+++04ffo0vva1rwEAbr31Vnz+85/H4cOHccstt+Do0aO499578c1vftMc8z3veQ9e85rX4FOf+hRuuOEGfOc738EPfvADR+mWP/3TP8U3vvENfOc730EsFjMMdgbStkvOZV2telNf0+CtBmYVXAKcLFP9joJ7yh7ld6jPCDIycKqAKvcA7vP8Dvc2BS9tsJ97nvbJoM7jZ+0ggL3fMqDJzCDbX7Cvmfpb/VA338sGqKk3bQBQAU0Km/fqHOqc2cCiAqY2GKn3QzO5+JqO0V4zrH9NvZnP540uo17UBq16HTY4qudQfaZrjJ/nemHwH1gq30n7wl4Teiy9Fl2fqleJY7AxOnUl9beyndXv1OMqQK0Max0bgXYdB4PjzDSLRCKIx+PGduM5OS98zmwcRZ8vZVtrloUGq/SZ0wCaYjYck5Ip+HntX6LPRLfgyFrxCDe7Ro+n12+TMe3nW+0ofeYVx9NrUlkPkN7zq7dXNgSkz8zM4D//5/+MH/7wh6Z5QrFYxGtf+1p861vfwtDQ0GaP87wRVZ7chMLhsFGuZEfxPVVeZCXPz8+bmududTYVYPb5fEgkEoaFFIvFEA6HTSSy20PGtNh4PI6RkRHTGOdsgenKimKKO+CsU6d1NJmupMABO2TrBr4ZY+amSIU6NzdnUoZnZ2fx9NNPIxKJ4ODBgxgcHMTAwAD27dtnAHWy9BgY0CCJXj8VFdPiFxYWkM/nUalUUCwWcfz4cdRqNZMu7vf7ceDAAQwMDBgGvxo+XFfhcBj5fN40Xmu328uYWhoxv/jii7F3714kEgkcOHDA1NhnQGOjIHo30WP19/cjmUyi3V5sWpJOp1GtVhGNRnH8+HE0Gg0TUS+VSpiZmTFKmkEWKmBNwVsPu4/rJxqNmgBCPB53gC001AkQMMjRbDbXXYv+XJKewt966enrsyfcl7UZJ/c/OhTK3qKuppPearVMmq7f70e9Xsfk5CSCwSCGh4cRiURMfWdmmvn9flQqFYeOWOnZqFarhr1TKpWMbqfTTxY6bYFgMGj6ObTbbZP5RfuCDgL3TO5z1GF9fX2oVqvI5/NmzKlUytgvOm8MIhDArNfrJrhPO0UBcn6nWq2iXq8jEAhgeHjY9PwgUE2Hsr+/37D64vE4BgYG0Gq1MDs7i3K5jL6+PgNw//KXv8Szzz6LQCCAsbExxONxDA4O4sCBAwiFQsZmYNk82h2ce9V9ZOPTrqKu5r0gIFAsFlEoFODxeJBOpxEOhwHAsPwYTPH7/RgYGIDX60WtVltTEMXn82F8fBwjIyMIhUIYHBxEMBhELBYzzLpCoYBqtWqasnLeFGDm+g2Hw2i3244mbZoVWa1WjaOubEECAby/mUzGND2nLmZJFV3LqvdZBuhMJRgMYnR01KxxAiMEYmq1mrEfznfZSl198803I5vN4qMf/SgmJydxxRVX4Hvf+x727dsHAJicnMSJEyfM5/fv34/vfe97eN/73ocvfOELGB8fx+c+9zncdNNN5jPXXHMNvvWtb+FDH/oQ7rjjDhw8eBD3338/rrrqKvOZL37xiwCAX//1X3eM5ytf+Qr+6I/+aJ1XfeZyLupqGyB3yzByK/2ge78SwHhMN6AbWF4egb+5f5MAVq1WHSQs7t88lxvBhkFB9n7QEhcej8fRdyESiZjMbQ0C0y7gddBP93q9JqO22Wyahtl63fY1K0gHwNEXhK/Rb6Y+oq5WIJC2CPEGXg9BWYKhJAcoIY32gOpzBXP13qqfRV2p4LvNpOZ10PbI5XIGRM1kMsYHZF+ORCLhYMYrPmAH25UE0el0TDkbXU/quzF7jAQ1kgNY3swOSNhZ55oRx7nR4Pjw8LDR0fQnGfynXlUcQ0mMtEu07Eq3IIY+Q1wDBNIBIBaLIZlMmjkulUqmfKqW+VGCmgbStWwfMxG5bm3CgE0QIZCvBADFrvR7xKBYmpW+PO+fbSNpeUCuUw0o2OvSFtrvNhHFPgff13Wv+xlJsXo+rRShgQEN/qwk2+FX33333fjMZz6DyclJXH755Thy5Ahe/epXd/38ww8/jMOHD+OJJ57A+Pg43v/+95tAOeXb3/427rjjDjz33HM4ePAgPv7xjzuy1H70ox/hM5/5DB5//HFMTk7igQceWFYOrdPp4CMf+Qjuuece5PN5XHXVVfjCF76Ayy+/fEPXuRbZEJD+rne9C6VSCU888QQuvfRSAMCTTz6Jt73tbXj3u9/tYAX0ZH2iTgwZOn19faZ2J7CkjFRhcpOl0+a22elvYMlR4WbNzau/v99EI90eMio8jkmbO5wNIF0VAY0brb/tBqQz4s5a7hoJdUvn2ajYkV0aDvV63ZQPyWQypj55qVRCrVYz9Wo12MFjuJV8sYMlBEPIPs/lcjhx4oRhwjOYMDQ0hGg06tjE1bClc0zlRWVmizrUqVQKo6OjBpjgdWw2gO42z5pe7/UuphXWajVMTU2ZueD8ax1/gtwEtqn018sO18g/m7Wxx4DW3+90Oo4xaG1em0VyPkkPSN966enrsyt0HugYkenMoC/3Ct0f1WlUJ18bMtK51b2f+tmuIb6S0HkmaKzptNTxdHxYToQOc6vVctQD18A0f7TMCx1RgsUMLFCvUM+RGKBsNiURqD2hQVDaOkw75txGo1GTekxRxhcARCIRxGIxtFotUwYMWAICqJsJghNsZuNszhcba5Pd7qbzbJCCwW0C6SzvQyCdTiPn2g4gsDQbMwvWome93sU+LXTOk8mkI+tsYWEB1WoVlUoFnU7H2Am81zbDi8EUtQ8UDFFdSlCJdgVrvbLBPdcM143abW715TdLfD6fCYDTcQVg9DgAV/vnfJSt1tW33XYbbrvtNtf37rvvvmWvXXvttas233zLW96Ct7zlLV3f32k2xbmoq3Ud2T6lAnAKEANL/q0dtFPWp60r9fM2i5SvURcqY9TOpOXewPPwmASZ7bFTuE9y/yNoZrNSbeBfM4/YY4sBaiVW2cAa51PHaAOVyqrmPNkYgNoh9rzzPc0Es9nePKaCjLa9o3pdGcs6HzoOe61oJhgAU6+bujEcDqPVahlQlv4YX+sWaGHwne/rmJrNJsrlMprNpgNI12PQtlOcQnEXvS82A5k+dTAYNPYJ7Rn6rHo/OM86Brfyv2RN65jc1omSCvUZ4Prj3DDobK95BqBUb9PGBGAIJKvZKyTpMQDF62evNZYc0rUTCARQrVbN86UED/t8ivvY0g1E12eEc6KBOTe8g+8rWUbvHW1hG4dzyw7h8XYikH7//ffjve99L+6++2686lWvwpe//GVcf/31ePLJJ7F3795lnz927Bje9KY34ZZbbsHXv/51/PjHP8Ztt92GoaEhE/w+evQobr75ZnzsYx/DjTfeiAceeABvfetb8cgjj5jgd7Vaxa/8yq/g7W9/uyNorvLpT38ad911F+677z5cfPHF+Mu//Eu84Q1vwC9+8Yuz1s9xQ0D6P/zDP+AHP/iBUfQAcNlll+ELX/gCrrvuuk0b3PksChorwMqa5ho5VwVE5RuLxVAoFADA1KkElkd5qWTJeqNzs5oz02w2TUpyoVAwjazszspnKrwm1kIlk4mggjrnwJKy0gZWtkHiFkncDLGDFVQuPP8LL7yATCaD2dlZ5PN5wxJLp9OGDchIuqb16zxQqZXLZczNzWF2dhbFYhHFYhGnTp0yLD0y9Mgg1DWikU5u+Nz0NSjCa/J4PEgkEhgZGUE0GsXY2JijcerZBNBXEoIonU4Ho6OjABYZPeVy2dR/JcDFhkc6F6spEGU0kB0ai8UMwz+ZTBqFz8g51xgBJt4D1owjcDI/P29qHNdqNeRyuV5KeU/OivT09dkVNWLpeHHPWVhYcJRlYxkRDX6XSiXkcjnDQuNewvqPzWbT6FTuaQSpdQz6222M1B20C+joAzCBZ61ryb1Smd58TR1uOufqPHPcZL9pEJspu/wcdU6z2UQoFDJOEueMTGUKv089z6A+ARJ1FG1bRAPxrBVPVjSPTbaTMoq0sZff78f09LSjCamWq+EcEjDn36VSyUF0UJ2rdV/1Rx1UAgTaMNZuoAYsMcoCgQBGRkaQSqUcoD71EwM/ZI4pKKOigIoSPTQYZDv2yjrlutIm5LFYzAD47Geia2ijepBrwufzIZlMYnh42LH+IpEIdu3aZcr32EH1+fl5HDx40KwL2sbT09MOFn1PT/fkbMi5qqvdfAQbFFawGFgijBEIt4FJ3Yv4ORU7IKs+IvcaZe/y2Oxpoc84j0Wgu6+vzzRVVKBWwWru/STS0BdTMJW/6TeQ9Q3AlKZkqQ9+xi6PRR3BcdpzxUwxBdTpn2rzZzsQwd/8Pvd1t7nmdWhZFZt0RH3Be63EQDvYovrEDejUMnjFYtFk2xWLRfT19ZkSKQycEowNhUIO/bKwsGBsB2WkU381Gg3kcjmjs4m/MBNZwVIeU20JOwuP64xECGa9sS8Ls/+0/Ileuw1Kqy/L9cWxKqBvB59oA6go5sP7wwbiPp/P6GvqRft63bJDOA86T7SlmAXHprqJRMLMF4MgnA8GG3ieVmuxNCuz6lkijzgRiQvshWPjZLp32PuK/Vza693+ngbyOL+KA+h947rlb13TdkBiJ9sYd911F97xjnfgne98JwDgyJEj+P73v48vfvGL+OQnP7ns81/60pewd+9eHDlyBABw6aWX4rHHHsNnP/tZA4gfOXIEb3jDG3D77bcDWCzn9vDDD+PIkSMmgHz99dfj+uuv7zquTqeDI0eO4C/+4i/w5je/GQDw1a9+FSMjI/jGN76BP/mTP9m0OVDZEJBOR8QWjfr15MyESg6AcRS5AWu6G7AcFG+32wgGg6aR4sjIiHEWNfJFpdtsNk3jJaY5c9PrJmTbNhoNzM7OmpRhboCbJWRNsQZ4LpdDp9MxTCc6ttz0qBD5XUZWlSlwNkWNCWYSsNP3zMwM2u02QqGQCTyk02nDZk6n0yaSToXP8XIeyKZjulkmk0GxWESj0TBN1YaHhzE6Omruq7K9VJnydW0goumMCnwMDAzg8ssvRywWw8GDB7Fr165lc7qVYDoNRBqW+/fvx8jICE6fPo18Po/+/n4Ui0XU63WjSClrUVB81gKBAAYGBnDo0CGMjo5i7969uPzyy809jEajywwcNfTtgMXs7Ky5Zz/96U9x8uRJTE5OGtBsLQD/i1m2OnLek56+3grhPFKH8rVOp2NqmlMfkNnD9dzf34/JyUmEw2EHSFqpVFAoFIyD5/V6DauZDSqBtT9TrVYLpVIJs7OzCIfDSKfTjlJn7XbbOP9aQ5xAPu0FspLpnCvLjfugZmVpIzCv12tsmEgk4ggAEKRQFr5mF9GZ8nq9xsHzeDyGBKBps3QE6cjw/jBTLxwOIxAIGBY7gRXOQzabBQBkMhlMTk4uK5HH+xmNRk3ZGpZjqdfrmJ6eNnNZLBaXBap57xicjUajBpAha9zv9ztK6RDMUKeT60DvfyqVwiWXXIJwOGyAdKZmEwTivWBaOe+PG7PcjeHItW2X9eEcE2ymvZZMJjE+Pg4ASKfTpoQKG8kpyKLg2HrF5/MZ5/ziiy/GoUOHHP1b/H6/AS7UZpyamjJN1+PxOHw+nwFmCoUC/u///b84fvy4Y72c69LT1Vsv56quVuKXzUhX8I77ijI7+Tkexy6Zwdd1D+NvzU7W47sF6zQ4bLOyuccyEwpY1M8MkjKAp/Y+S24S6KU+tBnF6suzTNz8/Dyy2Sw6nY7JVOKY7GtX4E0BU/r6mr2mWW7co7V3hzKWKWQDaxDdDqjqPVDAX++XkuI0YKoBa3u98F7YJDX1XdUWojDDiGU3SS5jJpISD4vFoqO2OueJeAqzx3gdxDpCoZAr+Yz+Jj9LIJ/veb1eUzt/cHAQF1xwgdE7bJbu5k/ar/Nea9kcArQcE8kSnGeuc86tlmZTkgfXDokdhUIBxWLRPAPEk5TNzx99PjXIwHtEQkAsFsNFF12EVCqFdDqNkZERs1Y5f/yfmAjP32otlQIkJlIsFh1B73w+jxMnTiwbo1tGupIxKZoVqGx+JbDyPdrL+r6SNnXO9ZnlfVU7VZ8FPmuryWbo6lKp5HidQTxb5ubm8Pjjj+MDH/iA4/XrrrsOjz76qOs5jh49uiwQ/MY3vhH33nuv6cFw9OhRR2Nwfobg+1rk2LFjmJqacpwrEAjg2muvxaOPPrqzgPTXve51eM973oNvfvObxjg+ffo03ve+9+H1r3/9pg7wfBY74qfKR6PqanxQAVDhM5WWSlM3dG6cjKyTLbWW1FqNQvL7/f39RiGdKetbwV6tgUVHWMdnKxp7vraaMW2zJmhY6fwCMIZGu9020W0F0lUIcszPzxsDbW5uznSEp6PMz/GeU+nZ5Xo4JjIctemmm9FKg4BGnTLRt4ONzvHx3NzwCUbV63UTfALW7thpIIQARyqVwuDgIIaGhsxPOBw2DdR0PG6i4AM/4/f7MTQ0ZNLcCcbos/pidpy6Sc8533rp6eutE90/lVmnutrWr9TBXq/XUSNUdQXZLdTrdA7WKtx3+H06o5rSq3YEWTV0/Ci6L6mzoMfnNdnOgtvndZ64P6oOV4DAZvdQeB51hO1j829N+9VjKXtOA596nVpjlQ4ej8egK4H0QqFggh6lUskc0waKCMATkFZdzHI2XAc2WKDzqL9Zp1dBcptEoE4h4MxUtI9t33u1Q932c9W5alPQZqBzrwGDjdQkt51WMh+TySRCoRDS6bSpO68BEGbS8WdhYcHYUAyQMwjDeU8kEqZBvLI4bRDlXJKert56ORd1teopXVN2wA5YDhypLmUAl8+lAtH6nW5BQVsv634GLNVQtkFH3bMJUnIPIPOXGVbqtzLAy73dza/mXmoD1PTNms2mKcXlpjvtedX51b/tshEaAOde1s3noG6kTaB2gU1S0mvS+6HlVG397XY+XQ82iK7XQLBbiWK8D2SsU4+qPuf1k5FOsp3efx6btpbqf9XJBJ+V9KbBZttPVjIBgxRa051j5GfXopv5vl1ShM+Lvfb1HG5/K5gNwKEvdQ3wPujaUVtNj0k7MxwOm+bn1KvxeNwEsNWW4f8K6vJ+ap14npfkQd6HaDRqfGwlfHC96160UbzKfmZ0/Lbda98DnS/7WDZTfiXZDF29Z88ex+sf/vCHceeddy77fCaTQavVwsjIiOP1kZER06zblqmpKdfPLywsIJPJYGxsrOtnuh2z23n4Pfs4L7zwwpqPs17ZEJD++c9/HjfccAMuuOAC7NmzBx6PBy+88AJe9rKX4X/9r/+12WM878XepFj/ynaM1Akul8s4duwYwuGwcSTIrg0Gg0ZBNJtNPPPMMzhx4gSy2axpdLIamM5zNZtNnDx5EoVCAUNDQ/B6vYZVp7Wz1yOMGjYaDZTLZTz77LMol8tmzGQ/KfOcyo0KjArOLU3mTAD+tYoN4usmSjCcDMNcLmecQGXT2WABN38aWATneZ18v1KpYGZmBs1mEydOnDApZPPz86b+a6ez2LDtxIkTKBQKOHnyJHK5HMrlskktZMQ8GAxiz5492Lt3r1GA2wmg2+LxLNVAHxgYwEUXXYTh4WE888wzJuCgqeYrSSwWw9jYGCKRCF72spfhJS95CaLRKPbu3WuU/cDAwLLyO6vNBZUpG8sODg4iGo2iVCohm83iyiuvRKlUws9//nM89dRTpiafnbb/Ypeec7710tPXWyv2OmW6KXUU4HT+6/U6pqamTHovS7vQ2WLmF8uDTUxMmJJQaxFNM52ZmUGxWEQsFkOtVjO1sxOJhPk8AUcG3dnIa2FhwaTP8hr42+PxoFqtYmpqyoyLzgltD+o2ZZRpYKFarSKbzWJhYcFxfjKSqO9JFGBgWJ1P7slkX6u46SwGsekkE1jVDCGeR3/o5JTLZdMATZlKzARTsMUtyEJ2VKPRwPPPP49CoYDBwUF0Oh3DeCMgkMvlDEjPkgHUD5xnpkOTlc1zADDN0chao4Pq8/lMyRICBsz2o8PMjLh2u21sSo6L86HMQdqY/A7vJe0XsjoHBwcRiUQMI3ytwLTHs1imJZ1Ow+/3Y3R0FENDQ4jH49i/fz+SySQikQiSySS8Xq9hjLVaLUxPT6PTWcxqTCQSDqBfg1nsRdPX14dXvvKVeOUrX4lcLofjx4+jUqnghRdewMmTJ43tda4Fvnu6euvlXNTV9FO6sci7BQQJDOpnAWeA2i04SD1hg672ublX6/e5p/E7GnDk70KhYIKmnU4Hfr/fNGxW3/P06dOYnp5GuVxGpVJZFvxWcE1fY98x+i7cQ9mTieVTCSRSz3C8HLOCyrwegsb1eh3ZbNYE8Dm/1IU69wDMawqw8n5y/9P7o+PQsjO6p3Bsbn4ydQ+vg9+zs/B4vzSwCSzaVFwL5XLZURaNdpiSDm1WP3WFAuK0SYLBIPL5PACYrDG9lyS4MRuNxAdgqYk4G9bGYjGTMcX3dI1rSRMGb+x6/vacs4JBp9NxsOH5OQak9FkguO8WuKINwtKuDCaTpc95tHW3PjfRaNRgCfv27cOuXbsQDocxPj5uxsgsfJtwoEEmDcTzmeh0Oqb8UavVQjqdNuVU9+zZg0ajgenpaWSzWdTrdUxOTi6zHWn/aZBHyw/ZBEPdWzSTRe0gt6w1XcMaCNR9wc6K4e+VZDN09cmTJxGPx83rbmx0FTeixWoBMrfzdgvorOWYmzW2M5UNAel79uzBT3/6U/zgBz/AU089hU6ng8suuwy/8Ru/sdnj68n/FzUe1iLVahWTk5MIBoOmNmUoFEKr1UIkEnGwvE+cOIHnnnsOlUrFKPy1jmlubg4zMzPI5XKYm5sz9dj7+vpMmtJ6r7PT6RiWO9NzcrmcUSpkvjNibDe8UBB9rQDqZogd9QecgLqyFajwq9XqsuPYRiWweg1cFaY7zc/PY3p6Gn6/H7VazbDmqKAIfORyOUxPT5sURK4x1m1PJBKmXMx210V3E1Wu8Xgcu3btQiqVQj6fh9/vX8ZMWUlYQzWdTuOaa67Bq1/9aqOk7br1a71+nSsawgAwNjaGTqeDYrGIgwcPolwuo91uY3Jy0gBBPSDd+d2erF96+np7pdVqGeDQbc8gSOrz+ZDNZk3piXg8boA9stsmJiZw/PhxEwRfi9C473Q6yOfzaLVaiMfjBqwlS0g/r7VOWbOStSjdmFEejweNRsME1ePxuKnNrXXElfVFHU6AoVwum3J08XgcsVjMAVDTeafzXK1WjfOvafu0H9gYjKxspt7qdRJIp7PGGucEn2ljAGe2d3UTBgSazSYmJydNE1SmefO8rLFeq9VQqVRMCSEFmqgDmcJOXU+2nYLoZGbx89pYjAC7zhUZlm6leuhY8of2AW0wOv+0gci8B4BEIoFwOIxisegALVYSjisYDJqA9Ete8hIcOHAAqVQKl112GQYHB02N1FarhWKxiGq1agIR7EfAsXY6HYcDzDXGuv0veclLMDIygsnJSaTTaVMOYGZmxgAx55r0dPXWy7moq+mj2SULNPgHOEuW2Exwm/yk7HFgCShXYMxmYAPOALauUWWoElTVfZJ+Z71eNz4y9Yrf7zckJWV4T01NYXp62pR10WCb7p/2nsfAJvcr3b/T6bQB0e091T62vScxYEsdMjU1hXq9bvxqEn20jJvui26BZD22grpK/LMz5932Bn7XraSJ3hPaPNTVmlXNv3XsfX19qNfrJjjM0i4E31U0w0jLd3EcZEWzlIgG3nWO5ubmUC6XHeVytWQKs6KCwaCxrZiZRTuNc0SA2SYC2lmCNpDeaDTQ6XQMCG+vd513O4jB+6vrKRwOI5VKmZrk+XzeEcTnXBNv0UxKEvl27dqFaDSKyy+/HBdddBECgYApa6uECs3gUJuEonsGr5lBpk6nY4gI1WoVw8PDaDQapscAiQ9KQmHAhUA651bPY2dX2nOqmX1aeplr3g7UAEtEEzv4oAA6sx5Xk83Q1SQKriaDg4Po6+tbxhSfmZlZxgSnjI6Oun7e5/NhYGBgxc90O2a38wCLzPSxsbENH2e9siEgHQD+8R//Ef/0T/9k6j7/7Gc/wze+8Q0AwF//9V9v2gB7sjGhMuGmNzExgWAwiEajgXA4bDbbZrOJTCaDUqlkANj1Cr9Tr9cxOzvrqHOqbDTAGV2jEaAscjprLFmSz+dRq9VMuQsqZBoAWjdO06A1nYfn2E7jfj3nXg9o3u37NCpYL1xZaZwP1nItFAool8vGGNMINdOvyIJYKb1wO4VriY1mgEUmJRWoKndbaEAGAgHs3r3b1G0bGhpyMCk345rtYxAAIWi0Z88eXHrppaZeWTabNQZFzzntyUalp683R3Tv20iAdiVmRKezlE0UCARQrVaNo0ZnkU78Wsqv6XGVyQUsNdmiU6hMLWCpjiUA4zDpeVmbFIABdjUIq7qc7CiyiOjcsn+FMprUPtDa28oMAmBeU6edLGJeJ51tZcLb4Io63cqO07kDzn7/D469r68P5XIZU1NTplwAQWmy21RXc3wEe/x+v8kGpL1H3UHbSK+FdoKCEepQqqOn6f2cE7W/eFwFovRYOs+0L+hs8lpXE+pLOucjIyOIx+NIJpMGDGKQhU3ZGQRQRhvHwPrGtn3IrAyyBcnIX1hYMKX3BgcHsXv3btTrdczMzCzrfdCTnmxEzjVdrX4D4CxZoExOfZ9ig4bA0t7ejcmuIDX9HYqyZO1a69yzFHS39zXdS5hVTB0dCASMD8pa52y47QY+axDSZsbzfNQL9KkrlYoB0oGl7Cx+1i51oQEBzfRWBjsDzMpKtpmz9LsZcFVGuto0uv9pbzL1w+ljalDDBuj5t45D9by9FjiHOsecd/WHtfScBh+U4KbBELvWu5bDrVarpiQrx8r5LBQKpsSqW08NljYjOE8sg9ehQSSb0a+vacka296x74v9DNli2z723ySSUW+rTck1pn/7fIvNSlkJYXh4GJFIxLDadf3oNdvPq9oi+r99HUpW5P0kUSQWi5nyqclk0jzLmiXCsXNdEFzX9+z9yLYd3GwYfcbd9izeW80AoL20ViB9K8Xv9+PlL385HnroIdx4443m9Yceegg33HCD63euvvpqfPe733W89uCDD+LQoUNmL7v66qvx0EMPOeqkP/jgg7jmmmvWPLb9+/djdHQUDz30EK688koAi/vQww8/jE996lNrPs56ZUNA+kc+8hF89KMfxaFDhzA2NrbjQLWewDiwjUYDv/zlL3Hq1Cn09/ebNBoqhFZrsQEZG1mut1YlFQdrHTUaDfT392N4eBhDQ0MIBoMYHh425yWbScEIOtylUgmZTAZzc3OYnJxENps1zUBY0oXMam40moasadRsAqPK2Y4Ebra4GUN2FHIrRKPCTz/9NI4fP45QKISBgQFHd/n5+XlkMhmj8JlmTmUcjUaxb98+jI+PY3h42IDKO/V5p7IfGBjA/Pw8xsfHMT7+/9h78xjbrupM/Lv31p3ne2uueoP9/IyxmRKcOEAzJQ206ZYIgQ5SWigdBdSW+w/Af9Bxd6w4AyB+ROgpAkJooSYoCqBWGkWtRmJQEiODk8Y2BGK3sZ/9xno13Xme7++P0rfrO7vOrenVe1X1fJdUqqo7nLPPPvvstda3vrXWPGq1mjGA3YSMs9nZWdxzzz34tV/7NaRSKaRSKVPGxk5LPUgJhUKYnZ1Ft9tFKBTCa17zGly7dg3/63/9Lzz33HOoVqumLtlxlzHL7ebLWF8fnGgapzoxe5FR67jf72NpaQn5fN7BxOYzQ0eYJTjU4d8O3FdwgOdmk3A3BvZgMEAqlTLNn7RZE/8mO7rX6xn9QdYd2ekEKcneCofDhgVcrVZRrVYdQDgAw8Imi58sMta6rNVqAIBsNotYLIZ2u4319XXTP0QbVpL9RceNgATngc4W2Vt0am3A42bob85prVYzxAety87AN6+Ruprj8/l8SKfTiMfjmJ+fx6lTp+Dz+bC2tmayELhuyEDkfSLYTvZ2IpEwgRVeO4FlTQv3eDwmaE0dORwOTfkAZiXS1mJABdgEEhhAXl1dNU7kdnPt8/mMc37q1Cm88Y1vRDKZdJStyeVyKBaLZi6Hw40yLupU06nVdc/roJOfSCQMG5Drtd/faPbKzLxTp04hn8/j8ccfx9LSkrGrbwUZ6+qbL7eirmbJCreg23ZrjDrQJkLxfwVz+b4CbWwwyfeVta4sVH1f9YEGdRWI5nnK5bLZKyKRCPx+v4MVrVk+gHuJBgXYFEAHNhs1KrBXq9VQKBQQDAYxNTWFWCxmxs3jKZDMMXNvZ8127dfFElwej8cQ1NT+aLVaZu/n3j4xMWHIVXr/dP/mPLEkynaMdILIdtBlOBwaXWeX0OKc8V7qeqH+53xqEITj5D3VEi/UkWSRh0IhJJNJAyJzHIVCwRyLY+X9Jpahddft4EA4HEYikUAymTQArwYwNJtLx8z7rOQGBuAZ9OXrXDNuILoN6tKu1YCA4iUkm5HkSPtBm29rNkC320U8Hsfi4iJmZmYwPz+Pu+66y9hyxIHsAIsGs3hf+SxyfHYJFlt4HC3V5vF4kEgkUKlU4PF4TFbZ2tqaozQu7VmWF2RmHsfJ9axBi1GiuIGSD+y9T9e1fV+4fneSm62rH3roIXzwgx/Evffeize84Q340pe+hMuXL+OBBx4AADz88MNYWlrCV7/6VQDAAw88gM997nN46KGH8OEPfxhPPPEEvvzlL+NrX/uaOeZHPvIRvOUtb8GnP/1pvOc978Hf/u3f4nvf+x4ef/xx85larYbz58+b/y9cuICf/OQnyGQyOHnyJDweDz760Y/ik5/8JM6ePYuzZ8/ik5/8JCKRCH7rt35rX/OzG9kXkP7FL34RX/nKV/DBD37woMczlgMSjQzSQeNmy9Qxgs5uaU57PRcVHtP4yABj80dGMPm6fpdR+1qthkqlYuqGEzzUjuLcdOkUK9OdTqYy0ukA3gjgfDdzchgMJZ2ncrmMWq1m0rfplDMAwk7XNjBDhRWLxYxDSQV2lIUGNJnprOW6ncLz+/1Ip9NG4Z8+fRqpVMrBbrmRQiOBLMt0Om1KMsViMQNIjYH0sXO+Hxnr64MT3RNuBMhBpvZ+RR1yFfs1Otgez0ZtczpGdNSYwkxnh44EfxN4peNBB52OAB1Oj8djjjMYbNTX5rnJSKdwD9Tv04liaRl+PhgMmjR0rU1uAyp0dlgOTnUjr4cOIHXEYYBX1McATH8MAA4QYrsSImSkU+fRziObDtgED2yWldqAdGKVhcj5tLPR3NLweVxlYZKpqXYc2Y/av4fHHrWGeR6WqGPJOdY559wQ7CL4DWxmWPD7nA8tN6HnsBnzzATh67QZGKThb31GjruMdfXNl1tRV9sAqQKl3CeAzTWjNrcCS7r3KTipQWT9rgKs9IWBzTIMCsTzmNybqGf0bxuwJunM693owUDfSslo+gy5+RLbMdJ172WNeQWU4/G4YZBznri/K0DMXg/0pbU+OO8Bx0wbgLpDgVUNDui9UdHPauCC125n/tjzoD4m9Q+Py2O6zZUen98jqG5fK3/boDUDDAqkA0AsFjNzrsEFZSMTx+D81ut1tNttR3Be7z+D/AwoE5Oh/tV5tBnpet38jj1/Guy27w/nx8Zh9PN6j/hM8Ljau4fj4rjtexSNRpFOp5FOp5HNZg2OoCQRft7NttEAhB2Q0PHaADWPSSyAASev14tUKmWeAT4rnEeuAy1bw+eK86DEFHv/GCX6npaL0WtwI8Fs97rKzdbVH/jAB5DP5/FHf/RHWF5exqte9Sp861vfwqlTpwAAy8vLuHz5svn8bbfdhm9961v42Mc+hs9//vOYn5/Hn/3Zn+F973uf+cwb3/hGfP3rX8fv//7v45FHHsGZM2fwjW98A/fdd5/5zJNPPom3v/3t5v+HHnoIAPDbv/3b+MpXvgIA+PjHP45ms4kHH3wQxWIR9913H77zne8gHo/v+Tp3K/sC0judzp7o9mPZKsqUBrYqz4MUZX3ZCpnK5KDPo45gs9k0GyidGWWJ69g0GsgUHI1Ic8xsxEignAaAbsqDwcAYAwog30hH2VYMqlgOw7ng3LJuG40LNSJ0bB6Px9QbY80sAunHgR2jAEosFjPlWVZWVhwOusfjQTweRyKRwPT0NF73utfhzjvvxMLCgiOl/mYLn5FMJoN7770XMzMz+PnPf456vW7W/HEG1MfO+c2Xsb6+MXJU1qPt9Oz1u61WC6VSyfxP55PggJbk0CAsM8nodKqDbqff0kGpVCqYmJgwtV91PyBozgB8IpEwoKdmTQ2HQ1QqFXi9XsOmVgaTsoppF9ip7woQcJxa7uao7LGaPk3bRe8xrzMajSKbzZqGXpVKxej6aDTqqNPK46neV+AYgClfoPpGCRIKwmvgQllbdgMv6lMy6jW9PZvN4sSJE2g0GigUClt6x/DciUQCr33tazE/P4/FxUWkUimEw2HU63VH41PaMewFAGysIQXvaf9wnm3whTIcDh3ZAWTTxWIxDIcbdWjvvfdeLCwsYGVlBS+88IJhfO7GCT6qMtbVN19uRV2t/obuEbqH7NXWtgN3CnYTKFSASn0xPvO6V/BvBcn4XRvo0/Op7lC9yfdsYH87vaKAIQBXPcRGoawrzYwZziEzczhOfoc+NMlmBM9VN/Kz2kSS+z2Dudqbg3syA4vcJ6lfeQ/sjHC3e20DofZ90TmhnaF7t95nxVJGifrkus/xnNQDqr/UnuFcqq7Q/V7LwXD82sOEADsJhnZwWufCLuPCY1H0+AxK06/XYIjbHOi90bWgtoGup8FgowxbPB532HEahEqlUojFYkilUrjjjjuMnmafAc12sEum8hzK5NaAmRvQ77aWuDY5/4lEAsFgELFYDL1eD1NTU7h69arpGcB+BAyGMyBlZ7Rwnu11aZ9/u/+5XtQGs68X2FzXR5GRDgAPPvggHnzwQdf3CGqrvPWtb8XTTz+97THf//734/3vf//I99/2trftOF6Px4NHH30Ujz766LafO0jZF5D+oQ99CH/913+NRx555KDH87IRMmPsVJr9potvJ27RTb5+0MINp1wumzSaK1eumE3HdqyUQawdqkOhEEKhkEnxZqkadjCvVCrGeLAZaZxbKnVN36MBoJv1QQk3YNv40gDAzRYdh13axG3z9Xg8Ju2M5U20KddxEK4vNjiJRCK4cOGCQwl7PB6TDrSwsIB/9a/+FV7zmtcYttthAOlctxMTE5iZmcGv/uqvol6v4+///u/x3HPPYTAYOOoQj2Usu5Gxvj44uR6D9UbK9YyJ9aRVCoUClpaWRrKDqXcBJ7NXHVLqXWU6K0tdAXoARlczbTybzWI43GBLNxoN0ySN+7fWo1Ww0+fzGadNwXEdkzLd6DQTpLCB1MMUZZwB2KLDtETKwsICZmdn4fP5UCwWzft0ellnl9enzqnXu9HMjveDn2XqNhtu09ZSEgYdTK4H3kO9HwpCtdttkwVB53ZmZgZ33HEHqtWqqXGuQoAgm83iDW94A1796lebgD8A05yb9iQ/T8CJ6ycSiSAcDiMcDjtSqpWFpqxTAA4HmiCCrrGZmRnE43HU63U8/fTTWFtbc4BYYxnLbuVW1NX0PXXfAJwgtbLD7b2Ox6CMep1/q32spV+UcaogKvc+kq14LAW6FNSmL6vAqoK7LBdll7Oh2LpFj0UfmfqI+5PdKNvj8aBUKm3pOcamlYov6Pwwq4n+oJbW0MAG91H1r6kXCMTzGlnWRsFU+/p0jPa9HAWQupXD4LkJfNpBFLvEjQ142j6dMsD1XHYdfbueN+tX897o/HE9EYDWe8OsMTYbZUBXwV/V9zyXYkNcV5px5/P5TAklvS4FZnUd6PF53ziP1IsAjC5VXR+JRJBKpRAMBk0FAWYuAsDMzAxOnz6NbDaLe+65B4uLiw5Q217vtk2jQQVtBu6WieB2TQqis0cCe6V1u10kk0nT0L1SqZiSwtyLtJxMMBgE4AySbJfdPmp8vB4lL+rrtJn0XhGnOi64y8tZdg2kk0IPbDx8X/rSl/C9733PgE8qn/3sZw9uhLegKGtWGcKj0lsOUnbrcI8yVnYrdjRYI/5UUPbGqim+Co7zN49LlroqGAXSdaO22QW8np0i5LsVN0POZqGr0XZYspfzq1KmknZTyEdRVCnzOjS1jvfD6/UaFl82m0U8Hjd1YQ+Ljc7x8/lgDULW0iMThIb6cZQxy+3myFhfH6xoWYtbbR2OeiY1Zdh2jlVXu2XJ0L6xGV8KpFMfK3BiO7Xcr93KmrjpVXWg6YjQZrBLk7mBJkdBV7uJOooqen9oUxEw0WCHOpmjWFQEBpSBZ6dU63gUYFJGle2UA5uOKF+3U7uV/GCPz+PxIBQKGT2o9c71vtn3U9egNqElUECnVkECFc4Fx8Dx6jXx+Gw6x+AAG5QeZxnr6psjt7qu3q7kgYqCerZ/poxgFQXd7L1F93v9rvqG9t7vJrr3cRxugU3+5vG4N+j3FZDlb7d9XZ8ft3ERuLSPz/3fDpIqK1mDAso8twMb+nmt4W2PUwls29lIo3xuN72ix7HPq7rHnnO1KWxQ3E14nZwfBlNoi9jlzDQgxMCr2/2x58HGfGyWvX7HTUbpfns92u+NEhtMV9H7CTjr+PN/+xr4OgCjq9lLhGC0HdTR+RkVKNPPq/3ndr387cbA1/lndgV7KHQ6HUfQyb5e2zayx+U2Vvt/ft/Nzhgle8Egxrr6cGXXQPqPf/xjx/+ve93rAAD/8i//4nj9OIBthymaDsV0E3X2qtWqacp0sxe4bsr2Rq+g8H7EdnbT6TSSyaSpBR0OhxEIBIyDxM/RiR4MBqaGervdxtraGsrlMrrdrmGs2wGJ4XBoUtkIrNK4UgN1lDGzW+Gc0DkjM0EbuipAcJTF4/GYumZ2OtZxEjrfrJ8ajUZNdJpR/Ve+8pV417vehUwmg5mZmUMF0G0h0B8KhXDmzBm87W1vw/r6On70ox+ZbIzjKGOFf3NkrK8PTqirA4EAWq2WyYrajy60nbLrEU3Lvd5juR2bzlIsFnMEGam/p6enDdPZLhvCWqqdTsc0ftR0cgU72bhRQXM2SGPvFALDLDGm7EFNE6bdQjtLGXdk2QGbNg0blCtLkddw1MRt72Qmn5ZSqdfrqFQqAIBUKoVoNIper2d0uQ1i2CC7NuELh8Nbsuv4edqrylgn+7vdbpt7S5Ydxx6NRhGPxxEMBlGr1Ux69ag593q9OHv2LF73utchmUwilUqZoDLvG7MOuPb6/T6i0SiSyaQZK22y1dVVeDwesx49Ho8hDmjWJL+n895sNk3ddQWoGCjw+XyYnZ1FJBLB8vLydfU7OGwZ6+qbI7e6riYbVOtwA1trobvpVA1kufWwcGM/U5/wswrGajNBzaRSsIz/q57R0k5kfNs+px2Q0wAu92e9Xg0uahBXdZYtdq15BQe9Xq8BMJkxQ1+X86FjY2k11vZm1jd1tPYkoY9sl+wC4GhWGgqFtgWubaKS4g26n+veyvFpwF7vlwKe/Ftrj7MJJ69plL+v18xzsVY8dRjLmLBvFe8t50IzD3geXgvxDeoa2ie1Wg1er9dk4SvDXdc97wXPZe+xXFPEHfh9BgsUILdBbf62Axlc97oW7UwzPtdkr8/OzuKOO+5APB43/b7soLX9DNsMbb5vN1UFsIUQoWPX518rAfA5V7JqKBTC5OQkAoEAarUayuXylvnUPgi8XmWnuwWGthMNCCjxYRSBdrc6eKyrD1d2DaT//d///Y0cx8tGVPFFIhGzyfBhZwMw4HAWuO2I3gjHkiDh5OQkwuEwFhYWEI1GTb1MZUBRqQ2HG82jKpWKSTdmGp3dtMw2bLiJ8m9VDgcFECuAT0D9sMu67EfIDIvH48YA2c4wOsri9/tNfVgaLzQkAoEAFhcX8Yu/+IvG4T5KwQKCPsPhEDMzM7jnnnuQy+VMiZrjsp5sGSv8myNjfb1/sfWv1+s1DRyHw43+HNcTGFXH73qOQT150CA6j08bgPUuld01Pz+P22+/HX6/3wCn6vzX63VUq1WTOcbasazVqudRZhDTtQmm09FstVoIh8OIx+PGPqHYjGmbqcVx0Snl+8xk03qrx03ozCvTjXMGbDiLTDFnhpk6kzpPWg/XJhqwuSxT1wl8sG49sAG6Axu9btiPpd1uO0AtOrqxWAwAzJrQusJu1zg3N4fXvva1DrCBJWLo2PI+a53+aDQKYKNnDxvSk4ChQDprrWvpQdv2JTGjUqk4nGmCJJy/VCqFiYkJFIvFg7rNhyJjXX1z5FbX1QzG0Q9yYyPvBE5xz9e9XYWva9Bb9zmKlm/iHsJzKnMU2AzQKluZvrs9bg02qvBauKcqsKkNugnM8vOjgDUFZhW8JeBLHUkAma+7NXOmX81AZq/XQ7lcNnXQ3Xo8KBlN54BZSQSIR4HV+prOL+0YHkvnXcvRuM2Fgul6bM45g7btdttcu1umlQLpDCZoYFXLyBFgZXkWm4RlEwc5Hga9qSsGg42681qOxM7W43za86rnUBtHy8sqiK6EAn5H93j7XiuLm/dH547j5zwx+zuVSmF2dtZkjilJwu1ejQoYKQDOhrkAHL1c9Bo4R7qe7GdTbdRAIIBEImECQG7j4meVXGD39uFcua13Ww/q+bmulIipshc8YqyrD1f2VSN9LPsXfegVZD1MwJUOlipfTbFVdjXZ326KbSdhLU82EiXjmc6eNgjTiDWdv0AgYMpdpNNp9Pt942RTgWi6F+CMQCoDwS0VHdhdZFHvkW7UPD7BdHXwrmeju5lCsIHpyW5pY8dFqOy1nhzXG+u8kSFw0PXyD1JCoRCmpqaMkx4Oh83zeCNAtLGMZSxOoR6hw6AM173K9QSnlSnEfYt7HB0P6kEbtN6LEBxU51wBbL/fb1jBPCftGOo66pFQKGQY7fV63aF76dAT0OZ76gzyutVR1+vi59SGUDCegIU2O1ddfRz0spuQxcayZHTKqOeAzXqeGugnWK5EA9WRrFHOtc7PKugBwJyHdmMkEjF2GHUvARx19AF3p1FrqxMUYy3ZVCqFZDJpShEw4MI1qmtHHVOCJwQQCGLQXuP7zIikHcix2g4yCTAKDDDY5Pf70el0UCgUEAqFsLKyYkq+HJeMxLGM5aCFdip/K4A5CiyywT3uK/zb9teoH5TMpMAnn8F+f7Pnls26pihzXDNT9Pz2a/bzrfsZwVPqJ/ai4PucF7vPiJ6fQrCVf3MuCNLSv+H/yh5Wn3ow2KwFrUEHgqIej8fRh8z2l+0SMpwX7vUaiFQ9ztd0jt3sIbUlFARVsYkIap8oEcDtOwoqEwhXkNoNVLXHp9dmr0f9rGY9aO8QnQO75It9fgW1FSxmRp3eh1G6RokQqueUMGKfz/4M9XIkEsFgMDDYDfuVsOcJ/+ZaU/vBDVx2m3MV3if+bQfj7GvQoJ19b2gfsqG91+s1ZWho09vsf/3tVuZI77tbQMKeVw1Q2MQQt7kZy9GWMZB+k0UVg6aJ2Ozlm/UAcROJxWIIBoOYnp42zBw6UI1GA91uF41GAysrK4aFRGdmJ+EmH4vFcOLECYTDYZw4cQKzs7OmgZMNoKtw4/P7/abrciAQwNTUFPL5vGFLNZtNE7WkI0RnSZUUFZsyDKgMt9vUbQAdgAMwZ4o4mfIEW46TE8XoLJ1z3eCPG6BORz8YDBrAPJPJ4PTp00gmk7jttttMI9WjCqR7PB6k02ncfffdKJfLePLJJzE5OYlms4lSqXTsmpmNI+djOeriZujT6fb5fKZcVLlc3jOQfr26nQ6Y3+9HMplEIBAwjHEApjRco9HA6urqlgbTuxWydYLBIGZmZjA7O+sw/gE4GkLqHJGdFolE0O/3MTk5iVAohFKpZNjAlMFgoywHbQk6P3QQ1aGZmJgwtgl1rQ0QE7BvNpumAWW5XDb7pKbla7bbcRPei1QqhcXFRaOre70e/H4/0um0cXpp8/DeEJweDocma0CbaBaLRVy7dg1+vx9zc3NIJBIGMFbx+XzIZDIGiCa7K5FIGIBaWVyNRsPcTzr/CqqxASjtMjZQjcfjOH36NE6cOIFer4fLly+jXC6bEnQE/2lrsdQQ2ePAZj1zMuYCgYAjQ0LBGh5He/hwjAT1aVt2Oh2EQiHMzMyY8fj9fpRKJeTzeaysrBjgf6/Ek8OWsa4ey0EIM7i0TAJ/KzimIJgNZPF5BpylYfi+ln3RXkLMVm6320YXUmdrIJU/DN4RXOXxqYuoMyhubFKOhX4HdTTBxkQigeFwaLKr6/W62XMUdHYLMii7nMIMHxLUstmsAyC29zAbcOW4BoMBwuGwKcdWKBSMjtVr1evUgIjNyFbwXMFrgvfaXNH+vO7H1FsaVFFwVDPnqaO0x5fbPPLYalOQ0cz7qUCyzpdeB8t96HzwszpfPF44HHY0vKYwGKIBChUNDPCzHo8HjUYDlUrFYBcsYaN7tx2w0ufGLgHjtl5sUJpZXqFQCOl02jTxzmQyCIfDmJ2dRSaTMXqca1sDLW4EDzuQoTpE51bXjBsrnDrczmJxW6uDwQD1eh2FQsFkY9jj1XOSHGAHkfTZVZ9cwXue235u9P6rEFvaDe4y1tWHK2Mg/RBEN2Oth6Yb9U5iK0X7YbMV03bHIHMsEomYaCKddWCTfQbAMGz2kgrN87DURjQaNeC9MoZ3cxwqEzrMrVbLsJLonGmEm5FtYCuDQMFzj2eTtbTdnPG7enx1xOygyHEC0SnKYjjOjHRg02igUqUjnUqlDIvMVmBHTZieyCh6KBRyrNXjJGOFP5bjJrrHU1/u5/kbpat5jt2sbztNmM56IpEwx6Euo/G+n+eG7F2m67LeKo+pafGqI+zUbOr9cDiMZrM50qlV4fjtOeH53Zx222lUVqJdZo0gqLLejqNwbskK471WR1vBb3Xuyd7UOeDc9Xo9NBoNw/QexZDks+DxeIwNpsw+fZ+gAx1z3gd1UG1GOpnuynDj2uh0Oqb+Op9HXguPyfqt/A7ng6xNTVnn52xWqL1e6Ujzb/4fiURM7fdkMonhcGiYoW7AyHGQsa4ey0GI7eNqMJZ6lK/b68YNcLOfSRtYU1/MLivGzwGb/qCucyVccW8h4Ko6w010D9X9jAApQfBwOGwAYo7RZs26iQKcdmaQnou6wAYReQzVExTuoXbmjg18Kii+nbhljCkA7TZ3+tv+vNs6sOfL/luv2+07ajOM+qx9LTa+oOOz8QOdM71X+kOxwevtrtUGcLvdrgH0t8N6dI1tNy/6Wb0Ofk/tT65t1n9nXy+uH51nPud7FQX69XjU3fY16RpyCyZxXXPM1NXBYNBkR7qNQedJ76e+x+dq1HVqEMN+hkedb7dzNNbVhydjIP0mCzfebrdr6jbqZsxo1qjFTUUfj8cxPz9vGirSkabhUKlUsLS0hHa7jWq1ampm6nEIImYyGUxNTZnGn3SaaUQEg0H0ej3zu9lsIpfLOdg8o8br9XqNQ5FIJJDNZk3zMi0fsxfhMVmDk4w3Rmh1LDRU1IBTh07TeBTQH7WZ2gwETQ9UJpQyDHYj6nTqud3K1dxo4ViOKkt7t6JGJ3/i8Thuu+02TE5OIpvNHgsHVyPZMzMzuOOOO0wDPzoHx0nGinssx0nImlZGnc08GSUKJE9MTCAejxvmLp8DMmK08aWb+Hw+JBIJJJNJBINB81sDgYlEAvF43ADX9XrdlDjZzpG1JRKJYH5+3rCXm82mw8EiWAFsNvYC4HBclH3m9W40myMbmAxiHZP+dDodVKtVBwjb6/VQrVYN2GunTGu2mTbcYvBRgeFWq+VgMe9F6IQRsD+MrCDOG+uRUwj42JkSmgGpKfONRgPlchn9fh/xeBzAZk8A1mu1e9CEQiGTBl2tVtFut00WBACTYcBmXsFgEM1mE5VKxQGg2yXXuHa0lN/09DTS6TS8Xi9yuZzJ9gNgegqR9cZ7SyBoMBgYu1evn8CYOrJupAclXZCpFggEHGV0uJY43m63i0QiAZ/Ph2w2i2QyacoL7acM1EGJHXjarYx19ViuVxSgs30wBXgp2605+3M265d7m+oCfkfLfvB/gp/MSo3FYpiennaUqABgamZzH2ODTepVBdxZ0iKdTiMejxsCGe0AZSIzqEcGfaPR2NKLwvZned0EK8PhsNGrkUjEUcPdBl+pm93mmHsvAMMwDoVCDmY/9SXHSxBVfWoVYh2qqxmwZNYSf7R0Du+VjlV/a+BYGfyaocBj2CW/bFCZjUWZad/v981+TZyEx9ceIxq014AQ39fAC9clj0dygs699jDRMkXMsqdtxLXGuVGby42soUC/bevwmPocKubghtEoxsG5DoVCyGQy5pkZBdQT8Fc70gaiNaDtZq/qdXIOdL7dwGnFdvh9PkODwcA0RvV6vVhbW9syT7SpRq0xHT/PodetgQ97X7Lnyl7vu5Wxrj48GQPpN1nUUdS0DX14tosqMvqcyWTw6le/GplMBidOnMDJkycBwKSJXbp0CU8++STK5TKWlpZQr9cdx9WyF9lsFvPz8yY1jOnAukkx7QuAaQJWLBa3pBHZwnQmpsROT08b9o4qy92Ibr48Zq/XM2m1rVYLuVzOMR4qf93IaCyo4qISVKPL3uA0aKC10BVIZxraXkq6UAnSmFNjh03crifiuBfhGuPPcQCatxObwZFIJHDmzBnMzc2ZFMijLrwHfr8fMzMzuPPOO7GysoKLFy8e+4ZmYxnLUZd+v496vT7S4B0lNKCpV0OhEObn57GwsOBg/Kyvr+OFF15Ao9FAtVp1BWa5LyeTSaOrk8kk/H6/YYd7vV4kEgnjrNdqNQSDQeOY76VsXDQaxcLCAiKRCOr1url+O9BLB5DXo3/TGSGzvd/vI5VKIRgMmmMqe1DB+Xa7bZhWBFlJELCZU6wVTweMzjv1fiwWcwTO2VtFHdS9CM/JTL3DKA9jA+m0FxhwsB1hm6FPu65er6NUKgGAWUMM+ACbQSQNRpO80Ww2sbq6ilKpZLK9hsMhyuUyarWaaVg+MTGBVquFUqlkSrdo7WCfz2fKHxDMIYFjZmbGBLxXV1dNAIs2GBu1aRo07Sg2I9V7w/nyeDyGQc61YQeauCa73S7y+Tzq9TpCoRASicSWLMpSqYThcIhUKoWZmRmEw2Fks1lks1lUq1Uzx4chbgzEsdM9lpslBAW5BjWINcq/sMEkBbD0M/qsApuAl/aG4BhsIIwyMTGBWCxmfNTbbrvNAMmZTAbAJpBeKpVw9epVNJtNrK+vI5/PO/zCUChkMl0nJydN6SkSv7jvARu6st/vm7IqnU4HuVzO9KdQUWBPy18RPGdQnfutBuop6ge56XECi17vRvkOkt5KpZLRG1rPnb8VbNXsJo5B63hrqTH2NlNg1L7fCpQTvObxCYLyexoI4JzRJtAgK4XnILBJwgGvk+vIZjUrI5prTq+DuknniOuD5EVm+uk94HPBe8vvaiN3DSKQDW3XVldGtB6f99DOTFNWNT9PkJzzqjYd7U1+hsGcbDaLRCJhgHRdg/q/G2Pbtkv5Wfs1DcLr+8RhOEbOpxu2w3tPG4O4QDqddqwZHVuj0UCr1TL3hwQA3gu30jNuOlfnfhT2sB/W/lgOV8ZA+iGKvVHsZNx6vV4T8ZucnMTU1BQymQyy2aypkcr0lHq9junpaYRCIdRqNRSLxS1RO26iWitcAVR7M2TqmCpyNZDchJuMNjRVZ3u/QKZGHbWRiz1mnVduZJpazGMpqD9qTHQ+bSDdLuWiDDu3cXPOVWnSEbWZGsqaUyOGzvtOwMj1MJFuBWdLr4HKj3XqaNAeB+GaIFOGWSPHTa5nXd0K63Esx1f2sv48Ho9JcU0kEpibm0M0GjU6m+AdGc1TU1OG9WWDf+rE2anBysZVnah6cRQDzU0UMOX5bOag7SDp/NggJNnBHA8BdU2zdxsbX7cBdl4Lf8hw4nxybJrmrO/bvWj2cj8V+GWGgY5HbQ8FdvZSBm8/oo6ZOtUcl1vpHK4pLQugP2ThaXkDAlEECbS+qn6HKd/MACDwTfuMdo42h+VcKVuL7E5mEyibleOiLamsQPsztFFt4EgdbGXh8X1gE0hgSUM6zhQenwQKgmlk6QWDQeOAH5YoS+9m2YBjXT0Wir0WdH/U8ko2i1N/23vadmCTsj5HjUGPSaBbSzPFYjEkEgnEYjEAMM93r9dDPB7HxMQEyuWyg/WsOkr3Vd2T3cA8kqh0DvT63eZP9a/u+zsFJ3QvcJsfPRYzknTv4NzznlGvMujNzynQu91Y9DoULNZzarCd/rbNLla/Wo9lg/Ru64Zj6Ha75vv9ft8xp7wWXvOogMR2a9Oed1vH2MGjUffJLUBi6zY30bWlz9V2Y+Nn7ECOBq7VZtip39h2Y9U1vxt9ZYPzvEcabNnueRhlV486z3a2sW2v69wBcMXJdgLMlWyzk4x19eHKGEg/RNnrAg6FQnj1q1+N2267DYuLi/jlX/5lpNNpw/AGNlNUFxYWcPr0aVSrVfz93/89ut0ums0mCoWCSckiq5vf1+ZKKnyN3ZrpSDKaZzsh9nfD4TCSyaRJOacjtV/hxk4HhwyhaDRq0mu1ZAuwmWJGJUxFSYaAgtq6cVIUHCeordF225ly2ySppJPJJGZmZkyzjmQyuQVIp3DMnU4HpVIJ7XYbuVwOa2trJs15lKNOJWdH9t1SvHSeeL7j2oiNokENruFIJILp6WnMzs6aOvvHRbxeLzKZDE6dOoXBYOBIDTwuMlb4Y3k5SCAQwKlTpzA7O4uTJ0/izW9+MzKZjGFK9/t9lMtltFotnD59Gr/wC7+AdruNv/u7v0Mul3PoU4LR2jxLjX/qBq93o3QVdXU4HDblLTTQO0p8Pp/RoSxfoY4+x0H9q2PkNWkJDTqmqVTKNC5tNpsIh8OO7C23PUFT2FlWhoCqx+MxDCGCvGTmK6OKY7T1MvX2boR2wMTEBE6fPo3FxUUAm8y7drvtaGTK8nf1eh29Xg+FQgGrq6sj7aPrFQWxNV1cgQcC2QQGNCCQTCaNTcJUftWJBIVrtZpZR8AmsOTxbDYanZycNPdGg0K1Ws1kUymbn+vK4/GgUChgbW0N1WoV/X7fHHNqagrT09NIJBJIJBKGJdhqtQxr0uv1Yn193ZyHdeNZjxhwAi78W0kV9XodzWbTlGFQZiOzP7xer7G5BoMBgsGgKVPE0jZszOvxeDA7O2vY9Hq8w5D9sNzGunosByHspaHsUcBZ49hm1Kp+s0FWN79EdZsNfPGc6gPxfe6FCwsLZq+55557EI/HzfPNfb7X65neSo1GA71eD7VazZS2arfbJjiozGMFp23fiyAtfWl+X4E3BQe5p+v1Myip+pFig7N6bgXqNLBBAJ26xOv1mutXYhfnhhnq6sPaZVJtQFsDvHpvqFcIzurcs/QN9QPZ97RZuO8ra5jXpPu9HfDn+ev1OsLhsMl0ol63We4AzNzodbj5ktRxvC+8//ZYeH/tNUqyogYG9EfBYgWNbYBaSw7p2uD9VLyD65Vzx/PwGWu326a80cTEhCk3mEqlzHOja85NbJa4AtMU2m6KvWhQQ+eAY3ULMvH49v3RwAv3Ad4rW1+TeGcTTSnEm2wcScXWwfyO237FvYmZmDvJWFcfroyB9GMkfr8fc3NzeMUrXoHFxUW84hWvQCqV2gL6AkAmk8H09DRqtRpeeuklPPvss/D5fKhUKgA2GT/25jEqIgds1lUnu8iOWLuJx+MxDg2dslGA/V5ElaSmyilDzRZlyfEYqgSpwHncUd/XdC0F0rcLKGjUPBwOY2pqCuFwGPPz85iamjKKzj4vlRfTqJmOzhR3rVU26nyaObCbDVcV13HfZPUauH5Zp/iwndu9isfjMSmn+Xz+2I0fGCv8sbw8xOfzYXJyEouLizh79ix+5Vd+BdPT08jn88jlcmi326bsSiQSQTqdRqfTwTPPPLNFdynDTUt/8YfNIanP+HnWLndz4N2EDkU4HHY45OqYqo5VoZ7SjC3qpUAggHg8jsFg4CjfsZ2+BLDFsST46vF4jGNNsMKeM+4VrDFqgw97EerRdDptSuixZj6dPe7N4XAYnU4HlUrFgOxra2t7OpeOf7ef5z1SFqTaB+poK9ARDofR7/cRjUZNSaB2u23sDDLvtc43bRTaFCRYpNNpTExMoNlsGtBjeXkZjUYDmUzG1Asm4MPx8x5Xq1VUq1UTGGG5F/5Eo1FHCnckEjElaIrFolnjNpDF+VGADtgo88LyQgTDwuEwYrGYAwRiMCgcDqNeryOfz6PX6xnwCth0+hmc8Hq9pl8B67gfNxnr6rEchFAnKXipoJgt1FkKdKnv1W63HexvN2CS7+1kIzOwx6DdzMwMFhcXTR8TnpslP5iR0mw2sbS0ZIAuzRi29bXt69rPBsfJvUvnRNnAysy2S6ioX+0GwOs51RfU/Zd2hWb70Jemz6vNxPk9vsd7xTnfroSPBkb0uqhj6NNzXulnM5jLwDrXSzgcNmVuNBtPQXzNyFf7gP67kgLdMBWdQ65RO0DjFqgAttYaV/9agXM9hgKsBI4VeHUDzt3mmmMJBoPm3ui60GxG/a06U58tJfAx84q2A21HFZ7LbUwULaHD+aH9Ys+/Hov3Vu1Vt3PpvNhAu9pMep90fLqeaFtxD+L94rpREF3vmT7LHId+x763SvLcSca6+nDl+CExL0PhA04G8+zsLFKplKOUiS2MaOl3QqEQ8vm8acCkilDZu6OED74qwJ0eQjvaNmq81yPc/NwihW6iyksdYSpatwg64GTJUYFqzdFRyoKKfXJyEtFoFKlUyjR3ZROa7TZLbqqxWMww7skEWFtbM7VHyZRSg4iBEjVWCACMmhuCFO1223GPD/q+3WjhtfCeqqJ0i1AfdaHBwKDU9QSixjKWsdw4YfYL9fTKyopp/lmr1RzNi/r9vqmNHolEcPr0aTQaDZM9Rn2jNoAy3JhdRoeXIC+BxN0G3Ohs0SlVFpQNpAPujaL4Om0PAg2VSsWw+PbDjgVgmGIE0nkuOsLA1tqaezmfAq0EkIPBoGG8EShWHUtSgY4RgAGLp6amzBgLhcKWpu8qtrMNOJmWo8ZMsJh1uJlqz/fD4bBxgBX44Ofa7bb5GQ6HqFQqKBQKZn0RsIhEIoYNGAqFttiOzIrQsncKZrCuKsv66LolqM3gCOurU0cTRFHbi81Oue653ngf1EalvqdNQFuWf0ejUfN9LXHINUGbi7Yig0lk9HGtdDodLC0twePZYOYr+34sY3k5Cp8pPstuvoeCzbqH28Aej8fv8H3qUsBZ6sq28+lLeb1eAwAyIMf+IjyGsnE1I4ufYWZxuVxGoVAw+6k2Q7avUTO5bBDZBlQ5DvVX+RqD3iwfp3Nng+SqD9WXU0CdfysTXoOO/F+JZgoOcl9m0FCvnRniNuNW/Wseg5lteh+pw3lPgE1gk/eDWUQahFF9rgFkPSZtGgDGt+r1eiiXy8ZeaTabjsCNYhpaPkzFZvkDm3rZbnBur097zdB/57gV4N4pSMNjqt7Tz9ol7rhWdD3pb64rrgV+V4P4owIQvAZ7PdvjtoFym2VuBxHc1rc9D26+vq47BgT4PLGUm71XKQmU80qbxwbYt5tnPZ7iTTpOPkO7YaSP5XBlDKQfA+EGn0wmcfr0adx9992G/TQKDJyYmDCpT4uLi7jnnnuwsrKCK1eumAYpdCS4Yexk7CuIvNtaozRc6JwcNICpAOluQVKNwpLhZG9m9qamBok6aDsxt5nunUgk8OpXvxpzc3OmYZWyFngttuh7k5OTGA6HyGQyuO2229BsNvHss8+aZrLr6+uGFUWlFo1GEQwGHfcNwMiyLQToy+UyAoGAo6a+GrbHQdioRRvdaK3+GxHUuZHi8XgM8491Go+bjCPnY3k5iM/nQzabxcmTJ+H1evHss88ax4sGN/f+druNcrmMXq+HbDaLX/qlX0KpVMLTTz9tmHB0+hKJBE6ePGnAaYJ4yWTSgItsQEynwC7XMUq83o0mYwQU1HnVYL7qLPuZpLOYSqVM3xY2UeVevN8yJ/pd/a2NUFVsxtdOQjA0GAxidnYWsVgMqVQKc3NzxkGn06SsIQ0stNttcz+8Xq/R1fV6HT/96U/RaDRc90Aey9ZLWjrGFo/H4yhfsrKygvX1dVPiheOIxWImHVvB836/b8qw+Hw+lMtl+P1+rKys4PLly5iYmMD8/LxhZ6bTaXNOBloIfLRaLVy9ehXD4WZDNY/HY0oEsW8Pz8nSLDMzM/D7/ahUKobpTYc2mUwaZ71er6NarTquv9lsmrIRXq/X0SCV16fgAYFwPjdaf31qagqJRMIAYf1+31wng1M8v64DXhPXR7VaxT//8z+bwEI0GjUsyeMmY109loMQ+iAELDWQpTpGQXGb2atsTQaHFURUsBXAFl9FgV+WaIhEIibThWx0rYnOYCqwmbHMYweDQSwuLmIwGCCXy2FpacnsHeVyGZ1Ox/hrGshTdi+D6W7XSzDaBpX1uSIpKxqNOnxrgo8a6OY4lMnOc1FsNq2y5Cma7aR6z9ZRzGLSwCrPoQCwArXMYmPZVT2+3kcSxBjQTafTjgwCfo7noC7ia8Ph0ASOCaDa648EtXq9jpWVFcMcdgvscH41qKw2G4WBBvaJ4b3R++Z2bJYsoa5T3MDGVNzwCPrueo/4PkF9BlHcAHTOmR5P7RQSJ7RknC0aQNLzu4Hh+r7bsRSs10wAO9ikMso+5PeCwSBSqRQ6nY6xP7hGOA59LnhP+Izpc0pshZgLX7eDASw/5zZXtK302dlOxrr6cOX4ITEvQ9FoKmuak5E6ykFWgJnM53A4bDYg3UQIiDO9xN68NKJos9ev5wE+CLkeINSOdKoicQsq2BHVna6b94DOeTweRyqVcrD1div8LB3Vfr/vYG7x/tmBAEaOlbGx05xpjdtRhulxEDXWAfdo9nETu8TDcZOxwh/Ly0EYuNOa2cPh0NEAlA6zsmO5pzMgSlHWGL/L7ylzjA6aMrH2Om79nu3sqShr0G1PZRk4ZZq5AQK7FXVY9Ptu7K79HJ97K/UzS4okEgmTCWYHoNXpVZuAgI0C7gSA7Ow/ZXnZzKTd3j86yjwGgW61A+x51LrxXH8ATBo9sJW5p+tDj83zK+ClpQ3I3iaYwPr2LKenoJoyCnn9yrLXbAyOWcF9G9DQeVYWuc4T7w+wUfJF7yO/x+/w+tUO5LwQ9Femm6asHycZ6+qxHISoL0rhPqFBST4/+hn9rcdT/Qk4gTl7f3b7jo6Jez5rElPc9nl+h6Wn2POL+6v6Tqrv6Fer3+zmR7sFfdX/Vtmvjrd9Xgrn39br/CznwAaIVacrc1sz7jTTm9dpE8hsf019TgUxuV+rL8QgjepS1VHUGfw+S9GQ5EdwlplG2sdFM8V47XYGAOdS9bqy+4HNhrSaZbAT6U9tPTc7y+01e++18Ru399xkO2zJ7Xy7JTG6/T9K19j2lW0f6fcpuyVOqHBtuLHq9XnQcfHHLgXkFtRTO4Hf199u16DPyk4y1tWHK2Mg/ZiIKrKdmONu37W/pw2xKpUKSqWSo3mo7cSQSVQsFtFsNk0N0J1qj1JpMa3XLVJ7PcJouEbvdmLJjxonj6f/259x27jdhEqZzeZisRgmJycRDoddU7j3IlwLwWAQCwsLiEajyOVyaLVahnWm6Xacd03tHiXD4RDNZhPFYhHBYNBhTBw3BjTZIZVKxTTEGZVSd1xEjcLjOP6brfC/8IUv4DOf+QyWl5dxzz334Ny5c3jzm9888vOPPfYYHnroITzzzDOYn5/Hxz/+cTzwwAOOz/zN3/wNHnnkEbz44os4c+YMPvGJT+C9732vef/73/8+PvOZz+Cpp57C8vIyvvnNb+LXf/3Xt1zLH/7hH+JLX/oSisUi7rvvPnz+85/HPffcs+drHMvREwXf6FANh0M0Gg1UKhWToUWwndk/+XwelUrFlH9R6fV6uHLlCobDoWFlqWNLwJosNzK4S6XSroxxfpZ6nXXNVdhjRdNeyXxnMHc43MhqWl5eNmBlIpFAvV5HuVw2jbz2+jxrzVxNAbfnfT/i8WywpycnJxEKhTAzM2P6aNTrdZPmzetm3fVIJGLYcKwNzvGR6RQKhTAcDjEzM4N+v492u41qtepoiMrrG8XQchOCtrlcDtFo1DTQJnGCoEC5XDY2AR1+lgxi1looFDLjSCQSOH36NLzezbIwer+Zvq/1ZYHNBmyafeg2z3RW2dSW5BCyv6mv0+k0vF6vKZlCgNsOiJMswu+zJJ2C73YTQAX8fT6faWLXarVMmYZ4PO7I9HAjT2h2Sa/Xc2QI0NY6amXYbLBxlIyd87EclOg+YftQuufZviEDehro4/e0ZIweS/1dBeH4/GoWC59dYHOP4Hjot+o5Nfioe3cwGDQZ4AwWlstlEzwlu12Bah6XGULKPtXPKRBH3T4xMYFGo2EYwApUK6ivY+c18zevX8fDe8SgMfdPzhH/1wAAx8TPqR5TEpP+tudUr1mPz2PSjiBGkUwmkUwmje1EcQuc6LUTvGc/MhsUJ3jJ3iFer9cEfpvNJhqNhhkfsRJ7Huh3qw8+MTFhdFiz2US1WoXX6zW6zwb+OR8KqNrrgZ/TbAsVG0BXkoV9zaMIGlwfHo/HUfKMx6dtFI/HzbHd9Iv+b7+nYDT/13XJ120SqD5DdiDOzd7V+eLxeG6yw90qJ9j3RMkFFA0SkkCi37FJBnpunRe9j3q9O8lYVx+uHC9k7GUqo6KQe/m+bViw9hfrgUUiEUQiEQSDQbMhckPodDrodDqo1+solUpoNBqo1+uOpi+jjHM692xOwU2dDt31Ohh08DhGjfjuVa5nM1JR9sLU1BTuuusuU87FLZVnr8cmkOLz+TA3N2dSEq9du2acQSp8BXNUuY+6TgLppVIJkUjEGHgAjhWQznVLUIpAup3aeNxkv8G0l6N84xvfwEc/+lF84QtfwJve9Cb8xV/8Be6//348++yzpmmgyoULF/Dud78bH/7wh/FXf/VX+MEPfoAHH3wQU1NTeN/73gcAeOKJJ/CBD3wAf/zHf4z3vve9+OY3v4nf/M3fxOOPP4777rsPAFCv1/Ha174Wv/M7v2O+Z8v/9//9f/jsZz+Lr3zlK7jzzjvxJ3/yJ3jHO96Bn//856ZEwViOtyiQB8CAreVy2egD9sig4+bxeFCtVk1TLft4S0tLKBQKiEQimJ+fN4Ap92Y6a61WC6VSyejt3eg12gL1eh0+nw/T09MAnJlo3Ed5PW7OCRtO1mo1+P1+LC4uIplMGhC50Wi4NsjeSZTlrSDGQYgC6ZFIBJOTk4jH42i326Y2tzLKqBej0Sji8bhx4pTVzbI7zEqbnp5GIBBArVbDysqKsYkIFtnsrJ3skcFgYO7VcDg05VBYM3Y4HKJUKqFWqzlY3rwOOses/0vdGI1GkclkMBhsNDmn/aApy5wP2lzhcNj0A9A55fUooKIsUJYoZIkV6uxisYj5+XkzdywNpAAOA1E8JzP1FEjXmvUMPDHwQZuR10lQIJfLmfunDi/tBgXz+b4GE1jSiH1mlLF62OLG5hvLWG6kKAgMOAOG9ueAzT2C+wvBZu6T+sy5PVda9oXBNQ1achzcE+mTcS9RIF1rUtusY/2be5meq1KpmD2KjZYpSohx81913vib+zaD5PTzgsGg2as0mG3rENXV1NOqS+1zkaDGvZb3kO8rUMzzMkhqiwK3bvfdBv2Vwatsce6lDHIymGKLjXnY+zV1n55T7wvL5vp8PmNPARvlxOxAEMfO+8I5UdBbyYStVgu1Ws3MBXuB6N6s12/77KpPud45dntPtwMeDJ4rWKsALtcQv6vv2/XxaX+Uy2VMTU057rMNgutrOnc6ZjZFH0USs0kGOg9u5V7cRJ87rleWk2OWib7PfYiieJeudd3TaAvz+Go3q+/OZ15FszR2w/Afy9GQ44OMvYyFGzEj3azLGIlERoKC3GQIrNZqNcO6sd/vdDpoNBoYDAbGoddNgAq1Xq8b1pVdKoQbom0EAJsGCZ0T7X683/nQ6+SGREWxH0Dc4/EYRgKNDE2t4XloWGznxCtDik4inbiD2BRVoahjGo1GjRPJ+8n7xHu9EyMd2ASAOK9kZfC+HWVRJct1TeBGgXTbuDwuoobrcXSEb2bk/LOf/Sx+93d/Fx/60IcAAOfOncO3v/1t/Pmf/zk+9alPbfn8F7/4RZw8eRLnzp0DALzyla/Ek08+iT/90z81gPi5c+fwjne8Aw8//DAA4OGHH8Zjjz2Gc+fO4Wtf+xoA4P7778f999+/7XWcO3cO/+2//Tf8xm/8BgDgL//yLzEzM4O//uu/xn/6T/9pT9c5lqMp3Gu1qSJ/tLyX1lzs9XqmhrSbjqGzNjExgXa7vYVZRoCX+paOHXWZiu208W9lSGudUjoP/J5dTkyv2efzmdrQTGknCKB6ei9iM7GAzYaVdIgIvNjZR6NEg94MaJD1xvFp2Rx1cPm6ZuXZ6fvK2ia7utfrmfXAe2bbNLsB0jkm1vKkjtOSbOo4ahBCz+fm7LrpSa5RLQHIOdF6rW7OPLDpZGtzN+oztTeU/c5jEXhXUIFZGbwPCpbpPbIBfV1DNiOPz6KSPUaBAbq2dR6Po152kzHLbSwHIdwfFHi0SzQAzkCirR/s/Uq/R/9GA13cb/k9+5jc47TZMmt663HdWKkcjwLgbnue9ltgto5eg92ni3spdZmWAXPbj7RclhtI6fYMqg3iRtBT8J7zw6Cr/vB+KsvbJuvpPHIfVh+S59FsYTf/lPpcS7kEAgFHzysbTFe2udtacxObbMY5oZ6ybSHbbrLPb685xUDsLHqdP9s3Vf3kpmPs58oev65hW+x1M2rN8H0NCvCZsfEXe+w6Z/zb1sFuz+6oMbuJzjXXrh00tvcEtfH0Pqr9th3wb2eM6jXotVJs/Eefo1HXs9sA/FhXH66MgfRjIASxy+UylpaWcP78eWSzWRPldgPTqWSbzSaWl5fx3HPPoVgsol6vA9g0JIbDIXK5HOr1OoLBoCMdjfUj2dSp1WqhWCwaw4FNzAg6qyJStg9LyMRiMXP+623AxI2m2WyiUCgYFh8d9d1uDtzMfD4fZmZmkMlkEAqFkM1mEQqFDJDc7XZx7do15PN5w/IbBaaHw2HMzMyYJjaxWMykDB20cCNOJBK4/fbbMT09jZdeegnFYhH9ft8EPwD3DvC2EISn8ZfL5UwjjqOWouwmXNcsU3D58mU0Gg0UCgUDUKnRfZyEwQHe04NkZN4suVkKv9Pp4KmnnsLv/d7vOV5/5zvfiR/+8Ieu33niiSfwzne+0/Hau971Lnz5y19Gt9uF3+/HE088gY997GNbPkPwfTdy4cIFrKysOM4VDAbx1re+FT/84Q/HQPotIMPhZsp2LBbDHXfcgWg0aliquv/QwQSAcrmMS5cumYC1fUw6LHS0A4GAYacBMCUyWq0WqtWqqZvN4+s5yabRMdGx83g8iMViCIfDhoXF13w+nwEJPB6PYfMAm85iMpk0zSmpp9nYstFojGyguZ2QFEBnhcy0u+++G+l0GoVCASsrK2i326ZR2E5C9pnf70cmk8H09LQJFjQaDRMQGA6HpokcABMY5z2jo8sSKdQzZDnTIc9kMsb2abfbWFpaQj6f37KXj3LQ7c/oXObzeQAwDHkCw2xMzaZqvA/2Xsxz0d4EnKUSyAYkeNHtdpHP500DNbuci64nYJM5x1R2r9eLZrOJZrOJfD6P1dVVsz4YdOH4otGoKR/EY7KuucfjQbvdRqFQcFyPz+dDPB6Hx+NxrB07jZ5MR5ZZItBBhrs+M7SrOSYNBtCuoIOtZfWOirO6F/07ds7HclDiBrLZvqMNour6U8BWey+oT6XBNxvIU79QfUcGnldXV5FMJpHJZJBOp03JGLfyChxnrVZDPp9HuVx2ZFgR0GT5U/pNoVDIAdRphpNm+7AUGPcNDWrS76R+4ueZ/cR90W0OgU2AWfc/LR3CMdF+IBubc8rAv5bZIeCvBDS9T8lk0jScTSaTxmahX03/nXqHx+Z1c7+dmJgw/eGSyaTJgCIBTku/6fW7Ad5uexv3fQ24Aps2Fcva2WtN1xX1GgMHaicx22o4HJrG1Uo4IO7CIHG/3ze6VtcBj633S69HyRDU+XYwhM8N7UklY2jgm9fCgD2zFtfW1tBsNrG6uopqtYpsNmueJ2V12xldqiNtYoaKbfdsl4VNW5tzoA1reXzeG64jDb5pQIzf5X5hEzLsMjoMvumPZgjwWuwAHY+la9S+djsosp2MdfXhytFGxSz51Kc+BY/Hg49+9KPmteFwiEcffRTz8/MIh8N429vehmeeeebwBnkDhA96u91GuVw2imc7wJibVafTQbVaxfr6OorFokPhcxNpNBoolUooFosoFArI5/PI5/MoFArm/0KhgGKxaEplUHHT+WCNKUaKdbPXdHMF2K9HNDpKZ4xR/b1sDKpYIpEIstkspqamcOLECZw+fRonT57EwsICZmdnkUwmHezyUTIxMYFEIoFkMmmUMNOnD1LUEAoEAkilUshms6auKQ00m7G209wz84HzSmf9uGy4NC5YRkHr8l5P1sJRED7TewkWHSXRSP9+foCNOs36w4wJlVwuh36/j5mZGcfrMzMzWFlZcR3bysqK6+d7vR5yudy2nxl1zFHn4feu5zhHWV6uupqi+6/X6zUg7dzcnNEnsVjMNDmjvqSOp2Npi7Lo6vU6arUaSqUS1tfXkc/nUavVjI5VXatsLq0VrYxewOmEUbfTGSHozLrWNitMM5/8fj/S6TRSqRS8Xq+DLT2qvvlu5pRODhlNLJly4sQJTE9PIx6PG4BzN0JgmNljbMrOuR4Oh+Z9dQjpwANwgA12jViCAwTTQ6EQwuEw4vG4AbjVaeOPOsjbiZuupp3Ftcf7GA6HHYz7USw9HpNsSjqhnAfWYCeooXVFSUrgHGipArL++f1QKGTYbSwXyOwxmwkWCAQQi8UQj8fNNYTDYTMGEj6U3Ujwn/eJa4drkJ8hwzEUCpkfBg3cwD1lvdvp1/r+cbYzgIPR1WM5HnIj9bUN8Nl7G58xPjO6B7qxa3Wv4Wu65hSwU4Bejwk4985arYZqtWoCjDy+zWrW4zPj1S17jLq/Xq+bMqj0g0lKo/6u1+vmdTLSuddyX9JmqByH+gKqt+15V6G/6Ma2V3CWALB9bdzbOQ4eS7O57c9FIhGkUimk02nMzMyYcqTpdBqZTAaxWMzoEdtHJo5BcF3xBmZ7MwiqezjtFh7TXiduP5xP279SG8fe2zQYokEfzTS0Mwi4FjSYYD8T9jq3AyD2WuP9d7Mf7GfF1v36jNjYgH6Ox2cZNBK6GJinjWcHFPQ5sp9PPY/e81F6ZJTdwudbewSpr2/vL7oPjfqx50WDGbpPcb3Y62AnEFzxG7dAgR3Q2U7Guvpw5dgw0n/0ox/hS1/6El7zmtc4Xn+51JolIH358mUEAgHk83mjoKLRKKLRKIDN9ONyuYxr166hVqvh/PnzyOVyZrMDnKwA3Rio9NnMBIAjUgfA4ZxpXTQtI8LjAZtsBEbwGaGlklNHfjuwmRsKmV4EHYrFImq1mmFe70UikQgymQzC4TDuvPNOnD59GpFIBDMzM4hEIobVz0hrMplEsVjESy+9ZOrY2gaM3+9HIpFAIpEwTvmNFoIcyrBzU1i7Ec5zu91GLpczhsHk5KRj4z9qwmeEYJKC6I1GwxiwVPwKkBwHIVhQKpVMIO3lKCdOnHD8/wd/8Ad49NFHXT9r39vhcPtMBLfP26/v9Zh7OddxWYvbyctRV9uOWq/Xw9LSEjweD3K5HHq9HhKJBCKRCKLRKAaDgWGMaxPvy5cv7yrYyXMAcOgfN8ZtMBg080v7QJ0RZSNR6vU6rly5YnSJAqM8P3WAOk0MMg+HQ1QqFQwGA+TzeVSrVUfT5/0Y78o+m56eNnXMp6enEYvFMDs7i2AwiHa7jcnJScNKX11d3cLu12PG43FTZodBObKvyEzUuVQHlPfb49lM1VfnR9mT/IyWMbleQgGFzD6OeX5+3jAFFWjxer0G/GGQgDYcmZPULcyUYO1yBhPT6TR8Pt9IR1VZbrx+wOkY8vOsE08gSeek2+2iUqkY0J2AOY87HA5NJoXN+hoOh4YcwfvJwAJBcgIb6vDz2dCxK1DEEgPD4XBLEMrWFWz+vl324ljGcthyM/W1DZjZpSdV7FIIts/Bz2uA0w3oBOAalNYxrK6uwuv1olarIRaLmX5WzEZiPzCSzlqtFlZXV41/wf0ScNY55jnItFbdwLHZ1z4qc5kMXpaJ4byRLEd7wAb/bQBWx8Xza3BASW8a+LbBRZ5Py1vxOoLBINLpNILBIObm5jA1NWUym4PBoNl7Weo1EomYXiRs7K04AYPQ7OVGfWBfp+pZez0o0KlzQduGelC/o2x9N5vF3v95bOoNBkb4vpIgPB4PwuGweQaU5a86TQF2+57ymqkXuZ7tprC8VrVdbJvPtgf1s91u15DRqKu73a7BG9hMtdlsGvtWz6l6m3Oi59Bx6N+8Jn1u7H1B76E9/xrw0WdDgwNk2zMQTztEnye938pyV3tYMTIN+vF9Elr0fbt8kx6T62JsOxx9ORZAeq1Ww3/4D/8B//2//3f8yZ/8iXl9OHx51ZptNpv46U9/ihdffBGnT59Gr9fD1NSUYbkBMI7J5cuX8eSTT6JUKuHZZ5/FpUuXzKbhtlEyotbpdFwVrm6ETKllozM6Y4xka7oaf7PUxpUrV0xzDTqvTD/ajTAqWi6XUalUkMvlsLKy4mhGtRdJpVJ4xStegWQyiV/8xV/E3Xff7SjtQqe33W5jYWEBS0tLuHr1KqrVKgaDgdmAVYLBIKamppBOpxGLxW4K6EwGYK/XQzweN2V59sP+46Zer9dx6dIllMtl+Hw+LCwsOAIgR0mofLvdLnK5HBqNBlZWVkyqP7MVyDypVCqGEXpchAGy5eVl0xDtuMn1RMD5vStXriCRSJjX3Rr4Tk5OwufzbWF4r62tbWGCU2ZnZ10/PzExgWw2u+1nRh1z1HmADWb63Nzcvo9zFOXlqKvVkKfT02q18Oyzz+KFF15AMpnEM888g0gkgjNnzuDMmTMANkqCtFotLC0t4Wc/+5lhp1FvbvecUPeQgcNx2KnFHo/HNM8EYBxkOlx01myHplgs4plnnkEgEMCJEyfM3q/6nTqbTovf70csFjO2xPr6uqMkmqZua6oxZae9gVlXkUgEv/Irv4JXv/rVZi4GgwGmp6dNaZXV1VWUSiVcu3YNP/zhDw1Abh8/FAphamrKZI4R9CTgz72Fc8QmnnapAM6/giMEqnkM3jN12vZT4sZNOp0OlpeXkc/nDVucpfrIsuSYqA+9Xi9isZix59LptKPcj8/nM4BFo9EwQZ5Tp04ZAEDLD/BaeE7aQbSj2u22mSOfz2dS3dvttgGcNfOt3W5jbW0NHo8H2WzWUW6BYAnLG/CYCkJwvQAw9qHf70ckEoHf70e1WkWpVDI2LEsj6P8E0wmsEVAgGKAgiTr6/f5GWT1mgh7Ufb6ZchC6eixHW26Gvh5FEHDL9gC2BoMBGDtdg5caTLbPB8ABJlL0uLofvvTSS7h69Srm5uYwHA6RSqWQyWRM02XuU9VqFWtra2i1Wrh27RpWV1cdwKWbH9vv91GpVBw6liAa/Wa9Dm2CqQAq34vFYg5GbL1eRz6fRzAYNDpHe4dwPwPg2LN1r+QeTnY+M4QUVFS9qPPM6yaRifPHrLvbb78di4uLjr2XY+p2u8hkMlhZWTFlU0ni43mIE8TjcdOgnZlnanNpwJrzbq8VBpA12EBAnuVV+FkNEI/KDmOwWQmJNtFMAxXUV6VSyZT9IeZCm0rXv2ZncR0o013LqSnjm7ad1ua3MR/7Hrqxo/mMUkez5A/tOLL1/X6/WTvxeNxxfA0w6VjtAIX+5vf4o5kkmpVhZ1YMh85SQ25BFhv36na7aDQaJsOZmXE8lwrXLYMkLO3GTDreR83MsIXj57F5r7jmFEhXu2I7Gevqw5WjhYaNkP/8n/8z/u2//bf41//6Xzte36nW7CjhpqA/x0H4wJKFnc/nkcvlTNkVlmbR93K5nDECuCnrBmVHBTU9iNFoOyWOG5O96bul8ihDi8wjMqLoVGqk2E6nsSPgTKVRZrHNmN+tcDOjYmbdtVQqhUQiYRR3Mpk072UyGeNUKVNPhcqOLKabIRpR13sw6rNu998WGk5k+3Oe1Zg9CuJmLNkp7lxjjD5fDzPyMIWO/KhmhEdd9F7t5weAyfbgjxuQHggE8PrXvx7f/e53Ha9/97vfxRvf+EbXsb3hDW/Y8vnvfOc7uPfee40jMuozo47pJrfddhtmZ2cdx+l0Onjsscf2dJyjKC93Xa26tNlsGrAul8uZ8ivU1aVSyaHL8/k8Go2G6946Kr1cAXc351jHxWMoS2fU3k+Qkg6CBsmVqUOnRe0DZcqR3abBzP1kSQGbjUWpr9PptEMXh0Ihh85OpVIGKHa7TmUe8/l2S493s33UeVPWlRuz0nbeboTO0flm8J/6TYGFUdkOtj1gM994Dk2LdltjFJ0ziq5NAu8sreBWlk9tPQJVvB+8FnvdjUp/t8enY9HnyL43ymaz54pzYrPvaHvRQSd4ctzkIHT1WI62HKS+3klXb0e+2W7d8HsasLa/N0q2e+40E6vdbqNWq6FWq5lMVoJqLMFCAk6lUjFlYOhf7MWv1rITCvbuxq/SueCx6VvTx9cSnm5gpb0PK1jMPVdZ6DYgPWqfVGEQluW3WFaM5VgikQhisRhisZjJqI9EIlv8V/3R7He7XIi957vdd3vMbtdg6z/dz22CIe+HjX/YwLauM71fWkbG9qvdAGY9ltu4FZNxuya3ebLvn/2em75Wf5rfoe/tFqixbYRR5xolo9beKDtM597tGvU4Wu5FMzDcsAH7fPazSFG7yH5+9oqd7PazY119uHLkGelf//rX8fTTT+NHP/rRlve2qzV76dKlkcf81Kc+hT/8wz882IHeBBkOh8bhWF5exg9+8ANEo1EDKAEwqbFkrrZaLZRKJaPoGU1TJaQNkUZFXin2RqxKhN8btUG2Wi2sr68bdkGj0UAoFMLk5KRJIdfUaTrkNAoajQZqtRra7TauXbtmmqeSybRbZ0WVYyqVwqlTp5DJZDA/P4+pqSn4/X4zHioRMte8Xi86nQ5mZmbQ7/cd4AfF5/MZw4AsqpshvDccPyO5zEIgoEDGAuCMhtrR716vZ+Y4mUyaGrRzc3OmkRzPe5hCQ5jp5+fPn0exWMTy8vKWuui1Wg0XL15Ev9/HbbfdhmQyeahj34v0+32sra3hhRde2LZcwVGW61Hce/3eQw89hA9+8IO499578YY3vAFf+tKXcPnyZTzwwAMAgIcffhhLS0v46le/CgB44IEH8LnPfQ4PPfQQPvzhD+OJJ57Al7/8ZXzta18zx/zIRz6Ct7zlLfj0pz+N97znPfjbv/1bfO9738Pjjz9uPsNyWpQLFy7gJz/5CTKZDE6ePAmPZ6MW6Sc/+UmcPXsWZ8+exSc/+UlEIhH81m/91r7m5ijIy1VX00Dm3/o6sKn3JiYm0Gw2cfXqVfM62W4s88Lv6XMSCoUQi8Xg8XhMUFMBRTXSWdZKGdP1et1kVtCRVXaWOnkUArODwcA0sWTplomJCaTTaczOzsLr9RrWrWaXsY4mS20x/Z3X7GZn7PR8p1Ip3HXXXUgkEkYXA5sMH7J/vF6vacRerVYN0E6ggDpQS9FoINrr9ZqGmHQMmWZLHcvyIMCmI8kSYl6v19Fwy3aIJyYmkEwmEYvFUCqVHOwsHs9mhO1mDdKOu3r1Kp566ilEIhGcPHkS09PTaLfbpkcO2ep6r9nw0+v1Gja6x+NxzNf8/DwGgwEikYgjkEvQhLpU2fiaws/Pk/lYLBbx3HPPoVaroVAobLnOdruNlZUVdDodY3/1ehv9Kur1uim9YDdqo43caDRw8eJFY0dms1lHwGE4HJrmuZrSb4ME/X4fxWLR8ZzpveH9CgQCiEajSKfT6HQ6KBQK+NnPfmbA9OMmN1NXj+Xmy0Hr6+10NcFRFe6r9nMFOFnbyvDka9yb7T1D/Rx9jm1WOn8zgEofptFo4Oc//zlCoZBhpAMwZVFZ2qXb7RrmNo9lg5zMutLzKpDW6XQMk5vj03JTbmxb+mp6TZ1OB6VSyfj2vV4PgUAA3W7XsK01c4bHoX7XOs+FQsH42NTfo7KvdL7p0/M3a6KTOc4a6GSX00ft9XpIpVLmXiaTSWPX1Go1M1bus2Q/a8kM/qZNwbmiqM5myVO9Dn5ebQiK3+9HNBpFv9939M3Qex4KhQxh0K08ka5pznu5XEaj0TBzouVk7e/r2qcvz/ng86DXwjEPh0NH6VJd9/o3n03aLBpw570ol8u4evUqKpUKSqWSaQhOIWEE2CA6tdvtLbXdlTGv12CL3g83oFzxC95LG+Dm2Gkr29/R391uF6VSCSsrKygUCsYO7/edjXs5Xq41PT/r8itYrqKZisqW53qzs9u4P+ymzKN+Zz8y1tXXL0caSL9y5Qo+8pGP4Dvf+Y4jRcUWtyjaduDeww8/jIceesj8X6lUttTdParCiBmdc27+fCCVXWMbJpoWpA8xxU0RjBI7AqivjXpf61TSeWNjTALPLBcDwGzqdL7J7Gu1Wrh69aqpCbqfki50nmKxGObm5jA5OYnp6WlT/1MbpwAboHI2m8XExATq9brpUt1sNresNa/Xa5ph3UxGOjdEKufhcOgw9GhoMV2bSp3Osh2F7fV6xtFPJpO4du2aYeozZfqwQXRgE0hn/fBLly4hl8sZg1fXRqPRwLVr1zAcDpHJZI6NEuHzXCgUcOnSJVOn8bjJzVT4H/jAB5DP5/FHf/RHWF5exqte9Sp861vfwqlTpwAAy8vLuHz5svn8bbfdhm9961v42Mc+hs9//vOYn5/Hn/3Zn+F973uf+cwb3/hGfP3rX8fv//7v45FHHsGZM2fwjW98A/fdd5/5zJNPPom3v/3t5n/qmt/+7d/GV77yFQDAxz/+cTSbTTz44IMoFou477778J3vfOfY1gp/uevqUfqHBjGNc9aZdvvcKGFvDo/HYzJSAHf2tBtoR+ZcIBBAMBh0lCsZxRKkHcGSbKwNTZ1GVrjH48HFixextLRkguIsBcJyGuwxQod9v+zceDxugp9sYgrAkeYMbKwxsuByuZwDZKXQ+dcyZZwPv99v6qY3Gg1jZ2gTTX5XSQS0B+jsKxjO+zMYDEzAgQ6Y1ubmOHgte3GkCLCsr6+j0+kgHo9jZmYGmUzGkBB6vR4ikQiSyaQJLGsDPD0/HXc65DMzM+Y8BKLpFLIZqMfj2WLn2c3U6Kjn83lcuHABhULBdf23223k83n0ej3MzMyYY+XzeRQKBSSTSUdTuUgk4kiL5hr0eDyYmZlBKpVCq9VCLpdDu9025QkVUFMwnWNisKvVajlAKdrifr/fjCUcDiORSJhG5+fPn9+RnHJUZeyc37pyI/T1drpaAT89tlv5Av5NkNkm7DD4CTj3R92/FRC0wS3dZ23912w2TaCAzbI5zsFgYABmZZZrKSgel/uijo3nVXCfAVq9Hu69Chjr/NsZNGTQ0q8n8YtBCGUs8/oJ2GuGLgFFlpdjeRUFDlVP8Xq5/1IHezwbJdCSyaTx66kvCdZqk9BEImH8+Gg0ahqxuomW1rBJCzahge+7AciaUaVgrK45rlmC71oGVIP4XI+8l3qP7PtOHVar1cw9ZvNtLa2m5fpGscR1TlRImCCAzDEp0YCv6Xtcw1olgPhArVbDysqKKUlDzIU/zNjw+XxoNBqmRA/FfoYV8HaTUVgS9S/via1bbfyi2WyaIIgNdPN+d7tdVKtVU72BNqqbbcxz2s82+wUqWURFbXWuE16DBgftwBuf0Z1krKsPV440kP7UU09hbW0Nr3/9681r/X4f3//+9/G5z30OP//5zwHsvdasOpPHWfThUaPffu2gz0cHe1TqsKb+2huKRtsYLSyVSuZ/OivcVKjoWWeuXC6bmuXKNL4e0Y16FMva/sxRAJBtcQtqUOmEQiEkEgkHK5EANBUODUabgTAcDk1mA6PTyWTSHMturnMzRB3eXq9n1kaxWHR0EbfXBpliDIi4GdhHTXg/WIOuUqmYRrdj2V4efPBBPPjgg67vEdRWeetb34qnn35622O+//3vx/vf//6R77/tbW/bcU/yeDx49NFH8eiIJqnHTca6eneyH11FHUh9uJ9zUhdryTatfTpqXAROyTACNhy3crmM1dVVeDwew67q9/sIBAKmrBYBB00Fvh5dzXlgUIIZY7RHlPGkziYBcJ07ZcVrnxd1tjlWvW46c9yTA4GAYX0pu42OMMEDHk9Z7XTk7AxBbfwJbE1JZoPaUWnCnCe/349yuWzq0zMAoMwnre+rtg8BE9vB4/Wrzuc9aDQaxobQzxLQob5m42+uj1Frgg48wXneKwYbY7GYCewwkMFr0nvPMfJcZBYS3BlVBs++p4PBwLDfOQcEkMh+q9VqKJVKxpY9CPt0LGM5aLkR+vp6dLXdtBFw2uMKCKtvqQCxft/Nrnfz6XSP5d7F573dbm8JSrvpyn6/b0A0BW3tz7kBfm66ajuxwWIF/HTfHw6HBhRUf5X7uQLpzNBh8JF/695sBwf0OkddI99XAp82a9RrUKxgJx9bMQjASR4cNQ5b3MBp7umqv+1rUXBYv2MD/BwPA848nn1ckiI9Ho/Jrrevn2Oyy5jtFqvQ8bvNj/rSHCPtQtXVdnaCno+ER4Loao/tBlsZJaOCCHYQxO2zvF/6W+dBcSzaq0omUJa4jWXZWTL6md1icBrAsa9Zn7ux/XD05UgD6b/2a7+Gn/3sZ47Xfud3fgd33XUX/st/+S+4/fbbTa3ZX/iFXwCwWWv205/+9GEM+VDkRoLnKox+ezwetFotszlpoyZG81if2t6AuGEz3XtiYsKkhQcCAZMmRQWsjjjZbWQGaN3MvYqyEvT/vcgoNt9RECp5ptVlMhmcOnXKMPWY+kUApFAo4MqVK2i1WigWi6a+IddWPp/HM888Yxq8dLtdxGIxLCwsIBwOX9c87lcYfGk2m3j++edx+fJlFItFXLt2zbDv7LVRKpXwk5/8BKlUCouLi7j33nu3pFEeNWm1WigUCiiVSrh8+TLOnz9vjN/jJuPI+a0pY11944QsWmCzgdpehHs4mVDU09Vq1TgS2wkZ7dTLBM+vXLkCj8djUty9Xi9WV1eNs83zar3u63mGmYXX7XYxNzeHVCplnD27Jqz2dmEauabmstxIIpFAOBxGLBZz9D2hjTEcDk3Qmc5Vp9NBsVhEq9VCMpk0jVy1RAjLvaVSKcNYo+2iDGgC7Qo2RKNRZDIZBINBJBIJxGIxAJssupWVFbz44ouGRWjfP2UasnRKLBbD4uKiySLg8VjHX8F7ziVBYzYu18ACM9r42U6ng6WlJUewgNeic9JqtUxjOZYQGCWNRgNXr15FKBTCmTNnMBgMEAwGcfbsWUdW5WAwwPr6OtbX1wHAUa6HJe56vZ65zkwms4Udy7UCbDLyeU+U1RoIBDA5Oeko+9dqtbC6uop6vY5SqYS1tTW02+2R2SduosDSQYkGAfZz3LGuvnXlMPS17aNyrwC21n4mwKSvKdBHsde4DTjyPZv9agc0mdnCPZ96pVarbWGc8/scJwFT7k/hcNic2y7dQiDSvg4FzkZlCGhAUM+t18XSbl6vF7lczuHn8jq5d9E/Z2Cde5mtqxWI5H6voLLOhRsjPBAImPrnZO4q4Ew/jgFyDSrbwRSOlT+cR+p+G2jmPbD9fRuTYJCWrGWSsLh2hsOhKZmivcKoCwOBgKPELsdTLpexvr5uGtXWajXHOZvNJtbX1826YYZTNBp1NGfldXa7XUdVAbeAgwYE3OZBa7JznASTAZhAfbfbNU3btS+ABjEY+On3+8jlcmg2m5iZmTF6nSV9VPT5t/Uef7sx1fmelolzu35eEzMD7bmhjVev11EsFlGr1bC+vo6lpSWz9pnRQRIE77k9breSSfyfhFKel2Pl3zY2ZhMmlLCwkxyGrv7CF76Az3zmM1heXsY999yDc+fO4c1vfvPIzz/22GN46KGH8Mwzz2B+fh4f//jHTZlVyt/8zd/gkUcewYsvvogzZ87gE5/4BN773vfu6bz/8T/+R/zlX/6l4zv33Xcf/vEf/3Ff17kbOdJAejwex6te9SrHa9FoFNls1rx+K9aa3Y/s9mHYLkq+m+/SGe33N5qPKeOHGzI3Wq3fagsVIUF5j8djaqRzo2HkXJucsSv4QYlufDtd+6jv7vV7N0psZURGGVPxs9mso7ELDUAaDcVi0QAj9nGpQAm0l8tlADB1+Khc3CKsN+IagU0GBse0traGcrnsiCzbQgeXzYV6vd6WMj5HTchGr9frpuHRblP+j5qMnfNbU8a6+saJGuT7FRr21Nna6Hs337M/12q1HMFWW48q2++gWLncB+nsBgIBc00AzN9qf9BesVnHOl5t0k22HK+b7wObzHbaIY1Gw4AmCh5zHLSX6BC5scMUEKauZpmUcDiMbDaLdDptrp8AP2vgugVB1EEulUqYmJjA5OQkFhcXjWOpDHGbkMD5s5ltClIoMEW7oF6vo9/vm1R+zofW7u31eqjX66hUKsam2O5+U0ezJrvP50MymTRlXDTbgcEcgjy0f3htBJjInNV5spmm6vBSuBZYlkDXPYMS2gycDPnjKmNdfevKYetr+7m3ATE3H8L2bxRwdQNGbSbsqKwT3X+1rjkzsTyejVIlFN3Lucfzx618jdt1KOiv16R7LF8fdQw33a2lLN32Vu7Nw+HQBA2Ugcy/beCPwKw9j/wcx6nBatV/doNQzfLRTHadM9UxOl92tjuPoa8p0D/KN9X7wPe0tIuyjjkuBoWpN5jxTdCbJfg0qM+sqmaz6QiscO5JZqvVaqZ/Cc9DW0evcxRLWRnfbixnW2/rHAAw2RjEZshEZwa0W0NwZWOz5CDtSgWR9Ty2HWTfH7fAl46XY1Abk8fVsakdwLXBTDSWoKEtR/uF65e2GLM7ODc6ZzrPmh1hj9Uep309o2wgt6yCUXKzdfU3vvENfPSjH8UXvvAFvOlNb8Jf/MVf4P7778ezzz6LkydPbvn8hQsX8O53vxsf/vCH8Vd/9Vf4wQ9+gAcffBBTU1OmdOoTTzyBD3zgA/jjP/5jvPe978U3v/lN/OZv/iYef/xxUzp1t+f9N//m3+B//I//Yf7Xskw3Qo40kL4budVqzd5I0U2IIDXgbDa6m2PwOyzDwqYpylDSiO5Ox+PmZdcF4waotTVHHc8tBWg7YeSx0WiYhlZTU1NIp9OGwcaUZF5PsVhEPp9HLpczDGE3Z4mOZbPZNJHNGy06Bq0bn0gkEAqFkM1mkUqlEIvFEIlEEIlEMBgMTOM5MrbC4TAajQbK5fIWY4WBj+XlZfT7fWQyGXg8HlM3nUaE1oc9CFBdDWiCKayxury8jHq9jqtXr5r6+dsxLFkPrd/faMr23HPPIZFIYH5+3rAJjoLoNedyOfzkJz9BLpfDysrKsa25Coyd85ezjHX1/kTZROr47SeQRsdBde1+nis9/yig3AY+rlfYVG043GB6kV1P9pTOC0vAMGOJqcn2WKgPBoOBqfGt8007ZzgcIhwOm9IFZNcps46gMVnmw+HQsLxpv9gNpLS/C7/H5mNsvK6BAgCGFR2NRpHP501Q25bBYIBqtYrhcINhx/rd1NWDwcA066TN4/P5jO0CbNQ5rlQqjjRnigICOn9k6AEbAZdWq4W1tTVcvXoVzWbT1CJVZtmo8dPmWFtbw/nz500WHPcMBWMIQMTjcQQCAZPBSPCBaz2fz28pJUHHn+On88t7w3mamJgwfWPY+8Dn8+HUqVM4e/YsXnzxRTz11FMolUpYXV3daUk7xnAjCAjXq2/HuvrlKwelr9Uv5P/AVhDJjWGrQLmyzG1xA950DbqBf+qjqJ/J97SJI/dQG/BTkEyBffu8+hn6ypqZRGEAUufKZqbymG5sVf0uP6vzpffBfr7t82iwl8fW5q8kINEv0++weXWn00E2m0UikTA6hox4MqMJ1NbrdVOW09aT1AUE3YkZbCf6GQ146hzq/VNwX8FrnS/aBqxBHolEkEqlTPZYJBLZMt+xWMw0mmdJNjfAtdlsolKpGBuDa4H6iPfCDkYQ5HUL7ug88If4jwLMw+FmVkWhUDBks/X19S3ldHWt8Z7T5iIRr1QqIRwOuwKY2tPPJlsozmNjPvbzbJdqse+rBsP1f85PqVTC888/b3rv2WtYg1VutrYG3Siazab7gdpOdilcO2C03/KNN1NXf/azn8Xv/u7v4kMf+hAA4Ny5c/j2t7+NP//zP8enPvWpLZ//4he/iJMnT+LcuXMAgFe+8pV48skn8ad/+qcGSD937hze8Y534OGHHwaw0XPjsccew7lz5/C1r31tT+cNBoOYnZ3d83XtV44dkP4P//APjv89nlur1uyNFHVMdON1Y5SNEj6w2qTSbVPey4OtSm5U6vp2x7KZU7YydDsWI+Asl1GpVDA5OYlUKoVQKGSUI5V9u93G2toalpeXsbS0hNXVVaytrZkUbhVGmelE0yG+0cLrZqMYr9eLRCKBbDaLqakpzM7OIhKJGEd9MNhsJkKmnaYna0BEP3vx4kUsLy8jk8mg3W4jlUphYWHB0XjloBnevJ+scd5sNnHx4kU8++yzqNfrWF1dRbFY3FEREYxpNBp44YUX8OSTT2JychKRSORIAenA5v28du0aHnvsMaytreHSpUsHxvAcy1hupBxXXa0OyVEQn8/ncLBtPbtbod5TAGO/17rTPns9hv0oYYmbVquFfD6PYrFomFBk0ZHxxTIizOAhoGwL9QmdSDp5ZC21220TVAZgGEr2bwaQ2TyUTCitoU1g3+PZrPnN83o8G/XLWXaNQDoA8x3+hMNhzM/Pm/J2lUrF9doGg4HJHisWi6a5+6lTp3DmzBkEAgHTOJz9TjyejbI9tMXYcyQYDCIWixlmuZ1arc3SWHuc2V/NZhNXr17F//t//8+A9Lz+7YJBdM77/T4uX75s+rw0m01MTk46gJ/BYGACHZlMBuFwGIVCwWRv0U5l9hodcc1+5Nxms1kDxDOzjcGHRqOBXC5nyiTV63VMT0/jl37pl3D77bejUqngRz/6Ea5du+bao2U7Oejn5ajsX2M5HnKj9LVdKoTPvRvIBcCwl/U1Df4SwCUYy7G6ge62aJaJzVrmvsbjsP+F7Sdxv9asHTdQj8e131eAXpnCtt+kpDebBa/z4DbX+jmei/OogLAGBBSsVdH3tJwL/7bvDctmXLt2DbFYDKlUCvF43JFJxvrz1L+lUskEx1n2laVeOD4GLgnEb+djqm5RO0Uzznhter1uGIYNTLMEDIO209PTpnE1S59q6RUC6PStbayE64j10ZltRtuBZXE0a41rRbPS7Abho/SrBsW53okZdDodrKys4Pz582i1WuZe2IEsOwBBO4yYyvr6uhm3srJ5fYoTkNTJcWuGgZYSUnxHAwKaheC2bu2AEOd8bW0NTz/9NAqFAlZWVrbsL25AOY/L9aPgO8+njcu1Z4R9LH0+1a7XY94MEuZepdPp4KmnnsLv/d7vOV5/5zvfiR/+8Ieu33niiSfwzne+0/Hau971Lnz5y182a/2JJ57Axz72sS2fIfi+l/P+wz/8A6anp5FKpfDWt74Vn/jEJzA9Pb2fy92VHL27NJYbKtxg7QjmQbBWdOO+GdExmz2m18SNeDtnjSyDWq2GiYkJVKtVE31lbUxNc2JDy0qlYkqiuIEJPO5uOy4fhCirStlVTGVijT+mK2mHd2Cz8zo/w3lUpQHAGJWDwUYqdbVahdfrRTweR61WM0w8Kj/bWN7ttfA3f2gwsG5ho9EwAAkbouxU61evgWnjuVwOPp/PGG620XpYwqwGNlItlUooFosGgDiuMma5jWUshyd7DXLfKFHQwHZaRwnBaKYcExwnq1vZfnQAtX66m1CfsSSLOmvq2NkBDHUC9zKnap/YAX/qawL5dm1y6lKy4YGdm9NxzNqgtFqtmkafZI2NyiCjfWWzrGynWoW6Wh1xBjYI/u/FLqIurFQqBpjgPbPnTYF1zpnah8p00+slOOZmG7sdn8em082SLgRMyOg/zjLW1WO50eLG6NTXFfQ+aOH+S/BR2ar2GHXf2M432Gmc9t7J3xrQs8dHP83tu6Oua7v37Pf1WG7j18CG6ir9nrK/GZTQjDBtRs4AKfuoMXOIukEboFPX2n69rTeVbTxqfvR+27iFG6FASXn80XIlWtaFPzZjnH9rYMgtc4I+KX3YRqMBv99vdCZ7vWiwSX1+GzS2fWgVDRbxPrG8CQMctq7mMUg20LHzfjNgzWMw+NHtdrdkX7gFefS324+bjaL3jqKfs489HA4d+AWD4Qx66z23sY/tnj83O2gnDEHtDZVRgYHt5CB0NUs1UkY1j87lcuj3+1uaTs/MzGBlZcX1HCsrK66f7/V6yOVymJubG/kZHnO3573//vvx7//9v8epU6dw4cIFPPLII/jVX/1VPPXUU/tuhr2TjIH0l5now+YWud5J+NAzDVlfY81qt03oIIVRPzqcjHxquhOBVTqQo8phlEol/PznP0c0GkWv18Pa2hrC4TBmZ2cRDoeNgmm1Wjh//jyWlpZQLBaRy+VGHpdN0VqtFnw+HxKJxA0xBFW63S7K5bJh4THzgAqfTqAq+uFwa/1XjXaPun+85nK5jPPnzyMYDGJ1dRVLS0tm7sjsTyaTDpDeBtcpCkaQVUEDi5stmeRLS0smfWxtbc2Ue9mLDAYDXLhwAa1WC/Pz8yZaGY/HMTU1tSXl8mYJ57xer+Nf/uVfsL6+jqeffhrnz583jVGOs4yd87EcdTlq64wAMnD9TG+bHXejsltsUABwBtpjsRiSySQAmIAhHe9RoDebfHJvLBaLpjapso263S6uXbuGQqFgGG6jhMdsNBpIp9OmPAkD5XQCPR6PCeTSuWemGZ1s6itgkx2mY6MRT/YXS7AxgAts3JNIJIKpqSmEw2Fje3i9XsN2Z6P3Vqu1a8eA7HJ+b21tDcFgEDMzM0gmk4hGo5ienobf7zfB6YmJCdOgXNcdbSsFpHkfB4MBLl++jNXVVWN7dTodk7a/n3r/w+EQhUIB3W4X2WwWJ06cQDabNfYfWfCsmV8sFtHr9RAIBIzTpfV/a7UaBoOB6cmjgXqC4nS4ef+ZocAyeWSbBYNBNJtNfPvb30apVMKzzz577HU0Zayrx3IQovWxVXSftwOCXD/0NRRk3I1/6QZY8rt8nYCajtEeA/UjiVFugUsNMCqxS8+vvqmW3FDQj2NWZr2WSuWY3QJ9Oma71IfOvRsQqGNW4FeBYdWvmh2s16LXwX4ZoVAIoVDIlDnNZrOOcqJkQOfzeVSrVRQKBQNwslQIfUcCswTdlfikZVjtwLaW5+Ax+b7OE/d4DZ4yiNvtdo0OI3EsEAgYHcDrZC19bQhLO4J2g86/lq2l387/vV6veY26iriLNvEmuB2JRLYEYvS54Rqk/TEYDJDP51GpVNBut1EqlcxvBtr5rLkFtDiffJ12ybVr1+Dz+cwcDYdDkwmvQRmdBw3UMHBEHc3XeG+09429jvmba9rGGxqNBi5duoRSqYQXX3zR9FVj5hnnzO3Z4jHt8+n4OS9uwZLtjqeBDbcMiZ3kIHT1iRMnHK//wR/8AR7dJhvJDgC4BQV2+rz9+m6OudNnPvCBD5i/X/WqV+Hee+/FqVOn8H/+z//Bb/zGb4wc3/XIGEh/mcp+HzpuSHRedAGrwrjRhjTBWQVruUmzbqoqXZv9RanX62i32wY4rVariEQiKBQKiEajJhW51WrhhRdewLVr19BqtVAul0eWoel2uyiVSuj1eshmszd0Hih0EMkA4IZsM8qU/QDAYbypMeUWYaVoChLTzllnLBwOo9lsYnp6GvF43AAOWiuW59UNULMH2DCHtdpbrRauXr2KlZUVVKtVXL582TQ/2S9DezAYYGVlBblcDsViEa9//euRyWQwGAyQyWQODUinNJtNvPTSS3jppZfw85//3AQPbgUZO9ljuV5xYxDdqqLG/fWKMreU7XPQ86jHpijTJxwOI5PJYDgcGqeZztgoUWD7woULyOVyiEQimJ6eRjgcNuVI2u02Lly4gJWVlR2vi71BCA6z9intB2UC9vt9A2ozKEx7A4DRRyps9E1Gls3a4w+dJ7LNE4kEYrEYKpUK+v2+OQ6zyxgE362e6vf7qNfrAIByuYyrV68iGAyi0WhgcnIS6XQaoVDIgPftdhs+nw/T09OYmpoyQQQ6z9TR/E0WWrPZxIULF/Dcc885GsPpWtiNY6gyHA5NrXYCGpxzbfRG22N9fR3VahVTU1OYmZkxJXoIhhHcCAaDjvsNwAANCq6rs6tp6ZOTk8hkMrh06RL+5//8n/jxj398y7DRKS+H/XUsN1YUSFewS4FPO/Bqg6B2M0nAvaY6X3cT+5gEcoFNENXj8TjKqBG4417gNlae0208BDlH+VOawW2XK1HAmj61Mr/dhPMEOME9N7EBLOoo6hUGgd2yx6gvdfwaPCDbnGXBut2uA1Dn53q9HtbX11EoFExjSwaPNYitZWQY7CTAzDHb5VI0e4z6e1QQ19bl9Jt5HiXUcSzUxfxNUJ3rTIPNWjZEbTm+r2tSG5ACMD1HmD3GQAQZ6pyfUCjkOC5tDOIUtFkYIO52u8jn81hfX0e73UY+nzcBCvaF4TriMYGt9cyV4e71ek0PkmQyiZmZGdNvTu0mt/nfLqNAgfTtss8Vv7CfSQL0q6urWF5exrVr1wyJQkmluie5VTug6POlwTNdc3ovbKa5HpfjtJ/v3QLpen/2K1euXHGUtx1F0picnITP59vCPl9bW9vCFqfMzs66fn5iYsJgZKM+w2Pu57wAMDc3h1OnTuGFF14Y+ZnrlTGQPpZ9iQ3I8rUb5Zjb5yWIzh9lXXPz4WZGZ1Nre1K4wdHRZEQc2FBgbDbW6XSMkndrWqbCWuPqkA8Gg5FKZL+im3+n00G5XDZp27wuOriRSMQ4x1QwTPlmJgEDBhzvbiOhAExqc7/fRz6fR7/fN/PFe8QarHREVTmpoUxFyfItnU4Hq6urpja6GnHXs844R81m09RgrdVqiEajiMViCIfDjkYvN0rUQGHpoLW1NVy+fBlXrlxBoVC4aSWCxjKWsdw42QlQVIfievc31cfUm3T6FBi4EaLBdNU5BJVjsRiCwaDRi2QD72avJchJZ49Nv+mwEkzfzbWp/tfeImQfa48QZR4qS4+f4XfowGpgmE4cx16pVAzwzLnS8iDMZtN09+FwiImJCdTrdVPz/HpAWwarWUectV9pB9CB15rpZOgx81AZk7Qd2ARW1+9BrTMyGAOBAJLJJHq9ngnS074gyWM4HKLZbBrghWtDASA65+p8q52mjEstlzccDrc45Cw5MwafxzKWTdkOTFY9ofrQfoYUiNqNEASzwT8VZrQATvCdz7mOTfd8LYVl/+ZY1QfWcjBudryWDHFjvNJXs31rnUsFd1W206l6TN4fsr8JDLuVxaLO1vnlddOHox9OUhtZz8ysovR6PeO3MjNN92YF8Akyqz7kNbiNU+fJ9rtHrUUNXthlZ7TWP3UfQXav12tIZdTlDLwTnKb+VNKY3nsyzHV8xBK0PjwDE8ruZt14Bik4Vp5X7xMzHPv9PsrlsmnWbj9fbiQIW2yQGICxBfx+P/L5vAHvyeBnMJpjd2tiPmq96ns6Prd1rpkctLVKpRKWlpawvLxs/Gq3Y6qNrgC/vtfv90cGGexr0fnR7A0tFaTvKcnwZkkikdhVn7hAIIDXv/71+O53v4v3vve95vXvfve7eM973uP6nTe84Q343//7fzte+853voN7773X4HNveMMb8N3vftdRJ/073/kO3vjGN+77vACQz+dx5coVzM3N7Xht+5UxkH4Lixtou1c2kIqC5wStbWWuSv9GsNzo6MTjcSQSCQQCAaTTaUd0djgcGic4EAgYJeTmeGr0milmBOoV0FAmFr8zSlqtFpaXl83Y5ubmTDOSg67RREVQrVbx0ksvoVgsolQqGVChVCqZdZBOp9HpdBAOhx1Msl6vh0KhgPX1ddTrdcP23g2YTkXKOuVer9ek3AcCAdOxPBKJGIYDAx9q4GhHdgYfWGO13+8bY4ZGwPWCTMCm0isUCvje976Hf/qnf8KrX/1q9Pt9TE5O4uTJk5ifnwewc72z6xEFXJ577jn87Gc/w+rqKh577DFcvnzZGGO3gqiBu5/vjmUswPZrwXYAjpIwDZnOlr2/ct8kYLnfABodFP6m0xWJROD3+40u1Pqa/B7lekBQ6kiy28j+zWazJggeDoeNM0rnjw74dvvEcDhEvV437HDqbHWgd7tfKqu6XC5jfX0dwWAQyWQSiUTClErT+6TNxqijgM1yNey7wuOSCU5dV61WcfHiRRMk5rEJNvj9fqytrTky4hS0LpfLWFlZQbvdNo0z9yO9Xg+rq6uGQXb+/HljvxEMeeaZZxAMBo3dBcD0KLFBMOoxlq1xA8OuV2q1Gv7pn/4J//zP/4zFxUXcc889SCaTuOuuu5BOp+Hz+QygPhgMsL6+7ji3BgTI8BsOh2YtaUkDsiI9Ho8BS/gs9ft9PPXUU/jxj39sStwVi0UHI/S4y1hXj+UghBk12r9AA5ijRMExBVjVr3Wzy1WfUdfqPqs+LI9FYJH7LD9HvaLlubT5I/WVZi8pW5f7B33iUT21mIlF39O+Fupyfp/H59hYZswuc0IgcDs2vG0fhEIh+P1+RKPRLfpY9wQFfm0wVYls+Xwe5XLZ+IHMzCIDnOQ1+n+8RoKtwOa+zeyxeDzuKF/KLC3+2IEQvqbrwo2wQB+zVquh1WqZ5tLtdtuUBFOCms/nMzW2mUHNe9Pv900TVdaE5+sMCigbXZuAEltpNBpYXV018wVgS1NzBj6SyaTJzKP+1ZrzGqjgs0cdZwPiHIdiALqeFPhVFv9gMDD2UL1ex2AwwJUrVzA1NYVarWYyEVlFgPiAPtNupV9sdre9nvXe8l5qxt+LL76ICxcuoFgs4ic/+QlWV1dNaT0G0vX8FF0fdgDLZpDbTHI9htvfBPlte8GuGLBbQsjN1NUPPfQQPvjBD+Lee+/FG97wBnzpS1/C5cuX8cADDwAAHn74YSwtLeGrX/0qAOCBBx7A5z73OTz00EP48Ic/jCeeeAJf/vKX8bWvfc0c8yMf+Qje8pa34NOf/jTe85734G//9m/xve99D48//viuz1ur1fDoo4/ife97H+bm5nDx4kX81//6XzE5OekA3w9axkD6LSpuzrEdKb+eY9vncHv/RoiC6UxzIstOmdbc4PgeFYCbcBNi6tb1iiotgqAej8ekEeu17EfsjZngcq1WMyxwXhOjw9rYhQYgAQcqFDrtGnnfyzVToRLE8Pv9ppErgXSv1+swptSQ1RqmnDsqY7dsgoMQzsHq6ipyuRyy2awBFqampoxxPApsup7zUpTdwIj52toaVldXsba2dt3nOkoyds7HcjPkRmZFXY8oo9XNGVCm8/XsM8pCp75Ux8t2FuzMsoOyFeikkMnHYDLBSnX+dZ/d6bz7qbW93Ri5BzPQTtuh1+s50rE5LoIwZMoBm6U/FCTg++p0sTyJlurS4zAl3uv1Ohp+afNpOucsTbDf62632yODDh6Px+hvlpzxeDyoVquHVmaMQX9gw76Yn583thvnm+uddg2DAoDTMeY12mtfU8L5HCgjnWsll8vhxRdfdGTO3Uoy1tVjOQjRfXqUTnNbLzYwu50P5yajSr/o+wQM6V8oAK0An+pTMtK5Lyooal+T2zNkA3JK1touSw3YzKCxmatqOygxh+Km31X0+3bzTO59ej2qr93KWPCYBJ3pdzLzTIMQbNBsj8Vm+RMgJlObulnvD8+vYKSNV9hzpcdXkJtsc45f55Nzws/wPvFvzhd1tPrUer/5t5styPO0Wi3HWpmYmHAEdu0a8hp05xiZca/kDdocCuTrenMbl5vYeARLJg0GA6MX/X6/KYfLDHWO0864GPXc2mD/TuPQ+1MqlUxW+/r6OnK53JbsMw0OuB3LFq7fUfa825hU9D64ZaTsRW62rv7ABz6AfD6PP/qjP8Ly8jJe9apX4Vvf+hZOnToFAFheXsbly5fN52+77TZ861vfwsc+9jF8/vOfx/z8PP7sz/4M73vf+8xn3vjGN+LrX/86fv/3fx+PPPIIzpw5g2984xu47777dn1en8+Hn/3sZ/jqV7+KUqmEubk5vP3tb8c3vvENxOPxfc3PbmQMpN9C4vFsNuHUFDTdlJX5u9d65uqEcLPUzYPHPgi28KjrUyWotffsxhLc5DgXO9WLO0hRtsX6+jqef/55RKNRnDp1Cul02ii//YxHlQkZZaxPXqlU0Gw2Haz5arVqlCrr3kWjUUSjUcOkYJ1YRvv9fj8ymYyDvcX7vdvoKLCZQkZlTcZ6rVbbAhZxvpTdYafw7VY0mqvGnh2xVvYY/15aWsJjjz2GVCqFq1ev4vbbb0csFsPJkycRj8cNu3IvBr3b3BAQabVayOfzuHDhAmq1Gn72s5/h2WefNffjVpOxcz6WmyHXs85upKiDqww56i9Nmb4efWUzaWjwkx1IgFE/7/b39QqBBwa87cApnd5gMGhqlNNRtFOSb6QMhxslYi5duoRoNGpYgRyDOuMsFaK1T/lZOpBsgkkdxFqwlUoFpVLJFXSlni6VSrhw4YKjdB11OTO1SBwgwE39agP+1ysckzrco3rD3GypVCo4f/48otEo2u02lpeXTX15BmtisZhhLJJJzrUfCAQc5WoYtGfAPxwOG/CnUqkgl8uhVquZRvPPP/88isWiWau3mox19VgOQmxGNIGz7Zjl/IwNRu8GTHdbt26AmZYNoT/i8/kcTSy5f5MBzFrVGpC2y0zybw32uoFmbsLyXhpg1rFSbypYqkEGPb76N6OAWs6VXbZEAV4dr56P10sdzzEpOK2/mW2swX0NNLsBm3aGHPVrqVRylF9jyTgCyqqzldVrg7FK5uLcDYcbJcHoTzPbWm0Wln/l3GjwQedee28wED0cDh3zRb9aAwG6lmwyF/1qFa93o7QMM6yURc7fbCA6SuygMV/js8hr06xJv9/vWIccC/8nqS+fz5s5qFarKJfLCIfDmJ2dNfYW/WqeU9epjSlpJqXH4zG2D0sJdTodVKtVrK2todVq4eLFi7h8+bIhCmp5IX3WuB7d9hn7WXILENo29056UJ+h65HD0NUPPvggHnzwQdf3vvKVr2x57a1vfSuefvrpbY/5/ve/H+9///v3fd5wOIxvf/vb237/RsgYSL+FhA4pa1AxNUsfcqbdksGzV9BbWVW20HDYC+i5F7EZdprWZEe5uSlqKu71blZ7EW7IKysrqFQqSCQSJpWYtef2Ox5Ny7p06RIuX75s6rtrbVjWQGPd9FarhWAwiFgshlgsZkAdRuCp9ILBoKPBGFnte61LzvXg8XiMcQjsjpViG2F7ER5f14adygdsNq9TEP/ixYu4du0aAoEAXnjhBZw9exazs7N461vfioWFBcRisS11CPcqXBtsdPr888/je9/7HvL5vGmqaoM0YxnLWHYnRxnEcWNSU2/rPrVfZgpllMOudTxHpZkflNDJIfigAUhmPwEbjhmztRgIBnDgoPB2MhxulPgqlUpGP9LWsBvlAZsAuwIH1KEEXtT5bbVayOVyuHjxooPFpufndRcKBVSrVUxMTGBubg5zc3OmTBsz5lgijveQrCs7Dfl654Rj4jXw9aMgpVIJ1WoVPp8PV69eRTqdRiaTwS/8wi9genoa6XTaBL8jkQgCgYBxtGnzEATXDAEyHSORCDKZDOr1Oq5evYpr167hypUrePLJJ01QY69klLGM5eUmbna/Ama2v6g+q83wVXDUDXwHnOChnfWk31Vflb6C+jiaKcXgHJs6qg9K1rIymoHN0hJ67XyN12JnjjFAq2VQKPRd6KfRd1HwX5ncqpsUnNb55jzQV94J8FemM79PFjuvnRgBwUg9JseuoLcCsJxbHSuvg0FQrolGo2GCodznGfwkBqLMZg0A2NlLHAffazQaRt9yr9cgAsviNRoNU+KFPbV07vU84XDY2DmxWAzD4dDUYCc4TlBdgxmcH7UJOR5+h2vYzqBgsId4gTK/+R0t42IHTngflIyo18HSMFqij8fSLLpGo4H19XX4/X4Ui0VMT08jkUiYhq4s+2pnGNjkNyUpcu1xHKwlT8LC2toannvuOdRqNeTzeZPJFgqFEIlEHM8nn397zjW44YZr8TnWjDfec95Dm8jnhn/shI2M5WjLGEi/BUSVMTcEKhJl5dqpUfrw78URGLWx3CyHQqO2ek7dvEZ9xo5W3+jxatkUll6hslfGmpthqNerRgCNCSpxguTcwFX4HYLhjCKHw2EAm4pU0+QAGPYi09ubzaZRyOpA7ia9/npA8d2KMs/J9mfQQl8DNueERoiy4AeDgTGKS6USCoWC6RTt9XoRj8fR6XQcNRKBTePfZlDwR9P8OZ8rKysoFAqmTm2xWES9Xt91o7zjKIcROR/LWI6icM/SmpfApjFNJ9pmgO1V9Ht0lg7jWaIec2P6cF+83mu9HuG5We+UYLbu9fbY1YkCYOwq1m8FYAIELK/mVj9cRVmMmtHl1l+FzbDpxGqJNOq4gyiBcyPuh53GrTqcIIqWFKAoMMO5aTab5l6VSiXz7JBY0u/3EYlETP16DVJz3tjglfXtJyYmTD149p4pl8uo1+umofutrJPGunosByG0q91Kn7iBU24lHdxet2U7Apf6fKO+Y4P7Gkjlj60L7PcAJ4Btn5fH5Hn4nlvGtK0Hdb70+PY8KgNbxQ0kdZsn+7edjWUHAexMcb1Ofo9jVNtDyW129rAKfVEdF/1qYCMozyAAf3MsOqcKkOo8cvwMcFMXsCyr6lM9P8fJ4IcGPbTWudv9pl7TGvA6RiWhcZwcq95vvSbaHhpssu0tBeXd1pu9DtSvJV5BfIn3lkEU+s06Vpvtz/vFzIRSqWTsFM4bCYf2WlAsgzgWr6HRaKBWq6HdbqNSqaBWq5kfMtQV49D1yetQseu0cyz2HOla1ntls9dtbEDvjy36ObfvuclYVx+ujIH0W0CoxP1+v2E+h0KhLYx0OmPdbtcRQd5PrU030PZGOxaa5sRI7nC40ViUmyQ3J7tGOdN57bQx3eBvBJNeI9PPPvssLl26hEQigbm5OYRCIaRSKaRSKRNF1/vF7xG8Zkp4Pp83KUtra2sol8sja4jznnQ6HUfDFzZ/m52dNew7NxYkjQTWASULfnV1Fe1229Q9O0zxer2GcRYOh5FOp836j8fjZm7tJjyNRsOAG8Vi0XT2rlQqGAwGJpsgHA6bFPJsNouFhQWEQiFMTU0hmUwiEAgYRgTnURV+q9VCuVw20XLeu6WlJZTLZVQqFdM8jmv6VpWxwh/LWDZ0NlNa4/E4pqenMTExgUqlgkajgcFggGg0iuFwaJpY7TWDzO1zNxP847m63a5xegl4AjAlS0qlktHR9Xrd2CWHBVS2221cvHgRq6urhs08MTGBdDqNRCJhmHMEbsnOUnaZMrHIaMvn84aRtxPoQ13OJmehUAinT59GLBYzDKvBYIBsNotUKmXOzyAty7Rdvnz5yPXZoBPLgD7nkA50PB5HOBxGo9FAoVBwNBdX244N5Jm2zj4xnK9YLIZsNotAIID5+Xlks1m0Wi2sr6+j0+ng5MmTOHv2LPr9PvL5vAmWX7lyBcBGZkA+n0en00GhUECz2US9Xt/SaPVWlbGuHstBCJ9RZU2r2GCzMpopGmR2AwF1rerxbaBZAVayaFXfkHGubF4yrnW/1+vgnhUKhUz5DLvsA0lturdrrxK+pmCrlpgYRXrbDUN/O5IW55aAL/0VJSFxrMPhZkkS3h/OGwOTmq2rpTI4JyRjKVjMueV90dIgSo7j2gBgMAvWDy+Xy4hGo8hkMggEAgYQ53rRY3LebGJVvV43Pu6VK1ewvr5urpFzr3POe9PpdByNWicmJpBMJk3JNZLOuI50vuhXs3Rbs9lEu9022eU2fqOZAJxj3g8Fvfk31ywJAvysihuOY2dqcN3TXiWbnKWEO52Osd0YhOA5lQgIbGSSNRoNTExMYGlpyeBWbEQfi8UcdoHW7ec88HhcW8ViEfl8Hu12G/l83gDr1WrV4BOxWMxcE9cZ17OKnbmir+meoevR3q/4fSVLjhLuCW7ZArutWjDW1YcrYyD9FhBloofDYVN+gukybhs/gURGYvcqu2UjH7RQAdORYjocx8TSLtzw+FuViCpXO4J60JuKstyuXr0Kr9eLyclJADCBDnZJd6uPqylSpVIJzWYT165dw4ULF4wjuZsgCJ16ZZ+Hw2HMzc1hcnJyZO1vzk2tVjMKkOx2pkMfJpDO+8n1HovFMDs7i0gkgkQigXQ6DZ/PZ2qmKhhVrVYNmO7z+VCpVODz+Uyt1GKxiFwuB4/HgxdffBEejwczMzM4ffo0IpEITp8+jdnZWYTDYWSzWcMqpbHEe1Or1bC6uopms4lLly7hwoULaDQaWFtbQ6VSObS5OwwZK/yxvNyFRjyDmclk0gDpdGYBOJzLWq1mnp3j9BzQiaJDSdYSM+YAOAKyBJoPU1h2C9hgNMfjcQOEUI8Am2wzNjNnMIBBawZo19fXDZCwlyAI54wl2aampjAzM4NcLmeY5jMzM5idnXWkILOed6PRMNdxlITAEIP6gUAAiUTCgA/ZbBaJRALlchler9c45QQS6LgDMOVmOPcMMgAb9TJZkqFQKGBubg6NRgPXrl0zOv/06dMYDDaaouXzeYdTe/HiRbz44ouHYuceBRnr6rEchDCISlauzZq1s4T5WZXtAGPAWa/YjYHLtcz3WNtcfclut4tgMGgC3DaYbgPqOkaWiiAgagt9dGAz00x7hthZStyHmP3KY4ximu/mNT22fsbOAup2u8YPpU9ts8sJqpPUxjHaTUiBjfuvmVvcv9Vv1BI8Ok7dg3TcWtaGc9nv9xEOhx2lNQiWcu4U7NQs/V6vh1qthmKxiGaziXw+j3w+7yj9oveSNhr/5nwxEJNOpxGJREwAifOlAREGgEluJOGrXq+jWq06giuanaXs8O1+FEjXOvSj1gPgLIuk95HXxdKwyWQSfr8f0WjUBC4IWrOULAl42jsPAOr1OsrlsjnPYDBAKpXC9PS0CUrFYjFMTEwgkUgYXCsSiRjAmsEIBtXX1tawsrJibC6WCKS9SdtNxc6G4Dy4AemaacFsBLXnNENBgXB9HtxEnyc3GfXMu93Dsa4+PBkD6beAUAHZaWb6EDMCb6en7Sbqpaktdj0xdTxutKhCJYhMBaUGjsfjQSgUQjgcRjQaRSqVcjASNA1N67qz3heVARu/UClc74ajjCqC4lSmVFRaXgWACQa0220Ui0XzXRoiu533cDiMTCaDUCiExcVFzM3NIRKJGLadG8uDQoUcDofh8XgwOzuL4XBomPBMqaJTS+E10QBluhaF6WCcExrcuxWC/36/H5OTk0gkEojH4+baEokEUqmUachGY4fKOx6Pm7I4Ho8HiUQCxWLRGLC1Ws10k+e9b7VaKBQKpi5etVo1Dc600S8Zgkw7J+uS4DlTzcYylrG8fERBRE375V7B9GQKnSnuX3aT0KMu1NW0P3h93JOHw6EBUwF3AEBtGAUeqDdyudwW3XNQQkeYYCt1YaVScdSzJ6ONjh3Lh4wqubad0JkkCz6TySAWi5k1wKAx04m1OR7Hx4aZCwsL8Pl8pqm1W8Nw2o/MqNL55vtqJ9Em2YuojZpKpYwDTtJHPB43znksFjOsUK4bpmj3+31Eo1H0+33UajUDQjAgboMCfF4IkrfbbZRKJfR6PSwtLeFf/uVfMBwOceXKFaytrTmABJZvGctYxrJ/YW1x+mZujFibtWkDnvbn+bqywil8TcvJ2GCs/bruT3xdmdqjSo8oU1rLZhBUtRnlFJtlq8ejvmB2k/rZOh8K3GmAwJ43La9h/+b+ys9pWUllInMetLxHOBzGYLDRnJmZv2QSq5ChTDJXr9czmT2cM2Wrkww3au+1Aw7K7i4Wi8YvJIDOoIidtcDzUUfT1yfAr+VFdE1pWR/qK7/fj1QqZYLBBJ21fI1baTgKgViS6hiM0TrenCdmWzGYY2c4aMBA1+V2ovYVf3w+HxKJBKLRqPFxGfSmX83s9na7jXA4jF6vh2g0aoDsWq3mKJXD+dYgCcFpljXVUm21Ws2sK9o0mtHQbrfR7XZRKpUMLkJWvwYfNAjhZmPaJXD4mooGJ2z2+G7mlwERJW7qsd3Oudv7N5bDlTGQfguIApWqRDTizY2RGwZZTqOilPbxGdmNRqMmwkkglzWoboZQcVarVZOC1m63zfVSiS0uLiKVSpm0eSoCpmbTICDrq9/vo1AooFKpoFKp4IUXXkCpVMLS0pJhf19vwIDf5dg1YkyFbzuxdFqHw83mMpx7O8LuJjxWNpvF6173OiSTSSwuLmJhYcEBMI9SCnwtFAqZNKh4PI677roLq6urmJiYQC6XQz6fx+rqqmM8oVAIs7OzpoRNNpt1nKder5t0rLW1NeRyuV1HVj0ejym1Eg6Hcfvtt2N2dhbxeByLi4uGjZZMJg34QOYglSqNuXa7jdXVVdRqNayvryORSKDRaODq1auGZcI1UiqVUK/X4fV68dJLLxnnQOfQThnkOQlCaKO6l5uMI+djeTnLxMSEKTOl5aYI0g6HG6XKCAIy+B2Px9Hv91GpVA69lNZehPZBs9k0Ngh1XSAQQCgUwokTJwy4yvRedTaUyUfggcDp+vo6Hn/8caysrNyQ8dPB83g8aDQaWF5eNjaTAs3AZsox93w6cHsF0hOJBO644w6Ew2Fjw2iwNhqNYnJy0jjp1WrVvM56+9R1mUwG3W4XKysr+L//9/8il8sZ50wdZ+pogid2ozY25Oz1eqhUKqhWq3u6Jtoa4XAYZ8+exeLionHEfT4fYrEYEomEYx5DoRDi8Ti8Xi+uXr2Kq1evAtgE5kqlElZWVtBsNnH16lUsLy8D2NQTDFZ7PB7U63VcvnzZUY6gWCzimWeeAQDjkOv390JUGCWcv4PSXbZ9sd/v70YPj3X1WA5CCLJSFPxTAMsGkwiy2SCXAocazKR/y+8yO0hBK2WUKtBOEFIbL1JPKRNd/TO17RXw5HcobgC8x+Mx4CD9E/qjJPKsr6876k7r3GhWtbLd7YCE23zbGTYKpmtpl263C6/XawhYJKjRz2ZJUC3Rkk6nDcDO+WHmL8tukAy2traGbrdrytkxeMEx6/XqtfB1Ze1z3MVi0WAVnFMt7cLv8zp4/na77WDle71eRKPRLeuS73G9xONxnDx5EuFwGNPT0ya7m2VQdPw8vzLK4/E4stksBoMBarUams2mYVT7/X5TqoznHgwGCAQCpjSsBnhoT7LEXK/XM6VDt9uPuZ45JpIQA4EAFhYWkMlkEAwGkU6nTWkXPtMkQTBzjuA2SYksiVatVpHL5UwgWwFiEiJIuFAcgushEomY+0GfnPeetrJbIJ06zL53Nnvcba1xvfFZo80UCoXM2uNzo9djNxjmdTBwo36/Bnb0WVJ8YDcZcWNdfbgyBtJvAVEAT+uMuTl8/J/KZbuoGt+nEUFHiEA6u5yTHaYO5I0UbjJ2RJ3GBDc7piCx/EYmk0E6nXYYXgqSxmIxE9kslUrwer2m3iaZX7aztR+xo8wcs22oAZs1yvd7Ph4vGAwilUohnU4jlUqZgMJejqOR9Vgshk6ng3g8jmaziVqtZowQVczskE3GmZ0uxbRAGj27WT96fBqjjJLH43GkUilT5iWRSDhAK41OE8whG35iYgLdbtfUf9MMATU0X44A+EHJWOGP5eUsCl5SD1OfaaYURQ15fn+7YytgwHNowFXZaDeL6aJMOrKo9dpDoZDJJpqcnEQgEHCwlZQNx/RcbbBNZ073loPIIAOcIMTN2vcnJiZMf49oNGoAcgWNGHRXMIfXq8AByRIERyYmJhyf1XuhBAwyPGlPcq6Zgr5X4f0jg4/sc+pYXi8A44QzkMAMADrSClKQsabNxHWNb3fvWFLo5Sa7BffHunosByHKlNVnkkKAi3uWLQp62axq27clUK5r3GZ/8pw2650/Ctzr69uRzvRZUV3tViaC+y1Jb0poon+hYK2OWZ8rW9frXOnfvB77mt3mUvdLNlymT0fAnwHwWCzmYEAHAgFks1lT0kSB31AoZFjpgUDAwUCu1Wquc2qD0G4BAt4vBda5BrQhJX19PZ5mHqsfSrGZ9Sqcc2aHRaNR05vOLgdEIUiqvr5eC6+x2Ww6suyp53mPCHZHo1GHnce1pHXYSQJwsxvt+dSgDAMN1Mu00fga7z0Bd5aLpc3BTDHaC9TVxJN4LfqckozpxsLWQLf2NdAa/XyfNpIbM98tcKf3gOPi/2qH6hyqTW4/P9vhKqPsd5uUwePulvE+1tWHK2Mg/RYQKgX90YYIypjSn+3qdiYSCROJPnXqFKampkxTCCplbl6rq6uoVCooFou4ePGiiYbeyNqSVJzctEOhEM6cOYOzZ88iEolgfn4eyWTS4YwxDckOOFDJ0ghgKjXrWL/yla9ErVbD888/j6tXrxrle5CONY0ABaKvNzAxMTGBTCaDSCRiWOjJZBLxeHzXG7SbULHHYjHccccdmJmZgd/vRz6fR6/XM8o1lUphYWHBGBqarq3HImvE6/WaiPWoMi8KcE9PT+PUqVOIxWI4ffo05ubmEIvFMD8/bwwNpoOpQcKfaDRqWCvBYBCNRgORSATD4dDUfmea2fr6+i3fCPRmyFjhj+XlLNTVmiaqZbXUgWDZKrKSWMtVhY4JnahkMml02Cte8QrEYjEUCgWsr6+bVFvuscvLy3sup3U94vV6zThvu+023H777QiFQkbvKgBMh10Dn9zHmY3E9Ol3v/vdqNfrpllYs9nECy+8gGvXrt20a9uPKDAEbNaX1cACAWMNKrDsCFmAdrCXa0wZhMzUikQipm67rjU6xZxTzVbkcdjUnQCEMsJ2us5oNIqpqSlHDx8CSBoQJ7Ou3+8bUKPX6xm93u12US6XDcORrDxmn2n5gKMge9VZtl1mf38/x3M7xm6OM9bVYzkI0XKggLMUiYobUxrYBJT4w6CZgltKrtIa2Sr6eTfmtspwODSZozbzXQFzkm94fdynya5Wdir3UQZ+5+bmDMuX/bJI1up0OojFYoatXSgUzOv2HNlAvx1Qt4PoumdzbvV7BIiTySQCgQBmZmYwPz/vaKrKJtxahmViYsL0E1E/KxgMIplMotPpIJFIGPsjnU6j2WwiFAphbW3N+L8K3Nts+u2E94PXoOC6EhJIiiOGQSIgf1MIutp78sTEhGmIydJr9HEZ4B4VbNb1quPj+NkzZ3Z2FrFYzGRpaz1/lpCJRqOO+WImxHA4NGVWYrEYBoPNOu0secLrVHIcdXIikcDU1BRCoRCmp6eRzWbNPWTwW0u28npJVGNQntn01PFkntvsdZIhlT1u/w24k9l4P/nc6Ws2KK33Vde+Cp9dm4CiILyC3rS9+BxwP+C4ybKnPcZzuAU37LXmFlAYJWNdfbgyBtJvAVFWEjcZVeLAZrRMo3la99KWRCKBU6dOIZlM4r777sOdd95palpyUyTIePHiReTzebz00ksmbQfYXUrKfsUGHGKxGM6ePYu3ve1tpnt3JBIx1z4qKqsKLxwOA9iYz9tuuw3D4UYd8Hw+j1KphG9/+9tot9uOWqg36poOQgikT05OYmFhwQQXRs3FboXfj8fjOHv2rEn7fv7559Hr9ZBOp809oPPudk6uJ80uqNfrpsaZmzCdjM3XTp8+jXg8boD0aDRqyskwBXHU9Wp6WDgcRqvVQjQahcfjMYATm6iwHA8wVjxjGctY9id0nLSPiRtbjs7sxMSEqd2p9UuBzX1YG2KePHkS2WwWZ8+exb/7d/8OMzMzOH/+PJ577jk0Gg0T9L5y5QoKhcKhAOnBYBB33HEH3vKWt5hyYXT2mMZMcGEwGJh6m3SEGUxl6v3rXvc6BAIBrK+v4+rVq6b81lEG0m02Fh1hBvwJpGuqLwMJg8FmOT0F22nTabAmHA4b++jEiRNG/6+vrzvYmgSzCabbOpvrlin0rCW7U7k7HiMSiWBmZsZkpxFEZ1Cc/WyUFcnzAkAsFkMkEkG9XkehUEC5XDbAFNPNU6mUSRE/KkC6ihuore/pb1v0e7u1P+z7p98f2zBjuVlis6qBrVkRNlNdAWAFeKkX7XIbdukSPZ5+l8feyf8hkM6yoVoui/qZQKJ9bQrG2aAZM6PD4TAWFxcdZUdZloN7WDweR7VaNeVQ2CNC9zYbAFeGsn3+wWDgKFelYCSFNgkBXYKpCwsLhhyl5CQV6mO3Ou3c19kXrFQqIZvNol6vmzltt9umf5R+l/fDvmc2IUsJU9SHw+HQ+OokHCiQTl1h6wseT+8j54pAeiKRMEA69axmldtjtkFSAsg8N/3V4XCI2dlZNJtNeDyeLcTEZDJpgHTNkuea5L3mHHg8HgejmziQjosBbOrS6elpU64mk8mY2uhkvCtzn0ImPG00rjU+OxxHo9FAtVp1BC4IOtsgupa15bxxLrhO7SwMDaToPdS51ywYvTejbBktB8Xv8Fmh6J5jl3vi+HUN6nj1OdH1e7OyRsdyfTIG0m8B4QNsA+luKXT6GZuRrgy5WCyGqakpJJNJ03CC0UhG3xhlSyQS6Pf7RqkAMI0fbpTBToUdj8cxMzNjGPSMFBOE2IvoZktjgMzmfr+PqakpzM/Po1gsmhqhtmFzlIQObDweN01F91LOZSeh4QTAlP0hI51A9nYNVqhAmFJHxtt2YyQYwzpubJiqIICdFufmpGoEm8oNgDnuYDAwLINut2vWmxrtY9m7jCPnY3k5Cx0EOrWsQ8p9ymbTaS8SfXbUaSXwGgqFjA4OhUKo1+tGD2tN8m63a7J1yBq6GXXXA4GAcToZ5B4Oh4bVxOsCnMFvBhv4W+0dj8fjYATSmU6n01hYWEC73TaNw48agKhjUb1GxiIdU64JNj9n+rTNfKPTrA6dXbuX+o0BbK49BjgIBtjj03GqA9tsNkden65RnpeMd+pplgpkyZlRYAnZbkwtJ5ONzwxtAJ73uIo6/TdirboBUtt9dqyrx3IQosC41+vdki3NICngrBus39ltsEmJZfY6VLCZAe1Ra9Vmo2o2t45Dg5c2E34wGDjKfDDbiP4Ks860fBlBynA4bMBnlrUii3cn4XOu8+f2N4UAHst6sD+FZhMzS0xrs2twUIF7Pa6Chn6/H/1+3/iLwAYwnMlkHL3WtBSHzrUtyjLnHOt16x6mgWP72NQZbuC3DXaSca2lXJQQYTO+9dgUBrq307G02Vj2h+tL/Vo9Pu+LlhfhcdzY9cq8pi1BG5H6lGuB957Xaj+rBNA5PsVPeL20FWgLtNtt0wNgO9HnTO8jnzW9Nwp0u82rij6r+p7bWrMB992I3hsll3K/cAP59fr2AqKPdfXhyhhIvwWEUbvBYGBSwEOhkDEU+KCSTcQUbzaP0i7Vk5OTCIfDeM1rXoO3ve1tpm4pa2Jp6hT/Z2SdNbBLpRL++Z//GT/96U9vCMg8MTGBqakpJBIJnDlzBu94xzswPT1tmn3YTIHrkVAohEwmg3g8jre+9a141atehYsXL+I73/kOrl27ZkraHMXNKBAIYH5+HmfOnEEqlXKNIl+PkEXn8/mQTqdx6tQp07Wbyng39VQ9Hg9isZhhV165csX1M3x/enoaiUQCi4uLWFxcRDwex/z8PGZmZkzNOkbARxnftoEUj8cNOMD0cHYcj0ajWF9fR6fTMY1cjuL9Pg4yVvhjeTmLAuIsUcFgrTpLZA1pqQpd/36/36Tfzs7O4vTp04hEIjhx4gQmJydRr9fxd3/3dyadmk2XWfplOBzizJkzqFarWF1dxdra2g1/vqampvCWt7wFU1NT8Hq9WFtbQzAYNI3Bud+SCEBng2PXjDrNWmKTSaa+DwYD3Hfffbjvvvtw9epVfP/733fs30dBFHygXltYWMDk5CQmJyeRTqdNMITB+rW1NQAbNsnCwgK8Xq9p5KpsP9plZK4TTI9GowgGgyYFWQkVsVgMk5OTCIVCjnmyxzkcDpFKpRAMBk1m3qg5ZVmAcDiM2dlZw3KbnZ01TelY2qBaraJWqxmwnKCIpo5rTdZOp4NOp2POTyClVCqhWCyi0WjchLu4N9nu+doP43w353M71m6PP9bVYzkIod9B0It/KzilhBvN2nJj8RIoVKCJ600Zq9w3CWjRV1FAkEAewU+tb67gGgFeDX5rzwkexy6vyvNHIhGEQiEsLi7i7rvvNgAl2alaFoYAO/2SaDSKiYkJNBoNUzp1VC1mBeA4ZgW89R7w89zbaVOkUilks1nceeediMfjxn4gwGuzd22QUcF1t0CGx+MxxLxut2vKx+ZyOTz77LOmnA11H8dol/TgmmGGggKXNmuf47IBdq/Xa5q7axkRgrbKcqcEg0FkMhnMzs6aYAOD3iQP0l5jYELPyftg61ZdAyS+pdNp03Se5dTY5JXPEH1dLWnC8fM4bMTKcZCcwWvzer1IpVKIxWKYnZ3FiRMnEIlEDJmS12H37+GYNQDEZ4ts9H6/j2q1asZBe6Ferxs9zvWkc6I15rXxJl/jHNjZH25BIiUH8HlX/aZr2Y1wqFiCipue06AgA0fKrt8uOGSD+3ovt5Oxrj5cGQPpt4DoBkSnAths8qKKj0A6y1Zoaq7P5zPNJWZnZ3HHHXcYJW4DohqdTSaT5rVarYZSqYQrV67sKXq3F6GiyWQymJ+fx913342FhYWRqfLXI36/3yjJUChkOnT/+Mc/NopADYejJGTsT05OGqDmIEUN4EgkglQqhW63axh1jKTv5jhkhHc6nW2/w9R3prclk0nEYjHE43FTdoiO+G7Oyx/txM3U82QyiVQqZVj2wWDQsOaO4v0+DjJW+GN5OYs6U81m0zheWuKE7DYCy5qOTaGuZubY4uIiotEoFhcXkc1mcenSJZw/fx75fB5nzpwxeyPZRclkEtlsFoFAAJVK5absabFYDLfffjvm5+extraGlZUVB9DARmSAMz2eY6ZDyTRwOtoEMZRVdfLkSUxPTyMej+MnP/kJSqXSkd27qUeTyaQBMggYs5wYgfJut4tsNmv6vrD0DeeEtcXpCNLG83g8hlWWSqWMY0cWYCwWM7XLeUzbqaNjp6na22X9sWYugZhYLGaa1BGASKfTBhjP5/PmXErWIFigmWvARlPSSqVimoZWq1WT8j2W65exrh7LQYhmG6mvCTgbZmqvB/qtBLXcsqttUVBMwXGbMU3/gPY+QVr9DP9WYJW6R1nN3IP5ng2mswkj961UKoW5uTlTwoM/HK8yiBn49Ho3+kg1m03kcrkd/VsFa20msoKOKgQt6cdls1nMzc2ZmuijwGCKG2FpFNt6OByaxtaDwUYZt3Q6jWAwiGvXrplACPWarhE9rrLouXaUYa3n1+t1A2x5H20wXfUQhaVd2ION+lZLq2mZkkAg4AhkKNBuH5vnpn5l9hizovnbzuRQX1xZ2pwbMsaVIW3rd5LfWCYtHA6bEnMa1NZ54vXqePR+sF4+ADQaDfh8PiQSCVO2xtbVGtjR+6b3gvPG6+D65VrX67czHDWjfDvGtxvxTmVUBoN+nuQPLbOsGS32ddtzoPdzJxnr6sOVscV5C4gaEKyjqnWnNbLFemFs9qCKIxAIYGpqCplMBul0egsQv5PQSfP7/Yb5RsD+IGo9UWlGo1HcfvvtOHPmDE6dOmVA4lFRw4MSnj+ZTOKuu+5CKpXCc889h2KxaJhyR2FTUgODKWiqfG+EsKYtu3QDB7tB0+DgeVjShT9MkbTT1/YiVF40GlmCgAx7lp65kfM4lrGM5dYVdTioE9vtNmq1mqNxF52xUQ3BNb2YAUs6GUydnp+fRywWQyqVMq9TmNkTjUZRKBQMQ/mgazIGg0HDkj9x4gQSiQT8fj9isRimp6eNA1upVFCr1Uw9UAIeg8EA5XIZgLMOKq+XTDrOCVmCbFhJxrvP58Pa2hqWlpaOhI6m6Fiorz0ej7HTms0mWq2WydryejfKsJRKJXi9XvOeOqVssO7xeBx2idaFpUNJUoXf7zfsdYLvABz12W3GqJ0lYYs653Z6OJ3MarVqHE0Gs/lssPyR1+s1TdIAGCBMs8YIztAOUGBkLGMZy+EJSzooI1MBKAXngNFN9myWs/0dvqc+rb1vcf9T4FNBVVv4mvrT9AFI+uE+pcxTjofB63Q6bcg+WnZDyTz2tXLfjUajyGazaLVaSKfTSCQSxjaw50LnQVnVCigSWFWwk+U2stkspqamTEaUZgfxmPaer2C9W0mV7fxyHU80GsX09DQCgQDW1tbQbDbNfbKv0Z43riEtr6OEQv6v+oXzsxvSlepAzofWY9dxMGjNDAcN4NuAJ311DRor6E87j8EkEga0LJEC8pwTDdBQz7sxtTUQwSCOG+uc+JFegwLpKlrulp9jaT1dD3YWAc9lg9+cT841ny+9/zZWpdnm+r+dnbFbG4HrRoMsuu4VtNc1wbXtBp7rfbYDUDwm95exHG0ZA+m3iBA8r9fraDQamJiYQK1Wc2wm3NDsmm6UcDiM2267DYuLi1hYWDAO+m6BQ6aWN5tNU+ql2Wwa1tD1it/vN81afvmXfxlvetObEI1GTXrwjRQ67BMTE5ibm8Pb3/521Ot1BINBvPTSS4adfhTqpVPJs54oI8puqYAHJXTetXzQXsQ2km1RVgfZlJlMBqlUygDr2zUW3U708+xMHwwGkUqlkMlk0O/3kUqlTEplqVTa0/HHsinjyPlYXo6i+5I65wBM7Wv93CinlcJsIzZ21h4O3W4X4XAYd911l0mHpiPF48diMZw5cwbdbheFQgEvvviiAV4PwnDneaLRKP7Vv/pXeP3rX2+aohJcmJ6eNmDqysoKWq0WqtWqYal5vV50u13kcjk0Gg2kUinMzs466nSSHcY+F2xCyvmNRCJ41atehRMnTuCnP/0pVldXTSrxURHe42AwaBpuVioVABvpz9Vq1ZTwSafTKBaLWFlZMexz3ns69qlUCidOnIDH40E+n0e5XEa32zXsc+pn6upGo4HhcGjsG2ATtKDTqo4619NOBAky0gn+UEdrSYRcLmfuN+sAM/NAdUWtVjPp4ewHUK1WkcvljE3L2ukMLjEoMNYb+5Oxrh7LQUixWHQwhBXcUmYp9xIFiOlHcH/jZ+nvuvmVPJbuiQQf+T0CgypuTG1g029WMHC7fU9ZvtR5CwsLyGQypvQog77ccwmWstkoxz8cDh3ZvsViEWtra47eH9zblRGvorXDWXdbS6Jor6lTp06Zsh70g5TVPAp45GtuZTNs39P2z6gPMpkM7rjjDjSbTZMpx1IgnBMFyhVU5/qyg7s2s9dmhCu4Omp8PJ/WLdd68Qo26zxzjem5FIzlubU0i509QL2oPe3UPuR8cK3bzW+pp7WEkQK8XIN2j5RRQLNNBNH+PTbTn9cAwNw/vkf7TcsF673lPOjaYQkeLQfD91g5gOfUQBp/699qx9jXOWoPsOdEsTF9RnQdcH1okEnPw3PpsXRdaiBpOxnr6sOVMZB+i4kd/bSBdE1Pc2O5hcNhR0rZXkBJKmeyg/x+vyl9chBCB5ygLVPTbzTbmqLRUUbsE4kEgsGgcUyPimiU+6CbjI46H0vgAHAYLTuJHd22v6MRXxoLNED5W6Px+1kLatyTwcDz6PkOujzOy03GCn8sL0exQXSKGuj2+9utdxrp6iQDMOyj4XDoaFZGx8Pe55TFTdvhoK6XwU+WLFE7hA4S7REGod1qm5I1rU6yzQxTfUAniewtgtORSMTBSDoqovpNWWZ2MIVOPO+VNsHW5mW8tzYwpMFqHlsddFv3qsOuelW/t9MaVbabvVbVHuX7tFltIYii91bHzHPpuhgzua5Pxrp6LAchNhg3ipyla0Z1IrCpt/Q13cfs0h0Um5lq7wn2fmeL+tI229oGDu0yDMqCJQksAYAtAADMFElEQVRIS1nYPhozc3TfVn3H47D0i5aM4LVxX7T3az2mgnTcmwmi0r/WLLe9+FS6f9v6xp5je+5YtlN7YWgGsJ11YK8rt/PZ51QQ09Y1O61JfkbBdxv8tH9GYS07iT03OgfKDNf5sOeAn6e+tEXHqexwex4IvtsscH3fbQxuxwCc696NhW+PEYDDv+da4bn4On1zravvJjbjXefZFr0WGwi3+zjYa07nUnEDt3O6nd9m7G8nY119uDIG0m9B4abmVoZiu42dEXSWAtmr6IbMKCdTc69XPB4PpqamcOeddyKbzZomVgddE303QoXv9/uxuLiIV7/61SgWi3jppZdMQ7DDlFGgzY0WGieVSgXlctnRKGc7GQ6HqFarKJVKqFQqrmx2BU1Yf53sNjIrDuJ6qZCVAU/jUuvhjWV/Mlb4Y3k5iu3g2H9v99qo4yn7jHqwWCyiUChscaQVJKdTRDDU5/OZjJtyubyr5kY7SSqVMo2q/H6/aWxOZhL31MFgs7b3cDg018Fa2iwDA2zol3q9bsD/bDZrbB0y12q1mvks52Bubg6DwQBLS0tIp9NoNBpoNBp7zpq6EcIgrZYno5AxxtrxBJ4BGF0Ui8UQDocN86zf76NSqWB5eRkej8eUa/F4PEgmkxgOh8jn86hWq+h0OqhUKiiVSshkMlhYWEA0GnU465xDsuUHg4E5PpmCo2Q4HJpxsQY868W2Wi1HkENZggSMeJ/JmmQGA68zHA5jYWEB/X4fhUIBpVLJlJKJxWJoNBqm1OFY9i43W1d/4QtfwGc+8xksLy/jnnvuwblz5/DmN7955Ocfe+wxPPTQQ3jmmWcwPz+Pj3/843jggQccn/mbv/kbPPLII3jxxRdx5swZfOITn8B73/te8/73v/99fOYzn8FTTz2F5eVlfPOb38Sv//qv73nsYxktWgLDBoe4v+jz7wa62+UmRgGNPB9/KyOdY+B37cCxG2iqY9TAIuCs+a7MUhUt88Lv29nges0KRvK7/DwDyvy/3W47Go+61ZvWcfAzGgDt9XpIJBJIp9Nm32RJF2XwMjA5Cgjn2O3yGradovNgC7ONer0epqamsLCwgFqthnq9jlKptOWec3xqP3AuGVjV+2XPr9t91mAuj8WsB4K+uiYVCNYAuN5bXRt2eRLqM2ZMaOYE7w9tG+o43iO9PwDMsUjq4/+tVstkoY3KKFC/V8lj2wVF9Fr1GVXbU6/d5/OZ7EsSOxKJhBkPsyooxJHsMm28B3rvSMrQ50aPo9dJ8oFmtOh12f79djY7v6e2NNeHPi87rUcdr+4XHs+42ehxkDGQfouK/cDuRmwgfT/ApEa6FeQ8CJmcnMTdd9+NbDaL6elpRCIRADcfMGZTlsFggLm5Odx1113I5XLI5XJHAkgHthqB+tqNOh+Nj1qthlwuh+FwiJmZGdNIdJQMhxuN9/L5vCmRY4uyJzS9zmakX6+o0ajlAuig87kYZQyOZSxjGYstN2KvUAec+1+xWEStVjPZW7bzp9+lo0UHdmJiAvV6/brH5fF4EI/HcfLkSQOGNxoNtFotlEol9Ho9R+12bXrO8YZCIROEjUajxlljw6pUKoVEImFK05DRTt1BUDoajWJmZgZ+v9+Um6NDdxSAdN4nrUcLbII0bHDNbC+C4kxzZsPZZrNp5rbRaGBtbc1xvxmQ4D2mE1mtVlEoFEyDcAXSFQBhYGM4HKLdbmN1dXXHmvoEDNg3hfXxCQgFg0EzplarZcobMZji9/vNGmb5gXa7jWvXrplyN/F43KzlRqNhvhOJRPaVYbEdsDaWGyff+MY38NGPfhRf+MIX8KY3vQl/8Rd/gfvvvx/PPvssTp48ueXzFy5cwLvf/W58+MMfxl/91V/hBz/4AR588EFMTU3hfe97HwDgiSeewAc+8AH88R//Md773vfim9/8Jn7zN38Tjz/+OO677z4AG6WTXvva1+J3fud3zPfGcrDCMl0KVNnAOIFiFWV0E5iiuLHV+Td1HX1QZbLapRlsH3m7/czNj7KBc3u/sQEuZdArIKllLfgdZv6wt5kNpBMk5bXaYK4eS4/JzzGwwSCnlshUgpoNkmoAwQak3RjLdjDBbV4JQHI/n5qaMoSsK1eujAQKh8OhCcpqMIFrxgYtbeKBXhM/Y49dAy/Kpta/7fvtBswSqNZyLzo+Bdr1nAyYlEolrK+vo9frYXJy0tw/noM2FAFzPlOtVsvYR9uVarFrpduZ7PY6HhX00r/1WaTdFYvFzDqOxWLGRmAPMo6Rzy+/ZwetdK3R1qA9aa83jpWBEgaUbBtG58O+PjdxA8LtYIzOh65BXSN6HDvT5SCILWO5sTIG0sdihJuwnUa912PoJk4GlRpF+rndnoMgfyqVQjKZNPWwD0u4ibMTe7fbRSAQ2PV33ZQBxVbGe70PVAC8D4yUcnO/EcK1w/VDx5jAjKbdU7hGut2uqe2vtdTsa3JT5DdC1Biwf7TR3V7W71g2ZBw5H8tYrl/oMDDjKxqNwuv1GhausqX4t35XnW8A5lgHVQ6DLGvNSCLDiL+BTQeOThfZWWQz2w3BbAaj2hfqSPPa6Wj3ej34/X5MTk4iGAwaVvoocQNUlfmo4M71lHRTHc3an3odCk7wM7SrFBRyA0220/V6HQwcN5tNw5oCNuvd8jqpm7VW8HaiAAkAR4BcX9NapfweWWh6jyYmJsya4Nhs5pn9w/PsVlffSBvpuMnN1NWf/exn8bu/+7v40Ic+BAA4d+4cvv3tb+PP//zP8alPfWrL57/4xS/i5MmTOHfuHADgla98JZ588kn86Z/+qQHEz507h3e84x14+OGHAQAPP/wwHnvsMZw7dw5f+9rXAAD3338/7r///n1d41h2L24sVQWR7PXi5hPZ7FhlENvnsety8zy7EQKUbmt41H4/quQjx6xBSbsWNq/J3sP1+/bnPR6PaahtM/YVsHObG71GfpcZvrae1fHbxxgl9n3a7jNaTkR1O/uhdTodRzkbe+waaLGPpbaPMrztMiMUDQ4oyO3GQqbvagPZ+hn7Huo57HnQ61d8gN/RDEINsACbQQG9fn6WIDxtBrWXKPo8boc72ODwdqJBInvd6lgVB9HPKE6koLYNpruJPrvbrUXdJ26GLz8KmB+1L+3FDhn71YcrYyB9LEbI7gqHw4Z9tBdh9JRNw8rlMtrttknNZaouAJOutBtAncpwamoKr3zlKw2Yftji8WyUm7nnnnuwurqKp59+etuNm0qOkVYCCjReVNkzqkoFuJd7we+RsdBsNk2E+UY5ie12G+VyGdVqFWtra1heXjZOcCQSQTabxeTkpEMB1mo1rK+vo91uY2lpybDc3NLFNQKuzXLVoT4oJ5iKnIx0bcbDEi8ej8eAGmPZvYwV/ljGcv3S6/VQq9XMPnX69OktfRzIgiEwS7CUzUgzmYzZM/P5vGEuHYQkEgmcOHHCMKYBOMqxkPkEbJQpATabrhIkL5fLxmm2G0lz7+WeoMdTBn6/38e1a9dMKZA3velNKJVKaLVaKBQKrmNXB0sdatYnJ/AwMTGBRqOBSqWy7wCEBgtyuZyjobqy04fDIer1urGvWMKG97zdbhtbgXretkXUkWNQmLZev9/HhQsXHNleWsKO89Xr9VCtVrcwE92EmXtkGRaLRQQCAdMgvNPpIJfLOVKiOaZgMIhKpYKlpSV0u11jP/D8ZGJqKRy7zBvHr4zO3chYz2zIQehqNs2lELBT6XQ6eOqpp/B7v/d7jtff+c534oc//KHr8Z944gm8853vdLz2rne9C1/+8pdN74AnnngCH/vYx7Z8huD7WG6O8LlWwEqBMTdw0QYf3YAz+/m3S13wu/Zr/H9UM2+WQbHZ4TY4p40euV8qkKuAZKfTQbPZNI2StbEz54IAKPWaBnB5nVq2Jp1Om6wuzmO73Ta+IzO9bGCSOp9jjUajmJycRCQSQSgU2nIf6E9SqF/cAgp67+xAgc1+1uxeBWiHwyHi8ThmZmYQCoUMO11L2eh9tLPglfik463VauZeqo7UIIY2pLWBe/U/tUl3OBx2ZDvo8dwC8jr/aqtobw+C/yxBov68kgC0nx0DIZ1Ox5DScrkc1tfXHQEgzhXPyTHyPHawmnPA79r3lqIBCjvwpXOj2eU2o99eQxoA4Pip49U2cwuoj3pdf/hcaQNTHfN+hPdPhevKLkGk57PnQOvC7yRjv/pwZQykj8UIHW06bHt9wLhRcNOjs6uKk4qaEV01ONxEI5jhcBjZbNYw0o+CRCIR4+DR2d/NtWjndDWogI1NnXOpTIDd3g81CqgYRzEsDkoY/aair9fr8Hq9KJfL6HQ6pvEdr8Xj8aDb7aJaraLZbKJaraJarW67Fmzje1Tk/CBEwXRNedOfo9Rc9jjJWHGPZSzXJ3SUmdbMWuQM9ukzRgeN+y+BglAoZPZi1q08KEZ6IBBAPB43QKw6uwpq0DbgfquNJBlQ1c+og6VsPLfUbl5rtVo1wYOFhQUzR9uJMrU5lz6fz+gDgiG0YfYr1GV0fhkcoTOmjibZ2xrwUEeT8+bGAtPz8TXtBTIcbvQ28Xg8jn4gBD673S5qtZqxJ3bLTKTdNxxulG/j+Tl3zWbTlHmhbcixsd47iQChUMiAUupccxy6DngMgjXbMdjGMlquV1efOHHC8f8f/MEf4NFHH3W8lsvl0O/3MTMz43h9ZmYGKysrrsddWVlx/Xyv10Mul8Pc3NzIz4w65lhujNgMVGXT7oaNzs+5geFuzFoFgBXYtD9H4X5p75PK6iawaoN0bvrSDUjW5sz0H2yQVQE2m0mu+xqvgWWv6HMrkKmgJbDJmKe+0OMweMvj2WKzq3fri+o51Pfja3Z5Fb1PLDPW6/XM2AjeK2Zgry3aF0oco/2hDHINJNjXaYPIKsoOJ1vevtf2nOn1ub1u/yixjutYcRX90UCJ6jv6/sRzdC2oDWaD3TYxze36t9OlbsEC+/lzW8+jRPcJXd9qz9rzyfnT0m60qfR8+izTJt5OdkPUs69fX+d4R5Vq0cAZr2UvMvarD0/GQPpYjLTbbdNEKpVKoVqtot/vO8BeN1EAmGyvbDaLX/qlX8JgsFHHUwFJAKYBRqfTMWy4UqmEtbU14yRz06ZTx/ptmoJ22ELWFaP5oVDI1K9TZcI6n9osk+w2G0hXRjodTbL9R7EobFFW3+rqKhKJhOmIfpBCJUnGRb/fx+LiIiYnJ5FMJnHq1CnDeJiamnIYMJVKBXNzc2i1WlhaWsK1a9dMFJ3N4AimqCFBlj3XEI1zNa72C27QSCJgwEg1f7gm7WjxWIntTsaR87GM5fql3+8bgHh1dRWXLl1CMBg0rCvbIbeBhVqthsuXL5vPLS4uYjgcGr3EIK8K9Xun00GtVkOpVBoJJExMTJhGmMCGbUGg3+v1otPpoN1um3H5fD7DOKPNEAwGDcuOjo46mXSMlBltgwTKOtpJ99kOLQCjq+2gN4/Fhp/U+aqH9iKDwQC1Ws2cLx6PIxwOo1AooFqtwufzIRqNGsad2gFkHpIZqcxsHQvZ/mzsrQz2SCSCqakpU3ectct5PrIpWTaIjjrBdTdhPfNGo4Hp6WkTfCBIwmC0lnXRJmAejwfZbBbdbheZTMY0JuP6ITgyHA4NE5P15EOhkNHZO9VyH4u7HISuvnLlChKJhHl9OwKMGxi1nR3n9nn79b0ecyw3TnQtMUPKza9UgE3ta/2+Gwg3CvhUfWCPZ7v1TVBuMBiYYKAeR9nnvA4F13l89n9ipkQ6nUYwGDR9KRRY03KoqscJ7kajUczPz5sx2fNAv4UBZOrZZrNpdCPnnb3QGFimf81SKtz7FQDWJpk2u92+b/o8uoHWNrCpgQubdOW2Dvg6r0PtA63xTSJBKBRCIpFw1KO377cCsTZjWL/HeeX9sxnp9jj1OLqG7DXMYAHvIbP2PR6PGfvk5CTm5+cRi8VM+TziC8yiJhGB95flUxmA5zjUbuI1ab+S4XDoCEbbdqSWKdqJBa5rmb68st/1fzcgWn17+xlzE80q0TnnOh4VFBuV2bIbvaHrVrMidLwqek1ux98tAXLsVx+ujIH0sRhpNBp48cUXsbS0hHg8jrvvvhvdbhfJZHJHIH04HKLRaGB1dRWtVgsLCws4deoUgsEg5ubmEIvFHMpUlf3Pf/5zFItFPPfcc/jHf/xHw4hnIzQ6lYlEwjjnR4Fh5PF4jHPYaDTM+Oj407mlQ8rO6FR2o6KyGhWu1Wom4FAsFkduyG7S7XaxtrYGv9+P2dlZTE1N7bqO+25Eo/cs5+PxePCKV7wCk5OTmJycxJ133ol4PG6cc71efqfdbuPixYu4evUqCoUCfvzjH2NlZcUYgjwHGZj1et0w2JnirmmM+w0WqJHEWu80LHgP9J4STB8rorGMZSw3UzqdDgqFArxeLy5cuICf/exnRrcQpLBBXXWAi8Wice4HgwFe+cpXIhAIYGZmBtFo1DT7VDC2Wq3i+eefR6lUwpUrV1Cr1UaWggmHw0ilUgiFQiiXy6jX64jH4yYQXiqVjLNGh50lS4bDoXHue72eYcsrk4rXp0FNXh+dEupXgsp2wNoW6g51GplxpmVz+v0+arWayULz+/0msEH2124D3pRer4disYh2u41EIoF0Oo1kMmlsIRIJkskkqtWqg+HI2uVa6zYUCjmc1263i3K5jG63i3w+b0qqMNCQzWbxmte8BslkEtls1pTgaTQaxsHm3xMTE0YXs6SMmzQaDZw/fx6BQAB33nmnqU/PzEJmCbBMUbVaBbCZbeDz+bCwsACv12sAg8FgYEDyZrOJSqVi1ghr4TO4wX4BB1n7fyx7k0Qi4QDS3WRychI+n28LU3xtbW0Lo5wyOzvr+vmJiQlks9ltPzPqmGO5MaIAFv+3s0QUSFK7WslFu2m6p+CzCtnJNoCo57dF9YjbNbFBNAN5ylbmmPnatWvXkM/nAcAAoJOTk0gkEg59oVlFyqbn9ROEV6Y1GeUKcrbbbUNIKhQKWFpaMrqaeisWixlwOZlMOppe85g2451+uYLdbqU5FCBUgFuFulrvGfdqO9vXBtL5PwP2kUjEof95DTy3Zilpqa/hcGjOqe9zfHo+rU9OW4V9O8i6twFee9xuwQWbUU2ig5LTvF6vycafmpoyfrUSCxkIKZfLiMfjaLVamJqaQqFQQL1ex8WLF1GpVAxBTUFmZsR5PB6ji3ktxCoIqJNgNhwOjY1hByCU5MDrstn82h/FJq0x601B7e2AcHuNuK0dCteWHQBzE11nPLYy4d3uHY9PnGAUAK/fd/vc9YDjY7m5MgbSx2JkMNhsKFWv1019Q9bxdtv8dePQDSoSiRhFnc1mEY/HjWLlZkSnh7Wz6Tz6fD7j9GnKEh3powCiUzR1jo621h2jA8/3eR3aeNO+Hp2jQCBgFJDWUNuNUcn7WavVTG1cKriDZOXQcKHhlclkMD09jUwmg2w2i1gshmg0+v+39+5hdlbl2fi998w+z57zJDOThJBArEDE0mApaEGr4rHiqdBqPfxaDxRRIPZDKXoZqEKxvfyoVbBY6qFW4fsutEo/awmtRilUFEEgYhJgkkkmc97n8+n9/ZHeK89e8+49e5JJ5pDnvq65Zmbv97DWete7nrXu536ehUgkUndfqh+pcKTajYtsj8djys2JFidb9g8nOa2GjNmQkznbuHNhL/PZKo4N6jlXKBYHXLhls1nE43GUSiVDgsuFGxeXckHOsbRWq9Wl8KAzOBAIGCIdgFk0dXV1oVarmTG9ra2tTkUHHF0kynztvIY9B2A9APfFjE2MuKnT5Jgif8vFrFQPShthL4Tkwl+q2WzlFe0wzwOObqgtCZCFkumS+LXrJMto18mtDnzmsv3kwo3zqra2NkOmdHZ2IhqNIhKJmGPl3KZUKqG7uxt9fX3IZrPI5/N1Tgw+V95Xzg1l3+OxfBYyJ718nrLt3Z61VLfZJNSxPgfFEZwsW+33+7Ft2zbs3LkTb3nLW8znO3fuxGWXXeZ6zoUXXoj777+/7rMHHngA559/volAufDCC7Fz5866POkPPPAALrroooVURbFIkIrPY+lX9jjo9p183xutAVgOW3HcSBEq7ymJUI6z8npyjJLq20KhYByTdBZzfSzPlWOjvA7vS4UxiXRGIUnHKYn9YrFo9s/gfWRkuFQLSyey249sJ7uejdbjbiIjtz7gZrNlG7vZN3uOQfGAfG7SXvM3yXUey+fTCG79TCrS3fKJ2+fxbzn3sdtU/i2fO4UAjECo1WpGlMZIfz5TRu6Xy2VEo1Hzt+McUZV3dXXBcY463m0byb9ZH1mvRu+SXTf7b7vdpPpczgVk28n+zntL3oLt4jYnbPYceU3pAGo0Dsm6EPKezWA/T/s680GmoWl1nNR19dJCiXSFQbVaNeqvvXv3wuPxIBqN4owzzsDQ0BACgYBRp3PxQrKWXu7h4WETFkailPnNJGgEOzo6sGnTJgwNDaGnpweDg4NIJBJ49NFH8cwzzyAYDJrUKX6/33WAW2pwIUgymZ8xLU1nZyd8Pp8hKOTA32wSwrbjAr1cLsPv95vQq/kGwHK5jPHxcaRSKZNypVqtmjC+44X06J599tnYsmUL2tvbsWbNGqNw6OnpMcZebgzk8RxR8zN3ejgcxvr165HL5XDmmWcinU5j//79+OUvf4l0Oo3R0VFMTEygWCwaZX40GsX09DQKhQI6OjqMCoGb27XqMJBKiHQ6bVINjY+PY2JiArFYDNPT0ybMXpLraoQWBjX4CsXiwXEcTExM4JFHHkEwGMTg4KBRgvf29sLv9xtVs4y2iUQieOELXzhHCSM3EstkMnXqrmAwiE2bNmHdunUYHBzEunXrkM1m8cwzz+DAgQPGHtgpRqQTnA5JhptzgUU7Ih3uTC3HaCCmHWEqL7kgZFvIBRhQH2LLOQmvxQWqzF3LOQtTywBHN0aXJDyjyqjMbmtrQ3d3N7q7u1Eul5FKpUxbt7KBa612dCPY2dlZHDhwAIlEAqlUypSJ6e8433IcB5FIBNFo1KQdY6ozOjASiQSy2awhzQOBALZs2YLTTz/dzL/YroODg3Vh4dJGS+f0pk2bTKj4c889h3g8blSPhUIBU1NTiMVi8Pl86O3tNTnXGYE2Pj5eFz3G/mY7gFgfAKYvOI5jbC8Ve4xsTKVSSKfTmJiYMPV2HMc4PVSVvjCcTFu9fft2vOtd78L555+PCy+8EHfddRdGR0dx5ZVXAgBuuOEGjI2N4etf/zoA4Morr8QXvvAFbN++He9///vxyCOP4O6778a3vvUtc81rrrkGF198MW677TZcdtll+O53v4sHH3wQDz30kDkmk8ng2WefNf+PjIzgiSeeQG9vL0477bRjqruiHrYTr5W9hex1EQk44Kg61D6O95GpnNyIYOnwk0SXnW6DpB3vYxNkcg0g1cocm+lg5thMIjSfzyMUCuH000/HmjVrjPqb9lEqWu2oKvlDu+BGfHM9zv2pgCOpveSG2WxHSTbzngDMmkiSnySQbcJbOszd1lxufUCqlqVTWG7mXa1Wzd8y7zntEm01I62bkfBsRxLLXPcxRR7rR+cA16vS9jG96uzsrOFKJD9BgZx8LnLTcJKj7CO2XWJbk/CPRqNYv3593bOJRqNYt24dAoGA6W/SlpNzqFarxtmdz+exceNGk9aNa+np6WmzCTjvT4e+1+vF7OwsarWaEVmw3WxHv03+2u86bX+pVEIymUQymTQ2m+p7ckLBYLAuvZHsU7aQknMrmTbYrf9J4QbfW0ZCSqcM7yH7spyncD4hHQ4SkhNxE33YDgf+dqvnQkSPuq5eWiiRrjBwHMcMTKOjo4jFYgiHw8hms8hms+jo6MDQ0BCCwaBJecEw51KphOHhYZxxxhmIRqN1BhmY66GVyi4a+oGBAWzYsMEM9M8//7zxtpNQbnVgOVmQAx/JhkqlYhZvXKTSQLSSWkW2m8x3SgUgvcrzDYDVahUzMzOIx+MIBoOIx+N1BP/xtqNUkpFYYQg6y9wqent7jXFmSqHdu3fD4/FgdnYWmUwGhw8fRrlcRiKRQKVSQW9vLxKJBKrVI5uSMU+9TP3TqhHihCSXyyEej5u8hpxoxONxJBKJOhWCKt0WDjX4CsXiYmZmBrOzs/D7/Uin0xgYGDBpxLxer8lnLtVk0WgUGzduREdHR11uSkYGkagEYNRvzN3d1taGNWvWYHBwEKlUCjMzMxgdHTULDrcNmmknGDVEAprXJ9FAFTw3ypKRQB6Px9SJocdSJSTHFpIA0hnNlCfBYNCkZOG4D6DOAUBHMCO6eH/giE2ms8Lj8RiihCHmuVzO1JNlmW/s4tyLC83x8XGzYTdtKQl0GQHA0Px8Po9kMjlHKUnyPRgMmhzonZ2dxsnCiDFJHDD1C1OqMH0d91jp7+9Hb28v0uk0fvWrX2FmZgYHDhww/S+fzyMej6O9vR1dXV1mgc/+xLQyfN5Mfce5EetHJ0etVjMbmMtnLFMOUOmZSqUwOzuLWCw2h0xbiMJLcXJt9RVXXIHZ2VncfPPNGB8fx9atW/H9738fGzduBACMj49jdHTUHL9p0yZ8//vfx3XXXYcvfvGLGB4exuc//3m87W1vM8dcdNFFuOeee/CJT3wCn/zkJ3HGGWfg3nvvxQUXXGCO+fnPf45XvOIV5v/t27cDAN7znvfgq1/96rFUXWFBOsYA1JFkjfoJx3aOoTbhKNN52ee5EekkwuWYweN5PVvRzPMbwb4O7y3ToDCqmmvCYrGIVCpVt3km06BJe0QHtiSVpQ1jzm87jZuMWg6HwyYFRrFYNDmyWT5b4W+Tz24KZUbqSmJQOjbchG7y2ct7kigk+cz5A4lZv9+PUqlklPdyk1a2GY+z96ySUdv8zK2cVO7bDhYSq9JxARzd8DuRSJhUKP39/ahWq+jo6DAEOMspI9kkqSr7s/wty0oVeW9vr7GR5Bg6OzvR3t5uuBmmdmFkAu9JB0+xWMTg4CCy2SwmJycRjUbNvIbns2x8zl6vF8lk0ojvOD9yi/i2SXSbSGfqmGKxiEwmY35yuZwREMjUfZy/2M6rRu3oxjXYfZDl5DvG90Rudm+DdWUflNGddn92I93lnJvllPMz2V/dIgxbha6rlxZKpCtcwYW01+tFLBYzyvBcLodAIGBUZe3t7SYsOBwOm8Gw1QHBVsPxPv39/RgaGjJGaSWQlnLCIMPCZSjdsULmWqdarJXy0DDm83lMTk4akkQqB5vlv7evxboBR/PTcUFMQy832VhoHVk2j+fI5iobNmxAOBzG2NiYyQnsOI7ZjTyZTMJxjuTvjUQiqFarxmFhe6mlAQaOTn6r1arJ/8rFOAn12dlZpFIpoz5oNBFVKBSKpQLH+Vwuh3Q6DcdxDGnMRYskRuX4TCWWDOOVP3LhJBcQXDREo1EMDAzUKbpkqLFU8HDBD8AQtzJiySY+7P+plKrVanVqIrl4ZDnl4s5OaQMcJU8kQc5NNmX7yLLYbeY4jiGA5eJPEioLBcl02kK2TXd3t8lXCqBuQSaJHM7NSKwwAq2zsxOhUAjRaNTM1Ujgy8WhLDefGR0G7e3txonBjWGpCly7di3C4TAmJibq0rbJ/VOCwaDZ56ZWqxmFoLTPdJxwfsEyZbNZ89zolOEiPJVKIZlMms1P7fQIaquXP6666ipcddVVrt+5kdqXXHIJfvGLXzS95tvf/na8/e1vb/j9y1/+cu0bJxg2kWqrLd3aX6bHlLDHefs7niuvIVOAAPUpXSTx22x9JstqRz3Z5WY9gaMRXlLtyrE6nU5jZmbGiK+k2retrc1Ef7P8bilZbNtktycdpEwHE4lEkMvljBrbLjOJT3ldOYbSUe5WR/tY+cPv3AR19jxDEqSNxnCb4Lb7UCuOa96X/ALbhLZQkp4sL0FbWyqVTMQ350NsD+6dIlOJ8Npy7Wl/L8UIHR0dxtEtIxTc+p9MbWb3UY/Hg0AgAMdxjCM9EAiY9a3H4zGEey6XQzKZrFtHezweI96TEW/yObK9pLOK8y4S/pwLpFIpEykphQZyridFEm73sgl0yb3IfiL/lvNCW+zp1ndk+8l2lfPOhdgP2Z/se7uNaW6qd8XygxLpCldwMZLL5fDkk09i7969xihzEPV4POju7sZll12GF7zgBSY/67Fu9hgIBNDT04NgMIhzzz0X7e3tSKVSOHDggAnjXo7gACvD+thOJDPskKRWISeHVHqXSqW6SWGzgZyD8OTkJP77v/8bwWAQL3zhC/GCF7wAoVDIbC43n+NDTrxImIfDYZx++ukmZQ3reSweVVlfEgenn346enp6kMlkEI1G0d/fj3g8jt27d2NmZsaUgRMNpngpl8tGmU6Fo1ueVbm5SSwWQy6XM+q6bDaLffv2Yf/+/cjlcpiamkI2m60jg5Zrf1zOUM+5YqXDdsgtF1SrVUxPTyORSKCtrQ3PPvss2trazGImHA5j69atZtNH2izuh8KFsr14pGOUYybVdlzwn3766QiHw0gkEti3bx+y2SxCoZAJW4/FYsYxT0KVqniv12sUzxJSySU3uZqdnTUKP6n+YpQVbZkM1ebmnADMuVSyycV9d3e3UcBms1lzjiQvWPZcLodcLmfCqGl/+MPvj8Xhmslk8Pzzzxv7xqi2/v5+9PT0GKKcZWP6GDqXufjyer0m131nZ6ex1bSvtVrNOI7ZjsDR8HXHcUzUIfOjA0fUwSTfmcZvaGgIfX19pt6JRMKQRalUyvQh5uqlulL+zdQuXFj39PTgtNNOQyAQwLPPPmsi6pgbNp/PY2JiArlcDhMTE5iamjKh+ifDRtsKyNUGtdWKxYBNeNkEqBvBSshxWxJQNrkpyTU6CElI2gSodBq6ldWt30u1tJ2aRjqV6Sy06yGPBY6srw8cOIDR0VGEQiH09PTUpW8JBoNmTW07pWmP5JrGJvQkURwOh7F27do6xy9Tt9VqNRMRbqeVkepjtjfXsxL28+R1bILUbY3JetiqYKZ+k3m6Zf1kO/C5yfa17+P2LNgXyFvQXstNYuW5NtlfqVSQTCYxOjoKv9+PgYEBDAwMIBAImA2PpUNAEq/8kbaczu1QKGTmaF1dXejp6QEAk+JO5oOX+cVlCjlJOPP/7u5uOM6R3PxdXV3I5/Mm4iGbzWJiYgKpVAqFQsFETXB9zEhArv0pHmCfl+n7CoUCstksarWaiYJIpVKYnJxEoVDA4cOHMTU1hVKpZFLVyXJLR5s9TvCedgRJrVYz9ZfiQDlmUKhnz/t4fUlYSzW5rKfcV6bRubIf2vWy3wPbEWA7kxa6yfKxQG318UOJdIUrpCFxy/HJhReNQFdXl9k9+liJVKlk7u3tNfnWx8bGzICy3F56lsf2ANtquVZU383Aa0hPaiuLN5arUChgenrabO46ODiIavXILvBM89KM6JfKNBohv9+PaDSKrq6upkqRhYD1A2A2KM3n81i/fj0mJyfrFOn0nNdqNaRSKaNeYwg4Db9tjGmkuHt5qVQyHnOZty2RSCAWixkvPVV+qkY/dqjBVyhODEh8yoWJBElwW53HnJjA0Q2gOX7ai1+p6uYx0Wi0bpFDEoMEOcdsjrmSIOcYzXva4dhShUUCn2Vys7FUbHPhJBXisuzyPIJpydiOJB7kvQimjmFKEiq2ZehwK7mA3UAyWJLkLKdUA7Jskrig84BEg3QocyNR1pt1YF51kk6ShHYLZWYucuZAJ+Hf1dWFXC5n0uIwj7pMBRAKhZDL5UwflTlpWUepwiLJIVPicTFLhwZtdiqVMu3gRogd79zEDY0UtasBaqsViwG3vkA7ISOCCKlstdc5bgS6PEcSXIzmscvRTIXq9Xrr0njZZZLqartutiJZ2gy+S3KjRUa5BoNBY0tom5mWRV5TksdyrWWX1W5vkrOMHMrn80YJzBQXPE/aSKn65j3knipu9+UzpR2UznkbNrEo17jSZvDadiSB/Rzl+m6+tai8NtuWdtZWu8v72Qr4crmMTCZjbGAkEjHzHLkHC0UHdpnlupptwmtRJMaNv2kXpfPWXtNK0lWKIACYeQp/6MDp6uoy1+M8kKIJrnu9Xq+JQJMOBjmX4t9MDVOpVExfy2azyGQyJlUe89IzwtuNHJewHW28pxxH3MYSOVeUKQbZdmwv+c6wDHKdL9vVjpzku9KqM10+a+kAs/vsQpzzS2Gr77jjDvz1X/81xsfHcc455+D222/H7/7u7zY8fteuXdi+fTt2796N4eFhXH/99WYPFOK+++7DJz/5STz33HM444wz8JnPfKZuA/JW7us4Dm666SbcddddiMfjuOCCC/DFL34R55xzzjHVsxUoka44JnBhNjAwgM7Ozrq0LscLj8eD3t5esxCt1Wom9YZUW52IhdFCQePFnK4kcT0eT93ADTTeWLRVSKMgyQU37ycHeZlqhaGBiUQCe/bsQTAYxPT0NMLhMAKBgMmDJieNjEzg5M/v92Pr1q0YHh42SvTFeOZukAqT9evXo1ar4cCBA9i3b5/pG5OTk0gmk/D7/SgWi4hGoyiXy2YSQrWgTDHANuMmLMViEZOTk8hkMpiZmcHIyIjJJZdKpUxewEZqFUXr0MW5YqVjpfVDju1MwcY0bMDclF1UK8tFBm2APJ4EOhXtuVzOLAqq1Sri8bhRVcfjcRSLRaNk4oKJ4ykJaFsBJEkR2gE6pLlAlWQ6navA0cVxsVisc8LbTlRGWXk8R0KX6Zilkor3YTvabVOpVJBKpQDAzFFqtRp6e3vR3d1t1OlyQW3DLcKB5aR9KhQKePLJJ+s2daXiPBAIIJfLYWxsDJlMBh0dHeju7japariAzufz5jlzEcxFvM/nM/nvqSZjOajgl4tJtgWvRQK/Uqlgy5Yt6OzsxNTUFB599FFMT0+bujG/7MTEhMmpzrQ1vb29hqhnu1UqFfj9fhw+fLjOae71ejE2NobR0VHjUKeS3g5vZxvzZzHV46tRiU6orVYsBmwFJ3BUMU3I9VyjftdsnSFV727X4dgl02rYx/J7QpLitnrVbS3Av+WxMnpbEo90HPNvjsWBQADhcBjRaBQdHR2IRqN1ayyOxVyfeb3eOvW3VL5KhzLvx+uTNKXjnVFHUhnuFp0m6ygJddZNit04H5DHukWr0x6ThAWObIzKsnHTbrfUHW7Xsu/ZqD/ZfYCfyR9J7tvkunQEMHqfm2yXy2UEAoG6+YN0CNPJINf0vb29Jv0aj+Wa1X62UpHO/igJeUKeS8g9SKLRKIaGhhAIBDA5OWnKynlRMpk0ooharWYi3BgFTvh8PpN6LZ1OmxRrcoPR8fFxFItFJJNJs6aWz9PNYWY/r0aCPfv5SHW3LQiRv9m+8h7y3WKZpANLtrt9f8m9yHLIZ8N32HYOuT23VnGybfW9996La6+9FnfccQde+tKX4u///u/xute9Dr/61a9cN+keGRnB61//erz//e/HN77xDfzXf/0XrrrqKgwMDJh9TR555BFcccUV+Mu//Eu85S1vwXe+8x1cfvnleOihh8y+Jq3c97Of/Sw+97nP4atf/Spe8IIX4NOf/jRe/epXY8+ePYhGo8fURvNBiXTFguHxeMzO0WvXrkVvb69RAy8Gud3e3o61a9eip6fHkLjpdNosrOiVXQ5EOkl0qpapJKOyr1EermNBs5ywEpwEtbW1IRKJIBAI1A3us7OzGB8fN8oH5mIbGBgwKg6GQVFRxolFV1cXtm7dio0bN5qNYO0J7GJBOgPOPPNMbNiwAQMDA3jyySeNcT5w4IBRE8TjcUSjUWSzWTMZ7e7uNuFrMhceyRKq4yYnJ5FOpzE9PY2RkRGTI52LdyXQFQrFSkQ4HEZfX5/JjdnT02Nsgr0g4GZWMs+l3NySdicUCqGjo8MQm0zfweOnpqawe/dus6cHo9qkUlwSszJ3OgCzkRrB76SSnvMALnYTiQRmZmbgOA66u7sRiUQMwSvJE8dxjL2WC02m9gJQlzOe3wcCAaMykyrwWCxmHAJMK7ZmzRoEg0HEYjFMT08bR7u9cGL5pfJOLrCoCPN4PBgfH4fX6zW2ze/3G5W5JNL7+vpQq9VMSD9tNJ0Dcu7EBV4gEDDku8fjMUpxlkM6AThvIPHC9srn8/B6vTj33HMxMDCAZ555Bs8++ywmJibqnvPMzAzK5bJR3vl8Pqxbt84QD5OTk0gkEvD7/ca+c95HwqJcLuPw4cN47rnnTLu6qUQbtfNi4ETNB9zmUW5iCf5uVm+FYqkho5ykshaoT70ghUGNSDU3JTJB0orjtbQxct4vScxm4Abb9vn2esAe0+1IJEnoSWKUTmuWuVKpmA2auXbh+kUSc9LBDRzdz4zqb5lGy1b+h8NhQ2IyhQdTqMgyyfZ2cyTYSnJJKEvCm4SkTWrKZyz7A9uGm0tzfcZoYFkm2TfozJXKZDfHqZuzg//bz5gqcqYtcYP8nByF1+vFzMwM2tvb61TkXV1dRl1Opy/3L6Ed5CbmTOMq3xtZb1uNzs/YJ+z5BPslbTvfAdr8UCiEgwcPmn7B1Hazs7MoFApm43Wm37VV2FIVTsU50wcxhcvhw4fr5lXNHCM26S2fuxwr5HeN5otSfOimoLcdevL95flSxOFmb+2yyesRdt9qxGHJOeBy5R0+97nP4U//9E/xvve9DwBw++2349///d9x55134tZbb51z/Je+9CWcdtppuP322wEAZ511Fn7+85/jb/7mbwyRfvvtt+PVr341brjhBgDADTfcgF27duH222/Ht771rZbu6zgObr/9dtx4441461vfCgD42te+hrVr1+Kb3/wmPvjBD56Q9lAiXXFMYA41ek0XU5UsB0UadU4guFhtNqE6WeCkkDt/S8LZxskoKwdmTgC54OUO8Ry0OXGSRrWtrQ2pVGoOkZ7JZMwEjxMzXl9uLHsi60QynSRCZ2cnOjs76xwr+Xwe6XQaAIzHW3qTGxHp3PAkkUiY8DNO3mj0l6MhW6lQlZtCcXJBopkLQmmvpdKJi25bVeamcrKJDi44bCUYgLqFjK0OsuG2AJSLFBLpMiqNC3P7h5Dke6lUMip1O5qLdZfqOSqx5aLcTYnEhaVNesg6ucFW2VM1GAqFDBFBtaEkgNrb241d9nq9dTZLOiVsdaEst2wre1Eq62STD5KscXuOdLBTWcfN9FguhoyTtGcKGG5imslkzCaibHsuviuViiHSGQGw0BQ6knxe6TiR9VBbrVgsuPWH+fqIm/NovrWGTaZLklGOc/aY1khpLcvSSD1qk3o2SS9JR5s05vckObl+k3bazf7a/9t2TNZFEnwyxYVdt0Yks9u97fvJdpRtJdvSJtLdrsNzKVBz2zjajQRvRkraxxLyubH+bpuES2ePXQ+uTe37s8xMm0ebzXtyfSqjuuQ8yX4ubs/Y7f1x64t2W9tRF4xqC4fDCIVCxk6Tb2FfyeVyqFardfMTWT5boMbULnSy06ZLNFNks5/Y9tpuF/sa8n2T6WjluY3uS8znoG7kROO9FwutjHksz/HaakZWEuwXNkqlEh577DF8/OMfr/v80ksvxcMPP+x6j0ceeQSXXnpp3Wevec1rcPfddxtRxSOPPILrrrtuzjEk31u578jICCYmJuruFQgEcMkll+Dhhx9WIl2xvMCNMKLR6JyNwhYDcudrTozS6TQOHz6MbDZrNjJbaqRSKYyPj5uNOmgobPXCYgywtlG1yQKq+AYGBrBmzRoT2s3BkO1I4r9YLGJ2dtaE0Y2OjhoFAQ0JF/FnnnkmzjnnHPT29mJoaKhOiX4ywIlgT08PXvziF2NgYACPPfYYRkdHUSgUMDExgUQigVAoZHLBM3+7bBsZakcinZuNZrNZ5PN5JBIJo2TUBeHiQhfnCsXJAxdwJDQ5OaajlDY2Go2iVqsZNVIgEDDhuyRtOXZyoSvTmHV0dKCjowOhUMjk3ly/fj0CgYBRvZFglWQ4y8fyyIUtx1/OBXg+/5aLmFrtSI7Z7u5uo7xj+hEq4LPZLGZnZ5FIJMxGa8DRRRtV5sz9zXuwHCRvpaqxo6OjbjMwqvdnZ2fNQpLnyPzvtLG9vb3o7++vI7YZHdbW1obJyUkTcp3P5037ZzIZAEdsmM/nMwQzF46RSATRaBSRSMSo2zhXoy2tVqvG/ns8HqRSKfh8PpMTlc/edgxIgoeLPDqrPZ4jKXJmZ2dRKpWwYcMGAMD09LTZ6yaRSJiULlS2JxIJ7N+/3zwHXpPPO5lMIplM1kVKsD1afQ9OpMO/lfsDrdmwxTrmeKG2WrEYyOfzZtwB5m7oaZPbtrMScCczG8EmR4GjxCYJWumIlfdqb283GytyLOJ50sFsO4xJ3Ml1nqwj14VMpyId1pLoHhgYQHd3t0npYpPEbnWnPXQcp85hTSetPJ5OzWKxiI6ODpPShXtWyI0keR6vxzLLda0sH22lnadbks/SHksiVBLoAEx0GNN8ye95L/nMeA87Wp2OW8dxjABMOhSY45zOXZLFsvxSiMD5C3POy6hsmQKUwrpisYjp6WlUKhXE43FzPtXmVJ9zLxDOjTiXkE4Xzi1k/Xgt9gE7fSnbSs7d5HtEoV2tVsPpp5+O7u5uHDhwADMzM8apQFvN3zMzM5iamjL9mX2e/ZWkOdP+McVLLBYzqdpsdbcUFLilwJPvkpvowxZUMFown8/DcRzjJJCwxQB8h+V+PTL1nxtsMp+KdrdxSvIlbg4h2eekQ+VkbTbKuRrxqU99Cjt27Jhz/MzMDKrVKtauXVv3+dq1a+uiDyUmJiZcj2eE4tDQUMNjeM1W7svfbsccOHDAtWyLASXSFccETjiYV3sxwYHEzpNaKBQQi8VQq9XMBhlLCcc5EoI2PT1tFqScmLhNBhfrnvxtGxeGnvf19ZnUK8yRymOAI8aPG2xSnZfP543Bt+HxeHDmmWfitNNOMxM9Gu+TAfYH4Ehu/k2bNqGzsxOHDh0yjgFGKfj9fiSTSfh8PpNjkIt8n89nJmO1Ws2ED5bLZbN7uAyTVCw+dHGuUJxcyOgxGbrN8FIuDGkTcrmcIbhJpEqi221RRoKe9+no6EBvb29dWjEZyi4XG3JBIze6lKo2WXabkGGYNDdT83rrNzClPWbql2QyaRbnEvKeTJ9SrVaNXWAebrlQ9fl86OnpQXt7u0nDkk6nMTExYRTWblFNJCc6OjqwZs0a4+QFgM7OTqxfv944GEh8eL1eQ8Qwjznzktqg40Q6T0KhUN2eKUy3w2vncjn4fL66lDlsdxtctMowZZnmhZuJ9ff3AzhCJI2NjZkIPi5Yi8Ui2traMDs7a+ZO0WjUOL6DwSCAI3uhTExMHJcNWGol+kLI9OUAtdWKxQDHTLeNPyXpbStcCUl0zUeiy3NpT/g5xyp702lZHto8Oo+lOreRitUeV6TD0XYOMHWGLIckmgGYVB9u+dndIAk6EnAc4/k3y0TiNxAIIBgMmrGfm4BLAlbWT6afsclq/ua8QNoPu5xsW2lrZTvws1KpZDa1pr1wE5JJFb9sC/lc6QSR7UG7xjoxUo3PRqr2eQ7bzOfzmVzmfr/fzDmY071cLiOZTJprJ5NJlEolZDIZQ8h3d3ebVGZ8JnJuwxRsfKbAUYJZrocl6cpnLlXc/OG6llFsfr+/LsKvs7MTa9euRTgcNqnquBko2yOfz5uNa6leZn25UTudMnSoMKKdaW/5HCnuk31IOvn57CRJLfuI/Ew+d/m8C4UCEomEcb7T4SUdQ3Y/lX/L6BHOJe3+bqv/bXEG50Qywk+OO/b7wetLgUkrYoHFsNUHDx5EZ2en+dxNjS5h16GZc6DR8fbnrVxzsY5ZTCxtbgzFigXDk+zcposFOfGhgU6n00b9LfObLgV4b7tM/E4qp+SGIMcDTuzszbRINDAHLtUMMu2ODL1neha/34+urq46BYS9YSwNTSgUMvl1ubhdCjCfO3PgckLEiQMnDMyBSyVbLBbD7Ows4vE4YrEYEokEUqmUIY7o/T9RDhDF0uCOO+7Apk2bEAwGsW3bNvzkJz9pevyuXbuwbds2BINBbN68GV/60pfmHHPffffh7LPPRiAQwNlnn43vfOc7C77ve9/73jq1jsfjwe/8zu8cX2UVCgtcEHGcc5yjCu9SqVSXDgQ4QsB2dnbC5/Mhl8vVpbqSKVHkopbKYi4MZY70X//610bpDBwhVRjuKxcM9oZ0chFVLBaRzWYNoc2FWSqVMgs1uYAiSU8ynbYhm81iamoKMzMzKBaLc9pKLuIqlYrZ8DKTyZjNs6RCjfVNJpOIx+OmjCSOeKytfOJCnLZa5oOnjeeivK2tzaj9Ozo66vY8aYRgMIienh709vbWCR0YYk1HOskFO00b5wcy532pVDI/Ml8554B23lO7nlxk2wpLKsfkpuZsdy6+SaYcD6TzY6nnjArFqQSbDLYdpUSj9ZG0BdIpaZOqbufJa8sxQJZLKsKBo2OwHJvkmNfonvIabtG6HFtJJtuRPdJB3Cz6uBHciG22mfwhccxc1nQwMyrXbV1vt5kcy+WzcSu7vAbvL8vD9pLkr3Tqy/Wz5AMkQdps3cbnIklYabv4A9Q7Uqhgp2ODm2xyriPFfnyudPbTeU07apPD/Glvbzf7nJCYt1Omynq79V8bNjcg+xzL6Pf7jWOAqnRyBtzoVBL7LAfts7TLfEc4n6T9LhQKdalybJLcfkayrm59Xr4nbvWW37HOFBPQeePWR9wcbJKgt0l6t/Q/bs/AfnZu9XYrB/uz5HtOBpgylz+NiPT+/n60tbXNUZ9PTU3NUYITg4ODrse3t7ejr6+v6TG8Ziv3HRwcBIAFlW0xoIp0xTGBKmYubhcT0jBSwVQoFHDw4EH89Kc/RX9/P4aGhrBhw4aTpop2K1+1WsXhw4fx05/+1JC1wNHNX6QXGKjfmGShkI4LuZil8ejo6DBKbXuTGruNpLqvra0Na9euxcGDB80O3SRXpFe0v78fZ555Jvr7+5c0GiAQCGB4eBi9vb0YHBxENBqty8Um86qm0+k6VYFUjtiT+WaKE8Xi4WSq3JbzzuIA8NrXvhZf+cpXzP+MHFEoFhMkkDnGhUIhsx9EqVQyKvL29naT0oQbLzOUWOYEBVA3rjJ9GDdmzuVy+OUvf4nR0VF0d3fjFa94Bc455xwUi0Ukk0kT3svFKP9mujG50CyXy4jH4ygUCiYSyus9splXPB5HIBBAX18fAoGAUSF5vV50d3ejo6PD2MparYapqSk8+eSThoS3IUPPmaebG7JVq1XTTl6v1yzOCoUC9u/fD8dxjF2lqk3aXqnmi0Qi2Lhxo1G9S+V7sVhEJpNBLBYztndoaAjlchmhUMhs6EmllQ2Px4Pu7m6ceeaZ6OrqqnNwpFIp1Go1s3hmmZn+TDrduVEancxy7JXOdrmg5EKXiqtgMGg2CI/FYvD5fCbnKtuFBLm8vnSySML9eEjo5UCirzScTFutWL2w595u82x7Xt7sWkC92ns+SFWuLINMJyHLSWKQ5GCj+rgp5al6BeqJTtpLn8+HSCRixnuZOoJ1ohDIjbSfr85SZSyJc66LZDTyxMQEpqenzf4UkUikbj8ppi+R96eNlHXjulQS3CwHlb9sc3ks7YS9qWatdiR12eTkpEm/aTtqWRcZtS5TpxFcw7K8koBnhBedy47jIBQKGXGAx+NBNBrFwMCAIWQjkUjdfMiOkmMkA3BkPs80eSwb24bPJhwOY3h42DicmW6HSnDOG6RQQPYt+bzdnMUyAoD9kPWgIIC/qUYmh+D3+5HJZMz1aKu5kajH4zFp41hmACa1i1QB12q1Orsv50fyt3y+NuSxNqfBOsi+JvuvvK585+XnFBawv/CahMx7z7alQIScg7ymBMtliyBl3aQYkMfK9D7NcDJttd/vx7Zt27Bz50685S1vMZ/v3LkTl112mes5F154Ie6///66zx544AGcf/75pg0vvPBC7Ny5sy5P+gMPPICLLrqo5ftu2rQJg4OD2LlzJ8477zwAR+aTu3btwm233bagei4Ey16Rfuutt+IlL3kJotEo1qxZgze/+c3Ys2dP3TGO42DHjh0YHh5GKBTCy1/+cuzevXuJSnxqQHrMTsQiRZLV/MnlcojFYojFYiaNylIoiFk2Kv2ocpaqKenlnM9j3gp4Lan+oqGiQQ+HwyZPLTeskYbHNr48JxKJIBKJ1J1D8HiG63d0dCxpbnqSGPSi23kApddYbnJC4ogTM7k5m9xUVBeAJxa2ImOhPwuB3OH7rLPOwu23344NGzbgzjvvdD1e7ix+1lln4X3vex/+5E/+BH/zN39jjpE7i7/whS/EDTfcgFe+8pVmQ5SF3DcQCGBwcND89Pb2Lqh+yw1qq5cnpMIZQF0aFVtpJ/OAMsenJBbkIkHaE5K+tFOJRAIHDx7EoUOHTPoRzhnkdaQaUI7hclHNc+QCnOonhn3LRYitJmKZCoUCkslk3V4mbuBiifXnj1TP0e44jmPyoMsNx+32kfVjWjyq0Wl32UZcQJFQlkqxUChk8rw2AvOhM62bJL1tBbmsi1zQyvBnW0HIc+3/7TlOW1ubmZNIFb1cCNvnSwU8BRRUth0v1LYvDCfTVitOLk6mrW5V7NRKn5lP2Wl/7va//IzjnBQd2eOaXOOyDPI6MlVDo7rK9Zfc8NPNPixkPW3fT95HioVsNbecE2SzWaRSKbOXiRt5x7LYZKabzW5Wdrfy2N/T/kmhnltdbLvR6J5sc5bTVrXLcshnI/Oi22nSeIydk16eJyO97PS3kuimXadDwU2RbnMIbkSs23OQ/cuum1Socw4if9OZwOtyXU3ng4xm5DPjj4x6k6l7bIW7LHuzd1u+I83eNencYrSdzMkur8d7yvdNOtt4PdkGdt+zr2djvndCXkM+W+k4WQiRfrJs9fbt2/EP//AP+Md//Ec888wzuO666zA6Ooorr7wSAHDDDTfg3e9+tzn+yiuvxIEDB7B9+3Y888wz+Md//Efcfffd+PM//3NzzDXXXIMHHngAt912G37961/jtttuw4MPPohrr7225ft6PB5ce+21uOWWW/Cd73wHTz/9NN773vciHA7jHe94x4Lr2SqWvSJ9165d+NCHPoSXvOQlqFQquPHGG3HppZfiV7/6FSKRCADgs5/9LD73uc/hq1/9Kl7wghfg05/+NF796ldjz549iEajS1yD1QfHcZDJZDAxMYFarWbCwRopoBeKWu1IuHQ6ncbMzIwxpkyjUiwW8dxzz6GnpwcdHR0YHBw8qelGstksDh06hHQ6jeeff96kdZHqKg7IDINiShI50LcCDqQMxaOR4uBHw0dyORwO1xnAZpAhetLbzkmW13s07zo3kZNh6EsBOg0AIBQKGdUhyQ7F8sZieM5b2V18Oe8sTvzoRz/CmjVr0N3djUsuuQSf+cxnsGbNmiYtsLyhtnp5QkZGOY5jFn3c54JjOwAkk0kkEgnUajV0d3cDcF/YkAh2HAdTU1M4cOAADh06NMcGUrFN+zIwMAC/319HqFP1TLJULkqZs5spZ7gRXG9vr1kokah1HMekX8nn84jH48hkMjh48KBR4EmFugRtdSKRgN/vR29vr0kdJhVX3DiPOWyZ8oxqJhIx0hEgVU9y/KO6LxgMmvkTcJSA5saf8XgcAExZ8vk8Ojo60N7eXhfxxvkXlfFUldNpEAqFzGKW57Dd5UawnGuw7FRKyjmHbBOOjTZp7ziOIRO4ISo/V7J1+WMxbLVieeJk2uqFksKN1kZ0/PFYN5LcbVy3x2GSckxtIddKUvxkb9RJSBJO3keS10C9SlWqcWWua46ZtHeO49RFdhWLRbO/BZ3b0qloq68lWSnJb/5P4pwbYDvOEaVxIpFAuVzGzMwMQqEQOjs7jWiqEbFIsJ1YXz4n1lmSzdKJz/uTuM9kMjh8+DCy2SwOHjyIqakpE6El7QajBpj6jNezyX27D9ApLJ8h5yB2v5FiM6ZUs50fbk4QthHTo1B8xhzjXFfTXnJtLdPA0HnOdqRDWzqvS6WSEQmwL9kiOJZFpuFhv8zlcnX9rlQqmTmhTAlLsSDna3L+yPvbfZKqe9lObHtJFtskvTyfkA4IOT7wmvxe7qdiz++k80heU74r8nj2Jfkuy/rIaBjpTLP7j/y+EeR4ZtdzITzaybbVV1xxBWZnZ3HzzTdjfHwcW7duxfe//31s3LgRADA+Po7R0VFz/KZNm/D9738f1113Hb74xS9ieHgYn//8502kNwBcdNFFuOeee/CJT3wCn/zkJ3HGGWfg3nvvNZHerdwXAK6//nrk83lcddVViMfjuOCCC/DAAw+c0PXlsifSf/CDH9T9/5WvfAVr1qzBY489hosvvhiO4+D222/HjTfeiLe+9a0AgK997WtYu3YtvvnNb+KDH/zgUhR71SOTyZhBnX87jtMyidsM1WoVs7OzmJycxOTkpFEkkVxPp9PYu3cvgsEgBgcHT3re7kwmg1//+teYmprCvn37MDk5aZRxwFHDUKvVkM/n6zZ7azQ5c4NUoTNPLBUDNLJciJNEl5vUzAeS0o7jGKKira3N5Hqn+lten+FSx/uMjxWss9frrSPS0+n0kpRHcfLRyu7iy3lncQB43etehz/4gz/Axo0bMTIygk9+8pP4vd/7PTz22GPzbvKyXKG2enlCLr6Ao6orEqeMSiqVSmaTzO7ubgwNDRkylosgLqa6u7uxdu1alMtl7N27F7t378bs7OycfUIKhQJGRkaQy+Vw+umnY/Pmzejt7UUsFsPMzIw5Tua25EIyGAwagrpcLpscpVzIRaNRQxxzUceQ6Fwuh3w+j9nZWezduxeJRMI44Rupm3lOJBLB4OAgurq6UC6X0dHRgUqlgqmpKSSTSQSDQbOIYzQXSQouSAHULXztxYoMDSdJQkU/F+/MU880LwMDAwiHwygUCiaPPUkR2/nAuQZV3XQ4+P1+o+Zne5HEZ79Ip9OIxWLweDwmUk0udllmtrVchEuCgvMH4GjIe7NIgNUON/JvIecCSlArFgcn01bbamE38sqNSJLgeCvJL/k+uSk75bjLvxuJh0iaMZJVkpPymnYKBrmZqX1PXtMm6m07LG2x4zgmbReJdKqbKSDiPW3SWBK7sk1kmZmqjORotVo1aTELhQImJyeNzV+3bp1JCyKjsdxU8PI4qT6W5LZMncZ61Go143xPJBIYGRlBIpHA/v37MT4+7mqrOQ9hmjM6PegMdgP7oG2L7RzUdnty77FoNGrqJ4lkHiefOdemXq/XpEmhY5qbu8ry0lFCAtveNJTOCXIMNvHMe9rqaUn8yuch7TCfB+cA7GdcY9ORUS6XTb56pqOTDhEbbBv2x0YRDtLJJd8luz7SQWCTz7VazbQhcNQ5Io+Rwg0557QhnS12n5C/2Z6N6u5m6xtxJgsVVy4XXHXVVbjqqqtcv/vqV78657NLLrkEv/jFL5pe8+1vfzve/va3H/N9gSPtuWPHjjmcwInEsifSbSSTSQAwYfAjIyOYmJioUxIGAgFccsklePjhh10Nvq1edcuXqWgOeiLL5TIymQwSiQSCwaBZ3B4LpHqAC0jmVOX3VFglk0lMT0+bfK5u4UiLCbnolJtX0lvuNqDKyRm96MwRCjT3OLItZLiUDEUj3LzjC4U0DI0mZ428/UsFlmWxoiAUJweL4TlfyO7ibuqUZn3F7Xj781auOd8xV1xxhfl769atOP/887Fx40b8v//3/8zCdaVDbfXyAp3e8XjcLCSoSpepU4D691SGv8rFC9NicVPIfD7vGqrNqCwu4IPBICqVSp2aiHaLCy4u7GTYrVSOyUWmVJzZ6d64EMxkMigUCvOOPbwGCWg7bFwuklhWHsP5gVyw2eOdtLVS0cWFrcz3Khdqto12eyYsPxfPMvSc5ZQbf7E+Ui1p15P3lOOgVBHaaQrYl8rlslHkcW7WaIGpaA0nm0xXRfqpgxNpqxdjfm7PvziONBpPbMWoJN/sTT7ta7ilCZlvfifJMKlEdwPHdekU4DVoD4rFInK5HLLZrIkg4jjsRuzJa8jxmvWhIpfXpW1jG5Ek5v4h3EdF7psh286NzJVlsH9sSIUy5x5yY2+Sxm7rataJ7Ww/a7f7sQ3tazbLxW2vpeV8qBWlsVSryxQvbir2Rn1OHiPV6bI89jlucwI5lttzO55DJ7y98bjtDLHnIbyv/M3yurWfXT83x488h/eR6nSpxgfqo/1sJbl9b9ZLfi7L4HZ/OV+TTo5WIccH+71zcyxKNPpcQm310mJFEemO42D79u142ctehq1btwI4ujurmwLwwIEDrte59dZbcdNNN53Ywq5y0Cglk0k8/fTTqNVqGB4exotf/OJjDqGgwc9ms3jmmWfws5/9DLOzs3PUxsViEU8//TQOHjyIzZs3o729HWvWrMHQ0BDWr1+/qMQqB5lyuWwmlwcPHsRPfvITTE5OGlKiEZgnjPlU6fGNRCJ1kxQJuTkMVejcUNPe+ItoRvS1Ardz3AznciKspYFbTuVSNMZiGHzuKt4My3lncTcMDQ1h48aN2LdvX9N6rRSorV5+yOfzeOKJJzA7O4vBwUG86EUvQigUQiKRwNTUlFkc0Dal02kT3kvim8q5dDqNRCKBfD6P/fv34+DBg0aRLVGtVjEzM4N0Oo1isYhoNIru7m4MDAxg7dq1xhb6/X6TV52pQqjAkwskufDi/3S0ZzIZpNNpY48dx8HMzAwOHDiAmZkZswHWfCiVShgbG8PMzIwJveYCiG1De8z8snbucLnvBsEFKuvGjUlJalSrVRPSHYvF4PV6USqVTBQYFWtMc0f1Pxd2Uu3N3OS8X7VaNZuNhkIhhMNhAEeJH5k2wOv1GsUZN4SVIc2pVAozMzNmg7Kenh7T3tVqFbFYDIlEAn19fTj99NPR09OD/v5+BINBE13YSt7P1YbjWbAuxfxGF+enBk60rea45Zb+oBWSiMc2IpClY9GOCuZ4xn2j6Fwkuclj5SaUMvWKJMDcHFm2OtYmFyWZSHvF+0qylQQp13q5XA779u3DzMwM1qxZY6Kf5HpROkB5P5l2S+ZCZ/7zsbExHDhwANls1jg9WN5isWiU4GvWrIHjOCYybXh42Ozt4ff7DSEvHdeSwGzkaOBzYZq0YrGIWCyGVCqFeDyO0dFRk6+ddZX2wiY+HedINLXbGlA6LHgsow34I6PtGonhZASXvJ6dwkYSz7SZtNuVSgXBYNBEnMmUNPl8vi6tCu9riwXotLYjBPjDNGr8XrY1uRXOEaRwgfVmZF0kEjERgaFQCJFIxMz/qP6W/ZoODc5vZB+VOehZJqnol3unsBzy+bDsPE4+Z37vpnaXUY4AzLsg5zTyXacYQPYxqZK309bJZ9So37iNH1J8IcUfvI4cn6SDaz6orV5arCgi/eqrr8aTTz6Jhx56aM53ragEiRtuuAHbt283/6dSqTnpAhTNwYEun8/j8OHDZtF71llnHfM15cA/MTGBPXv2mLxiEuVyGYcPH8bExAQqlQrOOOMME/pN47ZQj+F85apUKpidncWBAwdw4MABPPvss5icnJzXO83BmhO6UqlUt0mmnW+cC2LmhZe7ltubvRGLuciyvchyMthMYXCyoST6ysTJMvjLeWdxN8zOzuLgwYMYGhpquY7LGWqrlx/K5bLZ26NWq+G8885DNBrF7OwsEomECUXmooV7cdB+cbHX3t5u8olns1lMT0+bHKv2pL9WqxmVmcfjwf79+9HV1YVgMIiNGzciEAigs7PTkKzMySnV1PYCjYs2O1clVeSSwGZk28zMDIB6NWMjVKtHNksFjhDS3d3dJtKNi2AqB3n9VjbDdFOKk1BgeVgnSZKzzXkOw7wLhYJRkMmFKnBkHCIp7/F4zKbbTMPC+khHBJ+dTTax3H6/H47jGKI8FAphYGAAXV1ddfXI5/Pm+3A4bPaykXnbFcsfujg/NXCibTVTQbr1Jzc1pltUrU1KSUgCzv7Nv+kEtpXCrBPHT0ms2sSd3Q52G0nFKu/ZbD0lSVeCZGe5XMbU1BSy2Swcx8G6desAwDiyZR0kiW8TgrRThUIBuVwOiUQC09PTdXtryLaiLSuXyxgYGEA2m0U4HMbw8LCxAUxtxjzrtgODv5u1G6O+uAfI7Ows4vE4YrEY0um02WhbPiPWx3GcOYSyvTa1HS88l8SpTc43UrPL+Y+dHoiQEeZyXSp/uNanLZf9ns/bTgvkpuCWBLV0KvA7fi/fAengt5+LtPkkyEnIc55AEpn8jB1BSEe8LCOFg2x3PjepFJfp5Ox+JPsk555yPwP5PEny2/1evntyDsv3xB4fuMaT5xBsD1k+uedQM15GRjfIdpJtIsviFk0wH9RWLy1WDJH+4Q9/GN/73vfw4x//GOvXrzefDw4OAjjiQZckRDMFoNvmdIpjA/OZU/n07LPPYnZ2Ft3d3eju7p439Yb0Ks7OzmJ8fBzJZBKHDx82OeIaefxqtSMbnTKvGhVVwWAQfX19Jl84w9dbIVx5XXqgU6kUYrGYUcmPjIyYvO0LGYBokB3Hqdtgpru7G9FotC5Emh7wSqVi0tsUi0XMzs4ackOGNDHEXoaIt0Iwy4kJJ1skImSZaeQ5mXBT0Z9MSLVJsVhs6GBQnNrYvn073vWud+H888/HhRdeiLvuumvOzuJjY2P4+te/DuDIzuJf+MIXsH37drz//e/HI488grvvvhvf+ta3zDWvueYaXHzxxbjttttw2WWX4bvf/S4efPDBukXofPfNZDLYsWMH3va2t2FoaAj79+/HX/zFX6C/v7+OfF+pUFu9PEG75vV6MTk5id27d6Orqwsejwe9vb1mQ2y5kaRU5pFALpVKSCQSRtmWSCQabuIpUSqVMDs7i0KhYPKCc5He1dWFTCZjCA/m4WSZuSEmSX5umsZ5h9frNRugMw96pVLB9PR0XbqBY1k00JYynylVTR6PB+FwGL29vWYfBW4U5+ZgZ1vSKc6NUUlMyOPkxlyNIsIkASTvJ50RXFD6/X5Eo1GzOIzH40apGQgETJns3KFUcdm53rmZGhV1UnnGlC4+nw/JZBJerxfJZNLMz3ThtnA0IqbmcwopFM1wMmy1TEsCzJ+qwCbBgMYbAwL16xiZ4gFAnRPW7XhJyDUio3hduxw2YWcr0RuV2a6b2x4adECXy2WEQiFjx7q7u83aUebS5vU5vnNcLpVKSKVSmJ6eRi6XQzKZrMuPboNtVSqVMD09bWw1Fcp0ijL9jFRls+78LdXAUokLALFYzKyjJycnEYvFkMlkzJyDhK4kgWVf4PrP4/GYNHCSuKR9kmAaOjrduWbmfWX5OJfhMbbdshXyHIclWe31eusiHeSz55qVzgTp9Lb7gnSy85qy70mFtlwXS4eD7POynvJzOi+oMJd1Ytmkk11CqvHtKAmWnU509lkZxSfbhnWy1etuec3lu83+YH9P2JEIsp3dnClu/brRtXl9+368NsvJY/hbfk4nnFSvLxfhoqI5lj2R7jgOPvzhD+M73/kOfvSjH2HTpk1132/atAmDg4PYuXMnzjvvPABHBsxdu3bhtttuW4oin1IoFot47rnnMDo6isnJSVSrVfT09ODcc8/Fi170orqQZDdUKhWkUikUCgU89dRT+PGPf2w2HZmenq6bxEhwkJqcnMSuXbvg9/uxadMmbNmyBT09Pdi2bRs2bNhgFFH0kDYblDiYlkolxONx5PN57N27F7/4xS/MBqdjY2NGibYQ8paEABfh9LCuX7++TpkXCARQKpWMgT18+DCmp6cRj8fxzDPPGIeBDH1ibrtsNgufz4dwODzHu9qovpww5PN5pNPpOSF/NMgMw2OO98VU/C8EcrJQKBSQyWSQzWZP6U3MVhJOpud8ue4s3tbWhqeeegpf//rXkUgkMDQ0hFe84hW49957T+jO4icaaquXN6i0TqVSSKfTmJiYQCQSwUUXXYSLLrrIbCzt9/tNLnW5NwcdrrVaDaOjo/jv//5vZDIZpFIps0BtZhOz2Syef/55tLW1YXx8HHv37kVnZyfOPfdcDA8PA4DZPPv000/Hxo0bUSgUcODAAbMHS0dHB2q1GuLxOKampuD3+9HT0wO/34+pqSk89dRTyGazJvUJNyU7VnBBTKd3T09PXS7Rrq4u9Pf3o1wu45FHHsHTTz9tFrL2vIWLRqq3OQew33mSH9VqdU46ArnIBOpJCtrkQqGAmZkZE6XHCDimc5mYmMCBAwfQ1taGM844A729vSYtj61ak8SUJJ/ogKEjGziqyGT6G7/fj7GxMUxMTODw4cOG2FHid+FopFDk7xPRpqpyW704mbaakTNuSvNGcCOebVJaOiw5DnJtQIeejP6VylEpxpHjnZsyWRJwUrnK90OO89wY1B6rpeLUJuLljyRGabeKxSKCwSA6Oztx2mmnmXtwHSmdmFyj0dldKBQwPj6O5557DrlcDmNjY5idnQWAOuJTqum9Xi8KhYKx1RS4hUIhDA8Po7u7u26D1EgkgnA4PIdEZ92LxaJJS8rUNePj4xgZGUGhUDCbeNOZS7EZiXRuACvLyXV6rVYzKWY8Ho/Zk4ObfLKccrNrpjnj3i779+839prPlH0oEAiYDV9lmwFHHUR0iMvy8VlzHc91NB1KnC+l02kjIGCKNKqVZV/3eDxGECjJWNlv5PyC/ISdIo/gs+H8jg4J4Mh7ToKXbcG6y+crHQbst3aEvVTRS/KfZSHXIN9R2X+kw8RNnCDfa16LQkV5b5aXAgtg7mbEjSIKGmE+gSjHBjoRZJs6ztFUPbYDhmAav1agtnppseyJ9A996EP45je/ie9+97uIRqMmd1tXVxdCoRA8Hg+uvfZa3HLLLdiyZQu2bNmCW265BeFwGO94xzuWuPSrHyRZGc47PT2NarWKZDKJXC4Hn89nBg1bTQUcGUiz2SxyuRzi8TgmJibMYr8VcpSThra2NnR0dCAajRoivLu7G7VazZADdhicXRb+5kKX5DJV4SwXB8mFLmDkQM2JQjgcNgR6T08PAoGA8fbTINL4h0IhM7miMZDKdDlRcJsQ2s9NTiblj5zEyu/lJHUpIb3KLJsag5WDk/msluPO4qFQCP/+7//e9PyVCLXVyx8yvyNVb7lcri5cnJAEqlyk0XGbTCbNxmCtTPir1aqxX1xQ0FZHIhG0t7cbEoJELBc4hFxEc7FYLBYNqctUaNzY9HgWGCwnw5upymM529vbEY1G0dvbi3K5jGg0imAwaJwNXPxJ8kYqIEulklFwSlKDC0c3FaWbktCuH4l8EjC8LudAwBFyi2o1mQ5AXo/llfdmWWUeT/mMONfjfbm4lUSFYuVAn9fqxMm01bZyeyGwSXR5Tf621bZyzJcpseS5bmNaM2GQJCwb1ZFoFEHkdo6bkl4StcARQj2TyQCAidIGYPYSkesyKQZjWtRsNmsERxREuaWVsR0NjPhKJpMmX3YoFILjOEZBTaLWXl9L+8D1LMVPFKJls1kUCgVTTtsRz/I0Uhm72XaSk3SiSGEZbS6fJW0tneKyzWWOcRmJLfuYJLxtcpm/pepalpVrVukAsuvi5jilQ0C2gX0852skcWUZbUgbLzkBt3dJ8hdu17PfMVlufi85E3ltt+PldaUDwYZU5NtqeJvnkXMyu55S8W7zRI3e+2YckJujzS6brVKX97L73HxQW710WPZE+p133gkAePnLX173+Ve+8hW8973vBQBcf/31yOfzuOqqqxCPx3HBBRfggQceWNHqvpWIdDqNffv2IRQKIR6P49e//jUCgQB6e3sRDofNohQ4spBjns/JyUlks1mMj4/j0KFDJsXIQlCrHcnvBhxRmqZSKfT29qKjowP9/f2GtGY5ZLiQzIVle6QLhQIGBwfR3d2NQCCANWvWoFAoIBaLGU97K5uY+f1+rF27FpFIBENDQzjnnHNM2RhWz0U6JwOhUAgA0Nvbi3Q6jWg0inQ6jQMHDmDv3r1mUsBc6mNjY0gmkxgYGIDH4zGTHDsaQJIis7OzyOVymJqaQjqdNhMdtilJgXQ6jampKWOsgsHggp7PYkFGMCQSCZN/VxXpKwPqOV+9UFu9clCtVk0ar1/+8pfIZrN1qiAujoEjaUCYM5wLi/HxcRMSfSw5r4vFIlKpFIrFIp566imjkKZt/vWvf42hoSFDCsgc7bVaDdPT04jFYvD5fEgkEggEAshkMlizZg2KxSLC4bCJrmIUV6ug7Wxra8OaNWuwdetWhMNh45Cn4o05w9PpNCqVCtavX49wOIxKpWJs0vj4OA4cOFA3x6AKLxgMIpvNmhR4XPh7PB50dXWZunOeEYvFUK1WMTU11TAnO9Vuk5OTRknHNDocP+WmsTJXOs9nODsV98FgsE4tKFWcbCfO2UiykHDhgl72nZOJRukgVgNOtD1UW716cTJttSR4gXqVqiReSfrZzkcJm5yyVa4ylSfXfDapLfODuxFkds5jpniQKlVJ3rWqYLXrIUlWtpEk9DlmU1UeCARMtBGFVxxnpQiMjtRYLIZCoYDZ2VkcOnTIpPyS7S7rQUgVfaVSQTweN1EFiUQC4XAYkUgE/f398Pv9iEQixvkibQzX+hS7yecHHFnXlkol+Hw+dHZ2mkgs2hkqze1c1fzNZz0wMICOjg50dnZicHDQRHczZSptFB0PdAB0dnaiq6sLPp8PuVwO09PTGB8fN23IFKt0yJNwb2trMxuQM484AMMFsA29Xq9RnFPlzU0uOW8iKU71MecpdoQEn5GMDrS/k04n29FhO+Z5benIYbQAy8z2tx0XtgNGOiVs8lpG0kn1Nc/hdzKlCbkX2SclCW7X3S4P/3d7V4H69EfSccLjbFU9hY8sv/1c7DHKba5hp+qT7W/fS9ajVbGm2uqlxbIn0lt5yB6PBzt27MCOHTtOfIEUDcHUIB6PB/v27TMLqvXr16Orq6suvDiZTCKdTpudxDOZTJ2XcKEvt+M4SCQSSCaT8Hg82Lt3L7xeryGrg8GgIa25kGxvb6/L9cYBzufzoaOjA4FAwBDgjnMknDmbzSKZTOLZZ581G6i1ki/d5/NhaGgIg4OD2LhxI17ykpego6OjbvIl709Eo1E4zpHwuKGhIRQKBTz66KM4dOhQHcmQy+XMZMvj8SAajRrj4JaTj+dMTk4imUyaHHWcxLBNOQEgke44Djo7Oxf0bBYT1WrVGHr2IW7GqlAolg5qqxcfNvm4WJNeKrlJZO/evbvunv39/di0aRPa29tx4MABjI2Nwev1GlK2WCwuOL2ZBO/t8XgQi8XmKIl6enrMJqQDAwNm3sCyc+7AOQUX1n19fXAcB6FQCJlMxtiHhRLpfr8fPp8PAwMD2Lp1Kzo7O02Z/X6/mVPEYjGMjY2hVqth/fr1OOuss1Aul42z98knn8TY2FgdGVAsFjE9PW02bWUKl87OTpOPtrOzE16v12y+ViwWMTk5iUwmU+fsdoNM7cJ0NFIlx43FpLJctj+fbTAYxODgIHp6egw5Q3FBoVAwhAJVctxXJhQKIRKJmMW5m6rwZMCOQNQFo0JxBCfTVku1q1Rry7UPx1uudewNkO3ruKl1qdiVUUOMjJHkHAk0m7SyIUk4EnVuhDkdz25rLbfrS6WzFC3xOLke9Hg8KJVKJmKAm9L7fD709fUZQpfnyE2lSYAzTRtTldBB3siJABxNGcZ0cNwHbXx8HH6/H52dnVi/fr1pYwqrSHRKh4a8NucPAEzEeCgUQqFQMA5pqu65rrY3f6X98vv9ZsNr7sm2bt06Ux6uhaXCn6nFotGoiZjq6upCsVjEs88+a9qJ+7KUSiWk02lUq1UTlcbUOrSfbM90Om02cqddzWQyJgqA0Wfcf0Yq3em8YZoYtwh+2+nSqJ/JjcFJ/str2imR+E7ICIZcLmeIdHszc5u4Juz0JHZ0gp3iBajfw0CmkuF74aZet8tB5b+dFsZNPW+PATIygLDr5jYHt50YzcZTx3FMpgAAdeWU97DHI7eoA8XyxLIn0hUrCxwgJSGbSqUAwCwcAZgcplyUSY/1sUIOcFy8cbdyKtoA1C0kCRp5GspoNGoMJicJVDm0t7cjnU4jEokAgJn4uanzaCC4OO7t7TVqNhq4ZrAniB6Px3jf29razCZeNMIADNnv9/tRLpeNAedislwum9A/Gkyq293an6qIeDxunqH0gJ+MRTLLValUTJodEuhqaFYO1HOuULQOW8VyIiBVwwQX4G1tbUapxIULVVWL8T66hb7SbpOgZbozG1RwRyKRulziJNJpN0nyMnd6IzvHxQsd6czJ7raolaQyxzSpouLi2u/3mxQ1cmMtHs+8sdwwrVwuG7W41+s19pkkvlyQNQKVfR6PxyzMAcxZLFIBKjcpl8/YViYyPQsV61S88TipiCN5lUgkkMvljFBCsXKgtlqxGLDJLTdbJok6aY/kek7mhZaf8x62alwSdjLdhX2uLKe8PglRfmeXF8AcssuOvmGZGwnE5HH2b7d2kutMKSCiWpaR3uVy2ayr7Y1U5T3dyPRGqUCketneZJrl4G8SubSnjLLmD6/PY7ge55xDplqRCmZJKLMOVJ2TNG6Wu1qSvFRJM888iXkAhqeQxCufM+ckXq+3LtUpbTWfB78n3JxAjBLw+Xzo6uqak76kkfLZno/YpKzb85Pns1/K1C+c88n90HisfLaNwHfGbv9mc1iew3evWR3s60hlN+C+SbENWTabv3BzDshru6nReZ35nBuN6u72+bHM9dVWLy2USFecEEhPZ6VSwdTUVJ1XkgtChpgv9svMgYXqLK/Xi9nZWeMl5ySit7cX69atQ0dHB9avX48NGzaYcGh7cU6jXigUcOaZZ6JQKGDv3r3YvXu3UYQnk8m6cpCI7+/vx1lnnYUXvOAFiEQixlveKhhOXq1Wcdppp+G8885DKpXCr371K+MoSKVSxniT9GbeVuBoqCAdGOVy2Wyq2mgXd7bl5OQkfv7zn2NgYAADAwPYuHFjS4ZrsZFOp7F7924cOnQIzz33XFMHgGL5QQ2+QrFwnOy+n8lksH///rqIKxLIXPScqDLRRnHvk2QyWbfI8fl82LJlC0477TSEQiGjDs9kMkgkEnAcx0SeMUWax+PBL37xCzz88MPG8W0TKcyrGolEsGXLFvT396O7u9so17gxFzdyszf5ZL74trY2RKNRhMNh9Pb2or+/3+Rtl6HfbGdG8XG+EQ6H0dfXZxTpTOnCORMXniSL7OcwOzuLp556Ct3d3WYjc+a155yLKQ8Y+i8VXnTYM2KPqv7Z2dk6gYQMl3ccB5FIxIgNgCMCip/+9KdmU+eTnX5NEmlqOxYOtdWKxYDMo03YqRs4ttqKdDnWSLW03TdJiJK0stNgSBKdGz8C7mQj7z2f0InjJaO75PqJ60uqeblO4Wesk319pg6Ryn27fJlMBl6v10RkyXZgGeg85vpbpkRhW3JTRtsRLAlRll+SjW1tbSiVSpienjYkOIlz/kQiEZMONRgMoqenx0SPceNZlon/p9NpdHZ2IpvNIpFIIB6PG2U9o6XpkJV2ig5vqt25znWLyLbV/jLlSHd3NzZs2IBsNmvaDzgiTCOpXKvVjJiMpDvrzL3UGAnv9/tNWhipOJf9JJlMYt++fejq6jJiOzdHNu8jSWQZaSEjrtgvebx8vnyGsh/4fD7k83lMTk5ienoahw4dwsTEhLHXfr+/Tr0t+4lUybOMfD58l9iPG6VucptP8hxG50tnDeeC8lr2HgHy+fN6Mh2tjITh/7LM8j3g+2SndmpEjkvYjptmzg15rCTwWxEgqK1eWiiRrjghkJOXpcxfLfNg2eBgODg4CADo7OzE0NAQfD6fCQ90A0OmueHZxMQEfD6f2Q1dggY/HA5jYGAA69atM4P2QuDxeMyg393djaGhIYRCIYyMjACAmawBMIvm9vZ25PP5ug3HAJiNXqhqaOX5ZLNZHD582ExmGnlTTwSkUWF+2UOHDiEej6sifYVBDb5Csfwh85pKnKwUWtJuM68rQXvW09ODSCSCtWvXIhwOY2ZmxkRLMW8r9yTx+XzGTkuVtgRDoknODw0NGVJYLtrK5TKSySQKhYKx7VR/U1HO1AKhUAjhcBi1Ws2o1SRI0NOeejweQ0i3tbUhHo+bvV+4EHNbrMnPSJgzvzrzr1KdWKvVjKCAykISClxMcvHP8/L5vMlfy7KRvCK5QEKEc5tCoYBDhw5h//79SCaTS5J+TW3GsUNttWIxwLHFLVWBTRJJ8ZUNSQq6Rf5KpTNQH2klx283coplcyPs7OM4VktFt53LGzhKQEpyjqStJHLl2sxOt2VvAi3zu9vtxrqznegw4FjO60v1PsdvSRhKQtYt3YTH4zFrRzrauTaV5ZD7kcmNP2k3aC8YCR4KhZDL5Yzjm1FQTFUj03FI5T+jvmw1unQAuP2Wz4Dr9Gg0amy3dNwAMPZdqtgliZtMJpFMJuv2X5ORXiRm2b7Skc2UNiR/3ZToNtFsfy8h+5RcP/Nc6VDg3xQiJJNJI2RgihwZ2SF/S7LZLZJBOmXcCGH5mRwfZP9nBKR9fVvFzvuwz7MdeS2+e27tyLmPzAlvp5WS7WdHnDSKApD3aIUvcSPTW4Ha6qWFEumKUw70xre3t6O3txennXYaurq60NPTUxce1ux8TgKYS5Z55FKpVF0oGFVpPT09xjgfLwEtFWskFezJKSdEJMqlYWX4X6veTgDGYVCpVDA2NobR0VGEQiH09fXNyYW32KjVambzk4mJCYyOjuLAgQNGqadYOVCDr1AoWgUXQF6vF11dXRgYGEAwGMSaNWvqotsKhYIhoR3Hgd/vN6QwFXzBYBCbNm1CJpPBxMREXfQY70NVejQarcstzkUziQGmfpGh6iQp2tvbjaqyvb3dbDKey+WQTqcNsUJCQo6JjuMYFTg38ZQLYbk4dltg83/OAWKxGA4dOmTEAcFg0CySAdSRD5J0kkpQEgsdHR0mBJ4pXTgPYp0cxzFlnp6eNvuYSBWoYvFhkyaLAbXVihOBRiShVJS6nSPV5RyLqaq2Nz+Uv20Ccz5wPHdbA3J8t9NBUG0r11ksV7VarUsRYyuEeawbeSbzKrMOzYh05vEmiWtHXsnNN+WYbW/e2gy2iMo+HziiMudmqJ2dnYhEIsZRLZ2wst5tbW0mlRrvQQcu250bX3d0dKCnpwcdHR11G4HLTStl+dxU2ZJEdxzHpHKtVCp15LzdzjKKng4UitcYmVCpVODz+UwEmx2NwL7AjckBYHp6GtFoFB0dHRgcHITP56tz0JBUlkQ4HSG8nqwzP5P9Uc4lHMdBKpVCNpvF5OQkpqamMD09jVQqZcrMa0g1tt0W8l68j1v/sNP/yHK69Ws+F5unsOcr/ExGeNh9mP3H7VnS0SSV/G7tyHP4I9PisAyNnAV0rNikeitOjlbGLbXVSwsl0hWnHBh6HQwGsWHDBpx77rno7u5GV1eXUZc1I4YZfu04Dk477TREIhHEYjFMTEwgFovV7Y7e2dmJDRs2oK+vD+FweMFKdDdEo1Fs2LABHR0d6OjomDMoyxxyDMWXkBOLVgfRZDKJfD6PmZkZPP300+ju7kZ/fz9CoZAJ6ztRZHqtVsP09DQmJiawb98+PPXUU9i7d++ccEqFQqFQrB5Qseb3+7F582a85CUvMbaXNo45xqlSB47auGq1ipmZGQBAR0cHtm3bhlQqhUcffXROGjamdOvo6MCaNWswPDyMXC6HRCJhFnl0onOjcLkxFvdXkWlUAoEAzjjjDJMaZWpqqi56zG3RRgUe0Dw3ZyOw3sViEYcOHcLu3bvR39+Pc845B93d3ZiamkI8HgdwNM+8DN2Wi0c6F/i/4xwJ26fKng4D5lelWj+dTmN8fBxTU1MmdYwu2E4cGpEo9mcKxcmGVHsDRzcLtNWkMl0EIYkmO50Eo31IYEkC3SZO5f/8Xr4fNsHKv+16kIi2SXaphud3Mg2EdILaZZKQSmlbHMW2swlHEoIejwehUAihUMg4l22iz+/3m42g5QashFw7upVLKozdlP/8PBgMYmhoCB0dHeju7jb2Uiri6ZDg86OwrVKpGEd2Op3G1NQUZmZmTKpSABgYGMDw8DDC4TB6enoQjUYN8S3JUNnGtoOY95YpZkKhEBzHQTgcRkdHh6mTdORUKhWk02lkMpm6diSR7vf7USqVTEQCc9rLvcpYZyrSs9ksnn/+eRSLRaxZswa9vb3o6OgwUWUA6lTyMtWRmyNd2gBJSEuuoFKpYHJyEmNjY5iensbzzz+Pqakps1E6AMNZ8JnLd1nOsYC5G4fKfst5Ep+z3Hi0Wb+z8+Hzt4wMkZ/JNmDf5pyNaWIIzltshwrb13ZC8X5yo2I7CkYea9+L17UjPSQ5L0WczVTuiuUFJdIVpxyopGI4WSQSQSQSMeqGVsDjAoEAIpEIisWiWZTSYDmOY9Rcbt7tY4XctbxReaWBWgzQC+/z+ZBKpRCLxeDz+UxoujTsizXwc+JCx0Q8HkcikTDqdE3rsvKgnnOFQrEQcBHv9/uNszufz9dtCFatVk00GXB0cc8FIxVEkUjELFhscFHNhR4X+iSZZdi9nYtVlhM4SmZ7PB6zWSqvN98Y2Eh12CrkApeOAJaBbWIvQOUGsjYJwf/lQpb2nuWkQowh6iQa1Nl94nGs4eDzQW21YrHgRpzzc/7fiFh2g0zLIZWgbiR6M7itn9xyINvHyXFekt+Nyt2oHiT6Gt1HjtW2AEoS8620G9tLKl1topDXcyuLG+R9ZTtwjRoIBOryvsuxyraXLBfz6TOtmCwzr0PFOJ3XMuc3r287oO3nI/sQ7b20h271tNfVtkNCOiZkP5Q21Ca0uX7O5XJIpVIIh8N1e8i5OV5kOzYi1O3jpCOEG4Zz7xbug8K90+x0Jc3U5m4iOjdb1Gr0u7y2jOZwq0+zc3mcfV/2ITvljE1cS4eHDfu9tdHIFjdy4Mlxi8csROyotnppoUS64pRDe3s7enp60N3djd7eXpNP9ViIbr/fj2g0ikqlgp6eHvT39yOdThuiVxLprZL084ELfnpZ6TE+kQtWDraFQgFPP/00ksmk2Zh1eHgYfX19Jtz+eNXpvBdVfJlMBv/1X/+Fxx9/HDMzM5iennbdLE6x/KEGX6FQtAqqtsrlMkqlkrGnVGkB9eHqJAiKxSJKpVLdYpbnOo5jnN4krami6+3tRTQaNcrqZDKJmZkZlMtlE6JOcl3aXJYzn88bJbfP5zNOX24y6vf761KvnQiwToVCASMjI4jFYhgcHEStVkNfXx86Ozuxdu1aE2rPTUW5cGUu2Gq1ikQiYeYXVN5ns1njSO/u7kYkEkE+n0c2m0Umk8GePXvw/PPPI5PJmLB1RWuQBEGr8xu58LY/Px6orVYsBuSmf0DzftWIvJLkttxUUhJObqkj3MA1iq1i5X1sslAS0JKs5XfyXJnW007jwRzN0vEqNwGV5XFzBshz7TITFDzJ1Dey3G4EukxxIT+X5Kn9PCQ5zHN9Ph+6uroQCoXQ3d1tcp9LFa9s30ZpTkniktAmIS/XfIFAAN3d3QiHw4hGo8Yuu11POoClCp7OX9aBm5YStjOb6nKbbOf1qXi2nfmE3+9HOByus/3SVo+NjZn0sIODg2YuwWdnb57K+vD5MJUPzwGOpmaTNiIej2N8fBy5XA579uzByMgIMpkMYrEYstmsSfvKvsJzg8HgHOcA69joveFvPkf+bzsU3MD6MkWevWGudL7YdrMReS7LKf9nG1NwIYUQbG+S+lIQwUhIu080Gt/cxhu7LenIWIhDUG310kKJdMUpB6Z26e/vNwo3GTa1EDDMr1wumzzrjuPU5cY7UUS67YV3UyYsJhznyGY3e/fuxf79+7FlyxZs3LjR5D/t6+ubE7Z0LPcgmBM9Fovh8ccfxw9/+EMUi0WkUilVua1QqMFXKBStgqQ4AJN3VOblBuoV0pLM4EKUC6f29naEQiETds00LLQlgUDAkOUM304mk4jFYiZvKnOw24tkAIbwJ8Hs8/lMuDfrwHDvExlNxTG2VCrh0KFDOHToEKanp+Hz+UyKlzPPPNOkZAOObnrKxTiVcMwFK39IvEQiEUOYeL1e5PN5JJNJjIyM4Iknnjjh85HVChm23mr72YrJxYDaasViQKb9ANw3GJRwW0NIErajo6OOlOZ41OqaQKqmbeUxP5fqXUmSuSnRWT5brSy/531knmRZN3ujTNlOrKf9Psq/ea7Myc12lGtFW93LqCJ7k1FbneumlJcRX1zrhsNhdHd3IxqNGjW6JKzlPVhmWS9bdU3ikiS1THPC/Oj8aeSgZh0YhS7HVUlKM0JdErjyGLYTVfPyO3lNEr521AVzvMs9XVjmWq2GyclJo3A/88wz4TgOOjo6TDobW/Eu24epU2xy2S3lSSaTwf79+5FKpbBnzx4899xzKJfLJjWbnJvwedApAxx1ELgpue13l/OxRk4rNxsjxwc67wEYDkVu5iptpZ33X7Y9n6mcJ8qyyjFBHsv7S3Egz+Oz4txSOtua2T+3MY/taTu13JwCblBbvbRQIl1xysEeUPnZ8VxPGi1bIcAwqsVSUHOwlQvyhQyGtuFzU4s0WphJ1Vomk8HY2JgxMnRIMP+8JDjkfeW1CBrnSqWCbDaLUqmEw4cP4/nnn0c8Hsfs7KxRGaoSXaFQKFY/uIBrb29HJBKpc0rLxTAJgVwuZz5nvlNpI7koDAaDiEQiKJVKZuFVqVRQLBbNBl6cJ/D+DCenEokLOUm6UD3FRbtUygMLc3bbRKokcZodJ8HPS6WSyYs+OTmJw4cPG2cCFed2nXltEiE2SdHW1oaZmRkkEgmMj4/j8OHDSKVSyGQySqIfB7TdFKsZklyTaxBJhNtrBZkehOCYvdB7y3vY97H/d1NPy9QntnORazJgbo51opEjQbaJ2xggyT77OvJ6NtFKspUgaSdTkkingSQN7TZxixjgPZiShc5u2kG7Dedrd3s9y+vQdrPstuNX2ih5H+mUlMSqJCnl85XP1HYoyE1j3Z6NFLdJB4ydwoP3IVgWqtMTiQT8fr9JjSbnP1T/y/5ik66S/GffpKM/FoshFoshlUohl8uZMslnK69rR5TIOku1eyMOxV7/2/V2O086Xuz3rhWVNu/B4+Q4IfOfu6UxcovAaAQ3x5f9t9s5dp3dUgnpPGDlQIl0xSkHj8dTt9BtZgRauZb0/HPRTcNdKBSQTCaN13gxwNxmJJzt3KbzQe7+LXdwB47mbJOh3vZ1OXEZGxvDv/3bvyEcDmPLli3YunUrurq6sHXrVmzYsMGE39nhdBI04tlsFrlcDslkErt378bMzAyef/55PP7448hkMpiYmEAqlWrJiCqWL9RzrlgpsMNFFScfPp8Pg4OD6OzsxPr169HX14dQKIRUKmWeCRfasVgMo6Oj8Hg82Lx5M9atW4disYhYLGbSvNCh3dvbi9NOOw3JZBKlUgmlUgm5XA7j4+Po7u42acs6Ojqwdu1aOI6Dnp4edHV1oVgsYnJyEtls1qjYqJzP5XLw+XyIRqPo6upCuVxGPB43m4fKsN1GsFVd0mEgF7fyM6B5SgOmXPH5fJiamsKhQ4cQDAbR39+PaDSKaDSKwcFBBINBtLW1mVR3XV1dCAaDyOfzJhIsGAwiGAxiZmYGu3btwoEDB5BMJjExMYFisYhkMjnHAaBjd2vgnOtYzjsRZVFbrVgM2AIjN9ERSTlC2l+u1QiS6G7CGjflu60et0lpua6wVdiSEOVxjuOYMZ3kJ8k5qWS21adUNQOoI1ibnUPCkm0k62/bAbm+Yt2kEpubVzLayFZN2wQ5YW+mKolopnDhRt3cjDoSiZh0IDyXxLp0hNvzLF6bJGokEkFvb69JO0ISPZFIoFgsmggFCrxYZ67tZX/gM2OUGG28z+cz7SEjHLhOtlXGMue7TbLTEc10NMFg0LSrXK/bKJfLKBaLiMfj2L17N0ZHR9HZ2Yne3l4EAgEMDQ2ZCDBG53ENz9Qussy8X6lUQj6fx/j4ONLpNEZHR/HUU08hl8shm82a9uYz9nq9JoUN+4lUXbOOjuOYqAMJO6pAto/tMJFOJ74PhULBPCf5LnCfGr63fN9sst2NI5DRBNJhJN8f6STh9W1HioRUn8u2lxEthBxLZDvTgSgjSFgPtnsr8wG11UsLJdIVpyQaecaP9VpA44kTldSLlY6ERpOD90JJHqnIJ5EO1Hvim12Xn2cyGeTzeWMAmWN2cHAQvb29ZrInQ+7drsUJDjcpGxsbw9jYGJ599ln8+te/RjabNcoDxcqGGnzFSoFUNCmWBtwgtLOzEx0dHQgGgwgEAmYBI9VQXFzTBjNvKv+XY08wGDS50Gn/yuUy8vm8UX3RPoZCIXMOU7PIfKnM/cmFPBeYoVDIONalirCVMVD2PakWsxeM/Fyq7txAQh84uoALBoMolUro6elBtVrFwMCAaXMqDJl/1ufzGRtMsiSZTOLw4cPYs2cP0uk0YrHYnDnOYsyvFEsDtdWKEwGbyJaKWjelphQ+ce0j1ykcq3lteR9CKk8lGSjv55ZywVZTy8gmEnEkz932zXCDFC2x7vY9G0GSdDIiqhHRx3NkuhFbPW/Xr5EqX95ftif3HmG0GElsqtJtG+YmqGJ57fYFYARq5XLZtE21WkWxWDTpN7gWJkntll5DXpd7rtj9kP1KKrHd0qQ0UuyzPbiuZlod9glJ/trKdPblQqGAWCyGfD5vFOmhUAiRSMRExAUCAbPBOq/Fa8vnQxKd+40lEglMTU1henoa+Xx+DulMwZ98zjKFilSry/4g+xb/djtOtp/9HrjxJ/LZsY2kA0rey+14QhLmjBp0S5uy0PmKFDHIjdfd6mE7ueRx0mlFroT9ptl4QKitXlooka445VCr1YxxyefzdcZioQMpz+UinCpxDmy5XA7T09MAYDzqdqjdQpFOp3Ho0CEkEomWN/OSEwJOekgC2EQ6JwNSacHvJaQRTiQSePbZZ9HR0YFyuYw9e/YgEomgv7/f3I+EA8Fws2q1ilgshnQ6jXQ6jX379iEej2NycrJO6aFY+VCDr1gpsBc6ipOPSqVilGfDw8MoFApGMUXFUC6XM4u/tWvXGlJ9fHwcAMwCXxLg3HCLTloqyjOZDNra2pBIJBCNRk1edV4zFouhXC6jvb3dKNlI8FCB5vF4kEwmUalUMDExgUOHDi0o5QntqiQW2tvbjRqtVCoZuykVjq3A4/Egn89jZmbGtMns7CwOHz6MiYkJ+P1+dHZ2oqurC8DRBSjbhvUMBAKYnZ3F6Ogo0um0yaXuVhfFyoTaasViwFaf28RvK31FrkFImroJk+ZbJ0jSTZJUjdKkkOTjsTKFhlQuS7KYJBlTgdnXl2pdqbC1yUQ35bK9FpMOBElO2uQ4I6FkGhQ+Bzttjhs5z7rJTR/5HLhGY2o1uXm1G6Tzm//TMSD/lrm4bcI3n8+bSLM1a9aY+nBzVRkF7ZZShqpxr9dr2iaZTGJ6ehrpdNrMC/jsGjkZ7HYDjuZC53qXIjq2nXTC81nxPNaR9pTKZJ/PZyLhwuEw+vr66lLpAHBtq0wmY3iO6elp5HI5zM7O1qmt7U1T5TOR7wc31pTKdPkesq2ls0C+b7J+vAfb3o60sJ1q8lnaEQIUBdqRG1LYIK8jYQsT7HvJY2whg4RUmkunoDxeni/fAdlfWR/Z/qpIX/5QIl1xyqFarSKTyaC9vb1ug41jUahTTV0qlZDJZJBMJk24lOM4SKVSRpGeSCSQy+WMAW/F02jDcRwkEgns27cPiUQCqVSqpfOkAaR32+s9kn/WDm/0+/0mr7vc+MJtwKXRmJiYwMzMDLxeLx5//HGzQ/maNWtM3vRoNFo38eDmZOVy2eRa5WSM9242IVMoFIoTheOZnCoWB6VSCZOTk/B6vRgeHkYmk4HH4zGbY1arVSSTSaOA27hxIxzHQTKZxOzsLKLRKNavX49QKGQ2Di0UChgfH8fIyEidqjGfzxuCenJyEj6fD729vWbROj09jdnZWaOSj0QidU5xqfCbmZlBLBbDyMgI9uzZY2xdqw5h+7j29nasWbMGvb29yOVySKVSZs5BglvCTYXP+Q0X116vF+Pj40bVxsXehg0bsHHjRpRKJYyMjCAWixk7LxeAFCTIBfxiwFbjKxSKlQu3NC722kemGLHXYI7jGOchgDlEm034SiLRvo8dvSOdkJL0I3nuprYlGKEjlekk1Li2kmk9qJSW6SVYJq4JJUhOy/pJpbTc4NBuC9netVoNxWLR1FfmESe5K8l0brgo68zjfT4fgsEgABgRGm1AoVBAV1cXvF5vXdoY+1kSUs1rp3SRZZXkP8Fo6M7OTqxbtw7d3d2mvSXJStJVti3rwc9IWs/OzuLgwYPIZDJIJBLmc9lHbNKcwjRJkHJT8nA4bBzwdL5L4rRWq9UpkRkFxrZlrnSqsA8ePIhgMGiIdEbIkbBn23FewLkR0+AxLY6E3V/4YxPpvC4hyXtG9UkVu52qSDo0pOqfggjpoJHzKPZHSaCzr/J9opNLvnecI8r0RxLsm/a7JfsL/2b7u3FDsp34XrOdZJuwXeQ7KdXorIt8lxkRuViZDBQnDkqkK045yNAuEsacsCyU3KYh4XVoGOT3zEvHnGRUdS2EuJde+nw+j0wmYwjnVsB7SQNBI8VQKX5PAy+9qPOVUyrj6G1njq9AIIBCoYBisVjnhZVEeiwWQyKRMJ5vVaCvTqjnXKFQLATSrvDHtg9yoW8vACW5QnvNPKs8Rh7LdGyZTAaRSKTuHlzUSNvJhZBUSnExVSgUzM9ClePyt7wXF3pcOMrcnq2o3aV6ku3LdvF4POjs7EQ8Hjd2eXZ2tq6dFKcG1FYrFgNSiWmndpAkkRyHJTEJ1KfEkk4/t7VJs7WD7NO2ylme24zol9exSXuuoeQ4LUl5ux0IN+eB7QSV7bAQyHI2OtdWI9ufN1oby+fItZtcU0tCdT7Hh5uttjcR5XEkp7khJ9eaTMPmVhe73JL0ZaoY/tj3s69lP3N5Xfn8SdiT/HVLOeMGkrfSmeDxeExZ/X4/CoVCnfKdZS0Wi8hms6hUKkgmk0in03WOEZLRrXAP0unU7KfZe2SnXnGLznD7LccAtxQsjXgJ+3PbieJWv0b3sc+fL2WLDds5J9Xqbu+3fM/Yb1p9Tmqrlw5KpCtOOVQqFcRiMeRyOfT19eHw4cPIZrPo6+tDV1fXggauZDKJyclJxGIxTExMYHZ2ti4fulSmP/nkk5iensa6devwohe9COFw2OSRmw/5fB4TExPI5XLYu3cv9u7di2w2uyBFOvO2Ub0nDbydc4z/03PeyLPrBhrtXC5nPK2xWMyEOcoFPNuKCkNVoa1uqMFXKBQS9sK6EXK5HA4fPoxoNIp8Pm/yj4dCIbOxGMnx3t5e9Pf3o1qtIh6PY2ZmBul0GqlUCul0GpVKBX6/vy40nyiVSjh06BCSySQKhQIGBgYQDodN+hjaTNpP5i0lsV2tVs094vE4ksmksW+tgDlnpX1ub283G6eSOKBDnvnLE4mEIfDnUwPyf6o7JZHB9DWM3DvZ464bcbFcYZMoq2n+orZasRiQOaOBo32DqSNtdXYjMZPsU9J52Aw2AQ7U50u2STdJDLoRv/J8WV4pTCL5xbGZZbCje/k31bgkHUlMk9ClMtWNzLZTTdgqb1lO2W6SNJTrP0m2ymcm12xMpcb6Skf3zMwMMpkMQqEQhoeH0dHRge7ubkSjUVN+GXFEpTTXgfydzWaRTCaRz+eRSCSMQjyTyRhiuVwuI5vNYu/evYjFYujv78emTZuM7WTd7DaTz6BQKCAej6NYLGJ6ehrxeBy5XM70Sz4HO32P7RCx5wPckJtkf6VSMf2hra0N6XTaXJ955OkwYDvZjiaS+5VKBdls1jgO6DyQKY8o6KOyHUCdCt/tvWEfpiPErf+QDJYiBNk32Ifc3mGp+KZTQPYJ+U7wh859vifkE2Qkof1c2XZer9fkkpdllcS57ciTz1WmZ5Hjkts7xP+Boxvzsow2+K6T+5FtICHbZT6orV5aKJGuOOVQqVSQSqWQzWYRi8UwMzODarWKcDiMzs5OAPNP0DiIZrNZTE1NIRaLmUWzHKBpELLZLJ5//nlMT0+jWCzi9NNPrzO+892rVCphamoKiUQChw4dwsGDBxvmJXWDDIuS3nEZxsR7kTzn33JX6YUM6lQPKhSEGnyFQmGjFdtSKBQwOztbt88Ic5RzsVoqldDW1oaOjg50dXWZvKfZbBa5XM6EhJMUBzBnsVOpVMyiOhgMIpPJ1EVqSaUh7TcXeF6v1yzwGWLNv1uF1+s1Iep0FgBHFflSocfw7kKhgHQ6vcBWd09ZwL1KlhIraax3I+NWA9RWKxYDcs0BoG5tRMLPFvU0IquAuZuENgP7oRuhxbHcPl6msJJ1sK8pr0OClHaJRJl9fZ5PElBuQsl1l1RhMyWITAsibU+jNrLLB9SntWmkMOfzCAQCCAaDpt60z7Q7tHf8DjjyPNPptMnDPTs7i3K5jHA4XJe2wlY4l8tlQ1xTsEV7ls/nzTVlZFexWDQk8djYmCHY16xZY+psp9GQ4xmfMVXbuVwOiUTC7PchFek2qSzHe9kvSKLLHymU4zxFthlQ/z4wTYlMK2RHI3B+UavVTIo5KtGl4I2fcU4UiURMP+I7Z/dL9kXp9GHfkXnMZZml84af2XUkSIhL1b79PknHvtw8VhL1bhGJ8tny/nJMkcQ4z2klStB2lhG2E4v353shU9zIMrHt5Tjj5rSSZP58UFu9tFAiXXFKggNbJpPBxMQE8vm82USrra3NLGTlwCsH93w+j3K5jOnpaRw+fBipVMrsgu02MNHz7vV6EYvFcODAAcRiMXR2dqKjo6MuVFtOMgqFglGbjY2NIZlMmrxpCxk8G5WplfNWk8pKoVAoFMsLrdiXfD6PyclJpNNpRKPRujBue7FTLpdN+jMS0HScM82Y24KMZeGiMJPJYGxsDIlEwthBEh5cJPv9ftRqNUxPTyORSNQR6blcruU0ZXIBxXkHF7WSmJEkiL2wa9XZrVg82Ko4hUJxFFJlDRxVzkpFqsyR7EbwzpeeYb6/SUoBR4kuN7WyvIetjreJLTcyWtoUkqFSPWuraO2UF41SWNj14fGtpiJtFqFkp8GQ6ljbocA6SMLfrR60v16v1+xhIp3QPEc6U6T6m2lQC4VC3feyXXgtrsczmQxmZ2fNWp77f9lOCpLRvA+j02mr6ciRUWGNnoH8TKqpy+WyiUKTUV88ju0sHfFuzlipspbtK9XbtpOI5DTPsxXf0gki21P2a/4tyV37Rz5DWW6bgJfksa3klwQy3y0p5GO5+R7RueT2LrnBjfiWkH1R/pZ1cYtAcbuO/Qyl84F9QEI+I34v27PVd1ux9FAiXXHKgWS4x+PB4cOH8cgjjyASiSCRSCCTySAcDmPt2rXGk86BXoY7Hzp0yISVPfXUUygUCkbZ7gaGmFMJPzk5iUAggOHhYQwODiIQCKCrq8so62gwJicnEY/H6+5Jcn2hizZp6KVxohGQx/BvSaLrIlFxvFDPuWI1w174Hk+fbbZ4W01otX5TU1N49NFHEQgEsHnzZmzYsAGRSATd3d11juharWaU6NyHI5/PY2xsDM8//7xRwDVKJcaFdqVSwcGDB5FIJODz+dDf34+enh6zyJakdqVSwejoKCYnJ+vsJjdiawXSGcBFJ1V37e3t6OnpqUsrA6BO3cXz51MoKhYPy3lu5EYcLKScaqsViwFbwZpKpVAoFOoiZKneBVCnqAXcU7BwnG/kDHWDJC7lWMt78Vq8P8sFHE1b0YyMk4pe1lUqT+U9eC3aDx4jryMdC25pNniPRpBqfKkmlt+TNHZzDri9w8yBThvJOpIM5g8FaolEArlcDjMzM/D7/SadqdxYk+tqku8UqI2Pj6NYLBr7LclIqdBnqhmWye/3o6enB11dXWhrazP3LJVKxh5zHc11O1XoTFnCdHGSOJU532Ubsn+zHSqVCjKZjLk+93Shw51EOiPq+HmxWDTPVK7BZX/kXEP2J1kmW/zHa3g8nrr9XexjeH2S8Hy2sm9IstkmnlkOlpcReNJhwP/5/Nvb283GtYwyqFardal55ByY15TRhLZqX/Zb6SjipqGyzKyznXKGx9CBI58v08XI+9oRH7wvozocx6mLSpQOEKm053OS9+E7Mh/UVi8tlEhXnJLgwJPL5TA9PY1MJoPh4WEMDAygUqmgq6vLGBZuYMaBNZfLmd2wZ2ZmMDMzYzYpaXY/bpDC3IDMe8ecqNVqFcFg0ISol8tlTExMYHp6GrlcDpOTk8jn88dVZ0J6ud0Wgxzsl/NCUbHyoAZfsdqx2AS4qoyPgDbW5/NhYGDA2FBpp4Aj7V4oFOrCwakOTyaTZnHUDHJ+kMvlzOLG7/fD6/UaIgiAUcDPzMxgamoKQH14b6vPzi1XJhelctx0Wzzyf+0risWC2mrFYoH9gYRbqVSqy51OIstWZRI2mUcci8NQ5jO3yXmSnJJcY/mpMHeLBpLrJBJiMn0Gy9movG7qcnn/Y4VU9dqfu5XFtikkU5luRDovpCKYxDavyXUy073wGBLK0knB65VKJWPj8/k8stmsWTPbOd/l8+O52WzWXFc6aXhv5linMyefz6NYLCKRSJhocT4DKaCbLxWIfHZUVdNJz7LbIjagfnNaW+ksye/5RG481iZ32ZfdVNY2JJEsHTxu/d2NhJdgOdnv+LcsO69LDgSASUHD98YtMo99Szpwmr2Pdh3t42Rkg9sYI50PzdKsyGgV3pvXbTZGyfdFXks6WlqB2uqlhRLpilMazMdWrVYxMjKCfD6PUCiE0dFRhMPhusGM+cZyuRzGx8eRy+UwNTVlvOWtKs8Y+lWr1TAzM2NCz3g/TjYrlQoSiYTxZraSz6vZPWmASqVSHQFhT/IYCi93XleFm2IxoAZfsZphL3aO91pKjM4F7abjHMkRPjU1ZZRNbC+mdGGalUqlgtnZ2QUR2xKO4xhCwFYvATAqNKkClIvHVutF5HI5s4DkYs/n8xlHO20zVVwkHI61forVB9vxciznq61WHC9IUtI2yrVOMBicQ/bJMcwmuuUx88EtrQMJX0mmAzBpSjwejyF43fI3SzRa7zUjvyU57kZa83y3VBHyuEYkp9v8Q6qaCUm2upXRJl/dnolsU7nBInDUHtKJnUwm4ff7kUql4PP5jBIbOJqXXdpq5iqX6WN4L7t+0hbTSZ5Op037kazN5XJIpVLmmvwh6S2JUtsxD7g/bwrhpJLbcRzDGdiKao/HYzYLLxQKJuqd7c762HUiscpjGQEg+Qm3vjCf40mO8ZxrsAyyn9ltYkdd0nHB7+xNPGXOd6Z1kn2Y4wIdN3YEiHS08RnL/Oe2AMEmxd0iV+gc4vGSuOd3drSITEnVCPxOOhDc8svzf/Yh3pNOI74TrfBKy9VWx+NxfOQjH8H3vvc9AMCb3vQm/N3f/R26u7ubluemm27CXXfdhXg8jgsuuABf/OIXcc4555hjisUi/vzP/xzf+ta3kM/n8cpXvhJ33HEH1q9fv6B7uz3HO++8E1deeeWC6qlEuuKUBglwj8eDvXv34vnnn4fP5zNpVmT4ET3Y5XIZqVSqLtffQgYjhnF5PB6Mj49jYmLC1cvLwXE+NUOr9yyVSvB6vSgWi3VGSA7yjnN0gxKG8R1LGhmFQqE4FbGYY6WOu3NRrVYxPj4+R/0tw8Wles5W0h0LHMdBIpFAKpUyn9kKqOON4GL5qNgE6jcJDwaDiEQideHi3MzUVgguJtSZs7Khz06xlOBah4RQMBg0G1nKdFxybJapFGwcyxjONY7cDJQ/dtoUEq/2+stWnUpSzE4/Y6/l3OohI4K5hpSEoCS77Tpz3Wan2LBJN1l2XrORstgmym0lsUxJI9tOkqgy8iCRSMDj8SCZTJpj6EDhZpzAEacxHcJynzFb6CVV4vZzYZnptKnVashmswBg1q+ZTAaxWAy1Wg3BYHBO6haZ/kPCjjaQbcTzmNJFbgIuj+H6m2tqEukyjYlUYfPZymuQNGf/q1arZp8W6RiS6ne3fmc7Cex3z03JDtSrs0nkU2THZyvLJkV40pHG+Q3rJ50DbCcJloPfM2pBbuZqR4+wnLIvy5RR7NvyXZBKcIoY5PPjsy6Xy3XnNnv3OScjl2RvBksinakCZWYDRiK2KtBcjnjHO96BQ4cO4Qc/+AEA4AMf+ADe9a534f777294zmc/+1l87nOfw1e/+lW84AUvwKc//Wm8+tWvxp49exCNRgEA1157Le6//37cc8896Ovrw0c/+lG88Y1vxGOPPWb6Qav3/spXvoLXvva15v+urq4F11OJdMUpDw5oVHkxpxyV2zTEkmCWO3sfz31P5iBJAyLJf3siReMod+7WhZhiMaH9SaFQHA/k4o95TeUih4vwxbSxJ9Ne24t32mWZHo42Wu204kRB+5RiMUG1tSSzJdlF2CTisUKSgXYucHnPZgpkm1B3I7ZbQSOSXV7TLf1Eo3q5KdLt6xGSCHRDI2W627NpdIwshyQWSabKTbMl6cx1NdelNskpy+fmoLCPtR3oFLzxPlLNbJfVTsVjtxHb1a0v0UbzfM4X5N8A6j5rNL66Pf/5nDRuz8JuG7f3ijxAowiIRu1g/+32LBrdR35mv/eNYPct+5lLJ06jusr7zDeXk+r0ZvWxHRaNnh2Fi3TYNDtOtlWrNni52epnnnkGP/jBD/Df//3fuOCCCwAAX/7yl3HhhRdiz549+I3f+I055ziOg9tvvx033ngj3vrWtwIAvva1r2Ht2rX45je/iQ9+8INIJpO4++678U//9E941ateBQD4xje+gQ0bNuDBBx/Ea17zmgXdu7u7G4ODg8dVVyXSFYr/AQciueO49MzaKrflNnA1g/TCMmSOkxqpjABQFyZ+vM4ChULieB1PCsVKgdtCUHHiYKc1WQ1tzjoVCgVMTU0hHo/XEQ4nI53LamhHxcKhtlqxGKhUKggGgwiFQnPyGgNziTmgMaFmK2fdYKdoIDnFz0ii0jFpE502GSpJ1kYpIhqRmbYi3K0ubtcjuE6zldiSTJTkm9wQ062sfr8fwWCwLtWnVOsCR9W5drSAXRe5Gapdb5/PZxTKjOKm3WI97E00ZZtTfS6jtGRbyfzo0pnAlCGSNOczZhSX7Rhxe4424cs24h5lVBDLZ0KlOSPjarWa2auFG2SyjHb6Dt5Dpo1j/SVhy59GfV++P+wntuJdbpLe6H2yn4f8julwgKNpefhOyTQ3PF9GMtiOGarP/X4/AoGAUW/LcsmyMjJP5laXfUo+D0Y+NHqmPFYq5eV+DR6Px+yPZ/cX+zq2Wl/Czj3v1q5ybIpEIvM6Wmwshq2W0ZbAkT4eCASO+bqPPPIIurq6DJENAL/zO7+Drq4uPPzww65E+sjICCYmJnDppZfWleOSSy7Bww8/jA9+8IN47LHHUC6X644ZHh7G1q1b8fDDD+M1r3nNgu599dVX433vex82bdqEP/3TP8UHPvCBpg4dNyiRrlAIcPBqtnHoSoWtvPd6vSiXy3OIdDkZ0wWRYjGhi3PFaoebckj77onHSlJlSxXbfCBBYi90FIoTCbXVisWA4xxJFREIBOpIbXu8diPR3VTVPLaZilqmarB/ZBoSuf+TjPyx7yFzgEtyUyrDGynNbUWwnbJE/t3oOvacwu3e9nzDJo1JEvp8vjpVtGx76TRgW0ni125ru835f3t7uyHh5IabvA734fJ4PAiFQiZ9KsGysmxSzU3YhKUknG2CVD5r+Vzt/sW6sLysE8lhlplEv7yWTFnCdTXzocuIAxntLZ0Esi3tfsBj3Zwj8rnZsFPX8NqN8vQ3g+wrLL+MNpCbdrJd5A/PdSPZ7TRFNichHQIk2mX/le+xbB97E1x73iXblWB6QDoGZP9r1CZuER92OiW2O1PU2A4seU8AC9oXbzFs9YYNG+o+/9SnPoUdO3Yc83UnJiawZs2aOZ+vWbMGExMTDc8BgLVr19Z9vnbtWhw4cMAc4/f70dPTM+cYnt/qvf/yL/8Sr3zlKxEKhfAf//Ef+OhHP4qZmRl84hOfWEBNlUhXKE5ZyMkW/wfcwx0VCoVCMT/kuOmmglEsLlaiw2KllFOhUCiOB8xjLMkwN2Wo/N0sdUojAh2Ym/dbXksSl16v16QTse9rl0VeQxKfkvxrltLCDbaa1u07meJBEpSsr1uKDkkM2iSkrdBn+XkPu76sqx2V7OYEttObkIAHjuaJlmXnfd3Kbh9jPyN5DstqK0jl2pZ18Xq9CIfDAI6qbe01sNwk3O6DkmiXqV3cyHhJepMwtdXKsmzSCUFCulHaGrvdSqWSeZ52KhK360qy136GMsWL3d6yHG7EMuvu9Xrh9/vrHDCyreQ7KMcDqcyXZbPV4KwPHR6twFbf29eV5WF/l/3WLkOj6/HHfjfls5REulski7yfvdnuicTBgwfR2dlp/m+kRt+xYwduuummptf62c9+BqBxGqv5xkq3sW2+c+xjWrm3JMx/8zd/EwBw8803K5GuUCiao5FHXn6vUJwIqMpNcapgJSmkTwQWoro+nnvIMO+VvDGTQrGcoLZasRiIRqPw+XxzNgMk3JTZEm4kq02S2ISjfYxUI8u0nG791I20k+fKPTnsdBlupJtdTl5PRv5KoluSrXad7PpKIpLlY7oMqTqWZaXKlqlDgKMpNmS7kKB124PDzjFN0p9pQ2V5GQEtiWSSlZLIrlar5nypUHebR8k9vqQzwE5JItOg+f1+hMPhurQ1lUqlbr8zPltJKtsqeNnXeA5TtvAzzkXK5bLZSFVuiimPYTvzR85p+NxsYp7PjWlzvF4vgsEg/H5/HVnINie57ZYjn8e7pXuRKWJspbnsi2wHxzmSf76jo6NuHxdJzMt0MEyRIzfhlI4b+VumAmIfls9hPtW4/N9uS9ZJqvfle9zMnjXqI4wCkf3SdqRIZxcAQ5zL8aGZU7FRHRcCntvZ2VlHpDfC1VdfjT/8wz9seszpp5+OJ598EpOTk3O+m56enqM4J5irfGJiAkNDQ+bzqakpc87g4CBKpRLi8XidKn1qagoXXXSROWah9waOpH9JpVKYnJxsepwNJdIVilMYuuBRnEzo4lyhUCwm7PyTCoXi+KG2WrEYsHOiS0ildzNRTyMyye26zdTJJFYbqUYbpS/hddwIZaA+5cp8fd9WGNvllf/b37mljZN1lISeJAalMln+P9/mo25qcLe0PPyMZZBKa5kKx61ObAdbBTwfiWkr0hvZf6n+JQkr1dDyb+mskfeQhKdMG+R2DP+WdWrmIJIq5mZpghq9A/IZNWoL+7qttrEN+Zwb1ZnkPZ019vO2+8V85bHT8dh1aoVong+y7K1cbz6Hn9t8tFE7NCqLfJdaKf+xYqHn9vf3o7+/f97jLrzwQiSTSTz66KP47d/+bQDAT3/6UySTSUN429i0aRMGBwexc+dOnHfeeQCOOF527dqF2267DQCwbds2+Hw+7Ny5E5dffjkAYHx8HE8//TQ++9nPHvO9AeDxxx9HMBhEd3d3a43xP1AiXaFQKBQnBbo4VyhODZyM95ULVU2ho1AsLtRWKxYDjQgjqQaV6mw38q0REQ+gIQEpCT2pGpUpU+yUExIyfYcsB8lnquzt1GJuKR9sgtu+nszL7IZGBL8kn6X6WtZNpv0AjqaTsX/LdrPBz6SiXBKckqxmHWiX+WylKpcKaVkXj8dj8l/buaZtRa+d4oTH8ZrynrxWrVYzm4FyU0yWWW5eSbhdv5ETwE6dY6dYsduS6T3cyG1bpSzTydipR+RxvIbsy/JYPg+3lCWSkLZJa74vMq+7dIDx/eX1ZeQB8/HzWJbXdkhIRbh9vKxbI2eJLLN81+wUMvZztKNNZLSC3T5yjinLZjvG5B50Ml2MW1561kkq1zmucHPc5Uakt4qzzjoLr33ta/H+978ff//3fw8A+MAHPoA3vvGNdZt9vvCFL8Stt96Kt7zlLfB4PLj22mtxyy23YMuWLdiyZQtuueUWhMNhvOMd7wAAdHV14U//9E/x0Y9+FH19fejt7cWf//mf40UvehFe9apXtXzv+++/HxMTE7jwwgsRCoXwwx/+EDfeeCM+8IEPLHiTVSXSFQqFQqFQKBQrDouhSFIoFArFiYOt3GXqCzvNSTAYrCNXJeltq6AJN5LT3iyQ5JYk9u0NIyUkISjB+zD3u1Qf2wrdRt8RJHT5W6ZbIdzsmyQL2Y622p7EnE2kk5yzz2lGVMr2IMkoVbOS1JVEOnB0E05JBJM05zPguXQo2Gnh+BwkkS/T4vA3N7QtFAqm3WwinYQ7ryWdITLtjl13myiXz0aSxG4EOushUwGx/8h83zJCQaqj2TYyJQ3T7sh72MpuPh8S1LxmI1Kcv22Cl9eXz5zP2Cbq29vbjWOChCQdL3Z6JvZ5t70KJKSjRh5nt7Gd+obvvB2FANTnAJfpnuRnLKtdDhuSkOeYxncaqHcO2teTfUA6FuiEWsnz23/+53/GRz7yEVx66aUAgDe96U34whe+UHfMnj17kEwmzf/XX3898vk8rrrqKsTjcVxwwQV44IEHEI1GzTH/+3//b7S3t+Pyyy9HPp/HK1/5Snz1q1+tc8DNd2+fz4c77rgD27dvR61Ww+bNm3HzzTfjQx/60ILrqUS6QqFQKE4KlqPnXKFQKBQKxVGorVYsFmwSvREZJckkW3XcKM0G/17ofRup3pulICFsotftvnaqCjttiLyWraa3z7frY5eD122UCkbCLY0Kj3VTy9pOgEap1Fp55yUZ6qbelm0kyWe3FDhu9bQJa/4try3JVnkfuy3ttrfrbNfFra6SCJfXsElym5hvVFeWleQzf/N685GubnW168TjqBCXZZckr91O0rEhIyPoOOD1JFh23rNR+XkNt/ddloHlc0ujIvtRs6gL2Yfs8sl2cCsr3xdJ+PPZ2M+Y39tqdekwdHvP3LBcbXVvby++8Y1vLOj+Ho8HO3bswI4dOxqeEwwG8Xd/93f4u7/7u2O+92tf+1q89rWvbVq2VqFEukKhUChOCparwVcoFAqF4kRjPhJuuUBttWIxUC6XXZXdkqiTRBLzWFcqFaOadgOPkypEN1UpSWM3lautUJcqdDvthg1bDWwTinbaE6YskUppSSKTVLMJX7sc/Ezmnue9mDJEQjooeC43uXRLD8JrcuNN+TmVxraier5NvmUdZPml6lmqed2Ic9lP3EjMSqWCXC4H4EjfCAaDcBzHpBfhxqI831aAs0y8l+1Ykf2EBKl8DrwHy8LjZP3dIitknWQKHKrUZcojPn+Zmka+W7KMdtvL9CW8FlCfY50qaN5TXp/RAlLRLqMRuIkoNxLl9f1+v7merTy306DY5XZzKMi+JFXwNoktz+W7BTTfs4Ht7vY3gDrHBZ+PXdZGts9+R/iu0/HAcvEdZhTCSk3tcipBiXSFQqFQnBSowVcoFArFqQipMFvu9kxttWIxQPKuEdFkp8zw+/0NyVz7fJJPbmRTo9Qqbgpcwla+Nrqv/MxWmLrdW6rAbbLWLo9MKyKJc37Pz6Ta1ybdbVLercwkad2cB7LMNniMJPwalZdwcwQw3QXb2y2lin3PZupcEvHSGcN7k6yWkQ7slzIVjiwv20Gea9/PLh9QvzkmiV06Udra2uryy8t7sf4yDYhMsyKdHvJHErRuivZG6mupirdJZzuSgu8vSV95L16ffUn2T9ZFKq8lmS73LZB9RDp/mj136RCQ74QNPotGERpuYPllfXhP1lc6VFjPZsp6WRY+b9lvZAomu12aQW310kKJdIVCoVAoFAqFQqE4QdBFq+JUh00U2USmnXKDsAkyt837gLlKcfu+bht6yg0OSbDKMjVKZVIqlVzvZddXkqmyLpLQdKuz3Sb2eVJpzHMlKc7fNmEvz3cjiN2cD5LEl9fn37ZiW5LiJFSppJYkLssslc7zkYe280ESz279hIpx+7nK42UaD5axFRKT9ZVkrd1vpGLdjsRw61du9ZDkv+wHduoa+2+C5wJH+4i9qSufi03Ok7C3nSvy/m7kPR08zdKi2Olj3PqpfX23Orq1g/xf9is7AqPR++32TtvXkBEkjWDfQ5L/spyyrfi+zBftoVh6KJGuUCgUipMC9ZwrFAqF4lTFSrFjaqsViwmbIJWkpU3O2oSdJBz5NxW+kpyTqVRsSDJLknI8ljmnZaoJqb6WJBhJ9FKpVEc62ylX3O7ttsmlW53t9pApPWwSlgQtN/C0CU7+LQliN4eCJHBt0pykvSybPFameJGpdLzeI5t7trW1IRQKmTYuFouGpGVKEDuti+0AkG1il1u2oSSrbcWvTVjKclLRTqU825JtZ5PAkqRlmhM737V0eBSLxbp+0ugZyPrw/lK9bTsxbFW2vBbvTecGIz7s9pEKflk33ruZcl8+F/v+/C3V427jgLxOo/fH/kw6P2SfYX3lpqoynQzvx+syTZENO9rEHjtkdAevaV/HHhPsftWI+Jdj0HxQW720UCJdoVAoFCcFavAVCsVSwSYDFAqFO9RWKxYbNpkuya9milpJYDVTifPcZv3PJuVsYoyqXDfizibWZJoQ+1o2GqWdmK+cjVTqRCOl+HyKfre2WwhpJ9vDLqdbeSRBLdN7yPNaeXatlM9WdUuy1S1KgfcGjjp2bFW4fV6j52nXSTp55HXclNzN6tRMHd+Kgt9NBe5GQvN4t3fSjSyX17P/lv3ArYzys2OxGex3dlnd3hs75Ypsdz7zhUQh2KATpdmzstuJ/9vvgyT5W4Ha6qWFEukKhUKhOClQg69QtIbjXWQsJywXAns5lGG1QyrwtL1XLtRWKxYD5XIZjuMYpaat2Ja2QRLYMs8yj7OJPxJjtqJZ5qSWx9l/25AElq38ZU5jqr+5MSZVqCTGpCLVLqedZsVNoSvbolHaFXkc29RuWyqwbTLYVs/bKUlkXmheZ76cz/amrl6vF8FgcA5xSPWufFayvvyRJLUkJueLNCCq1SpyudwcR4fdXjxf3p/RBlSnU8XPCASv14tAIFC32Sr7cLlcNo4CtqNURcu2ZB1J7kvFMnO8l8tlFIvFumfLe9nt6hZl4UbSy8/Yn9nXWV/m7p6P5HdLTWOrqN36n9vmo9wI1u/3z9mIVj53eU+bQJff27noWS6Z4132cZbTLVLAdvbQIeT3+02kAY+TfU06bniOvA+fZalUMs+UfadRdIsb1FYvLZRIVygUCsVJgRp8haI12IvtlQq33JCK1QsZzq75PVcu1FYrFgPMIy5J1WZKS3vMsNWZ8m+ZlkXmKydsItuGJJblvey0CpLsLZfLdeSo1+uFz+erO8eNGLeVyPZ9W4Uk0nk+STkSsLJekhC180WTiJTpPeT5wBGytVQqGXLRfrdJntPRQKcJiXRJjtvEMs+XRLO8t5sqvFGqFxLQAFAsFlEsFlGpVIzDw+fzmTLJlEAydQqdPqVSyRDMfLaSdJb1I1kqc6CzLkz3wrLbqmn5t3w3mH6F17PrKnOl284mO62OVJHb7SdJbZ/PN8eZ4tYvbZW5TTrb95R1lESzfNbsY/L6btEEbm3m9XrNc+Nn7AesmyTkZYSArQSnA6hR3WUZ6KiTDjZJzrNf2GOV/a7Kd4eoVqsIBALw+/0NN+C1n8mxQm318UOJdIVCoVAoFIplhPnCeVcSdLJ+6oD9Vp+5QqEgqBInbJVnMzQi9Gxyzu2e9vH2dSXB5na+VL3aqUdacQ5IJbJbHmaWzS5vozrIv+1UFY2igNyu0UjV63Z+M1Vss5Q38rfXW59DvRlsgta2KfaGso0c9W7pMZrZJXms27l2tBXrJUn5Rn0VwJy0Nq2gmfNgPpDUtp9HK2jUbnYfldez1eON3o1WUjfJssrruinl7X7v5qSy/5d9TG7WK4lwN4eYWwSAJO/tCI9WQOcOzz+eFDOKkw8l0hUKhUJxUqCec4WiNTRaFK80rIY6KFrHQhfsKxVSjbga66u2WrEYkApZqZS2P5M5qklWuqVusMltAEZ57PF4EAgE6lImECSo+L19zUYEoa2YJqQidj7ClqkbgPq0IiynVDITchNWNxJTKt3ZJm75qSXh56b2lz9uxwD1ubNt0lMq2mU7lcvlOelGuDmrrcCWpD7bKBAI1LWdnQaGSl2Z7kSqd0lMyrQ7su3cHChUYlMpbKuJmXKjWq2iWCwaVTHLYadfkQp8j8djVN+VSqWOvGU72Buasi1l2iA30lheQ4KK+lqthlwuh0qlYhTRvB/rYBPDvLabfeP39jOQzg6Sy7a63b6OrSKXdZCbiRL2PXmcjHqQKVXsMsloBB5XLBbNJqTNxgL2Kb6bvEYwGJwTlWG/z83ANEFsZzelfzOorV5aKJGuUCgUipMCNfgKxVGoclexGnEq9OnVrhpTW61YDDTLsW0TdW7pJxopxeXfJCwl0SUJLR5HElESf5IwJWy7bBObBEm5ZuOAJFRJhErSkSlAGilo7b/t7+2yub17dl0kIe5Wf7fz3MhSWR5ZVzsXONuZKTBIsLql6gBQl/ZCEsvy2nY/cVM5S2dEK8SkJJhtyNQ9Nrnslg7Ibg+qjqUDxH5Wbg4J1m2+yAe7nDynvb3dkM/2PZvV1Y1MtqNK5Od2u0hyvFF9CdkX3JxsbAO3etrnuB0rIcl0vo/slzy3UVlkmWxlO6MNmo13EpKIl5/Z700rcwy11UsLJdIVCoVCcVKgBl+hOIpTrU/bodEKxUrFfOkQVjrUVisWA27kop2j2Iat6HQj06XaF6jfjLFRGdzUycDc3OiETaLJFAyShLQV1TaxR+KYRH6jOjcihu1c7Y2iYGzVtl0Hu03dyGc7dQmPtUlvebzcoJR5xGV7kUjnZp18dnY6FLe2oaMBOKqWp6LbJkTd2sOt/8koCakoJ+RGkG4RCW5OC/m9VFJTZS5V/LJdZLkqlYrZbFMS4DyPzgWpcm/WXryuvIZ9T7ZjI4eSvC7PcesLsm1Y/0KhYNpXEvCN0qDYaVNkf7ffWdmG8+URbyZYkcp8WTbWRarMZc57lkG2gYwKkY49u835mVu0jby+fb9GUFu9tGgt0dIKwB133IFNmzYhGAxi27Zt+MlPfrLURVIoFArFEmKhdmHXrl3Ytm0bgsEgNm/ejC996Utzjrnvvvtw9tlnIxAI4Oyzz8Z3vvOdBd/XcRzs2LEDw8PDCIVCePnLX47du3cfX2VXCNRWn5qQyp3VrORVnHgsh/7jtrBWHDuWq60+1XG87WOTsCSnSAbaCs9mBDHfN27sWCwWTfoWv98Pn88HYC55yFQhJCjtVBYk5WyluhthShvG+/l8PnOuDdaLG13ax9oKbluFzs9sklKqu9kekkCX58nj+Dc3d+Rv/i2fiSSbJWFuP0+SkNwcs1gsGvU9v2PbyzzQtiPEHtPtOpfLZZRKJRQKBbOxI8lK+UzlD58T7y3TzhQKBZTL5ToSVjpc/H6/cRCw7WSf4P+y7PJZyk08eS03wlVGJfCa7C+S1JXt3Ei5Tshn3Uz5Lt9Fm8S1iX+7b/EY+5nK97JQKCCTyaBQKJj0KR6Px2ykyf4k6ybfUTcHi3R68Tj7/WtU50YiDvbRYDCIYDCIcDiMYDAIv9+PQCBgCH4+V25gK9uKZWhvbzepWqSDQOZjt9NdyfeS1y8UCsjlcsjlclAsb6wKIv3ee+/FtddeixtvvBGPP/44fvd3fxeve93rMDo6utRFUygUCsX/QC6cjuVnIVioXRgZGcHrX/96/O7v/i4ef/xx/MVf/AU+8pGP4L777jPHPPLII7jiiivwrne9C7/85S/xrne9C5dffjl++tOfLui+n/3sZ/G5z30OX/jCF/Czn/0Mg4ODePWrX410Or3AFl1ZUFutUCgUyx9qqxUnun3ciLJGGym6qTfldeSmj43uJUlIW1XdSBVt30f+tFrWRkS7WxkJN0W4Tf7bDgo3Ip5/2yp5t3JKyOPte8vr2QplNwW0/XwWIyqtWfvLYwD3jUMlZDmkM6WZQts+Xl6nUcqUZnArg/2527HzXWu+zxv1G7doBFmmZhELvK506Nj9TxLM0vFhO5ka9RO7X8logGbt4/YM5bVkXWTqJrdoEHvckJEH0qHTLArFbjOZHmYhivSTZasVc+FxVkErXnDBBfit3/ot3Hnnneazs846C29+85tx6623znt+KpVCV1fXiSyiQqFQnBJIJpPo7Oys+0yOsceqJqSpcru+GxZqFz72sY/he9/7Hp555hnz2ZVXXolf/vKXeOSRRwAAV1xxBVKpFP7t3/7NHPPa174WPT09+Na3vtXSfR3HwfDwMK699lp87GMfAwAUi0WsXbsWt912Gz74wQ8upFlWFE41W90spPRUhL1IWmw0W3wqFIp6qK0+Pnu02nE87cN+dPrpp9dtyijV4M1SekgbIQk4Kn25kaKdHkQqR6Va3U6vQqWxPAc4aqOo6pXlc0sLI8vqpqaXZJ8kf6WS3C1VB8vEejaK5GI9a7VaXdu6kZxyU0ZJ/NkbMJbLZWSzWdRqNYRCIUQikbp7F4tFFAoFo572+XyoVCrIZDIol8uIRCLo6emp20CSSlu2a6FQAAAEAgGzISafF1XBtVoNhULBfG6n0/J4PHXKbT4HOQ+gytsm+O3c61SOy2dDhbBUx8sUQcFgEN3d3Whra8Ps7Czi8Tja2trQ2dmJYDCITCaDWCyGarWKrq4udHR0oFqtIp/Po1qtIhQKoaOjA47jIB6PI51OIxAIoLu7G4FAAOl0GslkEh6PB52dnQiFQqhWq6btWRbC6/UaJbjjOIhEIgiHw6hUKkilUigWi+aeXq8XuVzOpF8JBAJmbwGm4GEUhVTLM4KBkRZ8r+g4KBaLKBaLpl3td0CeI/cs4PNgmRznyCagfDZ2Ch6WT9bDfsZS5c7zWA+v1zvnebulAXIcB7lcDsVi0VyL/S4cDte9P+xvfKft8cUeB+wUNnyvbNJ+//79y8ZWK+ZixSvSS6USHnvsMVx66aV1n1966aV4+OGHl6hUCoVCoXDDyfCaH4tdeOSRR+Yc/5rXvAY///nPzY7ujY7hNVu578jICCYmJuqOCQQCuOSSS1a1zTrVbHUzkuBUhVsIu0JxKsFN6bhccarb6lMZi9U+jchnmS5E/kgVp/2usH9VKhV4PB6Ew2GEw2GEQiGTLsItdYuErfzlbzs9iK2mlqkbmJbCDZIgd3vP3WxgI0Uvj5HpLphugmko/H6/OddO1SIdBG5kP9tfXp9EIYlv3t9Oc8K0E8VisS7fOAlpmd4COOr4CAaDdelA7EgB2T/sVC1+v9/8BAIBk3bDPkc+O9kG/Mzv9yMYDCIQCJg6yWPk/3w+vD+dNzKNh030BoNBdHR0oL293aSkkeloZGoju1/wODvVjp2fXX4n+5AdtSBJat5bqs7lvew0Om59DzhKlss2s9vCTl0jU5ewzD6fz7y7fH8BoFwum3swBY90qrlFUsiURWxv2W6NxoRGYwy/k21p/89xge8Q+zh/OB5Jst0NMoLBTvMyX/53u9yqRj/5WPGbjc7MzKBarWLt2rV1n69duxYTExOu50iPGXDEE6NQKBSK48eJNs6pVKruf07CJI7FLkxMTLgeX6lUMDMzg6GhoYbH8Jqt3Je/3Y45cOBAw3qvdJxqtlonqScf2uaK5Y7l1EfVVi/svqcSFto+jWy1TRqSXJMpE9xSg0jCl8pOSXqS0JXkmE1Uyk0qG6VBaZTeRJ4vy9vW1laXq53fk7SzP7PPl2VgOSThZ5dR3kdGuEmVuU38ymuQAOex8p68jlTGuqWXIJkn21YSsW7/8xz+lhEAshyyLeX3bspetqPdBo3GMdlfpFOE/cG+t10+O2+43W/pbJDKd6lkd0vTIZ+XXU8ey1zisuw8Vl5DOgxYNrttG5VDXrO9vb3uM/uedv+W38vzZH+UZD1hn0OHmMxBbreRXWb7WnbUge0Qkfe0nQn293IscktjI9vbbhtZB15btpmtlreJbNkH5HfSEaZYnljxRDphe3oaeYMB4NZbb8VNN910MoqlUCgUpxTS6fSc9Bt+vx+Dg4PHvUDt6OjAhg0b6j771Kc+hR07drgevxC70Oh4+/NWrrlYx6xGqK1WKBSKpYfa6lPXDreKVtunka3ev3//iSqaQrEssW/fvjmfzc7OLkFJFKsFJ9JWDw4OmmgAxcKx4on0/v5+tLW1zelIU1NTczzpxA033IDt27eb/xOJBDZu3IjR0dEVlX+1GVKpFDZs2ICDBw+umrxHq61Oq60+gNZpJeBE1cdxHKTTaQwPD8/5LhgMYmRkxIQLHs897EWcrXADjs0uuE1Ipqam0N7ejr6+vqbH8Jqt3HdwcBDAEVXd0NBQS2VbDVBb7Y7VNr4Aq69Oq60+gNZppeBE1Elt9bHd91TCQttHbfXKxGqr02qrD6B1WilYqbaaqY4Ux4YVT6T7/X5s27YNO3fuxFve8hbz+c6dO3HZZZe5nuMWXggAXV1dq+aFJjo7O7VOyxyrrT6A1mkl4ETUp9mCiTkdTwaOxS5ceOGFuP/+++s+e+CBB3D++efD5/OZY3bu3Inrrruu7piLLrqo5ftu2rQJg4OD2LlzJ8477zwAR/KR7tq1C7fddtsi1H55Qm11c6y28QVYfXVabfUBtE4rBYtdJ7XVC7/vqYSFto/a6pWN1Van1VYfQOu0UrBabbXCHSueSAeA7du3413vehfOP/98XHjhhbjrrrswOjqKK6+8cqmLplAoFIolwHx24YYbbsDY2Bi+/vWvAwCuvPJKfOELX8D27dvx/ve/H4888gjuvvtufOtb3zLXvOaaa3DxxRfjtttuw2WXXYbvfve7ePDBB/HQQw+1fF+Px4Nrr70Wt9xyC7Zs2YItW7bglltuQTgcxjve8Y6T2EInH2qrFQqFQiGxXG31qQ5tH4VCoVAoGmNVEOlXXHEFZmdncfPNN2N8fBxbt27F97//fWzcuHGpi6ZQKBSKJcB8dmF8fByjo6Pm+E2bNuH73/8+rrvuOnzxi1/E8PAwPv/5z+Ntb3ubOeaiiy7CPffcg0984hP45Cc/iTPOOAP33nsvLrjggpbvCwDXX3898vk8rrrqKsTjcVxwwQV44IEHEI1GT0LLLB3UVisUCoVCYjnb6lMZ2j4KhUKhUDSBo3AKhYLzqU99yikUCktdlEWD1mn5Y7XVx3G0TisBq60+ilMHq7Hvap2WP1ZbfRxH67RSsBrrpFj9WI39Vuu0/LHa6uM4WqeVgtVYJ8X88DjO/2x1rlAoFAqFQqFQKBQKhUKhUCgUCoViDrxLXQCFQqFQKBQKhUKhUCgUCoVCoVAoljOUSFcoFAqFQqFQKBQKhUKhUCgUCoWiCZRIVygUCoVCoVAoFAqFQqFQKBQKhaIJlEhXKBQKhUKhUCgUCoVCoVAoFAqFoglOeSL9jjvuwKZNmxAMBrFt2zb85Cc/WeoitYxbb70VL3nJSxCNRrFmzRq8+c1vxp49e+qOcRwHO3bswPDwMEKhEF7+8pdj9+7dS1TiheHWW2+Fx+PBtddeaz5bifUZGxvDH//xH6Ovrw/hcBi/+Zu/iccee8x8v9LqVKlU8IlPfAKbNm1CKBTC5s2bcfPNN6NWq5ljlnudfvzjH+P3f//3MTw8DI/Hg3/5l3+p+76V8heLRXz4wx9Gf38/IpEI3vSmN+HQoUMnsRb1aFancrmMj33sY3jRi16ESCSC4eFhvPvd78bhw4frrrHc6qRQSKxUe622emXUR2318quT2mq11YqVB7XVyxdqr5cf1FYfwXKza2qrFfPCOYVxzz33OD6fz/nyl7/s/OpXv3KuueYaJxKJOAcOHFjqorWE17zmNc5XvvIV5+mnn3aeeOIJ5w1veINz2mmnOZlMxhzzV3/1V040GnXuu+8+56mnnnKuuOIKZ2hoyEmlUktY8vnx6KOPOqeffrpz7rnnOtdcc435fKXVJxaLORs3bnTe+973Oj/96U+dkZER58EHH3SeffZZc8xKq9OnP/1pp6+vz/nXf/1XZ2RkxPm///f/Oh0dHc7tt99ujlnudfr+97/v3Hjjjc59993nAHC+853v1H3fSvmvvPJKZ926dc7OnTudX/ziF84rXvEK58UvfrFTqVROcm2OoFmdEomE86pXvcq59957nV//+tfOI4884lxwwQXOtm3b6q6x3OqkUBAr2V6rrV7+9VFbvTzrpLZabbViZUFt9fKF2uvlWSe11Uew3Oya2mrFfDilifTf/u3fdq688sq6z174whc6H//4x5eoRMeHqakpB4Cza9cux3Ecp1arOYODg85f/dVfmWMKhYLT1dXlfOlLX1qqYs6LdDrtbNmyxdm5c6dzySWXGGO/EuvzsY99zHnZy17W8PuVWKc3vOENzp/8yZ/UffbWt77V+eM//mPHcVZenWzj2Er5E4mE4/P5nHvuucccMzY25ni9XucHP/jBSSt7I7hNYmw8+uijDgCzuFnudVKc2lhN9lpt9fKD2uojWM51Uluttlqx/KG2enlC7fXyrZPa6uVv19RWK9xwyqZ2KZVKeOyxx3DppZfWfX7ppZfi4YcfXqJSHR+SySQAoLe3FwAwMjKCiYmJujoGAgFccskly7qOH/rQh/CGN7wBr3rVq+o+X4n1+d73vofzzz8ff/AHf4A1a9bgvPPOw5e//GXz/Uqs08te9jL8x3/8B/bu3QsA+OUvf4mHHnoIr3/96wGszDpJtFL+xx57DOVyue6Y4eFhbN26dUXUETgyXng8HnR3dwNYHXVSrE6sNnuttnr5QW31ESz3OkmorV65dVKsTqitXr5Qe71866S2enXYNbXVpx7al7oAS4WZmRlUq1WsXbu27vO1a9diYmJiiUp17HAcB9u3b8fLXvYybN26FQBMPdzqeODAgZNexlZwzz334Be/+AV+9rOfzfluJdbn+eefx5133ont27fjL/7iL/Doo4/iIx/5CAKBAN797nevyDp97GMfQzKZxAtf+EK0tbWhWq3iM5/5DP7oj/4IwMp8ThKtlH9iYgJ+vx89PT1zjlkJ40ehUMDHP/5xvOMd70BnZyeAlV8nxerFarLXaquXZ33UVh/Fcq6ThNrqlVknxeqF2urlCbXXMP8vxzqprV75dk1t9amJU5ZIJzweT93/juPM+Wwl4Oqrr8aTTz6Jhx56aM53K6WOBw8exDXXXIMHHngAwWCw4XErpT4AUKvVcP755+OWW24BAJx33nnYvXs37rzzTrz73e82x62kOt177734xje+gW9+85s455xz8MQTT+Daa6/F8PAw3vOe95jjVlKd3HAs5V8JdSyXy/jDP/xD1Go13HHHHfMevxLqpDg1sNLHFEBt9XKsD6C2WmI518kNaquPYCXUSXFqYKWPKcDqsNWA2muJ5VontdWNsRLqqLb61MUpm9qlv78fbW1tczxCU1NTczxmyx0f/vCH8b3vfQ8//OEPsX79evP54OAgAKyYOj722GOYmprCtm3b0N7ejvb2duzatQuf//zn0d7ebsq8UuoDAENDQzj77LPrPjvrrLMwOjoKYOU9IwD4X//rf+HjH/84/vAP/xAvetGL8K53vQvXXXcdbr31VgArs04SrZR/cHAQpVIJ8Xi84THLEeVyGZdffjlGRkawc+dO4zUHVm6dFKsfq8Veq61envUB1FZLLOc6SaitXll1Uqx+qK1eflB7fRTLtU5qq1euXVNbfWrjlCXS/X4/tm3bhp07d9Z9vnPnTlx00UVLVKqFwXEcXH311fj2t7+N//zP/8SmTZvqvt+0aRMGBwfr6lgqlbBr165lWcdXvvKVeOqpp/DEE0+Yn/PPPx/vfOc78cQTT2Dz5s0rqj4A8NKXvhR79uyp+2zv3r3YuHEjgJX3jAAgl8vB660fOtra2lCr1QCszDpJtFL+bdu2wefz1R0zPj6Op59+etnWkcZ+3759ePDBB9HX11f3/Uqsk+LUwEq312qrl3d9ALXVxHKvk4Ta6pVTJ8WpAbXVyw9qr49gOddJbfXKtGtqqxU4GTuaLlfcc889js/nc+6++27nV7/6lXPttdc6kUjE2b9//1IXrSX82Z/9mdPV1eX86Ec/csbHx81PLpczx/zVX/2V09XV5Xz72992nnrqKeeP/uiPnKGhISeVSi1hyVuH3FnccVZefR599FGnvb3d+cxnPuPs27fP+ed//mcnHA473/jGN8wxK61O73nPe5x169Y5//qv/+qMjIw43/72t53+/n7n+uuvN8cs9zql02nn8ccfdx5//HEHgPO5z33Oefzxx81O262U/8orr3TWr1/vPPjgg84vfvEL5/d+7/ecF7/4xU6lUll2dSqXy86b3vQmZ/369c4TTzxRN14Ui8VlWyeFgljJ9lpt9fKvj9rq5VkntdVqqxUrC2qrlz/UXi8vqK0+guVm19RWK+bDKU2kO47jfPGLX3Q2btzo+P1+57d+67ecXbt2LXWRWgYA15+vfOUr5phareZ86lOfcgYHB51AIOBcfPHFzlNPPbV0hV4gbGO/Eutz//33O1u3bnUCgYDzwhe+0Lnrrrvqvl9pdUqlUs4111zjnHbaaU4wGHQ2b97s3HjjjXWGY7nX6Yc//KHru/Oe97zHcZzWyp/P552rr77a6e3tdUKhkPPGN77RGR0dXYLaHEGzOo2MjDQcL374wx8u2zopFBIr1V6rrV4Z9VFbvfzqpLZabbVi5UFt9fKG2uvlBbXVR7Dc7JraasV88DiO4xy7nl2hUCgUCoVCoVAoFAqFQqFQKBSK1Y1TNke6QqFQKBQKhUKhUCgUCoVCoVAoFK1AiXSFQqFQKBQKhUKhUCgUCoVCoVAomkCJdIVCoVAoFAqFQqFQKBQKhUKhUCiaQIl0hUKhUCgUCoVCoVAoFAqFQqFQKJpAiXSFQqFQKBQKhUKhUCgUCoVCoVAomkCJdIVCoVAoFAqFQqFQKBQKhUKhUCiaQIl0hUKhUCgUCoVCoVAoFAqFQqFQKJpAiXSFAsDLX/5yXHvttSvmuouN/fv3w+Px4IknnljqoigUCoVC4Qq11WqrFQqFQrG8obZabbVCsdrRvtQFUChWM7797W/D5/OdtPv96Ec/wite8QrE43F0d3eftPsqFAqFQrFSobZaoVAoFIrlDbXVCoViuUCJdIXiBKBcLsPn86G3t3epi6JQKBQKhcIFaqsVCoVCoVjeUFutUCiWGzS1i0LxP6jVarj++uvR29uLwcFB7Nixw3w3OjqKyy67DB0dHejs7MTll1+OyclJ8/2OHTvwm7/5m/jHf/xHbN68GYFAAI7j1IWg/ehHP4LH45nz8973vtdc584778QZZ5wBv9+P3/iN38A//dM/1ZXR4/HgH/7hH/CWt7wF4XAYW7Zswfe+9z0AR8LIXvGKVwAAenp66q79gx/8AC972cvQ3d2Nvr4+vPGNb8Rzzz13TO108803Y3h4GLOzs+azN73pTbj44otRq9WO6ZoKhUKhULQCtdWtQW21QqFQKJYKaqtbg9pqhWJlQol0heJ/8LWvfQ2RSAQ//elP8dnPfhY333wzdu7cCcdx8OY3vxmxWAy7du3Czp078dxzz+GKK66oO//ZZ5/F//k//wf33Xefa060iy66COPj4+bnP//zPxEMBnHxxRcDAL7zne/gmmuuwUc/+lE8/fTT+OAHP4j/7//7//DDH/6w7jo33XQTLr/8cjz55JN4/etfj3e+852IxWLYsGED7rvvPgDAnj17MD4+jr/9278FAGSzWWzfvh0/+9nP8B//8R/wer14y1veckwG+sYbb8Tpp5+O973vfQCAL33pS/jxj3+Mf/qnf4LXq0OKQqFQKE4c1Fa3BrXVCoVCoVgqqK1uDWqrFYoVCkehUDiXXHKJ87KXvazus5e85CXOxz72MeeBBx5w2tranNHRUfPd7t27HQDOo48+6jiO43zqU59yfD6fMzU1Nee611xzzZz7zczMOGeccYZz1VVXmc8uuugi5/3vf3/dcX/wB3/gvP71rzf/A3A+8YlPmP8zmYzj8Xicf/u3f3Mcx3F++MMfOgCceDzetL5TU1MOAOepp55yHMdxRkZGHADO448/3vQ84rnnnnOi0ajzsY99zAmHw843vvGNls5TKBQKheJYobZabbVCoVAoljfUVqutVihWO9TNpVD8D84999y6/4eGhjA1NYVnnnkGGzZswIYNG8x3Z599Nrq7u/HMM8+YzzZu3IiBgYF571Mul/G2t70Np512mvFsA8AzzzyDl770pXXHvvSlL627h13OSCSCaDSKqamppvd87rnn8I53vAObN29GZ2cnNm3aBOBIaN2xYPPmzfibv/kb3Hbbbfj93/99vPOd7zym6ygUCoVCsRCorW4daqsVCoVCsRRQW9061FYrFCsPutmoQvE/sHcB93g8qNVqcBwHHo9nzvH255FIpKX7/Nmf/RlGR0fxs5/9DO3t9a+gfR+3ezcqZzP8/u//PjZs2IAvf/nLGB4eRq1Ww9atW1EqlVoqsxt+/OMfo62tDfv370elUplTF4VCoVAoFhtqqxcGtdUKhUKhONlQW70wqK1WKFYWVJGuUMyDs88+G6Ojozh48KD57Fe/+hWSySTOOuusBV3rc5/7HO69915873vfQ19fX913Z511Fh566KG6zx5++OEF3cPv9wMAqtWq+Wx2dhbPPPMMPvGJT+CVr3wlzjrrLMTj8QWV28a9996Lb3/72/jRj36EgwcP4i//8i+P63oKhUKhUBwP1FbPhdpqhUKhUCwnqK2eC7XVCsXKg7q6FIp58KpXvQrnnnsu3vnOd+L2229HpVLBVVddhUsuuQTnn39+y9d58MEHcf311+OLX/wi+vv7MTExAQAIhULo6urC//pf/wuXX345fuu3fguvfOUrcf/99+Pb3/42HnzwwZbvsXHjRng8Hvzrv/4rXv/61yMUCqGnpwd9fX246667MDQ0hNHRUXz84x9fcDsQhw4dwp/92Z/htttuw8te9jJ89atfxRve8Aa87nWvw+/8zu8c83UVCoVCoThWqK2uh9pqhUKhUCw3qK2uh9pqhWJlQhXpCsU88Hg8+Jd/+Rf09PTg4osvxqte9Sps3rwZ995774Ku89BDD6FareLKK6/E0NCQ+bnmmmsAAG9+85vxt3/7t/jrv/5rnHPOOfj7v/97fOUrX8HLX/7ylu+xbt063HTTTfj4xz+OtWvX4uqrr4bX68U999yDxx57DFu3bsV1112Hv/7rv15Q2QnHcfDe974Xv/3bv42rr74aAPDqV78aV199Nf74j/8YmUzmmK6rUCgUCsXxQG31UaitVigUCsVyhNrqo1BbrVCsXHgcx3GWuhAKhUKhUCgUCoVCoVAoFAqFQqFQLFeoIl2hUCgUCoVCoVAoFAqFQqFQKBSKJlAiXaFQ1OHKK69ER0eH68+VV1651MVTKBQKheKUh9pqhUKhUCiWN9RWKxSrE5raRaFQ1GFqagqpVMr1u87OTqxZs+Ykl0ihUCgUCoWE2mqFQqFQKJY31FYrFKsTSqQrFAqFQqFQKBQKhUKhUCgUCoVC0QSa2kWhUCgUCoVCoVAoFAqFQqFQKBSKJlAiXaFQKBQKhUKhUCgUCoVCoVAoFIomUCJdoVAoFAqFQqFQKBQKhUKhUCgUiiZQIl2hUCgUCoVCoVAoFAqFQqFQKBSKJlAiXaFQKBQKhUKhUCgUCoVCoVAoFIomUCJdoVAoFAqFQqFQKBQKhUKhUCgUiiZQIl2hUCgUCoVCoVAoFAqFQqFQKBSKJlAiXaFQKBQKhUKhUCgUCoVCoVAoFIom+P8Be0R8LD5nDVAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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H++67L/r6+nD77bcDUBIDBx98MN71rnfhySefxBNPPIGJiQn86le/yjjW+8lPfoJjjz0WCxcuzLT/SSedBABN9fHGN75xSsvKqbB8+XK85CUvyVx75zvfieHhYTz44INJeQ866CC8/OUvbypvFEX4xS9+sUNpt7W1Nc0RDznkkKb5WVdXF974xjc25dHCwmLmw46/dvzdFqaztv3FL36B4447DosWLcrc+973vhdjY2NNjlG3B3bMT8trx3yL3QFWzsXiRYHnnnsOURQZncKYrlGGRL9fvw4ACxcuBKA023YEc+bMabqWz+cxPj6e/L9582b09PQ0hTNde74wlXFnYjrltbCwsLBQGB0dxebNm3HwwQcn1975znfi5z//OT75yU/i8MMPR7FYRFtbG04++eRpvUsbjQZOPPFErF+/Hp/85Cdx8MEHo7OzE41GA6tWrdpmHM8++yzWrFnTcpGk67pONa7ov3EsbTXe6huuOzJmveQlL9kux2bE3nvvjQ984APJ/9/+9rfxjne8A//8z/+M3/72t9OO5xe/+AWefPJJnHvuuZkF+9ve9jbccccd+MY3voEzzjhju/NHcM6g+2eZN28eXNfNzFeOO+44/OxnPwOgyI0TTjgBBx98MObPn4/bb78d++23X3J8mXj22Wfx4x//eKe0/7Yw1dyH5di8eTP6+vqawj3f+VmhUIDv+5lr+Xwe1Wo1+X/z5s3GeeQLMT+zsLD4y8KOvyns+Nsa013LvxA8Au+1Y74d8y12H1gS3eJFgdmzZ6Otrc2of25yBKEPQrNnz4bjONiwYUNT2PXr1wNAMiHgy3diYiLjiXq6zmJMmDNnjjGfL4QTC5NTVFkmiecz4FtYWFhYbBs//elPMTk5mWhTbt26FT/5yU9w4YUX4vzzz0/CTUxMYMuWLdOKc/Xq1fj973+Pm266Caeddlpyfc2aNdO6v1wuo6Ojo6VTaH2BbBpXWv3GxeiGDRvQ29ub+W39+vXbFfcLjbe97W24/PLLsXr16u2674YbbgAAfOYzn8FnPvMZ4+/Ph0SfM2cOfvOb3yCKokz9bNy4EWEYZurwuOOOww033IDf/va3+M1vfoNPfOITAJT14m233YannnoKQRBg1apVyT3lchmHHHIIPvWpTxnT50KWeD5tNNXch31lzpw52z0/k3i+8zMTgWOdjFlYzHzY8TeFHX+fH6Y7Tu1o3HbMt2O+xe4DK+di8aJAZ2cnVq5cif/+7/9GrVZLrlcqFfzkJz+Z1v1HHHEEvv/972d2lRuNBr761a+it7c3kUDhzuj//d//ZeKQHq63F8ceeyx+/vOfZzYBJicn8a1vfWu74yKxvz2W3/Pnz4fv+01l+uEPf7hT4rewsLCwaMbTTz+N8847D7NmzUoWdW1tbYiiKLNJCwD/+Z//icnJycy1Vu9jLnD0OL70pS9NK1+nnHIKnnjiCcyZMwcrV65s+pgshKYLHg3/6le/mrl+//3349FHH8Vxxx23w3HvKEyLNUDNIdatW9e0gJwKzz33HH7wgx/gFa94Be64446mz9/+7d/i/vvv325iQOK4445DpVLBf//3f2eu33zzzcnvMmxbWxs++clPwnGcxAna8ccfjzvuuAO33XYbXv3qV2cs0E455RSsXr0a++67r7H9t6c+toWHH34Yv//97zPXvv71r6Orqytx1nfcccfhkUceSY56y/K2tbXh2GOPBdB6fvajH/1oh/N37LHHYmRkpCmO7XX8bmFhsXvBjr8p7Pj7/HHcccfhF7/4RUL0EjfffDMKhUKGtN6RuO2Yb8d8i90H1hLd4kWDSy65BK9//evx2te+Fh/60IcwOTmJK6+8EkEQTMt64PLLL8cJJ5yAY489Fueddx48z8O1116L1atX4xvf+EYyKTr55JPR3d2N973vfbjkkkvgui5uuukmrFu3bofz/olPfAI/+tGP8JrXvAYXXHABCoUCrrnmGoyOjm53XDyS+LnPfQ6nnXYacrkcDjjgAHR1dbW8p62tDe9617vwX//1X9h3333xkpe8BL/97W+NA8aOxG9hYWGxp2P16tWJzuTGjRtx991348Ybb0R7ezt+8IMfYO7cuQCAYrGIV7/61bjyyitRLpfR19eHu+66CzfccANKpVImzhUrVgAArr/+enR1dcH3fSxZsgTLli3Dvvvui/PPPx9RFKG7uxs//vGPcdttt00rr2effTa+973v4dWvfjXOOeccHHLIIWg0Gnj66adx66234sMf/jCOOOKIHaqHAw44AP/wD/+A//iP/4DjODjppJOwdu1afPKTn8SiRYtwzjnn7FC8zwef+tSn8Otf/xpvf/vbceihh6KjowNPPvkkvvCFL2Dz5s248sorpx3X1772NVSrVXzwgx9MrBsl5syZg6997Wu44YYb8NnPfnaH8vue97wH11xzDU477TSsXbsWBx98MH71q1/hsssuw8knn5w5pj1v3jysWLECt956K4499lgUCgUAakG9ZcsWbNmypcla75JLLsFtt92Go446Ch/84AdxwAEHoFqtYu3atbjlllvwxS9+scmKcUexcOFCvPGNb8RFF12EBQsW4Ktf/Spuu+02XHHFFUlezznnHNx88814/etfj0suuQR77703fvrTn+Laa6/FBz7wgcTIoaenB8cffzwuv/xyzJ49G3vvvTd+/vOf4/vf//4O5+8973kPPvvZz+I973kPPvWpT2G//fbDLbfcgv/5n//ZKeW3sLB44WHHXwU7/j7/8bcVLrzwwkRb/IILLkB3dze+9rWv4ac//Sk+/elPY9asWTsctx3z7ZhvsZthV3gztbB4ofCDH/wgOvjggyPP86LFixdH//Zv/xZ98IMfjGbPnp2EgeYVXOLuu++OXvOa10SdnZ1RR0dHtGrVqujHP/5xU7jf/va30VFHHRV1dnZGe+21V3ThhRdG//mf/xkByHgS33vvvaPXv/71TffrXrejKIp+/etfR6tWrYry+XzU09MT/fM//3N0/fXXN8U5HXzsYx+LFi5cmHg6p9fqVvmJoijaunVr9P73vz+aP39+1NnZGb3hDW+I1q5dGwGILrzwwucVv6m8FhYWFnsCbrzxxghA8vE8L5o3b1509NFHR5dddlm0cePGpnv6+/ujU089NZo9e3bU1dUVve51r4tWr14d7b333tFpp52WCXv11VdHS5Ysidrb2yMA0Y033hhFURQ98sgj0QknnBB1dXVFs2fPjt761rdGTz/9tPGdbkKlUok+8YlPRAcccEDkeV40a9as6OCDD47OOeecaGBgIAnXakxlue+///6m3yYnJ6Mrrrgi2n///aNcLheVy+XoXe96V7Ru3bpMuKOPPjpavnz5NvNKXHjhhRGAaNOmTVOG0/N83333Rf/4j/8YveQlL4m6u7uj9vb2aO7cudHrXve66JZbbmkZjyl/hx56aDRv3rxoYmKi5X2rVq2KyuXylGH0dPQxdPPmzdGZZ54ZLViwIHJdN9p7772jj33sY1G1Wm26/5xzzokARJ/61Kcy1/fbb78IQPR///d/Tfds2rQp+uAHPxgtWbIkyuVyUXd3d/Syl70s+pd/+ZeoUqlEURRFTz75ZAQguvLKK6dVDh2cM3z3u9+Nli9fHnmeF/X19UWf+cxnmsI+9dRT0Tvf+c5ozpw5US6Xiw444IDoyiuvjCYnJzPhNmzYEL3lLW+Juru7o1mzZkXvete7ogceeCDzbERRFJ122mlRZ2dnUzrsQxJ8HoMgiLq6uqJTTz01uueee5ritLCw2L1gx187/urY3vF3e9a2f/jDH6I3vOEN0axZsyLP86KXvOQlOzRG2DHfjvkWuzfaokhzSW1h8SJCvV7HoYceir322gu33nrrrs6OhYWFhYWFhYWFhYWFhYWFhYWFxQyDlXOxeFHhfe97H0444QQsWLAAAwMD+OIXv4hHH30Un/vc53Z11iwsLCwsLCwsLCwsLCwsLCwsLCxmICyJbvGiwsjICM477zxs2rQJuVwOL33pS3HLLbdktMJmKhqNBhqNxpRhXNc+0hYWFhYWFjMBk5OTmOpAaFtbG9rb2/+COdo5eLGWy8LCwsLixYFdMU69WMfGF2u5LCxawdnVGbCw2Jn49re/jf7+fkxMTKBSqeCXv/wlXve61+3qbO0UXHLJJcjlclN+1q5du6uzaWFhYWFhYTEN7LvvvlOO6ccdd9yuzuIO4cVaLgsLCwuLFwd2xTj1Yh0bX6zlsrBoBauJbmExQ7B+/XqsX79+yjCHHHIIPM/7C+XI4sWCZ555Bh/96Efx//1//x/Gx8ex//7744YbbsDLXvYyAEAURbj44otx/fXX47nnnsMRRxyBa665BsuXL9/FObewsLCYufjDH/6AiYmJlr93dXXhgAMO+AvmaOfgxVqu3Q127LawsLDYMeyKcerFOja+WMv1QsGO3TMflkS3sLCw2IPx3HPP4bDDDsOxxx6LD3zgA5g3bx6eeOIJ9PX1Yd999wUAXHHFFfjUpz6Fm266Cfvvvz8uvfRS/PKXv8Tjjz+Orq6uXVwCCwsLCwuLPQt27LawsLCwsJhZsGP3iwOWRLewsLDYg3H++efj17/+Ne6++27j71EUYeHChTj77LPx0Y9+FAAwMTGB+fPn44orrsAZZ5zxl8yuhYWFhYXFHg87dltYWFhYWMws2LH7xQFLor+AaDQaWL9+Pbq6utDW1rars2NhYbEHoNFo4KmnnsLixYszTlzy+Tzy+XxT+IMOOgivfe1r0d/fj7vuugt77bUXzjrrLJx++ukAgD//+c/Yd9998eCDD+Kwww5L7nvTm96EUqmEr3zlKy98oXYQ1113Ha677rrEV8Dy5ctxwQUX4KSTTmp5z1133YVzzz0XDz/8MBYuXIiPfOQjOPPMM/9CObbYHWDHbgsLi7807NidxbXXXosrr7wSGzZswPLly3H11VfjVa96Vcvw2xq7v/zlL+Pmm2/G6tWrAQAve9nLcNlll+HlL3/580rXYveBHbstLCz+0rBjdxZ7zNgdWbxgWLduXQTAfuzHfuxnl38uvPBC43sqn89H+Xw++tjHPhY9+OCD0Re/+MXI9/3oK1/5ShRFUfTrX/86AhA988wzmftOP/306MQTT3yhX6PPCz/60Y+in/70p9Hjjz8ePf7449HHP/7xKJfLRatXrzaG//Of/xwVCoXoQx/6UPTII49EX/7yl6NcLhd997vf/Qvn3GJXwo7d9mM/9rO7fPbEsfub3/xmlMvloi9/+cvRI488En3oQx+KOjs7o6eeesoYfjpj9zvf+c7ommuuif73f/83evTRR6O/+7u/i2bNmhX19/fvcLoWuxfs2G0/9mM/u8vHjt0v7rHbWqK/gNi6dStKpdJOjdNxHLS1tSU77I7joL29He3t7fA8D+3t7ejo6IDruujo6EiuyW8AiKIIURRhfHwcYRiiWq1ifHwcURSh0WjAcZxMPK7ror29HY7jJHlg2lEUoVarYXJyEhMTEwjDEOxWbW1tyb2dnZ1wXReFQgH5fB6u68J1XURRhMnJSTQajcQpRWdnJzzPg+/7yOfzaG9vh+u6SbqA2vmLoggTExOo1WpNaba1tSU7gryfu4Lt7e3I5XJJXUZRhHq9jjAMUalUMDo6iuHhYQwODmJ8fBybN29GGIZJnXd3d6NYLGLWrFmYM2cOcrkcgiBAW1sb8vl8Uz0xzwTrq1qtJvXVaDSSD8vBsrCuWWe+72fKV6/XMTk5idHRUdRqNYyNjWFkZASTk5OYnJxM6oDt6nkeOjo6UCgU4HkegiBIysD2am9vR71eR71eR7VaRaVSQRRFTX2Q7SDzX6vVkvacnJzM3MP7eM/ExATq9TomJiaSz/j4ODzPw6xZs5DP5zFnzhx4npfUrSltpslrLIPjOHBdN0lLhq3VagjDEGEYJnlle/DDftFoNJLfa7Ua6vU6hoeHMTExga1bt2Ycqshng/lkW7NO+fcL9Qp++OGH0dvbm/zfakfc8zysXLkS99xzT3Ltgx/8IO6//37ce++9uOeee/CKV7wC69evx4IFC5Iwp59+OtatW4ef/exnL0j+Xyh0d3fjyiuvxPve976m3z760Y/iRz/6ER599NHk2plnnonf//73uPfee/+S2bTYheDYve6++1D0fXUxCIDOTmB0FNi6VV3r7FTfrqu+aYGyZQvwzDNAGAITE0A+D+y9two/axbAOMMQ2LwZ+POf1ff996vw//AP2FhcinkbVwN33gkcdhgem/MKPPss8Mc/quTvvFMlccUVwImzfqP++cMfgGXL8MC+b8f69cCcOSrJQ/fapOLeZx/U9lmGP/4R+OEPVRZe+1pg/vy0CNWqKuJBBwHOd74F5HLAq14FzJmD4YqDMAS6JzcBjzyiMvL006ocvg84DlAuq78PPhhjcxZh82Zg/XpVNUGg0omHUrhuWmWTk0CxCMxr26QCuC6Qz2MYRWzerH4PQ1We+fMBz20kEdXgIQyBSkVV36xZQBHDmfp/bGM3nnkmjWflSmDek79R9f5//6d+mDsX6OoCVq1Cba8l+OMfVZWyTubMAd5xak21V5xY48hX4I470vL4PjBvnsrnvHIDqFSwZmMRDz6ofvP9tMyuCyxenHYJ143LVa1mO2TcOMNVD9UqsHEj8Oyz6jK74MREWm2A6nKum3bbfD5Ng9Gzevg3u3QYqi4sw7Fs1aqqMkDVh+8DCxcCRb+GsdBLHg2mV3BraQSui1rooFpV8TCNp58G6nWgp0fVxUEHAc5TT6YFmzULw50LVN+b2ACMjmJ43lL88Y9pvioV1c/++EfgX//1OQCD+PnP98PKB6/Hb17yD3jzm4E3vQm49t+G8dj6Io444n4Aw9j60ktx1YMP4ryDDsJvrr4XR+y3Bfi3fwNe/3ocdvbReOYZYOPv1gH/8R8Y/sSncfnlwKmnAis3/4/qM/m8Kt/69SqvBx4I7LVX2nbVavq+mDWLLxh1nY3T2QnMmYNnNm3CQS99qR27ARxxxBF46Utfiuuuuy65duCBB+Kv/uqvcPnllzeF35Gxe3JyErNnz8YXvvAFvOc979mhdC12L3Ds/r//W4e9FwXqxRCGybPaCIoIQ/X48f0n35udnUAxiMcWBuKgISFvkgOZvCbeeYwOSNOUUU5OplHwf0Bd45gp7zNli9kIAqC7W70Xi35NJbx+vXrvVCpqMAtD9Q0AMS+AUkndNH++ernn86j5xeQVZkqT73mivT0dZ2QZ+f/oaDK0Z8IEQTxWyPplPhkJM8BKkfUcR9jwC0mzsc4YDcHxkGWZmFDVAqhim5LK51Ue2Y6Tk2m8s2apOKvVdDzl61pvx4kJFY7ZBtJ+IecGLJqsW1lcvf/IfBGjo9m09HaT7cC5CQA4YS2NVE9EJiAhMy4z77poIO3/bFbWOetDdklTH2ffls3OeRPLwOm1LFepZCiP7IRA+mB1dqaTHDYkE2WnlnURZ6QWOli//hm85CUH2bEbe9bY7W47iMWO4oU4SqYTkfJ/krX8kEQk8cq/gZRE5/8MSwK31f2mNEhitrW1JYQt4yeBSdLadV3kcjl4nmck0Zm+53kJaSoJdxOJznpoRaLzmyS67/tGEr29vT0hScMwRD6fRy6XQxiGST2xblgGEv25XA6+78NxnGmR6Eyrra0tIVK3RaKzDiSJznyRlOcGCAliE4nOOvV9PyHUC4UCcrlcstGhk+iMc1skOvPAvsQ+IetC3scw8vgT+wPbX+aTGyCMY3JyMkmbaU5ForMc8ptkNuOTfTqKoiTfLJ/sa67rJvfINuazwbbkNX0zQW4o7GwUi0UUi8VthluwYAEOOuigzLUDDzwQ3/ve9wAAPT09AICBgYHMYL5x40bMnz9/h/NXrVZRq9W2HVAD20Si1URFYnJyEt/5zncwOjqKI4880hjm3nvvxYknnpi59trXvhY33HAD6vU6crncdufXYuaB/asYBCh2dKiLQZASxVxIBIH6ljN7AKjVUjaSq8ogUJ9iMUuiM+z4uJrURxEQBBjvKqI4FqfZ2YkgUIvZjg41v+eiolgEip2dQKGg7i8U0NlZRKGgog0CoNhVVb8HAWrFIoIgXT90diremFnP5YC2NhWvUyioC11d8QW1GCqGVXVjva7yRxK9vV2l4/tAVxfcYjEpXisSnemGYZxMWzXL2kLFIRfbxaKZRG9rU3xAsQgUGWm1Cvg+grFi0iRJWqx3lqGjQ326upJ66uhQ8U5Oqr+LxVqm3RvFYmYvJS66ImOKDcBxEIwVk2qRazHXTas2Q6KT1CB4g+chlwPGxtQCVJLorFdJoudyabeTaTN6SeywTYJANWutlsZVr6u4dBKe8RaLiqxxQw8c8pieiUT3PBUf0ygUVBpJfy0CDh1nsf/F5FexWkk6aGdnmq8oSrseMAmgis7OIoodHejsLGb6RjBcBNAJYBLF9nb4AIrt7Sp8MVQBOzvR3q7uK3Z1JTfn83HbjhdUp/B9lXl2FBZCtp2sXFZ2e3saNi708Pg4ADt212o1/O53v8P555+fuX7iiSdmSAeJHRm7x8bGUK/X0d3dvcPpWuxeYP/q6iqiWAzUeM2Xvu8nJLrnZXlyvuuCQJDoDKSTiUAzia7PAQwkOt+7kuQ1Rcf/ZTRhqN51vK9en5pE7+qKxyu/phIejjeVo0h9whDJy5rPX6GgbmIEvo+aX4TnqaCmqpDkK6DS7+hoTaI7TpaTZFm6ugwkumw7XhNjSaaCSNj6hUyzVavqtSzzzPkP60++GjgXkv2CfGtXVxp+cjKNt6tLxZnLpW1sItFzORVvLpctAtPXSXSObRJTkejMF9HWlumGTe3GcjGdaZHo+nMgM6b/r5HobBfHSeugUlFlll1S9jO9yWWz63MqdltZrmLRUB7ZCYG0sTmh8bx0kqP1r0xdxL/XQgcjI8MA7Ni9p43dlkSfgZBksfxfh4nEl6Se/JuELK+RZCYRqVuhEyRNSdaSeJTEOv8nAcnwjuMkv5Go5MNCwl2mTfJULzfvlf/LcLK8k5OTidW2LD8tjicnJxMrY5LPkqzWiWhZRyZSWVrsm/IpCVsStaEYpExtrG+aAEi+c7kcGo1GQjizvDLvMs96m7KOmCYJZmmhrdcr21CWXV7Ty8J7ZNipyqZvMEgynnVmIqZN34yT5DfblX2T/ZXtIE9Z8CMt0bnhIvuxTFPmz0Si7w54xStegccffzxz7Y9//CP23ntvAMCSJUvQ09OD2267LdFmq9VquOuuu3DFFVfsUJrVahVLlizBwMDAdt8bBAEqNBuJceGFF+Kiiy4yhv/DH/6AI488EtVqFUEQ4Ac/+EHT5IUYGBhomqDMnz8fYRhicHAwM5mx2ANgWrFMtfKV95GY5f/yHrnCA1JybdmyZLHf1QXgmXT1oM/he3pS6ygS7dhrLyDuo5lFh8gzs1wuq/+5ACS4nqhUgOKCBUlZGnAwPq6MeLp7SyoDrgsMDaVl5IaBllkuIHWeQS6kgXgx6fqZVZOLbFVm5uxx/XluCM914c72UKnEa6Mwu8jWmwSAMlMCgH33Vd8LFqjKKJdRqSh+vWnNKBdfrtvEpUhrKd4s12AyrOla9o/0/wbU2DU5mW1bri+4eGYe5MKYmxe+DzhoAL6Ki69R2b3Z/npxZT75f6apwxCu6zW1tSwcF9Myv1qQJD+e3JyJf5D1WgwaWLDAQamkSK/hipPUxZFHzsbg4GwsWgTg8S709ADHHKNOH6C/H729BwE4AMAwUCqhFwB6ehSBQUtM38eKFaqbc8ei6NewdKmn4l0zpBKTG2lkQEzPvSys6SPvmyZ21dg9t6MDlW0HbcJ0x+7BwUFMTk4ax+JWc4YdGbvPP/987LXXXjj++ON3OF2L3ROZR0+8YHQekI+sbkjbHIm4pr/0+S3Dm+5tEZUejf6dGVOQWhfHe24ZTE6mlr1qrPDg+h68cjn70mYkMrGOjuwGP+JNXd9pshyXZdCJXxOfaioP40vu0SvExP5mNtjTcA1XbaRXK9l60Yl/5sN0uMA0TZPXpwqvX5e8nxzr9LaU8XDslNdbhW2Vr/b27FzANMwAKWHPDY9kbmCa58rGazW2mcauuBIdAJ7rwgvUPUHgJPMMnkqT8045NZbzEb27lkrNZZAb664bE+jyZJ+M0PStl7tpcpatE87Lthd27E4xk8fubb/lLXYbkOCTxKokrElGSlJXkpE6QSvD08KX6fCaybJaEqZShoXxk5CUeZTSHnJXSVq0A8oCva2tLbGMlkQ1rcslGo0GpHW9rCv5zfqpVqsJqcx7aM1MyQ7Kw3C3jhI5URQleZk1axZKpRIKhQI6OjoyJDrjl9b+0pqcBCrldWiNzzyQaCfJL9uZbSrjk2Vpa2tDLpdLNiHCMExkeuQGgOu6Sdoy76wD9hPWQ61Wy7SxzB/7V6tTApKkl31HpiXjkicfGFZuBBD8W99IYDr6aYAoiuB5XvIbNy64kSHLRYkjKVFE8px9JAxDjI6OZoh0neBnvdBanXUsn5FdjXPOOQdHHXUULrvsMrztbW/Db3/7W1x//fW4/vrrAaj8n3322bjsssuw3377Yb/99sNll12GQqGAd77znTuUZq1Ww8DAAJ5++ulp7doTw8PDWLx4MdatW5e5byor9AMOOAAPPfQQhoaG8L3vfQ+nnXYa7rrrrpZEummTyHTdYg+AvqriDH+qhQagZvClUnqPTpRVKmoyPzSkwvb2qt9XrUqI6UJlowoTa2ZIA9f2dhWUtyIsqR+XLAF6ejD5WNb4PVmlIDHMxooVKqm+PqCAsYQoZ9YGBoDiMcdkyvjss+p6GHro7TsIhZ4tWVJBfoIgWafTqFj+bLJYc12gEajn2oEaE9xQVaVpQZlkNm4Xr1xGd2zB0/ALKg6XCzdFiGbWSUuXqn8OPRRwXWypKBK+OpDKlugWaQ04cNi+rovBwbRsJKvzeVU2tjeDy67CMPI4NcssCecwBMJ4DSjJAS6CW1mqcUHZ0RFbVlarwKBaBHnxCtMXR9/lN7up66p8S2s1yb3MmqXiL/gNoBLC8xvo6nKSfABALXQAeIB4ZPTNCR6TJ78ThoCnWXP7bPuhuFLWrMFi3wceWgusWYPi/Pl46eGH46XLArzudQWEIVAc+CPQ24t9go34whfmYXFpGPjmr1BcMYQzzzwKmzfPBnqW4TW33QasWoXZs+N+c+SRQLmM978/zufAgKqIxx7D3/zNIege/COwdq3q2EuWqMLyOWclmhbgsnHYiLo13HZgV43dFQAfBjD12a8sJgD8e6WyXWO3aSyeahzenrH705/+NL7xjW/gzjvvhK89RNubrsXuh2Ss4B/xO4/vUvmTbujYgAO4nqLHdBI3EzmaBiWdVNNJXHmbHFcI04azzALfn5OT6fDH/3ViEkjHpDlz5qGrd56y9q5U0o986evEdfwe81wXs2d7xqlPK6JXH9cJuQHL/zN1IwNrJ/1YtyyftMaXpCtv4Xg1MqKucUye6jXLsW5oSN1H63PZBjpJzWUp+5G0IJft3Aq8n/mTzZDMCTR48VxEtkMup+qERLrc/JZ55FwlQ5pXDGy1qEjWfatNCVeS1rITcr4LJP97ADxZUCDNbK/Y8XdT+TemLaFv7jtoiLRC88NnehhNYWT5Rf/bziG6JezYve3wpuvA7jV2WxJ9BkKSxTqRCWSteSWJLCU5dCt0Eq/S6rsVeS2JT2pKV6vVjJSGtGyX1+v1ekIay3RImpKwpw67bpEu88AyyPxJklZClxxhXkxa2tKynvEH8WDu+z5c10000X3fR6FQSNIm4Ss3GEhYO46TSL5ImRm5MSEJeBKzzK8kpXVSnkQw69z3ffi+nxyFodwJ24V1y/plHur1eub0ADXK6/U6xsfHEx13abFOsprlbGUNLq28adFtsjCXfU/WKduC8UnyX7eolyS6JLNJorMPUrJGyt+QFB8fH0/04NnPGUZq2ksrfZkXuaHAdmG+KeezO+Dwww/HD37wA3zsYx/DJZdcgiVLluDqq6/G3/7t3yZhPvKRj2B8fBxnnXUWnnvuORxxxBG49dZb0UV2bAfR1dW1XXGwjqd7ZA5QG3NLly4FAKxcuRL3338/Pve5z+FLX/pSU9ienp6mXeuNGzfCdV3MoViixZ6DfD498hwaJuVyJSFXxJJEJ/gbFxZDQ8DgIFAuo9a7DwYGgDt/otYAZ57ZDee+e1TYuXMTy3RKmOZyyrK2ry8mSIdKaJS6MTAAVGJSlwR6qQRgIF5xum5Coh96aCy3MbReJVoqwfF9uG4xWV/39zvo7PRw8MGA7youcc0a4LnngP5+oLe3G/uvWJFdJcblawRF1DeleZHVkJyapSSLnx553xTf09HhwHUVQeuFVbXIizPfKHWndTg0pDI2NKQqpFpVluRuN1zXgR+T6aVSStYym3/u91CpeFi7VpV3ICbPe3uVpf7goFpIc1Hc3q5+LwQBxtwiRkayhvgsV0KCxImxHer1VCJ/9mwVhsZ/Dhpp+eBkFv+mY/vyCLPsgvJ6ogc6MJjWFzPkuvB7FyfdeGQkmw7lcUulWBIg7veFwIfrqjPrCTlfCZOFc0GwPw04GBpK5WBcVxE+UttXJ1QSsiIImkiCZIEehsBjj6kGeugh4O67gUWLlF+AahWF735XdaR/+ifghBOAX/0Kix94QDXy4CCwdi0+97mjMDAA4PZD0fuOdwDHHINZs+ITGMccA1QqODl8RF14rF9VxB/+gO5164AnngAef1x1FGnuT1QqzZ1eNhwLxftYpu08Zr0rx+4OAP42Q6XgCmU6Y3e5XEZ7e7txLG51lH17xu6rrroKl112GW6//XYccsghzytdi90TGRI9JuPCanYTEMhuQupkKd9zjoFES74FadsKrYg3E7nJTzI2hiFQVcQjbwrmqk1CuYnKjcnnnlP/SxkRQI036n3uoVTqRhB0o9Qbv1OHhsxzG2HB6/m+yoNgh0mskujV/3ddJ0Nwum66wUqC2HWdJpI5IWyRWpdz7GS2OJRJaRxZz6WS+oyPpy4pSHKbbB+YJ7mxPzqatku1mpLTvJ+Eu75fyte8g0Yyr5HQ92VkuwOy7YGmDicSclw3qT9CEulsLmO/Gqo0t7cckEWGSGTrY7fMTjoMevDYlkND8eb9oLqZVglyF0RmHEhNyWMLAC+fhydNzfWNHh1TPWytdnYI3WJC3CeNGkzVZTzJMgXs2J1iJo/dlkSfYTBJt5jkM0yyEbpls7TilhIfkqCVVu3S0SLjIeHID0ECVJLvYRiira0tuZ/kLslMan1Tu1zqsUtrdWm1zXt1y3dZV7K89Xo9IURpGU8iVJeDIdra2pL80EI+CAIUi8XEWl4SvbQ0pgyKrGNJ/LNMupa2lMaRbSbzJtuK9S3bk3rtUopEEu563co2Yl+hxTWt91lvY2NjSZwkq0mgswxyY0OvT8YtnXVKAlpKCMm2lv1Ut8znJpEuJaRvKOk+AZiutHxnejyRQO0tOj+V/Z0EvP7sSUKf+WU967JEuwNOOeUUnHLKKS1/b2trw0UXXdRSMmVHYZL92Vb4nZGmdAIrceSRR+LHP/5x5tqtt96KlStXWj30PRGuq0h0aQqjT8JNk3LXVSs401lUrgJJ6JVK5PXwk5+odce73gUU+2PiLiY8C34Dvp++q1esAJwHfpswvgMDwAMPpEnTEtn3kfE4Va2q693YoiyTBwczZmvt7cUkezTA3W8/Fc/goCLPR0bS9VC5XMwUvVgOgGo14+RK6oNSszWxGorrTllHe8lGAS2pCohXzGT2SyWAJHqlkjL6g4PpiiYIMJ5x0OUoa+k4rQYcDAyo2zZsAH79axXVhg1qkbhqlTJSp+OruGqS5oPvY3AgW3UkNGhZ7rpIrKHcmCtlnZCUp1O2xCIs7iv8s9WClX9L1RypfkLy26nG4ukkScg8xAGdsIYwVEQFLRlZHlr/BwGAwaG03wYBitQC4iJZbqDIRb7vo153kjokEaIbXevyMGEIjFXVonV8XFjTy+dv3TpFZN99N4YfegjFhx9WmR4cxOq778YWAK/+/e+Bd7wDWL0auOoqFfleewGDg/C++l9Y3NurNl7OPBNYtgwdvkqvv9/BsmVFOD/4gTp+0den4n7iiXTzq78/NfPk7glhYnXk4l8WVtadPHY+TeyqsdtBuriebvjpwvM8vOxlL8Ntt92Gv/7rv06u33bbbXjTm95kvGe6Y/eVV16JSy+9FP/zP/+DlStXPu90LXZPyHclScAwTHk7/XGUj2ozD9dshct4dM3r6YBkrLxHJ18zp960QcAJlHxZV1chU1aOGRy/JTZvVuHKZfWJVcuQz3uYP7ecJTdNou18t4tTNo6sZGDq/8MQjusmcxjqVDuum5KuyG5UVGLynA4yOR6MjiLZnOV8QU7DmGw+n9YHkNgRGMdT2QYMw6rga5ptzXRMBDq/WT7X9YzcrcyHLtWX2cAwkegisKrjdHMiDLN9SsavNkwq6dxJjjcmVjz+m/1J7yJA2oe5F+y6ULItcp7LiVV/fzofkRHKBpCb0vybR+rkUT/ArBGoHwswlMc4XzdBI9D15pB7ADsCO3YrzOSx25LoLwLsCLFkcngordDlt0mHXMbTiriW+ZPW1iatbGmFrDsvNWlIm+LQ09HrSMreSGtzkrkMI0lOaeks5XG2pW9t2tgwEYamdEgGm8h8eY9+XZczkbrnehlM1uIyf9JCXFrps+70Uwd6mFZ9Uv4+VThZFrmZsK161I8A6fWkt1Or9mM6JqepuvRLqzLoz4yuh2/xwpPoH//4x3HSSSdh0aJFGBkZwTe/+U3ceeediWfzj33sY3jmmWdw8803A1Aewb/whS/g3HPPxemnn457770XN9xwA77xjW9sV7oWewCmsmjRTYv0+/hdrwNhmKw3RkbE4leu9uPw7e1eEm1CkMZ5oKVQy4W8MJXJ5WBeMIchcn6aPZPBEBfqdJLFRSbDBIEDx1XH5vXqyVjryHoQgeS6vV6HMpkxkZJ6neof3TJI3M+Fp046cLHeisvU61ZfROnrOdkHWnUHU8GnMrBqtWZkNRrrWP7dog715sgEk3Xf6m/9pikLm0WroCT3m7JMRiPupDUAjXodTtyQFQDDQNo5q1VU63V4DEPruHxeybGUy4DvJ9GOjKiova1bVbi+vjSTOvlgetZb1YOJQZEMywzCC7kQB4Bzzz0X7373u7Fy5UoceeSRuP766/H000/jzDPPBLBjY/enP/1pfPKTn8TXv/519PX1JVZrQRAkp023la7FzMTzfbxMQ5B+mNRkWDtdNL0DTQS6Ycw0aZDL/Mg8Ut5E8uFALFHWaldBf081DQ6mzIsymMKbwhgu63/L8UB3rGqajumk93QwVVGm3mgx3+S4qcW+5HBboaUueQu00uTmhgHTTGRhTP1K5Nf4tyGI6VbjLXoDyYml3CwAWpPcQOoMhhMc/fepGln2XdO904xHn7rTIGKmwY7dOw+WRJ/hkCSgbnUudZel3rmUwsjlcomzSSnfsi0norS21sloyo9Qd1umxW86vZTf1OfWnYnqkjSS0JQOL3WNaZKuvEdKouhW3ZK05nWpS97W1pbRTac1fa1WSyyMmYaUJWE9snzymmw/nZhnvj3Py8jDeJ6HXC6XxKXLl+hOY03ks6mOWIfyGsPrMiWUWmH6lIlhXnRSmfWtOwmVlvYmIlrWi34aQd/AYLzyXuZXDyOdpkoNf2n9L/u2vmkgLfClHIuepgm6xJDez3aGlbVFFs8++yze/e53Y8OGDZg1axYOOeQQ/OxnP8MJJ5wAANiwYQOefvrpJPySJUtwyy234JxzzsE111yDhQsX4vOf/zxOPfXUXVUEi90B+qRaah63MmcDzISjgXRzXWVks99+KS+eOLkUGhddXR76+uLfwzCx1FG606mmJtclW7eqiX6hp0eRgb4P30TmhiHw5JPA5CS6V7pYsWJhIqsSBGm8lAgxSTmzmMrK2svod+sGQ051LGuG5fuowUvLLqoYc/1mMW7eR3Pp0VFFiNLEznWb9EWpyc0Ml0rdiOXT0deXGrmHobJC7+3NrvGYXBAAqFSRz3vo6kolV3K51CorKUNYANwChgazhoVyDdnRAYSUnUms19L6lsfYdQ7W99O2kWRJvZ7KziRsiWy0+PpY6Bk3DZhHGo11U0w+boOG62W1WqVpudbH5/f0oOEmYgQZY3h94S2zKPOg8uHAdQso9+6vrDVdVzVSEKDs++p5WbUKGBzEyrvvVs6zjjkGW6oFdC9dCr+nR4niH3yw6ic9PalYf5wJpjkxoQ44zKfz3JUrk7Tg++kpkmXLgKVLUYMHj8cSTO8EXbheNrIkqbZj4+HFjre//e3YvHkzLrnkEmzYsAErVqzALbfckjhe25Gx+9prr0WtVsNb3vKWTFrSQdq20rWYmTDxZ9vi3ab6H0ilM4CsnvVU97TilbPjnqN8QvCFpL8o42uFkgu/pN6tHKelDjhJZvlK5pDQ3p4dj1zXg+djagMAeX067ysSyPF4kZXnSq9tKxkge1JKnljSq4f3Us5FVptUBZGvZMbVah+41R6GtGw3VkWL+tH7iOtq2udTkb2x3FvSdmJP10Rip042HbPGvz4/5fgm8lAs+3BdByMj6XSi1fMxPq7SKvC0GieMscxecpTA1K/j8iUOc3S9HNlorfqnrFTGx3v1PjvVroahf+tVJx3kWqTYk8butsgyNy8YhoeHMWvWrJ0WXyvrYxKb1BEvFAoZp5VS11zKf0gnkySzpUQIoMhGaoZTzoOyFtSLbjQaGBsbS36bnJxEoVBAoVCA53no6OjIyHR0dXUhn8+js7MTnZ2d8DwPhUIBlHORUjKSrNVJTspsMF9Sa1zeI4lzSZhSzoXlk6BmtZSoaWtrS+p49uzZKBaL8Dwv49hAEvZso3w+n5Qrl8tlrObZRiTHGYeMh5byckOER1x4TZLDvJd1U6lUmjTRpd64lKIxWadLolvWlyTY9f5JSMkXnTCWDkUlmI7Uy6eMDr9ZZ/J0A/MjSf9WhDbzxX48NjaG4eFhjI2NYcuWLajVahgeHk7kXChrU61WM20j5YhYt3ITSrYJ640a63yOZF3uzNfxunXr0Nvbu9Pi25ngu3FwcHC7HYuWy2Vs3bp1u+6zsNgesH9uXbcORc/LLjQoyOm6alFgItH5f7VKT5zpJJ6LijVrgEcfBZYswfCKozA4CNx/vyLwTjkF6K6uz2YqXggMo4gwjH8fHASWLsWzIwVs3ZomRWt2cn6HHgoUBp8GymWMoaBIyIH1qWbL0JDSglm7FjjiCKUj3duLR9aq4+LLlqlF3j33OVizJs1SqRQ7NkVKJnR1NS9QyXV74ViawTBMWOnhioPBwfQnqaG9dClQCIdT/fNSCbUepeXtIdbI7u9Xv7HAQYBGeR4ATUebCVSrKu1yGVuGHKxenRK29Tpw4IGpr1c2K638u8ONiXD6liElC0PZG2mwHIbAn/6kNjJMxA3XirHkfUYRJFlUx4GHK06yKSFlAHxfacbTIay0kMqEcYXWtpseT+7vT/uMrB72HXZXUa2Z7l2obGw+ns0FuDznzU7g+0C5jOGql0mLjw2DMC9UTaHhuHy8li5VPPg+fQ2lix7HjTAE/vd/gYkJjL3uzbjvPuA1vX9Uci577w0ce2y6qGdkcSd/dryIzZvVY9DXBxzUfytQqWD9qjdj9Wrg+ONjCSW+A5YuxT1DykH1Ub1PqxvlxpcsDCuVBRabYPy9f/16LDrssBkxdl+C7dNVrQK4ALBjt8ULimTsfu65pJ/xfSffzfLxb0UItiK69Wt81I3WvoaIG34hQ2ADWXmY9nY1jrpurGHdSmZFJwpLpWSs2LpVBeMmLyW15KtZ+s9oStPEHMs0dcQVKUlxvQrk/3Ksk1FkLLHFDxlH22HqTFXGKdtYupuQm/NAdpwBUl6XdQGkUwryuDKv/GY8pv5T8DW/bC3seTN9xlC30pkqkG6OyK5gkheXmwX5fDovc4a2pIOrvgtBdlwnnlnQIEDD9YybNKa9YPa1JoMGmLtRq0emqV9opyczO0W8SXRu+piZqp5cV5srEvGP7H/6iU/Owfr7+7Fo773t2L2Hwe6fzHDolsy0mqa1dBiGCTkrCXRaE/u+nxDwUodckpwkrelYkR9JajOtarWKMAwT4lMSi4y/s7MzIdG7urqS9EkySx12fqSGNslv3fEjNw5o/U3ikvUhdd11R5YmeRPdWpq/ua6L0dHRZBOChLgk8BmOddzR0ZFJo16vZzS9uZEgCXJC1r8kW3XLekl4M+xUFs4k2uv1OkZHRzNksLQu57c8ISAt72Wa8nSCbDMpK9NK1keS3rIOSZhLHXepgy9lZXivjFPqyusbE9JhKPuw3r/1fq5b/cvTHbLvSsiTEfK5lc53dev2PQEvtJyLhcXzhpztS1JMkueSbdVXjXLCHxPvNb+oLM3iMCQsjzlGXarXgS3+QgRBTDwLk9xitT8l4IIADb+A0QF1X7msFjebNqlFM7n+IAD6+hbDd4ECF+V6Hu+7D7jzTsWqjo4CS5fioNe9LkM4lsuFzHredVO5bUY5OmomhT3UkGHKAaBUwljVSXyDMjtAuugfGQEKc4OMlS/Tq1Y9hKGHUs/+CJbGi6A4I05lOBshv2lFHCfaHQR49Sv7Mov0QnWLCkOCOAhQ7OkBJpCuyH0f3aUSqiUvI8fuuimf/Pjjaq+kpweYMydrkS+boKNDRcnFbkKcxBEXe3pAB2Js+kTjfagSa80WRb1kedpSyUv0Y2V1sBp0I696XVliT05m+xClSUmsFxgRdcKHhlLPsHKVSS3y2bOBMESxtzc5PUFrPkDF6bkNjFUVEcQ9nsFBJWtOCfyJCdXXlbWhg56elyIIVD2HIfDYYwuVxeJjKully/bHwje9CTjwQDw4tA9cFzhk2ZY0z3FjjI4icRY7OAjleADA//t/Sjd/cBB43etenhRp7Vr1yPg+cNSZZZVZHteYDukk+yXZjhmCF/pIuIXFzoKDBjyEoKNGz3VRjZ1WctjOkHUx6AgTaObodCvq5HSVJCRNsk8AnFIp8QECNDvNdN3UAXVHhzqllOhMS1ZYN0EOAhSDAEXfx/yeEuC6qLmFJk1rOQbIqQnTzOUcAF48LnhNm7qZNEX5pKU5kE1L15CXG+Va9TRvFoQhVI4ALw5UbAOMx+r0HWvfRXdJWW9v2qTST/yFBOoUH2+R5LTrpq9j/aSWOBzYpLpHNOBk+lLG0px5M+3KGDYMZJNXKukGgkkRReavszM9bMU5hieNPWRfpddQ10WyY88GqlSSSZ3j+yj4PgpzS5nNew6jMir+xvzKbsvN/lbSeVIaXc0lHZRKXjKfSaTSg2a58ySNcWBzf/MeFJ+xIEidvKu0PASBB9eP+4dsK6FtL5pKpRuG6YR1BsCO3TsPlkSf4ZhKzgVIHVWSlKQltLRgp9yItAKXMiZAs/yFlMHQJV1IQJLwIoFKq2vKuPi+j3w+n+SBltmSuJXQZVxoaS1JdKZHS2tKocj4TDrZrX6XBDGARMKFVsSSLJfxSBkS1i/B+2X6DJfL5eD7fhIHkBLutIZm2XV5EmkZrVuQk8jW65PtNT4+npDo8iQATzCQJGaepYwNCXvHcRJra9Y7+4IuY8O+Kb91q3Z5EkHKDEmC3yQDI+PU9d710wzMu+y/cjNA/+jW97refCt9db18soyyTk1lejHDkugWuzV0Ikw3W5MEuuke3Uwnvl6tpotBhCGcsIaC76JQHQIAPFvvxsCAIl7nzi3A4cy/UlEmUmGoTHHL5QyhTb5bLmAAxe3RULfb1xb3XAmuXYuBeh0999+vAtbrygRd6LfQGRm5ZaB5PQ80G/l6bgOoiJW8qA8uDBmfvk8xMaEcw3miDZjlwUEkZKvrAj09HoqBm9aVqS0rFcWU0kSvowOoVpWDM6iFeoZAp5m5zrjE8fv+PGPXqFZVU9Fyn4tCqR/P65T7ZHaSCiWDXSrB9wtJ1fk+sitD1403ZVIHeFzM6qS57IqUSjFZ05Fwl4cGpFW66yJdQbCuNm0Cnnmm2dyMN5DBqFbh+X6GpALE5gEKyQKbhPZjj6m/+/tTZ6NyoV0uK9WVkRHgW98CnnuujmOPzWHFCtUGCw87DM+2L8Rt31GL5mXv7YYnV/9I18JMt9GzEGEI/OY3iixfulRZqNO6bu1aFXcQKOtSR5r/mUgSE7Gub8jNENiFuMWMgc5I+j7iN32GF86EBxKSG8iGIaGckceQDN1UJLrrJtfdII1fkn96NoDYWSPBNLjTSVaSEhhdXcm47cWEvV8uJu97/QSQKU39dRQETqY+HJ1RRHas4bfUZZevRA4HtIpndGqzI2yuS1NCjEhCZlyMP47vo6NDbTQ7YS2pBM/3ATjJni83MwBVnfoY6rpmh5KmV72SUGk0d7IW80LTzzohTaMFOe/So5FDiqxbVb+GjIdhs1m/rEs+M/yOd9wd10UQFDPyLtLxKxXOOO8YGMhKtOmPiEyW5DnnHaWS2iT3/fQ7dmWS7D/Lfsa4OZVjvuTcctYs1caTk+kJBICb+dnNIqlt36SFbtoM2Y1hx+6dh5kzY7PYYUhiUScytwcmDetWhLQkFXXiUyesZRzSSlmmYXJgqmtv6986icw6kGhFwsqw20OMyvCSCNc3OGghbdoA0f+ntAzrkQQ279fJV13HW1pBb0/7m+rYtNGwrfu3pfeta7Xr8eu/7QjpapKlkZtArbTZZfq6vvp06mCq/E5V5j0FlkS3mDHQyXPT3xKtJtaucrrZxLiKRSnXxcaDKdoq12gZg0x0rfOl5d0Bms3DBKgB2d5u5glNvCFAqywzWu1TGLOp5Udf3E65ljGVybRw5P/1euL4tWWYMITrt6yuJs1MRsukZVTsAuPjyiLKkcKtcQSyvJ6sLIMVoF5HrSDzaKpzI8k0FfTKkDdrG/k66OCOTml1UkaPln+zTiUXAEwCyKX3uy4mw6y2rid1UrVniffpG0JA627j7SgRPoPIc8IuxC0sdgBi7HDQgOs6RlJWR+L8E0hf1rmceZJgGLMkh99qrJxqj2/KcWAH3l+8ZdqOGU1E9HQTEmN15t74e0dfv1PVSVqXToaABZC2ox7JdmSExaK2/Q5hqvSmM8c1oNVhan1jRb8uk+I1Tr8YzrRJI0lx03Vek/mSVd7qOfBkkWfg+DwV7Ni98/Di6hl7CFqRrUCWZKJFNqVUpM64rnlNK1xpFUyC0QR5vb29Hfl8Prl/cnISQRCgs7MzkTKh3jklV2iVLtPSiWBZFt2RqC6LwbLyXunkVBLpUpvbJJ2hE8W0ymdepNyM7uhSt5qWlve6ZAzzQOtzafEt60BuSvA+OjeVFt+8h5bUtVoNo6OjCMMQY2NjSVpMl3ritDr3fT/5n+lIa2/WqS6PYtqokBIoOokt+zD/l2WXGweynmX7yM0HU9yyf+qEOS35x8fHkz5FGaJqtZo5IcANC9d1MTk5iVwul8j36E5FpyLAJXGvE/Vys6XVs/ZihiXRLXZrmEgxWpvq7Girb3316rpqESHFwvv7Mxoog2vTSz09SH9z3dQqWpCsOnleKqUWxmGYWg8rC+Y4b4yPN/b1Yd6f/pQ4S8R++2UcdTbgIFdN4+rqSq2KpK4r/w6C7BFaj+UVsiyNoIj6ptSqiHmXVVcui6PQ8X20sGIaUoqkAUcR0HJ1xXuBtP4o7Ak0OdJK2i+Xaza/ZgHj72K5hr4+dbzYCWtx/hz4vqpCWkzrJCwt70V1JF1CSZUU0Lt0fwAqLC36uAjt6Cioe8rKuq4ylK1/Zl/vF7JK8vlsV5aLUX3Rmh6rTjdSkhup9cJECVmw3t5Ug8VXx+jlsXSio8PLqKww+p4elQTLMGtWqvPKsvX2qntWrgSGhvxEN911oU5trE27/Zo1QLlcxOJY/H4MhcRqjXHS0HPZMrW5sXRp2oWZNz4ilUrsgHUqosfEGMh3yAyyaGvD9i2u9zwTAYtdiYS0bLlL2zw065IbTRIcMt7MxqWXOgLl2DOVhjnzFYaJXAqQJfqYJ77zq1XhKJr3Sy01OdeQ8wWRJt/bJk5Q3xAkqtXmvU8VrQPXbbJrzhRTl9kAUgJV6ozLT+IAXOZdCphPB3ph4kGtwLqpiHapVOD4ftIOdBbLcXB8PDtGA8oyXGrMm17rnJOobyfzW3u7g1zOU2ka6kjGx3pidulsnGlUq6nmuF6fcnxPwIyxvzLyVn1Vznflcbr4f/029i8ZTG5G07pcv0+vP+mHRbpVkXXLetBPUujF4D3t7ep5Yr7oA0DmNZMJ8c3nXp/+Z/rsDIEdu3ceDK9Si90ZU1ks6xrZJKylY0vKpUjd77a2toR4JQkOZC3PdcKS16if7vt+4gyyra0NQRBg1qxZ8DwPXV1daG9vTzS/Ozo6kMvlEmkZ6dhS16wmZNkorSIJdCmpQhkRAImciHTOKe+bCiSYSWZTboYa3dyU0GVHKFHDbxmOGwesT2rT85v1Li3NZRhKwbiuizAMEzKY7SIdZQ4NDaFer6MSH1vjZgbrkUQx68Nk6U8SnW3B/sK/W51MkMQ120zW91QnEvT6khs7JOjZPiYCnXmR0i3UPOfGAp2GSikXbtLItgeQkeJhu5i00U0nPUySMLI/6ZskVs5l2+EtLHYpOMM3LIibmEeuenif66IGTy3AekrAokWKfXvsMbXiOfxwIAjQ3698jqakpwenXE5Z6zAEymU0giLCoeyEnoQ0CWZAaFj7DWCwAgRBqsvu+2qltmKFimDVKuCVr1SOM9GN6mB2DSWPvVKzWh4pHh1NiW250IPvwSuXAQC1WI92ZFO6qNlrL7WoKVQ2xmQEV5Y+UIkTFELrcqHF6iaJ7PsxkS7arBYqwqPAs8A8a0wBcECtRuWquLMzu5IDsmeQq1Vg7VosjMuFSpwxFOH7qiop67J2rQpCuRDdrxcJ5XJZEb9Kxz5dDJss9jSOxPibXAvquuxSXUe3uG7F97JNczlk62bu3PTMNZAyL1yp9vYmHbEWKi1VHq9mHrixQD+l4+NpOy9dmnJUjI7VLsvIR258XMm28FF9dpNKk4/RAw+ouE45ZR66e9I2kov0Z59V36tWKSJ9xQpVPKklv3Sp+h4cBFCeh+6gltVS0gkd+b/swDMMbdi+xbVdiFv8JRGGMG+kxn/rlzKEeStWM35WpbPHkRE66VRiYCRFE/8cMVFrjK9Sgef78FwX/lwveb/pp5USktD34Poe3N6iyi9J9FbMofjQ6p3yFfotmXpDc1SUyyZ0h5FTfZvi13/LOI7UG8dknGAqgM5o64VjO5A1FZryhVIpaVc2Wb2uFMqEJDiA7HjFeYck1ZmE/JbXOZ5zbCLBazoEyO/OzlT2TS++vjHB++QcjIRzw/fgBKJeSaabxiu9HTQGuQEH1UpWF56HI0h6y30l+v3mtEvPs15Pet3IunRdlQ6dfDLblHPX46RxBoso4xaKhUn69Nmi91N9TjUTYcfunYcZ2gX2XJgIdGnBLCU0pENPkugkMiVRToeJukWsyTJWkuiSaGS8dFTa2dmJzs7OxGEmtb5JMksHjCRZSWQ2Go3EYphpkrDXISVOZB5JoNLSeGJiArSkZr5JTpv011nHuVwus4lAUlwn0OW31JhnGgzLTQySwzqBKklrXieZzDTq9Tra29uTMnHjQJZ5fHwcw8PDmJiYSEh0OpllXLIs0sJaan9LK3NdEkeS9/JkANPi75LUlpAbH7rkkF5mQuahFQEr9c/Zt6mbX6lUUKvVsHnz5qSP6f2c+aKFvCwbNy/4nHBDp1U+pL8AWU+yj5lkf/YUstiS6Ba7NeSMWS7wdCuvViS6ZOTiGTo52BpiUrlSUWaxvg8cfjjGqg76+4EnnlCWzGmUHvwgJtPjxQ8XeswKZVYYZGQk1XtUVuhx4kGAwUFFNnfz5mXLFEN46KHAypUYc4v40x9U1ul4qbc3drgUx9NdKqE+p4DR0XR9Sr1LcqtSm9v3FVFA4l1aEXcHtVT8misjQDHJ0gTJdZOJKxe1cuHEdXHBF5aCYWpVjFIBhZ54VTcwoC7SIp0irYQU3pZsvVxlcyeBwty+DzcoAgAWuhuBcAhu3/6JlD0XkSSKWRe0gi6XVZGDQBG7tMLWjeEl6S3XtnJhLqdMrqt4biesJRkp9qSENklhOlPTF+TyEUiuMSGuUPWExU5HzVeavBhK20nXdZXO03g4gP2D0bIuli4FFvak42ktdDAwoO4n4c56am9XhHilouIaHATuv18199KlwMqV89C/WtV3qaSuAaptXFc9EtRfdcIalEPbtHsCafsFKzyltW4iJloJwPI3k1nebor2+LM94S0s/lJIHiWOw25W8oqPnec2sjfJl6aJvHVTLfUwVBuBQDo8cPjo6FCWxkn8+hxBDgauCycm00ulQuadKN+N8hXhug6CoDsh7TPEvSyggBwrpLX1VJLj0gqb4SWJKclfJplsSBjmQ4lJEtMw1bfOROvkuWSSOfHQ52I65Ji9aFEzG+37iXWuB3USjPUi/KBnotm6NavxLbMh61r/nw665eFCTjHkhrkeF6tEbrLoeyeyuCSJ5TXVng7gFgAXcINidgNJrzsRaQOOqpN4TiX7pwzOdGUVm6KX/UomxTyzHvRNJRlXGKb9kKfa9Dg5rzEdknNd1R5yA0PmU99MMh6KmGFsuh27dx5mVstbADDLuLQKp1v0trI4JtmnS020Iq10y2MS4nQW6vt+QiJTSoayJrrDSp30npycTAhiQlox64SjToKbyEuSqZK0plSLtHTWtbj1epbkv56+JL31epcWxyTTZV5M+uN0BipJZW4kkORub2/PWK4DSOqxWq1iYmIC4+PjiRU9kFqJy7wxXikPIyV2pNW16eSDLlMiw7fSGZdl1utRWvfrbav3XxMkuS8dhk5MTCR1Iq345bMiyygdrUpIq3KTNbzMmy5pI8ts+uxJsCS6xYyBvoLUoS8Y+bdGoks+zeMqanAwYYSrVUX4kpSTFjZhCBTjWbyUw5DZkXrlzAqP9aKS5quJzyuXEwZ3GEUMDqhFIpBanDloZL1CuS66ugpJHuSinIsr6ViMfz/3nLLckovHhMndsCFr1iUkZdLzwdlmoMUy03BdJIQJP7QAV694BwVJ9nI1SnMnzjeYpm65xgpkxKzoOJ9JewwMAOvWoXzs/k1dhdU4NJT6LyWxDqQbBLq1veumTrKYRUme5/PNi9IwVEVKNkBExXhBgCDwkqJ1dWXvdV3V7np9JiS6XF1Ki30SV4GSm6HhP/Oo+9/jpg+dqHGThfw8nYDRoH1hMAysXptk1CuVUC4vTJqB1U9ZGD5LbHaS6qxz/i0PmQwNic2j/qeTm32/O6mDWbNUWG4kVatoJtHZCPKb4EMzw2B1VS1mIoxDuPb8KR8eZmLRQSM51SQf8Zbcte9knRTqzKIk7l1XObh2XYTC6bKehj62A6k1fCFws4EEaI1umr6YsgWkrkE6OrK/cTjU40r+NzGsrd59LSOB+ffpvi/1+Rh10ebObdrE0P3SOK4LV2yYAKn+OC3P5bhRqWQJa5KyOolOw285xcnns/3StIE9VfXoVt2cA3GDg9dkVfBvlaZ8O6fyQmFMlsv/ZT/XnwE5B2S60uEn65CgjYIsk2nuop+W053VynLxI09Msm5MhgWumx5AlJhiL8HMmc8gIt2O3TsPM6fVLbYJSdRKkroVSL5K0lnXrZaklYybVsIkF0l26hbEuqWxTJvXpyJbpRUzyWuT/rVO6Er9aSmfIfNqqht9c0DWq74pIa3RZbl1iZdWRL9ONFOTXNYNyWx5P63rW8mpSEv7RqOBXDyaMc9TkdOSeGb80uJc7xOsW+Zb16efjs63TFtu8si209tH5lWvQykho39kn9gWTDrlet3JPmWSPdLT2ZOszKcDS6JbzAhMh+jKrDxa/AbNQmkoXhWUy8ocJghQHVKTeinFnSG8k43UbBLyeDWDkTgkR+xJIlrPEFc3Qi5F/zQhzoQk7nXZTEJf9Mi/wxBppXR1NQumajdwcUtyt6Oj+RivXj/8n9ZKBf18NsveSuZNrhRZaJo/8bomN0PvsAW/gVIpJV5cV1lUs306O9Pi9/Sk1tbcP5B9gfWqL7RJ/srFYlObyVWuIUCrojemWkYx4yQhNNZA4+wzi2TZR7jw5lFwIMvJy+PdJI4yCMNMkRw00N7uNHVvxiW5fn7Lemb63ETIlDdsbgfjczLVe2Oq98UMgF2IW8wITIfcmupZNBDshCQmGY18xyW3ypeM/hKcbh5hfp3QCrdeV++kZANgG3MW183KcJjKwGvbcv45OZmV1PBcZF+G25gTTYlthePEw8S+tqpvQuaLv4m5gOt7CdEbBGp+lculUxQ5jLYig2kBzf/pPkTKuZDElePU9nCy+gE6HSbHta32NkzdplUVyjFP9h/ZJDKuVpbg8n+guSy6UQjjlYQ+Ld85DeEGguzj2/PY6d2oFUx+E3Z32LF758GS6C8SUO+8vb0dvu9DOhMloQ5kCUsSjUBKUuu6ztJCltbT9Xod+Xw+sWCW8iTUO8/n85n0qbUuiWPdMl637pXXSJ7rZDiARNtakqq6Y1FqipOIl1bYOllrIkgpy0Jpms7OTnR0dCCfzyda75RH6ezsBC3vc2LUkG1A+RydFJQbIJSpoc65vFc6xJT15rouOjo6UK/XMXv2bNTrdRQKBQCpJnqhUEj6il7PlDipVquZOpX9QmrZy3xLp6SdnZ2ZfElpHl0iRlrVy3bQSXnTqQn5N/MrdfApEUSnofzWHYPq5LhsF5k2IeWS9PzIcuqbHHo/0K/tabAkusVuj+1ZhAPNKxLJ/gEI4n+9oY1KusR1gVNOAUol/LG/gIEBRZ6uXKmkIrxwDG5QwNCQWiwVe5QpUxgvIKT0dAFjQBii2KXSGxhwEiN3RcAWUOjrQy100gUbzX1pFh6b/rpuqjlNctEkOcHFo3SwODSU3pPLpdbVsnrkmnVoCHDLRRR6/WbzJkq5MDCUlRjDOK6LYmxNTR3tMMymSa106RCyPqeI+cuWqQu6sLtMXzcxlIy1DNvTg7FSbAntxlbfrIw1a/DKV+6fBK9WgQMOUNZog4Mqf5s3p/U2Z45qTzZJuZxdjNMSvdXCUxK7tFTr6BD9Uaz4G3AQVtNFp774NS2GmabqRx4c0XeGK2qzoFrJVp/u+46bBDQQDENVRmnY39mpjAYlwTB7dizBMFBJMxZH7FWH48C+coSbyz5+su+x6Ummx/5FUS4rfX5AncRwXWQ3WYIAbiWVcWFd0GK+4DfMlWWC3lENzu53V9iFuMXujobrqbECyEi5SGJMkuIMm+hzy93r+CXE1028P9pERuq8rTpxprGKcnzR4ud13/eS0zgyfgn9/3Soiq3NXa+J5JNjRi7X7LhS/m0iHjMb38a0gTBO3/P95oh1GJjN1Hlr1nFpk0wMG8LEAusfIPXKqb93XXViyqmOpXOB3l445TJ6e9X6mY6r46E+M5/IyuyobylJohPFHJNaaX1L6FXH8RzIGiaEYXb4kFViGlZMxHor6CS5/i1/k3rnTFu/lzDpwE9VfhpKSF8BOsnN8PK5YdzS5oHp69A3DEyEexNxLnc/ZgDs2L3zYEn0GYKpSDZaMJO0JolLORUSxwwrrXV1rWspS8JrJF9JGJM0lGGl41KmKa2eAWTIRuZZd+6p60ZL55Qk0PR7pGU2HT5yc0BakEt9cpM+vC6zASApA8tXLBbhui6KxSIKhQJ830ehUEhIc0q1SMt006ZAK0t5E5Fcq9US8pxEMYlnSUADSPoAyzc5OZk4WZXX9c0LEst0vDk6OoqJeDVLcp51wHoldMeqtH7XCW62Jf9muWT7Sut79qMoipLNCH2zhXXRaDQSeZaxsTFIy3M6FqWkj5RwkeVppY1v6qfUkGc+KIOjp6v3ZxOBrv+2J8GS6BYzAvosvdUCWJ5vlrN5YXLrVIZRqFYVgf7QQ8DKlfjNyEF4dk26fluxQukwL1sGYO0AnJ4eVKtqMbdlSBGXuoVPwa0Bj63JLBjr9f0TEj0tQmqdm8shZf/6+jCGQlK89nZFKLa3Cy3toUrTisgJayj4Lhb3IiHnKYPBBRbXqzyGSyNuHhUHKN/hISjvL/cc4IVCgkQ3axZEu+P7CMrzMD5uPkVOSQ8uegcHgc3lIoKgiN4V81T52ABS5FQn0aWZMi2wwxDo68MD9yki+IjDY6dvg4MqzvvvR3HRQJoZ38fCUgmY7QI9SFllxqc7pBvM9rci+1QIoFLNmnVJAXGkOqauS2dZXhOhDWRPMbTq4vzW9jQAFAC3gKF+Vezx8VQKSGaN/3Ovxln7Z8B1UYwFx0slJ6OVXwwaqv5kYqMiU77fLBYcm/OH8DJ9UCII1CZVGKo+TlJ9xYp48yP2cDq/t6RuoMdT38dwxYHvA/v0qnlVQ5yKyISVlca/dQ0luavj+5ZEt7DYiVCPm5N5/+jIvhtU2CYSPX7nk4jXydP4lW6MNwyBsaoas13fMxPBBubQcV34furckOSp/hqRUeh+MtTHaSL95KkxCY7P2+K9TWWURUgV7Bz4vpeS+aaBRWYYzTJsWaSq5ao+kWp6M7DuaEO2oxy4OEmJB4mhIaC75Cv/NHQQ4rro9qvoXuqraw8NwHNdLOSEKoD6tKoUUwWGSB2l64OqzrK7LhA7YWf76+0jkzD1cVM4oPVQU69P7cScabDO1akDJJkq+C7gi5u0jLWUSuJ3NVtfyTaK66I4Oxuf3Bgz5TfJJ8z9qhVhDzQ/G0aLc9OzOwNgx+6dh5nX+nswTHrdBEn0XC6XkN60jJaEp27NK0lqWv7q6TAeap3rIMlMolqS9pIMJqlIIlNayUtZGWm5y/uYT6CZRCdxWa/XUavVkrIByOixM29S09zk7FEnVuWGRGdnJ/L5PLq6utDZ2Qnf9xEEQXICgIQrCXGZhiSOaS2tW3pLGR7mY2JiApOTkxgfH0c1ngzohD+J+1wulzgP9TzPaAmt68STqA/DEOPj46jX66hUKpm0GL8uTyMlbriBACD5lm3OTQDZJwjmkf1A6tYzD1IuhfFKvfORkRFMTk5iZGQkyTvDcHNF1/7XJX6YvuwLUjdfXiNIyEu5GLnRQegEeivS3Eq+WFjsRtAnySZWTl8MyZm67ydEGwA4lYoiV9euVUT6oYfie/+dOjEMAuCYY4CDeoeBtYOJ00qS6HS4qDtfQqUCrFunTHljdnDSVyQ6rcJpwdzZqSxp1XCuGM0t1ULiyJKOr7iWc4a2mC2+gHSBWqnAc1309c1L1rHS+ZZcywZBahUUhqn1sUQ+r/KwdGkBBVdjLqQXU+YhCOAEAYBCJi0g5aNJnpPfZnmVI1QPi6XGCtOi+RPblgtomt7H4Z59zsPq1cqK/IjDwqzgeaWiPMVq/SLJOxdjJrNDuaiW5ujUNJmYSPOVzyunaUJs3nFdFHw/IdPDMLX8lvs7utGg1B0FsnJBbHpq8rOqWNyREWVZD6istLcry3ppDe5UxxRh4bqJJ9ruchkoxYTFUAVYM6g08qU5GzcbWP+sQ9dNfQv09GSaTF9Ql0pOYvXOKLyhjSo/kpWS3lzj57hSicn9x9ao+uUmlHRgICtJJ9Jl+7Kf6dpHFhYWzxu0iu3qmppEl1xYJpz2PEsyU3K0DMqhX04R5DikXu8pEez7ntq8ZcJy4ApDOFAbyzKPMk19jGPepYWverWkxCW/aa0uyXSdrJdV0Ir/NnHFMi/p0ObEZLp2o0awSv5bH4NMr1fWqe8rLW+vVMrqnOvMKBOgM/F4EFROpx04/f1qHNlrr3R856DJDV3p0ZzzLclwmypIdjAOppzk6B5GqWMWS8rI6PV+J+t4KjK9Faa6R5LpTCOzGSL7q+GUYlO5kZ72MGbCxHBPMaY6buyo1jTvlnGHIZwggOf78Hw/NibIJmHaEMpszkzxAmm43tSSdxYvWthZ24sIOqEpSU8JnbwkqahrbwOplAWAJpIXSK3gSdy2Iu1JDEry3uRMUScnpSTLpLZ1KglVxknZEzoRlem3kquR6clv/i6trFnGXC6X+ZC01glmWZ96vesW6fK6rHcSxfV6PbEO1x1fynJK4llKjPBbEvgMx3RJdJP05u+0atcdgjIvuiSKzL8ubaK3gTwJoUui6GnJOGW/kO2vS7bIfqG3sSSsW1mi644/Zd4J/QSBzL8el57ungxriW6xW2M6BHqrsOJ6Zh5OEnhkJLFa7e9Xn1JJLGrJ9ooZvr5GyOhnVqqKIedixvcRutn1Dp1rZrIar7TJ+bquItGBWDZDEsqtIBZTThAgCApTOqACUv1rWS6dY2Z+C75rDigXcFrb6ItMeYtu2M5q7unxlG68ZF71ihfWYrXQSY6sjw6pJvV9UR/y6LipEoCMk9Ym1pfpSQ+s+hlwWfZ8Xumd6AKtobJsDIVDvETWpp5dm/J3nQ+gUzWT9Zbc3xgZUd1waCjVKfX91DFdEifrBcgKn/M6GXnqGMnM1OtZmR8jk5U6SMsshuO6mD3by+aHm1v6orlaTU5ryC6X8VSq/z/d94beJ1q9W3ZDWGs2i90drXhNE+Srw3PRkjXmu5F/67riOpmph9Hj8vSLWoYU6Z19emQarTjHbemY05+ITnZL0GG4zmOaXlemV5xOxrsuWlaUtBSW6ZvGcRlFtZqOM2EIuIGjiFo5jprqWOMTGLc3Pq7GHJLj1aryhM5BDcgYDmR2kqd6l8s8cRwvlZo9hPNvueNsyKeeRKuxebqEugwrdfZl9o0ZkROKbc2N9bmNHp+MV59/6fHIuE1hCf0UAgDXVwYpqXV58+i0Ta1zLZ8z6BCZHbt3IiyJPkMhCT1JmrdymKlD15E2SU7oTkR18lNCl4bRSUepn07i3aRBLa14+ZFkqSQhdSeOknyV5ZC63LQMl2XJxxZdUsKFmwGSNG9VB4ybmxBSnkWS5SbLf709ZT7031iHJutmlknmRW6OSLJS/sb+Q51z3/cTzXu56cEym4htXcveBJ1kp8681LnXNxykBXm1Ws2ccJCOT+VJBKmFL/PbKk8yb3qf0onu6RC+er/X62k62JOIYkuiW8wo7CDJ5bqxHEo1TC1cFyxQGhI9PZg1K9Vn7uwkEZuNQBpd01iJ66zx8VjiI7kZQLkMF1lOj2szGjQnF5Fmy3XT32qhY3ZGyhvkYk8s+hw04PtOQtKWSiqPjEJqgsr1klxXZ5JkOvKYs1xNi8BSlkSCBuWdnVnOOp9Po/Zcg9YlvX6xcloQ7CSKXReouQV4PT2qjScm0iMAor4TSBJd/51lpDk3zQtZedNZuYlVN60a6chUGrbLtmEbyEW01C5ltLreKasLaHbcqWepKTL9+L2JhGhvT6332GgUV2eYIEDDLyCMq9QoIxCGcH11OiQhxRkX02f88mHT89Nqs2Uq8sZ0/3SZjt0IdiFusTvDdQG5VNMJXwkj2SjHNvGM8jWkv+/0x1gOUXxNj4+brHs1TXbt3UELVxNJraenQ772aI0uLWZ1MlaPn0PMdF5PrXhSfUMhcToqKz3e2GRd8KepZKZNecrMF1qR6GxXWnuLd3sYAt6CBSocTzoxHumlko2uy69xx6TVuKyP4bRGl3MsrQI5l9LJ81YbGVPVz7Yg24pFYBukQ5ujelArIrtVJ201zul9Qb9HJ9BNcbf6HWh+hl0380xsT1REA06TRf0MkkS3Y/dOxMybuVkASKVGPM+D53mJo0+T3IYkdHWHjJTxkCStlAahlEk+n884owSQIYt1XWtaaANIiF/KYNBKWHcwSVmRRqOBsbEx1Go11Gq1jBQH45PlYnwkUlk/koyWVtS0KKejVH5zs4C/yzRIousSOUy3vb09cRZKSNJaWnrrUh+SYDZZQzNvJPulFrduxc/yUwdcpinrguS11Ienc9kwDOG6LiYmJhKrbkroyLSZV9l/pOY68y43LWTd+r6fKQPrQ2qXs07Hx8ebyqqfVKAUzcTERJN1v26BzvzpJw1kH9ZPRZj+lpCnA6JI6bibCH2TFbqs1z0JlkS3eNGg1eKhWlWT0LVr1WKrXFYeqnp6gJUrsSVYjP32U3wroNZUQQBgIGb3YgFpniQG1O99fUpmZczvxugo0OjrhrNoUUr4lkrAaiUvImW8NcnsZJERQGVJWnUp6RgHftCdWc9kihoCruvBKyFDgjphCA/A/Nk+urq8RO5D3k8iVlaXztfX68BY6MEvdcdyLXFgarNo9c51cbNDNSeRwpFEe6kklEF4SoCLLi6iY53t5sKn95TK3Zg/X11avRooleZhn1e+UjUUTxVQDFwW8Lnnsmw0SVzGzUqR+jdJ3YfZRSYZGt0yP47Hc9Xx58LcIOtQD400TPwplFSnYTiZrC5Nz+JQUWZ0NOUcZPs2bYzk8yrA4KAqH+uH7UDWiZb39Cwat8eYW4Tf162kYVwXDVf1s5FNKtqeHmTN5MVC2glr8FwX1arqF91Le1ItfHaUnp4MiV4ZUpcbcFRfZKGA9D6271REuuzs8v8ZRKa3xZ/tCW9h8ZeC6wKelz6KfE/xkSOhTN5TPrK10IHrF+D46fu34XrJxrZ03Gw49GMkO2UYE+eorsUnm2LoBLp8bejW7fLVoRN6kgDVhw/5TcjNU8bFeYF+73TB/DdpYovIHKgxyg2czB45g9TrqYoZraUB88aEIx1i6JXE+UM8IRoL1ebyyAhQWLVKvceXLkWj1K2MHzgZ4gSG8ZrE8fXjD6aKYgeQWmr6ACnmAcp5ejoem6I2JSOv6Y5E9VOAre6T2U3/9+D5bnae0WrnxPQtIwZay9i1KhivT0XQi/k3wjAZw+lIXUfLqOJ/Wsq1uOq0yDYt13cj2LF752HmzNgsMiCx6nkeOjo6Mg4lpyJkAWSsd6vVKmq1WkIgSy31QqGQaKyTRHddN+OYUSeKpUPPfD6f0ckGFAlGgpQkubQ45//U5CahKq3jdaemUsojDMOMLrmUfKHlPeuJRH+pVEIul4Pv+5CW9zpR29bWltSz1GWXWtiSfJXkufywHkjoSxJX6qkT3ITgJoeu8a2TyWwHksnVatUoi8I2lRbuHR0diKIIvu9jYmICo6OjqFQqmbTkhgSQteimlIxOUuubLyyrzI90PErt+FAbQGU966S6dFSqS/9IQp11Kgl/5lPWp5Su0UlunfiXpxj0UxWyL+n5kuFMVv57Ava08lrMMJjM1FpN8E0rEi6yHntMLcKOPx5b0I21/cCf/qQWgCtWqKDPPaf+L5UA9Kfp1uChv1+FSUiAoS3AmjUorFyJoSEHAwPAwqVLAd/H0/0OqjHpvmBBauykG+0CqbPJYtBA0Q8TIjIMs2tGWTyu76SxbqlUgB8ox6mJdXVMRhZcpcvd3deDBhwMDWV1T7kIJocq+eLJSemr0Uui9f0CensXw/djfe04QqcynAp0i/Zb2NeHsZJyNkn5FiCtk4LfAAaGssd/maEgwLMjBeRyQHdQE4tHJGR+oVzGokUF5X/sIRXF0KHz0NMzDwsPbSi9nnIZz46oo8QkAagVnizWmXc2gOx/suKkVjuQ3f0wfWR/dV1lScXGYz+V3xzL41UlTyQ0XC/h/XU5d3IA3CsaHweeeUa1MU9AJMQIdzMoowIogtx1mwkL11URCkJ7GEWsXcO0C8meCpBuFAFo6ovJxkgYAr6ParUQS6k7WNjXp9KlWH5PD2pQOuj1kSzx4MXxNIJifMqkmm6UtDLD1xk1+ZlhcABsj/GdtWaz+EvCQQMFv5H4guB45UFokKN5I5evYNcFCrGMUy1MiTf9kdW5Q5640jcaJUz8bqrS5TS9DnSCj/dxo3Iq6RZ985PYlsE0kI13qlcZCVpTfDp5mxDpcqySmYvHJg9QRC3MTiFb8bTpb2pe4/qeItQhiNCgCCDe9xxM49q6FZgIDgICIBwCwkEgDNW2su8XUCrPy5SFY14ydm/rY0IrornFfYkOOLJON5NvrXPqdSej09urFe8vs8T+FgSplJ0k/JOAzcx762syEVOi28Pyc84c6557JZVHzmv1cnLu4qABWqYnp9c0Aj07fAtrdPkSmQGwY/fOw8ybuVlkiElJ/uo66CZpF2mFLolsneB0XRcdHR1J/CTWSXzqZKBuRUvrY2l5zHtJBAOKgJZW8tTiphNNSaKToJXx0RJaOpl09ZexyJ+UJaF1d1dXF/L5PDo7OzMONKVT04mJieQeSdBLuRSC/3OzwGRlzHywTnRrdAlJSNNSXFrmSzkVXdqEGuEmuZ5cLpdxnAogIdHb2tpQq9WSUwa08Ga6LDt/Z7lNEjWyvzK/7LtsiyiKkvRYd41GI9kI4KYB0zCR37p1vqnvy9/1jQvGpRP304Hc1JF9lX1SWuzLvMnnbk8kk60lusWMwLbeCa2sjfhbtaqIucFBwHUxMAA88ABw++1K0eVd71IT+Q0b1C1OdSwzw5c8I7nEhOw79FBUq17M3xVQGVR8fRgqK3TJ53V2AkW/BriuIgbE2sdzFQmoHCUWUK83+0rUFTfIMdNIOAyBIgPSoSZviBdcju8jKM1LiAapZOL7qnzkUcMw5UAZTaWi/HtJ6/pyuQAvEOSr1LcW9Vgol1EuFzJNk88DBbemNOWlVRkjjy3Vnn025mCXevCk9SEzNTSEUkmRuWvWpPnv6QGWLnXQ17cY/f3KSp3EglqMkndVjuaCoIByT7da2NGhKzPLipEL1kola3poWrzrIvCsgFIpFeKXFSz7MD9xhTuBi8nJlJiSwai0wsX26GjqYJSkD9FAbC0oCXNGKssoNzPErs3gIDKnM6rV1DlvX18iY56WSe28ZI93A6hWC0l38X1POTcdGgKCAMNVL7mVXZjcjxf/k2j2y1MMOsMjHyIZETv9VCTLbgp7JNxit0b8TMVUWrKRpz+bdFsBKPIsDJ3kAAxJNf09J/+mY1DpPJz7m61eqfyWXKPrNmuQmzhGeY88XCPT06uB5QSmtkCWhL5+TVez0vcDZbmZhrQWl/clS3RJpOvMpojccdUmrpR7mYrobU4re58cDrm3SmUWjs/c3JYbMJxLMb3UgFyN3a4rFFv8NEyTM05TRlt968yvqfL1RhLfju8ndSejq9fTOVurJPW65e/p3nss9yfbTG8/GcEUxLpOVKebVl4ivaZSFHWpxWXaMFCbUh6qleY+oyNx8isKy+fa9FwlDntn2Phtx+6dB0uizzBIYlASf62szqeCLnehy4u0khnR87AtqQsZD5AlhSWZqFv96rInUvdbT0vmX5c50fMmy0liWlrik1iV5DiJWhMJKollWZ8mC3Rdc5txm9pV/q+3i2mTRLdkltbPsq7kBoCp/0xOTiZ65dvSwje1nd42Olr1U/26ricv+4PMm7TE1+vPJJ0CIFMeWZfbImmlFXyrePXTEuw7U8W5p8KS6BYveuhkJtKF9eBgyu1JAlKfkMuFfjJfF/GG8XFk6ceyXlcketOechK313w9/rjagpkLcFkM3TFlcoLZbY4vWV3HNxn2uU1GVE0LovHxrI9TeZqYVmtJ2nK1KK75pULmOHwup+WViYuMVSvNeraJVZtoGGmJLZVBRkay/LC+3pJZdV0VHgAKkrXl6lVnTkxmg60gy9mKwNiBxSCzY9JXb2VolsC0kjdFLsl8123KNvtGy/4+Rdlkv5Z6p9OtjgYcOHz4plt/Opkww2AX4ha7NcSz6AqC20jyTRHFNm+LAzmCCJSvYjn8SIJ8qjSn4iMldMLdlO8dweRkljRvNV7LtFulqRvoTln1pgKLa7qm+1QwDec6UVytZi3lJbEuldEkqayPZ/qGg+sqNyjS74zrKiI3Y32/rc4wFbGuh2PCprBJJXiZYFLivRV0x6It+6apM+iEeStMEUYvqutSDki7zxDHdj8Dz/ehsST6HomZN3Pbw6HrTkuymyChK0lFeT+J4yiK4Hleot8sraxlXHQsKUlWPU8mgl1a4EptbJO0iNRIp8U4SW3ebyJZeT/rgHIslOuQluXS8lhK1OjlleU0OUvV86xLceiOPOVmh7Si10l7eQ3IEr2sE+mIk21MC3RabNMCnR/pRFTmRcqVsG1YVl0uiPUpyX/ZVjxNIPXX9XqVluLSOp/EeLVaTaz+pUW9tETnyQBasU9OToISKuyX+jMh202eAJDPj35CQ0rJ8LrpWWI9yHT050GmrRP++gbEnkYSWxLdYsbANEk2LWj0lTetaufPTwS4K2vVvJ963L29ygpmYkIdOW0ERTi9vclCZKuwvM6Q6LHFd2+vIoZJYtI/oq4VCiCJM6xm+e3i7JTx9NwGurocjI6myYRhuiCWEp708ZhopursJkS6MegsSxoGc6FKo2RpiT4+npL0UgaGFn6U4wiCbiWzoVeWIF9l2uSlUY3zKc324wbaMqTkZ1j2ahXK6j2O3hNC836oopg/P5WJKZWAuXNTqe/e3jRdtpW0auNmSlLRel2SSKf+dldXmndGKLVV9AU1/zcxzrIhdOsx/ua6KJUKCMNUGigINDka10Uh8FELvOTkhHRQRmKiQOa9s1P9WC6nRy2kiaDrKpN+jcGQJH0QqKpg0EQXX7JBUr4mros5c9Sf8+crNZlEDghAd7mG7lJaRyRwnKEtyseB76PQ0wNUNLaGposmFmAqzCDvZHYhbrFbQyPxUsfI2WdQSonxNkCNLyQRJybSd7buG7IQ38iTXePjaXh9+HPd1PWDvsGoHyZiHkzFMhWz1WZlKyKZG7amMMxLq9eVPpRQGkaeGGuVR2qad3Q48eu9sG1rXre1LElmMxzNMjVsL/mbvJ/uNtj+9I9SKqm8si05tJrKJNuyVV2psKnEDGBweq0XrhVM8yrT2C7Yf24MF1wX8NPOYqrXVu0ok0/U5IK4/TjpFHOAzA2ywvTERP48sRkli8fvpM70ygUFfFQcBQ77cWE8ndSX/1damN6L8FMO3dPdMNhNYMfunYeZ0+oWiUyErl0uHTkCWceRADJEIYBEp5zW1vy7ra0tIZVJikqdcxKtJrJQt1SW5Cjvy+VyTZbT/KZkCAlMqaXOuHU9cZ1UlwQ69dg7OzsTMpi/5XK5jHwLiW2SpZJQlnUpCWhJoJN4pWwKy+15HnK5XOL41XEcdHR0oL29PckfHZa2svbWLcuZT6nfTQ1xfpM8r9VqCSkNAL7vZ/TiSerq5WRdtrW1JfVH6R/pnJYEt6yLarWa9FO9n+jW4ST+x8bGkvgp5TM+Pp7UaRRFiZSMlO5hvZn0z5kGw+j50WWPpHQP5Y1Y72xPnSyXZZGyPHKTSvZVKYMjTwnIOt3TYEl0i90arVYSklTUw+vfsYQLDj8cCAL8uV9Jr7iukp1YtgxwVv8fEIZYfOih2DLkoL8fCMN5SfIDA+kCLkmWkilr1mCfFeqs8Viojq3PmpVak3EtFYZqQSgXTuPjSgcUAGbP9lKNy8FBFHwffX3FRGtaEtrpQjjlbHn8GQPVrCQK0MzmV6vwXBdBoMb4oaFU7luS6SY+nou3iYnUXyfrUzkV9dDZOQ9zV8xLHWaKhRaqVRSYFz9rGdboWYgwVNr0ExWgMpDmg7yo0rwVurV0JOn7CKqKwD3ssHTfpKsrdgK79s9YWC7DP7QI3wcKGMsuJhlhvKJvwMFY1QFPGQAe/KAABPNSHVaS6eyTOmS8/F+yPCyUvF+S9ZKAFhtCBYZlo3GjSJahVIIXBFi6dDGGhlRbycdlZAQocOE9d666d+lSbAmLgA+gnErChCGAfnXfrFmq37luk0w6SqWsk1RUwjQAdYL47MYZmh9swfy++CHpr2T19NnxBweBkRE4CxaoeAYGlGYSPfy6rnrgWB59A0Ci1YKbx1FmCOxC3GK3hnzO5GtQ28wNYieWnqvIOdf1kMtlX2vc0OUGZ8b/g696NglaKT+mWzBzqJDvMB20juYrenS0mfuTqh2um+bdM8xVEht8382M/YCZs90WGS/HYTmMSCU0ffiXcXZ2psQ1x+z2dgcdHUoSxZS2JHUlYS/LIPMphz1ZX3qZ+Rtl5LhRIjdMgOxcysTDmuqTmzCyD8iNklQuKJWCSTaiWWgmYiLaTdAbyXXT+YFpbgokUjmerCzfBwI1bkqfAuyXgOqXo6McKvl29+C6HtrbC4DY3NBVZhyd8Gd+4gp1XBee78Pzs4R6QqDrHc20uyLDyN0o3QGPzJi0DqH8IOtFr2O9I9ixe4+EJdFnGCQRTBJdErAkGk3kopSW8Dwv4/RQxi/johWwJC2lnrVuBS8txmu1WqIxTatpaUUvHXAyDZKKkvQkudnKEp7lYr0wf7lcDkEQwHVddHZ2JoQ2iXOdVCaRKi2MTVri0gKcmuO1Wg1bt24FLagBoKurCx0dHQiCICHNqTNPcpr5Zbz6poUkXCXhy3qm5na1Wk10yyWJzvikDE5bW1uyoSFJdNmWsp8VCoUkHuqUk+AmSU/r8VqtlukXrFvGxbZiPw3DEKOjoxknsxMTE6AePslz9g/mQZ420J3IyjRJaDOs7FdsY9kHZZoMo9c/45R9RG70kBzns8W2Yh5kX9Y3IfY0ktiS6BYzAqYVoZzE6xY2BIWaOzsxvOIorF2bkUZPSHTc94C6r1RCd28vHnvMw5o12aRJoie8aRgmJDpNnAvLlgFwMHdulh8FUqkXaTHHRX+9rhaR3UGAhPF0XThBBQXfR7ncjZGR7PqBaw1JLDhhLWURdAZBLjpiDZtioLQ6pe40F6Bbt6aL2VagTjyRrP8SztRBEBSSRbLnNlLNdD1vrtKqHxpC8s26ZpwkNaQlYrXqIAyL8EOgGChSho5RaRzurP2zaqeBAXT39KhI16zJasR0dqqKLJeBvj44QYChISdDTrBuFAGjFqy+X4TrAu1RqrHuurGuvq4dE4app9ZU+DdrCpktXBqhrvldqShrbDaeFI7N5xPz++KhPoo9JVQqXmZjZHQU2Z0h18Wz40U88UR2U0X6AyVnTTKKVoNFv6Y6w+2rVQXRQWlfH1AuY6yqThOkzvs8tRFQrSoyXPYHgs9upaKEcgcHlQfgZctUuVevVokPDqq2W7RI/U/mhKyZ7MStrNVEH5wpsAtxi90ZDTgZAi7dz8o+Yx5q8Fyo00hhCCdItbcpt0ZHynJ84TtIH/pHRlKiUY6X7e3ZV6NTHUsDxZHQ+SHHZRPBzXdhPi/KpA8S8ka5sQkkGtlwXVVuP0ust4JOEI+OqiRHRtKNTtaTHHYM+8NJHcqNBW6E6pb+Ms2JiWZuVFqW62lKPlSS3eRUu7rScaXg1gCEKJZFgfndFkI5ETcT2PSP4fk+an6hiew38d6y71D6JQhiR6iywls1AiPR55t6eF7Xd2dMOx3t7WpywUbp6YHj+yiUSiiUAgxXnKRMbGMprSfrnVHqba0MCERZdJJfc/ztxM9FxmqfiUnLC5aPHZOTDU50ZQbZKfbaKy2v3I2XDaTvysjNjfhTCx3UJ2fOCGfH7p2HmTNjswCQdYYoPxI6Mcr7AGQIQJKKuqQHw0ldb8arWyrrOueSJJWW2pJ81C3IAWRkNKRECEEyXZLa8m+mS1KaRHk+n0+szinfQqtw3TpZJ82l1rmUCJFkPkn0arWKrVu3JkQs89zW1oZ8Pp+UiSR+oVAALeSl1TLrSsrCyFMEjFdKqJDgluS5dARKQpdxSBkTad2uS5DI+pEkOk8wAEjKyzC0epf9i2npZDfjJAkvSXRa0VPGRcq6SNJZEtWMn5s1sj8zH/ppCKm5zz7Ia3IzSdYL/5blNPUTxqHnRd+k2lMJdMCS6Ba7Oaay+tF/N4XjRN91sXYt8NBD6Vze95Wlck8PFCkXhtQlwdDQPKxZk11XUAYkSZILEFrLui6wdGlMrDYbFrdabMo1CEpudmETE6OFpSWEoZM51s4FLyU0EisqubKSiw+9zmKi0fcLSVJy7cNvcstAs5WczGYYpoR+EGTJb66NukvIlo+RxQs3Eujc6JDxLlqUrqnILQOpNf+sWUABVThhiPmz/WxZ6QWTMiWVStoZuBgjIwwkJtaVipdZsEqDb0lMSBKC68Hukt98rJoE7/h4zGKLOuBKng2bEenXOgs3Ih57LP27Wk0dlQZByqb09ABhCN9fmFn/Jv1NLF6ffRZYtw545pmUwFIOc1X/l+QVkykGDaA/brSHHlJlXLRIBejpQQMORkbSjStWcbKQ7+9P24b10NGRbhIMDSkvwOvWpRW+Zo0qe2dn2nYLFqRSO/IjiY6pSHJLoltY7DRI7qvlYyXHKnHNdb0MR7d5c3bcY7yS8OUjnpycicH0czn1aunqMmxwxsSe4wO0HZf8nxas+YCLHFN11lZOBAQ5KX8jsd5Kb1yWR5LDJNB5Qo0b3/opMlkPOqEqs8i/6ZBU6pLrxTORuNJ5KvngbW0OJAeu9GMHkjTV53h63abeweGVfci3nbQn0OV55GkEOpX1faTSI7Ji5ORNtqUJMp/sSHLXY2KieYNdJ9FpWS2sGHy/mNzGkwj6/8wyo3Jd5ZtHnj5I5GRM/ZV5YflMDy8bXraZnJuMjKiHVj8ewfGYcw6WjxPNtKDZetTT5jMUPzO6zcjuDjt27zzMnBmbhVGPeUfjkJazuuyLDpN8iol41/OlE146Ka/LWOjOF3UJFeZR11MneSo3FaTFu4zDpJMtyyPzLUl1qUnOvEorcUmCStJbpr0teRO9rvR602GKTy+X3iamNORmgele3fp6qrQkdK142Wd0AlluuMhwMn9TEaim3/S+M1X5TXmcqny6Jbsedjp/W1gS3WIGotXEvhXiBYFpnZDqtJqTmSoLSSQkRuMEXN/L6E9vT1YTp02GTJji2aYvS8l670RrW7m+2tYiGUgXr01OqaRlkmijMMw6hJv2SV19E0FeM4WTDWQoyHT6Bv+mxi3/b2rLVpFKlkcyQzxe8HzbTZRfro1NeQirqZ4t20DyAUbfnSaGx1TnraAzOPzW9cnlw8o+wwdAkiozSNf8+aAt/mxPeAuLvzT0MVC9FxtTBxKQ44D81m/Xw045Xk733bSNoK6L5rKYIpjuRt408iFJ/enkcTrxSoPf6WRRJ6Nl+roj023VX/LdqlAyY6YwBiMBSgLpeXne0IllfewyGSzI31uJ3TMcnXxz0qNNNBiFqX3kuC2j5abIdPyebzckYy8xVWIzaKP6hYIdu3cebG+aIdBJ2G2FMf1P62LATBzSgldak/O6TEMn0KUjSUm2S4tqAE3EJK2mKTsCIMkf85jL5RJyNZfLNRHK0tpZtzKnrIuuea47N9XrVUpsUB+bden7fpPlOC2cgyDI1NXs2bMRBAGCIEgcmUodbz09maYkoHUCWbZjoVBAvV5PLPUp61KtVhNd8Xo842BctPJub29HrVZL6ol1yA0IKWOiy47ofYKSKfJkA8tG6RaGdRwH9XodY2NjkJbntESndbsss27tLa3p5f+6hIzJX4DMFy3dmQf95IXsH9KiXsYpSXcJ0+aIhYXFDIOJ/ZZ/myblieYKlAREqYSyr5QgKBXCW8fHgWJfX2rdEoYolZTlLZN33axBjesitfqV4p1hCCesoavLy5DB0nmlXhR5u7FMYWh0PsW4k/UcjyFL7ROZCC12RdwNv5A5Zcsj73KdqEtz8xrLxToCUks/WmRLy7ckDt0EznUxXPVQGUytn4HUX6csEovBhST/7+qKj4OvHUhvkOSzlDlhpvfaKyu2GgTKGkro9pTLHjo7U1UQGn2beHrdYjEhV2RGZ89OOxILKK3PWlWcbAR2wHxeWZlLqy1WUmen0ixgeVwXPXHTVyrKUGzOHAATYiPBdRNLc1p2kadmlMyyJNMbrgeH9btgQZqPmHDg80AruYQE8ON67ulJrfBlmwVB1tRuaEg9wL29KvP77ptK47A+XFfVJdtVNpC+eyDKbWFhsXOhP1p8H2asrVs8f/K1zWGZ101jkbxvKuK0Xo/Dy3Q1Yla+o3QuUDrBbCIzp5qjMKPyfS4KWgsdoMW+I6/RF4kc/xmFlJTm61ePSyYtx2meNJMW9lNJuLEexKHnzKuVUmqmqtAt3zlcZU7RSb8Wraz69chb9CE6kWW9tdq/lm3uujDLxshM6zfLDishN/WlpXa9jsRbul4+fewXmXbQQEeHen6q1dTSvNVGiGEqqX7XHzB5o95PXTf+yYHrijkm64Jhu7rSI4JhqDoWj0zIzXVmjsc79TmPfsyj1Xw//nbQQMZHjsUeBdvsMwAk8KRl9VTQSUVJqtNZJ5A69CRpmNNGbBM5KAlVSZrrzjFJPEoda0pySAeV0hKd0ie6bAqAxOGjzAeJX93BqvwwDOPgt07CMk4glSipVqsYHh5O6qKtrQ1BECRSMdLy3ff9JO9MMwgCFAqFRBdd6oLrDktJHks5EZ3MJUnNNBif1AwnGTw+Po7h4eGErJZkNoljaWHOMlFmhhsNDMN7pQNO9ikS6HRSK52rMpwkrFnHIyMjoCY6yXSGkWS2JLFd183ojMvnghsA0k8A20kvg9SLZ53zb5NMkJRR4t/Mm9w0oNSNzLv+TElsy5r/xQ5riW6x20PXGpWLUJ3FJSoVxZb39mLL0pejWgUW9zSwuCfEg6s9/OlP6cJxdBTAqlWZ1Xpvb5Yk3bRJKUuQswOgyL++vqzWY3xTwUdG67SjI10EUwqbR51J0La3q9sLkgQXsidci5n8WHLq4LoePH2RF8fXcJUmtnTyNr4pXduQjCZRKtcqkm+Q/8vj9GwOLsqDQDiiAlCDF1eVh/FxL9FXpfR1tZpKuEhZFK7TyJOSzOXi26tU4FF3ZCAm0SUhzf5CwpkssTxOzG+5gKtWUXRDFDtcoCsm+8teohdOPXhpJJeRepELRjYyA8qdCuappwcN10N/f9yNRDurKLpR7PVT2RaK946MCK+ySMuikfKFykYsdkNgaRnDVU/pmK+pZMo+q131fWarUkkd0TE6ElWSGyhQBH/ZsuyCPG7UQqmEnp4CJGooAKUCvBV+VueUdRSGqYbSypVpx4jbcqy0EIXKRuCmm9QinQK7jEcn0VtBT3OGoD3+bE94C4u/FJywBgeN1PI8eR96mrPCZkgOj3yc3Gzm3/Kwjny9yu/Mxh2vB77ZujkMQeea5PPkPEDf08w4zpaB9G9JiPo+Gm66yR5W0w3xVhsA8tVksimQe8aSvyRSXxTpeKrv0bI+ZdaneiXK4kmCul5vnhcwD3K/mlJ0BbeWSqy5brrRv2ZNVphejs0mAltk2gGSdqRMi6kchqbJEvqtdir09oyNEaTsuUyjfRbQEU8/vHAs3RTOaPmhmQGXZY4H3GLgw/cd5HKpIQOniqaDYOz/RL2u/PJ47AAEbxAdpBY6Sf9MsxXPMeUDKuOY6iPDcE5mIsn1TsV657f2GzeGZgrs2L3zYEn0GQBpbauTbiYJFJN+N4AMqUwySnfaKeMNxctJ6mlLEl3KuURRBGlJzmvSgWS9Xk8cSZJAM2mESyK0vb09IVkBJERmR0dHQiRLR6d6+tLZo4mYlZrytEqemJjA6OgoNm/enNznOA5mzZqFfD6PQqGAfD4P3/cTJ6GdnZ0JeU5CmhrotJBn/qRsSRRFqNVqmJiYyNS7bi3POKS2t17ftEQfGRmB67pJnYVhiPHx8UQ3XVqncxNAWqOTrGfaUhOd3yTZpba+lGTRSWr2K5L97A/j4+PJpoW0WNdPUZhOWEiyXG6qyA0n6QSUGzlMUxLnJoe1/DZJBTFfso+1IsWl5bou/bMnw5LoFrs9uEIgaydZ1VbaidUqsGED0NuL225Tl95+ZD/w2GPoW3liRoK5UgGG+w5SpO/AeiAMjXP7hx9OJS0BoBZ0w+vrUwz75s3ZvMaLImqder6LYuAmDhaBNP1SSS1scrm4GFzpCodPXGRzvSUtrIB0wey6SBdH8SJnrOpgcCDLyUviIQwVsS/XKFJrXa5p9DWeTCrjNHRrNUuMui68pUtRrToYGEgtzvnp7882IfW3y+VE0jshqDNOVMMwFVGvVFSb53Kpwyou9GRGS6U0ES6CJalTHUsXuBSXjctQ7OlBsbcEOmNlvtjldP6kadNHt/yKF6NbKh7Wrk791ErOgFLtqi4KcN0Cgt5uVX5Albu3t9mEXD4bYZg4VkVPD4q9vcBgNcv6uy66grQNSKKT8JD6sbI9qlWgUPITHfnMrgjJgjBM+2Vc5+sH6EyvCNctxhGnfQ8Aqn5MSsREQbmsNNh/c7+DH34BWLVqHt7Y06M6FNtZdlBmVmdRdMJA1tMMQRu2Tyt1R2Y61157La688kps2LABy5cvx9VXX41XvepVLcPfddddOPfcc/Hwww9j4cKF+MhHPoIzzzwz+f3hhx/GBRdcgN/97nd46qmn8NnPfhZnn312Jo6LLroIF198ceba/PnzMcANMouZAa4lSKDzvZT9WelPa6QYrUv1VxjhuilJy/Eqn88eQNElR4CUYGzAUadnpG+OOA+Mkz6KZfZSB5Rxdk160jKT8hO/k6WGs24fsK3Xj4n315NqlQ2+w/mK5Oaozk0DSDY/OLZtSyFLT5f1pueJw1KpBBT8eAOiUs04xh5DAWEVKAJZK2bAvPGtJxJXjut7mf4jozH58HbdeE7RSidc7gDE32NV5e9ja3+zNLjuDiWd7xXiOijCL6u6aEpXlCOpYD5DYQjPddEdqEyPdTmZaacIljwDut+AahVwAw9OoNWbIO7HqunpR/ksqQ31gnqu4wqWm0KMSs4V5Xcyd3NdDFdUGqVS1uAiM7GKb6ZBimfo5E7sxnimwI7dOw+WRJ8hMGlqmwh0fpss1nUpilZa2zoYTpKR0oqacic6ISYtwEmc1ut1VCqVhNzl/X78MqS0CAlLWl/TeSWAhGDv7OxELpdLSF/mDcjKiNAKXpZX1qFeB5SZGR8fx8jISJJ/kta0ZOf//O7o6EAul0NXVxc8z0uIaRK8pnzxQ+Jekrm6hAutziX5L4l1hqGV9cTEBNra2ppOH5BEJ9lNS22S3o7jIAzDjCNQKeOiW4nr1v+yL0qrcm5khGGIWq2WEP7SAp/pE/omgbSQl5ss8ltuwMjnQJL80hGrPA0h+4bs53oZmQ8pM7MtuaVW2JOJdEuiW+z24IxcXyEA6cpEWoPznRU7bnziiXjRdOAQsHYtuo9vwPedZFEchoqDDQJgcaAW1l44hnI5tZwlJ1itKiujXA547jlgPsluKYIuv4FMnly/iDDMWsbpxkZNzHWMyclURsOki5osdIJ4YRNbR9FyWpLoQm2jKZsmx2OMXy5CaZ3shLV0BScTkyR6cr7cS5xV8jMyouq3Xs9aFtIhKTcZGFViAch+UamojYzRURUhMyb7DiOkVX5MEIRhao2flqkAB0NZEl1WEoAg6E7kS4mmYNX0QsNVcwBH1CfCEDW3kByaoDPVNWtSY3Ny7jztLI2/fN9DIWbYa6V5sU9cD0G5mFSL6wLFIH4+Nm1KnXNmOlza+GxvOrDVSQBZVrnAroXqqDdKMbkvPdvpFRM3cLXqJRsRcrHv+2n/HhpSGzzPPqvKs2wZsNdeDh5/HLj9dhXdGw8NUu+n0jLOQKw0/T+DSHMdL7Rzsm9961s4++yzce211+IVr3gFvvSlL+Gkk07CI488gsWLFzeFf/LJJ3HyySfj9NNPx1e/+lX8+te/xllnnYW5c+fi1FNPBQCMjY1hn332wVvf+lacc845LdNevnw5br/99uT/9m2xeBa7H+S7Vz5vrtcc1kCMSfLXZBHOb/lekqQokH2v6FnzZEDxg0kegvmQ8WfGINN7TpLnQBOBrrlS2a49PElqykdD5lknijmu6uR5UhZWTAzHdWF6a8jmNJH2TNME2Z7GnQRxOSHRtXxlGrhFGwKqTJ4LhHFbtprvAKIPVafZnrFxAh278gQdlVro4FUvO/dtfF/Ne+g/Owi8eDwPsnNcfZzSNr3huigII4D29tQKfqq+lO5pxZtJvOi6yVwlDNPTk/oJCZUNB3TCW61kDw3IuabePGoK5mXCqOtqDqH6o1ZxkK8TJ+6byNbRThfAf+Fgx+6dB0uizyBIsk2SSVKHXA+rO1WcLmFH4nAqB4syDyRVpVSKri9t0iHX/yZ0a2NpdS0Jdmkd3Kpe5EfPB+OT90rrZJlfksrSwtkkIdPKGp71w/rS8yHLy7zLMugSNHrZTKcJWsXPb/0Ugu7kk79JC3QpRWNKR6Ynv2U/kPe0ul8S8uxP8n+9PK36KOPmZojcCDClrW9G6fWnx2/6W4d0SKunsyfDkugWMw766lb/W1s9NOmNh+mRbc675UJcB0k+GaZUSo8+w3VTM90WedgW9AWeyYxLP5ItF8EZS+F4FaoWR86UVmRN6YrrpvvIiUgnVibLIOONoXL4pZ+Glgt63bCMfzdZtukLXDIFJtO6FsQq11+mDQlT/ctrYYjEcZkehPF6KdudxJ/UlcZQy/W5/NarUH7r2W3Vzg3Ei86ODpVp7uDITSdRLknyaBx7E6nu+ypaDzWA+WrFHhjSArKEENs52eAYz7aP3MhJ8sPdFpn2dPqkju1hsXYDvNAL8c985jN43/veh/e///0AgKuvvhr/8z//g+uuuw6XX355U/gvfvGLWLx4Ma6++moAwIEHHogHHngAV111VbIQP/zww3H44YcDAM4///yWabuui56enu3MscVui1YDzXZGAWz7EZev/mlHbHrBbSMfRphe0mIjwXHdDEHPJMmNTucVxLGHoHwKYLa03lZcmfJso53ksCGldWSbsAwcH/T5yZR50gdT38/uoOsffeKmtSPJZVO96nl2XTTHPdUAKJDRU4c52HS6VzJWT6dzizJuq9/o0fH2JutvqM0T1hslY4hWc5DxcfNmkH4aRPYPuccmffsApOb1G9N/E4monfBe2RWwY/fOgyXRXwSg1AgJXcpaSJKXFsnSKleXoZBkpE6+AlmyWbfeliQxHV1ShoUfpkWrbOaREi46mU3Lan5I3NLSmBIwLKMkyCUJrltC0/qYkjKSDAaQOLmkvIjjOMjn86BkS0dHB7q6upK/KdtCq3OTBjs1wmX+aMmsy7LoBDvD5PP5JrkbaS3OumN6JIxZl7Tyl0S+tKqXuuWUtZEEtG7VLy3YdTkUtiUt9Wl9L+tf9glZVlqT834A0C3UpRY764T9Xm7isP2lA1Fa6ct2kX1cf47kyQh9k0Q+RyZCXvZF0+9yo0r2/T0FlkS32K3BWTYXS7pmhiQC9YVr/D17dvwvNTGGhrBs2bzE+tf3hRNLsYAKq8paaOtWZd1DeeZSSUVVdMdSs6Le3mbtEcNqQa6PMvrZInk9H/B9eG4DXV0O+vpS+eeCb7CEq4bKLCr+v1AuIwydxJKPupH6gk/y0OQjOZxI/WsTd+37jtLEzhQA2cVvGCba2L29SrpjZESlxVP+siml/jb5X0Cl5wxtSWV9uKNQLmedgrKRenpQ84uK5I3bqhY6ydFr0+Iz0fiWKz5mpKcHDb+A+qZst5TBqO3u+w58v5jRFPV9J64SZXFFffogUL4yaammW12S0NbVjJINk8ow5s8NYhOwKrwgQL3uiCaIrdbHx5XDUUk6BwEa5XmoVIDBfiQnBbJWYs3ypfSr6wxtAdYMZNualcs2KpdRi09hhKGy/tfLSTB9GQ2JosnJ1MK+ry8uxtKlaTo6K9SKmZJhZuAiHNjxhTj9DBH5fB55TVC2Vqvhd7/7XdNi+cQTT8Q999xjjP/ee+/FiSeemLn22te+FjfccAPq9XqTz6ep8Kc//QkLFy5EPp/HEUccgcsuuwz77LPPtO+32A3QirkT/yoSr/l3EoN895PM41hEH8J8N8quJflP/VUgNxoTSRctf0xbYkordHlCDmhm+TnQxwOaF38Q+AiCLAHKdx3QbFirE5iSrObfrAdZJ3J65LrNTj/VdbG209qN47yMj692Pe8yr1JRjHlIHImSAJXznHjMTtqJ3t1ZvzyWJuoyUzhtctIwvB1l3ba3N59U8H0PfuDBkYXT2rPhF5ous4+y7Pp0VC8qHbvKRyKN04Hrq1OQJokSfWOgOpTqsOtTT5m+Kl86ZXKqY2q+KAd6AAgCOK6LICgkXVpat8u5DtOQaeob3yZwXKelu++rNPL5eK7ne8oIwbCZkNa7INKBbWsO7UawY/fOgyXRZwhMFrC6RS+JPkmik4iWxCuAjJNHmYbUe9YlUmS6ofaGklrWksA3yYAwPzJfJMN1yZVcLodCodBUHsdx4Pt+RptaEudS/1wSmLVaDfV6HdVqFUNDQ5nfdIKbJC/11z3PQ7FYRGdnZ/ItNdF9389Yi3PzQNabtL4GkJDzJINN1v3ScSY3QqRVvLTSlg5b6SCV9SMJbTrAlCR7FEUYHx+H4zgYGxtrIsipJ860SF5LYlnmWUr9SEejExMTkNb+/EirfimTI63yZVnYh1jvut4/8yw3RKRzW9m3Wafs99zcMWmsm55B08kC/say61ryukxMK4v8FzMsiW6xW2NyEvC8lPQDtm3SI8fGahXlcrxYIsk9OIhuvwL07IPHHtOijFekJFqffVYR7YDi6jo7gf32i9VCVq9RbF9fH9Dbi5pbwEA/kM97mD3bUxrhPOcb59cJa4kVvFyEZYokNw64kKhUUHBdFKpDKr7N1ebFO78lO+y6CErdTQZbLdYncN2UaJ+cTEnLalUteLhQY1jG1dPTetGT3BCLoRd7e7Fs2bzk+HO1Csyfn3WIJZ2TysWn5zaAxwZUppYuRQ0evHK5eSUf3zCMIgbWqvwVY+eqlSGzUguzPzICuLMLcEuFlGSJM7FlyEFlUF2SvkGBdPH43HPqIw8n6BsWkmDgd1+fql9ueGzapP4n8cGm5r2eK7SGqTmZ6UhFTEyo4+auCyzu68seYxD1tHZtqk3PI+ok7Ht6VJ/o61MW54k0zdAWYO2QSnvNmqy5ITMZE+jDKGLtYylJNDmZdl+5EQGkJLr0tybl1p97Tv29bJl6DofL+6DY16fywU0tWT6y8SboZPoecCR80aJFmesXXnghLrroosy1wcFBTE5OYv78+ZnrU+mbDgwMGMOHYYjBwUEsWLBgWvk84ogjcPPNN2P//ffHs88+i0svvRRHHXUUHn74YcyZM2dacVjsZhAENZH4dIh/p66yfhuQDv+6alurMUx/pxCS6ATik0Hxi5950zf2JHmcDHG6tFwrHW0J/YSU68LzfWV1m3kfNxdOWhvzIzcVJH8vCU69fvTsSUvhNMmspbwk6uU0TMLUDlL7OpMBdUe2EV03IcjdUA0jjWAeHM3PRyNIpcrq42m0HR1KDoUbHHIjxFRfgHkDl1lpb3eQy3kAvLQuWIRKtijcHGCZGbdpc0c2q7TSZ3zN6jXki9R/8gSgXg5+m2T3mjYxKsNZpzQyk3FdOyXAjzcMmGZFK7tMS+4pyejktxxe9bD0NTAxwf2SrCNyeR/hzVAG1Y7dOw8ztAvsWTDJnrQi0UnI6jrZJAR1C13GI9OQBK0MJwlPXapFWi0DioiUu0eSnGb+aL0tyUSdRCe5zI8kN/mt621Lq2d+SGACagNhbGwMQ0NDkFbyzKescxK6+XweHR0dCIIAQRCgWCwiCAJ4noeOjo6k7mUbSYeVUudc1rl0WEkSmGCeWF+0dJd5k5InrF+p+c1rTEs6y5QEL8PROl8S3NKCnO3T1tbW5NhVll1at8u+xBMAzJskoHm/7MPcWGHdyTDs13LzghI10gKdTkxJ4Mtnh31d6mbJ+ua3PKkh+6j+PEhSWCfZdWt0kxyNfgrkxQ5Lolvs9pDWRvy/VTh9ARuGKJfjyT8ZwbVrgcFBlP5qn8SqNROH7yfrtsFBFbynR5HoPT1Asf8R4LEhxebFM/5nRwoYHVXEJ62mu7ocFICsQ7VQSZpwUVevx1ZBklCQZZbsAZDkvWnRLlczXMGVSkC5DMf34bqFDJGuExBczHHBBaTkuf7N5Do6UouqahXwAj/bVswPPaZu2pSsYp1KBQXfRyFeOJfLqUamTmx7rtBrHaoosnR8HFi6VKVL/XPZN+I67+9XxLDrAsU+NyPDOjKSWqTJ9CYmVNOqulALufE4LMldKohIgiHJ4lBWDp68taxzZpkL6WLQQNEP0XA95POp5VelkpLzUmIlIXL0jyDJJyeLSbupNIso9mU7QQNOQp5XKqp7jYyo9KtVZQgIqDb2hjYClUq6sTA4qAq7di2werXKFHc/+vqSwjeCIgbWqGDsO+wWklBgV2aUXGQLfiVDoi1dqu5bvRoolRwc1OOrmyWLNhWM5M7MwY4uxNetW4disZhc1y3ZJEzGQ1PJ4LWS29se6byTTjop+fvggw/GkUceiX333Rdf+cpXcO655047HovdBGHYRJC7LrKm1Mg+7/qmI18r8vSKfHx1nwq8xjCSUJbXFGHc7BRRfss8k6TNsNlyLDYR6fpuuR6hqcDyHea6cCThHvhZnx7j6ZArZWH0OmpVLloD63ryMptN5ZcRVatAaEjA4LhaDoAN10ut3n0fNbeQaSc1jnpKL7wUj2UDKZmrk9ip73Ankw1+y036bbW1LLduTU9w3tDervonoL4lqczwnFsBzeS6hLTi1jcB9LzqmwJ6eXw/zSO7k+8jPZXH42asTDl3iycmDtI5kKm7612dhhZyPinrkPFMZS3PrLQ6qZkl6QVfs92iJ7sOduzeebAk+gyGbtUsiThdP1t30GiSZGEc8j5ek6S5TFMShdJBoy7PIQlGKcVBot1kFS/zLmU7pIWwtHKW5dLJYklQMq90dMr7ZfmYb+ZP5pcfEvvS8p95l3khAa23m8m6WhLRzA/jp4W03taMU0raSOt/hqf1ubTqJ7FN0HKdJLe0QCfBrhPjMj69DnRdcUkk6ycA9HukFrpOzkt5FabNPi77JuuBzlblhgk3HkjUS5Jej1t3smqC6ZnSLdV1a3PTBtmeBEuiW+zWkJN7fRU81T1idk7nobXQUdbSsRmyE9ZQrTZbv6mFhPqTZOucOcCsWYrspEW1DL91SK1FRkfVwkYaRyd50qATuJnyMh9c1PDz3HMqbbnakt6fZNmFRbpcTMkqJWhEDGRJYSDrAE0/qksnq2Eo9DxNkOeNaS0sVsHFchmN2AJOLrKS6GQdjI+rig5DhIjbVZeTiQsoZVt0az5ZDmkNxnRl+8TJZbhqve1kU42MZK0e8/l08cxwGZ39uF4c38fs2crZaGdns2Ua89xE5OjskmhATn3Gx5FY6xE8YcB+TvkiuWBOIBfc/F96iJWbJ6IiGc/QULYsJB7kNVkcNnVHR7Yv8G9uRCQnCnq11bYeud5YOpj3GYK2+LM94QGgWCxmFuImlMtltLe3N1mubdy4sclijejp6TGGd133eVmhdXZ24uCDD8af/vSnHY7DYjeEzqCF5kcVSA+4SIfcvK0V+P6l3wp5b6t0Wl1vmY6J6ZRjsMksWI/YRKLrejTyOwxjctPLlFP8PHWeDeWUY1lm3NWyanSm2orx5Ytf3xiIIw/DWPtajNWyCuQGMJCOU6YNcCmdbiKVdW1uPbv67wTnNnqTyPmB3AzW4zdNWVsNPTL/+skC/m6yRDf1Wf4u5fuS76qYyHACwEriLpRM2M3mQc7Pxsez6cqhXyfO5bMnreaBdE4lN4FaPYt6fDMRduzeeZjB3WDPgW7ZCmRJN+lUU5KokkiWhLgkBSUBzTgk6SzT1/MjIUlVmaYke6ciCklaSmehJqkQWhozjyRZW31M5Lwk8fX6kgSxJFFl3ZCYpU631OuWRLJe53obstxSxoThW91PTGqLLSlBI4li2WZ6P5IW+vyWVv0mglPPi4yH/Um2mR4Py8sTCbqci2kDiMQ1CXu9PrkxwA0AefpAbiQQktQn8S4t2Vv1U73+dEtzfSNHpivj2xPJ8lawJLrFbg191TudmbO2MOW6wHMb6Ww9ZuDK5dSCCQDGqsoyjY6SgFTWZGJCELZhmJocDw2hp2de8q9cg+gWZVPm2bQykAvsMFQmwdTnoGmyXkdc2YjVSFeX+pOLTt0PquvG1vBhCFRDeAD8nmKyqCFHy6LzunRwmnFSBTR7QuVvFNPmjb6fIbjlQk0Rv6pNHGa0qyuJtx63k+e7iXUeABTiwjGPcuEn1/O0qJfre9lkRKZNRVNSp5vXaO2VaOxvo9mbEIZw43zI9SzjIRGUcUAmTd2pGxsEqAyoMktte4LWhL7vZNp19myVLuuK0vJJfeiECNuyXFbXeDSB+vS+n9mYkOWgJmwQqP9pwTY+rqJk+vm8il4eONDbolSC2TxOI59a1XkmYzME7fFne8JPF57n4WUvexluu+02/PVf/3Vy/bbbbsOb3vQm4z1HHnkkfvzjH2eu3XrrrVi5cuV2aarqmJiYwKOPPopXvepVOxyHxS6EIE0z78ImtizrTtD0uNKZswmm6/o1EydN+Q+ZpanykImAzJ9kWCUDKgcOfefalCE9YdN7LB58Pd+H57oolFzUwuwGsSRcZZSmV5zp0dTLnWZDjcWu76UW6fpObysCnfUV/x9W1bhtLL+oHukkVZLZejg5jsvyMhuS8NXHVQmTnry+xwGY90R0MA9T9alWGviybPyWmwL5fJZU19OU5ZL5SCSMdGfcTETXm0e23qVxgD5PCsPUgIDRcHwHmjccmLSYCm73IbKZCDt27zzM8K6w50ASciQTpewFoMhD3VKWxCC1tKV+trQcZ7xSI1taAMsw8pvQrYd1ORcAGUJaQlqZU5qDUiG0Omea9Xo9seqWZWd5pKSJTnZKq2nP89DZ2ZnIfegyG5JMpbY7LZqr1Woi8UGL7fb2duTz+YQkZt6k9bskDEna+74Pz/OSzQPT5oVuBc3y0ZpeEtFjY2OYmJjIWI1LslcS7RMTE6hWqxlZF6Yr72GdtBsWeLRQlw5JGV5aobMvUTueddZoNJLNDNlPaJVPwp31qG+qhGGIarWa2cQhec6TBiTZ2WdknUordqnNb9I/57fcRGFa8ls6kWU768+SyRp9T4Ql0S1mDFoR0qaVbqzFjCAAZTUTC/KenkRuYuXKbHTUP+f/0mCHxrYLly5V/zzwgPp+7DEUKxWly9zbnawhKxWgu7eUJQtcFxBEMdPg4jRj6cUViKsWyQDg9VbU9eeeU2S6zKwk08Mw9do5NIRCSf1U5JRgcCglHWVBmXEATqmEgu9jcbkMlAPlgDLWyKSFGBecxaCRFRl3XcXIuq6q75hwbbheIlGS4zoNAKrZbDBMGKYOp7q6PPilbjh9fUAYYswtiipQsiQ8IFAue0n19fSoawMDWeKfltBy8Z05qu6mTubY7bhw5QJa+k6VGunkEHQLNxOPkmlzqM2MIPAQBOkeCde8XV2StykozVha9gMJob1lyMHAQLYrZdKLiQ8HQHfJR1+fItPnzEn10Eli9/SoRwkVn5WbZooMPK2VuEru6VH9JShiZJP6iacdWP/sFjr30tmZRkMtdIZlHdIo3nVV+K4uAAOVZhJd9sdtrbxnGIm+o0fCp4tzzz0X7373u7Fy5UoceeSRuP766/H000/jzDPPBAB87GMfwzPPPIObb74ZAHDmmWfiC1/4As4991ycfvrpuPfee3HDDTfgG9/4RhJnrVbDI488kvz9zDPP4KGHHkIQBFi6dCkA4LzzzsMb3vAGLF68GBs3bsSll16K4eFhnHbaadtZAotdCvHMyccxeQybWD4vM1TKd6d8jKXVMTe4M1y8FrXkBZN3vKYn4SAmF2NyWm7IyvTVNQc+HT/KDLd692xr804Pq8dhYoz5zTwjq+9u3C3QKyX+mPTDpc62KQtIag2g3IdOLgc9sXSISDMh+yvsC05S3zp42qujI7tHwTFCYvZskVZVZZZbMl7gx/GlGw0k1CmbJqdOJtk7fnTLc9PUi+Df8qAgr5s2KUzDk5TRbwVTlxkfb3YEW63SV4uHYk9Pahkhd6dlovFA7cZTyXo9K1ejl5cbFvqJAtYn606Xg5H3m3Ti+ZuMU15juBl0iMyO3TsRu5REv+6663Dddddh7dq1AIDly5fjggsuSHRtoijCxRdfjOuvvx7PPfccjjjiCFxzzTVYvnx5EsfExATOO+88fOMb38D4+DiOO+44XHvtteilmGILXHvttbjyyiuxYcMGLF++HFdffXVmt2I6af+lIMltaektCT/HcRIik2QgSULKjpBAliR6q78BGPXKmQedLNWdMvIj46PlsU4auq6LQqEA13UTfXE6DSW5TgKZRDbrQEpv8Hcg3XQwWSHn83lEUYSuri7U6/VE5sQkrUGS10TYTk5OwvO8xHtwFEWJI1TqqOsbCbqFsnTO2Uob22SFToJ4eHg4Y1FOeRqp/a1bTIdhiFqthvHxcYyMjCTEM4AmK3oJSaJLS3Nd8oVh29rako0Fxsn6kOS5TvhLEluS8LJtSI5T+53gRovJEl1uJknHpexD7Lu6rI5Mn/UodeKlE1fWh+43QPa/VjJKuwoXXXQRLr744sw16QTkhX4XWmJ85mGPGbslqSVn0KZVnZxxk9jz/XSNsKZf6XK/6lVYXymiB8Bidz0QBBhGMdFiphY0Fw4k0MkRh+VulHu6UcADirUdHAQeegh42cvQe9LrMTSkLoUhsGXIge8XUfBrCeunL7SyBluOWgzHgcZCD2E1JYf36etTGeOGgCDam7ypSSFzZp4rmE2bFFsqvTzyw8QoRN3Tk1iOO52d8Do60E1L8t5eFWawkl1B0aQoCDBWXoyREeDx+7I8exCkJwHKZXWN5Cit+kdH06jI3ZbKiwGoKqBESC6nsr1mTbpoJFFeLqvNEWrb9/TEpxKoGVJpYcHsunBKJUVQxO1VmO0n+r6VStYyUtc9l+XQrbMyC0G9Q8SyLrNmpVqkUocdUPGFIVDULPq3DDnoX506CZX5SpKRGjfx6n1hEAAlF319hSS9SiVNU51SUA1R89UJBfgFuAHg9y6Gs6KWrb8gQANOppuRQO/tVdJI8zti52Y95YxmcrkMLFigwnZX18crbheoIEmjWC6jVu5OnfcOij5uYilM5m2SuZBMyXZgV47dbdi+xfX2znDe/va3Y/PmzbjkkkuwYcMGrFixArfccgv23ntvAMCGDRvw9NNPJ+GXLFmCW265Beeccw6uueYaLFy4EJ///Odx6qmnJmHWr1+Pww47LPn/qquuwlVXXYWjjz4ad955JwCgv78f73jHOzA4OIi5c+di1apVuO+++5J0Zzr2mLE7fpZIkPJxayLR4+fVFY+o3N8FUtJcvj/5OzdSdf+I8pOclqqOZcdFPWOuC5RKcFw3caoog5EQ5fvcdQvqNgPZmWzI8mb5txyjmYjcndZ9XUg5NLm7oFuJknnWmV4OorKc8cDglEpqzhEEQEyoMzty2tBqj0CH3PQtlbxMm+unwQAVRt/sTbIT/+2gkTqR1HdkXDfV9zYhLrcXfxD4CIJsObWumCmLTp7rQ0WmnUVfB5qdaMv+xN8JqefP+OWGsildk8NZXaKf9c5nZnQUGB930NFRhBsU4ZbmJXUMZJ9XyhpyDpH47tEfUCZe8s0W7vKewAfKfrKBw59knct5MaNh3egnEVoN+duCHbtfHGP3LiXRe3t78W//9m/JLsJXvvIVvOlNb8L//u//Yvny5fj0pz+Nz3zmM7jpppuw//7749JLL8UJJ5yAxx9/HF3xedWzzz4bP/7xj/HNb34Tc+bMwYc//GGccsop+N3vfme0nAWAb33rWzj77LNx7bXX4hWveAW+9KUv4aSTTsIjjzyCxYvVAmk6af8loespm+RGdFkOILWqzuVyCUEoydT29vaEQJVEK8lL6YxShtEhLX0lGSnDyvaQsikkzUlA61bd0tJ+cnIysZ4m0cqySRJd5kvXY6eFe0dHB3K5XFIXUv6GeSTJTqK5VqtldN6lI1OpO88y+L7fRMpL0lC3eNbrydQHSJZPTEygUqmgVqsl+ZBta7LEl/IrExMTGB8fT+Jj3fB7OlI8JMFJ6ldpYSbqiPUt+wblW6SMDutGngQw1Ymse35Yt3IjgfkDUpKaz4u+eaFbv/Me3XpcWqHzb5NVugwvNwOmIst3JZG+fPly3H777cn/sr53t3ehxa7HHjN2y5WpXD2bTHkkfF+ZJ/k+giAmTTdtAjZvRs0v4qFfAccfD3j/+7/AggXwV7wUQErMUsaCDhblupvk6CFA6lxxcBBob4dz5JEolboxMJAuAnwf8HuUE60GnCaHUNVqVpPcI7Hq+xiJfZfSJ6fvOyiX5ylSl0wxiVTJloZh6i1SegQdGlJMwDPPZK+zYJUKsGGDykippFZwJNHJqObzwNy5qQUTLd7lqo2r4FIJ/f2qeh54ILUGJzdPQyiuu2QdVyrCqi3IrpeBVBeVhtiVSho/q6OnByhgDEABmzaptDzUlINSOmiVhLLsZ9IMTPzm+D68IIAriN/2diEZFBeiu1SC63oYGlJ5lVE3QWcnwhBdXYpEn5jI7EkkXMvkJBAEHpzAxXDFwfgm1axsdu6xsIkyaentFW+cOG5s1RgEKAaBsuqrhBkihocgsotXD67rpcRHJZsMdYk7O2PnvEEDeOCx5OHiZgUAdLsuustxQ//qV6ow3G2hKH9PD7ylS9X93Plq9X5o2rVA6xX3dpLowIt77D7rrLNw1llnGX+76aabmq4dffTRePDBB1vG19fXt81N+29+85vblceZhj1l7G7AyRBykqzk747rJi8JReI5ycZ1K/AADJDVVOarQUqWSSLWcxvqvc9NZRPrLohmx3XhCut4OW7LoUJ+S7S3S5os1S9nnL4P5aSZ47JkDeUgSE/PFe2lSsjKlUwjM6YfAeKH8waGd13AzW7cyvFYOrhmnTOs/JbRy0NL8h4pA9eqrTMOxWWm9HkGkD2apL/j5TGEOEEnlsJxAy+pOraxbEudPGd0Gek60Rm8eI4nNw3k/gj3QuRthE6gAymxnjlBUUnb3wESWR/46saxqpN0GX2vhpCH15hePp/2V93pdyKFKCQM9flKMh+V+oiyjVgZQkqGknSFzD0uEOv96+8MEv3SoajMwvbCjt0pZurYvUvdyb7hDW/AySefjP333x/7778/PvWpTyEIAtx3332IoghXX301/uVf/gVvfvObsWLFCnzlK1/B2NgYvv71rwMAtm7dihtuuAH//u//juOPPx6HHXYYvvrVr+IPf/hDpmPq+MxnPoP3ve99eP/7348DDzwQV199NRYtWoTrrrsOAKaV9l8aps7TipjTZTikVa9J+1nXM5f361IgrfIj49HlUGQeTUShDK9rhJtkNaQuubT81SVcgGZLdJlP3ZJffqQ+tyyvTF/mQ9fF1nXVc7lcQlBLS32ZBtvURF7rdaBbRNMiW1pDm6RcJJEu61CXwGnlPFPWkawP2Ra6ZbYu2aH3RVkPOoHeql0k5OYRNzRk2+inJvT0dR12vZ/qfZ3pyDqV7b+tOtwd4bouenp6ks/cuXMBvPDvwlYbPVN9LHY97Ng9DcQTdJekdLwCpmUwgFS7IkYYpjIi0ppIrhO4oMws4LhqqVYzlknTtZKRi3P9aHMYZsn8ajUtmyQ3G66HWuig4Xpo+IXsypAsw/h4uiCXH5ra0zuqdBipO5DcujVrZt3qbG6cN7kJwehYZab6kdcmJ5vrn/HJjQh5ndlJFsRxoIkJYfml3yDrRze7kwnLBbyhu2XiDcOWpPl0VENcN7uA1++R/UX2EX1vpCX0CpXEjb75IhqI9S6rTXYVvUvItk0W5ww0MtLcDvwMDandI+5CVCrZB0EyPfpD26pC5Xer37cTu2rsdnbgY7HrsaeN3VM9kiYZD9N9HAcI+agyHN9LlHiZMm3T+30ag7WJN5RDg3wvTkxkP0aLZJOJMZAd9PQXbqt3davP6Gh20DW9a7Uym6pE5r/VsKkbHPBTr6fTENN0YZsVvq1K38GPg4ax+iV0PthIoOt/G4qhb8JMp9tJq3N9bmFsw2o1M2/QwXbQpzOyv8p2nrLu9YaWnUDOH1rl15T/+LOtoThr/b/jsGP3zMeOzdpeAExOTuI73/kORkdHceSRR+LJJ5/EwMAATjzxxCRMPp/H0UcfjXvuuQdnnHEGfve736Fer2fCLFy4ECtWrMA999yD1772tU3p1Go1/O53v8P555+fuX7iiSfinnvuAYBppW3CxMQEJrjNB2B4eHjHKkNDK/Jal5iQFuTbikcS1tz9Mul2S8tgXpfyHCQ1+RstjnVyWMJEDkvSk/+3+l0vqyRL5eYBLc9lusy31DrXLSd0AlVazFPrPZfLJTI51DX3fR/5fD6jcS5JekrtTEUGSuLVtJFBS2t+dPJaktjUS5dW6nodSamcVnVgyp++mUDozkFb6YubrPGl1bbUktfTBdRzXKvVmjYMdNkXaVEv80OpolYbNkyf7Q+kJyEkaW9yXCr7XitZIRN2NkE8MjKSeQfl83nk6S5dw5/+9CcsXLgQ+XweRxxxBC677DLss88+O/wunC62lxjfnrCXX345vv/97+Oxxx5DR0cHjjrqKFxxxRU44IADWt5z55134thjj226/uijj2LZsmXTTntPwh47duszbX0VEv+eTLh9H8jnUQwa6OuLrYVi0WWvOoxyuZg4NKQlb7WaamaHYWpg3dGBrOA2rWviBUSppCQvOjtTC3MEgZHU5DUTsUpLJBp9J5bV1DQXdaAvxgrSBAxQqyMp5E3TMi5wyILqJkrSuyS/83lVuFaLI+FItFxeiDBUVc28uW5aL4zCdbMa4AzHJKkuI8so12ft7Vllm44O9Zvnpg5GczlkNh4ylSf6TNKm+jUR3nWbHeG5rqesC+P73DgaOtGUOq9J1FVRIHYypL9TA5jasLLZeRsXxqwD9iXZfOUy4FW2pIy3bGNWrF4gvfxhiPZ2L3OJXchk5M22am9P81Fw45MAQOvVPpA2+sREKgbPvFIsPQyb+2ur8ujl0upahp8JY/cLratq8cJjTxq7dYvSBpwmMsxkwGraQJwOdN7PdYUzZn1s1DakuSktSeD/n73/D5Isq+4D8U+/evXqZdar7Oya7O6cpmZUM/QMDQx48AwLIyOJWEZgab22JXmttS1CRCDCBD9sNEKyZdkrVqGFtQNLeEMrsXgJEMHKcqwR8o/FCBT28EOAvzAwAQMaRCNKMzUzNdM109ndWVVZma+yv3/c97nv807eV1Xd9Mx0TeeJyMrKzPfuPffc+95993PO/Rx12tbpaX8LiToDhnlUzhm8Z1GvRqNM0MwI3jwv523bSFXAKsE5W+lfONlyAu33kbRjIE18HhlWxyLUFnxusXiyUmpzLvLPQKIO50Kea/OHjBEBsSQwtbxkaqu6e/1eCLkRNc8lcWyLThGANK3uUtMpiTa0dSjjjk51aYpJlF37XB+G4hhJOkaWubu95mmxp4Uen21x/jkiSxClmORUsQVqI4DJhzk+vISOkc+h7tR7hXW+aVumc/e1Jc86iP6Nb3wDd911FwaDAbIsw8c//nG86EUv8hPr8ePHK8cfP34cf/EXfwEAWFtbQ5IkOHLkyMQx5BWysr6+jp2dnWC5PIfvu9Udkve85z0THEdXUiyYWneMUmSEzgdK4JK0Kdvb2zh06FAFxCa/NalOFHRnZDU5pgF40FLBbdKGqBC4VACefO7D4dD/riCm0pAQuFSA0zoACJwqB7tSdQBlIta5uTko/YmNKgbgwfM0TdFsNtFsNtFoNNBoNLCwsIA4jtFqtTA7O4v5+XlP5UKwVvW0+mqUO+2toDfBcoK2o9EIW1tbHkgmYE6qG/Kdb2xsVPjACSiTboQc4UqBE4qEV91CelFfja7nOFCnSl2kdmj7p0a4cyzSgXDx4sVK8tQQ7ziASvS/7gKwkee77cbQqH1G16vTRvuKwvJsv9bJ0xVl/aIXvajy+Vd/9Vfxrne9a+K4V7ziFfjIRz6CW2+9FY8//jh+/dd/HT/4gz+Ib37zm5d9L9yvPJ0g+mc+8xm89a1vxctf/nLkeY5f+ZVfwWtf+1p861vfwrzNDGTk29/+Nlqtlv/MCIGplHLNzN2hBVAdgK4RLQqU5nm5t3t1Fa94Wddti73tNnfO6dNotttYXr4ZcewwxtVVF7hFEJfSbhegdrrsvihoYtDt+m2uJ5aLRUGxBXuIRTy6VuXL5P8hlZvFj6TIIICcrP45PFfM1lZlkcjtwpxOmu2200dXUnUAo0abkx6D2911AV6SnbrvqPz6ujPWk086/Todl7y118OJO9tot5uO/rpbBijpDt6zZ6sLWGWKYVVHj1Z5QFdXSyz47Fn3XuSLrexUH6fNCu66OYjc9mHLJc/vQlvDeZwsHNOsutCkudrtlgdrCKLT1+K5RX1kmRkAIkk8BtJy+cTz4oLPVWkPuKFidrZ0VrDLl5YcFtNcf8gZ7ezZkrPo+uur14mGoNnVdwHkNBoliMIAcAVU1NGhucuOHi3ssLpaLqaVp0eV5ji76aYSQIrjktQ+dknqKsn0FHmgwfgeul8o6qPf42DM3dOF+MGVa2XutqCzv/dB+JWLHwmMKTf0bn6wvXxkmvSZ96emArHq0db7RzHZhihMbHuoJ2k/9Lc6ffR/TjNp6p51E/Xiq4eY3+lnG9qsyKzew5n9m4rNzJTnbm2V5RbHJ2mKxSzDuJ144PzIkbIa9b1r+9SUgDwnifARzHJ953kZBa1978rWO5eby5ttcTiw7+w8Ghp8VvK8oO2ZfDYLJbesOGO0Hv7PpOxxwcGexmhlbq7iGLL2YxF0Kqhdmum42uf06mi91lHAPmyX3O86RJTySMe2FqlTP2lh0jRxz5Q8Qe2tF4RNDlPkR4kGm2EvFM+D8LEPymGr07MeQ7HX4nTuvrbkWQfRX/CCF+D+++9Hr9fDxz72Mfzsz/4sPvOZz/jfQ1Qle3EH7+eY/ZR7qXX/8i//Mu655x7/+fz587jhhht21WO/ooBcKBrdHmuPsYAeI6LTNPVgMgFWnq/gIQCf9DOKIh993Wg0KhHNBCYJrg70DlmIgvEEPJmYktHe5BenQ8BGWbMtCpQr6G4TU/J7rZPJVkejEeI4BnmtbZQ1dUqSxAPoCwsLaDabmJ+fx+HDh5EkCbIsQxzHyLLMR6uzHRZUtfXwewvQjsdjDAYDWM5tcpBvb297O49GI/T7fWxubqLf7+Ps2bM4dKjkjLegPnnbWQ9FbWfHnerFuvk7E8ESPNfIb6VYsWOWADdfNlkpE6my3eSA5zgI8Z1TByZ3VX20LyjqLFB6G7aTCW05ni3YruVoBDrbbq8/rU8B+ysJpH/rW9/C8573PP+5zhvOhFIA8JKXvAR33XUXnv/85+N3f/d38cpXvrKiL2U/99j9yNMJon/yk5+sfP7Qhz6EY8eO4b777sMP//AP73rusWPH0LZP4FOpyDUzd9sH7rpVqi4yNCKGvxOJXlkBHnwQuO02PIoTONHZBD73OaDRwMm7HYj+n/8z8N3vOkCS4OtoVEb0OhD9pEMolctifb1cAHP1nufIb/vv8PDDVZ5Wis1xlmVAM3Vt9HyUa2vuwAcecCAk21dwM47jpLLuBoBut4WIbS4WmkwKySh7roGyNtA56doVPfitkmBcw6a4upubc6tqKj8aAQ8/7Gzw3e+WGTyJmrfbaHY6uPPOEz4PK3OXsv4nnywx1ZkZT2ePTqeIvB8MgJUegIIHNI7R6dyIwcBh9+fOuWSVBNF1AczhwH50NDKR4/yWCKjNuIX1daDTaaGZPlVd5dKGwp8e5UMwGn1np0xwBwBZ1kQMBxg1U6AZF2XZBbAduwrm5HmZZFae46I0RZomFXU47Oks4NiZmwOOHymS2n7lG65vSNezvOxAdAWzSdGj15UB0tN2y/9kt6dTOGw4JrtdV120/oTTwRlpYvFfAbbjGCDveaHTZudGfONrbti7a7HpgDEF2i2YHhIO/ppjDsLcPV2IH1y5FubuELjqLzd7w5DrkJfwaOTu47tFBId+02KZi4HTVHwkcVG1QBhET1NsDiLkg1o2q0pbCHqG+KxVH3UuEsi0QKqTCECCublFzLLMdqlehUrEAqshwJ30bQqyU7T9s7Ol47ywQ1SA6eikGHeTyn3e+iBCQp90CJgN2YnzF5vHnCdqQ97ml5aaiNOymWnaQtppOceM2oKVaWcE/uc8rodacwWnC2uUGo+CA9Rdh7YWStDbJt310dYelB9MGt06Cew8TUMWdSYeZAfQTn0QAg9lEfyf1wqFO/rSFMizBGmaIOnE1XEnc+9YEvLmOTBYY/ua5fVi/Bq5GUuDQblxsmIX0xfKkc5nvuncfW3Jsw6iJ0niE5zceeed+PKXv4x/9a/+Ff7RP/pHAJx3+vrrr/fHP/HEE9470+12MRwOcfbs2YpX/IknnsAP/uAPBuvrdDqYmZmZ8JjbcveqOyS7bdu4ElIXsaoX1m6AnKVnoRC41IhiyzcOwIORBF9nZ2fRaDQq4CT1GI1GHpTX5J6sT18KzM7OzvrPCoyHotB3k9jMNiFucUZgz87OVgDjELc6o5lnZ2d9lHmj0UCz2USWZUiSBPPz84jjGGmaQulc1P7aZzaa3kbCK1hN6hLagBHojI7m/1tbW+j3+zh//jzOnj3rHR60x8WLF71ujBinvdSmIfoVGx3OujXym4D93NxchR4lxBvPeqgL+4H9rjsQWNfm5malvRYY584COmK0v1iH6qOifWI575k0VZ1CIaog/m/H/LMhCwsLlWjq/cr8/Dxe8pKX4Dvf+Q7+5t/8mwAu/V64X7lcEN1u2d3PvffcuXMAgMXFxT3rednLXobBYIAXvehF+Kf/9J8GKV6udbmm5m5dXYU+24WGAoM8juj1yooDok+exMoa0H1lE9EjjwBpila8iU6niY0Nd5hbsLl3LujabRch9PiZCNvbTbTbTWTdE4hWHyqzOlIHhiff5oBijZCmano4UCxgUhQRRcVihRlPqft118HvuY7jibU04P5vyvb0IRKsrLj6vvOdMnqZwD0dBrff/iJHuXH6dBmVzVULV/Osm1kmez3XwNVVd97WFjw3TgHGN+MYN3bbfsHKNXueuwWzJtFiBHoTmyWYz7DzwnjZ0o2I4zKa6vDhMpGZxaZJcwKUYHfrSOoXgMM8wtqqqwoAbuxmZecAk9QuxViL05LSRQO0ONwS5NVyVDFd/E6iKdXoNvM769V+H42qjDtpWkSyrRZj55FHyoSyvZ4LB6Qo8Ex9rE7F50Dgm98Uwc8aJMnfot5TpQcljsuLIQCk+638DKsvdnisrpbOraUl5yRocoeAlqPPn3VAfchJUHx3EObuQ8XrUo6fytUh19TcXYgHvWqARvMvgBJIv1TRS1zv/Zxnq4kMXcVDJMgH1YTVFhu14K9GDde1z9GJRZU8phYHV301cNzWG8eRnz/n5xM3Z7YlYlkzgep7CEznO0PAqYA6JAuDMRGnB2TTGGjHE1HB3CkGFDsN+gPX/rRZfsd6ctewsSSRVFsw/QVFMX7GBrB5ZJZL0wRJiskxxgp2ca7aaYLC/lAd4ziqtjzkWbDPq9aDEMd+x1qVGM6UWQfU18zPlTqoj9CwRAT04xhII0/XwyI0+Wmo+DwH2u1mSY1EEL14jur3yjI4JLe2SlpEPpoD1R0cKgrihwB0K1GRxhiYzt3Xmlx1DgZSNdx0003odrv49Kc/7X8bDof4zGc+4yfqO+64A7Ozs5VjHnvsMTzwwAO1k3mSJLjjjjsq5wDApz/9aX/Ofup+tuVywbkQaKUJSkNUHkp3YekvlBrDcl9b0Fr/D/FkAwgCjwri6m8hG9iI37pzVafd2qVc2ratlve7LqmqtXNdf4SikhW0trznocSWCvwqcL1fbu79jqvQGNLxEqJLqTt3tzpDEd82sWdIbL8pWK+/WSA8RF9TN3YOUuLQS5Ht7W386Z/+Ka6//vqn/V5or8/9vADghhtuwOHDh/3rPe95z5713HPPPXjVq16F2267rfa466+/Hh/4wAfwsY99DH/wB3+AF7zgBXjNa16Dz372s993W5/rMp279yFx7J7QCx6KCaxQPtgFrF3MK6XFYIDJBZp52reLcX2v1TVUQJ6XYToB0eRRWpECrpqf0a63L1wAhkgq/LAVpEDBdK72a3TxCon+5AjdCxyJY+y5kLSLXkbuh9bKc3Nhe2s0mC/aLni1sl0khD1PLHqDB+2zYIL3RhVVkXb1XKq6/T9Ut+3XUMG7CLtfi5idrfZvpS9DelgAoBAHplc7WU/1w243AP0ZlGdy7p65jNdUrk55rs/dtZfhLvfBvS5dGym8H6ncdvZ5b1fcOSSXe4sJ3QLpfOQtUnOC6/zNOZy/M6G4n5f13d6gd5urL0V5lOClBzHzvPI/Xwpy2vlQQdK6achUW3mkAKo7oiqF7VboJXTcJY81O9db8Dv0vf2tTi5nwIUA9+K7EA3RvqowB/E5KpRA1VR56XLZJ+5fpnP3wZRn9gnPyD/5J/8EP/ZjP4YbbrgBFy5cwO///u/j3nvvxSc/+UkcOnQI73jHO/Dud78bt9xyC2655Ra8+93vRrPZxN/9u38XAHD48GG88Y1vxC/8wi/guuuuw+LiIt75znfiJS95Ce6++25fz2te8xr8xE/8BN72trcBAO655x68/vWvx5133om77roLH/jAB/DQQw/hzW9+MwDsq+5nUkLANiOVGfHK7xU4BEqQSiOGAUAjk0Pc1IzMVqCy0Wh4r78CkKFkmAryslxGl6serGNmZmYCMA0BzPq/trsOmLX1UUctj7QrGgWv1Cmzs7NYWFjA3NyctwGTijLyWkF1W5fVSyPvWQ8jqzURKDnHyX1uqUyU5oTlMIqeUeYKGFNI36MR3uQat2PNAsgKSHMMabJOcqCTK1/HK8F9/ayiuxeoEyPuNXKcbSJFi+U9175h1L3tF20fryGlcWHf66sOSA/lH6hzBtU5Pi41IvtKyjvf+U78j//j/4gbb7wRTzzxBH79138d58+fx8/+7M8+7ffCy41Ef/jhhyve/r0ikd72trfh61//Oj7/+c/vetwLXvCCSuLRu+66Cw8//DDe+9737kkBcy3JNTV3W/Q5BALq/0qqyc88ZzBwocjnzgGDAfLcBcaeKLZvIk2R5p7OG8vLZXQzo3xdcE80UTWyzIXH6g/F4m19vaqO4oKdziRH+LiI3CprQlWJ664Dnvc8oNvFMG7i7FkXFM7oH8Dp31pqu/KKLbw0haWUoS40z+wssEieUU0mxT3a3W7ZoJIMvAwz6naBU6ecnjfc4H7rdrE5iLCxUQW99V1tkOcoI99UycKAEcZI08ir1WgUkW557iO7kiwtI+MwxjCPSv71otwIY8Rx5OlP4tjZSxOEDuG2szfJCZOWiedCdvSU6lmCKDNIv45fJfyUbdj+OEVO5LsojtFMU3Q6iQ/S52nb26590WCzGuq4sOAGGwnal5fd/1mGTTSBvODR5yBh3+tgiUuu4DguNxvwtKWl6iVKdqNuF0BPkG8eRO+NOmZQ6E6b8CIdDLB06kacPCn0MP3z1Z0Seq2zDnvPuILA+rM5d0+3hB9MuZbm7l3Bc16bxYEhkI0An/HDBm+lMzMTRVbKUPH5FIoDyVmd5+Ucqr5H1UHrZW7Osr1uB42KpczYrQ1sB3W27yp6XiXCXguO4+oWtZCRQ6A7/zeUW0oFgsBjWBRy/KsRLFVYoWOWuaTuMzNAK3MAfJYl3ilMthnrB2B1St82jBPEaVIC+dbTHmhLXpiHUdmWJohjS4upPJvs5owJAcCheSkkttz9gMm7oeHmWSpJY2RZ5CnlSVloH7nJ184E6Z7fXLY+RgCSNMWRI5H/CSinZH3Wsxv71Cz8XrvONuFKYOrTufu5Ic8qiP7444/j9a9/PR577DEcPnwYL33pS/HJT34SP/qjPwoA+KVf+iVsbW3hLW95C86ePYtXvOIV+NSnPoWFgocTAH7zN38TcRzjb//tv42trS285jWvwYc//OEKaPjd734X6yTBBPDTP/3TePLJJ/Frv/ZreOyxx3DbbbfhE5/4BH7gB37AH7Ofup9Jsfzeyg3O79M0nQBzCQYSoFSua+WGJogdxzHm5uZw8eJFn3yPQKhSlBCYJZBNkDFEk8HklcoxruD3oUMlr7lGGytoWhelzTL03dpN26kgM9vRbDYroDPLIrA6MzODRqPhwfRGo4H5+XkPqJM6JOQgsBHYSoVCEFyBco08JzC+sbGB7e1tbGxseDoTJtVUUN46Liw/ubaLZRCE3ypmbwu4h0Q55RWkJkc+7RUCrgnW2ySh2hZLW6P2YjlJknhdqAOphprNpueAZ9ttNLxto9qPjgX2jXKz10ldbgJL4aPfax8/m7Qvq6ur+Dt/5+9gfX0dR48exStf+Up86Utf8vfDp/NeeLkgeqvV2veWube//e34D//hP+Czn/0slggyXoK88pWvxEc/+tFLPu+5LNfU3F3HbRl6AlcQvVh1DXPHM5rwyb7X8xQheV5QNJ/8YfdwXzzs3367xxcrOTXjuApY6tpmM26hefvtlbrPDxJsbQGr33Vfc2u5qk7/E29vOztOPeKXJaeloJa33OJA6m7XM8g8+WSVwnx+Hh4c1kUL+cGzzGGTbA+j0MmesriUVXm6i0xhm2jiwfsBIEG3ezPSNrC4tOQKO3XKvbfbDuVMU4y7JxyDyGoZ7U6KFaY9sIsqv2jNZGFvV3WDAbKsiXbbtbndRjWsHgCyzC3si++STgdHjx4rjV+UGwGYm0v8ItFVFSFHE/mg5JBvtyOkaQujs9WFtuITxIT5fZpGSLNWdat/7LbCK5d52i6Sy8XFnKXG0v/puIhjNNttNNMU2VLLM+/0+wXuTZ5+LnQV6e52gU4H59HC1pbzKQHA4cNNNBpNpJnTY5iXNAQcV6TDmZ+vOpiiwWaZNLSQVrsNdFNgXUIo9frkNRnHXid/jWq7V1eBrS000xQ/+MpT7vO9p8s+JOBDo5v+rQwuPUfHVp7vTsAckGdz7p4uxA+mXCtzdxwjDGQaBGwcJ7WgmAXMeLtQdhKVubmSB52m0MtcVcjz6g4k+uJ4v2MCSJXZ2WoTmJtT83aqEITXBI4hWmstX9uu7/Z/nXO2t4s5PHXPOmm6iDgGEtJhaaVWigcb9oO+Br1JTJ560L7VXUiRm79sQQqmB/SIBgN0u8ecE3xlFchzLJ48ifP9yIHq/T7QSdFuV8fKzEx1x1Oel0nKXbLQxPfB7Kxbs6ozRFls1KbW/pwauIuu0NrRooQ60zoP7AC0x6voscDkw5E4AfYqTk+xuwMAuGciAAmAZgxH03OkPGmMqHqeztnWaMU8TtqfZurasZiJEf2LdqrqmxTBCSw2jk0D49gHPei9hXshLkWmc/dzQw5dfDbRm+e4nD9/HocPH/6+y1G+aEaBEzgk33Oapj6xZZqmlUSSjUYDqUQchTirCWYpQK4R1hQLYPM7AqIEO3m+Rl0PBoOJJIxACdySv3tmZgbz8/MeAFXOdoKaLFd1sjoqiK0A7c7OjrcTgfHZ2dkJLnONdqeOyn+epinm5uYwPz9fsakCtypqC4LhmiB0e3u7EplMW54/fx6DwQDnzp3D+fPnPQCuwD/bt7W15SPWNzc3J5J88iH30KFDnuObXOpAmWyVnPfaT7Y9mjyTIHaz2UQcxxMg+sbGBgaDAba3tzEYDDAcDrGxsREEym1UuNbF7+m4YAQ8gXu+Kye6vZYA+HMpTBpKvWgTRqErRc5uEqKfsTsLdIfB1tZWxaFwJeXhhx++LND4mRDeG//kT/4EWSgstUb6/T7+yl/5Kzh37tyeIPrFixfx9re/HR//+Mdx77334pZbbrksXf/W3/pbeOqpp/Bf/st/uazzp3LwhOPz3Pe+h1aSlKEsumgBys+WWJwP9e02nkpPII6B1tqfOYT4k58E7r8feOc78YnBf49ez4GkWQb81b9aJmYEShxvZsZFOm9tAY8/XmKS8/PlwncwKOmeeUmdPu3wQC7cbrnFAfQqjLodxk2PH/Z6ZUB3Mx27RKj9fsm5/spX4qnOrd4fwJ90fbS0BBw/7pJ0MvhYgQJSrTNKnvgmE6f+8KvG5Y95DnS7eKqfYHUV+NKXXHmHD7vopOVlF3TOxKnMscq6RiNUItDj2Nnu6NESn6cNdZF79GiRjJJ88ECZIXRpCeOs5fuv2wVa639edoQCx/QWMEJeFmuMqtqMW1hbc3oxmRbtRcdCiN1Gh6BGsrXbpcOE44GPgYr1Sy437ydJUyDpP4UKMs6BwQKVbmdpCWi38fiZCI884jYpHH/862HQ4s478a3TDlygvuRAtcUqb732y+ysy/nZwvlqQt3vfc8VxoIXFsoOJkBuM5dRXvhCDJduRjI4X47zM2fcwOHn5z/f7Wx4+GHg2992Cp86VV4sGomp3iMK7w8K6POcfh+rjz2GG+6880DM3V8DcCnL+QsAXgbsa+6eylQuV/zcffYsWs3mZHipcXIN42YQf1RR3mZGJVs+8dCuIN6feW+1HMxKPWFBdFteHFcd6kB5f2e5LE/bUsd7rmIjzS2OGhLrK7TvWoa2m3VTT9WPbVa7aF065+mcxinJO1TzYTWfCkV3DamBmJQlz908n+fAbbe57/iQwkQpaYpNhMeMbtrS30JjKuQcsO1k+/Q7TSbL77gDzu4Yq8y/oUpsZfxsHjIYNa+7Izhn2+B+LUb9y3z3yVf32qFggxdCngZ7rIrOvSGkP9Ruk+zdn7PbxVDYZ3V1FT/wAzdM5+5rTPa4TU7lapAQiGn5oUlLYWlfNOqVn/V8PYeANYF6vjNyXClDNKLYRtYS3CZISeBWk0Za0FtpMXbjQubx2o46qhdtr41y1t+YfLLZbFZoQPQ8AvIE3vkiGBuKdlZKGaub2pG0JRqRrVHKBNi3trawsbHhQXT2lQL2Gr1PgF353NUe3KFg+0wdCNpvLFedH3oO7UE6F61PnRM8n31Bihq+hyKzQ4k76VgiiK5UMtYZou3WsR4qP0Tnov1ZJ3ZcWv1DiWVDyVavJbncSPT9yFvf+lb83u/9Hv79v//3WFhY8EmtDh8+jEajAQD45V/+ZTzyyCP4yEc+AgB43/veh+XlZbz4xS/GcDjERz/6UXzsYx/Dxz72sUto1VSeUxJ6yNcHaxuuZhbrfsHK72Rxl+cOl7z/frc+e/Wri0XR+hM+cjlutyoLkpUVt4Ah8EshkB3Hbu03GrljCYhz0VmJSKYCee6ixgoaF0Yz+2axrQxb73SwtuYixzVoVxfoBGcZPQdU+bJ1raZrPtY9seUdiQesCdgfPlwCzr1eWf72dglGsJmM7OM6iXqkKVwiUwBIq9FieS6R+PxSFndRPkSaJiVeygYwO1wcV3cfpKlXLC/qSorGxwZzJd+sBkar3ebmymRZ1JVtpy114W3HymBQ5mXV9SMx3YSF6oJXw9xVIb+9oVWCCNaxVAAQj64n+MY3qmCQBRQ4XhUHUWyapkWvANAfftg15JFHqoORWzmWl8uGqf7muh4Miv5Wx8HGRukEYeUPP+w8VJ2O3/FQkb3uFxS7WA+hLVepTKPZpnLVC6+pPRBhiyvuJruldqjD6LRcBbB5r+ZtVssOgdQEni2+SLHc3BZHZf0KtNu5wQLglBBoHMImbXvtY5IF81XnENuKLdcyZun3E1JnDOvMBEqvbfE85LdUcR7gcVkGZCXdnc6/IXtrOykzM2W7Q2OOQHmoj5U6hpKEBho/2wJ2G7gBbwh3rPEZSvPZAOVzlR2vDPzQR8c0LXTVQc652kaXBzvUtKHuQgDCg8nOw6Eoe5Z3KUD6AZPp3H3l5LkzKq5xISinoKECzBaEtqJc17slzNTobHt+SKc6XfW30Ll7lacgpC0vFO2u4L1GZe8nKSjPVfvouSGQPCR1yUFDSULr2mc5y+vspMB/HbBbJ1pmHdBsj6mLUrf67AWa7gWSsp66RK9qk9BOgJBO1LcOzLZ5B/aSOmqXUL3XujydIPrv/M7vAABe/epXV77/0Ic+hDe84Q0AXEKshx56yP82HA7xzne+E4888ggajQZe/OIX4//7//4//PiP//i+653Kc1D2A27t50Gb5ZhQMC4w+JzvAcw49qckGCKOHeVHKJ9m3UKZvKl+K/A+2qIL6zEiR0lCRJwhSIXYRXKIPzW0tuN7aFE8cXBxANc4PKfRqEb5WWGXqK14fuFH29/aiApqIcXCL06TEnSoi+rSfdjFu28Wy2lX13R2a706HRiRqLawQIsKt4JrU2zZlNGo0IOFE1wIGdicTGB8YgzIMQoQWAeALbKubybGsvZLdc/7ZMGhkNG6Y9XI5Ny3C/jdJASk1w32A7ZIny7Ep/JcFIuhcU7bb3JHi8fxs9KPqD+WdaifNqQHy6ijbrkcmUi+HBDVQ29ddY8RdT7C0Hmzs9X5ytZVd7tV2+55G97rnmsjj0PHme9CfWN1pISeiexn7QfmbN9tHrRc+JXKQ22tA4d366xdxFalzxb7Kmavg3YbjCFF9jp2Ny/PfnV6Dsl07r5ycu2MmgMsCsDpuwULLeUEP49GIx8NrpHAmoyRACRpTRhZrMfzHAKVGqlsqT0UvKXOGgnOyHMLXltQ1EZZM0J7PB5jOBxWwGgtgwA3o7EJgBOcnp2dRbPZ9HQ3TIppgXHal983Gg0kSeIpdJQihRHL5KoPgfvk17Z0LoxGV3uR9oPtY/+ovdg+9rlGujORK8F0tYW2jXYCAI3gZj/qToWQ6HhQznl1XLCd7LsQ0Kz16E4CHa9KHWM536m72seOP+u8IXDOaH/2D+1lx64di9q3+q712nelJpoC6k+f7MeuH/7whyuff+mXfgm/9Eu/9DRpNJUDK6GVka7y9GW+m+UaTbevyirquuuAu+5ywCijyW/uugiocdrE6oPumOZsH0me40XLGRDH2MwTH72m0Vx2vbCwULJZVJiT7AqteM8yR2MClEFCi6QwEXoVYow2cRPXNUrNwe/It705KJNqkRudQgx0MACSMpMqkniMLIvQ7bqd1kBZNttGE9uIPeoTx5M0J3GMkgfT4KtJPC6NQNSdDfM850Cn03LblFkggVprdEkM6ssvaFIiAIuyvSBtNyvB3nnudFcAXXFhjeQjEB5aY+pnOmT02J0dF0gfH2khzlqIZBu7J78loMzvizaSin6xPa6CE6LIwoKjewFKQIljR3PDaq5obQdN7LlvtSPj2IXG9XqlriS/Z+E02He+U4YJAsBg4PTIUbYLcMdwezZ53hcWHJ+MTTBglVVOWpYVkl29SFOZylQuWfIcSJLJ7809KTeXKFANgtXbOcXvhDHV1QG9NbTSnibGOk/VCW65v23Z/M46T61jUv2NNjFmXUAu67eRxix/LyBd31mOZdnQl9LmhOpUOhvrUOZnTxeiJ6unWaOe7bY0oExarg825Ngrzk3iMeKs4MVGyQ+uKTeUtkaDoUP2s9PDbgC6nl9K5NqucxYPCBlcxT4kBCrmvzpuaMoQLXvduAJMUIbqpM/N9qQ6Y4QqsGKTAdRdpNRpL8MHJCpY0ady7cn0ie2AyH4AdAKUBIsVQAXgQUGNpCZQThCSvNb8TYFGJhKN49iDgKxbE4MSBLUgI8HDwWCAnZ0dT/+h4LmlBVHRMvI890A0dSFXfBzHvg1M/Ml3BdnJoU0wnCA6bcQ2Kb/8wsIC5ubmPJjOPtFElMqhbW1AkHZra8vzYpMzW6lY1ElA8JmAcZIkE0lXAVQSkiooTzA9SZIJoJq7C8gdTiBak6XSHruB6BQ6EJj4lGNE6WtoHx3LLFvr0+hvguPkrZ+bm/P9pbQudDTUgejUhzZXnn3S6VAPjkNtg1IWkeLI7j7Qa1UpW3T80kFyrYPoT2ck+lSm8rQIF2G6urarQfnOP7OT5oMSuyjz6693POV57mhd1taApaUmEjhK5tOn3fnH8zUH4n3vewCA5o/+KPL0mGev2NoqMUGl/swyB1recEMVzx0jAuLELbxkYcV15YULjn89jgF0W8g6LSTpit9zbsGBCxeqfNak5Miygld9MADWHFreLMDH1nKGYR55GlJd321tAWmaIElLmy9mMdrtxHOsWyDhzBmX4FSBWQaCX3+9W3AfPQpE/fOelzzPS+oXxVT9F6QlsaThp097Qvhkaan8TRuxtITNPEGTRikQfJ+Eikkr14q+JdCbpojabbTbx5Dnbl0fx8LPLmMIWYYkTZG3W55Ch32/245o9nW7XeXU5Xbtc+cI3LeQZS20ltNy3EuS0GEeIYHjZI16T2Gx3wd6ebVyWSA38/N44Qtb3tY7O6X9Sc/jtn2PAUTewUIdPe5Bj0kcl6T7tCmJ+inXX+8dGk/1E7SXgGhtzTWWKMdg4NrB8ui1iGNsZsdw4ULB8/6Vrziv1vOf78oOhQ3y3mBBdLVHCDkJRftfpTKNZpvKVS11gGHxPswjIJ/42t1nBRAbFwClAtF09GrkrQKH+p2mRgjdBiyGGLotBNSfALnrTBCqU8vgy0a523drzom5UiQE0tt2aTl8qbPC1mcBeeZXpG2jvAhC02AFnmAnQtJztdvlvZ6Tp5KtE2inh560Lr2eA4ILiQqFm3HsEmTGMZBWlSavuNrATg+kRtH5WEX7WSnnWIZLZgrEcYI4lWc780xaKwEAmYk0gWrKD/u+206Nid0ToQvCPkuHBrtF5+tAb1XOku7roNcHJfMwyWc0fy8IDUat7xKTgj+bMp27r5xMQfQDJCGeaAUKCbSSr9wCk/xOI4UVsCY4OT8/70FXoARECaITsNZEkAQ8CTjyf40AVsCSYKuNkLaR0JZyQ0F0JoKkHmwDk6wSRJ+dncX8/HwlKSsdBpY7XHnZWb+C6FmW+Sj02dlZr8/Fixc9IK4JKTURpSaVZBJRfrZUMixPo5bJga79qcAubWRBdAXA6UAgiM12sy8IQF9KJDp1YR9rokxLkaK8+nYc1wHp/I6ODgLoyoGues7IQpRJadVWtBEj0NlX5JlnuzU6n+ezH/hZAXTr6LJ5CyyQfq3zoQNTEH0qB0jsalIf9BXQM8f45/LBoMxuKQ/hnQ7QevD/B2QZ+v0X+RyGi3GMJ590WO3x4wC21x3J+b33ujJe8hLEJ4/5tcH2dgmCEmdl1PXRo8CJ7riy4uK/FT7N3NHGJPkAWGjhu98t1x7tNnAzOaEHg8raA3CLWuKOytc9N4eSp3ptrQJOIo6RxDGWl2/E2bMll3kcl2BunCXuIb4oIxoM0NKEYYJUHOmcwLlzrm6yzhDE6HbhkkaeXnOL6E4H0fIy4jjxi1fA4JjUW+tptx1w3Os5AJy/tdsYdk4AMZB03NePn03w5JPA0tKiA6HTFJsDN88106I/Hn64JK+fny+3DgwGaJ5qu6d0Js5cWXGeAq5KmRCt3Ua25BI+EZi2W8RDa7y5OZf4lU0geM7cqMy55vJnLqLZ6ZQgepbhodUI/T6wvJygidydtLJSJtq0i+Q8B3o9tNLCKdF2DgUdr5p4bGHhmG8Hx1MFRM/z0jmRphjGTQeCkXuex3Q6GLcXceYM8N3vOpPdaiPhJNnnJppADMQdx/H+x590Q/ctP3fKgehHj7qEonSCqOiin4NZgXXr+TmgIPqh4nUpx09lKs+YKJAWADMtgFkB0AUwi+CcuYqhWRoSi/lxF41GTZfllQB9HEcTuObc3N54nL1NhID0XfwH/kU9eW9N06p+qlwSx0BaBRgtkErxwDbLGgyAvjO43xtQKJTw/zTGuF3dObCbD9In0+wHUF090YKkQPmAlGV4dD1BliVopcUaOnPzaNQ/7x98hkiQxOfLLXNMsB16JuRgMOBuVDzrQN6bWdVDkmVRxZYW/6ZoX4f47Uu/bgQgcaC6Hdu7gemqQKF7HLvI+6QGNbRjQv+3zqE4joC0WTo+gCqfoQWsDcjN4IfBAH4nSRXET4qNgwmydtO1W5/Rq8q4gAqBiqvVR94GQa8UT9iPPa8Smc7dV06mIPpzRJSrXAE7ApEK/hE8VcoUBZc1GhkogVkLoipQD0wmpVSAzNJZ8KV628SoIU5rBSRt8ke2S6OC2Q6Cr3Ec++j7UFJQPV/txONJ+8L34XDoj7eJMgmQUxRYJ4iuUfRKRaKR7BbwtpQnQNVRorbi/xqFzfMtz3rIFrZf9hqDrFej8XWchDjsVUL846oX7WT7UW3Ac6xuVkcbFW5pgWhru6tCd3eEymc76+yj1+e1LlMQfSoHUjR0jJ/1N/N/HMM95TNzmEiaAnhgBeh0sLX1okoOKwY/7ewUHy5cgA/b7vcncDgKt0UT85yfR3XhF1cXbxNt6/c9gKgYn7ZZT1MwXQH0OC6i0rji0RB5iYBPul00Gkllm3yeMxpdOOKZ8LFIEuwPLkDdpFtGTfFnvhIMyyRhZ89KyHZSSfJlAWhvgAINIQiTFIAwAW/kue+3pIhmO7fmuqzfB7JusxIM5cve3nbOFQ09m5kpKWPiuKQROXOm3B6Qps5ARfKzJB4jTV3blXO1LkIsjl0/kWLHOXwSvx60ST17PbiIei4648RvrhgMgCaB7TNnqsbXClmw7HuP0hRpkWQ1yoeVRGNpp3q6+EsceKKL4LSJs4V/YZFOGtaVZej3nZmZH7QiJlsgaZIAd87qqrvsNvMETY4F9jvHolWU/attD4mCLqEF+lUqM8XrUo6fylSebbFTdvCSMyCeRuOqhHA1P+dJ2RNUDwactHroVMCy9D5uObV3iwIOAdA6N+tnD7KqkXT+KxobFQUlcRmpz8ODYK3uyqlTLk0RpajYvlnMt5Xy6IwMAa5A+D4r4CuAMoo8TdFbc9Nta8F95yns0hIEH/Ql2CAEltdFO2sb1dA6mYlEaYqYOhZjTp/BZmerZiS8EGryaFSOkzLwO6pE0FdstsfcE4zIls8sl3biuAjpxu8StZNTsLSfjjtjN010apPpshg+x9GZkKjNzfw8zKMJoF/LqhM/Ng+YTOfuKycH54ntGhcF4CylS+jYEL0Ez7FUMHWAdQiwsscRrIzj2FPJWD0saD8zM+OjrxWAPnTokI9gJsisEc6sj6Apo7JnZmaQ53kFLJ+dna1Q1WibrLMhZI86+1tqDuWht5HGfFFCFC+h+jQ6uQ40DPWp5ZHXyG7b7xrxHirHUqHUiR2Xyvmt0drq4NAxoXoyQt4C/CHufuVbV1spvZHaU8dSqM27tV0dN3q+HltnL7VJqP5rXaYg+lSuatnZAXbZgVORmrAhv6jc3nYvEkdnGdKBrKPyHEePukhgAEC7jdFj8nuaOtRT99TCgdZZ5hZNpO3Wrc7+/Dqgrvhuc+AWbIvtcpGhO5pd9C98FlRdzCkmbrlMBwMAnbQMadbVChdLBYCr4LWWlaQBvUPZ2VBtoq53x6SuIeVKEb2suk4kvRzkE1uHfTcLPQsNVAHH4xhpWkYjRoNNxGnTHzPMIyQsg9zp8/OuE+mFkLI86s3O4J52AfeVEx2YXGRrURS/EIxjIK9SwebSfN/mQiI4jnqg2AE/KCqhTjre1HEiAAalstg2K2+O6Ykd13YcQbeNG7BCxkWpQvEFCdhp16LfdDyzi+MY1X4JgU3W2NrI3VbkIUDoKpbplvCpHFSxfu6JyzKEjpuf7aEUzmET96oaBQikG1a1iXJDCbv30i0kWqYF32tBVi1Yfh/vdlVrA/a694VAZ1t+yOh7NRaYjELn+cUN3X/NeYL/WpBbP9tnKTq8+b++2+NDL7Y3TsRkk7sU9pvcFpikUPHPoYFn1KBHyfSDN7eA/P64QF9EGFccLIFiw7LPvo3j+msijstnuf1WfrnD6yDKdO6+cvIcHSLPPdkNRFcwz4LEFjC0QGsIYFWuc5Zp6yPNCQFtRqAnSQJG92q0tXJyz87OVkBVHkual0OHDuHChQsVihXWNTMzgyzLcPHiRTQaDU+RQvCVkfQLCwsgtQvPA0pubgVsmZjSgqIK/hIIHY1GPlpaE4IyMeX29jY2Nzf9d8r/HQJaFbi1YLPdNaC0OewDm2CU9lfKEtZDIJrvqg/7XBPJWiBfdQyB1OokYJ/ojogQwM1+YVuSQCIgpWlRHn3qMTJPFmpP1lcH6isHvjodSAMDoOIMYb/wWDpq+L3uPGDbaQuWUwfoX4syBdGnclXLYFCuRnRFo0/XitQyEonHxHGZnHJlxb1uv90lKjx5EifbBc1KUdedr3Y/DQbAQ3kLGxtlLkqkHRf2SxqJNEWCIY4fjXH0qKPEaDRcdA4pOnhuowG/mLTbbvv9CKORC3Du9YCTJyOcaGeI45Jq2ufHfACOyqLdRu/BEkfmMXleBnvTBM4cCbJsEa2TJ6tRWxLqTBCBXNxkMHF4d4SWgpc2qk1sHxeUHuwy2bmNLFtE++QiouVlDJFgfa3apUePKp1IUSbD+YtFtI8MW1523y0tORoXmAfqwQDtttNlMRsCK6uI2m2k7WOeLiVNm1i8807X6ao0UF2EE8FdWir5yMnfWnw+c6aksLFmIhg+NzeJBbjFYlREvrni5udLXIAR6f0+MOwecxH9RWT8jZ0M6MYlV2yauoSb1C0EnhRj0G+xznMkaVxsE48rTqIoHyLLEq9XHAsNjthpiATIlfM/9X2gMjfnTHj4MIDH49KWcQycPInzeRMZgObgKV921m1ieVl42m2Uu/S3N7YFkCwHbB2oNuVVncpUroxsb0842ggG2ktUL1Wf+HAX0Ut6NCojXoHqLiyguGcM8uo1b54jSPWRpPEEMB3CPEOfgertYy9/HWV7u6R14VzgKEAAxAnitP7curI5n1SUUC9BCKwNUGqUib4jc0qCKIvrldHv09RFGmvAQrfraVo4Z2zmbu3ZTJksNAEy912WAchRzb2huttnwABAHnr+yvOiXKo9qJooz8PAuTUj3yfpXNzLjcHCmDpP2bYYBwAjtHVnVimR1OPmX9ZHgJ3vngImz8v2Wi9RCMWuGfTN4jqxh4XsUgaplMV4vvOiP+Iac7BM62iZkAOGtk/n7isnB6vnr1FR0NJGTod4qi0Xs4qNVLaULhRGhrN+io0eTos7FI8lSLi9vQ0mIN3e3q7wiytIr6CjRvsS7CRommUZFhcXkSQJDh8+XIkwV3CZuijfuYKqymtNQJzHWQ5wBVvpCGDySfJnk75lOBxia2sLw+EQFy5cwPb2tv9M/cjRrkkwd4uqthHb1N+CvQCQJAl2dnYqSUeVdxuAtwWBX6B0fliHiY4j7R/bR1qPOjQ0St+OPQv806mi9g5FhiuFDZ0IOsaVP90C/iHwn+23PPH6IgiuDgnWodcLQXaOFbUL6WI4FmxC0WsZGJ6C6FO5qmVjYzIqGKhfPOlWVB5DTpbTp93r534OX8dLcXwE3LrswFUe13rgC2h1Oviz/FZ8+ctuMdTtFgsBckz3em7lS97nXg9Rv4/FTgfZqRMYDEo6F13Qj4Wqg5Lnjo3iwgXHFU1miu6rmgAc4JjEYwf+rw48kHs+b2JtzeVXXFpy52SZq3d9vWT0oA79vluoLy8vopEBrXizylk9GKDdbnnTEmQGSqaNbLmJSO08GpUh99IHGhTG9lFKXLq0RZqWToKo91RJnTIYOJsvLVXAl37PlXA+O4H09hNYWwNWv+Kw1VOnioVjwf/aSodoZTHwYNH3y8uIO8cAOL1GI+Do0Rsx/wM3+r5qYtP9qOOIodCMos8yjNNmZdg9+WQ1/ymBbwqDrZ1TwunJRbKzuwM+FjP3xfGjLvne6dOlY2RlBeh2E8dJr3wvrLTbxbh7onIJ6dZr4lp5Dhw+HOH4kbh0pgAYpo5CqCWc5p1OUtolz13SUgUr0tTnhGvGjmN1HCfI0wRJWgLdceycS0eOFNQ+54qOX1rCOGthbc2Z/dQpoPngg075TgdRluHFLz7h8JN+fxJEpwFlLCswNAGosF9D/KxTEH0qU7kyEgDoAj5uD1QypweBdMRJhaqB9Bpy29GivfCS91G/g7x6I7aTsEFCI0PxkRQFEojUqGALHupOLqWYDplFv+etaGOjtIsCsqF2hnDDKvAoXNKqlDnBz62DsG5KS6L1EOy3bXPVJf5z3nPtK6k9gKf6Cfr9pDInrq0553Ezd3PaIF1Er+emiCTfBNIU51E+o1QVgnc26GOftqW/PgmQV3Wux5TrhOdajnyOwSQuxmB/UE+Bw/81oSrcODt7tkz8bU+lfjxtfr5M/J0QlQ4h3Dz5UgaUnl+8e8dTDM/VT71VKhHzpsxhXlIpWUy/VsQApcOoRv+rVKZz95WTg9Pr17goqFdH00GxXNgaSayAZCginecr1UqIfkT5qRUQJXhKgFGjxAnMEzTd3t72SUsJ9g4GA+zs7GAwGCDPczCBJwC0221EUYQsy5AkCdI09SAw9awDSxXUJQDOqGwFptU2NqqfILHagSA6o+kJnm9tbWFzcxPb29seuJ+dnfW2sYk7Q5QjoV0HChQDZSQ3AfK5uTmvNwF1pcJRTnGNvlZgPASoq2ND3/VFh0nIGUJAnPUzgakm8rT213FF0WS2O2bBWRfxb51QtFtoF4Dyuevn4XA4QZtDu2tdNkJfk8mGkolOQeGpDaZyFQsXHhY0t0KUkAiwLhLIB7q66pJInjqF338X8Df+BnD83EqJFg8GLmlhmmLwylvx7W+7YOeTJ4uFfXvRLUhvuaWsg2hzkY00KRYWrYYuPgCkGTYH1Sg8LhrW193r9GlXzPOeR47rMZJ+r4yi39oCbroJ6HSwvu5AVWHA8FHDQJk/lXXxWFJjnDrVRJJWHQ1pp0odsr5e4rOzs+5/z42uUVUmIZXFJXu9qi5Kyc1I+na7WIj2em41zYM6HTx+xt3rlRqF5ZISe3XV9dOLTo2rq2euPtfWXN+nKaJTp5DniY+0f+QRdzj1eN7zmjjO8H5KllWA/F6vGu2v7eR3/b6zoS52+Tu5x+P2YiWYOolRMVgCIMturDQlTeFAdI67CxeA6693FSwtYWWltKuC571e+f9g4Jwvx4/Glf7M4xa2toDWkbIDs3ah79p69dqTgdzvFe0cOLBqkLp2LVpAKh5XBxaAp/IW1h4sbXryJMpkoYXD4vipNo4caQK9QdkwRRY4Fm0InHDIVzpLB6QOqhBv8FUqh3Bpi+tpcrKpPKNi5+5CLCjLRMwWmN4LEwtgcgAEuLTONQUB7fWvBYVQb6XRimOQN9ueWid7Ael1jgD9je822rlOB3VIRAY0tdHYfFkaMgp3WBEwruvDULt5W01T50CNMMb6eoTVVfd8dfRodT7FoQHQ72PQXvQpT5LBAOO06R3y5Y6nSWGSV9vdDG5Qu+021kL2t0Bv6LwKHR0r5oufQ8+wnKeK3/K8nKv5OGRz5MRx2SZAfMZZASzXzWvqWN4NTL+UgUsnVN2gLOqa2AkQqNI+Q5Zg+bh6kJZ9wJKCT+fuKyNTEP0AyW7Am41ID0WhA1X6EP0uJKHEhxoNbCOGWS6BYgXqCaRb2hSth4DjaDTyIDrbZYHgOI59olAFfZVWRIFoAq4hyo39RsNq2dRfAeQ6TnQeQ9tYzm+K9q/VR+lJbMS29oXdZUAngP3NHqNOA5u0VOtUEFgTw9pksXa3AZ0ddL6o/jZxKfvY7o6gvXXXgdWNn210f2gsq93UvqFIdnVe6VjW3Ro6LlSvOsfIVKaR6FO5yiUUsQOEw8D2KqfIbLiZJ1hddUAzZvtV0LXX8w/jxDK5MMnzIirNkzOjXKAwKtgCeRSz6uMagFHeXCD1+2W0MIDqD8L5OTB4Jqk5ohTI86SyVlM1GBGW5yZRFybXUqoX9UzsAXyX/+O0uqZR/wYDnPv90iSM2p+otGhAKHFVnjvseHu7TBzq14khAGVry3W4TyybTKxtKe02gG41jJAAer/vimBCT8BF+qu9SdmioISaS3WMMEYu0VgVnYuxFGdV0+S5tJGehK0tXzGDLlkf7acA+kSeOVFuZweVwRDlwyrSYqU41usFafssJlfIOjALeh7twzxH1UlTeCSSdlxF2kL6WCQuoGct4rWf+8hVJNNotqkcNOElNhrtD3hOAseE5qnK9xYw3887/w/dOwL3GdJQ7CW7Rdfa+cH+VleeJt7W+d3O9Sqk77J0LXy3APpuOtfJbnirNaHOuw4YjcqpWQ725xT/2F0Mti61t41E5/y3s+OeQ9T2oWh7K7v1Zejc2iSxdfNoiRZPfKV5t+1my13jS+rqqxsoITS77hl8r/lyHx6xiWfYy5il6u4TV7NM5+4rJwes66digUEFlRgVq++kKdFoX+UpZyJPCqPJQwCmBQ5tRLwVBUgVFOVLgX+NIlaAfG5uDmma+s+Ws1vB0ZCDQNtGAJZUKvo9qVpGo5Gvg3YmbYsC+Gwz+d/5Iu2H5XFnJDr7xEaWE3jXPrbAr905wGOtQ8RGm7Nc0rioDnwR9A6Bzxp1fvGiS/KqjgqNDLdj4+LFi5WIexVL4cL69Z1l2X625ajUjceQhAB3W07oWrBR79Z5ECon1PZrXaYg+lQOjOgTt4YF8X9LhmrPK0KNm/EQy8uJo0FZ7bnfu90SjIQDeJeX3VbiWj0YBq7AOlc4/CwgLH8K7KiusE5wi3Mlgo51SQJNsoskg/MOgSwoSJaWXurBXqXviGO35Zfb5pGllcrVTHFcRoYxEn0wAJo0CEPCeHAgeVij4Ras5Gon/fX8fHXb+sKC+21hAcB2Wp5QhK/pwjnPXdV57g67/nr3/7lz7vdhHiFhyHeel5FdCwvlfuc8r0T66XBiHeO0WUkCxqBnjbjjYpznsapGo2ynDk/2q+d3LQaC1cVL4WXIspIKdjRy5WJdOmswcO0q0A8dkjpcaSs6MTodlFHhhZI8dnMQoclK2Rf8X21btMFfmv7ZlwlJS+M4JpakXBCSLmb5GPLcqX/2bAFusA9pnCzDEIlz4tBLQKPZjIAhsUCCBRcOGIAOTBfiU7nKRecFwHMoW8BR6Ur0PuhzNoj4KFc5kLQllXqBKiAZira1CDTf9wGi26rqvtvZqQd8tVide4DJOUGTNO5Wp8Usde5MzHe664xO7Sxz9+Bez/3OKYCbw5aWgMX2GOf7EdbW3PHMKfPQaoR+31HMMTcM86tU9Mxz0IlNBRjZ755LMu8M3toqgXXqCVTntt3wYe3i0LhTuzExvLVvKC+qDXoODbmJHQC78fvoqzBMkqZot5PKpsetrUkaI91sRQf+BIC/mweAv+92jYTKsQ9QIYOYsm0+oLrTQkUrtRPF+BwOjEzn7isnB7D7r21hFC5BV02UScCWYCmpUAiaElwnlQojpZmAc25urpK4EqgmI7UUJCyLelEUeLx48SLm5uYm6FTIFc1IcoKQrIuc5s1mE41GA61WC41GwyeeVKqVUHQ720qAlPUQvFfQl3Qsg8BWWm17FEXY2try9iVFDe24tbXlAeZDhw554J8yMzODZrNZoQOxSSdVQuB4aDeCRlCz/3d2dpCmaSVCmnrzpZHkak+lr9FoektLwvPVzqqHjlkdT9RZAWeC7SHOfvaDjUDXcaBla7LSuih0tV+IXkWvAfahcr3rzgj+xnfaS0F4u4tgCgY7mYLoU7mqRdFNDWHWyG5drCt4bUOMu133eXUVd999M27tngfuPe34U1796hK0GwywvAzcfffkItcnKCtQ4U00HdhIUmjypjAZZnEsg8k7HaCZnwfiGJtoYna2BFfn593ryJGCG5wnAWV5S0t4qufAiOVl98KDD7pV7pe+BMQxXvSOJcTxojfB+rpjg5mZcUDq3JxT63w/QosgbJbhwgV3zPJyWXWv55JAttvFlmssYvnkIqJez+lDwngThU86EQVzjx93WDZpVC5ccIvsTqdYiJMDnf1U6HXmO+48mvmBB9xC/w1vAI5v/Dmy22/moVhfB2ZmIiwsJOBCfTQCji8tOTqXwjvQXMqQplFwXUuKmJmZCI2Gi+pfX/fqTKxHyS6S526Rq76Vo0erIPtiuunGkGxvaCIH4tTzg/pVYdHnLQzxl28DhkV7WukQ+PagHNNUuui0w4ePuTG0+hAQx2h3HU9/EwUP/rKjyDm+sAl85UGny6lTQJYh77niHDtRguVlYJHbHtjo9XXX0BtuqKA9oxGAI5kH8p1eMdDpYBPlVvxF6lsA+AmAG9tt5N1jePjhYocIx0BhzPN5E+trwM1x7gZzu129xlim3hMo7ED7vTq9WMaUE30qU7kyUmRHHu9BfcJ7ZTM1oLlulwmhbcVNhnzIlXqKm+4YkdtJo1LnOLOhy/Z+Igi3T4zogb0oCMJayhXbFOtYn3jeCAjr5O4oVc+aiybMMiDJyrktTYETHbfDqNdr4swZ4IUvBJL1R7HQPoFvfrN4Vuk9CgwGGAxuxuoqcNttAL70JbTuvBNfWnHzA+6/34Hcnf8Oa2vu+aHZexRZ94QH0bkTjTRmeS40Zv0+0mzRU8Ztxi3E7RbWH3B5RgiiDwZuCm80Jqc9oOTV1/m46K6Kk4KPhUp5xumBz2HcscVjdsOLVayDJE0lAatNFsOD6aEfDITTxim+2O0C7dT39dmz1QAE+xzip2PrJNZBUtOQCS5zW0nI6RQa3Gbw2uvfYvtANRHqfqPKq+M/2tfOkKtFpnP3lZMpiH7ARMFNgukawT07O4s0TaHR3Br5TVCRgDGjspWOREFjBTUVoI/juELpQWCcYGooUSmjmJMk8ecw+Sgj4hkh3Ww2MTs7iyzLMD8/j/n5eQ+iaxS3pSexyUE1Ynw8HkOjs7e3t70ttA2aCJTH0x5Jkng7EJgnuDoYDPxngugWhGfbCZ7bKHagBHDZNxp9T0oYBaCt80DBWwWQqQ/LI9e3reU/DAABAABJREFU8qDzXNZB3nq1F3neCYDqGLNOFktDY3cfAKi0T50fIWcNf9PrITQO2HbWYemOeK5GouuYol4K3rOP2G9WF45zBeUVRK+LeL+WZQqiT+WqFrvi5CLDUqpoUiYFdfWJ/XnPc+ecPo0fvj0D7v2SQ2Q7Hfy3ryXY2Unwg7ffDqyvI1r5c7woBYZLN+P0aYnuEWB+E02srAAvOrVUAvwSvj2OE0SDTSCOceECfJIsrK4CWYa4eyOAMjfnwoJ7HT6MMtyaK7mlJSDL8NBq5HlCT54EWv1HXRtOnwY++lG3krz9dtx6991+RfVnpyOsrLjF59KSW8g+fjZBrwe0ltveMXFuzdW9iKeAdoqHr3PJTY8ccXqfPu3w+jNngFfcchIA8PhoEdt94MZugWCnKZBXF64eHFlZAb7bw42dDtBpY3jyGE6fdpFrOH0aAPBU51b0esDN3a4rL8vw5JPOPidPOlW/8hW3mP7FXwTw7/4zFn/sx7C0dDOAMh8oF5qPPeYWxMdfueQ8COLoSNPFynpPfTXkGSXIvrZW8ojrIp1+Gw5DRqAvLLj/PRUKV/trPd+YIRK3i4COl2yxMu430cSFs8Dx7YeA06eRtNtIul1grV8uxpeWXEWnT/uErEeWit0JKytAHCNKUzTTtDTcLbfg+PIy8MCKA0C6XeDOO/FUL/Jb3FdWXF8DwGLaq0akkZD/6FEgLcH/nR0H9MdpMe6L63KMCGsrbtjHMbCYFsZ6+GHXOY89BjQaWPprP4kvfanwGy0tlUbOMqyuuvNvPpkD3/mOQ2qYUZfG107RF+8bCgLQ07ELqHC1y3QhPpWrWtpt5/jrhzm+Kf4+qckXgaoT2aLMllYtjhFlDjS3nMtxnCAucoVUolkVJK/wgcl3FqWW+0WeAwn4OfH+N95WWFwrG1fLEYdz099TXbuZxDRIfq16AIiyzB1ftCVOm54azcYdzMwALQHRsww40c6Bfh+9XhOPPAK87GUA7j+N5is7WF1NnMnXHnD85Ms3Y2UFiNafAP74j4GlJZw+faN7Vvlvn3f2+KsORG/GQ+CBBxDFMfK83GWU5/AKjUaLZa7XXs/tHiuSja+ulnMu2cqQ5z41zMKC8/kSk9ZuYrQ57R/HLmiAu8NII6SR9pweRiM3tzPSntNyK96szh2DfHLeKPqNuyKqjGxFAtY4QdKWccyXfVblswI7ME3BFK7HOx2Mlxcru+Q2cze2/XNWLuOE5dSB3SK2Sd5RxLLsux5sbSGiTgu9jKiCtmXix13mZ70cOcYOikzn7isnB+upbSqVSHQCfASWZmdnPYiuQK+C4SyD4CLBeALwBG95HMXShBAM1shcC8TaZJ2MziU4Sd0UXORvjUYDaZqi1Wphfn6+QulCkFcBTwXRFcAnQDszM1OJFiYoOh6PPXi+ubnpo613dnZ8O6nroUOH/OckSXyCVNqB5fBY3QVAQFwj7zVpJYF0G/lvdwRQLP+2HhOis6Hu1slAMJ50NZZfnu3K8xzkqbfOhrm5OT9m2HZLf2JpWyyHue5C0DETAtFtmTbS3ZZTB7zW8aRr/UpHozzvpO1RwJ3HaIS+ljOVqUzlgIk+7HO1E+JD4YsguvIu8/3oUXf82hrw5S8D3/iGW7FtbeGb33SHPf/5LRxfKgDH1dVicXfMnWci13o9hydWIroZkR7HLvq3qH9np4zGqkQboVx0E9dbWMBElspxexFnzpT4ZadTLO5WVz3iuf7wwxgDOPbAA2U21DhGp3OrNxMj3HdwAv1+SX+yOXB0M4cPw5WZpjhy5FZsb5eB//2+w2pnZoCXvcwBvitfczhop5M4oDaOgbzsimiwCZxeLU+m8u02kpMn0W7f6Lrl2w797sW3YmUFuPmVbe+IuHChiKSLx4jjCA8+6Joc9c8D993nQOG/dDPOnSvzUVKefNJVeX6QuD7iorXfx8yMA9E5VfL/jY0qKw9xY0dHMhnpRhCdi/mFhWJBy2xpSgAr0fr9PrCI3B0Tx0BWHdM8/Xjec44SjdCnc6WwJVZX/fcJhmWC1jguAenTp92LHAMcO3DjgJfWzIw79YEHHFiBdtEwNrzXc56UYmWsC1mqlRTtHWct71taXy+cSCnKa7nX86vf5O67kecusSlOdirtZPJdLOfA449XQ/y1cnakSp67bQ9KJqxbEOoW7VOZylQuW4Z5hLxfApcWROelFuXDiTwY/nrmTVXp0ngvUieYeDMVg9dLu6gNcVzkAwndJ/jO+vV/FqrPH34iSCrtIT1JHKOK8gIlsq33nFA7bZh6Xbh6oUOEMXZ2qnAbb7Oco0Yjefwo6uCmoDiGc2zefjt6vYJuZWUF6PUw6BYMbtwK1uthbe1Gl3D0wQeBPEd+d3GPpsN8eRl8dvJmKurc2Sn9I/wujhNsbZUMY5we8twVkBe3fgVL1c9CoJZCh/DsbDmPK9gax+4ZopmmYLaXNAXQ6yFrL/pHuUoGcVZa4+Bg9HaeRxPDyUXKR64+HVPap/TgP/lkmbxG56rBAJGOyTR1FHvqnNFxok4n+x2FOsv0WcWuo3ogvcbxbLn3bbX+WlEAvU4/C6TL57qcAlO5dmTqYDjAEoriVE7qEM2Jnksg1772ipYNRXpr3aFj9f9QxHjod8ujvltbbPtDn209KhpRfikvazvbbu0Hy2eu+lse8jq715Vh23YpL6vzbvqprWxS1u8XKA71LWUvwHs/iUPr2rdfvUIR8JY7fiqXJnbs7+c1lak86xKKrNntM7/jnuGNjUoGLa5JtrdRXdjahY485IcWBxNf+EWVLP4CCwUFGOIYE7/z/MqiQRXIc+RwQUgVQGIwCJoiqDuKSC7zg/pjB4PSbNRpov2F+Kg/XfQNBm6RWJCdWm5RX3WhdB2myYU1y+S2ebtw5WtrC5MRyjWi0VJ1dtJioiL+0balcqJ9rzuuTrgKt8fVhXiGjldgiP0QqFv7N6jWLivXyjlynezZtkIv34S6SDmtYJc+nDiRjeJFuNfC/QBIdBmvS5Xf/u3fxk033YQ0TXHHHXfgc5/73K7Hf+Yzn8Edd9yBNE1x88034/3vf3/l929+85v4qZ/6KSwvL+PQoUN43/ved0XqncrVJyHQrPbAumvQ3tDtOXUV7vK1zi/7P6G+/H1JqMy9XqH6d5sQvw/9/KnFfdIXNxr5+729/1bu81bf3dDNwLxvdanr3r2GSUgmnq0CJ1emUCnMH1839napODRdVz7XKaRJV7Q+zS5qjbHbw8rVLJf7PHTAZTp3XznZ51PgVJ5tqQP+dgMGFfwLcZnbSN88zzEzM+MjtEnZorQlSicDlPQVobrJ4a1Rz4zQZgQzE24SZGZ0epZlmJub8xQuGlEfirhmdDkj6zViHwAYKT8YDDAYDCoUKvoKgeMW2A4lylTaEAv4Ky85y1QOdI101/MtlUwIsNbdBZbfnNHSNuGn8rmzHEbqs22MLCflTihpKHcQqB14HMu1UdlqO9uf2i7rUNCxFwJTNfKc5TMaXvtBdw5QbFkhHWyd1Fl3X2ieAj1Xk5CyzeqQuJZlSucylatadJFBrgwbiQ6EwW7dP8qQpV7PRfQypLjdBmZn0e06THFuztRd1O8jknz4lDt2fr6I8lo3gHshjuoiqeDa3LKdxGPPyw24KF0fqMwPUh8Q4FhlBtQ8R/dzn3PnzM9XdGmlQ3S7idOziCbuny71aRYK+N3xRUh8WvzLqLE4dqbrdIpIawDdrou4amLTh401swzk+E6UHJ386Z2Oz4q60XNtWlxedtH7607ncZwgarcBuKDj4l+kKfDiFxcRzQwNz3MsppuIO47blYHW2qZz54Cjt73UU/JsookNF0Dn6XQOH3Z1kUPWBPphMHDVzc+XgY/z85BOc9FsvR4wSCNk7WPVJKcM+UPZFvTK3QvJ4Hx5XBwjaxdryUHqCOVpiDyvKDhGhIhZZrPM2Y6fOWDYecwpoGMcQIIh5ucTbGxU+zpNUSYvZTuYMbYwTpqVEex+bHq+4jGyLPJpAtptlM4pGp7Zb9fXcfvti2i3i/5P4cMSs2yxmmw3jstrhFH4Kjbcld+F0Jt9OleuNnm6t4T/23/7b/GOd7wDv/3bv42/8lf+Cv6v/+v/wo/92I/hW9/6Fm688caJ47/3ve/hx3/8x/GmN70JH/3oR/Enf/IneMtb3oKjR4/ip37qpwAAm5ubuPnmm/E//U//E37+53/+itQ7latT7OWkvi9ewnEMYCAnqGhCDRtJG7q+BwNEce6izAEkWYxhHoUv6X4ABFUkNfR9nY55jrxfqjZEglyClSvPISzDOj+VdoO/10Wiqy4so9i1ZW9vNCGfaxoNR5+2sFC2vd0uaN4w9vbudotdaUePAo0GZmaKObfddtuT2u1yGjl1Csjzchpst12+jCwDeu67uTlRP459jpMsA9BzX3NKYxJznaaQZcj7ZfJM1jUzUzVLJnMRh0mWwdPqJQCyLOFU6Q/SepMsqz6vcQ4PDWbbDwEh/k0/7jhrVqkBWb6Wpx3IscNdltLnExHhoeui7jPPK36r7J4QmaBbCb0jcI5vTnjmiWNMXmv8QW2q1435Tp+Hd3PMXG0ynbuvnBycJ7apTIgF0JW73EZAW7DSCvmvCeoRwCXfNb/TZJQAJgBGAqgESy0IzWhvpYXRJJ/kc1ded4LoBNsVpGZ9GkmuerBe0rQQRCeNC9s9Go08rUoI2GO51J11KViu4LdSsVgwnmA+bUlbsA/VqWCjoOnUUO5z3VVABwhpWPh+6JCjr2H5bAOdDcppTmF/U/fBYOB11chrnqMJN/mbOgo0SeihQ4e8nUiRY/uSZbI8SwGjYLwC+ZbTX/WiM0X1CwHZCrTX0RWpaIJY8qVbIF2BfUudc62Cw1MQfSpXteiTcZY5XmvLh07gUQBIfzyPIRno2hpw++0OeO73PVHz7be7j7q4pHD9ggdXyu2ycYwjR1xAu+e1nuBhT9DvuwUUaVh6PaDFld5ggGaaIs8in9CTeDOWTrqDC0J2XfPMzRVmIdL5ylc6+pYsc/XfcENJIzIaAd0uTp1ylC4PrUbI88TTk/R6QHu5iTh3i+Q0hU/YOLdV4qds9m23FVTURfk3k+PzwRV4svYCMe1jEUCErHMCcQxE5KovFoOb6WJpto57wObO6V4PWOx2EceuaQQB0hT4iZ8ofAu9nuO539oCHngAraUlXH/9iQqeSiaT1VXga18DZmcjpGkTeQ7PH8sh9bznlYw/EcYYI6qwBwwGztye45avPrdQV5OQOgCjWRm+J5YzD/h6xwxP4LgrxnCr00HabQK9tiOsJTDOk2MHEiGH40ovxkO/D0ddc9NNZcU0JM9X6pM8B9bWcHxpCf9tJcLjjzt733Zb4SRYXi7HIj0Jwike5UNkWZk8LM8BZKkHhxIA3a6zw/IygHvXnPGvu869SOmysoIXdQdA3MVTvWNotxOXwDbP0V260anMATM3h0f7LXS7LURf+pKjayk42icyyeW5u2B2dkrSXCpqAfQDtBJ/uhfiv/Ebv4E3vvGN+Lmf+zkAwPve9z780R/9EX7nd34H73nPeyaOf//7348bb7zRR6i98IUvxFe+8hW8973v9Qvxl7/85Xj5y18OAPjH//gfX5F6p3LwJInNLiUgDJxZAFOdaPb8ABiXhIBnLacOvASqdDH2XlGIOsnzHH6+J2DqDk/cKy3AxZB3gc5RdRbofUzPEaB9jAiIHd92CM+cm3NF87kmy1wy0EYD3nmxvFxU2+/7jJ3Pf76bFzF7EhgMsLDgjht3jiG6+26Ml25Ep1OY5+67nUn7rrsePxPh+O23Y9w9gbjv6lhYKB7HCt27HY/FOydxnqPTcdPCxkZpdh9D0Okg77njG43y+9nZauJPD47D5XKJY0nSXkir3Ua61HRjsFft6q0tIF5o+senXg+IOy3EMowSPmfZcVH0jVKZMCB/Z8eywESI4ybiFEi6qPLNEFinYiqay8MC+yp1ziZ9N8ckaU1Zeq3oM0rR3goQTucPf0MVnLf4vxf9oXh4GsdJeb2gTFAa+XLLcpJ4XM13cJXLdO6+cjIF0Z8DogClpa2wILombgSqQJ8CuwRqGSnOSFuNPLfgeQjI1MhfljEejzE7O4vRaIRGo+HPieMY8/PzFZBcQXxLBaPAq41Q5v+MxmYyTILoqhfbvb29DUayh2wDwOvOcxU0JxhMkJplaGQ7wVY6LNgeOglYjkbRs5w6uXjxYoXLnXqyXVvFap2JRemU0B0KFpQH4COrGV3NJLAE0a1Yrn19V+CfTgLaTftYAWtLk2Oj3nVMhL6jTa3zguOCYLoC/dou/d3SteixdMiwH1gehWNTHT90dFzrVDBTEH0qB0a4gAhF/kiyLgBV8moCb2trnu8bS0vu+yI54Y3t80CXkT1SbgFgN9OxT36JImo6gYvwxtq644jm6q+Yq1g1F2N83dhplyvuwQBp5ri5mfhzawt4aC3B0tIxRAVntagDP2XHMcadYxhkxxCfApI77yw5S8+cKStst3Hrj53EU70IDzzgDiFFNOlZ09RFrAPAuL2IPAcacIvUtTV3/JEjAjSfXi8jq7nyVQLWOEY/XvSgchwD7fYJZJ1yLba2WvKf0rTs3n4fyLrHkGCMG7vskBjNdIzXvqpY1N3fc32xve0SZPb76L7qhG8Th0GaOor7P/zDEmPNshJTbrddoPfxI0Ngdc3vAIiEb7TVEcXWpENdwwo0oOUBe7Z5bq5cQHc6wCte0cKRIy0kGLroOILoSh1EI6ytISnKHraPebttbQFPOt+Pd3wkxe6KYdxEfx0AErSKHQq+vJMn3Wtlxb00ZL/gTz937gROn3aHFbls8eh6gm73GKIHHnCV06gSLZlliQ59bA4ixHGCJHe2arXbSJdbiNYedYlEAeCFL3RlrK+7AbayAnz+88CrXoX+qdcijoFWrwdsbaF7Z1H2/T3v7SI///GHH3Z2ZDZXrq7VuQaUofL2/qHnhHa5XKVyuQvx8+fPV76fm5vDXGULjtsBed99900sll/72tfiC1/4QrD8L37xi3jta19b+e51r3sdPvjBD2I0GvndlbvJ5dQ7latTQlhxHEtkqwXm9LrVQngyMMlTrjcd3bUVcpDpKwS8a10WsK5plKqg/1tAe2bGzdtx7Jy4nmM6BIaqfrSJ/DYmYB7wH1gfIZOVu6STOaI8x3EGChQnNQdP4eZOXAHRX/jCQqVs2d3fe26qXVsDOq/677G64g/F48df6sy/5ua7xx8HcPxG7Ky533lbdc1w9lvMhkhPJW4HW9HeVuacsY1G1YcCuJwm9N8qTsvPnGsXFtyuKv98Jc9Z2mmJBmKgdNJvb5ePMhcuVIcnI9wbDQeApykmgNsxokrfW2q0UHkLCy2k3VY1Ot06iyjWQaydba8VFfsd69DyAzspK8fyf+t80v8NgA44cF6j0Sdwf3E8RHQOxUmhXuR3lrDaZnE5RBgXzojixwOUWXQ6d185mYLoB0gUqLZR6HU82Qo2h3i5CeAp+EcglRG1yhVuQT+NRrYgulKQaBQyweeZmRn/P0HPVqtVSQKqwK5GH2u7LU2Hgt8KWjPanIC4peDg/xrBzLYSJNWIcQWh1caM6NYofZZPsJt6EdhmVD6j7wF4B4DaO0TboxHa2q+sh7rbflJnhY4L9lue5z45qrZTwWxLdaN0NdofFE0mq7biWLD2shz9ISqdOiojgujUT/vAOkoIbOuYtnWHHEbaPzqOVCc7dnXc1ul/rcgURJ/KVS02AktDWGxUjY2u5WqLvxP4nJnBJppoEvyMY4d8knOC5WtUKs9X6fddGf1+ufIyixUuxi5cKHNGWf1dVF6EZuwSrOWzLTzyiFNnURYQkFNnZ92Hft9hj+73FrKshVvbq649vZ7jMXnySWBtDVnnRDVZF0p6ch+JlqboD5IK9prnrnlHjwLHj45LpwQj+YnIm77IU2kzqjmvAODs2bIYspwsL5dJTPt9YLENt3InDcxg4BObYTRy/ffII06PdhsJhkjTxA+FVjYG+n30ei3cf385hLrdcjt5mhaResxeyRB9KmvBF3oG1tZKeqHBAJgrh8namhs+s7NOTdKknDpV0NdkKOl6dAdDHJf77wtQaJgt+oSpcVxuquChaepAa6CJC2cFi+80iVkAAHr9BKMRcDzLqpy1o5HXg31x6pRzmGxsuM9ZBrTy3KEMR45M7AbRZHp6WSa8Lvt9R3bDLRmNRhlZz3bff7/zdiwvY7BclFPYJhpsot1ulraamcFjj8muiCefrIIAFmRgR6joIA9d81e5HMKlPbscKubuG264ofL9r/7qr+Jd73pX5bv19XXs7Ozg+PHjle+PHz+OtbW1YPlra2vB4/M8x/r6Oq6//vo9dbyceqdyMMTjfHa+ptj7bFzstAEQp0k1x4aea4E9BabtayIE1hwfAtUD+jldImxtVfOChJqkWCWxqDRNHGAYAB0rnw2IrrujbBNC4oFetZGhiPOebGYQR5G0PI4xzBa9aTqdcq7e2ioB7m9/uyy60SifL3RTkAfRB/DO6Kb0M40XxTmyrInBwNHJsOmM8GedajYF6d1zzKB06CsgbY3ECRQlQ5lOxUwGr0Mjjt335aNBlS7ITjmh/CL6ruU5aj+3YyvSaHOr8wQKHZA6oDukrB33u5UVAtFDx5vPjB4nWB66BAEgkUfjyndx7CmSxog8qDzhlDsgMp27r5xMQfQDKCGwHAhzO+8FUtVRYuj5GhHMSGfLD23LC4H6/F0j5/mdJg+1uijNR0jHUHstfYrqr5Qv/M5GA1t6Gm2bPcYCrdbuWq4CznXc4JbCpa6/9yu76RmyaV1kdKhfFbSv062ufAWpQ7zjoTbU/XYpNlGHQahPQ/a6VO7y0FgKiY6va1GmIPpUrnrRh/3QAmI/iwoeV9A6+HUpw4G0HrP4cIu/msgglqvbsCuUXG4xNTvrDqkEdOzy0C/5vHw51p/gzk8q5+S5HNxoOBR0F2BwZ8cE8BSLldFocr3l1bXKsFFa0D76hHyWc3OTmIHvH7HRGJFfjAEoQ/JDwIgRqstg5JmZgFlUiQrBd01h2ohd6g3mWNO62Oi5ubI84TK3mJBtD8Wuky0uRJoBXz7M/5js2krTQqCUtYc9x7aZ9ZEkVyPaxZYTl/pg4AGGiQtJed93E9tXdcDVQZE4Bi7lWfTiRSDP8fDDD6PVavmvbSSbSuh5f7dnvbr1waU+M19qvVO5OqUOYLykE0NiQb267+oUMWB9bfl7qKi30f3cPoLH1IGYRaFjE7OqzeOpnGOoTxWoLUDHOieBfl/TEDtX8nmmrrhAMyYP3EXqQNbdTp94PtmtwD1EaVhsdD9/t0PIYta75Vbdl9Q9a+42XvcCxOvq2G0A2waGbFs3fvZp90tV+8DLdO6+YnLAntqmolHbIdoQTVhpkzgCJRBKihYOLpZHQJsR44Aj9Ce1C/m1qQfBbxthbCPRNbqX9SmIy8jhNE09DYzqq8lMKRbstGA56TvyPMfW1ha2t7cxHA6xvb1diXAnXYcC7JZDnttJNCqbNqdsb2/735RbXEFiRrizvLm5OczOzmJ2dhZpmvq+ZdtDQHPIBrYum9CS5ekxIUBcdyGQ35tt1ESn1va6S0HLZES+0rkwUp2c7Ywwt7ZSnaywXTq+1NnC8TJjnr74mfbXcUldOB50J4YF0dnHbK/SAFmHAN+t48g6jC4HrD/oMgXRp3JVi406sw/9/Fy3omMZQLn/eG0N0ZeK7YVLS5Nl8fyCy+RFp9rAg6ddWFKjUe7dpiwtuYhahiC3256/8fDhMnqq3XbRvT5CiuFahYpx6pIpjs5K+4vfm/l5NAtKkTEiRLmLWs+yxHOT+uJ6qeOazjLHU3LTTY6jvF+C1rrIHgxcVPjRo4uVbemcWnVX9ONnIiwstNBcWirDtvg+GpXkp2mKGJUgOh9gzXK5CWBrq+QvXV52wd1HjhTbskty8UL/BE0mhtVxUPTD+UHiI9916llaAu68s8Srs8zVMT/v2nbhApAePVZGB3JHgo1UtmOEhWUZMHIfNbh6bs4FbzMHagXvj+Myq1uWufGpY7HgarlwtgwU7Hbd/4cPuyKOH3VRWNz+TL+Cgil8MX8nullJ8s7sqIVirCPLyki6SsJZ6syGpimGSHD2TAmsVMHv2B97Pm+itbTkuGLSFOOlGx1/e7cYKMvLPux9aanofw6g1VU0s577v+D/P8puYQZUXYnbe4HuKlAJXfsHRS5zId5qtSoL8ZB0Oh3MzMxMRJA98cQTE5FmlG63Gzw+jmNcd911+1LxcuqdysEQf5kp2Kk/kuPbnAPAzXechDhp6f95Xm554gTDrUChZwND5+GlLmxYfyfdVxxjse10Xlgop0DKzk6pgvU9TvA3G8oWoKQGqSQpRdlsqm99BjpVbW2x/oKXPWs6KhMVTXpi9KCFFhbK+ABL3211sNMmk3wCKPO2qLI8uNCLNB2zs27OYnu4eYnm4u1eI/PdPNlE0pYDaDAeEAiRz3tVlSx7hbbPsqDsJXb4sQ4+nzian5pxIZ2p48F9JYlAyR9uO0Q98IEyQ8849hpE2pyM9g5dFyzT0g/tk7ikFnsvPoS+O0g86BWZzt1XTA7gk9u1LQStkySpcFtr8kSCkwSfgWr0LSlDlM7DRmyT55nlkeubYDvrnp+fBxAGBQm2ElTk5yRJguCvcqJbcDfPc89nrck/9aU0KQR/NzY2MBqNcP78eQwGAw+ihyLeqZPScahDAEDF5qxTAVg6H+ioUOCUv2tfNJtN35fkQlf7KcUI6UJUV0utE3IkhCLBSbdC8NkmBGWS1cFg4MHkJEn8MQp+Kuc7AWg6Avi99qnSnszMzGA0GlWcEypqC9tPoUjxKIo8fc3MzIwHt9kPHPMXL16sJKrlNaN89eTRt5z8dHTouFP6HrUFz9PxpP+znJBdrwWZguhTueqFizyirrqC4gpFI8FD0WVx7MC3TscRcD/4oEvIeffdVaDerkZPn3avXs+t6AB4NDrPMUaEtX4L6+stHD9+AscLQI/ML8ePOjqR7u0tDAZAs/+EA9pZT7EqLFVIfDU7O3Bt7vedDkW7Ip6XpojyHCe6bb/N2zW7DdxySwWMfaqfeLpywDWFZiUGvrFRNSFVJId6HDuQoNEAOp1FZO1FtE7FVUoS8ou024j7VVCV2CvL9klUUXZZtPYocK4PnEN5ULuNcdpEb9Ud2+nc6Ls/z4FWp+PBaJq23S4WwkUjTp0C/tpfC4Mag4FjA9nYANJ00R1TYOitrMi7wQUsnRVLLSQFnz4BiNliWBw/XpqBQdbiC6jugiAAvLSER/tuccSpdqcP5L0SA8gyIFr5cyR5juM0atFnUbeLOG5W1rCa7E7Bhk00nRMEcKA1s89lGbrd6qWmaQg8gl903GZBH3PunCum3XZ2jvJh9Rpqt/HUoImVFaDbbeHE7bcDaUoae5w82UK700Lz9oEfO8nqn1ev6298o0RU7rgD6HaxVNgEp05VqZbsNaxGqfuN9jxAdC6XtRDfpyRJgjvuuAOf/vSn8RM/8RP++09/+tP4G3/jbwTPueuuu/Af/+N/rHz3qU99Cnfeeee+OFUvt96pXN2iYG/lHwPmDfNo4vL0/NZ6M+MNVScwgui6G0pBYb3hy41Y63SO7PIUD9yrZ5kv4ROJADTT1NGTzJq6QpKjfH7hOq3gOc9z9zv/1ySlIXzUF5lP1DJhYr6naVJRLcsSpO3WxHkRXLLGBHlBqQG0svJEgqN1fgaaQPXdzBPEaeIo7Awny1O9yM+5pAcj2Dw7W7LtcVogOM/uUR9+HEfIMpcQtEmvtu/k2Ac5EJSm88N0CwD32+xs2SZ5/PPPanXTB3VXu9rhUQG/B/kEoJ3nLmkrx4M6FrSONC3ykGRxdZyGEHx5cewN+u5n64tydbgEukBS2dDl9ZD2VKqrwe8r1ExAdYehUTP02XOos5zdrrerUaZz9xWTA9TrU2H0K4FsjUQH4IHR4XDoQUmCifxM0JBgMMtQbvThcAhGbyvVCQH8ubk5NJtNn1SAIKHyW2tUc4iqQ6k0+NvMzAwajYYHqRV83C5mC+XKtpzVCs6yDZubm9je3kav18PW1pZvn0bcawQ+nQw2EpqgMJOHav3kdec7I6A1KWtoJ0AURWg0Gj4anc4FC6Yq2K2f7XYXBXXVNhqJTocJxdKOKAc8I9F5vG7dsXYnaK/9qeUr2E1weTQaebtxTOoYBcrdFHq+jUC30fpxHGM0GlUogrQfaRN1Mih4TgcME7MyAl93W/DaUmog9jG/13HG31m3PR6oRsNfKzIF0adyVQuzMmqkVGgRzsgyGyWuoPrysvvtD//Qcy+fjxeRZkCy/mh5PMuMY0c4/uCD1Tq4iioOP33aFXfyJPCKVywCxUJnbg4OMF9fRxTHbpFNLm2jX56XEdR57hZFeQ6gnTmdV1ZKoDCOHaC5tOTbG8UxmmnqFugF8LyJJi5cALbXS5yBUwiLIi7B/xVv1Aj07e2SLjxNy/yhp06dQLMzLDOoFgDFOG1iZqtKn9LK3MI5KcCLZHAeOO1Q74T9trZWRvzPzQG33IJxt8rlTt1prxtuaOHEbbfhqV7kucIJArNhJ0+66G2uB1jVaFTFYXQIZBnw/OdHnk5bh6IrOykW9iWlThw77njShqdpyYmuQ6jSj90uHlqN8MUvuq8XFiYX24wAxFdWXMN7PdeYTqfkFs+aJTiRphgg8nTnBBl2dtypTQLiq6vOzoWyDDpS+2VZsVjtdAAA49Rx1pLenEN6aakAnuT6QJpinDaxftoN4X4fwMlFDHrusuLY63SAkydvdVGSvZ47WBX5/Oedrq9+NfDylwPtNjqFblheLvn560AD61kI/cbPB0WexoU4ANxzzz14/etfjzvvvBN33XUXPvCBD+Chhx7Cm9/8ZgDAL//yL+ORRx7BRz7yEQDAm9/8ZvzWb/0W7rnnHrzpTW/CF7/4RXzwgx/Ev/k3/8aXORwO8a1vfcv//8gjj+D+++9HlmU4efLkvuqdysEUArP2SwWQdepOUyBBXr1Bc54ZDErQnDk5Llyolk3UkYVxgotjfw9jcXpLKAOjXfR2k8CxgumWZ9s4BPZ8mXuOzj8ESq1fn5+VasTOWfb/UB+wagWn+T0ToDobRJM7AKQShqcx8SN/T9ptNNsZgBIEPnu2qvvRo1Vqts1B5PN0t1IOBDevcjMBfR8q9J0wJc3GRjllsLwsizAzk/iIfAXNd2OiI1Cu1Hq608CC7Wpbmf4mur50zuThzipOUv57dYRbGn2WOz9fgumec5+K2ufa4jXMIw/Q1w3tkFhHgMWwCYuFAPfgFGsqqyQNle+AckxVyuFD0kGR6dx9xeQAPbFNBZiMhtVIZ4KdSk2ilCo8XwFvjZbmMUyuqYkYx+NxJSKbdVuKEZbLuixIC1SpUqhfCEi1UeJ0ElixgKpGYhM0Hw6H2NraAmlELCiqkcrWXgR1lcJGgc8QdQqjr0P821oeaU4Y5U9bhOxmE1aqjW00+l7gpKUNUQCetqZTgn1vqVG0DALjIR1VJ+XV1zHF/7VN1pFiHQChNvEYdeAAqIxJjQTXHQQsg1QwlsrG8tmzDH3X9ipPPMe5in4fote5FmQKok/lqhY+zTPDkxV9MjcL5QlhRFKvh/F3voPowgUfQbvIFZPKzEwJ6NkwalllrK+7Q8hIUdkdyySVFCblpM7FisjzVcOowYKIVlK4Ldqu+AoEeYgE62vlKXbRaM2r0WOcZjTYT49hdLUHtNtJmWA1yzy1COvyCylBSRIWwqRmRDPOnHEIQrvtwN3BwAPAdhHJd7foj3wRoa6P+udxvCFfdDP0+5F3EFhcJs+dCgwUZ/s1USqPU2B8ZsYtZqkXt75r9LsCScPcLZQfe6zEjY8cKQEDAu/z88V5HD/r69VVu6Ip3ghJBfhXMGZzEDmny9ycoiZoNKo7BPyiv6iLC3viWnRu9HoFw44OqKLBtBsvAw4VzeFK/9IJhsLzS9Lq9Hrw2VULx0FW6OcVts4pNiBkJ4q9VxwkEH1mBoj2t1UeAHCJVHU//dM/jSeffBK/9mu/hsceewy33XYbPvGJT+AHfuAHAACPPfYYHnroIX/8TTfdhE984hP4+Z//efyf/+f/iRMnTuD/+D/+D/zUT/2UP+bRRx/Fy172Mv/5ve99L9773vfiR37kR3Dvvffuq96pHBwx+GrwRzOdVnDviflWUT7e3MhTxbBgCm+6gWte5xIClIw4toB62g4Akoo42jbZe47lIFEAHSUoqM8AoVgAC6pboFM/7waA2uB87i7jXLa1VfZbErK7rcx2stxTozgGaUc4X7JLEjk3H/jHB192XDUTZmYmb+OcrweD0oeiPlw+y2i3qG0UPLe+1Tob8hz6+lVsGVp3Eo+rUQuW1iZQsTU/n1GAST7+OHa68bEyTaPJCG85WHfXhfxDOh6t6I4+oLqZSx9FmTR1P8C5FZ7jEolKtH7hdDtIU/WETOfuKyYHeRhck2KBxZAQkLRg3n7L12hblgdUOdYpNjJcI3MtkKlgqKVTsTQndXzd+r2Csuo40EhsBa0JUjOq3CY41TJZp0ZCK+gZSkZaB7jzXUF0rVv7qQ5UVFDZ9pf2mf3fOiIsvYuNSt9NBwW6rQ7qXGFZ+k5wW3c9qCNCo/uVguVSQNa9rgnbbut0UScP3+varKJjxuqiY5XvIT2ts+BaAYunIPpUrmrhQ/9uWwrrwrHsioari04H0Q03AI2Gp4NGz4TgxHE1nFi5r+fmKtkwlaZjAjSwKzdNWCkrmIWFxIPFFVyPH5iIcTcQUBZIJW/m5KEFzu6jm4nHs9mMvK7DHZVS1P+exv5LXYRRRqNiK7gs4CrJzmgXrkqlz4m3E+CvwUUq2IU3c91iP8/B7eIhnJXt52s0KqP4Wb/119j/0xSIBptI0hQ2qgqoco/OzroIdLZX9aqcp0ZgdtTiANfe8lC1kQ3SiuPiz8JCpaF5r2y/r1sMHgGI46RySegmkTqhPRRHIvbNMVjRkWS+HEx6QnHNJJkxvBrK9vl+xN4zrnaJ46d1IQ4Ab3nLW/CWt7wl+NuHP/zhie9+5Ed+BF/96ldry1teXt7XM8Ru9U7l4EkQlCxuoPa34FSvyJwipLw5Ew2251h0z9wP9KfQO2Ci562y+jl00w4VKG1n+XFcXscEQnW64u1+L7GPFnUqh1Tiefqe6OSr3uS9xKCcIed9kob7QoXTGx8HNE4iz0vwX7/nPB3ioueUqXz1FE2mrg5na7+Ar2Di8242rozlkAHiKsDNAIL9Tk0TRU88lE7+pI+laq+6OkPD3LZZHRi7KmuVMXJgec93k+ncfcXkEp7wpvJsSwiABTAB8ik3tR10li7EgsAEmw8dOoT5+XlPb6Hc3Mp3Tf5pUr+Q/oLl8RxSmFjKFwUqlX4jNjc0/sao4YsXHfXMaDTyCUNZv7aNwHmz2UQURdje3vYc39puBcYZVR1FEQaDQQUMV050nqt2i6IIaZqCvPMaWa/9RqCbUd+0k+pkaVrUsaF2JTjN9ipvPW1Ee2m/2HGh/1tgWSP97XjTtoT63tpWeeOVEgaA35GgPON2t0CdhCLgdTxY5w8pXEhdoyB6kiSeGkaTxVoHlSajtdHzIbqWOvvbc0LX7lSmMpVnWAhWK8oZklBYFnksAHc+aS/uvttxr5w8ieOzTwG9IiKakdA7O8Dtt7sw5LNnfbJQnwCS74U+113naJkZNMvqBwOUCRypC/dPb22VROMrK2h2Omh2OhjHiU80NTfnuFKjLANuuMEBnhcuuIg7i9pL6PLmoORHZxAcTch8nAQ/Cyps9HouGpoLVUZQA6hEeHMhqkF+bpNABKCJQW9y4cVzzzcipKnjXs0HQJPoqa6Idb92UWE02ES73fS0KOQ916ZTZ+bmZHA11npliBsbXYyJdnsxuFhk3zESnUlcAQBHMwzzyNPA6/D00VL9PtAflHVlGVqFYuOCadauHTsdN4a0XXyfm3P2HiNCRCCZq3+Grec5osFmxW5cfFMUwI5jkI/H8ZvHLQx6VXoDDrN2u3B4FKHkCdwW/uXlRW/aPC/YXhT0lmfIdtvt0sgyeMoYAh9kpPGRp/0+8Pjj7nzuUT950nXG/HwZpV5U/lTeQhw30WL/MrydXPnasXZwsrMvBWyfylSmsi9RCga+V1YReY4ojpGmUeXy9KAcExPneXUy42d912sdKLlJOBGKd5L3wdGoLJZ18zFjIoJWn0M0RBiBY3Z7SdspWZZUiqR/QEHtEBZqq1eqFzWDUr9QTRVu9GM95fNDhDhuIus0y3lQw5fVBvouDwxROsnDDrjdUGoS74gtvmT709RFcbfb0YT5qEKWlVRz1tS6U8yCvpabmwco37vuANgtOpu2ruNAnzA+x7HZGjBGVKHS0yjxECc6i7HgeZ4bqh1VpLju4jjydqdDom4MqWgUvG1WnUw8J+nLPoTFcZAnfb91TeXakelQOCBiwVqlZLHRxgQOCURaqg99URSgJDc3ObAVhNVI852dHWxtbWE4HPoEkaQlsVHQpI4hLzWP0WhjBdKV050vJrZUPuo8z9Hv97GxsVGJPqfuqk+j0cBwOMRgMKhEsJMLm/YgDYwKAXJytrPcOI59QlB+Z5OF8nh1GihITQcAI7C1f9lvbJfaVkF09gu58mdnZz1fPdvLflSxzhTdHcCy9ZgQAGyj7PV/nk9qGHKVk3Nc9SJHuo4BgtZzc3OV5KGh3RV2t4KlNVJHkOozGo2804l9zbbuBJ5Y1CaWn5/6Ke2PRr1TT6WX4bvuWLjUCO2DKtNI9Klc1ULAMLRC0P9DD+RKo0KkMsuAn/kZx8m9/ihw//3lymFjo/x899347JcS/PAP/ZDLOkkQPU1Ljo80RQwHDiqPtS7usNwu9eI+Y67aH3/crYgefNCBgzfdhKjbRSvLkHYTf0qatpCcOlXyPpOQXFf7aYph3Kzg9Qze5bo/icfu/DzHzWkKZMVqeT1Ha2kJ5CAlUE78gbg9mT90MZnnzmykOeF3c3POJgTZ2U1kEJmdBdJ204HCuq2ZCtOIaeo45dMUx4+0MTyS4MyZskt92zDEYtuBMQDQTMflGOA4INhSrFDby4sVnlUuwFl1lhWJTsk9UiDrSZpisdNB1j1WXRTyGPYR+73gEkeaIm8fmwCLOKTINmQTgeo6OOl0SuUAVzbHgaAIwzzCxkZZj3LfkuN8M0+wPjiGvF+qSnphggEEKBKgykec54jabbTSFK2Ty8jzyIHjIU5ylM6aNAWasQNjjrdZMYAeygM4zmmc+Xnn1CIPDGlb4hhYWsL6wDlmWhxLq6uuL8ivBExyI+j/dQv5q13i+GmPZpvKVC5XKmBZ2qxO4QaxjeIYSQHqRSjyOvBS1DlTb4YWxLbXuEVMBUSMMPZzBQFBS3nmQfQQ+rwXPVSobhWDjkdxjiSOXQLPNEaWRRXw1s65Wt1+bllqqhAonOclLQrF+iDm5hIsLCRIM3Es84FDlbE0JXmOtL1Y0YfPC8Ikhvn5Kj1KhDGyrOBl7w8QAWhpg2MAnbSalFWEJk9iA5TnuXNyq66Gloe75Ogw9nNRo4r61yVXtTpUEmDqO6pJZfNByfHOZwGbXJZDW4egAukaUc8dDkFAOs+LpLg1MKTaq85zU2n3ZNvUMTDhsKg8KKN6jpx7SUj9QZHp3H3F5DkyIq4NIfhm6UFsRLcmtrQAHjAZjU6xlBwErTVZJ0FHlj0YDDxATmDSguiMLNdoagui2/ZZEH08HmN2dtZHXVMnJg89f/68bwcj3pk0FICPIt7e3va6sj3D4bACbDM6mQCvRpEPh0PMzs76hKC0EwHuJEnQaDSQJIlPGMqocG3vYDDwOmhiTk2oqRHeSidDO6lt2Gfb29vQSHQLHOcyYej5pDthv9BpoGMkFA2vgLF1lAAlZzzrZrsIXLPt1IvjRh0PdFZYh5G2g+fa7zVPAOuxIDp14LGzs7OV3RMKkquDSqPRQyC6OhFUF00wqm3Wa3A3aprnkkxB9Klc1eIzKmLyYV4l9KCvUehx7JJt5gnu/aTDrd/85hNoPvDvXB0LCw4kLED0TTTxxS8CS0st3HzyZAVEf6qfYHQBOFIUe/SoeynFNxfk5/sRWp1ONSkot0hvbZV8rnFchncPBkjabcRpE2trbiHVbh9DkwAqUCKi8iJ4ThB2fr4AQJlscr3nsqCqncRZ3e7c7P+PY6CJTSDP0SyirxNI0si2+25tzRVNPVncwkJJC3PunPuO/JgaFN7USPQ4BpaXXV35ZukN6PV8WF6SZWg0Wj5RZxwX27eLhXyTi6w+ylXo2bMlgC4oddQ/j2aaotnmOX2g10cSF0la1/tlw06fZke4spaXq4tr6kkOfQK9zOzpMpAC7WO+C0cjZ6cIYySDPpKine2lY7446x9KiLTrVoNizOi2+9D6lA6MNAUwGODChSYeeKD8jcVY/GkwAJIUJR87FSNQDWBp6WZ3ma4YAKWQVjYu+jMvbfXAA9XtEEtL7qUgOrcXnDyJx0eLOB7/eUkeX1wHZ7duxPY2cOudbVfuhQtuWwV3jFhgR0VtyUF6UGS6EJ/K1Sxy43IAWmCs0lHqweS4nMSKORtAGLzmjcoC6/zf6sJyivmGEfB6uorf+KZgfggQVwmBfjUA5oTOctP1icgLAJKm2q3pu6mlACvNTRNbp62qZwFuOtMXFoqEqzppaGUsnE6LfFgpOM+b2NioNpuBCP7LAuStAPUW9M4yRGnqwG5VGEAwcScbagnAa5y/XhfO84peF3UDArTvNT7gQHMV5eTn46BGotdh2Nrn9jvbjzb5a+X/OrJ9C3bbY2gbIEybxM9ZNgnia937AdHNdeudbVredO6+JmUKoh8QCfGD7yaW1gTAxP+h8vmuQHcoglrLZ31K06EvRpmz/BD/udXBJhZVSg8tVxNB6rkKrCoYrYAtwU+KbR+TSwLwtiAoz6hzG82v/Ov2pXpbENz2Qx1YqPZTahFrc8s5r2A5RSOptYxQEkzap44T3UZSa6S10p9YahtGjKtedHholLetb78SoqPRdu9WBx0//Gyj0m3fhewS0mG3Y+01cS3IFBifylUrdqVopW7hw9/MgnZQ4KLf+Y7D25rMRkX+5bNngZ0dDAYuAN0lzmx75JeJIHUhy6isNGtNYHF5XmxhDy2sGF7EBacCoYMBojSFo0nhGqZIBlkToTPamlz/xDGqC0eluFHp95EuVRfP6A38sQkjhCXaOS6iC1k817mAW3DbCCpdb3kdzSKJVCmL7XRykasL2OLUyvbpECAxGFQTzllUWnlq2D6uQmkzNu7ChXJfs4b7U3jshQvl6hco98qL3S19bwXcb7eRponH5ivrVrto1fql7bnpYjaxTPLq+kaTfbLZoXW2b9/WVun8GY28bbJlAUrU/nquIgLqdNBjiCbIdUCg/ckHgeNZPJHZlcCDb+TOTqnfHoBGpaH7PfZqERL87lcOEsgwleeG2BuK/Y3vFgi3njwr4nglyFxJ/hl6t2UJIFcnlYSGpt5KOyzwV6evSu2N1urgziflDUV3Tu1mKhbP6Ye3AXu/1/81JYWWq85Wn7gy1LaQAqadFvf0pstrzhEqtgnFtFB1mNv61Rj2vY6rRR8y1OB7GX2P31QlBdDV4WGxa/6vQ50JcevqiGNUI8G1QDsAgKqN7fNSHeDN/1UxGyofUq7umd56hw7i/LybTOfuKybPoVFxbUkIBLdAswLOep5SUdjzgSoYrLQhNlmm0rCwbKUW4TmabJK/W4CX5fAYpcMg1QejhzXil9zjaZr68ghaW05qbatG/lqglxHSBNIJWCuPewgoV3vZdipwrGAqo7ctAK8gs+pv6VVsIlUFi9mGOrBY+1VtXjd2aDu1Wwiw1zGjba2LvFbKIf2dx2gEuaX40Xq0XRqBb9tcB3zrOTourVhnT4jyxbbV6rebXGsA+jQSfSoHWkIP9naRUMyBo5GLqjp6tHjGX1hwD7NEEW+4wZ9b4WkOCNcYaZogyWK/9kjTkh89TQtwURdeksgx2JbimHEock8josxCfna2TEoWLFdXZ5bUMo4n8OdKnRa4zx0Hd6fTRJq6RbeumdK0BNBpF+LOee5w5sEAyDtNpGkTzW7qbToaiePBZqMsoqwpHhQe5JP6sjMYfl33spFp2i/8/7rrSmoRJuMM9UuWud+5Ih6NKslSA76PSeBfwB3lxnUR5KZPdcErrzRtVjh1VT0CQ0r3c/hwNY+nNt9fAyxwZ6fc51+Ip2DQVb8AXF4BrUA9CbZTqRh5zQcDtNtN18+su9ilMkesnPWzoeQeCqFMIVRiLyDoahPaYL9yDT3XTOUqEDORVD7WgZB2rlGQHOPK/5SJxIN2AtNrW+oNUXHoqZXoaXX+2ZNCEgLVQ8B77aRrjqspfi+hbch9rbdeNom7wlhdnpePB6xe53CeA7hnH79DTkFXVTTkWMh3uX1Zh4p+L7sWgoaou8/v9qylx9Sh1YFxWTmuTgL9R2cPnTdxXH0c4xTG03R5a+dzVUcfk6yaFTqZ3YDput9Cn3eT0Pi39Wt/1pUXOJ85ZSai0Q+STOfuKyZTEP2AyH7AI6XCUECWIB4B10OHDoH82wAmjiU4GOLxZj0WKOZngt0ExwgQa3Q3qVLYLv7GSO+ZmRmQ+oPtHo1GGAwGFRCdCTx3dnY89QzBeALo5NtWug2Wy6hyRhyTsmZ7exubm5ue7uPQoUNoNpveVgTum80m0jTF/Py85yBn2TxPAWyltWH7lTdd+03trOUojYhGVCvozwh6vjOZKrnXdbwofQ4TaioorlzhCrDbqHONMNd+tWC3jjmlDWL0OX9Tx8bFi4433jqIlOdcgWd1fGh5FlxXOwClg0LP03eKUgppm+yOAPajRvbb9zonRQh8fy7KFESfylUvddEqoUWWvvO3InJ1jAjb28D115cc0VhaciDrTTdVI2EBvOAFjkkCK9V60tQdQlYL95wfVRYwrXRYjXTiiYpmdrsOTe71SkLSYrU6TpuVoHGfb5NRVkQ3pQ6lFx8MSk7rhLZQChlmEKVOWVZZ21Qi8GIXIZ7ogqeIIG7FMVpxjBMv7/iEpgw6JtWLgueK4x854kzgmDyalfWSY0Bplm6EOHZ0PIPIY68VX0Rv4NtRsRPB2HYbm2ii2Yk9CnA+byLvAYspqpHRmoSOfOYsL+BM8PqRc58UJRJFzUV8lA+RZUk1kC5HdfVblJtlSWWNmUASnFI4CNXIAJIsx9JSq7L2TfLNkk4lz7HYSXHqVOJVj2O/EcMX1enwPGlHp1PtzDguo/iZ9LPQg8MuzhK/9X3ieh4M3IKy9Eo5LvO5OeC221y9vR5OZLnrZ9q5yPx6hHZcXy+pf5aXS5J5b2h5r5ODFPE1XYhP5SqWMaLK/SyOEzev8P6gwKbeU/l97JJk82elg3EAGoLzvwfHkcjPbn0a2qGj1Vd43HXSsgBxAJSfAFmL+7kmkvb3Y62PP9QBicX3dKxajNN+DpUZoUwI3Ww7nZizRGng+HjA07e23FzNzT98bGDz5uaAI0cSxHGC+fkWGjJ1elvGk7sFUpQ5o3VaBYBhHrmxojvzNNN5yJERArftdzq+bER7yAFg+8OWb48JgdKhz8V3tEUzLXdDUAWz2So4bcllUvk95C8I6iTzdOU3ndtDY36/Yq+HQkhnE2XxZJ/WnVu8eC2Vl98unO9Xs0zn7ismB6znp2KjcC0YyMjmmZkZD84qiE4qE4LoBP7IQa2R0AQ5FeDUiHWlRtHPCvCmaYo0TXHx4sXKsfY4gqQEVJmsVI/f3t72bZ6ZmfHlzs7OotVqTURn7+zs4MKFCxiNRhXKEpZLQJm0LOPxGMPhEFtbW9jY2PAgtE2ESk70+fl5NBoNZFmGOI59NDx12N7e9o4BArsE7gnszs3NgVzq5E2nEDAnlQj7j/ayEel0YqizYnt7uwKiax/Q1kzcqe+qg44vjahn9L2NYFcA3VLV0P7sQ0vnYscz20ZaHXUU0XESArl1DFh6GT1HgXobra/j34LdBNLp7FFhmazDcqbvRukSitZ/LgPHUxB9Kle1WLCS/+uD/W5AGbM0oVwLLy05jC3B0INtwyXHB550u/6B/oUvBBYzSaBVAAFcpJw54/i++Yzf6biyW+nQcasPBtUEoOSv5mciyNvbJaF4QSdy5oz7mmtIL5oFk4vBYhVMju88SzAzU66HkhhlGwjYExClfgWIDkjkvPTBoC9AKMFSlleQezfjGM2i3OHyzVhZKQ/d2nJ5VJmjs9dzKhw/7gK3T51yi+pbbnEqlbnKmkg7LmFqb61kZ+HCssL5LiFYDnwpouTSFOPOMayvAlmWYLHbxRAJVh50dSyeRKno+npp54LHfBwnGHRu9GaPMK4CxgRvOsfcb2la8oeT/kb2wUdxjmax06ASSaX78wFEeV4mNktjoNev0s8AZT8w+yv7rd9H0h446MgCBZQ8x83dAhBfWQXyHMcJUHeFB39t3TsiKmD/+nrZUXRAnD1bIiOoAgJp2kTCZLbUfzSa3MueZQDzELzyla6+Bx90vPTklu928RQWgYGLol9YAHD/mvPc8EIkiE6dFRSwtjiIMl2IT+UqljwH0CxBucgivwb4KiNME0SpA9suFEmk/Y6jQrxTWR15Rbl6iZOhow6jI1OH918SVdZ36wG2hXAi0u/TFJt54hNFApOAqEvc6dYoaZoAceLmMgtaSp0EC5O4tBmrBARAtyCx0T2KY08Nl3Wbfgo5d65KP06Afbcg83bbteXIkfKze6yIkGVJ5Xgmc41jd7yWGeVD96xROE5a7ANxQnMOrcyDoZcYhaBtOVQSIHNBeXHbBAyEwGPTB0Fgebe5xD6XBnRM9D0GWhYIDzlZfN0oHfGAywdTUw+l3CGWTNIg0bYhED30f6i95uUcI9VT4zgqc7xofSxDnufUEcWxqIccOJnO3VdMDmL3X7OiIFyIkkKTTtrIYCaOVIDRUrUo+E6gEkAFxLWJGq1emiySiTIVpCQwqIAlo6VZDykyCFwDJYhOUJqAOwCf4FOTRw4GAwyHQ6+XgnUEOdk+jWom+MzEn6PRyIPm6lxg5DmdBHReUFdrH+UT5zvPmZub82XxeALhSo/DNtJeIZoXG/HPNigdDmVnZ8fbks4TRqKzvJmZGR9Vry+l9aFdrANAbc9xpDQwBKdnZ2dr+capx7bwyqr9Qhzt6nCg88LusrDtUYcQ3+uS99KppBHpeh1oW/m/UttoZL4eH6Kmea7LFESfylUvdQ/sdQtbvue5W7kWwDQPXVoqkmb2Bw7I7nR8vs1O5wRm5oFZOHwd6+uVFXiEsV8IbmyUmCtB6+VluPq+8x0HDDK5IV8KohOUpM6y2t/YCON84zhBlGUYx0kBkMvKpFjxZp1jFROB4ECeV4FW6lIA6aMLAiSIXbmAGQw0cWcBEBMpn5sraTjm55G028jzRb8QZ65IzbtZBBJ7rLPTKWl2aHIGKA8GZdJWqs9IuIqh4tjZpviKoD4xbXdI4sF8vwAj5zmTgBblnR8k3gGwteUW/lkWodttIRI+b3K5j0YRjh49hoieAE0oSwCAzz0K7Oq4YMdpuDrP1cbTuPRScI5W0JhtC6EfMmawulp+LqK8k3a76gjodoE0xfl+hK0LcIA7gEonb2xUAOscVUr6JAsseXZ2nP4aib60BHQ6eHyrhZ0+cIJj7eRJ59hoL2Ll/jLovIlNB+D3++7cbrfqgKNt99o+fpBkuhCfylUslUsscP8Z5lUAuIqhJd5hWld2YgHMoiDeOhX81XwdwGRKiTgWMFWpvfT/UDt0d40BSQd9B+IzgaaCfzxse7u8FccxgLSIwi4A5b3ASqW4qRgnBArbNhRGiLLMAeoLTb8RRw+lr1xtqiagH143KnGjENultyqazbUxKafE4uA8d3ZrzZa68nknHzhnrJ87LTBtgGONXKbeyiLmIAPncHenJohTGQ9qQ+uYsPatkzoQnf1rjw0B1fuhEVIeF362uz2ACiDtvjbR3JYHTh0W1hu1V7sNAM5TfJ9zvMdSnw4auU/YoaxqJjEubS58tmU6d18xmYLoB0z2S+tiear3KqcOYNTf6r7XMpWTW6Nv637XRJ4E0QFMJCjlcXWJNG1E827tt+3gd6pbHS+22slSi9h2UrR9ofKUHoX119GFqI62P0ORznW7Fqw9QsBy3RjR/q/jTr8UUcCZ9uL3+pn9b2li6iRE38JxUjc2rO3sMXTw7NbekD2uNXB8vzIF0adyIGW3CCAbmmIWOXEMB6AXH8bFQk4CwT2+HSyvRg1fBcFq7oUOgfz8n58v5YF6H6E3ofXZxAFm0bkbk8VoVKzHtG5mvmLiTC1vMKjtBmKucexw6zStYqiqtyZo5eJ6P/gnjyG3unLA7rruZSUixHiDOwOMjVhEQpA7dPBuY7fuWP5vz+U7wxz1uJooRADVAUJkpM5I6qBAVOL1dWCALsBNtS76PtBGHf9SrgfRjB42GBGDvBrqqXpZEOO5IvTcTWUqB1j0EuVn+87fKwkULeBYU7a9pdclYQyeaBWp3Nvq799jRJU8lXW3x1rZcwLfh4SAdPu9/B6n5S3F3lq0CNpyZ6d6HKcb5kcxvu1KlbvpPBollc/7lhqDahGq+97FGXDZzich3erm2f2ca50m+3pgMXXb+i5BynwDqOpp59J9XgO7iT4rASYYROtH9Ws990DLdO6+YjK14gETBQYZ6U2x4K5SXzB6mhQes7Oznq/cRslSFOBWKhEFOW2iR42GV3CU0dXUW9vCiGGl2tDIdQKXpDdhJLBtp0Z6M7J6dnbWR8hr9LvSvvB/npOmqW836VYYca50JWoXbbsF11ku7c4+4XEsi9Hl1HN7e9tTmSjPuaVIsWKdA0q/osDw7Oys14N9MhqNKnziAMAobu5w4I4FvqwOFojWd3VQKBge2lkRAvGV3mY0GlVoWkKAtyZpVdGxHXKasF8UdFcg3opSHmn9Gl2u7/t1bj3XZQqiT+WqljwHkqT+N5W6B/GCMoJrgSQe+23C4/aiZyQByqgqskw0u133g+wbjQabaMYxOp3EY5V57qKi8xxlWFYcl0kQFTC3Yila4KhNNNiKP0W5o5eJ0tQtPGoSauqaaxwniNpt92F52b0zBLxCLE79q3pGGGNhIUIzHZeRy0C5Dz602Ov1cPLkCRw+7A7laYzcZwB8lpXcqKyf2981EJvAu7aL0Ug4Iv3e7yNKU6RpE3leRpM1Gq65zDE6GlV34COOnRJzc26FXXCNt9IUrU4GIPEB2cXGu8p4S+IYR464OS7Ji10OIRTBym7gRui4EKADwGcg5W8aMcb/LSe/tp10QuwUe2yaemcTAQjPkw+U5VmaoH5pWh/dR70bDYfGC6WQH0/Frg1efliTEMfYJWilqgkK+qFOp1ycsrMswqNttujdpUaHPduy2z1lKlO5GmSXMcqvS0qryV2toVuiz/WRVstmtLL6A/WcOh08VUwdIGijY23bAg7pCGM0GmUUNOewOgzVgopxXCTWVgA3YEeNQg8mWbQVqUI6kcI5fhcWEv/V7Gzp7Oa8rHiqPpuoavRl1k1XOveyPJ/XJE0xO5uUQKnYM4mFmo4dHfK+iM0iwEXZA54L3vpCrPh5SsvcDSy2gHPo+N2A7bpzLHhfdx7FdoR1Hhc6RBgjjqOKuqri2OYuqItE1wJ0/BtSdjWhbYqvv6Yj7C4LXq+2SgAHL5/JdO6+IjK14gERBW4JIhK4U2BOOaoVwG02mx4MjqLIf1bKFI0QJ3h48eJFz7NNXm3le1a6GI0EpxDEJg2J8pNTSD1CHQ4dOgQmDSVQa0F6Ar1KLaKUKQSr2U7ygiutCT+zfbTPzMwMGo2Gbxe5wqkPgAqIOxgMKkkklW5GwXylFbGR18PhsGLnnZ0dbG1tVZwXSqmj0eB1EeMU6qYc3wrwsw/UQaJUJATbSfWizhdtg4oC8Rrdb4FrpauxQHYoal+PZ99Y+iEdn9ov7Dfr0FEufwvsK+CvfabOEtaj16G2cbfdG5QQOHwtAMZTEH0qV7WEQPRQVM5ui6ntbWB7GwlcUkesr7tXp4MHH3Trgo0NB+Z2uw4kf3TQxPo68PjjEYATjnqEgF2BBJ86dSM6nZLju9Eo2DC6LUTdbkmNoYhxaFXZ6TjwkWSieY7j3cAirp+XSLIumHTxEsc+OBwo8eA0O4a0c6wE09vtcrEEYHMQefxwTJ5Mrnj6fbcQ7fVL2g7WOz/v3kl7w1X3976Hm1+S4eaXdHD0aBO9nuOufvJJR+Vy5kzJXqOU4RcKShlugdfFONsElAt8AOh2k8KhMHCFpymaS0sYp+W4aWVjZFnkQdz4SAKaooIGNBru8+qq+25lBUhTnLj9duBkxy/Co3xYLhYLRRIuMNXRoP2jEoo2qyPu1XNCSEwcF6TghdADRAMplQn322tyUI1YLyhbfB0CknNLfwV7SVtILAgexxhmi+j3yiJ8U7S9pDci8M7xL2MvWfmz8ljypBcIDDF1z/Nz8qSrkNc3rznayEoIEJuC6FOZyhWTMSIgbQYBXkuf4ijAqtQNFH6uMl4xmbfjt1YKc8vCQrFTJn1uE/cmPRgI0mJUEOTA/T1Nk0ob6J9VAFpP42275H+PimOaRZnhtnhbqqHqXnqMziOFwZpZhubRDI1G5KcP+iNDtC6hKUqnKYtz8/ORI46OJ4pjNy8PNn1S6LTdrNqXJ1p+Hm1TiBxb+7F4RXBRz4keu9/7aBD9RfU7a9u68/lZUWU7OEP/m/ao1DpUtN7igojEBupYKA8lh3niKNiUYy80pqiXXg+i424+BK97nJQtSNMKjYs1BatJYvOcfFBkOndfMZl0vT6D8p73vAcvf/nLsbCwgGPHjuFv/s2/iW9/+9u1x//9v//3cejQIbzvfe+rfL+9vY23v/3t6HQ6mJ+fx1//638dq6ure9b/27/927jpppuQpinuuOMOfO5zn6v8fvHiRbzrXe/CiRMn0Gg08OpXvxrf/OY3L6ut368QbGLUtkZRE1gKAegEgBuNBprNJrIsw8LCAprNJhqNRiWSPcSrTf5tTVKpyRqZEJNAsUZcp2nqecIJFG9vb2NjYwMXLlzAhQsXsLGxga2tLQwGA2xubuL8+fM4d+6cf+kxBJr5roCvAu7KM842z83NefAXQAVE1wSijUYDhw8fxrFjx3D8+HGcOHEC3W4XR44cwcLCgm8Pk3tub29jMBh4GzGiPkkSpGmKZrOJZrOJ+fl5/3+j0fA7AVjW9vY2Njc30e/30ev1cPbsWTz55JN48skncfbsWfR6PWxsbGB7exuaHNTSvdjIfIL4dBBwHHAMzM3N+QSZTKrK/qBjQ9vCdjQaDczNzXlgXcco9dLv1AFQB6bry0aoq7OGjp2trS0/JmkXguME/zkO2Pcsl+NI+83qorzq/N1G+VM3jkF92V0hoV0iddd5iJrnmZD3vOc9OHToEN7xjndUdHq67oPWYbKf11Sefblm5m6lqQB2XxzqIkYXMltbJV94BrdYe+wxIE3x4IMOJ+X6IFp/AlhdRZ47PPb++4H//J+Br3xFFuTr68DKCpLVP8eJ+AmcPFlij/2+A4ixtFQmN9Rob11JsEAmQiSYzspXV10yxdOnS6SeyS/5maA1FzBp6qOFST9SqIsHHwS+tX7MvU4nePBB4KHVCI+fiXDhQmniwcBFGXv7kUCcLwXRCf7zRNp6ZQX44z8G/viPcXP8EP7yqU380A8Br3oV8OpXAz/6oy5n5MmTJW18HDudz551zacJVlbc6/HH3YvtojkGA+l7ntjruYU5x0Gvh2j1If9bkm/iuuuA665D2ZY4dkjH1par+E//FLj3Xve6/37ggQcQrT2KZHBewuBRKsP+WlkBHn64tJVFclQU9VFucbWxHqu/85iZmZKENk2dgTY2XFu1vwqgetw5hodWIzy6nuCpQROb6aLPtjtsH8N5tDCMmyUw3m5jGDe9vTl8+33XV0/1ImzGLQzbx9xOh+VlXzVpgNJUEsDSHvPzTmcZ/0MkGGet8oK6/37ga19zepw6BXS7GKdNxLE7pBVv+jaeT4/hocExd+yZM1XEh/bSe0Uocs9E0V2KPNNz9wRYsZ/XVJ51uWbmbpSX3zCPMMyjEujT+x6vTaEAYzS5PZRzG09jGgvN42yTYQLhSyFNy81UPqFnyDmvJ2h+EzoNLZBelJNgiGY69ocuLJQ+wPl5N9XMzLjXzk45Z2t7mKv57NlJHHlC3f0C6GpUGkyfK9bW0MJ5nOiO8bznuVv6855XPtIUeZ3944qaINRfpGobDFw7/JxddHSUD0sder3SlLS56sr5nZ1sdS/09w8PfK2slHOzvvQYTlo6b9cRwV/qfdY6y+0r1Edx7MfZuL2IYdrCZtzCU4MmHj+b4NG1CA+tli82+8yZYk4eFNebfS62niZRcTBw1xSvq34fON+PMEQyOd71uVZf5rgQvm13hrArx3ECxC4CPXQNu5xExTWL4d7PV/uU6dx9cOVZBdE/85nP4K1vfSu+9KUv4dOf/jTyPMdrX/tabDAMSOQP//AP8d/+23/DiRMnJn57xzvegY9//OP4/d//fXz+859Hv9/HX/trf20iWaHKv/23/xbveMc78Cu/8iv42te+hh/6oR/Cj/3Yj+Ghhx7yx/yLf/Ev8Bu/8Rv4rd/6LXz5y19Gt9vFj/7oj+KCrvieQbFgI7+jWAoRAnZK5ZKmqQc/bUSx5ZpW4DMEJirFhwXuFVSnngrKE6Tli6Dm1tYWNjc3K2Auo8htVLbSyrBufRFEJchvqWs0upvRywTSFxYW0Gq1cPjwYbRaLTSbTR/FT1tp0k6NcKcuCuLyRacD7a9l0S60wcbGBvr9vnciECi2nOF11CAWTNcxwHFAu6gOtDfBeoLCth3WpjpOAVTA9dD4ZR/sBZha4JRjSceO9iOFbbbR/xYg17Gk59vxob9Zehgd/xrpH+JGr5NnGyD+8pe/jA984AN46UtfWvn+arsP7lcudbFI+cxnPoM77rgDaZri5ptvxvvf//5nQNuDJdfM3G0B9LrfdhMC8VykFwkQx4iwumpyh3JBhjIZ5je/6dZbvjqiicXiq4lNDwIPBgVfNMFlu8DQlSaFACgXH6qHAqEWZOUKQx60ycWq6yWuPRWT5/pxfd3hrZrAbWJxbuvVVU2jUfKbKMLx5JMOtf/GN3ylJ9qbOHnSYaG33eYW52SVUfBEgYQzZ6r+A2021fIR6nleog524UtvAlfw/T4WFpz63oZEVRi+//jjrg30tCgwrnZnXVSWAK4qG5LQwlbfQ8eORlUCeaAMXbR0Ltpfo5E/xvphej1gmLYwzloVkGMMtwgfIvHF6XqfOzgIZp096xKxPn4mMolcZQ2o7W00yrFfjH+qvDkoFv8EQNIUj5+JcL4flQ4vRi8Wiq2tuaGGOC4VC4EVdfJ9LFafa3P3VJ4+uWbmbkzidUHQlzcXc21qMyzGyHmt7jLXS12nK75P/G8PDp2gjre6eT3QtiQeI4nHlcNnZyeBRGsr/s/5eWurTD8StOVunRB6aVS3AtLirD96tEz4zdt0kTt8AlMNVcdHAn7m3F5nK3WkVDpK9bMdvrVVRX0vXJhoRwVg1zlaQXN9rrKemJCDRSU4qPbRD7Z8W0dRlu2ec+fK+dvO5UxKz6IrjquauvTxQgMw1AniAysuAfTV6PhQ0yiV+0MBvFtzcOdKlA/LXSx79cs+ZDp3H2x5VkH0T37yk3jDG96AF7/4xfhLf+kv4UMf+hAeeugh3HfffZXjHnnkEbztbW/D//P//D8eeKScO3cOH/zgB/Ev/+W/xN13342Xvexl+OhHP4pvfOMb+OM//uPaun/jN34Db3zjG/FzP/dzeOELX4j3ve99uOGGG/A7v/M7AByg9b73vQ+/8iu/gp/8yZ/Ebbfdht/93d/F5uYmfu/3fu/KG+MSZK+oTBuNHEqEqcftVYeta6+I0L3A3bryLYBvQf06mg0FZW0ks7WHfenvoSh+BURDyTdtm/fb7lDEdai9e9mLv+m7bbPqb9tR11+7gb97gb2Xw/cdaoul/aGE+NP3q1tdnXV11ZW9Gy+9tWNovO1VzzMddd3v9/H3/t7fw7/+1/8aR44cqejydN4Hn85I9EtZLFK+973v4cd//MfxQz/0Q/ja176Gf/JP/gn+wT/4B/jYxz72fbf1uSTX/Nx9OQ/OeV6JcAqtl/kbI+H0Gb3C08kdZMUBEwFp+9XvUqOYLlPsIveKFEQhiKuLSK1I9K8LXiKltzWD5b+sbL/fS69QY0UXxZ33tC9Xl6GFr21rSK+Qfer0tYtsrY8hi7vpu1v5xW8a5akvy2drrxGLJdm+2Noqy9DEehO2sScWv00A7jVNiDCe6IddMXAt6/tcdFt5tubuysWwn9dBoqp5Dss1P3eHJHDh2lzDKoGppXKrJDWKTUKot2BePvvmv97tXr7bzacoLyrcknpJKph+KT68S7qF1emrv9eI1Vf13ms+qJvyKtUFJhrWGWyknZR0krEdroNAv594oAuUezkSmmNC5e73u5qi96pSxY/vOt1ELC//ZZkj0JeqS0i/Sxn3V1qmc/fBl+9r6AwGA6Tfx/ZDK+fOnQMALC4u+u/G4zFe//rX4xd/8Rfx4he/eOKc++67D6PRCK997Wv9dydOnMBtt92GL3zhC3jd6143cc5wOMR9992Hf/yP/3Hl+9e+9rX4whe+AMCBKWtra5Vy5+bm8CM/8iP4whe+gL//9//+RLmM4qWcP39+v03ftzAKnJG0cRxXAEBGnjNymO+kD2H0Od8toMqI5IsXL3rKFEb6arSuRv3uxQGtQlCXSS7rgDHWSxAbKBM18vetrS0wkphc7RrhTAoZ0s+Q1/vixYuew3pU3LlJw6L242c9njbQiH5LZ6PR2axbOd9Zn4LnjDC3dCSMEqddGYWtfPDaXuVMTwou30aj4Tm92fczMzMVbnFGUFvedG2H8obbPiUtUAjQ5/kcT6FknrQJP6tzgTrqscr/HwKl6zjjQ6C25UnXOix3vY57O25t8lH2nU2+aq85297dnASXIhcuXKjcg7iLICRvfetb8T/8D/8D7r77bvz6r/+6//5y7oOXIpcKjF/KsZ/85Ccrnz/0oQ/h2LFjuO+++/DDP/zDwXPe//7348Ybb/Rbl1/4whfiK1/5Ct773vfip37qp/Zd99Uu07m7Kvuau0MraT6w05Z86NTf2m33ENrvI8nO+73I6+vA8ePllm4A2OzejHQZWL3XRfvEsePyJkvGZp6gubRU0qgAwGCAxXYKtGN0uwW/58qaq1+jy3W1D8BnTWQoNleiNhqqTtg+0SOKYxw5kvgIop2dsug8n+QVZ1QZP6sa6Bf6qm2pD9/ZJp6Ypi4ynZ8ZjV/0SZQ7Xnp2EYEPJiBdWKhi8CEg10a+7ezA2XkwKHlhLLqiK8OCLzshX7aNLic9Sp67MDyxr+czUQBXoyhpA/arDderZDNFWafqx/J6vfK70ciFmPV6ZTlqBM2kxwy3PIZc73J8KNGemowLai7C4yxCt+s+t9KhHxfKp0rzqJmOHnWfHWVCXB2Mtn9yl8jV22swKLnb+30cX2qXbWAInuw4WF4uxvK5fpg+yQIJIbSnOOYgzN2XjD48yzvtnisynburUjd3h4anu7wixHGCKDPXptiUly7xUTvdcHrUcu1UyVuNlqe34ZISwoSva2XaEC1AC95LdBLLXRLqJJV7D0peartZhhjxzEx9clQtx9/XQui1zuN6/9My9LtCoSRNkWVRxQRqf3t71eP00caqELwvF7qlaeISjercysgG22agNBIHjEWAQ54K6wmwL63D2pf/2zml7vmozlCqE/tFjVUcG8cOSyD2aovg4foYlmUoKe3UjqyjeJ6LshhxTJrdSdNV8Fv7DFtHcyPKNYvnBAXU/eeisixz7XPUPgMkWYYsi4LtrJVC0encfW3JJYPo4/EY/9v/9r/h/e9/Px5//HH82Z/9GW6++Wb8s3/2z7C8vIw3vvGNl6XIxYsXcc899+BVr3oVbrvtNv/9P//n/xxxHOMf/IN/EDxvbW0NSZJUvDgAcPz4caytrQXPWV9fx87ODo4fP157Dt9Dx/zFX/xFsNz3vOc9+F//1/91l1Z+f6Lg9Wg08oCvjbomsEvOa3Jxk86DYKlGqiugqAA034ESwB+Px9je3kYURR4kDkV+a9kEgQF4oJkAuD334sWLvk4Cv3o+257neYW+RUFXtRmFYHeapsjzHGmaYjQaYXZ2trIF0YLCGiXO9hBEV5taihMFx/lZHRMhQJB1zMzMYG5ubgJgV7DcUpSo42NmZsb3uSb5VJsqXUuaphUb63EK7KvjRdvKccg2qEOD7VU6oLpIfB0PFmgPgffqYLEAue64sAC32ludEMrBHtqpQZvlZqa3Y1xplejoshHqoZ0G6mT4fuVFL3pR5fOv/uqv4l3vetfEcb//+7+Pr371q/jyl7888dvl3AcvRS4XRLcg524PKpTQYtHKF7/4xcqDCwC87nWvwwc/+EF/rzioMp27v8+5mysETSrFlZwChkC5Gp2ZccTXs7MelHyqcysePO0OW15277Ozbn32x3/sgOYnnywxzBtucMUTGOx2b0aaAlH/fLmaLAC9iItxnnzbbZOLLqKKS0sYx0nl68EAaLcX0cyG5WrVosmhxS/bDCBJUyRpjCxLJoAFLWY0ctiqYpoTwALBWE2KqmjpYFDandQc8/OlZ0JX0tQPDlDNlluYn69u8dYFuC7e9LJnm5RbddxedMlQFVnhjwoasK9mZkrd19bKPoljh+R3OpMASb9fZn5rt6soQp6XSUk5FpU3t7DB+X5UWcMmslD2g+Cxx5xR+G77Pk3LMc3v5ubK7LgLC1WDGSB/JI4Ugx374Uy14th9GeU5Fgd9d8DqKgcqoixD0m4j6XSQx4mnq11bK30QzD2bxEWF5P3nmFDe9jx3v3e77v+TJ0uHAp0kWVYS/RNlGo0Q3f9VHOf9QZKc+oaxPruADSxmD8LcPV2IP3Mynbsvfe5O4nEQROdtOU0jAAnibLHi9wxhmPyNtwrNh8GpluXaXN6aW5g+3STfBAZ5lfLKguc8ySKTcYxx2qwF7qlzhWfdOl7VIHHsEz0mBu0f5lFwDp9Us1iHVW7cKOft3cBcK4rSFjovZinQLvWxgH+oKNpiZqagTRNppmOgj6punG/7fdcOnReK7z3vnILmdU5SSmg7GxUKofshMF0lVI/tY+1rjZa3hmL51InPfDpHxjHS4lludtaZgENEu5pDh2Pc052F6JLU2Z+mHqTX7rC+Fj4LVEB5flYl7EWcpiXlRqFDZJ5l7e/IcyRZhiQueNJDNqvpp+ncfW3JJYPov/7rv47f/d3fxb/4F/8Cb3rTm/z3L3nJS/Cbv/mblz2Zv+1tb8PXv/51fP7zn/ff3XffffhX/+pf4atf/eqeFAhWLCAYEvt76Jz9HEP55V/+Zdxzzz3+8/nz53HDDTdcitp7CgE2RlAnSTLBD86knuTxbjQaSNMUjEpXmhKeY4HMnZ0dbG1tVcBOHsuoZP6vYKPlgw5FBBNwtWCyAocEg5kwVKPBCWASyLegqEYp83cFuAkME0yP4xiMDFegnAC26sbzlRecQDWTbCqIDpSA62g0wubmZsVBwShlyyuvjgHuBBgMBt7229vbFUeFgsE7OzsevKWONvpbo6ABB0JevHhxgutbAWbahnWmaTqhK/tVy+F33C0QoupR3n0F0O3YoL1C9DQUC6DbSHTbfnVMUDj2rKOC79bxo0LAnu8KonNc2Gh2C6BfCrBcJ9/61rfwvOc9z38OgcwPP/ww/uE//If41Kc+tWt006XcBy9FLhdEt/fVugcVPS+0WLSytrYWfHDJ8xzr6+u4/vrr963r1SbTufsy5m5d/GmEKh/gL1xwKwsLMipZ9tGj7r1YUDy4fiv+8A+BO+5wSS4Bhz+eOQN8/vMOACQWed11wPXXlyA6E4c2GsDycgutdupOWFkpo2O5cFpYcCCgPixzQZNleHQ9qazbGT3e7wPdboIWgVpdBIWAdK52NJoaQJRlaDJC267EswxgJGDuwNxhHrmFf69fPSfLcH6QAEgKjLiJREFp9s38fJksklnUrGNDVt5R2sPxLAMaMcZLLa9ahHE9uBHHHuBO06Y395kzwNGjLUSMXK6z1WDg+gsoF7br687w119fgueMIlckn51DW3M3gi7E9VwBXYZIMOiXw4M/L7ZTTAwCenAeeKA8gQvrTqcERubmqtF0BujxgL9GZRe2sGs4xUz0laYo27qy4t5Pn+YgLZOCxjHi7JjnZn3ssZJavtMREEV3ZVCRlZUyI9q5c67c225z7SjKxunTDrxnnevr7jNQcvI//rgrd3m5uoNAGxkCMfiS3w/C3D1diD9zMp27L2/utk463jqBySlJb1PRYBMA0PRAnMv3oSD66mrpsFOcstt10xD9C3p7bGXj0nGnu70syKz3BQW2swxjuJwPo1Hp4wy1Ic4i59i1N1UbRs8TgaoDNk2RZBnirDkxpVmamlLlok69N+wGMtv7of2ec1zxTgc4AKAt9zzzPKJc2BU6EV+ufFZnt9rLJtawYfp1utcJAfS9ItAr4fKYtCP/D734fMBtgIyeD4H92kczM24wzc2Vu8b4HJumiAqgu87/G8flqc5B0S8DBvK8mhGc44uDpohG39lxfaa4uALpvh/1eZSBBUD5rGGvH73Q1dmgtlbkXufiNEVUP7UG58Dp3H1tySVY0clHPvIRfOADH8BrXvMavPnNb/bfv/SlL8WDDz54WUq8/e1vx3/4D/8Bn/3sZ7G0tOS//9znPocnnngCN954o/9uZ2cHv/ALv4D3ve99WFlZQbfbxXA4xNmzZyte8SeeeAI/+IM/GKyv0+lgZmZmwmP+xBNPeAClW2x1XltbqwAneoyV/URDfr+iYKmNMKYQQGWUNGlGCEgTqLUR6CxfEy8qYKiAbZ7nE9zqBINDEbwUBTEtnYX+TlBVI4SpC4HWECBKPQmYa/JU2oWgchRFPqGpgugUHqPfs33WrpY/XR0TChKzLoLoPJeOBaWSsVHmBGDZN6oP9dOocZbNMuiwUECazhBG3lsHAEF5Rv7zPI49Bcs1CtuOV+3Dut0CmsRTAW0bsW0B873GWuh7tS/twzrrKGJ4HPUIRcZr+epwsLsyQlHoWu6VANGZHHc3ue+++/DEE0/gjjvu8N/t7Ozgs5/9LH7rt37LJ+K8lPvgpcjlgugPP/xwpW173XdDi8U6CT24hL4/aDKdu6/A3G0fPMkRrb/bY/iQXCyc1+FyRZ465RbaeV4WsbLiXoxQ73bLdW0Yy00coEz0kMAeddMoHbOI6vVKHwBQrudnZ7lmjRyIYAC+2ofvEHjMBbBNCGqjhQAgbk5GURXHK9d1HJsIaqBcnDYaZcLITqfabt0CTISWi0QuqihcQPPldSyOyTKk7abvWkazt6iXdTpQcS4o47gMYbQJQLnIJHBNNJiLY0bDcVHO9of2UxdbmXMToJambu3ptjVLu0ajMjHaww87QJ0Lcdooz8tINYbs05HEdqZpdTFugZWAkBHGglv+Sx3j2ieFreJ22V0bG+GhBCBM59Lrubaur5d9E8fVXQwFDQ+yrETStE2M3CfvjF47IQldA4UchLl7uhB/5mQ6d1/G3F1z3dmveY/QBNMhwDGKY0CSHPMWwF0vxWF+Tmg0qixXvjwFsvmuKPzMTLlNi1Jca2NEFV+wpZSh8BaXxKje51R5PYn20+eFQhilnudR8HamU1scF3OKRVrtc0gdMKz/7wa0h+493uGRlkC+LU/A0drz9XmB26L4uY7k3uqoPDg8TgF0Pb8Oldbf93JA2PZpYhB6Wqi7jjOgumtMn/X4jFTUE+pSVdEztulYq3PasDAz2e/mo5hoJ/tDdwcoD5wC9Xq+7mCk6IOR2j60cyzQDiZBB6Zz97Uml5xY9JFHHsHJkycnvtfo6P3KxYsX8ba3vQ1/8Ad/gP/yX/4Lbrrppsrvr3/96/H1r38d999/v3+dOHECv/iLv4g/+qM/AgDccccdmJ2dxac//Wl/3mOPPYYHHnigdjJPkgR33HFH5RwA+PSnP+3Puemmm9DtdivHDIdDfOYzn6kt95mWEABlo7/1u93AR3uO/V/r04hcS8tho37ryggdZ6OgLTgeogAJff/9AJAa5UxAW1+Mug7pG6LkUAoW3S2g7Q71z1662QjsOhuqTeqA31AC1d0ivVmu7X9LyWL7pG6sWvBddxJYe4XE1qGOprp2W5vt1lZtr/2fn0PvoXbaNteB6c+UvOY1r8E3vvGNyj32zjvvxN/7e38P999/P26++ean9T5Y13e7vQCg1WpVXrsBoFws/tf/+l8ri8WQdLvd4CIvjmNcd91133d7n02Zzt1XYMyGnuz3m3SnWEhZhhGuMQj86TrIUnAGsUj7gx4QWtDs0rSJ5tWVq7awJ9qFTt3voXMD9Y3NY2qey3EEixcWXPifUpjYCGi7aAjZxkSrV0BpLS9gojQNlBk6f37egRYEvkk0GpLZWfc7j5mddefOzblzFxZKwNrqVugQ5cMJFRg1FuXDah/QEZFlzsPTbldftC/bpSABuehD9jZ9WTuWUS7QPQBl+9BQDrAvuW6miZVRpVJHyNkT6mMLkNhramurOr5DAIm+10kdyL6HPNtz9zQ52TMn07n7+xuzu12COjwnjtN5SKjP9hIeU0lsvJtSep/fr+II3270lryrsvqbVTTQ0JAqFs/1p+12H9B7rL237zYxhJ4bQmB7HfiuDdlljqp81ihhguChROb2f0ZF67F1Eegh4+72bGXbVvdMautsNKrjjHM927kbVaUA6Tq+Qo+DlR/r7v16cDGH2ql3Yu627QzZ3/YNz9vt9SzIdO5+7sgeT3eT8uIXvxif+9zn8AM/8AOV7//f//f/xcte9rJLKuutb30rfu/3fg///t//eywsLHjw4vDhw2g0GrjuuusmgIvZ2Vl0u1284AUv8Me+8Y1vxC/8wi/guuuuw+LiIt75znfiJS95Ce6++25/3mte8xr8xE/8BN72trcBAO655x68/vWvx5133om77roLH/jAB/DQQw95L/+hQ4fwjne8A+9+97txyy234JZbbsG73/1uNJtN/N2/+3cvzWhPgyhQS0CvDujdDRgPgbcKqjLyW6NjSbsBoMLNTeBZy2AdFuxmGyyAyjqVk5zULjxPHxptZLJGwttoYB5PXTRhqkabMgGr6m4BTraX7eDvGrFt9eP5pH5hOxWwD0Xo63mxTA51ACz1YcQ6+cjZXo1e150J2g/sK0aqa32M2Ga5Olb0PHLnK/c9+8jqfejQoUqi05DDhv2hn9lO0sno+KGtFNxm+0ixwn7WMcIdCBQLmKvdbYS96kxnQF3kuV4XV5LG5VJkYWFhgt5kfn4e1113nf/++70PPv7447Xe88uNRN/vsW9/+9vx8Y9/HPfee+/EYjEkd911F/7jf/yPle8+9alP4c477zzQfOjAdO6+rLmbD5F84Nao6lBEE49RUJK/F+DjySVH49JuA3/6py6QhkwsZIDZ2nLfbW87MJHAO1Di4H7Rn2Uu+pVUM4yCYqQ1t44bYNsebgPkBgOUkeh13KYsz36mHdQuuii1AHscA1nTRbFplHPRXgZW8bRh1kTSScu+4TkEe9MUmwMXOZdkw1IfC5RvbVUzu2pfKTWJinxH02QZEPWeKvm6ez3XQaT/UBvQfrr1uN8vM6wCrlM0utxKu+3oX6gjy6cwcr0oP4pjNNMUNy91XXm9nuOdX+tX+ylN3TaIra0y4pr2yjKnY6NRRi4SfMky912n417qiBBb9vvO3KRUt01jhHyeO2YVADh6tOCbZyT41lbZzwTu8xxR/zxuu61ViQ7lTo4sQ3W86XXKC4u7F44fdzQu3A3Ajk5TV/f6uns98kjZP3NzJZWQAi86nvSzLvQ12k0SJO4lz/bcHQS6dpMrkOflWpXp3H156+4IY5Cvu264cjlZyX1sgHMGi2sqDutb0/85tdhA3GEncQmllVOGJ6kzhHNSjdJcQtkNWcRC0zTgIN0NeOb/IaDX82pVuaGprvXhuCJsfKbhlQZAehAU1Xi6Dp0n99K3LKx854NNCBTX/y1IbgFV3stVJ+YdseXtBdgDYYd+CESvcwLUeixEqK++awcprYsdV+RFV53E/s4ZX82ho2r4IPAsKZOmsx94wSi1jHk+XOx2MUbku4/CxwcMRG8d+PYC0OTpduyoHqEHEJYdcqbrxW3PwR4OMyPTufu5I5cMov/qr/4qXv/61+ORRx7BeDzGH/zBH+Db3/42PvKRj+A//af/dEll/c7v/A4A4NUkBi3kQx/6EN7whjfsu5zf/M3fRBzH+Nt/+29ja2sLr3nNa/DhD3+4AtZ997vfxfr6uv/80z/903jyySfxa7/2a3jsscdw22234ROf+ETlIeWXfumXsLW1hbe85S04e/YsXvGKV+BTn/oUFhYWLqmdT4dYuo2LFy96ehHl1Q7xkwOTkcsKIir/t0b1anQ1gWKCgayXNCcK+CqoawFDpbhgnQA8oKxgt9apgC3PU9A+BKBr+9geTXRJcJnJWEmJo1HVll6G4HRd3yivu+ponRw8RvtIQVkCw8rDrtHWGnVNqhXqt729jZ2dHf/O/pmZmcHs7Gwwgp3nj0YjbG9vT9Rx8eJFDAaDir50uOg7bUA72/HI9tF5wTFHQF056S2YrZQvdOxwzJDKh7RDBLOVukZBfR6njh0d37ZOHYN2fFD0/qPnKKjPc5VO6JkG0veS7/c++JKXvAT/9//9f+Ov//W/PvHb0wmi77VYBByf5iOPPIKPfOQjAIA3v/nN+K3f+i3cc889eNOb3oQvfvGL+OAHP4h/82/+zb7rvVplOndfxtxNsFAXAgQWgcmFkVKG2AVpsWC7uXMe3f+5hW98A7jvvhJz7fVcNDrgAESC68TDicVy7dFoFAvPLHPAJ22kIG6v51bzGxvlYrjddvQXWYnfDgaOxWNjo8Jy4nhH1ZFgOVwtokAFdds4Q4OZEJOV0l6FXnm6WBTr+M/jtAnA6aWLKjJrABFmZhZdAFXnGIDCjg+Wx6YpcN11CRqNBK12XO7Bp37r65PgBQDcfrszeLuNYVxywlb8AH1n4lY2duD5+jrwne+4jltfdwe+/OV4dC3y7ep0bkTrznYVHKDBQ4sa5arlOwfD0hKGeYSzZ53ai+2Cb3d11bXxzBlHUaLjlBzfHBdc0DIpK3nAKdbZwe+IKj35pPtc2ArdLsadY45TmMcXYMRTvchzlC9mw7KOOMbmIJrAKc6edVVvbADtdguLy8vleLpwoWqjPAfW13FzNgA6Kc6fbGFrywXTJ/G4yqPK9vCdwD+/08SiSv3DHAG9nuNcevDBauj7LbeUNlSgQhf96kDR8X8ZIPp+5Omcu6cL8WdOpnP3ZczdMjYJ0CYAFtvFunS3jfipy9Fx9kzJpjUYOOccmb4shsnLYTQqL2XLnuJuX4nLFVLU4wuytBq8b5hrTO+Ro1H5iMLHjTQt7nl94zS0aCdlNKrSYGhIvjzTxFkVCLd87HU4shXe560/cWYmQqORII4TNNtp1dFpk2iHgEwLWNdVVvw+RgTEiUsiClKbjavzAucAS0diG2yBXK3L6hS6Z9qyQoC5Pc5KqJ32970Aej2WInNnlGWVhPF0ePN0Tl9ZliChc1rnXXUeaV8Wz4xRmmKx3Qay2Cf0nHCuAKUtFUy3c60+i9vndoLoFN1dRyC+zomlz22x210XGpLfr0zn7oMhhy5eBmLzR3/0R3j3u9+N++67D+PxGH/5L/9l/C//y/+C1772tU+HjgdWzp8/j8OHDz8tZROEbLVaaLfbOHz4MG688UY0m00cO3YMjUYDCwsLmJubQ5qmmJubAxNNEuBWEFfBcoLWm5ubyPMcm5ubGI1G/mXBWwC+3Ha77ROaMmECQeuNjQ1fvgVUedz29nYFOCcIa2lLFKjl90yoOjs7iyzLPEhM8DlJEn8+k3yyncpPTnsmSeLBNraXQPRwOPR86pubm8EobL6Tmz6OYzSbzQonvdKIEOhVQFZ3AqhoNL2CsNvb2xXbjUYjb/fBYIA8z/1YoL04LlQ0kpxtZl0E1a1Tg04J1Z3CNlnO95C97G4B6qPvGvnNyHG+kiTxfU/nhOWZt84hjm3lq7e7OBRA16h5y/VO4D2UkFQdOMPh0Efq06ZXEkB/+OGH96QveSbkX/7Lf4l/+k//KX72Z38Wv/mbv4mnnnoKf+fv/B187nOfw7/+1/8azWZz32Vtbm7iTW96E86dO7cn71wd/Y8uFt/whjdgZWUF9957r//9M5/5DH7+538e3/zmN3HixAn8o3/0jyo8pAdZpnP3/oRz97mHH0YrScqHcF3UhR66NfTM8irzwb9Ykf/50g/jf//fS8ybmKQCxq98JfCqVzmM/M47y9Pz3OF8CSTajMkOL1wowU0KM6IdPw7ccAOwvIzHj78UTz7pgAHiyf2+w1Cf9zwHQN560iTl0oRoBDS5WKIeLPCxx1yyxXbb1dtoVPmi2Zh+Hzh1CptLt1awTg38sQsU2oiR+/QRXLjgHBDsEmLCDNZPMHTg58MPO91WVqoLLsBV/Ff/KnDnndiMW/4Q6sSmUKdFPOXKXF8HvvENt5JcW3OVvutd+Orplh8W8/MugJzDIk2BaPWhyRDHfoHQLyxUx06R2HMzXfTU4L2e+/n5zy9A49PfcuOABPtskwXRFby97joPAo9P3lrZwKCBWe12kXSPlTPyfmnJ/bi8jEd7TbTbQLP/hKu73cY4Tnzu21OngOb6Q6VeaYphtuivAzpJ2C7We+pUUXdoDPLa4zXHTlfnli7k+T+PJWACOPsOIpcgjUlMOQBWV13f3n8/8F//q1Ps5S93g+vVr3bvHEt0TKgTynKv6n0iTbH62GO44VWvOhBz97k3vtHdG/cp54dDHP7gB/c1d09lUqZz9/7Ez91nzqC1uOi+VEevBd2MY4vgOpOG8n7Ee6L6YAmsa3HMt9xulwlGl5fLTT5zc8DxI8PSc84J3VKtcYIQerJx1griyIoheyBYn1XyvFSceTUUUGfwD+9LuhOLya47nUpSU865NGkIn2exIYCReL2C/2nq7EOWsqh/vtRd2xLaCqAUMfpcFgCXh3kUxKfjuHCK6wMI+yjkDNV3nhOKYA6B27YcbVfoZQ1pyw8EbFSOqYvetnXq72pbsek4TiaGl15G7MNmPCwvEl5INqgjTd1AOHLE1cek3PxNRefxkGg7uR2NA1ajVGgnjg9ea0ycnmUYp9V1aYRxqUOh+zgtAyweeWQVt956w3TuvsbkElwRpbzuda/D6173uiuty1QuQWw0KyOQ66Kv6wClUIS6RuRqFG4o6lajrjWiPERlYcsIUcmovlEUVeqw+hKo5Wcm51Qgci8e7RCFhk3MClTBUuplk7GGQHQeo1Hm2kf83lKi2L7V/tW2aTLMuj6zehHIZeS3gvoqpEmhXjZKXoFsdcAooGx3Gtg+npmZmdj5oMfPzMx4PWwyXbWNfsf/eQ6j020btR7axPLd27FhP1vnhpU6Shdb/9UYgX6l5Bd+4Rdw991342d+5mfw0pe+FE899ZRPqPJ007nsJR/+8IcnvvuRH/kRfPWrX913PQdJpnP39yF1q9fQMSHRRdmZM4iXy+SeyocOVHe9UrieUIps5CgXDlyU5Hm53/vCBVcQwTpD/UEMQV+a46ySJEwXg6EFHoXhSRsbpbOh3S6TT9bYlHXXbZXXUxhEzWPPni2Te164UM3JRqw0z4EkLQrULcW6eDULXgLzrIuAboXZieezI03kmgbm53m53X5mxn1uKVWOFbs4LhZ4F866Nm9slGDzxoZrd8I+YJ9TdCG8vl6CuTMzpSMkjj1mQJxcgWwXqN2cjKSURW+FCgjAOE4m175KkyKdbIcYz5uZof2biAjw2MV0nrvoe6CMBqfyOlYV+SnawAVzngN50ZVxHLmkvToYOUAJ2M/OVsdOSC9tmAXwVI88v7Q94c+A7DZ3T6PZnlmZzt3fp4TusfpZgFO9XWxtufsg6VlsslBe1qFHAaUhU5w1eO3o/QGBY8zx9icPoFtgdi/RBw2G0fN9l1O0nfrcYE2q71b/OK7m36Q0GjKH0Ggh4Fplr7ZyPiqcJFqM5gHNMvfMwwjjxILTdc89QBX4rYtIV33sXKRtqZuvAm2qfN7tZRtu67O7Cmt0iuIYcRzVqs3uGqcJojifHMs2izhtQceygud27gy12x6rCgGuc/kQp4NtPzYN2SHw1X4utWdSpnP3MyOXBaL3ej38u3/37/Dnf/7neOc734nFxUV89atfxfHjx/G85z3vSus4lYDYRIgECpVuxYKcFji24KaKgrGWGoUv3bZneccJqFoqGKuTpc+w9e8GaBJQZZSx5YO35Sqwa2llFExXqhflfmdkNL8niK/OC9qVoO3Ozg40GlqTZap9bd8oaK22thQhcRxXnAm2H2zbQ99bR4faYLd+U/Cc5yqYvZdQT9pEaVYsuGr50q0zxtqUxyq1ja1XhY4E9lso8p9lWVvVtdsC9lrWcxk0D8nNN9+MF7/4xfjYxz4GAPjJn/xJnzTlWrLDsy3TufsSJc+BUMTGbosTewxFKWAWFhDHLgCYeB8DdQYDB5BaIFkX6lysJnWLKA2V1sXK/LxH4BkBphSSaVrmk8yyclHut9Yqt7NGfmn7WDA5opW/2nKMy4Izjqv05NoF2jRd583MlAt6rsE0CE3zjMYxquAmG28REDE4deLXMzMlDbec4MpRPlEq0ethaanlgW7mA01ToeTMpV6eryF62qg0xRAJtrfDeGueF3U3GqXdWfbcXBkZTXSIIX/SL2QnmZur4tBsvy/PAgZyfpoC6BcL7v55NOMYnU4TcSzRaWzrYODoB9LIA+aWEUWHcwW10qi/OHbtUVuqTbVvjMMkwjgIriS2fYxm63Tcjo52u6Sy0ZNDwEndKlv1vAoTeNXO3bslxA3JVeYgOGgynbuvgOi9IzRvFwAhEPmfeRvWdxV1butcwcvZ4r95Ls7pSwGz8rzC8W6bNQGgqyL2XklFLfLNMHp1uBodtV2Kb9KPTyHVTAhwr5vqgrKbfeoAaq2QvxcVu4jiqNIvmi+UdiRQHPQKhHSzc46A6HZu4eFRFk+WWecEsZ/t3FanS0hXLdMC1LY+PnQZW6epm8/pb+Hh9MsDEr2tOjChKVCNKNBnitDcvh/RY3l+qI0cdKQZDIx3O5V7up/AmLsUFZ9Jmc7dT79ccrd//etfx913343Dhw9jZWUFP/dzP4fFxUV8/OMfx1/8xV94btmpPH1C6hLScBBEJj/3YDDAxYsXkaapp26xEbkhIE+jyBW4JW82RXnYFQAl+EjwnEC+AtCW+kLrs9HWBMNDDgHVi7Zg5Lg6F3i8UqLws9KeqD2UR5x1UfetrS3/P4Fd6kF9WY9GovN39pVNgEq7KiDL3xgNr8lMtR2kWCHwb9tUB37rjgLlE7948SKGw6GnHCG9iToXLl686PvZRoOrbUK7Iy5eLBOaqvPBcubT7iwz9KJoWRTr9CBPukbe0/lCe9PRURdpT/1V35Djg+VpX9modwXkLUD/XJI/+ZM/wc/8zM/guuuuw9e//nX8yZ/8CX7+53/+2VbrmpPp3H0ZoqFkfLC3n3UxHoqWsRFF5BYB8LKXlXjzxsb/n723j5OkKu/Fv1tzpqamp6bpHXp3m2WAARZYcMUlbBQQFBWNEF8To7kmxhg1l5jkKmhMMJoYA3k1XuI1al5MjHoTvdGr0Rsvor8ICRGim7BXV93IIhsY2AGG3Wa2d6amp6b398ep76mnnj7V88LusGA/n09P91SdOi/POVWnzvd5zvcB7rgj3zrOwIryfTdNi6BxamwgpiBt52X6Ak2Sz5lgL4qxDqXj04knAhs2ZHm27OIphQ0oBVMFalW3aHBbxyUXh9xCvWFDzjHN7bJclLES2TVyJ7lv7SjVT+9wjRlIp2BWgyB6mM7m7uBEasfHi673lKygKC7GlxwczLYpRx3nqYY0svnISnIL/N69OGMiBbY2MJParb+k45RDp7DVWnpnSQ70KMJMEqI5nataAhLMG7VaTtsiO9mYHOw9dMi67stt+5nCKqaNaEPoNjDIdawDkBjIlfV10b/sz0rUAfZN2zSTkwCAsVoNY3EM7G3mnvpiB0UYRRiLrVKGh4MCDS0X5UkCVAhO6LbJRbfkOJWLcx3PgIOn1UIQx0izbf50Wqtk9+mBpIIkATZvj4qL81oNeMpTcvCe2/4ViFLqRacpB7QF6XGWx3vu/uAHP4g//MM/xP79+/GUpzwFN954Iy677LLS9LfeeiuuvfZaR8X29re/vYuK7TOf+Qze9a534e6778aZZ56JG264AS9/+cvd+Xe/+934rd/6rcI1mzZtcjFVHg/pz92rEA0gLufeyu5TBr3UjxreqpzOCRpqOyyPyx1RvIbTX4UTi34/4LNCA9hZmsCYPEynmwDRPWnyOhk42QfM8ltOJhKIzBpNINgY267BwRxzp+gYljJr+ZEiMUk5J9pv010vWTdfRr5CKdk1xoSFpJwyCpzogDV0yDx8Rgp+i3mmg6DQtb5XDMDywA8OhogiW59KI87HgqR2A/we6T6daF1lHxcDwIQ5uC0Bcpmv5umReWWe4kEcI4wibNoQF9orq1XQu7xx5M5JPWfT0C8M+wW+etavTA+8IUnRxnHPdxZZSfnSybKjqED3I9vRQWA59IVBxmfUOh6kP3dbOdZz94pB9GuvvRY/+7M/iz/4gz8oENxfeeWVq4qe3ZeVCYG/wcFBDA0NORA9CAKQz5kc2FEUYWBgAAsLCw7opeduLy9wigTRCXQCRa9z1onHJNczgC6wkOkpkkJDfsIwdKA6Az8StF1YWHA6IK/30NCQA0m18Br+JrVJkiQFwJeGBvKcG2MKZZI/XQYSHRoawsjICAYGBpzRQlPRkG9b07dor2nJMy/TsH8liC77kB7XEjDWOxMkKCxpUbQxhJ73SZIUuMJ1YFkJqmt6FZmv5iKXbdZUMtL7nTqW40iD1hw7Uk9SN9IIwh0L1BWNLRJUZ91pDNLGAwna64CukpJGtlcHK5XUOtLQIcfMk02e+9zn4pprrsFv//ZvY3BwEOeeey527NiBCy644JjSufSlKP25exXSagHr1hVf9OVqmKvrspd6IH9xn5qyq6nLLsNs/VSgafnOazVgc20Ws6ig1bI0zJKKmc7Ceu1L2mxjYGkn5Kpfe9DKRVmWNkxnMRYbjMUoogLNJnB/K+fwiCIkpuLYQSSAXasFmJioIK5VEEjqljTN+c8zXtWOCfHww8BiYgNFRhEQCC7qIG3bdiRJDhJkbeqY0PJhN1sIjUFlfVxc3ci2UUFOWQZooUjeSdJaAgwUzu0ZwBHWOojj3CsxijJP6mYLQRQhNAadqIJWXEG1kdgOod4GBix39s6dwNatqE5M5AC3FA24ysVgpv9ZU8Whg5bGnTiwxIJZv/l5oL1+zHpQ1+vAli3FsjheicJTBzLDqSkExmBzo4F22u1BBwAziQ3+GjVOzY0pmVTjTk4efPAgcP/9xQUzAXd6g3k8zqpRhKoCJg40A8zNAZUNppgP9Qbk0XfVar4TVdBsArXaGALNj8ybKYqQJKHbaW6xCwue79xpm3T55RtxxvbIju1Nm+zNOTFh8yLpOw0oXUiCqKcGPSgr4CldC+k1d3vb10tW+G7zqU99Cm95y1vwwQ9+EM985jPxp3/6p7jyyivxne98B6eeempX+nvuuQdXXXUV3vjGN+ITn/gE/uVf/gVvetObsGHDBvz4j/84AOD222/Hq171Kvz2b/82Xv7yl+Ozn/0sXvnKV+K2227DM57xDJfXU57yFHzlK19x/+sdoGst/bn7MYgP8NTnFdhKL2QJovMyUrqccEJubJMiY3rww+wZw8PGRQ5Q4fzjAzL1/SWBdllv+b+8RryzyOCHEheVRenfXdXJviV2XeCJBgBtpxAALh+71IE2grO6XbeanhN9uvA1hA9xzzM4AHfx2fktjoFg+qHuiY75+H7L8rhLLA3QatrxQdp5DaZL0bHn4zhAFFlQPapXrSMD26yDq/r0I/UkdN9tvLDlOM9q3/tqmuYBvPnslnO2AJ4DY6xRSM9n0iAkj8lBqD3PYwHKiy6292Ol6N2u53nDoKchoizYvJfiSG5bVHpKhJrluw3fheyYCWGyvAmkM8Dv8SL9udvKsZ67Vwyif+Mb38Cf/umfdh0/+eSTH1dL/Q+aGGMwNDTkvLDpsU0QGEAhGCiBuzAMvZQUUgjoEQgkOEnOcXmdpieRgLP0RAdyb3XpJS4pPQiMa4BeemoTzKSHPYN/Dg8PuzoTuKZor2HqggC59KDmOQZ8ZBp6ZrdaLSwsLLh2yB0BlUrFBbSUIHaSJJidnXXtke1l/SQgTpCb4LmkqZGUOvq6IAgwPz9f8IjXlCOSBkV6c8vAnexDgvIaPOdvjiu9m4E6ZDtpgNEPM6lD6f0tvc9l/8lAshKAlmNWG2hkIFL+ZnBTzUsvd0OwXbyG51LxJiK90+U9oftYtk0aGXwUM70olp6ocvPNN+PZz3524dgZZ5wB4NhyovelKP25exUi3an0IlwuPnR0JZ6XL+3CU3vfPuvxfd7WjgXXb7oDlUYDW7Zc4tZ9XDP51tKyqNFR5KCzBmNlwFMuKqenc2AdyIN20QWe/N61mg02aQxS5PGhHn44pztn/MY0Bcb0QjfjhJlNAjSn7fWkrE5TZIsOuyBZv154irM+zK9Ws94/MsCXXkDLwJzyHIFoeY1cvEkvYKlU6ixJEGV82W5Hs+SXjyIkJsTUFFBt1HO3vBNPtN//8i/WE5vWh1NOKS4a9XiSi016T8cxpqeKge18Q5DVP3gQGB6uAKYCxEU1sXm18SoCdDDTCjA9CdRqIcZik4/TTMKsrmFWt3YaFJJQlXbxn3mgM+DnwYM24d69xUpPTNjItUQQuB9cojYKnECthoUjG7G4aBezob4pABfsDACqtU5hC7qkhx/THpkc8/V6gd2At0uSALt321i09Tow8owq1k9UEXJ3QFqxOx0IojNImayjphHSbfSBDceB9Jq7j/VC/H3vex9e//rX4w1veAMA4MYbb8SXvvQlfOhDH8Lv/u7vdqX/8Ic/jFNPPRU33ngjAODcc8/Fzp078d73vtctxG+88UY8//nPx3XXXQcAuO6663DrrbfixhtvxN/+7d+6vIwxaGS7EI4H6c/dKxfneQu4cep2DwkpmK3ESfk49oHp+hL56NIhH/iIW1zM43oCQLQhtBRpPuBRg5tAtzdyWewFMcfMtAL3LOOrSBn+7MOriZuymg6UlO820kPYk2EQRXYHUWSc1zVfSagbLg8L1C4a3Zf60J4FWuQ5n5ECAHs/QCd/75Be0778fXWKIswmdm7UATelI7nMUjeF40ZM+zAmRJVOCaxTL4cN1Yn0qC57NTUmsHeJb1AnSTEArSSvHx7Og8/IuvnebfR7jbxh5DyZefD7wtQAedHGBPa9RL2Tt9PAxTMB8m++YwLA4mJo9RtXXZ9ow0ZZV7PKcqoOV4ygrp305+61kRXvQ4iiCDMzM13H/+M//gMbNmw4KpXqS2/RfOGaukRTUQDo+tacz73K0fQXmhtdc1FLoFB+pOeu/uj66DbqcuRxgrGaJ17WR+tFU2pIcE6flzQmPo9oSX8jg5ISrCUI7tMb2yL7RusAgPdaHy++zrsX6KipSCRNiY8L3acrfe1KRfev/F0mPj35fmsDgvxfA/CybKlTPZYkP7/2wKdOfXXR9V+tvp6Iwol87969+NKXvoQ5uqKge/ws59OX1Ul/7l6F+FYf/Pah272ELq7G5GtNIpKZ1y4XUFJWUkThIrEw6QKMZdu4CCb4SaoP6SqG4roqSfJAa24h4llcEnTlZ34+v55FuGK0t5Csn++3rr9sg4wEJxf5ui/VIq6rA9K0sMA3prt+rIJrPwlWGXSy2bT1YcM16iL7q+Qjm85h5BuWaZoDNQyIpz9UUzsN3P+F/tN69a3CVTIdbM9VhG2WfcO+4OpVota+PhU6c2WU6GhuzqbpICiMR2ZbaGePhaTsIladhiMGsEWthna2Q8MHSnmlbOHqe54cB9Jr7l5qzHo/AGZmZgof7jaU0m638W//9m94wQteUDj+ghe8AF/72te8db399tu70v/Ij/wIdu7c6RwzytLoPO+66y5s3rwZp59+On7yJ38S3//+95ehrWMn/bn76EnXo8x334kE8tnv+2hH2l63sn70d9UDPf73NcL34XlxvYzj6Jsq+b/vsVuKn/Wap8ue4WmK0HQKj2Cp41Lp9dzslUbrRJ0rXKLTlr1seY7TYCPnZjlXs/m+15eyD9MW5rKSZ2rhXPa70wPeKwyXpcaZ5KKRL2y+ebtM17qOnhtHesxrPeri9PyuOedZTX7ke6fsiyTpTis/y33fpkf68ST9uXtt5u4Vv7G99KUvxXve8x78r//1vwBYoOjee+/Fr/3arzmLQV+OnUgKDMn/TNFgKL2p2+22o9eg1zKQg5j8zW9NSUFPYum13Uu0Z7XMF8j5zmUwTlkfH3DJcnkt6WykLmSZEjyXHtU8T89mAnOSr1zS3sh6+XjJJdhMWhLWhW2jF7b2fmb5PM6+kZ7oOlCq7mNtMGFdSW0jv3U+0lAAwO0iaLfbkFz0Pg92bfyQ40aWKb3p5ZhiHaS3OD3R6fnPfHXQUfapvgd6Aa0a1JY6JJ2LTKupWHxGJenNr0F76aWuDRDaiNLLaPBEl0ceeQSvfOUr8dWvfhXr1q3DXXfdhXq27b7vib520p+7j4JIoFEf9y2C6T6cppYfPEtTq2U0ymmae0uPjqIWW2/XiYnc03tkpOh0xcCW/N8Cu6p+Qmw6+5wMpVeTdLOjp5YxFvyVHvitFqLGRufUzoCTadpj8StAT70gknUvrPV13SUyKVf5rLNsA9MYU+oN5wrTiIev7vJ8mrO/RBEsT7zwbDP1MczNAW1TQUgPGH7X6znFB6O1+uouFcE0UYROXO1yPKOnnsxG9gP7xccZL3VPNdZqGXdvGXAgCks9OAnl8GFgbi7A+vVVhPU0p8oZH893AwwM2N0NmmZGbgNnJYUnOZDz8IawdDoaQAijCIODdpzbS0NXT4JFADA6WoGJKzaYqShvNg2d453+SNtKkth7QHq1zs0BVUlfJEUBWq5M+X2cSq+5e8XByTJlnXLKKYXDv/mbv4l3v/vdhWPT09NYXFzEpk2bCsd78ZtOTU1506dpiunpaZx00kmlaWSez3jGM/Cxj30MZ599Nh588EFcf/31uOSSS/Dtb38bJ3KHyRpLf+5euZSBWl23Xg+kzJjcU9p3Tj9X9WNdM1bJUA0uDnXieUjLQvT8pQvUx8Wnkz0DfUB42a2r52jqIP9fBEX11WmZ0iu5tywfJUgxcf7tA9cVyCwvLaTlp8zLX+aTdayeo+Vv+cpYhnv7Pl2P1zKFLfEM5iU+j2tjAEQhwtgzztLUz6PPdutBreOQyAr46q7GjgTQJWAud3XwEltURqtiUHgvka+uujh+iCf7XuPlu1TZtO2u883tx4n05+61mbtX3Pvvfe97cdVVV2Hjxo2Ym5vDs5/9bExNTeHiiy/GDTfccCzq2JdMyPssvZsJtALFgIWLi4tIkqQAVpLKRdKzAOjpJc10BLvJvS4BYAmESmCSdB+SXoaULYODg4VAm2yfrgtlaGioANwGQYAoigq6ILBKwwEB2U6n4+hZJJ82QXQC85qahroBctBXAuwAXDupF+pI6oaAv6R64TfTzM/Pgxzt1K8EvWWdZD9Lqh7Wi57w5MSX/SP7aXFxEXNzcwVQnXVgvhTpaU0dS/51aRxgf7Eu0mCi9evz+Jc87CyXnv38nwC6BLY5LmS9+b+8T9hvNHjoHQESPB8YGHD0OjQGsN+kAUPSvMgApFJ/Oj6AbzeCNNw8WeSaa67B4OAg7r33Xpx77rmFc30Qfe2kP3c/BpHAKtC9mNKLPF5DOgtjgCuucAEfz5gQAayiyIKKcYxGlkWzaYH0pz3NMoDIF3sGKyusHRP5Ty6O5zFhQMsQUW1zoQmBDPJIBJariWyLc2W8DdRCRzlN1hLu5l1cRP42KZ6FPOcDJrvWH1KfRD6pQx5bWMhJ4jOu9XYaWOOA9nqTqy8p0njAc74t0m5bdw42B8ksMNnMAX1jYBqn4tFHLdXNqdu32+snJmx+e/fm/09MODBdArAFMBc5LUmaAq3J7qZwEUlc2rcWos71Lnu9vq3VVHBYrQMBxmhPeO5GWFy0beeaO46Bk08ewyZagmi4oRFhYgIPTIcYGMiBpEpkdUDqgTjO6H0Ed0w1zvQ0OVXkuhVKiaIxd4kcQgTRSdM+OAjU65bHdHGxatszXfT2l3ocGsptArxdOPaJ9c+On41oy9kImgeK3AldqN0TR3rN3aWDr0yytPfddx+q1ao7PNSDTNbn+NDL4cCXXh9fKs8rr7zS/X7qU5+Kiy++GGeeeSb++q//Gtdee21p2cdS+nP3YxBT9Mp1z1spHsQtAGBMiOHhnNVCGrM11spbQdpJ5fOa35xLoiibTzRIqQDaroe2rLNvIs0KJb0Fn2Wy3kt5fsvq8BHGOZ8AZheQ3gssVaB3YAxo2B8cLD5vdR2MCRASzKU+fMBuWUM8CHVHGFid6nieEyuf4Twn0yjebs4ZHCdSZHfqY7qrOU4YsLWr28ueubr/lQ75Ld/FinUIMDBQsTRwAKL6ZvtuKPnEZUaZDgHPJj/1rmJMaLnd5dgo6bs0zT3GW60ioM48BwbyON6aEa8sa1nsUpsBNU+9PO/N/zgF0vtz99rM3Svu+Wq1ittuuw3/+I//iH//939Hp9PBD/3QD+GKK644FvXrixACqxJI93lpE/RbWFjA7OysAygHBwfR6XQcgO3zsuVvfksQnqAoAUzNky09cjWgLb1yK5UKjhw54ryUtae29LSXXr8a6Jfg/pEjR5z3NAOs0pBAsJje+NJTmDeqBEjlh0C2DF6pKWgIyBNEl3pj3uw3zZvOvGZnZwuGACD31teUKwS52T7tDT4wMOACs8qgs0eOHHFe3rJvpIf57OysSyvzoz6Yh+xf9h3pbGSQVR2UluOD45UGBOn9zg/T0mDC/9nXvgey9gpn/ZmPTitBdN13eqz7dmzIIK6ap13XRYLo1KXvnnuyAek333wzvvSlL2FcB9RDH0RfS+nP3asU+ZJMr20tenXN6wBgzx67Otq2Df++K8APbWtbcFXSh2zZAhiDzaaDRsPyS09PW8y90ciBQCAHPhnoKEjbeVlqgcmfxHy5iJAL+TF6zkqeSa6YiUY2m6jUaqjXw0ITuYixxXUvbFm+j1WF9XBrK4Piiz15znWktsVFh0YcaAZoNoEoCjEwEGJ4uAoTZ6As694DHC7oS6+OBHgRRUAw9UC+s0BsjTXbLTA7OQk0dpwPwP42Bjh161ab79atzlAy08q3HhsDIApgsgVpmgLTU3kMWu6WZRexyrVa7uzOamvOVb1tWfYb0wStGdsenvStQKPIdQHrxLwPH7a/JyfJxW7r2mwCGy4/FQE6zmBAavh9/8f+jiIbnM/G5rTBWycnrYo3bQJOOqmCer2CSpzm4yFNrXLIsa76MZoYszqczkFzGpCkl3kUWZvG0BDw6KPFsShVwc/oaA7w0KYjbTtJYuOKpilw0UVjqLB+8r4sW+nL449zAEstvebu1S7Eq9VqYSHuk3q9joGBgS7PtYceeqjLG43SaDS86Y0xzgutLE1ZngAwMjKCpz71qbjrrrt61vlYSn/uXoVk95ikejAGSwOw4lwUhc6IJh839DCXXur6VpAYuAZJK6ad72jqNTcZGSyxCP4ZE9pPpJqTFoFdHy0Fp3eK9lKWQKvPcGAMUIkUOC2VXKxol16lviTAy29Jv211aedIE2eArBYf0CsbIXQqg5wWhPqOrVE1jFHcoScUoHnmeUoPLepZOv5y7Eh9SqOLNC4XxpQ0VPikx/NYjgMdn7QXJ7jkE7cSFoweUnw75Nx4j0oAdPFbv6tKeiHJROKLRT40lDuX6PtRF+u79alaze5X0EQ27viuZp8lovIr5A0/1tKfu9dm7l4RiJ6mKaIowq5du/Dc5z4Xz33uc49VvfpSIr5AkxpIl7QY69blgUYJsnc6HQxnT5zBwcEumgnpdS6BXp4Hcs9aDY5qj2X5TY9tCUz7qGXYNul5L4FnCXYDKICukqpFArNzc3OYn58vANGavqNSqRR0q8FpCepLyhbJ+w7AgbbSm53e98PDww7k1t78zEt6vbN9ZfQ0NAxIAwPLl7odGBgo7AgAci96md/s7GzB4CF3O2jDCftVGwyCICh43vsCa8qxKg0fDGZK8Jp9LT3AZf9pShmOQyl6V4Oug9Q5+03TtmhPepZBoF3eF/pD8dHuPJk90CmHDx9GpVLxnuuD6Gsj/bn7MYpejOk3dd9Kmm/mU1NArYYHpgLs2QP80HbjjqHRcMEJDYBK8yEESYLzto5jphVY79tmE2GthoWFAIODOWgeOn4JtcJlfUXVNQdqIZZZPcpRREFTUkBfM8/fOB5zixp61XUtSjxAOr/527tFXoOMrDQ5xePYrpZEoTLWqFz8xHGAMXqn60WbXilp9FlkRgAmQCdHZgtE4vbc/LwF8/fts1k4EH1i3KLL9TraURVJK1/EDgwUY2qyGq1W7tnNYSc90ySQLhfCPtsOq0nHbXazExoFuOL1rDA7CJzHuQSWWWaS2CympooL56kpYGAgwKOP2mLuugt45BGrm337bLpGI3f8iyJ7nFg5u8ItwKl33ekcD1GUUTgEbtiwyyiHDlngn/WMIlvPVstuEigLG0AAjDr1GYT27bP5bN0KVHii1/OhF5B3nEivuXu1C/HlSBiGuPDCC/HlL38ZL3/5y93xL3/5y3jpS1/qvebiiy/GF77whcKxm2++GTt27HD0ghdffDG+/OUv45prrimkueSSS0rrMj8/j+9+97u47LLLll3/oyn9ufuxizMayqCYFN+4zO7dAB0YE2B42M55enoHckMnkAOkZeB5FDGIZavceKveM/iskdTUvEQ6gmrguYzWQjdZnre71YrnykDIDoQ3ei/RbXS/Q2/SMslV0n2diUSAUFlRCYKbIu+2nGNkAdT3WE04FYh2ttPABYfV9n1fO6hPDZzL36Rk83lBF2iJpIGiB5gud7rp9y9+5GuPnMsksO4bQ7pYvk9wjI+M5IA2z3WQBTD1dXCmQJYrXz3le6tI2rVJI47z8c+yZfb6tzYCSK//wn3q6t5xGZjMsHa8S3/uXpu5e0WBRY0xOO200wogW1/WVrTnaplocFYCeBK4koBiGUClvWR9Xuu6bN9vH4d7L9HlSSC7LAClr90a1JTt1cC/DwTtpZel+kGm0570mo7EFxzV99G6LdM3yy4TXzu1zmTapcYJRXLa6zpLI01ZPXz6W6otZSKv8dHJSFlq/K/0WJmBaSV1fjLIs571LHzsYx9z/9NzH+gHFl0r6c/dx0jKVqh6RZWmxbWkWuEwIKJc5VhPl5KFdlnZJaLfgbsuXcFLsjHdnmyFPMQ3FyulfJy9CllORVaaZjnXCOXIxdNyitELVaesKOrqxuXeir41cy97Tq/6edPoFbIav491wcjFsQThy9IxrQSMvLJEP+p+0GCQBArkNfpaWZwxvR3FvW17Iqy2e0ivubtrEC7nswK59tpr8Rd/8Rf4y7/8S3z3u9/FNddcg3vvvRdXX301AOC6667Dz/zMz7j0V199Nf7zP/8T1157Lb773e/iL//yL/GRj3wEb3vb21yaN7/5zbj55pvx+7//+9izZw9+//d/H1/5ylfwlre8xaV529vehltvvRX33HMP/vVf/xWveMUrMDMzg9e+9rWr0OBjl/7c/TiIZ6zKOc83/5U9G+iFbIwH5H2Mzwfpvb1c6TUleud1Ib7npstkhfd3Wd1Wk5UE9r2WDo+UeWB7u0Tls5S+l2qHPl82dowBugKEHiVdUzTYvtLzvT3ZlfgGnzrmtbeo83IOPwq30ZL98ESU/ty9NnP3iu/Ed77znbjuuuvwiU98AmNjY8eiTn3pIdojluLzRqdHNr2qNe/0kSPFYKH8n963ukyCWARJ5TEdeFLSZ4Rh6PJft24dhoeHC7Q00guYddHBNCk+ugxyaNOLWXNPs12ks6EXfaqevJLfXNK5ENRmftJDX3quy48PIGcZ1I3sM0mbosvhb8m5LoF23Q9SB5IeRXKNSx31MnToespx56Nx0UFhfV7jHIf0gmddpB5YtgwoqoN1yrEp6yW98OUYkm2R+pbXMJ0eP/I8x47P4CD7T+80WAoElnV5Mskf/uEf4vLLL8fOnTvRbrfx9re/Hd/85jcB9D3R11L6c/cqxfcS6UNNl7iu0bD0LB0ECDIPdKZbXMyCE2Zp2wgtTUkjdEuo+Xm7eOvUQgRGeEn7OFOz/0PTQa1m00VR7tUjPXEL7WGbDh2Cu0gEukwFLUgh9maKoquuyDNNrfevVNHwcO5NHcdZsMjpZtGdul63bk1xbI/TzahWc1w0I9njgN5PBS8iuomVup4hP0aPdOl5nx2vxnGuNHmdcJWamKgWnLkZY9K5SWVlSiCWgAXrG0W2n8h3zuyNydtGXu4TTrB9G2aL7CiyY4N9IZvF5nIbdGG4sH4Zv7y7CHAu4sxrYCCvuzF2AcuhceaZeZuHhiyPf71u/5+ft9+ML1qr2d+SzoXXpqk9dtJJ9vrC+KzX84ZJNzoOwjjGbBIUup27JjjehE3DUesz5qscFtS7jBGaprmq5L3DtCMjon9XsvhkG45D6TV3rzg42QoRile96lV45JFH8J73vAf79+/Htm3b8MUvfhGnnXYaAGD//v249957XfrTTz8dX/ziF3HNNdfgT/7kT7B582a8//3vLwTevOSSS/DJT34S73znO/Gud70LZ555Jj71qU/hGc94hkszOTmJ//Jf/gump6exYcMGXHTRRbjjjjtcuY+H9Ofu1Yn2yHVc3lLK/jdFj16guNOGzyuCiNLTmOXxWsZPMHEAGDt/l1LDlQjz53MeyG8pPafw98JC8ZmtX1WWCz5KI2xPoJ2FuJcCFBUivsPIwJigUC9dH/0YdYYIdIPLBYN3qcdz7sUudwzIwtyGLI+iyANOnUoGPN1M/Vu2QfefCzKr0jJvaxzIvO17PHOpky6ecpGnDAlTZlD2cdPrfKRoChpNtxKgUzwpFEG6Inr367J8ux25M89HmaRpWHz11fnJdwOdXu8E4LtaWeDi40X6c/fazN0rfnN7//vfj71792Lz5s047bTTMDIyUjj/7//+70etcn0pivZYlseBIthHoE8DuTyWJAnI7016ksXFRRhjXFBITStCIZe6pteQtCYEe8mPDcDRsYRh6AXJNUiqPbYJwrNMApntdhvtdhvz8/OOC12CnGyDbKcEdAELnJLuROYtKVx0H7AtYRgWjAIE3tlGGUCVFDtsi6RKGRoagqQGkdd3Oh1HRyMDXfJcGWiepqnTC3nhpbFBgtyyTAnQS3oY2S/SCKKBdIoM4CmNF6SikaC/HGuSsofXMT/Zb3qMsP9IXeTbAcD85X0ky5BULxrk5TGOD0njo8etLx+d35OZxoVy3nnn4Zvf/CY+9KEPYWBgAIcPH8ZLXvISvPe97+2D6Gso/bl7lSJXBfJ/onQynX7rF/+HU/fiKU85FWkKhFu3FvapOozWAEhTHDwI3HefXbBXs5UUi7JMFoFYaFneyijqDlKJZhMBgDFjgJpBp1Hxx9uUK56DB20hw8M5x0UcoxNVsHAoX9hJnm4AmEUFUb1iebbTFO00wOJizsgC2MUaebNrNWAsbud0LUxEnZHLUS+KCfgag1FYMLSSzhQ50OVqSrshS9H9JIOZkgNF7hkmhwo7IuvDiy6yPKrE3gkgowVH2SPB7MICM6Pn4Xbner3qAHTaMnRgukrUcYUFaYrQGNRq1QKXqFSBL2CZMcgtGfU6ZtIK0hSIatVcbUleZ6qJ093gYL5wnpjIyxoYANavB8KpewFjMD6+2dGlcAxQWBeOJRoPRkayfo06wLRFrw8kFTt+GlUYk/EKS+L3Ws3Rt5D+4NAhy3nOBbdcfNdqtq4E8Kencw71ZtPWYdOm/DZg+6nXOC7S6GzYYKsyPAxgUS2tfEC5/J9j6jjzNu41dy/bSEBZhaHgTW96E970pjd5z330ox/tOvbsZz97yXnsFa94BV7xileUnv/kJz+5ojquhfTn7tWLBjXDsnErAGBH+6Gm95GRbluq7zf/J33W3Jx97vC5HBJA9/KhdVdLJpOc1Bq412CufG3RQLWk7PAB/zJf/btnZZlR2cWCMi4wBqExCKMemcsKJflvt8rUFdPGVZEH6XkoUZQZ8JPUTUJBMmuJZnoog0aRgQGrv0JslxKRc74XqBU6kzz+3YB4sNSQAVCk5pHc7KQA8hlD5DjyHZeyVB0KALo6KAO7Ji1bV8ZX4biU414PJd/ruAbR5TXSyOQLYisNY7zO0bh4Gura5CvkOJH+3L02smLNvOxlLzsG1ejLUuKjAQG6PYcJShIcZxrN200QfXFx0YGk0oNXApASxAbgQFzN063Ba3JaR1FUCDYpA2YyDwlK+tpMkWAuweL5+XkkSVLqjX7kyBHHQc5rNBh75MgRB1J3Oh3HFS9BcALd5HUntzn5ziWILkFlCQZL7nYN6NK4IANb8lvyh0twXfOmS/Cc+pifn8fCwgKSJHEc8dIjnO0HUPAml3WXeqLnuwS6qSNZJ00Bw/rT0DE/P1/gVi/zZvfx60uhnnS5rJ/2QJdpfSC6HOva21yPc2kAkGOWevLt0NAir3mySqPRwG/91m+5/2dmZvog+hpLf+5epXS57yL/TRBPrqzlC6r8vXcvNm2LMJNsxOR0gPHxKsIMqHUBqmoA0hSPPgrcf7/16K1mc0YRRM8BQQKuIyPAhg2Cp5SoruAFD4xBRSKyuq6AdQd65BHgxBOd63AnrqLVyhceerGSphaENAao16sI0S7gm8TH6a07MpJ5VU9O5aTUtCQQ1ZyYcJ5fgAigagxm09ACqciA1L17bQWojAwY9i4WdH+xj7lYkug1Ud5azZJdR5EL6BoSdG82EUxOIqzXUZs4w9a1ecAuzK1CCiC69ASzHvPCqzpJENYNGpmxQ9B9I4qE0aEpjAJZwkojQlQLC0NSgsayi51KMlfsWVQcnzuFqiNwLwFkXk9v8bH0IduPTPRg4rjWgyhCNYqAVjMfDNpAkX1vjmOgxnsr+2SVYdBR3geNRohaLUStBlTiNmbT0GWdpnasHj5sq3HCCbkHOQOyso0TExkgn+3+kBzycuODBEnYzCSxIL0xeb5RBCBdGhxz0guJOw6kbO5ei4V4X6z05+7Vic92ShDV62GbgXvaQZxDd3S0eFzHRdD5SoO1nDtNHCKI1IUllfdxgMs6uTwVaBnyfJYPgWoJ0MopTwZfLhMvCFyGvPoMh7JDyq7T4quQ711MWwI4r3iiiPJZHpqMn54THWAnDAnCM78oQoo8qKQEebWxgmkKALKb49PuNvH/LKMgA5rlZaQ4842zMlWQJ1wavAvjRFpWZN00SbrnZgmlIvR7ZBQBqahI9gJC8DxpoSvOCt8ReaxsetHvIMy+lye6rqJ+LlRM9m5po9s7I5oxAQI96XuMNB0E3bQ7x4H05+5jLyvWzG/+5m8ei3r0ZZmiwXQe0yIBPwmYEryVQUHpgU4A00fxwfKkF7rPA157LBNolmA6ryOgLEFICS7LMjTIKMuTALIGiCU1Co0F9I7WdfV575NyhKAsQXQd4FUCwPzN87JtEpCVIKs0VlAH0tggPaslhYvsL70jQOuFoLoGdHVfSwC6DOD1eXXLMSn7iW337VgoC7ZJYNxHBcOgo9rIIwFx9oM0CPCcNED56FzKxEdNsxxQV99PZdf47uMnqrhtYyXSktHe+rIm0p+7H4P4wGbfAkO/bMq0zaYFGic24uGHM0/sLBmds5gXHbOTBO4tjQspvSCQtB9d5UrXZK72ZHRJuT+dkqYWRM4oUxBFpbE3ZdMJPMaxBQkKazNVPxcgVQYOZfDQbIHSRoiDD+fgQxSFiON86+/wMBAiyYNjTk3lyiDXiG9VJdvp6yeNJjCPDEA/eNDWaXOjZs9PT9uokmmKgN7zXIiPjxc4Qagz7yJW0MNU6hGMCdxa1i0a2VbZAcJaEcQGMtRRF8WNEMcfawwOPVwM6Mky+ZF9rtfKtRqAXZPWkBFFuUfW/LxF8ZtNq4OpqTzaJ6OQcozR4MFBJFfK2YfxRGU9qbpGI8ShQ8WhLtVKb0tXX+RjsmLawPQ04ngzADhqJRoPhoZgA/wmCRBHhcWy1EUXwKRvECm9zj3O0p+7jz/pz92rFw2Y+YB1isTI5DH5W04VUZTPyfo6OV/zeIE2Q04Gvgp76sbT+lWkCxj1NTRLHGS/Q2PpVGR+vo0wvQDNJZ9xvdrFjLXS9LuILz+f3srKkXpOU0AbJZIEiGO008CC6pyH9Va7kqYZk4HxWRnOeKGNI3pi8onuWNEcvpJIm4BWj28+0uddXaWXg3z/kMc0gk9EXmaquVtIvyePZ7phdnNz1sDNNsgi1HAFUHTyHhzMX+2U7cHLUsJzATpu0qcBwJggd2Qo9LnHcKXf+wWAfrxIf+5ee+mbF56AslxvTF86H0ezBmEJ4ko6Cg2kapoYSS1SBgZq8F1yjWtAVNdVg/eajqVMdF208cFHi1OmR03H4Tu+nL7RBgipXx+w3Ut8fbiccsvaCHQH4NTGDIqkuJHAvg90L6tbWX00FYoUaYjpxdnua7MG0GU/aJE61caopfpH62s5APmTyct6+/btbhzonSRS+p7ofXlCSC+gvCydTi8AXce9mJ1zIGn2f1kRegsuvYzT1IJ9XZ5PzMy32FWeNIFcCLGgbMEgm7AS7E/joQUqEZ9OPat1bpmGusQ5DukCfdtqyxb0cjFe1neiTmma16VLZzpvIdL7b8m6CZFtNwaF7ewuQ4UoDwyEXVXqCpCqFoCFMkTZy5E0VRQJ3DMtgQvtDSgReq589WJ8CaExhWt2DQDJrAYGckCc29ldddyiuLsMeStosTrN+1V7x/WU4xRAB5Y/dy+3nwrp+9KX41h63ZbLGe5lQPeKhr4HSF2qbl2ynMQZoCyLGxjwvyZQJL96aTm90P7lSBlArt8XCsC46Z7Ll7Ca9KzSCuahoyqirpb7POhqmn5d8VVRHuvi+V5qkPt+y8zkOaknjeKr844iKS2+z+ljWny4vW9Y6XRS7DuKv02WtqX7XC87zfEq/bl77WXFmlm/fr0XEFq3bh2iKMKWLVvwsz/7s3jd6153VCrYFysasNVUJAQby7zSSduiAXJ6gTOgpvSKplc2ucylEEAdHh4GPcrTNHW0KuvWWf5vKdLLWILmR44cwWD29JN0Jp1Ox1GTyDx4nrQ1bJPkLpcUNBqYJh3L0NBQwQNaepazvRLMZZmyDvRIZ3tZHwnsSsOAEQ8jer+zH6RxQHqk+4BkSSviC6aq9aUNHbJNTCfHj/yWbZXHpEGD10pPbd9Y9PWV7BtJlcLjkmpFUrcwDx7TxgTNzy77Q49pSRXDD732SYMj6yGNTb4dIexPaVyQ+vaByU8mueeee9zvO++8E29729vwK7/yK7j44osBAP/4j/+Ia665pg+ir6H05+5VCkFASXwp0WH5P9PqN31jgC1b0NlyNkwCbNmS4elRDR0EOPnkbAGQAIhjRIng1c6Ad/JFHzpk05KiolYTXjbTmaeJ3GfMNsg6xzE6tTGLbbbs4Yrk/iZPR+a5EtZqqNUqXV7AALq8paPIepnXaqFTCYun43scA2gJNyI2UAQxNaZ7+zzLdUFWG1W72KzVcg/yoaE8r7LFgs+1XjaC53l9HDvAmcB2mmaULuwEorkyP2PQTovB7ZhtmDk9FfpIeIKFUYTRUcV/KtvDzOi9nY279evDgrEiilD0issWtFIFSdIdrIsfCTpLNQG55/fmRqMIlEti8ozWxdWhXs+59umJrndDyM7O8hwassk4Bg8dyseCcOIv6GtkxNKsnHSS/a7VgE0b7Dx8YDCwnqHiAv5kIFJ+2mkAE1VcuiC1NEJRRnO0sGDZj0ZHs3Mc+Lotvt/yJjkOwPXlzt3HOjhZX3Lpz92PXTTOV3Z7LmUvD9BBR4CbMkC0zybLZ6h8Jhc8xnUhYu6Qz+lly3ITi3JZd82hLZPKZ3/XLqpeCG/ZOV1f3/OxLK0P7JXHdF7if2n8dA0yJr+cyuC7iHgX8D3WgdygLr2SA813r/u5V3uzuTI0Bohyr2hCKmXq0fOznsuNAZCiu3/40e8vvhcXeZ0uDCjwvxE4T1pdrzcFxhja3KUQFPfh8yMjvWMDaFXa4zZ2kEzTFaNAvS9a8N3+z7aE4vzx5IUO9Ofux0NWDKL/xm/8Bm644QZceeWVePrTn44jR47gG9/4Bm666Sb84i/+Iu655x78wi/8AtI0xRvf+MZjUecfWJF0GNpTt5enq/SgJfBMnm0CgASACchKcJiBMsn1zWODg4MIw9BxiBOIn5ubw5EjR1yemlKF+fgCN0rAW/O4S2905iePywCeMm99PSlmRkZGnA6k/jS1CmDBc9K7yDoSfD9y5IgLlEnKF5mXDggr+097NkuAWlK+aEMKg3NK6hYfiE4amzC0kzHTy7xk2fpbU6MQgKbOWPbi4mKX4US3iUYaAAUjBscL28QgrxQGux0aGirQ5XCcyvuC/S3LpL7ZDo5BCu8LAK4es7OzaLfbzjAkdSp/++49Y4wbMzrmgDZ2rHQnwxNBZDTsn/iJn8D73/9+XHXVVe7YxMREH0RfY+nP3asQCVJS9AJCr5zJ7S1B3DhGe8t52HkHcNFFQLDv+0CjgQNJBXEMbBo8UECaY2ODGg4NoQCit1qWCSNNLZdzmM5aXvHp6bxu5Kzgb9aRoHpG8jw5WbQNVGpxDmzyeffww45mJYhjxPGY23YL5BTXsqgQlqYlMAZVY1CdiNBoBEUAwXHVIAfuJcIeRQjSNipRvmrtmNDhslzvNJtAFFVQIY86V2lsB/NnZdlHXMlxe6vop0I+bFQcFxZ5AwNZEhOiUq/nilRgNaLILSAlp2kUAdUMRG/DUt84I4ZYbVYy8L6wM1yCruyAgQEX/TIEMBbZ9nRM6ABfeX0aha75VMHoaJGSnqJBHC5iFxftWFxYAGpP3YjKFpNnSJQbACYni6Sl4+PA+Dg6UQUPZ3Q9aavLZmHHkuk4slQaVNj8Bx+0/8dxPvylRJEN9jk6au+ViQmgGrWBvfsAYzA2MWH57ZsESfJ2im533Uq2IWMypUxPIzAGrZYNyDo6ClQw271bgGNOIjDyfpRpjgMQfblzd6mBqkxWkrYvBenP3asTH1aY79YKutIudfsRPCZgKvOVATp99BOsB4CikVaeMMbF3GBQUxlssQzcdvn6GuADnD1J+JHPYddmmbeP09vTDgmiystNVEzqYp1osm95TB8vK1Om1R9xnj87CBBoA272TG6bCpIWEMcVBMms0wUpcHx5yTFkg7+LmC7amC3bQpFAblbnMJs3a7Wcr5/vE2UyPFxoSj7uixUsJpK/l7oRNKot+jtJ8mChkvNc112e01kDRYN+2bfPxiKPLSx064kG8sLQiMOMCi/Ph+9p9GB3r6tR2OW5HmSs6I+39OfutZcVa+a2227D9ddfj6uvvrpw/E//9E9x88034zOf+QzOP/98vP/97+9P5kdZNAjn807WgJ4EXOldDqDgmQ7A8WUT/JMe4wTRh4eHMTQ0BAbPlACtMcaBjxKklUJQWHOFS490egPPz887QJUewfTuZV6slwyCKj3R6Y28bt26Qn2kFz1BXZ/nPPVHcFaC6LoeBNHlrgDZZglKS0CVXOWSG53gsAZfJeAsvaXpjV7mAc58mLdun+4f/mZ9qcMwDF0AVOnhTdCbutJe15qqR3uPA3B9zkCxh0mYBgtIG2NcEFcGYGU9CKJzzLC9BLhl/8kxOKjeSLUxgCD63Nwc2u12of5yx4I2wrBdAwMDXTtAfOn4rXdMPFnkW9/6Fk4//XTvuT6IvnbSn7tXKRLs1fza0tuoZMFGVHD3bmDnTuCSizrA7t1AFGG6VbEv6ozqOD4ORBEiWO/Z4WG4FTgxYWKUxmT/TE4Ce/bk0TqJhhIFZD25KohjzKYh9u+3Xrx0BkYjKgLwrRZw332WvDJzgw9rNQwPB5ibK6qH7+QDAyiC05lUtIeT/C09maTxQYHeQa2GNA0LXcLLCiC6aGeXSPA8A0JdxYeHcw9pCXJm9eEhekBRB9GGKoJ6vdj/WfvaadAVQMtHlTo3B1RGFXKd1TcwBiaq2mw1UCC3JrAjeG0cI+D2Bck1Li4lxzhtDrUaUEkOFMawqY25oF9s2uhonm2zCaxfDzQaY6g2MkS72cxJXMmlesopLrLn96cqLkSAvF3Y9fW63Wmxfn3gPL/YLazH9HQObI+M5EFA5SKb98z4OFDFDDA5bfnrs3EeykyRL7ilF3qQtpEkoVOh40efngZqNTSb1kO9EnVs/kTepcJ8v/Wzgro6jqTX3N1fiK+d9OfuVYgabw5IdK6pRd5jCUxq+oiuhADImyw3fS0s5HEo5PWOhzoVeXjAYJ/nuZzSyugqgO6YF13S4/6TwUhDk9VPguW9LAwSgY+ivA0KMKVw6U8v4zjOQElphC7jD9fGB9kuCT6X1Tm7Vl7OAJ5Of9mzWxqYq1ERME0zQ4d8dPuKlFUtUJ7p537ZRQJMD6IIoTEIY9MF2GvpMhj1StyDh6zM07rQNdmH70PcGaa7T4aakce10UbWP3t1LszHAKxRIxV3sMsg14scd7IsORZZZ3t5UOgSxjNAlL8zsa6hkcFH/Wp9vKU/d6+NrHgvwpe+9CVcccUVXcef97zn4Utf+hIA4KqrrsL3v//9x167vnRJGXi+nGt8HxnwUYK6MhAl/9fgML3KpVc6PX19NBq8jiCm/AwODkLSqbDekrJE10lTmGjqEunNTfCa6SSYy3IlwCyDVJbpyvchiCzLYn4aTJZe0tS9bJMO1OrzXpZlltG5yPZqT3idr6yz/F/Tp7DP6NEt9SWDh0oPcZap+0WC0HIMSgOBrgvHi/xoj3n50fqQgWB1eurWN+bYR2yrBtB1eb3qovX/ZAWIzz33XFx//fVIBHo0Pz8PYOkYDb5PX1Yn/bl7lSLf+n0LO73g8Z03xsVURJo6Tgq3YCaamaU1xnqhG5MvZIK07YpwOADz4ufwYT//tFyVZIAwk87N2e92GuQIJK+RXBlZu4wpLuZlcYODSl8SqdWRIXvVkXpn2aIQDUgnSbYQlistX3564SD3Fs/PW0XI9otPxxNoanFRxB+VHv+iLC7adH31mnl+HsV2q09hDaMXNPPz+X5oFqIjbMp8PV0kA9YWXNSzZ7b0L5Bq5Phh8o4Ji2AGQf7Dh/N6xDEefjiPL0rMXX5arXzoyXK14zZB/EcfLTaZQhvIyAjySjabtk6+zlBt5D3Wtd1cKDAlgMD2loEVUspAjeMMRO81d+tnyrI+fVmV9OfuxybLHXq+uArLyZffmn7C7abRqKLn/aGjAEB+5OOb0ssLeVVS9o4jn2lLvQOZIrgrp1Y5983Pd09ZXYpcqn6ai0z/1t8lz3jvqawthelBA/TLVKE8Xmifr52+i/SLQ1Yh6xFfHGf6UdtFueMT0W/y007tR74KyFcCTutzc/n8z36VVeW7Jftdv86VDSdZPcYzYZsKOxfUHF6mVqB7uue7mfaW10KdyOu8xoX+3P0DKSvWzNjYGL7whS/YLQFCvvCFL2BsbAwAcPjwYYz6yCz78phFgkmLi4vQnsWUMuBO0qQwXafTcV61muPaB0rLvCTQq2k0NGgr00mvZ17D+gC59zS9jMsMBRJk1lQ3Mj8fn3fqHrzG5aXb7ytP9oPuEwn881t63Wu9aQOABJMl+Cy99GX7qCdSh2jQn8dl3hrwlm2X4qOiKaMNkmONFDSyLG1Y0fXUADv7hL99QL02Lvh46KU++VvuzJB9Jg0Si2JC9N1Lsg5leuFYKsvDJ09GkPjDH/4wXvziF+OUU07B0572NACWrw3oe6KvpfTn7lXKUgs7+b/vO3OvnZgAnvKU7PiJJwL1Opr77EKjqvLQRboglrDetWmaAYr8MTJi3YNHRrpddzQwnqaIotCxthQ85/jCTA/dej2nCqGbcFpcuHVRj/OkBJQ9oIFXzz7QWywCJF0pm95V96Ve+lk/yWFOT3T2gWdFxaQaUE4SWI9muq2laQFEp8HDt2uaeQwNoQuAl0ouVEWC7UBOuOvTpa/tniIKwcfkHnBRtgczQRQV1dZqAVW6tNfrKGxZEEDE4GBY0An5TyWrz9BQdj6xmTNgKr3LWdWhoXyL9gkn2Hzokc42DQ8DaGYNoNu6VELWOLYFsHWgJ1+BczUt3gC8H7ueBVJxSy1ClwO8Pw7Sa+5e8eK6vxBftfTn7tWJF/d093z+L78lD/NSGfumfi321s/n7kIdZF165MM2yCx8HunyHcFbFtNkzzQJDEqeZ28lfAijrFh2LjAGxuTrMC59df2N6e1VX1Z/563uA6N9QHcJaE1ee9YNAEIxIDoIHG20e+aXNN1XTYpMUwhEvtTFut5LDEg9RxujxkOJXnx0OzrPMpH9J/nJZXG+adD3miynYo4Zvtt1BaMvQ7vTFDJQ7sJCkVZpmaosFe+zRMpxxhven7vXRlasmXe96134hV/4BXz1q1/F05/+dKxbtw5f//rX8cUvfhEf/vCHAQBf/vKX8exnP/uoV/YHXQiWAkCapjDGOCBdez5rr2ftgUx6CgYBld7Y2luXHN9hGLp0BBHJA05ubgDOU5h0Gz4qFIKY2nNdApIsa2hoyHkBS8oQ/tY85VJkIEoNFJOShmlIqyKDmUpQnfqTFB3SIMH6JUlS4GGXNDM+b3KCufxm++ayBSgBXhlElOmHhoYcnYwEpRcWFjAwMADSo9D7X3p1ky6HIscKPczp5c3xIEFk2RYfnY5vPGqPcxnAk/0zlEUyk3zsksNcBmMlpY/saw1Uk6tfGiRIEyQNO+12G5peR/Loy35jv0pDhGyzNljQUMVjErj38aQ/meTpT3867rnnHnziE5/Anj17cOTIEbzkJS/Bm9/8ZgB9YHytpD93r0KIEvM3F3C+VYv8n24vxlguiTTFGZP/hDMuaACtGvD85+Obe0Ls2mWTbOLiMHP9MfEYBgeL6wVjAkRRBsQDCJoHbPpaDTj33GJA0EYjXyClGchLabUQRim2bas6R3E3Lbion7D5ZjQpBzBmqTOaRTyeTSYQbAzQiauWZ7TMG1oAkE53ApXtGMv9aeIw346c6SY0KcZqMchLKgNtFdroAwUkbUymB9dWHwAq6k098XJ6RzGb6ekAjUYVlXQ6TxRFaDYtrfwpp1hV0oNreBhON9UIqMbGcnNTNxkKPNMKwO3ShbpzrLCfNIEo0/J/tfrTTD9U19wcEK0fA+J8mNPLjKwsBKnJn04jimPIMSHGJ85GKN3YOLayuhEIF8wzMMbmNzJi6WGcB2fGX7R+/RiGh4ELL7R1Ia2+bCL/p+2HC/0qZvIOPOWU4t7wbOzJ28fpJ+sjlhFFAJqt/L6OY2zdinwg8FsDPfr5IP+X7nHH2UK859zdD062ZtKfu1ch4r7N/gWQ81TL2zNAByYOutjatHQQAFGl1H7uKCBQvNWNyYNsA/ncpmk5+K0BZgZS1PXTZcn2uTwT/fj3eNFGOT1J0Ast9B0n10x2PjAGUZRTXczNdd/6si2FR4jPkKwVrWnaytBrnY9UVppmvNYVHDpk/Q/CxD6/27ENuD48LPjsVZwLIylg0hSG7yye147id+A4zr2AsM9Q0audJWXlbHqBSJOPe/nuRE9sLRLQlsFz9RjUNEJlwHya5jZ1TbMiy6TwnSCKhFe9pPiTkik/QMeNPX2aAeHltMX2+aZon1+CruPxLP25e21kxcPhjW98I8477zx84AMfwP/+3/8bR44cwdatW3HrrbfikksuAQC89a1vPeoV7UsOohN4JIVFGIYFIEpTh8gAi5ITXQpBbwLlR44ccSAqgVqeI0AoAez5+XkHJg4ODhZoSQg0k/OagCLpQGgMkIA26yCpTlgGvwlWz8/Po91uu+slr7isr/7mb4LpBE/n5uaQJEkB2CSYKr9l4FNpHJidnXX1l8YC6UHOPmE9CbzKdh4+fLgQPFQaSghoD2euUxKcplEjSRKQ357jhfrgWJAguuT5Jge5vMa3C4G/eb0ei1LP1Bt1Rx1LqiAC+BLEl0YWSbMiee8lVzv7kyINCnNzc+h0Opidne3aPcCdAwTT5c4KyU9PPUujjA7US33IPpM7HeTuibIYB08mqVQq+Pmf/3n3/8zMDN785jf3PdHXUPpz9yqE7rAE0BW3dAGgVYs0B6Rt2WI5K2680X5/9KP44s6N2LMH2LXLAoqXbM2uzVY/Ya0GenTJBUkUAZsWHyhQbaBet2VkXOdJAiRTNn1mj8ToaAVRvYKgNeOIqMM0RRhFiCbOxsGDWTlZ8M65OWB+PkSrVcWhQ5ZGen4e2LEjC2iaBXwci2NEE8UFpA0WGaBer1oPN5K4UzweURKlLOLuAcZqca6bLGhrNQNk23HFqTpBYI/LvGVZWR9xm3LUGENIZFvv8+X/2YoxSWy7AMtVzzVIFNku3bvXJj+baHccoxNVsP+7NgDmKacAwdQDqEQRKuuzOpJzdnq6WM84xkzGwU3cl6B1GyHC2BQR5JNOKhomNJ0N2y50HySzqMYRoihwXv3kEN23r9D0ri6jikPTQaNhF6xTUzkNC2n6t207H3EMhM2HuvZv12oWVFpYKILym2uzNqOpbnAhjCKESFF9dApotbA5ioAoq1wrBWo1dLaeYds3nZWZ7V52lavV7ADW4EQUIYocLo6xWrZob9l7zGQqDZKsfvU6ZswYIgCbTVYWgRY5diSKpxEWjk/ex8fp6rxs7i6Mr+XIcdq+J4L05+5VSJoiSNsIxTOfInFV0kMEUCzpWQIJCvowPAmg8395zOdxbW+FbrCvO02Xra+sqV4wnjzUOq3Oj8cscCk8mH3GZV9G6juMIsgAnL56O3BUl9Nt+VjZs0Mqgc9ZGTxUzu+1Ch59lDuVmvb9IwP+C4Zc1VGBMZY7nvQqUQ6s62pQ3NgxAaIoBIxyEvB14DKby08ZMC6nGV0f6UGuYwFItdEjvABoy2++WyC/j/T9VPUZlnsZCtIUaKG7T3soIYDdWWHi4nup9GuRbZLV8b2a+u4XLWXc8Y+39OfuYy+r0swzn/lMPPOZzzzadenLMoTgofRI1hQfmjpCewAzH/m/BAwJ6hHEZEBHApva45nfMo/BwcEu72ntRS1BSM1RrSkyJBB65MgRFwSUYGySJA7E1d7TMk+pI9aV4KsMjEoPfen1Lj2TCY5LChDqmnUhaOwDYTWHN9tL3S8sLODw4cNYWFhwxgemGRwcxPDwcMFIIL20JT2P7Bcf3zz1KL2jJfc5+50i9SDbw+vL6IWkSG9wjhGOCe48kAYitksakeQY1vzoURQ5UJu6oJEmSRJXtuY3l1Q8moddUtTIccBvHx2Oj07GtzPEZ5B4ssn3vvc93HLLLXjooYecAQzo07mstfTn7hWKXEn04NfsAtAliD4+DiQJ2p/9LKYAnNpq4bbbLFi5Z4/Fv7HNFK9JU3AJIosLkAUlJSAYx8D4uPUUn1Je5cidf+mBs2nU5EjnffcBw8MIGw0MD1ddtSU39b59NunevTa/LVuyjOmanSSoZPrpRBUkSc5PPTAAbNogvK58Uduk/jLQMWkV161xnAWXZOUIzEcRwkYDJqoITDlApWwOysAUcnBbFpwQw8MhqvXYzyWeVWJhwbYLsItr2lZC00GrFWDvXtsdZ9fyFVerZa+Zns4cee6fyilk5KptejrfUZB12HSm96mpIi6eJHYRXuG1xuTcJVKP2tNfeqOLhW9oDMLIoI0QBw/aw1NT9juOc5YbucCUC+mqSYHIYDozvkxNWQCdcVZHRoCzztqI2ni2cyIzEhGIX7/e5kcPcOzdlwd75Ydt4eDetYuWmuL92Ghk3oXGVkIFtwUA1Go4kFSwsABs2JC1I9NTaDqo1wNUTBvYN2nTZ7p0a06OkUYDk/vsrR3u2WOPNxp5J3EMyV0serzr58RxulAtm7v7C/G1lf7cvUIpzNVhF7Dt6Jr08149eyygnBu05+aKU5gEIaVvGh9P8pjGhPX/kk5Gg+gOvBQXSEoS+TiRx3yPQWYhpyGJXYfyoGc+9OpafidJRu0SFinXkGcbpG1/HlIhPgOknox8dZDKkPMhlZO1ifpJU7gfcwM2hMemDZ3cit2rvUonUnXytHz9yYeZ7b+Q87Mcg72A9DQFBGjP1yuOOW3s4bQl66dtA1rFekObG/O8kPMxFag/PikD0X3/6/Q6T3lMGyCMQRBlb9CpPRZFlWVNQVon8pHgS8fzxyOQ3p+7j72sSjN33303/uqv/grf//73ceONN2Ljxo246aabcMopp+Ap3Gvcl2MiEtBdCoDSvOMyD+kJq7mpfTQdOhimrIsPSJbXSe/dIAgKVCe+NshrKQRDmYcvkKX0dGY+PkMCUOTqJuAs6TbKuKult7GvL3xexvp6gvDMQ7Zbt0kGVWW7NX2P9CCnN7s2HugAl7o+kjNc6sinh6XGnPzW12g6GEmHo3nrCaRLnchrWX+dt6YxIv2O1LnmnKdIj3/dN3Lsy3ZJEF3StshrWSefnn3lPZnkz//8z/ELv/ALqNfraDQaXeO/D6KvnfTn7hXKUos36eoiRb7UZ6uQFoAmgFNhccKDB4sO5YVrPZJmiwHnoiyIwVvTReYMwC7Iu9YzcgV1+LDL2Kg1HANCsSiuJV1eGgCMoizYVU6zsrjYg6e1TIwpePR1KUA3KE0zby7h1VeyAJVb57XOoyjbZi0VqPLwdkuaYmEhdPqS7ZBVdv9wUZ/1W+F4llAHZ5OL8ELTFha6Pc5Ztk/nWi9iwR7GOVAkHcwKi2etg0IFw4JupRc9N29UBWJjomLVjRFef+S7KQP/Wy3gkUe6FcT+o5FFd3K2fUBS14dG6CW12/ILO06yPspB9Lw812WCcqa705chZc+Q40B6zd39hfjaSn/uXqF47kHOS4BnOPa4b32AqLYFl8UV1I/csvO9bo8CgM6LjClwe7NePiy5TCRIKLJdWnxIo6ob0rTAkU6va8mZvqzGl81vS1VWni8D28va1Kt+vg7N2grY+XspDFxn08Wbv5L5A/5As3q86mZ7XqV6qqlQhg80l8YW6RJPketiHYEXKP5fNrfI/ijTkUa3s//5nsjLtcjzZWmeSNKfu9dGVqyZW2+9FVdeeSWe+cxn4p/+6Z9w/fXXY+PGjfjmN7+Jv/iLv8CnP/3pY1HPvgghCEhP2jI+ZekxK8EqCUgCOUBYFkRSAuySjkKD4PyW4KQM9EmAUgK+ZSCtbpMGSyVX9tDQkPOC5ofe5QSTNUc1f/u89umF7as79S6BU00dI7nBfbQdAAogt/T+np+fx/z8fBdPuPZYLzMOMK3kbSfILgN0Sv3J6zT1jzY8yHGzHEOOrJf87fOiZ/0kB7r0bGffyd0Bkpe83W4X8mJZkjNfGh3YRt0/ZcFpfWPVZ2jpS1Guv/563HDDDfjVX/1Vd2xmZgYnnHBCH0RfQ+nP3asU36Kr1+JPLvJE2hhADQBaLWzbZk898oig+IwiSzVRqwFJgqpJMYsKpqet5/OGDQCmm108053ME5tArjHFWJPEayNSXxiT86dnROayGVFkPYSBnPOa659GA9ZTl/VQi9kwihBFYQGIrUhPMLmw8a3iUht0Uqo2TTNPrTguRozM8uWilZzsOj+CqUHaRhSFXeuH+Xk6tgeo1OtFj/QMxB0eDrucitMUMJGt6+hopt8MdG2nAebmcjUPD6NYb1kJzdUuhpHkBR0dFfVupv4IeOJ/gvHGCJ7bMgAhSbBhQwWDgzY4p+Q/l1u6AetdXnBjy8aP9ezPvcq7FuRx5AZogA7S1OO5laZFlEvry5g8sq6oOw4d6g46p906s2ubU/Z0o4Hy+1gt5sm1ihbcPedo6ZcyYqRp8V7RItt3nHGP9pq7+7yqayf9uXsVIsamvJ1LsVcN5mX3peYt18J5drmBMoXtsvRxLKtk0wbdXOUEB02Rg1x7ydMOqR9JnDNZd843Be/8Lgu/qJj+XwPWJc+GAoVLL8Tfh+zL+U1xvzuectnJepePnHOz3+6nem53ECCQO4s4JsrEgbU2L/aJtPGyWnq60Lzipe+VJYdkcG8el6qMIptGT5tlKudUJJvcNb6lLnU/lunJN0bK+kan8VWUZfIdk7/lDZ+JNDbJe1Cfl8NO/6/7bYW2jjWV/ty9NrICLVr5tV/7NVx//fW49tprC5HAn/Oc5+CP//iPj2rl+lIu5G4m4KiBcsDv7atBKALakhOd9BiaZ5u0G9qTV4Lq/J8BGhcWFhzvNr24JZWJpHHxAfQACrQqQO49TtqPgYEBRFHk8uH/AFw7tG60h70E3AnKS8oYgtpsC7296QEfhmEBHJZBOEmpInVIQHZubg4SPKa+6IVO44AEzvX/sj38TTodgsgAEIYh0jR1/SyNLLyOfSO5ymW5TOcbS7o+vnMsk/0nqWc0yK8Bdupdjj16f7fbbdc3DDaq+4E6kH1BgwW552UfUagPvavDZ/wpa79Mo3XyZAfiDx48iJ/4iZ94vKvxAy/9uXuVIsFYTazq4+7UIHF2TfjUp+LUu+4CpqbwqldZsLHZFGBeFOHf941hagq46tIU2LUL0aXPwt69FlvftPiApanYt8/muX07UK87Cg25fpEMH2lq34FdgCwGD63XXRtktaPInmo0LN22XHCcUTsA7JnMgXy69tELOI4xMnKGc8xtNoFKPXaGgcJiS+qSgHWSOKBbGgFmWgGi2kaE9Xqha9ppgDRb3zqQORF6J4CZ9U0Qx4iiSqHbJMZpTABjKqjVKojrOaAQxyEmJuw1BB44HIaHLa1HrQZnEWEwzvFx+6lG7bxjsjZ0ENj8Cdxniz/qOopcXFc0Gjl3r2tbL/A2TUF+e2OAilzTasAiKzAAMBbHOOussULX0H7BIhwvOOAQ9pERW8ehoZyihdfSMa1jQhtwVtRZYt6FuhHR0YVHkeUUIq95FFkOGfLekO6H55hHptvZeCO++107VrZuRVGPxtgFkb4+A2MCcXOQYj1JUERHZD05yORzQwF0XcjKSha2ayD9ufv4kP7c/dgkQAdxHBT+L0UPxf3ZTv0AurRflmHG+hFLb2D9eiDTs3hpxMwZTYJuYyhgn02w1BWhMajEIk1kgPUlgKwHkEdTzdFlKKtPZ/J9KPufwKTXk17zzOh5TQKimfiCVmqc33q6h4iiEGEtN2oDmTFeSgKceGI2xWRzBXWeJEAUV+1znzv/9Puey0dPZHDc3EVecKYXna2BWDl59hAC76EBwrj7vA80lt3pY+nRXS2nqXx6En0tB/RSwHnZbx+Iro5rqhQfwG2rEiKMirszIIwtPtB7qer57lHuAJFpynaiPF7Sn7vXRlb8xvatb30Lf/M3f9N1fMOGDXjkkUeOSqX60lsI8NKrOwzDAue2pLqQoJ70JpcezZL7uiyAqASiNWguvc4JcvpAdHpHVyqVgje0Dn7q8wZmu4Hcg3x4eBidTscF15TnfcElaUjwed5TD0eOHEEURQiCwIGqC2IfE4NhEqSlFzmFxwlGs6943fz8fKGPJPAvvaOlAWFoaKgABvfySCfoLNPSo5ygNUF0H4Arvb0lfYo01six4QPS5bfOV1LSsN+k17mmQpH5ynGxsLBQANQZNFQGzI2iCENDQwjD0BlVwjDs2l3APuIYkfqnoUTSEul68d7SuwK06PEs9bRSj+wnkvzET/wEbr75Zlx99dVd5/qe6Gsn/bl7lSKBS7nakCsLnid4Jrm1uVi8/HJg2zZgchKbdv4Dnve8H8WePRmInnEq/59PWoz8qotS4I47EFx0EfbsCS3uOrnX8pgz8mOGru67w3KWc13DII0Z9oc0tUBvmM7af7LF4Gw0BsCus7nIDQHniR0CqBoASIHmtD2+O/smeJ4klp96eNi2s17H+ovOwKOP5iB6HIeoEkT3rdyk/oxBFBcXLgcPWkDaXmLnEa6z6HFfq3n4bSWSL/oyrlfcKZlEVunEEy0Y3GgEqKYWQD21kQGxSeCawi7nBgLqtrnPntu6NQtyOZ06UPZAMxB0AAHGCKIL0X0p+cS7xh9FLfBThKSORxQFdlmpx7L8PwMKxrZsAWqx8/IPklkbYFMSq7LhtRo6JsToqB0C9XoezG7//twQMj/PSypu8S9vmcLiVloRuPtAguvbtqGdBjh40DISnXHpFsuTThJ5IN9C0Wg4JbbTAHv3AN/+tq3nFVcgJ3/XCFh2H7dNBQAQtpqOC70dVR1nfbOJ7gZIMIE3Iccg+4z89xp4WgI4WWvpNXevGPQ/zgwETyTpz92rFIFyuecfn7XyvpMgoLExIlJh+5J81hpAjyIPSAwgjHIvdmKsMuBjGUYtsUS5KSeKuumnNMe3E/mML7vvytDDpURW0AekZ8e6OKJ1w3111gZFpRwNBi8s5Kx0Wuz0UQzOzv9ZdBRlsTHStnvmG2MNGIcO2c+mQbgYLPR+D/S7i+wL3d6y/2ko9uhUGgu0EMTtmr+VjgPR92H2KcwxmYGBu6p4OfWqg24Whoicp8qQaXlR2ZjwXK6Hhf5fFyPFxnDxe5QvdX2aZpRyasyRolAD93yPWWqnyuMl/bl7bWTFmqnVati/fz9OP/30wvE777wTJ5988lGrWF96CwFAgokadNZexjxGQBwo0pkQxJRe4vo6ivY6lx96vUsub1JmSMDMB2JLEF1ScJC2g0JAk8cIREsjAT2XWW+pHw2iSy9uCusivc2lh732Qpfe4dSl1Am9ywmmS6oW1pfXELQluCxpV2T9Nc2IPk79Mcgmy+BOAy3aSCKNJUyvuerLxAcm00gg8wmCAAwEKg0Pkjdfton1kWA/we/Dhw8XxtvIyIgD2iW9j6S5oQc7+5Zjl/WV9db3kzZC+LzVffKDBgRv2bIF73rXu3DHHXfgqU99KgYHB9EPLLr20p+7VykadOQxvQAEuheASWJ5VgDgnHMs2jo9bYH0Cy5Ao7EZJ54I4LBd0e3caYONIk0tMp4kmJqyYCimp4H778+BwijCTMsGypya6gbQ6U2VJQWarfyfWg2Te+2/ExNACLEgazZtWUSZ5+aAu+/u9qYdGLBp77+/4DEcwtKmtFp2EdpqAVUJVDJf6b0lFoRB2raBzTJZXAxcXocP20sIntMxPUCnG2Sm/uWKGUDYyLnbgdwOQKCXl87PW2qT6mLW7szL2dQ3O2w0c77Hpk3ZluoowmwSOHA+mH4ILupoBpw29+VqtN8BoqjiFnCsVxRl1DnNlgvi6qTME120Pc2SFezSGoWgfghAS+/tWs1uZ+euA0kjxPGdARsFIClN0V5f6eLRz8di4Bag5Hkv1I+IFQ1SBKmz7QIPTAVoNm11bRkBzhgftwcYAVdYIWZRQdK0l09O5rs2CpXTkumWdQ5F+TKWQZLAghFyLOt3qzS1liAg3y6hvespx9m26V5zd38hvnbSn7tXIRohk8+7XuCvCZG0/Lgo0A0uugCZ3S7R1hs5ioAoKDyLe71GaPxYxggxJugGUOV8KjPlTrEy6cVLLfXne64xnTYYmm6vYW+DlwLRpUFYPTf05T6QNU0FTV4mAwN511NdjQYQtGZsguzFyRirGqbZ1DAukDuvD2Wb+Fv2vw9VlUYNrWdlEC8be4Bql37H8TkpyLR67jEGQa1mKfeMsR4VyD33KQXgmIPTtwMr+5bGIyB/BV5K6Jchs9W+K7peeujKXRwUGgXkK1Lp9NVVSFiwlRTSZBkcjyB6f+5eG1mxZl796lfjV3/1V/F3f/d3zgPzX/7lX/C2t70NP/MzP3Ms6tiXEpEgs/Q+L0snPYspPlBQA4GaS93Hha250VcDdmludZ2nriu/fcCu/J9gtw90lqL5v3t5Fpfxw8u26I/P6MB2ETiX4LWvbmV61frROpK60jsRZHppLJF5awONTy+6/VqfvvGldyMQ4JY6lNdIwwP55GUd9c4IyWWv67OcMco+1jo9mvJkp3P5sz/7M8RxjFtvvRW33norAHT18XKlD6KvXvpz9ypluW/HS6UbHbVv8tPTDpDkgo2E5s7ZlygtxCKCAKNaWHKxwnVQKQ4nruvydNKLcnmM6LJGZAcGctc6WbcyWc6Luw9RyH5KLz6nt6MsZYs1qZdeTSCAUFhsqcx8a9scIEGxDF+/+ESuJFe6mjPGH5HOV75aPDKJMSptDx2laeZRWSYK3HHBaQWwzXtiYYFAtmd1/FgWieJ6d+9lbV9c7I1Nuet1P3AAP4EWpL3m7v5CfO2kP3c/BvHdi0cb8dLzliy7++exE/kMllFUtQwMdEeq1ghj2bOqR0O8AHpZHfUx/X+PckqDj6O7K1ak92UktvOX0FWvhL2O+4yt6KZh0VXzzp9l726+csteQHRBvcQ3PtS75XKGYS8pu6V86XSVFxfz98Ml5+rHImkK0sUcb9Kfu9dGVqyZG264AT/7sz+Lk08+GUeOHMF5552HxcVFvPrVr8Y73/nOY1HHvpSIBHHpkUsvbg3CSr5s/i+DaBLIlMK8mZ+krvCBlxLwpFexBDol/zk9f8nrrj2O6UEsPdc11Yf0FJbewjqwJ89pwFjqQNLdsF3MW3qJ81uCwqSvmZubA72ZZV1JFcK8JXUN68Q6yLpKOh1Sykgday96qSsCyJJLnu0gxYnsC22wkP3Ja+WYk+VJvVH/ml5G9j3byjRDQ0OFMcN663xlWTxP3XC8+AKf0jtdjied3gfwa6OLrAdF31eybPnbZ8DyXfdklHvuuafrWD+w6NpLf+5ehaQpsG5d0aXbt+jk/2WApzEZbwus2/PUFJAkOOGEjJv59/4auOgiTE1di/37HwFqNrJnJ65ictLi7jDGujuLcom1N5vWK3vbtpwlgtVwXkCiTkFrBlu2VAEA4fQDOSk409FjiTQsQ0MWTNfCiFZRZAnUybOeVdcF3GTecZxXTHqxSa9j+cKephgYCL1JjbHVGh5GN18920CCd2Ocm/5MK+ja0b9hg72EjtYnn2yPbdgAYDItWCoCWE/2Ws0G0lxczFUjh8fCgqqssRy7EgCW3lFRZOsdRWFOqS3bpBc+1GHmOd42FaRpxn+ejY2REdsHQTJb9FJjwzWlyPBwXl+mlV6OcptDtr09peEn3zSO1nQ+7iSmzWLIJ7phg9jEkCR5XzE6aRTlBoY4RgcBHn44D8g7MZHdVvTUjyILEJF7PklQMSmimh3rJ55oKdXr9QyMiCJ0oorgxAdMVLEwRhQBrazOUy3H/ZMkodsFUa/D8tvKMcffUuR9ItPoZ8VxRqzaa+7uL8TXTvpz9yqFRjgdewDo4u+myOlcG6Tl9Osu4+2rx7ei5vDhlD5nXvmslP+HptPtaawNm9plthc4Kh/IXY1C97fOx3M/dwXIlPXQnvK+PGW95LfKjgBpL/YrN4eKbPU81NWeKELaUqf4DiFcwDsmBEyY70KQupcV1RX3tddzbCkbj42nohom8+jiSBNp9cAS7yjtNADSbi/4PPtAHCuCxyxOvt9IH4uSprrfvNd8rzl6WtVDXv6W8cTLytK/l6qcLE+Pcb4PHm/Sn7vXRlasmcHBQfzP//k/8Z73vAd33nknOp0OLrjgApx11lnHon598Yj0pJZ0IYcPH3bgKOk/CCJTJFgrAWL5kYAhAUAdVFSDiRJ0ldQjpNIAcsCRQDRpOFgPCbpKD2K2QQaglNQcmp5EgqGsF7nBgaI3s9SjbC/TMH8CvWmadhkbyPnebDYL9eS3zxOc+tDndBvWrVsHSdcj6yUDXpKvXLZbXhMEgeuHkZERAMD8/LzTtwx4qr3hGahTB3+lPgliyzrzGvYt+0jSuPB/flNXNK5IIF3SF/Gb4Lg0UshxSWoWctKzLD22qW/S3kid6R0APm9/9gP71Oe579u9oXcK/CBKH0RfO+nP3asQEm5KUFauCHRAKQk6ynRRlCPcd93l0p10ElDZ9TV87+/+Dmd/7nO4a+BaAHfggakfxWZYrH3nTuBpTwNwQWxRY7HVmWwb+/bZOKPP2jELAHjwUAWHDxfxT0GQDUxPI5zeY+tB6hYZyDGjHunUN1qwcXsnXwjLNmY86I4YPI4xm9g5KI7tgsZW1xT1J8F0qVsjg5FZYRw9Vov4vlwHYrpVpHORi8V63YGv880cb2ds1YWFIu02YIHWYPohYDLJ8xUggDEhxqJZAAlmhsewd6/Nb3BQ4fmSHsVYL+rpaXv+0KFi8NQ4trQhQTKLsVpU1LdcHCsgqBNXkaZ2rKQp0GiEqEQGITrYsCHjNOdJ6kYA760WMLat0c3PKvtajh3BGyRZXlhd+dsYCzwRtw+SWcdPHsdANc7GVSsri5FYCdAXsKLAjfVHHgEuvRQ4f1vHto27Oxg0d2ICM0mIavNeYGoKQaOBsUYDZ50Vuji4AIBazdHCkKbcSgXIgPVK1IHjTEpTp4qJCVtV7Bb3ehlwUavlv6V+tZTt4T8eZWBgZYvr44yq5okk/bl7ddLOnhk2YHRoPzUb54BgGIFDoHs4S9AcyGmrvAEzFWonn1+8rSXHuczXcjnnZRijA0krwLwXmkgpA9AVaOyru1eWAoI1UC6P+0B/5iHb4AN4s99yl5eaAgvfvZrMPo/jIM/D2AulsWNhwb5qDQ0BbYQIJyYsX3p2/uGHbX7Dw5bqI4rD3FAjDdXyf0mlwe8yA0UPobpCeb18n9L5aCsNJzm+b2Wc6GmSG745TssMSjLgLdPw2jx+jd/OI5sugW+Of37L6tObXIPyetjq+0gLh5u+jvl1EEB7lnfdYvo+jKLs2dJd3nEr/bn7qMmqu/3MM8/EmWeeeTTr0pdliM87lp7eSZIgTVMHBgKA9CCWHswEOSXASY907XVLoIsAJkFNzWcOwPGDS4oV7QVOwJPALetBIFMDuUwjvZkJJMugqBJE1iC69DTXgCiPMZCoz/udnNoEi6VeyHUu+deBPGDm0NAQhoaGYIxBFEVd7dCivZslQCw97Qne0ntdXqd562UbRkZG3BgwxkBy1/t2Fsg6sr3yMz8/7wB5CYhLz3MGrKVBgnqRwDvr2W63YYxx+WqqFoLrBNvb7XbB0EAd0gud/0sQnSLT876hl7o2amhqF9lXcrwxT5ZNvUk6H5kvv59MAPG1116L3/7t38bIyAiuvfbarvPzmetmH0Rfe+nP3SsQuqhKkBkovkRzwWRMN3c6JYpwINqM6Sng7A0bbLqDB1E/HcCnv4LPAXj6wgKShYMA/gX33ZeD6HfdleDuuyPgsjj3FM7KCNI2Wq0Qk5NZ1f7P/wFqNQzueIEDcx3OLwE6ovOHDuUrpkbDui6ffrrzvtq9m3h5ACBErWa9pE88cQzrG5b/nN7DncZmBw4DOZ5pA37CeYGnaYgoCmEi4V0nFspykZOprvB/aDL+8xQAjAUYJF+3LDyO8f19FkgheE0cOI6BsVqn2J+U3XtsENfR0XzRKQCCMDLAvklgehrxRZeg2bQLPbluBmzQrkCA6AxG2WrlgTeZfaORcW9L8FyvGJXHfjsN0GqiAM6PjFjQKEQbAfUyPZ0rM2v8bBpicp/lCG80AmzZckauW5khK0xvvGxsdEyI5mSetNWy1N+PPJJXNY7t4jpiXzft/ZSmoR07+/YV2jYTb8b0dNGLkLdfs2k/d99ty3vZywDccUcx4GlmGXlgOsT0NHB+lNggA9kYG4siXLIjBowNEDvTChwGL29pjpPxceQRRDPkJIsviomJLOBr2T0vUR7PDgsvYO7b7bHGsty52wsC9ZInFMpwfEp/7l6Z8PEnQTaAz92gMGXLqb0M5ySwHfCkvMAU+cClMVVTSkhvWX5CtIsGxV781rJ8XVHfd8kxzX3tw2ALAHQZiN/r2/fb1w4NoAuQX9PESPzf1U2TZ8t2q2JDYxDGtr/aaZG/W2+6stNhxb1SpCnw6KM2HY36Nq19rwmjTjEjzqfaAWM5hosSSVNB6cJ2SuBev49KgwTB88zI02rmwcD1lKR1IavuA7blsJWe6LrJepeAbEbhfssMVsyjjGNde54bk71vUFl5KV1UONwVJ5P6hikNaE5Eo010fIDo/bl77WVZmvF1Rpm8733vW3Vl+rJ80R7UABwITKCXgLSPQkOC0vLj874lqChpMQgyyjIkeCo9siW4K0F4egoTDJYUJyybwKgMSilBbuntXBaklOClpjeR5WiDAetB0FV+yzYwX60neuAPDg46XUnAVOpaGg0kcC91IClOJHhOsFhS2LA9PmoaeolLYFr2j+ZE197nvnotLCyg3W67fAYGBtwY5IfjIQxDMJDo8PBwF0Av+1jzxOuxo+laeEyOX7ZxYWGhML58HuY0KOjdAmW60X0leddlO3y/n+xy5513OgqiO++8s+u8L9jwcuQHRX9HS/pz92MUutUsLOQAIhclPiCsDByLIkxNWcDybOYxP28dVPfswSyA7wEAmgD+E488Auf1Ckxjenq8uMgUb/tJEjrgEXv2AI0GzEWe9bc80GrZIIwyUCR5UjPAcTYJMDlpQfFHHrGX1et24cggn7VaiLFGAx0TFpydmaXc7txBUPBSHhgARkcDVKKoi6M9TUkMYhcuIT2AAAsyMGolV2ISsRDuRm2EePhhu+glJswYn4Xr9AL8vvtswFR62ftcqzJwmoYMFqtxg1D0Gy9rNm2dgNyBOk3RfbEOBCVoVGTVmWc2rDL9CeOC7GdYL8lDB12MW2cjiuMAY3FULD/JXNTEYrwNG3xPln/wIPDgg3l8UgZ9dQtp0R7bxDQH9zM0otm09WEfscksg/YAN965ZXlxMb8/o8ilQT1D+LntgIXHMdA4w+V58KANIssqHjyY74BAmuYDPk2dzSdoHsiVzk/ZYlNGg9PjSO5mOQ7oXJY7d/cX4sdW+nP3YxcxnRVEgnic3nWaAhDHhDo4pHhW++YwCTRKIehX8Drn5MhvjUpqlFAi/xqBlCizAqJd1knvgI86gCq9+V1wSd97ThmI7gPQfQpX7emKMSKkK8CqNmZqpF2Xm6ZAVHGXaNCXl09P568b1bgDZO8xHFd8PRga4tRiQdlAvpvotvbQhT5F2jd53hhB6eLTo3xfIIAvPND1uxiHnXaWl+ND2gC0PVh+5uZyPcqNCXJ40hHaZwdy74xZYYEx9l3QGCAqp03pshX1QsRFI+TOEtkenbQw3jyJjwdn7f7cvfayLM3ozvi3f/s3LC4u4pxzzgEAfO9738PAwAAuvPDCo1/DvqxYfICwBPLK6CM0gCwB5qXALl+emjKl13Fdpq677+OrO3+X1UNSb/jO+erJ3z7PcU3d0Ut8beslPtDXRwEiAWaKpFvR6ShlPPmyL3x9J39LA4o0mkgjgfToluX3GotLid6RIfvWFzRVjmW9i8PXF9r4Q/34+vrJ5kV+NOSrX/2q9zelz4m+NtKfu4+ylL2QA/6FkkpvDHKOE+mWA4LGVQDrlfNzYrPRKzfxIux+Zmm860ifl5dMODyck5hngK+veV0LqewHFxGF86K+vrUHgUqJJbK+nUwjblt7tvgLjCcxV4HC6xtJgjCOMT8fONBermvTFN0AOn/rffcEDaQSxCItjkPHtKOxDZlOYeEujatbUwEThVVhUZitHE6FPpN1l6vYLK/BQf+CsXSceOrgYwOIIgso8PKuncOppcPpyjNNAVW0XJjK6hTWgFyxE+jP6pWx1nUDXtmFsg80YFQIWssD+l7TOluNUAdLLfjXUJY7d/cX4sdW+nP30ZGVDlHvLfgY70s+xySYvux6aaTRd0wjiCXzhm6GDs5N0F8+D3XRFrxV66CylwMN2smJXqfR12XvIJxPA3SQJIHDhIuBLEwxLgbnLKK48oWKGRpT4LPWupCAbxQV56IyFpyuPpXt1+30gd/dKihk1XVZSR7eeT873hVUXpUlsxwcLBqYdJa+5g0OFl+ffNXT44vTq3e3Q+H6sGvK9JWzEikL5Ho08l5r6c/day/L0ozsjPe9730YHR3FX//1X2P9ehv86uDBg3jd616Hyy677NjUsi8F8QGIGuSlhzC9Zwn0ac9ZSUkiqS+kaFoQ6XEseb99gGsZ5QupUSSFCCljKNqTnB/Jqc76sG0SJPXVpQzUpSey5tTmucXFxUIe1Bc5t0mPAgD0Wmc7JdXH/Py8C54pdacpUNiuTqfjaGFYP5mebWTfsa9YL7ZRtlt7tbOukkJHe7LLc5ouiG0nbUsQBI5DnvQ+LDdNU8zPzxf0KYWe5XrHgPRol/26uLiIMAxdn+hdCtQh9cNvyb0vdxXIcxI81974ZVLmgS6PlRlRfhDB+D6IfmylP3c/RnFIXCYaCabo1Y5EJzOSxozqGZjeAFx0ETAxYZOceSbOALAVwH/5Lyfib//2rbj0UgAfSDE1BQD/gVZrC/CVr1g33Xrdus3W62gjdAEWJyYAfHovYIzzsJ2YsE0I0LH/sE6NBrBjR9HLbWIidzWPY8wdzKu/ZUuxyaOjeQxIbglfvz5ftyYJECYz1tt9fBwHEstDG0V2UbZvn/WYrtUANJtII8srXqsBZzQsr/u905av+7yJFNi3D+0t52HnTmDr1grGJieBOMYDkfUmPnt62tLTXHEFZsbPQzU9ANx+O3DWWZicPBtTU5Yzvl63VdqzJ9PXnj22EY2GrfyePXYxHkX2GD9S6MJljK1ws4mLLqrknN/GoFbLDACTlpObe8HrjSrGx61+xsfhxkUcA2HrABwvjwz4qUFt5Jjx6CgsN/uGCM2m9S4jmN9BiLS+GWF0wNaB7YxjBOh00RkQZ3hgKkAUVTDWaFhdjI/nRoQMlKBj+/i4HVtpFjB1eDhXF3nnTzop62fqrNlEBc08AwF20GG80QCqURtthDh4MKdWqdVsIN563Z4HkG+VqNWsQqancfaOWXS2VIA9BjjzTLtKn5qyF05M2L6ZegBjxqBe34hWy47fRqPotN9sAnFcRSjGAPsLaZaI2whKkRR0r87lM4KgkvRWfyJIn1f1mEp/7n6MYgyGQwvo6aEnOZ0ly0VoLKjqbmcNCi+JmhaK7wJmZUDMUhxL73wCup8vGsX18W2ozLmjy0dfQVDSh3mzaHoItxEiTQNUOCdk1GB2/stA63od7TRA2HyID1H77OVWIyDfaiSCUrSjKsI0i+FRq2E6tZ7ip8YHgOlppPWzXfwX7NoF1Ou4Nz4PxgCb9+2zLxZXXIEDZiPG0gfsO0G9bt+3AHueL0ZZdOkwTRECqMTGgcxAbrzfNJwCw1SSQcUAW7eGBYze0ZVRZP/5EGhNC0iFJwmCNIXJ4pxEkfV+7yBwm57i2NatgxBthIW+ybfYZaLnFGGYkEYC/mZy7YTheQXpcvLX7ENSP6J4V46k1csp+tLueEJCP0EUWSqezKFC1lXm7y1U/N8LONfGC/fbB+6vFIw+XqQ/dx81WXHv/9Ef/RFuvvlmN5EDwPr163H99dfjBS94Ad761rce1Qr2xS8+AF2DhgRJJRitqUI06CpBSKaT3un86ECgZV7N0kNZgpMy6COBVdJu+CgwNOVMmqaOKuXIkSOFQJa6TKkf2XbtdU/6EElXQsCbupQg+vz8vAtqSW52UpawHqwL85+dncW6descyMz8eD31pwOp8nqKbgP7e35+3vGEJ9kMduSIpXshHzs51tkXEriXgDcNFaRCkRQyMu3w8LDrIwmqy3EijR6Li4ulNDc8L3XO+ksDCvuUbWf/+3ZNUC/8LcerD8QdVG++0kOf1/h4z+X9pKlvysDzH3RQuA+ir5305+5VyAknFHmKNYiuVxs8Pjdn6R+iyC1sqslDqDZiIBoHXvQii8ZNAbjsMrwSQFSr4aMfBd7whtMRfPp/AWmKvXsB4A5MT/8o8PGP2wXGdddZJHF8HAcP2kXI1q3ZwnLnTsBYWozpaeCS7bMWNd6wDQ8kYzaYY9S2FxHAzNowE220NBkA4sQ2mwuoLVvs77vusjQktRpQaT0ExDFmUgsgh60DCAGYeMzqYd8+u9CNIkymGxFFNp8ksacefBA45xyg+sgUksYYdu60588w00AUYffuCqamgPPGU2D3bhw88Tx87nOWC/uS3buBRgN3TD4dU1PA2Sfst3zwL3whPvc54Kd/egzBP/wD8MxnYu/dduH9ilcAp0YPYe/ejdi5E3jmMzN91WpuQY077rCVe+ELrVIbDczGGwugdWXfd/JIlLUaMDmJyu7d+TgwBlUCBPfcY9Nu2ADUagiNwTnnbMbgoL20sJV4514ga5dDarPVJvlLQ2M9tdLUDq/16wHs3AXEMTZtOh/G2DoGySwOJBXs2wds2TKGarZinYk2otUENtfbCFstDAyMFTjskwTZmAO2bx9DdaKGeycDTO6zcXGr+74JpClaGe4b7LG6mNh+CQ4dss0kVrJnj83z1PFO5iWYrU4nJy0h/OmnY2b8PDt2Jr8PpCnG4jaiKLRja98UwvFxLC6OYXg4u38McOmlG+32emthsnnv3ZsbgJIE+MpX7Hb6iQngh3/Ynt+9G4hjPDhXtf24Z6et+wt/DM2mVXsweS+qcYxofMxRxzSbwBlbtzqwfGIiAy0Sk6MAGgzRrnJ6n7ymEfJuJTjOpe/NtmbSn7tXJxJb1t/0po4iC5qHaANJisAYONqSHiB6h2my/31O2BpwZBovvzIvls8IfYz/K9Bcehe7xwjBb5ZDJznxjAl5PAOSHfVY9mwK4xgwFVtU01oXW7DPxvHxEEGaoo3QPX43R7ZO904GmQE8ts/piQl8fzLExMRGBHv3Wkqw2OYzlk7aeBNbt2bzVQVB9my/7+BGzM8Dp9angN27sfDss7FzZ/auc9NNwI4d2B1lIPqeXcBttwEvfCFuuw14yQ7Yd4IdO3Bg6yUwBqju3ZvzhdFILCm5MqC20C9TU/bTaNhJMEkQ7Ntn9UXLbrNVDL4t+y8Lwu0GBOd1Ecujk9HKhEkLaDYRRBEWFkL76jg5aetkNiJJsrmn2QTqGx1TWdXYvGcS+55Qq9l0bYTOOT9E29G4cAxGUU7ZIseoHKdSeI0ckoUAveJ6H4jOWADMx9EYURdpmn9TWJgsPI4RZMdDdX907ZRQ844MIKvFB8i7eygVCaQxxNgdDV27M45n6c/dR03KCYZKZGZmBg8++GDX8YceegiH+Da+TPmnf/onvPjFL8bmzZuxbt06fO5znyucb7Va+KVf+iWMj49jeHgY5557Lj70oQ8V0szPz+OXf/mXUa/XMTIygpe85CWYpKWzh3zwgx/E6aefjiiKcOGFF+Kf//mfC+ePHDmCd7/73di8eTOGh4dx+eWX49vf/vaK2ncsRYPoEgyVQReTJEGSJGi325ifn3fe09KznEAkAVN+2u22+03vYJbDIKQEfyUgLOsoudeZdmhoCJVKBcPDw6hUKi7A5JEjR1w9+ZF1kPViOn6zjbLeMlCo5E5nIFIGU+WHx8MwdB+m4/8EthcXFwv1INA8MDCAKIowPDyMoaEhB6wDFnCdm5vD7OwsZmdnMTc3h7m5OSRJ4nYNSIMB6zM0NIQoilCpVFCpVBBFkasb9c32zs7O4tChQ3j00Udx4MABPPLIIzhw4AAOHDiAVquFw4cPu0CcUi9sH8tg/zAgqvR8p46iKEIURYjjGLVarfCpVqsYHR119QTgdJYkiWs79cBxyj7lGNWGItmHYRi6oK1DQ0MYHh7G8PCwawvrTQA/SRKne35YpjQ6ySC7LM83PphGi/ZWXyk10VrLhz70IZx//vmoVquoVqu4+OKL8X//7/9154/3Z2EvWWqO0XLLLbd4n6179uxZmwofQ+nP3asQSQ8CFIEv/SYuX0wXF4sBpYyx4OyePeg0NuP7OAPfn7QLHmzfjuiP/xi45hqE73w7nvu5/wZ87nMCRN9lHbj27cPU1JQFWLdvx4GkgkcftZ7mW7YAlel7MXvffcC+fW5tiD17gH/+ZyBNsXu3rcJsGuLBQxX8+94q/vWuMXxzaiO+ObURX/mKXZfu3p3vjOaab3N0AGOte7Fhg20eF3dotXIWjYxgO0TbLr6mppxn99SU5ZkOkllUog7uv9+Ws7ho0yUJcOedmZNahlzu2QN84xuZzvfsweHDtn5792btmpzErl123YwHH0T6z/8MGIObbspiQH71q8D/+38OPx0fR5bYljU/Dwvy895OU/v/V79qy5yYwGy8Efv22TL/4z+yOJitVu7ZXa/b3zfdBNxyiwXl+bnjjvyiyQwomJzEpuEZjCUPILjlHy3Yu/d7CKYesBaKvXstH7twD59NLa/79HS2UM1A9CTJQPU9e4BvfYs4PSqRDbLWbNosp6bgOpLOeoDV8+CgBeMBWA9AuGGKffusV/rtt9vmffe7cPVy69y9e4GdO1FpPYT164EzJjqo7vsmTo0POEwbk5M58boxtiG7dgHNJvbuzepDkKHZRCWdySuRgSlRBLeF4IzaAfzQtnYOoj/6qG3ovn15ANDbbrM7NwA8kG60g3hyEkgS3H13dul3v2vrns5kQMSs66swscfIGT8bb3TbMYLJe3OSXC6kZdBhDXKxs3yueocO5VFZ03TFnOhP5rkbWPpZr+XWW2/FhRdeiCiKcMYZZ+DDH/5wV5rPfOYzOO+88zA0NITzzjsPn/3sZx9zucda+nP36iQ0HVSiDiqmjTCdRZjOIkjsh/dgkLYzAD1xc7ajEBPzewdBwUiWpvZYx4RdXrGWJqTo4R6aThE81BzeQBEsJNiaGWs7tTF0amNoR1XMJCEONAP3efhhO79yB5q00bmKyecQg2jwwyASDCLN/zPENUAecDpJctyZc8HkZP44hjGYnMzm6ey5TqB9agqu3cSm+dzndUkC551+//3ZO8HUFLBrFxYX7dQBwM7Te/fiW9/KpvDdu+1EBTv1ArBz8u7dOa85XwaaTdtnSZKD5PfcAzdBskHT0zb9LbfkAaqbTftiQoM7g93wGhnAI3uJ6piwgMrOpmHBS9wBzuyXJMHiYqb3rB4LC5nOs/5JU1u9uTm4uZVNSRJbDruy1YIbs0mSU7QYk1OpDAzkQ47nOPw4vcUx7L2Edn4fZeMnTGYQJjOoYBZVM4uxuF3YzNdoAJs2dDBW66CSziCYfijXve/DMciXH+5YkB+pZ97PCszuOIg7cAA6k+tpWYox9r7tehZo55lVSn/uLsoTde5eMYj+8pe/HK973evw6U9/GpOTk5icnMSnP/1pvP71r8eP/diPrSivw4cP42lPexo+8IEPeM9fc801uOmmm/CJT3wC3/3ud3HNNdfgl3/5l/H3f//3Ls1b3vIWfPazn8UnP/lJ3HbbbWi1WnjRi17UFQRQyqc+9Sm85S1vwa//+q/jzjvvxGWXXYYrr7wS9957r0vzB3/wB3jf+96HD3zgA/jGN76BRqOB5z//+St+YTmWormytfe4DLhI0FTSVsi0ABythe9b84hr3msffYqsm/RMl6AkgU6Wr4OO+uqjz+uPpOfQHOC9Pr5AqzpoKUXXU+pHgrCSCkRSlUgvfBmsVepO10sC/tIjnJ7RzFMaFghY08hA/ejdBcxfGw90/8q6STCbwD4BbZmP7F+pM/2R/cc2ac50X59pY4hPP8xX6kiWK/tPUxDpfpD/r1Q0xcvxIOPj4/i93/s97Ny5Ezt37sRzn/tcvPSlL3UT9rF8Fi7HwPBYDA5LzTFl8h//8R/Yv3+/+5x11lkruv54lP7cvYrxKsmdAf+WTi0SJBP0ZFxs0BObONysqVrP9AsvBL78ZeCjH80QS7swBuyL/gEAM4DbGs21w/BwFjNxehqzAHDoUL6Anp62ATLT1C2yk8QCp1wv6s/0tMcbKVvEyK3vzMx1Fxc0vJAruGyhPT+f64br9jQFMDeHNLWMHK0W3Kqm2bTe6gDcovGuuzK9ZQuoqakMhJ2bs7oxBvv2ZcBwtsBlWUEy67aSP/igAP6lJ9r+/ehMTblAshpnOHQIBQ+ygvs2F9REXvfvt78ffdSmP3w4XyRPTxcVLgs6fNihH1z0zc93xz51AMn0NPDIIwjStt11m7UlSaxODx6EG8POuCIycH2dWs52Btpkdz74oG3eo4/CVcDVhYvbVivjdG9aI8D0dO6UJ6Oe8uJHHnFAP8e4O8dKPvxwgd8fBw/m5WVlugZwQc3BPTlpdZsBC65h2bhqtbK6ZvkYI8rOGh6aTgFv6sRVW560MHm8QgEUnxmsoxQqfXFRoV0rk8dz7paA4rI/K5DlPOul3HPPPbjqqqtw2WWX4c4778Q73vEO/Lf/9t/wmc98xqW5/fbb8apXvQqvec1r8P/+3//Da17zGrzyla/Ev/7rv6663LWQ/ty9yvGqjVZ6B0jZh9fKb4opBrz02dOzZF31cB6rZRfxQvHpmBCzGRe4xMDl//Pz+RwhQkMUs9cgoNaH/mSFGINC2jQtBpxcWMgfiaw/H+H0Tk4S+/hOkrx8PlcB2MpnoK8DimUaBRynKex7zfS0CxqO6WnMHjqUP/MBN7+6RywN4HoiZeDoZtP+li9LmfHbFZwkdlKcnCxOlFSCRGizctJUDAhhgFHDo9sTm/+LuVrO3QziSZHFd1AcM7IsXkNjD5B7iEsPbP0JTae7jXpQCmCbQLsD3PXg1Vafubn8+9ChvF9arfxcr3tYS9YY3yl5TF/uu3cLvyWIvkogvT935/JEnrtXDKJ/+MMfxo/+6I/ip3/6p3HaaafhtNNOw0/91E/hyiuvxAc/+MEV5XXllVfi+uuvL30JuP322/Ha174Wl19+OSYmJvDzP//zeNrTnoadO3cCAB599FF85CMfwR/90R/hiiuuwAUXXIBPfOIT+Na3voWvZF4oPnnf+96H17/+9XjDG96Ac889FzfeeCNOOeUUZ20/cuQIbrzxRvz6r/86fuzHfgzbtm3DX//1X2N2dhZ/8zd/s6I2HivxAdYauKYQfJIAsw+AJpjbCziX3N2+j/RG14BXL0BM8mP76q7bXcan7QPiJXAtf0vQ1gei6o/09tfAto9vXe8SkB78GpDVbVjKSCD1shToLssCUACT5S4F6QHOfKVHvPTEJsguaWc0qC93O+h6+kRSoWg6FD0uliNlgKvsR1999Hhge6S+JciuedqXqs/xBqS/+MUvxlVXXYWzzz4bZ599Nm644QbEcYw77rjjMT0L3/GOd+DrX/96zzTHGkRfao4pk40bN6LRaLjPagwmx5v05+5VzN1pWhZ3//4AAQAASURBVHyJ1N9l6Sl08+ExAarTsyhJYFcvw8M5aWpG0Gw9v9djYgKoINt+/fDDzu1Lb8UNARet0xgUt8CqcumFJB3f6HXEKtOGwAwLwUOzbcpuTSG2Mrtrsv8ZbJIXj4zk5WBwEMbku5558ciI4NKOIhhj6UKiCM47j/XG8DDirGDGTWJjyNaCNAUGBhDHwKZNWT6sMz/r1yOo1YDhYbfIJX1kFGXdQ7ct2d+kdoljuMYNDxeVKxstx4NelIn6cD1JiSIASeL6yu10yPJdXEQh/0I/yL3ZWRp6m8lTUZTHl2XWJ5xQ7BtXhK+sbAy7IuVCTGZqjBv2XToAXMBQl+/wsB1ETGuMPSbzHRnJ9SzHPWmVsnEEwDaStDuAx32zWO2uoGfSqqGl18JeZjwwkH9WIY/n3H2sF+JLPeu1fPjDH8app56KG2+8Eeeeey7e8IY34Od+7ufw3ve+16W58cYb8fznPx/XXXcdtm7diuuuuw7Pe97zcOONN6663LWQ/tz9GNbdve5DKXqceub6jgcy0UOczwkClKTgWvYzocc9oy/lo0M+TvhI5HNrVWX2uF/lYxdpisFBNYekqZvv3RwBFxrGVczVj2Vl18lr6BnN6J6cat38HkXusY84RiUrIo6Rz8uSF5wTpzE5sM0XITnJy/e24eHuSZKBRyT1p+wItlN+S32X6FWed1NC9r+IbV041ktYJebFdzpHiZ3Nr5oiu+xR7Rv/pdJrzMnf8sNGDg5avfMdamioOLCXO69kdfAlKcuG35KqyXvRY5T+3J3LE3nuXvFIqFQq+OAHP4g//MM/xN13340jR45gy5YtGNEBuI6CXHrppfj85z+Pn/u5n8PmzZtxyy234Hvf+x7++I//GICNVr6wsIAXvOAF7prNmzdj27Zt+NrXvoYf+ZEf6cqz3W7j3/7t3/Brv/ZrheMveMEL8LWvfQ2AtYpMTU0V8h0aGsKzn/1sfO1rX8N//a//1VtfUpBQZmZmVt/4JUTTW+ggnzxH4HV+ft4BUAx4SWAoDEMX6JLgKvPV/Of0ViYFC3mxNYWF9L4mCKl5qFk3AC4oqDHGAbmSe9rH/06RIKf0hAiCAO1221GQlIGdEhD1AeKkxiEoTc9r1p9As7yW+QJw/cMgoTwm6yL5vZMkwbp16xxFjEwjdSG5wdmXQ0NDjqaFaWQw0iNHjjj6mfn5eczNzRV2B8RxXKC7YXBQGZxTgvGyLNm3PiBccrzLvmQ6DbpL0FSmk7+l7pi/zEd7+HO8ai97igbQKew/HUiXupF9Qj35wF+pHw3OH205dOhQ4RnEXQK9ZHFxEX/3d3+Hw4cP4+KLL171sxAA9u/fjxe96EUYGBjAi1/8Yrz0pS/FFVdcUajDSoFxptXP1uW0bSVywQUXIEkSnHfeeXjnO9+J5zznOUct78dL+nP3KubuxcV8ceUD+vhNjyEe4zVupQgLmM7Pu9NkfAGAeGIzwokkBwIvugi46CJcNAl8/OO/gze8ATjjG7Cv9f/f/wc8+CCGfupa7Ntn1xv1OoAoQrXRALZuzd97Gw3gzDPt9uGsmocP2+9aLf9mtUnHzXPGZAvUw3k7RkasU9bmbdsw0wrQmrbXV+t150UVYtYufBoNwBgRjNHq76yzRHC3eh1RZHlOt2yB09tTn5pxficJUK+jXgcuvzyjZTE7gIkJnEyA+ZxzED7nOUAU4Yd/OCvrssuALVuwI2sXWi1geBhbtwIvf3kWX3LrVouos8GXX25PbN2KJLGLSwZMdcB1M8rTkxt9x44C/70TAq1btth8I3Ht3Jz1wJuetunYkeyERgOT+/J+iCIgaM0AU1MI4xbOqNeAqWmbb62GA1lg0QPNAHE85uq8uAhgYiILCGer1TEhgjhG3Vi9M8hpFFmVpClw8skWZ96yxf7faABIbN/UaplR5MQTrbeYMTnGkIEWWdcDickpCaIKgvFxS4Zfr6NuLECP+6dt/9CSkiQFOoEkyfhnecO0Wjmw0WjYThJBQ9FsWt3GMRb2wzbwoouALVsQ78vqtW0b0GigXduIZDLL86677H06MQEkCWq1CubmgLFaJ98nb0xOAyDvbw2U+Z4X2liSGbwwOlpAO54Ic/eKF9dZ2uXM3ct51mu5/fbbC20FgB/5kR/BRz7yESwsLGBwcBC33347rrnmmq40XIivpty1kP7cvYq5OwNmC6LHq5zHhcHc8Z1nzzJ6VPsuJ9gWZqAkkvy+l5zj3gykZPNeO2VgUztnJy0/eC5BVfntAlzyIhoFy95dWLaogwOeoyins8ikXhdzUZpirNbBxERg30Eyo26jIQyPGdBdr2fvElkGztiNyAX2Hh/P6MgyoHtiIvM7ODwMjI+7qbYSddzEdc4JGZicngM85zmAMfY9whjg0kuBrVtzg/3WrXCVYXvrdVtvzsvUDXUyPm7nagYfB6w1f2Gh+x2P//vAR0n3pfq8AI5nuh8dhXMgoA4XFopp3JwbRWingXtNpQwP2/l/dNT2TWAMNmzIgp5nlEaV2MCYPNhsFBUN94Aaf3ou09++CzUQrfXEdgLFwO3yWmF89+pbl0m8QlUpBBDG/uCiXQB6Wf2zOvC5AAALi/Z3f+7+wZq7V21OGRkZwfnnn38069Il73//+/HGN74R4+PjDvT6i7/4C1x66aUAgKmpKYRhWAi2AgCbNm2y3KEemZ6exuLiIjZt2lR6Db99af7zP/+ztL6/+7u/i9/6rd9aWSNXIRI8lQEtJTgJwIF+nU7HeRkTDCZgzSCd0luZ+VUqFZcnj0v+aZbPsgggElQs+ybQLb2qGXyTPN/SQ5xAtfS01iCxFAYoBXKAXILoEoAPgsAF+ST/N3XBcsnFDtiHButFowOvl9QlzJsAexiGhePsI0klwnqnaVoAdn3GEeZHQFnyfw8MDLh+IvgtdyKQD3xmZgbNZtPVPQxDNBoNRFGEWq1W8DbnuJNjUHKrM29yuzNArNw5IHn0ecyXnxzDMrCoNoDoIKEcX8zD50EehqEbDxJE5y4FAAUQXNbPiElncHDQtY/1Y19okN/nTS099Y8VkH7eeecV/v/N3/xNvPvd7/am/da3voWLL74YSZIgjmN89rOfxXnnnecmn5U+CwHgr/7qr3DkyBHcdttt+MIXvoC3vvWtuP/++/H85z8fz3/+81261bT9lFNOWXbbViInnXQS/uzP/gwXXngh5ufn8fGPfxzPe97zcMstt+BZz3rWY87/eJD+3N0tpXN3mubeMD6PLj4T5EKVL/l86Wfgwfl5RxNhTFDYzdpqAdu3n2G9oEdHgSuuwINPeS5euA349rfHcV58L/ATP2EDbn3608BXv4rRq6/F3Xe7GKO2vOc/H9ixI19njY872ozBwSLWT4+uWs0uyk8+2R4fHbW8lzAGcRzYRfm8bRMdhKamgPvus8/d+Xmb7/i2jQhkwKzRUQtGZotaYwA0rf62b7flxTGARgO1GnDFFdnatmkXShdeaHFOtBLg5JNRNbN4xSsqFifd/kKgVsPEw3Y9iwsuAF7/eqBWw+WXZ/lmwVsvrwv6jjjGqdFDuPrqjdgcHciB1KiKNAUqr3iFBW+3bXMBN084Ie9uu7CNbaFpatOS8FMaW7gQ5GfbNjzQqmLgCDDYAsZqNau4ZtNuCydoMT5uAe/GqY4VJk1zkBvT05Zbha5lo6N2ANTrjjKU69G5uRzTJ/8+h2erZY0ejchiy3EMILEL6+3brT9fJZ0BWgm2b9+IRiPDpydt39QISpAk3xibRwuu0ErrIXsuuwdmUcGhh4FNHAy1GsbjbKzt3G/1QUMDQfRmEwMD2W2XAdvYt882gKTr+/bZ3+PjwLnn2ryze411uXcqxPgVLyjg9LjoIiBJXHZIm8C3v21vlMwjvV63Ad8c3y3vaVLyRJEtl0AAPdT5TOBv6cUo/5fPCuE5/0SYu8kxu1xh2uXM3ct51muZmprypk/TFNPT0zjppJNK0zDP1ZS7ltKfu7ulbO52Y9MH7ol3+U4GVsOEQBbAOU0AIAcWZRbG5IB6FzWL3qEij0vgShvaM+CeUwc3rNEnTGJ5EkCPY1WHJO0GzWWbJSBZBqTpOqbFPMPmQxiLopy+ZHoa27ZttGD7lE07Pm7tq7TSG2Mfk6OjANY1AADra9n/zRxE37IFWdBMC7Rv25rVaVcMbN2KCmZxxRUVq6TLLwe2bMEFjSxN/MPu4b59e9bGl70MmJiAszXRUs8JNaPGcyJfkKijrVvznWbsx5NPtp0jQV0NDmc65Di0hmv7v1N7Ng+4S4QhvmJsoFvpcZ9dBNRqbp4fGIAzkGucntNKiLYLDOue2GKMuuCcxgbVdbsGUBxGaQqbtgwwL94o+bf+zfed1DNeqX+Zh2yMOM57RhqzSsF9VbdA/A7djY7izSaF/wvDWqtVvE2A/tz9gzZ3rxpEXwt5//vfjzvuuAOf//zncdppp+Gf/umf8KY3vQknnXQSrrjiitLrJHhXJvq875rlpJFy3XXX4dprr3X/z8zMdA26xyqsgwRUy3i7CeZJj++FhQUHBpKWg4Aw80vTFMYY523OvCQgHEVRARAlB7kM0ijBc5/HsfZE57cE1SStiaZGkTqRgCS9uWU+DAoqqVlkuYODg4U2SX1qOpYwDB1wuri46LzLNZWONHJEUeR0KL30yVOu6VvYLuYjPfC5k0D2P9tCsLrT6WBoaKiQ7+zsrNuV0Gq1cPDgQTz00ENYt26dC14aZxMEQWMGSZXANvU8Oztb4HVvt9vu3MLCQsFzm/qkbpmP9Bind7yk0JGGIepNi/bGl0YkDVBL73reK5qzXV+nDQG+snhfcueDbJ/2Rpdj9Vh6on/nO9/ByUTGgJ7W8HPOOQe7du1Cs9nEZz7zGbz2ta/Frbfe6s6v9Fkor7vssstw2WWX4Q/+4A/w3e9+F1/4whfw0Y9+1OWzGk/0++67D9VqdVltW4mcc845OOecc9z/F198Me677z68973vfdKA6GshT5q5W4LikoORokF0HpMfCcC7BXa+YCZ1eK0GnEHv2h078A+fBn7uhQ8Av/duu9B74QuB3bvxwD//MzqHDmFzZPHDM8/MvIRhr6MnehQBM2kF1S1b0NyXv+i3WgV2C9Tr2SKkNZMB4HlTQtbdLbDsNaScls7XU1NArVZBJW3lhYyPA1GESnLAJsp0d8ZEB+PjGUBfqyFIZnF2Iy14JG1afMC+oSYJcNJJwPQ0rrjiVCQJ8ECyGckUwC6aiTej+vKXoxNVsGNHtmi89FLAGJzfyDzpdjVt3rt341QufjMAms7gIyeeh8FG7v0nA205sIKNJmI9MYHZ2uZC94fJTAFMeXCuij17cn2PbYmLC/ZDh2wbM090EYcUxgiv7qlpyzlOy8WWLcBFF2EmCR2dd5paL3Gu8dM0B+MliB43KghaMxhrNYE0cokrzXsLi9tqLUV1vGaNGRlI4MAbWmKMsToH8kLpqd1oAHGMQwczXnWE2LDlbATooNKaAaabuec40OWJTkz7wYUQo6Oh3bLfalljQ20jwkbD6m3TJnQmzrAL/Ql77WwSYHExj/smnRC/P1VBmlbyQGxJ0xoo6HGY0eYYA2DPtG1Po5GDKQ8/bFGgRiMf2/I+50cuxvnskLQ+2isPT4y522dT7CVMu5K5e6V196XXx4/F/PFkkifN3E3RALZHypiZOK1LDJBSAO3ks1zzRUvRIKI4zstkNvpSCu36zkucD34N4GvgnN/Zc0d60jpJgZDPJ1+e09PFdk9PI5DxKYxBtTaL6nrjJtEAHTQaGXhsagDsHB0SOG40gCRBmDzggHcYg3Dq3rzO4+PA9DTOm6hbQHjHDiCOcfZE1g/RVme0P+ec7JpLLwXiGMPZa8zs6KkAMfMUMFGIIJsQOghsn5L7XBo36/Ui4EvjsdQpv/UxyP5UOxqyNAXqEI4N9mnWF0HaRiUyQGrcdSxCjlPJDFbgMNcc4vLdVRhzgwjgLggNoBcqr0+WAetSMqNCXoUQJs7LMgbWEcNzz5Lj3SbOvkS7oyjwA+lLTVRq7i3oRp4X9ZC3N41arFt/7l5d3Z+oc/dxC6LPzc3hHe94Bz772c/iR3/0RwEA559/Pnbt2oX3vve9uOKKK9BoNNBut3Hw4MGCVfyhhx7CJZdc4s23Xq9jYGCgyzLx0EMPOQtGI3spnpqawkknneRN45OjTSmwlEhQGSjSfWgvWk2tUcZ9TiHoLQeyBu91cEWC5ZoiQ5bL32Vg6FLX6vQ6nfRe13Qg0mNYe+z7gnoyzzJudEkNslR7tBe99sCX9dVUNhp418FhJS2K5pX38Y/LwKakjuGDhoA786DhROqE+mK7Zbnau1rXU+cj0+gAn7Jftdd6ma578Ztrqh05DuR1mhrI17/SCMMx4qu7ry1lbTjaMjo6WpjwekkYhtiyZQsAYMeOHfjGN76BP/7jP8av/uqvAlj5s7BMzj33XJx77rm4+uqrccIJJ6waRGdE87WQiy66CJ/4xCfWpKwngzxp524uKqXbifxd9vLNNAsLjgN7YCDbqi2ccFotuFVQO6raGJjT08Att1gPqmc+E9i/Hy3YNcQ4OkgS+9yKIgAJujhA5+aAeEPFuxiSzq9B2s4RfVlvbRxAfliu8SR2WJG60Ks80Vjr/ZOlYWAuqS/pzZ2B+RXMAlHFgcKsf6sFIK4gSoFqlAELXPjKSKkDA3lZ9F6OY6RNe/qRR4pdB4jt8VyEypPZAtQFSMv0OhZHhbFxeDpnfkmSbMGu+dABx/3amsoDxRUcv9LUnpibs7w8c3NoI3SxuWR2WpWqGy3YzI4E8o4UgcwAeC92BgXZx1I3HAxiLLE97O44DhAwHY0CIJiBLt0QY6/wXsqAJ+dFNzjoFrZj2Xjh5WxmrZZj5DoOHI4sdAFfDgfxRWebn+8mpV3J6lSOI/7O3qefCHP3ahfiy5m7l/Os19JoNLzpjTE48cQTe6Zhnqsp98kkT6a5O02R8an0Fi9QuFzRIJ38v9cNot8jVPm9Lu2yB8jJ11cXscOlkIkqTxyGMQECOVHIyujnYI95UU429PZ1VDnyOpkX0L2jSzojEMSnAVfOX5IKBXAe7Sar/qFDRQdym7UEdQPrOMCJSoDLhXnRR83ie54r0a+IDrjXHeCbyHVHpTmAy1NdoTV841BbZ2Ra0RTffUEd9fRh1m0pSaLHdz5cQwQCbJfntVErr1OPevgK0lLWYJWxrBO/FxeLrwH9ufsHa+5ecWDRtRJSaGhQjPQkAHDhhRdicHAQX/7yl935/fv3Y/fu3aWTeRiGuPDCCwvXAMCXv/xld83pp5+ORqNRSNNut3HrrbeW5rtWoj2ptUivWf1bXiODV7bbbSwsLLhvnyeuzkOC8hII08Cx9s4uq48vL6YtA0bpAU3gV4Ky2htYB62UHtr0gOan3W47XdAzXILxkpZEfuQxHYzTF0y0Vx9K73ddluR2Z9vYfq0TCYizXqTjGR4exsjICCqViqNuYV7tdhtJkmBubg6HDx92FDBzc3NIksTtONC60YFXNf2ONkhoGiHWQ+8GYFtlgFjZ77I/9RiShgW5a8NXL6aR+tcAfi/vcXmt5oqX43Ol4PFaC3eVHOtnoc9bf6nPWsudd95ZeJHpS2950s3dZYsBvWDyeJR2pRUxO4zJMW8XtyqOgfXrEaLtHFxx4okW7D3rLOCss1AHUAOAZhNbM+ersWg2B4tbLYyP20u4LZcsFFGUx8TqWnDJNpS0lUlY5xNOyPMltunIw+WHF/B/DbbyQ4CbgL726kuSQlv4GRjwqF2vFDKg1SlBJC7rtoUFTzCtsgUuStSXpZWAdrMJ60afcZ9j0ya74N+wATDW05v9JI0WjldlYsJ6oZ9yilMR42nKuJpa7SeemHdHiHZxka091nzKyT4dBEWAQdIX1esFbvdZU8WDD5PnV6lFjpGRESCywVRlALixWscNGRfYNeMzSFPkZUo++qzsuTlnl3BJBNZSaDZOOinXbebd7lQho+6ynXosS/GBKfpby4oRPL+s1dztwwuX+ixXlvOs13LxxRd3pb/55puxY8cOt1u0LA3zXE25TyZ50s3dPtHPM/gfd/rRFkXWoBpkZAje/HwXeeiaCuBz9nCvRB33bGJQbf6v5ztX9aVuqrK53BQ9gn1TQBvh0u2g+OZxOYfzWJJYg732jGYevoeFD5wu+5Q0m6cHB4vvCqHpIEjbbm52xeo+lX0rO0XPC0I/HQRop0FBLXNzXerAbBKgjdDSCvn0rAH0rKJR1B27fHQ0o+OLOrmetV5120Q7OnEVbYQ4eNAaHPiuItuQJBkFUlkf6Lqr94blzA8aQNfC9HNz9h2N+nS6jCp5v+gbSX9k/y5zbpb/et89Vyn9uTuXJ8rcvexuf8c73oGXvexlePrTn37UCm+1Wti7d6/7/5577sGuXbswNjaGU089Fc9+9rPxK7/yKxgeHsZpp52GW2+9FR/72Mfwvve9DwBwwgkn4PWvfz3e+ta34sQTT8TY2Bje9ra34alPfWph29nznvc8vPzlL8cv/dIvAQCuvfZavOY1r8GOHTtw8cUX48/+7M9w77334uqrrwZgwba3vOUt+J3f+R2cddZZOOuss/A7v/M7qFQqePWrX33U2r9S0SCkppKQ6fjNlx96YlPI+3z48OEChQYt+vSslUEpNTAoubsleK2DL0rvXw1IynIkEE6RdCAsg+A2y/cBy7Jc6b3NdjAvgrGsB4OmSs5sXrtuXc6pTdobyf9Nypnh4WHHSy5BdblrQHLIE+iWgTqlviSfu9QvkNPo0PgxPz9fAJKlfqIowuDgoOM7lx7h5MU/csRSzBw6dAiLi4s4fPhwwQjA/NatW9cFCnOckPKG/QfAAdeSzkX2Fes8KEy6bK8EoGncoNFH7jrQtDg6oCjrxf6Q4DzHM4+RE196jss+1luNOK5Ig1R2bdk4l/VYa3nHO96BK6+8EqeccgoOHTqET37yk7jllltw0003HfNn4Wo90ZcrS80x1113He6//3587GMfA2AjgE9MTOApT3kK2u02PvGJT+Azn/kMPvOZz6yo3ONJ+nP3YxivaktuYTFn3Wbyc9qTRS846YqbpSP+RmA1jmG5NjMqi4su2mxPPvWpwEUX4Xs4G2e/bAJj27cD998P7N2Ln/zJp+OM6AHgKzvzVc/DD+PULd/HqeM1IIrRQYg4LtJ2G2MXYO5RzBOse8k+9krUgann22qHhvKYiGlqnaMfRAXDcQWmloPrDi9o5cE6C1uNyTEty6bw4mzBFxiDer1aCH41Oiq2t8ut5bJP2HAeE+5ocWzrxS5isblTmvCOll7JmV7oEeboP5Li6oNr1SSxNDgAcMmll1qKEAppSbKAn4D1jCezSbMJRNvOwKkvm3D16MRVTO+z/3KH+ciIrcvCgrXZSA5dBx60DgBTraJ3HdF60tSQ74eVpxC8RoAwozZpR1W0mlRNCOsCWkXaBCZ32+yyprkhdfAg8OijAer1jRjL+M479Y1oTgGVLLgr6Xcq9TrSeHOhDogiNKeAsfFxy22f8dwuLAD3zo+5rhoezoGp0dGM670W466FwIEEaQq72+NlLwO2b8f3mhuBpjUSDQ/DAuuMqEf6AQkayHqxgfL4Us8RijCyLUcez7l7pYvrldoIlnrW67n76quvxgc+8AFce+21eOMb34jbb78dH/nIR/C3f/u3Ls83v/nNeNaznoXf//3fx0tf+lL8/d//Pb7yla/gtttuW3a5ayn9ufsxjtce9yeNo9qR2u3Okh7CZYZF+VvvupIAZi/AmNdnu7PGogiIbH4+MNGYbJ5LPPnqhD5dZGAmQd3FxeKOOCDXwcBAgNHRCqJaxXqmp2mOqpbpBuje0SZ1Jd8zZPuXYxAoA9TLmp7lGxogjGwAzTTNApOmqaUoS1MEcYw0C77Ol5aOCYVeQkRRiHA8LryjdOKqa0KATgEkTlq5evhNB31Zx+KUEcAYW5bh+0yJ53+QpqhSb4nVndt40fSMNwo7l5NxHDt+7+aUTXrwoE3K6YjvEHwVGBoC1q+v2Dq6OnjuEY43EWuA1SLvv5SyKsum67RyCEkxJsh2fIYFT3HfEKIw2GpXBUQi1oP1p170FL+U9OfuJ8fcvWwQfVkRX1coO3fuxHOe8xz3P3nNXvva1+KjH/0oPvnJT+K6667DT/3UT+HAgQM47bTTcMMNNxQU8t//+3+HMQavfOUrMTc3h+c973n46Ec/WqAZufvuuzHNLUAAXvWqV+GRRx7Be97zHuzfvx/btm3DF7/4RZx22mkuzdvf/nbMzc3hTW96Ew4ePIhnPOMZuPnmmzHq9gmtrWjv5F6ezBRJ8UEQXQcAJZhOkJPBRCXliKYjIYC6bt26Lq8FApY+ihRNPyPpX6THtw4YKYNbyqChEtiWAUHLQHQGCpV0HfwOgsAFXa1UKiA/ugZ1WZb09Ja6DIIAcRw7wFZToch+kO3nMenBT7B6eHi4ADxLcJllMygnvcQpMjgo8wiCACMjIxgaGkIYhmi32y7qMjnTZ2ZmMDc310VfQzCeupFGHfaVpHfRBh/Wh+1gX5E/XRoA9Hg9cuQIkiRBkiTO+CFBdJmW56QhwRjjDBx6jHNsSYBb8/hLXn5NXcS0BNEJ8HP8ydgEx5oLfaXy4IMP4jWveQ3279+PE044Aeeffz5uuukmF4TkWD4LjzWIvtQcs3//ftx7773ufLvdxtve9jbcf//9GB4exlOe8hT8wz/8A6666qoVlXs8SX/ufgzjlTrSb52+hR//12/2RGLFQmNw0GKUZBwh1QcmJuxb+dQUzt8SA7sTy/15+eX45IeBc84J8arXvQ7YvRvYswdnNJruN+LYuhonCfCVr1jU8DnPARqbnTNOT48ZyY3iW7kDQLOJEMDExJgDa9k8cktPTeXNlwAu22kMcM45wPr1AUIenJ62F87NwXGjjI7aDBYX7cVDQ45ypDoeoRrnQbsKoIJGBCiSa1P2IYBq3EEUBZiezrFkjZdXJCCitnVTD2E66xa0TtLULbKazTwu5vj4GahtO8Ph080m0GraXQbVqA3TCPHww5ZHfHIy584fGgowOBhmi+3coWrLlgwcYP3Wx3brfNouGihotdHGCknQD+QgehyjbSpObYXFbFQBjKMvd90oPe3YnZdemgexNca26847Lfj/3B1bgFYLU1P2+s0TExbUThLgW98CTjoJ8eWbbblZv3WiClotYKY+hur27Q7on58Hbr/d5s+gu+PjQNh8yBoOmk2g0cD8/GYcOmT/nZsD/n1XgB96xStwb7OKW26y2e3YkbV5sApEVXs7GwC1Ckx9Y2604bjygQjHUB7PuZu35UrSr0SWetbrufv000/HF7/4RVxzzTX4kz/5E2zevBnvf//78eM//uMuzSWXXIJPfvKTeOc734l3vetdOPPMM/GpT30Kz3jGM5Zd7lpKf+5e/XhNUxS9eLN7lDzgEivL2KFyAJ1gmuYalx622UOdgUlzMD60SeJqN2e5zE/OU9r4luUfGFMI+tjVnpUAz3xuZuBwklijN6slg5myG4eHbRq7k6dqDd/yWcdGkydFP/98FlzJpSLrKDtFI82SPsX3kWWmaQ7sUjJQP4wiy8PeUhO8MZnxF4hjSyfSauVGhrxaFpgdHrZpp/fZyy2wnOMgNEzILjp0KKckk00Duo3wuTN6iEqsdC1d2akz/c7je38rZgzUaminAaanbFYHs5glfCVjNvK+kMZoANl7bMUanErmPxps0jTXJ/OWY41QCw0NPhC97CMBeTnP8PVRliGHjXZCr8ZRN5LPymQdJm9dSog2BuGxCvSQ/tz95Ji71x1ZASpx5Ege8fXzn/+8i/j6kpe8BC960YtQl1GO+4KZmRmccMIJRyUvAnZDQ0OO+oJALY/RS1p7h0tQUQLbEkgmAB3HMcbGxlCpVLBp0yZEUYR6vQ4GnSQISXBY07Vovmv+JgAtPeKlR3Gr1XIgMIFcgt46gKb07AXgwGB6GDMNweVF8QRgXgS96b0NAMPDwxgcHESlUnG/oygqAP+ybEmhQnA/CAJUq1UHoBMwloYBSSvTarVA2pTZ2Vnnrc1dAUEQuLrI8nk9vfIJLM/Ozrr2UOf0POf4IOB+6NAhPProo5idncWBAwdAnnQGS5UGB/Y5xwiNDZKCRRplKNKDnv/LYKEcBxJYlsA4x+7c3BwWFhbwyCOPuICmMo3uG7ljgf03ODiI0dFR144gCFwfchxSl3KXgOSAZz7Dw8OFmABsA+s7Pz/v+uTQoUOFsS0NRdpYcDTlvvvuwzij0B9nwmfjtddeu6IF4fz8PN73vvfh0UcfXTNO9CeD9OfulQnH56P796MaBMUXa7/bi/1mOgLDtZr1km21gI9/HHjwQeCXfgn/PrkRJ58MbBqdBaIIB5oBajUg+MrNNmDhhg056AvggS3Pwk//tAWfP/TL3wF27rRoLFHZe+6x9CDbt9trpqft9W94g0USKVxwiQUOxW1RZ93lgpcfeqFJ7vU0RSeqYM8ee3pqKndEGxjIPYHpUT00BPzwD1tn3krrITjkVCLFgC2DETIBR9FRcKuX/aDBCd03+n/5na2iDrTCwi50ueu3ihl7YnIyd1OPImB8HDNmzAbbnH6oOEayusxEG7F3L3DXXcC//IvN76KLiowgXGRv2QJsxgNAvY7v7LUBQ++4IweqFxbyxWC9bru80QB+aFsGllMH7KNmM48sqsGOoaF8SwKtIfRS3bbN8cbPZACDtBNRFhbs4rvVskORw/Lhh217OBZ+8idtXbmDeu9e4P/+XzvUX/EKa8j4zh5ryNixA6hMfs8q7N/+zRqXfvIn0UaIcO93gGYTs9svwa5dOcMNkBtxPvEJ2+TMsRyba7PW2JQkVgennIJ/nD4f+/bl43XbNnursI/qdXt9rZaPB/YXVTwykt3DSVIcF9Sn5I2R35KnRxhkJvfvxyk7djwh5u49ex7F6Ojy5+BDh2awdesJ/bl7hdKfu1cmHJ/79z+KjRuruUd5dm/OpnkgQwJw5MmOYxQNY9ymwv81dVUUoR1VC49WDdjFcQbKy10+fE4XLJIogs4a4etllJPPFx8QbYwzisoYGpzOZfgRebloJk48Uezk4TwtaVsk0CufcxIAty7Mxbr73Hg1MK5RT2kUoCFd7hyQIgFn+dyVxuRGA/cmGwHkgdYPHsx3pml7B5nD+MinvZnVkqogwPvoo92vJrp5dEyQTCO1WtY2ORaVU0YXiC7dpDV43mgAUYSZtIK5OftK2mzmH6pLZsHXQMmMwlcwtt0ZWDJx3udp7rlNI4LG2rW9Radhk2j00c3XHyn6tV3bczjGjbFtcveqNG6J9+WHH7b1kHlUMYPJ++/HKeed15+7f8BkRZzo69atc9Fe9+zZg69//eu46KKL8Od//uc4+eST8axnPQvvfe97cf/99x+r+vYFRb5zIAdoVyKSakLzhWtAryzYYxlPsY8Hvaz+vbzpy9L4PJW1B7MPkNT0GjofgqA+j2GZn+Z81x8fz7b01taBTeW37zrf93La5KuzpOaRfOtytwKQe16TrkYC7JqDvtf40+3s9dFURfJ4r370jQuZRorm6ffVTwL/vh0fukxfm30BXXWdl8rnB0HKxm2vT19WLv25+/gSYzLPm+ytP4qyhRKJLoEC2M116uHDyFcyXFDpqJISTJaLa3ksOy65Xcn2WlphSsmqRa7fWBxjYMqkBa8W7fWjj8l0PcpekVuNbo+6Xp9adh5LnOMh6of8qOw+EVvTKTCKclCGC/LDh4vdXmhDr5UlC+lFAVCiR5+aGSeXmDz/Z/a9itLDCUBhK3whkQaastVrr+Gu/y8kyiqsu4h9oViXCrpnu3msp4fWsgfSE1N8Q2ypT19WLv25+3GSFc41fA7ymdAVN1oafeVkIMtbXCxeuJqbyGfk79HEsv97TcOFNvAE603h/yKg+opdYFc4x/asvDzX6/+SbGQT9UfOCT7pNU/43o16dnWvdyHqukzU+GN3lLUH6O7Wsiq5OCli/Mn8ZNuOpZSppOw28t7aq5m7n0ATXH/uPnrymN7yGPH17W9/Ox5++GF8/vOfx+c//3kAwNve9rajUsG++EFbigbDfcE89TXyGLnGCabSq5iUFQw8SknT1AWnlDzpzNMHqPrKl+d0sElScOg2yDZLgJvUGdrDF8g5w30gN2lkmF56SROYp3e55E3X+UvjA4/Ri1vrQ1KYcPcAj0vecO42YBpSncj6AnD6p5c/aU94nvpksNB169Y5L39Sv9ADm17bmqJF61/2Aa/hGNH6kX0p+5bt5XEJ4JNeR+4goD65+4L1ZBpJByS9wiW1DHUIwJUt9UgKFx6nLpmf7kN5rBdwr0WnpU6ezADxxz/+cXz4wx/GPffcg9tvvx3rtTdKX9ZU+nP3CkS+UPtciXQa/i8jbgLWU3x4GEhTbNqUOammpni5dP2iq3GthtakdcKu1WD/jI/nblB0CRofz/lh0jQvW7qZycLStLDY4XdF0p7QQ8z3LTzBuPU5TXMbgPTEq9XsQoYeP1kMyTwvulxRvwMDLsimWwnJiJla79rrTjaqbOeA1kmaIorsfMyqSAcutLK0niBijjI88wrNaWZSIEmcKkdHbbNGRvJvqSfqCFMW0a3XLYXIli25R/TcXNETnVTqBcoB9p/26GNFeVyONX7o2ZoZa0hXQFVJjzJmwywOH7Yf6i5rvhuejUZGedNsYcuWjfjhH7bFVZv3ApMtTEychygCKskBO77n5x3v0UxiPSg3NxpAHLv4s6QP4hCivhj0Nk2RV4TbIeIYcS0fh2maB/MbH8+pZ8jjzg0R9LrjMLX3cFq8X5YCsXyrU6bvivb7+Et/7j6+pD93r1Kcm3D3KVJMedPztzwOuPu3161Lo6KJK9bDXc5ffJBoL2LfXCZFzNvdbUu7j4lnPQNdamBUPselyMeZ8xJOVR3lXMhgHHxgSi9zuS1N1p+F6Mro9sm2i4nFrVLLjBw+lFTqOitf2wQWF4tqlRuL5PRpTB6TxTdkWJScN7Rw7DC+jHzF6PKwl/M7f5chncqTuvBOh6IXNn9TtfJyzoGjo5IT3Z5zlEWJKlOoXoP1snxjull/pP5kU4C8X4DirSPfT5iuTNekjmF/SmeFrpcaVRFdT93m40n6c/exl6PW6xs2bMDrX/96vP71rz9aWfYlkzIvXSDn+wbyII4a/NS8zUwrgVuC5ww+ycCZgN0CYozB3Nyco7IgpYekl5Gc6RJ01R6/FAKXkq9btlVyhFO0hzw9owELjBr1ICMoTioOgtQsS3qxSwOEpN3wGS0kHYgOGMmAp7qvpLGBfTU8PIwwDB2YKusnA4gCcHQgrMe6desczQtBZrZVcq7LIJvMZ2FhAa1Wy9HJtNvtArhPL3UNrFM3ANw1NBpI/TAN6y8NBOxz5inBdPYr9Ungn20ivQ6940mdQuMAaVMkJ7ocE/Pz8y4vbbxhP9Cwwf6XZckgvdpoI3ctaGMCf2ug/ckMnFM+9KEP4Td+4zfwlre8BTfccEPBOLJS7/IfBH2ttfTn7h6SpkClYl+QfVzhMp0EZgXA6sDNrVvdCmVT9ACQxO4t3jAPvS94yxZ8b6+luNi+3Z5+8FAFm7ZvzyJNZttzWy273/qUU3IEc2Ag57dg/gVU2Ir0Fl5YANLhEFFtY6F5DJIV18bsokmA59yZTj5Ngo9clGzYYKso1VQxGYc5V2hycTsxYY9TF3S5VsC1VdwSYALz1yvkkgV3xbRRqZlumhsJdhBk5ko3SRC0ZtCJq7h3Mr/OGGBzbPu/EnUQxwEaDTsM4tiCtXIN7Hg5o7ZrcxWWp7N+xRgOHcoBai56uZgNTQeYzManJr+X3yKYWMFgQ10wcwAYH0fbVJC0ip5p8/NFxh1jisaAkRG7gJWUrVEE/ND2juVwyfaNV+IYz51o2P8/dxuQpqi+yKA6MQHctgvYtStX1MQE9u7NbsGJKuK4in27cw72vXvtGLvwwpwqh0MmSQBEKRzqPjkJADj5R3PnfI7TTZsybnnMFnRXTZpAmgBTdrxUajWY+pjV+1TLb7zQY1OiExJNkYDIcQai95q7V+qh1vdmO/rSn7vLxQdy8bmu8erSiyWgJh/SlDRFkLYRGoPh4cDNoUAO4uUBsEPEtTEAQCB3kmkk0Af0ivK8dfQB0dlznRzwSasYSFTjyMS+gRxk1CCrNSZ3l4EswLTLSGaggVxyofQyFvQCwiXC2+sa+T6mr5XlZg2U06AWXXXANpM+CyMjOStaWbVovJAiA3cCxTyMEe8e0nAi35fku45PJ3pMiPuAQ7rRKALQklIljtX7j/ukwCNKUerdIkVYsF37bMZsO2PKGJO9y0jF8XdkEMdFg/5y5xQfyA7kw3RZzwrk3UCHB3etMk4cD9Kfu9dGjj/TSV+8IkFHCc5KwI5gqQRfZUBQ6fXKYwTMyZktg3QyPwKZBFZHR0fR6XQc6EgAVYL4ZVQYGlwn+EswmEE7ed5HjSEpXCTdiAbRgyBAFEUOqJVtkOAtkBsjpC6ZN8tkOgKtg4ODBf5uelATRNd9JttEPUjjg6RXkUYSeo7LXQESRJfe0wScCfpKIwFB4NnZWcdDT75u9jE92jkG1q1bB3rhs07UBfOXFChSl9ILnX0jaXfY5zJAqRzT0hhBnnIGNaX3PIF8tq/dbmN2dtaNIZbDevA63ic8R93rtgC50aHdbhfKk6C51g3Lln0uj/8gUbn8j//xP/Dnf/7neNnLXobf+73fK5zrg+h9Oe7FtyrSC5euF/4oX1hmpNDtibORJEB18juWn3nrVrQnzi5683BVlsn39ga44w5bxPbt9vvuu4FHaiHOI9e5XOjThVoimRIYjWNbBhFGAGkaYm4up7E4fDjPlpexSnNzcEG1gJxuW9KO0jOa1zYaQDB5bxFElECvXKUb4y5um4rV10SnaMCQKzHfolF6+GX5kyrE6Vqu6mQeWTmBPCeR7qx+7dQaEJImsLkRAfv2IYhj7NsXONUODQGbL4hcu+O4gnrdcm9HkQVrA3Tw4MMB5udtsytRB5icyvlPp6YAY1CJIlQk+J0kwHTWvkfV6l+6lEk9UbdRBNTr6BjLt96azr3MTK2KMOuHA0nF9akUSVvPKtXr1gBRq4XOYCK7JDQdy+M/OWkvJvkqB87u3Xn9Wi0LoN9xh1XWtm3AxAT23ZLj4MbYrBh4dXoaOPNMm3R0FDivcQBIEhyINhMzzyu+bx+Qpti0vo358dDFXK3XgU3r2zZI70035YF54zgH/zke6nWEE1m+cgcAF9JlSIpvzMn79zhbiPeau491cLK+9OWxCr14JT+zlq5btswAqecbiY4agyiquOlNU7mQQ5nP04GBiuWalsE6+SnjF/chkBKczjibeUnS7Oah1piyzErbpmXW/Hb0bxI09L0oyGeazkQYtVlfKQ48lpWWFlkfwK4zkPXy6VAbRaIIkScZsyFQPhbb9fdsaoPHMsZjFIl3uJYA6gEbPB2w8/dgST1lW3Rgcple6VwGyfWpZiEz4FjqwGK/OqA+mbUnkwSYT4BHk/y98eDB3OrC8WhfAu3FAwN5APiTTiq+y6GIu/u6yZgcQHfjq4znHbABYwHwDTRU5wvX+XQXRcCwUgBQAMzbaeDNRtuD5Gt/x4THnQG8P3evjfRB9ONcyvijKdJbenFxEfT0leCzj/uZgCXBzaGhoUKQSEnNQjoLCd4PDw9jcXGxAKTzGgnwSzC/jFJGgvoSdNb0NFIkpQsBzjRNCwCopKiRNCLSgCD1wjw037f09pegqfZklwC8DCDqqz+vp9EiDEMH+Euvbl6/sLAA7WUPWHBae4lLw4bkNieAPD8/7wJf0ruddWZfUY+yPtJLH0DB2CEBY0mF4ms7wXHtfS9pXeSYZnsY8JRjYmFhwRkZOPZoPKCxgXpmXVkvSfHC9nHcky5mQEyK/C13HUh9y/tNG1z0uP1B8kIHgHvuuQcXXHCB91wfRO/LcS++hXOZiEVkJ8q2cGfg2+Sk/flDJgXuuw9oNFzykCsgFYhweq/F78bH8/igzOe8y+suSFThDV96ux48WKRI4Zu/+E7T4jqJxRMg1c1m8LTBwRyX5GKd19BD2RggaB6wYHCS2OhrgPWYl9GzqOcowgyqmDuUe7afeGKA0dEqojhb8OkVjg+QFMe4KGKbKlEJGMJ85Ufu4c4ymE2Cgld4oxEgyOpDr3x5GQs3Jo+VOjwMBK0ZIE0xPDyGxcUMQCfATCBF737gxbR6DA3ZxasE2I1Bx4R2wanRgEznpEbhJ4ryfl2/3gbKYzU4vKSaNKYRRQCmmwiMQYXlSRAoSeCieE5P5+2cnLRjYt8+O6DIo8IIpRMTQK2GTm0MU8K2YAxcgK+pKZtUOoJjzx6g1UJ8+WZbb1aa0dPiGJieRq22GXFsT8Ux7I+9e4FbbrGo+lOfaq+dnLQFjYzke+6pBOkZqUFwvcDXaIdG8DzveI+n9Jq7y0DJMllJ2r705WhLr/Faetv5wHRfxgBM1G1PX4r9LUmAOA5gTAhjQoRRp/sZIcrwot/Z/3Kem5vLgzCWZSWzkQwzPK49hB1I7GsIjbZ8AOuPeEdpI0SadDdJZhlFQfY7m8fKDAq+Ti0zdmglyLqJy2RQTZnl4CDcu5yJx2CMCKaZpH7wV9fP029dx90kps6LzuogcK8ofPcqw4/l5fK9xNVdvgjQuM33NWnslu2JIsuXNjiY7xjkvOjW2v4pTzfXvSfKAMDyW4rvfz0WfJZ/IH/flEadTBmBo/QJCq9/lIGBcvs4pTSe0OMk/bl7beT46vW+PGbRdCdaNHDtC+QoPYrlR3J/S+9rCThrfnDWR3rtauBMe6frY7p9SwFvvnM+OhkfN7XvXC/ppaOytvr0rw0Iuk94ra+vyj69DBBlbSvT+1Ltl8FofZ7aK/346qXbL3Un9SfT++rbKyCqL6ipry966UN+9wU4/fTTsWvXLu+5ozE2+tKXYyYrfdukrBQMK1mcyuKV84y/DL145X5V6SWjVjZHE7db0hlnKZcWT2Ukn2ZX+3pdL9LIdVZXACxffj36nUaHMqGtolC9TM/sEmPgFomFdHK1SyRjaKi7P30iMipUXVZGVczXRL3Y9alIHyt4DvLb91sqR7aJIItvAGWFUh28jM5wQ0P203ORy4uVO1nXwn4pwGNwsPteOlpynAHoQO+52weSLPXpS1/WSnpNj2X/ey/2JSq5V5edvxB5b7i5qUcZZdJrOtR16pU9H5XeNL0yKtOTusYH6GvQWqYpgJO+MpcrvRpsipzovmnWGPTu0KUG3HLreozmgdJs9QNaRxXtlRnb1+PFr+ydQc7nXfXoZe3y3WS+AlchT6Y5qj93r40cf29tffGKD1CV3xQJbGuebAkIksaFnugSiPSVJYH1hYUF58Es6UMGBgYwNDTkvLxZpgYfpYevD2SW9DOS3kN6fMvrJJAq+c5l22QdZDBQWR9pEJCe5iyD9ZX/ywCWpH4hbQnT0NtcUoXQ21ly0Mtgl/JDnQwODiKKIrfjgMe0bmW/a0CYXt1SXzrgJ+sgvcV9Y0zS7Egvdh9QrY0M9IyX1+m+krzq3GGhdzcAOTf78PCwo9MhBQ69zmWQXMnFTl1KkWOQ9woAMKiubqtul9zNwPbrPpD6fbLLr/zKr+AXf/EXHSXP17/+dXz7298G0PdE78sTQEgL0suVpkwEIEqHHbQit/XVbWFl3oLwup1tWmVwQ2ZXq2VrFumxpD2rSAtDD7Fm03r80svIGOe5XB0fR9yo4OGHix7HsmnENwlcMg2LIiuHMflverKNSXRT83Wz7uJ3Ne44TzTGJqNqEliPvahWgZQgzYOfF4JlRhGSJOelpfe8MSFMPIYwIzmdTQIYA4Sx8lrTi+CsMgMD1il5ZCQrs9FABwG2bMm7IopQ8KoK4zZCA0S10NY385iK6hsRRdZjPqQLvyRo9XnftVq2P4eHc+4c2f+w4EPA/7Pgs53aWEEf0gNRfoDiEApNx3m/DQ3lVSE7jMtE6k3tMHC7JkZH7SA+dMhuseD4jCLgh3/YHtu1Kwe9s8G/datVJ5s7PW3VsGULcO65IvAu4LzjHOVwrea82rn1/IDZiLvussVT7TDG1vOZz7QHs+Cq2LIlD+DL/Ck+D0cKb5YycEmOr+Nwpdpr7u57s/XliSRLYWtd5+UzWN/Hnu8AHRgTuGeofM5qoRHWy+DE5yU9fn03mp4/kwRBZHdZtVM7lzHGp57TmV0ZwM7leReQ7NOBzlymXeIdKU1t/agLaZiWgSYBoEqaOt9cqHXhA/V9O4S4ZSxLI99p0rTEkJC9vzhP+sjuIoAJ7Xucr7+W8+Bbag7wgMc+FZfhyVKfrsnUARPKvqTnAvtcgul8KeOkye8s4DdqNUtvknZXm+OL75FRlO0uTNKiB79us69PmVaOBb2dUoresSmUob3I9etL2XB+DHj9MZf+3L02sqohoCO+nnbaabjxxhtx+umn46UvfenRruMPvJQB5/qbYB7pMoAcnPR56WpOdMkLrb3DSZdBOhAGG52fn0cQBKhUKjDGuECZQ0NDYBBIAsiSK1zSX/gAcV4jg3XOk9wtE14jwX/ZVlK5EEyXFCu8VtJ2aIOEPKeDmzJtp9NxgG0i9v+QL52UNAzASqCY+iKILnVMKhJJBzMwMADygUuDgqZPGRgYcBQ7pDuR1CX8pm6Ghoa6KEmk4UICwVIPsv2+nQQSlGe7mJZGB+omSRKsW7cOQ0NDrq8Y/JNlhmHo+pn89wsLC65N5Cun7hgslf9zHM3NzRXqxH7SYK68b6hXSddDehnqoN1uF+hk2B/y/pE0RT9I8rrXvQ5pmuLtb387Zmdn8epXvxonnXQSgD6IvtbSn7tXKCQKlwsOwP9GLV/+M+kgQJABbmNoAzGANLKgXByX8nPPooLp6XxtIulRxsezAGC+RTa3TGdbu6PxDGDeuzffnpsTs9rVTJIgiGNsGh/HTJLznXPtxB27Bb7KOMZsEmBkJA9MRW7puTn7m+uV2kTFbpclIqv1JyVbBIXGYNOGmgteSpyVWCs/zjCRXduJq1nzQsCMIU1yRhRj8m3P+aIgn9ejCBgfrxaXU3rhFUVIm/bUhg12AdhBBTNmDCYBzt+mnu2TzbxdGZF4oP9vNBDHlQzwDQBUMTBSzYNzigVcvQ5U407Oo0MlAK5fOyZE0mKVAyCqwoxXrf1kXz5suFCUupR9XIUtG5MWrQ6iCJVaDZXRGKNbK0jTrC5pCqToDahQl3LruFzRZ+BEu74548GfyMH2Wg0BOrj00sDWjwNta91x07pgZJTxcQBAM0v64MEQmy64wJZ71lnA+DhuucXakyYmbEzeKMrqunWru94ZfcbH7be8fyhlgI7WgXTrkoYeqaPjbLXaa+7uL8TXVvpz98pE8ncH6CA0uVezvB21OOMjgE5ctXklItAwz2V5yV04AXIO7NAA1fV+tI3zsw/4dAckRUpZZaXxPcswNAYhgEotN6bKR9RK71uK9ZIPbavl88pn6PWBnqKhEkDX7Ce+y+fmAgwOVroemYV3Et1IiX5qZdMCK+KlUDR1meyfAy37fsT3ouJUEACZkd+nCi1lRgw3nmQm6iJpsPElK83X9Re632e1RR0oRh1lOqaVgUSzb+rSBaKP8yyNkbzxLf97tUwsXk7aIkjpwlzO809QfnAQgAGiuuBWL9GdBMzTFEhV8axCQW/opmvhueONxoXSn7vXRlYMopdFfK3Varjxxhv7k/lRlqXoTSTgTZBSgryS51kH09Qe3BKMllQg/J9gKwHDIAgwNzeHgYEBJEmCwcFBtNtt5y0tr5Me6dITmh7nsj0awCQoSRBd05SwTZLeQxoHJEUK85LGBhloVFOqSEBeAsTkEadH9cLCAmZnZx0oyPIJALPOrNPw8LD7DsOwwO8uvZupE4LoQ0NDrnwC5TLQJ3WxsLDg9UCnvshnz+Cp0utbeqBrkFOC32We1NKAw3QajKf+qU8AiKLIBbel0UHunNDBYTWfOfuB3uUEzOXYoSc6PdujKCoYVfQ4kkYUpmO9eUzyo8udCdI44aOG+UEChN/4xjfijW98I6anp9HpdBBFEU444YQ+iL6G0p+7VyF84ZerKrmSA5Z0R2nDAn3B1AMkQbXIHVAMIsb8jcGhQ3nwRjrwsqhGIwMNGVhSeVXNJgEmJ+W6McQZ9bpFDOfmLIE0yxoasr8z996qiArJwJNRlPF3qwhRJqpidDSn6CaAvrhosUbSdydJFlSLCy6WrRfgxuRAf/Y7iCJE8RgOHswptJmNxCHJKS+7y+fYBFiaeMkXy/Xa0JDFbKsSvBALOganowTNA7aTtpzt6Luxe3fRw40XEDiWbX/4YbcSD+sRkuT/Z+/voyTJzvJA/OmoW1FRWVHV2dU53TmtmlFpaEktMRKDNDASCCQMCIkVOoAx5ugceWVjQDYCC4llzWIvI1uLDto9WItsMNisxTHWwv7AZhELMtJihIUY64tZMZJmmBbTmmlpaqZrurO7sqqisqKyfn/ceG488ebN7I/paXXP5HtOnsyMj/vx3htx733e9z5v0qAi3dgAnniiuf5fXfUc7J3OEjorSyFp54Cltgs60O6q/LjEn+k0RjtK8Aij4nShSzLydju4nrdo1ekVof8Ms1aTh925hhEky4/AtY8ELtYBUqytVTTk99bqzjLgb6yshEYeuBZSDJF88uO18cCT0ftynDkDfOlLvlKveIU38LilBqXrE08A8yvHgBxwK75K99zjz6+seIOIc77MF7CEk+vLyDLgeNcfv/9+XruMbncZKQZ1OSahQPpbgSf7LFzkHfKVlHFj9zQ42bWT6dh9BaKdk3N2QJ61ZOxjRzCQr+w8b4XhqZTH3Q8NSQUQlqPesI386v9pliH1LxwPaKIKTsjr9T2q46QFHO3xyHsnqYB1AP67igmiwRTtczwJr3cxr2sdTPV9NuG9pjRtHHLo8KxxV4AROzYAnk+Q5y3AKT95Ue/oM1aKYeZ3sCUcaDGqugRDpCiBLG0M5f1+vYkvy5rBGe23pkuJdIOGgzz/O1cbejxXfn2NAuw0DAHeYDPS3wvzX8emWKPrmGQLrcp3PuaKxhndKwCIQYFtx11sIZgJvxVEtwYPekZU+W2VaRjHez3fRzY3m8XiroH5eVYhaejOss2wX7G/8b8Wwzu41M9W0m6PNZzBuabx4zqR6dj91Mtlz9zGRXy988478ZM/+ZNXtXBT8RLz9B3H98xv6xGsntkqlnJFPa5jonQxCpADtYcu4D2xSfFivW+1DhpEk8e0HGogUA97oPasVtDf0pQo37ilhiGgSm9tC84r8EkAV3VMkJT0HfR6Jr3I7u5uAFRt3dgWBI1JA0KAVmlQLCitXuIshwVqdUeBpXSx1xFgV8CX+VuvaV5jg6ZaQ4hSyjAdBeU1UCt/a5srLQ/TU0MAhd7kgAfh6fVP/dNjXNudxhT1Lrd9UPVNXfIeAI0ya5+Iee9rmnaXxzNNOp0OAODChQsApp7o11KmY/cVChdmsVXQOKnO6VolJZDabmOYL1XbWIvmCqxCEvdQL8IWFpqT/BQDoNdvloHjhiz+NZ7obZ1qtbazUy9kiFoStNabyhJJntcLkV6/CaJX3s/0+nEuwfnz/lYueCgBROcNwPjgTzFAIK891kgTQ2lQdlY6iAWG0qba2WkC7QSS89wvCLMs8WCD+ehCf3YW9aoOtUoDCq4BrOxuAWa8s1NXrCxRlmmI7UVwuWkMaWICMzP17d4xzAdhK4umXljf7e1Q3FAU0pGnbgj0xTOMnYgFOnPGJ2pX/pROpyqHeHFWfZExUAn2z8wkmJ1Nsb7u7Tnr6972UJYe0O50AJxoB90VBZBmZR1RV8Hrdtsfv/9+v7vjjjuALMPGRhoCv5alN0isrdV1X1vzt507Z+xizqFXlafdrg1Ya2v+Q4r6m25KR/3PLmdVasCdcP/lpHGNxY7dl1vc67hq171Mx+6rINoBnWvg1LFL7VBEEF0pSJTTOeWN1rA2zuBuXur0ag2erQSrVcx4H/22FbMDR/Wy43vaA+yj6/6IDSIkod76IzfFjsuxcfYB6lVxVeqZYy6HUovt+np54DRx5WidZQwH4INfRwJXOoLRZQmXpU1AFenI1IS3x5hmYkZ8zl3IiGL1Wpa1X4PSr/CaIZLR+CP62ypWz8cya1Q88i3WCxrEtzca05ZGe/GWLBM6Hs4ldGKqIDrzU8cDmfQU/dHbGPyVtDCULPPzDeYd6yfaryg02jS6rvWUL8vmMxnT6XUq07H7qZPLbvlxEV/n5uawSfPQVK6qXCpwFAPqLGCnXN6XIxaMjZVRgVICyJZr23rIq4e7DUxqwUhNxwL/6lGuwDm/bVljPN8WFLYe2LYe6vUOoPFtgXybF73Yy7IMOlBOdi2D8rTr8ZgHv91dQMBbDR30RLcc+La9lU881gf1mP0d87a2Hva2Hpb3nuWw11qvcdsvlBKItDD0PNd2i7W19mG2ue2PVu+q+0mULbHn5unqkf61X/u1E40FakSZgujXRqZj95MQu7gA4gtGWQQQXA74uyzsyhLBO6xxLxcT1aTVUjiGW8YtlEVGuE1HEoncp4jtReo/Dg/gIpCe6I0yx27W/7qV2Hj8z8w0Pacb6Rf1qiBWfJuVXSPSq31+vgKUuVKTVWKr2q4c0u77/NjOjTx1cWX1q8fNyq3RBcp6Rz+PLyw0F6m8JxgWyhJA2uCzZfIEIfSekPWkVZJzXjFUtvYFWbRnuWyvd85vbncOs7M1v30sae4MZ33zHI1nKyzEtZPFdoOMaXTnmnXnmlh1GLIrCszNpYEKvd32AMrCQoo899dfsVdW7B1yKeeusVzq2D1diF87mY7dT73E8GcaKq2MOz52jL1YxpWMUKXwe9zDNuldMu6/VrT6JgVJTC76StK0LvPmmLp0XNI5jL76eS+vde4inuhV+bLKE111qjZujgEoSyRZBueShiEjOhfDKAXMuLra/9YTXcfqS9Z7TGIA+yQZ137mHMdS/rbFsdOaBq2Kna+qN4Q2pFGuPayGFEvvw6mKnaZcTKyn+jiv8ujx62DMpkzH7msvl936jPj67Gc/u3H8D//wD/HCF77wqhVsKl4soKpg6jiAXL2dFVjWe+z/WHoKeGveMQCaD2dRFA2gkSCzlpnUHLOzs5idnQ1gsnJLk5aDXt5MSwHQGGDOOjNwp/XIBtAoH4Fbnit1QpMkARBnoFYbkFVpYuYq9IDc3nmeB7oW5kX6lc3NTezs7GBnZwdKscM6q1HBerPzm3VgORU0pn6oU9LwEEQnx7i2G9NVXWmbW+94enVbL3M9RyBe+x29uZVjnIYX2//YftYzXvXBvk4KGPLNUy+7u7uBtoW7D9iOFiSnQQPwOwrogU7ue+VzZ90AjAR4Zd3HgcUx/T6d5Lu/+7snni+KAp/5zGemIPo1lOnYfYVysUVxDCStANgkywAs1aB5NfMvCiDNZRHEc50OBmWC4nQNKGoMQ+fQXEmUZRNpr6TdNng4F5V57l196drlXB08URdlVZrMIo2s+EIwz6JAmmVot1Ps7np+aaAGGfIczZWvfguKPSgTuHwpcMiTPoVO64cO+W9uE85zYCmrvPIrfacYIM+9B5ll2Qgc7e3RxWu77UH5pfIscKrn3Y7X15tRsPIciXOeQgSoPaL7fXQ6Sz7NLPO6pct3t9uMvKoFEgSZW/nJTc6mOHy49lpzzjtbHz7s6cItKMz20LqVpa+CgvEAcPBgnUbgKVWFaL/sdGql2cCn4mEWuN510ewcltttIHfYKn17bm/X3bHdrg0DVFe7DeBUP2wtb5UXaq9zRdxZPvbpPA8AyuysB0u4Nm+3vZd7UXiv9GpDSJB+vwJhTp3C0ZUV3HnnsgfQP/lxoCzxoju+IegrPBMxLzTrlmdX+LYh7DXXgVzq2D1diF87mY7dVyCX8DzxVTcOAJ2dbQKaNsnGxu6Ya7GVcd7BsXJPAkJ1/LTHbKVi+RuukcRUMHUOadasg+V+rjnSh01qqphU6SonvU4JxoHQFgxVGzvPJf0LCBzfnDDYRFhPjR9SxSVJsgxpnqN0nh4Np0/7cysrPtC3TIk6nXiAdVtVMwTaYoz4KsSKa4eJumvV7ZDELlCxhpjYuZgwcw7URYHEOWSVd35ZjqdECRR7bth0RGDllPNcK209GpwLTgucI7C6agi34P38fJ0kszbQQkPUQGOn+o3y2WebFzOT64DzZDp2X3u57FlbLOLr//l//p9417vehX/7b//tU1HGZ7zEPHAp1vtcqUcU5LWAsnoLqzd3DEhXwJ2AsvWYVs/znZ0dDAYDFEUR6DW0rFmWBSCXQTAJZitgab1+CYwrNzZBeQLb5B9XUJPAKNOzHu61da4MwCf5xQn27+/XPOcE6RkYNM/zEHAVQOBhJ8e3gsYKzKrnsnqOKxc87yHQTOCWdVfOewVj5+bm0Gq1gl6HwyGKoghc6GwbNVBY2hmKelYrkE6dKQhNg4QGcqWRQD3btV/zP0FqljfWH633vOqCxguWK8syDAYDlGUZjDM0KCgdjfLb8z+NF+zbrBs561lmlmdubi70uSRJQr7jnlmt19MRGP7Zn/3ZiecvXLgQtiU/Het/Pcp07L4CsZP92Hn95uyUgSM7HZS5B9FbWeaDX7q0immV1MvSanX48Okk0F7kuQcVW8XZ+gB8UDIgbVKOAECeh7UP14ph0blW1gBkxX8eRZVldTdEEhYfLfUOr+rR2O5aFFiutowGcJngvBKLWoS3OrZV8YH7w02vON568KDXh3NAK6sWaKfWmnvuez0sdzq40E/Grt8WFvx/eidnmXC+33/Sp/vgg8Bjj9U3cnXmuTz8TST+7PXQ7S4hLbdq3Zw5Ewo/7Bzx6VtwQ0B0tXGw3bVbcV3ZKi94gH6vbvtAlVMlm2RAK/PtVxS+2NYzfSmnt305maqIIDqNLBaosVFfWb9S0q0K31pZwTD3AUl3dvypbter8UUvqhbexYWaQob96dSp+mL2m7Ksg8MtLHiEnJajogCyZht3OkBy6q/RynNkNx3Bzg5w9KixefR6nhZmbQ3HVlaAk+vAf/2vwN4eWnmOF544gbP9NPDKh15q+3QMMNCGpEUnpu/rQC517J7yql47mY7dly9KjTJCPVKWgASAvAj2O/I7JoOyohPJXdPQp3kqB4ZmfjFUy6J8/AjY2ABuq9dOI5gk38exPG3lrIsvUDkE1NztdX4KrjfH7pBsxG6oQSdtMSzIzO8Qt0PrZHaMjegYaEZId86Pzxsb/lwVX6PsLnl9nTzZ5D2rBuUUQxzroj7Oiun/WN72dwSpTRsB1/15l1k9N799MuIpH0P1Y5YJS9Zuy6Zpmf6WuNIbWBzQakeeqdAG0haa/zg+nnF9u7qEDhSc1/L2FIPR/myr124Hh4wYxQ6zigZ01fIYS0fj3RKhBvpKyHTsvvZy2TO3WMTXZz3rWfjf//f/HT/wAz/wVJTxGS0xT3SK9SK3/N0EF8fxY/O+cTQtMZBdvaOVakS5vA8cOIDd3V0URREAZ0qSJNjd3QU9hxVgV09tAqoKmhKYVQBZ+cXJMa7e79RXIQOH0obwP1AHhhwMBhgMBqHsTJuBGQAEAF15yqkT6p/e4QrSWs9vloHBNGdnZ5FlWQO8JSDLMqvHvQ0cy/ZiXuwze3t72N7eDuA9g5kSWFdub+1LLKPlwVcgWYFv8tZr8FHVu1LbqN75X0F57eNqHND6s39Th1pGgufUJQ0dw+EQ29vbocxqFGIZWB69l/1G+7I1TGmfUh7+GAWMyhRInspTLdOx+wplHEhG0Ym2AsuPPgo4h925W/0kNHPBC52OUC1JY5i1cOqUP7e66tduLWx5EJFIYJahdOLZzvJVKwEGPeNiI+lfAHoG0KQ71aFDTaAPCIA3A0eF113b62DgWij6wFKe1VEq7aLsZAVE0wubSHVsVVwd2zjnnb+Jy6pa6Z186BCQ9s967m4unAnYKwe5c8jbywHP1makHSHFoOI079cGj6IAHnnEL6AfeMB7pLHNmXaWecCWSHe7DfT7WFoZ+iCbLHSvF1zI19aATmep5sxlelWZt4okcLMTLGd1Ari/3qvBam+BqQEBfnhTVWnPaZ80VB28zntFc+FpDRy8oSz97oh8udFsStsCoAmgA3U0U8CD3BUHT9LpwLlWwJE7naot2GdI3q6u4mtr/vfq6iiNS7/vXerpJl4ZdJxgEjMzldHgk6eAdhtJlqHdXsJNNzWTCcaT+Xn/zPV6wJ/9ma/TV32Vfwxuf3Ew9kQBKH0eFB3idTFw7ToC0Kdyfcp07L58uRhIRA7ycfj1hOHqIlgl1zj1utc5wGVVHAU1Pup8QQc+TVgN2Aq2ujrAI7nagWZQTueSECsjCjSPA1RjLt/V70RAe37Gefqqd7LVLUFLBsdsgvrVMaFpC+VVoFpBdEuqvrvrrbW8l2PjzIx/t+/s1DFisgxlp8qTgToYECPLPBd5sTUayyWmT91xBvjxLKYUO3brPCnPkWTVNbIDIN5Pk2ZAbyvjUPhJEgO9x1mU1IDA/zHEX+usThXGOGOLEWh6yjIEkB/ZdWBBbAN+J3mONMvg8iaYzkuCcSY2Fqtlh0YV1FXOsqQOKjyVZ5xc0ezNRnw9cuTI1S7XVEQuFWCLAeM2EKOlgpnEn0SxdBwxLmwFVflfeaU1P3oLK9WMAr8EK5VmJeaBrEYDeojTC5zgJstBT2sLaKpxQg0Els9dg6aqBzTzZB7UkZ5XWg+WQYFo5mOpS9RD34K4yr1taW54nnlrOvTSJ61LjO5G+xB/sy1iVD6avvKIx/rBuD6jv0mPY721mW6sT6snve0rtkwKrlsqGrtDgW1FqhmlU1I9aeBTfnj+mS6HDh0aazSY0rlcW5mO3Vco6pkDjHrv2sl3WfoFmi5Sq2tG5ttynHQTZVnxNOqirMqzrK5tzN7EMyYsUO3iIraA0cXGJeigLP2acInrCLtg4sKm16vdqm1eWu/qP1VVFLUTGG9h8mF7sC6cNzZqvhMpg1+gJyPVck6CaNpIVQR+GQXTRuFUhLssa5BX9cwMd3bC/6LPxWCz7jEAZGbGZxHqSkMFo7VubNTgtPXujyI7aQOwaCz2FQWZtNsiy3DuXJNrNCzctd3Z1wlecEsF0AQ3UPfv1A29IWN9va7b7q6/gOT6VBIBjcqrLCVCDjRJz40eQhDYfj+AAlnVlNz6XfKhYvtvbwNPPOENYYD/3eshwRBlmTSzUD2o7u2iXr9vEJk0dl8ubjDFGJ6cTMfup1YUCI7FlBgnik9TLB7HV2yqA5odN8eBnLGdQpKuxYttEv5dGbkpVmitlPKKX+ThpbHdYqaa3EgVYuNVrEwXA9G3t0fBc/7Xc87VgVR4jI1eFHX7k+8r5lWu6Cv/WxCdHu62TBTlD6mtHfV55QyT47EmGIf7TtTzuPOxhPTcuL4yCURXsUC6GILKEsEBJFqfceC5thMjito82u06TosYJRpTXzXWXKy/V0ofqeZ1NsBNx+5rI09qRseIr1N56kWBPuvVqoCh5Qu3YKh+M10LbhJwVK9aBS6Zj3owW9oX3kcAViUGMBKgJV0GvZstMKwc6DEvekvXop7Y6n2u+athgNQcBOfpiU4vcQXn1RsfqD2qbTBTSx/DvNWDWY0CsetsmRUAVh71mFe0BYvVEKFe/DFOfdWL1sWCmfZlrYC19iO9z7aRNQBQlzb92DOg9dK+aQ0TTE+NLcPhMPxXjnd6xXNHAPNmGdUAos8OP9QfjUbjAo4+3eV//p//Z/wv/8v/gte+9rX4+q//euzv7+PP/uzP8H/9X//XFET/Csl07L5EGQcwx0BzfpsF0Pw8QW8XKFfCLQYIHWF74CJAPHBDkRS4LL23DVAtSKqt5SkpLghoMnEFtm09yhJJOUCWpQED5/Gs2mI8spDVBRI9dUjnot5G3AIrHk5Ds6ixzDHh9hjY0CClFZ0UBfK8FbBXoMZbh6gAYM2MFaV3t6UOYYGcq+lN6GlvqU5Y9yrdYr3adZALIGHbshLix9GTQJODxoLokesbALoFT+ziedxKqShw6FBrNC1rONAyqg40AqxzcJUqG03HPjM7WyNCarSodEzDQ1kCKVFw7ctVn9MmG6lbWYZuWhSCYWRZc794lnkPd8AT0Vfn2K2xjvjzY0Xb3J6/zkH1SWP3dCH+lZHp2H2FEnn+EgyRZUn0Naav03F47zi8LWYzC2Bd7OIYp4ktSAP1a94K1K9O/ual6sk78f1jf1/EyG5x393dZoDGGJ5JaYwjlEstn1ot1NKu8wIdQwiqz8z4lzcLxsJWRvj5+eredrsZiGRcGfRb229xsVmemMOFgueah+1wpadPGSIZ782vbWuB7Et56VpwW4+PO6eibaEPheatkzpNL5LmWExfr1dDw7ibeL25JvZcpzYzvc9ePGbudr2N5dOx+9rIJbX6xSK+qnz6059+UgWayqiM89gFasCRFBfjAm8SyItZphQA5H8CfkyHxygKVirgaMusXtiUvb09FEURQGkth6bBPEiLQsqSGLBOUJ10JTZopfJt8z7rJU/amYWFhQCCZlmGmZkZtFqtQO9CuhDyEwJNgFgpZWwQTdZL24Pe4RakV7Bb9cR6AZ5HnOWxBhVrXFAAmHQ6rZYnFNje3m7QrZAPnvft7u4GOhhL56PlVO50DdapfZPpWsoW27/Vs9/WhSC51plGF+anfZN14v0Ezy3/+v7+fuBPJ9WN0uKwPOr9r6LGF/Ydlou0MOPepTFP/6eDfPSjH8U73/lOvPnNbw7H3vSmN01B9Gsg07H7SYo+33bFrLNoww+uE/cltwXkGQZlC4Vrobdee1y7znJ1nb9lZaXeRTw7CwzzJSSrqyEtOIeWqwJ6rq03t4C323CdY4GdxF+ewrkUnU4Lrc6wCfgSuLTeVFVeiXNYyjK/8A8UIaXn4CatiHr4ctG5uurT6XSwVRHWlFJMLymANBwHPNY6N9fEtUn5keeo84wBwBQ5v9ztYthuYWbGr4lnZxXzbyFtywJcvcvZEL1evQCn/pUT/fhxoNsNW9uDl6Fz/lyW4WzRwtoaMQlynKZwAFpuiBQlkDW3GAOeXzdVr3fdRk6wWLntVRfahta4Y7c/qyHFXktZX0ca4/yMARfz883dBzzH/pZlcGXdtqEMKyuj6RF8qPoWqYTIHLPU7dYe6lWCg2zJP4LSJbMMng6H6fb7SLIMKytLujPb6/TEifoZZl92DnjBC4DbbwfKErd2SmBdOjTradtIKRFUv1YuZsT4CsqksXu6EH9qZTp2PzlJXb3mGuEvBkKHJMczqkCaNoCm4pHqUNykTamPx3DHkZ1h1srHMSgGLupYZ44nxZYfSbPIPfz0yuZ/tRiMA3HH0a+hro9ShU0yJNR1NxeNo5Kx+Vljv83MEl3bMcTqm/MXva/d9mNBUfp3PenoghcBRul0eMyC2ByzLbAdqx+/Y20vXu8JJJC16sGC5fabv2NtGQOcY/2g+rbPBUUDzEfLMiY9uDpui73c5ODnQkxHAXt+2511Nl+O54UPkOoq6hjmnZIKbpxe9GJXO35kWdW/JxkZvkIyHbuvjVxSq18s4utUnnpRzmYFWclvbYFKBZstd7SCdfpNANqmz3yVmzzmja0e7AqiK7CqtCcE/+lJzbIBgAYfVUBbAXOWXT2/tUwEf+lNTMCU+VgqE4Lf8/PzyLIs8JOrEYJpMHiqBpukoWF+fh4zMzPBe93S0LA+BM+1vdRQwd9Ky6LtpRzrbBP1xlZufAW1eS7LsgDwkt6EZWDgVN4zGAyQpil2dnaC8UCNI1o3Ba6p7xiHOYFw5dvXvsR7hsNh0JfqgGVXKhW2gQLuu7u7DSqY4XCI+WofN/Pb3d0NH3LQ9/v9UA8VSxvE+qtBhHVnv2GMAEtT80yQ//yf/zN+/ud/PnpuCqI/tTIdu5+kxNx/rCg4SWAZ8CvssvQcm+021nEsgNsMZqg7pufmgFtX/IJzq/Qet2fOAHt7Huxbysln3fPfDKzIRcTqKtJuF/0ywalTTUx8ZQXodBJ0OstodVGDfEyPwCHLb1c2ChQ617xuY8N7XzmHYb6EouJs761XhgJXr13J0GEDG5Vl7fTLIFLEiEMQTAav1HawbkWsQ6WXJMtwdGUFgzJp2Ao83psgy5b8vQRTu7eGZMPiH2jqpAJs/+pkgrWP+nIePgwcPSQLu9tvB9ptPPigp/Qmnqrr7Fs7Ps00z+EqIJ2e6EUBlC5Bi1uReTMX250O0OlgiCRgt3nVRIEf1fLsUod2samL/lgfX1sb9aaznmVsVHpz5zmG3WO+POQbzfMq+B6w3Ca9jF/gb+VHUJY1fexs3mzisgS2z/ks6ri1LSxX/PRnewn6vZrGvtv1VOzL+aDZb6iXXg+rq0vhVFEAW2ihdfvtXmdstOPHgZkZDO94CT7xCeCuF5XAvffW9aRBg31EAapxOz1YKf1YoOM6kUlj93Qh/tTKdOx+klKWgfe8+jvKv23fe87Vu5Sqj3NJY1iX5MPYxrjTeuuIlzDnB7yZeZqdYVtF0riEp0eChALjObo1AfubBVSg3Hida6BSvQ2ovcgDeFqWowCvzTcGAMfEArkVeKsY6e5uauYPIbLMiH0g6zTtnJS0uzVi9Ez6F/zvyliqY5xPNwHypUbxXWbmCap/tlHMAG2tD7aAMXA85mU+CbC2+tRz2uYU3TFoQOaiN4n3Pq2SS5v9VMsl+QY+fYO5W2ncmkmcAZcG5DJtR4xTMTBff2cZ0rzJ659lSQicq7pSkL9VnR9UdG6tbDhKrn4dyXTsvjZySa1+sYivU3lqRUFs5ZYGmvzcShtBINECtDEATz1xlZdagXClglHaEd5DL2/1ZldPYILPNi0AKMsycE4T+AW8R7dSpCjlBvWiVChK5aJBQpXn3PK7M23SuMzOzmJpaQmLi4tI0xTz8/MNQHZzczMA8kVRBHBUvZT39/cDkEpwlyAw623BakvbovpRnVsjhdKm8NoDBw4EnnalqlEDAnnRqd88zxsgOo0YzIN1JLhtPaq1rFo2tj0NFNS39mnWXz3XFYiOeaJrP1UAm/Qp1hihaWs/oz6LosDW1lYjICnb11LsaPpqJNDyUOi5r0abmDxdvdABYHl5GR/4wAfwEz/xEyPnpiD6UyvTsftJioJcekxXa0ATQOfEmq7PFXLec8cCNkccutfzwDIBZKytAf0+WsePoygSbG56IP2mm4B8NfWexcyHntnk8q68qHd3U5w5A5w/D7P49N+rq8uhyC5Lkbh+sw66QCOqySCkWl8uWDY3A81IUXggkzg28V/ijRRmo6CDAu2zsxXQWhQ+CKYC9sp/bRej29t+G/fcXA06ZxnSPEdZBbSkTvb26upxUc5y+WQ8JY4HSFK0234BXfT8NX/5l8BjjwWncwxvqtrHOWB1FWd7CR57zDd/u11Tc4a1e7sMCkoAOJeGneZUr3OVF5ZVUp7jQj9p4DIATydNIJ16Yua6groUT+lez1fCuXpnxvx80ys/dCgXgteeOuUPdzotbwOo9L6UD0eix677bo+NDd98zIpNbW0+gXZ/ZQnnzvh26PW8vWpuztsYZmerslfPVChjpZPl7gB5noYq9npAtnobkt7Zui7VtpCTJ4EvfAG460UAHnrI17vq1APyzlvDjjXwTBL7jrlOZNLYbQ1hFxOzIXUqF5Hp2P0kRTpno5/GgGgdjCxVlhvdKTQmGwAG8LYestbbVfK70E/CUK5pZhkZsRJkWeq9zu3usRiwasUa92Q8GeZL9e1FPV8AavYTOmGnDqP5xSwM9vfF3nERsJcgJkOfKAX2OMdnHdo4n7AGjna7hbxdg++pG4YxjgbdPK8MBc4BZRMLbw6fCRh7pAHw2p1+VrTwdpyItaudm9ldcjo+W6oYBczt+CT9YlAFw2UcF2Yxrgq1DlTPft4EIDgHhCqLXQEYD8xbsT4dvG9+3uflCOCXg3jQXupH9edSpcRHVgH1NBIRZK8f2WS0C6sXzHUm07H72sj11/JTico4sEkBc/7XcxawpsR4sxXg5rFx4J6lQ4nRxPCbQK2ldaEQRGca6jlty2zBVFs39diOgc30Rla9WGoYgt78KDCstB88plzr6vGtwK1tP5tvTO96Tq+3OwlihgmlVtE+oW2qBhgC2+q9rp76seClmp/tA9aQorpjP9C2YXltwNWLSaxfW90p1Y0aO9Rz3uqIZR9HOWPLNq4NVMe2DXnf013+6T/9p/gH/+Af4L/8l/+Cr//6r8eBAwfwZ3/2ZwCmIPpUblCxk+aLudOUJeCac+4aKJXbddJf8Zvb+KQjK8e9PUOmPRpXixShsUVnasu6u1sXioimrZ8u5GQlpGtqZcW4lAk79RK2x1tQIIZWxCS2YKr0b6tKUaex3V0PZCjewaBtTJZYPddrtlz0fN/eHsU1RnCOqryx6hBIb5hxK0WVxUizh6pHdTGu8hdrIFZYF+YjGY3qYBLGokKgpCjqvs5s9vaa+IJNk110e7tpv2qUnZ+ZmWZdiwJp7gDxVC2KyuNMwYc8b6a9szOy+i9LeY6MHqK6vQ4X3TGZNHZPwuticjnXTmUqV1vK0gTY5EH95u8JnTU2FCo+Gb1wXJrVe4aAncaiVHy1OV+Q8cC+FO14ad8zsfdOdWwcPgvU/gAj9Rs3Po8DjS9HqjEklt3ubnPM1vN6vVKV6/Cl9wOAy5NgAOccIMsQ+MhtFfj6n2QndU4ohJ6MxNpXg6fGMq8LMT5dC6JXxgLVY6w/xooH1H4Lk67R89ShhYUMQ+pIGtZ2oJLa+tgEJlRE+7e2uQ71E9U9ruBfIZmO3ddGLvvpjkV8BTwolGUZjh8/jje96U34u3/3716VAk6lKQo6TaKFsECneutSlN5CQT0FZtWbWak1LMe3TTsGMsbqcrF60jOYNCJAzZWt16p3swa/VLCY91qaG3oW0xM95mmsHu42QKmWQz2q1Rub+lVdKwVKDIRWsFxpWBTgpacz//Ozu7sb+NLp1U2xlD3UgXqix8Bp6m5ubi6UXYOa0hueBpGLAc+x9gYQKHIOHDiAwWDQaDMF/akPet1bg4Ly4rPN7G4JrZ+NL0COfC0jKWTUQHCpwnS1rZj30xkc/qEf+iG88IUvxL/8l/8S//E//kfs7+/jq77qqwBMQfRrKdOx+yrIpSwY7SS+QvvopKsYJL2uG/dW9/EnecKDd5sufhYWarCv3cbQpYFVg1hgWY56YtmiAmiSSDPq4u7uaPBOBQZnZ33iWYZh5bHHetaL/oYDNYCa1kU9jQOHLdO2XtN5Xns+awBIvYduyhEPLHVet5Svmk1RjC4eCYizzBSdipRl5X0m3t033eS/GRuWvz3/aoUCV277aZ5jOa/vDUIuWSowyzDMWsEjnuUmzk2gpcU21DaziDYLMwkQoce56ptebzEPuspNvFPx/SutbEPZVV+jGmi7YdtQt9qPtD8B3kOxLGuAhF6bI6IPBAtT8bh0OkdMOV2zwbIstP0wayHpdBrB48Lim7pVr78bfPU5aeyeLsSvnUzH7icnUSxR32XKda2fsqw4lGv+b928w8v0VRjNVNFce3MFFtMgOum5cm5MUM5JlbYFt3WMJMUxToHnMAcZ5wVv6xUrg/4eZ1SglCXSzAUwmmVh1gzYTpBTnQTsNAkY9Q2w2baqsXX3XBPYTTBE6mpQ3Kp0HE4dbSerB56zExHnRl3fbeW08jqh0opPoOwZmQyWJXwMnWbRtKh22qvNqzqLVQtoAud2qmGvtXptYP3VNVb/QySNoPWNbzMHKMtRAJ9zJ4Dtl0Snv2G8j+nxOpHp2H1t5LJbPhbx9ROf+AQ++MEP4kd/9Efx0EMP4R/8g3+AsizxQz/0Q09FmZ+xMg5wsoC5pb/Q48rhDNQAI9D0olaQlZ7CBFgJtmo6BDnV21x/a7nVi1s91SkKWO/s7KDf72N3dxdlWUa9owl+856YEBydnZ0NHPIESUkTQwoT8qADCJzqBKsHg0E4ZtvCBrIkZYoC4XZ3gBog7DneN1cNkBaY39vbw/b2Nvb29rC1tYXhcIjt7e3A6U3wfn5+vsG/nqZp0BePz8/PNzyyLa+4gvEHDx4Mhg0C3mwr1oU0OrYvqEFDaXlYv729vcDPTp1zRwD7IMvPdqMBgGnpzgflO9e+PS4g6N7eXugHu7u7cM4FferOg3FgvBWl8FGwH0Co3zMBGP7Gb/xGfOM3fmP4f+HCBfzO7/zOFES/hjIdu69AyhKgMc0CkHoNUC9UdHUhi82DB5vbkbPM007oehTtPKSXZR4jL0v/3dh2TiBZF0bdbuB35ikWTS8F6oXhCNJN3pF2e5S/lYXWVZZzvhLtdqBwyfMakwDq/3NzQtGCqsK9fpMGJ7bY4+Kn2wVWVjDMWhVX/OhCr338Vh941S44AST9C5491Tk/8y1LD1DLde3VIyO7gckgw6QOHvT/dZs44K9PK/qPovDAyuqq56NXbH13t6LvWS+avDcW1Xeu5sGh7tttbKGFjTM1vbmKJtFupz74pvbTPB/pr4GOpBw0txDwmptuqonqY32eGW5ve2VVyljuVnXojV8Vn+01t+vbNT/Vo8JrigJ44ok66YWFZqzVkF1Z+hMMREp9VwTqSycc8oriyC+cnW80ADh+HIMyweZHfZpnzgBHb7+9TkdAqFSPjVulXoeL7YvJuLF7uhC/djIdu69A+N6vwMzGeMfzfFkAo+M4jxUF8txTf5BiSpIfYRYLdBDO8zc3uMx1UOb7t9q1NM7rN/rcjLtgHGgdA1YblvZ4Eg3wXOO+aCEtak2lmLyUg9rm0wCc9dPvIwHQcg6tDEDu8xjlSa9pcPr9UU96BoFl3nYH2d4eUOZLQFED7ARUk9LPJ1gO5h8MCkUZbyT7rid1jvDNl9y/FHTi6WBSJ3RA4+hIbEcZB57bAdF+SzoMRJ5Wn1bbd27ygGuTxzzC9/biVCH2Xvttu6uoC8AopdCIgcK5UEYgRelDtsNlQNb219fnARSjNgpKUdRODmmWIcua8QFSN8qFPkQyNvDqV1KmY/dTL5c9o4tFfAWAX/mVX8Ef/dEf4Xd+53fw4he/GL/4i784HcyfIlFPdEtFwuP8toFGCR5afm4ClwRoATTASF6nXNIK2CuoniRJ4JK2IKGCnJNAbwadpPf5YDDATjW6sa42+KV6MFtvc94zNzeH+fl5OOcwNzfX4Fonbzf1pZzqGkRUDQ8qCnADHoCn9/7e3h6UHkZ1pTQyMe90GziVQDPLsLu7G8D07e1tbG5uYnt7GxsbG5iZmUGr1YJzDgsLC5idnQ385+qFroE2AcAG1CQIzHZW3TBgJoF8rYNtUwCNtmGeuotgZ2cngPRlWSJNUywsLICBRhnwlUFnNR81zCiArv2bdVEds8xMmxRD2r+U99+C6BYMtnQ67HuW1/6ZIsPhECdPnsTjjz+O4XCIzc1NAFNP9Gsp07H7CkRXfAr02vMUu3Dhiq4ocOiQX+iRf7mijkZReNpmALUXOPxkfXExwd5eBbr2BX2nEDHPcwyyJfRO14fUiTy2th4huiTS2+1iyy3VQS2VS9t63rC87XYA8Dsdf5je27wkz1EHqeT3+nod2bHXqy0LeR74qImMDrIlnDoFnDsHfOlLzW22HI67XaDTSZHnaTActFAFttSgmmw79QZ3DsnxEkvtNtrtVsBad3Zq6pa5OQ+iA7UXPRd729tAdqgKkFlhybetDkcBctVpv+/BXA18Se6dPPcoPA0IWYYttALFt4LLMzOjnvJlCWxvqydVGhbpZQnsbgN7VbN6g0DqOXfVWEIA2oBKI9y+XFH3enUnX1+Pe4CznzkXgoRWw0FwjOcOjbL0yaiovtkmi4veoNFuG69Qtneng79ea/lgpHl1nOT9WYakW4SOOkA60k37fZ/uE08Ai6u3oVWcBe6/HygKWZgXo8/Upaw+r/MV6rixe8qreu1kOnZfocTQPIr1zLZgMN91cinfPSOgr08opKd8yoEj2qVIsyZFqdJ+WVB4UjVG8xaxY3QMWCWgW3FXT5rGhMDQHD9jIDpv4g4x4VovCh9HxN6mU4iZmaTa0Cac7/o+NXklzgVAG1kGzDsMDi01hlXdPUZwl2A60JzK8b6ozllnKXSgaNGIsxbQtkA2mtRlzNeq0BtqPP+9y1IfE0WRZqsTlYjxwgL3WsSR4OksjGmk1LmaKiVrBnuljjUZ2zW0HayqxoHonCboY0qDxkhjtdsg/aHy55clp9M1ZZudivFR5xQ/UPlUD7BzrdF5czVvaHiLXIcyHbuferlsEH1cxNdv/dZvxdvf/nYAwHd+53fiH//jf/zkSzeVEYmBTjHqltg59Xa2tC5JkkCpQYAayFZqEfJma5BGpq33xMpt/2te4+4ZDAbhvA2eSSCVXsoMCKmGAgVsCZLSk3l+fj4KoiuoS8B8Z2enQZui7cC8rdEhxus+jutdjRH6+8CBA8FznvUuyxL0lN6RfWkE1wmgnz17FvSkphf53NxcqL/mRa98rZsaCpTyhUAwA78654J+tra2oPQ1pHxRqhVSoqhOWH7+Zh3ZD5gG24vtqN7ySmXDdNl+NrCt/mY5mZ/uVDhw4AB2dnZCmtq2lwIAq8GEBpTBYADnXNDT0x0Yvueee/CGN7wBX/ziFxs7Bvg9BdGvjUzH7isQO9u3K0C7cKJw9i8uZqkbYmEhCXipc/Be01VQJMB7ryUCWDqXIs+BVjb0nstMm9/VwnULLWycq9cWnN9b7HNmBk3PuBiokOdYP+3B6BYXCbposSueCiEv1ms2i5YbYGEhDbh4llUePFzhrq35Rfmjj/oIqCFSZF67ArESnQ4AD56fPu29gU+dGl0IcG3V79dBPNttoNUxi3JdhHLV1Ot51LYKzprntzYW2/zOc5+v0uNQ9vZ8Gfk7y+ARYBtZ1JZjfR3BtV63yrfb3r16cdGD6M5hY6O2OShrTaxrqtjuahfvWVYvlFIafyiVkaShM3ZiFZ6nxYEu/HQPNwamIZLGzgyWQzD2hseg2iLUS93i87H6I89x+l6f1vJq7st/pnLnP3q0BoCcQ+nSYNs5ebJpGOr1fB983moeB8xJgh+TmCXLIgjXmUwau6febNdOpmP3FUjM2G2B8sqDFaX3AiY4OoQEZ0Y1dmVJ7YUa6/yGW4KvgxDjA0AptDBAnZR6oV8ygD6pztbQHQN13SjnuN4aQEsdk9SgHqu/vs+cC7uMNjeb8R6B0cDRYbmZJXV8CUsdo17ZxiCQdquxq7pbmm/EK53/dUMyAfaKnS5UMdVCq36tQcHOBSPvdW1jAr3jdE8niDz3YHqiF+q3bfNqThY8/013bXaLMfz6uuPSjt2lpzjKstaIfcNOq6jXmIpioL4t30g31z6oFxcFXJ4GgzynVbxF+4IC87Y8jR0M1Ulnr2UZqjkMz11vQPN07L42ctn7Dxjx1coHPvABLC/7LZGbm5tYXFx88qWbyiXJpQJ59v8kwJ2inuSWhmVcvjFv6ljak8qp6VtPZS1T7BjvscdseS5Wtlh9bV66K8DuBrA6UAPGuLwn6dT+1/vHeX9TR7YusQCoel3Mu1rzHlcXBebteXsP0NxBMa78sTYZV07bBy41rVifjj0blnZnnFhDiU1P/1/Ks3Ejy5vf/GbceeeduO+++3D27FmcO3cODz/8MIDJ7Tmpnady+TIdu6+RjAPFxsxERy6XA2PxNbuwQXMSr2u4GHYXynMps2N7o8n3Ypc+KZHCczEW2zIcW8jp8eiFmr4iHdX3OC9+LrxjatAFegBDYgtfK0q8qZUwK1C9bFI3m5TVRSV207iGtfob19ki/YWL1svphooF0eOc3+r0HrzRxyVO1IYfAcZi2NvMjAfoA+hChKwo6kX5xSpxA65EJ43d2k0v9fNUyblz5/DGN74RBw8exMGDB/HGN74RvZihR2R/fx933303jh07hvn5ebzqVa/CZz/72cY1Ozs7+LEf+zF0Oh0sLCzg9a9/PU5XNECU1dXVkfXE1Qazp2P3VZQx4OZYIZjmLnZh8/yl9ncLmluJjtvj8h2HTE6Qi4GbI0Zz/T0u/QhIHxMbGPuSCjnuZSLtpGP0pOTGAZ9jyxTT87gX3UVeelftffikBntMmCCOOWbEUudMyuZSPuOK1Hj+IjpPMGykYftAxO4SZbmxEnad3GAyHbuvzdh92UucWMTXj3/84/iDP/gD/Ot//a8BAB/60Ifwyle+8nKTnspFRMFIinoMK+VHDJhToNHSuZB2RK/lf/UEpicuwUTSYlihZy/PqRc3+aljZbXX01OXHuL06OW9LJN6McfA9FjaykmtgUd5z+7uLoqiwN7eHoqiaNSBnOP8T4qQJEnQarUwOzuLhYUFZFkW9KV86/SgZ5mUZ1uBZ7YLwWl6gTNfpjs3N4f9/X3klecg06aXuVL50Gvc0qCo97v2FQXeWVbeowYFeonv7+9jcXEx0KgoSM/7SA3E/xZIV/ohpUEZDAbBG5+/uVtCqVzYV6yHv/L9a5nYnmxT5bLXIKOqb6VK0r6l5VAueN6vbal0P09XefDBB/Hbv/3bOH78eDj2dDccXI8yHbuvUMpydKY9abFqrxF+CYKgdGYZIB3Z/ppKWjw3KBMftBKot5BKGbggsPHRKH7LdEVtrWTasZlyUaDdbnlgUFcdZdkkPNdMyhLdrscVW9kQ6BeYna23ivd6QJknnqOb7sdzc/7+drt2VSONS57XZOLdLgauhf6aT4uAplU7ABw+XLPBHD7svcpGPL9Vh+pyRtqUdhvFWnDoCrz1zG9x0V96+HDNNsNrCAxvblZl67brfNS1KhBvoqatYRlJhQLUHOMV78nyiTbc8VaIn0osV3Fg3f6soLJWG2iCN/PzwFJeeVmu9+pycGsBRfu9ZlDtFmic44e87vJ/mC9hfa32GtPkWS4mTQ560vPQsb3T8XVn2bnFHKh2eJAuaH0dWFvDysoxLCzIbg/yw6+vI7j4dzpIV0o861lHkOe1w9sdd9Te6AmGwD33Ah/5iL9ed53EjCH8zfrr++Q6lxtl7H7DG96A06dP44Mf/CAA4Id/+Ifxxje+MQo8U9797nfjF37hF/C+970Pz3ve8/DOd74T3/7t344HHngggNFvfetb8YEPfAC/+Zu/icOHD+Ptb387Xve61+FTn/pUYzflP/tn/6xBo5Lrg3YVZDp2X6FYi5iM4wyErR8NHmmfU/83bXiojyBMxgAesy3aqcLsrH9/qz3SUrFxqPSe8OWoZ/a4usfeNVUh9H1Jug1bdedSZHn1vnRu1K3X1p3v+qwONK4BP8cZDGw7KHVIGCtZX4u6WrowKY71Ztb89fjsbB32w45H4f2uJ8chjTr3szoqfaBUGI5t3qZjt8ZbGeFH150Qtn2Fhi1xDmmWIc3cCB/9WEXZgkRQ7kBJ06uZ52zaatBmMpqcSkRNjapwqG7oQbeoscz9Plp5jltX2mGXG6eszpkdmP0+kLkROhZ3KG3Q/ETFIv7XqUzH7mszdl92L4hFfD1x4gQ+8pGP4Bu+4RsAIGwvm8rVk5inL4FApTlR3nIeo/B6y18O1ME1SceiYLcGzOTxnZ2dESoUAoz8JkCYpmkDeAZqWgwFbLWsBIkVWFZOblLK7O/vBx50pqHGAAV5CbYSsNzZ2WnwtzNwKYHQwWAA8n1vb2+PBUgppIdZXFzE3NwcDh48iFarFdpEDQsaIBXwIK6tB9tZucBJqcKyUB95nmNubg7kPt/e3g4vAxob2H79fj/oTfWcJEmDL95FBgmrA/5nOZVih22unPI0Xmjbs58xP4LW2o/YdltbWyiKosElz/tIWWN3WhDYV9Bc9cFyUh8E/tlvaIRI0zT0EZZPjUjUiQYh1b7ItqJxwDmH2dnZ8Fw8Xb2s77rrLpw8ebIxmFOmdC7XTqZj9xWIkjhOmjQrh7QVrqTLElmWYnfXY3YbG57+Q4eRogBKWchzW2xRAGnFN8oASS0JEpm6IfI8CYtvFpULiU6HlDAeUAwIMeumq6D1dSy12z4gJFcxpPMgaTVpV3hfUWDJncXSLIB1f8zlS6EMpN04enQJ8/kSlo67JnkpP0StK7SUQUTPn/dFZxGJ2XKBxPoePOjjYC4sAEcPDZpgsObBT6eDoUvR73sgYacPlL16HUlAXheBFbMKFhZ823U6QFpcAJzDIGuhKDx3tl+MpcjzI76MgN+WTWCA/Yb/SYHy2GM1WECO8bL0K+uiwFKng6VOB4PucmDG0WoqrsCyBy7fclB3LJTNjnLa6Gl9vXYVZANaFy6lauFvKkrogQCEhfy5c8ATp315GQuA5SRmr9vtb7nFH1t2F/wFJ71BYaksscQHpSyRZFlNP8S++eCDnkC/3cZtL2sDWYYL/cT3b1o+Tp70hel2vWVkdRVHv+7rcHS1Dec81dKtpz8G/MnJmpbn938feO97/T2896u+yiMxCl6p0YR6seCWRVSuI5k0do8FZcbIU1W1z3/+8/jgBz+Ie+65B3fddRcA4N/8m3+Dl7/85XjggQfw/Oc/f+Se/f19vOc978HP/MzP4Hu/93sBAL/+67+Oo0eP4v3vfz9+5Ed+BOfPn8ev/dqv4d//+3+Pb/u2bwMA/MZv/AZuueUWfPjDH8Z3fMd3hPQWFxfR1SC+V1mmY/fliwb7S/S9VJ3j2KjA6gjnuRH/GCdAFYy5wXNedXC+52L4bgDyKjCS55RWJAY8kvZkZLy0IHrMaBD5cA5BA+w4LJh1mJlJsbi4DJcDaWfQfPgVzKwqvFUkKA3eeymyu1vlnQmwy/GRv8fVi8C9sWWqWhRDVWH4kbL0dF3sF84BwyxFoiB+7Fsrad/rBnhPnUOee/3TYKLjncZaScqBj4XDAZ6Fl3lXo5L8cECtvpMs84C6q3nwKUMkPsBpFS9FJRhZNuL9xFZbP2SiyzLDq69GoAll5nMUKJT6RT1nZDAVzVy4eBLnsGQnwfbZ4byWzhN5jrTT8c4qWjag8nJP6rwMb5x9hq8HmY7d12bsviJTio34OpWnXtQLPeb5WpZlAMTp2TuOdkKBKAXRZ2ZmApiqHN8aTJMgONOnZzW9r9XDVgNHKpC6v7/f4PK2ZQLQ8ARm/Xd3dwOQTLBcjQjW+53pqJczgABY0luZH+qQoPLu7i62trawt7cXgmaOo3HRYJdLS0uYm5vDoUOHkOd54O/m9QpAU6/0etdysbwEkslnrvVjGy8sLGB/fx9ZlmEwGGAwGKDdbgcDwN7eHnq9Hvb29rC5uYmiKLCzsxOMAzQktNvtUIf5+fkRz3j19I6BxK2Wj2LPb15bFEUI8rmzs9Og42GaBMuzLAv9SoF8gujqGa+AdpZlDQCeugMQDDlqEGJ/5m/2c4L6CqJrf7OGKuutz/5ljSz6/CqQDmDkeXg6yY/92I/h7W9/O9bW1vCiF70Is7Oz6Fez2CmIfm1lOnZfgdhFamzlYEF0O8GvzrkKINzZ8cCw9cQlhyqFCyzvKZaEawDvNZNmrrE4O3TIX5M675lLiuZWNvSI5fo68MgjfrGxstKsn66KGDiJnmU83+lgq0jgMsDlS35xxAUNUe6K1Nq163Ver+eTYbLHjx9B3jnS9A6qdLVVem+g3hoC6Lq56dOhfhYXm4C2Xbi13KBGl+2Cl65N3S4ePu056k+dqk8xXXGqa+RBj6glV5V7vR/43NNuF6VLAw8sjRrEXn0aCZxrIW3XOgWA4F4OeK54dhKgBrF7PZ/QLbcgXV3FcqeDft4K1VPHcZY9LGILWUiqJxfTVyvF7q63BJRlM6rqoUMN4vKtMkXRB5xbhussh6bc3QV2elW3WW92MzqHM0uWU3EBdrks81mmxQXgvvv8TWfO1DsX2KH4UY/4sgS+8AXfD2j8WVlBH8fgXIoWo/qePu2DhHY6NSi+uOiB9+PH/TW/+7vAPfcAJ04Ax48DH/gAPlMUWDp1Cqu//dv+2JveBNx8c/N5oWWA7acIlSqFDXadyaSx+0oX4hcuXGgcn5ubw9zc3BWX8c///M9x8ODBsAgHgJe97GU4ePAgPvaxj0UX4g899BDW1tbw6le/ulGOV77ylfjYxz6GH/mRH8GnPvUp7O7uNq45duwYbr/9dnzsYx9rLMR//ud/Hv/8n/9z3HLLLfhbf+tv4X/4H/6HMOe/WjIduy9f6mHFc5xrQETLR81AxgCQ6qNY3Qc0uZ55T5Yl1bjQfH45XowEIa0GGg/sJwEkj4HnzvnXbpYBKQb1u0SBQc1Q0fuYuGbQU3oRUycE1SkEBj03t/+d5ylmZtLay16yKqriKOf3pbwnFLwvS+8o4JyA1xYFjxhr6fmubUOQWqdtrKfiz1kGpL3HkTqHLPPGaYLGfjxqIeXuJmAUPOeFsV1bOiesypxmWQBl1Vtexz0/ZstYTRCdnhfsjBaBt4A0x0YC6gz+ap4HjtsKlmt17TkrCwv1XGdhoRq3+2eBXjXGco6ocxB1uT98uI6hkmVIuMtL53Ds+5wD2ELxgQFqvWxs+I7OQCfyDIZdj9xqxvZSy1cl4TmOPFtJZbK7nmQ6dl+bsfuKZm3DYTPiq8o3f/M3X0mSU7mIWN4eCoE7gnyxAJ28zgJQCpBqsEelDyFwqIAjr1GgkvQW6vVNgJDpkzaFAKKlWrHc1kobYgOhWj5rBqKMcWNbOhegBnetTmlIoEfy7u4uSB0So9yghzk9ngmYEwien59HmqbhRWMNCkpxoh7M1KG2q+UV1zZUL+c0TbG7uxu+kyRpBFxl8NGtra0QCHR3dzeAuiw7jSKaj+rcguhquFFqIfYn7l7QOrNu7BsEwQmis58Q2Na2YFBT9l22v7YJdcI6UJ/UtQLoGliX9VT6FdaHbc1+ZHc+8BmJGasst7oaKJ6u8jf/5t8EAPy9v/f3Rs5NQfRrK9Ox+0mKrsZ0kq2TbgXPzAKQtyuuSynL0QBYPA7U4DntbUUBuDxpBJwKC/miqBbpaX0xg3dubtbHdJuyFmjMin5QJo1LlnK5nwuU6h4pSmAlUQcgGgY87UvaKGZZ+nv29moPa66X1FNL14hcT6cY1HXV1YL+zjJsFUkAdLnGo2PSwYO1Q3y7XS2gigIo4cFooOnZRDCjKOCytEGvonYWXeemuV89k9JnqVNduLhY7/G3HaUsffsJv4lzrRHnN2kyKbuA6FQqpd/3wD1QW3G4+GTieV4veKsMtOrM3y7KrfR6tb2AGLiKYg5ZBqTlVk1xQ/f1oqiD0zLwaZbVGRI9YWZnzvhPu42yWpe32HFoRWHB87w2KLTbvpD33Qfce2+t1Pvvx5cB9AGsPPigX0jF9oEryGJF++V1CKADk8duG5fgYsL32S233NI4/rM/+7O4++67r7SIWFtbw5EjR0aOHzlyBGvc6hC5BwCOHj3aOH706FF88YtfDNekaYpD5HmQazTdf/SP/hFe8pKX4NChQ/j4xz+On/7pn8ZDDz2Ef/tv/+0V1ykm07H7yQvfTzYWg3M12Oocxj6zpK+gKA2LD9I4bNwWjMTM3KSpdnd7nJ8AsNuXqw6KlgB6XKJovpttUgTVeZ0O/zQI65jGz8zM6HzFYsyTqFx0WqU86YHWJTbnqgpGz2qrXi379vZkT2HnAEZBd255pHwAvMOCNpidW1ilxc7Jbx+cM4mWuRHQlQ3E/zs71ba5nVHFcnwmSKz5A6PBX9E0nlgbgaWKs1XWaW+W1VOH2VkTSJ6g99qan1ecP9/cNrew0JyPckKnwdi1/9OIbguoSmRhOdehEwngdba4WEehX1314Lsa5ceMyTSoXe8yHbuvzdh92TO3WMRXCsHSqTy1cqmAmwKUFMt1Hkub4DlFAT97TvMiAEkqDwWDY/mQ1kTLRTBdy3E1AEZb7xiNjFLkKFXJuD59pQFC7cfuGlDA1X7Ug1s9ofX3uLzUgMGPBvZkujSWEIBnXrxOQWb12FY9sl0JcBPkHg6HgV6FhhKlSbHgspZLAW4F8dVwoudUZ9boMgmMtVRIWg41ZOh/NdTE+u3F+gN/Px1B4oceemjk2MbGBl70ohdNQfRrKNOx+ypJxEtlIhBmrue6QRd5vJ0g6+5ufGFqk7UStq9P8EQb+W0Xe5cI6JWlzy+JVajKnz/n5/0pXZ/YLdfjRBfrXBtqOqScrwEQjLaHRQRMe+hhtsHEAtkV5Jh0NW0u4sOWdZNk448uJrVdbJ3KcqT+se7ZuCdWH+fqRuLNBPOzzC8wFxZGlL+73UxGixzDlHl+bq7Zlry/AWTZ8s3MNKOJaifQm3QlH1HGCFDmnK8b6xdTIhf6PL+4iHRjAxkAx3PU78TGNXKdA+mTxu6yvLwt7KzqI488gqWlQMQz1pPt7rvvxjve8Y6JaX7iE58AEF8T6bx4nMTm7Re7x17zEz/xE+H3i1/8Yhw6dAjf933fh5//+Z/H4cOHJ6Z1qTIdu6+uzM427YM81pCJL9LmO90evyhAFd7pl7FbIfaSj73rdDyOfSL2AVbTdiMdv2IUKaoezlfGDY8xscVmPtH7rGIv0jb6zbKzfpovh5XY/RMlNpe6lEpTyhKo6MLGph8b+3WstuXgu5QW+1hfqCQGBtssbXB1vSY2b5o4/9DGuNhkhccmGZ9jBdT0OL4rtx1Bd50/6IQ0VhbT5xIMbwggfTp2j8pTMXZf9qyNEV//n//n/8HNN9/8tPagvF4kBuYBo4Apj6k3rD2vHub6rcCf9W4mLzlF6WNsfjw2OzsbqFTU63x/fz8AqjzOcisASs9jps2yWM9dC74roKugZFmWgTJDgWhNk57Xeo96PfNaftNzmh7o5DmnZzM92WfM24ogsxoM5ubmGpQgWgcFbvUaC8Cqx78aIrgjgPzs9NpnmRk0FUDgAz9//jy2trZGvMupJwss85te4JYr3DkX6FwYBJVtkGVZY3dBmqZI07TRJ6hLctRb8FyNHayD9TxnfkqjY0FcNRaod7/2r5jhIWao0LayOyTUEGU9959uQPGzn/3skWPcFjYF0a+dTMfuK5Bxq2QFqWMTfnXZEeLuBEO02wm6Xe/pzJ21zIo0JYGLcrvpUOOcv4drgMBxnWUYlAn6PRbJezgFTzmWOc9rzhOWM8ZPyY96K2N0jVEUQNY95svB1VXlHl6WTQp1Xc91u5WnUiXkaAVqz+bFxXoNpLt2lVVEKVca1DA8qG3CugifZZ77tLvd2jmJt3JR6MHgxG8tVuF/ekZXN5aFT1fB+Dz3aavn9lLmy5pmDs4lnvOTF6+u1q7xWnmLDhQFDq34fI4e9ZfPzdXrwbBbQVfH49qa9VFecaBuRHH9H7hWcAgrCr8Tm7rTbhVTF6ltuLub55yrqYu0CdF2Na8LFUpv/Dyvt4Tr1nCNpNpu+8JVwEOnU+1WoDv86mr9YOW55zZfXW0i/C99aR3sttMBvvqrcds992AZAL7ru/zxmRlfIT7YuqBXI5N16WN7Xo5r2DWSSWP3lS7El5aWGgvxcfKWt7wFP/ADPzDxmtXVVXzmM5/BY489NnLuzJkzI95qFHKgrq2t4eabbw7HH3/88XBPt9vFYDDAuXPnGh5tjz/+eOAhj8nLXvYyAMDJkyevGog+HbsvXzyPcf2fwBeP8d2vOFy43iKFlXCDkO4o4iuC40/i/Pu84YU+QSyWaF8FjIniGOCThVcOrNi73Ay6gzIBzHtZX0/8Hgew6zVAc3yzZSagrulNst/ylc3Xt3OGBscWRn6P8wFwrqa1UwdsHZv4ip+fB7DndZqh+W5r9AsdxPQdTm9p25C2zHq9c75HVv2loR87x9TCW372mEJ1a57ZqjfMWiNqZTsqEM7qxjzQgdFnx7DGNBuEBnkO/M7VUcF5s+XP07prAZzzc1hea/mVaNC2dC6dTj3h4MSSYzXHdU5WVcY8ENcbfYuV6dh9bcbuywbRYxFfp/LUyziPZwXgKOoFTgBPAVvlK9d7FVQn8Mn7NMAnqTD03GAwAKkxnHNQbmpyShMws4A4Rek1SKuyKy9ISxWiwKvS0VigbX9/P5SP6TMvTZsAsBoaCMwq9YsGvCSVjXp2M10C6fTA1jYk9QgBZ6UuUYOEtrNS6mgw2Ul9g1zqzjksLS1hd3cXzjm0Wi1sbm4iTdNA6VKWZeBl39zcHAHpWd/FxcUAwlMX1AeDcGZZ1gCbLfc467e/7/nx9/b2sLOzAw06SsPH3t4etre3sbOzE7jVFdxn31RQnOmw7dRrneetJzvzVfoX/WY/syA625ztSuBedzSMW/TYPJ/OHkWf+9zn8PDDD2MwGGBrawvAFES/ljIdu69AuKKwC2qLJtuJtrrikmqiuufwYf+TQTD11hQ+aJereK7LsqakXlvzyRCwDFt+KwD93Dkfk1KLTa/foUtrLkxd7LGcRdFEu1k/cjmLEYBcnmXp1yc+TmmKTufWkcU0OcsDoMCF33oTuE+zLAQiZfnV03x2tr6VILpzlQ7KEuiXo21gF0MG0HTwbcDDXPSzybhADOu3rObFLUtgd9d7ER7tdIKFY6vwdDeMWUnpdIAWtoCsFdZx2k8STTjPPfd2WY4GxVKKleretLiA5TzD6moadngz7+1tf2lL25tieXAi4MsQSTMu61oTCyZNz+oq0CrOBitBmmWeLoV6L8sQuPTWTge33t7BAGmwP5A9xQI8vi0StLjQ5ZbvjY2mkrVQztWWhF7PHz98OFis0t7j9QPV63ld3357ncbqqj/mXJ3fa18LvPzlbHjgm74Jq1/6kr/u7/99n/apUz5NfYZioIq2q22L61RiY/eVLsQvVTqdDjrKcT9GXv7yl+P8+fP4+Mc/jq//+q8HAPy3//bfcP78+bEL5uc85znodrv40Ic+hK/92q8F4J0vPvKRj+Dnf/7nAQAvfelLMTs7iw996EP4/u//fgDAo48+ivvuuw/vfve7x5bnL/7iLwCgscB/sjIdu69AyjIAXeo5akFha9tqAOlmnFewl8NLANPk5ZXYd63NiNdhCPKix4KL0hAe3uMKQmpa9n3jHAbwHOHKU07hMiNSRQCjALmWK3Y9xdoGrTFAvdn1tTgROFepbiKvOxA3ChBAR78fWj4FkOa5NyZglA4Org04h0zw6kZavEnf1RZEn1T2mMGjQqmTLEPq6n46RAK4FMiFF56i4Pk4EF06OdMMWHQ/rtbY9DZWFQXRgVEjVHBo6Ms8k4pWXbFD6DX6rYW2mYtRHEBtQCcYr5XgWHvmTD0fYMHpULK6OjJPD3krf2EFvtvne+jS69Y7fTp2P7Vj92WD6JMivk7lqZNJngeWBoRe4gq0qlctUHvCWioO/iaYTPB7HFCvHswEnhkAVIFtBR75naZp4JYGEMB49RCPeQrHPHetJ7qV3d3dwK29s7MTvJspCqITFLbn+JtgMoFk8sET1KWRgQE+6VVt20l/M13r1a9tRY9xgt32furF/mZQzSRJgpGDXO285sKFCwE8p8e4NSDMzMwEKpY0TTE/Px/6CHWXpinIq64BOLW/EHC2fOf9fj8A6cybILoGQqUhgMYH9US3nvzaRwiCU6cxahft97HdDDyn/ZrPh3LXK+CuOxmsB7q2k6UZerrIX//1X+N7vud78Jd/+ZfR5/TpVt/rVaZj9xWIXTHqImWciw5/09tFvVLLEjfdlPqgS9UiL9V8KjflBGgEn9re9kBjntcMG+gXAeQrS++cqwBtlnnckTymKVeN6v4O1O7RCv4pJwfQWNjo+mFz0/OJA7WnPD/dLvms+02U1C4kK9Tae/C1QvrMcimvOL3b3liQumG9GLIraU1TQXTnQuAxLtYdagcpWR8FAIEqYDBTpRXXqtx0UytQ2nBtyLKzeC03ANZ7yLqtUZoTSzhKj2g9x+Cta2t+MTgzUxeiUvzRTgdDlzbozgP3ZSYZKlIk7mPDzpFGVkyn16vBcqB2KNOksgzA6fWag1z1T0WQD/Wmm4BOB2m7jVs7HQzzpdAFLbY8N8e6JGh1u83+qO1PXbGg9D5qt70SFhfreyy3+iteUXv+FwWwsoIvr3sDSbfbQoIhtk68BGUJLJVn/X3kZP3qr8aFl70aZQksnz7tz9GzXZEJLa8arrTPXocyaey+Ul7Vqy0veMEL8JrXvAY/9EM/hF/5lV8BAPzwD/8wXve61zUCk504cQLvete78D3f8z04cOAA3vrWt+Lnfu7n8NznPhfPfe5z8XM/93NotVp4wxveAAA4ePAgfvAHfxBvf/vbcfjwYSwvL+Mnf/In8aIXvQjf9m3fBsAHRrvnnnvwLd/yLTh48CA+8YlP4Cd+4ifw+te/HrfeeutVq+N07L4CMai4xZtrw66/Ls1zwABhBGsVnOXYEHaBaV6XIsbgZ/F2+785xDEo9ei8pBEkshgNnGqzJ3CvLBZqWBjrSW8Nl/J7xKM6IgGQ1nlTiTrWyLjCVsI2sdMvHcOdQ3PblzRimmXI8zQMfUmxBZQueGhzqGyMReKsAAAlWlWmFcie56NRVCdZFPS/NDiD3zZvTYAqbkqlQTH0RJIuEWjW9nqjKgWaAWPtUNVoe+bT6KDVd276YOiokfppgNhxHvR2J5w+CLaxzdwuzGvzHOh0GkaWUB+O+9pvq84y7Bzx+i39J8vEEDau3fh7OnZfsTwdxu7Lbv1YxFeVF7/4xZeb5FQuUcbRusREwTp6wypVSQy4IqBnPXMJGirfOa9VMF49bzXNGD0JvbYtWE2vYoKj1sN6XN0v5tHKsjGAJKlaFPhk3gp624CSWg/LFW89lq2ns4oNNFm/3Oq3lYLuzF+BaJ5T4JX6VG9qvSZJEszNzeHAgQOBWmVnZ6ehA4L0NAYohcnc3BwGg0HQU6y/WE99NYZQ37obggYX3eGg9aY+1Qtf+596mqvYXQ7qiR7rL7Z9CZgzXT4DlhNdPc1tXS/2rNo8n47yj/7RP8JznvMcfPjDH8Ztt92Gj3/843j44Yfxmte8ZuqJfg1lOnZfRTELYbtgD+d4rexrTsqBB84JglvPF64EyxIMuLm359doXPw0qEvkVsWotQhlCbgs9QsDW0aN5MXyjqsTRourgSVZ1XALF7IM8kmQk9wf/FQrRF3IhXUV0yhLbwig3sYZLWwiUhQ6FmdZvbWfO4KVBsWKevGprrnVvmWuYzFCE0XaqvHHoAFbpW/3LE/rwKCsjz63LIhz3usuy5BlrQYDzEUXS5USGKeL4Pn6et106u2nFKuUUEbewMrrRQTY1WrhXDCeaJUoGhNgmKVIclls2wYBRoFpeqdpgxCt1x0Y3W59vN1G76QWMcHamj914sQy0nbhr19dBVZXcfKkz3KZ+bPeEQAo2t620teRTBq7yxJILsP57qms4n/4D/8BP/7jP45Xv/rVAIDXv/71+Jf/8l82rnnggQdw/vz58P+nfuqnsL29jX/4D/8hzp07h7vuugt/9Ed/hEVSXQH4F//iX8A5h+///u/H9vY2vvVbvxXve9/7wtx1bm4Ov/Vbv4V3vOMd2NnZwbOf/Wz80A/9EH7qp37qqtZvOnY/CSlLRLnH9UXO/6ivUyBOh/nwaPflXRNLe9y5MWK9tDVvoBlLw2XN+tgYlCPVknRjxdKhuDG3sCi1rWMUSB9fx5G0bQFtoTSPCmDW7CeKHRc42BcF0twBSLwhpHIgKKu2J8hPg4Bz8OAqxhc7zyu6N6t4axHRG20ji+Ji+eg8K1bVSccizYSZmWYMEh4PY/mkCttEJx3XDMqyBtRj98UMyuOu1Xu4i7LdxoV+0mgGbyhKsMxI8ZH7e72mY3xZAmnUgBCZ31+nMh27r83Yfdkgeiziq1KFPJ3pCL7SMgkAHycxIBGowVoFcxWUjAHo9JRWr2nrVW3pZTRPWxf1lrec7NZjnvfEgk8qAEkgXKlaCMJaz2MFShUAtjqL6U2vt179+iwQ/KWnvnoyW+9+6sEC7OqZr8E5tf1inuiqfwX2WS4FlSnWQGAB4pje1co5CaS2OuLvGKWKlkM9/2kkmJ2dDUC9ph3ra5pvzKhhdwjYPqD3xTzdVS9aPzV+2D6l3093+fM//3P88R//MW666aagp5e//OUApnQu11KmY/dTILoQii0eKLHFqHrf2FWOLCZ4WZ57THB3twroKVnZXcb0qF5YqL2iA/WJzUcDSsbKoVKWcOLRrewfSsGaZUArGwI9A3Yr6alNvygqEHgMP2iWeVqaDM0V5SSk2Oic2SkozEts96d9wQagoyg2yx/EQQIYQSAmcyNlaCwsTV1cVv9NHUZ1tbdXWwT4LffOztYBaoMhYhJY4VwACvQwN1KQ1Yf11h3XHmQom2nxpCJAGxv+Q17+CH2JfSR0c0TtHRfpl5q30WWwbPA83ejVm83ox1aB5UoxqAH4zCt6IgvLOEBgHJp1ncmksft6WogvLy/jN37jNyZeE5v33X333bj77rvH3pNlGd773vfive99b/T8S17yEtxzzz2XXd7LlenY/SRk0jMWewePkeglMXQy8n7y43WEqqT0HOpq1LXBxJ1reowr2Gl53suyyeUdw7n5UQ5yvsPCHCEGnsYS02PjjBWoyztSf9FDI137OwIw69ASSyrlAF2WTcuvjsMlxr+jK49vBf6zLB2ZbjhXAe/FBOPouDrb8UrKZrH2i3Vjm+W46aj1Qs8yszsgZkC5VAA9Vi973yQA3abPtlMS+3F9BQCKIrTRSNKsjyq0utBV/Xbk0aWCYnW6xPfGV0qmY/e1GbsvewYXi/g6ladeLgVsUq9qy9tsvYqBGrgmoGsDYCqYq0A6A2kqZ7gVAoikaFEgUYOHMvCmgsIsy/7+Pubm5hrgL69VL2etP6lAlKqFosA2UHtrMw0LyKr3N3+Tj5vBL1V/1oDAQJjaNuQRJxWKgvgaaJXl5b3UhVKnaJljxgTlICd9DalRyNfOa9SDfG9vL3iZs63YJqRrYT3UU78sy8B3boO9xvqv0sXQQEK6GO564H8GRXXOgbQqqndNT8vMMmiMADVi2Hazbaj9imXVa6gjcsGrp7y2u5VnEpC+t7eHvAIsOp0OvvzlLwfOsacaRP/TP/1T/K//6/+KT33qU3j00Ufxn/7Tf8J3f/d3T7znIx/5CN72trfhs5/9LI4dO4af+qmfwpvf/ObLyvd6lOnYfQVSlkCa+gkzgTaLmsUWYTob5wJAOa+rBR63B9vFGrIMW4U/x52qKyv1IrvfB5a4uOj3kTqHbncpZM0sWqjoVCjqKauoe+2i3Sy7bq2trksyIMv89uJ2G3j+82uKD8+FWYGMJ3s17YrmpwtjdaGrgPFOZ7mhwkGZAlV+Xn2pXyCLh/oIiKt6ZzNlXj8bG6NAhd5OXnEeVyc2bXLGoUrd0P/JssCn6reIlyidz3OAFGm73XAo3yoStOiVpZ+iQNoehu35gHi5OVejBgqeE9kuy5B+WBz3+6Ok45PAaKmf1U3MC9051EYN52qXfsCXtdfz9CcPPODLUQV4V1SIVWdA1sXFGsBfyoV2ARgFRNjfgGbAN/aPzc36HCvR63kOc+6QUK7UCiyhihV3x+nTwIMP+jpU6ZH3H/eVdWQ2q0xVJEXrIUaQ60kmjd3X00L86S7TsfsKhFQNhqKlHmLTyw4O2DDojQHNB2WCsogNS4nPk+8seSAS53zcisxhKXcNL3jNu54jVPdVJ0j7pmNUDG+0AVFTV6XXL0bHoRharJlovatvBfZVGgC6Dq7j3nlmgIlRuBQRHQP+uB9DUs8hz7yk7I0yCnDtvbDL6tIKjOWOpdIbPFLnY7gwjTDnsXMRFatHA+LqNwPTalta7DempouJNlWDqsfSydn2GZeQnRCNq3MMiLeV0QlWYysj6vmNzBFHysZzpd9PkDqHFulmeF2v35znyvwwby+P0OwNygSOsYRic0yZQ5XlU0d5cqUyHbuvjVw2E/6zn/3siZ/LkT/90z/Fd33Xd+HYsWM4cOAAfvd3fzec293dxf/4P/6PeNGLXoSFhQUcO3YMf+fv/B18+ctfbqSxs7ODH/uxH0On08HCwgJe//rX4zRJOifIL/3SL+E5z3kOsizDS1/6UvzX//pfG+f39/dx991349ixY5ifn8erXvUqfPazn72s+l1NiQGR4+gfFFhUXm0Cp0VRYGtrC1tbW9je3g5BGweDQeAOB9AAzNM0RZZlaLVaaLVayPMceZ5jcXEReZ5jfn4eWZYFD2GClwwOST7r7e1t9Pt9bGxshM/m5mYAeJnn/Pw88jzH0tISDh06hHa7jaWlJSwsLDSAXPVIJlC8vb2N8+fPNz6bm5vY3t7GYDAI9SNozPqpd7gGhiQoXxQFNjc3sbGxgX6/H9IsiqIRuJT3MpDDhQsX8MQTT+Ds2bM4e/Yser0etra2At+3tpvqXL2vGcB0YWEBeZ6j3W6j3W5jcXERi4uLIWoy20eNHAzIubW1hX6/H8q8s7MTuM/V65tc9XNzc+G3/U8QW4O5ss5M0wbz1L7LshHEJ9hs05+bm0OWZaGvab3zPEeWZaGvE8RnXy6KYuTDvqiGERo3tH5qqNC+YPsO24vlZN9k+dl2amy43oDzd73rXfi6r/s6LC4u4siRI/ju7/5uPPDAA41rnsz78Pbbb8dnPvMZAJ7b893vfnewAI/bwTDpczmyubmJr/marxnZHjZOHnroIXznd34nvumbvgl/8Rd/gf/pf/qf8OM//uP4nd/5ncvK93qU6dh9FUQ9VhXIswtK/c8F1vo6Ai9EWWJQJlhfr6kaL/STMMHfKpKA65FtYnXVf5OnupH++jrS3uNYXQVu7Q6w3H8YrdN/Bdx/vwcLydesi3dF548fD0BwA9jjNllDh5GUA2QZsJQNcPTA41juP4zk3k8Dn/wkcM89/nPffTVQSWR/ZcV/ut0ahdYFUq+HtH8WrfICUgyQYBhiQFJ96+toLoSUmkNFgdR+P1RrZ6dO69FHaw5wZSPp9Tz1+NpanWevV6vPOWA520J66q+Afh8XsIQBUrSyYcUD3wsLPjb/wLXCGg7wYP6FflKXleWtCpKUgxr3ZlsAtRVFC3jmTCgc2yYtLjTbnkB6zHIgC0HqqdPxTXXiBHDHHcCLj2/hee3HcVv+OG5tX8CtnS3c2tnCsfZW01hCqh6W9bHHfDnuu8/3j/vv94qXe7YrDtd2G7j5Zt/Xb+tuYdldqJWvfDztNtBuY5gv1eC48qLaxgQwzJdwtmjhc/cn+HKx7DN51rN8xky/6uN57m0B1P9SPsQSLgAnTwKf/ay//vBhIMuw7C54rnQbOY/6GPdbn7FxQPtF5Cs5do/D3CZ9pnJlMh27L18GZeIB7bKJ1+mrdlAmI0CwFYv9BeC5OsCggluF/5w7hzCuM3Zxv+/f90UhAC7HPF64tubf1adPIzn9MNLe4/7TP4u0fxZJ72ydmAC7KH0A1dQNw2uFLFZ8j83N+VfjoUO1YTKMU71es8BaHp7XD/Mf83Bbw8QIgK7jXAwRtwp3Lly+sVGPp/qxRXziCV+NC0XqP2ULW2hhq0iC40IjD0ig9uqTFFt+55FODqinfh9JsYWkf6FZAFuwcfMS+210k5QDpG4YPq1s2JiOLeVDLOVDtNzAf3zt6k95YeQT+tH643X7sr21zflfP7ZuOu+KtZ+1cIwD4/Wj2ym1stVY3/jovFQdQfR5qp6lRl+2/a6qT5gzVfrW5M72ElwoUs+Zr4a5qtyjHPaXJtOx++khl2HHaopGfFV5/etff8lpEOD4u3/374btapStrS18+tOfxj/9p/8UX/M1X4Nz587hrW99K17/+tfjk5/8ZLjurW99Kz7wgQ/gN3/zN3H48GG8/e1vx+te9zp86lOfGvGspvzWb/0W3vrWt+KXfumX8I3f+I34lV/5Fbz2ta/F5z73uUAo/+53vxu/8Au/gPe973143vOeh3e+85349m//djzwwAMN3p1rIRY8skASwUBLNWGDHAI13YnSdBCA3t/fh3OuAS7GgHqlCKGHO72dlUrFcoKzDAo4DwYDzM/PB090eglnWRbA+AMHDoQgnQp+EvTWfAjGb29vNyhC5ubmgvc071NvaktlQ7EBKFlPfpRDXcFfoA5mSk98Baiz6sVPwFUpbKxYz/kYbYl6Y9P7XHcCKM85dUR+eAXybR5M1xotYkAzQXHVpe2/3AmhoLQC00mShN0J7I8HDhwIfYRGCt5HIw3LS2OR5SNXOh62g+7AsPQ6lm5GaXy0rOqRzjKoR7zurLDPzvUCpn/kIx/Bj/7oj+Lrvu7rUJYlfuZnfgavfvWr8bnPfQ4LCwsAntz78J/8k3+Czcob8J3vfCde97rX4Tu+4zue8noBwGtf+1q89rWvveTr//W//te49dZb8Z73vAeAD3zyyU9+Ev/b//a/jYxRN6pMx+7LEOvtElvkjbuHk3vew0l85dlSln6+T/xvbg5YuskDyhvnarrmpNhCyzl0u94bm/EQG65XVdoJFwanTjUBf11wEBCvPOGL0ntcLbezpkeuGgLs4sg5z0/d63lgkSTa/N7e9iv5uTmfRrfr88uWAg7qHHx5dUGjnNbtNpBlKIo0OAwHb6Fu1lzAWYDSLujAQHBpUJeCK3RYo5P3zk693mJVAF+d4Ci1tub13G6jVywhz4FlJwtMAGjXRSBPOYu6s+P/L+VGx9SFcygrbvShS5E46XdcuLOPkde++rgsrReUrDBdvC/BfY0geopBFYSzX4MtBLGttxhvVKMIO/naGvDQQxieOoXk8GGPzldeUUMkgb+da+ZWUQXwtIYp2cFRVMm32y0kdncIlb69HdqCxT950tft2OqKv5b6kYCubOMQh4C6PH3at3mWeetCltU6VguEKtLqKFKfcP4y3dmezmP3VEZlOnZfuuhQbYcx/nYOSDM39p1owSMbi0TT5Dt+c7OBxTbsZPPz/tqUN+puKpVJBnqg6a1bluEd4lw6wj6hrxoPFJejeWu0bGAU+I05CcTKMW5smYTMxRpK8lWQkixmLLIt6szM6C4zFaWcS91w9P2sZeG8QoFXnbxwngHU4LJmbNtOdUldxQB1LY/oJBDlFEZvMZRz3P+Y3rW+Ov+aVBcqkd+XIrGH0aZlf1cS6JCszuy8dFz9NV+gOUep2jjJ5F7XQlnWdH51MYX5oCrTlQLN07H76SGXDaLHIr4CTRDvUmUSwHHw4EF86EMfahx773vfi6//+q/Hww8/jFtvvRXnz5/Hr/3ar+Hf//t/HyKu/sZv/AZuueUWfPjDHx7bYX7hF34BP/iDP4i///f/PgDgPe95D/7zf/7P+OVf/mW8613vwv7+Pt7znvfgZ37mZ/C93/u9AIBf//Vfx9GjR/H+978fP/IjP3LJdbyaciXAm1JqaDoUpd2wfNEKQlraFALvGmyR9yhPOPNT4FC5whXAt/zbBJcBD+LSAGABTStKv0FR/mzLOT7JWKAfDZw6ztPacmcTVGZ6zGtS0FFbFstNHgtCqRzozCdW/ljZY+nY9tRvzd+WY5LnsILS9p5Ye2hfUiOD8tvH+Ov1nPZ/y10/rs6xgKDWuKKiZdSyM+1x910v8sEPfrDx/9/9u3+HI0eO4FOf+hS++Zu/+Um/D/U9fNttt+Fzn/scTp06hec85zlXTOdy4cKFxvG5uTnMzc1dcjrj5M///M9DgBPKd3zHd+DXfu3XsLu7OxLQ60aS6dj9FRq7dZYtQTxjC3p7m/5IM4fSjW7zbiwk7IeoMMFsuZmL0+1tv/AcIvFbzRuZT6gTvxWs5LeubCUt66DUYFDVxZwszKzqQnJ2QWXq19C7KNeuPZ1rOhFrQE4u3Jls4xER0IEGj0YGVZ7jFlhRFdtOAeF8jXWQ3d0anZd7A9ijAMBlinOog7iqFxqJ1id5WavOpX+WAFLbCUyeWQagV9Yghi7czT2WDzdcp3qpjjEIbMDVmVlkAe4yk5X2czEkAWjq+BIMFNHyXkH7AF/ZsVufi0uR6227+40k07H7ysbucdjslaQT0hjT6e1rmOMVv6NliAGhfB+MAydjoHX1m3E4YpdEPcJjH1aGMjs7WrZxxkEjF6XLseNARFFaJDM0No451wTQY0NMdLwYV65JBoFYHWKTucsdE3QeEwObJ+UZK9M461HsXnZWlZjCYvOQSXUcNwGyhqGI0YG7NnwWMj+NzVNj9bHljZVzTGfR53mcxKiLLlWmY/fTQy67BzDi62OPPYZWq4XPfvaz+NM//VPceeed+JM/+ZOnoIi1nD9/HgcOHEC7chv61Kc+hd3d3QbocezYMdx+++342Mc+Fk1jMBjgU5/61AhQ8upXvzrc89BDD2Ftba1xzdzcHF75yleOTRfwW9wuXLjQ+FxtuVxag3HXWS9b5c8mLQZBVgKDpKewVCNKqRL7qCeuBZ1tkEsFqcmZTq910oRonejpa8ujNCT0ELeAq4L36hVtDQnWmKDe5gBCWUkVQqqcra2tQCGi5ad3OOl1SKuidVVaGAWdFdxWLnVLH6IUIwQYqRfyndsApPrNtNUDXb3PVS+xNmda9HzXuto628Ci1jtcDSrab21fspQ0rDdpWizdkJaRz4D+Zl/UfqY0LvrbBjll2tqfWT/L5c5rr7Zn+sbGRuNdtEOT+kWEUbCXl5cBXPn7cJIw7ZjR5WIfALjllltw8ODB8HnXu951ReWwsra2hqNHjzaOHT16FGVZYn19/ark8ZWS6dj9JMbuiy0S9NueI8BGrgrngH5/hClFXx+zs6NBKzlhD86rukggIEi3t07He3/fcov/HDo06oluZAR4tdt3Yws15j8764H6+fk6cGS77ctBD98YKMDy6rcukIoieCcfOuSrdPgw6nLZuo+pGxdJzGZhATh40Ke7sFAHWJud9e0gjCGhneiFrs7Dmt/uLkbq0cqG6HbrtKK4iN3GLPXPc1/vpNiKB1Pd2an3rtut1wRismy08JE8Nf5mlo0BXShW7xO8yLSPhKPSr5JiK/R9dr+RBbUA8VhfR9o/iyW3haOHBn5LvZ4vijqyarfrqYq6XZw/j0Ab41wkD+bT7yPtPY4lXECn4/Uf2ok8CWXZpEIgZVGMz3UcgMBP5PyNMHZfDIsbh89N5fJlOnZf2dhtH2997EZeU2UZ/tuAnQ2xhr0xQu5xSb5+DvR9yQEm9omNDTEAWxJXWpfGeDUOsNcPL15c9O86vu8mjBtj3/lWLvYiiIG8Uh9ubNNpzuJiXG0ctxm/msVkdSa2a0wnti1i942rq51LxShflPolRpGiadq8bdntudj/2EPACZA6CvH/uPaNgdiXIxPKNJYmJTawWN1NAu3HpSH36VzwYl3bnp+O3c8suchbb1RiEV9f8YpX4F3vehd+/Md/HH/xF3/xVJQTRVHgH//jf4w3vOENWFpaAuABjzRNcSjMcr0cPXoUa2tr0XTW19ext7cXBUp4D79j13zxi18cW8Z3vetdeMc73nF5FbtEsd7Q47xu9fpxQQ01PQZB1Ov1PsvLbWkpCPbqfQQg6T2uwCRQA/cEPAleAgigKjDq/U4gUmk0Dhw40KBpYXpK88J7FMgHauoTpmONBlYImgJoBFfd2dkJlDMqBK7n5uYwPz8f7h0Oh9je3g4AK6lFFAhXkNt6NWswUguqa98gtcnMzEzjm+mq94oaM5SGRA0o5PtWEFl55ckPTkog1a8aBtjnmDbbz4LgSg2kbaJc9UpHw/alkYD6VGMJOfGVR5352DZX44W2iQZWnZ2dbewsmJ2dbdAVaWBX24bqGT9pZ8KVygtf+MLG/5/92Z/F3ROiWbMsb3vb2/CKV7wCt99+O4Arfx9eilypJ/ojjzwSxgEAV8ULnRLbhRA7fqPJdOy+grFb+TfGgW72WxeoFSgH5zyYl+eeEmJ9HUmeo9NZDkk5V9+f52nIaug83UpZOXcz4GOM+iSsInkRy1rxeOpCUCfGMzMYvxhxrsnbDLmW54k67+35a3d3gZtuqtFj8q0XTVzUZRUVR56PBuaq9LbcdmivtmqQv98H1tabIDGVpeWkfiS9BEC328LcXNPLSBcLmiTXt7qeDqwf/cyDtHkOrPv0tsoUWZ7WNDVra1hios6h1W6jKJJGvgOkSDudUdC4KJDibFPX6uHPY6R1YT8oS69vu/U8y/xxBW4rXnEmOzPjgYfZWVGKpRyYna31HAMPtHwKsrTbSHo9rygF/bMM8/OtQKHT7wNL7awGq7U/lKV/fvQZI2qiegJ8RY4fB9ptXOjchlP3+FMEUkaAIJa51/N5LCwgfcEL6uvYj/lsPfhgs+7s67H3A2US+OZcsKTdCGN3WQKXMyROF+JXLtOx+/LHbrVROeeNgjZgo3Oo6TFQGw7Vw5Rp+P5bBRusqCViQJ++FhnIk0JDZdZuea/a2E4Ym4gWIiZ2DuJcXXpNw46Hep4Brm2aWha9NgI2j/XKjaFwF0PmeK4aQ1qZD+SpQ4JeAtROCNR3JkMIrwlBqjVraUeXVWN3Na4MygSu3fI0OJx7qN60rM2O0twJaIWF1LHZGiTYUZWfR/uBdm5bjnE6jYHsqlDbz2yak8D0WB+1fdPmzd9Sb7aFTi95eU1pU9Ttobx7gJ9fzMzUHHwxg4EF25lO1Wn4rqCoQczurhhWOz/YXNOx+5kllw2ixyK+Pv/5z8ezn/3sEVL8qyW7u7v4gR/4AQyHQ/zSL/3SRa+fRL1AuRS6hsuhdACAn/7pn8bb3va28P/ChQu45ZZbLlreSxELmCvYaUWPW0DacqcPBgMkSdKgVyFvNEFJAsD0ZGY6yj/NNHhOgVT1+iWIqsEsnXMBgB4MBgFgtGXd398PACvbgiCsekwTwGdeCtJZYFepPDQfBXVZJ6AGaNWgwECVTJtANgNN8nlREH1zczN4qCuITr505mE9l1kmBbfVK539hGA8KSjYtsorTwspjSnq6c88FDi3dVdueV43NzcXyqE7GiwPvvKSs79o32H+CqKrYYTgtPZb6oP9lbsRWKeyLIPxgse0P4zbgcDyaBtpsFeC8Pv7+yiKIvQZBdLVAKTPBvPVa66WfO5zn8OzGDgNlwY0v+Utb8FnPvMZfPSjHx05d7nvw0uRKwXRGUj3aku32x1ZCD7++ONwzuHw4cNXPb9rKdOx+0mM3XaxQ4mB6HpPWQLnzgHz87jglnH6JPDC3PnAilmG5eN+QbpVJI1kUwyQZh5AV6rNLKsWgkXhqTZ4EADyPACixBlZnBMnbkXae7xRh5F1bMz7vCj8guSWW0a5QNXbGai/mSgDiDoXArxZ8RTsCZxrIe9Ui1USlgtfa+JcXS5d8FVc6w2yUy2jXlstnNKswNHFLLTR0KU4c6YJqnc6QMsNMEAaGEz29ir9ZwOv+wqUHiANyZN6/Kablr33+H33+aCfdJlbWcHMzJGGLs6dA2ZnE7Tby96goJ5pNhgo24OrzF4PeOihelHebtfnaVBhmzhXGzOq9AZI0e/5S3TtmWVoGlQ2Nuo+rZ55PMY87MKUrlwEofkOPXOm5krPMmTZsaBDABh2W7UulKe3KIDPf77m7t/bA77qq4AXvajZZ6o8B7e/BPfdB6zd76nMGaQ3y1AD8Nqn+PB8+MO+vDs7Xme33940CG1sAF/4Qn1PlgF33VXrV58DFQtGjEHfboSxe7oQv3YyHbsvf+ymB7OKD8BZ/bFgKt8BxsCMyCXOJSNpq/AccTwK42B4w2wKl6VIAw+YFwWjFfds8LHbd63+Hmeg07FQj1+sIvadZcBOACiL0WQb5YqVmedV7H959yfOIa0+DeSqbUDk8C4vgX6JNMvg8iWvP46nZmccp2mzs8By7sfHrSIJw97sbIosS9HqGFTeVph5kxwfiPOCWCtLltUxSxT9V93HZFLbqU6ijVMJjd2x/mSv1fIocB4D0DX/caJ9qjKUEN8mzSDnJI2i6Pxne9s/WATRgXo+xBt1/q4IPduRnhJSN+c81mWnOSF/OeBcbcSZjt3PLLlsEJ0RX2+77bYQ8TVNU/zqr/4qbrvttqtewN3dXXz/938/HnroIfzxH/9xAzjpdrsYDAY4d+5cwyr++OOP4xu+4Rui6XU6HczMzESBElp8ut0uAG8JurkKfmSvicnV4uUdJwqkW/oNftvgiMB4D0pNT4M9DodD0ItcwWRSgTBNAt28h8c0bT0PjAbqVM96AqIKnGsAT/J8a0BLAqcES2kEsCA/81FvZnK620CYlhPb0rmoN7YCpUVRBM90AscEnufn50PZAe/hQX5llps6J1irgLmlWVGvaNvu/E8Q+sCBA4E6he04GAwaerR9Sg0cDPJK73AF8Qkuqyc64AFk6p/BP1mGnZ2dACQrEK3tyTqrQcV6omuQ0b29PczNzWFmZibsACB9i1K28EMDhnrgs38oTQ31E6PLsbsGaLxRnkr9aLvYQKZPRbDRxcXFywKaf+zHfgy/93u/hz/90z/FyspKOH6l78NLkSsF0Z8qefnLX44PfOADjWN/9Ed/hDvvvPOG5kMHpmP3FY3dY0CuxrlJQDrgF1Ozs1hb8/jcC293NRVE5UWc5ZVu6BUXKGCWQ3K7u1X2CnBrubIsrCtsXNF2G7iV3r3VAhh2YqxA4saGX5j0en4P9U03xYE/5g80DQwVwP3YuZr1nGtFVRGrwbVNnqd+kcxyqPfcuXOeuoQLz4UFD9RXoO7Q1Xklth2YniK1VX2SPMf8/BK2t+uFUMsNPG1InqPdXmrcHvRfAfdaRK7F5ueBpTyrG4MAbLsNLBxpFGtzs1Ztnre8JxyVw0W/XeRqhl/6UpODZmWl3pmgbpdZhoFroSCA40bp69mMqRs2G2lnp9n31Ytby2MNF7onmjQBQLP/9/twedM2UBRAS8D+cIL1ZTBPAiIveEET7K8sASdPAp/8ZB2HlQC6c1WwVktFUJYe4P/Lv/Q8LjIODsrEA17tts+HZej3fRnooa86HwdiaJ6hY9VyI4zd04X4tZPp2H3lY7dzAkCbc+OPpeHecZhv7JXM4zHR5Glf9B6uycg1Nj3/qbzg+eK2hmJbmEtB+cd9j2be4KcuZcwbl3RUATHQP3adVe7FjI+aps0ny2oubTvhqI6XZW3gIChf9P0xJpVlANq+DVI3BmSm7EkEVPWQtvWxILrVjTVixIwgNk0LbuuxmOg4a9OLdf5YOrH0bTvYe02bEkCnAz91r7sPUifp0vt8Z2c0KP3enp8fWj3Gnhmgvrex+y1t3DauzvRM5/d07H5myWVzov+Tf/JPAsD5zne+E1/84hfxTd/0TfiDP/gD/OIv/uJVLRwH8gcffBAf/vCHRzwBX/rSl2J2drYRCOXRRx/FfffdN3YwT9MUL33pS0eCp3zoQx8K9zznOc9Bt9ttXDMYDPCRj3xkbLrXShTotAA6gAb4fLnpjgv4OO4/RcFU9YqOUZHo7xgntKW2sJ9JwSHHBcC01CeXUkc9fjHgzpZbgVOti36rwUKvi117MaBxUn31Y3nGJ7WVXm950CdZQe2ug0ttV9Wj9Qi/FNG2JRDP4wpax2hyrAEh1rf4fbF+bXWg3+PkUq97qmR/fx9vectb8B//43/EH//xH+M5z3lO4/xT+T6M9YWLfS5H+v0+7r33Xtx7770APM/cvffei4cffhiA92L6O3/n74Tr3/zmN+OLX/wi3va2t+Hzn/88/o//4//Ar/3ar+Enf/Inn1Q9rweZjt1P4dg9DkCn7O6OrhsvtpDE6NZRp4sIe8K5kfUB4yvWN1+B2B0yExba+n/o0nphimYQMBUta1lG0ucFXIRepDwNnU2a/ZuFFG1kDSqTsqy29koVY4v7cdlpcEtzwSUtTBqKMTJ2dYe4jsb0jxj+0tiar/koSeiYdh/5KKeqiRvCOo7tmpNA+ljBR5OeuKt+pH6A112M64fXsg5Ke2TTuVjZrvRZNPKVHLs1iOKlfKbBya5cpmP3UzB2XyEyNGmo56th3OtxUppMj2M2P5fwmptc0HEyaRw3H6WtGVf/idldTRTOvpfHFSxWwKvV5pPG3pjYl9+llsPmY8da/T3JIHIpZR1336Q8Lyf9qyWXk9e4Pm1lTHtEL79KfXk6dj895LJ7fizi69mzZ3Ho0KHL3mLQ7/dx8uTJ8J8Ax/LyMo4dO4bv+77vw6c//Wn8/u//Pvb29oIVe3l5GWma4uDBg/jBH/xBvP3tb8fhw4exvLyMn/zJn8SLXvSiEDUcAL71W78V3/M934O3vOUtAIC3ve1teOMb34g777wTL3/5y/Grv/qrePjhh/HmN78ZgAfB3vrWt+Lnfu7n8NznPhfPfe5z8XM/93NotVp4wxvecLkqu2oSA2vpsUsgUNtgHK2LgobqaUsqDfWEBgByjNOrnKCkglrKoW695enBSU9hXqc84Bow1ALhPEYvX3oMsyykbaHwWqXLUFqXcbQdFgDWdKlb0sRYMD+WpnK3WwoSAr1lWTYCfpIjXT3t2Y6qa3ppK3hsqWdiXuv02KZ3eKvVwt7eHpaWlrC7u4u5uTns7++HtmK5VE9sd+qXfOv7+/vYrqzu1LdylrPfWDoYpqnc4QBCQFJ6nGsfYX10l4KC/aoP/lbaGHKZ8xy95rWtYwYIll13RlgDiO7C0PbQfhLjQD9w4EDj/7WSH/3RH8X73/9+/N//9/+NxcXF8J49ePAg5ufnn9L34VPtif7JT34S3/It3xL+c9vvf//f//d43/veh0cffTQA6oCfuPzBH/wBfuInfgL/6l/9Kxw7dgy/+Iu/iL/5N//mZeV7Pcp07L6Cvmq9cSiTgDwF0kQCm4VztadwdS8vTdUrqALossx7xeV55SG81vPn1Ns4yxqUKYcPI3BMl2VzIWC5S8ME+aY2QkbttvfumZurPWwtF6rqwepszEJHwVt11qIj0Ows0Moii52y9AgFyzA3572a2T5FgcRJWyi4GSu3pouLr8sSDNFypq0rQwHQdGBqpMVAq/PzYav27Gyt87Js0qEmxVYcpLcocLsNPOtZNV2LcwgRTEmjE8jbETLYONNcQKnnF4tL2prldnuU977qu1tuqfJoR1V/77WFir8/da7mjyWHeKfTfF5IPRPG5KazWCh7ntfeg7u7dXq9Xu3FzfIpIa5zITxAUfi6aYzbsqyet06n4rVf92nmOXDiRK3LKoG0f9Yf+9qv9dsHKj53rK7WCbPwtv0myZMAIL6SY/flYglXE0d7psl07L7CsftSzkU6pn2MJ4mNM8lXkNpPY68DNS7rfyvRDZAsYAwIt79j14wQwyOA5SplieB1rsdixeG3c5Gg1ONujMmkOowD/jUv5Xe3OuKL3xCmZ1nLVNrTeWjyjY1Xdr4X0ymvsQFY9BrdVac7mTSQaTWucK5hVeoc4AzlSEP/Wi8Va4zQ37ZT6+RmHMhuRSd2drublqU6l+Q5sqzuf6oy7sIcZimSDM2xtii8jjWA59yc30nmXFOXsXmr1lfqEquWdWp5MjIdu58eclXMR4z4erkyCeC4++678Xu/93sAgDvuuKNx33/5L/8Fr3rVqwAA/+Jf/As45/D93//92N7exrd+67fife97XwBrAeALX/gC1tfXw/+//bf/Np544gn8s3/2z/Doo4/i9ttvxx/8wR/g2c9+drjmp37qp7C9vY1/+A//Ic6dO4e77roLf/RHf4RFbkf9ColSoCh4yHME4tTLlmAfgWcVGzCRwKYCnIPBINCVKC80vwkIE0glBYeC0BrUc25uLgCWPKac0kANgJI328kbjdfu7u4GqhLrYaz86AqiK1WINQAoYK6GCoqlVrGAul6nOmHaaugoigIHDhwI9Wu1Wmi1Wpibmwv6JxULjQcEuS2fuALKWh/1KKcuDxw4gIWFhcbEmxzz5Cvf398P6bMupKfRb+pJwWjqRw0Yqi9LAaPXq/55zAbp1DYjVcv+/n74P25HxP6+p47hNQqc07DBfqt9VtuRQWLVyESds38pdZEaUSh2x0es/1xr+eVf/mUACO9Uyr/7d/8Ob3rTmwA8de/DpxpEf9WrXjXxnve9730jx175ylfi05/+9GXlc6PKdOy+RLGrYA1MFFtQ6YKjAve63cpTzTnPMe5coAUJa4wsadKZ9PtoMWgiOcFPn/bXdrsYtpc9ENqrt8ACHtdj/MbtbeGArsqva0tm9eW1BFm2DNdeRtYlYL9WL0wFiGaw0xSDUeC6ukbXXbOzvnwKmvP89rY/TzUu5665qKFy5udrjksb2JK81pZDmxQnlraD56tt8TaG5cjikzw5ejzLwq56NhF3bjvnjRUJDRIMSJllYe2su4/zvNJlJI8GNzngldXt+kbe3QW+5mvqhfj8fE3nUi0ewzbpHnD+fJNyvd+vuUeLwiexuuqTKYoEWdZC1rkV2cqtNVV+HzjzUN0kWQYcPOhtBBsbHl9eWGjh6MqKr8/qas09rkaibje059xck32mLIFB1kLayeo+wE7T7fqCZpnv4DTwkGKHessytNueMp07vtkcoRguReGWUWbAcrsEHnnEX/Bt3+Yvvv32Op/Tp4E778RfnUrxvONrnov+llu8/m++uQ4yy2fV0t2wsSf9v0z5So7d04X4V1amY/dkISAcgK+LAOf2WdShh7eMgJcCGKf5aP52iqCgYGzDS6xIaj/1+SY1PQkvihnurOG4+lZje1kCKJvluphoTEzNBqjmDEBzDB6nBAWaNUH9P64u9jqma0FzmycB6azlKcMcQswV51JogPW83YrW01MDoc6LICz/K1Bv6727OxpUVOcz4hjBMm5sNB0OYsadUVsDOftTMQKk8XsyUzfVpdXjJAOHLRQnGBHAPNpWvR6SLAsGjUBfCF//em6VoqWB0+38yOqUIHo1b02KrbrdNFCsMcik2bBhVBoB0Kdj91QAHNj/SnEIPAPkwoULOHjw4FVNk6AmQey5uTnMz883gFX1IgdGKTEUZCXfNT8KQhL0tB7a5JtO0xStViuA7wAavOYEPglSEmCkx7EGmtzc3ERZliiKogF2Z1kWykiAmUDmwsJCoywEztXjmGAvy0RubgVkKSwXy0pvbeXp1iCrahggz7aCsAsLC5ibm0OWZWi1Wg3vZYLqbDPyaGVZhna73QCZ+cmyDHNzc0EXzjnMz883AGQrBKL39vawvb2NsixDUNN+v4+NjQ0URYHz58+Ha/f390N61N9gMMDGxgbKssTW1lZjlwN1r17WCpxbowwNCeRCZ/8iYB7bcbG5udnwSre0KtpvLTc6hXz1CnZrH93Z2QnGItaLeic3PIO+al20r29sbKDf7wf9ajBTlp187Nvb241rrNHmycojjzzS4Fi7noTvxte+9rWXxTW+u7uLP/zDP8T58+efksCiU5kKUPfP8w895PtZDHUGmosCoAb0uEjo94EHH/QLpzvu8IsknfT3+0Cng8f2j2Bvr57vp+tfrr1iec/6ur/+1Clflm/7Njy2vRQ8zil5DhzreHD7sY0WNjc9fruUDxvBwLiOsPzpXODTEVfXF4qX0xm+VV6oywcEAPcClrC+Xq9jGGuLalHVEVg+dAg4frzyyD59ugavCZDqwkgXMYoKE9nmovT227FV1IAG63joELx3MRe+zgdABYC0uFDrP89rbvOyDAvd4eptWFurdOAGjSCwxE+T+z/n9dLpBL1suSVsbHhq791d4LnP9aeS9ccRFEbFaIBRBWZXVjw4bXnP4YOFal1JG1oUnlZ+Z8fHtd3crEF0gszttnfCzjK/m2F+vk6n1/M2FW0zrvlXVjyQ/sQTCDo5frwKgnvypL/5vvtqA9DMjL9odRW4+WacXX0JwwOE00DtyJ8UWz4dXZjff79P72u+BviWb/HH2Aepl9VVnO0loVvQpqIqPn3an/uGE2d9QNFOJ3iXb2XLcA5IP/rHwKlTeOy/+3v4D/8BeNt3/7VX1Fd/NfArv9LgTg/9VbwIA7hPiS3AyxKnv/Ql3PLCF94QY/fXfd15OHfpY3BZXsAnPnFwOnZP5SkV9s8zZ85jeXlpvPfoOFSca0ck4X2ut+zu1sbD1A2b6CYvAprpVcZMXjYJGLXYZJ43gys6JzErJuRJANAWz/7X2yeBs7Zc6txbg8syQVAdx5Bfm/m4TMYYAwBEQU47x0kwDGDpMK/jm5QlsJwP/ADQ6eCx7SUfWBRnw+RmmLVG+fSpRC2LGnlVmVbJOo+UeUcweGetMOzrlEbnTrFuG1Of/q759+viqFr54bhrcf5Yc1nDC4PoatMl/Qv1PEYnjlp4Bb2rAXro0jCM2npynugcfCB6nSdRpyxABZxr0+Q5kJZbzX5qDS8yr+IOgKQcNJVn5PTp07jl2c+ejt3PMHlyppSpXHNRoJIetgoGE3xUoFxpUnivDcypILqlUrHBF0mjAgCtVqtBfwEgBLTU/JWyhV7Tg4F/KRFcJKBOr2Cl/yCgq970SqOhHr3qfU3veAZ+JMCr/ynWWEBwVb37Caza4KMxUZ0wH+ZJYwGpUBi4M0mSEPST5bQfAMHAobQyls5Hy0C9AwjBYTXYKOtLEN0GTqUu1QOd6avXeew871UvFe1nauhg+zM97X/az/X+cVz4tl2VjkfblDpV2hnNw9K5qHd7jMpFdxzYstj2uhKe76eTPNWe6FOZypMSIsex/7FFrF5H4YqEXsYExhk0EqMchSnQDIDFVRU/VR6bm6NOOHmOkPah7q0jxaH4Rad/1ynQynhNFovWqnPNMjMDtA5lozpyLni42fWJdRzicefqdWhqV290S9ItztoGXG2eO+dXm3LNoEwCaKG3zM9X+UghXOVhPyJl6dsPCEYSXYOhKJBk3turcb/Q7XCR6FB75lNdCYa1UnQhaK0XlDzHoH0E/T6wfqpe99lF8sxMM+4W2/j8+VpVas8piprdxLlmn1hfr0F0LsqpDto4NjZqNflummCp06k7kUbU4xZ250KVeUqNOkAVpFU7I5+jLKvRJeqb384B/T6W236buDITaT/s9Srs/Y4sPHPD1du8btd8Usf6fWB9HZub3haAN7VR7O4iKwpgZQXD7jEPGFij2qWIfcfcIHK59v4pr+pUrgcZIml6p8dAXBE7ho3IOPDUgL/0dtZ09fJJGDODLjfKESuUq3eJKWhOw7jFMW1ZOCapX4vYAcZmPeLBPA5IjolN7CKosAY4bd5WGw2aLF+Jv8O5UH/uvkIuN2BppA4BpNVxOTbX08+4OWFsjkSgtwL3+70aMKeBWx0PNOAmk4ypy7KT6NyA34zFqzRqto0VuAbirDT8v7c3GvIkBAePdTithF5XlkiAxrOit1TDelXeFHAp0rYbmTvRmFL0a+PX3l5lhMojc1bbRrEH/6IvgxtDpmP31ZMbvzc8gyUGxClArqJAngXJbZoxIdA6qQw2CKYF7y1XtwKYFgRVkDRWh1j9lV5EgVLqJcZfriC3lsGWM5an1dmVAHwK3Gs5xwXlpAFDeeGBGgC26VrubcuR7pxDWZYBIOe3AtO2XQiGWxoX7R8EkXmMRhSVmL6YhwW6Y21/KXKxPBRAV4MLyxzri9reMfD8YmWxlC7PZJmC6FO5rqUsgTRt/r/U+2LXxlYgFSDbOGVd1HSRxtUPJsznJ0z0YwsvcmHzf2wRresM/g5BOHnhGP1YDvSLrUNRGGBD3fBsYcZVsvrwdi6kSMOdZQD6gjQASHKH1E1IV4J8NorS56q95lFtADWNRXatu7B9flK/0nYXUJ7gyDj7DQFpDrsXW/vpQtp6GeoxvTYWJ3QEi7Llv1KxQMXcnP+MAZNioJhNQuOkqmiX1jo6V9lmytIvni5jF9VYuQEBdMAX+3KG4+lCfCrXrUx4OV4SZnYFhjCL39tx+ZLzjiQwaXi07zu9pjEmTRCbbsMwcSk3xI6PAzLNsSTUsYlzMH9SmYyMw2UZEK/w2pZ0g6d2Kee0gWL1GDf2jANbnwQIy9v0PapJcaxSj3N73v623umxa2PnLja1pdEHWaRjk4LlYhlV6Vxs3Gi0GQtinoOpNGU6dl89mXaxG1QUuFNPYQ12CDS9aRUI5W/ldtZApQpuW2BbOdh3dnZA714FUi2grh7RyudOKhJScPA+losUJloH9RQnV7j1uCefOMtpPenVY1/pRnhsZmYm0LvYoKzM14L0ts5sG9WP0tRoPZm20t1YuhLAg73keme6pPJRowT1oH2BgLRS0QA+yCapZYqiCICyGhP29/eR53nQNXXAsvEYPcnp0c560ONeOcZ1xwHzUe573k9vea2P7sLgf6bFsmkfUKGe6O1P+po0TcFArwCgVDT0zI95nbNPjAsWSh2R0kWpXewzpn1qKlOZynUksQVUjPfSet1wVWq9VOVapWbMMtQczEDtcXv8eO2dDgBZhrm5MZ5BnXYA57lgJBdqWGP0ekicQ6ezBMZXRJX1+npN5Z1lnq4EZYlh3gr3M84nev2mKzS/y3rBpd7Meim9gwnQtttA0jtb60oLpvQYsbaxi+9Kqcmpv8YSM5gXBGG9V7tWU/mscGw7uloC4Lcqd7tLnhJmfR0oCqTdbnOVyfJ2OrhQtpCVfit+nicN+u5Gf7DoClDThbTbfrtz50jwQFc1BYoBlrkogG4bqHjRuRlC7wvAMGqGFY2TSen1PO23xRZ4TVH4tmy3EfplWQLIq3a7+WZ/Qa/n28as4AlqW1UE/agHumbIgKVG33CusbV/fr6il1lbA5zDUp5j6aYM588ndXqMH+OGQJZgZUW2jGcZbu0O8G3flgKnT8N1u75disLTzdBCpM9ATCxy9jTxbpvKVK43CVQc1bsgwfDiYK88i+MeSx4fIkGiHNhMQy+MGPRM/OPG7RbsjDGaBNA6UsAEnrbNGgxjUxQei9FmWLXwHL+VszpqNLV6iADiY3/btCLWhUSvEUmdq3eYFWVjMEnzHC4XrvxePd62O9wRJuVlIA2l6LP5snza/hczqpgyJxjCuSS0NUHLLKsNG5q8nR7wW73LY8UcV4RIF23sNuM16ohgg+bq77p8CZyrYpuwk4/zpDDu8s75/NWwo7vJnKvG5qKst9JJO9DYsiRUfeE5i+VvJVRCnGjUmDIds6eCKYh+Q4oF0BX4VlBSvYoVDARGvcMJNNNbmNcpx7p66hJ4HwwGDYDUerwzDS27AqgEMlutVgCIFcQnr7WmyzIq2DwcDuGcCyCv9WJXOhDr3UywlED0YDAIlCq8d3d3twHqWiGQqqA120bzpp4I1CrdCcHVwWAQ2lTLyeNqdNjZ2Wnwp6vu1VBC0Ji87qSmybIsUMvQI53lVeodgsh7e3uYn58fAYz39/cDcM7fSsuiup6dnQU5xkljY6lSaFSgDre3t0d4yJm/0gupN716vrMM1AUNCWo8Yf8nzZAC9+wH/LA/qK5YXjW86HPD8+R21/KN2xnydJepJ/pUrmvZ2QFardGVqF2BKFLMCTaP8zknL6Ryo1TXkwJjdVX4wD//+RoQ7Hbx12stOLeElTuPhIXyoUN+UUEQnsU720+h1CS6WA4Lsoo/eqlTYqmdhfJ3bz+Gkyc9x/VyPvCJV+h30u0izXOkuUOeJ57CgjzeBJAr/leqpt/3uGVR1HaB1dU6GCXXqMvtirv05Cl/EVHhiiOT9XCu0pFdrFLXCnb3eghk29aV+tw5T+KtbXr8eG20sKtW7mUm78naGtJ2ValHH/VEnVzdKajbbuOxcykeeww4ehQ4Ot9H6hy6XW+Q8Ly60n8oNBrkeQha+eW1BOtrwPZDNWsN1bSEC8Bary4f+2Kng+6JFwZadx7e22vaPQ4eBG66CbjzTqCVCRBfpXfrSg7c3sagTEIWyltKznEF34sCGOQpXHsZySp8PU6e9AWZIHw0QsDV0veHQZnQXoHb7ryzRvWJuFdt9+V1z4PKWKtZVtVpfd3nDwRC95WV5/nyFkW946EokJal77h8brMM+OQn8fqXHQf+5CTwdV/nO3K/37ROqFXCLrYt4hC1FtwYUpZTb7apXL8Sw7iiHtP2wuqZTTBEliUjp0awNwueRyyMSkGi04NLKXsMk9Y0KRpsVPmbU8DvruJpMS4yzY0N/03qEC2rvsIskK7zCuckKLreoGLR/EnHxilmHBpsQW0tMI9nmdeTDlxVmZP1x5tp8r2f5zjbS5DnKdKyGMkrGFLG1cX+jvW3okDiXKCDq4Yg7O7G8XtbTQLesSHETl21OBrnNFY8mx6HQaanhhTbnKSPA4C5uQRAisXFZbjcGPrHWAR0SLR0NN5oXc+fG0F2Ypw0ziHNMqRZVu8+tBWzCrBjd+QhDO8TneffADIdu6+eTEH0G0wsbYsCegpy87flpCZ4az3NCfwpEGt5uWPe1zvVW1LpP9QjXgFj9f7mf6UGGQ6HmJubG6HUUBBSvdAtiE7ANUYBQ7DO8lyznsq9zt8EhQl4Wi90FQXRFXTlvTym6WvZFDQmWM7rLdDonAvg+WAwCJ7o1gudAG6WZY08aZhgUNqtrS3s7u42OOoJBjNfemoPh8PgRc8goNRNkiQBIGaQUF7D/sV8ycuuQWlpGCDNDAFt1sMGHaX3O9udulWqFPXC53n2SQL4GhRVdyqwr7MNi6IIPPIMPmqBeuXZt4YQti8/vDdGG/RMkSmIPpXrWmKgl65e7MRaJ/J29RPzRK/Qb/VER78CZr/wBY9qHj+OYdbyXMzweO3iYoLVVe8hnueMcVEXg/EVDx6sqUy46OHCJiwkWae1NWBjA8kLCnS7t3kc8PRajYJrfZ2rF6MSgHNQJoBUj4vBM2c8DzdB9JWV2qOo3QaWsoHPq9fzF83MBHLus/0U6+tNr+mlLLKqVyHCqxWmizRQWx7oGs90Zma8zsnjbVeG29u1Hgiwnj4NPPKIX2nQG6pKb9heRr8PPPaYvyzLgKMHfD9Y6mR1Wbnq1Eh2NLhkGc6WS1i738fSrByp1b7i142nT/v69HoeFdnZ8eVdXUXS7SLPl0M/0SpXzu1YWfFptdb+ulmOtbXaEJHnSPMcyxWon3WXQxcgn3qnU/O6At5W4Z3Ol+HaQKvT8/qagCKpqonOn+37wK0nT3LNnKDbvRXL2ZYH5dkHkeKRR3z+dODPMukTBPArd/tWp4OVlWXgVL+5gGZQ4J2d2vL0F38B3HOPL9yJE15hTJf9Qt1LLQqlRhI+HBow9gaS6UJ8KjeKEDSOPmLjAF8IGB2AvVFQHQHMrtY0WW3wZVyQcVnqVKJBO6IXliVQeg/rGB94PSURQG/SvKMau4PHtnNwh2qDu3Ju87YYiMv5hEqaGQXHxuYYSm/HV74s9H3JiK42fSAeOVOvoWWX71mO3Rz8yrIeWLvdOu9+H1hZCcN6agwkw2qHF/uFc+moh7zOUWKKFF0kWWX0yByyLIlezj4T4qioLntmfKluTq0DARC488dJyEMkdcI1Xtl+bawZcs6rcwdVsbnpf8/NJZid9Y4eGnNb8xt5NkJ/LpuBSjlW28zsQ2Y56oBRK0HMImDP2zFdK3+DyHTsvnpyY83aptIQSymhwS4VaBzHbx0T9aBVwMqC6DaAJL2BlY7DgoOW79rSj8RoZKz3MX9rWZl3jA9ePZTpUc0y2rwI2Ntr1Rhhda564zktp+XKtvQxsett4FStr+rc7hBQj3/WSeurQDI91+ldTkMHjSIWrFTDDHWtHt8ahJX3KxivoDz7EmmImJb1midIrtQyqifV77jjSjFkd0lQD7orQI0hTDNGtaLGHNW57QOar+072q+eiQA6MAXRp3KDCSfKsa3b+j92nJP+yPVckIRj6uZbeloWAuPO+YXu6qq/0eVpuFeDhwEeRKfQQZtpNCJKKiDe6yFbqbx96OHDSFy6FZcgIIMxhgVlc31BQJVV0rVNoMlmOfp9DwALBzoPl2UdfHIp5rSrC5nd3WbkSJ5nAoAvFD2ISZOzuend4y2KoN9cIVLJGxv+vo2NOp3qWl3fbWwQWBkDbti8WE7nQvDLtTWPldNBfWHBtCcjeyqIXtGnpO02gGSEU3VurjZmtNsA7u81QfQzZ7zHvnLuVIvJrLs88igw0Ci9GhU/LkvgVl01m2qrhHJWJ6lHssFwA0T7eMsDF1VmRb9uWgZJDelQRxoFjbqx7cBK8PmYmfGJfulLPlEmroiCKja22I6J9tvpQnwqU7kqMpG2Bbg0o5UFtDGKC497ZPWxtlk1vGoV+NYEYwB0JDCp/k9tlSxIzXTNJ80dgKQRQ2NSvXS+Eavf+IOmXJpRWTYjVypIquetxNywNZCFnuN7muOADl5KlafvbzSLYauh38BosPKRdhx33lyXVGkRoA/9uSiAEs2dazq/5DxH86BTB/N3zgP2UpaRnQ3aL6XDknqGxSW1i3azvb3RppLpTEhSQ5rQEMS8G8+v7cfqeMDvGM+6nYzy2A1otL6aMh27r548c3vR01jGAU0KLPIagncKCMfAdgskW7Hg9TiQ0/KGswwKaGteyretALzNRzm6Y2KBVuvVzjwsRYctnwXDVawHPMtOYFh1GtNlrF7jdK5l53UK6KpBwLaF5fC29bVlif235dR8SdlCwFzbjqC1AuPahtTpYDBoeG4rVY41omgZ1fhiA4LG2sG2l5bHPiM2TdZJ+7gC5PY7pkdrGHomyjO57lO5ASS2erIT9pgnSmwFrYszgtNFES5N3bAG7YhqdjoNJxsCnlkGoFdWi43mWMS1A52zYvhgi95Z6s2lnNCFWRXZxfgE8K8sm15qzMY6hNXBvOR+AujmXq1TIyO7QJ2drTk8QoQr1Nwetk2IdFsKF105q/GD5SPtS7tdg+9mYZa6IdrtBO12ncUIsMoGIjeLGmr4KWq9KdtMwzHPubreVp+VkcPiKEyjwXZDnVIXi4t1OnNzNQdPlo1cSnxC8xnZYl6ibvhKD7Zf9PuCg1TXzM3VOIfdRk6PSpQlnEtHaGXKEkj54NxySw2mMLKoLazu0CARK9Ak8FdF2vfAuHcBfz8NFvB7e5e3EB9eBNOcylSuulyiUWqIxO+smvRsliUS5wLfOJO3zqtPWiYh7hPyaRjhOX7p/9iNl/geMkUIrzx1AB8ZxycaA9zobyaohNv6XzOMpRU7Fys8f5t5RmMHkVYSirlG0jJCMHocZz2A0bnhOH3E0tV+asd6Ox7xmOVDkWsseD5RqrwSAfeV3/9iz4Dl9x8Xl3sSVZE1cESNRCp2Aml1FLv+BjJmX4lMx+6rJzf+TO4ZKtZ72QJRBMtjtCM2WKYFHunBbIHii4HfBMBt4E5bbuuZbT3StawUekwT8NZ0SH8S87CPcbUzX6Cm44hRrDCvmZmZQDNjdaUUHhYsTdMUaZoG+hJ6hcd0aoH1mMGD6WqeyjtOb3RyfmdZ1vC0ZqBNDfqpQS5tuygXvebJY2wP3kdamf39fRw8eLABgKt3P6ln1COd5WEQ0SRJQP5wUs3EAr1qnWk4oIc5j2ubsQ20X+guBKZHT3r13nfOYW5uLhgsdCcE60o6G/5WHnXte+MMKc80mXqiT+W6FusBo58oQij30ctZI0hygk7X4i99CXjBC9DvV0wjp055vopOB3jpS4FXvAJ/1T+G9VM1bvv853v67WT98YDMOtdqrCNIT57nCCC70nhsbAAtBiqltznBUqKY/Kj3uXp2Aw1wOSkHyDJPuUEWFZ4mkwrxx06nthEk/QtNGhx6Olfp57nfdjw3VwO+jfIoajsz48Fyoqi7u3V5b7qp6YlOz3q9hhE1WXcutnjd7GzgJ0e3W0dfPXRodKswfHu2ALzwxCoOH05w000ATkUWvYDnY19dbZwLW657dZa6K1np9cMfBu+i+3tVTsveQ0x5ZcVX4Vi34gzv971xh7ogtY20zdB5zvHeuk+v3W44doemnJmp2rgc4EKR+t36JUY46tO1hz0ZQrXloHvixWHnBa9ZXKw3ANAgwS6QChd9K3PodpMQE44O5GtFgoWFW3HoNbciLbeAe+8NYPoQCRKtK59djaRalsCznuX7F738gZqUXqmBYh9g9P1h+8ANJGUJRKbtY2W6EJ/KNZWiACraSAX9rkgEVAsgJmqKlYvZxcbhe0GsUVePuVEaF3tJ4JjW8VC/NW1rLDaFot2Zhy3wSSxbNovBOdKLlKOFNHUZUYwFhfVeS+cSA9Bj71Etgw6YfPdyKxfzcK6mcdFrqvJwWBi6JQC1V3hpHOWbr3Klxk3hsrSmBxrXkKbN4zaJKi2lOOEFMW9s1Y0cH5Sjz8NI/7TjleSXZhlSB7TaNa0NL9VdjyqWdccOfbH6evGc6oDfdZmSCk946xu755iAZqQdWcnWYyL3jYuhUD/3lQHuBhrDp2P31ZMbp9Wn0hD1xrXUK+rBDNRgtwLcBE4J6mqgRYLJ/LZe2CoKorM8SkmidBpKraJlI082wUwAI+WkVzKDhw6HwxC8kmBnzHtaec5ZB15LwFaDkWqwSS1zVr1smR5BUPJ/K5c2z6dpilarhSzLAi85gWnrGa4Avno1WwDdgugEaouiCLQss7OzIfgngfz9/f1GQFQNlLqzswP19gYwAjIrBznFUrgkSYK5ubmgI6VwYXmpA5aZ55iW9jMe29vbw87OTgO0p4FB24zgOA0W2qeVLkZ3ByiITp52pVohncz+/n44z76k9WMwWoL9ZVlCA7bSIKPBb1UuF0x+usgURJ/KdS3kuVYhkEw0mMd0FcXFrHP1NXp/WXpejpMngTvuQK9XUWmcPOnBvTvuAO64A39V3ob/9J/8gpV800f3vgzct1avMvIcyFuBwSTPq4CgRQH0/KrGtY8AaK6tN7MlZNkSjnWyGkQmGEkw1dK5nDsX92yrEk5zh93dJLBg8DYC5kx+ZaUC+Htna8AS8DqlBaBKd6ntA7yFhbouQDWIJjlTuVgiysr2I9hpV3BFUXO+U06d8jQmQNPVbn4+oM4Pr7ewfh/Q7d6KY3d2m3mxnU+e9OmcPo2j3S5wHnXelQxKvxhbW0tx5kwanP4VJNffyhzCNeDuLmp+7k6nSY/T7QZe+e3tGlcAPCa8ulr1l5Onar4U9oVOBxfcsqdaXwPW7/N5Lyw01bKyAqT9s9jKlnHyZB2odHGxMvYUBZZWVgAkwJ5revEXBfCXf+m9+c+cAfb2kHY6cO5YXcmyRKvcQisD3GqrYePp94ElevBXnXt1dalhWzl1yn9I7/+sZ7Xwt79pNRDVlyU8pQv7/tqaz3dlpQbIicoXhQ9M8IUv1OBLltU8M+yb/FZww9I5mb5wI8l0IT6V61oMgMzgjza4KAHq4I0euaYBJCoYm2VIsgzkI7cBO/V2CxZOBNQNUHdRXFq5oZWmDYhTyCm4XCWq5bG/Y+B5lsl43C+aadv6xL5tebSCsQrHxILo9l4F57U/kIqLFHadDgbtI36Ocfrhehyt3vet4nEfIPxMitlZoN1uBoolaKwsMlb81KTmzW/MZYxOCEprdWizpR3BG4VSuSuFy1rjQenI0KOiqmzw88cMwEDz2QLQCsC0AzKTYEzkIbCBd5k89TrapZKqLy7BZUDWlr4YiQHQqKAxSlmsHajeFZF0wnvBuUZZnUsnN/51JtOx++rJjTl7mwqAUQ5oPTZOCJQyiCQ9b8mRrR7I1mMaGPVwt17elrJFgyvyPvV2JsCZZVmDU51gKQNUKiUI8yiKIgDJMd5rgr/K5V1OGJRZPnqOE3BV4JzALIXgrnpUa6BM9UQn0AsglEP1Nc4TXeulIPre3h62trZQliU2Njaws7OD+fl5pGmKsiwD6EsPdH5ruygQb+lgFGRWyhcF+9ku/NBjf3Z2ttEPFESfmZlpAPekblHAXY0EavxRb3jl1lfjB4OG8pu/Y5Q0uhPCBsDVXQp7e3uh7ZReiAYU9lUC6Py2HOv6zKgxZQqiX/r1U5nKNRUFyDm57vVqMmldVOsim6Abr1EgjaTOp08DvV5N9UFg/VWvwuDEi/HJ3wb+5E880PlN31Q5T5086QMzLi6G8ri2TzoMbwT9qndW2ulgb69eOBB8zDLg2Cvypie4XkBPHwbn1O/tbb8yVI+uLMPeXhoA9J0dXwRipsQbW9mw1iOJ0vmxOu33PV1HEUEmqEsF4anzdqUUggndbiOYFm0D8+0KhGV5WO8vfanOa2HB04DMzQGdDs4WLZw8GZoP/ZUUnU6K5cwsNE+f9uhtr+eBWUYClYVZUemKmxBYrsVF3+557rFcdjcKbeIctrdK76Hm8spbrn/B59vtonfaV4kLUtoSDh2CD8y5VvXF7e2aSyXPMWwv4+S9PqApq0BV0IaU5/Ce3adOIbtjecRmgVPrAZRwrlW3U6CqKTwgzQyKAnjFK+AyD6IPysS3f+WavtQGlroZHj6d4Nw5f/lWmaKVuQAiJUWBlqsDu/Z6CO11zz0+Jujf/lvd0PfKEnBZy4NofDbbbaDbxQCpDyhnPR2/8AVfPtdC0QeWeNyAd2NBIk3zBgTSpwvxqVzXsrMTPNEtCjcCksvxEZlgNAbgvVWr51ep1cbhwfZRV2DOHrdZaxp8fTTAQ91ZxnFRedXUk1sTkUMxGjagHjPC7ja7Qy0mMeA8BqLHJHZ8HDAau8fyivF8WQJ5ji23hFZeBgP62pofY4+yPnmOC0WKJRRgZPPz59PGFAVoAr0xG4Itfv3KTwIneYMCBvE216kXmy4mLJdylceGF8t+M3KN7e/a1lafQDxIp03UHqvm1KRJ0my5mzGGi1OUkXBmJsH8fIosS6P1DUU1Q7DWnWmlDs06ykX6jtBTl0WL8xWW6dh99eTGm7lNZaxYb3QrsQCMCkwDo8FAFUgfx0HN3zZ/5Yq21DJ6rYKwNoikUnEoGG+NBuM835me0pDoby0jryUoqgA2fxOsZTpKA6Lltp7PMWqdSaL1o25jxgp64u/s7GB3d7dRB0szo/zv2ge0/WNgL3/rt5ZBy2f7gwYT5TUEpcmXbrnZWSf17te+a8tp28lS7mg/UGOQrWesjpoH6xHjW9ddBXaXgX0OxuXxTJQpiD6VG0p0MXEx4CsGlOn9XB3s7tYT+qIfwGhieU884ZlIAn0zA1nOzo73ftEFbqScxMkBASntwogfXVUzvbKMuwiVJcrSAxe6gKP9Ic/Jt1408wCaF1odxvTJY1YUjBcZurSBtxNn2NkB8hUBUFk/BQZUh1mGotcMdEmO+uW2uZ6BPRtcNKPCDQ/nzvnyEPDe2KiTIh23Yh9sBl3E8/pWBVKTesViCjMzVXF4klaPsgz9iv2E7ENnzvhLDx3y52inoDITDFEUSbOa1GVZwulxbUc1qFTlcXl9e+owAtgQkOfhIRIkpr8wCJrWY23Ne8k3+n3QXeqXwru7wO6u113fb10PhOz6LKPZRcc+c5OQlRtU9vYub3E9Hbqn8hWVSxmzJ92rv8ch5ACa3sFXJkrlEQNm7TA4Mmbr2G15NSaNnUYUkGV+jYCTsTFc054Enl9q5ipaZkX3eU7RZfUmjoC5RQG09H/fXOYctreBpdl6zJnU7ON+U2L0JpZiSI07mganYFrFWH52bqDltCqYmRk1mIyIbd/Yt06oVP8xwNwWpnGsGTRXA9zabMnswyw1q4htaCQdTSumH98Ok8XefyMF35yO3VdPpiD6DSoKcse8wfU3QUmgBg8vBo5aahELpivFi+WWtgChpQaxQJjyXCvPtAXtbf1skEeKBSotCBvjadeyqliDgv5n4E56KGswTXrVxwKQWoDcBhxVOhOtB8F6HtNdA6SLIZUJjQn09KZ3+OzsLMqybBgi6DVt+dFVj6TTUdCbOlEKIKXsUaBePcgJntPzW8Fy5XrXslu9qZGCuwOU7zxG9WL7uBootI/E+qi9z4Lk2ibqIX8xmXqiT0H0qVznMsntZ9xxC5wrqLu+Hrxq0e0Ci4tot4HDhwE8Vt2f5+j1/C0nTniP5JtuqpK46aZolo2Yh6VxrSlLZFkatsgCTUA2gIQsq3JDO+fdotV1SWlRjOR5Uy10Cp+bq24pyxosXV/3yDELH/Pc11UhV03KM686j/GSV9foAszi/x4fTT0vNttFPfkIgM/PB/oVZkda9AAoa3lIOk6+c9a9cs0nL6mqk/rK8zpb0uHompbFYz1s81CSYgvdbiuwktDLndcM28sefF5crIOHVnv2VY1zcx48564CpYVHWYaG73bjnmCe7icf9crPMu/eTgVWYLXG+HQu8X20UhB3FCi/atA5lSjebQsLNcvN8eN1bNG0ivjaCkqrGvWWW0If8ztEer7cx4/jApawxEixGxtoZR6ob6zEY8aei4FCN9hKvCyBy/EFmA7dU7mmQuR3BHH2EvMcHXlMLTKnx2JgIBAMcioxOm9KgzqjSkuNf42ko0UQTmYNeFyW8cCTfHnLWKmg/eys/7DMmnSCYXPgsSBrTC4FPNc20vRYRgXDrc5jgKwFcqmHat41zFrorQHLK3m4LxSzun5QJt77e75pMFVd5HkS7L+k0uPQ8MgjPjkOJbQRHz7s6dOGLsWZM17Hy23vWDBwLfT7SWgiThe0KrZqqvqYM/g4sUHnQ/uyopcisecidkyvjx2LCKGYWJMrpbnWeWQ+cJlFvxK5VFVdTzIdu6+eTEH0G1SsR6yC2gT4lLNZva8VlFXaDnu/Xq8gtP63Xr8Eei24yHutMD/LE22DVlrAUnXg5A1o8wSaHNc2b63D3NxcyJuixoGYF7LSnShtDNMjwGu9vpm3cnZbznhLA6KBSXkPuc/J7c68Z2ZmQPqWzc3N8J+AsqVq2dnZCfzosR0LCmCrpz6DbZKuRrnnVffWK5tUKEo1o+B5URRQXnG2hYLizHd+fh7OOczPzwdDgoLrpNRRbnztW/q8MB9bb2sUYH0Y+JRtqQYQltcag5TWyHqvP9NA4imIPpXrWrgQtwGvnBv1AteFoJ6vXL6Hq7d5wLp/r+eAnp8HXvYyYGUFx10V1+qLReA9OflRD/z9d/8dcPPNPqDo/DyAlTt8midPNugoSJfSyoZAHyML6jxPg+c1UAeD7PWAMm9hyfX9n9VVXHDLnuKEKzhyQ3OrOFHdXs9zZMhKZ7k9xHIbeOyM5zE/fBg4eqgKqIVq5XL6tHdrfuyxGqhnBRjgi4t1csrTNV8BACLLNADYIGLOYZi1GpeQr92urZ0Dut1lD6TffnsNpNNlv1ohKxVOp+Ox8VvbF5r9g3p/7nN9473qVfhvn0hw19cNgd/+baDTwVaRkI4VqfO874DP9sQJn0SD435tHWm3i7JMArc3r5mZqR2laf9Avwhe3csAljttFMeXsLFRx1gtCk/Vsrp6BK1urzZQVO2ZFFtg0NrFxfo+ggUvXN3yBXEucLKfOJE2QP6UoAa57++/H/jkJ4Gv/mrgzjt9JW65xSfIuVe7jXa7ZpfZ3QWO3uT7wdle0mhu5/x1ziGA4VvO13OxaoZDhzx4zu7Vbldt3j2C1snPeMTjlltC0NjBy74ZAJBiiLQsAm/PheMvwe//PvCG1x2v633qlDcOsM31XaBcvGrk0Q7J64vCd84bRK6nhfi5c+fw4z/+4/i93/s9AMDrX/96vPe970V7xLKl5dnHO97xDvzqr/4qzp07h7vuugv/6l/9K3z1V391uOZXf/VX8f73vx+f/vSnsbGxgXPnzo2keSV5T+UaCMeJ6kUxKOu4Gso5TfCQxlGg2i0FjCKVitgZEJpyMUyZyQKRGB/ynWQ+cCPBdDu0aF4tNSrzXSJ1b6SvhurqHstYR0ndsHpvA+hVCtMYJpruOCN2DFzV4xocUunysgzDfKky8iMMhgOkod7OeQpuoH6dLuWu+b7NMlzoJ8hyIM3WgH4fZ854+rR2O8Vyla+rqsc5xdqaD31xa7d2MGDxk2LL08zlOdLMYb30MUcOHwaWeg8jX7kVf/Infly+62sHwNoaTvdvxb33Aq94BYCTJ5GsruKBB1qYmwPueoGf25xbuA2nTvlpw3L5OFznSJgecQp25owfJrj7jTvq1LeAtn8LlKvaLeCeumE917FjlLXkjDNmWINH0xLepHCrCkFHAgtk05ijuyA0JjzQ7MZMI8Ug/LGGMmvnVhmxjUUsVokbfdZVVTeKTMfuK8/byhREvwHFckCPA9EJHNOjV4FugorWU5ogaYz33HoEqzczgVket8Dg/v4+lAdcQUQboBJogq/Kx62ApPUaVw9pBXMtN7fqaRzliqWhoa71Wzm41SNdQXQGTNU6qQ7ViGH1Ri9sXpumKfb29hre5lmWBU90pUEhME3dJkkSwHSCy2x76pi83pZOhf3BUtQcOOA54rMsawDY1hiibUC+cOccyrIMfSIWlHMwGGBra6thAGm1Wtjf30eWZQFAz/M8GBTUSMA6pmna8NQ/cOBAqOfOzk74zWdEQXRtKwW9td+y71rOdNLWWHoifdY0vWciQDwF0adyXYsuFnTWPTfnZ/h2AQzUi1dG4KoWEGtrfm34wl4P+P/+Pw+gv+pVQLeLE+0KO/7DXSDLcLZo4f77gbvuqhZhYaXUxmfub6Esl/GSlZWweuIaNOmdBfrGVama5aduAFdRmijYur5elasDn167jU/+CbC62sJt1UVn27eh3wduPe4X1WexjFMngePHl7BEBJp6On0aKAocXV0FkHoA/f7760CNRQE8+KBfxVoQ/fhxH1TVuRqw56KfyDE9mAmiU/9jFnu9XtMTkMkcPFg3FYtQlsDcXIrZ9m1wnduwlPkFcECFqx0CGxveuznLgNtWh8BH763Jy7Xv3H47kGX4rf9fgt/5HeCtb03wDQ8+CBRF0Pux7hBYX0eWHcHenk/iJccvAFmGvz5deXWfOhUMCCWWgu1Cqom9vbpdk3JQ07Tw4nYbJ171N9DrVTzo/T7+Gkfwl3/pL3vJiRUMM8/1XpbAC48PgF4PMzOtYMtYXa2r1+nAE4yfOuWRgePHgfV1JCdPIu12sTtzzHt7s5Cs8H33AR/9qAdJshaQtZAcP163deV6v5QPUZYJvvQlrsX9/PKJJ5rdApCdBVU/OnXKtxF3cFTYeGMR/sgjvklf/MgjwP/7/wIvfalfeJ94IT74+/76v/GKqtyPPAI89hhOrgC/+ZvAq161hGMPPVQHj6WhR3d0sA+w06mBQlfd/N/vA1tbuFHkelqIv+ENb8Dp06fxwQ9+EADwwz/8w3jjG9+ID3zgA2Pvefe7341f+IVfwPve9z4873nPwzvf+U58+7d/Ox544AEsLi4CALa2tvCa17wGr3nNa/DTP/3TVy3vqTz1MkAaXvAEzbOsooaCAdFKT0Om16h3dsqBQkF1GmnlOqYb8zzf3Y1gy4qOx+YSzoHhI12WhtfI6CskQZrnoSxpe9g0/qLOa5i1/HiHSj2Ff1cC/l2ZlIO6DOu92lrLsp47V0fR1Epy8NEKxoBW5eBgutXYOkCKFEW4hrFVjh7y1w/gwWq1EzBLeoMDCZbyLICdHP7yHLityvexx/yUpNsFltv+nVz50AXg/tEH/fRk6FIklaKZZ4idUpWzLI/h0Uf9cI//di+SLMPJk0fqsfvee7Hx7Ftxzz1+eoP77wc6Hdx7bwt5Dtx1s9fzE+3b8NnPensu1k4jyXP0en4XWQtbwHofs7NHgrpb5QVk7aWwWW4p90aZrYpSj/R5aiAa63HeL+o+o8YYtVarjNvloHMxutOLAWdQJt4oUpaeYq8X91NRe7MGs+U1zklf1b4kgH0TQk9HuqMO02psCMYtW+eiQCLAP9PY3Z2C6FcqN/rYPQXRbzBR7+dxPOL09LW8z+q1bTmjeb16Q0+iidH/9L4msGwpMhRIVQ5tLS89khVw1vJb4wDrQE9fLbcaBuglbD2dtY6kAFHvZNV1zPucYK3yoytvNuADizrnooCs3Q2gx1hWBZkJJHMXAMvAgKxpmgZPcnp4MyjnTuV6SEoTBtxkf1DdMshnURQNkNg5hzRNweChWv/Z2dngda9GA21Hpk/AmZ7zCkKrLgeDAXZ2drC9vR3qToMF24H0NGmaBjBf20mNHDQ8ZNUorMYl7QsK2FuAVw0xlnYGQKPfxWiNVKxh5ZnohT6VqVz3okC0Emqr2IUEP8YrjCwuLywKv7B62cuAEycwdClW2kDae9wvTivgd22tYm756Ed92lkGrK7i5Em/CH7JD7RDXmVZTfzpOa5e6ALSzcwsNxzSAIRFKToANjawVSQ4edLfdttqHkDJ9XVgbi7B0ZvaOH2fx0KdA16sdQZqAu08x2L7WE3fQf6PsvSr0y9+EcGyQH3pokuJvPm/8lDTRVrgEVUQQhZuisXPzNTezbpI0x3qWeZBhZ0dYGUlxcrKrd67aW3Ne+2tVzzquffOxtqaB1KPHq3B/Ur3Z4sW1k754LC/+7vAa14DfMNjjwHz86E6/DEz4xfH7TaAe+8FOh20uy/0yd237vM5fhzIlsK9dhEYaOBVB4yq2W4jOXECy+12Hey0ewSnTvlF6okTfos72/WFJzzgQZyENgLq6+BBAB/6vA+weeedONtLsExQGQCedax+NHgT4BGNv/xL4I47au/Byot92D2Gfr8CA/p9OLekWEXorwQz5uaafPBUBvH6m2/2h5byIZaysmGIuX/zCM6fB1782GO+0t0u8JznoN/3jvLdLvA3XlF1oMceA9bWsL7u2/KRR4BjTzzhE19b86touunrO4G7N7SABJJii/8baSV+ncjnP/95fPCDH8Q999yDu+66CwDwb/7Nv8HLX/5yPPDAA3j+858/cs/+/j7e85734Gd+5mfwvd/7vQCAX//1X8fRo0fx/ve/Hz/yIz8CAHjrW98KAPiTP/mTq5b3VK6NFEXFbexcYxhREH13dzRYs+JmfO+kWf1OZ5r8bwH0sqw9ZUmLQsw5KrEEIseT3EE5tJ2r03YOQOaB0o0NYH4+QV4FmKYRmUEXz50Bzp+vacK0vCMBQzmWaxRrotW660sVNo5TRFFKW3/hHE/5fsyy8O4f3pQiQRHiWgjuHuySLJY3btfe+9zAtr0N3HaTCzrh1AMVpYsa2be3vY6YV7q72/BED04NUl/qFKdOAceP4/TpI77N19eBBx/ETtdj50UBPwb2+zh58ogf7ytquyLzp8oSAajv91t1wXo9uG4NomOtj8Q5zM62vJqrscbly77fVvFnPBVatROjEENtA0SXgVbnDwqKa5vado05nJh7BqU3Zrk8RVLpU3nZbbdJXZPWKCm2KocQB5R1fUfm28aBBPBGqFj3s9VpzFmCojF2bGYA1MEgenoqE+TpMHbfOOFkp9IQBdMpMcAvRsmhaViJAYc27VhZbJli5bP5xP5PAhMvpQyTRGlKrPfvOP71i6U9rr6W7iWWhtWbFauTmI4U6FevdvX0150ACv7GeM71nHqiW/oTBZtjOogF9bT/xxkorA5i/XZcvWPlid0T49K3uo55oseeMTU4WT5+m75tW/v7mSbj3lOTPlOZyjUV674CjCc4vYiUJerVu3OB2zl1zWBd6mUWVnXV4jZgbTrz1wzGrRS0DLFbJK3gTFvlUa0hfUAiJFqceBnsfyZgM9VzsbJzhWXzqISed0MkY6+xSe7tjV+48fqdnSaGH+sDwTPKIiciTGNnxxv0iwKNNlbh1uUsq28M2Rn9qDqjza26pJuUNUhU520Ar6Lw3c0m7pwHrYmbzM5WN1cdNejUtrX2U+1ItuAVILW9XbWnOW/X9Pac5sV0Ggti6kC25o+crw408GwBBHi80dRj2r5R/5jEKnKDcaJf7gcALly40PjQyeNK5c///M9x8ODBsBAGgJe97GU4ePAgPvaxj0Xveeihh7C2toZXv/rV4djc3Bxe+cpXjr3nauU9lWsnYWyIyKQxwKZxta6LTSVGEpiQ0MXysElweFVDo/6+hCybAPqkl69aIOzcZNw7cEz5Jx2LvVMutY1iaYzLL3ZN+D8pczEIhB/VO84CtGGorNJrDJ1j9NxQZXVfULdcMzJ3iCnAVjqW79UQKXRjrEZ8PhZ7NqOOEprguAYVeSqqdiPKdOy+emP31BP9BpFLAajpJateywAagKEFHCl7ZvKuXrnqCWyBT3oj8zvmhayeyErJwnTVi5vUIRbUjoGX/K352Dw0HaarNDD0aNYAmc654K2twUEVmC7LcqROZRjAXKB3YTmV6ob58lpbHpZ7bm6u4cmvFDXMi1QtdleAepnvmhFJg3jSk393dzdK5xKjGlGQnmVVPvGYcUcBeuavHuPkOC/LEvPz81hYWAiBR1WHrVYrcJyTlsb2ZdtPbRkU8GZb2zQ0poD17lfdcCcD+eZ1VwZpfnidNVA8UylcVKZ0LlO5rsV63MT+8zdQg3Hqfb26CrTbyHqV924vbwQuBICtIvF8zqdOAWtrI8E5Q+BN5+pzdD12rp7IqQc6pSzDvTv9+tDIuinPgZtuarBqAACKArfc4nml222/hfbQobQOpkn6mLL0+Vak08PuMaydAlZWjiF9QVF7mrOc9ExnZqR7UU9+XuscQvRV4StMyoGnCykiC6gsw9ClDUCUjkmzsx4QznP/rfy4gD8WXXAVBVZW6sCf3hsMoY0bXngVmLu767ecP/e5s56yZ80r7vBhUX7VrjffXKmoSid0Eee8i53zwT4XF+tt7FRXpyMUJ+sCmM/ONvlm6XFWFCHuabtdp0P1DpEgyTJ0Os3gtkzWOdT0Ne22x3+dC3uvw5RSeeuLouaFabc9fz8AnFz3YEDnSG2fqvowt9lLs4burbiOZztawvYZ//htbtbB3Jbbrsmxn+f1c0QlzMwA6+tYygZot9M6YGqWebqassT2I37LfruNpuKUuNW+J7SRKA0lyjVj3VWvRxlif3+880nsegC45ZZbGkd/9md/FnffffcVl2JtbQ1HjhwZOX7kyBGskYoicg8AHD16tHH86NGj+OIXv/iU5j2VayN85Bik0z6Ck+zglqKF3uc8zmCeCtBr2mQ64THNi8cCpYY9Yce96j1BSg6+kgiIW9F8+TqWKUAw1MZs355qQ3bTlGUdyJz/uf2HYzaFru36/rPXWEVRydUxb5RG4z7n/LDRynMUa81kNClmMz/f9GDm692Pi32g3w+xvjudul5ZVnOtM72Fhep/FdU95MfBoypEVo2/WYYwgB4/Xr3Oq0G105Gxo7r/uc+VMX5uDocO+aExz2udcFxn4VrZEO12EryyURRY6uT1GAvvsd1SarGyrHdTxNohNlax72nnYZtOMgyPE1fv2vAO/J7KhY7j3KjGIhcFgCpOjD5zI3WYNN7KcWVZY5qERqL2EJ3TG9DeOU+Xo6pI0/FVv/5kOnZfrbF7CqLfAKIAdoyXGhjl/gbQAIA1IKMC6QpoEwhUsJ1AI8FmBTxJlUHAkGAu0yJYqPQkCoLxegWnWRbW29K+WFoX/U+Q0wLn6o2slDOW35w82+Qy5++YnhWIZr3If00aF+ra8nTHvKK13ix/mqYBBNc6EdhlfpbSh+2lxghtEwvelmWJzc3NRoBR1Z/2I+sxzvJRnwT1bbBX1Zvy9ZOnPEkSLCwsNAKtElBX3TCI6MLCQmgvNXDEjEXUw4EDnide68J+bD3vaVAoyxIbGxvY2dkZ6V80QlBPNAYxbzV2sD7KU6/t8UyVKYg+letaFBzjYgKoF1C6QFQ0b33d01qsrmKw+jycO+cXQgcPAjjf9sSYKyvVQjrBxgbQcs5zTD/4IDod49BLdLMCNbOsAt67XR8Ui9fp4g6oVw55joFroSjqdO2i5Ww/xfLqKoqUWV9xAAEAAElEQVSiBom5mjmWfxloOx+wFMDKyhGUZcXjfn9jZYSt9jFsbACnPgF86UveLtDt3oYMQLtXcZAePuz3nXMivLhYg+TUKcHXdhsD10Ja1TVIzLNaF+7VdnBSs7C+lTqwuOiDoPrs/YJ7e7t2riZOEDy8uGDt/xWWgBqo7nSqaGFoLrycC17ox48D3/d9/hv940Ce4+jiFm66qQX0fL/qZH6R3ekAOO0zTnpnfXpZ5q0Y1U8C8LSjzMz406kb1tzjCtSy03DPewWkL7sLuOOOpYD/53mdNjO7tT1Ep5N4TtaTp5HmOVZWjvnzd94J3H47Bt1bUZwG0M4Ckh88uVcrsHltzed74oT/Xl2tuVkefBDY20OyugrAc7CnWQYUFWWOiBqYtAusr4dd8o1vAGjfkSBZW/PGndXbSP1fKbHr61EUwOc/Dxw/jq/6qhdT3UC7jc8Uz8OpU56P/bu/u+r3d9xRWy3m5+t+J+BKKKhyRrC/Kv+sNRzdELJXfS7neuCRRx7B0tJSODpnrSSV3H333XjHO94xMcVPfOITAMbvqr3YDtWYI9Kl7GqdlMaVpjOVqysWU9NHEDCPmnNAWYPdFjfzQF8N6Plb4h7u+jjbcjT4qCcV1tW71MqK31qNwTHwT187zlV80QDyPA3nEgyxuDiegMBzrqdVMVK0ullzzsOLWE5jNOZLlcaFsgTKQsqk70Wlx6iOpRiEMd/HQfGnNzaAokgaQKjaG2ljXlioqMB6Pc8l7xxSAEvdSoGPPALs7OB5LxsASH2MjzWfSavtedQZyJrjw/Y2sLS6ii206mYKiLmXHH5sT8pBCIz+ildUzeocsLKC1VVP57a6inDNy15WJeMccOgQVlb8sLLcHob5z+oqjfVVxdfXfTDU9fUmVZjqVC0t2pF5LtZBFSBXg47OJ2nRiIHoSgXDfNQ7wdVOBbu7vk1p6GayvI3C0EPBkGL7os73NG/n+fNL6WJ8frTf6C48lQZlUwR9Z5yC1AHIHLIsuZHCmWA6dsfTuJJ0piD6DSIESa1nNEU9sZVHWwFcet1aHnT9aMBM3sfjmo4GatT0CBKyTBZEV7HeygAawLAen+R5rvkwGCfLoJ7yCpyTR5zXJIkP0kn+bHo6MxAlgd/d3d0AtBIU1QCg6klOzm4F1JneOJoP2+b7+/vBO5z84QrC0vOZ7apBQ+khrf1C60BdFkWBXq8HBvRk2WlYoF4sFQrbg+mqfulFrlz16rVPwPnAgQNI0zSA0WmaBv0PBgPkeR7aF0DQJw0dapRQj3W2Nz8E0QmQEzxnGmxHAKEP7+zsYHd3F5ubm9jc3AztzfT5XCRJ0jAOqEGD5R4MBtjc3Ax1URD9mQwMT0H0qVzXogtFXaly0m4n9Zypkye708H99/uFwp13VsGhOh3/p+IHL8sU588DRzsO/QcfxBqA4/2z2Nmp+cstiJ7n1Zql7YNXBSejGKhfgdvnzjSBZP1NENJ1j6C/htrLnBeSzHN1Feh00MoynDix5EFe6qRaRJ085YHz06cDjTiyzKf5spd58GHp5ps9uszFcrtdRzrl4oUL8nypCj61BNdZ8t7LJHLnItIGIXUOW4VfEJ8/X6+Ddnc92EynOQZNa1U6zm86Eppwe1uKQ0/3ft8DrZubNZLxXd+Fz5xsBc5wggWD0udP3PhrvkZAdAA4eRIJC5LnWM6HyG9PkBYXasSHHjFEuJ1DgiFuuinB4iK8LgiMn+rHF82KupMzn3o7fRovvv2EXJoEHvFw78mTaBHsrtDnpEKlHpu7FTsAilPV9dR/nqPo+SwePu35eZe78O123BsRsLBQA+sPPODvv/NOIGuFPk/wS/ur5bJnH+71fDfd2PAU5sRiytLrv/Xoo0Ceh8CpwWs/X/GJfPKTnhv9BS/A6uqLMT+PkMgv/6yPPfqmNwGve10VmPWlL63rrIt5oAk0WARPQXTryTlmUXp9ypUtxJeWlhoL8XHylre8BT/wAz8w8ZrV1VV85jOfwWOPPTZy7syZMyPeapRuBWKtra3hZunwjz/++Nh7xqVzuXlP5dpI6oYBsG4EIKyey5iDtGK8fIVehFGscb/9be8hsB3EIt+oAWi+opk/KcZieZFJha8hz23uK5G4sgrujKrerZC1BQ85PFCyLEGWLVcZVXnkR8ber57iQA1Y0sZIPvowX7AGR1aQVt3q/b65WaVfoCFa98XFSi8cdBX0Jyk6B4b77sPzVlaAtaKh1CTLUIXCDq/1nR3gQvsIeut1fS4UKbJ82RutAeQlx/Z+AMi/+RWVseS+DOh20ep9Gd/5mq4f86prvuFlFUXJKZ9hqziLl9zRDjFlUJZYdhd8W1JfDK6jLtujDVcrKGKg0f910NHUf0qEvuJcK7Q7YIwgtvHt3INlk/lyWXh98jI21f+fvb8PkiQrz0PxZ7JPZWXXZPfU9NTM1O72rhqY3R1ghRe0YpcPIWEBQnEx+rA+fLEl47CQHPwcCBQ2MpYUAoTBSA7FRlgSErZDyIEUVlxjruRrSwZFXJC5sEJIbIiVtCtGYsQ2bC/Tu1MzXdOdXZ1d8/vj5JP55Fsnq3uWWZieqTeio7qqMs/He07lOec5z3leFo1GP7N7VGB6VGxMxL6PC8g/dnEJfI8RIRvWi8h5nebD9Le2quClNV/leb2f8iatL/yGfyvfwsGx2dh9tcbuGYh+AGyafrRlaitYqmCevV/NSpQQmAXqTG6mo3InygQGKhDcssSVcWuZzPq//UzLqPXkZ1YqhHVgoFIGDFWwlRsFKuPCevFzraP1r7Levc5pVgNwuTundVaGNYFXW2ca/alBYAm4Mlgo39tAmgS/NT3ew3xUrmV3dxdZluHSpUvY2dkpA3lyE4GbDSqDYjc/CCCz7JadTV9YbWtlb5O5rZscOzs7pSQK21XbSDeEbH+y/VeZ8KrRTn+x72o5GXg0yzJsbW2VftPflUrpKOOdZWE99DelWvQKItvf841gMxB9Zte8ceFgAxZZxg4XYwTICoBwddUDe0kCYHW9ki0hwofYLyT6DgMA6wBODYfI86V6GQqQjos7JfmUshgW1C9ex4jKRbiVNuXr+fPVuqHdLvC8vLjg7FnUKOqDASIil7qBkKbl/gHXevx6eRm4807Pxl8kKNzrlX/j3gm/MNJVlat0qFnmXi/yTHBd4GhQrMIH/FoVTAC/2Ka2N9YFeHYOUZoiSTzjrLao0g2JL33JVwzVd8V+Sbn251Fysv57Pb/IjvNiE0UXvmzQ4RCxLjwVJdYNhjxHlOce+F8v+pk6KcQyW1iobzqQcj8Y+N2OAsyPnUO3W8kGxEni6/rww3U/FADwl77ksz92rGCMy++C2bBrLJ1KK5CB/XQ49B2P/iw6i68GA7SX7im7eLvtN0cUZFIGOq9dX5c2LPrqeuF2KgnBdf1Fn/mMr8xjj+H43cV9eY6xi/H//X/A5z//txgOvwF33134XY8222dCk4SB9iNluPH7AyXn8tQW4vu1Xq+HHjWLptiLXvQiXLhwAZ/+9Kfxwhe+EADwR3/0R7hw4QJe/OIXB+95xjOegX6/j49+9KN4/vOfD8ATHT7+8Y/jve99777L+FTyntnXyBRB0wGv+K0RYPdQe6RfTQDoqoip14SAdf2sxjoPlMEa5WL4Z/XMdcxWPJN7b86JdnRoQ9U5RPmolC7RR1DolhDp2D7KQn7T4ce5iuFfMnxtRryh2PBHkmCUV21iYzMDk3I81eaBmTBwjONEIM+r6J18ViuAj7pUB3FrBfAZpmZhwYO6sRsjzrNqdxaoAmw75ycGPCEG1K/ROWQRTLR8z8y18rpBYP1o53/c2DWMcJbL9rcm420x/WXHLy2L5q8d1bnyd6ThSUIbI9pntMjaXWyAXwBln9EpDk27hE1P5fvKodtu7OhnWgntuwfGZmP31Rq7Z4FFD4g16aADVwdYCoG5V3KvgmH6qkB3KP2QFrVNwzKYNW1rVm7EyraoH+3mgK2PlccJ1bfpL2SWgW7LqbI9+/kL+XOvMu3VzqHTAVaORZn4+l7baFreTT5pkh7SjQcF2Zt81bQBpPWyfcq2d0j2xvrItt00X17JdzeS7ee3tJ/f1sxm9nU3y/gpqa7eaoGR5NoSc8syRKiYDTXpyeIiGyRtz+Bokg+ztjidZeYADXqxTZnZDYX9mIKNArrWAoQWr9MWd8EySHr8iqo8T8WmHr9vqLf9uHxv0Y9p9QlZaKEaKk/TZo+9b6/Vc1NZGu4ZI5qumRpKcx/BNENVpdm+Gqp2nqMuFhy6gZ2k1arurV0vP5wmEChU4Gn5HWgbP4W/q2/Pfvaz8epXvxpveMMb8MADD+CBBx7AG97wBrzmNa/BnXfeWV53+vRpfPjDHwbg519vfvOb8e53vxsf/vCH8dBDD+H1r389Op0OXve615X3rK2t4cEHH8SZAhD73Oc+hwcffBBPPvnkFeU9s2vL9JkefL4b4zPGPtOvyEJjhbwPBUDV+Mv2kRJ63/jY2dcgOplGCGBtymefWVQWelAX/zcFg/2qbdo8pfiOAG5oDNeN9dq4Y8eCaTsP+p6fTQxWmLxu2til3+913R5GuZXQdK9sl6Y537Qy7MOmFdNKLdly7XcaM82lwbyfyvz2mrfZ2H21xu7rqVdcdxYCVvk5LcT2bgL3mrSXQ2CufmevZb6WlQugBFYVmNxvnQiQWbBcmeYKbmow1JAON6VCrK63ArHqF8rNbG9vl9IfFrDWMqi2N8uv9VDAz/o35HP7R9+Soc30NIinXqOSOeqzLMtKVrVKowAoweokSaDyPZRDAargpRsbG5ibm8OlS5dq5VStfEqzWF1yC8irX7Q9mHer1UKe54jjuFZ2e2pAdc9Z/jiOy/8tu5vtx2Ch9NNoNCp1z7MsA+VdeKKBddS60aax9O3mD/tq6ETIjWgzJvrMrmmz9KymmboCo4OBp7l+93cDd92FZK2Yf6+tVfomMjGPMcJzljPgwYfRB0AiTBlgam4OSFM8mXWQDcLMHZZrXOiZ1hg6SVJj/PBotVWOWFiopDOSpGAWX0K1IcCIZPqnAtVFfY4c8dVXTXfnPJm61LfuditmuwTkzPOC7STlzwd1llA5zBOE5xdkgVFOZK2qC+vHrMuj39zkEFCfzL5acFXmlaaeUq5ssG53wp8RxogdMD8flUTzWj5adjLkmr6jY/jesLvQ6/nPKOJqF+RkvTtXCcQraCz9cYyoxuByLsJir1dp19ORhb+7Xd/ORegSDIdeP3aULJbp0G0TzGtlefX7ZT/n8Xz+3Hj8m1VicVXPlF2UJH8W9/hxrxoDoNSxTRJf5ko5oOOhgZtuKgOeanvluW/ys2eXceRIUcc896cz2m3PSNf+QSq87Z9ab/7ArJbrgbKnl812Jfabv/mbeNOb3oRXvepVAIDXvva1+KVf+qXaNY888gguXLhQvn/rW9+Kra0tvPGNb8T58+dx77334iMf+QgWRIT/V3/1V2vari972csAAL/+67+O17/+9fvOe2ZfB7sC0IvBR4H6s4ZBOQ3GW0t+OqAc1YoRaQKBstopxtzc9GkHy1Y9r4WhqwU0eTIWS6AIE2nrK6/Tz6dhuDrMVGnG9QCRJmENBm7rPg309HMH1J+7vIjU5O1t/5kGhQZqcmkur6YFlomv8UecE2KEFjak220LvR/0VgdBFsY2BvNpkm9RvXAztwix0C1wHpIyim1Dc+6l5QnkPUaEVsv3VW0i3UvWfW7Om3SKoll4V3h18jwLF0HTZz9k0F/WL7RhwrSjcvISMPXpgQPZZ2P3fvPeyw5Sq99w1sSipoUYwgrqhhjLCrJbgNsC2xbkU7B4d3e3lKpQoJIgumqjq1yFTYt5WLCeYKdqkE/Tj6aMCrWurYa8rZetCwFo5jEajdBut0v5D4KmlObQ8qikB/2qvm1iLmv9NeApTQOgqi49QW3rC4LB9Bk1vLe2tmqSKJap3W63S+1xBs6kzwHUNNlDmwfz8/Ol7w8fPlyTyaH/WW8rXxJi32sfYADVzc3N2qaGgufqJ+q46yaAti3bb7uYTCmrnqA5pVuyLCt93W63y7Jr36E/qZUeOhmgGxcEz6mpfqPbDESf2TVtlgpmF0Zc1CjoORgA992HL3afV8oed7vwR3fPni11xblgjFa/CHzsY8CjjyJ65SuxVCyK7rmnCDK5sFBqq+e5xxuZfbnoGGY1INov3mNEBci9seHxU8CXpd2uWFWcd5aSHIV1knFVN6KTEoFy5DqIe2Z14xyOH6+TiwnaU8t9fh5Aq1c5Jk39go6bA2VgM18XgvFcz3jWvqy0+FocAc9zYONcpTTDgGOsb21BVrTDZlYwmYZVmSc0Mnnj3XfX+sQo8YE5eb1vj2Hhaw8ml0fNifbqApmLY/pWF6X8X69lfYvrx92liS5KfyUJEA0vVh8+8YRPo9eb2BQZIyoDoeZ5dap88dRKrX31td/3vqVU0Po6MHCLGA69jq1zXr5nYQGTx6JR+YnR08bdJWyvVovcra3qBDzdX8kXRZibq/LudoFbbvF9T+O+lW1YACdpVtWvOuXeQWdlBXjuc4Hl5fLecdJBNgTuvde7/Kab/D7YIjIv/9Lr+Q+Ldh0nnUrmSPRay4KwAtrW/O7AAeljXNni+umb7ywtLeGDH/zg1GtCp2Df/va34+1vf3vjPXt9v9+8Z3YNWeh35lwNSDdflX+102QFfGIDjtps6p9Hdrgsx7smEDAEZNt8NC8GQS03o0Oazs5NAOCh/PU1hM/yXgKVoVNB+gpIkFbH2BcybmV+vCfWHVK8sqYMZSDyEmQcAJ2TKOmob9qSAFBce3Ho23Ex8fr5adqZeHSXgU8Hw3qh1MfTQPQmwHUSwa2/Z31sY+s1mo4tuPyRaGE3KSgdZE03cnzV6nPLxo4pky1lihd75aVyj3UByQgKbof6J+eGQDVND/URm075OxazJx+qqVYElyyW5atJJYX8e2BsNnbvN++97CC1+g1nBAlD0hVW7iQE1CkoGZLZUMBSgVV9bZLDIAtdP+PnlrFtTQHQEMP+8uUq+CUZwgBq9dP7eS8ZzBrwVJnaasqUV8Ae8KDx3Nxc+Z4Ma5aB9eP/qv2t/raM62l/LHvIH8pMn5ubKwOZsuzapgqAKyisbUGmtjLZDx8+XPMFAWRqv49GoxLIZnr0f5qm6HQ66HQ6ZZBN6snT96oBbzd6NBCqSrjopgCZ6aG+ZAPhKhv+0KFD5SYAgBqYrlI0GuyTzH2+MpCp9nU9DaF9gP2Sn2udrW68lZu5EQHiGYg+s2veLF0ntDjigm1nxwNop07hV9/lAcRXvtLjd/jsox5EP3asBG/zHIjX14Hf/32f7rd+q6fPArjrLvggkwUCfeb3fVZcE3L41Yl9EUKsLG6nWKVcWvWgJvE7BZO5CD56tFgkMoFBVtWT+u0FWD1OOjh/rmBad5dq4ALT5yKt3fYgKsF75wCkvhIjxMiGk+5mtnlegaSHD/vsywWQgJJPDmMM1+ukLW4akHV+5EjFmKZt5j5/yo4eO+YXXWmKMmhYmVfhsE23WFsADtYNa50FANBuL2JhwSh3WPY8aXcEVtUBGjyM6apmepqWcq+8hAxwkvx7vUXEZEhvbHgxcTqT9ZLTCsxyY8Onc+RIhJMrK+VCOHbjEtnuYBOdow5PDr3G7mBQl3DlpknHjSbBHNYnSUqAmzFSqVG6ve3T1JMBncTr3i6mCZyLauw14tnPepbPV/XR0e9jnC6WLh5Kv0sSoLO8DDz72UCvV7smz4HnP98Tzns9L+GO+cwHIV1eBr7t2zxQkHQwGABLQF3E1+jQlhspNtjogbNrh802s5lZK4GxJnQamACWCaTrHibAwKQIAmiRq8uP2A1NayFM1d7XZCEwUT/jKR4AcElcbdxay/MSvG8yy0pWMJ/vyeqd2LAMmAX6mUeT5ru+NmGU9rM8L+J4AECSYDOLAAe4/mLt+sHAB0FN0xhJUs0BkgRYzP3uatxPkLuo2gBngFK7ma0b4nyua2WMc8Yubtxwcc63W20The0Y6lBNnSUAprOPqq9DQLRNZnfX/3EOU1VnyomCsjKu3JzXr4DqhNh+QG5/b1ROITjHsZr+ob2DSs+devnSfsUNkWHMs4l1Y8E3dYRO4sJte6BA9NnYfbXsILX6DWkhAJ3WpBccYqKr7YcBG7o3xBTWslht6VCaNh191XsIUKo8DNm+KoWhviHzmaCnlcxQHzE9ZRQrwEumPQFrBYRDetkETm27NPlA36vMTMjPBLrVv1bGhfkpIKwguJZXA5ZaVr4GTmV9dJMhz3NcunQJKuWj7Gq2EVnjGmhWN0ksG183ErhpwVMABNC5qaH+Zb6HDnnJFdZP+wnLwHZXwNyC6CodQ8CdzHq2s5UaampX5jeNfX+j2wxEn9k1bXZlN23RwhVHlmHkOvjMZzzB9u/9vQIvGwz8H+U0iiTjLPMs9bk54DWv8doRzqHfA7Dmkb5RHpWLvGAR5EMt5rhYgBEcteTXiUXGMJusJy/iKqUA/3d3q8CRurjguvLw4TqAXrLQwQBQPqAqF3FBHXZTnzoTEGXhub5VQGJ7uwLtVT1DF5D0y/nz/r40FXa/BpIVP6yv1dVX6JbaWrnIYL7r35d1M2nVQPI8DwbLqjnCROMaIyoZ1cRtrdxPkgBLTIs7Etq2ARBINy8uXPCbJVxwz89HWCTAXyDRSXoCQIUzMN/y1Ub1Ynl2dqqdnW63vEylWrS4c3PyoXNIkriWDPve8eNAlG3CdTvlveN0sXY8n/Up3bzcrY5LoH6NEhcvXQLQyoFz52pyQFx01wpNP4WAdHX2gbTZQnxmB9wUeTNAOq025oR+r/Kctl9Ni1liAfGQUf4iVGxNI/S6l9kNgyZTADVUPn2cceM5xOzV/xU8t0CovZ711FdeE8JwOYaO8qh6Jhu7dKmex9aWSLfIBME5AbB5ytBupFiGOB1ikWMD0Ib8Y4eGqj2jusxdyLEhhwE18JyvFkC3Zqd/gG+vVst+V0ie2MbghoEw0C3GrvUt55/A5GY7gW7nQAkiu/HSBKTXiqMMgcAcyN7EPspL5+b8/LU2R9P5zIGKcTIbu6+WzUD0a9j2C7JdDWDJSmQ05aNAK0HS0DWWHb1X2e31TUC1BZ1D0h668cD7tAwhhriV4lDfK4BPpn0Ty17TDMm3hORImtj+vJe+1jxDmwhWYkT9QB+oj8nsVka1nmoItZ1lj9u62oCdzIcbBE0bPras9hQG29b2F5u/3TSxJzCsf0N6+zT1sUrtqJ9teqFNFnttCEi/kcHhG7nuMzugFgLEgBKldq5iAgMFWE7Rca6MsgydbrFAs/TwhiztV+V7+VKvIaNHVDvKS3m4ppFEY1dSDRfa9VPsxnBphKNHPQCredukeXRb7+eUgmsSLpJ2dwN6rwIA23UQXU0Gc5J4RmHsXAkesByUseG1nnmY11eyRZs5F9f8KJhuxVorEuZnSYLJFZ9+WRSUR92dQ6XHacvABpX2TpIKEOaalv2v2wVwNguvYgsjoEJ/kQ3O90qu6yRjv7kj6TFfZeTXurLuMtC4syENlA8nNzj0vXPACDHibre2QOfmTK/nyx0NLwLDITr9BHnq+wfT46bD+rp/5QmNfj9Gp6gsWfjqsjRFyZRHr+dZ69RWKnxQmi6qA+BCzQ4skH6lAcdm8nUz+zpb02/RgI5BIHofv1MFxpsupQ7zlVgTKG6r8FQCaDc9qgiwqoa0NY1f4RwmNmOthYBbi0eGvtPxVo2KZDr2R7k/9RSnDkkSTaSf59UYqepptfGKIHoST47ZWjg7EUgSL9GH8SQDfYoGvZ1OWjC4xkTXCjWZlDcqXuOifXQqof01ZNbftu18lSPYUw32N2A3DZhWY1+1kzlXMdoZz4RzAhI5dN5IsJ9925ezg7ibTArdM58pP5wr/U1d+zYbu6+WXXdd43qxEGs7ZJZBbcFHBekIFjrnyustiDtNXkI1pWlNgPteGubKnOb1lHGhjEdTQEbKnthgj61Wq/yM12jZLUuewHKr1aoBx/QD5WlUH9yCr+ozC9xrG5AhT5a01T8niG/LrKaa7WxDsq8tW5w+UXkdZVjr5gf9re3CvGy9AZT+IkO73W5Dtc+pZc6ysG3IGLcgOSVc2H5kpOuGQZIktfaZJhNkgWyWg3WykjfqFwW86dt2u13+T1/Mzc3VTkiwTGTM6yvzZV9hnflbnLYhM7OZzezrbCFajoLKOsPu9YDTpxFhjFe8ogoqORwCS6dPV4gkkTyicvfd59E/ijk7V5Mtid0Y3W5Uy25+3ic1RoSoABU9V8cHtVTrdkvZ6ZqKByALHEvtUcaOkZ6I3RhpWj/2miSRX7CtrSHKMtyxsox+Py4Xzspao0sp18G1KNPTtSLjgfG6JIm8TI2rtMx5PRfFesy8g01/AYFf54/gx84hThJ0Uofu6SIg6+BJYCgIrvaBYpHcP7VYat3HbozNLMLGhseD43yzYvPDA87dbuTLsD6og+C9nm+7fh8A8Pi5CBcu+Hr4o84RWq0TcAmQ9gtwYDDwfadoxAhj9HoRdna8FA1Q+XkxGXmk+OGBF/KmLgo7jy7G8xxxniN2DulyB3nuu+LODnDy6AhYXUMMIGYDra1V/SJJECcJbu53MTdXnw+WJLWk4zc/9Lckuu6j7olSemVry19GaZj19arYScITB1FJGk8SLxeziAy4sOZliNbXfULPeAYW+32M0iWcPetPHJw96zd3zpypn87Ic+BlL13B4+ci/Pmf10lmDPK7vg6srgJ/sXIbnvOud02gL/PzqHaoGAzA7iKpH4TNDyBMO53ZzGZ2xVY+apqQWQE39bGk/5fs2BCALkhhJBuzNg1gOpvbFs1ipPq4UOkNNdUkn4oHmoJxQ9lUpxGr1c+IRbJuKqvVZNYvFiu13+k4znpNnAywVHb+DYd+UzTgkHHXz0tieMB9KU2QJH5zHGeH5XM5Auo7uToPcq4WFB3wG7znzwFAhPn5OrCsdWV9olzk8wAvNZIXony2U9qGsN/vY6eFBIQYheyNA5D4zxhPRrMKSb1MG6JC/X/a9DlYbNMpRnmErJgXXLjg76GqDucLykznrUqi4GuSREiSDg4f7kzIyPjCVsXgkp/Ny/cTpyFtJWd2Q9kMRL8GzYKyISDdMrcVRFc2tmpR6700e52aBcEpr6LAIY1MY5Y5BAxanXV+rxrVBDRV61vTJSBL3XMFpxWkpmQJ66AAKYFhlf1QtrcawWXWu0nHneVSbW4F/RUcPnToEOI4nmA0hzYttOwEfrmBQHBeNbi3t7exvb2NOI5LGZRDhw7V2o1a6QR6CXor85w+tkbNcQBIkgRzc3MliM6TCbqpw82CJEkmfEBfUcOcYDXbkf4OscTpK8qw6Ge8TkFsrZ/+bra3t7G5uVk7XcGyJUlSbhKoXr1uVrAvUT99Z2cH29vbE37W34sC53Ecl9/fiIzsmZzLzA6E6XinKzpLkVpeLoNwft/3dbCz45muwyGwdM89Xqrl7Fm/ChgOPVKYpl7GpZrpT+afZej3vTQFs0tTz7gdJYs4fz7CwkKhPc3VBVAy3Je6Yyx1IRTf3IPFAJLeCV89u1BTEF0jNRblSdNOqVCTJIWmep77+q2tAWfPYpFSHcvLnk2UdGr4IYFSYoxkCE+zNAV6vRjIq9PVbJbDh0XbfX29AlTtCkvbDkBkZFWCPigyidIUi2kKrA38aYJeD+7okgfQ19bqCMP6enHtWlWxYvH9+OUTRd+IsLVV9RNiyzs7ftFI96dpjF7vBLorJ6oNljzHUpIDCSo9lwvDSldlddVfx5UmI6aSZq6bJYXOuRfaAZYIGDy0VvVX1Y5xroosmudAt4uT3S6O3rVUti9f/f9x8edv3drpYBtLyIfAcK3aLNHF8aVLXjWl1aoClbKad9/tf05pCmDV9zf8wR/Uj9E/8QSwvAz3bX8XZ854d3z2s/7jBx6Y3MDp9SKsrvrvAO+mo0d9fIIlPIk8X8LDD3sAPkmeg1OngB/+ByNgOKw2ppyrtIPsnyIG6nfefKAW47Mj4TO7tm2MqB5Do3glcK7Dgv3p+U1S1IFZs/FYG/tdDOeqIcQGatwLSGcyBO5DQU6bwEmbRgk26xchlNx5pnLHOSDxzG07FE7zkw6poVea1tueNAttAJSAeS7zmXLu0gCahxpQJ0tiUQEk6459p9sFBpKXzdNPPGqVvJjF2DpfgbgMOwJMBjJvt2Vjv4jrMZGX/bM7GbbP2QbZa+fDOlgKGBeb4Uhcqdtu5dWUW8AiWl17m70WgQQHDo/6XUn8EPAcqKbJnNJwD59Dp5Wxy7IqDzY9pVhIIuHBzyNHfJvwBJtyCxTo1/fBigEHbAN8NnZfLZuB6NeoTdMPV7PSGCp9YQHfJq1uvZ7XhUDwEHhOU+CYshshKRCbHoAScGQwRwXvQ3ItBHiTJClBWAXPVZ7Fssj5XuvL8lq/KBOd5SKwTPY0y0g9dgWPrRSJgvwE3JWxr2xlPTmguvDj8bgEoukDlWphfmSjEyR3zpXg7s7ODrIsw/b2NrIsQ57n2NzcLOt86JAPNKrMfu0rrAOBcT0NoMC/ZdUnSVIC4/rH+hD4VxCd7aP9Ra1JkoYMdALZ6j/9y7IMW1tbNRkcbhKw/twkYP1ZBwZ5ZbnYVuzLqkvPcmqdyea/kW0Gos/smja7ONPVQImaVTbunUCWAZ3sSSydeQjo9/FFdxuGQ+CvzkRwbgnPTNc94Le66l/vvhuju1+ILAMWcbEeOFJWJUeO+Hk6ixBlm34FkSziwgW/GOgkeZ3dShqOrjYUxU4SRN0unIs9sK51VjCY4L4sYqNsE851sLFhFk2sF/21vFyyuKLlZTgXlwseLpC4uNHYXXbNx+SKpMoYrnxP7DLONysAeTj0KKwiGFwpcaXFVZn6hqu97e3KlwRCSYHmig4Fq0ujahIQVe2QtbUate6JrCqmYgOM4TocVm685RZf1OGQ2UfodguFUJaDpxrW1vyFq6v+T0FcbobMz9f1ckJB09i319a8DwcDjz7zGn7f7Xr/DgZAt4u45wH2uOgzj5+PS311xQVYxNDPa329qg7dz/vpr17PY/gRxr6MZ874AL0bG14UncBJliF66Utx9myMs2eBP/kTf/mjj/4tgIvY2JgDMIcHHrgTL32pT+bBB6uqUQ8dD51Fni9hdRX43OeAhx++hOc//zC+4ztinFxIqhMgSn8L6Sjpylw3JJybLcRnNrOrZPyJlRJgxcPH6lJzf1FtQhbFbsJqBpzbu0pazALotH0B6UVeNd3louyxXFg+azApFRIKzDiBcOrDuLA4TRGnnpVshvuJ/eWQREcTmK5jONMIgeelHBo3+jkI6J/V2uIrnW71XTi5CJlONDjQMD0LoicJxuliNT0SMJcbwAoysxgEbCntlySY1BZjPRWVDsmOqFPpQPqCcxr7mdaV11inm78oTb0Ge1LN1dQVlOGzew0h/J91VmCb1QjtfdgNLgXRz53zm+mcA3CqY/upc35vX08xJEkVo+fYMT8FkoOfZRfRzY/AT+Q6sdnYfbXsuuwe14vtR8qFr3uB7fYemtV9Jghp2eK8V7/T9FR6w8qmqM56iFGv8hoKegMowcuQvjjLTACXwLZeS8CzyUJ65bZsKoFCoFX9YOsU0kPXDQ0FkEMbG/azkEa81l9lTOgPLbuV7yHgrGxpgu1sQwLHIdDSyrDYzRqrQU6GOoO1Wn3wkAa6nqrgpkfo98D2DQHsyuIPaZTryYQQy52+5J/2Ectct31G0wz95qadMrmRbAaiz+zAmWXyAOVKIM/9Gq/Tgp/hOwf0bis/b7UAdF2dhZrnJQ66uNzARs1ztFoBbdI8L5Pa2QHg8vrKxIJ19jO+p6al5m2QbK/1mU1cpyykWOtFo8i2WY3s7NQXPYrb6wKdixtdWFn2kwLuGMoCdTj0qy6CviyDnj3f3vaNs71dL9z8fEUt04haiuoyIpkWvvSpqyrHShEozXPkqIrIAKvEAHSNzU2C3V2/EM8yvxis+ZsXb21VAWz1mACd2CSaq+XX+s3NVT68dKnataA/FdygQC3zLK3OamM7cXGsn6nEkC6OgcqNiqvkOeq/pbW1igrIz4q+SPxlMPCLcWAA4GJRxgjr63eW39vu6xzKgmxsAI8/DgB/gzNnvtF3m6Ou/tsJ7f6EqGwhxOHA2GwhPrNr22o/reIfO8RpEGO5LDgM126cYtP2whigcc+0WfgQ0mieGVHT86OpnJp24HrnqhNDCvoH9vb3baHsdBh2zmh/69jCgZDjtI61LIgmrgj9tOcrJxMhLRqdXMj8jn+UHbMAOjW6WScOlRPF0MR4o05qWD+768LOYwFyzUDrRdN6qgaPmm3g4nSFdZduiodAdJskXwlqa5GbtNHtfC/L6n7mvIldQv2vLuLUhEx0ElE41wL8PaEDoLbu14/Nxu6rZQdx5nbDmMpiTDOraW6B5hBQ3CQPYwMt6ncWGG0qc+h/ypCErlcAk8ApLRRwkqbgsmWTWxB7bm6udl2o7nq9raMFcZU1zrIR7NX0lWVO4FoZ4yFgXIFmZZizrropoCxoBYS179BvCggrAK73M4/t7e0SmNe2aUpH2ffaV0L32JMRoT4R2kgISd+of7XvKjNd/W7T4ckG9b9lxGt56Stltjf1+VD9Qr9V+/mNZDMQfWbXtOksOjSj1tc8h+t6rcWSepSmaLfN0VVl/6Qp0G5Xiw9BE0d55Nm8xSpM0yiLUnyYJMXCobYAciXzrhakcgJ1lvoRGA34Ic8LxjXrm2VIe4ulLGiSwB+HpoQLzZ7bNe61i3SrkENGUeHOqq6FKRg/HBYyJHle1yvVBSZf1SfCVKytuIj0KsLNgvB6jbxJsJr5WkYyLcvQ7VcYvBaz260YUsTdyYamWlCSFMEzFWhgu7OMdIz2tW7X142+0booYh2K/nb4sC+Itisp8rae9GeWYWFhsWwv1odu6/Umm8Qy1biHoev7rS1fHOfgN3d6PeDkSa/vUtH1/WtB7e/3Y+zseGmWXg/41KfuhAfRvWTfXXcBt9/um/nMGZ/28nLBds82Aedw7Ji/5sIF4FOfeiZOny5csrZWUQ5DwLgFxtSv+rcf4eRrxsa4ssX1LO7LzK4t40+OwJoFykKBrGvjpPx2yaAluNqUX9PnJYi8X9Nx3u4A2MRDQDIzbUI9UQGfNunQbaFrQsVlUexGg/8+gnMxokQy0fGEfg+htRyzLDKvY5IF3qcVPrABqpcsLFT77vPzdYBZ5zHKRE+SYizRY1Zal7m5aqO7aUyw9bNzPqCO5FtTsW8tqJUbk+TIJGeR5ucrzF+G+hq5QU1jmnD+xrTs/gWDnHPjIUmqwO95XhEICHzbfRXmzSmohoHRaZAezKO8DK9R1074Y1q/OTA2G7uvlh3E1r/hzDLOaRYQtyC5ZdIq4GklRyzQq3IuBAyZT1MwUgvEW8BSr+X1FgQlCK3MdVtmzU91wnm9DfB56FClIc5Al7xWQXwrwWI3FEKa8Konr9fqHxnfqhtPuRMFoG3QUfqNADDlQZgGP2O9NFCm+pxs/na7jUOHDtUCabLsVnaEEjZJkqDT6cA5h3a7XdNAn5+fL/XMQ5syzPvQoUNot9ul7A79zHaz/Vq18tX3ITa5Bu5kvRiclsFNKeui9zItSrfo74N1olSOMtbZHqPRqGTvWxa8/R1Y09+s9h+7aXEj2AxEn9k1bXahyQkz33NRVrxG+QhJEgMu8YBekmAhqRYdAIBDiUfeOOs/fryc/BM4f3IQYTgEut0lLK6kGCGuHQd2DhgXwRodvLZjtwtgzdB9k8SDtN0lRARLVYdTTRdR1GkmQOBiDAdel9ulsQ/Aub6OKM/xnNP96p4s83VTwJjItyw28rxaRFsdSgKrvI2vPI7LRRcx4u3tajGb50DWjdHtnkDndFIHmJXhlWUVWi2+2szj2tq22wWW0iKg59mz/jpGmaT+zPIyHj8XYWFhCZ3lvFr5AUAR8DWyTPEkwW2nL6LfX8Stt9abgos8LhRbLWAp2azOL68OKmeRXq1GoPuWW+o0S6LxdGqBEvuyn0DnVFEfnlFnebkxsLxcIQK8n+np6pW/l4Iq1oHX3E2Xl2rKJb2eLyIwua5nQFEl5LH5VlZ82zNQ7mAAdE8/x/fvwcB3BnYabhIMh/i2b/Ma/t/8zf6yBx5IMBgkZXVf/3rgBclf4K7ve05ZlnvuKfpjIdz/jaeAf/gP/ed33XUY990HxJ/5pP/+rruqhmOBtcNbYz+hT9nBD4yNcWWL69lCfGZfO9OhutQ2lzGIIJ0CoEBd7cLj5MVmto6rAu6OipM2eVaNR/uROuH/NQDdIs2hTTf7LLHPmf2C5aE0DB1XsdVWqy5TE3rM7WUh7gHHbjKGAaDViuFcjKTn48CUAV6tpgwLaU2cPUZU7ncwkGjtOjY0jx/pZyxk4YQo25TIHsDycqc27NEijOvzrGEGrGeTm9VaFxtBVTdsQqC+OjXkWJu+9U9gk8Cmy/oupa62UWSnUzqtVEK9Nc7fODyTYFKC08RVUEVQ6fQTdLtRmQebivGGbHlIuLd7ArpXoBIuKjMTu7GcmvSSSaxzrZvxA/29HLgN8NnYfTVsBqJfo3YlYNFeUieWeawSHLzPSnxoGULM9b3kWQjSEugMldcyjZluSO5CQUeaaqozHZWn0bJTg5zyIARmeT+A0kd81Y0F+ufSpUtlncg8J0hNYFzTp09tIEoCzwSkVWucQUeVkX/58mWMRiNsb2+XYDk/0/IQ3FX/se4EwXd2dkoAnnkQbN7c3MTly5extbWFubk5pGmK3d1dzM/Pgxr01ELvdDplmtx0UEb7oUOHakFD4ziuta3qmWvfASpmvG5aaNBSyzxXLXL6iD4fjUbYKhAs2y+SJEG73Ua73S5Bc+0rAJBlWekzDVSqjHR76sKe5mDeKgEzk3SZgegzu8at3QaiaJK5zRk734tudqfbxSiPkaU3I0mATr4JOGDxeDHhHiYeAWQ6/X5tIZIkURmH0mOAcQ1rY5b+Hv/d8eMFM9nod4/yqMRZd3djpGmMxV4ysRiNMMYoj5DnMTq6MCgWChpTcm4OOHko92ArJTS0YP2+B1ytFSsXLnTyvFIA0TUcF1jEZ7vdgr0lZdLAV8R7d3aqwJyehLwIYLG4B+gV6UWDJ325ez08mS+W+Phw6BnIKk968iSwshLj1ltP4MX39YA8x5fXY2TrQL9/Ap1eD19ei/Dooz4A5fLyiRpeMSgA2mf2+5V2yfa21xN58EHEaYqbu10gTWRjJgfO+oot0umrqxUAf+mSp2FzV4WIT7fr+yvZ1/zLMt+ZeI34b/Wsbz7v5w663Q6Wll3lBGk3nDqF0ennlVh+ngPZWgVoH+3JIpSaKHle5h2vrGCp16utvBcvDesL/uLzm3s93NxP6xs5EpSWl1IX/+xZoNu9Dc/8J/+k/ruUDnJzto6bswzPSTJgJcH3vuIUxukiHn7YF/Fl3T8DfvU/In7pS/Hd3/0DPkjv//V/eX8X9PXOmT/Dva0c9756Ga95zQks5V8BfvUPfB7ckFHdmiYQQ0Er7holiW/XA2OzI+Ezu3atBF7BAKOVKX7InyKHTmXGlnhaEsMV+tB5Bni5kxh5NqnCMZRHmsW/Q5IRgOiAA+FnRhOQvg8AvdRLLyRaQre4JIY1C/pPw2mtBnxT8UL7ivbzsLH1OmX1SUqwoH4oP+Lgy8uxJzkUxrlDrxfjtr5vHI6NcSbxaWiGOR7leRWg1E5GLl2qv4aE9xcW/Gu3W0eXOYlJEoxcpzbe7e4CyOvJzKf+f3vor7YxY8cj21dCDdJQ31jvTxIgcUCv2lTib0nl3fUWJUMkCft/AzrvHDpFERcBn9eymyy/NbuTFeogw+KzQ9362K1Ie9KpZ8Vyym/rik6QXBM2G7uvls1A9GvQyMzdj5zLNCZ6KKBlk/60Bb9tHjZYqDMPMMqMWFBcwXRNj/VUyY9Dhw6BzGVlbes9VlJF/UPJlibGvNbXynGo9IkGtrx8+XKZLsFpAuqj0Qiq2a7pWRkX3kv2tQVSWT4G2dRNEK0fmeRkQRNk5nuC4ayD3kfAO45j5HkOlSohW5sBVNXveoKBIHMcxzUmOsF5gs1sBwbnJAtd2fchjXTtTxYotxsa9jv6gAFUCXKzXpq3Bnidm5vD/Pw8Dh8+XP5e9Pen9dJNES2D9lX2bQXXQ7rt+jucgej7v35mM/uamXOA/m65ELGrRE6+C8ZalsVYXfUBjE4eGlaaHVw19HqVqGO3i3y90n0EqhiVvK3fB27uUy+UWcbl92WQUXPEXNd1FQEqQpp2vMSLWJ4Xeu5HJxfilB2hneyiLhzdbnsUOUkqprZuMJDZXpgylXQqwcBPaVrUdzj0zGulMKcpol4PzlUB4uhKyptTzpzfAx7X95IoS4gBbLpFnD3j/fyZz/giPvigf09gZHnZHyg4fRpYXvZHzR99tFoPHz4cYX3dx9vkMW6tD9twebnjpXCc84kzA11I0zG0wQB49FG/CGcELTZkv1/tMqhcC0H0fh+Pn49x9o+Bo0c7WF6+DUBFWif7zwfY9P4q4oJi6e5uvfCkiS8v47Of9VImZ8/W24++6nYjLKZFPa2ouRW3HwyAxx6rdwY9AsBNguI3E3W7PthZliHOc3ROn8aTwxiDAfD5z/s6rK11aoSwlZUOjveB6OG/8I28vl5Faz15ElG3i+d88zcDp5aB//h/+8CkWYbO6dPeOb/zO75Mt9ziK/jQQ/7v2DEs3Xqrd95nPuPL+JKXeN8rEECbBnodWDbbbCE+s2vfLLZcAxiBkm3qXKXPzGF+Z6diqBNjU6UrAoT6k9dApXtJc5fjRQhJ1huaNuRCzxhbUZN8KCsLvoZAWELPceK/JIDIKQf91VTEacC6ZbjrtCp0rz1gptey/LY8PHGgMUYHAz8mEkgHgI3z/ruTx9MqMTa+xgTRVybEuCF6nI2vNdC5+NveriY9qjfS62HkOrV43zaeKotG9TkdSqp2jDA3pxsk8YQUHu91hf55xLGb9bOScaF+JhTvOE29pJ5zQMFgvzicjEvH22o6+OpTzh/szrnmb79npYD6XIqaTfx/fr5eEM5ZtX6FEyOM64F7eU1BCKGfbXDfa9tmY/fVshmIfoDtSoG3aYzXkPSKmtWo3gvct/dYrWgF7pvkYZrY7jYdrcM0U+C6SWbDBgC1zOG96mtf9U83GpjmNLkbBa7pjybA1YK2oTRD4L0FfjUtoGJVh/yoaRH05waMyu9M07UP9TerXa7/2w0K6+tp9+lGidZFN5tUTsdu0oTatOk3M60dmuxGBNJnIPrMDow1MV9Ci9/iiPf2NjAR7BOodMqLlYQuIsnSJqherp8DE/wyyT2oXFzc8wh7nsMDknJf+e9eDB9ebBctUjayuQBU4HHAQvG8asCCLuCUrt5QHK5V7ToXqNc/dq7mY+L8/NM9EcWuk6QCTvhedc1tM7AtS3/TmAg/4wpfb8wyj1hz8a67IXylwxQtKHy0ve3X9K1WdakNxppl1WcEiEZ5VC8r00wSXLoEnD9fP5XA9Gtd3C58bSPxc7L01Cc7Ox5YUGmTPK8QglrDeoBge7vSZ1UgiNIKHaIQRCVoly4Bt97qL15frzoAG113VLgBws2jVqu6lh1jj99ho1n06kDYbCE+s2vY9votynhFHWYaxxDLcg4xri2Gx8eA/pRtjMenZJYSHqpL6HM3yTKfdvt+H0EEF+kr3rezU8cs98ozBKCHgHcbTJxsZ2WkazlCLrHE8iyr9LRt+5WnF0JzjtBYtr1dnyyEQPRQOlbCyxAgONzZctqiqXa97X/Myk5BrJ8m2l4bQOtqb2DiDRMht48+OGHWx9rofG87BTAJouvGNOPaaOekHJ1Ojvc5jof8fDBsNnZfLTuQzX8jmQUAaQRgraayZZjvBfzuBTyzDMoKJsAYAmVDearmOo2yJpq2gsXKKg+Br1o2fVV9c7LImab6jmmxHFovsrttvQGUDHUFiC1jXPOxfrHlIJN6NBohiqIak5xMbWVus54q9aKMec1Xfc16kUHNtMjKppSJ+gRAKd1CFn2e58iyrJSSsax/st/5nhIwZHzbjRxl+bMPqEQKXzUwKOusrHSWzUqsKBOcbRb6s22nYC1PCOzu7pbtr5r5WlbmQX1/7ZMh2wuMv97tawGi/8qv/Ap+4Rd+AY899hie+9zn4v7778e3fMu3BK/92Mc+hpe//OUTn//lX/4lTp8+fcV5z+w6MF2p7DXJlu9qoLTOtPMcUT7ynxUBEFut+iI7Tb1EiyY7gj9Sznudk/VXIA/kOZwwk8iG42njk8e7ADxoymPp29uVLnu5YMkyHF8xIMBAVqMsiLxXEDtNY8SFjIiXjKnH2bTrsNqiJKQRCgDDIdJ0sVTQALwShjKyDh+uL6yPH/efddwIGAyR9hexsOBxVJKQGMCTbUdlGn5O+RktN+N0MtBl4bJJEIYFY5QsjuEVFaxCH5gJX8ns4gpZdebZuCaql3O+TCGis/paiezsIzGDcmobJwmcq04KaJFUQaYElvVotGamn6t8iYLo3a4/2eBclREphGQDJgl2zlfsUZUGUu3VTlKMv91uRZnX1e/GRiV3c+xYxfJ3zh9ByLIqbza2nrTYzzNhr5X2gVyNzxbiM7uGzVWBIEu5hQJUtvIL1D3mJZbwGmJYSzaNP/0Qjqf3OefjqNSeHyx08eDWDWn/fQyXBBi8FvAE617oSxcZ2rpa4+2+GLxbL5i8Xo3zBA5JihGHJF9svHOgAsY5doXwVGKe9oCTfSyrO/lng5lznLR5kABQBpjVNgq9181f2yY6BvLUmAb4lgAwbHPrWx0i+L9Ko9hN5ND1tKmHniwSrw0R2mBnvXRCof0tm1aXqOybtS9Cc1rdndHvGPVUy2orq5NBvUbL25BvFKqzsYMl6TIbu6+WHbRZ2w1jKgVCsJymQCQBPppqaysLmPfZ9IFJkI8AIQErgrkEMpVJ3SpGTMs8VgDZgpr8X8FazReoWNgKVFsJGa2L9ZcG8lT5Dtp4PEar1cJ4PC6BUZaT2tohJnKSJKUcyjRWvrKe9c8GDSXoy0Ceqsk+Pz9fC3Spmxi8hnVotVolyKuBYQnQM/2dnR1sb2+XQTipwb6wsIDd3V10Op1af6BWuHOulDS5dOlSKWWT5znSNC3lWtrtdm1jIUmSiX5o+5xK0SgYzj5h+4gy+K32ufqT7ac68yrjor8Vy8jX31jLUEhU4kfLzfLxepWr0b6+Hyb7jWJPN4j+27/923jzm9+MX/mVX8FLXvIS/Nqv/Rq+8zu/E3/xF3+B2267rfG+Rx55BIuLi+X744pozuzGswBAHbSCgeS6i5WSRVcm7GTrkBK1soKR60zM+/t9D0ySHLu15dUlvGpKDAcTb8oldZoVQXTBLZn1xoZfdG1v15+tBA+GQ2CJUhrr68BwiGj1i1jSQg4Gk6s3WaUOB3WZFecqmY1WC1jqeg3Y7kqnJjlDF83NefAgcrJa1sXNYIAoy7CycgIbG5X0ty4k6R+ypZkn1oo6JQmOHVtCnntfs/hcoPOU9bFjwE03VfE6L1you9ni2XlelxQv19pcLDMh27d0IccFY7frG98ywLjgVpDZaAukaVwq7Gj3JVCe576OGoOTfQWI0FlergHpTw6i2l6JFnl5GVhyF4Gz62WfKRnbx45V4LPt6DfdVF+Ys/xE5dMUo2Sx1vxR0UibeVzK0rDtuJFCefildOTbG/C7KGzQ7e1Kk+bxx/1OinPAs58N3HknRsvPRLy8DLz61f4HQ93ybreKZ0Ajim/bNDRfrR0tOeh2GVcWcOzGnefM7Bqw4iES5aPqPTDxO1UJF9rubphZbUFK/Vlr8paFzvEpykf1kzUC5BFI5Vhq05ybizA/7zW+YzealNzggKpBwuHlOggO83WUV9Isms+0zYNQnZqwUKAZZGTeCl5bgrH9M/vFUzcDtGwcHzhe0GXdrg86Wure51Taq2KvLCYyNlt0v92u/t/eruZFFvUH/IDLgnDiUZAp0OthnHRqhHbWod2uDkQB9QC4lmtA4zXT2tH6LOb9uqvATEKbPXbClaYTwXbtppStQ5JESJJOfbNLGe0sAzsdP1c2hiaomdBUQk9P/nEOpewJdaROLG29i8sPFoAOzMbuq2fXPIj+pS99CT/5kz+J3/u938PW1hbuuOMO/Kf/9J/wTd/0TQA8qPKOd7wD73//+3H+/Hnce++9+OVf/mU897nPnZruhz70IfzMz/wM/vqv/xrPetaz8G/+zb/B93zP99SuuRIG49NhCnQTPLIa3yGAnQCmBXD5vepQW5BeAWErQaKsWwATTF4FTxVsV2DTsrtDAP6hQ1XQSbKY+b/1jS0b89G60BetVqtWf15PlvHm5mZNQ1vzIFNbGdsERwla202B0KaCDRZKMH57e7vMiz5M0xTOOczPz0+AvRbYZf1HoxFGoxE0+CXZ7QTRqalOtnQcxzhy5EiwDnoCQLXG2S67u7tot9u4fPkynHPodDq1DQCC6lpnu+HBNDc2NrC1tQUy5lViRYOusn8BqNWTbaabPewDBM3ZB/jebm5oOVl3XqvlZ4BaBf15jwXRqYcf8kFI9uVrbX/4h3+IX/iFX8Cf/Mmf4LHHHsOHP/xhfPd3f3f5/VN9xu7Hnm4Q/Rd/8RfxT//pP8WP/MiPAADuv/9+/K//9b/wvve9D+95z3sa7ztx4gS6BBJn9pTsuhi78xzodKr/LWVKjYum4RCuJ7inPd5KxHhlBV8edGrrK1q/Xyyw4TWft7c9NpkkXHT4v9j5I9WjPKrLphSZx84fU2cR1terOKBcWxMLp/nPYyxyUZFllY601kNp37KCYzBTKmPQ5uY8uThN4Quyvo4oSdBxDp1eD0kROHJrSxjRdpHEnYCCOdzpA535BMdPL9ZAa75G+ag6Tn1mMCEPc/JUF0BUrmFXVvx9J0/6sjKdbheI1r8COIcjR5Zqbj582GOzLLuC6GTJ7eygAr4JotsFIr/Xhdzjj1fMNjLU2c8oaKpUPMrAFCC6xeutIsr8vE+WxHPtngyex6TzvCLR89HI5lnCk8DZVd8u1DkfDCrmPdnbWla7+8DMgVITdm3Ndz22QZIA/f4SksQHbR0OK7ckSSXL3+97UASrq1UnJLucnZ87VIyS6pwPIPqN34iHHvL1f95rXlPXdqeTVNbFHidXs8y9EOoTYvft076eY/eMzXb92nUxdhdW+2mFUG5jVmmLl3KcDu2Xhcxip3bzsQTQrRZWcSG/4sEeDsUKnjLYeK8XI3bF/XpjCElW5m2RQNztlprOxBqnSWBrctyoBqrHe+yEJT80CZkEKR0Wa8IL1bhA5nwIRJ9mui+h4zVQDUdML00BDIaI0hRZFmNry19Dv+/sAIup7JJYEJ0D6c6OT6xpY5UxUDiYEURfXgaSBJt5jGxQkQ/UVfa0oj3gBdQxZG2raWaB95J1H6pn6MbiWgb+zrLJYLv2dtuHqrgDxTxWQXSzuQSgStzOH3S+qHMlpmM7BN9zLsK68hotvNa36JdPcdgGMBu7rxe7pkH08+fP4yUveQle/vKX4/d+7/dw4sQJ/PVf/3UN3Pj5n/95/OIv/iI+8IEP4I477sC73vUuvPKVr8QjjzyCBc74jX3qU5/CD/7gD+Lnfu7n8D3f8z348Ic/jB/4gR/AJz7xCdx7770AnjqD8WqaSkSE5FyAOjOXn1tJl2mmoKkCfWSeAxVrWPW8LSPY5s3yE1jU8lsZDN0sUPY489zd3Z0An0O62srUJqjpnKuVmaA2TbW/t7a2ShB2e3u7BMmdc4jjuARINSCngri64WHZzMrU5yaByseQSU3/MQ8yqMn0JgjM71WCReuZZRlGI8+4IFObYDrf02/K0rcgJduI9xH8npubw/b2dgkms61Yzk6nU/ObgsZsJ4LP29vb2NnZwaVLlzAcDmsgOkFzvgL1DQQrUWM3euh7trsF5UO/Ee1f2mbK9Gd6rIe2OUF+llNBf+2rtv9+vYD0S5cu4e/8nb+Df/JP/gn+/t//+xPfP5Vn7NNtFy9erL1vt9toG23B0WiEP/mTP8G/+lf/qvb5q171Knzyk5+cmv7zn/98ZFmG5zznOfjpn/7poMTLzJrtuhu7dUINNK8si2tql9tVnEzm19bqpGIugqJsE1hfR5reVt42GHjA9tgxAYizDEg9gBwnbrKcqK8/mI5zFVit6wdi1FtbwOJRVyHDql0N+EXgwkJVeMmMVaQENd8nScUCQzacYMh1lpdLuZfKxYGjvsVGBZwrdxaiLEOnXASh1hZlgEwWhgUqJGsWFhZLJjtlW5aXgQ42q/JlGbDm85zvLpUgA+Dr1MEmkCbIC1YdY4xxfba7C2De1X1ltcItyKwoPFCh3e22T1yDreo1xf1RPkKaVkw6oFqXcjql2D1BBRs7jc3OtuM9JHQlCSoGOqOssk7UWOExdS7QWWbWj69FppvoYFDg8QTRl5d91ZVFqPgQP09TIM4360L31Joh5Z47UkC147G87HdP+n2snWG6EdJ0Ebf1XNWJez1/z6VL/iKKAFeddjqyZt/rtVe4Kj+IY/fMrm27XsbuiWCA9n/57dmv7c9YH9X2J7pXLODG/TKdC9gL5WtlJdvTVnzu5bmA0Hrjzk4VRZoTDKViawIF851jG5+vlFahWemQkDxKjV2v/zdtYlhUVcDRyDnExR8Sz9APuGuiXULjHoNzdrt+jhUDQOI80F883/PihBMf9yx2qZFuy2t9S7q4perTUQqiF6/jYg63sVHF8WDZdfzV7BU/1o0VvSY0HDXsHU06U4FsuwshyD03ObJhvZnV/3babJsbkH6VxnUt+mnjo3UEJ1x81UyrY5FV/UI/pFBHsnnKNc4V1+y1q2NsNnZfH3ZNh5N973vfi1tvvRW//uu/jhe+8IVYWVnBt3/7t+NZz3oWAA863X///fipn/opfO/3fi/uuusu/MZv/AY2NzfxW7/1W43p3n///XjlK1+Jt73tbTh9+jTe9ra34du//dtx//33l9cog/HZz3427r//ftx666143/ve93RXu9EsyGZBWv1sL/DcAo37tZDudwjQ3itty7C3ZsHGJumLvTTd7fX6fj8s2Cb/WrkcK2ETAmZtHfT/pnpa1nIoWOZedbBl0j6imvEh+ZlpfyH/hOqsfrcM7JBv7EkFC47bTZivxvbyX+g7+xtTrfeQP5p+j1fKwn667Du/8zvxrne9C9/7vd878d1Tfcbu10J9ea8/ALj11ltx5MiR8i/EKl9fX8fu7i5OnjxZ+/zkyZNYW1sLluemm27C+9//fnzoQx/Cf/tv/w133nknvv3bvx1/+Id/+FXX9Uay627sblp52El+YT5QmXxlF10NRjJTU1ah//eR7IRxzr/fY76l6bl2u5r/amg506zJd5bmFNrkCDGogCDyMVEVXQ1KOqH1s03DAg217KbdOK3+/F8TpxZowypZjxmHym33J/ay0KK9XEQq4k4zjig3lQIFGiPadz/aV1cLbXjZRlFwQHyr+E+WIbxYZ/0sRXCv8ny114h9Pcfuis12JX8zu9btehm7axILezww9vM8Ce1/hUwfKzbtxmftFfzumwD/p9uuECOsmy3oXs4MVax4z3a143WEce1Pr+H/jWWT/+0jnu05VbIjNCGzAy0HTTtmNxTMDjE2Wb637HO9d9r0bN/jf2ieImN2qP89LX1yPz7e669J9+ZKJkFXyWZj9/VhX7se8xTsd3/3d/Ed3/Ed+P7v/358/OMfxy233II3vvGNeMMb3gAA+MIXvoC1tTW86lWvKu9pt9v41m/9Vnzyk5/Ej/3YjwXT/dSnPoW3vOUttc++4zu+oxzMnyqDcXt7G9vc9cUkW/KpmEpfKFjbBNiGQO2QFrrqjyvjvUknHagCfjrnyj+VK+F9O8VCikzukB46v9e0bdlZX2UukxFs9cWV+a1gMNOjrIky7Pk5meDMk9reLC/lVSjroXXWerE87Xa7ZJCTIU0Wt+argLDVslfmvQY/pQ+YPxnpWk/61WrJKytc8yC72ra19pk4jmu64nNzc1hcXESSJDXdc/bVra2tmpRJiInNvqEsdzWeCmBZtD+wzra+QAVg2xMS+jnz1j6zu7tbA8HtCQeWjxIv4/EYnU6nLD8Z+8yjielug6NqHa6mbWxs1J5BIbb2XvZUn7H7tSvdSOC1jz76aE2zfFq9pp00sHbnnXfizjvvLN+/6EUvwqOPPop/9+/+HV72spftu5w3ul03Y7el0zQhkRawyzJ0ux0ve1GAsWQbddJh+Vm3G9fm88xqfLyDqNst2T2qWU0COApAMsIYSRJVx6alXGNEE6QwstkqPcq6vHmaFqwnBRbtsdmjR+tC4jTnagxtDaypaxgkaXUWHQDSFJtZVHM3v6ox0S1Yrj7X97pQogPYTvy818MoWcTG+epWlcmZaPPifa0eKAJXDrLi/k6p2EL1jzJI7Nqw5qeJRlf6lorE05lkdjtXSbywMEAFYOd5xbp3XvfWpZ69xyB2R492yhMHbH9bLFWWoW9I7mL9Y4yATNqD8jJMhAzwNMVm7qWJer3YBy5lhhDWaPHeofIbGf1K3uskY+SipasnHpwDkMrvcmcnTIW76aZKN4gdvygr2Ze89clhjG7vhNdjJy2U6ahMDD/bi3Gu5QjQBQ/C2D07En592nUzdlsLaSwX/0fOwbmoZPs2DS92fOIjOZQ8YxlqwGO+TgQFteYcYB4LIcxQCeUT9/MLi8DyJj0h5LwWeGhTXQOFajIhYLaWv81TnWifkfsBQ4vvLXBbPT6jWlKalV4XPDkgg0evt1iOM7UxUPVV7JgdYGiXUVXVce12PZB4UThPuqj6n53yhDBfEt9tv2B6Ew4CPJN/v9xZJ9e6enDZPAfyLHwb+3ue1/3X1M3t38RmBX3KibD2H8419AdWfEZ5mTJNHisoK2DGZP1cpWC0HNYv+l3RsWZj941l1zSI/jd/8zd43/veh5/4iZ/Av/7X/xqf/vSn8aY3vQntdhs//MM/XLIJQ0zDv/3bv21Md21tbSo78akwGAHgPe95D97xjndcUR2nWQhwtCC6ZQWrxIemowAlgWQL8O3FIlfwvN1u14BlpkMwsYlVTABTQVvVo7ba1AoOb29v1+Q1VJrDBiIlcExfKFBufWODULJuKmNDOZFWq1ULdqp58VoG4jx06FAJqvI7gsX0j7ahDXbJdEejURD003tYPpWVoYxIu92GBk9ttVqlRjrlcnQSCqDWrlbmh6D54cOHEccx5ufnS4CefmZ62l8tW5t9Ymtrq1Zu/Z4bCFp/5qUyKdovbPvqZyrfo3XSstK07ynoTnmZKIpw5MiREkSnrr4GpNW0WR5uDlg99asNpD/nOc+pvf/Zn/1ZvP3tb7+iNJ7qM3a/9lRB9MXFxRqIHrJer4e5ubmJZ/ZXvvKVifpMs/vuuw8f/OAH9339zK6jsXt7GxiP6xNrYJLRwsm5AKArK0XA0MEQcA6rq34d9pxTvRKIW16udCRVceTcOeDo0UUMVqu5/smTIh0yzGt5R7q6pxWAtv2zwCgXi0wuTYHFtACGddFdpFkChwV4uJn5Z1sn8frsO+eqtK0ebHn0WwF05zBOFzFYqwBRXSQicdUCyAq18sy1XRQ5h5HrwCXwvtGVXK+HEWIMh8D62TooUgLFNj1JI3ZjpGnkJUOyDFivFtJx16HbjfGsZ3kApd8vpHnWBpV8jYIYzJjp88gxJUYoFTI3V0U6JepNhIaRwziGUw9XAIyo262AgjxHnGSIkwSLx5MysJt0m9q6VPtKTes2M+g14MvFjkqNmH4fI9fB6lng/Hl/eb/fqfULutgfjo0RuzHiJEeyHOPw4eqaVgvo5BeBtSGW+n1ANonoPgBAX5AF6qeqtmqSAKdO+Qaifi37dK+H7W3/kbrUOWB5eRFLXVdl1G5XQvoMLhcCfwJA+QSQLn3sIIzds4X49WnXzdiNCpCjTFiSxBX8pWNCnqOTVM/CpjAHVlKZr3NzKJ9TOhzZzeNyfzE0GE8xBeqByZjePk0D5Fo5iwDQiDTF2MXlY9zW22KMISCfOL0+6mJ9zmoZaE2ZTAPR3aQ+uiZt20Sz4ivx7OAphWIMu3ml52PM5JvA+tBLvsjYCaB6/uvgQ8p4ktR3I5ip3UnRjYUsQ+wcXBrX+o8toq1nbTMGqDa0Q8h10RmjUII66EMkWrLJZIBw9fR96GBW06lHGxw12DdYPvt5AETfzP2G0HC9mrYDEebmOmi1/Jy8k4zrmjmBMdg/C2LEab1DKYBu+xowG7tvNLumQfTxeIx77rkH7373uwF4ndo///M/x/ve9z788A//cHndlTANr+SeK033bW97G37iJ36ifH/x4kXceuutU8uxlxE0Iuit4KvKiSg4aTW/yTi2bHMykhW0bDLmF8cxWq1WDVRmWmTiWlCQeepmgPWlBie1khgWeGdZeJ+y25XJrX5h8M8QMGs3GMi6DgG0BMcJprdarbIuqldOcJr5KlOe7Qn43UXqrKtPte6j0ah2IkH7ADXIDx8+XILuLDd91G63oSz+OI5BXW/LnmZ9WSYb3JOBTtkH9DSCgsOj0ajmV20TZdkzGCrvUyNLne3PdmCf1rbmd1Y7X/2u/mvalLKa+QBqfZZ9hBsT9Cl13XlagGXUV21TlkF/L1fb/uIv/gK33HJL+f5Kd8PVnsozdj/2VEH0/Vgcx/imb/omfPSjH60Fr/roRz+K7/qu79p3Op/97Gdx00037fv6mV1HY3eWVStLroJDrCOgvnAZDrGYJB7sHg6BNMVjj3m56OXl2H+X54izi0XwzBg7OxWGmiQ+242NCic+ebyY+K+v+3wIRFtwWRZFXAgx3a0t/zc/X2eeMynnigUG2ba6QFEUoN/H4+dj7O5W68kjRyK0Wh545OKFi327lh4hhusule/Pn6u0uFWrPc/9YqZclLOuyrq2C8WibQjGL5oAwV9cjUqZdF0ksrxJguaoagAwHPo2Y2BKXYAOh4gAnFwoKnxmtQ40qwB9mlYa4XleRZBjsEtddZKipgxCNS7iSZ225ZKy1/pIknhWuPPMLWbB7kXmur/X+ISotTpSdfL7fSBJcDHvYFhUaTDwxbT9wuAYiJMcWF9HDOBkUc5xuuhBgzNr5U7LUq+HwaDOSAekz+jCWP8IovP3tLvrgw30+7g4nARqvvSlqjmSU51Kf39hoQLRbUS3EGBu31sgvZgDHYSxGxgXf1dy/cyudbtuxm75zSkg2HGY3BwtN0gdkHhtcD5K7SNPgXGg/izjcMz7uaThXmeaFsDnIJtMvAEF1kcLlzcKPOrjvfwwhMAaEL08pZbVSdb2UVVL3xTTuTqzm77x0lzFhoUtj17MG2wGzFQGiL0CjFrf2Ky07MGysOFWV30/0HE7tJsScpjOk6zDQlIiph9GLkcZzjuR72p5m86odWjqsDYNLYNu6su8kXMxjf/CJNWPJEZMO6nQNPxpcWqn/+yXthNqXy7+HyPyxIj1KiYPg46rdbtArxfBuQ46qSmg+IrzRyRh5n65iWFsNnbfWHZNg+g33XTTxK7Os5/9bHzoQx8CAPT7fQB+10ZBjr2Yhv1+fyo78akyGJ/KsY39mAJNCiJZ5rkFUfV+C+ABKKUxCKZSwqIJTFegUqVHANTAXWUVaz6WWa8AfmgDQE2ZwDQCwwr+qk8s65lArQVraXZTQk3TC13HDQbNm/UkiG/LDqAGwqtvWWZlLrP8KgHD69rtdnAyqrI3Wo+5uTns7OyAgVeV7a6AuYLpZOgvLCzUTiFoG/Fve3u7Vn6mwfopsK1sbDXtS/QX/cA06Xvbd5i29n1ubNjTBwTC7SZTU9+zrHcN6moDltr+re1qZWKuti0sLOzJ1t7Lnuozdr/2dILoAPATP/ET+KEf+iHcc889eNGLXoT3v//9+OIXv4h/9s/+GQC/APvSl76E//yf/zMAr9u5srKC5z73uRiNRvjgBz+ID33oQ+WYM7P92XU1disASrMAZWiRZaJqXrrkJ/hbW8AiQTNekyyVtykrvTZUWcCS5eCNgcWcLjaJ0VlJc7N+mmTJWfZUwfa5dKlarCgrjrfx5HKSFLIfzk1ItvBaAu+CI5blrtVTL9JgjoFF+s6O/0uSCmQFKoxab9GT7zW2tTVdlBLwJjCuFaP/1tfrsiwCCpD1GNu6qZQLUQq+hhbi1gchcELLpc7ga5J4qRJUR+LLwLV6jz0OrcFad3Yq9nmxMzPKIwwHdYJ9t+vbm/HXmJ9WpYaIF6B3pL8Z+S0416kVqbHPSHuOESF3HbhuBxHbr9gA2NqadCUPBjDbDtthbq6+A0W/hwD0EJJgUYai8x+EsXvGZrs+7boau8V44ASJq/8WLZCeeGkXVYHiZQRtLTAH1MdT/rR1CC1Bt2kbtA3Gx8TOTjgkRslI1htCx8AKgJfMfO5HNxUn9HzWz5qCqpaPNou2TsukKVNXyWdYAJ2f1fIMJGXbobHQOu5YHTz1qUWVNUPrd+v/UJ2bxoimz7S89n3oT9OxuyBa9sKJOs2y3VX9rH1cQXS6IMr9vC+eaJdowm1BP+iYqhcL8K+seU4N8tzLwPFzLScgt3fjyd9O8T/7l5UXbOw/hc3G7hvLrunAoi95yUvwyCOP1D77q7/6K3zDN3wDAOAZz3gG+v0+PvrRj5bfj0YjfPzjH8eLX/zixnRf9KIX1e4BgI985CPlPcpgVPvoRz86Nd2vlTVpiu/nnhBgZ4Epq0GtoDFQBwRVukUlXPQ7+36/pvlZCRULDitTO8/zkuGrUhy2nNYsCG9Z7SEfa/o2D023SdZEgdaQhTYFmoBZZdrrny2TbgYQ1KaOO08aEDxXXzwVa5L1sWW0baT1s5tFX00ZrC9C0kPadvYEQajtVPbG/um9+lsI5XMt2lN9xu7XQs+Pvf6uxH7wB38Q999/P975znfi7rvvxh/+4R/if/7P/1mOIY899hi++MUv1ur2L/7Fv8Dznvc8fMu3fAs+8YlP4H/8j/8RDP4ys2a77sbuvWbOIVMw07n6miXARgrt7RI3TRJMLpqYh01PzB6h1fS4FuFCPHbjCjidlhfq6xgC5YojcgFWJuPCTDI1BQaU1JvnxdFaMpz5t7BQ07EuwezEa7wCFeAQu3GNcBWSmuHCvJYX07JsLS20Fp7lsRlZUDXLEGPkAfvQxogA0WX9muqroLo2wH7BGmkQL1VT+EZlZYgea7q28QJar7Zt2+0wvmyvrXWSKwCd2O+GQ1RtqO1RZM7Av+UCemfH7watr+Pk0RFOnQJOn/Ykc/4tL/u/pXTk/XIlFgJJnsoz5Qrs6R67PTvtSgKTzdhsB8Guu7F7mgWQsSa5Bm6yauBQ+/gPMVNrP/OmZ1gowcBXfMTasXdPADVgez1Om+o4UaeGdPPcBIvWgut7O37ZvynltXMmPv+5ga6Au76fKFeobNO+t3Ww34U+a6rzNAvNwULgeBNgbu8LWYO/m8xWo92ufhOscoRxdYrNjN+heWCW+XlXOdeSuVyd4SF1KeYn0fAi4nwTi24T/b4fo2+5xb/quF0otZXTJ5azrJSt4JQmsa5kONurZbOx++DY0zuL+yrtLW95C1784hfj3e9+N37gB34An/70p/H+978f73//+wF4QOvNb34z3v3ud+P222/H7bffjne/+93odDp43eteV6bzwz/8w7jlllvwnve8BwDw4z/+43jZy16G9773vfiu7/ou/M7v/A7+4A/+AJ/4xCfKe/ZiMH6tjWCcsr0tuKnMZDXV9ibT1r6SzUtwVe8jWE0JEyuzAqAmZdHERFdJFd6rutZWm1rBc5ZNv1ddbOY/Go1KJrWVVlEgTlnkys7WemqZbDtYNrFKeChDW3XkFfTme0qiqFSPBeC1bcjCJsOdfYKSKJQ/CR0Dsv0gSZIaG1wtpA+urH6yt7WsFgS3wDXLbPXBKf2iwVaZJ2V1VL7HbjyENiFC4LjKw1iWOiVtlCWveYVONrAeu7u7ICueMjfj8RhxHNd8yfLs7OzUJGyeLib6fm04HOLMmTPl+y984Qt48MEHsbS0hNtuu21fz9ivxp7uDYQ3vvGNeOMb3xj87gMf+EDt/Vvf+la89a1vfVrLcyPYdTN2J0kVfNCCocAku0fZVjwPWiwEiOdtbwNY7lWrhzTFxrlJPNw5j5u2Wl5pogZkKljKPPR9sUrYXq8zaY4erY6V9/v+9ejRgim+PpjUuLb1pKa2c7i5nwJAyahm0QYDj0fSHUkC5GlUrnuYXJ7XMfE0rUt7AxVj2afVQdK7DckyEC0vA0AZrHUwALI1AccH1RH7aPBkpRsPYGXltrJ57DH2xx8HLlzweTnXQT5kE3Vw8/IykOel7ElEmRg2Vr+PvzrrpQC6Xa+9ubTiKtkXAtKUWrHOYFr9vpcaoQNlxTp2cdnEJQDMfrG6Wge8te3sQlT7LK8vyhOzPGtrlbaQ9usQuBACHYp+kqYRkqRUd0G3WwUy1U0e5wotfgBYNX2Rmkbs9zShhOqi/OxZYD2NsHL6eYiLvlJa4ZsyjsBg4Glrf/In/v3HPoa/u7Lir33UawvdsbIC9FPgwTPAo4969KrbresisRChRTgLliR1bfav0r6+Y/eMzXY92nUzdrt6AEAC0KXUiN3NM79b/Vnrq2J7c3N+jHaukr6KkwRk2k7dMwsBd6YMevhIL6Wqmo+PIhRc1iWUvhgvUTmUpsudm2ScUzmM/zNNTbe61mtLOwlO6RzgAtMpgprabvvZAw5dF3rE8jMvGVcFde8wZkhobNE5l86zQmY3zZucGgK57es0QLzp//3cz7Kwfgao5ikFO9zzf5IcSt8lYxmncy9haDs+/9K0vHRry8/v7FQTiOAccPjwot8kShnAPKs0/1ZXq8kmj0NubyMCsFSkcpI/Eh6JJCHBOSDjXMLMYYr25ZzWzk9sM1iG/ZXYbOy+PuyaBtG/+Zu/GR/+8Ifxtre9De985zvxjGc8A/fffz/+4T/8h+U1b33rW7G1tYU3vvGNOH/+PO6991585CMfwcLCQnnNF7/4xRoY+uIXvxj/5b/8F/z0T/80fuZnfgbPetaz8Nu//du49957y2t+8Ad/EE888QTe+c534rHHHsNdd91VYzB+rU0BU5UJUYDZBjG0gLUyZAGUgShVzsVqVzdJmFh2L5nflPGwwCDBUpbFAug0y+olI1pZ6JbZSxA4yzLs7OzUAFAG+VQf2frwM0qOKAtb5WrUpwQ/qf/NvObm5tDpdKBa4QRWWQatp5XFUYYyryUzPM9zMECoZTJvbW3h8uXL2Nraws7OTk1bXRnldrPgcBENR/uRtpGy+tmmqvM+Z2ZXtl9YBrpeR9B8NBqV/ROoNh5YdvqAvgxtgIRkWPS3opss6lvbhzQgLYOH2n6i9/Fa/h6TJCnrYn+jzINBXZtkbL7W9pnPfAYvf/nLy/fUl/zH//gf4wMf+MC+nrFP1Z5uOZeZfX3suhm7222g40HTaYvS8jNOxBX46/VKEL3f95PvL69FSNMYaeqDIFEaRZNxzoPn8/PAotsEvrReAXFA/YY0reeZpmXwTOpaOueLsrzsLy+K5QNfEoTlImVnp1p4AJMbA4I+e/3OBPHKCkbw9blwwdeJ6zMq4ehJ6eI2AMBSt9BhTxxQBLtcW6uqpNqcvrqL2NmpjuyeP+8XZFwn9XqehdRxowoMLhZc8V057ji1gicHEVZXfdpMh2szVpkgQ7cLZKdjJElcLiKXFEQH8Pj5GL//+/6j5WV/z3Ofu4STp7rAmTPVgi/Pq0zpZ6CiSa2s4K9WO2W9gap8NO9T34f6/SWkfSBi221sePF9VkK12C3grTsJLNu5cz5DRsI9dsyXiytnDXDKnRh2psBCfjH1R653dioAfWGhLlnU7VIreFDplOsCmYFKFUSXo9z8mATx9XX/2dmzwJEjSzh+3BdxMR2j7FjcPFhb83+f+Qzw4IMYb2xgCH9MtwMgarWAZz3LF/pcId7/8pcD/+gfVf5QJEk7trLm2Fe4k8DnBCvwFOzrOXbPdFWvT7tuxm6EgcDq8cSA2NVGHJ8nNIuFWrKxcyL/JWNi5JwPFOkqGRLnAOSBhBXALxIniNxqVcMD8y2fk8Ns8qSQFrxh41SZwJbJbQFzjVthTR/FBBtVlk2HgdB+hT0J5vOOgprm2nY6LmseqkUfMpUboXFYzJIISXqi3p5aYLvJ0rBRMcqrQNcAsLMVzp9NU26EK1u76S9k08D4pnvsho1M0hRAZ0xzYBJvj/JR0edyYG1Y7//2R8cJoIDoDP/CqQqnROorPYx37FiM48djP8cZDv3APhgADz3kB/vVVeCxx/xnTzxR6R7NzQG33uqj/t5+e0VLv+sun/jysv+9JR1pt7icbwKVciCro9VTmaEGleBGm43d14cdujxDJZ42u3jxIo4cOXJV0tLAlmRaR1GEJElKoJhALY1gJ18VGCdYqK/8Y4BLgoi8xwJeyjImeE5WcQjQ3N7eroGnBJmdc0iSpARJFeROkqQWzFRBTAKjWZZhNBpha2sLw+GwBhYfPnx4QsOd9WKezIvBIpkf71MQmIDvzs4O8jzHaDRClmW1+xcWFhDHcRk0tNVqlZrl/FPGszKpFSjWDQUyv5tkUQieX7p0CVmW1QJ/8lWBaBvMVH0LoEwvyzJsbW0hz3NsFxRBpkOfWsmS3d1dbG5u1voD/abgOvsl21HbnG2jGwDApOQO+w5QbXTQuAnADRYF0TV+gG5aOOfQ6XQwNzeH+fn5sg9oOZTlzjowsOjW1ha2traQZRk2NjawubmJxx57DJubm3jiiSewubmJwWCA4XBYlu/pegQ/+uijWLYsvGvE+GxcWVm5Ipme8XiMs2fP4sKFC1+17tzMZtZk7J8XHnus3s9C7B5dSBH1HQz85L7bBU6fxjjplNgkLyHYu7Xl5/28DagD3fHwSf/F2bP+SwJwXAGmKcZJx4Ph6+tAkmDUPVGuNcoASUB53DXGqALMWZ+HH/aLEdYpTaugiQr2OVetgrQy990H9Hr45AMRHn20HvzsyJG6vibX+6dP+/J08os+b+abpvjycBHr65WsOMncLJ6u2/h68iRw/Lgncr/0pQUL/cEH/Y2PPeYb4O67/QXLy/jimt9oIK4aInPnuW+Hu+/2a7GjRyv8OMaoXDj+2dlF/Nt/69+ePu3vueceX7/bkq8Af/zHlROyzDdOnlft+KxnAc94Bp7sPwf/9b/WQQGC6Lom5YbBqVP+9eb8i74iDz5Yib7nuS/o6dNhaZO1tUqonw7+27/1rwTReRZad0R6Pf96111e+7x7Auvr/qPFdFxvZBa8WKQTPLg4jEqwe3m52MzhhgcbnoA6K6q/tdOnsZksYW2t6udsP2JLaeox7mc/27ug3wcW1/8GZaMPh95fq6vABz+Iv9jZwTqAM/AMoyUAibzmxd8LFhaAD3ygHiiWvyXuoPCz4RD4/Of9e+083W7V2Yp6rT72GG69554DMXYD/xl+m2G/tgngh5+Wsfv8+fN405vehN/93d8FALz2ta/Fv//3/x5dE1RY7fLly3jHO96B97///SVI8cu//Mt47nOfW17z/ve/H7/1W7+FP/3TP8XGxgbOnz8/kebKygr+9m//tvbZT/7kT+Lf/tt/e9XqN7P9G/vn+fMX0Okslo+f2I1rADIfT7o3Z4d3gmn8jo/PGtiqG2X6PAfK3/XYGe3lhhgmcK6UIuMlJeBaApdZ9Ww0TNwaQstycIOzKPzIdTAc1lno9JFlGStubFniivsyLQ0OruO9xZ6Bqpiap25SAJMBKxUkZ7swDwaSVNdq3loXtv2RI/6RrM3Q602CpUwrhEsroK/7pfZaxZQPH66AYueAaHixutnKpoXmnCGzwL81FkAAbThX6pvoBgDrRBCdpy1ijKqNd5aVja7H+mxe7IMrK7g49OQF7pNz7mUV0ngLpx/Ly8DSsJjjfOIT/vVjHwM+/3kMBwOsAxgCWEcFETsAPfhRqg+gc/y4n8i9+tV+svj85/u692/D+fN1oobtA/Y5oQf08hz40pdWcccdt87G7iu0gz52u70vmdnX20LSHE0awU0yF3o9AXF9VVPmrQaCVDZySLJDX/UaoAq+qLIqKolh66BAKdnxrWI0VUCfMiQEZVWWhMAxgXnWLeQzzU+Bdb7awKDKYNa6kRGtLGWrj63pWl9q+toeTe1K4FhZ49ysYDqtVqvGXGd5FGBvt9sTJxgAvxEzNzc3wSK30i1q9jOVc1EWP6VVCKJr2qynbohoeiEJl1Cetq00Hz3Zob6lXI2V1dE/C75ru2oQVZU/stI29jTBjWgzJvrMrnkLrQDtysowkux3eV6xuhR75pFWLlx04j43h7pmdmhhJOnHUgamp8HRuDCI88168ErewIURV65KQQuhC7p4Asoy5nlcBjC1TCbFHGqB25hOjYK2WH61tVVhq2oWRHfOM/dKUh5vZnkvXao2AIZDOLc0sbhWlhSN7Qb4BSXrECeuVhbGxKNiCwNpIpU+Qf1t3cQQRIP1DK2nSXrWtcDGRuHfpKjIzk59AZ5lxtnG8tx3wkuXUEa/HQ6rTQdSwvT+NK0a17myO83NeYZ8ZH8DRePEzoFsTCfSNKXpD4P12NryFecpiyLPUDVYXWJLXBAfPy4/IV7EtIv/N3d28CSANQBfhGeib8KD51nxysX5aGPDn8AIsRP5ua1ciPZ5oO3aORL+ute9Dqurq/j94ijIj/7oj+KHfuiH8N//+39vvOfnf/7n8Yu/+Iv4wAc+gDvuuAPvete78MpXvhKPPPJIyfbb3NzEq1/9arz61a/G2972tsa03vnOd+INb3hD+T7VINQz+7qZBUL5mYKx+8UoJ4BlfbaF/uTGkFZ6c8KTY3YtyLOeHuJRs+1tapT4G5OkPqEwlVO2dlO9y7xN4EU+221wSPVjaMO8Kb8kmZwr6ByIpOK92keBbKtmZh/HukFhhigcPuznENpHNI9QXTTfJhBdDyzZISPWiZ8mGOpP00znEzQNMGNNxy8xraduqmCY1fsh51fb2/VJGFDpHdGZUge+5bxOk9OipWnVxSfmdMWEazQYYADgSQAD+PGb43QEP91I4cHOE+fOIXn00SogfDGn0D6r0wx16fVls7Gb9tWO3Qd9FnfDmLJ9mzSgLSg9DXBSgLIpGKJlGVtJGQskTwMFFQwl8NsEoNv60kLsdr2WAKbNk9cqeK+bAlongsZ8pZ673RwIAd/T6hKSjrHSIOpzTZvt2gTqKnhLPW4ytJWBrpr4BN3JFLda4zYIZ6jOuimjoPNeALf1kdW4135Bv9h0FUi3mw+WGW83O2wdQu1l/ftU6qWmMkozILiyGYg+s2va9gK6mr7XRXGWIU790W4eC7XByZzzixWSyUpTBFUv5ncAMBwi5oKg+J6LAi1OuQ9JOpQ9p93t1kF0pd1pYdUoYC4ouXNxmRexeGpg83YuNEv5UC1TAdrmg0mX6rFixQj4qkUuNxbm53055+fr1Ls8L9PjvFkVWlTalMTrw4frrhujAIyLCynXQwY02eLI80n97GPHfLmYeK8H9HoYrjcTyejPw4crYvnCgn+PS8UK0Ir52g0e257O+cIW0m6lbujx4z5xyszQKWQ4UmRYkqZrnYvqmsP8QvLMs+rj4dDrzpc681mGcieGegYWjckyuLTyB1BJuPNyVu/wYSmqLpK63XKjoHPrrVh+9FE4ACP4BfgJeLmitHiNir/41lurKKmheoaQOf4o+FmIjWrFh69puzYW4n/5l3+J3//938cDDzxQSoP8h//wH/CiF70IjzzyCO68886Jey5fvoz7778fP/VTP1UGDf+N3/gNnDx5Er/1W7+FH/uxHwMAvPnNbwYAfOxjH5tahoWFBfT7/atXqZldFathk3kOFCxvPkubjOBqq1Xpf1MSrQSWmzJUU3SW74E6RZuv5QZhPHFpksSIOFxbYJwIJKmy+pwmEilC7nzuhrTQa4Cp1sHWqQTSJ7/WocXuI+p7fSXWqtMNfsZrmJ4C37oRwn3e0BQpNH1pKovep32kSSYmhHUH9lGwu1tX/ZrwT2icvlKb5ny7WTMl/cZhSPsXG4LzGlaMliSTgvlZhiTplG2V55O66ExC46aX7cATFcePe4eeOoV4bg79s2cRA+jCj9U5KhD9BDzn+gQAd+ut/lja8nI175L5gM77dM7aFEz44Nps7KZ9tWP3ge4GN5KFpD+AurY0vwvpa2s6vEZlXSyQroCmZUIr85oyHRo0lMCnZU+rBjpZ26F6hjSoCYLaevOeVqtVA8lpCspqGpRBoXa1gttkuDvngnlaUHYaG5tpUkLG6pRbcF/bSNO23ynoDKCUknHO4fDhw6WEjLYj01AAfWtrC3EcTwSqDbUr24yvqjWvQLoyx5s2fIAq4Kzqnqv0DgDwBIAFuFWrnjr4NNXt5/dk59sTAvyNKDvf9n3th7pZMI2pHtpAsT6YAcIzm9kBstCsOUTF4rVcxBYUmyT1IY84Z9fFAS/VxXqrheroti60OctXqhdZ3MU1Wxt1yXRdkG7mPshX3Per8jGiim1m5T5YsGKBP4JnD3dWRpVkzRe+UNG/h0OkaaeGr6apX/MwOaAC0cv1C8/tOodRslgygtRPKiWtTGyVO+Ga6PBhf39MzRznKno4F5jDIZaWuxh341Keut2u0tIFOJVt5ub88W9dbGdZVO493HNPVS8qtJycvwgMsjoQPRxWlHZKe5w6hS+vx6U0iZZBwf40BW66yZen1/MqJ3G+CfzteuXYdrsOomi/1AU0nckIs3qqoNfzrywf71FN9CItxYAIXidJp+pXStVL05qsQp57Br9zQL9/Mzr9fiUzs7bmqfatVnXem07p9xH3euh24zKdXq8C5TX9Y8cqCfJR72agV/WvxbvuKo8MrPzBH2Dl0Udx35//uW/Q22/3N5Lafsst3vl33111BAUstJ70N1D9+GhWv/gGsosXL9bet9tttIlYPAX71Kc+hSNHjtS0te+77z4cOXIEn/zkJ4ML8S984QtYW1vDq171qlo5vvVbvxWf/OQny4X4fu29730vfu7nfg633norvv/7vx//8l/+ywl5zZl9fawEvfMckfOb2QRj+b19jfIRYgck3QrQ5j21Z5pmQlCRCYVOj/F3b58PUpgodUDxfNTHxNxcjFYrRpIuomMfdMoC1jSdA7pdjFFIdQwr4rAWXQ4VlVWZqKemX2yW8yMLMGt6ti3UXZyX8P2EzEk+El8W8VfKggJIq2DblKghKGubSI1yMxyntWpc8nOI02bUPXi+6h8PT+k1/J9psfg6VMRpUqdAN6HxIWeGLNQ/1fENQHooSd0oie39uqvR5HSdLA0GiJMMz1zp4uIwQr/vpxdbWz6ODpNhqJii+1Zl6Pd8fvfd58fk5WVgfR1ufR0n1tdxYjjEMzlf5hEGyqv1+34icOutlbxdr4exi7FxzhdRp03WPaENmAhjMO7BjTCMz8buSbsBmv1gWwic0z+yp21gSH4HTIJ9KtES0kRXKQoFamnKZN7e3gZ1nS1IqRIqzGMaWzkEECuIvlOM1BZ81I0AapgrwK5G8JwWRRF2dnZqQC4DcxL8tqCt1SUnUzzEjKaPCXLzgWM1ua0sjJWqsT4hM10Z/goGU8aGdvnyZWRZVgZ/pYY9AMRxXPYh9gsNtkmfqd94TYilrX1m2gkK2xcJovN+AuBqTJebEuwXFqzW3wa13bU8uqlw+fLlWp8P/QauRLfbnuSwJzxmVtmMiT6za9p0Jh1alPBzu+hRpLfQKY/TFEjiCVY22W5UrNjakuQHAx/MkCtLBbipO0Jj1NAkwfZ6ReYFqriMTNJbFRQJiNDt3oZ0+bZSDmNuDpgvFi+D1bpMRrcbI01PYHnlhFdWFDA/TU/UGN1cvyQJEK19GchzLMkCf5RH2Mxj5G7J68OvhtdhKuFJqXauOW1ctUp+JcIig19ubFQUIxHhjJzz5Ukces9fKtnMFlQpgYr1CsTN86jEeufmgFe8olwrwjngZOtJ4KGHKyekKUbJok+zYL9sdm/2kvefAR59FHj88bqUDDcQnKvrhN51VxGQ9cyZSkuGTiACQFRCj/krqK47E1zlEzwnKsG07M6C9EW+3dmpDjN4MD1CksRwSez16QugIMvjUmGGMQEA39UXFiIsL9+MRa6ez53zyAi124kAFWKpnSTBbctpcAGv+r+Av/Xhh30Z6bZudwlJsoS7X/MmPPNHfqTqy0mCTeclhToPftL797778PjczTh5vPB7CDQgoqOdyJ4PV9DL0i8PjI1xZQw1P9+69dZba5/+7M/+LN7+9rc/5VKsra3hxIkTE5+fOHECa9RXCtwDACdPnqx9fvLkyQmN1L3sx3/8x/GCF7wAR48exac//Wm87W1vwxe+8AX8x//4H68onZldfSvH0RpVOJ4AyBT7jvJRbWyAc4hRAIgyxkxkojokipBaaS39ngXhM7l4pjoXl4/gkE71/HwE52I4x435ExOPjt0cQA5kZ+uf6yNcfaDjTJSPjM/k5uJZFjmHJIlqwLDOaWr+R/0aC57LATQP3uvmANAsZ+ccoiRBzDF83gFHE4wL/7Fsqv/O5LRM9lDe7m4xX8k2kRRBJwFfbg1oanXhQ4FVbf2tX9ptIEkiuKTjZ2TOVTvR00DpAAgerKCdt+pOidyve8EKnKuNXQykAX1/+6r9PbRbvraGxSTBYpri5lN+XruZRTVZl9Cw+uW1CMAi5k4+D61lwN39smA11ULp5TnghoAz/aDWB+1vVja6YgCUpePG3IEaumdjN4CrM3bPQPQDYE0sV6DOROe1+0lPwUq+V4DWMnHVCJqTia7SHyGgUNnvVnLGSrQoC97qYGs9eS3LSyY6AWEtY0gbWzXMAZRMbA1wyTT5P8tr0wmZ+iKKolJ7nAEzrWzK7u5uCRxbKRWaguJ2g4J+YzBTyriwbmSUk2HOzY88z2sbBaofz3utnjh9pW1O32md9XSE7bPqH4LWGsBT28XqsVuJHauRrvkTiGdAUfar0EmIaacwQidArDWx0Jt+tzObgegzO4BmVxmhxQ5n5LoSHg4Rd7tYWPDPbF1oEr90zhypJti7sFAHOyHf6YJ8ZaUWICpUZLuuIcA4GPikz5/3rKA0reRJGGOS8cy42Mgy4HnL/ZqQ9+GTdf3PbhfouBGwPvARIMm4ShKPhqdLNS1yzp1JILfrvmPHyr0Cz8AGMF72C13GV9P19yLZS0ePVmeqFcgUJ8X9DEvOAWvDuqNCfaDbRZ53yjLfcgvwvLt88LozZ4rrzp71YGsRgHMTHZw942/v928GADz0oHfd6moV65I4Mde4GkuW7bLkLgJn16uomlofguPUOaHpwpmASJHwZh7DpfCbPdpB6Hwr+CoLcQXR2Y+VtZckRRirYkGa59X329sV/r+xURH0l5c7WOr3/QeMWqf11PehIGcA4n4fS8VphItZjEuXvLvW131sMj2c8IlP+Dx9V14sD4EkCfAjP/JiPPclwO/8jo9l9prXRHjdK7qTsQWkjmUD0jnqU/at0CmBA2NUnr2S633Acw1O1sRke/vb3453vOMdU1P84z/+YwDNJx73Wg+F5nVXSnR4y1veUv7/vOc9D0ePHsX3fd/34b3vfS+OHTt2RWnN7OpZhHElu1J7nsXl94A/iVX72elv2qKedkBS00GWf5YprpFKeY9qlJliWAl0bq5yjhDah9PiqNkgnToU6JxjAkAPoZll5pOMzaYy6F6D5k8N8jQtgksrkspXndDYiY1lVqcpoiRBJ0nQ6frn6yivCAMcjyx7nrI9HHu5ce5BUs92V3kXO4dSTfYmt+l9HCeBakoUa38IJaSNrOOKNbuhoxbqy0Vb2vaywHP1PkKeF5tRSdEHSA5Q/X6Ox2Ru2DmXyA510hSdJMFSr1uC6vzJ8HQg99EvXKgOaTLovMYhD1WZ0nrdbjV/XF6uH6xLEgnyakH0EEW9cFLk9oh7cM3ZbOwGrs7YfZBmbDMLWJNm9ZV2JgVlrXSGfq9sawWS+d6y1u09ljG/l1kt8KZ7tKxko6vki5qWXcFfy6ZX9rXVQw+B3NPKFWIna3k0LW07Zfbb7/S9gr8KojNd1Ty3OuFaTwscW/3zUHmntck0v6isiwWc1Tfqh6b+3sQUD7WTgvi2TE8XWzyU1wwQnoHoMzvA1kTX0e9DKxHEQT3WEEsJQFi81C7W7fFwhBcSesvWVpW8Xsvj0Aq6a3AnZUlvbaG+qChsgomm5WTGsirTGJLMm5rn1j9kzMVu7ANdFYuYTuKQ55GVPa/rltv5iS40nasYxEoBVMDDtgPq7sdgUCy6i4UGHVakn2dV/YinECTZ2Jgk3BH4YNH5miSYjMhly+pcFdQrUG7r3GzoP6qB6NZC/ggkF7ykKU35igBGGUgsdXUtcYtcsN0UcdIdFLZnlsGJ1jAvJYiu5RgMKlDfM+P9Bke3618Jwl8x4L3X8+LA2VPTVV1cXKwtxJvsn//zf45/8A/+wdRrVlZW8Gd/9md4/PHHJ747d+7cBFuNRg3UtbU13HTTTeXnX/nKVxrv2a/dd999AIAzZ87MQPRr1JyDPKQCR/ebfqdX8pu3qCl3Du0zdErQZzvEM2ZKKMClYvChoYrfh+Sr910XU/+oVJ6uvuYjGaik6Wg6/HKIYnkmpL+a/miU67BtEtjEdKE2lmRojEtTq7cAzF/N43uve/O8CFSumxRqdjLUOGHchwXusW05rZx81bZmkTsMcF4D6N3kkQo1boRIR06STi0P/Qlx6nPunAfYqf5GMN22qXPVAbs8r+LJ6MG90i3NOwf1zw/Uhre12dgdsqcydh/kXnBDW2gXxoKaTYDrtDQtC1lNAXOgku0gKGvlNLRsasoEpoSI5nHo0KEag1zlWqbJwPB/gqoEj5WxHPID66Rl0nKrbrreoyx4Ba9V11sZ1YcOHcLOzk7tBICWSf2p5WcaZJnrpoRtI96nwV4VKFeZG2X8WzkagtbUTt/Z2SmlVdRPTNOyxbWt9TPVqKcPbf5aPkoAaVuwDCGpF9VZpxyO9jEGWVW/6kmMaScwQn1G/aUSR/QXT0RYnX7mb09i3Gg2A9FndiDsSlZPdnKtAt6oE8nJQFKWFif6wyGwyNm+leAAqkhaW1slo2fsYmTDao6v2qQEYW3xNDlLctJ1EBfwCwvVImR+Xi4u5DdCC/xx0kGU5tVKJrAAtPivbjTomswevS5vdnVGdC35gqE2kYlNTDVjQuKmcu846WDrXNWO29uYOCKNJClPEVzM4pI51W5XR/Mp0UKyvBIhrYxLr+fv7fUArGeTq1i+hiLXKgXQ+I6nFzxA76UC4Pyx7XGxsC43IiwjPc+RIy7lg9i8zL7bLdhpXOUCWOz1sNjvIkkipGnFIuMi+dIl/1m3u4jOrbfWNPfL3Ydbb/WUfC1Xt1tnvpHCD38aoteLsbLiPx4M/B+b/FnP8gcjuCgfDj1gniTAd3wHcPLQV9B9/Qncc0+hfb+6Gn4uhHYTrF6y/RGGQKJr3p7e4GS9Xg+9Xm/P6170ohfhwoUL+PSnP40XvvCFAIA/+qM/woULF/DiF784eM8znvEM9Pt9fPSjH8Xzn/98AD4O0Mc//nG8973vvaJyWvvsZz8LALUF/sy+ziYDiP3pBRmk+wHImjZXQ2OM/rZ3dqqohUT4AlRyvm23p+OpwKT8WKg6IbkJFmturtq0di6uwFygDkozMxTSHmgcHvXSie9C85DaZrdlUiuobDNUhjDfm7EutP+qm+2cb+hJqlq7BMzWodWqhl1bdL1eyNfluF9mY48J8saQ85wr28BaUM9+Wn8t/o9d9VuYOKGByaaw3wHAZhYhSTpVYHFOcnVzW8dFVl7mXhozBfC/AeqV53k1zC8v++TPn/fjNjfGQ32Sp/iOHPFhTTjNYxHK34f2PR23tcEPNIAOzMbusD2Vsfug94QbxprkXACUALaydlVOwwJPBPWUHW31m3kdAzvyPV+tjEpIrkMZ3UAFglO2Q+umGuHU4Wb6KrES8oFldmsZ+D/BXmVus9wsJ6VOaFmWlaC1BgRVkFWlWg4fPoy5uTnMz8+DTHCC4hqgk/dYyRBlkxNAp0VRhPn5ebTb7RpLXPXTla2tMiw7Ozul9A7ldxS8Vi1y+o1tsL29jSzLyuCc6p/t7W1QN54a5aq1f+jQIbTb7RLUZllVioX9le2o7amyLLrRcfny5ZrOuQWi6X+mNTc3V8roWLkY/s86sG9aVrxl4bMO9DsB8yzLsLm5idFohM3NzRJQp9/YL5IkKT+/UiD5erIZiD6za9r2ArbsikIXJlyNaeT3LEPkcnScQ+7iUj86ykeIARw+7BlPJNT277oZ0alTddpP4jU/IwqN87t+v9Qsp7VaHn9Mkiq2oa2OSoUwOU2DqhlF7MUSQOcfVyijdMmvk1br6ZMxND+/iMXTp6vFVEEJIkBPANWezraLz2pdE1ULtaLQnSQBA8fVj7zHiHq9yc0IfW+dUqRdgsgMclYYWVCUre/1/GZBzcdp6vVnVlbw0GeqE84MDpokPsYV/aSnoHUNTxA9xqi6gI2tuwZ5Xumgq/NET3+U14N6ApX+rnPVZomffnDTGkiS2P/1FhFpAlkGpJ2aqopmG6192SPiZ87418OHy42FpeVlL7dy9xLOnQP++q+rRfC5cx4v/8ZvfB46p0eVthCde/fd+OKqShfFADxL6tjJ22oav3F2ETh7FotJghes+NX06dO+3v0+0EnGXs/lD/63/6F0u0CvC7zGM5PwGx8EHn4YzwTwTAD4RA/4hm/w17EhgcnNGfnNlmYX5crUG9X72LVtT+9CfL/27Gc/G69+9avxhje8Ab/2a78GAPjRH/1RvOY1r6kFJjt9+jTe85734Hu+53tw6NAhvPnNb8a73/1u3H777bj99tvx7ne/G51OB6973evKe9bW1rC2toYzhT7T5z73OSwsLOC2227D0tISPvWpT+GBBx7Ay1/+chw5cgR//Md/jLe85S147Wtfi9tuu+1pqe/MnoLJw3QCNOcDRMcSC3zbtHjdNDBNnwE0C2wq2KtSbagHOVSpEWZJfW5m1VSUaYCn7hPXn/0RWi1qx8cT1fb/1NnBLAerpXlp2fi/VYLIcyBWf1jk2/qRiYWevc7VgNgsqwcg12S0vDr3uTiMsFgQEyDZNdVzmr+1eO12XQ++pgFv+4fdkeD8rwgUmxWY9KQ0DTXzKbmCurSRLWjAvzVEhSB78X+cOGxmdYkcSzD384UlQLq1nUPBuXI+Ukqqr/lXnsIjP6OKVZADbuhfu0XmvSq9Wn/Q8jch68XrGJH3kZXTsZsYmjbT1R/BgbDZ2H21xu4ZiH4ArUnuQkFHvg/dw/ssC9qCh1ZPnGkoAM8otnyv+tkEGBm0UgOMKvCtZSN7WOs1Ho8Rx3EJZFvAXNnNAGr/hzYRLGhrNd2tbxQ8J0CroCuDeHY6nQnAlkA4WdPb29vlZ3NzcyX4HAKOqWlO1vLhw4cxPz9fAsMEky9fvoytrS2QNa7sZwXRCeiS6a7tTtCZ91AzfWtrq0w7y7ISOL58+XLpE0Zopua7Bua0gUq1/2leqgWvvqCv+F2WZWCw0M3NzdqGAYHqTqeDVquF+fn5sjydjgc32Ie0vRVE1zI3geg2CCxZ8QT2t7a2amC6bgqxzVnW7eLs4I0KDs9A9JkdKNsPA0Un1s5h3POBc6Lhxdqq1XVvLtUm+PnRo3EpL76+7uf4zzx92i8eVj06PYJnmy8qKJznGHeXMDhbXw8pyYdrAx6HJROaYHg0vAisDxED6BQA97i7VJO+KGI5otfzjB4y0TezqJQ7t65SiRLnPBDb73cQu3EN0OW9lPFWsIB16HarxXeeFxqiZqEdJwnixAc943V5DqB7okwzwhibWSRBVn0+R4/6vAiQK76SJPWAdMSw19d9YExuNtS6SAGyfnEtxoMPVvVSJZLO+heB9fUqQGuaAr20csowA86sW0dWbc9CEWk5erQOQshphhEmA9ZNI9zTdD1/7BhwkkcmCsQ8T+ry5AoA4cwZT+3+/Od91FQmzgix/T6eed996PcXcfZstT+Q51771Ovzx1hZeU4tsNvqAz5ZBUfOn/duIGN/ZcW7444+fP6kygO4+fBh//7/LsD9D34Qq48+6osHIAWQfsu3+IX+//v/Yh3AFwGsAXgBgNv+z//TZ0ARd9se6jj5nU6A6Pr5pUs4OHZtLMQB4Dd/8zfxpje9Ca961asAAK997WvxS7/0S7VrHnnkEVy4cKF8/9a3vhVbW1t44xvfiPPnz+Pee+/FRz7yESzwbD+AX/3VX61pu77sZT6Q3a//+q/j9a9/PdrtNn77t38b73jHO7C9vY1v+IZvwBve8Aa89a1vfdrqOrMrtzEi8GTNxIPOgujAJHOWphtfQP15bAE2oKLONmlU211iSYeb3hZPtllo1mqaXQiLzvNKWc3Kr+2FGwJhZvvcnP+zAPk0+Totb55H8IFfYzgBqCc2PsQRBMvLNLK6izXgpyUZaHLa5Hx1BYBu/c16Wnw7VC/9vOx/WeaBeZ4ma9qw0cSLVw3AWQLPpk62S1XBaP08xvsUkx2iCWC3HSJJanIrQCUDqD8nu/Gisjp23qfZcC5WBvocPOnH6M9/3o+RfOWEgXp4u7uTOwpJ4jugMkB6Pf93+rT/bGUFUfGdp7MAXvM9RtxN6v4INXBT212zNhu7r9bYPQPRD5A16TUrwK0AepOOtl5n09Y8bPBMCygqU5r/EyzMzUNFgWsyuwGU8htM1zKOmSfTV4DVSpNo3aiLTrDZSo5YX1hmtK23guIEuAnyEuAnkEzJEILMQLU5oEx09aUy0QloK3uZAH6n0ymvUxCXba75EDAniE4wV3XWWSeVSWG6DMhJ4Jp/ZLMTbGYZWKd2u13Wi76wALnVZNd2YB+hb1kfBl9lPbIsKzdpLl++XALS4/G4lNaxDHTdLNC8uJGgUiva3xV4t4C6SrmQsb+9vV3K4HAzSfNiANzQJs+NZDMQfWbXtFk2CjAdSA8gjwRqlxxqKwfXE1yUAHCaIkmi8ojquXNAtxt5tu7qKuCcsIZjJGm1KOECMYTTOeeP6uYumihmmhYAuqKXhTRGlKaYm6szlXkk9vhxfz9ZUaur/jayptUNxCJUviZN6yA3Gcx2QdpqVQx1lZyp+VxXc8WqLGJiqJhOXF+1WlG5MaDJkPl04UIlMcKNjjSta5RTVYSy5yEyGW987DHvH2XTl7a6WkVu5Wqe55VV/FPFWk+e9HImehyaDuTKU2nhRaYsG7/a2qqzG5uAGtufTvbrN2gbqp6+c6iipn7pSx715oW9nv9/YwPo99FZXgawVCsb02N1WM08r4KKMfAYA7w655OuHTLo5R7A18i1TOjBB4FHH8VfPfoo/hRVuK0ugLv/9/9GAuAMgCcB/CmAETz+cdtf/qXPbGOjrpukQJvuZvGVPwK1Elg5SIvxpxac7OmwpaUlfPCDH5x6jZ07HDp0CG9/+9vx9re/vfGevb5/wQtegAceeOBKijqzr5P5n100qfpsUb8QKm3nAA2feTZrYaHngN6r1wTydK4e0oJMXw0QGspKs6BES+jxoszhmg44KuAVqMuOh4qrxZ6IhYL6daHHWwjDnZxi2Varg7GKmxIwV5+oRJ6qyVl/6bU6lmld8ryqJ1/LE09uPNneWQCoNnOV8nXa7ojzrG0GT+fcxErT8FYPnNfnU9o+NSGYEKJtEzRliuTUH2VwOG7bveCmjQvbdoDfk2bfL8vOydrZs56x8OCDfgLwuc8hHwwwBLAJPy7bs1wxfO9J4YObx90ucPvt1QlRnSx0u5OgfsKNHdTk7WgHK6AobTZ2X62xewaiXye2F7gUAtL1viaAcD+metYKijflaZnkeo9a6DMt317WlP9+gEsrjcPPFFyfll/otID6OPRQUHkdssX5XYitrsBsk9RPk4XKassX6hP0ifpGTx9oOnqP1sPqrzfJ9GjZQhslTfnYsof8Y+Vcpv1v05/mKxto1/Zhu7F1JW12PdoMRJ/ZgTBdMIcW1FOsXCTwlj2AMpu0XWRokeyieMqaZ28LLfTyXI5119MLLSia6hAqt3XDtMX5ftKtZSAX6mKNIISqn+ynjE0YiLWJWGcNDTBRRxZIUQ+iyCx4U6Y2MdtY8pcHFrOsjwXNmVSTD6Y1xtQubpELCbYXihMXaoutreozjdUXAmR2dwNlVe2c4s8uwEfFXwS/OB8Xr5DXsgBNR7mndeImVOnA2BhXxlA7iGDDzK4H+6p/ZvscRGuAWtNApg/bfQzQFrgNFanpkcINaHvdfvzBsdIWucl0HzFULvvdtDLsVT6th86PrpaiBv1og5vvp0wTA+aVTiJqiaHsI3auZYfRvUwld/ZdDr0m4IjQraGYODYJvU+vn5ub0td0sOe8qBi3OXZzjOavkB5zxec5gJj3soEbJzjXs83G7qtlMxD9ANk0AMmCsPqqADfTUX1usoHzPC81zvV6m6Yyh0OgJFAxx5XlTDkLfQ/UwXKVhVGddGp6q5a5+kXBUvu5ZZY31Y3vyaYnkz3kO7KOVXJEpVJsW2mdtV1UB52M8yYmutUHV8kclp0BTpWtT9+Q/a8SO/qebG7VT1cN9FD/Y9swPcrWkK3OPqBtw/es9+7uLpxztXu0LegLlVGx6SqAr0FbVSbG+p7+Ul17e43WW1nn9E2WZaVUDpnnejpAgfVQm+sJjhvRZiD6zK5pC6GJuhIITbzt9/bYc3FuOMYIz3pWjIUFAHm1UMrzKsjkwkJ1rJs0bF0EkZ3rnF8wq2qEc14ahfqbZDGplEftSHZIS3I4RLfXwalTleyLc34tc3HI48H+MzKs+32fr8V987xifoeO/LbbFROc6fpAl/7abhdYTO1kPkDBK+owdnFtvdVq1YGI3d1JBQ6WhYxxypLweLG6h3Xo9z2x6dixyYXqOOkg6vXQGngpnDT1xCeeKO4k40ogXftXmlba50RQlM6ljWHp+WRWMY2iMpTO0aCrCrIoYKKv6h8j21sa/UV/q486rCwp/fxddLveeaI/3+tVwcK2trzPWZ3lZZ8mmXgE3LPMl51Vdq7S+a/9dpaXqzLw6IRzvgxJguW1NQxRLRc78FKrDsAygCX4hfrfALgDAJ7xDJ/Ro4/6QrFx6aQQu9A61Dr6QNm1cyR8ZjOzNkZU6RyHTB/2ekRqGgoMNO+uhsBxjk12/mDnEnp/niNyXo5srylG4NYJs+A2r1OFmSYLuUPT0fmDhuEIlXOvPUMLiO930yDkC93MzvMqkDdQPZaZF0+50brdSfCc6bD8tpx5zqElgiviokxokOv/Tbu+tr8VAytP++V5xbS3/lILHYwiM730WRboXNaZIUcXEwHOJ/RSzhG0DEDz5gbnC5oG532cf5V+5MAOoIwOvruL5Nw5JE88gaWNjQkQHaiY6NHCgp+YcsLGidjhw1Xhs6yUuwn11TI+TpFDubGx352Wa8ZmY/fVsoPW8jesqaREiK1rAToFvfWakLyIBRN3dnZAyQkGnlTgj6Dp5cuXYTXMmRZBYUpraB14TQgQI4BMEJvAMQFrgvgKyE8D1pQlrfIvQAXwU5qFf5QtATCxYaCAvkqRULed7+fm6siJ+s+2D2VL6G+VoKFPeB8BWQWV+ZkC6Hmeg8E8mR/lQ+gP1kd10y9duoQ8z0tNdLazgsH25MB4PMZoNCp9RNkV51xZDsqrqA80gCrTUNkYrZ/q67Nt1Ce272hAV9ZZfU4/aTBRleFR0/pT5mZra6vmL0reMAirlldPdWibq6wLr7vRbAaiz+xAWdOCiKYL5xKpNosIrhTW13Eyy4DdxE/onStvkdOl/n9+6BzifBNxmuDxcxG2t+uBqrjGYDaL6RjIMmyig/PnvUzJ+fMVwM2ixnrz+npVp8EAUZ7jvvtursnD5nkVI5JVZtzHZ66MPfh71AflijAuQYovr0UlNkzcgtgF9ci5iDp+vAgmpYjs+rDuZy4wc7/wSRJ/z9jFGAwqPVSgAujVPyrPoutE1ovWbvsNDTWC83QdQWSgAnP9psEi0hT4pm+qg8Lx8ElgLav0Oa2ODQXXKX2iejRMSEH04u9i3sFwAMzNddBifQd10CAEhFjQggt13SQpg8maC2OM0OvFJZhARlmWwXcMOmljo7qPDSLIPIFyGvM8fBg4edyPoU8OolLiRSV3LEig7fnkIMLSXXdVou3sgMMhcNddQJ5j0Tm88LOfre8qdLtAq4XbitMAtz36KJ4EsNzv+wbd3QUeecSX//bbJwME6k5R03OiCSm75m22EJ/ZtWtTf1LOS2MARTBLoL4ZyZvtxvK036vuHha/b2qx+88qAY0JbXY7lyiA9Ng5UDIjdNl+6quSI1dqId1zHSemAezApNSF1gUIA9N8/CoTXmVImHbsmtmx5eZJ8fwl2KkbvaFYIM75YZW2vV2NKbyWEmgcG4Eqra0tPYlGHnTV7qUmuEOp+e4camVl+ZlfNqxPJ1V3XP3FtOgvHQN1jliTnLEdSsF7fW8G1FFegfq6KW/k2xsxeTXbb/XnqPGCkKbVpvvcnG+ElZWSbBBlGaI8r0BN/Q1rYXo9n067XZfEK+6J800/z04m2wKY7Hf8fjwXEP6/Zm02dl8tm4Ho17g1SVaoWQY6AVwFA62EigVHyd5tt9slC5yfEfBTAFjLYwFWlejQOhBY1GCTFqRWBjXBVYLV+hkDWgJ1fWsFmhU8Vya6+kI3C5i3BXF1s4Bg6Gg0qm1qKIhupUe0LVQrXQHVVquFJElq9zLvVqtVtinLQtCa5WL5CVa3Wi1QOxxAqSWuJw8soz3Pc1y6dKkEs61ES5NpeocOHSrZ6ATxqU0OoKzL3Nxc2X7UUCcAvb29ja0i4g3Z6SwTwX7bv9WUOc6yME89WcC2p89swFsL8OoJBJZ1OByW2vHcXCGIrj6msb35O+AG042qjT4D0Wd2TVtoxaerp2krda52TJCtkrb78MPAZz8L3Horxv0KpCZeTmnsKNusQEiilMMhdndPYDAocb4angoUC7O1Na+/vvxMXLjgb33iCf997fHpPHO7ZA6z7OvrwGCApe4QS0mC8fJtOHeuAuPzvAK/ieni7Fmfb6+HqNutQMs0Rf/UHWWM1PX1CiBdWKgkKrkIi9a/Ul8tcgXJC5IEo2SxxELpt243LrFSuovu6yTjku1PfwEFWJ/ngEswRlSeAEgSWXSygYoFWafrrz16tLaWA1C1A+Ne9fv+mji76CuvzLPTp3Exi2uBSZ0DDh/u4OSpbp2RrrssZHAnvhzDIbC1UUl/2+7G/7XtnQszCOluGplhpd79sH4tBgN00hTdbgfnzlXsvDxHyfSuNTA3CZStnedYXq6q5VzB1Cfg/dA6AGBpeRnoea1+kkgHA9+PVlbqpH3qpq+vA65/M1xX+tbZs1XHTRLg1Cng1a/2NzJaKdtqZQU4fBid7W10ssyj/adP+wBn/8//4x30mteEQXQ1dbyy8g+kzRbiM7t2rTrtVK3F8ryCNPmMiNOkNv55oNGAiZpAk8Czebgqe1jNP9siOBvoNARoOleC6f6jSUDd/h8yPYVlgW77f5NZnNXWCWhgX4tFZlOAprre9nYF8jkulBvzerHcFIXeO4dF54DEYdztlMOPqqVxnuBctUFb1cuXm3FQBPMOnqxrapOQ/9ptr7sNTPpBq8fuZzcc1BjwlEOMPeFX85vdQeCr/HG+lOco5eAInocAfGWihwgK2ldCcoATvwmWl3OIPK/iqfA6Bqhhplo4ZTRkmZ8o6LFC/dMfLH9/ZXAVKas2fvH2YB0mn43dV8tmIPoBMWWgh9joQF1fWeVUQmkpwExmM+/V4ItMlwC6fqeyJ7YMKh/SpE8dMgXftQwKwitgvJeedEhSg6aMaA0+afNWRjwZ3CFwmoA22dcaDFMlSKx8jG5UEMRVsJAbGNwM0I0BG1yV7a1tx7Z1ztVkTdQ3PF2gMi7K3t4LRGdeo9EIrVYLOzs7Nc10ygTpZof6m+UgyM/PWV8F9a3mvpUHUla3bS+mrT5XYF/7puZjfbWzs1OC6XxPH4Y2b9R/VtLlRpVymdnMrnnT1RVQp2/TmlaXU1ZRY0SIhkMfbHF+fmI9MzdXSZlgWGzgJh0/jS+BR7924EKpBvjyj9otqL8NLfS4qOykwojmomIw8AuKbhet1iLy3GOMlMsgIB3lI7/6XF+v0hgOgcceA44eRdTvI00r4Jtrl4WFOgYRuzFqSLguqgxYoQEo2TSKN9fYYXleLuTLxZou1ArQIkmKMSUfAQNB44FqNVqkFec54jxH0l0qq812yApw/JZbgHjwlSrIJs+bpylGiLG+7vcdBoMKg01THwA1STro9Fy9UoFj3lwjEoxXIhbdZRezoT+6WfsG/ddJAoHTeEOWwblOjanomejpRKZjF/vNofX1WjJxvomllAB7DgyyqmK8tkDOj/dvxu5u1SzdLnDyaLEZ0vXHzRl4NMuqoKPOAcvLJ/zieGvL6/AwSq52oPV14DOf8TfffXe1GaDOWlvzQV/pg5AzQ7bX9zOb2cyeHnOuxi4dI0LkXDmG+J+4/yz4G7XjvZ0TFPfpMFwFtLZDmAQibULHBchvAqH3qO7E/zUwOlS3QAK6GWFtAjyfks4009ubLq+N2yGUOZSglsE5RF2gI4ExOR8BqjFubi6eSFJjqdisOf+wc6sQTm0/lylF8HtNy4LXmnZIl17f19qpqY0K9L0GnueTeYemvSwD54XsujGjjeQ5vDi5T7DWo5p2H7QzkEhiy6uFsccDCKIXhJBysgrUiRHqF9tQxfs8B2Knla+FZ53ZDWizWdwBtyZN8mlmAx42BU3U7/TVXh/Km4AkP9cAnSEgPZT+XhrwtrwKlltTZnhIM94yxzUfzavpur3KqWW8EvatrZcFY6f9H/JvKG39PtQXQnWy/lRZIPWZ1RbXuvAe+53dlGEeetLBygFN25ix5Z52nWWeW9+H2iLUtqGguFqfmXmbMdFndiBsP6vWhpVfTSZDFtgxUK7KdKE9cU9DWebkiG4JEhNRNX8sFsF2KYpPLvFBJ3d20Lx6LW8Ir8FqC3LdvA+sJgn8U/cyJMc+kbf6VxY1eknT8fNaugX4PeGnaYv8phWt3EO2WshaLXhQ2BZKwJaQUS5mnMQlm46VDS1y98IwLC7UhBNp3zAun/SFZKxr2PIemwD7P3cLdAGsGRKZ0L+5uRLpijDG3Fw0FbNuAi2yDOgcPuz/OXq0rsWjG0jtdlUp7myUzDRwp2MyM2tXgnodGJux2WZ2nZt9JvGzK0zC/l8Dsfd6eJv3BNJDl+5lU4e5psSKz6MrGR9D4LUAjxYwtpddlf3FafWR8Yqvdv4QOqGlMUSm7a+EPg/V2Q5309Jgnjs7VTlDZdnPcFRe8BTGpbm5uiQcP2vK3znUgHMAjZsbe5bX3jsNRKepxoz+6QbVU9E6Qp1Nf7CG+NnYfbVsBqIfALMAowWjVWaF7NYQMGzBPU3T6oGrLAelQ8jWVQY0Qc04jmtMb6strbIn1lR2RIFV3se6WuY4zbKxmQ5BVwC18pGtrextBXObzAK16n/9s8EryfRnXVk+BYK1HVgOlQOxeuj0u8rENIHqGjiWvlZWu/6FQH9ttxBTnv5rtVpot9ulVAv7AOu5s7NTO0XAejA/6rCT1U0faZu2Wq2axI/2aXvCQoF8liPUTlpfTUeDhKr/9HMriaNs9L0Ade1DNyq4PgPRZ3bNWwNoWppzFetFX4vJ/VLqNbqRV8fG86wAEefngXYbeV6XYua8v5IZkQVEwcw9eXcfCwudshgxRtUZZOqaF+WJhhfR7S6WwRb5VZKQtRw+el7Wj8znpIPhekUS39oSMEBZ0gsL9cWN0JdiN8bKSlTGdaJ0Tak1mef1oFehRVHxR78dPuy/Uma8rpOcKxhmw4pxrv6p8vXtFhVMrPKzEEjAdpf+waBU1MJNEl82r+1ZlJ2iqwUjyqqa6GL+wgXP+PdBy2L/lxaY8qB+FF33TwJNV8oDabNoMFG7lxBc1/Iie0Kg+K6TjNHvR2W55uZQX+A6h83MB7idn4/g0hNwzjDV1KfDoWfuq1YOxem7XSwsLNbqOnYxIufKoGdJ4jHyLKvk2MkGveNFL/LpLy/j4jDCYv6kz4u6NQyGmmWVFuvKCp4cVuyzpbsyL/WS55WTm5B7OteiagdW1mUMWCbrntfPbGZfG9sLl9Mh1Uq++GEgqmRd+Gp/31UCE4nGzsGlddZ4eVJM5wl249sW0J5+cQ5xmiJ2KHXdQ7eHgk5a3LR2IosJab2aHGbrb/+3G5LSGATjQ8z2EDirsUw8INuwazwNuA/VI8tAEZU4TQD4DVm2TZrGxav/LE6S8oTafkB+FontEGoP1X0PFZH/hzZiQkOLXm/vYz+PbGK2rQqLMJ5oI15OxRRLUNB8y36VmXmWTbD4YyB4VM1T1avBB6GylXOppIM8B7aKGEDd7gl0er3y9J5z/uTbRIKBfkSgvKndD9zQPRu7r5rNQPQDYlZSRAFlG7DRAugK1PK9Ao4KnkZRhNHIL2h2dnZK8Jza1hYgVnCb9/NVWclMR8FF5mkDWPJ++xcC0Ql25vIUCwXu5HuCqWrqnxDLXqVQrISI6sVTV1vz5nUEXgkoU3tewWoFxylfY5nrTbItFkRXQJjALiVI1N8qC2MDrzaBwFYzvNVqlX/z8/OlXr32gcuXL5f65nmeY25urtSAp2nZKJFDSSDbZgqWs58qeK1tR815fbX9wDLMx+MxNjc3S51z+kqlXLR9FGhXTXT1HaVqtO9oH7lRbQaMz+yaNgFXJ1YnIXDVgqxnzyJKEmB5GZt5XIKfHbJa5+cnZFaSpNBCV50S5rG6WiKCnW63AvzW1isR7rNn/bWMKLW2ht5KBThS3oKYvNooj6pQWKxHEQ3z3Dl//WBQaZqX1xFYVaBYfSQA7G1pDiTFPYOsAi5DC2SNPirvxy7G1jl/Wbc7yYBut6vqe1mcYaUhQyfwPdnFtQ2TuH7Om6s0bXPNNM/Rbsdl0vz6+PHiMvqGgTaLegxWJ3DmcqODeyEE1il7E8IMqJWq3Y9+YFdbTIpNmSJoVpLEaLUq3XCWQavG93Nz4jMLovPiwQBLzgFpghHiApTw11BzeGPDbw488UTVtXu92JfPGRB9MPCxAwiic1ci832ms5zAHY1L9pv3ewVcEddeW6tiAdA36+tLAJaw/qCvzrd/+xJOujWg18PfrC8iSRZx80tfWu02JQn+7OEYDz5Ytce3fMsduPnuu30CthNKv6hp+evzg581gQzXtI1xZQy12UJ8Zl8740/RgoEaE8OCYjo0OAe41G+Gls/ndLE5KKi+LzIola51U1A3Ci2IbgujfzoOFunH5bhY3wRQ2Q3dnCfwaetbK7f939ZzGmhuP7d1kOceg6bqo4/gLP+3wHBJKrBltXlqBUMIJxuY41maYpFzlmKjPU7gpcWGVft0kgQujctmnDYNZD0YhDRkTCMURNWeGLRV289+h/3eD2VxdRJPCzyJuDcC6aHy1eXxzDxOC2j6M+cFg/U6CC5KhDVTrXVNzoLo9id3+LDfuNdu0u12yrEcYLpxWbygTexexBMfX/s2G7uvls1A9ANg05joFlxWpnVTWpZBa/WzCUhbEJ0gMFnHGiTTsuEVyCaISjY1wUetj4K6BGYJomrwTQLaIcY266FgKcvF4KEEe1XvnJsGNoimlSSxbGGrrW2Dr6r2OH1H3yqgbtng/JwgLMFhtjfrQF9omfm/lldZ1Brw0rK5QyC6brJY4J7mnEO73Ua73cb8/HzpfwWHFXje3t7G3NwcRqNRDfy3wTgZ8JMbC7oxoVrp29vbJdhNkFtPVygjnunpH03lW5guA5raTQcFyXUTQn1spWnUt6HNKP5ObiS70vreaP6Z2TVgGswxtOrWxUggWCLZraP+bWV8SAA1iQjeWi7881GFbtsFI7Udd3f9DadP+9f1deDRRz1a+OCD/trlZb96WF9HvLxcLhrjNMXWkSVsb1dEXz3lGmuefrUBdLu4cLYC0c+fl0WOAgNc2HMVZBdTwyFw5kyldZ0VbF4NxmiBaok2WeqAZ36BCkgAyqISzkWl1E3Hjap8dVWmwKyCE1xA8j2Bdqu1GegHdTC3SjrKRzVfcuGYDevFUqyEwDYX5K1WGKfl8eomLKGQXvf7BOuD2oZQXAQD3dmpFv3U46+B56AkTVYBD5qRtm3RmWJGmi38yS5y6ZLvO5cueXCbfa7bBRaXzUYFN4S0rfp9X5giWG2cpkhTzzpTtjng6xy7MdbWovK3x3Lw0Mb5gqn2rGcBJxeAcbqIhz9RYCynlvxP1PnF0kMPAZ/4RNW28/PAa0+fRhnhV+Vp1Ohzovq27zRR3K5pmx0Jn9m1axHGk5rfmHw+2uFGAdAwzuiDgjKNSBPSi+24zeemAudbW9UgtrXlX1UPgwgf6dg6B+GzNUk8mI5ooh77thAgvRdYHbomND9S0NS8V2kavSV0+1QtdK2HPk/5PiRYnufVZEYHYJplFxTfxQnQbleSNCGfh06I2aKqsZh241o/C8nwaXpNWun8rl7Gqg+XMnG2cAZIDw1RVZugvjEUAs81H5IWnCs31tkMnFrwPZNUnzBYqt2fsd1jZ6faxLDDLKcSoQCoeqhsQmqQVRL/5PlBI8LNxu6rZQdx5nZDmtVfplkQTgF2q+9s0wmlTeDPSmOoEeRVsFbBYyuTQhDUBoy0choK4CtArPVU4NOyrdU3DGSpoCXBZw2OqgBok9zMNAA91Ab8TNuBoK5ln9s21usty5kgtvpdGfn6naZpGdaav91IscbvrQa65qmSNiqbYqV1NA/dFFAQ3coNaZ8CUILgTJMbP5QiUt9oG4RONFg/qfQOme0aNFTbw7ZhaDOkSZ9f7UaVcaHNQPSZHQjba6FoF5b6ORfHMPhrUlFgdOLvHMILEF64tVUHdpUVfOlSfUVC8FnBz2JxON9dqi30nAsfOfZlrRhDzEqB21oFiAKHwGYFR9fXKxDh2LHw4px+NAC6sq7Le2SlpQuimqa2+lQ3POj40EKyqR0CK2ibRLXwqhp3BC/ns7FR+dAu8viqGMv29mQRLS6gr9YPpeQOd0yKtoowRqvlx2oy4pStOKHPrwtlCwgTSAcmgAsuarNi84NdkV2ltiGj+djV9caG79dySiPuehBJp6q1OAHo1PruxobfPCKpfmfH/3Rw1NWUY1QCfW6u2qdi2mtrAHrdsC/UJ03oiW3wA2WzhfjMrg+zQ7YFI0NgafXTLU5fhxK0qJ4y0YnwbW3VB1OljvNBo+LXfGAqcoh6+UNF4fdAFWuj0Rl7gej2c5bHZm5OaoV3LsLBGXVsm5CcaQLQr8SYjrLSNVOdv+hnzqHVihsB66ZsphUx5GI13dS2J+72W23dGNL7Ytnobuo8UTH3Cgai3etPM22o++7u5P68Hniz+0vcS0qSSWY6f07KaA/Nr/K8mn+E9nlU6m7qb2U2dt/QdhBbf2bGQgDvfmwvxrWChiEt7FA+BCOVCax5WWkSZQpregRjp+lFE/S0TF7dSAAwwTbW8vA6DY5pWcEh0FtlayxIS6DXSoaEtLhVIoSfK0NdGdjqB5ZVpWi0Dbg5QGCazOgmIL2pXdQ/TScAVMJG/RUC7237NbWT1tleF2pDrTv/LHC+Hz10lbexG0p202Hahojts02bVzc6KDwD0Wd2TRuf3yG0khZaxQSAdl5GjBnDrNQamVgfNiGjzvkVg9KFOfvn38JCBTIq4Mn7ywJ4a7f912TfeP3urEI9ixVHhDGSJCoJcROsaF2U6iupQ1aShSuh+fkqQqoa0csCwN/TzQK06/ottgt5i1qrr+WzUR4hZkVZHr2f5TcFU0ZYSTzOqzcWkLGsR/2fGu80bTrrD3UtMV2rHhKUE0HVDHZNqJsqOzsA5qXxxeeluD1Qp40FjN0hTf3eCbtz7QIWhNJAhXxLqY/D/KTtXKFBH9oISlPPOuPiPEn8gY25uapZlfHHpG3AtF7PH5og5qUxRhsBHdvP9BoLNhwom+mqzuz6M/7uG2VPAu/LD3WcDZmeVLGI3tbWZKZamJAeO1A+P8gstpe12/7VEtydkxNS055DoTy1rvUdhcnrFJ20iCWAPAsnpW4aI6pOhmkeerFShu2Y3/S5HST1Gs5hdNANPcfFHSFXqfxaaAjQLNnc6s4pwgJB0F3Cz0yU0f7vX0X7f4qFTnVMJAzUN3lsw2rGrtIa50eMB5TnVb8Nda809ddyfmRUjpDnk7HtbRo6JWVeDc07kcbBt9nYfbXsuukSM6uDkCGzYCDvsUx2BXhDIK3eY5nhylC3eatGtLKYnXOYn58vrwEwAaArgGqZ2KoRzmtDALwN5qna1JaFbgHT3IxICpArC5vBNZ1zSJIEcRyXoKwC0dyQYDDN7e1tbG1tldcwD62jgr4M+hoClPk95UiyLIPV6raBRZUFzjZR37VaLcRxXMuD98RxDJVdsaxtK0mzn7Zinhr0VesZYtBbwJunD9rtNpIkQavVQpIktXZo8gc3NtRvujlByRgbYFSNGzNaPr7qvU333wg2A9Fndk2bc0Ac12fQNdps4HqzUCBNhgupm/tjT2EdDj2y1+shW6/W0OVQo0B0kiDLgA41oXd2KoHsXg8X8w4W+32P8JFGu7EBfOlLlW6FcxW1Nk2xdc5fdvSo/ztyxCcZrX25zhIHSuZ7v79YJtHr+eLXFld5XmmQqA9uuqlCJwFfTqUGF5rrpX9lUXsxi8uyUjNViXlzc6htDijxHADyNIZzMVx30QMHw4sVPclK9BSrqs08LtRCFpGe8lryZT3l3rGLEa1/xbdnmiKHdxmboNcrglclCTYLNjSJ2ly4scrahbi4K1R0aotHHj5QqXagWjTK3kNt/T/KI7jeCUQY1xavfnMEOHmyzoxn+gSlL1wA5ucXvR97N9fKPRwC+TrQ73f8poUunovK5FmlQHTTTf6r5eV6vSdAjOVl4N57qwanfBF3cJjXcIjIOSx1U4zhpVsUTL9teYxeLypZ5sOh/xmR3T835+VccMkhyjZx112d8udH3zoH3HOP77rr616RaGUFwKpBK+yJB4uSsG7MwF5/YGzGZpvZtW1jI3EC7A2EcUPRuSrYtXOT6dA4NWAw6SiRL/QiO4ewm2h7AdihzTjZsIyc1xmPASBxWEwDiK2WRQM+7pV/E7qoQKktb+h+VwV9zovxRQ7qBU9CVfhrBJd0JjXpbf0EoOW93CzgZrxzxXiucxLAb9h3l5DnhaQdQfQy+ItPn490HYNDh7I4/HEs0kMGymS3kiKK60/bzGE+nBPZ5gz1fduNfNyYqPgsLj8v5zvTxiaLTDdtJIc2jlydWCIhggBMchb09sOH6+xznQrYPxtygHVnjBnrF763QVNZjThQ12ng+7Vps7H7atmBavYb2ZrAoxBrWL9TUFHZyBZItozqQ4cOYWdnpwSXVUpEgc0QiM68NV/9Xstqddy1njbgpQXnQwCs/Z51saC4BeDVV8xLAWbqw2teGqyUIHOr1cLhw4dLwJbAs2X907a2tmrfOecQxzHm5uZw+PDh2nsFkDVIp8qtsPwEz3d2dmoBMslOtwFFWT8th0qzsD7arkxP2d3MnxIoBJtZbt18sPIqLXNunOmqWakhTdu239zcXKnTvrCwgLm5OczPz9c2I/I8x2g0KjXb9Xeyvb1dvqq/uEkxHo/LV2Xaq4a7NftbUBD9RrQZiD6za9qSBOh06gtQCirTCE6HFrkC7pYT+dVVr1lOFLrXw9ajHtC7dKlYjOliuwh8lWVA0u0g4oJuft6vgHo9rK4Cy8tLWDx1yt+zvOzRwv/6Xz3Ay9VVmuLiMEI+qADEft/nGWcXgcHQa1Cvrvrr+fwtBKUXuzkWeylu7vvFsHOofENAcHXVX3/ypEfnibgXK6UxIuBUFeTUWpQkGCMqi63KIZbVRYB5jAhRmmIzq7SvucDlK21lZdEvkNk+NMlkfTXUxFHx6sH4QbEP8pxTXY+oZhl22vXu4RyA1TVs9p+Jhx/2n+3s+KY7ebKuZ671IjaiMUjJvHKu2hfRJuLik65Wf7F6PphsBaD3esBiCsQY4eRRX+CLw6hWHqoOnD9fSZ9QZ1Sl0b1vgdOnl7xG/dpaBWi4GFtb1b5FWuTJ89SPby1id7doR0UjTp2qnFDsIDx+LsL2NtBLRQufuxbDIaIkgXNL2NkRGYCHH0ZndRWdNMXS8jKwnOLkSR8TgP6KhheBbQcMBnhm1zPf/+qslzAiEfSO9Mu4Y7AKvOIufLLXwenTAM6YDTU6RpGV0GkIPb9+IG22EJ/ZtW0hAI7gIDcSQ0BjktQlRMoAoTJe6P31fAq9bFS66WW+3W794hDQru9Dn4cytZv6dlyzr3uRAHQeQ0S3AJvHrgqeneeooUi2mDsGIG+qji2Cgs2ADSRZtoaplv9MAeuKO1Avs08rQpIsIuku+vYePAkkCVZX/TXP7BXP7DTFk8Mq2GSeaVnqJOsmkDvkG/3MYtGhfdcQoKvjswLGqhBk/c10qSuucwU9MOAZ6uPGjRLb/73F8n5Spsfpq0mScV05vW3SeCf4rf6I3XhCOJ2bKKGDkVYSZy+r/b7TqB4HIa9OgRwcm43dV8tmIPp1YlaKxZoyypUtHgLPlYnOe/M8L78jUBliSCtTXO+1ZrXT2wUty8qcjEajMj0LTGpAVRvEVINkWhb77u4uyEQmEK/Ap+q18x6rt01Qm/8TSFfwfH5+HnEcT5RLA1OORqOyjuPxGGSwE5RXKRUtC0Fq9Y+2NZntOzs7ZXBMZU9byRICuZbhr5sDnU6n9Al9yH5BY3osX5ZlJQtfgfYmmRuVDLIbHBY8t+CzlVkh+M+2oG/JQOd19FGWZbU+rFI4lnmuZbAguv0dWjkb3QjQ0xk3os1A9Jld06YzbtV6Jmin1CEiliE2lnPVunVtDfjc54Bv/mag38fFLJ4IQjVGhIgz940ND9AWetKdhYWK0pskGCWLWF8vFounbq4YRGtr/pURQJ3DZhaVH7Poi8nI121tzddrtUCQZeFc1j3Py7rHRES5+qFvHnvMa2W02xVbvt+vyawQBOZ7ZVw75xnDJOtz+nD4cMUcIt5dLmxzAIjoqloTadEAz7g/uSBULwU9Cx9Rsl32MNDr1dlL/P7UqRhxUZndAgA/VzDnGSA263q3Msvd3YqFzXZXNtnOTqVkwmaI8lHpjDTt1AN+SlftdotgqlkGrGeI0xQj1ynBfcVPksSD6KXDnMNiv49RHtXY7or5bm0Bjz9eB9EVQOh2gV4vwiJvLBhn7OPlhg3p3N0ujt79wjItyu9Q/36ARWRD4NwXKi31PPeE9JWVyC/VtSBJAre8VFUyz73zP/YxX7hTp4BuFydPnfKFebjY9On1/B8D3/Z6SNLn1IGOz3wG+IM/AF7xCtz1ba/FotucBL600+yFgPDag0dlw2whPrNr3XRjks+e0BhtgVB91k4AiMW91InWfOzldZCUbPY4CLo5s982cfLJ/tmMbeb6vRaED3GgPngw8waJk1EBkGaykWoP0VgfBEi7k/U2jz6dcrFdyFBnkRVgbdpH4KOV6iL8TJufr8eOASfnfUE4Rj5zuRiHkg4Ga3tzJaw2d8g0bwLgalZyx/opZAqWc+5w6VL1nk1tWf6qK66EfFUJDBZeKqFzVhtMld9r+W03s58BAdmYUB8fDOvOlPgozCxy/lRGzIx6xcnBfDRx2lFNwXdrLErs5IPi/oM1fM/G7qtlB6rZZ3Z1zbKjQ+Cfvtp7Qvrh9hrLEA5JzliJD/18x44ygWv4qqxzGwzVlq0pjRCjvUn3Wu9R8FlBYWXKq1+4sWCBWADI87wEk62/+d7Kj9hgq6qJrnIh9iSC1k3BeN2UULka9XUIxNb/FXTWDQmtj5UGsn1wr02hvUBV3RDQ9mCZLDhv5WhCuuhav2l9Y5qF2uFGtBmIPrMDY1yAAs2rwtCq0c6us8yvcgq9cWqCBhlaRB8hrByu2IoFgAKcfm3gFwEdlUeR5CYkq20CGrEzzyvRdL3ZohNaZ66uZQxWAJ3gqwLHypi2RaLVApkGmiCEK+j/TKs2NQitVKWqW1s1Ej+cqxahypK3kEgtj6IAlOuxdZ1mXOSWoE7x51ynBBNsVZIEXm9fF5Zpp1bmsqpaDh5dzz2zyq4vmT2BbIIUuqGxH3US56R8BYCs+ajUDLH9jQ2/L1OT6cnF99q5THoAagF1a69J4sF8r91TJbyxUVDm677FYOB3d9bX/eXrw+aKTrNQBz1Yq3B4ndQrWVzfuGSBmX19bQI3z3OggT06EcRyz8TCXxFYVIk2Pq/5Occ/BTcrQN+ffIoUBbZ523KGJhGKHOd5JdPW9LyxaQhgyjpxiNdnvY5Flkne9Nl+TIF0TctW2RabJ590vkOGNv2tUxbMoyQ7ZBlq/uHQyDqGXGf3I0Jmgfe9rmmypvFVgWz+2RMArL/6j1O+yU2QqFk+x5TFEkHs0Fb16/oGQrlxxXxCE7vQ5FATtxMbTVgs4uSLnSJAfPEs86iWlNYzZAdu6J6N3VfNDlzTz2wS7AYmpVRC9wAIgrM2HQskW3ayAuEK6gIeACZATNaxspwJ/hKQJbCp+t9MzwK7vJ762yoFY5no1qax41XvWoFgBffJBNfgn01/IT9ZP1vQXd+zLKOR13Ejm1vlZSjnsl1o1jJf227KslcJESvpYjXvVWKF9bL9wkr5MB+tUxzHJRNd/WY3H7T+Wg4rU2PrYZn9BMetRA+lWtjvmIb2v1arVUr9qA4+09UNCd0Esf0ttBGj/bBJkuZGtBmIPrNr2kJgMVCffOuqObRyKCb35eKRFOMikmOSxOh2/QKPyWUZ0GFw0AJoLJMmklvoQud5pR+5tuaTv7k3AtbWK4SzYCMP1n1yGpRps9tBp59UQtvz8572PT8/EbxxQiPERnQiXWtrq7ZS8+yiirVnSUAhZhJlNmiUAQHqayBDjppgslnWWpZ5dtmE5qfzEjUE92lmz2KCxZZlQKffB9IUc5er6wF/9D3qdksJF00jyybj1jrnm3VhodI/9/nUMx2uVyxxuyjOsmIDhY5I05I9yMBdzLfdRr2Py8ZMCEvRJmZa2g1Uv50X6wYKu/MS+3ZRySgfIU3jcjFNIJ2LewVNLEmy1hhFRnG+iePHO1WfVkRla6uKbMo/LsKpw3PrrUCaot+vihnlI//P8jKQ54ge+rOqI2oUNLvgV7PvtRMfuJX4LDjZzK59a8K8+ZyZgrkFdhox9Xdqx4ZpZgFPCzJXQRNjxKmrxl+dj+hAx9cmEF2vM9KZQdOHdpIgScPJ89LQ5vB+/KDAatM9+p0F0EPpAb6KHBv5GYNn81Sfzjs4VtFGeYS4250gHTDWelNdppktb4gjqBsvNr1Q/9IusLBQ942dnto0+Ge7Qz2NusRaWMZlsoyh//ne67CbcdwSVEJ9mh2lCdFu6jx6n90tsPNXk5TOUSeyLb6cBrBfmzYbu6+WHbRZ2w1vli2tjFkCiFZihdeqnIYF0Zt0nBX8JICtgCoBQNWRVqkMZaGz7NTbpkxIq9XC/Px8Lagm72u329je3i5BUwKz04BKBWMt6KaAJYHVkN4560wQ2ILzlGxpt9tot9vl/6wPgX7Lrrb/s76W5ZznOS5duoQsyzAajeCcqwWiVP1uAGi327U2AlBKzmgeDGKqPuUr5U9Yb7YRNdn1tADTVaBaWdVxHJeSKuo3tn2Tf+wGhMq7KJBuJW309dChQ2i1WuWmzWg0wubmJpxzJXCu6bfb7fIzbl7wPv6uKLtDf2mQUNvPCMxz84CmADw12FmHmc1sZtegZVmlia6Tb6K5Fs1Tlgsn5wXjdeEuT0BH0islJZBl6KQOy8sxdnZ88MY890RYLJxAp49KE73AAhe58lteBrpd5EMPOK+vAw884HWpbx6eBT7/+Wpl1e/jzJmKSKvYnU8+wh39fj3qZatVZ+dmWRUAVIC/MYOp8VrVbJd74yRBlvnnIatAF3a7QAebQA4gy9FxDnecSmqB4WI3Ls9njwrud+w8e4nBnubnq+ftkSN1kOLcuYpB5yVtGFg8xvz8CQDA1nl/D+/j4nJhoXKJNrlzRUzRU8/x+ReA68JClc/NKyvYXa/kYACfjrLsnfNHyikhTzA6xggAg7HFJcBP8rRdCCeJ/zxLIgCefb5zrpKJqYAZf8/CAurodJKUGwl27cpLvM+qEwQE1dmO5b5L6vsB8Wklg2fdGDdTtx8ABoNSxgUodPGdlzki651l6fUqDJzgR3lmnxevrvqFP99fulQ5/PHHvbP6fWyig06/6POUcVlZweiuFyB2Y8Rn/srLFnFHZ3kZeOlLvWTRBz/oC8FAp6wsHZbne+ud03n6fmYzm9lXbQqI1kwAtQg+UGDJuAXq6KUF17iWMnrqIeBQgWEF2UJs4SYwkn/tttcBn5/3417SXazlNbEhrK+WGsxnIr+3oKLeR8vzMnBptxvXkp3GQN8rKKaaTYvphYpn/w+lbzdbtUnX1yupF24Ij5NO7XG9tgb0eiewsVHdTwK/3VwObbI37Z9qPallbq/XtOymivYvArt8bbe9z0iqsFIrzIfp1rXm6xvx3Jj330/+lmyftmVX43vmyflfyT7P8sl+GsrI/qj2u/uiG0Kh7+VefW4EfvrVb63muIMIpM/sathsxnaArEluhOxZmpMHgmVE83oLLhMYVADesofJ1lXmN+8lGLi1tVULYGnLStCS6Tnn0G630el0ShBXr6de9c7ODihzwmvIILdSJcwzJCOj7F/qiZPNraxrZWITFLcgOvXPW61WCabr9ZZhznbSP4LJrIMCrPTf5uYmoiiCZY+zPPyOQD7rasF0BdG3trYmNl7oN7YRAfRWq1XTK+cfy25PGyhwz7Zkua3kjfYl3QTQPmwlUwj6U+udTP2sGCCpf8582b5sQ24SKOOefY+A9u7uLjY3N8s+yI0HKy9kT4VoP9M2199eKJjpjcqwnjHRZ3ZNG1FXwMhjFMCXDRjIa5QiXYDonWQM5yKg16/uKRa0S0kCLCRYWKi0q7/0JeDuu08gWl8Hul1k68W8vVj5baYnsHrWF6Xf97LP//t/++xf3HsYePhhn3+3Cywv48yDVQxGBdFXV/3ia/nvLXoGs4pxc3V29my1Wuv1Ss1sLjKSJK7YSgQcufqTjYU895sF6j7ngDjfrEfyLO6JBCAv00kSxFo+oCwIA4glSQGwAwUAXTUjddN5LF19weT4fatVybqTOaWBudhWg0FNbrusn9dX9WXq96tqMA/Fr2+6CbhteVxpmFTOxTDrlKRq5knZerK6Dh/273mrPfGsYDebueNGXlqFhUiSmoyNvZ9dgCA/g9IyPfVzqaGbVVUaDKry53mE20j1JpheO5JQv5/t0etVGvrlglYz39nxjax6N+fOVdozxe/pYt7B2bPA804v+8/X1oCHHgKWl/GJTwDLyxHuePBBf++LXuQr6yOnAh/4APDv/73//13vqhrXsj6bECCrPcyyX6nWwdfVZrqqM7u2LYS/lXrGsuFVWyE3IeJAbVPXml5qWe6KWbdadT1xHYtC6anUiwVqq+tYg7h4H5dA6cLCYrUhSxCdz0IWwm72hWTb6KskQewc4sSVQUZpvHRCEif0HFSHOQekSRkHg5fvNxipJmP9p//zwBBBdI4lQDU2MR8F2q0KiM3Xbq5bn2j5Q4FA9Tugnh6HCNsHIvi5Quyc79MOWEx95ptZdfortGkzzbTJ7aaB1k3BeDWSDdQ/tqm9Lnled4LOszUT62xb2JDTQ/fqBrctlGxk2zlPTWaGE6DigrGL9+XTa89mY/fVshmIfgAtxEQHKjBdwVoFxhUctzIS+9H7Vgax3kdQUwM0Eii0rF+WgfIoBNPJdk6SpHbPaDRCq9UqwV8y0QGUwD1BXPpAfaSmICzLR5DValMTXCWj2sqKEDjXwJv8CwU7bfojsKvtuL29XUqQUPrm0KFDJbBuNwdUr5zgvf1O9b4JCqueOjcmCHJzc4MbAtontKzURrd9yErVqMwP77P+Ud1yy9BXqR6VpNG66KYN66xlY9+lP3VzYG5urvQ5+waNabMPal/Wcmu/C+m7q4SLniq4kYHhGYg+s2vaFDjX1YVFXW0ERgXXNzbKz9vtDkaug5gsXIKHBSLd6feRdD24t7bm8buTxSpua6tItsh3fd0D4KdOAYvpGFkW4ZFHvBIF8jUPfAMl8L26WsdnrWT6+jpwWyCgWG2R43zgzY0Nz5onoJrnVUDIEmENLKJ3djy7eH5egG4WShfyeQ48+mi1iiP1Ocs8WozCDwTTi3ui1CFJIp9uAcrHaYo4SZAkUUlWJuOfdS9jT/Wqoms5QxIiXOiyGVdWgOecHgNJVALsrBZjVtLfxHJVF7TXA8pG4uq98KUyuYdDHyuWmxg0LpQZSIzka+c8K58HC9rtAoBmgC09154k2DpXZ8gB1ULZMsmWl4sNkOEQWB/6zY1ksYbLaPfZ2Kjw7TQFbrurW9y7Xm34ALVFrQXzfeDS6hRC+QVQIRQaaJTOYCEKJ/JUxqlTsZdO2try/s9zPPywv/WOs2f97+j224FuF+OVZ2J1Fbgtz3E2y3Dbgw8imp8PM9GbmHX8ngtyIjRa3gNhs4X4zA6wBQDi0nT8xnQJC3s7QbdILnCu0ljWR4IC6BpE2xbH5hEqMk1Z4M75R59/bsZeGqbrqucTUEeG1Se626uDnwCXkSlA7FzFKmZ6TCO0majzjCRBlCToJAk2szrxbxpQ2eQLi62qhJ0d+zleabE5Nev3/ZTDPspD4PBeZgFttr9l3Ktb+J4bADWnWGZ1YZ0kARKfiA2USV14Tcb6tukzrSffczpG4/yApvcEwXN1BlDvK2ohB4d+JKGCa7p6DFL7pHMTLPQagM77Xd2v+92cuLZsNnZfLTtIM7aZNdg0LXRrBNDt/3vd2/S9BYCtznMI0A+la2VorGa4bgZY1rSVYmkqp5Y3FBiySduaviKLme+1zE31mGYWdLZmJUNskEtl21tZEVtf/b/pz/re6pRb7e4miRrrH/Yzq5++V5vt5bemOoTAaz2pYH1k7wmd9rB52N9cSAqpqdw2vRvZZiD6zA6kfRVglwecZUFqvqR+uP2q1AJ1/rraxD3PAcTV+kODg5qyKt7IWyeqE2LryKtdhE1Y04LGWtPqgyi2fpfnexQ6kK6iFhL+kx/pwaIkqfRO7To/dFxb0wqt+0Lp5Hldv7yx3Py/wUIKYFofXZQ7598TVKnpn34VKz/nuAmSV2B8nperCvWtbeadneJ7dWZgRdrkisb+atNh46lJ2mVWe/2e5fdUAi3FX9mr9uPLg7XS3sNmC/GZzexqWRNoeSX3qFFXO/gMDSHAFjxvynCvcVf/t+9Dhd4rz0DSdtjQ4l+p2f3+vYr7dFhTHjs7E/s5028KtGFUbOLox+QkPNXy6XvqzodM2yNSXe2Qo0MZ2UxDoLm+308ffqpmAPeDbbOx+2rZQe8JN7SFACWVZQFQgteqZ22DN/K+EChsgzvmeV67jgC36kXzPrKQQ6xdDfzIMpOZa5nuqvNNdjj/Z95zc3OgFjsAhBjblkVNn5BtTha3DaKpQDLTUk10ysCoj8gg17rrd9Tbtm2omw6UYbEMca0XP6e8CVBpvVudetXftlIzZJzz1UqQqIUkTHSTwfYP/d+C3dpnWW5+Zvu0le5RiRiyz0MnLpgGfaLseZWi0UClrKeelGD7hcqm+YXKq+x5leW50W0Gos/smjZlyNgVaWiyTvqQLlAXFvwqI8uwsNCBc57ZFunilekUdDTnlqqkC3CSsiBYXQU2NnD4nueVRaFGeHk8mSsk6necPYtXvOI5OH8eeOIJlExywLOUFxYKJvQ66uxkZlAEMWW9NFgXg3SVYCLz5AUM3pgkaIkrswxAEsOlcZ3RRrYQqWHqJ6ZFtrv6WpoAOWqC3ZR0IRaa556RxSP0u7sVgE4GN5tYFW5YX5LiVWIly6pj1DQlGq+vVyo3WvQaqK2BXGtRz6qvGdCzSZafLHKDR5eupQ/ipl0B9SOqOlurbeCIHrlL/UehclBHPUl8vccogpYp7U4KbRl5QMXI73YjREonpBg9ZVycK+MG4NIlTztnoxayNSz2Yi8FnvEMn3Ca4r77CoWW9VMVy3x9HdGpU8jzCOj10AMQHz9e1d8u8PX5YJ8Ves9XgwB9XW22EJ/ZwTH+xGpjLxD+3ZlxneMTGeVqJctWdcnN/a6QPdFHu72fjxn9PKRb3VTcvXBDjtMuLWKYMFM9acfEbIb6EDYSauU9tvB2bsOB0FZc82hg+OoGMesCVGMnpwyhxyjf7+5WLGyO685VJwB4L6uqRdWxLFRdnv5qeoRrVW0aeV7Vg3MLbXfb5mUd7ZipGankiCFkhGLKhogB9r3tFhZI53s9XVe3qJpzaKL8X09kNXVm7Xuh91p4+6rjrP0rrvO/82iy7OrrwI/wwA3ds7H7qtmBa/ob1SzL1QKKNjCofhYCzXmdgtkKOIZ0ngkAU8tZwVPVQWeeIW3uQ4cOIUmSErBmvryvCXR3ziFJklK7GqjkXFQbm5IeLKsClRa0pDwNgVgb7JJ1J5jKa+bm5tDpdJAkCebn50sNbg1CSZ1yu5lBiRbqsVtQWX1gddhbrVaQ+Q+gDHxJHW+C5iofsr29XYLHKiNDkFiDvVog3Uq4sD0VONZ+Z8F0lS7R4Jx8z3QpN6MAPPPXjRYFo/XEgvYz/m+Z+0xf+5n+lug3AuhJkkwE0FXtdLaZSsew/9lgsNvb26Ue+iyg6AxEn9k1boxsaBeDOuEH6t/ZBfqxY/7/4RCdJMHYed3zjl1QZJkH+pIEzi0hy4rjx+vrwPIyBoPYL7QffhhYW8PRV/4fTBbnz/ukVlY8boizRXm6XX/e9oEHcEf3YX/BS0/hIhbxsY/5S176UmCpO64kNYgMW/HrYiGRuDpmeOmSf108lFeAu6xGxy720h7DSeCdi8/5+Q7SfkcCTZmj5rogT9NSd4W6251kXCLfJUBSrJKfHEQlYErwmwC6Dc6purRc11F/e8ldBACM0sXSRdRNJbC7sVHtX/Be7gesrvpiLy/X86A5hwqxlw2AsQAw3W6d9bW76/PlpoAG4qSfVZ6e9/gNjMhL8ASQBwUSeOQcqIMWeS7/DAZee6jbhetXmwZ6VF3x7mPHfFWzrDh6TjCHGjAFosQAouon+vyuu4C4CLo7ShZ9XWzl77kHXx4u4ub7nP9tsSN0u9jYqNotTWMs3nOP10bKMrxg/U+BQQK84hU+rQce8EFH77oLGxuLwKlT6J4+7bWTdnZK8L22O2IRB+3DqsVjwfcDY2Nc2eJ6RhqY2dfW9OdUVyWJaoGMAYTHcwXgnCuCa2LyvtCrGEH4JIlq2bB8+ozTmBsqnQUYNi8qmRmNG8ExTJWlAP85n8VJEsMlMWKNQGnrSzNgY1AX3nkN9lrQxdBOrgVIDUg/dnE5PnOc0vfWd1R7A6qYGTZZxT+feMLfPzdX2xutGU+kMUgnfRmqhnahJvBb8V5q5ev0JtRm2gRNmybOxYCL6/rz0ka8zrp9WrqavpoGJrXfh/5Xdrq2Se4YWF7meixIyMlqISBbSAYqxVLzierJhfqeFj7P4YzOf+1HGjAd3g+Ozcbuq2UHqtln5q1JFkJZvARtgTrznEbA0QbyVJBTjaAwUAG0ytAmKMw0CEBaWZa5uTm02+0JEF3zVHkNXssgnmRNAyjBzizLSmY3g0sSYLW+YvqqjW1Be6s3zjzJQI+iCAsLC+h0OojjGEmSlD4kMBqSFVEGujLluelA8FcZ6LyPadp2J6jL4JoEaLe3t0tfaBsyDdU5p/65vtrNFQX7udFAn1izgDb9ohsmDASq7HytX6iNlC1udcvZ39SPaspAj6Kodq+agul6UoHfKfhtmes2HcuCH41GMxDd2AxEn9k1bUYP3DJXJkS17UzaOeDo0SqttTVEKyvI86jUrKwtNM+dA1otuJXnVEB1AW4PBgWY+dBDwOqq16JGB1tbFat8ZaVg0J4pZvW9np/8P/CAB99Pnwbuuw+Ld9+NkydfAABYWv0z4GNnPLrb63mAb22tWjSkKcan7vDk9GKhnCRRTcvVOQAJShB9BA+cD9fqC05qjhPr1IWdZ1lHmJ+P4VyMTl9WJ/RT4evNLEIuIHySRBWbvbhnhBjZkME963shyiTXwG1sTmLZdOFiMgIeOgM4h/j0abhisX/uXBUkjlrrNC7U09Rft7pabXSEugoAjFwHLgGiNK0YedLFej3RIC8A5N6pExgOvXT39rYH8qmDqzqq6kL6LU7dRGEsEMF7lBFZW98SFH7iCeCmmxBhjJ2daCJQm0qSHj9egfSjPPJgjnNVdLcsQ4QxWq0IaVoPdMqAqqdPFyB6t4vz54H5+RiLDFRaXPxXa4v4xCeAf/SPnod45UHvnCIKLE8cFHF/cXYYYzA4gZfd9STw7/4dcOed+NMX/f8wGAB/F/AOzjJsby8Cp1aAV77SNy53LvQZ0MS6pFntdN5zoAKLjnFli+vZQnxmX1tTfG7iNI2Ly+8nAmHS7P/2oahmEcsAgh/BnwBKkgDbFdVjIMJYAiEPJwHG4mIG8+50u8VmpB8blVyuxdLpjH/URGi1/HjrHGqIUJlV7v/43m6o1stO4lwB8OajSb+E0FznSgDdjs+hQJxyWy0ZDaFitbm511vsoWJhofqM8w+gzpHgfSyH1lWatVYmPsaZftmessnfKS7udF0QVJ9mk/s7nuGtDP7QPdp9tQ72f02b/2sdbflsfraJdZnLdvFpRr7fpXFY6qUJRHdVQFv+RCzIPzfn+zXgYwGo/4MbOfJ+2kYV27PpEXBwbDZ2Xy2bRMBmdiAspAcd+p7/05RprUEclY0cCvgIVFIlyqjlH9npCiJr+vaV+ViGMQFSy2onIzhJErTbbbTb7fK9sqdplrGsYK0FyTU4KIF+1kGlVegj5suyMG/6nD5ioNWQn+yfllPLZtvL+lKZ3js7O9ja2sKlS5cwHA4xGAxw8eJFXLp0CVtbW7X2sW1v/xRw//+3d+bRUVxX/v+q1d1qLUhCyNCSQYjFbBZewBt4gSQ2MQ5eE4Njx2PPJPzsEGywPWPjcXLAyfiEcXI4ZI7XyTCOZ+wZPBOW4yQeYjEBYgzYrGP2AJYsy0hgCUmt7laru9Tv98erV/26ultIWEJq8f2cU6CuelXvvqXerbrv1n32fmSvEzXJoRZbVWFuVNuo3+qY3s/UdfXFO3Vjsx76xB4aRfeA178kSDaBoedj92i3t4My+Ks6UjLr/UOf4NCvqxvh7fLqfZvhXCTJxrGzbd3llVdewahRo+DxeDB16lR88MEHnabfsmULpk6dCo/Hg9GjR+O111471+KRgYDuDmV/CE/2QG5/ylYhSDRLrmEguVE+EAD8/njvFvMca060vj62ACViNjynM+Y1bb3p5OVJT/TqahgffAB88AGwaxdw+DAGDTLt+0eOSMO8cmlWcmpW0ObmmDFaFTOpVzKAqNONpiaZvqEhthimfq46RxkxVXYtLTGP7rAzB9G8fISdOQgabgQhF2X1+R3WYp6BQCz8ih3DkE2m7M1606kmDYViRnC92Prn2Xl5gFUBZiU4nTKN2Vxx5+tGZxVlRHmM63Mx6mU9xxOVnvRaV1OGELvzlBthKUN9vTTqmpMpygBgGLHyKGOEkkVvK+XVF4UjpQuV3jWTfQJutbmqaCWw1idbW2NtoE+m5ObGYsMbBuLdLbVCq3rKzo7Jo69DqvJT7RDNy4/NgOTlobpadu3qasRujuxsIDc3rg+oSYh9+xCbdDp6FEeOADt3moKYbW8YkLMZ48cDF18cK39nRnP9Hk9WZ+rctDKid5zD1js0NTXhwQcfREFBAQoKCvDggw+iWa0MmAIhBJYtW4bS0lJkZ2dj5syZOHjwoHX8zJkzeOyxxzB+/Hjk5OSgrKwMjz/+OFr0mbJzzJucP9Stp0J22fVAynFQT9jdzX6+7bcewUpteXlSFziaz8R0jdrq6+X2+edyU7/r6xMUrbqevo6Hnr0acpTuVLpUbWqe0b4pPdreHps81z3gk+nXuGcc/QurJPv0YVH9bV90Uz+uD53233pb66gy6EZzvz9++LaLCMS+YlNbqma269eE/qSUjaZ8HKGg9QyQlyfVk765XLEt2eVCIVjh6ux2YvvjqbofdEO62xmFwwjHbW5nFG5nNKGPque+VGrKfl/Zn7X0LwH19orCEZ+RnqH2O+rJsSZb9C8A1XOj2mf/HTYciHpyEr/062z2IEld630zVZ2mB9Td55q3HRrR04yzGZE6MzR1ZmBMlvZs2BdqtBsFk4VlOZcFJe0La9q3ZOXVY6/rHsp2o7TdE12XV5fbLrM9nI4+CaAMpPb6UefZsV9b/91ZnmczNNpjyycz9qbKM1lddLbZr9OVNtaN87psqcpql1s35ttj7evydFZ/qRaXTXUP6XWU7B7Rz7cvsksS6W0j+jvvvIPFixfjueeew969e3HjjTdi9uzZqKmpSZq+qqoKt912G2688Ubs3bsXf//3f4/HH38ca9as6YniknSkM0uiItmTtf18La21y/6Gqz2Nxz2Y69fWDI7qHUM58saJoS5gWiDjRquOjthl1NumfSVI7U0yznvJlN8WrjtBTl1+lYX9pV4VJ1kVJnsJtBuVk1a76ZVlx/5CqIy5ao5Df79STZ2ZaftEXWub7Ozk73talVtl1dsIkNdUL6z2QoRC0pChf2FvGSX0N1R1Mb8fboRjMW+difZofV5HeSFa3na2SR/983L1sqxCjKukCd5y9rgDSQ6lIq79bNaLVIYA+wWcTtuLvdZvrbyVhQIAOjoSjAlxdOIO6HSa/wwaJLeUhTkLycaTtPo6rf+8iN9///3Yt28fNmzYgA0bNmDfvn148MEHOz3nxRdfxIoVK/DSSy9h586d8Hq9uOWWW9Da2goAOHnyJE6ePIlf/vKX2L9/P37zm99gw4YN+P73v/+V8ybnlx43cKW6YKr7P8n+LsvUnTEFUrfYjZ3J5vT0sU8/frYhKNl4rIzWdiw9bLdK239r6OFQVBmS6Va7LVT/bY8lr7CrKKdTfq2l62v9evp1VV0q3an0p1LFKr2aMNbThA1HTPkqQYC4BxpVV/ZHQoW1GLfWDsnij9ufs/Sypvx9FitwV7tgSn3aCWfV7xp2L/HOFny3Xz9hsixVn9ToSjx5PZ/0gbr7XPO2k1ZzJ0SSyuinhwDR/9YNjfqCkuqYwh7bWt9vj6Wu0ENb6OgGWLfbHRf+Qw8XksxInSxWtYozbq8DAJbHcjQateKG5+bmxpVB93xWeahQNvYFKfW6VMdUWmUcjUQiCIVClgez2qfLrzzUVb0r72YhhCWnLo8e/z1Ze9jbXeWpvND1RUPtsbwBQMVoV2FNlFe1kknVk/Is143TyhisyqWH59Hb2/63XX77ZIMue7JFPVXdqLLp7ZydnZ20HVW+dqO8vhip3rdVW9uN//YJmmSTLpmZmZbnul5W5VGv/58sZNGFTneN4t1Nv2LFCnz/+9/HD37wAwDAypUr8cc//hGvvvoqfv7znyekf+2111BWVoaVK1cCACZOnIhdu3bhl7/8Jb797W93K28yANCtcPaHbrsrkn2fsmiquKPK07uhAfmFhUBIO6ZepsrLLRcprxdwhOQaFwiF4PWaNsCKCulRaxgYPlxGYckJnYFhFOH4cXneZYWF0h2nsFB6zY4dC8eECXLxRPP65eXmS4laactOVpZM5PFYRmb52Tvg8PuQ4/fD4y1Ffb15uulqLV90HCgulrIop2mXy/SkDhkwDBmG5qKLpIjK2U6tGdrRIZ3tDUPGzx48GNaiqHoIbeUdX1wM5OTJBVb9zfHVrl40hw+X+xoa5LWShS1RDv7KAy47G7EXXxVEtbkZ7jwDY8cWAYjFV1dt4XTGrqE+5S4vd1iO0G6EAX8ottClab13FxcjBAfq62X47bw84NJLpRxmJBGMK/RLd+nhw2X87uZm6TU9fDi++GIKqquBq66Sh7duldt11wFz5sjzN26U8t5+O5DTUINgcRmOHHGgvNyN/GbZkCdO5MAwpKP14MHAqVOy3svLgXFjo/D5HThwQBr6LUvB2LEyU6/X8qJX3vxer3zZ/eKLWPic7OxYt/d4EHsTUSvjFhcjGHLEeZ0ni2gAAPD74fXmw+mEnJQArMYvL5cx/wEAM2dKt/QDB4CGBgy/LtZ3VL8rLjbbu6QEKCxEXp75tYbqUH4/ysvN+3vChNiMhylHnPXEjv2FXRVE/4xCD8rf7+lA9/yweudF/PDhw9iwYQN27NiBa6+9FgDw61//GtOmTcPRo0cxfvz4hHOEEFi5ciWee+453HPPPQCAN998E8OGDcN//Md/4JFHHkFFRUXc5PmYMWPwwgsv4Hvf+x4Mw4DT6TynvMn5w+2Uz/qG0xE36ZtyAk3X7Xajmv3etT8H2NH36+cYBmCPu6xjF0w3uKpJwFSGP3OAVCFj1Fc8QGw8VsZlpRt1w6DSleqRQH+MsRdXGauV17W+2LgKf+LxyBOiTre19oRaXDKIHBghID/PDLXhdKOtTcpjPt4kVLWaTI6bHLVVm0IZx5XMkYhUU3oZCgvl84XLhbivuZI1SUOD1MPZ2cCkCbJffbTTgcZGqWOLQidxxlOKzZulriwzPgUMA/WecWhoAKYMN6x1NU4256CwEMhp+BRwOnHGkOuseL1AjjOMMyE3amulTF6vlEk9LpiPZGhtlXp5yBAg3xmE2+NBg99hlcthhBGEqvf4R00gZvRXXuyyWdx6N7J3q7g+kqre7W2mY9+v5+H2pDhge852OJ0ydBFk+CA9RJE9L7v8qowJIWRMdKO5Kof+W/1vL1uyLyH7N9TdPaW7aURPM+xe2cqwq/4HEhcGtYds6U4eAOLihNsNf8k8m3XjuIqzrWKh6x68yTyc7QtSCiEsA6S+KKd+viqzimOtFiC1p1HpVP3YFw21G3/1xVHV9ZQ8yoCuh1NRi3pmZWXB4XBYMdPVZi+z+m3frwzuyuiql1v3co9Go2hvb0/weHY4HFaYGT2MTXt7uxXLXhnD3W53nCFcGdDV+frisYZhJEwsJPPSt0/M6CFUVPgUPWSP+t3e3h5n6FchcpJN9CjZVZ/WJ130BXB1w3ZGRkZCLHQ9Jr3qE3pb2731VTx+VSfqf7Wp9gmHwwgEAlZfSWVE1+/bC5VzKb/P54v7rcIF6YTDYezevRtLliyJ2z9r1ixs27Yt6XW3b9+OWbNmxe375je/iVWrViESiVjtTy4QsrISjWL2QJtA/JumsqIqC6F6O1MrItbWWouFhguHwu0JxQIzV1TAF3LD2SxflizDu2m8y8uDtAqalsVxY6MyPMu+fSj/3v/Drl0yzR1jCuW5Xq/crrhCyqCshQAcRw7ZLJmIf/MYPBinxFCgDRgWOi3TKXft2lqgqgqOiSF4PKPlS4WyaIdCcLlykO8MArW1KPaOw86diKUxDABlCASAiROBfP9JRApKceCAPHTxxfLF/Phx+aJ49dXS2N7YCOzfL9dy9Hrly5wyLo8dCxR5DPjNl08Vx9zjcVuedWWFPqC2FkVeL6Jji+Rn89XVQMiwjJ+X3Szrv7pavrQWFiJmHFXxts3P5ydN8MDrzbGaurhYlgUwF8t0ATA8gAEUNdejqKEBMAoBFMu2OXBAWjZGjIitQIp8VFcDv/2tbP/ycin7vn3ylNvGNktL+He+g5MoRWmeAWzYAIwdi2ORKfjoI+A73wHK6j/Gzqxr8Pvfm5MxW/+MnOHDsWvXaHz5JTDv9iCwYwdCN5dh82ZpX74sJGcx9u2TZbriCsDdfBrAUNTWSpsxtm5F/tix8PtLAZjxzD0e+DxDZaxyP5AViX32n5UFjBkjDdEqBIDyTFf1lpkJwCnbIDq8zLJLd3TIqq6rk3WQLAavmpxyqzdkLY4/AIz2BDF6uBOf1rqxdr0Dc+ZcBrf/LaCuDhMmyFMKCqRhxOvV7unycqC42DLmwOm0ZnvKhvsAeBCsuAY5CMrGUbFr1D2iu7/pszTJLHf67IBNr/VvwuherFQ5tnRFd3eH7du3o6CgwHoRBoDrrrsOBQUF2LZtW9KX4aqqKtTX18fp+qysLMyYMQPbtm3DI488kjSvlpYW5OfnW44u55I3OT/ocZBz8vIAxMchTzDy6fdpykQp9tstr6kskOa50ls8cfFHp1Nb20N3dVZ52mWzjyfmswKUoVE7Jz9PS2MYgCcWW1qhQpsor2u7L4B+uvqwR0UncTrlpEUUDktlDh4MuA3DWmalsBAoLZRy15sT1hMmOOAIhYC8HLS3y497ckJnAKcTecPzYRiAO+QzlWweop4c6VxgD2auW4c9Hn3VaPicRTJ0WvNJIBRCePho1NeburHhtHwGME93689Cqj6bm1FaUYHjRo7MbutWAEA7bsKRI8BtN/iA1atRtHAhtm6VdTrFL9McHz4O+/YBU+4z5LPahAnYsUP6Qoyrl5by2uYy1Neba574m+EPDcXhw7FnAPXYGAqZzg9+H9rb81Fbaz42dDQDhYXw++UEeGmxGfatsFQtnQKH3wd3Xp5laFcT/CFzslr3nFcTLjr2LxRUl1N9RR3PzIxfi8W+IKn9VolEZD9SnvhW/9fCw1knK8y+7XY64QaAwjzp7W9rNnV9fXIotgZOzIicbIJIF0PL0pqY0xMbRpLFdvs11N09pbsHTDiX3oh7u2bNGkyaNAlZWVmYNGkS1q1b11vid4lUoTz0Y8nCHijDp25It8elVtg9d1OFS1FplNHQ7kWsDJ/KiO7xeJCdnW0ZlvWY3vbQKXqsb2UMVUZJn8+HpqYmnDlzBi0tLVbMb7WwqTIA5+fno7CwEEVFRRg8eDAKCgqQn5+PvLw8DBo0CLm5uVZMcz2Gt9qys7MteXNyciyvclXWUCiEYDCI1tZWtLS04MyZMzhz5gwaGxvR1NSElpYW+P1+qEVP7XG2lZwejwc5OTnQY72r+OK6l7gyhre1taGtrQ3BYBCBQMDalAEagBXHOycnB9nZ2ZahXHlFq5jjygPc7v2tZNPbTY8Br/efrsRTt3ugq9jnejxytRBqMBhEW1sbQqEQQqGQtV+VW8nscrmstlRtm5ubi5ycHKsO7bHLdaO9Hqdej7+ux/bX7zN9wkXvn/pEi+7trr5UULIrQ7rd613Ve3+gu2PoV8HtdsNrWS26R15eHkaMGGHFMSsoKEjqVd7Q0ICOjg4MGzYsbv+wYcNQX1+f9Nr19fVJ0xuGgYaGhnOSl3ROv9bdygiub3YXFf2pXY/Bod4k1eqSap9yNfb70dCAmFtRJIKaereMywzNiA4Afj8uvliGH8F110mrJyCvs2MHsGEDckJnLEdba+Us5Q5eUSGN7xMmmO62kLHQjxyJvQHrgbMBIC8PJ05ID2I0N0uDsypTfb00HtbWxrzCmpvlCpqqDmprgQMHkO8Jo7HR3G0GWFUv5/meMHDkCAYPjs0xKDGqq4GDB+U+B6JobgZOnJBZq5ArqipVlqp6/X5YscutlxtVV7t2wXHgE2DzZmD16tj2298ClZXIP74H5eXSuJqXh9jFi4vl1txsla2o4S8oNWowOu808uv/IvNQW3V1bBLk8GHg97+X+asgswcOyAKqGLdmLPzq6lhS1aWs2NzNzTKuvd8v2zkvTxrVd+zAiRPynLLhUWD9erhcQF1dFerrYcXBP3AA+Ogj8zq7diEUkvaA2lpIGUIh7N8vm9bplPtUc+fmmtc5fhyhkPTmD4Vk/923T4px5Eh8nHiXCxiW7YO79lPL1qG/iIZCpvO12Ui1tXKi5NSpWBj6U6fkZg8fbqH6pik/gFj88yNHZGUCWL8e2LvXTH/qFBzVn2LsWDlBk5cXu1WsmZwhQ2JzTk6nrADVbpB1/ZfanNiY0N4u+789AL/9zVyPAaQqQQV5DQRwLvSN7t4IYEM3to1d1t3dob6+HkOHDk3YP3To0E71PIBuPRs0NjbiZz/7WdxL+rnkPZDo17pbKRlzy/HE4jy7nVEZUkvb1F/6ZHjU6Y7b4ibKU7qz22RQ/9sM68k8Ya1r6ZsaK+zPIfYYYvYyKyNyqvjqDQ1w+H2xLRS0LqPH4E7mlRxb0Nu2toVZLpW9Ok8ZgdXjTNTptsQwDFjjtnoUUQcdDaflRK5at+XIETiO/0X+vWuXVIpHjkh9q9IcPy4zq662/vZ4ZLx57N0LbN8ujfKAnEhX55hrjKC2NrbujFpUY+dOc2ENUw/u2AHs2AG/Hzh2DDLt+vVAQwN27DD16a5dwK5dqK42be6GIRWrYahHJ6sd1Nd6AICGBoRC8lnn1CnpUe50xkQyDFmhoZCm8kyFq+Zy1Sdcyp8DgPWloGob1Zj6ujHqC7JAQH7IqOLf6+us2PuD6iuqm9rjuas+kiyOuooxnxBr3O6urvq0HqRf9WWzX7uNINxGEDnOsHV7qC8lOjpi8f9VvPTO4v7b4+6rcHZuZzS+EszKTTY50FWou9Nbd6cY+dMLFff2lVdewfXXX4/XX38ds2fPxqFDh1BWVpaQXsW9nT9/Pt566y18+OGHWLBgAS666CLrk/3t27dj3rx5+NnPfoa7774b69atw9y5c7F169a4mYvzjd1onuyYnWShQgD0iBesPYyLPV8ACQZWI8lok8ygqHvA6x7LoVAozvNceXkrw7zap8qbkZFheSXrEwO617PdIKpPAqj8k4VW0b3F29raLCOpy+WCx+NJ6oEMwDLEJpvIABK/JlB5KS9rPRSKCm+ie7XrseD1BVU7OjoSwpUkCyujL16amZkZF/akK/HiU8VsTxaLXBmsVRvrdaX6ih5uRuWpFvtUM6H6ArK64dsul11GPbyLKnuyOOb2vpEs9rtuSNcnCuyTUfo1+oMnenfH0K+Kx+NBVVVVwpcBXcF+bwDodDbcnjbZ+WdLn2w/+er0e92tv8za3yR17EZ17X/5Yu6WHgu6sd3QFhg1n/TVe255ufnSpxnlBw2SP0996YDLlY8iozlm0LWM8kXSMJ+dbQXvjHpy4Cgulnk3NclzlJuvffUtm6dbQ4NpSHaGYgZhQJahsdH0djfTWxZR7bfpnR4KycWg1FuJkae9PGsve21tsfcn9WLT1gazrtyWoV158umLhqosrbkMy1DhiF2wri7WntXV8sVbd60z3eDzr7gC2dnyc3g0hIC8PESdbjg8kAJpkxvW22NIqyP1LbnyMG9sjMVoUS9f+oqqKr6JKWZTUwCff55rlUsZkxEKyVmNtjbU+xErh9eLZsh3SjQ3A8ePw3kdAJyC3z9K9pFhw9DQYL68m2/ghiF/t7YCyJB9srExFqEEfj8imdrkRn29NQnS3h7z0lafuuflyagucS+V5guvJ2+06lbmwcSvItWiYB0dsSpVIWAMI4mtSmWk2kO/5wA4lGBXyOo/dQpx3oWO5jOWEcrjccu+bhhWvHMVNx+AbCM1e4DYZM64QlOotrbEVfDs2D3SNZkBnFNM9IGqu5ctW4bnn3++0+vt3LkTQHLdfDY9n+y8VOf4fD5861vfwqRJk7B06dJOr9HVvNOdfq+7Ffr9aHqBd5bU6XTI0cPptKvDhLU2HIgmPhsku6i6QBdEtUZF3UBvLWDRhQuo/9U5yeJcKIuo7nXtdMKZl2MlU9nbw3cYRuIwZYlm/mEY7pg3s/mcYzkWmxfWHoPkl0Q6avVP/WHgyy9jsisDqh6vRn8QUOeoL/yUbI2Nsdl65Meu5XRKna3iujidMWtqQ4PUuaaOcblgKlHAmCD1FZqbpTXd70dtrXl50xDX3Gz+qWajDSNuoh9+P/yw1Ip8PvLEP/s4ne6YgVyrU+tZxykr0ppkthl4rT/M8+wqR01eqDB2qstYkxraJZJ141RfeHS1y+r6PQpH7B7V791kfVnPVOsv0ps9dq/aL2Mvg97VUpXBem7R7zHrt/ucjOjU3emvuweEEb034t6uXLkSt9xyC5599lkAwLPPPostW7Zg5cqV+M///M/zU7A04nw/NPaG4fGryG83kva2YdQe87u75/ZEW53rNXproc2B/tLSm3R3DO0J1NcNvUVxcTEyMzMTZpVPnz6dMIut8Hq9SdM7nU4MGTKk12S9UKHu7mW68OJOkpDCyNu181Ls72wS5qvSyUKYZGAzEHU3ACxcuBD33Xdfp2nKy8vxySef4NSpUwnHvvzyy071PCC90UpKSqz9yZ4NWltbceuttyIvLw/r1q2LC+nm9Xq7nfdAIS11dyorIOkXpFSLyfSbFXS9/9PT6r47WFXXl0KQpFB3p7/uTntt0ltxb7dv344nnngiIY16AEhGe3s72jVPrJaWlm6WJjW6B7r6X/futccW1z1dldcygDgvcD3euEKlVR7CilQzNiqNMswqT2flhau8wNXipLpntH4dPfa2kj+ZJ3pHRwfC4bBVZhU/XC3wqHtOp/JEV/WWzBPd7hFv90Rvb2+PW0hS/a1Cg6hzVAxyPca7Cqmi6udsnugqDExbWxvC4bAVHkT3dFb9TZVXDzfidDrR0dFhhW4Jh8PW4qdAzOsagOXd39bWZtVtJBKJq08VT72zWPJKdmXgV/Wux7XXy6Uv0KruHz30i73fq/pS5dM919U5KnSKCqWjQrbYwx/pnuh6vdm/rlCy28O+6Iui6jHe1d96PPhUnvnJvijpKXw+X1wMs1Txy85lDE0H3G43pk6disrKStx9993W/srKStx5551Jz5k2bRp+97vfxe17//33cdVVVzEeeg+TDrrb19qaGOYEiHmcKtcf9dvjAYSQ344GAtIT3LwHHYEAEAxK9yLTA6k14oMvJyz3ud3w+30IBqXHkc8XjXlDBQII+nxWdk4n4Ay0xoJPd3QAra0QwgfDAHyBgDyvtRXRHB8cra1SnmBQbtFo7DtV5ZWi4nCoc/1+BIM+ZGQAPo8flmBut/y7vR0IBuH3++B2A05VNp8PPjjhVLKbcgeDgM+U1w9ZTp/PBwSDiOppfFKEUEiKFggAPl8YgYAb7e3qknKBS5XG7wd8viD8fgPBoPrtAxwOBAIyFq4vFIj/XlmVX7VfJCKPmUL4/W74fIDh9wNCyHo0wrE2dLuBjIxY25v1YfWPaFTWl8Mh00ci8WE72tul8CrP1lYE3T7T2SqAjo4OyyFPeYj5AgHZv4JBtEXN8V0IwDAQifgghFnuSATBoA9AAJGIDz5TNsPwIRo1+3U4jNZW2V+CQcCXIWWIRMw+5AOMQACBTB/a2+Vvp3mdoOFTTQunI4pg0GEVX+9CwaCZl98PP3zWp+M+Xxi+kPSuEwLwRX1ARwf8fg8CAVmtSi4VN13F6m1vl3VitY3K2OWS9R2NQj1eOsx2jiun6u9SENkGTieChlvedzBjm5t9OxAAfDD7ssMhhfP5EAy65TGnP3ZvRaPyb3W/qQZU6N/EKxkCAcsVrtXsPxe67gbkJHixCj3VCdOmTUNLSws+/vhjXHPNNQCAjz76CC0tLZg+fXrSc0aNGgWv14vKykpceeWVAGRdbtmyBf/4j/9opfP5fPjmN7+JrKwsvPvuuwnGh3PJeyCQNro7I0OOD9FoTM+lMKKrRQWdTnOBYqfTirOc6jS50LbmJaveHXRvVXWy02mNNVa4GCTGUnYjHO/lqj75SeYCrlDxJvSLKd1m995VrsaGIXWY2peZiXDUidbWmIhqGFOO2kDskUM9CpjDOzIzAZ87CDid8PvNsdEHGCEfWgNOTS+HEXW6LRXo8wFGayvCOT5rSHarcVPFAFHPLUo5KB3sdMZCYAWDskxKYDUmBwII+3yyXtWzV2srWg0ffM5WmUZ9Deh0yutnZsaeEdTK4YEAAoZP6jyzLwWDPkQipl6Oyue1aFTqcJ/5rh0K+aTubm2V8rW2Ihw2n39MnRKEeY6p0/yGz1JrPl8QYadhie7zyWc/f0DTp07pph4IeKTuNhsl7PNZ7aAqVzns+3yyr7W2xvRw1FxnXoV70ZvAfh+o/1WXth+XZY+/DdRtqq6rHp3CYZm/6ncORGMPgvrztRLMftNkZkrhtfvM53PEnapC0thlTFbOSEReTu9OOTnmuGD3RI9G4fO74ffLzwSouy8w3S3SnC+++EIAEB9++GHc/hdeeEGMGzcu6TmXXHKJeOGFF+L2ffjhhwKAOHnypBBCCJfLJd5+++24NG+//bZwu90pZVm6dKkAwI0bN279blu6dGmPjaHpwurVq4XL5RKrVq0Shw4dEosXLxa5ubmiurpaCCHEkiVLxIMPPmil//TTT0VOTo544oknxKFDh8SqVauEy+USv/3tb/uqCAMW6m5u3LhxO/t2Ieru7nDrrbeKyy67TGzfvl1s375dTJ48WcyZMycuzfjx48XatWut38uXLxcFBQVi7dq1Yv/+/eK73/2uKCkpET6fTwghhM/nE9dee62YPHmyOH78uKirq7M2wzC6lfdAg7qbGzdu3M6+UXd3Trrr7rT3RFf0Rtzb7l7z2WefxZNPPmn9bm5uxsiRI1FTU4OCgoKzFyLN8Pl8GDFiBD7//HPk5+f3tTg9DsuXvgzksgGdly8ajeKzzz5DWVmZ9aUB0HnccKD74106MG/ePDQ2NuKnP/0p6urqUFFRgffeew8jR44EANTV1aGmpsZKP2rUKLz33nt44okn8PLLL6O0tBT/9E//ZH1uTHoe6u7zz4U8Pg4EBnL5BnLZAOru3uDtt9/G448/bnk633HHHXjppZfi0hw9ejTu6+Cnn34abW1tWLBgAZqamnDttdfi/fffx6BBgwAAu3fvxkcffQQAGDt2bNy1qqqqUF5e3uW8ByrU3eefC3l8HAgM5PIN5LIB1N29Qbrr7rQ3ovdW3NtUaTqLlZPqs42CgoIBOaAo8vPzWb40ZiCXbyCXDUhdvsLCwi5f41zG0HRiwYIFWLBgQdJjv/nNbxL2zZgxA3v27OllqQh1d99zoY6PA4WBXL6BXDaAursnKSoqwltvvdVpGmELm5eRkYFly5Zh2bJlSdPPnDmzS6H2upL3QIO6u++5UMfHgcJALt9ALhtA3d2TpLvuTgzGnGbocW91KisrU8a1mTZtWkJ6e9zbVGkGcpw7QsiFx7mMoYR8Vai7CSHk3KHuJn0BdTchhJw71N0DhG4Ff+mn9Ebc2w8//FBkZmaK5cuXi8OHD4vly5cLp9MpduzY0WW5WlpaBADR0tLSc4XtR7B86c1ALt9ALpsQPV++s42hhPQG1N19A8uX3gzk8g3ksglB3U0GBtTdfQPLl94M5PIN5LIJQd1NEhkQRnQhhHj55ZfFyJEjhdvtFlOmTBFbtmyxjj300ENixowZcek3b94srrzySuF2u0V5ebl49dVXE6753//932L8+PHC5XKJCRMmiDVr1nRLplAoJJYuXSpCodA5lam/w/KlNwO5fAO5bEL0Tvk6G0MJ6S2ou88/LF96M5DLN5DLJgR1Nxk4UHeff1i+9GYgl28gl00I6m6SSIYQXQgcQwghhBBCCCGEEEIIIYRcgKR9THRCCCGEEEIIIYQQQgghpLegEZ0QQgghhBBCCCGEEEIISQGN6IQQQgghhBBCCCGEEEJICmhEJ4QQQgghhBBCCCGEEEJSQCN6L/HKK69g1KhR8Hg8mDp1Kj744IO+Fumc+PnPf46rr74agwYNwtChQ3HXXXfh6NGjcWkefvhhZGRkxG3XXXddH0ncPZYtW5Ygu9frtY4LIbBs2TKUlpYiOzsbM2fOxMGDB/tQ4u5RXl6eUL6MjAz86Ec/ApB+bffnP/8Zt99+O0pLS5GRkYH169fHHe9Ke7W3t+Oxxx5DcXExcnNzcccdd6C2tvY8liI5nZUtEongmWeeweTJk5Gbm4vS0lL81V/9FU6ePBl3jZkzZya053333XeeS0JI+kLd3X/Hfx3q7vRqO+pu6m5CehPq7v47/utQd6dX21F3U3eT5NCI3gu88847WLx4MZ577jns3bsXN954I2bPno2ampq+Fq3bbNmyBT/60Y+wY8cOVFZWwjAMzJo1C4FAIC7drbfeirq6Omt77733+kji7nPppZfGyb5//37r2IsvvogVK1bgpZdews6dO+H1enHLLbegtbW1DyXuOjt37owrW2VlJQDg3nvvtdKkU9sFAgFcfvnleOmll5Ie70p7LV68GOvWrcPq1auxdetW+P1+zJkzBx0dHeerGEnprGzBYBB79uzBT37yE+zZswdr167FX/7yF9xxxx0JaefPnx/Xnq+//vr5EJ+QtIe6u3+P/3aou9On7ai7qbsJ6S2ou/v3+G+Hujt92o66m7qbpECQHueaa64Rjz76aNy+CRMmiCVLlvSRRD3H6dOnBQCxZcsWa99DDz0k7rzzzr4T6iuwdOlScfnllyc9Fo1GhdfrFcuXL7f2hUIhUVBQIF577bXzJGHPsmjRIjFmzBgRjUaFEOnddgDEunXrrN9daa/m5mbhcrnE6tWrrTRffPGFcDgcYsOGDedN9rNhL1syPv74YwFAfPbZZ9a+GTNmiEWLFvWucIQMUKi70wfq7vRtO+pu6m5CehLq7vSBujt92466m7qbxKAneg8TDoexe/duzJo1K27/rFmzsG3btj6SqudoaWkBABQVFcXt37x5M4YOHYpx48Zh/vz5OH36dF+Id04cO3YMpaWlGDVqFO677z58+umnAICqqirU19fHtWVWVhZmzJiRlm0ZDofx1ltv4W/+5m+QkZFh7U/nttPpSnvt3r0bkUgkLk1paSkqKirSrk1bWlqQkZGBwsLCuP1vv/02iouLcemll+Jv//Zv08Z7g5C+hLo7/cZ/6u70bTsd6m4JdTch3Ye6O/3Gf+ru9G07HepuCXX3hYmzrwUYaDQ0NKCjowPDhg2L2z9s2DDU19f3kVQ9gxACTz75JG644QZUVFRY+2fPno17770XI0eORFVVFX7yk5/g61//Onbv3o2srKw+lPjsXHvttfi3f/s3jBs3DqdOncI//MM/YPr06Th48KDVXsna8rPPPusLcb8S69evR3NzMx5++GFrXzq3nZ2utFd9fT3cbjcGDx6ckCad7s9QKIQlS5bg/vvvR35+vrX/gQcewKhRo+D1enHgwAE8++yz+L//+z/rc0JCSHKou9Nr/KfuTt+2s0PdTd1NyLlC3Z1e4z91d/q2nR3qburuCxka0XsJfcYRkIrQvi/dWLhwIT755BNs3bo1bv+8efOsvysqKnDVVVdh5MiR+MMf/oB77rnnfIvZLWbPnm39PXnyZEybNg1jxozBm2++aS30MVDactWqVZg9ezZKS0utfencdqk4l/ZKpzaNRCK47777EI1G8corr8Qdmz9/vvV3RUUFLrnkElx11VXYs2cPpkyZcr5FJSTtGCjjvQ51tyRd25K6OzXp1KbU3YT0HgNlvNeh7paka1tSd6cmndqUupskg+Fcepji4mJkZmYmzK6dPn06YaYunXjsscfw7rvvYtOmTRg+fHinaUtKSjBy5EgcO3bsPEnXc+Tm5mLy5Mk4duyYtVr4QGjLzz77DBs3bsQPfvCDTtOlc9t1pb28Xi/C4TCamppSpunPRCIRzJ07F1VVVaisrIybDU/GlClT4HK50rI9CTmfUHen9/hP3Z2+bUfdnQh1NyFdg7o7vcd/6u70bTvq7kSouy8caETvYdxuN6ZOnZrwGUdlZSWmT5/eR1KdO0IILFy4EGvXrsWf/vQnjBo16qznNDY24vPPP0dJScl5kLBnaW9vx+HDh1FSUmJ9nqO3ZTgcxpYtW9KuLd944w0MHToU3/rWtzpNl85t15X2mjp1KlwuV1yauro6HDhwoN+3qVLkx44dw8aNGzFkyJCznnPw4EFEIpG0bE9CzifU3ek9/lN3p2/bUXcnQt1NSNeg7k7v8Z+6O33bjro7EeruC4jzvZLphcDq1auFy+USq1atEocOHRKLFy8Wubm5orq6uq9F6zY//OEPRUFBgdi8ebOoq6uztmAwKIQQorW1VTz11FNi27ZtoqqqSmzatElMmzZNXHzxxcLn8/Wx9GfnqaeeEps3bxaffvqp2LFjh5gzZ44YNGiQ1VbLly8XBQUFYu3atWL//v3iu9/9rigpKUmLsik6OjpEWVmZeOaZZ+L2p2Pbtba2ir1794q9e/cKAGLFihVi79691krZXWmvRx99VAwfPlxs3LhR7NmzR3z9618Xl19+uTAMo6+KJYTovGyRSETccccdYvjw4WLfvn1x92J7e7sQQojjx4+L559/XuzcuVNUVVWJP/zhD2LChAniyiuv7POyEZIOUHf37/Ffh7o7vdqOupu6m5Degrq7f4//OtTd6dV21N3U3SQ5NKL3Ei+//LIYOXKkcLvdYsqUKWLLli19LdI5ASDp9sYbbwghhAgGg2LWrFnioosuEi6XS5SVlYmHHnpI1NTU9K3gXWTevHmipKREuFwuUVpaKu655x5x8OBB63g0GhVLly4VXq9XZGVliZtuukns37+/DyXuPn/84x8FAHH06NG4/enYdps2bUraHx966CEhRNfaq62tTSxcuFAUFRWJ7OxsMWfOnH5R5s7KVlVVlfJe3LRpkxBCiJqaGnHTTTeJoqIi4Xa7xZgxY8Tjjz8uGhsb+7ZghKQR1N19PxZ2Beru9Go76m7qbkJ6E+ruvh8LuwJ1d3q1HXU3dTdJToYQQpyjEzshhBBCCCGEEEIIIYQQMqBhTHRCCCGEEEIIIYQQQgghJAU0ohNCCCGEEEIIIYQQQgghKaARnRBCCCGEEEIIIYQQQghJAY3ohBBCCCGEEEIIIYQQQkgKaEQnhBBCCCGEEEIIIYQQQlJAIzohhBBCCCGEEEIIIYQQkgIa0QkhhBBCCCGEEEIIIYSQFNCITgghhBBCCCGEEEIIIYSkgEZ0QmzMnDkTixcvTpvr9jTV1dXIyMjAvn37+loUQgghpEtQd1N3E0IISS+ou6m7CUk3nH0tACEXCmvXroXL5Tpv+W3evBlf+9rX0NTUhMLCwvOWLyGEEDJQoO4mhBBC0gvqbkJIb0EjOiG9TCQSgcvlQlFRUV+LQgghhJAuQN1NCCGEpBfU3YSQ3obhXAhJQjQaxdNPP42ioiJ4vV4sW7bMOlZTU4M777wTeXl5yM/Px9y5c3Hq1Cnr+LJly3DFFVfgX//1XzF69GhkZWVBCBH3WdnmzZuRkZGRsD388MPWdV599VWMGTMGbrcb48ePx7//+7/HyZiRkYF/+Zd/wd13342cnBxccsklePfddwHIT8O+9rWvAQAGDx4cd+0NGzbghhtuQGFhIYYMGYI5c+bgxIkTPV+JhBBCyHmEupsQQghJL6i7CSHpBI3ohCThzTffRG5uLj766CO8+OKL+OlPf4rKykoIIXDXXXfhzJkz2LJlCyorK3HixAnMmzcv7vzjCRzdfgAABMdJREFUx4/jv/7rv7BmzZqkMc6mT5+Ouro6a/vTn/4Ej8eDm266CQCwbt06LFq0CE899RQOHDiARx55BH/913+NTZs2xV3n+eefx9y5c/HJJ5/gtttuwwMPPIAzZ85gxIgRWLNmDQDg6NGjqKurw69+9SsAQCAQwJNPPomdO3fif//3f+FwOHD33XcjGo32Qk0SQggh5wfqbkIIISS9oO4mhKQVghASx4wZM8QNN9wQt+/qq68WzzzzjHj//fdFZmamqKmpsY4dPHhQABAff/yxEEKIpUuXCpfLJU6fPp1w3UWLFiXk19DQIMaMGSMWLFhg7Zs+fbqYP39+XLp7771X3HbbbdZvAOLHP/6x9dvv94uMjAzxP//zP0IIITZt2iQAiKampk7Le/r0aQFA7N+/XwghRFVVlQAg9u7d2+l5hBBCSH+Bupu6mxBCSHpB3U3dTUi6QU90QpJw2WWXxf0uKSnB6dOncfjwYYwYMQIjRoywjk2aNAmFhYU4fPiwtW/kyJG46KKLzppPJBLBt7/9bZSVlVkz1gBw+PBhXH/99XFpr7/++rg87HLm5uZi0KBBOH36dKd5njhxAvfffz9Gjx6N/Px8jBo1CoD8XI4QQghJV6i7CSGEkPSCupsQkk5wYVFCkmBfzTsjIwPRaBRCCGRkZCSkt+/Pzc3tUj4//OEPUVNTg507d8LpjL8d7fkkyzuVnJ1x++23Y8SIEfj1r3+N0tJSRKNRVFRUIBwOd0lmQgghpD9C3U0IIYSkF9TdhJB0gp7ohHSDSZMmoaamBp9//rm179ChQ2hpacHEiRO7da0VK1bgnXfewbvvvoshQ4bEHZs4cSK2bt0at2/btm3dysPtdgMAOjo6rH2NjY04fPgwfvzjH+Mb3/gGJk6ciKampm7JTQghhKQT1N2EEEJIekHdTQjpj9ATnZBucPPNN+Oyyy7DAw88gJUrV8IwDCxYsAAzZszAVVdd1eXrbNy4EU8//TRefvllFBcXo76+HgCQnZ2NgoIC/N3f/R3mzp2LKVOm4Bvf+AZ+97vfYe3atdi4cWOX8xg5ciQyMjLw+9//Hrfddhuys7MxePBgDBkyBP/8z/+MkpIS1NTUYMmSJd2uB0IIISRdoO4mhBBC0gvqbkJIf4Se6IR0g4yMDKxfvx6DBw/GTTfdhJtvvhmjR4/GO++8063rbN26FR0dHXj00UdRUlJibYsWLQIA3HXXXfjVr36FX/ziF7j00kvx+uuv44033sDMmTO7nMfFF1+M559/HkuWLMGwYcOwcOFCOBwOrF69Grt370ZFRQWeeOIJ/OIXv+iW7IQQQkg6Qd1NCCGEpBfU3YSQ/kiGEEL0tRCEEEIIIYQQQgghhBBCSH+EnuiEEEIIIYQQQgghhBBCSApoRCeEEEIIIYQQQgghhBBCUkAjOiGEEEIIIYQQQgghhBCSAhrRCSGEEEIIIYQQQgghhJAU0IhOCCGEEEIIIYQQQgghhKSARnRCCCGEEEIIIYQQQgghJAU0ohNCCCGEEEIIIYQQQgghKaARnRBCCCGEEEIIIYQQQghJAY3ohBBCCCGEEEIIIYQQQkgKaEQnhBBCCCGEEEIIIYQQQlJAIzohhBBCCCGEEEIIIYQQkoL/DxhrK7a3t1eDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08df8487f0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class DataDiscrepancyCallback(callbacks.Callback):\n",
+    "    def __init__(self, A, data):\n",
+    "        self.f = LeastSquares(A, data)\n",
+    "        self.save_values=[]\n",
+    "\n",
+    "    def __call__(self, algorithm):\n",
+    "        self.save_values.append(self.f(algorithm.get_output()))\n",
+    "\n",
+    "mycallback_FISTA_lower_bound= DataDiscrepancyCallback(A, absorption)\n",
+    "algo1=FISTA(initial=ig.allocate(0), f=F, g=alpha*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo1.run(500, callbacks=[mycallback_FISTA_lower_bound])\n",
+    "\n",
+    "        \n",
+    "mycallback_FISTA_no_lower_bound= DataDiscrepancyCallback(A, absorption)\n",
+    "algo2=FISTA(initial=ig.allocate(0), f=F, g=alpha*TotalVariation(), update_objective_interval=10)         \n",
+    "algo2.run(500, callbacks=[mycallback_FISTA_no_lower_bound])\n",
+    "\n",
+    "\n",
+    "show2D([ground_truth, algo1.get_output(), algo2.get_output()], title=['ground_truth', 'FISTA_lower_bound', 'FISTA_no_lower_bound'], num_cols=3)\n",
+    "show2D([absorption, A.direct(algo1.get_output())-absorption, A.direct(algo2.get_output())-absorption], title=['ground_truth', 'Data error FISTA_lower_bound', 'Data error FISTA_no_lower_bound'], fix_range=[[0,3], [-0.02, 0.02], [-0.02, 0.02]], cmap=['gray', 'seismic', 'seismic'], num_cols=3)\n",
+    "plt.plot(range(10,501), mycallback_FISTA_lower_bound.save_values[10:], label='FISTA TV with lower bound ')\n",
+    "plt.plot(range(10, 501), mycallback_FISTA_no_lower_bound.save_values[10:], label='FISTA TV without lower bound ')\n",
+    "plt.yscale('log')\n",
+    "plt.ylabel('Data discrepancy $\\|Ax-y\\|_2^2$')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the without the lower bound, the reconstruction overfits to the noisy absorption data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculating a noise approximation for each iteration  (A custom callback example) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08dfcb7670>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import skimage\n",
+    "\n",
+    "class SigmaEstimateCallback(callbacks.Callback):\n",
+    "    def __init__(self):\n",
+    "\n",
+    "        self.save_values=[]\n",
+    "\n",
+    "    def __call__(self, algorithm):\n",
+    "        self.save_values.append(skimage.restoration.estimate_sigma(algorithm.get_output().as_array()))\n",
+    "\n",
+    "mycallback_FISTA_TV_alpha_01= SigmaEstimateCallback()\n",
+    "algo1=FISTA(initial=ig.allocate(0), f=F, g=0.1*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo1.run(500, callbacks=[mycallback_FISTA_TV_alpha_01])\n",
+    "\n",
+    "        \n",
+    "mycallback_FISTA_TV_alpha_1= SigmaEstimateCallback()\n",
+    "algo2=FISTA(initial=ig.allocate(0), f=F, g=1*TotalVariation(lower=0), update_objective_interval=10)         \n",
+    "algo2.run(500, callbacks=[mycallback_FISTA_TV_alpha_1])\n",
+    "\n",
+    "\n",
+    "show2D([ground_truth, algo1.get_output(), algo2.get_output()], title=['ground_truth', 'FISTA_TV_alpha_01', 'FISTA_TV_alpha_1'], num_cols=3)\n",
+    "show2D([absorption, A.direct(algo1.get_output())-absorption, A.direct(algo2.get_output())-absorption], title=['ground_truth', 'Data error FISTA_TV_alpha_01', 'Data error FISTA_TV_alpha_1'], fix_range=[[0,3], [-0.02, 0.02], [-0.02, 0.02]], cmap=['gray', 'seismic', 'seismic'], num_cols=3)\n",
+    "plt.plot(range(10,501), mycallback_FISTA_TV_alpha_01.save_values[10:], label='FISTA TV alpha=0.1 ')\n",
+    "plt.plot(range(10, 501), mycallback_FISTA_TV_alpha_1.save_values[10:], label='FISTA TV alpha=1.0 ')\n",
+    "plt.ylabel('Noise Estimate')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()\n",
+    "                                   \n",
+    "                            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see with a larger regularisation parameter, the resulting image is less noisy. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image metric callbacks (custom callback example) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 MSE                  MAE                 PSNR\n",
+      "         1.07888e-06          5.48145e-04          9.48530e+00\n",
+      "         5.85316e-07          6.22034e-04          1.21411e+01\n",
+      "         5.05844e-07          5.72563e-04          1.27749e+01\n",
+      "         4.31374e-07          5.19819e-04          1.34665e+01\n",
+      "         3.64704e-07          4.67054e-04          1.41956e+01\n",
+      "         3.06416e-07          4.16492e-04          1.49519e+01\n",
+      "         2.56388e-07          3.70092e-04          1.57261e+01\n",
+      "         2.14156e-07          3.28810e-04          1.65077e+01\n",
+      "         1.78987e-07          2.92725e-04          1.72868e+01\n",
+      "         1.50022e-07          2.60981e-04          1.80535e+01\n",
+      "         1.26383e-07          2.33361e-04          1.87981e+01\n",
+      "         1.07187e-07          2.09652e-04          1.95136e+01\n",
+      "         9.16141e-08          1.89309e-04          2.01954e+01\n",
+      "         7.89449e-08          1.72049e-04          2.08418e+01\n",
+      "         6.85910e-08          1.57283e-04          2.14524e+01\n",
+      "         6.00884e-08          1.44610e-04          2.20271e+01\n",
+      "         5.30737e-08          1.33746e-04          2.25662e+01\n",
+      "         4.72670e-08          1.24414e-04          2.30695e+01\n",
+      "         4.24393e-08          1.16364e-04          2.35374e+01\n",
+      "         3.84176e-08          1.09416e-04          2.39697e+01\n",
+      "         3.50543e-08          1.03451e-04          2.43676e+01\n",
+      "         3.22300e-08          9.82989e-05          2.47324e+01\n",
+      "         2.98493e-08          9.39156e-05          2.50657e+01\n",
+      "         2.78304e-08          9.01341e-05          2.53698e+01\n",
+      "         2.61075e-08          8.68679e-05          2.56474e+01\n",
+      "         2.46249e-08          8.40164e-05          2.59013e+01\n",
+      "         2.33423e-08          8.14809e-05          2.61336e+01\n",
+      "         2.22266e-08          7.92211e-05          2.63463e+01\n",
+      "         2.12462e-08          7.72332e-05          2.65422e+01\n",
+      "         2.03792e-08          7.54337e-05          2.67232e+01\n",
+      "         1.96080e-08          7.38151e-05          2.68907e+01\n",
+      "         1.89173e-08          7.23520e-05          2.70464e+01\n",
+      "         1.82934e-08          7.10307e-05          2.71921e+01\n",
+      "         1.77264e-08          6.98001e-05          2.73288e+01\n",
+      "         1.72101e-08          6.86725e-05          2.74572e+01\n",
+      "         1.67384e-08          6.76756e-05          2.75779e+01\n",
+      "         1.63068e-08          6.67997e-05          2.76913e+01\n",
+      "         1.59109e-08          6.59966e-05          2.77981e+01\n",
+      "         1.55498e-08          6.52429e-05          2.78978e+01\n",
+      "         1.52207e-08          6.45565e-05          2.79907e+01\n",
+      "         1.49199e-08          6.39012e-05          2.80774e+01\n",
+      "         1.46448e-08          6.32729e-05          2.81582e+01\n",
+      "         1.43935e-08          6.26837e-05          2.82334e+01\n",
+      "         1.41640e-08          6.21308e-05          2.83032e+01\n",
+      "         1.39533e-08          6.16084e-05          2.83683e+01\n",
+      "         1.37602e-08          6.11234e-05          2.84288e+01\n",
+      "         1.35827e-08          6.06739e-05          2.84852e+01\n",
+      "         1.34200e-08          6.02613e-05          2.85375e+01\n",
+      "         1.32710e-08          5.98831e-05          2.85860e+01\n",
+      "         1.31342e-08          5.95365e-05          2.86310e+01\n",
+      "         1.30086e-08          5.92132e-05          2.86727e+01\n",
+      "         1.28935e-08          5.89066e-05          2.87113e+01\n",
+      "         1.27882e-08          5.86154e-05          2.87469e+01\n",
+      "         1.26929e-08          5.83402e-05          2.87794e+01\n",
+      "         1.26069e-08          5.81077e-05          2.88090e+01\n",
+      "         1.25294e-08          5.79025e-05          2.88357e+01\n",
+      "         1.24593e-08          5.77139e-05          2.88601e+01\n",
+      "         1.23964e-08          5.75408e-05          2.88821e+01\n",
+      "         1.23400e-08          5.73717e-05          2.89019e+01\n",
+      "         1.22899e-08          5.72179e-05          2.89196e+01\n",
+      "         1.22457e-08          5.70800e-05          2.89352e+01\n",
+      "         1.22065e-08          5.69476e-05          2.89491e+01\n",
+      "         1.21716e-08          5.68219e-05          2.89616e+01\n",
+      "         1.21399e-08          5.67079e-05          2.89729e+01\n",
+      "         1.21121e-08          5.66082e-05          2.89828e+01\n",
+      "         1.20881e-08          5.65168e-05          2.89914e+01\n",
+      "         1.20672e-08          5.64386e-05          2.89990e+01\n",
+      "         1.20490e-08          5.63735e-05          2.90055e+01\n",
+      "         1.20338e-08          5.63197e-05          2.90110e+01\n",
+      "         1.20213e-08          5.62742e-05          2.90155e+01\n",
+      "         1.20117e-08          5.62335e-05          2.90190e+01\n",
+      "         1.20049e-08          5.61994e-05          2.90215e+01\n",
+      "         1.20006e-08          5.61720e-05          2.90230e+01\n",
+      "         1.19991e-08          5.61517e-05          2.90236e+01\n",
+      "         1.19998e-08          5.61385e-05          2.90233e+01\n",
+      "         1.20029e-08          5.61309e-05          2.90222e+01\n",
+      "         1.20088e-08          5.61240e-05          2.90201e+01\n",
+      "         1.20170e-08          5.61242e-05          2.90171e+01\n",
+      "         1.20275e-08          5.61325e-05          2.90133e+01\n",
+      "         1.20408e-08          5.61499e-05          2.90085e+01\n",
+      "         1.20565e-08          5.61750e-05          2.90028e+01\n",
+      "         1.20747e-08          5.62071e-05          2.89963e+01\n",
+      "         1.20954e-08          5.62405e-05          2.89888e+01\n",
+      "         1.21182e-08          5.62744e-05          2.89806e+01\n",
+      "         1.21432e-08          5.63137e-05          2.89717e+01\n",
+      "         1.21702e-08          5.63569e-05          2.89620e+01\n",
+      "         1.21990e-08          5.64026e-05          2.89518e+01\n",
+      "         1.22295e-08          5.64532e-05          2.89410e+01\n",
+      "         1.22611e-08          5.65052e-05          2.89297e+01\n",
+      "         1.22934e-08          5.65577e-05          2.89183e+01\n",
+      "         1.23272e-08          5.66137e-05          2.89064e+01\n",
+      "         1.23621e-08          5.66716e-05          2.88941e+01\n",
+      "         1.23983e-08          5.67352e-05          2.88814e+01\n",
+      "         1.24357e-08          5.68040e-05          2.88683e+01\n",
+      "         1.24743e-08          5.68758e-05          2.88549e+01\n",
+      "         1.25140e-08          5.69482e-05          2.88411e+01\n",
+      "         1.25548e-08          5.70229e-05          2.88269e+01\n",
+      "         1.25965e-08          5.71005e-05          2.88125e+01\n",
+      "         1.26388e-08          5.71802e-05          2.87980e+01\n",
+      "         1.26821e-08          5.72615e-05          2.87831e+01\n",
+      "         1.27264e-08          5.73452e-05          2.87680e+01\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "class MetricsDiagnostics(callbacks.Callback):\n",
+    "    \n",
+    "    def __init__(self, reference_image, metrics_dict, print_interval=1):\n",
+    "\n",
+    "        # reference image as numpy (level) array\n",
+    "        self.reference_image = reference_image      \n",
+    "        self.metrics_dict = metrics_dict\n",
+    "        # if data_range is None:\n",
+    "            # self.data_range = np.abs(self.reference_image.max() - self.reference_image.min())\n",
+    "        self.computed_metrics = []    \n",
+    "        self.print_interval=print_interval\n",
+    "\n",
+    "        super(MetricsDiagnostics, self).__init__()  \n",
+    "\n",
+    "    def __call__(self, algo):\n",
+    "\n",
+    "            \n",
+    "        for metric_name, metric_func in self.metrics_dict.items():\n",
+    "\n",
+    "            if not hasattr(algo, metric_name):\n",
+    "                setattr(algo, metric_name, [])   \n",
+    "                \n",
+    "            metric_list = getattr(algo, metric_name)\n",
+    "            metric_value = metric_func(self.reference_image, algo.get_output())\n",
+    "            metric_list.append(metric_value)\n",
+    "            \n",
+    "            self.computed_metrics.append(metric_value)\n",
+    "            \n",
+    "        if algo.iteration == 0:\n",
+    "                    \n",
+    "            print (self.callback_header())\n",
+    "        \n",
+    "        print(self.callback_iteration())  \n",
+    "                        \n",
+    "    \n",
+    "            \n",
+    "               \n",
+    "    def callback_header(self):\n",
+    "        return \" \".join(\"{:>20}\".format(metric_name) for metric_name in self.metrics_dict.keys())\n",
+    "\n",
+    "    def callback_iteration(self):\n",
+    "        if isinstance(self.computed_metrics, list):\n",
+    "            # Handle list of metrics\n",
+    "            return \" \".join(\"{:>20.5e}\".format(metric) for metric in self.computed_metrics[-len(self.metrics_dict):])\n",
+    "        else:\n",
+    "            # Handle single metric\n",
+    "            return \"{:>20.5e}\".format(self.computed_metrics)     \n",
+    "        \n",
+    "\n",
+    "from cil.utilities.quality_measures import mae, psnr, mse \n",
+    "metric_callback= MetricsDiagnostics(ground_truth, {'MSE':mse, 'MAE':mae, 'PSNR':psnr})\n",
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(100, callbacks=[metric_callback])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More complex example, image metric callbacks with region of interests \n",
+    "\n",
+    "Warning - this is a complex example! But the code may be useful to adapt and reuse "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ImageQualityCallback(callbacks.Callback):\n",
+    "    \"\"\"\n",
+    "      Parameters\n",
+    "    ----------\n",
+    "\n",
+    "    reference_image: CIL or STIR ImageData\n",
+    "      containing the reference image used to calculate the metrics\n",
+    "\n",
+    "    roi_mask_dict : dictionary of ImageData objects\n",
+    "      list containing one binary ImageData object for every ROI to be\n",
+    "      evaluated. Voxels with values 1 are considered part of the ROI\n",
+    "      and voxels with value 0 are not.\n",
+    "      Dimension of the ROI mask images must be the same as the dimension of\n",
+    "      the reference image.\n",
+    "      \n",
+    "    metrics_dict : dictionary of lambda functions f(x,y) mapping\n",
+    "      two 1-dimensional numpy arrays x and y to a scalar value or a\n",
+    "      numpy.ndarray.\n",
+    "      x and y can be the voxel values of the whole images or the values of\n",
+    "      voxels in a ROI such that the metric can be computed on the whole\n",
+    "      images and optionally in the ROIs separately.\n",
+    "\n",
+    "      E.g. f(x,y) could be MSE(x,y), PSNR(x,y), MAE(x,y)\n",
+    "\n",
+    "    statistics_dict : dictionary of lambda functions f(x) mapping a \n",
+    "      1-dimensional numpy array x to a scalar value or a numpy.ndarray.\n",
+    "      E.g. mean(x), std_deviation(x) that calculate global and / or\n",
+    "      ROI mean and standard deviations.\n",
+    "\n",
+    "      E.g. f(x) could be x.mean()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "      \"\"\"\n",
+    "      \n",
+    "    def __init__(self, reference_image, \n",
+    "                       roi_mask_dict   = None,\n",
+    "                       metrics_dict    = None,\n",
+    "                       statistics_dict = None,\n",
+    "                       ):\n",
+    "\n",
+    "        # the reference image\n",
+    "        self.reference_image = reference_image\n",
+    "\n",
+    "\n",
+    "        self.roi_indices_dict = {}\n",
+    "        self.roi_store=[]\n",
+    "\n",
+    "\n",
+    "\n",
+    "        self.roi_mask_dict=roi_mask_dict\n",
+    "        \n",
+    "        \n",
+    "        self.metrics_dict = metrics_dict\n",
+    "        self.metrics_store={}\n",
+    "        for key, value in self.metrics_dict.items():\n",
+    "                self.metrics_store['global_'+key] = []\n",
+    "                if roi_mask_dict is not None:\n",
+    "                  for roi_name, value in roi_mask_dict.items():\n",
+    "                    self.metrics_store[roi_name+'_'+key] = []\n",
+    "\n",
+    "        self.statistics_dict = statistics_dict\n",
+    "        self.stat_store={}\n",
+    "        for key, value in self.statistics_dict.items():\n",
+    "                self.stat_store['global_'+key] = []\n",
+    "                if roi_mask_dict is not None:\n",
+    "                  for roi_name, value in roi_mask_dict.items():\n",
+    "                    self.stat_store[roi_name+'_'+key] = []\n",
+    "                \n",
+    "    def __call__(self, algorithm):\n",
+    "            if self.metrics_dict is not None:\n",
+    "                for metric_name, metric in self.metrics_dict.items():\n",
+    "                    ans = metric(self.reference_image, algorithm.x)\n",
+    "                    self.metrics_store['global_'+metric_name].append(ans)\n",
+    "                    \n",
+    "                    \n",
+    "                    for roi_name, roi in self.roi_mask_dict.items():\n",
+    "                      ans = metric(self.reference_image, algorithm.x, mask=roi)\n",
+    "                      self.metrics_store[roi_name+'_'+metric_name].append(ans)\n",
+    "                       \n",
+    "                    \n",
+    "                    \n",
+    "            if self.statistics_dict is not None:\n",
+    "                for statistic_name, stat in self.statistics_dict.items():\n",
+    "                    ans = stat( algorithm.x.array, np._NoValue)\n",
+    "                    self.stat_store['global_'+statistic_name].append(ans)\n",
+    "                    \n",
+    "                    \n",
+    "                    for roi_name, roi in self.roi_mask_dict.items():\n",
+    "                        ans = stat( algorithm.x.array, roi.array.astype('bool'))\n",
+    "                        self.stat_store[roi_name+'_'+statistic_name].append(ans)\n",
+    "                        \n",
+    "        \n",
+    "                    \n",
+    "                    \n",
+    "       \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mse(dc1, dc2, mask=None):\n",
+    "    ''' Calculates the mean squared error of two images\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dc1: `DataContainer`\n",
+    "        One image to be compared\n",
+    "    dc2: `DataContainer`\n",
+    "        Second image to be compared\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number, the mean squared error of the two images\n",
+    "    '''\n",
+    "    dc1 = dc1.as_array()\n",
+    "    dc2 = dc2.as_array()\n",
+    "\n",
+    "    if mask is not None:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "\n",
+    "        mask = mask.astype('bool')\n",
+    "        dc1 = np.extract(mask, dc1)\n",
+    "        dc2 = np.extract(mask, dc2)\n",
+    "    return np.mean(((dc1 - dc2)**2))\n",
+    "\n",
+    "\n",
+    "def mae(dc1, dc2, mask=None):\n",
+    "    ''' Calculates the Mean Absolute error of two images.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dc1: `DataContainer`\n",
+    "        One image to be compared\n",
+    "    dc2: `DataContainer`\n",
+    "        Second image to be compared\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero.\n",
+    "\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number with the mean absolute error between the two images.\n",
+    "    '''\n",
+    "    dc1 = dc1.as_array()\n",
+    "    dc2 = dc2.as_array()\n",
+    "\n",
+    "    if mask is not None:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "\n",
+    "        mask = mask.astype('bool')\n",
+    "        dc1 = np.extract(mask, dc1)\n",
+    "        dc2 = np.extract(mask, dc2)\n",
+    "\n",
+    "    return np.mean(np.abs((dc1-dc2)))\n",
+    "\n",
+    "\n",
+    "def psnr(ground_truth, corrupted, mask=None):\n",
+    "    ''' Calculates the Peak signal to noise ratio (PSNR) between the two images.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ground_truth: `DataContainer`\n",
+    "        The reference image\n",
+    "    corrupted: `DataContainer`\n",
+    "        The image to be evaluated\n",
+    "    data_range: scalar value, default=None\n",
+    "        PSNR scaling factor, the dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values). We take the maximum value in the ground truth array.\n",
+    "    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`\n",
+    "        The pixelwise operation only considers values where the mask is True or NonZero..\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A number, the peak signal to noise ration between the two images.\n",
+    "    '''\n",
+    "    \n",
+    "\n",
+    "    if mask is None:\n",
+    "        data_range = ground_truth.as_array().max()\n",
+    "\n",
+    "\n",
+    "    else:\n",
+    "\n",
+    "        if isinstance(mask, DataContainer):\n",
+    "            mask = mask.as_array()\n",
+    "        data_range = np.max(ground_truth.as_array(),\n",
+    "                                where=mask.astype('bool'), initial=-1e-8)\n",
+    "\n",
+    "    \n",
+    "    tmp_mse = mse(ground_truth, corrupted, mask=mask)\n",
+    "\n",
+    "    return 10 * np.log10((data_range ** 2) / tmp_mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAAAAAAAABMBGOgAAAAAAAAAAAbCRDgAAAAAAAABAAGykAwAAAAAAAAAQwP8BErYreQJZftMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08dcf79120>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#%% create masks\n",
+    "top = ig.allocate(0)\n",
+    "bottom = ig.allocate(0)\n",
+    "\n",
+    "top.fill(\n",
+    "    np.asarray(ground_truth.array > 0.8 * ground_truth.max(), \n",
+    "               dtype=np.float32)\n",
+    "    )\n",
+    "bottom.fill(\n",
+    "    np.asarray(np.invert(ground_truth.array < 0.4 * ground_truth.max()), \n",
+    "               dtype=np.float32)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "roi_image_dict = {\n",
+    "    'top' : top,\n",
+    "    'bottom' : bottom\n",
+    "}\n",
+    "\n",
+    "show2D([ground_truth, top, bottom], num_cols=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_qual_callback = ImageQualityCallback(ground_truth,\n",
+    "                                              roi_mask_dict = roi_image_dict,\n",
+    "                                              metrics_dict = {'MSE':mse, \n",
+    "                                                              'MAE':mae, \n",
+    "                                                              'PSNR':psnr},\n",
+    "                                              statistics_dict = {'MEAN': (lambda x, y: np.mean(x, where=y)),\n",
+    "                                                                 'STDDEV': (lambda x, y: np.std(x, where=y)),\n",
+    "                                                                 'MAX': (lambda x, y: np.max(x, where=y, initial=0))},\n",
+    "                                              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cil.utilities.display.show2D at 0x7f08dcda0af0>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo=FISTA(initial=ig.allocate(0), f=F, g=G, update_objective_interval=10)         \n",
+    "algo.run(500, callbacks=[img_qual_callback])\n",
+    "show2D([ground_truth, recon, algo.solution], title = ['Ground Truth', 'FDK Reconstruction', 'TV solution'], origin = 'upper', num_cols = 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f08dec65b10>]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(501), img_qual_callback.metrics_store['global_MSE'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f08dec67bb0>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(501), img_qual_callback.metrics_store['top_PSNR'], label='Top')\n",
+    "plt.plot(range(501), img_qual_callback.metrics_store['global_PSNR'], label='Global')\n",
+    "plt.plot(range(501), img_qual_callback.metrics_store['bottom_PSNR'], label='Bottom')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cil_testing2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/v24.2.0/demos/callback_demonstration/index.html b/v24.2.0/demos/callback_demonstration/index.html
new file mode 100644
index 0000000000..5be42ca0ba
--- /dev/null
+++ b/v24.2.0/demos/callback_demonstration/index.html
@@ -0,0 +1,1626 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>CIL Callback demonstration &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
+  </script>
+  <!-- 
+    this give us a css class that will be invisible only if js is disabled 
+  -->
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+<li class="toctree-l2"><a class="reference internal" href="../00_CIL_geometry/">A detailed look at CIL geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../deriv2_cgls/">1D inverse problem demo using deriv2 from regtools</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">CIL Callback demonstration</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Load-Data">Load Data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Default-behaviour">Default behaviour</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Other-provided-CIL-callbacks">Other provided CIL callbacks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Early-stopping-(a-custom-callback-example)">Early stopping (a custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Calculating-data-discrepancy-at-each-iteration-(A-custom-callback-example)">Calculating data discrepancy at each iteration (A custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Calculating-a-noise-approximation-for-each-iteration-(A-custom-callback-example)">Calculating a noise approximation for each iteration (A custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Image-metric-callbacks-(custom-callback-example)">Image metric callbacks (custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#More-complex-example,-image-metric-callbacks-with-region-of-interests">More complex example, image metric callbacks with region of interests</a></li>
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+  <div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">#  Copyright 2024 -  United Kingdom Research and Innovation</span>
+<span class="c1">#  Copyright 2024 -  The University of Manchester</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1">#   Authored by:    CIL contributors</span>
+</pre></div>
+</div>
+</div>
+<section id="CIL-Callback-demonstration">
+<h1>CIL Callback demonstration<a class="headerlink" href="#CIL-Callback-demonstration" title="Link to this heading">#</a></h1>
+<p>This notebook runs on CIL Master (built on 14/03/2024) and demonstrates the new callback functionality</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cil.utilities</span> <span class="kn">import</span> <span class="n">dataexample</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.display</span> <span class="kn">import</span> <span class="n">show2D</span>
+<span class="kn">from</span> <span class="nn">cil.recon</span> <span class="kn">import</span> <span class="n">FDK</span>
+<span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">TransmissionAbsorptionConverter</span><span class="p">,</span> <span class="n">Slicer</span>
+<span class="kn">from</span> <span class="nn">cil.utilities.quality_measures</span> <span class="kn">import</span> <span class="n">psnr</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.tigre</span> <span class="kn">import</span> <span class="n">ProjectionOperator</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">FISTA</span><span class="p">,</span> <span class="n">Algorithm</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">LeastSquares</span><span class="p">,</span> <span class="n">IndicatorBox</span><span class="p">,</span> <span class="n">ZeroFunction</span><span class="p">,</span> <span class="n">TotalVariation</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">GradientOperator</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.utilities</span> <span class="kn">import</span> <span class="n">callbacks</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">DataContainer</span>
+
+<span class="kn">from</span> <span class="nn">cil.utilities.quality_measures</span> <span class="kn">import</span> <span class="n">mse</span><span class="p">,</span> <span class="n">mae</span><span class="p">,</span> <span class="n">psnr</span>
+
+<span class="c1"># set up default colour map for visualisation</span>
+<span class="n">cmap</span> <span class="o">=</span> <span class="s2">&quot;gray&quot;</span>
+
+<span class="c1"># set the backend for FBP and the ProjectionOperator</span>
+<span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;gpu&#39;</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<section id="Load-Data">
+<h2>Load Data<a class="headerlink" href="#Load-Data" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="c1">#%% Load data</span>
+<span class="n">ground_truth</span> <span class="o">=</span> <span class="n">dataexample</span><span class="o">.</span><span class="n">SIMULATED_SPHERE_VOLUME</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">dataexample</span><span class="o">.</span><span class="n">SIMULATED_CONE_BEAM_DATA</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="n">twoD</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">if</span> <span class="n">twoD</span><span class="p">:</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="s1">&#39;centre&#39;</span><span class="p">)</span>
+    <span class="n">ground_truth</span> <span class="o">=</span> <span class="n">ground_truth</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="s1">&#39;centre&#39;</span><span class="p">)</span>
+
+<span class="n">absorption</span> <span class="o">=</span> <span class="n">TransmissionAbsorptionConverter</span><span class="p">()(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">absorption</span> <span class="o">=</span> <span class="n">Slicer</span><span class="p">(</span><span class="n">roi</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;angle&#39;</span><span class="p">:(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)})(</span><span class="n">absorption</span><span class="p">)</span>
+
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ground_truth</span><span class="o">.</span><span class="n">geometry</span>
+
+<span class="c1">#%%</span>
+<span class="n">recon</span> <span class="o">=</span> <span class="n">FDK</span><span class="p">(</span><span class="n">absorption</span><span class="p">,</span> <span class="n">image_geometry</span><span class="o">=</span><span class="n">ig</span><span class="p">)</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+<span class="c1">#%%</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">recon</span><span class="p">],</span> <span class="n">title</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Ground Truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FDK Reconstruction&#39;</span><span class="p">],</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;upper&#39;</span><span class="p">,</span> <span class="n">num_cols</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># %%</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+FDK recon
+
+Input Data:
+        angle: 60
+        horizontal: 128
+
+Reconstruction Volume:
+        horizontal_y: 128
+        horizontal_x: 128
+
+Reconstruction Options:
+        Backend: tigre
+        Filter: ram-lak
+        Filter cut-off frequency: 1.0
+        FFT order: 8
+        Filter_inplace: False
+
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_4_1.png" src="../../_images/demos_callback_demonstration_4_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show2D at 0x7f0908e75840&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Default-behaviour">
+<h2>Default behaviour<a class="headerlink" href="#Default-behaviour" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><br/><span></span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">ProjectionOperator</span><span class="p">(</span><span class="n">image_geometry</span><span class="o">=</span><span class="n">ig</span><span class="p">,</span>
+                       <span class="n">acquisition_geometry</span><span class="o">=</span><span class="n">absorption</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+
+<span class="n">F</span> <span class="o">=</span> <span class="n">LeastSquares</span><span class="p">(</span><span class="n">A</span> <span class="o">=</span> <span class="n">A</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">absorption</span><span class="p">)</span>
+<span class="n">G</span> <span class="o">=</span> <span class="n">alpha</span><span class="o">*</span><span class="n">TotalVariation</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">algo</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">G</span><span class="p">)</span>
+<span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">)</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">recon</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">solution</span><span class="p">],</span> <span class="n">title</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Ground Truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FDK Reconstruction&#39;</span><span class="p">,</span> <span class="s1">&#39;TV solution&#39;</span><span class="p">],</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;upper&#39;</span><span class="p">,</span> <span class="n">num_cols</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "6355b4a0be0c4297a02f41b49e6f7e3d", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_6_1.png" src="../../_images/demos_callback_demonstration_6_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show2D at 0x7f0908e74b80&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Other-provided-CIL-callbacks">
+<h2>Other provided CIL callbacks<a class="headerlink" href="#Other-provided-CIL-callbacks" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">algo</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">G</span><span class="p">,</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ProgressCallback</span><span class="p">(),</span> <span class="n">callbacks</span><span class="o">.</span><span class="n">TextProgressCallback</span><span class="p">()])</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">recon</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">solution</span><span class="p">],</span> <span class="n">title</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Ground Truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FDK Reconstruction&#39;</span><span class="p">,</span> <span class="s1">&#39;TV solution&#39;</span><span class="p">],</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;upper&#39;</span><span class="p">,</span> <span class="n">num_cols</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "43acfefb45534d8093aa0174846f19eb", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+     0/500        ?it/s
+    10/500    29.53it/s, objective=+8.586e+01
+    20/500    29.53it/s, objective=+9.047e+00
+    30/500    28.65it/s, objective=+2.640e+00
+    40/500    28.53it/s, objective=+1.188e+00
+    50/500    18.75it/s, objective=+6.928e-01
+    60/500    21.45it/s, objective=+4.585e-01
+    70/500    23.55it/s, objective=+3.451e-01
+    80/500    25.28it/s, objective=+2.741e-01
+    90/500    26.58it/s, objective=+2.337e-01
+   100/500    27.57it/s, objective=+2.062e-01
+   110/500    28.17it/s, objective=+1.870e-01
+   120/500    20.05it/s, objective=+1.729e-01
+   130/500    22.35it/s, objective=+1.623e-01
+   140/500    24.32it/s, objective=+1.541e-01
+   150/500    25.89it/s, objective=+1.476e-01
+   160/500    26.91it/s, objective=+1.421e-01
+   170/500    27.82it/s, objective=+1.375e-01
+   180/500    28.58it/s, objective=+1.336e-01
+   190/500    20.24it/s, objective=+1.303e-01
+   200/500    22.51it/s, objective=+1.275e-01
+   210/500    24.47it/s, objective=+1.250e-01
+   220/500    26.06it/s, objective=+1.228e-01
+   230/500    27.32it/s, objective=+1.208e-01
+   240/500    28.08it/s, objective=+1.190e-01
+   250/500    28.55it/s, objective=+1.173e-01
+   260/500    28.69it/s, objective=+1.159e-01
+   270/500    28.86it/s, objective=+1.145e-01
+   280/500    29.03it/s, objective=+1.134e-01
+   290/500    29.44it/s, objective=+1.123e-01
+   300/500    29.27it/s, objective=+1.113e-01
+   310/500    29.51it/s, objective=+1.104e-01
+   320/500    21.28it/s, objective=+1.096e-01
+   330/500    22.71it/s, objective=+1.088e-01
+   340/500    24.61it/s, objective=+1.081e-01
+   350/500    26.25it/s, objective=+1.075e-01
+   360/500    27.47it/s, objective=+1.068e-01
+   370/500    28.42it/s, objective=+1.063e-01
+   380/500    29.10it/s, objective=+1.057e-01
+   390/500    20.90it/s, objective=+1.052e-01
+   400/500    23.14it/s, objective=+1.048e-01
+   410/500    24.79it/s, objective=+1.043e-01
+   420/500    25.48it/s, objective=+1.039e-01
+   430/500    26.67it/s, objective=+1.035e-01
+   440/500    27.03it/s, objective=+1.031e-01
+   450/500    27.67it/s, objective=+1.028e-01
+   460/500    27.81it/s, objective=+1.025e-01
+   470/500    28.66it/s, objective=+1.022e-01
+   480/500    29.28it/s, objective=+1.019e-01
+   490/500    29.76it/s, objective=+1.016e-01
+   500/500    30.16it/s, objective=+1.013e-01
+   500/500    26.07it/s
+
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_8_2.png" src="../../_images/demos_callback_demonstration_8_2.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show2D at 0x7f0908e75030&gt;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ProgressCallback</span><span class="p">(),</span> <span class="n">callbacks</span><span class="o">.</span><span class="n">TextProgressCallback</span><span class="p">()])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "6d4cfa5b60cf48b7acfd008e3c40655f", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+   501/600        ?it/s
+   511/600    30.76it/s, objective=+1.011e-01
+   521/600    30.74it/s, objective=+1.008e-01
+   531/600    30.69it/s, objective=+1.006e-01
+   541/600    30.26it/s, objective=+1.004e-01
+   551/600    28.95it/s, objective=+1.002e-01
+   561/600    20.01it/s, objective=+1.000e-01
+   571/600    22.57it/s, objective=+9.986e-02
+   581/600    24.69it/s, objective=+9.969e-02
+   591/600    26.41it/s, objective=+9.953e-02
+   600/600    26.40it/s, objective=+9.938e-02
+
+</pre></div></div>
+</div>
+</section>
+<section id="Early-stopping-(a-custom-callback-example)">
+<h2>Early stopping (a custom callback example)<a class="headerlink" href="#Early-stopping-(a-custom-callback-example)" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">EarlyStopping</span><span class="p">(</span><span class="n">callbacks</span><span class="o">.</span><span class="n">Callback</span><span class="p">):</span>
+         <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+               <span class="k">if</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">objective</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mf">2e-1</span><span class="p">:</span>  <span class="c1"># arbitrary stopping criterion</span>
+                  <span class="k">raise</span> <span class="ne">StopIteration</span>
+
+<span class="n">algo</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">G</span><span class="p">,</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">callbacks</span><span class="o">.</span><span class="n">TextProgressCallback</span><span class="p">(),</span> <span class="n">EarlyStopping</span><span class="p">()])</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">recon</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">solution</span><span class="p">],</span> <span class="n">title</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Ground Truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FDK Reconstruction&#39;</span><span class="p">,</span> <span class="s1">&#39;TV solution&#39;</span><span class="p">],</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;upper&#39;</span><span class="p">,</span> <span class="n">num_cols</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+     0/500        ?it/s
+    10/500    31.71it/s, objective=+8.586e+01
+    20/500    31.18it/s, objective=+9.047e+00
+    30/500    29.77it/s, objective=+2.640e+00
+    40/500    30.06it/s, objective=+1.188e+00
+    50/500    30.28it/s, objective=+6.928e-01
+    60/500    30.35it/s, objective=+4.585e-01
+    70/500    30.46it/s, objective=+3.451e-01
+    80/500    30.57it/s, objective=+2.741e-01
+    90/500    22.27it/s, objective=+2.337e-01
+   100/500    22.69it/s, objective=+2.062e-01
+   110/500    24.61it/s, objective=+1.870e-01
+   110/500    26.86it/s
+
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_11_1.png" src="../../_images/demos_callback_demonstration_11_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show2D at 0x7f08d5daaec0&gt;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">EarlyStopping</span><span class="p">(</span><span class="n">callbacks</span><span class="o">.</span><span class="n">Callback</span><span class="p">):</span>
+         <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">array</span><span class="o">-</span><span class="n">ground_truth</span><span class="o">.</span><span class="n">array</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mf">3e-8</span><span class="p">:</span>  <span class="c1"># arbitrary stopping criterion</span>
+                  <span class="k">raise</span> <span class="ne">StopIteration</span>
+
+<span class="n">algo</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">G</span><span class="p">,</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">callbacks</span><span class="o">.</span><span class="n">TextProgressCallback</span><span class="p">(),</span> <span class="n">EarlyStopping</span><span class="p">()])</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">recon</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">solution</span><span class="p">],</span> <span class="n">title</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Ground Truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FDK Reconstruction&#39;</span><span class="p">,</span> <span class="s1">&#39;TV solution&#39;</span><span class="p">],</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;upper&#39;</span><span class="p">,</span> <span class="n">num_cols</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+     0/500        ?it/s
+    10/500    23.79it/s, objective=+8.586e+01
+    20/500    26.96it/s, objective=+9.047e+00
+    23/500    26.89it/s
+
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_12_1.png" src="../../_images/demos_callback_demonstration_12_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show2D at 0x7f08d5b68d90&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Calculating-data-discrepancy-at-each-iteration-(A-custom-callback-example)">
+<h2>Calculating data discrepancy at each iteration (A custom callback example)<a class="headerlink" href="#Calculating-data-discrepancy-at-each-iteration-(A-custom-callback-example)" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DataDiscrepancyCallback</span><span class="p">(</span><span class="n">callbacks</span><span class="o">.</span><span class="n">Callback</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">f</span> <span class="o">=</span> <span class="n">LeastSquares</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">save_values</span><span class="o">=</span><span class="p">[]</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">save_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">f</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">get_output</span><span class="p">()))</span>
+
+<span class="n">mycallback_FISTA_lower_bound</span><span class="o">=</span> <span class="n">DataDiscrepancyCallback</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">absorption</span><span class="p">)</span>
+<span class="n">algo1</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">alpha</span><span class="o">*</span><span class="n">TotalVariation</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo1</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">mycallback_FISTA_lower_bound</span><span class="p">])</span>
+
+
+<span class="n">mycallback_FISTA_no_lower_bound</span><span class="o">=</span> <span class="n">DataDiscrepancyCallback</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">absorption</span><span class="p">)</span>
+<span class="n">algo2</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">alpha</span><span class="o">*</span><span class="n">TotalVariation</span><span class="p">(),</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo2</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">mycallback_FISTA_no_lower_bound</span><span class="p">])</span>
+
+
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">algo1</span><span class="o">.</span><span class="n">get_output</span><span class="p">(),</span> <span class="n">algo2</span><span class="o">.</span><span class="n">get_output</span><span class="p">()],</span> <span class="n">title</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ground_truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FISTA_lower_bound&#39;</span><span class="p">,</span> <span class="s1">&#39;FISTA_no_lower_bound&#39;</span><span class="p">],</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">absorption</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">algo1</span><span class="o">.</span><span class="n">get_output</span><span class="p">())</span><span class="o">-</span><span class="n">absorption</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">algo2</span><span class="o">.</span><span class="n">get_output</span><span class="p">())</span><span class="o">-</span><span class="n">absorption</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ground_truth&#39;</span><span class="p">,</span> <span class="s1">&#39;Data error FISTA_lower_bound&#39;</span><span class="p">,</span> <span class="s1">&#39;Data error FISTA_no_lower_bound&#39;</span><span class="p">],</span> <span class="n">fix_range</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.02</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.02</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]],</span> <span class="n">cmap</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="s1">&#39;seismic&#39;</span><span class="p">,</span> <span class="s1">&#39;seismic&#39;</span><span class="p">],</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">501</span><span class="p">),</span> <span class="n">mycallback_FISTA_lower_bound</span><span class="o">.</span><span class="n">save_values</span><span class="p">[</span><span class="mi">10</span><span class="p">:],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;FISTA TV with lower bound &#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">501</span><span class="p">),</span> <span class="n">mycallback_FISTA_no_lower_bound</span><span class="o">.</span><span class="n">save_values</span><span class="p">[</span><span class="mi">10</span><span class="p">:],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;FISTA TV without lower bound &#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Data discrepancy $\|Ax-y\|_2^2$&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Iteration&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_14_0.png" src="../../_images/demos_callback_demonstration_14_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_14_1.png" src="../../_images/demos_callback_demonstration_14_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x7f08df8487f0&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_14_3.png" src="../../_images/demos_callback_demonstration_14_3.png" />
+</div>
+</div>
+<p>We see that the without the lower bound, the reconstruction overfits to the noisy absorption data</p>
+</section>
+<section id="Calculating-a-noise-approximation-for-each-iteration-(A-custom-callback-example)">
+<h2>Calculating a noise approximation for each iteration (A custom callback example)<a class="headerlink" href="#Calculating-a-noise-approximation-for-each-iteration-(A-custom-callback-example)" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">skimage</span>
+
+<span class="k">class</span> <span class="nc">SigmaEstimateCallback</span><span class="p">(</span><span class="n">callbacks</span><span class="o">.</span><span class="n">Callback</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">save_values</span><span class="o">=</span><span class="p">[]</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">save_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">skimage</span><span class="o">.</span><span class="n">restoration</span><span class="o">.</span><span class="n">estimate_sigma</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span><span class="o">.</span><span class="n">as_array</span><span class="p">()))</span>
+
+<span class="n">mycallback_FISTA_TV_alpha_01</span><span class="o">=</span> <span class="n">SigmaEstimateCallback</span><span class="p">()</span>
+<span class="n">algo1</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="mf">0.1</span><span class="o">*</span><span class="n">TotalVariation</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo1</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">mycallback_FISTA_TV_alpha_01</span><span class="p">])</span>
+
+
+<span class="n">mycallback_FISTA_TV_alpha_1</span><span class="o">=</span> <span class="n">SigmaEstimateCallback</span><span class="p">()</span>
+<span class="n">algo2</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="mi">1</span><span class="o">*</span><span class="n">TotalVariation</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo2</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">mycallback_FISTA_TV_alpha_1</span><span class="p">])</span>
+
+
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">algo1</span><span class="o">.</span><span class="n">get_output</span><span class="p">(),</span> <span class="n">algo2</span><span class="o">.</span><span class="n">get_output</span><span class="p">()],</span> <span class="n">title</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ground_truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FISTA_TV_alpha_01&#39;</span><span class="p">,</span> <span class="s1">&#39;FISTA_TV_alpha_1&#39;</span><span class="p">],</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">absorption</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">algo1</span><span class="o">.</span><span class="n">get_output</span><span class="p">())</span><span class="o">-</span><span class="n">absorption</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">algo2</span><span class="o">.</span><span class="n">get_output</span><span class="p">())</span><span class="o">-</span><span class="n">absorption</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ground_truth&#39;</span><span class="p">,</span> <span class="s1">&#39;Data error FISTA_TV_alpha_01&#39;</span><span class="p">,</span> <span class="s1">&#39;Data error FISTA_TV_alpha_1&#39;</span><span class="p">],</span> <span class="n">fix_range</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.02</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.02</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]],</span> <span class="n">cmap</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="s1">&#39;seismic&#39;</span><span class="p">,</span> <span class="s1">&#39;seismic&#39;</span><span class="p">],</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">501</span><span class="p">),</span> <span class="n">mycallback_FISTA_TV_alpha_01</span><span class="o">.</span><span class="n">save_values</span><span class="p">[</span><span class="mi">10</span><span class="p">:],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;FISTA TV alpha=0.1 &#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">501</span><span class="p">),</span> <span class="n">mycallback_FISTA_TV_alpha_1</span><span class="o">.</span><span class="n">save_values</span><span class="p">[</span><span class="mi">10</span><span class="p">:],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;FISTA TV alpha=1.0 &#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Noise Estimate&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Iteration&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.
+  return _methods._mean(a, axis=axis, dtype=dtype,
+/home/bih17925/miniconda3/envs/cil_testing2/lib/python3.10/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in divide
+  ret = ret.dtype.type(ret / rcount)
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_17_1.png" src="../../_images/demos_callback_demonstration_17_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_17_2.png" src="../../_images/demos_callback_demonstration_17_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x7f08dfcb7670&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_17_4.png" src="../../_images/demos_callback_demonstration_17_4.png" />
+</div>
+</div>
+<p>We see with a larger regularisation parameter, the resulting image is less noisy.</p>
+</section>
+<section id="Image-metric-callbacks-(custom-callback-example)">
+<h2>Image metric callbacks (custom callback example)<a class="headerlink" href="#Image-metric-callbacks-(custom-callback-example)" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="k">class</span> <span class="nc">MetricsDiagnostics</span><span class="p">(</span><span class="n">callbacks</span><span class="o">.</span><span class="n">Callback</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reference_image</span><span class="p">,</span> <span class="n">metrics_dict</span><span class="p">,</span> <span class="n">print_interval</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+
+        <span class="c1"># reference image as numpy (level) array</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_image</span> <span class="o">=</span> <span class="n">reference_image</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span> <span class="o">=</span> <span class="n">metrics_dict</span>
+        <span class="c1"># if data_range is None:</span>
+            <span class="c1"># self.data_range = np.abs(self.reference_image.max() - self.reference_image.min())</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">computed_metrics</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">print_interval</span><span class="o">=</span><span class="n">print_interval</span>
+
+        <span class="nb">super</span><span class="p">(</span><span class="n">MetricsDiagnostics</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algo</span><span class="p">):</span>
+
+
+        <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_func</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">algo</span><span class="p">,</span> <span class="n">metric_name</span><span class="p">):</span>
+                <span class="nb">setattr</span><span class="p">(</span><span class="n">algo</span><span class="p">,</span> <span class="n">metric_name</span><span class="p">,</span> <span class="p">[])</span>
+
+            <span class="n">metric_list</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">algo</span><span class="p">,</span> <span class="n">metric_name</span><span class="p">)</span>
+            <span class="n">metric_value</span> <span class="o">=</span> <span class="n">metric_func</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_image</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">get_output</span><span class="p">())</span>
+            <span class="n">metric_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">metric_value</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">computed_metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">metric_value</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">algo</span><span class="o">.</span><span class="n">iteration</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+
+            <span class="nb">print</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">callback_header</span><span class="p">())</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">callback_iteration</span><span class="p">())</span>
+
+
+
+
+    <span class="k">def</span> <span class="nf">callback_header</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:&gt;20}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metric_name</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+    <span class="k">def</span> <span class="nf">callback_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">computed_metrics</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="c1"># Handle list of metrics</span>
+            <span class="k">return</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:&gt;20.5e}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">computed_metrics</span><span class="p">[</span><span class="o">-</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span><span class="p">):])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Handle single metric</span>
+            <span class="k">return</span> <span class="s2">&quot;</span><span class="si">{:&gt;20.5e}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">computed_metrics</span><span class="p">)</span>
+
+
+<span class="kn">from</span> <span class="nn">cil.utilities.quality_measures</span> <span class="kn">import</span> <span class="n">mae</span><span class="p">,</span> <span class="n">psnr</span><span class="p">,</span> <span class="n">mse</span>
+<span class="n">metric_callback</span><span class="o">=</span> <span class="n">MetricsDiagnostics</span><span class="p">(</span><span class="n">ground_truth</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;MSE&#39;</span><span class="p">:</span><span class="n">mse</span><span class="p">,</span> <span class="s1">&#39;MAE&#39;</span><span class="p">:</span><span class="n">mae</span><span class="p">,</span> <span class="s1">&#39;PSNR&#39;</span><span class="p">:</span><span class="n">psnr</span><span class="p">})</span>
+<span class="n">algo</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">G</span><span class="p">,</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">metric_callback</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+                 MSE                  MAE                 PSNR
+         1.07888e-06          5.48145e-04          9.48530e+00
+         5.85316e-07          6.22034e-04          1.21411e+01
+         5.05844e-07          5.72563e-04          1.27749e+01
+         4.31374e-07          5.19819e-04          1.34665e+01
+         3.64704e-07          4.67054e-04          1.41956e+01
+         3.06416e-07          4.16492e-04          1.49519e+01
+         2.56388e-07          3.70092e-04          1.57261e+01
+         2.14156e-07          3.28810e-04          1.65077e+01
+         1.78987e-07          2.92725e-04          1.72868e+01
+         1.50022e-07          2.60981e-04          1.80535e+01
+         1.26383e-07          2.33361e-04          1.87981e+01
+         1.07187e-07          2.09652e-04          1.95136e+01
+         9.16141e-08          1.89309e-04          2.01954e+01
+         7.89449e-08          1.72049e-04          2.08418e+01
+         6.85910e-08          1.57283e-04          2.14524e+01
+         6.00884e-08          1.44610e-04          2.20271e+01
+         5.30737e-08          1.33746e-04          2.25662e+01
+         4.72670e-08          1.24414e-04          2.30695e+01
+         4.24393e-08          1.16364e-04          2.35374e+01
+         3.84176e-08          1.09416e-04          2.39697e+01
+         3.50543e-08          1.03451e-04          2.43676e+01
+         3.22300e-08          9.82989e-05          2.47324e+01
+         2.98493e-08          9.39156e-05          2.50657e+01
+         2.78304e-08          9.01341e-05          2.53698e+01
+         2.61075e-08          8.68679e-05          2.56474e+01
+         2.46249e-08          8.40164e-05          2.59013e+01
+         2.33423e-08          8.14809e-05          2.61336e+01
+         2.22266e-08          7.92211e-05          2.63463e+01
+         2.12462e-08          7.72332e-05          2.65422e+01
+         2.03792e-08          7.54337e-05          2.67232e+01
+         1.96080e-08          7.38151e-05          2.68907e+01
+         1.89173e-08          7.23520e-05          2.70464e+01
+         1.82934e-08          7.10307e-05          2.71921e+01
+         1.77264e-08          6.98001e-05          2.73288e+01
+         1.72101e-08          6.86725e-05          2.74572e+01
+         1.67384e-08          6.76756e-05          2.75779e+01
+         1.63068e-08          6.67997e-05          2.76913e+01
+         1.59109e-08          6.59966e-05          2.77981e+01
+         1.55498e-08          6.52429e-05          2.78978e+01
+         1.52207e-08          6.45565e-05          2.79907e+01
+         1.49199e-08          6.39012e-05          2.80774e+01
+         1.46448e-08          6.32729e-05          2.81582e+01
+         1.43935e-08          6.26837e-05          2.82334e+01
+         1.41640e-08          6.21308e-05          2.83032e+01
+         1.39533e-08          6.16084e-05          2.83683e+01
+         1.37602e-08          6.11234e-05          2.84288e+01
+         1.35827e-08          6.06739e-05          2.84852e+01
+         1.34200e-08          6.02613e-05          2.85375e+01
+         1.32710e-08          5.98831e-05          2.85860e+01
+         1.31342e-08          5.95365e-05          2.86310e+01
+         1.30086e-08          5.92132e-05          2.86727e+01
+         1.28935e-08          5.89066e-05          2.87113e+01
+         1.27882e-08          5.86154e-05          2.87469e+01
+         1.26929e-08          5.83402e-05          2.87794e+01
+         1.26069e-08          5.81077e-05          2.88090e+01
+         1.25294e-08          5.79025e-05          2.88357e+01
+         1.24593e-08          5.77139e-05          2.88601e+01
+         1.23964e-08          5.75408e-05          2.88821e+01
+         1.23400e-08          5.73717e-05          2.89019e+01
+         1.22899e-08          5.72179e-05          2.89196e+01
+         1.22457e-08          5.70800e-05          2.89352e+01
+         1.22065e-08          5.69476e-05          2.89491e+01
+         1.21716e-08          5.68219e-05          2.89616e+01
+         1.21399e-08          5.67079e-05          2.89729e+01
+         1.21121e-08          5.66082e-05          2.89828e+01
+         1.20881e-08          5.65168e-05          2.89914e+01
+         1.20672e-08          5.64386e-05          2.89990e+01
+         1.20490e-08          5.63735e-05          2.90055e+01
+         1.20338e-08          5.63197e-05          2.90110e+01
+         1.20213e-08          5.62742e-05          2.90155e+01
+         1.20117e-08          5.62335e-05          2.90190e+01
+         1.20049e-08          5.61994e-05          2.90215e+01
+         1.20006e-08          5.61720e-05          2.90230e+01
+         1.19991e-08          5.61517e-05          2.90236e+01
+         1.19998e-08          5.61385e-05          2.90233e+01
+         1.20029e-08          5.61309e-05          2.90222e+01
+         1.20088e-08          5.61240e-05          2.90201e+01
+         1.20170e-08          5.61242e-05          2.90171e+01
+         1.20275e-08          5.61325e-05          2.90133e+01
+         1.20408e-08          5.61499e-05          2.90085e+01
+         1.20565e-08          5.61750e-05          2.90028e+01
+         1.20747e-08          5.62071e-05          2.89963e+01
+         1.20954e-08          5.62405e-05          2.89888e+01
+         1.21182e-08          5.62744e-05          2.89806e+01
+         1.21432e-08          5.63137e-05          2.89717e+01
+         1.21702e-08          5.63569e-05          2.89620e+01
+         1.21990e-08          5.64026e-05          2.89518e+01
+         1.22295e-08          5.64532e-05          2.89410e+01
+         1.22611e-08          5.65052e-05          2.89297e+01
+         1.22934e-08          5.65577e-05          2.89183e+01
+         1.23272e-08          5.66137e-05          2.89064e+01
+         1.23621e-08          5.66716e-05          2.88941e+01
+         1.23983e-08          5.67352e-05          2.88814e+01
+         1.24357e-08          5.68040e-05          2.88683e+01
+         1.24743e-08          5.68758e-05          2.88549e+01
+         1.25140e-08          5.69482e-05          2.88411e+01
+         1.25548e-08          5.70229e-05          2.88269e+01
+         1.25965e-08          5.71005e-05          2.88125e+01
+         1.26388e-08          5.71802e-05          2.87980e+01
+         1.26821e-08          5.72615e-05          2.87831e+01
+         1.27264e-08          5.73452e-05          2.87680e+01
+</pre></div></div>
+</div>
+</section>
+<section id="More-complex-example,-image-metric-callbacks-with-region-of-interests">
+<h2>More complex example, image metric callbacks with region of interests<a class="headerlink" href="#More-complex-example,-image-metric-callbacks-with-region-of-interests" title="Link to this heading">#</a></h2>
+<p>Warning - this is a complex example! But the code may be useful to adapt and reuse</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">ImageQualityCallback</span><span class="p">(</span><span class="n">callbacks</span><span class="o">.</span><span class="n">Callback</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">      Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    reference_image: CIL or STIR ImageData</span>
+<span class="sd">      containing the reference image used to calculate the metrics</span>
+
+<span class="sd">    roi_mask_dict : dictionary of ImageData objects</span>
+<span class="sd">      list containing one binary ImageData object for every ROI to be</span>
+<span class="sd">      evaluated. Voxels with values 1 are considered part of the ROI</span>
+<span class="sd">      and voxels with value 0 are not.</span>
+<span class="sd">      Dimension of the ROI mask images must be the same as the dimension of</span>
+<span class="sd">      the reference image.</span>
+
+<span class="sd">    metrics_dict : dictionary of lambda functions f(x,y) mapping</span>
+<span class="sd">      two 1-dimensional numpy arrays x and y to a scalar value or a</span>
+<span class="sd">      numpy.ndarray.</span>
+<span class="sd">      x and y can be the voxel values of the whole images or the values of</span>
+<span class="sd">      voxels in a ROI such that the metric can be computed on the whole</span>
+<span class="sd">      images and optionally in the ROIs separately.</span>
+
+<span class="sd">      E.g. f(x,y) could be MSE(x,y), PSNR(x,y), MAE(x,y)</span>
+
+<span class="sd">    statistics_dict : dictionary of lambda functions f(x) mapping a</span>
+<span class="sd">      1-dimensional numpy array x to a scalar value or a numpy.ndarray.</span>
+<span class="sd">      E.g. mean(x), std_deviation(x) that calculate global and / or</span>
+<span class="sd">      ROI mean and standard deviations.</span>
+
+<span class="sd">      E.g. f(x) could be x.mean()</span>
+
+
+
+
+<span class="sd">      &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reference_image</span><span class="p">,</span>
+                       <span class="n">roi_mask_dict</span>   <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">metrics_dict</span>    <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">statistics_dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="p">):</span>
+
+        <span class="c1"># the reference image</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_image</span> <span class="o">=</span> <span class="n">reference_image</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">roi_indices_dict</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">roi_store</span><span class="o">=</span><span class="p">[]</span>
+
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">roi_mask_dict</span><span class="o">=</span><span class="n">roi_mask_dict</span>
+
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span> <span class="o">=</span> <span class="n">metrics_dict</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">metrics_store</span><span class="o">=</span><span class="p">{}</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="s1">&#39;global_&#39;</span><span class="o">+</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">if</span> <span class="n">roi_mask_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                  <span class="k">for</span> <span class="n">roi_name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">roi_mask_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="n">roi_name</span><span class="o">+</span><span class="s1">&#39;_&#39;</span><span class="o">+</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">statistics_dict</span> <span class="o">=</span> <span class="n">statistics_dict</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">stat_store</span><span class="o">=</span><span class="p">{}</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">statistics_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">stat_store</span><span class="p">[</span><span class="s1">&#39;global_&#39;</span><span class="o">+</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">if</span> <span class="n">roi_mask_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                  <span class="k">for</span> <span class="n">roi_name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">roi_mask_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">stat_store</span><span class="p">[</span><span class="n">roi_name</span><span class="o">+</span><span class="s1">&#39;_&#39;</span><span class="o">+</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metrics_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="n">ans</span> <span class="o">=</span> <span class="n">metric</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_image</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="s1">&#39;global_&#39;</span><span class="o">+</span><span class="n">metric_name</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span>
+
+
+                    <span class="k">for</span> <span class="n">roi_name</span><span class="p">,</span> <span class="n">roi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi_mask_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                      <span class="n">ans</span> <span class="o">=</span> <span class="n">metric</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_image</span><span class="p">,</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">roi</span><span class="p">)</span>
+                      <span class="bp">self</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="n">roi_name</span><span class="o">+</span><span class="s1">&#39;_&#39;</span><span class="o">+</span><span class="n">metric_name</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span>
+
+
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">statistics_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">statistic_name</span><span class="p">,</span> <span class="n">stat</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">statistics_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="n">ans</span> <span class="o">=</span> <span class="n">stat</span><span class="p">(</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">_NoValue</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">stat_store</span><span class="p">[</span><span class="s1">&#39;global_&#39;</span><span class="o">+</span><span class="n">statistic_name</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span>
+
+
+                    <span class="k">for</span> <span class="n">roi_name</span><span class="p">,</span> <span class="n">roi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">roi_mask_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                        <span class="n">ans</span> <span class="o">=</span> <span class="n">stat</span><span class="p">(</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">roi</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;bool&#39;</span><span class="p">))</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">stat_store</span><span class="p">[</span><span class="n">roi_name</span><span class="o">+</span><span class="s1">&#39;_&#39;</span><span class="o">+</span><span class="n">statistic_name</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mse</span><span class="p">(</span><span class="n">dc1</span><span class="p">,</span> <span class="n">dc2</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Calculates the mean squared error of two images</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    dc1: `DataContainer`</span>
+<span class="sd">        One image to be compared</span>
+<span class="sd">    dc2: `DataContainer`</span>
+<span class="sd">        Second image to be compared</span>
+<span class="sd">    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`</span>
+<span class="sd">        The pixelwise operation only considers values where the mask is True or NonZero.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    A number, the mean squared error of the two images</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">dc1</span> <span class="o">=</span> <span class="n">dc1</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+    <span class="n">dc2</span> <span class="o">=</span> <span class="n">dc2</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
+        <span class="n">dc1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc1</span><span class="p">)</span>
+        <span class="n">dc2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(((</span><span class="n">dc1</span> <span class="o">-</span> <span class="n">dc2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+
+
+<span class="k">def</span> <span class="nf">mae</span><span class="p">(</span><span class="n">dc1</span><span class="p">,</span> <span class="n">dc2</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Calculates the Mean Absolute error of two images.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    dc1: `DataContainer`</span>
+<span class="sd">        One image to be compared</span>
+<span class="sd">    dc2: `DataContainer`</span>
+<span class="sd">        Second image to be compared</span>
+<span class="sd">    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`</span>
+<span class="sd">        The pixelwise operation only considers values where the mask is True or NonZero.</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    A number with the mean absolute error between the two images.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">dc1</span> <span class="o">=</span> <span class="n">dc1</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+    <span class="n">dc2</span> <span class="o">=</span> <span class="n">dc2</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;bool&#39;</span><span class="p">)</span>
+        <span class="n">dc1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc1</span><span class="p">)</span>
+        <span class="n">dc2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">dc2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="n">dc1</span><span class="o">-</span><span class="n">dc2</span><span class="p">)))</span>
+
+
+<span class="k">def</span> <span class="nf">psnr</span><span class="p">(</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">corrupted</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Calculates the Peak signal to noise ratio (PSNR) between the two images.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ground_truth: `DataContainer`</span>
+<span class="sd">        The reference image</span>
+<span class="sd">    corrupted: `DataContainer`</span>
+<span class="sd">        The image to be evaluated</span>
+<span class="sd">    data_range: scalar value, default=None</span>
+<span class="sd">        PSNR scaling factor, the dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values). We take the maximum value in the ground truth array.</span>
+<span class="sd">    mask: array or `DataContainer` with the same dimensions as the `dc1` and `dc2`</span>
+<span class="sd">        The pixelwise operation only considers values where the mask is True or NonZero..</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    A number, the peak signal to noise ration between the two images.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+
+    <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">data_range</span> <span class="o">=</span> <span class="n">ground_truth</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+
+
+    <span class="k">else</span><span class="p">:</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">DataContainer</span><span class="p">):</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">as_array</span><span class="p">()</span>
+        <span class="n">data_range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ground_truth</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span>
+                                <span class="n">where</span><span class="o">=</span><span class="n">mask</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;bool&#39;</span><span class="p">),</span> <span class="n">initial</span><span class="o">=-</span><span class="mf">1e-8</span><span class="p">)</span>
+
+
+    <span class="n">tmp_mse</span> <span class="o">=</span> <span class="n">mse</span><span class="p">(</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">corrupted</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">((</span><span class="n">data_range</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">tmp_mse</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#%% create masks</span>
+<span class="n">top</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">bottom</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">top</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ground_truth</span><span class="o">.</span><span class="n">array</span> <span class="o">&gt;</span> <span class="mf">0.8</span> <span class="o">*</span> <span class="n">ground_truth</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span>
+               <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+    <span class="p">)</span>
+<span class="n">bottom</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">invert</span><span class="p">(</span><span class="n">ground_truth</span><span class="o">.</span><span class="n">array</span> <span class="o">&lt;</span> <span class="mf">0.4</span> <span class="o">*</span> <span class="n">ground_truth</span><span class="o">.</span><span class="n">max</span><span class="p">()),</span>
+               <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="p">)</span>
+
+
+
+<span class="n">roi_image_dict</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;top&#39;</span> <span class="p">:</span> <span class="n">top</span><span class="p">,</span>
+    <span class="s1">&#39;bottom&#39;</span> <span class="p">:</span> <span class="n">bottom</span>
+<span class="p">}</span>
+
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">top</span><span class="p">,</span> <span class="n">bottom</span><span class="p">],</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_24_0.png" src="../../_images/demos_callback_demonstration_24_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show2D at 0x7f08dcf79120&gt;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">img_qual_callback</span> <span class="o">=</span> <span class="n">ImageQualityCallback</span><span class="p">(</span><span class="n">ground_truth</span><span class="p">,</span>
+                                              <span class="n">roi_mask_dict</span> <span class="o">=</span> <span class="n">roi_image_dict</span><span class="p">,</span>
+                                              <span class="n">metrics_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;MSE&#39;</span><span class="p">:</span><span class="n">mse</span><span class="p">,</span>
+                                                              <span class="s1">&#39;MAE&#39;</span><span class="p">:</span><span class="n">mae</span><span class="p">,</span>
+                                                              <span class="s1">&#39;PSNR&#39;</span><span class="p">:</span><span class="n">psnr</span><span class="p">},</span>
+                                              <span class="n">statistics_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;MEAN&#39;</span><span class="p">:</span> <span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="n">y</span><span class="p">)),</span>
+                                                                 <span class="s1">&#39;STDDEV&#39;</span><span class="p">:</span> <span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="n">y</span><span class="p">)),</span>
+                                                                 <span class="s1">&#39;MAX&#39;</span><span class="p">:</span> <span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">initial</span><span class="o">=</span><span class="mi">0</span><span class="p">))},</span>
+                                              <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">algo</span><span class="o">=</span><span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">f</span><span class="o">=</span><span class="n">F</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><span class="n">G</span><span class="p">,</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">img_qual_callback</span><span class="p">])</span>
+<span class="n">show2D</span><span class="p">([</span><span class="n">ground_truth</span><span class="p">,</span> <span class="n">recon</span><span class="p">,</span> <span class="n">algo</span><span class="o">.</span><span class="n">solution</span><span class="p">],</span> <span class="n">title</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Ground Truth&#39;</span><span class="p">,</span> <span class="s1">&#39;FDK Reconstruction&#39;</span><span class="p">,</span> <span class="s1">&#39;TV solution&#39;</span><span class="p">],</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;upper&#39;</span><span class="p">,</span> <span class="n">num_cols</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_26_0.png" src="../../_images/demos_callback_demonstration_26_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;cil.utilities.display.show2D at 0x7f08dcda0af0&gt;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">501</span><span class="p">),</span> <span class="n">img_qual_callback</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="s1">&#39;global_MSE&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&lt;matplotlib.lines.Line2D at 0x7f08dec65b10&gt;]
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_27_1.png" src="../../_images/demos_callback_demonstration_27_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">501</span><span class="p">),</span> <span class="n">img_qual_callback</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="s1">&#39;top_PSNR&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Top&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">501</span><span class="p">),</span> <span class="n">img_qual_callback</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="s1">&#39;global_PSNR&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Global&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">501</span><span class="p">),</span> <span class="n">img_qual_callback</span><span class="o">.</span><span class="n">metrics_store</span><span class="p">[</span><span class="s1">&#39;bottom_PSNR&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Bottom&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x7f08dec67bb0&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_callback_demonstration_28_1.png" src="../../_images/demos_callback_demonstration_28_1.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
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+
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+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">1D inverse problem demo using deriv2 from regtools</p>
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diff --git a/v24.2.0/demos/deriv2_cgls.ipynb b/v24.2.0/demos/deriv2_cgls.ipynb
new file mode 100644
index 0000000000..3cce6a2e50
--- /dev/null
+++ b/v24.2.0/demos/deriv2_cgls.ipynb
@@ -0,0 +1,645 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "602dbdfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "#  Copyright 2023 United Kingdom Research and Innovation\n",
+    "#\n",
+    "#  Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "#  you may not use this file except in compliance with the License.\n",
+    "#  You may obtain a copy of the License at\n",
+    "#\n",
+    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "#  Unless required by applicable law or agreed to in writing, software\n",
+    "#  distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "#  See the License for the specific language governing permissions and\n",
+    "#  limitations under the License.\n",
+    "#\n",
+    "#   Authored by:  Bill Lionheart (University of Manchester),\n",
+    "#   Edited by:  Margaret Duff (STFC - UKRI)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8acbea",
+   "metadata": {},
+   "source": [
+    "# 1D inverse problem demo using deriv2 from regtools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc422b7",
+   "metadata": {},
+   "source": [
+    "We roughly translated deriv2 (P. C. Hansen, Regularization Tools Version 4.0 for Matlab 7.3, Numerical Algorithms, 46 (2007), pp. 189-194.) to Python. The righthand side vector b is made as Ax so is \"exact\" as a vector. We will look at the singular valued decomposition (SVD) and regularized solution as an example of a mildly ill posed problem and show how to recostruct using the Core Imaging Library (CIL. See Jørgensen, Jakob S., et al. \"Core Imaging Library-Part I: a versatile Python framework for tomographic imaging.\" Philosophical Transactions of the Royal Society A 379.2204 (2021): 20200192. and https://tomographicimaging.github.io/CIL/nightly/index.html). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f0222cb",
+   "metadata": {},
+   "source": [
+    "This notebook was developed as part of the CCPi CIL Hackathon https://ccpi.ac.uk/events/byod-cil-hackathon/ in March 2023 in Cambridge as part of the Rich Nonlinear Tomography programme at the Isaac Newton Institute for Mathematical Sciences https://www.newton.ac.uk/event/rnt/.  The CIL is supported by the CCPi EPSRC grant EP/T026677/1 and the Isaac NewtonInstitute by EP/R014604/1 The author would like to thank the Isaac Newton Institute for support  and hospitality.(c) W.R.B. Lionheart 2023. Apache License"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b2a54694",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from cil.optimisation.algorithms import CGLS\n",
+    "from cil.optimisation.operators import MatrixOperator\n",
+    "from cil.framework import VectorData, BlockDataContainer\n",
+    "from deriv2 import deriv2\n",
+    "from cil.optimisation.operators import BlockOperator,IdentityOperator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d33631d9",
+   "metadata": {},
+   "source": [
+    "### CIL version 23.0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "751f22b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cil\n",
+    "print(cil.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "250103e8",
+   "metadata": {},
+   "source": [
+    "We set up a 1D inverse problem, in which the forward model integrates twice.  The notebook will first set up and solve a basic formulation of the problem using just python/numpy, and after that using CIL.\n",
+    "\n",
+    "Consider a discretization of a first kind Fredholm integral equation whose kernel K is the  Green's function for the second derivative:\n",
+    " $$\n",
+    "    K(s,t) = \\begin{cases} s(t-1)  ,  s <  t \\\\\n",
+    "              t(s-1)  ,  s \\geq t \\end{cases} $$\n",
+    "\n",
+    "and $$ \\int_0^1K(s,t)x(t)dt=b(s). $$\n",
+    "\n",
+    " For this notebook, consider the case\n",
+    "$$ b(s) = (s^3 - s)/6          , \\ \\  x(t) = t$$\n",
+    "\n",
+    " where the integral is disretised using the Galerkin method with orthonormal box functions, with interval size $1/n$.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "27770924",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Test of one dimensional inverse problems using deriv2 from reg tools\n",
+    "n = 100\n",
+    "A,b,x = deriv2(n,1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "113e7dc1",
+   "metadata": {},
+   "source": [
+    "The functions $b$ and $x$ can be plotted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "336dc870",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),x, label='x')\n",
+    "plt.plot(np.linspace(0,1,n),b, label='b')\n",
+    "plt.plot()\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84dfaf27",
+   "metadata": {},
+   "source": [
+    "This has made a A, x, and b where Ax=b. We can check check this is the case up to floating point error: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6d854bd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(A@x-b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554b487c",
+   "metadata": {},
+   "source": [
+    "Now make a version of b with noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "35d30bb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bn= b + 0.001*np.random.randn(n)\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),b, label='Noise free b')\n",
+    "plt.plot(np.linspace(0,1,n),bn, label='Noisy b')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd88fee8",
+   "metadata": {},
+   "source": [
+    "$A$, $x$ and $b$ are stored as Numpy arrays. Just as in Matlab we can look at the singular value decomposition (SVD).   On a log scale we see the singular values decay as a negative power as expected for an operator that approximates the inverse of two derivatives.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "761ac845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
+    "plt.figure()\n",
+    "plt.plot(s)\n",
+    "plt.title('The singular values of the forward operator A')\n",
+    "plt.show()\n",
+    "plt.figure()\n",
+    "plt.loglog(s)\n",
+    "plt.title('A log-log plot of the singular values of the forward operator A')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad79f585",
+   "metadata": {},
+   "source": [
+    "Solving the least squares problem $\\min_x\\|Ax-b\\|_2^2$ demonstrates the ill-posedness of the problem:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cab55e6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xlq=np.linalg.lstsq(A,bn,rcond=None)[0]\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),xlq, label='Least squares solution')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.figure()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44ed725c",
+   "metadata": {},
+   "source": [
+    "We see the solution matches the observed data well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "28589f81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.213659415696782e-15"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(A@xlq-bn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c64567",
+   "metadata": {},
+   "source": [
+    "However this is not a desired solution to our inverse problem. Now, instead, solve using Tikonov regularization: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e14a076f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg_param= 0.005\n",
+    "\n",
+    "Atik=np.vstack((A,reg_param*np.eye(n))) \n",
+    "\n",
+    "btik = np.hstack((bn,np.zeros(n)))\n",
+    "\n",
+    "xtik =  np.linalg.lstsq(Atik,btik,rcond=None)[0]\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),xtik, label='Tikhonov regularised solution- numpy')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.figure()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e35764d",
+   "metadata": {},
+   "source": [
+    "This solution is an improvement, although we still have this odd behaviour at the end of the interval. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c93e53f",
+   "metadata": {},
+   "source": [
+    "Now convert the matrix and operators to CIL types so we can use inbuilt optimisation routines and regularisers in CIL: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7ab3314c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Aop = MatrixOperator(A)\n",
+    "bop= VectorData(bn) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a523f4b",
+   "metadata": {},
+   "source": [
+    "We run CGLS to solve the un-regularised least squares problem $$\\min_x \\|Ax-b\\|_2^2.$$ We choose a small number of iterations to implicitly regularise via early stopping: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a8bd2985",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Iter   Max Iter     Time/Iter            Objective\n",
+      "                               [s]                     \n",
+      "        0          4         0.000          2.16038e-01\n",
+      "-------------------------------------------------------\n",
+      "        4          4         0.081                     \n",
+      "Stop criterion has been reached.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#bop = VectorData(np.reshape(b,[-1]))\n",
+    "op = VectorData(bn)\n",
+    "cgls = CGLS(operator=Aop, data=bop, max_iteration=4, update_objective_interval=10)\n",
+    "cgls.run()\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,100),cgls.solution.as_array(), label='Least squares solution with early stopping')\n",
+    "plt.plot(np.linspace(0,1,100),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90dcba3f",
+   "metadata": {},
+   "source": [
+    "We can also do Tikhonov regularisation using the CIL framework. For example, in the block framework below: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "15ff9d79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Iter   Max Iter     Time/Iter            Objective\n",
+      "                               [s]                     \n",
+      "        0       1000         0.000          2.16038e-01\n",
+      "        1       1000         0.130          3.73899e-03\n",
+      "        2       1000         0.108          9.73406e-04\n",
+      "        3       1000         0.109          8.16896e-04\n",
+      "        4       1000         0.111          8.07526e-04\n",
+      "        5       1000         0.106          8.07019e-04\n",
+      "        6       1000         0.105          8.07002e-04\n",
+      "        7       1000         0.106          8.07002e-04\n",
+      "        8       1000         0.110          8.07002e-04\n",
+      "Tolerance is reached: 1e-06\n",
+      "-------------------------------------------------------\n",
+      "        8       1000         0.110          8.07002e-04\n",
+      "Stop criterion has been reached.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ig = Aop.domain_geometry()\n",
+    "L = IdentityOperator(ig)\n",
+    "operator_block = BlockOperator(Aop, reg_param*L)\n",
+    "\n",
+    "zero_data = L.range.allocate(0)\n",
+    "data_block = BlockDataContainer(bop, zero_data)\n",
+    "\n",
+    "cglsb = CGLS(operator=operator_block, data=data_block, max_iteration=1000, update_objctive_interval=10)\n",
+    "cglsb.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "74107db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n), cglsb.solution.as_array(), label='Tikhonov regularised solution- CIL')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fa3e6b0",
+   "metadata": {},
+   "source": [
+    "We can compare the results of Tikhonov regularisation implemented by numpy and CIL: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7c6e22c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(np.linspace(0,1,n),  cglsb.solution.as_array(),'o', label='Tikhonov regularised solution- CIL')\n",
+    "plt.plot(np.linspace(0,1,n),xtik, '+', label='Tikhonov regularised solution- numpy')\n",
+    "plt.plot(np.linspace(0,1,n),x, label='Ground truth solution')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc199d9",
+   "metadata": {},
+   "source": [
+    "You can see that the solutions are identical!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c27a9218",
+   "metadata": {},
+   "source": [
+    "We think the reconstruction error near the boundary at 1 is caused by the discretisation of the integral - for future investigation "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/v24.2.0/demos/deriv2_cgls/index.html b/v24.2.0/demos/deriv2_cgls/index.html
new file mode 100644
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+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../00_CIL_geometry/">A detailed look at CIL geometry</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">1D inverse problem demo using deriv2 from regtools</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#CIL-version-23.0.1">CIL version 23.0.1</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../callback_demonstration/">CIL Callback demonstration</a></li>
+</ul>
+</li>
+</ul>
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+
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+                <article class="bd-article">
+                  
+  <div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">#  Copyright 2023 United Kingdom Research and Innovation</span>
+<span class="c1">#</span>
+<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1">#  you may not use this file except in compliance with the License.</span>
+<span class="c1">#  You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1">#  See the License for the specific language governing permissions and</span>
+<span class="c1">#  limitations under the License.</span>
+<span class="c1">#</span>
+<span class="c1">#   Authored by:  Bill Lionheart (University of Manchester),</span>
+<span class="c1">#   Edited by:  Margaret Duff (STFC - UKRI)</span>
+</pre></div>
+</div>
+</div>
+<section id="1D-inverse-problem-demo-using-deriv2-from-regtools">
+<h1>1D inverse problem demo using deriv2 from regtools<a class="headerlink" href="#1D-inverse-problem-demo-using-deriv2-from-regtools" title="Link to this heading">#</a></h1>
+<p>We roughly translated deriv2 (P. C. Hansen, Regularization Tools Version 4.0 for Matlab 7.3, Numerical Algorithms, 46 (2007), pp. 189-194.) to Python. The righthand side vector b is made as Ax so is “exact” as a vector. We will look at the singular valued decomposition (SVD) and regularized solution as an example of a mildly ill posed problem and show how to recostruct using the Core Imaging Library (CIL. See Jørgensen, Jakob S., et al. “Core Imaging Library-Part I: a versatile Python framework
+for tomographic imaging.” Philosophical Transactions of the Royal Society A 379.2204 (2021): 20200192. and <a class="reference external" href="https://tomographicimaging.github.io/CIL/nightly/index.html">https://tomographicimaging.github.io/CIL/nightly/index.html</a>).</p>
+<p>This notebook was developed as part of the CCPi CIL Hackathon <a class="reference external" href="https://ccpi.ac.uk/events/byod-cil-hackathon/">https://ccpi.ac.uk/events/byod-cil-hackathon/</a> in March 2023 in Cambridge as part of the Rich Nonlinear Tomography programme at the Isaac Newton Institute for Mathematical Sciences <a class="reference external" href="https://www.newton.ac.uk/event/rnt/">https://www.newton.ac.uk/event/rnt/</a>. The CIL is supported by the CCPi EPSRC grant EP/T026677/1 and the Isaac NewtonInstitute by EP/R014604/1 The author would like to thank the Isaac Newton Institute for support and hospitality.(c) W.R.B. Lionheart 2023.
+Apache License</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">CGLS</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">MatrixOperator</span>
+<span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">VectorData</span><span class="p">,</span> <span class="n">BlockDataContainer</span>
+<span class="kn">from</span> <span class="nn">deriv2</span> <span class="kn">import</span> <span class="n">deriv2</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.operators</span> <span class="kn">import</span> <span class="n">BlockOperator</span><span class="p">,</span><span class="n">IdentityOperator</span>
+</pre></div>
+</div>
+</div>
+<section id="CIL-version-23.0.1">
+<h2>CIL version 23.0.1<a class="headerlink" href="#CIL-version-23.0.1" title="Link to this heading">#</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cil</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">cil</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+23.0.1
+</pre></div></div>
+</div>
+<p>We set up a 1D inverse problem, in which the forward model integrates twice. The notebook will first set up and solve a basic formulation of the problem using just python/numpy, and after that using CIL.</p>
+<p>Consider a discretization of a first kind Fredholm integral equation whose kernel K is the Green’s function for the second derivative:</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}K(s,t) = \begin{cases} s(t-1)  ,  s &lt;  t \\
+          t(s-1)  ,  s \geq t \end{cases}\end{split}\]</div>
+<p>and</p>
+<div class="math notranslate nohighlight">
+\[\int_0^1K(s,t)x(t)dt=b(s).\]</div>
+<p>For this notebook, consider the case</p>
+<div class="math notranslate nohighlight">
+\[b(s) = (s^3 - s)/6          , \ \  x(t) = t\]</div>
+<p>where the integral is disretised using the Galerkin method with orthonormal box functions, with interval size <span class="math notranslate nohighlight">\(1/n\)</span>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="c1"># Test of one dimensional inverse problems using deriv2 from reg tools</span>
+<span class="n">n</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">A</span><span class="p">,</span><span class="n">b</span><span class="p">,</span><span class="n">x</span> <span class="o">=</span> <span class="n">deriv2</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<p>The functions <span class="math notranslate nohighlight">\(b\)</span> and <span class="math notranslate nohighlight">\(x\)</span> can be plotted:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">x</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">b</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_10_0.png" src="../../_images/demos_deriv2_cgls_10_0.png" />
+</div>
+</div>
+<p>This has made a A, x, and b where Ax=b. We can check check this is the case up to floating point error:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="nd">@x</span><span class="o">-</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+0.0
+</pre></div></div>
+</div>
+<p>Now make a version of b with noise</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bn</span><span class="o">=</span> <span class="n">b</span> <span class="o">+</span> <span class="mf">0.001</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">b</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Noise free b&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">bn</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Noisy b&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_14_0.png" src="../../_images/demos_deriv2_cgls_14_0.png" />
+</div>
+</div>
+<p><span class="math notranslate nohighlight">\(A\)</span>, <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(b\)</span> are stored as Numpy arrays. Just as in Matlab we can look at the singular value decomposition (SVD). On a log scale we see the singular values decay as a negative power as expected for an operator that approximates the inverse of two derivatives.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;The singular values of the forward operator A&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;A log-log plot of the singular values of the forward operator A&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_16_0.png" src="../../_images/demos_deriv2_cgls_16_0.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_16_1.png" src="../../_images/demos_deriv2_cgls_16_1.png" />
+</div>
+</div>
+<p>Solving the least squares problem <span class="math notranslate nohighlight">\(\min_x\|Ax-b\|_2^2\)</span> demonstrates the ill-posedness of the problem:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">xlq</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="n">bn</span><span class="p">,</span><span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">xlq</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Least squares solution&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">x</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ground truth solution&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Figure size 640x480 with 0 Axes&gt;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_18_1.png" src="../../_images/demos_deriv2_cgls_18_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Figure size 640x480 with 0 Axes&gt;
+</pre></div></div>
+</div>
+<p>We see the solution matches the observed data well:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="nd">@xlq</span><span class="o">-</span><span class="n">bn</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+4.213659415696782e-15
+</pre></div></div>
+</div>
+<p>However this is not a desired solution to our inverse problem. Now, instead, solve using Tikonov regularization:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">reg_param</span><span class="o">=</span> <span class="mf">0.005</span>
+
+<span class="n">Atik</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">A</span><span class="p">,</span><span class="n">reg_param</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span>
+
+<span class="n">btik</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">bn</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span>
+
+<span class="n">xtik</span> <span class="o">=</span>  <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">Atik</span><span class="p">,</span><span class="n">btik</span><span class="p">,</span><span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">xtik</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Tikhonov regularised solution- numpy&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">x</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ground truth solution&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Figure size 640x480 with 0 Axes&gt;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_22_1.png" src="../../_images/demos_deriv2_cgls_22_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Figure size 640x480 with 0 Axes&gt;
+</pre></div></div>
+</div>
+<p>This solution is an improvement, although we still have this odd behaviour at the end of the interval.</p>
+<p>Now convert the matrix and operators to CIL types so we can use inbuilt optimisation routines and regularisers in CIL:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Aop</span> <span class="o">=</span> <span class="n">MatrixOperator</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+<span class="n">bop</span><span class="o">=</span> <span class="n">VectorData</span><span class="p">(</span><span class="n">bn</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<p>We run CGLS to solve the un-regularised least squares problem</p>
+<div class="math notranslate nohighlight">
+\[\min_x \|Ax-b\|_2^2.\]</div>
+<p>We choose a small number of iterations to implicitly regularise via early stopping:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#bop = VectorData(np.reshape(b,[-1]))</span>
+<span class="n">op</span> <span class="o">=</span> <span class="n">VectorData</span><span class="p">(</span><span class="n">bn</span><span class="p">)</span>
+<span class="n">cgls</span> <span class="o">=</span> <span class="n">CGLS</span><span class="p">(</span><span class="n">operator</span><span class="o">=</span><span class="n">Aop</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">bop</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">update_objective_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">cgls</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span><span class="n">cgls</span><span class="o">.</span><span class="n">solution</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Least squares solution with early stopping&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span><span class="n">x</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ground truth solution&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+     Iter   Max Iter     Time/Iter            Objective
+                               [s]
+        0          4         0.000          2.16038e-01
+-------------------------------------------------------
+        4          4         0.081
+Stop criterion has been reached.
+
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_27_1.png" src="../../_images/demos_deriv2_cgls_27_1.png" />
+</div>
+</div>
+<p>We can also do Tikhonov regularisation using the CIL framework. For example, in the block framework below:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ig</span> <span class="o">=</span> <span class="n">Aop</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span>
+<span class="n">L</span> <span class="o">=</span> <span class="n">IdentityOperator</span><span class="p">(</span><span class="n">ig</span><span class="p">)</span>
+<span class="n">operator_block</span> <span class="o">=</span> <span class="n">BlockOperator</span><span class="p">(</span><span class="n">Aop</span><span class="p">,</span> <span class="n">reg_param</span><span class="o">*</span><span class="n">L</span><span class="p">)</span>
+
+<span class="n">zero_data</span> <span class="o">=</span> <span class="n">L</span><span class="o">.</span><span class="n">range</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">data_block</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="n">bop</span><span class="p">,</span> <span class="n">zero_data</span><span class="p">)</span>
+
+<span class="n">cglsb</span> <span class="o">=</span> <span class="n">CGLS</span><span class="p">(</span><span class="n">operator</span><span class="o">=</span><span class="n">operator_block</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">data_block</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">update_objctive_interval</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">cglsb</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+     Iter   Max Iter     Time/Iter            Objective
+                               [s]
+        0       1000         0.000          2.16038e-01
+        1       1000         0.130          3.73899e-03
+        2       1000         0.108          9.73406e-04
+        3       1000         0.109          8.16896e-04
+        4       1000         0.111          8.07526e-04
+        5       1000         0.106          8.07019e-04
+        6       1000         0.105          8.07002e-04
+        7       1000         0.106          8.07002e-04
+        8       1000         0.110          8.07002e-04
+Tolerance is reached: 1e-06
+-------------------------------------------------------
+        8       1000         0.110          8.07002e-04
+Stop criterion has been reached.
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span> <span class="n">cglsb</span><span class="o">.</span><span class="n">solution</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Tikhonov regularised solution- CIL&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">x</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ground truth solution&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_30_0.png" src="../../_images/demos_deriv2_cgls_30_0.png" />
+</div>
+</div>
+<p>We can compare the results of Tikhonov regularisation implemented by numpy and CIL:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span>  <span class="n">cglsb</span><span class="o">.</span><span class="n">solution</span><span class="o">.</span><span class="n">as_array</span><span class="p">(),</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Tikhonov regularised solution- CIL&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">xtik</span><span class="p">,</span> <span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Tikhonov regularised solution- numpy&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">n</span><span class="p">),</span><span class="n">x</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ground truth solution&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/demos_deriv2_cgls_32_0.png" src="../../_images/demos_deriv2_cgls_32_0.png" />
+</div>
+</div>
+<p>You can see that the solutions are identical!</p>
+<p>We think the reconstruction error near the boundary at 1 is caused by the discretisation of the integral - for future investigation</p>
+</section>
+</section>
+
+
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+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="00_CIL_geometry/">A detailed look at CIL geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="deriv2_cgls/">1D inverse problem demo using deriv2 from regtools</a></li>
+<li class="toctree-l2"><a class="reference internal" href="callback_demonstration/">CIL Callback demonstration</a></li>
+</ul>
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+  <div><img alt="" src="../_images/demos_00_CIL_geometry_28_5.png"></div>
+  <div>A detailed look at CIL geometry</div>
+</a>
+<a class="reference internal" href="deriv2_cgls/">
+  <div><img alt="" src="../_images/demos_deriv2_cgls_32_0.png"></div>
+  <div>1D inverse problem demo using deriv2 from regtools</div>
+</a>
+<a class="reference internal" href="callback_demonstration/">
+  <div><img alt="" src="../_images/demos_callback_demonstration_28_1.png"></div>
+  <div>CIL Callback demonstration</div>
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diff --git a/v24.2.0/developer_guide/index.html b/v24.2.0/developer_guide/index.html
new file mode 100644
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+</li>
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+    Optimisation framework
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+    Introduction
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+</li>
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+    Framework
+  </a>
+</li>
+
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+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
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+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
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+    Processors
+  </a>
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+    Utilities
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+
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+    CIL Plugins
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+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Developers’ Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#conventions-on-new-cil-objects">Conventions on new CIL objects</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#creator">Creator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#setter-methods-and-properties">Setter methods and properties</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#other-methods">Other methods</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#documentation">Documentation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#docstrings">Docstrings</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#building-documentation-locally">Building documentation locally</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#notebooks-gallery">Notebooks gallery</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#contributions-guidelines">Contributions guidelines</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
+</ul>
+</div>
+        <div class="sidebar-primary-item">
+
+  
+  <div class="tocsection editthispage">
+    <a href="https://github.com/TomographicImaging/CIL/edit/master/docs/source/developer_guide.rst">
+      <i class="fa-solid fa-pencil"></i>
+      
+      
+        
+          Edit on GitHub
+        
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+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
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+      
+      <main id="main-content" class="bd-main" role="main">
+        
+        
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+            <div class="bd-article-container">
+              
+              <div class="bd-header-article d-print-none">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
+<nav aria-label="Breadcrumb" class="d-print-none">
+  <ul class="bd-breadcrumbs">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Developers’ Guide</span></li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="developers-guide">
+<h1>Developers’ Guide<a class="headerlink" href="#developers-guide" title="Link to this heading">#</a></h1>
+<p>CIL is an Object Orientated software. It has evolved during the years and it currently does not fully adheres to the following conventions. New additions must comply with
+the following.</p>
+<section id="conventions-on-new-cil-objects">
+<h2>Conventions on new CIL objects<a class="headerlink" href="#conventions-on-new-cil-objects" title="Link to this heading">#</a></h2>
+<p>For each class there are <strong>essential</strong>, and <strong>non-essential</strong> parameters. The non-essential can be further be divided in <strong>often configured</strong> and <strong>advanced</strong> parameters:</p>
+<ul class="simple">
+<li><p>essential</p></li>
+<li><p>non-essential</p>
+<ul>
+<li><p>often-configured</p></li>
+<li><p>advanced</p></li>
+</ul>
+</li>
+</ul>
+<p>The definition of what are the essential, often-configured and advanced parameters depends on the actual class.</p>
+<section id="creator">
+<h3>Creator<a class="headerlink" href="#creator" title="Link to this heading">#</a></h3>
+<p>To create an instance of a class, the creator of a class should require the <strong>essential</strong> and <strong>often-configured</strong> parameters as named parameters.</p>
+<p>It should not accept positional arguments <cite>*args</cite> or key-worded arguments <cite>**kwargs</cite> so that the user can clearly understand what parameters are necessary to
+create the instance.</p>
+</section>
+<section id="setter-methods-and-properties">
+<h3>Setter methods and properties<a class="headerlink" href="#setter-methods-and-properties" title="Link to this heading">#</a></h3>
+<p>Use of <cite>property</cite> is favoured instead of class members to store parameters so that the parameters can be protected.</p>
+<p>The class should provide setter methods to change all the parameters at any time. Setter methods to set multiple parameters at the same time is also accepted.
+Setter methods should be named <cite>set_&lt;parameter&gt;</cite>. The use of <cite>set_</cite> helps IDEs and user to find what they should change in an instance of a class.</p>
+</section>
+<section id="other-methods">
+<h3>Other methods<a class="headerlink" href="#other-methods" title="Link to this heading">#</a></h3>
+<p>Methods that are not meant to be used by the user should have a <cite>_</cite> (underscore) at the beginning of the name.
+All methods should follow the convention of small caps underscore separated words.</p>
+</section>
+</section>
+<section id="documentation">
+<h2>Documentation<a class="headerlink" href="#documentation" title="Link to this heading">#</a></h2>
+<section id="docstrings">
+<h3>Docstrings<a class="headerlink" href="#docstrings" title="Link to this heading">#</a></h3>
+<p>The Core Imaging Library (CIL) follows the <a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard">NumpyDoc</a>
+style with the <a class="reference external" href="https://pydata-sphinx-theme.readthedocs.io/en/latest/">PyData Sphinx HTML theme</a>.
+When contributing your code please refer to <a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard">this</a> link
+for docstring formatting and this rendered <a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/example.html#example">example</a>.</p>
+<section id="example-from-cil">
+<h4>Example from <code class="docutils literal notranslate"><span class="pre">cil</span></code><a class="headerlink" href="#example-from-cil" title="Link to this heading">#</a></h4>
+<p>The following provides an example of the docstring format used within <code class="docutils literal notranslate"><span class="pre">cil</span></code>, and the rendered documentation generated from it.</p>
+<section id="source">
+<h5>Source<a class="headerlink" href="#source" title="Link to this heading">#</a></h5>
+<div class="literal-block-wrapper docutils container" id="id1">
+<div class="code-block-caption"><span class="caption-text"><cite>FBP.run method from cil.io.recon.FBP</cite></span><a class="headerlink" href="#id1" title="Link to this code">#</a></div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Runs the configured FBP recon and returns the reconstruction</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        out : ImageData, optional</span>
+<span class="sd">           Fills the referenced ImageData with the reconstructed volume and suppresses the return</span>
+
+<span class="sd">        verbose : int, default=1</span>
+<span class="sd">           Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ImageData</span>
+<span class="sd">            The reconstructed volume. Suppressed if `out` is passed</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">AcquisitionType</span><span class="o">.</span><span class="n">DIM2</span> <span class="o">&amp;</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">dimension</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only 3D datasets can be processed in chunks with `set_split_processing`&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">system_description</span> <span class="o">==</span> <span class="s1">&#39;advanced&#39;</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only simple and offset geometries can be processed in chunks with `set_split_processing`&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only default image geometries can be processed in chunks `set_split_processing`&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">allocate</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">out</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_inplace</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_pre_filtering</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="p">)</span>
+
+            <span class="n">tot_slices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">pixel_num_v</span>
+            <span class="n">remainder</span> <span class="o">=</span> <span class="n">tot_slices</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span>
+            <span class="n">num_chunks</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slices_per_chunk</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                <span class="n">pbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="n">num_chunks</span><span class="p">)</span>
+
+            <span class="c1">#process dataset by requested chunk size</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_setup_PO_for_chunks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tot_slices</span><span class="o">-</span><span class="n">remainder</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">):</span>
+
+                <span class="k">if</span> <span class="s1">&#39;bottom&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="n">i</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="n">tot_slices</span> <span class="o">-</span><span class="n">i</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">tot_slices</span> <span class="o">-</span> <span class="n">i</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">array</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_chunk</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices_per_chunk</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                    <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+            <span class="c1">#process excess rows</span>
+            <span class="k">if</span> <span class="n">remainder</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_setup_PO_for_chunks</span><span class="p">(</span><span class="n">remainder</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="s1">&#39;bottom&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">panel</span><span class="o">.</span><span class="n">origin</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="n">tot_slices</span><span class="o">-</span><span class="n">remainder</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">tot_slices</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">start</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">end</span> <span class="o">=</span> <span class="n">remainder</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">array</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_chunk</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">remainder</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                    <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                <span class="n">pbar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">ret</span>
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_inplace</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+                <span class="n">proj_filtered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">proj_filtered</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_pre_filtering</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">)</span>
+
+            <span class="n">operator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_PO_class</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">image_geometry</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">acquisition_geometry</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">proj_filtered</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="rendered">
+<h5>Rendered<a class="headerlink" href="#rendered" title="Link to this heading">#</a></h5>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.FBP.run">
+<span class="sig-prename descclassname"><span class="pre">FBP.</span></span><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/recon/FBP/#FBP.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.recon.FBP.FBP.run" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured FBP recon and returns the reconstruction</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><em>optional</em>) – Fills the referenced ImageData with the reconstructed volume and suppresses the return</p></li>
+<li><p><strong>verbose</strong> (<em>int</em><em>, </em><em>default=1</em>) – Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The reconstructed volume. Suppressed if <cite>out</cite> is passed</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+</section>
+<section id="building-documentation-locally">
+<h3>Building documentation locally<a class="headerlink" href="#building-documentation-locally" title="Link to this heading">#</a></h3>
+<p>The easiest way to test documentation changes is to open a pull request and <a class="reference external" href="https://github.com/TomographicImaging/CIL/blob/master/.github/workflows/README.md">download the rendered documentation from the CI</a>.</p>
+<p>Alternatively, to build the docs locally, you will need a working <code class="docutils literal notranslate"><span class="pre">cil</span></code> installation.
+For development of the documentation embedded within the source code itself (e.g. docstrings), you should build <code class="docutils literal notranslate"><span class="pre">cil</span></code> from source.</p>
+<p>The following steps can be used to create an environment that is suitable for building <code class="docutils literal notranslate"><span class="pre">cil</span></code> and its documentation, and to start
+a HTTP server to view the documentation.</p>
+<ol class="arabic simple">
+<li><p>Clone <code class="docutils literal notranslate"><span class="pre">cil</span></code> repo</p></li>
+<li><p>Update conda with <code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">update</span> <span class="pre">-n</span> <span class="pre">base</span> <span class="pre">-c</span> <span class="pre">defaults</span> <span class="pre">conda</span></code></p></li>
+<li><p>Follow the instructions <a class="reference external" href="https://github.com/TomographicImaging/CIL/tree/master#building-cil-from-source-code">here</a> to create a conda environment and build <code class="docutils literal notranslate"><span class="pre">cil</span></code> from source</p></li>
+<li><p>Go to <code class="docutils literal notranslate"><span class="pre">docs</span></code> folder</p></li>
+<li><p>Install packages from <code class="docutils literal notranslate"><span class="pre">docs_environment.yml</span></code></p></li>
+<li><p>[Install Ruby version 3.2](<a class="reference external" href="https://www.ruby-lang.org/en/documentation/installation/#installers">https://www.ruby-lang.org/en/documentation/installation/#installers</a>)</p></li>
+<li><p>Install the web dependencies with <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">web-deps</span></code></p></li>
+<li><p>Build the documentation with <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">dirhtml</span> <span class="pre">web</span></code></p></li>
+<li><p>Start an HTTP server with <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">serve</span></code> to access the docs via <a class="reference external" href="http://localhost:8000">localhost:8000</a>.</p></li>
+</ol>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>git clone --recurse-submodule git@github.com:TomographicImaging/CIL
+cd CIL
+sh scripts/create_local_env_for_cil_development_tests.sh -n NUMPY_VERSION -p PYTHON_VERSION -e ENVIRONMENT_NAME
+conda activate ENVIRONMENT_NAME
+cmake -S . -B ./build -DCMAKE_INSTALL_PREFIX=${CONDA_PREFIX}
+cmake --build ./build --target install
+cd docs
+conda update -n base -c defaults conda
+conda env update -f docs_environment.yml # with the name field set to ENVIRONMENT_NAME
+make web-deps  # install dependencies (requires ruby 3.2)
+make dirhtml web serve
+</pre></div>
+</div>
+</section>
+<section id="notebooks-gallery">
+<h3>Notebooks gallery<a class="headerlink" href="#notebooks-gallery" title="Link to this heading">#</a></h3>
+<p>The <code class="docutils literal notranslate"><span class="pre">mkdemos.py</span></code> script (called by <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">dirhtml</span></code>):</p>
+<ul class="simple">
+<li><p>downloads notebooks from external URLs to <code class="docutils literal notranslate"><span class="pre">source/demos/*.ipynb</span></code></p></li>
+<li><p>uses the <code class="docutils literal notranslate"><span class="pre">demos-template.rst</span></code> file to generate the gallery in <code class="docutils literal notranslate"><span class="pre">source/demos.rst</span></code></p></li>
+</ul>
+<p>The <code class="docutils literal notranslate"><span class="pre">nbsphinx</span></code> extension will convert the <code class="docutils literal notranslate"><span class="pre">*.ipynb</span></code> files to HTML.</p>
+</section>
+</section>
+<section id="contributions-guidelines">
+<h2>Contributions guidelines<a class="headerlink" href="#contributions-guidelines" title="Link to this heading">#</a></h2>
+<p>Make sure that each contributed file contains the following text enclosed in the appropriate comment syntax for the file format. Please replace <cite>[yyyy]</cite> and <cite>[name of copyright owner]</cite> with your own identifying information. Optionally you may add author name and email.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Copyright</span> <span class="p">[</span><span class="n">yyyy</span><span class="p">]</span> <span class="n">United</span> <span class="n">Kingdom</span> <span class="n">Research</span> <span class="ow">and</span> <span class="n">Innovation</span>
+<span class="n">Copyright</span> <span class="p">[</span><span class="n">yyyy</span><span class="p">]</span> <span class="n">The</span> <span class="n">University</span> <span class="n">of</span> <span class="n">Manchester</span>
+<span class="n">Copyright</span> <span class="p">[</span><span class="n">yyyy</span><span class="p">]</span> <span class="p">[</span><span class="n">name</span> <span class="n">of</span> <span class="n">copyright</span> <span class="n">owner</span><span class="p">]</span>
+<span class="n">Author</span><span class="p">(</span><span class="n">s</span><span class="p">):</span> <span class="p">[</span><span class="n">Author</span> <span class="n">name</span><span class="p">,</span> <span class="n">Author</span> <span class="n">email</span> <span class="p">(</span><span class="n">optional</span><span class="p">)]</span>
+<span class="n">Licensed</span> <span class="n">under</span> <span class="n">the</span> <span class="n">Apache</span> <span class="n">License</span><span class="p">,</span> <span class="n">Version</span> <span class="mf">2.0</span> <span class="p">(</span><span class="n">the</span> <span class="s2">&quot;License&quot;</span><span class="p">);</span>
+<span class="n">you</span> <span class="n">may</span> <span class="ow">not</span> <span class="n">use</span> <span class="n">this</span> <span class="n">file</span> <span class="k">except</span> <span class="ow">in</span> <span class="n">compliance</span> <span class="k">with</span> <span class="n">the</span> <span class="n">License</span><span class="o">.</span>
+<span class="n">You</span> <span class="n">may</span> <span class="n">obtain</span> <span class="n">a</span> <span class="n">copy</span> <span class="n">of</span> <span class="n">the</span> <span class="n">License</span> <span class="n">at</span>
+    <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">www</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="n">licenses</span><span class="o">/</span><span class="n">LICENSE</span><span class="o">-</span><span class="mf">2.0</span>
+<span class="n">Unless</span> <span class="n">required</span> <span class="n">by</span> <span class="n">applicable</span> <span class="n">law</span> <span class="ow">or</span> <span class="n">agreed</span> <span class="n">to</span> <span class="ow">in</span> <span class="n">writing</span><span class="p">,</span> <span class="n">software</span>
+<span class="n">distributed</span> <span class="n">under</span> <span class="n">the</span> <span class="n">License</span> <span class="ow">is</span> <span class="n">distributed</span> <span class="n">on</span> <span class="n">an</span> <span class="s2">&quot;AS IS&quot;</span> <span class="n">BASIS</span><span class="p">,</span>
+<span class="n">WITHOUT</span> <span class="n">WARRANTIES</span> <span class="n">OR</span> <span class="n">CONDITIONS</span> <span class="n">OF</span> <span class="n">ANY</span> <span class="n">KIND</span><span class="p">,</span> <span class="n">either</span> <span class="n">express</span> <span class="ow">or</span> <span class="n">implied</span><span class="o">.</span>
+<span class="n">See</span> <span class="n">the</span> <span class="n">License</span> <span class="k">for</span> <span class="n">the</span> <span class="n">specific</span> <span class="n">language</span> <span class="n">governing</span> <span class="n">permissions</span> <span class="ow">and</span>
+<span class="n">limitations</span> <span class="n">under</span> <span class="n">the</span> <span class="n">License</span><span class="o">.</span>
+</pre></div>
+</div>
+</section>
+</section>
+
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+    <a class="left-prev"
+       href="../plugins/"
+       title="previous page">
+      <i class="fa-solid fa-angle-left"></i>
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">CIL Plugins</p>
+      </div>
+    </a>
+    <a class="right-next"
+       href="../demos/"
+       title="next page">
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">Tutorials</p>
+      </div>
+      <i class="fa-solid fa-angle-right"></i>
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+</div>
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+              
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+            
+            
+              
+            
+          </div>
+          <footer class="bd-footer-content">
+            
+          </footer>
+        
+      </main>
+    </div>
+  </div>
+  
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script defer src="../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+<script defer src="../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+
+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
+      
+        <div class="footer-item">
+
+  <p class="copyright">
+    
+      © Copyright 2017-2024.
+      <br/>
+    
+  </p>
+</div>
+      
+    </div>
+  
+  
+  
+</div>
+
+  </footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/v24.2.0/framework/index.html b/v24.2.0/framework/index.html
new file mode 100644
index 0000000000..5c425f817a
--- /dev/null
+++ b/v24.2.0/framework/index.html
@@ -0,0 +1,2727 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>Framework &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
+  </script>
+  <!-- 
+    this give us a css class that will be invisible only if js is disabled 
+  -->
+  <noscript>
+    <style>
+      .pst-js-only { display: none !important; }
+
+    </style>
+  </noscript>
+  
+  <!-- Loaded before other Sphinx assets -->
+  <link href="../_static/styles/theme.css?digest=fd05d3ddc9a6e3fa7c66" rel="stylesheet" />
+<link href="../_static/styles/pydata-sphinx-theme.css?digest=fd05d3ddc9a6e3fa7c66" rel="stylesheet" />
+
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
+    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
+  
+  <!-- So that users can add custom icons -->
+  <script src="../_static/scripts/fontawesome.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66" />
+<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66" />
+
+    <script src="../_static/documentation_options.js?v=6cb63398"></script>
+    <script src="../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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+    <script src="../_static/copybutton.js?v=f281be69"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'framework';</script>
+    <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.16.0rc0';
+        DOCUMENTATION_OPTIONS.theme_switcher_json_url = '/CIL/versions.json';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '24.2.0';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
+        </script>
+    <link rel="index" title="Index" href="../genindex/" />
+    <link rel="search" title="Search" href="../search/" />
+    <link rel="next" title="Read/ write AcquisitionData and ImageData" href="../io/" />
+    <link rel="prev" title="Introduction" href="../introduction/" />
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+  <meta name="docsearch:version" content="24.2.0" />
+  </head>
+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
+
+  
+  
+  <div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
+  
+  <div id="pst-scroll-pixel-helper"></div>
+  
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>Back to top</button>
+
+  
+  <dialog id="pst-search-dialog">
+    
+<form class="bd-search d-flex align-items-center"
+      action="../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form>
+  </dialog>
+
+  <div class="pst-async-banner-revealer d-none">
+  <aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
+</div>
+
+  
+    <header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
+<div class="bd-header__inner bd-page-width">
+  <button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
+    <span class="fa-solid fa-bars"></span>
+  </button>
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
+    
+      <div class="navbar-item">
+
+  
+     
+  
+
+<a class="navbar-brand logo" href="/">
+  
+  
+  
+  
+  
+    
+    
+      
+    
+    
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB.svg" class="logo__image only-light" alt="CIL - Home"/>
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB-reversed.svg" class="logo__image only-dark pst-js-only" alt="CIL - Home"/>
+  
+  
+</a></div>
+    
+  </div>
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item current active">
+  <a class="nav-link nav-internal" href="#">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+            <li class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
+                    More
+                </button>
+                <ul id="pst-nav-more-links" class="dropdown-menu">
+                    
+<li class=" ">
+  <a class="nav-link dropdown-item nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+                </ul>
+            </li>
+            
+  </ul>
+</nav></div>
+      
+    </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+      
+        <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+      
+    </div>
+    
+  </div>
+  
+  
+
+  
+</div>
+
+    </header>
+  
+
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      
+      
+      <dialog id="pst-primary-sidebar-modal"></dialog>
+      <div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
+        
+
+  
+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          
+          
+            <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item current active">
+  <a class="nav-link nav-internal" href="#">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+  </ul>
+</nav></div>
+          
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+        
+      </div>
+    
+  </div>
+  
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item">
+<form class="bd-search d-flex align-items-center"
+      action="../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+        <div class="sidebar-primary-item">
+<div class="version-switcher__container dropdown pst-js-only">
+  <button id="pst-version-switcher-button-2"
+    type="button"
+    class="version-switcher__button btn btn-sm dropdown-toggle"
+    data-bs-toggle="dropdown"
+    aria-haspopup="listbox"
+    aria-controls="pst-version-switcher-list-2"
+    aria-label="Version switcher list"
+  >
+    Choose version  <!-- this text may get changed later by javascript -->
+    <span class="caret"></span>
+  </button>
+  <div id="pst-version-switcher-list-2"
+    class="version-switcher__menu dropdown-menu list-group-flush py-0"
+    role="listbox" aria-labelledby="pst-version-switcher-button-2">
+    <!-- dropdown will be populated by javascript on page load -->
+  </div>
+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Framework</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#acquisitiongeometry">AcquisitionGeometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.AcquisitionGeometry"><code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#parallel2d-geometry">Parallel2D Geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#parallel3d-geometry">Parallel3D Geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cone2d-geometry-fanbeam">Cone2D Geometry (Fanbeam)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cone3d-geometry">Cone3D Geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#configure-the-geometry">Configure the geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#use-the-geometry">Use the geometry</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#imagegeometry">ImageGeometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.ImageGeometry"><code class="docutils literal notranslate"><span class="pre">ImageGeometry</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#blockgeometry">BlockGeometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.BlockGeometry"><code class="docutils literal notranslate"><span class="pre">BlockGeometry</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#data-containers">Data Containers</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#datacontainer">DataContainer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#acquisitiondata">AcquisitionData</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#imagedata">ImageData</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#vectordata">VectorData</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#blockdatacontainer">BlockDataContainer</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#partitioner">Partitioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.Partitioner"><code class="docutils literal notranslate"><span class="pre">Partitioner</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#labels">Labels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.labels.ImageDimension"><code class="docutils literal notranslate"><span class="pre">ImageDimension</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.labels.AcquisitionDimension"><code class="docutils literal notranslate"><span class="pre">AcquisitionDimension</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.labels.FillType"><code class="docutils literal notranslate"><span class="pre">FillType</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.labels.AngleUnit"><code class="docutils literal notranslate"><span class="pre">AngleUnit</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.labels.AcquisitionType"><code class="docutils literal notranslate"><span class="pre">AcquisitionType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#dataprocessor">DataProcessor</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.DataProcessor"><code class="docutils literal notranslate"><span class="pre">DataProcessor</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.Processor"><code class="docutils literal notranslate"><span class="pre">Processor</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
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+                  
+  <section id="framework">
+<h1>Framework<a class="headerlink" href="#framework" title="Link to this heading">#</a></h1>
+<section id="acquisitiongeometry">
+<h2>AcquisitionGeometry<a class="headerlink" href="#acquisitiongeometry" title="Link to this heading">#</a></h2>
+<p>The <code class="code docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code> class holds the system acquisition parameters.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">AcquisitionGeometry</span></span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry" title="Link to this definition">#</a></dt>
+<dd><p>This class holds the AcquisitionGeometry of the system.</p>
+<p>Please initialise the AcquisitionGeometry using the using the static methods:</p>
+<p><cite>AcquisitionGeometry.create_Parallel2D()</cite></p>
+<p><cite>AcquisitionGeometry.create_Cone2D()</cite></p>
+<p><cite>AcquisitionGeometry.create_Parallel3D()</cite></p>
+<p><cite>AcquisitionGeometry.create_Cone3D()</cite></p>
+</dd></dl>
+
+<p>We create the appropriate <code class="code docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code> for our data by using the static methods:</p>
+<section id="parallel2d-geometry">
+<h3>Parallel2D Geometry<a class="headerlink" href="#parallel2d-geometry" title="Link to this heading">#</a></h3>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.create_Parallel2D">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">create_Parallel2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ray_direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_position</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_direction_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation_axis_position</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'units</span> <span class="pre">distance'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.create_Parallel2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.create_Parallel2D" title="Link to this definition">#</a></dt>
+<dd><p>This creates the AcquisitionGeometry for a parallel beam 2D tomographic system</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ray_direction</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 2D vector describing the x-ray direction (x,y)</p></li>
+<li><p><strong>detector_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 2D vector describing the position of the centre of the detector (x,y)</p></li>
+<li><p><strong>detector_direction_x</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 2D vector describing the direction of the detector_x (x,y)</p></li>
+<li><p><strong>rotation_axis_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 2D vector describing the position of the axis of rotation (x,y)</p></li>
+<li><p><strong>units</strong> (<em>string</em>) – Label the units of distance used for the configuration, these should be consistent for the geometry and panel</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="parallel3d-geometry">
+<h3>Parallel3D Geometry<a class="headerlink" href="#parallel3d-geometry" title="Link to this heading">#</a></h3>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.create_Parallel3D">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">create_Parallel3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ray_direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_position</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_direction_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1,</span> <span class="pre">0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_direction_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation_axis_position</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation_axis_direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'units</span> <span class="pre">distance'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.create_Parallel3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.create_Parallel3D" title="Link to this definition">#</a></dt>
+<dd><p>This creates the AcquisitionGeometry for a parallel beam 3D tomographic system</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ray_direction</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the x-ray direction (x,y,z)</p></li>
+<li><p><strong>detector_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the position of the centre of the detector (x,y,z)</p></li>
+<li><p><strong>detector_direction_x</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 3D vector describing the direction of the detector_x (x,y,z)</p></li>
+<li><p><strong>detector_direction_y</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 3D vector describing the direction of the detector_y (x,y,z)</p></li>
+<li><p><strong>rotation_axis_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the position of the axis of rotation (x,y,z)</p></li>
+<li><p><strong>rotation_axis_direction</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the direction of the axis of rotation (x,y,z)</p></li>
+<li><p><strong>units</strong> (<em>string</em>) – Label the units of distance used for the configuration, these should be consistent for the geometry and panel</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="cone2d-geometry-fanbeam">
+<h3>Cone2D Geometry (Fanbeam)<a class="headerlink" href="#cone2d-geometry-fanbeam" title="Link to this heading">#</a></h3>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.create_Cone2D">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">create_Cone2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source_position</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_position</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_direction_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation_axis_position</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'units</span> <span class="pre">distance'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.create_Cone2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.create_Cone2D" title="Link to this definition">#</a></dt>
+<dd><p>This creates the AcquisitionGeometry for a cone beam 2D tomographic system</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>source_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 2D vector describing the position of the source (x,y)</p></li>
+<li><p><strong>detector_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 2D vector describing the position of the centre of the detector (x,y)</p></li>
+<li><p><strong>detector_direction_x</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 2D vector describing the direction of the detector_x (x,y)</p></li>
+<li><p><strong>rotation_axis_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 2D vector describing the position of the axis of rotation (x,y)</p></li>
+<li><p><strong>units</strong> (<em>string</em>) – Label the units of distance used for the configuration, these should be consistent for the geometry and panel</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="cone3d-geometry">
+<h3>Cone3D Geometry<a class="headerlink" href="#cone3d-geometry" title="Link to this heading">#</a></h3>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.create_Cone3D">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">create_Cone3D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source_position</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_position</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_direction_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1,</span> <span class="pre">0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_direction_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation_axis_position</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation_axis_direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">0,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'units</span> <span class="pre">distance'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.create_Cone3D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.create_Cone3D" title="Link to this definition">#</a></dt>
+<dd><p>This creates the AcquisitionGeometry for a cone beam 3D tomographic system</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>source_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the position of the source (x,y,z)</p></li>
+<li><p><strong>detector_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the position of the centre of the detector (x,y,z)</p></li>
+<li><p><strong>detector_direction_x</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 3D vector describing the direction of the detector_x (x,y,z)</p></li>
+<li><p><strong>detector_direction_y</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em>) – A 3D vector describing the direction of the detector_y (x,y,z)</p></li>
+<li><p><strong>rotation_axis_position</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the position of the axis of rotation (x,y,z)</p></li>
+<li><p><strong>rotation_axis_direction</strong> (<em>list</em><em>, </em><em>tuple</em><em>, </em><em>ndarray</em><em>, </em><em>optional</em>) – A 3D vector describing the direction of the axis of rotation (x,y,z)</p></li>
+<li><p><strong>units</strong> (<em>string</em>) – Label the units of distance used for the configuration, these should be consistent for the geometry and panel</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="configure-the-geometry">
+<h3>Configure the geometry<a class="headerlink" href="#configure-the-geometry" title="Link to this heading">#</a></h3>
+<p>This gives us an acquisition geometry object configured with the spatial geometry of the system.</p>
+<p>It is then necessary to configure the panel, angular data and dimension labels:</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.set_panel">
+<span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">set_panel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_pixels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1,</span> <span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">origin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bottom-left'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.set_panel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.set_panel" title="Link to this definition">#</a></dt>
+<dd><p>This method configures the panel information of an AcquisitionGeometry object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_pixels</strong> (<em>int</em><em>, </em><em>list</em><em>, </em><em>tuple</em>) – num_pixels_h or (num_pixels_h, num_pixels_v) containing the number of pixels of the panel</p></li>
+<li><p><strong>pixel_size</strong> (<em>int</em><em>, </em><em>list</em><em>, </em><em>tuple</em><em>, </em><em>optional</em>) – pixel_size_h or (pixel_size_h, pixel_size_v) containing the size of the pixels of the panel</p></li>
+<li><p><strong>origin</strong> (<em>string</em><em>, </em><em>default 'bottom-left'</em>) – the position of pixel 0 (the data origin) of the panel ‘top-left’, ‘top-right’, ‘bottom-left’, ‘bottom-right’</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.set_angles">
+<span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">set_angles</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_angle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angle_unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'degree'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.set_angles"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.set_angles" title="Link to this definition">#</a></dt>
+<dd><p>This method configures the angular information of an AcquisitionGeometry object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>angles</strong> (<em>list</em><em>, </em><em>ndarray</em>) – The angular positions of the acquisition data</p></li>
+<li><p><strong>initial_angle</strong> (<em>float</em><em>, </em><em>optional</em>) – The angular offset of the object from the reference frame</p></li>
+<li><p><strong>angle_unit</strong> (<em>string</em>) – The units of the stored angles ‘degree’ or ‘radian’</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.set_labels">
+<span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">set_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.set_labels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.set_labels" title="Link to this definition">#</a></dt>
+<dd><p>This method configures the dimension labels of an AcquisitionGeometry object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>labels</strong> (<em>list</em><em>, </em><em>optional</em>) – The order of the dimensions describing the data.Expects a list containing at least one of the unique labels: ‘channel’ ‘angle’ ‘vertical’ ‘horizontal’
+default = [‘channel’,’angle’,’vertical’,’horizontal’]</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.set_channels">
+<span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">set_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.set_channels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.set_channels" title="Link to this definition">#</a></dt>
+<dd><p>This method configures the channel information of an AcquisitionGeometry object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of channels of data</p></li>
+<li><p><strong>channel_labels</strong> (<em>list</em><em>, </em><em>optional</em>) – A list of channel labels</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a configured AcquisitionGeometry object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="use-the-geometry">
+<h3>Use the geometry<a class="headerlink" href="#use-the-geometry" title="Link to this heading">#</a></h3>
+<p>We can use this geometry to generate related objects. Including a 2D slice <code class="code docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code>, an <code class="code docutils literal notranslate"><span class="pre">AcquisitionData</span></code> container, and a default <code class="code docutils literal notranslate"><span class="pre">ImageGeometry</span></code>.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.get_slice">
+<span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">get_slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertical</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizontal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.get_slice"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.get_slice" title="Link to this definition">#</a></dt>
+<dd><p>Returns a new AcquisitionGeometry of a single slice of in the requested direction. Will only return reconstructable geometries.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.allocate">
+<span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">allocate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.allocate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.allocate" title="Link to this definition">#</a></dt>
+<dd><p>allocates an AcquisitionData according to the size expressed in the instance</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>value</strong> (<em>number</em><em> or </em><em>string</em><em>, </em><em>default None allocates empty memory block</em>) – accepts numbers to allocate an uniform array, or a string as ‘random’ or ‘random_int’ to create a random array or None.</p></li>
+<li><p><strong>dtype</strong> (<em>numpy type</em><em>, </em><em>default numpy.float32</em>) – numerical type to allocate</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionGeometry.get_ImageGeometry">
+<span class="sig-prename descclassname"><span class="pre">AcquisitionGeometry.</span></span><span class="sig-name descname"><span class="pre">get_ImageGeometry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_geometry/#AcquisitionGeometry.get_ImageGeometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionGeometry.get_ImageGeometry" title="Link to this definition">#</a></dt>
+<dd><p>returns a default configured ImageGeometry object based on the AcquisitionGeomerty</p>
+</dd></dl>
+
+</section>
+</section>
+<section id="imagegeometry">
+<h2>ImageGeometry<a class="headerlink" href="#imagegeometry" title="Link to this heading">#</a></h2>
+<p>The <code class="code docutils literal notranslate"><span class="pre">ImageGeometry</span></code> class holds meta data describing the reconstruction volume of interest. This will be centred on the rotation axis position defined
+in <code class="code docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code>, with the z-direction aligned with the rotation axis direction.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.ImageGeometry">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">ImageGeometry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">voxel_num_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_num_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_num_z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_size_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_size_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_size_z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_geometry/#ImageGeometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageGeometry" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageGeometry.get_slice">
+<span class="sig-name descname"><span class="pre">get_slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertical</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizontal_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizontal_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_geometry/#ImageGeometry.get_slice"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageGeometry.get_slice" title="Link to this definition">#</a></dt>
+<dd><p>Returns a new ImageGeometry of a single slice of in the requested direction.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageGeometry.clone">
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_geometry/#ImageGeometry.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageGeometry.clone" title="Link to this definition">#</a></dt>
+<dd><p>returns a copy of the ImageGeometry</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageGeometry.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_geometry/#ImageGeometry.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageGeometry.copy" title="Link to this definition">#</a></dt>
+<dd><p>alias of clone</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageGeometry.allocate">
+<span class="sig-name descname"><span class="pre">allocate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_geometry/#ImageGeometry.allocate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageGeometry.allocate" title="Link to this definition">#</a></dt>
+<dd><p>allocates an ImageData according to the size expressed in the instance</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>value</strong> (<em>number</em><em> or </em><em>string</em><em>, </em><em>default None allocates empty memory block</em><em>, </em><em>default 0</em>) – accepts numbers to allocate an uniform array, or a string as ‘random’ or ‘random_int’ to create a random array or None.</p></li>
+<li><p><strong>dtype</strong> (<em>numpy type</em><em>, </em><em>default numpy.float32</em>) – numerical type to allocate</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="blockgeometry">
+<h2>BlockGeometry<a class="headerlink" href="#blockgeometry" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.BlockGeometry">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">BlockGeometry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockGeometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockGeometry" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockGeometry.get_item">
+<span class="sig-name descname"><span class="pre">get_item</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockGeometry.get_item"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockGeometry.get_item" title="Link to this definition">#</a></dt>
+<dd><p>returns the Geometry in the BlockGeometry located at position index</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockGeometry.allocate">
+<span class="sig-name descname"><span class="pre">allocate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockGeometry.allocate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockGeometry.allocate" title="Link to this definition">#</a></dt>
+<dd><p>Allocates a BlockDataContainer according to geometries contained in the BlockGeometry</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="data-containers">
+<h2>Data Containers<a class="headerlink" href="#data-containers" title="Link to this heading">#</a></h2>
+<p><code class="code docutils literal notranslate"><span class="pre">AcquisitionData</span></code> and <code class="code docutils literal notranslate"><span class="pre">ImageData</span></code> inherit from the same parent <code class="code docutils literal notranslate"><span class="pre">DataContainer</span></code> class,
+therefore they largely behave the same way.</p>
+<p>There are algebraic operations defined for both <code class="code docutils literal notranslate"><span class="pre">AcquisitionData</span></code> and <code class="code docutils literal notranslate"><span class="pre">ImageData</span></code>.
+Following operations are defined:</p>
+<ul class="simple">
+<li><p>binary operations (between two DataContainers or scalar and DataContainer)</p>
+<ul>
+<li><p><code class="code docutils literal notranslate"><span class="pre">+</span></code> addition</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">-</span></code> subtraction</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">/</span></code> division</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">*</span></code> multiplication</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">**</span></code> power</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">maximum</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">minimum</span></code></p></li>
+</ul>
+</li>
+<li><p>in-place operations</p>
+<ul>
+<li><p><code class="code docutils literal notranslate"><span class="pre">+=</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">-=</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">*=</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">**=</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">/=</span></code></p></li>
+</ul>
+</li>
+<li><p>unary operations</p>
+<ul>
+<li><p><code class="code docutils literal notranslate"><span class="pre">abs</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">sqrt</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">sign</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">conjugate</span></code></p></li>
+</ul>
+</li>
+<li><p>reductions</p>
+<ul>
+<li><p><code class="code docutils literal notranslate"><span class="pre">sum</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">norm</span></code></p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">dot</span></code> product</p></li>
+</ul>
+</li>
+</ul>
+<section id="datacontainer">
+<h3>DataContainer<a class="headerlink" href="#datacontainer" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.DataContainer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">DataContainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deep_copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimension_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer" title="Link to this definition">#</a></dt>
+<dd><p>Generic class to hold data</p>
+<p>Data is currently held in a numpy arrays</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#cil.framework.DataContainer.shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the DataContainer</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#cil.framework.DataContainer.ndim" title="Link to this definition">#</a></dt>
+<dd><p>Returns the ndim of the DataContainer</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.number_of_dimensions">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">number_of_dimensions</span></span><a class="headerlink" href="#cil.framework.DataContainer.number_of_dimensions" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the  of the DataContainer</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.dtype">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#cil.framework.DataContainer.dtype" title="Link to this definition">#</a></dt>
+<dd><p>Returns the dtype of the data array.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.size">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">size</span></span><a class="headerlink" href="#cil.framework.DataContainer.size" title="Link to this definition">#</a></dt>
+<dd><p>Returns the number of elements of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.get_dimension_axis">
+<span class="sig-name descname"><span class="pre">get_dimension_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension_label</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.get_dimension_axis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.get_dimension_axis" title="Link to this definition">#</a></dt>
+<dd><p>Returns the axis index of the DataContainer array if the specified dimension_label(s) match
+any dimension_labels of the DataContainer or their indices</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dimension_label</strong> (<em>string</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>ints</em>) – Specify dimension_label(s) or index of the DataContainer from which to check and return the axis index</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The axis index of the DataContainer matching the specified dimension_label</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int or tuple of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.as_array">
+<span class="sig-name descname"><span class="pre">as_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.as_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.as_array" title="Link to this definition">#</a></dt>
+<dd><p>Returns the pointer to the array.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.get_slice">
+<span class="sig-name descname"><span class="pre">get_slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.get_slice"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.get_slice" title="Link to this definition">#</a></dt>
+<dd><p>Returns a new DataContainer containing a single slice in the requested direction.         Pass keyword arguments &lt;dimension label&gt;=index</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.reorder">
+<span class="sig-name descname"><span class="pre">reorder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">order</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.reorder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.reorder" title="Link to this definition">#</a></dt>
+<dd><p>reorders the data in memory as requested.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>order</strong> (<em>list</em><em>, </em><em>sting</em>) – ordered list of labels from self.dimension_labels</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.fill">
+<span class="sig-name descname"><span class="pre">fill</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">dimension</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.fill"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.fill" title="Link to this definition">#</a></dt>
+<dd><p>fills the internal data array with the DataContainer, numpy array or number provided</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>array</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><em>subclasses</em><em>, </em><em>numpy array</em><em> or </em><em>number</em>) – number, numpy array or DataContainer to copy into the DataContainer</p></li>
+<li><p><strong>dimension</strong> – dictionary, optional</p></li>
+</ul>
+</dd>
+</dl>
+<p>if the passed numpy array points to the same array that is contained in the DataContainer,
+it just returns</p>
+<p>In case a DataContainer or subclass is passed, there will be a check of the geometry,
+if present, and the array will be resorted if the data is not in the appropriate order.</p>
+<p>User may pass a named parameter to specify in which axis the fill should happen:</p>
+<p>dc.fill(some_data, vertical=1, horizontal_x=32)
+will copy the data in some_data into the data container.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.get_data_axes_order">
+<span class="sig-name descname"><span class="pre">get_data_axes_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.get_data_axes_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.get_data_axes_order" title="Link to this definition">#</a></dt>
+<dd><p>returns the axes label of self as a list</p>
+<p>If new_order is None returns the labels of the axes as a sorted-by-key list.
+If new_order is a list of length number_of_dimensions, returns a list
+with the indices of the axes in new_order with respect to those in
+self.dimension_labels: i.e.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">:</span><span class="s1">&#39;vertical&#39;</span><span class="p">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">new_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">]</span>
+<span class="go">returns [1,0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.clone">
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.clone" title="Link to this definition">#</a></dt>
+<dd><p>returns a copy of DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.copy" title="Link to this definition">#</a></dt>
+<dd><p>alias of clone</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.sapyb">
+<span class="sig-name descname"><span class="pre">sapyb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.sapyb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.sapyb" title="Link to this definition">#</a></dt>
+<dd><p>performs a*self + b * y. Can be done in-place</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> (<em>multiplier for self</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>y</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>b</strong> (<em>multiplier for y</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>) – out can be self or y.</p></li>
+<li><p><strong>num_threads</strong> (<em>number</em><em> of </em><em>threads to use during the calculation</em><em>, </em><em>using the CIL C library</em>) – It will try to use the CIL C library and default to numpy operations, in case the C library does not handle the types.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.exp">
+<span class="sig-name descname"><span class="pre">exp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.exp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.exp" title="Link to this definition">#</a></dt>
+<dd><p>Applies exp pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.log">
+<span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.log" title="Link to this definition">#</a></dt>
+<dd><p>Applies log pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.squared_norm">
+<span class="sig-name descname"><span class="pre">squared_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.squared_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.squared_norm" title="Link to this definition">#</a></dt>
+<dd><p>return the squared euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.norm">
+<span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.norm" title="Link to this definition">#</a></dt>
+<dd><p>return the euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.dot">
+<span class="sig-name descname"><span class="pre">dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.dot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.dot" title="Link to this definition">#</a></dt>
+<dd><p>returns the inner product of 2 DataContainers viewed as vectors. Suitable for real and complex data.
+For complex data,  the dot method returns a.dot(b.conjugate())</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.sum">
+<span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.sum" title="Link to this definition">#</a></dt>
+<dd><p>Returns the sum of values in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘sum’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The sum as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.min">
+<span class="sig-name descname"><span class="pre">min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.min"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.min" title="Link to this definition">#</a></dt>
+<dd><p>Returns the minimum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘min’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary.  See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The min as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.max">
+<span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.max"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.max" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘max’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The max as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataContainer.mean">
+<span class="sig-name descname"><span class="pre">mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/data_container/#DataContainer.mean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataContainer.mean" title="Link to this definition">#</a></dt>
+<dd><p>Returns the mean pixel value of the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘mean’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The mean as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="acquisitiondata">
+<h3>AcquisitionData<a class="headerlink" href="#acquisitiondata" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">AcquisitionData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deep_copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_data/#AcquisitionData"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionData" title="Link to this definition">#</a></dt>
+<dd><p>DataContainer for holding 2D or 3D sinogram</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.as_array">
+<span class="sig-name descname"><span class="pre">as_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.as_array" title="Link to this definition">#</a></dt>
+<dd><p>Returns the pointer to the array.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.clone">
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.clone" title="Link to this definition">#</a></dt>
+<dd><p>returns a copy of DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.copy" title="Link to this definition">#</a></dt>
+<dd><p>alias of clone</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.dot">
+<span class="sig-name descname"><span class="pre">dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.dot" title="Link to this definition">#</a></dt>
+<dd><p>returns the inner product of 2 DataContainers viewed as vectors. Suitable for real and complex data.
+For complex data,  the dot method returns a.dot(b.conjugate())</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.dtype">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#cil.framework.AcquisitionData.dtype" title="Link to this definition">#</a></dt>
+<dd><p>Returns the dtype of the data array.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.exp">
+<span class="sig-name descname"><span class="pre">exp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.exp" title="Link to this definition">#</a></dt>
+<dd><p>Applies exp pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.fill">
+<span class="sig-name descname"><span class="pre">fill</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">dimension</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.fill" title="Link to this definition">#</a></dt>
+<dd><p>fills the internal data array with the DataContainer, numpy array or number provided</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>array</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><em>subclasses</em><em>, </em><em>numpy array</em><em> or </em><em>number</em>) – number, numpy array or DataContainer to copy into the DataContainer</p></li>
+<li><p><strong>dimension</strong> – dictionary, optional</p></li>
+</ul>
+</dd>
+</dl>
+<p>if the passed numpy array points to the same array that is contained in the DataContainer,
+it just returns</p>
+<p>In case a DataContainer or subclass is passed, there will be a check of the geometry,
+if present, and the array will be resorted if the data is not in the appropriate order.</p>
+<p>User may pass a named parameter to specify in which axis the fill should happen:</p>
+<p>dc.fill(some_data, vertical=1, horizontal_x=32)
+will copy the data in some_data into the data container.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.get_data_axes_order">
+<span class="sig-name descname"><span class="pre">get_data_axes_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.get_data_axes_order" title="Link to this definition">#</a></dt>
+<dd><p>returns the axes label of self as a list</p>
+<p>If new_order is None returns the labels of the axes as a sorted-by-key list.
+If new_order is a list of length number_of_dimensions, returns a list
+with the indices of the axes in new_order with respect to those in
+self.dimension_labels: i.e.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">:</span><span class="s1">&#39;vertical&#39;</span><span class="p">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">new_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">]</span>
+<span class="go">returns [1,0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.get_dimension_axis">
+<span class="sig-name descname"><span class="pre">get_dimension_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension_label</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.get_dimension_axis" title="Link to this definition">#</a></dt>
+<dd><p>Returns the axis index of the DataContainer array if the specified dimension_label(s) match
+any dimension_labels of the DataContainer or their indices</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dimension_label</strong> (<em>string</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>ints</em>) – Specify dimension_label(s) or index of the DataContainer from which to check and return the axis index</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The axis index of the DataContainer matching the specified dimension_label</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int or tuple of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.get_slice">
+<span class="sig-name descname"><span class="pre">get_slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertical</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizontal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_data/#AcquisitionData.get_slice"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionData.get_slice" title="Link to this definition">#</a></dt>
+<dd><p>Returns a new dataset of a single slice in the requested direction.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.log">
+<span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.log" title="Link to this definition">#</a></dt>
+<dd><p>Applies log pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.max">
+<span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.max" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘max’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The max as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.mean">
+<span class="sig-name descname"><span class="pre">mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.mean" title="Link to this definition">#</a></dt>
+<dd><p>Returns the mean pixel value of the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘mean’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The mean as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.min">
+<span class="sig-name descname"><span class="pre">min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.min" title="Link to this definition">#</a></dt>
+<dd><p>Returns the minimum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘min’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary.  See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The min as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#cil.framework.AcquisitionData.ndim" title="Link to this definition">#</a></dt>
+<dd><p>Returns the ndim of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.norm">
+<span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.norm" title="Link to this definition">#</a></dt>
+<dd><p>return the euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.number_of_dimensions">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">number_of_dimensions</span></span><a class="headerlink" href="#cil.framework.AcquisitionData.number_of_dimensions" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the  of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.partition">
+<span class="sig-name descname"><span class="pre">partition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_batches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.partition" title="Link to this definition">#</a></dt>
+<dd><p>Partition the data into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches using the specified <code class="docutils literal notranslate"><span class="pre">mode</span></code>.</p>
+<p>The modes are</p>
+<ol class="arabic simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sequential</span></code> - The data will be partitioned into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches of sequential indices.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">staggered</span></code> - The data will be partitioned into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches of sequential indices, with stride equal to <code class="docutils literal notranslate"><span class="pre">num_batches</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">random_permutation</span></code> - The data will be partitioned into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches of random indices.</p></li>
+</ol>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_batches</strong> (<em>int</em>) – The number of batches to partition the data into.</p></li>
+<li><p><strong>mode</strong> (<em>str</em>) – The mode to use for partitioning. Must be one of <code class="docutils literal notranslate"><span class="pre">sequential</span></code>, <code class="docutils literal notranslate"><span class="pre">staggered</span></code> or <code class="docutils literal notranslate"><span class="pre">random_permutation</span></code>.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The seed to use for the random permutation. If not specified, the random number
+generator will not be seeded.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Block of <cite>AcquisitionData</cite> objects containing the data requested in each batch</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../optimisation/#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer">BlockDataContainer</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<p>Partitioning a list of ints [0, 1, 2, 3, 4, 5, 6, 7, 8] into 4 batches will return:</p>
+<ol class="arabic simple">
+<li><p>[[0, 1, 2], [3, 4], [5, 6], [7, 8]] with <code class="docutils literal notranslate"><span class="pre">sequential</span></code></p></li>
+<li><p>[[0, 4, 8], [1, 5], [2, 6], [3, 7]] with <code class="docutils literal notranslate"><span class="pre">staggered</span></code></p></li>
+<li><p>[[8, 2, 6], [7, 1], [0, 4], [3, 5]] with <code class="docutils literal notranslate"><span class="pre">random_permutation</span></code> and seed 1</p></li>
+</ol>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.sapyb">
+<span class="sig-name descname"><span class="pre">sapyb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.sapyb" title="Link to this definition">#</a></dt>
+<dd><p>performs a*self + b * y. Can be done in-place</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> (<em>multiplier for self</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>y</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>b</strong> (<em>multiplier for y</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>) – out can be self or y.</p></li>
+<li><p><strong>num_threads</strong> (<em>number</em><em> of </em><em>threads to use during the calculation</em><em>, </em><em>using the CIL C library</em>) – It will try to use the CIL C library and default to numpy operations, in case the C library does not handle the types.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#cil.framework.AcquisitionData.shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the DataContainer</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.size">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">size</span></span><a class="headerlink" href="#cil.framework.AcquisitionData.size" title="Link to this definition">#</a></dt>
+<dd><p>Returns the number of elements of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.squared_norm">
+<span class="sig-name descname"><span class="pre">squared_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.squared_norm" title="Link to this definition">#</a></dt>
+<dd><p>return the squared euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.sum">
+<span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.AcquisitionData.sum" title="Link to this definition">#</a></dt>
+<dd><p>Returns the sum of values in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘sum’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The sum as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.AcquisitionData.reorder">
+<span class="sig-name descname"><span class="pre">reorder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">order</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/acquisition_data/#AcquisitionData.reorder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.AcquisitionData.reorder" title="Link to this definition">#</a></dt>
+<dd><p>Reorders the data in memory as requested. This is an in-place operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>order</strong> (<em>list</em><em> or </em><em>str</em>) – Ordered list of labels from self.dimension_labels, or string ‘astra’ or ‘tigre’.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="imagedata">
+<h3>ImageData<a class="headerlink" href="#imagedata" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.ImageData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">ImageData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deep_copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_data/#ImageData"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageData" title="Link to this definition">#</a></dt>
+<dd><p>DataContainer for holding 2D or 3D DataContainer</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.get_slice">
+<span class="sig-name descname"><span class="pre">get_slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertical</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizontal_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizontal_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_data/#ImageData.get_slice"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageData.get_slice" title="Link to this definition">#</a></dt>
+<dd><p>Returns a new ImageData of a single slice of in the requested direction.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.apply_circular_mask">
+<span class="sig-name descname"><span class="pre">apply_circular_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_place</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_data/#ImageData.apply_circular_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageData.apply_circular_mask" title="Link to this definition">#</a></dt>
+<dd><p>Apply a circular mask to the horizontal_x and horizontal_y slices. Values outside this mask will be set to zero.</p>
+<p>This will most commonly be used to mask edge artefacts from standard CT reconstructions with FBP.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>radius</strong> (<em>float</em><em>, </em><em>default 0.99</em>) – radius of mask by percentage of size of horizontal_x or horizontal_y, whichever is greater</p></li>
+<li><p><strong>in_place</strong> (<em>boolean</em><em>, </em><em>default True</em>) – If <cite>True</cite> masks the current data, if <cite>False</cite> returns a new <cite>ImageData</cite> object.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>If <cite>in_place = False</cite> returns a new ImageData object with the masked data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.as_array">
+<span class="sig-name descname"><span class="pre">as_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.as_array" title="Link to this definition">#</a></dt>
+<dd><p>Returns the pointer to the array.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.clone">
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.clone" title="Link to this definition">#</a></dt>
+<dd><p>returns a copy of DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.copy" title="Link to this definition">#</a></dt>
+<dd><p>alias of clone</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.dot">
+<span class="sig-name descname"><span class="pre">dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.dot" title="Link to this definition">#</a></dt>
+<dd><p>returns the inner product of 2 DataContainers viewed as vectors. Suitable for real and complex data.
+For complex data,  the dot method returns a.dot(b.conjugate())</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.ImageData.dtype">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#cil.framework.ImageData.dtype" title="Link to this definition">#</a></dt>
+<dd><p>Returns the dtype of the data array.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.exp">
+<span class="sig-name descname"><span class="pre">exp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.exp" title="Link to this definition">#</a></dt>
+<dd><p>Applies exp pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.fill">
+<span class="sig-name descname"><span class="pre">fill</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">dimension</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.fill" title="Link to this definition">#</a></dt>
+<dd><p>fills the internal data array with the DataContainer, numpy array or number provided</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>array</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><em>subclasses</em><em>, </em><em>numpy array</em><em> or </em><em>number</em>) – number, numpy array or DataContainer to copy into the DataContainer</p></li>
+<li><p><strong>dimension</strong> – dictionary, optional</p></li>
+</ul>
+</dd>
+</dl>
+<p>if the passed numpy array points to the same array that is contained in the DataContainer,
+it just returns</p>
+<p>In case a DataContainer or subclass is passed, there will be a check of the geometry,
+if present, and the array will be resorted if the data is not in the appropriate order.</p>
+<p>User may pass a named parameter to specify in which axis the fill should happen:</p>
+<p>dc.fill(some_data, vertical=1, horizontal_x=32)
+will copy the data in some_data into the data container.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.get_data_axes_order">
+<span class="sig-name descname"><span class="pre">get_data_axes_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.get_data_axes_order" title="Link to this definition">#</a></dt>
+<dd><p>returns the axes label of self as a list</p>
+<p>If new_order is None returns the labels of the axes as a sorted-by-key list.
+If new_order is a list of length number_of_dimensions, returns a list
+with the indices of the axes in new_order with respect to those in
+self.dimension_labels: i.e.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">:</span><span class="s1">&#39;vertical&#39;</span><span class="p">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">new_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">]</span>
+<span class="go">returns [1,0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.get_dimension_axis">
+<span class="sig-name descname"><span class="pre">get_dimension_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension_label</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.get_dimension_axis" title="Link to this definition">#</a></dt>
+<dd><p>Returns the axis index of the DataContainer array if the specified dimension_label(s) match
+any dimension_labels of the DataContainer or their indices</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dimension_label</strong> (<em>string</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>ints</em>) – Specify dimension_label(s) or index of the DataContainer from which to check and return the axis index</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The axis index of the DataContainer matching the specified dimension_label</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int or tuple of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.log">
+<span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.log" title="Link to this definition">#</a></dt>
+<dd><p>Applies log pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.max">
+<span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.max" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘max’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The max as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.mean">
+<span class="sig-name descname"><span class="pre">mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.mean" title="Link to this definition">#</a></dt>
+<dd><p>Returns the mean pixel value of the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘mean’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The mean as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.min">
+<span class="sig-name descname"><span class="pre">min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.min" title="Link to this definition">#</a></dt>
+<dd><p>Returns the minimum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘min’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary.  See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The min as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.ImageData.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#cil.framework.ImageData.ndim" title="Link to this definition">#</a></dt>
+<dd><p>Returns the ndim of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.norm">
+<span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.norm" title="Link to this definition">#</a></dt>
+<dd><p>return the euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.ImageData.number_of_dimensions">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">number_of_dimensions</span></span><a class="headerlink" href="#cil.framework.ImageData.number_of_dimensions" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the  of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.sapyb">
+<span class="sig-name descname"><span class="pre">sapyb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.sapyb" title="Link to this definition">#</a></dt>
+<dd><p>performs a*self + b * y. Can be done in-place</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> (<em>multiplier for self</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>y</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>b</strong> (<em>multiplier for y</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>) – out can be self or y.</p></li>
+<li><p><strong>num_threads</strong> (<em>number</em><em> of </em><em>threads to use during the calculation</em><em>, </em><em>using the CIL C library</em>) – It will try to use the CIL C library and default to numpy operations, in case the C library does not handle the types.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.ImageData.shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#cil.framework.ImageData.shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the DataContainer</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.ImageData.size">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">size</span></span><a class="headerlink" href="#cil.framework.ImageData.size" title="Link to this definition">#</a></dt>
+<dd><p>Returns the number of elements of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.squared_norm">
+<span class="sig-name descname"><span class="pre">squared_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.squared_norm" title="Link to this definition">#</a></dt>
+<dd><p>return the squared euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.sum">
+<span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.ImageData.sum" title="Link to this definition">#</a></dt>
+<dd><p>Returns the sum of values in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘sum’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The sum as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.ImageData.reorder">
+<span class="sig-name descname"><span class="pre">reorder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">order</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/image_data/#ImageData.reorder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.ImageData.reorder" title="Link to this definition">#</a></dt>
+<dd><p>Reorders the data in memory as requested. This is an in-place operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>order</strong> (<em>list</em><em> or </em><em>str</em>) – Ordered list of labels from self.dimension_labels, or string ‘astra’ or ‘tigre’.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="vectordata">
+<h3>VectorData<a class="headerlink" href="#vectordata" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.VectorData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">VectorData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/vector_data/#VectorData"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.VectorData" title="Link to this definition">#</a></dt>
+<dd><p>DataContainer to contain 1D array</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.as_array">
+<span class="sig-name descname"><span class="pre">as_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.as_array" title="Link to this definition">#</a></dt>
+<dd><p>Returns the pointer to the array.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.clone">
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.clone" title="Link to this definition">#</a></dt>
+<dd><p>returns a copy of DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.copy" title="Link to this definition">#</a></dt>
+<dd><p>alias of clone</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.dot">
+<span class="sig-name descname"><span class="pre">dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.dot" title="Link to this definition">#</a></dt>
+<dd><p>returns the inner product of 2 DataContainers viewed as vectors. Suitable for real and complex data.
+For complex data,  the dot method returns a.dot(b.conjugate())</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.VectorData.dtype">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#cil.framework.VectorData.dtype" title="Link to this definition">#</a></dt>
+<dd><p>Returns the dtype of the data array.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.exp">
+<span class="sig-name descname"><span class="pre">exp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.exp" title="Link to this definition">#</a></dt>
+<dd><p>Applies exp pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.fill">
+<span class="sig-name descname"><span class="pre">fill</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">dimension</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.fill" title="Link to this definition">#</a></dt>
+<dd><p>fills the internal data array with the DataContainer, numpy array or number provided</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>array</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><em>subclasses</em><em>, </em><em>numpy array</em><em> or </em><em>number</em>) – number, numpy array or DataContainer to copy into the DataContainer</p></li>
+<li><p><strong>dimension</strong> – dictionary, optional</p></li>
+</ul>
+</dd>
+</dl>
+<p>if the passed numpy array points to the same array that is contained in the DataContainer,
+it just returns</p>
+<p>In case a DataContainer or subclass is passed, there will be a check of the geometry,
+if present, and the array will be resorted if the data is not in the appropriate order.</p>
+<p>User may pass a named parameter to specify in which axis the fill should happen:</p>
+<p>dc.fill(some_data, vertical=1, horizontal_x=32)
+will copy the data in some_data into the data container.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.get_data_axes_order">
+<span class="sig-name descname"><span class="pre">get_data_axes_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.get_data_axes_order" title="Link to this definition">#</a></dt>
+<dd><p>returns the axes label of self as a list</p>
+<p>If new_order is None returns the labels of the axes as a sorted-by-key list.
+If new_order is a list of length number_of_dimensions, returns a list
+with the indices of the axes in new_order with respect to those in
+self.dimension_labels: i.e.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span><span class="o">.</span><span class="n">dimension_labels</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">:</span><span class="s1">&#39;vertical&#39;</span><span class="p">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">new_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;vertical&#39;</span><span class="p">,</span><span class="s1">&#39;horizontal&#39;</span><span class="p">]</span>
+<span class="go">returns [1,0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.get_dimension_axis">
+<span class="sig-name descname"><span class="pre">get_dimension_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension_label</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.get_dimension_axis" title="Link to this definition">#</a></dt>
+<dd><p>Returns the axis index of the DataContainer array if the specified dimension_label(s) match
+any dimension_labels of the DataContainer or their indices</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dimension_label</strong> (<em>string</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>ints</em>) – Specify dimension_label(s) or index of the DataContainer from which to check and return the axis index</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The axis index of the DataContainer matching the specified dimension_label</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int or tuple of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.get_slice">
+<span class="sig-name descname"><span class="pre">get_slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.get_slice" title="Link to this definition">#</a></dt>
+<dd><p>Returns a new DataContainer containing a single slice in the requested direction.         Pass keyword arguments &lt;dimension label&gt;=index</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.log">
+<span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.log" title="Link to this definition">#</a></dt>
+<dd><p>Applies log pixel-wise to the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.max">
+<span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.max" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘max’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The max as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.mean">
+<span class="sig-name descname"><span class="pre">mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.mean" title="Link to this definition">#</a></dt>
+<dd><p>Returns the mean pixel value of the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘mean’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The mean as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.min">
+<span class="sig-name descname"><span class="pre">min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.min" title="Link to this definition">#</a></dt>
+<dd><p>Returns the minimum pixel value in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘min’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary.  See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The min as a scalar or inside a DataContainer with reduced dimension_labels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.VectorData.ndim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ndim</span></span><a class="headerlink" href="#cil.framework.VectorData.ndim" title="Link to this definition">#</a></dt>
+<dd><p>Returns the ndim of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.norm">
+<span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.norm" title="Link to this definition">#</a></dt>
+<dd><p>return the euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.VectorData.number_of_dimensions">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">number_of_dimensions</span></span><a class="headerlink" href="#cil.framework.VectorData.number_of_dimensions" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the  of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.reorder">
+<span class="sig-name descname"><span class="pre">reorder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">order</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.reorder" title="Link to this definition">#</a></dt>
+<dd><p>reorders the data in memory as requested.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>order</strong> (<em>list</em><em>, </em><em>sting</em>) – ordered list of labels from self.dimension_labels</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.sapyb">
+<span class="sig-name descname"><span class="pre">sapyb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.sapyb" title="Link to this definition">#</a></dt>
+<dd><p>performs a*self + b * y. Can be done in-place</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> (<em>multiplier for self</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>y</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>b</strong> (<em>multiplier for y</em><em>, </em><em>can be a number</em><em> or </em><em>a numpy array</em><em> or </em><em>a DataContainer</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>) – out can be self or y.</p></li>
+<li><p><strong>num_threads</strong> (<em>number</em><em> of </em><em>threads to use during the calculation</em><em>, </em><em>using the CIL C library</em>) – It will try to use the CIL C library and default to numpy operations, in case the C library does not handle the types.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.VectorData.shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape</span></span><a class="headerlink" href="#cil.framework.VectorData.shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the DataContainer</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.VectorData.size">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">size</span></span><a class="headerlink" href="#cil.framework.VectorData.size" title="Link to this definition">#</a></dt>
+<dd><p>Returns the number of elements of the DataContainer</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.squared_norm">
+<span class="sig-name descname"><span class="pre">squared_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.squared_norm" title="Link to this definition">#</a></dt>
+<dd><p>return the squared euclidean norm of the DataContainer viewed as a vector</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.VectorData.sum">
+<span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.VectorData.sum" title="Link to this definition">#</a></dt>
+<dd><p>Returns the sum of values in the DataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>axis</strong> (<em>string</em><em> or </em><em>tuple</em><em> of </em><em>strings</em><em> or </em><em>int</em><em> or </em><em>tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – Specify the axis or axes to calculate ‘sum’ along. Can be specified as
+string(s) of dimension_labels or int(s) of indices
+Default None calculates the function over the whole array</p></li>
+<li><p><strong>out</strong> (<em>ndarray</em><em> or </em><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – <p>Provide an object in which to place the result. The object must have the correct dimensions and
+(for DataContainers) the correct dimension_labels, but the type will be cast if necessary. See
+<a class="reference external" href="https://numpy.org/doc/stable/user/basics.ufuncs.html#ufuncs-output-type">Output type determination</a> for more details.
+Default is None</p>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The sum as a scalar or inside a DataContainer with reduced dimension_labels
+Default is to accumulate and return data as float64 or complex128</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>scalar or <a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="blockdatacontainer">
+<h3>BlockDataContainer<a class="headerlink" href="#blockdatacontainer" title="Link to this heading">#</a></h3>
+<p>A <code class="code docutils literal notranslate"><span class="pre">BlockDataContainer</span></code> can be instantiated from a number of <a class="reference internal" href="#datacontainer">DataContainer</a> and subclasses
+represents a column vector of <code class="code docutils literal notranslate"><span class="pre">DataContainer</span></code> s.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">bdc</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="n">DataContainer0</span><span class="p">,</span> <span class="n">DataContainer1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This provide a base class that will behave as normal <code class="code docutils literal notranslate"><span class="pre">DataContainer</span></code>.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">BlockDataContainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer" title="Link to this definition">#</a></dt>
+<dd><p>Class to hold DataContainers as column vector</p>
+<p>Provides basic algebra between BlockDataContainer’s, DataContainer’s and
+subclasses and Numbers</p>
+<ol class="arabic simple">
+<li><p>algebra between <a href="#id16"><span class="problematic" id="id17">`</span></a>BlockDataContainer`s will be element-wise, only if
+the shape of the 2 <a href="#id18"><span class="problematic" id="id19">`</span></a>BlockDataContainer`s is the same, otherwise it
+will fail</p></li>
+<li><p>algebra between <cite>BlockDataContainer`s and `list</cite> or <cite>numpy array</cite> will
+work as long as the number of <cite>rows</cite> and element of the arrays match,
+independently on the fact that the <cite>BlockDataContainer</cite> could be nested</p></li>
+<li><p>algebra between <cite>BlockDataContainer</cite> and one <cite>DataContainer</cite> is possible.
+It will require all the <cite>DataContainers</cite> in the block to be
+compatible with the <cite>DataContainer</cite> we want to operate with.</p></li>
+<li><p>algebra between <cite>BlockDataContainer</cite> and a <cite>Number</cite> is possible and it
+will be done with each element of the <cite>BlockDataContainer</cite> even if nested</p></li>
+</ol>
+<p>A = [ [B,C] , D]
+A * 3 = [ 3 * [B,C] , 3* D] = [ [ 3*B, 3*C]  , 3*D ]</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.next">
+<span class="sig-name descname"><span class="pre">next</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.next"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.next" title="Link to this definition">#</a></dt>
+<dd><p>python2 backwards compatibility</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.is_compatible">
+<span class="sig-name descname"><span class="pre">is_compatible</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.is_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.is_compatible" title="Link to this definition">#</a></dt>
+<dd><p>basic check if the size of the 2 objects fit</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.add">
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.add" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: add method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placehold for the resul.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.subtract">
+<span class="sig-name descname"><span class="pre">subtract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.subtract"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.subtract" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: subtract method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.multiply">
+<span class="sig-name descname"><span class="pre">multiply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.multiply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.multiply" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: multiply method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.divide">
+<span class="sig-name descname"><span class="pre">divide</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.divide"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.divide" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: divide method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.power">
+<span class="sig-name descname"><span class="pre">power</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.power"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.power" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.maximum">
+<span class="sig-name descname"><span class="pre">maximum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.maximum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.maximum" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.minimum">
+<span class="sig-name descname"><span class="pre">minimum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.minimum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.minimum" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.sapyb">
+<span class="sig-name descname"><span class="pre">sapyb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.sapyb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.sapyb" title="Link to this definition">#</a></dt>
+<dd><p>performs axpby element-wise on the BlockDataContainer containers</p>
+<p>Does the operation .. math:: a*x+b*y and stores the result in out, where x is self</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> – scalar</p></li>
+<li><p><strong>b</strong> – scalar</p></li>
+<li><p><strong>y</strong> – compatible (Block)DataContainer</p></li>
+<li><p><strong>out</strong> – (Block)DataContainer to store the result</p></li>
+</ul>
+</dd>
+</dl>
+<section id="example">
+<h4>Example:<a class="headerlink" href="#example" title="Link to this heading">#</a></h4>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bdc1</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bdc2</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">bdc1</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">bdc2</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.axpby">
+<span class="sig-name descname"><span class="pre">axpby</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float32'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads=2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.axpby"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.axpby" title="Link to this definition">#</a></dt>
+<dd><p>Deprecated method. Alias of sapyb</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.binary_operations">
+<span class="sig-name descname"><span class="pre">binary_operations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.binary_operations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.binary_operations" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: generic method of algebric operation with BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<p>Provides commutativity with DataContainer and subclasses, i.e. this
+class’s reverse algebraic methods take precedence w.r.t. direct algebraic
+methods of DataContainer and subclasses.</p>
+<p>This method is not to be used directly</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.unary_operations">
+<span class="sig-name descname"><span class="pre">unary_operations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operation</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.unary_operations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.unary_operations" title="Link to this definition">#</a></dt>
+<dd><p>Unary operation on BlockDataContainer:</p>
+<p>generic method of unary operation with BlockDataContainer: abs, sign, sqrt and conjugate</p>
+<p>This method is not to be used directly</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.copy" title="Link to this definition">#</a></dt>
+<dd><p>alias of clone</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+<section id="partitioner">
+<h2>Partitioner<a class="headerlink" href="#partitioner" title="Link to this heading">#</a></h2>
+<p>This method partitions an instance of tomography <code class="code docutils literal notranslate"><span class="pre">AcquisitionData</span></code> into a number of batches. For example, to use with a stochastic optimisation method.</p>
+<p>The partitioning is done by taking batches of angles and the corresponding data collected by taking projections along these angles. The partitioner method chooses what angles go in which batch depending on the <cite>mode</cite> and takes in an <cite>AquisitionData</cite> object and outputs a <cite>BlockDataContainer</cite> where each element in the block is  <cite>AquisitionData</cite> object with the batch of data and corresponding geometry.
+We consider a <strong>batch</strong> to be a subset of the <code class="code docutils literal notranslate"><span class="pre">AcquisitionData</span></code> and the verb, <strong>to partition</strong>, to be the act of splitting into batches.</p>
+<p>For example:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cil.utilities</span> <span class="kn">import</span> <span class="n">dataexample</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.astra.operators</span> <span class="kn">import</span> <span class="n">ProjectionOperator</span>
+
+<span class="c1"># get the data</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">dataexample</span><span class="o">.</span><span class="n">SIMULATED_PARALLEL_BEAM_DATA</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="n">data</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="s1">&#39;astra&#39;</span><span class="p">)</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="s1">&#39;centre&#39;</span><span class="p">)</span>
+
+<span class="c1"># create the geometries</span>
+<span class="n">ag</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+
+<span class="c1"># partition the data into batches contained in the elements of a BlockDataContainer</span>
+<span class="n">data_partitioned</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="n">num_batches</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;staggered&#39;</span><span class="p">)</span> <span class="c1"># Choose mode from `sequential`, `staggered` or `random_permutation`</span>
+<span class="c1"># From the partitioned data build a BlockOperator container the projectors for each batch</span>
+<span class="n">A_partitioned</span> <span class="o">=</span> <span class="n">ProjectionOperator</span><span class="p">(</span><span class="n">ig</span><span class="p">,</span> <span class="n">data_partitioned</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The total number of angles is &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The first 30 angles are &#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">[:</span><span class="mi">30</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;In batch zero the number of angles is &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_partitioned</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The angles in batch zero are &#39;</span><span class="p">,</span> <span class="n">data_partitioned</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The angles in batch one are &#39;</span><span class="p">,</span> <span class="n">data_partitioned</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">angles</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-RST notranslate"><div class="highlight"><pre><span></span>The total number of angles is  300
+The first 30 angles are  [ 0.   1.2  2.4  3.6  4.8  6.   7.2  8.4  9.6 10.8 12.  13.2 14.4 15.6
+16.8 18.  19.2 20.4 21.6 22.8 24.  25.2 26.4 27.6 28.8 30.  31.2 32.4
+33.6 34.8]
+In batch zero the number of angles is  30
+The angles in batch zero are  [  0.  12.  24.  36.  48.  60.  72.  84.  96. 108. 120. 132. 144. 156.
+<span class="m">168.</span> 180. 192. 204. 216. 228. 240. 252. 264. 276. 288. 300. 312. 324.
+<span class="m">336.</span> 348.]
+The angles in batch one are  [  1.2  13.2  25.2  37.2  49.2  61.2  73.2  85.2  97.2 109.2 121.2 133.2
+145.2 157.2 169.2 181.2 193.2 205.2 217.2 229.2 241.2 253.2 265.2 277.2
+289.2 301.2 313.2 325.2 337.2 349.2]
+</pre></div>
+</div>
+<p>The <code class="code docutils literal notranslate"><span class="pre">partition</span></code> method is defined as part of:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.Partitioner">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">Partitioner</span></span><a class="reference internal" href="../_modules/cil/framework/partitioner/#Partitioner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.Partitioner" title="Link to this definition">#</a></dt>
+<dd><p>Interface for AcquisitionData to be able to partition itself in a number of batches.</p>
+<p>This class, by multiple inheritance with AcquisitionData, allows the user to partition the data,
+by using the method <code class="docutils literal notranslate"><span class="pre">partition</span></code>.
+The partitioning will generate a <code class="docutils literal notranslate"><span class="pre">BlockDataContainer</span></code> with appropriate <code class="docutils literal notranslate"><span class="pre">AcquisitionData</span></code>.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.Partitioner.partition">
+<span class="sig-name descname"><span class="pre">partition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_batches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/partitioner/#Partitioner.partition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.Partitioner.partition" title="Link to this definition">#</a></dt>
+<dd><p>Partition the data into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches using the specified <code class="docutils literal notranslate"><span class="pre">mode</span></code>.</p>
+<p>The modes are</p>
+<ol class="arabic simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sequential</span></code> - The data will be partitioned into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches of sequential indices.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">staggered</span></code> - The data will be partitioned into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches of sequential indices, with stride equal to <code class="docutils literal notranslate"><span class="pre">num_batches</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">random_permutation</span></code> - The data will be partitioned into <code class="docutils literal notranslate"><span class="pre">num_batches</span></code> batches of random indices.</p></li>
+</ol>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_batches</strong> (<em>int</em>) – The number of batches to partition the data into.</p></li>
+<li><p><strong>mode</strong> (<em>str</em>) – The mode to use for partitioning. Must be one of <code class="docutils literal notranslate"><span class="pre">sequential</span></code>, <code class="docutils literal notranslate"><span class="pre">staggered</span></code> or <code class="docutils literal notranslate"><span class="pre">random_permutation</span></code>.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The seed to use for the random permutation. If not specified, the random number
+generator will not be seeded.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Block of <cite>AcquisitionData</cite> objects containing the data requested in each batch</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../optimisation/#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer">BlockDataContainer</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<p>Partitioning a list of ints [0, 1, 2, 3, 4, 5, 6, 7, 8] into 4 batches will return:</p>
+<ol class="arabic simple">
+<li><p>[[0, 1, 2], [3, 4], [5, 6], [7, 8]] with <code class="docutils literal notranslate"><span class="pre">sequential</span></code></p></li>
+<li><p>[[0, 4, 8], [1, 5], [2, 6], [3, 7]] with <code class="docutils literal notranslate"><span class="pre">staggered</span></code></p></li>
+<li><p>[[8, 2, 6], [7, 1], [0, 4], [3, 5]] with <code class="docutils literal notranslate"><span class="pre">random_permutation</span></code> and seed 1</p></li>
+</ol>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="labels">
+<h2>Labels<a class="headerlink" href="#labels" title="Link to this heading">#</a></h2>
+<p>Classes which define the accepted labels</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.labels.ImageDimension">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.labels.</span></span><span class="sig-name descname"><span class="pre">ImageDimension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names=&lt;not</span> <span class="pre">given&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qualname=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/labels/#ImageDimension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.labels.ImageDimension" title="Link to this definition">#</a></dt>
+<dd><p>Available dimension labels for image data.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">reorder</span><span class="p">([</span><span class="n">ImageDimension</span><span class="o">.</span><span class="n">HORIZONTAL_X</span><span class="p">,</span> <span class="n">ImageDimension</span><span class="o">.</span><span class="n">VERTICAL</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">reorder</span><span class="p">([</span><span class="s2">&quot;horizontal_x&quot;</span><span class="p">,</span> <span class="s2">&quot;vertical&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.ImageDimension.CHANNEL">
+<span class="sig-name descname"><span class="pre">CHANNEL</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'channel'</span></em><a class="headerlink" href="#cil.framework.labels.ImageDimension.CHANNEL" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.ImageDimension.VERTICAL">
+<span class="sig-name descname"><span class="pre">VERTICAL</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'vertical'</span></em><a class="headerlink" href="#cil.framework.labels.ImageDimension.VERTICAL" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.ImageDimension.HORIZONTAL_X">
+<span class="sig-name descname"><span class="pre">HORIZONTAL_X</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'horizontal_x'</span></em><a class="headerlink" href="#cil.framework.labels.ImageDimension.HORIZONTAL_X" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.ImageDimension.HORIZONTAL_Y">
+<span class="sig-name descname"><span class="pre">HORIZONTAL_Y</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'horizontal_y'</span></em><a class="headerlink" href="#cil.framework.labels.ImageDimension.HORIZONTAL_Y" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionDimension">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.labels.</span></span><span class="sig-name descname"><span class="pre">AcquisitionDimension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names=&lt;not</span> <span class="pre">given&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qualname=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/labels/#AcquisitionDimension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.labels.AcquisitionDimension" title="Link to this definition">#</a></dt>
+<dd><p>Available dimension labels for acquisition data.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">reorder</span><span class="p">([</span><span class="n">AcquisitionDimension</span><span class="o">.</span><span class="n">CHANNEL</span><span class="p">,</span>
+<span class="go">              AcquisitionDimension.ANGLE,</span>
+<span class="go">              AcquisitionDimension.HORIZONTAL])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">reorder</span><span class="p">([</span><span class="s2">&quot;channel&quot;</span><span class="p">,</span> <span class="s2">&quot;angle&quot;</span><span class="p">,</span> <span class="s2">&quot;horizontal&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionDimension.CHANNEL">
+<span class="sig-name descname"><span class="pre">CHANNEL</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'channel'</span></em><a class="headerlink" href="#cil.framework.labels.AcquisitionDimension.CHANNEL" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionDimension.ANGLE">
+<span class="sig-name descname"><span class="pre">ANGLE</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'angle'</span></em><a class="headerlink" href="#cil.framework.labels.AcquisitionDimension.ANGLE" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionDimension.VERTICAL">
+<span class="sig-name descname"><span class="pre">VERTICAL</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'vertical'</span></em><a class="headerlink" href="#cil.framework.labels.AcquisitionDimension.VERTICAL" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionDimension.HORIZONTAL">
+<span class="sig-name descname"><span class="pre">HORIZONTAL</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'horizontal'</span></em><a class="headerlink" href="#cil.framework.labels.AcquisitionDimension.HORIZONTAL" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.labels.FillType">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.labels.</span></span><span class="sig-name descname"><span class="pre">FillType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names=&lt;not</span> <span class="pre">given&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qualname=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/labels/#FillType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.labels.FillType" title="Link to this definition">#</a></dt>
+<dd><p>Available fill types for image data.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.FillType.RANDOM">
+<span class="sig-name descname"><span class="pre">RANDOM</span></span><a class="headerlink" href="#cil.framework.labels.FillType.RANDOM" title="Link to this definition">#</a></dt>
+<dd><p>Fill with random values.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.FillType.RANDOM_INT">
+<span class="sig-name descname"><span class="pre">RANDOM_INT</span></span><a class="headerlink" href="#cil.framework.labels.FillType.RANDOM_INT" title="Link to this definition">#</a></dt>
+<dd><p>Fill with random integers.</p>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">FillType</span><span class="o">.</span><span class="n">RANDOM</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="s2">&quot;random&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.labels.AngleUnit">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.labels.</span></span><span class="sig-name descname"><span class="pre">AngleUnit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names=&lt;not</span> <span class="pre">given&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qualname=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/labels/#AngleUnit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.labels.AngleUnit" title="Link to this definition">#</a></dt>
+<dd><p>Available units for angles.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angle_data</span><span class="p">,</span> <span class="n">angle_units</span><span class="o">=</span><span class="n">AngleUnit</span><span class="o">.</span><span class="n">DEGREE</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angle_data</span><span class="p">,</span> <span class="n">angle_units</span><span class="o">=</span><span class="s2">&quot;degree&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AngleUnit.DEGREE">
+<span class="sig-name descname"><span class="pre">DEGREE</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'degree'</span></em><a class="headerlink" href="#cil.framework.labels.AngleUnit.DEGREE" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AngleUnit.RADIAN">
+<span class="sig-name descname"><span class="pre">RADIAN</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'radian'</span></em><a class="headerlink" href="#cil.framework.labels.AngleUnit.RADIAN" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.labels.</span></span><span class="sig-name descname"><span class="pre">AcquisitionType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names=&lt;not</span> <span class="pre">given&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qualname=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/labels/#AcquisitionType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.labels.AcquisitionType" title="Link to this definition">#</a></dt>
+<dd><p>Available acquisition types &amp; dimensions.</p>
+<p>WARNING: It’s best to use strings rather than integers to initialise.
+&gt;&gt;&gt; AcquisitionType(3) == AcquisitionType(2 | 1) == AcquisitionType.CONE|PARALLEL != AcquisitionType(‘3D’)</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType.PARALLEL">
+<span class="sig-name descname"><span class="pre">PARALLEL</span></span><a class="headerlink" href="#cil.framework.labels.AcquisitionType.PARALLEL" title="Link to this definition">#</a></dt>
+<dd><p>Parallel beam.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType.CONE">
+<span class="sig-name descname"><span class="pre">CONE</span></span><a class="headerlink" href="#cil.framework.labels.AcquisitionType.CONE" title="Link to this definition">#</a></dt>
+<dd><p>Cone beam.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType.DIM2">
+<span class="sig-name descname"><span class="pre">DIM2</span></span><a class="headerlink" href="#cil.framework.labels.AcquisitionType.DIM2" title="Link to this definition">#</a></dt>
+<dd><p>2D acquisition.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType.DIM3">
+<span class="sig-name descname"><span class="pre">DIM3</span></span><a class="headerlink" href="#cil.framework.labels.AcquisitionType.DIM3" title="Link to this definition">#</a></dt>
+<dd><p>3D acquisition.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType.validate">
+<span class="sig-name descname"><span class="pre">validate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/labels/#AcquisitionType.validate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.labels.AcquisitionType.validate" title="Link to this definition">#</a></dt>
+<dd><p>Check if the geometry and dimension types are allowed</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType.dimension">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dimension</span></span><a class="headerlink" href="#cil.framework.labels.AcquisitionType.dimension" title="Link to this definition">#</a></dt>
+<dd><p>Returns the label for the dimension type</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.framework.labels.AcquisitionType.geometry">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">geometry</span></span><a class="headerlink" href="#cil.framework.labels.AcquisitionType.geometry" title="Link to this definition">#</a></dt>
+<dd><p>Returns the label for the geometry type</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="dataprocessor">
+<h2>DataProcessor<a class="headerlink" href="#dataprocessor" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.DataProcessor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">DataProcessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/processors/#DataProcessor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.DataProcessor" title="Link to this definition">#</a></dt>
+<dd><p>Basically an alias of Processor Class</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataProcessor.check_input">
+<span class="sig-name descname"><span class="pre">check_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.DataProcessor.check_input" title="Link to this definition">#</a></dt>
+<dd><p>Checks parameters of the input DataContainer</p>
+<p>Should raise an Error if the DataContainer does not match expectation, e.g.
+if the expected input DataContainer is 3D and the Processor expects 2D.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataProcessor.get_input">
+<span class="sig-name descname"><span class="pre">get_input</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.DataProcessor.get_input" title="Link to this definition">#</a></dt>
+<dd><p>returns the input DataContainer</p>
+<p>It is useful in the case the user has provided a DataProcessor as
+input</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataProcessor.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.DataProcessor.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.DataProcessor.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.framework.DataProcessor.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.Processor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">Processor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/processors/#Processor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.Processor" title="Link to this definition">#</a></dt>
+<dd><p>Defines a generic DataContainer processor</p>
+<p>accepts a DataContainer as input
+returns a DataContainer
+<cite>__setattr__</cite> allows additional attributes to be defined</p>
+<p><cite>store_output</cite> boolian defining whether a copy of the output is stored. Default is False.
+If no attributes are modified get_output will return this stored copy bypassing <cite>process</cite></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.Processor.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/processors/#Processor.set_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.Processor.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.Processor.check_input">
+<span class="sig-name descname"><span class="pre">check_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/processors/#Processor.check_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.Processor.check_input" title="Link to this definition">#</a></dt>
+<dd><p>Checks parameters of the input DataContainer</p>
+<p>Should raise an Error if the DataContainer does not match expectation, e.g.
+if the expected input DataContainer is 3D and the Processor expects 2D.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.Processor.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/processors/#Processor.get_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.Processor.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.Processor.get_input">
+<span class="sig-name descname"><span class="pre">get_input</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/processors/#Processor.get_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.Processor.get_input" title="Link to this definition">#</a></dt>
+<dd><p>returns the input DataContainer</p>
+<p>It is useful in the case the user has provided a DataProcessor as
+input</p>
+</dd></dl>
+
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
+</section>
+</section>
+
+
+                </article>
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+  <script defer src="../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+<script defer src="../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+
+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
+      
+        <div class="footer-item">
+
+  <p class="copyright">
+    
+      © Copyright 2017-2024.
+      <br/>
+    
+  </p>
+</div>
+      
+    </div>
+  
+  
+  
+</div>
+
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+  </body>
+</html>
\ No newline at end of file
diff --git a/v24.2.0/genindex/index.html b/v24.2.0/genindex/index.html
new file mode 100644
index 0000000000..1ca15f74ee
--- /dev/null
+++ b/v24.2.0/genindex/index.html
@@ -0,0 +1,2928 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Index &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
+  </script>
+  <!-- 
+    this give us a css class that will be invisible only if js is disabled 
+  -->
+  <noscript>
+    <style>
+      .pst-js-only { display: none !important; }
+
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+  </noscript>
+  
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+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
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+  <!-- Pre-loaded scripts that we'll load fully later -->
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+    <script>DOCUMENTATION_OPTIONS.pagename = 'genindex';</script>
+    <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.16.0rc0';
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+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '24.2.0';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
+        </script>
+    <link rel="index" title="Index" href="#" />
+    <link rel="search" title="Search" href="../search/" />
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+  <meta name="docsearch:version" content="24.2.0" />
+  </head>
+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
+
+  
+  
+  <div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
+  
+  <div id="pst-scroll-pixel-helper"></div>
+  
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>Back to top</button>
+
+  
+  <dialog id="pst-search-dialog">
+    
+<form class="bd-search d-flex align-items-center"
+      action="../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form>
+  </dialog>
+
+  <div class="pst-async-banner-revealer d-none">
+  <aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
+</div>
+
+  
+    <header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
+<div class="bd-header__inner bd-page-width">
+  <button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
+    <span class="fa-solid fa-bars"></span>
+  </button>
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
+    
+      <div class="navbar-item">
+
+  
+     
+  
+
+<a class="navbar-brand logo" href="/">
+  
+  
+  
+  
+  
+    
+    
+      
+    
+    
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB.svg" class="logo__image only-light" alt="CIL - Home"/>
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB-reversed.svg" class="logo__image only-dark pst-js-only" alt="CIL - Home"/>
+  
+  
+</a></div>
+    
+  </div>
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+            <li class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
+                    More
+                </button>
+                <ul id="pst-nav-more-links" class="dropdown-menu">
+                    
+<li class=" ">
+  <a class="nav-link dropdown-item nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+                </ul>
+            </li>
+            
+  </ul>
+</nav></div>
+      
+    </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+      
+        <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+      
+    </div>
+    
+  </div>
+  
+  
+
+  
+</div>
+
+    </header>
+  
+
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      
+      
+        
+      
+      <dialog id="pst-primary-sidebar-modal"></dialog>
+      <div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
+        
+
+  
+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          
+          
+            <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+  </ul>
+</nav></div>
+          
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+        
+      </div>
+    
+  </div>
+  
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item">
+<form class="bd-search d-flex align-items-center"
+      action="../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+        <div class="sidebar-primary-item">
+<div class="version-switcher__container dropdown pst-js-only">
+  <button id="pst-version-switcher-button-2"
+    type="button"
+    class="version-switcher__button btn btn-sm dropdown-toggle"
+    data-bs-toggle="dropdown"
+    aria-haspopup="listbox"
+    aria-controls="pst-version-switcher-list-2"
+    aria-label="Version switcher list"
+  >
+    Choose version  <!-- this text may get changed later by javascript -->
+    <span class="caret"></span>
+  </button>
+  <div id="pst-version-switcher-list-2"
+    class="version-switcher__menu dropdown-menu list-group-flush py-0"
+    role="listbox" aria-labelledby="pst-version-switcher-button-2">
+    <!-- dropdown will be populated by javascript on page load -->
+  </div>
+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
+</ul>
+</div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main" role="main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article d-print-none"></div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#Q"><strong>Q</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ | <a href="#X"><strong>X</strong></a>
+ | <a href="#Z"><strong>Z</strong></a>
+ 
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.__call__">__call__() (cil.optimisation.functions.BlockFunction method)</a>
+
+      <ul>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__call__">(cil.plugins.ccpi_regularisation.functions.FGP_dTV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.__call__">(cil.plugins.ccpi_regularisation.functions.TGV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.__call__">(cil.plugins.ccpi_regularisation.functions.TNV method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__delattr__">__delattr__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__delattr__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__dir__">__dir__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__dir__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__eq__">__eq__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__eq__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__format__">__format__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__format__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__ge__">__ge__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__ge__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__getattribute__">__getattribute__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__getattribute__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.__getitem__">__getitem__() (cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__getstate__">__getstate__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__getstate__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__gt__">__gt__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__gt__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__hash__">__hash__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__hash__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__iadd__">__iadd__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__idiv__">__idiv__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__imul__">__imul__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__init__">__init__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__init__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.__init__">(cil.optimisation.functions.BlockFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.__init__">(cil.optimisation.operators.BlockOperator method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__init__">(cil.plugins.ccpi_regularisation.functions.FGP_dTV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.__init__">(cil.plugins.ccpi_regularisation.functions.TGV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.__init__">(cil.plugins.ccpi_regularisation.functions.TNV method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__init_subclass__">__init_subclass__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__init_subclass__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__isub__">__isub__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__iter__">__iter__() (cil.framework.BlockDataContainer method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__iter__">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__iter__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__itruediv__">__itruediv__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__le__">__le__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__le__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__lt__">__lt__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__lt__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__ne__">__ne__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__ne__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__neg__">__neg__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__new__">__new__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__new__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__next__">__next__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__next__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__radd__">__radd__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__rdiv__">__rdiv__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__reduce__">__reduce__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__reduce__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__reduce_ex__">__reduce_ex__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__reduce_ex__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__repr__">__repr__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__repr__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__rmul__">__rmul__() (cil.framework.BlockDataContainer method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.__rmul__">(cil.optimisation.functions.BlockFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.__rmul__">(cil.optimisation.operators.BlockOperator method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__rmul__">(cil.plugins.ccpi_regularisation.functions.FGP_dTV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.__rmul__">(cil.plugins.ccpi_regularisation.functions.TGV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.__rmul__">(cil.plugins.ccpi_regularisation.functions.TNV method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__rpow__">__rpow__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__rsub__">__rsub__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__rtruediv__">__rtruediv__() (cil.framework.BlockDataContainer method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__setattr__">__setattr__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__setattr__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__sizeof__">__sizeof__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__sizeof__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__str__">__str__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__str__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__subclasshook__">__subclasshook__() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__subclasshook__">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.framework.BlockDataContainer.__weakref__">__weakref__ (cil.framework.BlockDataContainer attribute)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.__weakref__">(cil.optimisation.algorithms.FISTA attribute)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.__weakref__">(cil.optimisation.algorithms.ISTA attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../processors/#cil.processors.AbsorptionTransmissionConverter">AbsorptionTransmissionConverter (class in cil.processors)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData">AcquisitionData (class in cil.framework)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionDimension">AcquisitionDimension (class in cil.framework.labels)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry">AcquisitionGeometry (class in cil.framework)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType">AcquisitionType (class in cil.framework.labels)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.preconditioner.AdaptiveSensitivity">AdaptiveSensitivity (class in cil.optimisation.utilities.preconditioner)</a>
+</li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.add">add() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.add">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.adjoint">adjoint() (cil.optimisation.operators.BlockOperator method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.ChannelwiseOperator.adjoint">(cil.optimisation.operators.ChannelwiseOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.CompositionOperator.adjoint">(cil.optimisation.operators.CompositionOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.DiagonalOperator.adjoint">(cil.optimisation.operators.DiagonalOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.FiniteDifferenceOperator.adjoint">(cil.optimisation.operators.FiniteDifferenceOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.GradientOperator.adjoint">(cil.optimisation.operators.GradientOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.IdentityOperator.adjoint">(cil.optimisation.operators.IdentityOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.LinearOperator.adjoint">(cil.optimisation.operators.LinearOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.MatrixOperator.adjoint">(cil.optimisation.operators.MatrixOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ProjectionMap.adjoint">(cil.optimisation.operators.ProjectionMap method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ScaledOperator.adjoint">(cil.optimisation.operators.ScaledOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.SumOperator.adjoint">(cil.optimisation.operators.SumOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.SymmetrisedGradientOperator.adjoint">(cil.optimisation.operators.SymmetrisedGradientOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.WaveletOperator.adjoint">(cil.optimisation.operators.WaveletOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ZeroOperator.adjoint">(cil.optimisation.operators.ZeroOperator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm">Algorithm (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.allocate">allocate() (cil.framework.AcquisitionGeometry method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.BlockGeometry.allocate">(cil.framework.BlockGeometry method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageGeometry.allocate">(cil.framework.ImageGeometry method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionDimension.ANGLE">ANGLE (cil.framework.labels.AcquisitionDimension attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AngleUnit">AngleUnit (class in cil.framework.labels)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.preconditioner.AdaptiveSensitivity.apply">apply() (cil.optimisation.utilities.preconditioner.AdaptiveSensitivity method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.utilities.preconditioner.Preconditioner.apply">(cil.optimisation.utilities.preconditioner.Preconditioner method)</a>, <a href="../optimisation/#id55">[1]</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.utilities.preconditioner.Sensitivity.apply">(cil.optimisation.utilities.preconditioner.Sensitivity method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.ImageData.apply_circular_mask">apply_circular_mask() (cil.framework.ImageData method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.approximate_gradient">approximate_gradient() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.approximate_gradient">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.approximate_gradient">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.approximate_gradient">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.approximate_gradient">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.approximate_gradient">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction">ApproximateGradientSumFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule">ArmijoStepSizeRule (class in cil.optimisation.utilities.StepSizeMethods)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.as_array">as_array() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.as_array">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.as_array">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.as_array">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.axpby">axpby() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.axpby">[1]</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule">BarzilaiBorweinStepSizeRule (class in cil.optimisation.utilities.StepSizeMethods)</a>
+</li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.binary_operations">binary_operations() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.binary_operations">[1]</a>
+</li>
+      <li><a href="../processors/#cil.processors.Binner">Binner (class in cil.processors)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.BlockDataContainer">BlockDataContainer (class in cil.framework)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction">BlockFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../framework/#cil.framework.BlockGeometry">BlockGeometry (class in cil.framework)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator">BlockOperator (class in cil.optimisation.operators)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.calculate_Lipschitz">calculate_Lipschitz() (cil.optimisation.functions.TotalVariation method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.ChannelwiseOperator.calculate_norm">calculate_norm() (cil.optimisation.operators.ChannelwiseOperator method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.DiagonalOperator.calculate_norm">(cil.optimisation.operators.DiagonalOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.GradientOperator.calculate_norm">(cil.optimisation.operators.GradientOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.IdentityOperator.calculate_norm">(cil.optimisation.operators.IdentityOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.LinearOperator.calculate_norm">(cil.optimisation.operators.LinearOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.Operator.calculate_norm">(cil.optimisation.operators.Operator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.WaveletOperator.calculate_norm">(cil.optimisation.operators.WaveletOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ZeroOperator.calculate_norm">(cil.optimisation.operators.ZeroOperator method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.calculate_objective_function_at_point">calculate_objective_function_at_point() (cil.optimisation.algorithms.FISTA method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.calculate_objective_function_at_point">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.calculate_objective_function_at_point">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.callbacks.Callback">Callback (class in cil.optimisation.utilities.callbacks)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.centered_at">centered_at() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.ConstantFunction.centered_at">(cil.optimisation.functions.ConstantFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Function.centered_at">(cil.optimisation.functions.Function method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.centered_at">(cil.optimisation.functions.IndicatorBox method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.KullbackLeibler.centered_at">(cil.optimisation.functions.KullbackLeibler method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Norm.centered_at">(cil.optimisation.functions.L1Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Sparsity.centered_at">(cil.optimisation.functions.L1Sparsity method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L2NormSquared.centered_at">(cil.optimisation.functions.L2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LeastSquares.centered_at">(cil.optimisation.functions.LeastSquares method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.centered_at">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL11Norm.centered_at">(cil.optimisation.functions.MixedL11Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL21Norm.centered_at">(cil.optimisation.functions.MixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.OperatorCompositionFunction.centered_at">(cil.optimisation.functions.OperatorCompositionFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Rosenbrock.centered_at">(cil.optimisation.functions.Rosenbrock method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.centered_at">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.centered_at">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ScaledFunction.centered_at">(cil.optimisation.functions.ScaledFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.centered_at">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm.centered_at">(cil.optimisation.functions.SmoothMixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumFunction.centered_at">(cil.optimisation.functions.SumFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction.centered_at">(cil.optimisation.functions.SumScalarFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.centered_at">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.centered_at">(cil.optimisation.functions.TotalVariation method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TranslateFunction.centered_at">(cil.optimisation.functions.TranslateFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.centered_at">(cil.optimisation.functions.WeightedL2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ZeroFunction.centered_at">(cil.optimisation.functions.ZeroFunction method)</a>
+</li>
+      </ul></li>
+      <li><a href="../processors/#cil.processors.CentreOfRotationCorrector">CentreOfRotationCorrector (class in cil.processors)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS">CGLS (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionDimension.CHANNEL">CHANNEL (cil.framework.labels.AcquisitionDimension attribute)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.labels.ImageDimension.CHANNEL">(cil.framework.labels.ImageDimension attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.ChannelwiseOperator">ChannelwiseOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.check_convergence">check_convergence() (cil.optimisation.algorithms.PDHG method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.DataProcessor.check_input">check_input() (cil.framework.DataProcessor method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.Processor.check_input">(cil.framework.Processor method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.check_input">(cil.plugins.ccpi_regularisation.functions.TNV method)</a>
+</li>
+      </ul></li>
+      <li>
+    cil.utilities.quality_measures
+
+      <ul>
+        <li><a href="../utilities/#module-cil.utilities.quality_measures">module</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.clone">clone() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.clone">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.clone">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageGeometry.clone">(cil.framework.ImageGeometry method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.clone">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.column_wise_compatible">column_wise_compatible() (cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.CompositionOperator">CompositionOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.preconditioner.Sensitivity.compute_preconditioner_matrix">compute_preconditioner_matrix() (cil.optimisation.utilities.preconditioner.Sensitivity method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType.CONE">CONE (cil.framework.labels.AcquisitionType attribute)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Padder.constant">constant() (cil.processors.Padder static method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ConstantFunction">ConstantFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.ConstantStepSize">ConstantStepSize (class in cil.optimisation.utilities.StepSizeMethods)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.convex_conjugate">convex_conjugate() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.convex_conjugate">(cil.optimisation.functions.BlockFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ConstantFunction.convex_conjugate">(cil.optimisation.functions.ConstantFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Function.convex_conjugate">(cil.optimisation.functions.Function method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.convex_conjugate">(cil.optimisation.functions.IndicatorBox method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.KullbackLeibler.convex_conjugate">(cil.optimisation.functions.KullbackLeibler method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Norm.convex_conjugate">(cil.optimisation.functions.L1Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Sparsity.convex_conjugate">(cil.optimisation.functions.L1Sparsity method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L2NormSquared.convex_conjugate">(cil.optimisation.functions.L2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LeastSquares.convex_conjugate">(cil.optimisation.functions.LeastSquares method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.convex_conjugate">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL11Norm.convex_conjugate">(cil.optimisation.functions.MixedL11Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL21Norm.convex_conjugate">(cil.optimisation.functions.MixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.OperatorCompositionFunction.convex_conjugate">(cil.optimisation.functions.OperatorCompositionFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Rosenbrock.convex_conjugate">(cil.optimisation.functions.Rosenbrock method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.convex_conjugate">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.convex_conjugate">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ScaledFunction.convex_conjugate">(cil.optimisation.functions.ScaledFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.convex_conjugate">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm.convex_conjugate">(cil.optimisation.functions.SmoothMixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumFunction.convex_conjugate">(cil.optimisation.functions.SumFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction.convex_conjugate">(cil.optimisation.functions.SumScalarFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.convex_conjugate">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.convex_conjugate">(cil.optimisation.functions.TotalVariation method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TranslateFunction.convex_conjugate">(cil.optimisation.functions.TranslateFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.convex_conjugate">(cil.optimisation.functions.WeightedL2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ZeroFunction.convex_conjugate">(cil.optimisation.functions.ZeroFunction method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV.convex_conjugate">(cil.plugins.ccpi_regularisation.functions.FGP_dTV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TGV.convex_conjugate">(cil.plugins.ccpi_regularisation.functions.TGV method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TNV.convex_conjugate">(cil.plugins.ccpi_regularisation.functions.TNV method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.copy">copy() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.BlockDataContainer.copy">(cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.copy">[1]</a>
+</li>
+        <li><a href="../framework/#cil.framework.DataContainer.copy">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.copy">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageGeometry.copy">(cil.framework.ImageGeometry method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.copy">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.create_Cone2D">create_Cone2D() (cil.framework.AcquisitionGeometry static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.create_Cone3D">create_Cone3D() (cil.framework.AcquisitionGeometry static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.create_Parallel2D">create_Parallel2D() (cil.framework.AcquisitionGeometry static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.create_Parallel3D">create_Parallel3D() (cil.framework.AcquisitionGeometry static method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.data_passes">data_passes (cil.optimisation.functions.ApproximateGradientSumFunction property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.data_passes">(cil.optimisation.functions.LSVRGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.data_passes">(cil.optimisation.functions.SAGAFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.data_passes">(cil.optimisation.functions.SAGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.data_passes">(cil.optimisation.functions.SGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.data_passes">(cil.optimisation.functions.SVRGFunction property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.data_passes_indices">data_passes_indices (cil.optimisation.functions.ApproximateGradientSumFunction property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.data_passes_indices">(cil.optimisation.functions.LSVRGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.data_passes_indices">(cil.optimisation.functions.SAGAFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.data_passes_indices">(cil.optimisation.functions.SAGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.data_passes_indices">(cil.optimisation.functions.SGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.data_passes_indices">(cil.optimisation.functions.SVRGFunction property)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.DataContainer">DataContainer (class in cil.framework)</a>
+</li>
+      <li><a href="../framework/#cil.framework.DataProcessor">DataProcessor (class in cil.framework)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AngleUnit.DEGREE">DEGREE (cil.framework.labels.AngleUnit attribute)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.DiagonalOperator">DiagonalOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType.DIM2">DIM2 (cil.framework.labels.AcquisitionType attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType.DIM3">DIM3 (cil.framework.labels.AcquisitionType attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType.dimension">dimension (cil.framework.labels.AcquisitionType property)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.direct">direct() (cil.optimisation.operators.BlockOperator method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.ChannelwiseOperator.direct">(cil.optimisation.operators.ChannelwiseOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.CompositionOperator.direct">(cil.optimisation.operators.CompositionOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.DiagonalOperator.direct">(cil.optimisation.operators.DiagonalOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.FiniteDifferenceOperator.direct">(cil.optimisation.operators.FiniteDifferenceOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.GradientOperator.direct">(cil.optimisation.operators.GradientOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.IdentityOperator.direct">(cil.optimisation.operators.IdentityOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.MatrixOperator.direct">(cil.optimisation.operators.MatrixOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.Operator.direct">(cil.optimisation.operators.Operator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ProjectionMap.direct">(cil.optimisation.operators.ProjectionMap method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ScaledOperator.direct">(cil.optimisation.operators.ScaledOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.SparseFiniteDifferenceOperator.direct">(cil.optimisation.operators.SparseFiniteDifferenceOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.SumOperator.direct">(cil.optimisation.operators.SumOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.SymmetrisedGradientOperator.direct">(cil.optimisation.operators.SymmetrisedGradientOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.WaveletOperator.direct">(cil.optimisation.operators.WaveletOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ZeroOperator.direct">(cil.optimisation.operators.ZeroOperator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.divide">divide() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.divide">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.domain_geometry">domain_geometry() (cil.optimisation.operators.BlockOperator method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.Operator.domain_geometry">(cil.optimisation.operators.Operator method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.dot">dot() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.dot">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.dot">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.dot">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.LinearOperator.dot_test">dot_test() (cil.optimisation.operators.LinearOperator static method)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.KORN.download_data">download_data() (cil.utilities.dataexample.KORN class method)</a>
+
+      <ul>
+        <li><a href="../utilities/#cil.utilities.dataexample.SANDSTONE.download_data">(cil.utilities.dataexample.SANDSTONE class method)</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.dataexample.USB.download_data">(cil.utilities.dataexample.USB class method)</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.dataexample.WALNUT.download_data">(cil.utilities.dataexample.WALNUT class method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.dtype">dtype (cil.framework.AcquisitionData property)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.dtype">(cil.framework.DataContainer property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.dtype">(cil.framework.ImageData property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.dtype">(cil.framework.VectorData property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../processors/#cil.processors.Padder.edge">edge() (cil.processors.Padder static method)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Normaliser.estimate_normalised_error">estimate_normalised_error() (cil.processors.Normaliser static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.exp">exp() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.exp">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.exp">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.exp">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../plugins/#cil.plugins.astra.FBP">FBP (class in cil.plugins.astra)</a>
+
+      <ul>
+        <li><a href="../plugins/#cil.plugins.tigre.FBP">(class in cil.plugins.tigre)</a>
+</li>
+        <li><a href="../recon/#cil.recon.FBP">(class in cil.recon)</a>
+</li>
+      </ul></li>
+      <li><a href="../recon/#cil.recon.FDK">FDK (class in cil.recon)</a>
+</li>
+      <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_dTV">FGP_dTV (class in cil.plugins.ccpi_regularisation.functions)</a>
+</li>
+      <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.FGP_TV">FGP_TV (class in cil.plugins.ccpi_regularisation.functions)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.display.show1D.figure">figure (cil.utilities.display.show1D attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.fill">fill() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.fill">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.fill">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.fill">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.labels.FillType">FillType (class in cil.framework.labels)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.FiniteDifferenceOperator">FiniteDifferenceOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA">FISTA (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.flag">flag() (cil.optimisation.algorithms.CGLS method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.from_function">from_function() (cil.optimisation.utilities.Sampler static method)</a>, <a href="../optimisation/#id50">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.full_gradient">full_gradient() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.full_gradient">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.full_gradient">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.full_gradient">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.full_gradient">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.full_gradient">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.Function">Function (class in cil.optimisation.functions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.GD">GD (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType.geometry">geometry (cil.framework.labels.AcquisitionType property)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.KORN.get">get() (cil.utilities.dataexample.KORN class method)</a>
+
+      <ul>
+        <li><a href="../utilities/#cil.utilities.dataexample.SANDSTONE.get">(cil.utilities.dataexample.SANDSTONE class method)</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA.get">(cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA class method)</a>, <a href="../utilities/#id1">[1]</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA.get">(cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA class method)</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA.get">(cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA class method)</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.dataexample.USB.get">(cil.utilities.dataexample.USB class method)</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.dataexample.WALNUT.get">(cil.utilities.dataexample.WALNUT class method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.get_as_list">get_as_list() (cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.get_data_axes_order">get_data_axes_order() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.get_data_axes_order">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.get_data_axes_order">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.get_data_axes_order">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../io/#cil.io.NEXUSDataReader.get_data_offset">get_data_offset() (cil.io.NEXUSDataReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.NEXUSDataReader.get_data_scale">get_data_scale() (cil.io.NEXUSDataReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.utilities.HDF5_utilities.get_dataset_metadata">get_dataset_metadata() (cil.io.utilities.HDF5_utilities static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.get_dimension_axis">get_dimension_axis() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.get_dimension_axis">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.get_dimension_axis">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.get_dimension_axis">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../recon/#cil.recon.FBP.get_filter_array">get_filter_array() (cil.recon.FBP method)</a>
+
+      <ul>
+        <li><a href="../recon/#cil.recon.FDK.get_filter_array">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../io/#cil.io.NEXUSDataReader.get_geometry">get_geometry() (cil.io.NEXUSDataReader method)</a>
+
+      <ul>
+        <li><a href="../io/#cil.io.NikonDataReader.get_geometry">(cil.io.NikonDataReader method)</a>
+</li>
+        <li><a href="../io/#cil.io.ZEISSDataReader.get_geometry">(cil.io.ZEISSDataReader method)</a>
+</li>
+      </ul></li>
+      <li><a href="../plugins/#cil.plugins.TomoPhantom.get_ImageData">get_ImageData() (in module cil.plugins.TomoPhantom)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.get_ImageGeometry">get_ImageGeometry() (cil.framework.AcquisitionGeometry method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.DataProcessor.get_input">get_input() (cil.framework.DataProcessor method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.Processor.get_input">(cil.framework.Processor method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.BlockGeometry.get_item">get_item() (cil.framework.BlockGeometry method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.get_item">(cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.get_last_loss">get_last_loss() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.get_last_loss">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.get_last_loss">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.get_last_loss">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.get_last_loss">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.get_last_loss">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.get_last_loss">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.get_last_loss">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.get_last_loss">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.get_last_loss">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.get_last_objective">get_last_objective() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.get_last_objective">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.get_last_objective">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.get_last_objective">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.get_last_objective">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.get_last_objective">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.get_last_objective">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.get_last_objective">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.get_last_objective">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.get_last_objective">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+      <li><a href="../io/#cil.io.ZEISSDataReader.get_metadata">get_metadata() (cil.io.ZEISSDataReader method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.get_norms_as_list">get_norms_as_list() (cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.DataProcessor.get_output">get_output() (cil.framework.DataProcessor method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.Processor.get_output">(cil.framework.Processor method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.get_output">(cil.optimisation.algorithms.Algorithm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.get_output">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.get_output">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.get_output">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.get_output">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.get_output">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.get_output">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.get_output">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.get_output">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.get_output">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.astra.FBP.get_output">(cil.plugins.astra.FBP method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.tigre.FBP.get_output">(cil.plugins.tigre.FBP method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.AbsorptionTransmissionConverter.get_output">(cil.processors.AbsorptionTransmissionConverter method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Binner.get_output">(cil.processors.Binner method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.CentreOfRotationCorrector.get_output">(cil.processors.CentreOfRotationCorrector method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Masker.get_output">(cil.processors.Masker method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.MaskGenerator.get_output">(cil.processors.MaskGenerator method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Normaliser.get_output">(cil.processors.Normaliser method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Padder.get_output">(cil.processors.Padder method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.PaganinProcessor.get_output">(cil.processors.PaganinProcessor method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.RingRemover.get_output">(cil.processors.RingRemover method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Slicer.get_output">(cil.processors.Slicer method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.TransmissionAbsorptionConverter.get_output">(cil.processors.TransmissionAbsorptionConverter method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.get_output_shape">get_output_shape() (cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      <li><a href="../io/#cil.io.NikonDataReader.get_roi">get_roi() (cil.io.NikonDataReader method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.get_samples">get_samples() (cil.optimisation.utilities.Sampler method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.utilities.SamplerRandom.get_samples">(cil.optimisation.utilities.SamplerRandom method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.get_slice">get_slice() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.AcquisitionGeometry.get_slice">(cil.framework.AcquisitionGeometry method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.DataContainer.get_slice">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.get_slice">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageGeometry.get_slice">(cil.framework.ImageGeometry method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.get_slice">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule.get_step_size">get_step_size() (cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule.get_step_size">(cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.ConstantStepSize.get_step_size">(cil.optimisation.utilities.StepSizeMethods.ConstantStepSize method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.StepSizeRule.get_step_size">(cil.optimisation.utilities.StepSizeMethods.StepSizeRule method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.gradient">gradient() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.gradient">(cil.optimisation.functions.BlockFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ConstantFunction.gradient">(cil.optimisation.functions.ConstantFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Function.gradient">(cil.optimisation.functions.Function method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.gradient">(cil.optimisation.functions.IndicatorBox method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.KullbackLeibler.gradient">(cil.optimisation.functions.KullbackLeibler method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Norm.gradient">(cil.optimisation.functions.L1Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Sparsity.gradient">(cil.optimisation.functions.L1Sparsity method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L2NormSquared.gradient">(cil.optimisation.functions.L2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LeastSquares.gradient">(cil.optimisation.functions.LeastSquares method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.gradient">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL11Norm.gradient">(cil.optimisation.functions.MixedL11Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL21Norm.gradient">(cil.optimisation.functions.MixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.OperatorCompositionFunction.gradient">(cil.optimisation.functions.OperatorCompositionFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Rosenbrock.gradient">(cil.optimisation.functions.Rosenbrock method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.gradient">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.gradient">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ScaledFunction.gradient">(cil.optimisation.functions.ScaledFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.gradient">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm.gradient">(cil.optimisation.functions.SmoothMixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumFunction.gradient">(cil.optimisation.functions.SumFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction.gradient">(cil.optimisation.functions.SumScalarFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.gradient">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.gradient">(cil.optimisation.functions.TotalVariation method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TranslateFunction.gradient">(cil.optimisation.functions.TranslateFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.gradient">(cil.optimisation.functions.WeightedL2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ZeroFunction.gradient">(cil.optimisation.functions.ZeroFunction method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.gradient_operator">gradient_operator (cil.optimisation.functions.TotalVariation property)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.GradientOperator">GradientOperator (class in cil.optimisation.operators)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../io/#cil.io.utilities.HDF5_utilities">HDF5_utilities (class in cil.io.utilities)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.herman_meyer">herman_meyer() (cil.optimisation.utilities.Sampler static method)</a>, <a href="../optimisation/#id51">[1]</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionDimension.HORIZONTAL">HORIZONTAL (cil.framework.labels.AcquisitionDimension attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.ImageDimension.HORIZONTAL_X">HORIZONTAL_X (cil.framework.labels.ImageDimension attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.ImageDimension.HORIZONTAL_Y">HORIZONTAL_Y (cil.framework.labels.ImageDimension attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.operators.IdentityOperator">IdentityOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../processors/#cil.processors.CentreOfRotationCorrector.image_sharpness">image_sharpness() (cil.processors.CentreOfRotationCorrector static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.ImageData">ImageData (class in cil.framework)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.ImageDimension">ImageDimension (class in cil.framework.labels)</a>
+</li>
+      <li><a href="../framework/#cil.framework.ImageGeometry">ImageGeometry (class in cil.framework)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox">IndicatorBox (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Masker.interpolate">interpolate() (cil.processors.Masker static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.is_compatible">is_compatible() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.is_compatible">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.is_linear">is_linear() (cil.optimisation.operators.BlockOperator method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.CompositionOperator.is_linear">(cil.optimisation.operators.CompositionOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.LinearOperator.is_linear">(cil.optimisation.operators.LinearOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.Operator.is_linear">(cil.optimisation.operators.Operator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ScaledOperator.is_linear">(cil.optimisation.operators.ScaledOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.SumOperator.is_linear">(cil.optimisation.operators.SumOperator method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.IdentityOperator.is_orthogonal">is_orthogonal() (cil.optimisation.operators.IdentityOperator method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.Operator.is_orthogonal">(cil.optimisation.operators.Operator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.WaveletOperator.is_orthogonal">(cil.optimisation.operators.WaveletOperator method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.is_provably_convergent">is_provably_convergent() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.is_provably_convergent">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.is_provably_convergent">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.is_provably_convergent">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.is_provably_convergent">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.is_provably_convergent">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.is_provably_convergent">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.is_provably_convergent">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.is_provably_convergent">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.is_provably_convergent">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../utilities/#cil.utilities.jupyter.islicer">islicer (class in cil.utilities.jupyter)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA">ISTA (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.iterations">iterations (cil.optimisation.algorithms.Algorithm property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.iterations">(cil.optimisation.algorithms.CGLS property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.iterations">(cil.optimisation.algorithms.FISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.iterations">(cil.optimisation.algorithms.GD property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.iterations">(cil.optimisation.algorithms.ISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.iterations">(cil.optimisation.algorithms.LADMM property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.iterations">(cil.optimisation.algorithms.PD3O property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.iterations">(cil.optimisation.algorithms.PDHG property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.iterations">(cil.optimisation.algorithms.SIRT property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.iterations">(cil.optimisation.algorithms.SPDHG property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../utilities/#cil.utilities.dataexample.KORN">KORN (class in cil.utilities.dataexample)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.KullbackLeibler">KullbackLeibler (class in cil.optimisation.functions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.L">L (cil.optimisation.functions.ApproximateGradientSumFunction property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.L">(cil.optimisation.functions.BlockFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ConstantFunction.L">(cil.optimisation.functions.ConstantFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Function.L">(cil.optimisation.functions.Function property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.L">(cil.optimisation.functions.IndicatorBox property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.KullbackLeibler.L">(cil.optimisation.functions.KullbackLeibler property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Norm.L">(cil.optimisation.functions.L1Norm property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Sparsity.L">(cil.optimisation.functions.L1Sparsity property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L2NormSquared.L">(cil.optimisation.functions.L2NormSquared property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LeastSquares.L">(cil.optimisation.functions.LeastSquares property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.L">(cil.optimisation.functions.LSVRGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL11Norm.L">(cil.optimisation.functions.MixedL11Norm property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL21Norm.L">(cil.optimisation.functions.MixedL21Norm property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.OperatorCompositionFunction.L">(cil.optimisation.functions.OperatorCompositionFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Rosenbrock.L">(cil.optimisation.functions.Rosenbrock property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.L">(cil.optimisation.functions.SAGAFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.L">(cil.optimisation.functions.SAGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ScaledFunction.L">(cil.optimisation.functions.ScaledFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.L">(cil.optimisation.functions.SGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm.L">(cil.optimisation.functions.SmoothMixedL21Norm property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumFunction.L">(cil.optimisation.functions.SumFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction.L">(cil.optimisation.functions.SumScalarFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.L">(cil.optimisation.functions.SVRGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.L">(cil.optimisation.functions.TotalVariation property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TranslateFunction.L">(cil.optimisation.functions.TranslateFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.L">(cil.optimisation.functions.WeightedL2NormSquared property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ZeroFunction.L">(cil.optimisation.functions.ZeroFunction property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.L1Norm">L1Norm (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.L1Sparsity">L1Sparsity (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.L2NormSquared">L2NormSquared (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM">LADMM (class in cil.optimisation.algorithms)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.LeastSquares">LeastSquares (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Padder.linear_ramp">linear_ramp() (cil.processors.Padder static method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.LinearOperator">LinearOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.jupyter.link_islicer">link_islicer (class in cil.utilities.jupyter)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.Lmax">Lmax (cil.optimisation.functions.ApproximateGradientSumFunction property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.Lmax">(cil.optimisation.functions.LSVRGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.Lmax">(cil.optimisation.functions.SAGAFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.Lmax">(cil.optimisation.functions.SAGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.Lmax">(cil.optimisation.functions.SGFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumFunction.Lmax">(cil.optimisation.functions.SumFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction.Lmax">(cil.optimisation.functions.SumScalarFunction property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.Lmax">(cil.optimisation.functions.SVRGFunction property)</a>
+</li>
+      </ul></li>
+      <li><a href="../utilities/#cil.utilities.dataexample.TestData.load">load() (cil.utilities.dataexample.TestData method)</a>
+</li>
+      <li><a href="../io/#cil.io.NEXUSDataReader.load_data">load_data() (cil.io.NEXUSDataReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.NikonDataReader.load_projections">load_projections() (cil.io.NikonDataReader method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.log">log() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.log">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.log">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.log">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.callbacks.LogfileCallback">LogfileCallback (class in cil.optimisation.utilities.callbacks)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.loss">loss (cil.optimisation.algorithms.Algorithm property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.loss">(cil.optimisation.algorithms.CGLS property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.loss">(cil.optimisation.algorithms.FISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.loss">(cil.optimisation.algorithms.GD property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.loss">(cil.optimisation.algorithms.ISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.loss">(cil.optimisation.algorithms.LADMM property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.loss">(cil.optimisation.algorithms.PD3O property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.loss">(cil.optimisation.algorithms.PDHG property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.loss">(cil.optimisation.algorithms.SIRT property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.loss">(cil.optimisation.algorithms.SPDHG property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction">LSVRGFunction (class in cil.optimisation.functions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../utilities/#cil.utilities.quality_measures.mae">mae() (in module cil.utilities.quality_measures)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Masker">Masker (class in cil.processors)</a>
+</li>
+      <li><a href="../processors/#cil.processors.MaskGenerator">MaskGenerator (class in cil.processors)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.MaskOperator">MaskOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.MatrixOperator">MatrixOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.max">max() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.max">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.max">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.max">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.max_iteration">max_iteration (cil.optimisation.algorithms.Algorithm property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.max_iteration">(cil.optimisation.algorithms.CGLS property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.max_iteration">(cil.optimisation.algorithms.FISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.max_iteration">(cil.optimisation.algorithms.GD property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.max_iteration">(cil.optimisation.algorithms.ISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.max_iteration">(cil.optimisation.algorithms.LADMM property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.max_iteration">(cil.optimisation.algorithms.PD3O property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.max_iteration">(cil.optimisation.algorithms.PDHG property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.max_iteration">(cil.optimisation.algorithms.SIRT property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.max_iteration">(cil.optimisation.algorithms.SPDHG property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.max_iteration_stop_criterion">max_iteration_stop_criterion() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.max_iteration_stop_criterion">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.max_iteration_stop_criterion">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.max_iteration_stop_criterion">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.max_iteration_stop_criterion">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.max_iteration_stop_criterion">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.max_iteration_stop_criterion">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.max_iteration_stop_criterion">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.max_iteration_stop_criterion">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.max_iteration_stop_criterion">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.maximum">maximum() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.maximum">[1]</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.mean">mean() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.mean">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.mean">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.mean">(cil.framework.VectorData method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Masker.mean">(cil.processors.Masker static method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.MaskGenerator.mean">(cil.processors.MaskGenerator static method)</a>
+</li>
+      </ul></li>
+      <li><a href="../processors/#cil.processors.Masker.median">median() (cil.processors.Masker static method)</a>
+
+      <ul>
+        <li><a href="../processors/#cil.processors.MaskGenerator.median">(cil.processors.MaskGenerator static method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.min">min() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.min">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.min">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.min">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.minimum">minimum() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.minimum">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.MixedL11Norm">MixedL11Norm (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.MixedL21Norm">MixedL21Norm (class in cil.optimisation.functions)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="../utilities/#module-cil.utilities.quality_measures">cil.utilities.quality_measures</a>
+</li>
+      </ul></li>
+      <li><a href="../utilities/#cil.utilities.quality_measures.mse">mse() (in module cil.utilities.quality_measures)</a>
+</li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.multiply">multiply() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.multiply">[1]</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.AcquisitionData.ndim">ndim (cil.framework.AcquisitionData property)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.ndim">(cil.framework.DataContainer property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.ndim">(cil.framework.ImageData property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.ndim">(cil.framework.VectorData property)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.next">next() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.next">[1]</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.next">(cil.optimisation.utilities.Sampler method)</a>
+</li>
+      </ul></li>
+      <li><a href="../io/#cil.io.NEXUSDataReader">NEXUSDataReader (class in cil.io)</a>
+</li>
+      <li><a href="../io/#cil.io.NEXUSDataWriter">NEXUSDataWriter (class in cil.io)</a>
+</li>
+      <li><a href="../io/#cil.io.NikonDataReader">NikonDataReader (class in cil.io)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.norm">norm() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.norm">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.norm">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.norm">(cil.framework.VectorData method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.norm">(cil.optimisation.operators.BlockOperator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.Operator.norm">(cil.optimisation.operators.Operator method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.operators.ScaledOperator.norm">(cil.optimisation.operators.ScaledOperator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../processors/#cil.processors.Normaliser">Normaliser (class in cil.processors)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.num_threads">num_threads (cil.optimisation.functions.IndicatorBox property)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.number_of_dimensions">number_of_dimensions (cil.framework.AcquisitionData property)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.number_of_dimensions">(cil.framework.DataContainer property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.number_of_dimensions">(cil.framework.ImageData property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.number_of_dimensions">(cil.framework.VectorData property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.objective">objective (cil.optimisation.algorithms.Algorithm property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.objective">(cil.optimisation.algorithms.CGLS property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.objective">(cil.optimisation.algorithms.FISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.objective">(cil.optimisation.algorithms.GD property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.objective">(cil.optimisation.algorithms.ISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.objective">(cil.optimisation.algorithms.LADMM property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.objective">(cil.optimisation.algorithms.PD3O property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.objective">(cil.optimisation.algorithms.PDHG property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.objective">(cil.optimisation.algorithms.SIRT property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.objective">(cil.optimisation.algorithms.SPDHG property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.objective_to_dict">objective_to_dict() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.objective_to_dict">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.objective_to_dict">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.objective_to_dict">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.objective_to_dict">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.objective_to_dict">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.objective_to_dict">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.objective_to_dict">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.objective_to_dict">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.objective_to_dict">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.objective_to_string">objective_to_string() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.objective_to_string">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.objective_to_string">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.objective_to_string">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.objective_to_string">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.objective_to_string">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.objective_to_string">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.objective_to_string">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.objective_to_string">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.objective_to_string">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.Operator">Operator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.OperatorCompositionFunction">OperatorCompositionFunction (class in cil.optimisation.functions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../processors/#cil.processors.Padder">Padder (class in cil.processors)</a>
+</li>
+      <li><a href="../processors/#cil.processors.PaganinProcessor">PaganinProcessor (class in cil.processors)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType.PARALLEL">PARALLEL (cil.framework.labels.AcquisitionType attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.partition">partition() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.Partitioner.partition">(cil.framework.Partitioner method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.Partitioner">Partitioner (class in cil.framework)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O">PD3O (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG">PDHG (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../recon/#cil.recon.FBP.plot_filter">plot_filter() (cil.recon.FBP method)</a>
+
+      <ul>
+        <li><a href="../recon/#cil.recon.FDK.plot_filter">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.power">power() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.power">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.LinearOperator.PowerMethod">PowerMethod() (cil.optimisation.operators.LinearOperator static method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.preconditioner.Preconditioner">Preconditioner (class in cil.optimisation.utilities.preconditioner)</a>
+</li>
+      <li><a href="../io/#cil.io.utilities.HDF5_utilities.print_metadata">print_metadata() (cil.io.utilities.HDF5_utilities static method)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Binner.process">process() (cil.processors.Binner method)</a>
+
+      <ul>
+        <li><a href="../processors/#cil.processors.Slicer.process">(cil.processors.Slicer method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.Processor">Processor (class in cil.framework)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.callbacks.ProgressCallback">ProgressCallback (class in cil.optimisation.utilities.callbacks)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.ProjectionMap">ProjectionMap (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../plugins/#cil.plugins.astra.ProjectionOperator">ProjectionOperator (class in cil.plugins.astra)</a>
+
+      <ul>
+        <li><a href="../plugins/#cil.plugins.tigre.ProjectionOperator">(class in cil.plugins.tigre)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.proximal">proximal() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.proximal">(cil.optimisation.functions.BlockFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ConstantFunction.proximal">(cil.optimisation.functions.ConstantFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Function.proximal">(cil.optimisation.functions.Function method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.proximal">(cil.optimisation.functions.IndicatorBox method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.KullbackLeibler.proximal">(cil.optimisation.functions.KullbackLeibler method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Norm.proximal">(cil.optimisation.functions.L1Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Sparsity.proximal">(cil.optimisation.functions.L1Sparsity method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L2NormSquared.proximal">(cil.optimisation.functions.L2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LeastSquares.proximal">(cil.optimisation.functions.LeastSquares method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.proximal">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL11Norm.proximal">(cil.optimisation.functions.MixedL11Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL21Norm.proximal">(cil.optimisation.functions.MixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.OperatorCompositionFunction.proximal">(cil.optimisation.functions.OperatorCompositionFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Rosenbrock.proximal">(cil.optimisation.functions.Rosenbrock method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.proximal">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.proximal">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ScaledFunction.proximal">(cil.optimisation.functions.ScaledFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.proximal">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm.proximal">(cil.optimisation.functions.SmoothMixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumFunction.proximal">(cil.optimisation.functions.SumFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction.proximal">(cil.optimisation.functions.SumScalarFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.proximal">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.proximal">(cil.optimisation.functions.TotalVariation method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TranslateFunction.proximal">(cil.optimisation.functions.TranslateFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.proximal">(cil.optimisation.functions.WeightedL2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ZeroFunction.proximal">(cil.optimisation.functions.ZeroFunction method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.proximal_conjugate">proximal_conjugate() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.BlockFunction.proximal_conjugate">(cil.optimisation.functions.BlockFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ConstantFunction.proximal_conjugate">(cil.optimisation.functions.ConstantFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Function.proximal_conjugate">(cil.optimisation.functions.Function method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.proximal_conjugate">(cil.optimisation.functions.IndicatorBox method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.KullbackLeibler.proximal_conjugate">(cil.optimisation.functions.KullbackLeibler method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Norm.proximal_conjugate">(cil.optimisation.functions.L1Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L1Sparsity.proximal_conjugate">(cil.optimisation.functions.L1Sparsity method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.L2NormSquared.proximal_conjugate">(cil.optimisation.functions.L2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LeastSquares.proximal_conjugate">(cil.optimisation.functions.LeastSquares method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.proximal_conjugate">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL11Norm.proximal_conjugate">(cil.optimisation.functions.MixedL11Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.MixedL21Norm.proximal_conjugate">(cil.optimisation.functions.MixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.OperatorCompositionFunction.proximal_conjugate">(cil.optimisation.functions.OperatorCompositionFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.Rosenbrock.proximal_conjugate">(cil.optimisation.functions.Rosenbrock method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.proximal_conjugate">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.proximal_conjugate">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ScaledFunction.proximal_conjugate">(cil.optimisation.functions.ScaledFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.proximal_conjugate">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm.proximal_conjugate">(cil.optimisation.functions.SmoothMixedL21Norm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumFunction.proximal_conjugate">(cil.optimisation.functions.SumFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction.proximal_conjugate">(cil.optimisation.functions.SumScalarFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.proximal_conjugate">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation.proximal_conjugate">(cil.optimisation.functions.TotalVariation method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.TranslateFunction.proximal_conjugate">(cil.optimisation.functions.TranslateFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.WeightedL2NormSquared.proximal_conjugate">(cil.optimisation.functions.WeightedL2NormSquared method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.ZeroFunction.proximal_conjugate">(cil.optimisation.functions.ZeroFunction method)</a>
+</li>
+      </ul></li>
+      <li><a href="../utilities/#cil.utilities.quality_measures.psnr">psnr() (in module cil.utilities.quality_measures)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Q">Q</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../processors/#cil.processors.MaskGenerator.quantile">quantile() (cil.processors.MaskGenerator static method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.labels.AngleUnit.RADIAN">RADIAN (cil.framework.labels.AngleUnit attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.FillType.RANDOM">RANDOM (cil.framework.labels.FillType attribute)</a>
+</li>
+      <li><a href="../framework/#cil.framework.labels.FillType.RANDOM_INT">RANDOM_INT (cil.framework.labels.FillType attribute)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.TestData.random_noise">random_noise() (cil.utilities.dataexample.TestData static method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.random_with_replacement">random_with_replacement() (cil.optimisation.utilities.Sampler static method)</a>, <a href="../optimisation/#id48">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.random_without_replacement">random_without_replacement() (cil.optimisation.utilities.Sampler static method)</a>, <a href="../optimisation/#id49">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.range_geometry">range_geometry() (cil.optimisation.operators.BlockOperator method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.operators.Operator.range_geometry">(cil.optimisation.operators.Operator method)</a>
+</li>
+      </ul></li>
+      <li><a href="../io/#cil.io.RAWFileWriter">RAWFileWriter (class in cil.io)</a>
+</li>
+      <li><a href="../io/#cil.io.NEXUSDataReader.read">read() (cil.io.NEXUSDataReader method)</a>
+
+      <ul>
+        <li><a href="../io/#cil.io.NikonDataReader.read">(cil.io.NikonDataReader method)</a>
+</li>
+        <li><a href="../io/#cil.io.TIFFStackReader.read">(cil.io.TIFFStackReader method)</a>
+</li>
+        <li><a href="../io/#cil.io.utilities.HDF5_utilities.read">(cil.io.utilities.HDF5_utilities static method)</a>
+</li>
+        <li><a href="../io/#cil.io.ZEISSDataReader.read">(cil.io.ZEISSDataReader method)</a>
+</li>
+      </ul></li>
+      <li><a href="../io/#cil.io.TIFFStackReader.read_as_AcquisitionData">read_as_AcquisitionData() (cil.io.TIFFStackReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.TIFFStackReader.read_as_ImageData">read_as_ImageData() (cil.io.TIFFStackReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.NEXUSDataReader.read_as_original">read_as_original() (cil.io.NEXUSDataReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.TIFFStackReader.read_rescaled">read_rescaled() (cil.io.TIFFStackReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.TIFFStackReader.read_scale_offset">read_scale_offset() (cil.io.TIFFStackReader method)</a>
+</li>
+      <li><a href="../io/#cil.io.utilities.HDF5_utilities.read_to">read_to() (cil.io.utilities.HDF5_utilities static method)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Padder.reflect">reflect() (cil.processors.Padder static method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.AcquisitionData.reorder">reorder() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.reorder">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.reorder">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.reorder">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../recon/#cil.recon.FBP.reset">reset() (cil.recon.FBP method)</a>
+
+      <ul>
+        <li><a href="../recon/#cil.recon.FDK.reset">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../processors/#cil.processors.RingRemover">RingRemover (class in cil.processors)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.Rosenbrock">Rosenbrock (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.row_wise_compatible">row_wise_compatible() (cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.run">run() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.run">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.run">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.run">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.run">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.run">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.run">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.run">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.run">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.run">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+        <li><a href="../recon/#cil.recon.FBP.run">(cil.recon.FBP method)</a>
+</li>
+        <li><a href="../developer_guide/#cil.recon.FBP.FBP.run">(cil.recon.FBP.FBP method)</a>
+</li>
+        <li><a href="../recon/#cil.recon.FDK.run">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction">SAGAFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction">SAGFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler">Sampler (class in cil.optimisation.utilities)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.SamplerRandom">SamplerRandom (class in cil.optimisation.utilities)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.SANDSTONE">SANDSTONE (class in cil.utilities.dataexample)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.sapyb">sapyb() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.BlockDataContainer.sapyb">(cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.sapyb">[1]</a>
+</li>
+        <li><a href="../framework/#cil.framework.DataContainer.sapyb">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.sapyb">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.sapyb">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../utilities/#cil.utilities.display.show1D.save">save() (cil.utilities.display.show1D method)</a>
+
+      <ul>
+        <li><a href="../utilities/#cil.utilities.display.show2D.save">(cil.utilities.display.show2D method)</a>
+</li>
+        <li><a href="../utilities/#cil.utilities.display.show_geometry.save">(cil.utilities.display.show_geometry method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ScaledFunction">ScaledFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.ScaledOperator">ScaledOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.TestData.scikit_random_noise">scikit_random_noise() (cil.utilities.dataexample.TestData static method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.preconditioner.Sensitivity">Sensitivity (class in cil.optimisation.utilities.preconditioner)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.sequential">sequential() (cil.optimisation.utilities.Sampler static method)</a>, <a href="../optimisation/#id0">[1]</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.set_angles">set_angles() (cil.framework.AcquisitionGeometry method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.set_channels">set_channels() (cil.framework.AcquisitionGeometry method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.ApproximateGradientSumFunction.set_data_partition_weights">set_data_partition_weights() (cil.optimisation.functions.ApproximateGradientSumFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.LSVRGFunction.set_data_partition_weights">(cil.optimisation.functions.LSVRGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.set_data_partition_weights">(cil.optimisation.functions.SAGAFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.set_data_partition_weights">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SGFunction.set_data_partition_weights">(cil.optimisation.functions.SGFunction method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction.set_data_partition_weights">(cil.optimisation.functions.SVRGFunction method)</a>
+</li>
+      </ul></li>
+      <li><a href="../recon/#cil.recon.FBP.set_fft_order">set_fft_order() (cil.recon.FBP method)</a>
+
+      <ul>
+        <li><a href="../recon/#cil.recon.FDK.set_fft_order">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../recon/#cil.recon.FBP.set_filter">set_filter() (cil.recon.FBP method)</a>
+
+      <ul>
+        <li><a href="../recon/#cil.recon.FDK.set_filter">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../recon/#cil.recon.FBP.set_filter_inplace">set_filter_inplace() (cil.recon.FBP method)</a>
+
+      <ul>
+        <li><a href="../recon/#cil.recon.FDK.set_filter_inplace">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.set_gamma_fconj">set_gamma_fconj() (cil.optimisation.algorithms.PDHG method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.set_gamma_g">set_gamma_g() (cil.optimisation.algorithms.PDHG method)</a>
+</li>
+      <li><a href="../recon/#cil.recon.FBP.set_image_geometry">set_image_geometry() (cil.recon.FBP method)</a>
+
+      <ul>
+        <li><a href="../recon/#cil.recon.FDK.set_image_geometry">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.DataProcessor.set_input">set_input() (cil.framework.DataProcessor method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.Processor.set_input">(cil.framework.Processor method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.astra.FBP.set_input">(cil.plugins.astra.FBP method)</a>
+</li>
+        <li><a href="../plugins/#cil.plugins.tigre.FBP.set_input">(cil.plugins.tigre.FBP method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.AbsorptionTransmissionConverter.set_input">(cil.processors.AbsorptionTransmissionConverter method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Binner.set_input">(cil.processors.Binner method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.CentreOfRotationCorrector.set_input">(cil.processors.CentreOfRotationCorrector method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Masker.set_input">(cil.processors.Masker method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.MaskGenerator.set_input">(cil.processors.MaskGenerator method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Normaliser.set_input">(cil.processors.Normaliser method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Padder.set_input">(cil.processors.Padder method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.PaganinProcessor.set_input">(cil.processors.PaganinProcessor method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.RingRemover.set_input">(cil.processors.RingRemover method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.Slicer.set_input">(cil.processors.Slicer method)</a>
+</li>
+        <li><a href="../processors/#cil.processors.TransmissionAbsorptionConverter.set_input">(cil.processors.TransmissionAbsorptionConverter method)</a>
+</li>
+        <li><a href="../recon/#cil.recon.FBP.set_input">(cil.recon.FBP method)</a>
+</li>
+        <li><a href="../recon/#cil.recon.FDK.set_input">(cil.recon.FDK method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.set_labels">set_labels() (cil.framework.AcquisitionGeometry method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.Operator.set_norm">set_norm() (cil.optimisation.operators.Operator method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.set_norms">set_norms() (cil.optimisation.operators.BlockOperator method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.set_num_threads">set_num_threads() (cil.optimisation.functions.IndicatorBox method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionGeometry.set_panel">set_panel() (cil.framework.AcquisitionGeometry method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.set_relaxation_parameter">set_relaxation_parameter() (cil.optimisation.algorithms.SIRT method)</a>
+</li>
+      <li><a href="../recon/#cil.recon.FBP.set_split_processing">set_split_processing() (cil.recon.FBP method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.set_step_sizes">set_step_sizes() (cil.optimisation.algorithms.PDHG method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.IndicatorBox.set_suppress_evaluation">set_suppress_evaluation() (cil.optimisation.functions.IndicatorBox method)</a>
+</li>
+      <li><a href="../io/#cil.io.NEXUSDataReader.set_up">set_up() (cil.io.NEXUSDataReader method)</a>
+
+      <ul>
+        <li><a href="../io/#cil.io.NEXUSDataWriter.set_up">(cil.io.NEXUSDataWriter method)</a>
+</li>
+        <li><a href="../io/#cil.io.ZEISSDataReader.set_up">(cil.io.ZEISSDataReader method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.set_up">(cil.optimisation.algorithms.Algorithm method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.set_up">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.set_up">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.set_up">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.set_up">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.set_up">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.set_up">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.set_up">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.set_up">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.set_up">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.SGFunction">SGFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.shape">shape (cil.framework.AcquisitionData property)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.shape">(cil.framework.DataContainer property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.shape">(cil.framework.ImageData property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.shape">(cil.framework.VectorData property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.should_stop">should_stop() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.should_stop">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.should_stop">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.should_stop">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.should_stop">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.should_stop">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.should_stop">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.should_stop">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.should_stop">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.should_stop">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+      <li><a href="../utilities/#cil.utilities.display.show1D">show1D (class in cil.utilities.display)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.display.show2D">show2D (class in cil.utilities.display)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.display.show_geometry">show_geometry (class in cil.utilities.display)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA">SIMULATED_CONE_BEAM_DATA (class in cil.utilities.dataexample)</a>, <a href="../utilities/#id0">[1]</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA">SIMULATED_PARALLEL_BEAM_DATA (class in cil.utilities.dataexample)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT">SIRT (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.size">size (cil.framework.AcquisitionData property)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.size">(cil.framework.DataContainer property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.size">(cil.framework.ImageData property)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.size">(cil.framework.VectorData property)</a>
+</li>
+      </ul></li>
+      <li><a href="../io/#cil.io.ZEISSDataReader.slice_metadata">slice_metadata() (cil.io.ZEISSDataReader method)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Slicer">Slicer (class in cil.processors)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.SmoothMixedL21Norm">SmoothMixedL21Norm (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.solution">solution (cil.optimisation.algorithms.Algorithm property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.solution">(cil.optimisation.algorithms.CGLS property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.solution">(cil.optimisation.algorithms.FISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.solution">(cil.optimisation.algorithms.GD property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.solution">(cil.optimisation.algorithms.ISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.solution">(cil.optimisation.algorithms.LADMM property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.solution">(cil.optimisation.algorithms.PD3O property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.solution">(cil.optimisation.algorithms.PDHG property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.solution">(cil.optimisation.algorithms.SIRT property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.solution">(cil.optimisation.algorithms.SPDHG property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.SparseFiniteDifferenceOperator">SparseFiniteDifferenceOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG">SPDHG (class in cil.optimisation.algorithms)</a>
+</li>
+      <li><a href="../processors/#cil.processors.MaskGenerator.special_values">special_values() (cil.processors.MaskGenerator static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.squared_norm">squared_norm() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.squared_norm">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.squared_norm">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.squared_norm">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.Sampler.staggered">staggered() (cil.optimisation.utilities.Sampler static method)</a>, <a href="../optimisation/#id47">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.StepSizeMethods.StepSizeRule">StepSizeRule (class in cil.optimisation.utilities.StepSizeMethods)</a>
+</li>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.subtract">subtract() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.subtract">[1]</a>
+</li>
+      <li><a href="../framework/#cil.framework.AcquisitionData.sum">sum() (cil.framework.AcquisitionData method)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.DataContainer.sum">(cil.framework.DataContainer method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.ImageData.sum">(cil.framework.ImageData method)</a>
+</li>
+        <li><a href="../framework/#cil.framework.VectorData.sum">(cil.framework.VectorData method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.functions.SumFunction">SumFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.SumOperator">SumOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.SumScalarFunction">SumScalarFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.SVRGFunction">SVRGFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Padder.symmetric">symmetric() (cil.processors.Padder static method)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.SymmetrisedGradientOperator">SymmetrisedGradientOperator (class in cil.optimisation.operators)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA">SYNCHROTRON_PARALLEL_BEAM_DATA (class in cil.utilities.dataexample)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.operators.BlockOperator.T">T (cil.optimisation.operators.BlockOperator property)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.TestData">TestData (class in cil.utilities.dataexample)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.utilities.callbacks.TextProgressCallback">TextProgressCallback (class in cil.optimisation.utilities.callbacks)</a>
+</li>
+      <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TGV">TGV (class in cil.plugins.ccpi_regularisation.functions)</a>
+</li>
+      <li><a href="../processors/#cil.processors.MaskGenerator.threshold">threshold() (cil.processors.MaskGenerator static method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../io/#cil.io.TIFFStackReader">TIFFStackReader (class in cil.io)</a>
+</li>
+      <li><a href="../io/#cil.io.TIFFWriter">TIFFWriter (class in cil.io)</a>
+</li>
+      <li><a href="../plugins/#cil.plugins.ccpi_regularisation.functions.TNV">TNV (class in cil.plugins.ccpi_regularisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.TotalVariation">TotalVariation (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.TranslateFunction">TranslateFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../processors/#cil.processors.TransmissionAbsorptionConverter">TransmissionAbsorptionConverter (class in cil.processors)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.BlockDataContainer.unary_operations">unary_operations() (cil.framework.BlockDataContainer method)</a>, <a href="../optimisation/#cil.framework.BlockDataContainer.unary_operations">[1]</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.update">update() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.update">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.update">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.update">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.update">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.update">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.update">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.update">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.update">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.update">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.update_objective">update_objective() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.update_objective">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.update_objective">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.update_objective">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.update_objective">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.update_objective">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.update_objective">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.update_objective">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.update_objective">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.update_objective">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.update_objective_interval">update_objective_interval (cil.optimisation.algorithms.Algorithm property)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.update_objective_interval">(cil.optimisation.algorithms.CGLS property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.update_objective_interval">(cil.optimisation.algorithms.FISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.update_objective_interval">(cil.optimisation.algorithms.GD property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.update_objective_interval">(cil.optimisation.algorithms.ISTA property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.update_objective_interval">(cil.optimisation.algorithms.LADMM property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.update_objective_interval">(cil.optimisation.algorithms.PD3O property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.update_objective_interval">(cil.optimisation.algorithms.PDHG property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.update_objective_interval">(cil.optimisation.algorithms.SIRT property)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.update_objective_interval">(cil.optimisation.algorithms.SPDHG property)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.update_step_sizes">update_step_sizes() (cil.optimisation.algorithms.PDHG method)</a>
+</li>
+      <li><a href="../utilities/#cil.utilities.dataexample.USB">USB (class in cil.utilities.dataexample)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionType.validate">validate() (cil.framework.labels.AcquisitionType method)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Masker.value">value() (cil.processors.Masker static method)</a>
+</li>
+      <li><a href="../framework/#cil.framework.VectorData">VectorData (class in cil.framework)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.verbose_header">verbose_header() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.verbose_header">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.verbose_header">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.verbose_header">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.verbose_header">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.verbose_header">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.verbose_header">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.verbose_header">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.verbose_header">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.verbose_header">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.algorithms.Algorithm.verbose_output">verbose_output() (cil.optimisation.algorithms.Algorithm method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.CGLS.verbose_output">(cil.optimisation.algorithms.CGLS method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.FISTA.verbose_output">(cil.optimisation.algorithms.FISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.GD.verbose_output">(cil.optimisation.algorithms.GD method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.ISTA.verbose_output">(cil.optimisation.algorithms.ISTA method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.LADMM.verbose_output">(cil.optimisation.algorithms.LADMM method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PD3O.verbose_output">(cil.optimisation.algorithms.PD3O method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.PDHG.verbose_output">(cil.optimisation.algorithms.PDHG method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SIRT.verbose_output">(cil.optimisation.algorithms.SIRT method)</a>
+</li>
+        <li><a href="../optimisation/#cil.optimisation.algorithms.SPDHG.verbose_output">(cil.optimisation.algorithms.SPDHG method)</a>
+</li>
+      </ul></li>
+      <li><a href="../framework/#cil.framework.labels.AcquisitionDimension.VERTICAL">VERTICAL (cil.framework.labels.AcquisitionDimension attribute)</a>
+
+      <ul>
+        <li><a href="../framework/#cil.framework.labels.ImageDimension.VERTICAL">(cil.framework.labels.ImageDimension attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../utilities/#cil.utilities.dataexample.WALNUT">WALNUT (class in cil.utilities.dataexample)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.functions.SAGAFunction.warm_start_approximate_gradients">warm_start_approximate_gradients() (cil.optimisation.functions.SAGAFunction method)</a>
+
+      <ul>
+        <li><a href="../optimisation/#cil.optimisation.functions.SAGFunction.warm_start_approximate_gradients">(cil.optimisation.functions.SAGFunction method)</a>
+</li>
+      </ul></li>
+      <li><a href="../optimisation/#cil.optimisation.operators.WaveletOperator">WaveletOperator (class in cil.optimisation.operators)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.WeightedL2NormSquared">WeightedL2NormSquared (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../processors/#cil.processors.Padder.wrap">wrap() (cil.processors.Padder static method)</a>
+</li>
+      <li><a href="../io/#cil.io.NEXUSDataWriter.write">write() (cil.io.NEXUSDataWriter method)</a>
+
+      <ul>
+        <li><a href="../io/#cil.io.RAWFileWriter.write">(cil.io.RAWFileWriter method)</a>
+</li>
+        <li><a href="../io/#cil.io.TIFFWriter.write">(cil.io.TIFFWriter method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="X">X</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../processors/#cil.processors.CentreOfRotationCorrector.xcorrelation">xcorrelation() (cil.processors.CentreOfRotationCorrector static method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Z">Z</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../io/#cil.io.ZEISSDataReader">ZEISSDataReader (class in cil.io)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="../optimisation/#cil.optimisation.functions.ZeroFunction">ZeroFunction (class in cil.optimisation.functions)</a>
+</li>
+      <li><a href="../optimisation/#cil.optimisation.operators.ZeroOperator">ZeroOperator (class in cil.optimisation.operators)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
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+        <div class="sidebar-primary-item">
+<h3><a href="#">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="developer_guide/">Developers’ Guide</a></li>
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+  <section id="welcome-to-cil-s-documentation">
+<h1>Welcome to CIL’s documentation!<a class="headerlink" href="#welcome-to-cil-s-documentation" title="Link to this heading">#</a></h1>
+<p>The aim of this package is to enable rapid prototyping of optimisation-based
+reconstruction problems, i.e. defining and solving different optimization problems to enforce different properties on the reconstructed image, while being
+powerful enough to be employed on real scale problems.</p>
+<p>Firstly, it provides a framework to handle acquisition and reconstruction
+data and metadata; it also provides a basic input/output package to read data
+from different sources, e.g. Nikon X-Radia, NeXus.</p>
+<p>Secondly, it provides an object-oriented framework for defining mathematical
+operators and functions as well a collection of useful example operators and
+functions. Both smooth and non-smooth functions can be used.</p>
+<p>Further, it provides a number of high-level generic implementations of
+optimisation algorithms to solve generically formulated optimisation problems
+constructed from operator and function objects.</p>
+<section id="demos-and-examples">
+<h2>Demos and Examples<a class="headerlink" href="#demos-and-examples" title="Link to this heading">#</a></h2>
+<p>A number of demos can be found in the <a class="reference external" href="https://github.com/vais-ral/CIL-Demos">CIL-Demos</a> repository.</p>
+<p>For detailed information refer to our articles and the repositories
+with the code to reproduce the article’s results.</p>
+</section>
+<section id="cite-this-work">
+<h2>Cite this work<a class="headerlink" href="#cite-this-work" title="Link to this heading">#</a></h2>
+<p>If you use this software please consider citing one or both of the articles below.</p>
+<p>1. Jørgensen JS et al. 2021 Core Imaging Library Part I: a versatile python framework for tomographic imaging
+<a class="reference external" href="https://doi.org/10.1098/rsta.2020.0192">https://doi.org/10.1098/rsta.2020.0192</a> . Phil. Trans. R. Soc. A 20200192.
+The code to reproduce the article results. <a class="github reference external" href="https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-I">TomographicImaging/Paper-2021-RSTA-CIL-Part-I</a></p>
+<p>2. Papoutsellis E et al. 2021 Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral
+tomography <a class="reference external" href="https://doi.org/10.1098/rsta.2020.0193">https://doi.org/10.1098/rsta.2020.0193</a> Phil. Trans. R. Soc. A 20200193.
+The code to reproduce the article results. <a class="github reference external" href="https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-II">TomographicImaging/Paper-2021-RSTA-CIL-Part-II</a></p>
+</section>
+<section id="table-of-contents">
+<h2>Table of Contents<a class="headerlink" href="#table-of-contents" title="Link to this heading">#</a></h2>
+<div class="toctree-wrapper compound" id="mastertoc">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="introduction/">Introduction</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="introduction/#ct-geometry">CT Geometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="introduction/#parallel-geometry">Parallel geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="introduction/#fan-beam-geometry">Fan-beam geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="introduction/#cone-beam-geometry">Cone-beam geometry</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="introduction/#multi-channel-data">Multi channel data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="introduction/#block-framework">Block Framework</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="framework/">Framework</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="framework/#acquisitiongeometry">AcquisitionGeometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.AcquisitionGeometry"><code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#parallel2d-geometry">Parallel2D Geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#parallel3d-geometry">Parallel3D Geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cone2d-geometry-fanbeam">Cone2D Geometry (Fanbeam)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cone3d-geometry">Cone3D Geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#configure-the-geometry">Configure the geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#use-the-geometry">Use the geometry</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="framework/#imagegeometry">ImageGeometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.ImageGeometry"><code class="docutils literal notranslate"><span class="pre">ImageGeometry</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="framework/#blockgeometry">BlockGeometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.BlockGeometry"><code class="docutils literal notranslate"><span class="pre">BlockGeometry</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="framework/#data-containers">Data Containers</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="framework/#datacontainer">DataContainer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#acquisitiondata">AcquisitionData</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#imagedata">ImageData</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#vectordata">VectorData</a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#blockdatacontainer">BlockDataContainer</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="framework/#partitioner">Partitioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.Partitioner"><code class="docutils literal notranslate"><span class="pre">Partitioner</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="framework/#labels">Labels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.labels.ImageDimension"><code class="docutils literal notranslate"><span class="pre">ImageDimension</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.labels.AcquisitionDimension"><code class="docutils literal notranslate"><span class="pre">AcquisitionDimension</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.labels.FillType"><code class="docutils literal notranslate"><span class="pre">FillType</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.labels.AngleUnit"><code class="docutils literal notranslate"><span class="pre">AngleUnit</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.labels.AcquisitionType"><code class="docutils literal notranslate"><span class="pre">AcquisitionType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="framework/#dataprocessor">DataProcessor</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.DataProcessor"><code class="docutils literal notranslate"><span class="pre">DataProcessor</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="framework/#cil.framework.Processor"><code class="docutils literal notranslate"><span class="pre">Processor</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="io/">Read/ write AcquisitionData and ImageData</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="io/#nexus">NeXuS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.NEXUSDataReader"><code class="docutils literal notranslate"><span class="pre">NEXUSDataReader</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.NEXUSDataWriter"><code class="docutils literal notranslate"><span class="pre">NEXUSDataWriter</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="io/#nikon">Nikon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.NikonDataReader"><code class="docutils literal notranslate"><span class="pre">NikonDataReader</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="io/#zeiss">ZEISS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.ZEISSDataReader"><code class="docutils literal notranslate"><span class="pre">ZEISSDataReader</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="io/#tiff-reader-writer">TIFF Reader/Writer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.TIFFStackReader"><code class="docutils literal notranslate"><span class="pre">TIFFStackReader</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.TIFFWriter"><code class="docutils literal notranslate"><span class="pre">TIFFWriter</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="io/#raw-file-writer">RAW File Writer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.RAWFileWriter"><code class="docutils literal notranslate"><span class="pre">RAWFileWriter</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="io/#hdf5-utilities">HDF5 Utilities</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="io/#cil.io.utilities.HDF5_utilities"><code class="docutils literal notranslate"><span class="pre">HDF5_utilities</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="optimisation/">Optimisation framework</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#algorithms-deterministic">Algorithms (Deterministic)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#base-class">Base class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#gd">GD</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#cgls">CGLS</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#sirt">SIRT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#ista">ISTA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#fista">FISTA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#pdhg">PDHG</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#ladmm">LADMM</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#pd3o">PD3O</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#algorithms-stochastic">Algorithms (Stochastic)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#spdhg">SPDHG</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#approximate-gradient-methods">Approximate gradient methods</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#stochastic-gradient-descent-example">Stochastic Gradient Descent Example</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#note">Note</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#memory-requirements">Memory requirements</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#operators">Operators</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#operator-base-classes">Operator base classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#trivial-operators">Trivial operators</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#gradientoperator">GradientOperator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#waveletoperator">WaveletOperator</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#functions">Functions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#base-classes">Base classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#simple-functions">Simple functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#composition-of-operator-and-a-function">Composition of operator and a function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#indicator-box">Indicator box</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#kullbackleibler">KullbackLeibler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#l1-norm">L1 Norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#l2-norm-squared">L2 Norm Squared</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#least-squares">Least Squares</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#l1-sparsity">L1 Sparsity</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#mixed-l21-norm">Mixed L21 norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#smooth-mixed-l21-norm">Smooth Mixed L21 norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#mixed-l11-norm">Mixed L11 norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#total-variation">Total variation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#approximate-gradient-base-class">Approximate Gradient base class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#stochastic-gradient-function">Stochastic Gradient function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#sag-function">SAG function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#saga-function">SAGA function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#stochastic-variance-reduced-gradient-function">Stochastic Variance Reduced Gradient Function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#loopless-stochastic-variance-reduced-gradient-function">Loopless Stochastic Variance Reduced Gradient Function</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#utilities">Utilities</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#samplers">Samplers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#callbacks">Callbacks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#step-size-methods">Step size methods</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#preconditioners">Preconditioners</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="optimisation/#block-framework">Block Framework</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#blockdatacontainer">BlockDataContainer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#cil.framework.BlockDataContainer"><code class="docutils literal notranslate"><span class="pre">BlockDataContainer</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#block-function">Block Function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#cil.optimisation.functions.BlockFunction"><code class="docutils literal notranslate"><span class="pre">BlockFunction</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimisation/#block-operator">Block Operator</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#cil.optimisation.operators.BlockOperator"><code class="docutils literal notranslate"><span class="pre">BlockOperator</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimisation/#references">References</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="processors/">Processors</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="processors/#data-manipulation">Data Manipulation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="processors/#data-slicer">Data Slicer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#data-binner">Data Binner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#data-padder">Data Padder</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#mask-generator-from-data">Mask Generator from Data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#data-masking">Data Masking</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="processors/#pre-processors">Pre-processors</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="processors/#centre-of-rotation-corrector">Centre Of Rotation Corrector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#data-normaliser">Data Normaliser</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#transmission-to-absorption-converter">Transmission to Absorption Converter</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#absorption-to-transmission-converter">Absorption to Transmission Converter</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#ring-remover">Ring Remover</a></li>
+<li class="toctree-l3"><a class="reference internal" href="processors/#paganin-processor">Paganin Processor</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="recon/">Recon</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="recon/#analytical-reconstruction">Analytical Reconstruction</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="recon/#fbp-reconstructor-for-parallel-beam-geometry">FBP - Reconstructor for parallel-beam geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="recon/#fdk-reconstructor-for-cone-beam-geometry">FDK - Reconstructor for cone-beam geometry</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="utilities/">Utilities</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="utilities/#test-datasets">Test datasets</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#a-set-of-simulated-volumes-and-ct-data">A set of simulated volumes and CT data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#a-ct-dataset-from-the-diamond-light-source">A CT dataset from the Diamond Light Source</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#simulated-image-data">Simulated image data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#remote-data">Remote data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#walnut">Walnut</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#usb">USB</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#korn">KORN</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#sandstone">SANDSTONE</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="utilities/#module-cil.utilities.quality_measures">Image Quality metrics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#cil.utilities.quality_measures.mse"><code class="docutils literal notranslate"><span class="pre">mse()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#cil.utilities.quality_measures.mae"><code class="docutils literal notranslate"><span class="pre">mae()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#cil.utilities.quality_measures.psnr"><code class="docutils literal notranslate"><span class="pre">psnr()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="utilities/#visualisation">Visualisation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#show2d-display-2d-slices">show2D - Display 2D slices</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#show1d-display-1d-slices">show1D - Display 1D slices</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#show-geometry-display-system-geometry">show_geometry - Display system geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#islicer-interactive-display-of-2d-slices">islicer - interactive display of 2D slices</a></li>
+<li class="toctree-l3"><a class="reference internal" href="utilities/#link-islicer-link-islicer-objects-by-index">link_islicer - link islicer objects by index</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="plugins/">CIL Plugins</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="plugins/#ccpi-regularisation">CCPi Regularisation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="plugins/#total-variation">Total variation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plugins/#other-regularisation-functions">Other regularisation functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="plugins/#tomophantom">TomoPhantom</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="plugins/#cil.plugins.TomoPhantom.get_ImageData"><code class="docutils literal notranslate"><span class="pre">get_ImageData()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="plugins/#tigre">TIGRE</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="plugins/#fbp">FBP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plugins/#projection-operator">Projection Operator</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="plugins/#astra">ASTRA</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="plugins/#id6">FBP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="plugins/#id7">Projection Operator</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="developer_guide/">Developers’ Guide</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="developer_guide/#conventions-on-new-cil-objects">Conventions on new CIL objects</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="developer_guide/#creator">Creator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="developer_guide/#setter-methods-and-properties">Setter methods and properties</a></li>
+<li class="toctree-l3"><a class="reference internal" href="developer_guide/#other-methods">Other methods</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="developer_guide/#documentation">Documentation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="developer_guide/#docstrings">Docstrings</a></li>
+<li class="toctree-l3"><a class="reference internal" href="developer_guide/#building-documentation-locally">Building documentation locally</a></li>
+<li class="toctree-l3"><a class="reference internal" href="developer_guide/#notebooks-gallery">Notebooks gallery</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="developer_guide/#contributions-guidelines">Contributions guidelines</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="demos/">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="demos/00_CIL_geometry/">A detailed look at CIL geometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="demos/00_CIL_geometry/#The-structure-of-an-AcquisitionGeometry">The structure of an AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/00_CIL_geometry/#Create-a-simple-AcquisitionGeometry">Create a simple AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/00_CIL_geometry/#Create-an-offset-AcquisitionGeometry">Create an offset AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/00_CIL_geometry/#Create-a-more-complex-AcquisitionGeometry">Create a more complex AcquisitionGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/00_CIL_geometry/#The-structure-of-an-ImageGeometry">The structure of an ImageGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/00_CIL_geometry/#Create-a-simple-ImageGeometry">Create a simple ImageGeometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/00_CIL_geometry/#Create-a-custom-ImageGeometry">Create a custom ImageGeometry</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="demos/deriv2_cgls/">1D inverse problem demo using deriv2 from regtools</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="demos/deriv2_cgls/#CIL-version-23.0.1">CIL version 23.0.1</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="demos/callback_demonstration/">CIL Callback demonstration</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#Load-Data">Load Data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#Default-behaviour">Default behaviour</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#Other-provided-CIL-callbacks">Other provided CIL callbacks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#Early-stopping-(a-custom-callback-example)">Early stopping (a custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#Calculating-data-discrepancy-at-each-iteration-(A-custom-callback-example)">Calculating data discrepancy at each iteration (A custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#Calculating-a-noise-approximation-for-each-iteration-(A-custom-callback-example)">Calculating a noise approximation for each iteration (A custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#Image-metric-callbacks-(custom-callback-example)">Image metric callbacks (custom callback example)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="demos/callback_demonstration/#More-complex-example,-image-metric-callbacks-with-region-of-interests">More complex example, image metric callbacks with region of interests</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="contacts">
+<h2>Contacts<a class="headerlink" href="#contacts" title="Link to this heading">#</a></h2>
+<p>Please refer to the main <a class="reference external" href="https://www.ccpi.ac.uk">CCPi website</a> for up-to-date information.</p>
+<p>The CIL developers may be contacted:</p>
+<ul class="simple">
+<li><p>by joining the <a class="reference external" href="https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=CCPI-DEVEL">devel mailing list</a></p></li>
+<li><p>on the CIL’s GitHub repository page <a class="github reference external" href="https://github.com/TomographicImaging/CIL">TomographicImaging/CIL</a> or</p></li>
+<li><p>on the CIL Discord channel <a class="reference external" href="https://discord.gg/9NTWu9MEGq">https://discord.gg/9NTWu9MEGq</a></p></li>
+</ul>
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+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Introduction</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#ct-geometry">CT Geometry</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#parallel-geometry">Parallel geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fan-beam-geometry">Fan-beam geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cone-beam-geometry">Cone-beam geometry</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#multi-channel-data">Multi channel data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#block-framework">Block Framework</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
+</ul>
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+                  
+  <section id="introduction">
+<h1>Introduction<a class="headerlink" href="#introduction" title="Link to this heading">#</a></h1>
+<p>The goal of the Core Imaging Library is to allow the user to simply create iterative reconstruction methods which
+go beyond the standard filter back projection technique and which better suit the data characteristics.
+The framework comprises:</p>
+<ul class="simple">
+<li><p><code class="code docutils literal notranslate"><span class="pre">cil.framework</span></code> module gives the building blocks used to describe and handle the data</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">cil.io</span></code> module which provides a number of loaders for real CT machines, e.g. Nikon. It also provides reader and writer to save to NeXuS file format.</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">cil.optimisation</span></code> module allows the user to create iterative methods to reconstruct acquisition data applying different types of regularisation, which better suit the data characteristics.</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">cil.plugins</span></code> module which allows CIL to use selected functionality from ASTRA, TIGRE, TomoPhantom and the Regularisation Toolkit</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">cil.processors</span></code> module contains tools for data manipulation and common CT pre-processing steps</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">cil.recon</span></code> module contains an optimised FDK/FBP reconstructors, making using the both CIL accelerated libraries and the Tigre/ASTRA back-projectors</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">cil.utilities</span></code> module contains a selection of display tools for 2D and 3D data, as well as real and simulated test datasets</p></li>
+</ul>
+<section id="ct-geometry">
+<h2>CT Geometry<a class="headerlink" href="#ct-geometry" title="Link to this heading">#</a></h2>
+<p>Please refer to <a class="reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/demos/1_Introduction/00_CIL_geometry.ipynb">this</a> notebook on the CIL-Demos
+repository for full description.</p>
+<p>In conventional CT systems, an object is placed between a source emitting X-rays and a detector array
+measuring the X-ray transmission images of the incident X-rays. Typically, either the object is placed
+on a rotating sample stage and rotates with respect to the source-detector assembly, or the
+source-detector gantry rotates with respect to the stationary object.
+This arrangement results in so-called circular scanning trajectory. Depending on source and detector
+types, there are three conventional data acquisition geometries:</p>
+<ul class="simple">
+<li><p>parallel geometry (2D or 3D),</p></li>
+<li><p>fan-beam geometry, and</p></li>
+<li><p>cone-beam geometry.</p></li>
+</ul>
+<section id="parallel-geometry">
+<h3>Parallel geometry<a class="headerlink" href="#parallel-geometry" title="Link to this heading">#</a></h3>
+<p>Parallel beams of X-rays are emitted onto 1D (single pixel row) or 2D detector array. This geometry
+is common for synchrotron sources. 2D parallel geometry is illustrated below.</p>
+<figure class="align-center" id="id1">
+<img alt="alternate text" src="../_images/parallel.png" />
+<figcaption>
+<p><span class="caption-text">2D Parallel geometry</span><a class="headerlink" href="#id1" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+<figure class="align-center" id="id2">
+<img alt="alternate text" src="../_images/parallel3d.png" />
+<figcaption>
+<p><span class="caption-text">3D Parallel geometry</span><a class="headerlink" href="#id2" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+</section>
+<section id="fan-beam-geometry">
+<h3>Fan-beam geometry<a class="headerlink" href="#fan-beam-geometry" title="Link to this heading">#</a></h3>
+<dl class="simple">
+<dt>A single point-like X-ray source emits a cone beam onto 1D detector pixel row. Cone-beam is typically</dt><dd><p>collimated to imaging field of view. Collimation allows greatly reduce amount of scatter radiation
+reaching the detector. Fan-beam geometry is used when scattering has significant influence on image
+quality or single-slice reconstruction is sufficient.</p>
+</dd>
+</dl>
+<figure class="align-center" id="id3">
+<img alt="alternate text" src="../_images/fan.png" />
+<figcaption>
+<p><span class="caption-text">Fan beam geometry</span><a class="headerlink" href="#id3" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+</section>
+<section id="cone-beam-geometry">
+<h3>Cone-beam geometry<a class="headerlink" href="#cone-beam-geometry" title="Link to this heading">#</a></h3>
+<p>A single point-like X-ray source emits a cone beam onto 2D detector array.
+Cone-beam geometry is mainly used in lab-based CT instruments. Depending on where the sample
+is placed between the source and the detector one can achieve a different magnification factor <span class="math notranslate nohighlight">\(F\)</span>:</p>
+<div class="math notranslate nohighlight">
+\[F = \frac{r_1 + r_2}{r_1}\]</div>
+<p>where <span class="math notranslate nohighlight">\(r_1\)</span> and <span class="math notranslate nohighlight">\(r_2\)</span> are the distance from the source to the center of the sample and
+the distance from the center of the sample to the detector, respectively.</p>
+<figure class="align-center" id="id4">
+<img alt="alternate text" src="../_images/cone.png" />
+<figcaption>
+<p><span class="caption-text">Cone beam geometry</span><a class="headerlink" href="#id4" title="Link to this image">#</a></p>
+</figcaption>
+</figure>
+</section>
+</section>
+<section id="multi-channel-data">
+<h2>Multi channel data<a class="headerlink" href="#multi-channel-data" title="Link to this heading">#</a></h2>
+<p>CIL is designed to work with 4D data.</p>
+<p>Both <code class="code docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code>, <code class="code docutils literal notranslate"><span class="pre">AcquisitionData</span></code> and <code class="code docutils literal notranslate"><span class="pre">ImageGeometry</span></code>, <code class="code docutils literal notranslate"><span class="pre">ImageData</span></code>
+can be defined for multi-channel (spectral/time) CT data using <code class="code docutils literal notranslate"><span class="pre">channels</span></code> attribute.</p>
+</section>
+<section id="block-framework">
+<h2>Block Framework<a class="headerlink" href="#block-framework" title="Link to this heading">#</a></h2>
+<p>The block framework allows writing more advanced <a href="#id5"><span class="problematic" id="id6">`optimisation problems`_</span></a>. Consider the typical
+<a class="reference external" href="https://en.wikipedia.org/wiki/Tikhonov_regularization">Tikhonov regularisation</a>:</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}}\begin{Vmatrix}A u - b \end{Vmatrix}^2_2 + \alpha^2\|Lu\|^2_2\]</div>
+<p>where,</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(A\)</span> is the projection operator</p></li>
+<li><p><span class="math notranslate nohighlight">\(b\)</span> is the acquired data</p></li>
+<li><p><span class="math notranslate nohighlight">\(u\)</span> is the unknown image to be solved for</p></li>
+<li><p><span class="math notranslate nohighlight">\(\alpha\)</span> is the regularisation parameter</p></li>
+<li><p><span class="math notranslate nohighlight">\(L\)</span> is a regularisation operator</p></li>
+</ul>
+<p>The first term measures the fidelity of the solution to the data. The second term measures the
+fidelity to the prior knowledge we have imposed on the system, operator <span class="math notranslate nohighlight">\(L\)</span>.</p>
+<p>This can be re-written equivalently in the block matrix form:</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}}\begin{Vmatrix}\binom{A}{\alpha L} u - \binom{b}{0}\end{Vmatrix}^2_2\]</div>
+<p>With the definitions:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\tilde{A} = \binom{A}{\alpha L}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(\tilde{b} = \binom{b}{0}\)</span></p></li>
+</ul>
+<p>this can now be recognised as a least squares problem which can be solved by any algorithm in the <code class="code docutils literal notranslate"><span class="pre">cil.optimisation</span></code>
+which can solve least squares problem, e.g. CGLS.</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}}\begin{Vmatrix}\tilde{A} u - \tilde{b}\end{Vmatrix}^2_2\]</div>
+<p>To be able to express our optimisation problems in the matrix form above, we developed the so-called,
+Block Framework comprising 4 main actors: <code class="code docutils literal notranslate"><span class="pre">BlockGeometry</span></code>, <code class="code docutils literal notranslate"><span class="pre">BlockDataContainer</span></code>,
+<code class="code docutils literal notranslate"><span class="pre">BlockFunction</span></code> and <code class="code docutils literal notranslate"><span class="pre">BlockOperator</span></code>.</p>
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+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Read/ write AcquisitionData and ImageData</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#nexus">NeXuS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.NEXUSDataReader"><code class="docutils literal notranslate"><span class="pre">NEXUSDataReader</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.NEXUSDataWriter"><code class="docutils literal notranslate"><span class="pre">NEXUSDataWriter</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#nikon">Nikon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.NikonDataReader"><code class="docutils literal notranslate"><span class="pre">NikonDataReader</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#zeiss">ZEISS</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.ZEISSDataReader"><code class="docutils literal notranslate"><span class="pre">ZEISSDataReader</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#tiff-reader-writer">TIFF Reader/Writer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.TIFFStackReader"><code class="docutils literal notranslate"><span class="pre">TIFFStackReader</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.TIFFWriter"><code class="docutils literal notranslate"><span class="pre">TIFFWriter</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#raw-file-writer">RAW File Writer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.RAWFileWriter"><code class="docutils literal notranslate"><span class="pre">RAWFileWriter</span></code></a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="#hdf5-utilities">HDF5 Utilities</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.io.utilities.HDF5_utilities"><code class="docutils literal notranslate"><span class="pre">HDF5_utilities</span></code></a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Read/ write AcquisitionData and ImageData</span></li>
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+                  
+  <section id="read-write-acquisitiondata-and-imagedata">
+<h1>Read/ write AcquisitionData and ImageData<a class="headerlink" href="#read-write-acquisitiondata-and-imagedata" title="Link to this heading">#</a></h1>
+<section id="nexus">
+<h2>NeXuS<a class="headerlink" href="#nexus" title="Link to this heading">#</a></h2>
+<p>The CCPi Framework provides classes to read and write <code class="code docutils literal notranslate"><span class="pre">AcquisitionData</span></code> and <code class="code docutils literal notranslate"><span class="pre">ImageData</span></code>
+as NeXuS files.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># imports</span>
+<span class="kn">from</span> <span class="nn">cil.io</span> <span class="kn">import</span> <span class="n">NEXUSDataWriter</span><span class="p">,</span> <span class="n">NEXUSDataReader</span>
+
+<span class="c1"># initialise NEXUS Writer</span>
+<span class="n">writer</span> <span class="o">=</span> <span class="n">NEXUSDataWriter</span><span class="p">()</span>
+<span class="n">writer</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">my_data</span><span class="p">,</span>
+            <span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;tmp_nexus.nxs&#39;</span><span class="p">)</span>
+<span class="c1"># write data</span>
+<span class="n">writer</span><span class="o">.</span><span class="n">write</span><span class="p">()</span>
+
+<span class="c1"># read data</span>
+<span class="c1"># initialize NEXUS reader</span>
+<span class="n">reader</span> <span class="o">=</span> <span class="n">NEXUSDataReader</span><span class="p">()</span>
+<span class="n">reader</span><span class="o">.</span><span class="n">set_up</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;tmp_nexus.nxs&#39;</span><span class="p">)</span>
+<span class="c1"># load data</span>
+<span class="n">ad1</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+<span class="c1"># get AcquisitionGeometry</span>
+<span class="n">ag1</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">get_geometry</span><span class="p">()</span>
+</pre></div>
+</div>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.</span></span><span class="sig-name descname"><span class="pre">NEXUSDataReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader" title="Link to this definition">#</a></dt>
+<dd><p>Create a reader for NeXus files.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>file_name</strong> (<em>str</em>) – the full path to the NeXus file to read.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Initialise reader.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>file_name</strong> (<em>str</em>) – Full path to NeXus file</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader.get_geometry">
+<span class="sig-name descname"><span class="pre">get_geometry</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader.get_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader.get_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Parse NEXUS file and return acquisition or reconstructed volume
+parameters, depending on file type.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Acquisition or reconstructed volume parameters. Exact type
+depends on file content.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry">AcquisitionGeometry</a> or <a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry">ImageGeometry</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader.get_data_scale">
+<span class="sig-name descname"><span class="pre">get_data_scale</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader.get_data_scale"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader.get_data_scale" title="Link to this definition">#</a></dt>
+<dd><p>Parse NEXUS file and return the scale factor applied to compress
+the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>scale</strong> – The scale factor applied to compress the dataset</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader.get_data_offset">
+<span class="sig-name descname"><span class="pre">get_data_offset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader.get_data_offset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader.get_data_offset" title="Link to this definition">#</a></dt>
+<dd><p>Parse NEXUS file and return the offset factor applied to compress
+the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>offset</strong> – The offset factor applied to compress the dataset</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader.read_as_original">
+<span class="sig-name descname"><span class="pre">read_as_original</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader.read_as_original"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader.read_as_original" title="Link to this definition">#</a></dt>
+<dd><p>Returns the compressed data from the file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>output</strong> – The raw, compressed data. Exact type depends on file content.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a> or <a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader.read">
+<span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader.read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader.read" title="Link to this definition">#</a></dt>
+<dd><p>Returns the uncompressed data as numpy.float32.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>output</strong> – The uncompressed data. Exact type depends on file content.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a> or <a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataReader.load_data">
+<span class="sig-name descname"><span class="pre">load_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataReader/#NEXUSDataReader.load_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataReader.load_data" title="Link to this definition">#</a></dt>
+<dd><p>Alias of <cite>read</cite>.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#cil.io.NEXUSDataReader.read" title="cil.io.NEXUSDataReader.read"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read</span></code></a></p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataWriter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.</span></span><span class="sig-name descname"><span class="pre">NEXUSDataWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataWriter/#NEXUSDataWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataWriter" title="Link to this definition">#</a></dt>
+<dd><p>Create a writer for NEXUS files.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>,</em>) – The dataset to write to file</p></li>
+<li><p><strong>file_name</strong> (<em>os.path</em><em> or </em><em>string</em><em>, </em><em>default None</em>) – The file name to write</p></li>
+<li><p><strong>compression</strong> (<em>str</em><em>, </em><em>{'uint8'</em><em>, </em><em>'uint16'</em><em>, </em><em>None}</em><em>, </em><em>default None</em>) – The lossy compression to apply, default None will not compress data.
+uint8 or unit16 will compress to 8 and 16 bit dtypes respectively.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataWriter.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataWriter/#NEXUSDataWriter.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataWriter.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Set up the writer</p>
+<dl class="simple">
+<dt>data: AcquisitionData, ImageData,</dt><dd><p>The dataset to write to file</p>
+</dd>
+<dt>file_name: os.path or string, default None</dt><dd><p>The file name to write</p>
+</dd>
+<dt>compression: int, default 0</dt><dd><p>The lossy compression to apply, default 0 will not compress data.
+8 or 16 will compress to 8 and 16 bit dtypes respectively.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NEXUSDataWriter.write">
+<span class="sig-name descname"><span class="pre">write</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NEXUSDataWriter/#NEXUSDataWriter.write"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NEXUSDataWriter.write" title="Link to this definition">#</a></dt>
+<dd><p>write dataset to disk</p>
+</dd></dl>
+
+</dd></dl>
+
+<div class="line-block">
+<div class="line"><br /></div>
+</div>
+</section>
+<section id="nikon">
+<h2>Nikon<a class="headerlink" href="#nikon" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.NikonDataReader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.</span></span><span class="sig-name descname"><span class="pre">NikonDataReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fliplr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NikonDataReader/#NikonDataReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NikonDataReader" title="Link to this definition">#</a></dt>
+<dd><p>Basic reader for xtekct files</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file_name</strong> (<em>str</em>) – full path to .xtekct file</p></li>
+<li><p><strong>roi</strong> (<em>dict</em><em>, </em><em>default=None</em>) – <p>dictionary with roi to load:
+{‘angle’: (start, end, step),</p>
+<blockquote>
+<div><p>’horizontal’: (start, end, step),
+‘vertical’: (start, end, step)}</p>
+</div></blockquote>
+</p></li>
+<li><p><strong>normalise</strong> (<em>bool</em><em>, </em><em>default=True</em>) – normalises loaded projections by detector white level (I_0)</p></li>
+<li><p><strong>fliplr</strong> (<em>bool</em><em>, </em><em>default = False</em><em>,</em>) – flip projections in the left-right direction (about vertical axis)</p></li>
+<li><p><strong>mode</strong> (<em>str: {'bin'</em><em>, </em><em>'slice'}</em><em>, </em><em>default='bin'</em>) – In bin mode, ‘step’ number of pixels is binned together,
+values of resulting binned pixels are calculated as average.
+In ‘slice’ mode ‘step’ defines standard numpy slicing.
+Note: in general output array size in bin mode != output array size in slice mode</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl>
+<dt><cite>roi</cite> behaviour:</dt><dd><p>Files are stacked along axis_0. axis_1 and axis_2 correspond
+to row and column dimensions, respectively.</p>
+<p>Files are stacked in alphabetic order.</p>
+<p>To skip projections or to change number of projections to load,
+adjust ‘angle’. For instance, ‘angle’: (100, 300)
+will skip first 100 projections and will load 200 projections.</p>
+<p><code class="docutils literal notranslate"><span class="pre">'angle':</span> <span class="pre">-1</span></code> is a shortcut to load all elements along axis.</p>
+<p><code class="docutils literal notranslate"><span class="pre">start</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span></code> can be specified as <code class="docutils literal notranslate"><span class="pre">None</span></code> which is equivalent
+to <code class="docutils literal notranslate"><span class="pre">start</span> <span class="pre">=</span> <span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span> <span class="pre">=</span> <span class="pre">load</span> <span class="pre">everything</span> <span class="pre">to</span> <span class="pre">the</span> <span class="pre">end</span></code>, respectively.
+Start and end also can be negative.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NikonDataReader.get_geometry">
+<span class="sig-name descname"><span class="pre">get_geometry</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NikonDataReader/#NikonDataReader.get_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NikonDataReader.get_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Return AcquisitionGeometry object</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NikonDataReader.get_roi">
+<span class="sig-name descname"><span class="pre">get_roi</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NikonDataReader/#NikonDataReader.get_roi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NikonDataReader.get_roi" title="Link to this definition">#</a></dt>
+<dd><p>returns the roi</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NikonDataReader.read">
+<span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NikonDataReader/#NikonDataReader.read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NikonDataReader.read" title="Link to this definition">#</a></dt>
+<dd><p>Reads projections and returns AcquisitionData with corresponding geometry,
+arranged as [‘angle’, horizontal’] if a single slice is loaded
+and [‘vertical, ‘angle’, horizontal’] if more than 1 slice is loaded.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.NikonDataReader.load_projections">
+<span class="sig-name descname"><span class="pre">load_projections</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/NikonDataReader/#NikonDataReader.load_projections"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.NikonDataReader.load_projections" title="Link to this definition">#</a></dt>
+<dd><p>alias of read for backward compatibility</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="zeiss">
+<h2>ZEISS<a class="headerlink" href="#zeiss" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.ZEISSDataReader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.</span></span><span class="sig-name descname"><span class="pre">ZEISSDataReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/ZEISSDataReader/#ZEISSDataReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.ZEISSDataReader" title="Link to this definition">#</a></dt>
+<dd><p>Create a reader for ZEISS files</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file_name</strong> (<em>str</em>) – file name to read</p></li>
+<li><p><strong>roi</strong> (<em>dict</em><em>, </em><em>default None</em>) – dictionary with roi to load for each axis:
+<code class="docutils literal notranslate"><span class="pre">{'axis_labels_1':</span> <span class="pre">(start,</span> <span class="pre">end,</span> <span class="pre">step),'axis_labels_2':</span> <span class="pre">(start,</span> <span class="pre">end,</span> <span class="pre">step)}</span></code>.
+<code class="docutils literal notranslate"><span class="pre">axis_labels</span></code> are defined by ImageGeometry and AcquisitionGeometry dimension labels.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl>
+<dt><cite>roi</cite> behaviour:</dt><dd><p>For ImageData to skip files or to change number of files to load,
+adjust <code class="docutils literal notranslate"><span class="pre">vertical</span></code>. E.g. <code class="docutils literal notranslate"><span class="pre">'vertical':</span> <span class="pre">(100,</span> <span class="pre">300)</span></code> will skip first 100 files
+and will load 200 files.</p>
+<p><code class="docutils literal notranslate"><span class="pre">'axis_label':</span> <span class="pre">-1</span></code> is a shortcut to load all elements along axis.</p>
+<p><code class="docutils literal notranslate"><span class="pre">start</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span></code> can be specified as <code class="docutils literal notranslate"><span class="pre">None</span></code> which is equivalent
+to <code class="docutils literal notranslate"><span class="pre">start</span> <span class="pre">=</span> <span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span> <span class="pre">=</span> <span class="pre">load</span> <span class="pre">everything</span> <span class="pre">to</span> <span class="pre">the</span> <span class="pre">end</span></code>, respectively.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.ZEISSDataReader.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/ZEISSDataReader/#ZEISSDataReader.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.ZEISSDataReader.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Set up the reader</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file_name</strong> (<em>str</em>) – file name to read</p></li>
+<li><p><strong>roi</strong> (<em>dict</em><em>, </em><em>default None</em>) – dictionary with roi to load for each axis:
+<code class="docutils literal notranslate"><span class="pre">{'axis_labels_1':</span> <span class="pre">(start,</span> <span class="pre">end,</span> <span class="pre">step),'axis_labels_2':</span> <span class="pre">(start,</span> <span class="pre">end,</span> <span class="pre">step)}</span></code>.
+<code class="docutils literal notranslate"><span class="pre">axis_labels</span></code> are defined by ImageGeometry and AcquisitionGeometry dimension labels.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl>
+<dt><cite>roi</cite> behaviour:</dt><dd><p><code class="docutils literal notranslate"><span class="pre">'axis_label':</span> <span class="pre">-1</span></code> is a shortcut to load all elements along axis.</p>
+<p><code class="docutils literal notranslate"><span class="pre">start</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span></code> can be specified as <code class="docutils literal notranslate"><span class="pre">None</span></code> which is equivalent
+to <code class="docutils literal notranslate"><span class="pre">start</span> <span class="pre">=</span> <span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span> <span class="pre">=</span> <span class="pre">load</span> <span class="pre">everything</span> <span class="pre">to</span> <span class="pre">the</span> <span class="pre">end</span></code>, respectively.</p>
+<p><strong>Acquisition Data</strong></p>
+<p>The axis labels in the <cite>roi</cite> dict for <cite>AcquisitionData</cite> will be:
+<code class="docutils literal notranslate"><span class="pre">{'angle':(...),'vertical':(...),'horizontal':(...)}</span></code></p>
+<p><strong>Image Data</strong></p>
+<p>The axis labels in the <cite>roi</cite> dict for <cite>ImageData</cite> will be:
+<code class="docutils literal notranslate"><span class="pre">{'angle':(...),'vertical':(...),'horizontal':(...)}</span></code></p>
+<p>To skip files or to change number of files to load,
+adjust <code class="docutils literal notranslate"><span class="pre">vertical</span></code>. E.g. <code class="docutils literal notranslate"><span class="pre">'vertical':</span> <span class="pre">(100,</span> <span class="pre">300)</span></code> will skip first 100 files
+and will load 200 files.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.ZEISSDataReader.slice_metadata">
+<span class="sig-name descname"><span class="pre">slice_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/ZEISSDataReader/#ZEISSDataReader.slice_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.ZEISSDataReader.slice_metadata" title="Link to this definition">#</a></dt>
+<dd><p>Slices metadata to configure geometry before reading data</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.ZEISSDataReader.read">
+<span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/ZEISSDataReader/#ZEISSDataReader.read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.ZEISSDataReader.read" title="Link to this definition">#</a></dt>
+<dd><p>Reads projections and return Acquisition (TXRM) or Image (TXM) Data container</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.ZEISSDataReader.get_geometry">
+<span class="sig-name descname"><span class="pre">get_geometry</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/ZEISSDataReader/#ZEISSDataReader.get_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.ZEISSDataReader.get_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Return Acquisition (TXRM) or Image (TXM) Geometry object</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.ZEISSDataReader.get_metadata">
+<span class="sig-name descname"><span class="pre">get_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/ZEISSDataReader/#ZEISSDataReader.get_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.ZEISSDataReader.get_metadata" title="Link to this definition">#</a></dt>
+<dd><p>return the metadata of the file</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="tiff-reader-writer">
+<h2>TIFF Reader/Writer<a class="headerlink" href="#tiff-reader-writer" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.TIFFStackReader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.</span></span><span class="sig-name descname"><span class="pre">TIFFStackReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roi=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode='bin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float32'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFStackReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFStackReader" title="Link to this definition">#</a></dt>
+<dd><p>Basic TIFF reader which loops through all tiff files in a specific
+folder and loads them in alphabetic order</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>file_name</strong> (<em>str</em><em>, </em><em>abspath to folder</em><em>, </em><em>list</em>) – Path to folder with tiff files, list of paths of tiffs, or single tiff file</p></li>
+<li><p><strong>roi</strong> (dictionary, default <cite>None</cite>) – <p>dictionary with roi to load:
+<a href="#id1"><span class="problematic" id="id2">``</span></a>{‘axis_0’: (start, end, step),</p>
+<blockquote>
+<div><p>’axis_1’: (start, end, step),
+‘axis_2’: (start, end, step)}``</p>
+</div></blockquote>
+<p>roi is specified for axes before transpose.</p>
+</p></li>
+<li><p><strong>transpose</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to transpose loaded images</p></li>
+<li><p><strong>mode</strong> (<em>str</em><em>, </em><em>{'bin'</em><em>, </em><em>'slice'}</em><em>, </em><em>default 'bin'.</em>) – <p>Defines the ‘step’ in the roi parameter:</p>
+<p>In bin mode, ‘step’ number of pixels
+are binned together, values of resulting binned pixels are calculated as average.</p>
+<p>In ‘slice’ mode ‘step’ defines standard numpy slicing.</p>
+<p>Note: in general output array size in bin mode != output array size in slice mode</p>
+</p></li>
+<li><p><strong>dtype</strong> (<em>numpy type</em><em>, </em><em>string</em><em>, </em><em>default np.float32</em>) – Requested type of the read image. If set to None it defaults to the type of the saved file.</p></li>
+</ul>
+</dd>
+</dl>
+<section id="notes">
+<h3>Notes:<a class="headerlink" href="#notes" title="Link to this heading">#</a></h3>
+<dl>
+<dt>roi behaviour:</dt><dd><p>Files are stacked along <code class="docutils literal notranslate"><span class="pre">axis_0</span></code>, in alphabetical order.</p>
+<p><code class="docutils literal notranslate"><span class="pre">axis_1</span></code> and <code class="docutils literal notranslate"><span class="pre">axis_2</span></code> correspond
+to row and column dimensions, respectively.</p>
+<p>To skip files or to change number of files to load,
+adjust <code class="docutils literal notranslate"><span class="pre">axis_0</span></code>. For instance, <code class="docutils literal notranslate"><span class="pre">'axis_0':</span> <span class="pre">(100,</span> <span class="pre">300)</span></code>
+will skip first 100 files and will load 200 files.</p>
+<p><code class="docutils literal notranslate"><span class="pre">'axis_0':</span> <span class="pre">-1</span></code> is a shortcut to load all elements along axis 0.</p>
+<p><code class="docutils literal notranslate"><span class="pre">start</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span></code> can be specified as <code class="docutils literal notranslate"><span class="pre">None</span></code> which is equivalent
+to <code class="docutils literal notranslate"><span class="pre">start</span> <span class="pre">=</span> <span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">end</span> <span class="pre">=</span> <span class="pre">load</span> <span class="pre">everything</span> <span class="pre">to</span> <span class="pre">the</span> <span class="pre">end</span></code>, respectively.</p>
+<p>Start and end also can be negative.</p>
+<p>roi is specified for axes before transpose.</p>
+</dd>
+</dl>
+</section>
+<section id="example">
+<h3>Example:<a class="headerlink" href="#example" title="Link to this heading">#</a></h3>
+<p>You can rescale the read data as <cite>rescaled_data = (read_data - offset)/scale</cite> with the following code:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">reader</span> <span class="o">=</span> <span class="n">TIFFStackReader</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;/path/to/folder&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">rescaled_data</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read_rescaled</span><span class="p">(</span><span class="n">scale</span><span class="p">,</span> <span class="n">offset</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Alternatively, if TIFFWriter has been used to save data with lossy compression, then you can rescale the
+read data to approximately the original data with the following code:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">writer</span> <span class="o">=</span> <span class="n">TIFFWriter</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;/path/to/folder&#39;</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="s1">&#39;uint8&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">writer</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">original_data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">reader</span> <span class="o">=</span> <span class="n">TIFFStackReader</span><span class="p">(</span><span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;/path/to/folder&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">about_original_data</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read_rescaled</span><span class="p">()</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.TIFFStackReader.read">
+<span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFStackReader.read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFStackReader.read" title="Link to this definition">#</a></dt>
+<dd><p>Reads images and return numpy array</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.TIFFStackReader.read_as_ImageData">
+<span class="sig-name descname"><span class="pre">read_as_ImageData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFStackReader.read_as_ImageData"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFStackReader.read_as_ImageData" title="Link to this definition">#</a></dt>
+<dd><p>reads the TIFF stack as an ImageData with the provided geometry</p>
+<p>Notice that the data will be reshaped to what requested in the geometry but there is
+no warranty that the data will be read in the right order!
+In facts you can reshape a (2,3,4) array as (3,4,2), however we do not check if the reshape
+leads to sensible data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.TIFFStackReader.read_as_AcquisitionData">
+<span class="sig-name descname"><span class="pre">read_as_AcquisitionData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">acquisition_geometry</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFStackReader.read_as_AcquisitionData"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFStackReader.read_as_AcquisitionData" title="Link to this definition">#</a></dt>
+<dd><p>reads the TIFF stack as an AcquisitionData with the provided geometry</p>
+<p>Notice that the data will be reshaped to what requested in the geometry but there is
+no warranty that the data will be read in the right order!
+In facts you can reshape a (2,3,4) array as (3,4,2), however we do not check if the reshape
+leads to sensible data.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.TIFFStackReader.read_scale_offset">
+<span class="sig-name descname"><span class="pre">read_scale_offset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFStackReader.read_scale_offset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFStackReader.read_scale_offset" title="Link to this definition">#</a></dt>
+<dd><p>Reads the scale and offset from a json file in the same folder as the tiff stack</p>
+<p>This is a courtesy method that will work only if the tiff stack is saved with the TIFFWriter</p>
+<section id="returns">
+<h4>Returns:<a class="headerlink" href="#returns" title="Link to this heading">#</a></h4>
+<p>tuple: (scale, offset)</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.TIFFStackReader.read_rescaled">
+<span class="sig-name descname"><span class="pre">read_rescaled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFStackReader.read_rescaled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFStackReader.read_rescaled" title="Link to this definition">#</a></dt>
+<dd><p>Reads the TIFF stack and rescales it with the provided scale and offset, or with the ones in the json file if not provided</p>
+<p>This is a courtesy method that will work only if the tiff stack is saved with the TIFFWriter</p>
+<section id="parameters">
+<h4>Parameters:<a class="headerlink" href="#parameters" title="Link to this heading">#</a></h4>
+<dl class="simple">
+<dt>scale: float, default None</dt><dd><p>scale to apply to the data. If None, the scale will be read from the json file saved by TIFFWriter.</p>
+</dd>
+<dt>offset: float, default None</dt><dd><p>offset to apply to the data. If None, the offset will be read from the json file saved by TIFFWriter.</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h4>Returns:<a class="headerlink" href="#id3" title="Link to this heading">#</a></h4>
+<p>numpy.ndarray in float32</p>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.TIFFWriter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.</span></span><span class="sig-name descname"><span class="pre">TIFFWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">counter_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFWriter" title="Link to this definition">#</a></dt>
+<dd><p>Write a DataSet to disk as a TIFF file or stack of TIFF files</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em> or </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a>) – This represents the data to save to TIFF file(s)</p></li>
+<li><p><strong>file_name</strong> (<em>string</em>) – This defines the file name prefix, i.e. the file name without the extension.</p></li>
+<li><p><strong>counter_offset</strong> (<em>int</em><em>, </em><em>default 0.</em>) – counter_offset indicates at which number the ordinal index should start.
+For instance, if you have to save 10 files the index would by default go from 0 to 9.
+By counter_offset you can offset the index: from <cite>counter_offset</cite> to <cite>9+counter_offset</cite></p></li>
+<li><p><strong>compression</strong> (<em>str</em><em>, </em><em>default None. Accepted values None</em><em>, </em><em>'uint8'</em><em>, </em><em>'uint16'</em>) – The lossy compression to apply. The default None will not compress data.
+‘uint8’ or ‘unit16’ will compress to unsigned int 8 and 16 bit respectively.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If compression <code class="docutils literal notranslate"><span class="pre">uint8</span></code> or <code class="docutils literal notranslate"><span class="pre">unit16</span></code> are used, the scale and offset used to compress the data are saved
+in a file called <code class="docutils literal notranslate"><span class="pre">scaleoffset.json</span></code> in the same directory as the TIFF file(s).</p>
+<p>The original data can be obtained by: <code class="docutils literal notranslate"><span class="pre">original_data</span> <span class="pre">=</span> <span class="pre">(compressed_data</span> <span class="pre">-</span> <span class="pre">offset)</span> <span class="pre">/</span> <span class="pre">scale</span></code></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In the case of 3D or 4D data this writer will save the data as a stack of multiple TIFF files,
+not as a single multi-page TIFF file.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.TIFFWriter.write">
+<span class="sig-name descname"><span class="pre">write</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/TIFF/#TIFFWriter.write"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.TIFFWriter.write" title="Link to this definition">#</a></dt>
+<dd><p>Write data to disk</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="raw-file-writer">
+<h2>RAW File Writer<a class="headerlink" href="#raw-file-writer" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.RAWFileWriter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.</span></span><span class="sig-name descname"><span class="pre">RAWFileWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/RAWFileWriter/#RAWFileWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.RAWFileWriter" title="Link to this definition">#</a></dt>
+<dd><p>Writer to write DataContainer (or subclass AcquisitionData, ImageData) to disk as a binary blob</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em> or </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a>) – This represents the data to save to TIFF file(s)</p></li>
+<li><p><strong>file_name</strong> (<em>string</em>) – This defines the file name prefix, i.e. the file name without the extension.</p></li>
+<li><p><strong>compression</strong> (<em>str</em><em>, </em><em>default None. Accepted values None</em><em>, </em><em>'uint8'</em><em>, </em><em>'uint16'</em>) – The lossy compression to apply. The default None will not compress data.
+‘uint8’ or ‘unit16’ will compress to unsigned int 8 and 16 bit respectively.</p></li>
+</ul>
+</dd>
+</dl>
+<p>This writer will also write a text file with the minimal information necessary to
+read the data back in. This text file will need to reside in the same directory as the raw file.</p>
+<p>The text file will look something like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">MINIMAL</span> <span class="n">INFO</span><span class="p">]</span>
+<span class="n">file_name</span> <span class="o">=</span> <span class="n">filename</span><span class="o">.</span><span class="n">raw</span>
+<span class="n">data_type</span> <span class="o">=</span> <span class="o">&lt;</span><span class="n">u2</span>
+<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">is_fortran</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="p">[</span><span class="n">COMPRESSION</span><span class="p">]</span>
+<span class="n">scale</span> <span class="o">=</span> <span class="mf">550.7142857142857</span>
+<span class="n">offset</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.0</span>
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">data_type</span></code> describes the data layout when packing and unpacking data. This can be
+read as numpy dtype with <code class="docutils literal notranslate"><span class="pre">np.dtype('&lt;u2')</span></code>.</p>
+<p class="rubric">Example</p>
+<p>Example of using the writer with compression to <code class="docutils literal notranslate"><span class="pre">uint8</span></code>:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.io</span> <span class="kn">import</span> <span class="n">RAWFileWriter</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">writer</span> <span class="o">=</span> <span class="n">RAWFileWriter</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="n">fname</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="s1">&#39;uint8&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">writer</span><span class="o">.</span><span class="n">write</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<p>Example of reading the data from the ini file:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">config</span> <span class="o">=</span> <span class="n">configparser</span><span class="o">.</span><span class="n">ConfigParser</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inifname</span> <span class="o">=</span> <span class="s2">&quot;file_name.ini&quot;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">config</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">inifname</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">read_dtype</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;MINIMAL INFO&#39;</span><span class="p">][</span><span class="s1">&#39;data_type&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">read_dtype</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fname</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;MINIMAL INFO&#39;</span><span class="p">][</span><span class="s1">&#39;file_name&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">read_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fromfile</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">read_dtype</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">read_shape</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;MINIMAL INFO&#39;</span><span class="p">][</span><span class="s1">&#39;shape&#39;</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">scale</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;COMPRESSION&#39;</span><span class="p">][</span><span class="s1">&#39;scale&#39;</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">offset</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;COMPRESSION&#39;</span><span class="p">][</span><span class="s1">&#39;offset&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If compression <code class="docutils literal notranslate"><span class="pre">uint8</span></code> or <code class="docutils literal notranslate"><span class="pre">unit16</span></code> are used, the scale and offset used to compress the data are saved
+in the <code class="docutils literal notranslate"><span class="pre">ini</span></code> file in the same directory as the raw file, in the “COMPRESSION” section .</p>
+<p>The original data can be obtained by: <code class="docutils literal notranslate"><span class="pre">original_data</span> <span class="pre">=</span> <span class="pre">(compressed_data</span> <span class="pre">-</span> <span class="pre">offset)</span> <span class="pre">/</span> <span class="pre">scale</span></code></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Data is always written in ‘C’ order independent of the order of the original data,
+<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html#numpy.ndarray.tofile">https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html#numpy.ndarray.tofile</a>,</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.RAWFileWriter.write">
+<span class="sig-name descname"><span class="pre">write</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/RAWFileWriter/#RAWFileWriter.write"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.RAWFileWriter.write" title="Link to this definition">#</a></dt>
+<dd><p>Write data to disk</p>
+</dd></dl>
+
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
+</section>
+<section id="hdf5-utilities">
+<h2>HDF5 Utilities<a class="headerlink" href="#hdf5-utilities" title="Link to this heading">#</a></h2>
+<p>Utility functions to browse HDF5 files. These allow you to browse groups and read in datasets as numpy.ndarrays.</p>
+<p>A CIL geometry and dataset must be constructed manually from the array and metadata.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.io.utilities.HDF5_utilities">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.io.utilities.</span></span><span class="sig-name descname"><span class="pre">HDF5_utilities</span></span><a class="reference internal" href="../_modules/cil/io/utilities/#HDF5_utilities"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.utilities.HDF5_utilities" title="Link to this definition">#</a></dt>
+<dd><p>Utility methods to read in from a generic HDF5 file and extract the relevant data</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.utilities.HDF5_utilities.print_metadata">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">print_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'/'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/utilities/#HDF5_utilities.print_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.utilities.HDF5_utilities.print_metadata" title="Link to this definition">#</a></dt>
+<dd><p>Prints the file metadata</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<em>str</em>) – The full path to the HDF5 file</p></li>
+<li><p><strong>group</strong> (<em>(</em><em>str</em><em>)</em><em>, </em><em>default: '/'</em>) – a specific group to print the metadata for, this defaults to the root group</p></li>
+<li><p><strong>depth</strong> (<em>int</em><em>, </em><em>default -1</em>) – depth of group to output the metadata for, -1 is fully recursive</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.utilities.HDF5_utilities.get_dataset_metadata">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_dataset_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dset_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/utilities/#HDF5_utilities.get_dataset_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.utilities.HDF5_utilities.get_dataset_metadata" title="Link to this definition">#</a></dt>
+<dd><p>Returns the dataset metadata as a dictionary</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<em>str</em>) – The full path to the HDF5 file</p></li>
+<li><p><strong>dset_path</strong> (<em>str</em>) – The internal path to the requested dataset</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>ndim, shape, size, dtype, nbytes, compression, chunks, is_virtual</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>A dictionary containing keys, values are <cite>None</cite> if attribute can’t be read.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.utilities.HDF5_utilities.read">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dset_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_sel=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float32'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/utilities/#HDF5_utilities.read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.utilities.HDF5_utilities.read" title="Link to this definition">#</a></dt>
+<dd><p>Reads a dataset entry and returns a numpy array with the requested data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<em>str</em>) – The full path to the HDF5 file</p></li>
+<li><p><strong>dset_path</strong> (<em>str</em>) – The internal path to the requested dataset</p></li>
+<li><p><strong>source_sel</strong> (<em>tuple</em><em> of </em><em>slice objects</em><em>, </em><em>optional</em>) – The selection of slices in each source dimension to return</p></li>
+<li><p><strong>dtype</strong> (<em>numpy type</em><em>, </em><em>default np.float32</em>) – the numpy data type for the returned array</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The requested data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy.ndarray</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>source_sel takes a tuple of slice objects to defining crop and slicing behaviour</p>
+<p>This can be constructed using numpy indexing, i.e. the following lines are equivalent.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">source_sel</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">source_sel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">s_</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">10</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.io.utilities.HDF5_utilities.read_to">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dset_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/io/utilities/#HDF5_utilities.read_to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.io.utilities.HDF5_utilities.read_to" title="Link to this definition">#</a></dt>
+<dd><p>Reads a dataset entry and directly fills a numpy array with the requested data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<em>str</em>) – The full path to the HDF5 file</p></li>
+<li><p><strong>dset_path</strong> (<em>str</em>) – The internal path to the requested dataset</p></li>
+<li><p><strong>out</strong> (<em>numpy.ndarray</em>) – The output array to be filled</p></li>
+<li><p><strong>source_sel</strong> (<em>tuple</em><em> of </em><em>slice objects</em><em>, </em><em>optional</em>) – The selection of slices in each source dimension to return</p></li>
+<li><p><strong>dest_sel</strong> (<em>tuple</em><em> of </em><em>slice objects</em><em>, </em><em>optional</em>) – The selection of slices in each destination dimension to fill</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>source_sel and dest_sel take a tuple of slice objects to defining crop and slicing behaviour</p>
+<p>This can be constructed using numpy indexing, i.e. the following lines are equivalent.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">source_sel</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">source_sel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">s_</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">10</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
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diff --git a/v24.2.0/objects.inv b/v24.2.0/objects.inv
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+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Optimisation framework</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#algorithms-deterministic">Algorithms (Deterministic)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#base-class">Base class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#gd">GD</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cgls">CGLS</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sirt">SIRT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#ista">ISTA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fista">FISTA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#pdhg">PDHG</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#ladmm">LADMM</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#pd3o">PD3O</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#algorithms-stochastic">Algorithms (Stochastic)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#spdhg">SPDHG</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#approximate-gradient-methods">Approximate gradient methods</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#stochastic-gradient-descent-example">Stochastic Gradient Descent Example</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#note">Note</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#memory-requirements">Memory requirements</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#operators">Operators</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#operator-base-classes">Operator base classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#trivial-operators">Trivial operators</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#gradientoperator">GradientOperator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#waveletoperator">WaveletOperator</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#functions">Functions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#base-classes">Base classes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#simple-functions">Simple functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#composition-of-operator-and-a-function">Composition of operator and a function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#indicator-box">Indicator box</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#kullbackleibler">KullbackLeibler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#l1-norm">L1 Norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#l2-norm-squared">L2 Norm Squared</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#least-squares">Least Squares</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#l1-sparsity">L1 Sparsity</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#mixed-l21-norm">Mixed L21 norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#smooth-mixed-l21-norm">Smooth Mixed L21 norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#mixed-l11-norm">Mixed L11 norm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#total-variation">Total variation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#approximate-gradient-base-class">Approximate Gradient base class</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#stochastic-gradient-function">Stochastic Gradient function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sag-function">SAG function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#saga-function">SAGA function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#stochastic-variance-reduced-gradient-function">Stochastic Variance Reduced Gradient Function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#loopless-stochastic-variance-reduced-gradient-function">Loopless Stochastic Variance Reduced Gradient Function</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#utilities">Utilities</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#samplers">Samplers</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#callbacks">Callbacks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#step-size-methods">Step size methods</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#preconditioners">Preconditioners</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="#block-framework">Block Framework</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#blockdatacontainer">BlockDataContainer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.framework.BlockDataContainer"><code class="docutils literal notranslate"><span class="pre">BlockDataContainer</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#block-function">Block Function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.optimisation.functions.BlockFunction"><code class="docutils literal notranslate"><span class="pre">BlockFunction</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#block-operator">Block Operator</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.optimisation.operators.BlockOperator"><code class="docutils literal notranslate"><span class="pre">BlockOperator</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#references">References</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
+</ul>
+</div>
+        <div class="sidebar-primary-item">
+
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+  <div class="tocsection editthispage">
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+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Optimisation framework</span></li>
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+                <article class="bd-article">
+                  
+  <section id="optimisation-framework">
+<h1>Optimisation framework<a class="headerlink" href="#optimisation-framework" title="Link to this heading">#</a></h1>
+<p>This package allows rapid prototyping of optimisation-based reconstruction problems, i.e. defining and solving different optimization problems to enforce different properties on the reconstructed image.</p>
+<p>Firstly, it provides an object-oriented framework for defining mathematical operators and functions as well a collection of useful example operators and functions. Both smooth and non-smooth functions can be used.</p>
+<p>Further, it provides a number of high-level generic implementations of optimisation algorithms to solve generically formulated optimisation problems constructed from operator and function objects.</p>
+<p>The fundamental components are:</p>
+<ul class="simple">
+<li><p><code class="code docutils literal notranslate"><span class="pre">Operator</span></code>: A class specifying a (currently linear) operator.</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">Function</span></code>: A class specifying mathematical functions such as a least squares data fidelity.</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">Algorithm</span></code>: Implementation of an iterative optimisation algorithm to solve a particular generic optimisation problem. Algorithms are iterable Python object which can be run in a for loop. Can be stopped and warm restarted.</p></li>
+</ul>
+<section id="algorithms-deterministic">
+<h2>Algorithms (Deterministic)<a class="headerlink" href="#algorithms-deterministic" title="Link to this heading">#</a></h2>
+<p>A number of generic algorithm implementations are provided including
+Gradient Descent (GD), Conjugate Gradient Least Squares (CGLS),
+Simultaneous Iterative Reconstruction Technique (SIRT), Primal Dual Hybrid
+Gradient (PDHG), Primal dual three-operator (PD3O),  Iterative Shrinkage Thresholding Algorithm (ISTA),
+and Fast Iterative Shrinkage Thresholding Algorithm (FISTA).</p>
+<p>An algorithm is designed for a particular generic optimisation problem accepts and number of
+instances of <code class="code docutils literal notranslate"><span class="pre">Function</span></code> derived classes and/or <code class="code docutils literal notranslate"><span class="pre">Operator</span></code> derived classes as input to
+define a specific instance of the generic optimisation problem to be solved.
+They are iterable objects which can be run in a for loop.
+The user can provide a stopping criterion different than the default max_iteration.</p>
+<p>New algorithms can be easily created by extending the <code class="code docutils literal notranslate"><span class="pre">Algorithm</span></code> class.
+The user is required to implement only 4 methods: set_up, __init__, update and update_objective.</p>
+<ul class="simple">
+<li><p><code class="code docutils literal notranslate"><span class="pre">set_up</span></code> and <code class="code docutils literal notranslate"><span class="pre">__init__</span></code> are used to configure the algorithm</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">update</span></code> is the actual iteration updating the solution</p></li>
+<li><p><code class="code docutils literal notranslate"><span class="pre">update_objective</span></code> defines how the objective is calculated.</p></li>
+</ul>
+<p>For example, the implementation of the update of the Gradient Descent
+algorithm to minimise a Function will only be:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">+=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">rate</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective_function</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">objective_function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>The <code class="code docutils literal notranslate"><span class="pre">Algorithm</span></code> provides the infrastructure to continue iteration, to access the values of the
+objective function in subsequent iterations, the time for each iteration, and to provide a nice
+print to screen of the status of the optimisation.</p>
+<section id="base-class">
+<h3>Base class<a class="headerlink" href="#base-class" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">Algorithm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">update_objective_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm" title="Link to this definition">#</a></dt>
+<dd><p>Base class providing minimal infrastructure for iterative algorithms.</p>
+<p>An iterative algorithm is designed to solve an optimization problem by repeatedly refining a solution. In CIL, we use iterative algorithms to minimize an objective function, often referred to as a loss. The process begins with an initial guess, and with each iteration, the algorithm updates the current solution based on the results of previous iterations (previous iterates). Iterative algorithms typically continue until a stopping criterion is met, indicating that an optimal or sufficiently good solution has been found. In CIL, stopping criteria can be implemented using a callback function (<cite>cil.optimisation.utilities.callbacks</cite>).</p>
+<blockquote>
+<div><p>The user is required to implement the <code class="code docutils literal notranslate"><span class="pre">set_up</span></code>, <code class="code docutils literal notranslate"><span class="pre">__init__</span></code>, <code class="code docutils literal notranslate"><span class="pre">update</span></code> and <code class="code docutils literal notranslate"><span class="pre">update_objective</span></code> methods.</p>
+<p>The method <code class="code docutils literal notranslate"><span class="pre">run</span></code> is available to run <code class="code docutils literal notranslate"><span class="pre">n</span></code> iterations. The method accepts <code class="code docutils literal notranslate"><span class="pre">callbacks</span></code>: a list of callables, each of which receive the current Algorithm object (which in turn contains the iteration number and the actual objective value) and can be used to trigger print to screens and other user interactions. The <code class="code docutils literal notranslate"><span class="pre">run</span></code> method will stop when the stopping criterion is met or <cite>StopIteration</cite> is raised.</p>
+<dl class="simple">
+<dt>update_objective_interval: int, optional, default 1</dt><dd><p>The objective (or loss) is calculated and saved every <cite>update_objective_interval</cite>.  1 means every iteration, 2 every 2 iterations and so forth. This is by default 1 and should be increased when evaluating the objective is computationally expensive.</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Set up the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.update" title="Link to this definition">#</a></dt>
+<dd><p>A single iteration of the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.should_stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.max_iteration_stop_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.get_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The current solution</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.is_provably_convergent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.get_last_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>calculates the objective with the current solution</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.objective_to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.objective_to_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.verbose_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.Algorithm.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/Algorithm/#Algorithm.verbose_header"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.Algorithm.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="gd">
+<h3>GD<a class="headerlink" href="#gd" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">GD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objective_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/GD/#GD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.GD" title="Link to this definition">#</a></dt>
+<dd><p>Gradient Descent algorithm</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData</em><em>)</em>) – The initial point for the optimisation</p></li>
+<li><p><strong>objective_function</strong> (CIL function (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Function()</span></code></a>. ) with a defined gradient method) – The function to be minimised.</p></li>
+<li><p><strong>step_size</strong> (positive real float or subclass of <code class="xref py py-meth docutils literal notranslate"><span class="pre">StepSizeRule()</span></code>, default = None) – If you pass a float this will be used as a constant step size. If left as None and do not pass a step_size_rule then the Armijio rule will be used to perform backtracking to choose a step size at each iteration. If a child class of <code class="xref py py-meth docutils literal notranslate"><span class="pre">cil.optimisation.utilities.StepSizeRule()</span></code>’ is passed then it’s method <cite>get_step_size</cite> is called for each update.</p></li>
+<li><p><strong>preconditioner</strong> (class with a <cite>apply</cite> method or a function that takes an initialised CIL function as an argument and modifies a provided <cite>gradient</cite>.) – This could be a custom <cite>preconditioner</cite> or one provided in <code class="xref py py-meth docutils literal notranslate"><span class="pre">preconditioner()</span></code>. If None is passed  then <cite>self.gradient_update</cite> will remain unmodified.</p></li>
+<li><p><strong>rtol</strong> (<em>positive float</em><em>, </em><em>default 1e-5</em>) – optional parameter defining the relative tolerance comparing the current objective function to 0, default 1e-5, see numpy.isclose</p></li>
+<li><p><strong>atol</strong> (<em>positive float</em><em>, </em><em>default 1e-8</em>) – optional parameter defining the absolute tolerance comparing the current objective function to 0, default 1e-8, see numpy.isclose</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objective_function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/GD/#GD.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.GD.set_up" title="Link to this definition">#</a></dt>
+<dd><p>initialisation of the algorithm</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData</em><em>)</em>) – The initial point for the optimisation</p></li>
+<li><p><strong>objective_function</strong> (<em>CIL function with a defined gradient</em>) – The function to be minimised.</p></li>
+<li><p><strong>step_size</strong> (positive real float or subclass of <code class="xref py py-meth docutils literal notranslate"><span class="pre">StepSizeRule()</span></code>, default = None) – If you pass a float this will be used as a constant step size. If left as None and do not pass a step_size_rule then the Armijio rule will be used to perform backtracking to choose a step size at each iteration. If a child class of <code class="xref py py-meth docutils literal notranslate"><span class="pre">cil.optimisation.utilities.StepSizeRule()</span></code>’ is passed then it’s method <cite>get_step_size</cite> is called for each update.</p></li>
+<li><p><strong>preconditioner</strong> (class with a <cite>apply</cite> method or a function that takes an initialised CIL function as an argument and modifies a provided <cite>gradient</cite>.) – This could be a custom <cite>preconditioner</cite> or one provided in <code class="xref py py-meth docutils literal notranslate"><span class="pre">preconditioner()</span></code>. If None is passed  then <cite>self.gradient_update</cite> will remain unmodified.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/GD/#GD.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.GD.update" title="Link to this definition">#</a></dt>
+<dd><p>Performs a single iteration of the gradient descent algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/GD/#GD.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.GD.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>calculates the objective with the current solution</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/GD/#GD.should_stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.GD.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>Stopping criterion for the gradient descent algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.calculate_objective_function_at_point">
+<span class="sig-name descname"><span class="pre">calculate_objective_function_at_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/GD/#GD.calculate_objective_function_at_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.GD.calculate_objective_function_at_point" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the objective at a given point x</p>
+<div class="math notranslate nohighlight">
+\[f(x) + g(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The current solution</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.GD.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.GD.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.GD.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.GD.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.GD.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.GD.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.GD.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.GD.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="cgls">
+<h3>CGLS<a class="headerlink" href="#cgls" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">CGLS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/CGLS/#CGLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.CGLS" title="Link to this definition">#</a></dt>
+<dd><p>Conjugate Gradient Least Squares (CGLS) algorithm</p>
+<p>The Conjugate Gradient Least Squares (CGLS) algorithm is commonly used for solving large systems of linear equations, due to its fast convergence.</p>
+<p>Problem:</p>
+<div class="math notranslate nohighlight">
+\[\min_x || A x - b ||^2_2\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operator</strong> (<a class="reference internal" href="#cil.optimisation.operators.Operator" title="cil.optimisation.operators.Operator"><em>Operator</em></a>) – Linear operator for the inverse problem</p></li>
+<li><p><strong>initial</strong> (<em>(</em><em>optional</em><em>) </em><em>DataContainer in the domain</em><em> of </em><em>the operator</em><em>, </em><em>default is a DataContainer filled with zeros.</em>) – Initial guess</p></li>
+<li><p><strong>data</strong> (<em>DataContainer in the range</em><em> of </em><em>the operator</em>) – Acquired data to reconstruct</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Passing tolerance directly to CGLS is being deprecated. Instead we recommend using the callback functionality: <a class="reference external" href="https://tomographicimaging.github.io/CIL/nightly/optimisation/#callbacks">https://tomographicimaging.github.io/CIL/nightly/optimisation/#callbacks</a> and in particular the CGLSEarlyStopping callback replicated the old behaviour.</p>
+<p><a class="reference external" href="https://web.stanford.edu/group/SOL/software/cgls/">https://web.stanford.edu/group/SOL/software/cgls/</a></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/CGLS/#CGLS.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Initialisation of the algorithm
+:param operator: Linear operator for the inverse problem
+:type operator: Operator
+:param initial: Initial guess
+:type initial: (optional) DataContainer in the domain of the operator, default is a DataContainer filled with zeros.
+:param data: Acquired data to reconstruct
+:type data: DataContainer in the range of the operator</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/CGLS/#CGLS.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.update" title="Link to this definition">#</a></dt>
+<dd><p>single iteration</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/CGLS/#CGLS.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>calculates the objective with the current solution</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/CGLS/#CGLS.should_stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.flag">
+<span class="sig-name descname"><span class="pre">flag</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/CGLS/#CGLS.flag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.flag" title="Link to this definition">#</a></dt>
+<dd><p>returns whether the tolerance has been reached</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The current solution</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.CGLS.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.CGLS.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="sirt">
+<h3>SIRT<a class="headerlink" href="#sirt" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">SIRT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SIRT/#SIRT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SIRT" title="Link to this definition">#</a></dt>
+<dd><p>Simultaneous Iterative Reconstruction Technique, see <span id="id1">[<a class="reference internal" href="#id72" title="Avinash C. Kak and Malcolm Slaney. Principles of Computerized Tomographic Imaging. Society for Industrial and Applied Mathematics, January 2001. URL: https://doi.org/10.1137/1.9780898719277, doi:10.1137/1.9780898719277.">8</a>]</span>.</p>
+<p>Simultaneous Iterative Reconstruction Technique (SIRT) solves
+the following problem</p>
+<div class="math notranslate nohighlight">
+\[A x = b\]</div>
+<p>The SIRT algorithm is</p>
+<div class="math notranslate nohighlight">
+\[x^{k+1} =  \mathrm{proj}_{C}( x^{k} + \omega * D ( A^{T} ( M * (b - Ax^{k}) ) ) ),\]</div>
+<p>where,
+<span class="math notranslate nohighlight">\(M = \frac{1}{A*\mathbb{1}}\)</span>,
+<span class="math notranslate nohighlight">\(D = \frac{1}{A^{T}\mathbb{1}}\)</span>,
+<span class="math notranslate nohighlight">\(\mathbb{1}\)</span> is a <code class="code docutils literal notranslate"><span class="pre">DataContainer</span></code> of ones,
+<span class="math notranslate nohighlight">\(\mathrm{prox}_{C}\)</span> is the projection over a set <span class="math notranslate nohighlight">\(C\)</span>,
+and <span class="math notranslate nohighlight">\(\omega\)</span> is the relaxation parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default = None</em>) – Starting point of the algorithm, default value =  DataContainer in the domain of the operator allocated with zeros.</p></li>
+<li><p><strong>operator</strong> (<a class="reference internal" href="#cil.optimisation.operators.LinearOperator" title="cil.optimisation.operators.LinearOperator"><em>LinearOperator</em></a>) – The operator A.</p></li>
+<li><p><strong>data</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The data b.</p></li>
+<li><p><strong>lower</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, default = None) – Lower bound constraint</p></li>
+<li><p><strong>upper</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, default = None) – Upper bound constraint</p></li>
+<li><p><strong>constraint</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a><em>, </em><em>default = None</em>) – A function with <code class="code docutils literal notranslate"><span class="pre">proximal</span></code> method, e.g., <a class="reference internal" href="#cil.optimisation.functions.IndicatorBox" title="cil.optimisation.functions.IndicatorBox"><code class="xref py py-class docutils literal notranslate"><span class="pre">IndicatorBox</span></code></a> function and <a class="reference internal" href="#cil.optimisation.functions.IndicatorBox.proximal" title="cil.optimisation.functions.IndicatorBox.proximal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">IndicatorBox.proximal()</span></code></a>,
+or <a class="reference internal" href="#cil.optimisation.functions.TotalVariation" title="cil.optimisation.functions.TotalVariation"><code class="xref py py-class docutils literal notranslate"><span class="pre">TotalVariation</span></code></a> function and <a class="reference internal" href="#cil.optimisation.functions.TotalVariation.proximal" title="cil.optimisation.functions.TotalVariation.proximal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">TotalVariation.proximal()</span></code></a>.</p></li>
+<li><p><strong>kwargs</strong> – Keyword arguments used from the base class <a class="reference internal" href="#cil.optimisation.algorithms.Algorithm" title="cil.optimisation.algorithms.Algorithm"><code class="xref py py-class docutils literal notranslate"><span class="pre">Algorithm</span></code></a>.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If <code class="code docutils literal notranslate"><span class="pre">constraint</span></code> is not passed, <code class="code docutils literal notranslate"><span class="pre">lower</span></code> and <code class="code docutils literal notranslate"><span class="pre">upper</span></code> are used to create an <a class="reference internal" href="#cil.optimisation.functions.IndicatorBox" title="cil.optimisation.functions.IndicatorBox"><code class="xref py py-class docutils literal notranslate"><span class="pre">IndicatorBox</span></code></a> and apply its <code class="code docutils literal notranslate"><span class="pre">proximal</span></code>.</p>
+<p>If <code class="code docutils literal notranslate"><span class="pre">constraint</span></code> is passed, <code class="code docutils literal notranslate"><span class="pre">proximal</span></code> method is required to be implemented.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The preconditioning arrays (weights) <code class="code docutils literal notranslate"><span class="pre">M</span></code> and <code class="code docutils literal notranslate"><span class="pre">D</span></code> used in SIRT are defined as</p>
+<div class="math notranslate nohighlight">
+\[M = \frac{1}{A*\mathbb{1}} = \frac{1}{\sum_{j}a_{i,j}}\]</div>
+<div class="math notranslate nohighlight">
+\[D = \frac{1}{A*\mathbb{1}} = \frac{1}{\sum_{i}a_{i,j}}\]</div>
+</div>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[\underset{x}{\mathrm{argmin}} \frac{1}{2}\| x - d\|^{2}\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sirt</span> <span class="o">=</span> <span class="n">SIRT</span><span class="p">(</span><span class="n">initial</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">operator</span> <span class="o">=</span> <span class="n">A</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">d</span><span class="p">,</span> <span class="n">max_iteration</span> <span class="o">=</span> <span class="mi">5</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SIRT/#SIRT.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Initialisation of the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.set_relaxation_parameter">
+<span class="sig-name descname"><span class="pre">set_relaxation_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SIRT/#SIRT.set_relaxation_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.set_relaxation_parameter" title="Link to this definition">#</a></dt>
+<dd><p>Set the relaxation parameter <span class="math notranslate nohighlight">\(\omega\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>value</strong> (<em>float</em>) – The relaxation parameter to be applied to the update. Must be between 0 and 2 to guarantee asymptotic convergence.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SIRT/#SIRT.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.update" title="Link to this definition">#</a></dt>
+<dd><p>Performs a single iteration of the SIRT algorithm</p>
+<div class="math notranslate nohighlight">
+\[x^{k+1} =  \mathrm{proj}_{C}( x^{k} + \omega * D ( A^{T} ( M * (b - Ax) ) ) )\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SIRT/#SIRT.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the objective</p>
+<div class="math notranslate nohighlight">
+\[\frac{1}{2}\|A x - b\|^{2}\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The current solution</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SIRT.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SIRT.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="ista">
+<h3>ISTA<a class="headerlink" href="#ista" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">ISTA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#ISTA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.ISTA" title="Link to this definition">#</a></dt>
+<dd><p>Iterative Shrinkage-Thresholding Algorithm, see <span id="id2">[<a class="reference internal" href="#id70" title="Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009. URL: https://doi.org/10.1137/080716542, arXiv:https://doi.org/10.1137/080716542, doi:10.1137/080716542.">1</a>]</span>, <span id="id3">[<a class="reference internal" href="#id69" title="Amir Beck and Marc Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11):2419-2434, 2009. doi:10.1109/TIP.2009.2028250.">2</a>]</span>.</p>
+<p>Iterative Shrinkage-Thresholding Algorithm (ISTA)</p>
+<div class="math notranslate nohighlight">
+\[x^{k+1} = \mathrm{prox}_{\alpha^{k} g}(x^{k} - \alpha^{k}\nabla f(x^{k}))\]</div>
+<p>is used to solve</p>
+<div class="math notranslate nohighlight">
+\[\min_{x} f(x) + g(x)\]</div>
+<p>where <span class="math notranslate nohighlight">\(f\)</span> is differentiable, <span class="math notranslate nohighlight">\(g\)</span> has a <em>simple</em> proximal operator and <span class="math notranslate nohighlight">\(\alpha^{k}\)</span>
+is the <code class="code docutils literal notranslate"><span class="pre">step_size</span></code> per iteration.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>For a constant step size, i.e., <span class="math notranslate nohighlight">\(a^{k}=a\)</span> for <span class="math notranslate nohighlight">\(k\geq1\)</span>, convergence of ISTA
+is guaranteed if</p>
+<div class="math notranslate nohighlight">
+\[\alpha\in(0, \frac{2}{L}),\]</div>
+<p>where <span class="math notranslate nohighlight">\(L\)</span> is the Lipschitz constant of <span class="math notranslate nohighlight">\(f\)</span>, see <span id="id4">[<a class="reference internal" href="#id74" title="Patrick L. Combettes and Valérie R. Wajs. Signal recovery by proximal forward-backward splitting. Multiscale Modeling &amp; Simulation, 4(4):1168-1200, 2005. URL: https://doi.org/10.1137/050626090, arXiv:https://doi.org/10.1137/050626090, doi:10.1137/050626090.">4</a>]</span>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Initial guess of ISTA.</p></li>
+<li><p><strong>f</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – Differentiable function. If <cite>None</cite> is passed, the algorithm will use the ZeroFunction.</p></li>
+<li><p><strong>g</strong> (Function or <cite>None</cite>) – Convex function with <em>simple</em> proximal operator. If <cite>None</cite> is passed, the algorithm will use the ZeroFunction.</p></li>
+<li><p><strong>step_size</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code> or child class of <code class="xref py py-meth docutils literal notranslate"><span class="pre">cil.optimisation.utilities.StepSizeRule()</span></code>’,  default = None) – <dl class="simple">
+<dt>Step size for the gradient step of ISTA. If a float is passed, this is used as a constant step size.  If a child class of <code class="xref py py-meth docutils literal notranslate"><span class="pre">cil.optimisation.utilities.StepSizeRule()</span></code>’ is passed then it’s method <cite>get_step_size</cite> is called for each update.</dt><dd><p>The default <code class="code docutils literal notranslate"><span class="pre">step_size</span></code> is a constant <span class="math notranslate nohighlight">\(\frac{0.99*2}{L}\)</span> or 1 if <cite>f=None</cite>.</p>
+</dd>
+<dt>preconditioner: class with a <cite>apply</cite> method or a function that takes an initialised CIL function as an argument and modifies a provided <cite>gradient</cite>.</dt><dd><p>This could be a custom <cite>preconditioner</cite> or one provided in <code class="xref py py-meth docutils literal notranslate"><span class="pre">preconditoner()</span></code>. If None is passed  then <cite>self.gradient_update</cite> will remain unmodified.</p>
+</dd>
+</dl>
+</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="simple">
+<dt>kwargs: Keyword arguments</dt><dd><p>Arguments from the base class <a class="reference internal" href="#cil.optimisation.algorithms.Algorithm" title="cil.optimisation.algorithms.Algorithm"><code class="xref py py-class docutils literal notranslate"><span class="pre">Algorithm</span></code></a>.</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If the function <cite>g</cite> is set to <cite>None</cite> or to the <cite>ZeroFunction</cite> then the ISTA algorithm is equivalent to Gradient Descent.</p>
+<p>If the function <cite>f</cite> is set to <cite>None</cite> or to the <cite>ZeroFunction</cite> then the ISTA algorithm is equivalent to a Proximal Point Algorithm.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[\underset{x}{\mathrm{argmin}}\|A x - b\|^{2}_{2}\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">f</span> <span class="o">=</span> <span class="n">LeastSquares</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">ZeroFunction</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">Aop</span><span class="o">.</span><span class="n">domain</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ista</span> <span class="o">=</span> <span class="n">ISTA</span><span class="p">(</span><span class="n">initial</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(),</span> <span class="n">f</span> <span class="o">=</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span> <span class="o">=</span> <span class="n">g</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ista</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#cil.optimisation.algorithms.FISTA" title="cil.optimisation.algorithms.FISTA"><code class="xref py py-class docutils literal notranslate"><span class="pre">FISTA</span></code></a>, <a class="reference internal" href="#cil.optimisation.algorithms.GD" title="cil.optimisation.algorithms.GD"><code class="xref py py-class docutils literal notranslate"><span class="pre">GD</span></code></a></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#ISTA.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#ISTA.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Set up of the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#ISTA.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.update" title="Link to this definition">#</a></dt>
+<dd><p>Performs a single iteration of ISTA</p>
+<div class="math notranslate nohighlight">
+\[x_{k+1} = \mathrm{prox}_{\alpha g}(x_{k} - \alpha\nabla f(x_{k}))\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#ISTA.get_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#ISTA.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>Updates the objective</p>
+<div class="math notranslate nohighlight">
+\[f(x) + g(x)\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.calculate_objective_function_at_point">
+<span class="sig-name descname"><span class="pre">calculate_objective_function_at_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#ISTA.calculate_objective_function_at_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.calculate_objective_function_at_point" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the objective at a given point x</p>
+<div class="math notranslate nohighlight">
+\[f(x) + g(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__delattr__">
+<span class="sig-name descname"><span class="pre">__delattr__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__delattr__" title="Link to this definition">#</a></dt>
+<dd><p>Implement delattr(self, name).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__dir__">
+<span class="sig-name descname"><span class="pre">__dir__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__dir__" title="Link to this definition">#</a></dt>
+<dd><p>Default dir() implementation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__eq__">
+<span class="sig-name descname"><span class="pre">__eq__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__eq__" title="Link to this definition">#</a></dt>
+<dd><p>Return self==value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__format__">
+<span class="sig-name descname"><span class="pre">__format__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">format_spec</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__format__" title="Link to this definition">#</a></dt>
+<dd><p>Default object formatter.</p>
+<p>Return str(self) if format_spec is empty. Raise TypeError otherwise.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__ge__">
+<span class="sig-name descname"><span class="pre">__ge__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__ge__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&gt;=value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__getattribute__">
+<span class="sig-name descname"><span class="pre">__getattribute__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__getattribute__" title="Link to this definition">#</a></dt>
+<dd><p>Return getattr(self, name).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__getstate__">
+<span class="sig-name descname"><span class="pre">__getstate__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__getstate__" title="Link to this definition">#</a></dt>
+<dd><p>Helper for pickle.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__gt__">
+<span class="sig-name descname"><span class="pre">__gt__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__gt__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&gt;value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__hash__">
+<span class="sig-name descname"><span class="pre">__hash__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__hash__" title="Link to this definition">#</a></dt>
+<dd><p>Return hash(self).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__init_subclass__">
+<span class="sig-name descname"><span class="pre">__init_subclass__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__init_subclass__" title="Link to this definition">#</a></dt>
+<dd><p>This method is called when a class is subclassed.</p>
+<p>The default implementation does nothing. It may be
+overridden to extend subclasses.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__iter__">
+<span class="sig-name descname"><span class="pre">__iter__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__iter__" title="Link to this definition">#</a></dt>
+<dd><p>Algorithm is an iterable</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__le__">
+<span class="sig-name descname"><span class="pre">__le__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__le__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&lt;=value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__lt__">
+<span class="sig-name descname"><span class="pre">__lt__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__lt__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&lt;value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__ne__">
+<span class="sig-name descname"><span class="pre">__ne__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__ne__" title="Link to this definition">#</a></dt>
+<dd><p>Return self!=value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__new__">
+<span class="sig-name descname"><span class="pre">__new__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__new__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__next__">
+<span class="sig-name descname"><span class="pre">__next__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__next__" title="Link to this definition">#</a></dt>
+<dd><p>Algorithm is an iterable</p>
+<p>This method triggers <code class="code docutils literal notranslate"><span class="pre">update()</span></code> and <code class="code docutils literal notranslate"><span class="pre">update_objective()</span></code></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__reduce__">
+<span class="sig-name descname"><span class="pre">__reduce__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__reduce__" title="Link to this definition">#</a></dt>
+<dd><p>Helper for pickle.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__reduce_ex__">
+<span class="sig-name descname"><span class="pre">__reduce_ex__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protocol</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__reduce_ex__" title="Link to this definition">#</a></dt>
+<dd><p>Helper for pickle.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__repr__">
+<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__repr__" title="Link to this definition">#</a></dt>
+<dd><p>Return repr(self).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__setattr__">
+<span class="sig-name descname"><span class="pre">__setattr__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__setattr__" title="Link to this definition">#</a></dt>
+<dd><p>Implement setattr(self, name, value).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__sizeof__">
+<span class="sig-name descname"><span class="pre">__sizeof__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__sizeof__" title="Link to this definition">#</a></dt>
+<dd><p>Size of object in memory, in bytes.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__str__">
+<span class="sig-name descname"><span class="pre">__str__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__str__" title="Link to this definition">#</a></dt>
+<dd><p>Return str(self).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__subclasshook__">
+<span class="sig-name descname"><span class="pre">__subclasshook__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__subclasshook__" title="Link to this definition">#</a></dt>
+<dd><p>Abstract classes can override this to customize issubclass().</p>
+<p>This is invoked early on by abc.ABCMeta.__subclasscheck__().
+It should return True, False or NotImplemented.  If it returns
+NotImplemented, the normal algorithm is used.  Otherwise, it
+overrides the normal algorithm (and the outcome is cached).</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.__weakref__">
+<span class="sig-name descname"><span class="pre">__weakref__</span></span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.__weakref__" title="Link to this definition">#</a></dt>
+<dd><p>list of weak references to the object</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.ISTA.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.ISTA.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="fista">
+<h3>FISTA<a class="headerlink" href="#fista" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">FISTA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#FISTA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.FISTA" title="Link to this definition">#</a></dt>
+<dd><p>Fast Iterative Shrinkage-Thresholding Algorithm, see <span id="id5">[<a class="reference internal" href="#id70" title="Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009. URL: https://doi.org/10.1137/080716542, arXiv:https://doi.org/10.1137/080716542, doi:10.1137/080716542.">1</a>]</span>, <span id="id6">[<a class="reference internal" href="#id69" title="Amir Beck and Marc Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11):2419-2434, 2009. doi:10.1109/TIP.2009.2028250.">2</a>]</span>.</p>
+<p>Fast Iterative Shrinkage-Thresholding Algorithm (FISTA)</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{cases}
+    y_{k} = x_{k} - \alpha\nabla f(x_{k})  \\
+    x_{k+1} = \mathrm{prox}_{\alpha g}(y_{k})\\
+    t_{k+1} = \frac{1+\sqrt{1+ 4t_{k}^{2}}}{2}\\
+    y_{k+1} = x_{k} + \frac{t_{k}-1}{t_{k-1}}(x_{k} - x_{k-1})
+\end{cases}\end{split}\]</div>
+<p>is used to solve</p>
+<div class="math notranslate nohighlight">
+\[\min_{x} f(x) + g(x)\]</div>
+<p>where <span class="math notranslate nohighlight">\(f\)</span> is differentiable, <span class="math notranslate nohighlight">\(g\)</span> has a <em>simple</em> proximal operator and <span class="math notranslate nohighlight">\(\alpha^{k}\)</span>
+is the <code class="code docutils literal notranslate"><span class="pre">step_size</span></code> per iteration.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Starting point of the algorithm</p></li>
+<li><p><strong>f</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – Differentiable function.  If <cite>None</cite> is passed, the algorithm will use the ZeroFunction.</p></li>
+<li><p><strong>g</strong> (Function or <cite>None</cite>) – Convex function with <em>simple</em> proximal operator. If <cite>None</cite> is passed, the algorithm will use the ZeroFunction.</p></li>
+<li><p><strong>step_size</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code> or child class of <code class="xref py py-meth docutils literal notranslate"><span class="pre">cil.optimisation.utilities.StepSizeRule()</span></code>’,  default = None) – Step size for the gradient step of ISTA. If a float is passed, this is used as a constant step size. If a child class of <code class="xref py py-meth docutils literal notranslate"><span class="pre">cil.optimisation.utilities.StepSizeRule()</span></code>’ is passed then it’s method <cite>get_step_size</cite> is called for each update.
+The default <code class="code docutils literal notranslate"><span class="pre">step_size</span></code> is a constant <span class="math notranslate nohighlight">\(\frac{1}{L}\)</span> or 1 if <cite>f=None</cite>.</p></li>
+<li><p><strong>preconditioner</strong> (class with a <cite>apply</cite> method or a function that takes an initialised CIL function as an argument and modifies a provided <cite>gradient</cite>.) – This could be a custom <cite>preconditioner</cite> or one provided in <code class="xref py py-meth docutils literal notranslate"><span class="pre">preconditoner()</span></code>. If None is passed  then <cite>self.gradient_update</cite> will remain unmodified.</p></li>
+<li><p><strong>kwargs</strong> (<em>Keyword arguments</em>) – Arguments from the base class <a class="reference internal" href="#cil.optimisation.algorithms.Algorithm" title="cil.optimisation.algorithms.Algorithm"><code class="xref py py-class docutils literal notranslate"><span class="pre">Algorithm</span></code></a>.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If the function <cite>g</cite> is set to <cite>None</cite> or to the <cite>ZeroFunction</cite> then the FISTA algorithm is equivalent to Accelerated Gradient Descent by Nesterov (<span id="id7">[<a class="reference internal" href="#id64" title="Yurii Nesterov. Introductory lectures on convex optimization: A basic course. Volume 87. Springer Science &amp; Business Media, 2003.">9</a>]</span> algorithm 2.2.9).</p>
+<p>If the function <cite>f</cite> is set to <cite>None</cite> or to the <cite>ZeroFunction</cite> then the FISTA algorithm is equivalent to Guler’s First Accelerated Proximal Point Method  (<span id="id8">[<a class="reference internal" href="#id63" title="Osman Güler. New proximal point algorithms for convex minimization. SIAM Journal on Optimization, 2(4):649–664, 1992.">6</a>]</span> sec 2).</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[\underset{x}{\mathrm{argmin}}\|A x - b\|^{2}_{2}\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">f</span> <span class="o">=</span> <span class="n">LeastSquares</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">ZeroFunction</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">Aop</span><span class="o">.</span><span class="n">domain</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fista</span> <span class="o">=</span> <span class="n">FISTA</span><span class="p">(</span><span class="n">initial</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(),</span> <span class="n">f</span> <span class="o">=</span> <span class="n">f</span><span class="p">,</span> <span class="n">g</span> <span class="o">=</span> <span class="n">g</span><span class="p">,</span> <span class="n">max_iteration</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fista</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#cil.optimisation.algorithms.FISTA" title="cil.optimisation.algorithms.FISTA.FISTA"><code class="xref py py-class docutils literal notranslate"><span class="pre">FISTA</span></code></a>, <a class="reference internal" href="#cil.optimisation.algorithms.GD" title="cil.optimisation.algorithms.GD"><code class="xref py py-class docutils literal notranslate"><span class="pre">GD</span></code></a></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#FISTA.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/FISTA/#FISTA.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.update" title="Link to this definition">#</a></dt>
+<dd><p>Performs a single iteration of FISTA</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{cases}
+    x_{k+1} = \mathrm{prox}_{\alpha g}(y_{k} - \alpha\nabla f(y_{k}))\\
+    t_{k+1} = \frac{1+\sqrt{1+ 4t_{k}^{2}}}{2}\\
+    y_{k+1} = x_{k} + \frac{t_{k}-1}{t_{k-1}}(x_{k} - x_{k-1})
+\end{cases}\end{split}\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__delattr__">
+<span class="sig-name descname"><span class="pre">__delattr__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__delattr__" title="Link to this definition">#</a></dt>
+<dd><p>Implement delattr(self, name).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__dir__">
+<span class="sig-name descname"><span class="pre">__dir__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__dir__" title="Link to this definition">#</a></dt>
+<dd><p>Default dir() implementation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__eq__">
+<span class="sig-name descname"><span class="pre">__eq__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__eq__" title="Link to this definition">#</a></dt>
+<dd><p>Return self==value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__format__">
+<span class="sig-name descname"><span class="pre">__format__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">format_spec</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__format__" title="Link to this definition">#</a></dt>
+<dd><p>Default object formatter.</p>
+<p>Return str(self) if format_spec is empty. Raise TypeError otherwise.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__ge__">
+<span class="sig-name descname"><span class="pre">__ge__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__ge__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&gt;=value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__getattribute__">
+<span class="sig-name descname"><span class="pre">__getattribute__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__getattribute__" title="Link to this definition">#</a></dt>
+<dd><p>Return getattr(self, name).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__getstate__">
+<span class="sig-name descname"><span class="pre">__getstate__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__getstate__" title="Link to this definition">#</a></dt>
+<dd><p>Helper for pickle.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__gt__">
+<span class="sig-name descname"><span class="pre">__gt__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__gt__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&gt;value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__hash__">
+<span class="sig-name descname"><span class="pre">__hash__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__hash__" title="Link to this definition">#</a></dt>
+<dd><p>Return hash(self).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__init_subclass__">
+<span class="sig-name descname"><span class="pre">__init_subclass__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__init_subclass__" title="Link to this definition">#</a></dt>
+<dd><p>This method is called when a class is subclassed.</p>
+<p>The default implementation does nothing. It may be
+overridden to extend subclasses.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__iter__">
+<span class="sig-name descname"><span class="pre">__iter__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__iter__" title="Link to this definition">#</a></dt>
+<dd><p>Algorithm is an iterable</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__le__">
+<span class="sig-name descname"><span class="pre">__le__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__le__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&lt;=value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__lt__">
+<span class="sig-name descname"><span class="pre">__lt__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__lt__" title="Link to this definition">#</a></dt>
+<dd><p>Return self&lt;value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__ne__">
+<span class="sig-name descname"><span class="pre">__ne__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__ne__" title="Link to this definition">#</a></dt>
+<dd><p>Return self!=value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__new__">
+<span class="sig-name descname"><span class="pre">__new__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__new__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__next__">
+<span class="sig-name descname"><span class="pre">__next__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__next__" title="Link to this definition">#</a></dt>
+<dd><p>Algorithm is an iterable</p>
+<p>This method triggers <code class="code docutils literal notranslate"><span class="pre">update()</span></code> and <code class="code docutils literal notranslate"><span class="pre">update_objective()</span></code></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__reduce__">
+<span class="sig-name descname"><span class="pre">__reduce__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__reduce__" title="Link to this definition">#</a></dt>
+<dd><p>Helper for pickle.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__reduce_ex__">
+<span class="sig-name descname"><span class="pre">__reduce_ex__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protocol</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__reduce_ex__" title="Link to this definition">#</a></dt>
+<dd><p>Helper for pickle.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__repr__">
+<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__repr__" title="Link to this definition">#</a></dt>
+<dd><p>Return repr(self).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__setattr__">
+<span class="sig-name descname"><span class="pre">__setattr__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__setattr__" title="Link to this definition">#</a></dt>
+<dd><p>Implement setattr(self, name, value).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__sizeof__">
+<span class="sig-name descname"><span class="pre">__sizeof__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__sizeof__" title="Link to this definition">#</a></dt>
+<dd><p>Size of object in memory, in bytes.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__str__">
+<span class="sig-name descname"><span class="pre">__str__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__str__" title="Link to this definition">#</a></dt>
+<dd><p>Return str(self).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__subclasshook__">
+<span class="sig-name descname"><span class="pre">__subclasshook__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__subclasshook__" title="Link to this definition">#</a></dt>
+<dd><p>Abstract classes can override this to customize issubclass().</p>
+<p>This is invoked early on by abc.ABCMeta.__subclasscheck__().
+It should return True, False or NotImplemented.  If it returns
+NotImplemented, the normal algorithm is used.  Otherwise, it
+overrides the normal algorithm (and the outcome is cached).</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.__weakref__">
+<span class="sig-name descname"><span class="pre">__weakref__</span></span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.__weakref__" title="Link to this definition">#</a></dt>
+<dd><p>list of weak references to the object</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.calculate_objective_function_at_point">
+<span class="sig-name descname"><span class="pre">calculate_objective_function_at_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.calculate_objective_function_at_point" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the objective at a given point x</p>
+<div class="math notranslate nohighlight">
+\[f(x) + g(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Set up of the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>Updates the objective</p>
+<div class="math notranslate nohighlight">
+\[f(x) + g(x)\]</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.FISTA.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.FISTA.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="pdhg">
+<h3>PDHG<a class="headerlink" href="#pdhg" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">PDHG</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_fconj</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG" title="Link to this definition">#</a></dt>
+<dd><p>Primal Dual Hybrid Gradient (PDHG) algorithm, see <span id="id9">[<a class="reference internal" href="#id65" title="Antonin Chambolle and Thomas Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision, 40(1):120-145, May 2011. URL: https://doi.org/10.1007/s10851-010-0251-1, doi:10.1007/s10851-010-0251-1.">3</a>]</span>, <span id="id10">[<a class="reference internal" href="#id66" title="Ernie Esser, Xiaoqun Zhang, and Tony F. Chan. A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. SIAM Journal on Imaging Sciences, 3(4):1015-1046, 2010. URL: https://doi.org/10.1137/09076934X, arXiv:https://doi.org/10.1137/09076934X, doi:10.1137/09076934X.">5</a>]</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>f</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A convex function with a “simple” proximal method of its conjugate.</p></li>
+<li><p><strong>g</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A convex function with a “simple” proximal.</p></li>
+<li><p><strong>operator</strong> (<a class="reference internal" href="#cil.optimisation.operators.LinearOperator" title="cil.optimisation.operators.LinearOperator"><em>LinearOperator</em></a>) – A Linear Operator.</p></li>
+<li><p><strong>sigma</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, or <cite>np.ndarray</cite>, <cite>DataContainer</cite>, <cite>BlockDataContainer</cite>, optional, default=None) – Step size for the dual problem.</p></li>
+<li><p><strong>tau</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, or <cite>np.ndarray</cite>, <cite>DataContainer</cite>, <cite>BlockDataContainer</cite>, optional, default=None) – Step size for the primal problem.</p></li>
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Initial point for the PDHG algorithm.</p></li>
+<li><p><strong>gamma_g</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, optional, default=None) – Strongly convex constant if the function g is strongly convex. Allows primal acceleration of the PDHG algorithm.</p></li>
+<li><p><strong>gamma_fconj</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, optional, default=None) – Strongly convex constant if the convex conjugate of f is strongly convex. Allows dual acceleration of the PDHG algorithm.</p></li>
+<li><p><strong>**kwargs</strong> – <p>Keyward arguments used from the base class <a class="reference internal" href="#cil.optimisation.algorithms.Algorithm" title="cil.optimisation.algorithms.Algorithm"><code class="xref py py-class docutils literal notranslate"><span class="pre">Algorithm</span></code></a>.</p>
+<dl>
+<dt>max_iteration<span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">int</span></code>, optional, default=0</span></dt><dd><p>Maximum number of iterations of the PDHG.</p>
+</dd>
+<dt>update_objective_interval<span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">int</span></code>, optional, default=1</span></dt><dd><p>Evaluates objectives, e.g., primal/dual/primal-dual gap every <code class="docutils literal notranslate"><span class="pre">update_objective_interval</span></code>.</p>
+</dd>
+<dt>check_convergence<span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">boolean</span></code>, default=True</span></dt><dd><p>Checks scalar sigma and tau values satisfy convergence criterion</p>
+</dd>
+</dl>
+</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<p>In our <a class="reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/binder/TomographyReconstruction.ipynb">CIL-Demos</a> repositoryyou can find examples using the PDHG algorithm for different imaging problems, such as Total Variation denoising, Total Generalised Variation inpaintingand Total Variation Tomography reconstruction. More examples can also be found in <span id="id11">[<a class="reference internal" href="#id68" title="J. S. Jörgensen, E. Ametova, G. Burca, G. Fardell, E. Papoutsellis, E. Pasca, K. Thielemans, M. Turner, R. Warr, W. R. B. Lionheart, and P. J. Withers. Core imaging library - part i: a versatile python framework for tomographic imaging. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2204):20200192, 2021. URL: https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2020.0192, arXiv:https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0192, doi:10.1098/rsta.2020.0192.">7</a>]</span>, <span id="id12">[<a class="reference internal" href="#id67" title="Evangelos Papoutsellis, Evelina Ametova, Claire Delplancke, Gemma Fardell, Jakob S. Jörgensen, Edoardo Pasca, Martin Turner, Ryan Warr, William R. B. Lionheart, and Philip J. Withers. Core imaging library - part ii: multichannel reconstruction for dynamic and spectral tomography. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2204):20200193, 2021. URL: https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2020.0193, arXiv:https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0193, doi:10.1098/rsta.2020.0193.">10</a>]</span>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Currently, the strongly convex constants are passed as parameters of PDHG.
+In the future, these parameters will be properties of the corresponding functions.</p>
+</div>
+<p class="rubric">Notes</p>
+<p>A first-order primal-dual algorithm for convex optimization problems with known saddle-point structure with applications in imaging.</p>
+<p>The general problem considered in the PDHG algorithm is the generic saddle-point problem</p>
+<div class="math notranslate nohighlight">
+\[\min_{x\in X}\max_{y\in Y} \langle Kx, y \rangle + g(x) - f^{*}(x)\]</div>
+<p>where <span class="math notranslate nohighlight">\(f\)</span> and <span class="math notranslate nohighlight">\(g\)</span> are convex functions with “simple” proximal operators.</p>
+<p><span class="math notranslate nohighlight">\(X\)</span> and <span class="math notranslate nohighlight">\(Y\)</span> are two two finite-dimensional vector spaces with an inner product and representing the domain of <span class="math notranslate nohighlight">\(g\)</span> and <span class="math notranslate nohighlight">\(f^{*}\)</span>, the convex conjugate of <span class="math notranslate nohighlight">\(f\)</span>, respectively.</p>
+<p>The operator <span class="math notranslate nohighlight">\(K\)</span> is a continuous linear operator with operator norm defined as</p>
+<div class="math notranslate nohighlight">
+\[\|K\| = \max\{ \|Kx\| : x\in X, \|x\|\leq1\}\]</div>
+<p>The saddle point problem is decomposed into the primal problem:</p>
+<div class="math notranslate nohighlight">
+\[\min_{x\in X} f(Kx) + g(x),\]</div>
+<p>and its corresponding dual problem</p>
+<div class="math notranslate nohighlight">
+\[\max_{y\in Y} - g^{*}(-K^{*}y) - f^{*}(y).\]</div>
+<p>The PDHG algorithm consists of three steps:</p>
+<ul class="simple">
+<li><p>gradient ascent step for the dual problem,</p></li>
+<li><p>gradient descent step for the primal problem and</p></li>
+<li><p>an over-relaxation of the primal variable.</p></li>
+</ul>
+<div class="math notranslate nohighlight">
+\[y^{n+1} = \mathrm{prox}_{\sigma f^{*}}(y^{n} + \sigma K \bar{x}^{n})\]</div>
+<div class="math notranslate nohighlight">
+\[x^{n+1} = \mathrm{prox}_{\tau g}(x^{n} - \tau K^{*}y^{n+1})\]</div>
+<div class="math notranslate nohighlight">
+\[\bar{x}^{n+1} = x^{n+1} + \theta (x^{n+1} - x^{n})\]</div>
+<p class="rubric">Notes</p>
+<ul class="simple">
+<li><p>Convergence is guaranteed if <span class="math notranslate nohighlight">\(\theta\)</span> = 1.0,  the operator norm <span class="math notranslate nohighlight">\(\|K\|\)</span>, the dual step size <span class="math notranslate nohighlight">\(\sigma\)</span> and the primal step size <span class="math notranslate nohighlight">\(\tau\)</span>, satisfy the following inequality:</p></li>
+</ul>
+<div class="math notranslate nohighlight">
+\[\tau \sigma \|K\|^2 &lt; 1\]</div>
+<ul>
+<li><p>By default, the step sizes <span class="math notranslate nohighlight">\(\sigma\)</span> and <span class="math notranslate nohighlight">\(\tau\)</span> are positive scalars and defined as below:</p>
+<ul class="simple">
+<li><p>If <code class="docutils literal notranslate"><span class="pre">sigma</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> and <code class="docutils literal notranslate"><span class="pre">tau</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>:</p></li>
+</ul>
+<div class="math notranslate nohighlight">
+\[\sigma = \frac{1}{\|K\|},  \tau = \frac{1}{\|K\|}\]</div>
+<ul class="simple">
+<li><p>If <code class="docutils literal notranslate"><span class="pre">tau</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>:</p></li>
+</ul>
+<div class="math notranslate nohighlight">
+\[\tau = \frac{1}{\sigma\|K\|^{2}}\]</div>
+<ul class="simple">
+<li><p>If <code class="docutils literal notranslate"><span class="pre">sigma</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>:</p></li>
+</ul>
+<div class="math notranslate nohighlight">
+\[\sigma = \frac{1}{\tau\|K\|^{2}}\]</div>
+</li>
+<li><p>To monitor the convergence of the algorithm, we compute the primal/dual objectives and the primal-dual gap in <a class="reference internal" href="#cil.optimisation.algorithms.PDHG.update_objective" title="cil.optimisation.algorithms.PDHG.update_objective"><code class="xref py py-meth docutils literal notranslate"><span class="pre">update_objective()</span></code></a>.</p>
+<p>The primal objective is</p>
+<div class="math notranslate nohighlight">
+\[f(Kx) + g(x)\]</div>
+<p>and the dual objective is</p>
+<div class="math notranslate nohighlight">
+\[- g^{*}(-K^{*}y) - f^{*}(y)\]</div>
+<p>The primal-dual gap (or duality gap) is</p>
+<div class="math notranslate nohighlight">
+\[f(Kx) + g(x) + g^{*}(-K^{*}y) + f^{*}(y)\]</div>
+<p>and measures how close is the primal-dual pair (x,y) to the primal-dual solution. It is always non-negative and is used to monitor convergence of the PDHG algorithm. For more information, see <a class="reference external" href="https://en.wikipedia.org/wiki/Duality_gap">Duality Gap</a>.</p>
+</li>
+</ul>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<ul class="simple">
+<li><p>The primal objective is printed if <cite>verbose=1</cite>, <code class="docutils literal notranslate"><span class="pre">pdhg.run(verbose=1)</span></code>.</p></li>
+<li><p>All the objectives are printed if <cite>verbose=2</cite>, <code class="docutils literal notranslate"><span class="pre">pdhg.run(verbose=2)</span></code>.</p></li>
+</ul>
+<p>Computing these objectives can be costly, so it is better to compute every some iterations. To do this, use <code class="docutils literal notranslate"><span class="pre">update_objective_interval</span> <span class="pre">=</span> <span class="pre">#number</span></code>.</p>
+</div>
+<ul>
+<li><p>PDHG algorithm can be accelerated if the functions <span class="math notranslate nohighlight">\(f^{*}\)</span> and/or <span class="math notranslate nohighlight">\(g\)</span> are strongly convex. In these cases, the step-sizes <span class="math notranslate nohighlight">\(\sigma\)</span> and <span class="math notranslate nohighlight">\(\tau\)</span> are updated using the <a class="reference internal" href="#cil.optimisation.algorithms.PDHG.update_step_sizes" title="cil.optimisation.algorithms.PDHG.update_step_sizes"><code class="xref py py-meth docutils literal notranslate"><span class="pre">update_step_sizes()</span></code></a> method. A function <span class="math notranslate nohighlight">\(f\)</span> is strongly convex with constant <span class="math notranslate nohighlight">\(\gamma&gt;0\)</span> if</p>
+<div class="math notranslate nohighlight">
+\[f(x) - \frac{\gamma}{2}\|x\|^{2} \quad\mbox{ is convex. }\]</div>
+<ul class="simple">
+<li><p>For instance the function <span class="math notranslate nohighlight">\(\frac{1}{2}\|x\|^{2}_{2}\)</span> is <span class="math notranslate nohighlight">\(\gamma\)</span> strongly convex for <span class="math notranslate nohighlight">\(\gamma\in(-\infty,1]\)</span>. We say it is 1-strongly convex because it is the largest constant for which <span class="math notranslate nohighlight">\(f - \frac{1}{2}\|\cdot\|^{2}\)</span> is convex.</p></li>
+<li><p>The <span class="math notranslate nohighlight">\(\|\cdot\|_{1}\)</span> norm is not strongly convex. For more information, see <a class="reference external" href="https://en.wikipedia.org/wiki/Convex_function#Strongly_convex_functions">Strongly Convex</a>.</p></li>
+<li><p>If <span class="math notranslate nohighlight">\(g\)</span> is strongly convex with constant <span class="math notranslate nohighlight">\(\gamma\)</span> then the step-sizes <span class="math notranslate nohighlight">\(\sigma\)</span>, <span class="math notranslate nohighlight">\(\tau\)</span> and <span class="math notranslate nohighlight">\(\theta\)</span> are updated as:</p></li>
+</ul>
+<div class="math notranslate nohighlight">
+   \begin{aligned}
+
+       \theta_{n} &amp; = \frac{1}{\sqrt{1 + 2\gamma\tau_{n}}}\\
+       \tau_{n+1} &amp; = \theta_{n}\tau_{n}\\
+       \sigma_{n+1} &amp; = \frac{\sigma_{n}}{\theta_{n}}
+
+   \end{aligned}</div><ul class="simple">
+<li><p>If <span class="math notranslate nohighlight">\(f^{*}\)</span> is strongly convex, we swap <span class="math notranslate nohighlight">\(\sigma\)</span> with <span class="math notranslate nohighlight">\(\tau\)</span>.</p></li>
+</ul>
+</li>
+</ul>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The case where both functions are strongly convex is not available at the moment.</p>
+</div>
+<div class="admonition-todo admonition" id="id13">
+<p class="admonition-title">Todo</p>
+<p>Implement acceleration of PDHG when both functions are strongly convex.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.set_gamma_g">
+<span class="sig-name descname"><span class="pre">set_gamma_g</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.set_gamma_g"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.set_gamma_g" title="Link to this definition">#</a></dt>
+<dd><p>Set the value of the strongly convex constant for function <cite>g</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>value</strong> (<em>a positive number</em><em> or </em><em>None</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.set_gamma_fconj">
+<span class="sig-name descname"><span class="pre">set_gamma_fconj</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.set_gamma_fconj"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.set_gamma_fconj" title="Link to this definition">#</a></dt>
+<dd><p>Set the value of the strongly convex constant for the convex conjugate of function <cite>f</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>value</strong> (<em>a positive number</em><em> or </em><em>None</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Initialisation of the algorithm</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>f</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A convex function with a “simple” proximal method of its conjugate.</p></li>
+<li><p><strong>g</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A convex function with a “simple” proximal.</p></li>
+<li><p><strong>operator</strong> (<a class="reference internal" href="#cil.optimisation.operators.LinearOperator" title="cil.optimisation.operators.LinearOperator"><em>LinearOperator</em></a>) – A Linear Operator.</p></li>
+<li><p><strong>sigma</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, or <cite>np.ndarray</cite>, <cite>DataContainer</cite>, <cite>BlockDataContainer</cite>, optional, default=None) – Step size for the dual problem.</p></li>
+<li><p><strong>tau</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, or <cite>np.ndarray</cite>, <cite>DataContainer</cite>, <cite>BlockDataContainer</cite>, optional, default=None) – Step size for the primal problem.</p></li>
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Initial point for the PDHG algorithm.</p></li>
+<li><p><strong>theta</strong> (<em>Relaxation parameter</em><em>, </em><em>Number</em><em>, </em><em>default 1.0</em>)</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.get_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.update" title="Link to this definition">#</a></dt>
+<dd><p>Performs a single iteration of the PDHG algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.check_convergence">
+<span class="sig-name descname"><span class="pre">check_convergence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.check_convergence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.check_convergence" title="Link to this definition">#</a></dt>
+<dd><p>Check whether convergence criterion for PDHG is satisfied with scalar values of tau and sigma</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>True if convergence criterion is satisfied. False if not satisfied or convergence is unknown.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.set_step_sizes">
+<span class="sig-name descname"><span class="pre">set_step_sizes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.set_step_sizes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.set_step_sizes" title="Link to this definition">#</a></dt>
+<dd><p>Sets sigma and tau step-sizes for the PDHG algorithm. The step sizes can be either scalar or array-objects.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sigma</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, or <cite>np.ndarray</cite>, <cite>DataContainer</cite>, <cite>BlockDataContainer</cite>, optional, default=None) – Step size for the dual problem.</p></li>
+<li><p><strong>tau</strong> (positive <code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, or <cite>np.ndarray</cite>, <cite>DataContainer</cite>, <cite>BlockDataContainer</cite>, optional, default=None) – Step size for the primal problem.</p></li>
+</ul>
+</dd>
+</dl>
+<p>The user can set either, both or none. Values passed by the user will be accepted as long as they are positive numbers,
+or correct shape array like objects.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.update_step_sizes">
+<span class="sig-name descname"><span class="pre">update_step_sizes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.update_step_sizes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.update_step_sizes" title="Link to this definition">#</a></dt>
+<dd><p>Updates step sizes in the cases of primal or dual acceleration using the strongly convexity property.
+The case where both functions are strongly convex is not available at the moment.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PDHG/#PDHG.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>Evaluates the primal objective, the dual objective and the primal-dual gap.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PDHG.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PDHG.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="ladmm">
+<h3>LADMM<a class="headerlink" href="#ladmm" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">LADMM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/ADMM/#LADMM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.LADMM" title="Link to this definition">#</a></dt>
+<dd><p>LADMM is the Linearized Alternating Direction Method of Multipliers (LADMM)</p>
+<p>General form of ADMM : min_{x} f(x) + g(y), subject to Ax + By = b</p>
+<p>Case: A = Id, B = -K, b = 0   ==&gt; min_x f(Kx) + g(x)</p>
+<p>The quadratic term in the augmented Lagrangian is linearized for the x-update.</p>
+<p>Main algorithmic difference is that in ADMM we compute two proximal subproblems,
+where in the PDHG a proximal and proximal conjugate.</p>
+<p>Reference (Section 8) : <a class="reference external" href="https://link.springer.com/content/pdf/10.1007/s10107-018-1321-1.pdf">https://link.springer.com/content/pdf/10.1007/s10107-018-1321-1.pdf</a></p>
+<blockquote>
+<div><p>x^{k} = prox_{      au f } (x^{k-1} - tau/sigma A^{T}(Ax^{k-1} - z^{k-1} + u^{k-1} )</p>
+<p>z^{k} = prox_{sigma g} (Ax^{k} + u^{k-1})</p>
+<p>u^{k} = u^{k-1} + Ax^{k} - z^{k}</p>
+</div></blockquote>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/ADMM/#LADMM.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Set up the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/ADMM/#LADMM.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.update" title="Link to this definition">#</a></dt>
+<dd><p>A single iteration of the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/ADMM/#LADMM.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>calculates the objective with the current solution</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The current solution</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.LADMM.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.LADMM.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="pd3o">
+<h3>PD3O<a class="headerlink" href="#pd3o" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">PD3O</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PD3O/#PD3O"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PD3O" title="Link to this definition">#</a></dt>
+<dd><p>Primal Dual Three Operator Splitting (PD3O) algorithm, see “A New Primal–Dual Algorithm for Minimizing the Sum
+of Three Functions with a Linear Operator”.  This is a primal dual algorithm for minimising <span class="math notranslate nohighlight">\(f(x)+g(x)+h(Ax)\)</span> where all functions are proper, lower semi-continuous and convex,
+<span class="math notranslate nohighlight">\(f\)</span> should be differentiable with a Lipschitz continuous gradient and <span class="math notranslate nohighlight">\(A\)</span> is a bounded linear operator.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>f</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A smooth function with Lipschitz continuous gradient.</p></li>
+<li><p><strong>g</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A convex function with a computationally computable proximal.</p></li>
+<li><p><strong>h</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A composite convex function.</p></li>
+<li><p><strong>operator</strong> (<a class="reference internal" href="#cil.optimisation.operators.Operator" title="cil.optimisation.operators.Operator"><em>Operator</em></a>) – Bounded linear operator</p></li>
+<li><p><strong>delta</strong> (Float, optional, default is  <cite>1./(gamma*operator.norm()**2)</cite>) – The dual step-size</p></li>
+<li><p><strong>gamma</strong> (Float, optional, default is <cite>2.0/f.L</cite>) – The primal step size</p></li>
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional default is a container</em><em> of </em><em>zeros</em><em>, </em><em>in the domain</em><em> of </em><em>the operator</em>) – Initial point for the  algorithm.</p></li>
+</ul>
+</dd>
+</dl>
+<section id="reference">
+<h4>Reference<a class="headerlink" href="#reference" title="Link to this heading">#</a></h4>
+<p>Yan, M. A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator. J Sci Comput 76, 1698–1717 (2018). <a class="reference external" href="https://doi.org/10.1007/s10915-018-0680-3">https://doi.org/10.1007/s10915-018-0680-3</a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PD3O/#PD3O.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.set_up" title="Link to this definition">#</a></dt>
+<dd><p>Set up the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PD3O/#PD3O.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.update" title="Link to this definition">#</a></dt>
+<dd><p>Performs a single iteration of the PD3O algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/PD3O/#PD3O.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>Evaluates the primal objective</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The current solution</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.objective" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.PD3O.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.PD3O.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+</section>
+<section id="algorithms-stochastic">
+<h2>Algorithms (Stochastic)<a class="headerlink" href="#algorithms-stochastic" title="Link to this heading">#</a></h2>
+<p>Consider optimisation problems that take the form of a separable sum:</p>
+<div class="math notranslate nohighlight">
+\[\min_{x} f(x)+g(x) = \min_{x} \sum_{i=0}^{n-1} f_{i}(x) + g(x) = \min_{x} (f_{0}(x) + f_{1}(x) + ... + f_{n-1}(x))+g(x)\]</div>
+<p>where <span class="math notranslate nohighlight">\(n\)</span> is the number of functions.  Where there is a large number of <span class="math notranslate nohighlight">\(f_i\)</span> or their gradients are expensive to calculate, stochastic optimisation methods could prove more efficient.
+There is a growing range of Stochastic optimisation algorithms available with potential benefits of faster convergence in number of iterations or in computational cost.
+This is an area of continued development for CIL and, depending on  the properties of the <span class="math notranslate nohighlight">\(f_i\)</span> and the regulariser <span class="math notranslate nohighlight">\(g\)</span>, there is a range of different options for the user.</p>
+<section id="spdhg">
+<h3>SPDHG<a class="headerlink" href="#spdhg" title="Link to this heading">#</a></h3>
+<p>Stochastic Primal Dual Hybrid Gradient (SPDHG) is a stochastic version of PDHG and deals with optimisation problems of the form:</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[\min_{x} f(Kx) + g(x) = \min_{x} \sum f_i(K_i x) + g(x)\]</div>
+</div></blockquote>
+<p>where <span class="math notranslate nohighlight">\(f_i\)</span> and the regulariser <span class="math notranslate nohighlight">\(g\)</span> need only be proper, convex and lower semi-continuous ( i.e. do not need to be differentiable).
+Each iteration considers just one index of the sum, potentially reducing computational cost. For more examples see our [user notebooks]( <a class="github reference external" href="https://github.com/vais-ral/CIL-Demos/blob/master/Tomography/Simulated/Single%20Channel/PDHG_vs_SPDHG.py">vais-ral/CIL-Demos</a>).</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.algorithms.</span></span><span class="sig-name descname"><span class="pre">SPDHG</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SPDHG/#SPDHG"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG" title="Link to this definition">#</a></dt>
+<dd><p>Stochastic Primal Dual Hybrid Gradient</p>
+<p>Problem:</p>
+<div class="math notranslate nohighlight">
+\[\min_{x} f(Kx) + g(x) = \min_{x} \sum f_i(K_i x) + g(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>f</strong> (<a class="reference internal" href="#cil.optimisation.functions.BlockFunction" title="cil.optimisation.functions.BlockFunction"><em>BlockFunction</em></a>) – Each must be a convex function with a “simple” proximal method of its conjugate</p></li>
+<li><p><strong>g</strong> (<a class="reference internal" href="#cil.optimisation.functions.Function" title="cil.optimisation.functions.Function"><em>Function</em></a>) – A convex function with a “simple” proximal</p></li>
+<li><p><strong>operator</strong> (<a class="reference internal" href="#cil.optimisation.operators.BlockOperator" title="cil.optimisation.operators.BlockOperator"><em>BlockOperator</em></a>) – BlockOperator must contain Linear Operators</p></li>
+<li><p><strong>tau</strong> (<em>positive float</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Step size parameter for Primal problem</p></li>
+<li><p><strong>sigma</strong> (<em>list</em><em> of </em><em>positive float</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – List of Step size parameters for Dual problem</p></li>
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Initial point for the SPDHG algorithm</p></li>
+<li><p><strong>prob</strong> (<em>list</em><em> of </em><em>floats</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – List of probabilities. If None each subset will have probability = 1/number of subsets</p></li>
+<li><p><strong>gamma</strong> (<em>float</em>) – parameter controlling the trade-off between the primal and dual step sizes</p></li>
+<li><p><strong>**kwargs</strong></p></li>
+<li><p><strong>norms</strong> (<em>list</em><em> of </em><em>floats</em>) – precalculated list of norms of the operators</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<p>Example of usage: See <a class="github reference external" href="https://github.com/vais-ral/CIL-Demos/blob/master/Tomography/Simulated/Single%20Channel/PDHG_vs_SPDHG.py">vais-ral/CIL-Demos</a></p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Convergence is guaranteed provided that [2, eq. (12)]:</p>
+<div class="math notranslate nohighlight">
+\[\]</div>
+<p>|sigma[i]^{1/2} * K[i] * tau^{1/2} |^2  &lt; p_i for all i</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<dl class="simple">
+<dt>Notation for primal and dual step-sizes are reversed with comparison</dt><dd><p>to PDHG.py</p>
+</dd>
+</dl>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<dl class="simple">
+<dt>this code implements serial sampling only, as presented in [2]</dt><dd><p>(to be extended to more general case of [1] as future work)</p>
+</dd>
+</dl>
+</div>
+<p class="rubric">References</p>
+<p>[1]”Stochastic primal-dual hybrid gradient algorithm with arbitrary
+sampling and imaging applications”,
+Chambolle, Antonin, Matthias J. Ehrhardt, Peter Richtárik, and Carola-Bibiane Schonlieb,
+SIAM Journal on Optimization 28, no. 4 (2018): 2783-2808.</p>
+<p>[2]”Faster PET reconstruction with non-smooth priors by randomization and preconditioning”,
+Matthias J Ehrhardt, Pawel Markiewicz and Carola-Bibiane Schönlieb,
+Physics in Medicine &amp; Biology, Volume 64, Number 22, 2019.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.set_up">
+<span class="sig-name descname"><span class="pre">set_up</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SPDHG/#SPDHG.set_up"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.set_up" title="Link to this definition">#</a></dt>
+<dd><p>set-up of the algorithm
+:param f: Each must be a convex function with a “simple” proximal method of its conjugate
+:type f: BlockFunction
+:param g: A convex function with a “simple” proximal
+:type g: Function
+:param operator: BlockOperator must contain Linear Operators
+:type operator: BlockOperator
+:param tau: Step size parameter for Primal problem
+:type tau: positive float, optional, default=None
+:param sigma: List of Step size parameters for Dual problem
+:type sigma: list of positive float, optional, default=None
+:param initial: Initial point for the SPDHG algorithm
+:type initial: DataContainer, optional, default=None
+:param prob: List of probabilities. If None each subset will have probability = 1/number of subsets
+:type prob: list of floats, optional, default=None
+:param gamma: parameter controlling the trade-off between the primal and dual step sizes
+:type gamma: float
+:param **kwargs:
+:param norms: precalculated list of norms of the operators
+:type norms: list of floats</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SPDHG/#SPDHG.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.update" title="Link to this definition">#</a></dt>
+<dd><p>A single iteration of the algorithm</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.update_objective">
+<span class="sig-name descname"><span class="pre">update_objective</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/algorithms/SPDHG/#SPDHG.update_objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.update_objective" title="Link to this definition">#</a></dt>
+<dd><p>calculates the objective with the current solution</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.objective">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">objective</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.objective" title="Link to this definition">#</a></dt>
+<dd><p>alias of loss</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.get_last_loss">
+<span class="sig-name descname"><span class="pre">get_last_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.get_last_loss" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.get_last_objective">
+<span class="sig-name descname"><span class="pre">get_last_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.get_last_objective" title="Link to this definition">#</a></dt>
+<dd><p>Returns the last stored value of the loss function. “Loss” is an alias for “objective value”. If <cite>update_objective_interval</cite> is 1 it is the value of the objective at the current iteration. If update_objective_interval &gt; 1 it is the last stored value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>return_all</strong> (<em>Boolean</em><em>, </em><em>default is False</em>) – If True, returns all the stored loss functions</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Last stored value of the loss function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The current solution</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.is_provably_convergent">
+<span class="sig-name descname"><span class="pre">is_provably_convergent</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.is_provably_convergent" title="Link to this definition">#</a></dt>
+<dd><p>Check if the algorithm is convergent based on the provable convergence criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Outcome of the convergence check</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.iterations">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">iterations</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.iterations" title="Link to this definition">#</a></dt>
+<dd><p>returns the iterations at which the objective has been evaluated</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.loss">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">loss</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.loss" title="Link to this definition">#</a></dt>
+<dd><p>returns a list of the values of the objective (alias of loss) during the iteration</p>
+<p>The length of this list may be shorter than the number of iterations run when the <cite>update_objective_interval</cite> &gt; 1</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.max_iteration">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_iteration</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.max_iteration" title="Link to this definition">#</a></dt>
+<dd><p>gets the maximum number of iterations</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.max_iteration_stop_criterion">
+<span class="sig-name descname"><span class="pre">max_iteration_stop_criterion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.max_iteration_stop_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.objective_to_dict">
+<span class="sig-name descname"><span class="pre">objective_to_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.objective_to_dict" title="Link to this definition">#</a></dt>
+<dd><p>Internal function to save and print objective functions</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.objective_to_string">
+<span class="sig-name descname"><span class="pre">objective_to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.objective_to_string" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#cil.optimisation.utilities.callbacks.Callback" title="cil.optimisation.utilities.callbacks.Callback"><span class="pre">Callback</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.run" title="Link to this definition">#</a></dt>
+<dd><p>run upto <code class="code docutils literal notranslate"><span class="pre">iterations</span></code> with callbacks/logging.</p>
+<p>For a demonstration of callbacks see <a class="github reference external" href="https://github.com/TomographicImaging/CIL-Demos/blob/main/misc/callback_demonstration.ipynb">TomographicImaging/CIL-Demos</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iterations</strong> (<em>int</em><em>, </em><em>default is None</em>) – Number of iterations to run. If not set the algorithm will run until <code class="code docutils literal notranslate"><span class="pre">should_stop()</span></code> is reached</p></li>
+<li><p><strong>callbacks</strong> (list of callables, default is Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>) – List of callables which are passed the current Algorithm object each iteration. Defaults to <code class="code docutils literal notranslate"><span class="pre">[ProgressCallback(verbose)]</span></code>.</p></li>
+<li><p><strong>verbose</strong> (<em>0=quiet</em><em>, </em><em>1=info</em><em>, </em><em>2=debug</em>) – Passed to the default callback to determine the verbosity of the printed output.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.should_stop">
+<span class="sig-name descname"><span class="pre">should_stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.should_stop" title="Link to this definition">#</a></dt>
+<dd><p>default stopping criterion: number of iterations</p>
+<p>The user can change this in concrete implementation of iterative algorithms.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.solution">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solution</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.solution" title="Link to this definition">#</a></dt>
+<dd><p>Returns the current solution.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.update_objective_interval">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">update_objective_interval</span></span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.update_objective_interval" title="Link to this definition">#</a></dt>
+<dd><p>gets the update_objective_interval</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.verbose_header">
+<span class="sig-name descname"><span class="pre">verbose_header</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.verbose_header" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.algorithms.SPDHG.verbose_output">
+<span class="sig-name descname"><span class="pre">verbose_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">__</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.algorithms.SPDHG.verbose_output" title="Link to this definition">#</a></dt>
+<dd><p>Do not use: this is being deprecated</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="approximate-gradient-methods">
+<h3>Approximate gradient methods<a class="headerlink" href="#approximate-gradient-methods" title="Link to this heading">#</a></h3>
+<p>Alternatively, consider that, in addition to the functions <span class="math notranslate nohighlight">\(f_i\)</span> and the regulariser <span class="math notranslate nohighlight">\(g\)</span> being proper, convex and lower semi-continuous, the <span class="math notranslate nohighlight">\(f_i\)</span> are differentiable. In this case we consider stochastic methods that replace a gradient calculation in a deterministic algorithm with a, potentially cheaper to calculate, approximate gradient.
+For example, when <span class="math notranslate nohighlight">\(g(x)=0\)</span>, the standard Gradient Descent algorithm utilises iterations of the form</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[x_{k+1}=x_k-\alpha \nabla f(x_k) =x_k-\alpha \sum_{i=0}^{n-1}\nabla f_i(x_k).\]</div>
+</div></blockquote>
+<p><span class="math notranslate nohighlight">\(\nabla f(x_k)=\sum_{i=0}^{n-1}\nabla f_i(x_k)\)</span> with <span class="math notranslate nohighlight">\(n \nabla f_i(x_k)\)</span>, for an index <span class="math notranslate nohighlight">\(i\)</span> which changes each iteration, leads to the well known stochastic gradient descent algorithm.</p>
+<p>Replacing, <span class="math notranslate nohighlight">\(\nabla f(x_k)=\sum_{i=0}^{n-1}\nabla f_i(x_k)\)</span> with <span class="math notranslate nohighlight">\(n \nabla f_i(x_k)\)</span>, for an index <span class="math notranslate nohighlight">\(i\)</span> which changes each iteration, leads to the well known stochastic gradient descent algorithm.</p>
+<p>In addition, if <span class="math notranslate nohighlight">\(g(x)\neq 0\)</span> and has a calculable proximal ( need not be differentiable) one can consider ISTA iterations:</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[x_{k+1}=prox_{\alpha g}(x_k-\alpha \nabla f(x_k) )=prox_{\alpha g}(x_k-\alpha \sum_{i=0}^{n-1}\nabla f_i(x_k))\]</div>
+</div></blockquote>
+<p>and again replacing <span class="math notranslate nohighlight">\(\nabla f(x_k)=\sum_{i=0}^{n-1}\nabla f_i(x_k)\)</span> with an approximate gradient.</p>
+<p>In a similar way, plugging approximate gradient calculations into deterministic algorithms can lead to a range of stochastic algorithms. In the following table, the left hand column has the approximate gradient function subclass, <span class="xref std std-ref">Approximate Gradient base class</span> the header row has one of CIL’s deterministic optimisation algorithm and the body of the table has the resulting stochastic algorithm.</p>
+<p>*In development</p>
+<p>The stochastic gradient functions can be found listed under functions in the documentation.</p>
+</section>
+<section id="stochastic-gradient-descent-example">
+<h3>Stochastic Gradient Descent Example<a class="headerlink" href="#stochastic-gradient-descent-example" title="Link to this heading">#</a></h3>
+<p>The below is an example of Stochastic Gradient Descent built of the SGFunction and Gradient Descent algorithm:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cil.optimisation.utilities</span> <span class="kn">import</span> <span class="n">Sampler</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">GD</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">LeastSquares</span><span class="p">,</span> <span class="n">SGFunction</span>
+<span class="kn">from</span> <span class="nn">cil.utilities</span> <span class="kn">import</span> <span class="n">dataexample</span>
+<span class="kn">from</span> <span class="nn">cil.plugins.astra.operators</span> <span class="kn">import</span> <span class="n">ProjectionOperator</span>
+
+<span class="c1"># get the data</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">dataexample</span><span class="o">.</span><span class="n">SIMULATED_PARALLEL_BEAM_DATA</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="n">data</span><span class="o">.</span><span class="n">reorder</span><span class="p">(</span><span class="s1">&#39;astra&#39;</span><span class="p">)</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="s1">&#39;centre&#39;</span><span class="p">)</span>
+
+<span class="c1"># create the geometries</span>
+<span class="n">ag</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span>
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+
+<span class="c1"># partition the data and build the projectors</span>
+<span class="n">n_subsets</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">partitioned_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="n">n_subsets</span><span class="p">,</span> <span class="s1">&#39;sequential&#39;</span><span class="p">)</span>
+<span class="n">A_partitioned</span> <span class="o">=</span> <span class="n">ProjectionOperator</span><span class="p">(</span><span class="n">ig</span><span class="p">,</span> <span class="n">partitioned_data</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+
+<span class="c1"># create the list of functions for the stochastic sum</span>
+<span class="n">list_of_functions</span> <span class="o">=</span> <span class="p">[</span><span class="n">LeastSquares</span><span class="p">(</span><span class="n">Ai</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">bi</span><span class="p">)</span> <span class="k">for</span> <span class="n">Ai</span><span class="p">,</span><span class="n">bi</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">A_partitioned</span><span class="p">,</span> <span class="n">partitioned_data</span><span class="p">)]</span>
+
+<span class="c1">#define the sampler and the stochastic gradient function</span>
+<span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">staggered</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">list_of_functions</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">f</span> <span class="o">=</span> <span class="n">SGFunction</span><span class="p">(</span><span class="n">list_of_functions</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">sampler</span><span class="p">)</span>
+
+<span class="c1">#set up and run the gradient descent algorithm</span>
+<span class="n">alg</span> <span class="o">=</span> <span class="n">GD</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">objective_function</span><span class="o">=</span><span class="n">f</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">f</span><span class="o">.</span><span class="n">L</span><span class="p">)</span>
+<span class="n">alg</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">300</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="note">
+<h3>Note<a class="headerlink" href="#note" title="Link to this heading">#</a></h3>
+<blockquote>
+<div><p>All the approximate gradients written in CIL are of a similar order of magnitude to the full gradient calculation. For example, in the <code class="code docutils literal notranslate"><span class="pre">SGFunction</span></code> we approximate the full gradient by <span class="math notranslate nohighlight">\(n\nabla f_i\)</span> for an index <span class="math notranslate nohighlight">\(i\)</span> given by the sampler.
+The multiplication by <span class="math notranslate nohighlight">\(n\)</span> is a choice to more easily allow comparisons between stochastic and non-stochastic methods and between stochastic methods with varying numbers of subsets.
+The multiplication ensures that the (SAGA, SGD, and SVRG  and LSVRG) approximate gradients are an unbiased estimator of the full gradient ie <span class="math notranslate nohighlight">\(\mathbb{E}\left[\tilde\nabla f(x)\right] =\nabla f(x)\)</span>.</p>
+<blockquote>
+<div><p>This has an implication when choosing step sizes. For example, a suitable step size for GD with a SGFunction could be
+<span class="math notranslate nohighlight">\(\propto 1/(L_{max}*n)\)</span>, where <span class="math notranslate nohighlight">\(L_{max}\)</span> is the largest Lipschitz constant of the list of functions in the SGFunction and the additional factor of  <span class="math notranslate nohighlight">\(n\)</span> reflects this multiplication by  <span class="math notranslate nohighlight">\(n\)</span> in the approximate gradient.</p>
+</div></blockquote>
+</div></blockquote>
+</section>
+<section id="memory-requirements">
+<h3>Memory requirements<a class="headerlink" href="#memory-requirements" title="Link to this heading">#</a></h3>
+<p>Note that the approximate gradient methods have different memory requirements:
++ The <cite>SGFunction</cite> has the same requirements as a <cite>SumFunction</cite>, so no increased memory usage
++ <cite>SAGFunction</cite> and <cite>SAGAFunction</cite> both store <cite>n+3</cite> times the image size in memory to store the last calculated gradient for each function in the sum and for intermediary calculations.
++ <cite>SVRGFunction</cite> and <cite>LSVRGFunction</cite> with the default <cite>store_gradients = False</cite> store 4 times the image size in memory, including the “snapshot” point and gradient. If <cite>store_gradients = True</cite>, some computational effort is saved, at the expensive of stored memory <cite>n+4</cite> times the image size.</p>
+</section>
+</section>
+<section id="operators">
+<h2>Operators<a class="headerlink" href="#operators" title="Link to this heading">#</a></h2>
+<p>The two most important methods are <code class="code docutils literal notranslate"><span class="pre">direct</span></code> and <code class="code docutils literal notranslate"><span class="pre">adjoint</span></code>
+methods that describe the result of applying the operator, and its
+adjoint respectively, onto a compatible <code class="code docutils literal notranslate"><span class="pre">DataContainer</span></code> input.
+The output is another <code class="code docutils literal notranslate"><span class="pre">DataContainer</span></code> object or subclass
+hereof. An important special case is to represent the tomographic
+forward and backprojection operations.</p>
+<section id="operator-base-classes">
+<h3>Operator base classes<a class="headerlink" href="#operator-base-classes" title="Link to this heading">#</a></h3>
+<p>All operators extend the <code class="code docutils literal notranslate"><span class="pre">Operator</span></code> class. A special class is the <code class="code docutils literal notranslate"><span class="pre">LinearOperator</span></code>
+which represents an operator for which the <code class="code docutils literal notranslate"><span class="pre">adjoint</span></code> operation is defined.
+A <code class="code docutils literal notranslate"><span class="pre">ScaledOperator</span></code> represents the multiplication of any operator with a scalar.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">Operator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator" title="Link to this definition">#</a></dt>
+<dd><p>Operator that maps from a space X -&gt; Y</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>domain_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em> or </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry"><em>AcquisitionGeometry</em></a>) – domain of the operator</p></li>
+<li><p><strong>range_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em> or </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry"><em>AcquisitionGeometry</em></a><em>, </em><em>optional</em><em>, </em><em>default None</em>) – range of the operator</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.is_linear">
+<span class="sig-name descname"><span class="pre">is_linear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.is_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.is_linear" title="Link to this definition">#</a></dt>
+<dd><p>Returns if the operator is linear
+:rtype: <cite>Bool</cite></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.is_orthogonal">
+<span class="sig-name descname"><span class="pre">is_orthogonal</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.is_orthogonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.is_orthogonal" title="Link to this definition">#</a></dt>
+<dd><p>Returns if the operator is orthogonal
+:rtype: <cite>Bool</cite></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Calls the operator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the Operator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.norm">
+<span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.norm" title="Link to this definition">#</a></dt>
+<dd><p>Returns the norm of the Operator. On first call the norm will be calculated using the operator’s calculate_norm
+method. Subsequent calls will return the cached norm.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>norm</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>positive:<cite>float</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.set_norm">
+<span class="sig-name descname"><span class="pre">set_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.set_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.set_norm" title="Link to this definition">#</a></dt>
+<dd><p>Sets the norm of the operator to a custom value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>norm</strong> (<em>float</em><em>, </em><em>optional</em>) – Positive real valued number or <cite>None</cite></p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The passed values are cached so that when self.norm() is called, the saved value will be returned and not calculated via the power method.
+If <cite>None</cite> is passed, the cache is cleared prompting the function to call the power method to calculate the norm the next time self.norm() is called.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Returns the norm of the Operator. Note that this gives a NotImplementedError if the SumOperator is not linear.
+:returns: <strong>Scalar</strong>
+:rtype: the norm of the Operator</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.range_geometry">
+<span class="sig-name descname"><span class="pre">range_geometry</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.range_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.range_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Returns the range of the Operator: Y space</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.Operator.domain_geometry">
+<span class="sig-name descname"><span class="pre">domain_geometry</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#Operator.domain_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.Operator.domain_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Returns the domain of the Operator: X space</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.LinearOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">LinearOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#LinearOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.LinearOperator" title="Link to this definition">#</a></dt>
+<dd><p>Linear operator that maps from a space X &lt;-&gt; Y</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>domain_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em> or </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry"><em>AcquisitionGeometry</em></a>) – domain of the operator</p></li>
+<li><p><strong>range_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em> or </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry"><em>AcquisitionGeometry</em></a><em>, </em><em>optional</em><em>, </em><em>default None</em>) – range of the operator</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.LinearOperator.is_linear">
+<span class="sig-name descname"><span class="pre">is_linear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#LinearOperator.is_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.LinearOperator.is_linear" title="Link to this definition">#</a></dt>
+<dd><p>Returns if the operator is linear</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.LinearOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#LinearOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.LinearOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns the adjoint/inverse operation evaluated at the point <span class="math notranslate nohighlight">\(x\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the Operator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Only available to linear operators</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.LinearOperator.PowerMethod">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">PowerMethod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#LinearOperator.PowerMethod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.LinearOperator.PowerMethod" title="Link to this definition">#</a></dt>
+<dd><p>Power method or Power iteration algorithm</p>
+<p>The Power method computes the largest (dominant) eigenvalue of a matrix in magnitude, e.g.,
+absolute value in the real case and modulus in the complex case.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operator</strong> (<a class="reference internal" href="#cil.optimisation.operators.LinearOperator" title="cil.optimisation.operators.LinearOperator"><em>LinearOperator</em></a>)</p></li>
+<li><p><strong>max_iteration</strong> (positive:<cite>int</cite>, default=10) – Number of iterations for the Power method algorithm.</p></li>
+<li><p><strong>initial</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default = None</em>) – Starting point for the Power method.</p></li>
+<li><p><strong>tolerance</strong> (positive:<cite>float</cite>, default = 1e-5) – Stopping criterion for the Power method. Check if two consecutive eigenvalue evaluations are below the tolerance.</p></li>
+<li><p><strong>return_all</strong> (<cite>boolean</cite>, default = False) – Toggles the verbosity of the return</p></li>
+<li><p><strong>method</strong> (<cite>string</cite> one of <cite>“auto”</cite>, <cite>“composed_with_adjoint”</cite> and <cite>“direct_only”</cite>, default = <cite>“auto”</cite>) – The default <cite>auto</cite> lets the code choose the method, this can be specified with <cite>“direct_only”</cite> or <cite>“composed_with_adjoint”</cite></p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>dominant eigenvalue</strong> (positive:<cite>float</cite>)</p></li>
+<li><p><strong>number of iterations</strong> (positive:<cite>int</cite>) – Number of iterations run. Only returned if return_all is True.</p></li>
+<li><p><strong>eigenvector</strong> (<em>DataContainer</em>) – Corresponding eigenvector of the dominant eigenvalue. Only returned if return_all is True.</p></li>
+<li><p><strong>list of eigenvalues</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">list</span></code>) – List of eigenvalues. Only returned if return_all is True.</p></li>
+<li><p><strong>convergence</strong> (<cite>boolean</cite>) – Check on wether the difference between the last two iterations is less than tolerance. Only returned if return_all is True.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The power method contains two different algorithms chosen by the <cite>method</cite> flag.</p>
+<p>In the case <cite>method=”direct_only”</cite>, for operator, <span class="math notranslate nohighlight">\(A\)</span>, the power method computes the iterations
+<span class="math notranslate nohighlight">\(x_{k+1} = A (x_k/\|x_{k}\|)\)</span> initialised with a random vector <span class="math notranslate nohighlight">\(x_0\)</span> and returning the largest (dominant) eigenvalue in magnitude given by <span class="math notranslate nohighlight">\(\|x_k\|\)</span>.</p>
+<p>In the case <cite>method=”composed_with_adjoint”</cite>, the algorithm computes the largest (dominant) eigenvalue of <span class="math notranslate nohighlight">\(A^{T}A\)</span>
+returning the square root of this value, i.e. the iterations:
+<span class="math notranslate nohighlight">\(x_{k+1} = A^TA (x_k/\|x_{k}\|)\)</span> and returning  <span class="math notranslate nohighlight">\(\sqrt{\|x_k\|}\)</span>.</p>
+<p>The default flag is <cite>method=”auto”</cite>, the algorithm checks to see if the <cite>operator.domain_geometry() == operator.range_geometry()</cite> and if so
+uses the method “direct_only” and if not the method “composed_with_adjoint”.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mf">1.</span><span class="p">,</span><span class="mf">2.</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Mop</span> <span class="o">=</span> <span class="n">MatrixOperator</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Mop_norm</span> <span class="o">=</span> <span class="n">Mop</span><span class="o">.</span><span class="n">PowerMethod</span><span class="p">(</span><span class="n">Mop</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Mop_norm</span>
+<span class="go">2.0000654846240296</span>
+</pre></div>
+</div>
+<p><cite>PowerMethod</cite> is called when we compute the norm of a matrix or a <cite>LinearOperator</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">Mop_norm</span> <span class="o">=</span> <span class="n">Mop</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+<span class="go">2.0005647295658866</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.LinearOperator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#LinearOperator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.LinearOperator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Returns the norm of the LinearOperator calculated by the PowerMethod with default values.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.LinearOperator.dot_test">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dot_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#LinearOperator.dot_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.LinearOperator.dot_test" title="Link to this definition">#</a></dt>
+<dd><p>Does a dot linearity test on the operator
+Evaluates if the following equivalence holds
+.. math:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Ax</span>\<span class="n">times</span> <span class="n">y</span> <span class="o">=</span> <span class="n">y</span> \<span class="n">times</span> <span class="n">A</span><span class="o">^</span><span class="n">Tx</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operator</strong> – operator to test the dot_test</p></li>
+<li><p><strong>range_init</strong> – optional initialisation container in the operator range</p></li>
+<li><p><strong>domain_init</strong> – optional initialisation container in the operator domain</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>default = 1</em>) – Seed random generator</p></li>
+<li><p><strong>tolerance</strong> (<em>float</em><em>, </em><em>default 1e-6</em>) – Check if the following expression is below the tolerance</p></li>
+<li><p><strong>math::</strong> (<em>..</em>) – <a href="#id75"><span class="problematic" id="id76">|Ax\times y - y \times A^Tx|</span></a>/(|A||x||y| + 1e-12) &lt; tolerance</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>boolean, True if the test is passed.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ScaledOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">ScaledOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scalar</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#ScaledOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ScaledOperator" title="Link to this definition">#</a></dt>
+<dd><p>A class to represent the scalar multiplication of an Operator with a scalar.
+It holds an operator and a scalar. Basically it returns the multiplication
+of the result of direct and adjoint of the operator with the scalar.
+For the rest it behaves like the operator it holds.</p>
+<p>Parameters</p>
+<blockquote>
+<div><p>a scalar multiplier</p>
+</div></blockquote>
+<p class="rubric">Example</p>
+<p>The scaled operator behaves like the following:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">sop</span> <span class="o">=</span> <span class="n">ScaledOperator</span><span class="p">(</span><span class="n">operator</span><span class="p">,</span> <span class="n">scalar</span><span class="p">)</span>
+<span class="n">sop</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="n">scalar</span> <span class="o">*</span> <span class="n">operator</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">sop</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="n">scalar</span> <span class="o">*</span> <span class="n">operator</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">sop</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">norm</span><span class="p">()</span>
+<span class="n">sop</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">range_geometry</span><span class="p">()</span>
+<span class="n">sop</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span> <span class="o">=</span> <span class="n">operator</span><span class="o">.</span><span class="n">domain_geometry</span><span class="p">()</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ScaledOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#ScaledOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ScaledOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>direct method</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ScaledOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#ScaledOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ScaledOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>adjoint method</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ScaledOperator.norm">
+<span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#ScaledOperator.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ScaledOperator.norm" title="Link to this definition">#</a></dt>
+<dd><p>norm of the operator</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ScaledOperator.is_linear">
+<span class="sig-name descname"><span class="pre">is_linear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#ScaledOperator.is_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ScaledOperator.is_linear" title="Link to this definition">#</a></dt>
+<dd><p>returns a <cite>boolean</cite> indicating whether the operator is linear</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.CompositionOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">CompositionOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">operators</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#CompositionOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.CompositionOperator" title="Link to this definition">#</a></dt>
+<dd><p>Composes one or more operators.
+For example, <cite>CompositionOperator(left, right).direct(x)</cite> is equivalent to <cite>left.direct(right.direct(x))</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>args</strong> (<cite>Operator</cite> s) – Operators to be composed. As in mathematical notation, the operators will be applied right to left</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.CompositionOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#CompositionOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.CompositionOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Calls the composition operator at the point <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the CompositionOperator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the CompositionOperator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.CompositionOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#CompositionOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.CompositionOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Calls the adjoint of the composition operator at the point <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the range of the CompositionOperator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the adjoint of the CompositionOperator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.CompositionOperator.is_linear">
+<span class="sig-name descname"><span class="pre">is_linear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#CompositionOperator.is_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.CompositionOperator.is_linear" title="Link to this definition">#</a></dt>
+<dd><p>Returns if the operator is linear
+:rtype: <cite>Bool</cite></p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.DiagonalOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">DiagonalOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">diagonal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/DiagonalOperator/#DiagonalOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.DiagonalOperator" title="Link to this definition">#</a></dt>
+<dd><p>Performs an element-wise multiplication, i.e., <a class="reference external" href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)#:~:text=In%20mathematics%2C%20the%20Hadamard%20product,elements%20i%2C%20j%20of%20the">Hadamard Product</a>
+of a <code class="xref py py-class docutils literal notranslate"><span class="pre">DataContainer</span></code> <cite>x</cite> and <code class="xref py py-class docutils literal notranslate"><span class="pre">DataContainer</span></code> <cite>diagonal</cite>, <cite>d</cite> .</p>
+<div class="math notranslate nohighlight">
+\[(D\circ x) = \sum_{i,j}^{M,N} D_{i,j} x_{i, j}\]</div>
+<p>In matrix-vector interpretation, if <cite>D</cite> is a <span class="math notranslate nohighlight">\(M\times N\)</span> dense matrix and is flattened, we have a <span class="math notranslate nohighlight">\(M*N \times M*N\)</span> vector.
+A sparse diagonal matrix, i.e., <code class="xref py py-class docutils literal notranslate"><span class="pre">DigaonalOperator</span></code> can be created if we add the vector above to the main diagonal.
+If the <code class="xref py py-class docutils literal notranslate"><span class="pre">DataContainer</span></code> <cite>x</cite> is also flattened, we have a <span class="math notranslate nohighlight">\(M*N\)</span> vector.
+Now, matrix-vector multiplcation is allowed and results to a <span class="math notranslate nohighlight">\((M*N,1)\)</span> vector. After reshaping we recover a <span class="math notranslate nohighlight">\(M\times N\)</span> <code class="xref py py-class docutils literal notranslate"><span class="pre">DataContainer</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>diagonal</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – DataContainer with the same dimensions as the data to be operated on.</p></li>
+<li><p><strong>domain_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a>) – Specifies the geometry of the operator domain. If ‘None’ will use the diagonal geometry directly. default=None .</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.DiagonalOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/DiagonalOperator/#DiagonalOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.DiagonalOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Returns <span class="math notranslate nohighlight">\(D\circ x\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.DiagonalOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/DiagonalOperator/#DiagonalOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.DiagonalOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns <span class="math notranslate nohighlight">\(D^*\circ x\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.DiagonalOperator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/DiagonalOperator/#DiagonalOperator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.DiagonalOperator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Returns the operator norm of DiagonalOperator which is the <span class="math notranslate nohighlight">\(\infty\)</span> norm of <cite>diagonal</cite></p>
+<div class="math notranslate nohighlight">
+\[\|D\|_{\infty} = \max_{i}\{|D_{i}|\}\]</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ChannelwiseOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">ChannelwiseOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimension</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'prepend'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ChannelwiseOperator/#ChannelwiseOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ChannelwiseOperator" title="Link to this definition">#</a></dt>
+<dd><p>ChannelwiseOperator:  takes in a single-channel operator op and the
+number of channels to be used, and creates a new multi-channel
+ChannelwiseOperator, which will apply the operator op independently on
+each channel for the number of channels specified.</p>
+<p>ChannelwiseOperator supports simple operators as input but not
+BlockOperators. Typically if such behaviour is desired, it can be achieved
+by creating instead a BlockOperator of ChannelwiseOperators.</p>
+<blockquote>
+<div><dl class="field-list simple">
+<dt class="field-odd">param op<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Single-channel operator</p>
+</dd>
+<dt class="field-even">param channels<span class="colon">:</span></dt>
+<dd class="field-even"><p>Number of channels</p>
+</dd>
+<dt class="field-odd">param dimension<span class="colon">:</span></dt>
+<dd class="field-odd"><p>‘prepend’ (default) or ‘append’ channel dimension onto existing dimensions</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ChannelwiseOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ChannelwiseOperator/#ChannelwiseOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ChannelwiseOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Returns D(x)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ChannelwiseOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ChannelwiseOperator/#ChannelwiseOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ChannelwiseOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns D^{*}(y)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ChannelwiseOperator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ChannelwiseOperator/#ChannelwiseOperator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ChannelwiseOperator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Evaluates operator norm of DiagonalOperator</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SumOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">SumOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operator1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#SumOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SumOperator" title="Link to this definition">#</a></dt>
+<dd><p>Sums two operators.
+For example, <cite>SumOperator(left, right).direct(x)</cite> is equivalent to  <cite>left.direct(x)+right.direct(x)</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operator1</strong> (<cite>Operator</cite>) – The first <cite>Operator</cite> in the sum</p></li>
+<li><p><strong>operator2</strong> (<cite>Operator</cite>) – The second <cite>Operator</cite> in the sum</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Both operators must have the same domain and range.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SumOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#SumOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SumOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Calls the sum operator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the SumOperator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the SumOperator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SumOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#SumOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SumOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Calls the adjoint of the sum operator, evaluated at the point <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the range of the SumOperator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the adjoint of the SumOperator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SumOperator.is_linear">
+<span class="sig-name descname"><span class="pre">is_linear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/Operator/#SumOperator.is_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SumOperator.is_linear" title="Link to this definition">#</a></dt>
+<dd><p>Returns if the operator is linear
+:rtype: <cite>Bool</cite></p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="trivial-operators">
+<h3>Trivial operators<a class="headerlink" href="#trivial-operators" title="Link to this heading">#</a></h3>
+<p>Trivial operators are the following.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.IdentityOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">IdentityOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/IdentityOperator/#IdentityOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.IdentityOperator" title="Link to this definition">#</a></dt>
+<dd><p>IdentityOperator:  Id: X -&gt; Y,  Id(x) = xin Y</p>
+<p>X : gm_domain
+Y : gm_range ( Default: Y = X )</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.IdentityOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/IdentityOperator/#IdentityOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.IdentityOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Returns Id(x)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.IdentityOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/IdentityOperator/#IdentityOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.IdentityOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns Id(x)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.IdentityOperator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/IdentityOperator/#IdentityOperator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.IdentityOperator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Evaluates operator norm of IdentityOperator</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.IdentityOperator.is_orthogonal">
+<span class="sig-name descname"><span class="pre">is_orthogonal</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/IdentityOperator/#IdentityOperator.is_orthogonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.IdentityOperator.is_orthogonal" title="Link to this definition">#</a></dt>
+<dd><p>Returns if the operator is orthogonal
+:rtype: <cite>Bool</cite></p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ZeroOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">ZeroOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ZeroOperator/#ZeroOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ZeroOperator" title="Link to this definition">#</a></dt>
+<dd><p>ZeroOperator:  O: X -&gt; Y,  maps any element of <span class="math notranslate nohighlight">\(x\in X\)</span> into the zero element <span class="math notranslate nohighlight">\(\in Y,  O(x) = O_{Y}\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>gm_domain</strong> – domain of the operator</p></li>
+<li><p><strong>gm_range</strong> – range of the operator, default: same as domain</p></li>
+</ul>
+</dd>
+</dl>
+<p>Note:</p>
+<div class="math notranslate nohighlight">
+\[O^{*}: Y^{*} -&gt; X^{*} \text{(Adjoint)}
+&lt; O(x), y &gt; = &lt; x, O^{*}(y) &gt;\]</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ZeroOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ZeroOperator/#ZeroOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ZeroOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Returns O(x)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ZeroOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ZeroOperator/#ZeroOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ZeroOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns O^{*}(y)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ZeroOperator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ZeroOperator/#ZeroOperator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ZeroOperator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Evaluates operator norm of ZeroOperator</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.MatrixOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">MatrixOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/MatrixOperator/#MatrixOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.MatrixOperator" title="Link to this definition">#</a></dt>
+<dd><p>Matrix wrapped into a LinearOperator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>a numpy matrix</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.MatrixOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/MatrixOperator/#MatrixOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.MatrixOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Calls the operator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the Operator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.MatrixOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/MatrixOperator/#MatrixOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.MatrixOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns the adjoint/inverse operation evaluated at the point <span class="math notranslate nohighlight">\(x\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the Operator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Only available to linear operators</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.MaskOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">MaskOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/MaskOperator/#MaskOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.MaskOperator" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mask</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Boolean array with the same dimensions as the data to be operated on.</p></li>
+<li><p><strong>domain_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a>) – Specifies the geometry of the operator domain. If ‘None’ will use the mask geometry size and spacing as float32. default = None .</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ProjectionMap">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">ProjectionMap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ProjectionMap/#ProjectionMap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ProjectionMap" title="Link to this definition">#</a></dt>
+<dd><p>Projection Map or Canonical Projection (<a class="reference external" href="https://en.wikipedia.org/wiki/Projection_(mathematics">https://en.wikipedia.org/wiki/Projection_(mathematics</a>))</p>
+<p>Takes an element <span class="math notranslate nohighlight">\(x = (x_{0},\dots,x_{i},\dots,x_{n})\)</span> from a Cartesian product space <span class="math notranslate nohighlight">\(X_{1}\times\cdots\times X_{n}\rightarrow X_{i}\)</span>
+and projects it to element <span class="math notranslate nohighlight">\(x_{i}\)</span> specified by the index <span class="math notranslate nohighlight">\(i\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\pi_{i}: X_{1}\times\cdots\times X_{n}\rightarrow X_{i}\]</div>
+<div class="math notranslate nohighlight">
+\[\pi_{i}(x_{0},\dots,x_{i},\dots,x_{n}) = x_{i}\]</div>
+<p>The adjoint operation, is defined as</p>
+<div class="math notranslate nohighlight">
+\[\pi_{i}^{*}(x_{i}) = (0, \cdots, x_{i}, \cdots, 0)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>domain_geometry</strong> (<cite>BlockGeometry</cite>) – The domain of the <cite>ProjectionMap</cite>. A <cite>BlockGeometry</cite> is expected.</p></li>
+<li><p><strong>index</strong> (<em>int</em>) – Index to project to the corresponding <cite>ImageGeometry</cite></p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ProjectionMap.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ProjectionMap/#ProjectionMap.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ProjectionMap.direct" title="Link to this definition">#</a></dt>
+<dd><p>Returns the ith (<cite>index</cite>) element of the Block data container, <span class="math notranslate nohighlight">\(x\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<cite>BlockDataContainer</cite>)</p></li>
+<li><p><strong>out</strong> (<cite>DataContainer</cite>, default <cite>None</cite>) – If <cite>out</cite> is not <cite>None</cite> the output of the <cite>ProjectionMap</cite> will be filled in <cite>out</cite>, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><cite>DataContainer</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.ProjectionMap.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/ProjectionMap/#ProjectionMap.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.ProjectionMap.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns a <cite>BlockDataContainer</cite> of zeros with the ith (<cite>index</cite>) filled with the <cite>DataContainer</cite>, <span class="math notranslate nohighlight">\(x\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<cite>DataContainer</cite>)</p></li>
+<li><p><strong>out</strong> (<cite>BlockDataContainer</cite>, default <cite>None</cite>) – If <cite>out</cite> is not <cite>None</cite> the output of the adjoint of the <cite>ProjectionMap</cite> will be filled in <cite>out</cite>, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><cite>BlockDataContainer</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="gradientoperator">
+<h3>GradientOperator<a class="headerlink" href="#gradientoperator" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.GradientOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">GradientOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'forward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bnd_cond</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Neumann'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/GradientOperator/#GradientOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.GradientOperator" title="Link to this definition">#</a></dt>
+<dd><p>Gradient Operator: Computes first-order forward/backward differences on
+2D, 3D, 4D ImageData under Neumann/Periodic boundary conditions</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>domain_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a>) – Set up the domain of the function</p></li>
+<li><p><strong>method</strong> (<em>str</em><em>, </em><em>default 'forward'</em>) – Accepts: ‘forward’, ‘backward’, ‘centered’, note C++ optimised routine only works with ‘forward’</p></li>
+<li><p><strong>bnd_cond</strong> (<em>str</em><em>, </em><em>default</em><em>,  </em><em>'Neumann'</em>) – Set the boundary conditions to use ‘Neumann’ or ‘Periodic’</p></li>
+<li><p><strong>**kwargs</strong> – <dl class="simple">
+<dt>correlation: str, default ‘Space’</dt><dd><p>’Space’ will compute the gradient on only the spatial dimensions, ‘SpaceChannels’ will include the channel dimension direction</p>
+</dd>
+<dt>backend: str, default ‘c’</dt><dd><p>’c’ or ‘numpy’, defaults to ‘c’ if correlation is ‘SpaceChannels’ or channels = 1</p>
+</dd>
+<dt>num_threads: int</dt><dd><p>If backend is ‘c’ specify the number of threads to use. Default is number of cpus/2</p>
+</dd>
+<dt>split: boolean</dt><dd><p>If ‘True’, and backend ‘c’ will return a BlockDataContainer with grouped spatial domains. i.e. [Channel, [Z, Y, X]], otherwise [Channel, Z, Y, X]</p>
+</dd>
+</dl>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>a BlockDataContainer containing images of the derivatives order given by <cite>dimension_labels</cite>
+i.e. [‘horizontal_y’,’horizontal_x’] will return [d(‘horizontal_y’), d(‘horizontal_x’)]</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer">BlockDataContainer</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<p>2D example</p>
+<div class="math notranslate nohighlight">
+ \begin{eqnarray}
+ \nabla : X \rightarrow Y\\
+ u \in X, \nabla(u) &amp;=&amp; [\partial_{y} u, \partial_{x} u]\\
+ u^{*} \in Y, \nabla^{*}(u^{*}) &amp;=&amp; \partial_{y} v1 + \partial_{x} v2
+ \end{eqnarray}</div><dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.GradientOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/GradientOperator/#GradientOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.GradientOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Computes the first-order forward differences</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a>)</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>optional</em>) – pre-allocated output memory to store result</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>result data if <cite>out</cite> not specified</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer">BlockDataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.GradientOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/GradientOperator/#GradientOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.GradientOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Computes the first-order backward differences</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Gradient images for each dimension in ImageGeometry domain</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><em>optional</em>) – pre-allocated output memory to store result</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>result data if <cite>out</cite> not specified</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.GradientOperator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/GradientOperator/#GradientOperator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.GradientOperator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Returns the analytical norm of the GradientOperator.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}(\partial_{z}, \partial_{y}, \partial_{x}) &amp;= \sqrt{\|\partial_{z}\|^{2} + \|\partial_{y}\|^{2} + \|\partial_{x}\|^{2} } \\
+&amp;=  \sqrt{ \frac{4}{h_{z}^{2}} + \frac{4}{h_{y}^{2}} + \frac{4}{h_{x}^{2}}}\end{split}\]</div>
+<p>Where the voxel sizes in each dimension are equal to 1 this simplifies to:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>2D geometries <span class="math notranslate nohighlight">\(norm = \sqrt{8}\)</span></p></li>
+<li><p>3D geometries <span class="math notranslate nohighlight">\(norm = \sqrt{12}\)</span></p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.FiniteDifferenceOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">FiniteDifferenceOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'forward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bnd_cond</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Neumann'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/FiniteDifferenceOperator/#FiniteDifferenceOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.FiniteDifferenceOperator" title="Link to this definition">#</a></dt>
+<dd><p>Computes forward/backward/centered finite differences of a DataContainer
+under Neumann/Periodic boundary conditions</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>domain_geometry</strong> – Domain geometry for the FiniteDifferenceOperator</p></li>
+<li><p><strong>direction</strong> (<em>string label from domain geometry</em><em> or </em><em>integer number</em>) – Direction to evaluate finite differences</p></li>
+<li><p><strong>method</strong> (<em>'forward'</em><em>, </em><em>'backward'</em><em>, </em><em>'centered'</em>) – Method for finite differences</p></li>
+<li><p><strong>bnd_cond</strong> – ‘Neumann’, ‘Periodic’</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.FiniteDifferenceOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/FiniteDifferenceOperator/#FiniteDifferenceOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.FiniteDifferenceOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Calls the operator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the Operator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.FiniteDifferenceOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/FiniteDifferenceOperator/#FiniteDifferenceOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.FiniteDifferenceOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns the adjoint/inverse operation evaluated at the point <span class="math notranslate nohighlight">\(x\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the Operator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Only available to linear operators</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SparseFiniteDifferenceOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">SparseFiniteDifferenceOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bnd_cond</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Neumann'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/SparseFiniteDifferenceOperator/#SparseFiniteDifferenceOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SparseFiniteDifferenceOperator" title="Link to this definition">#</a></dt>
+<dd><p>Create Sparse Matrices for the Finite Difference Operator</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SparseFiniteDifferenceOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/SparseFiniteDifferenceOperator/#SparseFiniteDifferenceOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SparseFiniteDifferenceOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Calls the operator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – Element in the domain of the Operator</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><a class="reference internal" href="#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a><em>, </em><em>default None</em>) – If out is not None the output of the Operator will be filled in out, otherwise a new object is instantiated and returned.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> or BlockDataContainer containing the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SymmetrisedGradientOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">SymmetrisedGradientOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bnd_cond</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Neumann'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/SymmetrisedGradientOperator/#SymmetrisedGradientOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SymmetrisedGradientOperator" title="Link to this definition">#</a></dt>
+<dd><p>The symmetrised gradient is the operator, <span class="math notranslate nohighlight">\(E\)</span>, defined by <span class="math notranslate nohighlight">\(E: V \rightarrow W\)</span> where <cite>V</cite> is <cite>BlockGeometry</cite> and  <cite>W</cite> is the range of the Symmetrised Gradient and</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[\begin{split}E(v) = 0.5  ( \nabla v + (\nabla v)^{T} ) \\\end{split}\]</div>
+</div></blockquote>
+<p>In 2 dimensions, let <span class="math notranslate nohighlight">\(v(x,y)=(v_1(x,y),v_2(x,y))\)</span> which gives</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[\begin{split}\nabla v =\left( \begin{matrix}
+    \partial_{x} v_1 &amp; \partial_x v_2\\
+    \partial_{y}v_1 &amp; \partial_y v_2
+\end{matrix}\right)\end{split}\]</div>
+</div></blockquote>
+<p>and thus</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[\begin{split}E(v) = 0.5  ( \nabla v + (\nabla v)^{T} )
+=\left( \begin{matrix}
+    \partial_{x} v_1 &amp; 0.5  (\partial_{y} v_1 + \partial_{x} v_2) \\
+    0.5  (\partial_{x} v_1 + \partial_{y} v_2) &amp; \partial_{y} v_2
+\end{matrix}\right)\end{split}\]</div>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>domain_geometry</strong> (<cite>BlockGeometry</cite> with shape (2,1) or (3,1)) – Set up the domain of the function.</p></li>
+<li><p><strong>bnd_cond</strong> (str, optional, default <code class="code docutils literal notranslate"><span class="pre">Neumann</span></code>) – Boundary condition either <code class="code docutils literal notranslate"><span class="pre">Neumann</span></code> or <code class="code docutils literal notranslate"><span class="pre">Periodic</span></code></p></li>
+<li><p><strong>correlation</strong> (str, optional, default <code class="code docutils literal notranslate"><span class="pre">Channel</span></code>) – Correlation either <code class="code docutils literal notranslate"><span class="pre">SpaceChannel</span></code> or <code class="code docutils literal notranslate"><span class="pre">Channel</span></code></p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SymmetrisedGradientOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/SymmetrisedGradientOperator/#SymmetrisedGradientOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SymmetrisedGradientOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Returns <span class="math notranslate nohighlight">\(E(v) = 0.5 * ( \nabla v + (\nabla v)^{T} )\)</span></p>
+<section id="parameters">
+<h4>Parameters:<a class="headerlink" href="#parameters" title="Link to this heading">#</a></h4>
+<p>x: BlockDataContainer
+out: BlockDataContainer, default None</p>
+<blockquote>
+<div><p>If out is not None the output of direct will be filled in out, otherwise a new object is instantiated and returned.</p>
+</div></blockquote>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.SymmetrisedGradientOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/SymmetrisedGradientOperator/#SymmetrisedGradientOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.SymmetrisedGradientOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns the adjoint of the symmetrised gradient operator</p>
+<section id="id14">
+<h4>Parameters:<a class="headerlink" href="#id14" title="Link to this heading">#</a></h4>
+<p>x: BlockDataContainer
+out: BlockDataContainer, default None</p>
+<blockquote>
+<div><p>If out is not None the output of adjoint will be filled in out, otherwise a new object is instantiated and returned.</p>
+</div></blockquote>
+</section>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="waveletoperator">
+<h3>WaveletOperator<a class="headerlink" href="#waveletoperator" title="Link to this heading">#</a></h3>
+<p>We utilise PyWavelets (<a class="reference external" href="https://pywavelets.readthedocs.io/en/latest/index.html">https://pywavelets.readthedocs.io/en/latest/index.html</a>) to build wavelet operators in CIL:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.WaveletOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">WaveletOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">domain_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'haar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/WaveletOperator/#WaveletOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.WaveletOperator" title="Link to this definition">#</a></dt>
+<dd><p>Computes forward or inverse (adjoint) discrete wavelet transform (DWT) of the input</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>domain_geometry</strong> (<em>cil geometry</em>) – Domain geometry for the WaveletOperator</p></li>
+<li><p><strong>range_geometry</strong> (<em>cil geometry</em><em>, </em><em>optional</em>) – Output geometry for the WaveletOperator. Default = domain_geometry with the right coefficient array size deduced from pywavelets</p></li>
+<li><p><strong>level</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default= log_2</em><em>(</em><em>min</em><em>(</em><em>shape</em><em>(</em><em>axes</em><em>)</em><em>)</em><em>)</em>) – integer for decomposition level. Default = log_2(min(shape(axes))), i.e. the maximum number of accurate downsamplings possible</p></li>
+<li><p><strong>wname</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='haar'</em>) – label for wavelet used.</p></li>
+<li><p><strong>axes</strong> (<em>list</em><em> of </em><em>ints</em><em>, </em><em>optional</em><em>, </em><em>default=`None`</em>) – Defines the dimensions to decompose along. Note that channel is the first dimension: for example, spatial DWT is given by axes=range(1,3) and channelwise DWT is axes=range(1)
+Default = <cite>None</cite>, meaning all dimensions are transformed. Same as axes = range(ndim)</p></li>
+</ul>
+</dd>
+</dl>
+<section id="kwargs">
+<h4><a href="#id15"><span class="problematic" id="id16">**</span></a>kwargs<a class="headerlink" href="#kwargs" title="Link to this heading">#</a></h4>
+<dl class="simple">
+<dt>correlation: str, default ‘All’. Note: Only applied if <cite>axes = None</cite>!</dt><dd><p>‘All’ will compute the wavelet decomposition on every possible dimension.
+‘Space’ will compute the wavelet decomposition on only the spatial dimensions. If there are multiple channels, each channel is decomposed independently.
+‘Channels’ will compute the wavelet decomposition on only the channels, independently for every spatial point.</p>
+</dd>
+<dt>bnd_cond: str, default ‘symmetric’. More commonly known as the padding or extension method used in discrete convolutions. All options supported by PyWavelets are valid.</dt><dd><p>Most common examples are ‘symmetric’ (padding by mirroring edge values), ‘zero’ (padding with zeros), ‘periodic’ (wrapping values around as in circular convolution).
+Some padding methods can have unexpected effect on the wavelet coefficients at the edges.
+See <a class="reference external" href="https://pywavelets.readthedocs.io/en/latest/ref/signal-extension-modes.html">https://pywavelets.readthedocs.io/en/latest/ref/signal-extension-modes.html</a> for more details and all options.</p>
+</dd>
+</dl>
+<p>true_adjoint: bool, default <cite>True</cite>. For biorthogonal wavelets the true mathematical adjoint should no longer produce perfect reconstructions, setting <cite>true_adjoint`as `False</cite> the reconstruction (using the adjoint) should be (almost) the original input.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The default decomposition level is the theoretical maximum: log_2(min(input.shape)).  However, this is not always recommended and pywavelets should give a warning if the coarsest scales are too small to be meaningful.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We currently do not support wavelets that are not orthogonal or bi-orthogonal.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.WaveletOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/WaveletOperator/#WaveletOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.WaveletOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the WaveletOperator applied to <span class="math notranslate nohighlight">\(x\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the WaveletOperator applied to <span class="math notranslate nohighlight">\(x\)</span> or <cite>None</cite> if <cite>out</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.WaveletOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Wx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/WaveletOperator/#WaveletOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.WaveletOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the adjoint of the WaveletOperator applied to <span class="math notranslate nohighlight">\(x\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the adjoint of the WaveletOperator applied to <span class="math notranslate nohighlight">\(x\)</span> or <cite>None</cite> if <cite>out</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.WaveletOperator.calculate_norm">
+<span class="sig-name descname"><span class="pre">calculate_norm</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/WaveletOperator/#WaveletOperator.calculate_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.WaveletOperator.calculate_norm" title="Link to this definition">#</a></dt>
+<dd><p>Returns the norm of WaveletOperator, which is equal to 1.0 if the wavelet is orthogonal</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>norm</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.WaveletOperator.is_orthogonal">
+<span class="sig-name descname"><span class="pre">is_orthogonal</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/WaveletOperator/#WaveletOperator.is_orthogonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.WaveletOperator.is_orthogonal" title="Link to this definition">#</a></dt>
+<dd><p>Returns if the operator is orthogonal</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><cite>Bool</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Link to this heading">#</a></h2>
+<p>A <code class="code docutils literal notranslate"><span class="pre">Function</span></code> represents a mathematical function of one or more inputs
+and is intended to accept <code class="code docutils literal notranslate"><span class="pre">DataContainers</span></code> as input as well as any
+additional parameters.</p>
+<p>Fixed parameters can be passed in during the creation of the function object.
+The methods of the function reflect the properties of it, for example, if the function
+represented is differentiable the function should contain a method <code class="code docutils literal notranslate"><span class="pre">gradient</span></code>
+which should return the gradient of the function evaluated at an input point.
+If the function is not differentiable but allows a simple proximal operator,
+the method <code class="code docutils literal notranslate"><span class="pre">proximal</span></code> should return the proximal operator evaluated at an
+input point. The function value is evaluated by calling the function itself,
+e.g. <code class="code docutils literal notranslate"><span class="pre">f(x)</span></code> for a <code class="code docutils literal notranslate"><span class="pre">Function</span> <span class="pre">f</span></code> and input point <code class="code docutils literal notranslate"><span class="pre">x</span></code>.</p>
+<section id="base-classes">
+<h3>Base classes<a class="headerlink" href="#base-classes" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Function">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">Function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">L</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#Function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Function" title="Link to this definition">#</a></dt>
+<dd><p>Abstract class representing a function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>L</strong> (<em>number</em><em>, </em><em>positive</em><em>, </em><em>default None</em>) – Lipschitz constant of the gradient of the function F(x), when it is differentiable.</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The Lipschitz of the gradient of the function is a positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L \|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Function.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#Function.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Function.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of function <span class="math notranslate nohighlight">\(F\)</span>  evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if it is differentiable</p>
+<div class="math notranslate nohighlight">
+\[F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a>, the value of the gradient of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Function.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#Function.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Function.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Function.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#Function.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Function.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Function.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#Function.proximal_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Function.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Function.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#Function.centered_at"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Function.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Function.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.Function.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">SumFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">functions</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#SumFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SumFunction" title="Link to this definition">#</a></dt>
+<dd><p>SumFunction represents the sum of <span class="math notranslate nohighlight">\(n\geq2\)</span> functions</p>
+<div class="math notranslate nohighlight">
+\[(F_{1} + F_{2} + ... + F_{n})(\cdot)  = F_{1}(\cdot) + F_{2}(\cdot) + ... + F_{n}(\cdot)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>*functions</strong> (<em>Functions</em>) – Functions to set up a <a class="reference internal" href="#cil.optimisation.functions.SumFunction" title="cil.optimisation.functions.SumFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">SumFunction</span></code></a></p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>ValueError</strong> – If the number of function is strictly less than 2.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[F(x) = \|x\|^{2} + \frac{1}{2}\|x - 1\|^{2}\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">L2NormSquared</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageGeometry</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">f1</span> <span class="o">=</span> <span class="n">L2NormSquared</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">f2</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">L2NormSquared</span><span class="p">(</span><span class="n">b</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">F</span> <span class="o">=</span> <span class="n">SumFunction</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="math notranslate nohighlight">
+\[F(x) = \sum_{i=1}^{50} \|x - i\|^{2}\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">F</span> <span class="o">=</span> <span class="n">SumFunction</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">L2NormSquared</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">)])</span>
+</pre></div>
+</div>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.SumFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.SumFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#SumFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SumFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the sum of the gradient of functions evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if all of them are differentiable.</p>
+<div class="math notranslate nohighlight">
+\[(F'_{1} + F'_{2} + ... + F'_{n})(x) = F'_{1}(x) + F'_{2}(x) + ... + F'_{n}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Point to evaluate the gradient at.</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the sum of the gradients evaluated at point <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SumFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SumFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SumFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SumFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ScaledFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">ScaledFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scalar</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ScaledFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ScaledFunction" title="Link to this definition">#</a></dt>
+<dd><p>ScaledFunction represents the scalar multiplication with a Function.</p>
+<p>Let a function F then and a scalar <span class="math notranslate nohighlight">\(\alpha\)</span>.</p>
+<p>If <span class="math notranslate nohighlight">\(G(x) = \alpha F(x)\)</span> then:</p>
+<ol class="arabic simple">
+<li><p><span class="math notranslate nohighlight">\(G(x) = \alpha  F(x)\)</span> ( __call__ method )</p></li>
+<li><p><span class="math notranslate nohighlight">\(G'(x) = \alpha  F'(x)\)</span> ( gradient method )</p></li>
+<li><p><span class="math notranslate nohighlight">\(G^{*}(x^{*}) = \alpha  F^{*}(\frac{x^{*}}{\alpha})\)</span> ( convex_conjugate method )</p></li>
+<li><p><span class="math notranslate nohighlight">\(\text{prox}_{\tau G}(x) = \text{prox}_{(\tau\alpha) F}(x)\)</span> ( proximal method )</p></li>
+</ol>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ScaledFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.ScaledFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ScaledFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ScaledFunction.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ScaledFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the convex conjugate of the scaled function.</p>
+<div class="math notranslate nohighlight">
+\[G^{*}(x^{*}) = \alpha  F^{*}(\frac{x^{*}}{\alpha})\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the scaled function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ScaledFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ScaledFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ScaledFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the gradient of the scaled function evaluated at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[G'(x) = \alpha  F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Point to evaluate the gradient at.</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the gradient of the scaled function evaluated at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ScaledFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ScaledFunction.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ScaledFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the scaled function, evaluated at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau G}(x) = \text{prox}_{(\tau\alpha) F}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the scaled function evaluated at <span class="math notranslate nohighlight">\(x\)</span> with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ScaledFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ScaledFunction.proximal_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ScaledFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>This returns the proximal  conjugate operator for the function at <span class="math notranslate nohighlight">\(x\)</span>, <span class="math notranslate nohighlight">\(\tau\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal conjugate operator for the function evaluated at <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ScaledFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ScaledFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">SumScalarFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#SumScalarFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction" title="Link to this definition">#</a></dt>
+<dd><p>SumScalarFunction represents the sum a function with a scalar.</p>
+<div class="math notranslate nohighlight">
+\[(F + scalar)(x)  = F(x) + scalar\]</div>
+<p>Although SumFunction has no general expressions for</p>
+<ol class="lowerroman simple">
+<li><p>convex_conjugate</p></li>
+<li><p>proximal</p></li>
+<li><p>proximal_conjugate</p></li>
+</ol>
+<p>if the second argument is a ConstantFunction then we can derive the above analytically.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#SumScalarFunction.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the convex conjugate of a <span class="math notranslate nohighlight">\((F+scalar)\)</span>, evaluated at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[(F+scalar)^{*}(x^{*}) = F^{*}(x^{*}) - scalar\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate evaluated at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#SumScalarFunction.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of <span class="math notranslate nohighlight">\(F+scalar\)</span></p>
+<div class="math notranslate nohighlight">
+\[ext{prox}_{     au (F+scalar)}(x) =     ext{prox}_{     au F}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the evaluation of the proximal operator evaluated at <span class="math notranslate nohighlight">\(x\)</span> and :math:`       au`.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the sum of the gradient of functions evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if all of them are differentiable.</p>
+<div class="math notranslate nohighlight">
+\[(F'_{1} + F'_{2} + ... + F'_{n})(x) = F'_{1}(x) + F'_{2}(x) + ... + F'_{n}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Point to evaluate the gradient at.</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the sum of the gradients evaluated at point <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SumScalarFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SumScalarFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TranslateFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">TranslateFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#TranslateFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TranslateFunction" title="Link to this definition">#</a></dt>
+<dd><p>TranslateFunction represents the translation of function F with respect to the center b.</p>
+<p>Let a function F and consider <span class="math notranslate nohighlight">\(G(x) = F(x - center)\)</span>.</p>
+<p>Function F is centered at 0, whereas G is centered at point b.</p>
+<p>If <span class="math notranslate nohighlight">\(G(x) = F(x - b)\)</span> then:</p>
+<ol class="arabic simple">
+<li><p><span class="math notranslate nohighlight">\(G(x) = F(x - b)\)</span> ( __call__ method )</p></li>
+<li><p><span class="math notranslate nohighlight">\(G'(x) = F'(x - b)\)</span> ( gradient method )</p></li>
+<li><p><span class="math notranslate nohighlight">\(G^{*}(x^{*}) = F^{*}(x^{*}) + &lt;x^{*}, b &gt;\)</span> ( convex_conjugate method )</p></li>
+<li><p><span class="math notranslate nohighlight">\(\text{prox}_{\tau G}(x) = \text{prox}_{\tau F}(x - b)  + b\)</span> ( proximal method )</p></li>
+</ol>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TranslateFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.TranslateFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TranslateFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.TranslateFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TranslateFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.TranslateFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TranslateFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#TranslateFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TranslateFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the gradient of the translated function.</p>
+<div class="math notranslate nohighlight">
+\[G'(x) =  F'(x - b)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Point to evaluate the gradient at.</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the gradient of the translated function evaluated at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TranslateFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#TranslateFunction.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TranslateFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the translated function.</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau G}(x) = \text{prox}_{\tau F}(x-b) + b\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the translated function at <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TranslateFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#TranslateFunction.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TranslateFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the convex conjugate of the translated function.</p>
+<div class="math notranslate nohighlight">
+\[G^{*}(x^{*}) = F^{*}(x^{*}) + &lt;x^{*}, b &gt;\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the translated function at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="simple-functions">
+<h3>Simple functions<a class="headerlink" href="#simple-functions" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ConstantFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">ConstantFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ConstantFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ConstantFunction" title="Link to this definition">#</a></dt>
+<dd><p>ConstantFunction: <span class="math notranslate nohighlight">\(F(x) = constant, constant\in\mathbb{R}\)</span></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ConstantFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ConstantFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ConstantFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of the function, <span class="math notranslate nohighlight">\(F'(x)=0\)</span>
+:param x: Point to evaluate the gradient at.
+:type x: DataContainer
+:param out:
+:type out: return DataContainer, if None a new DataContainer is returned, default None.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>A DataContainer of zeros, the same size as <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ConstantFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ConstantFunction.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ConstantFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>The convex conjugate of constant function <span class="math notranslate nohighlight">\(F(x) = c\in\mathbb{R}\)</span> is</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}F(x^{*})
+=
+\begin{cases}
+    -c, &amp; if x^{*} = 0\\
+    \infty, &amp; \mbox{otherwise}
+\end{cases}\end{split}\]</div>
+<p>However, <span class="math notranslate nohighlight">\(x^{*} = 0\)</span> only in the limit of iterations, so in fact this can be infinity.
+We do not want to have inf values in the convex conjugate, so we have to penalise this value accordingly.
+The following penalisation is useful in the PDHG algorithm, when we compute primal &amp; dual objectives
+for convergence purposes.</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \sum \max\{x^{*}, 0\}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The maximum of x and 0, summed over the entries of x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ConstantFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ConstantFunction.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ConstantFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the constant function, which is the same element, i.e.,</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = x\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, equal to <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ConstantFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.ConstantFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ConstantFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ConstantFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ConstantFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ConstantFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ZeroFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">ZeroFunction</span></span><a class="reference internal" href="../_modules/cil/optimisation/functions/Function/#ZeroFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ZeroFunction" title="Link to this definition">#</a></dt>
+<dd><p>ZeroFunction represents the zero function, <span class="math notranslate nohighlight">\(F(x) = 0\)</span></p>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ZeroFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.ZeroFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ZeroFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ZeroFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ZeroFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ZeroFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>The convex conjugate of constant function <span class="math notranslate nohighlight">\(F(x) = c\in\mathbb{R}\)</span> is</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}F(x^{*})
+=
+\begin{cases}
+    -c, &amp; if x^{*} = 0\\
+    \infty, &amp; \mbox{otherwise}
+\end{cases}\end{split}\]</div>
+<p>However, <span class="math notranslate nohighlight">\(x^{*} = 0\)</span> only in the limit of iterations, so in fact this can be infinity.
+We do not want to have inf values in the convex conjugate, so we have to penalise this value accordingly.
+The following penalisation is useful in the PDHG algorithm, when we compute primal &amp; dual objectives
+for convergence purposes.</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \sum \max\{x^{*}, 0\}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The maximum of x and 0, summed over the entries of x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ZeroFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ZeroFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of the function, <span class="math notranslate nohighlight">\(F'(x)=0\)</span>
+:param x: Point to evaluate the gradient at.
+:type x: DataContainer
+:param out:
+:type out: return DataContainer, if None a new DataContainer is returned, default None.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>A DataContainer of zeros, the same size as <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ZeroFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ZeroFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the constant function, which is the same element, i.e.,</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = x\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, equal to <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ZeroFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ZeroFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Rosenbrock">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">Rosenbrock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Rosenbrock/#Rosenbrock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Rosenbrock" title="Link to this definition">#</a></dt>
+<dd><p>Rosenbrock function</p>
+<div class="math notranslate nohighlight">
+\[\]</div>
+<p>F(x,y) = (alpha - x)^2 + beta(y-x^2)^2</p>
+<p>The function has a global minimum at .. math:: (x,y)=(alpha, alpha^2)</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Rosenbrock.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/Rosenbrock/#Rosenbrock.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.Rosenbrock.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Gradient of the Rosenbrock function</p>
+<div class="math notranslate nohighlight">
+\[\]</div>
+<p>nabla f(x,y) = left[ 2*((x-alpha) - 2beta x(y-x^2)) ; 2beta (y - x^2)  right]</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Rosenbrock.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.Rosenbrock.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Rosenbrock.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.Rosenbrock.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Rosenbrock.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.Rosenbrock.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Rosenbrock.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.Rosenbrock.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.Rosenbrock.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.Rosenbrock.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="composition-of-operator-and-a-function">
+<h3>Composition of operator and a function<a class="headerlink" href="#composition-of-operator-and-a-function" title="Link to this heading">#</a></h3>
+<p>This class allows the user to write a function which does the following:</p>
+<div class="math notranslate nohighlight">
+\[F ( x ) = G ( Ax )\]</div>
+<p>where <span class="math notranslate nohighlight">\(A\)</span> is an operator. For instance the least squares function can
+be expressed as</p>
+<div class="math notranslate nohighlight">
+\[F(x) = || Ax - b ||^2_2 \qquad \text{where} \qquad G(y) = || y - b ||^2_2\]</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">F1</span> <span class="o">=</span> <span class="n">Norm2Sq</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
+<span class="c1"># or equivalently</span>
+<span class="n">F2</span> <span class="o">=</span> <span class="n">OperatorCompositionFunction</span><span class="p">(</span><span class="n">L2NormSquared</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">),</span> <span class="n">A</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.OperatorCompositionFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">OperatorCompositionFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/OperatorCompositionFunction/#OperatorCompositionFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.OperatorCompositionFunction" title="Link to this definition">#</a></dt>
+<dd><p>Composition of a function with an operator as : <span class="math notranslate nohighlight">\((F \circ A)(x) = F(Ax)\)</span></p>
+<blockquote>
+<div><dl class="field-list simple">
+<dt class="field-odd">parameter function<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="code docutils literal notranslate"><span class="pre">Function</span></code> F</p>
+</dd>
+<dt class="field-even">parameter operator<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="code docutils literal notranslate"><span class="pre">Operator</span></code> A</p>
+</dd>
+</dl>
+</div></blockquote>
+<p>For general operator, we have no explicit formulas for convex_conjugate,
+proximal and proximal_conjugate</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.OperatorCompositionFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.OperatorCompositionFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.OperatorCompositionFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/OperatorCompositionFunction/#OperatorCompositionFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.OperatorCompositionFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Return the gradient of <span class="math notranslate nohighlight">\(F(Ax)\)</span>,</p>
+<p><span class="math notranslate nohighlight">\((F(Ax))' = A^{T}F'(Ax)\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.OperatorCompositionFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.OperatorCompositionFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.OperatorCompositionFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.OperatorCompositionFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.OperatorCompositionFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.OperatorCompositionFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.OperatorCompositionFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.OperatorCompositionFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="indicator-box">
+<h3>Indicator box<a class="headerlink" href="#indicator-box" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">IndicatorBox</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accelerated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/IndicatorBox/#IndicatorBox"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox" title="Link to this definition">#</a></dt>
+<dd><p>Indicator function for box constraint</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[\begin{split}f(x) = \mathbb{I}_{[a, b]} = \begin{cases}
+                                   0, \text{ if } x \in [a, b] \\
+                                   \infty, \text{otherwise}
+                            \end{cases}\end{split}\]</div>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lower</strong> (<em>float</em><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><em>numpy array</em><em>, </em><em>default None</em>) – Lower bound. If set to None, it is equivalent to <code class="docutils literal notranslate"><span class="pre">-np.inf</span></code>.</p></li>
+<li><p><strong>upper</strong> (<em>float</em><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><em>numpy array</em><em>, </em><em>default None</em>) – Upper bound. If set to None, it is equivalent to <code class="docutils literal notranslate"><span class="pre">np.inf</span></code>.</p></li>
+<li><p><strong>accelerated</strong> (<em>bool</em><em>, </em><em>default True</em>) – Specifies whether to use the accelerated version or not, using numba or
+numpy backends respectively.</p></li>
+</ul>
+</dd>
+</dl>
+<p>If <code class="docutils literal notranslate"><span class="pre">lower</span></code> or <code class="docutils literal notranslate"><span class="pre">upper</span></code> are passed a <code class="docutils literal notranslate"><span class="pre">DataContainer</span></code> (or derived class
+such as <code class="docutils literal notranslate"><span class="pre">ImageData</span></code> or <code class="docutils literal notranslate"><span class="pre">AcquisitionData</span></code>) or a <code class="docutils literal notranslate"><span class="pre">numpy</span> <span class="pre">array</span></code>, the bounds
+can be set to different values for each element.</p>
+<p>In order to save computing time it is possible to suppress the evaluation of
+the function. This is achieved by setting <code class="docutils literal notranslate"><span class="pre">suppress_evaluation</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code>.
+<code class="docutils literal notranslate"><span class="pre">IndicatorBox</span></code> evaluated on any input will then return 0.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">accelerated</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code> (default), the Numba backend is used.
+Otherwise, the Numpy backend is used. An optional parameter to set the number of
+threads used by Numba can be set with <code class="docutils literal notranslate"><span class="pre">set_num_threads</span></code>. Setting the number of
+threads when <code class="docutils literal notranslate"><span class="pre">accelerate</span></code> is set to <code class="docutils literal notranslate"><span class="pre">False</span></code> will not have any effect.
+The default number of threads is defined in the <code class="docutils literal notranslate"><span class="pre">cil.utilities.multiprocessing</span></code>
+module, and it is equivalent to half of the CPU cores available.</p>
+<section id="example">
+<h4>Example:<a class="headerlink" href="#example" title="Link to this heading">#</a></h4>
+<p>In order to save computing time it is possible to suppress the evaluation of the
+function.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">ib</span> <span class="o">=</span> <span class="n">IndicatorBox</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ib</span><span class="o">.</span><span class="n">set_suppress_evaluation</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">ib</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># returns 0</span>
+</pre></div>
+</div>
+</section>
+<section id="id17">
+<h4>Example:<a class="headerlink" href="#id17" title="Link to this heading">#</a></h4>
+<p>Set the number of threads used in accelerated mode.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">num_threads</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">ib</span> <span class="o">=</span> <span class="n">IndicatorBox</span><span class="p">(</span><span class="n">lower</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ib</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="n">num_threads</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.set_suppress_evaluation">
+<span class="sig-name descname"><span class="pre">set_suppress_evaluation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/IndicatorBox/#IndicatorBox.set_suppress_evaluation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.set_suppress_evaluation" title="Link to this definition">#</a></dt>
+<dd><p>Suppresses the evaluation of the function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>value</strong> (<em>bool</em>) – If True, the function evaluation on any input will return 0, without calculation.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/IndicatorBox/#IndicatorBox.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Proximal operator of IndicatorBox at x</p>
+<div class="math notranslate nohighlight">
+\[prox_{\tau * f}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Input to the proximal operator</p></li>
+<li><p><strong>tau</strong> (<em>float</em>) – Step size. Notice it is ignored in IndicatorBox</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Output of the proximal operator. If not provided, a new DataContainer is created.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">tau</span></code> is ignored but it is in the signature of the generic Function class</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/IndicatorBox/#IndicatorBox.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.gradient" title="Link to this definition">#</a></dt>
+<dd><p>IndicatorBox is not differentiable, so calling gradient will raise a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code></p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.num_threads">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">num_threads</span></span><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.num_threads" title="Link to this definition">#</a></dt>
+<dd><p>Get the optional number of threads parameter to use for the accelerated version.</p>
+<p>Defaults to the value set in the CIL multiprocessing module.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.set_num_threads">
+<span class="sig-name descname"><span class="pre">set_num_threads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/IndicatorBox/#IndicatorBox.set_num_threads"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.set_num_threads" title="Link to this definition">#</a></dt>
+<dd><p>Set the optional number of threads parameter to use for the accelerated version.</p>
+<p>This is discarded if <code class="docutils literal notranslate"><span class="pre">accelerated=False</span></code>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.IndicatorBox.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.IndicatorBox.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+<section id="kullbackleibler">
+<h3>KullbackLeibler<a class="headerlink" href="#kullbackleibler" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.KullbackLeibler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">KullbackLeibler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'numba'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/KullbackLeibler/#KullbackLeibler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.KullbackLeibler" title="Link to this definition">#</a></dt>
+<dd><p>Kullback Leibler</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}F(u, v)
+= \begin{cases}
+u \log(\frac{u}{v}) - u + v &amp; \mbox{ if } u &gt; 0, v &gt; 0\\
+v &amp; \mbox{ if } u = 0, v \ge 0 \\
+\infty, &amp; \mbox{otherwise}
+\end{cases}\end{split}\]</div>
+<p>where the <span class="math notranslate nohighlight">\(0\log0 := 0\)</span> convention is used.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>b</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>non-negative</em>) – Translates the function at point <cite>b</cite>.</p></li>
+<li><p><strong>eta</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default = 0</em>) – Background noise</p></li>
+<li><p><strong>mask</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default = None</em>) – Mask for the data <cite>b</cite></p></li>
+<li><p><strong>backend</strong> (<em>{'numba'</em><em>,</em><em>'numpy'}</em><em>, </em><em>optional</em>) – Backend for the KullbackLeibler methods. Numba is the default backend.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The Kullback-Leibler function is used in practice as a fidelity term in minimisation problems where the
+acquired data follow Poisson distribution. If we denote the acquired data with <code class="code docutils literal notranslate"><span class="pre">b</span></code>
+then, we write</p>
+<div class="math notranslate nohighlight">
+\[\underset{i}{\sum} F(b_{i}, (v + \eta)_{i})\]</div>
+<p>where, <span class="math notranslate nohighlight">\(\eta\)</span> is an additional noise.</p>
+<p>In the case of Positron Emission Tomography reconstruction <span class="math notranslate nohighlight">\(\eta\)</span> represents
+scatter and random events contribution during the PET acquisition. Hence, in that case the KullbackLeibler
+fidelity measures the distance between <span class="math notranslate nohighlight">\(\mathcal{A}v + \eta\)</span> and acquisition data <span class="math notranslate nohighlight">\(b\)</span>, where
+<span class="math notranslate nohighlight">\(\mathcal{A}\)</span> is the projection operator. This is related to <a class="reference external" href="http://stir.sourceforge.net/documentation/doxy/html/classstir_1_1PoissonLogLikelihoodWithLinearModelForMean.html">PoissonLogLikelihoodWithLinearModelForMean</a> ,
+definition that is used in PET reconstruction in the <a class="reference external" href="https://github.com/SyneRBI/SIRF">SIRF</a> software.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The default implementation uses the build-in function <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.kl_div.html">kl_div</a> from scipy.
+The methods of the <a class="reference internal" href="#cil.optimisation.functions.KullbackLeibler" title="cil.optimisation.functions.KullbackLeibler.KullbackLeibler"><code class="xref py py-class docutils literal notranslate"><span class="pre">KullbackLeibler</span></code></a> are accelerated provided that <a class="reference external" href="https://numba.pydata.org/">numba</a> library is installed.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.optimisation.functions</span> <span class="kn">import</span> <span class="n">KullbackLeibler</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.framework</span> <span class="kn">import</span> <span class="n">ImageGeometry</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="s1">&#39;random&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">F</span> <span class="o">=</span> <span class="n">KullbackLeibler</span><span class="p">(</span><span class="n">b</span> <span class="o">=</span> <span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.KullbackLeibler.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.KullbackLeibler.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.KullbackLeibler.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.KullbackLeibler.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.KullbackLeibler.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.KullbackLeibler.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.KullbackLeibler.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.KullbackLeibler.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of function <span class="math notranslate nohighlight">\(F\)</span>  evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if it is differentiable</p>
+<div class="math notranslate nohighlight">
+\[F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a>, the value of the gradient of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.KullbackLeibler.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.KullbackLeibler.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.KullbackLeibler.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.KullbackLeibler.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="l1-norm">
+<h3>L1 Norm<a class="headerlink" href="#l1-norm" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Norm">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">L1Norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Norm/#L1Norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L1Norm" title="Link to this definition">#</a></dt>
+<dd><p>L1Norm function</p>
+<p>Consider the following cases:</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||x||_{1}\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||x - b||_{1}\]</div>
+</li>
+</ol>
+<p>In the weighted case, <span class="math notranslate nohighlight">\(w\)</span> is an array of non-negative weights.</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||x||_{L^1(w)}\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||x - b||_{L^1(w)}\]</div>
+</li>
+</ol>
+<p>with <span class="math notranslate nohighlight">\(||x||_{L^1(w)} = || x  w||_1 = \sum_{i=1}^{n} |x_i| w_i\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>weight</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>numpy ndarray</em><em>, </em><em>default None</em>) – Array of non-negative weights. If <code class="code docutils literal notranslate"><span class="pre">None</span></code> returns the L1 Norm.</p></li>
+<li><p><strong>b</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default None</em>) – Translation of the function.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Norm.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Norm/#L1Norm.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L1Norm.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the convex conjugate of the L1 Norm function at x.</p>
+<p>This is the indicator of the unit <span class="math notranslate nohighlight">\(L^{\infty}\)</span> norm:</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*})\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*}) + \langle x^{*},b\rangle\]</div>
+</li>
+</ol>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*})
+= \begin{cases}
+0, \mbox{if } \|x^{*}\|_{\infty}\leq1\\
+\infty, \mbox{otherwise}
+\end{cases}\end{split}\]</div>
+<p>In the weighted case the convex conjugate is the indicator of the unit
+<span class="math notranslate nohighlight">\(L^{\infty}(w^{-1})\)</span> norm.</p>
+<p>See:
+<a class="reference external" href="https://math.stackexchange.com/questions/1533217/convex-conjugate-of-l1-norm-function-with-weight">https://math.stackexchange.com/questions/1533217/convex-conjugate-of-l1-norm-function-with-weight</a></p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(x^{*})\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(x^{*}) + \langle x^{*},b\rangle\]</div>
+</li>
+</ol>
+<p>with <span class="math notranslate nohighlight">\(\|x\|_{L^\infty(w^{-1})} = \max_{i} \frac{|x_i|}{w_i}\)</span> and possible cases of 0/0 are defined to be 1..</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – where to evaluate the convex conjugate of the L1 Norm function.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>the value of the convex conjugate of the WeightedL1Norm function at x</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>DataContainer.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Norm.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Norm/#L1Norm.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L1Norm.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the proximal operator of the L1 Norm function at x with scaling parameter <cite>tau</cite>.</p>
+<p>Consider the following cases:</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_\tau(x)\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_\tau(x - b) + b\]</div>
+</li>
+</ol>
+<p>where,</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}(x) = sgn(x) * \max\{ |x| - \tau, 0 \}\]</div>
+<p>The weighted case follows from Example 6.23 in Chapter 6 of “First-Order Methods in Optimization”
+by Amir Beck, SIAM 2017 <a class="reference external" href="https://archive.siam.org/books/mo25/mo25_ch6.pdf">https://archive.siam.org/books/mo25/mo25_ch6.pdf</a></p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_{\tau*w}(x)\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}_{\tau*w}(x - b) + b\]</div>
+</li>
+</ol>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>float</em><em>, </em><em>real</em><em>,  </em><em>ndarray</em><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default None</em>) – If not None, the result will be stored in this object.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>The value of the proximal operator of the L1 norm function at x</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>DataContainer.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Norm.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.L1Norm.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Norm.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L1Norm.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Norm.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L1Norm.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of function <span class="math notranslate nohighlight">\(F\)</span>  evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if it is differentiable</p>
+<div class="math notranslate nohighlight">
+\[F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a>, the value of the gradient of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Norm.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L1Norm.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="l2-norm-squared">
+<h3>L2 Norm Squared<a class="headerlink" href="#l2-norm-squared" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L2NormSquared">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">L2NormSquared</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#L2NormSquared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L2NormSquared" title="Link to this definition">#</a></dt>
+<dd><p>L2NormSquared function: <span class="math notranslate nohighlight">\(F(x) = \| x\|^{2}_{2} = \underset{i}{\sum}x_{i}^{2}\)</span></p>
+<p>Following cases are considered:</p>
+<blockquote>
+<div><ol class="loweralpha simple">
+<li><p><span class="math notranslate nohighlight">\(F(x) = \|x\|^{2}_{2}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(F(x) = \|x - b\|^{2}_{2}\)</span></p></li>
+</ol>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>b</strong> (<cite>DataContainer</cite>, optional) – Translation of the function</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>For case b) we can use <code class="code docutils literal notranslate"><span class="pre">F</span> <span class="pre">=</span> <span class="pre">L2NormSquared().centered_at(b)</span></code>, see <em>TranslateFunction</em>.</p>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">F</span> <span class="o">=</span> <span class="n">L2NormSquared</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">F</span> <span class="o">=</span> <span class="n">L2NormSquared</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">F</span> <span class="o">=</span> <span class="n">L2NormSquared</span><span class="p">()</span><span class="o">.</span><span class="n">centered_at</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L2NormSquared.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#L2NormSquared.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L2NormSquared.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of the L2NormSquared function at x.</p>
+<p>Following cases are considered:</p>
+<blockquote>
+<div><ol class="loweralpha simple">
+<li><p><span class="math notranslate nohighlight">\(F'(x) = 2x\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(F'(x) = 2(x-b)\)</span></p></li>
+</ol>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L2NormSquared.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#L2NormSquared.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L2NormSquared.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the convex conjugate of the L2NormSquared function at x.</p>
+<p>Consider the following cases:</p>
+<blockquote>
+<div><ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \frac{1}{4}\|x^{*}\|^{2}_{2}\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \frac{1}{4}\|x^{*}\|^{2}_{2} + \langle x^{*}, b\rangle\]</div>
+</li>
+</ol>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L2NormSquared.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#L2NormSquared.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L2NormSquared.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the proximal operator of the L2NormSquared function at x.</p>
+<p>Consider the following cases:</p>
+<blockquote>
+<div><ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \frac{x}{1+2\tau}\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \frac{x-b}{1+2\tau} + b\]</div>
+</li>
+</ol>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L2NormSquared.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.L2NormSquared.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L2NormSquared.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L2NormSquared.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L2NormSquared.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L2NormSquared.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.WeightedL2NormSquared">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">WeightedL2NormSquared</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#WeightedL2NormSquared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.WeightedL2NormSquared" title="Link to this definition">#</a></dt>
+<dd><p>WeightedL2NormSquared function: <span class="math notranslate nohighlight">\(F(x) = \|x\|_{W,2}^2 = \Sigma_iw_ix_i^2 = \langle x, Wx\rangle = x^TWx\)</span>
+where <span class="math notranslate nohighlight">\(W=\text{diag}(weight)\)</span> if <cite>weight</cite> is a <cite>DataContainer</cite> or <span class="math notranslate nohighlight">\(W=\text{weight} I\)</span> if <cite>weight</cite> is a scalar.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>**kwargs</strong></p></li>
+<li><p><strong>weight</strong> (a <cite>scalar</cite> or a <cite>DataContainer</cite> with the same shape as the intended domain of this <cite>WeightedL2NormSquared</cite> function)</p></li>
+<li><p><strong>b</strong> (a <cite>DataContainer</cite> with the same shape as the intended domain of this <cite>WeightedL2NormSquared</cite> function) – A shift so that the function becomes  <span class="math notranslate nohighlight">\(F(x) = \| x-b\|_{W,2}^2 = \Sigma_iw_i(x_i-b_i)^2 = \langle x-b, W(x-b) \rangle = (x-b)^TW(x-b)\)</span></p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.WeightedL2NormSquared.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#WeightedL2NormSquared.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.WeightedL2NormSquared.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of <span class="math notranslate nohighlight">\(F'(x) = 2Wx\)</span> or, if <cite>b</cite> is defined,  <span class="math notranslate nohighlight">\(F'(x) = 2W(x-b)\)</span>
+where <span class="math notranslate nohighlight">\(W=\text{diag}(weight)\)</span> if <cite>weight</cite> is a <cite>DataContainer</cite> or <span class="math notranslate nohighlight">\(\text{weight}I\)</span> if <cite>weight</cite> is a scalar.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.WeightedL2NormSquared.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#WeightedL2NormSquared.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.WeightedL2NormSquared.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the convex conjugate of the WeightedL2NormSquared function at x.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.WeightedL2NormSquared.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.WeightedL2NormSquared.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.WeightedL2NormSquared.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.WeightedL2NormSquared.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.WeightedL2NormSquared.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L2NormSquared/#WeightedL2NormSquared.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.WeightedL2NormSquared.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the proximal operator of the WeightedL2NormSquared function at x.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.WeightedL2NormSquared.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.WeightedL2NormSquared.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="least-squares">
+<h3>Least Squares<a class="headerlink" href="#least-squares" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LeastSquares">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">LeastSquares</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/LeastSquares/#LeastSquares"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.LeastSquares" title="Link to this definition">#</a></dt>
+<dd><p>(Weighted) Least Squares function</p>
+<div class="math notranslate nohighlight">
+\[F(x) = c\|Ax-b\|_2^2\]</div>
+<p>or if weighted</p>
+<div class="math notranslate nohighlight">
+\[F(x) = c\|Ax-b\|_{2,W}^{2}\]</div>
+<p>where <span class="math notranslate nohighlight">\(W=\text{diag}(weight)\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference internal" href="#cil.optimisation.operators.LinearOperator" title="cil.optimisation.operators.LinearOperator"><em>LinearOperator</em></a>)</p></li>
+<li><p><strong>b</strong> (<em>Data</em><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>c</strong> (<em>Scaling Constant</em><em>, </em><em>float</em><em>, </em><em>default 1.0</em>)</p></li>
+<li><p><strong>weight</strong> (<em>DataContainer with all positive elements</em><em> of </em><em>size</em><em> of </em><em>the range</em><em> of </em><em>operator A</em><em>, </em><em>default None</em>)</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>L is the  Lipshitz Constant of the gradient of <span class="math notranslate nohighlight">\(F\)</span> which is <span class="math notranslate nohighlight">\(2 c ||A||_2^2 = 2 c \sigma_1(A)^2\)</span>, or <span class="math notranslate nohighlight">\(2 c ||W|| ||A||_2^2 = 2c||W|| \sigma_1(A)^2\)</span>, where <span class="math notranslate nohighlight">\(\sigma_1(A)\)</span> is the largest singular value of <span class="math notranslate nohighlight">\(A\)</span> and <span class="math notranslate nohighlight">\(W=\text{diag}(weight)\)</span>.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LeastSquares.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/LeastSquares/#LeastSquares.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.LeastSquares.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of <span class="math notranslate nohighlight">\(F(x)\)</span>:</p>
+<div class="math notranslate nohighlight">
+\[F'(x) = 2cA^T(Ax-b)\]</div>
+<p>or</p>
+<div class="math notranslate nohighlight">
+\[F'(x) = 2cA^T(W(Ax-b))\]</div>
+<p>where <span class="math notranslate nohighlight">\(W=\text{diag}(weight)\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LeastSquares.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.LeastSquares.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LeastSquares.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LeastSquares.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LeastSquares.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LeastSquares.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LeastSquares.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LeastSquares.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LeastSquares.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LeastSquares.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="l1-sparsity">
+<h3>L1 Sparsity<a class="headerlink" href="#l1-sparsity" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Sparsity">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">L1Sparsity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Sparsity/#L1Sparsity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L1Sparsity" title="Link to this definition">#</a></dt>
+<dd><p>L1Sparsity function</p>
+<p>Calculates the following cases, depending on if the optional parameter <cite>weight</cite>  or data <cite>b</cite> is passed. For <cite>weight=None</cite>:</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||Qx||_{1}\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||Qx - b||_{1}\]</div>
+</li>
+</ol>
+<p>In the weighted case, <cite>weight</cite> = <span class="math notranslate nohighlight">\(w\)</span> is an array of non-negative weights.</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||Qx||_{L^1(w)}\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F(x) = ||Qx - b||_{L^1(w)}\]</div>
+</li>
+</ol>
+<p>with <span class="math notranslate nohighlight">\(||x||_{L^1(w)} = || x \cdot w||_1 = \sum_{i=1}^{n} |x_i| w_i\)</span>.</p>
+<p>In all cases <span class="math notranslate nohighlight">\(Q\)</span> is an orthogonal operator.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Q</strong> (<em>orthogonal Operator</em>) – Note that for the correct calculation of the proximal the provided operator must be orthogonal</p></li>
+<li><p><strong>b</strong> (<em>Data</em><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default is None</em>)</p></li>
+<li><p><strong>weight</strong> (<em>array</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – non-negative weight array matching the size of the range of operator <span class="math notranslate nohighlight">\(Q\)</span>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Sparsity.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Sparsity/#L1Sparsity.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L1Sparsity.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the convex conjugate of the L1Sparsity function at x.
+Here, we need to use the convex conjugate of L1Sparsity, which is the Indicator of the unit
+<span class="math notranslate nohighlight">\(\ell^{\infty}\)</span> norm on the range of the (bijective) operator Q.</p>
+<p>Consider the non-weighted case:</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(Qx^{*})\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(Qx^{*}) + \langle Qx^{*},b\rangle\]</div>
+</li>
+</ol>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbb{I}_{\{\|\cdot\|_{\infty}\leq1\}}(x^{*})
+= \begin{cases}
+0, \mbox{if } \|x^{*}\|_{\infty}\leq1\\
+\infty, \mbox{otherwise}
+\end{cases}\end{split}\]</div>
+<p>In the weighted case the convex conjugate is the indicator of the unit
+<span class="math notranslate nohighlight">\(L^{\infty}( w^{-1} )\)</span> norm.</p>
+<p>See:
+<a class="reference external" href="https://math.stackexchange.com/questions/1533217/convex-conjugate-of-l1-norm-function-with-weight">https://math.stackexchange.com/questions/1533217/convex-conjugate-of-l1-norm-function-with-weight</a></p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(Qx^{*})\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \mathbb{I}_{\{\|\cdot\|_{L^\infty(w^{-1})}\leq 1\}}(Qx^{*}) + \langle Qx^{*},b\rangle\]</div>
+</li>
+</ol>
+<p>with <span class="math notranslate nohighlight">\(\|x\|_{L^\infty(w^{-1})} = \max_{i} \frac{|x_i|}{w_i}\)</span> and possible cases of 0 / 0 are defined to be 1.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Sparsity.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Sparsity/#L1Sparsity.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.L1Sparsity.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the proximal operator of the L1 Norm function at x with scaling parameter <cite>tau</cite>.</p>
+<p>Consider the following cases:</p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = Q^T \mathrm{ShinkOperator}_{\tau}(Qx)\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = Q^T \left( \mathrm{ShinkOperator}_\tau(Qx- b) + b \right)\]</div>
+</li>
+</ol>
+<p>where,</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau | \cdot |}(x) = \mathrm{ShinkOperator}(x) = sgn(x) * \max\{ |x| - \tau, 0 \}\]</div>
+<p>The weighted case follows from Example 6.23 in Chapter 6 of “First-Order Methods in Optimization”
+by Amir Beck, SIAM 2017 <a class="reference external" href="https://archive.siam.org/books/mo25/mo25_ch6.pdf">https://archive.siam.org/books/mo25/mo25_ch6.pdf</a></p>
+<ol class="loweralpha">
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = Q^T \mathrm{ShinkOperator}_{\tau*w}(Qx)\]</div>
+</li>
+<li><div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = Q^T \left( \mathrm{ShinkOperator}_{\tau*w}(Qx-b) + b \right)\]</div>
+</li>
+</ol>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>float</em><em>, </em><em>ndarray</em><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>default None</em>) – If not None, the result will be stored in this object.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>The value of the proximal operator of the L1 norm function at x</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>DataContainer.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Sparsity.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.L1Sparsity.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Sparsity.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L1Sparsity.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Sparsity.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L1Sparsity.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of function <span class="math notranslate nohighlight">\(F\)</span>  evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if it is differentiable</p>
+<div class="math notranslate nohighlight">
+\[F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a>, the value of the gradient of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.L1Sparsity.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.L1Sparsity.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="mixed-l21-norm">
+<h3>Mixed L21 norm<a class="headerlink" href="#mixed-l21-norm" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL21Norm">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">MixedL21Norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/MixedL21Norm/#MixedL21Norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.MixedL21Norm" title="Link to this definition">#</a></dt>
+<dd><p>MixedL21Norm function: <span class="math notranslate nohighlight">\(F(x) = ||x||_{2,1} = \sum |x|_{2} = \sum \sqrt{ (x^{1})^{2} + (x^{2})^{2} + \dots}\)</span></p>
+<p>where x is a BlockDataContainer, i.e., <span class="math notranslate nohighlight">\(x=(x^{1}, x^{2}, \dots)\)</span></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL21Norm.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/MixedL21Norm/#MixedL21Norm.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.MixedL21Norm.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the convex conjugate of the MixedL21Norm function at x.</p>
+<p>This is the Indicator function of <span class="math notranslate nohighlight">\(\mathbb{I}_{\{\|\cdot\|_{2,\infty}\leq1\}}(x^{*})\)</span>,</p>
+<p>i.e.,</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbb{I}_{\{\|\cdot\|_{2, \infty}\leq1\}}(x^{*})
+= \begin{cases}
+0, \mbox{if } \|x\|_{2, \infty}\leq1\\
+\infty, \mbox{otherwise}
+\end{cases}\end{split}\]</div>
+<p>where,</p>
+<div class="math notranslate nohighlight">
+\[\|x\|_{2,\infty} = \max\{ \|x\|_{2} \} = \max\{ \sqrt{ (x^{1})^{2} + (x^{2})^{2} + \dots}\}\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL21Norm.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/MixedL21Norm/#MixedL21Norm.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.MixedL21Norm.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the proximal operator of the MixedL21Norm function at x.</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \frac{x}{\|x\|_{2}}\max\{ \|x\|_{2} - \tau, 0 \}\]</div>
+<p>where the convention 0 · (0/0) = 0 is used.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL21Norm.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.MixedL21Norm.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL21Norm.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.MixedL21Norm.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL21Norm.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.MixedL21Norm.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of function <span class="math notranslate nohighlight">\(F\)</span>  evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if it is differentiable</p>
+<div class="math notranslate nohighlight">
+\[F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a>, the value of the gradient of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL21Norm.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.MixedL21Norm.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="smooth-mixed-l21-norm">
+<h3>Smooth Mixed L21 norm<a class="headerlink" href="#smooth-mixed-l21-norm" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SmoothMixedL21Norm">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">SmoothMixedL21Norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epsilon</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/MixedL21Norm/#SmoothMixedL21Norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SmoothMixedL21Norm" title="Link to this definition">#</a></dt>
+<dd><p>SmoothMixedL21Norm function: <span class="math notranslate nohighlight">\(F(x) = ||x||_{2,1} = \sum |x|_{2} = \sum \sqrt{ (x^{1})^{2} + (x^{2})^{2} + \epsilon^2 + \dots}\)</span></p>
+<p>where x is a BlockDataContainer, i.e., <span class="math notranslate nohighlight">\(x=(x^{1}, x^{2}, \dots)\)</span></p>
+<p>Conjugate, proximal and proximal conjugate methods no closed-form solution</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SmoothMixedL21Norm.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/MixedL21Norm/#SmoothMixedL21Norm.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SmoothMixedL21Norm.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of the SmoothMixedL21Norm function at x.</p>
+<p>frac{x}{<a href="#id77"><span class="problematic" id="id78">|x|</span></a>}</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SmoothMixedL21Norm.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.SmoothMixedL21Norm.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SmoothMixedL21Norm.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SmoothMixedL21Norm.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SmoothMixedL21Norm.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SmoothMixedL21Norm.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SmoothMixedL21Norm.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SmoothMixedL21Norm.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SmoothMixedL21Norm.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SmoothMixedL21Norm.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="mixed-l11-norm">
+<h3>Mixed L11 norm<a class="headerlink" href="#mixed-l11-norm" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL11Norm">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">MixedL11Norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Norm/#MixedL11Norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.MixedL11Norm" title="Link to this definition">#</a></dt>
+<dd><p>MixedL11Norm function</p>
+<div class="math notranslate nohighlight">
+\[F(x) = ||x||_{1,1} = \sum |x_{1}| + |x_{2}| + \cdots + |x_{n}|\]</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>MixedL11Norm is a separable function, therefore it can also be defined using the <a class="reference internal" href="#cil.optimisation.functions.BlockFunction" title="cil.optimisation.functions.BlockFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">BlockFunction</span></code></a>.</p>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#cil.optimisation.functions.L1Norm" title="cil.optimisation.functions.L1Norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">L1Norm</span></code></a>, <a class="reference internal" href="#cil.optimisation.functions.MixedL21Norm" title="cil.optimisation.functions.MixedL21Norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MixedL21Norm</span></code></a></p>
+</div>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL11Norm.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.MixedL11Norm.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL11Norm.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.MixedL11Norm.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL11Norm.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.MixedL11Norm.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL11Norm.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.MixedL11Norm.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of function <span class="math notranslate nohighlight">\(F\)</span>  evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if it is differentiable</p>
+<div class="math notranslate nohighlight">
+\[F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a>, the value of the gradient of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL11Norm.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/L1Norm/#MixedL11Norm.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.MixedL11Norm.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the proximal operator of the MixedL11Norm function at x.</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}(x)\]</div>
+<p>where,</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = \mathrm{ShinkOperator}(x) := sgn(x) * \max\{ |x| - \tau, 0 \}\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.MixedL11Norm.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.MixedL11Norm.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="total-variation">
+<h3>Total variation<a class="headerlink" href="#total-variation" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">TotalVariation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">correlation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'c'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">isotropic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strong_convexity_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/TotalVariation/#TotalVariation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TotalVariation" title="Link to this definition">#</a></dt>
+<dd><p>Total variation Function</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{TV}(u) := \|\nabla u\|_{2,1} = \sum \|\nabla u\|_{2},\, (\mbox{isotropic})\]</div>
+<div class="math notranslate nohighlight">
+\[\mathrm{TV}(u) := \|\nabla u\|_{1,1} = \sum \|\nabla u\|_{1}\, (\mbox{anisotropic})\]</div>
+<p class="rubric">Notes</p>
+<p>The <code class="code docutils literal notranslate"><span class="pre">TotalVariation</span></code> (TV) <code class="code docutils literal notranslate"><span class="pre">Function</span></code> acts as a composite function, i.e.,
+the composition of the <a class="reference internal" href="#cil.optimisation.functions.MixedL21Norm" title="cil.optimisation.functions.MixedL21Norm"><code class="xref py py-class docutils literal notranslate"><span class="pre">MixedL21Norm</span></code></a> function and the <a class="reference internal" href="#cil.optimisation.operators.GradientOperator" title="cil.optimisation.operators.GradientOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientOperator</span></code></a> operator,</p>
+<div class="math notranslate nohighlight">
+\[f(u) = \|u\|_{2,1}, \Rightarrow (f\circ\nabla)(u) = f(\nabla x) = \mathrm{TV}(u)\]</div>
+<p>In that case, the proximal operator of TV does not have an exact solution and we use an iterative
+algorithm to solve:</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau \mathrm{TV}}(b) := \underset{u}{\mathrm{argmin}} \frac{1}{2\tau}\|u - b\|^{2} + \mathrm{TV}(u)\]</div>
+<p>The algorithm used for the proximal operator of TV is the Fast Gradient Projection algorithm (or FISTA)
+applied to the _dual <a href="#id79"><span class="problematic" id="id80">problem_</span></a> of the above problem, see <span id="id18">[<a class="reference internal" href="#id70" title="Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009. URL: https://doi.org/10.1137/080716542, arXiv:https://doi.org/10.1137/080716542, doi:10.1137/080716542.">1</a>]</span>, <span id="id19">[<a class="reference internal" href="#id69" title="Amir Beck and Marc Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11):2419-2434, 2009. doi:10.1109/TIP.2009.2028250.">2</a>]</span>, <span id="id20">[<a class="reference internal" href="#id73" title="Mingqiang Zhu, Stephen J. Wright, and Tony F. Chan. Duality-based algorithms for total-variation-regularized image restoration. Computational Optimization and Applications, 47(3):377-400, Nov 2010. URL: https://doi.org/10.1007/s10589-008-9225-2, doi:10.1007/s10589-008-9225-2.">12</a>]</span>.</p>
+<p>See also “Multicontrast MRI Reconstruction with Structure-Guided Total Variation”, Ehrhardt, Betcke, 2016.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>max_iteration</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">int</span></code>, default = 5) – Maximum number of iterations for the FGP algorithm to solve to solve the dual problem
+of the Total Variation Denoising problem (ROF). If warm_start=False, this should be around 100,
+or larger, with a set tolerance.</p></li>
+<li><p><strong>tolerance</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, default = None) – <p>Stopping criterion for the FGP algorithm used to to solve the dual problem
+of the Total Variation Denoising problem (ROF). If the difference between iterates in the FGP algorithm is less than the tolerance
+the iterations end before the max_iteration number.</p>
+<div class="math notranslate nohighlight">
+\[\|x^{k+1} - x^{k}\|_{2} &lt; \mathrm{tolerance}\]</div>
+</p></li>
+<li><p><strong>correlation</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, default = <cite>Space</cite>) – Correlation between <cite>Space</cite> and/or <cite>SpaceChannels</cite> for the <a class="reference internal" href="#cil.optimisation.operators.GradientOperator" title="cil.optimisation.operators.GradientOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientOperator</span></code></a>.</p></li>
+<li><p><strong>backend</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, default = <cite>c</cite>) – Backend to compute the <a class="reference internal" href="#cil.optimisation.operators.GradientOperator" title="cil.optimisation.operators.GradientOperator"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientOperator</span></code></a></p></li>
+<li><p><strong>lower</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">'float</span></code>, default = None) – A constraint is enforced using the <a class="reference internal" href="#cil.optimisation.functions.IndicatorBox" title="cil.optimisation.functions.IndicatorBox"><code class="xref py py-class docutils literal notranslate"><span class="pre">IndicatorBox</span></code></a> function, e.g., <code class="code docutils literal notranslate"><span class="pre">IndicatorBox(lower,</span> <span class="pre">upper)</span></code>.</p></li>
+<li><p><strong>upper</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">'float</span></code>, default = None) – A constraint is enforced using the <a class="reference internal" href="#cil.optimisation.functions.IndicatorBox" title="cil.optimisation.functions.IndicatorBox"><code class="xref py py-class docutils literal notranslate"><span class="pre">IndicatorBox</span></code></a> function, e.g., <code class="code docutils literal notranslate"><span class="pre">IndicatorBox(lower,</span> <span class="pre">upper)</span></code>.</p></li>
+<li><p><strong>isotropic</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">boolean</span></code>, default = True) – <p>Use either isotropic or anisotropic definition of TV.</p>
+<div class="math notranslate nohighlight">
+\[|x|_{2} = \sqrt{x_{1}^{2} + x_{2}^{2}},\, (\mbox{isotropic})\]</div>
+<div class="math notranslate nohighlight">
+\[|x|_{1} = |x_{1}| + |x_{2}|\, (\mbox{anisotropic})\]</div>
+</p></li>
+<li><p><strong>split</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">boolean</span></code>, default = False) – Splits the Gradient into spatial gradient and spectral or temporal gradient for multichannel data.</p></li>
+<li><p><strong>strong_convexity_constant</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, default = 0) – <p>A strongly convex term weighted by the <code class="code docutils literal notranslate"><span class="pre">strong_convexity_constant</span></code> (<span class="math notranslate nohighlight">\(\gamma\)</span>) parameter is added to the Total variation.
+Now the <code class="code docutils literal notranslate"><span class="pre">TotalVariation</span></code> function is <span class="math notranslate nohighlight">\(\gamma\)</span> - strongly convex and the proximal operator is</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}} \frac{1}{2\tau}\|u - b\|^{2} + \mathrm{TV}(u) + \frac{\gamma}{2}\|u\|^{2} \Leftrightarrow\]</div>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}} \frac{1}{2\frac{\tau}{1+\gamma\tau}}\|u - \frac{b}{1+\gamma\tau}\|^{2} + \mathrm{TV}(u)\]</div>
+</p></li>
+<li><p><strong>warm_start</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">boolean</span></code>, default = True) – If set to true, the FGP algorithm used to solve the dual problem of the Total Variation Denoising problem (ROF) is initiated by the final value from the previous iteration and not at zero.
+This allows the max_iteration value to be reduced to 5-10 iterations.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>With warm_start set to the default, True, the TV function will keep in memory the range of the gradient of the image to be denoised, i.e. N times the dimensionality of the image. This increases the memory requirements.
+However, during the evaluation of <cite>proximal</cite> the memory requirements will be unchanged as the same amount of memory will need to be allocated and deallocated.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In the case where the Total variation becomes a <span class="math notranslate nohighlight">\(\gamma\)</span> - strongly convex function, i.e.,</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{TV}(u) + \frac{\gamma}{2}\|u\|^{2}\]</div>
+<p><span class="math notranslate nohighlight">\(\gamma\)</span> should be relatively small, so as the second term above will not act as an additional regulariser.
+For more information, see <span id="id21">[<a class="reference internal" href="#id71" title="Julian Rasch and Antonin Chambolle. Inexact first-order primal–dual algorithms. Computational Optimization and Applications, 76(2):381-430, Jun 2020. URL: https://doi.org/10.1007/s10589-020-00186-y, doi:10.1007/s10589-020-00186-y.">11</a>]</span>, <span id="id22">[<a class="reference internal" href="#id65" title="Antonin Chambolle and Thomas Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision, 40(1):120-145, May 2011. URL: https://doi.org/10.1007/s10851-010-0251-1, doi:10.1007/s10851-010-0251-1.">3</a>]</span>.</p>
+</div>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + \alpha\|\nabla u\|_{2,1}\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">TV</span> <span class="o">=</span> <span class="n">TotalVariation</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sol</span> <span class="o">=</span> <span class="n">TV</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">alpha</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + \alpha\|\nabla u\|_{1,1} + \mathbb{I}_{C}(u)\]</div>
+<p>where <span class="math notranslate nohighlight">\(C = \{1.0\leq u\leq 2.0\}\)</span>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">TV</span> <span class="o">=</span> <span class="n">TotalVariation</span><span class="p">(</span><span class="n">isotropic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sol</span> <span class="o">=</span> <span class="n">TV</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">alpha</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + (\alpha\|\nabla u\|_{2,1} + \frac{\gamma}{2}\|u\|^{2})\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">2.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">gamma</span> <span class="o">=</span> <span class="mf">1e-3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">TV</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="n">TotalVariation</span><span class="p">(</span><span class="n">isotropic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">strong_convexity_constant</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sol</span> <span class="o">=</span> <span class="n">TV</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/TotalVariation/#TotalVariation.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the TotalVariation function at <code class="code docutils literal notranslate"><span class="pre">x</span></code> .</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/TotalVariation/#TotalVariation.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of convex conjugate of the TotalVariation function at <code class="code docutils literal notranslate"><span class="pre">x</span></code> .</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.calculate_Lipschitz">
+<span class="sig-name descname"><span class="pre">calculate_Lipschitz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/TotalVariation/#TotalVariation.calculate_Lipschitz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.calculate_Lipschitz" title="Link to this definition">#</a></dt>
+<dd><p>Default value for the Lipschitz constant.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of function <span class="math notranslate nohighlight">\(F\)</span>  evaluated at <span class="math notranslate nohighlight">\(x\)</span>, if it is differentiable</p>
+<div class="math notranslate nohighlight">
+\[F'(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a>, the value of the gradient of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.TotalVariation.gradient_operator">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">gradient_operator</span></span><a class="headerlink" href="#cil.optimisation.functions.TotalVariation.gradient_operator" title="Link to this definition">#</a></dt>
+<dd><p>GradientOperator is created if it is not instantiated yet. The domain of the <cite>_gradient</cite>,
+is created in the <cite>__call__</cite> and <cite>proximal</cite> methods.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="approximate-gradient-base-class">
+<h3>Approximate Gradient base class<a class="headerlink" href="#approximate-gradient-base-class" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">ApproximateGradientSumFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/ApproximateGradientSumFunction/#ApproximateGradientSumFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction" title="Link to this definition">#</a></dt>
+<dd><p>ApproximateGradientSumFunction represents the following sum</p>
+<div class="math notranslate nohighlight">
+\[\sum_{i=0}^{n-1} f_{i} = (f_{0} + f_{2} + ... + f_{n-1})\]</div>
+<p>where there are <span class="math notranslate nohighlight">\(n\)</span> functions. This function class has two ways of calling gradient</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><cite>full_gradient</cite> calculates the gradient of the sum <span class="math notranslate nohighlight">\(\sum_{i=0}^{n-1} \nabla f_{i}\)</span></p></li>
+<li><p><cite>gradient</cite> calls an <cite>approximate_gradient</cite> function which may be less computationally expensive to calculate than the full gradient</p></li>
+</ul>
+</div></blockquote>
+<p>This class is an abstract class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>functions</strong> (<cite>list</cite>  of functions) – A list of functions: <span class="math notranslate nohighlight">\([f_{0}, f_{2}, ..., f_{n-1}]\)</span>. Each function is assumed to be smooth with an implemented <a class="reference internal" href="#cil.optimisation.functions.Function.gradient" title="cil.optimisation.functions.Function.gradient"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradient()</span></code></a> method. All functions must have the same domain. The number of functions (equivalently the length of the list) must be strictly greater than 1.</p></li>
+<li><p><strong>sampler</strong> (An instance of a CIL Sampler class ( <code class="xref py py-meth docutils literal notranslate"><span class="pre">sampler()</span></code>) or of another class which has a <code class="code docutils literal notranslate"><span class="pre">__next__</span></code> function implemented to output integers in <span class="math notranslate nohighlight">\({0,...,n-1}\)</span>.) – This sampler is called each time <code class="code docutils literal notranslate"><span class="pre">gradient</span></code> is called and sets the internal <code class="code docutils literal notranslate"><span class="pre">function_num</span></code> passed to the <code class="code docutils literal notranslate"><span class="pre">approximate_gradient</span></code> function.  Default is <code class="code docutils literal notranslate"><span class="pre">Sampler.random_with_replacement(len(functions))</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We ensure that the approximate gradient is of a similar order of magnitude to the full gradient calculation. For example, in the <code class="code docutils literal notranslate"><span class="pre">SGFunction</span></code> we approximate the full gradient by <span class="math notranslate nohighlight">\(n\nabla f_i\)</span> for an index <span class="math notranslate nohighlight">\(i\)</span> given by the sampler.
+The multiplication by <span class="math notranslate nohighlight">\(n\)</span> is a choice to more easily allow comparisons between stochastic and non-stochastic methods and between stochastic methods with varying numbers of subsets.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Each time <code class="code docutils literal notranslate"><span class="pre">gradient</span></code> is called the class keeps track of which functions have been used to calculate the gradient. This may be useful for debugging or plotting after using this function in an iterative algorithm.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes_indices</span></code> is a list of lists holding the indices of the functions that are processed in each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending a list of the indices of the functions used to calculate the gradient.</p></li>
+<li><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes</span></code> is a list of floats that holds the amount of data that has been processed up until each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning: if your functions do not contain an equal <cite>amount</cite> of data, for example your data was not partitioned into equal batches, then you must first use the <a href="#id23"><span class="problematic" id="id24">`</span></a>set_data_partition_weights” function for this to be accurate.</p></li>
+</ul>
+</div></blockquote>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The <a class="reference internal" href="#cil.optimisation.functions.ApproximateGradientSumFunction.gradient" title="cil.optimisation.functions.ApproximateGradientSumFunction.gradient"><code class="xref py py-meth docutils literal notranslate"><span class="pre">gradient()</span></code></a> returns the approximate gradient depending on an index provided by the  <code class="code docutils literal notranslate"><span class="pre">sampler</span></code> method.</p>
+</div>
+<p class="rubric">Example</p>
+<p>This class is an abstract base class, so we give an example using the SGFunction child class.</p>
+<p>Consider the objective is to minimise:</p>
+<div class="math notranslate nohighlight">
+\[\sum_{i=0}^{n-1} f_{i}(x) = \sum_{i=0}^{n-1}\|A_{i} x - b_{i}\|^{2}\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">list_of_functions</span> <span class="o">=</span> <span class="p">[</span><span class="n">LeastSquares</span><span class="p">(</span><span class="n">Ai</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">bi</span><span class="p">)]</span> <span class="k">for</span> <span class="n">Ai</span><span class="p">,</span><span class="n">bi</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">A_subsets</span><span class="p">,</span> <span class="n">b_subsets</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">f</span> <span class="o">=</span> <span class="n">ApproximateGradientSumFunction</span><span class="p">(</span><span class="n">list_of_functions</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">list_of_functions</span> <span class="o">=</span> <span class="p">[</span><span class="n">LeastSquares</span><span class="p">(</span><span class="n">Ai</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">bi</span><span class="p">)]</span> <span class="k">for</span> <span class="n">Ai</span><span class="p">,</span><span class="n">bi</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">A_subsets</span><span class="p">,</span> <span class="n">b_subsets</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">sequential</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">list_of_functions</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">f</span> <span class="o">=</span> <span class="n">SGFunction</span><span class="p">(</span><span class="n">list_of_functions</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">sampler</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">f</span><span class="o">.</span><span class="n">full_gradient</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="go">This will return :math:`\sum_{i=0}^{n-1} \nabla f_{i}(x)`</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="go">As per the approximate gradient implementation in the SGFunction this will return :math:`\nabla f_{0}`. The choice of the `0` index is because we chose a `sequential` sampler and this is the first time we called `gradient`.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">f</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="go">This will return :math:`\nabla f_{1}` because we chose  a `sequential` sampler and this is the second time we called `gradient`.</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.full_gradient">
+<span class="sig-name descname"><span class="pre">full_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/ApproximateGradientSumFunction/#ApproximateGradientSumFunction.full_gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.full_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the  full gradient of the sum of functions at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\nabla_x(f_{0} + f_{1} + ... + f_{n-1})(x) = \nabla_xf_{0}(x) + \nabla_xf_{1}(x) + ... + \nabla_xf_{n-1}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the gradient of the sum function at x or nothing if <cite>out</cite></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.approximate_gradient">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">approximate_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function_num</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/ApproximateGradientSumFunction/#ApproximateGradientSumFunction.approximate_gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.approximate_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the approximate gradient at a given point <code class="code docutils literal notranslate"><span class="pre">x</span></code> given a <cite>function_number</cite> in {0,…,len(functions)-1}.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+<li><p><strong>function_num</strong> (<cite>int</cite>) – Between 0 and the number of functions in the list</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code> given a <cite>function_number</cite> in {0,…,len(functions)-1}</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/ApproximateGradientSumFunction/#ApproximateGradientSumFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Selects a random function using the <cite>sampler</cite> and then calls the approximate gradient at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.set_data_partition_weights">
+<span class="sig-name descname"><span class="pre">set_data_partition_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/ApproximateGradientSumFunction/#ApproximateGradientSumFunction.set_data_partition_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.set_data_partition_weights" title="Link to this definition">#</a></dt>
+<dd><p>Setter for the partition weights used to calculate the data passes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>weights</strong> (<em>list</em><em> of </em><em>positive floats that sum to one.</em>) – The proportion of the data held in each function. Equivalent to the proportions that you partitioned your data into.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.data_passes_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes_indices</span></span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.data_passes_indices" title="Link to this definition">#</a></dt>
+<dd><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes_indices</span></code> is a list of lists holding the indices of the functions that are processed in each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending a list of the indices of the functions used to calculate the gradient.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.data_passes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes</span></span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.data_passes" title="Link to this definition">#</a></dt>
+<dd><p>if your functions do not contain an equal <cite>amount</cite> of data, for example your data was not partitioned into equal batches, then you must first use the <a href="#id25"><span class="problematic" id="id26">`</span></a>set_data_partition_weights” function for this to be accurate.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes</span></code> is a list of floats that holds the amount of data that has been processed up until each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.ApproximateGradientSumFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.ApproximateGradientSumFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="stochastic-gradient-function">
+<h3>Stochastic Gradient function<a class="headerlink" href="#stochastic-gradient-function" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">SGFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SGFunction/#SGFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SGFunction" title="Link to this definition">#</a></dt>
+<dd><p>Stochastic gradient function, a child class of <code class="code docutils literal notranslate"><span class="pre">ApproximateGradientSumFunction</span></code>, which defines from a list of functions, <span class="math notranslate nohighlight">\({f_0,...,f_{n-1}}\)</span> a <cite>SumFunction</cite>, <span class="math notranslate nohighlight">\(f_0+...+f_{n-1}\)</span> where each time the <cite>gradient</cite> is called, the <code class="code docutils literal notranslate"><span class="pre">sampler</span></code> provides an index, <span class="math notranslate nohighlight">\(i \in {0,...,n-1}\)</span>
+and the <code class="code docutils literal notranslate"><span class="pre">gradient</span></code> method returns the approximate gradient <span class="math notranslate nohighlight">\(n \nabla_x f_i(x)\)</span>. This can be used with the <code class="code docutils literal notranslate"><span class="pre">cil.optimisation.algorithms</span></code> algorithm <code class="code docutils literal notranslate"><span class="pre">GD</span></code> to give a stochastic gradient descent algorithm.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>functions</strong> (<cite>list</cite>  of functions) – A list of functions: <code class="code docutils literal notranslate"><span class="pre">[f_{0},</span> <span class="pre">f_{1},</span> <span class="pre">...,</span> <span class="pre">f_{n-1}]</span></code>. Each function is assumed to be smooth with an implemented <a class="reference internal" href="#cil.optimisation.functions.Function.gradient" title="cil.optimisation.functions.Function.gradient"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradient()</span></code></a> method. Although CIL does not define a domain of a <code class="code docutils literal notranslate"><span class="pre">Function</span></code>, all functions are supposed to have the same domain. The number of functions must be strictly greater than 1.</p></li>
+<li><p><strong>sampler</strong> (An instance of a CIL Sampler class ( <code class="xref py py-meth docutils literal notranslate"><span class="pre">sampler()</span></code>) or of another class which has a <code class="code docutils literal notranslate"><span class="pre">__next__</span></code> function implemented to output integers in {0,…,n-1}.) – This sampler is called each time gradient is called and  sets the internal <code class="code docutils literal notranslate"><span class="pre">function_num</span></code> passed to the <code class="code docutils literal notranslate"><span class="pre">approximate_gradient</span></code> function.  Default is <code class="code docutils literal notranslate"><span class="pre">Sampler.random_with_replacement(len(functions))</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.approximate_gradient">
+<span class="sig-name descname"><span class="pre">approximate_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function_num</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SGFunction/#SGFunction.approximate_gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SGFunction.approximate_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the gradient of the function at index <cite>function_num</cite> at <code class="code docutils literal notranslate"><span class="pre">x</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+<li><p><strong>function_num</strong> (<cite>int</cite>) – Between 0 and the number of functions in the list</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code> given a <cite>function_number</cite> in {0,…,len(functions)-1}</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.data_passes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes</span></span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.data_passes" title="Link to this definition">#</a></dt>
+<dd><p>if your functions do not contain an equal <cite>amount</cite> of data, for example your data was not partitioned into equal batches, then you must first use the <a href="#id27"><span class="problematic" id="id28">`</span></a>set_data_partition_weights” function for this to be accurate.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes</span></code> is a list of floats that holds the amount of data that has been processed up until each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.data_passes_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes_indices</span></span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.data_passes_indices" title="Link to this definition">#</a></dt>
+<dd><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes_indices</span></code> is a list of lists holding the indices of the functions that are processed in each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending a list of the indices of the functions used to calculate the gradient.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.full_gradient">
+<span class="sig-name descname"><span class="pre">full_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.full_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the  full gradient of the sum of functions at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\nabla_x(f_{0} + f_{1} + ... + f_{n-1})(x) = \nabla_xf_{0}(x) + \nabla_xf_{1}(x) + ... + \nabla_xf_{n-1}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the gradient of the sum function at x or nothing if <cite>out</cite></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Selects a random function using the <cite>sampler</cite> and then calls the approximate gradient at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SGFunction.set_data_partition_weights">
+<span class="sig-name descname"><span class="pre">set_data_partition_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SGFunction.set_data_partition_weights" title="Link to this definition">#</a></dt>
+<dd><p>Setter for the partition weights used to calculate the data passes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>weights</strong> (<em>list</em><em> of </em><em>positive floats that sum to one.</em>) – The proportion of the data held in each function. Equivalent to the proportions that you partitioned your data into.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="sag-function">
+<h3>SAG function<a class="headerlink" href="#sag-function" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">SAGFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SAGFunction/#SAGFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SAGFunction" title="Link to this definition">#</a></dt>
+<dd><p>The stochastic average gradient (SAG) function takes a index <span class="math notranslate nohighlight">\(i_k\)</span> and calculates the approximate gradient of <span class="math notranslate nohighlight">\(\sum_{i=1}^{n-1}f_i\)</span> at iteration <span class="math notranslate nohighlight">\(x_k\)</span> as</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\sum_{i=1}^{n-1} g_i^k \qquad \text{where} \qquad g_i^k= \begin{cases}
+                                                            \nabla f_i(x_k), \text{ if } i=i_k\\
+                                                            g_i^{k-1},\text{ otherwise }
+                                                            \end{cases}\end{split}\]</div>
+<p>The idea is that by incorporating a memory of previous gradient values the SAG method can achieve a faster convergence rate than black-box stochastic gradient methods.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Compared with the literature, we do not divide by <span class="math notranslate nohighlight">\(n\)</span>, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</p>
+<p>Schmidt, M., Le Roux, N. and Bach, F., 2017. Minimizing finite sums with the stochastic average gradient. Mathematical Programming, 162, pp.83-112. <a class="reference external" href="https://doi.org/10.1007/s10107-016-1030-6">https://doi.org/10.1007/s10107-016-1030-6</a>.</p>
+<dl>
+<dt>functions<span class="classifier"><cite>list</cite>  of functions</span></dt><dd><p>A list of functions: <span class="math notranslate nohighlight">\([f_{0}, f_{1}, ..., f_{n-1}]\)</span>. Each function is assumed to be smooth with an implemented <a class="reference internal" href="#cil.optimisation.functions.Function.gradient" title="cil.optimisation.functions.Function.gradient"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradient()</span></code></a> method. All functions must have the same domain. The number of functions (equivalently the length of the list <cite>n</cite>) must be strictly greater than 1.</p>
+</dd>
+<dt>sampler: An instance of a CIL Sampler class ( <code class="xref py py-meth docutils literal notranslate"><span class="pre">sampler()</span></code>) or of another class which has a <cite>next</cite> function implemented to output integers in <span class="math notranslate nohighlight">\({0,...,n-1}\)</span>.</dt><dd><p>This sampler is called each time <cite>gradient</cite> is called and sets the internal <cite>function_num</cite> passed to the <cite>approximate_gradient</cite> function.  Default is <cite>Sampler.random_with_replacement(len(functions))</cite>.</p>
+</dd>
+</dl>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The user has the option of calling the class method <cite>warm_start_approximate_gradients</cite> after initialising this class. This will compute and store the gradient for each function at an initial point, equivalently setting <span class="math notranslate nohighlight">\(g_i^0=\nabla f_i(x_0)\)</span> for initial point <span class="math notranslate nohighlight">\(x_0\)</span>.  If this method is not called, the gradients are initialised with zeros.</p>
+<p>This function’s memory requirements are <cite>n + 3</cite> times the image space, that is with 100 subsets the memory requirement is 103 images, which is huge.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.approximate_gradient">
+<span class="sig-name descname"><span class="pre">approximate_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function_num</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SAGFunction/#SAGFunction.approximate_gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.approximate_gradient" title="Link to this definition">#</a></dt>
+<dd><p>SAG approximate gradient, calculated at the point <span class="math notranslate nohighlight">\(x\)</span> and updated using the function index given by <cite>function_num</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData object</em><em>)</em>) – Element in the domain of the <cite>functions</cite></p></li>
+<li><p><strong>function_num</strong> (<cite>int</cite>) – Between 0 and the number of functions in the list</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.warm_start_approximate_gradients">
+<span class="sig-name descname"><span class="pre">warm_start_approximate_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SAGFunction/#SAGFunction.warm_start_approximate_gradients"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.warm_start_approximate_gradients" title="Link to this definition">#</a></dt>
+<dd><p>A function to warm start SAG or SAGA algorithms by initialising all the gradients at an initial point. Equivalently setting <span class="math notranslate nohighlight">\(g_i^0=
+abla f_i(x_0)\)</span> for initial point <span class="math notranslate nohighlight">\(x_0\)</span>.</p>
+<blockquote>
+<div><dl class="simple">
+<dt>initial: DataContainer,</dt><dd><p>The initial point to warmstart the calculation</p>
+</dd>
+</dl>
+<p>When using SAG or SAGA with a deterministic algorithm, you should warm start the SAG-SAGA Function with the same initial point that you initialise the algorithm</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.data_passes_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes_indices</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.data_passes_indices" title="Link to this definition">#</a></dt>
+<dd><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes_indices</span></code> is a list of lists holding the indices of the functions that are processed in each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending a list of the indices of the functions used to calculate the gradient.
+This is overwritten from the base class to first check to see if the approximate gradient was warm started and, if it was, ensure that the first element of <cite>data_passes_indices</cite> contains each index used to warm start and the index used in the first call to <cite>gradient</cite>. Thus the length of <cite>data_passes_indices</cite> is always equal to the number of calls to <cite>gradient</cite>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.data_passes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.data_passes" title="Link to this definition">#</a></dt>
+<dd><p>if your functions do not contain an equal <cite>amount</cite> of data, for example your data was not partitioned into equal batches, then you must first use the <a href="#id29"><span class="problematic" id="id30">`</span></a>set_data_partition_weights” function for this to be accurate.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes</span></code> is a list of floats that holds the amount of data that has been processed up until each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.full_gradient">
+<span class="sig-name descname"><span class="pre">full_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.full_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the  full gradient of the sum of functions at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\nabla_x(f_{0} + f_{1} + ... + f_{n-1})(x) = \nabla_xf_{0}(x) + \nabla_xf_{1}(x) + ... + \nabla_xf_{n-1}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the gradient of the sum function at x or nothing if <cite>out</cite></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Selects a random function using the <cite>sampler</cite> and then calls the approximate gradient at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGFunction.set_data_partition_weights">
+<span class="sig-name descname"><span class="pre">set_data_partition_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGFunction.set_data_partition_weights" title="Link to this definition">#</a></dt>
+<dd><p>Setter for the partition weights used to calculate the data passes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>weights</strong> (<em>list</em><em> of </em><em>positive floats that sum to one.</em>) – The proportion of the data held in each function. Equivalent to the proportions that you partitioned your data into.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="saga-function">
+<h3>SAGA function<a class="headerlink" href="#saga-function" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">SAGAFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SAGFunction/#SAGAFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction" title="Link to this definition">#</a></dt>
+<dd><p>SAGA (SAG-Ameliore) is an accelerated version of the stochastic average gradient (SAG) function which takes a index <span class="math notranslate nohighlight">\(i_k\)</span> and calculates the approximate gradient of <span class="math notranslate nohighlight">\(\sum_{i=1}^{n-1}f_i\)</span> at iteration <span class="math notranslate nohighlight">\(x_k\)</span> as</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}n\left(g_{i_k}^{k}-g_{i_k}^{k-1}\right)+\sum_{i=1}^{n-1} g_i^{k-1} \qquad \text{where} \qquad g_i^k= \begin{cases}
+                                                           \nabla f_i(x_k), \text{ if } i=i_k\\
+                                                           g_i^{k-1},\text{ otherwise}
+                                                           \end{cases}\end{split}\]</div>
+<p>SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates. Compared to SAG it is an unbiased estimator.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Compared with the literature, we do not divide by <span class="math notranslate nohighlight">\(n\)</span>, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</p>
+<p>This function’s memory requirements are <cite>n + 3</cite> times the image space, that is with 100 subsets the memory requirement is 103 images, which is huge.</p>
+<p>Defazio, A., Bach, F. and Lacoste-Julien, S., 2014. SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. Advances in neural information processing systems, 27. <a class="reference external" href="https://proceedings.neurips.cc/paper_files/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">https://proceedings.neurips.cc/paper_files/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf</a></p>
+</div>
+<section id="id31">
+<h4>Parameters:<a class="headerlink" href="#id31" title="Link to this heading">#</a></h4>
+<dl>
+<dt>functions<span class="classifier"><cite>list</cite>  of functions</span></dt><dd><p>A list of functions: <code class="code docutils literal notranslate"><span class="pre">[f_{0},</span> <span class="pre">f_{1},</span> <span class="pre">...,</span> <span class="pre">f_{n-1}]</span></code>. Each function is assumed to be smooth function with an implemented <a class="reference internal" href="#cil.optimisation.functions.Function.gradient" title="cil.optimisation.functions.Function.gradient"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradient()</span></code></a> method. Each function must have the same domain. The number of functions must be strictly greater than 1.</p>
+</dd>
+<dt>sampler: An instance of one of the <code class="xref py py-meth docutils literal notranslate"><span class="pre">sampler()</span></code> classes which has a <cite>next</cite> function implemented and a <cite>num_indices</cite> property.</dt><dd><p>This sampler is called each time gradient is called and  sets the internal <cite>function_num</cite> passed to the <cite>approximate_gradient</cite> function.  The <cite>num_indices</cite> must match the number of functions provided. Default is <cite>Sampler.random_with_replacement(len(functions))</cite>.</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The user has the option of calling the class method <cite>warm_start_approximate_gradients</cite> after initialising this class. This will compute and store the gradient for each function at an initial point, equivalently setting <span class="math notranslate nohighlight">\(g_i^0=\nabla f_i(x_0)\)</span> for initial point <span class="math notranslate nohighlight">\(x_0\)</span>. If this method is not called, the gradients are initialised with zeros.</p>
+</div>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.approximate_gradient">
+<span class="sig-name descname"><span class="pre">approximate_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function_num</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.approximate_gradient" title="Link to this definition">#</a></dt>
+<dd><p>SAG approximate gradient, calculated at the point <span class="math notranslate nohighlight">\(x\)</span> and updated using the function index given by <cite>function_num</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData object</em><em>)</em>) – Element in the domain of the <cite>functions</cite></p></li>
+<li><p><strong>function_num</strong> (<cite>int</cite>) – Between 0 and the number of functions in the list</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.data_passes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.data_passes" title="Link to this definition">#</a></dt>
+<dd><p>if your functions do not contain an equal <cite>amount</cite> of data, for example your data was not partitioned into equal batches, then you must first use the <a href="#id32"><span class="problematic" id="id33">`</span></a>set_data_partition_weights” function for this to be accurate.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes</span></code> is a list of floats that holds the amount of data that has been processed up until each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.data_passes_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes_indices</span></span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.data_passes_indices" title="Link to this definition">#</a></dt>
+<dd><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes_indices</span></code> is a list of lists holding the indices of the functions that are processed in each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending a list of the indices of the functions used to calculate the gradient.
+This is overwritten from the base class to first check to see if the approximate gradient was warm started and, if it was, ensure that the first element of <cite>data_passes_indices</cite> contains each index used to warm start and the index used in the first call to <cite>gradient</cite>. Thus the length of <cite>data_passes_indices</cite> is always equal to the number of calls to <cite>gradient</cite>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.full_gradient">
+<span class="sig-name descname"><span class="pre">full_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.full_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the  full gradient of the sum of functions at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\nabla_x(f_{0} + f_{1} + ... + f_{n-1})(x) = \nabla_xf_{0}(x) + \nabla_xf_{1}(x) + ... + \nabla_xf_{n-1}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the gradient of the sum function at x or nothing if <cite>out</cite></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Selects a random function using the <cite>sampler</cite> and then calls the approximate gradient at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.set_data_partition_weights">
+<span class="sig-name descname"><span class="pre">set_data_partition_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.set_data_partition_weights" title="Link to this definition">#</a></dt>
+<dd><p>Setter for the partition weights used to calculate the data passes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>weights</strong> (<em>list</em><em> of </em><em>positive floats that sum to one.</em>) – The proportion of the data held in each function. Equivalent to the proportions that you partitioned your data into.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SAGAFunction.warm_start_approximate_gradients">
+<span class="sig-name descname"><span class="pre">warm_start_approximate_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SAGAFunction.warm_start_approximate_gradients" title="Link to this definition">#</a></dt>
+<dd><p>A function to warm start SAG or SAGA algorithms by initialising all the gradients at an initial point. Equivalently setting <span class="math notranslate nohighlight">\(g_i^0=
+abla f_i(x_0)\)</span> for initial point <span class="math notranslate nohighlight">\(x_0\)</span>.</p>
+<blockquote>
+<div><dl class="simple">
+<dt>initial: DataContainer,</dt><dd><p>The initial point to warmstart the calculation</p>
+</dd>
+</dl>
+<p>When using SAG or SAGA with a deterministic algorithm, you should warm start the SAG-SAGA Function with the same initial point that you initialise the algorithm</p>
+</div></blockquote>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+<section id="stochastic-variance-reduced-gradient-function">
+<h3>Stochastic Variance Reduced Gradient Function<a class="headerlink" href="#stochastic-variance-reduced-gradient-function" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">SVRGFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snapshot_update_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_gradients</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SVRGFunction/#SVRGFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction" title="Link to this definition">#</a></dt>
+<dd><p>The Stochastic Variance Reduced Gradient (SVRG) function calculates the approximate gradient of <span class="math notranslate nohighlight">\(\sum_{i=1}^{n-1}f_i\)</span>.  For this approximation, every <cite>snapshot_update_interval</cite> number of iterations, a full gradient calculation is made at this “snapshot” point. Intermediate gradient calculations update this snapshot by taking a index <span class="math notranslate nohighlight">\(i_k\)</span> and calculating the gradient of :math:<a href="#id34"><span class="problematic" id="id35">`</span></a>f_{i_k}`s at the current iterate and the snapshot, updating the approximate gradient to be:</p>
+<div class="math notranslate nohighlight">
+\[n*\nabla f_{i_k}(x_k) - n*\nabla f_{i_k}(\tilde{x}) + \nabla \sum_{i=0}^{n-1}f_i(\tilde{x}),\]</div>
+<p>where <span class="math notranslate nohighlight">\(\tilde{x}\)</span> is the latest “snapshot” point and <span class="math notranslate nohighlight">\(x_k\)</span> is the value at the current iteration.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Compared with the literature, we multiply by <span class="math notranslate nohighlight">\(n\)</span>, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In the case where <cite>store_gradients=False</cite> the memory requirements are 4 times the image size (1 stored full gradient at the “snapshot”, one stored “snapshot” point and two lots of intermediary calculations). Alternatively, if  <cite>store_gradients=True</cite>  the memory requirement is <cite>n+4</cite> (<cite>n</cite> gradients at the snapshot for each function in the sum, one stored full gradient at the “snapshot”, one stored “snapshot” point and two lots of intermediary calculations).</p>
+<p>Johnson, R. and Zhang, T., 2013. Accelerating stochastic gradient descent using predictive variance reduction. Advances in neural information processing systems, 26.https://proceedings.neurips.cc/paper_files/paper/2013/file/ac1dd209cbcc5e5d1c6e28598e8cbbe8-Paper.pdf</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>functions</strong> (<cite>list</cite>  of functions) – A list of functions: <code class="code docutils literal notranslate"><span class="pre">[f_{0},</span> <span class="pre">f_{1},</span> <span class="pre">...,</span> <span class="pre">f_{n-1}]</span></code>. Each function is assumed to be smooth with an implemented <a class="reference internal" href="#cil.optimisation.functions.Function.gradient" title="cil.optimisation.functions.Function.gradient"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradient()</span></code></a> method. All functions must have the same domain. The number of functions must be strictly greater than 1.</p></li>
+<li><p><strong>sampler</strong> (An instance of a CIL Sampler class ( <code class="xref py py-meth docutils literal notranslate"><span class="pre">sampler()</span></code>) or of another class which has a <cite>next</cite> function implemented to output integers in {0, 1, …, n-1}.) – This sampler is called each time gradient is called and  sets the internal <cite>function_num</cite> passed to the <cite>approximate_gradient</cite> function.  Default is <cite>Sampler.random_with_replacement(len(functions))</cite>.</p></li>
+<li><p><strong>snapshot_update_interval</strong> (<em>positive int</em><em> or </em><em>None</em><em>, </em><em>optional</em>) – The interval for updating the full gradient (taking a snapshot). The default is 2*len(functions) so a “snapshot” is taken every 2*len(functions) iterations. If the user passes <cite>0</cite> then no full gradient snapshots will be taken.</p></li>
+<li><p><strong>store_gradients</strong> (bool, default: <cite>False</cite>) – Flag indicating whether to store an update a list of gradients for each function <span class="math notranslate nohighlight">\(f_i\)</span> or just to store the snapshot point :math:` tilde{x}` and its gradient <span class="math notranslate nohighlight">\(\nabla \sum_{i=0}^{n-1}f_i(\tilde{x})\)</span>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SVRGFunction/#SVRGFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Selects a random function using the <cite>sampler</cite> and then calls the approximate gradient at <code class="code docutils literal notranslate"><span class="pre">x</span></code> or calculates a full gradient depending on the update frequency</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData object</em><em>)</em>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> (e.g. ImageData object)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.approximate_gradient">
+<span class="sig-name descname"><span class="pre">approximate_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function_num</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SVRGFunction/#SVRGFunction.approximate_gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.approximate_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the stochastic gradient at the point <span class="math notranslate nohighlight">\(x\)</span> by using the gradient of the selected function, indexed by <span class="math notranslate nohighlight">\(i_k\)</span>, the <cite>function_number</cite> in {0,…,len(functions)-1}, and the full gradient at the snapshot <span class="math notranslate nohighlight">\(\tilde{x}\)</span></p>
+<div class="math notranslate nohighlight">
+\[n*\nabla f_{i_k}(x_k) - n*\nabla f_{i_k}(\tilde{x}) + \nabla \sum_{i=0}^{n-1}f_i(\tilde{x})\]</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Compared with the literature, we multiply by <span class="math notranslate nohighlight">\(n\)</span>, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData object</em><em>)</em>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+<li><p><strong>function_num</strong> (<cite>int</cite>) – Between 0 and n-1, where n is the number of functions in the list</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code> given a <cite>function_number</cite> in {0,…,len(functions)-1}</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> (e.g. ImageData object)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.data_passes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes</span></span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.data_passes" title="Link to this definition">#</a></dt>
+<dd><p>if your functions do not contain an equal <cite>amount</cite> of data, for example your data was not partitioned into equal batches, then you must first use the <a href="#id36"><span class="problematic" id="id37">`</span></a>set_data_partition_weights” function for this to be accurate.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes</span></code> is a list of floats that holds the amount of data that has been processed up until each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.data_passes_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes_indices</span></span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.data_passes_indices" title="Link to this definition">#</a></dt>
+<dd><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes_indices</span></code> is a list of lists holding the indices of the functions that are processed in each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending a list of the indices of the functions used to calculate the gradient.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.full_gradient">
+<span class="sig-name descname"><span class="pre">full_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.full_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the  full gradient of the sum of functions at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\nabla_x(f_{0} + f_{1} + ... + f_{n-1})(x) = \nabla_xf_{0}(x) + \nabla_xf_{1}(x) + ... + \nabla_xf_{n-1}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the gradient of the sum function at x or nothing if <cite>out</cite></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.SVRGFunction.set_data_partition_weights">
+<span class="sig-name descname"><span class="pre">set_data_partition_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.SVRGFunction.set_data_partition_weights" title="Link to this definition">#</a></dt>
+<dd><p>Setter for the partition weights used to calculate the data passes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>weights</strong> (<em>list</em><em> of </em><em>positive floats that sum to one.</em>) – The proportion of the data held in each function. Equivalent to the proportions that you partitioned your data into.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="loopless-stochastic-variance-reduced-gradient-function">
+<h3>Loopless Stochastic Variance Reduced Gradient Function<a class="headerlink" href="#loopless-stochastic-variance-reduced-gradient-function" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">LSVRGFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snapshot_update_probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_gradients</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SVRGFunction/#LSVRGFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction" title="Link to this definition">#</a></dt>
+<dd><dl>
+<dt>“”</dt><dd><p>A class representing a function for Loopless Stochastic Variance Reduced Gradient (SVRG) approximation. This is similar to SVRG, except the full gradient at a “snapshot”  is calculated at random intervals rather than at fixed numbers of iterations.</p>
+<p>Kovalev, D., Horváth, S. &amp;; Richtárik, P.. (2020). Don’t Jump Through Hoops and Remove Those Loops:  SVRG and Katyusha are Better Without the Outer Loop. Proceedings of the 31st International Conference  on Algorithmic Learning Theory, in Proceedings of Machine Learning Research 117:451-467 Available from <a class="reference external" href="https://proceedings.mlr.press/v117/kovalev20a.html">https://proceedings.mlr.press/v117/kovalev20a.html</a>.</p>
+<blockquote>
+<div><dl>
+<dt>functions<span class="classifier"><cite>list</cite>  of functions</span></dt><dd><p>A list of functions: <code class="code docutils literal notranslate"><span class="pre">[f_{0},</span> <span class="pre">f_{1},</span> <span class="pre">...,</span> <span class="pre">f_{n-1}]</span></code>. Each function is assumed to be smooth with an implemented <a class="reference internal" href="#cil.optimisation.functions.Function.gradient" title="cil.optimisation.functions.Function.gradient"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradient()</span></code></a> method. All functions must have the same domain. The number of functions <cite>n</cite> must be strictly greater than 1.</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl>
+<dt>sampler: An instance of a CIL Sampler class ( <code class="xref py py-meth docutils literal notranslate"><span class="pre">sampler()</span></code>) or of another class which has a <cite>next</cite> function implemented to output integers in {0,…,n-1}.</dt><dd><p>This sampler is called each time gradient is called and  sets the internal <cite>function_num</cite> passed to the <cite>approximate_gradient</cite> function.  Default is <cite>Sampler.random_with_replacement(len(functions))</cite>.</p>
+</dd>
+<dt>snapshot_update_probability: positive float, default: 1/n</dt><dd><p>The probability of updating the full gradient (taking a snapshot) at each iteration. The default is <span class="math notranslate nohighlight">\(1./n\)</span> so, in expectation, a snapshot will be taken every <span class="math notranslate nohighlight">\(n\)</span> iterations.</p>
+</dd>
+<dt>store_gradients<span class="classifier">bool, default: <cite>False</cite></span></dt><dd><p>Flag indicating whether to store an update a list of gradients for each function <span class="math notranslate nohighlight">\(f_i\)</span> or just to store the snapshot point :math:`        ilde{x}` and it’s gradient :math:<a href="#id38"><span class="problematic" id="id39">`</span></a></p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>abla sum_{i=0}^{n-1}f_i(       ilde{x})`.</p>
+<blockquote>
+<div><p>In the case where <cite>store_gradients=False</cite> the memory requirements are 4 times the image size (1 stored full gradient at the “snapshot”, one stored “snapshot” point and two lots of intermediary calculations). Alternatively, if  <cite>store_gradients=True</cite>  the memory requirement is <cite>n+4</cite> (<cite>n</cite> gradients at the snapshot for each function in the sum, one stored full gradient at the “snapshot”, one stored “snapshot” point and two lots of intermediary calculations).</p>
+</div></blockquote>
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \sum_{i} L_{i}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.Lmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Lmax</span></span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.Lmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the maximum Lipschitz constant for the SumFunction</p>
+<div class="math notranslate nohighlight">
+\[L = \max_{i}\{L_{i}\}\]</div>
+<p>where <span class="math notranslate nohighlight">\(L_{i}\)</span> is the Lipschitz constant of the smooth function <span class="math notranslate nohighlight">\(F_{i}\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.approximate_gradient">
+<span class="sig-name descname"><span class="pre">approximate_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function_num</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.approximate_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the stochastic gradient at the point <span class="math notranslate nohighlight">\(x\)</span> by using the gradient of the selected function, indexed by <span class="math notranslate nohighlight">\(i_k\)</span>, the <cite>function_number</cite> in {0,…,len(functions)-1}, and the full gradient at the snapshot <span class="math notranslate nohighlight">\(\tilde{x}\)</span></p>
+<div class="math notranslate nohighlight">
+\[n*\nabla f_{i_k}(x_k) - n*\nabla f_{i_k}(\tilde{x}) + \nabla \sum_{i=0}^{n-1}f_i(\tilde{x})\]</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Compared with the literature, we multiply by <span class="math notranslate nohighlight">\(n\)</span>, the number of functions, so that we return an approximate gradient of the whole sum function and not an average gradient.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData object</em><em>)</em>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+<li><p><strong>function_num</strong> (<cite>int</cite>) – Between 0 and n-1, where n is the number of functions in the list</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code> given a <cite>function_number</cite> in {0,…,len(functions)-1}</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> (e.g. ImageData object)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.centered_at">
+<span class="sig-name descname"><span class="pre">centered_at</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.centered_at" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns a translated function, namely if we have a function <span class="math notranslate nohighlight">\(F(x)\)</span> the center is at the origin.</dt><dd><p>TranslateFunction is <span class="math notranslate nohighlight">\(F(x - b)\)</span> and the center is at point b.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>center</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The point to center the function at.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The translated function.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.data_passes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes</span></span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.data_passes" title="Link to this definition">#</a></dt>
+<dd><p>if your functions do not contain an equal <cite>amount</cite> of data, for example your data was not partitioned into equal batches, then you must first use the <a href="#id40"><span class="problematic" id="id41">`</span></a>set_data_partition_weights” function for this to be accurate.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes</span></code> is a list of floats that holds the amount of data that has been processed up until each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.data_passes_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data_passes_indices</span></span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.data_passes_indices" title="Link to this definition">#</a></dt>
+<dd><p>The property <code class="code docutils literal notranslate"><span class="pre">data_passes_indices</span></code> is a list of lists holding the indices of the functions that are processed in each call of <cite>gradient</cite>. This list is updated each time <cite>gradient</cite> is called by appending a list of the indices of the functions used to calculate the gradient.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.full_gradient">
+<span class="sig-name descname"><span class="pre">full_gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.full_gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the  full gradient of the sum of functions at <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\nabla_x(f_{0} + f_{1} + ... + f_{n-1})(x) = \nabla_xf_{0}(x) + \nabla_xf_{1}(x) + ... + \nabla_xf_{n-1}(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the gradient of the sum function at x or nothing if <cite>out</cite></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of function <span class="math notranslate nohighlight">\(\tau F\)</span>  evaluated at x</p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F}(x) = \underset{z}{\text{argmin}} \frac{1}{2}\|z - x\|^{2} + \tau F(z)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the proximal operator of the function at x with scalar <span class="math notranslate nohighlight">\(\tau\)</span>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the proximal operator of the convex conjugate of function <span class="math notranslate nohighlight">\(\tau F\)</span> evaluated at <span class="math notranslate nohighlight">\(x^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x^{*}) = \underset{z^{*}}{\text{argmin}} \frac{1}{2}\|z^{*} - x^{*}\|^{2} + \tau F^{*}(z^{*})\]</div>
+<p>Due to Moreau’s identity, we have an analytic formula to compute the proximal operator of the convex conjugate <span class="math notranslate nohighlight">\(F^{*}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\text{prox}_{\tau F^{*}}(x) = x - \tau\text{prox}_{\tau^{-1} F}(\tau^{-1}x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p></li>
+<li><p><strong>tau</strong> (<em>scalar</em>)</p></li>
+<li><p><strong>out</strong> (<em>return DataContainer</em><em>, </em><em>if None a new DataContainer is returned</em><em>, </em><em>default None.</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>DataContainer, the value of the proximal operator of the convex conjugate at point <span class="math notranslate nohighlight">\(x\)</span> for scalar <span class="math notranslate nohighlight">\(\tau\)</span> or None if <cite>out</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.set_data_partition_weights">
+<span class="sig-name descname"><span class="pre">set_data_partition_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.set_data_partition_weights" title="Link to this definition">#</a></dt>
+<dd><p>Setter for the partition weights used to calculate the data passes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>weights</strong> (<em>list</em><em> of </em><em>positive floats that sum to one.</em>) – The proportion of the data held in each function. Equivalent to the proportions that you partitioned your data into.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.LSVRGFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/SVRGFunction/#LSVRGFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.LSVRGFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Selects a random function using the <cite>sampler</cite> and then calls the approximate gradient at <code class="code docutils literal notranslate"><span class="pre">x</span></code> or calculates a full gradient depending on the update probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> (</em><em>e.g. ImageData objects</em><em>)</em>)</p></li>
+<li><p><strong>out</strong> (return DataContainer, if <cite>None</cite> a new DataContainer is returned, default <cite>None</cite>.)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the value of the approximate gradient of the sum function at <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a> (e.g. ImageData object)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+<section id="utilities">
+<h2>Utilities<a class="headerlink" href="#utilities" title="Link to this heading">#</a></h2>
+<p>Contains utilities for the CIL optimisation framework.</p>
+<section id="samplers">
+<h3>Samplers<a class="headerlink" href="#samplers" title="Link to this heading">#</a></h3>
+<p>Here, we define samplers that select from a list of indices {0, 1, …, N-1} either randomly or by some deterministic pattern.
+The <code class="code docutils literal notranslate"><span class="pre">cil.optimisation.utilities.sampler</span></code> class defines a function <code class="code docutils literal notranslate"><span class="pre">next()</span></code> which gives the next sample. It also has utility to <code class="code docutils literal notranslate"><span class="pre">get_samples</span></code> to access which samples have or will be drawn.</p>
+<p>For ease of use we provide the following static methods in <cite>cil.optimisation.utilities.sampler</cite> that allow you to configure your sampler object rather than initialising the classes directly:</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.from_function">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Sampler.</span></span><span class="sig-name descname"><span class="pre">from_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.from_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.from_function" title="Link to this definition">#</a></dt>
+<dd><blockquote>
+<div><p>Instantiate a sampler that wraps a function for index selection.</p>
+<dl class="simple">
+<dt>num_indices: int</dt><dd><p>The sampler will select from a range of indices 0 to num_indices.</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl>
+<dt>function<span class="classifier">callable</span></dt><dd><p>A deterministic function that takes an integer as an argument, representing the iteration number, and returns an integer between 0 and num_indices. The function signature should be function(iteration_number: int) -&gt; int</p>
+<dl class="simple">
+<dt>prob_weights: list of floats of length num_indices that sum to 1. Default is [1 / num_indices] * num_indices</dt><dd><p>Consider that the sampler is incremented a large number of times this argument holds the expected number of times each index would be outputted,  normalised to 1.</p>
+</dd>
+</dl>
+<dl class="simple">
+<dt>Sampler</dt><dd><p>An instance of the Sampler class which samples from a function.</p>
+</dd>
+</dl>
+<p>This example creates a sampler that always outputs 2.  The probability weights are passed to the sampler as they are not uniform.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">num_indices</span><span class="o">=</span><span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_sampling_function</span><span class="p">(</span><span class="n">iteration_number</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">return</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">my_sampling_function</span><span class="p">,</span> <span class="n">prob_weights</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">12</span><span class="p">)))</span>
+<span class="go">[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]</span>
+</pre></div>
+</div>
+<p>This example creates a sampler that outputs sequential indices, starting from 1.  The probability weights are not passed to the sampler as they are uniform.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">num_indices</span><span class="o">=</span><span class="mi">10</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_sampling_function</span><span class="p">(</span><span class="n">iteration_number</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">return</span> <span class="p">(</span><span class="n">iteration_number</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">10</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">my_sampling_function</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">25</span><span class="p">)))</span>
+<span class="go">[1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5]</span>
+</pre></div>
+</div>
+<p>This example creates a sampler that samples in order from a custom list. The num_indices  is 6, although note that the index 5 is never output by the sampler. The number of indices must be at least one greater than any of the elements in the custom_list.
+The probability weights are passed to the sampler as they are not uniform.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">custom_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">num_indices</span> <span class="o">=</span> <span class="mi">6</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_sampling_function</span><span class="p">(</span><span class="n">iteration_number</span><span class="p">,</span> <span class="n">custom_list</span><span class="o">=</span><span class="n">custom_list</span><span class="p">]):</span>
+<span class="gp">&gt;&gt;&gt; </span>   <span class="k">return</span><span class="p">(</span><span class="n">custom_list</span><span class="p">[</span><span class="n">iteration_number</span><span class="o">%</span><span class="nb">len</span><span class="p">(</span><span class="n">custom_list</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">my_sampling_function</span><span class="p">,</span> <span class="n">prob_weights</span><span class="o">=</span><span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">25</span><span class="p">)))</span>
+<span class="go">[0, 0, 0, 0, 0, 0, 3, 2, 1, 4, 0, 0, 0, 0, 0, 0, 3, 2, 1, 4, 0, 0, 0, 0, 0]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="p">)</span>
+<span class="go">Sampler that wraps a function that takes an iteration number and selects from a list of indices {0, 1, …, N-1}, where N is the number of indices.</span>
+<span class="go">Type : from_function</span>
+<span class="go">Current iteration number : 0</span>
+<span class="go">number of indices : 6</span>
+<span class="go">Probability weights : [0.6, 0.1, 0.1, 0.1, 0.1, 0.0]</span>
+</pre></div>
+</div>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.sequential">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Sampler.</span></span><span class="sig-name descname"><span class="pre">sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.sequential" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler that outputs sequential indices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class that will generate indices sequentially.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">sequential</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="go">[0 1 2 3 4]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.staggered">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Sampler.</span></span><span class="sig-name descname"><span class="pre">staggered</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.staggered"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.staggered" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs in a staggered order.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p></li>
+<li><p><strong>stride</strong> (<em>int</em>) – The stride between returned indices. The stride should be less than the num_indices.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class that will generate indices in a staggered pattern.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">staggered</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">4</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+<span class="go">[ 0  4  8 12 16]</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">staggered</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">17</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">8</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+<span class="go">[ 0  8  16 1 9 2 10 3 11 4]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.herman_meyer">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Sampler.</span></span><span class="sig-name descname"><span class="pre">herman_meyer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.herman_meyer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.herman_meyer" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs in a Herman Meyer order.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices. For Herman-Meyer sampling this number should not be prime.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class which outputs in a Herman Meyer order.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<section id="id42">
+<h4>Reference<a class="headerlink" href="#id42" title="Link to this heading">#</a></h4>
+<p>With thanks to Imraj Singh and Zeljko Kereta for their help with the initial implementation of the Herman Meyer sampling. Their implementation was used in:</p>
+<p>Singh I, et al. Deep Image Prior PET Reconstruction using a SIRF-Based Objective - IEEE MIC, NSS &amp; RTSD 2022. <a class="reference external" href="https://discovery.ucl.ac.uk/id/eprint/10176077/1/MIC_Conference_Record.pdf">https://discovery.ucl.ac.uk/id/eprint/10176077/1/MIC_Conference_Record.pdf</a></p>
+<p>The sampling method was introduced in:</p>
+<p>Herman GT, Meyer LB. Algebraic reconstruction techniques can be made computationally efficient. IEEE Trans Med Imaging.  doi: 10.1109/42.241889.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span><span class="o">=</span><span class="n">Sampler</span><span class="o">.</span><span class="n">herman_meyer</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
+<span class="go">[ 0  6  3  9  1  7  4 10  2  8  5 11  0  6  3  9]</span>
+</pre></div>
+</div>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.random_with_replacement">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Sampler.</span></span><span class="sig-name descname"><span class="pre">random_with_replacement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.random_with_replacement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.random_with_replacement" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs an index between 0 - num_indices with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices</p></li>
+<li><p><strong>prob</strong> (<em>list</em><em> of </em><em>floats</em><em>, </em><em>optional</em>) – The probability for each index to be selected by the ‘next’ operation. If not provided, the indices will be sampled uniformly. The list should have a length equal to num_indices, and the values should sum to 1</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – Used to initialise the random number generator where repeatability is required.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the <cite>RandomSampler</cite> class that will generate indices randomly with replacement</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><cite>RandomSampler</cite></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">random_with_replacement</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+<span class="go">[3 4 0 0 2 3 3 2 2 1]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">random_with_replacement</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+<span class="go">[0 1 3 0 0 3 0 0 0 0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.random_without_replacement">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Sampler.</span></span><span class="sig-name descname"><span class="pre">random_without_replacement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.random_without_replacement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.random_without_replacement" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs an index between 0 - num_indices. Once sampled the index will not be sampled again until all indices have been returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – Used to initialise the random number generator where repeatability is required.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the <cite>RandomSampler</cite> class that will generate indices randomly without replacement</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><cite>RandomSampler</cite></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span><span class="o">=</span><span class="n">Sampler</span><span class="o">.</span><span class="n">randomWithoutReplacement</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
+<span class="go">[6 2 1 0 4 3 5 1 0 4 2 5 6 3 3 2]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<p>They will all instantiate a Sampler defined in the following class:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.</span></span><span class="sig-name descname"><span class="pre">Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler" title="Link to this definition">#</a></dt>
+<dd><p>Initialises a sampler that returns and then increments indices from a sequence defined by a function.</p>
+<p>Static methods to easily configure several samplers are provided, such as sequential, staggered, Herman-Mayer, random with and without replacement.</p>
+<p>Custom deterministic samplers can be created by using the <cite>from_function</cite> static method or by subclassing this sampler class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>function</strong> (<em>Callable</em><em>[</em><em>[</em><em>int</em><em>]</em><em>, </em><em>int</em><em>]</em>) – A function that takes an integer iteration number and returns an integer between 0 and num_indices.</p></li>
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p></li>
+<li><p><strong>sampling_type</strong> (<em>str</em><em>, </em><em>optional</em><em>,  </em><em>default = None</em>) – The sampling type used. This is recorded for reference and printed when <cite>print</cite> is called.</p></li>
+<li><p><strong>prob_weights</strong> (<em>list</em><em> of </em><em>floats</em><em> of </em><em>length num_indices that sum to 1.  Default is</em><em> [</em><em>1 / num_indices</em><em>] </em><em>* num_indices</em>) – Consider that the sampler is incremented a large number of times this argument holds the expected number of times each index would be outputted,  normalised to 1.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class representing the desired configuration.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">random_with_replacement</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">20</span><span class="p">))</span>
+<span class="go">[3 4 0 0 2 3 3 2 2 1 1 4 4 3 0 2 4 4 2 4]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">staggered</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">4</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+<span class="go">[ 0  4  8 12 16]</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">sequential</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="go">[0 1 2 3 4]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">herman_meyer</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
+<span class="go">[ 0  6  3  9  1  7  4 10  2  8  5 11  0  6  3  9]</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<p>This example creates a sampler that outputs sequential indices, starting from 1.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">num_indices</span><span class="o">=</span><span class="mi">10</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_sampling_function</span><span class="p">(</span><span class="n">iteration_number</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">return</span> <span class="p">(</span><span class="n">iteration_number</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">10</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">my_sampling_function</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">25</span><span class="p">)))</span>
+<span class="go">[1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5]</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The optimal choice of sampler depends on the data and the number of calls to the sampler.  Note that a low number of calls to a random sampler won’t give an even distribution.
+For a small number of samples (e.g. <cite>&lt;5*num_indices</cite>) the user may wish to consider another sampling method e.g. random without replacement, which, when calling <cite>num_indices</cite> samples is guaranteed to draw each index exactly once.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.next">
+<span class="sig-name descname"><span class="pre">next</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.next"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.next" title="Link to this definition">#</a></dt>
+<dd><p>Returns a sample from the list of indices <a href="#id43"><span class="problematic" id="id44">`</span></a>{0, 1, …, N-1}, where N is the number of indices and increments the sampler.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.Sampler.get_samples">
+<span class="sig-name descname"><span class="pre">get_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_samples</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.get_samples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.Sampler.get_samples" title="Link to this definition">#</a></dt>
+<dd><p>Generates a list of the first num_samples output by the sampler. Calling this does not increment the sampler index or affect the behaviour of the sampler .</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>num_samples</strong> (<em>int</em>) – The number of samples to return.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The first <a href="#id45"><span class="problematic" id="id46">`</span></a>num_samples” output by the sampler.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>List</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="id0">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler that outputs sequential indices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class that will generate indices sequentially.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">sequential</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="go">[0 1 2 3 4]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="id47">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">staggered</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.staggered"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id47" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs in a staggered order.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p></li>
+<li><p><strong>stride</strong> (<em>int</em>) – The stride between returned indices. The stride should be less than the num_indices.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class that will generate indices in a staggered pattern.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">staggered</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">4</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+<span class="go">[ 0  4  8 12 16]</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">staggered</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">17</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">8</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+<span class="go">[ 0  8  16 1 9 2 10 3 11 4]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="id48">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">random_with_replacement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.random_with_replacement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id48" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs an index between 0 - num_indices with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices</p></li>
+<li><p><strong>prob</strong> (<em>list</em><em> of </em><em>floats</em><em>, </em><em>optional</em>) – The probability for each index to be selected by the ‘next’ operation. If not provided, the indices will be sampled uniformly. The list should have a length equal to num_indices, and the values should sum to 1</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – Used to initialise the random number generator where repeatability is required.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the <cite>RandomSampler</cite> class that will generate indices randomly with replacement</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><cite>RandomSampler</cite></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">random_with_replacement</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+<span class="go">[3 4 0 0 2 3 3 2 2 1]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="n">sampler</span><span class="p">))</span>
+<span class="go">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">next</span><span class="p">())</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">random_with_replacement</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+<span class="go">[0 1 3 0 0 3 0 0 0 0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="id49">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">random_without_replacement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.random_without_replacement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id49" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs an index between 0 - num_indices. Once sampled the index will not be sampled again until all indices have been returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – Used to initialise the random number generator where repeatability is required.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the <cite>RandomSampler</cite> class that will generate indices randomly without replacement</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><cite>RandomSampler</cite></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span><span class="o">=</span><span class="n">Sampler</span><span class="o">.</span><span class="n">randomWithoutReplacement</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
+<span class="go">[6 2 1 0 4 3 5 1 0 4 2 5 6 3 3 2]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="id50">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.from_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id50" title="Link to this definition">#</a></dt>
+<dd><blockquote>
+<div><p>Instantiate a sampler that wraps a function for index selection.</p>
+<dl class="simple">
+<dt>num_indices: int</dt><dd><p>The sampler will select from a range of indices 0 to num_indices.</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl>
+<dt>function<span class="classifier">callable</span></dt><dd><p>A deterministic function that takes an integer as an argument, representing the iteration number, and returns an integer between 0 and num_indices. The function signature should be function(iteration_number: int) -&gt; int</p>
+<dl class="simple">
+<dt>prob_weights: list of floats of length num_indices that sum to 1. Default is [1 / num_indices] * num_indices</dt><dd><p>Consider that the sampler is incremented a large number of times this argument holds the expected number of times each index would be outputted,  normalised to 1.</p>
+</dd>
+</dl>
+<dl class="simple">
+<dt>Sampler</dt><dd><p>An instance of the Sampler class which samples from a function.</p>
+</dd>
+</dl>
+<p>This example creates a sampler that always outputs 2.  The probability weights are passed to the sampler as they are not uniform.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">num_indices</span><span class="o">=</span><span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_sampling_function</span><span class="p">(</span><span class="n">iteration_number</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">return</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">my_sampling_function</span><span class="p">,</span> <span class="n">prob_weights</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">12</span><span class="p">)))</span>
+<span class="go">[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]</span>
+</pre></div>
+</div>
+<p>This example creates a sampler that outputs sequential indices, starting from 1.  The probability weights are not passed to the sampler as they are uniform.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">num_indices</span><span class="o">=</span><span class="mi">10</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_sampling_function</span><span class="p">(</span><span class="n">iteration_number</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">return</span> <span class="p">(</span><span class="n">iteration_number</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="mi">10</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">my_sampling_function</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">25</span><span class="p">)))</span>
+<span class="go">[1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5]</span>
+</pre></div>
+</div>
+<p>This example creates a sampler that samples in order from a custom list. The num_indices  is 6, although note that the index 5 is never output by the sampler. The number of indices must be at least one greater than any of the elements in the custom_list.
+The probability weights are passed to the sampler as they are not uniform.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">custom_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">num_indices</span> <span class="o">=</span> <span class="mi">6</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_sampling_function</span><span class="p">(</span><span class="n">iteration_number</span><span class="p">,</span> <span class="n">custom_list</span><span class="o">=</span><span class="n">custom_list</span><span class="p">]):</span>
+<span class="gp">&gt;&gt;&gt; </span>   <span class="k">return</span><span class="p">(</span><span class="n">custom_list</span><span class="p">[</span><span class="n">iteration_number</span><span class="o">%</span><span class="nb">len</span><span class="p">(</span><span class="n">custom_list</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">from_function</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="n">num_indices</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">my_sampling_function</span><span class="p">,</span> <span class="n">prob_weights</span><span class="o">=</span><span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">25</span><span class="p">)))</span>
+<span class="go">[0, 0, 0, 0, 0, 0, 3, 2, 1, 4, 0, 0, 0, 0, 0, 0, 3, 2, 1, 4, 0, 0, 0, 0, 0]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="p">)</span>
+<span class="go">Sampler that wraps a function that takes an iteration number and selects from a list of indices {0, 1, …, N-1}, where N is the number of indices.</span>
+<span class="go">Type : from_function</span>
+<span class="go">Current iteration number : 0</span>
+<span class="go">number of indices : 6</span>
+<span class="go">Probability weights : [0.6, 0.1, 0.1, 0.1, 0.1, 0.0]</span>
+</pre></div>
+</div>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="id51">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">herman_meyer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#Sampler.herman_meyer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id51" title="Link to this definition">#</a></dt>
+<dd><p>Instantiates a sampler which outputs in a Herman Meyer order.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices. For Herman-Meyer sampling this number should not be prime.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class which outputs in a Herman Meyer order.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<section id="id52">
+<h4>Reference<a class="headerlink" href="#id52" title="Link to this heading">#</a></h4>
+<p>With thanks to Imraj Singh and Zeljko Kereta for their help with the initial implementation of the Herman Meyer sampling. Their implementation was used in:</p>
+<p>Singh I, et al. Deep Image Prior PET Reconstruction using a SIRF-Based Objective - IEEE MIC, NSS &amp; RTSD 2022. <a class="reference external" href="https://discovery.ucl.ac.uk/id/eprint/10176077/1/MIC_Conference_Record.pdf">https://discovery.ucl.ac.uk/id/eprint/10176077/1/MIC_Conference_Record.pdf</a></p>
+<p>The sampling method was introduced in:</p>
+<p>Herman GT, Meyer LB. Algebraic reconstruction techniques can be made computationally efficient. IEEE Trans Med Imaging.  doi: 10.1109/42.241889.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span><span class="o">=</span><span class="n">Sampler</span><span class="o">.</span><span class="n">herman_meyer</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
+<span class="go">[ 0  6  3  9  1  7  4 10  2  8  5 11  0  6  3  9]</span>
+</pre></div>
+</div>
+</section>
+</dd></dl>
+
+</dd></dl>
+
+<p>In addition, we provide a random sampling class which is a child class of  <cite>cil.optimisation.utilities.sampler</cite> and provides options for sampling with and without replacement:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.SamplerRandom">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.</span></span><span class="sig-name descname"><span class="pre">SamplerRandom</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random_with_replacement'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#SamplerRandom"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.SamplerRandom" title="Link to this definition">#</a></dt>
+<dd><p>The user is recommended to not instantiate this class  directly but instead use one of the static methods  in the parent Sampler class that will return instances of different samplers.</p>
+<p>This class produces Samplers that output random samples with and without replacement from the set {0, 1, …, N-1} where N=num_indices.</p>
+<p>Custom random samplers can be created by subclassing this sampler class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_indices</strong> (<em>int</em>) – The sampler will select from a range of indices 0 to num_indices.</p></li>
+<li><p><strong>sampling_type</strong> (<em>str</em><em>, </em><em>optional</em><em>,  </em><em>default = 'random_with_replacement&quot;</em>) – The sampling type used. This is recorded for reference and printed when <cite>print</cite> is called.</p></li>
+<li><p><strong>prob_weights</strong> (<em>list</em><em> of </em><em>floats</em><em> of </em><em>length num_indices that sum to 1.  Default is</em><em> [</em><em>1 / num_indices</em><em>] </em><em>* num_indices</em>) – Consider that the sampler is incremented a large number of times this argument holds the expected number of times each index would be outputted,  normalised to 1.</p></li>
+<li><p><strong>replace</strong> (<em>bool</em><em>, </em><em>default is True</em>) – If True, sample with replace, otherwise sample without replacement</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – Used to initialise the random number generator where repeatability is required.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of the Sampler class representing the desired configuration.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#cil.optimisation.utilities.Sampler" title="cil.optimisation.utilities.Sampler">Sampler</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="o">.</span><span class="n">random_with_replacement</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">20</span><span class="p">))</span>
+<span class="go">[3 4 0 0 2 3 3 2 2 1 1 4 4 3 0 2 4 4 2 4]</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sampler</span><span class="o">=</span><span class="n">Sampler</span><span class="o">.</span><span class="n">randomWithoutReplacement</span><span class="p">(</span><span class="n">num_indices</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">get_samples</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
+<span class="go">[6 2 1 0 4 3 5 1 0 4 2 5 6 3 3 2]</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.SamplerRandom.get_samples">
+<span class="sig-name descname"><span class="pre">get_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_samples</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/sampler/#SamplerRandom.get_samples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.SamplerRandom.get_samples" title="Link to this definition">#</a></dt>
+<dd><p>Generates a list of the first num_samples output by the sampler. Calling this does not increment the sampler index or affect the behaviour of the sampler .</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>num_samples</strong> (<em>int</em>) – The number of samples to return.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The first <cite>num_samples</cite> produced by the sampler</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="callbacks">
+<h3>Callbacks<a class="headerlink" href="#callbacks" title="Link to this heading">#</a></h3>
+<p>A list of <code class="code docutils literal notranslate"><span class="pre">Callback</span></code> s to be executed each iteration can be passed to <a href="#id81"><span class="problematic" id="id82">`Algorithms`_</span></a> <code class="code docutils literal notranslate"><span class="pre">run</span></code> method.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cil.optimisation.utilities.callbacks</span> <span class="kn">import</span> <span class="n">LogfileCallback</span>
+<span class="o">...</span>
+<span class="n">algorithm</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="o">...</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">LogfileCallback</span><span class="p">(</span><span class="s2">&quot;log.txt&quot;</span><span class="p">)])</span>
+</pre></div>
+</div>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.callbacks.Callback">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.callbacks.</span></span><span class="sig-name descname"><span class="pre">Callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/callbacks/#Callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.callbacks.Callback" title="Link to this definition">#</a></dt>
+<dd><p>Base Callback to inherit from for use in <code class="code docutils literal notranslate"><span class="pre">Algorithm.run(callbacks:</span> <span class="pre">list[Callback])</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>verbose</strong> (<em>int</em><em>, </em><em>choice</em><em> of </em><em>0</em><em>,</em><em>1</em><em>,</em><em>2</em><em>, </em><em>default 1</em>) – 0=quiet, 1=info, 2=debug.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<p>Built-in callbacks include:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.callbacks.ProgressCallback">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.callbacks.</span></span><span class="sig-name descname"><span class="pre">ProgressCallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tqdm_class=&lt;class</span> <span class="pre">'tqdm.asyncio.tqdm_asyncio'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**tqdm_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/callbacks/#ProgressCallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.callbacks.ProgressCallback" title="Link to this definition">#</a></dt>
+<dd><p><code class="code docutils literal notranslate"><span class="pre">tqdm</span></code>-based progress bar.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tqdm_class</strong> (default <code class="code docutils literal notranslate"><span class="pre">tqdm.auto.tqdm</span></code>)</p></li>
+<li><p><strong>**tqdm_kwargs</strong> – Passed to <code class="code docutils literal notranslate"><span class="pre">tqdm_class</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.callbacks.TextProgressCallback">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.callbacks.</span></span><span class="sig-name descname"><span class="pre">TextProgressCallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tqdm_class=&lt;class</span> <span class="pre">'cil.optimisation.utilities.callbacks._TqdmText'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**tqdm_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/callbacks/#TextProgressCallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.callbacks.TextProgressCallback" title="Link to this definition">#</a></dt>
+<dd><p><code class="code docutils literal notranslate"><span class="pre">ProgressCallback</span></code> but printed on separate lines to screen.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>miniters</strong> (int, default <code class="code docutils literal notranslate"><span class="pre">Algorithm.update_objective_interval</span></code>) – Number of algorithm iterations between screen prints.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.callbacks.LogfileCallback">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.callbacks.</span></span><span class="sig-name descname"><span class="pre">LogfileCallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'a'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/callbacks/#LogfileCallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.callbacks.LogfileCallback" title="Link to this definition">#</a></dt>
+<dd><p><code class="code docutils literal notranslate"><span class="pre">TextProgressCallback</span></code> but to a file instead of screen.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>log_file</strong> (<em>FileDescriptorOrPath</em>) – Passed to <code class="code docutils literal notranslate"><span class="pre">open()</span></code>.</p></li>
+<li><p><strong>mode</strong> (<em>str</em>) – Passed to <code class="code docutils literal notranslate"><span class="pre">open()</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<p>Users can also write custom callbacks.</p>
+<p>Below is an example of a custom callback implementing early stopping.
+In each iteration of the <code class="code docutils literal notranslate"><span class="pre">TestAlgo</span></code>, the objective <span class="math notranslate nohighlight">\(x\)</span> is reduced by <span class="math notranslate nohighlight">\(5\)</span>. The <code class="code docutils literal notranslate"><span class="pre">EarlyStopping</span></code> callback terminates the algorithm when <span class="math notranslate nohighlight">\(x \le -15\)</span>. The algorithm thus terminates after <span class="math notranslate nohighlight">\(3\)</span> iterations.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cil.optimisation.algorithms</span> <span class="kn">import</span> <span class="n">Algorithm</span>
+<span class="kn">from</span> <span class="nn">cil.optimisation.utilities</span> <span class="kn">import</span> <span class="n">callbacks</span>
+
+<span class="k">class</span> <span class="nc">TestAlgo</span><span class="p">(</span><span class="n">Algorithm</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configured</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">-=</span> <span class="mi">5</span>
+
+    <span class="k">def</span> <span class="nf">update_objective</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">2</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
+
+<span class="k">class</span> <span class="nc">EarlyStopping</span><span class="p">(</span><span class="n">callbacks</span><span class="o">.</span><span class="n">Callback</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">algorithm</span><span class="p">:</span> <span class="n">Algorithm</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">x</span> <span class="o">&lt;=</span> <span class="o">-</span><span class="mi">15</span><span class="p">:</span>  <span class="c1"># arbitrary stopping criterion</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+
+<span class="n">algo</span> <span class="o">=</span> <span class="n">TestAlgo</span><span class="p">()</span>
+<span class="n">algo</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ProgressCallback</span><span class="p">(),</span> <span class="n">EarlyStopping</span><span class="p">()])</span>
+</pre></div>
+</div>
+<div class="highlight-raw notranslate"><div class="highlight"><pre><span></span>Output:
+ 15%|███                 | 3/20 [00:00&lt;00:00, 11770.73it/s, objective=3.05e-5]
+</pre></div>
+</div>
+</section>
+<section id="step-size-methods">
+<h3>Step size methods<a class="headerlink" href="#step-size-methods" title="Link to this heading">#</a></h3>
+<p>A step size method is a class which acts on an algorithm and can be passed to  <cite>cil.optimisation.algorithm.GD</cite>, <cite>cil.optimisation.algorithm.ISTA</cite>  <cite>cil.optimisation.algorithm.FISTA</cite> and it’s method <cite>get_step_size</cite> is called after the calculation of the gradient before the gradient descent step is taken. It outputs a float value to be used as the step-size.</p>
+<p>Currently in CIL we have a base class:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.StepSizeRule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.StepSizeMethods.</span></span><span class="sig-name descname"><span class="pre">StepSizeRule</span></span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#StepSizeRule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.StepSizeRule" title="Link to this definition">#</a></dt>
+<dd><p>Abstract base class for a step size rule. The abstract method, <cite>get_step_size</cite> takes in an algorithm and thus can access all parts of the algorithm (e.g. current iterate, current gradient, objective functions etc) and from this  should return a float as a step size.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.StepSizeRule.get_step_size">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_step_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#StepSizeRule.get_step_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.StepSizeRule.get_step_size" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>the calculated step size</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<p>We also have a number of example classes:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.ConstantStepSize">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.StepSizeMethods.</span></span><span class="sig-name descname"><span class="pre">ConstantStepSize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#ConstantStepSize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.ConstantStepSize" title="Link to this definition">#</a></dt>
+<dd><p>Step-size rule that always returns a constant step-size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>step_size</strong> (<em>float</em>) – The step-size to be returned with each call.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.ConstantStepSize.get_step_size">
+<span class="sig-name descname"><span class="pre">get_step_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#ConstantStepSize.get_step_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.ConstantStepSize.get_step_size" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>the calculated step size</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.StepSizeMethods.</span></span><span class="sig-name descname"><span class="pre">ArmijoStepSizeRule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmstart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#ArmijoStepSizeRule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule" title="Link to this definition">#</a></dt>
+<dd><p>Applies the Armijo rule to calculate the step size (step_size).</p>
+<p>The Armijo rule runs a while loop to find the appropriate step_size by starting from a very large number (<cite>alpha</cite>). The step_size is found by reducing the step size (by a factor <cite>beta</cite>) in an iterative way until a certain criterion is met. To avoid infinite loops, we add a maximum number of times (<cite>max_iterations</cite>) the while loop is run.</p>
+<section id="id53">
+<h4>Reference<a class="headerlink" href="#id53" title="Link to this heading">#</a></h4>
+<ul class="simple">
+<li><p>Algorithm 3.1 in Nocedal, J. and Wright, S.J. eds., 1999. Numerical optimization. New York, NY: Springer New York. <a class="reference external" href="https://www.math.uci.edu/~qnie/Publications/NumericalOptimization.pdf">https://www.math.uci.edu/~qnie/Publications/NumericalOptimization.pdf</a>)</p></li>
+<li><p><a class="reference external" href="https://projecteuclid.org/download/pdf_1/euclid.pjm/1102995080">https://projecteuclid.org/download/pdf_1/euclid.pjm/1102995080</a></p></li>
+</ul>
+<dl class="field-list simple">
+<dt class="field-odd">param alpha<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The starting point for the step size iterations</p>
+</dd>
+<dt class="field-even">type alpha<span class="colon">:</span></dt>
+<dd class="field-even"><p>float, optional, default=1e6</p>
+</dd>
+<dt class="field-odd">param beta<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The amount the step_size is reduced if the criterion is not met</p>
+</dd>
+<dt class="field-even">type beta<span class="colon">:</span></dt>
+<dd class="field-even"><p>float between 0 and 1, optional, default=0.5</p>
+</dd>
+<dt class="field-odd">param max_iterations<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The maximum number of iterations to find a suitable step size</p>
+</dd>
+<dt class="field-even">type max_iterations<span class="colon">:</span></dt>
+<dd class="field-even"><p>integer, optional, default is numpy.ceil (2 * numpy.log10(alpha) / numpy.log10(2))</p>
+</dd>
+<dt class="field-odd">param warmstart<span class="colon">:</span></dt>
+<dd class="field-odd"><p>If <cite>warmstart = True</cite> the initial step size at each Armijo iteration is the calculated step size from the last iteration. If <cite>warmstart = False</cite> at each  Armijo iteration, the initial step size is reset to the original, large <cite>alpha</cite>.
+In the case of <em>well-behaved</em> convex functions, <cite>warmstart = True</cite> is likely to be computationally less expensive. In the case of non-convex functions, or particularly tricky functions, setting <cite>warmstart = False</cite> may be beneficial.</p>
+</dd>
+<dt class="field-even">type warmstart<span class="colon">:</span></dt>
+<dd class="field-even"><p>Boolean, default is True</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule.get_step_size">
+<span class="sig-name descname"><span class="pre">get_step_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#ArmijoStepSizeRule.get_step_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.ArmijoStepSizeRule.get_step_size" title="Link to this definition">#</a></dt>
+<dd><p>Applies the Armijo rule to calculate the step size (<cite>step_size</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>the calculated step size</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.StepSizeMethods.</span></span><span class="sig-name descname"><span class="pre">BarzilaiBorweinStepSizeRule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'short'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stabilisation_param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#BarzilaiBorweinStepSizeRule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule" title="Link to this definition">#</a></dt>
+<dd><p>Applies the Barzilai- Borwein rule to calculate the step size (step_size).</p>
+<p>Let <span class="math notranslate nohighlight">\(\Delta x=x_k-x_{k-1}\)</span> and <span class="math notranslate nohighlight">\(\Delta g=g_k-g_{k-1}\)</span>. Where <span class="math notranslate nohighlight">\(x_k\)</span> is the <span class="math notranslate nohighlight">\(k\)</span> th iterate (current solution after iteration <span class="math notranslate nohighlight">\(k\)</span> ) and <span class="math notranslate nohighlight">\(g_k\)</span> is the gradient calculation in the <span class="math notranslate nohighlight">\(k\)</span> th iterate, found in <code class="code docutils literal notranslate"><span class="pre">algorithm.gradient_update</span></code>.  A Barzilai-Borwein (BB) iteration is <span class="math notranslate nohighlight">\(x_{k+1}=x_k-\alpha_kg_k\)</span> where the step size <span class="math notranslate nohighlight">\(\alpha _k\)</span> is either</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\alpha_k^{LONG}=\frac{\Delta x\cdot\Delta x}{\Delta x\cdot\Delta g}\)</span>, or</p></li>
+<li><p><span class="math notranslate nohighlight">\(\alpha_k^{SHORT}=\frac{\Delta x \cdot\Delta g}{\Delta g \cdot\Delta g}\)</span>.</p></li>
+</ul>
+<p>Where the operator <span class="math notranslate nohighlight">\(\cdot\)</span> is the standard inner product between two vectors.</p>
+<p>This is suitable for use with gradient based iterative methods where the calculated gradient is stored as <cite>algorithm.gradient_update</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>initial</strong> (<em>float</em><em>, </em><em>greater than zero</em>) – The step-size for the first iteration. We recommend something of the order <span class="math notranslate nohighlight">\(1/f.L\)</span> where <span class="math notranslate nohighlight">\(f\)</span> is the (differentiable part of) the objective you wish to minimise.</p></li>
+<li><p><strong>mode</strong> (<em>One</em><em> of </em><em>'long'</em><em>, </em><em>'short'</em><em> or </em><em>'alternate'</em><em>, </em><em>default is 'short'.</em>) – This calculates the step-size based on the LONG, SHORT or alternating between the two, starting with short.</p></li>
+<li><p><strong>stabilisation_param</strong> (<em>'auto'</em><em>, </em><em>float</em><em> or </em><em>'off'</em><em>, </em><em>default is 'auto'</em>) – In order to add stability the step-size has an upper limit of <span class="math notranslate nohighlight">\(\Delta/\|g_k\|\)</span> where by ‘default’, the <cite>stabilisation_param</cite>, <span class="math notranslate nohighlight">\(\Delta\)</span> is  determined automatically to be the minimium of <span class="math notranslate nohighlight">\(\Delta x\)</span> from the first 3 iterations. The user can also pass a fixed constant or turn “off” the stabilisation, equivalently passing <cite>np.inf</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<section id="id54">
+<h4>Reference<a class="headerlink" href="#id54" title="Link to this heading">#</a></h4>
+<ul class="simple">
+<li><p>Barzilai, Jonathan; Borwein, Jonathan M. (1988). “Two-Point Step Size Gradient Methods”. IMA Journal of Numerical Analysis. 8: 141–148, <a class="reference external" href="https://doi.org/10.1093/imanum/8.1.141">https://doi.org/10.1093/imanum/8.1.141</a></p></li>
+<li><p>Burdakov, O., Dai, Y. and Huang, N., 2019. STABILIZED BARZILAI-BORWEIN METHOD. Journal of Computational Mathematics, 37(6). <a class="reference external" href="https://doi.org/10.4208/jcm.1911-m2019-0171">https://doi.org/10.4208/jcm.1911-m2019-0171</a></p></li>
+<li><p><a class="reference external" href="https://en.wikipedia.org/wiki/Barzilai-Borwein_method">https://en.wikipedia.org/wiki/Barzilai-Borwein_method</a></p></li>
+</ul>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule.get_step_size">
+<span class="sig-name descname"><span class="pre">get_step_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/StepSizeMethods/#BarzilaiBorweinStepSizeRule.get_step_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.StepSizeMethods.BarzilaiBorweinStepSizeRule.get_step_size" title="Link to this definition">#</a></dt>
+<dd><p>Applies the B-B rule to calculate the step size (<cite>step_size</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>the calculated step size</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+<section id="preconditioners">
+<h3>Preconditioners<a class="headerlink" href="#preconditioners" title="Link to this heading">#</a></h3>
+<p>A preconditioner is a class which acts on an algorithm and can be passed to  <cite>cil.optimisation.algorithm.GD</cite>, <cite>cil.optimisation.algorithm.ISTA</cite> or <cite>cil.optimisation.algorithm.FISTA</cite> and it’s method <cite>apply</cite> is called after the calculation of the gradient before the gradient descent step is taken. It modifies and returns a passed <cite>gradient</cite>.</p>
+<p>Currently in CIL we have a base class:</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.preconditioner.Preconditioner">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.preconditioner.</span></span><span class="sig-name descname"><span class="pre">Preconditioner</span></span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#Preconditioner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.preconditioner.Preconditioner" title="Link to this definition">#</a></dt>
+<dd><p>Abstract base class for Preconditioner objects. The <cite>apply</cite> method of this class takes an initialised CIL function as an argument and modifies a provided  <cite>gradient</cite>.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.preconditioner.Preconditioner.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#Preconditioner.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.preconditioner.Preconditioner.apply" title="Link to this definition">#</a></dt>
+<dd><p>Abstract method to call the preconditioner.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="id55">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#Preconditioner.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id55" title="Link to this definition">#</a></dt>
+<dd><p>Abstract method to apply the preconditioner.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>algorithm</strong> (<a class="reference internal" href="#cil.optimisation.algorithms.Algorithm" title="cil.optimisation.algorithms.Algorithm"><em>Algorithm</em></a>) – The algorithm object.</p></li>
+<li><p><strong>gradient</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The calculated gradient to modify</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>,</em>) – Container to fill with the modified gradient.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In CIL algorithms, the preconditioners are used in-place. Make sure this method is safe to use in place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The modified gradient</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<p>We also have a number of already provided pre-conditioners</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.preconditioner.Sensitivity">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.preconditioner.</span></span><span class="sig-name descname"><span class="pre">Sensitivity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#Sensitivity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.preconditioner.Sensitivity" title="Link to this definition">#</a></dt>
+<dd><p>Sensitivity preconditioner class.</p>
+<p>In each call to the preconditioner the <cite>gradient</cite> is multiplied by <span class="math notranslate nohighlight">\(1/(A^T \mathbf{1})\)</span> where <span class="math notranslate nohighlight">\(A\)</span> is an operator, <span class="math notranslate nohighlight">\(\mathbf{1}\)</span> is an object in the range of the operator filled with ones.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>operator</strong> (<em>CIL Operator</em>) – The operator used for sensitivity computation.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.preconditioner.Sensitivity.compute_preconditioner_matrix">
+<span class="sig-name descname"><span class="pre">compute_preconditioner_matrix</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#Sensitivity.compute_preconditioner_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.preconditioner.Sensitivity.compute_preconditioner_matrix" title="Link to this definition">#</a></dt>
+<dd><p>Compute the sensitivity. <span class="math notranslate nohighlight">\(A^T \mathbf{1}\)</span> where <span class="math notranslate nohighlight">\(A\)</span> is the operator and <span class="math notranslate nohighlight">\(\mathbf{1}\)</span> is an object in the range of the operator filled with ones.
+Then perform safe division by the sensitivity to store the preconditioner array <span class="math notranslate nohighlight">\(1/(A^T \mathbf{1})\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.preconditioner.Sensitivity.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#Sensitivity.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.preconditioner.Sensitivity.apply" title="Link to this definition">#</a></dt>
+<dd><p>Update the preconditioner.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>algorithm</strong> (<em>object</em>) – The algorithm object.</p></li>
+<li><p><strong>gradient</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The calculated gradient to modify</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>,</em>) – Container to fill with the modified gradient</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The modified gradient</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.preconditioner.AdaptiveSensitivity">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.utilities.preconditioner.</span></span><span class="sig-name descname"><span class="pre">AdaptiveSensitivity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#AdaptiveSensitivity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.preconditioner.AdaptiveSensitivity" title="Link to this definition">#</a></dt>
+<dd><p>Adaptive Sensitivity preconditioner class.</p>
+<p>In each call to the preconditioner the <cite>gradient</cite> is multiplied by <span class="math notranslate nohighlight">\((x+\delta) /(A^T \mathbf{1})\)</span> where <span class="math notranslate nohighlight">\(A\)</span> is an operator,  <span class="math notranslate nohighlight">\(\mathbf{1}\)</span> is an object in the range of the operator filled with ones.
+The point <span class="math notranslate nohighlight">\(x\)</span> is the current iteration, or a reference image,  and <span class="math notranslate nohighlight">\(\delta\)</span> is a small positive float.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operator</strong> (<em>CIL object</em>) – The operator used for sensitivity computation.</p></li>
+<li><p><strong>delta</strong> (<em>float</em><em>, </em><em>optional</em>) – The delta value for the preconditioner.</p></li>
+<li><p><strong>reference</strong> (<em>DataContainer e.g. ImageData</em><em>, </em><em>default is None</em>) – Reference data, an object in the domain of the operator. Recommended to be a best guess reconstruction. If reference data is passed the preconditioner is always fixed.</p></li>
+<li><p><strong>max_iterations</strong> (<em>int</em><em>,  </em><em>default = 100</em>) – The maximum number of iterations before the preconditoner is frozen and no-longer updates. Note that if reference data is passed the preconditioner is always frozen and <cite>iterations</cite> is set to -1.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>A reference for the freezing of the preconditioner can be found: Twyman R., Arridge S., Kereta Z., Jin B., Brusaferri L., Ahn S., Stearns CW., Hutton B.F., Burger I.A., Kotasidis F., Thielemans K.. An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction. IEEE Trans Med Imaging. 2023 Jan;42(1):29-41. doi: 10.1109/TMI.2022.3203237. Epub 2022 Dec 29. PMID: 36044488.</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.utilities.preconditioner.AdaptiveSensitivity.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/utilities/preconditioner/#AdaptiveSensitivity.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.utilities.preconditioner.AdaptiveSensitivity.apply" title="Link to this definition">#</a></dt>
+<dd><p>Update the preconditioner.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>algorithm</strong> (<em>object</em>) – The algorithm object.</p></li>
+<li><p><strong>gradient</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The calculated gradient to modify</p></li>
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>,</em>) – Container to fill with the modified gradient</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The modified gradient</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+</section>
+<section id="block-framework">
+<h1>Block Framework<a class="headerlink" href="#block-framework" title="Link to this heading">#</a></h1>
+<p>To be able to express more advanced optimisation problems we developed the
+<a class="reference internal" href="#block-framework">Block Framework</a>, which provides a generic strategy to treat variational
+problems in the following form:</p>
+<div class="math notranslate nohighlight">
+\[\min \text{Regulariser} + \text{Fidelity}\]</div>
+<p>The block framework consists of:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#blockdatacontainer"><span class="std std-ref">BlockDataContainer</span></a></p></li>
+<li><p><a class="reference internal" href="#block-function"><span class="std std-ref">BlockFunction</span></a></p></li>
+<li><p><a class="reference internal" href="#block-operator"><span class="std std-ref">BlockOperator</span></a></p></li>
+</ul>
+<p>The block framework allows writing more advanced optimisation problems. Consider the typical
+<a class="reference external" href="https://en.wikipedia.org/wiki/Tikhonov_regularization">Tikhonov regularisation</a>:</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}}\begin{Vmatrix}A u - b \end{Vmatrix}^2_2 + \alpha^2\|Lu\|^2_2\]</div>
+<p>where,</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(A\)</span> is the projection operator</p></li>
+<li><p><span class="math notranslate nohighlight">\(b\)</span> is the acquired data</p></li>
+<li><p><span class="math notranslate nohighlight">\(u\)</span> is the unknown image to be solved for</p></li>
+<li><p><span class="math notranslate nohighlight">\(\alpha\)</span> is the regularisation parameter</p></li>
+<li><p><span class="math notranslate nohighlight">\(L\)</span> is a regularisation operator</p></li>
+</ul>
+<p>The first term measures the fidelity of the solution to the data. The second term measures the
+fidelity to the prior knowledge we have imposed on the system, operator <span class="math notranslate nohighlight">\(L\)</span>.</p>
+<p>This can be re-written equivalently in the block matrix form:</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}}\begin{Vmatrix}\binom{A}{\alpha L} u - \binom{b}{0}\end{Vmatrix}^2_2\]</div>
+<p>With the definitions:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\tilde{A} = \binom{A}{\alpha L}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(\tilde{b} = \binom{b}{0}\)</span></p></li>
+</ul>
+<p>this can now be recognised as a least squares problem which can be solved by any algorithm in the <code class="code docutils literal notranslate"><span class="pre">cil.optimisation</span></code>
+which can solve least squares problem, e.g. CGLS.</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}}\begin{Vmatrix}\tilde{A} u - \tilde{b}\end{Vmatrix}^2_2\]</div>
+<p>To be able to express our optimisation problems in the matrix form above, we developed the so-called,
+Block Framework comprising 4 main actors: <code class="code docutils literal notranslate"><span class="pre">BlockGeometry</span></code>, <code class="code docutils literal notranslate"><span class="pre">BlockDataContainer</span></code>,
+<code class="code docutils literal notranslate"><span class="pre">BlockFunction</span></code> and <code class="code docutils literal notranslate"><span class="pre">BlockOperator</span></code>.</p>
+<section id="blockdatacontainer">
+<h2>BlockDataContainer<a class="headerlink" href="#blockdatacontainer" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="#blockdatacontainer"><span class="std std-ref">BlockDataContainer</span></a> holds <a class="reference external" href="../framework/#datacontainer">DataContainer</a> as column vector. It is possible to
+do basic algebra between <a class="reference internal" href="#blockdatacontainer"><span class="std std-ref">BlockDataContainer</span></a> s and with numbers, list or numpy arrays.</p>
+<div class="math notranslate nohighlight">
+\[ \begin{align}\begin{aligned}x = [x_{1}, x_{2} ]\in (X_{1}\times X_{2})\\y = [y_{1}, y_{2}, y_{3} ]\in(Y_{1}\times Y_{2} \times Y_{3})\end{aligned}\end{align} \]</div>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.framework.</span></span><span class="sig-name descname"><span class="pre">BlockDataContainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer" title="Link to this definition">#</a></dt>
+<dd><p>Class to hold DataContainers as column vector</p>
+<p>Provides basic algebra between BlockDataContainer’s, DataContainer’s and
+subclasses and Numbers</p>
+<ol class="arabic simple">
+<li><p>algebra between <a href="#id56"><span class="problematic" id="id57">`</span></a>BlockDataContainer`s will be element-wise, only if
+the shape of the 2 <a href="#id58"><span class="problematic" id="id59">`</span></a>BlockDataContainer`s is the same, otherwise it
+will fail</p></li>
+<li><p>algebra between <cite>BlockDataContainer`s and `list</cite> or <cite>numpy array</cite> will
+work as long as the number of <cite>rows</cite> and element of the arrays match,
+independently on the fact that the <cite>BlockDataContainer</cite> could be nested</p></li>
+<li><p>algebra between <cite>BlockDataContainer</cite> and one <cite>DataContainer</cite> is possible.
+It will require all the <cite>DataContainers</cite> in the block to be
+compatible with the <cite>DataContainer</cite> we want to operate with.</p></li>
+<li><p>algebra between <cite>BlockDataContainer</cite> and a <cite>Number</cite> is possible and it
+will be done with each element of the <cite>BlockDataContainer</cite> even if nested</p></li>
+</ol>
+<p>A = [ [B,C] , D]
+A * 3 = [ 3 * [B,C] , 3* D] = [ [ 3*B, 3*C]  , 3*D ]</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__iter__">
+<span class="sig-name descname"><span class="pre">__iter__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__iter__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__iter__" title="Link to this definition">#</a></dt>
+<dd><p>BlockDataContainer is Iterable</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.next">
+<span class="sig-name descname"><span class="pre">next</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.next"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.next" title="Link to this definition">#</a></dt>
+<dd><p>python2 backwards compatibility</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.is_compatible">
+<span class="sig-name descname"><span class="pre">is_compatible</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.is_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.is_compatible" title="Link to this definition">#</a></dt>
+<dd><p>basic check if the size of the 2 objects fit</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.add">
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.add" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: add method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placehold for the resul.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.subtract">
+<span class="sig-name descname"><span class="pre">subtract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.subtract"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.subtract" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: subtract method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.multiply">
+<span class="sig-name descname"><span class="pre">multiply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.multiply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.multiply" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: multiply method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.divide">
+<span class="sig-name descname"><span class="pre">divide</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.divide"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.divide" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: divide method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.power">
+<span class="sig-name descname"><span class="pre">power</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.power"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.power" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.maximum">
+<span class="sig-name descname"><span class="pre">maximum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.maximum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.maximum" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.minimum">
+<span class="sig-name descname"><span class="pre">minimum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.minimum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.minimum" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param<span class="colon">:</span></dt>
+<dd class="field-odd"><p>other (number, DataContainer or subclasses or BlockDataContainer)</p>
+</dd>
+<dt class="field-even">Param<span class="colon">:</span></dt>
+<dd class="field-even"><p>out (optional): provides a placeholder for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.sapyb">
+<span class="sig-name descname"><span class="pre">sapyb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.sapyb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.sapyb" title="Link to this definition">#</a></dt>
+<dd><p>performs axpby element-wise on the BlockDataContainer containers</p>
+<p>Does the operation .. math:: a*x+b*y and stores the result in out, where x is self</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> – scalar</p></li>
+<li><p><strong>b</strong> – scalar</p></li>
+<li><p><strong>y</strong> – compatible (Block)DataContainer</p></li>
+<li><p><strong>out</strong> – (Block)DataContainer to store the result</p></li>
+</ul>
+</dd>
+</dl>
+<section id="id60">
+<h3>Example:<a class="headerlink" href="#id60" title="Link to this heading">#</a></h3>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ig</span> <span class="o">=</span> <span class="n">ImageGeometry</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">ig</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bdc1</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bdc2</span> <span class="o">=</span> <span class="n">BlockDataContainer</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">bdc1</span><span class="o">.</span><span class="n">sapyb</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">bdc2</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.axpby">
+<span class="sig-name descname"><span class="pre">axpby</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float32'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads=2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.axpby"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.axpby" title="Link to this definition">#</a></dt>
+<dd><p>Deprecated method. Alias of sapyb</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.binary_operations">
+<span class="sig-name descname"><span class="pre">binary_operations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.binary_operations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.binary_operations" title="Link to this definition">#</a></dt>
+<dd><p>Algebra: generic method of algebric operation with BlockDataContainer with number/DataContainer or BlockDataContainer</p>
+<p>Provides commutativity with DataContainer and subclasses, i.e. this
+class’s reverse algebraic methods take precedence w.r.t. direct algebraic
+methods of DataContainer and subclasses.</p>
+<p>This method is not to be used directly</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.unary_operations">
+<span class="sig-name descname"><span class="pre">unary_operations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">operation</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.unary_operations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.unary_operations" title="Link to this definition">#</a></dt>
+<dd><p>Unary operation on BlockDataContainer:</p>
+<p>generic method of unary operation with BlockDataContainer: abs, sign, sqrt and conjugate</p>
+<p>This method is not to be used directly</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.copy" title="Link to this definition">#</a></dt>
+<dd><p>alias of clone</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__radd__">
+<span class="sig-name descname"><span class="pre">__radd__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__radd__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__radd__" title="Link to this definition">#</a></dt>
+<dd><p>Reverse addition</p>
+<p>to make sure that this method is called rather than the __mul__ of a numpy array
+the class constant __array_priority__ must be set &gt; 0
+<a class="reference external" href="https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__">https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__rsub__">
+<span class="sig-name descname"><span class="pre">__rsub__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__rsub__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__rsub__" title="Link to this definition">#</a></dt>
+<dd><p>Reverse subtraction</p>
+<p>to make sure that this method is called rather than the __mul__ of a numpy array
+the class constant __array_priority__ must be set &gt; 0
+<a class="reference external" href="https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__">https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__rmul__">
+<span class="sig-name descname"><span class="pre">__rmul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__rmul__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__rmul__" title="Link to this definition">#</a></dt>
+<dd><p>Reverse multiplication</p>
+<p>to make sure that this method is called rather than the __mul__ of a numpy array
+the class constant __array_priority__ must be set &gt; 0
+<a class="reference external" href="https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__">https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__rdiv__">
+<span class="sig-name descname"><span class="pre">__rdiv__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__rdiv__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__rdiv__" title="Link to this definition">#</a></dt>
+<dd><p>Reverse division</p>
+<p>to make sure that this method is called rather than the __mul__ of a numpy array
+the class constant __array_priority__ must be set &gt; 0
+<a class="reference external" href="https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__">https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__rtruediv__">
+<span class="sig-name descname"><span class="pre">__rtruediv__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__rtruediv__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__rtruediv__" title="Link to this definition">#</a></dt>
+<dd><p>Reverse truedivision</p>
+<p>to make sure that this method is called rather than the __mul__ of a numpy array
+the class constant __array_priority__ must be set &gt; 0
+<a class="reference external" href="https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__">https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__rpow__">
+<span class="sig-name descname"><span class="pre">__rpow__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__rpow__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__rpow__" title="Link to this definition">#</a></dt>
+<dd><p>Reverse power</p>
+<p>to make sure that this method is called rather than the __mul__ of a numpy array
+the class constant __array_priority__ must be set &gt; 0
+<a class="reference external" href="https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__">https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__iadd__">
+<span class="sig-name descname"><span class="pre">__iadd__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__iadd__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__iadd__" title="Link to this definition">#</a></dt>
+<dd><p>Inline addition</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__isub__">
+<span class="sig-name descname"><span class="pre">__isub__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__isub__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__isub__" title="Link to this definition">#</a></dt>
+<dd><p>Inline subtraction</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__imul__">
+<span class="sig-name descname"><span class="pre">__imul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__imul__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__imul__" title="Link to this definition">#</a></dt>
+<dd><p>Inline multiplication</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__idiv__">
+<span class="sig-name descname"><span class="pre">__idiv__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__idiv__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__idiv__" title="Link to this definition">#</a></dt>
+<dd><p>Inline division</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__itruediv__">
+<span class="sig-name descname"><span class="pre">__itruediv__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__itruediv__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__itruediv__" title="Link to this definition">#</a></dt>
+<dd><p>Inline truedivision</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__neg__">
+<span class="sig-name descname"><span class="pre">__neg__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/framework/block/#BlockDataContainer.__neg__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.framework.BlockDataContainer.__neg__" title="Link to this definition">#</a></dt>
+<dd><p>Return - self</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.framework.BlockDataContainer.__weakref__">
+<span class="sig-name descname"><span class="pre">__weakref__</span></span><a class="headerlink" href="#cil.framework.BlockDataContainer.__weakref__" title="Link to this definition">#</a></dt>
+<dd><p>list of weak references to the object</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="block-function">
+<h2>Block Function<a class="headerlink" href="#block-function" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="#block-function"><span class="std std-ref">BlockFunction</span></a> acts on <a class="reference internal" href="#blockdatacontainer"><span class="std std-ref">BlockDataContainer</span></a> as a separable sum function:</p>
+<blockquote>
+<div><div class="math notranslate nohighlight">
+\[ \begin{align}\begin{aligned}f = [f_1,...,f_n] \newline\\f([x_1,...,x_n]) = f_1(x_1) +  .... + f_n(x_n)\end{aligned}\end{align} \]</div>
+</div></blockquote>
+<div class="math notranslate nohighlight">
+\[ \begin{align}\begin{aligned}\begin{split}Y = \begin{bmatrix}
+y_{1}\\
+y_{2}\\
+y_{3}\\
+\end{bmatrix}, \quad  F  = [ f_{1}, f_{2}, f_{3} ]\end{split}\\F(Y) : = f_{1}(y_{1}) + f_{2}(y_{2}) + f_{3}(y_{3})\end{aligned}\end{align} \]</div>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.functions.</span></span><span class="sig-name descname"><span class="pre">BlockFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">functions</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction" title="Link to this definition">#</a></dt>
+<dd><p>BlockFunction represents a <em>separable sum</em> function <span class="math notranslate nohighlight">\(F\)</span> defined as</p>
+<div class="math notranslate nohighlight">
+\[F:X_{1}\times X_{2}\cdots\times X_{m} \rightarrow (-\infty, \infty]\]</div>
+<p>where <span class="math notranslate nohighlight">\(F\)</span> is the separable sum of functions <span class="math notranslate nohighlight">\((f_{i})_{i=1}^{m}\)</span>,</p>
+<div class="math notranslate nohighlight">
+\[F(x_{1}, x_{2}, \cdots, x_{m}) = \overset{m}{\underset{i=1}{\sum}}f_{i}(x_{i}), \mbox{ with } f_{i}: X_{i} \rightarrow (-\infty, \infty].\]</div>
+<p>A nice property (due to it’s separability structure) is that the proximal operator
+can be decomposed along the proximal operators of each function <span class="math notranslate nohighlight">\(f_{i}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = ( \mathrm{prox}_{\tau f_{i}}(x_{i}) )_{i=1}^{m}\]</div>
+<p>In addition, if <span class="math notranslate nohighlight">\(\tau := (\tau_{1},\dots,\tau_{m})\)</span>, then</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) = ( \mathrm{prox}_{\tau_{i} f_{i}}(x_{i}) )_{i=1}^{m}\]</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">functions</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.L">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">L</span></span><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.L" title="Link to this definition">#</a></dt>
+<dd><p>Lipschitz of the gradient of function f.</p>
+<p>L is positive real number, such that <span class="math notranslate nohighlight">\(\|f'(x) - f'(y)\| \leq L\|x-y\|\)</span>, assuming <span class="math notranslate nohighlight">\(f: IG \rightarrow \mathbb{R}\)</span></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.__call__" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the BlockFunction <span class="math notranslate nohighlight">\(F\)</span></p>
+<div class="math notranslate nohighlight">
+\[F(x) = \overset{m}{\underset{i=1}{\sum}}f_{i}(x_{i}), \mbox{ where } x = (x_{1}, x_{2}, \cdots, x_{m}), \quad i = 1,2,\dots,m\]</div>
+<p>Parameter:</p>
+<blockquote>
+<div><p>x : BlockDataContainer and must have as many rows as self.length</p>
+<p>returns ..math:: sum(f_i(x_i))</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the convex conjugate of the BlockFunction at <span class="math notranslate nohighlight">\(x^{*}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \overset{m}{\underset{i=1}{\sum}}f_{i}^{*}(x^{*}_{i})\]</div>
+<p>Parameter:</p>
+<blockquote>
+<div><p>x : BlockDataContainer and must have as many rows as self.length</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.proximal">
+<span class="sig-name descname"><span class="pre">proximal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction.proximal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.proximal" title="Link to this definition">#</a></dt>
+<dd><p>Proximal operator of the BlockFunction at x:</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F}(x) =  (\mathrm{prox}_{\tau f_{i}}(x_{i}))_{i=1}^{m}\]</div>
+<p>Parameter:</p>
+<blockquote>
+<div><p>x : BlockDataContainer and must have as many rows as self.length</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.gradient">
+<span class="sig-name descname"><span class="pre">gradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction.gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.gradient" title="Link to this definition">#</a></dt>
+<dd><p>Returns the value of the gradient of the BlockFunction function at x.</p>
+<div class="math notranslate nohighlight">
+\[F'(x) = [f_{1}'(x_{1}), ... , f_{m}'(x_{m})]\]</div>
+<p>Parameter:</p>
+<blockquote>
+<div><p>x : BlockDataContainer and must have as many rows as self.length</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.proximal_conjugate">
+<span class="sig-name descname"><span class="pre">proximal_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction.proximal_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.proximal_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Proximal operator of the convex conjugate of BlockFunction at x:</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau F^{*}}(x) = (\mathrm{prox}_{\tau f^{*}_{i}}(x^{*}_{i}))_{i=1}^{m}\]</div>
+<p>Parameter:</p>
+<blockquote>
+<div><p>x : BlockDataContainer and must have as many rows as self.length</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.functions.BlockFunction.__rmul__">
+<span class="sig-name descname"><span class="pre">__rmul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/functions/BlockFunction/#BlockFunction.__rmul__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.functions.BlockFunction.__rmul__" title="Link to this definition">#</a></dt>
+<dd><p>Define multiplication with a scalar</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>other</strong> – number</p>
+</dd>
+</dl>
+<p>Returns a new <a class="reference internal" href="#block-function"><span class="std std-ref">BlockFunction</span></a> containing the product of the scalar with all the functions in the block</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="block-operator">
+<h2>Block Operator<a class="headerlink" href="#block-operator" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="#block-operator"><span class="std std-ref">BlockOperator</span></a> represent a block matrix with operators</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}K = \begin{bmatrix}
+     A_{1} &amp; A_{2} \\
+     A_{3} &amp; A_{4} \\
+     A_{5} &amp; A_{6}
+\end{bmatrix}_{(3,2)} *  \quad \underbrace{\begin{bmatrix}
+x_{1} \\
+x_{2}
+\end{bmatrix}_{(2,1)}}_{\textbf{x}} =  \begin{bmatrix}
+A_{1}x_{1}  + A_{2}x_{2}\\
+A_{3}x_{1}  + A_{4}x_{2}\\
+A_{5}x_{1}  + A_{6}x_{2}\\
+\end{bmatrix}_{(3,1)} =  \begin{bmatrix}
+y_{1}\\
+y_{2}\\
+y_{3}
+\end{bmatrix}_{(3,1)} = \textbf{y}\end{split}\]</div>
+<p>Column: Share the same domains <span class="math notranslate nohighlight">\(X_{1}, X_{2}\)</span></p>
+<p>Rows: Share the same ranges <span class="math notranslate nohighlight">\(Y_{1}, Y_{2}, Y_{3}\)</span></p>
+<div class="math notranslate nohighlight">
+\[K : (X_{1}\times X_{2}) \rightarrow (Y_{1}\times Y_{2} \times Y_{3})\]</div>
+<p><span class="math notranslate nohighlight">\(A_{1}, A_{3}, A_{5}\)</span>: share the same domain <span class="math notranslate nohighlight">\(X_{1}\)</span> and
+<span class="math notranslate nohighlight">\(A_{2}, A_{4}, A_{6}\)</span>: share the same domain <span class="math notranslate nohighlight">\(X_{2}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\begin{split}A_{1}: X_{1} \rightarrow Y_{1} \\
+A_{3}: X_{1} \rightarrow Y_{2} \\
+A_{5}: X_{1} \rightarrow Y_{3} \\
+A_{2}: X_{2} \rightarrow Y_{1} \\
+A_{4}: X_{2} \rightarrow Y_{2} \\
+A_{6}: X_{2} \rightarrow Y_{3}\end{split}\]</div>
+<p>For instance with these ingredients one may write the following objective
+function,</p>
+<div class="math notranslate nohighlight">
+\[\alpha ||\nabla u||_{2,1} + ||u - g||_2^2\]</div>
+<p>where <span class="math notranslate nohighlight">\(g\)</span> represent the measured values, <span class="math notranslate nohighlight">\(u\)</span> the solution
+<span class="math notranslate nohighlight">\(\nabla\)</span> is the gradient operator, <span class="math notranslate nohighlight">\(|| ~~ ||_{2,1}\)</span> is a norm for
+the output of the gradient operator and <span class="math notranslate nohighlight">\(|| x-g ||^2_2\)</span> is
+least squares fidelity function as</p>
+<div class="math notranslate nohighlight">
+\[ \begin{align}\begin{aligned}\begin{split}K = \begin{bmatrix}
+          \nabla \\
+          \mathbb{1}
+        \end{bmatrix}\end{split}\\F(x) = \Big[ \alpha \lVert ~x~ \rVert_{2,1} ~~ , ~~ || x - g||_2^2 \Big]\\w = [ u ]\end{aligned}\end{align} \]</div>
+<p>Then we have rewritten the problem as</p>
+<div class="math notranslate nohighlight">
+\[F(Kw) =   \alpha \left\lVert \nabla u \right\rVert_{2,1} + ||u-g||^2_2\]</div>
+<p>Which in Python would be like</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">op1</span> <span class="o">=</span> <span class="n">GradientOperator</span><span class="p">(</span><span class="n">ig</span><span class="p">,</span> <span class="n">correlation</span><span class="o">=</span><span class="n">GradientOperator</span><span class="o">.</span><span class="n">CORRELATION_SPACE</span><span class="p">)</span>
+<span class="n">op2</span> <span class="o">=</span> <span class="n">IdentityOperator</span><span class="p">(</span><span class="n">ig</span><span class="p">,</span> <span class="n">ag</span><span class="p">)</span>
+
+<span class="c1"># Create BlockOperator</span>
+<span class="n">K</span> <span class="o">=</span> <span class="n">BlockOperator</span><span class="p">(</span><span class="n">op1</span><span class="p">,</span> <span class="n">op2</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="p">)</span>
+
+<span class="c1"># Create functions</span>
+<span class="n">F</span> <span class="o">=</span> <span class="n">BlockFunction</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">MixedL21Norm</span><span class="p">(),</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">L2NormSquared</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="n">noisy_data</span><span class="p">))</span>
+</pre></div>
+</div>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.optimisation.operators.</span></span><span class="sig-name descname"><span class="pre">BlockOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator" title="Link to this definition">#</a></dt>
+<dd><p>A Block matrix containing Operators</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>*args</strong> (<a class="reference internal" href="#cil.optimisation.operators.Operator" title="cil.optimisation.operators.Operator"><em>Operator</em></a>) – Operators in the block.</p></li>
+<li><p><strong>**kwargs</strong> (<em>dict</em>) – shape (<code class="xref py py-obj docutils literal notranslate"><span class="pre">tuple</span></code>, optional): If shape is passed the Operators in vararg are considered input in a row-by-row fashion.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The Block Framework is a generic strategy to treat variational problems in the
+following form:</p>
+<div class="math notranslate nohighlight">
+\[\min Regulariser + Fidelity\]</div>
+</div>
+<p>BlockOperators have a generic shape M x N, and when applied on an
+Nx1 BlockDataContainer, will yield and Mx1 BlockDataContainer.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>BlockDatacontainer are only allowed to have the shape of N x 1, with
+N rows and 1 column.</p>
+<p>User may specify the shape of the block, by default is a row vector</p>
+<p>Operators in a Block are required to have the same domain column-wise and the
+same range row-wise.</p>
+</div>
+<p class="rubric">Examples</p>
+<p>BlockOperator(op0,op1) results in a row block</p>
+<p>BlockOperator(op0,op1,shape=(1,2)) results in a column block</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.column_wise_compatible">
+<span class="sig-name descname"><span class="pre">column_wise_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.column_wise_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.column_wise_compatible" title="Link to this definition">#</a></dt>
+<dd><p>Operators in a Block should have the same domain per column</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.row_wise_compatible">
+<span class="sig-name descname"><span class="pre">row_wise_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.row_wise_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.row_wise_compatible" title="Link to this definition">#</a></dt>
+<dd><p>Operators in a Block should have the same range per row</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.get_item">
+<span class="sig-name descname"><span class="pre">get_item</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">row</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">col</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.get_item"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.get_item" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Operator at specified row and col
+:param row: The row index required.
+:type row: <cite>int</cite>
+:param col: The column index required.
+:type col: <cite>int</cite></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.norm">
+<span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.norm" title="Link to this definition">#</a></dt>
+<dd><p>Returns the Euclidean norm of the norms of the individual operators in the BlockOperators</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.get_norms_as_list">
+<span class="sig-name descname"><span class="pre">get_norms_as_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.get_norms_as_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.get_norms_as_list" title="Link to this definition">#</a></dt>
+<dd><p>Returns a list of the individual norms of the Operators in the BlockOperator</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.set_norms">
+<span class="sig-name descname"><span class="pre">set_norms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norms</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.set_norms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.set_norms" title="Link to this definition">#</a></dt>
+<dd><p>Uses the set_norm() function in Operator to set the norms of the operators in the BlockOperator from a list of custom values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>norms</strong> (<em>list</em>) – A list of positive real values the same length as the number of operators in the BlockOperator.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.direct">
+<span class="sig-name descname"><span class="pre">direct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.direct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.direct" title="Link to this definition">#</a></dt>
+<dd><p>Direct operation for the BlockOperator</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>BlockOperators work on BlockDataContainers, but they will also work on DataContainers
+and inherited classes by simple wrapping the input in a BlockDataContainer of shape (1,1)</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.adjoint" title="Link to this definition">#</a></dt>
+<dd><p>Adjoint operation for the BlockOperator</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>BlockOperator may contain both LinearOperator and Operator
+This method exists in BlockOperator as it is not known what type of
+Operator it will contain.</p>
+<p>BlockOperators work on BlockDataContainers, but they will also work on DataContainers
+and inherited classes by simple wrapping the input in a BlockDataContainer of shape (1,1)</p>
+<p>Raises: ValueError if the contained Operators are not linear</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.is_linear">
+<span class="sig-name descname"><span class="pre">is_linear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.is_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.is_linear" title="Link to this definition">#</a></dt>
+<dd><p>Returns whether all the elements of the BlockOperator are linear</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.get_output_shape">
+<span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xshape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adjoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.get_output_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.get_output_shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns the shape of the output BlockDataContainer
+:param xshape:
+:type xshape: BlockDataContainer
+:param adjoint:
+:type adjoint: <cite>bool</cite></p>
+<p class="rubric">Examples</p>
+<p>A(N,M) direct u(M,1) -&gt; N,1</p>
+<p>A(N,M)^T adjoint u(N,1) -&gt; M,1</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.__rmul__">
+<span class="sig-name descname"><span class="pre">__rmul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scalar</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.__rmul__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.__rmul__" title="Link to this definition">#</a></dt>
+<dd><p>Defines the left multiplication with a scalar. Returns a block operator with Scaled Operators inside.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>scalar</strong> (<em>number</em><em> or </em><em>iterable containing numbers</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.T">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">T</span></span><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.T" title="Link to this definition">#</a></dt>
+<dd><p>Returns the transposed of self.</p>
+<p>Recall the input list is shaped in a row-by-row fashion</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.domain_geometry">
+<span class="sig-name descname"><span class="pre">domain_geometry</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.domain_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.domain_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Returns the domain of the BlockOperator</p>
+<p>If the shape of the BlockOperator is (N,1) the domain is a ImageGeometry or AcquisitionGeometry.
+Otherwise it is a BlockGeometry.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.range_geometry">
+<span class="sig-name descname"><span class="pre">range_geometry</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.range_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.range_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Returns the range of the BlockOperator</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.__getitem__">
+<span class="sig-name descname"><span class="pre">__getitem__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.__getitem__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.__getitem__" title="Link to this definition">#</a></dt>
+<dd><p>Returns the index-th operator in the block irrespectively of it’s shape</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.optimisation.operators.BlockOperator.get_as_list">
+<span class="sig-name descname"><span class="pre">get_as_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/optimisation/operators/BlockOperator/#BlockOperator.get_as_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.optimisation.operators.BlockOperator.get_as_list" title="Link to this definition">#</a></dt>
+<dd><p>Returns the list of operators</p>
+</dd></dl>
+
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
+<section id="references">
+<h3>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h3>
+<div class="docutils container" id="id62">
+<div role="list" class="citation-list">
+<div class="citation" id="id70" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id5">2</a>,<a role="doc-backlink" href="#id18">3</a>)</span>
+<p>Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. <em>SIAM Journal on Imaging Sciences</em>, 2(1):183–202, 2009. URL: <a class="reference external" href="https://doi.org/10.1137/080716542">https://doi.org/10.1137/080716542</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1137/080716542">arXiv:https://doi.org/10.1137/080716542</a>, <a class="reference external" href="https://doi.org/10.1137/080716542">doi:10.1137/080716542</a>.</p>
+</div>
+<div class="citation" id="id69" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id3">1</a>,<a role="doc-backlink" href="#id6">2</a>,<a role="doc-backlink" href="#id19">3</a>)</span>
+<p>Amir Beck and Marc Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. <em>IEEE Transactions on Image Processing</em>, 18(11):2419–2434, 2009. <a class="reference external" href="https://doi.org/10.1109/TIP.2009.2028250">doi:10.1109/TIP.2009.2028250</a>.</p>
+</div>
+<div class="citation" id="id65" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id9">1</a>,<a role="doc-backlink" href="#id22">2</a>)</span>
+<p>Antonin Chambolle and Thomas Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. <em>Journal of Mathematical Imaging and Vision</em>, 40(1):120–145, May 2011. URL: <a class="reference external" href="https://doi.org/10.1007/s10851-010-0251-1">https://doi.org/10.1007/s10851-010-0251-1</a>, <a class="reference external" href="https://doi.org/10.1007/s10851-010-0251-1">doi:10.1007/s10851-010-0251-1</a>.</p>
+</div>
+<div class="citation" id="id74" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">4</a><span class="fn-bracket">]</span></span>
+<p>Patrick L. Combettes and Valérie R. Wajs. Signal recovery by proximal forward-backward splitting. <em>Multiscale Modeling &amp; Simulation</em>, 4(4):1168–1200, 2005. URL: <a class="reference external" href="https://doi.org/10.1137/050626090">https://doi.org/10.1137/050626090</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1137/050626090">arXiv:https://doi.org/10.1137/050626090</a>, <a class="reference external" href="https://doi.org/10.1137/050626090">doi:10.1137/050626090</a>.</p>
+</div>
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+<p>Ernie Esser, Xiaoqun Zhang, and Tony F. Chan. A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. <em>SIAM Journal on Imaging Sciences</em>, 3(4):1015–1046, 2010. URL: <a class="reference external" href="https://doi.org/10.1137/09076934X">https://doi.org/10.1137/09076934X</a>, <a class="reference external" href="https://arxiv.org/abs/https://doi.org/10.1137/09076934X">arXiv:https://doi.org/10.1137/09076934X</a>, <a class="reference external" href="https://doi.org/10.1137/09076934X">doi:10.1137/09076934X</a>.</p>
+</div>
+<div class="citation" id="id63" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id8">6</a><span class="fn-bracket">]</span></span>
+<p>Osman Güler. New proximal point algorithms for convex minimization. <em>SIAM Journal on Optimization</em>, 2(4):649–664, 1992.</p>
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+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id11">7</a><span class="fn-bracket">]</span></span>
+<p>J. S. Jörgensen, E. Ametova, G. Burca, G. Fardell, E. Papoutsellis, E. Pasca, K. Thielemans, M. Turner, R. Warr, W. R. B. Lionheart, and P. J. Withers. Core imaging library - part i: a versatile python framework for tomographic imaging. <em>Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences</em>, 379(2204):20200192, 2021. URL: <a class="reference external" href="https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2020.0192">https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2020.0192</a>, <a class="reference external" href="https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0192">arXiv:https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0192</a>, <a class="reference external" href="https://doi.org/10.1098/rsta.2020.0192">doi:10.1098/rsta.2020.0192</a>.</p>
+</div>
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+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">8</a><span class="fn-bracket">]</span></span>
+<p>Avinash C. Kak and Malcolm Slaney. <em>Principles of Computerized Tomographic Imaging</em>. Society for Industrial and Applied Mathematics, January 2001. URL: <a class="reference external" href="https://doi.org/10.1137/1.9780898719277">https://doi.org/10.1137/1.9780898719277</a>, <a class="reference external" href="https://doi.org/10.1137/1.9780898719277">doi:10.1137/1.9780898719277</a>.</p>
+</div>
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+<p>Yurii Nesterov. <em>Introductory lectures on convex optimization: A basic course</em>. Volume 87. Springer Science &amp; Business Media, 2003.</p>
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+<p>Evangelos Papoutsellis, Evelina Ametova, Claire Delplancke, Gemma Fardell, Jakob S. Jörgensen, Edoardo Pasca, Martin Turner, Ryan Warr, William R. B. Lionheart, and Philip J. Withers. Core imaging library - part ii: multichannel reconstruction for dynamic and spectral tomography. <em>Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences</em>, 379(2204):20200193, 2021. URL: <a class="reference external" href="https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2020.0193">https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2020.0193</a>, <a class="reference external" href="https://arxiv.org/abs/https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0193">arXiv:https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2020.0193</a>, <a class="reference external" href="https://doi.org/10.1098/rsta.2020.0193">doi:10.1098/rsta.2020.0193</a>.</p>
+</div>
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+<p>Julian Rasch and Antonin Chambolle. Inexact first-order primal–dual algorithms. <em>Computational Optimization and Applications</em>, 76(2):381–430, Jun 2020. URL: <a class="reference external" href="https://doi.org/10.1007/s10589-020-00186-y">https://doi.org/10.1007/s10589-020-00186-y</a>, <a class="reference external" href="https://doi.org/10.1007/s10589-020-00186-y">doi:10.1007/s10589-020-00186-y</a>.</p>
+</div>
+<div class="citation" id="id73" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id20">12</a><span class="fn-bracket">]</span></span>
+<p>Mingqiang Zhu, Stephen J. Wright, and Tony F. Chan. Duality-based algorithms for total-variation-regularized image restoration. <em>Computational Optimization and Applications</em>, 47(3):377–400, Nov 2010. URL: <a class="reference external" href="https://doi.org/10.1007/s10589-008-9225-2">https://doi.org/10.1007/s10589-008-9225-2</a>, <a class="reference external" href="https://doi.org/10.1007/s10589-008-9225-2">doi:10.1007/s10589-008-9225-2</a>.</p>
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+<h3><a href="../">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">CIL Plugins</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#ccpi-regularisation">CCPi Regularisation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#total-variation">Total variation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#other-regularisation-functions">Other regularisation functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#tomophantom">TomoPhantom</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.plugins.TomoPhantom.get_ImageData"><code class="docutils literal notranslate"><span class="pre">get_ImageData()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#tigre">TIGRE</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fbp">FBP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#projection-operator">Projection Operator</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#astra">ASTRA</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#id6">FBP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#id7">Projection Operator</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
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+                  
+  <section id="cil-plugins">
+<h1>CIL Plugins<a class="headerlink" href="#cil-plugins" title="Link to this heading">#</a></h1>
+<section id="ccpi-regularisation">
+<h2>CCPi Regularisation<a class="headerlink" href="#ccpi-regularisation" title="Link to this heading">#</a></h2>
+<p>This plugin allows the use of regularisation functions from the <a class="reference external" href="https://github.com/vais-ral/CCPi-Regularisation-Toolkit">CCPi Regularisation toolkit</a>
+(<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S2352711018301912">10.1016/j.softx.2019.04.003</a>,
+a set of CPU/GPU optimised regularisation modules for iterative image reconstruction and
+other image processing tasks.</p>
+<section id="total-variation">
+<h3>Total variation<a class="headerlink" href="#total-variation" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.FGP_TV">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.ccpi_regularisation.functions.</span></span><span class="sig-name descname"><span class="pre">FGP_TV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">isotropic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonnegativity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strong_convexity_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#FGP_TV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.FGP_TV" title="Link to this definition">#</a></dt>
+<dd><p>Fast Gradient Projection Total Variation (FGP_TV)</p>
+<p>The <a class="reference internal" href="#cil.plugins.ccpi_regularisation.functions.FGP_TV" title="cil.plugins.ccpi_regularisation.functions.FGP_TV"><code class="xref py py-class docutils literal notranslate"><span class="pre">FGP_TV</span></code></a> computes the proximal operator of the Total variation regulariser</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{prox}_{\tau (\alpha TV)}(x) = \underset{z}{\mathrm{argmin}} \,\alpha\,\mathrm{TV}(z) + \frac{1}{2}\|z - x\|^{2} .\]</div>
+<p>The algorithm used for the proximal operator of TV is the Fast Gradient Projection algorithm
+applied to the _dual <a href="#id8"><span class="problematic" id="id9">problem_</span></a> of the above problem, see <span id="id1">[<a class="reference internal" href="../optimisation/#id70" title="Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009. URL: https://doi.org/10.1137/080716542, arXiv:https://doi.org/10.1137/080716542, doi:10.1137/080716542.">1</a>]</span>, <span id="id2">[<a class="reference internal" href="../optimisation/#id69" title="Amir Beck and Marc Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11):2419-2434, 2009. doi:10.1109/TIP.2009.2028250.">2</a>]</span>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In CIL Version 24.1.0 we change the default value of nonnegativity to False. This means non-negativity is not enforced by default.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">Number</span></code> (positive), default = 1.0 .) – Total variation regularisation parameter.</p></li>
+<li><p><strong>max_iteration</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">int</span></code>. Default = 100 .) – Maximum number of iterations for the Fast Gradient Projection algorithm.</p></li>
+<li><p><strong>isotropic</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">boolean</span></code>. Default = True .) – <p>Isotropic or Anisotropic definition of the Total variation regulariser.</p>
+<div class="math notranslate nohighlight">
+\[|x|_{2} = \sqrt{x_{1}^{2} + x_{2}^{2}},\, (\mbox{isotropic})\]</div>
+<div class="math notranslate nohighlight">
+\[|x|_{1} = |x_{1}| + |x_{2}|\, (\mbox{anisotropic})\]</div>
+</p></li>
+<li><p><strong>nonnegativity</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">boolean</span></code>. Default = False .) – Non-negativity constraint for the solution of the FGP algorithm.</p></li>
+<li><p><strong>tolerance</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, Default = 0 .) – <p>Stopping criterion for the FGP algorithm.</p>
+<div class="math notranslate nohighlight">
+\[\|x^{k+1} - x^{k}\|_{2} &lt; \mathrm{tolerance}\]</div>
+</p></li>
+<li><p><strong>device</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">str</span></code>, Default = ‘cpu’ .) – FGP_TV algorithm runs on <cite>cpu</cite> or <cite>gpu</cite>.</p></li>
+<li><p><strong>strong_convexity_constant</strong> (<code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>, default = 0) – <p>A strongly convex term weighted by the <code class="code docutils literal notranslate"><span class="pre">strong_convexity_constant</span></code> (<span class="math notranslate nohighlight">\(\gamma\)</span>) parameter is added to the Total variation.
+Now the <code class="code docutils literal notranslate"><span class="pre">TotalVariation</span></code> function is <span class="math notranslate nohighlight">\(\gamma\)</span> - strongly convex and the proximal operator is</p>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}} \frac{1}{2\tau}\|u - b\|^{2} + \mathrm{TV}(u) + \frac{\gamma}{2}\|u\|^{2} \Leftrightarrow\]</div>
+<div class="math notranslate nohighlight">
+\[\underset{u}{\mathrm{argmin}} \frac{1}{2\frac{\tau}{1+\gamma\tau}}\|u - \frac{b}{1+\gamma\tau}\|^{2} + \mathrm{TV}(u)\]</div>
+</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="math notranslate nohighlight">
+\[\underset{u\qeq0}{\mathrm{argmin}} \frac{1}{2}\|u - b\|^{2} + \alpha TV(u)\]</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="n">FGP_TV</span><span class="p">(</span><span class="n">max_iteration</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;gpu&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sol</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">proximal</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The <a class="reference internal" href="#cil.plugins.ccpi_regularisation.functions.FGP_TV" title="cil.plugins.ccpi_regularisation.functions.FGP_TV"><code class="xref py py-class docutils literal notranslate"><span class="pre">FGP_TV</span></code></a> regularisation does not incorparate information on the <code class="xref py py-class docutils literal notranslate"><span class="pre">ImageGeometry</span></code>, i.e., pixel/voxel size.
+Therefore a rescaled parameter should be used to match the same solution computed using <a class="reference internal" href="../optimisation/#cil.optimisation.functions.TotalVariation" title="cil.optimisation.functions.TotalVariation"><code class="xref py py-class docutils literal notranslate"><span class="pre">TotalVariation</span></code></a>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G1</span> <span class="o">=</span> <span class="p">(</span><span class="n">alpha</span><span class="o">/</span><span class="n">ig</span><span class="o">.</span><span class="n">voxel_size_x</span><span class="p">)</span> <span class="o">*</span> <span class="n">FGP_TV</span><span class="p">(</span><span class="n">max_iteration</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;gpu&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G2</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="n">TotalVariation</span><span class="p">(</span><span class="n">max_iteration</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="../optimisation/#cil.optimisation.functions.TotalVariation" title="cil.optimisation.functions.TotalVariation"><code class="xref py py-class docutils literal notranslate"><span class="pre">TotalVariation</span></code></a></p>
+</div>
+</dd></dl>
+
+</section>
+<section id="other-regularisation-functions">
+<h3>Other regularisation functions<a class="headerlink" href="#other-regularisation-functions" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TGV">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.ccpi_regularisation.functions.</span></span><span class="sig-name descname"><span class="pre">TGV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TGV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TGV" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TGV.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TGV.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TGV.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Creator of Total Generalised Variation Function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>number</em><em>, </em><em>default 1</em>) – regularisation parameter</p></li>
+<li><p><strong>gamma</strong> (<em>number</em><em>, </em><em>default 1</em><em>, </em><em>can range between 1 and 2</em>) – ratio of TGV terms</p></li>
+<li><p><strong>max_iteration</strong> (<em>integer</em><em>, </em><em>default 100</em>) – max number of sub iterations. The algorithm will iterate up to this number of iteration or up to when the tolerance has been reached</p></li>
+<li><p><strong>tolerance</strong> (<em>float</em><em>, </em><em>default 0</em>) – minimum difference between previous iteration of the algorithm that determines the stop of the iteration earlier than max_iteration. If set to 0 only the max_iteration will be used as stop criterion.</p></li>
+<li><p><strong>device</strong> (<em>string</em><em>, </em><em>default 'cpu'</em><em>, </em><em>can be 'gpu' if GPU is installed</em>) – determines if the code runs on CPU or GPU</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TGV.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TGV.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TGV.__call__" title="Link to this definition">#</a></dt>
+<dd><p>Call self as a function.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TGV.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TGV.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TGV.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TGV.__rmul__">
+<span class="sig-name descname"><span class="pre">__rmul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scalar</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TGV.__rmul__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TGV.__rmul__" title="Link to this definition">#</a></dt>
+<dd><p>Define the multiplication with a scalar</p>
+<p>this changes the regularisation parameter in the plugin</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.FGP_dTV">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.ccpi_regularisation.functions.</span></span><span class="sig-name descname"><span class="pre">FGP_dTV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">isotropic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonnegativity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#FGP_dTV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.FGP_dTV" title="Link to this definition">#</a></dt>
+<dd><p>Creator of FGP_dTV Function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>reference</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a>) – reference image</p></li>
+<li><p><strong>alpha</strong> (<em>number</em><em>, </em><em>default 1</em>) – regularisation parameter</p></li>
+<li><p><strong>max_iteration</strong> (<em>integer</em><em>, </em><em>default 100</em>) – max number of sub iterations. The algorithm will iterate up to this number of iteration or up to when the tolerance has been reached</p></li>
+<li><p><strong>tolerance</strong> (<em>float</em><em>, </em><em>default 0</em>) – minimum difference between previous iteration of the algorithm that determines the stop of the iteration earlier than max_iteration. If set to 0 only the max_iteration will be used as stop criterion.</p></li>
+<li><p><strong>eta</strong> (<em>number</em><em>, </em><em>default 0.01</em>) – smoothing constant to calculate gradient of the reference</p></li>
+<li><p><strong>isotropic</strong> (<em>boolean</em><em>, </em><em>default True</em><em>, </em><em>can range between 1 and 2</em>) – Whether it uses L2 (isotropic) or L1 (anisotropic) norm</p></li>
+<li><p><strong>nonnegativity</strong> (<em>boolean</em><em>, </em><em>default True</em>) – Whether to add the non-negativity constraint</p></li>
+<li><p><strong>device</strong> (<em>string</em><em>, </em><em>default 'cpu'</em><em>, </em><em>can be 'gpu' if GPU is installed</em>) – determines if the code runs on CPU or GPU</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.FGP_dTV.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">isotropic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonnegativity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#FGP_dTV.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.FGP_dTV.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#FGP_dTV.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__call__" title="Link to this definition">#</a></dt>
+<dd><p>Call self as a function.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.FGP_dTV.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#FGP_dTV.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.FGP_dTV.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.FGP_dTV.__rmul__">
+<span class="sig-name descname"><span class="pre">__rmul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scalar</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#FGP_dTV.__rmul__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.FGP_dTV.__rmul__" title="Link to this definition">#</a></dt>
+<dd><p>Define the multiplication with a scalar</p>
+<p>this changes the regularisation parameter in the plugin</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TNV">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.ccpi_regularisation.functions.</span></span><span class="sig-name descname"><span class="pre">TNV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TNV"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TNV" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TNV.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TNV.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TNV.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Creator of TNV Function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>number</em><em>, </em><em>default 1</em>) – regularisation parameter</p></li>
+<li><p><strong>max_iteration</strong> (<em>integer</em><em>, </em><em>default 100</em>) – max number of sub iterations. The algorithm will iterate up to this number of iteration or up to when the tolerance has been reached</p></li>
+<li><p><strong>tolerance</strong> (<em>float</em><em>, </em><em>default 0</em>) – minimum difference between previous iteration of the algorithm that determines the stop of the iteration earlier than max_iteration. If set to 0 only the max_iteration will be used as stop criterion.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TNV.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TNV.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TNV.__call__" title="Link to this definition">#</a></dt>
+<dd><p>Call self as a function.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TNV.convex_conjugate">
+<span class="sig-name descname"><span class="pre">convex_conjugate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TNV.convex_conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TNV.convex_conjugate" title="Link to this definition">#</a></dt>
+<dd><p>Evaluation of the function F* at x, where F* is the convex conjugate of function F,</p>
+<div class="math notranslate nohighlight">
+\[F^{*}(x^{*}) = \underset{x}{\sup} \langle x^{*}, x \rangle - F(x)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The value of the convex conjugate of the function at x.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TNV.__rmul__">
+<span class="sig-name descname"><span class="pre">__rmul__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scalar</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TNV.__rmul__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TNV.__rmul__" title="Link to this definition">#</a></dt>
+<dd><p>Define the multiplication with a scalar</p>
+<p>this changes the regularisation parameter in the plugin</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.ccpi_regularisation.functions.TNV.check_input">
+<span class="sig-name descname"><span class="pre">check_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/ccpi_regularisation/functions/regularisers/#TNV.check_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.ccpi_regularisation.functions.TNV.check_input" title="Link to this definition">#</a></dt>
+<dd><p>TNV requires 2D+channel data with the first dimension as the channel dimension</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+<section id="tomophantom">
+<h2>TomoPhantom<a class="headerlink" href="#tomophantom" title="Link to this heading">#</a></h2>
+<p>This plugin allows the use of part of <a class="reference external" href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a>
+(<a class="reference external" href="https://doi.org/10.1016/j.softx.2018.05.003">10.1016/j.softx.2018.05.003</a>,
+a toolbox written in C language to generate customisable 2D-4D phantoms (with a
+temporal capability).</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="cil.plugins.TomoPhantom.get_ImageData">
+<span class="sig-prename descclassname"><span class="pre">cil.plugins.TomoPhantom.</span></span><span class="sig-name descname"><span class="pre">get_ImageData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geometry</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/TomoPhantom/#get_ImageData"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.TomoPhantom.get_ImageData" title="Link to this definition">#</a></dt>
+<dd><p>Returns an ImageData relative to geometry with the model num_model from tomophantom</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_model</strong> (<em>int</em>) – model number</p></li>
+<li><p><strong>geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a>) – geometrical info that describes the phantom</p></li>
+</ul>
+</dd>
+</dl>
+<p>Example usage:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">ndim</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">N</span><span class="o">=</span><span class="mi">128</span>
+<span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">360</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">offset</span> <span class="o">=</span> <span class="mf">0.4</span>
+<span class="n">channels</span> <span class="o">=</span> <span class="mi">3</span>
+
+<span class="k">if</span> <span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+    <span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone2D</span><span class="p">((</span><span class="n">offset</span><span class="p">,</span><span class="o">-</span><span class="mi">100</span><span class="p">),</span> <span class="p">(</span><span class="n">offset</span><span class="p">,</span><span class="mi">100</span><span class="p">))</span>
+    <span class="n">ag</span><span class="o">.</span><span class="n">set_panel</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
+
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">ag</span> <span class="o">=</span> <span class="n">AcquisitionGeometry</span><span class="o">.</span><span class="n">create_Cone3D</span><span class="p">((</span><span class="n">offset</span><span class="p">,</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="n">offset</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+    <span class="n">ag</span><span class="o">.</span><span class="n">set_panel</span><span class="p">((</span><span class="n">N</span><span class="p">,</span><span class="n">N</span><span class="o">-</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="n">ag</span><span class="o">.</span><span class="n">set_channels</span><span class="p">(</span><span class="n">channels</span><span class="p">)</span>
+<span class="n">ag</span><span class="o">.</span><span class="n">set_angles</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">angle_unit</span><span class="o">=</span><span class="n">AngleUnit</span><span class="o">.</span><span class="n">DEGREE</span><span class="p">)</span>
+
+<span class="n">ig</span> <span class="o">=</span> <span class="n">ag</span><span class="o">.</span><span class="n">get_ImageGeometry</span><span class="p">()</span>
+<span class="n">num_model</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">phantom</span> <span class="o">=</span> <span class="n">TomoPhantom</span><span class="o">.</span><span class="n">get_ImageData</span><span class="p">(</span><span class="n">num_model</span><span class="o">=</span><span class="n">num_model</span><span class="p">,</span> <span class="n">geometry</span><span class="o">=</span><span class="n">ig</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+<section id="tigre">
+<h2>TIGRE<a class="headerlink" href="#tigre" title="Link to this heading">#</a></h2>
+<p>This plugin allows the use of <a class="reference external" href="https://github.com/CERN/TIGRE">TIGRE</a>
+(<a class="reference external" href="http://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010">10.1088/2057-1976/2/5/055010</a>
+for forward and back projections and filter back projection reconstruction.</p>
+<section id="fbp">
+<h3>FBP<a class="headerlink" href="#fbp" title="Link to this heading">#</a></h3>
+<p>This reconstructs with FBP for parallel-beam data, and with FDK weights for cone-beam data</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.tigre.FBP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.tigre.</span></span><span class="sig-name descname"><span class="pre">FBP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acquisition_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/tigre/FBP/#FBP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.tigre.FBP" title="Link to this definition">#</a></dt>
+<dd><p>FBP Filtered Back Projection is a reconstructor for 2D and 3D parallel and cone-beam geometries.
+It is able to back-project circular trajectories with 2 PI angular range and equally spaced angular steps.</p>
+<p>This uses the ram-lak filter
+This is provided for simple and offset parallel-beam geometries only</p>
+<dl class="simple">
+<dt>acquisition_geometry<span class="classifier">AcquisitionGeometry</span></dt><dd><p>A description of the acquisition data</p>
+</dd>
+<dt>image_geometry<span class="classifier">ImageGeometry, default used if None</span></dt><dd><p>A description of the area/volume to reconstruct</p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.plugins.tigre</span> <span class="kn">import</span> <span class="n">FBP</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fbp</span> <span class="o">=</span> <span class="n">FBP</span><span class="p">(</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fbp</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">reconstruction</span> <span class="o">=</span> <span class="n">fbp</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.tigre.FBP.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.plugins.tigre.FBP.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.tigre.FBP.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.plugins.tigre.FBP.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="projection-operator">
+<h3>Projection Operator<a class="headerlink" href="#projection-operator" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.tigre.ProjectionOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.tigre.</span></span><span class="sig-name descname"><span class="pre">ProjectionOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acquisition_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direct_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interpolated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adjoint_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'matched'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/tigre/ProjectionOperator/#ProjectionOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.tigre.ProjectionOperator" title="Link to this definition">#</a></dt>
+<dd><p>ProjectionOperator configures and calls TIGRE Projectors for your dataset.</p>
+<p>Please refer to the TIGRE documentation for further descriptions
+<a class="github reference external" href="https://github.com/CERN/TIGRE">CERN/TIGRE</a>
+<a class="reference external" href="https://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010">https://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>image_geometry</strong> (<cite>ImageGeometry</cite>, default used if None) – A description of the area/volume to reconstruct</p></li>
+<li><p><strong>acquisition_geometry</strong> (<cite>AcquisitionGeometry</cite>, <cite>BlockGeometry</cite>) – A description of the acquisition data. If passed a BlockGeometry it will return a BlockOperator.</p></li>
+<li><p><strong>direct_method</strong> (<em>str</em><em>,  </em><em>default 'interpolated'</em>) – The method used by the forward projector, ‘Siddon’ for ray-voxel intersection, ‘interpolated’ for interpolated projection</p></li>
+<li><p><strong>adjoint_weights</strong> (<em>str</em><em>, </em><em>default 'matched'</em>) – The weighting method used by the cone-beam backward projector, ‘matched’ for weights to approximately match the ‘interpolated’ forward projector, ‘FDK’ for FDK weights</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.plugins.tigre</span> <span class="kn">import</span> <span class="n">ProjectionOperator</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">PO</span> <span class="o">=</span> <span class="n">ProjectionOperator</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">forward_projection</span> <span class="o">=</span> <span class="n">PO</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">backward_projection</span> <span class="o">=</span> <span class="n">PO</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+</section>
+<section id="astra">
+<h2>ASTRA<a class="headerlink" href="#astra" title="Link to this heading">#</a></h2>
+<p>This plugin allows the use of <a class="reference external" href="https://github.com/astra-toolbox/astra-toolbox">ASTRA-toolbox</a>
+(<a class="reference external" href="http://dx.doi.org/10.1364/OE.24.025129">10.1364/OE.24.025129</a>)
+for forward and back projections and filter back projection reconstruction.</p>
+<section id="id6">
+<h3>FBP<a class="headerlink" href="#id6" title="Link to this heading">#</a></h3>
+<p>This reconstructs with FBP for parallel-beam data, and with FDK weights for cone-beam data</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.astra.FBP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.astra.</span></span><span class="sig-name descname"><span class="pre">FBP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acquisition_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/astra/processors/FBP/#FBP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.astra.FBP" title="Link to this definition">#</a></dt>
+<dd><p>FBP configures and calls an appropriate ASTRA FBP or FDK algorithm for your dataset.</p>
+<p>The best results will be on data with circular trajectories of a 2PI angular range and equally spaced small angular steps.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>image_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em>, </em><em>default used if None</em>) – A description of the area/volume to reconstruct</p></li>
+<li><p><strong>acquisition_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry"><em>AcquisitionGeometry</em></a>) – A description of the acquisition data</p></li>
+<li><p><strong>device</strong> (<em>string</em><em>, </em><em>default='gpu'</em>) – ‘gpu’ will run on a compatible CUDA capable device using the ASTRA FDK_CUDA algorithm
+‘cpu’ will run on CPU using the ASTRA FBP algorithm - see Notes for limitations</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.plugins.astra</span> <span class="kn">import</span> <span class="n">FBP</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fbp</span> <span class="o">=</span> <span class="n">FBP</span><span class="p">(</span><span class="n">image_geometry</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fbp</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">reconstruction</span> <span class="o">=</span> <span class="n">fbp</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>A CPU version is provided for simple 2D parallel-beam geometries only, any offsets and rotations in the acquisition geometry will be ignored.</p>
+<p>This uses the ram-lak filter only.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.astra.FBP.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/astra/processors/FBP/#FBP.set_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.astra.FBP.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.plugins.astra.FBP.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/astra/processors/FBP/#FBP.get_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.astra.FBP.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="id7">
+<h3>Projection Operator<a class="headerlink" href="#id7" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.plugins.astra.ProjectionOperator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.plugins.astra.</span></span><span class="sig-name descname"><span class="pre">ProjectionOperator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acquisition_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gpu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/plugins/astra/operators/ProjectionOperator/#ProjectionOperator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.plugins.astra.ProjectionOperator" title="Link to this definition">#</a></dt>
+<dd><p>ProjectionOperator configures and calls appropriate ASTRA Projectors for your dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>image_geometry</strong> (<code class="docutils literal notranslate"><span class="pre">ImageGeometry</span></code>, default used if None) – A description of the area/volume to reconstruct</p></li>
+<li><p><strong>acquisition_geometry</strong> (<code class="docutils literal notranslate"><span class="pre">AcquisitionGeometry</span></code>, <code class="docutils literal notranslate"><span class="pre">BlockGeometry</span></code>) – A description of the acquisition data. If passed a BlockGeometry it will return a BlockOperator.</p></li>
+<li><p><strong>device</strong> (<em>string</em><em>, </em><em>default='gpu'</em>) – ‘gpu’ will run on a compatible CUDA capable device using the ASTRA 3D CUDA Projectors, ‘cpu’ will run on CPU using the ASTRA 2D CPU Projectors</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.plugins.astra</span> <span class="kn">import</span> <span class="n">ProjectionOperator</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">PO</span> <span class="o">=</span> <span class="n">ProjectionOperator</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">forward_projection</span> <span class="o">=</span> <span class="n">PO</span><span class="o">.</span><span class="n">direct</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">backward_projection</span> <span class="o">=</span> <span class="n">PO</span><span class="o">.</span><span class="n">adjoint</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>For multichannel data the ProjectionOperator will broadcast across all channels.</p>
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
+</section>
+</section>
+</section>
+
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+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Processors</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#data-manipulation">Data Manipulation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#data-slicer">Data Slicer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#data-binner">Data Binner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#data-padder">Data Padder</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#mask-generator-from-data">Mask Generator from Data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#data-masking">Data Masking</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#pre-processors">Pre-processors</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#centre-of-rotation-corrector">Centre Of Rotation Corrector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#data-normaliser">Data Normaliser</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#transmission-to-absorption-converter">Transmission to Absorption Converter</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#absorption-to-transmission-converter">Absorption to Transmission Converter</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#ring-remover">Ring Remover</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#paganin-processor">Paganin Processor</a></li>
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+  <section id="processors">
+<h1>Processors<a class="headerlink" href="#processors" title="Link to this heading">#</a></h1>
+<p>This module allows the user to manipulate or pre-process their data.</p>
+<section id="data-manipulation">
+<h2>Data Manipulation<a class="headerlink" href="#data-manipulation" title="Link to this heading">#</a></h2>
+<p>These processors can be used on <cite>ImageData</cite> or <cite>AcquisitionData</cite> objects.</p>
+<section id="data-slicer">
+<h3>Data Slicer<a class="headerlink" href="#data-slicer" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.Slicer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">Slicer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">roi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Slicer/#Slicer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Slicer" title="Link to this definition">#</a></dt>
+<dd><p>This creates a Slicer processor.</p>
+<p>The processor will crop the data, and then return every n input pixels along a dimension from the starting index.</p>
+<p>The output will be a data container with the data, and geometry updated to reflect the operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>roi</strong> (<em>dict</em>) – <p>The region-of-interest to slice {‘axis_name1’:(start,stop,step), ‘axis_name2’:(start,stop,step)}
+The <cite>key</cite> being the axis name to apply the processor to, the <cite>value</cite> holding a tuple containing the ROI description</p>
+<p>Start: Starting index of input data. Must be an integer, or <cite>None</cite> defaults to index 0.
+Stop: Stopping index of input data. Must be an integer, or <cite>None</cite> defaults to index N.
+Step: Number of pixels to average together. Must be an integer or <cite>None</cite> defaults to 1.</p>
+</p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">Slicer</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">roi</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;horizontal&#39;</span><span class="p">:(</span><span class="mi">10</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span><span class="s1">&#39;vertical&#39;</span><span class="p">:(</span><span class="mi">10</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">)}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Slicer</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_sliced</span><span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">Slicer</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">roi</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;horizontal&#39;</span><span class="p">:(</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span><span class="s1">&#39;vertical&#39;</span><span class="p">:(</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="mi">2</span><span class="p">)}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Slicer</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">geometry_sliced</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The indices provided are start inclusive, stop exclusive.</p>
+<p>All elements along a dimension will be included if the axis does not appear in the roi dictionary, or if passed as {‘axis_name’,-1}</p>
+<p>If only one number is provided, then it is interpreted as Stop. i.e. {‘axis_name1’:(stop)}
+If two numbers are provided, then they are interpreted as Start and Stop  i.e. {‘axis_name1’:(start, stop)}</p>
+<p>Negative indexing can be used to specify the index. i.e. {‘axis_name1’:(10, -10)} will crop the dimension symmetrically</p>
+<p>If Stop - Start is not multiple of Step, then
+the resulted dimension will have (Stop - Start) // Step
+elements, i.e. (Stop - Start) % Step elements will be ignored</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Slicer.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Slicer/#Slicer.set_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Slicer.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data or geometry to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>Geometry</em>) – The input DataContainer or Geometry</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Slicer.process">
+<span class="sig-name descname"><span class="pre">process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Slicer/#Slicer.process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Slicer.process" title="Link to this definition">#</a></dt>
+<dd><p>Processes the input data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed output and suppresses the return</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The downsampled output is returned. Depending on the input type this may be:
+ImageData, AcquisitionData, DataContainer, ImageGeometry, AcquisitionGeometry</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Slicer.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Slicer.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="data-binner">
+<h3>Data Binner<a class="headerlink" href="#data-binner" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.Binner">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">Binner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">roi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accelerated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Binner/#Binner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Binner" title="Link to this definition">#</a></dt>
+<dd><p>This creates a Binner processor.</p>
+<p>The processor will crop the data, and then average together n input pixels along a dimension from the starting index.</p>
+<p>The output will be a data container with the data, and geometry updated to reflect the operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>roi</strong> (<em>dict</em>) – <p>The region-of-interest to bin {‘axis_name1’:(start,stop,step), ‘axis_name2’:(start,stop,step)}
+The <cite>key</cite> being the axis name to apply the processor to, the <cite>value</cite> holding a tuple containing the ROI description</p>
+<p>Start: Starting index of input data. Must be an integer, or <cite>None</cite> defaults to index 0.
+Stop: Stopping index of input data. Must be an integer, or <cite>None</cite> defaults to index N.
+Step: Number of pixels to average together. Must be an integer or <cite>None</cite> defaults to 1.</p>
+</p></li>
+<li><p><strong>accelerated</strong> (<em>boolean</em><em>, </em><em>default=True</em>) – Uses the CIL accelerated backend if <cite>True</cite>, numpy if <cite>False</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">Binner</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">roi</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;horizontal&#39;</span><span class="p">:(</span><span class="mi">10</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span><span class="s1">&#39;vertical&#39;</span><span class="p">:(</span><span class="mi">10</span><span class="p">,</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">)}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Binner</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_binned</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">Binner</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">roi</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;horizontal&#39;</span><span class="p">:(</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span><span class="s1">&#39;vertical&#39;</span><span class="p">:(</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="mi">2</span><span class="p">)}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Binner</span><span class="p">(</span><span class="n">roi</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">geometry_binned</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The indices provided are start inclusive, stop exclusive.</p>
+<p>All elements along a dimension will be included if the axis does not appear in the roi dictionary, or if passed as {‘axis_name’,-1}</p>
+<p>If only one number is provided, then it is interpreted as Stop. i.e. {‘axis_name1’:(stop)}
+If two numbers are provided, then they are interpreted as Start and Stop  i.e. {‘axis_name1’:(start, stop)}</p>
+<p>Negative indexing can be used to specify the index. i.e. {‘axis_name1’:(10, -10)} will crop the dimension symmetrically</p>
+<p>If Stop - Start is not multiple of Step, then
+the resulted dimension will have (Stop - Start) // Step
+elements, i.e. (Stop - Start) % Step elements will be ignored</p>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Binner.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Binner.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Binner.process">
+<span class="sig-name descname"><span class="pre">process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Binner.process" title="Link to this definition">#</a></dt>
+<dd><p>Processes the input data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed output and suppresses the return</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The downsampled output is returned. Depending on the input type this may be:
+ImageData, AcquisitionData, DataContainer, ImageGeometry, AcquisitionGeometry</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Binner.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Binner.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data or geometry to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>Geometry</em>) – The input DataContainer or Geometry</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="data-padder">
+<h3>Data Padder<a class="headerlink" href="#data-padder" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.Padder">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">Padder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder" title="Link to this definition">#</a></dt>
+<dd><p>Processor to pad an array with a border, wrapping numpy.pad. See <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html">https://numpy.org/doc/stable/reference/generated/numpy.pad.html</a></p>
+<p>It is recommended to use the static methods to configure your Padder object rather than initialising this class directly. See examples for details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mode</strong> (<em>str</em>) – The method used to populate the border data. Accepts: ‘constant’, ‘edge’, ‘linear_ramp’, ‘reflect’, ‘symmetric’, ‘wrap’</p></li>
+<li><p><strong>pad_width</strong> (<em>int</em><em>, </em><em>tuple</em><em>, </em><em>dict</em>) – The size of the border along each axis, see usage notes</p></li>
+<li><p><strong>pad_values</strong> (<em>float</em><em>, </em><em>tuple</em><em>, </em><em>dict</em><em>, </em><em>default=0.0</em>) – The additional values needed by some of the modes</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl class="simple">
+<dt><cite>pad_width</cite>  behaviour (number of pixels):</dt><dd><ul class="simple">
+<li><p>int: Each axis will be padded with a border of this size</p></li>
+<li><p>tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</p></li>
+<li><p>dict: Specified axes will be padded: e.g. {‘horizontal’:(8, 23), ‘vertical’: 10}</p></li>
+</ul>
+</dd>
+<dt><cite>pad_values</cite>  behaviour:</dt><dd><ul class="simple">
+<li><p>float: Each border will use this value</p></li>
+<li><p>tuple(float, float): Each value will be used asymmetrically for each axis i.e. (before, after)</p></li>
+<li><p>dict: Specified axes and values: e.g. {‘horizontal’:(8, 23), ‘channel’:5}</p></li>
+</ul>
+</dd>
+</dl>
+<p>If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,
+and the last two angles in geometry.angles. The user should ensure the output is as expected.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 0. 1. 2. 2.]</span>
+<span class="go">[0. 0. 1. 2. 2.]</span>
+<span class="go">[3. 3. 4. 5. 5.]</span>
+<span class="go">[6. 6. 7. 8. 8.]</span>
+<span class="go">[6. 6. 7. 8. 8.]]</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;horizontal_y&#39;</span><span class="p">:(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span><span class="s1">&#39;horizontal_x&#39;</span><span class="p">:(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)},</span> <span class="n">constant_values</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[-1. -1. -1. -1.  1.  1.]</span>
+<span class="go">[-1.  0.  1.  2.  1.  1.]</span>
+<span class="go">[-1.  3.  4.  5.  1.  1.]</span>
+<span class="go">[-1.  6.  7.  8.  1.  1.]</span>
+<span class="go">[-1.  1.  1.  1.  1.  1.]</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.constant">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">constant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pad_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder.constant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder.constant" title="Link to this definition">#</a></dt>
+<dd><p>Padder processor wrapping numpy.pad with mode <cite>constant</cite>.</p>
+<p>Pads the data with a constant value border. Pads in all <em>spatial</em>
+dimensions unless a dictionary is passed to either <cite>pad_width</cite> or <cite>constant_values</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pad_width</strong> (<em>int</em><em>, </em><em>tuple</em><em>, </em><em>dict</em>) – The size of the border along each axis, see usage notes</p></li>
+<li><p><strong>constant_values</strong> (<em>float</em><em>, </em><em>tuple</em><em>, </em><em>dict</em><em>, </em><em>default=0.0</em>) – The value of the border, see usage notes</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl class="simple">
+<dt><cite>pad_width</cite>  behaviour (number of pixels):</dt><dd><ul class="simple">
+<li><p>int: Each axis will be padded with a border of this size</p></li>
+<li><p>tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</p></li>
+<li><p>dict: Specified axes will be padded: e.g. {‘horizontal’:(8, 23), ‘vertical’: 10}</p></li>
+</ul>
+</dd>
+<dt><cite>constant_values</cite>  behaviour (value of pixels):</dt><dd><ul class="simple">
+<li><p>float: Each border will be set to this value</p></li>
+<li><p>tuple(float, float): Each border value will be used asymmetrically for each axis i.e. (before, after)</p></li>
+<li><p>dict: Specified axes and values: e.g. {‘horizontal’:(8, 23), ‘channel’:5}</p></li>
+</ul>
+</dd>
+</dl>
+<p>If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,
+and the last two angles in geometry.angles. The user should ensure the output is as expected.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">constant_values</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 0. 0. 0. 0.]</span>
+<span class="go">[0. 0. 1. 2. 0.]</span>
+<span class="go">[0. 3. 4. 5. 0.]</span>
+<span class="go">[0. 6. 7. 8. 0.]</span>
+<span class="go">[0. 0. 0. 0. 0.]]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.edge">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pad_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder.edge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder.edge" title="Link to this definition">#</a></dt>
+<dd><p>Padder processor wrapping numpy.pad with mode <cite>edge</cite>.</p>
+<p>Pads the data by extending the edge values in to the border. Pads in all <em>spatial</em>
+dimensions unless a dictionary is passed to <cite>pad_width</cite>.</p>
+<dl class="simple">
+<dt>pad_width: int, tuple, dict</dt><dd><p>The size of the border along each axis, see usage notes</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl class="simple">
+<dt><cite>pad_width</cite> behaviour (number of pixels):</dt><dd><ul class="simple">
+<li><p>int: Each axis will be padded with a border of this size</p></li>
+<li><p>tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</p></li>
+<li><p>dict: Specified axes will be padded: e.g. {‘horizontal’:(8, 23), ‘vertical’: 10}</p></li>
+</ul>
+</dd>
+</dl>
+<p>If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,
+and the last two angles in geometry.angles. The user should ensure the output is as expected.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 0. 1. 2. 2.]</span>
+<span class="go">[0. 0. 1. 2. 2.]</span>
+<span class="go">[3. 3. 4. 5. 5.]</span>
+<span class="go">[6. 6. 7. 8. 8.]</span>
+<span class="go">[6. 6. 7. 8. 8.]]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.linear_ramp">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">linear_ramp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pad_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder.linear_ramp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder.linear_ramp" title="Link to this definition">#</a></dt>
+<dd><p>Padder processor wrapping numpy.pad with mode <cite>linear_ramp</cite></p>
+<p>Pads the data with values calculated from a linear ramp between the array edge
+value and the set end_value. Pads in all <em>spatial</em> dimensions unless a dictionary
+is passed to either <cite>pad_width</cite> or <cite>constant_values</cite></p>
+<dl class="simple">
+<dt>pad_width: int, tuple, dict</dt><dd><p>The size of the border along each axis, see usage notes</p>
+</dd>
+<dt>end_values: float, tuple, dict, default=0.0</dt><dd><p>The target value of the linear_ramp, see usage notes</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl class="simple">
+<dt><cite>pad_width</cite> behaviour (number of pixels):</dt><dd><ul class="simple">
+<li><p>int: Each axis will be padded with a border of this size</p></li>
+<li><p>tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</p></li>
+<li><p>dict: Specified axes will be padded: e.g. {‘horizontal’:(8, 23), ‘vertical’: 10}</p></li>
+</ul>
+</dd>
+<dt><cite>end_values</cite> behaviour:</dt><dd><ul class="simple">
+<li><p>float: Each border will use this end value</p></li>
+<li><p>tuple(float, float): Each border end value will be used asymmetrically for each axis i.e. (before, after)</p></li>
+<li><p>dict: Specified axes and end values: e.g. {‘horizontal’:(8, 23), ‘channel’:5}</p></li>
+</ul>
+</dd>
+</dl>
+<p>If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,
+and the last two angles in geometry.angles. The user should ensure the output is as expected.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">linear_ramp</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">end_values</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0.  0.  0.  0.  0.  0.  0. ]</span>
+<span class="go">[0.  0.  0.  0.5 1.  0.5 0. ]</span>
+<span class="go">[0.  0.  0.  1.  2.  1.  0. ]</span>
+<span class="go">[0.  1.5 3.  4.  5.  2.5 0. ]</span>
+<span class="go">[0.  3.  6.  7.  8.  4.  0. ]</span>
+<span class="go">[0.  1.5 3.  3.5 4.  2.  0. ]</span>
+<span class="go">[0.  0.  0.  0.  0.  0.  0. ]]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.reflect">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reflect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pad_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder.reflect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder.reflect" title="Link to this definition">#</a></dt>
+<dd><p>Padder processor wrapping numpy.pad with mode <cite>reflect</cite>.</p>
+<p>Pads with the reflection of the data mirrored along first and last values each axis.
+Pads in all <em>spatial</em> dimensions unless a dictionary is passed to <cite>pad_width</cite>.</p>
+<dl class="simple">
+<dt>pad_width: int, tuple, dict</dt><dd><p>The size of the border along each axis, see usage notes</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl class="simple">
+<dt><cite>pad_width</cite> behaviour (number of pixels):</dt><dd><ul class="simple">
+<li><p>int: Each axis will be padded with a border of this size</p></li>
+<li><p>tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</p></li>
+<li><p>dict: Specified axes will be padded: e.g. {‘horizontal’:(8, 23), ‘vertical’: 10}</p></li>
+</ul>
+</dd>
+</dl>
+<p>If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,
+and the last two angles in geometry.angles. The user should ensure the output is as expected.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">reflect</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[4. 3. 4. 5. 4.]</span>
+<span class="go">[1. 0. 1. 2. 1.]</span>
+<span class="go">[4. 3. 4. 5. 4.]</span>
+<span class="go">[7. 6. 7. 8. 7.]</span>
+<span class="go">[4. 3. 4. 5. 4.]]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.symmetric">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">symmetric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pad_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder.symmetric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder.symmetric" title="Link to this definition">#</a></dt>
+<dd><p>Padder processor wrapping numpy.pad with mode <cite>symmetric</cite>.</p>
+<p>Pads with the reflection of the data mirrored along the edge of the array.
+Pads in all <em>spatial</em> dimensions unless a dictionary is passed to <cite>pad_width</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>pad_width</strong> (<em>int</em><em>, </em><em>tuple</em><em>, </em><em>dict</em>) – The size of the border along each axis</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl class="simple">
+<dt><cite>pad_width</cite> behaviour (number of pixels):</dt><dd><ul class="simple">
+<li><p>int: Each axis will be padded with a border of this size</p></li>
+<li><p>tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</p></li>
+<li><p>dict: Specified axes will be padded: e.g. {‘horizontal’:(8, 23), ‘vertical’: 10}</p></li>
+</ul>
+</dd>
+</dl>
+<p>If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,
+and the last two angles in geometry.angles. The user should ensure the output is as expected.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">symmetric</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 0. 1. 2. 2.]</span>
+<span class="go">[0. 0. 1. 2. 2.]</span>
+<span class="go">[3. 3. 4. 5. 5.]</span>
+<span class="go">[6. 6. 7. 8. 8.]</span>
+<span class="go">[6. 6. 7. 8. 8.]]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.wrap">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">wrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pad_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder.wrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder.wrap" title="Link to this definition">#</a></dt>
+<dd><p>Padder processor wrapping numpy.pad with mode <cite>wrap</cite>.</p>
+<p>Pads with the wrap of the vector along the axis. The first values are used to pad the
+end and the end values are used to pad the beginning. Pads in all <em>spatial</em> dimensions
+unless a dictionary is passed to <cite>pad_width</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>pad_width</strong> (<em>int</em><em>, </em><em>tuple</em><em>, </em><em>dict</em>) – The size of the border along each axis</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<dl class="simple">
+<dt><cite>pad_width</cite> behaviour (number of pixels):</dt><dd><ul class="simple">
+<li><p>int: Each axis will be padded with a border of this size</p></li>
+<li><p>tuple(int, int): Each axis will be padded with an asymmetric border i.e. (before, after)</p></li>
+<li><p>dict: Specified axes will be padded: e.g. {‘horizontal’:(8, 23), ‘vertical’: 10}</p></li>
+</ul>
+</dd>
+</dl>
+<p>If padding angles the angular values assigned to the padded axis will be extrapolated from the first two,
+and the last two angles in geometry.angles. The user should ensure the output is as expected.</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">Padder</span><span class="o">.</span><span class="n">wrap</span><span class="p">(</span><span class="n">pad_width</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_padded</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[0. 1. 2.]</span>
+<span class="go">[3. 4. 5.]</span>
+<span class="go">[6. 7. 8.]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">data_padded</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
+<span class="go">[[8. 6. 7. 8. 6.]</span>
+<span class="go">[2. 0. 1. 2. 0.]</span>
+<span class="go">[5. 3. 4. 5. 3.]</span>
+<span class="go">[8. 6. 7. 8. 6.]</span>
+<span class="go">[2. 0. 1. 2. 0.]]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Padder/#Padder.set_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Padder.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>Geometry</em>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Padder.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Padder.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="mask-generator-from-data">
+<h3>Mask Generator from Data<a class="headerlink" href="#mask-generator-from-data" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">MaskGenerator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'special_values'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(None,</span> <span class="pre">None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(None,</span> <span class="pre">None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/MaskGenerator/#MaskGenerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.MaskGenerator" title="Link to this definition">#</a></dt>
+<dd><p>Processor to detect outliers and return a mask with 0 where outliers were detected, and 1 for other pixels. Please use the desiried method to configure a processor for your needs.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator.special_values">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">special_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/MaskGenerator/#MaskGenerator.special_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.MaskGenerator.special_values" title="Link to this definition">#</a></dt>
+<dd><p>This creates a MaskGenerator processor which generates a mask for inf and/or nan values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nan</strong> (<em>bool</em><em>, </em><em>default=True</em>) – mask NaN values</p></li>
+<li><p><strong>inf</strong> (<em>bool</em><em>, </em><em>default=True</em>) – mask INF values</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator.threshold">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/MaskGenerator/#MaskGenerator.threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.MaskGenerator.threshold" title="Link to this definition">#</a></dt>
+<dd><p>This creates a MaskGenerator processor which generates a mask for values outside boundaries</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_val</strong> (<em>float</em><em>, </em><em>default=None</em>) – lower boundary</p></li>
+<li><p><strong>max_val</strong> (<em>float</em><em>, </em><em>default=None</em>) – upper boundary</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator.quantile">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">quantile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/MaskGenerator/#MaskGenerator.quantile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.MaskGenerator.quantile" title="Link to this definition">#</a></dt>
+<dd><p>This creates a MaskGenerator processor which generates a mask for values outside boundaries</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_quantile</strong> (<em>float</em><em>, </em><em>default=None</em>) – lower quantile, 0-1</p></li>
+<li><p><strong>max_quantile</strong> (<em>float</em><em>, </em><em>default=None</em>) – upper quantile, 0-1</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator.mean">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/MaskGenerator/#MaskGenerator.mean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.MaskGenerator.mean" title="Link to this definition">#</a></dt>
+<dd><p>This creates a MaskGenerator processor which generates a mask for values outside a multiple of standard-devaiations from the mean.</p>
+<p>abs(A - mean(A)) &lt; threshold_factor * std(A).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>threshold_factor</strong> (<em>float</em><em>, </em><em>default=3</em>) – scale factor of standard-deviations to use as threshold</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>string</em>) – specify axis as int or from ‘dimension_labels’ to calculate mean. If no axis is specified then operates over flattened array.</p></li>
+<li><p><strong>window</strong> (<em>int</em><em>, </em><em>default=None</em>) – specify number of pixels to use in calculation of a rolling mean</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator.median">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">median</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/MaskGenerator/#MaskGenerator.median"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.MaskGenerator.median" title="Link to this definition">#</a></dt>
+<dd><p>This creates a MaskGenerator processor which generates a mask for values outside a multiple of median absolute deviation (MAD) from the mean.</p>
+<p>abs(A - median(A)) &lt; threshold_factor * MAD(A),
+MAD = c*median(abs(A-median(A))) where c=-1/(sqrt(2)*erfcinv(3/2))</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>threshold_factor</strong> (<em>float</em><em>, </em><em>default=3</em>) – scale factor of MAD to use as threshold</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>string</em>) – specify axis as int or from ‘dimension_labels’ to calculate mean. If no axis is specified then operates over flattened array.</p></li>
+<li><p><strong>window</strong> (<em>int</em><em>, </em><em>default=None</em>) – specify number of pixels to use in calculation of a rolling median</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.MaskGenerator.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.MaskGenerator.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.MaskGenerator.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="data-masking">
+<h3>Data Masking<a class="headerlink" href="#data-masking" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.Masker">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">Masker</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'value'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Masker/#Masker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Masker" title="Link to this definition">#</a></dt>
+<dd><p>Processor to fill missing values provided by mask.
+Please use the desired method to configure a processor for your needs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mask</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><em>numpy.ndarray</em>) – A boolean array with the same dimensions as input, where ‘False’ represents masked values.
+Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.
+Mask can be generated using ‘MaskGenerator’ processor to identify outliers.</p></li>
+<li><p><strong>mode</strong> (<em>{'value'</em><em>, </em><em>'mean'</em><em>, </em><em>'median'</em><em>, </em><em>'interpolate'}</em><em>, </em><em>default='value'</em>) – The method to fill in missing values</p></li>
+<li><p><strong>value</strong> (<em>float</em><em>, </em><em>default=0</em>) – Substitute all outliers with a specific value if method=’value’, otherwise discarded.</p></li>
+<li><p><strong>axis</strong> (<em>str</em><em> or </em><em>int</em>) – Specify axis as int or from ‘dimension_labels’ to calculate mean, median or interpolation
+(depending on mode) along that axis</p></li>
+<li><p><strong>method</strong> (<em>{'linear'</em><em>, </em><em>'nearest'</em><em>, </em><em>'zeros'</em><em>, </em><em>'linear'</em><em>, </em><em>'quadratic'</em><em>, </em><em>'cubic'</em><em>, </em><em>'previous'</em><em>, </em><em>'next'}</em><em>, </em><em>default='linear'</em>) – Interpolation method to use.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Masker.value">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Masker/#Masker.value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Masker.value" title="Link to this definition">#</a></dt>
+<dd><p>Returns a Masker that sets the masked values of the input data to the requested value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mask</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><em>numpy.ndarray</em>) – A boolean array with the same dimensions as input, where ‘False’ represents masked values.
+Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.
+Mask can be generated using ‘MaskGenerator’ processor to identify outliers.</p></li>
+<li><p><strong>value</strong> (<em>float</em><em>, </em><em>default=0</em>) – Values to be assigned to missing elements</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Masker processor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Masker.mean">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Masker/#Masker.mean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Masker.mean" title="Link to this definition">#</a></dt>
+<dd><p>Returns a Masker that sets the masked values of the input data to the mean of the unmasked values across the array or axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mask</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><em>numpy.ndarray</em>) – A boolean array with the same dimensions as input, where ‘False’ represents masked values.
+Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.
+Mask can be generated using ‘MaskGenerator’ processor to identify outliers.</p></li>
+<li><p><strong>axis</strong> (<em>str</em><em>, </em><em>int</em>) – Specify axis as int or from ‘dimension_labels’ to calculate mean.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Masker processor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Masker.median">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">median</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Masker/#Masker.median"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Masker.median" title="Link to this definition">#</a></dt>
+<dd><p>Returns a Masker that sets the masked values of the input data to the median of the unmasked values across the array or axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mask</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><em>numpy.ndarray</em>) – A boolean array with the same dimensions as input, where ‘False’ represents masked values.
+Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.
+Mask can be generated using ‘MaskGenerator’ processor to identify outliers.</p></li>
+<li><p><strong>axis</strong> (<em>str</em><em>, </em><em>int</em>) – Specify axis as int or from ‘dimension_labels’ to calculate median.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Masker processor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Masker.interpolate">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Masker/#Masker.interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Masker.interpolate" title="Link to this definition">#</a></dt>
+<dd><p>Returns a Masker that operates over the specified axis and uses 1D interpolation over remaining flattened array to fill in missing values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mask</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><em>numpy.ndarray</em>) – A boolean array with the same dimensions as input, where ‘False’ represents masked values.
+Alternatively an integer array where 0 represents masked values, and any other value represents unmasked values.
+Mask can be generated using ‘MaskGenerator’ processor to identify outliers.</p></li>
+<li><p><strong>axis</strong> (<em>str</em><em>, </em><em>int</em>) – Specify axis as int or from ‘dimension_labels’ to loop over and perform 1D interpolation.</p></li>
+<li><p><strong>method</strong> (<em>{'linear'</em><em>, </em><em>'nearest'</em><em>, </em><em>'zeros'</em><em>, </em><em>'linear'</em><em>, </em><em>'quadratic'</em><em>, </em><em>'cubic'</em><em>, </em><em>'previous'</em><em>, </em><em>'next'}</em><em>, </em><em>default='linear'</em>) – Interpolation method to use.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Masker processor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Masker.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Masker.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Masker.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Masker.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+<section id="pre-processors">
+<h2>Pre-processors<a class="headerlink" href="#pre-processors" title="Link to this heading">#</a></h2>
+<p>These processors can be used with <cite>AcquisitionData</cite> objects</p>
+<section id="centre-of-rotation-corrector">
+<h3>Centre Of Rotation Corrector<a class="headerlink" href="#centre-of-rotation-corrector" title="Link to this heading">#</a></h3>
+<p>In the ideal alignment of a CT instrument, the projection of the axis of rotation onto the
+detector coincides with the vertical midline of the detector. In practice this is hard to achieve
+due to misalignments and/or kinematic errors in positioning of CT instrument components.
+A slight offset of the center of rotation with respect to the theoretical position will contribute
+to the loss of resolution; in more severe cases, it will cause severe artifacts in the reconstructed
+volume (double-borders).</p>
+<p><code class="code docutils literal notranslate"><span class="pre">CentreOfRotationCorrector</span></code> can be used to estimate the offset of center of rotation from the data.</p>
+<p><code class="code docutils literal notranslate"><span class="pre">CentreOfRotationCorrector</span></code> supports both parallel and cone-beam geometry with 2 different algorithms:</p>
+<ul class="simple">
+<li><p>Cross-correlation, is suitable for single slice parallel-beam geometry. It requires two projections 180 degree apart.</p></li>
+<li><p>Image sharpness method, which maximising the sharpness of a reconstructed slice. It can be used on single</p></li>
+</ul>
+<p>slice parallel-beam, and centre-slice of cone-beam geometry. For use only with datasets that can be reconstructed with FBP/FDK.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.CentreOfRotationCorrector">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">CentreOfRotationCorrector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/CentreOfRotationCorrector/#CentreOfRotationCorrector"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.CentreOfRotationCorrector" title="Link to this definition">#</a></dt>
+<dd><p>This class contains methods to create a CentreOfRotationCorrector processor using the desired algorithm.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.CentreOfRotationCorrector.xcorrelation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">xcorrelation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">slice_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'centre'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">projection_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ang_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/CentreOfRotationCorrector/#CentreOfRotationCorrector.xcorrelation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.CentreOfRotationCorrector.xcorrelation" title="Link to this definition">#</a></dt>
+<dd><p>This creates a CentreOfRotationCorrector processor using the cross-correlation algorithm.</p>
+<p>For use on parallel-beam geometry it requires two projections 180 degree apart.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>slice_index</strong> (<em>int</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – An integer defining the vertical slice to run the algorithm on or string=’centre’ specifying the central slice should be used (default is ‘centre’)</p></li>
+<li><p><strong>projection_index</strong> (<em>int</em><em> or </em><em>list/tuple</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – An integer defining the index of the first projection the cross correlation algorithm will use, where the second projection is chosen as the projection closest to 180 degrees from this.
+Or a list/tuple of ints specifying the two indices to be used for cross correlation (default is 0)</p></li>
+<li><p><strong>ang_tol</strong> (<em>float</em><em>, </em><em>optional</em>) – The angular tolerance in degrees between the two input projections 180 degree gap (default is 0.1)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">CentreOfRotationCorrector</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">CentreOfRotationCorrector</span><span class="o">.</span><span class="n">xcorrelation</span><span class="p">(</span><span class="s1">&#39;centre&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_centred</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_ouput</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">CentreOfRotationCorrector</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">CentreOfRotationCorrector</span><span class="o">.</span><span class="n">xcorrelation</span><span class="p">(</span><span class="n">slice_index</span><span class="o">=</span><span class="mi">120</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">get_ouput</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.processors</span> <span class="kn">import</span> <span class="n">CentreOfRotationCorrector</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">logging</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">WARNING</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">cil_log_level</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;cil.processors&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">cil_log_level</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">CentreOfRotationCorrector</span><span class="o">.</span><span class="n">xcorrelation</span><span class="p">(</span><span class="n">slice_index</span><span class="o">=</span><span class="mi">120</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_centred</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>setting logging to ‘debug’ will give you more information about the algorithm progress</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.CentreOfRotationCorrector.image_sharpness">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">image_sharpness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">slice_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'centre'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tigre'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.005</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_binning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/CentreOfRotationCorrector/#CentreOfRotationCorrector.image_sharpness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.CentreOfRotationCorrector.image_sharpness" title="Link to this definition">#</a></dt>
+<dd><p>This creates a CentreOfRotationCorrector processor.</p>
+<p>The processor will find the centre offset by maximising the sharpness of a reconstructed slice.</p>
+<p>Can be used on single slice parallel-beam, and centre slice cone beam geometry. For use only with datasets that can be reconstructed with FBP/FDK.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>slice_index</strong> (<em>int</em><em>, </em><em>str</em><em>, </em><em>default='centre'</em>) – An integer defining the vertical slice to run the algorithm on. The special case slice ‘centre’ is the default.</p></li>
+<li><p><strong>backend</strong> (<em>{'tigre'</em><em>, </em><em>'astra'}</em>) – The backend to use for the reconstruction</p></li>
+<li><p><strong>tolerance</strong> (<em>float</em><em>, </em><em>default=0.005</em>) – The tolerance of the fit in pixels, the default is 1/200 of a pixel. This is a stopping criteria, not a statement of accuracy of the algorithm.</p></li>
+<li><p><strong>search_range</strong> (<em>int</em>) – The range in pixels to search either side of the panel centre. If <cite>None</cite> a quarter of the width of the panel is used.</p></li>
+<li><p><strong>initial_binning</strong> (<em>int</em>) – The size of the bins for the initial search. If <cite>None</cite> will bin the image to a step corresponding to &lt;128 pixels. The fine search will be on unbinned data.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<p>from cil.processors import CentreOfRotationCorrector</p>
+<p>processor = CentreOfRotationCorrector.image_sharpness(‘centre’, ‘tigre’)
+processor.set_input(data)
+data_centred = processor.get_output()</p>
+<p class="rubric">Example</p>
+<p>from cil.processors import CentreOfRotationCorrector</p>
+<p>processor = CentreOfRotationCorrector.image_sharpness(slice_index=120, ‘astra’)
+processor.set_input(data)
+processor.get_output(out=data)</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>For best results data should be 360deg which leads to blurring with incorrect geometry.
+This method is unreliable on half-scan data with ‘tuning-fork’ style artifacts.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.CentreOfRotationCorrector.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.CentreOfRotationCorrector.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.CentreOfRotationCorrector.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.CentreOfRotationCorrector.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="data-normaliser">
+<h3>Data Normaliser<a class="headerlink" href="#data-normaliser" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.Normaliser">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">Normaliser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flat_field</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dark_field</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Normaliser/#Normaliser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Normaliser" title="Link to this definition">#</a></dt>
+<dd><p>Normalisation based on flat and dark</p>
+<p>This processor read in a AcquisitionData and normalises it based on
+the instrument reading with and without incident photons or neutrons.</p>
+<p>Input: AcquisitionData
+Parameter: 2D projection with flat field (or stack)</p>
+<blockquote>
+<div><p>2D projection with dark field (or stack)</p>
+</div></blockquote>
+<p>Output: AcquisitionDataSet</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Normaliser.estimate_normalised_error">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">estimate_normalised_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">projection</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dark</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_flat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_dark</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/Normaliser/#Normaliser.estimate_normalised_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.Normaliser.estimate_normalised_error" title="Link to this definition">#</a></dt>
+<dd><p>returns the estimated relative error of the normalised projection</p>
+<p>n = (projection - dark) / (flat - dark)
+Dn/n = (flat-dark + projection-dark)/((flat-dark)*(projection-dark))*(Df/f + Dd/d)</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Normaliser.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Normaliser.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.Normaliser.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.Normaliser.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="transmission-to-absorption-converter">
+<h3>Transmission to Absorption Converter<a class="headerlink" href="#transmission-to-absorption-converter" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.TransmissionAbsorptionConverter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">TransmissionAbsorptionConverter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_intensity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">white_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/TransmissionAbsorptionConverter/#TransmissionAbsorptionConverter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.TransmissionAbsorptionConverter" title="Link to this definition">#</a></dt>
+<dd><p>Processor to convert from transmission measurements to absorption
+based on the Beer-Lambert law</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>white_level</strong> (<em>float</em><em>, </em><em>optional</em>) – A float defining incidence intensity in the Beer-Lambert law.</p></li>
+<li><p><strong>min_intensity</strong> (<em>float</em><em>, </em><em>optional</em>) – A float defining some threshold to avoid 0 in log, is applied after normalisation by white_level</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns AcquisitionData, ImageData or DataContainer depending on input data type, return is suppressed if ‘out’ is passed</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a>, <a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a> or <a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+<p>Processor first divides by white_level (default=1) and then take negative logarithm.
+Elements below threshold (after division by white_level) are set to threshold.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.TransmissionAbsorptionConverter.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.TransmissionAbsorptionConverter.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.TransmissionAbsorptionConverter.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.TransmissionAbsorptionConverter.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="absorption-to-transmission-converter">
+<h3>Absorption to Transmission Converter<a class="headerlink" href="#absorption-to-transmission-converter" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.AbsorptionTransmissionConverter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">AbsorptionTransmissionConverter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">white_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/AbsorptionTransmissionConverter/#AbsorptionTransmissionConverter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.AbsorptionTransmissionConverter" title="Link to this definition">#</a></dt>
+<dd><p>Processor to convert from absorption measurements to transmission</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>white_level</strong> (<em>float</em><em>, </em><em>optional</em>) – A float defining incidence intensity in the Beer-Lambert law.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns AcquisitionData, ImageData or DataContainer depending on input data type</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a>, <a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a> or <a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+<p>Processor first multiplies data by -1, then calculates exponent
+and scales result by white_level (default=1)</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.AbsorptionTransmissionConverter.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.AbsorptionTransmissionConverter.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.AbsorptionTransmissionConverter.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.AbsorptionTransmissionConverter.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="ring-remover">
+<h3>Ring Remover<a class="headerlink" href="#ring-remover" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.RingRemover">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">RingRemover</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decNum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'db10'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/RingRemover/#RingRemover"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.RingRemover" title="Link to this definition">#</a></dt>
+<dd><p>RingRemover Processor: Removes vertical stripes from a DataContainer(ImageData/AcquisitionData)
+using the algorithm in <a class="reference external" href="https://doi.org/10.1364/OE.17.008567">https://doi.org/10.1364/OE.17.008567</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>decNum</strong> (<em>int</em>) – Number of wavelet decompositions - increasing the number of decompositions, increases the strength of the ring removal
+but can alter the profile of the data</p></li>
+<li><p><strong>wname</strong> (<em>str</em>) – Name of wavelet filter from pywt e.g. ‘db1’ – ‘db35’, ‘haar’ - increasing the wavelet filter increases the strength of
+the ring removal, but also increases the computational effort</p></li>
+<li><p><strong>sigma</strong> (<em>float</em>) – Damping parameter in Fourier space - increasing sigma, increases the size of artefacts which can be removed</p></li>
+<li><p><strong>info</strong> (<em>boolean</em>) – Flag to enable print of ring remover end message</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Corrected ImageData/AcquisitionData 2D, 3D, multi-spectral 2D, multi-spectral 3D</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.RingRemover.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.RingRemover.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured processor and returns the processed data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The processed data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer">DataContainer</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.RingRemover.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.processors.RingRemover.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – The input DataContainer</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="paganin-processor">
+<h3>Paganin Processor<a class="headerlink" href="#paganin-processor" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.processors.PaganinProcessor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.processors.</span></span><span class="sig-name descname"><span class="pre">PaganinProcessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">energy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">energy_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'eV'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_retrieval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'paganin_method'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cm'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/PaganinProcessor/#PaganinProcessor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.PaganinProcessor" title="Link to this definition">#</a></dt>
+<dd><p>Processor to retrieve quantitative information from phase contrast images
+using the Paganin phase retrieval algorithm described in [1]</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>delta</strong> (<em>float</em><em> (</em><em>optional</em><em>)</em>) – Real part of the deviation of the material refractive index from 1,
+where refractive index <span class="math notranslate nohighlight">\(n = (1 - \delta) + i \beta\)</span> energy-
+dependent refractive index information for x-ray wavelengths can be
+found at [2], default is 1</p></li>
+<li><p><strong>beta</strong> (<em>float</em><em> (</em><em>optional</em><em>)</em>) – Complex part of the material refractive index, where refractive index
+<span class="math notranslate nohighlight">\(n = (1 - \delta) + i \beta\)</span> energy-dependent refractive index
+information for x-ray wavelengths can be found at [2], default is 1e-2</p></li>
+<li><p><strong>energy</strong> (<em>float</em><em> (</em><em>optional</em><em>)</em>) – Energy of the incident photon, default is 40000</p></li>
+<li><p><strong>energy_units</strong> (<em>string</em><em> (</em><em>optional</em><em>)</em>) – Energy units, default is ‘eV’</p></li>
+<li><p><strong>full_retrieval</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, perform the full phase retrieval and return the thickness. If
+False, return a filtered image, default is True</p></li>
+<li><p><strong>filter_type</strong> (<em>string</em><em> (</em><em>optional</em><em>)</em>) – The form of the Paganin filter to use, either ‘paganin_method’
+(default) or ‘generalised_paganin_method’ as described in [3]</p></li>
+<li><p><strong>pad</strong> (<em>int</em><em> (</em><em>optional</em><em>)</em>) – Number of pixels to pad the image in Fourier space to reduce aliasing,
+default is 0</p></li>
+<li><p><strong>return_units</strong> (<em>string</em><em> (</em><em>optional</em><em>)</em>) – The distance units to return the sample thickness in, must be one of
+‘m’, ‘cm’, ‘mm’ or ‘um’. Only applies if full_retrieval=True (default
+is’cm’)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>AcquisitionData corrected for phase effects, retrieved sample thickness
+or (if <code class="code docutils literal notranslate"><span class="pre">full_retrieval=False</span></code>) filtered data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">PaganinProcessor</span><span class="p">(</span><span class="n">delta</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">energy</span><span class="o">=</span><span class="mi">18000</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">thickness</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">PaganinProcessor</span><span class="p">(</span><span class="n">delta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">beta</span><span class="o">=</span><span class="mf">10e2</span><span class="p">,</span> <span class="n">full_retrieval</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">filtered_image</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">PaganinProcessor</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">thickness</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="n">override_filter</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;alpha&#39;</span><span class="p">:</span><span class="mi">10</span><span class="p">})</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">phase_retrieved_image</span> <span class="o">=</span> <span class="n">thickness</span><span class="o">*</span><span class="n">processor</span><span class="o">.</span><span class="n">mu</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>This processor will work most efficiently using the cil data order with
+<cite>data.reorder(‘cil’)</cite></p>
+<p class="rubric">Notes</p>
+<p>This processor uses the phase retrieval algorithm described by Paganin et
+al. [1] to retrieve the sample thickness</p>
+<div class="math notranslate nohighlight">
+\[T(x,y) = - \frac{1}{\mu}\ln\left (\mathcal{F}^{-1}\left
+(\frac{\mathcal{F}\left ( M^2I_{norm}(x, y,z = \Delta) \right )}{1 +
+  \alpha\left ( k_x^2 + k_y^2 \right )}  \right )\right ),\]</div>
+<p>where</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(T\)</span>, is the sample thickness,</p></li>
+<li><p><span class="math notranslate nohighlight">\(\mu = \frac{4\pi\beta}{\lambda}\)</span> is the material linear</p></li>
+</ul>
+<p>attenuation coefficient where <span class="math notranslate nohighlight">\(\beta\)</span> is the complex part of the
+material refractive index and <span class="math notranslate nohighlight">\(\lambda=\frac{hc}{E}\)</span> is the probe
+wavelength,
+- <span class="math notranslate nohighlight">\(M\)</span> is the magnification at the detector,
+- <span class="math notranslate nohighlight">\(I_{norm}\)</span> is the input image which is expected to be the
+normalised transmission data,
+- <span class="math notranslate nohighlight">\(\Delta\)</span> is the propagation distance,
+- <span class="math notranslate nohighlight">\(\alpha = \frac{\Delta\delta}{\mu}\)</span> is a parameter determining
+the strength of the filter to be applied in Fourier space where
+<span class="math notranslate nohighlight">\(\delta\)</span> is the real part of the deviation of the material
+refractive index from 1
+- <span class="math notranslate nohighlight">\(k_x, k_y = \left ( \frac{2\pi p}{N_xW}, \frac{2\pi q}{N_yW}
+\right )\)</span> where <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(q\)</span> are co-ordinates in a Fourier
+mesh in the range <span class="math notranslate nohighlight">\(-N_x/2\)</span> to <span class="math notranslate nohighlight">\(N_x/2\)</span> for an image with
+size <span class="math notranslate nohighlight">\(N_x, N_y\)</span> and pixel size <span class="math notranslate nohighlight">\(W\)</span>.</p>
+</div></blockquote>
+<p>A generalised form of the Paganin phase retrieval method can be called
+using <code class="code docutils literal notranslate"><span class="pre">filter_type='generalised_paganin_method'</span></code>, which uses the
+form of the algorithm described in [2]</p>
+<div class="math notranslate nohighlight">
+\[T(x,y) = -\frac{1}{\mu}\ln\left (\mathcal{F}^{-1}\left (\frac{
+\mathcal{F}\left ( M^2I_{norm}(x, y,z = \Delta) \right )}{1 - \frac{2
+\alpha}{W^2}\left ( \cos(Wk_x) + \cos(Wk_y) -2 \right )}  \right )
+\right )\]</div>
+<p>The phase retrieval is valid under the following assumptions</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>used with paraxial propagation-induced phase contrast images which</p></li>
+</ul>
+<p>can be assumed to be single-material locally
+- using intensity data which has been flat field corrected
+- and under the assumption that the Fresnel number
+<span class="math notranslate nohighlight">\(F_N = W^2/(\lambda\Delta) &gt;&gt; 1\)</span></p>
+</div></blockquote>
+<p>To apply a filter to images using the Paganin method, call
+<code class="code docutils literal notranslate"><span class="pre">full_retrieval=False</span></code>. In this case the pre-scaling and conversion
+to absorption is not applied so the requirement to supply flat field
+corrected intensity data is relaxed,</p>
+<div class="math notranslate nohighlight">
+\[I_{filt} = \mathcal{F}^{-1}\left (\frac{\mathcal{F}\left (
+I(x, y,z = \Delta) \right )}
+{1 - \alpha\left ( k_x^2 + k_y^2 \right )}  \right )\]</div>
+<p class="rubric">References</p>
+<ul class="simple">
+<li><p>[1] <a class="reference external" href="https://doi.org/10.1046/j.1365-2818.2002.01010.x">https://doi.org/10.1046/j.1365-2818.2002.01010.x</a></p></li>
+<li><p>[2] <a class="reference external" href="https://henke.lbl.gov/optical_constants/getdb2.html">https://henke.lbl.gov/optical_constants/getdb2.html</a></p></li>
+<li><p>[3] <a class="reference external" href="https://iopscience.iop.org/article/10.1088/2040-8986/abbab9">https://iopscience.iop.org/article/10.1088/2040-8986/abbab9</a></p></li>
+</ul>
+<p>With thanks to colleagues at DTU for help with the initial implementation
+of the phase retrieval algorithm</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.PaganinProcessor.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/PaganinProcessor/#PaganinProcessor.set_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.PaganinProcessor.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Set the input data to the processor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a>) – The input AcquisitionData</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.processors.PaganinProcessor.get_output">
+<span class="sig-name descname"><span class="pre">get_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">override_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">override_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/processors/PaganinProcessor/#PaganinProcessor.get_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.processors.PaganinProcessor.get_output" title="Link to this definition">#</a></dt>
+<dd><p>Function to get output from the PaganinProcessor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>optional</em>) – Fills the referenced DataContainer with the processed data</p></li>
+<li><p><strong>override_geometry</strong> (<em>dict</em><em>, </em><em>optional</em>) – Geometry parameters to use in the phase retrieval if you want to
+over-ride values found in <cite>data.geometry</cite>. Specify parameters as a
+dictionary <code class="code docutils literal notranslate"><span class="pre">{'parameter':value}</span></code> where parameter is
+<code class="code docutils literal notranslate"><span class="pre">'magnification',</span> <span class="pre">'propagation_distance'</span></code> or
+<code class="code docutils literal notranslate"><span class="pre">'pixel_size'</span></code> and value is the new value to use. Specify
+distance parameters in the same units as <code class="code docutils literal notranslate"><span class="pre">return_units</span></code>
+(default is cm).</p></li>
+<li><p><strong>override_filter</strong> (<em>dict</em><em>, </em><em>optional</em>) – Over-ride the filter parameters to use in the phase retrieval.
+Specify parameters as <code class="code docutils literal notranslate"><span class="pre">{'parameter':value}</span></code> where parameter
+is <code class="code docutils literal notranslate"><span class="pre">'delta',</span> <span class="pre">'beta'</span></code> or <code class="code docutils literal notranslate"><span class="pre">'alpha'</span></code> and value is the new
+value to use.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>AcquisitionData corrected for phase effects, retrieved sample
+thickness or (if <code class="code docutils literal notranslate"><span class="pre">full_retrieval=False</span></code>) filtered data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">PaganinProcessor</span><span class="p">(</span><span class="n">delta</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">energy</span><span class="o">=</span><span class="mi">18000</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">thickness</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">PaganinProcessor</span><span class="p">(</span><span class="n">delta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">beta</span><span class="o">=</span><span class="mf">10e2</span><span class="p">,</span>
+<span class="go">full_retrieval=False)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">filtered_image</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span> <span class="o">=</span> <span class="n">PaganinProcessor</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">processor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">thickness</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="n">override_filter</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;alpha&#39;</span><span class="p">:</span><span class="mi">10</span><span class="p">})</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">phase_retrieved_image</span> <span class="o">=</span> <span class="n">thickness</span><span class="o">*</span><span class="n">processor</span><span class="o">.</span><span class="n">mu</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>If <code class="code docutils literal notranslate"><span class="pre">'alpha'</span></code> is specified in override_filter the new value will
+be used and delta will be ignored but beta will still be used to
+calculate <span class="math notranslate nohighlight">\(\mu = \frac{4\pi\beta}{\lambda}\)</span> which is used for
+scaling the thickness, therefore it is only recommended to specify
+alpha when also using <code class="code docutils literal notranslate"><span class="pre">get_output(full_retrieval=False)</span></code>, or
+re-scaling the result by <span class="math notranslate nohighlight">\(\mu\)</span> e.g.
+<code class="code docutils literal notranslate"><span class="pre">thickness*processor.mu</span></code> If <code class="code docutils literal notranslate"><span class="pre">alpha</span></code> is not specified,
+it will be calculated <span class="math notranslate nohighlight">\(\frac{\Delta\delta\lambda}{4\pi\beta}\)</span></p>
+</dd></dl>
+
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
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+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+            <li class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
+                    More
+                </button>
+                <ul id="pst-nav-more-links" class="dropdown-menu">
+                    
+<li class=" ">
+  <a class="nav-link dropdown-item nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+                </ul>
+            </li>
+            
+  </ul>
+</nav></div>
+      
+    </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+      
+        <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+      
+    </div>
+    
+  </div>
+  
+  
+
+  
+</div>
+
+    </header>
+  
+
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      
+      
+        
+      
+      <dialog id="pst-primary-sidebar-modal"></dialog>
+      <div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
+        
+
+  
+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          
+          
+            <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../recon/">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+  </ul>
+</nav></div>
+          
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+        
+      </div>
+    
+  </div>
+  
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item">
+<form class="bd-search d-flex align-items-center"
+      action="../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+        <div class="sidebar-primary-item">
+<div class="version-switcher__container dropdown pst-js-only">
+  <button id="pst-version-switcher-button-2"
+    type="button"
+    class="version-switcher__button btn btn-sm dropdown-toggle"
+    data-bs-toggle="dropdown"
+    aria-haspopup="listbox"
+    aria-controls="pst-version-switcher-list-2"
+    aria-label="Version switcher list"
+  >
+    Choose version  <!-- this text may get changed later by javascript -->
+    <span class="caret"></span>
+  </button>
+  <div id="pst-version-switcher-list-2"
+    class="version-switcher__menu dropdown-menu list-group-flush py-0"
+    role="listbox" aria-labelledby="pst-version-switcher-button-2">
+    <!-- dropdown will be populated by javascript on page load -->
+  </div>
+</div></div>
+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
+</ul>
+</div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main" role="main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article d-print-none"></div>
+              
+              
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+
+   <h1>Python Module Index</h1>
+
+   <div class="modindex-jumpbox">
+   <a href="#cap-c"><strong>c</strong></a>
+   </div>
+
+   <table class="indextable modindextable">
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-c"><td></td><td>
+       <strong>c</strong></td><td></td></tr>
+     <tr>
+       <td><img src="../_static/minus.png" class="toggler"
+              id="toggle-1" style="display: none" alt="-" /></td>
+       <td>
+       <code class="xref">cil</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="../utilities/#module-cil.utilities.quality_measures"><code class="xref">cil.utilities.quality_measures</code></a></td><td>
+       <em></em></td></tr>
+   </table>
+
+
+                </article>
+              
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+</div>
+                </footer>
+              
+            </div>
+            
+            
+              
+            
+          </div>
+          <footer class="bd-footer-content">
+            
+          </footer>
+        
+      </main>
+    </div>
+  </div>
+  
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script defer src="../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+<script defer src="../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+
+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
+      
+        <div class="footer-item">
+
+  <p class="copyright">
+    
+      © Copyright 2017-2024.
+      <br/>
+    
+  </p>
+</div>
+      
+    </div>
+  
+  
+  
+</div>
+
+  </footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/v24.2.0/recon/index.html b/v24.2.0/recon/index.html
new file mode 100644
index 0000000000..4309d07f73
--- /dev/null
+++ b/v24.2.0/recon/index.html
@@ -0,0 +1,926 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../" >
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>Recon &#8212; CIL 24.2.0 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
+  </script>
+  <!-- 
+    this give us a css class that will be invisible only if js is disabled 
+  -->
+  <noscript>
+    <style>
+      .pst-js-only { display: none !important; }
+
+    </style>
+  </noscript>
+  
+  <!-- Loaded before other Sphinx assets -->
+  <link href="../_static/styles/theme.css?digest=fd05d3ddc9a6e3fa7c66" rel="stylesheet" />
+<link href="../_static/styles/pydata-sphinx-theme.css?digest=fd05d3ddc9a6e3fa7c66" rel="stylesheet" />
+
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
+    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
+  
+  <!-- So that users can add custom icons -->
+  <script src="../_static/scripts/fontawesome.js?digest=fd05d3ddc9a6e3fa7c66"></script>
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=fd05d3ddc9a6e3fa7c66" />
+<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=fd05d3ddc9a6e3fa7c66" />
+
+    <script src="../_static/documentation_options.js?v=6cb63398"></script>
+    <script src="../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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+    <script src="../_static/copybutton.js?v=f281be69"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'recon';</script>
+    <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.16.0rc0';
+        DOCUMENTATION_OPTIONS.theme_switcher_json_url = '/CIL/versions.json';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '24.2.0';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
+        </script>
+    <link rel="index" title="Index" href="../genindex/" />
+    <link rel="search" title="Search" href="../search/" />
+    <link rel="next" title="Utilities" href="../utilities/" />
+    <link rel="prev" title="Processors" href="../processors/" />
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+  <meta name="docsearch:version" content="24.2.0" />
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+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
+
+  
+  
+  <div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
+  
+  <div id="pst-scroll-pixel-helper"></div>
+  
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>Back to top</button>
+
+  
+  <dialog id="pst-search-dialog">
+    
+<form class="bd-search d-flex align-items-center"
+      action="../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form>
+  </dialog>
+
+  <div class="pst-async-banner-revealer d-none">
+  <aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
+</div>
+
+  
+    <header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
+<div class="bd-header__inner bd-page-width">
+  <button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
+    <span class="fa-solid fa-bars"></span>
+  </button>
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
+    
+      <div class="navbar-item">
+
+  
+     
+  
+
+<a class="navbar-brand logo" href="/">
+  
+  
+  
+  
+  
+    
+    
+      
+    
+    
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB.svg" class="logo__image only-light" alt="CIL - Home"/>
+    <img src="https://ccpi.ac.uk/wp-content/uploads/2022/11/CIL-logo-RGB-reversed.svg" class="logo__image only-dark pst-js-only" alt="CIL - Home"/>
+  
+  
+</a></div>
+    
+  </div>
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item current active">
+  <a class="nav-link nav-internal" href="#">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+            <li class="nav-item dropdown">
+                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
+                    More
+                </button>
+                <ul id="pst-nav-more-links" class="dropdown-menu">
+                    
+<li class=" ">
+  <a class="nav-link dropdown-item nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+                </ul>
+            </li>
+            
+  </ul>
+</nav></div>
+      
+    </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+      
+        <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+      
+    </div>
+    
+  </div>
+  
+  
+
+  
+</div>
+
+    </header>
+  
+
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      
+      
+      <dialog id="pst-primary-sidebar-modal"></dialog>
+      <div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
+        
+
+  
+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          
+          
+            <div class="navbar-item">
+<nav>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../introduction/">
+    Introduction
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../framework/">
+    Framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../io/">
+    Read/ write AcquisitionData and ImageData
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../optimisation/">
+    Optimisation framework
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../processors/">
+    Processors
+  </a>
+</li>
+
+
+<li class="nav-item current active">
+  <a class="nav-link nav-internal" href="#">
+    Recon
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../utilities/">
+    Utilities
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../plugins/">
+    CIL Plugins
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../developer_guide/">
+    Developers’ Guide
+  </a>
+</li>
+
+
+<li class="nav-item ">
+  <a class="nav-link nav-internal" href="../demos/">
+    Tutorials
+  </a>
+</li>
+
+  </ul>
+</nav></div>
+          
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+
+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button></div>
+        
+      </div>
+    
+  </div>
+  
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item">
+<form class="bd-search d-flex align-items-center"
+      action="../search/"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
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+<h3><a href="../">Table of Contents</a></h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Recon</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#analytical-reconstruction">Analytical Reconstruction</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fbp-reconstructor-for-parallel-beam-geometry">FBP - Reconstructor for parallel-beam geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fdk-reconstructor-for-cone-beam-geometry">FDK - Reconstructor for cone-beam geometry</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
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+                  
+  <section id="recon">
+<h1>Recon<a class="headerlink" href="#recon" title="Link to this heading">#</a></h1>
+<p>This module allows the user to run pre-configured reconstruction algorithms on their data.</p>
+<section id="analytical-reconstruction">
+<h2>Analytical Reconstruction<a class="headerlink" href="#analytical-reconstruction" title="Link to this heading">#</a></h2>
+<p>The CIL analytical reconstructions use CIL to filter and prepare the data using highly optimised routines. The filtered data is then
+backprojected using projectors from TIGRE or ASTRA-TOOLBOX.</p>
+<p>Standard FBP (filtered-backprojection) should be used for parallel-beam data. FDK (Feldkamp, Davis, and Kress) is a filtered-backprojection
+algorithm for reconstruction of cone-beam data measured with a standard circular orbit.</p>
+<p>The filter can be set to a predefined function, or a custom filter can be set. The predefined filters take the following forms:</p>
+<figure class="align-center">
+<img alt="FBP Filters" src="../_images/FBP_filters1.png" />
+</figure>
+<section id="fbp-reconstructor-for-parallel-beam-geometry">
+<h3>FBP - Reconstructor for parallel-beam geometry<a class="headerlink" href="#fbp-reconstructor-for-parallel-beam-geometry" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.recon.FBP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.recon.</span></span><span class="sig-name descname"><span class="pre">FBP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ram-lak'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tigre'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/recon/FBP/#FBP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.recon.FBP" title="Link to this definition">#</a></dt>
+<dd><p>Creates an FBP reconstructor based on your parallel-beam acquisition data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a>) – The input data to reconstruct. The reconstructor is set-up based on the geometry of the data.</p></li>
+<li><p><strong>image_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em>, </em><em>default used if None</em>) – A description of the area/volume to reconstruct</p></li>
+<li><p><strong>filter</strong> (<em>string</em><em>, </em><em>numpy.ndarray</em><em>, </em><em>default='ram-lak'</em>) – The filter to be applied. Can be a string from: {’<cite>ram-lak</cite>’, ‘<cite>shepp-logan</cite>’, ‘<cite>cosine</cite>’, ‘<cite>hamming</cite>’, ‘<cite>hann</cite>’}, or a numpy array.</p></li>
+<li><p><strong>backend</strong> (<em>string</em>) – The backend to use, can be ‘astra’ or ‘tigre’. Data must be in the correct order for requested backend.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.utilities.dataexample</span> <span class="kn">import</span> <span class="n">SIMULATED_PARALLEL_BEAM_DATA</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.recon</span> <span class="kn">import</span> <span class="n">FBP</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">SIMULATED_PARALLEL_BEAM_DATA</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fbp</span> <span class="o">=</span> <span class="n">FBP</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">fbp</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>The reconstructor can be further customised using additional ‘set’ methods provided.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.set_split_processing">
+<span class="sig-name descname"><span class="pre">set_split_processing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">slices_per_chunk</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/recon/FBP/#FBP.set_split_processing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.recon.FBP.set_split_processing" title="Link to this definition">#</a></dt>
+<dd><p>Splits the processing in to chunks. Default, 0 will process the data in a single call.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>out</strong> (<em>slices_per_chunk</em><em>, </em><em>optional</em>) – Process the data in chunks of n slices. It is recommended to use value of power-of-two.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This will reduce memory use but may increase computation time.
+It is recommended to tune it too your hardware requirements using 8, 16 or 32 slices.</p>
+<p>This can only be used on simple and offset data-geometries.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/recon/FBP/#FBP.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.recon.FBP.run" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured FBP recon and returns the reconstruction</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><em>optional</em>) – Fills the referenced ImageData with the reconstructed volume and suppresses the return</p></li>
+<li><p><strong>verbose</strong> (<em>int</em><em>, </em><em>default=1</em>) – Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The reconstructed volume. Suppressed if <cite>out</cite> is passed</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.reset">
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/recon/FBP/#FBP.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.recon.FBP.reset" title="Link to this definition">#</a></dt>
+<dd><p>Resets all optional configuration parameters to their default values</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.get_filter_array">
+<span class="sig-name descname"><span class="pre">get_filter_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FBP.get_filter_array" title="Link to this definition">#</a></dt>
+<dd><p>Returns the filter array in the frequency domain.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>An array containing the filter values</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy.ndarray</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The filter length N is 2^self.fft_order.</p>
+<p>The indices of the array are interpreted as:</p>
+<ul class="simple">
+<li><p>[0] The DC frequency component</p></li>
+<li><p>[1:N/2] positive frequencies</p></li>
+<li><p>[N/2:N-1] negative frequencies</p></li>
+</ul>
+<p>The array can be modified and passed back using set_filter()</p>
+<p class="rubric">Notes</p>
+<p>Filter reference in frequency domain:
+Eq. 1.12 - 1.15 T. M. Buzug. Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Berlin: Springer, 2008.</p>
+<p>Plantagie, L. Algebraic filters for filtered backprojection, 2017
+<a class="reference external" href="https://hdl.handle.net/1887/48289">https://hdl.handle.net/1887/48289</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.plot_filter">
+<span class="sig-name descname"><span class="pre">plot_filter</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FBP.plot_filter" title="Link to this definition">#</a></dt>
+<dd><p>Returns a plot of the filter array.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>A plot of the filter</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.pyplot</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.set_fft_order">
+<span class="sig-name descname"><span class="pre">set_fft_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FBP.set_fft_order" title="Link to this definition">#</a></dt>
+<dd><p>The width of the fourier transform N=2^order.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>order</strong> (<em>int</em><em>, </em><em>optional</em>) – The width of the fft N=2^order</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If <cite>None</cite> the default used is the power-of-2 greater than 2 * detector width, or 8, whichever is greater
+Higher orders will yield more accurate results but increase computation time.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.set_filter">
+<span class="sig-name descname"><span class="pre">set_filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ram-lak'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FBP.set_filter" title="Link to this definition">#</a></dt>
+<dd><p>Set the filter used by the reconstruction.</p>
+<p>Pre-set filters are constructed in the frequency domain.
+Pre-set filters are: ‘ram-lak’, ‘shepp-logan’, ‘cosine’, ‘hamming’, ‘hann’</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filter</strong> (<em>string</em><em>, </em><em>numpy.ndarray</em><em>, </em><em>default='ram-lak'</em>) – Pass a string selecting from the list of pre-set filters, or pass a numpy.ndarray with a custom filter.</p></li>
+<li><p><strong>cutoff</strong> (<em>float</em><em>, </em><em>default = 1</em>) – The cut-off frequency of the filter between 0 - 1 pi rads/pixel. The filter will be 0 outside the range rect(-frequency_cutoff, frequency_cutoff)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If passed a numpy array the filter must have length N = 2^self.fft_order</p>
+<p>The indices of the array are interpreted as:</p>
+<ul class="simple">
+<li><p>[0] The DC frequency component</p></li>
+<li><p>[1:N/2] positive frequencies</p></li>
+<li><p>[N/2:N-1] negative frequencies</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.set_filter_inplace">
+<span class="sig-name descname"><span class="pre">set_filter_inplace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FBP.set_filter_inplace" title="Link to this definition">#</a></dt>
+<dd><p>False (default) will allocate temporary memory for filtered projections.
+True will filter projections in-place.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>inplace</strong> (<em>boolean</em>) – Sets the inplace filtering of projections</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.set_image_geometry">
+<span class="sig-name descname"><span class="pre">set_image_geometry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FBP.set_image_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Sets a custom image geometry to be used by the reconstructor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>image_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em>, </em><em>default used if None</em>) – A description of the area/volume to reconstruct</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FBP.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FBP.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Update the input data to run the reconstructor on. The geometry of the dataset must be compatible with the reconstructor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a>) – A dataset with a compatible geometry</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="fdk-reconstructor-for-cone-beam-geometry">
+<h3>FDK - Reconstructor for cone-beam geometry<a class="headerlink" href="#fdk-reconstructor-for-cone-beam-geometry" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.recon.FDK">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.recon.</span></span><span class="sig-name descname"><span class="pre">FDK</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ram-lak'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/recon/FBP/#FDK"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.recon.FDK" title="Link to this definition">#</a></dt>
+<dd><p>Creates an FDK reconstructor based on your cone-beam acquisition data using TIGRE as a backend.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a>) – The input data to reconstruct. The reconstructor is set-up based on the geometry of the data.</p></li>
+<li><p><strong>image_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em>, </em><em>default used if None</em>) – A description of the area/volume to reconstruct</p></li>
+<li><p><strong>filter</strong> (<em>string</em><em>, </em><em>numpy.ndarray</em><em>, </em><em>default='ram-lak'</em>) – The filter to be applied. Can be a string from: {’<cite>ram-lak</cite>’, ‘<cite>shepp-logan</cite>’, ‘<cite>cosine</cite>’, ‘<cite>hamming</cite>’, ‘<cite>hann</cite>’}, or a numpy array.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.utilities.dataexample</span> <span class="kn">import</span> <span class="n">SIMULATED_CONE_BEAM_DATA</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.recon</span> <span class="kn">import</span> <span class="n">FDK</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">SIMULATED_CONE_BEAM_DATA</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fdk</span> <span class="o">=</span> <span class="n">FDK</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">fdk</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>The reconstructor can be futher customised using additional ‘set’ methods provided.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/recon/FBP/#FDK.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.recon.FDK.run" title="Link to this definition">#</a></dt>
+<dd><p>Runs the configured FDK recon and returns the reconstruction.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>out</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><em>optional</em>) – Fills the referenced ImageData with the reconstructed volume and suppresses the return</p></li>
+<li><p><strong>verbose</strong> (<em>int</em><em>, </em><em>default=1</em>) – Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The reconstructed volume. Suppressed if <cite>out</cite> is passed</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.get_filter_array">
+<span class="sig-name descname"><span class="pre">get_filter_array</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.get_filter_array" title="Link to this definition">#</a></dt>
+<dd><p>Returns the filter array in the frequency domain.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>An array containing the filter values</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy.ndarray</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The filter length N is 2^self.fft_order.</p>
+<p>The indices of the array are interpreted as:</p>
+<ul class="simple">
+<li><p>[0] The DC frequency component</p></li>
+<li><p>[1:N/2] positive frequencies</p></li>
+<li><p>[N/2:N-1] negative frequencies</p></li>
+</ul>
+<p>The array can be modified and passed back using set_filter()</p>
+<p class="rubric">Notes</p>
+<p>Filter reference in frequency domain:
+Eq. 1.12 - 1.15 T. M. Buzug. Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Berlin: Springer, 2008.</p>
+<p>Plantagie, L. Algebraic filters for filtered backprojection, 2017
+<a class="reference external" href="https://hdl.handle.net/1887/48289">https://hdl.handle.net/1887/48289</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.plot_filter">
+<span class="sig-name descname"><span class="pre">plot_filter</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.plot_filter" title="Link to this definition">#</a></dt>
+<dd><p>Returns a plot of the filter array.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>A plot of the filter</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib.pyplot</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.reset">
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.reset" title="Link to this definition">#</a></dt>
+<dd><p>Resets all optional configuration parameters to their default values</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.set_fft_order">
+<span class="sig-name descname"><span class="pre">set_fft_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.set_fft_order" title="Link to this definition">#</a></dt>
+<dd><p>The width of the fourier transform N=2^order.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>order</strong> (<em>int</em><em>, </em><em>optional</em>) – The width of the fft N=2^order</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If <cite>None</cite> the default used is the power-of-2 greater than 2 * detector width, or 8, whichever is greater
+Higher orders will yield more accurate results but increase computation time.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.set_filter">
+<span class="sig-name descname"><span class="pre">set_filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ram-lak'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.set_filter" title="Link to this definition">#</a></dt>
+<dd><p>Set the filter used by the reconstruction.</p>
+<p>Pre-set filters are constructed in the frequency domain.
+Pre-set filters are: ‘ram-lak’, ‘shepp-logan’, ‘cosine’, ‘hamming’, ‘hann’</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filter</strong> (<em>string</em><em>, </em><em>numpy.ndarray</em><em>, </em><em>default='ram-lak'</em>) – Pass a string selecting from the list of pre-set filters, or pass a numpy.ndarray with a custom filter.</p></li>
+<li><p><strong>cutoff</strong> (<em>float</em><em>, </em><em>default = 1</em>) – The cut-off frequency of the filter between 0 - 1 pi rads/pixel. The filter will be 0 outside the range rect(-frequency_cutoff, frequency_cutoff)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If passed a numpy array the filter must have length N = 2^self.fft_order</p>
+<p>The indices of the array are interpreted as:</p>
+<ul class="simple">
+<li><p>[0] The DC frequency component</p></li>
+<li><p>[1:N/2] positive frequencies</p></li>
+<li><p>[N/2:N-1] negative frequencies</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.set_filter_inplace">
+<span class="sig-name descname"><span class="pre">set_filter_inplace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.set_filter_inplace" title="Link to this definition">#</a></dt>
+<dd><p>False (default) will allocate temporary memory for filtered projections.
+True will filter projections in-place.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>inplace</strong> (<em>boolean</em>) – Sets the inplace filtering of projections</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.set_image_geometry">
+<span class="sig-name descname"><span class="pre">set_image_geometry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.set_image_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Sets a custom image geometry to be used by the reconstructor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>image_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em>, </em><em>default used if None</em>) – A description of the area/volume to reconstruct</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.recon.FDK.set_input">
+<span class="sig-name descname"><span class="pre">set_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.recon.FDK.set_input" title="Link to this definition">#</a></dt>
+<dd><p>Update the input data to run the reconstructor on. The geometry of the dataset must be compatible with the reconstructor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a>) – A dataset with a compatible geometry</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
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+        <div class="sidebar-primary-item">
+<form class="bd-search d-flex align-items-center"
+      action="#"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+        <div class="sidebar-primary-item">
+<div class="version-switcher__container dropdown pst-js-only">
+  <button id="pst-version-switcher-button-2"
+    type="button"
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+        <div class="sidebar-primary-item">
+<h3><a href="../">Table of Contents</a></h3>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../utilities/">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
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+</ul>
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+              
+  <div class="bd-search-container">
+    <h1>Search</h1>
+    <noscript>
+      <div class="admonition error">
+        <p class="admonition-title">Error</p>
+        <p>Please activate JavaScript to enable the search functionality.</p>
+      </div>
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+    
+<form class="bd-search d-flex align-items-center"
+      action="#"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
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+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form>
+    <div id="search-results"></div>
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+  <script>
+    // Activate the search field on page load
+    let searchInput = document.querySelector("form.bd-search input");
+    if (searchInput) {
+        searchInput.focus();
+        searchInput.select();
+        console.log("[PST]: Set focus on search field.");
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+  </script>
+
+              
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diff --git a/v24.2.0/searchindex.js b/v24.2.0/searchindex.js
new file mode 100644
index 0000000000..8e23cc450b
--- /dev/null
+++ b/v24.2.0/searchindex.js
@@ -0,0 +1 @@
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+<h3><a href="../">Table of Contents</a></h3>
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+<li class="toctree-l1"><a class="reference internal" href="../introduction/">Introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../framework/">Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../io/">Read/ write AcquisitionData and ImageData</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/">Optimisation framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../optimisation/#block-framework">Block Framework</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../processors/">Processors</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../recon/">Recon</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Utilities</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#test-datasets">Test datasets</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#a-set-of-simulated-volumes-and-ct-data">A set of simulated volumes and CT data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#a-ct-dataset-from-the-diamond-light-source">A CT dataset from the Diamond Light Source</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#simulated-image-data">Simulated image data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#remote-data">Remote data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#walnut">Walnut</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#usb">USB</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#korn">KORN</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sandstone">SANDSTONE</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#module-cil.utilities.quality_measures">Image Quality metrics</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#cil.utilities.quality_measures.mse"><code class="docutils literal notranslate"><span class="pre">mse()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.utilities.quality_measures.mae"><code class="docutils literal notranslate"><span class="pre">mae()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cil.utilities.quality_measures.psnr"><code class="docutils literal notranslate"><span class="pre">psnr()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#visualisation">Visualisation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#show2d-display-2d-slices">show2D - Display 2D slices</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#show1d-display-1d-slices">show1D - Display 1D slices</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#show-geometry-display-system-geometry">show_geometry - Display system geometry</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#islicer-interactive-display-of-2d-slices">islicer - interactive display of 2D slices</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#link-islicer-link-islicer-objects-by-index">link_islicer - link islicer objects by index</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../plugins/">CIL Plugins</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../developer_guide/">Developers’ Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demos/">Tutorials</a></li>
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+                  
+  <section id="utilities">
+<h1>Utilities<a class="headerlink" href="#utilities" title="Link to this heading">#</a></h1>
+<section id="test-datasets">
+<h2>Test datasets<a class="headerlink" href="#test-datasets" title="Link to this heading">#</a></h2>
+<p>A range of small test datasets to generate and use</p>
+<section id="a-set-of-simulated-volumes-and-ct-data">
+<h3>A set of simulated volumes and CT data<a class="headerlink" href="#a-set-of-simulated-volumes-and-ct-data" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">SIMULATED_CONE_BEAM_DATA</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SIMULATED_CONE_BEAM_DATA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA.get">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SIMULATED_CONE_BEAM_DATA.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA.get" title="Link to this definition">#</a></dt>
+<dd><p>A cone-beam dataset generated from SIMULATED_SPHERE_VOLUME</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The simulated spheres dataset</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">SIMULATED_PARALLEL_BEAM_DATA</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SIMULATED_PARALLEL_BEAM_DATA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA.get">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SIMULATED_PARALLEL_BEAM_DATA.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA.get" title="Link to this definition">#</a></dt>
+<dd><p>A simulated parallel-beam dataset generated from SIMULATED_SPHERE_VOLUME</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The simulated spheres dataset</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="id0">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">SIMULATED_CONE_BEAM_DATA</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SIMULATED_CONE_BEAM_DATA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="id1">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SIMULATED_CONE_BEAM_DATA.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id1" title="Link to this definition">#</a></dt>
+<dd><p>A cone-beam dataset generated from SIMULATED_SPHERE_VOLUME</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The simulated spheres dataset</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="a-ct-dataset-from-the-diamond-light-source">
+<h3>A CT dataset from the Diamond Light Source<a class="headerlink" href="#a-ct-dataset-from-the-diamond-light-source" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">SYNCHROTRON_PARALLEL_BEAM_DATA</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SYNCHROTRON_PARALLEL_BEAM_DATA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA" title="Link to this definition">#</a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA.get">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SYNCHROTRON_PARALLEL_BEAM_DATA.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SYNCHROTRON_PARALLEL_BEAM_DATA.get" title="Link to this definition">#</a></dt>
+<dd><p>A DLS dataset</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The DLS dataset</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData">AcquisitionData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="simulated-image-data">
+<h3>Simulated image data<a class="headerlink" href="#simulated-image-data" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.TestData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">TestData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#TestData"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.TestData" title="Link to this definition">#</a></dt>
+<dd><p>Class to return test data</p>
+<p>provides 6 dataset:
+BOAT = ‘boat.tiff’
+CAMERA = ‘camera.png’
+PEPPERS = ‘peppers.tiff’
+RESOLUTION_CHART = ‘resolution_chart.tiff’
+SIMPLE_PHANTOM_2D = ‘hotdog’
+SHAPES =  ‘shapes.png’
+RAINBOW = ‘rainbow.png’</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.TestData.load">
+<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">which</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">1)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#TestData.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.TestData.load" title="Link to this definition">#</a></dt>
+<dd><p>Return a test data of the requested image</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>which</strong> (<em>str</em>) – Image selector: BOAT, CAMERA, PEPPERS, RESOLUTION_CHART, SIMPLE_PHANTOM_2D, SHAPES, RAINBOW</p></li>
+<li><p><strong>size</strong> (<em>tuple</em><em>, </em><em>optional</em>) – The size of the returned ImageData. If None default will be used for each image type</p></li>
+<li><p><strong>scale</strong> (<em>tuple</em><em>, </em><em>optional</em>) – The scale of the data values</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The simulated spheres volume</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.TestData.random_noise">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">random_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#TestData.random_noise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.TestData.random_noise" title="Link to this definition">#</a></dt>
+<dd><p>Function to add noise to input image</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>image</strong> – input dataset, DataContainer of numpy.ndarray</p></li>
+<li><p><strong>mode</strong> – type of noise</p></li>
+<li><p><strong>seed</strong> – seed for random number generator</p></li>
+<li><p><strong>clip</strong> – should clip the data.</p></li>
+</ul>
+</dd>
+</dl>
+<p>See <a class="github reference external" href="https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py">scikit-image/scikit-image</a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.TestData.scikit_random_noise">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scikit_random_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#TestData.scikit_random_noise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.TestData.scikit_random_noise" title="Link to this definition">#</a></dt>
+<dd><p>Function to add random noise of various types to a floating-point image.
+:param image: Input image data. Will be converted to float.
+:type image: ndarray
+:param mode: One of the following strings, selecting the type of noise to add:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>‘gaussian’  Gaussian-distributed additive noise.</p></li>
+<li><dl class="simple">
+<dt>‘localvar’  Gaussian-distributed additive noise, with specified</dt><dd><p>local variance at each point of <cite>image</cite>.</p>
+</dd>
+</dl>
+</li>
+<li><p>‘poisson’   Poisson-distributed noise generated from the data.</p></li>
+<li><p>‘salt’      Replaces random pixels with 1.</p></li>
+<li><dl class="simple">
+<dt>‘pepper’    Replaces random pixels with 0 (for unsigned images) or</dt><dd><p>-1 (for signed images).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>‘s&amp;p’       Replaces random pixels with either 1 or <cite>low_val</cite>, where</dt><dd><p><cite>low_val</cite> is 0 for unsigned images or -1 for signed
+images.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>‘speckle’   Multiplicative noise using out = image + n*image, where</dt><dd><p>n is uniform noise with specified mean &amp; variance.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – If provided, this will set the random seed before generating noise,
+for valid pseudo-random comparisons.</p></li>
+<li><p><strong>clip</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True (default), the output will be clipped after noise applied
+for modes <cite>‘speckle’</cite>, <cite>‘poisson’</cite>, and <cite>‘gaussian’</cite>. This is
+needed to maintain the proper image data range. If False, clipping
+is not applied, and the output may extend beyond the range [-1, 1].</p></li>
+<li><p><strong>mean</strong> (<em>float</em><em>, </em><em>optional</em>) – Mean of random distribution. Used in ‘gaussian’ and ‘speckle’.
+Default : 0.</p></li>
+<li><p><strong>var</strong> (<em>float</em><em>, </em><em>optional</em>) – Variance of random distribution. Used in ‘gaussian’ and ‘speckle’.
+Note: variance = (standard deviation) ** 2. Default : 0.01</p></li>
+<li><p><strong>local_vars</strong> (<em>ndarray</em><em>, </em><em>optional</em>) – Array of positive floats, same shape as <cite>image</cite>, defining the local
+variance at every image point. Used in ‘localvar’.</p></li>
+<li><p><strong>amount</strong> (<em>float</em><em>, </em><em>optional</em>) – Proportion of image pixels to replace with noise on range [0, 1].
+Used in ‘salt’, ‘pepper’, and ‘salt &amp; pepper’. Default : 0.05</p></li>
+<li><p><strong>salt_vs_pepper</strong> (<em>float</em><em>, </em><em>optional</em>) – Proportion of salt vs. pepper noise for ‘s&amp;p’ on range [0, 1].
+Higher values represent more salt. Default : 0.5 (equal amounts)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>out</strong> – Output floating-point image data on range [0, 1] or [-1, 1] if the
+input <cite>image</cite> was unsigned or signed, respectively.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ndarray</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside
+the valid image range. The default is to clip (not alias) these values,
+but they may be preserved by setting <cite>clip=False</cite>. Note that in this case
+the output may contain values outside the ranges [0, 1] or [-1, 1].
+Use this option with care.
+Because of the prevalence of exclusively positive floating-point images in
+intermediate calculations, it is not possible to intuit if an input is
+signed based on dtype alone. Instead, negative values are explicitly
+searched for. Only if found does this function assume signed input.
+Unexpected results only occur in rare, poorly exposes cases (e.g. if all
+values are above 50 percent gray in a signed <cite>image</cite>). In this event,
+manually scaling the input to the positive domain will solve the problem.
+The Poisson distribution is only defined for positive integers. To apply
+this noise type, the number of unique values in the image is found and
+the next round power of two is used to scale up the floating-point result,
+after which it is scaled back down to the floating-point image range.
+To generate Poisson noise against a signed image, the signed image is
+temporarily converted to an unsigned image in the floating point domain,
+Poisson noise is generated, then it is returned to the original range.</p>
+<p>This function is adapted from scikit-image.
+<a class="github reference external" href="https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py">scikit-image/scikit-image</a></p>
+<p>Copyright (C) 2019, the scikit-image team
+All rights reserved.</p>
+<p>Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:</p>
+<ol class="arabic simple">
+<li><dl class="simple">
+<dt>Redistributions of source code must retain the above copyright</dt><dd><p>notice, this list of conditions and the following disclaimer.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>Redistributions in binary form must reproduce the above copyright</dt><dd><p>notice, this list of conditions and the following disclaimer in
+the documentation and/or other materials provided with the
+distribution.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>Neither the name of skimage nor the names of its contributors may be</dt><dd><p>used to endorse or promote products derived from this software without
+specific prior written permission.</p>
+</dd>
+</dl>
+</li>
+</ol>
+<p>THIS SOFTWARE IS PROVIDED BY THE AUTHOR <a href="#id2"><span class="problematic" id="id3">``</span></a>AS IS’’ AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="remote-data">
+<h3>Remote data<a class="headerlink" href="#remote-data" title="Link to this heading">#</a></h3>
+<p>Remote data classes can be used to access specific datasets from zenodo. These
+datasets are not packaged as part of CIL, instead the <cite>download_data(data_dir)</cite>
+method can be used to download the dataset to a chosen data directory then loaded
+from that data directory using <cite>get(data_dir)</cite>.</p>
+</section>
+<section id="walnut">
+<h3>Walnut<a class="headerlink" href="#walnut" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.WALNUT">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">WALNUT</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#WALNUT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.WALNUT" title="Link to this definition">#</a></dt>
+<dd><p>A microcomputed tomography dataset of a walnut from <a class="reference external" href="https://zenodo.org/records/4822516">https://zenodo.org/records/4822516</a></p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;my_PC/data_folder&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">WALNUT</span><span class="o">.</span><span class="n">download_data</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># download the data</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">WALNUT</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># load the data</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.WALNUT.get">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#WALNUT.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.WALNUT.get" title="Link to this definition">#</a></dt>
+<dd><p>Get the microcomputed tomography dataset of a walnut from <a class="reference external" href="https://zenodo.org/records/4822516">https://zenodo.org/records/4822516</a>
+This function returns the raw projection data from the .txrm file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em>) – The path to the directory where the dataset is stored. Data can be downloaded with dataexample.WALNUT.download_data(data_dir)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The walnut dataset</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.WALNUT.download_data">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prompt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.utilities.dataexample.WALNUT.download_data" title="Link to this definition">#</a></dt>
+<dd><p>Download a dataset from a remote repository</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory where the downloaded data should be stored</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="usb">
+<h3>USB<a class="headerlink" href="#usb" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.USB">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">USB</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#USB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.USB" title="Link to this definition">#</a></dt>
+<dd><p>A microcomputed tomography dataset of a usb memory stick from <a class="reference external" href="https://zenodo.org/records/4822516">https://zenodo.org/records/4822516</a></p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;my_PC/data_folder&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">USB</span><span class="o">.</span><span class="n">download_data</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># download the data</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">USB</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># load the data</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.USB.get">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#USB.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.USB.get" title="Link to this definition">#</a></dt>
+<dd><p>Get the microcomputed tomography dataset of a usb memory stick from <a class="reference external" href="https://zenodo.org/records/4822516">https://zenodo.org/records/4822516</a>
+This function returns the raw projection data from the .txrm file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em>) – The path to the directory where the dataset is stored. Data can be downloaded with dataexample.WALNUT.download_data(data_dir)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The usb dataset</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.USB.download_data">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prompt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.utilities.dataexample.USB.download_data" title="Link to this definition">#</a></dt>
+<dd><p>Download a dataset from a remote repository</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory where the downloaded data should be stored</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="korn">
+<h3>KORN<a class="headerlink" href="#korn" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.KORN">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">KORN</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#KORN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.KORN" title="Link to this definition">#</a></dt>
+<dd><p>A microcomputed tomography dataset of a sunflower seeds in a box from <a class="reference external" href="https://zenodo.org/records/6874123">https://zenodo.org/records/6874123</a></p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;my_PC/data_folder&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">KORN</span><span class="o">.</span><span class="n">download_data</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># download the data</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">KORN</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># load the data</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.KORN.get">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#KORN.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.KORN.get" title="Link to this definition">#</a></dt>
+<dd><p>Get the microcomputed tomography dataset of a sunflower seeds in a box from <a class="reference external" href="https://zenodo.org/records/6874123">https://zenodo.org/records/6874123</a>
+This function returns the raw projection data from the .xtekct file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em>) – The path to the directory where the dataset is stored. Data can be downloaded with dataexample.KORN.download_data(data_dir)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The korn dataset</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.KORN.download_data">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prompt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.utilities.dataexample.KORN.download_data" title="Link to this definition">#</a></dt>
+<dd><p>Download a dataset from a remote repository</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory where the downloaded data should be stored</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="sandstone">
+<h3>SANDSTONE<a class="headerlink" href="#sandstone" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SANDSTONE">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.dataexample.</span></span><span class="sig-name descname"><span class="pre">SANDSTONE</span></span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SANDSTONE"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SANDSTONE" title="Link to this definition">#</a></dt>
+<dd><p>A synchrotron x-ray tomography dataset of sandstone from <a class="reference external" href="https://zenodo.org/records/4912435">https://zenodo.org/records/4912435</a>
+A small subset of the data containing selected projections and 4 slices of the reconstruction</p>
+<p class="rubric">Example</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;my_PC/data_folder&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">SANDSTONE</span><span class="o">.</span><span class="n">download_data</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># download the data</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">dataexample</span><span class="o">.</span><span class="n">SANDSTONE</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="c1"># load the data</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SANDSTONE.get">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/dataexample/#SANDSTONE.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.dataexample.SANDSTONE.get" title="Link to this definition">#</a></dt>
+<dd><p>Get the synchrotron x-ray tomography dataset of sandstone from <a class="reference external" href="https://zenodo.org/records/4912435">https://zenodo.org/records/4912435</a>
+A small subset of the data containing selected projections and 4 slices of the reconstruction
+:param data_dir: The path to the directory where the dataset is stored. Data can be downloaded with dataexample.SANDSTONE.download_data(data_dir)
+:type data_dir: str
+:param file: The slices or projections to return, specify the path to the file within the data_dir
+:type file: str</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The selected sandstone dataset</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData">ImageData</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.dataexample.SANDSTONE.download_data">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prompt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.utilities.dataexample.SANDSTONE.download_data" title="Link to this definition">#</a></dt>
+<dd><p>Download a dataset from a remote repository</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data_dir</strong> (<em>str</em><em>, </em><em>optional</em>) – The path to the data directory where the downloaded data should be stored</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+<section id="module-cil.utilities.quality_measures">
+<span id="image-quality-metrics"></span><h2>Image Quality metrics<a class="headerlink" href="#module-cil.utilities.quality_measures" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="cil.utilities.quality_measures.mse">
+<span class="sig-prename descclassname"><span class="pre">cil.utilities.quality_measures.</span></span><span class="sig-name descname"><span class="pre">mse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dc1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dc2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/quality_measures/#mse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.quality_measures.mse" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the mean squared error of two images</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dc1</strong> (<cite>DataContainer</cite>) – One image to be compared</p></li>
+<li><p><strong>dc2</strong> (<cite>DataContainer</cite>) – Second image to be compared</p></li>
+<li><p><strong>mask</strong> (array or <cite>DataContainer</cite> with the same dimensions as the <cite>dc1</cite> and <cite>dc2</cite>) – The pixelwise operation only considers values where the mask is True or NonZero.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A number, the mean squared error of the two images</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="cil.utilities.quality_measures.mae">
+<span class="sig-prename descclassname"><span class="pre">cil.utilities.quality_measures.</span></span><span class="sig-name descname"><span class="pre">mae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dc1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dc2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/quality_measures/#mae"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.quality_measures.mae" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the Mean Absolute error of two images.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dc1</strong> (<cite>DataContainer</cite>) – One image to be compared</p></li>
+<li><p><strong>dc2</strong> (<cite>DataContainer</cite>) – Second image to be compared</p></li>
+<li><p><strong>mask</strong> (array or <cite>DataContainer</cite> with the same dimensions as the <cite>dc1</cite> and <cite>dc2</cite>) – The pixelwise operation only considers values where the mask is True or NonZero.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A number with the mean absolute error between the two images.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="cil.utilities.quality_measures.psnr">
+<span class="sig-prename descclassname"><span class="pre">cil.utilities.quality_measures.</span></span><span class="sig-name descname"><span class="pre">psnr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ground_truth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">corrupted</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/quality_measures/#psnr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.quality_measures.psnr" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the Peak signal to noise ratio (PSNR) between the two images.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ground_truth</strong> (<cite>DataContainer</cite>) – The reference image</p></li>
+<li><p><strong>corrupted</strong> (<cite>DataContainer</cite>) – The image to be evaluated</p></li>
+<li><p><strong>data_range</strong> (<em>scalar value</em><em>, </em><em>default=None</em>) – PSNR scaling factor, the dynamic range of the images (i.e., the difference between the maximum the and minimum allowed values). We take the maximum value in the ground truth array.</p></li>
+<li><p><strong>mask</strong> (array or <cite>DataContainer</cite> with the same dimensions as the <cite>dc1</cite> and <cite>dc2</cite>) – The pixelwise operation only considers values where the mask is True or NonZero..</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A number, the peak signal to noise ration between the two images.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="visualisation">
+<h2>Visualisation<a class="headerlink" href="#visualisation" title="Link to this heading">#</a></h2>
+<section id="show2d-display-2d-slices">
+<h3>show2D - Display 2D slices<a class="headerlink" href="#show2d-display-2d-slices" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.display.show2D">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.display.</span></span><span class="sig-name descname"><span class="pre">show2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datacontainers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slice_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fix_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">origin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lower-left'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gray'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(15,</span> <span class="pre">15)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/display/#show2D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.display.show2D" title="Link to this definition">#</a></dt>
+<dd><p>This plots 2D slices from cil DataContainer types.</p>
+<p>Plots 1 or more 2D plots in an (n x num_cols) matrix.
+Can plot multiple slices from one 3D dataset, or compare multiple datasets
+Inputs can be single arguments or list of arguments that will be sequentially applied to subplots
+If no slice_list is passed a 3D dataset will display the centre slice of the outer dimension, a 4D dataset will show the centre slices of the two outer dimension.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>datacontainers</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageData" title="cil.framework.ImageData"><em>ImageData</em></a><em>, </em><a class="reference internal" href="../framework/#cil.framework.AcquisitionData" title="cil.framework.AcquisitionData"><em>AcquisitionData</em></a><em>, </em><em>list</em><em> of  </em><em>ImageData / AcquisitionData</em><em>, </em><a class="reference internal" href="../optimisation/#cil.framework.BlockDataContainer" title="cil.framework.BlockDataContainer"><em>BlockDataContainer</em></a>) – The DataContainers to be displayed</p></li>
+<li><p><strong>title</strong> (<em>string</em><em>, </em><em>list</em><em> of </em><em>strings</em><em>, </em><em>optional</em>) – The title for each figure</p></li>
+<li><p><strong>slice_list</strong> (<em>tuple</em><em>, </em><em>int</em><em>, </em><em>list</em><em> of </em><em>tuples</em><em>, </em><em>list</em><em> of </em><em>ints</em><em>, </em><em>optional</em>) – The slices to show. A list of integers will show slices for the outer dimension. For 3D datacontainers single slice: (direction, index). For 4D datacontainers two slices: [(direction0, index),(direction1, index)].</p></li>
+<li><p><strong>fix_range</strong> (<em>boolean</em><em>, </em><em>tuple</em><em>, </em><em>list</em><em> of </em><em>tuples</em>) – Sets the display range of the data. <cite>True</cite> sets all plots to the global (min, max).</p></li>
+<li><p><strong>axis_labels</strong> (<em>tuple</em><em>, </em><em>list</em><em> of </em><em>tuples</em><em>, </em><em>optional</em>) – The axis labels for each figure e.g. (‘x’,’y’)</p></li>
+<li><p><strong>origin</strong> (<em>string</em><em>, </em><em>list</em><em> of </em><em>strings</em>) – Sets the display origin. ‘lower/upper-left/right’</p></li>
+<li><p><strong>cmap</strong> (<em>str</em><em>, </em><em>list</em><em> or </em><em>tuple</em><em> of </em><em>strings</em>) – Sets the colour map of the plot (see matplotlib.pyplot). If passed a list or tuple of the
+length of datacontainers, allows to set a different color map for each datacontainer.</p></li>
+<li><p><strong>num_cols</strong> (<em>int</em>) – Sets the number of columns of subplots to display</p></li>
+<li><p><strong>size</strong> (<em>tuple</em>) – Figure size in inches</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a matplotlib.pyplot figure object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>matplotlib.figure.Figure</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.display.show2D.save">
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.utilities.display.show2D.save" title="Link to this definition">#</a></dt>
+<dd><p>Saves the image as a <cite>.png</cite> using matplotlib.figure.savefig()</p>
+<p>matplotlib kwargs can be passed, refer to documentation
+<a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html">https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html</a></p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="show1d-display-1d-slices">
+<h3>show1D - Display 1D slices<a class="headerlink" href="#show1d-display-1d-slices" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.display.show1D">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.display.</span></span><span class="sig-name descname"><span class="pre">show1D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slice_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_colours</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_styles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('Index',</span> <span class="pre">'Value')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">6)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/display/#show1D"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.display.show1D" title="Link to this definition">#</a></dt>
+<dd><p>This creates and displays 1D plots of pixel values by slicing
+multi-dimensional data.</p>
+<p>The behaviour is as follows: if provided multiple datasets and a single
+slice set (see first example below), one line plot will be generated
+per dataset; if provided a single dataset and multiple sets of slices
+(see second example below), one line plot will be generated per slice
+set;  if provided multiple datasets and multiple slice sets, the
+<span class="math notranslate nohighlight">\(i\)</span>-th set of slices will apply to the <span class="math notranslate nohighlight">\(i\)</span>-th dataset, with
+a line plot generated in each case.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>list</em><em> of </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em>, </em><em>tuple</em><em> of </em><a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a>) – Multi-dimensional data to be reduced to 1D.</p></li>
+<li><p><strong>slice_list</strong> (<em>tuple</em><em>, </em><em>list</em><em> of </em><em>tuple</em><em> or </em><em>list</em><em> of </em><em>list</em><em> of </em><em>tuple</em><em>, </em><em>default=None</em>) – A tuple of (dimension, coordinate) pair, or a list, or nested list, of
+such pairs for slicing <cite>data</cite> (default is None, which is only valid when 1D
+data is passed)</p></li>
+<li><p><strong>label</strong> (<em>'default'</em><em>, </em><em>str</em><em>, </em><em>list</em><em> of </em><em>str</em><em>, </em><em>None</em><em>, </em><em>default='default'</em>) – Label(s) to use in the plot’s legend. Use <cite>None</cite> to suppress legend.</p></li>
+<li><p><strong>title</strong> (<em>str</em><em>, </em><em>default None</em>) – A title for the plot</p></li>
+<li><p><strong>line_colours</strong> (<em>str</em><em>, </em><em>list</em><em> of </em><em>str</em><em>, </em><em>default=None</em>) – Colour(s) for each line plot</p></li>
+<li><p><strong>line_styles</strong> (<em>{&quot;-&quot;</em><em>,</em><em>&quot;--&quot;</em><em>,</em><em>&quot;-.&quot;</em><em>,</em><em>&quot;:&quot;}</em><em>, </em><em>list</em><em> of </em><em>{&quot;-&quot;</em><em>,</em><em>&quot;--&quot;</em><em>,</em><em>&quot;-.&quot;</em><em>,</em><em>&quot;:&quot;}</em><em>, </em><em>default=None</em>) – Linestyle(s) for each line plot</p></li>
+<li><p><strong>axis_labels</strong> (<em>tuple</em><em> of </em><em>str</em><em>, </em><em>list</em><em> of </em><em>str</em><em>, </em><em>default=</em><em>(</em><em>'Index'</em><em>,</em><em>'Value'</em><em>)</em>) – Axis labels in the form (x_axis_label,y_axis_label)</p></li>
+<li><p><strong>size</strong> (<em>tuple</em><em>, </em><em>default=</em><em>(</em><em>8</em><em>,</em><em>6</em><em>)</em>) – The size of the figure</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="cil.utilities.display.show1D.figure">
+<span class="sig-name descname"><span class="pre">figure</span></span><a class="headerlink" href="#cil.utilities.display.show1D.figure" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>matplotlib.figure.Figure</p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Examples</p>
+<p>This example creates two 2D datasets (images), and uses the provided
+slicing information to generate two plots on the same axis,
+corresponding to the two datasets.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.utilities.display</span> <span class="kn">import</span> <span class="n">show1D</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.utilities.dataexample</span> <span class="kn">import</span> <span class="n">PEPPERS</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">PEPPERS</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_channel0</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data_channel1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">get_slice</span><span class="p">(</span><span class="n">channel</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">show1D</span><span class="p">([</span><span class="n">data_channel0</span><span class="p">,</span> <span class="n">data_channel1</span><span class="p">],</span> <span class="n">slice_list</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;horizontal_x&#39;</span><span class="p">,</span> <span class="mi">256</span><span class="p">)],</span>
+<span class="gp">... </span>       <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Channel 0&#39;</span><span class="p">,</span> <span class="s1">&#39;Channel 1&#39;</span><span class="p">],</span> <span class="n">line_styles</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="s2">&quot;-&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>The following example uses two sets of slicing information applied to a
+single dataset, resulting in two separate plots.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.utilities.display</span> <span class="kn">import</span> <span class="n">show1D</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">cil.utilities.dataexample</span> <span class="kn">import</span> <span class="n">PEPPERS</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">PEPPERS</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">slices</span> <span class="o">=</span> <span class="p">[[(</span><span class="s1">&#39;channel&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;horizontal_x&#39;</span><span class="p">,</span> <span class="mi">256</span><span class="p">)],</span> <span class="p">[(</span><span class="s1">&#39;channel&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;horizontal_y&#39;</span><span class="p">,</span> <span class="mi">256</span><span class="p">)]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">show1D</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">slice_list</span><span class="o">=</span><span class="n">slices</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Channel 0&#39;</span><span class="p">,</span> <span class="s1">&#39;Channel 1&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.display.show1D.save">
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.utilities.display.show1D.save" title="Link to this definition">#</a></dt>
+<dd><p>Saves the image as a <cite>.png</cite> using matplotlib.figure.savefig()</p>
+<p>matplotlib kwargs can be passed, refer to documentation
+<a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html">https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html</a></p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="show-geometry-display-system-geometry">
+<h3>show_geometry - Display system geometry<a class="headerlink" href="#show-geometry-display-system-geometry" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.display.show_geometry">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.display.</span></span><span class="sig-name descname"><span class="pre">show_geometry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">acquisition_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elevation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">azimuthal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-35</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(10,</span> <span class="pre">10)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/display/#show_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.display.show_geometry" title="Link to this definition">#</a></dt>
+<dd><p>Displays a schematic of the acquisition geometry
+for 2D geometries elevation and azimuthal cannot be changed</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>acquisition_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.AcquisitionGeometry" title="cil.framework.AcquisitionGeometry"><em>AcquisitionGeometry</em></a>) – CIL acquisition geometry</p></li>
+<li><p><strong>image_geometry</strong> (<a class="reference internal" href="../framework/#cil.framework.ImageGeometry" title="cil.framework.ImageGeometry"><em>ImageGeometry</em></a><em>, </em><em>optional</em>) – CIL image geometry</p></li>
+<li><p><strong>elevation</strong> (<em>float</em>) – Camera elevation in degrees, 3D geometries only, default=20</p></li>
+<li><p><strong>azimuthal</strong> (<em>float</em>) – Camera azimuthal in degrees, 3D geometries only, default=-35</p></li>
+<li><p><strong>view_distance</strong> (<em>float</em>) – Camera view distance, default=10</p></li>
+<li><p><strong>grid</strong> (<em>boolean</em>) – Show figure axis, default=False</p></li>
+<li><p><strong>figsize</strong> (<em>tuple</em><em> (</em><em>x</em><em>, </em><em>y</em><em>)</em>) – Set figure size (inches), default (10,10)</p></li>
+<li><p><strong>fontsize</strong> (<em>int</em>) – Set fontsize, default 10</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>returns a matplotlib.pyplot figure object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>matplotlib.figure.Figure</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="cil.utilities.display.show_geometry.save">
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cil.utilities.display.show_geometry.save" title="Link to this definition">#</a></dt>
+<dd><p>Saves the image as a <cite>.png</cite> using matplotlib.figure.savefig()</p>
+<p>matplotlib kwargs can be passed, refer to documentation
+<a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html">https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html</a></p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="islicer-interactive-display-of-2d-slices">
+<h3>islicer - interactive display of 2D slices<a class="headerlink" href="#islicer-interactive-display-of-2d-slices" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.jupyter.islicer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.jupyter.</span></span><span class="sig-name descname"><span class="pre">islicer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slice_number</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gray'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">origin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lower-left'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">play_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/jupyter/#islicer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.jupyter.islicer" title="Link to this definition">#</a></dt>
+<dd><p>Creates an interactive slider that slices a 3D volume along an axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference internal" href="../framework/#cil.framework.DataContainer" title="cil.framework.DataContainer"><em>DataContainer</em></a><em> or </em><em>numpy.ndarray</em>) – A 3-dimensional dataset from which 2-dimensional slices will be
+shown</p></li>
+<li><p><strong>direction</strong> (<em>int</em>) – Axis to slice on. Can be 0,1,2 or the axis label, default 0</p></li>
+<li><p><strong>title</strong> (<em>str</em><em>, </em><em>list</em><em> of </em><em>str</em><em> or </em><em>tuple</em><em> of </em><em>str</em><em>, </em><em>default=''</em>) – Title for the display</p></li>
+<li><p><strong>slice_number</strong> (<em>int</em><em>, </em><em>optional</em>) – Start slice number (default is None, which results in the center
+slice being shown initially)</p></li>
+<li><p><strong>cmap</strong> (<em>str</em><em> or </em><em>matplotlib.colors.Colormap</em><em>, </em><em>default='gray'</em>) – Set the colour map</p></li>
+<li><p><strong>minmax</strong> (<em>tuple</em>) – Colorbar (min, max) values, default None (uses the min, max of
+values in <cite>data</cite>)</p></li>
+<li><p><strong>size</strong> (<em>int</em><em> or </em><em>tuple</em><em>, </em><em>optional</em>) – Specify the figure size in inches. If <cite>int</cite> this specifies the
+width, and scales the height in order to keep the standard
+<cite>matplotlib</cite> aspect ratio, default None (use the default matplotlib
+figure size)</p></li>
+<li><p><strong>axis_labels</strong> (<em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em>) – The axis labels to use for each of the 3 dimensions in the data
+(default is None, resulting in labels extracted from the data, or
+[‘X’,’Y’,’Z’] if no labels are present)</p></li>
+<li><p><strong>origin</strong> (<em>{'lower-left'</em><em>, </em><em>'upper-left'</em><em>, </em><em>'lower-right'</em><em>, </em><em>'upper-right'}</em>) – Sets the display origin</p></li>
+<li><p><strong>play_interval</strong> (<em>int</em><em>, </em><em>default=500</em>) – The interval of time (in ms) a slice is selected for when iterating
+through a set of them</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>box</strong> – The top-level widget container.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ipywidgets.Box</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="link-islicer-link-islicer-objects-by-index">
+<h3>link_islicer - link islicer objects by index<a class="headerlink" href="#link-islicer-link-islicer-objects-by-index" title="Link to this heading">#</a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="cil.utilities.jupyter.link_islicer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cil.utilities.jupyter.</span></span><span class="sig-name descname"><span class="pre">link_islicer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/cil/utilities/jupyter/#link_islicer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cil.utilities.jupyter.link_islicer" title="Link to this definition">#</a></dt>
+<dd><p>Links islicer’s slice-selection widgets</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>*args</strong> (<em>tuple</em><em> of </em><em>ipywidgets.Box</em>) – The widget containers returned from <cite>islicer</cite>, from which the slice
+selection sliders will be extracted and linked.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<p><a class="reference internal" href="../#mastertoc"><span class="std std-ref">Return Home</span></a></p>
+</section>
+</section>
+</section>
+
+
+                </article>
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\ No newline at end of file
diff --git a/versions.json b/versions.json
index 2ee752d81a..7ab4e84cb3 100644
--- a/versions.json
+++ b/versions.json
@@ -1 +1 @@
-[{"name": "nightly", "version": "24.1.1.dev117+g2962b920", "url": "/CIL/nightly/"}, {"name": "v24.1.0", "version": "24.1.0", "url": "/CIL/v24.1.0/"}, {"name": "stable", "version": "24.1.0", "url": "/CIL/v24.1.0/", "preferred": true}, {"name": "v24.0.0", "version": "24.0.0", "url": "/CIL/v24.0.0/"}, {"name": "v23.1.0", "version": "23.0.1", "url": "/CIL/v23.1.0/"}]
\ No newline at end of file
+[{"name": "v24.2.0", "version": "24.2.0", "url": "/CIL/v24.2.0/"}, {"name": "stable", "version": "24.2.0", "url": "/CIL/v24.2.0/", "preferred": true}, {"name": "nightly", "version": "24.1.1.dev117+g2962b920", "url": "/CIL/nightly/"}, {"name": "v24.1.0", "version": "24.1.0", "url": "/CIL/v24.1.0/"}, {"name": "v24.0.0", "version": "24.0.0", "url": "/CIL/v24.0.0/"}, {"name": "v23.1.0", "version": "23.0.1", "url": "/CIL/v23.1.0/"}]
\ No newline at end of file